esa-sceva/satcom-chunk-collection huggingface dataset:
# Autostereoscopic 3D Display System for 3D Medical Images
## 3 Results and Discussion
### Experimental Results on Light Field 3D Subpixel Rendering  
We performed a visual quality evaluation using light field 3D subpixel rendering with eye positions. To validate the visual quality of a human, we set up a stereo camera for quantitative validation and attached a printed image of a human face to make the camera lens look like the iris of the eyes. We then moved the stereo camera to different positions within a distance range of 300 to 600 mm.  
In this experiment, we use extrinsic crosstalk as the metric of visual quality. We encoded each view using colors, e.g., red to the left and blue to the right image. If rendered ideally, only red pixels are visible on the left eye and blue pixels are visible on the right eye. In contrast, the users see an image of each color being mixed; the image with this effect is referred to as extrinsic crosstalk, defined as:  
\[\text{Extrinsic crosstalk}(\%)=\frac{\text{Incorrect view luminance}}{\text{Correct view luminance}}\times 100 \tag{4}\]  
We captured 100 images at four positions of different distances with the 3D rendered image displayed in two ways, one that takes eye position into account and one that does not. Figure 10 shows the left and right captured image at different camera positions. When using the proposed rendering method, the left and right views are obviously separated. The visual quality evaluation results are summarized in Table 2. The average external crosstalk value of the proposed rendering method was 8.32%, which is within an acceptable range for a two-view autostereoscopic display [42].  
Figure 9: Proposed implemented prototypes of an autostereoscopic 3D display system for 3D medical images ((**left**) 31.5”, (**middle**) 18.4”, (**right**) 10.1”). In the 31.5” monitory prototype (**left**), the light field 3D subpixel rendering is processed with a GPU and the eye tracking algorithm is processed with only CPU computations in Windows PC. In the 18.4” (**middle**) and the 10.1” (**right**) tablet display prototypes, the light field 3D subpixel rendering and eye tracking are processed in the FPGA board.
## 4 Experiment
### Results of Off-Grid Arrays
#### 4.1.2 Performance of Archimedean Spiral Strategy  
In this part, we compare three search path planning strategies, including the original spiral position update, ASS, and Levy flight strategy. The Archimedean spiral spacing is set to 10 m. The beampatterns and the iterative process are shown in Figures 7 and 8, respectively. It can be seen that the PSLL of ASS is \(-21.02\) dB, while the PSLL of Levy flight strategy is \(-20.76\) dB. After adding ASS, the PSLL is \(2.37\)dB lower than the original strategy.  
seen that the PSLL of array without RL optimization is \(-21.02\) dB, and the PSLL of array with RL optimization is \(-22.47\) dB. Compared with WOA, the PSLL obtained by GEWOA is reduced by 3.81 dB.  
Figure 8: The iterative process under three search path planning strategies.  
Figure 10: The iterative process with and without RL.  
Figure 9: Beampatterns with and without RL.  
In order to test the influence of RL parameter on the search accuracy, experiments with different RL parameters are performed. The three sets of RL parameters are (1) \(k=1\); (2) \(k_{max}=1.2,k_{min}=0.8,t_{max}=5\); and (3) \(k_{max}=1.2,k_{min}=0.8,t_{max}=10\). The array beampatterns and iterative process are shown in Figures 11 and 12, where the PSLLs of three sets of RL parameters are \(-21.47\), \(-22.30\), and \(-22.47\) dB. The PSLLs of the designed arrays improve as the search range of the inverse solution is expanded.
## 7 Open Issues and Research Challenges
### Environmental Conditions  
The current environmental conditions in which the UAVs operate pose a challenge, as they may change the predetermined path to the destination, which results in mission failures.
