esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of FTSW Prediction Using the LSTM Model Performance with Other Models
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Quinta de Pancas**} & \multicolumn{2}{c}{**Colinas do Douro**} \\ \cline{2-5}  & **RMSE** & \(\mathbf{R^{2}}\)**-Score** & **RMSE** & \(\mathbf{R^{2}}\)**-Score** \\ \hline BiLSTM & 10.36 & 0.87 & 8.3 & 0.93 \\ \hline RF & 16.8 & 0.67 & 12.88 & 0.85 \\ \hline SVR & 19.43 & 0.58 & 14.15 & 0.82 \\ \hline LR & 22.5 & 0.43 & 18.68 & 0.69 \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison of the performance of the trained BiLSTM model with RF in predicting the FTSW one day ahead on the Quinta de Pancas and Colinas do Douro validation sets. Figure 14 shows the boxplots for the fifth, twenty-fifth, seventy-fifth, and ninety-fifth percentiles for the observed and predicted FTSW with BiLSTM and RF using the Colinas do Douro dataset. While the whiskers cover the values from the 5 th to the 95 th percentile, the boxes indicate the 25 th and 75 th percentiles. The mean of the measured data in the test phase is represented by the solid line within each box. As you can see from the figure, the FTSW predicted by the RF model was within the box between 20 and 80%, but the boundaries of the box for the observed FTSW and those predicted by the BiLSTM are almost identical (between 15 to 85). Moreover, in both models, the mean of the predicted FTSW was close to the mean of the observed values.
## 2 Materials and Methods
### Maximum Particle Size and Specimen-Scaling Methods  
Given the shear box dimensions (350 mm \(\times\) 250 mm) for the large-scale laboratory shear test, international standards diverge notably in their \(d_{\rm max}\) stipulations: ASTM D3080 mandates \(d_{\rm max}\leq 35\)mm (1/10 of the specimen length), whereas DL/T 5356-2019 per  
Figure 3: (**a**) Large-scale laboratory direct shear test apparatus; (**b**) schematic diagram.  
Figure 2: (**a**) Field photos of the test equipment; (**b**) schematic view of the in situ direct shear test.  
mits \(d_{\rm max}\leq 70\)mm (1/5 of the specimen length). Per DL/T 5356-2019, the original CGS gradation (\(d_{\rm max}=60\)mm) is suitable for direct testing in the large-scale apparatus. In contrast, ASTM D3080 necessitates scaling to comply with its criteria. To systematically assess the influence of \(d_{\rm max}\) values and scaling techniques on CGS shear strength, this study employs two prevalent methods--the Equivalent Substitution Method (ESM) and the Similar Gradation Method (SGM)--to adjust the original gradation (\(d_{\rm max}=60\)mm) to ASTM's requirement (\(d_{\rm max}=30\)mm), followed by large-scale direct shear testing.  
The ESM, depicted in Figure 4, replaces oversized particles with coarse ones (5-60 mm) to keep the scaled gradation's mechanical properties as similar as possible to the original by regulating fine particle content. The formula for calculating each particle group's content in ESM is  
\[\Delta P_{i}=\frac{P_{>5}}{P_{>5}-P_{>d_{\rm max}}^{0}}\Delta P_{0i} \tag{1}\]  
where \(\Delta P_{i}\) is the content of a particle group after scaling (%), \(\Delta P_{0i}\) is the content of a particle group in the original gradation (%), \(P_{>d_{\rm max}}^{0}\) is the content of oversized particles (%), and \(P_{>5}\) is the content of particles larger than 5 mm (%). \(\Delta P_{i}\), \(\Delta P_{0i}\), and \(P_{>d_{\rm max}}^{0}\) are illustrated in Figure 4.  
