esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Security Considerations and Proposed Zero-Trust Security Model
### Security Considerations to Support Zero-Trust Model
#### 7.1.2 OT Network Segmentation via Firewall  
Network segmentation of the OT environment from the IT network must be guarded by firewalls which will enable the system owners to exactly understand how and what data are transferred between the segments. One of the options to enable this is Isolated Computing Environment Firewalls. Figure 10 shows the working of such firewalled network.  
After the installation of the firewall and configuring the rules by the administrator, all data transferred between the IT and OT network are controlled by the defined rules as to whether allow or deny the data. All the traffic that was filtered is then monitored for analysis to build foundational firewall rules. Always use implicit deny rules and monitor the policies for a certain period of time for example 90 days. Any change in firewall rules must go through a change management process. The benefit of separating IT and OT networks is to provide a barrier between them which will ensure increased resiliency against cyber threats passing from one isolated network to another.
# Cornering Stiffness Prediction Based on Geometric Method
## 2 Method Description
### Local Load Calculations
#### 2.1.2 The Rotating Method  
A tire under the camber will show two important characteristics compared to a non-cambered tire. One is the load transfer between the two sides, and the other is the contact patch width change. The tilting method only shows the load transfer. To cover the contact patch width change, the author put forward the rotating method.  
The FE simulation results in Figure 3 show the contact patch changing with increasing camber angle for a rolling tire (details of FE simulation are presented in the Results section below). Ignoring the contact patch shape sharpness change for different camber angles, the shape of the contact patch is only shifted in the lateral direction. Based on this observation we assume that the contact patch always keeps the same shape as the non-cambered tire. Keeping the centre of the tire fixed, as well as the contact patch area, rotating the tire according to an applied camber angle will shift the contact patch laterally with respect to the tire tread. The tire shoulders will limit the contact shape to expand beyond its width. A schematic drawing in Figure 3 illustrates the idea.  
The load on the outer half of the contact will decrease and the contact patch will shift away from the shoulder of the tire. Rotating the tire will shift the contact patch from the outer half to the inner, leading to a decreased volume for the outer half. We can regard the inner side as the remaining part of the volume (colored yellow in Figure 3). We illustrate this as a volume outside the width of the tire and ignore that the real volume has a different shape on the inner side.  
According to Figure 3, we have:  
\[e=R_{L}\cdot\tan(\gamma). \tag{15}\]  
Taking the point of intersection of the Y-axis and \(R_{L}\) as the origin of the integral coordinate system, we have:  
\[d_{y}=\sqrt{R_{0}^{2}-{\bf y}^{2}}-R_{L} \tag{16}\]  
\[R_{y}=R_{L}+d_{y} \tag{17}\]  
\[\theta_{y}=arccos\left(\frac{R_{L}}{R_{y}}\right) \tag{18}\]  
\[w(x)=\sqrt{R_{0}^{2}-{R_{L}}^{2}},w\leq w_{0}, \tag{19}\]  
Figure 3: Rotating method diagrammatic drawing and contact patch shapes under different camber angles.  
\[V_{z1}=\int_{-w}^{-\mathrm{e}}R_{y}^{2}\cdot\left(\theta_{y}-\frac{sin\left(2 \theta_{y}\right)}{2}\right)dy \tag{20}\]  
\[V_{z2}=\int_{-e}^{w}R_{y}^{2}\cdot\left(\theta_{y}-\frac{sin\left(2\theta_{y} \right)}{2}\right)dy \tag{21}\]  
\[F_{z1}=F_{z}\cdot\frac{V_{z1}}{V_{z1}+V_{z2}} \tag{22}\]  
\[F_{z2}=F_{z}\cdot\frac{V_{z2}}{V_{z1}+V_{z2}} \tag{23}\]  
where \(d_{y}\) is the radial deformation of an arbitrary point along the width of the contact patch. \(\theta_{y}\) is half of the central angle corresponding to the chord length of contact patch length on this point.
# Application of a New Polymer Particle Adhesive for Lithium Battery Separators
## 3 Results and Discussion
### Electrochemical Performance of Assembled Lithium Battery
#### 3.5.2 Electrochemical Impedance Spectroscopy  
Figure 10a shows the Ac impedance diagram of the assembled lithium batteries LG3, LG4 and LS. It can be seen from the Figure 10a that the AC impedance diagrams of the three lithium batteries are composed of a semicircle in the high-frequency region and a straight line in the low-frequency region. The semicircle diameter of the high-frequency region is the resistance of charge transfer (R\({}_{\rm CT}\)), which reflects the interface resistance between the positive electrode material and the electrolyte in the lithium battery. The straight line in the low-frequency region is mainly related to Warburg impedance, which can reflect the diffusion ability of lithium ions in iron phosphate/lithium iron phosphate materials [30; 31; 32]. Figure 10b shows the relationship between Z\({}^{\prime}\) and \(\omega^{-1/2}\)/S\({}^{-1/2}\) at low frequencies of the assembled lithium batteries. In this figure, the slope of the straight line represents the Warburg coefficient.  
