esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Exploiting FPGA Features
#### 5.4.2 Custom Clock Gating  
FPGAs are highly customizable, allowing designers to use clock-gating strategies for a specific application at the fine-grained level. By selectively enabling clocks of different modules only when needed, custom clock gating can minimize significant power consumption in unnecessary clock transitions. The authors of [87] implemented their design using a modular approach for P-wave, T-wave, and QRS extractors. They incorporated the method of custom clock gating to turn off the modules which were not required to be active. Likewise, the authors of [81] also used clock gating to activate only 1 of the 15 multiplier instances at a time, reducing dynamic power consumption by exploiting the fact that these multipliers were used serially.  
Clock gating reduces unnecessary switching activities, which in turn lowers dynamic power consumption. Dynamic power consumption mainly arises from the charging and discharging of capacitive loads during clock transitions. By minimizing the number of active clock signals, these transitions are reduced, leading to lower power consumption. Thus, selectively enabling or disabling the clock signal to different parts of the circuit effectively reduces dynamic power consumption. This approach is highly useful for designing various event-driven and sleep mode strategies for applications to optimize power consumption.
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 6 Related Works
P2 VoD presents a caching strategy, a generation concept, and a distributed directory service, among other things. Analytical investigation demonstrates that P2 VoD is reliable and efficient. [PERSON] et al. [31] bring new insights into research with the use of a caching scheme on clients and the introduction of generation for better client management. [PERSON] et al. [32] has as a relevant factor in its research a simple pre-search, where it works through measurement, being able to extract certain properties of user behavior. To ensure reliable and efficient operation, cloud computing architectures for video streaming services must have high availability, scalability, security, and fault tolerance. Designing these systems involves identifying weaknesses and proposing solutions through careful planning and analysis, and techniques such as sensitivity analysis, modeling hierarchies, and the study of redundancy mechanisms can improve performance. [PERSON] et al. [33,34] studied the use of redundancy mechanisms to enhance the performance of cloud computing systems in video streaming services. Surveillance monitoring systems are essential for preventing various social issues in smart cities, but determining the key components and devices that influence their dependability is complex due to advanced video stream analysis, high availability, and the impact of various factors on trustworthiness. [PERSON] et al. [35] use stochastic Petri net models to analyze the reliability and availability of a surveillance monitoring system under different parameters to determine the highest level of dependability. The availability of a cloud video system (CVS) was analyzed by comparing Markovian and Semi-Markovian modeling based on the CVS architecture and component dependability [36]. Data from cloud system research and operations were used to model the CVS, and the results informed the choice of modeling technique and the assessment of availability. It was suggested that the comparison of CVS availability models using Markovian and Semi-Markovian modeling processes should consider the architecture and component failures during different life-cycle phases, and that the results could be used to improve DevOps engineering procedures for the CVS. As a result, all the titles mentioned below contain metrics for determining whether or not the service is suitable for a certain video. Table 7 presents a comparison of the works according to the following criteria: metrics used for the evaluation, capacity variation, sensitivity analysis, and multiple redundancy strategies. Our paper attracts attention since it presents all of these qualities.
# Sensorimotor Control Using Adaptive Neuro-Fuzzy Inference for Human-Like Arm Movement
## 6 Conclusions  
This study designs and implements a sensorimotor controller that enables a robotic system to mimic human-like circular motion. By optimizing the internal forces of the muscles, we achieve accurate hand trajectory following along the desired path using joint-space feedback. Utilizing the ANFIS structure, we advance the methodology by treating muscle-force parameters as learning variables, allowing for dynamic adjustment based on the muscle's rate of change. This approach leverages the power of adaptive learning and fuzzy logic to mimic the control of human motor function. The results underscore the effectiveness of the proposed sensorimotor controller, demonstrating improvements in the motion performance. Future work will focus on refining the controller's adaptability and exploring its application in more complex movement patterns and different robotic platforms. Additionally, further investigation into the integration of sensory feedback mechanisms could enhance the system's responsiveness and versatility, paving the way for more intuitive and lifelike robotic assistance in various applications.  
