esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### ADMM Principle
From the gradient formula of the augmented Lagrangian of \(W_{i}\) and \(b_{i}\), we have  
\[\frac{\partial L_{\rho}\Big{(}\{W_{i}\},\{bi\},\left\{Z_{i}^{k}\right\}, \left\{U_{i}^{k}\right\}\Big{)}}{\partial W_{i}}=\frac{\partial f(\{W_{i}\}, \{bi\})}{\partial W_{i}}+\rho i\Big{(}W_{i}-Z_{i}^{k}+U_{i}^{k}\Big{)} \tag{9}\]  
\[\frac{\partial L_{\rho}\Big{(}\{W_{i}\},\{b_{i}\},\left\{Z_{i}^{k}\right\}, \left\{U_{i}^{k}\right\}\Big{)}}{\partial b_{i}}=\frac{\partial f(\{W_{i}\}, \{b_{i}\})}{\partial b_{i}} \tag{10}\]  
Since \(g_{i}(\cdot)\) is an indicator function of the \(\text{S}_{i}\) set, the global optimal solution to the problem can be expressed as Formula (11). \[L_{\rho}(\{Z_{i}\},\{W_{i}\},\{\tau_{i}\},\{b_{i}\})=f(\{W_{i}\},\{b_{i}\})+\sum_{ i=1}^{N}g_{i}(Z_{i})+\sum_{i=1}^{N}\frac{\rho_{i}}{2}\|W_{i}-Z_{i}+U_{i}\|_{F}^{2}+ \sum_{i=1}^{N}\frac{\rho_{i}}{2}\|U_{i}\|_{F}^{2} \tag{3}\]0  
where \(\prod_{S_{i}}\left(\cdot\right)\) denotes the Euclidean projection of \(Z_{i}^{k+1}+U_{i}^{k}\) on the \(S_{i}\) set, and \(S_{i}\) is a non-convex set, so calculating \(\prod_{S_{i}}\left(\cdot\right)\) is a difficult problem. However, considering the special structure of \(Si=\{W|card(W)\leq li\}\), the solution of Equation (4) is to maintain the maximum importance of the element \(l_{i}\) of \(W_{i}^{k+1}+U_{i}^{k}\) and set the remaining elements to 0. Finally, the bivariate \(U_{i}\) is updated according to Equation (6). In this way, an iteration of ADMM is completed. The pruning algorithm based on ADMM is as follows (Algorithm 1).
# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
## 3 Protocol Design
### Road Segment Selection  
At intersections, there are many factors affecting the data forwarding decision, such as vehicle density, signal lights, the direction of the target node, road width (number of lanes), etc. Here, traffic flow, direction, and the number of lanes are selected to complete intersection decision making. The three key factors are described in detail as follows.  
Number of lanes: Number of lanes in the road segment;  
Direction: The angle between a candidate road and the line from the current intersection to the destination;  
Traffic flow: The traffic flow of each road section in different time periods.  
In this paper, we choose a nine-point fuzzy scale to quantify the factors we selected. The TFN is denoted as follows:  
\[\widetilde{A}=(l,m,u) \tag{6}\]  
where \(l\) and \(u\) represent the lower and upper bounds of the TFN A, respectively, and \(m\) represents the modal value. As mentioned earlier, the purpose of TFN is to convert linguistic variables to definite values that can be used for calculations. Herein, we transfer the linguistic variables to definite values as follows in Table 3.  
In the next subsections, we will describe the detail on why and how to evaluate the selected factors.
## 2 Basic Theory
### Optimized ACMD Method  
Like other algorithms, the STA is based on the iterative search of individuals in the neighborhood. The difference is that each individual in the search species uses the group optimal value, that is, the elite individual. The search process is only related to the group optimal value of the previous generation, and the search is carried out within the group optimal neighborhood. For example, when performing the expansion operation, the optimal individual of the group is first taken as an input, the optimal individual is then copied to the number of individuals \(SE\) of the search species, and the expansion operation is performed on these optimal individuals. The optimal individual in the group is calculated by performing the expansion operation, and the optimal individual is used as the input for the next operation.  
