esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 4 Data and Methodology  
In this section, we introduce the data and preprocessing method used in this paper. We designed three types of comparative models to test the effect of NODE. Through many experiments, we choose the three models that perform the best in three metrics (root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE)) for electricity load forecasting. After that, we compare the models with NODE and without NODE. The detailed discussion is given later.
## 3 Results  
### Municipality Selection  
The flood hazard map provided by [PERSON] et al. [31] detects the municipalities located on the Ionian coast as the most exposed to flood hazard in the case of the LPH scenario. These municipalities are Bernalda, Pisticci, Scanzano Jonico, Policoro, Rotondella, and Nova Siri. In order to include municipalities intersected by the landslide hazard layer (Figure 1) and affected by past significant events (Section Multi-Hazard Context in the Basilicata Region), seven neighboring municipalities were also selected: Montescalioso, Pomario, Craco, Montalbano Jonico, Tursi, Colobraro, and Valsinni. As shown in Figure 4 and Table 5, thirteen municipalities in total were identified for the investigation. In particular, the study area is characterized by 1391.95 km\({}^{2}\) and 1331 census tracts.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Indicator** & **Variable** & **Description** & **References** \\ \hline Population density & Pop\_density & Number of inhabitants/km\({}^{2}\) & [35; 36; 37; 38; 39; 40; 41] \\ \hline Build up density & Build\_density & Number of buildings/km\({}^{2}\) & [37; 42; 33] \\ \hline \multirow{2}{*}{Age} & Eld & \% of inhabitants with age \textgreater{} 65 & [16; 44; 45] \\ \cline{2-4}  & Child & \% of inhabitants with age \textless{} 15 & [43; 46] \\ \hline Gender & Wom & \% of women residents & [34; 47; 4] \\ \hline \multirow{2}{*}{Education} & \multirow{2}{*}{Low\_ed} & \% of illiterate residents or & \\  & & with low education & [38; 49; 47] \\ \hline Employment & Unemp & \% of unemployed residents & [38; 50; 51; 52] \\ \hline Foreign & For & \% of foreign people & [16; 39; 47; 53] \\ \hline \multirow{2}{*}{Household} & \multirow{2}{*}{House6+} & \% of households with 6 or & \\  & & more family members & [54; 55] \\ \hline \end{tabular}
\end{table}
Table 4: Selection of indicators that influence social vulnerability. The variables were extracted from the open dataset ISTAT’s available at the census tract level.
# Each-Phase Metering with Gas-Liquid Stratified Flow Based on the Multi-Frequency Coriolis Principle
## 1 Introduction
[11] combined Coriolis flowmeter with a water cut meter to realize the measurement of three-phase flow (water content from 0 to 100%, gas volume fraction (GVF) from 0 to 50%). [PERSON] et al. [12] proposed a new method of CO\({}_{2}\) flow measurement under carbon capture and storage (CCS) condition based on the Coriolis flowmeter and LSSVM model, using a classifier to identify mixed fluid flow patterns, and using a single Least Square Support Vector Machine (LSSVM) model to measure and predict CO\({}_{2}\) mass flow and volume fraction. It can be seen from the above analysis that for the application of Coriolis flowmeters to measure the gas-containing fluids, many researchers and research institutions have made theoretical progress and some practical application results. Some companies have also launched relevant Coriolis flowmeters applicable to the gas-containing conditions, among which the multi-frequency Coriolis flowmeters by E+H company can compensate for the bubble effect and resonance effect caused by the presence of the gas [13]. However, the measurement correction method of uniform bubble flow based on the multi-frequency Coriolis principle applied by this flowmeter has higher accuracy only under the ideal flow pattern with more evenly distributed bubbles [14]. However, for the low gas-containing fluids with complex and variable flow patterns, it is challenging to meet the current requirements for phase-separation metering only by using the existing research results and the measurement accuracy of products. Therefore, how to improve the applicability and metering accuracy with a multi-frequency Coriolis flowmeter is the critical content of this paper. The single-frequency Coriolis flowmeters that are widely used in the oilfields can directly measure the oil-water two-phase separated from the gas-liquid two-phase separators. However, it has a higher requirement for the effect of gas-liquid separation. The entrainment of gas will significantly increase the measurement errors of Coriolis flowmeters. Therefore, this paper uses the Finite Element Analysis method to simulate the measurement characteristics of Coriolis flowmeter for gas-liquid two-phase flow under different working conditions (gas content, flow pattern). A multiphase metering technology based on the multi-frequency Coriolis principle is proposed. Using the multi-frequency response characteristics of Coriolis flowmeters and advantages of traditional single-frequency Coriolis flowmeters with high accuracy and multi-parameter output, a stratified flow deviation compensation model based on the multi-frequency Coriolis principle can be established to reduce the measurement deviations caused by bubble effect and resonance effect. Only one multi-frequency Coriolis flowmeter can realize the online non-separation metering of each phase flow.
