esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Interpretation of Error Estimation Analysis  
The following table (Table 1) shows the calculated standard error values between the PDSI and vegetation parameters during 2015-2019 that were under consideration. The results showed that the estimated errors ranged from 70.10 to 105.98 between GPP and PDSI, 11.23 to 18.01 between LAI and PDSI, 9.07 to 18.11 between fAPAR and PDSI, 0.18 to 0.26 between NDVI and PDSI, and 0.07 to 0.12 between EVI and PDSI. EVI continuously managed to show the least estimated standard error values for all five years, clearly indicating the highest level of long-term sensitivity compared to the other parameters. The low error values between EVI and PDSI showed the most accurate negative agreement in all five years and, also, better represented the sensitivity compared to the NDVI, LAI, GPP, and fAPAR.  
Figure 3: Correlation of the vegetation parameters with the PDSI in the years 2015 (**a**), 2016 (**b**), 2017 (**c**), 2018 (**d**), and 2019 (**e**). Significance codes 0 *** 0.001 *** 0.01 *** 0.05 *** 0.1 *** 1.
## 3 Results and Analysis
### Disc Cutter Breakage Analysis  
#### 3.2.1 Morphological Characteristics of Tool Breakage  
The breakage morphologies of the disc cutter are shown in Figure 15. Figure 15a,b show the chipping and cracking breakage, respectively, and it can be found that the broken parts of the disc cutter are mainly concentrated at the edge of the cutting edge. Chipping is the most common morphology of tool breakage during ultrasonic cutting of honeycomb core with a disc cutter, and with the increase in cutting length, the chipping edge will expand to form a continuous chipping edge. Especially when cutting honeycomb material with high density, continuous chipping and cracking breakage will occur on the flank face of the disc cutter. Analysis of its cause is that the higher density of aramid paper honeycomb as a whole presents high-hardness and flexibility [31; 32], so the disc cutter's cutting edge in the rotary cutting process, when encountering a hard, brittle phenolic resin layer, will lead to chipping and even cracking breakage.  
Figure 14: Variation of flank wear \(VB\) of position 3 with cutting length.  
Figure 13: Flank wear band SEM observation and EDS result analysis of the position 3. **(a)** Flank wear band; **(b)** The energy spectrum analysis of zone A.  
Figure 15: Breakage morphologies of disc cutter. **(a)** Chipping; **(b)** Crack breakage.
###### Abstract  
Background: Non-alcoholic fatty liver disease (NAFLD) and the presence of low muscle mass (sarcopeniaia) represent noteworthy health issues. Handgrip strength, a muscle function indicator, is vital for sarcopenia diagnosis. We investigated the link between handgrip strength and hepatic steatosis in Italian adults. Methods: We retrospectively assessed 388 adults (\(\geq\)50 years), measuring muscle function and hepatic steatosis using a dynamometer and transient elastography. We divided participants into handgrip strength tertiles. Results: 207 had NAFLD. The lowest handgrip strength tertile had a higher NAFLD prevalence (64% vs. 46%, \(p=0.02\)). Tertiles I and II exhibited increased odds of NAFLD in comparison to tertile III, with an odds ratio of 5.30 (95% confidence interval: 2.24-12.57, \(p<0.001\)) and 2.56 (95% confidence interval: 1.17-5.59, \(p=0.01\)), respectively. rHGS predicted NAFLD with an AUC of 0.41 (SE = 0.029, \(p=0.003\)). An rHGS of 1.22 achieved 18% sensitivity and 80% specificity for hepatic steatosis prediction. Conclusion: Low handgrip strength is linked to an increased susceptibility to NAFLD among the Italian population, implying its potential utility in the identification of risk for hepatic steatosis.  
handgrip strength; sarcopenia; hepatic steatosis; liver transient elastography; CAP score; nutrition-related diseases +
Footnote †: journal: Applied
## 4 Classification of MAC Protocols Based on Channel
### Cluster-Based Multi-Channel MAC Protocols
#### 4.9.2 Lmmc  
A multi-channel MAC based on learning automata is designed for optimized utilization of channel access for VANET applications. The multi-channel scheme with the use of a radio transceiver and a smart model of learning automaton is used to learn about the cluster members' traffic parameters. Nodes are equipped with GPS, unlike other MACs that use CCH for safety applications and SCH for both safety and non-safety applications. The learning automaton model is implemented for the polling of cluster members to find out its parameter using the cluster head, with dynamic TDMA allocation of resources within the cluster [97].  
