esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Failure Characteristics and Numerical Analysis  
### Failure Characteristics  
LPL5-1 was excavated and supported by June 2015 when the main powerhouse was excavated at layer II. From then until November 2016, when the excavation of the main powerhouse was at layer III-V, cracks and peeling of shotcrete were found on the upstream arch of LPL5-1 at stake from ZC0-080 m to ZC0+250 m. Cracks extended discontinuously, with a total length of 80 m, accounting for 15% of the LPL5-1 length. To address this problem, reinforcement measures were taken covering supplementary bolts, reinforcement mesh and re-shotcrete, which were finished in January 2017. At the end of March 2017, the shotcrete in LPL5-1 cracked again. The shotcrete on the upstream arch cracked, ballooned and peeled off (Figure 5a). The upstream side floor concrete cracked and swelled, thus separating from the structure below (Figure 5d) [15]. These failures grew with the continuous excavation of the main powerhouse. Shotcrete cracks extended and expanded, shotcrete ballooning evolved into peeling off and the upward displacement of the floor obviously increased (Figure 5f). During the excavation of layers VI\({}_{2}\)-VII\({}_{1}\), these failures developed most remarkably. After the main powerhouse was excavated at layer VII\({}_{2}\) in June 2017, the change and development of failures gradually slowed down. Later, in the excavation process of the unit pit, the changing rate decreased significantly. After the excavation of the main powerhouse was completed, there was no apparent change in these failures.  
Figure 4: Basic information about the drainage gallery LPL5-1: (**a**) location, (**b**) spalling at the upstream arch during excavation and (**c**) a geological cross-section along the axis and the final axial distribution of failures.  
After the excavation was finished, shotcrete failures in LPL5-1 were at stake from ZC0-093 m to ZC0+235 m and the most serious section was from ZC0-026 m to ZC0+130 m. Failures were on the upstream arch, spandrel and sidewall and extended along the axis. The types of failures covered cracks, ballooning and peeling of shotcrete and bending of rebar, as shown in Figure 5a-c. Figure 5d presents cracks on the upstream side floor concrete. At stake from ZC0-15 m to ZC0+125 m, the floor concrete uplifted and separated from the structure beneath. Consequently, cracks, bumps and dislocations arose on the floor, as reflected in Figure 5e. The final distribution of failures in LPL5-1 along the axis is demonstrated in Figure 4c.  
Figure 5: Failures in the drainage gallery LPL5-1: (**a**) a shotcrete crack and (**b**) exposed bent rebar at the upstream arch, (**c**) peeling of shotcrete on the upstream sidewall, (**d**) cracks and swelling of floor concrete, (**e**) dislocation on the floor and (**f**) failure distribution at stake from ZC0–030 m to ZC0+130 m in 2017.
## 3 System Model
### Service Coverage Model
#### 3.3.2 LEO Satellite Service Coverage  
Unlike stationary UAVs, LEO satellites exhibit continuous orbital motion, creating time-varying coverage windows for ground IoTDs. The visibility duration \(T_{k,n}^{vis}\) during which IoTD \(k\) can communicate with satellite \(n\in\mathcal{N}\) is determined by  
\[T_{k,n}^{vis}=\frac{l_{k,n}}{v_{k,n}}, \tag{4}\]  
where \(v_{k,n}\) represents the linear velocity of the accessible LEO satellite \(n\) for IoTD \(k\). Let \(l_{k,n}=2(re+d_{k,n})\omega_{k,m}\) denote the arc length of the ground-to-satellite communication link between IoTD \(k\) and its accessible LEO satellite \(n\), where \(\omega_{k,m}\) is the coverage angle of the accessible LEO satellite \(n\), calculated as follows:  
\[\omega_{k,m}=\arccos\frac{re}{re+d_{k,n}}\cos\theta_{k,n}-\theta_{k,n}, \tag{5}\]  
where \(d_{k,n}\) signifies the altitude of the accessible LEO satellite \(n\) from the ground for IoTD \(k\), \(re\) denotes the Earth's radius, and \(\theta_{k,n}\) represents the elevation angle between IoTD \(k\) and its accessible LEO satellite \(n\).
