esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup and Procedures  
As shown in Figure 3, the pipeline was fixed by a holding device, a spring-loaded impact hammer applied an impact on the middle position of the pipeline, and a microphone with a frequency band of 10 Hz-20 kHz was placed about 5 cm away from the impact position to capture the percussive acoustic signal generated by the impact. During the experiments, the sampling rate of the data acquisition device was set to 100 kHz.  
Figure 3: (**a**) Schematic of the experimental setup; (**b**) experimental setup.  
In the tests, six pipelines specimens with different dimensions were fabricated; the dimensions of these specimens are listed in Table 1.  
During the test, to simulate different ponding states of the pipelines, the specimens were filled with different volume percentages of water. There were a total of six experimental cases, which are listed in Table 2. The energy of each impact of the spring-loaded hammer was constant at 1). Only the selected signal was filtered with a band-pass filter matching the microphone frequency, and 100 experiments were performed for each case.
## 3 Results and Discussion
### Carcass and Physico-Chemical Traits

Table 2: Effect of genotype and diet on carcass traits of broiler chickens. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Diet (D) a**} & \multicolumn{2}{c}{**Genotype (G) b**} & \multicolumn{4}{c}{**Significance**} \\ \cline{2-9}  & **SOY** & **PEA** & **KB** & **NR** & & **D** & **G** & **D \(\times\) G** \\ \hline pH\({}_{24}\) & 5.84 & 5.84 & 5.80 & 5.88 & 0.02 & ns & * & ns \\ Color 24 h & & & & & & & & \\ \(L^{*}\) & 53.98 & 53.91 & 54.16 & 53.73 & 0.51 & ns & ns & ns \\ \(a^{*}\) & 3.22 & 3.19 & 3.52 & 2.89 & 0.17 & ns & ns & ns \\ \(b^{*}\) & 5.81 & 5.54 & 5.49 & 5.85 & 0.39 & ns & ns & ns \\ WHC, \% & 13.90 & 13.78 & 14.28 & 13.39 & 0.26 & ns & ns & ns \\ \hline \hline \end{tabular}  
* SOY = control diet including flaked soybean (3.39 g/100 g DM); PEA = experimental diet including pea beans (6.78 g/100 g DM); b KB = Kabir Rosco Plus; NR = New Red. WHC = Water holding capacity; SEM = standard error mean; ns = not significant (\(p>0.05\));  
\end{table} 
Table 3: Effect of genotype and diet on the physico-chemical properties of the breast muscle of broiler chickens. The use of pea beans did not significantly affect meat pH, color and WHC. Similarly, other studies [8; 23; 30] on the use of pea as a substitute of soybean meal did not show differences in meat quality traits (pH and color). Differently, [PERSON] et al. [31] reported that a fava bean diet increased the pH values of breast muscle as compared to a soybean diet, but it did not influence meat color. [PERSON] and [PERSON] [30] observed higher WHC values in the meat of birds fed with peas in comparison with birds fed the soybean diet. Genotype influenced only the pH of breast meat: NR birds showed a higher (\(p<0.05\)) ultimate pH value than the KB birds (Table 3). The ultimate pH is known to affect the structure of myofibrils and consequently the WHC and the color of the meat [34]; this relationship is not confirmed in our study. However, pH values found in the present study were in normal ranges [35] and do not indicate any problems. Differences in ultimate pH were generally found between slow-growing chickens and fast-medium-growing ones [33; 36; 37].
# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 4 Discussion and Conclusions
Therefore, future research will focus on developing a faster method for 3D point cloud segmentation of wall spalls in civil buildings, utilizing a lightweight network to reduce computational load and enhance processing speed. * In addition, only one major building defect has been considered in the segmentation dataset. In future research, we plan to expand the dataset to include cracks, changes in structural dimensions, curvatures, and tilting. These issues, which may indicate material fatigue, settlement, or alignment problems, are crucial for identifying potential risks to building integrity. The accuracy and robustness of the detection system are aimed to be enhanced by incorporating these additional defect categories, enabling more comprehensive health assessments of buildings. * Finally, to achieve continuous tracking of structural changes in buildings, future experiments will involve periodic scans and comparisons, providing recommendations for timely repairs and maintenance of the buildings. **Author Contributions:** Conceptualization, J.W. and Y.P. ; methodology, J.W. and Y.P. ; software, Y.P. ; validation, X.T. ; formal analysis, Y.P. ; investigation, J.W. and Y.P. ; resources, J.W. ; data curation, Y.P. ; writing--original draft preparation, Y.P. ; writing--review and editing, J.W. and Y.P. ; visualization, X.T. ; supervision, J.W. and Y.P. ; project administration, X.T. ; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Qingdao, grant number 23-2-1-208-zyyd-jch. **Infrared Consent Statement:** Not applicable. **Data Availability Statement:** Some or all data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to confidentiality agreements. The authors would like to express many thanks to all the anonymous reviewers. The authors declare no conflicts of interest.