# Research on Multi-Robot Formation Control Based on MATD3 Algorithm
## 4 Multi-Robot Formation Strategy Design Based on Improved MATD3
### State and Action Space Design  
The state space is defined according to the specific types of robots in the formation. For the leader robot \(\mathbf{r}_{lea}\), its main goal is to find an optimal path to reach the target point, so the state information includes its own position \(S_{lea}^{p}\) and velocity \(S_{lea}^{v}\), the target point position \(S_{g}^{p}\), and the detection information of the obstacle position \(S_{lea}^{v}\), specifically expressed as:  
\[S_{leader}=\Big{[}S_{lea}^{p},S_{lea}^{v},S_{g}^{p},S_{lea}^{v}\Big{]}=\Big{[} p_{lea}^{x},p_{lea}^{y},v_{lea}^{x},p_{lea}^{y},p_{lea}^{x}-p_{g}^{x  
\[S^{i}_{follower}=\left[S^{p}_{i},S^{p}_{i},S^{p}_{i},S^{q}_{i}\right]=\left[p^{x}_ {i},p^{y}_{i},v^{x}_{i},v^{y}_{i},p^{x}_{i},p^{y}_{i},p^{x}_{i}-p^{x}_{oj},p^{y}_ {i}-p^{y}_{oj}\right],j=1,\ldots,M \tag{24}\]  
where \(i\in[1,\ldots,N-1],l\in[1,\ldots,i-1,i+1]\).  
The action space of robot \(\mathbf{r}_{i}\) is the magnitude of the force, i.e.,  
\[A_{i}=\left[F^{x}_{i},F^{y}_{i}\right]_{i=1,\ldots,N} \tag{25}\]
# Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading
## 3 System Model and Problem Formulation
### Example  
An example of configuration is shown in the graph of Figure 3, which shows all the possible connections and, highlighted, a specific resource assignment. The dimensions of this example are \(N_{\text{AV}}=4\), \(N_{\text{BS}}=3\), and \(N_{\text{DC}}=2\), while the latencies \(d_{k,m}\), \(l_{m}\), and \(\ell_{i}\), the loads \(\gamma_{m}\), the bit rates \(r_{i,k}\) and \(\rho_{i}\), and the connection matrixes \(D_{i,k}\) and \(K_{k,m}\) are printed in Table 1 (a null value in \(d_{k,m}\) corresponds to a missing link between the BS and the AV).  
A possible assignment \((\mathbf{f},\mathbf{S})\) is specified in Table 2, and it has been highlighted by using different colors and thicker lines in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(l_{m}\) (ms) & \([100\) \(100\) \(100\) \(100\)] \\ \(\gamma_{m}\) & \([1/4\) \(1/4]\) \\ \(\rho_{i}\) (Mbit/s) & \([5\) \(5\) \(5\) \(8\) \(5]\) \\ \(r_{i,k}\) (Mbit/s) & \(\begin{bmatrix}14&0&2\\ 0&12&8\\ 7&9&8\\ 0&8&0\end{bmatrix}\) \\ \(D_{i,k}\) & \(\begin{bmatrix}1&0&1\\ 0&1&1\\ 1&1&1\\ 0&1&0\end{bmatrix}\) \\ \(d_{k,m}\) (ms) & \(\begin{bmatrix}24&6\\ 18&0\\ 0&21\end{bmatrix}\) \\ \(K_{k,m}\) & \(\begin{bmatrix}1&1\\ 1&0\\ 0&1\end{bmatrix}\) \\ \hline \end{tabular}
\end{table}
Table 1: Input parameters of the example in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(\mathbf{f}\) & \([0\) \(1\) \(0\) \(0\)] \\ \(\mathbf{S}_{1}\) & \(\begin{bmatrix}0&0\\ 0&0\\ 1&0\end{bmatrix}\) \\ \(\mathbf{S}_{2}\) & \(\begin{bmatrix}0&0\\ 0&0\\ 0&0\\ 0&0\end{bmatrix}\) \\ \hline \end{tabular}
\end{table}
Table 2: A possible assignment for the example in Figure 3.  
Figure 3: Graph of assignment in Table 2 for the example in Table 1.  
