esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Solution Proposal for UAV Applications in Greenhouses
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Study** & **Information** & **Sensors** & **App. Environment** & **Result** \\ \hline \multirow{3}{*}{1} & [PERSON] et. al. & UAV study based on an extended Kalman filter (EKF) that can navigate independently in a closed indoor environment, create an area map using 2D laser scan data for navigation and record live video. & LIDAR, ultrasonic sensor & ROS & The map was created by a planar laser scanner using a UAV indocs, and it was shown that the UAV avoided obstacles correctly. & \\ \hline \multirow{3}{*}{2} & UAV and UAV cover use used together for autonomous exploration, mapping and navigation of the indoor environment. To take advantage of heterogeneous robots, the exploration and mapping tasks are divided into two layers. In the first layer, the aim is to carry out a preliminary exploration and produce a rough mapping with the UAV mounted on LIDAR. The map created by the UAV is shared with the UAV. The UAV then performs complementary prescription mapping using an inclined 2D laser module and visual sensors, filling the remaining gaps in the previous map. The application was applied both in simulation and experimentally. & \\ \hline \multirow{3}{*}{3} & Future-based monocular ORS-SLAM was presented for indoor and outdoor environments. For feature extraction, ORS with directed multiscale FAST corners was used. While the ORR provided good invariance from the point of view, its calculation and matching were extremely fast. This enabled the powerful optimization of mapping. The system combined monitoring, local mapping and loop closing threads running in parallel. The distributed spoof words (DBW) location recognition module was used in the system to perform loop detection. & \\ \hline \multirow{3}{*}{4} & This study presented the monocular large-scale direct SLAM & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\ (LSD-SLAM), which is popular among direct SLAM methods. Direct SLAM algorithms do not extract key points in the image, but instead, use image densities to predict location and map. That is, they are more robust and detailed than feature-based methods (MonSLAM, PTAM, ORS-SLAM, etc.), but this causes high computational costs. The map of the environment is created based on specific key frames containing the camera image, an inverted depth map and the variance of the inverted depth map. The depth map and its variance are created not for all pixels, but only for pixels located near large image density gradients, which therefore have a semi-dense structure. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Previous popular SLAM studies in indoor environments.
## 3 New Sets of Benchmark Instances for the ACVRP
### New Benchmark Instances
#### 3.2.2 Depot Locations  
In the two cities, depot locations are based on UDCs, hub terminals for national postal services, and some large shopping malls. Deliveries starting from the UDCs or postal hubs are reasonable from a logistics point of view, but setting a shopping mall as depot location may seem contrary. However, this reflects the booming demand in last-mile delivery services, which have increased significantly during the COVID-19 pandemic.  
The spatial data for the depot locations are obtained from shared data produced by various public institutions in Korea. Locations for UDCs, postal hub terminals and mega shopping malls are shared by the Korean National Logistics Information Center. Spatial data for postal hub centers and shopping malls are from the Korean Ministry of Science and ICT (Information and Communications Technology) and the Korean Land and Geospatial Informatrix Corporation, respectively.  
In Figures 5 and 6, large markets are denoted as blue squares. Among them, one for Seoul and one for Busan are randomly chosen for the benchmark creation. Similarly, for the UDCs and postal hubs, one of each are selected for the Seoul instances, and one of each is selected for the Busan instances.
## 4 Validation and Error Analysis of Extracting Transmission Characteristics
### Error Analysis  
From the above analysis, we can see that the two key points, the transfer function \(H_{T}(f)\) and the input signal \(V_{IN}(f)\), affect the accuracy of \(H_{L}(f)\). In practice, \(H_{T}(f)\) is correlated to the coupling capacitance, which is dependent on the distance between the printed trace being tested and the near-field probe tip, but the amplitude of \(V_{IN}(f)\) is normalized, which partially cancels out the error caused by the height. To investigate the influence of the distance on transmission characteristic extraction, the variable height and height resolution were considered. The via trace was chosen as the validated structure; the compared results of the transmission characteristic extraction at 1 mm, 5 mm, and 7 mm distances and those obtained using the VNA are depicted in Figure 16. We can see that at different distances between the printed trace and the probe, the extracted results agree well. On the other hand, we considered the height resolution of the tested distance at 1 mm, but the transfer function exchanged with \(H_{T}(f)\) at 0.3 mm and 0.5 mm. Figure 17 displays the compared results. It shows that when height deviation is about 0.5 mm, it seems to be easier to control and the obtained transmission characteristic is still in good agreement with the standard vector network analyzer (VNA) measurements. Furthermore, considering the random perturbations in the testing process, there are \(\pm 3\) dB random fluctuations added to every harmonic amplitude at a 1.0 mm distance in order to verify the method against conventional random interference, respectively. Figure 18 depicts the result of the random perturbations condition, which is compared to no perturbations and VNA measurement.  