## 3 Experimental Activities
### Advanced Shielding for Spacecraft
#### 3.2.2 Self-Healing Advanced Panels  
In the framework of the IMpact BEhavior of MUltifunctional materialS (IMBEMUS) project, supported by the CARIPARO Foundation, the mechanical behavior of multifunctional panels was studied under impact conditions. The investigation required both high-velocity impact experiments and numerical simulations, performed with ANSYS(tm) Autodyn. In particular, multi-layer panels composed of ionomeric polymers with self-healing properties were subjected to 14 impact experiments; the self-healing (see Figure 5) was successful in all but one ionomer samples and the primary damage on ionomeric polymers was found to be significantly lower than that on aluminum. On the other hand, aluminum plates exhibited slightly better debris fragmentation abilities, even though the protecting performance of ionomers seemed to improve at increasing impact speed. Further data and the main experimental campaign results can be found in [39; 40; 41].  
Figure 4: Frames from the video of an impact on CFRP panels. The projectile is impacting from the left; two debris clouds are generated respectively from the panel front and back faces.
# Fault Injection Emulation for Systems in FPGAs: Tools, Techniques and Methodology, a Tutorial
## 8 ACME: Speeding Up the Injection Performance  
Essential bits defined by Xilinx programs are related to the whole system, including the SEM IP, the ROM, the DUT, the golden copy and the checker (Figure 7). As the objective is to inject in the DUT to validate its fault tolerance against SEUs in the configuration memory, the injection process must be driven selecting only those bits from the configuration memory that may have impact on the behavior of the design under test. It is an attempt to optimize the campaign performance eliminating injections at insignificant areas.  
In order to carry out a driven fault injection campaign that can be able to improve the time emulation, a tool called ACME [13] is used. ACME is an open-source tool designed to translate the configuration memory essential bits of a Xilinx SRAM-based FPGA region into injection addresses for the Xilinx SEM IP controller. ACME needs the EBD file of the complete design together with the pBlock coordinates of the design under test as inputs to generate a text file containing the injection addresses of the specified range as is shown in the Figure 24.  
Figure 24: Integration of the ACME tool into fault injection process.  
ACME currently supports the ZedBoard, the Basys3 Artix-7, the Nexys 4 DDR, the ZC706, and the KCU105 UltraScale, but it can be easily configured to work with other boards. When ACME is started (Figure 25), the required data are the following:  
* Model of board (Nexys 4 DDR for this tutorial).
* An EBD file that lists all the essential bits in the bitstream.
* Coordinates of the pBlock where the DUT is allocated into the FPGA. This information might be defined in the constraint file, through the routine:  
_resize_pblock_-_pblock_MY_DUT_-_add_SLICE_XOY100:SLICE_X5Y149  
ACME computes these inputs generating as a result, a text file named FrameRange. It collects a subset of essential bits belonging to the pBlock area. For instance, in the above Figure 9974 essential bits are identified from the.ebd file. These bits are translated by ACME into configuration memory addresses that will be used by the SEM IP controller to conduct one injection per address, in the following way:  
_N [10-digit hex value]: N C00 XXXXXXXX_  
As it can be noticed, this routine is composed of the N injection command and the address translated by ACME that corresponds with an essential bit belonging to the pBlock. More details about ACME tool, can be found in the web site of the ARIES research group [46].