The protocol provides better PDR for safety applications in real-time scenarios. It provides proper bandwidth and channel access utilization and, thus, increases throughput in the network. Still, there are not many analyses on QoS for non-safety applications. The protocol does not deal with best-effort, video, background, and other non-safety-based services.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### PRF Bounds
The lower bound of PRF is often defined by the nominal Doppler bandwidth (\(\mathrm{PRF}_{\mathrm{min}}\geq\mathrm{B}_{\mathrm{d}}\)), while the upper bound is constrained by factors such as the radar's operating range. The maximum (\(\mathrm{PRF}_{\mathrm{max}}\)) can be described by the formula (\(\mathrm{PRF}_{\mathrm{max}}\leq\frac{c}{2(\mathrm{R}_{\mathrm{e}}-\mathrm{R}_ {\mathrm{m}})}\)), where \(\mathrm{R}_{\mathrm{n}}\) and \(\mathrm{R}_{\mathrm{f}}\) are the near and far ranges, respectively, and \(\mathrm{c}\) is the speed of light. These PRF bounds are important in determining the SAR system's performance, including its resolution and coverage capabilities. The choice of PRF directly impacts the azimuth resolution and the swath width, highlighting the trade-offs that must be considered in SAR system design and operation. When determining the PRF bound for SAR, various parameters significantly affect system performance. Sensor velocity (\(\mathrm{v}\)) impacts the Doppler frequency shift and influences the PRF range to avoid Doppler ambiguities. Azimuth resolution (\(\mathrm{\delta}_{\mathrm{x}}\)) is tightly linked with PRF, since the azimuth resolution impacts the Doppler bandwidth and consequently the PRF limits. Considering swath length (swlen), longer swaths typically require higher PRFs to adequately image the entire area, but this can introduce range ambiguities. Grazing angle (grazang) affects the geometry of the SAR system and the relationship between the satellite's path and the ground swath, influencing the PRF limits. Pulse width dictates the time interval of each pulse, influencing the maximum PRF to avoid pulse overlapping. The Doppler cone angle relates to the direction of the scene with respect to the sensor's motion, affecting the Doppler bandwidth and PRF limits. These factors collectively define the operational constraints for PRF in SAR, balancing the need for resolution, swath coverage, and ambiguity avoidance. The swath width affects the area the radar can cover, which in turn influences the required PRF to adequately image that area. A wider swath generally requires a higher PRF to ensure full coverage without missing any area, but this increase in PRF can lead to range ambiguities. There are mainly two types of ambiguities to be considered. Range ambiguity occurs when the PRF is too high, causing the overlapping of successive pulses. To avoid this, the maximum PRF is typically limited to ensure that a pulse returns before the next pulse is transmitted. Azimuth (or Doppler) ambiguity happens when the PRF is too low, leading to Doppler spectra from different targets overlapping. The minimum PRF is set to avoid this overlap and ensure clear differentiation between targets based on their Doppler shifts. The employed PRF bounding Algorithm 1 is given below.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 4 Experiments
### Comparison with State-of-the-Art Methods  
**CASIA-B**: Table 1 presents the comprehensive comparison results between the proposed method and other state-of-the-art methods on CASIA-B dataset. From Table 1, it can be clearly seen that our method attains outstanding performance across nearly all viewpoints compared to other methods, such as GaitNet [12], GaitSet [4], GaitPart [7], MT3D [29], and RPNet [21]. In particular, the proposed method exhibits significantly higher average accuracies than other gait recognition methods, especially under BG and CL conditions. Our proposed method achieves average accuracies of 97.7%, 93.7%, and 83.8% under these conditions, outperforming GaitPart [7] by +1.5%, +2.2%, and +5.1%, respectively. Furthermore, our proposed method demonstrates superior performance in some specific view angles. For example, under the view angles of 90\({}^{\circ}\) and 180\({}^{\circ}\), the NM accuracy of our method reaches 95.9% and 94.2%, surpassing GaitPart [7] by +3.4% and +3.8% separately. The experimental results illustrate that our framework exhibits substantial robustness and advantages under unfavorable conditions, which can be attributed to the fact that multi-scale convolution can simultaneously observe more detailed gait features in spatial and temporal domain. Furthermore, the effective combination of multi-scale spatial-temporal features can obtain discriminative features even in the presence of occlusion, thus enhancing the recognition ability of the proposed method.  