The SGM, outlined in Figure 5, scales down particle sizes of the original gradation using geometric similarity principles, preserving the gradation curve's shape while reducing \(d_{\rm max}\) to the target value. The scaling formulas are  
\[d_{i}=\frac{d_{0i}}{B} \tag{2}\]  
\[B=\frac{d_{0\rm max}}{d_{\rm max}} \tag{3}\]  
\[P_{\rm x}=P_{0} \tag{4}\]  
where \(d_{0i}\) is a certain particle size in the original gradation (mm), \(d_{i}\) is the particle size after scaling the original gradation by a factor of \(B\) (mm), \(B\) is the particle size reduction coefficient, \(d0\)max is the maximum particle size of the original gradation (mm), \(d_{\rm max}\) is the allowable maximum particle size of the specimen (mm), \(P_{0}\) is the percentage of particles  
Figure 4: Diagram of the Equivalent Substitution Method (modified from [PERSON] et al. [25]).  
smaller than a certain size in the original gradation (%), and \(P_{X}\) is the percentage of particles smaller than a certain size after scaling by \(B\) (%).
# Large-Aperture Real-Time Compensated Collimating Wavefront Error Detection Method
## 3 Experiment and Analysis
Due to manufacturing-related errors of the optical components, experimental analysis can be performed by increasing the step resolution based on the desirable detection accuracy requirements. Compared with the traditional pentaprism scanning method, the advantages of this method are: (1) The error compensation subsystem separates the measurement error caused by changes in the direction of the incident beam, which results from the pitch, yaw, and roll of the pentaprism, and (2) the method performs the compensation in real time, thereby avoiding the system error of the guide rail caused by manufacturing or assembly errors. The method is simple and provides high measurement accuracy. The measurement residual for the PV and RMS values was 0.072 \(\lambda\) and 0.041 \(\lambda\), respectively.
## 4 Results
### IV-CatBoost Model Results
#### 4.1.3 Landslide Susceptibility Mapping Using the IV-CatBoost Model  
After successive model fitting iterations, the optimal learning rate was determined to be 0.091, with a max depth of nine, and a minibatch fraction of nine. This resulted in an RMSE of 0.24, an R\({}^{2}\) of 0.77, and an MAE of 0.16 for the top-performing model. All data from the study area were input into the trained model for landslide susceptibility prediction. The predicted values (ranging from 0 to 1) were obtained, and ArcGIS's natural breaks method was used to divide them into five susceptibility levels: very high susceptibility (0.85 to 1.0), high susceptibility (0.6 to 0.85), moderate susceptibility (0.36 to 0.60), low susceptibility (0.13 to 0.36), and very low susceptibility (0 to 0.13). The results are shown in Figure 7.  
According to Table 3, the area proportions of different susceptibility levels from very high to very low are 23.78%, 17.52%, 17.44%, 19.63%, and 21.63%, respectively. Through  
Figure 6: Landslide susceptibility map of IV: regions with very low and low susceptibility, and the distribution of non-landslide points.  
Figure 7: Landslide susceptibility maps of the (**a**) IV-CatBoost and (**b**) IVCI models.  
validation with existing landslide points, it was found that 90.71% of the existing landslide points fall within the high and very high susceptibility zones, while only 1.06% are located in the very low susceptibility zone. Using Python (3.10) with sklearn-learn (1.3.0), the model was evaluated using the 30% validation dataset, and the ROC curve was plotted. The ROC curve results are shown in Figure 8. The AUC value on this validation dataset is 0.88, indicating that the model's predictions for new feature data are reasonably accurate and reliable.  
