esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement  
Let \(\Psi=\{S1,S2,\ldots,Sn\}\) be the source information to be detected and \(U=\{u_{1},u_{2},\ldots,u_{l}\}\) be a user collection on social media. Each \(S_{i}\in\Psi\) consists of a sequence of \(l_{i}\) tokens \(\{w_{1},w_{2},\ldots,w_{li}\}\), where each token \(w_{li}\in R^{d}\) is a d-dimensional vector denoting the token feature. Each \(u_{i}\in R\) represents the authority of user \(i\) calculated based on multiple metadata features, including whether the account is verified, and geolocation information and homepage introduction exist, and the numbers of fans, friends and favorites. The specific calculation process is provided in Appendix A.  
When a news story \(S_{i}\) is posted, it causes users to discuss the story and generate comments or forwarded information. We describe the information propagation process from the perspective of temporal and spatial structures, as shown Figure 2. The spatial structure reflects user-content interaction (e.g., commenting, forwarding) in message propagation, while the temporal structure is associated with a series of posts (e.g., comments or forwarded messages) over time. We denote the temporal structure as \(P(S_{i})=\big{\{}(c_{0},\ t_{0}),\ldots,(c_{j},t_{j}),\ldots\big{\}}\) where \(c_{j}\) is a d-dimensional vector representing the post (comment or forwarded) content at time \(j\) in the propagation of information \(S_{i}\) and \(t_{j}\) is the time at which post \(c_{j}\) is generated. \(c_{0}\) denotes \(S_{i}\) semantic feature. The spatial structure is denoted as \(G(S_{i})=<V,E>\), where \(G(S_{i})\) represents the propagation graph of news \(S_{i}\). \(V\) is the node collection of \(G(S_{i})\) denoting posts in source information propagation. Each node in \(V\) is defined as \(v_{j}=\big{(}u_{j},\ c_{j}\big{)}\in V\), where \(u_{j}\in R\) represents the authority of the user who posted post \(j\), and \(c_{j}\in R^{d}\) characterizes the post content. E denotes the edge collection describing the association relationship between nodes in \(G(S_{i})\). If \(c_{j}\) is a comment or forwarded message to \(c_{i}\), a directed edge from node \(i\) to node \(j\ e_{ij}=\big{\langle}v_{i},\ v_{j}\big{\rangle}\) will be added in \(E\).  
Figure 2: A piece of news on PHEME, and the meta-data of users participating in the discussion, the relevant comments or forwarded message on social media over time. The unit of time axis is minutes. “V” represents whether the account is verified or not, and “FL”, “FR” and “F” represent the numbers of followers, friends and favorites, respectively. The bold words are the false portions of source information, and some explainable comments can directly confirm the falsehood of these words in the news.  
The interpretable fake news detection task can be described as learning a decision function \(f:\ f(S,P,G)\to y\) that maximizes prediction accuracy with reliable evidence and marks the false portions of source information with explainable evidence-related words.
## 3 Results
### Connection with the Thalamus  
The main basal ganglia output pathway originates in the SNr and the inner segment of the globus pallidus (GPI) and leads to the thalamus. These pathways are referred to as the nigrothalamic and pallidothalamic pathways, respectively [64]. [PERSON] et al. (2021) [64] utilized super-resolution track-density imaging (TDI) on 7T MRI to generate three-dimensional maps of the nigrothalamic and pallidothalamic projections and to identify the SNr substructures in detail. The study demonstrates that SNr projects directly to disparate thalamic nuclei. Two such pathways have been identified: the lateral pathway, which originates in the SN, leading through the reticular nucleus and internal capsule and to the ventral anterior nucleus (VA) and ventral lateral posterior nucleus (VLp); and the medial pathway, which originates in the caudal SNr, traverses the SNc, and terminates at the ventromedial nucleus (VM) of the thalamus. As demonstrated in the latter work, the superior and inferior parietal lobes provide parallel but topographically distinct outputs to the thalamus.  
