esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.3 Prediction of Reference Points
In the waist area (C1 and C2):  
* is computed the midpoint between points C1 and C2 (C_mid_);
* In the neck and waist area (P1, P2, C1 and C2):
* is computed the vertical length of the back (_dist_y_) by difference of the \(y\) coordinate of the intermediate points calculated earlier (_P_mid_, _C_mid_);
* Only at the neck and waist on the left side (_P_1 and C1):
* is computed the vertical length of the back using the \(y\) coordinate of these points \(P\)1 and C1 (the difference between these two points) (_disty_);
* Only at the neck on the right side and at the waist on the left side (_P_2 and C1):
* is computed the vertical length of the back through the \(y\) coordinate of these points \(P\)2 and C1 (difference between these two points) (_disty_);
* Only at the neck on the left side and at the waist on the right side (_P_1 and C2):
* is computed the vertical length of the back through the \(y\) coordinate of these points \(P\)2 and C2 (difference between these two points) (_disty_);  
The algorithm is designed in such a way that it will only proceed to the next step when it has found at least four reference points, two of which must be the points \(S\)1 and \(S\)2, because that is the only way it can calculate the horizontal length of the back. It must find a point at the neck (_P_1 or \(P\)2) and another one at the bottom of the back (_C_1 or C2), to be able to calculate the vertical length of the back. The calculation of the auscultation points' 2D coordinates, represented in Figure 9 in green, are based on the measurements of the length of the back (vertical and horizontal). If these are successfully obtained, and depending on the key points found, the following equations apply:  
Figure 9: Dorsal mapping of auscultation points (green), reference points (red), and distances. \[L1_{x} =S\_mid_{x}-(dist_{x}/2)*0.30 L1_{y} =Ref\_point_{y}+dist_{y}*0.15 \tag{1}\] \[R1_{x} =S\_mid_{x}+(dist_{x}/2)*0.30 R1_{y} =Ref\_point_{y}+dist_{y}*0.15\] (2) \[L2_{x} =S\_mid_{x}-(dist_{x}/2)*0.15 L2_{y} =Ref\_point_{y}+dist_{y}*0.35\] (3) \[R2_{x} =S\_mid_{x}+(dist_{x}/2)*0.15 R2_{y} =Ref\_point_{y}+dist_{y}*0.35\] (4) \[L3_{x} =S\_mid_{x}-(dist_{x}/2)*0.35 L3_{y} =Ref\_point_{y}+dist_{y}*0.50\] (5) \[R3_{x} =S\_mid_{x}+(dist_{x}/2)*0.35 R3_{y} =Ref\_point_{y}+dist_{y}*0.50\] (6) Figure 8:0 (7) Figure 8:1  
The variable \(Ref\_point_{y}\) corresponds to whatever point is found in the neck area, i.e., \(P\_mid_{y}\) if both are found, or \(P1_{y}\), \(P2_{y}\), depending on which one is found. This algorithm is detailed in the flowcharts shown in Figures 10 and 11. Figure 8:2 First part of the algorithm for calculating auscultation points’ 2D coordinates.
## 4 Validation
### Comparative Analysis  
Table 8 in the paper presents a comparative analysis between existing prioritization techniques and the newly proposed framework. The existing methods are characterized by subjective grouping, while requirements are typically categorized without a standardized criterion, leading to potential inconsistencies. In contrast, the proposed framework utilizes objective grouping, which bases the categorization on defined, measurable criteria, enhancing consistency and transparency. Existing techniques often lack a structured pairwise comparison, while this is a methodological feature of the proposed framework. This enables a more detailed and comparative analysis of requirements, helping to establish clearer priorities. Similarly, while traditional methods do not employ a ratio scale, the proposed framework integrates this scale to quantify the differences in importance between requirements more accurately. The management of hierarchy levels, which is absent or unstructured in current practices, is systematically addressed in the proposed framework. This structured approach allows for clearer definition and understanding of requirement layers and their interdependencies.  
