esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.3.1 Principal Component Analysis (PCA)  
PCA is a commonly used data dimensionality reduction for transforming high-dimensional data into low-dimensional data while retaining the primary information of the original data. It maps the original data into a new coordinate system through a linear transformation, such that the variance of the data is maximized under the new coordinate system. The specific steps are as follows:  
1. Data normalisation: The original data is normalized to feature the mean value of 0 and a variance of 1 to eliminate the difference in magnitude between different features.
2. Calculate the covariance matrix: The covariance matrix describes the correlation between the different features.
3. Calculate the eigenvalues and eigenvectors: The covariance matrix is decomposed to obtain the eigenvalues and the corresponding eigenvectors. The eigenvalues represent the variance in the data in the direction of the eigenvectors, while the eigenvectors represent the projection direction of the data in the new coordinate system.
4. Selecting principal components: According to the magnitude of the eigenvalues, the eigenvectors correspond to the top \(k\) eigenvalues that are selected as principal components. The number of principal components \(k\) is usually selected based on the proportion of variance retained, and the calculation formula is the following: \[F_{k}=\sum_{i=1}^{k}\lambda_{i}/\sum_{i=1}^{mk}\lambda_{i}\] (1)
5. Data transformation: The original data are projected onto the selected principal components to obtain the reduced-dimensionality data. The dimensionality of each sample in the new data set is reduced from the number of features in the original data to the number of selected principal components [36].
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 4 Results and Discussion
### Discussion  
As described previously, multiple experiments were conducted on mammogram classification using several partitioning criteria, hyperparameter optimizations, incremental pre-processing, and balanced data sampling strategies to acquire the best performance for all classifiers examined. The initial experiment did not produce satisfactory results due to the lack of training data. When we received more data in the second experiment, the accuracy increased slightly, but we were still suffering from model overfitting. The study's limitation is data availability--the availability of large and diverse datasets for training deep learning models.  
Acquiring a comprehensive dataset that includes various breast cancer cases, demographics, and imaging modalities can be challenging. As a result, we attempted to use the cross-validation technique in conjunction with a stratified strategy in order to adequately address the imbalanced class. Thus, we conducted the third experiment and observed a significant improvement in accuracy. To address the class imbalance, we attempted undersampling to reach the minority class; nevertheless, this experiment was not successful. Additionally, cross-validation was computationally intensive, which is why we chose to employ pre-trained models. Since it takes a long time to train from scratch, extract the features, and perform the validation splits.  
In the fourth experiment, we addressed the overfitting problem by using pre-trained models. We experimented with dropout values to boost the findings further; for each classifier, a particular dropout value was ideal. We have achieved outstanding achievements by utilizing the resources available to us. However, if we had additional data, the models would likely perform much better. On the other hand, the BI-RADS descriptor classification model yielded a high testing score compared to the previous studies. However, the study has a restriction in terms of the number of examinations included in the dataset. We cannot assure that the sample selected was generalizable to the entire population because we removed a considerable proportion of those examinations that had missing results during data cleaning. Additionally, the data extraction tool built to extract features from examination reports was exclusively designed to function with the report structure used by King Fahad University's Hospital. As a result, our methodology may not be suited to all practices. The suggested deep learning AMAN system is depicted in the AMAN system and is based on the results of all experiments. The maximum accuracy was achieved while consuming the fewest computational resources and using the fewest resources for image and clinical data processing.
# Transient Attitude Motion of TNS-0#2 Nanosatellite during Atmosphere Re-Entry
###### Abstract  
Attitude motion reconstruction of the Technological Nano Satellite TNS-0 #2 during the last month of its mission is presented in the paper. The satellite was designed to test the performance of the data transmission via the Globalstar communication system. This system successfully provided telemetry (even during its atmosphere re-entry) up to an altitude of 156 km. Satellite attitude data for this phase is analyzed in the paper. The nominal satellite attitude represents its passive stabilization along a geomagnetic field induction vector. The satellite was equipped with a permanent magnet and hysteresis dampers. The permanent magnet axis tracked the local geomagnetic field direction with an accuracy of about 15 degrees for almost two years of the mission. Rapid altitude decay during the last month of operation resulted in the transition from the magnetic stabilization to the aerodynamic stabilization of the satellite. The details of the initial tumbling motion after the launch, magnetic stabilization, transition phase prior to the aerodynamic stabilization, and subsequent satellite motion in the aerodynamic stabilization mode are presented.  
