esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Visualization and Forecasting from Australian Building and Construction Statistics
## 2 Related Works
### Time Series Forecasting  
Time series forecasting or prediction [4; 11] is an active research branch that lasts for long time due to the fact that in reality many data are obtained over time. Statistical methods have been proposed and used since 1970s. The Box-Jenkins-based methodologies [12] were the most popular ones at that time. Decomposition-based methods have the longest history and are still in use [1; 2]. A time series data are composed of up to four components: trend (T), seasonally (S), cyclical (C), and noise (N) components. Decomposing the time series helps for analysing and understanding historical time series, but it is also proved useful when attempting a forecasting analysis. To decompose the time series data, the moving average (MA) will remove the seasonal effects from the time series. MA is a type of filter that takes a cycle of data and gets the average value of the numbers. MA includes two kinds: simple moving average (SMA) and weighted moving average (WMA). SMA puts equal weights on each number, while WMA puts different weights at different timestamps, determining different periods to make the trend more accurate. The length of a cycle needs to be decided manually. The size of a cycle can be either one month, two months, six months, etc. The longer the cycle is, the smoother the trend will be. The autoregressive integrated moving average (ARIMA) model only supports non-seasonal data by proposing an integrated MA. ARIMA shows good performance because of the small number of parameters required to build the model. However, the ARIMA model has the assumption of stationarity, which makes the method inflexible to use. The SARIMA model is an extension of the ARIMA model, which has both non-seasonal and seasonal parts. The non-seasonal is the same as the ARIMA model. The SARIMA model will be introduced in more detail in Section 4. The vector autoregressive moving average (VARMA) model is a generalization of ARIMA that can be applied on multivariate time series. However, it requires a stationary time series, so it could not be used for a seasonal time series.  
With the development of machine learning methods, time series forecasting is formed as a regression task and many supervised learning models could be adjusted to forecast time series. Support vector machine (SVM), and its variants, were the popular models adopted [8]. In recent years, deep learning methods achieved superior results, and models that were designed for dealing with sequence data were adopted to time series forecasting. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that can memorize short-term values. It is well-adopted in the time series community [5; 9; 10; 11; 13; 14]. In our work, we will follow this direction and apply LSTM on the time series forecasting with multiple variables.
## 3 Results
### Alveolar Bone Crest Level (RCF Versus Non-RCF Teeth)

Table 2: Distance CEJ—alveolar bone crest in the matching pairs related to the upper and lower jaw; both the differences within the jaws and those between the jaws are shown. CEJ = cemento-enamel-junction; RCF = root canal filling; CI = confidence interval [95%]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & \\ \cline{3-6}