Furthermore, while existing methods use subjective assignment of requirements to categories, the proposed framework adopts numerical assignment, which enhances the precision of requirement categorization. Traditional techniques often do not use an ordinal scale, while the proposed framework incorporates this aspect, allowing for the ranking of requirements in a standardized manner. This addition helps in systematically determining the relative importance of each requirement. Additionally, existing methods typically organize requirements into three broad groups. The proposed framework expands this by introducing a fourth group, enabling a more nuanced categorization which better addresses the complexity of IoT systems. The proposed framework also addresses the lack of stakeholder management in current methods by explicitly incorporating stakeholder inputs into the requirement prioritization process, ensuring that all stakeholder needs are considered. Lastly, the proposed framework includes mechanisms for managing requirements with the same priority, an aspect often overlooked in existing methods. This feature ensures that critical requirements are not overlooked simply because they share similar priority levels, thereby supporting more effective project management and execution.
## 4 Conclusions  
Calibrated capacitances values in the millimeter-wave regime considering a frequency of operation of 30 GHz (free-space wavelength of 1 mm) have been presented. The scanning probe instrumentation proposed built entirely from scratch is based on a combined AFM/SMM integrated inside an SEM. The stability of the microwave path is ensured by keeping the microwave probe and cables fixed during the scanning operation. Only the sample under test is moved under the probe. A high signal-to-noise ratio is obtained by choosing appropriate electrical parameters for the VNA, i.e., the RF power set to 0 dBm and IFBW to 50 Hz. No external electrical matching network commonly found in SMM set-ups is used. Instead, we increased the frequency of operation up to 30 GHz to obtain a good compromise between the collimation of the electrical fields in the vicinity of the tip  
Figure 11: Relative error in the determination of the microwave capacitance at 30 GHz. (**a**) Non calibrated topography 1D profile. (**b**) Topography 1D profile in the range of 17-25 μm. (**c**) Extracted 1D microwave capacitance on the silicon oxide steps.  
apex and moderate losses in the microwave path between the microwave source and the apex tip. We focused mainly on MOS capacitors whose theoretical values are in the range of 0.5-5 fF. A dedicated calibration is developed to extract the capacitances values with a median error of 10%. To that end, a dedicated standard operation procedure, including the measurement protocol and data analysis, is used to derive the quantitative data of interest. The measurement performance is demonstrated with capacitance fluctuations in the order of \(\pm\)5 aF.
# A Quantum Detectable Byzantine Agreement Protocol Using Only EPR Pairs
###### Abstract
The distinguishing feature of the EPRQDBA protocol, which does not utilize a quantum signature scheme, is the fact that it only relies on EPR pairs. Exotic multi-particle entangled states employed in other protocol are not so easy to produce with our current quantum apparatus. This increases preparation time and complicates their use in situations where the number \(n\) of players is large. Similarly, although for small values of \(n\)\(|GHZ_{n}\rangle\) states are easily generated by contemporary quantum computers, when the number \(n\) of players increases, it becomes considerably more difficult to prepare and distribute \(|GHZ_{n}\rangle\) tuples. Thus, protocols that require \(|GHZ_{n}\rangle\) tuples for \(n\) players dot not facilitate scalability. In contrast, Bell states are, without a doubt the easiest to generate among maximally entangled states. The EPRQDBA only requires EPR pairs, specifically \(|\Psi^{+}\rangle\) pairs, irrespective of the number \(n\) of players. This leads to a reduction in preparation time, increases scalability, and, ultimately, practicability. Another strong point of the EPRQDBA protocol is the fact that it completes in a constant number of rounds, specifically 4 rounds. Thus, the completion time of the protocol is independent of the number \(n\) of players, which also enhances its scalability.