annosatellite; attitude determination; passive magnetic stabilization; aerodynamic stabilization +
Footnote †: journal: selected topics
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Digital Core Analysis
#### 3.3.2 X-Ray Diffraction  
X-ray diffraction (XRD) is a non-destructive method with which to analyze the structural parameters of crystalline materials by comparing them with a reference database of X-ray diffraction patterns [48; 49]. Petroleum industry practitioners use XRD to analyze rock composition, identify mineralogy and clay composition [50], study the effects of temperature and pressure on clay swelling, and measure particle density. XRD analysis has also been applied to clay swelling damage [51] and the effect of KCl brine injection [52]. [PERSON] et al. [53] performed XRD and SEM analysis on 18 rock samples from the LAM field offshore western Turkmenistan, and the XRD data showed a high content of clay minerals. Therefore, they concluded that clay swelling was the main factor causing formation damage in this region. In a recent study, [PERSON] et al. [54] used the XRD technique to analyze the water sensitivity of the formation by examining the clay mineral fractions in the core. The results of their XRD analysis are shown in Figure 3.  
Figure 3: Schematic diagram of the results of XRD analysis of the cores [54].  
Figure 2: (**a**) Nano-late invades into the pores, (**b**) nano-latex changes the formation porosity, (**c**) hydrophobic groups in the molecular chain of nano-latex are exposed, (**d**) nano-latex film is formed on the rock surface [47].
## 2 An Overview of the Cellular Networks
### General Features of Cellular Networks
#### 2.1.3 Cell Identifiers  
The cells of a network can be characterized by means of several identifying attributes at two different levels, which partially depend on the technology under consideration.  
First of all, for each specific technological solution, there is a public land mobile network (PLMN), composed by two attributes:  
* The mobile country code (MCC), which identifies the country where a network is located. It consists of 12 bits, or equivalently, three digits, and is assigned by the International Telecommunication Union (ITU). Some countries, e.g., USA and India, have more than one MCC;
* The mobile network code (MNC), which identifies a network in a country. It consists of 8-12 bits, or equivalently, 2-3 digits, and is assigned by the national authority.  
The cell global identifier (CGI) is a unique global identifier that identifies each cell among all cells of all networks. It pairs the PLMN identifier with additional logical attributes that depend on the specific network generation. A mobile device can get these logical attributes by listening to information across the Broadcast Common Control Channel (BCCH). Since this operation can take some milliseconds, in order to avoid delays, networks provide other physical identifiers that make it possible to quickly discriminate cells without the need for decoding the BCCH. The physical-layer cell identifier provides quick discrimination between neighboring cells. It is a local identifier within a small part of an access network, and its definition depends on the specific technology.
## 4 Body-to-Body Communication and Connectivity
### Impact of Dissemination Strategies on Connectivity  
Data dissemination defines the interconnection logic hierarchy. First, we define the flat wireless network which is a set of nodes communicating with any reachable neighbors using the same wireless technology. Second, a hierarchical wireless network is a set of nodes which communicate with other neighbors from the same hierarchical level only. Specific or elected nodes only could communicate with higher level nodes. The interconnection hierarchy could be set physically (different frequencies to separate nodes into groups, different wireless technologies: more than one WiFi standard, one standard and different channels, etc.) or logically (i.e., same wireless technology and frequencies but different nodes groups related to one specific gateway node). We identify two main dissemination strategies in BBN communications related to the two different interconnection hierarchies.  
The clustered data dissemination strategy consists of dividing the network into small groups of sub-networks called clusters. Each cluster leader is called a cluster head (CH). Nodes in a same cluster only communicate with their CH. CHs communicate between each others to reach wide networks. This strategy fulfills relevant operational requirements for indoor scenarios [36]. Even though this strategy defines a clear hierarchical communication charter, but it still restricts the connection capabilities only through CH. Indeed, when a CH of a cluster \"A\" is out of range from a neighbor cluster \"B\", a connection can not established even if a normal node from \"A\" is too close to \"B\". Electing new CH for the cluster \"A\" causes a significant delay, in addition to disconnection during the election process. Therefore, defining members for each cluster is always challenging. Consequently, connectivity of the entire BBN network depends on the connectivity between the CHs.  
