esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Accelerated Environmental Conditions and Test Methods
### Flexural Test Methods  
In this study, the performance of the hybrid panel of the improved moveable weir was evaluated. Improved moveable weirs is a structure receiving a water load. The load exposure condition is that of a structure subjected to a flexural load. Therefore, in this study, the flexural test was carried out considering the application conditions of the improved movable weir (Figure 4). The flexural strength of our hybrid panels was evaluated in accordance with ASTM D7264 (American Society for Testing and Materials D 7264, Standard Test Method for Flexural Properties of Polymer Matrix Composite Materials, ASTM International, 2015, West Conshohocken, PA, USA). When a flexural load was applied to our hybrid panels the interface between the glass fiber and the resin and the adhesion interface between the GFRP panel and the steel panel were most affected. The ASTM D7264 was considered the most appropriate method for determining the mechanical limits of the glass fiber/resin and the GFRP/steel interfaces in our hybrid panels. The test specimens for these tests were prismatic in shape, with dimensions of 40 \(\times\) 40 \(\times\) 160 mm. The mechanical performance of the hybrid panels, in terms of residual strength after exposure to an accelerated deterioration environment, was also evaluated. The residual strength was calculated as shown in Equation (1).  
\[\text{Residual strength}(\%)=\frac{\text{Flexural strength after accelerated environmental conditions}}{\text{Flexural strength of control conditions}}\times 100(\%) \tag{1}\]  
Previous studies have specified a residual strength of 65% to analyze durability. While not directly applicable to this study, previous research results have shown that GFRP composite materials composed of glass fiber should retain \(\geq\)65% of their initial strength after environmental exposure if they are to be used as concrete-reinforcing materials [14; 15; 1]. In the current study, flexural tests were performed on six specimens under each environmental condition.
## 3 Results
### Sensitivity Analysis  
In order to know what input variables have a significant effect on the pile-bearing capacity, with the assistance of the DNN model, a sensitivity analysis was performed by utilizing the step-by-step technique [60]. In this method, each normalized input parameter varies at a constant rate, one at a time, while the other variables are held constant. Diverse constant rates (0.3, 0.6, and 0.9) were chosen in this study. For every input, the percentage of variation in the output, as a result of the variation in the input, was computed. The sensitivity of each input was computed based on Equation (7):  
\[\text{Sensitivity level of }X_{j}(\%)=\frac{1}{K}\sum_{i=1}^{K}\biggl{(}\frac{ \%\;change\;in\;output}{\%\;change\;in\;input}\biggr{)}_{i} \tag{7}\]  
where \(K\) refers to the number of the datasets used in the study (\(K=100\)). The outcomes of the sensitivity analysis of the proposed \(DNN\) model are illustrated in Figure 4. It can be noticed that the pile-bearing capacity was significantly influenced by the pile area, and its sensibility ratio ranged between 26.3% and 38.06%. This parameter is closely followed by the pile length, which showed a moderate sensitivity level that ranged between 15% and 19%. In addition, the cohesion and friction angle had a moderate effect on the pile-bearing capacity, with a sensibility ratio ranging between 9% and 15%. Finally, other parameters had little effect on the target values.
## 3 Capacity Enhancement by Channel State Information
### Statistical Channel State Information
achieves the maximum capacity but is unfortunately unfeasible in practical systems, the proposed statistical CSI can be a solution to increase the capacity rather than using equal launch power at the transmitter. This method has a low-cost complexity since it works independently of the channel variations, thus it can be feasible for real implementation. Nonetheless, it is important to determine how many channel acquisitions are enough to properly use the average channel. Also, the time duration of the use of a given statistical CSI before having to compute a new one.
## 6 Conclusions and Future Work  
In conclusion, this work aimed at testing the hardware and software performance in terms of the number of operations performed and the time needed to obtain satisfactory anonymity. The real world example concerning moving bicycle riders proved satisfactory results using different additional ancillary methods of space partitioning and indexing. Also, a GeoSpark-based approach for evaluating the vulnerability of spatial \(k\)-anonymity in large-scale spatio-temporal data is provided. Experiments show that GeoSpark constitutes an appropriate Spark-based system for the evaluation of robust \(k\)-NN queries. The GeoSpark spatial RDDs are exploited to store trajectory data of the mobile objects as PointRDD in order to acquire the anonymity sets of all query mobile objects issuing iterative spatial \(k\)-NN queries for all mobile objects in consecutive timestamps. It should be noted that the experiments were conducted in one Virtual Machine (VM) in local mode, with specific capabilities as described in the experimental evaluation.  