G.G. and M.A.; Methodology, G.G. and M.A.; Software, G.G. and M.A.; Writing--original draft, G.G. and M.A.; Writing--review and editing, G.G. and M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Karabuk University Scientific Research and Publication Board (protocol code 313756 and 2023-2024).  
Informed Consent StatementInformed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to legal reason.  
Thanks to all the participants who volunteered in this study and provided valuable data for our research.  
The authors declare no conflicts of interest.  
Figure 15: Performance of the ANFIS structure for the reaching movement. The locations (A–D) represents the arm-end points during the reaching movement.
## 2 Materials and Methods
### Characterization of Nanocomposite Films
#### 2.5.3 Water Vapor Permeability  
Film water vapor permeability (WVP) was calculated based on ASTM E96-65 method. Glass permeation cups with an internal diameter of 2 cm and length of 4.5 cm were used. A total of 3 g of silica gels were placed inside the cups, and then the film pieces were placed on the top of the cups and sealed using parafilm. Films were conditioned at RH of 55% for 24 h before the experiments. Glass cups sealed with the films were placed into a desiccator containing saturated potassium sulfate and stored at room temperature. The weight of glasses was recorded during time intervals. The water vapor transmission rate (WVTR) of the films was defined based on the slope of (g/s) divided by the surface area (m\({}^{2}\)). WVP (g\(\cdot\)mm/kPa\(\cdot\)h\(\cdot\)m\({}^{2}\)) was calculated using the equation below:  
\[\text{WVP}=\frac{\text{WVTR}}{\text{P(R}_{1}\;-\text{R}_{2}\;)}\times\text{X} \tag{3}\]  
where WVTR represents the water vapor transmission rate (g/m\({}^{2}\)-day), X represents the film thickness (m), P represents the saturation water pressure at 25 \({}^{\circ}\)C (Pa), R\({}_{1}\) represents the relative humidity in the desiccator (%), and R\({}_{2}\) represents the relative humidity in glass cups (%) [24].
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 3 The Data Storage and Exchange Method Based on Superscalar Pipeline  
The data storage method of linear mapping in the traditional spaceborne SAR imaging system makes the azimuth data access efficiency extremely low. When accessing the data matrix under two-dimensional conditions, the balance between the range direction and the azimuth direction is very low. The method of partitioning a matrix and mapping it to a three-dimensional storage array establishes a great balance during the two-dimensional  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance** & **Range** & **Azimuth** \\ \hline Writing efficiency & 66\% & 66\% \\ Reading efficiency & 80\% & 80\% \\ Writing bandwidth & 8.45 GB/s & 8.45 GB/s \\ Reading bandwidth & 10.24 GB/s & 10.24 GB/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the matrix block three-dimensional cross-mapping method for range and azimuth access in the paper [35].  
Figure 4: Schematic diagram of comparison between non-pipelined design and scalar pipeline. (**a**) The process of non-pipelined design; (**b**) The process of scalar pipeline design.  
access of the matrix, which signficantly improves efficiency in azimuth. Furthermore, the scalar pipeline design effectively improves the system's processing time. However, the method's bandwidth in both directions still creates limitations for high real-time SAR imaging systems, and the scalar pipeline design continues to have more idle time, wasting pipeline resources. As a result, we propose a superscalar pipeline-based method and architecture for efficient data storage and exchange in SAR imaging systems in order to improve real-time processing. A data storage and exchange architecture suitable for an SAR imaging system is designed based on the efficient storage mapping and superscalar pipeline data exchange method. The coordinate address of the two-dimensional data matrix are mapped to physical addresses in two DDR3s in equal amounts, a new mapping method is adopted to replace the traditional linear mapping method, and the spatial parallelism is increased by the design of a superscalar pipeline. Since the method and architecture have improved data access bandwidths in both the azimuth and range directions, the real-time processing of spaceborne SAR imaging is now more feasible.
