esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Analysis
### IEEE-118 System Simulation Analysis  
1. Basic data  
The standard IEEE-118 distribution system was adopted for simulation analysis [34]. The distribution network contained 19 distributed photovoltaics with a capacity of 0.5 MW, which were connected to nodes 7, 9, 14, 31, 35, 36, 52, 54, 59, 60, 63, 78, 85, 87, 95, 96, 113, 114, and 117. The distribution network also contained 10 distributed wind power generation with a capacity of 0.5 MW, which were connected to nodes 4, 18, 25, 26, 28, 30, 32, 40, 41, and 42. The rest of the data is consistent with the IEEE-33 system, which is not repeated here.  
2. Results analysis  
(1) Optimal scheduling results of ETSHE considering the load characteristics  
The same method was used for node sensitivity analysis, and the set of nodes that were selected to obtain the energy storage access included [3, 6, 10, 35, 38, 44, 47, 48, 61, 66, 77, 78, 79, 91, 92, 93, 114, 115, 117], a total of 19 nodes. In order to highlight the impact of load characteristics on the planning settings, it was considered that nodes 3, 6, 10, 61, 66, 77, 91, 92, and 93 were residential loads; nodes 35, 38, 78, and 79 were commercial loads; nodes 44, 47, 48 were industrial loads; and nodes 114, 117, and 119 were educational loads.  
According to the optimization model constructed in this paper, the IMOPSO algorithm was used to solve the calculation example. The installation position and capacity of the electric regenerative heating equipment are shown in Table 6.  
(2) Node sensitivity analysis before and after comparison  
This system is more complex than IEEE-33, so it takes a longer time to calculate. The comparative analysis before and after the node sensitivity analysis is shown in Table 7.  
\begin{table}
\begin{tabular}{c c} \hline
**Node Number** & \(\mathbf{P_{DG}}\)/\(\mathbf{MW}\) \\ \hline  
\end{tabular}
\end{table}
Table 6: Optimized configuration results.  
Figure 14: Percentage improvement of voltage deviation relative to the standard value.  
Figure 12: Voltage fluctuation curve.  
Figure 13: Percentage improvement of current harmonic distortion rate relative to the standard value.
# Efficient Transcoding and Encryption for Live 360 CCTV System
## 2 Related Work
### Video Transcoding  
Video transcoder studies are reviewed in [8; 9]. Regarding transcoding, there are some concepts commonly used in multimedia tasks:  
* Transcoding is the process at a high level of retrieving compressed (or encoded) content, the next is decompressing (or decoding) and then changing somehow to recompress it again. For example, we can change the audio/video format (codec) from MPEG-2 video source to H.264/AVC video and Advanced Audio Coding (AAC) audio. Other tasks might be adding various digital contents to a video stream.
* Trans-rating specifically focuses on changing the bitrate, such as taking the 4K Ultra-HD video input stream at 10 Mbps and converting it to one or more lower bitrates: HD at 4 Mbps, 2 Mbps, 1 Mbps, etc.
* Trans-sizing refers specifically to video frame change (can be understood as up-sampling, down-sampling). Normally, the video source often has high video frame size such as 4K (4096 \(\times\) 2048), 8K, or even 12K. Thus, the trans-sizing usually is downgrading the resolution, for example downgrading from 4K ultra-high-definition (UHD) resolution (3840 \(\times\) 2160) to 1080p (1920 \(\times\) 1080) or 720p (1080 \(\times\) 720).  
Generally, transcoding is the combination or one or all of the above methods. The video conversion requires intensive computational power, so transcoding often requires acceleration capabilities of central processing units (CPUs) or graphics processing unit (GPU). [PERSON] et al. [10] proposed a solution for H.264/AVC to HEVC transcoding using parallel processing based on multicore platforms. Their proposed method speeds up the transcoding time to 5 frames/s for 720p and 1.5 frames/s for 1080p with the specified hardware platform. [PERSON] et al. [11] proposed a method to reduce the transcoding complexity based on deriving an optimal strategy. They proposed their system for using various transcoding techniques to reduce the complexity in both prediction unit (PU) and coding unit (CU) at optimization levels. The authors achieved an effective approach that can reduce the complexity by around 82% while maintaining the bitrate loss below 3%. [PERSON] et al. [12] proposed an adaptive fast quadtree level decision (AFQLD) algorithm. The main idea is AFQLD algorithm to exploit the information gathered at the H.264/AVC decoder in order to make faster decisions on CU splitting in HEVC using a Naive-Bayes probabilistic classifier, which is determined by a supervised process of data mining. The AFQLD algorithm for the three levels achieves a quantitative speedup  
Figure 5: (**a**) Front face of 3 \(\times\) 2 cubemap projection (CMP); and (**b**) front face of 3 \(\times\) 2 adjusted cubemap projection (ACP).  
