esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 3 Materials and Methods
### Classifier Models
#### 3.4.4 Gated Recurrent Unit  
The GRU is another RNN derivative model. It has less complexity but a similar performance to LSTM. GRU adds reset and update gates to simple RNN. Figure 7 shows a Gated Recurrent Unit.  
Figure 5: Simple RNN structure [11] (© 2018 IEEE. Reprinted, with permission, from IEEE Computational Intelligence Magazine).  
Figure 6: Long short-term memory [11] (© 2018 IEEE. Reprinted, with permission, from IEEE Computational Intelligence Magazine).  
The high training accuracies (100% for some models) suggest overfitting, where the model memorizes the training data rather than learning generalizable patterns. This leads to poor performance on unseen data.  
Regularization methods, such as L1, L2 and elastic net, impose penalties on excessive model complexity, serving as a deterrent against overfitting to particular data points in the training set [45].  
Dropout Layers: Randomly dropping out neurons during training forces the model to rely on other features and prevents overfitting to individual neurons [46].  
Balanced Dataset: An imbalanced dataset, wherein there is a prevalence of either positive or negative tweets, can result in the model exhibiting bias toward the majority class. This may lead to high training accuracy but might not ensure effective generalization [47; 48].  
Oversampling/Undersampling: Employing techniques such as oversampling, which involves replicating data points from the minority class, or under sampling, which entails removing data points from the majority class, aids in balancing the dataset. These approaches aim to alleviate bias, fostering a more equitable learning experience for the model from both classes [49].
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Terrestrial Links
#### 3.4.4 Signal Attenuation  
The signal found in the receiving antenna is altered by the path in which it travels. Typically, these alterations result in signal attenuation that can be divided into the following elements:  
* Free space attenuation;
* Diffraction attenuation;
* Atmospheric attenuation;
* Rain attenuation;
* Signal fading;
* Signal reflection.  
Each step represents a different window on the project page, where the user may insert necessary information (such as polarization, for example) or change automatically acquired information (such as rain intensity). The global real-life elements necessary for signal attenuation calculations are acquired by two different processes. Either by the use of the OpenWeatherMap API [20], used for real-time weather variable acquisition, or using the digital maps provided in multiple ITU-R Recommendations. These digital maps may vary in precision and meaning; whereas one digital map has leaps between the latitude and longitude angles of 0.75 degrees, other digital maps may have an angle leap of 1 degree. For a faster search of the values in these digital maps, the content inserted in the.txt files provided is rearranged to a tree-search-like structure exemplified in Figure 5, dividing the latitude and longitude values into different categories and eliminating the need to loop through all the elements of the file content to find a searched value of a given latitude and longitude coordinate.  
To detect diffraction and reflection points within the first and second Fresnel ellipsoids, two ray-tracing algorithms were designed; in both algorithms, the camera displayed in the Cesium window is used as a starting position, and they can detect both urban building entities and terrain points. The first algorithm, denominated _optical ray-tracing_, only considers the first element intersected in each ray, ignoring the remaining obstacles that may be found after the intersected obstacle. Alternatively, the second algorithm,  
Figure 4: Radiation pattern window.  
denominated _complete ray-tracing_, considers all the obstacles found in a ray direction and inside the Fresnel ellipsoids. To do so, after a ray intersects a building entity, the visible element of each entity is intersected and new rays are cast until either no entities are found or the entities are positioned outside the Fresnel ellipsoids.
