esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
To identify all articles of interest for this review and meta-analysis, a systematic search of literature was conducted in PubMed, Medline, Scopus, Web of Sciences. The search string is shown in Table 1. The following search terms were used: \"Third molar surgery\", \"mandible\", \"surgical wound infection\", \"Postoperative infection\", \"antibiotic prophylaxis\", \"humans\", \"randomized clinical trial\", \"RCT\". The search has been updated up to July 2021. To find out any studies not identified by the electronic search, references of the included articles were evaluated, and numerous paper journals were manually searched.  
In the present work our aim was to summarize the available evidence on antibiotic prophylaxis efficacy in preventing surgical wound infections in patients undergoing mandibular third molar extraction by conducting a meta-analysis. To achieve this, the following PICO format was applied: extraction of a mandibular third molar (Population); administration of antibiotic prophylaxis (Intervention); placebo/no administration (Comparison); appearance of signs of surgical wound infection (Outcome).  
The exclusion criteria were: systematic reviews, meta-analyses, case reports, case series, letters to the editor, expert opinions, retrospective studies, prospective non-randomized studies, studies in which it was not possible to specifically identify data concerning mandibular third molars (e.g., studies on generic extraction of impacted teeth or extraction of mandibular and maxillary third molars), absence of placebo, articles in other languages than English, French, Spanish, and German. No limitations were applied for publication date. All articles were review initially by 2 experts (GO and JL). In case of discrepancies, these were resolved by SML.
## 5 Conclusions  
This study presented the Electric-Potential Estimation (EPE) method, an approach that can accurately predict the potential distribution in the vicinity of cylindrical electrodes, regardless of electrode size, position, and orientation in a uniformly distributed rectangular grid. High grid size-independent accuracy is achieved by more correctly estimating the electric-potential values of the voxels neighboring on the electrode voxels based on the actual physical distance to the electrode. This EPE method provides a possibility to considerably shorten the simulation time of treatment planning by using large voxel-sized models without largely affecting the accuracy of the electric-field distribution. Thus, real-time clinical IRE treatment planning in realistic heterogeneous target volumes becomes feasible.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12031471/s1](https://www.mdpi.com/article/10.3390/app12031471/s1)]([https://www.mdpi.com/article/10.3390/app12031471/s1](https://www.mdpi.com/article/10.3390/app12031471/s1)), Table S1: Application in heterogeneous tumor models, additional simulation data.  
Conceptualization, H.P.K., J.C. and P.A.; methodology, H.P.K., J.C. and P.A.; validation, P.A.; formal analysis, P.A.; investigation, H.P.K., J.C. and P.A.; data curation, P.A.; writing--original draft preparation, P.A.; writing--review and editing, H.P.K., J.C., P.A., K.P.v.L., M.G.B. and T.M.v.G.; visualization, P.A.; supervision, H.P.K. and J.C.; project administration, J.C.; funding acquisition, J.C., M.G.B. and T.M.v.G. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Dutch Cancer Society (grant UvA 2014-7244).  
Not applicable.  
Data Availability Statement: Data is contained within the article and supplementary material.  
Authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article. [PERSON] and [PERSON] are paid consultants for Angio-Dynamics. Angio-Dynamics had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
During that period in the THUMS simulation, the occupant's head also moved backward relative to the chest, began to contact the headrest at 61 ms, and remained in contact with headrest. From 120 to 160 ms, as the PMHS occupant's head continued moving backward to the maximum position relative to the chest, it rebounded forward and gradually separated from the headrest. During that period, the THUMS simulation showed a similar movement, and the occupant's head rebounded forward and separated from the headrest at 133 ms. Therefore, the kinematic responses of the THUMS were in good agreement with the PMHS tests during the whole collision period. In terms of the time-history data, the head accelerations, head rotation angle, and thoracic spine vertebral accelerations were processed in Hypergraph, and they were compared with the PHMS test data recorded by sensors in the study conducted by [PERSON] et al. [17]. As shown in Figure 3, the predicted head acceleration in the x-direction, the head rotation about the y-axis, and the first thoracic spine vertebral (T1) acceleration in the x-direction all have a good agreement with the PMHS test, and the errors between the peak values are 5.5%, 0.9% and 11.6%, respectively. Further, a Correlation Analysis (CORA) [19] was conducted by Hypergraph to quantitatively evaluate the time histories between and simulation results and the PMHS test. The phase, amplitude, and slope of the time-history curves of the PMHS test and predicted responses were compared, and then the correlation scores were computed to evaluate the similarity of the two curves. The relative score ranges from 0 to 1, with 1 indicating a perfect match. Table 2 lists the corridor scores, cross-correlation scores, and CORA scores of the time-history curves of the head acceleration in the x-direction, the head rotation angle about the y-axis, and T1 acceleration in the x-direction, and specifically, the corresponding CORA scores are 0.73,  
Figure 2: Comparison of kinematic postures between PMHS test (**upper row**) [18] and THUMS simulation (**lower row**) under rear impact. 0.74, and 0.79, respectively. Overall, the simulation results have good agreement with the PMHS test data. Therefore, the effectiveness of the THUMS-based finite element model of occupant restraint systems with a 3-point seat belt system and a conventional seat was sufficiently validated. Accordingly, the THUMS-based finite element model assembly was able to provide good accuracy for the subsequent computational biomechanics analysis of occupants in zero-gravity seats with different restraint systems.
