esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluations and Results Analysis
### Results and Analysis
#### 4.2.4 Data Transmission Time  
Previous Simulation shows the similarity between real environment and prediction model. In this simulation, we analyzed transmission time regarding network capacity using prediction model. Total transfer time was tested by throughput as compared to network capacity using the BPR function presented in Section 3.5 and shown in Figure 13. \(\alpha\)- and \(\beta\)- values calculated in Section 3.5.1 were used and a test was conducted depending on the network speed and the number of channels. The below Figure 13a-c show throughput compared to network capacity when the number of channels is more than one, two, and three in high-, medium-, and low-speed networks, respectively. High speed network is assumed to 100% speed when middle speed network is assumed to 70% speed and low speed network is assumed to 45% speed of high speed network.  
Figure 12: _Cont._In the high speed network, 2 channel network shows average 11.95% faster transmission time than 1 channel network, 3 channel network shows average 4.74% and 16.69% faster transmission time than 2 channel network.  
Figure 13: Congestion and transmission time comparison: (**a**) High speed network data transmission time; (**b**) Middle speed network data transmission time; and (**c**) Low speed network data transmission time.  
2 channel network and 1 channel network. In middle speed network, 2 channel network shows average 7.96% faster transmission time than 1 channel network, 3 channel network shows average 0.25% and 8.21% faster transmission time than 2 channel network and 1 channel network.  
In the low speed network, 2 channel network shows average 5.58% faster transmission time than 1 channel network, 3 channel network shows average 2.65% and 8.23% faster transmission time than 2 channel network and 1 channel network.  
This study shows that the estimated congestion shows a sensitive response to changes. It will therefore be able to predict congestion and can be used in the congestion zone in the future.
## 4 Implementation, Simulation, and Real Tests  
### Description of the Implementation Environment  
To implement different control strategies for communicating material with the SADHoa-WSN framework, we developed an instrumented and controlled WSN data collection platform which can be divided into three parts:  
* The **WSN nodes** (hardware systems to be controlled and depicted in Figure 11): The nodes are programmed to detect a physical phenomenon (in most of our tests, the ambient temperature is used) and periodically send the measured value to the base station using multi-hop routing. Communication protocols like the primary routing define energy prediction models used by the control system.
* The **Energy Measurement System** (EMS, hardware): This system is calibrated to measure the real-time consumed energy of each node.
* The **control system** (set of software implementing SADHoa-WSN): The control system, thanks to the EMS, continuously observes the energy consumed. It compares prediction models with real measurements to detect significant deviations. The SADHoa-WSN principle is used to implement the network reconfiguration functionality. Technically, this control system is implemented using NetLogo ([[https://www.northwestern.edu/](https://www.northwestern.edu/)]([https://www.northwestern.edu/](https://www.northwestern.edu/))), a multi-agent programmable modeling environment.  
Figure 10: SADHoa-WSN architecture formalized in UML.  
The average power of a node in an active state is around 600 mW, and 320 mW in a sleep state where the power of Arduino alone (without the XBee shield) is 275 mW. Therefore, the power of the XBee module with its shield in active/sleep mode is 325 mW and 45 mW, respectively. In addition, the computing ability of nodes could also be used to reduce the data transfer and thus the time for a radio to stay active.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 3 Results
### Misclassification Errors  
Some obvious misclassification errors were observed (letters A, B, and C in Figure 12). As described above, reoccurring boat traffic, e.g., ferries, around harbors or harbor occupancy can be detected in percentile images and classified as ITZ depending on the frequency of boat traffic. This is a major issue in more populated regions in southern Norway [49].  
Under extreme reoccurring windy condition along the coast, ocean noise cannot be totally excluded when processing larger areas. However, such ocean noise generally appears as single pixels offshore.  
Due to its side-looking geometry, SAR imagery does not provide reliable data behind or in front of steep slopes due to radar shadow and overlay. If these areas are not well masked because of errors in the DEM, they can potentially appear as false detections of ITZs. Another misclassification due to the topography was observed in east-west-directed fjords with very steep mountain slopes (north-south) as in Ersfjord, indicated as \"B\" in Figure 12. It seems that the backscatter from the radar side-lobes reflected from the hill sides parallel to the radar range direction dominated the received radar signal compared to the low backscatter from water.  