## 5 Summary and Consideration for Implementation of Optical Link
However, considering the results of other researchers including the author of this paper, the dispersion map should be optimized for high-speed and high-capacity optical transmission depending on the bit rate, the modulation format, transmission method, transmission distance, and network topology. In other words, the optimization of the dispersion map, which affects the performance of the dispersion-managed link, is inevitably process of finding the best rRD, TRD, unit dispersion profile and the overall dispersion profile of whole link suitable for various requirements, and it should be understood that this paper also went through such a process.
# A Prospective Study on Accuracy of Computer-Based Fully Guided Versus Pilot-Guided Implant Surgery
###### Abstract  
The aim of this study is to compare the implant placement deviations and evaluate the 1-year post-implant placement bone loss of pilot and fully guided implant placement protocols. In the first method, the pilot drill is used for guide surgery, and the following procedure is a method for performing implant surgery in a non-guided protocol. The second method is to perform fully guided surgery. A total of 74 implants in 20 patients were included. Postoperative CT scans were used to compare the implant placement deviations with the preoperative plan. In addition, bone loss was compared one year after surgery. In shoulder area, the implant deviations from the planned positions for dx(mesio-distal), dy(bucco-lingual), dz(vertical) dimensions, mean deviations with pilot-guided protocol (\(n=31\)) were \(0.50\pm 0.42\) mm, \(0.61\pm 0.55\) mm, \(0.87\pm 0.71\) mm, and \(1.33\pm 0.75\) mm, respectively. The corresponding deviations for fully guided protocol (\(n=41\)) were \(0.50\pm 0.52\) mm, \(0.29\pm 0.27\) mm, \(0.56\pm 0.51\) mm, and \(0.96\pm 0.57\) mm. In apical area, the corresponding deviations for pilot-guided protocol were \(0.75\pm 0.72\) mm, \(0.61\pm 0.46\) mm, \(0.98\pm 0.76\) mm, and \(1.54\pm 0.87\) mm. Deviations for fully guided protocol were \(0.91\pm 0.90\) mm, \(0.44\pm 0.39\) mm, \(0.54\pm 0.51\) mm, and \(1.38\pm 0.76\) mm, respectively. Angular deviations were \(3.33\pm 3.23^{\circ}\) with pilot-guided protocol and \(3.90\pm 1.85^{\circ}\) with fully guided protocol. The average bone loss after 1 year was \(0.50\pm 0.29\) mm and \(0.50\pm 0.24\) mm, respectively. In the shoulder area, dy(bucco-lingual) of horizontal deviations, vertical deviations, and mean value of the deviations showed a statistically significant difference between fully guided and pilot-guided (\(p=0.005\), \(p=0.033\), and \(p=0.023\), respectively). In the apical area, vertical deviations showed a statistically significant difference. However, the mean value of the deviations did not show a statistically significant difference (\(p=0.347\)). There was no statistically significant difference in angular deviations (\(p=0.59\)).  
implant; CT-guided surgery; computer-assisted surgery; angular deviations; distance deviations +
Footnote †: journal: Accepted: 10 March 2020; Published: 13 March 2020
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 3 Problem and Algorithm Description
### DC-KH Algorithm Description  
#### Dynamic Hierarchical and Non-Uniform Clustering Stage  
At first, UWSNs are initialized. Then, a random number (\(\zeta\)) varying between 0 and 1 is assigned to each node. The relationship between the size of \(\zeta\) and the threshold (\(\tau\)) is judged. If \(\zeta\) is less than \(\tau\), then the node candidate is the master cluster head node. Otherwise, it becomes the node in the cluster until the end of cluster head election. To solve the problem of \"hot spots\", the non-uniform clustering of UWSNs can be realized in the latter stage by setting the competition radius [14]. It is formulated as follows:  
\[\tau=\frac{\mathrm{p}}{\left[1-\mathrm{pr}\cdot\mathrm{mod}\left(\frac{1}{ \mathrm{p}}\right)\right]}\cdot\frac{E(n_{i})}{E_{n\_max}},\ n\in G \tag{24}\]  
\[R_{c}=[1-\frac{c(d_{max}-d_{i\to Bs})}{d_{max}-d_{min}}]\cdot R_{c}^{max} \tag{25}\]  
where in Equation (24), \(p\) is the probability of being selected as the cluster head, \(r\) is the number of cycles, \(E_{n\_max}\) is the initial energy, \(E(n_{i})\) is the current energy, and \(G\) is the current cluster head node set. In Equation (25), \(d_{max}\) and \(d_{min}\) are, respectively, the farthest and nearest distance between the node and base station, \(d_{i\to Bs}\) is the distance between candidate cluster head and base station, \(R_{c}^{max}\) is the maximum competitive radius, and \(c\) is a random value ranging from 0 to 1.  
