esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dataset and Preprocessing  
The dataset information used in this study is shown in Table 1. For ease of the overlay processing, the dataset's resolution was uniformly resampled to 500 m, using ArcGIS 10.7 software.  
Among them, the GPP remote-sensing data are derived from MODIS and PML, with a temporal resolution of 8 days and a spatial resolution of 500 m. GPP_MODIS utilizes the concept of light-use efficiency, which estimates GPP as the product of incoming photosynthetically active radiation, the fraction of the photosynthetically active radiation absorbed by plants, and the actual light-use efficiency of vegetation [32]. The PML-V2 model, building upon previous versions, incorporates vapor pressure deficit (VPD) to further improve accuracy on a global scale [33]. ERA5 is a global land-surface meteorological dataset developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) [34; 35]. Due to its high temporal and spatial resolution and global coverage, the ERA5 dataset is widely used in various climate studies, hydrological simulations, and environmental monitoring [36; 37]. This dataset includes multiple meteorological variables, with a temporal resolution of hourly and a spatial resolution of 0.1 degrees. Furthermore, this paper takes the average temperature and the sum of precipitation and radiation to explore the impact of meteorological variables on GPP. GlobeLand30 is the world's first land-cover dataset with a spatial resolution of 30 m. The land types include forests, shrublands, grasslands, farmland, tundra, water bodies, wetlands, artificial surfaces, bare land, and permanent ice and snow. It is worth noting that we employed a masking approach to extract regions where the vegetation type remained unchanged between 2000 and 2020. This allowed us to investigate the lag effects of different land-use and land-cover changes (LUCCs).  
Additionally, we utilized carbon-flux observation data from the ChinaFLUX Observation Network ([[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/)) (accessed on 17 November 2023)) to assess the accuracy of the GPP estimates for China, derived from the PML_V2 and MODIS products at 8-day and annual scales. Information about the flux stations used in this study is listed in Table 2.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## Architecture of the Real-Time Wide-Area Rain-Rate Measurement System
### NEFOCAST's Space Segment  
The NEFOCAST system uses a bi-directional geostationary satellite network. The link connecting the ISTs to the gateway is used by the ISTs to access the terrestrial infrastructure via the satellite. The forward link transports the traffic generated by remote servers on earth and addressed to the ISTs via the satellite. EUTELSAT 10A (EUT 10A) geostationary satellite is used by NEFOCAST SmartLNBs  
Figure 4: Architecture of the Earth station rain gauge sensor.  
for both the return and the forward links. The satellite is located at ten degrees east longitude and provides Ku-band widebeam footprints serving Europe. Table 1 describes the characteristics of the space segment.
# Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate
## 2 Materials and Methods
### Data and Methods for Ionosphere  
Specific data from a three-spacecraft Swarm mission has been used, which marks the status of art for monitoring the Earth's magnetic field from space [76; 77]. As of 17 April 2014, Swarm Alpha (SW-A) and Swarm Charlie (SW-C) are located at 460 km altitude. Swarm-Bravo (SW-B) travels in an orbit placed at 510 km altitude with an inclination of 87.8\({}^{\circ}\). In the current study, 1 Hz magnetic field data was used from Swarm satellites, utilising the vector field magnetometer (VFM) data. Original data (selected magnetic satellite product name: MAGX_LR_1B) were provided in local and the North-East-Centre (NEC) coordinate reference frames. The key criteria for anomaly selection are: (1) its occurrence during geomagnetic quiet time to exclude external sources of ionospheric disturbance comes out of it, so the quietest days in the month have been utilised ( | Dst l index less or equal than 20 nT and ap index less or equal than 10 nT). (2) The satellite must pass over the location of the epicentre in the Dobrowolsky area between 6:00 p.m. local time and 6 a.m. [44]. Nighttime was chosen to avoid disturbing daily ionospheric activity. Dobrovolsky's field is considered to be the best approximation of the earthquake preparation field, where it is commonly used by scientists [44; 51; 78]. Analysis of the Magnetic data from the SWarm (AMSW) algorithm [75] is applied to the Swarm magnetic data. The steps of the AMSW algorithms are as follows: First, we confirm that the data are in the quiet time of the day. Next, we extract the data from the original 1 Hz time resolution common data format (CDF). Next, we calculate the corresponding local time (LT) using (LT = UT + longitude/15). Then, convert geographic latitude to geomagnetic latitude. The VFM data represent the total magnetic field, which consists of crustal and external fields. Consequently, we used the CHAOS-6 model [79] of the Earth's geomagnetic field to get the core, crustal and external components. To identify more information from the data, the first-time derivative and the cubic spline function are applied to the residual data. The anomalous signals, the object of discussion, were marked with red dashed ovals.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 2 Problem Formulation