As a future work, our aim is to apply the proposed anonymity method in a fully distributed environment with many VMs so as to verify the expected results from previous findings about the execution time; in this case, the input data will be partitioned and in following distributed to different VMs. In this way, several parallel tasks will be executed and thus, the execution time for the anonymity set computation is expected to be much lower than in the current single VM case when considering large-scale databases. The locality of the data and load balancing are important when applying efficient \(k\)-NN queries. GeoSpark also provides a grid-type partitioning based on \(KDB\)-tree that can ensure workload balancing.  
In conclusion, it should be pointed out that the current experimentation gave us an insight about the design issues and possible constraints of the proposed approach so as to optimize its performance in terms of execution time cost and memory utilization for real-time scenarios, where the mobile objects identity security, when issuing a \(k\)-NN query, is of high importance.  
**Author Contributions:** [PERSON], [PERSON], M.T., [PERSON], S.S. and A.T. conceived of the idea, designed and performed the experiments, analyzed the results, drafted the initial manuscript and revised the final manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work is funded by General Secretariat for Research and Technology (GSRT), Hellenic Foundation for Research and Innovation (HFRI), and supported by University of Patras.  
**Acknowledgments:** The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved both content and presentation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Preliminaries and Models
### Security Requirement and Attack Model  
Within the system model, there are four types of actors involved in the meter data reporting process: the \(i\)th user (self), other users in the same neighborhood (BAN), the GW, and the CC. The CC requires the spatially aggregated fine-grained neighborhood usage data to optimize power delivery efficiency and the temporally aggregated user-specific utility usage data for the billing purpose. Hence, we stipulate the following security/privacy requirements:  
_Requirement_**R1**. Fine-grained, individual utility data are private and should not be disclosed to CC, GW, or other users.  
_Requirement_**R2**. Temporal aggregation for an individual user and spatial aggregation in one neighborhood cannot be tampered by the malicious aggregator or other internal nodes. For this, we envision a secure and reliable communication model comprising a verifiable publically method, which is customized to the correctness verification of the aggregation value of SG.  
For this, our attack model is based on the malicious aggregator who attempts to tamper the aggregation value in a neighborhood and the billing value for individual users, or infers fine-grained meterings of the individual user by colluding with other \(n\)-2 compromised nodes at most. Following the above security requirements, different compositions of the attackers and actions may be grouped into the following attack types:  
1. External attack  
External attackers may compromise the meterings of the object users by eavesdropping the communication flow between communication nodes through various eavesdropping malware.
2. Malicious attack  
False aggregation report. The aggregator may alter or drop maliciously any individual data, or tamper the aggregation data to the CC; any malicious user node may provide false local aggregation to the GW.  
Collusion with compromised nodes. The aggregator may collude with compromised users to attempt to infer the uncompromised users' data.
3. Semi-trustable internal attack  
The curious CC or any user node can also acquire data through the public communication flow, such as the message from the user node to the GW or from the GW to the CC. They may infer the object user's fine-grained data by the public communication flow.  
An attack is an arrangement that enables unauthorized parties to gain access to private data or to tamper secured data (even by the user itself) without being detected. In this work, we assume the SMs are tamper-resistant [7; 20; 23], and can perform the measurement and reporting operations normally, but do not exclude the possibility of tampering with local aggregation values by itself.
## 5 Conclusions  
The multiband, high gain and low-profile-based superstrate antenna are presented in this manuscript. The different approaches such as the varying height of superstrate layers, the feed position, the size of metamaterial rings, and the number of metamaterial rings, are considered and analyzed to identify the structure's optimized performance. Simulation and fabrication are analyzed by considering the low-profile FR4 material. Comparison among the measured and simulated responses of the reflection coefficient, radiation patten, and directivity is incorporated in the manuscript. The proposed design provides six  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Total Bands** & **Size of Structure** & **Resonating Frequency (GHz)** & **Peak Gain** \\ \hline Proposed & 6 & 48.2 \(\times\) 48.2 & 4.8/5.22/7.771/8.881/10.2/10.891 & 5.77 \\
[42] & 2 & 45 \(\times\) 40 & 2.4/5.5 & 2.34 \\
[43] & 3 & 40 \(\times\) 50 & 1.65/1.93/2.20 & 2.93 \\
[44] & 2 & 75 \(\times\) 140 & 0.698–0.960/1.710–2.690 & - \\
[45] & 3 & 22 \(\times\) 25 & 2.4/3.5/5.8 & 3.15 \\
[46] & 5 & 24.8 \(\times\) 30 & 2.28/2.65/4.80/5.89/8.73 & 3.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparative study of the presented design with other articles.  