## 3 System Performance
### The Throughput of the System  
It is essential for us to analyze the throughput for the system, especially the mobile user \(S_{2}\). According to [31], the definition of throughput for two time slots networks can be expressed as  
\[\mathrm{T}=\frac{R_{s}}{2}\times\left[1-P_{out-r}\left(x_{0}\right)\right],r \in\left\{AF,DF\right\}. \tag{46}\]  
By substituting (24) and (31) with analytical and asymptotic OP expressions into (46), the analytical and the asymptotic expressions of throughput are derived. In order to reduce the length of the paper, we do not give the final expressions here.
###### Abstract  
Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients' social lives. As a result, accurate diagnosis of SZ has attracted researchers' interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients' and 74 healthy controls' (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample \(t\)-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.  
sticizophrenia; static functional connectivity; dynamic functional connectivity; sliding window; cuneus; variability; mean strength; standard deviation; rsfMRI; COBRE
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 4 Consecutive Error Prevention Performance: Evaluation  
This section evaluates the reliability improvement that the proposed collision feedback scheme can bring if it is allowed to use a collision pointer in the SCI of the transmitted packet. First, the duration and the number of consecutive packet collision events are compared in the original SPS and the proposed scheme. To put the proposed scheme in a broader perspective, the average PRR is also measured. In the comparison, the alternating reservation scheme [9] and the blind retransmission from the LTE/NR V2X standards [2; 11], as well as SPS, are included.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Impact of Encoding Parameters
#### 4.2.2 Impact of Quantization Parameter (QP)  
At the resolution of 4 K, it can be seen that the differences of QoE aspects between QP = 22 and QP = 28 are trivial. Specifically, the average difference over the three contents is 0.19 MOS for the perceptual quality, 0.24 MOS for the presence, and 0% for the acceptability rates. For the versions with the QP value of 40, the acceptability rates are very low (i.e., <30%). In addition, the corresponding presence and perceptual quality scores are smaller than 3 MOS. This implies that the versions encoded at the QP value of 40 or higher are annoying to the viewers. To achieve good QoE, the maximum QP values at the 4 K and 2.5 K resolutions are respectively 32 and 32 for device set D1, and 32 and 28 for device set D2.
# An Efficient Advantage Distillation Scheme for Bidirectional Secret-Key Agreement
###### Abstract  
The classical secret-key agreement (SKA) scheme includes three phases: (a) advantage distillation (AD), (b) reconciliation, and (c) privacy amplification. Define the transmission rate as the ratio between the number of raw key bits obtained by the AD phase and the number of transmitted bits in the AD. The unidirectional SKA, whose transmission rate is 0.5, can be realized by using the original two-way wiretap channel as the AD phase. In this paper, we establish an efficient bidirectional SKA whose transmission rate is nearly 1 by modifying the two-way wiretap channel and using the modified two-way wiretap channel as the AD phase. The bidirectional SKA can be extended to multiple rounds of SKA with the same performance and transmission rate. For multiple rounds of bidirectional SKA, we have provided the bit error rate performance of the main channel and eavesdropper's channel and the secret-key capacity. It is shown that the bit error rate (BER) of the main channel was lower than the eavesdropper's channel and we prove that the transmission rate was nearly 1 when the number of rounds was large. Moreover, the secret-key capacity \(C_{s}\) was from 0.04 to 0.1 as the error probability of channel was from 0.01 to 0.15 in binary symmetric channel (BSC). The secret-key capacity was close to 0.3 as the signal-to-noise ratio increased in the additive white Gaussian noise (AWGN) channel.  
