esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Problem Formulation  
### Objective Function  
The objective function is defined based on the minimization of the total expected cost (EC) of an isolated residential MG which includes cost of energy and reserve provision as well as the operating cost in different working scenarios.  
\[\begin{array}{ll}EC&=\sum\limits_{t=1}^{T}\sum\limits_{i=1}^{Ng}[(A_{i}\cdot u _{i,t}+B_{i}\cdot P_{i,t})+SUC_{i}\cdot y_{i,t}+SDC_{i}\cdot z_{i,t}\\ &+(C_{i,t}^{RD}\cdot R_{i,t}^{D}+C_{i,t}^{RU}\cdot R_{i,t}^{UI}+C_{i,t}^{NS} \cdot R_{i,t}^{NS})\Big{]}\\ &+\sum\limits_{w=1}^{Nw}C_{w,t}\cdot P_{w,t}+\sum\limits_{p=1}^{Np}C_{p,t}\cdot P _{p,t}\\ &+\sum\limits_{k=1}^{Nk}SPR_{t}\cdot P_{k,t}^{R}-\sum\limits_{k=1}^{Nk}SPR_{t }\cdot P_{k,t}^{c}\\ &+\sum\limits_{t=1}^{T}\sum\limits_{j=1}^{Nj}C_{j,t}^{RD}\cdot R_{j,t}^{D}+C_{ j,t}^{RU}\cdot R_{j,t}^{UI}-\sum\limits_{j=1}^{Nj}C_{j,t}\cdot L_{j,t}\\ &+\sum\limits_{s=1}^{Ng}\sum\limits_{t=1}^{T}\sum\limits_{i=1}^{Ng}[SUC_{i} \cdot(y_{i,t,s}-y_{i,t})+SDC_{i}\cdot(z_{i,t,s}-z_{i,t})\\ &+C_{i,t}\cdot(r_{i,t,s}^{UI}+r_{i,t,s}^{NS}-r_{i,t,s}^{D})\Big{]}\\ &+\sum\limits_{s=1}^{Ng}\sum\limits_{t=1}^{T}\left[\sum\limits_{i=1}^{Nj}C_{j, t}\cdot(r_{i,t}^{UI}-r_{j,t,s}^{D})\right.\\ &+\sum\limits_{w=1}^{Nw}C_{w,t}\cdot\Delta P_{w,t,s}+\sum\limits_{p}^{Np}C_{p,t }\cdot\Delta P_{p,t,s}\Bigg{]}\\ &+\sum\limits_{j=1}^{Nj}V^{LOL}\cdot L_{j,t,s}^{shcd}\end{array} \tag{17}\]  
In Equation (17), the first line of the objective function states the costs associated with energy provided from the generating units and the start-up and shut-down costs, and the second line expresses the commitment of the generating units to provide reserves. The third line denotes the costs associated with energy provided from the WT and PV units. The fourth line expresses the cost associated with charge/discharge of EVs and the fifth line considers the utility of the demand loads and their up and down reserve provision.  
The rest of the terms in the objective function deal with the operating cost in different working scenarios. In this regard, the sixth and the seventh lines consider cost of unit commitment and the cost of deploying reserves from those units in different scenarios. The eighth line represents the cost of deploying reserves from DR programs and the ninth line states the costs associated with energy provided from WT and PV units. Here, it is assumed that the MG operator would pay for energy provided by WT and PV. Finally, the last term stands for the expected cost of energy not served for the inelastic loads.
