esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 1 Introduction
[13] proposed to extract buildings based on contextual and spectral difference segmentation. First, a single-class support vector machine (SVM) is used to determine artificial structures, such as buildings, roads, and then a conditional threshold is used for texture segmentation to extract buildings, and finally noise, vegetation and shadows are removed from the candidate building areas. Currently, the basic concept of optical remote sensing image building target detection research is to use feature differences, that is, the geometric structure, color, contrast and spectral characteristics of the building target itself to separate the building target from the background, but when applied to remote sensing images with low resolution, or when the contrast between the building area and the background area in the image is low, most of the methods are difficult to maintain a strong applicability. Local occlusion of buildings, different lighting conditions, and different types of interference, such as stacks, ships, playgrounds, and flower beds, all seriously affect the detection results. Moreover, most of the methods aim to detect the top surface of the building target, which is difficult to detect when there is a silhouette of the building target in the image. The existing building target detection methods, deep learning-based building detection methods are more widely used. Among them, the pixel-based classification and recognition methods can hardly consider the neighborhood information around the pixels, so the contours of buildings cannot be well guaranteed. The existing wavelet transform-based building detection methods, which is difficult for the detection of complex building backgrounds and the study of complex building roofs. Combining the above problems and shortcomings, it is important to study building detection techniques that resist low contrast between building targets and background, detect the contours of building targets, and remove various types of interference. The remainder of this paper is organized as follows. Section 2 introduces the experimental data used in this work, details the specific workflow of research, including building detection based on rectangular approximation and region growing, building detection based on multifeature saliency, fusion of the rectangular approximation-based and saliency-based detection results, and evaluation. Section 3 is a comparative analysis of experimental results. Discussions and conclusions are presented in Sections 4 and 5, respectively.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies  
In this section, we give an overview of the long-range wireless technologies that are available presently. Classifying those based on their proprietary or open aspects is not appropriate as some may be part of both worlds, as LoRa [8]. Instead, we propose to gather them based on which frequency band they use: (i) either the licensed mobile frequencies (a.k.a. cellular frequencies) (ii) or the unlicensed ISM bands.  
On the one hand, using the licensed mobile frequencies ensures that the band is used by authorized devices in a sane manner. However, on the other hand, the use of these frequencies requires subscription costs and they may already be very busy because of the wide spread of cellular devices, especially in crowded urban areas.  
On the contrary, using the unlicensed ISM bands [9] does not ensure a sane use of the frequencies, as anybody is allowed to emit and a lot of devices may interfere. Still, the duty cycle restrictions must be respected depending on the used frequency band, the power and the geographical location of the device. However, at the same time, using unlicensed bands also implies that we can freely expand the network with new base stations, create private networks and use those technologies to possibly communicate in a peer-to-peer fashion between devices. One can wish to deploy their own infrastructure for two main reasons : (i) either because the area to monitor is not covered by any other technology (ii) or to keep control over the infrastructure management.  
Managing the energy consumption of LPWAN devices is a key factor, because those devices are usually expected to run autonomously for long periods of time, commonly several years. Replacing or recharging batteries is often unpractical, especially when numerous devices are deployed in different places, hostile or unreachable environments. Additionally, those devices may have to receive firmware updates, e.g., for optimization or security reasons. As with battery replacement, mandatory physical access to the devices for firmware updates is highly unpractical. Thus, supporting remote firmwareupdates, known as Firmware over the Air (FotA) updates, may turn out to be a key factor for choosing a long-range technology.  
Figure 1 summarizes this section by positioning each main technology with regards to its maximum data rate and theoretical urban range (based on data available in datasheets referenced further). Tables 1 and 2 summarize the technologies' technical information.
