esa-sceva/satcom-chunk-collection huggingface dataset:
# Pose Measurement Method Based on Machine Vision and Novel Directional Target
## 2 Materials
\begin{array}{l}h_{1}^{T}K^{-T}h_{2}=0\\ h_{1}^{T}K^{-T}h_{1}=h_{2}^{T}K^{-T}h_{2}\end{array} \right. \tag{7}\]  
Two basic constraints on the camera's internal reference have thus been obtained. Let  
\[B=K^{-T}K^{-1}= \begin{bmatrix}B_{11}&B_{12}&B_{13}\\ B_{21}&B_{22}&B_{23}\\ B_{31}&B_{32}&B_{33}\end{bmatrix}  \tag{8}\]  
where \(B\) is a symmetric matrix that can be expressed as a six-dimensional vector  
\[b= \begin{bmatrix}B_{11}\;\;,B_{12}\;,\;B_{22}\;\;,B_{13}\;,\;B_{23}\;,\;B_{33 }\end{bmatrix} ^{T} \tag{9}\]  
Let the \(i\)th column vector of the homography matrix \(H\) be \(h_{i}=[h_{i1},h_{i2},h_{i3}]^{T}\); then, we have  
\[h_{i}^{T}Bh_{j}=v_{ij}^{T}b \tag{10}\]  
In this way, Equation (7) can be written as two chi-square equations with \(b\) as an unknown  
\[ \begin{bmatrix}v_{12}^{T}\\ (v_{11}-v_{12})^{T}\end{bmatrix} b=0 \tag{11}\]However, the coefficient matrix of Equation (11) has only two rows, which is not enough to solve the 6-dimensional vector \(b\). Therefore, we need to shoot \(n\) targets to obtain \(n\) single responsivity matrices and coefficient matrices with \(2n\) rows. When \(n\geqslant 3\), then we can solve for the 6-dimensional vector \(b\). That is, we need to obtain at least 3 pictures of the target to complete the calibration of the camera, and the total system of equations obtained at this time can be expressed as:  
\[Vb=0 \tag{12}\]  
\(V\) is a \(2n\times 6\) matrix of coefficients. At this point, Equation (12) is scale-equivalent, i.e., solving for \(b\) multiplied by any multiple is still the correct solution, so the matrix \(B\) consisting of \(b\) does not strictly satisfy \(B=K^{-T}K^{-1}\), but rather there exists a scale factor \(\lambda\) that satisfies \(B=\lambda K^{-T}K^{-1}\). By deduction, all camera parameters can be calculated from Equation (13). \[\left\{ \begin{array}{l}v_{0}=\left(B_{12}B_{13}-B_{11}B_{23}\right)\left(B_{ 11}B_{22}-B_{12}^{2}\right)\\ \lambda=B_{33}-\left[B_{13}^{2}+\
u_{0}(B_{12}B_{13}-B_{11}B_{23})\right]/B_{ 11}\\ \alpha=\sqrt{\lambda/B_{11}}\\ \beta=\sqrt{\lambda B_{11}/\left(B_{11}B_{12}-B_{12}^{2}\right)}\\ \gamma=-B_{12}\alpha^{2}\beta/\lambda\\ u_{0}=\gamma\
u_{0}/\beta-B_{13}\alpha^{2}/\lambda\end{array} \right. \tag{13}\]  
For each image, the outer parameters \(R\), \(t\) can be calculated by Equation (14). \[\left\{ \begin{array}{l}r_{1}=\lambda K^{-1}h_{1}\\ r_{2}=\lambda K^{-1}h_{2}\\ r_{3}=r_{1}\times r_{2}\\ t=\lambda K^{-1}h_{3}\end{array} \right. \tag{14}\]  
Among them, \(\lambda=1/||K^{-1}h_{1}||=1/||K^{-1}h_{2}||\).
## 4 Discussion
### Influencing Factors
#### 4.4.2 Liquid Absorption  
Many polymers exhibit reduced mechanical properties upon water absorption, which can flatten the stress-strain curve and lower the modulus of elasticity [44]. Water or saliva may disrupt polymer chain cohesion, altering the material's mechanical behavior. [PERSON] et al. [30] reported that while water storage alone did not affect force levels, its combination with mechanical loading led to significant force decay, particularly during the initial hours. This suggests that water exposure may reduce the creep resistance of PET-G specimens by increasing polymer chain mobility and weakening mechanical cohesion within the material. [PERSON] et al. [29] observed that PET and PET-G materials exhibited significant performance changes when exposed to beverages compared to artificial saliva, whereas TPU showed no such variation, indicating potentially greater moisture resistance in some formulations.  