**Tooth Type** & **No of Teeth** & **RCF** & \multicolumn{2}{c}{**Mesial**} & \multicolumn{2}{c}{**Diasual/**} & \multicolumn{2}{c}{**Buccal**} & \multicolumn{2}{c}{**Pooled**} \\  & & & & & & & & \\ \hline molar & 82 & yes & mean \(\pm\) sd & 2.56 \(\pm\) 1.02 & 2.78 \(\pm\) 1.16 & 2.62 \(\pm\) 0.91 & 2.52 \(\pm\) 0.89 & 2.62 \(\pm\) 1.00 \\  & & & 95\% CI & 2.33–2.78 & 2.49–3.08 & 2.43–2.82 & 2.35–2.79 & 2.46–2.79 \\  & & & range & 0.45 & 0.59 & 0.39 & 0.44 & 0.33 \\ molar & 82 & no & mean \(\pm\) sd & 2.55 \(\pm\) 1.08 & 2.71 \(\pm\) 1.03 & 2.57 \(\pm\) 0.96 & 2.48 \(\pm\) 0.88 & 2.58 \(\pm\) 1.02 \\  & & & CI & 2.31–2.79 & 2.49–2.92 & 2.36–2.78 & 2.28–2.67 & 2.41–2.75 \\  & & & range & 0.48 & 0.43 & 0.42 & 0.39 & 0.36 \\ \hline molar & & & \(p\)-value & 0.896 & 0.725 & 0.802 & 0.837 & 0.870 \\ premolar & 101 & yes & mean \(\pm\) sd & 2.56 \(\pm\) 1.11 & 2.68 \(\pm\) 1.55 & 2.96 \(\pm\) 1.50 & 2.64 \(\pm\) 1.32 & 2.71 \(\pm\) 1.37 \\  & & & 95\% CI & 2.34–2.78 & 2.38–2.99 & 2.66–3.26 & 2.36–2.88 & 2.47–2.94 \\  & & & range & 0.44 & 0.61 & 0.60 & 0.42 & 0.47 \\ premolar & 101 & no & mean \(\pm\) sd & 2.51 \(\pm\) 1.24 & 2.64 \(\pm\) 1.42 & 2.87 \(\pm\) 1.21 & 2.70 \(\pm\) 1.23 & 2.68 \(\pm\) 1.28 \\  & & & CI & 2.26–2.75 & 2.36–2.92 & 2.48–2.95 & 2.46–2.94 & 2.47–2.90 \\  & & & range & 0.49 & 0.56 & 0.47 & 0.48 & 0.43 \\ \hline premolar & & & \(p\)-value & 0.758 & 0.506 & 0.522 & 0.435 & 0.757 \\ anterior & 52 & yes & mean \(\pm\) sd & 2.55 \(\pm\) 1.17 & 2.43 \(\pm\) 1.16 & 2.68 \(\pm\) 1.14 & 2.69 \(\pm\) 1.13 & 2.59 \(\pm\) 1.12 \\  & & & 95\% CI & 2.22–2.87 & 2.12–2.75 & 2.40–2.96 & 2.37–3.00 & 2.34–2.84 \\  & & & range & 0.55 & 0.63 & 0.56 & 0.63 & 0.50 \\ anterior & 52 & no & mean \(\pm\) sd & 2.52 \(\pm\) 1.36 & 2.42 \(\pm\) 1.11 & 2.71 \(\pm\) 1.09 & 2.62 \(\pm\) 1.15 & 2.57 \(\pm\) 1.18 \\  & & & 95\% CI & 2.14–2.90 & 2.13–2.76 & 2.41–3.01 & 2.31–2.94 & 2.34–2.81 \\  & & & range & 0.76 & 0.63 & 0.60 & 0.63 & 0.47 \\ \hline anterior & & & \(p\)-value & 0.648 & 0.834 & 0.727 & 0.559 & 0.916 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distance CEJ—alveolar bone crest in the matching pairs—related to tooth type; CEJ = cemento–enamel-junction; the table displays all measuring points of RCF and n-RCF teeth and the pooled data of the different tooth types with mean values, standard deviation and 95% confidence interval and the corresponding range of the values. RCF = root canal filling; sd = standard deviation; CI = confidence interval.
# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 2 Literature Review  
In-network storage plays a crucial role in Information-Centric Networking (ICN), significantly improving data delivery speeds compared to traditional internet caching mechanisms. Data is no longer dependent solely on the original content provider; rather, it is temporarily stored in nodes distributed near to users, reducing the need for extensive communication and storage resources. Caching stands as the primary feature of ICN; research continues to focus on refining caching techniques. Figure 4 shows the The progression historically.  
The idea of ICN was introduced in the Translating Relaying Internet Architecture integrating Active Directories (TRIAD) in 1999. Since then, name-based routing has evolved through many projects, such as the Data-Oriented Network Architecture (DONA). The major contribution was made by U.S.-funded initiatives like Named Data Networking (NDN) and MobilityFirst. Other notable efforts include COMET, PURSUIT, SAIL, GreenICN, 4 WARD, and ICN-2020 [13]. Amongst these strategies, Content-Centric Networking (CCN) [14] has appeared as a pivotal ICN model by employing Interest and Data packets for effective content fetching. ICN addresses many complexities in conventional IP networks, such as mobility, crowdedness, security, scalability, and adaptability.  
