esa-sceva/satcom-chunk-collection huggingface dataset:
# Regularized Chained Deep Neural Network Classifier for Multiple Annotators
## 3 Methods  
### Chained Deep Neural Network  
Let us consider an input-output dataset\(=\{\mathbf{X}\in\mathcal{X},\mathbf{y}\in\mathcal{Y}\}\), where \(\mathbf{X}=\left\{\mathbf{x}_{n}\in\mathcal{X}\subseteq\mathbb{R}^{P}\right\}_{n=1}^{N}\) and \(\mathbf{y}=\{y_{n}\in\mathcal{Y}\}_{n=1}^{N}\) hold the input and output spaces, respectively, (with \(N\) instances and \(P\) features). Inspired by the chained Gaussian processes model (CGP) [25], a likelihood function with \(J\) parameters is written as:  
\[p(\mathbf{y}|\mathbf{X},\mathbf{\theta})=\prod_{n=1}^{N}p(y_{n}|\theta_{1}(\mathbf{x}_{n}), \ldots,\theta_{J}(\mathbf{x}_{n})), \tag{1}\]  
where \(\mathbf{\theta}=\left[\mathbf{\theta}_{1}\ldots,\mathbf{\theta}_{J}\right]^{\top}\in \mathbb{R}^{NJ}\) is a parameter vector, and \(\mathbf{\theta}_{j}=\left[\theta_{j}(\mathbf{x}_{1})\ldots\theta_{j}(\mathbf{x}_{N}) \right]^{\top}\in\mathbb{R}^{N}\). Here, each \(\theta_{j}(\mathbf{x})\in\mathcal{M}_{j}\) maps an input sample to the parameter space, being \(\mathcal{M}_{j}\) the domain for the \(j\)-th parameter (\(j\in\{1,2,\ldots,J\}\)). A chained deep neural network (CDNN) can be introduced linking each likelihood parameter \(\theta_{j}(\mathbf{x})\) to one of the \(J\) outputs of a deep neural network comprising \(S\) hidden layers. Accordingly, let \(\mathbf{f}(\mathbf{x})=[f_{1}(\mathbf{x}),\ldots,f_{J}(\mathbf{x})]^{\top}\in\mathbb{R}^{J}\) be a vector containing the \(J\) outputs of a deep network:  
\[\mathbf{f}(\mathbf{x})=(\epsilon_{S}\circ\epsilon_{S-1}\circ\cdots\circ\epsilon_{1})( \mathbf{x}), \tag{2}\]  
where \(\circ\) stands for function composition. Then, each parameter is computed as: \(\theta_{j}(\mathbf{x})=h_{j}(f_{j}(\mathbf{x}))\), where \(h_{j}:\mathbb{R}\rightarrow\mathcal{M}_{j}\) is a deterministic function that maps each output \(f_{j}(\mathbf{x})\) to the appropriate domain \(\mathcal{M}_{j}\). Besides, each layer \(\epsilon_{s}\), with \(s\in\{1,2,\ldots,S\}\), depends on a set of variables (neural network weights and bias) \(\mathbf{\phi}=\left[\mathbf{\phi}_{1},\ldots,\mathbf{\phi}_{S}\right]^{\top}\), which can be estimated by minimizing the following log likelihood cost (for i.i.d samples):  
\[-\log(p(\mathbf{y}|\mathbf{X},\mathbf{\theta},\mathbf{\phi}))=-\sum_{n=1}^{N}\log\bigl{(}p(y_{ n}|\theta_{1}(\mathbf{x}_{n}),\ldots,\theta_{J}(\mathbf{x}_{n}),\mathbf{\phi})\bigr{)}. \tag{3}\]  
Remarkably, the deep model in Equation (2) allows exploiting the representation learning capability of neural networks within a chained framework through the likelihood in Equation (3).
## 3 The Integrated Sensing and Communication Platform
### LoRaWAN Communication Platform: LoRa Node, GW and Server
#### 3.2.2 LPS8 GW  
LPS8 is an open source LoRaWAN GW [27] which acts as a bridge between the ED and the network infrastructure. It has a backhaul Internet connectivity that connects it to the remote network server. The LPS8 uses a Semtech packet forwarder, a software responsible for forwarding packets to the server and includes a SX1308 LoRa concentrator. It allows users to send data and reach extremely long ranges at low data-rates providing 10 parallel demodulation paths. The receiver has a sensitivity of up to \(-140\) dBm with SX1257 Tx/Rx front-end.
