esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconstruction of the Physiological Behavior of Real and Synthetic Vessels in Controlled Conditions
## 3 Results
### Tortuosity  
Initially, the analysis focused on the tortuosity and tortuosity index. Notably, the average tortuosity and tortuosity index for the iliac arteries were comparable, reaching the level of 0.86 \(\pm\) 0.02. Conversely, for ePTFE vascular prostheses, both the average tortuosity and tortuosity index were almost 3.5 times lower (0.24 \(\pm\) 0.06) compared to the iliac arteries (\(p\) = 0.0001). Subsequently, the modified tortuosity index was examined, revealing that, for iliac arteries, the average modified tortuosity index was 0.87 \(\pm\) 0.02. In contrast, for ePTFE vascular prostheses, the average modified tortuosity index and tortuosity indexes were 0.28 \(\pm\) 0.05 (\(p\) = 0.0001).  
The Bland-Altman analysis demonstrated minimal significant differences between Ti and MTi for iliac arteries. The discrepancy between Ti and MTi was 0.01, within the range of 0.02 (Figure 7a). In contrast, for ePTFE vascular prostheses, the difference between Ti and Mti was 0.04, within the range of 0.09 (Figure 7b).  
Figure 6: An example of ePTFE vascular prosthesis movement for: (**a**) EV = 70 mL, FP = 60 min\({}^{-1}\); (**b**) EV = 70 mL, FP = 75 min\({}^{-1}\); (**c**) EV = 85 mL, FP = 60 min\({}^{-1}\); (**d**) EV = 85 mL, FP = 75 min\({}^{-1}\). For each analyzed case, two centerlines were added. The centerlines illustrate how ePTFE vascular prosthesis works for different hemodynamic parameters. Moreover, triangles were added to illustrate the degree of ePTFE vascular prosthesis’s deformation/bent as the area under the centerlines. Colors for each case are as follows: orange—EV = 70 mL, FP = 60 min\({}^{-1}\); blue—EV = 70 mL, FP = 75 min\({}^{-1}\); green—EV = 85 mL, FP = 60 min\({}^{-1}\); red—EV = 85 mL, FP = 75 min\({}^{-1}\).  
Figure 7: Comparison of tortuosity index (Ti) and modified tortuosity index (MTi) for: (**a**) iliac arteries with the use of Bland–Altman analysis; (**b**) ePTFE vascular prosthesis with the use of Bland–Altman analysis. The analyzed cases consisted of \(n\) = 120. Statistical significance was considered for all analyses with a threshold of \(p\) < 0.05.  
Moreover, Wd was compared to T, Ti as well as MTi. Since the results of T and Ti were the same, Ti was used for further calculations. For the iliac artery, a positive correlation between Wd and Ti was calculated (R2 = 0.981). Similarly, for ePTFE vascular prostheses, a positive correlation between Wd and MTi (R2 = 0.994) (Figure 8a), Wd and Ti (R2 = 0.942), and Wd and MTi was observed (R2 = 0.922) (Figure 8b).
# The Subsea Micro-Drilling Vehicle's Dynamic Analysis during Landing
## 1 Introduction
[PERSON] [15] proposed a three-dimensional dynamic model of a submarine-tracked vehicle traveling on cohesive soil in 2002 and simplified the tracked vehicle to a single rigid body with six degrees of freedom to solve for its pressure-sink and shear displacement characteristics. [PERSON] [16] entered the soil characteristics of the simulated deep-sea soil into the multi-body model of the subsea mining collector after conducting pressure subsidence and shear displacement experiments on the soil. The kinetic software RecurDyn models the interaction between the deep-sea mining vehicle and the seabed. [PERSON] [17] integrated the role of hydrodynamic forces on the collector on this basis. It conducted underwater experiments on the laboratory mining vehicle model to verify the dynamics model's feasibility. After multi-body dynamics modeling of the crawler deep-sea mining vehicle, [PERSON] [18] demonstrated the consistency between straight-line travel and simulation through sea trials, which provided the basis for subsequent functions such as turning, climbing, and overcoming obstacles. There are fewer studies on the landing impact of tracked subsea equipment. [PERSON] [19] established a 3D model of the mining vehicle in SolidWorks in 2004 and performed a landing simulation in ADAMS. [PERSON] [20] considered the hydrodynamic and sandy substrate for a tracked ROV, established a mathematical model of its dynamics, and conducted walking experiments in both a water tank and the sea. The results showed that the calculated and experimental results had a high agreement. [PERSON] [21] analyzed the varying conditions of the full ocean depth landing vehicle, contrasted the effect of the vehicle on rock and mud surfaces, refined and enhanced its structure, and conducted numerical simulation analysis. These analyses resulted in a noteworthy decrease in stress concentration regions, enhancing the vehicle's landing security. The study of the dynamic response characteristics during the walking of land-based tracked equipment and subsea non-tracked landers has made significant progress. However, the analysis of the hard landing dynamics of subsea-tracked equipment is still in its infancy. In the crawler multi-body dynamics modeling, the previous multi-body dynamics modeling ignores water resistance and the influence of sediment soil properties on its interaction. It cannot simulate the real-world environment. In addition, most multi-body dynamics modeling approaches cannot evaluate tracked equipment's impact acceleration and overall stability. This study uses multi-body dynamics simulation software to build a system dynamics model of the AMDV, which is the subject of the study. The grounding process is then dynamically analyzed to see how different heights and slopes affect the vehicle's response. This paper offers a theoretical framework for the SMDV's safe deployment and process control.