## 1 Introduction
Orthogonal frequency-division multiple access (OFDMA) [15] is employed in 5G networks to achieve the advantage of multi-carrier OFDM signals as multiple access technique; however, it also has limitations due to its high-peak power fluctuations. The SC-FDMA scheme [16] is another variant of the multi-carrier scheme to deal with multipath fading channels. Additional FFT operation in SC-FDMA spreads the single symbol over all the sub-carriers resulting in a low peak-to-average power ratio (PAPR) [17] structure. OFDMA and SC-FDMA are the multi-carrier orthogonal multiple access (OMA) methods suggested in 4G and 5G networks. For future generations of networks, these OMA schemes can be converted in to NOMA schemes with the integration of IDMA resulting in OFDM-IDMA [18] and SC-FDMA-IDMA [19, 20, 21, 22, 23, 24] schemes. Distinguished orthogonal interleavers [25, 26, 27] in IDMA allow the overlapped sharing of bandwidth while keeping the multi-carrier nature of the signal to deal with multipath fading of the channel. As stated, any IDMA-based multiple access system depends upon orthogonal interleavers to distinguish the simultaneous users. Various orthogonal interleavers suggested in the literature provide multiple access to the users in any IDMA-based NOMA system. This work analyzes the performance of progressive pattern interleaver with SC-FDMA-IDMA and OFDM-IDMA schemes. The performance of both multi-carrier NOMA schemes with PPI is analyzed and compared under multipath fading channel conditions to justify the possible implementation of the interleaver in next generation's networks. The rest of the paper is structured as follows. Section 2 presents some previous and related works. Section 3 offers an overall description of our proposed system model. Section 4 presents the simulation parameters and results. Section 5 explains the conclusion and future works.
## 4 Tether Simulation
### Variable Length Cables  
Underwater simulation of ROVs will also require modelling of cables connected to a spool that are released or retracted according to a naive logic that whenever there is a tension in the cable the cable is released. In the following, we outline a method to extend our model to a generate a variable length cable.  
A flag is associated with each vertex of the cable model, and it represents whether the vertex is free to move according to the Verlet integration and the constraint mechanisms. By default, both ends of a cable will be flagged as non-free and the rest are free, since the cable is attached to both ends. In the case of a spooled cable, all the particles of the rope that are currently not released are flagged as non-free particles.  
Figure 7: A small underwater OpenROV robot connected through a thin cable for video and control transmissions [25].  
Since our model is position based, whenever the first segment from the spool side is stretched enough, typically by 10 percent of the total length, we will release a particle/vertex. After that, the Verlet integration and the constraint mechanism will move into action and will adjust the particles accordingly.  
We have made further modifications in the model, particularly in the areas closer to end points. Random forces were not be applied on the first free segment, to avoid the spontaneous release of the cable due to random change of the length of the first free segment.  
The spooled cable extension is done with the intention to lay a simulated foundation for the development and testing of managed tethered system. The simulation can report in real-time the current length of the cable and the estimated tension of the cable at each point along the cable. Specifically, in the beginning and the end of the cable wherein a real system we can place tension sensors as an input to the controller of the tethered system. The tension can be measured as a function of the distance between every two particles.
## 4 Data Treatment
### Technical and Organizational Measures
As such, the user accesses to the remote front-end of the application, in turn accessing to the back-end, potentially located on different servers, to retrieve data or execute data elaboration. Health360 implements two levels of data caching (Figure 4), the first of which is on the user browser, using the IndexedDb technology, commonly implemented in any browser nowadays [23], allowing for locally saving data inserted in a form before sending such information to a server, in order to retrieve them if something goes wrong. More specifically, the IndexedDb caches all the data among work sessions, with the main aim for this being the prevention of accidental data breaks. A second level of caching is implemented on the front-end, at the graphical user interface level, to save the information retrieved from the back-end, making them available in case of connection absence with the latter, through Redis (classified as associative database). At the back-end level, a MongoDB is available, to manage all the data with the highest level of flexibility, seeing as the database is schemeless.
