esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implemented MCDM Methods
### TOPSIS Method  
The method TOPSIS [46; 56] is based on vector normalization:  
\[\widetilde{r}_{ij}=\frac{r_{ij}}{\sqrt{\sum\limits_{i=1}^{n}r_{ij}^{2}}}\quad(i =1,\ \ldots,\ n;\ j=1,\ \ldots,\ m), \tag{17}\]  
where \(\widetilde{r}_{ij}\) is the normalized value of \(j\)-th criterion for \(i\)-th alternative.  
The best alternative \(V^{*}\) and the worst alternative \(V^{-}\) are calculated by:  
\[V*=\{V_{1}^{*},\ V_{2}^{*},\ \ldots,V_{m}^{*}\}=\{(\max\limits_{i}\omega_{j} \widetilde{r}_{ij}\ /\ j\in\ J_{1}),\ (\min\limits_{i}\omega_{j}\widetilde{r}_{ij}/\ j\in\ J_{2})\}, \tag{18}\]  
\[V^{-}=\{V_{1}^{-},\ V_{2}^{-},\ \ldots,V_{m}^{-}\}=\{(\min\limits_{i} \omega_{j}\widetilde{r}_{ij}/\ j\in\ J_{1}),\ (\max\limits_{i}\omega_{j} \widetilde{r}_{ij}/\ j\in\ J_{2})\}, \tag{19}\]  
where \(J_{1}\) is a set of indices of the maximized criteria, and \(J_{2}\) is a set of indices of the minimized criteria.  
The distance \(D_{i}^{*}\) of every considered alternative to the ideal (best) solutions and its distance \(D_{i}^{-}\) to the worst solutions are calculated:  
\[D_{i}^{*}=\sqrt{\sum\limits_{j=1}^{m}(\omega_{j}\widetilde{r}_{ij}-V_{j}^{*}) ^{2}}, \tag{20}\]  
\[D_{i}^{-}=\sqrt{\sum\limits_{j=1}^{m}(\omega_{j}\widetilde{r}_{ij}-V_{j}^{-}) ^{2}}, \tag{21}\]  
The criterion \(C_{i}^{*}\) of the method TOPSIS is calculated by:  
\[C_{i}^{*}=\frac{D_{i}^{-}}{D_{i}^{*}+D_{i}^{*}}(i=1,\ \ldots,\ n),(0\leq C_{i}^{*} \leq 1). \tag{22}\]  
The largest value of the criterion \(C_{i}^{*}\) corresponds to the best alternative.
## 3 Remote-Sensing System Framework and Problem Defined
### The Predefined Flight Path Planning  
Drones are suitable for performing drone remote sensing, such as air quality and pollution monitoring. Most drone remote-sensing applications need drones to fly around a wide area and sense environmental information. Therefore, drones need a predefined path and must fly along it. Thus, a drone plans a local trajectory using the LMFM to fine-tune its motion. Here, we applied the mission planning software to plan a flying path in the GCS. The drone was powered by one battery, and the standard flight time was 27 min. The onboard computer and all sensors in the sensor layer were powered by another 4S 1000-mAh battery. Both the Zenmuse X4S (RGB camera) and Raptor OWL 640 SWIR cameras were mounted on the drone. The predefined flying path was planned on the GCS, as shown in Figure 3. Then, the drone was deployed to capture only the SWIR images in the predefined flying path.
## 5 Experimental Results and Discussion
### Non Deep Learning Classifiers  
We tried four other traditional classifiers on the same dataset for comparison, which are Support Vector Machine (SVM), Kernal SVM, Random Forest (RF), and XGBoost. These are well-known classifiers for their high performances and also considered as baseline models in classification tasks [74]. SVM can perform nonlinear classification using kernel functions by separating hyperplanes. A widely used RF classifier is an ensemble of decision trees based on the bagging approach [82, 83]. XGBoost is state of the art classifier based on gradient boosting model of decision trees, which attracted much attention in the machine learning community. RF and SVM have been widely used in remote sensing applications [29, 30, 46]. Each classifier involves hyperparameters that need to be tuned at the time of classification model development.  
