esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of the Proposed Methodology Under Uncertainty
### Criteria and Alternatives  
Being a local and/or global stakeholder in the supply chain network can affect all stages. In addition, determining the location correctly can affect the success and investment life of all stages, from the design of the facilities to be invested to the delivery process. Therefore, the analysis of cities is important in the first stage of determining the potential for the logistics centers and analyzing the current situations. The ten main criteria and sixty-six sub-criteria are determined for this particular purpose, and details are provided below. In addition, the evaluations performed in this section were compiled from expert opinions, academic studies and ministry official statistical reports [50; 51; 52]. Please see Figure 2 for alternatives. **C1. Economic. ** While supply chain in its traditional definition means transmitting a product or service from the manufacturer to the end customer, in modern terms, it is important to offer these products at an affordable cost by improving the living standards of society. The economic dimension is vital in the supply chain. The existence of increased competition, possible unpredictable financial changes and volatility in prices and costs may affect the investment environment and the time value of money and lead to uncertainty. This may cause costs to increase and control to become difficult. **C2. Safety. ** The report published by the International Labor Organization (ILO) estimates that more than 2.7 million workers die every year from work accidents and occupational diseases. Additionally, the report states that more than 160 million employees lost their health due to workplace risks [53]. It is stated that the economic dimension of this social problem is more than 4% of the global GDP. Reducing and eliminating occupational diseases can be achieved by reducing the hazards in the workplace and by knowing and preventing the hazards that may arise from work processes, operations and equipment. Additionally, road traffic accidents kill approximately 1.2 million people every year, with more than 90% of deaths occurring in low- and middle-income countries [54]. **C3. Hazards risk. ** As the global supply chain expands and becomes more connected, disrupting a particular part of the network could lead to significant shortages that disrupt the supply network. With the concern of reducing costs, the network spread over a wide area has become interdependent, and unforeseen delays can cause severe disruptions and fluctuations in economies [55]. From this perspective, identifying potential critical risks and mitigating risks in order to reduce exposure to assets is essential for business continuity [56]. Risks such as earthquakes, floods, pandemics, landslides, forest fires, etc., are considered minor but increasing and frequently encountered risks in this logistics network.
## 4 Results
### Pilot Group 1
#### 4.1.1 Pilot Group 1 Usage of the Different Worlds  
To analyze use ('time of usage' and 'number of activities') among the classes' groups in Pilot Group 1, all descriptive statistics were calculated by aggrupation into Special Needs classes, 1 st Cycle and 2 nd Cycle classes (Table 11). It was detected how primary education 1 st Cycle learners were more engaged users, with more time spent working with the different materials of the platform. It is important to mention that, in the Pilot Group 1, classes grouped in 1 st Cycle were more numerous than groups of 2 nd Cycle or Special Needs. No significant differences were found when comparing between averages of activities done in worlds by 1 st and 2 nd Cycles. A possible explanation is that 1 st Cycle students would need more time to complete the suggested activities. One exception to this is that 1 st Cycle learners showed to be the most dedicated group at the Multiplayer world, while the highest use in both factors, 'time of usage' and 'number of activities', was achieved by 2 nd Cycle classes. If we  
Figure 3: Pilot Group 1’s use of worlds (Arts, Emotion, Logic, Science, Literacy, Spatial, Multiplayer) at Smile and Learn’s platform. (**a**) General time of usage (seconds) with the platform for Pilot Group 1; (**b**) General number of activities played by Pilot Group 1 with the platform.  
focus on 'number of activities', Science world activities were in first position (mean: 77.51), however the world with the maximum of activities played was Logic (number: 802). The less used world at Pilot 1 was Emotions, with its highest average reaching 11.39 'number of activities' and 2063.91 seconds as 'time of usage'--results from 1 st Cycle population.  
In order to compare whether there were any major differences among groups in Pilot 1, a one-way ANOVA analysis was run. At first place, results obtained from 'time of usage' of worlds in the platform were significant in those of Logic, F(2) = 3.167, \(p\) = 0.044 (\(<\) 0.05) and Science F(2) = 3.248, \(p\) = 0.041 (\(<\) 0.05), as represented in Figure 4. This can be explained by the huge usage of such worlds in the 1 st Cycle of elementary education. Nonetheless, no significance was found among all other worlds.  
