esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Outcomes  
At every time point of the study (T0, T1, T2, and T3), clinical evaluations of the patient's compliance for oral hygiene were performed assessing the bleeding index (BI), modified gingival index (MGI), and plaque index (PI) for all Ramfjord teeth (maximally right first molar, maxillary left central incisor, maxillary left first premolar, mandibular left first molar, mandibular right first incisor, and mandibular right first premolar) [31]. After probing three sites (mesio-buccal, buccal, and disto-buccal) of the gingival sulcus for each tooth examined (Probe UNC 15 R198; PDT, Missoula, MT, USA), the BI index was scored recording the absence of bleeding as 0, bleeding observed after 30 s as 1, and immediate bleeding as 2 [32]. On the same sites, the MGI was visually assessed assigning a grade from 0 to 3 denoting absent, mild, moderate, and severe inflammation of the buccal marginal gingiva, respectively [33]. Finally, the PI was determined by making the patients chew a disclosing agent (Gum Red-Cote, Sunstar Italiana Srl, Saronno, Italy) according to the instructions of the manufacturer. The buccal surfaces of each tooth considered were divided into vertical and horizontal thirds with the bracket at the center [34] and for both the two boxes alongside (mesial and distal) and for the three gingival (mesial-gingival, center-gingival, and disto-gingival) to the bracket, a score 0 (absence of plaque) or 1 (presence of plaque) was assigned by the operator [35].  
As regards the questionnaire submitted, the correct answers and those related to the optimal oral practice or dietary behaviour were scored. Percentages were calculated for the three different sections of the questionnaire.  
Clinical assessments and questionnaires visualization were respectively performed by operators who were not involved in the previous phases and therefore did not know the participants' allocation.  
No changes to the outcomes occurred during the trial.
## 4 POD with Ambiguity Resolution  
The GNSS observation model and the methods employed to resolve the SD and DD ambiguities are introduced in this part. FA, DD AR, and SD AR schemes are applied to calculate the GRACE-FO orbits. The generated orbits are assessed with JPL's orbit, SLR, and KBR data. Effects of PCV corrections on POD and PBD are also analyzed.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 9 Conclusions and Research Opportunities
### Backwashing and Relaxation  
Backwashing and relaxation are effective methods for fouling mitigation. However, the chosen protocols are likely to be conservative and suboptimal. Improved performance could involve an adjustable protocol (backwash frequency, duration, flux, etc.) that responds to changes to feed conditions and required production rate. Such a system could use an online monitor for 'fouling propensity' linked to a neural-network-based control system.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Aegerolysins from Virus, Ascoviridae  
Aegerolysins from _Trichoplusia ni_ ascovirus 2c (TnAV2c)  
_Trichoplusia ni_ ascovirus 2c (TnAV2c) (renamed to TnAV6a1) is an obligate viral pathogen of _T. ni_, _C. includes_, and _H. zea_ larvae and selected other lepidopterans of the family _Noctuidae_[47; 49; 50]. Ascoviruses are a family of insect viruses with circular, double-stranded DNA genomes that cause chronic, lethal infections in insects of the order _Lepidoptera_. The name of this family of viruses is derived from the unique cellular pathology they cause, i.e., the formation of large, virion-containing vesicles 5-10 um in diameter in the hemolymph of infected insects. While ascoviruses are poorly infectious by the oral route, they are spread among larvae in lepidopteran populations by parasitic waps during oviposition. Certain parasitoid waps pick up the virus after oviposition in an infected host and transmit the virions to new hosts during subsequent ovipositions [51].  
High rates of infection with ascovirus TnAV-2 in a population of the corn earworm _H. zea_ (as high as 74%) were found in a cotton growing area in Blackville, SC; observations showed that ascovirus infection alters the feeding behavior of _H. zea_ larvae [49]. The complete genome sequence of TnAV-6a1 (TnAV-2c) was determined; the circular genome contains 165 open reading frames of greater than 180 bp [50]; ORF29 was identified as aegerolysin **TnAV2c gp029**[50; 149]. Similar ORFs were also sequenced in another _T. ni_ ascovirus **6b**[150], and another moth tobacco budworm _Chloridea virescens_ (_Heliothis virescens_) ascovirus **3f** and **3h**[49; 151]. RNA-sequencing was performed to compare the expression of TnAV-6a1 core genes during the first week of infection, with emphasis on the first 48 h, comparing transcript levels in major somatic tissues (epidermis, tracheal matrix, and fat body), the sites infected initially, with those of the hemolymph, where viral vesicles circulate and most replication occurs. In addition to the ascovirus core genes, several genes were identified in the contigs of the TnAV6a1 are unique. Only three of them were identified as highly expressed in hemolymph 48 h or 7 days after infection, namely the aegerolysin gene **ORF29** and two coding hypothetical protein genes. In somatic tissues, only the aegypti gene was identified as highly expressed 48 h after infection [152].  
