esa-sceva/satcom-chunk-collection huggingface dataset:
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
###### Abstract  
The energy consumption of buildings presents a significant concern, which has led to a demand for materials with better thermal performance. Thermal conductivity (TC), among the most relevant thermal properties, is essential to address this demand. This study introduces a methodology integrating a Multilayer Perceptron (MLP) and a Generative Adversarial Network (GAN) to predict the TC of concrete based on its mass composition and density. Three scenarios using experimental data from published papers and synthetic data are compared and reveal the model's outstanding performance across training, validation, and test datasets. Notably, the MLP trained on the GAN-augmented dataset outperforms the one with the real dataset, demonstrating remarkable consistency between the model's predictions and the actual values. Achieving an RMSE of 0.0244 and an R\({}^{2}\) of 0.9975, these outcomes can offer precise quantitative information and advance energy-efficient materials.  
artificial neural networks; MLP; GAN; concrete; thermal conductivity
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 2 Methodology
### Light Gradient Boosting Machine  
Light Gradient Boosting Machine (LGBM) is a framework based on the Gradient Boosting Decision Tree [28] (GBDT) algorithm, which approximates residuals through the Taylor expansion of the loss function (including first-order and second-order derivative information) and uses regularization terms to control model complexity. The most distinctive features of LGBM are its histogram-based decision tree algorithm and the leaf-wise growth algorithm with depth constraints.
## 3 Threat Model and Security Requirements
### Threat Model Definition  
#### 3.1.1 Adversary Capabilities and Assumptions  
We consider a sophisticated adversary with signal injection capability enabling precisely timed electrical signals on ESC communication lines, complete protocol knowledgeunderstanding DShot specifications and timing tolerances, environmental control capacity to introduce controlled electromagnetic interference, potential firmware access through supply chain or physical compromise, and coordination ability to synchronize attacks across multiple channels simultaneously. Adversary limitations include the inability to physically damage hardware components, the restriction to exploiting vulnerability levels at the protocol level, and the operation within existing electrical and timing constraints.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
###### Abstract  
The use of sensor applications has been steadily increasing, leading to an urgent need for efficient data compression techniques to facilitate the storage, transmission, and processing of digital signals generated by sensors. Unlike other sequential data such as text sequences, sensor signals have more complex statistical characteristics. Specifically, in every signal point, each bit, which corresponds to a specific precision scale, follows its own conditional distribution depending on its history and even other bits. Therefore, applying existing general-purpose data compressors usually leads to a relatively low compression ratio, since these compressors do not fully exploit such internal features. What is worse, partitioning a bit stream into groups with a preset size will sometimes break the integrity of each signal point. In this paper, we present a lossless data compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural architecture named multi-channel recurrent unit (MCRU). Each channel in the proposed MCRU models a specific precision range of each signal point without breaking data integrity. During compressing and decompressing, the mirrored network will be trained on observed data; thus, no pre-training is needed. The superiority of our approach over other compressors is demonstrated experimentally on various types of sensor signals.  
lossless compression; sensor signals; context-based compressor; entropy coding; recurrent neural networks +
Footnote †: 2021: 11, 10240. [[https://doi.org/10.3390/app112110240](https://doi.org/10.3390/app112110240)]([https://doi.org/10.3390/app112110240](https://doi.org/10.3390/app112110240))
## 3 Materials and Methods
### Evaluation Criteria for the Algorithms  
For the comparison among our conducted experiments, a variety of different error metrics are provided by the literature, such as Mean Absolute Error (\(MAE\)) see Equation (1), Mean Square Error (\(MSE\)), or Root Mean Square Error (\(RMSE\)) see Equation (2), a derivation of the \(MSE\).  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}(P_{i}-O_{i}) \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(P_{i}-O_{i})^{2}} \tag{2}\]  
\(P_{i}\) is the predicted value for the \(i\)th observation in the dataset  
\(O_{i}\) is the observed value for the \(i\)th observation in the dataset  
\(n\) is the sample size.  
