esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 MAC Layer in WMSN
### The Affecting Characteristics of WMSN on MAC Protocol Design  
In WMSNs, a sensor node may have various kind of sensors, as described in the Hardware section, that gather different types of data with different levels of importance. Therefore, WMSNs generate different traffic classes and these classes require classification, buffering, and different type of services (Figure 2). In addition, a WMSN normally demands larger bandwidth and entails higher network throughput to transport large volume of data to remote data sink rapidly and reliably. However, data rates provided by existing commercial sensor products, e.g., 250 Kbps in MICAz, pose some limitations to support multimedia traffic. On the other hand, current sensor nodes, such as MICAz and WINS, already support multiple channels for communication, for example, 40 channels in WINS [19]. Thus, the development of multi-channel MAC protocols, which can effectively utilize the available channel capacity appears as a research direction to achieve a better support for multimedia applications over WSNs. Moreover, the design of the MAC layer depends on a trade-off between complexity/cost and the network throughput, which is reflected in the literature of MAC protocol design for wireless sensor networks.  
The work in [19] argues that a flat architecture is not suitable for multimedia applications because the transmission of the large volume of data resulting from multimedia applications will quickly drain the battery of the sensor nodes, thus, significantly reducing the network lifetime. Of course, multimedia transmission poses a research challenge and it is true that the multi-tier paradigm is very used in the literature of WMSNs. Although they can be of interest for some scenarios, we do not think, however, that the research focus should be put on the design of MAC protocols that assume some kind of _supernodes_ (_i.e._, abundant power supply, out-of-band channel to communicate with the sink, _etc._) that allow to get rid of the constraints of WMSNs (such as [19]).
# Assessment of Simplified Rating Factor Methodology for Shallow Cover Metal Culverts
## 1 Background  
Existing simplified tools used to load rate metal culverts include spreadsheet-based approaches developed by the Ohio Department of Transportation [1] and the Michigan Department of Transportation [2]. The earliest version of the simplified approach is the original ODOT spreadsheet, which is based on procedures outlined in the _National Corrugated Steel Pipe Association Design Data Sheet 19_[3] and the AASHTO _Standard Specifications for Highway Bridges_[4]. However, regardless of the robustness of the structure and its performance, that method rates culverts as unsafe if the cover depth is less than the minimum cover required by AASHTO design specifications. The ODOT subsequently recognized and discussed the conservatism of their spreadsheet in \"Verification of ODOT's Load Rating Analysis Programs for Metal Pipe and Arch Culverts\" [1]. Therefore, the rating methodology was revised in 2012 by ODOT using the arch buckling formulations presented in the _Guide to Stability Design Criteria for Metal Structures_[5], which was then evaluated by Ohio University for corrugated metal culverts under shallow soil covers [6]. The Michigan DOT subsequently developed its own load rating procedure that included replacing the rating factor equation that was based on the minimum cover requirement in the ODOT spreadsheet with an equation that is based on the moment equations presented in \"Design Procedures for Flexible Metal Culvert Structures\" [2; 7].  
After carefully reviewing and examining the source and definition of each parameter used in these approaches and how the parameters affect the rating factors, it was found that some of these parameters were incorrectly applied. Furthermore, for shallow cover culverts, pavement can play a significant role in reducing the moment due to live load [8; 9; 10; 11; 12; 13], yet is merely counted as an additional layer of backfill in currently used rating methodologies.  
These shortcomings and the exclusion of pavement effect artificially amplify the structural response represented by the load rating procedures, and for culverts under shallow fill, can ultimately result in rating factors that are significantly lower than their true values.  
The work presented in this paper, therefore, aims to address these challenges and define an improved rating factor approach for culverts under shallow fill. The responses and rating factors calculated from the presented procedures are then compared with their corresponding finite element results from a two-dimensional (plane strain) finite element software developed specifically for culvert analysis and design to verify the improvement offered by these modifications, and an example and comparisons are provided.
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 8 Conclusions  
In this study, we introduced a hybrid positioning method that integrates IMEs radio strength information and PDR information to deal with the problems of indoor positioning. The signal propagation of the IMEs radio was modeled and identified in order to understand its CNR _versus_ distance relation. The result of the pre-experiment showed that IMEs radio-based trilateration using the radio propagation model worked well in the test indoor environment with sub-meter-level accuracy. With this result, we tried to improve the localization accuracy by using a hybrid positioning method. The trilateration and the hybrid positioning method were evaluated by applying them to robot localization and pedestrian localization in a test environment. Experiments showed that the hybrid positioning method can gradually rectify the estimation error arising from an incorrect initial position, and it achieved an average estimation error of 217 mm for robot localization. However, the proposed method was not effective for pedestrian localization because of the influence of the human body on the radio propagation. The result of the experiment conducted to investigate the human body influence suggests that it can be modeled. The improvement of pedestrian localization based on such a model is future work.  