We followed the \"random search\" approach to optimize major hyperparameters [84]. Best values of hyperparameters were selected based on classification accuracy achieved for the validation set, and are highlighted with bold letters in Table 6. Further details about hyper parameters and achieved overall accuracy (OA) for SVM, Kernal SVM, RF, and XGBoost are reported in Table 6 From these non-deep learning classifiers, SVM stands highest with OA = 79.6% while RF, Kernel SVM, and XGBoost achieved 77.5%, 76.8%, and 77.2% respectively. From the results presented in Table 6, our proposed Pixel R-CNN based classifier achieved OA = 96.5%, which is far better results than the non deep learning classifiers. Learning temporal and spectral correlations from multi-temporal images considering large data set is challenging for traditional non-deep learning techniques. The introduction of deep learning models in the remote sensing domain brought more flexibility to exploit temporal features in such a way that it can increase the amount of information to gain much better and reliable results for classification tasks.  
Figure 12: Visual representation of the activation of the internal neurons of Pixel R-CNN, where darker color are values close to zero and vice versa. (**a**). four samples of the same class “artificials”, (**b**). related activations inside the network at the output of the TimeDistributedDense layer \(Y_{(timeD)}\). It is interesting to notice how the four inputs are pretty different from each other, but the network representations already at this level are similar.
## 2 Materials and Methods
### IDW Interpolation  
Essentially, all spatial interpolation methods compute weighted averages of sampled data, as estimations for unknown data [2,3]. Given a set of spatial data of an attribute \(z\) at \(n\) locations, the general estimation formula is:  
\[z(\hat{s_{0}})=\sum_{i=1}^{n}w_{i}(s_{0})\cdot z(s_{i}) \tag{1}\]  
where \(z(\hat{s_{0}})\) is the interpolated value estimated for the variable of interest at the station \(s_{0}\), \(z(s_{i})\) is the sample value at the station \(s_{i}\), \(w_{i}(s_{0})\) is the weight attached to the station \(s_{i}\) and \(n\) is the stations' number.  
The main difference between all spatial interpolation methods relies in computing the weights \(w_{i}\) used in the interpolation.  
The problem we tackled consists of estimating precipitation values for some locations where these values are unknown, using as input data the precipitation values recorded at several locations in the neighborhood. The general Formula (1) is also valid for IDW. The simplest version of weights estimation uses the inverse distances from all the points to the target one [18]:  
\[w_{i}(s_{0})=\frac{1/d(s_{0},\ s_{i})^{\beta}}{\sum_{i=1}^{n}\bigl{(}1/d(s_{0}, \ s_{i})^{\beta}\bigr{)}},\ \beta>1, \tag{2}\]  
where \(d(s_{0},\ s_{i})\) is the distance from \(s_{0}\) to \(s_{i}\) and \(\beta\) is a parameter that must be determined. Thus, the weights decrease as the distance increases, especially if the value of \(\beta\) is large. The parameter \(\beta\) determines the degree of influence the neighboring stations have upon the estimates for a given station (it is expected that nearer neighbors have more influence upon the estimated value than the more distant ones).  
