esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Neural Network Tests
#### 4.2.1 Ablation Study  
In neural networks, ablation is employed to evaluate the importance of the neurons or filters in the classification performance [55]. Therefore, the different non-linear layers, with a certain number of layers for each descriptor according to its dimensional space comprising the network filters, were modified. Tests were applied to determine the number of layers and the neurons necessary for better performance. These results are displayed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Descriptor** & **Characteristic Vector Dimension** & & **Structure** & **Accuracy** \\ \hline \multirow{3}{*}{LBP} & \multirow{3}{*}{256} & \(\bullet\) & Input layer & \multirow{3}{*}{98.3333} \\  & & \(\bullet\) & 1 hidden layer & \\  & & \(\bullet\) & Output layer & \\ \hline \multirow{3}{*}{OC\_LBP} & \multirow{3}{*}{32} & \(\bullet\) & Input layer & \multirow{3}{*}{81.6667} \\  & & \(\bullet\) & Output layer & \\ \hline \multirow{3}{*}{CS\_LTP} & \multirow{3}{*}{9} & \(\bullet\) & Input layer & \multirow{3}{*}{36.6667} \\  & & \(\bullet\) & Output layer & \\ \hline \multirow{3}{*}{ICS\_TS} & \multirow{3}{*}{18} & \(\bullet\) & Input layer & \multirow{3}{*}{55} \\  & & \(\bullet\) & Output layer & \\ \hline \multirow{3}{*}{CCR} & \multirow{3}{*}{512} & \(\bullet\) & Input layer & \multirow{3}{*}{98.3333} \\  & & \(\bullet\) & Output layer & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation test.  
Figure 10: Classification efficiency of carcinogenic laceration images deploying neural networks and the LBP, OC-LBP, CS-LTP, ICS-TS, and CCR spectra as an input signal.  
Table 2 presents a comparison between accuracy and the techniques employed for the classification of cervical cancer images.
## Appendix B Mathematical Model Used in ATM Algorithm  
If \(\tau im_{i}=Tim_{i}\times Ni\) is the total processing time and \(rim_{i}\) is the starting time of order \(i\) on machine \(m_{i}\) while \((Tim_{i})^{-1}\times t\) is the production rate of \(i\) as a function of time \(t\), then the units of \(i\) are equal to:  
\[Uti(t)=STi+(Tim_{i})^{-1}\times t \tag{1}\]  
If \(tcrit\) is the time when the quantity of \(i\) is sufficient for production of \(j\) to commence, i.e., \(ni(tcrit)=Ni\), then Equation (1) is formulated as:  
\[Ni=STi+(Tim_{i})^{-1}\times tcrit\Leftrightarrow \tag{2}\] \[tcrit=(Ni-STi)\times Tim_{i}\]  
Since \(Tim_{i}=\tau im_{i}/Ni\), then Equation (2) can be transformed as:  
\[tcrit=\tau im_{i}-\left(STi\times\frac{\tau im_{i}}{Ni}\right)=\tau im_{i} \bigg{(}1-\frac{STi}{Ni}\bigg{)} \tag{3}\]  
Due to (3), the minimum starting time of father \(j\) becomes:  
\[\text{min}rjm_{j}=rim_{i}+\tau im_{i}\times\left(1-\frac{STi}{Ni}\right) \tag{4}\]  
Equation (4) holds true only if \(mj\
eq mi\). For that reason, we insert in (4) the integer parameter \(\delta m_{i}m_{j}\) such that if \(mj=mi\) then \(\delta m_{i}m_{j}=0\), else \(\delta m_{i}m_{j}=1\). Given the above, we have Equation (5):  
\[\text{min}rjm_{j}=rim_{i}+\delta m_{i}m_{j}\times\tau im_{i}\times\left(1- \frac{STi}{Ni}\right)+\left(1-\delta m_{i}m_{j}\right)\times\tau im_{i} \tag{5}\]  
When viewed from the makespan perspective of the classic job shop scheduling problem, i.e., \(\text{min}(\text{max}r2m_{z}+\tau zm_{z})\), the traditional precedence constraint can be replaced by (5), and the following additional constraints must also be satisfied:  
\[rjm_{j},rim_{i},\tau im_{i},STi,Ni\geq 0 \tag{6}\]  
\[rim_{i}-rkm_{i}\geq\tau km_{i}\lor rkm_{i}-rim_{i}\geq\tau im_{i},\forall(i,k )\ m_{i}\text{ sequentially processed on} \tag{7}\]  
\[rjm_{j}-rlm_{j}\geq\tau lm_{j}\lor rlm_{j}-rjm_{j}\geq\tau jm_{j},\forall(j,l )\text{ sequentially processed on}\ m_{j} \tag{8}\]
## 2 Materials
For the sake of clarity, standard deviations among the measurements were not included in plots if they were negligible. Several configurations of multi-layer resonant structures were investigated in the study to test the reliability of the prediction methods. Table 2 summarizes the analyzed combinations, starting from the layer nearest to the sound source to the farthest one. The combinations could be divided into four classes (Figure 3): double perforated panels (PP-PP), one perforated panel and a micro-perforated membrane (PP-MPM), double micro-perforated membranes (MPM-MPM), and a triple leaf panel with one PP and two MPM. All of the layers will be described, starting from the outer layer (exposed to sound) to the backing rigid surface, for the sake of clarity and consistence throughout the paper. \begin{table}
