esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Photogrammetric and Lidar Applications of UAV
## 6 Discussion
### Discussion on UAV Carriers, Sensors, and Software  
We further divided the articles and literature that we studied in this publication according to the UAV carriers (Table 10), the type of sensor (Table 11), and the software that was used in data processing (Table 12). Most authors use the UAV DJI Phantom Pro for scientific purposes, but UAV DJI Phantom 4 and UAV DJI Mavic are also very common. Canon and Sony cameras are the most used digital cameras. The Velodyne lidar scanner is most commonly used for lidar data acquisition. Finally, the authors most often used Agisoft products and Pix4D software for data processing.  
\begin{table}
\begin{tabular}{l c} \hline
**UAV** & **References** \\ \hline DJI Phantom 4 Pro & [59,61,69,71,77,79,89,107,108,115,127,129,142,161,170,198] \\ \hline DJI Phantom 4 & [58,82,103,120,135,138,157] \\ \hline DJI Phantom 3 & [68,78,80,93,131,137,179] \\ \hline DJI Phantom 2 & [73,131,159,164] \\ \hline DJI Mavic & [94,100,104,129,131,148,187] \\ \hline DJI Matrice & [96,109,110,115,116,151] \\ \hline DJI Inspire & [62,119,176] \\ \hline
3 DR Solo & [60,105,120] \\ \hline Droidworx Skyjib Oktokopter & [63,66] \\ \hline DJI S1000 & [81,88] \\ \hline \hline \end{tabular}
\end{table}
Table 10: Classification by UAV carrier.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sensor** & **Type of Sensor** & **References** \\ \hline Canon EOS & digital camera & [63,87,103,106,111,126,156] \\ \hline Canon PowerShot & digital camera & [101,120,146,147,182] \\ \hline Sony Alpha & digital camera & [125,132,135,150,165,177] \\ \hline Sony EXMOR & digital camera & [68,93,168,179] \\ \hline Sony ILCE & digital camera & [81,87,88] \\ \hline DJI Phantom 4 integrated camera & digital camera & [58,82,103,120,135,138,157] \\ (12,4 MP) & digital camera & [59,61,69,71,77,79,89,107,108,115,127,129,142,161,170,198] \\ \hline DJI Phantom 4 Pro integrated camera (20 MP) & digital camera & [59,61,69,71,77,79,89,107,108,115,127,129,142,161,170,198] \\ \hline DJI Zenmuse & digital camera & [62,88,110,119,148,176] \\ \hline Nikon & digital camera & [70,91,101,178,199] \\ \hline GoPro & digital camera & [102,105,131,186,188] \\ \hline Parrot Sequoia & multispectral camera & [59,60,64,69,190] \\ \hline Panasonic & digital camera & [119,154] \\ \hline Velodyne & lidar & [64,67,96,107,116] \\ \hline Leica & lidar & [76,103,158] \\ \hline Riegl VUX & lidar & [65,96,116] \\ \hline Ibeo LUX & lidar & [63,66] \\ \hline Odroid XU4 & lidar & [109] \\ \hline LiteMapper & lidar & [147] \\ \hline \end{tabular}
\end{table}
Table 11: Classification by sensor.
# Energy Optimization in Hotels: Strategies for Efficiency in Hot Water Systems
## 3 Materials and Methods  
The availability of domestic hot water (DHW) is a fundamental requirement for comfort in the hotel sector, where its consumption represents a significant part of the energy expenditure. This consumption varies according to factors such as climate, hotel category, and occupancy level. In this context, it is crucial to address energy efficiency in air conditioning and water heating systems, as their optimization can significantly reduce both energy consumption and operating costs and environmental impact [19].  
Studies indicate that air conditioning loads in hotels are variable throughout the year; in the case of hot water demand, in addition to seasonal variation, there are daily peaks, mainly in the morning and afternoon [20]. This requires heating and storage systems to be adequately sized. However, a common problem is the oversizing of these systems, where the installed capacity exceeds the actual demand, resulting in inefficient operation [21].  
