esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 3 Architecture Overview
### Network Elements  
Each network element (data-collecting units, mobile nodes, or gateways) is based on a central processing unit (Raspberry Pi board) with the hardware specifications described in Table 4.  
To achieve the multi-technology communication, allied with the Raspberry Pi embedded WiFi interface, a SX1272 LoRa module manufactured by Libelium is used. In order to establish interaction between the SX1272 LoRa module and the Raspberry Pi, a Multiprotocol Radio Shield (also manufactured by Libelium) must be connected along with the module. This shield will work as a connection bridge between both components. A summary description of the SX1272 Long Range (LoRa) module characteristics is presented in Table 5.  
The selected hardware provides a low-cost and easy-to-repair structure.
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
## 3 Results
### Analytical Results  
#### 3.3.1 Color Change: Colorimetric Measurements  
The colorimetric measurements substantiated our empirical evaluation, with the data reported in Table 7.  
Figure 3 illustrates that samples containing a styrene-acrylic binder layer (StyY) exhibit higher \(\Delta\)E values, indicating a substantial color difference between the bottom layer after cleaning and its original color. This observation corresponds to a poor cleaning result, leading to a higher number of cleaning cycles and lower scores for the \"effectiveness\" criterion. Conversely, samples using the blue spray as the bottom layer, such as AcrB-Alkyl, AcrB-AcrY, AlkB-AlkY, and AlkB-AcrY, show satisfactory \(\Delta\)E values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \multicolumn{2}{c}{**AcrY–AcrFB**} & \multicolumn{2}{c}{**StyY–AcrB**} \\ \hline
**Instrument** & **QS** & **LQS** & **Raking Light** & **QS** & **LQS** & **Raking Light** \\ \hline Unaged & & & & & & \\ \hline Aged & & & & & & \\ \hline Radar chart & Pigment & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cleaning results of samples AcrY–AcrFB and StyY–AcrB.  
It is also worth noting that the error bars in Figure 3 are correlated with the \"residue\" criterion, where larger \(\sigma\Delta\)E values suggest a greater color difference between different points measured within the same cleaning section. This aligns with our empirical evaluation: in samples containing StyY, not only is it difficult to reveal their original color, but they also exhibit an uneven surface with significant residue after multiple cleaning cycles.
## 3 Methods
### DEM Generation and Mosaicking  
The aligned and co-registered 3D point clouds are individually rasterized using the (_point2 dem_) function, thereby preserving the native resolution of the PSSs in geographic coordinates. This is possible due to the fact that a high number of points will fall into one cell of the raster as a result of the multi-view principle in the region of interest, yielding high redundancy. During this process, the intersection errors provided by ASP in meters for each 3D point are analyzed to detect and remove outliers. Points with intersection errorsexceeding three times the 75 th percentile of all points in the error map are identified as outliers and excluded from further processing. Furthermore, isolated points are removed.  
Small gaps in the resulting DEMs are interpolated using a search radius factor of three pixels, and a 11x11 median filter is applied to remove spikes. Error maps are generated during this step, showing the intersection error for each point in the corresponding DEM. These maps depict the mean divergence of ray intersections for object points within each raster cell, defined as the shortest distance between two image rays that theoretically intersect at a single point in object space. They provide a quantitative measure of the uncertainty in 3D object point calculations and facilitate the detection of potential systematic errors. Moreover, the _point2 dem_ tool facilitates the orthorectification of, by default, the left image from each stereo pair, which is subsequently used to generate an orthophoto mosaic. This is not the core task of _Planet4 Stereo_ but may be an interesting by-product for the user. The DEMs, error maps, and orthophotos are finally merged using functions of the _rasterio_ library for orthophoto mosaicking and ASP's _dem_mosaic_ for DEM and error map mosaicking.