\begin{tabular}{c c} \hline \hline
**Identifier** & **Panel Arrangement: From the Structure Surface to the Backing Rigid Surface** \\ \hline C/10/B/50 & C + 10 mm cavity + B + 50 mm cavity + rigid surface \\ B/20/M5/40 & B + 20 mm cavity + M5 + 40 mm cavity +rigid surface \\ M4/25M5/25 & M4 + 25 mm cavity + M5 + 25 mm cavity + rigid surface \\ D/10/M4/15/M5/30 & D + 10 mm cavity + M4 + 15 mm cavity + M5 + 30 mm cavity + rigid surface \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configurations of multi-leaf resonant absorbers used in the preliminary test. Figure 2: (**a**) Magnified image of a sample of the holes of the micro-perforated panels with major dimensions and effective diameter (in mm). Red line highlights the hole perimeter obtained by means of the segmentation algorithm; and, (**b**) Frequency distribution of effective hole diameters.
## 3 Results and Discussion
The regression plot corresponding to the testing process is presented in Figure 5.  
To demonstrate the predictive accuracy of the ANN model, a comparison was made with other AI models, including GPR, SVM, and LR. As shown in Table 3, the ANN model produced the best R and MSE value, indicating that it outperformed the other models. As can be seen in Table 3, both the ANN and GPR models demonstrated close performance in predicting compressive strength. The decision to choose the ANN model in this study is due to its high flexibility, complexity in data, and ability to capture nonlinear relationships. This capability is especially valuable in predicting compressive strength, where various factors interact in nonlinear ways. Additionally, ANNs are more scalable with larger datasets, allowing them to refine and improve predictions as more data become available. ANN models are also more robust for applications requiring rapid predictions and can generalize better with diverse, complex input conditions typically seen in compressive strength studies. In contrast, GPR, while effective, can become computationally intensive with larger datasets, making it less practical for extensive applications. Figure 5: The test regression plot. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **NN** & **GPR** & **SVM** & **LR** \\ \hline R Value & 0.90 & 0.88 & 0.87 & 0.68 \\ \hline MSE Value & 0.14 & 0.16 & 0.17 & 0.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of R values among different AI models. The question of the model's generalizability to real-world data and its reliability is a critical consideration. Consequently, a cross-validation strategy is employed [38; 39]. This approach not only mitigates the risk of training a model that is overly reliant on the specific dataset, but also facilitates the assessment of the model's generalizability. In the cross-validation technique, the dataset is partitioned into smaller subsets, with each subset alternately functioning as the test set while the remaining subsets are used for training. The model undergoes training on the training subsets and is then evaluated on the specified test set. This procedure is reiterated for each iteration of the cross-validation process. The performance metrics derived from each iteration are subsequently consolidated and averaged to compute the total efficacy of the system. In this research, a five-fold cross-validation approach was employed to impartially assess the model's performance. Consequently, the model's R value was calculated as 0.90. Figure 6 displays the results and depicts the optimal error rate between the predicted data obtained from system output using the five-fold cross-validation and the actual values.
## 2 Models and Definitions
### Network Connectivity Rate  
The network connectivity rate is an important criterion of the service quality of sensor networks and the premise of sensor network application. This criterion refers to the ratio of the number of nodes that connect with the sink node by one or several hops to the total number of nodes by using the _Cer_ expression; the formulation is expressed as follows [26]:  
\[Cer=\frac{N_{connection}}{N_{all}} \tag{7}\]  
where \(N_{connection}\) is the number of nodes connected to the sink node and \(N_{all}\) is the total number of nodes in the UWSN.