In relation to the Multiplayer world, comparing the 'number of activities' done by class groups via one-way ANOVA, a significant effect is observed in this world world: F(2) = 3.329, \(p\) = 0.039 (\(<\) 0.05) (Figure 5). Results associate with the explanation followed through descriptive statistics, outlining 2 nd Cycle in Pilot 1 as the one most involved with the Multiplayer world. No significance between other groups and worlds in 'number of activities' was found.  
Figure 4: Descriptive statistic representation of significant results with one-way ANOVA. (**a**) Figure showing major results for ‘time of usage’ among class groups in the world Logic. (**b**) Figure showing major results for ‘time of usage’ among class groups in the world Science.
## 6 Conclusions  
In this paper, we designed the Kronecker-product-based frame with low mutual coherence and a small number of phases, which is advantageous in practical applications. Utilizing the block-wise circulant feature of the Gram matrix, we derived a novel objective function to optimize the mutual coherence with low computational complexity. To find a subsampling index set for the Kronecker-product-based frame using the objective function, we used Algorithm 2 in [41] by modifying the optimization problem for the Kronecker-product-based frame. Simulation results show that the optimized Kronecker-product-based frames have similar mutual coherence as the optimized harmonic frames, although ours have a smaller number of phases. In particular, we obtained the ETFs in \(N=64\), \(m=28\) for \(q=4,8\). To the best of our knowledge, these ETFs are new ones that have never been reported. These ETFs with a small number of phases can be easily generated by the Kronecker product of Fourier and Hadamard matrices, which is suitable for practical applications. Finally, we applied the Kronecker-product-based frames for sparse signal recovery in compressed sensing. We demonstrated that the optimized Kronecker-product-based frames achieve similar performances of success rate in support set recovery and MSE of the optimized harmonic frames.  
The novelty of our work is to present an efficient objective function to optimize mutual coherence with low complexity for the Kronecker product of Fourier and Hadamard matrices. Furthermore, the algorithmic method for frame design allows us to obtain the frames with arbitrary frame vector length \(m\). In conclusion, the Kronecker-product-based frames designed by a novel objective function can be suitable for practical applications thanks to the low mutual coherence and a small number of phases. Applying our designed frames for real-life applications will remain for future work.  
Conceptualization, J.K. and N.Y.Y.; methodology, N.Y.Y.; software, J.K.; validation, J.K.; formal analysis, J.K.; investigation, J.K.; resources, N.Y.Y.; data curation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, N.Y.Y.; supervision, N.Y.Y.; project administration, N.Y.Y.; funding acquisition, N.Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Research Foundation of Korea (NRF) Grant through the Korea Government (MSIT) under Grant NRF-2021R1F1A1046282 and in part by the GIST Research Project grant funded by the GIST in 2022.  
Not applicable.  
Figure 5: The distribution of average magnitude of inner products for 30-frame vectors randomly selected from the optimized harmonic and Kronecker-product-based frames, where \(N=256\) and \(m=64\).
# Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service
## 4 Results and Discussion  
To analyze the practical applicability of the BDS PPP-B2b service in water vapor monitoring, ZTD and PWV retrieved from the monitoring system for a total of 25 days from day-of-year (DOY) 076 to 100 in 2025 are analyzed in this section. Firstly, the consistency of NRT processing mode and post-processing mode for ZTD estimation are firstly verified. Subsequently, to analyze the performance of the PPP-B2b service, ZTD estimated from two RT ephemeris (i.e., CAS and PPP-B2b) based on NRT processing mode are evaluated through comparison with post-processed ZTD. Finally, the accuracy of PWV from the monitoring system is assessed with that from ERA5 reanalysis to prove the feasibility of PPP-B2b service in water vapor monitoring.
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 4 Results
### QoE Quantization Performance
#### 4.4.2 Loss Rate  
The sensitivity of QoE to packet loss rate (LR) is  
\[\Psi_{LR}=\frac{\partial QoE}{\partial LR}=\frac{\alpha\beta c}{(LR+d)^{2}} \cdot\exp(-\beta\cdot QoS) \tag{17}\]  
Figure 4: Effects of the packet time delay on QoE attenuating.  