## 3 Architectural Overview
### Multi-Metric Scoring Engine in MMS-DSR  
The Multi-metric Scoring Engine is the core component that evaluates and ranks the discovered routes based on multiple performance metrics. The engine operates according to the following features:  
* Dynamic Metric Evaluation: the engine processes each route to compute scores based on five key metrics: latency, bandwidth, reliability, beamforming efficacy, and Channel State Information.
* Weighted Scoring: each metric is assigned a dynamic weight reflecting its current importance based on network conditions. These weights are adjusted in real time using feedback from the Machine Learning-Based Prediction Unit to ensure that scoring aligns with the predicted network state. This approach allows the system to adapt to the urban dynamics effectively, prioritizing different metrics as the urban environment evolves.
* Score Computation: the overall score for each route, \(S\), is computed as follows: \[S(route)=w_{1}\cdot\frac{1}{L(route)}+w_{2}\cdot B(route)+w_{3}\cdot R(route)+w_{4} \cdot BE(route)\] (1) where \(w_{1},w_{2},w_{3}\), and \(w_{4}\) are the weights for latency, bandwidth, reliability, and beamforming efficacy, respectively. This formula ensures that routes are evaluated comprehensively, incorporating both traditional and advanced metrics to select the most efficient path.
* Optimization: the engine uses a combination of heuristic and machine-learning techniques to fine-tune the weights based on ongoing network performance data. This adaptive approach ensures that the engine remains responsive to changing network dynamics, particularly in adjusting beamforming strategies for optimal route performance in smart cities.  
This scoring process enables MMS-DSR to select the most efficient and reliable routes, considering both current network metrics and future state predictions. The inclusion of beamforming efficacy as a metric ensures that the protocol can adaptively manage and utilize advanced beamforming techniques in 6G networks for enhanced communication quality.
# A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves
## 2 Vibration Mechanism of Saturable Reactors  
The structure of a saturable reactor is shown in Figure 1. It mainly consists of windings, iron cores, cover plates and screws. A single iron core is fixed as a whole by two U-shapedlaminated silicon steel sheets through tensioning straps. According to reference [18], the voltage borne by a saturable reactor is as follows:  
\[U=\sum_{k=0,1,2,\cdots}^{K}U_{k}\sin(k\omega t+\varphi_{k}) \tag{1}\]  
where \(U_{k}\) and \(\varphi_{k}\) are the voltage amplitude and phase of the \(k\)-th harmonic, respectively, \(\omega=50\) Hz and \(K\) is the total harmonic number.  
The small deformations of saturable reactor components caused by magnetostriction satisfies the following relationship:  
\[\frac{\Delta L}{L}.\frac{1}{\mathrm{d}H}=\frac{2\varepsilon_{s}}{H_{c}^{2}}|H| \tag{2}\]  
where \(\Delta L\) is the deformation and expansion amount of silicon steel sheet, \(L\) is the original size of the silicon steel sheet, \(\varepsilon_{s}\) is the saturated magnetostriction rate of the silicon steel sheet, \(H_{c}\) is the coercive force and \(H\) is the magnetic field strength in the iron core.  
Therefore, the vibration formula of the saturable reactor core is as follows:  
\[a=\frac{d^{2}\Delta L}{dt^{2}}=\gamma\Big{\{}\sum_{1}2U_{k}^{2}\cos(2k\omega t +2\varphi_{k})+\sum_{2}q_{1}\cos[(k_{1}+k_{2})\omega t+\varphi_{k1}+\varphi_{k2 }]+\sum_{2}q_{2}\cos[(k_{1}-k_{2})\omega t+\varphi_{k1}-\varphi_{k2}]\Big{\}} \tag{3}\]  
\[q_{1}=\frac{(k_{1}+k_{2})^{2}U_{k1}U_{k2}}{k_{1}k_{2}} \tag{4}\]  
\[q_{2}=\frac{(k_{1}-k_{2})^{2}U_{k1}U_{k2}}{k_{1}k_{2}} \tag{5}\]  
where \(U_{k}\) and \(\varphi_{k}\) are the voltage amplitude and phase of the k-th harmonic, respectively, and \(\omega\) is the angular frequency of 50 Hz voltage. \(\sum_{1}*=\sum_{k=1,2,3,\cdots}^{K}\), \(\sum_{2}*=\sum_{k_{1}\
eq k_{2}=1,2,3,\cdots}^{K}*\). \(\gamma\) is related to the saturation magnetic flux density of the iron core, winding turns, saturation magnetostriction rate of the silicon steel sheet, cross-sectional area of the iron core, original size of the silicon steel sheet and other parameters.  
