esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 MVDB Implementation
Furthermore, to maximize the damping efficiency in the MVDB, a mass damping mode with \(\beta\) equal to 0 is adopted for the absorption layer. Therefore, for the proposed method, \(\alpha\) is the key parameter to realize the simulation of the seismic wave field in infinite space. This parameter can be estimated experimentally and depends on the source frequency, the number of absorbing layers, the physical properties of the propagation medium, etc. In this study, an empirical formula for MVDB-based viscous damping (Equation (11)) is derived after extensive numerical argumentation, and the damping scheme is easy to implement and more efficient under the same conditions. \[\alpha_{n}=(2\pi f)^{k}.\left(\frac{\sum_{i=1}^{n}\Delta l_{i}}{L}\right)^{2} \tag{11}\]  
where \(\left(\frac{\sum_{i=1}^{n}\Delta l_{i}}{L}\right)^{2}\) is the primary function, \(\left(2\pi f\right)^{k}\) is the damping amplitude control function, \(f\) is the excitation frequency, \(k\) is the damping amplitude control coefficient, \(n\) is the number of absorbing layers, \(\Delta l\) is the thickness of a single absorbing layer, and \(L\) is the range of absorbing layers (\(L=\Delta l\times n\)). The control parameters of the MVDB determine the absorption effect and the computational cost. The CALM divides the absorbing region into 1-5 layers and continuous layers equally in this study, and the NRB sets the absorbing region into 10 layers. For the reader's ease of reference, the proposed method defines individual absorbing material thicknesses as grid cell dimensions and standardizes them in subsequent comparisons of the calculations. In order to fully consider the \"shock\" problem when the step size is below the lower limit and ensure the accuracy of the solution [53; 54], the grid size is set as follows:  
\[\Delta l=\frac{1}{10}\lambda_{min} \tag{12}\]  
where \(\lambda_{min}\) is the minimum wavelength (i.e., transverse wave wavelength). The working mechanism of the MVDB is shown in Figure 3. This study defines the MVDB as a locally attenuated artificial boundary, which gradually attenuates the outgoing scattered wave energy through viscous damping in the MVDB to ensure that the pulsed wave is not reflected into the analysis area after entering the absorbing layer. In this way, the transmission process of the pulsed wave from the finite domain to the infinite space is simulated. The core idea of the MVDB is to change only the damping value of each absorbing material layer on the boundary to attenuate the outgoing wave energy at the boundary without changing the physical properties of the absorbing layer itself, thus avoiding reflections due to physical differences between the absorbing layer and the analysis area.
## 2 Data and Methods
### Methods
#### 2.2.4 Stability Analysis  
The selection of indicators is crucial for a comprehensive evaluation. In extreme cases, one particular indicator may control the final score completely. However, previous studies have paid little attention to this aspect. Here, we proposed a strategy to test the stability of the model. We deleted each indicator and calculated the mean absolute error of the score before and after subtracting the indicator for each year. Then, we calculated an average of the mean absolute error of these four years, which represents a certain indicator's influence on the final result. Indicators that have greater impacts on the results may be related to their importance (weights) or extreme data (some samples perform extremely well or poorly).
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects  
The future generation of wireless systems, i.e., 6G, is anticipated to possess multiple new specifications, requirements, and potential uses. We looked at 6G from multiple angles based on the following hierarchy: the highest level includes a general discussion of the aspects of communications, from the social, technical, and economic points of view. The medium level presents the main points about the network requirements, such as services, technologies, and research problems. Lastly, we take a look at the network's technical operational improvements, such as modified radio frame structures and altered RA methods, at the lowest level of our approach. This approach we took to describe 6G is further clarified in Figure 4.
# Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation
###### Abstract  
Mining enterprises are widely introducing digital technologies and automation is one of such tools. Granularity monitoring, namely, the size determination of rock mass pieces is a common operational component of the processes that extract minerals by open-pit mining. The article proposes an approach that, in addition to the lump size distribution, makes it possible to estimate the lump form distribution as well. To investigate the effectiveness of monitoring the form of blasted rock mass lumps, the authors conducted experiments in four stages related to the rock condition. They include geological occurrence, explosive crushing, prommelling, and mill crushing. The relationship between these stages is presented and the change in the lumps fragment form is traced. The present article proposes an informational and analytical model of the processes at mining enterprises, extracting minerals by open-pit mining, as well as an algorithm for determining the lumps form and obtaining their distribution in the rock mass.  
digitalization; rock fragmentation; fragmentation; form; blasting Article
Sensors_**2014**, _14_, 15182-15202; doi:10.3390/s140815182  
_Series_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**The Open Service Signal in Space Navigation Data Comparison of the Global Positioning System and the BeiDou Navigation Satellite System**  
**[PERSON] * and [PERSON]**  
Institute of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-6-275-7575 (ext. 63629); Fax: +886-6-238-9940.  
_Received: 17 June 2014; in revised form: 24 July 2014 /Accepted: 15 August 2014 / Published: 19 August 2014_  
**Abstract:** More and more Global Navigation Satellite Systems (GNSSs) have been developed and are in operation. Before integrating information on various GNSSs, the differences between the various systems must be studied first. This research focuses on analyzing the navigation data differences between the Chinese BeiDou Navigation Satellite System (BDS) and the United States' Global Positioning System (GPS). In addition to explaining the impact caused by these two different coordinate and time systems, this research uses an actual open service signal in space (SIS) for both GPS and BDS to analyze their current system performance. Five data quality analysis (DQA) mechanisms are proposed in this research to validate both systems' SIS navigation data. These five DQAs evaluate the differences in ephemeris and almanac messages from both systems for stability and accuracy. After all of the DQAs, the different issues related to GPS and BDS satellite information are presented. Finally, based on these DQA results, this research provides suggested resolutions for the combined use of GPS and BDS for navigation and guidance.  
**Keywords:** Global Navigation Satellite System (GNSS); BeiDou Navigation Satellite System (BDS); Global Positioning System (GPS); navigation data; ephemeris; almanac
## 6 Discussion of the Results
Similar considerations and examples can be given for other combinations of digits. Cross comparison of the overall accuracy of ADR-Det algorithm with Existing License Plate Recognition tools for short distances varies from 82 to 92% for low contrast, dirty, reflected, or deformed images up to 92-97% for clear images. Nevertheless, in most cases, these images include 150-300 dpi 3-channel color images, 7-20 times the size of the toll post images used. This limitation of single channel 96 dpi and 40-65 KB in size toll post images, provided the mean accuracy of the ADR-Det algorithm's detection close to 92% making it a very useful tool for the EGNATIA SA motorway operators. As mentioned by the operators, such an automated tool of at least 85% accuracy, using images taken from their existing infrastructure, shall provide significant statistical results as well as real-time information on the ADR-denoted cargo on the road in cases of catastrophic tunnel incidents. Additionally, the ADR-Det algorithm has the potential to achieve better than 97% if trained using a high-resolution imagery input. The authors have raised these concerns to the motorway operators for further improvements of their toll post cameras or even the acquisition of the author's proposed embedded 8-core ARM ADR-Det implementation, including at least 8 or 16 MegaPixel (MP) camera modules. Regarding HMRT-EF application performance, it achieved a mean processing time of 0.45 sec per photo for its three stages using a 12-core CPU that is real-time efficient and 2.6-3.2 s for a single-core CPU close to real-time efficiency. Furthermore, using 8 or 16 cores makes it possible for the authors' proposed algorithm to be also implemented in portable embedded ARM devices used for real-time detection. However, if this is not possible, the authors also have implemented a cloud-based version of their ADR-Det algorithm. The developed HMRT-EF application tool has also been implemented as a standalone application of the ADR-Det algorithm. In addition, the HMRT-EF application tool also includes an integrated database containing all ADR substances and their UN numbers. This database can eventually be used to query and identify the carried substance offline, or even if the ADR-Det algorithm identifies fewer digits during the ADR-Det stage 3 detection process.