The lithium ions diffusion coefficient D\({}_{\rm Li}\) is calculated from Equation (1):  
\[\rm D_{Li}=R^{2}T^{2}/\left(2\sigma^{2}A^{2}n^{4}F^{4}C^{2}\right) \tag{1}\]  
where R is the gas constant, R = 8.3138462 \(\approx\) 8.314 J/(mol\(\cdot\)K); T is the absolute temperature, T = 298.15 K; \(\sigma\) is the slope of the straight line in the low-frequency region; A is the surface area of the battery electrode (cm\({}^{2}\)); F is [PERSON]'s constant, F = 96485.3; \(\omega\) is the AC angular frequency; S is the cross-sectional area between the electrolyte and the electrodes; C is the lithium ion concentration, which can be calculated from Equation (2):  
\[\rm C=\frac{n}{V}=\frac{m/M}{V}=\frac{\rho V/M}{V}=\frac{\rho}{M} \tag{2}\]  
Table 5 shows the charge transfer resistance (Rct), Warburg factor (\(\sigma\)) and lithium ions diffusion coefficient (D\({}_{\rm Li}\)) of the assembled lithium batteries. It can be seen from Table 5 that the lithium ion diffusion coefficients of lithium batteries LG3 and LG4 are greater than that of lithium battery LS. This shows that the transmission performance of lithium ions in LG3 and LG4 lithium batteries is better, which is conducive to charging and discharging under high current densities. This may be because the battery separator used by lithium battery LS is coated with too many S-1 polymer particle adhesives, which affects the transmission of lithium ions.  
Figure 10: (**a**) The Ac impedance diagram of the assembled lithium batteries; (**b**) relationship between Z\({}^{\prime}\) and \(\omega^{-1/2}\)/S\({}^{-1/2}\) at low frequencies of the assembled lithium batteries.
## 4 Biological Activities of Ginger Polysaccharides
### Anti-Oxidant Effects
The results showed that the anti-oxidant capacity of HUE-GSLPs and UE-GSLPs polysaccharides was concentration dependent, and different preparation methods of polysaccharides could affect the anti-oxidant activity of ginger polysaccharide [38]. With the confirmation of the anti-oxidant activity of ginger polysaccharides from stems, people are also interested in the polysaccharides from ginger leaves. The anti-oxidant activity of ginger polysaccharides from stems and leaves (GSLP) was measured by free radical scavenging activity, including the anti-oxidation ability of ABTS, DPPH, hydroxyl, and superoxide free radicals, as well as the chelating activity and reduction ability of ferrous ions. By comparing the activity of ginger polysaccharides obtained by four different extraction methods, including hot water extraction (HWE), ultrasound-assisted extraction (UAE), alkaline solution extraction (ASE), and enzyme-assisted extraction (EAE), it was found that compared with other samples, the yield of ginger polysaccharides obtained by alkali extraction was the highest (11.38%), the molecular weight was the lowest, and the anti-oxidant activity was better [32]. The anti-oxidant activity of polysaccharides may be affected by various factors, such as molecular weight, monosaccharide composition, and glycoside linkage type. Therefore, this study shows that alkali solution extraction is a feasible method, which can extract polysaccharides from ginger stems and leaves with good anti-oxidant activity.
# A Searchable Encryption with Forward/Backward Security and Constant Storage
## 3 System Model  
The system model adopted by our proposed scheme is shown in Figure 3 and has three entities: the client, the PKG, and the server.  
Client: Data encryption and decryption are performed on the client. The client user uploads the ciphertext to the server after encrypting the plaintext with the public key. When searching, the client user can use the keywords to generate trapdoors for a precise search. After receiving the ciphertext result returned by the server, the private key is used to decrypt it.  
Figure 3: Model of entities and data interaction.  
Server: The server has powerful computing and storage capabilities and can store encrypted data. It is an honest but curious entity. It executes the preset program honestly, but it may try to learn the information from the stored messages and infer what the client user is searching for. In addition, the server processes the search request through the check trapdoor. After the verification, the server sends the encrypted data to the corresponding user.  
