esa-sceva/satcom-chunk-collection huggingface dataset:
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 6 Discussion  
In the IoT concept, LLN is one of the core network infrastructure for smart city applications. The effectiveness of these applications depends on the reliability, longevity, and delivering required information timely. Route selection plays an important role in meeting the application objectives in LLNs. Context-awareness in the IoT paradigm envisions the ability of a system that can provide efficient services to the users. Contextual information collected from the requirements of the application can be used to provide context-awareness. Our proposed COOF can select the efficient route under high and dynamic traffic scenarios. Simulation results and performance analysis show that COOF performs outstanding when compared to others. This performance superiority comes from the metrics composition mechanism, along with other aspects. Before we conclude, we emphasize the important aspects of the proposal, which are as follows:  
* Unlike default RPL OF, COOF uses multiple routing metrics for selecting the best parent through which a node forwards the data towards the root. The proposed OF is based on the combination of different metrics for IoT network environments. The routing metrics used in COOF efficiently utilizes the network resources since the IoT devices are resource-constrained.
* QFI and REI metrics are used to prefer the nodes with high resource availability. In addition, QFI is considered as a simple indicator of traffic tendency in the network, where it monitors the queue fluctuation in per unit time. Thus, it is designed to respond to the dynamic needs of the network. The ETX metric is used to indicate the availability of the quality of the link(s). According to the application requirements, these metrics values are used in the fuzzy system as an input variable to construct a single output variable.
* The single routing metric based problem in RPL is addressed using fuzzy logic techniques that simultaneously use multiple routing metrics. This technique estimates the quality of the route based on the metric values that are present in the OF.
* Another aspect that enhances the effectiveness of COOF is context information. It utilizes knowledge regarding the application requirements, and it is aware of the context through its ability to gather contextual information regarding the condition of a given link and node in the network.  
Overall, for the evaluated scenarios, it is clear that the COOF can improve network performance for use in IoT based smart city applications. In this proposition, we only consider a static network scenario, even though some smart city network may consist of the mobility of nodes. However, in the future, we plan to optimize and evaluate the COOF in mobility scenarios.
# Evolution and Testing of NB-IoT Solutions
## 5 End-to-End NB-IoT Testing
### NB-IoT Performance Results
WLAN, GPRS, UMTS, and HSDPA results have been extracted from [24] and LTE measurements are from [25]. Continuous transmission doesn't take advantage of eDRX and PSM, the mechanisms used in NB-IoT to reduce power consumption. The effect of these mechanisms are analyzed in [26]. The results obtained in this paper are for the worst case but enable comparing the consumption with other technologies when using continuous transmission. Table 1 shows that NB-IoT power consumption is still lower than the rest of the technologies. It is not the purpose of this article to provide a detailed comparative of power consumption, but demonstrate the versatility offered by the testbed to execute tests in atypical conditions in order to obtain non nominal values, looking for the borders of the applicability of the technology and the system under test. Figure 11: Power and round trip time in robust scenarios. Figure 12: Robust vs. Normal scenario power consumption patterns. As an important aspect, due to the extremely reduced capacity of NB-IoT, we have verified that any traffic, even a residual from equipment connected to a NB-IoT device, can be very harmful. Therefore, in scenarios where NB-IoT devices work as modems, we have identified the need to systematically use firewalls or other mechanisms to filter any undesired traffic. Another interesting lesson learned is that some of the traditional traffic generation test tools have problems operating in scenarios of a very low rate of transmission and particularly in the presence of very high peaks of delay as those present in some configurations NB-IoT, so it is advisable to use solutions which have been specifically verified in those scenarios.
