esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Classification and Localization Scheme for Power Distribution Network
## 2 Wavelet Transforms and Feature Extraction
### Standard Deviation and Mean Value  
The standard deviation \((\sigma)\) of the signal is a measure of how the signal deviates from its own mean value and is mathematically expressed as:  
\[\sigma(t_{1},t_{2})=\sqrt{\left(\int_{t_{1}}^{t_{2}}(x(t)-\overline{x})dt \right)} \tag{3}\]  
where \((t_{1},t_{2})\) denotes the time limit of signal \(x(t)\), and \(\overline{x}\) is the mean average and is mathematically expressed as:  
\[\overline{x}=\frac{1}{t_{2}-t_{1}}\int_{t_{1}}^{t_{2}}x(t)\;dt \tag{4}\]  
Under no-fault conditions, \(\sigma=1\), and under fault conditions the value deviates from one. The mean (\(\overline{x}\)) value of the signal is the average value of the signal and is equal to zero under normal conditions; the (\(\overline{x}\)) value is not equal to zero under fault conditions.
## 3 System and Channel Model  
### System Model  
Consider a wireless mobile ad-hoc network comprising \(N\) pairs of device-to-device (D2D) users with the assistance of \(M\) RIS and utilizing \(J\) RBs, as illustrated in Figure 1. Each D2D pair consists of transmitters (Tx) and receivers (Rx), equipped with \(N_{T}\) and \(N_{R}\) antennas, respectively. Additionally, each RIS is equipped with \(R\) electronically controlled elements, serving as passive relays in the network.  
In this scenario, the \(N\) pairs of D2D users possess no information about the RISs or other D2D pairs beyond themselves. At each time slot, the \(i\)-th D2D pair has the flexibility to select any RIS or RB. Let \(D_{i}^{r}\) and \(D_{i}^{l}\) represent the receiver and transmitter of the \(i\)-th D2D pair, respectively. The received signal at \(D_{i}^{r}\) from \(D_{i}^{l}\) with the assistance of RIS \(m\) on RB \(j\) can be expressed as follows:  
\[y_{i}(t)=\mathbf{h}_{i,j}^{H}(t)\mathbf{x}_{i}(t)+\mathbf{f}_{i,m,j}^{H}(t) \mathbf{\Theta}_{i,m,j}(t)\mathbf{g}_{i,m,j}(t)\mathbf{x}_{i}(t)+n_{i}(t), \tag{1}\]  
where \(\mathbf{h}_{i,j}^{H}(t)\) represents the direct wireless channel from the \(i\)-th transmitter (\(Tx\)) to the \(i\)-th receiver (\(Rx\)) using the \(j\)-th \(RB\). The phase shift matrix \(\mathbf{\Theta}_{i,m,j}(t)\) corresponds to the \(m\)-th RIS and is utilized for the \(i\)-th pair of transmitter-receiver using the \(j\)-th RB. It is defined as \(\mathbf{\Theta}_{i,m,j}(t)=diag[e^{\mathbf{i}_{0}(t)},e^{\mathbf{i}_{0}(t)},\dots,\)\(e^{\mathbf{i}_{R}(t)}]\in\mathbb{C}^{R\times R}\). Here, \(\mathbf{f}_{i,m,j}(t)\in\mathbb{C}^{M\times 1}\) and \(\mathbf{g}_{i,m,j}(t)\in\mathbb{C}^{M\times N_{Tx}}\) represent the wireless channels between the \(i\)-th transmitter and the \(m\)-th RIS, as well as between the \(m\)-th RIS and the \(i\)-th receiver, respectively, both using the \(j\)-th RB. The received signal \(y_{i}(t)\) at the \(i\)-th receiver is affected by noise \(n_{i}(t)\), where \(n_{i}(t)\) follows an additive white noise distribution \(\mathcal{CN}(0,\sigma_{k}^{2})\).  
Figure 1: Multi-RIS-assisted ad-hoc wireless network.  
