esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 4 Proposed EMS for SEEB
### The Impact of Using EACRA on the WSN Topology and Data Packets Routing Process  
Sensors are battery constraint devices, and it is essential to optimize their energy consumption to minimize maintenance dispatch frequency to change the batteries. Therefore, we developed an Energy-Aware Context Recognition Algorithm (EACRA) (Figure 13) that dynamically configures sensors to send specific data under specific conditions and at a particular time, thus avoiding redundant data transmissions. In designing this algorithm, we took advantage of the CBR model's declared knowledge about the different buildings and rooms. Hence, this algorithm will have the sensor node send data only when the context changes. The EACRA algorithm decreased the sensor's energy consumption as the number of samples, used modules, and transmissions were minimized [21]. The deployed algorithm takes advantage of the wireless mesh network topology's benefits [22; 23] to deliver better results.  
Energy consumption in the deployed WSN nodes can be optimized at the following three components: XBee RF, Arduino Nano programming board, and the attached modules. The XBee RF can consume less energy in the transmission mode if it sends a minimum number of packets with less data payload. The EACRA algorithm identifies the building type and the room's type of usage-based on room ID. Then, it generates the sensor schedule and configuration that will be valid during a period of time (lease). This configuration is then sent to the sensor node. The lease schedule is updated twice a day; the first schedule is attributed in the morning at 6 a.m., and the second schedule is updated at 7:00 p.m.  
As EACRA schedules sensors based on the declared knowledge of the building, the WSN graph changes frequently. Hence, in some scenarios, some sensor nodes ended up isolated with no link to the rest of the network as the neighbor sensors were put to sleep by EACRA. Therefore, we investigated the different ways which can accommodate this problem.  
As a result, we enhanced the EACRA to maintain the network connectivity by turning on some sensor nodes that were put to sleep by the EACRA. The selection of which nodes to wake up is based on different criteria to maintain network connectivity with fewer nodes being ON.  
This study investigated two network topologies: The full mesh network and the cluster tree mesh topology. There were no isolated nodes in the cluster tree mesh as the router nodes were always ON while only the sensor nodes were put to sleep. The problem of isolated nodes raised in the full mesh network; hence, EACRA was enhanced to have an initial view on the topology, connected nodes, graphs, trees, and routing tables of each node. As a result, when EACRA finishes  
Figure 12: Finite State Machine (FSM) State diagram that controls Linux Laboratory heater.  
scheduling sensor nodes, it checks the topology looking for potentially isolated nodes and then turns on the nodes that can provide total WSN connectivity.
# DIFshilling: A Diffusion Model for Shilling Attacks
## 2 Related Work  
Recommender systems (RSs) have become integral to various online platforms, providing personalized suggestions that enhance user experience and drive commercial success. However, their widespread adoption has also exposed them to significant vulnerabilities, particularly adversarial attacks [13] and shilling attacks [6]. While adversarial attacks are beyond the scope of this paper, we focus on shilling attacks, which involve injecting fake user profiles to manipulate recommendation outcomes. These attacks can degrade recommendation quality, undermine user trust, and cause substantial economic consequences [14]. Addressing these risks is crucial for ensuring the robustness and reliability of RSs. Shilling attacks can be broadly categorized into **algorithm-agnostic** and **algorithm-specific** approaches. Recently, generative adversarial networks (**GANs**) [15] have been increasingly utilized to enhance the effectiveness of shilling attacks, enabling the generation of more sophisticated user profiles that are harder to detect. We review all these categories in detail and show the baselines compared with our approach in the experiments in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method Name** & **Description** & **Type** & **References** \\ \hline Random Attack & Chooses items at random, applying ratings from a normal distribution with parameters matching the overall ratings’ mean and variance. & Algorithm-agnostic & [16; 17] \\ \hline Average Attack & Uses a normal distribution for ratings with parameters based on sampled items’ ratings. & Algorithm-agnostic & [16] \\ \hline Segment Attack & Assigns maximal ratings to selected items and minimal to others, targeting specific system segments. & Algorithm-agnostic & [16] \\ \hline Bandwagon Attack & Exploits item popularity by assigning maximal ratings to popular items and others randomly. & Algorithm-agnostic & [16; 18] \\ \hline AIA & Generates profiles maximizing an attack objective using a bilevel optimization framework under a black-box scenario. & Algorithm- specific & [19] \\ \hline DCGAN & Treats shilling attacks as a repeated general-sum game, using DCGAN for fake profile generation. & GAN-based & [20] \\ \hline AUSH & Trains models considering shilling, reconstruction, and adversarial losses to balance attack impact and detectability. & GANs-based & [9] \\ \hline Leg-UP & Improves AUSH by focusing on attack transferability and invisibility, leveraging direct loss functions. & GAN-based & [9; 21] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The benchmark of shilling attack methods used in experiments.