## 2 Materials and Methods
### GPR Methodology  
Ground-based GPR systems incorporate a range of essential components designed to optimize the accuracy and functionality of subsurface investigations. The GPR unit, which consists of radar transmitter and receiver antennas, is central to this setup. These antennas emit electromagnetic waves into the ground and capture reflected signals from subsurface structures, enabling the collection of precise data about underlying features. The success of GPR surveys heavily depends on properly calibrating these antennas to minimize noise and maximize signal clarity. The selection of antennas is carefully aligned with the specific requirements of the investigation, such as the desired resolution and depth of exploration. For this study, a GPR system equipped with 500 MHz and 250 MHz central frequency antennas was employed, with the 500 MHz antenna providing high-resolution imaging for shallow targets (up to 5 m) and the 250 MHz antenna enabling deeper penetration (up to 12 m) into the subsurface. This configuration balances depth penetration with the ability to resolve finer subsurface details. Figure 7 shows the location of the GPR profiles. Developed by Sensors Software Inc. (a division of Radiodetection, Bristol, UK), the GPR system includes advanced enhancements such as proprietary signal processing algorithms and real-time visualization tools to maximize its capabilities.  
To ensure spatial accuracy, the system integrates Real-Time Kinematic (RTK) technology, which associates subsurface data with precise geographic coordinates, facilitating detailed spatial mapping of the investigated area. Using RTK technology enhances the precision of measurements and ensures that results can be seamlessly integrated with other geospatial datasets for comparative analysis. Data were collected at two distinct depth intervals, reaching 5 m for near-surface conditions and 15 m for deeper structural insights. These intervals were selected based on preliminary assessments of subsurface conditions, ensuring that key structural features and potential voids were adequately captured. This dual-depth approach offered a comprehensive understanding of the subsurface, aiding in the diagnosis of structural issues and supporting the development of targeted remediation strategies for the affected road segment. Survey parameters such as scanning speed, an  
Figure 7: The 2023 subsidence location is marked by a yellow star, highlighting the affected area. Green lines represent the 200 m (500 and 250 MHz) GPR measurement profiles overlaid on a satellite image of the region for spatial context and visualization.  
tenna spacing, and data sampling rate were optimized to ensure consistent coverage and high-quality data acquisition across the investigation area.
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 6 Conclusions  
In wireless communication, AMR plays a crucial role in efficient signal demodulation. Traditional AMR methods face challenges with complexity and high computational demands. Recent advancements in deep learning have offered promising solutions to these challenges. This study highlights the adaptability of the Transformer architecture, originally developed for language processing, to the field of signal modulation recognition. By adapting the Transformer's encoder with various tokenization strategies, ranging from non-overlapping and overlapping segmentation to the integration of convolutional layers, this research introduces a novel architecture, TransIQ, for AMR tasks. A thorough examination of the TransIQ model, utilizing the RadioML2016.10b and CSPB.ML.2018+ datasets,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Latency (ms/Sample)** & **Throughput (Sample/s)** \\ \hline TransIQ (small variant) & 3.36 & 297.61 \\ TransIQ (large variant) & 5.93 & 168.63 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measured latency and throughput of two variants of TransIQ on NVIDIA Jetson AGX Orin Developer Kit.  
was presented. Moreover, the experiments underlined the necessity of balancing model size and performance, especially for deployment in resource-constrained environments like IoT devices. Comparative analysis with baseline models showed that TransIQ exhibits superiority in terms of accuracy and parameter efficiency.  
Conceptualization, N.R. and K.W.; methodology, N.R., G.M. and V.K.; software, N.R. and K.W.; investigation, N.R. and G.M.; resources, K.W.; writing--original draft preparation, N.R. and K.W.; writing--review and editing, N.R. and K.W.; visualization, N.R., K.W. and N.M.P. ; supervision, G.S. and H.T.; project administration, G.S. and H.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
RadioML2016 is publicly available at [[https://www.deepsig.ai/datasets/](https://www.deepsig.ai/datasets/)]([https://www.deepsig.ai/datasets/](https://www.deepsig.ai/datasets/)) (accessed on 1 August 2023).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMR & automatic modulation recognition \\ IoT & Internet of Things \\ DN & deep learning \\ SNR & signal-to-noise ratio \\ RNNs & recurrent neural networks \\ NLP & natural language processing \\ ViT & vision Transformer \\ CNN & convolutional neural network \\ RF & Radio Frequency \\ \end{tabular}
## 5 Results and Analyses
### Main Results
#### 5.1.1 Interpretation and Significance of Performance Metrics
(1) Reduced manual verification effort: a 3.40% improvement in F1 score means approximately 189 fewer incorrectly classified relations per 1000 predictions. (2) Enhanced knowledge graph quality: Higher precision reduces propagation of erroneous relations in downstream applications. (3) Broader applicability: Consistent improvements across both the BERT and RoBERTa backbones indicate the architecture-agnostic benefits of our approach.
