esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Methodologies for Robustness Quantification  
### Essence of Structural Robustness  
Similar to in other disciplines, robustness, as one of the fundamental terms for civil engineering, is extensively used in codes and literature [4]. However, as mentioned in [PERSON] et al. [2] and [PERSON] et al. [26], the meaning of robustness is often unclear and there is no consensus with respect to neither the definition nor the measure of structural robustness in its application within civil engineering. Therefore, structural robustness is often an issue of controversy and is frequently used as a synonym for stability, ductility, reserve strength capacity, redundancy or as an opposite of vulnerability, sensitivity and fragility. Although in the majority of literature no consensus is found with respect to neither the definition nor the measure of the concept of robustness, agreement on the essentials or the context of robustness in codes (e.g., Eurocode EN 1990 [27]) has been already reached:  
* Accidental event: structural robustness refers to the capacity of the considered structure to resist accidental events such as explosion, impacts, earthquakes and so on.
* Local damage: the structure subjected to the specified accidental event is partially damaged. The damage due to the event is restricted to a small portion (with respect to volume or area) of the structure and the degree of the damage is seen to be proportionate to the accidental event. The damage is often referred to as local (or direct) damage (or failure or consequence) and a notional central column removal scenario is the most frequently adopted damage scenario.
* Disproportionality: a structure of adequate robustness will not suffer from disproportionate damage which may be the direct failure or collapse (i.e., the local damage) due to the accidental event or the indirect/progressive failure or collapse triggered by the local damage.
# Route Prefix Caching Using Bloom Filters in Named Data Networking
## 4 Evaluation
There is no cache hit in Type 4 because it is not cached. The conventional cache misses can be classified into compulsory, capacity, and conflict misses. In our cache scheme, a conflict miss does not occur because the cache memory is fully associative. The capacity and compulsory misses occur when there is no matching prefix in the cache due to the replacement or being never loaded before, respectively. In the Bloom filter-based cache scheme, we have two more miss types: BF miss and non-cacheable miss. In the BF miss, there is a matching prefix in the cache, but its Bloom filter asserts a positive. The non-cacheable miss occurs if the matching prefix in the FIB is not cached because it corresponds to Type 4. The capacity misses do not include the non-cacheable misses in this context. Figure 10(a) shows the distribution of the cache miss counts according to their types. For a small cache, the capacity misses and the compulsory misses are a substantial portion of the total misses. There are very few capacity and compulsory misses if the cache size is greater than 2048. The BF misses and the non-cacheable misses become a relatively large portion as the cache size increases. The actual miss count for various Bloom filter configurations is shown in Figure 10(b). There is no doubt that the compulsory and the capacity misses do not change over the Bloom filter configuration. The BF miss \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cache Size**} & \multicolumn{5}{c}{**Bloom Filter Parameters**} \\ \cline{2-6}  & \(m\) = 4, \(k\) = 1 & \(m\) = 4, \(k\) = 2 & \(m\) = 8, \(k\) = 1 & \(m\) = 8, \(k\) = 2 & \(m\) = 8, \(k\) = 3 \\ \hline
256 & 0.57660 & 0.57663 & 0.58304 & 0.58608 & 0.58690 \\
512 & 0.66347 & 0.66320 & 0.67254 & 0.67663 & 0.67757 \\
1024 & 0.75574 & 0.75514 & 0.76698 & 0.77201 & 0.77309 \\
2048 & 0.85337 & 0.85257 & 0.86665 & 0.87268 & 0.87393 \\
4096 & 0.88660 & 0.88567 & 0.90075 & 0.90708 & 0.90840 \\
8192 & 0.88660 & 0.88567 & 0.90075 & 0.90708 & 0.90840 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cache hit ratio with Bloom filters having size \(m\) and the number of hash functions \(k\). Figure 10: Observations with regard to the type of matching prefixes: (a) distribution of matching prefixes in the traces with \(\alpha\), from 0.6 to 1.1; (b) cache hit count on matching prefix types. count is lower when \(m\) is 8 than when \(m\) is 4. For \(m=8\), the BF miss counts are nearly similar when compared for \(k=2,3\). Furthermore, for \(m=4\), the BF miss counts are similar for \(k=1,2\).