## 3 Localization Algorithms in WSNs
### Range Free Localization Algorithm
#### 3.1.1 Hop Count Based
Then finally adjusting the average hop distance by \(HopSize_{eff}^{i,j}\)=HopSize\({}_{i}-\frac{e^{ij}+e^{i,j}}{h^{ij}+h^{ij,n}}\), where \(m\) is the closest anchor node to anchor node \(i\) and \(HopSize_{i}\) is calculated as \(HopSize_{i}=\frac{\sum_{j\
eq i}\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i} -y_{j}\right)^{2}}}{\sum_{j\
eq i}h_{ij}}\), where \(\left(x_{i},y_{i}\right)\left(x_{j},y_{j}\right)\) are the coordinates of anchor nodes \(i\) and \(j\) and \(h_{ij}\) is the number of hops between anchors \(i\) and \(j\). The algorithms [67; 68], made improvements on distance estimation and consequently the accuracy of the DV-Hop algorithm. **Improvement based on node information and nearest anchors:** There are still some disadvantages in the improved algorithms that are based on the average hop distance, such as no obvious improvement on localization accuracy, especially when the transmission route is not straight but detoured. These approaches are accurate insofar only when the topology is isotropic, i.e., shortest paths between anchors and sensors approximate to their Euclidean distances. However, there may be large errors in the distance estimates if the topology is not isotropic or contains a hole (aka anisotropic environment) [71]. Therefore, some modified methods were proposed using the anchor node information and the relationship between anchor node and sensor node or topological structure information to improve the DV-Hop localization method. In order to alleviate the influence of holes (obstacle shape), [PERSON] et al. [72] suggest using only four nearest anchors, assuming that the shortest paths to the nearest anchors may be less affected by irregularities, and this does produce good results in some cases but with a drawback of the possibility to falsely discard some good anchors which can improve the localization accuracy.
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 3 Proposed MIA-LSTM Model
### Iterative Imputation Using Extra Tree Regressor  
The iterative imputation method was used for replacing missing data in the available dataset, where every feature was modeled as a function of other remaining features. The function/model was created with the help of various regressors available. In this process, the missing values were identified using the regressor model and repeated with multiple iterations. This was performed in order to get a more accurate value of the missing data. As many iterations were performed, this process is called iterative imputation. Here, the rows and columns where the missing values were present were identified, and the respective rows were removed. This created two datasets: one which did not contain missing values and the other that contained missing values. The target was to replace these missing values. Using the first set of data and applying the machine learning regression algorithm, the missing values from the later set could be identified. In this paper, during imputation, an extra tree regressor was used to find the missing values. For the same dataset, the experimentation was carried out for the prediction of particulate matter by using various regression techniques, such as LightGBM, gradient boosting regressor, KNN, decision tree, extra tree, and thirteen more. Out of these techniques, extra tree regressor provided the least RMSE and MAE values for prediction. Hence, the ET regressor was chosen as an estimator in the iterative imputation. This was the first iteration; after imputation, the dataset was merged, and after merging the dataset, the regression was applied to obtain the new imputed values. Iterations were carried out until the difference in the imputed values was the least, as shown in the flowchart.
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 4 LQA-AODV Routing Protocol
### Multicast Extensions  
The routing protocols in tactical MANETs need to efficiently support multicast traffic. For instance, a tree-based multicast AODV (MAODV) is extension of AODV protocol to extend its support for unicast communications while modifying the existing RREQ and RREP (route reply) messages. Our proposed routing protocol exploits the same idea. Thus, we assume that multicast routing tables contain extra fields such as address of multicast group, leader node address, multicast leader group HELLO message, and the sequence number of the multicast group [15].
# Tumor Microenvironment and Dermatological Conditions in Prostate Cancer
## 2 The Cutaneous Manifestations Associated with PCa
### Cutaneous Metastases Derived from Pca  
The skin was defined as \"a mirror of metastatic destiny\" [35]. Metastasis is a laborious process that occurs when the primary tumor grows large enough that it can invade other tissues. Several stages of metastasis have been described. The first stage consists of local invasion, and then the tumor cells penetrate the vessels, manage to survive in circulation, and thus reach distant organs where extravasation and the formation of metastases take place [14; 18; 37; 38]. Regarding solid cancers, it was reported that cutaneous metastases could be the first sign of malignancy (early metastases); they can develop concomitantly with the primary malignancy (synchronous metastases) or they can indicate the recurrence or progression of malignancy (metachronous metastases) [34; 35].  
Cutaneous metastases develop predominantly through vascular invasion (lymphatic or hematogenous) or direct tumor invasion (contiguity) [14]. It is estimated that 0.7-9%  
Figure 2: Dermatological conditions related to PCa (EMT—epithelial–mesenchymal transition; TME—tumor microenvironment; PCa—prostate cancer).  
(global mean incidence of 5.3%) of metastases derived from solid tumors occur in the skin [35]. In addition, skin metastases associated with carcinomas of the genitourinary tract come from the kidneys (66%), bladder (17%), prostate (12%), and testicles (4%) [39]. Cutaneous metastases developed from PCa are rare (0.36%), cause considerable discomfort, and have a serious impact on survival [39; 40; 41].  
