esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Workflow-Based Access Control with Authorization Policies
### Framework Implementation  
The current proof-of-concept framework is implemented in Java on top of the Spring Cloud ([[http://cloud.spring.io/](http://cloud.spring.io/)]([http://cloud.spring.io/](http://cloud.spring.io/))) framework and, more particularly, Service Release 3 (SR3) of the Dalston release train. It simplifies the development of microservices and offers capabilities to realize data integration and real-time data processing pipelines with the Spring Cloud Data Flow ([[http://cloud.spring.io/spring-cloud-dataflow/](http://cloud.spring.io/spring-cloud-dataflow/)]([http://cloud.spring.io/spring-cloud-dataflow/](http://cloud.spring.io/spring-cloud-dataflow/))) toolkit. Our framework also leverages Hystrix ([[https://spring.io/guides/gs/circuit-breaker/](https://spring.io/guides/gs/circuit-breaker/)]([https://spring.io/guides/gs/circuit-breaker/](https://spring.io/guides/gs/circuit-breaker/))), the circuit breaker implementation provided by Spring. Each of the microservices runs in a Docker CE Edge 17.05 container ([[https://docs.docker.com/edge/](https://docs.docker.com/edge/)]([https://docs.docker.com/edge/](https://docs.docker.com/edge/))). The isolation of microservices simplifies the allocation and monitoring of memory and processor resources, as well as the communication between microservices. Access control usually involves authentication (verifying whether the identity of a subject is correct) and authorization. The focus of this research was on the latter. For authentication, we rely on ForgeRock's OpenAM 14.1 ([[https://www.forgerock.com/platform/access-management/](https://www.forgerock.com/platform/access-management/)]([https://www.forgerock.com/platform/access-management/](https://www.forgerock.com/platform/access-management/))) Identity and Access Management (IAM) framework. This IAM framework is also deployed in a Docker container with password-based authentication of the subjects handled by AM's RESTful interfaces. While OpenAM also provides authorization capabilities on top of XACML, it is not able to offer the functionality discussed in this paper.
## 3 Technical Overview
### Calibration Procedure  
Of utmost importance in the ARA mix measurements, especially for the feature extraction block, is the calibration of the signal inputs. This procedure converts the signals from the normalized amplitude levels of the signal processing programming environment (Matlab-Simulink [39]), which range from \(-1\) to \(1\), and displays them as the desired dB values. Similarly, the calibration procedure in the \(BL\) ARA mix calibrates the signals of the real environment and the signal of the virtual source. The same calibration procedure in dB SPL values is also applied on the pure tones, which were used for \(BL\) transform matrix construction. Tests during the simulation have shown that adjusting the calibration term to an 82 dB reference to have a value slightly greater than that helps the function of the \(BL\) ARA mix model.
## 5 Discussion
Thus, we evaluate the topographic correction effect separately, and make the atmospherically-corrected reflectance serve as the baseline scenario. Nevertheless, special emphasis should be put on the advantages of combined correction methodologies. The interfering effect of topography is evident in a single satellite scene and introduces even stronger distortions in multi-temporal approaches [5]. Multi-temporal studies require a previous radiometric homogenization of input images to better identify true changes, and topographic correction is one of the key steps to produce consistent and radiometrically stable multi-temporal time series [14]. In this study, the improved model possesses a solid physical base and successfully avoids the scene-dependent empirical regression. Also, the BRDF effects are accounted for when performing topographic correction. All these attributes favor the radiometric correction for time series images and tend to promise a much higher accuracy on quantitative applications of multi-temporal or multi-sensor images [70]. The execution speed of the algorithm is also an important factor to be considered when automatically processing a tremendously large volume of imagery in near-real time is a necessity [71]. In this study, the spring TM scene contains 8051 \(\times\) 7151 pixels (column \(\times\) line), and the autumn scene contains 8031 \(\times\) 7151 pixels. The processing time needed for each scene is about 11 hours on a typical personal computer (configuration: Inter(R) Core(TM) i7-3770 CUP _@_ 3.40 GHz, and 4 GB memory). Efforts will be put into enhancing the algorithm to further shorten the processing time in the future.