Information is distinguished by names and retrieved via obvious requests with a unique approach called pervasive caching. The outcome leads to the development of various caching  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Acronym** & **Definition** \\ \hline SARAC4N & Social and Resource-Aware Caching for Clustered CCN \\ CCN & Content-Centric Networking \\ CCAR & Caching Cost-Aware Routing \\ PCSBCC & Popularity-Based Cooperative Strategy for CCN \\ DLRUK & Dynamic LRU with Utility-Based Knowledge \\ IoT & Internet of Things \\ CSQR & Centrality-Based Social Queue Routing \\ CB-PC-DMM & Content-Based Priority-Centric Delay Minimization Mechanism \\ CCndnS & Centralized CCN with Degree-Aware Node Selection \\ LCD & Leave Copy Down \\ MCD & Most Common Data \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions of acronyms used in the paper.  
mechanisms such as cooperative caching, non-cooperative caching, on-path caching, and off-path caching. These strategies are generally divided into five main categories.
## 3 Development of ANN Model
### Data Selection for the Training and Validation Set  
One of the most important elements in developing a neural network model is selection of the dataset, which is divided into two subsets: training data and testing data. Both stability and precision of the neural network model depend on the training phase [38; 39]. In this research, a large experimental dataset of 159 specimens reported in the existing literature [9; 11; 14; 15; 22; 40; 41; 42; 43], was used for the training and validation of the ANN model. Almost 95% of the total data (151 specimens) was used for training and the remaining 5% (8 specimens) was used for validation. In this research, ten independent variableswere used as input parameters for the ANN models. These input parameters include two sectional properties: width (\(b\)) and depth (\(h\)), both in millimeters; three variables related to closed stirrup: width (\(b^{\prime}\)), depth (\(h^{\prime}\)), and spacing (\(s\)), all in millimeters; the concrete compressive strength (\(\hat{t}_{c}\)), in MPa; and four reinforcement related properties: yield strength of the longitudinal reinforcement (\(f_{yt}\)) in MPa, longitudinal reinforcement ratio (\(\rho_{l}\)) in percent, yield strength of transverse reinforcement (\(f_{yt}\)) in MPa, and transverse reinforcement ratio (\(\rho_{l}\)) in percent. The output variable was set to be the maximum torsional strength (\(T_{\max}\)) of the RC beams. The sectional and material details of training and validation datasets are given in Appendix A Table A1. Before learning, the raw data was preprocessed for more efficient training and a better outcome.
# An Experimental Study of Industrial Site and Shaft Pillar Mining at Jinggezhuang Coal Mine
###### Abstract  
Engineering site and shaft pillars are excavated to prolong the life of collieries and excavate more underground coal. The Jinggezhuang colliery (JGZ') is a resource-exhausted coal mine in eastern China. It was determined that the industrial site and shaft pillar of JGZ would be extracted in 2008. This study excavated an experimental panel to examine the effect of pillar excavation on surface buildings in complicated geological conditions. A new pillar design was proposed based on surface monitoring to increase the recovery ratio. To maintain the safety of the shaft and engineering facilities, panel 0091 was mined and surface deformation was monitored during the experiment. The deformation characteristics and parameters were obtained using a back analysis method. A new pillar was designed using the parameters measured from panel 0091. The design maintained the safety of the shaft but relaxed the restriction of the influence of constructions at the engineering site. The prediction results of the surface subsidence and the deformation of the main building were analyzed. The maximum subsidence of the surface was 7419 mm, but the surface subsidence of the shafts was less than 10 mm. The shafts were weakly influenced by the pillar excavation. The prediction results can be used as basic information for the monitoring and maintenance of buildings in the future. Using the new pillar design, 254 million tons of coal resources were mined. This study provides an engineering example and a reference for shaft pillar excavation in the future.  
mine subsidence; shaft pillar; protection pillar; probability integral method; deformation
## 4 Results
### Avatar Creation  
The criterion of _avatar creation_ examines in greater detail whether an application allows users to create or modify avatars and if so then to what extent (see Table 2). Three levels are distinguished: (1) not available, (2) selection of predefined avatars, and (3) modification of individual features. It is important to note that avatar creation is considered available only if it is integrated within the application itself, without requiring modifications or making an additional effort to implement it in the software.  