The clinical presentation of PCa metastases is diverse. The metastases appear as single or multiple nodules or plaques, edema, a nonspecific rash, or ulcers. The recently reported atypical cases are described as telangiectatic lesions or vesicular lesions [37; 39; 42; 43; 44]. PCa skin metastases show varied morphological models: solid/poorly differentiated, glandular/ductal, infiltrative, mucinous with a signet ring, telangiectatic, lymphangitic, and with epidermotropism [45; 46; 47]. Cutaneous metastases developed from PCa are located in the groin and penis (28%), abdomen (23%), head and neck (16%), chest (14%), extremities (10%), and back (9%) [48]. Cutaneous metastases from PCa are positive for PSA, prostatic acid phosphatase, and negative for CK7, CK20, and thrombomodulin [14]. From the data presented, we deduce that there is no clear predilection for the skin region affected by PCa metastases.
## 3 Filtering Algorithm
### LDLT Decomposition  
At present, the square root method \(\mathrm{L}^{\mathrm{T}}\mathrm{L}\) that is the LU decomposition method is commonly used in computers. In this method, symmetric positive definite equations are solved by trigonometric decomposition of symmetric positive definite matrices. This method splits the coefficient matrix \(A\) into two symmetric trigonometric matrices \(A=\mathrm{L}^{\mathrm{T}}\mathrm{L}\). When elements of \(L\) are determined, the elements of \(L^{T}\) can be obtained. In the process of computer processing, only non-zero elements of \(L\) can be stored. Additionally, the order of magnitude of \(L\) does not increase during the decomposition, and the diagonal elements are always positive. Therefore, the square root method is a numerically stable decomposition method. However, through the analysis of the calculation process, it is found that the back-generation process of the algorithm takes almost half of the whole solving-process time, and the symmetry and sparsity of the decomposed matrix are not fully utilized. In addition, the decomposition process requires the square root calculation, which can cause precision loss. In order to avoid calculating the square root, the improved square root method (LDLT) can be used to decompose the sparse matrix \(A\). The decomposition formula is shown in Equation (9).  
Figure 7: Flow chart of LU decomposition method.  
\[A=\left[\begin{array}{ccccc}1&&&&\\ Lb_{1}&1\\ Lc_{1}&Lb_{2}&1\\ &\ddots&\ddots&\ddots\\ &&Lc_{n-2}&Lb_{n-1}&1\end{array}\right]\left[\begin{array}{ccccc}d_{1}&&&&\\ &d_{2}&&&\\ &&d_{3}&&\\ &&&\ddots&\\ &&&d_{n}\end{array}\right]\left[\begin{array}{ccccc}1&Lb_{1}&Lc_{1}&&\\ &1&Lb_{2}&\ddots&\\ &&&1&\ddots&Lc_{n-2}\\ &&&\ddots&Lb_{n-1}\\ &&&1\end{array}\right], \tag{9}\]  
The LDLT decomposition method decomposes matrix \(A\) into two symmetric triangular matrices and one diagonal matrix. Therefore, the interference caused by rooting operation is avoided and improves the stability of the filtering result. In addition, the sparse matrix \(A\) is a symmetric matrix. Compared with the square root method, the LDLT decomposition method simplifies the program design. As shown in Figure 8, it is the program design flow chart of LDLT decomposition method.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Statistical Analysis  
In addition to the neural network model, we conducted traditional statistical analyses:  
* One-way ANOVA to compare QE durations across skill levels.
* Pearson correlation coefficients to examine relationships between QE metrics and shot accuracy.
* Multiple regression analysis to assess the combined effect of QE and biomechanical variables on shot accuracy.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 5 Numerical Experiment
### Sensitivity Analysis
#### 5.3.2 Truck Speed and Drone Speed  
Set the speed growth rates of trucks and drones respectively as \(\rho^{\text{T}}\) and \(\rho^{\text{D}}\), and both \(\rho^{\text{T}}\) and \(\rho^{\text{D}}\) increase from \(-20\%\) to \(20\%\) by \(5\%\). The sensitivity analysis results are shown in Figure 7. Only increasing \(\rho^{\text{T}}\) or \(\rho^{\text{D}}\) can reduce \(\tau\). The main reason is that the increase in \(\rho^{\text{T}}\) or \(\rho^{\text{D}}\) can shorten the time trucks wait for drones or drones wait for trucks at rendezvous points. However, just increasing either \(\rho^{\text{T}}\) or \(\rho^{\text{D}}\) has only a small effect on reducing \(\tau\), and the decreasing rate \(\tau\) slows down as \(\rho^{\text{T}}\) or \(\rho^{\text{D}}\) increases. In addition, only increasing either \(\rho^{\text{T}}\) or \(\rho^{\text{D}}\) cannot obtain minimum \(\tau\). Only when both \(\rho^{\text{T}}\) and \(\rho^{\text{D}}\) are \(20\%\) can we obtain minimum \(\tau\), which is \(162\) min. It shows that only when the truck speed and drone speed increase simultaneously can the delivery time be significantly reduced. Our result is in line with the conclusion given by [PERSON] and [PERSON] [29].  