According to the mechanism analysis process mentioned above, magnetostriction is the core factor causing the vibration of a saturable reactor [19]. The voltage borne by the saturable reactor during operation contains many higher-order harmonics. Resonance occurs when the natural frequency of the iron core approaches the excitation frequency. In addition, the material and structure of the iron core can affect its nonlinear characteristics. These factors will further affect the vibration mechanism of the iron core [20].  
Figure 1: Saturable Reactor Structure.
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### MRI-to-PET Translation:
#### 3.1.3 Image Super-Resolution
The medical image arbitrary-scale super-resolution (MIASSR) method coupled with GAN executes the super-resolution for modalities such as cardiac MR scans and chest CTs by exercising transfer learning [55]. Similarly, in [56], simultaneous super-resolution and segmentation are performed for 3D neonatal brain MRI on the simulated version of low-resolution. The learned model then upgrades and segments the real clinical low-resolution images. In 2D MR acquisition, the pulse sequence decides the slice thickness. The exact characteristics of signal excitation are not explicitly known; giving less information about slice selection profiles (SSP) creates insufficient training data. This problem can be solved by predicting a relative SSP from the difference between in- and through-plane image patches. The thicker slices and larger slice distance are maintained to decrease scan timing and achieve a high signal-to-noise ratio, resulting in a lower through-plane resolution than in-plane resolution. The GAN-based method focuses on improving the resolution of the through-plane slices where training data is a degraded version of in-plane slices to match the through-plane resolution [57]. A high signal-to-noise ratio in an MRI scan can assist in correctly detecting [PERSON]'s disease. Utilizing the GAN-based SR technique, image quality equivalent to a 3-T scanner can be achieved without altering scanner parameters, even if the scans are obtained through 1.5-T scanners. The generator creates a transformation mask, and the discriminator differentiates the synthetic 3-T image from the original 3-T image [58]. In [59], fine perceptive generative adversarial networks (FPGANs) adopt the divide-and-conquer scheme to extract the low-frequency and high-frequency features of MR images separately and parallelly. The model first decomposes an MR image into low-frequency global approximation and high-frequency anatomical texture subbands in the wavelet domain. The subband GAN simultaneously performs a super-resolving process on each subband image, resulting in finer anatomical structure recovery. Study [60] uses end-to-end GAN architecture to produce high-resolution 3D images. The training is performed in a hierarchical manner producing a low-resolution scan and a randomly selected part of the high-resolution scan simultaneously. It provides two-fold benefits: first, the memory requirement during training of high-resolution images is divided into small parts. Next, high-resolution volumes are converted to a single low-resolution image keeping anatomical consistency intact. Spatial resolution images are produced by direct Fourier encoding from three short-duration scans [61]. Table 7 presents the summary of GAN-Synthesized images used for super-resolution.