### Measuring the Performance
#### 2.4.3 Model 3  
A final third model of the efficiency is proposed, and it is based on the time the team of agents need to complete the search. The ideal performance would be reached if the search area was completely observed without observing any of the discretization cells more than once and if during the search process, no part of the agent's footprint is outside the search area. If this were the case, the time needed to observe a specific area would be:  
\[t_{i}=\frac{A_{o}-A_{i}}{2R_{f}\varphi_{n}N_{a}} \tag{12}\]  
where \(A_{i}\) is the observed area at \(t=0\) and \(A_{o}\) is the actual observed area at the end of the mission. Note that in some missions, depending on the search area per agent, the footprint sensor radius and the configuration of the algorithm, it may be \(A_{o}<A_{s}\) if the available energy is limited. If this is the case, we consider that the search is not completed. If a group of \(N_{a}\) agents needs a time \(t_{n}\) to completely observe that area, we can define the efficiency as:  
\[E_{3}=\frac{t_{i}}{t_{n}} \tag{13}\]  
where \(E_{3}\in(0,\ 1)\). Note that reaching an efficiency equal to one, i.e., \(t_{n}=t_{i}\), is not possible since the footprint invades part of the adjacent cells, and therefore, when flying over the cells at the boundaries, part of the sensor footprint would be outside the search area, implying a loss of efficiency. It is also very likely that some of the discretization cells are observed more than once. Probably if we chose the search cells so that the footprint is inscribed inside them, the efficiency would increase. However, the corners of the search area would never be observed.
# A Survey of Defensive Measures for Digital
## 3 The Scope: Examples of Digital Persecution in the Global South
### Pakistan  
Pakistan is a large, Islamic traditionalist country that has struggled with internet adoption. A 2013 publication by [PERSON] describes issues surrounding internet adoption, surveillance, and censorship in Pakistan [28]. There is a significant gap between urban and rural internet access, with rural neitzens representing only 3% of Pakistan's internet users. Many users still rely on dial-up and mobile phone connectivity. The Pakistani government's heavy-handed approach to internet regulation,Haque argues, is one reason for this hampered development. The Pakistani Telecommunication Authority (PTA) oversees most og the matters involving the internet, including regulation, licensing, and censorship.  
The Pakistan government employs sweeping powers to surveillance citizens while using internet technology. Most of Pakistan's internet traffic is routed through a single backbone, called the Pakistan Internet Exchange (PIE). This makes monitoring a simpler task as most user traffic is funneled through a single point. The use of virtual private networks (VPN) and encryption are illegal, and ISPs are required in order to retain records of traffic for 90 days, further simplifying the state's surveillance efforts. At least one command and control (C&C) server for FinFisher spyware has been found on the government's network. This is proprietary spyware that takes advantage of common unpatched vulnerabilities.  
The PTA is incredibly censorious, and it will block entire websites hosting content deemed as offensive, even if the content is only hosted on one part of the site. It has been estimated that the PTA blocks anywhere between 20,000 and 40,000 websites, but the true number is probably much higher. In 2012, the PTA sought to purchase filtering software from Netsweeper, a Canadian firm, which can categorize 10 million new websites each day. Blocking can be done at the ISP level, and ISPs that fail to comply with the PTA's blocking requests risk losing their license. Further, the censorship decision-making process is not transparent and website blocking appears arbitrary.  
At least three different blocking methods are used: first is DNS level blocking, wherein the users are given the impression that a requested page does not exist. Second is HTTP level blocking, where users are redirected to a warning page. Third is forged HTTP response injection, wherein the user receives a packet with an injected warning message, and the connection with the blocked site times out [7].