## 3 Proposed Methods
### Gradient Constraints
#### 3.1.2 Ball-Type Gradient Constraint  
Let the sets of indexes \(\mathcal{G}_{1},\ldots,\mathcal{G}_{N}\in\mathcal{G}\) be \(\mathcal{G}_{n}:=\{n,N+n,\ldots,5N+n\}\) for \(n=1,\ldots,N\). We introduce the ball-type gradient constraint with respect to the \(n\)-th pixel as  
\[\|(\mathbf{D}\mathbf{x})^{\mathcal{G}_{n}}\|_{2}\leq\|(\mathbf{D}\mathbf{x}_{ \text{ref}})^{\mathcal{G}_{n}}\|_{2}, \tag{14}\]  
where \((\mathbf{D}\mathbf{x})^{\mathcal{G}_{n}}\in\mathbb{R}^{6}\) is the \(n\)-th sub-vector of \(\mathbf{D}\mathbf{x}\) with the entries specified by \(\mathcal{G}_{n}\), such as  
\[\begin{split}(\mathbf{D}\mathbf{x})^{\mathcal{G}_{n}}& :=\left[(\mathbf{D}\mathbf{x})_{n}\;(\mathbf{D}\mathbf{x})_{N+n} \;(\mathbf{D}\mathbf{x})_{2N+n}\;(\mathbf{D}\mathbf{x})_{3N+n}(\mathbf{D} \mathbf{x})_{4N+n}\;(\mathbf{D}\mathbf{x})_{5N+n}\right]^{\top}\\ &=\left[(\mathbf{D}_{v}\mathbf{x}_{R})_{n}\;(\mathbf{D}_{h} \mathbf{x}_{R})_{n}\;(\mathbf{D}_{v}\mathbf{x}_{G})_{n}\;(\mathbf{D}_{h} \mathbf{x}_{G})_{n}(\mathbf{D}_{v}\mathbf{x}_{B})_{n}\;(\mathbf{D}_{h} \mathbf{x}_{B})_{n}\right]^{\top}.\end{split} \tag{15}\]  
The ball-type gradient constraint limits the magnitude of the gradients of an output image to less than that of a reference image. Note that the signs of the gradients are not considered explicitly in these constraints.  
Figure 2b illustrates an example of the ball-type gradient constraint with the same reference image as in Figure 2a. We see from this figure that the ball-type constraint is defined by a circle whose radius is the gradient magnitude of the reference image (see the dotted blue circle). Furthermore, the range of the ball-type constraint is more relaxed compared with the box-type one in Figure 2a, even with the same reference image. Therefore, even when we use a noisy image as a reference, in which the sign of the gradients varies locally, the ball-type constraint enables us to generate a smooth image that is locally smoother and more natural than the box-type one.  
As described above, the ball-type constraint can obtain better results than the box-type one for a noisy image. However, when it is applied to a noise-free image, gradient reversals may occur, especially in the case of small-magnitude texture.
# Strained Silicon Single Nanowire Gate-All-Around TFETs with Optimized Tunneling Junctions
## 4 Conclusions  
By proposing the advantage of a single nanowire over an array of nanowires in studying the effect of different process steps, we have investigated how ion implantation and dopant activation parameters impact the performance of strained silicon single NW GAA TFETs. The goal was to improve the TFETs in terms of I\({}_{\text{on}}\)-current and average subthreshold swing. The best result was achieved for n-TFETs with ion implantation in the normal direction to the nanowire surface and dopant activation at a moderate temperature of 500 \({}^{\circ}\)C, as compared to the annealing temperature of 550 \({}^{\circ}\)C for p-TFETs. Such optimized n-TFETs deliver high I\({}_{\text{on}}\)-currents of 15 \(\upmu\)A/\(\upmu\)m at V\({}_{\text{dd}}\) = 0.5 V and a low average subthreshold swing of 76 mV/dec over 4 decades of drain current.  
[PERSON] fabricated and measured the devices and wrote the paper; [PERSON] performed the e-beam lithography; [PERSON] deposited the gate oxide and metal layers; [PERSON] and [PERSON] contributed to the analysis of data and writing of the paper.  
This work is partially supported by the BMBF project UltraLowPow (16 ES0060K) and the European project E2 SWITCH.  
The authors declare no conflict of interest.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on the Basic Framework of Vulnerability Analysis  
To address the growing concerns about the security of IoT systems, it is vital to perform an accurate analysis of firmware binaries, even when the source code or the hardware documentation is not available [43]. However, vulnerability analysis in the IoT security field is obstructed by the lack of dedicated the basic framework. For example, the dynamic analysis relies on the ability to execute software in a controlled environment, often an instrumented emulator [43]. Thus, the basic framework mainly provides the features of dynamic debugging by semi-simulation and full simulation methods. It can perform complex dynamic analyses to support IoT security research.  
