esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling a Brushed DC Motor  
### Classic Brushed DC Motor Modeling  
The first step in modeling a brushed DC motor is to create an equation that includes the electrical and mechanical components of the DC motor. The denominations of the electrical and mechanical elements are listed in Table 1, and a simplified diagram containing them is shown in Figure 1.  
In the brushed DC motor electrical elements shown in Figure 1, the applied voltage can be expressed as the sum of the differential equation of the current and the back EMF via [PERSON]'s voltage law:  
\[V_{in}(t)=R_{a}i_{a}(t)+L_{a}\frac{di_{a}}{dt}+e_{a}(t) \tag{1}\]  
Then, for the mechanical element shown in Figure 1, using [PERSON]'s second law, the torque produced by the motor can be expressed as a differential equation of the angle position:  
\[\tau(t)=B\frac{d\theta}{dt}+J\frac{d^{2}\theta}{dt^{2}} \tag{2}\]  
The back EMF generated by the motor is proportional to its rotational speed and can be expressed as the product of the back EMF constant \(k_{b}\) and angular velocity \(\omega\), as expressed in Equation (3). A description of the back EMF is provided in Section 2.2.  
\[e_{a}(t)=k_{b}\omega(t) \tag{3}\]  
The torque generated by the motor is proportional to the current flowing through the motor and can be expressed as the product of \(i_{a}(t)\) and torque constant \(k_{\tau}\):  
\[\tau(t)=k_{\tau}i_{a}(t) \tag{4}\]  
The Laplace transform can then be performed using Equations (1)-(4), and thus, a block diagram for modeling can be generated. Refer to Appendix A for a complete breakdown.
## 2 Datasets and Preprocess
### MODIS NDVI Product  
The NDVI dataset from MOD13A1 comprises two bands, the NDVI and EVI, which are derived from atmospherically corrected bidirectional reflectance. It has a temporal resolution of 16 days, dividing the year into 23 periods, with a spatial resolution of 500 m, reflecting the spectral and phenological features of the vegetation [52]. In this study, the maximum value composite method [53] was used to generate the monthly NDVI.
## 2 Data Description and Key Observations  
This paper uses the same real wind generation data from a large wind farm as our previous works [36; 37; 38; 40]. The wind farm has a rated capacity of 300.5 MW, where two classes of wind turbines are installed: Mitsubishi and GE turbines. There are 221 Mitsubishi turbines with a rated capacity of 1 MW and 53 GE turbines with a rated capacity of 1.5 MW (see in Figure 2).  
Each class of wind turbines has distinct power curves as well as a cut-in and cut-off speed. For each class, a meteorological tower (MET), collocated with a wind turbine, is deployed to collect weather information. The instantaneous power outputs of each turbine together with the weather information are saved every 10 min for the years 2009 and 2010. In this paper, we use the power outputs of the Mitsubishi turbines \(P_{init}(t)\) and GE turbines \(P_{ge}(t)\), the wind speed \(W_{s}(t)\), and the wind direction \(W_{dir}(t)\) to develop the proposed NNs.  
From the measurements of the power outputs, we find (1) the non-stationarity of the power measurements and (2) the heterogeneous dynamics of the wind non-ramp and ramp events across each class of turbines as illustrated in Figure 3, where the cumulative distribution functions (CDFs) of the wind power measurements of two classes of turbines over different seasons of a year and different ramp events are presented.  
Figure 3: Empirical distribution of power outputs of GE and Mitsubishi turbines in 4 seasons and ramp events, where season 4 is from October to December.  
Figure 2: Locational distribution of the Mitsubishi and GE wind turbines in the wind farm.  
In addition, it is shown in Figure 4 that the distributions of the ramps in different time windows \(l\) and different time periods are different and follow the generalized Pareto distribution (GPD).  
In the previous work [38], the non-stationarity is considered by developing seasonal self-evolving neural networks, where the ramp events are defined using fixed thresholds. As observed from Figure 4, fixed thresholds cannot fully capture the dynamics of wind ramp events. To address this challenge, we redefine the ramps by using dynamic thresholds, which change over time based on the dynamics of the ramp events, in order to reduce the forecast error of the wind farm generation, especially for ramp events.