## 4 Results and Discussion
### \(k\)-Fold Cross-Validation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fold** & **Data Type** & **Mean** & **Standard** & **Minimum** & **Maximum** \\ \hline \multirow{3}{*}{Fold 1} & Training & 0.0870 & 0.6500 & \(-1.1556\) & 6.4422 \\  & Validation & 0.2624 & 1.4909 & \(-2.3569\) & 12.4731 \\  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 2} & Training & 0.1042 & 0.6668 & \(-1.083\) & 6.4581 \\  & Validation & 0.2731 & 1.3162 & \(-2.1941\) & 12.2122 \\  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 3} & Training & 0.1228 & 0.6995 & \(-1.5363\) & 6.5456 \\  & Validation & 0.0054 & 0.6352 & \(-1.5293\) & 2.3126 \\  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 4} & Training & 1.6254 & 0.8436 & \(-1.7872\) & 8.3321 \\  & Validation & 0.0488 & 0.6029 & \(-1.1615\) & 3.9717 \\  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 5} & Training & 0.1475 & 0.8598 & \(-1.9181\) & 9.0759 \\  & Validation & \(-0.1290\) & 0.7534 & \(-1.5396\) & 6.1157 \\  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 6} & Training & 0.1372 & 0.7552 & \(-0.4369\) & 7.6077 \\  & Validation & 0.1283 & 0.7686 & \(-1.1311\) & 6.9952 \\  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 7} & Training & \(-0.1565\) & 0.8117 & \(-1.7292\) & 8.1865 \\  & Validation & 0.0776 & 0.7495 & \(-1.4006\) & 3.3819 \\  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 8} & Training & 0.1349 & 0.8198 & \(-1.8158\) & 8.3387 \\  & Validation & 0.0327 & 0.6069 & \(-0.9845\) & 2.5030 \\ \cline{1-1}  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 9} & Training & 0.1435 & 0.7756 & \(-1.6429\) & 7.5061 \\ \cline{1-1}  & Validation & 0.0513 & 0.6240 & \(-0.9654\) & 2.1172 \\ \cline{1-1}  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \multirow{3}{*}{Fold 10} & Training & 0.1328 & 0.7367 & \(-1.6206\) & 7.0720 \\ \cline{1-1}  & Validation & 0.1702 & 0.7270 & \(-1.1042\) & 2.9059 \\ \cline{1-1}  & Test & 0.0071 & 0.7469 & \(-1.5295\) & 3.1866 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Range of scaled data in each fold of the 10-fold cross-validation.
## 2 Construction of a Numerical Model of Particle Flow
### Model Building  
In order to investigate the effect of symmetrical fissure dip angle and length on the mechanical properties of hole-containing sandstones under conventional triaxial compression, a two-dimensional numerical model of particle flow was established with the standard cylindrical specimen (50 mm \(\times\) 100 mm) longitudinal cross-sectional dimensions, and defects in the model were achieved by deleting particles, as shown in Figure 3. Symmetrical fissures of differing lengths and dip angles were prefabricated in the model and penetrated a single circular hole located at the model's center. The settings for some of the model parameters are presented in Table 1. Four walls were generated around the model, while \(\sigma\)1 was loaded axially for the top and bottom loading walls, and \(\sigma\)3 was loaded transversely for the left and right servo walls. The loading method was displacement-controlled, with a loading rate of 0.02 mm/s. The axial loading pressure is set to stop when it is reduced to 70% of the peak strength of conventional compression, and a total of 29 different working conditions are simulated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Numerical Values** \\ \hline Number of particles & 11137 \\ Hole diameter (d) & 10 mm \\ Fissure width & 0.8 mm \\ Fissure length (2a) & 0 mm,4 mm,8 mm,12 mm,16 mm \\ Fissure dip angle (\(\alpha\)) & \(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\), \(75^{\circ}\), \(90^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters.  
Figure 2: Principle of linear parallel bonded contact model [24].
