esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Imperfect Maintenance Improvement Factor
#### 4.4.5 Maintenance Interval Optimization Model  
The objective function is to minimize the maintenance cost per unit of time within the lifespan of transformers, which is expressed in the following equation. The corrective maintenance cost in the total maintenance cost takes into account the imperfect maintenance improvement factor, which can provide a more accurate estimation of the number of occurrences of defects of each severity in each maintenance cycle, making the results of the optimization model more convincing [45].  
\[\min\frac{C_{pm}+C_{cm}+C_{pr}}{NT} \tag{15}\]  
and  
\[\min\frac{(N-1)c_{pm}+\sum\limits_{i=1}^{N-1}\sum\limits_{s=1}^{3}\left(c_{cm} ^{s}\cdot n_{i}^{s}\right)+\left\lfloor\frac{N}{k}\right\rfloor\cdot c_{pr}}{ NT} \tag{16}\]  
The main constraint is that the unreliability of the defective component of the transformer during each maintenance cycle should be lower than a certain threshold \(F_{0}\).  
Since the threshold provided by the power grid company is the unreliable threshold \(F_{0}^{j}\) for each defect mode of the transformer, it is assumed that each defect mode is independent of each other. By adding up the unreliable thresholds of all defect modes contained in the target defect component, the unreliable threshold \(F_{0}\) for that defect component is obtained as follows:  
\[F_{0}=\sum\limits_{j=1}^{n}F_{0}^{j} \tag{17}\]  
where \(n\) is the number of all possible defect modes that may occur in the defective component. Also, due to \(R_{0}=1-F_{0}\) and the fact that the minimum value of the Weibull distribution fault probability density function in any interval can only appear at both ends of the interval, the constraint condition for \(\lambda_{0}^{i}=\min\left\{\frac{f(T_{i}^{-})}{R_{0}},\frac{f(T_{i}^{+})}{R_{0 }}\right\}\) is as follows:  
\[\begin{cases}\lambda_{i}(t,\delta_{i},T)\leq\lambda_{0}^{i}\\ T=\frac{T_{0}}{N}\\ N\in N_{+}\end{cases}i=1,2,\ldots,N \tag{18}\]  
Among them, \(\lambda_{i}(t,\delta_{i},T)=\frac{m}{t_{0}}\left(\frac{t-\frac{i}{j-1}\delta _{j}\cdot T}{t_{0}}\right)^{m-1}\) represents the failure rate function of the defective component in the \(i\)-th maintenance cycle.
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 3 Simulation and Analysis
### Result Simulation
#### 3.2.1 Weight Optimization Performance Analysis  
Set the optical signal modulation format to DP-QPSK and DP-16 QAM, analyze the effects of the optical signal to noise ratio (OSNR) to TD-CDE and FD-CDE, with weight-optimized time-domain chromatic dispersion equalization (WO-TD-CDE), such as Equation (12). The comparison is mainly reflected in two categories; the first is the longitudinal comparison to three kinds of filters, and the second is the transverse comparison to the same kind of filter. The control variable method is used to ensure that the simulation experiment is based on fairness. The variable in the first type of comparison is the CDE algorithm in DSP, and the variable in the second type of comparison is the length of the optical fiber link. Each test independently repeats thirty times to reduce random errors, as shown in Figure 5.  
From Figure 5, we can observe that the equalization performance of the filter in Equation (12) is better than that in Equations (2) and (4), which is more prominent in short-medium distance transmission and high OSNR, as shown in Figure 4(a). The performance advantage of Equation (12) is greater under the high-order modulation format in Figure 4(b). Therefore, using the second-order Nuttall self-convolution window function to optimize the weight of the filter coefficients can effectively improve the CDE effect of a short-medium distance coherent optical communication system, which is also suitable for long-distance transmission systems under high OSNR.