In this study, we use \(RMSE\), a commonly used metric for evaluating the regression performance of forecasting models, to compare the quality of the predictions [71]. The \(RMSE\) is a non-negative metric based on prediction errors, which penalizes undesirable significant errors but remains easily interpretable by being in the same unit as the target variable (EUR/MWh). Accordingly, each model is evaluated in all predefined configurations at different points in time. This is done in increments of 24 h before the end of the forecast horizon of 168 h.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 3 Effects of Si Content on NGO Electrical Steels
### High Silicon Electrical Steel Ordering  
Brittleness of high Si steels depends on the grain size, ordered phase structure, and grain boundary impurities [24, 25, 26]. As shown in Figure 3a, when the Si content of the steel increases, A\({}_{2}\), B\({}_{2}\), and D0\({}_{3}\) ordering are observed. A\({}_{2}\), B\({}_{2}\), and D0\({}_{3}\) are the Strukturbericht symbols that designate different crystal ordering structures. These structures are best explained by the superlattice structure, as presented in Figure 3b. In Fe-Si alloys, two types of ordering phases were studied: B\({}_{2}\) structure (Fe-Si) and D0\({}_{3}\) structure (Fe\({}_{3}\)Si ordering type) are formed with a Si content of 5.3-11 wt.% [27]. A\({}_{2}\) represents the simply \(\alpha\)-Fe BCC structure.  
X-ray diffraction (XRD) analysis has shown that D0\({}_{3}\) has a special superlattice peak, which corresponds to {111} planes. B\({}_{2}\) and D0\({}_{3}\) {100} planes share a [200] superlattice peak [1, 28]. Dislocations interfere with these orderings and cause a strengthening effect [29]. Superdislocation slip deformation may affect mechanical properties. This deformation mechanism is also observed in B\({}_{2}\) and D0\({}_{3}\) lattices in high Si electrical steels [30]. In some studies, B\({}_{2}\) and D0\({}_{3}\) ordering increased the magnetic properties. The growth of B\({}_{2}\) leads to a higher specific magnetization, while the growth of D0\({}_{3}\) leads to a low coercive force and maximum permeability [31].  
Rapid cooling at a critical cooling rate can suppress D0\({}_{3}\) ordering and reduce the size of B2 ordering in Fe-Si alloys [32]. An exponential relationship between the Si content and the critical cooling rate can be observed in Figure 4 where A1 to A5 are air quenched, B1 to B3 are oil quenched, C1 to C3 water quenched, and D1 to D4 are brine quenched. In another study, 5-6 wt.% Si 2 mm thick strip samples were hot-rolled at 1200-900 \({}^{\circ}\)C and cooled by air. The strip samples were then annealed at 850 \({}^{\circ}\)C for 1 h and cooled at different cooling rates (as shown in Figure 5). As seen in this figure, the high cooling rate can reduce the domain size of B2 phase and suppress the D0\({}_{3}\) phase [33]. In addition, deformation decreases high Si ordering and reduces brittleness [34].  
Figure 3: (**a**) Section of the binary phase diagram of the Fe-Si alloy system [20]. (**b**) Superlattice structures of A2, B2, and D0\({}_{3}\) structures in high Si electrical steels. A2 is disordered, allowing Fe and Si to allocate in any available site. In B2 ordering, solid gray and black dots represent the sites where Fe is present, whereas gradient and open dots represent the sites where Si is observed. In the D0\({}_{3}\) texture, open dots represent the sites where Si is located, while Fe were located on other sites [21].
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 4 Performance Evaluation, Results, and Discussion
The storage required by product code in [6] is \(255\times Storage(LDPC)+36\times Storage(RS)\), while our proposed scheme needs \(Storage(LDPC)+Storage(RS)\). Therefore, product code in [6] introduces much more latency and needs much more storage than our proposed concatenated code. For our proposed scheme, RS decoding only needs to wait for one LDPC code to finish decoding. Additionally, the HEEC iterative decoding exchanges information  
Figure 10: 4-BCJR, BP max. itr. = 50, turbo eq. itr. = 3. Figure 9: 4-BCJR, BP max. itr. = 20, turbo eq. itr. = 3.  
between the LDPC and RS decoders. Therefore, decoding latency increases in proportion to iterations. Our proposed scheme does not use iterative decoding between LDPC and RS code considering the increase in latency. In [11], [PERSON] and coworkers achieved a similar level (\(10^{-8}\) to \(10^{-7}\)) of noise floor with their RS-SC-LDPC algorithm. Spatially coupled LDPC code may have better performance; however, it has much more complexity and latency, even when utilizing sliding window decoding. Meanwhile, SC or sliding window decoding needs more storage for multiple LDPC code words. Product code scheme or spatially coupled code scheme will have better performance than our proposed concatenated code scheme. However, while considering constraints on latency, complexity, and storage, our proposed scheme will be a better choice.