## 6 Discussion and Conclusions  
### Discussion  
In the pursuit of understanding the ME and EMR signal characteristics stemming from the compression rupture of sandstone specimens, uniaxial compression tests were conducted utilizing the RMT-150C rock mechanics testing machine, coupled with ME sensors, strain gauges, and EMR antennas. The ensuing analysis delved into the distinct features of EMR and ME signals generated during the static failure of dolomite under compressive stress, unraveling the intricate coupling processes between them. Additionally, the test data laid the foundation for a comprehensive coupling analysis of ME and EMR generated by sources in different directions within the surrounding rock.  
While the current work holds notable practical significance in the realm of damage and collapse prediction for rock masses, it is not without inherent limitations. The orientation of bedding planes within the rock mass emerges as a significant factor influencing plastic deformation characteristics and failure patterns [32; 33]. Furthermore, the establishment of specific yield criteria, tailored to the in situ state of stresses in rock beds, is imperative for a more nuanced analysis of complex failures [34; 35]. These aspects, acknowledging their importance, are slated for incorporation by the authors in future studies.
## 3 Drilling
### Extended Drilling  
While most drilling operations in the region typically employ horizontal wells with lateral extensions ranging from 2500 to 2800 m, the Loma Campana Project took an unconventional approach. The project aimed to exploit more challenging reservoir zones  
Figure 2: Ternary mineralogical diagram across various sub-basins of the Vaca Muerta formation. Reprinted with permission from Ref. [42]. Copyright 2018, Society of Petroleum Engineers.  
by designing wells with lateral stretches exceeding 4000 m. A comprehensive feasibility study was conducted by [PERSON] et al. from YPF and Chevron, which included generating prospective well designs, outlining specialized procedures, and identifying the requisite engineering solutions and toolsets.  
Initial test wells were drilled and swiftly fine-tuned to facilitate broader development using the innovative well architecture. A host of challenges emerged during the drilling process, particularly concerning wellbore tortuosity, most notably between the measured depths of 18,000 feet and the total depth (TD). Elevated tortuosity led to complications such as ineffective hole cleaning, weight distribution issues, augmented torque requirements, and increased energy consumption by the drilling rig. These challenges were particularly pronounced in the first two wells of Pad A, where torque demands nearly exceeded the capacity of the Top Drive system, necessitating the maximum power output from the rig's generators.  
During the lateral drilling phases, time-dependent instabilities were noted, particularly around the landing zones. These instabilities led to pipe-sticking tendencies. The prolonged exposure of the lateral section (exceeding 48 h) due to slow tripping operations resulted in alterations to fluid properties and rheology, triggering the emergence of cavings and gas influxes that required management. To address these issues, the team deployed a specialized mining rig in order to install deeper conductor casings at depths between 40 and 50 feet, a significant departure from the previous 9-12 foot depths in Pad A. Fluid specifications were also re-evaluated, leading to the introduction of potassium sulfate as a clay stabilizer to enhance wellbore integrity [45; 46].  
The extended drilling strategy employed in the Vaca Muerta formation underscored the necessity for tailored approaches that manage unique challenges, like tortuosity and geomechanical instabilities. Insights gleaned from the wells that were drilled to the total depth were cataloged for future reference. Key action items for subsequent projects included the adoption of casing-while-drilling (CWD) techniques [47; 48], the selection of rigs with an adequate operational capacity, improved tortuosity control measures, and optimized bottomhole-assembly (BHA) configurations for the production intervals.
## 4 Control Strategy  
### PID Controller  
The PID controller was used in this study to control the FBBA position and completed a closed-loop system. PID is a popular controller in the industrial sector due to its simplicity and dependability. Figure 7 depicts the discrete PID structure used in the simulation and experimentation. The study's results were determined by the optimal PID parameters \(K_{p}\), \(K_{i}\), and \(K_{d}\).  
\[u(z)=\Big{[}K_{p}+K_{i}T_{s}\frac{1}{1-z^{-1}}+\frac{K_{d}}{T_{s}}\Big{(}1-z^{-1} \Big{)}\Big{]}e(z) \tag{8}\]  
Figure 5: Measured and simulated model output.  
Figure 6: ARX331 model poles(x) and zeros (o) plot.  
Figure 7: PID controller structure.  