## 2 Methodology
### Simulation Model
#### 2.3.4 Propulsion and Constellation Modules  
Because a ReCon is intended for highly agile Earth observation, its satellite mass is largely broken down into the propulsive part and the non-propulsive part. First, the propulsive mass comprises the propellant mass (_propellant_mass_) and the propulsion subsystem mass (_prop_dry_mass_), which are calculated by the Propulsion Module in Table 2, Figure 2, and Table 6. The propulsion subsystem mass is further broken down into the tankage mass (\(m_{tank}\)) and the valve and nozzle mass (\(\varepsilon m_{p}\)) which is proportional to the propellant mass (\(m_{p}\)) as depicted in Figure 7. Second, the non-propulsive mass (\(m_{dry,non-optics}\)) is calculated by the Constellation Module. It consists of the optics subsystem and the non-optics subsystem; the term \"non-optics\" here collectively refer to parts for communications, electric power management, etc.  
The empirical relationship between \(m_{tank}\) and \(m_{p}\) are given in Equations (14) and (15) for two propellant types, cold gas and monopropellant respectively. The dataset and fitting lines are provided in Appendix B. It is also notable that the exponent value of 0.59 is close to 0.67 = 2/3 indicating that the mass of propellant tank, basically a thin shell, grows a two-dimensionally while its volume grows three-dimensionally.  
\[m_{tank(cold)}=61.7V_{tank}^{0.594}=\;61.7\left(\frac{m_{p}}{\rho_{p}}\right)^{0.594}[\text{kg}], \tag{14}\]  
\[m_{tank(mono)}=38.7V_{tank}^{0.592}=\;38.7\left(\frac{m_{p}}{\rho_{p}}\right)^{ 0.592}[\text{kg}], \tag{15}\]  
Figure 7: Mass breakdown of a ReCon satellite.  
With the aforementioned terms, the satellite mass can be calculated using the Tsiolkovsky rocket equation, as shown in Equation (16). Solving the equation requires numerical iterations, such as the Newton-Rapshon method, indicative of tight coupling between the Propulsion Module and Constellation Module.  
\[\frac{m_{dry,optics}\ +\ m_{dry,non-optics}\ +\ m_{tank}\ +\ (\varepsilon\ +\ 1)m_{p}}{m_{ dry,optics}\ +\ m_{tray,non-optics}\ +\ m_{tank}\ +\ \varepsilon m_{p}}\ \, \tag{16}\]
## 2 Materials and Methods
### Method Validation  
A matrix blank was injected in each batch of samples to identify the presence of any cross-contamination and interference during the analysis. To evaluate the applicability of the method, the linearity, LOD (limit of detection), LOQ (limit of quantification), precision, accuracy, and matrix effect were investigated under the optimized extraction conditions. The linearity was estimated using matrix-matched calibration curves by spiking blank samples. The peak areas of each analyte were plotted against their corresponding concentration levels (0.01-100 ng g\({}^{-1}\)), and then, linear regression analyses were performed on the resulting curves following the minimum least-square method. LOD was determined as the lowest concentration of the spiked analyte with a signal to noise ratio (S/N) of 3 for the target ions. The method LOQ was set at the minimum concentration that could be quantified with acceptable accuracy and precision [33]. For determining the precision of the method, five similar experiments were carried out for blank extracts spiked at 10 ng g\({}^{-1}\) on the same day and the three consecutive days. To test the accuracy, recovery tests were carried out in real tomato, cucumber, and packhoi samples at three different levels (5, 25, and 50 ng g\({}^{-1}\)) with six replicates. The recovery was determined by comparing the calculated amounts of insecticides in the samples with the spiked amounts. The matrix effect was calculated from the calibration curve slope in the matrix and solvent according to the following equation.  
\[\text{Matrix effect}\;(\%)\;=\;(\frac{\text{Slope of calibration curve in matrix}}{\text{Slope of calibration curve in solvent}}-1)\times 100\]
## 4 Discussion
The greatest advantage of linking data from SDI to the Big Data resource is obtaining more sources of information, thorough studies and analyses, as well as improved decision quality in the area of spatial planning tasks. All the elements mentioned above are improved thanks to the possibility of using data that were almost impossible to acquire before. The time of processing and analyzing these data is much shorter than that of traditional data obtained from the SDI. Moreover, the quality of the decision may be significantly improved, mainly due to the possibility of analyzing data that are acquired independently from the user, to a large extent. At the same time, one should bear in mind that there are numerous sources of Big Data (such as observations from sensors, on-vehicle sensors, social media from citizens), but also of companies and entities that collect these data. Each of these datasets requires using the adequate software, technical and organizational solutions to store and manage the data and to process them so that they may be used later in various thematic areas. An important issue related to the processing of Big Data are personal and sensitive data, whose processing is governed by several national and international legal regulations. From the point of view of integrating SDI and Big Data, one should also pay attention to the format of the studies prepared based on Big Data, so that, in various practical applications (e.g., in town planning for the purposes of this paper), it will be possible to integrate and analyze various types of data, including those obtained from SDI, considering their interoperability and the related standards. Both geoporals and numerous types of GIS software offer the possibility to add data sources in specific formats, and to perform specific operations and analyses on these data.