While Bodyswaps and Mursion do not offer avatar creation, five providers, including Polycular and Talespin, provide a limited selection of predefined characters. In Polycular, users can apply basic adjustments such as selecting the gender of their counterpart. In contrast, Talespin offers a more diverse range of options allowing users to choose not only between male and female characters but also to differentiate them by age, skin tone, and clothing style.  
Figure 5: Avatar style from (**left**) to (**right**): PIXOVR, 3 spin, and WondaVR.  
EngageVR, Virbela, and WondaVR offer avatar creation, allowing users to modify more individual features. In Virbela, users can only personalize their own character, whereas EngageVR and WondaVR also enable the creation of virtual counterparts. Consequently, the customization options vary significantly depending on the provider. In Virbela, users have extensive customization options for their avatars. The avatar creation process is structured into five adjustable categories: (1) body, (2) hair, (3) face, (4) clothing, and (5) accessories. Within the body category, users can modify both height and weight, as well as specific attributes such as hip width, chest size, abdominal shape, and muscle definition. Additionally, skin tone can be finely adjusted using a coordinate system, where the Y-axis ranges from cooler to warmer tones and the X-axis from darker to lighter shades. For hair, users can choose from various hairstyles and colours. Facial hair can be added or removed, and eyebrows as well as eyelashes can be customized. The face section allows for the selection of different head shapes, eye shapes and colours, nose structures, lip and mouth styles, ear shapes, and makeup options. Further refinements can be made using coordinate-based adjustment systems. Additionally, users are able to define the avatar's apparent age. Clothing customization follows a three-step process. Users can select from a variety of tops, pants or skirts, and shoes, with further options to modify individual garment colours. The available styles range from casual and sporty to formal and elegant. Finally, in the accessories section users can equip their avatar with various items, including hats, glasses, earrings, scarves, and belts, providing additional personalization options.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 2 BDS-3 Integrity Monitoring Architecture
### BDS-3 Integrity Monitoring Method  
The BDS-3 integrity monitoring method includes SAIM and ground integrity monitoring. Among them, SAIM can monitor the satellite clock performance deterioration and the signal anomaly at the satellite in orbit. Ground integrity monitoring can comprehensively monitor the prediction accuracy level, spatial signal error level, system integrity status, message integrity status and signal integrity status of the broadcast ephemeris with observation data from the monitoring network on the ground and the intersatellite link on the orbit. The BDS-3 integrity monitoring process is shown in Figure 1. This section provides a basis for the following integrity risk tree modeling, according to the BDS-3 integrity monitoring process.
# SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation
## 2 Materials and Methods
### Simplified Mask R-CNN for Rolled Rice Stubble Row Segmentation
#### 2.2.2 Optimization Method for SMR  
Since there was no region proposals correction part in the second stage of SMR, the quality of region proposals directly determines the segmentation quality. The region proposals are usually coarse, and they do not cover the target well. In order to improve the quality of the region proposals, it is necessary to analyze the process of generating the region proposals.  
The region proposals are transformed from the anchors, which are rectangular boxes distributed aimlessly over the input image. The detection process only detects the area covered by the anchors instead of traversing the whole image to improve the speed of the operation. During the prediction process, each anchor box is assigned a score, with higher scores indicating a higher probability of the presence of the target object. Then, the region proposals are transformed from the top 100 highest-scoring anchors by the following transformation formula:  
\[\begin{array}{l}x\ =\ t_{x}\ w_{a}+x_{a}\ \ \ w\ =e^{l_{w}}\ w_{a}\\ y\ =\ t_{y}\ h_{a}+y_{a}\ \ \ \ h\ =e^{l_{h}}\ h\end{array} \tag{1}\]  
\(e\) is a natural constant. \(x\) and \(y\), \(w\) and \(h\) indicate the coordinates of the center of the region proposals on the image and the width and height of the proposed region proposals. The \(t_{x}\) denotes the amount of variation in the x-coordinate of the center of the anchors predicted by the model; \(w_{a}\) indicate the width of the anchors; \(x_{a}\) indicate the x-coordinate of the center of the anchors, and so on for the meaning of the other variables. From Equation (1), it is clear that the size and position of the region proposals are related to the value of the model prediction and the size and position of the anchors.  
