esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study Area and Data  
### Study Area  
Shanghai (120\({}^{\circ}\)52\({}^{\prime}\) E-122\({}^{\circ}\)12\({}^{\prime}\) E, 30\({}^{\circ}\)40\({}^{\prime}\) N-31\({}^{\circ}\)53\({}^{\prime}\) N) is located in the easternmost part of the Yangtze River Delta in China. The average altitude is approximately 4 m above sea level. It is at the confluence of the Yangtze River and the Huangpu River. The place borders the Yangtze River to the north and the East China Sea to the east. Shanghai is a typical soft soil area with complex geological structure and large pore ratio. A large number of infrastructure construction can easily lead to abnormal subsidence in the area. Many faults exist in the Shanghai area, but these faults are mainly the early and middle Pleistocene faults and are classified as inactive. Therefore, the geological structure of this area is relatively stable, and it is not easy for major structural changes to occur.  
Shanghai is one of the largest industrial bases in China. Many scientific research and production bases of light and heavy industrial products are located in Shanghai. Shanghai has a population of 24.28 million, thereby making it the second most populous city in China by 2019. The geographical location of the study area in this paper is represented by the area in the red rectangle in Figure 2a, which contains most of Shanghai. Figure 2b shows the backscattering intensity image of Sentinel-1A SAR image that covers the study area, and the measures in east-west and south-north directions are approximately 79.46 km and 78.43 km, respectively. The background shows the Google image that covers Shanghai area.  
Due to its geographical location, Shanghai enjoys convenient transportation, advanced culture, and rapid industrial development. In recent years, many infrastructure and rail transits have been constructed in Shanghai, thereby signifying an uneven subsidence phenomenon. Shanghai has a subtropical monsoon climate with abundant rainfall, and the typical rainy season occurs in September of each year, with approximately 60% of the annual precipitation. The rainfall change has a great impact on regional surface subsidence, and the seasonal variation of subsidence is evident. To alleviate the excessive groundwater in the region, the Shanghai municipal government adopts tap water recharge to maintain the basic balance of groundwater. It has also become one of the measures to alleviate the surface subsidence.  
Figure 2: (**a**) The black area is the municipal area of Shanghai, including Baoshan (BS), Jiading (JD), Qingpu (QP), Minhang (MH), Pudong New Area (PD), Songjiang (SJ), Jinshan (JS), and Fengxian (FX); and (**b**) Sentinel-1A image intensity map of the study area. The red rectangle depicts the study area of this paper. The white line corresponds to the urban area of Shanghai (SQ); The black lines represent the division of Shanghai.
## 4 Model Predictive Control for Power Optimization and DDPG-Based Optimization Scheduling
### Model Predictive Control for Power Optimization  
The MPC process comprises three key stages: model development, rolling optimization, and feedback correction. The model includes four state vectors, two input vectors, and electrical power as the output vector, designed to optimize power consumption [21].  
MPC is an advanced control strategy extensively applied in fields such as process control, automation, and robotics. Its core principle involves utilizing the system's mathematical model to predict future system behavior. Optimization algorithms are then employed to determine the optimal control inputs that ensure the system achieves peak performance while adhering to specified constraints.  
In the context of UAV operations, MPC leverages flight state parameters to predict and optimize power consumption. The detailed process is illustrated in Figure 4.
## 2 Potential Model Improvement Directions
### Define and Collect New Relevant Input Attributes  
Additional relevant input attributes may be the key to increased modeling accuracy. They can be defined in different ways, come from different sources, and be different in their physical nature, e.g., laboratory analyses of pollution deposit samples, database of main polluters such as the National Emission Information System (NEIS) [16], recordings of acoustic emissions of polluted insulators, visual information extracted from satellite images, etc. Laboratory analysis of collected pollution deposit samples is the standard and established method, but is also very time-consuming and costly. Therefore, cheaper and simpler alternatives of comparable quality are very much needed. One extremely useful source of data that comes virtually free is satellite imagery. Since we already possess quite extensive satellite imagery from Sentinel-2 satellites, we chose to use it for this purpose. Our main motivation is that abrupt color changes in the vicinity of our field measurement locations should correlate well with agricultural and other invasive interventions, such as plowing, sowing, building, or surface mining activities, which tend to increase local pollution levels.
# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## 4 Results
### Receiver Operating Characteristic (ROC)
#### 4.5.1 ResNet-50  
Test  
The area under the curve (AUC) values for the test set across different classes and folds demonstrate the consistently high performance of the ResNet-50 model in classification tasks, as shown in Figure 13. Across all folds, the AUC scores for each class, including Moderate, Severe, and Normal, consistently exceeded 0.98, indicating strong discriminative power and robustness in distinguishing between different severity levels of sinus pathologies. Notably, the Severe class consistently exhibited the highest AUC scores, often surpassing 0.99, suggesting that the model excels particularly in identifying severe cases with high confidence levels. These results underscore the effectiveness of the ResNet-50 model in accurately classifying sinus pathologies based on severity levels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Train** & **Test** & **Valid** \\ \hline \multicolumn{4}{c}{Loss} \\ \hline ResNet & 0.111 & 0.174 & 0.297 \\ \hline ResNeXt-50 & 0.088 & 0.180 & 0.285 \\ \hline \multicolumn{4}{c}{Accuracy} \\ \hline ResNet & 96.448 & 95.210 & 91.154 \\ \hline ResNeXt-50 & 97.047 & 94.961 & 91.154 \\ \hline \multicolumn{4}{c}{Precision} \\ \hline ResNet & 0.965 & 0.954 & 0.913 \\ \hline ResNeXt-50 & 0.971 & 0.951 & 0.917 \\ \hline \multicolumn{4}{c}{Recall} \\ \hline ResNet & 0.964 & 0.952 & 0.912 \\ \hline ResNeXt-50 & 0.970 & 0.950 & 0.912 \\ \hline \multicolumn{4}{c}{F1-score} \\ \hline ResNet & 0.964 & 0.952 & 0.912 \\ \hline ResNeXt-50 & 0.970 & 0.949 & 0.913 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Models’ results in 5-fold cross-validation.  
Validation  
Similar to the test set, the validation set's results also demonstrate the strong performance of the ResNet-50 model in terms of AUC values across different classes and folds, as shown in Figure 14. The AUC scores for each class consistently remained above 0.97, reaffirming the model's ability to generalize well to unseen data and maintain high discriminative power. Once again, the Severe class exhibited the highest AUC scores, underscoring the model's proficiency in identifying severe sinus pathologies with high confidence levels. These findings highlight the robustness and reliability of the ResNet-50 model in accurately categorizing sinus pathologies based on severity levels, making it a valuable tool for medical diagnosis and decision-making.  
Figure 14: ROC curves of each fold for the ResNet-50 model—validation data.  
Figure 13: ROC curves of each fold for the ResNet-50 model—test data.
## 4 Slope Excavation Stability Analysis
### Analysis of the Influence of the Goaf Collapsed Area
#### 4.2.3 Sensitivity Analysis of Material Parameters in Collapsed Mining Face  
The sensitivity analysis method is an uncertainty analysis method, which studies the degree of influence of a factor change on one or a group of key indicators from the perspective of quantitative analysis. Its essence is to explain the size law of key indicators affected by these variables by changing the values of related variables one by one. The sensitivity calculation formula is as follows:  
\[S_{i}=\frac{m}{M} \tag{4}\]  
In the above formula, \(m\) is the relative change rate of the slope stability coefficient corresponding to the influencing factor \(i\); \(M\) is the relative change rate of influencing factor \(i\). The larger the \(S_{i}\) is, the greater the sensitivity of the influencing factor \(i\) is, and the greater the influence of this factor on slope stability.  
From the analysis in Section 4.2.2, it can be seen that the existence of the collapsed mining face is the main reason that affects the stability of the slope. According to the material parameters of rock mass in the collapsed mining face, the models shown in Tables 9-12 are established, respectively, to analyze the sensitivity of material parameters such as elastic modulus, cohesion, internal friction angle, and bulk density to assess the relative importance of input variables of rock mass material parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model No.1** & **Model No.2** & **Model No.3** & **Model No.4** \\ \hline Cohesion/Kpa & 7 & 14 & 21 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Different cohesion value models.  
Figure 9: Collapsed mining face exposed to the side slope.  
