esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parametric Study of Wind Pressure Distribution on Facades Using Computational Fluid Dynamics
## 3 Model Convergence  
### Mesh Sensitivity Study  
A mesh sensitivity study using a bracket length 'g' = 240 mm and a fin length 'd' = 480 mm was performed. The wind's direction was set at 0\({}^{\circ}\) to the horizontal, with the result that while a sheltering effect occurred for two of the fins, the leading fin had full wind exposure. The sensitivity study focused on this leading fin, as it is the critical fin to establish solution convergence. Three meshes were analyzed differing by their minimum cell size, with the cell size of Mesh 1 set at 2 mm, the cell size of Mesh 2 set at 1 mm and the cell size of Mesh 3 set at 0.5 mm. The simulation was performed for six residence times, equal to approximately 6 s (based on a mean wind velocity of 24.9 m/s and a domain length of 24.48 m). The first three residence times allowed the solution to reach a statistically stable condition. Representative data was collected in the subsequent three residence times. In all cases, readings were taken every 0.04 s. The selected numerical solver is the Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm.  
The non-dimensional wall distance, Y-plus (Y\({}^{+}\)), is an important measure of mesh quality at wall features within the computational domain. For values less than 5, modeling takes account of turbulence dampening at the viscous sub-layer [12]. However, with the application of WMLES, turbulence was resolved directly at wall regions where Y\({}^{+}<\) 300 [23; 24]. The minimum cell sizes of 2 mm, 1 mm and  
Figure 3: Detail of structured mesh (**a**) around the building (**b**) around the leading fin (**c**) around the lower edge of the leading fin for a mesh with cell size of 2 mm.  
0.5 mm do not cause Y\({}^{+}\) to exceed 300, with peak values of 290.1, 157.2 and 78.8, respectively (Table 1). This means for these respective cell sizes involving the sub-grid scale, the WMLES model was within operating limits for each mesh.  
The Courant Number, Co, is a measure of mesh instability that affects solution convergence and therefore affects model accuracy. Choosing the time step for a particular mesh is based on meeting the tolerance sought for the Courant Number parameter. In the case of implicit transient formulations, it is required to be less than 20-40 for sensitive regions [24], which in this problem represents areas nearest the fins. Table 1 displays maximum Courant Numbers of 38.1 (2 mm cell size), 38.4 (1 mm cell size) and 33.4 (0.5 mm cell size), and indicates that values do not exceed the upper limit of Co = 40. These results were produced for time-steps \(8\times 10^{-4}\) s, \(4\times 10^{-4}\) s and \(2\times 10^{-4}\) s, with respect to the 2 mm, 1 mm and 0.5 mm cases. There appeared to be little variation in Co between the meshes, as the ratio of time-step to cell size remained the same in each case. For completeness, the number of cells, ratio and refinement factors for each of the three mesh sizes are given in Table 2.
## 2 Materials and Methods
### Geostatistical Interpolation of Ground Sensor Data and Cross-Validation for Evaluating Performances  
Geostatistical interpolation is generally employed to estimate weather data over a regularly spaced prediction grid by using ground sensor measurements available over irregularly spaced networks [59]. Among the wide collection of geostatistical interpolation methods, ordinary kriging is the most used to interpolate weather data, such as wind speed [60; 61], relative humidity [61] and solar radiation [62; 63; 64]. However, ordinary kriging only uses data available from observations and its performance greatly depends on the density of the available measuring network and the spatial structure of the variable of interest [65]. For this reason, in some other applications where an evident statistical correlation exists between the weather variable of interest and an auxiliary external variable sampled with a higher spatial resolution, regression kriging is preferred [66]. This is the case of temperature [66; 67; 68; 69], which is highly correlated with the elevation.  
Regression kriging [70] (also named residual kriging [71], mathematically equivalent to universal kriging, often said kriging with external drift [72]) couples a regression model of the variable of interest on auxiliary independent variables, with the ordinary kriging applied to the regression residuals.  
Appendix A provides theoretical details of kriging estimation that can be exhaustively found in Journel & Huijbregts [73] and [PERSON] [74], among others.  