This work was supported in part by Hitachi Industrial Equipment Systems Co., Ltd.  
[PERSON] conceived of, designed and performed this work. He was involved in writing the manuscript. [PERSON], [PERSON] and [PERSON] helped [PERSON] conduct the experiment. [PERSON] and [PERSON] gave him advice. [PERSON] supervised the overall project.  
The authors declare no conflict of interest.
# A Novel Finite Element Method Approach in the Modelling of Edge Trimming of CFRP Laminates
## 5 Results and Discussion
### Fibre Orientation of 90\({}^{\circ}\)
A gradual and significant increase in the forces \(F_{N}\) and \(F_{Y}\) caused by tool wear was observed to increase in maximum forces of 38.79% and 47.45%, respectively. This rise is less pronounced for \(F_{X}\) forces, achieving a maximum increment of 16.12%, while the \(F_{T}\) forces do not show a significant variation due to the wear of the cutting tool. This effect is due to the increased contact area between the workpiece and the worn cutting tool. All the data obtained in the simulations performed are illustrated in Figure 21, while the force increases obtained in comparison with the forces obtained with the unworn tool with a tool edge radius of 10 \(\upmu\)m, are shown in Table 6.  
A deeper analysis of the results simulated in 45\({}^{\circ}\) and 90\({}^{\circ}\) laminates yield the following conclusions. The increment of the machining forces occurs because the tool edge wear decrease the fibre cutting effectiveness. A worn tool, instead of shearing the fibres away, tends to bend the fibres, increasing the machining forces and sub-surface damage. Generally, the increment of machining forces occurs because the tool/workpiece contact area is increased. This factor increases the spring back and frictional forces. The spring back \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tool Edge Radius** & **Average \(F_{X}(N)\)** & **Average \(F_{Y}(N)\)** & **Average \(F_{T}(N)\)** & **Average \(F_{N}(N)\)** \\ \hline
10 \(\upmu\)m & 123.17 (\(-\%\)) & 155.04 (\(-\%\)) & 23.01 (\(-\%\)) & 201.26 (\(-\%\)) \\
20 \(\upmu\)m & 129.71 (5.31\%) & 180.41 (16.36\%) & 23.44 (1.86\%) & 228.45 (13.51\%) \\
30 \(\upmu\)m & 135.51 (10.02\%) & 196.43 (26.69\%) & 23.62 (2.65\%) & 245.66 (22.06\%) \\
40 \(\upmu\)m & 139.03 (12.88\%) & 213.74 (37.85\%) & 22.34 (\(-\)2.92\%) & 263.60 (30.97\%) \\
50 \(\upmu\)m & 143.02 (16.12\%) & 228.62 (47.45\%) & 22.09 (\(-\)3.99\%) & 279.34 (38.79\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average forces and increment percentage because of the tool wear simulated. Figure 21: Representation of the average machining forces simulated with a fibre orientation of 90\({}^{\circ}\): (**a**) \(F_{X}\) and \(F_{Y}\) (**b**) \(F_{T}\) and \(F_{N}\). forces increase significantly because a bigger area of the tool penetrates inside the laminates, adding a substantial force in the normal component (\(F_{N}\)). The tangential force (\(F_{T}\)) changes are smaller because, despite the friction forces increasing due to contact area increments, the tangential component of the spring back forces is contrary to the frictional force. Therefore, both effects are counteracted, avoiding a clear trend in the tangential forces. Horizontal and vertical force (\(F_{X}\) and \(F_{Y}\)) increments are the result of the projections of the significant spring back force increment. In general, the increments of \(F_{Y}\) caused by tool wear are more severe than the force increments observed in \(F_{X}\).