Salmon fish farms were clearly falsely detected in the ITZ_AtmExp product. These are generally offshore, however, and their pattern allows them to be easily recognized as errors. An example is shown in Figure 16.
## 3 Results
### Anthropometric Characteristics of Older Persons  
In Table 2, the blood pressure and weight statuses of the participants are compared by gender. The overall prevalence of hypertension, overweight and obesity were 45%, 46% and 36%, respectively. Significant differences in proportions for BMI (\(p\leq 0.0001\)), WC (\(p\leq 0.0001\)) and WHtR (\(p=0.039\)) were observed between older men and women. Abdominal obesity by WC was observed among 57% of the participants, with women being more affected (87%, \(p\leq 0.0001\)) compared to men (23%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **All, \(n=350\)** & **Men, \(n=165\)** & **Women, \(n=185\)** & \multirow{2}{*}{\(p\)**-Value**} \\  & \(n\) (\%) & \(n\) (\%) & & \\ \hline Household income & & & & \\ \(<\)R5000 (\(<\)§323.31) & 127 (36) & 49 (30) & 78 (42) & 0.015 * \\ \(\geq\)R5000 (\(\geq\)§323.31) & 223 (64) & 116 (70) & 107 (58) & \\ \hline Access to electricity & & & & \\ No & 0 (0) & 0 & 0 & 1.000 \\ Yes & 350 (100) & 165 (100) & 185 (0) & \\ \hline Access to water & & & & \\ No & 0 (0) & 0 & 0 & 1.000 \\ Yes & 350 (100) & 165 (100) & 185 (0) & \\ \hline Type of toilet & & & & \\ Flush & 8 (2) & 4 (2) & 4 (2) & 1.000 \\ Pit & 242 (98) & 161 (98) & 161 (98) & \\ \hline Smoking & & & & \\ No & 194 (55) & 83 (50) & 111(60) & 0.068 \\ Yes & 156 (45) & 82 (50) & 74 (40) & \\ \hline Alcohol use & & & & \\ No & 225 (64) & 91 (55) & 134 (72) & 0.001 * \\ Yes & 125 (36) & 74 (45) & 51 (28) & \\ \hline Family history of & & & & \\ hypertension & & & & \\ No & 279 (80) & 140 (85) & 139 (75) & 0.024 * \\ Yes & 71 (20) & 25 (15) & 46 (25) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **All, \(n=350\)** & **Men, \(n=165\)** & **Women, \(n=185\)** & \multirow{2}{*}{\(p\)**-Value**} \\  & \(n\) (\%) & \(n\) (\%) & \(n\) (\%) & \\ \hline Blood pressure & & & & \\ (mmHg) & & & & \\ Normal & 189 (54) & 87 (53) & 102 (55) & 0.652 \\ High & 161 (46) & 78 (47) & 83 (45) & \\ \hline SBP (mmHg) & & & & \\ Mean & 135 (\(\pm\)SD = 21) & 135 (\(\pm\)SD = 21) & 135 (\(\pm\)SD = 22) & 0.912 \\ Normal & 108 (242) & 48 (29) & 60 (32) & 0.499 \\ High & 242 (69) & 117 (71) & 125 (68) & \\ \hline DBP (mmHg) & & & & \\ Mean & 81 (\(\pm\)SD = 13) & 82 (\(\pm\)SD = 11) & 81 (\(\pm\)SD = 11) & 0.588 \\ Normal & 178 (51) & 82 (50) & 96 (52) & 0.682 \\ High & 172 (49) & 83 (50) & 89 (48) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of blood pressure and weight statuses of participants by gender. Table 3 presents the comparison of anthropometric indices by blood pressure status. Significant differences in BMI and WC proportions were observed by blood pressure status among men and women.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 3 Traveling Wave Head Detection Algorithm Based on CEEMDAN-TEO
### Simulation Verification  
In this paper, a simulation line was set up using ATP/EMTP, with a fault point located 45 km away from the M end. The wave within 1 ms after the fault was selected for analysis. The \(\alpha\)-mode component and \(\beta\)-mode component were detected at M and N ends, respectively, after improving CEEMDAN-TEO. As shown in Figure 2, the time of the \(\alpha\)-mode component arriving at the M and N ends was 154.2 \(\mu\)s and 260.3 \(\mu\)s, respectively; the time of the \(\beta\)-mode feature arriving at the M and N ends was 150.8 \(\mu\)s and 254.6 \(\mu\)s, respectively.  