of approximate 2.31 times on average with the time reduction of around 56.7%. In this paper, the proposed method uses an efficient library from Intel to implement transcoding task with advanced points. This library supports multiple threads for transcoding in various kinds and allows transcoding both video and audio streams.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 3 Overview of Radio Propagation Measurements in Industrial Environments
The PLE values were 1.98 and 2.15 at 28 GHz for light and heavy industry, respectively, along a 35 m LoS Tx-Rx separation distance. For NLoS scenario at 28 GHz, the PLEs were 2.3 and 5.3 for light and heavy industry, respectively. At 60 GHz, the PLEs were 2.1 (light industry), 1.9 (heavy industry) and 2.4 (light industry), 5.7 (heavy industry) for the LoS and NLoS scenarios, respectively. For LoS scenarios, the mean RMSDSs at 28 GHz and 60 GHz were almost identical with values 13.7 ns (at 28 GHz) and 13.4 ns (at 60 GHz) for light industry and 38.5 ns (at 28 GHz) and38.3 ns (at 60 GHz) for heavy industry. For NLoS scenarios, the RMSDS values were also almost identical at 28 GHz and 60 GHz with values of 29.1 ns (at 28 GHz) and 29.3 ns (at 60 GHz) for light industry and 49.4 ns (at 28 GHz and 60 GHz) for heavy industry. The physical layer channel characteristics like the multipath delay profile and the link-layer parameters like throughput were investigated based on the conducted measurements at 60 GHz in different industrial sites [52]. The reported studies of mmWave channel measurements in industrial indoor environments are summarized in Table 2.  
After a thorough literature review, it can be found that measurements of radio channels in industrial environment are very limited, particularly at high frequencies. In various industrial settings, there is an absolute need for more measurements to understand the wave propagation mechanism accurately. The radio channel characterizations in different indoor industrial environments need more studies at different candidate frequencies in the sub-THz band as it is the promising spectrum for 5G, 6G, and beyond. To the authors best knowledge, industrial radio channel propagation studies at frequency bands above 100 GHz are lacking in the literature. This paper presents a study of wideband channel characteristics in three different industrial environments in the 107-109 GHz frequency band. The path loss models and the K-factor are investigated. The temporal characteristics are analyzed with respect to various metrics, like the power delay profile (PDP), mean excess delay (MED), and RMSDS.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 2 Background
### Taxonomy
#### 2.1.2 Data Modality Specific vs. Data Modality Agnostic  
Data modality specific refers to explanation methods that are only applicable for a certain data type. For example, some methods only work with MR images and some methods only work for tabular clinical records. An example of a method that only works for images is Grad-CAM [84] which does not apply to other data modalities such as textual or tabular data. These data modality specific approaches are usually coupled with model-specific explanation approaches. For example, some explanation methods use feature maps from convolution operations to calculate what information the model uses to make a prediction. Data modality agnostic refers to explanation methods that work for any data type. An example of this is LIME [85] which can provide explanations for image, tabular, and textual data. These are useful for clinical implementation due to the ability to handle a wide range of data used to make clinical decisions. These methods commonly use surrogate or perturbation based approaches to create a general approach for model explanation.
## 7 Ablation Study
### Robustness Analysis Under Varying Training Sample Sizes  
To assess the robustness of our SpikeCAN model with respect to the number of training samples per class presented in [47], we conducted experiments on the CWRU  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Without** & **Accuracy** & **F1 Score** \\ \hline SpikeCAN & - & 96.31\% \(\pm\) 0.76\% & 0.9560 \\ SpikeCAN One AM & Attention Module 2 & 96.08\% \(\pm\) 0.70\% & 0.9526 \\ SpikeCAN No AM & Attention Module 1, 2 & 95.34\% \(\pm\) 0.57\% & 0.9436 \\ SpikeCAN No dilation & dilation & 94.09\% \(\pm\) 1.32\% & 0.9305 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of ablation methods.  
