esa-sceva/satcom-chunk-collection huggingface dataset:
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis  
This section outlines the experimental evaluation process and analyses the obtained results. All latency results depend on the used equipment, namely the node computational capacity. Therefore, nodes with high computational capacity will decrease the latency overheads presented. The testing environment is depicted in Figure 11.  
On the left is a representation of the cellular network (e.g., 5G) serving soft-mobility users with two smartphones (X and Y). In the middle, the ITS centre is hosting the MQTT  
Figure 11: Representation of the testing environment.  
broker connected to the ISEL network. This is the university's network where all the tests were conducted, thus bringing the evaluation closer to a real-world scenario. On the right, and also connected to ISEL's network, are the laptop (M_RSU) and the Siemens P_RSU. This one communicates with the OBU, both representing the ITS stations (e.g., a traffic light and a car) connected by ITS-G5.  
In this experimental setup, the environment is structured in the context of the EC and FC models [15; 47]. Smartphones X and Y, along with the Unex OBU and the RSU, operate at the edge of the hybrid network and process data within their respective local application. In addition, the ITS centre acts as an intermediary in the fog layer due to its proximity to the edge [17; 48]. Table 6 describes each equipment computational environment.  
The developed work is assessed in different aspects. Firstly, in Section 4.1, the local computation time of each execution environment application (RSU, OBU and smartphone) is measured. Next, in Section 4.2, communication latency is measured for three security approaches. Finally, in Section 4.3, the end-to-end times of each workflow are calculated.
# Biomechanical Analysis of Gait Compensation Strategies as a Result of Muscle Restriction
## 5 Conclusions  
The compensation strategies in human gait appearing under restrictions of the lower-limb muscles were observed and analyzed in this study. The muscle restriction was achieved using the MARTT, which includes wearable belts for restricting lower-limb muscles. The gait motion was investigated under various conditions of walking speed and muscle restrictions.  
The compensation behavior did not appear to be dependent on the walking speed, but the significance of the compensation as evaluated among the subjects increased with the speed. The joint motion weaknesses and compensations seen in the C-restriction agreed with those seen in the elderly, suggesting that human compensation behaviors against muscle weakness have similar purposes, regardless of age. The results of this study provide an essential inspection for understanding the mechanisms of gait compensation against limitations of gait ability.  
Supplementary materials can be found at [[https://www.mdpi.com/article/10.3390/app11188344/s1](https://www.mdpi.com/article/10.3390/app11188344/s1)]([https://www.mdpi.com/article/10.3390/app11188344/s1](https://www.mdpi.com/article/10.3390/app11188344/s1)).  
Conceptualization, Y.Y., Y.A., S.O. and J.B.U.; methodology, Y.A., S.O. and J.B.U.; software, Y.A. and J.B.U.; validation, Y.A., S.O. and Y.Y.; formal analysis, J.B.U., Y.A. and S.O.; investigation, J.B.U., Y.A. and S.O.; resources, Y.Y.; data curation, J.B.U.; writing--original draft preparation, J.B.U., Y.A.; writing--review and editing, Y.A., J.B.U.; visualization, J.B.U.; supervision,Y.A., S.O., Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS KAKENHI grant number 26750121.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Nagoya University, Japan (approval number 14-4 and date of approval).  
Written informed consent was obtained from all subjects involved in the study.  
The subjects' joint angles in every gait section analyzed in this study are compiled and available in the submitted supplementary material; as well as, the statistical analysis of the relevance of the found compensatory motions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
Ref  
rectus femoris  
vastii  
BFS  
biceps femoris short head  
GAS  
gastrocnemius  
SO  
soleus  
TA  
tibialis anterior  
HAM  
hamstring  
ILPS  
iliopsoas  
MARTT  
Muscle Activity Restriction Taping Technique  
MTC  
minimum toe clearance  
HC  
heel contact  
TO  
toe off  
C-restriction  
calf restriction
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 5 Environmental Impacts on Hardware-Based LQEs
### Environmental Impacts on RSSI Based LQEs
#### 5.2.2 Environmental Impacts on RSSI Based LQEs  
Using Equation (10), the LR model between \(\mu_{\textit{rssi}}\) and \(PRR\) in three environments was plotted, as shown in Figure 10. It is clear that only the measured data in the runway are nearly coincident with the LR model. There are obvious translations from the model curve to the measured data in the artificial lawn and corridor. Using Equation (11), the PR model between \(\mu_{\textit{rssi}}\) and \(PRR\) in three  
Figure 8: \(\mu_{\textit{rssi}}\) vs. \(PRR\) in different environments.  
