esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works and Contribution
### Data Compression  
In order to reduce the amount of data transmitted in wireless sensor network, the collected data can be compressed before transmission. Data compression can be performed directly at sensor nodes or at intermediate nodes (cluster heads) [13; 14; 15]. The compression is usually more effective, when implemented at the cluster heads that receives data from multiple sensor nodes. The data are collected by cluster heads over a period of time, thus the spatial and temporal correlation can be exploited in the compression process. A decompression has to be executed at sink (or base station) to recover the original data.  
The compression-based approach leads to delay in collecting data at the sink node. A delay is related to the fact that the cluster head has to hold the data before a data block is accumulated and ready for compression. This approach can also affect the accuracy of collected data, if a lossy compression method is used.  
A data compression method for body area sensor network was presented in [16]. The method was designed by taking into account the possibility of overhearing transmissions between the sensor nodes that are attached to human body. It was also observed that the compression is facilitated by strong temporal and spatial correlations among accelerometer readings collected during movement of the body. According to that method, each sensor node samples its data, overhears transmissions of neighbouring nodes, compresses the data, and transmits them. The temporal and spatial correlations were modelled by differential coding and linear regression. An offline procedure was introduced to learn these correlations and adjust model parameters.  
In [17] a wearable sensor network was presented for monitoring and quantitative assessment of stroke patients' upper limb motor function. An accelerometer data compression was implemented in that network to reduce the amount of data during sampling and transmissions. It was shown that raw accelerometer signals can be compressed and precisely reconstructed by the sink node for automatic classification.  
The possibility of compressing continuous bio-signals in a wearable sensor network was analysed in [18]. The authors have used a binary permuted block diagonal matrix encoder to compress electrocardiogram and photoplethysmogram data. In that method, the sink node dynamically determines compression parameters to adapt to changing sparsity level of the monitored signal, and transmits the parameters to sensor node.  
Another method for electrocardiogram data compression was based on empirical mode decomposition and feature dictionary construction [19]. This solution has demonstrated that the dedicated compression algorithms can achieve higher compression ratio by exploiting self-similarities and inherent properties of the monitored signals. That method was designed for compressing the data transmitted from a single wearable node.
# AetherGeo: A Spectral Analysis Interface for Geologic Mapping
## 1 Introduction
This work aims to present the AetherGeo software, its interface, main components, and functionalities, while showcasing some processing workflows that can be achieved entirely within the software. The current state of the software allows for the use of classical remote sensing capabilities, such as dimensionality reduction algorithms [14], band ratios [15], clustering [16], and endmember extraction and classification [17]. The last-mentioned techniques have shown extensive potential across multiple geological applications [18; 19]. Additionally, AetherGeo serves as a tool for testing and implementing advanced analysis algorithms in the future and, no less importantly, implementing preprocessing algorithms essential for addressing the range ambiguity and noise associated with sensor data [20; 21; 22]. Overall, this free, open-source software can serve as an alternative to proprietary software for dimensionality reduction, endmember extraction, spectral analysis, and image classification of multi- and hyperspectral data, with a special focus on (but not limited to) geological applications. The user-friendly interface (GUI) allows it to reach a broader audience with ready-to-use tools and no programming knowledge required. Additionally, the software can be installed on Windows via an executable installer ([[https://www.fc.up.pt/RS-GISLab/aethergeo.html](https://www.fc.up.pt/RS-GISLab/aethergeo.html)]([https://www.fc.up.pt/RS-GISLab/aethergeo.html](https://www.fc.up.pt/RS-GISLab/aethergeo.html)), accessed on 23 May 2025). As for users with more programming experience, since the program is open source, there is the opportunity to use the functions provided in independent programs/workflows or make alterations to those functions based on user-specific needs.
