esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Final Results  
The causal path contribution degree in Table 2 is converted to percentages (the total impact of all nodes in Table 2 on the target node is 100%), and the top 15 influencing factors that have a greater impact on the target variable are screened out and sorted according to the ideas in Section 3.4. The results are shown in Figure 6. In the figure, node No. 372 is ranked first, and its causal pathway contribution degree is 19.97%. The second node is No. 118, whose causal pathway contribution degree is 18.60%. By analogy, the 15 th ranked node is No. 72, whose causal pathway contribution degree is 1.37%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Node**} & **Number of Local** & **Number of Global** & **Average Causal** & **Causal Pathway** \\  & **Causal Pathways** & **Causal Pathways** & **Pathway Length** & **Contribution Degree** \\ \hline
2 & 1 & 1 & 2 & 0.33 \\ \hline
4 & 2 & 10 & 4 & 2 \\ \hline
6 & 2 & 12 & 3 & 3 \\ \hline
22 & 2 & 2 & 2.5 & 0.57 \\ \hline
27 & 6 & 18  
In addition, if only the number of causal pathways to the target variable is considered, the selected key influencing factors of the target variable are shown in Figure 7. At this time, the first node is No. 118, and there are 126 global causal pathways through this node to the target variable. The second node is No. 95, and there are 84 global causal pathways to target variables through this node. By analogy, the fifteenth is node 4, and there are 10 global causal pathways through this node to the target variable.  
Figure 6: Top fifteen key influencing factors of the target variable.  
Figure 7: Key influencing factors of the target variable (the number of causal pathways considered only).  
In order to verify the influence of the values of \(\alpha\) and \(\beta\) on the experimental results, we perform calculations when \(\alpha=0.5\), \(\beta=1\) and \(\alpha=1\), \(\beta=0.5\). It was found that under the different values of A and B, the identified key influencing factors basically did not change, but their order was slightly adjusted.
# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
## 2 Related Work
SRCM is designed to balance randomness with structured mobility. As shown in Figure 1f, it enables nodes to semi-randomly select start and end points along the arc of a circular trajectory within a fixed radius and to move accordingly [34]. SRCM enables the model to preserve the diversity of random movement while ensuring more uniform coverage across the entire area. Unlike simple random models, SRCM can be effectively applied to purpose-driven experiments such as scanning. Moreover, compared to path-based models that enforce repetitive trajectories, it offers the advantage of generating a wider variety of experimental scenarios. However, while SRCM is effective for scanning-oriented experiments, it has limitations in FANET scenarios requiring relay path alignment, making it unsuitable for relay-based communication experiments.
## 4 Descriptions of Different Topologies of WSNs
### Chain-Based Topology
#### Advantage  
* Chain-oriented topology saves more energy than cluster-based topologies do. For example, PEGASIS saves 50% more energy compared with LEACH citepchap 4-16.
* Energy distribution in a chain-oriented topology is even
* Because of better energy conservation, chain-oriented topologies offer longer lifetime for WSNs.
# Protection Circuit Design for Ultrasound Transducers
## 3 Design Topology and Analysis of the Developed Protection Circuits
There are similar approaches based on gate-source connected MOSFET devices. Gate-source connected MOSFETs with additional gate-source shunt PMOS. The measured IL was 64 dB at 10 MHz. The switches controlled by the PMOS are used to shut down the gate-source connected MOSFETs during the transmitting and receiving times, reducing the static power consumption to 12.1 \(\upmu\)W [52]. In addition, the gate-source connected MOSFETs were implemented with a shunt MOSFET to improve the isolation between the transmitting and receiving times for a portable ultrasonic system with a piezoelectric micromachined ultrasonic transducer (PMUT). The simulated IL of the device was 62 dB with a capacitive load of 20 pF [53]. Figure 17 shows a protection circuit that uses a dual-diode limiter to provide a higher discharge pulse for ultrasound system applications. The protection circuit, which is based on a diode limiter, is very simple. Therefore, it is still widely used for laboratory equipment and multichannel ultrasound imaging systems, and different configurations have been developed to improve the suppression capability. Compared to a single-diode limiter, the dual-diode limiter showed higher voltage suppression and faster recovery. When 50 V\({}_{\text{P-P}}\) and 1 MHz pulses were transmitted to the protection circuit, the measured IL, THD, recovering time, and discharge pile were \(-\)3.87 dB, 0.29%, 6.1 \(\upmu\)S, and 1.15 V, respectively. The resistance values (R\({}_{1}\) and R\({}_{2}\)) were adjusted for impedance matching using a receiver. Compared to the measured discharged pulse and the recovery time of the single diode limiter, the values of the dual diode limiter were improved by 28.69% and 1.61%, respectively. Figure 3:3 Schematic design of the protection circuit based on the complementary MOSFET and gate-source connected MOSFET devices. Adapted with permission from Hsia [51]. Copyright 2015, IEEE. [PERSON] et al. have developed a protection circuit that uses a return-to-zero (RTZ) circuit and switch, as shown in Figure 3:4 This design was developed to save chip space for multichannel ultrasound transducer applications and to improve impedance-matching conditions without any external DC bias voltages [55]. In the designed protection circuit, the n-channel double-diffused MOS (DMOS) transistors (N\({}_{2}\) and N\({}_{3}\)), a p-channel DMOS transistor (P\({}_{2}\)), and a CMOS transistor (N\({}_{0}\)) were used. In high-voltage fabrication, the size of the CMOS transistor (N\({}_{0}\)) is significantly smaller than the size of the DMOS transistors (N\({}_{2}\), N\({}_{3}\), and P\({}_{2}\)), so that the chip die area can be reduced. The obtained discharged pulse voltage and recovery time were 4.5 V and 77.23 ns, respectively.