Figure 9: Cumulative distribution functions (CDFs) of the fluctuation range of \(RSSI\) in different environments.  
environments was plotted, as shown in Figure 11. Unlike the LR model, there is no obvious translation from the PR model to the measured data in the artificial lawn and corridor.  
To describe the environmental impact on \(RSSI\) based LQEs quantitatively, RMSE of the estimated \(PRR\) and real \(PRR\) in three environments were calculated, as shown in Table 4. It can be seen that RMSEs of the LR model in the artificial lawn and corridor are much higher than that in the runway. Compared with the runway, RMSEs of the LR model in the artificial lawn and corridor increase by 40.74% and 131.96%, respectively. It can be concluded that the LR model is not suitable for both the artificial lawn and corridor. That is to say, the LR model between \(\mu_{rssi}\) and \(PRR\) constructed in a specific environment cannot be directly used in other environments.  
On the other hand, the RMSEs of the PR model in the three environments are quite close. Compared with the runway, RMSE of the PR model in the artificial lawn increases by 13.32%, but RMSE in the corridor reduces by 12.07%. Thanks to the normalization of \(RSSI\), the PR model is adaptive to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Runway** & **Artificial Lawn** & **Corridor** \\ \hline LR model & 0.1161 & 0.1634 & 0.2693 \\ PR model & 0.1284 & 0.1455 & 0.1129 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSEs of \(RSSI\) based link quality estimators (LQEs) in different environments.  
Figure 11: Effects of PR (polynomial regression) model in different environments.  
Figure 10: Effects of the logistic regression (LR) model in different environments.  
environment to a certain extent. However, it is obvious from Figure 11 that the PR model is not in good agreement with the measured data when _PRR_ changes from 0.8 to 1.0. The environment in which the PR model was constructed is a typical industrial environment [26], where high interference may exist. This is also explained from the side why the RMSE in the corridor is the smallest, as the corridor is the closest to the industrial environment.
## 3 Results  
### Release Tests  
An exceptionally low amount of Met was found in the liquid phase obtained during the release test. Met-BIO interaction is apparently stronger than Met-ADS. The maximum release of Met was 6% and 3% for Met-ADS and Met-BIO, respectively, at pH 3. Little pH effect can also be inferred considering that, for both solids, the minimum Met released was observed at near- neutral pH, i.e., 5.8 (Table 1).  
Solids after release at pH 5.8, were also analysed by FTIR spectroscopy (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Carrier \({}^{*}\)** & **pH** & **Initial Met (g)** & **Released Met [g]** & **Release \% (w/w)** \\ \hline  & 3.0 & & 0.03 & 6 \\ ADS & 5.8 & 0.50 & 0.01 & 2 \\  & 8.9 & & 0.02 & 4 \\ \hline  & 3.0 & & 0.01 & 2 \\ BIO & 5.8 & 0.50 & 0.006 & 1.2 \\  & 8.9 & & 0.01 & 2 \\ \hline \hline \end{tabular} \({}^{*}\)ADS: Adsorbene; BIO: BioKi; Met: Methionine.  
\end{table}
Table 1: Released methionine at different pH from two different carriers (Adsorbene and BioKi). Tests were performed on 2 g of Met-carrier, which correspond to 0.5 g of total initial methionine in the analysed sample.  
Figure 2: Spectral regions of infrared spectra obtained by Fourier-transform infrared spectroscopy (FTIR) of released methionine at different pH conditions; (**A**) Met-ADS and (**B**) Met-BIO before and after release at pH 5.8 (dashed areas: bands of interest). Dashed rectangular: weak IR bands characteristic of the organic moiety near 2920–2950 cm\({}^{-1}\) (CH stretching modes) and in the region 1620–1300 cm\({}^{-1}\) (CO and CN stretching modes, CH deformation modes).  