Hot water demand is influenced by cultural and social norms, as well as region-specific climatic conditions, making generalization across countries difficult [22]. In addition, current regulations on hot water demand in non-residential buildings are outdated and do not adequately address the selection of necessary equipment [23; 24].  
Despite the fact that in tropical countries the need for air conditioning and hot water is always present, there is limited research on the simultaneous optimization of both systems  
Figure 2: Physical and functional diagram of the installation with heat recovery and thermal storage tanks for domestic hot water.  
in hotels. Understanding consumption patterns is essential for designing efficient systems. A recent study has compiled information on DHW consumption profiles in different types of buildings, providing tools to accurately estimate the energy consumption associated with these systems [19].  
The implementation of more efficient technologies and a proper design that considers peak demand are essential to ensure an adequate supply of domestic hot water without incurring unnecessary costs.
# Gesture-to-Text Translation Using SURF for Indian Sign Language
###### Abstract
Using SURF ensures that the vocabulary is invariant to the scale, orientation, and rotational changes of signs, which is crucial for sign language recognition, where different people may sign the same word differently. The BoVW method is especially relevant for sign language recognition, as it can handle object appearance, pose, and illumination variations. In addition, the classification phase of the study was performed using support vector machine (SVM) naive Bayes, logistic regression, K nearest neighbors (k-NN), as well as convolutional neural network (CNN). The utilization of these techniques led to the high accuracy achieved during this study. This research paper presents an ISL translator that utilizes SURF for the gesture to text translation. The paper's organization is as follows: the study is divided into sections: Introduction, Prior Research, Research Methodology, Results and Discussion, and Conclusion. Section 2, Prior Research, aims to cover a range of studies and present a comparative analysis of their methodologies, techniques, and outcomes. By comparing prior research, the paper aims to build upon existing knowledge and identify gaps this study seeks to address. Section 3 of the research methodology provides a detailed description of the system setup and experimental procedures, including image collection, pre-processing, and feature extraction, where SURF and BoVW methods are discussed and incorporated. In Section 3.5, SVM, CNN, k-NN, naive Bayes, and logistic regression are utilized. The subsection on image pre-processing discusses color space concepts, conversion of RGB to the HSV color space, and edge detection using Canny edge. Section 4, Results and Discussion, presents a comparative analysis and the final output. Finally, the conclusion section summarizes the key findings and contributions of the study and highlights the implications of the research; moreover, this section discusses possible future work.
## 2 Materials
### Data Preprocessing  
For SM modeling, we created a dataset that combines static and dynamic features, as previously shown in Table 1. The static features are soil texture and topography; the dynamic features are climate and satellite-derived time-series data. In addition, we added a time variable as a dynamic feature. Because the LSTM framework requires time-series data, we repeated the static features as the sequence length before feeding it to the LSTM framework.  
For dynamic features, we prepared a three-year dataset that includes in situ observations acquired from ISMN stations from 31 December 2017 to 1 January 2021. In this dataset, we applied data cleaning to reduce the data-originated uncertainty and eliminate the inconsistency within the measurements. Data cleaning involves a two-step elimination criteria. The first criterion is related to the record length. The record length condition requires that those stations be discarded if more than 10% of the measurements were missing in any station. The second criterion is developed to ensure sequential dependence in the observations. The SM stations with more than 60 consecutive days of missing measurements are also excluded from the analysis because a solution like interpolation was unrealistic considering the complex nature of the problem. According to these criteria, we found 103 stations, shown by red dots in Figure 1, out of 1611 with time series of SM measurements suitable for the analysis. Because dynamic features are gathered from various sources with different temporal resolutions, we upsampled all data into daily sampling by using the linear interpolation method for temporal matching. The ground measurements are resampled into daily SM values to ensure the matching temporal resolution.  
For the training of the LSTM model, we formed five different scenarios to determine the contribution of feature groups. As previously shown in Table 1, in SM monitoring, climate data, soil texture, and topographical data are the main drivers of SM. Beginning with the climate data (Case I), we consecutively included soil texture (Case II), topographical data (Case III), and satellite data (Case IV and Case V) and listed them below.  
