esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Second Step: Regional Pole Placement
### Control Gain Synthesis  
In accordance with the LMI theory proposed, \(\dot{\mathbf{k}}\) ensures that the closed-loop poles of the system in Equation (11) lie in the prescribed LMI region \(\mathcal{D}\) if Equation (18) holds for \(\hat{\mathbf{A}}_{\text{CL}}=\hat{\mathbf{A}}-\hat{\mathbf{B}}\hat{\mathbf{k}}^{T}\):  
\[\mathbf{R}\otimes\mathbf{X}+\mathbf{S}\otimes\left(\hat{\mathbf{AX}}-\hat{ \mathbf{B}}\hat{\mathbf{k}}^{T}\mathbf{X}\right)+\mathbf{S}^{T}\otimes\left(\hat{ \mathbf{AX}}-\hat{\mathbf{B}}\hat{\mathbf{k}}^{T}\mathbf{X}\right)^{T} \tag{19}\]  
It should be noted that such a matrix inequality becomes non-linear. Indeed, both \(\hat{\mathbf{k}}\) and \(\mathbf{X}\) are unknown and hence Equation (19) has bilinear terms of the unknowns. Linearity is restored, defining the auxiliary variables \(\mathbf{l}=\hat{\mathbf{k}}^{T}\mathbf{X}\), thus leading to the following LMI:  
\[\mathcal{F}(\mathbf{X},\mathbf{l})=\mathbf{R}\otimes\mathbf{X}+\mathbf{S}\otimes \left(\hat{\mathbf{AX}}-\hat{\mathbf{B}}\mathbf{l}\right)+\mathbf{S}^{T}\otimes \left(\hat{\mathbf{AX}}-\hat{\mathbf{B}}\mathbf{l}\right)^{T}<0 \tag{20}\]  
The LMI can be exploited as a constraint in the control gain synthesis, by defining a suitable cost function to be accounted for in a semidefinite programming (SDP) problem. It is here proposed to solve the following problem, since it is convex and hence global optimal solution can be effectively found:  
\[\min_{\begin{subarray}{c}\mathcal{F}(\mathbf{X},\mathbf{l})\prec 0\\ \mathbf{X}\succ 0\end{subarray}}\left|\mathbf{l}\right|^{2}. \tag{21}\]  
Figure 1: Examples of Linear Matrix Inequalities (LMI) regions.  
Once \(I\) and \(\mathbf{X}\) are obtained, \(\hat{\mathbf{k}}\) is computed from the relation \(\hat{\mathbf{k}}^{T}=I\mathbf{X}^{-1}\). Finally, \(\mathbf{f}\) and \(\mathbf{g}\) are computed from Equation (7). Under mild hypotheses, which will be discussed in the Section 4.4, the closed-loop system with such gains (i.e., \(\mathbf{A}_{\text{CL}}=\mathbf{A}-\mathbf{B}\mathbf{k}\)) features a set of poles \(\Sigma\) which includes \(\Sigma_{\text{d}}=\left\{\mu_{1},\ldots,\mu_{p}\right\}\), and the other ones belong to \(\mathcal{D}\), i.e., \(\Sigma\backslash\Sigma_{\text{d}}\subset\mathcal{D}\). Additionally, it should be noticed that  
\[\|\mathbf{l}\|=\|\hat{\mathbf{k}}^{T}\mathbf{X}\|=\|\mathbf{X}\hat{\mathbf{k}}\| \tag{22}\]  
and, since \(\mathbf{X}\) is a positive definite matrix, minimizing the norm of \(I\) guarantees that the controller effort is kept small.