Substituting the data into (11) yields.  
\[d_{M}=\frac{t_{M1}-t_{M2}}{t_{M1}-t_{M2}+t_{N1}-t_{N2}}L_{MN}=\frac{154.2-150. 8}{154.2-150.8+260.3-254.6}\times 120=44.83\;km \tag{22}\]  
The modified wave ranging method employed by [PERSON] et al. [19] only eliminates the effect of wave speed, and it also needs to be combined with the moment of fault occurrence to achieve ranging. The accuracy of the method proposed in this paper was verified by comparing it with [PERSON] et al.'s method.  
Substituting into the double-ended traveling wave localization Equation of [PERSON] et al. [19]:  
\[d_{M}=\frac{t_{M}-t_{0}}{t_{M}+t_{N}-2t_{0}}L_{MN}=\frac{154.2-0}{154.2+260.3-0} \times 120=44.64\ km \tag{23}\]  
As can be seen from Equation (23), the positioning error of the proposed method is 170 km, while the calculation error of [PERSON] et al.'s method is 360 m. In order to further evaluate the accuracy of the algorithm proposed in this paper, comparative experiments were conducted at different fault locations, and the results are shown in Figure 3.  
From Figure 3, the accuracy of the improved algorithm is better than that of the algorithm utilized by [PERSON] et al. [19], excluding when the fault occurs near the measurement point. Meanwhile, [PERSON] et al. [19] accurately obtained the time of failure; the fault signal at fault 0 s is directly studied for convenience consideration. A comparison of the wavehead capture accuracy is also made, from which it is evident that [PERSON] et al. [19] have greater errors. Further, by comparing the localization errors of the two localization methods, the proposed method has higher ranging accuracy.  
Figure 2: Time of arrival of the fault traveling wave mode component.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Scientific and Technological Challenges
#### 3.2.5. Orographic Enhancement of Precipitation  
Precipitation retrievals from space generally do not include correction for the orographic enhancement; this is a major error source. The physical mechanisms involved encompass fluid dynamics, thermodynamics, and microphysical cloud processes, as well as the larger-scale patterns of the atmospheric general circulation [211; 212]. The necessary ingredient is the moisture-laden airflow towards a mountain, but a detailed knowledge of the orographically-modified flow is crucial for predicting the intensity, location, and duration of orographic precipitation [213; 214]. The small-scale release of buoyant instability by the upslope flow has always been associated with precipitation enhancement in these cases, but Houze and Medina [215] show that cells may appear even if buoyant instability is weak or nonexistent, so that even a stable flow may form cells that will enhance precipitation over the windward slopes. From the satellite-based precipitation retrieval perspective, early attempts to cope with orographic corrections to real-time, high-resolution satellite rainfall rate distributions date back to 2002 [216]; furthermore, diagnostic models, driven by reanalysis data, were used shortly afterwards to improve satellite rainfall estimates in data-sparse regions [217]. More recently, specific campaigns were carried out in the coastal Chilean Andes region [218; 219] and in the Southern Appalachian Mountains [220] dwelling on CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) instrument. These data have provided a deeper understanding of the microphysical processes leading to orographic enhancement. GPM has offered the opportunity of modeling low-level seeder-feeder interactions to address underestimations and missed detections in QPEs over complex terrains [221]. However, a specific implementation of an orographic/nonrographic rainfall classification scheme for MW radiometer data has, to date, only been implemented for the GSMaP products [222; 223; 224]. Studies have been conducted on small catchments to try to characterize the error structure, and have confirmed the difficulty of the problem, which remains open [225; 226; 227].
## 5 Collagen Type I and III Turnover in Normal Pregnancy
### MMP-1 Dysregulation  
MMP 1 is a member of the MMP family. The zymolytes of MMP 1 are collagen and metagelatin, which play major roles in trophoblast invasion [53]. TIMP 1 is a natural inhibitor of MMP 1 [54; 55]. Different methods may be used to determine their circulatory levels.  
[PERSON] et al., in 2011, have shown an increased expression of matrix metalloproteinase-1 in systemic vessels of women with preeclampsia. Vessel expression of MMP-1 and circulating MMP-1 levels were increased in women with preeclampsia, whereas vascular expression of collagen or tissue inhibitor of metalloproteinase-1 were down-regulated or unchanged [56].  
