esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Thermal Comfort Analysis  
Based on the temperature data measured in the classrooms, thermal comfort conditions during occupied hours were analysed according to two of the most widely used standards in the scientific community [29]: EN ISO 7730 and ASHRAE Standard 55.  
The main international standards are based on [PERSON]'s comfort studies [30], whose methodology was incorporated into the EN ISO 7730 standard [31]. These standards form the basis for the initial comfort analysis conducted in this study. Based on the conditions specified in Table 1 of EN ISO 7730 for classrooms under winter conditions, the following conditions were established:  
* Thermal environment category: B, indicating a predicted percentage of dissatisfied (PPD) \(<\) 10%;
* Metabolic rate: 1.2 MET;
* Clothing level: 1.0 clo.  
Under these conditions, an optimal comfort temperature of 22 \({}^{\circ}\)C was established, with an acceptability range of \(\pm\)2 \({}^{\circ}\)C. Temperatures above or below these conditions were considered states of indoor overheating or undercooling, respectively.  
However, numerous studies establish that when analysing comfort in buildings using natural ventilation through operable windows managed by the occupants, as in the case studies examined here, adaptive standards are more suitable than the steady-state model [32; 33]. Therefore, in the second comfort analysis conducted in this study, we employed the adaptive method defined in ASHRAE Standard 55 [34], which considers the user's ability to adapt in terms of clothing (between 0.5 and 1.0 clo) and the use of ventilation systems. For this method to be applicable, the occupants' activity should correspond to a metabolic rate of between 1 and 1.5 MET. In this case, an optimal comfort temperature (\(T_{co}\)) is established, which depends on the running outdoor temperature (\(T_{out}\)), according to Equations (1) and (2), and an acceptability range of \(\pm\)2.5 \({}^{\circ}\)C (resulting in PPD \(<\) 10%). Temperatures above or below these conditions are considered states of indoor overheating or undercooling, respectively.  
\[T_{co}\left[{}^{\circ}\mathrm{C}\right]=0.31\times T_{out}+17.8 \tag{1}\]  
\[\begin{array}{c}T_{out}\left[{}^{\circ}\mathrm{C}\right]=(1-0.6)\times(T_{e (d-1)}+0.6\times T_{e(d-2)}+0.6^{2}\times T_{e(d-3)}+0.6^{3}\times T_{e(d-4)}+ 0.6^{4}\times T_{e(d-5)}\\ +0.6^{5}\times T_{e(d-6)}+0.6^{6}\times T_{e(d-7)})\end{array} \tag{2}\]  
where \(T_{e(d-n)}\) is the daily mean external air temperature for the previous days \(n\) (1-7).
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 4 Data Collection Technologies  
According to [42], traffic data collection technologies can be classified into three groups based on functionality: point sensors, point-to-point sensors, and area-wide sensors. Here, the word sensor refers to a traffic flow sensor, i.e., device or system that can collect traffic flow data. Point sensors include various technologies such as: inductive loops, piezoelectric sensors, video image sensors, radars, infrared sensors, acoustic sensors, pneumatic road tubes, and magnetic sensors. These sensors are usually limited in spatial coverage and are used for measuring traffic volume, speed, occupancy, and other traffic flow parameters [43; 44; 45]. Point-to-point sensors detect vehicles at multiple locations throughout the network and are often called automated vehicle identification (AVI). Major technologies used for point-to-point detection are Bluetooth, Wi-Fi, RFID, and Automatic License Plate Recognition (ALPR). These technologies are suitable for computing travel times, route choice fractions paths, and origin-destination (O-D) flows [46; 47; 48]. Some technologies do not necessarily belong to only one group and can be used as either point or point-to-point sensors. There are several papers where researchers use inductive loops for vehicle reidentification and travel time estimation [49; 50; 51]. Cameras and video- and image-processing are used for collecting traffic data as point and point-to-point sensors [52; 53; 54; 55]. Area-wide sensors include data collection technologies that allow tracking of vehicles over a large area. The most promising are Floating Car Data (FCD) and Cellular Floating Car Data (CFCD). FCD data are generated by smartphones or vehicles equipped with GNSS receivers, also known as GNSS probe data. These vehicles are mostly part of the fleet, e.g., taxi service, public transport service, or some company fleet. FCD and CFCD are used for calculating a wide range of useful traffic parameters, such as space mean speed, travel time, O-D matrices [56; 57; 58; 59], and queue length [60; 61; 62]. An often used term in the literature, crowdsourced data, are data gathered from social media [63]. Posts from social media (e.g., Facebook, Twitter, WeChat, Sina Weibo) must be geotagged to collect various traffic events, such as traffic accidents and jams. Posts from social media usually have low
## 3 Results and Discussion
### Robustness Analysis  
The results analysis of this paper found that the NHLBVNet model outperforms the baseline models on the small-scale dataset. To substantiate the NHLBVNet model's reliability and sturdiness concerning various datasets, this paper reports robustness experiments on XGBoost, XGBoost+MLP, and the NHLBVNet model.  