# Research on Risk Assessment Method for Land Subsidence in Tangshan Based on Vulnerability Zoning
## 2 Materials and Methods
### Ground subsidence Hazard Assessment Methods
#### 2.2.2 Information Value Method  
Urban ground subsidence is a multifactorial phenomenon, with the contributing factors exerting varying degrees of influence. To accurately assess the risk of ground subsidence, it is essential to calculate the information value of each factor, thereby determining their individual contributions to the occurrence of ground subsidence disasters. The formula for calculating the information value of a single factor is as follows:  
\[I_{(Y,u_{i})}=\ln\frac{P(Y,u_{i})}{P(Y)},i=1,2,\cdots n \tag{7}\]  
where \(Y\) is the ground subsidence disaster; \(u_{i}\) is the \(i\)-th evaluation factor among the influencing factors; \(n\) is the number of influencing factors; and \(P\) is the probability of the occurrence of the disaster.  
The probability of a ground subsidence disaster occurring is determined by the number of ground subsidence events or the area of ground subsidence disasters within the study area:  
\[I_{(Y,u_{i})}=\ln\frac{N_{i}/N}{S_{i}/S},i=1,2,\cdots n \tag{8}\]  
where \(I_{(Y,u_{i})}\) is the information value provided by the indicator (\(u_{i}\)) for ground subsidence disasters; \(N_{i}\) is the number of grid cells with ground subsidence disasters within the range of the indicator (\(u_{i}\)); \(N\) is the total number of grid cells with ground subsidence disasters in the study area; \(S_{i}\) is the number of grid cells with the indicator (\(u_{i}\)); and \(S\) is the total number of grid cells in the study area.  
By calculating the information value of each factor, we can assess its relative importance in contributing to ground subsidence disasters.
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 3 1 \(\times\) 2 Array Antenna
### Design Methodology  
As depicted in Figure 5, the single-element antenna is modified to a 1 \(\times\) 2 array antenna where the single feed feeds the power. The antenna elements are kept apart at a center-to-center distance of 0.375A (less than \(\lambda/2\)) to avoid inter-element coupling (\(\lambda\) is computed corresponding to the lowest operating frequency). The spacing between the neighboring elements of the array is optimized progressively to have minimal degradation of its performance. Finally, a value of 0.375A is chosen as an optimal spacing that provides satisfactory gain, scattering parameters, and radiation efficiency. The proposed array antenna has an overall dimension of 26 \(\times\) 50 \(\times\) 0.1 mm\({}^{3}\). The main aim of designing the array is to boost the gain of the single-element antenna. As discussed in the earlier sections, the antenna gain is not high in Sub-6 GHz bands, when compared to the higher frequencies like the millimeter wave bands [2]. This is due to the comparatively higher wavelength of this band than the higher-frequency bands. Figure 5 shows the structure of the suggested 1 \(\times\) 2 array antenna. In the proposed array structure, the power is fed using a corporate feed technique where the power divider involved is optimized to ensure that the same amount of power is delivered to each element to achieve efficient radiation characteristics. The dimensions of the 1 \(\times\) 2 array are tabulated in Table 2. The dimensions of the transmission line width that correspond to 50 ohms (z5) and 100 ohms (z3) are included in the table, calculated using Equation (1).  
Figure 4: Parameter optimization of different antenna design variables: (**a**) width of the feedline, (**b**) ground plane length, (**c**) length and width of the strip, and (**d**) length and width of the ground plane rectangular slot.  
The width of the transmission line of the array antenna can be estimated using the equation to guarantee an equal power distribution [13]:  
\[w_{z_{e}}=\left(\frac{377}{Z_{c}\sqrt{\varepsilon_{r}}}-2\right)h \tag{1}\]  
where \(Z_{c}\) defines the transmission line's characteristic impedance, and \(\sqrt{\varepsilon_{r}}\) defines the relative permittivity.  
The reflection coefficients of the proposed single-element antenna and array antenna are compared in Figure 6. As discussed earlier, the single element has a bandwidth of 3.68-3.85 GHz and 5.84-6.06 GHz. The array antenna, on the other hand, provides bandwidth from 3.59-3.84 GHz to 5.65-5.95 GHz, resonating at 3.75 GHz and 5.76 GHz, respectively. In the array antenna design, there is a slight resonance frequency variation towards the second band due to the alteration of the ground. However, the antenna still operates in the desired frequency range of operation.