Case-I Climate data Case-II Climate data, soil texture Case-III Climate data, soil texture, topographical data Case-IV Climate data, soil texture, topographical data, satellite data (SMAP) Case-V Climate data, soil texture, topographical data, satellite data (SMAP, S1)  
In each case, time variables (sine and cosine of time) are kept within the features set because they are independent variables that represent the positional encoding of input features in a time series.
## 3 Results and Discussions
### Alfacrux Digital Twin Pilot
#### 3.2.3 A Preliminary Outcome Example  
As a simple example to illustrate the AlfaCrux DT model outcome, a first analysis of the attitude reconstruction by means of stored telemetry data is shown in Figure 10, where the Euler angles are parameterized in the 3-1-3 rotation sequence. Currently, the AlfaCrux ground station is undergoing technical adjustments and hardware upgrade in order to improve the signal downlink. Moreover, for the early orbit phase and commissioning performed in these first months of operation, the in-orbit data are gathered into parameter report structures using a low data sampling rate. Both scenarios lead to missing information in the sensors;' time series, impacting the quality of the attitude reconstruction. In this case, to evaluate the DT kinematic pilot model, preliminary studies were carried out considering telemetry data sampled within short time intervals. In order to illustrate the first results, consider the AlfaCrux data from the 4 June 2022 within a time interval of 180 s, where the telemetry is uniformly sampled at each 30 s during a passage over the command and control station. A quadratic interpolation was performed in order to generate new points at each 5 s time interval providing a smooth and more appropriate time series for attitude reconstruction. For future analysis, the AlfaCrux sampling rate will be properly adjusted, increasing the knowledge database, and consequently improving the quality of the estimation.  
Since the AlfaCrux satellite does not have an attitude control subsystem, the only torques acting on the CubeSat are the disturbance ones, such as those due to the gravity-gradient and residual magnetic dipole. For these first analyses, the influence of a possible residual magnetic dipole was not considered. This will be estimated later along with the magnetometer data analysis and calibration. By using the least squares method, it was possible to estimate an initial magnetometer bias with an order of magnitude of \(10^{4}\) nT. With respect to the solar panels, since they do not have available information about sensor characterization, the uncertainties must be estimated. As regular coarse sensors are inaccurate in the order of degrees, the same order of magnitude is expected for the estimation of the Sun vector direction based on the power and thermal analysis. Finally, the evolution of the state error standard deviations can be calculated, providing the results shown in Figure 11.  
Figure 11: Estimated state vector error for the angles parameters.  
Figure 10: Euler angles from attitude reconstruction.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 4 Experimental Results
### Results  
#### 4.4.1 Reduction of the Primary Quantities  
The PD reduction aspect in our framework comprises global data compression with AET and residual post-processing with MGARD. In AET compression, we varied the size of AE bottleneck layers and the number of bits in the PQ dictionary (PQ bits) to represent codewords. Let \(N\) be the number of nodes, \(L\) be the bottleneck layer size, and \(B\) be PQ bits. Then, the compressed data size is \(N\times L\times B\) bits, as discussed in Section 3.1.3. When we increase the number of PQ bits, codeword cardinality in the PQ dictionary is increased, and thus, the accuracy of quantization is improved. The compression ratio, however, is decreased due to the increased number of bits for quantization. In the XGC application, using four bits for PQ and a small size for the bottleneck layer (\(\sim\)30) is sufficient, since we have an additional processing step with residuals.  
Residual post-processing enables us to guarantee the PD error in our framework. We compress residuals with MGARD to bound the reconstruction error of residuals, and thus the overall PD error is bounded. In this step, any error-bounded lossy compressors can be used. We call the reduction of PD part in our framework AETM (AET followed by MGARD). To optimize the amount of compression achieved, we limit this step to only nodes where the errors are high, and therefore it is conditional post-processing. As discussed in Section 3.1.4, the residual of a node is processed if the NRMSE of the node is larger than a user-defined PD threshold. Therefore, we can achieve different compression amounts by changing the PD threshold and MGARD error bound parameters. Figure 2(a) shows that the AETM outperforms the two versions of MGARD in PD NRMSE. This is due to the efficient compression in AET followed by selective residual post-processing on a subset of the output.