The experimental setup was devised to incrementally diminish the training set's volume, all the while keeping the training parameters unchanged, thereby investigating the dependency of each model on the data. The size of the training set was controlled by adjusting the proportion it represents within the entire dataset, with models being tested at training set proportions of 70%, 60%, 50%, and 40%, while the test set remained fixed. The \(R^{2}\) similarity coefficient was utilized as the evaluation metric to assess the overall accuracy of each model's output. The robustness experiment's findings are illustrated in Figure 9.  
Figure 8: Training epochs experiment results (**a**) \(R^{2}\); (**b**) MAE; (**c**) RMSE.  
As the size of the training set decreased, the performance of all models showed a significant decline. Notably, the combined model of XGBoost + MLP experienced the most substantial decrease, while the NHLBVNet model exhibited the least reduction in overall accuracy. This indicates that compared to other baseline models, the NHLBVNet model is capable of delivering superior performance even with small-scale datasets. The NHLBVNet model outperformed the baseline models across various datasets, once again demonstrating the superiority and effectiveness of the NHLBVNet model.
# Virtual and Physical Prototyping of Reconfigurable Parallel Mechanisms with Single Actuation
## 3 Mobility Analysis
### Analysis for Several Kinematic Chains  
Now, let us consider the output link connected to the base by several kinematic chains. In the discussed mechanisms, all the chains are coupled through the driving wheel. To operate properly, the mechanism number of DOFs in a general (nonsingular) configuration should be equal to zero when the drive is fixed [22] (p. 40). If we fix the drive, each chain will impose wrench (4) on the output link. We can compose matrix \(\mathbf{W}\), whose columnsrepresent these wrenches, and the number of columns will depend on the number of kinematic chains.  
In the mechanisms with three, four and five kinematic chains (Figure 2a-c), there will be three, four and five wrenches \(\zeta_{i}\), respectively, acting on the platform. Hence, the rank of matrix \(\mathbf{W}\) will always be less than six. This means we can find reciprocal twists, which will correspond to the uncontrolled movements (or uncontrolled DOFs) of the mechanism output link. Thus, the mentioned structures shown in Figure 2a-c cannot operate correctly with a single drive.  
Fixing the drive in the mechanism with six kinematic chains shown in Figure 2d will lead to six wrenches \(\zeta_{i}\), and matrix \(\mathbf{W}\) will have six columns. If the matrix has a full rank, the platform configuration is completely determined. The mechanism operates properly and has one (controlled) DOF. In some configurations, matrix \(\mathbf{W}\) can be rank deficient, and the platform will obtain additional and uncontrolled DOFs. Such configurations represent another type of singularities common for parallel mechanisms [34] that should also be avoided for proper mechanism performance.  
Table 1 summarizes the results of the mobility analysis, describing the reconfigurable solutions of the proposed kinematic chain. Thus, the only workable mechanism structure of the suggested ones is the one with six kinematic chains and single DOF shown in Figure 2d. We will study exactly this mechanism in the following sections.  
Though the considered mechanism has only one DOF, a simple reconfiguration procedure, implying disconnecting the belts and reorienting the cranks (Figure 1b), allows for reproducing various output link trajectories, as we will see later in the paper. This is the key feature of the proposed design and the major benefit among other known reconfigurable mechanisms mentioned earlier in the introduction.
