esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## 4 BEP Analysis
### Linear EH Model  
In this subsection, the BEP of the considered DH AF relaying system is derived for the linear EH. Here, it is assumed that the harvested energy is linearly dependent on the received energy and increases by increasing the energy transferred from PB. At high SNR values, the overall BEP for the linear EH model is given as  
\[P_{b}^{L}\!\!\approx\!\!\frac{1}{\theta}\int_{0}^{\infty}\underbrace{\int_{0 }^{\infty}P_{s}^{L,NL}(e|z,w)f_{Z}(z){\rm d}z}_{P_{s}(e|w)}f_{W}(w){\rm d}w. \tag{24}\]  
In (24), \(P_{s}(e|w)\) is calculated by substituting \(f_{Z}(z)\) and \(P_{s}^{L,NL}(e|z,w)\) from (21) and (14) as  
\[P_{s}(e|w)=\frac{a}{2}(E_{0}-F_{0}(w)), \tag{25}\]  
where  
\[E_{0}=\int_{0}^{\infty}f_{Z}(z){\rm d}z=1, \tag{26}\]  
and after simplifying  
\[F_{0}(w)=\frac{D}{\sqrt{\rm B}}\int_{0}^{\infty}\frac{z^{m_{z}-0.5}}{\sqrt{1+ z/G}}\exp(-z\phi_{z}){\rm d}z, \tag{27}\]where \(G=B/C\). Using ([56], eq. 10), (27), is rewritten as  
\[F_{0}(w)=\frac{D}{\Gamma(0.5)\sqrt{B}}\int_{0}^{\infty}z^{m_{z}-0.5}G\,\tfrac{11} {11}\left(\frac{Z}{G}\left|\begin{matrix}0.5\\ 0\end{matrix}\right.\right)\exp(-z\phi_{z})\mathrm{d}z. \tag{28}\]  
Using ([50], eq. 7.813-1), and substituting \(G=B/C\), (28) is obtained as  
\[F_{0}(w)=\frac{D}{\Gamma(0.5)\sqrt{B}}\phi_{z}^{-(m_{z}+0.5)}G\,\tfrac{12}{21} \left(\frac{1+w\bar{y}}{B\phi_{z}w\bar{y}}\left|\begin{matrix}0.5-m_{z},0.5\\ 0\end{matrix}\right.\right). \tag{29}\]  
Then, (25) is calculated by substituting (26) and (29). Moreover, substituting (25) and (23) in (24) we have  
\[P_{b}^{L}\approx\frac{1}{\vartheta}\frac{a}{2}\int_{0}^{\infty}(1-F_{0}(w))f_ {W}(w)\mathrm{d}w=\frac{a}{2\vartheta}(P-J), \tag{30}\]  
where  
\[P=\int_{0}^{\infty}f_{W}(w)\mathrm{d}w=1 \tag{31}\]  
and  
\[J=\frac{DI}{\Gamma(0.5)\sqrt{B}}\phi_{z}^{-(m_{z}+0.5)}\int_{0}^{\infty}w^{m_ {w}-1}\exp(-w\phi_{w})G\,\tfrac{12}{21}\left(\frac{1+w\bar{y}}{B\phi_{z}w\bar{ y}}\left|\begin{matrix}0.5-m_{z},0.5\\ 0\end{matrix}\right.\right)\mathrm{d}w. \tag{32}\]  
Please note that no closed-form solution is available for (32) as it is calculated numerically. Finally, the overall BEP for the linear EH model is obtained from (30) which depends on only \(J\) in (32).
