esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Experiments and Result Analysis
### Result Analysis
Viewing the experimental results, some methods performed well on certain programs or patterns, e.g., [PERSON] et al. [28] achieved 100% precision and recall for two patterns on JUnit, but the performance was very poor on some other programs or patterns. This is because different programs and patterns have their own characteristics, and it is hard for researchers to take a comprehensive consideration of these different characteristics in the process of manual feature extraction and feature selection. In addition, like non-machine learning methods, these design pattern detection methods based on traditional machine learning algorithms do not perform well on behavioral patterns, and most of them cannot distinguish between the Adapter/Command patterns and the State/Strategy patterns. This is because it is more difficult to select appropriate and effective features for behavioral patterns than for creation and structural patterns. In general, the method proposed in this paper has achieved higher precision and recall than the design pattern detection methods based on traditional machine learning algorithms on the three programs. For different programs and their patterns, the precision and recall were stable at more than 85% in most cases, including the identification of behavioral patterns such as Observer and State. According to the above analysis, the method in this paper can achieve better identification results than the non-machine learning methods and the design pattern detection methods based on traditional machine learning algorithms, and can maintain stability for different projects and different patterns. In addition, this paper can also distinguish instances of behavioral patterns that have similar structural features to other patterns (e.g., there are similar structural features between the State pattern and the Strategy pattern, as well as the Command pattern and the Adapter pattern).
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 5 Testbed Implementation
### Communication Protocol
In particular, the experimentation phase showed that in an indoor environment, the use of the value \(n=1.6\), as also shown in the figure above, is very adequate because it provides RSSI values very close to the ideal ones. Instead, for the outdoor environment, the value \(n=1.9\) was chosen for the same reasons. Once these parameters have been determined and the distances have been calculated by exploiting the RSSI index, the localization phase comes into play, and dedicated algorithms can be applied.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 2 Methods
### Grid Network Topology Model and Routing Algorithm
#### 2.3.2 Dynamic Routing Planning Algorithm  
The low-orbit satellite constellation system has an improvement in on-board processing capability and will help realize communication between satellites. However, choosing the shortest path in a highly time-varying situation involves a dynamic planning algorithm. This is often used to solve the problem of optimization in a multi-stage, multi-task, and multi-objective decision-making process [1,20]. Therefore, satellite routing planning is an NP-hard (Non-deterministic Polynomial hard) problem, and the satellite route planning algorithm for a complete data transmission process will be given (Figure 8a).  
Based on the idea of spatial grid virtualization, the satellite moving period \(T\) in the study period is divided into \(K\) sub-periods. When the time (\(\Delta t=T/K\)) is sufficiently small, the satellite topology can be considered static, and equivalently regarded as the topology of the grids where the satellite is located (Figure 8b). Therefore, a correlating matrix of topology containing satellites and ground targets can be established according to the situation where the cells are occupied in the particular time slice. The size of the entire correlating matrix is \((M+N)\times(M+N)\), where \(M\) and \(N\) represent the number of satellites and ground targets established, respectively. The main correlating matrix of the satellite topology exists on-board and is updated regularly [26]. The shortest path algorithm is developed based on this matrix, and its time complexity and space complexity are \(O((M+N)^{2})\). The number of satellites and ground targets are large, and the cost of calculation and storage is too high, not meeting the requirements of on-board algorithms and storage. Considering that visibility is a necessary condition for communication, each time a connection graph is constructed, a step of querying the visibility table is added. This can filter out satellites that are inaccessible and less likely to communicate, and thus reduce the cost of the algorithm and the pressure of on-board storage. Finally, the path is selected based on different decisions, such as minimum steps and shortest distance. Because there must be unconnected satellites in the calculation process, the size of the newly established correlating matrix must be smaller than \((M+N)\times(M+N)\). This algorithm removes the satellites that are not involved in the communication in each time period, and reduces the dimension of the main correlation matrix. The path planning time of the shortest path planning algorithm is shortened, and the time and space complexity of the shortest path planning algorithm is greatly reduced.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