[PERSON] et al., 2016, compared serum values of MMP-1, TIMP-1, and their ratio in the second and third trimester of normal and pre-eclamaptic pregnancy. In that study, 30 females progressing to normal pregnancy were compared with 16 females who developed preeclampsia. MMP-1 and TIMP-1 concentrations were measured in serum samples (2 nd and 3 rd trimester) of the females by enzyme linked immuno-sorbent assay. There was no significant difference in the levels of MMP-1, TIMP-1, and ratio of MMP-1 and TIMP-1 in pre-eclamaptic and normal pregnancy females. There is a lack of alteration in the levels of MMP-1, TIMP-1, and their ratio during the progression of pre-eclampsia when compared with normal pregnancy [57].  
Figure 1: Schematic presentation of impaired collagen I and III turnover in pathological pregnancy.  
[PERSON] et al., 2017, tested the hypothesis that placental ischemia could target vascular and uteroplacental MMPs through an inflammatory cytokine-mediated mechanism. \"MMP levels and distribution were measured in the aorta, uterus, and placenta of normal pregnant (Preg) rats and pregnant rats with reduced uterine perfusion pressure (RUPP). Gelatin zymography showed prominent uterine MMP-2 and MMP-9 activity that was dependent on the amount of loaded protein. This study showed that placental ischemia, possibly through the release of TNF-\(\alpha\), causes increases in the levels of matrix metalloproteinase MMP-1 and MMP-7, which could alter collagen deposition and cause inadequate uteroplacental and vascular remodeling in hypertension in pregnancy. The data suggest that targeting MMP-1 and MMP-7 and their upstream modulators, such as TNF-\(\alpha\), could provide a new approach in the management of hypertension in pregnancy and preeclampsia\" [58].  
[PERSON] et al., 2015, measured levels of MMP-1 in maternal umbilical serum of women with preeclampsia and compared the concentrations with those of normotensive pregnant females. MMP-1 levels were decreased in maternal umbilical serum of patients with preeclampsia [59]. Table 1 summarizes the available literature data for studies measuring levels of MMP-1 in samples of patients with hypertensive disorders of pregnancy.
## 3 The Present: 2010-2015
### Applications of UPS and UBPS
#### 3.3.3 Fatigue Damage  
The UPS method was also applied to fatigued composites. Fatigue loading typically leads to the initiation, progression and accumulation of micro defects. At the macroscopic level, these defects manifest themselves in a directional reduction of the stiffness properties [31,32]. Such a directional stiffness reduction is visible as a stretching (along the direction of loading) of the UPS contours. This is explicitly demonstrated in Figure 11 for a woven C/E laminate (ultimate tensile strength of 770 MPa), which was fatigued along \(\varphi=0^{\circ}\): 314,111 load cycles with stress range 0-625 MPa at a frequency of 5 Hz [33]. The P-UPS experiment yielded a stiffness reduction of 12.8% along the loading direction, which is in good agreement with extensometer data yielding a stiffness reduction of 11% [33].  
Figure 11: P-UPS recording of woven C/E laminate: before (**a**) and after (**b**) applying tension fatigue.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
###### Abstract  
The rapidly growing number of space activities is generating numerous space debris, which greatly threatens the safety of space operations. Therefore, space-based space debris surveillance is crucial for the early avoidance of spacecraft emergencies. With the progress in computer vision technology, space debris detection using optical sensors has become a promising solution. However, detecting space debris at far ranges is challenging due to its limited imaging size and unknown movement characteristics. In this paper, we propose a space debris saliency detection algorithm called SDebrisNet. The algorithm utilizes a convolutional neural network (CNN) to take into account both spatial and temporal data from sequential video images, which aim to assist in detecting small and moving space debris. Firstly, taking into account the limited resource of the space-based computational platform, a MobileNet-based space debris feature extraction structure was constructed to make the overall model more lightweight. In particular, an enhanced spatial feature module is introduced to strengthen the spatial details of small objects. Secondly, based on attention mechanisms, a constrained self-attention (CSA) module is applied to learn the spatiotemporal data from the sequential images. Finally, a space debris dataset was constructed for algorithm evaluation. The experimental results demonstrate that the method proposed in this paper is robust for detecting moving space debris with a low signal-to-noise ratio in the video. Compared to the NODAMI method, SDebrisNet shows improvements of 3.5% and 1.7% in terms of detection probability and the false alarm rate, respectively.  