## 5 Method
### Problem Modeling
#### 5.3.2 Task Edge Layer Model  
In the task edge layer model, the entire process of diverting rescue tasks to the task edge layer is divided into three steps: upstream transmission, task solving, and result distribution. After the rescue mission is completed and analyzed, it is encoded and compressed to a smaller scale before being distributed. Due to the fast transmission rate of the downstream in 5G networks, which has an advantage of orders of magnitude over the upstream, and the smaller scale of data distribution after encoding, the delay of downstream distribution can be ignored. Therefore, the total time delay for rescue tasks to be diverted to the task edge layer is  
\[T_{i,mac}^{j}=T_{i,trans}^{j}+T_{j,exc} \tag{6}\]  
where \(T_{j,exc}\) is the time for task \(i\) to complete processing at the MEC platform, and the MEC server allocates the corresponding computational resources for task \(i\). \(f_{i,mac}\), and thus there is  
\[T_{j,exc}=\frac{C_{i}}{f_{i,mac}} \tag{7}\]  
\[\text{s.t.}\ \sum_{i=1}^{n}f_{i,mec}\leq f_{mec,total} \tag{8}\]  
When the rescue task is diverted from the rescue area to the task edge layer, the energy consumption is mainly generated by the rescue equipment, which can be calculated from the rescue equipment transmit power:\[E_{i,trans}=P_{i}T_{i,trans}^{j}=P_{i}\frac{D_{i}}{R_{i}^{j}} \tag{9}\]  
Due to the deployment of MEC servers and rear rescue center cloud servers based on mobile communication base stations, there is sufficient power guaranteed. Therefore, the total energy consumption of the task being diverted from the rescue area to the task edge layer needs to include the transmission energy consumption, while if the task edge layer is diverted to the rescue command center layer, the transmission and calculation energy consumption of both do not need to be considered. Therefore, the total cost of task allocation to the MEC servers is  
\[F_{i,mec}^{j}=\delta T_{i,mec}+(1-\delta)E_{i,trans} \tag{10}\]  
Based on the foregoing, the total overhead of the system for processing computational tasks is the weighted sum of the overheads of each layer:  
\[F_{system}=\sum_{i=1}^{n}{(1-\alpha_{i})F_{i,local}+\alpha_{i}F_{i,mec}^{j}} \tag{11}\]
## 3 Methodology
### Performance Evaluation  
For the performance evaluation of the heuristic, a numerical experimentation was conducted using a set of 54 instances employed by [PERSON] (1966), consisting of 21 to 50 customers and 2 to 4 satellites, alongside the best-known solutions from the literature (BKS) identified by them along with their respective computational execution times. The heuristic's performance is measured based on the runtime and the percentage gap between the heuristic's result and the best-known literature solution. Additionally, the heuristic is assessed with a case study involving a major e-commerce retailer in Latin America, which allowed the use of real delivery data, including customer locations in the city of Sao Paulo, and their package quantity demands. To maintain the company's anonymity, the distribution center locations were randomly chosen in a region equidistant from the city center, and the satellite locations were selected randomly based on a uniform distribution in the city. The case study evaluation was conducted by comparing the heuristic's result with the Incumbent of the mathematical model optimization [PERSON] (2010), using the Gurobi solver with a time limit of 1 h.  
All numerical experiments were conducted in Python v13.11 on a machine with the following hardware specifications: an 11 th Gen Intel(R) Core(TM) i7-1165G7 processor @ 2.80 GHz with a clock frequency of 1.69 GHz, 16 GB of RAM, and a 1 TB SSD storage capacity.
## 2 Results and Discussion
### Influence of Reaction Time  
Temperature and time also play a significant role in reaction kinetics. In order to study the effect of these two parameters, a varied range of temperature (40-100 \({}^{\circ}\)C) was used to carry out the model reaction for different time periods (15-120 min) and by using 0.05 g of Na\({}_{2}\)CaP\({}_{2}\)O\({}_{7}\) (Table 1, entries 9-14). The first period, time ranges from 15 to 30 min, was characterized by significant changes in the yield of the product. During this period, the product yield increased by 12% after 5 min (from 15 to 20 min) and by 29% during the following 10 min (from 20 to 30 min). The highest yield (94%) was achieved at 80 \({}^{\circ}\)C after 30 min. The yield of 5**a** remained unchanged even after extending the reaction time and increasing the temperature (Table 1, entries 11, 13, and 14).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Entry** & \begin{tabular}{c} **Amount of** \\ **Catalyst (g)** \\ \end{tabular} & \
\begin{tabular}{c} **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} &
\begin{tabular}{c} **Time** \\ **(Min.)** \\ \end{tabular} & **Yield (\%)**\({}^{\mathrm{La[h]}}\) \\ \hline Absence of a catalyst & 1 & 0 & 80 & 120 & - \\ \hline  & 2 & 0.01 & 80 & 30 & 20 \\  & 3 & 0.02 & 80 & 30 & 40 \\ Influence of the amount & 4 & 0.03 & 80 & 30 & 60 \\ of the catalyst & 5 & 0.04 & 80 & 30 & 84 \\  & 6 & 0.05 & 80 & 30 & 94 \\  & 7 & 0.06 & 80 & 30 & 94 \\  & 8 & 0.07 & 80 & 30 & 94 \\ \hline  & 9 & 0.05 & 80 & 20 & 65 \\  & 10 & 0.05 & 80 & 15 & 53 \\ Influence of temperature & 11 & 0.05 & 80 & 40 & 95 \\ and reaction time & 12 & 0.05 & 40 & 30 & 75 \\  & 13 & 0.05 & 60 & 30 & 85 \\  & 14 & 0.05 & 100 & 30 & 94 \\ \hline \hline \end{tabular} \({}^{\mathrm{[a]}}\) Isolated yields; \({}^{\mathrm{[b]}}\) reaction conditions: benzaldehyde (1 mmol), malomonitrile (1.1 mmol), acetophenone (1 mmol), and ammonium acetate (1.5 mmol).  