There is no MACPF-like protein encoded in this virus.
## 4 Discussion
### AI Applications in Safety Management Across Industries  
#### 4.1.1 Construction Sector  
In the construction sector, AI and smart technologies address safety factors such as real-time hazard detection, predictive analytics, and enhanced training. These innovations help mitigate workplace accidents, improving worker safety and reducing the economic burden of injuries. For example, AI-powered safety management systems utilize IoT sensors and machine learning algorithms to monitor safety data in real time, predicting potential hazards and enabling proactive interventions. Furthermore, computer vision technologies detect unsafe behaviors through automated image recognition, while Virtual Reality (VR) and Augmented Reality (AR) provide immersive training environments, improving hazard recognition and response. These systems enhance both operational efficiency and worker protection, highlighting AI's societal and practical impact on construction safety [78; 79].
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 3 ID-Based Side-Channel Authentication System
### ID-Based Side-Channel Authentication Scheme
#### 3.1.1 Comparison of Expected Authentication Speed  
Table 1 shows the comparison of the expected authentication speed between a straightforward Challenge-S approach using normal AES and the ID-based Challenge-S using \(n\)-round AES. By denoting the clock period as \(T_{clk}\), the acquisition time of the side-channel information for each method is \(n\cdot 11T_{clk}\) and \(nT_{clk}\), respectively. Denote the data processing time of each AES round to obtain the intermediate values as \(T_{p}\); then, the total data processing time can be calculated. For the straightforward Challenge-S approach, the data processing time can be represented by \(q\cdot 5n\cdot T_{p}\) since only 5 rounds of intermediate values are calculated. For the ID-based \(n\)-round Challenge-S approach, the data processing time is \(n\cdot T_{p}\). The total time for authentication consists of the acquisition time and the data processing time. It can be seen that ID-based \(n\)-round approach is much more efficient.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Straightforward Challenge-S [1]** & **ID-Based \(n\)-Round Challenge-S** \\ \hline Side-channel information & \(n\) traces of 10-round AES-128 & \(1\) trace of \(n\)-round AES \\ Acquisition time & \(n\cdot 11T_{clk}\) & \(nT_{clk}\) \\ ID system & Not used & Used \\ \# of trials & \(q\) (1 acceptance and \(q-1\) false trials) & 1 (Only acceptance trial) \\ Data processing time & \(q\cdot 5n\cdot T_{p}\) & \(n\cdot T_{p}\) \\ Total time & \(n\cdot 11T_{clk}+q\cdot 5n\cdot T_{p}\) & \(nT_{clk}+n\cdot T_{p}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between two side-channel authentication schemes.  
Figure 4: Comparison of frameworks of the conventional (a) and proposed (b) ID-based side-channel authentication.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 4 Evaluating Mixed Traffic Safety with CAV Degradation and Driving Behaviors
### Mixed Traffic Flow Considering the Degradation of CAV  
In the evolving landscape of Intelligent Transportation Systems (ITS), CAVs leverage Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications to achieve efficient cooperative control and information sharing, significantly enhancing the overall performance of the vehicle fleet. CAV automation is classified into six levels [4], and the proportion of fully automated CAVs has not yet been achieved in real-world traffic. Over the next few decades, mixed traffic flow--where CAVs coexist with HDVs--is expected to become a key component of road transportation systems [21].  
In mixed-traffic environments, when a CAV follows an HDV, V2V communication cannot encompass the HDV, and real-time information about the HDV ahead cannot be obtained. This limits the CAV's ability to fully utilize cooperative control, leading to a phenomenon known as system degradation, where the CAV effectively becomes a degraded CAV (DCAV), as illustrated in Figure 10.  
CAVs are equipped with a CACC system. The primary advantage of CACC lies in its ability to share real-time data on the speed, acceleration, and other parameters of the vehicle ahead through V2V communication, enabling precise distance control and cooperative car-following. However, when a CAV degrades into a DCAV, it must rely solely on onboard sensors and the ACC system for car-following, which may hinder its ability to respond  
Figure 10: The degradation phenomenon of CAV.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TTC (s)** & \(R_{\text{total}}\) & **PICUD** & **WS2 DR** \\ \hline
1.50 & 0.52 & \(-0.59\) & 0.40 \\
2.00 & 0.50 & 0.03 & 0.45 \\
2.50 & 0.48 & 0.45 & 0.48 \\
3.00 & 0.47 & 0.73 & 0.51 \\
3.50 & 0.46 & 0.95 & 0.53 \\
4.00 & 0.45 & 1.16 & 0.56 \\
4.50 & 0.43 & 1.43 & 0.60 \\
5.00 & 0.40 & 1.84 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Suggested safety thresholds for metrics.  
quickly to sudden events. As the penetration rate of CAVs decreases, the probability of such degradation increases, thereby elevating the risk of traffic accidents.
