esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
At lower microwave frequencies (i.e., 900 and 1900 MHz), studies performed to assess the influence of the source/phantom interactions on the transmitted field, demonstrated that the electric field may be significantly modified depending on the position of the antenna in respect to the phantom (decrease down to 25% and enhancement up to two times of the electric field amplitude at 900-MHz for a dipole and mobile terminal, respectively) [15; 16]. On the other hand, a study conducted mainly at 24 GHz, with some results at 60 GHz, showed that the source/body interaction is relatively weak (enhancement by 10% of the squared E-field for a dipole array with four elements at 60 GHz) [19]; however, only electrically small antennas were investigated in that study. The impact on the transmitted to the body field is expected to be higher for wireless devices equipped with larger antennas (e.g., patch antenna arrays). The main purpose of this study is to analyze the impact of the antenna/human body interactions in the near-field on the transmitted power density (TPD) at 60 GHz. For the first time, the fundamental limits in terms of enhancement and decrease in the TPD are investigated. Sources of increasing complexity are compared, including plane waves with and without free-space losses, antenna-equivalent sources, and patch antenna arrays. For antenna arrays, the role of the directivity and ground plane dimensions are also investigated.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 1 Introduction  
A MANET [1] is a network where nodes are mobile and can be organized sporadically in arbitrary network topologies. A MANET allows people and devices to interconnect without any infrastructure as an independent network or to connect with some external network scenarios through a proper gateway. In a MANET, the nodes have a limited transmission range so to reach their destinations other nodes might route the packets. A MANET can use different wireless technologies such as Zigbee, Bluetooth, WiMAX, Wi-Fi [2]. Among the most notable features of MANETs are low cost, flexibility, decentralized architecture, easy deployment, and auto-configuration.  
Despite MANETs being very versatile networks, they hardly achieve a good quality of service [2] since they are affected by the variable and unpredictable nature of the wireless channel and its dynamic topology. The transmission power is an important parameter because it determines if a transmission is successful or not, in which case, a power control would be necessary. If the transmission power is very high, the node would sense and interfere with a high number of neighbors, and it would cause channel saturation, contentions, and collisions. On the contrary, if the power is low, a node could detect very few neighbors or none, which would lead to a failed transmission. By controlling transmission power, we obtain advantages such as: increasing the number of successful transmissions, using the necessary battery energy, avoiding channel overloads and collisions. From the previous scenarios, it is reasonable to say that the power control in a MANET should be related to the operation of the upper layers, such as the networklayer. Cooperation between layers would have great efficiency in the network, however, this approach has not been very much considered for the design of ad-hoc networks.  
This paper proposes a mechanism to control the transmission power based on the detection of neighbors performed by AODV (Ad-hoc On-Demand Distance Vector). The mechanism is based on a cross-layering criterion to allow coordination, interaction, and exchange of information between the physical layer and network layer. The rest of this paper is organized as follows: Section II describes a background about routing protocols with an emphasis on AODV. Then, Section III reviews power control algorithms for MANETs. Afterward, in Section IV, we show our technique for power control using the cross-layering principle. Next, the performance evaluation is shown in Section V. Finally, we present some conclusions.
## 3 Methodology
### Data Labeling  
Data labeling, also known as data annotation, is a critical step in the sentiment analysis process, essential for enabling accurate decision-making. It involves labeling data to prepare them for supervised learning algorithms. There are several methods available for labeling data; one option is manual annotation by experts, which, while precise, can be both costly and time-consuming. Alternatively, automated tools, such as TextBlob and VADER, or pre-trained models like BERT and RoBERTa, can be used to streamline the process and minimize human error.  
The English dataset is labeled using the TextBlob 0.17.1 library in Python 3.12. TextBlob is a simple, powerful tool for processing textual data and offers solutions to various natural language processing tasks, such as translation, classification, sentiment analysis, and more [18]. TextBlob relies on a rule-based lexicon approach called Pattern Analyzer. This method assigns a polarity score to each review based on the presence of predefined positive and negative words and their grammatical structure. Unlike the machine learning and deep learning classifiers used in our study, which are trained to learn features from data distributions, TextBlob does not rely on statistical learning or model training. In our dataset, each review is classified into one of two categories: negative, with 42,906 reviews, and positive, with 45,732 reviews. The categories are well balanced, with 48.40% of the reviews being negative and 51.60% positive (Figure 2). Although this method enables efficient automatic labeling, it does not rely on supervised learning or deep contextual analysis, and the resulting labels may reflect surface-level sentiment patterns rather than nuanced human interpretation. To address this, we manually reviewed a sample of labeled comments to verify the general polarity consistency and observed a satisfactory alignment with the expected sentiment in the reviews.  