## 3 Resonator Design
### Data Coil Tuning  
Resonant tuning of the data coil is required to provide an output impedance level that allows for a subsequent matching to the receiving amplifier. Here, we attempt to obtain closed-form expressions for the tuning components that provide both impedance matching and fast transient settling. The basic tuning topology is shown in Figure 5: The receiver coil inductance \(L_{\mathrm{c}}\) and series resistance \(R_{\mathrm{c}}\) are complemented by a parallel tuning capacitor \(C_{\mathrm{P}}\) and a resistance \(R_{\mathrm{L}}\gg R_{\mathrm{c}}\), which represents the input resistance of the subsequent low-pass filter and receiving amplifier. The system is optimized for the case of a large coil separation distance (small \(M_{\mathrm{3d}}\)), so that the effect of reflected impedance of (4) can be neglected and the input voltage \(u_{\mathrm{in}}\) is determined by [PERSON]'s law as formulated in (2). Then, the maximum power theorem (see [30], p. 78) requires the impedance resulting from the parallel combination of \(C_{\mathrm{P}}\) and \(R_{\mathrm{L}}\) to be the conjugate complex of the coil series impedance:  
\[\mathrm{Re}\left(\frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L }}}\right)\overset{!}{=}R_{\mathrm{c}}\;\;\mathrm{and}\;\;\mathrm{Im}\left( \frac{R_{\mathrm{L}}}{1+j\omega_{0}C_{\mathrm{P}}R_{\mathrm{L}}}\right) \overset{!}{=}-\omega_{0}L_{\mathrm{c}}, \tag{8}\]  
which results in the optimal values of parallel capacitance and load resistance of  
\[C_{\mathrm{P,opt}} =\frac{L_{\mathrm{c}}}{R_{\mathrm{c}}^{2}+\omega_{0}^{2}L_{\mathrm{ c}}^{2}}, \tag{9}\] \[R_{\mathrm{L,opt}} =R_{\mathrm{c}}+\frac{\omega_{0}^{2}L_{\mathrm{c}}^{2}}{R_{\mathrm{ c}}}. \tag{10}\]  
Apart from impedance matching, the resonator's properties must allow for a fast transient response in order to achieve high symbol rates. Consequently, the settling time of the resonator is determined in the following:  
Figure 4: Positional analysis of the data coils’ mutual inductance and the cross-coupling to the energy coils. First, the frequency behavior of the resonant network is characterized by the transfer function  
\[G(s)=\frac{u_{\text{out}}(s)}{u_{\text{in}}(s)}=\frac{R_{\text{L}}}{C_{\text{P}}L _{\text{c}}R_{\text{L}}s^{2}+C_{\text{P}}R_{\text{c}}R_{\text{L}}s+L_{\text{c}} s+R_{\text{c}}+R_{\text{L}}}. \tag{11}\]  
Any symbol transition, either being the activation of the carrier in on-off keying or a phase change in phase-shift keying, is formally represented by an additional sinusoidal input signal \(x_{\text{c}}(t)\) adding up to the overall input signal  
\[u_{\text{in}}(t)=x_{0}(t)+\Theta(t)\cdot x_{\text{c}}(t)=x_{0}(t)+\Theta(t) \cdot a_{\text{c}}\cdot\sin(\omega_{0}t+\phi_{\text{c}}), \tag{12}\]  
which is enabled for \(t>0\) by the Heaviside step function \(\Theta(t)\).
## 3 Results
### Amino Acid Content  
The total amino acid content and composition of twenty amino acids in raw and cooked _T. papillosa_ are presented in Table 4. Our results indicate that late nymphs had a higher total amino acid content compared to adults. Furthermore, the roasting process significantly increased the amino acid content of _T. papillosa_, while the grilling process led to a slight increase in the total amino acid content. Hence, the roasting method proved to be more effective than grilling in enhancing the amino acid content. Among the amino acids, tyrosine (70 mg/g) was the most abundant in late nymphs, followed by methionine and histidine (53 and 25 mg/g, respectively). Conversely, methionine was the most abundant amino acid in adults, followed by tyrosine and histidine (46 and 19 mg/g, respectively). No presence of asparagine was detected in any of the samples.  