Furthermore, it is hardly guaranteed that the positions in each test are consistent. We have employed the technique of averaging several measurements to minimize the error. Furthermore, the harmonic spectra of \(V_{IN}(f)\) reconstructed using mathematical formulae cannot be exactly the same as the practical harmonics due to the signal processing flow, including analog-to-digital conversion, the filter, and so on. It could introduce errors and affect the calculation of the transmission characteristics. We have amended the deviation using preliminary test data. Generally, for a transmission characteristic error less than 25% in the experiment, the maximum vertical distance error is controllable at 0.5 mm. It should be noted that the allowed maximum vertical distances depend on the size of the tip and width of the printed trace being tested.  
Figure 15: The transmission characteristic of the trace from port 5 to port 6 obtained by the proposed method and smoothed by wavelet packet were compared with measured by a VNA.  
Figure 16: The transmission characteristic extraction at 1 mm, 5 mm, and 7 mm distances.  
Figure 17: The transmission characteristic extraction at 1 mm distance with exchanged \(H_{\rm T}(f)\).  
Figure 18: The transmission characteristic extraction at 1 mm distance with random perturbations.
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 2 Related Research Works
### Problem Statement  
The aforementioned related research works propose significant changes to currently implemented RACH, which improve its performance in various applications and context. However, neither of the works propose to replace UD and discusses the limitations posed by the UD distribution. It has been observed that with the continuous evolution of wireless communication technologies and the massive increase in connected devices, UD-based channel access mechanisms have already been proved less efficient. One of the reasons behind the still use of UD-based mechanisms is the backward compatibility and ease of use. However, due to channel scarcity and resource constraints, we need to move on to better options. Keeping this in mind, in this paper, we expand the capabilities of the current RACH by implying Poisson process-based exponential distribution. As described earlier, from Figure 2, the users' dispersion in the earlier channel access slots allows the system to permit early channel access with low collision chances.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 2 System Model
### Communication Model
To simplify the illustration, assume that receiver \(DR_{j,2}\) has worse channel conditions than receiver \(DR_{j,1}\), i.e., receiver \(DR_{j,1}\) is a weak user and can demodulate its own signal, and in order for \(DR_{j,1}\) to remove \(DR_{j,2}\) signal to decode its own signal, the following constraints must be satisfied:  
\[\frac{\left|g_{i,DT_{j},DR_{j,1}}^{u}\right|^{2}P_{i,D_{j}}^{u}}{P_{i}^{u}\left| g_{i,DR_{j,1}}^{u}\right|^{2}+\sigma^{2}}\geq\frac{\left|g_{i,DT_{j},DR_{j,2}}^{u} \right|^{2}P_{i,D_{j}}^{u}}{P_{i}^{u}\left|g_{i,DR_{j,2}}^{u}\right|^{2}+\sigma ^{2}} \tag{16}\]After simplification, the inequality can be transformed into  
\[S_{i,D_{j}}^{u}=\left|g_{i,D\mathcal{T}_{j},D\mathcal{R}_{j,1}}^{u}\right|^{2} \left(p_{i}^{u}\left|g_{i,D\mathcal{R}_{j,2}}^{u}\right|^{2}+\sigma^{2}\right)- \left|g_{i,D\mathcal{T}_{j},D\mathcal{R}_{j,2}}^{u}\right|^{2}\left(P_{i}^{u} \left|g_{i,D\mathcal{R}_{j,1}}^{u}\right|^{2}+\sigma^{2}\right)\geq 0 \tag{17}\]  
Thus, based on Equation (15) and the NOMA principle, the SINR of receiver \(DR_{j,2}\) in a D2D cluster can be expressed as  