Figure 2: Distribution of labeled datasets.  
The French and Arabic datasets are annotated based on reviews classified by the users themselves as either \"liked\" or \"unliked\" comments, with verification through the associated ratings. Additionally, a manual check was conducted on those datasets to confirm that the assigned labels were generally consistent with the expressed sentiment. In the French dataset, there are 6302 positive reviews, representing 63.54% of the total, compared to 3616 negative reviews, which make up 36.46%. In the Arabic dataset, we have 2686 positive reviews and 1594 negative reviews, accounting for 62.76% and 37.24% (Figure 2), respectively. These results indicate that the distribution of positive and negative categories in both the French and Arabic datasets is not balanced.
# Implementation of the 3-30-300 Green City Concept: Warsaw Case Study
## 3 Results
### Visibility of 3 Trees and 300 m Distance to Park or Forest  
Analyzing the individual criteria, one might have the impression that Warsaw is a fairly green city and that the inhabitants of significant areas of the city have fairly good access to various forms of nature. However, the situation seems a little more complicated when the conditions of the adopted concept are analyzed together.  
Figure 11 shows the percentage of buildings with 100 percent visibility of three trees and at the same time located at a maximum distance of 300 m from the nearest forest or park. In this case, almost 71% of buildings in the city do not meet these two criteria at the same time.  
The spatial distribution of buildings meeting the two conditions is presented in Figure 12. The cartogram shows that five districts--three on the western side of the Vistula (Bemowo, Wola, and Ursus) and two on the eastern side (Targowek, Praga Polnoc)--have a very low percentage of buildings that meet the two criteria together--less than 15%. The highest percentage of buildings meeting both conditions at the same time is in the Wesola district, located in the south-east of the city, again above 70%. Most districts have between 15 and 30% of buildings meeting the two criteria together.  
Figure 10: Buildings within 300 m from a park or forest larger than 1 ha by districts.  
Figure 11: Buildings that meet both conditions: 3 and 300 m requirements for the whole Warsaw (green (yes)—percentage of buildings meeting both conditions simultaneously, red (no)—percentage of buildings not meeting both conditions simultaneously).  
Figure 12: Buildings that meet both conditions of 3 trees and 300 m requirements by district.
# Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
## 4 Discussion
Thus, a signal from a diseased system is expected to exhibit a lower complexity than that from a healthy system. Considering heart rate time series as an example, diseased systems suffering congestive heart failure (CHF) exhibit relatively regular behavior. On the contrary, diseased atrial fibrillation systems exhibit highly erratic fluctuations with statistical properties resembling uncorrelated noise. As a result, under large scales \(\tau\) around 20, the MSE values of the RR interval series from a healthy group are higher than those of the two diseased groups of CHF and AF. However, under between two and five scales, the MSE values for CHF, healthy, and AF subjects are small, medium, and large, respectively [14; 15; 19]. For comparison, the complexity of the healthy remain higher than those of the CHF and AF at every scale for another analysis called entropy of entropy (EoE) [20]. These particular features imply that MSE is a measure of disorder under relatively small scales and a measure of complexity under relatively large scales. Therefore, the EEG signals from AD patients exhibit low disorder and high complexity. Further explorations are needed to clarify the meaning of complexity of EEG signals. Three limitations of our study exist. First, although HC and AD2 groups were well separated, as shown in the Results Section, the accuracy in differentiating all the HC, AD1, and AD2 groups still needs to be improved. Second, the EEG signals were only recoded with length of 10 s; longer EEG segment might provide more information and benefit classification. Third, the EEG signals are combinations of cortical electric activities and cannot clearly localize the underlying pathology. Future study of AD classification with EEG source localization may help to identify the specific brain regions of interest.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 4 Results and Discussion
### Experiments on the TGRS-HRRSD Dataset  
#### 4.5.1 Experimental Results  
To verify the generalization of RS-DETR in remote sensing image object detection, we conducted experiments on the TGRS-HRRSD dataset, with results shown in Table 3 and Figure 10. Table 3 presents the detection results of RS-DETR for all categories in the TGRS-HRRSD dataset. Our algorithm achieved an average mAP50 of 91.6% and an average mAP of 65.4% across all categories. As shown in Table 3, RS-DETR achieves high detection accuracy for most categories, with mAP50 exceeding 90%, demonstrating the model's effectiveness. However, detection performance is relatively poor for categories such as basketball court, parking lot, and T-junction. This may be because the instance features of these categories are not distinct and resemble instances of other categories. For example, basketball courts and playgrounds, as well as tennis courts, appear quite similar at low resolutions, making it difficult for the model to distinguish between them, leading to lower detection accuracy.  