The model's predictions show that the high and very high susceptibility zones are concentrated in the range of elevation 542 m and slope 17\({}^{\circ}\), mainly located within 0 to 300 m from both sides of the road, often found in cultivated areas. On the other hand, the low and very low susceptibility zones have a broader distribution, mainly located beyond 450 m from the road, often found in forested areas. These areas are generally at higher elevations, less affected by human activities, and in a stable susceptibility state.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 2 Theory and Method
### Loss Function and Training Details  
The loss function is determined based on the scale-invariant signal-to-distortion ratio. It achieves good general performance across a range of popular speech enhancement evaluation measures [37]. The scale-invariant signal-to-distortion ratio is defined as [38]  
\[\mathrm{R_{std}}=10\mathrm{log}_{10}\frac{\left\|\mathbf{v_{target}}\right\|_{2 }^{2}}{\left\|\mathbf{e_{\mathrm{noise}}}\right\|_{2}^{2}}, \tag{12}\]  
where \(\mathbf{v_{\mathrm{target}}}=(\mathbf{\hat{v}}\cdot\mathbf{v})\mathbf{v}/ \left\|\mathbf{v}\right\|_{2}^{2}\), \(\mathbf{e_{\mathrm{noise}}}=\mathbf{\hat{v}}-\mathbf{v_{\mathrm{target}}}\), \(\mathbf{v}\) is the ground-truth direct wave (or reflected wave) and \(\mathbf{\hat{v}}\) is the estimated direct wave or reflected wave in the time domain. Zero mean normalization is applied to \(\mathbf{v}\) and \(\mathbf{\hat{v}}\) to ensure scale invariance. \(\mathrm{R_{std}}\) is defined in decibels (dB). The larger the \(\mathrm{R_{std}}\), the closer \(\mathbf{v}\) and \(\mathbf{\hat{v}}\). In order to ensure that the model parameters are optimal when the loss function is at its minimum in the optimization process, we define the loss function as \(-\mathrm{R_{std}}\). The optimization goal is to minimize the loss function, which is equivalent to maximizing the similarity between \(\mathbf{v}\) and \(\mathbf{\hat{v}}\).  
Batch normalization is used to speed up the separation process. During the training process, Adam [39] is used to optimize the parameters of the network such as the encoder basis functions, filters and decoder basis functions. The number of epochs is set to 60. The batch size is 512. The initial learning rate is 0.001. If the loss function on the validation set does not decrease for three consecutive epochs, the learning rate is halved. The remaining parameters of the network are consistent with the original Conv-TasNet paper [22]. The permutation problem of the direct wave and the reflected wave is solved by using utterance-level permutation-invariant training [18]. After training for 30 epochs, the reflected waves predicted by MC-ConvTasNet are closest to the theoretical reflected waves in the test set, with an average \(\mathrm{R_{std}}\) of 19.5.
# Application and Evaluation of Surrogate Models for Radiation Source Search
## 2 Physical Model
### Numerical Model  
We employ a ray-tracing scheme to determine the intensity of gammas reaching each detector at location \(r_{i}\). For each detector, we construct a ray from the detector location to the source location. We compute the number of intersections with the \(\mathcal{N}\) buildings, and the length \(\ell_{h}\) of the ray segment through each of the \(h=1,\ldots,\mathcal{N}\) buildings. The buildings in the domain are assumed to be homogeneous, each having a mean free path \(\lambda_{T}^{h},\ h=1,\ldots,\mathcal{N}\)[2]. These assumptions yield  
\[\hat{u}_{i}(q)=I_{0}\Delta t_{i}e_{i}\frac{A_{i}}{4\pi||r_{i}-r_{S}||_{2}^{2} }\text{exp}\bigg{(}-\sum_{h=1}^{\mathcal{N}}\frac{\ell_{h}}{\lambda_{T}^{h}} \bigg{)},\ i=1,\ldots,d. \tag{3}\]  
We employ the Python code gefry[4] to implement this numerical model, which is a significant simplification of the original problem derived from [PERSON] transport theory. Even so, because of the buildings in the geometry, this model is non-smooth and any extension of this model--to 3D or considering particle scattering-would yield a model that is computationally infeasible for Bayesian inference and uncertainty propagation.