\[r_{i,D\mathcal{R}_{j,2}}^{u}=\frac{\left|g_{i,D\mathcal{T}_{j},D\mathcal{R}_{ j,2}}^{u}\right|^{2}P_{i,D_{j}}^{u}a_{i,D\mathcal{R}_{j,2}}^{u}}{\left|g_{i,D \mathcal{T}_{j},D\mathcal{R}_{j,2}}^{u}\right|^{2}P_{i,D_{j}}^{u}a_{i,D \mathcal{R}_{j,1}}^{u}+P_{i}^{u}\left|g_{i,D\mathcal{R}_{j,2}}^{u}\right|^{2} +\sigma^{2}} \tag{18}\]  
The receiver \(DR_{j,1}\) in the D2D cluster can demodulate its own signal, so the SINR of \(DR_{j,1}\) can be expressed as  
\[r_{i,D\mathcal{R}_{j,1}}^{u}=\frac{\left|g_{i,D\mathcal{T}_{j},D\mathcal{R}_{ j,1}}^{u}\right|^{2}P_{i,D_{j}}^{u}a_{i,D\mathcal{R}_{j,1}}^{u}}{P_{i}^{u} \left|g_{i,D\mathcal{R}_{j,1}}^{u}\right|^{2}+\sigma^{2}} \tag{19}\]  
According to [PERSON]'s theorem, the achievable rate for cellular user \(C_{i}\) using sub-channel \(S_{i}\in\mathcal{S}_{u}\) is  
\[R_{i}^{u}=B\log_{2}(1+r_{i}^{u}) \tag{20}\]  
where \(B\) denotes the bandwidth. Similarly, the achievable rates of receivers \(DR_{j,1}\) and \(DR_{j,2}\) in D2D cluster \(D_{j}\) can be expressed as, respectively,  
\[R_{i,DR_{j,1}}^{u}=B\log_{2}\left(1+r_{i,D\mathcal{R}_{j,1}}^{u}\right) \tag{21}\]  
\[R_{i,DR_{j,2}}^{u}=B\log_{2}\left(1+r_{i,D\mathcal{R}_{j,2}}^{u}\right) \tag{22}\]  
According to Equations (21) and (22), the sum rate expression in D2D cluster \(D_{j}\) is  
\[R_{i,D_{j}}^{u}=B\left[\log_{2}\left(1+\frac{\left|g_{i,D\mathcal{T}_{j},D \mathcal{R}_{j,1}}^{u}\right|_{i,D\mathcal{D}_{j,1}}^{u}a_{i,D\mathcal{R}_{j, 1}}^{u}}{P_{i}^{u}\left|g_{i,D\mathcal{R}_{j,1}}^{u}\right|^{2}+\sigma^{2}} \right)+\right. \tag{23}\]  
Thus, the whole system throughput can be expressed as  
\[y_{i,DR_{j,n}}^{u}=\sqrt{P_{i,D}^{u}}x_{i,DT_{j}}^{u}g_{i,DT_{j},DR_{j,n}}^{u} +\sqrt{P_{i}^{u}}x_{i}^{u}g_{i,DR_{j,n}}^{u}+\eta \tag{15}\]0
## 5 Implementation and Results
### Verification of Intersection Solver  
Before showing the optimization results, the intersection solver proposed in Section 4 is verified against solutions of Equation (5), solved as a linear programming problem, using the solver provided in the \"QCAT\" toolbox [19]. The toolbox helps to translate the problem into a standard linear programming problem and then solve it using the inherit _linprog_ function from MATLAB [11; 20].
# Anisotropy and Directivity Effects on Uniaxial Compression of Carbonaceous Slate Form Jinman Mine
## 3 Results and Discussion
### Loading Effect of Mechanical Parameters  
The bedding effect and stress loading directions of the slate samples have a significant effect on the deformation and damage evolution characteristics. To quantify the deformation characteristics, Tables 1 and 2 show the peak strength (\(\sigma_{c}\)), expansion strength (\(\sigma_{cd}\)), elastic modulus (\(E\)), axial peak strain (\(\epsilon_{zz}\)), hoop peak strain (\(\epsilon_{xx}\) and \(\epsilon_{yy}\)), and volume peak strain (\(\epsilon_{xy}\)) for vertical loading and parallel loading.
## 4 Results and Discussion
### Selection of Hyperparameters  
The role of hyperparameters, such as the number of convolution filters, batch size, learning rate, pooling and kernel size, optimizer type, and epochs, has been observed in model performance during classification. To maximize the convolution filter number, the model learns more complicated features, which raises the number of parameters and generates overfitting problems. As a result, the exact and appropriate filter selection at each layer is critical. Sixty-four filters are used in the first layer, and to compensate for downsampling induced by the pooling layer, the number of filters in the second convolutional layer is doubled.  