Choosing \(\beta\) is an optimization process by itself. Usually, the search for the optimal \(\beta\) is a grid search: a specific range is set (arbitrarily or based on some intuition of the researcher), and then \(\beta\) takes all values in that range, with a certain step-size, also arbitrarily chosen. The value yielding the lowest prediction error (among the searched values) is finally attributed to the parameter.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 4 Detailed Algorithm Description
### Forwarding Process and Loop Avoidance  
In the routing strategy mentioned above, a total of three routing tables are involved, and their priorities are: 1. detour path routing table, 2. main path routing table, and 3. alternate path routing table. During the forwarding process, the conditions for entering the next priority are as follows:  
1. The satellite is not in the state of the failure area, that is, there is no failure link in the area. 1-> 2
2. The corresponding value of the destination address of the data packet in the detour routing table is \(-1\), that is, its main path does not pass through the failed link. 1-> 2
3. According to the current routing table, the forwarding interface is in an unestablished state (point to the seam) or the link to which it points has failed. 1-> 2, 2-> 3
4. According to the current routing table, the forwarding interface direction is the same as the incoming interface direction of the data packet, which means the direction leads packets to turn back. 1-> 2, 2-> 3  
As shown in Figure 6, the data packet enters from node 4 to node 5, p1 is the main path, and p2 is the alternate path. In the case where the main path and the alternate path are both unavailable, the direction p3 opposite to the alternate path is selected for forwarding. At this time, it is no longer considered whether the forwarding interface is in the same direction as the incoming interface, that is, the data packet is allowed to turn back. If p3 is unavailable, the remaining p4 is used for forwarding at this time, and the local satellite is marked as invalid at the head of the data packet. Then, the data packet will no longer be sent to this satellite when it passes through the satellite adjacent to this satellite.  
Figure 6: Forwarding interface selection.
## 2 Experimental Research
### Main Experiment and Analysis of Obtained Results
In this way, we delve into the very principles of achieving, in this case, coatings of the desired thicknesses and obtain a clearer picture of the process itself. From this, it is possible to further speed up and improve the production processes themselves. Graphic presentations, as in Figures 5-7, show how coating thickness changes on different base materials (carbon steel, galvanized steel and aluminum) when influenced by the input variables. It is interesting that this type of analysis and data processing led to the determination of optimal values of voltage and current that provide a certain thickness of the coating. Coating manufacturers recommend the thickness in which the coating should be applied; the thickness of coating can also be requested by the end user of a coated product. Since coating is applied on different materials, it is clear that the same combination of parameters (current and voltage) will not always result in the same thickness of applied coating. Therefore, defined mathematical models and the possibility of optimization as presented in this paper can speed up the whole process of coating application. The targeted, i.e., desired, coating thickness in this research was 60 \(\upmu\)m. Statistical analysis and subsequent optimization of the observed process parameters proved that the target coating thickness on carbon steel can be achieved by setting the current to approximately 74.813 \(\upmu\)A and the voltage to 43.984 kV. In order to achieve the target coating thickness on galvanized steel, the optimal current would be 69.150 \(\upmu\)A and voltage is 80.236 kV, while the same parameters applied on the coating of aluminum would be 98.405 \(\upmu\)A and 63.420 kV, respectively.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 6 Summary of Some Important Milestones of This Work  
The methodology applied in this study problem is divided into two parts, as illustrated by two graphical schemes:  
1. The methodology used for analysis, correction, and validation of solar irradiance time series was presented in Figure 4. This part of the methodology was developed in Section 2.3, and the results obtained were presented in Section 2.4.
2. The methodology used for characterization, clustering, and decision of the number of clusters from Koppen-Geiger classification is presented in Figure 20. This part of the methodology was developed in Section 3 (characterization); in Section 4, we presented the Koppen climatic classification and clustering methods; and finally, in Section 5, we presented the maps and results.  
It should be noted that, in future work, if high-quality GHI data are available, the first part of the methodology (i.e., the one presented in Figure 4) may be unnecessary to implement. As a result, only the implementation of the second part of the methodology would be necessary in a future work (without the reduction of the number of characteristic variables from five to three (the three independent variables are sufficient). In conclusion, it can be evidenced that the second part of the proposed methodology is easy to implement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **k-Means** & **Spectral** & **k-Medoids** & **Hierarchical** & **Köppen** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline k-means & 100 & 96 & 67 & 53 & 58 \\ Spectral & 96 & 100 & 63 & 49 & 58 \\ k-medoids & 67 & 63 & 100 & 54 & 55 \\ Hierarchical & 53 & 49 & 54 & 100 & 62 \\ Köppen & 58 & 58 & 55 & 62 & 100 \\ \hline \end{tabular}
\end{table}
Table 6: Jaccard Index (JI) for 3 clusters.  
\begin{table}
\begin{tabular}{c|c c c c c} \hline  & **k-Means** & **Spectral** & **k-Medoids** & **Hierarchical** & **Köppen** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline k-means & 100 & 95 & 70 & 41 & 51 \\ Spectral & 95 & 100 & 69 & 40 & 52 \\ k-medoids & 70 & 69 & 100 & 44 & 60 \\ Hierarchical & 41 & 40 & 44 & 100 & 53 \\ Köppen & 51 & 52 & 60 & 53 & 100 \\ \hline \end{tabular}
\end{table}
Table 5: Rand Index (RI) for 3 clusters.