A recent finding revealed the existence of a direct connection between SNc dopaminergic neurons and the thalamus. The conventional model proposed by [PERSON] (1990) posited that the SNc exerts its influence on the thalamus solely indirectly, through the intermediary of the striatum and subsequent downstream circuits [28]. As demonstrated in the study by [PERSON] et al. (2025) [28], the authors conducted a selective monitoring of connections through the SNc, thereby ascertaining that, on average, approximately 12% of all streamlines passing through the SNc proceed directly to the thalamus. This nigrothalamic pathway was reproducibly identified in all young healthy subjects in the study and formed a separate bundle not involving other structures (i.e., it was not a branch to the striatum, cortex, or cerebellum). This constitutes quantitative in vivo evidence of a direct SNc link to the thalamus in humans. This finding extends the established model of basal ganglia circuits, indicating that, in addition to the expected inhibitory pathway from the SNr, there is also modulation of the thalamus by dopamine from the SNc. This represents a significant advancement in our understanding of basal ganglia circuits. The authors illustrated the nigrothalamic system in schematic representation as previously seen in Figure 1B [28].  
In addition to the direct connections of SNr and SNc to the thalamus, a recent study using generalized q-sampling imaging (GQI) tractography has allowed the complex involvement of the ansa lenticularis (AL) to be described. [PERSON] et al. (2022) [65] reconstructed four subcomponents of the AL, one of which, the globus pallidus-substania nigra (GPSN), demonstrates a direct anatomical connection between these structures. This connection indicates the presence of additional, more precisely organized circuits that integrate basal ganglia output activity not only to the thalamus but also in the opposite direction towards the SN.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 1 Introduction
Researchers have thus focused on microsize and low power consumption for implantable sensors. Furthermore, the RF spectrum hosts many devices, such as Wi-Fi and Bluetooth devices. The interference generated can affect the reliability of intrabody networks and the security of patients. Information about patients could be eavesdropped upon, which causes a security concern. Ultrasound could propagate along the body or inside the body, which enhances security. In addition, the frequency choice is more flexible compared with that of RF communication. From research conducted over the past decades, ultrasonic waves are known to have better propagation characteristics than RF waves in medical applications. Previous studies have provided software-based ultrasonic sensor networks; however, there was no real experimental realization [14; 15; 16] on digital communication. Therefore, we attempted to use ultrasonic wave communication between sensors on the basis of an experimental setup used for body sensor networks. The rest of this paper is organized as follows. In Section 2, we introduce the principles of ultrasonic waves, including the attenuation of ultrasonic waves and the fundamentals of propagation along the body. In Section 3, we illustrate the modulation method of communication and the design of the experimental setup, and discuss the experimental and simulation waveforms. In Section 4, we present the results and discuss the performance of ultrasonic waves through a BSN. The conclusion is provided in Section 5.
# Effect of Non-Homogeneous Soil Characteristics on Substation Grounding-Grid Performances: A Review
###### Abstract  
Designing an effective grounding system for AC substations needs predetermination of ground resistance and ground potential distribution caused by fault current's presence in the ground. Therefore, it is necessary to have a suitable grounding grid structure in the soil properties in which the grid is buried. Though the soil composition where the grounding grid is located is typically non-homogeneous, the soil is often presumed to be homogeneous due to the complexities of grounding system analysis in non-homogeneous soil. This assumption will lead to inaccuracies in the computation of ground resistance and ground potentials. Although extensive research has been done on non-homogeneous soil structure, comprehensive literature on grounding system performance in non-homogeneous soil is yet to be reviewed. Thus, this paper reviews the effect of non-homogeneous soil on the grounding system, with different soil characteristics in horizontal and vertical two-layer soil structure and the horizontal three-layer soil structure. In addition, the effect of design parameters on the grounding performance in non-homogeneous soil conditions for non-transient fault conditions is also studied. The significance of this study is that it provides a comprehensive review of grounding performance as grounding design changes and their effects as soil layers and their corresponding features change. This knowledge will be useful in developing safe grounding designs in non-homogeneous soil.  
homogeneous soil; non-homogeneous soil; two-layer soil; three-layer soil; soil characteristics; substation grounding +
Footnote †: journal: Applied  
_sciences_
## 3 Optimizing In-Orbit SAR Operational Parameters for Altitude Variation
### PRF Optimization
#### 3.2.2 PRF Optimization Using the Altitude Ratio  
In this approach, PRF is allowed to change according to the altitude variation to overcome the limitation of the constant PRF selection method. For this, PRF is optimized using the ratio between the nominal and predicted altitudes. As shown in Figure 13, the allocable PRF value decreases for the increasing altitude. Using such proportional relation between PRF and the altitude, the optimized PRF, \(PRF_{\text{opt}}\), is given as follows.  
