esa-sceva/satcom-chunk-collection huggingface dataset:
# Concentration in Saliva and Antibacterial Effect of Xylitol Chewing Gum: In Vivo and In Vitro Study
## 2 Materials and Methods
### Xylitol Concentration In Saliva  
#### 2.2.1 Sample Size  
In the literature, only a few studies reported data on the concentration of xylitol in saliva [7, 8, 9]. Since no study reported a sample size calculation and two of them have a very small number of participants, it was decided to use the study performed in Finland as reference [9]. Since the present study has a double-blind cross-over design, the sample size calculation was performed through G*Power 3.1.3 for Apple, using logistic regression with an odds ratio of 1.8 and an error probability of 0.04. The minimum sample size was set to 28 subjects; the number was then increased taking into account the uncertainty of the effect and the no-responders rate; the number of subjects was set to 38.  
\begin{table}
\begin{tabular}{c c c} \hline
**Ingredients** & **100\% Xylitol** & **22\% Xylitol** \\ \hline  & g/\% & g/\% \\ \hline Xylitol & 0.903/64.5 & 0.308/22 \\ Sorbitol & - & 0.434/31 \\ Malitol Syrup & - & 0.084/6 \\ Mannitol & - & 0.056/4 \\ Gum base & 0.421/30.1 & 0.392/28 \\ Other components & 0.076/5.4 & 0.126/9 \\ \hline \end{tabular}
\end{table}
Table 1: Percentage Composition of the two chewing gums. Each chewing gum weights 1.4g.  
Figure 1: Flow-chart of the study design (in vivo and in vitro phases).  
Fifty dental students (age range 19-25 years), attending the Dental School of the University of Sassari, were invited to participate with a leaflet explaining the purpose of the study together with an informed consent; thirty-three accepted. The inclusion criteria were good general health, absence of diseases that could alter the saliva secretion rate, as assessed by a medical questionnaire, a stimulated saliva flow of at least 1 mL/min, and no medication in the six weeks before the beginning of the study. Smokers were not included. One of the authors (OM) performed all the examinations and registered medical and dental histories.
## 4 Implementation and Optimization
### QAT Offloading Module
#### 4.2.2 Load Balance and Availability  
**Load balance:** In scenarios of high-performance computing, upper-layer applications have large-scale data, resulting in extremely high compression bandwidth. If the computing resources of the QAT accelerator are exhausted, the QAT instances cannot be obtained by the QAT offloading module. To ensure the full utilization of computing resources in the system, the load balancing between QAT hardware-assisted compression and traditional compression performed by CPUs is required in the QAT offloading module. Therefore, if the QAT instances in the QAT offloading module are insufficient, the QAT offloading module transfers all compression operations to the software compression algorithms processed by the CPU. When the computing resources of the QAT accelerator are replenished, compression resumes using the QAT accelerator, achieving a seamless transition between the two schemes.  
**High availability:** The QAT session is a service abstraction that describes the configuration information and parameters of the compression service in the QAT instances. In the requests and responses of the QAT instances, specific data manipulation is responsible for the QAT session, and each session represents a (de)compression service. After the QAT offloading module completes instance initialization and data reconstruction, the QAT session is responsible for the information transfer to the QAT accelerator. Thus, if asoftware-level error occurs during compression, such as passing the wrong DMA (Direct Memory Access) address or the wrong number of parameters, it is handled and recovered in the QAT session. If a hardware-level error occurs, the QAT offloading module clears all instances and sessions, reclaims the memory of all the corresponding data structures, sets the available identifier location of this module to false, and then restarts the QAT driver and the QAT offloading module. Finally, all relevant buffer data structures, logical instances, and compression sessions are re-initialized for use in the next phase of continued work. As a mature commercial ASIC device, the QAT accelerator device rarely experiences errors at the hardware level. However, the implementation of a perfect error-handling mechanism in the QAT offload module can help avoid potential risks and improve system availability and robustness.