Here, we assume that LR is between 0 and 50%, the QoE changing slope values in Figure 5 can be described as \(\left\{|\Psi_{LR}|(\text{MV}),|\Psi_{LR}|(\text{OV})\right\}>\left\{|\Psi_{LR}|( \text{FS}),|\Psi_{LR}|(\text{LO}),|\Psi_{LR}|(\text{LV}),|\Psi_{LR}|(\text{VC}) \right\}>\left\{|\Psi_{LR}|(\text{WB}),|\Psi_{LR}|(\text{OA}),|\Psi_{LR}|(\text{ LA}),|\Psi_{LR}|(\text{OG})\right\}\). Further, we can draw the following conclusions.  
1. The values of all the curves are less than 0 in the entire packet loss rate analysis domain, which indicates that the increasing packet loss rate has a negative effect on all QoEs of the data service, which will lead to the attenuation of QoE.
2. For most types of data services, in the range of \([0,10\%]\), a slight increase in the packet loss rate leads to a sharp drop in QoE. When the packet loss rate reaches to a relative high level, the impact on QoE will be less obvious than it in the range of \(0-20\%\), which is mainly reflected in the range of 20 to 30%.
3. From high to low order, it can be seen that the more data to be transmitted, the less dependable a certain kind of service is. As example, live audio requires less data flow due to its high compression rate.
## 3 The GNSS Jamming as a Terrorist Attack on USVs  
Jamming masks and blurs satellite signals with more powerful interference. Thus, the blurry receiver can no longer position itself, or it can position itself erroneously. Although the use of jamming is prohibited, low-power jamming is often used by citizens concerned with protecting their privacy. Drivers who do not want to be monitored also use jamming.  
In addition, terrorist use of jamming cannot be ruled out. Powerful jamming systems for GNSS have already been observed in various conflict zones.  
For example, the US Navy accidentally disabled GPS for three days at the Port of San Diego in January 2007 [3]. Additional examples are given in [4; 5]. A GNSS jamming for USVs (Figure 3) is intentional interference that emits electromagnetic signals at GNSS frequencies in order to jam GNSS signals. Military silencers are widely known, but so-called Personal Protective Devices (PPDs) are becoming more common in everyday practice. PPDs are small, lightweight jammers that are readily available online.  
The problem of jamming detection is relatively easy to solve. A detailed explanation of this issue is presented in [6]. Among the many technologies for partial or complete suppression of jamming, we note [7] (Figure 4), which can be bought for under $20.  
Interference signals are sent to a spectrum analyzer and an attenuator (VA) is added to the GPS simulator and GNSS signals. VA scales the interference factor.  
Figure 1: Main known sources of GNSS interference [2].  
Figure 2: The signal energy decreases with distance from the satellite.
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
## 1 Introduction
Finally, stepper motor controllers were implemented in syringe mechanisms for hemodialysis systems in [10], demonstrating high accuracy but lacking validation with real peristaltic pumps. State-space modeling and advanced techniques such as Adaptive Network-Based Fuzzy Inference Systems (ANFIS) have also been applied to improve system identification and fluid regulation. In [4], state-space models outperformed ARX and ARMAX methods for peristaltic pump identification, while [11] demonstrated that ANFIS controllers achieved systematic errors as low as 1 mL. Despite these advancements, many studies relied on generic test fluids or simulated conditions, underscoring the need for validation under realistic hemodialysis scenarios. Building upon this body of research, the present study addresses these limitations by focusing on the identification, characterization, and control of a real peristaltic pump integrated into the blood circuit of a hemodialysis machine. Unlike prior studies, this work employs synthetic blood to replicate the viscosity and behavior of real blood, ensuring realistic testing conditions. Tubing and filters specifically designed for hemodialysis are used to reflect actual clinical conditions, while a P.I. controller is implemented to regulate roller speed and minimize flow variability. The pump's transfer function was identified using the non-linear least squares method, and a redundant mass and volume measurement system was used to ensure comprehensive characterization and validation. These measures allow for precise control implementation and reliable fluid delivery, addressing the challenges outlined in related works and improving upon their limitations. The structure of this article is organized as follows: Section 2 outlines the methodology used in the study. Section 3 presents a comprehensive analysis of the results. In Section 4, the obtained results are discussed in detail. Finally, Section 5 summarizes the conclusions derived from the analysis.
