esa-sceva/satcom-chunk-collection huggingface dataset:
Huge amount of information about an individual is collected and distributed. Individuals generally need to restrict the details of personal information. Therefore, countermeasures for privacy threats have to cover both needs: enable data collection and restrict the storage of some private parts. Network and database communities approach the privacy problem from different aspects. The network community mostly thinks that hiding sender and/or receiver in network communications is the prominent privacy problem. Privacy threat models presented by this community are based on concealing communication profiles against traffic analysis attacks. These attacks focus on external threats like global or local eavesdropping. On the other hand, database community uses \"data\" per se as the subject of privacy. They bring solutions for privacy preserved storage and sharing of data. However, privacy models are not suitable for the actual needs of network applications, where data is gathered from users and relayed to data collectors. There are some models that fully trust data collection parties, which are not very realistic in most of the cases. There are some other models that consider data collector as an un-trusted entity. However, in such models, privacy preservation is provided by sending perturbed data to the data collector, which limits the types of analysis that can be performed by data collector. Also, data collection models do not meet the requirements of having many data collectors with different privacy levels, which may be the case in a WSN. This issue has to be dealt by WSN designers. In most of the WSNs, minimization of energy consumption is one of the primary criteria due to limited battery capacity or unavailability of battery replacements. All other security countermeasures as well as the privacy preserving solutions have to perform their works with minimum energy. In this study, privacy preserving data collection framework is proposed for WSNs. The framework is based on a network model which has multiple un-trusted sinks. Privacy requirement level of each sink is assumed to be different from each other, which can be a realistic scenario in recent WSN applications. Our proposed framework meets all the privacy requirements while consuming low amount of energy. This paper is organized as follows: In Section 2, motivation of the study and some background information are given. This section also includes the description of threat/network model and statement of our contributions. Section 3 discusses the details of proposed anonymization method. Section 4 shows the experimental results of simulations. Literature review of the topic is presented in Section 5 Section 6 concludes the study.
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Deep-Learning-Based Fault Diagnosis Method
#### 5.3.1 Autoencoder (AE)  
An AE is an unsupervised learning neural network that uses the back-propagation algorithm to make the output value equal to the input value [116], which can effectively extract low-dimensional data features. Encoding and decoding are the two components of an AE. The encoder compresses the input into a representation of latent space, and the decoder reconstructs the information from the latent space representation. With the advent of deep learning, researchers have created additional AE model types. For instance, to extract abstract features from data, several AEs are stacked to form a Stacked Autoencoder (SAE), and a Denoising Autoencoder (DAE) was developed to improve the anti-noise capability of neural networks [117].  
Under the premise of training on small data samples, AE can achieve efficient fault diagnosis when combined with other classification methods [118]. The use of AE for fault diagnosis has received considerable attention in recent years. [PERSON] et al. [119] designed an AE-distributed fault diagnosis system with only three layers of the network, which was used for fault diagnosis in sensors, and the model was trained in the cloud to address the issue that deep learning methods consume a great deal of computing and communication resources. [PERSON] et al. [120] proposed a normalized sparse AE model for intelligent fault diagnosis, learning various meaningful features from the input signal. The translation invariant features were obtained in the feature layer, and then the fault was identified in the output layer, and the accuracy rate of the model test reached 99.92 percent. In addition, due to the model training phase, AE automatically extracts non-fault-related features, which hinders fault diagnosis performance. [PERSON] et al. [121] proposed a supervised SAE model capable of extracting fault-related deep features and configuring fine-tuned network initial parameters. This method improved classification precision with fewer iterations. The local minimum problem exists in the traditional AE method for fault diagnosis. Through batch regression, hyperparameter optimization, and other techniques, the model can be made to closely resemble the actual working conditions of the sensor, which is advantageous for resolving this issue. [PERSON] et al. [122] propose a two-stage fault diagnosis method that uses an LSTM autoencoder to perform a separate fault detection stage in advance. This method can effectively catch rare faults.  