The roasting treatment exhibited a tendency to increase the free amino acid content in late nymphs. Specifically, the levels of arginine, isoleucine, leucine, and tyrosine increased by approximately 4.48%, 4.52%, 6.43%, and 5.83%, respectively, compared to the raw samples. Similarly, in adults, methionine, glutamic acid, leucine, and aspartic acid increased by approximately 8.45%, 12.06%, 8.30%, and 11.89%, respectively, after roasting. These findings are consistent with the results of [PERSON] et al. [31], who reported that roasting treatment in _Terminalia catappa_ significantly improved the availability of the total amino acid content by up to 11%. Additionally, [PERSON] et al. [32] observed a significant increase in the total amino acid content of Barrosa-PDO veal after grilling.  
These findings highlight the impact of cooking methods, particularly roasting, on the amino acid content and composition of _T. papillosa_. The enhanced amino acid profiles contribute to the nutritional value of this edible insect, making it a potential source of essential amino acids in human diets.
# Centralized Energy Prediction in Wireless Sensor Networks
## 2 Related Work
### Energy Consumption Prediction Implementation  
As our proposal is for WSN, we decided to implement our energy consumption model on TelosB motes, an IEEE 802.15.4-compliant platform with an MSP430 microprocessor and 48 kB of flash memory. We modeled the TelosB mote energy consumption considering the device would run a temperature monitoring application, considering that the whole WSN was programmed using Contiki-3.0 [40]. Next, we describe the states of each module.  
The temperature sensor of the TelosB is a Sensirion STH11 and it operates in two states: active and sleep. Regarding the processing energy consumption, Contiki avoids staying idle and it supports only two low-power modes: LPM1 and LPM3. Using the default configuration, the microprocessor never enters in LPM3 state. Thus, the processing module operates in two states: active and LPM1. The communication module behavior is defined by the CC2420 Contiki implementation. Contiki uses a radio duty cycle mechanism named ContikiMAC [41]. This mechanism uses a power-aware wake-up mechanism with a set of timing constraints aiming to keep the transceiver turned off as long as possible. An important issue is that using ContikiMAC, the communication module does not enter in the _idle_ state. Furthermore, whenever ContikiMAC turns off the radio, the frequency synthesizer is disabled, which means the module is in the power down state. Based on these characteristics, the communication module operates in three states: transmitting, receiving, and power down.  
Based on the TelosB energy consumption characteristics, we defined five states for the Markov chain: Sensing, Processing, LPM1, Transmitting, and Receiving. Each state is composed of three substates, one for each module. Table 1 describes the configuration of each Markov chain state.  
Figure 1: Communication module: simplified diagram.  
The probability matrix is constructed after a training period where each node monitors its transitions. Each node constructs a transitions matrix to organize the number of transitions between states and a transitions vector to store the total of transitions of each state. Then, dividing each row of the transitions matrix by the total number of transitions, as shown in Equation (10), we obtained the probability matrix. Then, we can calculate Equations (2) and (4) to obtain the energy consumption prediction. Note that \(P\) depends on the node's behavior, which means that it can change every time it is calculated, according to the transitions (\(a\) and \(v\)) counted.  
\[P=\begin{pmatrix}a_{11}/v_{1}&a_{12}/v_{1}&a_{13}/v_{1}&a_{14}/v_{1}&a_{15}/v_{ 1}\\ a_{21}/v_{2}&a_{22}/v_{2}&a_{23}/v_{2}&a_{24}/v_{2}&a_{25}/v_{2}\\ a_{31}/v_{3}&a_{32}/v_{3}&a_{33}/v_{3}&a_{34}/v_{3}&a_{35}/v_{3}\\ a_{41}/v_{4}&a_{42}/v_{4}&a_{43}/v_{4}&a_{44}/v_{4}&a_{45}/v_{4}\\ a_{51}/v_{5}&a_{52}/v_{5}&a_{53}/v_{5}&a_{54}/v_{5}&a_{55}/v_{5}\\ \end{pmatrix} \tag{10}\]
## 5 Optimization Result
### Implementation of the Methodology  
The inverse dynamic algorithm presented in Equations (1)-(6) was implemented as a function using the kinematics and the dynamics parameter of the robot given in Tables 1 and 2, respectively. In order to evaluate the objective function given in Equation (28), it is necessary to calculate the acceleration factor in Equation (10) for the given motion profile. Therefore, the inverse dynamics algorithm must be called as much as the number of sampling points for numerical integration calculation in Equation (10). That means the inverse dynamics function must be executed \(\frac{t_{t}}{\Delta t}\times Number\ of\ iterations\) times during an optimization, where \(\Delta t\) is the time step (10 ms in this study) for the numerical integration.  