###### Abstract  
Background and Objectives: Although gender and age are high-profile risk factors for meniscal injuries, their interplay is not yet fully understood. Little information is available about patients undergoing first-time meniscus surgery and the epidemiology of meniscus tears in Eastern Europe. This study investigated sex- and age-related trends in meniscal injuries (medial and/or lateral, with or without co-occurring knee chondropathy) in a substantial cohort of patients undergoing first-time meniscus surgery at a high-volume medical center in western Romania. Materials and Methods: This retrospective study included 488 patients of different ages. Logistic regression was applied across the age continuum, followed by analyses stratified by age quartiles to investigate age-related variations. Results: Increasing age was significantly associated with increased odds of having medial meniscus tears (O.R. = 1.04 (1.01-1.07)) and associated knee chondropathy (O.R. = 1.03 (1.00-1.05)). Women displayed significant, twofold lower odds of having lateral meniscus damage (O.R. = 0.54 (0.31-0.96)). Men tended to have a higher prevalence of associated knee chondropathy (O.R. = 1.71 (0.96-3.04)). The frequency of medial meniscus tears and patellar damage rose significantly, starting from the third age quartile (33-42 years) and the second quartile (23-32 years), respectively. Significant positive associations between medial and lateral meniscal damage were found across all age quartiles (\(r_{s}\geq 0.42\)), with their magnitude increasing with age. Medial damage also correlated significantly with patellar damage, but weaker and only from the third quartile onward (\(r_{s}\geq 0.30\)). Conclusions: Increasing age was linked to a higher risk of medial meniscal injuries and knee chondropathy, with the latter tending to occur earlier. Male sex was associated with a higher likelihood of lateral meniscal damage. These findings highlight the importance of considering age and sex in the diagnosis and management of meniscal injuries and provide valuable data for an Eastern European population.  
m +
Footnote †: journal: Applied sciences
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## Image Rectification Using a Perspective Transformation Model  
When the camera displays a 3D world on a two-dimensional (2D) image plane, the projection matrix is used to achieve dimension reduction. Among various projection types, such as orthogonal projection, parallel projection, perspective projection, etc., perspective projection is the most suitable for human sensory vision. Under the perspective projection model, a rectangular planar object in the object space is projected onto the image plane to become trapezoidal or approximately trapezoidal.  
According to the perspective transformation model as described above, when the photograph of the same building is captured by two aerial cameras, the rectangular building structure may present the following three situations, as shown in Figure 1.  
It can be seen that the two rectangles in Figure 1a are similar; there is no distortion difference, and they have uniform internal grayscale distribution. However, in Figure 1b, the left rectangle has no deformation, but the right rectangle becomes a trapezoid, and its upper region is compressed while its lower region is stretched. However, in Figure 1c, opposite compression and stretching effects appear in the upper and lower areas of the two rectangles. For the red to-be-matched segments, the two line segments' neighborhood information is consistent in Figure 1a, and matching is easy. In Figure 1b, right line segment's neighborhood information changes relative to the left, which will cause a certain degree of matching difficulty. In Figure 1c, the relative changes in the neighborhood information of the two line segments are larger, which will lead to greater matching difficulty.  
The oblique camera platform and the method of eliminating the perspective distortion effects are described below.
# NTT and Inverse NTT Quantum Circuits in CRYSTALS-Kyber for Post-Quantum Security Evaluation
## 2 Background
### Barrett Reduction  
The Barrett reduction, introduced by [PERSON] in 1986 [PERSON] (1986), is an optimization in modular arithmetic, which facilitates modular reduction without direct division. This technique, primarily used in cryptographic algorithms, employs multiplication and shifts, making it efficient for large-number arithmetic. When computing instances at \(r=k\) mod \(n\) (\(n\): constant, \(k<n^{2}\)) for a fixed modulus \(n\), long division causes slowness. For these calculations, the Barrett reduction performs modular reductions. The Barrett reduction replaces division with multiplication for the \(r=k\) mod \(n\) operation.
## 2 Materials and Methods
### Classification  
After extracting the features, the next important step is to train and choose the classification algorithm. We applied and optimized the SVM with polynomial, radial base function (RBF) and Gaussian kernels, naive Bayes, and decision tree. [PERSON], in 1979, proposed SVM for classification problems. SVM has recently gained much popularity, being widely used in large margin classification problems, including medical diagnosis areas [47; 48], machine learning [49], and pattern recognition [50; 51]. SVM has also been successfully used in many other applications, such as signature and text recognition, face expression recognition, speech recognition, biometrics, emotion recognition, and several content-based applications, as detailed in [52; 53; 54]. Naive Bayes belong to the family of probabilistic networks based on Bayes' theorem. Based on its performance, naive Bayes is used in many applications, detailed in [55; 56; 57; 58]. Moreover, the decision tree algorithms have also been used in many applications of medical, economic, and scientific applications [59].
# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 1 Introduction
When the system is operated using the SMM, the therapist may remotely assess patient movement based on video and data from the wearable sensors, particularly if methods for automatic quantitative assessment are used [18; 19]. In order to validate the tele-rehabilitation component of this system, series of simulated experiments were conducted to verify the feasibility of our technical solution. This paper is organized as follows. Section 2 presents the gait simulator, including details on its mechanical design, low-level control, and additional development required to implement new operation modes, as well as the proposed control strategies, SMM and TMM. The method employed to validate the tele-rehabilitation component is also described in this Section. Experimental evaluation is presented in Section 3, including data from tests to validate each subsystem separately and preliminary experiments designed to evaluate the feasibility of the overall system when using the novel operation modes. These trials involved healthy subjects. These results are discussed in Section 4. Finally, we draw conclusions and discuss future works in Section 5.
## 3 Test Results and Discussions
### Effect of CN on Sulfate Corrosion Resistance of ECC  
When the hydration products are corroded by sulfate, SO42- will react with the hydration products to produce expandable ettringe and gypsum. These products will not only generate expansion stress and expand local pores but also dissolve C-S-H and Ca(OH)2 in the ECC matrix, destroy the ECC structure, and reduce its mechanical strength and durability [36].  
Figure 4: The uniaxial tension results of the ECC.  
The compressive strength results of an ECC subjected to sulfate corrosion at 30, 60, and 120 d are presented in Figure 5. As shown in the figure, the compressive strength of the ECC progressively decreases with increasing corrosion duration. For the sample group without a CN, the compressive strength after 30, 60, and 120 d of sulfate exposure decreased by 0.53%, 2.66%, and 6.78%, respectively. The compressive strength of the 0.1% CN sample group decreased by 0.49%, 1.59%, and 6.11%, while that of the 0.15% CN sample group decreased by 0.47%, 1.31%, and 5.42%, respectively. The structure of the ECC remains dense, and during the initial stages of sulfate exposure (30 and 60 d), the penetration of SO4\({}^{2-}\) and water molecules into the matrix is limited. Consequently, the compressive strength loss in all sample groups is minimal at these time points. However, at 120 d, a significant reduction in compressive strength is observed. Notably, the CN-containing samples exhibit a smaller strength loss compared to the control group. This improvement is attributed to the role of the CN in effectively filling the pores, enhancing the matrix density [37; 38], restricting the diffusion of water and SO4\({}^{2-}\), and mitigating the degradation of hydrated products by sulfate and water, thereby extending the service life of the ECC in aggressive environments.  
The uniaxial tensile test results of the ECC samples following sulfate corrosion are presented in Figure 6 (The arrow marks the ultimate strain rate on the stress-strain curve). For the control sample (Figure 6a), after 60 and 120 d of sulfate corrosion, the strain rate decreased to 3.51% and 3.32%, respectively, from the initial 3.62%, resulting in a strain loss of 3.04% and 8.29%. For the 0.10% CN-ECC sample group (Figure 6b), the strain rate reduced to 4.44% and 4.22% from the original 4.53%, with strain losses of 1.99% and 6.84%, respectively. For the 0.15% CN-ECC sample group (Figure 6c), the strain rate decreased to 5.51% and 5.26% from the initial 5.61%, showing strain losses of 1.78% and 6.23%.  
It is obvious that the strain rate loss of the CN-ECC sample under sulfate corrosion is lower than that of the control group, and the CN can effectively alleviate the damage of sulfate on the ECC's tensile properties. Combined with the compressive strength, strain rate, and sulfate corrosion resistance, it can be seen that when the content of the CN is 0.15%, it has the strongest effect on improving the ECC's performance.  
Figure 5: Compressive strength results of ECC after sulfate corrosion. (**a**) Compressive strength; (**b**) loss rate.
## 5 Discussion of Numerical Simulation Results  
### The Influence of the Extension Length of the Outer Ring Tubes  
Due to the influence of the extension length of the outer ring freezing pipe, the bottom of the formed cup-shaped freezing wall will extend a part of the freezing area, as shown in Figure 11 below. This part of the extension area will have a certain impact on the bottom of the freezing cup. The following analysis of the influence of the extension length of the outer ring on the bottom of the cup.  
Figure 10: Temperature of the weak point varies with space.  
Figure 9: Intuitive diagram of temperature distribution of section C.  