Private Key Generator (PKG): The PKG is the core of the IBE system. Its functions are as follows: when the IBE system is initialized, it generates the master key and public parameters, and publishes the public parameters. The PKG verifies the user's identity and sends it to the corresponding user through a secret channel as a private key.  
The client is responsible for encrypting/updating the documents and uploading them to the storage server (double-layer structure), as well as generating keyword-specific search tokens for keyword searches. The server is responsible for constructing the double-layer system for storage and retrieval and uploading the encrypted data to the DIC part of the double-layer structure.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 3 Result and Discussion
### LoRa Performance based on Range-Distance RSSI  
This scenario was examined in the LoS and N-LoS condition. This test aimed to measure the LoRa performance distance based on the strength of the RSSI signal devices between sensor node and gateway that can be detected by the system. As seen in Figure 10a-c, the LoS testing was examined in a large and open area of the soccer field near Telkom University. Meanwhile, the N-LoS testing was examined in a small forest inside the Telkom University area. The testing was also conducted in a forest in a suburb area near Batu Kuda Manglayang. The sensor nodes on the proposed system were moved to a range of 0.5 m from the gateway until the communication between the sensor node and the gateway could not be detected and lost connection. Sensor nodes and the gateway were located two meters from the ground.  
Figure 10: Scenario testing of (**a**) line of sight (LoS) at soccer field; (**b**) non-line of sight (N-LoS) at Telkom University area; (**c**) N-LoS in the forest.  
Figure 9: Prototype of the Proposed System.  
The testing results of LoRa RSSI signal performance in the LoS and N-Los area shown in Figure 11. In LoS area at the soccer field, the communication between gateway OLG01 and sensor nodes connected in the range of 0-900 m for Tx1 and 0-750 m for Tx2. In Non-LoS area at Telkom University, the RSSI signal between gateway OLG01 and sensor nodes Tx1 and Tx2 are connected in the range of 0-350 m. The signal cannot be read when the Tx1 or Tx2 are moved more than 350 m. Meanwhile, for the testing result in the suburb forest area Batu Kuda Manglayang, the communication distance between gateway OLG01 and sensor nodes Tx1 and Tx2 can only reach around 300 m. It is the density and thickness of the environment around the sensor nodes which affected the signal strength between the transmitter and receiver.  
The testing also conducted a measure of the most appropriate position for placing the gateway and node sensors in the tree; the best position in which to place the sensor nodes and gateway is estimated at 1.8 to 2 m from the ground.
## 1 Introduction
Through the use of those algorithms, a satisfactory solution effect was achieved during the resource allocation in CRNs [20; 21; 22; 23; 24; 25]. However, there is still room to further improve the optimization effect due to the inherent disadvantages of those algorithms in the practical engineering applications of IoT, such as the possible ease of falling into the local optimal solutions in some cases, the difficulty adjusting many key parameters during the implementation process of the algorithms, and many drawbacks [26; 27; 28; 29; 30; 31; 32]. The Jaya algorithm has been widely used in engineering optimization fields due to its excellent searching ability with an algorithm-specific parameterless feature [33]. In consideration of the satisfactory optimization performance achieved by the Jaya algorithm, there is a general idea to utilize the Jaya algorithm to better serve CRN-aided IoT. In this article, motivated by our previous work in ref. [34], the power resource allocation in the downlink of a CRN-aided IoT system is modeled as a constrained optimization problem, and then a solution method is accordingly presented by incorporating the proposed Jaya algorithm into the cognitive OFDM radio network model to achieve a satisfactory power allocation result. Specifically, compared with the traditional Jaya, the proposed Jaya algorithm, named PA-Jaya, achieved faster convergence speed by using a parallel structure and an asynchronous iteration strategy. Actually, the simulation results show that compared with other popular optimization algorithms, our PA-Jaya algorithm can achieve a better power allocation with faster converge speed, which means that it can improve the efficiency of spectrum utilization more effectively in IoT. This article is organized as follows. Section 2 presents the mathematical model used in the optimization problem we discuss. In Section 3, in addition to analysis for the basic idea and implementation of the traditional Jaya algorithm, the solution method using the proposed PA-Jaya is also presented. Section 4 shows the simulation results to verify the advantages of using the PA-Jaya algorithm, with a comparison with other algorithms. Finally, we provide a conclusion of this article in Section 5.