## 8 The Defense Model
#### ARDS--The Proposed IDS/IPS Model  
The operation model of the proposed ransomware protection system is shown in Figure 12. An essential feature of the system design is that it performs multi-step filtering, classification, and analysis in real time. Incoming requests from the Internet go through a pre-qualification, as a result of which they are placed in event sets. These event sets are classified based on the inbound port, source IP address, and network delimiter device (usually router and/or firewall) rules based on the probability of the threat.  
The system hosts the MITM Agent, which is responsible for protecting the shared resources available on the network. The first step is a packet scan on the agent, followed by a real-time behavior scan and request/event validation. The reliability decision is then made. Events and requests classified as trusted by the agent are forwarded to the server corresponding to the original request. Requests that are marked as untrusted are further analyzed using the additional parser components of the agent. As a result of the analysis, the rules system of the autonomous agent is updated.  
Protecting backup servers, file and database servers is a key task for the proposed ARDS. In the server layer, the data protection agent represents another line of defense using a ZFS file system, which is able to recover data from time-stamped snapshots.  
Figure 12: Operation model of autonomous ARDS.  
The next element of the protection strategy of the proposed ARDS system is the inspection of the outbound data flow from the internal network to the Internet. With the help of the Firewall agent, the C2 activity can be detected based on the analysis of the outgoing network traffic.  
In this way, ARDS is a complex system that can implement autonomous defense in several steps, at special intervention and access points, in the application and service layers, and it complements other protection technologies already available in the network.  
In addition to the formal description based on the planned ARDS model the validation of the operation takes place in the next phase of the research, followed by the physical implementation and testing in a live environment. The designed and modeled system is able to work with already available software solutions, hardware solutions, and as a standalone, autonomous device, physically installed on the internal network, can automatically provide protection for servers storing and providing critical business data.
## 2 Materials and Methods
### Digital Versus Conventional Workflow for Maxillofacial Prosthesis Design and Manufacturing  
#### 2.2.1 Conventional Workflow  
A conventional workflow for maxillofacial prosthesis production includes the following steps (Figure 2). An accurate impression of the area requiring prosthesis is achieved by selecting a suitable impression material (hydrocolloid alignates or elastic silicone polymers are the most-used materials) according to the type of defect, size and presence or absence of any undercuts in the respective area, with a custom ray often being required. Some anatomic undercuts are blocked so as to remove the impression without damaging the surrounding tissue. After pouring the impression, the gypsum cast is obtained, and a wax model of the anatomic part to be replaced is fabricated. For reproducing the natural morphological details of the defect, the wax is carved, followed by a try-in of the maxillofacial prosthesis wax-up with the corresponding adjustments for marginal fit and esthetic appearance. The molds are produced using the final retouched wax-up by applying the lost wax method, where gypsum  
Figure 1: (**A**) Classification of the maxillofacial defects in extraoral, complex and intraoral cases (including extraoral complex and intraoral prostheses). (**B**) The intraoral maxilla and midface defects, classified according to [PERSON] and [PERSON] classification in six classes [8]: vertical classification, with a maxillectomy not causing an oronasal fistula (**I**); not involving the orbit (**II**); involving the orbital adnexae with orbital retention (**III**); with orbital enucleation or exenteration (**IV**); with an orbitomaxillary defect (**V**); and with a nasomaxillary defect (**VI**), and for horizontal classification, only a palatal defect not involving the dental alveolus (**a**); less than or equal to a half unilateral (**b**); less than or equal to a half bilateral or transverse anterior (**c**); a greater than half maxillectomy (**d**). (**C**) The intraoral mandibular defects, classified according to Cantor and Curtis classification in six classes [10;11]: radical alveoletctomy with preservation of mandibular continuity (**I**); lateral resection of the mandible distal to the cusp area (**II**); lateral resection of the mandible to the midline (**III**); lateral bone graft and surgical reconstruction (**IV**); anterior bone graft and surgical reconstruction (**V**); and anterior mandibular resection without surgical reconstruction (**VI**).  