## 7 Proposed Methodology for Metrological Verification of ATLAS-LG Systems  
According to ATLAS-LG data system, the Lithuanian public railway infrastructure manager JSC \"Lithuanian Railways\" detects defects in the rolling stock and, upon their identification, sends the rolling stock for repairs. The rolling stock is often owned by independent carriers and may lead to various legal disputes regarding the reliability of the measuring equipment. In order to avoid this, an ATLAS-LG metrological check is required. Depending on the experiment results and the existing ATLAS-LG service instructions, an ATLAS-LG metrological inspection methodology is proposed.  
ATLAS-LG systems are installed at certain points of the Lithuanian railway network. The distances between the installation points are large; therefore, it is recommended that all ATLAS-LG systems be grouped according to their geographical location. It is recommended to use a single freight wagon for testing each system group with wheelsets that have wheels with artificial flat spots using the parameters presented in Table 5.  
The metrological test should be carried out once a year in winter, when testing all systems of the same group with the same test train containing the traction device and a loaded wagon with an axle load of 230 kN, where the wheels have artificial flat spots as indicated in Table 5. The pilot train should then perform 5-10 trips per system at the speed of 40-80 km/h. The mean values of the axles of the test train and the derived values of the vertical forces can be evaluated. Next, a metrological certification is carried out according to two indicators.  
The derived value of the vertical force determines the probability of wheel detection up to 60-70% according to the average of the base values of the vertical forces of the test train. This parameter allows the evaluation of systematic deviations of the post operation. If the value obtained for a particular post differs from other posts in the same group by more than 10%, then there is a systemic post-work error that needs to be investigated and removed. An error may occur due to the reduced stiffness of the rail base.
## 4 Experimental Results and Analysis
### Seismic Datasets and Experimental Details  
To verify the performance of our algorithm, examples of hyperbolic events-synthesized signals were utilized, and the synthetic data generated with 150 traces and a 1 ms sampling interval are presented in Figure 6a. The Ricker wavelet is expressed by  
\[x(t)=\left(1-2\pi^{2}f^{2}t^{2}\right)\cdot e^{-\pi^{2}f^{2}t^{2}}. \tag{13}\]  
The basic data for the experiments were synthesized seismic signals of 150 traces at a 1000 Hz sampling frequency with linear features, curvilinear features, different dip angles, and fault events. Figure 6a presents an example of the prepared records. In addition, we collected and obtained forward model data from 12 wells, including very weak effective signals, which aimed to verify the effectiveness of the proposed method for weak signal mining. Figure 6b presents an example of the forward models. This paper rotated the synthetic data and forward models by 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 270\({}^{\circ}\), and 360\({}^{\circ}\), and then added white Gaussian random noise and linear noise of various levels into the rotated synthetic data, as well as the original data, to generate additional expanded versions, which verified the effectiveness of the proposed method in removing random noise and linear noise simultaneously. Meanwhile, we added real background noise (Figure 6c) collected from unexcited seismic sources into the rotated forward models, as well as the original forward data, which verified the effectiveness of the proposed method for mining weak signals submerged in strong background noise. In total, 80% of the data were utilized as training sets, while the remaining data were adopted as testing sets. During the training process, the batch size was 64, and the initial learning rate was 10\({}^{-4}\) for each layer and halved every 50 epochs. The experimental process was located in the HP 7920 series tower server, equipped with an NVIDIA RTX3090 graphics card, which can simultaneously process a large amount of noisy seismic data.
## 3 Control Methods  
Obtaining the semi-active and active forcing components is achieved via the use of control algorithms. In this study, for the purely active control case, the algorithm of choice is the optimal LQR that was proven suitable in a series of studies [3; 24; 25; 26] for use on flexible structural applications. The design of the controller (i.e., the determination of the weighting matrices which are required in the determination of the control gains) is based on the performance index defined in [25]. For the case of semi-active control, the displacement based groundhook algorithm that belongs to the category of direct output feedback controllers (i.e., the control actions are calculated based on a limited number of measurements) is selected. The choice of this direct output feedback controller for the case of semi-active control is based on the reduction of the computational effort required for the online calculation of Equations (7) and (8), requirement of minimum state measurements as well as  
Figure 4: Control signals as a function of relative velocity for the (**a**) purely active system; (**b**) semi-active component; and (**c**) active component of the hybrid configuration subjected to a white noise excitation.  