space debris detection; space-based surveillance; lightweight neural network; small object detection; video salient object detection +
Footnote †: journal: Applied  
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Footnote †: journal: Applied  
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# Vortex: Network-Driven Opportunistic Routing for Ad Hoc Networks
## 1 Introduction
Against the above-stated backgrounds, this paper proposes VORTEX, a novel routing protocol that uses hierarchical OR, to overcome the above problems from the following perspectives, and also stated as the research objectives. * Opportunistic forwarding with hierarchical structure;
* Network-driven adaptive packet forwarding;
* Efficient and effective route reuse,  
VORTEX employs an opportunistic forwarding strategy to improve reliability aiming to adapt to the dynamic communication environments of ad hoc networks. Moreover, the hierarchically structured networks contribute to managing forwarding routes without initiating conventional route discovery procedures to find an individual guide for opportunistic forwarding. In addition to that, there is the advantage that the structured networks also become guides for other end terminal pairs. In other words, once the structured networks are established, the information can be a guide for every end terminal pair in the networks, namely, network-driven packet forwarding, which does not require end-to-end pair-based routing procedures, can be realized by the structure for better routing and network management flexibility. The expected contributions derived from VORTEX can be summarized as follows. * End-to-end reliability improvement derived from multi-path forwarding strategy of OR;
* Prompt forwarding initiation omitting conventional route discovery procedure;
* Reduction of the number of control packets for route management;
* Relieve burdens of end pairs by network-driven forwarding strategy based on hierarchical structures;
* Effective reuse of routing information with the hierarchical structure. In other words, the OR in VORTEX contributes to improving end-to-end reliability with multi-path forwarding and the hierarchization contributes to effective and efficient routing information management across the network. The rest of this paper is organized as follows. In Section 2, conventional routing protocols for wireless multihop networks are briefly introduced. Section 3 describes the details of the proposed OR, VORTEX, along with its hierarchization and forwarding procedures. The performance evaluations are conducted in Section 4 and Section 5 concludes this paper.
## 7 Taking One Step Further down the Road on the Hybrid Computational Platform
Table 5 shows a comparison of the wellbore hydraulics model against the numerical model and compares the results against the field data with respect to the level of accuracy achieved. A computational performance comparison of the full numerical model (numerical reservoir model coupled with numerical wellbore hydraulics model) against the neuro-simulation model (numerical reservoir model coupled with ANN-based wellbore hydraulics model) was made. The full numerical wellbore model was run for 24 h with the input variables randomly chosen in a protocol like the one followed to generate data for ANN training. The numerical model was able to study 91 cases within this time span. These 91 cases were re-simulated on the same computer using the ANN model. Although the ANN can take inputs simultaneously in the form of a 43 \(\times\) 91 matrix and predict a result as a 1 \(\times\) 91 vector, an iterative loop was used to run the neuro-simulation model. This was done in order to account for the ANN call time, which will be a factor in each iteration of the coupled reservoir-wellbore simulation. It was observed that the total time taken by the ANN toolbox was 2.13 s to re-simulate the 91 runs. Since the numerical model was run for 86,400 s, the proposed ANN model outperformed the numerical model speed-wise by more than a factor of 40,000. The ANN model did not encounter any stability issues that may lead to time-step cuts or failure of the program. This speed and robustness of the ANN model provides a significant advantage in coupled reservoir-wellbore simulation studies. This comparison was performed on a computer with a 2.20 GHz processor and 8 GB RAM [11]. Further computational time experiments were conducted for gas-lift operations. Table 6 shows the computational time comparison for full reservoir numerical model (numerical reservoir model linked to numerical wellbore hydraulics model), numerical-ANN coupled model (numerical reservoir model linked to ANN-based wellbore hydraulics model), and full ANN-based model (full ANN-based reservoir model linked to ANN-based wellbore hydraulics model). Once again, the time comparisons are remarkably different. The numerical-ANN coupled gas lift model provides a speed-up of about 160 when compared to a full numerical model. Furthermore, the ANN-based gas lift model provides a speed up of six orders of magnitude greater when compared to the full numerical model.