esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Framework
### Network Model Compression Based on VGG16  
With the development of deep learning theory, convolutional neural networks possessing powerful performance emerged in an endless stream, such as LeNet, AlexNet, GoogLeNet, VGG, ResNet, and so on. According to achievements published by the study field, to extract more abstract feathers, much deeper networks are utilized to be trained and more accurate recognition results are obtained simultaneously. However, improvement of the results' accuracy also brings some problems, which cannot be ignored, such as massive datasets and large memory devices. As a result of it, we decided to compress the VGG16 network, and the compressed network was used to extract feathers in the field of lip pictures. VGG is a deep network with a more perfect performance developed based on convolutional neural networks, which was born in the Computer Vision Group of Oxford University, and is a major research achievement. First of all, the accuracy of the VGG which won the championship in the 2014 ImageNet competition is better than the other models. Then, VGG possesses strong transfer learning capabilities, high scalability, and generalization capabilities. Moreover, VGG16 is a simple network model formed by a pure convolutional layer simultaneously. From the experience of AlexNet, a smaller convolution kernel and more network layers can improve accuracy. The network structure diagram of VGG16 is shown in Figure 2 (Conv, convolutional layer; Relu, rectified linear unit; FC, fully connected layer).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Baseline** & **Prune0.6** & **Baseline** & **Prune0.6** \\  & **-CIFAR10** & **-CIFAR10** & **-CIFAR100** & **-CIFAR100** \\ \hline Top1-acc (\%) & 93.77 & 93.78 & 72.12 & 73.32 \\ Parameters (M) & 20.04 & 2.25 & 20.04 & 4.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pruning performance verification of VGG16 on the CIFAR dataset.  
Figure 1: Schematic diagram of pruning filter method.  
Furthermore, a 3 \(\times\) 3 size fixed convolution kernel and 2 \(\times\) 2 size pooling layer are utilized to build a network model with a depth of 16-19, which is the foremost feather of VGG. Its design makes the VGG possess an advantage in the part of parameters and calculations. This paper used its powerful feature extraction capabilities to extract visual features of lip language, and Figure 3 shows the ratio of time occupied by the convolutional layer involved in feature extraction during the extraction process.
# Positron Emission Tomography in Cerebral Amyloid Angiopathy: A Scoping Review
## 3 Results
### Hereditary CAA  
Five papers collectively provided a deeper understanding of hereditary CAA subtypes, emphasizing their unique clinical and imaging profiles. Hereditary CAA results from mutations such as Dutch-type CAA (APP E693Q), APP duplications, ATTR mutations, and others. PET imaging with Pittsburgh Compound B--[11C]Pb--serves as a consistent marker for amyloid pathology across all forms of hereditary CAA. In particular, two studies focused on hereditary ATTR amyloidosis, providing insights into amyloid-related imaging features such as cSS and CMBs. An elevated amyloid burden detected via [11C]PiB PET highlights parallels with hereditary CAA in amyloid deposition [44; 45].  
Studies on Dutch-Type CAA [46; 47] identified frequent cortical microinfarcts, extensive WMH, and CMBs, underscoring the impact of this mutation on cerebrovascular integrity. In addition, a strong correlation was proposed between amyloid deposition, as measured by PiB PET, and imaging markers of cerebrovascular injury.  
Patients with hereditary APP duplication exhibited severe WMH, cSS, and numerous CMBs. Notably, [11C]Pib PET demonstrated extensive amyloid deposition, including parenchymal areas beyond blood vessels. This finding highlights the diffuse impact of APP mutations [48].  
