esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real Model of a Micro-Grid to Improve Network Stability
## 3 Description of the DC Micro-Grid
### Energy Sources
#### 3.1.2 Fuel Cell  
The research laboratory is also equipped with a fuel cell (FC) [32], an H-200 FC Stack (Singapore) by Horizon Fuel Cell Technologies (Figure 2). The H-200 exhibits the following parameters: a rated power of 200 W; performance of 28.8 V @ 7.2 A and 48 cells.
# A Reconfigurable Formation and Disjoint Hierarchical Routing for Reshapeable Bluetooth Networks
## 6 Scatternet Performance
### Energy Efficiency  
In the hierarchical rechargeable WSNs [29], the mesh subnet topology simulated was uniformly distributed by a two dimensional 7 by 7-square lattice with 49 masters as the upper tier and their affiliated slaves as the lower tier for the energy efficiency simulation. The root node as the sink node was located at the center of the sensing field, and all other nodes served as sensors. Each sensor constantly reports its sensor data to the sink, and the traffic pattern is periodical. The initial energy of nodes is set to 0.6 J, the energy consumption per bit of the transmitting circuit is 100 nJ/bit, the packet length is 2000 bits and the simulation duration is calculated by rounds. In addition, the energy harvesting rate was close to the power consumption rate for packet transmission. Three conditions were compared, including the conventional shortest path, two disjoint paths routing, and the rechargeable two disjoint paths routing algorithms.  
In terms of energy efficiency, the network lifetime is defined as the time taken for the first node to deplete its battery capacity in the mesh-subnet. From the simulation, the first dead node is located around the sink node, and three simulation cases were generated for this hot-spot node. In Figure 14, the conventional shortest path case with single path routing depletes its battery capacity first. The two disjoint paths routing case improves the energy efficiency in terms of network lifetime by about 43% over that of the conventional shortest path case. With energy harvesting, the two paths routing algorithm achieves the best network lifetime performance, since the rechargeable batteries effectively improve the power consumption for packet transmission.
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 4 Industrial Applications of Cavitating Jets
### Well Drilling Test Using Cavitating Jets  
[PERSON] et al. [10] investigate the performance of well drilling for the construction of salt caverns using cavitating jets; drilling performance was compared to a conventional mining method. Their field test indicates that rapid solution mining technology with self-resonating cavitating jets can significantly speed up the construction at the pocket stage of cavern development.  
The experimental results showed that the pressure amplitude peak occurs at a distance of 5-13 times the nozzle outlet diameter. They also found that under the same operating conditions, the cutting potential of self-resonating cavitating jets is twice that of conventional jets.  
The field test showed that the use of self-resonating cavitating jets would increase the salt production rate at least by two-fold as compared to the conventional method.  
Figure 18: CAVIJET nozzle vs. Smith nozzle: (**a**) Nozzle geometry, (**b**) Rock-cutting comparison [26].
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Conventional Machine Learning
#### 2.1.2 Random Forest (RF)  
Random forests (RF) were used in the article [45] along with 5 basic classification models. In [36] RF was used among the six basic models. It is an ensemble learning method that combines multiple DTs to improve classification accuracy and robustness. The equation of an RF can be represented [46] as:  
\[I_{G}(n)=1-\sum_{i=1}^{J}(p_{i})^{2} \tag{2}\]  
where the node \(n\) is equal to 1 minus the total of the fraction of cases in each class \(pi\) squared over all classes \(J\). The values of the Gini impurity index for the two split nodes are lower than the value for the parent node for a particular node split. The equivalent Gini significance value for each variable can be obtained by adding the Gini impurity decreases for three variables in a dataset across all trees in an RF model. This value can then be utilized for feature selection.  
Each tree in the forest is trained on a random subset of the data, and a final prediction is made by averaging the predictions of all the trees. Although RF is more robust than individual DT, it is still susceptible to overfitting, especially with high-dimensional data. The paper [47] proposes an ML model as a first-level decision model using RF, BalancedRF, Bagging, and Extra Tree with smoothed clustering decisions followed by voting classifications. It explains the results using SHAP and LIME interpretations. Step-by-step data processing involves balancing the data using SMOTEENN, then transforming the data using Quantile Transformer, PCA dimensionality reduction technique for six features, and a Meta ML model to obtain 97.6% performance, 95.8% accuracy, 97.9% recall, and 96.8% F1 score. RF and DT algorithms [24] were applied to brain MRI images acquired from normal controls (NC) and AD subjects. The KNIME analytics platform was used to calculate the DT, and the R project was used for the RF.  