AE has the advantages of strong learning ability, simple structure, and easy training. However, traditional AE only uses a single-layer encoder for feature extraction, which struggles to extract deep features and has limited data processing capabilities. As the modern agricultural production process gradually presents the characteristics of modularization and specialization, it is suitable for application in distributed fault diagnosis and processing small samples of univariate data.
## 2 Materials and Methods
### Data Acquisition and Processing  
The NDVI data used in this study were derived from the GIMMS-NDVI 3g.v1 time series dataset, which was based on an advanced very high-resolution radiometer (AVHRR) and currently ranges from 1981 to 2015 with a spatial resolution of 8 km and temporal resolution of 15 days [18]. Using MATLAB software, the semi-monthly original data were first transformed to monthly data by calculating the maximum value in each pixel and then the monthly maximum data were transformed to yearly data by calculating the mean value of 12 months in a year.  
NPP data were acquired from the global annual summed GPP/NPP dataset generated by the revised EC-LUE model, which covers a period from 1980 to 2018 with a spatial resolution of 0.05\({}^{\circ}\)[19; 20].  
The annual mean temperature (MAT) and precipitation (MAP) were derived from the dataset of 1 km monthly mean temperature and precipitation dataset for China (1901-2017) ([[https://data.tpdc.ac.cn/zh-hans/](https://data.tpdc.ac.cn/zh-hans/)]([https://data.tpdc.ac.cn/zh-hans/](https://data.tpdc.ac.cn/zh-hans/))) (accessed on 22 May 2021). The dataset was spatially downscaled from CRU TS v4.02 with WorldClim datasets based on the delta downscaling method. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for investigations related to climate change across China [21].  
All of these data were resampled to a spatial resolution of 10 km with the Albers_WGS_1984 coordinate system using ArcGIS 10.5 software.  
Figure 1: The study area. I-VI represents northeast China (NEC), north China (NC), east China (EC), northwest China, southwest China (SWC) and central south China (CSC), respectively.
## 3 Study Areas and Data Process
### Data Introduction  
In this study, 103 scenes of InSAR data were used, including 9 scenes of X-band descending orbit TerraSAR images (2020-2021) and 94 scenes of C-band Sentinel-1A images (2016-2022). The detailed parameters are shown in Table 1. During the InSAR data process, DEM and precise orbit information can improve the accuracy of the time series deformation results. Thus, the terrain phase was removed by using SRTM DEM with a resolution of 30 m provided by NASA ([[https://dwtkns.com/srtm30m/](https://dwtkns.com/srtm30m/)]([https://dwtkns.com/srtm30m/](https://dwtkns.com/srtm30m/)), accessed on 30 June 2023), and the precise orbit data released by ESA were used to refine the baseline of the data image and reduce the orbit error ([[https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)]([https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)), accessed on 30 June 2023).  
The land use type and water system are also useful for risk assessment. The detailed parameters of other data are described in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Sentinel-1A** & **TerraSAR-X** \\ \hline
**Wavelength** & C & X \\ \hline
**Polarization mode** & VV & VV \\ \hline
**Orbit direction** & Ascending & Descending \\ \hline
**Incidence** & 39.28\({}^{\circ}\) & 42.8\({}^{\circ}\) \\ \hline
**Image number** & 94 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the InSAR data.  
Figure 1: The scope and data of the experimental study. The experimental data cover 15 districts of Shanghai city.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hazard Indicators** & **Data Sources** & **Vulnerability Indicators** & **Data Sources** \\ \hline Type of land use & Global land cover data official website & Population density & \\ \cline{2-3} Water system density &
\begin{tabular}{c} ([[http://www.globallandcover.com/](http://www.globallandcover.com/)]([http://www.globallandcover.com/](http://www.globallandcover.com/)), \\ accessed on 30 June 2023) \\ \end{tabular} & GDP per unit area & Shanghai Statistical Yearbook 2022 \\ \hline \multirow{3}{*}{Vertical velocity} & \multirow{3}{*}{SBAS-InSAR results} & Green space density & \\ \cline{2-3}  & & Road density & OpenStreet’s official website \\ \cline{1-1} \cline{2-3} Subsidence & & Building density & ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)),  accessed on 30 June 2023) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Other data sources.