# The Application of Blockchain in Social Media: A Systematic Literature Review
## 4 Methodology
### Eligibility Criteria
#### 4.1.1 Inclusion Criteria  
Blockchain and social media are recent fields of inquiry, and studies have only emerged in recent years; hence, publication date was not used as an eligibility criterion. However, papers were included in this SLR if they satisfied the criteria below:  
* Papers published in journals or conference proceedings, as these are much better indexed in scholarly databases and are easier to find;
* Papers written in English;
* Papers containing the following keywords: (\"Blockchain\" OR \"Blockchain platform\" OR \"Blockchain application\") AND (\"Social media\" OR \"Social network\" OR \"Social platform\" OR \"Online community\" OR \"media platform\");
* Papers must have proposed methods to resolve an issue in social media by applying blockchain to improve privacy and security or have proposed a model using one of the characteristics or components of blockchain.
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
###### Abstract  
Federated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data. FL-based systems can achieve much stronger privacy preservation since the distributed devices deliver only local model parameters trained with local data to a centralized server. However, there exists a possibility that a centralized server or attackers infer/extract sensitive private information using the structure and parameters of local learning models. We propose employing homomorphic encryption (HE) scheme that can directly perform arithmetic operations on ciphertexts without decryption to protect the model parameters. Using the HE scheme, the proposed privacy-preserving federated learning (PFFL) algorithm enables the centralized server to aggregate encrypted local model parameters without decryption. Furthermore, the proposed algorithm allows each node to use a different HE private key in the same FL-based system using a distributed cryptosystem. The performance analysis and evaluation of the proposed PPFL algorithm are conducted in various cloud computing-based FL service scenarios.  
privacy preserving; homomorphic encryption; federated learning Article  
Privacy-Preserving Federated Learning Using Homomorphic Encryption. _Appl. Sci._**2022**, _12_, 734. [[https://doi.org/10.3390/app12020734](https://doi.org/10.3390/app12020734)]([https://doi.org/10.3390/app12020734](https://doi.org/10.3390/app12020734))
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 5 Open Issues
### Large-Scale Integration with 6G Networks  
Since the discussion of decentralized architectures has started taking place, it is expected that maritime networks will evolve in the 6G era, incorporating key enabling technologies, such as network function virtualization and software-defined networks (SDNs) [32]. Recently, FL-based solutions have emerged to optimize data routing and traffic control in challenged SDNs, being characterized by varying network conditions and the high data volumes of edge devices [33].
# A Polarization-Based Method for Maritime Image Dehazing
## 3 Experiments and Proposed Method  
Images acquired over the sea often show large sky areas and noticeable polarization in the water. Traditional polarimetric dehazing methods face difficulties in achieving satisfactory results in maritime environments because of challenges in estimating infinite atmospheric light and the presence of dark areas in the haze-free image above the sea surface. To tackle these challenges, this paper introduces the following approaches.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 2 Background
### Tsch  
Time-Slotted Channel Hopping (TSCH) media access divides time into timeslots and assigns them in order to avoid contention. In addition, channel hopping combats link failures caused by external interference and multi-path fading. Together, this enables TSCH to achieve high reliability. Furthermore, TSCH opens for low-power operation as a device may sleep if the schedule of slots does not indicate otherwise. The length of a timeslot is typically 10 ms and timeslots are grouped into repeating slotframes commonly consisting of 101 timeslots [10]. A single element in the TSCH schedule is known as a cell. A cell in the slotframe is identified by a timeslot- and channel-offset [11]. It allows for one transmission opportunity with an optional acknowledgment. The allocation of cells in the slotframe is typically referred to as the schedule. Figure 1 shows a slotframe which is three timeslots long where four cells have been scheduled. Cells can be organized in what the 6 TiSCH architecture denote as tracks [5] (which can also be part of a DetNet flow). These are in principle reserved resources between two entities, e.g., a node and a sink. In essence, cells are reserved for a specific track, and only traffic associated with this track may utilize the cells. Multiple cells bundled together increase the probability of successful transmission within a slotframe. Tracks are shaped as a Directed Acyclic Graph (DAG), and support multi-path forwarding and route around failures [5]. An illustration of a track is provided in Figure 1.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.2 Configuration of DSRC  