The distributed data dissemination strategy allows any node in the network to communicate with any reachable node. As an example, an on-body sensor placed on the right wrist could communicate with the on-body coordinator of the neighboring body. This strategy decreases the average delay of the packet compared to the clustered approach [36], whereas, it decreases the link spectral efficiency and the network overall throughput. Accordingly, the average end-to-end connectivity decreases with the link spectral efficiency.  
Distributed data dissemination strategies have higher routing overhead than the clustered strategies, since in the first category, any node is allowed to send data to any node. However, in the clustered strategy, the routing overhead remains only between CHs. A delay in route refreshment in the routing table may increase routes unavailability. Therefore, a node with unavailable routes in its routing table is considered as a disconnected node.
# Performance Study of Black Shale Modified Soil for Road Use Based on Eshelby-Mori-Tanaka Theory
## 3 Study on Cement-Soil Mix Proportions  
### Experimental Design  
According to the statistical analysis of the mechanical performance indicators of cement-treated soil by [PERSON] [31], the commonly used cement mixing ratio in engineering falls between 10% and 25%. To expand the research scope on cement mixing ratios, this experiment adopts a mass ratio denoted as S = M\({}_{\text{cement-soil}}\) (where M\({}_{\text{cement-soil}}\) = M\({}_{\text{day}}\) + M\({}_{\text{cement}}\)), with cement mixing ratios of 10%, 15%, 20%, 25%, and 30%. After mixing with water and compaction, cylindrical cement-soil specimens with a diameter of 100 mm and height of 120 mm were prepared. The optimal moisture content (\(\omega_{\text{op}}\)) of the clay was used as the reference to control the moisture content of the cement specimens. For each mixing ratio, at least five specimens with different moisture contents were prepared, with a 1-3% difference between adjacent moisture contents. In the case of higher cement content (30%), a 3% difference was chosen, while for lower cement contents, 1% or 2% differences were selected. For instance, for a cement content of 10%, target moisture contents were set as (\(\omega_{\text{op}}\) - 2%), (\(\omega_{\text{op}}\) - 1%), \(\omega_{\text{op}}\), (\(\omega_{\text{op}}\) + 1%), and (\(\omega_{\text{op}}\) + 2%), followed by mixing with water and compaction, and the actual moisture content was measured.  
After compaction tests according to the specifications of the \"Geotechnical Testing Procedures for Highway Engineering\" (JTG 3430-2020), the compacted specimens were placed in a standard curing chamber at a temperature of \(20\pm 2\)\({}^{\circ}\)C and a relative humidity of over 95% for a curing period of 7 days. Subsequently, the specimens were taken out for unconfined compressive strength testing.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 2 Diagnosability Architecture for Control Systems Based on Structural Analysis
### A Structural Model for Diagnosability Based on Structural Analysis
#### 2.1.5 Structural Modeling and Structural Modeling of Diagnosability  
The structural model is a bipartite graph consisting of variables and constraint equations. It is an abstract statement of the analytical model. It describes the constraint connections between the variables and the equations but disregards the precise analytical formulation. The structural model comprises the system's essential traits and properties, regardless of the parameters [23].  
Let \(E=\{e_{1},e_{2},\ldots\}\) be the set of constraint equations and \(V=\{v_{1},v_{2},\ldots\}\) be the set of internal variables and parameters. Then, the system \(M\) can be represented by a tuple \((E,V)\). The types of equations in \(E\) are algebraic equations, differential equations, and difference equations. The variables in \(V\) are qualitative and quantitative. \(V\) can be further categorized into known variables \(Z\) and unknown variables \(X\), i.e., \(V=Z\cup X\).  