As a result, quick processing of the inverse dynamics routine is critical for the computation time. To this end, we implemented the inverse dynamics routine with C++. The matrix operation required for the calculation of dynamics was implemented using the free software library Eigen ([[https://eigen.tuxfamily.org](https://eigen.tuxfamily.org)]([https://eigen.tuxfamily.org](https://eigen.tuxfamily.org))).  
To solve the optimization problem formulated as in Equations (28)-(30), an optimization algorithm was implemented. The optimization algorithm initializes the matrix \(A\) and vectors \(b,\ c\) in Equation (29) using the problem data given in Tables 3 and 4. The bound \(\underline{x}\) and \(\overline{x}\) in Equation (30) are initialized using the problem data given in Table 3. The objective function for the optimization problem which is a part of the algorithm receives the B-spline control points as an input and evaluates the joint torques for each sampling points by invoking the inverse dynamic algorithm. Then, the objective function calculates the acceleration factor defined in Equation (10) for all joints of robot and returns AM-GM ratio of the acceleration factors.  
The optimization algorithm was implemented in Python 3.5.2 environment. In particular, the interpolation module for SciPy library (version 1.2.1) was used to evaluate the B-spline value at the sampling points. Sequential Least Squares Programming (SLSQP) solver provided by the optimize package was also utilized to solve the constrained optimization problem ([[https://docs.scipy.org/doc/scipy/reference/index.html](https://docs.scipy.org/doc/scipy/reference/index.html)]([https://docs.scipy.org/doc/scipy/reference/index.html](https://docs.scipy.org/doc/scipy/reference/index.html))).
## 4 Engineering Example
### Analysis of Monitoring Information  
Figure 6 depicts the reservoir water level storage process curve. During the period from 21 October 2021 to 2 November 2021, the reservoir storage level fluctuated between 2369.08 m and 2369.95 m and basically remained stable, so this period was chosen for the inverse modeling.  
Several seepage monitoring points were installed on the left and right shoulders of the dam. This study aimed to invert the hydraulic conductivities of the poorly impermeable parts on the right bank; therefore, the following inversion process only considers the right bank monitoring data as the basis. Figure 7 presents the installation location of the seepage monitoring points on the right bank.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Materials** & **Hydraulic Conductivity (m/s)** \\ \hline Q = 5–10 Lu rock layer & 1.5 \(\times\) 10\({}^{-7}\)–1.5 \(\times\) 10\({}^{-5}\) \\ Q = 10–100 Lu rock layer & 3.0 \(\times\) 10\({}^{-7}\)–3.0 \(\times\) 10\({}^{-5}\) \\ Q \(\geq\) 100 Lu rock layer & 3.0 \(\times\) 10\({}^{-6}\)–3.0 \(\times\) 10\({}^{-4}\) \\ Fracture zone & 3.0 \(\times\) 10\({}^{-6}\)–1.0 \(\times\) 10\({}^{-4}\) \\ Defective grout curtain & 1.0 \(\times\) 10\({}^{-7}\)–3.0 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range of hydraulic conductivity for each material.  
Figure 5: Maximum cross-section.  
Figure 6: Process line of reservoir level change.