## 2 Materials and Methods
### Sampling and Models Generation
A classifier is a system that can predict, based on the previous learning, the class of a new input instance. Some of the methods that have been discussed in the literature and that will be used for our work are the following: C5.0, Logit Boost, Bayesian Generalized Linear Models (BayesGLM), Multilayer Perceptron, Random Forests (RF) and Support Vector Machine models (SVM). C5.0 [65] (p. 5) is an improvement of the classic C4.5 algorithm [66], which generates decision trees based on the concept of information entropy. LogitBoost [67], also known as additive logistic regression, applies a boosting approach to build a logit model using decision trees as weak learners [68]. BayesGLM [69] uses an approximate Expectation-Maximization (EM) to fit GLM with the Student-t prior to distribution. The Multilayer Perceptron [70,71] is a class of artificial neural network that uses hidden layers and the back-propagation error algorithm. Random Forests [72] is a metaclassifier that builds multiple decision trees and combines their outputs by a voting process. Finally, SVM [73] build models as function estimation and optimization problems, in a linear or non-linear way, separating classes by hyperplanes. Some of the literature works related to health sentiment analysis have used machine learning methods such as SVM [29,39], Naive-Bayes [74], Random Forests and Random Decision Trees [48]. Six supervised learning algorithms were implemented to obtain the models: C5.0, Logit Boost, Bayes GLM, Multilayer Perceptron, Random Forest and SVM. The hyperparameters of each algorithm were either set in their default values or optimized between 4 and 12 parametrizations, for further indications code and packages are provided as it is indicated below. The Multilayer Perceptron used a Weighted Decay in the los function and SVM was implemented with a linear kernel. Each of them was evaluated using 10-fold cross-validation and their accuracies were computed by the mean and standard deviation of ROC values. All the sampling and classification methods were implemented in R, mainly by UBL ([[https://www.rdocumentation.org/packages/UBL](https://www.rdocumentation.org/packages/UBL)]([https://www.rdocumentation.org/packages/UBL](https://www.rdocumentation.org/packages/UBL))) and caret ([[https://www.rdocumentation.org/packages/caret](https://www.rdocumentation.org/packages/caret)]([https://www.rdocumentation.org/packages/caret](https://www.rdocumentation.org/packages/caret))) packages, respectively. All the code is included in the Supplementary Materials.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 4 Experimental Results  
### Experimental Setup  
Our implementation was carried out on the Vitis Unified Platform, using the PYNQ (Python Productivity for Zynq) framework [34] (version 3.0.0), which provides a Python-based environment (version 3.10) for FPGA development. The entire code base was developed and executed through PYNQ's Jupyter notebook(version 3.4.4) interface, enabling interactive development and real-time result visualization.  
The server CPU is a 16-core, 3.0 GHz AMD EPYC 7302P [35] with 128 GB of system memory, and operating with both 32-bit and 64-bit architectures. The CPU configuration includes a substantial cache hierarchy, featuring 512 KiB L1 cache (sixteen instances), 8 MiB L2 cache (sixteen instances), and 128 MiB L3 cache (eight instances). The processor operates at a base frequency of 1500 MHz with a maximum boost frequency of 3000 MHz, utilizing AMD-V virtualization technology. The OS is Ubuntu 18.04 LTS with the Linux 4.15.0-20-generic kernel.  