A filter with reduced size is initially used to learn low-level characteristics; nevertheless, big-size filters work better for specified features and high levels. Layer 1 uses a large kernel size while the other layers use less kernel size. The filter size is selected first because it reads generic information in one value and has a greater global influence on the entire image, but it misses local features. A tiny filter size is used in the following layers to learn local and individual characteristics. The kernel lengths of the convolutional layers are first fixed at 5 and 3. Experiments are then performed with different network widths (which include LSTM units) to observe how they affect the results. The number of filters (C) is doubled every time from 16 to 256, whereas the LSTM units (N3) are generally half or equal to the preceding layer's kernel width [47]. According to the results, the model performance is increased with the increase of the LSTM units and the number of filters. The Adam optimization algorithm is used for weight adjustments, the weights are then updated accordingly.
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 4 Single-Modality Data Representation and Event Detection
### Text Based Data Representation  
#### 4.1.1 Term Based Methods  
In most existing works, text based event detection mainly used the Natural Language Processing (NLP) based methods to represent data. The most widely used representing model is the Vector Space Model (VSM) [20], which is an efficient model and is simple. By VSM, a document is represented by a group of key terms and the corresponding term weights. Words with higher weights are more important to a document [21]. In general, terms can be extracted by Term Frequency-Inverse Document Frequency (TF-IDF) [22], Named Entities Recognition (NER) [23] or Textrank [24]. However, VSM totally ignores words order and words relationship and lack of semantic information. We call this kind of event detection methods the term based methods for that in VSM representation, these terms are isolated. For example, the Group Average Clustering (GAC) proposed by [PERSON] et al. [25] used VSM to represent data, in which only the top k terms are selected to represent the document and the rest terms are discarded. The value of k is set by experience and the event detection performance is optimized gradually. Then the similarity measurement is used to measure the documents similarity and vectors are clustered. Finally, the documents are clustered to generate events. The basic idea of term based event detection methods is shown in Figure 3. In addition, the method in [26; 27; 28; 29; 30] all used this idea for events detection on document datasets. In these researches, different feature selection and parameter settings may influence the results [31]. So preliminary experiments are required to determine the parameter values.
## 5 Sentiment Analysis Results
### Recurrent Neural Network (RNN)  
In Figure 4, the results of RNN, when compared to the previous two (TextBlob and NLTK-VADER), has a neutral section of 0 percent in both August and September results, which is a significant improvement. In addition, small changes in distribution were observed in both the positive and negative sections compared to the previous models. In the case of the previous models, especially in the case of the NLTK-VADER results, there is a similarity in the result categories, both in positive and negative sections, the huge difference, and of course the neutral category. Our model was able to place all tweets in a category, as we expected, which significantly increases the establishment of a clearer picture of these specific periods.  
The value of'strongly positive' was 7.60 percent in August, down from 6.80 percent in September. The 'positive' section was 21.40 percent in August, but it was 19.60 percent in September; the 'weakly positive' values rising from 17.40 percent in August to 20.20 percent in September. Overall, in addition to the changes in ratios, the positive section increased by 0.2 percent overall, but there was a shift toward the 'weakly positive' section.  
For the negative sections, the'strongly negative' value was unchanged at 10.20 percent in both August and September. The 'negative' value fell from 25.4 percent in August to 25 percent in September. The 'weakly negative' value rose from 18 percent in August to 18.2 percent. Despite a small 0.2 percentage increase in positive values, and even in the case of minimal movements inside of the negative section, the negative sections still represent a larger overall section, plus in the case of positive values, a shift toward a 'weakly' value should be highlighted.  
In summary, the results of the RNN model and the results of previous models show a strong division; there is some kind of \"boundary line\" based on the studied periods, which is very difficult to move. People have their own opinions about the pandemic, which has lasted for almost two years. Due to the significant neutral result seen in the TextBlob result, it is difficult to write a conclusion, but the results of the subsequent NLTK-VADER and then the RNN results, where the neutral values decreased significantly and then disappeared, already give some picture. They show a shift in the negative direction; during the period under review, the negative sections provided a higher percentage value overall, and in the case of the RNN model, the shift to the already mentioned 'weakly positive' section can be highlighted again.  
Vaccinations, and the relatively 'free summer', also provide a basis for the positive parts in the studies, and the uncertainties of starting school and the fourth wave continue to maintain a more negative attitude.