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 2 System Description
### Conventional Frequency Offset Estimation Methods
#### 2.2.1 IFO Estimation Method  
The IFO estimation method is generally based on the autocorrelation of the pilot sequence [4]. According to (3), the received pilot sequence \(\mathbf{Y}\) can be obtained by cyclically right-shifting \(\epsilon_{I}\) the elements in the transmitted pilot sequence \(\mathbf{X}\), regardless of the noise. Therefore, by calculating the correlation between \(\mathbf{X}\) and \(\mathbf{Y}\) with several cyclically shifted elements, the number of shifted elements \(\epsilon_{I}\) corresponding to the maximum correlation value would be the IFO estimation result; that is,  
\[\hat{e}_{I}=\operatorname*{arg\,max}_{\epsilon_{I}}\left|\left| \sum_{k\in\mathcal{S}}\mathbf{Y}[\operatorname{mod}(k+\epsilon_{I},N)]\cdot\mathbf{X }^{*}(k)\right|\right|^{2}\] \[\text{\it s.t. }\epsilon_{I}\in\{-\lceil\epsilon_{\max}\rceil,- \lceil\epsilon_{\max}\rceil+1,\ldots,-1,0,1,\ldots,\lceil\epsilon_{\max} \rceil-1,\lceil\epsilon_{\max}\rceil\} \tag{4}\]  
where \(\mathcal{S}\subseteq\{0,1,2,\ldots,N-1\}\) is the index set of pilot subcarriers, \(|\mathcal{S}|=N_{\text{s}}\) denotes the number of elements in \(\mathcal{S}\), \(\epsilon_{\max}>0\) denotes the maximum frequency offset in the system, \(\lceil\epsilon_{\max}\rceil\) denotes the upper integer of \(\epsilon_{\max}\), and \(\mathbf{X}^{*}(k)\) denotes the conjugation of \(\mathbf{X}(k)\).
## 5 Social Media Applications and e-Government
### The Role of Social Media in Public Health and e-Government  
Public health is one of the most critical areas of government responsibility, absorbing a significant proportion of national spending worldwide [46]. In recent years, as governments attempt to adopt e-Government models to improve transparency, participation and service delivery, social media have emerged as important tools for communication and interaction.  
In reality, public health organizations use platforms such as Facebook, Twitter, and YouTube to communicate with citizens, disseminate information, promote participation in public consultations, gather opinions and enhance accountability. This use of social media is part of the broader context of e-Government, which seeks to bring citizens closer to decision-making and enhance the transparency of public organizations.  
The main objectives of using social media fall into four basic categories of e-Government:  
* Transparency and accountability: Social media act as platforms where public organizations share information about services, actions, programs and policies. This enhances transparency and citizen awareness;
* Democratic participation: Through social media, citizens can comment, express opinions and participate in public consultations on health issues;
* Co-operation: Although a less developed practice, some organizations use social media to solicit proposals or invite citizens to joint actions;
* Evaluation: Here social media posts feedback on the quality of services provided.  
Particularly important is the category of evaluation, which emerged as a new theme from the review, showing the importance that citizens attach to providing comments and evaluations regarding their experience of public health services [46].  
On the other hand, using social media for e-Government purposes cannot always be implemented successfully due to the following reasons:  
* Absence of a clear strategy: Use is fragmented, without a unified strategic plan;
* Untapped data: Data resulting from social media interactions is not systematically analyzed to provide useful information for improving services;
* Ethical issues: Using personal data or citizen comments for research purposes raises privacy and consent issues;Risks of manipulation: Organized campaigns by interest groups can distort deliberations and disproportionately influence political decisions.
# Infrastructure Elements for Smart Campuses: A Bibliometric Analysis
## 4 Implications of Findings
### Implications of Findings on Procurement of Smart Campus Infrastructure
#### 4.2.4 Implications on Smart Learning and Teaching Systems  
The governance of a smart campus is hinged on services delivered through learning and teaching. Smart learning and teaching systems division of a smart campus infrastructure depends on on-campus information portals with the attachment of automated attendance; education data mining; collaborative learning; learning analytics; and smart classrooms with smart boards [89; 94; 122]. The learning management system governs how students and other campus end-users interact with the smart technology, smart buildings, and energy usage on campus while learning.  