Meanwhile, the SLR-only orbit overlap of 9-day arcs for the SLR-only GLOVE-A satellite were about 10 cm, 0.5 m, and 1 m [10]; the same results were obtained in Flohrer [11]. For BDS, the 3D-RMS of BeiDou-G1, -I3, -I5, and -M3 were on the level of one metre to a few metres, while the radial accuracy was on the level of decimetres [12]. In [PERSON] et al. [13], the 7-day arcs of SLR-only orbits for GPS, GLONASS, Galileo, and BDS were implemented, and the orbit accuracy were evaluated and compared, based on the different satellite types, as well as on different satellite constellations. For MEO, the radial accuracy was around 4-10 cm while the 3D-RMS are tens of cm, either for orbit overlaps or for comparisons with microwave-based orbits. Overall, most previous studies show that for a selected arc, the SLR-derived orbit for the GNSS satellites is not universal. It is not quite clear what accuracy can be achieved under different conditions. The multi-GNSS orbit of long-term time series--using solely SLR observations--was determined by [PERSON] et al. [14] and [PERSON] et al. [15]. The contribution demonstrated that 60 SLR observations--as many SLR sites as possible--and optimal 5-day to 7-day arc length were necessary in order to provide a better geometry of observations and preclude deterioration of the along-track component caused by excessive arc length. However, the SLR-only orbit for BDS is not analysed in detail, especially for the BDS GEO satellites. Moreover, the SLR-only orbit determination for BDS-3 satellites, as a fresh constellation, has hardly been explored. Based on these, the SLR-only orbit for BDS-2 and BDS-3 satellites are determined from 1 January 2019 to 30 June 2019 in this contribution. The 3-, 5-, 7-, and 9-day arc solutions are calculated to test and determine the magnitude of accuracy of the SLR-only orbit of BDS-2 and BDS-3 satellites, including a total of nine BDS satellites, which cover all the three types of satellites: GEO, IGSO, and MEO. Among them, five satellites belong to BDS-2--namely C01, C08, C10, C11, and C13. The remaining four satellites belong to BDS-3, which are C20, C21, C29, and C30. The accuracy of SLR-only orbit determination is evaluated by the orbit overlaps and by the comparison with the microwave-based precise orbit of WUM. Besides, the dependency of SLR-only orbit determination accuracy on the number of SLR observations and on the number of SLR sites is explored in detail. The paper is organized as follows. In Section 2, the methodology of SLR-only orbit determination and the corresponding accuracy assessment strategy are described. Then, Section 3 presents the time series of processed SLR data and the SLR validation residuals derived from WUM while the results of SLR-only orbit determination are shown in Section 4. Section 5 discusses the accuracy dependency of the SLR-only orbit determination on the number of SLR observations and on the number of SLR sites. Finally, we summarize and comment on the conclusions in the last section.
## 4 Results and Discussion
### Feature Ranking Analysis  
We computed the feature ranking list resulting from each classification scheme over the cross-validation rounds for the clinical interpretability of the results. Figure 4A shows the feature ranking for the six schemes. It can be noted that only the diameter results are relevant, regardless of the adopted scheme. The correlation between tumor size and lymph node status has been evaluated in several studies. In patients with breast cancer, increasing tumor diameter at diagnosis is associated with an increasing number of metastatic lymph nodes [39,40].  
Moreover, it is worth noting that the three Lasso Logit models agree much more with each other on the final average feature ranking, outlining the features histology, diameter, ER and multifocality as the most important features. As a matter of fact, Logit Lasso exhibits greater stability of feature ranking compared to RF. Stability refers to the feature ranking's sensitivity to perturbation of training samples and it is associated with the reproducibility power of the feature selection method [41]. When analyzing feature selection algorithms in different clinical scenarios, high stability could be just as crucial as high classification accuracy [42, 43, 44]. We used the quartile coefficient to objectively measure the stability of these features for each classification scheme. It can be seen in Figure 4B that the feature diameter is the most stable among all the features, regardless of the adopted scheme, while the HER2 feature exhibits the highest dispersion coefficient, showing high instability. Moreover, the weights of Age, PgR and Ki67 are shrunk to zero from the Lasso algorithm, highlighting their low impact on the classification task.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 6 Numerical Results
### Network Overhead Performance  
Finally, to further verify the superiority of our proposed method, we define a network overhead function in two aspects (i.e., user panel and control panel). Figure 10 shows the corresponding network overhead of these two panels for different user access quantities. It can be seen that with the same user access volume, the proposed method causes the least network overhead.  