## 2 Related Works  
### Single Image Super-Resolution  
In 2014, SRCNN [10] was the first to apply deep learning techniques to super-resolution reconstruction, marking the rapid development of deep-learning-based super-resolution methods and their widespread adoption thereafter. The SISR method utilizes deep learning models to learn end-to-end mapping between LR and HR images, and its performance is significantly better than traditional algorithms [11; 12; 13]. Researchers have found that increasing the number of network layers can effectively expand the receptive field. [PERSON] et al. [14] proposed the VDSR model, which further expands the receptive field by using a 20 layer deep network and accelerates the convergence of the network through residual connections, successfully alleviating the gradient explosion problem. [PERSON] et al. [15] proposed RCAN, which improves residual blocks by adding channel attention mechanism and is the first attention mechanism to be applied to each resolution image. In addition to improving the network structure to enhance super-resolution reconstruction performance, the selection of loss function is also an important factor in determining the quality of reconstructed images. [PERSON] et al. [16] proposed SRGAN, which uses a general adversarial network (GAN) [17] as the adversarial loss function to produce super-resolution results that are more in line with natural image distribution [18; 19; 20; 21]. Recently, research has explored knowledge distillation frameworks [22; 23] to further improve super-resolution performance.
# Target Recognition and Navigation Path Optimization Based on NAO Robot
## 2 NAO Robot's Monocular Vision Spatial Target Localization  
Because the NAO robot can only work with a single camera, this paper proposed to use the monocular vision positioning technology for target positioning. First, a monocular vision positioning model is established [22]. Through the geometric calculation, the two-dimensional coordinates in the image are transformed into three-dimensional coordinates in the robot coordinate system to achieve the spatial target positioning. As a typical system of visual positioning system [23], the monocular vision system has achieved many research results on the target position positioning model. However, the height of the actual target object has been ignored in many studies. In the grasping experiment based on the Nao robot, the object should appear at a certain height, so the positioning based on the plane target cannot meet the requirements. In this paper, the position coordinates of the target center in the image coordinate system are obtained with the image recognition method, and then the monocular vision positioning model is used to establish the relationship between the positions coordinates and the image coordinates at a fixed height in space. Finally, the position coordinates of the target in the NAO robot coordinate system are obtained.
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 3 Audio Datasets
### Data Analysis
#### 3.4.3 Uttering Speed  
As an effort to measure the variability of the multiple audio files that compose the different corpora, the uttering speed was also computed. Using the information obtained from the forced alignment, more specifically the duration of individual phones, the speed of each utterance \(S\) can be easily obtained by dividing the number of uttered phones by the sum of the durations of each of the phones, as shown in Equation (1):  
\[S=\frac{n}{\sum_{i}^{n}\text{dur}(p_{i})} \tag{1}\]  
where \(\text{dur}(p_{i})\) is the duration of the individual phone \(p_{i}\) and \(n\) is the total number of them. \(S\) is, therefore, measured in phones per second. Notice that the duration of silences and pauses was not computed.  
Using this metric, the variability of each corpus in terms of speed can be measured. The obtained results can be found in Figure 3 and in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speaker** & **Min.** & **Max** & **Mean** & **Median** & **Stdev** \\ \hline XRev & 0.72 & 32.81 & 21.15 & 21.27 & 3.84 \\ Tax _valid_ & \(-\)20.34 & 97.90 & 36.48 & 37.36 & 8.75 \\ Hi-Fi TTS 92 & \(-\)17.97 & 73.86 & 40.57 & 39.48 & 9.62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum, maximum, average, median, and standard deviation values of the SNR calculated on _XRev_, on the _valid_ subset of the _Tux_ corpus, and on speaker 92 of the _Hi-Fi TTS_ corpus.  