The transmitted signal is given as  
\[\mathbf{x}_{i}(t)=\sqrt{p_{i}(t)}\mathbf{q}_{i}(t)s_{i}(t) \tag{2}\]  
where \(p_{i}(t)\), \(\mathbf{q}_{i}(t)\), and \(s_{i}(t)\) denote the transmit power, the beamforming vector at the transmitter (\(Tx\)), and the transmitted data to the receiver (\(Rx\)), respectively. Combining these elements, let \(\mathbf{W}_{i}=\sqrt{p_{i}(t)}\mathbf{q}_{i}(t)\), representing the power of the transmit signal.  
Considering the maximum transmit power constraint, the expression for the power of the transmit signal is given by  
\[\mathbf{E}[|\mathbf{x}|^{2}]=tr(\mathbf{W}_{i}^{H}\mathbf{W}_{i})\leq P_{max} \tag{3}\]
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 4 Experiments and Analysis
### Clustering Evaluation Metrics  
In order to quantitatively estimate the effectualness of clustering in a computational model, the performance was assessed using different evaluation metrics. We used the values of the Fowlkes and Mallows Index (FM-index) [37] and the Adjusted Rand Index  
Figure 3: Schematic diagram of the 6 protein–protein interaction networks (here, only the network composed of randomly selecting up to 110 protein interactions is shown). The colored lines indicate the extent of protein-protein interactions, and the closer the color to purple, the more similar the biological information contained.  
(ARI) [38]. Before introducing these two types of evaluation metrics, let's first introduce the relevant terms involved. Consider a dataset with n points. For binary classification, the rare class is referred to as the positive class, and the majority class is referred to as the negative class. The confusion matrix, summarizing the correct and incorrect predictions of the classification model, is shown in Table 1 below:  
From the above, clustering usually uses indicators to evaluate the degree of consistency between clustering results and actual data. For network data, we want to assign two nodes to the same cluster if and only if they are similar. Therefore, in cluster evaluation metrics, TP represents the assignment of two similar nodes to the same cluster. TN represents the assignment of two dissimilar nodes to different clusters. FP represents the assignment of two dissimilar nodes to the same cluster. FN represents the assignment of two similar nodes to different clusters.  
The equation of FM-index value [37] is  
\[FM-index=\sqrt{\frac{TP}{TP+FP}\cdot\frac{TP}{TP+FN}} \tag{8}\]  
The equation of ARI value [38] is  
\[ARI=\frac{RI-E[RI]}{\max(RI)-E[RI]} \tag{9}\]  
where \(RI=(TP+TN)/C_{2}^{n_{samples}}\); \(C\) is the total number of possible element pairs that can be formed in the dataset. The value range of the FM-index is [0, 1]. The larger the value of the FM-index, the better the clustering effect, indicating a more accurate representation of the factual situation. The value range of ARI is [\(-\)1, 1]. Specifically, a value of 1 indicates that the clustering result matches the fact almost perfectly, a value of 0 indicates that the clustering performance is the same as that generated randomly, and a value of \(-\)1 indicates that the clustering result completely contradicts the fact.
## 7 Conclusions  
To leverage the advantages of CAV in mixed traffic flow and enhance intersection efficiency, this study thoroughly analyzes the car-following characteristics of mixed traffic and develops a speed guidance strategy. The main contributions and conclusions of this study are as follows. First, considering the different driving characteristics of CAV and HDV, the study incorporates reaction time and driver car-following behavior to construct a mixed-traffic-flow car-following model. This model improves the applicability of various car-following models in mixed traffic flow, aligning better with future realistic mixed traffic environments.  
Additionally, based on real-time information such as vehicle speed, position, and traffic signal status, and considering the uncertainty factors of leading vehicles, a speed guidance algorithm is constructed. This algorithm enables vehicles to pass through intersections without stopping or with minimal stopping, thereby enhancing intersection efficiency. While the manuscript uses a crossroad as an example, this model is applicable to all signal-controlled intersections, whether they are crossroads, roundabouts, or \"T\" intersections. Furthermore, simulation experiments were conducted where detectors were set up to measure the delay time and the number of stops for vehicles at intersections under both guided and unguided conditions. These experiments verify the effectiveness of the proposed speed guidance algorithm. The results indicate that the proposed speed guidance algorithm can reduce the number of stops and delays for vehicles at intersections to a certain extent. Moreover, when the mixed traffic flow is constant, a higher penetration rate of CAVs leads to a better guidance effect.  