The main findings are summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Probable CAA** & **Possible** & **Controls** & \multirow{2}{*}{**Pathological Data**} & \multirow{2}{*}{**Key Findings**} \\  & **with ICH (N)** & **CAA (N)** & & & \\ \hline [PERSON] et al., & & & & & Higher amyloid deposition & PET imaging predicted ICH \\
2024 [41] & 56 & 18 & 34 & in occipital regions; SUVR & recurrence with 83\% sensitivity. & Occipital-to-global SUVR \\  & & & & 1.65 \(\pm\) 0.2 (\(p<0.001\)). & distinguished CAA from AD. \\ [PERSON] et al., & & & & & Regional amyloid correlated & Amyloid burden highest in \\
2021 [43] & 62 & 20 & 30 & with cortical microbleeds & occipital lobes in CAA patients, \\  & & & & (r = 0.58, \(p<0.01\)). & correlating with disease & severity and microbleeds. \\ [PERSON] et al., 2021 [42] & 59 & 21 & 28 & probable CAA (1.7 \(\pm\) 0.3) vs. AD. Atrophy patterns also & PET accurately differentiated & probable CAA from AD with \\  & & & & & ADC of 0.89 (\(p<0.001\)). & AUC of 0.89 (\(p<0.001\)). \\ [PERSON] et al., & & & & & Probable CAA with cSS \\
2018 [32] & 48 & 16 & 30 & cSS associated with increased & showed higher occipital & amyloid and greater atrophy in \\  & & & & occipital amyloid burden. & posterior cortices compared to \\  & & & & non-cSS. & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Findings of selected studies on pathology-proven CAA and amyloid PET.
## 7 Challenges, Opportunities, and Future Trends
### Future Trends in Energy Harvesting and Security
#### 7.3.3 Advances in Security Mechanisms  
The recent advances in security mechanism related to EH-WSNs are the following:  
* Quantum Cryptography: Quantum cryptography provides a new level of security by enforcing quantum mechanics rules during data transmission [135; 136; 137]. This technology could be built into EH-WSNs to improve data security.
* Bio-Inspired Security Approaches: Investigations into bio-inspired security mechanisms--that is, security based on biological processes--may yield novel approaches to protecting EH-WSNs from cyber attacks [138; 139].
* Adaptive Security Protocols: Future security protocols are predicted to be more adaptable, able to change their protection methods in response to real-time threat analysis and network conditions [140; 141].
# Low-Cost Goethite Nanorods for As (III) and Se (VI) Removal from Water
## 3 Results and Discussion
### Structural and Morphological Study
Furthermore, the systematic growth of \(\alpha\)-FeOOH NRs could be ascribed to the formation of O-H groups that is responsible for electron diffusion and then the plausible growth of small clusters. Of course, these clusters, in the absence of surfactants, grow in a particular surface to form 1-D \(\alpha\)-FeOOH NRs due to the highly attractive forces among the particles. Certainly, the uniformity of the nanorods is directly proportional to the uniform distribution of OH species in the solution (no local gradient of pH). Likewise, the Transmission electron microscope (TEM, FEI TECNAI G2) micrograph (Figure 3b,c) shows a uniform and straight nanorods resembling a cigar-like feature of ~50 nm diameter and ~400 nm length. The above dimensions correlate well with the obtained from the scanning electron microscopy (SEM) image. Finally, Figure 3d depicts the SAED pattern obtained from the nanorods. Indeed, the pattern shown in bright dots indicates a single crystalline structure of \(\alpha\)-FeOOH NRs. Obviously, the pattern is identified in two orthogonal directions (010) and (200) of the goethite structure, which is marked by inclined and perpendicular dotted lines [19], as shown in Figure 3d. Observably, the uniform distribution of the bright dots is an indication of the pure phase of \(\alpha\)-FeOOH [19]. To conclude, it can be inferred that the grown \(\alpha\)-FeOOH NRs are single crystalline structure and of a single pure phase. Figure 3: Plane view (**a**) Scanning electron microscope (SEM) micrograph; (**b**) and (**c**) Transmission electron microscope (TEM) micrographs; (**d**) Electron diffraction pattern of \(\alpha\)-FeOOH NRs (water was used as a solvent).