The above different material parameter models are calculated, and the other parameters remain unchanged during the calculation. The calculation results are shown in Table 13. It can be seen from the table that the cohesion, internal friction angle, and unit weight will affect the calculation results of the slope stability coefficient among the material parameters of the slope collapsed mining face, while the elastic modulus of the rock mass has little effect on the calculation results.  
According to the sensitivity calculation formula, the sensitivity of elastic modulus, cohesion, internal friction angle, and unit weight is calculated, and the calculation results are shown in Table 14. According to the calculation results, among the four material parameters, the sensitivity relationship is internal friction angle > cohesion > unit weight > elastic modulus; that is, the influence of internal friction angle, cohesion, unit weight, and elastic modulus of the collapsed mining face on slope stability decreases in turn. Within the normal fluctuation range, the greater the internal friction angle and cohesion of the collapsed mining face, the more favorable it is for slope stability, and the smaller the unit weight of the collapsed mining face, the more favorable it is for slope stability.
# Smart Parking: A Literature Review from the Technological Perspective
## 4 Discussion
### Software Solutions  
After finishing the review of all the retrieved research papers, as shown in Figure 8, it was noticed that 51% of the reviews did not mention any kind of solution, as most of them just focused on sensing methods and different algorithms used for smart parking.  
Among those that contain a detailed description of how these were implemented, only a 45% have a detailed explanation of how parking information was handled, being the classification with  
Figure 8: Software Solutions of smart parking.  
Figure 7: Camera usage.  
most samples. Almost all of these research papers explained the modules and structure that raw data had to pass through from sensors up until how that resulting information was presented to final users. Also, some of them mentioned that a cloud storage is an important factor for Smart Parking implementations, as shown in [42], because stored information would be accessed by users via the Internet.  
The most used software solution is Information Management with 38% among the revised papers. From the reviewed work it can be seen that the proposed or designed software solutions are looking forward to handle information rather than providing a whole set of services as in E-Parking solutions.  
The second most mentioned classification is E-Parking, where most solutions focus on the interaction between users and parking slot reservations, and one of the main issues is that users could go after the same space as mentioned in [56]. We also found that the information is presented through this medium but the interaction that users can have with it is not shown in great detail apart from the visualization and reservation of the parking spaces.  
Finally, implementing algorithms for prediction of reservation and assignment of parking spaces only reaches ~11% of the research papers.
## 5 Implementation and Deployment
### Graphical User Interface  
There are two routes for implementing a GUI: application and web development. Application development refers to installing a program on a local computer and utilizing hardware resources to enable the implementation of more sophisticated functionalities. However, this requires higher development costs, has poorer environment compatibility, and leads to a relatively more challenging deployment and maintenance. On the other hand, web development decouples the front end and back end and allows access through a web browser, offering better cross-platform compatibility and user-friendliness. Web development is an excellent choice to achieve GUI.  
Abandoning traditional web development methods (HTML, CSS, JavaScript), the MERN stack [27], which consists of four key technologies--MongoDB, ExpressJS, ReactJS, and NodeJS--has gained more traction as it allows for smooth development and faster deployment. However, due to its short development time, MERN needs to be further developed and the key technologies must be improved. Considering the small size, low complexity, and high flexibility required for its development, the front-end and back-end architecture are also a good choice for web interface development.  
Within the domain of front-end development, frameworks like Vue.js, Angular.js, and React.js have garnered substantial acclaim among developers for their ability to streamline the development process. Vue.js, a progressive JavaScript framework for building user interfaces, follows a data-driven and component-based paradigm, providing excellent flexibility and user-friendliness. Notably, Vue.js excels in the realm of lightweight framework development.  
Regarding back-end development, popular frameworks such as Django, Flask, and FastAPI come to the forefront. Django, in particular, stands out as one of the most widely embraced and extensively utilized frameworks. Its primary objective is to facilitate the swift and straightforward development of database-driven websites. In comparison, Django is a feature-rich framework that offers an array of built-in functionalities and extensions, making it well-suited to large-scale applications.  
Django is famous for its security and scalability, while Vue.js has a low learning curve that attracts the developer community [28]. Skin cancer detection interactive applications [29], the Apache Airavata science gateway middleware project [30], and the public health inspection application [31] have used Django and Vue.js to build their systems.  