The impact of liquid exposure was further explored in studies by [PERSON] et al. [20] and [PERSON] et al. [21], both of which reported notable stress relaxation following water immersion. [PERSON] et al. [26] tested samples in both water and antibacterial solutions and found no significant influence on initial force or stress values, though prolonged exposure did affect some formulations. [PERSON] and [PERSON] [28] and [PERSON] et al. [25] used water storage and thermocycling to simulate intraoral aging. The former observed reduced flexural strength, while the latter found no further degradation beyond the effects of thermoforming alone.  
Collectively, these findings highlight that aligner materials respond differently to hydration and chemical exposure. This reinforces the importance of material selection based on intraoral conditions and patient-specific habits, such as dietary intake or the presence of antibacterial agents in hygiene routines.
###### Abstract  
The present study investigated the impact of peat moss as a feed additive on the emission of methane (CH\({}_{4}\)) and carbon dioxide (CO\({}_{2}\)) from piggery slurry stored in slurry pits. There is no well-known study on the relationship between pig manure generated after feeding peat moss as a feed additive and CH\({}_{4}\) and CO\({}_{2}\) released during the storage period. A lab-scale experiment was conducted for two months using a slurry pit simulator composed of six vessels--three for pig slurry collected after feeding 3.0% peat moss as a feed additive (PFS) and three for pig slurry without feeding peat moss (CTL). PFS reduced CO\({}_{2}\) and CH\({}_{4}\) emissions (\(p<0.05\)) from stored pig slurry by approximately 23% and 44%, respectively. PFS exhibits substantially elevated concentrations of humic substance (HS) such as humic acid, fulvic acid, and humin compared with CTL, with fold differences of 2.3, 1.8, and 1.1, respectively. Elevated HS levels in the PFS seemed to limit hydrolysis, resulting in lower total volatile fatty acid concentrations compared with CTL. A dominance of CH\({}_{4}\) in total carbon emissions was observed (\(p<0.05\)), with CH\({}_{4}\) accounting for approximately 93% and 95% of total carbon emissions in PFS and CTL, respectively. PFS had a roughly 43% lower impact on cumulative carbon emissions than CTL, primarily due to decreased CH\({}_{4}\) emissions. These findings suggest that PFS may be a promising approach for mitigating carbon emissions and potentially impacting environmental sustainability and climate change mitigation efforts.  
pig slurry; slurry pit; storage; peat moss; humic substance; methane emission; carbon dioxide emission +
Footnote †: journal: Applied Sci. 2023  
1
###### Abstract  
This article describes the process of implementing a transportable radar MSSR-S for Peruvian civil aviation (ACP) to minimize the operational impact in emergencies that affects air traffic without causing structural damage and restore data from the radar in a short time. In recent years, ACP has shown constant falls in the radars, causing radar data to be lost for long periods of time and putting air safety at risk due to the lack of maintenance and overlapping radar coverage of more than three radars. The deployment of the transportable radar in Mode S of Monopulse Secondary Surveillance (MSSR-S) has allowed for work that involves the prolonged stoppage of the radar to be carried out and provided coverage to eight more radars during maintenance and modernization, covering the areas without coverage in the Peruvian air space (EAP). For the implementation, this was divided into three SPRINTS using the SCRUM methodology; the first sprint refers to the equipment and radar coverage study, the second the implementation and service test phase, and the third the operational analysis phase with the eight modernized radars. As a result of the implementation and integration with the other ACP radar systems, they were able to operate together, providing highly reliable radar data, performing a continuous analysis of radar performance through the PASS software, complying with the thresholds established by ICAO and EuroControl, and guaranteeing that the systems operate under perfect conditions and with full coverage at all time.  
civil aeronautics; MSSR-S radar; radar data; SCRUM; ICAO; PASS software Article
## 6 Experiments and Results  
To evaluate the proposed approach, two experiments were carried out:  
* Experiment A, which uses a randomly generated operating procedure as the initial solution for the metaheuristic hybrid algorithm. The results were then compared against a benchmark solution [17] and a solution obtained with the micro genetic algorithm described in [10].
* Experiment B, which compares the metaheuristic hybrid algorithm, the simulated annealing algorithm, and the tabu search algorithm. The comparison considers two different initial solutions: a randomly generated solution; and a feasible solution generated by the procedure explained in Figure 6.