## 2 Materials and Methods
### Camera and Camera Controller Modules
Subsequently, these values are relayed every 100 ms to the MCU (nrf52840, Nordic Semiconductor ASA Co.) on the receiver circuit board within the control module through wireless Bluetooth low energy (BLE) communication protocols. The receiver module translates these yaw and pitch values into corresponding angles based on the motor's positioning. The activation of the foot switch triggers an interrupt signal to the receiver's MCU, which, in turn, issues a universal asynchronous receiver/transmitter (UART) signal to the motor (XC330M288T, Robotics Co., Seoul, Republic of Korea). This motor is selected for its integrated driver-based MCU (Cortex-M0+ 32 bit, 64 MHz), which is optimized for its compact size. It employs a half-duplex asynchronous serial communication protocol utilizing a time-to-live (TTL) level multi-drop bus for simplified control. Furthermore, precise control is achievable by implementing a proportional integral-differential (PID) control algorithm. Including a contactless absolute 12-bit, 360\({}^{\circ}\) encoder (AS5601, ams-OSRAM, Premstaetten, Austria) facilitates precision control predicated on position, enabling the motor to operate according to the converted angle. Figure 4: Design of the camera module. The camera module consists of a backbone spring, two pairs of wires, two pairs of wire guides, a spring for manipulating the head module, and a guide tube for guiding the manipulating spring. The two pairs of wires are used to control the vertical and horizontal positions of the camera, and the spring for manipulating the head module is used for the cleaning motion of the camera lens.
## 4 Discussion  
We conducted charge-discharge cycle experiments on multiple different 18,650 LiFePO4 batteries, collecting various sets of LIB charge-discharge cycle experimental data. Through the selection and improvement of the RF model, research on LIB SOH prediction was carried out. In terms of feasibility verification for the selection of the RF model, we constructed four traditional machine learning models, namely the DT model, CNN model, LSTM model, and RF model, using Python programs. We performed SOH prediction and comparative analysis on three LIB with relatively large datasets. The results showed that different LIB datasets produced varying model prediction results, leading to different rankings of model prediction results. However, among these four traditional machine learning models, regardless of which LIB charge-discharge cycle experimental data was used, the prediction of the Class RF model consistently outperformed the other three models. This finding validates the viewpoints presented in References [32; 33; 34], indicating that the RF model tends to exhibit higher prediction accuracy in certain application scenarios compared to the DT model, CNN model, and LSTM model. These results demonstrate the feasibility of using the RF model for LIB SOH prediction in this study.  
To enhance the prediction accuracy of the model, we further proposed the RF improved model. To verify the effectiveness of the RF improved model, we constructed RF improved model 1 and RF improved model 2 for SOH prediction on multiple LIBs, comparing them with the Class RF model. In the SOH prediction results for #1, #6, and #26 LIB, RF improved model 1 achieved a reduction in _RMSE_ of 19.82%, 17.07%, and 17.61% compared to the Class RF model. It also achieved decreases in _MAPE_ of 19.94%, 17.39%, and 17.59%, along with increases in \(R^{2}\) of 1.24%, 0.82%, and 1.14%. This improvement can be attributed to the fact that parameter tuning for the Class RF model was based on subjective human judgment without adaptive optimization. In contrast, RF improved model 1 used a method inspired by Reference [40] for adaptive optimization of max_depth, n_estimators, and max_features. This confirmed that using the PSO algorithm, as discussed in References [40; 41], to optimize parameters of the RF model is indeed beneficial for improving prediction accuracy.  
Building upon this, RF improved model 2, proposed in this study, further adapted max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf. In the SOH prediction results for #1, #6, and #26 LIB, RF improved model 2 achieved a reduction in _RMSE_ of 21.93%, 15.90%, and 24.00% compared to RF improved model 1. It also achieved decreases in _MAPE_ of 23.24%, 18.21%, and 24.25%, along with increases in \(R^{2}\) of 0.92%, 0.61%, and 0.92%. These results indicate that the evaluation index for RF improved model 2 are superior to RF improved model 1. In conclusion, the RF improved model 2, based on the PSO algorithm for adaptive optimization of five parameters, has proven to be effective in LIB SOH prediction. It can further enhance the prediction accuracy of LIB in new-energy electric vehicles.
## 3 System Description
### Specifications for Real-Time Wireless Transmission  
The EUITS was based on V.42 bis [26]. This is because V.42 bis functions as a wireless transmission environment for real-time compressing and sending data for dictionary-based Lempel-Ziv Welch (LZW) variant methods, enabling the proposed solution to transmit data in real-time packet units. In other words, the proposed algorithm is not a static method that saves, compresses, and transmits EMG signals. The proposed algorithm dynamically compresses and sends real-time EMG signals by data packets. The main specifications of V.42 bis are shown in Figure 6.  