Figure 6: Sensitivity analysis of the number of truck–drones.  
Figure 7: Sensitivity analysis of truck speed and drone speed.
# VDMS: An Improved Vision Transformer-Based Model for PM\({}_{2.5}\) Concentration Prediction
## 6 Conclusions  
Given the severe health risks posed by PM\({}_{2.5}\) and the limited availability of ground monitoring stations, comprehensively monitoring PM\({}_{2.5}\) concentrations across different regions presents a challenge. Therefore, accurately predicting the spatiotemporal distribution of PM\({}_{2.5}\) is particularly critical. In this study, we propose an integrated model that combines the ViT with SimCLR self-supervised contrastive learning to effectively process complex data with spatiotemporal dependencies. Through a series of experiments, the model demonstrated significant advantages in feature extraction, temporal modeling, and capturing global dependencies. By leveraging the SimCLR self-supervised contrastive learning framework, the model effectively learns deep features from images even in the absence of extensive labeled data, thereby establishing a solid foundation for subsequent prediction tasks. Furthermore, the introduction of an LSTM layer enables the model to capture long-term dependencies in temporal data, enhancing its performance in time series forecasting. Additionally, the incorporation of a multi-head self-attention mechanism significantly bolsters the model's ability to capture global dependencies among input features, resulting in outstanding performance in managing complex spatiotemporal data.  
Experimental results indicate that, in predicting PM\({}_{2.5}\) concentrations, the proposed model exhibits higher accuracy and greater generalizability compared to traditional CNNs and standard LSTM models. Moreover, comparisons with other advanced models such as RF and standard deep neural networks (DNNs) further validate the superiority of our model in handling spatiotemporally dependent data. Specifically, the model achieves significantly lower MAE and RMSE on the test set relative to benchmark models, demonstrating its robust performance in complex prediction tasks.  
Overall, the integrated model based on ViT, SimCLR, LSTM, and self-attention mechanisms provides an effective solution for spatiotemporal data prediction tasks. Future research will focus on optimizing the model's computational efficiency and extending its application to a broader range of real-world scenarios, applicable not only limited to PM\({}_{2.5}\) detection but also to the monitoring of other pollutants.  
This manuscript was designed and written by [PERSON], while [PERSON] supervised the study and contributed to the analysis and discussion of the algorithm and experimental results. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Key Lab of Information Network Security, Ministry of Public Security and the Shenzhen Fundamental Research Program under Grant JCYJ2030807094104009.  
Not applicable.  
The original contributions presented in this study are included in the article.  