We configure the Wi-Fi class available in NS3 using DSRC parameters. First, we set the Wi-Fi standard as IEEE 802.11p and set the remote station as a constant rate Wi-Fi manager. We set the Request to Send-Clear to Send (RTS-CTS) threshold of 65,535 bytes in order to disable the RTS-CTS flow control mechanism, as DSRC transmission is limited to one-hop broadcasts in this research. Furthermore, we set the maximum retransmission attempts for packets smaller than the RTS-CTS threshold to seven and that for packets larger than RTS-CTS threshold to four. Retransmission attempts are set to a higher value (seven as the packet size is always less than the RTS-CTS threshold) in order to increase the reliability of DSRC communication. According to the Federal Communication Commission (FCC), the maximum allowable Equivalent Isotropic Radiated Power (EIRP) at 5.9 GHz band is 44 dBm [47]. We consider the transmission powers 33 dBm, 41 dBm, and 44 dBm to satisfy the minimum coverage requirements of 139 m, 278 m, and 347 m for urban, non-urban, and autobahn vehicular environments, respectively. The preceding coverage has been standardized considering the maximum relative speed in each of the mobility scenarios [48]. A minimum throughput of 10 Mbps is required if only status information such as safety-related driving maneuvers are collected [48]. As our simulations involve the collection of such data, we choose the physical layer Orthogonal Frequency Division Multiplexing (OFDM) data rate of 12 Mbps to satisfy the throughput requirement. The propagation loss model of the DSRC channel is set as Cost-Hata (Cost 231) for the urban scenario and as the \"Three Log Distance Propagation Loss\" model for suburban and autobahn scenarios. Both Cost231 and Log Distance are deterministic path loss models. Cost 231 has been used to model path loss in urban scenarios in DSRC [49]. The three log distance propagation loss model assumes an exponential path loss over the distance, which is designed for suburban scenarios having three exponents for near, middle, and far fields [50]. An error rate model mark packets as errors based on a predetermined method. We set the error rate model as the \"Nist Error rate model,\" which models different error rates for different OFDM modulations in IEEE 802.11 [51].
## 3 Materials and Methods
### Methodology  
#### 3.2.1 General Outline of the Proposed Method  
In this work, we evaluated four different encoder-decoder-based basic Semantic Segmentation Models (SSM), namely U-net [21], LinkNet [8], PSPNet [9], and FPN [22], to segment the multi-class images. Variants of each base model were formed by replacing the encoder blocks with five distinct ImageNet pre-trained backbone networks, such as VGG19 [2], Resnet50 [3], Efficientb7 [23], MobilenetV2 [4] and InceptionV3 [24]. These modified networks were trained with the IDD-Lite segmentation training dataset. The trained networks generated a multi-class segmentation mask for every test image. The segmentation accuracy of each SSM base model can be improved by weighting the masks obtained from the different backbones of each base model and ensembling them via the meta-model. The framework for getting the final multi-class segmentation mask using a stacked ensembling algorithm comprises two levels, as depicted in Figure 3. First, the base model is trained with five backbones on the same IDD-Lite dataset. The predictions of these five models on the same training data are used to create the training set for the meta-model in the next stage. Therefore, the final predictions of the stacked ensembling framework are more accurate than the predictions of the individual models.  
The development of the model architecture with different encoders and the method used to ensemble the predictions of these models to create the final meta-model are described in the following subsections.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class Names** & **Class Labels** & **Proportion of Pixels** \\ \hline Drivable area & Class 1 & 0.32 \\ Non-drivable & Class 2 & 0.02 \\ Living things & Class 3 & 0.01 \\ (Two-wheeler, auto-rickshaw, large vehicle) Vehicles & Class 4 & 0.1 \\ (Barrier) Roadside objects & Class 5 & 0.12 \\ (Construction) Far objects & Class 6 & 0.28 \\ Sky & Class 7 & 0.15 \\ \hline \end{tabular}
\end{table}
Table 1: IDD-Lite dataset class labels with class names and the proportion of pixels in each class.  