The state transition algorithm has five important parameters, that is, the number of individuals in the search species \(SE\), rotation operator \(\alpha\), translation operator \(\beta\), expansion operator \(\gamma\), and axesion operator \(\delta\). In the case where the number of individuals \(SE\) is constant, the larger the value of \(\alpha,\beta,\gamma,\delta\), the larger the search range of the corresponding operator, and the larger the search range, which is beneficial to the global search. When its value is small, the search accuracy is high, which is advantageous for the local search. In References [36; 37], the species \(SE\) is set to \(SE=20\) and \(SE=10\), respectively, because the number of artificial species will affect the accuracy of the search. This paper proposes the Rastrigin optimization test equation to optimize the number of different species, and detects the time of each optimization (the number of species is 1-100, in which the rotation operator, translation operator, expansion operator, and axesion operator are unchanged and are set to \(\alpha=1,\beta=1,\gamma=1,\delta=1\), respectively). Through multiple inspections, the number of species of the state transition algorithm when the average optimization time is the shortest is finally determined.  
Rastrigin function:  
\[f=\sum_{i=1}^{n}{(x_{i}^{2}-10\cos(2\pi x_{i})+10)[-5.12,5.12]} \tag{15}\]  
where \(x_{i}\) is the \(i\)-th element of the \(n\)-dimensional Euclid space element.  
This paper uses the improved STA method to optimize in the ACMD method. The optimization process is shown in Figure 2.  
* Determine the species of the STA when the average optimization time is the shortest through multiple detections.
* Bring the number of optimal species groups into the STA algorithm.
* Employ the optimized STA algorithm to find the optimal parameter.  
Figure 2: Optimized adaptive chirp mode decomposition (ACMD) method.  
* The optimal parameter is input into ACMD, and the optimized ACMD is used to process the chirp signal.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 5 Voxel-Based Navigation
### Outdoor Navigation
[108] developed a method that automatically extracts road features in urban environments, using dense Airborne Laser Scanning (ALS) data. By leveraging the high-resolution data provided by ALS, the method creates a detailed and accurate representation of the urban road network, significantly enhancing the reliability and safety of autonomous vehicle navigation systems. Recent studies [109; 110; 111] have increasingly focused on combining Bird's-Eye View (BEV) and voxel representations to improve the performance of autonomous drivingsystems and computer vision applications. This combination leverages the intuitive representation of BEV in the planar view and the detailed modeling capability of voxels in 3D space, leading to a more accurate and comprehensive understanding of the environment.
## 3 The Proposed Distributed SPARQL Query Processing Scheme
### Query Decomposition
To overcome this, subqueries are grouped with related subqueries, thereby reducing the communication cost during query processing. If only related nodes of subqueries are known through the index, subqueries are only sent to the related nodes. For only adjacently connected subqueries, related node ID are compared. For example, if \(SG_{1}\) is related to \(N_{1}\) and \(N_{2}\) and \(SG_{2}\) is related to Nodes \(N_{1}\), \(N_{2}\), \(N_{3}\), and \(N_{4}\), \(SG_{1}\) only knows predicate and object. Thus, \(SG_{1}\) has a small number of related nodes compared to other subqueries. On the other hand, \(SG_{2}\), which knows only predicates, may have many related nodes depending on how the stored data are distributed. In such a case, the communication cost may increase according to the number of related nodes during query processing. Thus, it is important to reduce the number of related nodes of \(SG_{2}\) compared to that of \(SG_{1}\), which is an adjacent subquery. The unnecessary data transfer during query processing and data parsing cost can be decreased by reducing the number of related nodes of \(SG_{i}\) through only adjacent nodes that are shared in common via the comparison of related nodes of \(SG_{1}\) and \(SG_{2}\). Grouping of related nodes can be conducted through the related node and adjacent subqueries. In addition, related and unrelated subqueries are distinguished even inside each node. During grouping, S, P, and O of each \(SQ_{i}\) are compared. The second grouping is conducted based on \(SQ_{i}\) that have the same S, P, or O, or the same variables during the comparison. The join cost can be reduced by query processing of related \(SQ_{i}\) through second grouping. Figure 4 shows the subquery grouping by the node. In \(N_{1}\), \(N_{2}\), and \(N_{3}\), only a single group is made since grouping is conducted through common variables in subqueries included in the related nodes. In contrast, in \(N_{4}\), although \(SQ_{3}\), \(SQ_{4}\), and \(SQ_{6}\) are related to \(N_{4}\), \(SQ_{3}\) and \(SQ_{4}\) can be grouped into a single group through the \"?W\" variable. However, \(SQ_{6}\) does not have S, P, and O that are related to \(SQ_{3}\) and \(SQ_{4}\), which cannot be grouped into a single group. Thus, in \(N_{4}\), two groups are produced. Figure 4: Grouping of subqueries by node. Figure 3: Example of a SPARQL query.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 2 Technical Introduction to AI for Clinicians
### Machine Learning (ML)  
Briefly, Machine Learning is an AI paradigm in which software programs can extract relationships and traits/characteristics as a learning procedure, but often without being specifically programmed for this activity. For example, automated processing of eye images as a diagnostic aid is a challenge of research in order to make the best paradigm with the least computational effort [12].  
