esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The Industry 4.0 revolution has affirmed the centrality of the human operator, proposing a new way to automate production processes based on human and robotic expertise synergy. The impact of new technologies, such as collaborative robots (co-bots), virtual reality (VR) and augmented reality (AR) [1], wearable devices, Internet of Things (IoT) and artificial vision [2, 3], allows the companies to streamline their processes, ensuring better quality and enhancing the flexibility of their production potential [4, 5]. From this perspective, the human-centered approach has once again been reaffirmed in the automotive sector in the era of electric vehicles [6]; as an example, the technological processes of battery pack assembly require several manual procedures, especially in the completion of electrical connections. The continuous evolution of production processes, not only in the automotive industry [7] but in the whole world of automation, together with the problem of employee turnover, has led to a progressive and ceaseless need to train novel operators on new techniques and to assess the quality of their job. The assembly and maintenance of industrial assets are complex tasks that require a large number of training hours in classrooms and hands-on sessions. Furthermore, especially in times of restrictions related to the pandemic, only a few trainees can access training on a physical asset at the same time, and their availability is always a trade-off with production line efficiency. Training all the operators of a plant on a piece of new equipment in a reasonable lead time is a huge challenge. In addition to the personnel training, one of the key factors of success in the new industry is the traceability of manual operations to ensure product quality [8]. Indeed, in most of the manufacturing processes, it is important to collect data from the manual assembly and maintenance interventions and to store these data in a centralized data management system for subsequent analysis of the performance of the plants [9]. In this scenario, the companies that can accurately understand workers' behaviors and evaluate their performance in real-time will outperform their competitors. Smart industrial workstations for training and evaluating the performance of the workers are drawing attention as an innovative approach to facing the problem. These systems are designed to guide a non-experienced operator in achieving the same level of precision and performance as the expert [10; 11]. In addition, they ensure that procedures are carried out in the right way while collecting anonymous real-time data that can be used to verify technical parameters and to improve the working efficiency where needed.
###### Abstract  
In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Thus, the prediction model is unreliable although the overall model accuracy can be acceptable. Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class. However, their effectiveness depends on several factors mainly related to data intrinsic characteristics, such as imbalance ratio, dataset size and dimensionality, overlapping between classes or borderline examples. In this work, the impact of these factors is analyzed through a comprehensive comparative study involving 40 datasets from different application areas. The objective is to obtain models for automatic selection of the best resampling strategy for any dataset based on its characteristics. These models allow us to check several factors simultaneously considering a wide range of values since they are induced from very varied datasets that cover a broad spectrum of conditions. This differs from most studies that focus on the individual analysis of the characteristics or cover a small range of values. In addition, the study encompasses both basic and advanced resampling strategies that are evaluated by means of eight different performance metrics, including new measures specifically designed for imbalanced data classification. The general nature of the proposal allows the choice of the most appropriate method regardless of the domain, avoiding the search for special purpose techniques that could be valid for the target data.  
imbalanced data classification; undersampling; oversampling; SMOTE; ROS; RUS; OSS; CNN; ENN; TL +
Footnote †: journal: Applied Statistics and Applied Statistics
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 4 Discussion
### Results from Indoor Short Distance Experiment  
For this experiment by using WiFi technology in indoor scenarios, we obtained \(\overline{\theta}\), \(\delta\), and jitter as a function of the distance and the traffic direction (uplink or downlink). Figure 10 shows the performance results obtained by using the D-ITG software. The results show a lower \(\overline{\theta}\) performance for TCP-NACK, but a better \(\delta\) and jitter performance when compared to Generic TCP.
## 4 Road Sensing
### Detecting and Monitoring Moving Vehicles
#### Accelerometers  
Accelerometers can sense vibrations of the road caused by passing vehicles. Recently they have been suggested as accurate sensors for detecting and classifying vehicles [66], with potential applications in WSNs [11]. Although their possibilities are yet under study, they are very promising sensors. They provide a signature where output peaks coincide with vehicles axles, which can be used to estimate the number of axles and the wheel-base of a vehicle, and, using this information, its speed (by means of a single sensor). In addition, it is possible to use accelerometers to estimate the lateral offset of vehicles thanks to the different attenuation suffered by the different frequencies [66]. The main weakness of these sensors is that very high sampling rates are required since the signal has a spectral bandwidth of a few KHz, therefore requesting that this sensor be used only on demand, when other less accurate and less energy consuming sensor notifies a potential detection.
## 5 Analysis of the Results  
During the measurements, 87,252 system loss values were collected with 40 ms interval between the consecutive measurements, for NB and UWB, for both motion scenarios and all three MN placements. It should be mentioned that the preliminary results, including the mean and the standard deviation system loss values are presented in [29; 30], hence, they are not analyzed in detail in this article. The corresponding RDM were collected simultaneously. It should be pointed out that, in this article, the propagation attenuation is understood as the system loss [31], i.e., the difference in power delivered to the transmitting antenna and the power available at the terminals of the receiving one.  