## 3 Results and Discussion
### Model Comparison and Discussion  
Five ML algorithms were utilized to construct a predictive model for the RSSI parameter of an FSO link. The size of the data set and the considerable variance of the independent variables made the task of fitting a traditional linear or non-linear model (i.e., polynomial) very challenging. In particular, a second-order polynomial was initially fitted to the data set with a very poor performance, specifically an R\({}^{2}\) = 0.36 for the training subset and an R\({}^{2}\) = 0.05 for the testing subset. Therefore, more sophisticated models were required to achieve adequate prediction accuracy of the RSSI parameter. We trained and tested five different ML algorithms and used the determination of coefficient R\({}^{2}\) and RMSE as the performance metrics to evaluate each algorithm and compare among each other. Table 2 summarizes the results for each model for the training, validation (if applicable) and testing phase. The ANN proved to be the most accurate model, with an R\({}^{2}\) = 0.94867 but also a very high computational time cost (it takes approximately 3 h to train, validate and test the model). On the other hand, all three tree-methods provided comparable results within a relatively short training period of time. Finally, the KNN algorithm resulted in a slightly less accurate model, yet it was much better than the polynomial model and was still statistically significant for RSSI prediction.  
Figure 14: Regression Fits for the proposed ANN architecture for RSSI prediction.  
Figure 13: Error histogram of the proposed ANN architecture for RSSI prediction for the training, validation and testing phases.  
Comparing to previous studies, the ML algorithms that were used in this study proved to be much more robust and accurate than the polynomial model developed elsewhere [27; 28], despite referring to a much larger data set that spanned over a year and included measurements from different seasons. As shown in Table 1, the value range of the measured meteorological parameters is much higher compared to those in [27; 28]. However, the prediction accuracy performance of the models used in this study, outperformed the previous ones since they achieved an R\({}^{2}\) > 0.9, which is significantly higher comparing to an R\({}^{2}\)-0.7 of the previous models [27; 28]. Regression models for RSSI prediction developed in [24; 25] also did not achieve an accuracy higher than 70%. It is clear that the ML algorithms provide a more efficient method to predict the RSSI parameter of an FSO link. However, the ML algorithms provide in a less intuitive prediction model and require a deeper comprehension in order to interpret the results.
## 2 The Principle of Remanence Generation and Elimination  
### Remanence Generation  
The core of a transformer is typically constructed from stacked ferromagnetic materials that exhibit high permeability and low loss, and all magnetic materials tend to retain their magnetism. Changes in magnetic flux density consistently lag behind changes in magnetic field strength, a phenomenon known as hysteresis. As illustrated in Figure 1, the magnetization process of the iron core can be broadly classified into four stages [11; 12; 13]:  
Initial unmagnetized state (point a): The material is in an unmagnetized state where H = 0 and B = 0.  
Magnetization process (a to b to c): As the applied magnetic field intensity H gradually increases, the magnetic induction intensity B also increases. In the initial phase, B increases linearly with H, corresponding to the reversible arrangement of the magnetic domains. As H continues to increase, the magnetic domains gradually and irreversibly align, resulting in a slowdown in the rate at which B increases, until B reaches the saturation value Bs when H is sufficiently large.  
Demagnetization process (c to d): As H gradually decreases from the maximum value to zero, B does not immediately return to zero; instead, due to the hysteresis phenomenon, B gradually decreases, and ultimately, when H = 0, the material still maintains a certain magnetic induction intensity Br, which is the residual magnetic induction intensity. The point d here is B\({}_{\rm r}\) (d).  
Reverse magnetization process (d to e): The magnetic induction intensity B decreases gradually until B = 0 when the reverse magnetic field H is applied. At this point, the required reverse H value is called coercivity (\(H_{\rm c}\)).  
Transformers possess multiple sources of remanence, with the most common being the DC resistance test conducted after the circuit breaker is opened. For large devices, the actual current measured during the DC resistance test is relatively high (typically \(\geq\) 20 A), and the remanence in the core is considerably greater [14; 15]. In practical engineering applications, AC excitation is frequently employed to gradually reduce the hysteresis loop.  
Figure 1: Schematic diagram of the magnetization process of the core.  
This can be accomplished by maintaining the excitation voltage for a specific duration while gradually decreasing the excitation voltage, or by gradually increasing the excitation frequency while keeping the excitation voltage constant [16].