###### Abstract  
The probabilistic analysis of streamflow and drought event durations plays a crucial role in the efficient and sustainable management of existing water resources in the region. This approach involves the collection of historical hydrological data from river gauging stations, the use of statistical and probabilistic models, and the assessment of hydrological projections at different return periods to provide valuable information for society to understand the potential impacts of extreme events. The analysis is carried out on the Sinu River in Colombia, with consideration given to both the presence and absence of the Hydropower Plant Urra I. The results reveal that, under natural conditions, a higher number of return periods correspond to less extreme drought flows and longer temporal durations. However, when the hydropower plant is operational, the occurrence and duration of drought are influenced by the regulations implemented during energy generation. The results of this analysis can guide water resource management policies, considering the operation of the hydroelectric plant, thereby enabling decisions that enhance the resilience and sustainability of the river's hydrological conditions and communities that depend on it.  
low flows; drought hydrograph; statistical analysis; hydropower effect +
Footnote †: journal: Applied Sciences
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 4 Experimental Results
### Comparison with the State of the Art
#### 4.4.2 Qualitative Comparison  
To further highlight the performance of the proposed model and quality of tge saliency map, qualitative analysis of the proposed model on five datasets was also performed. Figure 8 shows the quality of the maps and segmented salient objects from five widely used datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Criteria** & **Proposed** & **NLDF** & **C2S** & **MDF** & **ELD** & **GCBR** & **Amulet** \\ \hline \multirow{3}{*}{MSRA-B} & MAE & **0.0345** & 0.0477 & 0.0662 & 0.104 & - & 0.0373 & - \\  & \(F_{\beta}\) & **0.935** & 0.910 & 0.8309 & 0.885 & - & 0.8904 & - \\ \hline \multirow{3}{*}{DUTS} & MAE & **0.0653** & 0.066 & 0.0663 & 0.094 & 0.093 & 0.0695 & 0.075 \\  & \(F_{\beta}\) & **0.816** & 0.812 & 0.790 & 0.730 & 0.738 & 0.801 & 0.773 \\ \hline \multirow{3}{*}{DUT-OMRON} & MAE & **0.0753** & 0.0795 & 0.0790 & 0.0915 & 0.0909 & 0.0763 & 0.0830 \\  & \(F_{\beta}\) & **0.764** & 0.753 & 0.733 & 0.694 & 0.719 & 0.7010 & 0.7370 \\ \hline \multirow{3}{*}{PASCAL-S} & MAE & 0.106 & 0.113 & 0.0991 & 0.143 & 0.133 & **0.0356** & 0.092 \\  & \(F_{\beta}\) & **0.829** & 0.807 & 0.827 & 0.771 & 0.768 & 0.801 & 0.826 \\ \hline \multirow{3}{*}{HKU-IS} & MAE & **0.0432** & 0.0485 & 0.0527 & 0.135 & 0.074 & **0.0432** & 0.052 \\  & \(F_{\beta}\) & **0.918** & 0.914 & 0.897 & 0.867 & 0.839 & 0.8988 & 0.889 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of proposed method with SOA methods on five datasets in terms of F-measure (larger is better) and MAE (smaller is better). Note: NLDF, nonlocal features; C2S, contour to saliency; MDF, multiscale deep CNN features; ELD, deep saliency with encoded low-level distance map and high-level features; GCBR, salient-object detection in low-contrast images via global convolution and boundary refinement; Amulet, aggregating multilevel convolutional features for salient-object detection.  
Figure 7: (**left**) Precision–recall (PR) and (**right**) F-measure curves of proposed model (SODL) and state-of-the-art (SOA) models.  
Qualitative comparison of the proposed model on the five datasets showed that the proposed model could accurately segment objects in different challenging scenarios, containing images that had much less color difference from their background, and objects that were large and touching image boundaries. Most strategies can produce good results for images with simple scenarios, while the proposed model could achieve better results even in complex scenarios. Many deep-learning models cannot segment and locate objects in complex backgrounds, but the proposed technique was successful in capturing most salient regions.