## 4 Evaluation Performance
### Evaluation in Captured Data
#### 4.2.2 Denoising for Captured Data  
Post-processing software of the portable DHSPI system can process the recorded results to improve the visualization for extracting useful information. The basic post-processing tools include denoising interferogram, phase unwrapping, displaying phase unwrapped image, and 3D surface deformation  
Figure 8: Captured data: (**a**) Measured sample; (**b–f**) estimated phase maps at 12 s, 53 s, 103 s, 151 s, and 218 s, respectively.  
Figure 7: Schematic diagram of the measuring device.  
map, etc. [13]. The noisy data of Figure 8b-f are processed by the post-processing of the portable DHSPI system as shown in column (a) of Figure 9. Then, the proposed method is further used for denoising as shown in column (b) of Figure 9. From the visual inspection, the clean phase maps are obtained by the proposed method.  
To clearly compare performance, in Figure 9 the enlarged phases in the red regions of column (a) and blue regions of column (b) are shown in columns (c) and (d), and the defect state can be observed more intuitively from the enhanced wrapped phase map using the proposed method. It can  
Figure 9: Experiment results: (**a**) Denoising result by DHSPI system; (**b**) denoising result by the proposed method; (**c**) enlarged region of (**a**); (**d**) enlarged region of (**b**).  
be seen that the proposed method can handle the high-frequency regions of the phase well, which shows it to be a useful post-processing method to estimate the depth of the defects in artworks. In the proposed method, the calculation time is 3.60 s for a captured wrapped phase map with size \(1017\times 1030\) pixels. Figure 10a,c shows the phase unwrapping results of the noisy data (Figure 8) and filtered data (Figure 9b) using DCT. The phase unwrapping effect is further illustrated by visualization of the contours in Figure 10b,d, and it further proves that the smooth phase can be obtained after denoising of the proposed method.  
Figure 10: Phase unwrapping results using discrete cosine transform (DCT): (**a**) Unwrapping results of Figure 8b–\(f\); (**b**) contour of (**a**); (**c**) unwrapping results of Figure 9b; (**d**) contour of (**c**).
# Identification and Evaluation of Composition in Food Powder Using Point-Scan Raman Spectral Imaging
## 2 Materials and Methods
### Sample Preparation and Acquisition of Spectral Image  
A sample mixture was prepared at ten different concentration levels, 1% through 10%, by mixing powdered non-dairy creamer (Nestle USA Inc., Glendale CA, USA) with equal weights of vanillin (Sigma-Aldrich, St. Louis, MI, USA), granulated sugar (sucrose) (Domino Food, Baltimore, MD, USA), and melamine (Sigma-Aldrich, St. Louis, MI, USA). The 1% concentration sample thus contained 1% vanillin, 1% sugar, 1% melamine, and 97% non-dairy creamer, while the 10% concentration sample contained 10% vanillin, 10% sugar, 10% melamine, and 70% non-dairy creamer. Samples for the concentration levels between 1% and 10% were prepared similarly. The total weight of each sample mixture was approximately 8.3 g to 8.5 g.  
To produce thorough particle distribution throughout the sample mixtures, each mixture was subjected to 20 min in an acoustic mixer (Resodyn Acoustic Mixers, Inc., Butte, MT, USA). Since particle size can influence the mixing quality [36], the particle sizes of the mixture components were examined using a Horiba Jobin Yvon LabRam Aramis Raman Spectrometer (Horiba, Ltd., Tokyo, Japan) with a charged couple device camera and Olympus BX41 microscope. From the microscope images, the particle sizes of vanillin, melamine, sugar, and non-dairy creamer were observed to be in the ranges of 26-30 \(\upmu\)m, 15-20 \(\upmu\)m, 45-50 \(\upmu\)m, and 20-25 \(\upmu\)m, respectively.  
For imaging, each sample mixture was packed into a nickel-plated sample holder (interior dimensions 100 mm \(\times\) 50 mm \(\times\) 3 mm), and the powder surface was leveled flush with the upper edge of the sample holder, as shown in Figure 1. A Raman spectral image of the sample surface was acquired by point-scanning across a 96 mm \(\times\) 46 mm area that excluded the edges of the sample, using a CCD exposure time of 0.1 s, a spatial resolution of 0.5 mm, and a laser power of 250 mW. The final sample image acquired was a 192 \(\times\) 92 \(\times\) 1024 hyperspectral cube with 17,664 spatial pixels at each of 1024 spectral wavenumbers. One hyperspectral cube was acquired for each of the ten concentration samples.