In learning and teaching on campus, the WI-FI system, mobile applications, and communications mostly through emails must be effective. The campus equipment management system also supports learning and teaching when visitors and material waste are managed. Finally, the education management system produces an opportunity for artificial intelligence to be cohesive with E-learning platforms for students and mobile learning applications. IT project management is required to deliver the infrastructure required for smart learning and teaching. In this infrastructure category, IT networks and applications can be developed either through contractors or the existing IT services on campuses. Other tools such as smart classrooms and boards can be procured and installed by the IT service or specialist contractors.
## 2 Materials and Methods
### Human-Assisted Feature Extraction Methods
#### 2.2.4 Unit Dimensional Local Binary Patterns (ULBP)  
Each ECG sample is transformed into an 8-bit binary code by analyzing the eight neighbor samples (four to the left, four to the right). The bit sequence is eventually converted into the respective integer value and mapped to a predefined 59-dimensional descriptor, which summarizes the frequency of each 8-bit sequence in a Uniform LBP histogram. This method, also suggested by [PERSON] et al. [11], is based on a 1-dimensional variant of the 2-dimensional LBP, which is commonly used in image processing to highlight patterns of the input image in a lower resolution format.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 2 Background to Interference Monitoring
### Detection
 \tag{3}\]  
where \(m(x[n])\) is an arbitrary metric that depends on the complex digital samples \(x[n]\), \(\lambda\) is the detection threshold, \(\mathcal{H}_{0}\) is the null hypothesis and assumes that the received signal has no interference signal, and \(\mathcal{H}_{1}\) is the alternative hypothesis and assumes that an interference signal is present. A common approach is to use an energy detector, as a tuned energy detector is the optimal detector for a signal in noise with no a priori information [46] and is a popular choice for interference detection [13; 47; 48]:  
\[m(x[n])=e[n]=T_{\text{s}}\sum_{k=0}^{K-1}x[n-k]x^{*}[n-k] \begin{array}{c} \stackrel{{\mathcal{H}_{1}}}{{\underset{\mathcal{H}_{0}}{\leq}} }\lambda\\ \stackrel{{\mathcal{H}_{1}}}{{\underset{\mathcal{H}_{0}}{\leq}} }\lambda\end{array}  \tag{4}\]  
where \(e[n]\) is the energy calculated over \(K\) samples, \(T_{\text{s}}\) is the sample time, and \((\cdot)^{*}\) is the complex conjugate. A significant issue with this approach is the detector tuning and calibration in practical systems. An implicit energy detection method is monitoring the automatic gain control (AGC) of a GNSS receiver, as it regulates the front end gain based on the received signal energy [43; 49]. However, it is risky that the AGC is considered a black box, and unknown effects could deteriorate detection performance. More advanced detection methods consider spectral techniques [50]. Here, the raw IQ samples are transformed to the spectral domain using a fast Fourier transform (FFT)-based method [50; 51], but filter-based approaches such as a spectrum analyzer architecture also exist [52]. Spectrum monitoring approaches are generic, apply to numerous areas beyond GNSS, and have been studied in detail [53; 54; 55]. The GNSS receiver outputs monitoring and signal spectrum monitoring methods complement each other [13; 49; 56; 57]. For example, GNSS receiver monitoring may yield false detection when a receiver is indoors and has low CN0 values, but spectrum monitoring would reveal no interference. Therefore, a common and advisable approach is to leverage both approaches for improved performance.
## 2 Method
### Notation and Frame Definition  
The following notations and definitions are used throughout this paper:  
* The vector \(\mathbf{f}\in\mathbb{R}^{3}\) is the force vector composed of the \(x\), \(y\), and \(z\) axes.
* The vector \(\boldsymbol{\tau}\in\mathbb{R}^{3}\) is the torque vector composed of the \(x\), \(y\), and \(z\) axes.
* The vector \(\mathbf{w}\in\mathbb{R}^{6}\) is the wrench vector composed of \(f\) and \(\tau\).
* The vector \(\mathbf{g}\in\mathbb{R}^{3}\) is the gravity vector composed of the \(x\), \(y\), and \(z\) axes.
* The vector \(\mathbf{r}\in\mathbb{R}^{3}\) is the CoM vector of the end tool in sensor coordinates.
* The matrix \(\mathbf{C}\in\mathbb{R}^{3\times 6}\) is the crosstalk matrix.
* The subscript \(\mathbf{A}_{i}\) represents the coordinates on which the vector and matrix \(\mathbf{A}\) are defined.