## 2 Materials and Methods
### Intervention  
The whole therapeutic intervention was characterized by the combination of US-guided RF genicular ablation and a rehabilitative physical exercise protocol.  
After enrollment, all patients underwent RF with selective ablation of the supero-lateral, supero-medial, and infero-medial geniculate nerve. A US machine (Esaote Myd-LabSix, Genoa, Italy) was used to identify the genicular nerves that travel together with the genicular arteries in the neurovascular bundle. A 20 G specific needle was introduced percutaneousously, and the correct positioning was confirmed by US guidance. A sensory test with 50 Hz and a motor test with 2 Hz was performed using an RF generator (Cosman G4, Burlington, MA, USA) to confirm the correct positioning of the cannula. The procedure was performed by the same expert physician under complete aseptic conditions. The nerve was anesthetized using a local anesthetic (2 mL lidocaine 2%) and a conventional RF thermoagulation was performed with a temperature of 90 \({}^{\circ}\)C for 180 s [16]. The procedure was reproduced for each targeted nerve. Acetaminophen 1 g and topical cryotherapy were allowed if patients experienced pain in the treated region the day after the procedure. Moreover, peri-procedural and late complications were recorded.  
All study participants started a 2-week physical therapy beginning the day after the procedure. All patients underwent five daily sessions per day for 2 weeks, supervised by an expert physical therapist. Each session lasted 30 min and consisted of three phases:  
* Phase I (lasting 5 min): short warm-up including static and dynamic stretching exercises for knee flexor and extensor aimed at adapting the musculoskeletal system to the load and reduce the possibility of injuries.
* Phase II (lasting 20 min): resistance training characterized by isometric and isotonic exercises, including three free weight exercises (single leg raise, single leg extension on the chair, hip thrust), performed in 3 sets of 20 repetitions daily. Time under tension was set between 3 and 5 s in both concentric and eccentric contraction, while isometric contraction was set for 5 s. A one minute rest period was performed between each set.
* Phase III (lasting 5 min): cooling down including dynamic stretch exercises. After the exercise intervention, all patients were strongly encouraged to continue the training at home. Figure 1 depicts a study participant undergoing the physical exercise program.  
Figure 1: Physical exercise performed by a study participant.
# Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles
## 2 Materials and Methods
### Path Generation  
In order to reach a set target point, it is necessary to create an optimal path. The A* algorithm is used for path generation [32; 33]. A weight is assigned to each cell of the grid according to the result of classifying the free space in which driving is possible and the space which cannot be driven in, and according to the distance from the target point. The weights of cells adjacent to the current location of the vehicle are calculated, and a path is generated to the cell with the smallest weight, that is, the lowest cost. By repeatedly executing this process, it is possible to generate the shortest optimal path considering the collision risk with obstacles.  
As shown in Figure 6, when the weight of the cell \(P_{1}\cdots P_{8}\) is calculated at the current position \(P_{0}\), if the cell with the lowest weight is \(P_{3}\), the path is created in the direction from  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Relative Speed of the Obstacle (S)** & **Possibility of Driving** \\ \hline No obstacles & CA \\ S \(\geq\) 0 & A \\ -(vehicle speed) \(<\) S \(<\) 0 & UA \\ S \(<\) -(vehicle speed) & CUA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Driving possibility according to object speed.  
\(P_{0}\) to \(P_{3}\). In the next step, the weights of adjacent cells are calculated based on \(P_{3}\), and then a path is created.  