Table 2 provides a detailed comparison of the different methods used in studies of AD detection. Various models, such as SVM, CNN, and random forest, have been applied to datasets such as ADNI [48] and OASIS [49] using MRI, DTI, and fundus images. Model-agnostic methods like SHAP and LIME provide general explanations, while model-specific methods like Grad-CAM and Score-CAM provide detailed insights. The table highlights that combining different models and explanation methods leads to different accuracies, with some studies achieving high accuracies (more than 90%), depending on the approach used.
# Parameterized Algorithms in Bioinformatics: An Overview
## 2 Genome Comparison and Completion
### Genome Completion
#### 2.3.2 Breakpoint Median  
**Problem Description.** In the Breakpoint Median problem, the goal is to build a most-likely common ancestor from three genomes, which can in turn be used to recreate all ancestral genomes in a phylogenetic tree [39].  
\(\left[\begin{array}{l}\text{\bf{Breakpoint Median (BM)}}\\ \text{\bf{Input:} genomes $G_{1},G_{2},G_{3}$ (signed permutations), some $d\in\mathbb{N}$}\\ \text{\bf{Question:} Is there a genome $G$ with $\sum_{i=1}^{3}d_{\text{bp}}(G,G_{i})\leq d$?}\end{array}\right.\)  
**Results.** The problem can be seen as a special case of Traveling Salesman[39] and can be solved in \(O(2.15^{d}n)\) time.
# Assessment of Spatial Variability in Ground Models Using Mini-Cone Penetration Testing
## 2 Theoretical Background
### Correlation Length (CL)  
To quantitatively evaluate spatial variability, [PERSON] and [PERSON] [24] proposed the concept of _CL. CL_ is a measure that indicates how far the data from a specific point can influence other points. The greater the correlation, the higher the correlation coefficient between adjacent points, and the longer the _CL_ becomes. This implies that certain points can be correlated even if they are far apart from each other. Conversely, if the correlation is low, the _CL_ becomes shorter, indicating that certain points are only correlated when they are in close proximity to each other [25]. The calculation of this _CL_ involves determining the correlation coefficient (\(\rho\)) of cone penetration resistance between two points separated by a separation distance \(\tau\), as shown in the following Equation (2):  
\[\rho(\tau)=\frac{\sum\limits_{h=0}^{\tau_{max}-\tau-1}{(q_{c,h}-\overline{q_{c }})(q_{c,h+\tau}-\overline{q_{c}})}}{\sum\limits_{h=0}^{\tau_{max}-1}{(q_{c,h}- \overline{q_{c}})^{2}}} \tag{2}\]  
where  
\(q_{c,h}\), \(q_{c,h+\tau}\)= a pair of \(q_{c}\) values at two points with a separation distance \(\tau\).  
_CL_ (\(\theta\)) is defined as the area under the function based on the relationship between the correlation coefficient \(\rho\) and the separation distance \(\tau\), and it is calculated according to [21] as follows:  
\[\theta=\int_{-\infty}^{\infty}\rho(\tau)d\tau=2\int_{0}^{\infty}\rho(\tau)d\tau \tag{3}\]  
The correlation coefficient \(\rho\) of ground properties converges to 0 as the separation distance increases, and this can be represented through various regression models. The commonly used regression models include the Markovian model (Equation (4)) and the Gaussian model (Equation (5)), where in each model, the _CL_ (\(\theta\)) is defined by a specific value of the correlation coefficient \(\rho\). Although the value of \(\rho\) at \(\theta\) may differ according to each model, generally, in all models, \(\rho\) takes a value less than 0.14. Equations (4) and (5) indicate that the regression coefficient of the regression models is \(\theta\), which can be derived through regression analysis such as the least squares method.  
\[\rho(\tau)=\exp\left\{\frac{-2|\tau|}{\theta}\right\} \tag{4}\]  
\[\rho(\tau)=\exp\left\{-\pi\left(\frac{|\tau|}{\theta}\right)^{2}\right\} \tag{5}\]
# Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers
## 1 Introduction
After generating a radiance map, the proposed method is used to achieve the Stevens effect in a color appearance model [6] to enhance local contrast and details. Tone scaling is adopted for color compensation in the proposed method. Thus, with regard to color compensation, the proposed method calculates the brightness gap between the input VIS image and the generated radiance map. Consequently, compared with conventional methods, the proposed method results in less damage to the edge information in the NIR images improved local contrast and details, and better preservation of the color chrominance of the input VIS image. The proposed method is to preserve the detailed information by a higher value of PCA and enhance local contrast and details according to the visual properties of the Stevens effect. Thus, the proposed method preserves more detailed information and enhances the details effectively.