Lastly, the research focuses on intersections outside urban areas, where the algorithm is applicable under free-flow and stable-flow conditions. However, it should be noted that when traffic volume increases to the point of causing congestion, the effectiveness of the guidance is significantly reduced.  
Conceptualization, H.L.; methodology, H.L.; software, K.N.; validation, K.N. and H.W.; formal analysis, H.W. and Z.W.; investigation, A.S.; resources, A.S. and Z.W.; data curation, A.S., Z.W. and Z.Z.; writing--review and editing, H.W.; visualization, H.W.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Science and Technology Project of Hebei Education Department, project No. QN2020151. The aim was to investigate the economic performance optimization of autonomous vehicles under platooning mode. We would like to express our gratitude to Shijiazhuang Tiedao University for providing the research infrastructure and technical support.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications
## 4 Discussion  
The system implemented with the proposed framework received positive ratings from the participants. The lowest scores in the questionnaire also match with the highest time required to recognize some gestures. For example, a participant gave a neutral rating for Q2 because he had difficulties replicating the Z gesture. Another participant had difficulties performing S9 and gave a neutral rating to Q1. As shown in Figure 8, most static gestures were detected about 1 s after being displayed to the participant. Dynamic gestures require a longer time to complete than static gestures, which is due to the fact that there is hand motion involved. Furthermore, in order for the participant to understand which gesture to perform, a short video is displayed, which needs to be understood first.  
Additionally, some recognition times are impacted by the hand configuration itself. For example, some subjects had problems shaping the hand properly due to either congenital characteristics of the hand or previous injuries (especially S3, S10, and S17). Along with the settings that a gesture had to correspond quite closely to the recorded gesture, this resulted in an overall longer recognition time. However, all participants were able to perform the desired gestures, and the questionnaire results are overall very positive. The measurements from the first part of the experiment helped reveal some potential problems with certain gestures. S1, for example, took a surprisingly long time to recognize. The participants had problems turning the hand exactly as it was shown to them in the video.  
The proposed implementation shows promising results for recognizing one-handed gestures. To the best of our knowledge, there is no one-handed gesture that cannot be designed, performed, and recognized with the framework. In addition, recording static and dynamic gestures is as simple as performing the desired gesture and subsequently pressing a key. The simplicity of the framework was confirmed when the gestures for the experiment were defined. It took no more than five minutes to design and implement the 25 gestures used in the evaluation. The framework can be used to implement various applications and is not bound to specific hardware. In order to support other hand tracking devices, the hand data provider has to be adjusted to support the target hand tracking SDK.  
Figure 10: Answers from participants to the given questionnaire. SA = Strongly Agree, A = Agree, N = Neutral, D = Disagree, SD = Strongly Disagree.
# Properties of Green Tea Waste as Cosmetics Ingredients and Rheology Enhancers
## 3 Result and Discussion
### Characterization of CNF as Cosmetic Raw Materials  
#### 3.4.1 As Humectant  
After being treated by DS on green tea waste, the water retention value (WRV) increased, that shown in Figure 3. The results show that the final amount of water removed in the green tea waste CNF treated has different effects with GTW DS 0 that do not have any pretreatment. The highest water retention rate in CNF DS 0.3 shows that at the same time and speed, the percentage of water in the sample after centrifugation is much higher than in other formulations.  
The mixture of sodium alginate (SA) and TEMPO-mediated oxidation of micro fibrillated cellulose (OMFC) from the cotton fiber was obtained from Whatman filter paper in ratios of 10/90, 20/80, and 30/70, and its WRV (%) is 734.6 \(\pm\) 17.1, 808.1 \(\pm\) 14.3, 846.7 \(\pm\) 19. [44], which has a higher WRV than green tea waste DS. Microcrystalline cellulose and homogeneous cellulose WRV purchased from MikroTechnik GmbH (C200) (Burgstadt (Miltenberg), Bavaria-Germany) increased significantly with the applied pressure and the degree of homogenization. The results also show that high treatment intensity (pressure: 500 b and homogeneity higher than 12.9) has a more significant effect on the increase in WRV (275% for C200 (50/12.9) and 330% for C200 (50/32.1)). These increases in WRV indicate that, based on the microscopic observations achieved, the increase in specific surface area is combined with the swelling caused by cellulose hydration [45]. However, hyaluronic acid (HA) with MW = 1000 kDa, Rg = 125 nm exhibits a relative water retention capacity retaining >30% of its initial water content after HA was hydrated in 82% relative humidity at 37 \({}^{\circ}\)C for ten days prior to desiccation conditions [46].  