Figure 6: Confusion matrices illustrating the classification performance across different ablation scenarios compared with the original SpikeCAN model. (**a**) SpikeCAN. (**b**) SpikeCAN One Attention block. (**c**) SpikeCAN No Attention block. (**d**) SpikeCAN No dilation.  
10-class classification dataset. We systematically reduced the number of samples per class to 50, 40, and 30, and evaluated the model's performance under each condition using five-fold cross-validation, summarized in Table 6.  
Our results reveal that SpikeCAN maintains robust performance, with only a moderate decline in F1 Score of approximately 0.0847 when reducing the training samples from 50 to 40 per class, although this decrease is statistically significant (t-statistic = 2.9093, \(p=0.0196\)). This suggests that 40 samples per class can be a practical lower bound for reliable model training under the current experimental conditions. However, further reduction to 30 samples per class induces a pronounced degradation in performance, with an F1 Score drop of approximately 0.2119 relative to the 50-sample baseline (t-statistic = 5.9901, \(p=0.0002\)). Additionally, the difference in F1 Score between 40 and 30 samples per class is about 0.1272 (t-statistic = 2.8178, \(p=0.0225\)), reinforcing that the performance decline accelerates beyond the 40-sample threshold. These findings highlight an important trade-off between training data availability and diagnostic accuracy, emphasizing the necessity of sufficient sample sizes to ensure dependable fault diagnosis in practical industrial applications.
###### Abstract  
A comparison between a traditional packed column and a novel membrane contactor used for CO\({}_{2}\) absorption with carbonate production is addressed in this paper. Membrane technology is generally characterized by a lower energy consumption, it offers an independent control of gas and liquid streams, a known interfacial area and avoids solvent dragging. Those advantages make it a potential substitute of conventional absorption towers. The effect of the concentration and the flow rates of both the flue gas (10-15% of CO\({}_{2}\)) and the alkaline sorbent (NaOH, NaOH/Na\({}_{2}\)CO\({}_{3}\)) on the variation of the species present in the system, the mass transfer coefficient, and the CO\({}_{2}\) removal efficiency was evaluated. Under the studied operation conditions, the membrane contactor showed very competitive results with the conventional absorption column, even though the highest mass transfer coefficient was found in the latter technology. In addition, the membrane contactor offers an intensification factor higher than five due to its compactness and modular character.  
CO\({}_{2}\) capture; membrane contactor; packed column; intensification +
Footnote †: journal: Applied Physics Letters
## 7 Recommended Framework
### Enabling Technologies  
IoT systems encounter significant challenges, including inefficient clustering, poorly planned node deployment, and the lack of effective coordination among limited resources. Addressing these issues is essential for improving energy efficiency and overall system performance. Several enabling technologies offer promising solutions to these challenges, playing a crucial role in advancing energy-efficient and high-performing IoT systems. These technologies are particularly effective in tackling the complex demands of modern IoT networks:  
* Wireless Sensor Networks (WSNs): WSNs are comprised of sensor nodes that detect and relay data. Improving WSNs through optimised clustering, strategic node distribution, and efficient path selection can increase energy efficiency while meeting Quality of Service (QoS) requirements. By managing constraints such as limited energy resources and memory, WSNs can significantly contribute to sustainable IoT operations.
* Fuzzy Logic Systems: As a predictive technology, fuzzy logic is adaptable for various IoT tasks, such as load balancing and task scheduling. Its ability to adjust to specific system requirements makes it a valuable tool for energy optimisation, allowing for tailored solutions that reduce unnecessary power use while ensuring task prioritisation and resource allocation.
* Bio-Inspired Algorithms: These algorithms provide solutions inspired by biological processes to solve computational problems in IoT. By implementing bio-inspired optimisation techniques, IoT systems can improve routing and clustering efficiency, leading to better energy management and enhanced overall performance.