**OUMVLP:** Similar experiments are executed on OU-MLVP dataset to prove the generalization efficacy of our approach. As demonstrated in Table 2, our method consistently attains higher accuracies across most camera views than other methods such as GaitSet [4], GaitPart [7] and RPNet [21], which displays a more outstanding recognition ability in the large-scale dataset. Specifically, the recognition ability of our method is superior to the state-of-the-art RPNet [18] by a margin of +4.4% (85.0% vs. 89.4%). Due to the addition of view embedding, the accuracies of our method under the view angles of 0\({}^{\circ}\) and 180\({}^{\circ}\) have been greatly improved. Meanwhile, it can be also observed that our results may not surpass GaitPart [7] under certain view angles, such as 45\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\), this is mainly due to the fact that our training dataset does not include as many samples as other works contained under these view angle. Although our results may not surpass GaitPart [7] under some specific view angles, our method has still achieved substantial overall improvement. In conclusion, our method can perform better recognition ability and effectively improve the recognition accuracy on the large and worldwide dataset for cross-view gait recognition.
### \"Airborne and Terrestrial Situational Awareness\"--A Modular System by DLR  
Real-time aerial imagery could significantly support situational awareness especially during major and large-scale disasters [7]. DLR's \"Airborne and Terrestrial Situational Awareness\" imaging and analysis system builds upon this fact and depicts an interdisciplinary process chain for swift decision support in crisis management (Figure 1): Module 1 is the Ground Control Station U-Fly operating the research aircraft D-CODE as a remotely piloted vehicle [8]. Module 2 is DLR's 3K aerial camera system [9]. Module 3 is the Center for Satellite-based Crisis Information (ZKI), which analyzes the acquired aerial imagery and generates crisis-related information products, such as situational awareness maps [10]. Module 4 is called KeepOperational and provides traffic analysis and route planning capabilities, taking into account airborne and/or ground data [11]. By interlinking these four modules, it provides a way to rapidly transform raw high-resolution aerial image data into quickly appropriable information for non-expert users. The application-oriented products and user interfaces are tailored to support specific disaster response tasks. The solution can be applied as a complete system or by using only individual modules standalone or as a subsystem.  
Within the tabletop trial \"The Netherlands\", the data utilization and visualization part of DLR's all-in-one system was applied (Modules 3 and 4) [12], rather than the data acquisition. In a subsequent DRIVER+ trial, Modules 1 and 2 were also assessed [13].  
.2.1 Improving Situational Awareness through the Center for Satellite-Based Crisis Information (ZKI)  
ZKI is a 24/7 service that aims to rapidly collect, process, and analyze earth observation data, spaceborne or airborne, in order to generate up-to-date situational awareness information in support of CM before, during, or after natural disasters, technical accidents, humanitarian crisis situations, or civil security emergencies. Within the introduced data acquisition and analysis system, the task of ZKI is to transform the plain image information into an easily accessible format for the practitioner, technically and content-wise. Accordingly, all crisis information is produced in close consultation with the ZKI users to meet individual requirements. Results are provided for instance as maps, geo-pdf, web services, or text dossiers. The main national and international user groups are political decision makers, governmental organizations, situation centers, and relief organizations. Special emphasis by ZKI is put on joint research activities together with users to continuously improve its services and products. Therefore, consultancy, technology transfer, as well as training and exercises are provided. The close interlocking between research, development, and service is an enormous strength of the ZKI.
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.12 CRC  
The CRC block in the NPDSCH receiver uses the same technique as the transmitter, and if the remainder is zero, that means the data are correct; otherwise, the data are incorrect. The CRC block receives the data from the viterbi decoder and passes them to the upper layer [10].