## 3 Findings
### Feedback on App Usability and Workflow Modifications
#### 3.3.5 Report Generation  
Regular sputum smear microscopy is an important medical aspect in ensuring adherence to the treatment protocol and declaring the treatment outcome of a TB patient. It is extremely difficult toreview paper-based records and prepare a list of patients whose sputum smear microscopy is due in the next month. This time-consuming exercise is iterative and considered as a fundamental responsibility of the field workers for tracking treatment progress of the registered patients. With the ODK Scan process, generating this actionable information became easier by running a customized follow up visit report on the smartphone in offline mode (Figure 7). This enabled field workers to keep a constant check on the treatment progress of the patients registered in his area of control. Moreover, all field workers talked very positively about the benefits of this report. One of them said, \"My existing practice is to review TB03 in the beginning of month to identify patients with follow up appointments. This task takes lot of time and mental energies as it is difficult to develop such list. With that view point, if I see this application, this is an excellent idea that I open app and all follow up visits will be appeared.\"  
Others said:  
\"This is the best feature of this application, as now we know in advance when we have to call the patient for follow-up.\"  
\"Previously my practice was to visit all the GPs to enlist patients who have follow up visit due or overdue. Now with this report I do not have to visit the GPs to know patient's follow up visit date.\"  
\"I would say that, follow up visit report has revolutionized our work. Now, we will never miss out any patient and upon click we view all needed information required for follow up.\"
###### Abstract  
The aim of this comparative study is to analyze the efficacy of EndoVac and Endoactivator endodontic irrigation systems for removing the endodontic smear layer present in the coronal, middle, and apical root third of the root canal system (RCS) and the isthmus from mesial roots from the first lower molar teeth using micro-computed tomography (micro-CT) analysis. The study was performed on 40 mesial roots that were randomly assigned to the following groups: Group A, EndoVac endodontic irrigation system (EV) (\(n=10\)); Group B, Endoactivator endodontic irrigation system (EA) (\(n=10\)); Group C, Root canal systems performed with F2 ProTaper Gold endodontic rotary system but not irrigated (Positive control) (\(n=10\)); Group D, Root canal systems not performed or irrigated (Negative control) (\(n=10\)). The samples were exposed to micro-CT analysis and the mesial roots were segmented using an advanced image segmentation technique based on partial differential equations (ROI: \(10\times 10\times 10\) mm) and the root canal systems and isthmus were reconstructed at a resolution of 25 microns per voxel. The EA study group showed statistically significant different residual endodontic smear layer volume (\(0.48\pm 0.24\) mm\({}^{3}\)) compared to the EV study group (\(0.18\pm 0.15\) mm\({}^{3}\)) (\(p=0.016\)). The EV endodontic irrigation system performed a higher smear layer removal at coronal and middle root thirds, compared to the EA endodontic irrigation system; however, the EA endodontic irrigation system performed a higher smear layer removal at the apical root third, compared to the EV endodontic irrigation system.  
micro-computed tomography; non-surgical endodontic retreatment; smear layer removal; isthmus; EndoVac; Endoactivator
## 3 Proposed Routing Algorithm
### Operation of the Radio-Aware Routing Algorithm  
In this paper, we proposed a radio-aware (RA) routing algorithm to address lossy situation. RA not only maintains the advantages of DD, but also applies radio information to set up the route with cross-layer designs. In order to address the problem mentioned above, RA is designed to improve the reliability of DD in lossy WSNs. Firstly, RA can exchange routing information among nodes independent of the period. In RA, the route will be adjusted to the situation caused by unexpected errors in lossy WSNs.  
Secondly, RA introduces radio information which represents the air time cost and route failure as new metrics of gradient. Since these new metrics can reflect current status of networks, RA can maintain consistent connectivity and achieve reliability in lossy WSNs. In addition to the metrics, the position of each sensor node is used to decide the closest direction from source nodes to a sink.  
Finally, the fast re-route scheme is adopted to address the lossy situation in addition to the link asymmetry. In the fast re-route scheme, the lossy situation can be fetched through the RTS/CTS mechanism of the CSMA/CA at the 802.11b MAC. The route failure flag indicates the lossy situation of the error-prone node. RA can choose an alternative node instead of a failed node. Moreover, the fast re-route scheme gives reduction of link asymmetry problem of wireless network.  