## 2 Materials and Methods
### Sequence Filtration and sRNA Clustering  
The raw data were first processed to remove low-quality reads and contaminants, including sequences with a poly N or 5\({}^{\prime}\) adaptor, without a 3\({}^{\prime}\) adaptor insert tag, and containing poly A/T. Thereafter, clean reads were obtained. To classify the sRNA comprehensively, clean reads were further aligned to the repeat sequence and the GenBank ([[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)), accessed on 19 March 2021), Rfam ([[http://rfam.xfam.org/](http://rfam.xfam.org/)]([http://rfam.xfam.org/](http://rfam.xfam.org/)), accessed on 21 March 2021), and miRBase ([[http://www.mirbase.org/](http://www.mirbase.org/)]([http://www.mirbase.org/](http://www.mirbase.org/)), accessed on 26 March 2021) databases. Through the alignment with GenBank and Rfam, rRNA, scRNA, snoRNA, snRNA, and tRNA were excluded from the clean reads. Additionally, sRNAs derived from the exon and intron of the mRNA were also discarded. To obtain a unique annotation for each sRNA, the following criterion was applied: rRNA > conserved miRNA > repeat > exon > intron, with a preference for GenBank annotation over that of Rfam.
## 4 Discussion
The assessment of motor symptoms using MDS-UPDRS (motor examination score), Timed Up and Go, Berg balance Scale and Purdue Pegboard Test is an endorsed combination of scores used in Parkinson's disease patients [32; 33]. In our study we combined the MDS-UPDRS with an objective muscle assessment tool (MyotonPRO). As a future research direction, we have in view the comparison of MDS-UPDRS score and myotonometric parameters in patients with Parkinson's disease before and after a 3-month rehabilitation program. The patients' follow-up is a limitation of the study; it was done between the treatment administration periods and involved functional status in consecutive hours and days. Another element that limited the research is that the time and means of data collection were dependent on the physical and emotional status of the patients. In the future, we aim to include in the analysis a larger group of patients for a longer period, as well as the assessment of postural stability in relation to myotonometric parameters. As the postural stability and physical activity was already studied in healthy subjects [34; 35], it can be extended to patients suffering from Parkinson's disease. The correlations among dynamic balance, physical activity and quality of life have been investigated [36], as well as the efficacy of particular trunk exercises in the balance training in patients with Parkinson's disease [37]. We envisage a future study aiming to assess postural stability in relation to myotonometry in Parkinson's disease patients who follow a physical exercise program for postural correction as well as improved static and dynamic balance. We also intend to extend the muscle groups assessed by MyotonPRO to muscles involved in posture (ventral trunk muscles rectus and obliquus externus abdominis, and dorsal trunk muscles longissimus vercivis and lumbar erector spinae).