# Multi-Descriptor Random Sampling for Patch-Based Face Recognition
## 4 Conclusions  
This paper has proposed a novel face recognition algorithm using the concept of random patching. The method operates by dividing the face images into a number of non-overlapping patches. Next, LBP operator is employed as a local descriptor and then combined with HOG technique to extract a concatenated descriptor of the image patches. A dimensionality reduction step using KPCA method is then applied to the inherent high dimensional descriptors. Once done, a random patch sampling operation  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Occlusion \%** & **10** & **20** & **30** & **40** & **50** \\ \hline \multirow{2}{*}{subset3} & SSR-P [36] & 100 & 100 & 100 & 97.8 & 85.4 \\  & Our Method & 94.26 & 94.53 & 94.57 & 94.89 & 90.78 \\ \hline \multirow{2}{*}{subset4} & SSR-W [36] & 99.8 & 99.4 & 99.4 & 99.6 & 98.1 \\  & Our Method & 87.04 & 85.52 & 90.90 & 89.96 & 90.29 \\ \hline \multirow{2}{*}{subset5} & SSR-W [36] & 98.0 & 97.3 & 95.8 & 95.4 & 88.6 \\  & Our Method & 89.38 & 86.53 & 90.54 & 90.02 & 90.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of the proposed approach using the Extended Yale B Dataset.  
Figure 15: Results for each Extended Yale B dataset subset averaged over the different levels of occlusion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Occlusion \%** & **60** & **70** & **80** \\ \hline subset3 & 90.07 & 89.17 & 90.08 \\ \hline subset4 & 89.03 & 93.85 & 89.85 \\ \hline subset5 & 90.58 & 86.66 & 86.44 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Further Results on the Extended Yale B Dataset.  
is employed allowing us to build a number of sub-SVM classifiers. Finally, the results from the classification obtained from the SVMs are fused using a simple union rule. The experiments carried out suggest that the proposed algorithm performs favourably when compared against conventional global SVM face classifiers when the lower part of the face is missing (up to 98.5%). Furthermore, the algorithm outperforms other similar state-of-the-art techniques, thus clearly demonstrating its potential recognition performances, even when working in an under-sampled and challenging operational environment.  
Conceptualisation, I.C. and A.B. ([PERSON]); methodology, I.C.; formal analysis, I.C.; implementation, I.C.; writing-original draft preparation, I.C.; writing-review and editing, I.C., A.B. ([PERSON]); supervision, A.B. ([PERSON]), N.A.-M. and R.J.; funding acquisition, N.A.-M., A.B. ([PERSON]) and A.B. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by NPRP grant # NPR 8-140-2-065 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Strategies for Upgrading the Dielectric Strength Prediction Protocol  
The PIM protocol was further developed using three distinct strategies to improve the prediction accuracy for the dielectric strengths of 168 PFC and non-PFC compounds. These development processes involved the reparameterization of PIM (strategy I), incorporation of a novel factor into PIM (strategy II), and appropriately combining highly correlated factors into an equation-based protocol (strategy III).
# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 2 Materials and Methods
### Wise IOU Loss  
The original YOLOv7 suffers from issues with its bounding box regression loss function, CIOU. These issues include the inability to simultaneously increase or decrease the width and height of bounding boxes during parameter updates, as well as the lack of balance between high and low-quality samples. In the training data, low-quality samples hinder the generalization of the object detection model, while high-quality samples dominate and overshadow the influence of lower-quality ones. As a result, the convergence speed of the loss function is slow, leading to a decrease in detection accuracy. To address these issues, this paper utilizes the Wise IOU Loss function, which is based on a dynamic non-monotonic focusing mechanism, for the enhanced method, replacing the CIOU loss function. The Wise IOU Loss function v3 [26] (hereafter referred to as WIOU) is defined as shown in Equation (9):  
\[L_{WIOUv3}=\frac{\beta L_{IOU}}{\delta\alpha^{\beta-\delta}}exp\left(\frac{ \left(x-x_{gt}\right)^{2}+\left(y-y_{gt}\right)^{2}}{\left(W_{g}^{2}+H_{g}^{2} \right)^{s}}\right) \tag{9}\]  
Here, \(\beta=\frac{L_{IOU}^{*}}{L_{IOU}}\in(0,+\infty)\), \(L_{IOU}=1-IOU\), and \(IOU\) is the ratio of the intersection of the prediction box and the ground truth box. \(\overline{L_{IOU}}\) is the sliding average with momentum m; the superscript * indicates that it is separated from the computational graph, i.e., not involved in backpropagation and weight updating. The \(\alpha\) and \(\delta\) are the given hyperparameter, which is taken as \(\alpha=1.9\), \(\delta=3\) in this paper. The \(x\) and \(y\) are the coordinates of the prediction box, while \(x_{gt}\) and \(y_{gt}\) are the coordinates of the ground truth box, \(W_{g}\) and \(H_{g}\) is the width and length of the outer rectangular box at the intersection of the prediction box and the ground truth box. The quality of the anchor boxes is evaluated using the outlier \(\beta\) instead of \(IOU\), and a gradient gain assignment strategy is provided. This strategy reduces the competitiveness of high-quality anchor boxes while reducing the detrimental gradients generated by low-quality examples. This allows WIOU to focus on average-quality anchor boxes and improves the method's generalization ability and overall performance.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 3 Methods
### Download and Preprocessing of Sentinel-1 Data  
The first block of the AUTOWADE tool performs the operations of searching, downloading, and unzipping S1 L1 GRD data by using a script in Python language, as shown in the left side of Figure 4. It takes advantage of the freely available Sentinelsat tool ([[https://pypi.org/project/sentinelsat/](https://pypi.org/project/sentinelsat/)]([https://pypi.org/project/sentinelsat/](https://pypi.org/project/sentinelsat/)), accessed on 31 March 2021) that enables users to automatically search and download Sentinel-1/2/3/5p data. For the script, the following Sentinelsat inputs are used: (1) URL of the Copernicus Open Access Hub; (2) start date; (3) product type (GRD in this case); (4) Search polygon in GeoJSON format (corresponding to Italy in this case).  
The downloading script is scheduled to run every hour (365 days-a-year) to continuously verify the availability of new S1 images; in this way, possible delays in the production and delivery of L1 GRD products are managed.  
The preprocessing of S1 data is schematized in the right side of Figure 4; it is performed using the SARscape COTS software, available as a tool of the IDL/ENVI commercial software package. In particular, a SARscape batch script in IDL language has been created; it is scheduled to be executed twice a day (at 02:00 AM for the afternoon passes of the previous day and 06:00 PM for morning passes) 365 days-a-year, provided that new GRD products are available. The S1 data are initially imported into SARscape and the slices belonging to the same orbit are mosaicked. Then, the data are radiometrically calibrated (backscattering coefficient \(\sigma^{0}\)) according to the radar equation. The calibration involves correction for the scattering area (each output pixel is normalized for the real illuminated area of each resolution cell), the antenna gains pattern, and the sensor-to-ground distance variation from near range to far range. Moreover, a correction factor based on a modified cosine model [36] is applied to the backscattering coefficient to compensate for range variations.  
Calibrated data are geocoded using the SRTM DEM introduced in Section 2.2; a pixel size of 15 \(\times\) 15 m\({}^{2}\) has been chosen for the geocoded data. To reduce the speckle noise characteristic of any SAR image, the Enhanced Lee Filter [37] with a window size of 5 \(\times\) 5 pixels is applied. Finally, the backscattering data are converted in dB units. An example of the output of the preprocessing phase is shown in Figure 5.  
Figure 4: Block diagram of the script for S1 data downloading and preprocessing implemented in AUTOWADE.  
In order to distinguish between floodwater and permanent water, AUTOWADE adopts a change detection approach (Section 3.2.2) that analyzes pairs of S1 images acquired from the same orbit; to this aim, the preprocessing step creates a data archive where calibrated, geocoded, and filtered data acquired from different orbits are distinguished.