To fully assess the detection performance of our proposed improved algorithm, we conducted a visual analysis of detection results on the TGRS-HRRSD dataset. As shown in Figure 10, RS-DETR accurately locates and identifies targets of various categories in remote sensing images, achieving high confidence scores with almost no omissions or false detections. Additionally, dense small targets, such as storage tanks, are accurately detected without omissions. For ships with significant scale variations, the bounding box  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Backbone** & **FLOPs (G)** & **Params (M)** & **mAP50 (\%)** & **mAP (\%)** \\ \hline Faster R-CNN & ResNet50 & 180 & 42 & 74.4 & 60.4 \\ DETR & ResNet50 & 86 & 41 & 73.4 & 58.6 \\ Deformable DETR & ResNet50 & 173 & 40 & 75.2 & 60.4 \\ Conditional DETR & ResNet50 & 90 & 44 & 74.0 & 60.1 \\ DAB-DETR & ResNet50 & 202 & 44 & 75.0 & 55.9 \\ DN-DETR & ResNet50 & 94 & 44 & 74.7 & 60.3 \\ DINO-4 scale & ResNet50 & 279 & 47 & 74.8 & 60.3 \\ RT-DETR & ResNet18 & 60 & 20 & 76.2 & 62.0 \\ RS-DETR (ours) & ResNet18 & **53.3** & **17.5** & **78.2** & **63.1** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparative results of different models on the SIMD dataset.  
can accurately regress to correctly locate the target's position in the original image. These results demonstrate the robustness of our model.  
Figure 9: The visual results of RT-DETR (**left**) and RS-DETR (**right**) on the SIMD dataset.
# Weight Adjustment Framework for Self-Attention Sequential Recommendation
## 3 Methodology
### Problem Formulation  
In this section, we will provide the problem definition for sequential recommendation. Assuming that we had a series of user-item historical interaction sequences, our goal was to predict the item that a user was most likely to be interested in next, based on these historical sequences. Define U as the set of users, and \(I\) as the set of items. At each time step, the user u \(\in\) U interacts with an item from the set \(I\), forming a sequence \(S_{u}=\left\{i_{1}^{u},\ldots,i_{|S_{u}|}^{u}\right\}\), where \(i_{k}^{u}\in I(1\leq k\leq|S_{u}|)\) represents the item \(u\) interacted with at the \(k\)-th time step and \(|S_{u}|\) is the length of the sequence for user u. Our model attempts to learn patterns from the sequence \(S_{u}\) and use these patterns to predict the item that user u is most likely to choose next. Mathematically, we can represent this prediction task as an optimization problem, where we seek a probability model \(P\) to maximize the likelihood of the next item \(i_{|S_{u}|+1}^{u}\) being a specific item \(i\), given the known sequence \(S_{u}\). This can be formulated as follows:  
\[\operatorname*{argmax}_{i\in I}P\left(i_{|S_{u}|+1}^{u}=i\mid S_{u}\right) \tag{1}\]  
Furthermore, to capture and express the characteristics of each item and the user's interactions with them more accurately, we introduced an embedding matrix to map the IDs of the items to a continuous vector space. Create an item-embedding matrix \(M\in\mathbb{R}^{|I|\times d}\), where \(d\) is the dimension of each vector and \(|I|\) represents the number of all items. Given the input sequence \(S_{u}\), embedding this sequence, \(S_{u}\), results in \(\mathrm{e}^{u}\in\mathbb{R}^{n\times d}\), where \(e^{u}=\left\{m_{s_{1}},m_{s_{2}},\ldots,m_{s_{n}}\right\}\); here, \(m_{s_{k}}\in\mathbb{R}^{d}\) represents the embedding of the item at position k in the sequence and n represents the length of the sequence.