# An Agent-Based Simulation of How Promotion Biases Impact Corporate Gender Diversity
## 3 The Simulation
### Core Elements of Our Simulation
#### 3.1.2 Simulating the Promotion Process  
When a vacancy appears at any rank above the entry level, promotions are simulated by identifying a pool of \"promotion candidates\" from the rank immediately below, and then randomly choosing one employee from that pool.  
By default, the promotion pool is set as a percentage of the total number of employees at that rank and is based on the \"promotion score\" of each employee, a normalized value based on the amount of time the employee has spent at the current rank (\"rank seniority\") relative to the amount of time spent by the most senior employee at that rank. In other words, in the absence of a bias, promotions are based on rank seniority, with some randomness to reflect the fact that seniority is not an exact indicator of merit. It is worth noting that seniority has been widely used as a salient criterion for promotions in many industries, and it has been suggested that promoting the most senior employee instead of promoting the candidate with best performance or ability can reduce possible psychological disturbances [48].  
When an employee is promoted, their seniority at the new rank is set to zero, while the simulation still tracks the total amount of time that the employee has been with the organization.  
Gender biases in the promotion process are simulated by adding a \"promotion-bias\" parameter, a value that is added to the seniority of each employee to calculate the promotion scores. The promotion-bias parameter can be positive or negative to simulate biases that favor men or women, respectively. The promotion-bias parameter is applied uniformly at all ranks.  
It is important to note that we are not explaining how promotion biases come into play; we are simply interested in understanding how gender biases in the promotion process,if present, would impact the overall outcomes for the company, regardless of their source or nature.
# WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication
## 5 Conclusions  
In recent years, hospitals and medical research centers have been transformed into digitized environments. This evolution has made the security of the usage, management, and transmission of digital medical images an issue of extreme importance. Here, we proposed WMNet, which assists in confirming the ownership of watermarks in a lossless medical image watermarking system. The primary objective of WMNet was to examine the extracted watermark when authenticating suspected medical images. The advantages of WMNet are as follows: (1) WMNet interpretation makes the determination more objective. (2) Even if the extracted watermark is distorted, WMNet interpretation is also relatively successful. (3) WMNet is flexible and can be used to examine distorted watermarks that have been extracted using other schemes. Finally, the experimental results show that the WMNet in this paper has good efficacy, which can be extended to different multimedia applications in the future. The corresponding copyright attribution of the watermark extracted from a protected image can be read directly through WMNet, making the proposed system more objective and suitable for real-time systems.  
Conceptualization, Y.-P.C.; Data curation, T.-Y.F.; Formal analysis, Y.-P.C. and H.-C.C.; Funding acquisition, H.-C.C.; Investigation, T.-Y.F. Methodology, Y.-P.C. and T.-Y.F.; Project administration, Y.-P.C.; Resources, T.-Y.F.; Software, T.-Y.F.; Supervision, H.-C.C.; Validation, Y.-P.C. and H.-C.C.; Visualization, Y.-P.C.; Writing--original draft, T.-Y.F.; Writing--review and editing, H.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chang Gung Memorial Hospital, Taiwan, R.O.C. (grant number CLRPG3H0012 and CIRPG3H0012); the Ministry of Science and Technology, Taiwan, R.O.C. (grant number MOST 109-2221-E-130-012).  
The author would like to express his earnest appreciation to the anonymous referees for their constructive suggestions. The authors thank the statistical assistance and wish to acknowledge the support of the Maintenance Project of the Center for Artificial Intelligence in Medicine (Grant CLRPG3H0012, CIRPG3H0012) at Chang Gung Memorial Hospital for study design and monitoring, data analysis, and interpretation. In addition, this research was partly supported by the Ministry of Science and Technology, Taiwan, R.O.C., under Contract No. MOST 109-2221-E-130-012.  
The authors declare no conflict of interest.