The authors declare no conflicts of interest regarding the publication of this paper.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol Type** & **Protocol** & \begin{tabular}{c} **Communication** \\ **Type** \\ \end{tabular} & \
\begin{tabular}{c} **Routing Type** \\ \end{tabular} &
\begin{tabular}{c} **Data** \\ **Transmission** \\ \end{tabular} \\ \hline AODV [19] & Topology-based & Uni/Multi-cast & Reactive & Multi-hop \\ DSDV [20] & Topology-based & Uni-cast & Proactive & Multi-hop \\ DSR [21] & Topology-based & Uni-cast & Reactive & Multi-hop \\ OLSR [22] & Topology-based & Uni-cast & Proactive & Multi-hop \\ GPCR [23] & Position-based & Uni-cast & Reactive & Greedy \\ ZRP [24] & Position-based & Uni-cast & Hybrid & Multi-hop \\ VADD [25] & Position-based & Uni-cast & Reactive & Greedy \\ GPSR [26] & Position-based & Uni-cast & Reactive & Greedy \\ IVG [27] & Geo-based & Multi-cast & Reactive & Multi-hop \\ Geo-DTN [28] & Position-based & Uni-cast & Reactive & Greedy \\ CBF [29] & Position-based & Multi-cast & Reactive & Multi-hop \\ DV-Cast [30] & Broadcast-based & Broadcast & Proactive & Multi-hop \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification and Comparison of Routing Protocols. a distributed broadcast routing protocol mostly suited for connected highways; broadcasting happens based on local topological information. The broadcast routing protocols are frequently used in VANET for traffic management, weather monitoring, road conditions, and for advertising and announcements of messages between vehicles. The protocols like AODV, GPSR, OLSR, DSDV are modified in [31; 32; 33; 34; 35] for an efficient adaptive routing protocol implementation of VANET scenario. The optimized routing protocols provide enhanced network performances. In terms of packet delivery ratio, the improved GPSR model [36] performs better than the existing GPSR. A vulnerability attack on the DV-CAST protocol is described, along with safety specifications in secure DV-CAST [37]. Authentication, legitimacy, and non-repudiation of safety messages along with vehicle privacy are included in secure DV-CAST, providing less latency than traditional DV-Cast.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 3 Adversarial Perturbation for the CJ  
In this section, we design the white-box perturbation for the CJ using a targeted FGM to solve (4). We first assume that the CJ has one antenna, \(q=1\). We will relax the assumption in Section 4. For the targeted attack, the CJ minimizes \(L(\mathbf{\theta},\mathbf{r}^{\prime}_{te}(\delta),\mathbf{y}^{target})\) with respect to \(\delta\) where \(\mathbf{y}^{target}\) is the one-hot-encoded desired target class. We fix \(\mathbf{y}^{target}\) as _noise_ label since the CJ always tries to add perturbation to fool the eavesdropper into misclassifying a received signal as noise. We use FGM to linearize the loss function as \(L(\mathbf{\theta},\mathbf{r}^{\prime}_{te}(\delta),\mathbf{y}^{target})\approx L(\mathbf{ \theta},\mathbf{r}_{te},\mathbf{y}^{target})+(\mathbf{H}_{ce}\mathbf{\delta})^{T}\
abla_{ x}L(\mathbf{\theta},\mathbf{r}_{te},\mathbf{y}^{target})\) and then minimize it by setting \(\mathbf{H}_{ce}\mathbf{\delta}=-\alpha\
abla_{x}L(\mathbf{\theta},\mathbf{r}_{te},\mathbf{y}^ {target})\), where \(\alpha\) is a scaling factor to constrain the adversarial perturbation power to \(P_{max}\). The details of determining the CJ's perturbation signal are presented in Algorithm 1. After we obtain the \(\delta\) that causes misclassification at the eavesdropper and satisfies the power constraint, we check the BER at the receiver. The perturbation power can further be adjusted to meet a target BER level. Specifically, if the BER level at the receiver is more important than fooling the eavesdropper, we can decrease the adversarial perturbation power. On the other hand, if fooling the eavesdropper is the priority, we can increase the adversarial perturbation power.  
```
Inputs: input \(\tau_{te}\), desired accuracy \(\varepsilon_{acc}\), power constraint \(P_{max}\), and \(L(\theta,\cdot,\cdot)\). Initialize: \(\varepsilon\gets 0,\varepsilon_{max}\leftarrow\sqrt{P_{max}},\varepsilon_{min} \gets 0\). \(\delta_{norm}=\frac{\
abla_{\mathbf{r}}L(\theta,\tau_{te},y^{target})|_{2}}{( ||\
abla_{\mathbf{r}}L(\theta,\tau_{te},y^{target})|_{2})}\). while\(\varepsilon_{max}-\varepsilon_{min}>\varepsilon_{acc}\)do \(\varepsilon_{avg}\leftarrow(\varepsilon_{max}+\varepsilon_{min})/2\) \(x_{adv}\leftarrow\tau_{te}-\varepsilon_{avg}\delta_{norm}\) if\(l(x_{adv})==noise\)then\(\varepsilon_{min}\leftarrow\varepsilon_{avg}\) else\(\varepsilon_{max}\leftarrow\varepsilon_{avg}\) end while \(\varepsilon^{*}=\varepsilon_{max}\), \(\delta^{im}=-\varepsilon^{*}\delta_{norm}\)
```  
**Algorithm 1**Generating the perturbation of the CJ