\end{table}
Table 1: Optimization of reaction conditions for the synthesis of 2-amino-3-cyanopyridine 5**a**.  
Figure 6: Influence of the amount of the Na\({}_{2}\)CaP\({}_{2}\)O\({}_{7}\) catalyst and reaction time on the synthesis of 2-amino-3-cyanopyridine 5**a**.
## 2 Methodology
### Creating Target-Oriented Resistivity Models Based on Field borehole Data Using GemPy  
To create a deep-learning model optimized for fault zone detection, we employed a method that involved generating subsurface resistivity models based on field borehole data using GemPy, an open-source Python library for 3D geological modeling [28]. The potential-field method developed by [PERSON] [29] was used to create a 3D geological model in GemPy. To create a stratigraphic model, it is important to interpolate lithological interfaces that show changes in physical properties using a scalar field value [30]. The input values for the scalar field include layer interface points that represent interfaces between two layers and gradients of the scalar field that indicate poles of the layer. The layer interface points are also known as surface contact points, and the gradients of the scalar field are referred to as orientations. The scalar values are subsequently calculated for all interfaces in the scalar field to identify the lithology of every point in the mesh. By discretizing 3D space, we generated a layered model, as shown in Figure 2a, which is a vertical section of the 3D model.  
Figure 1: Proposed workflow for deep learning-based ERT inversion (DL-ERT) and integration with deterministic inversion.  
To create a fault structure, we added surface contact points and orientations for a fault to the layered model, as depicted in Figure 2b. To simulate the assumption that the fault zone is saturated with groundwater, we increased the thickness of the existing fault line, which resulted in the saturated fault structure shown in Figure 2c.  
In this work, field borehole data and resistivity properties were used to create 3D resistivity models in GemPy. We assigned the same coordinates as the field borehole data and set the depths of each stratum as surface contact points with upward-facing surface orientations. This approach allowed us to generate realistic 3D layered models reflecting field conditions, as shown in Figure 3. Additionally, to create a fault structure, the surface points and displacements for the faults were assigned randomly, and the surface orientations were set close to perpendicular to the fault plane. Assuming the fault zone to be saturated with groundwater, a fault structure with a random thickness and low resistivity was created. From the 3D models, we randomly selected two-dimensional (2D) sections along survey lines to generate 2D resistivity models.  
Figure 2: Geological structures created using GemPy: (**a**) layered structure, (**b**) fault structure, and (**c**) saturated fault structure.
# Automated Destination Renewal Process for Location-Based Robot Erands
## 3 Methods
### Data Collection of Indoor Space
#### 3.1.1 Drawing-Based Map Building  
To facilitate the automation of the indoor space data collection process with fewer steps, the proposed system adopts drawing-based map building instead of generating a typical map through SLAM. This suggestion is motivated by the desire to base initial tasks such as exploration and spatial partitioning on a more accurately dimensioned grid map. CAD drawings encompass immutable elements of the indoor environment, such as walls and pillars, providing precise measurements related to spatial purposes, room layout, area, and wall lengths. Users are then involved in a process of simplifying the grid map by removing unnecessary dimensions, annotations, guide lines, door information, etc., to transform it into a grid map suitable for the robot's navigation. Subsequently, the processor completes the map generation process for building floor frames, aligning the transformed grid map with the specified resolution settings and format.