The preliminary analysis of the empirical system loss values for both frequency bands showed that the propagation model should include the estimation of the system loss separately for LOS and NLOS conditions, and separately for each MN placement. The results of the distance estimation by RDM indicated the significant differences in the radio wave propagation across the environment. In this environment the waves propagate along the corridor due to the metal structure and the waveguide effect, unlike in the typical indoor environments, where the radio waves can propagate along the direct line (even through the walls) between MN and RN [29; 30].  
The system loss (\(L_{\text{SL(dB)}}\)) is modeled by the general following expression:  
\[L_{\text{SL(dB)}}=\overline{L_{\text{S(dB)}}}+\Delta L_{\text{BS(dB)}}+\Delta L _{\text{MF(dB)}}, \tag{1}\]  
where:  
* \(\overline{L_{S}}\) is the MSL component,
* \(\Delta L_{\text{BS}}\) is the body shadowing component represented by a random variable, and
* \(\Delta L_{\text{MF}}\) is the multipath component represented by a random variable.
## 6 Conclusions and Future Work  
In this paper, we have presented two graph models to represent sharing among files in a deduplicated storage system, and have illustrated and evaluated these two graph models with two real-world applications. One application solved the problem of partitioning a deduplicated set of files into multiple deduplicated subsets with minimal deduplication loss (chunk replication between subsets). The other application measures the actual size of a subset of all files in a deduplicated storage system. Our contributions can be summarized as follows:  
* We introduced two novel graph model to represent deduplicated data in a deduplicated storage system. The first graph model represents files as vertices, and sharing between files as edges. This model produces sparse, low memory footprint graphs, a key enabler for efficient graph processing. The second graph model represents files and shared chunks as vertices, and chunk ownership as edges between files and shared chunks. This model produces dense graphs which are resilient to various data manipulation operations in deduplicated storage systems.
* Based on the first graph model, we have designed and evaluated a novel algorithm to partition a deduplication domain based on graph partitioning. This algorithm places the chunks of the same file in the same deduplication domain. For real-world traces, the partition cost (defined as the amount of replicated data over the total amount of removable duplicate data) is less than 6% even when the data is partitioned into 15 domains.
* For the second graph model, we developed optimization methods to mitigate the problem of high memory footprint with various techniques, which proved to be efficient for two real-world traces. In particular, after applying these optimization techniques, the memory footprint can drop to below 10% of the original memory used by _DCSG_ graphs with no optimization.
* Based on the second graph model, we developed an efficient method to calculate the accurate size of a subset of files from a deduplicated storage system. For the same subset of files, our optimized graph-based algorithm (_GraphOpt_) has an elapsed time less than 1% of what can be achieved with current methods that access deduplication metadata in a database.  
In addition to the partitioning and size-calculation use cases, the graph models and related graph-based algorithms can be applied to other use-cases such as (1) to prevent failure propagation with a minimal deduplication loss and (2) migration of deduplicated files in a tiered-storage environment [24]. We are currently working on these practical problems with the help of expressive graph models and related graph-based algorithms.
## 4 Results and Discussions
### High-Intensity Laguerre-Gaussian Beam
Figure 11: The radial distributions of the normalized light field intensities of focal spots in the nominal focal plane for incident Laguerre-Gaussian beams of LG\({}_{1}^{2}\) mode with three different light field intensities, \(E_{0}=1\times 10^{8}\) V/m, \(5\times 10^{8}\) V/m and \(1\times 10^{9}\) V/m, respectively. Figure 10: (**a**) The normalized cross-sectional light field distribution of the incident Laguerre-Gaussian beam (LG\({}_{1}^{2}\)) in the lens plane. The three field patterns of focal spots correspond to the incident Laguerre-Gaussian beams with different light field intensities: (**b**) \(E_{0}=1\times 10^{8}\) V/m, (**c**) \(E_{0}=5\times 10^{8}\) V/m, and (**d**) \(E_{0}=1\times 10^{9}\) V/m.
## 5 Experimental Result
### Testbed
#### 5.1.2 Analog Input  
The power and performance monitoring activity of the pipeline has two requirements: (i) Measure the current sumk by the system-on-chip at any time of the data acquisition and processing and (ii) precisely split the current waveform into each pipeline stage, to detect the analysis stages that are the more power hungry or require most MCU cycles to be accomplished. Both requirements are met by using a PXIe-4309 ADC device reading 2 M Samples per second and featuring 32 channels 18 bit wide. We used two of the available input channels to perform the following synchronous activities:  
* Sample the current sunk by the MCU using a 1 Ohm shunt resistor. Such measurement exploits a jumper available on the development board to monitor current consumption on the VDD of the system-on-chip.