# Advanced Unified Earthquake Catalog for North East India
## 2 Seismicity of North East India  
Figure 1 displays a seismotectonic map indicating the epicenters of M\({}_{\rm wg}\geq 2\) occurrences from 1737 to 2012, as well as the geological aspects of the NE India region ranging from 20-30\({}^{\circ}\) N latitude to 87-98\({}^{\circ}\) E longitude. The Shillong earthquake of 12 June 1897 (M\({}_{\rm wg}=8.9\)) and the Assam earthquake of 15 August 1950 (M\({}_{\rm wg}=8.6\)) are the two recent big earthquakes in this area. Furthermore, a succession of significant earthquakes (M\({}_{\rm wg}\geq 7.0\)) has occurred in this area, resulting in the loss of lives and the damage of property. Ref. [25] classified the area into four primary seismogenic root zones: the Eastern Syntax (zone I), the  
Arakan-Yoma Subduction Belt (zone II), the Shillong Plateau (zone III), and the Himalayan Frontal Thrusts Main Central Thrust (MCT) and Main Boundary Thrust (MBT) (zone IV). Seismicity in this region is caused by the collision of the Indian Plate and Tibet in the north and the Burmese landmass in the east. As a result of such collisions, the Himalayan Thrust system was formed in the north, the Arakan-Yoma Mountain Arc, the Naga Hills, the Tripura Folded Belt in the east, and the Shillong Plateau rose.  
The Shillong massif stands out as a plateau with an average elevation of 1500 m near the basin's SW outlet. The seismic zones of Ref. [25] are further classified into nine seismogenic zones, as illustrated in Table 1 and Figure 2[20]. The subdivision focuses on tectonic and geological faults, focal mechanism solutions, and the geographical distribution of earthquake events [20].  
\begin{table}
\begin{tabular}{c c c} \hline
**Seismogenic Zone** & **Major Division** & **Subdivision** \\ \hline I & Indo Burma Fault Belt & NS Indo-Burma Fold Belt \\ \hline II & Indo Burma Fault Belt & NE-SW Indo Burma Fold Belt \\ \hline III & Plateau Region & Sagging Fault Region \\ \hline IV & Mishmi Massif & NW-SE trending feature \\ \hline V & Plateau Region & Tibetan Plateau \\ \hline VI & Himalayan Mountain Belt & Eastern MCT \\ \hline \end{tabular}
\end{table}
Table 1: Different Seismogenic zones for Northeast India.  
Figure 1: Seismotectonic map exhibiting seismicity for M\({}_{\rm wg}\geq 2\) with epicenters and tectonic characteristics of the NE India region on a GIS platform.
## 3 Results
### What Is Possible with Cloud-Free Imagery  
In cloudy regions, the best imagery for some years will still contain some cloud, especially since imagery is required for a narrow window during the green up period in order to produce a strong relationship. This was the case for Landsat 8 in 2013 and 2014 which meant that the resulting ANPP maps included gaps due to cloud cover. To illustrate what is possible under ideal conditions, two cloud-free Landsat 5 images acquired on 28 April 2011 covering most of Wales were downloaded (Figure 9). The images gave an R\({}^{2}\) of 0.507 and exhibited more scatter than previous relationships, which is probably a consequence of using Landsat-5 and the time lag between the in situ data collection and the satellite imagery. However, the relationship was still significant and had the form:  
\[\textit{In(ANPP)}=0.888\times\textit{NDVI}_{\textit{LT5}}+5.50, \tag{3}\]  
Figure 8: Scatter plots showing the validation of the three models summarised in Table 5 Solid line shows the 1:1 line and error bars represent \(\pm\) root-mean-square error.  
Figure 9: ANPP (g dry mass m\({}^{-2}\) year\({}^{-1}\)) map for Wales for all habitat types listed in Table 1 produced using Landsat 5 TM imagery captured on 28 April 2011.