## 4 Experiment
### Performance Evaluation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm Recall20** & **Recall30** & **Recall50** & **Precision10** & **Precision20** & **Ndcg20** \\ \hline ItemCF & 0.0184 & 0.0196 & 0.0452 & 0.0186 & 0.0195 & 0.0356 \\ MF & 0.0250 & 0.0267 & 0.0624 & 0.0254 & 0.0201 & 0.0518 \\ NCF & 0.0265 & 0.0288 & 0.0665 & 0.0262 & 0.0225 & 0.0542 \\ NGCF & 0.0348 & 0.0387 & 0.0731 & 0.0328 & 0.0257 & 0.0630 \\ PUP & 0.0382 & 0.0415 & 0.0754 & 0.0316 & 0.0285 & 0.0624 \\ PinSAGE & 0.0283 & 0.0357 & 0.0710 & 0.0317 & 0.0229 & 0.0545 \\ \hline ATGCF & 0.0408 & 0.0426 & 0.0775 & 0.0358 & 0.0298 & 0.0651 \\ ATGCN & 0.0416 & 0.0433 & 0.0783 & 0.0367 & 0.0309 & 0.0658 \\ \hline HetGNN & 0.0412 & 0.0429 & 0.0780 & 0.0381 & 0.0371 & 0.0662 \\ HAN & 0.0432 & 0.0455 & 0.0729 & 0.0377 & 0.0359 & 0.0649 \\ \hline MIMA & **0.0475** & **0.0503** & **0.0834** & **0.0410** & **0.0381** & **0.0719** \\ \hline Improvement & **9.9\%** & **10.5\%** & **6.5\%** & **7.6\%** & **2.7\%** & **8.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance evaluation on the Amazon-book dataset and improvement compared to the optimal baseline. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm Recall20** & **Recall30** & **Recall50** & **Precision10** & **Precision20** & **Ndcg20** \\ \hline ItemCF & 0.0214 & 0.0238 & 0.0512 & 0.0332 & 0.0327 & 0.0396 \\ MF & 0.0384 & 0.0425 & 0.0748 & 0.0559 & 0.0445 & 0.0425 \\ NCF & 0.0401 & 0.0554 & 0.0864 & 0.0532 & 0.0496 & 0.0489 \\ NGCF & 0.0443 & 0.0647 & 0.1026 & 0.0604 & 0.0584 & 0.0504 \\ PUP & 0.0455 & 0.0665 & 0.1048 & 0.0588 & 0.0596 & 0.0548 \\ PinSAGE & 0.0431 & 0.0607 & 0.1015 & 0.0496 & 0.0478 & 0.0537 \\ \hline ATGCF & 0.0482 & 0.0689 & 0.1070 & 0.0645 & 0.0627 & 0.0581 \\ ATGCN & 0.0491 & 0.0695 & 0.1093 & 0.0656 & 0.0635 & 0.0593 \\ \hline HetGNN & 0.0533 & 0.0702 & 0.1033 & 0.0771 & 0.0747 & 0.0572 \\ HAN & 0.0529 & 0.0677 & 0.0995 & 0.0767 & 0.0729 & 0.0559 \\ \hline AFHGCN & **0.0545** & **0.0712** & **0.1170** & **0.0820** & **0.0755** & **0.0621** \\ \hline Improvement & **2.3\%** & **1.4\%** & **7.0\%** & **6.4\%** & **1.1\%** & **4.7\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance evaluation on the MovieLens-1m dataset and improvement compared to the optimal baseline. through widespread comparison of the three datasets, the MIMA we propose can effectively utilize node attributes to improve recommendation.
## 4 Discussion
The included studies reported that patients presenting severe pain unresponsive to nonoperative management and associated with functional limitation and radiographic signs of GHOA were eligible for replacement. Patients with secondary GHOA, such as history of trauma, instability, rheumatoid arthritis, avascular necrosis of the humeral head, and prior shoulder surgery were not eligible in most of the studies [21; 27; 30; 38; 41; 48; 56; 57; 61]. Some authors required a period of persistent pain [19; 31; 39; 43; 44; 48; 52; 53; 55], others required 3 to 6 months of nonoperative managements, including analgesics, anti-inflammatory medications, physiotherapy, and home range-of-motion exercise programs [27; 31; 45; 49]. Two studies performed the intervention in patients younger than 50 years of age [39; 54]. However, many studies did not report any inclusion or exclusion criteria [17; 22; 29; 40; 42; 46; 47; 50; 58; 59; 60]. The status of the rotator cuff influences the outcome of shoulder replacement [82]. In rotator cuff-deficient patients, the unopposed action of the deltoid causes superior migration of the humeral head toward the acrom and coracoacromial arch, leading to poor functional results, glenoid component loosening, and reduced implant survivors [87]. The average arm forward elevation of patients with impaired rotator cuff was \(88^{\circ}\), whereas \(150^{\circ}\) was achieved by patients with intact rotator cuff [88]. The status of the rotator cuff in patients included in the studies considered for the present investigation was homogeneous and often biased, thus representing a further limitation. In some of the studies included in our article, the rotator cuff was intact before surgery [21; 22; 27; 28; 30; 32; 38; 41; 48; 49; 51; 56; 57; 61]. Other authors repaired intraoperatively rotator cuff lesions [17; 19; 29; 31; 40; 42; 44; 46; 47; 50; 52; 53; 54; 55; 58; 59; 60]. Two studies did not specify the status of the rotator cuff in their patients [43; 45]. The age-adjusted Constant score has been used for analysis; however, although widely used to assess outcome for shoulder implants, the score itself is not validated [35]. Given these limitations, the data from the present study must be interpreted with caution.