In conclusion, we selected the Vue.js and Django schemes to implement the GUI.
# Subwavelength-Cavity High-Gain Circularly Polarized Antenna with Planar Metamaterials
## 1 Introduction
These types of antennas have the advantages of high-gain, ultra-thinness, good circular polarization radiation, simple feed network, and convenience of assembly. In the next section, we describe the design of a high-gain circularly polarized MNM antenna with a simple linearly polarized patch source. All the simulations were performed using CST Studio Suite software. The metamaterial antenna was machined and its radiation performance was measured in a microwave anechoic chamber. Finally, the last section includes some conclusions.
## 3 Results and Discussion
### Combined Effect of the Bit, Shear, and Inertial Forces  
As can be seen in Figure 15, the soil tillage using one spade assembly will result a crankshaft torque by overlapping the loads of the three types of forces.  
To prove the correctness of our model, a comparison between the calculated energy values and the measurements from [8] was made. In this, the energetic need for soil tillage is determined in real conditions: the MSS-140-type spading machine was pulled by U-445-type tractor on loam soil in greenhouse conditions. During the experimental tillage, average advancing speed values were measured, and the associated soil chip thickness was measured. By the results, cases I, V, and IX from Table 1 may be identified. Using the presented Matlab(r) Simulink model, the variation of the crankshaft torque was calculated for the above-mentioned three cases. For the simulation, one further refinement must be made.  
According to Figure 16, the rear limit (_depth_limit_ block-Figure 13) value must be set. The inertial load on the spade is acting from the very first moment of soil displacement (beginning of the soil shear process, at the maximum depth of the spading cycle) until the modulus of the acceleration of the spade decreases. In order to determine this limit  
Figure 14: Effect of inertial load on the crankshaft torques using working depth of 0.28 m and machine advancing speed of 0.35 m/s (first cycle presented).  
Figure 15: The overlapping effects of the bit, shear, and inertial loads on the crankshaft torques using working depth of 0.28 m and machine advancing speed of 0.35 m/s (first cycle presented).  
point, the acceleration modulus and the variation of the spade-tip vertical displacement is compared in Figure 16 for three examined cases. As may be observed, regardless of the investigated cases, the acceleration maximum occurs at the depth of 0.22 m, so this value should be used for the rear limit in the _depth_limit_ block.
## 3 Our Proposed TCDE Scheme
### Exploring Topographic Profiles by Using Niching
#### 3.3.4 Searching for Peaks  
In this step, a more detailed search of the obtained niches is conducted. In each niche, the TCDE algorithm runs DE to search for peaks. The DE/current-to-pbest/1 strategy, which is a greedy strategy that can greatly speed up convergence, is employed as the mutation strategy for DE.  
In order to avoid repeated searches, the TCDE algorithm uses the hill-valley strategy (Algorithm 3) on the niche before searching for peaks. Multiple individuals within the niche under investigation and all peaks already explored are uniformly generated. If the fitness of the generated individuals is lower than that of the aforementioned individuals, then it is determined that the current niche range has not reached the peak, and a detailed search is required. In the opposite case, no further search is required. The TS is updated according to the results at the end of the algorithm.  
As shown in Algorithm 4, the input is \(PEAK^{*}\), which is the potential peak set obtained by Algorithm 2. The TCDE algorithm screens and clusters this peak set to generate niches, where a new population is generated within each niche. A detailed search is conducted within the population to obtain the peak, and the taboo region TS is updated based on this information.  
```
Input:\(niche_{i}\) //The niche to be examined currently \(PEAKS\_found\) //The set of all peaks that have been found Output:\(needs\_search\) //Boolean: true indicates that a detailed search is required; false indicates the opposite.