**Definition 1**.: _(Structural Model) The structural model can be represented by a bipartite graph \(G\big{(}E,V,\overline{A}\big{)}\), where \(\overline{A}\) is the set of system edges:_  
\[\overline{A}=\big{\{}\big{(}e_{i},v_{j}\big{)}\big{|}e_{i}\in E,v_{j}\in V\big{\}} \tag{1}\]The above equation denotes that \(v_{j}\) is in \(e_{i}\). (Incidence Matrix) The structural model can be described by the incidence matrix \(A\) of \(G\left(E,V,\overline{A}\right)\):  
\[A_{ij}=\left\{\begin{array}{l}1,\left(e_{i},v_{j}\right)\in\overline{A};\\ 0,\text{ else}.\end{array}\right. \tag{2}\]  
The incidence matrix rows represent the constraint equations, and the columns represent variables. \"\(A_{ij}=1\)\" denotes that \(v_{j}\) is included in \(e_{i}\), i.e., \(\left(e_{i},v_{j}\right)\in\overline{A}\). \"\(A_{ij}=0\)\" means that \(v_{j}\) is not included in \(e_{i}\), i.e., \(\left(e_{i},v_{j}\right)\
otin\overline{A}\).  
The structural model of \(M=\left(E,V\right)\) is mainly the bipartite graph or its incidence matrix. They are equivalent and are used according to the specific application context. Variables and parameter vertices connected to the vertices of the same equation must satisfy the constraint equations represented by the vertices of that equation.  
In summary, the structural model of diagnosability can be described by a tuple: \(\mathbb{M}=\langle E,\mathbf{X},\mathbb{X},\mathbf{Z},\mathbb{Z},\mathbf{B}, \text{assump}\rangle\).
## 3 Validation of the Proposed Semi-Analytical Solution
### Validation of the Proposed Semi-Analytical Solution Regarding Numerical Results
The purpose of this numerical analysis is the validation of the proposed semi-analytical solution, which will be done based on the force-displacement relationships of the analyzed models. The force-displacement relationship obtained by numerical analysis, and the results based on the proposed semi-analytical solution, are shown side-by-side in Figure 15. For the deflection limit of the free end of the cantilever beam, the value of 0.15 h was adopted, which is 30 mm for h = 200 mm and 75 mm for h = 500 mm. The values of the shear forces at the limit displacements, as well as the elastic stiffness and post-yield stiffness, are shown in Table 6. Based on the comparative analysis of the results, it can be concluded that the semi-analytical solution is satisfactorily validated by numerical results, considering that the difference between the semi-analytical and FEM results, in cases of force at limit displacement and elastic stiffness, is less than 5%. It should be mentioned that post-yield stiffness differences are less than 10%, which may be considered as significant, but taking into account that this stiffness is a small value, obtained differences are expected and can be taken as satisfying. Figure 14: The finite element method analysis results for model C1: (**a**) total deformation; (**b**) von Mises stress; (**c**) maximum principal plastic strain. Figure 15: Force–displacement diagrams for cantilever beam models: (**a**) C1; (**b**) C2; (**c**) C3; (**d**) C4; (**e**) C5; (**f**) C6.
# Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions
## 3 Results
### Location 3--Vegetation
The mean difference between the measured and estimated values was 1.2 dB, and the standard deviation was 3.6 dB.
## 3 Network Model
### Heterogeneous and Homogenous Network  
In a homogenous network, all nodes have the same performance in terms of communication range, power level, processing ability, mobility, memory, _etc_. Otherwise, it is called a heterogeneous network. Sometimes, in order to simplify the problem, one aspect is stressed, and others are ignored. For example, if each node has the same range, the network is homogenous; otherwise, it is heterogeneous. Inpractice, WSNs are very likely to be heterogeneous. For instance, the base station and cluster heads are more powerful than other nodes; hence, they have a longer communication range than regular nodes; especially, the sink node has no constraints with respect to energy, as it is usually not a battery-powered device.