## 3 The Proposed Scheme
### Training Specification  
Based on the IM/DD channel, the training set is obtained using a simulation under different system configurations in terms of training SNR, the CP usage, and the clipping effect. In each training epochs, 100 DCO-OFDM symbols are random generated and one of them is chosen to carry the pilots. After channel propagation, the received symbol and the original transmitted data are formed as the training set. In order to accelerate the network convergence rate, we should collect the diverse and abundant training set as much as possible, which are beneficial to the parameters learning. Note that the DC gain is removed from the training set so that we can evaluate the BER performance of the proposed scheme more fairly [31]. The full channel effect, which include the LED nonlinearity, multipath propagation of optical model, and the channel noise, is involved in the training procedure.  
In the training phase, the proposed scheme is trained to optimize the equalization and demodulation performance by tuning the weights \(\mathbf{\Theta}=\left\{\mathbf{W}^{\mathcal{S}1},\mathbf{b}^{\mathcal{S}1}, \mathbf{W}^{\mathcal{S}2},\mathbf{b}^{\mathcal{S}2}\right\}\) so that the reconstructed \(\hat{\mathbf{t}}_{0}\) is closer to the original \(\mathbf{t}\). Therefore, we employ the mean squared error (MSE) between the original \(\mathbf{t}\) and the estimated \(\hat{\mathbf{t}}_{0}\) as the training loss function, demonstrated by the following:  
\[loss=\left\|\mathbf{t}-\hat{\mathbf{t}}_{0}\right\|_{2}^{2}. \tag{16}\]  
The goal is to learn the optimized \(\hat{\mathbf{\Theta}}\), which minimizes the objective MSE during each training epochs, shown as follows:  
\[\hat{\mathbf{\Theta}}=\underset{\mathbf{\Theta}}{\text{argmin}}\Big{(}\left\| \mathbf{t}-\hat{\mathbf{t}}_{0}\right\|_{2}^{2}\Big{)} \tag{17}\]  
Considering the computational efficiency and stability, the adaptive moment estimation (Adam) is used as the optimizer. Furthermore, the TensorFlow is adopted since it can easy implement and train the complex DL model on fast concurrent graphics processing unit (GPU) architectures. We train the DL model on a work station running with a GPU of NVIDIA GeForce 2080 Ti driven by CUDA 10.0. As for the testing phase, each batch contain 100 DCO-OFDM symbols and 8000 batches are totally employed for each SNR values. Note that, the testing data should be set different from the training ones.
## 2 Method
### SLIC Superpixel Generation and Superpixel Constraint
#### 2.3.2 Superpixel Constraint  
For PolSAR images, the target decomposition parameters are an important feature to describe ground information. The Pauli decomposition's three components have obvious physical significance, representing odd scattering, dihedral angular scattering, and \(\pi/4\) even scattering, respectively. Meanwhile, a PauliRGB composite image is the standard display mode of PolSAR images. Therefore, in this paper, Pauli features are used to replace spectral features in traditional optical images.  
Aiming at the effect of speckle noise in PolSAR data on building extraction and the over-smoothing phenomenon caused by CNN, a superpixel constraint method based on the CNN classification result is proposed to extract buildings more accurately. After obtaining the superpixel area and the preliminary extraction area of the building, we modify the building area with the superpixel constraint. First, the extracted results in the same superpixel area are counted. For the statistical results, if the number of buildings in the statistics is less than the number of non-buildings, the buildings in the superpixel will be classified as non-buildings; otherwise, the pixels in the superpixel will not be modified.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types
### Radar Sensors
#### 2.6.1 SAR Backscattering Technique  
SAR backscattering data show the strength of the returned signal from the source, which depends on the source characteristics. The acquired data are recorded in digital numbers, which are then processed into the backscattering measurements for analysis, and this process is known as radiometric measurement. A major benefit is that the estimation method can be easily applied over another sensor [118]. However, this transformation from one sensor to another requires the same model of the system and is not compatible with all models. For continuous and accurate calibration without interruption, there is a need to develop an application that can be compatible for all models.