is poured over the wax model and the wax is then simply removed with hot water [12]. The final prosthesis is obtained using the adequate material. For intraoral and complex defects including a part or the complete dental arch, an impression of the opposite arch and the mounting in a semi-adjustable articulator is also necessary before the try-in. Complex defects, including intraoral and extraoral missing anatomical parts, require the use of materials with different characteristics, such as acrylic resins or silicones.
## 3 Results
### Study's Methodology Quality  
All 34 eligible studies were scored using the MCMS scale. An average score of 69.5 (range: 58-82) was obtained, with 33 studies (97%) meeting the 60% criteria for methodological quality. The one excluded study failed to describe participant inclusion criteria, report power, alpha error, and had a small sample size of 8. Out of the 33 studies that were included in this review, only two of them performed a sample size calculation [29; 40].
## 5 Discussion
An evaluation of model performance reveals that the hybrid ARIMA-LSTM model consistently achieves the lowest error rates across most geographic and age group categories, making it the most suitable choice for cancer incidence forecasting. This model outperforms the ARIMA, LSTM, and Prophet models in key accuracy metrics, including MAE, MSE, RMSE, MAPE, and R2.
# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 3 Materials and Methods
### Defect Classification  
The classification of bearing surface defects is completed by the classifier, and an error back-propagation training algorithm is used to complete the non-linear mapping from the input signal to the output mode. The steps are as follows:  
* Choose an initial value of the weight coefficient.
* Input a sample \(X=(x_{0},x_{1},\ldots,x_{N-1})^{T}\) and its expected output \(d=(d_{0},d_{1},\ldots,d_{M-1})^{T}\).
* Calculate the actual output \(y=(y_{0},y_{1},\ldots,y_{M-1})^{T}\).
* Backward-adjust the weights from the output layer forward, layer by layer, using the adjustment formula [36]: \[\omega_{ij}(t+1)=\omega_{ij}(t)+\eta\delta_{j}x_{i}^{\prime},\] (14)where \(\omega_{ij}\) is the weight of the connection from node \(i\) in the hidden layer or input layer to node \(j\) at time \(t\), \(x_{i}^{\prime}\) can either be the output of node \(i\) or the input of node \(i\), \(\eta\) is the gain term, and \(\delta_{j}\) is the error term of node \(j\). If node \(j\) is an output layer node, then:  
\[\delta_{j}=y_{j}\big{(}1-y_{j}\big{)}\big{(}d_{j}-y_{j}\big{)}, \tag{15}\]  
where \(d_{j}\) is the expected output of node \(j\), and \(y_{j}\) is the actual output of node \(j\). If node \(j\) is on an intermediate hidden layer, then:  
\[\delta_{j}=x_{j}^{\prime}\Big{(}1-x_{j}^{\prime}\Big{)}\sum_{k}\delta_{k} \omega_{jk}, \tag{16}\]  
Here, \(k\) represents all the nodes on the layer before node \(j\). To improve the convergence, the following formula may be used for weight update \(\Delta\omega_{ij}\) correction:  
\[\omega_{ij}(t+1)=\omega_{ij}(t)+\eta\delta_{j}x_{i}^{\prime}+a\big{(}\omega_ {ij}(t)-\omega_{ij}(t-1)\big{)}, \tag{17}\]  
Here, 0 < _a <_ 1; that is, a subsequent adjustment of the weight value takes into account the last update of weight value.  
Step 5: Go back to Step 3 and repeat the execution until the error requirements are met.