# Should the Application of Diode Laser Completely Replace Conventional Pulpotomy of Primary Tenth?
## 5 Conclusions  
Due to the minimal invasiveness and increased patient cooperation, lasers can be used as an adequate replacement, supplementary diagnostic, or therapeutic choice in preventive and restorative dental medicine, as well as in endodontics. During endodontic laser procedures on primary teeth, clinicians must pay attention to the influence of the laser on the pulp and root canals of primary teeth, bearing in mind the anatomy of the apical openings and the depth of penetration of the laser into the tissues. The limiting factors for the use of lasers in dental medicine are the necessary education and experience of clinicians, as well as the price and availability of lasers on the market. Given that it provides numerous benefits from the patient's point of view, as well as the dentist's, in pedodontics, a laser is a valuable tool in the provision of dental care. Although a statistically significant difference between laser pulpotomy and conventional, i.e., vital pulpotomy was not confirmed in this study, a slightly higher success rate in favor of the diode laser was noted. However, this research indicates that the quality of the final restoration of an endodontically treated tooth immensely contributes to the success of the therapeutic intervention, regardless of the pulpotomy method used. To sum up, regardless of the fact that this is a significant shift in this scientific field in terms of longer follow-up compared to most of the earlier research, we believe that 2 years is not enough to give a definitive conclusion about the durability of an endodontically treated tooth. Guided by this thought, future studies should be conducted on a larger number of subjects and with a longer period of follow-up (e.g., 5 years).  
**Author Contributions:** Conceptualization, R.B. and D.N.V.; methodology, D.N.V.; software, L.S.; validation, D.N.V.; formal analysis, L.S.; investigation, R.B.; data curation, L.S.; writing--original draft preparation, L.S., B.S. and L.V.; writing--review and editing, L.S., B.S., L.V. and D.N.V.; visualization, L.S.; supervision, D.N.V.; project administration, L.S. and B.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**In Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board, University of Zagreb, School of Dental Medicine (05-PA-15-10/2017).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 3 Iterative Positioning Algorithm for the Target Node Based on Distance Correction
### Basic Concept  
**Definition 1**.: _WSN node positioning. WSN node positioning refers to when the position of the unknown target node is calculated based on the communication between the anchor nodes whose position information is known in the network by specific techniques, algorithms, and schemes [37]._  
**Definition 2**.: _Anchor node. The anchor node is the node whose coordinates or position information is known in WSN [38]._  
**Definition 3**.: _Target node. The target node refers to the node whose coordinates or positioning information is unknown in the WSN [39]._
# An Adaptive Back-Off Mechanism for Wireless Sensor Networks
## 2 Review of Related Work
### Critical Technologies of S-MAC Protocol  
#### 2.1.1 Periodic Listen and Sleep  
The S-MAC protocol's operation cycle includes a listen period and a sleep period. The periodic listen and sleep mechanism is shown in Figure 1. During sleep period, nodes may turn off their transceivers for some time in order to reduce power consumption. During the listen period, each node wakes up from a sleep state and listens to check if any other node has data delivered to it. It is worth noting that, before each node starts periodic listening and sleep, it needs to choose or generate a schedule that determines the time to sleep and exchanges it with its neighbors by sending a synchronization (SYNC) packet. Each node maintains a schedule table that stores the schedules of all its known neighbors [14]. Finally, nodes adopting the same schedule achieve synchronization and form a virtual cluster in which nodes can communicate directly.
# 3D In Situ Stress Estimation by Inverse Analysis of Tectonic Strains
## 2 Inverse Analysis for In Situ Stress Determination
### Tectonic Displacement-Based Approach  
Similarly to the concept that the in situ stress field is composed of the gravity and tectonic stress field, the displacement field on the boundary of a region can also be assumed as the combination of the displacements induced by both gravity and tectonic stress fields. The displacement \((u,v,w)\) on the boundary of 3-D calculating model can be expressed as Equation (13):  
\[\left(\begin{array}{c}u\\ v\\ w\end{array}\right)\ =\ \left(\begin{array}{c}u^{0}\\ v^{0}\\ w^{0}\end{array}\right)+\left(\begin{array}{c}u^{t}\\ v^{t}\\ w^{t}\end{array}\right) \tag{13}\]  
where \(\left(u^{0},v^{0},w^{0}\right)\) is the displacement of corresponding \(X\), \(Y\) and \(Z\) axes at any point on the lateral boundary induced by the gravity stress field in the 3-D calculating model and \(\left(u^{t},\ v^{t},w^{t}\right)\) is the displacement induced by tectonic stress field.  
For the displacement \(\left(u^{0},\ v^{0},w^{0}\right)\) at boundary, it is easy to obtain the gravity stress field based on the theory of elasticity.  