# Concealed Conduit Routing in Building Slabs
## 1 Introduction
### Methodology Overview  
In this article, we propose a piping algorithm for designing concealed conduits. To conquer the aforementioned challenges, we first invent an innovative representation method  
Figure 1: (**a**) Exposed conduits and (**b**) concealed conduits. The exposed conduits are built in open spaces or on the surfaces of objects while the concealed conduits are constructed inside the walls, ceilings, and floors of a building. The working domain of concealed conduits is more complicated, and the routing process would be challenging.  
to encode the workspace. Then we design a two-stage routing algorithm to compute the pipe paths. As a result, the proposed method achieves the following goals: (1) The resultant pipes are confined inside the walls, floors, and ceilings and are at a safe distance from the open space; (2) the length and number of bends of the resultant pipes are optimized; (3) the functionalities and safety regulations of the resultant pipes are satisfied; and (4) the computations are efficiently carried out without performing exhaustive search in the workspace.  
The proposed piping method is composed of two major tasks, the preprocessing and the pipe routing computations, as shown in Figure 2. The preprocessing builds a hierarchical representation of the workspace. The walls, ceiling, and floors of the target building are regarded as slabs. By representing the slabs as vertices and creating edges between them, the working environment (the target building) is converted into a weighted graph, which serves as the top level of the hierarchical representation. Then, each individual slab is decomposed into voxels and transformed into a 3D image. In the following step, the voxels of the slabs are labeled according to their positions and usability. For example, the voxels, occupied by windows, doors, and other openings, are marked as obstacle voxels, and the voxels within a predefined distance from the slab surfaces are labeled as infeasible voxels. As the labeling ends, the bottom level of the hierarchical representation is generated, as shown in Figure 1(a).  
The routing computation is comprised of two stages, as shown in Figure 1(b). In the first stage, the proposed algorithm calculates an abstract path in the weighted graph for each pipe. Then, the pipes are routed one by one in the following computations. Before arranging a pipe, the proposed piping method creates a feasible space by using the abstract path of this pipe and the relevant slabs stored in the bottom-level representation. Next, the pipe path is calculated in the feasible space. Then, the geometric model of the pipe is generated. In the final step, the workspace is updated to reflect the existence of the pipe and the routing computations are repeated for the next pipe.
# Responses of Laterally Loaded Single Piles Subjected to Various Loading Rates in a Poroelastic Soil
## 4 Parametric Study on Pile Cap Geometry
### Effect of Pile Cap Embedment Depths  
The pile cap embedment depths were parameterized for 0.0 m (no embedment), 0.3 m, 0.6 m, and 1.0 m in the ground with a lateral load applied at the top of the pile cap. The cap thickness was held constant at 0.3 m in this parametric study. Three cases of loading conditions were considered: undrained (loading rate = 2 kN/s), partially drained (loading rate = 2 kN/h), and drained (loading rate = 2 kN/d). An uncoupled analysis, represented by a dry soil with a step-loading of 2 kN/step is also included for comparison purposes. The corresponding results, plotted in terms of lateral load versus pile cap lateral displacement, immediately after the completion of the incremental load, are presented in Figure 4 and Table 3.  
In all circumstances, the increment of the embedment depths of the pile cap is seen to have minor influences on the lateral displacement of the pile cap, where the displacement of the pile cap decreases as the depth of embedment of the pile cap increases. The effect of cap embedment depth begins to vanish when the embedment depth is deeper than 0.3 m. This is because most the resistance was indeed provided by the pile itself. In addition, it was observed that the pile cap displacement increases for all the cap embedment depths as the loading rate decreases from fast (2 kN/s) to almost stationary (2 kN/day). In all circumstances, the displacement of the pile cap at the end of the incremental lateral loading is smaller than the pile cap displacement in the case of the dry soil. This result is consistent with the findings of [15].