# A GNN-Based False Data Detection Scheme for Smart Grids
## 2 Related Work
### Spatial Temporal Graph Neural Networks, STGNNs  
A Spatio Temporal Graph Neural Network (STGNN) has become a category of neural network devised to model data that has both space and time dependencies. These networks are exceedingly beneficial to tasks where the data has a dynamic structure that changes over time and involves interactions across different locations or entities, such as traffic prediction, social network analysis, and climate modeling. In an STGNN, the spatial component obtains the correlations among discrepant nodes, while the temporal component models captures how these relationships evolve over time. The network is typically composed of graph convolutional layers that operate on the spatial graph and recurrent or temporal-based layers that capture the time dynamics. The graph structure in STGNN refers to a data structure containing nodes and edges, wherein nodes stand for spatial positions, with edges indicating relationships and temporal dependencies between spatial positions. The data processing module utilizes raw input data to construct spatiotemporal graph data, providing input data for the spatiotemporal graph learning module. The spatiotemporal graph learning module captures the spatiotemporal dependencies present in complex data, and organically combines spatial and temporal networks through a certain spatiotemporal fusion neural network architecture.  
Graph neural networks could efficiently model spatial information. In [19], the authors designed an unsupervised anomaly detection model using a sequence-to-sequence (seq2 seq) embedding method and GCNs for the spatio-temporal information capture in the time series of water treatment networks (WTNs). The authors in [20] used multiscale one-dimensional convolutions for the temporal pattern capture and subsequently utilized the graph attention network (GAT) to make predictions of time series. In [21], the authors employed two graph attention layers to capture the correlations between various features and temporal dependencies across different timestamps. The anomaly score is formulated on the reconstruction error from the prediction model and the reconstruction model itself, which suggests whether further adjustments are needed. In [22], the authors used a graph convolutional gated recurrent unit (GCGRU) and LSTM to obtain the high correlation of traffic dynamics in the temporal and spatial dimensions and the trend of traffic dynamics, respectively. While using the adversarial framework, the strong correlation between adjacent nodes in both space and time dimensions, as captured by GCGRU and LSTM, made generators and discriminators effectively learn the spatial-temporal patterns of traffic data and anomalies, respectively. In smart grid, a series of historical data in communication networks, due to the influence of air humidity, gradually became abnormal over time. The node in the physical transmission network corresponding to these abnormal data had malfunctioned, which would affect the quality of neighboring nodes.
# An Interactive Transient Model Correction Active Sonar Target Tracking Method
## 2 Description of the TMC Tracking Method
### System Model
#### 2.1.3 Measurement Debiasing  
In the system model described in Figure 2, when the sonar acquires the radial distance and azimuth measurement values of underwater targets, \(D_{m}(k)\) and \(\theta_{m}(k)\), the measurement vector \(\mathbf{Z}(k)\) needs to be solved according to Formula (1). Due to the nonlinear transformation included in Formula (1), the measured vector in the Cartesian coordinate system is biased, and it needs to be debiased through compensation.  