The weight for each divided area is calculated according to the calculation constant (\(u\)) in Table 3. The weight is the product of the linear distance to the target point multiplied by the calculation constant u. In the region where there is a high possibility of driving without obstacles, the smallest value is 1, and when the relative speed of the obstacle is greater than or equal to 0, the weight is 2. In the area where the relative speed of the obstacle is less than 0, but it is running in the same direction as the vehicle, the calculated constant value is 3, and the area where the obstacle is running in the opposite direction to the vehicle is set to 4.
## 2 Materials and Methods
### Data Extraction and Analysis  
To study the potential of C-band InSAR data for monitoring crop growth and detecting crop field operations (e.g., seeding or harvest), SAR data, including averages of the backscattering coefficient in VV and VH polarizations, and the VV interferometric coherence, were extracted at the field level within polygons of field boundaries from all SAR acquisitions throughout the season. To prevent interference from headland and different land cover or crop type of neighbouring fields, the polygons were shrunk inward by 50 m using the ArcGIS system software (Esri, CA, USA). It is assumed that the fields were under uniform management condition at any time of SAR image acquisition, although occasionally this may not be true. A dataset consisting of time-series field-level SAR data was then establishedfor visual interpretation and algorithm development to determine the dates of field activities such as seeding and harvest.
# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
## 4 Case Studies Using the Proposed System
### Experimental Comparison and Result Analysis
As aforementioned, the Four-opamp double second-order high-pass filter circuit fault diagnosis dataset has 26,000 samples, and each sample size, i.e., the input size of each network, is 1 \(\times\) 300. We also take 70% of data as the training set and the test data as the testing set. Table 5 shows the comparison results of Four-opamp double second-order high-pass filter circuit fault diagnosis. It is easy to see from the table that the average fault diagnosis accuracy of the RNN network is 92.13%, which is much lower than the other four models. This is because the RNN network is prone to gradient dispersion and gradient explosion. The average fault diagnosis results obtained by BPNN, LSTM and 1 DCNN are close to about 99%, while the performance of the LSTM network is slightly better than that of the BPNN and 1 DCNN networks. The diagnostic accuracy achieved by MSCNN-SK is higher than the other four network models, reaching 99.87%. At the same time, the standard deviation is also the smallest among the five test methods. This shows that the MSCNN-SK network can effectively extract the essential characteristics of the fault. A good fault diagnosis effect can also be achieved in complex circuit fault diagnosis.
## 1 Introduction
It is, therefore, an approach not used thus far in the post-earthquake diagnosis of cities and also benefits greatly from the major development experienced in recent years by GIS (Geographic Information System) analysis computer programs, which today enable the implementation of accurate analysis tools that were not available just a few years ago. In this article, the geostatistical approach will be put into practice with the assessment of the Lorca earthquake that occurred in Lorca, Spain, on May 11, 2011. This earthquake has been analyzed from different points of view in recent years [11], giving answers to questions such as the influence of the geology of the area on the earthquake [14], the impact of the shallow focal depth [15], or the effect of some factors related to the configuration of the urban plot and building design [16]. This situation has given rise to numerous theories regarding the causes of the major damage from the earthquake, although none of these theories has enabled the development of a comprehensive risk mitigation strategy in the city. The geostatistical approach will spatially determine the existing correlation between variables of different fields of knowledge and the damage detected. The analysis through different statistical tools and GIS indicators will help us to rank the importance of each one of the different phenomena detected in 2011 in the post-earthquake scenario of the city.