The FPGA device is part of the Alveo U200 (XCU200-2 FSGD2104E) [36]. The platform features extensive computational resources, including 1,182K lookup tables (LUTs), 2364K registers, 6840 DSP slices, and 960 UltraRAMs. The memory subsystem provides 64 GB DDR total capacity with a maximum data rate of 2400 MT/s and a total bandwidth of 77 GB/s. Connectivity is facilitated through a PCIe Gen3 x16 interface and dual QSFP28 network interfaces. The processor's architecture supports advanced security features and mitigations. This robust host configuration, combined with the FPGA resources, creates a powerful platform for accelerating complex computational workloads, particularly in quantum computing applications where both CPU processing power and FPGA acceleration capabilities are crucial. A comparison of the FPGA and CPU resources is given in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Alveo U200** & **CPU** \\ \hline Processing System & Same as host & AMD EPYC 7302 (16-Core) \\ \hline System Memory (GB) & 64 & 128 \\ \hline System Frequency (MHz) & 300 & 3000 \\ \hline FPGA Device & XCU200-2 FSGD2104E & N/A \\ \hline LUTs & 1182K & N/A \\ \hline UltraRAM & 960 & N/A \\ \hline DSP Slice & 6840 & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: FPGA accelerator board and CPU resources.
## 3 Methodology
### Result Analysis: SHAP Explainable and MK Test Method
#### 3.4.2 MK Test  
The MK test is a non-parametric statistical method used to analyze trends in time-series data [63; 64]. It employs \(p\)-values and \(Z\)-values to ascertain the presence of significant trends in the dataset. The research employs the MK test to analyze the trend of landslide susceptibility probability over a span of 22 years in China. Specifically, while \(Z<0\) signifies a downward trend, \(Z\) equals zero signifies the absence of a significant trend, and \(Z>0\) signifies an upward trend. The significance level, represented by \(p\), reflects the probability of erroneously rejecting the null hypothesis. For a significance level of \(p=0.01\), the confidence level is 2.58, whereas for \(p=0.05\), the confidence level is 1.96. In the study, when \(0.01<p<0.05\) and \(1.96<|Z|<2.58\), a trend is regarded as significant, while a trend isconsidered highly significant if \(p<0.01\) and \(\mid\!Z\!\mid\) exceeds 2.58. In all other cases, the trend is viewed as not significant.
# Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
## 2 Biological Perception and Agricultural Aviation  
Agricultural aviation aircraft need to collect information about the surrounding environment in the process of operation, and high-precision and high-reliability sensors are the main means of environmental perception and data acquisition for agricultural aircrafts [14]. Facing the complex and harsh farmland operating environment, how to ensure the efficient and accurate information acquisition of aircraft is an urgent problem for researchers. After hundreds of millions of years of development, the sensing system of organisms can cope with the complex and changeable natural environment, providing ideas for the research of agricultural aviation sensors. At present, relevant Chinese scholars have put forward optimization schemes for how to improve the accuracy and reliability of sensor information acquisition by studying the mechanism of biological system perception. This section introduces the research progress of biological perception in China agricultural aviation through three aspects: echolocation and obstacle avoidance, binocular stereo vision, and compound eyes.