## 1 Introduction
Several studies demonstrating the photocatalytic anti-bacterial efficiency were also reported (Table S1) [33; 34; 35; 36; 37; 38; 39; 40]. However, in most of the cases, a high concentration of Cu was used to display the antimicrobial activity and did not study the effect of calcination temperature. They either calcined at a single temperature or kept the temperature below that of the anatase-rutile phase transition. So, this work provides new insights into higher temperature anatase phase stability of Cu-doped TiO\({}_{2}\). Additionally, Cu dopants were deposited on morphologically tuned TiO\({}_{2}\) nanotubes and nanofibers. This involves a series of cumbersome synthesis techniques. In the present study, we demonstrate a facile one-step sol-gel technique to prepare 0.5 mol % of Cu-TiO\({}_{2}\). The as-prepared samples were calcined at different temperatures ranging from 500 to 700 \({}^{\circ}\)C. Density Functional Theory (DFT) simulations were also performed to understand any bandgap alteration of TiO\({}_{2}\) lattice and the formation of oxygen vacancies after Cu doping. The anatase phase of TiO\({}_{2}\) was maintained (55%) up to 650 \({}^{\circ}\)C. The sample calcined at 650 \({}^{\circ}\)C illustrated high visible light absorption, and Cu doping resulted in lowering of TiO\({}_{2}\) band gap from 3.2 eV (for anatase TiO\({}_{2}\)) to 2.8 eV. Cu-TiO\({}_{2}\) was further assessed for the photocatalytic disinfection efficiency towards _Escherichia coli_ (Gram-negative) and _Staphylococcus aureus_ (Gram-positive) bacterial strains. A 5-Log reduction by the Cu-TiO\({}_{2}\) was attained at a very short time span of 30 min.
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 4 Experimental Results
### Performance Evaluation
#### 4.3.1 Experiments with the Pavia University Data Set  
In this section, the experimental results on the Pavia University data set were reported firstly, where the number of training samples per class was set to 0.1%, while the others were used for testing. Table 1 lists the average quantification results for each class. Table 1 shows that RPNet achieves the lowest accuracy. The reason for this may be that it is difficult to choose appropriate random patches when the training sample set is small. In addition, we can also find that CEGCN achieves low accuracy. This may be because the algorithm  
Figure 6: Effects of different \(L\), \(S\) and \(K\) on the OA, AA, and Kappa on KSC dataset. (**a**) Setting \(S\) and \(K\) to \(0.8L^{2}\), the influence of the change of \(L\) on OA, AA, Kappa; (**b**) Setting \(L\) to \(9\), \(K\) equals \(S\), the effect of \(S\) on OA, AA, Kappa; (**c**) After the values of \(L\) and \(S\) were determined, the effect of \(K\) change on OA, AA, and Kappa was determined.  
based on deep learning needs a large number of training samples, and 0.1% of the training samples are not enough. Thus, it does not provide satisfactory results. It is easy to find that the proposed SPCM obtains the best performance. Compared with the advanced method LCEM, the improved SPCM is about 2% higher in OA, AA, and Kappa. Both LCEM and SPCM can extract the spectral correlation features, but the performance of SPCM method is higher than that of LCEM. This is because SPCM adopts the spatial perception technique, which can better reflect the nonlinear relationship between spectral bands in HSI compared with the LCEM method. Additionally, SPCM emphasizes intraclass spectral correlation more than LCEM. Consequently, the local correntropy matrices obtained by the spatial perception method can extract more discriminative features, which facilitates the final classification.  
In addition to the objective experimental results comparison, we visualize the results to verify the effectiveness of the method. The full classification maps of different methods are shown in Figure 7, and they are generated by one of the random experiments. As we can observe in Figure 7, the proposed method has achieved the best performance using only 0.1% samples for training, and some methods produced maps with more noise and lost some structural details. For example, LCMR and SCMK generate salt-and-pepper noise with a small training set. For PRNet, its classification map loses many edge structures. CEGCN produces an over-smooth classification map, which may be caused by its larger filter size. From Figure 7, it can be seen that the borders between classes are generally well defined and without salt and pepper noise.  
Figure 7: Classification maps of different methods on the Pavia University data set. (**a**) False Colour. Image; (**b**) Ground Truth; (**c**) Training set; (**d**) Test set; (**e**) LCMR; (**f**) SCMK; (**g**) CEGCN; (**h**) PRNet; (**i**) LCEM; (**j**) SPCM.