# Recent Progress on Aberration Compensation and Coherent Noise Suppression in Digital Holography
## 5 Coherent Noise Suppression
Based on the above analyses, it is essential to study a digital processing method to reduce the effect of coherent noise without a loss of spatial resolution, which provides a facility for real-time measurement. [PERSON] et al. [116] presented a method of coherent noise reduction using a laterally shifting hologram aperture in 2016. The digital method was similar to a laterally shifting CCD to reduce the coherent noise in the optical arrangement. The hologram with the larger size (compared with the hologram size to be reconstructed) was captured by a camera as the original hologram. The digital aperture was shifted in the original hologram. To suppress the loss of the spatial resolution of the reconstructed image, the aperture size was required to contain the whole specimen to be reconstructed, instead of extracting the specimen's part information. Each sub-hologram sampled at a different position was reconstructed. The average effect was employed for different reconstructed images to reduce the coherent noise. The experimental results of the microhole array showed that the proposed method decreased the phase standard deviation and distribution range of the reconstructed phase. At the same time, the reconstruction of the resolution chart demonstrated that the spatial resolution was immune to the average effect in speckle reduction. Since each sub-hologram remains the total specimen, the method with a single hologram can realize the coherent noise reduction without a loss of spatial resolution. Table 2 shows several kinds of coherent noise suppression methods. The low coherent source, incoherent DH, and digital processing methods all record one hologram. Thus they may be suitable for dynamic measurement. But these methods have respective limitations. The multiplexing holograms method requires capturing multiple holograms and utilizing the average effect to reduce the coherent noise, which results in a longer capture time and is not applied in the rapid dynamic measurement.
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 3 Analytical Method for Safety Factor of Slope with Crack
### Analytical Method for Safety Factor of Base Circle Slope
#### 3.2.2 Determination of the Safety Factor  
The safety factor is also calculated by the limit equilibrium method, as in Equation (15).  
(1) Calculation of the sliding force  
The sliding force of base circle slope needs to increase the calculated region of \(AEI\), and the other region is consistent with the toe circle slope. The sliding force of \(AEI\), \(T_{base\_AEI}\) can be written as follows:  
\[\begin{array}{ll}T_{base\_AEI}&=\int_{s-EI}\sin\theta\text{d}w=\int_{x_{4}} ^{0}\gamma\frac{x-a}{r}\bigg{\{}0-\left[b-\sqrt{r^{2}-\left(x-a\right)^{2}} \right]\bigg{\}}\text{d}x\\ &=\frac{\gamma}{r}\left[\frac{b\left(r^{2}-b^{2}-a^{2}\right)}{2}+\frac{\left( b^{2}\right)^{\frac{2}{2}}-\left(r^{2}-a^{2}\right)^{\frac{2}{2}}}{3}\right] \end{array} \tag{23}\]  
Thus, the total sliding force of base circle slope \(T_{base}\) is expressed as  
\[T_{base}=T_{toe}+T_{base\_AEI} \tag{24}\]  
Figure 5: Base circle slope calculated model.  
(2) Calculation of resisting force  
The resisting force of \(AEI\) is also calculated in two parts, including the resistance \(T^{\prime}_{\text{{base-AEI-c}}}\) caused by cohesion and the resistance \(T^{\prime}_{\text{{base-AEI-q}}}\) caused by friction angle.  
\(T^{\prime}_{\text{{base-AEI-c}}}\) acting on the slip surface \(EI\) can be written as follows:  
\[\begin{array}{ll}T^{\prime}_{\text{{base-AEI-c}}}&=\int_{EI}c\text{d}s=\int_{ x_{4}}^{0}\frac{r}{\sqrt{r^{2}-(x-a)^{2}}}\text{d}x\\ &=cr\left(\arcsin\frac{\sqrt{r^{2}-b^{2}}}{r}-\arcsin\frac{\theta}{r}\right) \end{array} \tag{25}\]  
Similarly, the \(T^{\prime}_{\text{{base-AEI-q}}}\) acting on the slip surface \(EI\) can be written as follows:  
\[\begin{array}{ll}T^{\prime}_{\text{{base-AEI-q}}}&=\tan\varphi\int_{EI}dN= \int_{x_{4}}^{0}\tan\varphi\text{d}w\cos\theta\\ &=\frac{\gamma\tan\varphi}{r}\left\{\begin{array}{l}\frac{\sqrt{r^{2}-b^{2}} }{6}+\frac{a\phi}{2}\sqrt{r^{2}-a^{2}}+\\ \frac{b^{2}}{2}\left[\arcsin\left(\frac{a}{r}\right)-\arcsin\left(\frac{\sqrt{ r^{2}-b^{2}}}{r}\right)\right]\\ -ar^{2}+\frac{a^{3}}{3}\end{array}\right.\end{array} \tag{26}\]  
Thus, the total resisting force \(T^{\prime}_{\text{{base}}}\) of base circle slope is expressed as  
\[T^{\prime}_{\text{{base}}}=T^{\prime}_{\text{{to}}}+T^{\prime}_{\text{{base- AEI-c}}}+T^{\prime}_{\text{{base-AEI-q}}} \tag{27}\]  
Therefore, the safety factor of base circle slope is given by the following expression:  
\[F=\frac{T^{\prime}_{\text{{base}}}}{T_{\text{{base}}}}=\frac{T^{\prime}_{ \text{{to}}}+T^{\prime}_{\text{{base-AEI-c}}}+T^{\prime}_{\text{{base-AEI-p}}}} {T_{\text{{to}}}+T_{\text{{base}}}} \tag{28}\]
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 8 Optimization Using the Modified GA Approach
### Elitism  
Elitism selects some of the strongest chromosomes from the population and ensures that they are carried over to the next iteration unchanged, without undergoing any other GA  
Figure 7: Mutation operation.  