Figure 2: Density of the SNR values calculated by using the information of the forced alignments of _XRev_ (green), subset _valid_ of the _Tux_ dataset (blue), and the speaker 92 from _Hi-Fi TTS_ (orange).  
Based on the data, it can be seen that the utterance speed conformed to a normal distribution in the three corpora.
# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 2 Review of Related Algorithms  
### Review of LSTM Algorithm  
LSTM is a specialized type of recurrent neural network (RNN) that addresses the gradient vanishing and exploding issues encountered by traditional RNN when handling long-sequence data. As shown in Figure 1, each LSTM unit consists of a forget gate, an input gate, an output gate, and a cell state. These components work collaboratively to control the selective memory and forgetting of information, enabling the dynamic feature extraction of sequential data.  
1. Forget gate: Determines the portion of information in the current cell state that needs to be forgotten. The calculation formula is as follows:  
\[f_{t}=\sigma(W_{f}\cdot[H_{t-1},X_{t}]+b_{f} \tag{1}\]  
where \(f_{t}\) is the output of the forget gate; \(W_{f}\) and \(b_{f}\) are the weights and biases of the forget gate; \(H_{t-1}\) is the hidden state of the previous moment; \(X_{t-1}\) is the input of the current moment; and \(\sigma\) is the Sigmoid function [24].  
2. Input gate: Determines the new information that needs to be updated into the current memory. The calculation formula is as follows:  
\[i_{t}=\sigma(W_{i}\cdot[H_{t-1},X_{t}]+b_{i} \tag{2}\]  
\[z_{t}=\tan\h(W_{c}\cdot[H_{t-1},X_{t}]+b_{c} \tag{3}\]  
where \(i_{t}\) is the output of the input gate and \(z_{t}\) is the candidate state.  
3. Update cell state: Updates the memory unit based on the information from the forget gate and the input gate. The formula is as follows:  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot z_{t} \tag{4}\]  
4. Output gate: Determines the hidden state \(H_{t}\) at the current time step, providing output information for the next layer.  
\[o_{t}=\sigma(W_{o}\cdot[H_{t-1},X_{t}]+b_{o} \tag{5}\]  
\[H_{t}=o_{t}\odot\tan\h(C_{t}) \tag{6}\]  
where \(o_{t}\) is the output of the output gate and \(H_{t}\) is the output at the current moment.  
Through the above mechanisms, LSTM dynamically remembers and forgets information, enabling it to effectively capture both short-term and long-term dependencies in time-series tasks. This characteristic allows for LSTM to track the degradation patterns of battery performance over time, making it particularly advantageous in scenarios where the battery aging process spans a long period.  
Figure 1: Basic structure of LSTM neural units.
## 3 Mathematical Modeling  
### Equations of Motion  
A synodic (co-rotating) reference frame fixed at the center of mass of a regularly rotating symmetric NEO will be adopted for the analysis of the motion of a nearby spacecraft. Assuming that the NEO has constant density and is rotating uniformly about the fixed axis \(\mathbf{\dot{z}}\), which corresponds to the body's maximum moment of inertia, the equations of motion for the NEO spacecraft 2-Body Problem are written as ([75])  
\[\ddot{x}\,-\,2\omega\dot{y}\ =\ \omega^{2}x\,-\,\frac{\partial U}{\partial x}\ \ +\ A_{x}\]  
\[\ddot{y}+2\omega\dot{x}\ =\ \omega^{2}y\,-\,\frac{\partial U}{\partial y}\ \ +\ A_{y} \tag{1}\]  
\[z\ \ \ \ \ \ \ \ \ \ \ =\ \ \ \ \ \ \ -\frac{\partial U}{\partial z}\ \ +\ A_{z}\,,\]  
in which \(\omega=\|\omega\|\) is the spin rate of the asteroid, \(U\) is a plane-symmetric potential field (\(U(x,y,z)=U(x,y,-z)\)) and \(A_{(\cdot)}\) are the components of an acceleration vector comprising control and possible disturbances.  