## 3 Results & Observations
### Size of Payload & Transmission Interval
The main impact is caused by the overhead each different payload has, which increases the overall ToA. In detail, we have achieved a PDR of 0.96 when sending 50 byte payloads in 50 s interval, 0.93 when sending 20 byte payloads every 20 min and 0.88 by sending 10 byte payloads in 10 min interval. Figure 6 demonstrates the PDR differentiation which is derived from payload size and transmission interval relation. It is worth mentioning that for medium-sized or larger networks, the GW starts to run out of available channels if the transmission interval is set to 1 min or less. Those observations imply that when we want to scale our network and have a capacity of several thousand EDs with medium coverage, devices should be clustered into groups of data acquisition frequency. End devices that have to report data rarely could be set to a lower DR setting while the EDs from those from which we need data more often should be set to a higher DR setting, with the premise of exhausting the 50 byte size limit in each payload. Nevertheless, the impact that short transmission intervals have on PDR and thus on network capacity is so crucial that even in DR5 there are limits. We can see that when transmission is very frequent, even with 10 byte payloads, the capacity for DR5 also drops to around 100 EDs (Figure 7). It is obvious that for the avoidance of interference, transmission interval is a much more important parameter than higher spreading factor distribution. Figure 6: PDR differentiation derived from payload size and transmission interval relation. In general, we can summarize that if there is a certain amount of data to be sent it is better to send them in one big payload in more rare intervals, than to send data when they are available with shorter payloads. This is not the perfect choice, but one with the least possible drawbacks related to transmission interval context issues (Figure 8). In addition, it is proven that transmission intervals less than 600 s can exponentially harm network performance, when there are more than 100 devices or when a lot of devices use lower data rates. We are claiming that a DR-based distance relative transmission interval distribution method will be vital to scale a network, but this is only applicable if we can break down the EDs, within the application layer, in different classes of data acquisition frequency.
# Non-Contact Dermatoscope with Ultra-Bright Light Source and Liquid Lens-Based Autofocus Function
## Materials and Methods
### Full Polarization Control  
The concept of using polarized light for skin examination is well known in the literature [19; 20; 21]. There are two polarizers in the setup presented in this work: one in front of the illumination unit and one in front of the liquid lens.  
The polarizer in front of the camera is mounted on a rotation stage such that the operator can switch between cross polarization and parallel polarization by means of custom software. In cross-polarization mode, the orientation of the two polarizers is 90 degrees. Because the directly reflected light does not change its polarization for the most part, this light will be filtered out. Only light that is scattered in the skin is detected in this case. If the orientation of the polarizers is parallel to each other, the effect is the opposite: the scattered light is filtered out and the light that is directly reflected will be detected. An image taken with parallel setting of the polarizers can provide information about the surface topography and structure while a cross polarization image will provide information from deeper skin layers. Due to the rotation stage, also any orientation between cross and parallel can be set. In Figure 2, images of a scar taken with parallel and cross-polarization are shown. On the picture taken with a parallel polarization setup, facets of the skin on the scar appear to be larger than those of the surrounding skin areas. The image with the crossed polarization shows a darker reddened area around the scar, which is not clearly visible in the other image. The scar itself appears brighter and whiter.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## 5 Conclusions  
To improve supply chain efficiency, we developed a highly accurate method for predicting the arrival time of marine transportation vessels, which account for approximately 90% of the total volume managed in international logistics and consume considerable time resources. The impact of en route factors, such as weather conditions, often prevents marine transportation vessels from arriving at the destination port as scheduled. The arrival time has been predicted in previous studies by calculating the route to the destination port and the en route voyage speed without considering the influence of future weather conditions. Therefore, the prediction accuracy may become low when weather conditions change.  
In this study, a prediction method for the vessel arrival time was developed to consider weather conditions in two steps: (1) route calculation and (2) voyage speed calculation. (1) Past routes of vessels with similar GRTs and types as the considered vessel are obtained, the difference between future and past weather conditions is calculated for each route, and the route with the smallest difference is selected. (2) For each site along the route calculated in (1), past operation data under similar future weather conditions are acquired for vessels of similar type and GRT to the considered vessel. Bayesian learning is then used to calculate the voyage speed by considering the bow direction and future weather conditions at each site. The time required to travel to each site is then calculated by dividing the distance between sites by the voyage speed at each site and used to predict the time of arrival at the destination port.  