Network user panel overhead, that is, the burden brought by the data service carried in the network, which can be measured by the data flow in the network, as shown in the following formula.  
\[Bd_{u}=E(\frac{RB_{used}}{RB_{total}})=E(\frac{\sum_{k\in M}B_{kb}}{C_{ch}}) \tag{45}\]  
The control surface overhead is defined by the transmission volume of the signaling, as shown in the following formula.  
\[I\leq-log(\frac{1}{L}) \tag{46}\]  
\[Id_{max}=-log(\frac{1}{L})M \tag{47}\]  
\[I_{actual}\leq\sum_{i=0}^{\infty}-log(\frac{1}{L_{i}})MAS^{i}=-log(\frac{1}{ L})M\sum_{i=0}^{\infty}AS^{i} \tag{48}\]  
\[Bd_{c}=10 log(\frac{I_{actual}}{Id_{max}}) \tag{49}\]  
where \(L\) indicates the total signaling type in order to describe the cost of a single message on the control panel.  
Figure 9: MS successful access ratio of proposed slicing allocation method with different RB amounts.  
By the way, since in this study we tried to design a way that can marginalize the decision-making process to reduce the overhead on the core network controller, which may challenge the computing power of the mobile station, we also conducted experiments on the CPU computing load in experiments and simulations. Table 5 shows the CPU and cache usage rate during the simulation loop. The results show that the average mobile device is fully capable of running the model generated in our proposed way, which is lower than what many other edge computing-related studies require for mobile devices [46; 47; 48; 49; 50].
# Indentation Measurement in Thin Plates under Bending Using 3D Digital Image Correlation
###### Abstract
[28] have also incorporated static contact laws for the analysis of impact problems even when it was reported that for dynamic condition the contact law differed from the static contact law [15; 28]. DIC is a full-field and non-contact optical technique for strains and displacements measurements in structural components under loading. During the deformation, a sequence of images showing a random gray intensity (speckle) over the surface are captured during the test. Each image is divided into square virtual subsets or facet in pixels, centered at a point P (\(X_{0}\),\(Y_{0}\),\(Z_{0}\)). Every facet presents a different gray scale of intensity. During the deformation, some algorithms of correlation [29] track the speckle contained into a facet. Each facet at the reference image is searched in the deformed image \(P\)(\(X^{\prime}\),\(Y^{\prime}\),\(Z^{\prime}\)). The result is a displacement vector located in the center of each virtual facet. When a stereoscopic system is adopted (3D-DIC), a minimum of two cameras or a mirror-based system is required. Thus, out-of-plane displacement and strains over the surface of the specimen can be computed. In this case, the system must be previously calibrated [30; 31]. Although many factors are involved, errors in measurements are kept lower than 0.1 mm for most DIC-3D applications [32; 33]. DIC technique has been widely employed in many fields in experimental mechanics; however, due to the nature of the technique (non-contacting and full-field), its application is highly interesting for the analysis of contact problems. However, no information has been found in the literature related to the application of 3D-DIC to the analysis of contact problems. In this study, the relative out-of-plane displacements in the thickness direction of the specimen have been measured using 3D-DIC. The indentation during a quasi-static test between a plane specimen and a rigid sphere has been experimentally measured and evaluated in thin aluminum plates. As a result, the experimental contact law behavior and the effect of bending have been quantified.
## 3 System Requirements for BVLOS Flight Operations
### RAVEN-Proposed Small UAS DAA System
#### 3.5.1 ADS-B for Cooperative Traffic  
Since 2011, the RAVEN project has partnered with R-Cubed Engineering for the use of their All-Weather Sense and Avoid System (AWSAS) shown in Figure 9a. AWSAS is an ADS-B technology-based system which transmits ADS-B positional information on 978 MHz and receives ADS-B radio transmissions on both 978 MHz ES and 1090 MHz [14; 15].
# Recent Developments and Emerging Trends in Paint Industry Wastewater Treatment Methods
## 3 Paint Industry Wastewater Treatment Methods  
Paint industry wastewater contains various organic solvents, pigments, surfactants, and other chemicals but also solid particles, heavy metals, oils, etc. Therefore, treatment before discharge into the environment is needed to suit the prescribed standards.  
It was reported that many paint manufacturing industries discharge effluents without treatment; even though wastewater treatment plants are inefficient in bringing wastewater conditions to environmentally acceptable safe limits [30]. Industrial wastewater treatment techniques may vary depending on their composition, origin, and the specific company.  