# Simultaneous Electrochemical Removal of Selenium and Strontium from Aqueous Solution
## 3 Results
### Strontium Removal  
The results show that Sr was far more challenging to remove than Se. Similar to the trends observed in the Se removal, lower current strengths (below 15 A), shorter durations, and unfavorable pH levels adversely affected the Sr removal efficiency. However, the influence of these factors on the Sr removal was more pronounced, reflecting its more resistant nature to electrochemical treatment. The obtained model showed that all of the factors were significant with \(p\)-values less than 0.0001, with the exception of the electrode number, which had a \(p\)-value of 0.0466. This suggests that while the number of electrodes played a role, its impact was less critical compared with the other factors.  
Furthermore, several interactions were particularly significant in the context of Sr removal, including the applied current and pH, treatment time and pH, and electrode material and pH. These interactions underscore the complex interplay between the electrochemical settings and the chemical environment.  
As expected, longer treatment times and higher current led to higher Sr removal rates. However, the pH value of the solution had significantly more influence on the efficiency of the Sr removal compared with Se. This heightened sensitivity to pH changes could be particularly observed in interactions between the applied current and treatment time, as shown in Figure 8. When the highest values of the treatment time and current were used, high efficiencies were achieved by using both Fe and Al electrodes, regardless of the pH. However, by comparing Figure 9a,b, the effect of the pH value is evident when the treatment time and current were low. The maximum pH range, meaning the most alkaline environment, drastically improved the performance of the Fe electrode. This effect can also be observed in Figure 10, where it can be seen that both materials show similar performances in alkaline conditions, as opposed to neutral conditions, where Al performed better. It is important to note that this is only valid for short treatment times and/or low applied currents.  
Figure 7: pH–applied current interaction for Se removal in the case of Al electrodes when the treatment time was (**a**) 5 min and (**b**) 25 min. The red line represents experiments with no pH adjustment, along with green line middle and blue line maximum ranges of pH values.  
Figure 8: FSM plots showing interactions of factors A (applied current) and C (treatment time) for Sr for (**a**) no pH control and (**b**) maximum pH range.  
Figure 10: Electrode material–applied current interaction for Sr removal when treatment time was 5 min for (**a**) no pH adjustment and (**b**) maximum pH range. The red line represents Al electrodes, green line Fe electrodes.  
Figure 9: pH–electrode material interaction for Se removal in the case of Al electrodes when treatment time and applied current were set to (**a**) 5 and (**b**) 25. Red line represents experiments with no pH adjustment, along with green line middle and blue line maximum ranges of pH values.
## 2 Methodology
### Sun Detection
#### 2.2.2. Dome Detection  
In this section, the dome detection method, for the region where the Sun should be, is described. The dome detection is carried out as follows. Let \(I(m,n,p)\) be the matrix, which represents an image with dimensions \(m\times n\times p\), where \(i=1,2,...,m\) and \(j=1,2,...,n\) represent the spatial position of the pixel and \(k=1,...,p\) is the channel. Hence, \(I(i,j,k)\) is a function that returns the intensity value of image \(I\) in position \((i,j)\) in the \(k\) channel; in this case, the image has three channels, red, green, and blue. The image \(I\) is converted to a gray scale image \(G\). This conversion is given by \(G(i,j)=0.299\cdot I(i,j,1)+0.587\cdot I(i,j,2)+0.114\cdot I(i,j,3)\)[16]. Once the gray scale image has been computed, the system performs a noise reduction, resulting in a new image, denoted as \(G_{\mathrm{s}}(m,n)\). The noise reduction was done through the well-known Gaussian filter. This filter works with a convolution mask with dimensions of \(11\times 11\), which has the values of the center pixels weighted more (for more details about the Gaussian filter, see [17]). Then, \(G_{\mathrm{s}}(m,n)\) is binarized by an adaptive thresholding algorithm, which calculates the threshold for a small region of the image, getting different thresholds, according to its local neighborhood, for different regions of the same image. Image thresholding segments a digital image based on a certain characteristic of the pixels (for example, intensity value) based on a threshold (\(th\)); that means the new pixel value will depend on if the pixel value is below or above this threshold, as follows:  
\[B(i,j)=\left\{ \begin{array}{ll}0&\mathrm{if}\ \ G_{\mathrm{s}}(i,j)<th\\ 1&\mathrm{if}\ \ G_{\mathrm{s}}(i,j)\geq th\end{array} \right.\]  