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 4 Methods for Assigning Weights to Evaluation Indicators  
### Analytic Hierarchy Process (AHP) Method  
The construction of the judgment matrix is a crucial step in applying the AHP method [52]. The proportional scale method is employed to quantitatively process the importance of each hierarchical evaluation indicator. Here, \(a_{ij}\) represents the importance value of the element in the judgment matrix. After obtaining the judgment matrix \(\mathrm{P}\), a consistency check is performed, and the final weight values are derived [53].  
Step 1: Calculate the Consistency Index (\(CI\)):  
\[CI=\frac{\lambda_{max}}{n-1}. \tag{2}\]  
Step 2: Find the Random Index (\(RI\)).  
Step 3: Calculate the Consistency Ratio (\(CR\)):  
\[CR=\frac{CI}{RI}. \tag{3}\]  
The Consistency Ratio should be less than \(0.1\) for the judgments to be considered acceptable.  
Step 4: Calculate the weight.  
Taking the geometric mean, the formula is as follows, where \(w_{i}\) represents the weight vector obtained by the AHP method:  
\[w_{i}=\frac{\sqrt[n]{\prod_{j=1}^{n}a_{ij}}}{\sum_{i=1}^{n}\sqrt[n]{\prod_{j=1 }^{n}a_{ij}}},i=1,2,\ldots,n. \tag{4}\]
# A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
## 1 Introduction  
Recent advances in high-throughput technologies, along with collaborative efforts such as The Cancer Genome Atlas (TCGA) [1], have allowed for the generation of large datasets that include diverse omic profiles and detailed clinical annotations in extensive sample collections. The unprecedented availability of multiple omics data types (e.g., transcriptomics, proteomics, metabolomics) for the same set of samples, a concept widely known as multi-omics, offers a robust platform for investigating molecular mechanisms underlying diseased phenotypes. In machine learning, it is widely recognized that the combination of various omic sources increases predictive model performance and allows for more reliable biomarker discovery, contributing to understanding the characteristics of complex diseases [2]. However, integrating multi-omics data is fraught with challenges [3], such as data heterogeneity, the inherent noise in biological datasets, high dimensionality, and missing data issues.  
Multi-omics experiments often encounter missing data problems [4], especially when large portions of data are absent from one or more omics sources, resulting in block-wise missing data. For instance, an examination of sample availability across various experimental strategies in TCGA projects shows a significant imbalance, with RNA-seq and whole-slide imaging samples far exceeding those from other omics such as whole genome or whole exome sequencing. As a result, compiling a sufficiently large dataset with complete information across all omics for each individual is often impractical.  
A straightforward solution is to exclude samples with missing values; however, this leads to a significant loss of valuable data. Another approach is to impute missing values, which is a challenging task, especially when the generative process behind the missing data is unknown [4, 5]. To overcome this challenge, we adopted an available-case approach, leveraging distinct complete data blocks and avoiding the need for imputation [6]. To support this approach, we extended the functionality of the bmw R package [6], enabling it to handle regression and binary and multi-class classification.  
In the following sections, we outline our methodological framework for dealing with block-wise missing data (Section 2), provide the gradient expressions of the loss functions required for multi-class classification (Section 3), and expand upon our previous work in regression and binary classification [6]. Finally, in Section 4, we present two real-world case studies that illustrate the advantages of our approach, while Sections 5 and 6 contain the discussion and conclusions, respectively.
## 2 Theoretical Derivation of Short-Code Systems
Therefore, the sequence \(A_{j}(m)\) can be regarded as a sequence of linearly increasing \(\Delta\theta\) in phase, varying according to the sinusoidal law. According to the characteristics of a sine wave, from \(A_{j}(m)\) to \(A_{j}(m+1)\) the effective phase increases by \(<\)2\(\pi\Delta\)/T\({}_{b}\)\(>\)\({}_{2\pi}\), where \(<\)\(>\)\({}_{2\pi}\) represents taking the remainder for 2\(\pi\). Therefore, the angular frequency of \(A_{j}(m)\) is  
\[\omega=\left\langle 2\pi\Delta fT_{b}\right\rangle_{2\pi}/T_{b} \tag{11}\]  
In particular, the sequence \(A_{j}(m)\) is a fixed value when \(\omega\) = 0, that is, \(\Delta f\) is an integer multiple of \(R_{b}\), and \(A_{j}(m)\) = \(\mathrm{Cos}(\alpha+\Delta\theta)\). It can be seen that with the increase in the interference frequency offset, the angular frequency of the interference sequence changes periodically in the range of \([0,\pi\cdot R_{b}]\); in other words, the frequency varies periodically in the range \([0,R_{b}/2]\). This result shows that there is a large difference between the performance of the long-code system and the short-code system.