# Current Harmonic Suppression Algorithm for Asymmetric Dual Three-Phase PMSM
## 4 Proposed Current Harmonics Suppression Strategy
### Adaptive Feedback Current Control Loop
\right]^{T} \tag{20}\]  
Adding the feedforward voltage compensations to the phase voltages obtained from Equation (20), the desired reference phase voltage vector is  
\[ \begin{array}{cccccccc}\left[\begin{array}{cccccccc}U_{AN}&U_{BN}&U_{CN}&U_{ DN}&U_{EN}&U_{FN}\end{array}\right]^{T}=\left[\begin{array}{cccccccc}U_{AN}^{*}&U_{ BN}^{*}&U_{CN}^{*}&U_{DN}^{*}&U_{EN}^{*}&U_{FN}^{*}\end{array}\right]^{T}+\\ \left[\begin{array}{cccccccccccc}U_{Ad}&U_{Bd}&U_{Cd}&U_{Dd}&U_{Ed}&U_{Fd} \end{array}\right]^{T}\end{array}  \tag{21}\]  
Figure 8: Bode diagram for different values of \(\mathrm{w_{c}}\) when \(\mathrm{K_{R}}\) is constant. Figure 9: Bode diagram for different values \(\mathrm{K_{R}}\) when \(\mathrm{w_{c}}\) is constant. where \(U_{Ad}\), \(U_{Bd}\), \(U_{Cd}\), \(U_{Dd}\), \(U_{Ed}\), and \(U_{Fd}\) are the feedforward voltage compensations of phases A, B, C, D, E, and F, respectively. Due to the isolated neutral points of ADT-PMSM, the three-phase zero-sequence voltage injection PWM technique can be adopted. The output PWM waveform is modulated by comparing the final reference voltage waveform of each phase with the triangular carrier waveform, which is also known as the carrier-based pulse width modulation (CBPWM). There are many methods for calculating zero-sequence components. Different zero-sequence components are corresponding to different PWM strategies [32]. In this paper, the commonly used mean zero sequence component injection, i.e., half of the middle value of the three-phase voltage values is used, as shown in Equation (22). \[\left\{ \begin{array}{l}U_{o1}=-0.5(U_{\text{max1}}+U_{\text{min1}})=0.5U_{mid1} \\ U_{o2}=-0.5(U_{\text{max2}}+U_{\text{min2}})=0.5U_{mid2}\end{array} \right. \tag{22}\]  
where \(U_{max}\), \(U_{mid}\) and \(U_{min}\) are the maximum, middle, and the minimum values of the three-phase voltages. Since the sum of three-phase voltages is zero, the zero-sequence voltage is equal to the half of the middle value of three-phase voltage values. It has been proved that the zero sequence voltage injection method is equivalent to the space vector pulse width modulation (SVPWM) algorithm, which can achieve the same linear voltage modulation range as proved in [14; 15; 32]. Compared to the four vector SVPWM algorithm, a four-dimension current control for ADT-PMSM can be implemented. Besides, it is easy to implement and the PWM waveform is centrally symmetrical and each power device is switched only once in each PWM cycle. Besides, it will not introduce even harmonics and can reduce switching losses. Figure 11 shows the double zero-sequence injection pulse width modulation (PWM) strategy. Figure 10: Diagram of control algorithm for asymmetric dual three-phase permanent magnet synchronous motor (ADT-PMSM).
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 8 Submarine Networks Security  
As submarine networks are the main component of Earth's global backbone network, security issues are of critical importance. Submarine networks are \"the world's information super-highways\" and carry more than 99% of international traffic between continents. Keeping in mind that the submarine environment is not as friendly, stable, and predictable as the terrestrial one, all possible fail causes have to be explored and faced. Fishing techniques with large ships, anchor dragging, and more rarely earthquakes are among the causes for most damage done to cables [87].  
In addition, major \"digital threats\" such as backdoors during the cable manufacturing process, targeting onshore landing stations and facilities linking cables to networks on land, or tapping the cables at sea are of important interest. The nightmare scenario of a hacker who gains control, or administrative rights, of the submarine network management system may result in the hacker discovering vulnerabilities, interrupting or diverting data traffic,  
Figure 34: Cable cost per bit for 8, 12 and 16 FPs [70].  
Figure 33: (**a**) Global cable construction costs for the period ranging from 2010–2020. (**b**) Costs by region [86].  
or even executing a \"kill click\" by which he or she might delete the wavelengths which transmit data has to be prevented. For all these reasons, live cable monitoring techniques are proposed to predict, diagnose, and program the proper restore actions. Figure 35 shows the percentages of various threats for a submarine cable system. As is evident, fishing and anchorage are responsible for about the two thirds of cable damages.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 3 Key Aspects of the BSI Technical Guideline with Relevance for Energy Management
### Transparent Data Tunnel  
The second key function relevant for energy management--besides access to meter readings--is the transparent data tunnel for communication between EMPs and CLSs. It is configured using _Proxy Communication Profiles_. These profiles specify combinations of CLS and EMP that may use the data tunnel in order to communicate, and also hold the certificates necessary for authentication. There are three possible ways (identified as HKS3, HKS4 and HKS5, where HKS stands for HAN communication scenario (\"HAN-Kommunikationsszenario\")) to initiate the establishment of a transparent data tunnel.  