# SMS: A Secure Healthcare Model for Smart Cities
## 2 Need for Transition from Cloud to Edge in a Smart City Ecosystem
### Mobile Edge Computing (MEC)  
\"Mobile Edge Computing (MEC)\" is a computing paradigm wherein the data are processed in close proximity to its original source of generation. MEC involves a distributed intermediate server having some compute, storage, and networking facilities to handle the real-time processing of data for producing fast and instant results. The data that require real-time processing can be easily handled by such intermediate servers to provide instant results. What is important to mention is that, as of 2019, 45% of the data created under IoT solutions is expected to be stored and processed at network edges [15]. One of the main features of EC is the ease of adoption to multi-component and peripheral scenarios, which is commonly found in IoT solutions [11]. In the EC architecture, we assist a part of the processing and storage resources to be at the edges, near sensors or mobile devices, allowing for \"agile-connectivity,\" \"real-time services,\" \"data optimization,\" \"security,\" and \"privacy.\" Real-time response, low latency, reduction of network traffic, storage, energy consumption, and bandwidth cost are some of the advantages that can be pointed out to the EC paradigm [10]. The need to adopt a different paradigm from cloud computing to current and future IoT solutions is mainly due to three reasons:  
* The large amount of data generated at the edges of the network (sensors, devices, etc.) requires, in a cloud architecture, the transmission of a large amount of data across the network, which is the origin of the bottleneck that is currently seen in the cloud paradigm. If we use the example of a Boeing 787, which is estimated to generate 5 gigabytes of data per second, it is easy to conclude that a system using the cloud paradigm will not scale to support such or similar situations [16].
* Data producers in an IoT solution (such as sensors, mobile devices, traffic lights, lights, appliances, etc.) will grow exponentially and reach billions in the coming years. In a cloud computing architecture, data consumers obtain the information they want after requesting it to the cloud, which will no longer be possible considering the amount and increasing number of data producers.
* In today's scenarios with the proliferation of mobile devices and even other wearables, data consumers will also act as producers of data (uploading and sharing multimedia contents) and hence the cloud paradigm will not be enough. In addition, the data being produced on the edges tend to be more personal (photos, videos, etc.), raising privacy issues that will be more easily addressed by maintaining edge processing.  
Although, MEC seems to be an appropriate solution for handling real-time requests, which the typical cloud computing paradigm fails to acknowledge. There are several vulnerabilities associated with this technology.
## 1 Introduction  
### Research Background  
Virtual and augmented reality (VR and AR) technologies are rapidly evolving, driven by advances in computational algorithms and the increasing accessibility of advanced hardware such as GPUs and high-resolution VR headsets [1]. These technologies, historically utilized for training in high-stakes environments such as aviation, are now penetrating other domains, including neuropsychology, neurorehabilitation, and complex surgical procedures [2]. At the heart of this technological shift is the growing capability to simulate intricate dynamic interactions in virtual spaces, making them increasingly relevant for both educational [3] and therapeutic applications [4].  
This manuscript builds upon foundational work by [PERSON] [5] and [PERSON] [6], who distinguished between exploratory and performative activities. They proposed that both action and awareness can be scientifically studied and enhanced through virtual environments. Accordingly, this research aims to explore and improve both the physical actions(such as movements and responses) and the awareness (the perceptual and cognitive understanding) of individuals within virtual environments. However, a significant limitation has been the lack of explicit mathematical foundations in theories of motion perception, particularly those proposed by [PERSON] [6]. Our study addresses this gap by applying the \"Theory of Screws\" [7], a sophisticated mathematical model historically used to describe the dynamics of rigid bodies, to the domain of VR and AR.  
Our study formulates the issues that the Theory of Screws aims to resolve within the context of VR and AR [8], providing a structured method for handling the complexities of motion and interaction in virtual environments. By integrating [PERSON]'s conceptual framework with the mathematical rigor of screw theory, we explore how harmonic ratios and stimulation invariants can be leveraged to improve the design and functionality of VR and AR systems. Specifically, this approach allows for modeling visual interception behaviors critical in scenarios requiring precise coordination and motion control, such as training simulations for sports or rehabilitative applications. Harmonic ratios and stimulation invariants serve as the backbone for developing algorithms that enhance the realism and responsiveness of virtual environments, facilitating more effective training and rehabilitation protocols.
# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 4 Datasets & Metrics
### Datasets
As Reference [143] indicated, this dataset suffers from pretty inaccurate calibration parameters and synchronization issues. **SynthHands**[136] contains 63.5 K RGB and depth synthetic images from an egocentric viewpoint of two subjects, captured using the Intel RealSense SR300 with \(640\times 480\) resolution. Real object textures and background images were used, capturing the variations in skin color, shape, background clutter, camera viewpoint, and hand-object interactions. In total, there exist interactions with 7 different virtual objects. Thus, the subject was able to see the rendered scene in real time leading to the above mentioned interactions with objects. As for the annotated data, it provides accurate locations of 21 joints.
# Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm
## 3 Study Area and Data
### Data  
The landslide monitoring data from 2000 to 2013 in the Three Parallel Rivers were taken as the experimental data. The total data is 19.6 G, covering a total of 175,000 instances. There are three landslide displacement monitoring stations (the ShangQiaotou, XiangDuo and JuDian landslide monitoring stations), representing the upstream, midstream and downstream areas of the Three Parallel Rivers, respectively.  
The Three Parallel Rivers have high rainfall and a clear climate. The rainfall peak in the Three Parallel Rivers area appeared in 2004, and the rainfall has gradually decreased in recent years, as suggested from the rainfall data from 2000 to 2013 (see Figure 6). The groundwater levels in the upstream, midstream and downstream areas of the Three Parallel Rivers area were recorded by ShangQiaotou, XiangDuo and JuDian landslide displacement monitoring stations, respectively. The monitoring station measures the data once a month. Figure 7 shows the annual average rate of change of groundwater level in the area of the Three Parallel Rivers.  
Figure 6: Annual average rainfall in the Three Parallel Rivers from 2000 to 2013.  
Figure 7: Annual average rate of change of groundwater level in the area of the Three Parallel Rivers.  
Because of the special geographical location, geological disasters frequently occur in the Three Parallel Rivers area. Table 1 lists the types of landslide for each landslide hazard, the nature of the landslide, and the maximum rainfall, etc.
## 6 Discussion and Implications
### Stakeholder-Driven Integration and Sustainability  
Though SUCM significantly correlates with CSCI, BI and RL have not shown their greater potential than FI to enhance the degree of integration. Forward integration is mainly driven by project stakeholders, while backward integration and reverse logistics are objectively about material flows, which stakeholders may do not pay attention. The stakeholder's ideas actively lead the project implementation rather than the demands of the whole supply chain. Regarding improving sustainability in construction by reaching a higher degree of supply chain integration, the most crucial task is to cultivate a positive attitude of project stakeholders towards supply chain backward integration and reverse logistics [73; 74].  
Among the formative indicators of BI and RL, off-site process monitoring (bi_2), transfer partial on-site activities into off-site (bi_3), and purchase permit of recycled materials (rl_1) received high indicator weights (0.605, 0.580, 0.615). Stakeholders' decisions can influence if the off-site process should be adequately monitored [75] and if pre-fabrication is adopted in the project [76]. The purchase permit of recycled materials may be issued by the industry alliance or the government, but project stakeholders also have a strong voice. Therefore, project stakeholders should understand the  
\begin{table}
\begin{tabular}{c c c c} \hline
**Path** & **Hypothesis** & **Construct Order** & **Result** \\ \hline CSCI \(\rightarrow\) SUCM & H1 & 2 \(\rightarrow\) 2 & Supported \\ BI \(\rightarrow\) CSCI & H2 & 1 \(\rightarrow\) 2 & Loosely supported \({}^{*}\) \\ FI \(\rightarrow\) CSCI & H3 & 1 \(\rightarrow\) 2 & Supported \\ RL \(\rightarrow\) CSCI & H4 & 1 \(\rightarrow\) 2 & Supported \\ SUCM \(\rightarrow\) BED & H5 & 2 \(\rightarrow\) 1 & Supported \\ SUCM \(\rightarrow\) MPC & H6 & 2 \(\rightarrow\) 1 & Loosely supported \({}^{*}\) \\ SUCM \(\rightarrow\) CST & H7 & 2 \(\rightarrow\) 1 & Supported \\ \hline \end{tabular} Note: \({}^{*}\) All the structural model evaluation criteria are satisfied, but not all the measurement model evaluation criteria are satisfied.  
\end{table}
Table 11: Summary of hypothesis tests.  
Figure 3: Testing results of the research model. Note: The label of a path represents the path coefficient/loading/weight and its significance level (t-value).  
contribution of supply chain integration to the sustainability in the construction field, and further develop strategies to support backward integration and reverse logistics.  
As a new concept [27], supply chain integration can enhance the implementation of strategies such as the construction industrialization. This can be further accelerated by stakeholders. It adds to the body of knowledge of stakeholder management for sustainability.