esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### The State-of-the-Art Methods
#### 2.2.4 Level Set Active Contour Model (Lascm)  
The LASCM is a level set method that relies on the region for image segmentation in the presence of intensity inhomogeneity. This method models the inhomogeneous objects as Gaussian distributions of different means and variances. Using a sliding window, the original image is mapped into another domain. In this area, every object's intensity distribution is still Gaussian but more distinct. It is possible to approximate the means of the Gaussian distributions in the transformed domain by multiplying a bias field with the original signal inside the window. A functional energy of maximum likelihood is then defined in the entire image region, which combines the bias field, the level set function, and the partly constant function which approximates the true image signal [10].  
The method works by combining information from neighbouring pixels that belong to the same class. In this approach, the desired object is separated from its background. Given \(NR_{x}\), a neighboring region centered at location \(x\), i.e., \(NR_{x}=\{y||y-x|\leq\rho\}\), where for the region \(NR_{x}\), \(\rho\) is its radius. The whole image domain \(\Omega\) can be represented as \(\Omega=\cup_{i=1,...,n}\Omega_{i}\) with \(\Omega_{i}\cap\Omega_{i}=\emptyset\), for all \(i\
eq j\), where \(\Omega_{i}\) is the \(ith\) object region. A mapping \(\mathcal{T}:I(\mathbf{x}|\mathbf{\theta}_{i},B)\rightarrow\mathcal{I}(\mathbf{x}|\mathbf{ \theta}_{i},B)\) from original image intensity domain \(ID(\mathcal{T})\) to another domain \(RD(\mathcal{T})\) by averaging image intensities is defined as  
\[\mathcal{I}(\mathbf{x}|\mathbf{\theta}_{i},B)=\frac{1}{L_{i}(\mathbf{x})}\int_{\Omega_{i} \cap NR_{x}}I(\mathbf{y}|\mathbf{\theta}_{i},B,\mathbf{x})d\mathbf{y} \tag{11}\]  
where the number of pixels in region \(\Omega_{i}\cap NR_{x}\) is obtained with \(L_{i}(x)=|\Omega_{i}\cap NR_{x}|\). The intensity of pixel \(x\) is assumed to be independently distributed with a Gaussian distribution, that is, \(P(\mathcal{I}(\mathbf{x}|\mathbf{\theta}_{i},B))=\mathcal{N}\Big{(}I|U_{i}(\mathbf{x}), \frac{\sigma_{i}^{2}}{L_{i}(\mathbf{x})}\Big{)}\) where \(U_{i}\) and \(\sigma_{i}\) are the spatial varying mean and the standard deviation subject to the object in region \(\Omega_{i}\), for all \(\mathcal{I}(\mathbf{x}|\mathbf{\theta}_{i})\in RD(\mathcal{I})\).  
The advantages of this method are: it achieves soft classification, it is robust against noise and it mitigates the over-smoothing of object boundaries problem.  
The disadvantages are: only the neighbouring intensities belonging to the same class contribute to each class and the overlapping parts of the statistical distributions among different classes of intensities are suppressed.
###### Abstract  
This contribution describes the methodology applied to evaluate the suitability of a Long-Range Mobile Mapping System to be integrated with other techniques that are currently used in a large and complex landslide deformation monitoring project carried out in Cortes de Pallas, in Valencia (Spain). Periodical geodetic surveys provide a reference frame realized by 10 pillars and 15 additional check points placed in specific points of interest, all with millimetric accuracy. The combined use of Close-Range Photogrammetry provides a well-controlled 3D model with 1-3 cm accuracy, making the area ideal for testing new technologies. Since some zones of interest are usually obstructed by construction, trees, or lamp posts, a possible solution might be the supplementary use of dynamic scanning instruments with the mobile mapping solution Kaarta Stencil 2 to collect the missing data. However, the reliability of this technology has to be assessed and validated before being integrated into the existing 3D models in the well-controlled area of Cortes de Pallas. The results of the experiment show that the accuracy achieved are compatible with those obtained from Close-Range Photogrammetry and can also be safely used to supplement image-based information for monitoring with 3-8 cm overall accuracy.  
deformation; environmental monitoring; long-range mapping; MMS; close-range photogrammetry; sub-millimetric EDM geodetic techniques +
Footnote †: journal: Applied Sciences
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 3 Results
### Common Wearable Sensors Used in Gait, Balance, and RoM Analysis  
Wearable sensors are devices that are mounted to a person's body in order to gather information; such as movement or heart rate. Wearable sensors typically are inexpensive and small in size. Wearable sensors are playing an increasing role in balance and gait assessment in rehabilitation research. Three important advantages of wearable sensors for assessment of gait and balance disorders include [12]:  
* obtain objective measures that characterize how and why functional performance of gait and balance are impaired,
* increase the sensitivity of gait and balance measures,
* increase the opportunity for immediate biofeedback provided to patients.
# Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting
## 3 Results and Discussion
### Computation Time  
The deep learning models proposed in this study have complex architectures that involve a high computational cost. Therefore, it is also essential to evaluate the models in terms of computational efficiency. Table 4 presents the training and prediction time for all the best TCNs and LSTMs that were presented in the previous subsection. It also reports the number of trainable parameters to further enhance the comparative study. For obtaining these computation times, the batch size has been fixed to 128 instances for all models in order to perform fair comparisons. As can be seen, TCNs have far more trainable parameters since their stacked blocks have many convolutional operations. This results in higher training time compared to the LSTM models. The differences in training times are slightly larger in the case of the electric demand data, considering that an epoch of this dataset involves many more instances than the EV data. However, with respect to prediction time, TCN models are always faster than recurrent networks. Regardless of the considerably higher number of parameters, TCNs are able to provide very quick forecasts once they are trained. As it was expected, parallel convolutions of TCNs can process long input sequences faster than the sequential processing of recurrent networks. This seems an essential advantage for real-time applications, in which predictions need to be obtained as soon as possible in order to make informed decisions.  
Figure 8: Evolution of the training and validation loss function of the best TCN and LSTM models for each possible value of past history. First row corresponds to the electric demand data (**a**–**c**), while the second row corresponds to the EV power consumption data (**d**–**f**).
## 4 Experimental Scenarios
### Scenario 1: Testing Assembly Stations  
In the first scenario, the experiments were carried out in the assembly stations of a production line using a physical model built with Lego(r) Mindstorms(r) EV3 and reported in Figure 2.  
This is a prototype of an assembly line built by the authors and programmed using the Lego(r) Mindstorms(r) EV3 software to assemble two 2x2 Lego(r) bricks (one yellow on the top and one green at the bottom) (bottom left in Figure 2).  
The Lego(r) physical model is characterized by a modular structure and it is composed by three modules as detailed in Figure 2 and described below:  
* Module 1: Warehouses for raw materials (RM) and Main Conveyor Belt (highlighted in pink in Figure 2). It includes two warehouses for raw materials--one for yellow Lego(r) bricks and the other one for green Lego(r) bricks--and the Main Conveyor Belt (length: 95 cm) which connects the warehouses for raw materials to the other modules of the processing line.
* Module 2: Assembly Station 1 (AS1) + Distribution system 1 (highlighted in yellow in Figure 2). It includes the first assembly station--made up of two wheels rotating in opposite directions that assemble a yellow brick on a green Lego(r) brick--and the first distribution system made up of a rack moved by a medium motor which pushes all the necessary Lego(r) bricks towards the assembly station.
* Module 3: Assembly Station 2 (AS2) + Distribution system 2 (highlighted in light blue in Figure 2). It includes the second assembly station and the second distribution system working exactly as the Module 2.  
Every module is controlled by an EV3 Brick, which is the brain of the physical prototype (highlighted in green in Figure 2). The EV3 Bricks are connected via Bluetooth, and for this kind of connection the EV3 Bricks are divided into Master Bricks and Slave Bricks. Master EV3s and Slave EV3s can send messages between each other via Bluetooth.
## 4 The Algorithm
### Beams in 3D  
So far, all the work has been carried out in 2D workspace. When it comes to work in 3D space, the first task is to generate the potential field in 3D, then to find the middle path for a point robot from the start point to the goal point as seen in Figure 10.  
Figure 10: Generating potential field in 3D environment (Obstacles are not shown).  
Figure 9: Mapping the manipulator onto its path.  
Sending beams in 3D is not as easy as in 2D. The left and right beams in 2D cover all the free space which the robot moves within. On the other hand, sending just a couple of beams shown in Figure 11a barely covers the free space. Therefore, it cannot be used in 3D as in 2D. To cover the free space, it is possible to send beams in various directions at 90\({}^{\circ}\) angles to the line tangent to the middle path. The more couples included, the more space is covered. In Figure 11, there are 1,2,3 and 4 couples shown. Although increasing the number of couples makes the resolution higher, not only it causes high computational costs but also it does not contribute to the maneuvering ability of the manipulator.