## 2 Materials and Methods
### Crop Model--Dssat  
DSSAT CERES-Rice (4.7.5 version) was used to estimate the rice yield. CERES-Rice was integrated into the DSSAT-CSM (cropping system model) platform and required meteorological, soil, soil-plant-atmosphere (SPAM), and management modules to simulate crop development, yield, carbon, and water balance [45]. DSSAT crop models require weather, soil, and crop management practices.
# Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset
###### Abstract  
Shoplifting poses a significant challenge for shop owners as well as other stakeholders, including law enforcement agencies. In recent years, the task of shoplifting detection has gained the interest of researchers due to video surveillance generating vast quantities of data that cannot be processed in real-time by human staff. In previous studies, different datasets and methods have been developed for the task of shoplifting detection. However, there is a lack of a large benchmark dataset containing different behaviors of shoplifting and standard methods for the task of shoplifting detection. To overcome this limitation, in this study, a large benchmark dataset has been developed, having 900 instances with 450 cases of shoplifting and 450 of non-shoplifting with manual annotation based on five different ways of shoplifting. Moreover, a method for the detection of shoplifting is proposed for evaluating the developed dataset. The dataset is also evaluated with methods as baseline methods, including 2D CNN and 3D CNN. Our proposed method, which is a combination of Inception V3 and BILSTM, outperforms all baseline methods with 81 % accuracy. The developed dataset will be publicly available to foster in various areas related to human activity recognition. These areas encompass the development of systems for detecting behaviors such as robbery, identifying human movements, enhancing safety measures, and detecting instances of theft.  
bidirectional long short-term memory; convolutional neural network; human activity recognition; shoplifting detection +
Footnote †: journal: Applied Sciences
# Image Analysis of Dynamic Brain Activity Based on Gray Distance Compensation
## 2 Automated Detection of Cellular Activity
### Activity Center Tracing in Serial Images  
During imaging there can be time-dependent changes in signal that occur independent of cellular activity, due to, for example, the inherent-honeycomb pattern noise of the fiber bundles [11] and changes in tissue absorption characteristics over time. To limit these cellular activity-independent effects on center-tracing, an offset of 15-20 pixels between potentially-matching centers was tolerated from frame to frame. Several active regions were first selected from an image that was used as a reference frame for the rest of the time series. This tolerance of offset within a time series morphs the activity centers of each frame into matching activity areas among frames. These activity regions were then refined by selecting pixels with a mean gray distance larger than the mean of the full frame intensity.  
Figure 3 displays the extracted activity areas. The first row is a short four-frame series of fluorescence changes exhibited by GeaMP3-expressing astrocytes in the imaging field. The second row is the extracted activity centers for each corresponding frame, marked with red dots. This demonstrates that, in the same brain region, there can be 17, 15, 17, and 20 activity centers detected. With the offset-tolerance approach (offset: 20), Figure 4a shows the 12 matched activity centers among these four frames. The matched activity centers are marked with different colors and shapes. Figure 4b is a zoom-in view of the highlighted region-of-interest in Figure 4a.  
Figure 3: Extracted active centers in a series of images. The first row shows four images of GeaMP3-expressing astrocytes taken at the same field of view at different times. The second row shows the extracted cellular active centers of each image in the first row. The scale shown in the first panel corresponds to all figures.