## 2 Message Replication in OppNets for Sensed Data Collection
### Higher Degrees of Spatial Locality and Dynamicity in Human Movement  
Freedom of user movement coupled with short transmission ranges results in short-lived encounters, frequent disconnections and a highly dynamic network topology. User movement also exhibits high degrees of spatial and temporal locality. Studies show that users regularly visit only one or few regions(s) in the network. [PERSON] and [PERSON] (2010) analyse the mobility patterns of 275 users equipped with handheld PDAs in a campus wireless network. Their results show that only few users--less than 2% of them--were associated with a very high number of access points in the network. A similar study conducted by [PERSON] et al. (2016) on Dartmouth College's WLAN mobility trace shows that nodes visit their first two preferred locations at a probability of 70%. [PERSON] et al. (2017) also observe that most of the time, nodes visit only few locations in the network with high probability of return, which often corresponds to their home and workplace. [PERSON] et al. (2017) study movement traces in a campus and find that the students usually take relatively few trips--less than four--and visit relatively few different locations--less than five--per day.  
It is also evident from studies in literature that a strong correlation exists between encounter frequency and geographic proximity. [PERSON] et al. (2017) study real world movement traces and show that the number of encounters between node pairs decreases rapidly when the distance between their home regions increases. No node pair whose home proximity is greater than 4 km had more than 100 encounters. Thus, nodes tend to move within a local area, and this influences the encounters it experiences. The results also show that encounters between nodes from the same region are significantly more than encounters between nodes from different regions, since only a small proportion of node movement spans longer distances. Based on this intuition, [PERSON] et al. (2017) attempt to improve delivery rate by sending messages to the home region of the destination. These characteristics indicate that the mobility of portable handheld user devices and their encounters are not random, and OppNet solutions need to be designed accordingly since they rely on node movement and encounter opportunities.
## 2 Overview of VISFFB
### Illumination and Image Acquisition  
In a visual inspection system, lighting configurations vary greatly depending on the target object and inspection environment. In this study, the objective is to detect floating foreign particles inside flexible fluid bags, requiring careful consideration of the bag material and the positioning of lights to effectively distinguish foreign particles from air bubbles.  
Direct lighting from the camera side is problematic for this application due to severe surface reflections from the fluid bag making it difficult to view the interior. To address this, a planar light source is positioned behind the fluid bag, eliminating surface reflection issues and providing clear visibility of the bag's interior. This setup ensures that air bubbles, which generally transmit light, appear as bright, while foreign particles, which often do not transmit light, appear as dark in the captured images.  
However, even with this backlighting configuration, small air bubbles at the edges of the fluid bag can appear to be similar to dark foreign particles due to reduced light transmission resulting from the interaction between the bag's surface and the direction of  
Figure 3: The proposed gripper designed for holding a flexible fluid bag.  
the light beam. To resolve this, as shown in Figure 4, an additional light source is placed beneath the fluid bag, directing light upwards.  
This approach takes advantage of the convex lens properties of air bubbles, causing them to refract the light from a wide-angle direction and appear bright when the additional light source is positioned differently from the backlight. This differentiation from foreign particles is illustrated in Figure 5.  
High-resolution cameras are installed both above and below the fluid bag to visually detect even the smallest foreign particles. The upper camera covers the top portion of the fluid bag, while the lower camera covers the bottom portion, ensuring comprehensive coverage.  
As the gripper rotates the fluid bag and brings it to a halt in front of the cameras, the fluid inside continues to rotate, causing any foreign particles to move. The cameras capture a sequence of images over approximately 2 s, and then a deep learning-based detection algorithm processes these image sequences to effectively distinguish between foreign particles and air bubbles. This study demonstrates that the use of strategic lighting and high-resolution imaging, combined with advanced deep learning algorithms, enhances the detection of foreign particles in flexible fluid bags.
## 3 Results and Discussion
### Spectroscopy Analysis  
#### 3.4.1 FT-NIR  
The FT-NIR spectra obtained from the algae samples analysed are represented in Figure 4. The bands shown in the FT-NIR region contain information about the hydrogen-containing organic constituents (e.g., N-H, C-H, O-H) present in the sample. The NIR region encompasses wavelengths between 12,500 and 4000 cm\({}^{-1}\)[78; 79; 80].  
The most crucial signature of the near-infrared (NIR) spectra was the sensitive region of 6000-5500 cm\({}^{-1}\), combined with the bands situated around 4350-4250 cm\({}^{-1}\) within a broad band at 5775 cm\({}^{-1}\), and two small sharp bands at 4330 and 4257 cm\({}^{-1}\). The combined bands depict the absorption of saturated fatty acids, with C-H groups adjacent to C=C double bonds [81].  