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 4 Evaluation Metrics and Results
### Results
They proposed an approach based on an accurate energy model that correctly estimates the energy needed to complete the coverage mission. They showed that their work achieves minimum energy consumption. Our work achieves 11.3% energy gain over the work in [31] which in turn achieves a gain up to 17% over the approach in [10]. Moreover, our work achieves complete area coverage with shorter path over the work in [31], as they avoid the whole obstacle cell which results in more number of turns and less coverage rate. Figure 23a shows the gain in completion time in our 3D work vs. 2D work while increasing both the speed and rotation rate. Figure 23b shows the gain in completion time while increasing the speed and decreasing the rotation rate. Results show that the mission completion time is reduced by an average of 15.95% in EAOA compared to the results obtained in [31]. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **(a) Ref. [31]** & **(b) Our work (EAOA)** \\ \hline Route length \(\top\) in meters & 1075.5555 & 1033.702 \\ \hline UAV speed \(v\) in m/s & 8 & 8 \\ \hline Turns in degree & 2025 & 1260 \\ \hline Rotation rate \(\omega\) in degree/s & 30 & 30 \\ \hline Completion-time \(\top\) in sec & 201.944 & 171.212 \\ \hline Energy consumption \(E_{total}\) in KJ & 160.229 & 142.122 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results obtained by CPP in Ref. [31] (Figure 22a) vs. EAOA (Figure 22b). Figure 22: Trajectory Planning in our work versus the work in [31]. (**a**) trajectory planning by [PERSON] et al. [31]; (**b**) trajectory plan by our work.
## 5 Discussion
### Model Limitations and Future Directions
#### 5.1.4 Generalization of the Results  
To increase the accuracy of the system and its capability, the accuracy of the input data for prediction training would need to be expanded and improved. A true orthophoto (all objects are rectified to an orthogonal projection) view of the building shape would be used, which is more accurate and corresponds to the building outline lines. The data could be supplemented with oblique images and ALS LiDAR data.  
To improve the robustness of the model to a variety of conditions and to increase its generalizability, data augmentation techniques (e.g., rotations, scaling, changes in brightness or contrast, offsets) [22] were employed in this study to increase the size and diversity of the training set. These methods helped the model to generalize better by simulating different scenarios. In future studies, further improvements could be achieved by incorporating additional augmentation strategies and more diverse input data. For example, utilizing images captured at different times of day and in different weather conditions will increase the diversity due to changing lighting and different roof appearances (e.g., dry and wet roof textures). Including data with these variables in the training set will improve the model's ability to identify data in different circumstances.  
At present, the system has only been developed for one region, which significantly limits its versatility. Expanding the training set with data from different regions will increase the variety of roof shapes and their textures. The use of data from different sources in the future development of the multimodal system will be crucial for its universality. Data from different sensors (aerial imagery, satellite imagery, and drone data) can support a better understanding of the specifics of different roof types. Future plans include the use of data from OpenStreetMap (OSM), GoogleEarth, and INSPIRE. The next phase of system development will be the use of synthetic data. ALS LiDAR data will be used to create 3D roof models and render them under different lighting and weather conditions.  
Taking the above into account will allow a better YOLO model to be created, thus improving the multimodal system. Data classification will be able to handle a greater variety of data in practical applications.
## 7 Conclusions  
Because of rain attenuation, the equivalent baseband transfer functions of ultra-wideband radio-links to/from satellites orbiting in GeoSurf satellite constellations will not be ideal. We have evaluated both the output of a direct channel and the interference coming from the orthogonal channel.  
By calculating the rain attenuation and phase delay with the Synthetic Storm Technique, we have found that in a 10-GHz bandwidth centered at 80 GHz, possibly used in future internet services with spread-spectrum coding techniques, both direct and orthogonal channels will introduce significant amplitude and phase distortions, both increasing with rain attenuation. Only \"narrow band\" channels (\(100\,\sim 200\) MHz) will not be significantly affected.  
We have found that the ratio \(\rho\) between the probability of bit error with rain attenuation and the probability of bit error with no rain attenuation increases as rain attenuation increases. If interference is present, there are significant differences as \(\rho\) tends to decrease when the impulses are of different signs and tends to increase when the impulses are of the same sign, an unexpected result that should be due to a significant amplitude distortion of the interfering impulse coming from the orthogonal channel.  
We have found that, considering only average transfer functions and no interference from the cross channel, the linear distortions can give rise to an average loss of \(1\,\sim 2\) dB in the SNR radio link budget. If the cross-channel interference is also considered, the loss can reach \(3\,\sim 4\) dB. All results depend on the site, Tampa being the worst one.  
Figure 14: Probability ratio \(\rho\) between the probability of bit error with rain and the probability of bit error with no rain (ideal case) vs. probability of bit error in the direct channel with no interference, according to the average SNR (continuous lines) and \(-1\) standard deviation of SNR (dashed lines), at Spino d’Adda, Prague, Madrid, Tampa. Blue lines, \(A=10\) dB; red lines, \(A=20\) dB.  
Future work will run a full Monte Carlo digital simulation to confirm these findings.  