# Enhanced Modeling for Analysis of Fine Particulate Interactions with Coated Surfaces
###### Abstract  
The adhesion of Martian surface dust to solar panels has been a longstanding challenge for Mars mission researchers. Anti-dust coatings have been developed to deter the adsorption of dust particles onto these solar panels. However, current ground testing methods struggle to accurately verify and assess the coating's ability to inhibit dust particle adsorption. Consequently, this paper introduces a computational model capable of calculating the contact force between fine particles and the coated surface. This model, grounded in the classical adhesive elastic interactions paradigm, establishes a normal force solution by approximating the pressure distribution on the contact area between the sphere and the coating, subsequently computing the composite force acting on the particle. Utilizing the API module of the EDEM simulation platform, we conducted simulations of the motion of fine particles on both smooth and coated plates. The results reveal that van der Waals forces are more prominent for light-mass particles, and the application of the coating can diminish the pull of these forces, facilitating easier detachment of dust particles from the plate surface.  
Martian dust; fine particles; particle interactions; discrete element method +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions
### Effect of Concentration and Adsorption Models  
The concentration effect was determined at 25; 50; 75; 100 and 125 mg/L at a constant temperature (25 \({}^{\circ}\)C), and the plots are indicated in Figure 6. The ibuprofen adsorption trend of Raw-RGL and AC-RGL illustrates increased adsorption with increased concentration. The adsorption capacities of Raw-RGL and AC-RGL increase with maxima of 74. 12 and 113.76 mg/g, respectively. The plots show an increased uptake up to 75 mg/L and a decrease after that. The adsorption trend indicates that the AC-RGL had better performance than Raw-RGL. The higher sorption of ibuprofen on AC-RGL could be associated with more active sites being exposed and a higher surface area after the chemical activation of carbon [53].  
A comparison of the two models is shown in Table 4 and attained at 298 K (25 \({}^{\circ}\)C). [PERSON] and [PERSON]'s isotherms are fitted to estimate the interaction behaviour of ibuprofen onto Raw-RGL and AC-RGL. Table 3 shows that the adsorption data of ibuprofen by Raw-RGL and AC-RGL best fitted the [PERSON] model with high regression coefficient (r\({}^{2}\)) values of 0.998 and 0.997, respectively. The Freundlich model proposes that the adsorption forms a multilayer adsorbent on the heterogenous surface [46]. A similar observation was made by [PERSON] et al. (2019) [54] during the adsorption of IBP.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbent**} & **Surface** & **Micropore** & **Mesopore** & **Total Pore** & **Zeta** \\  & **Area** & **Volume** & **Volume** & **Volume** & **pH(pzc)** & **Potential** \\  & **(m\({}^{2}\)/g)** & **(cm\({}^{3}\)/gh)** & **(cm\({}^{3}\)/g)** & **(cm\({}^{3}\)/g)** & & **(mV)** \\ \hline Raw-RGL & 1.70 & - & - & - & 7.32 & 22.56 \\ AC-RGL & 17.69 & 0.500 & 0.103 & 0.606 & 6.61 & 41.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physicochemical characterization of Raw-RGL and AC-RGL adsorbents.  
Figure 5: Proposed interaction of Raw-RGL and AC-RGL adsorbents with IBP.  
Figure 6: Effect of concentration on ibuprofen uptake by Raw-RGL and AC-RGL adsorbents.
# A Critical Review on Obesity: Herbal Approach, Bioactive Compounds, and Their Mechanism
## 4 Herbs That Control Obesity
### Zingiber officinal  
Commonly known as ginger, it is a herbaceous perennial plant that grows up to 1 m in height [105]. The herb is native to Southeast Asia, the Indian subcontinent, China, and New Guinea. This herb is consumed all over the world for culinary and medicinal uses. The main bioactive compounds for herb's pharmacological properties such as anti-inflammatory, anti-diabetic, anti-cancer, anti-obesity, anti-arthritis, anti-bacterial, anti-fungal, etc. [106] are zingerone, gingerols, shogaols, and paradols [107]. The primary active compounds responsible for its anti-obesity effects are 6-gingerol and 6-shogaol. It is reported that 6-gingerol and 6-shogaol can effectively suppress the differentiation of 3T3-L1 preadipocytes into adipocytes and decrease the levels of triglycerides. They inhibit lipid accumulation and reduce glycerol-3-phosphate dehydrogenase (GPDH) activity. They also decrease the mRNA expression levels of adipogenesis-related transcription factors such as PPAR-\(\gamma\), C/EBP-\(\alpha\), and their main lipogenic enzymes such as fatty acid synthase (FAS) and acetyl-CoA carboxylase (ACC). Additionally, they inhibit the activity of pancreatic lipase and amylase, which leads to a decrease in plasma and tissue lipids [108; 109; 110].  