The discrete PID controller equation is shown in Equation (8). In the z domain, u(z) is the control signal and e(z) is the error signal. \(K_{p}\) denotes the proportional increase, \(K_{i}\) shows the integration gain, and \(K_{d}\) symbolizes the derivative increase. Ts is the sampling time, that was set to 0.005 s.  
Based on Table 1, each parameter plays an important role in providing a stable output. Therefore, the most optimal PID parameters had to be determined. Thus the Genetic Algorithm optimization technique was utilised in finding the parameters
## 3 Results  
### Biochemical Properties  
In the current study, among all dried samples, the highest EC\({}_{50}\) (ABTS) values were obtained for CD dried Weiki cultivars and ranged from 0.25 to 0.83 mg d.m./mL (Table 2). Using DPPH assay, the lowest antioxidant activity amid Weiki samples was determined for kiwiberry dried (CD) at 50 and 70 \({}^{\circ}\)C; these values were significantly different (\(p<0.05\)) from all other dried samples. In turn, amid Geneva samples, the lowest antioxidant activity was characteristic for samples dried also by the convective method although at temperatures above 60 \({}^{\circ}\)C. The lowest values of EC\({}_{50}\) (DPPH), which point at strong antioxidant properties, were measured for VD Geneva at 50 \({}^{\circ}\)C. In this case, antioxidant capacity was equal to 0.16 mg d.m./mL. In all tested conditions, there were no statistical differences between ABTS and DPPH values from fresh and VD samples dried at 50 \({}^{\circ}\)C.  
Total carotenoid content (TCC) of dried fruits ranged between 36.55-90.27 and 35.08-61.88 \(\upmu\)g d.m. for Geneva and Weiki, respectively. Among Geneva fruits, the highest concentration of carotenoids not significantly different from the fresh samples was obtained for the VD method performed at 60 \({}^{\circ}\)C. In the case of Weiki samples, the highest values were stated for VD performed at 50 \({}^{\circ}\)C and 80-50 \({}^{\circ}\)C; in these cases, carotenoid content was equal to 61.49 and 61.88 \(\upmu\)g d.m., respectively. The lowest concentration of carotenoids, equal to 35.08 \(\upmu\)g/g d.m., was found in Weiki dried convection method at 50 \({}^{\circ}\)C. Such results are linked to the long exposure to oxygen and to oxidation of carotenoids resulting from the longest drying time. [PERSON]'s correlation analysis proved that the relationship between drying time and total carotenoid content was negative and statistically significant (r = \(-0.475\)).  
The highest statistically different TPC value in comparison to other dried samples was stated for extracts of Geneva cultivar vacuum dried at 50 \({}^{\circ}\)C: 44.89 mg GAE/g d.m. It is worth emphasizing that this sample did not differ significantly from fresh fruits. In comparison, the lowest values were measured for CD dried Weiki, which were characterized by TPC equal to 4.77 and 4.83 mg GAE/g d.m. for drying at 50 and 60 \({}^{\circ}\)C, respectively. The TPC values correlated significantly with both antioxidant activity determinants analyzed by ABTS and DPPH assays. The values of coefficients of correlation were equal to r = \(-\)0.673 and r = \(-\)0.640, respectively. Average correlation was discovered between EC\({}_{50}\) parameters and total carotenoid content, r = \(-\)0.330 for ABTS and r = \(-\)0.402 for DPPH, which indicates that free radical scavenging activity resulted mainly from phenolics presence.
## 6 Results and Discussion
### Complexity Analysis  
To analyse the complexity of the RFR(F) and RFR(S) models over ANN-based models, we deployed these models on Raspberry Pi 4 Model B. We focused on analysing the lightweightness of the models through key parameters such as CPU utilisation, average of four cores in (%), and memory usage in (MBs) during the training and testing phase. From Table 4, we can see that the RFR(F) demonstrates a considerable advantage in terms of resource efficiency. During the training phase, it consumes significantly less memory, 923 MBs, compared to the ANN(F) model with 1095.68 MBs. Also, it utilises only 2.1% of CPU, while the ANN(F) utilises 99.25%. During testing, the RFR(F) maintains lower memory usage and CPU utilisation compared to the ANN(F) model, which shows no reduction in memory usage but a drastic decrease in CPU utilisation to 7.65%. Finally, we compare the CPU utilised by the RFR(S) with the ANN(S) model for the selected RSSI series, as illustrated in Figure 15, indicating the CPU utilisation in the training and testing phase of the RFR(S) and ANN(S) models. It is visibly clear that the ANN(S) utilises a higher CPU during training. However, the testing usage was less than RFR(S) only for the sequence with features \([2,0,2]\). In the testing phase, both models show decreased CPU usage; however, the ANN Model still exhibits overall higher resource consumption than the RFR Model. Considering the combination of training time, memory usage, and CPU utilisation in training and testing as complexity parameters, we can observe the significantly greater efficiency of the RFR(S) model over the ANN(S) model. The reduced CPU usage during the testing phase of both models is obvious, as this task involves only evaluating the already trained models.  