\[PRF_{\text{opt}}=\frac{H_{\text{opt}}}{H_{\text{nominal}}}PRF_{\text{nominal}} \tag{6}\]  
Here, \(H_{\text{opt}}\) is the predicted altitude. Also, \(H_{\text{nominal}}\) and \(PRF_{\text{nominal}}\) represent, respectively, the nominal satellite altitude and the PRF value at the nominal altitude, which are stored in the database. This method was used for KOMPSAT-5 before this study.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 3 LoRaWAN Overview
### LoRa Channel Activity Detector  
LoRa Channel Activity Detection (CAD) is a mechanism implemented in the LoRa transceiver to detect activity on the channel before transmission. LoRa CAD does not use the traditional Received Signal Strength Indicator (RSSI) approach of channel activity detection implemented in other transceivers, as LoRa transceivers can demodulate transmissions below the noise floor.  
The LoRa CAD mechanism requests the transceiver to attempt to capture preamble symbols on a specified frequency and with specific SF/BW settings. The LoRA radio post-processes the received signal and checks for a correlation between the transceiver's captured data and an ideal preamble waveform [7]. This CAD method allows LoRa CAD to differentiate between random noise and a LoRa signal. If a preamble is detected, the transceiver will switch to Rx mode and receive the payload. The time (\(Rx_{time}\)), in seconds, that the LoRa radio receiver should be active can be calculated according to Equation (1) [8], with the SF, BW, and CR settings corresponding to the settings used to setup the CAD mechanism. It is also possible for the transceiver to detect the RSSI of the detected preamble message. LoRa CAD relies on detecting whether or not a preamble is currently being transmitted, and cannot detect channel activity when the payload of a message is being transmitted.  
\[Rx_{time}=\frac{2^{SF}+32}{BW} \tag{1}\]  
The LoRa CAD mechanism is used as a key mechanism in the proposed LoRaWAN relay specification [9], which allows a relay node to periodically scan whether or not a child node requires a packet to be forwarded to a gateway. The proposed multi-hop network protocol in this research relies on Real-Time Clock (RTC) synchronisation in the network to ensure that relay nodes are in receive mode when a child node requires a packet to be forwarded.
# Application of Label Correlation in Multi-Label Classification: A Survey
## 2 Overview of Label Correlation
### Introduction to Label Correlation
#### 2.2.2 Classification Based on Granularity of Sharing  
In MLL, the granularity of sharing for label correlations defines the extent or scope to which label correlations are shared across the entire multi-label dataset. The granularity of sharing describes whether label correlations are shared among all samples or only among a subset of samples. Based on the granularity of sharing, label correlations can be classified into global label correlations and local label correlations.  
Global label Correlation  
Global label correlations refers to label correlations that apply to the entire dataset. Traditional MLC methods that incorporate label correlations typically impose these correlations on all data samples [20; 21; 22; 23; 24], assuming that the mined label correlations are universally applicable. However, in various real-world scenarios, this global assumption often has limitations. Its overly broad applicability can lead to unnecessary erroneous predictions. These mispredictions usually manifest as predictions of irrelevant labels, thereby compromising the model's performance.  
In real-world tasks, there are cases where certain label correlations only apply to local subsets of data, rather than to the entire dataset. For example, in a news dataset, from technology-related articles, we may observe a high correlation between the labels \"Apple\" and \"[PERSON]\", while in lifestyle news, the \"Apple\" is highly correlated with \"Fruit\". This example illustrates that in different contextual environments, such as technology and lifestyle domains, \"Apple\" exhibits different label relationships, indicating that label relationships derived from specific domains and contexts are not generalizable and should not be shared by all data.  
Addressing this issue, [PERSON] and [PERSON] et al. [34] first introduced the concept of local label correlations in 2012, suggesting that there are label correlations that are only applicable to local subsets of data in practical tasks, rather than to the entire dataset. Subsequent work has studied local label correlations [32; 35; 36] and experimentally demonstrated that considering local label correlations positively impacts model performance.