## 3 Vision System for Therapeutic Adaptation: Analysis of Functional Requirements
### Functional Requirements of the Vision Module  
The vision module is responsible for acquiring, analyzing, and interpreting the patient's movements during rehabilitation sessions. This module is not limited to a simple monitoring system that captures images but uses computer vision and artificial intelligence techniques to extract relevant biomechanical parameters and provide a detailed analysis of the patient's progress.  
The implementation of this module is developed as a software prototype based on ROS2, tested in simulation to verify its correct functioning and the precision in detecting movements and to guarantee an accurate analysis of the movement. The system uses RGB-D cameras with a minimum resolution of 1080p at 30 fps, allowing a smooth and detailed acquisition, which allows the detection of even the smallest variations in the amplitude and precision of movements, providing fundamental data for therapeutic evaluation.  
The vision module calculates and transmits the following biomechanical parameters to the suggestion module.  
To ensure efficient information transmission, the vision module uses a publisher-subscriber architecture in ROS2, where the vision module publishes the biomechanical data analyzed (Figure 5) and the suggestion module subscribes to the data and generates personalized therapeutic recommendations.  
ROS2 is an open-source software framework widely used in robotics and automation systems. It provides a set of tools, libraries, and conventions for writing robot software in a modular and scalable way. ROS2 helps manage communication between sensors, actuators, processing modules, and control algorithms, making it easier to develop complex systems that integrate hardware and software. In the context of rehabilitation or systems with multiple sensors (such as cameras or Motion Capture devices), ROS can be used to synchronize and coordinate data coming from different sources in real time.  
If the system detects positive progress, the suggestion module \"suggests\" a reduction in motor assistance and an increase in exercise difficulty. When the data show difficulty in movement, the system can suggest an increase in support or alert the therapist.  
The analysis of human movement through computer vision presents some critical issues such as the occlusion of body parts; to overcome this limitation, the system uses  
Figure 4: Flowchart of the biomechanical data processing and therapy adaptation system.  
Figure 5: Publisher/subscriber communication flow.  
prediction algorithms to reconstruct the movement even when some joints are temporarily not visible.  
Another critical factor concerns postural variations; to improve the system's ability to adapt to different postures, deep learning models are trained on diversified datasets.  
Furthermore, advanced filters are applied to remove irrelevant information from the analysis caused by the presence of disturbing objects.
## 4 Experiments and Results
### Training Details  
The webcam was used to collect the CNC machine and HMI images. The dataset includes training and testing images with a resolution of 960 \(\times\) 1080. Images were gathered from different positions in our lab. Two hundred images, including 160 training images and 40 testing images, are used to train and test the developed models. The experiments were conducted on a laptop equipped with an Intel Core i7-8750H 4.0 GHz CPU and a single NVIDIA GeForce GTX 1060 under the Ubuntu 18.04 64-bit operating system.  
During the training process, Stochastic Gradient Descent (SGD) is adopted to train the proposed SiameseRPN based on the pre-trained SNN model using ImageNet. In addition, picking positive and negative training samples is also necessary. The criterion used in the target CNC machine detection and HMI detection tasks is based on intersection over union (_IoU_) and two thresholds \(T_{high}\) and \(T_{low}\). When the predicted bounding boxes that have _IoU_ \(>T_{high}\) with respect to the ground truth, they are considered positive samples, which means  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Weight & 25 kg \\ Maximum Payload & 5 kg \\ Max Reach & 1096 mm \\ Voltage & DC48 V \\ Maximum Speed of TCP & 3 m/s \\ Communication & TCP/IP, WIFI \\ Power & 150 W \\ Axes & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of robot manipulator.  
Figure 5: The structure of robot system.  
the correct results. When the \(IoU<T_{low}\), those are considered negative samples and will be removed from the results; the \(T_{high}\) and \(T_{low}\) are set as 0.3 and 0.6 in this study, respectively.