two-way wiretap channel (TWWC); secret-key agreement (SKA); transmission rate; secret-key capacity +
Footnote †: journal:  
Article
## 3 Numerical Simulation of Oil Spill
### Interaction of Ice and Oil  
Considering current, wind and waves, the movement of ice floes with 6 DOF included rolling, pitching and having motions on the water surface. The overset grid [31] technique was used to simulate the flow field of moving ice with large displacement. This technique simplified the construction of computational grids about complex geometries and allowed relative motion between embedded  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position of y/m** & **Time(t)/s** & **Width of the Oil Film/m** & **Thickness of the Oil Film/m** \\ \hline y = 5 & 2.34–10.14 & 2.67–6.46 & 1.82–1.99 \\ y = 10 & 5.64–10.14 & 2.96–9.51 & 1.44–1.94 \\ y = 15 & 9.48–10.14 & 2.72–4.10 & 1.23–1.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical results of drift and diffusion of oil film.  
Figure 9: Three phase distribution of oil–water–air for icy waters at 10.14 s.  
grids. Based on interpolating connections in every step in the time direction, at the state of ice floes flow associated with the non-linear wave, the background mesh established in the static domain was adopted to simulate the far field in water and the valid configurations of overset mesh were refined around boundaries consisting of ice particles.  
In the oil-spill model, oil density and ice diameter were set at 840 kg/m\({}^{3}\) and 1.5 m, respectively. As shown in Figures 10 and 11, there were two main movements: (a) oil trapped under the ice floe while it rose to the surface and encountered the ice; (b) oil trapped by slush or brash (broken) ice and diffused to the surface of ice, which was consistent with the features of oil spreading in medium ice-density waters [9; 11]. At the plane section of y = 0 m and y = 0.25 m, the oil drifted to 4.06 m at 3 s, and the horizontal and vertical displacement of the ice flow in the vicinity of the water surface were 1.61 m and 0.19 m, respectively. The pitch amplitude only increased from 0 to 6.0 deg. This is a small increase mainly affected by currents and waves. Due to the fact that the velocity in the \(y\) direction was limited to 10% of the wind velocity, the roll motion was completely negligible.  
Figure 10: Oil-spreading characteristics of the ice particle with six-degrees-of-freedom (6 DOF) movement at y = 0 m plane section.
## 4 FIL Component Quantitative Analysis: Experimental Testbed Design
### Testing Environment Design and Configuration  
In the experimental testbed design, we employed Docker to create isolated environments for both FIL and the F4I OPC UA Agent, ensuring comparable testing conditions. The monitoring tools cAdvisor and Prometheus were configured for real-time metrics collection, tracking resource usage, latency, and throughput. While cAdvisor provides insights in real time of the software tested, Prometheus allows one to set up a historical vision of each container performance, for example, to test the memory usage over time. Grafana provided the visualization platform for the data collected by cAdvisor and Prometheus, enabling us to analyse and compare the performance of FIL and F4I OPC UA Agent across various simulated IoT scenarios. The evaluation metrics focused on scalability, efficiency, and interoperability, critical factors for assessing semantic interoperability solutions in Industry 4.0 settings.  
We used two Virtual Machines (VMs) to perform the experiments. We have used the application versions that come from FIWARE documentation recommendations or the latest available application version. In VM #1, we deployed the modules that enable the performance analysis OPC-UA Servers, and in VM #2, we deployed the applications under test: the interoperability solution, FIWARE, and MongoDB. We configured both VMs as the standard Amazon AWS t2.medium instance configuration (2 vCPU-4 GB of RAM). The Node.js OPC UA client serves as an implementation of an OPC UA client within the Node.js runtime environment. OPC UA, a widely adopted communication standard in industrial automation and control, facilitates seamless interoperability between various devices and systems. Leveraging the power of the Node.js ecosystem, the Node.js OPC UA client leverages the JavaScript or TypeScript programming languages, taking advantage of its asynchronous and event-driven nature. This client enables real-time access to data, subscriptions to events, and execution of operations on OPC UA servers. By leveraging the extensive library ecosystem and modular architecture of Node.js, the client enables developers to build robust industrial applications, including real-time monitoring and control systems, asset management solutions, and data acquisition systems. In industrial environments, the Node.js OPC UA client provides a reliable and efficient means to establish connections and facilitate seamless communication with OPC UA servers, ensuring optimised integration and interoperability.  
In the other server, a FIWARE-based platform has been deployed to facilitate the automatic management of component 1 and component 2 simulating different actions on a shop floor, i.e., to facilitate the automatic management of a robot using the automation control enablers.