There are broad absorption regions around 5174 cm\({}^{-1}\) and 6923 cm\({}^{-1}\). These findings indicate the presence of alcohols and organic acids, both containing O-H functionality,  
Figure 4: FT-NIR spectra of selected macroalgae samples _Gracilaria multipartita_, _Ulta_ sp., and _Sargassum muticum_ (from Buarcos and San Marcos).  
the second most important signature after the C-H bond, while the O-H bond is highly influenced by C-H that are typical of hydrocarbon mixtures [79].  
Furthermore, the absorption bands at 4760-4450 cm\({}^{-1}\) and the sensitive region at 4000-3800 cm\({}^{-1}\) (peak at 3983 cm\({}^{-1}\)) are due to aromatic C-H groups, appearing as combined bands [46; 48]. The ester group C=O also makes a weak appearance as the second overtone at around 4650 cm\({}^{-1}\); however, there is an overlap of aromatic C-H features [81].  
Figure 5 represents the score plot of the PCA performed on all samples (left side). PC1 and PC2 account for 96% of the total variation and can discriminate between different genera of algae samples.  
PC1 discriminates between the three different species _G. multipartita, Ulva_ sp., and _S. muticum_ and explains 83 per cent of the total variance. PC2, which explains 13 per cent of the total variance, is responsible for the clear distinction between _S. muticum_ from San Marcos and Buarcos. This analysis therefore highlights the variations between species, with _G. multipartita, Ulva_ sp., and _S. muticum_ separated, while _S. muticum_ collected in different sites shows distinct differentiation.  
The most important region for discriminating between samples for PC1 and PC2 is the region between 6500 cm\({}^{-1}\) and 4000 cm\({}^{-1}\) (Figure 5, right side). Another promising region for PC2, accounting for 13 per cent of the total variation and effectively distinguishes _S. muticum_ species collected in different regions, is the peak at 5174 cm\({}^{-1}\). This peak corresponds to the water absorption band of the first and second vibrational overtones of the -OH stretch and is also associated with carbohydrates, starch, and proteins. Supporting the above, the different taxa and locations are clearly identified through the FT-NIR spectral analysis.
# Impact of Device Topology on the Performance of High-Speed 1550 nm Wafer-Fused VCSELs
## 1 Introduction
Minimizing the volume of the mode to increase the D-factor (by reducing the optical cavity length and BTJ size) and reducing the parasitic capacitance of the reverse-biased p-n junction to increase the parasitic frequency cutoff (by applying the double-mesa structure and reducing the size of the mesa-containing BTJ) made it possible to demonstrate a 23 GHz [22; 23; 24; 25; 26; 27] small-signal modulation bandwidth and 50 Gbps data transmission speed in a back-to-back (BTB) configuration without equalization or forward error correction [24]. The maximum SM output power (with a side mode suppression ratio of 40-45 dB) is about 1.6-3.0 mW for VCSELs with 3-4 BTJs [24; 26; 27; 28]. The minimum DC power consumption is below 10 mW [24; 29] due to the low differential resistance (\(\sim\)60 Ohm) and ultra-low threshold current (\(\sim\)1 mA for 4 \(\upmu\)m buried tunnel junction devices). The 40 Gbps monolithic Coarse Wavelength Division Multiplexing (CWDM) C-band VCSEL arrays enable multicore, multi-level WDM for terabit board-to-board and rack-to-rack parallel optics (terabit-capacity active optical cable) [30]. To date, the total data throughput of 700 Gbps across a 7-core fiber is demonstrated for 20 GHz modulation bandwidth short-cavity VCSELs [31]. It should also be noted that a record SM output power of up to 8 mW has been demonstrated for larger (7 \(\upmu\)m) BTJ diameters by insertion of mode-selective mirror losses and inhibition of spatial hole burning [32]. However, this approach is associated with strong deterioration of the modulation bandwidth due to the increase in the mode volume. Wafer-fused VCSELs demonstrate large single-mode output power (\(\geq\)6 mW at 20 \({}^{\circ}\)C) [33; 34; 35]. The modest high-speed performance of 1550 nm range WF VCSELs [34; 35; 36] is noticeably inferior to short-cavity VCSELs fabricated by the TUM/Vertilas team. It is related to the long cavity (\(\sim\)3\(\lambda\)) as well as the device topology inherent to short-cavity VCSELs with minimized mode volume. The change from a semiconductor DBR design to a dielectric/metal mirror allows the TUM/Vertilas team to reduce the active volume by a factor of 5 [21; 37]. The differential gain is increased by enhancing the strain-induced mismatch. In this paper, we provide a study of continuous-wave (CW) and dynamic characteristics of 1550 nm SM VCSELs, fabricated by wafer fusion of molecular-beam epitaxy (MBE)-grown structures. The same static and modulation performances as compared to previous records for WF metalorganic vapor-phase epitaxy (MOVPE)-grown VCSELs are demonstrated for large-type device topology. Optimization of device topology as well as structure design allows us to reach modulation bandwidths of 13 GHz and 37 Gbps data transmission rate (in BTB configuration for NRZ modulation) at room temperature.