In the IR spectrum of both samples before the release test, several weak IR bands characteristic of the organic moiety near 2920-2950 cm\({}^{-1}\) (CH stretching modes) and in the region 1620-1300 cm\({}^{-1}\) (CO and CN stretching modes, CH deformation modes) are detectable. A detailed spectroscopic investigation on the interaction with methionine and clay matrix has been recently reported by [PERSON] et al. (2021, accepted on 14 June). After the release tests, these bands are barely detected in the high-frequency region of the spectrum, namely in the spectrum of the MET-BIO sample (Figure 2B), suggesting the presence of residual Met, strongly bounded at the surface of the carrier.
## 4 Performance Evaluation of Post-Installed Anchors  
A static performance evaluation was conducted by installing M10 and M12 set anchors, among various types of post-installed anchors, into two types of concrete specimens (non-cracked and cracked concrete). The main purpose of the experiment was the development of a dynamic load protocol for seismic performance evaluation. The load protocol applied to the dynamic tests was proposed by investigating the structural behavior under a pull-out and shear load and using the average maximum load from the results.
## 4 Uptake and Bioaccumulation of Microplastics and Nanoplastics in the Human Body
### Gastric Exposure  
The most significant way that humans consume plastic particles is by ingestion [66]. Although there are no studies particularly examining the toxicity of NPs in people, there is evidence that MPs are being consumed through food and drink [46]. The discovery of plastic particles in human feces samples during initial examination confirms this [67]. Yet no research has investigated what happens to the MPs and NPs after they get into the gastrointestinal (GI) system. It is important to investigate how the particles move through the GI tract and if they move past the gut epithelia or remain in the gut lumen. Given that the tight junction channels' important pores have a maximum functional size of about 1.5 nm, it is doubtful that MPs can penetrate at the paracellular level [68]. The possibility of their entering through lymphatic tissue and infiltrating the microfold (M) cells in the peyer's patches through phagocytosis or endocytosis is particularly probable [69]. Following intraperitoneal injections into mice, 1, 5, and 12 \(\upmu\)m polymethacrylate and PS particles were observed to be phagocytosed by peritoneal macrophages [70; 71], and the intestinal absorption was observed to be low i.e., 0.04-0.3% [72].  
In comparison to MPs (2-7%), the oral bioavailability level of 50 nm PS NPs is ten to one hundred times higher [73; 74]. There is no clear relationship between the absorption, size, and structure of NPs, which is consistent with the findings for MPs [75]. Some studies have demonstrated that different in vitro intestinal models' ability to absorb NPs (50-500 nm) varies substantially, with rates ranging from 1.5 to 10% depending on the size, chemical composition, and type of model utilized [11; 75; 76]. NPs might change after being ingested, and this will affect how well and how quickly they can be absorbed. In general, nanoparticles may interact with a variety of substances in the GI tract [77], while a group of proteins known as a \"corona\" is known to surround them [78; 79]. An in vitro model of human digestion has been found to modify the protein corona, which increases the translocation of nanoparticles [74]. A recent analysis looked at how dispersed organic materials interact with metal (oxide) nanoparticles and discovered that these interactions greatly affect agglomeration and deposition [80]. Furthermore, organic matter prevalent in water will stick to the surface of nanoparticles. More research should be conducted to fully understand the mechanisms involved in the uptake of MPs and NPs via the gastric route.
# Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times
## 4 Heuristic Algorithms  
The MIP can be used to find an optimal solution using a commercial optimization solver. However, because the considered scheduling problem is NP-hard, the solver may require a significant computation time to obtain an optimal solution for large or practical-sized problems. Therefore, this study focuses on proposing heuristic algorithms that can find a good solution close to optimal or acceptable. Two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are adopted in this study.  