# Wedge Design and Dynamic Characteristics Analysis of Unequal-Length Wedge Slant Support Clutch
## 3 Analysis of Dynamic Characteristics of Differential Engagement of Slant Support Clutch
### Analysis of Simulation Results  
#### 3.2.1 Conventional Slant Support Clutch  
The time-domain curves of the internal and external ring speeds of the conventional slant support clutch during engagement are displayed in Figure 7. It can be observed that a long period elapses from the first synchronization of the inner and outer ring speeds to the maximum speed difference, which defines the engagement response time. The speed of the outer ring of the clutch suddenly changes at approximately 0.528 s. The maximum speed difference between the inner and outer rings was 74.52 rad/s and the instantaneous angular acceleration of the outer ring was 7673.64 rad/s\({}^{2}\). Based on the analysis of the wedge pose during the simulation, the following can be concluded.  
Figure 6: Multibody dynamic model of partial clutch with unequal-length wedges: (**a**) three A wedges; (**b**) six B wedges; (**c**) nine C wedges; (**d**) twelve D wedges; (**e**) nine E wedges; (**f**) three B and three D wedges.  
1. Approximately 0.1 s after the speed of the clutch outer ring is withdrawn, the speed of the inner and outer rings remains the same. During this period, the speed decrease rate of the outer ring of the clutch remains constant until 0.526 s. At this point, the wedge is completely detached from the inner ring until the speed of the outer ring decreases to approximately 1900 rad/s and the bonding conditions are re-established.
2. The phenomenon of PCE on the wedge block is speculated to be caused by the excessive deformation between the inner and outer rings of the clutch and wedge block.
3. Owing to the excessive impact, the wedge deflects further, which is similar to the phenomenon of overload deflection in an all-phase clutch. There is always a certain difference between the speed of the outer ring and that of the inner ring of the clutch, and the torque transmission performance of the slanted support clutch declines significantly.
4. During the simulation process, there was an overload deflection phenomenon, and under actual working conditions, the clutch could have been in a failed or damaged state and, thus, unable to meet the usage requirements.
# Cognitive Radio Network Technology for IoT-Enabled Devices +
## 5 Conclusions  
There is a rising demand for IoT devices that are both intelligent and CRN-enabled in order to monitor surrounding radio environments, assess available frequencies, and determine when and where to transmit. As a result of this requirement, there has been a great deal of effort to create CR-based IoT systems as the standard paradigm for intelligent IoT systems by making use of CR's cognitive capacities. This study has supplied the current frameworks of CR-based IoT systems, investigated the most recent SS and spectrum sharing methodologies, and emphasized the benefits and drawbacks of each. This research has also investigated the advantages of incorporating various new technologies, such ML methods, into CR-based IoT setups. Lastly, several obstacles, future research areas, and outstanding issues in development of CR-based IoT systems have been addressed and highlighted by this work.  
**Author Contributions:** The authors of this paper worked together to construct it. The idea and framework for the article process were proposed by [PERSON], [PERSON] and [PERSON]; [PERSON] and [PERSON] were responsible for the literature search, data extraction, data analysis and manuscript writing; [PERSON] and [PERSON] did a thorough literature search, analyzed the data, and edited the early proposal. [PERSON] gathered the information, prepared the article, and analyzed and organized the data. The paper was proofread by all authors. Each contributor has reviewed the final manuscript and given their approval. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Polytechnic University of Bucharest [PUB Art] grant number [HG 37/19.6.2019] and the APC was funded by [PUB Art].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 4 Eviction Set Construction Algorithms
### Eviction Set Construction Algorithms
#### 4.2.3 Prime-Prune-Probe Algorithm  
The _Prime-Prune-Probe_ (PPP) [53] algorithm has shown that faster eviction set construction is possible. As shown in Algorithm 3, the PPP technique begins with the _Prime_ step, where the attacker generates a set of random addresses and accesses them to fill in either a subset or the entire cache, i.e., the candidate set. To eliminate false positives, the attacker accesses the candidate set again in the _Prune_ step and removes any addresses that result in a high access latency. This process is repeated many times to remove self-evicted addresses until all remaining addresses in the Prime set are concurrently cached. Regarding the number of cache accesses, our experiments showed that the pruning process is normally completed in fewer than two rounds. After pruning, the PPP returns a set of \(n^{\prime}\) addresses, where \(n^{\prime}\) is less than or equal to the initial size \(n\). The pruning step aims to absorb noise so that congruent addresses can be more easily identified in the Probe step. These three steps are repeated until enough congruent addresses are found and added to \(E\).  