Figure 3: A schematic representation of the procedure followed for modeling the semi-active hybrid mass damper.  
its enhanced performance over other conventional direct output feedback controllers as shown in the studies of [21; 27]. The mathematical expressions describing the control algorithm used in the derivation of the control actions are found in [28; 29].  
With reference to Section 2.2, and because of the fact that semi-active control precedes the design of the active controller, the incorporation of semi-active control to the system results in a configuration that is no longer linear but piecewise linear, generating the need for linearisation before a purely active controller is designed. In this study, the linearisation of the piecewise linear system is performed via input/output subspace SSARX identification using MATLAB's (MATLAB2016a, The MathWorks Inc., Natick, MA, USA, 2016) system identification toolbox. To this end, a purely harmonic signal with known frequency and amplitude is used as the external input to the system. The displacement of the structural mass was used as the output. From this, a four-state equivalent linear system is constructed and the state matrices are extracted for use in the active controller design procedure.
## 5 Numerical Simulation Analysis of the Damage Rule of the Loose Layer
### Study on the Damage Law of the Loose Layer
#### 5.2.2 Study on the Fractal Shape of the Fissure Evolution of the Loose Layer  
In order to analyze the evolution process of the crack in the loose layer, the numerical simulation results of PFC2D data were processed. Images from the specified area were imported into MATLAB (version R2023b) for grayscale and binary image processing. Following this, the box-cutting dimension (as shown in Equation (1)) was used. The relationship between the development of loose layer cracks and the fractal dimension was calculated in the process of working face advancement (Figure 11).  
\[\text{D}=-\lim_{r\to 0}\frac{\text{lgN}(r)}{\text{lg}(r)} \tag{1}\]  
Figure 10: Illustrates cracks observed at varying excavation distances: (**a**) excavate 80 m; (**b**) excavate 100 m; (**c**) excavate 140 m; (**d**) excavate 180 m; (**e**) excavate 200 m; and (**f**) excavate 300 m.  
Coal seam mining induces stress release and transmission in the loose layer, leading to the upward development of cracks that eventually form ground fissures on the surface. The observed damage patterns in the loose layer reveal that the evolution of these fissures progresses through four distinct stages.  
1. Stage I: the gestation stage. During this initial stage, the mining-induced damage had not yet propagated to the loose layer. The overlying rock layer experienced damage, but it remains confined within the covered rock. This stage involves advancing the working face to a distance of 60 m;
2. Stage II: the early stage of development. At this stage, as the working face progressed, cracks started to develop in the loose layer. The fractal dimension of the cracks increased from 0.957 to 1.36, indicating that between 60 m and 100 m of working face advancement, the loose layer experienced damage, and the cracks rapidly developed upward. These cracks exhibited local and discontinuous characteristics;
3. Stage III: the rapid development stage. During this stage, the cracks in the loose layer continued to develop upward in a stepwise manner until they reached the surface. This corresponded to the working face advancing from 100 m to 200 m. Coal mining-induced instability and deformation caused the loose layer to develop through cracks that reached the surface, resulting in surface fissures;
4. Stage IV: the stability stage. In the final stage, periodic crack development occurred in the loose layer as the working face advanced from 200 to 300 m. During this period, the loose layer stabilized and reached a state of stress equilibrium.
## 7 Future Directions
### Deep Space Communication  
Improvements in THz-FSO systems may play a role in furthering future deep space communication architectures for high-data-rate and low-latency interplanetary platforms. This capability is inherent for further and subsequent space exploration and colonization missions, as colonization would require time-effective and secure communication.