The goal is to create a binary representation of the image, let \(B(m,n)\) be such a representation, classifying each pixel into one of two categories, such as \"dark\" (\(B(i,j)=0\)) or \"light\" (\(B(i,j)=1\)) [18]. In order to find the dome, the system uses \(B(m,n)\) as the input of Algorithm 1. Here, the parameters \(m\), \(n\), and \(\Delta y\) are also needed, \(m\) and \(n\) are the image dimensions, and \(\Delta y\) is a  
Figure 2: Sun position detection process. slight vertical displacement that can take values in a range of 50-100 pixels with respect to the center of the image. What Algorithm 1 does is find the circle that best fits the dome; this circle is defined by three points, which are obtained through a search that starts at the center of the image and ends once a pixel, with a value of one, is found. The way to find the pixel is by moving along the image from the center, both to the left and to the right. As a result of this search, two of the three needed points are found. The third point is found by moving along the image to the left starting at the center, but adding a slight translation on the vertical axis (80 pixels). Having these three points, the parameters of the circle (center and radius) are computed through the procedure DomeParameters, which is composed of classical circle formulas.
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 3 The Mean Square Convergence Analysis
### Mean Square Convergence Condition  
To achieve the convergence condition, substitute the error relation  
\[e_{p}(n)=e_{a}(n)-\mu e(n)||\varphi(\mathbf{x}(n))||^{2} \tag{23}\]  
into the energy conversion relation (22) and obtain  
\[||e_{w}(n+1)||^{2}=||e_{w}(n)||^{2}+\mu^{2}e(n)^{2}||\varphi(\mathbf{x}(n))||^{2}- 2\mu e_{a}(n)e(n) \tag{24}\]  
By taking the expectations of both sides of (24), the following is obtained:  
\[\begin{split}& E\left[||e_{w}(n+1)||^{2}\right]=E\left[||e_{w}(n)|| ^{2}\right]+\\ &\mu^{2}E\left[||\varphi(\mathbf{x}(n))||^{2}|e(n)|^{2}\right]-2\mu E \left[e_{a}(n)e(n)\right]\end{split} \tag{25}\]  
Assume that the disturbance noise \(v(n)\) is independent and identically distributed (iid), and has a different distribution to the input sequence \(\mathbf{x}(n)\); this assumption is commonly used in convergence analysis [14,26]. Thus, we have  
\[E[e(n)]=E[|e_{a}(n)|^{2}]+\sigma_{v}^{2}, \tag{26}\]  
where \(\sigma_{v}^{2}\) is the variance of \(v(n)\). Equation (25) can be rewritten as  
\[\begin{split}& E\left[||e_{w}(n+1)||^{2}\right]=E\left[||e_{w}(n)|| ^{2}\right]+\\ &\mu^{2}\left(E\left[||\varphi(\mathbf{x}(n))||^{2}e_{a}(n)^{2} \right]+||\varphi(\mathbf{x}(n))||^{2}\sigma_{v}^{2}\right)-2\mu E\left[|e_{a}(n)| ^{2}\right]\end{split} \tag{27}\]  
To ensure the convergence of the learning system, the following should be satisfied:  
\[E\left[||e_{w}(n+1)||^{2}\right]\leq E\left[||e_{w}(n)||^{2}\right] \tag{28}\]i.e.,  
\[\begin{split}\mu^{2}&\left(E\left[||\varphi(\mathbf{x}(n))|| ^{2}e_{a}(n)^{2}\right]+||\varphi(\mathbf{x}(n))||^{2}\sigma_{v}^{2}\right)\\ &-2\mu E\left[|e_{a}(n)|^{2}\right]\leq 0\end{split} \tag{29}\]  
Therefore, with the monotonic decrease of the weight error power \(E\left[||e_{w}(n)||^{2}\right]\), the step size should satisfy the following convergence condition:  
\[0<\mu\leq\frac{2E\left[|e_{a}(n)|^{2}\right]}{E\left[||\varphi(\mathbf{x}(n))||^{2 }e_{a}(n)^{2}\right]+\sigma_{v}^{2}E\left[||\varphi(\mathbf{x}(n))||^{2}\right]} \tag{30}\]
## 2 Materials and Methods
### Method
#### 2.2.7 Sensory Analysis  
A questionnaire-based sensory evaluation was conducted in a controlled laboratory environment, free from confounding factors, with panel members spaced in time and/or location to prevent bias. The samples used in the evaluation were the analytical samples, which were cut into small, uniform pieces. Each sample was assigned a random three-digit numerical code, and the panel members received the samples in a randomized sequence to minimize the potential influence of sequential tasting on their perceptions. To neutralize their palates between samples, panel members were provided with water and bread. The panel consisted of 30 participants, all of whom were non-expert regular consumers who had not undergone specific training for the test. The participants, a balanced mix of men and women aged 18 to 45, were not asked to provide demographic information. Panel members were instructed to evaluate and rank their preferences for colour, aroma, texture,taste, and overall characteristics using a 10-point category scale in a descriptive sensory test. They indicated their preferences on pre-printed forms, with ratings ranging from 1 to 10 for each attribute. The order of attributes was arranged according to the typical sequence in which human senses perceive them. Participants were also encouraged to provide written comments. The collected data were subsequently entered into a spreadsheet programme for further analysis.