# Pigment Dyeing of Atmospheric Pressure Plasma-Treated Cotton Fabric
## 1 Introduction  
The use of pigment dyeing in textile materials is an environmentally-friendly process compared with the application of other dyes, because of the low use of chemicals, as well as the lack of a subsequent steaming and washing-off process [1]. Moreover, the adhesion of pigment in dyeing depends on the properties of the binder used, and thus, the final rubbing fastness is not generally good. In addition, a low uptake in pigment dyeing may occur if pigment application is not properly conducted. Pigment-dyeing performance is greatly dependent on the physical and chemical properties of the binder and the fabric. During pigment dyeing, the pigment solution is absorbed first on the fabric surface through capillary action by the padding process, followed by drying and curing. The size of pigments affects the absorption; the micrometer-sized pigment particles are normally deposited on the fibre surface because their molecular size is too large. Therefore, the pigments are adhered mechanically to the textile material surface by the binder. Since pigment application depends on the surface behaviour of the fibre, surface modification of the textile fibre helps enhance the pigment application. Among different surface modification techniques, plasma technology has been discussed for several decades. Textile materials can be treated with plasma to achieve the least fibre damage, but that can affect the outermost surface in regard to the depth of the nanometer scale. Plasma treatment can modify textile materials and improve properties such as hydrophilicity [2; 3; 4; 5], hydrophobicity [6; 7; 8], dyeability [9; 10; 11; 12], shrinkage resistance [13; 14], and adhesion to different chemical finishing materials [15; 16; 17]. In the case of dyeing textile materials with pigment, plasma has been proved to enhance the dyeing quality of silk and polyester [18; 19; 20]. However, the importance of pigment dyeing cotton is increasing in the market, and the effect of atmospheric plasma treatment on the colour quality of pigment dyed cotton fabric has apparently not been examined. This research is expected tobenefit cotton apparel manufacturers by providing valuable plasma treatment information related to pigment dyeing. Thus, the aim of this paper is to study the colour quality of pigment-dyed cotton fabric under the influence of different operational parameters of atmospheric pressure plasma treatment.
## 3 Control System Design
### AEB Control System Design  
In the design of the AEB system, Time to Collision (TTC) is a crucial metric for assessing the potential danger of colliding with obstacles [42; 43]. \(TTC\) can be calculated using Equation (5), where \(d\) represents the relative distance between the subject vehicle and obstacle vehicles, and \(v_{rel}\) represents the relative velocity between the two vehicles.  
\[TTC=\left|\frac{d}{v_{rel}}\right| \tag{5}\]  
Based on the calculated \(TTC\), corresponding control strategies can be formulated. The typical AEB control logic diagram based on the \(TTC\) model is shown in Figure 5.  
The specific control logic is as follows:  
1. \(TTC\)\(>2.6\) s: the AEB control system not triggered;
2. \(1.6\) s \(<TTC\leq 2.6\) s: the FCW control system gives an audible alarm;
3. \(0.6\) s \(<TTC\leq 1.6\) s: the AEB control system intervenes moderately\(\rightarrow\)40% braking;
4. \(TTC\leq 0.6\) s: the AEB control system intervenes seriously\(\rightarrow\)100% braking.  
Figure 4: Trajectory tracking control results.
## 9 Discussion, Contributions, and Future Directions
### Semantic Interoperability and Standardization  
To address semantic conflicts and ensure data consistency across heterogeneous sources, the system relies on standardized data management practices, including unified naming conventions and real-time data transformation. Incorporating industrial communication standards (e.g., ModbusTCP, OPC UA, Profibus) aids in delivering a seamless integration pathway. These measures minimize discrepancies, reduce data-cleaning overhead, and ultimately improve the overall reliability of downstream analytics and knowledge-generation pipelines.
## 4 Experiments
### Experimental Steps
#### 4.2.1 Baseline: LLM Agora  
Our study builds on the _LLM Agora_ framework, which applies the multi-agent debate method introduced by [PERSON] et al. [32]. This framework focuses on improving the quality of responses from open-source language models (LLMs) through iterative debates. The main steps are as follows:  
Step 1: Initial response generation  
Multiple open-source LLMs, such as Llama2, Vicuna2, and WizardLM2, are used to generate responses independently for the following tasks:  
* Math problems: Simple arithmetic operations involving six numbers.
* GSM8K dataset: Grade school-level math word problems.