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 2 Deep Learning Models
### NASNet  
[PERSON] et al. [28] proposed a new category of convolutional networks called NASNet based on the idea of directly training the architecture over the desired dataset. In order to overcome the issue of computational cost for relatively larger datasets, authors proposed to search for an architectural building for a smaller dataset, often called proxy dataset and then transferring it to a larger dataset. The search space which enables the transfer from a smaller dataset to a larger dataset is referred to as NASNet search space inspired from neural architecture search (NAS) [29]. Furthermore, to improve the generalization of the NASNet model, the authors proposed a novel normalization approach called ScheduledDropPath. In NAS, a recurrent neural network (RNN) controller samples the child architectures, which are trained over proxy datasets and based on the training accuracy, the controller improves the architecture. The main contribution of this approach is the decoupling of architecture complexity from depth.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products  
Anissa Belkheiri  
1 CNRS, SIGMA Clermont, Institut Pascal, University Clermont Auvergne, F-63000 Clermont-Ferrand, France; anissa.belkheiri@ethu.uca.fr (A.B.); alina.violeta.ursu@uca.fr (A.V.U.); pascal.dubessay@uca.fr (D.D.);guillaume.pierre@uca.fr (G.D.); edric.delattre@uca.fr (C.D.); gholamraza.delveb@sigma-clermont.fr (G.D.)  
Cedric Delattre  
1 CNRS, SIGMA Clermont, Institut Pascal, University Clermont Auvergne, F-63000 Clermont-Ferrand, France; anissa.belkheiri@ethu.uca.fr (A.B.); alina.violeta.ursu@uca.fr (A.V.U.); pascal.dubessay@uca.fr (D.D.);guillaume.pierre@uca.fr (G.P.); edric.delattre@uca.fr (C.D.); gholamraza.delveb@sigma-clermont.fr (G.D.)  
Gholamraza  
2 Food Science and Technology Department, College of Agriculture, Isfahan University of Technology, Isfahan 84156, Iran; a.forouhar@ag.itu.ac.ir (A.F.); hamdamani@cc.iut.ac.ir (N.H.)  
Shim Abdelkafi  
4 Laboratoire de Genie Enzymatique et Microbiologie, Equipe de Biotechnologie des Algues, Ecole Nationale d'Ingenieurs de S fax, Universite de S fax, Sfax 3029, Tunisia; sim.abdelkafi@enis.tn  
Nasser Hamdami  
5 Correspondence: philippe.michaud@uca.fr; Tel.: +347340742  
components, which are the flesh, seed, and rind. The watermelon rind, the area of white-colored flesh between the colored flesh and the outer skin, accounts for approximately one-third of the total fruit mass [6]. The rind contains mineral salts (13.09%), fat (2.44%), protein (11.17%), carbohydrates (56%), vitamins, and phytochemicals [7]. Carbohydrates are the main compounds of the watermelon rind which can be a raw material for pectin extraction. However, it is considered as waste and has no commercial value [8].  
Pectins are a family of complex polysaccharides present within the primary cell wall and intercellular regions of dicotyledons, that impart flexibility and mechanical strength to plants [9]. In the 1920s and 1930s, many companies began producing pectin because of the large quantities of fruit left over from the juice and wine industries, especially apple or citrus pulps [10]. Pectins are used in the cosmetics, pharmaceutical, and food industries to stabilize acidified milk drinks or juice and as a gelling or thickening agent. In Europe, they are an approved food additive, coded E440a for low- and high-methoxyl pectins and E440b for amidated pectin [11]. Pectins have also been the subject of special attention from nutritionists. They are used as dietary fiber and exert physiological effects on the intestinal tract by increasing the transit time and the absorption of glucose [12].