## 3 Determining the Pressure Wave Speed in the Tested System  
In order to perform calculations on the water hammer in the analyzed flow system, it is necessary to know the pressure wave propagation speed in individual pipes of different diameters. One of the possible ways of determining this speed in the tested cases is based on the use of [PERSON]'s theory [48], which links the first pressure amplitude (maximum increase) with the initial flow velocity and the pressure wave speed:  
\[\Delta H_{1}=\frac{a\cdot V_{0}}{g}=\frac{4a\cdot Q_{0}}{g\cdot\pi D^{2}} \tag{2}\]  
where \(\Delta H_{1}\) is the first pressure amplitude [m wc] (\(\Delta H_{1}=H_{1}-H_{init}\)), \(H_{1}\) is the pressure head of the first pressure rise caused by the water hammer [m wc], \(H_{init}\) is the pressure head in the initial steady-state condition before the water hammer [m wc], \(V_{0}\) is the initial mean flow velocity [m/s], \(Q_{0}\) is the initial volumetric flow [m\({}^{3}\)/s] and \(a\) is the pressure wave speed [m/s].  
Formula (2) is correct for cases where the cut-off of the liquid flow in the pipeline takes place during time \(T_{c}\), which is not longer than the time of the pressure wave circulating forth and back along the pipeline where the quick-closing valve is installed, i.e., in the time equal to half of the period \(T\) of the pressure wave in this pipeline:  
\[T_{c}\leq\frac{T}{2}\quad\rightarrow\quad T_{c}\leq\frac{2L}{a} \tag{3}\]  
where \(T\) is the pressure wave period [s] (\(T=4L/a\)) and \(L\) is the length of pipeline [m].  
For the analyzed laboratory system, condition Equation (3) is met: the valve closing time \(T_{c}\) is about 50 times shorter than the ratio \(2L/a\) for pipeline #2. Therefore, the analysis of the uncertainty of determining the valve closing time, which is very small depending on resolution of potentiometer and data acquisition, is insignificant in relation to the analysis conducted in this paper.  
The main problem with this method is that it requires knowing the exact values of \(Q_{0}\) and \(\Delta H_{1}\) to be able to accurately determine the wave speed \(a\) using Equation (2). As shown in Figure 3, due to high frequency pressure oscillations, the most probable result of the unfavorable effect of vibrations in the structure caused by the abrupt closing of the shut-off valve, it is not possible to determine the value of \(\Delta H_{1}\) with the required precision. Additionally, the determination of the initial flow may be subject to a significant error resulting from the accuracy class of the flow measurement transducer used. Therefore, this paper proposes a method for determining the speed of pressure wave propagation based on the measurement of the timing of the passage of the pressure wave between two cross-sections in which pressure changes were measured during the tests.  
In the later part of the paper, the second method is presented, which uses the frequency of free pressure oscillations measured in the final phase of their damping in the tested pipeline system to determine the speed of the pressure wave. This method was also applied to the analyzed cases of water hammer.
## 3 Case Study (Quseir Road)  
One of Egypt's significant mega projects towards sustainable development and optimal resource utilization is the Golden Triangle Project. The Golden Triangle, illustrated in Figure 7, connects Quseir on the Red Sea to Safaga and Qena cities along the Nile Valley. The project's primary goal is to establish links between mining and industrial zones with the Red Sea while fostering the development of new tourism and industrial communities. The Golden Triangle stands out due to its distinctive geographic, sociological, and geological features. To the west, it is bordered by the Nile Valley and Qena City, providing access to a skilled workforce. On the eastern coastal side, the area is bordered by three major mineral seaports: Abou-Tartour Port at Safaga, Quseir Port, and Hamraween Port.  
As part of the major development initiatives in the region, the existing 160-km-long two-lane two-way Quseir Road, highlighted in yellow and red in Figure 7a, will be upgraded to become a major multilane freeway with a design speed of 110 km/h. This upgrade necessitates substantial improvements and widening of the road alignment, particularly due to the current industrial developments in this mountainous region and the essential movement of cargo between the ports and the Nile Valley. The project focuses on the redesign, rehabilitation, widening, and enhancement of Quseir Road. The case study outlined in the paper specifically focuses on the 90-km complex mountainous segment of the road, delineated in yellow in Figure 7b.  
Figure 7: (**a**) Road Location in Golden Triangle Region; (**b**) DEM Model showing Existing Alignment of 180-km Segment of Quseir Road and 90-km Case Study in Yellow.