* MMLU benchmark: Questions covering 57 subjects from STEM to humanities and social sciences.  
Step 2: Summarization of responses  
The responses from all models are summarized using a summarizer model, such as ChatGPT. The summary synthesizes key points from each model, removes redundancy, and serves as the input for the next round of debate.  
Step 3: Iterative debate  
Based on the summarized response, the models refine their answers over multiple rounds:  
* Each model updates its response using the summarized feedback.
* The _chain-of-thought (CoT)_ method is optionally applied to enhance logical reasoning and improve response quality.  
Step 4: Evaluation metrics  
The quality of the responses is assessed using the following metrics:  
* Accuracy: Correctness of responses compared to the ground truth.
* Reduction in hallucination: The extent to which factual errors are reduced across debate rounds.
* Efficiency: Resource usage, including the total tokens and computation time.
* Completion time: Time taken to reach the final response.  
Step 5: Results and Observations  
The baseline results show that multi-agent debate improves factuality and reasoning in open-source models. However, the effectiveness depends on the quality of initial responses, and challenges remain in ensuring consistent improvements.
## 1 Introduction
Subsequently, average frequency, kurtosis, the power of signal, median frequency, coefficient of dissymmetry, deciles, and peak frequency of PSD were calculated as frequency domain features. Furthermore, the length or duration of data is calculated as a new feature through dividing the signal into 10 equal length segments and calculating the root mean squre (RMS) of each segment. The K-nearest neighbors (kNN), linear discriminant analysis classifier (LDA), and ensemble of classifier have been applied to optimize the results of the system. The system will grant/deny access to the user from the captured sEMG biometrics identity as a signature that is based on hand gestures. Performance analysis of the biometrics system has been presented to validate the capacity of the system by estimating both the false acceptance rate (FAR) and the false rejection rate (FRR). The results presented in this paper showed that the KNN classifier exhibits a testing accuracy of 97.4% with a FAR of 0.2% and an FRR of 2.9%. Thus, the KNN classifier was found to be the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification.
## 3 Results and Discussion
### DBNPG and TBNPA Degradation  
DBNPG and TBNPA (Figure 1) degradation by the four-strain consortium (_Pseudomonas citronellosis_, _Gordonia siltwensis Shinella zoogloeides_, _Microbacterium oxydans_) was tested with and without the amendments yeast extract (YE), vitamin mix, or glucose. As degradation did not occur without any additions, the amendments were chosen to unravel the limiting factor for degradation and to examine whether metabolic complementation between the four strains exists. Degradation kinetics were tested with separate and mixed substrates: yeast extract (YE, 100 mg L\({}^{-1}\)), DL-vitamin mix (2 mL/l), and glucose (100 mg L\({}^{-1}\)) (Table 2).  
Simultaneous utilization of substrate mixtures is often accompanied by a decrease in degradation rates in relation to rates obtained when supplemented separately [51]. This phenomenon is due to a competitive inhibitory effect. In this study, competitive inhibition was observed, while TBNPA and DBNPG degradation rates decreased in the mixed substrate experiments. Various BTEX mixtures exhibited similar processes [19] during the biodegradation of toluene and phenol by _Burkholderia_ sp. JS150 A [24] and in cis-dichloroethene (cDCE) degrading microcosoms, where the addition of different substrates led to decreased cDCE degradation rates [21; 52].