## 3 The Hierarchical Multi-Dimensional Aggregate Pyramid (MAP) Model  
By reviewing the characteristics of the target datasets, the generic multi-dimensional aggregate pyramid (MAP) model is built from two well-known graphics concepts: namely, the Spatio-temporal Cube and the 2D Tile Pyramid. The former is extended with the attribute dimension to Space-Time-Attribute Cube and provides the building blocks for the proposed MAP model; while the latter implements the idea of 2D spatial aggregation and provides a good implication for the further simultaneous aggregation of space, time, and attributes.
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 3 Implementation of the Proposed Method
### NLM Filter Denoising in High-Frequency Sub-Bands  
The coefficients of the high-frequency sub-bands contain detailed information from the remote sensing images, but also contain a lot of noise. In this paper, the non-local means filter as described in Section 2.2 is adopted to suppress the noise in the first high-frequency sub-band, because it has the most noise. The other high-frequency sub-bands coefficients are not treated.
# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 2 State of the Art
[18] introduced an adaptive data-driven model-based approach to detecting regime-changing point by using K-means clustering. Additionally, there are a lot of applications for regime changing point detection in time series in different areas, such as financial data analysis, etc. [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. Detection of change in time series is a very widely investigated problem. Signal segmentation is frequently used in various signal processing applications to divide original data into homogeneous segments or to extract the pattern, etc. [PERSON] et al. [39] used stochastic modeling for seismic signal segmentation. [PERSON] et al. [40] used segmentation for shock extraction in a sieving screen vibrations. A challenge in signal segmentation is when the difference between regimes is not so clear. One may say that regime A is smoothly transforming to regime B. This issue was investigated by [PERSON] et al. [41]. It is also the case here, especially as there is \"no jump\" from good condition to warning state, that the machine in good condition slowly starts the degradation process. Another issue is multiple change points and unit heterogeneity [42].
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 2 Review of Related Literature
### KSSP Algorithms
[43] leveraged the \(k\) shortest path framework in computer vision to effectively track players in sports video analysis. Routing applications, including public transit routing with transfer penalties and alternate route choices [29] and autonomous driving decision-making under dynamic traffic scenarios [8], often benefit from KSSP formulations. Ref. [30] explored the use of \(k\) feasible paths for navigation within large multi-story buildings, demonstrating the utility of KSSP in addressing complex spatial challenges. Another similar problem to KSSP is finding the set of reasonably dissimilar alternative paths on time-dependent networks which are useful in the context of route planning [44], where, in addition to the length/travel time of the paths, their degree of similarity is considered for \"meaningful\" alternatives. \"Terminal paths\", introduced in [45], is a related concept that is also a set of multiple simple paths to several destinations. While the terminal path set does not consider the length of the paths or the size of the set, the multi-destination KSSP problem differs from it in terms of the objective, which is to minimize the path lengths and find a given number of simple paths to each destination. Analysis of the literature reveals many different algorithms for KSP problems, dominated by deviation/replacement path types for KSSP problems. Previous studies have primarily focused on algorithms for single-origin-to-single-destination scenarios despite several practical applications that could benefit from an efficient single-origin-to-multiple-destinations algorithm. Among the existing studies, compared to the popularity of deviation-based and path-replacement algorithms for KSP and KSSP, the literature on label-setting algorithms is comparatively sparse. This could be attributed to the fact that while the existing label-setting algorithms can solve the KSP problem exactly, they cannot guarantee optimality for the KSSP problem since the optimality principle underpinning general labeling procedures fails for the KSSP problem. Thus, in contrast to labeling algorithms, existing deviation/replacement path type KSSP algorithms designed primarily for a single OD case need to be run for each OD pair separately and do not take advantage of the paths found to other destinations in the earlier runs. Among the labeling methods, as noted by [23], label-setting methods have a better worst-case complexity than label-correcting methods. This gap presents an opportunity to explore label-setting algorithms for potential computational efficiency gains for the multi-destination KSSP problem.