## 5 Performance Evaluation Using OPNET Testbed
### Small Network
#### 5.1.2 Buffer Overflow Percentage  
The percentage of buffer saturation of the highway scenario is shown in Figure 19. As expected, the percentage of time for which the buffer is full decreases with respect to increasing buffer size. Note also the larger bandwidth also reduces the percentage as packets can be served faster.
## 3 Numerical Simulation
### Model Building  
The half-span cross-section of an actual orthotropic deck steel box girder is shown in Figure 4. Considering the local force feature of the orthotropic deck, a partial structure is intercepted here to establish a local finite element model. The model has two lanes with a width of 7.5 m in the horizontal direction, five-span diaphragms with a length of 16 m in the longitudinal direction, and part of the diaphragm with 2 m down from the deck in the vertical direction. The deck, U-rib, and diaphragm thicknesses are 14 mm, 12 mm, and 8 mm, respectively. All components of the model are simulated using shell elements. The elasticity modulus of the material is \(\text{E}=2.1\times 10^{11}\) Pa-. Here, the research focuses on the load identification of a vehicle driving in one lane, so the mesh size of the middle lane is refined with an element size of 5 cm, and the other part of the deck is meshed with an element size of 15 cm. The bottom of the diaphragm is simply supported at the outermost end. The entire finite element model is shown in Figure 5. The response at the position of measurement points shown in Figure 1 is calculated by finite element analyses.  
For the built FEM model of the orthotropic deck steel box girder, the deck comprises a panel and U-rib, and it has different stiffnesse in longitudinal and transverse directions. The deck is supported on multiple diaphragms, and its mechanical performance in longitudinal direction is similar to continuous rigid frame bridges under local wheel loads. The strain influence line of U-rib is short, and it will vary with the transverse position of the vehicle. Because the transverse stiffness of the deck is smaller than longitudinal stiffness, the strain influence line of the panel is more localized, which is suitable for axle identification.
## 2 Statistical Model
### Directional Statistics  
Let us consider a general reverberant enclosure. The target speaker signal \(S\) is captured by an array of \(M\) microphones and possibly contaminated by competing interference \(I\) and background noise \(N\). In the Short-Time Fourier Transform (STFT) domain, the observed signal is written as:  
\[Y_{m}(t,f)=H_{s,m}(t,f)S(t,f)+H_{i,m}(t,f)I(t,f)+N_{m}(t,f) \tag{1}\]  
where \(t\) is the time index, \(f\) is the frequency index, and \(H_{s,m}\), \(H_{i,m}\) are respectively the multiplicative transfer functions from the target signal and the interference to the \(m\)-th microphone. We rewrite (1) in the vector form:  
\[\mathbf{y}(t,f)=S(t,f)\mathbf{h}_{s}(t,f)+I(t,f)\mathbf{h}_{i}(t,f)+\mathbf{n }(t,f) \tag{2}\]  
where \(\mathbf{y}=[Y_{1},Y_{2},...,Y_{M}]^{T}\) is the observation vector, \(\mathbf{h}_{s}=[H_{s,1},H_{s,2},...,H_{s,M}]^{T}\) is the target transfer function vector, \(\mathbf{h}_{i}=[H_{i,1},H_{i,2},...,H_{i,M}]^{T}\) is the interference transfer function vector, and \(\mathbf{n}=[N_{1},N_{2},...,N_{M}]^{T}\) is the noise vector with \({}^{T}\) denoting transpose.  
To infer the location of the target speaker from the observations, we rely on the directional statistics, which is defined as:  
\[\mathbf{z}(t,f)=\frac{\mathbf{y}(t,f)}{\sqrt{||\mathbf{y}(t,f)||_{2}}} \tag{3}\]  
where \(||\cdot||_{2}\) denotes the \(l2\) norm. \(\mathbf{z}\) is the normalized observation vector of size \(M\times 1\) that lies on the \(\mathbb{R}^{M}\) complex unit hypersphere. It is assumed that signals coming from different spatial directions would naturally form different clusters [26]. Note that the normalizing operation keeps the level and phase differences unchanged between microphones, which are widely-used features for localization. The vector length is discarded because it is mainly caused by the source power.