## 4 Experiment
### Ablation Experiment  
To demonstrate the effectiveness of incorporating entity category information and the Multi-Relation-aware Multi-hop Attention Network (MRAN), we conducted ablation experiments, as shown in Table 5. The experiments consisted of four groups: (1) CAKGC model, (2) CAKGC model without entity category information, (3) CAKGC model without MRAN, and (4) CAKGC model without both entity category information and MRAN. The results in Table 5 indicate that the model's performance decreased when MRAN and entity category information were removed separately. This demonstrates the effectiveness of incorporating entity category information and the MRAN in improving the model's performance.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline C-LMKE\({}_{(BERT_{base})}\) & 0.593 & 0.534 & 0.623 & 0.683 \\ CAKGC\({}_{(BERT_{base})}\) & 0.612 & 0.566 & 0.635 & 0.703 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Link prediction experiments on the Thangka dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**FB15k-237**} & \multicolumn{4}{c}{**WN18 RR**} \\ \cline{2-9}  & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE & 0.279 & 0.198 & 0.376 & 0.441 & 0.243 & 0.043 & 0.441 & 0.532 \\ RotatE & 0.338 & 0.241 & 0.375 & 0.533 & 0.476 & 0.428 & 0.492 & 0.571 \\ DistMult & 0.241 & 0.155 & 0.263 & 0.419 & 0.430 & 0.390 & 0.440 & 0.490 \\ AutoBLM & 0.364 & 0.270 & - & 0.553 & 0.492 & 0.452 & - & 0.567 \\ Pretrain-KGE & 0.332 & - & - & 0.529 & 0.235 & 0.263 & 0.423 & 0.557 \\ KG-BERT & - & - & - & 0.420 & 0.216 & 0.041 & 0.302 & 0.524 \\ KRA & 0.296 & 0.205 & 0.322 & 0.482 & 0.401 & 0.243 & 0.491 & 0.709 \\ MEM-KGC\({}_{(BERT_{base})}\) & 0.346 & 0.253 & 0.381 & 0.531 & 0.557 & 0.475 & 0.604 & 0.704 \\ C-LMKE\({}_{(BERT_{ling})}\) & 0.406 & 0.319 & 0.445 & 0.571 & 0.545 & 0.467 & 0.587 & 0.692 \\ C-LMKE\({}_{(BERT_{base})}\) & 0.404 & 0.324 & 0.439 & 0.556 & 0.598 & 0.480 & 0.675 & 0.806 \\ SimKGC & 0.336 & 0.249 & 0.362 & 0.511 & 0.666 & 0.587 & 0.717 & 0.800 \\ CAKGC\({}_{(BERT_{ling})}\) & 0.416 & 0.335 & 0.451 & 0.571 & 0.579 & 0.512 & 0.614 & 0.704 \\ CAKGC\({}_{(BERT_{base})}\) & - & - & - & - & 0.636 & 0.544 & 0.698 & 0.795 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Link prediction experiments on the FB15k-237 and WN18 RR datasets.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Packet Types in ERPMSA-UWSN
#### 4.6.2 Node Density in PFZ Levels  
The node density in a certain place indicates less probability of void hole occurrence and the chances of getting the best nodes for forwarding. This results in two regions, i.e., the region with dense nodes but with more distance from a sink (PFZ1), and the region with less depth but with a sparse setup (PFZ2). The first choice PFZ1 can result in attaining high PDR on the cost of high APD and high energy use. On the other hand, in the second choice (PFZ2), low energy consumption and high PDR can be achieved because our proposed system topology outperforms in a sparse environment in terms of PDR and energy [43].  
In ERPMSA-UWSN, the next forwarder (Master) is selected only if it has PFNs in its PFZs and has enough energy for transmission. Consequently, this minimizes the probability  
Figure 3: Region division of PFZ1 and PFZ2.  
of void holes and energy holes in a less dense region. Therefore, to gain the desired output we prefer the first choice i.e., prioritizing PFZ1 over PFZ2 for master selection.  
We established the knowledge about node density in PFZ1 and PFZ2 based on the theoretical analysis that we carried out with the volumetric information and then justified it by experimental results. By applying Equation (6), we calculated the volume for the upper cap of the hemisphere (PFZ1) using parameters h = 1000 m and r = 2000 m. Similarly, for the lower partial hemisphere, we used Equation (7), for the calculation of volume. Comparing the volumes, we found out that PFZ2 has more capacity than PFZ1, indicating that the probability of nodes is higher in PFZ2 as shown in Figure 4.  
\[V_{upper}=\frac{1}{3}\pi(3 hr^{2}-h^{3}) \tag{5}\]  
\[V_{lower}=\frac{1}{3}\pi h(3r^{2}-h^{2}) \tag{6}\]