## 4 Results
### Input Data Availability and Influence on ESTARFM Predictions  
To analyze the influence of the temporal distance (number of days between Landsat input observations and prediction dates) on the error level, a Landsat scene from the middle of the year (DOY 143, 195/52) was predicted not only with its nearest shoulder scenes, but also with all other, more distant shoulder combinations. The MAE between these predictions and the observed Landsat NDVI have been calculated and visualized against the total temporal distance of the input shoulder combinations (Figure 12). It is evident that the error level increases with increasing temporal distance, from less than 0.02 MAE to over 0.05 MAE. However, it can also be observed that the temporal distance is not the only factor determining the MAE. The symbols of Figure 12 highlight that the right shoulder (_i.e._, MODIS-Landsat combination after the prediction date) is significantly more important for the quality of prediction than the left shoulder (MODIS-Landsat combination before the prediction date). In the presented case, the left shoulder dates are all from the dry season at the beginning of 2014 (Figure 2), where changes in phenology between the scenes are minor, while the right shoulders are all  
Figure 11: Median deviation of NDVI (blue line) and 1 st to 3 rd quartile (light-blue area) between ESTARFM predictions for the overlapping areas of path 195 and path 194 for the year 2014.  
Figure 10: Absolute difference images created between observed and ESTARFM-predicted NDVI for a focus area of tile 194/52 (location indicated in Figure 1). The lower three images show the absolute difference for three selected dates of 2014 with corresponding Landsat false color composites (RGB: 5-4-3) in the upper three images.  
from different phenological stages: DOY 159 is from approximately the same phenological stage as the target scene, DOY 319 from the period of browning after the vegetation peak, DOY 351 includes widespread burned areas, and DOY 18 (2015) is from the dry season of the following year. Hence, every time the right shoulder date (_i.e._, the phenological stage) is changed, a pronounced jump in MAE is observed. This analysis emphasizes the importance of using a shoulder scene from the same phenological stage if possible.
## 3 Results
### Effect of pH and Adsorbent Dose
The decrease in lead adsorption efficiency at higher pH levels can be attributed to the formation of soluble hydroxylated species, such as Pb(OH)\({}^{+}\) and Pb(OH)\({}_{2}\), which remain in the solution rather than binding to the adsorbent surface [74]. The paired \(t\)-test analysis indicated a significant difference in Pb removal rates at pH 3, 5, and 7 (\(p\) = 1.04 \(\times\) 10\({}^{-5}\) to 0.02 \(<\) 0.05). The extent of Pb removal was found to be significantly dependent on the adsorbent dose, with all pH levels displaying a positive correlation in terms of performance at higher doses. This enhancement is due to the higher dosages resulting in a greater surface area and more active adsorption sites. However, for adsorbent dosages in the range of 40-50 g/L, the removal efficiency becomes relatively constant, indicating the saturation of active sites  
Figure 12: Efficiency (%) of lead removal by DAC at different pH levels (3, 5 and 7). Adsorbents are introduced at 5, 10, 25, and 50 g/L. on the DAC surface. This plateau is consistent with the Langmuir isotherm formulation, which explains the limited adsorption capacity of adsorbents. Therefore, optimizing the adsorbent dose is crucial to maximizing the efficiency of the adsorbent [75].
## 3 Channel Estimation
### Finding \(\mathbf{H_{d}}\) and \(\mathbf{V}\)  
After estimating the channel noise, we can estimate the channel parameters \(\mathbf{H_{d}}\) and \(\mathbf{V}\). Switching off the IRS, from (6), we obtain:  
\[\mathfrak{F}(z)=\mathbf{H_{d}}\mathfrak{F}(x)+\mathbf{\mathcal{N}}_{\mathbb{C}} \tag{9}\]  
\[\mathbf{H_{d}}=(\mathfrak{F}(z)-\mathbf{\mathcal{N}}_{\mathbb{C}})\mathfrak{F}(x)^{-1} \tag{10}\]  
With the knowledge of \(\mathbf{H_{d}}\), we can proceed to estimating \(\mathbf{V}\). Let \(\mathbf{\phi}\) be the matrix \([\theta_{1}\theta_{2}...\theta_{4096}]\), \(\mathbf{Z}\) be the matrix \([\mathfrak{F}(z)_{1}\mathfrak{F}(z)_{2}...\mathfrak{F}(z)_{4096}]\) and \(\mathbf{N}\) be the matrix \([\mathcal{N}_{\mathbb{C}1}\mathcal{N}_{\mathbb{C}2}...\mathcal{N}_{\mathbb{ C}4096}]\). From (6), we obtain:  
\[\mathbf{Z}=(\mathbf{H_{d}}+\mathbf{V\phi})\mathfrak{F}(x)+\mathbf{N} \tag{11}\]  
\[\mathbf{H_{d}}+\mathbf{V\phi}=(\mathbf{Z}-\mathbf{N})\mathfrak{F}(x)^{-1}=\mathbf{\Lambda} \tag{12}\]  
\[\mathbf{V}=(\mathbf{\Lambda}-\mathbf{H_{d}})\mathbf{\phi}^{-1} \tag{13}\]