To avoid the nodes being repeatedly selected as cluster heads, all nodes are dynamically stratified. The nodes are divided into H/(Rt + 1) layers, and each layer is numbered from 1. H is the depth of the water, and d is a fixed value. After the Rt/d round again, the network is restored to the initial state of stratification. Given that the 0-th layer node is in the range of sink nodes, the cluster cannot be formed. In this case, the first round of the first floor is the ratio of water depth (\(d\)) and interval. At the end of each round, the network layer will adjust the distance (\(d\)) downwards. Equation (26) represents the nodes in the first round of the hierarchy.  
\[L_{ni}=[\frac{h_{i}+d\cdot mod(n-1,d/d)}{d}] \tag{26}\]  
Figure 1 is a sketch map of the node dynamic hierarchical and non-uniform clustering. The figure shows that suitable cluster heads are selected by dynamic hierarchical and non-uniform clustering. Thus, it solves the problem of premature death of cluster head nodes and the network \"hot spots\". In addition, the network energy consumption can be balanced, and the survival time of UWSNs can be prolonged.
## 3 Results
### Variable Importance Plots  
The top performing UAV and PlanetScope regression models with all variables were both from RF and were multi-date combinations of the 12, 20, and 27 May datasets. Modelling of RF in RStudio can be visualized with a variable importance plot using the \"varImp-Plot()\" function. The higher a variable's IncNodePurity value is, the more important the explanatory variable is in the prediction of CNW. In the UAV model plot (Figure 5) with 28 variables, plant height was by far the most important predictor, followed by LAI. Red-edge VIs (NDRE, RVI2, CI_RE) were the third, fourth, and sixth most important predictor variables, respectively. Of the MicaSense band reflectances, NIR was the most important. Soil moisture also appeared to be among the top group of important variables with a greater difference in IncNodePurity compared to the other variables in the plot below. Of the topographic metrics, TWI #2 and TWI #1 held more importance than the rest.  
The satellite RF regression model plot with 24 variables indicates height as the most important predictor for CNW (Figure 6). Of the four PlanetScope band reflectances, the blue band was most important and second-most important overall of all variables, followed closely by LAI. Overall, VIs and PlanetScope bands were among the most important variables, while topographic metrics and soil moisture were the least important.  
Figure 5: Variance Importance plot produced by the UAV RF regression model of data from 12, 20, and 27 May and 28 variables, using the function varImpPlot() in RStudio. Higher IncNodePurity values indicate more impact on canopy nitrogen estimation. (1) Please refer to Table 1 for the full name of vegetation indices. TWI_1, total wetness index #1; TWI_2, total wetness index #2.
# Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
## 5 Conclusions  
In this study, an innovative hyperspectral anomaly detection method, called the spectra-based selective searching (Triple-S) algorithm, utilizing image oversegmentation and merging, is proposed, fully combining and utilizing the spatial and spectral information of hyperspectral images. The adaptive regions composed of adjacent pixels with the same spectral characteristics are referred to as the processing unit rather than a single pixel. In addition to the difference between the anomaly and background, the spectral similarities between the anomaly pixels and between the background pixels are both exploited to
# Flexural Capacity of a New Composite Beam with Concrete-Infilled Tubular Lower Flange
## 3 Flexural Strength Estimation
### Non-Composite Specimens
\]  
The yield moment of the non-composite beam section with web openings illustrated in Figure 10 can be computed by determining the location of the elastic neutral axis (\(y_{NA}\)) as follows:  
\[y_{NA}=\frac{\sum A\overline{y}}{\sum A}, \tag{10}\] \begin{table}
\begin{tabular}{c c} \hline
**Internal Axial Force Component** & **Force Equation** \\ \hline Compression force at upper flange (\(C_{Af}\)) & \(C_{Af}=b_{f}\times t_{f}\times F_{y}\) \\ Compression force at web (\(C_{Aw}\)) & \(C_{Aw}=t_{w}\times(h_{A}-t_{f})\times F_{y}\) \\ Compression force at upper flange of tube (\(C_{tw}\)) & \(C_{tw}=b_{u}\times t_{u}\times F_{y}\) \\ Compression force at web of tube above PNA (\(C_{tw}\)) & \(C_{tw}=2t_{u}\times(y_{PNA}-h_{A}-t_{u})\times F_{y}\) \\ Tension force at web of tube below PNA (\(T_{tw}\)) & \(T_{tw}=2t_{u}\times(H-y_{PNA}-t_{u})\times F_{y}\) \\ Tension force at lower flange of tube (\(T_{tbf}\)) & \(T_{tbf}=b_{u}\times t_{u}\times F_{y}\) \\ Tension force at main rebars (\(T_{re}\)) & \(T_{re}=n\times A_{re}\times F_{y,re}\) \\ \hline \end{tabular}
\end{table} 
Table 6: Internal force components in case the plastic neutral axis exists at the web of the tube. where  
\[ \begin{array}{ll}\sum A\overline{y}=b_{f}t_{f}\times\frac{t_{f}}{2}+t_{w}(h_{A}- t_{f}-h_{h})\times\frac{(h_{A}+t_{f}-h_{h})}{2}+b_{u}t_{u}\times(h_{A}+\frac{t_{f}}{2})+2(h_ {u}-2t_{u})t_{u}\times(h_{A}+\frac{h_{u}}{2})\\ \qquad+b_{u}t_{u}\times(H-\frac{t_{u}}{2})+nA_{re}\times(H-h_{re}),\\ \text{and }\sum A=b_{f}t_{f}+t_{w}(h_{A}-t_{f}-h_{h})+2b_{u}t_{u}+2(h_{u}-2t_{u})t_{ u}+nA_{re}.\end{array} \]  
From this equation, the moment of inertia of the entire composite section (\(I_{T}\)) can be written as:  
\[ \begin{array}{ll}I_{T}=&\frac{b_{f}t_{f}^{3}}{12}+b_{f}t_{f}\times(y_{NA}- \frac{t_{f}}{2})^{2}+\frac{t_{w}(h_{A}-t_{f}-h_{h})^{3}}{12}+t_{w}(h_{A}-t_{f} -h_{h})\times(y_{NA}-\frac{h_{A}+t_{f}-h_{h}}{2})^{2}\\ &+\frac{b_{u}t_{u}}{12}+b_{u}t_{u}\times(y_{NA}-h_{A}-\frac{t_{u}}{2})^{2}+2 \times\left(\frac{t_{u}(h_{u}-2t_{u})^{3}}{12}+(h_{u}-2t_{u})t_{u}\times(y_{NA} -h_{A}-\frac{t_{u}}{2})^{2}\right)\.\\ &+\frac{b_{u}t_{u}}{12}+b_{u}t_{u}\times(y_{NA}-H+\frac{t_{u}}{2})^{2}+n\times \left(\frac{\pi\times r_{re}}{4}+\pi\times{r_{re}}^{2}\times(H-h_{re})^{2}\right) \end{array}  \tag{11}\]  
Finally, the yield moment can be expressed as:  
\[M_{y}=\frac{F_{y}I_{T}}{y_{\max}}, \tag{12}\]  
where \(y_{\max}\) is the larger of the distances from the elastic neutral axis to the top and bottom of the section. In the derivation of Equation (12), it is assumed that the rebars have a higher yield strength than that of steel plate components, thus yielding first occurs at the top or bottom fiber of the section.
# Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area
## 2 Multiple Linear Regression Inversion Principle of Initial Ground Stress Field  
Regarding conventional mountain tunnels, the designed depth of the tunnel is large. The surrounding rock of the tunnel area is weak. The geological conditions are complex, and the tectonic action is strong. The poor geology is developed. Especially when the whole tunnel axis passes through several large fault fracture zones, geological disasters, such as landslides and collapses, very easily occur [31; 32]. For the comprehensive action of the above factors, the ground stress in the tunnel area is complex. To grasp the distribution law of the ground stress field of the tunnel area, understand the stratum environment where the tunnel structure is located, and ensure the safety of tunnel construction and operation, it is necessary to measure the ground stress in the tunnel area [33]. Due to the limitation of geological topography and the input of material resources and manpower, it is impossible to set up boreholes with sufficient density to fully measure the ground stress in the tunnel area. Through the regression inversion of the ground stress, the distribution law of the initial ground stress field can be grasped as a whole, which can provide some reference for the route selection and construction of the tunnel [34].  