## 2 Materials and Methods
### Flight Planning and Acquisition of Data with UAV  
A DJI Phantom 4 RTK [34] drone with GNSS RTK/PPK technology was used to survey the study sites. The UAV weighs 1391 g, has a diagonal distance of 350 mm, and maximum flight time of 30 min. A camera that is mounted on the UAV has a 1-inch, 20-megapixel CMOS sensor with lens FOV of 84\({}^{\circ}\) 8.8 mm/24 mm (35 mm format equivalent) and f/2.8-f/11 auto-focus at 1-\(\infty\) m. The ISO range for photos is 100-3200 for auto mode, and 100-12,800 for manual. The maximum image size is 5472 \(\times\) 3648 (3:2), with a pixel size of approximately 2.4 \(\mu\)m. Multi-Frequency Multi-System High-Precision RTK GNSS enables GPS (L1/L2), GLONASS (L1/L2), and Galileo (E1/E5a) tracking. The manufacturer states that it enables a positional accuracy (RMSE) of 0.015 m (vertical) and 0.010 m (horizontal),  
Figure 4: Example of verification point (VP)/ground control point (GCP) target set out in the field.  
both with 1 ppm, which means that the error has a 1 mm increase for each 1 km of movement by the UAV.  
The UAV surveys were performed on three separate days for each location: Krnica rockfall was surveyed on 29 August 2019, Mangart rockfall on 20 September 2019, and Kekec rockfall on 7 November 2019. UAV flights were planned such that the flying height was kept constant, using LiDAR-derived digital terrain model (DTM) with a spatial resolution of 1 m. This feature allows for the acquisition of images with the same scale, despite the terrain being very rugged with changing elevation (Figure 3). The flight pattern was perpendicular to the surface slope. The flying height was set to 80 m at all study sites, to achieve a ground sampling distance of 2-3 cm. The image overlaps were 80%. Pictures were taken at 45\({}^{\circ}\) and 90\({}^{\circ}\) angles, as the combination of oblique and nadir images contributes to a more accurate representation of terrain [62]. For planning the UAV mission, we used the DJI GS RTK application.  
The surveyed area for each site is provided in Table 1. We surveyed all sites in PPK mode, such that GNSS data were recorded during the flight and post-processed by using correctional data from the base station that was located within or next (<1 km) to the surveyed area. The location of the base station was calculated with RTK VRS method based on the Slovenian SIGNAL service (interval observations were performed with time spacing of more than one hour and a half). The cadence of observations was one observation per second, while the number of station satellites varied between 4 and 7. GNSS data from the flights were post-processed, using Leica GeoOffice and RTKlib.
## 4 Simulation and Discussion
### Simulation Results of Energy Storage Participating in Peak Shaving  
**Peak shaving.** Table 5 shows the results of energy storage configuration and economic evaluation under participation in the load peak shaving. In addition, each cost and income term for the service with four different energy storage batteries is illustrated in Figure 5. Energy storage participation in the load peak shaving is a loss. Through the analysis of the income items, the auxiliary service revenue accounts for a large proportion, while the subsidy price of the current peak-filling valley is low, resulting in a loss of energy storage investment. As a result, it is not recommended that the user only select the load peak shaving service as the energy storage income item.  
**Energy Storage Charging and Discharging Results.** Under the best battery selection, the user load power is shown in Figure 6. The charge and discharge power of VRLA is shown in Figure 7. Energy storage can effectively cut down the peak power to obtain the peak shaving revenue, however, the revenue cannot cover the whole investment.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 3 Delay-/Disruption-Tolerant Networking  
DTN originated from a generalization of the requirements of Interplanetary Networking (IPN), when it was identified that the same approach could be applied to challenged networks (where the ordinary TCP/IP architecture has strong drawbacks), on Earth and deep-space [34].  
The DTN architecture relies on the introduction of the new Bundle Layer above the Transport Layer (or other lower layers). This overlay depends on the introduction of multiple DTN hops between sender and destination, to deal with long propagation delay and disruption [35].  
For the transport layer to interact with bundles, it is necessary to use a convergence layer adapter (CLA) below the BP. The DTN architecture is presented on Figure 3.  
In DTN, there is no more need for end-to-end connectivity, in contrast to TCP/IP networks, which stand for Transmission Control Protocol/Internet Protocol. In the presence of link disruptions or scheduled intermittence, the nodes along the path can store the for long periods of time and assure the transfer when the link to the next node becomes available again.  
Currently, in addition to IETF on going standardization [37], space standards for DTN systems are promoted by CCSDS (Consulative Committee for Space Data System), a standardization [38] body made by all of the most important space agencies, including NASA, ESA, and DLR. In particular, they have published two \"blue books\" (the recommended standards) for the Bundle Protocol (BP) [39] and the Licklider Transmission Protocol (LTP) [40].