# Biochars as a Solution for Silver Removal and Antimicrobial Activity in Aqueous Systems
## 3 Results and Discussion
### Antibacterial Activity of Silver-Loaded Biochar  
The ARHB sample, produced by the pyrolysis of rice husks and subsequent chemical activation, exhibited the best structural characteristics and adsorption capacity among the tested biochars. SEM analysis revealed its well-defined, porous structure (Figure 4b1,b2). Figure 11a demonstrates the distribution of silver on the surface of the ARHB sample, with elemental mapping (visible as magenta highlights) indicating a relatively uniform dispersion of silver nanoparticles across the biochar structure. This suggests effective adsorption and integration of silver ions, likely facilitated by the material's surface functional groups and porosity. Additionally, the EDS spectrum in Figure 11b confirms the presence of silver (Ag) peaks, alongside other elements such as oxygen (O), carbon (C), and trace metals, indicating a complex surface chemistry. The prominent Ag peaks further support the conclusion that silver is effectively adsorbed onto the biochar surface. Consequently, ARHB was selected for antibacterial activity testing.  
Table 7 presents the summarized results of testing the effect of the biochars with silver on _E. coli_ in a suspension with a density of 10\({}^{6}\) cells mL\({}^{-1}\) after different exposure intervals.  
Figure 11: SEM (**a**) and-EDX (**b**) of the ARHB sample after silver adsorption.  
Figure 10: Binding energy of the Ag 3d5/2 and Ag 3d3/2 orbitals.  
It can be seen that after 30 and 60 min, there was a decrease in the number of colonies, but in this exposure time interval, a full bactericidal effect was not observed.  
The summarized results reflecting the effect of the tested plant material with silver on the experimental strains, determined by the suspension method, are presented in Table 8. From the data, it can be seen that the diameters of the inhibitory zones during their application were an average of 13.25 + 1.3 mm, while the control antibiotic showed higher activity with zones 18.80 + 0.4 mm. This is an indicator of medium-high sensitivity in vitro of the studied strains of _E. coli_ to the silver loaded biochar.  
The results show that the rice husk biochar with Ag\({}^{+}\) exhibits an inhibitory effect against the tested _E. coli_ strains, although this was not strongly expressed. The differences in the results of the three products with Ag\({}^{+}\) and the control antibiotic were significant (\(p<0.01\)). The results are consistent with those of other authors, who have studied the effects of silver-loaded biochar products obtained from various plants and other materials by different methods [52; 53; 54].
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 4 Experiment and Results
### Real-World Datasets
The F1 score is a useful metric for classification tasks because it balances two critical performance metrics: precision and recall, which are often at odds due to imbalanced classes (normal and anomaly) in anomaly detection tasks. On the other hand, AUC measures the model's ability to rank positive instances higher than negative ones across all possible thresholds. This reflects the overall discrimination capability of the model. Both scores are key performance metrics. We can see from the tables that, for the MSL and SMAP datasets, the FNN with bandpass filtering does not perform well. This is because both the FNN and BP assume that the time series is derived from a continuous function, which is not valid for these datasets, where binary features are the majority. In contrast, for the other datasets (SMD, PSM, SWAN, SWAT, and WADI), where the time series are scalar functions, bandpass filtering significantly improves the results for each method, increasing the F1 scores by 0.25-20% and the AUC scores by 1-15%. Among these five datasets, the FNN Autoencoder models consistently outperform the alternatives, demonstrating their robustness in handling a scalar time series with bandpass filtering. We performed a Monte Carlo simulation to estimate the variability in the F1 and AUC scores for each dataset using the FNN (BP) model. This involved repeatedly resampling the test data and computing the metrics to assess their variance. The maximum observed standard deviation for the F1 score was approximately 0.018, while for the AUC, it was around 0.010. Using these values, we constructed 95% confidence intervals for the performance comparison. Our analysis shows that for the SMD, PSM, SWAT, and WADI datasets, the FNN (BP) model not only outperforms the alternatives but does so with statistical significance. In contrast, for the SWAN dataset, while FNN (BP) performs well, the improvements are not statistically significant. For the MSL and SMAP datasets, the results indicate no strong performance advantage across the different models since the differences fall within the confidence interval.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 2 Materials  
Several data were used in this study to implement the proposed methodology and assess its performance. A comprehensive list of the considered data, together with their main characteristics, is presented in Table 1, and their description is provided in the following subsections.