The assignment results in the following radio usage ratios  
\[U_{1} =\frac{\rho_{3}}{r_{3,1}}=\frac{8}{7}\approx 1.14, \tag{7}\] \[U_{2} =\frac{\rho_{4}}{r_{4,2}}=\frac{5}{8}=0.625,\] (8) \[U_{3} =0, \tag{9}\]  
which shows that the radio link deployed at BS1 is overexploited. Similarly, the CPU loads are calculated as  
\[\Gamma_{1} =2\gamma_{1}=0.5, \tag{10}\] \[\Gamma_{2} =0, \tag{11}\]  
so the two DCs are not overloaded. In terms of experienced latency, AV1 does not have a route toward any service, so it is \(L_{1}=\infty\). AV2, instead, uses its internal service, experiencing a latency \(L_{2}=\ell_{2}=100\,\mathrm{ms}\). The third AV offloads its task to SVC1 on DC1, passing through BS1, which is overloaded at 114% of the link capacity; the typical latency \(l_{1}\) is thus multiplied by \(U_{1}\approx 1.14\) and increased by the wired connection latency \(d_{1,1}=24\,\mathrm{ms}\), achieving \(L_{3}=l_{1}U_{1}+d_{1,1}\approx 61.62\,\mathrm{ms}\). Finally, AV4 also has a link to the service SVC1 that passes through a nonoverloaded BS; the experienced latency is \(L_{4}=l_{1}+d_{2,1}\approx 51\,\mathrm{ms}\).
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Intrusion Detection
#### 2.1.2 Model Selection  
The process of choosing a model for the SFSS's intrusion detection component is critical [26]. The model used for this job is critical to the system's effectiveness since its ability to reliably recognize and categorize intruders, whether they are animals or people, determines the system's overall performance. Inaccurate or poor models may jeopardize the system's core goal of protecting agricultural land. Figure 7 presents the model selection procedure.  
Figure 6: Sample images from real farm dataset.  
Figure 7: Model selection.  
Model selection is based on a set of comprehensive criteria that take into consideration the system's diverse needs. The models' object identification skills, computational efficiency, the depth of their pretrained knowledge, and their adaptability to a cloud-based environment are all critical requirements. The models adopted must also be compatible with the system's scope, which encompasses extensive agricultural areas and a variety of incursion situations.  
A comparison study was performed to determine which model best meets these requirements. The performance of five separate models was evaluated in this analysis: Inception, Xception, VGG16, AlexNet, Yolov6, Yolov7, and Yolov8. The models were rigorously tested and evaluated, modeling real-world incursion situations frequent in the agricultural environment. The capacity of each model to identify intruders, its effectiveness in processing massive amounts of data, and its adaptation to cloud infrastructure were all assessed.  
The selection process is driven by the critical necessity of finding a model that can integrate easily into the cloud-based architecture of our system, manage real-time video feeds from farms, and identify and react to intrusions quickly. Finally, the model adopted acts as the foundation of the SFSS, determining the system's capacity to preserve agricultural land and reduce the harm caused by animals and people.
## 5 Interpretation and Discussion
Negative PS velocities of Sentinel-1 could be explained by a short-term subsidence event included inside a longer uplifting trend visible since the first ERS SAR image. Unfortunately, the piezometric measurements after 2012 are not available and consequently it is not possible to confront the decreasing velocity trend of PS with the evolution of the deep aquifers. Figure 9: Time series of a PS (mm) located on the Tour & Taxis building. It should be noted that the rising groundwater levels in the Paleozoic and Mesozoic aquifers since the early/mid-1990s are part of a general trend that started in the 1970s. From historical records [33] it is known that, due to the abundant presence of water-demanding industries in the city centre of Brussels, groundwater levels had dropped drastically between the 1880s and 1960s (circa 50 m, see Figure 11). It can be reasonably argued that overexploitation of the aquifers would have led to wide-spread ground subsidence in the period of heavy industrialisation. Since the 1970s, heavy industries moved out of the city centre [34] and groundwater levels began to recover (circa 30 m by the end of the 1980s, Figure 11). Several local subsidence features can be identified on the maps of ground displacements (Figure 3A-D) and may correspond to local groundwater extraction activities or ground settlements. The subsiding phenomenon in Forest/Uccle is observed only in the Envisat data and is probably related to intense groundwater pumping, which lowers the Quaternary aquifer level for civil engineering works locally (confidential report, Brussels Environment Agency). Subsid  