The multiple linear regression of the initial ground stress field is based on the multiple linear regression numerical analysis method, which decomposes the complex geological processes in the formation of ground stress into a variety of simple basic factors acting alone [35]. Using the superposition principle of elasticity, the results of many basic factors are regarded as independent variables and the ground stress at the corresponding points in the ground stress field is regarded as a dependent variable. Then, a linear relationship between the two variables will be established. The initial ground stress field analysis by using multiple linear regression method is only based on the on-site measured data. The differences of methods and technical operations in the process of obtaining on-site measured data are not taken into account. The main implementation method is shown in Figure 1.  
Figure 1: Principle of regression inversion of initial ground stress.
## 4 Modeling and Results
### Improved PNN-SVM System Results  
The results of modeling of the improved PNN-SVM smart system from Figure 6 based on a given dataset are shown in Table 1  
It should be noted that these results were obtained with the optimal value of the smooth factor (\(\sigma=0.12\)) for PNN and SVM with _linear_ kernel. Practical experiments have shown that the nonlinear _rbf_-kernel, which demonstrates the highest accuracy among other kernels when using a simple SVM [14], in the proposed system provides significantly lower accuracy (93% vs. 97%). This is due to the additional modeling of input data before submission to the SVM based on the Ito decomposition, which is provided by the improved PNN-SVM system. According to [PERSON]'s theorem [42], nonlinear expansion of the input data space, in this case, based on this decomposition type, increases the probability of correct classification by linear models. That is why the linear kernel for SVM here demonstrates the highest accuracy among other kernels  
As can be seen from Table 1, the improved PNN-SVM smart system provides high classification accuracy according to all performance indicators.
# Is Malware Detection Needed for Android TV?
## 3 Methods
### Dataset Description  
We collected 1107 Android TV applications from the Androzoo [20] dataset, and 3 of them were labeled as malicious based on VirusTotal's antivirus scanners. We excluded these 3 applications and injected a malicious payload into some of the benign applications. We obtained errors on payload injection for some of the applications and could not convert them. As a result, we obtained 471 Android TV malwares and 567 benign applications. We named this collection the Androzoo Payload-Injected TV dataset and used it in training the malware detection models.  
We collected 370 Android TV applications from the APKMirror website, and all of them were labeled as benign. A total of 188 TV applications from the APKMirror collection were converted into malware, and the remaining 182 TV applications were kept as benign. We named this collection the APKMirror Payload-Injected TV dataset and used it in testing the malware detection models.  
The number of malicious and benign applications in the datasets is shown in Table 3 with the malware ratio. The malware ratio of the Androzoo Payload-Injected TV dataset was calculated as 45%, including 154 malwares with reverse_http, 154 malwares with reverse_http, and 163 malware with reverse_tcp payloads. The malware ratio of the APKMirror Payload-Injected TV dataset was calculated as 51%, including 188 malicious TV applications consisting of 67, 64, and 57 malwares with reverse_http, reverse_https, and reverse_tcp payloads, respectively.  
In order to select the best features using the TF-IDF method, we split 10% of the applications randomly in the Androzoo Payload-Injected dataset. We used these 103 applications only for feature selection, and they were excluded in the model training and testing datasets. We used the remaining 935 applications for the training of the classification models and saved the extracted features as the model training dataset. To test the model, we used 370 applications in the APKMirror Payload-Injected dataset. The features extracted from the test applications were saved as the model testing dataset. We exported the extracted features as training and testing spreadsheet files. In this way, we shared the features extracted from Android TV applications for use by future studies on malware detection in the literature.  
Both the training and testing spreadsheets include 1000 features, which are normalized TF-IDF values, and class labels, where benign applications are represented with 0 and malicious applications with 1. The features extracted from the decoded \"AndroidManifestxml\" files are placed in the first 500 columns. The features extracted from binary source (classes.dex) files are placed in the next 500 columns. The last column shows the class labels of the application. The features extracted from the applications are placed in rows. Also, the header row in spreadsheet files includes feature names, which are shown in string format for XML files and in hexadecimal format for DEX files.