## 4 Discussion
Currently, NVC is the standard imaging modality for the assessment of the microvasculature in SSc and has been incorporated into the classification criteria [13]. In NVC, the only parameter that has been consistently associated with organ manifestations and disease progression over time is the loss of capillaries [14]. With NVC it is, however, impossible to directly measure flow velocities; rather, NVC relies on patterns (early, active, late) [4]. While this study did not have the purpose of testing the diagnostic properties of MVI in SSc, it has to be regarded as complementary to NVC. Further, longitudinal data are required for an association of MVI (or changes thereof) with organ manifestations or disease progression. Overall, due to the excellent visualization of microvascular tissue and organ perfusion, MVI has the potential to avoid invasive or radiation-assisted examinations. Still, more extensive studies in rheumatic conditions are not available. In our experience, visualization with MVI was better suited for superficial blood vessels than conventional PD US. Some examination modalities, such as thermography and others, are already available for SSc [6]. However, these are not available everywhere; some are cumbersome to perform, expensive, or require additional equipment. The advantage of MVI is that rheumatologists are already used to performing US examinations and would need only an additional software application to use MVI. Our study has several limitations: The study cohort included relatively few individuals. However, it included a representative sample of SSc patients. Currently, we cannot ascertain if there are differences between different diseases, such as mixed connective tissue disease or systemic lupus erythematosus, which frequently show RP. We will address this in a further analysis of the method. Finally, based on our results, we cannot recommend estimating flow velocity in less than all four fingers. This is in line with what is frequently found in NVC, where different findings are also present in different fingers of the same individual. Nevertheless, the interrater agreement in healthy control patients was excellent. The strengths of our study are the feasibility of MVI in clinical practice, the relatively short examination time required, and the novelty of the presented data. Thus, MVI may offer potential applications in assessing microvascular alterations in patients with inflammatory rheumatic diseases, such as SSc and others.
## 3 Methodology
### The TOPSIS Algorithm
#### 3.1.7 Calculation of the Relative Proximity Score  
The relative proximity score \(r\) for each satellite lies within the range:  
\[0\leq r\leq 1 \tag{18}\]  
The satellite with the highest \(r\) score is the optimal candidate for handover, as it is the closest to the PIS and the farthest from the NIS [23; 24; 25; 26; 27; 31; 32; 33; 34; 35].  
\[r_{i}=\frac{dist^{-}}{dist^{+}+dist^{-}},\forall i=1,...,m \tag{19}\]  
The method used by each TOPSIS variant influences the way \(r\) is calculated. These differences in scoring methods ultimately guide the handover decision.
## 1 Introduction
### Outline of the Present Paper  
The industrial research work presented in this study will show how the \"5 GT System\" was designed, developed, tested and validated up to field trials.  
The contribution of this work is the following: Section 2 provides an overview of the enabling satellite technologies and related works. Section 3 presents the materials and methods for realizing the 5 GT System. Section 4 describes the results of the validation in an operational environment. Finally, Section 5 contains concluding remarks and future applied research directions.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 2 Related Work
### Rule-Based Methods  
The main idea of the rule-based methods for estimating viewpoints from object images is to analyze image features, such as statistical features and geometric features, and establish a mapping between these statistical features and the corresponding viewpoints. For example, in 1997, [PERSON] et al. [18] proposed a viewpoint estimation method based on moment features representing non-local image properties, including gravity center and breadth. This method demonstrated a lower computational cost and notable robustness to noise. It was found to be a connection between the 3D shape contour and the viewpoint [19]. To improve the viewpoint estimation accuracy, in 2018, [PERSON] et al. [2] proposed a domain adaptation approach that aligns the domain of the synthesized images with the domain of the actual images. In 2000, [PERSON] et al. [20] presented a method for estimating the viewpoint by identifying the minimum distance point to the set of step rays. In 2024, [PERSON] et al. used a Gaussian viewpoint estimator and an extended Kalman filter to predict the three-dimensional (3D) object's position [21].  