In ophthalmology, choosing the best method of representation, analysis and diagnosis using fundus images of the eyes is a complex computational problem [13].  
Types of Learning Methods for ML  
ML algorithms are mainly organized according to their learning model.  
In this respect, _supervised_ and _unsupervised_ training are extensively used in relation to still images and videos. Similarly, _reinforcement learning_ can be used without human intervention when performing eye surgery.  
The characteristics of the three learning methods are shown in Figure 3:  
* _Supervised learning:_ The network has current inputs and desired outputs and delineates the connection of the inputs and those wanted outputs;
* _Unsupervised learning:_ The network has certain input data, and it must group them into \(n\) classes based on similarity between those data;
* In [14], it is shown how retinal blood vessels are detected by means of an extreme machine learning (ELM) method and probabilistic neural networks;
- [PERSON] et al. [13] studied fundus images with a three-layer ANN and Support Vector Machine (SVM) to classify retinal images;
- [PERSON] et al. classified with a data mining technique to provide useful predictions to diabetics diagnosed with retinopathy (DR) [15].  
Figure 2: (**a**) Machine Learning (ML) and (**b**) Deep Learning (DL) for Diabetic Macular Edema (DME) assessment [11].  
In spite of good outcomes, the principal issues are that the datasets were small, and the annotations are expensive to perform (in computing resources, and the work is cumbersome).
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 3 Uncertainty Estimation Approach
### Correction Factors Calculation to Compensate Estimators  
This procedure removes the constraints that exist to achieve an adequate estimate of reliability and provides the factors that must be included to eliminate the estimators' bias. These factors are  
1. Additive factor for \(\overline{Y}_{k}\) (\(\left[\Phi^{\mathsf{T}}\Phi\right]^{-1}\Phi^{\mathsf{T}}\Xi\))
2. Multiplicative factor for \(S_{k}\) (\(C_{k}\))  
In our case, the \(\Phi\) and \(\Xi\) turned out to be uncorrelated, so this additive term is close to zero and was not considered in this study.  
The multiplicative factor for \(S_{k}\) is obtained from the Cauchy-Bunyakowskii-Schwartz inequality described in Equation (10). In many cases where \(E\{S_{k}\}<\sigma\), the question is how far is \(E\{S_{k}\}\) from \(\sigma\), so we will introduce a factor \(c_{k}\) that eliminates this bias, that is to say  
\[E\{c_{k}S_{k}\}=\sigma \tag{24}\]  
Figure 4: Simulation procedure with the Monte-Carlo method (MMC) to determine the variance \(S_{k}^{2}\).  
The new standard deviation estimator \(\sigma\) will be \(c_{k}S_{k}\), this factor is estimated using the Monte-Carlo method and the relation  
\[c_{k}=\frac{\sqrt{E\{\left(Y-E\{Y\}\right)^{2}\}}}{\sqrt{\frac{1}{k-1}\sum_{i=1} ^{k}\left(Y_{i}-\overline{Y}_{k}\right)^{2}}}=\frac{\sigma}{S_{k}}. \tag{25}\]
# Optimizing a Reverse Supply Chain Network for Electronic Waste under Risk and Uncertain Factors
## 1 Introduction
[48], risks in a supply chain operation can be considered as a potential variation from the initial plan, which can cause non-value-added activities at different stages. In supply chain risk management, the probability is defined as a measure of how often an unexpected event results in a potential loss while impact indicates the importance of the loss to the company [49]. Studies about risks in forward supply chains including demand risks, transportation risks, supply risks, delay risks, information risks, etc., were conducted by some researchers [50; 51; 52; 53]. Note that risks are included in some forward supply chains but are rarely examined in RSC models. According to the literature, existing models are insufficient to represent a practical e-waste RSC. To overcome this gap, this study aims to propose a multi-product, multi-tier RSC model for e-waste with risk costs incorporated. Risk factors in RSC models can lead to a significant impact on the entire RSC cost [54]. Risks normally occur during treatment processes and shipping activities due to many types of dangerous substances in e-waste [55; 56]. In addition, most input data (e.g., all costs involved, the capacity of centers, return rate, risk factors, etc.) in the proposed model are considered as fuzzy in nature, which can handle uncertain parameters in real situations. The proposed model aims to minimize the total cost including collection, set-up, processing, shipping, disposal, and risk costs as well as the profit obtained from selling recovery materials and used items. Furthermore, the proposed model also applies an interactive fuzzy solution which can generate a practical and satisfactory solution to balance the conflict between minimizing the objective value and improving the degree of satisfaction constraints. This research will aid managers in the decision-making process particularly in selecting the optimal locations and the flow of materials. Compared to existing research, this proposed model suggests the actual total cost of the RSC network, which is closer to the reality as risks and uncertainties are simultaneously considered. The body of this paper is organized as follows. The description of the problem is presented in Section 2, whereas the mathematical model is elaborated in Section 3. The proposed solution approach is reported in Section 4. In Section 5, an example with three scenarios is used to show the usefulness of the proposed model. Lastly, conclusions and some future directions are detailed in Section 6.