## 6 Applications and Simulations
### Data Storage and Retrieval  
Our scheme guarantees greedy and stateless peer-to-peer routing in a 3D sensor network. Another possible application of the proposed scheme is for data centric networking, which supports in-network data storage and query. Traditionally, the underlying network used for data storage and retrieval needs to store a great amount of routing information. Let us consider a network without or with one internal hole first. With the proposed techniques, the whole network consists of tetrahedrons and is mapped into a unit sphere in a 3D space, which supports greedy peer-to-peer routing. Therefore, we propose to uniformly map a datum to a point inside of the unit sphere and let the tetrahedron, which contains the point to store the datum. Both data insertion and retrieval are naturally supported by greedy routing.
# Application of the Improved Entry and Exit Method in Slope Reliability Analysis
## 1 Introduction
The traditional method of grid search [24] is a type of violent search method that can quickly search for a limited number of slip surfaces in grid points with a rough division form. But it is difficult to determine the true critical slip surface using this method. In order to obtain more accurate search results, it is usually necessary to exponentially increase the number of trial slip surfaces by reducing the spacing between grid points. However, this significantly increases computational costs. In order to improve search efficiency, [PERSON] et al. [25] proposed the binary method based on the grid search method, which uses the previously searched objective function value information to find the possible positions of extreme points to improve the descent speed of the function. [PERSON] et al. [26] used a similar approach and predetected each search direction from the central point to determine the next center point movement direction, which overcame the limitations of the search area and improved the reliability of slope stability analysis. After using traditional approaches to find the circular slip surface, [PERSON] et al. [27] simplified the second search process by changing the position of some points on the slip surface and applying the random jumping method and two-point random walking method. This achieved an optimized search for the key slip surface, improving the accuracy of the safety factor. The traditional grid search method is independent of each trial slip surface [28] and has strong adaptability. [PERSON] et al. [29] used the grid search method to determine the critical sliding surface of expansive soil slopes and established a reliability calculation model for expansive soil slopes. [PERSON] et al. [30] combined the grid search method with the pattern search method to search for noncircular slip surfaces of complex soil slopes. [PERSON] et al. [31] expanded the grid-based search approach for identifying critical failure surfaces and predicting the safety factor of slopes by developing supplementary analytical expressions for the slip center grid. These traditional methods are extensively applied to determine the critical slip surface in the stability analysis of slopes because of their simple principles and reliable results. However, if a high accuracy of the safety factor is necessary, these methods can be very time-consuming. The contradiction between the precision and efficiency of traditional approaches in searching for the minimum safety factor limits their application in slope reliability analysis. To settle this problem, this paper proposes an improved entry and exit approach to determine the critical slip surface. Meaningful comparisons are made to verify the performance of the approach.