## 2 Materials and Methods
### Development of the Integrated RJD Efficiency Forecasting Method  
When doing a long-term forecast of the technological indexes, one should consider the mutual influence of the wells, the water intrusion rate, the reservoir pressure changes as well as the geological and technological operations taking place on other wells. The integrated method is designed to resolve the mentioned problems. The method includes the following stages:  
Stage 1. Forecasting the liquid production rate increment after RJD. The technological efficiency of the RJD technology may be assessed with a number of methods: statistics, machine learning, or hydrodynamic modeling. However, only a hydrodynamic simulator allows for long-term forecasting of the technological indexes taking into account the mutual influence of the wells. There are two ways of forecasting with a hydrodynamic simulator: by setting the liquid withdrawal or the bottom hole pressure limits. In the authors' practice, the method of setting the liquid rates is more common. For such estimation, it is necessary to know the changes of well operation mode after RJD to set the liquid production rate after RJD for the forecast period. As a rule, it requires a geological analysis and calculation of the specific productivity factors that determine the production rate change after RJD [26]. The authors suggest forecasting the liquid production rate increment using machine learning methods and including these models into the hydrodynamic simulator. To develop the machine learning models, it is necessary to reduce the number of variables, in other words, to select the most relevant parameters that influence the liquid production increment. To reduce the scale, linear discriminant analysis was used for finding the set of the parameters that influence particular values. For the Bsh site, the linear discriminant function was as follows (1):  
\[\text{Z}=0.12\cdot\text{h}_{\text{oil}}+7.96\cdot\text{}NTG+0.33\cdot\text{P}_{ \text{bh}}+0.07\cdot\upmu+0.16\cdot\epsilon_{\text{farfield}}+0.03\cdot\text{W}- 0.012\cdot\text{S}-6.08;\ \text{R}=0.71 \tag{1}\]  
The result of the linear discriminant function classification was mostly influenced by, in descending order: oil-saturated thickness, net-to-gross sand ratio, oil viscosity, farfield hydroconductivity, water cut, and skin factor.
# Positron Emission Tomography in Cerebral Amyloid Angiopathy: A Scoping Review
## 3 Results
### CAA-Related Inflammation  
CAA-ri is an uncommon and often reversible encephalopathy, characterized by an inflammatory response to amyloid-beta deposits within the cerebral vasculature. Few studies have addressed the performance of amyloid PET in patients with this condition [49; 50; 51]. The first one [49] examined 12 patients with CAA-ri, highlighting MRI findings on FLAIR and gradient-recalled echo (GRE) sequences indicative of vascular inflammation and damage associated with CAA-ri, but without quantitative details on amyloid PET imaging. Another group [50] studied eight patients with asymmetric white matter hyperintensities, cortical swelling, and leptomeningeal enhancement, along with microbleeds identified on susceptibility-weighted imaging (SWI) on MRI. Notably, amyloid PET imaging demonstrated elevated uptake in cortical regions, suggesting significant amyloid deposition contributing to the inflammatory process. A third study [51] assessed 10 patients with CAA-ri. MRI findings included extensive white matter hyperintensities, cortical edema, and leptomeningeal enhancement, with microbleeds observed on GRE sequences. Amyloid PET showed increased cortical uptake, corroborating amyloid involvement, while FDG-PET revealed hypometabolism in affected regions, indicating functional impairment associated with inflammatory changes.  
The main findings are summarized in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Population** & **Imaging Findings** & **PET Findings** \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] \\ et al., 2016 [49] \\ \end{tabular} } & \multirow{2}{*}{12 patients with CAA-ri} & MRI: leukoencephalopathy; multiple cortical and subcortical hyperintensities & Not specified \\  & & on FLAIR; microbleeds on GRE sequences & \\  & & MRI: asymmetric white matter & \\ \multirow{4}{*}{\begin{tabular}{c} [PERSON] et al., \\ 2018 [50] \\ \end{tabular} } & \multirow{2}{*}{8 patients with CAA-ri} & hyperintensities, cortical swelling, leptomeningeal enhancement; & Amyloid PET: elevated uptake in cortical regions \\  & & microbleeds on SWI & \\ \multirow{4}{*}{\begin{tabular}{c} [PERSON] et al., \\ 2018 [51] \\ \end{tabular} } & \multirow{2}{*}{10 patients with CAA-ri} & MRI: extensive white matter & \\  & & hyperintensities, cortical edema, leptomeningeal enhancement; & \\ \multirow{4}{*}{\begin{tabular}{c} [PERSON] et al., \\ 2018 [51] \\ \end{tabular} } & \multirow{2}{*}{10 patients with CAA-ri} & \multirow{2}{*}{\
\begin{tabular}{c} hyperintensities, cortical edema, \\ leptomeningeal enhancement; \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} hyperintabolism in affected \\ regions \\ \end{tabular} } \\  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Findings of selected studies on CAA-ri and amyloid PET.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 2 Principles and Methodology
### Data Preparation  
The conveyor belt damage dataset used in this paper is provided in Ref. [3], which contains 3000 images. The damage types are divided into four categories: surface wear, surface damage, tear and breakdown, and each type of damage occupies 1/4. The establishment of the dataset was completed through manual labeling, and finally stored in the format of VOC2007.  