Before introducing the metaheuristic algorithms, we provide the following equations to calculate completion times of jobs in a sequence obtained in procedures of the proposed heuristics. Note that, because this study considers only permutation schedules, the completion times are calculated sequentially one by one from the first to the last. Equations (15) and (16) calculate the completion times of normal jobs and urgent jobs on the first machine, respectively, while Equation (17) calculates the completion times on the second machine. In addition, Equation (18) calculates the tardiness of urgent jobs. Here, it is assumed that \(c_{0,1}=c_{0,2}=0\).  
\[c_{t1}=\max\{c_{(t-1)1}+p_{[t]1},\,c_{(t-1)2}-w_{[t]}\},\,\text{if the $t$th position job is a normal job} \tag{15}\]  
\[c_{t1}=\max\{\max\{c_{(t-1)1},\,r_{[t]}\}+p_{[t]1},\,c_{(t-1)2}-w_{[t]}\},\, \text{if the $t$th position job is an urgent job} \tag{16}\]  
\[c_{t2}=\max\{c_{t,1},\,c_{(t-1)2}\}+p_{[t]2},\,\text{for $t=1,\,\ldots,\,n$} \tag{17}\]  
\[\tau_{[t]}=\max\{c_{t2}-d_{[t]},\,0\},\,\text{if the $t$th position job is an urgent job} \tag{18}\]
## 4 Taxonomy of Clustering Schemes
### Classification of Clustering Attributes in WSNs
#### 4.1.2 Cluster-Head Characteristics  
_Existence_: Based on whether there exist cluster-heads within a cluster, clustering schemes can be grouped into cluster-head based and non-cluster-head based clustering. In the former schemes, there exist at least one CH within a cluster, but there aren't any CHs within a cluster in the latter schemes, such as some chain based clustering algorithms.  
_Difference of Capabilities_: Based on uniformity of energy assignment for sensor nodes, clustering schemes in WSNs can be classified into homogeneous or heterogeneous ones. In homogeneous schemes, all the sensor nodes are assigned with equal energy, computation, and communication resources and CHs are designated according to a random way or other criteria. However, sensor nodes are assigned with unequal capabilities in heterogeneous environment, in which the roles of CHs are pre-assigned to sensor nodes with more capabilities.  
_Mobility_: According to the mobility attributes of CHs, clustering approaches in WSNs also can be grouped into mobile and stationary manners. In the former manners, CHs are mobile and membership dynamically change, thus a cluster would need to be continuously maintained. Contrary to it, CHs are stationary and can keep a stable cluster, which is easier to be managed. Sometimes, a CH can travel for limited distances to reposition itself for better network performance [32].  
_Role_: A CH can simply act as a relay for the traffic generated by the sensor nodes in its cluster or perform aggregation/fusion of collected information from sensor nodes in its cluster. Sometime, a cluster head acts as a sink/BS that takes actions based on the detected phenomena or targets [32]. It is worth mentioning, sometimes a CH acts in more than one role.
It should be noted that beams reinforced with FRP bars subjected to tension at elevated temperatures often fail through anchorage degradation. The threshold temperature for fiber operation is approximately 880 \({}^{\circ}\)C for glass fibers and approximately 1250 \({}^{\circ}\)C for basalt fibers. Moreover, the mechanical properties of composite rods deteriorate as the temperature increases, but if it does not exceed the glass transition temperature of the fibers, they return almost completely to their initial values. In prestressed structures, it should be considered that due to the relatively high coefficient of transverse thermal expansion, additional stresses may arise at the bar-wood interface. Moreover, long-term exposure to negative temperatures may cause hardening of the matrix or microcracks as well as reduced adhesion between the matrix and individual composite fibers. Likewise, freeze-thaw cycles combined with the action of salt may also result in the deterioration of the resin properties, visible in the form of swelling of the polymer on the surface of the rod. To prevent the possibility of decreased adhesion between FRP rods and concrete due to the expansion and contraction of the rods under the influence of temperature cycles and degradation resulting from frost, the rod cover and composite building blocks should be selected appropriately to the type of rod and the element's operating environment. At the same time, to ensure proper functioning of the structure at elevated temperatures, it is necessary to select a cover that protects the rod against the influence of high temperatures as well as to properly know the type of polymer resin material and the type and size of fibers from which the composite rods are made. According to [41], fiber-reinforced polymer composites have very good properties for replacing steel in bridge cables or underground oil extraction and ocean engineering. All these are related to advantages such as low weight, high strength, and the desired resistance to corrosion and fatigue. In this work, two types of carbon fiber and glass hybrid wafers were developed, including random mode hybrid (RH) and core-shell hybrid (CH). Based on the tests, it was found that the maximum percentage increase in tensile and bending strength for the RH board after 360 days was 51.3% and 39.7%, respectively. In [42], rods made of hybrid fiber-reinforced polymer (HFRP) with a glass fiber coating (GFS) and a carbon fiber core (CFC) were used as an alternative solution to steel materials and used in a bridge cable. Based on the research, it was found that the degradation of hybrid bars increased with increasing temperature or bending load levels. It was also recommended that the level of cyclic bending load applied should not exceed 50% to allow for the desired durability of bridge structures.