## 2 Methodology
### Computational Process  
The solution process for the interface-corrected diffuse interface model and the flux solver is shown below:  
1. Set up the length of \(\delta_{x}\) (\(\delta_{x}=\delta_{t}\)) at the surfaces of each control volume;
2. Based on the flow variables \(\rho\), \(\mathbf{u}\), \(p\), and \(C\) of each control volume, compute \(f_{\alpha}^{eq,m}(\mathbf{r}-\mathbf{e}_{\alpha}\delta_{t},t-\delta_{t})\) applying Equation (28), then acquire \(f_{\alpha}^{eq}(\mathbf{r},t)\) using Equation (27);
3. Compute the fluxes \(\mathbf{R}_{k}\) at the surfaces using Equation (25) and acquire source terms using Equation (20);
4. Compute the interface correction term \(q_{1}\) given by Equation (11) and the mass correction term \(q_{2}\) using Equation (16);
5. Discretize Equation (5) by applying the central difference scheme to the diffuse term and the Lax-Wendroff scheme to the convective term;Use the second-order TVD Runge-Kutta temporal scheme to solve Equations (5) and (23); in this way, the flow variables of the next time step can be obtained;
* Repeat steps (2-6) until the final results are obtained. As shown in the above computational sequence, a major improvement was made in this study. For the tracking of the phase interfaces, compared to the original DI model with merely the mass conservation term proposed in [32], the approach proposed in this study has the advantage of maintaining interface shapes. Overall, the proposed approach has good properties for mass conservation and interface correction. It was tested in the next part of the study by simulating several multiphase flow problems.
# Joint Resource Allocation in Secure OFDMA-Based Networks Taking a Base Station as a Two-Way Relay
## 1 Introduction
When there was an untrusted intermediate relay, the system sum secrecy rate, in which cell-edge mobile stations transmit confidential messages to the base station, was maximized by means of power allocation [17]. When a multi-antenna base station simultaneously communicated with multiple potentially malicious users in the presence of randomly located external eavesdroppers, the achievable secrecy rate was studied [18]. A cognitive relay selection algorithm was put forward for secure communication in cognitive decode-and-forward relay networks against eavesdropping [19]. When there was a sophisticated multiple antenna eavesdropper and a multiple antenna transmitter, transmit antenna selection was used in the physical layer security scheme [20]. Secure transmission in two-hop amplify-and-forward untrusted relay networks was investigated, taking power allocation into account [21]. Opportunistic multi-hop routing [22] which adopted the best of multiple receivers to forward each packet was suggested to improve throughput for a wireless network, the model of which had multiple nodes. Our model takes one base station as a relay, which is different from the scenario with multiple relays. The problem of selecting the optimal constrained candidate set was considered in the opportunistic routing paradigm [23], which also took into account of the network scenario with multiple candidate nodes. However, these two schemes place an emphasis on a routing algorithm, which is different from our objective that puts an emphasis on physical security. Several existing schemes merely lay an emphasis on either Amplify and Forward (AF) or Decode and Forward (DF) relay to improve the secrecy rate. However, these schemes do not take normal users into account in one cell that transmits and receives information in traditional way. Our objective is to maximize the total secrecy rate by means of joint power allocation, subcarrier pairing and subcarrier assignment, when one cell has both relay users and normal users in the OFDMA network taking the base station as a two-way relay. In the next section, the system model is set up. In Section 3, the optimization problem is solved using the dual method. In Section 4, the experiments are conducted in order to test the performance of the proposed scheme. In the last section, some conclusions are given.
# Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
## 5 Conclusions  
This research addressed the high energy consumption problem in modern mobile communication systems by controlling the transmission power of eNodeB. The two parameters that play a vital role in improving energy efficiency are the adaptive initial and final thresholds, where these two parameters were adjusted by considering a stochastic traffic arrival pattern of UEs. In addition, the proposed SET algorithm was formulated as a MOP to determine the trade-off between the SE and the EE. Similarly, an interference approach was developed by applying antenna patterns to provide efficient energy management at eNodeB. Several simulation experiments were conducted, which demonstrated the ability of the proposed algorithm to outperform the DRX power-saving algorithm in terms of SE, average response delay, and energy consumption.  