## 2 Materials and Methods
### Yield Data  
Yield data were acquired by yield monitors equipped with commercial combine harvesters. Five combines were selected. The software used was provided by Topcon (Topcon, Tokyo, Japan) [56] and Trimble (Trimble Navigation Limited, Westminster, CO, USA) [57] companies. Each software estimates the volumetric grain flow with two optical sensors equipped on the combine, before the hopper. The result is a yield map in kg\(\cdot\)ha\({}^{-1}\), using a spatial resolution of 7.6 m (cutting width). The possible variability due to the machine operator, software, and conditions of each field was corrected with the postprocessing protocol proposed by [PERSON] et al. [58]. Thus, a uniform grid of 10 \(\times\) 10 m polygons was obtained, wherein for each polygon, a yield value was recorded, considered as the reference yield value in kg\(\cdot\)ha\({}^{-1}\). A negative buffer of 10 m was used to remove polygons near the edges of the fields (wrong satellite measurement, following the [PERSON] et al. [58] protocol). Figure 2 shows an example of the original yield map (raw data) and the postprocessed map.
## 5 Results and Discussion
### Sensitivity Analysis of Input Variables  
Both Section 5.1 and 5.2 demonstrate the decent performance of the PSVM model. In order to determine the most influential input variables on the ultimate axial capacity \(N_{u}\), the sensitivity analysis is performed using the cosine amplitude method (CAM) [58; 59] in this section. The method has achieved good sensitivity analysis results in most research [60; 61; 62]. The express similarity relation between the target function and the input parameters is used to obtain by CAM [63]. In this method, each data pair \((x_{i},x_{j})\) can be considered as a specific point in the \(m\)-dimensional space, where each  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Indexes** & **PSVM** & **PSVM after Error Correction** \\ \hline \(R^{2}\) & 0.914 & 0.953 \\ \(MAPE\) & 0.145 & 0.107 \\ \(MAE\) (\(\times 10^{3}\) kN) & 0.227 & 0.176 \\ \(RMSE\) (\(\times 10^{3}\) kN) & 0.304 & 0.226 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction performance comparison of the original PSVM model and error corrected PSVM model in the test set.  
Figure 8: Measured and estimated \(N_{u}\) values obtained from the original PSVM model and error corrected PSVM model in the test set.  
point requires \(m\)-coordinates to be fully described. Thus, each input variable is directly connected to the corresponding output. The strength of this relationship between \(x_{i}\) and \(x_{j}\) is calculated by:  
\[r_{ij}=\frac{\sum\limits_{k=1}^{m}x_{ik}x_{jk}}{\sqrt{\sum\limits_{k=1}^{m}x_{ ik}^{2}\sum\limits_{k=1}^{m}x_{jk}^{2}}}, \tag{17}\]  
where \(x_{ik}\) and \(x_{jk}\) are the \(k\)-th input and corresponding \(k\)-th output variable of the model, respectively; and \(m\) is the number of experimental data. The larger the \(r_{ij}\) value, the greater the influence of the corresponding input variable on the output.  
The sensitivity analysis results are presented in Figure 9. From a single variable, the side length \(D\) of square CFST specimens has the greatest influence on the ultimate axial capacity. In terms of different variable combinations, it is obvious that the geometric properties \((D,t,L)\) of square CFST specimens have the relatively highest effects on \(N_{u}\) values. Therefore, the size effect (or scale effect) of square CFST short columns under axial compression is not negligible, which has long been discussed by [PERSON] et al. [64].
## 3 Simulation Analysis of Tire-Asphalt Pavement Frictional Contact
### Tread Deformation  
Tire braking behavior has a distinct impact on tangential tire deformation. Therefore, the tangential deformation was examined to investigate tire-pavement frictional behaviors.  
Figure 6 depicts tangential deformation of the tire tread along vehicle's direction of motion. It can be inferred from the nephogram that:  
When the slip ratio is only 7%, the tread's tangential deformation is insignificant. Nevertheless, as the pavement's elasticity is improved, the deformed area has become longer and wider, extending to the marginal region of sidewall in the direction of the tire's motion. These phenomena account for the enlargement of the tire-pavement contact area; the effect tire adhesion on highly elastic pavement is a superior braking agent in comparison to that of common pavement with no crumbed rubber additives.  