## 3 Materials and Methods
In this aspect, message delivery performance metrics and recommendations to the user that are made daily are analyzed. In the same sensor system, we implement two types of algorithms: collaborative and cooperative. The initial stage in the formation of a sensor network involves the process of neighbor recognition and acknowledgment. When all the nodes in the network are turned on, they start flooding the network with packets in order to recognize their neighboring nodes and establish communication links with them. This process is essential for the nodes to form their neighbor tables and routing tables, which are used to determine the best path for data transmission within the network. However, during this initial stage, the network may experience unnecessary collisions due to the flooding of these packets. This is because there are no traffic packets yet, which are packets carrying the measurement information from the sensors. As a result, this flood of packets can cause congestion and delays in the network, which can impact the overall performance and reliability of the sensor network. Once the nodes have established their neighbor tables and routing tables, the network enters a stable state. At this point, the network is updated with respect to the control packets and the nodes can start sending information packets carrying the measurement data from the sensors. This stable state allows for the efficient transmission of data within the network, ensuring that the data are delivered to the appropriate nodes without any unnecessary collisions or delays.
## 3 Results of the Parametric Study
### Effect of the pile Spacing Ratio (S/D) on the Behaviour of the Pile Group  
The effects of pile spacing on the pile group behaviour can be examined by the changes in the \(p_{si}\)/\(p_{ii}\) and \(p_{bi}\)/\(p_{li}\) ratios with load levels under different pile spacing ratios, _S/D_. In Figure 12b, the results for a 9 \(\times\) 9 pile group with _S/D_ = 3 and _L/D_ = 20 are presented. To research the pile interaction effect under different _S/D_ ratios, analyses are conducted for the two additional cases with identical geometries and soil conditions but only changing the _S/D_ ratios to 6 and 9. The results are shown in Figure 17a,b for _S/D_ ratios of 6 and 9, respectively. The overall behaviours of the three cases are very different. As previously mentioned, owing to the more pronounced interaction effects along the pile shaft than at the pile base for the case with a closer pile spacing (i.e., _S/D_ = 3), more loads are transferred to the bottom of the pile groups and the group behaves similar to an equivalent rigid block. The behaviours of the pile groups are, therefore, very different from that of the single pile as shown in Figure 12b. As the pile spacing increases to _S/D_ values of 6 and 9 as shown in Figure 17a,b, the shaft interaction effects reduce significantly. As a result, the overall behaviours of the pile groups are closer to that of a single pile. In fact, for _S/D_ = 9, the interaction effect is very small and all the piles in the 9 \(\times\) 9 group behave similar to a single pile. Figure 18 shows that the normalised stiffness of the pile groups varies with different pile spacing ratios (_S/D_) under different pile group configurations of 3 \(\times\) 3, 5 \(\times\) 5, and 9 \(\times\) 9. The effects of the pile spacing on the normalised group stiffness are more significant for large pile groups under low _S/D_ ratios. As the _S/D_ ratios increase, the efficiency of the pile groups increases in response to lower shaft interaction effects [35]. It is interesting to note that the normalised group stiffness values are almost identical regardless of the size of the pile groups at large _S/D_ ratios. Under such conditions, the pile group behaviours are simply the sum of the individual isolated single piles, and the pile interaction effect has little relevance.  
Figure 16: Normalised pile stiffness with different pile slenderness ratios (**a**) single pile; (**b**) 2 \(\times\) 2 pile group; (**c**) 3 \(\times\) 3 pile group; (**d**) 5 \(\times\) 5 pile group; (**e**) 9 \(\times\) 9 pile group; (**f**) 15 \(\times\) 15 pile group.  
Figure 17: Load ratios developed at the pile shaft and the pile base with load level for a 9\(\times\)9 pile group and a single pile (**a**) \(S/D=6\); (**b**) \(S/D=9\).