Figure 11: Historical groundwater levels in the Cretaceous aquifer. Figure 10: Comparison of the evolution of the water table (blue) and time series of averaged velocity of PS located in a buffer of 50 m around the selected piezometers. The different colours refer to the different radar satellites (red for ERS, green for ENVISAT, light blue for TerraSAR-X and purple for Sentinel 1). in the TerraSAR-X and Sentinel 1 datasets may be related to engineering works or settlement of the railway embankments at Schaerbeek station, although conclusive records are not available to the authors. Subsidence in the Maelbeek and Woluwe river valleys (Figure 1) (TerraSAR-X data: linear features \"Lb\" and \"Lc\"), as well as in the Senne river valley (Sentinel 1 data: apparent reversal of the direction of movement from uplift to subsidence), may be attributed to the compaction of alluvial deposits [35; 36; 37]. The natural settlement of the river deposits is intensified by the load of buildings. The ground settlements probably existed during previous observation periods [38], but may have been obscured by the stronger signal of uplift in the area. As for local phenomena observed in the TerraSAR-X data, their absence in the other datasets may be related to differences in ground resolution.
###### Abstract  
Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, which are hard to realize in actual engineering. In this study, we propose a novel data fusion method. Measured inclinations across multiple cross-sections of the target bridge and accelerations at a subset of these sections were used to estimate accelerations at the remaining sections. Theoretical analysis of a typical vehicle-bridge interaction (VBI) system has shown parallels with the blind source separation (BSS) problem. Based on this, Independent Component Analysis (ICA) was applied to derive surrogate inclination mode shapes. This was followed by calculating surrogate displacement mode shapes through numerical integration. Finally, a surrogate inter-section transfer matrix for both measured and unmeasured accelerations was constructed, enabling the estimation of the target accelerations. This paper presents three key principles involving the relationship between the surrogate and actual inter-section transfer matrices, the integration of mode shape functions, and the consistency of transfer matrices for low- and high-frequency responses, which form the basis of the proposed method. A series of numerical simulations and a large-scale laboratory experiment were proposed to validate the proposed method. Compared to existing approaches, our proposed method stands out as a purely data-driven technique, eliminating the need for finite element analysis assessment. By incorporating the ICA algorithm and surrogate mode shapes, this study addresses the challenges associated with obtaining accurate mode shape functions from low-frequency responses. Moreover, our method does not require partial measurements of the target responses, simplifying the data collection process. The validation results demonstrate the method's practicality and convenience for real-world engineering applications, showcasing its potential for broad adoption in the field.  
b 2024 14 8556. [[https://doi.org/10.3390/app1488556](https://doi.org/10.3390/app1488556)]([https://doi.org/10.3390/app1488556](https://doi.org/10.3390/app1488556))
## 4 Geometrical Analysis
### Dimensional Analysis
The normalized deflection values calculated for each specimen allow for a deeper understanding of the role of slenderness and tool position in the deviations observed during machining. The results show that the maximum bending deflection occurs near the center of the specimen, with progressively lower values towards the extremes. This distribution is characteristic of a fixed-supported beam subjected to transverse loading, and it aligns with the actual boundary conditions of the part during the turning process. When comparing specimens with different nominal diameters, the data reveal that smaller diameters lead to significantly larger normalized deflections, confirming the structural weakening associated with increased slenderness. In particular, the deflection for \(D=14\) mm is markedly higher than that for \(D=16\) mm or \(D=18\) mm under similar force conditions. These calculated deflections correlate directly with the experimental diameter deviations discussed earlier. In both cases, the largest errors are observed in the central region of the specimen, while the areas near the chuck and tailstock exhibit lower deviations. The spatial distribution of the predicted deformation matches the measured pattern of dimensional error, confirming that flexural deflection is the main contributor to the loss of dimensional accuracy. Moreover, the quantitative relationship between higher deflec  
Figure 8: Normalized deflection of the turned surface as a function of axial position. tion and higher deviation from the nominal diameter strongly supports the conclusion that specimen geometry, particularly diameter, is a critical factor in the machinability and achievable tolerances of the samples.