# Antibacterial Bio-Based Polymers for Cranio-Maxillofacial Regeneration Applications
## 3 Scaffolds and Implanting Medical Devices for Soft and Hard Maxilofacial Tissues
### Polymeric and Bioactive Glass-Based Composites  
Bioactive glasses (BGs) are materials with the ability to induce and conduct the mineralization of tissues, which are currently widely employed in maxillofacial reconstruction, cranial and periodontal defects repair, alveolar ridge augmentation, as bone substitutes in orthopedics, and as bioactive coatings in dental implants, among other applications [39,96,97,98,99,100,101,102]. The incorporation of these glasses  
Figure 4: Layer by layer coating of polycationic and polyanionic polymers to make bacteriostatic surfaces. Reproduced and adapted from [PERSON] et al. 2019 [35], with permission from Elsevier.  
into various restorative dental materials has been recently investigated with the aim of providing bioactivity and antimicrobial properties against oral bacteria such as _Streptococcus sanguis_, _S. mutans_ and _Actinomyces viscosus_[96].  
Bioactive glass nanocomposites (58S-BG) consisting of SiO\({}_{2}\), CaO and P\({}_{2}\)O\({}_{5}\) have been widely applied for bone tissue engineering due to their excellent biocompatibility, bioactivity, antimicrobial activity and tight bonding between the dental implants and the surrounding bone [41; 97]. Other examples include the BG-chitosan composite which was deposited on PEEK/BG layers, providing an antibacterial and bioactive coating [98], or a self-gelling pectin-BG composite, which was reported as an injectable composite with antibacterial activity [99]. In addition, some BGs have been incorporated into membranes along with bio-based polymers such as poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) to provide them with a favorable surface roughness, hydrophilicity and flexibility, but also to develop devices with potential applicability as multidrug carriers (e.g., antibacterial and/or antiosteoporotic drugs), making them promising candidates to be applied in GTR/GBR applications [100]. This has led to the study of composites based on BG and other natural polymers such as gelatin and collagen biomimetic electrospun fish collagen, which possess antibacterial activity against _S. aureus_ and which could be used as a functional skin wound dressing [101].
## 4 Numerical Results and Discussion
Besides, it should be noted that (2 \(\times\) 2 \(\times\) 2) NR-D&F  
Figure 12: Throughput vs. SNR of the cooperative system; 64-QAM signal. protocol with LS and PCE have obtained 97.00 Mbps and 99.13 Mbps, respectively. From Figure 13, it can be noted that the maximum throughput of the mmWave SISO cooperative network was achieved from \(\text{SNR}=6.5\) dB, such that a capacity gain of about 28.16 Mbps was obtained when the (2 \(\times\) 2 \(\times\) 2) MIMO NR-D&F protocol with PCE was considered. It worth noting that the impact of decoding and encoding steps that the received signal is subjected to before being re-transmitted to the NR-UE by the NR-D&F protocols are noticed when the MIMO technique is considering. Therefore, we also have presented the throughput of the mmWave (4 \(\times\) 4 \(\times\) 2) MIMO NR-D&F strategies, as are shown in Figure 12 and 13. Furthermore, it can be seen that, when NR-D&F with channel knowledge is employed, the better capacity between the considered cooperative systems is achieved, for both 64-QAM and 256-QAM constellations. However, when mmWave (4 \(\times\) 4 \(\times\) 2) NR-D&F with LS channel estimation is considered, the achievable throughput is nearest to the reached with channel knowledge. Furthermore, it should be noticed that the maximum performances of our system are 113.85 Mbps and 151.90 Mbps, which were reached by mmWave (4 \(\times\) 4 \(\times\) 2) NR-D&F from \(\text{SNR}=12\) dB and \(\text{SNR}=16\) dB for 64-QAM and 256-QAM cases, respectively. Finally, the achieved maximum performance by mmWave (4 \(\times\) 4 \(\times\) 2) NR-D&F protocol with the 256-QAM constellation was 38.05 Mbps higher than the 64-QAM scheme.