## 2 Problem Formulation and Preliminaries  
### Graph Theory  
The network among the \(N\) followers is represented by a directed graph \(\mathcal{G}=\{\mathcal{V},\mathcal{E},\mathcal{A}\}\), where \(\mathcal{V}=[\hat{\theta}_{1},\hat{\theta}_{2},\cdots,\hat{\theta}_{N}]\) is the set of nodes that represent the followers, with \(\hat{\theta}_{i}\) representing the \(i\)th follower, and \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\) is a directed edge set. \(\mathcal{A}=(a_{ij})\in\mathbb{R}^{N\times N}\) is the adjacency matrix with \(a_{ij}\in\{0,1\}\), \(i,j=1,2,\cdots,N\). In particular, if follower \(\hat{\theta}_{i}\) has access to follower \(\hat{\theta}_{j}\) information, then \(a_{ij}=1\), which also means that edge \((\hat{\theta}_{j},\hat{\theta}_{i})\) exists, otherwise \(a_{ij}=0\), and in adjacency matrix \(\mathcal{A}\), \(a_{ii}=0\) for any \(i\). For a directed graph, the Laplace matrix \(L=[l_{ij}]_{N\times N}\) related to the adjacency matrix is defined as \(l_{ij}=-a_{ij}\) for \(i\
eq j\); \(l_{ii}=\sum_{j=1}^{N}a_{ij}\). Matrix \(B=\text{diag}\{b_{1},\cdots,b_{n}\}\) with \(b_{i}\in\{0,1\}\). In this paper, we only consider the case where there is only one leader. It is defined that when information flows from the leader to the follower \(\hat{\theta}_{i}\), \(b_{i}=1\), otherwise, \(b_{i}=0\). To distinguish the leader node from the follower nodes, we use \(\hat{\theta}_{0}\) to denote the leader and \(\hat{\theta}_{1},\hat{\theta}_{2},\cdots,\hat{\theta}_{N}\) denotes the follower nodes. Then, the whole topology can be described by \(\mathcal{G}=\left\{\bar{\mathcal{V}},\mathcal{E},\bar{\mathcal{A}}\right\}\) with \(\bar{\mathcal{V}}=\mathcal{V}\cup\{\hat{\theta}_{0}\}\).  
A directed path is an ordered sequence of edges. For example, the edge sequence of a directed path from node \(\hat{\theta}_{j}\) to node \(\hat{\theta}_{i}\) has the form \((\hat{\theta}_{j},\hat{\theta}_{k}),(\hat{\theta}_{k},\hat{\theta}_{m}),\cdots,(\hat{\theta}_{n},\hat{\theta}_{i})\). In a directed graph, if every node except the root node (the root node has no parent node and has a directed path to other nodes) has a parent node, we call such a directed graph a spanning tree. If there is a (directed) spanning tree that is a subset of a graph, the graph is said to have (or contain) a (directed) spanning tree.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 2 Array Signal Processing Model Based on Compressive Sensing  
According to the theory of compressive sensing, the reconstruction of the sparse signal mainly includes the following three steps: the first is the sparse transformation of the signal, that is, by selecting a suitable sparse dictionary, and according to this dictionary, the coordinates of the original signal with sparsity can satisfy the sparse structure, during the processing of the space array signal, the number of signals received by the array often only accounts for a few in the entire airspace grid, so it can be considered that the target signal in the airspace grid is sparse; the second step is the structure of the observation matrix, after constructing an observation matrix that is not related to the sparse dictionary, the high-dimensional sparse signal is reduced to the low-dimensional signal that is convenient for the transmission to complete the sample of the sparse signal; and the last step is the sparse reconstruction of the transmitted signal, that is, the reconstruction of the original signal is completed by using the low-dimensional signal obtained by transmission, so as to realize the purpose of estimating the direction of arrival of the wave. This section will introduce the compressive sensing model of the array signal processing based on the above steps.
## 2 Methods
### Point Cloud Alignment and Parameter Optimization Point Cloud Pose Estimation and Optimization
#### 2.3.2 Fine Registration by J-ICP
The subscript letters indicate the transformation direction of this matrix, and the \(T_{ji}\) subscript letter indicates the direction of transformation of the matrix, which is denoted as \(p_{i}\) frame to \(p_{j}\) frame to frame transformation, assuming that the current acquisition of three frames of the target point cloud data \(P_{i}\), \(P_{j}\), \(P_{k}\) That is, the joint alignment optimization formula is expressed as:  
\[\Delta T_{kj}=\frac{\left(T_{ki}-T_{ji}\right)+T_{kj}}{2} \tag{9}\]  
where \(\Delta T_{kj}\) denotes the transformation matrix obtained by joint alignment of three frames from \(j\) frame to \(k\) frame. As the calculation process shown in Figure 6, we can find that this method can effectively avoid the local optimization problem caused by the symmetry feature of single-frame data alignment for it relies on the interrelationship of cross-frame data to guide the alignment process. From the perspective of computational complexity, the proposed J-ICP algorithm has a complexity of \(O(kn\log n)\), where \(k\) is the number of iterations and \(n\) is the number of points in the point cloud. This complexity encompasses nearest neighbor search and transformation matrix computation, which is comparable to the complexity of the ICP algorithm. In contrast, the GICP algorithm, which incorporates point covariance information, has a computational complexity of \(O(kn\log n+kn)\). Although the theoretical complexity of J-ICP is similar to that of ICP, J-ICP reduces cumulative error and enhances registration accuracy by combining multiple ICP processes. Consequently, J-ICP maintains computational efficiency while typically achieving superior registration accuracy.
# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Location-Based Routing Protocols
If a node leaves the network or if a link failure occurs, the route maintenance phase is performed using a cache to set up new routes. In LEER, it was proven [135] that the sum of the energy transmitted over multiple-hops (routing) is less than the transmission energy consumed for a single hop (routing). Finally, the Zone-based routing with a parallel Collision-Guided broadcasting protocol (ZCG) [136] employs a parallel and distributed broadcasting method to reduce redundant broadcasting and accelerate the path discovery process. This broadcasting technique guarantees low node energy expenditure andpreserves a high reachability ratio. In ZCG, a one-hop clustering algorithm is used for splitting the network into zones guided by powerful Cluster-heads (zone leaders). Zone leaders have high battery power and are normally static (they have zero/low mobility). The broadcasting technique used in ZCG reduces redundant broadcasting by the use of the zone-to-live (ZTL) technique. The ZTL technique decides the number of zones a broadcast needs to propagate from end-to-end before it is discarded by member nodes. The main drawbacks of ZCG are the following:  
* The cluster-heads (CHs) can probably perform selfish behavior. Therefore, there is a need to increase fairness among nodes to protect zone members from such selfish CHs. * ZCG generates higher routing overheads compared with other protocols. The main reason is that inactive member nodes change their location/status frequently. * The clustering procedure of ZCG and the zone selection mechanism may limit the scalability of ZCG in a highly dynamic MANET. Vehicular Ad hoc Networks (VANETs) and Flying Ad hoc Networks (FANETs) are subclasses of MANETs. Recently, [PERSON] et al. [137] presented various location routing protocols which are suitable for VANETs, while [PERSON] et al. [138] conducted an interesting performance analysis of position-based packet routing algorithms for FANETs. Hereafter, we discuss multicast-based routing protocols which additionally consider the energy consumption issue. This category of power-aware optimization solutions is specifically designed for group-oriented applications.
# Synthesis and Characterization of Highly Crystalline Vertically Aligned WSe\({}_{2}\) Nanosheets
## Materials and Methods
### Materials
#### 2.1.1 Synthesis of WSe\({}_{2}\) Nanosheets  
The WSe\({}_{2}\) nanosheets were grown via double selenization using an atmospheric pressure CVD technique. A 50-nm thick W film was deposited on a sapphire substrate by direct current magnetron sputtering (sputter current 100 mA), using a commercial sputter deposition system (Quorum Q15T/ES) in an Argon (99.9995%) atmosphere. A pure W target with a 57 mm diameter was used, and the substrates were placed on a rotating holder with a 90 mm target. The pressure of the argon in the deposition chamber was \(1\times 10^{-3}\) mbar. The W film thickness was monitored using a quartz microbalance mounted in the deposition system.  
The selenization was performed on a quartz tube (reactor). Firstly, the W film on the sapphire substrate was introduced into the reactor with the Se powder, and this was placed in two predefined zones of the tube (0.350 g were used in each zone) in order to be in the correct temperature zones when the reactor was inserted into the furnace. The Se powder was used without any further purification. The reactor was flushed for one hour to remove (reduce) oxygen, using a 0.475 L min\({}^{-1}\) argon flow outside of the furnace, prior to the selenization process. After that, the reactor was put inside of the furnace. As in a typical selenization process (Figure S1), the Se powder was placed into two temperature zones, one part at 40 \({}^{\circ}\)C and the other at 850 \({}^{\circ}\)C (in total 0.70 g), along with the W film sample, which was placed at 850 \({}^{\circ}\)C with the argon flow, into the reactor. A 0.150 L-min\({}^{-1}\) H\({}_{2}\) flow was inserted to help with the reaction. The presence of a strong reducer is indeed mandatory for the selenization reaction, compared with the sulfurization reaction. In the first selenization step of 30 min, the Se powder was moved to the 850 \({}^{\circ}\)C zone. The optimized second selenization step was performed by inserting the quartz tube into the hot zone of the furnace, such that the Se powder placed at 40 \({}^{\circ}\)C reached the 400 \({}^{\circ}\)C temperature zone; during an additional 30 min, the sample remained in the good temperature zone of 850 \({}^{\circ}\)C (moving only a few centimeters). After the reaction, the H\({}_{2}\) flow was stopped, and the quartz tube was removed from the reactor and was cooled with the argon flow for 1 h.