## 3 The Location-Based Interactive Model
### Location-Based On-Demand Sensing Model
#### 3.4.2 Multiple Mobile Users within a Geographical Region  
The IoT cloud tracks all mobile users who are currently using sensing services in a geographical region. When the cloud receives a request from a new user, it first checks if there are available data from the region of interest requested by other users. If there are available data meeting requirements of new users, the cloud reuses the data for later coming users.
## 2 Related Work
### Motivation and Contribution  
Most of the work presented in the literature using spatial signal correlation models in satellite communication revolves around performance investigations of communications in terms of bit error or symbol error probability, outage probability, etc. To the authors' knowledge, a framework for DoA modeling with respect to the elevation angle and using a spatial signal correlation model to analyze the performance of DoA techniques in satellite communication, especially at higher DoA angles with respect to the antenna boresight and elevation angle, is unique and has not been previously addressed in the available literature. Based on the knowledge gained from the literature review, this work considered a geographic coordinate system for the uplink, enabling a precise representation of locations and a simplified geometry for effective modeling of the downlink. However, the focus of this work leans towards the downlink as environmental influences, i.e., fading and correlation, are typically present only in the downlink because of the terrain and infrastructure in the receiver environment. In contrast, the uplink lacks such environmental influences, resulting in a scenario where only additive white Gaussian noise (AWGN) can be considered, thus indicating only one result for the uplink. Similarly, due to the nature of free space in the uplink, the concept of correlation is not applicable. In this scenario, the signals are inherently fully correlated in space, meaning they remain 100% correlated at all times.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 4 5G and Healthcare Opportunities
### In-Home Health Monitoring  
Wearable sensors and implantable medical devices (to monitor and transmit health recorded data) will feature prominently in the future of wireless healthcare systems. There are already many examples of wearable/implantable medical devices (e.g., cochlear implants, cardiac defibrillators/pacemakers, insulin pumps) that are having a large impact on patients and bringing them much ease. In the future, interfacing these wireless sensing devices with 5G will present unprecedented opportunities, as well as formidable challenges.  
Currently, there are several technological constraints (i.e., low data rates, limited connectivity, security concerns, etc.) that prevent the large-scale adoption of in-home health monitoring services. The future 5G infrastructure will open up a new set of possibilities to tackle these constraints by providing extended security along with higher bandwidth, transmission reliability and ubiquitous access. This will provide always-available communication services for health monitoring, with a great increase in transmitting capability between health service providers and patients.  
Already, there have been exciting advances in terms of the ability to conveniently perform in-home health monitoring. For example, patients can now measure their vitals (e.g., AliveCor ECG application at home using mobile devices at a reduced cost with more convenience, compared to previous options that typically entailed a visit to the local hospital. The advances in 5G can spur a decentralization trend in which healthcare facilities can be delivered locally in homes, nursing centres, surgery centres, clinics,rehabilitation places, remote areas and even in ambulances (e.g., when shifting critical patients to central healthcare facilities). 5G will also open up new opportunities for radar technologies to monitor the health of elderly people (i.e., fall detection and quantitative gait measurement) in a non-invasive manner both in a home and the clinical environments.