In RA, the air time cost and the position of each sensor node were collected to set up gradients whenever interest and data packets were received on each node. RA can calculate the air time cost, and the position of each node is included in the interest. If a source node has a problem in sending data due to route failure, RA can select an alternative route instead of failed one by utilizing fast re-reroute scheme.
# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 4 Problem Formulation for Optimal Solution
Aiming to maximize field coverage and minimize movement distance, the deployment patterns of \(S_{1}\) and \(S_{2}\) are exchanged over the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Combination**} & \multicolumn{4}{c}{**Parameters**} \\  & \multirow{2}{*}{**Zone Priority**} & **Residual Energy** & **Movement Distance** & **Movement Distance** & **Consumed Energy** \\ \hline Case 1 & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) \\ Case 2 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) \\ Case 3 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Proposed parameter combinations. Figure 6: Initial deployment before the movement operation. entire horizon (t\({}_{1}\) and t\({}_{2}\)). Now, the residual energy of S\({}_{1}\) is zero, which impacts the network lifetime and the proper operation of the WSN. In Case 2, besides the zone priority, the deployment decision is also affected by residual energy to avoid poor-energy nodes. Thus, avoiding poor-energy nodes to improve network lifetime is its second priority. Figure 7b shows the movement operation during the time interval, t\({}_{2}\), under parameter combination of Case 2. The deployment patterns of sensor nodes S\({}_{3}\) and S\({}_{4}\) are swapped over time periods t\({}_{1}\) and t\({}_{2}\) for field coverage maximization and energy saving. Now that S\({}_{4}\) has no residual energy, depending on it alone to increase energy efficiency will be insufficient. Finally, in Case 3, besides zone priority and the movement distance, the expected residual energy after the movement is added to improve energy efficiency. Figure 7c shows the crossover operation during the time interval, t\({}_{2}\), under the parameter combination of Case 3. Aiming to maximize field coverage and energy efficiency, the deployment patterns of sensor nodes S\({}_{2}\) and S\({}_{3}\) are exchanged during the time intervals t\({}_{1}\) and t\({}_{2}\). Here, the residual energy on S\({}_{2}\) and S\({}_{3}\) are 2 J and 5 J, respectively. Thus, it is possible to observe that considering the expected residual energy after the movement, the movement distance and zone priority into deployment decision turn into much better energy efficiency than the other cases.
# Prototyping the Symmetry-Based Chaotic Communication System Using Microcontroller Unit
## 2 Materials and Methods
### Symmetry Coefficient Modulation and Parameter Modulation
\begin{split} x_{n+s}&=x_{n}+h_{1}(\sigma(y_{n}-x_{n })).\\ y_{n+s}&=y_{n}+h_{1}(x_{n+s}(r-z_{n})-y_{n});\\ z_{n+s}&=z_{n}+h_{1}(x_{n+s}y_{n+s}-bz_{n});\\ z_{n+1}&=(z_{n+s}+h_{2}x_{n+s}y_{n+s})(1+h_{2}b)^{-1}; \\ y_{n+1}&=(y_{n+s}+h_{2}x_{n+s}(r-z_{n+1}))(1+h_{2}) ^{-1};\\ x_{n+1}&=(x_{n+s}+\sigma h_{2}y_{n+1})(1+h_{2} \sigma)^{-1};\end{split}  \tag{5}\]  
Finally, the slave finite-difference model for the Lorenz system, obtained by the VSCD method, is as follows:  