## 2 Composite Signal's Distribution and the Dataset
### Dataset  
To train machine learning algorithms for multi-output regression, a dataset \(D\) of \(R\) samples was simulated, where \(R=10^{5}\)[3]. First, \(R\) composite signals were generated by summing a random number of component signals, ranging from two to ten. Where each component signal has four underlying parameters: modulation order, data rate, power level, and normalized frequency, values of which are randomly sampled from the parameter ranges described in Table 1. To ensure that categorical features have the same distance among each other in the feature space, we used the one-hot-encoding technique to encode parameters modulation order and number of component signals. For modulation order, we employ one-hot encoding with nine bits: eight bits to represent the eight possible modulation orders and one additional bit reserved for the case where the slot is unused (e.g., if fewer than ten signals are present). Consequently, each component signal is represented by 12 features: nine bits for the one-hot-encoded modulation order, plus one feature each for data rate, power level, and normalized frequency.  
When fewer than ten component signals are present (e.g., only seven signals are added), the feature vectors for the remaining slots (signals 8, 9, and 10) are set to zero, ensuring the input vector consistently maintains \(12\times 10=120\) features for the maximum possible number of component signals. Moreover, we one-hot-encode the total number of added signals (ranging from 2 to 10) into nine bits. This yields an additional nine features, leading to a total of \(p=120+9=129\) features per composite signal.  
A GGD distribution was fit on each of the composite signals to calculate GGD's \(q=3\) parameters, shape parameters \(a\) and \(b\), and a scale parameter \(s\). Each sample in \(D\) has an input vector of \(p\) descriptive variables \(\mathbf{x}^{(r)}=(x_{1}^{(r)},...,x_{i}^{(r)},...,x_{p}^{(r)})\) and an  
Since each component waveform is generated in the SDR's digital domain, we do not add any noise prior to summation. This assumption reflects a key benefit of SDR-based design: the ability to combine signals noise-free before analog impairments.
## 4 The LAGOON NLG Results and Discussion
### Multiple 2D Cross-Sections Acoustic Results  
#### 4.1.1 The Upper Part of the LAGOON NLG  
Figure 7 illustrates the effect of the SCL on the predicted far-field noise of Sec.1 and Sec.2. Three different values are assumed for SCL (SCL1 = \(5.0D_{\text{cl},2}\) and SCL2 = \(2.5D_{\text{cl},2}\)) as suggested in previous studies of flow past a circular cylinder [21; 22], whereas SCL3 is estimated using Equation (2) for each C-S. Note that, the vortex shedding frequency, \(f\), is calculated for all resulting C-Ss, as shown in Table 3. [PERSON] [30] demonstrated that for a wide spectrum of Re (\(2.0\times 10^{2}-1.0\times 10^{5}\)), the vortex shedding frequency occurs at St \(\approx 0.2\). Therefore, the St is assumed as 0.2 for all the 2D C-Ss.  
Figure 7: Effect of different SCL values on the acoustic prediction due to the upper part of the LAGOON NLG at: (**a**) Sec.1; and (**b**) Sec.2.  
Overall, the results in Figure 7 show that the SCL affects the amplitude of the predicted noise spectrum. The PSD noise levels of different values of the SCL are within constant offset from each other. Figure 7a shows that the peak of the first harmonic of Sec.1 occurs at low frequency, \(f_{1}\approx 250\) Hz, and exhibits a significant broadening. For Sec.2, the fundamental frequency is shifted to a higher frequency, \(f_{1}=350\) Hz (Figure 7 These tones could be attributed to the flow impingement and circulation at the frontal portion of the inner cavities and the axle, as illustrated by the C-S of Sec.4 in Figure 9b.  
Upon comparing Figures 9c and 9a, it can be noted that Sec.5 exhibits different peak frequencies compared to Sec.3 although they have similar C-Ss. For Sec.5, three peaks at \(f_{1}\approx 245,f_{2}\approx 559\), and \(f_{3}\approx 830\) Hz are visible in the low frequency range. However, in the medium frequency and high frequency ranges, there is only one peak detected (at each range) at \(f_{4}\approx 1.2\) kHz and \(f_{6}\approx 1.7\) kHz, respectively, as shown in Figure 9c. Thus, this study highlights important insights about the noise contribution of each C-S to the overall noise of the LG. Based on these preliminary results, the SCL should be assumed as small as possible (i.e., SCL \(<<\) L\({}_{a}\)) to ensure that the flow coherence level is met. Therefore, the spectrum result of each C-S based on the SCL3 value is selected to obtain the overall noise of the LG.