**Technical requirements:** The ability to fetch firmware of the IoT device.  
For the lack of specialized analysis tools for firmware, especially dynamic analysis tools, _Avatar_[43] proposed a framework to analyze firmware combining both simulated execution mode on simulators and the actual execution mode on real devices. When the firmware is running in the simulation mode, _Avatar_ forwards the operation to the actual device in the case of input/output (I/O) access. The real device returns the results to the simulator after dealing with operation so that the simulator can continue the execution. It effectively solves the problem of specific peripheral components without source code and documentation. Then, _Prospect_[44] and _Surrogate_[45] also proposed similar dynamic analysis frameworks. Four years later, the author's team of _Avatar_ re-developed _Avatar2_[46], which allows security researchers to inter-operate between different dynamic analysis frameworks, debuggers, simulators, and real devices. In addition, the authorsalso show how to use _Avatar2_ to record the execution flow of the device. [PERSON] et al. [47] proposed _Firmadyne_, focusing on Linux-based devices. The first use software for system-wide simulation, then adopt dynamic analysis methods such as scanning and probing to discover vulnerabilities. The simulation features of the above frameworks are based on QEMU [48]. For sensor operations that are not easy to simulate, semi-simulation frameworks [43; 44; 45; 46] are to guide I/O operations to physical hardware by software agent methods when executing firmware instructions in Table 1.
## 5 Case Analysis
### EPS Performance Evaluation under Multi-Level System
#### 5.1.3 Cloud Parameters of Various Factors in EPS System  
In the EPS system, the principle of a reverse cloud generator is used to obtain the cloud parameters. That is, the scores of nine experts regarding the actual work of an EPS are substituted into Equation (14), and the cloud parameters of each index in Figure 1 are obtained. The specific values are shown in Tables 3 and 4.  
As can be seen from Tables 3 and 4, the \(Ex\) of the performance indicators at the factor level exceeds 70 points. According to the grading of the performance evaluation of the EPS system, we can determine that most indicators are in a good stage. However, the \(Ex\) of the steering wheel angle (\(C_{2}\)) and torque fluctuation at low speeds (\(D_{3}\)) is lower, which indicates that the good working condition of the EPS system cannot be satisfied; this affects the comfort and stability of the vehicle. On the whole, the \(Ex\) of the working indicators of the state layer is between 60 and 80, which indicates that the system is in good working condition.  
In addition, the \(En\) of the working index of the EPS system's factor layer is basically between 5 and 6, which shows that the scores of the nine experts are relatively concentrated and that the scoring results have good data adhesion. However, the \(En\) of performance \(C\) and \(D\) of the steering wheel is large, and the evaluation of appraisers is random and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor Layer** & **Subjective** & **Objective** & **Combination** & **Index Cloud** \\
**Indicators** & **Weight** & **Weight** & **Weight** & **Parameters** \\ \hline A & 0.325 & 0.402 & 0.361 & (79.46, 5.25, 1.07) \\ B & 0.243 & 0.199 & 0.222 & (77.68, 6.52, 0.83) \\ C & 0.191 & 0.134 & 0.164 & (66.85, 8.27, 0.97) \\ D & 0.122 & 0.102 & 0.113 & (68.80, 7.19, 0.99) \\ E & 0.119 & 0.163 & 0.140 & (76.02, 6.85, 0.49) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation parameters of factor layer in the EPS multi-level system.  
score range is large. Therefore, it is necessary to conduct an experimental review of these two performances.