## 3 Discrete Formulation of Topology Optimization Problem Statements and Sensitivities
### Stress Functional
This procedure can be performed through an ABAQUS\({}^{\otimes}\) FEA;
* Find \(\lambda\) such that: \(K(\rho)\lambda=\frac{\partial\sigma^{PN}(\rho)}{\partial\sigma_{a}^{\
u M}} \frac{\partial\sigma_{a}^{\
u M}(\rho)}{\partial\sigma_{a}}\mathbb{C}_{0}B_{a}\);
* Compute: \(\partial\sigma_{u}^{PN}(\rho)=\frac{\partial\sigma^{PN}}{\partial\sigma_{a}^{ \
u M}}\frac{\partial\sigma_{a}^{\
u M}}{\partial\sigma_{a}}\Big{(}-\rho^{1} \xi_{adj}^{T}(\rho)\mathbb{C}^{\prime}(\rho)\xi_{s}(\rho)\Big{)}\). As demonstrated in Equation (55) through (58), determining \(\partial\sigma_{u}^{PN}(\rho)\) is significantly more complex and computationally expensive, due to the necessity of using the adjoint model. Some researchers [100] have proposed approximating this term as \(\partial\sigma_{u}^{PN}(\rho)=0\), in an attempt to reduce the complexity and computational cost of stress-dependent topology optimization problems. While doing so does have evident advantages, the results obtained for each element of the benchmark problem described in Section 7.2 show that, on average, \(|\partial\sigma_{u}^{PN}(\rho)|>|\partial\sigma_{sp}^{PN}(\rho)|\). As a result, assuming that \(\partial\sigma_{u}^{PN}(\rho)=0\) would lead to an average error greater than 50% in the calculation of the maximum stress sensitivity, at least for the particular case of the benchmark problem described in Section 7.2. The error in this approximation is aggravated by the fact that \(\partial\sigma_{u}^{PN}(\rho)\) has a negative sign from Equation (53), and as a consequence, ignoring this term could potentially point the optimization algorithm in the opposite direction of the gradient. Therefore, the simplification proposed in [100] was not adopted in this work.
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 5 Results and Discussion
For Task 1, consisting of three delay lines, four attenuators, and four phase shifters, the estimated power consumption is 97 mW. For Task 2, consisting of four filters, four delay lines, and four phase shifters, the estimated power consumption is 122 mW. In comparison, classical DSP methods such as FFE and DFE often exceed 700 mW and can reach up to 1 W as baud rates increase [44; 45]. Thus, the AOP is presented as a power-efficient solution, achieving superior performance at only 10% of the power consumption of its digital counterparts. The results highlight the promising potential of the all-optical processor and the superiority of the combination of optical and electrical equalization in high-speed PON systems, ensuring higher ROP tolerance and longer fiber reach. With the help of the equalizer, the fiber reach can be increased, making PONs more suitable for broader deployment areas. More significantly, this increase in received optical power tolerance--at least 3 dB--could provide the flexibility to double the number of users that a single PON can serve. Consequently, this advancement not only extends the operational range but also enhances the scalability and efficiency of network deployments, addressing the ever-growing demand for high-speed, reliable internet access. This approach dictates that future PONs can rely on leveraging legacy IM/DD equipment to achieve cost-effective and high-speed solutions.
## 4 Experimental Results
### Experimental Results  
In the second set of experimental results, the same loss functions and optimizers used in the first set were maintained, namely, binary cross entropy with logits loss (BCEWithLogitsLoss), mean squared error loss (MSELoss), Huber loss, and L1 loss, as well as the optimizers Stochastic Gradient Descent (SGD) and ADAM. This decision was made with the aim of consistently evaluating the performance of the UNET architecture under controlled conditions.  
For this set of experimental results, a dataset with higher-resolution images (512 \(\times\) 512 pixels) was used. This increase in spatial resolution is critical because it enables the capture of more details in the input images.  
The results obtained from this second set of experimental results are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Epochs**} & \multicolumn{4}{c}{**Jaccard Index**} & \multirow{2}{*}{**Optimizer**} \\ \cline{2-2} \cline{4-6}  & **MSELoss** & **BCEWithLogitsLoss** & **HuberLoss** & **L1 Loss** \\ \hline \multirow{2}{*}{5} & 0.497790 & 0.453614 & 0.477699 & 0.210285 & SGD \\ \cline{2-6}  & 0.875501 & 0.554724 & 0.902015 & 0.621233 & Adam \\ \hline \multirow{2}{*}{10} & 0.222385 & 0.626545 & 0.187804 & 0.258824 & SGD \\ \cline{2-6}  & 0.928157 & 0.879121 & 0.931167 & 0.915113 & Adam \\ \hline \multirow{2}{*}{20} & 0.526851 & 0.467208 & 0.105790 & 0.625047 & SGD \\ \cline{2-6}  & 0.942854 & 0.890057 & 0.935855 & 0.929915 & Adam \\ \hline \multirow{2}{*}{50} & 0.443066 & 0.176306 & 0.262248 & 0.502235 & SGD \\ \cline{2-6}  & 0.943741 & 0.925606 & 0.943469 & 0.937352 & Adam \\ \hline \end{tabular}
\end{table}
Table 1: Jaccard index with post-processing images.  