## 3 Results
### Visible (Vis) and Ultraviolet (UV) Light Imaging  
The observations of the painted canvases allow for the definition of different response to the UV-vis imaging. The untreated canvases revealed visible brush strokes. Under ultraviolet light, the oil-painted canvas exhibited a subtle fluorescence indicative of oil  
\begin{table}
\begin{tabular}{c c} \hline
**ID** & **Composition** \\ \hline Gel 1 & Ligroin/White spirit \\ \hline Gel 2 & Methyl Myristate \\ \hline Gel 3 & Methyl Stearate \\ \hline Gel 4 & Methyl Palmitate \\ \hline Gel 5 & Methyl Oleate \\ \hline Gel 6 & Agnique® ME \\ \hline Gel 7 & Methyl Caprate \\ \hline Gel 8 & Methyl Laurate \\ \hline Gel 9 & poly-hydroxybutyrate (PHB) \\ \hline \end{tabular}
\end{table}
Table 4: Selected FAME used in the experimentation and the gels’ names.  
Figure 3: FAME plotted on a Teas chart. Yellow area: synthetic resins; green area: natural resins; grey area: waves; blue area: oils; red area: polymerized oils; pink area: polysaccharides and proteins.  
aging, as discussed by [PERSON] et al. [34], while canvases painted with acrylic showed no fluorescence [35].  
Under visible light, microcrystalline wax displayed an opaque appearance, while Regalrez 1094 varnish exhibited a glossy appearance. In ultraviolet light, neither displayed any fluorescence response, which was attributed to the marker's presence (Fe\({}_{3}\)O\({}_{4}\)). All cleaning areas exhibited varnish residues. Table 5 presents the images acquired for the oil-based canvases. Gel 3 emerged as the most effective cleaning agent for both microcrystalline wax and Regalrez varnish. It resulted in a level of cleanliness comparable to ligroin for the wax and superior to white spirit for Regalrez 1094.  
In the evaluation of acrylic canvases (Table 6), all tested gels obtained poorer cleaning outcomes in the designated areas. This outcome might be attributed to a potential interaction between the varnishes and the paint layers, influencing the efficacy of the cleaning process.  
Figure 4 shows the percentages of residual black aspect after the cleaning treatment for the various treated areas. The images confirm the result obtained with the macroscopical observations, establishing Gel 3 as the most effective treatment. Based on the percentage of black aspect remaining, the following order for the removal of microcrystalline wax on both oil and acrylic paint layers can be established:  
\[\text{Gel 3}>\text{Gel 1}=\text{Gel 2}>\text{Gel 4}=\text{Gel 5}=\text{ Gel 6}>\text{Gel 8}>\text{Gel 7}=\text{Gel 9}\]  
Regarding the removal of Regalrez 1094 varnish (Figure 5), the following order can be established based on the percentage of remaining black substance after cleaning treatment:  
\[\text{Gel 3}=\text{Gel 5}>\text{Gel 7}>\text{Gel 6}=\text{Gel 8}>\text{ Gel 1}=\text{Gel 4}>\text{Gel 9}\]
# Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models
## 5 Results and Discussion  
In this paper, many experiments were conducted to study the effect of using multiple approaches for automatic abusive content detection in Arabic. The Arabic language has a rich morphological structure and many dialects; thus, it is a challenging task to detect abusive content in Arabic, especially when dealing with daily and informal tweets. Accordingly, we used the recent proposed pretrained LMs in DA (see Table 3) in our proposed framework. On the other hand, the binary classification task was not enough to capture the targeted groups and other related aspects of abusive content in tweets; thus, subsequently tackling this problem using multiclass classification task is useful in order to detect many aspects of abusive content. Therefore, we used the multi-aspect annotated dataset with 3.3 K tweets in Arabic and all the compared algorithms used the same split ratio train:dev:test = 8:1:1, as this split was used in the previous work. Additionally, due to the low resource scenario, we relied on a data augmentation method using the NLPaug library. The results for the proposed framework are presented in Section 5.1 and the results for the MTL models are presented in Section 5.2, where the best results are shown in bold. For the performance evaluation, we selected the macro-F1 metric to compare the achieved results. Macro-F1 has been adopted as the official evaluation measure for the imbalance in class distributions [55]; it is computed as a simple arithmetic mean of per-class F1 scores. Furthermore, the computational complexity measures the amount of computing resources (time and space) consumed by a particular algorithm when it runs. A set of experiments was undertaken using different parameters each time. The specific details of the hyperparameters in each experiment were reported previously in Section 4.1. Simultaneously, we performed the experiments using Google Colab Pro+ with high RAM requirements and a GPU accelerator; however, since we were dealing with a low number of samples, each experiment took 7 to 8 min to run.