For a trained SMR model, the predicted value of the model cannot be changed, so the only way to improve the region proposals is to improve the anchors. For the five feature maps in SMR, each position on each feature map corresponds to a set of anchors, and the size and position of each set of anchors on the image is determined by the hyperparameters \(SCALE\), \(RATIO\), and \(STRIDE\). In fact, the value of \(STRIDE\) has been taken to be the most optimum, for the 5-stage feature maps P2-P6, \(STRIDE=\{4,8,16,32,64\}\) to ensure that the generated anchors are evenly distributed over the input images, while for \(SCALE\) and \(RATIO\), the area and aspect ratio of the anchors are controlled respectively, the effect of these three hyperparameters on the anchors is shown in part (a) of Figure 3. The key hyperparameters for improving the performance of segmentation were finally determined experimentally to be \(SCALE\), which is described in detail in Section 3.3.  
In addition, there is one detail that needs to be clarified here before the region proposals are output, they need to be subjected to NMS (Non-Maximum Suppression) [49] based on IoU to reduce the redundancy of the region proposals, and the IoU threshold is 0.5 in SMR.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 2 Requirement Analysis
### Current Requirements
#### 2.2.2 More Efficient Data Sharing  
The management regulation of special equipment inspection agencies [44] mentions that inspection agencies should realize real-time data sharing with the supervisor, but the actual situation does not meet such regulation. As mentioned by many researchers (e.g., [PERSON] [45]), the supervision system and the inspection system are not integrated in some provinces of China, and in other provinces, data sharing relies on independent databases, which leads to the delayed and high technical requirements of data sharing. In addition, the distrust between equipment users and inspection agencies, caused by inefficient data sharing, also deserves our attention. In practical terms, based on the current supervision system, equipment users cannot know the latest information about the inspection license of inspection agencies and the inspection technology being used, which makes them unable to fully trust the inspection results carried out by the inspection agencies.
## 1 Introduction
While previous literature estimates the future driving conditions from a receding horizon and then searches of an optimal control policy, the proposed methodology does not rely on the estimation of future driving conditions. Instead, the proposed methodology is based on the offline optimization of a previously covered driving cycle by dynamic programming, and then uses the obtained cost-to-go as an approximation for the cost-to-go in the actual route, which is a novelty with regards to previous works. Once the cost-to-go is estimated, the optimal power-split at every time-step of the real route can be computed by applying the one-step look-ahead roll out algorithm [31]. The simulation results obtained show near-optimal performance for the proposed strategy, sustaining the energy in the battery and, in terms of fuel consumption, showing a 1.9% increase with regards to the theoretical optimum, while the standard ECMS, with the same information as the rollout, shows an increase of 11%. According to the previous paragraph, the main novelties and contributions of the proposed strategy are:  
* The paper shows that the resemblance of the different loops covered by an urban bus in its route can be used to obtain a control strategy that minimizes its fuel consumption while fulfilling the restrictions on the energy in the battery. * The proposed strategy produces near-optimum results, improving the fuel consumption obtained with other algorithms in the literature and without relying on information about future driving conditions. * Unlike most of the available literature on the EMS of HEVs, the proposed strategy does not require the estimation of the future driving conditions and does not follow the ECMS approach for optimization. The paper is organized as follows: after this introduction in Section 1, the case study and the powertrain model used in the paper are presented in Section 2. Then, Section 3 introduces two standard algorithms (DP and ECMS) to be used as reference strategies for offline and online optimization in order to assess the performance of the proposed strategy. Section 4 analyzes the measured vehicle-speed profiles and the optimal controls from DP in order to find the main patterns. Section 5 describes the proposed algorithm based on the dynamic programming optimization of a reference loop previously registered and one-step look-ahead rollout. Finally, Sections 6 and 7 discuss the obtained results and highlight the main paper conclusions, respectively.