* Read the logic level of a debug GPIO (DBG pin, Figure 8) that is toggled by the application software each time a processing stage is started or completed, to associate each phase (e.g., SPI transfer, FFT computation, SNN inference) to its current consumption waveform (with negligible overhead on the application performance).
## 3 Framework Design and Deployment
### FL with Secure Aggregation  
FL enables collaborative model training while keeping data distributed on edge devices such that \(w_{t+1}=w_{t}+\eta\sum_{k=1}^{K}\Delta w_{t}^{k}\), where \(w_{t}\), \(\Delta w_{t}^{k}\), and \(\eta\) represent the model weights at iteration \(t\), updates from the \(k^{th}\) device, and the learning rate, respectively. Here, the local model updates are aggregated from multiple devices to create a new global model (\(w_{t+1}\)).
## 3 Model Description
### Motion Information Detection Channel
#### 3.1.1 Omnatidia  
As illustrated in Figure 1b, ommatidia are crucial components of the pathway for detecting motion information [57; 58] and function as sensory receptors to detect the brightness value within the actual visual environment. In addition, they are designed with Gaussian filters for processing the information with Gaussian blurring. Mathematically, we define a function \(I(x,y,t)\) on the real number domain to describe the light stimuli received by the ommatidia neurons. The definition of ommatidia responses outputs \(P(x,y,t)\) as shown below:  
\[P(x,y,t)=\iint I(u,v,t)G_{\sigma_{1}}(x-u,y-v)dudv, \tag{1}\]  
where  
\[G_{\sigma_{1}}(x,y)=\frac{1}{2\pi\sigma_{1}^{2}}e^{\frac{-(x^{2}+y^{2})}{2 \sigma_{1}^{2}}} \tag{2}\]  
is a Gaussian kernel, and \(\sigma_{1}\) denotes the standard variance.
# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
## 5 Conclusions  
In this paper, an artificial intelligence model for cooling load forecasting is developed using the LSTM algorithm at the first step and its reliability is illustrated. Based on the TOU tariff, a peak shifting control strategy with two optional modes which combine temperature setpoint control and operation mode control is then proposed to realize peak shifting as well as cost reductions. With the proposed strategy, the feasibility and applicability are evaluated by taking the actual operating data of five consecutive working days. Finally, the peak shifting effect and economy of the proposed strategy are demonstrated by comparing the ratio of the cooling capacity provided in different periods and the cost reduction rate. The detailed conclusions are as follows:  
1. The cooling load of the cold store has a significant periodicity during continuous working days, and therefore LSTM is suitable for load prediction. The results are of good accuracy and have engineering practicality.
2. Mode A is a complete shifting strategy, which means the entire day's cooling load is met during the valley period. As a consequence, this mode offers a better economy, with an average cost reduction rate of approximately 40%. Nevertheless, this mode may result in larger temperature fluctuations and require a higher capacity of the compressor unit which brings much more initial investment.
3. Mode B is a load-balancing strategy, which means the cooling load throughout the day is assigned to multiple appropriate periods to be met. Compared to Mode A, the average cost reduction rate for this mode is only 13.4%. However, this model causes smaller temperature fluctuations and does not require a higher capacity of the compressor unit.  
Conceptualization, C.W. and Z.X.; Data curation, D.R. and D.W.; Formal analysis, D.W.; Investigation, Y.L. and C.W.; Methodology, Z.L. and H.W.; Software, Y.L.; Supervision, Z.L. and Z.X.; Validation, D.R.; Writing--original draft, Y.L.; Writing--review and editing, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51976148. The authors gratefully acknowledge the financial support from the China Scholarship Council (No. 202306280272).  
Not applicable.  
The data that support the findings of this study are not available for privacy reasons.
## 6 Discussion  
We conducted an experiment comparing the educational effect and experience of the proposed system with those of the video method. Two tests were conducted to evaluate the educational effectiveness and participants' scores in each group were averaged and compared. In the two tests, the results of the proposed system group were 9.5 and 15.25 points higher than those of the video group, respectively. When the difference in scores between the post-test and retention-test was compared by group, we found that the proposed system group scored 6 points higher. This indicates that the proposed system group retained more knowledge than the video group. These results prove that the proposed system is more suitable than video training in terms of knowledge acquisition and retention in technical training.  