# Use of _Chlorella vulgaris_ and _Ulva lactuca_ as Biostimulant on Lettuce
## 3 Results and Discussion
### Chemical Composition
#### 3.3.1. Algal Chemical Composition
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Dry Matter**} \\ \hline  & _C. vulgaris_ & _U. lactuca_ \\ \hline MOISTURE (g/100 g) & 4.94  & 11.16  \\ \hline ASHES (g/100 g) & 9.33 \(\pm\) 0.04  & 26.25 \(\pm\) 0.08  \\ \hline FATS (g/100 g) & 1.36 \(\pm\) 0.05  & 0.99 \(\pm\) 0.02  \\ \hline FIBER (g/100 g) & 1.09 \(\pm\) 0.03  & 7.51 \(\pm\) 0.08  \\ \hline PROTEINS (g/100 g) & 37.11 \(\pm\) 0.06  & 13.58 \(\pm\) 0.06  \\ \hline NON-NITROGEN EXTRACTIVES (g/100 g) & 51.11 \(\pm\) 0.00  & 51.67 \(\pm\) 0.08  \\ \hline ENERGY (Kcal/100 g) & 365 \(\pm\) 0.20  & 270 \(\pm\) 0.07  \\ \hline ENERGY (KJ/100 g) & 1529 \(\pm\) 0.85  & 1130 \(\pm\) 0.31  \\ \hline N (\%) & 5.94 \(\pm\) 0.01  & 2.17 \(\pm\) 0.15  \\ \hline P (\%) & 1.78 \(\pm\) 0.03  & 0.26 \(\pm\) 0.01  \\ \hline Ca (\%) & 1.13 \(\pm\) 0.06  & 0.64 \(\pm\) 0.06  \\ \hline Mg (\%) & 0.21 \(\pm\) 0.01  & 0.82 \(\pm\) 0.10  \\ \hline K (\%) & 0.75 \(\pm\) 0.05  & 1.57 \(\pm\) 0.04  \\ \hline Na (\%) & 0.13 \(\pm\) 0.05  & 0.20 \(\pm\) 0.04  \\ \hline Cu (mg/Kg) & 25.75 \(\pm\) 1.24  & 12.75 \(\pm\) 2.28  \\ \hline Zn (mg/Kg) & 30.51 \(\pm\) 1.90  & 23.64 \(\pm\) 1.53  \\ \hline Fe (mg/Kg) & 38.24 \(\pm\) 5.60  & 210.70 \(\pm\) 10.84  \\ \hline Mn (mg/Kg) & 93.21 \(\pm\) 2.82  & 33.53 \(\pm\) 1.91  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Moisture value (g/100 g) and the chemical composition of _Chlorella vulgaris_ and _Ulvia lactuca_ dry matter. The results are expressed by mean \(\pm\) standard deviation. Statistically significant differences in the same element content among the species are expressed by different letters. more carbohydrates. Moisture (4.49%) was higher than in the research of [PERSON] and [PERSON] [25], while fat content (1.36%) was much lower than the 13.32% of the same study. According to [PERSON] [26], who analyzed dried _Ulva lactuca_ for its nutritional composition, this is considered a good source of carbohydrates, since it has 58.1% of non-nitrogen extractives, while, in this study, we found 51.67%. Protein presented a percentage of 13.6 in this study, which is the same as in [PERSON] [26], but higher than in [PERSON] et al. [27]. The iron (Fe) and sodium (Na) content results were similar to [PERSON] [26]. In the present study, moisture was 11.16% (Table 4), which is lower than the considered study where the same parameter is 16.9%, as shown by [PERSON] al. [28]. The same was true for fiber (7.51% in this study against 28.4% in [PERSON] [26]) and calcium (Ca) (0.64% while in [PERSON] [26] it was 1.83%). Other parameters were higher in the present study than in [PERSON] [26]. The main differences concern ashes: 26.25% in this study and 11.2% in the cited work. This parameter seems to be unstable in the different studies in which it is analyzed, for example, in [PERSON] et al. [27] it was 19.59%. Fats and potassium (K), as ashes, were higher in the characterized _U. lactuca_ compared to Rasyid [26].
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 2 Approaches for Inference Time Reduction
### Channel Shuffling  
Another way to reduce computational cost significantly while preserving accuracy is channel shuffling. This approach was introduced in ShuffleNet [7]. Although in standard group convolution every input channel is associated with only a single output channel, in the case of channel shuffling, a group convolution acquires data from different input groups. In particular, every input channel will correlate with every output channel, as shown in Figure 2. Specifically, for the feature map generated from the previous group layer, we can first divide the channels in each group into several subgroups and then feed each group in the next layer with different subgroups.  
This can be efficiently implemented by a channel shuffle operation that is described as follows: suppose a convolutional layer with \(g\) groups whose output has \(g\times n\) channels; we first reshape the output channel dimension into \((g,n)\), transposing and then flattening it back as the input of next layer. Channel shuffle is also differentiable, which means it can be embedded into network structures for end-to-end training. Overall, this approach comprises appealing characteristics, but one should be careful with the selection of the parameter \(g\) which defines the number of groups. For optimal results, experimentation is required to achieve the best \(g\) value.