Based on this analysis, the choice of model should be based on the alignment of the model with resource availability and performance requirements of the specific application. Thus, we consider the RFR(F) and RFR(S) as lightweight models based on their training time, testing time, and CPU utilisation. While the models show significant promise in improving estimation accuracy and reliability, scaling up their deployment in larger, more dynamic IoT environments may present challenges such as computational resource constraints, real-time processing limitations, and adaptability to diverse network conditions. Addressing these challenges will be a critical step toward ensuring the practical applicability of the models. This analysis is critical for understanding the accuracy and reliability of RSSI estimation, which is pivotal for designing robust and adaptive IoT systems in dynamic environments.
###### Abstract
[63] compared all IMERG products over Spain at multiple temporal scales using a high-resolution gridded precipitation product derived from \(\sim\)2300 rain gauges. [PERSON] et al. [64] conducted a comparative study of the IMERG ER and IMERG FR over the extreme precipitation of typhoons in China. They demonstrated a good correlation on spatiotemporal between IMERG data and GP observations. Likewise, [PERSON] et al. [65] concluded that IMERG captures precipitation during hurricane (tropical cyclone) events and storm cores reasonably well. In addition, the above comparative studies have reported that FR is generally verified to have high accuracy and reasonable practicability on a monthly scale in comparison to ER and LR based on the study conducted in China [53; 66], East Asia [67], Austria (temperate climate) [68], Saudi Arabia [69], Pakistan [70], and Italy [71]. However, few studies have contradicted this superiority of FR as studies have reported either no significant improvement of FR run or even outperformance by ER, at least in some aspect (i.e., POD, correlation, extreme events) [72; 73]. Challenges exist for precipitation retrieval algorithms to detect very light/heavy precipitation events, precipitation over rugged topography, and differentiate rain from snow. So, evaluation results are regional and climate specific; it is always recommended to carry out such evaluation tasks over distinct geographical regions [74]. In the process, few past studies using ER data have been conducted; for instance, [PERSON] et al. [75] noticed the better performance of IMERG-V05B to capture the light and heavy precipitation amount compared to IMERG-V06B for the evaluated regions, though they have used ten stations. [PERSON] et al. [74] found a good agreement between IMERG and APH_V18 (as a reference) over the Nepal Himalayas. However, [PERSON] et al. [47] explored the ability of IMERG FR of V06 to capture the drought event frequency in the Nepal Himalayas. In addition, most evaluations of SPEs on the topography of Nepal are based on previous-generation satellite products, i.e., the TRMM era. Managing water resources is the utmost and most challenging task in the climatic change-prone and topographic variant region [57; 76; 77]. The above-mentioned counterintuitive behavior of IMERG at geography and lack of comprehensive evaluation motivates us to compare the four datasets: (i) MW only, (ii) IR only, (iii) multi satellites but gauge uncalibrated, and (iv) multi satellites gauge calibrated. We have computed 28 precipitation indices, including threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, and proportion-based indices. These precipitation indices help understand the dynamic characteristics of precipitation amount, intensity, wet days, and other relevant information. The study findings are the first ones, per our knowledge, to serve as feedback to the end-users and algorithm developers in understanding the performance of each product with potential improvement, if needed.
# 5G-MEC Testbeds for V2X Applications
## 2 Background  
An MEC system provides the backend support for 5G to implement critical latency applications such as URLLC in which latency can be required to be a maximum of 1 ms. The 5G-MEC paradigm can provide latency and other critical requirements. The MEC system helps to analyze real-time scenarios in the case of V2X applications and can perform the required computation tasks. The MEC system provides computational capabilities using virtualization and softwarization technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV).  