In summary, some typical features are listed in Table 1 as examples of these rule-based methods. Rule-based viewpoint estimation methods often perform feature analysis based on image content, so these methods are generally class-dependent. We did not adopt this viewpoint estimation method based on image feature analysis to make the construction method more versatile.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Statistical Feature** & **Geometric Feature** \\ \hline
[2] & HOG, CNN feature vector & – \\
[18] & Moments & Gravity center and breadth \\ \hline \hline \end{tabular}
\end{table}
Table 1: The examples of statistical features and geometric features used in references.
## 3 Overview of Study Site
### Monitoring System  
A complete landslide monitoring system was established in April of 1998. The aim is to analyze its stability and deformation tendency. The monitoring items include a precision geodetic survey of the surface displacement, as well as the borehole, flat tunnel, and meteorological monitoring. The distribution of monitoring points can be seen in Figure 2.  
The precision geodetic survey includes a plane control network and an elevation control network. It is used to monitor the horizontal and vertical displacement to determine the displacement direction and law of the surface displacement in different parts of the landslide. There is a total of 21 monitoring points, the accuracy of which is of a second order. In the borehole monitoring, high precision inclinometers (inclination angle accuracy 0.01\({}^{\circ}\)) are used to monitor the lateral displacement of the upper and lower rock mass of the sliding surface. On these grounds, the location and displacement of the sliding surface can be determined. There are a total of 8 borehole monitoring points on the landslide body. Among them, 6 points are located in Block 2.  
Figure 2: Geological section of the landslide (modified from [PERSON] and [PERSON] 1991; [PERSON] 2002).  
A footprints is a good geology window for deeply analyzing landslides. Several precision instruments including a rock mass deformation instrument (accuracy of 0.05 mm), clinometer (inclination angle accuracy of 0.01\({}^{\circ}\)), displacement meter (accuracy of 0.1 mm), and triangulated water weir are fixed in the footprint. Using these instruments, the deep deformation and groundwater level change of the landslide body can be clearly observed. Footfill #5 is located above the elevation of 1220 m and is a long-term monitoring point during the reservoir operation.  
The meteorological observation of the landslide uses a 110-WS-16 automatic meteorological instrument, which can provide data on the rainfall, temperature, and wind speed for a landslide.  
Initially, the monitoring frequency of the items is mostly once per week. When the reservoir water level fluctuates sharply and abnormal conditions occur, the monitoring frequency is appropriately increased. Thus far, the monitoring frequency of most items has been once a month.  
The system has been checked and serviced regularly since 1998. To date, the system has recorded a considerable amount of comprehensive and real-time observation data concerning the change in movement of the landslide body and its influencing factors.  
Figure 3: Plan of the landslide and its monitoring points.
###### Abstract  
Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.  
blast-induced ground vibration; random forest; Harris hawks optimization; Monte Carlo simulation; sensitive analysis +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Analysis
### Normalization of Data  
In the process of data collection, incomplete data are often encountered. To ensure data integrity and improve the prediction accuracy of the model, this study employs interpolation methods to address missing values. Simultaneously, to simplify model computation and enhance efficiency, the min-max normalization method is applied to scale the data to the range of [0, 1]. The normalization formula is as follows:  
\[X^{\prime}=\frac{X-\min}{\max-\min} \tag{28}\]  
Here, the maximum and minimum values in the sample data are denoted as \(\max\) and \(\min\), respectively.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 2 Green Energy Utilized in WWTPs
### Wind Energy  
Wind energy, a significant part of green energy, is kinetic energy produced by air flow under solar radiation. Wind energy is widely distributed and abundant in reserves. It is especially important for islands and areas that are far away from the main power grid. For the poor stability and randomness of wind energy itself, it is difficult to use it alone for the power supply of WWTPs. To make up for the shortcomings of wind energy and solar power generation, wind power is mainly combined with solar energy in WWTPs under the current technical conditions, which can realize wind-solar complementary power generation to supply energy to WWTPs [58; 59]. This technology has been widely used in the field of wind energy utilization [60; 61].