It has been shown that many paint industries, especially small-scale ones are not able to sufficiently treat the effluents before their discarding into the environment because they do not have suitable treatment systems [9]. On the other hand, the empirical data indicates that despite the installation of treatment systems in the paint industries a high level of pollution is still released into the environment. In addition to COD removal, minimizing sludge production from the coagulation process is crucial [9].  
The recently used treatment techniques for the paint industry effluents can be divided according to the treatment's characteristic into physicochemical, biological, and electrochemical treatment and advanced oxidation processes (Figure 3).  
The commonly used methods include coagulation, adsorption [29], flocculation, filtration, electrochemical processes [30], advanced oxidation processes, and biological processes such as composting [31; 32; 33].  
Nowadays, a combination of different effective treatment methods is also used to safely dispose of or, recycle the treated wastewater [32; 34].  
The treatment of the paint industry effluents can be separated into four stages: preliminary, primary, secondary, and tertiary treatment (Figure 4). During the preliminary treatment neutralization and equalization take place [35]. The primary treatment includes simple and traditional techniques, such the sedimentation, chemical coagulation, flocculation, magnetic separation, and flotation. Physicochemical separation or biological oxidation is applied during the secondary treatment for the reduction of the organic compounds. Tertiary treatment is used for improving effluent treatment [36]. Some reviews of treatment methods have also shown the application of different molecular imprinted polymers for the elimination of heavy metal ions in everyday toxic paints [37].
# Development of a High-Precision Touch-Trigger Probe Using a Single Sensor +
## 4 Experimental Results and Discussion
### Sensitivity and Permissible Range  
Figure 10 shows the sensitivity and permissible range of the probe. It can be found that the sensitivities of the probe in horizontal and vertical directions were nearly equal and coincident with the design target quite well when the ball tip's displacement was within 1 \(\upmu\)m. In other words, the probe has a uniform sensitivity within a trigger range of 1 \(\upmu\)m that is large enough for the touch-trigger measurement. Figure 10 also illustrates that the probe has a permissible range up to 8 \(\upmu\)m, which provides a large safety margin for the application of the probe. The difference between two sensitivity curves could be caused by the error of manufacturing and assembly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{5}{c}{**Residual Errors (nm)**} \\ \cline{2-6}  & \(X\)**+** & \(X\)\(-\) & \(Y\)**+** & \(Y\)\(-\) & \(Z\) \\ \hline
1 st & \(-\)0.6 & \(-\)1.1 & \(-\)2.7 & \(-\)1.4 & \(-\)1.9 \\
2 nd & 2.9 & \(-\)0.8 & 0.2 & 1.3 & \(-\)1.4 \\
3 rd & 0.6 & 1.9 & \(-\)1.8 & \(-\)0.3 & \(-\)1.2 \\
4 th & \(-\)2.2 & 0.7 & 1.6 & 1.3 & 0.1 \\
5 th & 0.6 & \(-\)2.6 & 2.1 & 0.7 & \(-\)2.4 \\
6 th & \(-\)3.2 & 0.8 & 0.1 & \(-\)0.3 & \(-\)2.8 \\
7 th & 1.3 & 2.0 & 1.2 & \(-\)1.0 & \(-\)1.0 \\ standard deviation & 2.0 & 1.7 & 1.8 & 1.1 & 1.0 \\ repeatability (\(K=2\)) & 4.0 & 3.4 & 3.6 & 2.2 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the trigger test.  
Figure 10: The sensitivity and permissible range of the probe.  
Figure 9: Stability test results of the probe.
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 4 Discussion  
In order to display and discuss the experimental results in this paper in a more convenient and vivid way, we plotted the experimental results MAE and MSE with the prediction scale of 1 day, 3 days, and 7 days, respectively, on the Bohai sea data set and the South China Sea data set of the six models in Figure 3 for further comparison.  