First, the CLS may use a SOCKSv5 [32] command to tell the SMGW to establish a connection (HKS3). Second, an EMP can trigger the establishment with the help of the _SMGW Administrator_ (HKS4). In this case, the EMP signals the _SMGW Administrator_ that a transparent data tunnel is needed. If there is no open _management connection_ between the _SMGW Administrator_ and the SMGW, a connection is initiated using the _wake-up service_ of the SMGW. Once the management connection is available, the _SMGW Administrator_ is able to trigger the tunnel establishment by the SMGW. Third, the SMGW itself may initiate the transparent data tunnel whenever a predefined event occurs, for instance time based or periodically (HKS5).
## 1 Introduction
### Organization  
The rest of the article is organized as follows. Section 2 presents related works that discuss the SG environments and then Section 3 presents system model for the SG environments. In Sections 4 and 5, we review of [PERSON] et al.'s scheme and analyze its security problems. In Section 6, we present a privacy-preserving lightweight authentication protocol for demand response management in SG environments to address the security shortcomings of [PERSON] et al.'s scheme and enhance efficiency. In Section 7, we perform the security analysis of the proposed scheme utilizing informal and formal analysis. Section 8 evaluates the security and performance features of the proposed scheme compared with existing schemes. Finally, we summarize the conclusion in Section 9.
## 2 Theories of BSS Based on BCA  
### Bss  
Assuming that the transmission of the signal is instantaneous, the mixed signals can be expressed as follows [19; 20]:  
\[x_{i}(t)=\sum_{j=1}^{n}h_{ij}s_{j}(t)+n_{i}(t) \tag{1}\]  
where \(x_{i}(t)\) denotes the mixed signals received by sensors,\(s_{j}(t)\) denotes the source signals, \(n_{i}(t)\) denotes the observation noise of the \(i\)-th sensor, and the above equation can be expressed as Equation (2) using a matrix.  
\[x(t)=As(t)+n(t) \tag{2}\]  
where \(s(t)=\left[s_{1}(t),s_{2}(t),s_{3}(t)\ldots s_{m}(t)\right]^{T}\) denotes an m-dimensional column vector, \(x(t)=\left[x_{1}(t),x_{2}(t),x_{3}(t)\ldots x_{n}(t)\right]^{T}\) and \(n(t)\) denote n-dimensional column vectors, \(\text{A}=\left(a_{1},\ a_{2},\ a_{3}\ldots a_{n}\right)\) denotes the mixing matrix, and \(a_{1}=h_{1j}\), \(a_{2}=h_{2j}\), \(a_{3}=h_{3j}\), \(\ldots\), \(a_{n}=h_{1j}\), \(j=1\), \(2\), \(3\), \(\ldots\), \(m\), assuming we believe that the noise was processed to a negligible level before blind source separation.  
\[x(t)=As(t) \tag{3}\]  
From the above equation, it can be seen that the process of blind source separation is actually conducted by estimating the mixing matrix \(A\) when \(x(t)\) is known and the mixing matrix \(A\) and the source signal \(s(t)\) are unknown, and by deriving a matrix \(M\), such that \(M\) carries out a linear transformation of \(x(t)\) to satisfy Equation (4).  
\[y(t)=Mx(t) \tag{4}\]  
where \(y(t)\) is the estimate of the source signal \(s(t)\), which is the source signal separated from \(x(t)\) and \(M\) is called the separation matrix. Thus  
\[y(t)=Mx(t)=M\times As(t)=Ws(t) \tag{5}\]  
where \(W\) is positive definite and is called the complex separation matrix. In particular, when \(m=n,M\) is the inverse matrix of \(A\). The whole model of linear mixed signal separation is shown in Figure 1.
# DSCope: Development of Automatic Program for Detecting Fractures and Measuring Dip Angles
## 2 Development of an Automated Program (DSCope)
### Measuring the Dip Angle of a Fracture  
Linear fitting was performed based on the pixel position of the detected edge. The polyfit module of the Numpy package [9] was used for linear fitting. This module uses the least squares method to estimate values corresponding to the coefficients and constants. Fitting was performed in such a way that the squared residual (r), composed of the expected function \(f(x_{i})\) and the actual observation \(y_{i}\), became the smallest according to Equation (2). Subsequently, the arctangent (\(\tan^{-1}(a)\)) could be obtained from the coefficient (\(a\)), derived through linear fitting, and was converted to an angle:  
\[\mathrm{r}=\sum_{i=1}^{n}(y_{i}-f(x_{i}))^{2} \tag{2}\]  
Three or more data points were required to perform the linear fitting. Thus, the code was written such that fitting was not performed at the edges with fewer than six pixels. This can be useful in cases where the spots on drilling cores or patterns on rocks can also be captured as edges. In other words, linear fitting may be performed for four or five pixels when a small scanbox and a low threshold are applied to detect as many fractures as possible. However, since it is difficult to consider them as real fractures, linear fitting with fewer than six pixels is excluded from the fractures.  