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 3 Our Method
### Adaptive Frame Sampling
#### 3.1.2 Adaptive Frame Sampling Based on Mutual Information  
Inspired by the literature [35], we use the cumulative distribution function for frame sampling, and adaptively sample frames according to the motion information distribution of the entire set of consecutive frames to be sampled. As shown in Figure 2, given a set of consecutive frames to be sampled containing T + 1 frames, a set of \(MI\) scores can be obtained by calculating the \(MI\) scores of adjacent frame pairs, \(S=[\hat{M_{0}},\hat{M_{1}},\ldots,\hat{M_{T}}]\), where each element \(\hat{M_{t}}\in[0,1],t\in\{0,1,\ldots,T\}\). Then, we accumulate the obtained \(MI\) scoresto obtain the cumulative motion distribution function of the set of consecutive frames to be sampled:  
\[M_{d}(t)=\sum_{i\leq t}\hat{M}_{i} \tag{8}\]  
The obtained motion accumulation distribution function is shown in Figure 2, where the \(X\)-axis represents the frame index and the \(Y\)-axis represents the total motion accumulation up to the current frame. Finally, to sample \(N\) frames from the consecutive frames set, we divide the \(Y\)-axis evenly into \(N\) segments, and the frames whose indices fall within the segments corresponding to the \(X\)-axis are regarded as sub-sampling sets, with a total of \(N\) sub-sampling sets. Then, in order to increase the diversity of the input model data, a frame is randomly sampled from each subsampling set rather than a frame at a specific location, and in particular, for the subsampling set where the frame to be detected \(F_{T}\) is located, the frame to be extracted is fixedly set to the frame to be detected, \(F_{T}\). Then, they form the final sampled frame set, which is the input of the network for detection. As shown in Figure 2, this sampling method can sample more frames during periods of significant motion and fewer frames during periods of relatively static motion.
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion
### Mechanisms by Which Walnut Contributes to the Management of NAFLD
#### 3.2.1 Effects of Walnut on Metabolism
Insulin Resistance and Glucose Metabolism  
Insulin resistance, which is closely associated with T2 DM and obesity, is a manifestation of NAFLD [70], and individuals with NAFLD is known to exhibit impaired hepatic glucose production similar to patients with overt T2 DM [71]. Thus, NAFLD is independently associated with increased levels of fasting blood glucose and HbA1c [72]. In a randomized clinical trial in 20 patients with T2 DM, supplementation with \(\alpha\)-linoleic acid, which is rich in walnut, improved glucose homeostasis and insulin sensitivity with an increase in the level of adiponectin that is known to decrease hepatic and systemic insulin resistance and attenuate hepatic inflammation and fibrosis [73]. In a crossover randomized clinical trial conducted with ten obese subjects, walnut consumption significantly decreased insulin resistance along with a reduction in the blood levels of ceramides and sphingomyelins that are known to promote insulin resistance [74]. A randomized controlled clinical trial in 100 patients with T2 DM demonstrated that consumption of walnut oil (15 g/day for 3 months) significantly reduced HbA1c and fasting glucose levels with respect to the baseline [64]. Collectively, these studies indicate that walnut may also exert beneficial effects on NAFLD-associated insulin resistance and impaired glucose metabolism.
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
## 6 Results and Discussion
Finally, we investigate the effect of different queue delay thresholds on network performance. As shown in Table 1, a high threshold value increases packet loss and end-to-end delay because, as the sojourn time of packets in the queue increases, it results in queue overflow and more packets being dropped. The increased sojourn time also reflects the average end-to-end delay. Packet loss and end-to-end delay decrease when the threshold is reduced from 150 ms to 100 ms and then to 50 ms. A lower queue delay threshold of 10 ms slightly increases flow completion time and end-to-end delay without significantly affecting packet loss. This is because such a low queuing delay in a high-load network  
Figure 4: Packet loss comparison in OSPF and OSPF with PBR module. Figure 5: Throughput comparison in OSPF and OSPF with PBR module. causes rapid switching of the QoS flow to a new low-latency route. This rapid switching adversely affects the QoS of the flow and increases end-to-end delay, as shown in Table 1.  
Besides the significant performance gains discussed above, one potential limitation of this work is the absence of a mechanism that allows real-time applications to communicate their specific latency requirements directly to the SDN controller. This feature is important for enabling the controller to make more informed and dynamic routing decisions based on real-time application needs, which we plan to incorporate in future work. Additionally, a future study could focus on further optimizing the PPBR module. For instance, integrating machine learning algorithms could significantly enhance the  
Figure 8: End-to-end delay comparison in OSPF and OSPF with PBR module. Figure 6: Jitter comparison in OSPF and OSPF with PBR module. Figure 7: Flowcompletion time in OSPF and OSPF with PBR module. system's ability to predict network congestion and make proactive routing adjustments, thereby improving QoS for real-time media flows. Machine learning techniques could also enable more adaptive and intelligent routing strategies, allowing the network to self-optimize based on historical traffic patterns. These improvements would extend the current framework's capabilities and offer more sophisticated, data-driven approaches to managing network performance.