```
Input:\(n\)=candidate set size, \(x\)=target address Output:\(E\)=minimal eviction set
1\(E\leftarrow\{\}\)
2 whileTunedo
3\(C\leftarrow\) generate n random addresses /* Prime step */
4 Access C\(\cup\)E /* Prune process */
5 whilethy are no self-eviction in C\(\cup\)Edo
6 foreach\(c\in C\)do
7 ifget redundancy to access \(c>\)thresholdthen
8\(C\gets C\backslash\{c\}\)
9 end if
10
11 end if
12/* Probstep */
13 forach\(c\in C\)do
14 iflatency to access \(c>\)thresholdthen
15\(E\gets E\cup\{c\}\)
16 end if /* Test eviction */
17 if\(E\) evict \(x\)then
18 break
19
20 end if
21 returnE
```  
**Algorithm 3**The Prime-Prune-Probe algorithm  
This algorithm can be used to find eviction sets in LLCs using different replacement policies. The number of cache accesses is estimated as \(\mathcal{O}(SW)\) when the LLC uses an LRU as the replacement policy, i.e., the smallest of the three fast algorithms. However, when using a random replacement policy, the number of cache accesses increases to \(\mathcal{O}(W\times n)\). This increase in complexity is due to the candidate set size after the pruning step being much smaller than the cache size \(SW\). Using a small set of addresses reduces the chance of finding a congruent address in each PPP iteration.
## 2 Related Works
### Magnetic Anomaly Signal Processing Methods
The Stochastic Resonance Method uses noise to enhance abnormal signal information, thus improving the output signal-to-noise ratio of a nonlinear dynamic system. It does not require deterministic parameters of the signal and is applicable for random noise. But only one type of abnormal signal detection employsthis approximate performance detection method. The wavelet transform approach reduces noise well, but it has trouble retrieving information from signals with high noise due to signal amplitude decay. The detected magnetic anomaly signal is usually composed of tens of thousands of copies of magnetic field noise. Numerous elements influence magnetic field noise, resulting in variance that is difficult to eradicate and lacks regularity. Although the deep learning-based method moderates the demand for manual processing, the currently used deep neural network is not as capable as expected in signal recognition in low signal-to-noise ratio conditions. Therefore, considering the above defects of the currently used magnetic anomaly detection methods, the maximized retainment of magnetic anomaly signals and the removal of magnetic field noise will become the focus of future research. Currently, there are a large number of magnetic anomaly localization algorithms; however, most of them fail to properly detect weak signals. Therefore, it is necessary to improve the existing algorithms or develop innovative algorithms and put the improved or new magnetic anomaly detection technologies into practice. In this paper, synchrosqueezed wavelet transform and extraction of time-frequency ridges calculated from the greedy algorithm are combined to extract target-induced magnetic anomaly disturbances. Compared with wavelet transform, this method produces a clearer time-frequency display of magnetic anomaly signals; it can effectively detect the presence of targets even with a low signal-to-noise ratio, which is impossible for minimum entropy filtering, deep learning, and higher-order crossings. Compared with the Orthogonal Basis Algorithm, it does not require multiple magnetometers to perform the difference and is less computationally intensive.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 5 A Blockchain-Based Cement Factory  
Our blockchain-based architecture for cement plant is illustrated in Figure 4, from the start to the end of the process, a clinker product represented by a raw material passes through a specially designed rotary kiln, a cooler and iron conveyors. A VFD gear-motor controls the rotation of the kiln.  
In cement processes, the temperature of the rotary kiln, the pressure of the inlet air actuator, the rotation speed of the kiln and the moisture of the mixture are highly important factors that are directly related to the quantity and quality of clinker production.  