\[ \begin{split} x_{n+s}&=x_{n}+h_{1}(\sigma(y_{n}-x_{n })).\\ y_{n+s}&=y_{n}+h_{1}(x_{n+s}(r-z_{n})-y_{n}+k(y_{M}-y_{n }));\\ z_{n+s}&=z_{n}+h_{1}(x_{n+s}y_{n+s}-bz_{n});\\ z_{n+1}&=(z_{n+s}+h_{2}(x_{n+s}y_{n+s}))\cdot(1+h_{ 2}b)^{-1};\\ y_{n+1}&=(y_{n+s}+h_{2}(x_{n+s}(r-z_{n+1})+k(y_{M}-y _{n})))(1+h_{2})^{-1};\\ x_{n+1}&=(x_{n+s}+\sigma h_{2}(y_{n+1}))\cdot(1+h_{ 2}\sigma)^{-1};\end{split}  \tag{6}\]An example of test message (1010110010) transmission by developed chaotic communication systems based on PM and SCM modulation, and their synchronization errors on transceivers, are shown in Figure 2a and Figure 2b, respectively.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Dataset Descriptions
#### 3.1.2 DeepGlobe Road Dataset  
This dataset comprises images collected from Thailand, Indonesia, and India, cumulatively covering an area of 2220 square kilometers, which includes urban and suburban locales. It consists of 6226 training images, each with a resolution of 1024 \(\times\) 1024 pixels and a spatial resolution of 0.5 m per pixel. In this dataset, roads are annotated as the foreground, while other objects are marked as the background. The data are partitioned into training, validation, and testing sets at an 8:1:1 ratio, adhering to standard academic practices.
## 2 Materials and Methods
### Experimental Procedure  
Uniaxial compression tests were conducted at real-time temperatures of 25 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C. The specimens were placed on the test platform, and the high-low temperature chamber was closed and sealed with high-temperature cotton to prevent heat loss during warming [23]. The wiring was then connected, and circuit safety was verified. The specimen was heated at a rate of 5 \({}^{\circ}\)C/min. Upon reaching the target temperature, it was maintained for 2 h to ensure uniform internal heating [24]. After reaching thermal equilibrium, the specimen was loaded at the target temperature using displacement control at a rate of 0.1 mm/min until failure. The basic mechanical parameters of the specimen are presented in Table 1.  
Based on this, cyclic loading and unloading tests with constant and variable lower limits were conducted under high-temperature conditions. The heating method was identical to that of the first set of tests. Once the target temperature was reached, according to the recommendations for rock tests issued by the International Society for Rock Mechanics (ISRM) [22], cyclic loading and unloading tests were performed at a rate of 0.1 mm/min. The two cyclic loading and unloading paths are illustrated in Figure 3. Constant lower limit cyclic loading and unloading: Loading begins at 0 MPa and increases to 10 MPa, followed by unloading to 0 MPa at the same rate, completing one cycle. The target stress for each subsequent loading increased by 10 MPa, while the unloading stress remained at 0 MPa. This process was repeated until specimen failure. Variable lower limit cyclic loading and unloading: The target stress for each loading was identical to that of the constant lower limit test. However, only the first unloading reached 0 MPa. In subsequent cycles, the unloading target was set to the maximum stress achieved in the previous loading, continuing until specimen failure. To minimize errors and ensure test accuracy, three specimens were tested under each condition [25; 26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**T** (\({}^{\circ}\)**C**) & **\(\sigma_{\text{s}}\) (MPa)** & **\(\epsilon_{\text{s}}\) (10\({}^{-3}\))** & **E (GPa)** \\ \hline
25 \({}^{\circ}\)C & 149.42 \(\pm\) 12.25 & 6.53 \(\pm\) 0.12 & 31.87 \(\pm\) 1.40 \\
200 \({}^{\circ}\)C & 171.64 \(\pm\) 13.73 & 6.67 \(\pm\) 0.11 & 33.39 \(\pm\) 1.79 \\
400 \({}^{\circ}\)C & 134.09 \(\pm\) 13.34 & 7.91 \(\pm\) 0.44 & 23.20 \(\pm\) 1.02 \\
600 \({}^{\circ}\)C & 78.81 \(\pm\) 2.74 & 11.63 \(\pm\) 0.31 & 8.98 \(\pm\) 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Uniaxial compression test results at real-time temperature.  
Figure 3: Cyclic loading and unloading path diagram.