###### Abstract  
The purpose of this paper is to compare the performance of human listeners against the selected machine learning algorithms in the task of the classification of spatial audio scenes in binaural recordings of music under practical conditions. The three scenes were subject to classification: (1) music ensemble (a group of musical sources) located in the front, (2) music ensemble located at the back, and (3) music ensemble distributed around a listener. In the listening test, undertaken remotely over the Internet, human listeners reached the classification accuracy of 42.5%. For the listeners who passed the post-screening test, the accuracy was greater, approaching 60%. The above classification task was also undertaken automatically using four machine learning algorithms: convolutional neural network, support vector machines, extreme gradient boosting framework, and logistic regression. The machine learning algorithms substantially outperformed human listeners, with the classification accuracy reaching 84%, when tested under the binaural-room-impulse-response (BRIR) matched conditions. However, when the algorithms were tested under the BRIR mismatched scenario, the accuracy obtained by the algorithms was comparable to that exhibited by the listeners who passed the post-screening test, implying that the machine learning algorithms capability to perform in unknown electro-acoustic conditions needs to be further improved.  
Keywords:spatial audio scene classification; spatial audio information retrieval; convolutional neural networks; deep learning +
Footnote †: journal: Accepted: 25 August 2020; Published: 28 August 2020
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Model Performance Comparison
#### 5.2.3 Further Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **Encoder** & **Decoder** & **RMSE** & **MAE** & **ADE** & **FDE** \\ \hline VEPO-S2S & BiGRU & GRU & \(3.60\times 10^{-4}\) & \(1.99\times 10^{-4}\) & \(3.15\times 10^{-4}\) & \(5.28\times 10^{-4}\) \\ VEPO-S2S & LSTM & LSTM & \(3.46\times 10^{-4}\) & \(1.72\times 10^{-4}\) & \(2.72\times 10^{-4}\) & \(4.94\times 10^{-4}\) \\ VEPO-S2S & BiLSTM & LSTM & \(4.19\times 10^{-4}\) & \(2.48\times 10^{-4}\) & \(3.39\times 10^{-4}\) & \(5.97\times 10^{-4}\) \\ VEPO-S2S & GRU & GRU & \(\mathbf{3.17\times 10^{-4}}\) & \(\mathbf{1.60\times 10^{-4}}\) & \(\mathbf{2.52\times 10^{-4}}\) & \(\mathbf{4.57\times 10^{-4}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Exploration results on different Seq2 Seq structure. The proposed VEPO-S2S model consists of the Multi-level Vessel Trajectory Representation Module (Multi-Rep) and the Feature Fusion and Decoding Module (FFDM). The Multi-Rep is specifically designed not only to integrate trajectory information but also to fully consider the vessel features and behavioral preferences, encoding them with distinct encoders to enrich the representation of features. Additionally, the FFDM selects and integrates the above information and features based on the current prediction environment, which allows the model to leverage them efficiently. These two advantages make VEPO-S2S more accurate than the other baselines.
# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 4 Experiment and Results
### Ablation Experiment  
Ablation experiments were conducted under various network framework designs to verify whether the three-branch feature fusion network framework based on Raman spectrum pair comparison proposed in this study can enhance the accuracy of mixture component identification.  
Firstly, the multi-head self-attention mechanism was removed while keeping the basic network framework unchanged. A one-dimensional convolutional network was used to extract spectral characteristic peaks, verifying the effectiveness of the multi-head self-attention mechanism in extracting correlations between Raman spectrum characteristic peaks. This operation is labeled as \"without attention\". This setup established a benchmark to clearly observe and analyze the specific role of the multi-head self-attention mechanism in enhancing model performance.  
Figure 5: Boxplots of our results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Network** & \
\begin{tabular}{c} **Qualitative Detection** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Quantitative Detection** \\ **Accuracy** \\ \end{tabular} \\ \hline U-Net [36] & 92.25\% \(\pm\) 1.44\% & 86.74\% \(\pm\) 1.77\% \\ ResNext [37] & 94.96\% \(\pm\) 1.73\% & 90.76\% \(\pm\) 3.57\% \\ DeepRaman [33] & 96.06\% \(\pm\) 0.79\% & 90.38\% \(\pm\) 0  
Secondly, the middle branch was removed, leaving only the upper and lower branches intact. The Raman spectrum pairs were fed into the upper and lower branches respectively. After feature extraction, the two branches were compared before being sent to the classifier. Feature fusion was performed on the extracted features from both branches, and the result is labeled as \"fusion after two branches\".  
Then the spectral pairs were also used as input. After the first dimensionality reduction stage, the key difference was that feature fusion was performed immediately afterward. This time, only the middle branch was retained and utilized for subsequent processing. This fusion step is labeled as \"single-branch pre-fusion\".  
Finally, the network framework proposed in this study is labeled as \"three-branch fusion\".  