## 3 Methodology
### Application Type for Supervised Learning
#### 3.1.2 Feedforward Neural Network  
The proposed FFNN architecture is also known as a multilayer perceptron (MLP) or simply a neural network [27]. During the computation process, a group of layers, defined as two consecutive layers within the network, processes the inputs by multiplying them with their respective weights. The resulting products are then summed to compute the total input for a single neuron [28]. During the development of each machine learning algorithm, all information on contextual variables used as inputs was standardized using Z-score normalization.  
\[X_{i}^{*}=\frac{(X_{i}-\mu)}{\sigma} \tag{2}\]  
where \(x_{i}\) represents the input variables and \(X_{i}^{*}\) is the new input of the standardized variables, while \(\mu\) and \(\sigma\) are the mean and standard deviation [29]. The data used in this study consists of five main input categories, which are signal strength, latency, resource allocation, required bandwidth, and application type. The prediction output from the BRFFNN is the allocated bandwidth, as shown in Figure 2.  
The structure of the feedforward neural network uses a regression model with five input features and a single output. The network consists of three hidden layers with 5, 10, and 15 neurons and employs a Bayesian regularization algorithm to predict the bandwidth allocations. These predictions will be used in the simulation of the UAV-GScommunication system. The QoS parameters from the simulation, including packet delivery ratio, throughput, and delay, will be recorded for analysis.  
Bayesian regularization [30] adjusts the FFNN's cost function by adding both a data-fitting component that reduces prediction errors and a regularization component that discourages large weights. This strikes a balance between prediction accuracy and model complexity.  
Figure 3 shows the convergence of training and validation loss over 500 epochs to demonstrate the model's training performance. The regression plots between the training and testing sets' actual and predicted values show excellent model performance, with an overall R-value of 0.9997. In order to forecast bandwidth allocations and subsequently analyze QoS metrics like the PDR, throughput, and delay, the trained BRFFNN model is incorporated into the UAV-GS communication simulation.
###### Abstract  
Flying an unmanned aerial vehicle (UAV) is a challenging task, due to the absence of direct sensory information such as a view of surroundings, sound, vibrations, and motion. It is important to provide these sensory stimulations to an operator for better situational awareness and efficient control. In this research, we study the effects of incorporating haptic feedback in UAV flight control. We present an experimental evaluation of three flight control scenarios, that is, in the absence of haptic feedback, in the presence of realistic haptic feedback, and an exaggerated haptic feedback. These three conditions are tested for an altitude hold, gain, and descent task in the flight simulation environment. A linear mathematical model is used to simulate the flight dynamics of a UAV controlled by a 6 DOF Touch 3D Stylus haptic device. Fifteen untrained users are recruited to participate in the flight simulation experiment. The analysis of the results of the experiment indicates an improvement in the longitudinal flight control performance. Specifically, the altitude hold task is better performed with no feedback, the altitude gain with exaggerated haptic feedback, and the altitude descent with realistic haptic feedback. A subjective evaluation in the end corroborates these findings.  
haptic feedback; unmanned aerial vehicles (UAVs); flight control; flight simulation; human-machine interaction +
Footnote †: journal: Applied Sciences
## 4 Mobile Localization Algorithms
### Distributed Localization Algorithms  
In this section, six distributed localization schemes are surveyed: (1) Multi-frequency Active Localization Method base on TDoA (MFALM), (2) Dive and Rise (DNR) positioning scheme, (3) \"multi-stage DNR\" (MS-DNR) positioning scheme, (4) AUV-Aided localization technique, (5) Multi-stage AUV-aided Localization (MS-AUV) scheme and (6) Scalable Localization scheme with Mobility Prediction (SLMP).