The loss function curve for every sample of 50 epoch is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Epochs**} & \multicolumn{4}{c}{**Jaccard Index**} & \multirow{2}{*}{**Optimizer**} \\ \cline{2-2} \cline{4-6}  & **MSELoss** & **BCEWithLogitsLoss** & **HuberLoss** & **L1 Loss** & \\ \hline \multirow{2}{*}{5} & 0.210011 & 0.566471 & 0.416691 & 0.448628 & SGD \\ \cline{2-6}  & 0.720174 & 0.278763 & 0.472072 & 0.627168 & Adam \\ \hline \multirow{2}{*}{10} & 0.249043 & 0.310350 & 0.289211 & 0.499403 & SGD \\ \cline{2-6}  & 0.774610 & 0.583088 & 0.657412 & 0.779585 & Adam \\ \hline \multirow{2}{*}{20} & 0.624645 & 0.283813 & 0.124204 & 0.539284 & SGD \\ \cline{2-6}  & 0.770497 & 0.615353 & 0.103587 & 0.774108 & Adam \\ \hline \multirow{2}{*}{50} & 0.571249 & 0.289641 & 0.485721 & 0.413464 & SGD \\ \cline{2-6}  & 0.792976 & 0.699045 & 0.709324 & 0.803244 & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 2: Jaccard index with post-processing images.  
Figure 6: Loss function curve for 50 epoch and input of 256 px (UNET).  
Figure 7: Segmented image for 50 epoch and input of 256 px (UNET).
## 6 Conclusions and Future Work  
It is critical to secure VANET communication to save lives. VANET aims to ensure safe driving by assisting drivers in recognizing risks and improving road safety and traffic conditions. Adversary nodes may launch a variety of attacks in VANET. These attacks have a substantial impact on VANET's performance and reliability. As a result, this research introduces a novel trust model for identifying and excluding attacker nodes in a VANET by monitoring node activity. It is based on a fuzzy logic model. Several criteria are fed into the model to calculate the final value of trust. Our scheme provides a practical solution for dealing with malicious and selfish nodes in VANETs without needing additional VANET components. When malicious nodes have a low trust value, the scheme evicts them from VANET.  
Furthermore, evicting any selfish node encourages selfish nodes to collaborate. Through simulations, we demonstrated that our scheme could detect and evict all malicious nodes from the VANET while simultaneously increasing throughput and minimizing delay. Our solution encourages all nodes to participate in providing accurate information without being self-serving. We want to use a verification tool in the future to ensure that our scheme is robust to various attacks and that it only selects the most trustworthy nodes in the VANET. Furthermore, we would like to conduct a similar analysis on a real-world system.  
Conceptualization, M.A.; Formal analysis, A.F.A.-S.; Investigation, A.F.A.-S.; and B.I.; Methodology, M.A., A.A. and B.I.; Software, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results and Discussion
### Performance Units
#### 5.2.7 Safety Message Travel Time  
It is the time period taken by a safety message forwarded by a hop to reach its single-node neighbor. Travel time decrease based on the number of nodes is shown in Figure 12. Besides this, as the hop scale is maximized, hopping is incremented, which results in faster message distribution. Furthermore, safety message travel time is increased with a decrease in the node density, which results in clashing of hops in finding a surrounding hop to transfer the message ahead. MAMC-MAC is perceived as having good accomplishments compared to the other protocols because of two limits. Primarily, cluster-head selection is not required in MAMC-MAC. This minimizes the extra periods that would have been taken in the process. Next, because the vehicular density is regarded as stable for the simulation extent, when the nodes are on the roads, HELLO message control is accomplished only once, which helps in minimizing the travel time.