Figure 16: Surface charge distribution **(a)** 5.22 GHz, **(b)** 7.771 GHz, **(c)** 8.881 GHz, **(d)** 10.891 GHz. The notation used for electric field: 1.2903e+004 = 1.29 \(\times\) 10\({}^{4}\). The same notation used for all figures.  
band response with sufficient bandwidth. The performance is analyzed regarding the reflection coefficient response, gain, directivity, E-field and radiation pattern. The FR4-based low-profile material is used in the presented work. The proposed design provides a maximum bandwidth of 490 MHz, a minimum reflection coefficient response of \(-49\) dB, a peak gain of 5.77 dB, six bands response, proper directivity, an electric peak field of \(1.29\times 10^{4}\) V/m and a broader radiation pattern. Similarity among the simulated and measured results is observed. The low-profile material helps with the cost reduction in the proposed structure. The design highlights are compared with other published articles to identify the improvement. The multiband response over the 4 GHz to 12 GHz helps to attain multiple wireless communication applications such as WiFi and Microwave links.  
Conceptualization, A.A. and S.K.P.; methodology, A.A. and S.L.; software, S.L. and M.A.; validation, S.L., S.K.P. and M.G.D.; formal analysis, A.A. and S.L.; investigation, A.A. and K.A.; writing--original draft preparation, A.A., S.L., M.A., K.A., M.G.D. and S.K.P.; writing--review and editing, A.A. and S.K.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data will be made available at a reasonable request to the corresponding author.  
The authors declare no conflict of interest.
## 6 Applications of VSNs
### Rock Slides and Animal Crossing Monitoring  
VSNs can also be used in the application like rock sliding and animal crossing monitoring. Figure 5 represents a mountain terrain where there are different types of animals crossing a road.  
In particular situations monitoring this type of event can be really critical. There may be rock slides on the mountain. To protect animals from rock slides, sensor nodes are deployed along the mountain areas [22]. There are emergency signaling systems to make the people and animals aware. A single  
Figure 4: Application of VSNs in battlefield monitoring.  
Figure 5: Applications of VSN in rock slide and animal monitoring.  
physical WSN is deployed, but it is used by two VSNs. One VSN monitors rock slides and another VSN monitors animals crossing the mountainous terrain. Both applications rendered by the two VSNs use the same physical sensor nodes and relay the data to the signaling systems and/or to its members. In this scenario only two applications have been considered. Some other sophisticated applications may also be added to the existing system.
## 3 Case Study
\begin{split} Accuracy=\frac{1}{N}\sum\limits_{i=1}^{N}1(\mu_{Tci }=\hat{n}_{Tci}),Precision=\frac{TP}{TP+FP},\ Recall=\frac{TP}{TP+FN},\\ F1-score=\frac{2\cdot Precision\cdot Recall}{Precision+Recall}, \ AUC-ROC=\int\limits_{0}^{T}TPR\cdot\Big{(}FPR^{-1}(t)\Big{)}dt.\end{split}  \tag{25}\]  
According to the comparative analysis data (Table 8), the developed neural network is trained significantly faster and shows improved quality indicators. Thus, the training time is only 1 min 12 s (72 s), which is approximately 2 times faster than that of the traditional RBF network, whose training time reaches 2 min 25 s (145 s) [20; 56]. In comparison with the Jordan and Elman architectures, the training time is reduced by approximately 3.5 times (72 s vs. 257 and 252 s, respectively) [21; 57; 58]. Moreover, in terms of metrics such as accuracy and F1-score, the developed network shows an about 18\(\ldots\)22% improvement compared to the Hopfield (accuracy 0.837, F1-score 0.833) and Hamming (accuracy 0.811, F1-score 0.819) architectures [23; 24; 59; 60]. These quantitative improvements indicate that the proposed architecture combines high accuracy and significantly reduced training time, making it more effective for real-time applications. Figure 11: Diagram of the quality metrics: (a) accuracy metric diagram, (b) loss metric diagram.