1 generated_individuals = Generate uniformly distributed individuals between the \(niche_{i}\) and \(PEAKS\_found\) \(min\_fitness\_generated=min(\text{Fitness(individual)}\) for individual in \(generated\_individuals)\) \(min\_fitness\_niche=min(\text{Fitness(individual)}\) for individual in \(niche\_i\_individuals)\(min\_fitness\_peaks=min(\text{Fitness(peak)}\) for peak in \(PEAKS\_found)\) if\(min\_fitness\_generated<min(min\_fitness\_niche,min\_fitness\_peaks)\)then
2 return true
3 end if
4 else
5 return false
6
7 end if
```  
**Algorithm 3**Hill-valley checkThe TCDE method can be employed to obtain the final result by running the aforementioned Algorithms 1-4 in sequence until the FEs reach MaxFEs.
## 5 Imperfections and Side Channels in Practical CVQKD
### Local Oscillator Manipulation  
As was mentioned above, the presence of the intense phase reference signal, for which the no-cloning theorem does not apply, in the quantum channel is specific to standard CVQKD implementations and opens the way to potential security loopholes. Attacks based on the LO typically involve control of its intensity [75, 76], and so, a monitoring detector at the entrance of [PERSON]'s site is useful in this case. The eavesdropper can also exploit a subtle link between the local oscillator calibration procedure and the clock generation procedure employed in practical setups, such as the one illustrated in Figure 1. In this case, suitable manipulation of the LO leads to an overestimation of the shot noise by [PERSON] and [PERSON], who then underestimate the excess noise present in the system and establish a key under conditions where no key could normally be securely generated [77]. A suitable countermeasure for this attack consists of implementing a rigorous and robust real-time measurement of the shot noise [78]. Another possible countermeasure against this threat is to generate the LO locally in [PERSON]'s lab, and preliminary results have recently been obtained in this direction [79, 80].
# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 2 Methods
### Data Processing and Classification
#### 2.1.1 Support Vector Machines (SVMs)  
SVMs are originally presented as binary classifiers, that assign each data instance \(X\in\mathbb{R}^{d}\) to one of two classes described by a class label \(y\in\{-1,1\}\) based on the decision boundary that maximizes the margin \(2/\|\mathbf{w}\|_{2}\) between the two classes. Generally, a feature map \(\phi:\ \mathbb{R}^{d}\ \Rightarrow\ \mathbb{R}^{p}\) is used to transform the geometric boundary between the two classes to a linear boundary \(L:\ \mathbf{w}^{T}\varphi(x)+b=0\) in feature space, for some weight vector \(\mathbf{w}\in\mathbb{R}^{p\times 1}\)_and_\(b\in\mathbb{R}\). The class of each instance can then be found by \(y=sign\Big{(}\mathbf{w}^{T}\varphi(x)+b\Big{)}\), where \(sign\) refers to the sign function [30].  
The estimation of the boundary \(L\) is performed based on a set of training examples \(x_{i}\ (1\ \leq\ i\ \leq\ N)\) with corresponding class labels \(y_{i}\in\ \{-1,\ 1\}\). An optimal boundary is found by maximizing the margin that is defined as the smallest distances between \(L\) and any of the training instances. In particular, one is interested in constants \(\mathbf{w}\) and \(b\) that minimize a loss-function:  
\[\min_{\mathbf{w},\ \mathbf{b};\ \underline{\xi},\ \underline{1}\ \mathbf{w}^{T} \mathbf{w}+\ C\sum_{i=1}^{N}\xi_{i}} \tag{1}\]  
and are subject to:  
\[y_{i}\Big{(}\mathbf{w}^{T}\varphi(xi)\ +\ b\Big{)}\geq\ 1\ -\ \xi_{i}\ \text{and}\ \xi_{i}\ \geq\ 0,\ i\ =\ 1,\ 2,\...,\ N. \tag{2}\]  
The constant \(C\) denotes the penalty term that is used to penalize misclassification through the slack variables \(\xi_{i}\) in the optimization process.  
The so-called kernel-trick avoids the explicit introduction of a feature map \(\ \phi\) and implicitly allows for the use of feature spaces of infinite dimensionality. A commonly used kernel is given by the Gaussian kernel:  
\[k\Big{(}x_{i}\,x_{j}\ \Big{)}=exp\left(\frac{\left\|x_{i}-x_{j}\right\|^{2}}{2 \sigma^{2}}\right) \tag{3}\]  
where \(\sigma\) denotes the kernel bandwidth. Both \(\sigma\) and \(C\) can be optimized as hyper-parameters in a cross-validation experiment.