* The superscript \(\mathbf{A}^{i}\) describes the type of vector and matrix \(\mathbf{A}\).
* The operator \([\cdot]^{+}\) is the pseudo-inverse matrix.
* The operator \([\cdot]_{\times}\) is the skew-symmetric matrix.  
The coordinates described in Figure 1 are used throughout this paper. Each rotation matrix between coordinates satisfies the following relationship: \(\mathbf{g}_{f}=\mathbf{R}_{s}\mathbf{g}_{e}=\mathbf{R}_{s}\mathbf{R}_{f} \mathbf{g}_{b}=\mathbf{R}_{s}\mathbf{R}_{f}\mathbf{R}_{0}\mathbf{g}_{g}\).  
Figure 1: Coordinate definition on our mobile manipulator Moby from Neuromeka.
## 2 Materials and Methods
### Methods
#### 2.2.3 Dimensionality Reduction  
Further dimensionality reduction to two-dimensional embedding space is conducted with respect to the latent space produced by the autoencoder using t-SNE and UMAP. We chose two dimensions for simplicity and ease of downstream visualisation and interaction [37]. We use the Barnes-Hut approximation of t-SNE for its much faster computational advantages [38]. In addition, we implemented the algorithm with variable tail distribution in low dimensions to both overcome the overcrowding problem and the ability to make it heavy-tailed for tighter clusters. Our implementation enables user interaction with the number of iterations and to replay iterations by recording each iteration's variables. The user can navigate between optimisation steps for any patterns that are presented earlier [39]. Data can be labelled at any optimisation step, with those labels then available to previous and future iterations. The first process of t-SNE is to optimise the embeddings with a lower learning rate and once it achieves an embedding that does not change for a few iterations, variable on data content and size, the learning rate is increased to expand and optimise the embedding. Visualising earlier iterations of t-SNE would allow a user to explore any emerging clusters without needing to wait for the finished process. As time complexity increases with dataset size earlier iterations may provide expedient clusters to the user. Finally, we added the ability to change the perplexity parameter of t-SNE for the user to assign depending on if they require more global or local attention. We defined a custom version of t-SNE based on existing libraries for implementation. The resulting Python scripts are embedded into our application.  
We do not make any changes to UMAP and therefore refer to the original paper for the implementation [31]. We do however allow the user to input parameters such as minimum distance and the N nearest neighbours that UMAP should compute. Both parameters  
Figure 2: Architecture for the autoencoder. Yellow rectangles represent convolutional layers. Green rectangles represent batch normalisation layers. Purple represents max pooling layers and grey represents dense layers. The encoder and decoder segments of the architecture have multiple N layers in each segment.  
govern how the initial fuzzy graph is computed by UMAP and therefore can change the visualisation and its ability to show coherent clusters or manifolds.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 1 Introduction
* In order to avoid void holes, the forwarder node sends data packet to that sensor node which further has one or two hop neighbor nodes in its transmission range. * Every data packet passing through sensor nodes lies on the layer which reduces the chance of retransmissions due to the reception of more than one copies at the sink. The remainder of the paper is organized as follows: Section 2 provides an overview of existing routing protocols in UWSNs. In Section 3, a brief overview of FLMPC-One and FLMPC-Two is provided along with problem description in LMPC. Linear optimization of energy and throughput are discussed in Section 4. Section 5 consists of simulation results and performance evaluation of both proposed schemes and LMPC. In Section 5.3, performance trade offs are discussed. Finally, Section 6 presents conclusion.
## 5 Results and Discussion
### Detection Experiments for Various Attacks Based on KL Divergence
In this experiment, KL divergence is employed to compare the differences between original samples and the reconstructed samples generated by the AE, as well as the differences between original samples and adversarial examples. The observed results reveal that the KL divergence between original samples and reconstructed samples is low, indicating that the AE effectively learns and replicates the probability distribution of the original samples. Consequently, the proximity in probability space between original and reconstructed samples is evident. However, for adversarial examples, the KL divergence is notably higher. This discrepancy may arise due to the intentional manipulation of features in adversarial examples during their design to deceive machine learning models. These adjustments are made to maintain a striking similarity to the original samples in human perception while inducing significant discrepancies in machine learning models. Hence, the gap between the probability distributions of adversarial examples and the original samples might widen. Given this behavior of KL divergence, it reveals the distinctive representations of original and adversarial examples in the probability space, providing a robust foundation for detecting adversarial examples. By establishing an appropriate KL divergence threshold, we can reasonably distinguish between original and adversarial examples. Generally, if a sample's KL divergence from the original sample surpasses this threshold, the sample could be classified as an adversarial one.