Figure 2: Some road scene images from structured (Cityscape) and unstructured (IDD) environments are in the first and second rows, respectively.
# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 3 Efficiency Multi-Attribute Feature Generation Framework
### Optimization  
In this study, the Softmax regression was used for whole network optimization. At the training phase, the above three kinds of attribute networks were trained via the Softmax function, which was followed by a Cross-entropy loss. The stochastic gradient descent (SGD) algorithm was used for the network training. Training errors were calculated via each mini-batch and back propagated to the primary layers. The single attribute network loss is denoted as \(L(\theta_{a})\), \(L(\theta_{b})\), and \(L(\theta_{c})\), which is shown in Equations (2)-(4), where \(\theta_{a}\), \(\theta_{b}\), and \(\theta_{c}\) are the parameters for each network correspondingly, and \(r\), \(s\), and \(t\) denote the amount of the attribute for each one. In this study, \(r\) is 4, which means four kinds of accessory attributes (head, top, middle, and bottom) for each person. \(s\) was set as 4, which denotes 4 types of body attributes (profile head, torso, and legs) for a person. \(t\) was 3, which represents three kinds of color attributes.  
\[L(\theta_{a})=-\frac{1}{r}[\sum_{i=1}^{r}\sum_{j=0}^{1}1\{y^{(i)}=j\}\log p(y^ {(i)}=j|x^{(i)};\theta_{a})] \tag{2}\]  
\[L(\theta_{b})=-\frac{1}{s}[\sum_{i=1}^{s}\sum_{j=0}^{1}1\{y^{(i)}=j\}\log p(y^ {(i)}=j|x^{(i)};\theta_{b})] \tag{3}\]  
\[L(\theta_{c})=-\frac{1}{t}[\sum_{i=1}^{t}\sum_{j=0}^{1}1\{y^{(i)}=j\}\log p(y^ {(i)}=j|x^{(i)};\theta_{c})] \tag{4}\]  
Figure 5: The color attribute network reduces the extracted convolutional feature map \(A\in R^{H\times W\times C}\) into \(H\times W\times 3\) correspondingly. Then, the three channels feature map is divided into \(X\), \(Y\), and \(Z\) for the global average pooling operation, respectively.  
The above equations update the gradient, which is shown in Equation (5). \(\bigtriangledown_{\theta_{j}}L(\theta)\) is a vector, and its \(l\)-th element is \(\frac{\partial{J(\theta)}}{\partial{\theta_{jl}}}\), the partial derivative of \(J(\theta)\) with respect to the \(l\)-th element of \(\theta_{j}\), which is the Cross-entropy loss to each single attribute (descriptor) network for optimization. The proposed method can be seen as a multi-task learning framework. The whole loss is \(L_{\text{cross}}\) and is described as Equation (6).  
\[\bigtriangledown_{\theta_{j}}L(\theta)=-\frac{1}{m}\sum_{i=1}^{m}[x^{(i)}(1\{ y^{(i)}=j\}-p(y^{(i)}=j|x^{(i)};\theta))] \tag{5}\]  
\[L_{\text{cross}}=\lambda_{a}L(\theta_{a})+\lambda_{b}L(\theta_{b})+\lambda_{c }L(\theta_{c}) \tag{6}\]  
where \(\lambda_{a}\), \(\lambda_{b}\), and \(\lambda_{c}\) are the accessory, body, and color attribute factors, respectively.
## 3 Evolution Process and Parametric Study  
This section details the progression of the proposed antenna's design, with an emphasis on the surface current distribution for better understanding. Additionally, it examines the influence of crucial design parameters on the reflection coefficients' frequency response.