In this study, we used (i) ordinary kriging--Equations (A1-A4)--for interpolating daily wind speed, relative humidity and solar radiation and (ii) regression kriging for daily mean, maximum and minimum air temperature. By implementing daily regression models of temperature with elevation, regression kriging requires the application of Equations (A1-A4) to the random field of the regression residuals, instead of the observed weather variable.  
Then, the performances of kriging estimators were assessed by means of the cross-validation method [74; 75]. Indeed, a very common way to assess the predictive ability of a statistical model and, specifically, of a spatial interpolation technique, such as kriging, is to test model results on a set of data that were not used for estimation. Cross-validation serves this scope and it is based on a jackknife kriging estimation of the variable of interest at a sampling site, obtained by applying the kriging interpolator to the variable observations available at the remaining sampling sites (leave-one-out cross-validation).  
Then, after applying the described procedure for all sampling sites, i.e., 18 AWSs sites and weather variables, the (jackknife) kriging estimations were used for the performance analysis and to evaluate the predictive ability of kriging in deriving gridded data from weather observations in the study area.
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
###### Abstract  
The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol's self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications.  
Keywords: unmanned aerial network; flying ad hoc network; cognitive radio network; routing protocol; dynamic topology +
Footnote †: journal:  
00 2024
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 6 Research Outlook
In a dynamical system, for the same residual, its transient response for one fault may overlap with the quick response of the residual for another subsequent fault. Even the former fault response may mask the latter, making some faults challenging to observe and making fault detection more difficult. Therefore, the coupling of faults and the mutual influence of fault phenomena in the diagnosis of multi-fault systems need to be further investigated. A faster and more effective diagnostic method needs to be employed to identify the faults and to clarify the diagnosable characteristics of the system under multi-fault conditions. Residual observer design based on structural analysis and advanced control theory  
Combine the existing estimation techniques (e.g., sliding mode observer) with structural analysis to design a more robust residual generator to achieve the best fault sensitivity while suppressing disturbances. Reconfigurability design based on structural analysis  
It is more important for the system's reliability to reconfigure the system faults and to detect and isolate them. Hybrid intelligent fault diagnosis method based on structural analysis  
Sensitive relationships with the system faults are constructed through structural analysis, which is used to build the training set of intelligent diagnosis algorithms and to improve the richness of the dataset of intelligent fault methods. As the structural and functional complexity of control systems gradually increases, future diagnosability studies based on structural analysis may face the following challenges: First, the difficulty of searching the system MSO set will increase, and in the future, it may be necessary to work with intelligent optimization algorithms to improve the speed and accuracy of MSO search algorithms. Second, there will be a rise in the number of mathematical parameters and the degree of interaction between them. The outcomes of the diagnostic analysis of the system based on the structural analysis will not be impacted by the complexity of the system's data or structure. However, this will make it much more challenging to apply the results of the diagnostic theory to the control system's diagnostic structure and function design reasonably and efficiently. Finally, system faults will increase, especially those in the underdetermined part of the system structure. The degree of coupling between the faults will become complex, which poses a severe challenge to the evaluation and design of diagnosability based on structural analysis.
## 3 Materials and Methods
### JumpShot-HTS-DART-QqQ-MS/MS Conditions  
For SPMESH-DART-QqQ-MS/MS, a JumpShot-DART with a high throughput screening (HTS) package (IonSense; Saugus, MA, USA) was coupled to a Bruker EVOQ Elite QqQ MS (Bruker Daltronics, Billerica, MA, USA). During analyses of IBMP extracted onto SPMESH sheets, the JumpShot-HTS X-Y positioning stage scanned across a row a constant rate of 0.5 mm/s, and moved from row to row automatically. A program was also written to allow the X-Y positioning stage to scan at 0.5 mm/s over extracted spots, and at 10 mm/s over inter-well regions. The MS was operated in positive ion mode with a scan speed of 100 ms in multiple reaction monitoring (MRM) mode. The collision energy (CE = 20 eV) for the \(m\)/z 167.4 \(\xrightarrow{}\)\(m\)/z 124.6 transition was optimized by dropping 10 \(\upmu\)L standard solutions of IBMP in the DART reaction zone while operating the MS in MRM mode.  