## 2 Results and Discussion
### Water Activity Analysis  
Water activity (a\({}_{\text{w}}\)) measures the extent of water removal and the amount of residual moisture of dried products [10]. To eliminate the possibility of microbial growth and unfavorable enzymatic activities, dried products should have low a\({}_{\text{w}}\), ranging from 0.60 to 0.80. Spoilage bacteria are inhibited below a\({}_{\text{w}}\) of 0.91 and most types of mold are inhibited at a\({}_{\text{w}}\) of 0.80 [51]. However, for dried leaves or tea products, the suggested a\({}_{\text{w}}\) should be below 0.267 [52]. The lowest a\({}_{\text{w}}\) recorded was for convective-dried (CD) samples at 60 \({}^{\circ}\)C (Table 7). Leaves dried at CD 50 and 60 \({}^{\circ}\)C and CPD-VMFD have significantly lower a\({}_{\text{w}}\), because drying temperatures coupled with extended drying durations of CD ensured minimal residual moisture in leaves. Increasing the hot-air temperature during CD corresponded to lower a\({}_{\text{w}}\), because the temperature difference between the leaf material and surrounding heated air increased, which resulted in greater heat and moisture transfer. Thus, higher evaporation of water occurs, leaving material with a lower amount of moisture. The low a\({}_{\text{w}}\) values obtained for all drying methods suggest that samples were microbiologically stable, and that a limited amount of residual water was present for undesirable chemical reactions to occur.
# Study of Deformation and Fault Mechanisms in Bedding-Locked Rock Slope
## 7 Conclusions  
1. Under the influence of vertical load at the top of the slope, the rocky slope with down-layer locking exhibits local instability, primarily focused on the slope surface. The upper part of the slope features a linear slip surface, while the lower part features a circular arc slip surface, resulting in \"linear + circular arc\" damage geometry and a relatively flat shear surface.
2. For vertical loading at the top of the slope, the displacements at the measurement points on the sliding surface increase proportionally along with loading. Maximum and minimum lateral displacement occurs around the top and the foot of the slope, respectively, leaving the intermediate values at the middle. In other words, the slope experiences maximum deformation near the top. Therefore, special attention should be paid to potential sliding areas during the construction and management of this type of project.
3. Under vertical loading at the top of the slope, abrupt changes in the displacement of each measuring point on the sliding surface indicate that there was less shear strength than shear stress, resulting in slope deformation, collapse, and the occurrence of the sliding surface. The load strength required for the sudden change in displacement of the measurement points is highest at the foot of the slope, followed by the middle, and lowest at the top. The results indicate that the shear strength is greater at the locking section, with the highest value found at the foot of the slope, followed by the middle and the top.
4. The evolution of slope deformation can be divided into three stages: the loose deformation stage at the slope top and back; the stage of crack development, extension, and interlayer misalignment; and the stage of complete through crack formation and complete instability damage of the slope. Thus, the process of the deformation and damage of a rocky slope under loading conditions can be described as follows: load-induced loosening and deformation at the top and back of the slope and crack development, expansion, and interlayer misalignment, as well as tension crack expansion, at the foot of the slope. The latter can expand and intensify interlayer misalignment, leading to the destruction of the locking section and ultimately causing the destabilization of the entire slope. Therefore, it is important that the monitoring procedures of the locking section are focused on the bottom of the slope to effectively predict slope stability.  
Figure 22: Horizontal displacement cloud diagram when the model is unstable.
# Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory
## 3 Method
### Animal Movement Modeling
In this work, a simplified version of [39] is implemented, where the center of the animal herds and individual animals in the herds follow CRW and BRW with a bias toward the herd center, respectively. The implemented model helps ensure the following: The members of the animal herds are biased to move towards the herd center, which ensures herd integrity. The herd centers and the overall herd maintain a stochastic free foraging behavior. The CRW and BRW models employed in this paper to model the group behavior of animal herds are  
* Group center movement model (CRW): \[ \begin{split}& u_{T+1}=u_{T}+[r_{u,T+1}\cos(\theta_{u,T+1})\quad r _{u,T+1}\sin(\theta_{u,T+1})]\Delta t\\ &\theta_{u,T+1}=\theta_{u,T}+v_{u,T+1}\end{split} \] (3)
* Individual animal movement model (BRW): \[ \begin{split}& z_{T+1}^{q}=z_{T}^{q}+[r_{q,T+1}\cos(\theta_{q,T+1}) \quad r_{q,T+1}\sin(\theta_{q,T+1})]\Delta t\\ &\theta_{q,T+1}=\arctan\left(\frac{u_{y,T+1}-z_{y,T+1}^{q}}{u_{x, T+1}-z_{x,T+1}^{q}}\right)+v_{q,T+1},\end{split} \] (4) where \(T\in\mathbb{N}\) is discrete time, \(u_{T}=[u_{x,T},u_{y,T}]\) is the herd center location, \(z_{T}^{q}=[z_{x,T}^{q},z_{y,T}^{q}]\) is the \(q\)th animal position. Also, \(r_{(\cdot),T+1}\) denote speeds of herd centers and individual animals, where \(r_{(\cdot),T+1}\sim\Gamma(\mu_{\gamma},\sigma_{\gamma})\) are random variables with the gamma distribution \(\Gamma\) with a mean \(\mu_{\gamma}\) and standard deviation \(\sigma_{\gamma}\). To introduce randomness in movement directions of both herd centers and individual animals, the random variables \(v_{(\cdot),T+1}\) are added to the heading directions \(\theta_{(\cdot),T+1}\), where \(v_{(\cdot),T+1}\sim V(\mu_{vm},\kappa_{vm})\) follow von [PERSON] distribution \(V\) with a mean \(\mu_{vm}\) and concentration measure \(\kappa_{vm}\). The time interval between consecutive time steps is denoted by \(\Delta t\).
## 2 Materials and Methods
### Localization of PD Sources Using the TDoA Technique  
For each of the power transformers discussed in this article, in addition to online monitoring, the location of PD sources was determined using the TDoA method. This is one of the most popular and widely used location techniques, the detailed principles of which are presented, among others, in [36; 37; 38]. The basis of the TDoA method lies in the observation that the acoustic wave generated by PDs propagates inside the oil-filledtransformer tank in a manner close to spherical. The wave reaches different transducers with varying time delays, depending on the distance from the PD source to each sensor.  
The TDoA localization procedure consists of the following steps: (1) Registration of the time waveforms of acoustic emission signals generated by PDs using at least four piezoelectric transducers mounted on the transformer tank. (2) Estimation of the onset times of AE pulses. (3) Determination of the time differences of arrival of the acoustic wave between the reference transducer (i.e., the sensor located closest to the PD source, which is the first to register the AE pulse) and the other transducers. (4) Based on the known coordinates of the transducers, the time differences of arrival of the signals at each transducer, and the velocity of the acoustic wave propagation in transformer oil, a nonlinear system of equations describing the spheres is formulated. The centers of these spheres correspond to the positions of the transducers, and their radii represent the shortest geometric distances between the transducers and the PD source. The intersection point of these spheres indicates the location of the PD source. (5) Solving the nonlinear system of equations using either an iterative or non-iterative method [36].  
To locate PD sources in the monitored transformers, the PDtracker Portable system and a dedicated program developed in the MATLAB environment were used. The onset times of AE pulses were automatically determined using the Akaike Information Criterion (AIC) method [39; 40]. The AIC method determines signal onset by identifying the point where the statistical properties of the signal, such as variance or autocorrelation, change significantly, marking the transition from noise to the signal. Nonlinear systems of equations were solved using the fsolve function with its built-in iterative Trust-Region-Dogleg algorithm [41; 42]. This algorithm is very efficient as it involves only a single linear solve per iteration to compute the Gauss-Newton step [43]. Moreover, it often proves to be more robust than the Gauss-Newton method combined with a line search.