## 6 Stability Evaluation of Slope under Reinforcement Scheme
### Analysis of Monitoring Data  
Figure 17 shows the variation in the horizontal deformation of some of the typical surface monitoring points over time. Three slope deformation stages can be observed in Figure 17. In the initial excavation stage, the slope deformed at a low rate because of the small volume of excavation. After 100 days, the slope was excavated rapidly, causing the deformation rate at the surface monitoring points to increase dramatically. When excavation finished, the slope deformation rate slowed down and showed a tendency towards a constant value. A similar deformation trend was also observed for a typical multi-point of M3-DBL, as shown in Figure 18.  
Figure 16: Layout of various monitoring points on the Dabenliu quarry slope.  
Figure 17: Deformation evolution of typical surface monitoring points (HD4-DBL, HD7-DBL-HD12-DBL, and HD16-DBL) with time.  
Figure 19 shows the anchoring force and the variation in loss ratio (the ratio of real-time force to the locked force) of SF5_DBL during the excavation process. Initially, the rapid increase in anchoring force is associated with the lack of deep support treatment regarding the excavated slope. When the reinforcement scheme was completed above 1985 m elevation, the anchoring force remained stable. Widespread rainfall in the summer of 2011 led to an obvious increase in anchoring force again. Later, after the completion of excavation and the reinforcement measurements, the anchoring force showed a tendency towards a convergence state, and its anchoring loss ratio was 5.2%, which is acceptable in engineering. Through combining the deformation results of surface deformation monitoring and shallow deformation monitoring, it was shown that the slope is in a stable condition under the implementation of the reinforcement design scheme.  
The DDA simulation results show that the excavated slope is stable under the action of gravity after the reinforcement scheme was implemented. This conclusion is consistent with that of field monitoring studies and the actual behavior of slope. Thus, the use of the DDA method for evaluating the stability of the slope proved to be reasonable.  
Figure 19: Evolution of anchoring force and loss ratio for dynamometer SF5_DBL.  
Figure 18: Deformation of evolution of extensometers M2-DBL.  
It should be noted that the monitoring results only reflect the deformation behavior of the slope under gravity loading, and the state of the slope under seismic loading is not known. The DDA method brings the advantage of providing a dynamic solution for discontinuous deformation; therefore, the stability of this slope was extensively evaluated via DDA simulation. The results described in Section 5 also indicate that the slope is stable under the action of a magnitude 7 earthquake.
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 2 OFDM Equivalent Signal Model  
### Principle of OFDM  
OFDM is a multicarrier data transmission, where a single stream of information is divided over a large number of subcarriers [21]. In this section, we explain the principle of OFDM from the perspective of continuous-time domain. Consider the time-limited complex exponential signals \(\{\exp(2\pi f_{k}t)\}_{k=0}^{N-1}\) which represent the different subcarriers at \(f_{k}=k/T_{sym}\) in the OFDM signal, where \(0\leq t\leq T_{sym}\). These signals are defined to be orthogonal if the integral of the products for their fundamental period is zero, that is,  
\[\frac{1}{T_{sym}}\int_{0}^{T_{sym}}\exp(j2\pi f_{k}t)\exp(-j2\pi f_{i}t)dt= \left\{\begin{array}{ll}1,&\mbox{$\forall$integer}k=i\\ 0,&\mbox{otherwise}\end{array}\right.. \tag{1}\]  