# Anti-Atopic Dermatitis Activity of _Cornus walteri_ and Identification of the Bioactive Compounds
## 4 Conclusions  
In this study, the MeOH extract of CWS exerted anti-AD potential in an AD-like HaCaT model. Further phytochemical examination of the MeOH extract resulted in the isolation of six compounds, lupeol (**1**), betulinic acid (**2**), 5\(\alpha\)-stigmast-3,6-dione (**3**), 3-O-acetylletulin (**4**), betulinic acid methyl ester (**5**) and norphytan (**6**). The structures of the isolates were elucidated through NMR and LC/MS analyses. These isolated compounds also showed anti-AD effects by reducing chemokines in HaCaT cells. In particular, lupeol (**1**), betulinic acid (**2**) and 5\(\alpha\)-stigmast-3,6-dione (**3**) exhibited superior inhibitory effects against AD. Further studies aimed at identifying the molecular mechanisms of these active compounds will contribute to the management of patients with AD.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12178857/s1](https://www.mdpi.com/article/10.3390/app12178857/s1)]([https://www.mdpi.com/article/10.3390/app12178857/s1](https://www.mdpi.com/article/10.3390/app12178857/s1)), General experimental procedure; Plant material; NMR and physical data for compounds **1-6**. Figure S1: 1'H NMR spectrum of lupeol (**1**) (CDCl3, 700 MHz), Figure S2: 13C NMR spectrum of lupeol (**1**) (CDCl3, 175 MHz), Figure S3: 1'H NMR spectrum of betulinic acid (**2**) (CDCl3, 700 MHz), Figure S4: 1'H NMR spectrum of 5\(\alpha\)-stigmast-3,6-dione (**3**) (CDCl3, 175 MHz), Figure S6: 1H NMR spectrum of 3-O-acetylletulin (**4**) (CDCl3, 700 MHz), Figure S7: 1'H NMR spectrum of betulinic acid methyl ester (**5**) (CDCl3, 700 MHz), Figure S8: 1'H NMR spectrum of northytan (**6**) (CDCl3, 700 MHz).  
**Author Contributions:** Conceptualization, Y-R.C., S.B. and K.H.K.; formal analysis, B.S.L., Y-R.C., M.J., Y.S.J. and J.-C.K.; investigation, B.S.L., Y-R.C., M.J. and Y.S.J.; writing--original draft preparation, B.S.L., Y-R.C. and K.H.K.; writing--review and editing, S.B. and K.H.K.; visualization, B.S.L., Y-R.C. and K.H.K.; supervision, S.B. and K.H.K.; project administration, S.B. and K.H.K.; funding acquisition, S.B. and K.H.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT; grant numbers 2020R1A2C1010703, 2019R1A5A2027340 and 2021R1A2C2007937) and a Korea Institute of Science and Technology Intramural Research Grant (2E31881).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
Circuit designs using fractional-order devices require a special type of analysis and evaluation because the resulting behavior of these systems is quite different and more complex than in cases of standard integer-order designs. Frequency responses influenced by the fractional-order behavior of used components are studied more frequently in recent years [1]. Many works in this field focus on novel solutions of integral and derivative two-ports (for example [2; 3; 4; 5; 6; 7; 8; 9]), proportional integral and derivative controllers (for example [10; 11; 12; 13; 14; 15; 16; 17; 18] and so-called bilinear two ports [8; 9; 19; 20; 21] serving for various purposes. Two ports, known as integrators and differentiators, have started to be interesting for designers of fractional-order systems, and especially for proportional, integral and derivative controllers (PIDs). However, these systems are analyzed as complete solutions, and their transfer functions are considered in overall form (sum of all three responses). The overview of typical examples dealing with the design of fractional-order integrators and differentiators (from the above discussed groups) is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Reference** & \begin{tabular}{c} **Design Target** \\ **(Both = Integrator** \\ **and Differentiator**) \\ \end{tabular}  & \begin{tabular}{c} **Simple Single** \\ **Parameter Electronic** \\ **Time Constant** \\ **Tuning** \\ \end{tabular}  & \begin{tabular}{c} **Active** \\ **Devices** \\ **Tining** \\ \end{tabular}  & \begin{tabular}{c} **Solution of** \\ **Fractional-Order** \\ **Perf** \\ \end{tabular}  & \begin{tabular}{c} **Topological** \\ **Circuit** \\ **Complepleity** \\ **on Required** \\ \end{tabular}  & \begin{tabular}{c} **SW Support** \\ **not Required** \\ **Bandwidth** \\ \end{tabular}  & \ \begin{tabular}{c} **Tested** \\ **Operational** \\ **Handwidth** \\ \end{tabular}  & \begin{tabular}{c} **Application**, \\ **if Any** \\ **if Any** \\ **Fractional-Order** \\ \end{tabular}  \\ \hline
[2] & integrator & No & OAs & passive & Low & Yes & \(<\)1 MHz & N/A & - \\
[3] & both & Yes & DVCCTA & passive & Low & Yes & \(<\)100 kHz & N/A & - \\  & & & & & & Sum & & \\
[4] & both & No & \begin{tabular}{c} CMOS current \\ mirrors \\ \end{tabular}  & active & Low & Yes & \
## 4 Experiments and Results
### Network Depth  
The DNN topology was also investigated in detail to improve the modelling performance.  