Figure 3: SST prediction results of the six models at different scales and sea areas.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**Prediction Length (Day(s))**} \\ \cline{3-5}  & & **1** & **3** & **7** \\ \hline \multirow{2}{*}{CGMP} & _MSE_ & 0.0613 & 0.1058 & **0.1658** \\  & _MAE_ & 0.1782 & 0.2411 & **0.3099** \\ \hline \multirow{2}{*}{FC-LSTM} & _MSE_ & 0.0765 & 0.1387 & 0.1721 \\  & _MAE_ & 0.1792 & 0.2788 & 0.317 \\ \hline \multirow{2}{*}{CFCC-LSTM} & _MSE_ & 0.1057 & 0.1234 & 0.1839 \\  & _MAE_ & 0.2502 & 0.2663 & 0.3531 \\ \hline \multirow{2}{*}{GED} & _MSE_ & 0.0818 & 0.1402 & 0.2460 \\  & _MAE_ & 0.2146 & 0.2823 & 0.3881 \\ \hline \multirow{2}{*}{SVR} & _MSE_ & 0.0953 & 0.1569 & 0.2416 \\  & _MAE_ & 0.2233 & 0.2955 & 0.3726 \\ \hline \multirow{2}{*}{AGLNM} & _MSE_ & **0.0485** & **0.1057** & 0.1907 \\  & _MAE_ & **0.1520** & **0.2292** & 0.3156 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction results of the South China Sea data set (2307 points).
## 3 Results
### Evaluations of Three Models in Modeling GPP  
The performances of the H-LUE model and the two LUE models in estimating the GPP of the test dataset are summarized in Figure 5. The results demonstrate that on the test dataset, the H-LUE model yields significantly higher R\({}^{2}\) and lower RMSE (R\({}^{2}\) = 0.86 and RMSE = 1.79 gC m\({}^{-2}\) d\({}^{-1}\)) values as compared to VPM (R\({}^{2}\) = 0.73 and RMSE = 2.79 gC m\({}^{-2}\) d\({}^{-1}\)) and VPRM (R\({}^{2}\) = 0.68 and RMSE = 3.26 gC m\({}^{-2}\) d\({}^{-1}\)). The H-LUE also performed better than VPM and VPRM on the training and validation datasets (Appendix C, Figure A2). The results emphasize that combining the LUE model with machine learning can lead to improved performance compared to conventional LUE models.
## Materials and Methods
### Characterization Techniques  
#### 2.5.1 Impedance Spectroscopy  
In Impedance Spectroscopy technique, a sinusoidal voltage is applied to the material under test. The excitation signal has the form of Equation (1).  
\[\mathrm{E_{t}}=\mathrm{E_{0}}\sin(\omega\mathrm{t}) \tag{1}\]  
where \(\mathrm{E_{t}}\)(V) is the voltage at a specific time, \(\mathrm{E_{0}}\)(V) is the voltage amplitude and \(\omega\)(rad/s) is the angular velocity. The relation between angular velocity and frequency (f) is described by the equation \(\omega=2\pi\mathrm{f}\).  
The applied excitation forces the conductivity mechanisms to adapt to the external field. These mechanisms are divided into three categories, (i) dipole relaxation which occurs from the orientational movement of the dipolar respecting to the excitation field, (ii) electrical conduction due to free electron or ion migration, and (iii) interfacial polarization. Interfacial polarization arises from trapped free charges in the interfacial areas between two phases (e.g., polymeric matrix and reinforcement at composite materials).  
Therefore, forcing the excitation field through a dielectric material results in an output current of the same frequency with a phase delay \(\varphi\) which is described by Equation (2).  
\[\mathrm{I_{t}}=\mathrm{I_{0}}\sin(\omega\mathrm{t}+\varphi) \tag{2}\]  
Finally, the impedance can be estimated by applying an expression analogue to [PERSON]'s general law for direct current \(\mathrm{R}=\mathrm{E/I}\); Equation (3).  
\[Z=\frac{\mathrm{E_{t}}}{\mathrm{I_{t}}}=\frac{\mathrm{E_{0}}\sin(\omega \mathrm{t})}{\mathrm{I_{0}}\sin(\omega\mathrm{t}+\varphi)}=|Z|\frac{\sin( \omega\mathrm{t})}{\sin(\omega\mathrm{t}+\varphi)} \tag{3}\]  
Impedance measurements were performed using the Advanced Dielectric Thermal Analysis System (DETA-SCOPE) supplied by ADVISE, Greece. The specimens were placed between two parallel copper plates with dimensions of 52 mm \(\times\) 12 mm. A sinusoidal voltage of 10 V was applied to the capacitor. Scans were performed between two frequency values (0.01 Hz to 100 kHz) at 14 different frequencies with the duration of each scan to be approximately 275 s. All measurements were performed under stable temperature of 21 \(\pm\) 0.1 \({}^{\circ}\)C controlled by an EXTECH VIR50 IR thermometer. The experimental setup for the impedance spectroscopy measurements is schematically illustrated in Figure 1.