The lengths of the outer ring are 7.207, 7.707, 8.207 (initial length), 8.707, and 9.207 m, which correspond to the calculated extension lengths of 3.53 m, 4 m, 4.5 m, 4.99 m, and 5.47 m, respectively. The thickness and average temperature of the frozen wall at the bottom of the frozen cup are analyzed as shown in Figure 12.  
It can be seen from Figure 12 that the thickness and average temperature of the frozen wall at the bottom of the frozen cup change approximately linearly with the increase in the extension length of the outer ring pipe. The thickness of the frozen wall increases by 0.25 m on average, and the average temperature decreases by 1.25 \({}^{\circ}\)C when the extension length increases by 1 m on average. In practical engineering, the extension length of the outer ring pipe affects the brine circulation consumption of the freezing pipe. Therefore, it is important to find a suitable extension length of the outer ring pipe to meet the requirements of freezing strength and stability and to reduce the freezing construction cost as much as  
Figure 11: Extension length of cup bottom frozen.  
Figure 12: Curve of thickness and average temperature variation with the length of the outer pipe.  
possible. From the above diagram, the thickness of the frozen wall of path 3, which is the weakest, can reach 5.24 m to meet the design requirements when the extension length of the outer ring pipe is 5 m, and too long will cause excessive waste of cooling capacity and high cost.  
The boundary point is the midpoint of the two freezing pipes, and its temperature change is of great significance for judging whether the freezing wall intersects. It can be approximated as the intersection time when the midpoint of C2C1, which is the weak point of the first quadrant, drops to 0 \({}^{\circ}\)C due to the symmetry of the model. The intersection time is shown in Table 4.  
It can be seen from Table 4 that the crossover time decreases logarithmically with the increase in the extension length of the outer ring tube. The crossover time only decreases by 5 days when the extension length increases from 3.53 m to 4.00 m, while the crossover time decreases by 13 days when the extension length increases from 4.99 m to 5.47 m.
# Feature Interaction in Terms of Prediction Performance
## 2 Proposed Interaction Measure
\begin{array}{l}PP_{M}( DS)-PP_{M}\Big{(}DS\_Perm\Big{(}\Big{\{}F_{i},F_{j}\Big{\}}\Big{)}\Big{)},\text{ if $M$ is a classification model}\\ PP_{M}\Big{(}DS\_Perm\Big{(}\Big{\{}F_{i},F_{j}\Big{\}}\Big{)}\Big{)}-PP_{M}( DS),\text{ if $M$ is a regression model}\end{array} \right. \tag{8}\]  
The reduced performance of two features can be defined in the context of a single feature. We can permute the two features and consider the modified dataset \(DS\_Perm(\{F_{i},F_{j}\})\). Definition 2 can also be extended to include three or more features of \(Err()\). **Definition 3**. : _The feature interaction Interact between features \(F_{i}\) and \(F_{j}\) is defined as_  
\[\left\{ \begin{array}{l}\Big{(}Err(F_{i})+Err\Big{(}F_{j}\Big{)}\Big{)}-Err \Big{(}\Big{\{}F_{i},F_{j}\Big{\}}\Big{)},\text{ if $M$ is a classification model}\\ Err\Big{(}\Big{\{}F_{i},F_{j}\Big{\}}\Big{)}-\Big{(}Err(F_{i})+Err\Big{(}F_{ j}\Big{)}\Big{)},\text{ if $M$ is a regression model}\end{array} \right. \tag{9}\]  
In Definition 3, feature interaction is defined as the difference in the performance reduction. \(Err\) (\(\{F_{i},F_{j}\}\)) is equal to \(Err(F_{i})+Err\) (\(F_{j}\)) only when there is no interaction between the features \(F_{i}\) and \(F_{j}\). In the context of this study, the prediction performance is influenced by the feature interaction. The prediction errors caused by \(F_{i}\) and \(F_{j}\) independently decrease/increase when there is interaction between these two features. Three cases of \(Interact(F_{i},F_{j})\) can be defined as  
\(Interact(F_{i},F_{j})=0\): no interaction between \(F_{i}\) and \(F_{j}\)  
\(Interact(F_{i},F_{j})>0\): positive interaction between \(F_{i}\) and \(F_{j}\)  
\(Interact(F_{i},F_{j})<0\): negative interaction between \(F_{i}\) and \(F_{j}\)  
Negative interaction implies that the connection between \(F_{i}\) and \(F_{j}\) reduces the prediction performance, whereas positive interaction leads to an increase in the prediction performance. In other words, positive or negative interactions decrease or increase the prediction error, respectively.