To control these critical factors effectively, four high-accuracy temperature sensors (kiln inlet, outlet, mixture, clinker) are used with an STM32 board. During the burning process of the raw material, the temperature sensors, rotation sensor, and pressure sensors  
Figure 3: Collecting and storage process of sensor data.  
are interfaced with the STM32 board to provide control over the state of the burner valve, the inlet air actuator, the raw material feed and the rotation speed. In the second stage, burning raw material at a high temperature (1450 \({}^{\circ}\)C) passed through the rotary kiln to produce the clinker. In this case, all of the sensors and actuators are used to ensure ideal burning conditions inside the rotary kiln, while the moisture sensor is used to check and control the moisture of the mixture before feeds to the kiln. Weight sensors are used with the STM32 board to record the weight of the product (clinker) in tons per hour. In the final stage (after cooling), iron chains are used to convey the product to the clinker store. Our architecture consists of several modules, all of which interact with the lightweight nodes (four STM32F427) of the blockchain service layer. Each of these modules has a copy of the recorded data, meaning that if a node crashes for any reason, then another node can provide a backup of the required data. Through this interaction, lightweight nodes can efficiently record the data and the states of actuators provided by the sensors. Real-time encryption is provided through the use of the ECC algorithm; this is applied by lightweight nodes to the acquired data, which are then sent to the private blockchain. The ECDSA was chosen for our architecture due to its low storage requirements and low complexity, which make it suitable for resource-constrained devices [34]. It also used to create public and private keys and to provide an authentication mechanism for devices. Encrypted data are stored in a secure, private blockchain network, and only the administrator and authorized users can connect to and access services. Blockchain is a promising technology for use in a factory, as it can ensure the security required in a smart industrial environment.
## 4 IFC Data Model for Supporting Safety Risk Identification in Deep Excavation
### Workflow of the Automatic Safety Risk Identification Focusing on the Entire Deep Excavation Process  
An IFC data model-based approach is proposed in this research to realize automatic safety risk identification focusing on the entire deep excavation process, which can be divided into five steps.  
1. After establishing a deep excavation information model meeting the level of details for risk identification (i.e., meeting the information requirements according to the IFC data model), a complete IFC file can be produced and input into the established risk identification system application based on IFC.
2. Users can select a certain construction phase for safety risk identification. Then, a list with construction information requirements that reflect the dynamic construction condition on site is correspondingly generated by the risk identification system application.
3. In accordance with the information requirement list, necessary dynamic construction parameters (which can be obtained from field sensors and monitoring databases) are input based on the real-time construction states.
4. Based on risk identification rules, the risk identification system application retrieves and extracts required construction parameters from the IFC file and information  
Figure 11: Framework of the IFC data model for safety risk identification of deep excavation projects.  
requirement list. Then, the exported engineering parameters are converted into extensible markup language for risk reasoning.
5. Thereafter, the risk identification system application outputs the results, which mainly consist of two parts (selected by users): (1) identified safety risk events, which have high possibilities of occurrence, and (2) similar risk cases.  
With the above process, safety risks in deep excavation projects can be identified automatically, aiming at the entire deep excavation process. Risk identification in the preconstruction stage is unrelated to the dynamic construction condition, which can be automatically conducted by the risk identification platform based on the IFC data model without dynamic information input.