# Link Prediction with Hypergraphs via Network Embedding
## 1 Introduction
Considering that hypergraphs can represent higher-order systems more conveniently, the interaction information of nodes is characterized into vectors with network embedding, so that the link prediction on hypergraphs can be converted into a classification problem. Therefore, we provide a novel idea of link prediction with hypergraphs with network embedding (HNE) in this paper. Our motivation is to predict the relationships of students based on the library loan records of universities, instead of higher-order relationships of students. Thus, we investigate the link prediction with hypergraphs. We use a hypergraph to model all types of objects and relations of the library loan record networks. Firstly, we construct different kinds of nodes associations in a heterogeneous network with a hypergraph according to the library loan records of seven universities. Secondly, a network embedding method, Deepwalk, is utilized to extract structural information and represent nodes by vectors. Thirdly, a machine learning model, a random forest [40], is applied as a classifier for the link prediction. The experiments are conducted on seven sizes of heterogeneous networks and compare several typical link prediction methods to verify the performance of the proposed approach and achieve the promising results on the seven datasets. The innovations in this paper are as follows: We propose a link prediction method using hypergraphs based on network embedding. The representation of the features of library loan record associations are novel in the process of our overall algorithm for link prediction of the relationship of students, which means that learning technology is applied to human behavior dynamics networks, that is, network embedding technology is introduced into human behavior dynamics networks. Then, a vector of each student for library loan records is constructed as a training set. Our method achieves promising results on the seven different datasets.
# Assessing the Seismic Demands on Non-Structural Components Attached to Reinforced Concrete Frames
## 5 Results and Discussion
### Component Dynamic Amplification Factor  
This section investigates the amplification in the acceleration of the component relative to the floor acceleration to which it is attached. The top-story FRSs of a regular and irregular building model under different soil types normalized by the corresponding peak floor accelerations (PFAs) are shown in Figure 13. The ratio of FRS to the PFA is known as a component dynamic amplification factor (CDAF). The CDAF of the building models in the present study is compared with the definitions of ASCE 7-16 [38] and FEMA P-750 [39]. As per the definition of ASCE 7-16, the component amplification factor is 2.5 for flexible NSCs whose time period is larger than 0.06 sec. For rigid NSCs (\(T<\) 0.06 s), the value of the amplification factor is 1. From Figure 13, it is clear that the definition of ASCE 7 underestimates the CDAF for periods closer to the vibration periods of the regular and irregular building models for hard soil, while it underestimates the CDAF for a period closer to the fundamental vibration period of the building models for medium and soft soil conditions. The maximum amplification factors for the top story of a regular building model are 4.47, 4.93, and 5.19 for soil A, B, and C, respectively. The corresponding values for an irregular building are 4.32, 4.83, and 4.85 for soil A, B, and C, respectively. As shown in Figure 13, this underestimation is also included in the definitions of FEMA P-750.  
The effect of the damping ratio of NSC (\(\xi_{NS}\)) on the seismic behavior of non-structural components must be assessed, as NSCs are characterized by different periods and damping ratios [45]. Hence, in this particular study, component dynamic amplification factors are evaluated for different \(\xi_{NS}\) (0.1%, 0.2%, 0.5%, 1%, 2%, 5%, 10%, and 20%). Figure 14 shows the top floor CDAF spectrum of regular and irregular building models for different NSC damping ratios (0.5%, 2%, and 10%). Lower \(\xi_{NS}\) values resulted in higher amplification factor values, as expected. The effect of the damping ratio of NSC is found to be more significant at modal periods of the building models for all soil types. It is worth noting that the effect of \(\xi_{NS}\) is insignificant over both very short and very long periods. As a result, this study attempted to develop a CDAF spectrum prediction model using one of the machine learning techniques known as artificial neural networks (ANNs).  
Figure 12: Normalized PCA values for (**a**) regular buildings; (**b**) irregular buildings.  
Figure 14: CDAF spectrum for a top floor at various damping ratios of NSC.  
Figure 13: CDAF for a top floor level (**a**) regular building; (**b**) irregular building.
# Monitoring of Surgically Treated Upper Arm Fracture by Implanted Antenna at 402 MHz
## 6 Conclusions  
In this study, an application for the post-surgery monitoring of humerus fracture healing was proposed. Different types of fractures were considered in monitoring. The monitoring depended on measuring the difference between the far-field APD of an implanted planar and the wire half-wave dipole antennas in cases of a normal and fractured humerus. The measuring also depended on selecting the best position for recording this  
Figure 13: Comparison between measurements and simulation results in case of (**a**) wire antenna and (**b**) planar antenna.  
difference. The return loss of the implanted antenna was also taken into consideration. Two different positions of observation were taken into account according to the simulation of the far-field power pattern of the implanted antenna. The maximum difference in APDs was observed at \(\Phi=90^{\circ}\) and \(\theta=91^{\circ}\). The results show a reduction of 11.57% to 15.45% of the transmitted APD when the humerus was broken. This considerable difference for all of the studied fracture types provides information about bone healing. The return loss showed a remarkable difference between the normal and fractured humerus cases. The specific absorption rate was considered in the antenna design to guarantee its safety for human tissues. The technique was verified experimentally using a lifeless animal model, and the results showed good matching with the simulation.  