Figure 5: Verification of simulation model against experimental data on tire static test. (**a**) Tire static loaded test; (**b**) Load-tire vertical deformation comparison of numerically simulated results and experiment measurements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Pavement Types**} & \multicolumn{3}{c}{**Slip Ratio (\%)**} \\ \cline{2-4}  & **7** & **10** & **14** \\ \hline low-E & low-SR & med-SR & high-SR \\ med-E & low-SR & med-SR & high-SR \\ high-E & low-SR & med-SR & high-SR \\ \hline \hline \end{tabular} Note: In this table, E represents pavement Elasticity with or without crumb rubber addition, namely, in accordance with the pavement type in Table 3. Besides, SR is the abbreviation of Slip Ratio.  
\end{table}
Table 5: Conditions of brake efficiency in different slip ratios.  
Secondly, under each of the nine conditions, the direction of tread deformation is opposite to the tire's motion path. Under conditions of high-E, the tread deformation area is significantly larger in comparison to conditions of low-E and med-E. This pattern is also found in the slip direction of the tire head's mesh. The above findings explain that while stiffness decreased and elasticity improvements were made in the paving materials, the deformity and braking force applied to the tires increases. Therefore, the tire-pavement contact area enlarges with elasticity. This trend indicates that appropriate decreases in pavement stiffness could facilitate enough tire-pavement contact to enhance tire adhesion, improving brake efficiency.  
By examining the tread deformation along the direction of tire movement under 10% and 14% slip ratio, we see that when the slip ratio is 14%, the tread deformation slightly decreases, indicating that tire adhesion does not always improve with the increased slip ratio. This change agree with the law that as the slip ratio is between 10% and 15%, the tire braking force coefficient will reach the maximum value [18].  
Figure 6: Tangential deformation nephogram of tire tread under different slip ratios.
# Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm
## 6 Experimental Analysis
### Discussion of the Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Water Cycle Algorithm**} \\ \cline{2-13}
**Instance** & \multicolumn{4}{c}{**Autonomous Approach**} & \multicolumn{10}{c}{**Default Approach**} \\ \cline{2-13}
**ID** & \multicolumn{4}{c}{\(\mathbf{N_{pop}\in\{10,100\}}\)} & \multicolumn{4}{c}{\(\mathbf{N_{pop}=30}\)} & \multicolumn{4}{c}{\(\mathbf{N_{pop}=50}\)} & \multicolumn{4}{c}{\(\mathbf{N_{pop}=80}\)} & \multicolumn{4}{c}{\(\mathbf{N_{pop}=100}\)} \\ \cline{2-13}  & **Stime** & **It** & **Opt** & **Stime** & **It** & **Opt** & **Stime** & **It** & **Opt** & **Stime** & **It** & **Opt** & **Stime** & **It** & **Opt** \\ \hline CF61 & 4787 & 67.34 & 35.01 & 4581 & 98.23 & **34.56** & 5512 & 53.23 & 38.34 & 6987 & 19.23 & 36.12 & 6213 & 45.12 & 35.34 \\ CF62 & 4899 & 122.56 & 32.58 & 4198 & 34.23 & **32.45** & 5982 & 31.24 & 37.63 & 6349 & 149.23 & 32.89 & 6098 & 59.23 & 33.56 \\ CF63 & 4700 & 83.57 & 71.69 & 5929 & 67.60 & 72.35 & 4627 & 45.12 & 78.23 & 5224 & 98.34 & 71.23 & 6928 & 49.12 & 71.35 \\ CF64 & 4698 & 75.29 & **53.68** & 4721 & 56.78 & 55.32 & 4982 & 56.23 & 59.23 & 4012 & 71.23 & 54.12 & 6340 & 111.35 & 54.34 \\ CF65 & 4677 & 87.31 & 11.67 & 5892 & 77.89 & 11.01 & 4635 & 85.30 & 17.23 & 5821 & 161.24 & **10.23** & 6329 & 51.57 & **10.23** \\ CF66 & 4986 & 52.58 & 15.56 & 4912 & 56.78 & 15.57 & 5872 & 34.67 & 18.33 & 5898 & 104.23 & 15.23 & 6213 & 71.23 & **14.23** \\ CF67 & 4872 & 81.23 & **43.99** & 4719 & 43.32 & 47.45 & 4983 & 25.67 & 49.12 & 6318 & 76.34 & 57.23 & 6391 & 33.47 & 46.89 \\ CF68 & 4798 & 75.23 & **46.46** & 4689 & 76.34 & 57.34 & 4982 & 92.34 & 51.23 & 5981 & 207.23 & 50.47 & 6305 & 55.58 & 48.23 \\ CF69 & 4821 & 65.12 & **318.08** & 4966 & 87.34 & 331.32 & 5093 & 56.45 & 362.73 & 6102 & 87.23 & 302.23 & 6092 & 97.24 & 318.54 \\ CF70 & 4986 & 54.67 & 43.65 & 4700 & 120.30 & 47.77 & 5928 & 134.12 & 46.23 & 6124 & 41.23 & 41.23 & 42.63 & 6469 & 87.23 & 41.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results solving complex instances (3/3).
## 3 Materials & Methods
### Model Architecture  
This section presented a model that clarifies the stages of work required to achieve the final objectives. The proposed model is shown in Figure 3. This model consists of five primary phases: data collection, data pre-processing, topic modeling, users modeling and graph modeling. The first phase consisted in collecting data and building the dataset and including the essential factors in implementing the proposed idea. Next, the data is cleaned and prepared for work. The proposed idea consists of three phases: topic modeling, user modeling, and graph modeling. These are examined in the following subsections.  
Extracting users' interests through the posts they publish on social media sites or discovering the topics in which they are interested is not an easy process. It requires a lot of time to read all the user's tweets and then judge their interest. Accordingly, extracting interests requires analyzing and mining texts by topic modeling. Topic modeling identifies keywords clusters that often co-occur in user posts. Moreover, the keywords in a topic with the highest probability reveal what the topic is about [42].  
Figure 3: Proposed Model.  
Figure 2: Proposed Approach to Find Similar Users and Topics.  
Let \(\text{D}=\{d_{1},d_{2},\ldots,d_{n}\}\) be a set of documents, where each document represents a collection of texts generated by a single user. For example, document one represents all texts generated by the first user only, and document two is generated by the second user, and so on. In order to transform the documents, bag-of-word (BOW) vector representations were applied. Subsequently, latent Dirichlet allocation (LDA) was employed to extract a set of interested topics \(Z_{i}\)_from_\(d_{n}\)[43]. This is subsequently carried out for each user \(Z=\{z_{1},z_{2},\ldots,z_{n}\}\). In order to ascertain the close relationships and semantic similarities between the topics, fasttext word embeddings were applied [44]. In fasttext, each word appears as a bag of character n-gram, which is the adjacent series of n items from a specific character or word sample. This process is reliant on the skip-gram model, where the objective of this model is to maximize the log-likelihood and scoring function \(s\), and is calculated as follows:  
\[s(w,c)=\sum_{g\in\mathbb{C}_{w}}z_{g}^{T}v_{c} \tag{1}\]  
where \(G_{w}\) is the set of n-grams appearing in word \(w\), associate each vector \(Z_{g}\) to each n-gram and \(v_{c}\) represent word vector. Additionally, the k-means algorithm is applied to cluster extracted topics based on semantic similarities.