In general, a quantity associated with the NEO's dimension, such as its _Brillouin_ radius \(r_{\rm{B}}\) (the radius of the sphere circumscribing the asteroid's surface), is taken as the reference distance ([10]). When using such a normalization, the Stokes coefficients ([76]) remain constants, irrespective of the NEO's physical characteristics, such as size, mass distribution, and rotation state. On the other hand, when adopting as reference distance the point-mass-equivalent synchronous radius (1:1 spin-orbit resonance) for the rotating body  
\[R_{\rm{s}}=\sqrt{\frac{Gm}{\omega^{2}}}\, \tag{2}\]  
(\(m\) is the asteroid's mass and \(G\) the gravitational constant), the Stokes coefficients become a function of these NEO's physical properties, which allows the study of how the dynamics are affected by these NEO's characteristics, for instance through the effects of the harmonic coefficients \(C_{20}\) and \(C_{22}\) usually adopted in studying slightly oblate symmetric bodies ([66; 77; 78; 79]).  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**NEO** & **Discovery** & **Mass (\(\times 10^{15}\) kg)** & **Dimension (km)** & **Rotation Period (h)** & **Density (g/cm\({}^{3}\))** \\ \hline (4) Vesta & 1807 & 259,000 & \(569\times 555\times 453\) & 5.3 & 3.4 \\ \hline (16) Psyche & 1852 & 24,000 & \(280\times 230\times 190\) & 4.2 & 4.0 \\ \hline (253) Mathilde & 1885 & 103.3 & \(66\times 48\times 46\) & 417.7 & 1.3 \\ \hline (433) Eros & 1898 & 6.7 & \(33\times 13\times 13\) & 5.3 & 2.7 \\ \hline (153591) 2001 SN263 (main body “Alpha”) & 2001 & 0.00917 & \(2.8\times 2.7\times 2.9\) & 3.4 & 1.1 \\ \hline \end{tabular}
\end{table}
Table 1: Asteroid properties ([73; 74]).
# Simulation of an Indoor Visible Light Communication System Using Optisystem
## 3 Optisystem Simulation Tool for Indoor VLC System
### NLOS Propagation Model  
Both the direct path and the reflected path of light are considered in the NLOS propagation model. By changing the number of LEDs used on the transmitter side, the simulation identifies two instances of NLOS propagation models. We consider a single LED as the transmitter in the first scenario and two LEDs as the transmitters in the second scenario. Then, considering the received signal's shape, we evaluated the VLC system's performance at the receiver side.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 2 Taxonomy of Broadcast-Related Problems in IPv4 Networks  
In the literature, all the existing work dealing with IPv4 security issues review only related work specific to their problem. However, this may not help us to provide a holistic solution to attacks against the IP protocol. Thus, we present a taxonomy of broadcast-related problems due to IPv4.  
Broadcasting introduces several system and network performance issues and consumes the network bandwidth. Every active host's Network Interface Card (NIC) must interrupt its CPU (Central Processing Unit) in order to process a received broadcast packet. This will affect the CPU performance, especially when a host needs to process several broadcast packets within a short burst of time. The same is applicable to switches, as well. Most often, a host does not benefit from processing the broadcast packet. This is because the host is not the destination being sought; it does not care about the service that is being advertised; or it already knows about the service.  
Since broadcasting affects both the individual host's CPU and the network performance, it is exploited often to launch a Denial of Service (DoS) attack. In the absence of a destination-based authentication, any malicious node can pretend to be the destination node and launch a Man-In-The-Middle (MITM) attack.  
The attacks that are related to IPv4 broadcasting can be broadly classified into two categories:  
1. Attacks that are originated external to a Local Area Network (LAN). The attackers can be anywhere on the Internet and launch an attack towards a network.
2. Attacks that are originated internal to a LAN. Here, an attacker is a host inside a LAN.