Conceptualization, G.L.S. and T.L.B.; Methodology, G.L.S., T.L.B., H.F., J.R., B.Y., B.R.J.M. and W.F.; Validation, T.L.B., H.F., B.Y., B.R.J.M. and B.S.P.; Formal Analysis, T.L.B. and G.L.S., Investigation, G.L.S.; Data Curation, T.L.B.; Writing--Original Draft Preparation, T.L.B.; Writing--Review and Editing, G.L.S.; Visualization, T.L.B.; Funding Acquisition, G.L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by New York Wine and Grape Foundation--Enology Program.  
Not applicable.  
Raw data can be made available upon request to the corresponding author.  
We acknowledge [PERSON] and [PERSON] for their assistance.  
[PERSON] is an employee of Bruker Daltonics, which owns IonSense, the manufacturer of the DART SVP and DART JumpShot HTS ion sources. [PERSON] contributed to this publication by providing access to a DART JumpShot HTS w/QQQ-MS and assisting T.L.B. with methods development and analysis on these systems. No financial support was provided by Bruker Daltonics.
# Modeling the Power Consumption of Function-Level Code Relocation for Low-Power Embedded Systems
## 4 Code Profiling Using the Source Code Insertion Technique
### Design of Code Profiling  
An inline assembler is one of the features of a compiler that merges code written in assembly language into a program that already contains a programming language. The feature has the advantage of clearly inscribing the assembly code in the designated position for execution. Unfortunately, it is not offered by all compilers. However, the TI compiler, which is used to measure power consumption, is an exception in that it supports the feature. Therefore, the technique we implemented entailed inserting the source code by using an inline assembler to execute the desired assembly code. The source code insertion was performed during code profiling to interpret the power consumed by the instruction groups for the different memory devices.  
The target function is placed in Flash memory without any particular modification, or is relocated via modification of the program code and placed in the SRAM. This section describes our intention to examine whether the relocation of function-level code enables the power consumption to be reduced depending on the instruction groups. Thus, as shown in Figure 4(a),(b), experimental and control groups are specified. In both of these cases, the target function contains different assembly language depending on the instruction groups, and the following experimental examples of instructions are performed on each of the instruction groups.  
Figure 4: Executed assembly codes in both environments.  
* DP group: The group comprises a set of instructions that execute data processing operations in logical arithmetic units, and examples in which a set of instructions are configured are presented in Table 2. We used code profiling to define the most typical addition operation in the target function, i.e., the ADDS instruction, to validate the effect of simple arithmetic operations.
* RLS group: The instructions to access memory for the transmission of data belong to this group. Because it has a heterogeneous memory structure, the same experiment was conducted by separating the address of the target memory to detect the apparent influence. Other than this, the experiments were conducted according to the purpose, i.e., read and write, of accessing the memory. The TI compiler, however, does not provide a feature that allows an inline assembler to gain direct access to a specific memory address. We created an additional DP group by combining the MOVW, MOVT, and LDR or STR instructions, to generate the necessary instructions and define the target function.
* BC group: The instructions contained in this group control the flow of the program. As listed in Table 2, instructions such as B, BL, and BX exist. In the same way, a general instruction is selected from the DP group, in which case B is the instruction comprising the target function.