## 4 Problem Formulation
### Notation
Let \(\tilde{L}(d,f,e)\) denote the overall latency of flows in set \(\mathcal{H}(d,f,e)\); \(\tilde{L}(d,f,e)=\sum_{(\tilde{d},\tilde{f})\in\mathcal{H}(d,f,e)}L(\tilde{d},\bar{f},e)\). Let \(\mathcal{T}(d,f,e)\) be the set of possible values of latency \(L(d,\bar{f},e)\) produced by lower priority flows \((\bar{d},\bar{f})\in\mathcal{L}(d,f,e)\). Let subset \(\mathcal{L}(d,f,e)\subseteq\mathcal{L}(d,f,e)\) comprise the flows producing latency \(t\in\mathcal{T}(d,f,e)\) and \(L(t)\) denote the value of this latency. Routing paths: Set \(\mathcal{P}(d,f)\) includes candidate routing paths for flow \(f\) of demand \(d\). Subset \(\mathcal{P}(d,f,v)\subset\mathcal{P}(d,f)\) comprises the paths terminated in PP node \(v\), and subset \(\mathcal{P}(d,f,e)\subset\mathcal{P}(d,f)\) comprises the paths going through link \(e\). Set \(\mathcal{P}=\bigcup_{d\in\mathcal{D},f\in\mathcal{F}}\mathcal{P}(d,f)\) represents all candidate paths. In this work, we assume that all the candidate MH paths of the demands belonging to slice eMBB are terminated in the hub node, where the CU is placed, and only such paths are included in set \(\mathcal{P}(d,f)\) for these flows. Eventually, set \(\mathcal{E}(p)\) comprises the links belonging to path \(p\). The notation is summarized in Table 1.
## 2 Materials and Methods
### Hand Gesture Recognition and Control  
Gesture recognition in this study was implemented using Google's MediaPipe Hands, an open-source framework that leverages machine learning to detect and track 21 key hand landmarks using a single RGB webcam (Bitcraze, Malmo, Sweden), as illustrated in Figure 4. Each landmark corresponds to specific joints and tips of the fingers and wrist. Once detected, these landmark coordinates are used to classify predefined hand gestures by calculating joint angles and Euclidean distances between specific landmark pairs. The system evaluates angles at key finger joints by applying geometric calculations to three-point sequences. For example, to detect if a finger is extended or bent, the angles formed at the carpometacarpal (CMC), metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints are measured. An extended finger typically shows an angle greater than 120\({}^{\circ}\), whereas a bent finger produces a significantly smaller angle (usually \(<\)100\({}^{\circ}\)). In addition to angles, distances between landmark points (e.g., between the thumb tip and the base of the index finger) are used to determine the spread of the hand or proximity of fingers, further enhancing gesture classification. To control the drones using the different hand gestures, furthermore, we select eight common gestures in this paper, including \"pointing\", \"peace\", \"thumbs up\", \"pinky\", \"open hand\", \"fist\", \"okay\", and \"love\", to indicate the corresponding drone commands, as described below and demonstrated in Figure 5.  
* A \"pointing\" gesture moves the drone forward. * A \"peace\" gesture moves the drone backward. * A \"thumbs up\" gesture moves the drone to the left. * A \"pinky\" gesture moves the drone to the right. * An \"open hand\" gesture triggers the drone to ascend (up). * A \"fist\" gesture triggers the drone to descend (down). * An \"okay\" gesture triggers the drone to land. * A \"love\" gesture triggers the drone to rotate. Figure 4: Hand landmark points used for gesture recognition [9]. First, the system detects \(i=0,\;1,\;2,\;\dots,\;20\) hand landmarks per frame and each landmark is represented by \(v_{i}\in\mathbb{R}^{2}\) in 2D image coordinates:  
\[\mathbf{v}_{i}=[x_{i},y_{i}]^{T}\text{ for }i=0,\;1,\;\dots,\;20 \tag{1}\]  
where \"\(T\)\" indicates the transpose. Then, a gesture in a frame can be represented by a \(21\times 2\) matrix \(\mathbf{V}= \begin{bmatrix}\mathbf{v_{0}}&\dots&\mathbf{v_{20}}\end{bmatrix} ^{T}\), which are the raw data for the gestures. To classify hand gestures into the eight classes, such that \(\mathcal{G}=\{G_{pointing},\;G_{peace},\;G_{thumbsUp^{\prime}},\;G_{pinky},\;G_{ openHand},\;G_{fist},\;G_{okay},\;G_{love}\;\}\) as shown in Figure 5, in the next step, the system extracts two types of features from the matrix \(\mathbf{V}\), including the distance feature \(d_{ij}\) and the angle feature \(\theta_{ijk}\) where \(i,\;j,\;k\in\{0,\;1,\;\dots,\;20\}\).