## 2 Outdoor Positioning in the IoT Ecosystem
### Location-Enabled IoT Technologies
#### 2.3.1 LoRaWAN
In Over-The-Air Activation, the end nodes are identified with an end-device identifier, application identifier, and AppKey. In Activation by personalization, end nodes have two keys determined by the LoRaWAN network server as session keys. Therefore, in this way, the end nodes directly join the network without going through the Join procedure steps. After Joining the LoRaWAN network, end nodes can exchange data with the LoRaWAN network server throw LoRaWAN gateways. LoRaWAN defines three classes of end devices: Class A, Class B, and Class C [25]. * _Class A_: when an end node needs to send data (Uplink), it selects a channel randomly. Then, end nodes send packets through the ALOHA technique. To enable bidirectional communication, Uplink transmission is followed by to short packet from the LoRaWAN network server (Downlink). Class A is useful for IoT devices with power limitations and low downlink data because the end node will be in sleep mode most of the time. * _Class B_: this class is useful for IoT devices with more downlink data and power limitations. The uplink transmission is the same as class A, but end nodes will open additional receive windows at scheduled times to receive downlink transmission. End node and LoRaWAN gateway establish a parameter. This parameter is used to calculate the ping slot. Throw ping slot determination, end device knows times that must wake up and wait for a downlink. Additionally, the server knows when end nodes are listening to the medium. * _Class C_: this class is not useful for devices with power limitation. In this class, IoT devices are continuously listening to the channel and are open for downlink transmission.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Terrain Model Generation  
In the literature, DTM generation approaches are described for different kind of input data. Most of them are designated for airborne LiDAR data, some for DSMs extracted from high-resolution airborne optical images and spaceborne optical imagery. In this work, we aim for a DTM extraction approach custom-tailored to DSMs that are generated by a state-of-the-art photogrammetric workflow from VHR satellite stereo or tri-stereo images, as presented in Section 2.2. Thus, a novel algorithm will be proposed within this paper which is especially suitable for this case.
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 4 Core Based Multicast Tree
### Security Analysis
#### 4.2.4 Single Point Failure  
In the case of a PKI based system, the certificate authority is a single-point failure. In our case, the certificate authority is not needed once the system is created. Since we use k-threshold cryptography as the underlying cryptosystem, a new set of keys can be generated through collaborating with k-hosts in the network. Thus, the job of the CA is distributed among k-nodes in the network. Hence, it is impossible to bring the network down by attacking a single host or a server in the network.
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### Grid Aspects with Influence in PLC Systems Design
#### 5.2.3 Isolation of MV and LV Grid  
The electricity grid is a fully connected network. This is strictly true if electricity signals (50 Hz or 60 Hz) are considered, as electricity can flow from the MV to the LV segment through the transformersin the substations. However, PLC systems make use of higher frequency signals, and depending on the range of the frequency to be considered and the physical characteristics of existing assets (mainly transformers), the connectivity of the systems presents itself in a different way, ranging from full isolation to complete communication transparency. This means that a PLC signal of some kHz could be partially transmitted from the MV segment to the LV segment, while a signal of some MHz would not be capable of going through the same transformer. The frequency threshold of this signal transfer from MV to LV depends on the transformers characteristics and other installation factors.  
Thus the only way to ensure interference-free coexistence of PLC systems in the same grid is to fully separate frequency bands. If this separation cannot be achieved, the PLC system must offer a channelization of its resources (different possibilities; a coexistence mechanism is the preferred option), and in any case, this PLC system internal segmentation brings a reduction of the available throughput. However, and as it happens with any other communication system, self-interference of PLC systems is largely unavoidable due to the shared media characteristics of electricity grid, and the varying degrees of isolation of the different parts of the grid.