# An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids
## 6 Experiments
### Results in the Uninformed Setting
\begin{table}
\begin{tabular}{c c c c c} \hline
**Instance** & **FAT-RLS** & **CEGO** & **UMM** & **RS** \\ \hline
10\_42 & 346.7 & **346.7** & **346.7** & 389.6 \\
10\_73 & 329.4 & **324.7** & 329.9 & 343.6 \\
15\_42 & **505.4** & 515.7 & 516.9 & 583.1 \\
15\_73 & **515.1** & 523.7 & 530.7 & 573.0 \\
20\_42 & **698.9** & 726.9 & 729.1 & 777.8 \\
20\_73 & **676.3** & 730.8 & 810.1 & 813.2 \\
25\_42 & **837.4** & 945.9 & 966.8 & 1075.1 \\
25\_73 & **889.0** & 988.7 & 1028.7 & 1089.6 \\
30\_42 & **1062.3** & 1183.0 & 1260.6 & 1271.0 \\
30\_73 & **1098.2** & 1212.1 & 1232.8 & 1344.2 \\ \hline \end{tabular}
\end{table} 
Table 2: Best objective values for uninformed experiments. The results highlighted in bold indicate the highest performance achieved for each instance.
## 3 Results and Discussion
### Critical Month Identification  
In Spain, the critical month for cereal crops in CL and CM (Figure 2) is predominantly May, except for wheat in CM, where it is April. In both regions and for both crops, the critical month is in the spring phenological stages, i.e., during the reproductive and maturation phases of the crop. The R values are within the range of 0.60 to 0.80, which shows that SM is a fundamental variable for the development of cereals. These results are also consistent with those of [99], which observed that in the main cereal-growing areas of Spain, the critical periods of impact on the wheat and barley yields of SM were concentrated in the spring phases. Several studies [100; 101] have shown that grain yields are strongly affected by water stress during anthesis in May. In addition, [102] observed that the dry conditions in those months in Spain led to a marked decrease in yields.  
Analyzing the agricultural drought trends for each month of the year in all districts of the study regions in Germany (Figure 3), a generalized negative trend is observed in all months and areas. This result is consistent with that reported in the study [41], in which the authors observed a trend toward drier conditions in central Europe over the last three decades. The month with the maximum number of districts with a significant trend was April, followed by May. Thus, April was selected as a critical month in the regions of Germany to identify drought years. Several studies [103; 104] prove that for winter cereals  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multicolumn{2}{c}{**Predominant Month**} & \multicolumn{2}{c}{**Average R**} \\ \cline{2-5}  & **GPP** & **LAI** & **GPP** & **LAI** \\ \hline CL & May & May & 0.50 & 0.69 \\ CM & April & April & 0.29 & 0.69 \\ BY & May & July & \(-\)0.57 & 0.16 \\ NS & June & July & \(-\)0.44 & 0.57 \\ NW & May & July & \(-\)0.48 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Predominant month and mean R of the predominant month for each study region (Castilla y Leon, CL; Castilla-La Mancha, CM; Bayern, BY; Niedersachsen, NS and Nordrhein-Westfalen, NW) resulting from the correlation analysis between biophysical parameters and the agricultural drought index.  
that dominate agricultural production in central Europe, the development stage in early spring is crucial for determining grain yield, as the cereals are very susceptible to drought in that period. Thus, [105] studied the effect of water stress on winter wheat production in central Europe and found that during the month of April, the crop is very sensitive to water stress, causing low yields.