operations such as crossover or mutation. This prevents the loss of the best chromosomes in that iteration. By utilizing elitism, the fittest chromosomes from the current generation are automatically included in the next iteration. In the modified GA, the constraints are formulated as follows:  
1. Bound constraint: \(0\leq\rho_{CU_{i}}\leq\rho_{max}\)
2. Linear constraint: \(SIR_{CU_{i}}>SIR_{T}\)  
Algorithm 4, shows the process of modified GA used for power optimization model. Population size, number of chromosomes and number of genes in a chromosome are taken as input parameter and optimum weights are returned as output. Initial population of size \(N\) is generated and random weights between \(-1\) and \(1\) are initialized to each chromosome and binary encoded. The fitness of each chromosome is computed until a criteria for a fitness function is met. The function of selection, crossover and mutation takes place in a loop and in each loop fitness function is checked and then finally returns optimum weights.  
**Input:** N (population size), n (number of randomly selected chromosomes), k (number of genes in a chromosome)  
**Output:** optimum weights \(w_{i_{k}}\)  
**Initialization:**\(gen=0\), r = 0.85 (crossover fraction)  
random weights assigned to each chromosome: \(w_{i_{k}}\)  
_Generate the initial population_  
_Binary Encode_  
\(Compute\ fitness(i)\) _for each_ \(i\in w_{i_{k}}\)  
**while (fitness criteria are not met) do**  
\(gen=gen+1\)  
roulette wheel selection  
crossover  
mutation  
compute fitness (i) for each \(i\in w_{i_{k}}\)  
**end while**  
Return the fittest individual from \(w_{i_{k}}\)  
**Algorithm 4** Power optimization model using the modified GA.
## 3 Methodology
### Impervious Surface Analysis  
In order to improve the extraction of ISA for a wide range of surface materials and the heterogeneity of the land use classes based on medium resolution images, this study adopted object-based SVM algorithm to extract the pervious/impervious area first. Then, the LSMA algorithm was used in the impervious area to further estimate the ISA percentage.  
ESP2 tool [38] was applied to generate an optimized segmentation scale = 50 for the HJ-1B images. Then the multi-resolution segmentation algorithm divided the original images into more than 10,000 objects with the optimized scale of 50. For supervised classification, SVM uses specific training and reference data to generate the model, and then estimates the target value based on the properties of the training data set [39]. Hence, as a compromise between statistical strictness and practical limitations, 120 samples which are 1% percentage of all the objects were selected by stratified random sampling in the SVM model. The samples were divided into two major types: 60 samples of impervious layers (mainly roads, parking lots, residential area, central business districts, rooftop, etc.) and the other 60 samples of pervious layers (including grassland, forest and farmland). These samples were used to identify training areas and train the prior classifier for SVM classification.  
The kernel function Radial Basis Function (RBF) of svm.train in LIBSVM [40] was used to train the samples in a high dimensional training space. Then we used the easy.py, grid.py and gnuplot functions of LIBSVM to calculate the optimized parameters C (error tolerance) and gamma (data in high dimension distribution). Finally, the parameters-C = 3 and gamma = 0.33 were imported into the eCognition software (version 9.1, Definiens, Munich, Germany)-SVM classifiers to classify further the pervious and impervious features.  