Several clinical trials have been conducted on different herbs for treating obesity in humans and to observe the efficacy of herbs such as _Nigella sativa_, _Hibiscus sabdariffa, flex paraquariensis, Coffee arabica_, _Caralluma fimbriata, Panax ginseng_, etc., as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Herb** & **Dose** & **Duration** & **Outcome** & **References** \\ \hline \multirow{4}{*}{_Nigella sativa_} & 1500, 1600, 2000, & \multirow{4}{*}{6–8 weeks} & Reduction in body weight, BMI, waist and hip & \multirow{4}{*}{[111; 112; 113; 114]} \\  & 3000 mg/day & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \multirow{4}{*}{_Hibiscus sabdariffa_} & \multirow{4}{*}{75, 500, 1000 mg/day} & \multirow{4}{*}{4–12 weeks} & Decreased body weight, BMI, body fat percentage, fat & \multirow{4}{*}{[115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 1
## 6 Discussion
### System-Level Performance Evaluation
#### 6.3.2 Flow Stability Enhancement  
Comparative analysis identifies Configuration 4 (24% wave spring stiffness enhancement) as optimal, eliminating pressure decay through enhanced damping. Spring optimization reduces pressure perturbations by 90% during valve transitions while maintaining volumetric efficiency. Transient flow simulations (Figure 9) reveal 6.3 \(\pm\) 0.1% flow rate deviations from theoretical predictions (\(Q_{t\_{\text{max}}}\) = 40.0316 kg/s), attributable to kinematic hysteresis effects (0.04-0.05 s valve lag). The cushioning-induced valve oscillation during closure creates measurable flow disturbances, emphasizing the need for balanced spring dynamics. The variation in flow rate due to stroke number demonstrates the importance of balancing drilling pump performance enhancement and delaying failure of key components.
## 4 Conclusions  
In conclusion, we measured the absolute frequency and studied the hyperfine structure of the R(106)50-0, R(100)49-0, R(84)47-0, R(59)45-0, P(82)47-0, and P(71)46-0 lines of molecular iodine at 514 nm using high-resolution spectroscopy and a narrow-linewidth diode laser. The hyperfine splitting of each line was fitted to a four-term Hamiltonian with an uncertainty of several kilohertz to obtain accurate hyperfine constants for the line. The measurement of absolute frequencies and hyperfine structures of these lines are valuable for practical applications, such as telecommunication and optical frequency combs.  
Conceptualization, F.-L.H.; methodology, S.M., D.A. and F.-L.H.; investigation, S.M., Y.I. and F.-L.H.; data curation, Y.I., D.A. and F.-L.H.; writing--original draft preparation, S.M. and F.-L.H.; writing--review and editing, D.A.; funding acquisition, F.-L.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Line** & \(\Delta\)_eQq_ **(MHz)** & \(\Delta\)_C_ **(kHz)** & \(\Delta\)_d_ **(kHz)** & \(\Delta\delta\) **(kHz)** \\ \hline R(106)50-0 & 1886.649 (17) & 398.308 (10) & \(-\)221.07 (56) & 55.12 (45) \\ R(100)49-0 & 1887.605 (9) & 355.587 (7) & \(-\)199.36 (43) & 38.10 (45) \\ R(84)47-0 & 1889.559 (8) & 284.410 (5) & \(-\)158.50 (23) & 15.97 (21) \\ R(59)45-0 & 1891.696 (3) & 228.863 (3) & \(-\)126.39 (7) & 3.80 (5) \\ P(82)47-0 & 1889.654 (4) & 281.774 (4) & \(-\)156.49 (13) & 15.16 (13) \\ P(71)46-0 & 1890.699 (3) & 252.942 (2) & \(-\)140.33 (12) & 8.56 (9) \\ \hline \end{tabular}
\end{table}
Table 4: Hyperfine constants obtained from fitting.  
**Funding:** This research was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI 24K01382 and the Japan Science and Technology Agency (JST) Moonshot R&D (Grant No. JPMJMS226C).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The authors thank [PERSON] for her technical assistance with the experiments.  
**Conflicts of Interest:** The authors have no conflicts of interest to declare.