## 1 Introduction
These analyses correspond to satellite broadcasts of satellite location information, signal arrival at the receiver, and receiver calculation of ranging measurements based on the received signal and receiver's ability, respectively. The satellite data, signals, and measurements used in this research were recorded from the 10 th of July to the 13 th of August 2014. The GPS and BDS signals were recorded at the same time on the roof of the building of the Department of Aeronautics and Astronautics, National Cheng Kung University, Taiwan. The GNSS antenna was a NovAtel GPS-703-GGG, and the receiver was a NovAtel FlexPak6. In order to present the original performance of GPS and BDS, this research used the raw measurement from the NovAtel receiver without any smoothing filter. The raw data was tested in Matlab R2014a on a 3.4-GHz Intel(r) Core (TM) i7-3770 CPU with 4 GB of RAM. Sections 2-4 detail DQA, SQA, and MQA, respectively. GPS and BDS SIS data were analyzed using these three analysis methods.
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type
### The Calculation Capacity of the Membrane Considering Perforated Holes  
Capacitance of parallel plate thickness \(t\), as shown in Figure 9 is defined by Equation (28) presented in [74]:  
\[C=\frac{\varepsilon_{0}w}{g_{0}}\bigg{[}1+\frac{2g_{0}}{\pi w}\ln\bigg{(}\frac {\pi w}{g_{0}}\bigg{)}+\frac{2g_{0}}{\pi w}\ln\bigg{(}1+\frac{2t}{g_{0}}+2 \sqrt{\bigg{(}\frac{t}{g_{0}}+\frac{t^{2}}{g_{0}^{2}}\bigg{)}}\bigg{)}\bigg{]} \tag{28}\]  
Each of the perforated square holes of the membrane can be defined as a separate parallel flat capacitor. There are three components of the container: \((a)\) the capacitance of the membrane; \((b)\) the capacitance in the area of the membrane thickness edging; \((c)\) the capacitance in the area of the hole edging.  
In this case, the total capacitance in the up state is calculated using Equation (29) [75]:  
\[\begin{split} C_{total}=(a)-(b)+(c)\\ (a)=\frac{\varepsilon_{0}w}{(g_{0}+\frac{t_{d}}{\varepsilon_{r}})} +\frac{2\varepsilon_{0}W}{\pi}\bigg{[}\ln\bigg{(}\frac{\pi w}{(g_{0}+\frac{t_ {d}}{\varepsilon_{r}})}\bigg{)}+\ln\bigg{(}1+\frac{2t_{b}}{(g_{0}+\frac{t_{d}} {\varepsilon_{r}})}+\\ +2\sqrt{\bigg{(}\frac{t_{b}}{(g_{0}+\frac{t_{d}}{\varepsilon_{r}} )}+\frac{t_{b}^{2}}{(g_{0}+\frac{t_{d}}{\varepsilon_{r}})^{2}}\bigg{)}}\bigg{)} \bigg{]};\\ (b)=n_{l}n_{w}\frac{\varepsilon_{0}w_{h}^{2}}{(g_{0}+\frac{t_{d}} {\varepsilon_{r}})};\\ (c)=\frac{2n_{l}n_{w}\varepsilon_{0}w_{h}}{\pi}\bigg{[}\ln\bigg{(} \frac{\pi w}{(g_{0}+\frac{t_{d}}{\varepsilon_{r}})}\bigg{)}+\ln\bigg{(}1+\frac {2t_{b}}{(g_{0}+\frac{t_{d}}{\varepsilon_{r}})}+2\sqrt{\bigg{(}\frac{t_{b}}{ (g_{0}+\frac{t_{d}}{\varepsilon_{r}})}+\frac{t_{b}^{2}}{(g_{0}+\frac{t_{d}}{ \varepsilon_{r}})^{2}}\bigg{)}}\bigg{)}\bigg{]}.\end{split} \tag{29}\]  
where \(w\)--the signal line width; \(W\)--the beam width; \(t_{d}\)--the dielectric film thickness; \(t_{b}\)--the beam thickness; \(n_{l}\)--the number of holes along the length; \(n_{w}\)--the number of holes along the width; \(w_{h}\)--the hole area.  
To find the down state capacitance air height \(g_{0}=0\) will be considered in Equation (29).