This section provides detailed insights into 5G-MEC and V2X applications and is divided into four subsections that discuss 5G features and capabilities, the fundamentals of MEC, V2X applications, and the general elements of the 5G-MEC V2X testbed. The MEC fundamentals include integrating MEC deployment into 5G and softwarization technologies such as SDN and NFV with MEC. The 5G-MEC V2X testbed's general elements are divided into two parts: hardware and software components.
## 2 Materials and Methods
### Reverberation Time Prediction Methodology Based on Artificial Neural Network
This methodology is based on the following steps (Figure 3):  
* Reverberation time measurement using the integrated impulse response method. * Recordings of a series of signals. Figure 2: Artificial neural network architecture. * Features extraction of the recorded signals. * Construction of a dataset containing the processed data. * Training of a regression algorithm based on artificial neural networks. * Regressor test. Once the prediction system has been set up it will only be necessary to record a signal in a room to derive the reverberation time.
# Evaluation of Measurement Strategy for Track Side Monitoring of Railway Wheels
## 3 Methodology
### In-Workshop Measurements  
For the manual measurements, wheel profiles were measured along the circumference of the wheel during in-workshop inspections using a handheld measurement system. The measurement system used for this study is a laser-based equipment CALIPRI manufactured by the NEXSENSE company, Austria. The accuracy of the measurement equipment is \(<\)\(\pm\)80 \(\upmu\)m, and the repeatability is \(<\)\(\pm\)35 \(\upmu\)m. The manual measurements of the wheel were  
Figure 1: Original and worn wheel profiles, with the wheel profile parameters. qR: flange slope; Sd: flange width; Sh: flange height; Th: tread hollowing.  
performed at a workshop owned by the operator of the iron ore traffic in northern Sweden, LKAB Malmtrafik, Sweden.  
Seven wheels were measured using the handheld laser-based equipment with a resolution of 35 measurement points equally spaced along the circumference of each of the wheels, giving approximately 100 mm between the measurement points. Table 1 shows the manually measured wheels in the workshop along with the information regarding the age of the wheel in terms of distance travelled in km. Figure 2 depicts the profiles plotted along the circumference of the wheel (HP6448) illustrating the different measurement positions for that wheel.  
For the in-workshop measurements, both the reproducibility and the representability of the wheel profile measurements were tested. The reproducibility of the measurements was tested by comparing two operators performing the same measurement task on the same wheel using the same measurement device. The reproducibility was analysed with the wheel profile parameters. The representability was tested for both newly grinded wheels and wheels with different operating hours. For the representability test, measurements carried out by only one of the operators were used. The representability was also analysed with respect to wheel profile parameters.
## 2 Materials and Methods
### Measurement of Boundary Layer Profile
#### 2.3.2 Stability Study  
To establish a stable boundary layer airflow that represents a farmland terrain, the characteristics of wind profiles within the region of interest were assessed. The arrangement of the roughness elements was adjusted accordingly until reaching a desirable boundary layer flow following the VDI-guideline 3783/12 [40]. Air velocities at two vertical lines, one at the upstream edge of the scaled model (i.e., Line 1 in Figure 3) and the other at the downstream end of the region of interest (i.e., Line 2 in Figure 3), from 0.003 m to 0.6 m height along the symmetry line of the model were measured. Measurements were carried out using the 2D LDA without the presence of the scaled model. Free stream wind speed  
Figure 4: Reproducibility study for air velocity measurements in the wind tunnel without scaled model for free stream wind speed of 8 m\(\cdot\)s\({}^{-1}\). (**a**) and (**b**) represent streamwise (\(U\)) and vertical (\(W\)) air velocity components, respectively.  
Figure 3: Isometric view of the scale model and air velocity measurement positions. Lines 1 and 2 indicate vertical lines for wind profile measurements. The black dots at Planes 1–3 represent actual velocity measurement positions downwind the building. \(X\), \(Y\) and \(Z\) represent axes of the coordinate system. The red dot represents the origin of the coordinate system.  
in the wind tunnel was 8 m\(\cdot\)s\({}^{-1}\). Parameters that define the roughness class of the boundary layer, e.g., the wind profile power exponent, roughness length, and turbulence intensity were examined.