The object-based SVM adjustment algorithm incorporated spectral and textural properties to enhance further classification performance. One of the most prevalent techniques, the Grey-Level Co-occurrence Matrix (GLCM) [10; 41], was used to derive textural characteristics. For coarse resolution satellite images, the anisotropy of each urban landscape represents a general isotropy [41]. Therefore, we did not combine the multiple directional influences with the textural features analyses. The calculated texture measures (including contrast, correlation, dissimilarity, mean and variance angular) were chosen to train the SVM classifier through integrating spectral information.
# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 4 Methodology
### Classification and Accuracy Assessment  
To examine the capability of the multi-index approach in extracting the built-up area and determining the other LCU classes, the SVM classification method with radial basis function (RBF) kernel was applied on the multi-index images. The SVM was initially developed as a binary classifier, thus, a pairwise classification approach was implemented for multiclass classification requirements, by creating a binary classifier for each possible pair of classes [73]. The classification process was performed using the ENVI software, which requires a set of parameter definitions. The gamma is the most critical parameter and was the only parameter changed in this research (determined by division of 1 by the number of data layers). The penalty parameter reduces misclassification in the training step when set to 100. The classification probability threshold should be set to zero to guarantee that each pixel is assigned to a class. A pyramid level of zero allows for the classification to be performed directly on the original image pixels, instead of a first-pass classification on low-resolution pyramid layers [74].  
The built-up index data listed in Table 2 and the NDTI were separately layer-stacked with vegetation and water index data and were classified using the same training samples, defined by region of interest (ROI). In addition, the ten-band Sentinel-2A image was classified and compared with the multi-index image classification results to analyze and illustrate the improvements in the classification accuracy, using the index images. Lastly, the NDTI-based multi-index dataset was layer-stacked with the ten-band original image and this thirteen-band combination was also classified for further comparison.  
To evaluate the accuracy of the classification results, the overall accuracies with the user's, the producer's, and the overall accuracy metrics and Kappa statics were derived from the confusion matrix [75]. The accuracy assessment of the classification results was performed with stratified random points, and the original Sentinel-2A image and Google Earth(r) imagery as the reference data. A random point distribution was designed, according to the heterogeneity potential and areal coverage of the classes. The training sample distribution and number of points used in the accuracy assessment are provided for each class and region in Table 3.
## 1 Introduction
Nonetheless, they treated the entire system and the coupling behavior as linear terms; i.e., they did not take into account the influence of complex coupling terms of the nonlinear terms created by external disturbances in actual flexible structures. Determining whether the same vibration-suppression effects could be obtained in actual rotary systems will require further investigation. Horizontal thin-plate rotors have been proposed by previous researchers [12; 13]. Our primary objective in this study was to determine the effectiveness of an active vibration-suppression control system based on the motor-driven rotation of thin flexible plate held vertically. This system was developed in conjunction with a dynamic model that takes into account nonlinear coupling effects as well as nonlinear terms. We attached piezoelectric devices to the thin flexible plate, which underwent excitation through the periodic motion of the motor. Feedback signals from the piezoelectric sensors were used to examine active vibration-suppression control in the flexible structure. The simulation results in [13] indicate that taking system coupling into consideration during the controller design stage and applying a multi-input-multi-output controller will result in better suppression effects on the vibrations created by periodic disturbances. However, this approach will also greatly increase the complexity of the overall controller design. We sought to overcome these difficulties by assuming that the coupling of vibrations caused by rotary motion could be regarded as external disturbances and systemic uncertainties associated with the thin flexible plate. This led us to design single-input-single-output controllers for the motor-driven rotation system as well as for the piezoelectric vibration suppression system. Adaptive fuzzy sliding mode control (AFSMC) was used in the latter to deal with complex vibrations generated by the rotation of the thin flexible plate. This control method can effectively handle nonlinear systems with uncertain parameters and achieve the expected control performance [15; 16; 17]. To minimize the influence of the periodic disturbances caused by the periodic motions of the motor, we adopted a hybrid RC framework [13; 14] to combine RC with AFSMC and enhance piezoelectric vibration-suppression control. Experimental resultsdemonstrate the effectiveness of the proposed hybrid intelligent control method in suppressing the complex vibrations generated in flexible structures subjected to periodic motor-driven rotations.