Conceptualization, E.M. and C.R.; methodology, E.M. and C.R.; software, E.M. and C.R.; investigation, E.M. and C.R.; data curation, E.M. and C.R.; writing--original draft preparation, E.M. and C.R.; writing--review and editing, E.M. and C.R.; visualization, E.M. and C.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We wish to thank [PERSON], at NASA years ago, for providing the rain rate data of Tampa; [PERSON], Institute of Atmospheric Physics in Prague, for providing the rain rate data of Prague; and [PERSON], Universidad Politecnica de Madrid, for providing the rain rate data of Madrid.  
The authors declare no conflict of interest.
## 4 Experimental Evaluation
### Case Study  
To further illustrate the effect of the UA-BFET model on cross-modal sentiment analysis in the unaligned data scenarios, we visualized the attention weight of interaction between text and video cross-modal elements in the dataset. As shown in Figure 7, in Case a, when the words \"unfortunately\", \"horrible\", and \"disappointing\" expressed negative emotions, they were also accompanied by a series of facial expressions, such as \"frowned\" and \"angry\" in the video modality, so the model could accurately predict the negative emotional attitude.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Modal** & **Acc-2/\%** & **F1-Score/\%** & **MAE** & **Corr** \\ \hline Without CCA & 84.1 & 83.9 & 0.413 & 0.794 \\ With CCA & 84.7 & 84.7 & 0.424 & 0.793 \\ \hline text & 82.8 & 82.8 & 0.708 & 0.800 \\ video & 82.9 & 75.2 & 0.702 & 0.801 \\ audio & 83.2 & 75.5 & 0.697 & 0.811 \\ \hline audio + video & 83.8 & 83.8 & 0.716 & 0.801 \\ text + video & 84.7 & 84.7 & 0.424 & 0.793 \\ text + audio & 84.5 & 84.5 & 0.727 & 0.793 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation results (taking MOSI data set as an example).  
In Case b, the overall polarity of the video modality is biased towards negative emotion due to the drooping eyebrows presented by the people in the video. Nevertheless, the polarity of cross-modal fusion features is consistent with the polarity of the true emotion. It can be seen that the video modality is canceled out in the final cross-modal fusion. It is shown that the model can explore the correlation and interaction between text representing emotions and corresponding video segments in long sequences. In Case c, the text modality \"deliver a lot of intensity\" exhibits ambiguous emotions, and the smiling facial expression removes ambiguities that appear in the text modality, as shown by the section circled in the black block diagram in Case c, which exhibits positive emotions overall. These further indicate that the model can enhance the unimodal features and effectively improve the cross-modal sentiment analysis effect in the unaligned data scenarios by using the correlation and interaction between different modalities.  
Through experiments executing on unaligned public datasets MOSI and MOSEI, the UA-BFET model has obtained better sentiment analysis results in unaligned data scenarios, which proves the advantages of sentiment analysis from the perspective of extracting effective unimodal features and interaction between different modalities. Notwithstanding, when sarcasm, comparison, and irregular text appear in the text modality, the model cannot always accurately predict the sentiment, so it can be considered to introduce an external knowledge base to solve the above problems. We believe that this study is an important step forward in solving cross-modal sentiment analysis in unaligned data scenarios.
## 6 Open Issues and Future Trends
### System Scalability of Dima Optimization  
As the ILP problem itself is NP-hard, the standard solvers for optimal solution will suffer from low computational efficiency in solving optimization problems of a large size. Although it may be efficient for simple problems, it becomes infeasible when the number of constraints and decision variables are large. In DIMA optimization, the number of variables can increase significantly with the number of devices, switches, functions and signals. Such a system setting renders DIMA optimization to be a large-scale mapping problem [65]. Thus, it is still an open issue on how to tackle the scalability of solving the large scale optimization problem in DIMA for achieving the solution close to the optimum within an acceptable time.  
To extend the scalability of DIMA optimization, we can rely on the following approaches. First, Lagrangian relaxation method can be adopted to obtain the approximate solution for DIMA optimization that is close to the optimum. The Lagrangian relaxation method is a common technique to solve the ILP problem efficiently, which has been experienced on network engineering [38; 39; 66; 67]. The purpose of the relaxation is trying to decompose the original optimization problem into a set of subproblems. By decomposition, each of the subproblems will have much less number of integer variables and constraints, which is easier to solve. For each of the subproblems, subgradient optimization scheme can be used to update the multipliers for obtaining the feasible solution. Second, we can try to restrict the search space and reduce the number of objectives to have an optimization problem of smaller size. For example, in Reference [68], the authors reduced the search space and proposed a heuristic algorithm inspired from game theory for ILP problem. In Reference [65], the authors leveraged backtracking approach to obtain better solutions from a search tree to avoid finding similar solutions. In addition, [PERSON] et al. in Reference [69] proposed to leverage the set of subproblems that have been solved to avoid solving a large number of integer programs, thus improving lexicographic optimization. By taking these approaches, the solutions could be provided in an acceptable execution time given a full scale optimization problem.