Figure 3: Different water retention values from different DS\(-\)treated green waste biomass.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Width (nm)** & **Length (nm)** \\ \hline DS 0 & 591.7 \(\pm\) 2.3 & 2491.7 \(\pm\) 34.3 \\ DS 0.3 & 206.2 \(\pm\) 4.1 & 1318.7 \(\pm\) 55.9 \\ DS 0.4 & 70.9 \(\pm\) 7.5 & 840.5 \(\pm\) 108.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The particle size of green tea waste biomass.
## 6 Discussion
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature** & **Hand-Crafted Firewall Rules** & **AI-Generated Firewall Rules** \\ \hline
**Attack Generation Capabilities** & Limited to known signatures; updates are manual and reactive. & Can adapt to novel and evolving threats \\ \hline
**Defense Adaptability** & Slower to adapt; changes require human intervention to add or refine rules. & Faster adaptability; models can incorporate new threat intelligence and retrain on evolving attack patterns with only a few examples. \\ \hline
**Technical Expertise Required** & High; crafting and maintaining rules demand deep understanding of protocols, vulnerabilities, and patterns. & Moderately lower; leveraging pre-trained models reduces the need for low-level rule-writing, though ML expertise may be needed for fine-tuning. \\ \hline
**Computational Efficiency** & Generally efficient; simple pattern-matching is computationally inexpensive. & Variable; depending on model complexity and inference cost, may require more computational resources. \\ \hline
**Scalability** & Scales by adding more rules, but management complexity can increase substantially over time. & Potentially more scalable; a well-trained model can generalize broadly, though high throughput might require more computing resources. \\ \hline
**Limitations** & Prone to false positives if rules are too broad; limited coverage of novel threats due to slow update cycles. & LLM-based approaches may face token constraints and may be prone to hallucinations. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of hand-crafted and AI-generated firewall rules. newly observed and even previously unseen attacks. However, validation mechanisms are essential to address the risk of incorrect outputs caused by LLM hallucinations.
## 2 Materials and Methods
### Gilder Deployment and Data Collection
#### 2.2.3 Sea Mammal Vocalizations and Anthropogenic Noise  
To passively scan for marine mammal vocalizations and anthropogenic sound, two of the three automated vehicles were used. The Seaglider(r) was equipped with an Observer/AMAR G4 from JASCO [11; 50; 51] and recorded continuously down to 100 m depth. The Wave Glider was equipped with a hydrophone SoundTrap from OceanInstruments. To date, only the Seaglider(r) data were analyzed. To determine the different types of biological and anthropogenic sounds that occur in the LoVe area, the recordings were inspected visually and aurally using automatic detection and visualization software (i.e., PAMguard [52] and Raven [53]). Ship noise consists of broadband noise depending on the size and speed of the vessels, but most of the energy is at low frequencies (<1 kHz). Typically, the sound of a ship passing by can last for tens of minutes. Automatic detectors and manual spectrogram analysis identified biological sounds as well as weather events, vessel passages, and seismic activities. Ambient noise was defined as all sound recorded, except sound associated with a specified signal and the self-noise produced by the vehicle (e.g., pitch/roll maneuvers, apogee pump activity, etc.). Prior to data analysis, all self-noise was detected and removed from the dataset using template matching [11].
## 3 Proposed OCC IoMT Platform (Design and Implementation)  
The IoMT is the practical application of IoT devices combined with medical technology for healthcare, facilitating the transfer of data between devices and health applications. The implementation of OCC within IoMT involves the use of light signals and camera sensors to create a secure and fast data communication system. In practice, OCC enables remote patient monitoring and real-time transmission of medical data to healthcare providers. Figure 3 shows a block diagram of the IoMT OCC platform.  