The prediction accuracy of the proposed method was verified for vessels carrying cargoes for the home appliance and automobile industries. The mean prediction accuracy for ten vessels was 90% using the proposed method and 62% using the method of [PERSON] et al. [6]. Hence, the prediction accuracy for the arrival time is projected to increase by 28% compared with that of [PERSON] et al.  
Figure 9: Comparison of ship routes from KEELUNG to TAICHUNG.  
The congestion status en route and at the destination port will be considered in a future study to improve the prediction accuracy of the proposed method.  
Conceptualization, T.O. and T.I.; methodology, T.O. and T.I.; software, T.I.; validation, T.O. and T.I.; formal analysis, T.O. and T.I.; investigation, T.O. and T.I.; resources, T.O. and T.I.; data curation, T.I.; writing--original draft preparation, T.I.; writing--review and editing, T.O.; visualization, T.O., T.I. and N.U.; supervision, T.O.; project administration, T.O.; funding acquisition, T.O. All authors have read and agreed to the published version of the manuscript.  
No external funding was obtained to conduct this study.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 1 Introduction
This particularly meets the demand of the GNSS community that wants to assess the contributions of IGSO and GEO satellites as their altitude is much higher than that of the traditional MEO ones, and answers the question if these satellites can be used for PPP solutions. In this study, we mainly aim at quantifying and analyzing different contributions of BDS GEO, IGSO, and MEO satellites to PPP solutions using different cases, e.g., static PPP using different types of BDS satellites, static and kinematic PPP using GPS and different types of BDS satellites. The possible contributions of BDS observations to shorten the convergence time and to the improvement in position accuracy, particularly in kinematic mode, are investigated. Importantly, these contributions to PPP Integer Ambiguity Resolution (IAR) of GPS satellites are also analyzed. The study focuses on the Asia-Pacific region as it makes more sense for the BDS satellites. Furthermore, those GPS and BDS combined static and kinematic PPP results are compared with that of GPS and GLONASS combined solutions to assess the contribution of BDS and GLONASS to GPS PPP. The three constellations combined solutions are also presented. The paper is organized as follows. In the strategy section, the observations, orbit and clock products as well as PPP and PPP IAR processing methodology are described. Numerical results with different PPP cases are presented and analyzed in results and analysis section. Finally, the study will be summarized in the last section.
## Part B: Network Design for Energy Optimization
### Distributed Approaches  
As opposed to centralized processing, in a distributed model sensor networks distribute the computation among sensor nodes. Each sensor unit acquires local, partial, and relatively coarse information from its environment. The network then collaboratively determines a fairly precise estimate based on its coverage and multiplicity of sensing modalities. Several such distributed approaches have been proposed. Although we are not comparing with them, we shortly discuss some of them, for completeness.  
In [32], a cluster-based distributed tracking scheme is provided. The sensor network is logically partitioned into local collaborative groups. Each group is responsible for providing information on a target and tracking it. Sensors that can jointly provide the most accurate information on a target (in this case, those that are nearest to the target) form a group. As the target moves, the local region must move with it; hence groups are dynamic with nodes dropping out and others joining in. It is clear that time synchronization is a major prerequisite for this approach to work. Furthermore, this algorithm works well for merging multiple tracks corresponding to the same target. However, if two targets come very close to each other, then the mechanism described will be unable to distinguish between them.  
Another nice distributed approach is the dynamic convoy tree-based collaboration (DCTC) framework that has been proposed in [51]. The convoy tree includes sensor nodes around the detected target, and the tree progressively adapts itself to add more nodes and prune some nodes as the target moves. In particular, as the target moves, some nodes lying upstream of the moving path will drift farther away from the target and will be pruned from the convoy tree. On the other hand, some free nodes lying on the projected moving path will soon need to join the collaborative tracking. As the tree further adapts itself according to the movement of the target, the root will be too far away from the target, which introduces the need to relocate a new root and reconfigure the convoy tree accordingly. If the moving target's trail is known a priori and each node has knowledge about the global network topology, it is possible for the tracking nodes to agree on an optimal convoy tree structure; these are at the same time the main weaknesses of the protocol, since in many real scenaria such assumptions are unrealistic.  