## 2 Results and Discussion
### Characterization of Silica Particles
#### 2.1.1 Fourier Transform Infrared (FTIR) Analysis  
The surface functional groups on the surface of silica particles were confirmed based on the presence of the characteristic peaks for different moieties, such as C=O, N-H, and \(-\)CH\({}_{2}\) (Figure 1). For both bare and mPEG-grafted silica particles, the peaks at \(\sim\)1045 cm\({}^{-1}\) are attributed to asymmetric vibrations of Si-O-Si [33]. A characteristic peak of carbonyl group (C=O) stretching in the range of 1715 \(\pm\) 100 cm\({}^{-1}\) appeared for mPEG-functionalized silica particles due to presence carbonyl groups in the mPEG-silane structure (Figure 1). Note that the carbonyl moiety seen in the mPEG structure is not the part of the repetitive unit, however, is present in the structure due to the coupling chemistry used to prepare mPEG-silane. It was also anticipated that the characteristic broad peak in the range of 3000-3600 cm\({}^{-1}\) for mPEG-grafted silica would appear due to O\(-\)H stretching [34]. The residual moisture upon grafting and washing steps of mPEG-grafted silica particles with water and ethanol caused the broad \(-\)OH peak to appear in the aforementioned region. The presence of the residual moisture content is supported by the very small mass loss seen in the initial phase of the thermogravimetric (TGA) profile where the silica particles were heated to 120 \({}^{\circ}\)C and remained for \(\sim\)3 min (Figure 2). Although an N\(-\)H stretching peak is expected in 3000-3600 cm\({}^{-1}\) region, due to the strong \(-\)OH peak, the signal associated with the N\(-\)H stretching is likely masked. An \(-\)NH group is also present in the structure of mPEG silane due to coupling chemistry used. Moreover, the peak around 2800 \(\pm\) 100 cm\({}^{-1}\) appeared is attributed to \(-\)CH\({}_{2}\) stretching vibration from the repetitive units of mPEG grafted on silica particles [33; 35]. The small peaks at \(\sim\)1462 and 1347 cm\({}^{-1}\) are attributed to \(-\)CH\({}_{2}\) bending and wagging vibration, respectively [33; 35]. In summary, identification of characteristic peaks of carbonyl (C=O), N\(-\)H, and \(-\)CH\({}_{2}\) suggest that successful coupling of mPEG-silane to silica particles was achieved.  
Figure 1: Fourier transform infrared (FTIR) spectra of bare silica and polyethylene glycol (mPEG)-grafted silica particles. Insets show the zoomed in spectra and the chemical structure of mPEG–silane.
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
## 2 Background  
A reliable storage system is needed to provide a high availability of data that meets user needs with an automatic process. This is because various data are produced by the instruments continuously, such as every 5 min, 15 min, hourly, and daily. It would be impossible for a human to conduct this as a manual process.  
The researchers in LAPAN need data from space weather observations as input to their research. When the necessary data is not available, the production of the latest information is hampered. We indicated that the centralized method of the existing system has several drawbacks, such as low data availability, low throughput, and increased time for data updates. Reliance on one server can result in a single point of failure when the destination server is down. Currently, the main server in Bandung is the central destination for storing data. A large number of users accessing the Bandung server can create a high network load on the server.  
Furthermore, the process of synchronizing data between the Bandung server and all the site servers at the same time also affects network performance on the server. If one elaborates on those factors, the main problems associated with data updates arise from synchronization at the same time from the central server to all site servers or between site servers and PCs. The current system employs two crontabs (cron tables), which are configuration files that specify shell commands for running a script program on a schedule. First, a script on the site server runs the main program every 5 min for data synchronization between the site server and all PCs (Figure 2). Second, a crontab job on the central server runs every 15 min. It synchronizes the data between the central server and all site servers (Figure 3). All synchronization processes run at one time to synchronize all data through the default secure shell (SSH) port utilizing the Rsync tool. Thus, this process creates a queue of jobs that have to be executed, while the various data from each site and instrument should be stored safely in real-time and without damage. This lowers throughput in sending data because of the scripts running at the same time on one port. Low throughput affects the duration of data synchronization due to the high network load; thus, synchronization takes longer. A delay in updating files also occurs when data with small size cannot be sent immediately because of the need to wait for the larger data in the queue to finish. Of course, these situations emerge as a problem when all concurrent access is directed to one port. The queue builds and causes a delay for data updates due to the high network load.