## 3 Proposed Method
### Temperature Extraction  
Figure 2 compares the existing grayscale mean-shift tracking with the proposed temperature-based mean-shift tracking algorithm. The 14-bit digital count values should be converted to a Planck radiance (\(T_{BB}\)) value through a blackbody-based radiometric calibration to extract the temperature data. The temperature can be obtained based on the converted Planck radiance value. In this paper, the blackbody apparent temperature information provided by the FLIR camera was used.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 2 Related Work
### Resource Allocation in Network Slicing  
The authors of [23] specifically studied the problem of provisioning slice links with splittable flows. Since this problem is NP-hard, using the idea of the multipartite graph, they proposed a polynomial heuristic algorithm based on linear relaxation and randomized rounding. It was verified by simulation that the algorithm could achieve good performance. However, this study did not solve the slice node provisioning. The work in [24] used a mixed integer linear programming (MILP) model for the dynamic slicing problem and proposed several heuristic algorithms for it considering temporal variations of the virtual resource requirements. The simulation results showed that the dynamic slice provisioning could increase the slice acceptance ratio and, thus, enabled the physical network provider to increase their revenue. However, dynamic slicing comes at a cost of service quality degradation. [PERSON] and [PERSON] [25] proposed an optimal slice allocation strategy for the 5G core network concerning the intra-slice isolation and delay requirement of slices. They formulated the problem as a MILP model and solved it with CPLEX. Their results showed that the resource utilization of the physical network would improve if the slice isolation was not considered, and stricter delay requirement also affected the slice acceptance ratio as well as resource utilization. In the very latest study [26], the authors presented a latency-optimal resource allocation method for 5G transport network slices to support URLLC services. They introduced the network resource attributes and topology attributes to resource allocation in network slicing, but they did not delve into the impact of network resource attributes and topology attributes on slice provisioning.
## 2 Rainfall Data Source  
### Rain Gauges  
Rain gauges are classified into recording and non-recording types [37]. Non-recording rain gauges commonly in operation in Indonesia are the manual observatory (OBS) and Hellman types, due to their simplicity and low maintenance costs. With non-recording rain gauges, data collection is conducted manually; hence, the result is highly dependent on the reading accuracy of operators, and the timing of rainfall occurrence is unknown because the data obtained are cumulative [38]. Furthermore, some examples of recording  
Figure 1: The distribution of automatic rain gauges includes 1233 units (green triangles), and the integrated weather radar network consists of 42 units (yellow) operating in the Indonesian region.  
rain gauges include mechanical mechanisms (based on floatation or weight), electric instruments (tipping bucket and capacitance sensor), and point rainfall calculation-based sensors (vibration-based disdrometer and optical rain gauge) [39]. Although many precipitation measurement mechanisms have been developed, the tipping bucket is the world's most widely used rain gauge [40] due to its major advantages--low cost, simplicity, and low energy consumption [41]. In addition to the tipping bucket rain gauge, several countries use optical rain gauges for precipitation measurement [42] because this instrument can observe the size and falling velocity of the precipitation particles and then classify the type of rain [43]. In Indonesia, most tipping bucket rain gauges are installed on equipment systems, such as automatic rain gauges (ARGs), automatic weather systems (AWSs), agroclimate automatic weather systems (AAWS), and marine automatic weather systems (MAWSs). Furthermore, the tipping bucket and optical rain gauge are installed simultaneously on weather observation equipment systems for aviation, namely, the automated weather observing system (AWOS). This is to meet aviation safety requirements by covering the entire rain range. Under light rainfall conditions, optical rain gauges can more accurately measure rainfall than tipping bucket rain gauges [44]. Conversely, tipping bucket rain gauges are more accurate than optical rain gauges during heavy rainfall [1]. Figure 2 illustrates the types of rain gauges in operation in Indonesia.
## 3 Result and Discussion
### Short-Term State of Health Estimation
Moreover, the method used for optimization of the parameter of ANFIS, is a combination of back-propagation and least-square estimation. Note that the trained dataset consists of all of the voltage intervals, starting from 0% to 100% SOC. The dataset, related to the battery No. 06, is utilized to test the developed algorithm. The errors between the experimental data (actual SOH) against the estimated SOH at different voltage levels are illustrated in Figure 5. The mean squared error (MSE) and root mean squared error (RMSE) are 0.009 and 0.094, respectively. As can be inferred from the results, the ANFIS has better performance compared with the GMDH. The results, shown in Figure 4, have a maximum error below 0.3. Moreover, the overestimation and underestimation is lower than that of the previous technique, which demonstrated the adaptive capability of the ANFIS technique. Figure 4: Experimental and estimated results of state of health (SOH) vs. battery voltage by employing GMDH (No. 06) for two cycles.