Two critical DNN topology parameters are the network depth (number of hidden layers) and width (number of neurons per hidden layer) [43]. We tested different DNN topologies, where the number of hidden layers varies between 1 and 6 and the number of neurons per hidden layer varies between 30 and 300.  
The performance impact of the network depth is shown in Figure 12. The MAE of 10 independent runs is shown. The number of neurons per hidden layer is 300, and the number of layers varies between 1 and 6. It can be observed that when the number of hidden layers is increased, the modeling performance of the deep neural network is improved. The median MAE for the number of beach visitors improves from 0.04655 with one hidden layer to 0.03045 for six hidden layers, with a 34.6% improvement. However, the improvement slows down when more layers are added. The improvement becomes insignificant with more than four hidden layers. The median MAE for the number of beach visitors shows a 34.2% decrease from 0.04655 with one hidden layer to 0.03065 with four hidden layers; however, when the number of hidden layers is increased to six, only a 0.65% decreased is achieved, with 0.00036 modeling accuracy variation. We observe that the deep neural network can maintain a good modelling performance when the number of layers is increased and does not suffer overfitting. We conclude that the model is not biased with the training data and keeps improving when the number of neurons raises.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline
**0–49** & 0.891 & 0.927 & 0.91 \\
**50–99** & 0.935 & 0.912 & **0.923** \\
**100–149** & 0.931 & 0.856 & 0.892 \\
**150–199** & 0.941 & 0.905 & 0.922 \\
**200–249** & 0.893 & 0.903 & 0.90 \\
**250–299** & 0.931 & 0.981 & **0.955** \\
**300+** & 0.945 & 0.927 & **0.936** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Per-class precision, recall, and F1-scores for the time period 2018–2020.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\
**(Beach Visitors)** & **Precision** & **Recall** & **F1-Score** \\ \hline
**0–49** & 0.931 & 0.917 & 0.924 \\
**50–99** & 0.915 & 0.931 & 0.923 \\
**100–149** & 0.951 & 0.888 & 0.918 \\
**150–199** & 0.932 & 0.861 & 0.895 \\
**200–249** & 0.934 & 0.94 &
# On the Control and Validation of the PARA-SILSROB Surgical Parallel Robot
## 2 Materials and Methods
### Experimental Model of the Parallel Robot PARA-SILSROB
Figure 5 illustrates the development stage of the experimental model of the PARA-SILSROB robot (1), which features a detailed view of the mobile platform (2) and torso training (3). This control architecture includes two graphical interfaces (4 graphical interfaces for the engineer and 5 graphical interfaces for the surgeon). The surgeon operates the robot (slave) using a keyboard and two commercially available 3D Space Mouse devices (6), which are integrated into the command-and-control logic detailed in Section 2.2. The figure also illustrates the movements of the three serial modules (7, 8, 9) attached to the mobile platform. These modules are used for orienting and inserting the active instruments, which are commercial products developed by Intuitive Surgical, as well as the laparoscopic camera. These motions are achieved through seven actuators attached to the mobile platform (three for modules A and C, and one for module B). Additionally, the training simulator kit used for the experimental tests is shown in the figure. At this stage of the robot's development, the active instruments used are commercial models for which custom mounting brackets and tip control mechanisms were designed using two printed circuit boards (PCBs). These boards control the actuators (four per active instrument) that manipulate the tips of the active instruments. A more detailed explanation of these components can be found in [46] and in the next subsection.