## 6 Main Steps of the Proposed NSDI Method  
The following are the main steps in the suggested NSDI approach utilizing the ESMA algorithm:  
1. Create two parallel ESMA model based on Table 1 and set the initial number of variables equal to the total number of elements in structure.
2. Employ the ESMA to determine the optimal solution.
3. Determine the total number of intact elements using finding \(i\) as \(X_{i}\cong 0\) for all components of the damage vector.
4. Reduce the number of variables from the optimization problem by removing the intact elements from the damage vector.
5. Run ESMA again, this time using the revised optimization size from d.
6. Check the convergence by computing \(1-MDLAC\) based on Equation (26). If the two response vectors are almost identical, save the results and end the optimization process; otherwise, go to the step c.
7. Based on determined damage variables, calculate PoDE and DMI based on Equations (17) and (18).
## 1 Introduction
In the present study, we explored the potential of Raman spectroscopy combined with chemometrics approaches for exploratory analysis and multivariate statistical regression methods to identify adulterants and quantify their concentration in EVOO. Sunflower oil, pomace olive oil and refined olive oil were chosen as stretching agents as they are likely adulterant choices and provide varying degrees of difference in chemical composition when compared to EVOO. Sunflower oil was expected to be the easiest to detect, followed by pomace oil, and finally refined olive oil, the most chemically similar adulterant to EVOO being the most challenging.
## 2 Data and Methods
### Methods
#### 2.2.2 Forecast Performance Evaluation  
The performance of the NN forecasts was evaluated on the test set using the MAE metric. Since we had an ensemble of NN models, we obtained a distribution of MAE values for every setup. We could calculate various statistical parameters from these distributions, such as the average value and the 10 th and 90 th percentile of MAE.  
The performance of the NN forecasts was also compared to the persistence and climatological forecasts. The persistence forecast assumes that the value of \(T_{\text{max}}\) or \(T_{\text{min}}\) for the next day (or any other day in the future) will be the same as the previous day's value. The climatological forecast assumes the value for the next day (or any other day in the future) will be identical to the climatological value for that day in the year (the calculation of climatological values is described is Section 2.1.2).
# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 3 Gait Recognition Using GERF  
The pipeline of our proposed method is shown in Figure 2. Given two raw sequences of a gallery and probe, first extract gait silhouettes using a background subtraction-based graph-cutsegmentation [38] and obtain registered and size-normalized silhouettes using the region center information [16]. Second, average the silhouettes over a gait period to get a GEI. Third, transform the GEI using learned GERF from the training set with covariates, e.g., clothing or carrying status variations. Then, after two post-processing techniques, i.e., Gabor filtering and spatial metric learning, calculate the L2 distance as a dissimilarity score between the gallery and probe. Finally, do the comparison by comparing the score with an acceptance threshold for verification (one-to-one comparison) scenarios, or by using the most commonly used nearest neighbor classifier for identification (one-to-many comparison) scenarios.
# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 3 Predictive Modelling Design  
The structure of the prediction model is shown in Figure 1, which consists of four parts: an input layer, a BiLSTM layer, an attention layer, and an output layer.  
In this case, the input layer pre-processes the cost data to transform it into a shape that the BiLSTM can process. The BiLSTM layer is used to capture inter-temporal interactions in the costing data, and the attention layer is used to obtain inter-feature interactions in costing data. The output layer contains the dropout layer and the dense layer, where the dropout layer randomly switches off some neurons with probability \(p\) during training to set their outputs to zero, thus reducing the risk of overfitting. The weight matrix W of each neuron is multiplied by the probability \(p\) to maintain consistency during testing to compensate for the random switching-off operation during training, thus improving the model generalisation and robustness. In the dense layer, the neurons establish connections with each neuron in the previous layer, each connection has a corresponding weight, and each feature in the input data is passed through these connections to the dense layer where a weighted summation is performed in the neuron and nonlinear mapping is performed on the weighted output sequence to produce the output of the neuron.