## 3 Cryptography and the Erosion of the Security Perimeter
### Higher Cryptographic Protocols for User Privacy  
The next milestone in the dissolution of the safely centralized data processors was the wide uptake of computing in society, and especially in businesses and public administration. Both the processing of large amounts of personal data, and the wide and deep usage of computers by many humans in many places made the control of personal data increasingly difficult. This was a relevant phase where both privacy and identity management challenges blossomed, and both research and practice thrived with the e-Commerce boom from the 1980s into the 1990s. Here, anonymizing MIXes for unobservable communication [13] were invented and extended from e-mail protocols to ISDN, mobile phones and IP [14], and researchers' imagination turned public-key algorithms into protocols with many peculiar properties in Identity Management (IdM), privacy preservation [15], secure electronic payments, and other areas. Some of the research results where reaching out into practice, for example the CAFE [16] and SEMPER [17] projects that developed or prototyped a number of technologies for electronic marketplaces including anonymous electronic cash, anonymous e-mail and Internet communication, revocable anonymity, e-cash with double spending control and blinding of certified information. The common assumption for most of these public-key cryptography based security protocols is that the private key part is safely, and exclusively, stored and used by the respective user or device only [17]. This assumption has made its way into regulatory frameworks such as the EU directive on electronic signatures [18].
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### CN Slicing
#### 3.1.2 Slice-Aware Device Connectivity  
According to 3 GPP specifications, the Allowed NSSAI can include a maximum of eight S-NSSAI values [10]. This means that a device can establish Packet Data Unit (PDU) sessions with up to eight slices at the same time.  
The device can have different client applications (e.g., internet browsing applications, enterprise applications, XR applications), each requiring the connection to a different slice. To make this possible, the device needs to be made slicing aware, something that is achieved with the introduction of the UE Resource Selection Policy (URSP) [28]. The URSP is a network slicing feature enabled by the Policy Control Function (PCF), which informs the network slice status to the UE via the Access and Mobility management Function (AMF). It is composed of a number of URSP rules that map application information (e.g., client application ID, device Operation System ID, IP descriptors) with network slice information (e.g., S-NSSAI, Session and Service Continuity, Data Network Name). The device uses the URSP to determine which PDU session shall be chosen for a particular application based on URSP rules. For further information on the URSP and its use for slicing support at the device side, please see [29].
# Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds
## 4 Experimental Results
### Experimental Results
#### 4.2.3 Various Ratios of Server Capacities  
MRG performance under various combinations of server capacities with a fixed ratio of resource demands was evaluated. Two servers were used with various ratios of CPU cores and memory capacities in the range from (8,2), (8,4), (8,8), (8,16) to (8,32). Two applications, Spark Pi and Spark PageRank, were used in the system with the ratio of [1:2]. Other ratios were not conducted in this experiment because of the limitation of server capacities. When the ratio of resource demands is smaller, there are more jobs submitted to the experimental system. Therefore, the lower ratio of resource demands can be experimented with more performance results to evaluate the resource provisioning. One application required 1 CPU core and 2 GB memory to execute Spark PageRank, and the other application required 2 CPU cores and 1 GB memory for running Spark Pi. Each round of the experiment had nine applications submitted to the system with varied server capacities.  
In general, the higher resource capacities the server has, the more performance gain the MRG approach will be for the corresponding application. Figure 7 illustrates that the overall execution time can be reduced when one of the server resource capacities increases from (8,2) to (8,4). The results showed that the performance improvement was not highly relevant to the increasing memory capacities, which is because Spark Pi is CPU-intensive. Increasing memory capacities does not considerably reduce the execution time. On the contrary, Figure 8 depicted performance improvement when running the memory-intensive application Spark PageRank with various sizes of memory capacities. Results revealed that, when one of the server capacities was (8,2), the completion time could be improved as the other server capacities increased from (8,2) to (8,16). Accordingly, the higher capacities the memory has, the more performance gain the MRG approach will have.  
Finally, in this study, MRG performance was evaluated after submitting nine Spark Pi and nine Spark PageRank applications to the system. As depicted in Figure 9, the proposed MRG approach could considerably reduce the overall execution time when the resource capacities were increasing. This is because the server could allocate resources for both CPU- and memory-intensive applications with sufficient resource capacities and efficient MRG allocation method. The performance gain was achieved more than 60% when the ratios of resource capacities were over (8,8). In particular, the improved performance can be up to 64.7% in the case of (8,8) and (8,16) for running both CPU- and memory-intensive applications.  
Figure 6: Performance comparisons of mixed ratios of application demands.  
Figure 8: Performance evaluations of various server capacities to run Spark PageRank.  
Figure 7: Performance evaluations of various server capacities to run Spark Pi.  
Figure 9: Performance evaluations of various server capacities to run both Spark Pi and Spark PageRank.