Figure 3 shows the block diagram of the IoMT OCC form, and the process begins when the patient activates the health device. This process involves collecting data from oxygen, heart rate, and temperature sensors, which are vital indicators in health monitoring and are equipped with a panic button. A data encoder then encrypts the data collected by these sensors. This encrypted data is then transmitted through a matrix of LEDs using the On-Off Keying (OOK) modulation technique. Each LED has its own identity and acts as an anchor in data communication, allowing the identification of specific LED locations and ensuring the accuracy of the received data source.  
The diagram also shows the process of detecting missing bits and decoding data, demonstrating an error correction system that ensures data integrity. The system also uses a camera to receive the encrypted light signals. Once the data is decoded, the system reassembles the data that may be divided into several packets, and then sends it to the edge server. This server acts as a bridge between IoT devices and larger cloud infrastructure, performing initial data processing, reducing communication latency, and enabling rapid response in emergencies.  
Finally, the edge server sends the data to the IoMT platform, which can be accessed by healthcare providers. This provides the possibility for real-time patient monitoring, which is crucial for proactive and responsive healthcare.
## 3 Materials and Methods
### Deblurring Pipeline
#### 3.2.2 Deblurring  
The process of deblurring was achieved using high-resolution reconstruction. For the images in the low-resolution group, the features that were extracted by the neural network were particularly ambiguous, which was not conductive to subsequent classification. Thus, we utilized the super-resolution reconstruction framework SRGAN [34], with a scaling factor of 4, to generate high-quality images. Table 3 lists the resolution ranges of the categories in the dataset after deblurring.  
In the existing classification method that is based on deep learning, bilinear interpolation is a common means for enhancing images with different resolutions. Thus, we compared the deblurring effects of bilinear interpolation and SRGAN. Figure 6 shows several low-resolution images and their deblurred counterparts. From the figure, it can be seen that the images that were processed using the deblurring pipeline were obviously clearer than those that were processed using bilinear interpolation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Minimum Size** & **Maximum Size** \\ \hline _Artemisia_ & \(136\times 136\) & \(352\times 352\) \\ Chenopodiaceae & \(208\times 208\) & \(480\times 480\) \\ Cupressaceae & \(119\times 119\) & \(472\times 472\) \\ Poaceae & \(94\times 94\) & \(368\times 368\) \\ Moraceae & \(188\times 188\) & \(384\times 384\) \\ Pinaceae & \(161\times 161\) & \(640\times 640\) \\ _Salix_ & \(136\times 136\) & \(388\times 388\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The resolution ranges of the categories in the dataset after the deblurring process.
## 5 Conclusions and Future Work  
In this paper, an efficient geocast routing (EGR) approach using the temporal dependence of link quality for a highway traffic environment is presented. A geometrical localization for GPS outage problem and a temporal link quality estimation model are presented to support the EGR. We develop a framework for analysis that quantifies the likelihood of choosing the longest-lasting link out of a list of predetermined links between a transmitting vehicle and its surrounding vehicles. The EGR performs better under the shadowing fading channel situation while choosing a next forwarding vehicle using the probability measure. The performance of EGR in highway traffic environments is measured for both increasing vehicle density and increasing vehicle velocity. A comparative performance study based on simulations has been done to assess the efficacy and scalability of EGR in terms of resolving the issue of wireless communication link breakage and assuring message delivery in a highway traffic environment. It is evident from the examination of simulation results that EGR is superior than contemporary routing approaches for different vehicle traffic settings. In the future, we measure the performance of the proposed EGR in the presence of interfering vehicles for a highway traffic environment.  
Conceptualization R.Y.; formal analysis S.K.; investigation, R.Y.; methodology, R.Y.; supervision, S.K. and O.K.; validation, A.K. and D.; writing, R.Y.; review and editing, S.K., O.K., M.A. and J.L. All authors have read and agreed to the published version of the manuscript.  
The research is funded by the School of Computer and Systems Science, Jawaharlal Nehru University, India.  
The authors declare that they have no conflict of interest.