The experimental results are shown in Table 4. The analysis indicates that adding the multi-head self-attention mechanism significantly improves the detection accuracy. However, removing the feature fusion of the middle branch, whether at the beginning or the end, results in a significant drop in accuracy. This is because the middle branch design can gradually embed the features of each spectrum at multiple scales, adding in better learning of the correlation between spectral pairs. All these findings verify that our proposed three-branch feature fusion network framework, based on Raman spectrum pair comparison, can effectively enhance the network's detection performance.
# Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs
###### Abstract  
In recent years, UAVs (Unmanned Aerial Vehicles) have been widely applied for data collection and image capture. Specifically, UAVs have been integrated with wireless sensor networks (WSNs) to create data collection platforms with high flexibility. However, most studies in this domain focus on system architecture and UAVs' flight trajectory planning while event-related factors and other important issues are neglected. To address these challenges, we propose a cloud-assisted data gathering strategy for UAV-based WSN in the light of emerging events. We also provide a cloud-assisted approach for deriving UAV's optimal flying and data acquisition sequence of a WSN cluster. We validate our approach through simulations and experiments. It has been proved that our methodology outperforms conventional approaches in terms of flying time, energy consumption, and integrity of data acquisition. We also conducted a real-world experiment using a UAV to collect data wirelessly from multiple clusters of sensor nodes for monitoring an emerging event, which are deployed in a farm. Compared against the traditional method, this proposed approach requires less than half the flying time and achieves almost perfect data integrity.  
cloud-assisted; Emerging event; Flying parameters; UAV; WSN +
Footnote †: journal: Sensors
# SIFT-Based Depth Estimation for Accurate 3D Reconstruction in Cultural Heritage Preservation
## 2 Materials and Methods
### Recent Works on 3D Reconstruction
In terms of implementation, SIFT benefits from well-established libraries, while SfM implementations might necessitate greater technical expertise, and MVS frequently depends on specialized software. Ultimately, the choice between SIFT, SfM, and MVS hinges on a balance between several factors. SIFT provides a reasonable compromise between speed and accuracy for less demanding applications. SfM generally facilitates more robust camera pose estimation and scales well to larger image sets. MVS excels in generating highly detailed 3D models, albeit at the expense of greater computational requirements. Project-specific considerations such as the desired level of detail, computational resources, and the complexity of the target object or scene will guide the optimal 3D reconstruction technique.
## 3 Results and Discussion
### Evaluation of Memory Effect
#### 3.2.3 Memory Effect Accuracy Assessment  
According to the research question \"whether VR can enhance the instant knowledge memory effect compared with the traditional teaching method\", when 95% is the percentage of confidence interval (CI) is 95%, Sig = 0.142 (Sig \(\geq\) 0.05), and the variance is homogeneous, then t(9) = \(-\)1.6, \(p\) = 0.117 (\(p\geq\) 0.05), showing no significant difference. Therefore, compared with the traditional teaching method, VR is not effective in enhancing the effect of instant knowledge memory and knowledge mastery.  
Correspondingly, in the evaluation of whether VR enhances the effect of delayed knowledge memory, when CI = 95%, Sig = 0.134 (Sig \(\geq\) 0.05), and the variance is homogeneous, then t(9) = \(-\)1.309, \(p\) = 0.197 (\(p\geq\) 0.05), and thus the results showed no significant difference. Consequently, VR technology is not effective in enhancing students' delayed memory retention compared with the traditional teaching.  
However, when the students' performance in the specific process practice memories were analyzed separately, the two groups showed a certain degree of difference. From the point of view of immediate memory evaluation, 95% is the percentage of confidence interval (CI = 95%), Sig = 0.000 (Sig \(\leq\) 0.05), and when the variance is not uniform, then t(9) = 2.711, \(p\) = 0.011 (\(p\leq\) 0.05), and thus the effect is statistically significant. In the delayed memory evaluation performance, 95% is the percentage of confidence interval (CI = 95%), Sig = 0.000 (Sig \(\leq\) 0.05), and when the variance is not uniform, then t(9) = 3.118, \(p\) = 0.004 (\(p\leq\) 0.05), and thus the effect is statistically significant. Therefore, compared with traditional teaching methods, VR teaching is more effective in immediate and delayed mastery of specific process practice memories.  
Figure 5: Delayed memory test results: (**a**) Delayed-memory-test overall effect; (**b**) Delayed-memory-effect score.  
Combined with the previous analysis and summary, and compared with the traditional teaching method, VR teaching has no obvious advantages for the immediate and delayed mastery of theoretical knowledge, but it is more beneficial to the immediate and delayed mastery of the specific-process practice memories.