The OFDM transmitter maps the message bits into a sequence of PSK or quadrature amplitude modulation (QAM) symbols which are subsequently converted into \(N\) parallel streams. Each \(N\) symbol from serial-to-parallel (S/P) conversion is carried out by a different subcarrier. Let \(X_{l}[k]\) represent the \(l\)th transmit symbol at the \(k\)th subcarrier, \(l=0,1,2,\cdots,\infty\), \(k=0,1,2,\cdots N-1\). Due to the S/P conversion, the duration of transmission time for \(N\) symbols is extended to \(NT_{s}\), which forms a single OFDM symbol with length of \(T_{sym}=NT_{s}\). Let \(\psi_{l,k}(t)\) represent the \(l\)th OFDM signal at the \(k\)th subcarrier, which is given as  
\[\psi_{l,k}(t)=\left\{\begin{array}{ll}\exp\bigl{[}j2\pi f_{k}\bigl{(}t-lT_{ sym}\bigr{)}\bigr{]},&0<t<T_{sym}\\ 0,&\mbox{elsewhere}\end{array}\right.. \tag{2}\]  
Then, the baseband OFDM signal in the continuous-time domain can be expressed as  
\[x_{l}(t)=\sum\limits_{l=0}^{\infty}\sum\limits_{k=0}^{N-1}X_{l}[k]\exp\bigl{[} j2\pi f_{k}\bigl{(}t-lT_{sym}\bigr{)}\bigr{]}, \tag{3}\]  
where \(t=lT_{sym}+nT_{S}\), \(T_{s}=T_{sym}/N\), and \(f_{k}=k/T_{sym}\). Consider the received baseband OFDM symbol \(y_{l}(t)=\sum\limits_{k=0}^{N-1}X_{l}[k]\exp\bigl{[}j2\pi f_{k}\bigl{(}t-lT_{ sym}\bigr{)}\bigr{]},lT_{sym}<t<lT_{sym}+nT_{s}\), from which the original transmitted symbol \(X_{l}[k]\) can be reconstructed using the orthogonality among the subcarriers in Equation (1) as follows:  
\[\begin{array}{ll}Y_{l}[k]&=\frac{1}{T_{sym}}\int_{-\infty}^{\infty}y_{l}(t) \exp\bigl{[}-j2\pi f_{k}\bigl{(}t-lT_{sym}\bigr{)}\bigr{]}dt\\ &=\sum\limits_{l=0}^{N-1}X_{l}[i]\Bigl{\{}\frac{1}{T_{sym}}\int_{0}^{T_{sym}} \exp\bigl{[}j2\pi(f_{i}-f_{k})\bigl{(}t-lT_{sym}\bigr{)}\bigr{]}dt\Bigr{\}}\\ &=X_{l}[k]\end{array} \tag{4}\]
## 3 Key Technologies
### Real-Time Data Collection and Driving  
#### 3.2.1 Data Collection and Processing  
Twin data are the foundation of the online monitoring system for the magnetic levitation knitting needle. Twin data include attribute data of the physical entity devices and virtual models of the magnetic levitation knitting needle system (physical properties of the knitting needle system), drive data, status process data (environmental data and operational data), and user information data [18]. The data connection and interaction flow of the digital twin model of the magnetic levitation knitting needle are shown in the diagram.  
Using sensor technology and image recognition perception technology, data from physical entities, user information, and virtual models are collected, extended, integrated, and transformed. These data are classified into component-level, unit-level, and system-level to form the data required by users. Unity3D communicates using the OPC-UA protocol through C# drive scripts for the drive components. In this setup, Unity3D acts as the server and the STC80C51 chip acts as the client. Real-time data are transmitted to the memory of the STC80C51 chip through communication ports and are stored in the OPC-UA address space.  
The OPC-UA communication protocol is a platform-independent method for communication between devices. With the help of the OPC-UA protocol, standardized access to machines, equipment, and other systems in industrial environments is possible, enabling data exchange that is similar and independent of the manufacturer. A complete OPC-UA protocol setup requires an OPC-UA server and an OPC-UA client, as shown in Figures 5 and 6. Additionally, data are saved as SQL files, forming a database on the local server [19].  
Figure 4: Diagram of the elements of the virtual model of the magnetic levitation weaving needle system.  
Figure 5: OPC-UA client architecture.
## 2 Materials and Methods
### Experimental Setup for Bench Scale AGMD  
The bench scale AGMD module includes a thin plate of stainless steel inserted as a condensation plate, an effective membrane area of 0.19 m\({}^{2}\), with an 80% porosity and a 0.2 mm thickness. Figure 3 shows a photograph of the Elixir500 lab unit tested at KTH.  