In addition, subjects who participated in the proposed system training evaluated the presence and system usability. The SUS score was 77.6, which corresponds to \"Good\" according to the adjective rating scale. Therefore, the proposed system was judged to be suitable for use. In the sense of presence evaluation, all items scored above the average out of six points. SP (M = 5.08, SD = 0.417) and G (M = 4.95, SD = 0.759) presence exhibited the highest scores. Therefore, it was determined that the participants received the feeling of presence in a virtual space through the proposed system. According to the two evaluation results, it is determined that the proposed system is more appropriate for learning than the video method in technical training, such as aircraft maintenance.
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
###### Abstract  
The increasing number of floods and the severity of their consequences, which is caused by phenomena, such as climate change and uncontrolled urbanization, create a growing need to develop operational procedures and tools for accurate and timely flood mapping and management. Synthetic Aperture Radar (SAR), with its day, night, and cloud-penetrating capacity, has proven to be a very useful source of information during calibration of hydrodynamic models considered indispensable tools for near real-time flood forecasting and monitoring. The paper begins with the analysis of radar signatures of temporal series of SAR data, by exploiting the short revisit time of the images that are provided by the Cosmo-SkyMed constellation of four satellites, in combination with a Digital Elevation Model for the extraction of flood extent and spatially distributed water depth in a flat area with complex topography during a flood event. These SAR-based hazard maps were then used to perform a bi-dimensional hydraulic model calibration on the November 2010 flood event at the mouth of the Bradano River in Basilicata, Italy. Once the best fit between flood predictions of hydrodynamic models was identified and the efficacy of SAR data in correcting hydrodynamic inconsistencies with regard to reliable assessment of flood extent and water-depth maps was shown by validation with the December 2013 Bradano River event. Based on calibration and validation results, the paper aims to show how the combination of the time series of Synthetic Aperture Radar (SAR) and Digital Elevation Model (DEM) derived water-depth maps with the data from the hydrodynamic model can provide valuable information for flood dynamics monitoring in a flat area with complex topography. Future research should focus on the integration and implementation of the semi-automatic proposed method in an operational system for near real-time flood management.  
SAR; flood risk management; Cosmo-SkyMed; hydraulic modeling; digital elevation model; calibration; flood monitoring 2018 7 105 12 March 2018 29 January 2018; Accepted: 12 March 2018; Published: 14 March 2018
## 1 Introduction
### Flaw in Security Proof in Previous Research Literature
's assertion [12] is that _because of the soundness of the NIZK proof system, there exists one query \((A_{1}^{*},A_{2},B_{1}^{*},B_{2},\pi^{*})\) (with overwhelming probability) such that \(\pi^{*}\) is valid among the queriedinputs_\(\{(A_{1}^{*},\star,B_{1}^{*},\star,\pi^{*})\}\)_, in which case the entry_ \(A_{2}\) _is the solution to the given CDH instance._ Thus, if their assertion was true, the adversary who succeeds in breaking the CCA-security of the variant must make the query, including the CDH solution, to \(H_{2}\); otherwise, the adversary has no information on the challenge key. However, we show that their assertion is not true for the following reasons. Let \(\mathsf{CT}^{*}=(A_{1}^{*},\pi^{*}=(c^{*},s^{*}))\) and \(\mathsf{key}^{*}\) be the challenge ciphertext and key, respectively. First, the adversary generates the value \(B_{1}^{*}\) by computing \(g^{s^{*}}(A_{1}^{*})^{-c^{*}}\), which is the same as the value that the simulator calculated. Next, the adversary can generate the values \(A_{2}\) and \(B_{2}\) as follows: pick a random \(R\in\mathbb{Z}_{p}\), and set \(A_{2}=h^{R}\) and \(B_{2}=h^{s^{*}}(A_{2})^{-c^{*}}\). Finally, the adversary issues \((A_{1}^{*},A_{2},B_{1}^{*},B_{2},\pi^{*})\) to the \(H_{2}\) query. We can see that \(\pi^{*}=(c^{*},s^{*})\) is valid with respect to the _false_ statement \((A_{1}^{*},A_{2})\) and the relevant commitment \((B_{1}^{*},B_{2})\); furthermore, polynomially-many such simulated queries can be made after \(\mathsf{CT}^{*}\) and \(\mathsf{key}^{*}\) are given to the adversary. If the adversary issues the correct query such that \(A_{2}=h^{a}\) among the polynomially-many simulated queries, the simulator cannot specify which query relates to the correct statement \((A_{1}^{*}=g^{a},A_{2}=h^{a})\) associated with the CDH solution, unless it is given an oracle to solve the decisional Diffie-Hellman (DDH) problem. Moreover, the implicitly predetermined challenge key \(\mathsf{key}^{*}\) will be mapped to a certain wrong \(H_{2}\) query input \((A_{1}^{*},A_{2}^{\prime},B_{1}^{*},B_{2}^{\prime},\pi^{*})\) with \"high\" probability. Clearly, such inconsistent mapping will allow the adversary to distinguish between a real attack and a simulation.