## 2 Materials and Methods
### Data Augmentation Method  
Diffusion models have proven to be highly effective in producing realistic output based on text queries (prompts) [45]. In our study, we employed AudioLDM, which is built upon a latent diffusion architecture to generate synthetic audio. We leveraged ChatGPT using prior knowledge about the target domain for generating prompts. Each prompt can consist of multiple words; for instance, \"add sharp and metallic screeching sound created by metal-cutting saws\". During the model pretraining phase, both the original samples and their augmented versions, using a randomly selected prompt from ChatGPT prompts, were fed into the model for pre-training purposes. The workflow of the suggested model is as follows:  
To generate audio, instead of creating it from scratch, we integrated real audio into the generation process of the diffusion model, which is a reverse process with S steps. Firstly, we insert a real audio \(x_{0}^{ref}\) with noise \(\epsilon\sim N(0,1)\) at timestep \(\lfloor S_{t_{0}}\rfloor\), where \(t_{0}\in[0,1]\) is a hyperparameter controlling the insertion position of the audio. Then, the process of reverse diffusion is applied by utilizing the spliced audio at timestep \(\lfloor S_{t_{0}}\rfloor\), before we proceed iteratively to apply Equation (1) until a sample is generated at timestep 0.  
\[x_{\lfloor S_{t_{0}}\rfloor}=\sqrt{\tilde{x}_{\lfloor S_{t_{0}}\rfloor}x_{0}^{ ref}}+\sqrt{1-\tilde{x}_{\lfloor S_{t_{0}}\rfloor}\epsilon} \tag{1}\]  
This conditioned generation process is guided by a prompt that includes the embedding \(\overrightarrow{w_{i}}\) for the prompt. We demonstrate this type of augmentation surpasses traditional augmentation methods without diffusion and helps the improvement in few-shot audio classification tasks. In this context, four key challenges associated with Generative augmentation were identified [46; 47], which are as follows: prompt ambiguity--certain prompts possess multiple meanings, leading to the generation of incorrect samples; diversity--ensuring the generation of audio samples with sufficient variation poses another challenge; domain shifting--augmentation methods must demonstrate flexibility in adapting to new distributions; and finally, fidelity--Generative methods must produce high-quality samples that closely resemble real audio. In this study, we address these challenges by employing clear and diverse prompts, generating high-quality samples.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 4 Design of Control Strategies
### System Identification  
Implementing robot control requires prior knowledge of the desired trajectory for task execution. Trajectory planning presents multiple challenges, including obstacle avoidance and ensuring actuator limitations for voltage and torque are not exceeded. The fundamental control objective is to guide the robot along a desired trajectory defined by a set of end-effector coordinates.  
In robotics, tasks are typically specified in the workspace or Cartesian space, where manipulator movements can be naturally described relative to the environment and workpiece. However, trajectory tracking control is more effectively implemented in joint space, which facilitates the formulation of arm dynamics. Consequently, inverse kinematics becomes essential for converting the desired Cartesian trajectory into joint space coordinates \(q_{d}(t)\).  
Recent years have seen increased research interest in redundant robot manipulators. Kinematic redundancy occurs when robot manipulators possess more DoF than required for a given task. This characteristic enhances robot dexterity, enabling additional tasks such as obstacle avoidance or singularity prevention while maintaining the primary motion task. For instance, a 3-DoF robot manipulator exhibits functional redundancy when controlling only end-effector position (two coordinates), but not when both position and orientation are required.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation  
This section presents the comparative performances of the proposed Priority Aware Utility Maximization (PAUM), Basic Radio Resource Management (bRRM) [8], and Intelligent Radio Resource Management (iRRM) [12] with diversified parameters in network simulator version-3 [NS-3] [32; 33] using a typical LTE network cell for a smart healthcare system.
## 7 Applications and Future Research Directions of RIS
### RIS-Assisted Visible Light Communication (VLC) System and Hybrid VLC-RF Networks
#### 7.4.1 Dynamic Channel Gain Modeling  
In RIS-assisted LiFi systems, it would be possible to dynamically change the perceived channel gains at different users by controlling the multi-path channel propagation, and performance can be enhanced in two different ways:  
1. Increased reliability: By introducing and managing channel gains, ideal conditions for effective power allocation and, as a result, successful consecutive interference cancellation can be created.
2. Improved fairness: NOMA users with lower decoding order, i.e., lower channel gain, must always decode their signals with interference present, which means that their achievable data rates may not be sufficient to meet their QoS needs. Dynamic RIS tuning allows users to adjust their decoding order regardless of their location, resulting in increased fairness.