## 3 Results
The Braccio di Ferro, a planar robotic manipulandum with 2 degrees of freedom that can assist or contrast patients during a reaching task with either resistive, assistive, or perturbating force fields was the most tested device in this population [29; 33; 35; 36]. The Amadeo(r) (Tyromotion-Austria), a hand robotic device with each finger attached to the robotically driven slide, was used in a recent RCT [30]. The HapticMaster robot (MOOG, the Netherlands), an end-effector with a 3D workspace for upper limb training, was used in a case series and an RCT study [32; 37]. Finally, the Armeo Spring (Hocoma AG, Zurich, CH), a passive rehabilitation exoskeleton with sensors for the shoulder, elbow, and wrist joints with gravity support for the arm in a virtual learning environment, was used in two case series studies [31; 34]. Overall, 161 PwMS (EDSS 4.5-8; 29 primary progressive, 81 secondary progressive, 49 relapsing-remitting, and 2 relapsing-progressive) were included in the clinical studies. The RCTs included, respectively, 22 [33], 17 [32], 44 [30], and 36 [29] patients. Eight patients were included in the crossover study [35], and the remaining 34 patients were included in the four case series studies [31; 34; 36; 37]. The systematic review [16] that only considered robotic studies included 41 patients. Considering the included studies, the duration of the robotic treatment varied from 30 to 60 min. The frequency of the therapy was about 2-5 per week for 4-8 weeks, depending on the study, with a total number of sessions that ranged between 8 and 40. Only one study [32] associated the robotic treatment to the conventional one, whereas conventional therapy [30], the adjunction of object manipulation to robot-assisted reaching tasks [33] or sensorimotor training [29] were selected as comparators. Within-group significant differences in the robotic group were highlighted for the following outcome measures: handgrip strength; active range of movement (ROM); Nine Hole Peg Test (9 HPT) for dexterity; Wolf Motor Function Test (WMFT) and Action Research Arm Test (ARAT) for arm function; Tremor in Activity of Daily Life (TADL) questionnaire and Tremor Severity Scale (TSS) for tremor; Fugl-Meyer assessment Scale (FMA) for upper limb impairment; Life Habits assessment--general short form (LifeH), Motor Activity Log (MAL) for upper limb use; and TEMPA for arm function during daily living activities. In addition to clinical outcome measures, a set of robotic-driven measurements for motor control and surface electromyography upper limb muscle activity were included in the analysis.
## 2 You-Ye-Lin landslide
### Drainage Well for Long-Term Remediation
#### 2.5.2 Construction of the Drainage Well  
Figure 10 illustrates a typical construction sequence of a drainage well in Taiwan. The shaft is assembled by galvanized corrugated steel ring liners. One ring liner consists of seven segments and is erected every 0.5 m till the design depth. The completed liner is reinforced by steel hoop and the shaft entrance is also fixed by backfilling pure concrete. Then, PVC pipes with diameter of 100 mm are equipped at the bottom of the shaft to drain off the collected groundwater into the stilling pool. Subsequently, the auxiliary works such as maintenance ladder, entrance cover, and safety fence are setting up.  
In Taiwan, a drainage well is usually designed with a diameter of 3.5 m or 4.0 m and a depth of 15-30 m or deeper. A drainage well should not penetrate through the PSS to avoid the shearing failure of well shaft caused by the downward sliding of soil mass. According to the past works and experiences, in case of the drainage capacity of PVC pipes at the shaft bottom is not enough to accommodate the collected inflow water from horizontal drains, water pumps are equipped to pump out water into the stilling pool.