The interested reader is encouraged to refer to [52], the nice book by [PERSON] and [PERSON], that even presents the tracking problem as a \"canonical\" problem for wireless sensor networks. Also, several tracking approaches are presented in [43]. Other tracking methods, including interesting algorithmic components, have appeared in [53; 55; 56; 57]. This work has appeared in [37].
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 4 Experiments
### Ablation Study  
#### 4.4.1 The Effect of Balanced Factor \(\alpha\)  
In this section, the effect of \(\alpha\) and different setting values on the performance of the model are explored. In TuSimple and CULane datasets, we tested different settings (\(\alpha=0.6,0.65,0.7,0.75,0.8\)) and compared them to no settings of \(\alpha\). As shown in Table 5, the result gets better as the setting value is increased. However, if the setting value is too high, the model may pay too much attention to positive samples, reducing the supervision of negative samples, which make the performance slightly attenuated. Therefore, we finally chose 0.7 as the setting value of \(\alpha\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & **Proportion (\%)** & **SCNN [13]** & **ENet-SAD [12]** & **ERFNet-E2E [17]** & **UFNet [16]** & **PINet [10]** & **FPLane (Ours)** \\ \hline Normal & 27.7 & 90.6 & 90.1 & 91.0 & **90.7** & 90.3 & 90.6 \\ Crowded & 23.4 & 69.7 & 68.8 & 73.1 & 70.2 & 72.3 & **73.4** \\ Night & 20.3 & 66.1 & 66.0 & 67.9 & 66.7 & 67.7 & **69.5** \\ No line & 11.7 & 43.4 & 41.6 & 46.6 & 44.4 & **49.8** & 48.2 \\ Shadow & 2.7 & 66.9 & 65.9 & 74.1 & 69.3 & 68.4 & **74.4** \\ Arrow & 2.6 & 84.1 & 84.0 & 85.8 & 85.7 & 83.7 & **86.3** \\ Dazzle light & 1.4 & 58.5 & 60.2 & 64.5 & 59.5 & 66.3 & **68.4** \\ Curve & 1.2 & 64.4 & 65.7 & **71.9** & 69.5 & 65.6 & 67.2 \\ Crossroad & 9.0 & 1990 & 1998 & 2022 & 2037 & **1427** & 1578 \\ \hline Total & - & 71.6 & 70.8 & 74.0 & 72.3 & 74.4 & **75.2** \\ \hline Runtime(ms) & - & 116 & 51 & - & **5.7** & 40 & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results on CULane dataset. F1-score is evaluation metric of the dataset. For crossroad, only FP is shown.  
Figure 8: Visualized results of FPLane on CULane testing set. The second row is labels and the third row is predicted results of FPLane.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **TuSimple** & **CULane** \\ \multicolumn{1}{c}{\(\alpha\)} & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline - & 96.68 & 73.8 \\
0.60 & 96.73 & 74.4 \\
0.65 & 96.77 & 74.8 \\
0.70 & 96.82 & 75.2 \\
0.75 & 96.78 & 75.1 \\
0.8 & 96.73 & 74.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The testing results of different \(\alpha\) settings.
## 4 The Modified MSRS Method  
### Problems in the MSRS Method  
The response, \(z(t)\), in Equation (11) is obtained by the product of the transfer vector, \(\mathbf{q}\) and the displacement vector, \(\mathbf{X}\) at the non-support degrees of freedom. However, not all responses are caused by the non-support degrees of freedom completely, in other words, the displacements at the structural support degrees of freedom also have a certain contribution to structural response. For example, various internal forces in components around structural supports can not be obtained by the linear combination of the displacements at the non-support degrees of freedom only, which results in the great deviation of internal forces obtained by Equation (11). Hence, it is necessary to investigate the contribution of displacements at the structural support degrees of freedom to the structural response. The following section introduces three methods to improve the expression of the support contribution to the structural response.