It is assumed that the measurement errors of the radial distance \(D_{m}(k)\) and azimuth \(\theta_{m}(k)\) are uncorrelated and follow a zero-mean Gaussian distribution, with variances of \(\sigma_{\mathrm{D}}^{2}\) and \(\sigma_{\theta}^{2}\), respectively. The projected targets' radial distances \(d_{x}(k)\) and \(d_{y}(k)\) on the X- and Y-axis after debiasing compensation are:  
\[\begin{cases}d_{x}(k)=D_{m}(k)\cdot\sin{(\theta_{m}(k))}/\lambda_{\theta},\\ d_{y}(k)=D_{m}(k)\cdot\cos(\theta_{m}(k))/\lambda_{\theta}.\end{cases} \tag{15}\]In the formula, the value of \(\lambda_{\theta}\) is:  
\[\lambda_{\theta}=e^{-\sigma_{\theta}^{2}/2} \tag{16}\]  
The noise covariance matrix of the compensated position measurement is:  
\[\mathbf{R}(k)=\begin{bmatrix}\sigma_{x}^{2}&\sigma_{xy}\\ \sigma_{xy}&\sigma_{y}^{2}\end{bmatrix}. \tag{17}\]  
in which:  
\[\left\{\begin{aligned} \sigma_{x}^{2}&=\frac{\big{(}D_{m}^{2}(k)+ \sigma_{D}^{2}\big{)}\big{(}1-\lambda_{\theta}^{\prime}\text{cos}(2\theta_{m}( k))\big{)}}{2}+\Big{(}\lambda_{\theta}^{-2}-2\Big{)}D_{m}^{2}(k)sin^{2}( \theta_{m}(k)),\\ \sigma_{y}^{2}&=\frac{\big{(}D_{m}^{2}(k)+\sigma_{D}^{2} \big{)}\big{(}1+\lambda_{\theta}^{\prime}\text{cos}(2\theta_{m}(k))\big{)}}{2 }+\Big{(}\lambda_{\theta}^{-2}-2\Big{)}D_{m}^{2}(k)cos^{2}(\theta_{m}(k)),\\ \sigma_{xy}&=\frac{\big{(}D_{m}^{2}(k)+\sigma_{D}^{2} \big{)}\lambda_{\theta}^{\prime}\text{sin}(2\theta_{m}(k))}{2}+\Big{(} \lambda_{\theta}^{-2}-2\Big{)}D_{m}^{2}(k)sin(\theta_{m}(k))sin(\theta_{m}(k) ).\end{aligned}\right. \tag{18}\]  
in which:  
\[\lambda_{\theta}^{\prime}=e^{-2\sigma_{\theta}^{2}}. \tag{19}\]
# Enhanced Electrokinetic Remediation for the Removal of Heavy Metals from Contaminated Soils
## 1 Introduction
The use of organic acids as facilitating agents in electrokinetics shows various benefits for the remediation of metal-contaminated soils. Organic acids are recognized as effective complexing and chelating agents that can keep metals in solution in a wide range of pH, even in an alkaline environment [15]. Moreover, the mild conditions of organic acids do not induce dramatic changes in the soil properties. Organic acids are usually biodegradable and can be removed by natural biodegradation at the end of the remediation process. Furthermore, the residual organic acids in the soil may help to enhance the activity of the natural soil microflora. This paper studies the remediation of six metals (Cd, Cr, Co, Cu, Pb, and Zn) from agricultural soil with aged contamination. The different chemical nature of the metal species and the presence of these metals in high concentrations make the remediation of soil more difficult. The removal of metals was studied using organic acids (EDTA and citric acid) as facilitating agents with the objective to develop a feasible technology to be applied at a large scale. The aim of the study is to identify the facilitating agent and the experimental conditions to achieve the mobilization and removal of the six metallic species from the soil.
## 7 Research Directions
### Identity Privacy  
IoT networks are dynamic due to the mobility of some smart devices, such as wearable devices and smart vehicles. Every device of an IoT system must be identified uniquely. The identifiers of these devices should be cryptographically proctored to defend against impersonation-type attacks. Moreover, the identifiers, such as host identities [117] and certificates [128], of a device used for authentication should be unique from locations to locations, sessions to sessions, and networks to networks. The use of static identifiers makes IoT devices and device owners vulnerable to cyber espionage and user-targeted attacks, such as movement profiling (Figure 22) and communication relation (Figure 23).  
Figure 22: An adversary profiles the movement of a target by tracking the static identifier of the target. RSU = Roadside Unit. VANET = Vehicular Ad Hoc Network.  
As shown in Figure 22, an adversary can learn the locations of a smart car and its owner by tracking the identifier of the car used for network authentication (when the car joins a network) and service authentication (when the car communicates with other cars and roadside services). Similarly, adversaries being co-located with victim devices can record device identifiers during device-to-device communications for device targeted attacks. For instance, an adversary can disrupt communication channels when it identifies that a target device starts communication with other devices and services. Figure 23 shows such an attack.  
To protect location and communication privacy, IoT devices should be enabled to compute or use a one-time device identifier, which is similar to the one-time password [173], for network authentication and device-to-device mutual authentication. Authentication schemes based on Zero Knowledge Proof algorithms [174; 175] can be adopted to preserve identity privacy.