Conceptualization, W.Z. and R.U.; methodology, W.Z. and R.U.; software, W.Z.; experiments and measurements, W.Z.; validation, W.Z. and R.U.; writing original draft preparation, W.Z.; writing review and editing, W.Z. and R.U.; supervision, R.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were waived for this study, due to the study was conducted following the ethical guidelines of Eastern Mediterranean University [[https://bayek.emu.edu.tr/en/regulations](https://bayek.emu.edu.tr/en/regulations)]([https://bayek.emu.edu.tr/en/regulations](https://bayek.emu.edu.tr/en/regulations)), which were announced on 12 May 2022.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.  
The authors declare no conflict of interest.
# Analysis of Hydrogen Filling of 175 Liter Tank for Large-Sized Hydrogen Vehicle
## 3 Result and Discussion  
### Numerical Validation  
The numerical validation was performed to verify the adaptability of the numerical models by comparing the obtained results with the experimental results of Zheng et al. [6] with a Type III hydrogen tank. Numerical conditions and details were considered with the experimental conditions in the work of Zheng et al. [6]. In our previous studies [3; 16], the Redlich-Kwong gas model of hydrogen showed good adaptability for CGH\({}_{2}\) filling behavior. For the adaptability of the turbulence model, the turbulence models of realizable k-epsilon [17], renormalization group (RNG) k-epsilon (RNG), shear stress transport (SST) models, and Reynolds stress model (RSM) were considered for the CGH\({}_{2}\) behavior.  
Figure 2 shows a comparison of the average gas pressure and temperature of hydrogen in the tank during the filling processes for 200 s obtained using the different turbulence models. Increasing trends in pressure and temperature distributions were observed. In the early stage of the filling process, the pressure and temperature deviations from the experimental results increased with maximum deviations of 11% at 40 s and 1% at 100 s, respectively. However, the deviations decreased with the filling time.  
Figure 2: Time histories of pressure (**a**) and temperature (**b**) distributions with different turbulence models during filling processes [6].  
Among the different turbulence models, results obtained using the realizable k-epsilon and RSM models showed the closest agreement with the experimental values, while those from the RNG and SST models showed the largest deviation. These results are similar to those reported by Suryan et al. [18].  
For the pressure distribution, the realizable k-epsilon and RSM models showed slight deviations of 2% and 1.6%, respectively. The realizable k-epsilon and RSM models showed minor deviations of 0.2% and 0.4%, respectively, in the temperature distribution. Between two turbulence models, reasonable solutions can be obtained in the RSM model. However, more computational effort and cost are required to solve six additional transport equations to calculate the Reynolds stress [19]. Therefore, the realizable k-epsilon model was found to be a suitable turbulence model considering the accuracy, convergence, and computational cost for the calculation of the CGH\({}_{2}\) filling phenomenon.
## 2 Study Area and Input Data
### Water Occurrence Masks  
Water occurrence or probability masks are available from different sources. In this study, we use the Global Surface Water dataset (GSW) published by [PERSON] et al. (2018), as this has a global coverage and a high spatial resolution of about 30 m. It has been derived based on about three million Landsat satellite images taken between 1984 and 2015. For each pixel, the water probability given in percentages is provided, where a value of 50% might indicate either a permanent water occurrence in half of the pixels for all 32 years of data or a full water pixel for half of the time period. These values can be used to extract water masks for predefined water probability values; e.g., to derive lake shapes for permanent water bodies in dry seasons (with a threshold of 100%) or for areas that are temporarily flooded (with a threshold \(<\) 50%). More information on the methods used to convert GSW to land-water masks is provided in Section 3.1.