For the tectonic displacement \(\left(u^{t},\ v^{t},w^{t}\right)\) at the boundary, it can be explained by a 3-D calculating model (Figure 3). For boundaries ABEF and OCDG, the normal displacement \(u_{n}\) can be assumed to be Equation (14). For the shear displacement, there are also two types of displacement \(u_{\tau y}\) and \(u_{\tau z}\) in red color, and they can be expressed as Equations (15) and (16):  
\[\mathbf{u}_{\tau y}\ =\ \mathbf{b}_{1}\times\mathbf{y}+\mathbf{b}_{2}\times\mathbf{z} \tag{14}\]  
\[\mathbf{u}_{\tau z}\ =\ \mathbf{c}_{1}\times\mathbf{y}+\mathbf{c}_{2}\times\mathbf{z} \tag{15}\]For boundaries BCDE and AOGF, similarly to Equations (14)-(16), Equation (17) can account for the normal displacement \(u_{\text{my}}\), and the Equations (18) and (19) are for shear displacements \(u_{\tau\text{x}}\) and \(u_{\tau\text{z}}\), as shown in Figure 3:  
\[\mathbf{u}_{\text{\tiny{xx}}}\ =\ \mathbf{d}_{0}+\mathbf{d}_{1}\times\mathbf{y}+\mathbf{d}_{2} \times\mathbf{z} \tag{17}\]  
\[\mathbf{u}_{\text{\tiny{xy}}}\ =\ \mathbf{e}_{1}\times\mathbf{y}+\mathbf{e}_{2}\times\mathbf{z} \tag{18}\]  
\[\mathbf{u}_{\tau\text{z}}\ =\ \mathbf{e}_{3}\times\mathbf{y}+\mathbf{e}_{4}\times\mathbf{z} \tag{19}\]  
Once the displacement boundary conditions are set, the rest of the forward modeling process is the same as the stress boundary-based approach. It should be noted that this approach is still more extensively applied than stress-based approach, because the stress is not usually consistent on the boundary when there is different rock mass or fault on the boundary.
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 4 Experimental Results and Analysis
### Average PSR Analysis
#### 4.4.1 Static EDs Scenario  
The average \(PSR\) for a different number of static EDs is presented in Figure 10. Here, Figure 10 shows a decreasing trend in \(PSR\) with an increasing number of EDs in a confirmed mode due to high interference among the SFs when packets are transmitted with a high SF. These higher SFs (e.g., 11 and 12) are weak to interference, owing to the high ToA, which negatively influences the capacity of the communication channel [37; 38; 39; 40; 41]. Thus, retransmissions from the EDs are increased, which results in substantial congestion and massive packet loss. However, the proposed EMA-ADR with and without the I-SFA scheme outperforms the other schemes in terms of \(PSR\) because EMA-ADR frequently changes the SF and TP parameters by employing a low-pass filter and averaging function. In addition, for a similar scenario, as presented in Figure 10, Table 6 shows the average \(PSR\) improvement for ADR+ and the proposed schemes when compared to a typical ADR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**N** & **ADR** & **ADR+** & **G-ADR** & **G-ADR (I-SFA)** & **EMA-ADR** & **EMA-ADR (I-SFA)** \\ \hline
200 & - & +12.5\% & +17.3\% & +19.7\% & +22.8\% & +24.3\% \\
400 & - & +8.9\% & +17.7\% & +20.7\% & +23.8\% & +25.0\% \\
600 & - & +5.5\% & +14.7\% & +19.5\% & +20.9\% & +22.7\% \\
800 & - & +5.1\% & +12.9\% & +21.5\% & +21.0\% & +23.0\% \\
1000 & - & +3.1\% & +11.2\% & +20.5\% & +22.5\% & +25.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: PSR improvement for static EDs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**N** & **ADR** & **ADR+** & **G-ADR** & **G-ADR (I-SFA)** & **EMA-ADR** & **EMA-ADR (I-SFA)** \\ \hline
200 & 22 & 21 & 15 & 16 & 3 & 0 \\
400 & 23 & 20 & 17 & 15 & 6 & 0 \\
500 & 15 & 14 & 16 & 13 & 3 & 3 \\
600 & 13 & 14 & 14 & 13 & 3 & 3 \\
800 & 20 & 21 & 22 & 15 & 4 & 3 \\
1000 & 29 & 23 & 17 & 17 & 4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Convergence period in hours for mobile EDs (with starting SF = 12, uplink period = 24 packets/day, total simulation time = 4 days).