## 2 Materials
### In Situ Measurements
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Channel No.**} & \multirow{2}{*}{**Channel Name**} & \multicolumn{2}{c}{**Wavelength**} & \multirow{2}{*}{**Resolution**} \\  & & & **(Full Width at Half Maximum)** & & \\ \hline \multirow{4}{*}{Visible channels} & 1 & VIS (VIS0.4) & 0.431–0.479 μm & 1.0 km \\  & 2 & VIS (VIS0.5) & 0.5025–0.5175 μm & 1.0 km \\  & 3 & VIS (VIS0.6) & 0.625–0.66 μm & 0.5 km \\ \hline \multirow{4}{*}{Near-infrared channels} & 4 & VNIR (VIS0.8) & 0.8495–0.8705 μm & 1.0 km \\  & 5 & SWIR (NR1.3) & 1.373–1.383 μm & 2.0 km \\  & 6 & SWIR (NR1.6) & 1.601–1.619 μm & 2.0 km \\ \hline \multirow{4}{*}{ Mid-wave infrared channels} & 7 & MWIR (IR3.8) & 3.74–3.96 μm & 2.0 km \\  & 8 & MWIR (IR6.3) & 6.061–6.425 μm & 2.0 km \\  & 9 & MWIR (IR6.9) & 6.89–7.01 μm & 2.0 km \\  & 10 & MWIR (IR7.3) & 7.258–7.433 μm & 2.0 km \\ \hline \multirow{4}{*}{ Long-wave infrared channels} & 11 & TIR (IR8.7) & 8.44–8.76 μm & 2.0 km \\  & 12 & TIR (IR9.6) & 9.543–9.717 μm & 2.0 km \\  & 13 & TIR (IR10.5) & 10.25–10.61 μm & 2.0 km \\  & 14 & TIR (IR11.2) & 11.08–11.32 μm & 2.0 km \\  & 15 & TIR (IR12.3) & 12.15–12.45 μm & 2.0 km \\  & 16 & TIR (IR13.3) & 13.21–13.39 μm & 2.0 km \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the GEO-KOMPSAT 2A Advanced Meteorological Imager (GK2A/AMI) spectral channels. The commonly used unit for SSI is W m\({}^{-2}\), meaning the radiation energy over unit area and unit time. However, the KMA preprocessed the operational in situ SSI measurements by cumulating them over an hour and converting them to the unit of MJ m\({}^{-2}\). The unit of MJ m\({}^{-2}\) indicates the radiation energy over the unit area and unit time, similar to W m\({}^{-2}\). In this study, the unit of SSI was unified as MJ m\({}^{-2}\), which is the standard of the KMA in situ SSI measurements as the reference value of the model. For the in situ SSI data observed from IORS and NIFoS flux towers, after their unit of W m\({}^{-2}\) was converted into MJ m\({}^{-2}\), the in situ SSI data were used to test the model.
## 3 Application
### Artificial Intelligence Model Results
#### 3.2.4 PSO-GP Results  
Before using the PSO to find the hyper-parameter of the \(GP\) model for achieving the best predictive performance, several parameters including the number of particles and the maximum number of iterations of PSO should be defined by the user. In the study of [PERSON] et al. [78], [PERSON] et al. [79] and [PERSON] et al. [80], it can be found that the optimization algorithm such as GA, PSO and ABC can usually get stable results with the maximum number of iterations of 1000. Therefore, a parameter investigation for 9 PSO-GP models with the various number of particles from 20 to 300, the maximum number of iterations of 1000 and a search range of \(-10\) to 10 was carried out, and it was found that the PSO model marked by 300 particles and 1000 iterations yield the lowest fitness curve. Moreover, no change of the fitness curve can be found after 200 iterations, so 300 and 200 were determined to be optimal parameters combination of the PSO-GP model. After calculation, the optimal PSO-GP model is showing the _VAF_ of 94.3008, \(R^{2}\) of 0.9438 and _RMSE_ of 0.1387 for training datasets, and the _VAF_ of 89.2192, \(R^{2}\) of 0.9153 and _RMSE_ of 0.1591 for testing datasets, and the actual _BRZT_ and predicted _BRZT_ were shown in Figure 7.