Figure 5 shows an example of the detection of fractures and dip angle measurements. The red solid line indicates the edge detected using Canny edge detection. The result of linear fitting to the pixel coordinates of the edge is indicated by a blue dashed line. In the lower right, the dip angles measured through linear fitting are depicted. The DSCope program detected five fractures, as shown in Figure 5, but the wood chip that was added to indicate the depth could not be considered a fracture. Therefore, the other four results are presented.  
Figure 4: Example of detecting fractures from a grayscale core image.
## 3 Theoretical Framework
### Bee Colony Optimization Algorithm
#### 3.1.2 Backward Pass  
Bees return to the hive after exploring the search space and comparing their solutions to further adjust their search strategies to avoid getting trapped in local optima. Thus, the Backward Pass Equations are described as:  
1. Comparison of Solutions The comparison solution can be expressed in Equation (11) as:  
\[Compare\left(S_{ij},S_{kj}\right)=\left\{\begin{array}{l}1,\;if\;f\left(S_{ ij}\right)<f\left(S_{jk}\right)\\ 0,\;otherwise\end{array}\right. \tag{11}\]  
The backward pass compares the current solution \(S_{ij}\) found by honeybees with the neighbor solution \(S_{jk}\) concerning their fitness values. If the fitness of the neighbor solution is good (lower in the case of minimization problems), the honeybee updates its solution to move towards a better solution. The comparison process helps honeybees adjust their search direction based on the quality of solutions found by other bees.
2. Update Solution  
The update strategy is defined in Equation (12) as:  
\[S_{ij}=S_{ij}+\Delta S_{ij}\times Compare\left(S_{ij},S_{kj}\right) \tag{12}\]  
where the Honeybee updates its solution \(S_{ij}\) by integrating a change \(\Delta S_{ij}\) based on the comparison result. If the neighbor's solution \(S_{kj}\) is better, the honeybee adjusts its solution toward the neighbor's solution. This update mechanism allows bees to learn from the collective search behavior and improve their solutions iteratively.
# Insights into the Potential of Buckwheat Flow Fractions in Wheat Broad Dough
###### Abstract  
Buckwheat flour fractions with different particle sizes (PS), comprising various concentrations of valuable nutritional components, represent an opportunity to enhance refined wheat bakery products. The aim of this research was to assess the potential of buckwheat flour (BF) fractions (large, L > 300 \(\upmu\)m, medium, 180 \(\upmu\)m < M < 300 \(\upmu\)m and small, S < 180 \(\upmu\)m) to substitute refined wheat flour at 0, 5, 10, 15, and 20% in wheat bread dough and to establish the optimal amount for each fraction. The results revealed significant changes during different bread-making stages and on the finished product. A decrease in falling number index, water absorption, starch gelatinization, elastic modulus, and bread hardness with increasing PS was observed. The increase of BF amount led to an increase in dough development time, speed of protein weakening, gel starch stability, alveograph ratio, rheofermentation properties, maximum creep-recovery compliance, and bread hardness. The optimal values for falling number, mixing-heating-cooling dough parameters, dough biaxial extension, rheofermentation, storage and loss moduli, creep-recovery compliance, loaf volume, and bread hardness were obtained depending on PS based on the generation of predictive models. It was established that the best formulations, with respect to dough rheology and bread characteristics, included BF at 9.13% for large, 10.57% for medium, and 10.25% for small PS.  