## 5 Machine Learning, Dependability, and 6G
### Dependability of Federated Learning
#### 5.2.2 Availability  
A lack of, or improper, criteria when selecting the clients for local training does not only affect reliability, but availability also. Untrusted clients using low quality data for training hinders the whole learning process and may severely affect predictions. In this manner, a FL framework whose accuracy is not as desired cannot be deployed, nor can services trust it, thereby rendering it unavailable. Availability in FL systems is complex to achieve due to the distributed nature of the model training, and the centralization of global model aggregation; in other words, it is not possible to hide a \"faulty\" or badly trained model when several untrusted clients have performed training with corrupted data. Moreover, this centralization of the aggregation process renders a FL framework vulnerable to weak aggregation algorithms, which are incapable of discerning high-quality trained models from those coming from suspicious clients. Availability is also hindered by security issues discussed in Section 4.
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
## 3 Group IV Semiconductors
### Carbon  
The carbon family comprises fullerene [72], carbon nanodots (CQDs) [73], carbon nanotubes (CNTs) [74; 75], graphene [76], and graphene quantum dots (GQDs) [77]. Considering the superior and uniquely chemical, optical, physical, mechanical, and electronic properties of these innovative materials [78], they have attracted a growing research interest in the last couple of decades.  
In particular, CNTs and graphene have shown unique potential for a broad range of photodetectors from the ultraviolet to the THz [79]. In this section we introduce the basic properties of both CNTs and graphene based photodetectors that have been realized in the last few years. The performance status of these detectors is reported, and their potential for further improvement is discussed.
# Application of Fly Ash as an Adsorbent for Removal of Air and Water Pollutants
###### Abstract  
Air pollutants such as volatile organic compounds (VOCs), nitrogen oxides (NOx), and sulfur dioxide (SO\({}_{2}\)), as well as water pollutants (e.g., heavy metals phosphorous, fluoride, boron, phenolic compounds, and dyes), are harmful to humans and the environment. Effective control and reduction of their pollution is therefore an important topic for today's scientists. Fly ash (FA) is a type of industrial waste that can cause multiple environmental problems if discharged into the air. On the other hand, because of its high porosity, large specific surface area, and other unique characteristics, FA can also be used as a low-cost and high efficient adsorbent for treatment of environment pollutants. This paper reviews the effects of FA on treatment of the air and water pollution, including to the current status of global FA utilization, physicochemical properties, principle of adsorption, and the application direction of FA in the future. Since most researchers only studied the adsorption capacity of pure FA or zeolite (synthesized from FA), the research on the fabrication of nanofiber membranes using FA is still lacking, especially the adsorption of VOCs from air and heavy metals from wastewater using FA nanofiber membranes. Therefore, in this paper, we focus on reviewing and summarizing that FA can be spun into a fiber membrane via electrospinning with the ability to adsorb vOCs and heavy metals from air and wastewater. Moreover, we also evaluate the future application value of FA nanofiber membranes in the field of environmental pollution control. Utilization of nanofiber technology to fabricate multi-functional FA emerging composite materials to mitigate air and water pollution has great potential in the future, especially the use of pollutant materials to control other pollutants.  
fly ash; composite material; environmental pollution; volatile organic compounds; heavy metal