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Design Parameters & \multicolumn{2}{c}{Drainage Well-1} & \multicolumn{2}{c}{Drainage Well-2} \\ \hline Depth of well shaft (m) & \multicolumn{2}{c}{20} & \multicolumn{2}{c}{16} \\ \hline Diameter of shaft (m) & \multicolumn{2}{c}{4} & \multicolumn{2}{c}{4} \\ \hline Length of horizontal drains (m) & \multicolumn{2}{c}{50} & \multicolumn{2}{c}{50} \\ \hline \multirow{6}{*}{Elevation and number of horizontal drains installed at 1 st-4 th levels (Elevation below ground surface)} & Level No. & Elevation (m) & Number of horizontal drains \\ \cline{2-6}  & 1 st level & 5 & 6 & 1 st level & 4 & 6 \\ \cline{2-6}  & 2 nd level & 10 & 5 & 2 nd level & 8 & 5 \\ \cline{2-6}  & 3 rd level & 15 & 5 & 3 rd level & 12 & 5 \\ \cline{2-6}  & 4 th level & 19 & 6 & 4 th level & 15 & 6 \\ \hline Elevation angle of horizontal drains (\({}^{\circ}\)) & \multicolumn{2}{c}{8\({}^{\circ}\)-10\({}^{\circ}\)} & \multicolumn{2}{c}{8\({}^{\circ}\)-10\({}^{\circ}\)} \\ \hline Central angle of horizontal drains with fan-shape radial array (\({}^{\circ}\)) & \multicolumn{2}{c}{120\({}^{\circ}\), 150\({}^{\circ}\)} & \multicolumn{2}{c}{120\({}^{\circ}\), 150\({}^{\circ}\)} \\ \hline Diameter of horizontal drains (mm) & \multicolumn{2}{c}{7.62} & \multicolumn{2}{c}{7.62} \\ \hline Diameter of drainage pipe (m) & \multicolumn{2}{c}{10} & \multicolumn{2}{c}{10} \\ \hline Depression angle of drainage pipe (\({}^{\circ}\)) & \multicolumn{2}{c}{8\({}^{\circ}\)-10\({}^{\circ}\)} & \multicolumn{2}{c}{8\({}^{\circ}\)-10\({}^{\circ}\)} \\ \hline \hline \end{tabular}
\end{table}
Table 2: The design parameters of drainage wells in You-Ye-Lin landslide.  
Figure 9: Typical configuration of well shaft with multi-level drainage boreholes (or horizontal drains) for large landslide (SWCB, 2003) [25].
## 3 Results and Discussions  
### Damage Identification Based on Natural Frequency  
Structural damage leads to changes in stiffness, natural frequency, and damping. The damage identification of the structural sealant in HFGCW could be assessed based on variations in dynamic properties. Specifically, structural damage was identified by analyzing changes in natural frequency under different damage conditions [35; 36; 37].  
The variation in natural frequency changes in the structural sealant in the HFGCWs under unilateral damage conditions was tested, as shown in Figures 5 and 6. The natural frequencies typically demonstrate negligible deviations as damage of equivalent extent occurs at different locations. However, a strong relationship exists between damage extent and natural frequency reduction. First-order natural frequency exhibits limited sensitivity  
Figure 4: Schematic diagram of the layout and numbering of sampling points.  
to debonding damage, whereas higher-order natural frequencies (modes 4-6) demonstrate significantly enhanced responsiveness to such damage.  
Nevertheless, the fundamental frequency remains easier to obtain. As shown in Figure 5, the fundamental frequency continued to decrease from 27.284 Hz (undamaged) to 22.679 Hz (side A debonding (100%)) in this experimental environment, representing a 16.88% reduction. The data exhibit a pronounced linear relationship between fundamental frequency and sealant debonding extents. For the tested sample configuration, the reduction in fundamental frequency correlates with the percentage of sealant debonding length (Figure 5). It is important to note that the observed relationship between fundamental frequency reduction and sealant debonding extent is specific to the tested sample configuration (600 \(\times\) 600 mm glass panels with 6 mm sealant thickness). While this demonstrates the proof of concept for damage severity assessment, further validation is required to generalize these findings to panels with different geometries, glass thicknesses, or sealant properties.  
Figure 5: Natural frequencies (modes 1–6) of side A under different damage degrees (**a–f**): (**a**) first order; (**b**) second order; (**c**) third order; (**d**) fourth order; (**e**) fifth order; (**f**) sixth order.  
Figure 6: Natural frequencies (modes 1–6) under identical damage degree.  
As shown in Figure 5, the higher-order natural frequencies (modes 4 and 5) represent a 53.13% reduction as the structural sealant transitions from undamaged states to instances of complete unilateral debonding. Consequently, as structural sealant damage initiates, higher-order frequencies serve as a reliable indicator of its occurrence. However, as shown in Figure 6, natural frequency values remain statistically invariant when equivalent damage occurs at different locations. In summary, while changes in natural frequency can reflect the structural damage state and serve as an indicator for assessing the percentage of sealant debonding length, they are insufficient for accurately identifying the exact location of the damage.