All authors of this article have contributed to the work as follows: conceptualization, validation, formal analysis, methodology, software, visualization, and original draft preparation have been done by M.M.; technical review, proofreading, editing, and writing the final draft have been done by M.A.A.; project administration, supervision, and funding acquisition have been done by Z.M.H. All authors have read and agreed to the published version of the manuscript.  
This work is funded by Universiti Putra Malaysia under Putra Berimpak Grant number (9659400).  
Not applicable.  
This work is supported by Universiti Putra Malaysia.  
The authors declare no conflict of interest.
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## 3 Results and Discussions
### Blood Flow and Platelets Migration in a Parent Vessel without an Aneurysm  
To demonstrate the accuracy of the model, an initial simulation in a straight vessel was performed (See Figure 2). To reduce computational cost, we assume that the flow is two-dimensional. The inlet velocity is 0.5 m/s, and the hematocrit numerically studied are 36% and 42%, respectively. The platelets bulk concentration is \(3.0\times 10^{14}\) PLTs/m\({}^{3}\). Using the vessel height, inlet velocity, plasma density and plasma viscosity as reference parameters, the Reynolds number of the current problem is about 50. The resulting steady-state concentration profile is provided in Figure 3, for the case of 36% hematocrit, demonstrating excellent agreement with [PERSON]'s experiments [40]. The relative concentration is defined here as the local platelets concentration divided by the bulk concentration. The simulations also demonstrate that an increase in hematocrit leads to greater enrichment of platelets near the wall.
# Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures
## 2 Problem Formulation and Simulation Methodology
### Scenario: Network Deployment Requirements  
Herein, our planning process was employed in a rectangular area with dimensions 1 Km \(\times\) 1 Km. Due to the area characteristics, the requirements/constraints of the planning process were defined against a set of 25 equi-spaced CNs (separation distance of 200 m). This grouping of CNs represented the high-capacity requirements in a small area, i.e., a demanding network planning operational scenario that requires a dense network deployment.  
Since the trade-off between performance and network density was our main concern, a clear preexisting network layout was considered and new transmitting sites were explored, i.e., microcells with various density deployments were examined. Scenarios with 30 (5 x 6 grid), 100 (10 x 10 grid) and 400 (20 x 20 grid) candidate microcells uniformly distributed in the area were studied in the analysis below in order to investigate the capabilities of a dense network. However, it should be pointed out that the locations of the candidate network sites were an external parameter to our platform, thus could be defined by any interested stakeholder.
## 4 Illustrative Example
### Four-Dimensional Trajectory Control  
The application section concludes with an example of a complex trajectory following task. Here, Figure 4 shows the desired/commanded (solid red) vs. the actually flown (dashed blue) trajectory for a complex climb and turn maneuver including a holding. Furthermore, the planar projections (dashed black) of the maneuver are shown for convenience and to get a better overview of the maneuver structure.  
Then, Figure 5 shows the position, velocity, and acceleration deviations obtained from following the trajectory in Figure 4. It can be seen that maximum vertical errors of \(\pm 0.02\) m occur in climb/descent  
Figure 3: Maximal absolute lateral specific force over turn radius for 180\({}^{\circ}\) turn maneuver.  
Figure 2: Maximal absolute lateral deviation over turn radius for 180\({}^{\circ}\)-turn maneuver.  
segments, while the maximum lateral deviation is around \(\pm\)0.01 m. In general, the horizontal deviation shows the overall best tracking.  
This overall shows the high accuracy of the controller. Additionally, the errors are rapidly reduced, which is specifically obtained due to the error controller that already starts acting at the acceleration deviation level. In this context, it can be seen that maximum vertical acceleration deviation of \(\pm\)0.05 m/s\({}^{2}\) and of \(\pm\)0.01 m/s for velocity is achieved. This shows both the fast response of the controller to deviations as well as the smoothness, i.e., that small deviations are controlled with small control effort.  
Figure 4: Commanded vs. actually flown trajectory for four-dimensional trajectory control case.  
Figure 5: Trajectory position, velocity, and acceleration deviations in all three axes along the trajectory.  
Finally, Figure 6 shows the specific forces and their derivatives commanded to the model when following the trajectory in Figure 4. It is specifically seen that a high smoothness for the specific forces as well as the decoupling of the axes by nonlinear dynamic inversion is achieved, which results in a more physical command for controlling the aircraft. Still, it is clearly seen that the specific force derivatives directly react to the deviations in Figure 5. Specifically, it can be observed that the specific force derivatives counteract the acceleration errors in Figure 5. Furthermore, it is seen that there are generally no high-frequency oscillations in the command signal, which is a specific consequence of the fact that the nonlinear aircraft kinematics are considered by the controller. Due to these, it is known how the aircraft can move, and thus the controller does not try to compensate deviations by large control effort but by reasonable magnitudes and dynamics that the aircraft is able to follow.