During the training process, the Mosaic and CutMix data enhancement strategies were used to increase the variability of the input image, enrich the image background information, and improve the robustness and generalization ability of the model. At the same time, the gradually decreasing learning rate was used to train 100 Epoch, the initial learning rate was \(1\times 10^{-3}\), and dropped to 1/10 of the previous value at 50 Epoch and 80 Epoch, and the Batch size was set to 16.
# A Review of the Water Desalination Technologies
## 1 Introduction
In the same area, in 1985 another big desalination plant was realized, having an installed capacity equal to 56,800 m\({}^{3}\)/day [11]. Great technological progress occurred in the process of reverse osmosis thanks to the increase in membrane lifetimes and the adoption of energy recovery devices to reduce the energy requirements for the process. So, nowadays RO is applied to seawater and is economically competitive with the other technologies. This improvement was achieved during the 1990s thanks to the introduction of an energy recovery system based on the introduction of hydro turbines or similar systems before returning the brackish water to the sea [8]. Currently, RO is the most widespread technology for desalination, followed by MSF and Multi-Effect Distillation (MED). According to the statistics, in the last year the total installed capacity was based essentially on three technologies: RO (68.7%), MSF (17.6%), MED (6.9%). The other technologies had a marginal role (6.8%) [12]. Desalination plants are installed around the world but are mainly concentrated in Middle East and North Africa (47.5% of global capacity). The main raw water source is represented by seawater (70.5% of global capacity) [12]. Several technologies are currently under investigation, with the goal being to reduce the energy demand for freshwater production.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities  
Medical imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), ultrasound, and optical coherence tomography (OCT), have unique characteristics and generate different types of images. Therefore, DL techniques need to be tailored to each modality to achieve optimal performance. In this section, we will discuss the current state-of-the-art DL techniques and applications for each modality, as well as the challenges and future directions.  
Before diving into the application of DL in specific imaging modalities, it is important to clarify the focus of this section. The intention is to discuss how DL is applied in the analysis of images generated by these different modalities, such as MRI, CT, PET, ultrasound imaging, and OCT, rather than its application in the process of image acquisition. Specifically, the discussion will center around how DL has been utilized to extract meaningful insights from these images, for example, through tasks such as segmentation, classification, detection, and prediction. This includes the ability to identify and classify pathologies, measure anatomical structures, and even predict treatment outcomes.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 3 Design and Implementation of CONE
### Neighbor Discovery and Degree Exchange
#### 3.2.1 Constructing a Dominating Set  
In this step, nodes create a DS by electing a particular node to become dominator. Algorithm 4 gives the pseudo-code of the main process of a DS construction. Besides, on the sender's side, the pseudo-code of the election process is shown in Algorithm 5. Nodes use an election_message which contains the identification (ID) of the sender's dominator.  
```
1:if receive degree_message then
2:while degree is lower than threshold and timer for degree exchange has not expired yet do
3: broadcast degree_message
4: set timer for degree_message
5: wait until timer for degree_message expires
6:endwhile
7:endif
8:if receive election_message then
9: set callback timer for election
10: pass election_callback as a parameter
11: proceed with DS connection
12:endif
```  
**Algorithm 4** Dominating set construction.