particle size; refined wheat flour; Mixolab; alveograph; rheometer; bread characteristics
## 3 Small-Signal Modulation Response of EAM-VCSELs  
### MTF of EAM-VCSEL Photon Density  
We consider small modulation \(\Delta\alpha_{EAM}\) of the losses in the quantum well region of the EAM, which leads to small-signal modulation of the photon and carrier densities \(\Delta n_{ph}\) and \(\Delta n\), the total losses \(\Delta\alpha_{tot}\) and the confinement factor in the VCSEL QW region \(\Delta\Gamma\), i.e.,  
\[\frac{d\Delta n_{ph}}{dt}=\Gamma v_{g}(n_{ph}\Delta g+g\Delta n_{ph })+v_{g}g\pi n_{ph}\Delta\Gamma\] \[-v_{g}\alpha_{tot}\Delta n_{ph}-v_{g}n_{ph}\Delta\alpha_{tot}, \tag{8}\]  
\[\frac{d\Delta n}{dt}=-\frac{\Delta n}{\tau_{\Delta n}}-v_{g}(n_{ph}\Delta g+g \Delta n_{ph}). \tag{9}\]  
Here, \(\Delta\alpha_{tot}=\frac{da_{tot}}{da_{EAM}}\Delta\alpha_{EAM}\), \(\Delta\Gamma=\frac{d\Gamma}{da_{EAM}}\Delta\alpha_{EAM}\) and \(\Delta g=a\Delta n-a_{p}\Delta n_{ph}\), i.e.,  
\[\frac{d\Delta n_{ph}}{dt}=\Gamma v_{g}(an_{ph}\Delta n-a_{p}n_{ph} \Delta n_{ph}+g_{0}\Delta n_{ph})+\] \[v_{g}g\pi n_{ph}\Delta\Gamma-v_{g}\alpha_{tot}\Delta n_{ph}-v_{g} n_{ph}\Delta\alpha_{tot}, \tag{10}\]\[\frac{d\Delta n}{dt}=-\frac{\Delta n}{\tau_{\Delta n}}-v_{g}(an_{ph}\Delta n-a_{p}n_{ ph}\Delta n_{ph}+g_{0}\Delta n_{ph}) \tag{11}\]  
Taking into account that \(\Gamma g_{0}=\alpha_{tot}\) from Equation (1) and considering \(\Delta\alpha_{tot}\) and consequently \(\Delta n_{ph}\) and \(\Delta n\) proportional to \(e^{i\omega t}\), with \(\omega\) being the modulation frequency, the left-hand side of the equation for \(\Delta n_{ph}\) (\(\Delta n\)) becomes proportional to \(i\omega\Delta n_{ph}(i\omega\Delta n)\) or in matrix form  
\[\left[\begin{array}{cc}i\omega+\Gamma g_{g}a_{p}n_{ph},&-\Gamma g_{g}an_{ph} \\ \frac{v_{g}a_{tot}}{\Gamma}-v_{g}a_{p}n_{ph},&i\omega+\frac{1}{\tau_{\Delta n} }+v_{g}an_{ph}\end{array}\right]\left[\begin{array}{c}\Delta n_{ph}\\ \Delta n\end{array}\right]\]  
\[=v_{g}n_{ph}\left[\begin{array}{c}(\Delta\alpha_{tot}-g\Delta\Gamma)\\ 0\end{array}\right]. \tag{12}\]  
Solving for \(\Delta n_{ph}\), we obtain  
\[\Delta n_{ph}=\frac{\left|\begin{array}{cc}v_{g}n_{ph}(\Delta\alpha_{tot}-g \Delta\Gamma),&\Gamma v_{g}an_{ph}\\ 0,&-i\omega-\frac{1}{\tau_{\Delta n}}-v_{g}an_{ph}\\ \hline\end{array}\right|\begin{array}{cc}i\omega+\Gamma v_{g}a_{p}n_{ph},&- \Gamma v_{g}an_{ph}\\ \frac{v_{g}a_{tot}}{\Gamma}-v_{g}a_{p}n_{ph},&i\omega+\frac{1}{\tau_{\Delta n} }+v_{g}an_{ph}\end{array}\right|}. \tag{13}\]  
From here the modulation transfer function (MTF) \(H(\omega)=\frac{\Delta n_{ph}}{\Delta\alpha_{EAM}}\) is expressed as  
\[H_{EAM}(\omega)=-\frac{n_{ph}(i\omega+\frac{1}{\tau_{\Delta n}}+v _{g}an_{ph})}{-\omega^{2}+\omega_{R}^{2}+i\gamma\omega}\times\] \[\times(\frac{d\alpha_{tot}}{d\alpha_{EAM}}-v_{g}g\frac{d\Gamma}{d \alpha_{EAM}}), \tag{14}\]  
with  
\[\omega_{R}^{2} = \frac{\Gamma v_{g}a_{p}n_{ph}}{\tau_{\Delta n}}+\frac{v_{g}an_{ph }}{\tau_{ph}}, \tag{15}\] \[\gamma = \frac{1}{\tau_{\Delta n}}+v_{g}an_{ph}+\Gamma v_{g}a_{p}n_{ph}. \tag{16}\]