After initiating a request and confirming that initiation, the algorithm processes dictionary codes by EMG signal data packets. In scenario #1, the code word and dictionary are calculated, as long as there are incoming signals transmitted in data packets. If the signal sample is not found in the dictionary, it calculates a new code word size, and adds it to the dictionary. The end procedure always matches the newly initialized string signal sequence, so that strings are matched real-time. If the signal sample is found in the dictionary, there is no need for further dictionary addition, so it escapes and waits for another signal sequence. This way, the EMG signal dictionary is trained in real-time through our proposed algorithm. There is no need for delays deriving from statat  
Figure 6: Flow chart specifications of compressing & transmitting real-time data of EUITS.  
If there is no reply in the incoming character, check first if it's due to a link error. If an error has occurred, re-initiate the request. If it is not an error and it is the end of transmission, request and confirm the flush and end algorithm. The main role of the flush function is to meet the needs of the real-time dynamic bit allocation transmission of LZW. If an error occurs and an empty bit occurs, the flush function fills the empty bit with 0 integer values. Constructed on the real-time specifications of V.42 bis, the proposed algorithm supports dynamic real-time transmission in packet units. In the evaluation Section, the compression performance of the developed algorithm is evaluated.
## 4 Conclusions  
The work described above has made it possible to evaluate the performance of the developed calibration system in a non-ideal context and to assess how well it works. The application of the calibration method on four different materials has allowed to evaluate the influence of the ageing of different materials in the outcomes of the calibration of the images. Mathematical criteria have been taken in order to select the most adequate support material to build the structure. Under an analysis of color mathematics over the test results, very mild differences have been found between the four of them. Robustness to externally-induced chromatic deviation is practically analogue in every studied case, with a revealed decay that is non-noticeable by human eye.  
Thus, the calibration process allows to extract valid information from any taken images, guaranteeing that no errors originating from the color change of the reference charts may hinder the proper operation of the entire system.  
Finally, the results support the possibility of manufacturing customized reference color charts using low-cost commercial materials, as the developed calibration process can easily overcome the small color drift introduced by aging. These reference color charts can be used to design low-cost conservation tools, affordable for small museums with limited budget.
## 4 Discussion  
### Sensitivity Analysis of SIF, EVI, NDVI to Drought  
Figure 4 shows the correlation between SIF, EVI and NDVI at multiple sites and sc_PDSI during peak growth. The correlation between SIF and sc_PDSI was higher than that between EVI and NDVI at all sites observed, due to the significant time-lag effect between reflectance-based vegetation indices and water stress [4] and has been widely discussed. In addition, it is worth noting that EVI and NDVI were significantly and positively correlated with sc_PDSI in most areas of Southwest Yunnan, mainly because continuous drought exacerbates the reduction in forest greenness and is more pronounced in evergreen broadleaf forests [30]. Vegetation indices, such as EVI and NDVI, are only sensitive to changes in canopy structure and pigment concentration, and they are not directly related to photosynthesis [7], but SIF is directly related to photosynthesis, so the decrease in photosynthesis due to drought stress is expected to be more accurately reflected by SIF.  
In addition, Figure 4 shows that the correlation between SIF and sc_PDSI is different in different regions, and the R\({}^{2}\) of SIF and sc_PDSI in the Lijiang region is 0.52, which is lower than the R\({}^{2}\) of the other three sites, probably because the Lijiang site is close to Yulong Snow Mountain, with an elevation that is 2700 m higher than the rest of the sites. The average annual temperature is lower by about 11 \({}^{\circ}\)C. The effect of drought is limited because evaporation is relatively small and moisture is relatively abundant. The highest correlation is in Zhaotong because the Zhaotong site is close to the dry hot valley area,  
Figure 15: Scatter plot of the total value of GPPd and the mean value of sc_PDSI in all months, 2009-06–2011-06.  
where high temperature and evaporation make moisture a major stressor for the vegetation, and the vegetation cover is low and mainly grasses, scrub, and low trees [31], so it is more sensitive to drought. Similarly, the Wenshan site is mostly scrub, which is more affected by drought, so the correlation is also higher. The vegetation at the Lincang site is lush and mostly trees, meaning that the vegetation has a stronger ability to regulate itself [32], so the response to drought is less sensitive than that at the Zhaotong site. The correlation is in the middle.  
The spatial correlation between SIF and sc_PDSI confirms this (Figure 3a). The abundance of water resources and high vegetation cover in Western Yunnan, and the decrease in cloud cover during drought led to an increase in solar radiation, which was favorable for vegetation growth [33]. Therefore, the correlation between SIF and sc_PDSI in some areas of Western Yunnan is negative. In contrast, the vegetation cover in Central and Eastern Yunnan is lower than that in Western Yunnan, and precipitation is relatively low, so the vegetation in this region is more sensitive to drought.