Figure 3: Bench scale Elixir500 AGMD test unit at KTH Royal Institute of Technology, Energy Department laboratory.  
Figure 2: The Xzero AGMD unit used and the different streams considered.  
In Figure 4, the schematic diagram shows the main components and water streams (1-8) considered during analyses. The red lines show streams on the hot feed side of the module and the blue lines for the streams on the cooling side. Stream 8 refers to the permeate collected from the module. The bench scale MD unit consists of one MD unit module, a 20 L capacity heating, a recirculating tank, two pumps for each of the coolant, and feed streams. Two heaters immersed in the tank with a combined heating rate of 4.5 kW provide temperature control for the feed water.  
The obtained permeate was returned to the feed tank so that feed concentration would be constant over the course of experimentation. In order to measure the feed and cold-side temperatures, thermocouples were installed at the inlets and outlets of the module and were connected to a data logger (Keithley 2701 DMM with a 7706 card). Once-through tap water was used as a heat sink, which exchanges heat with an external water to vary coolant temperature. Rotameters with built-in control valves measured feed and coolant water flow rates. Product water was measured with a graduated cylinder and stopwatch, typically during a 30 min period of steady operation.  
The reference state and experimental conditions are given in Table 1. Data for the exergy analysis is taken for one cascade of MD modules containing two modules connected in series. Pressure drops across the MD modules are very low--less than 0.019 MPa. The concentration of total dissolved solutes in the feed water is also relatively low in comparison to seawater desalination applications (two orders of magnitude or less). As the three different cases have different feed and cooling temperatures, the exergy rates will be mainly affected by them.
## 3 Results
### Parametric Study of the Combination PV Peak Power/Thresholds Related to Electrical Architecture
#### 3.2.2 Analysis of the Combination of Peak Power/Thresholds  
The simulated use cases are a combination of these thresholds with three values of peak power: 312 W, 433 W and 205 W. The other parameters are 30% shading and without battery saturation.  
Figure 10 presents the simulation results for the cities of Malaga and Paris. For the calculation of the losses, the reference is the yellow bar including the system's efficiency and shading effects.  
Figure 9: Graphical display of power threshold effect, schematic application onto a typical sunny day.  
Figure 10 shows the following:  
* Lower peak power induces higher losses of PV energy and distance due to the thresholds. Therefore, even if the graphs present the distance per kW peak, the values are different in function of peak power;
* The losses due to the thresholds are very high. The minimum is 12% (Malaga, 433 W peak, threshold 13 W) and the maximum is 85% (Paris, 205 W peak, thresholds 20 W and 40 W);
* With the same shading level, the percentage of use of the PV depends on the location (1 st order), the thresholds (2 nd order) and the peak power (3 rd order);
* The mileage covered by the VIPV per unit of peak power in Malaga is up to 5569 km/kW and down to 2218 km/kW depending on the peak power and the threshold values;
* The distance covered by VIPV per unit of peak power in Paris is between 604 and 3243 km/kW depending on the peak power and threshold values.
## 2 Materials and Methods
### Hyperspectral Image Acquisition and Correction  
Half-hour preheating of the hyperspectral imaging system was essential prior to data acquisition to remove errors such as dark currents. Next, the SpecVIEW software was used (SpecView Ltd., Uckfield, UK) that comes with the system and executes a variety of tests, such as focusing. The exposure time of the system was finally determined as 0.09 s, the sampling interval was 3.37 nm, and the moving speed of the electronic control mobile platform was 0.55 cm/s. When the data were collected, 4 white shrimp samples for a group were placed on the electric mobile platform, and 15 hyperspectral images were collected every day; altogether 105 hyperspectral images were collected within 7 days. To avoid disturbance from outside light during data acquisition, a special glass baffle was used to shield the windows around the system. Besides, reflectance correction of the original hyperspectral images was required to reduce the influence of the experimental conditions [19], the correction formula is:  
\[R=\frac{O-H}{B-H} \tag{1}\]  
In Formula (1): O represents the original hyperspectral image. H represents the all-black calibration image (camera lens cap closed, all four lights off, the black image captured by the camera, the theoretical reflectance is 0%). B represents the all-white calibration image (camera lens cap open, all four lights on, the white image captured by the camera on the whiteboard, the theoretical reflectance is 99%). R represents the corrected hyperspectral image.