## 2 Materials and Sample Preparations
### Materials
#### 2.1.2 Bentonite  
The bentonite material was processed from highly plastic clay. Bentonite is used in many applications, which include drilling operations and water barrier layers. When inundated, it becomes semi-impermeable due to the expansion of the clay particles. The quality and index properties can vary between one type of clay and another. In clay liners, natural clay with high plasticity can also be a good additive to sand for the purpose of reducing its permeability. However, bentonite is ideal due to the homogeneous size and components of the product.  
OCMA-grade commercial bentonite supplied by the Arabian Minerals & Chemical Co., Ltd. in Saudi Arabia was selected for use in this study. The liquid limit of the bentonite was measured as 316 and the plastic limit as 62. A typical chemical analysis of OCMA-grade bentonite manufactured in the Arabian Gulf region is presented in Table 1. Figure 2 presents the grain size distribution.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Oxides** & **Concentration (wt \%)** \\ \hline SiO\({}_{2}\) & 55.20 \\ Al\({}_{2}\)O\({}_{3}\) & 17.00 \\ FeO\({}_{3}\) & 2.90 \\ Na\({}_{2}\)O & 1.90 \\ MgO & 4.60 \\ CaO & 0.90 \\ TiO\({}_{2}\) & \textless{}0.01 \\ K\({}_{2}\)O & 0.10 \\ Loss of Ignition & 16.70 \\ Total & 99.30 \\ \hline \hline \end{tabular} Source one—Arabian Gulf Region, OCMA-grade (1998).  
\end{table}
Table 1: Chemical composition of OCMA grade bentonite.
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 2 Literature Review
### Feature Importance
#### 2.5.1 Local Interpretable Model-Agnostic Explanation (LIME)  
The LIME explains the individual predictions of any model by approximating it locally [74]. The output of LIME provides a quantitative and visual understanding of an instance and the model predictions in order for a naive or expert person to build trust in a model and make effective decisions. LIME provides local fidelity of predictions, which may provide different results than global explanatory methods; in other words, features that LIME considers important for a local prediction might not be considered important by the global explainers and vice versa. LIME has some advantages over global model explainers such as SHAP; for example, it is computationally fast and converges quickly. Explaining an individual model prediction is faster compared to finding and aggregating global permutations. LIME uses an intuitive approach to analyze model output by providing variations in the input data. The variation in input data is generated by creating new perturb data from the original input. The new data are then used to train the model and interpret its predictions. An important pre-consideration must be made to specify the number of features we want to interpret the model when interpreting models. The higher the features, the better the trustworthiness of the model. Equation (1) represents a local interpretation of input data sample x:  
\[interpretation(x)=argmin_{g\in G}\mathcal{L}(f,g,\pi_{x})+\Omega(g) \tag{1}\]  
where \(g\) represents an individual model from the list of available explainable models \(G\) (e.g., decision tree, linear regression) for the sample instance, \(x\) and \(f\) represent the original model (e.g., MLP) being explained. \(\pi_{x}\) defines the proximity measure or weight between the original and sample data. \(\mathcal{L}(f,g,\pi_{x})\) represents the fidelity function that will try to reduce the loss \(\mathcal{L}\) (e.g., categorical cross-entropy) and measures the difference between the predictions made by the original model \(f\) and its representation \(g\). Finally, \(\Omega(g)\) represents the complexity of model \(g\). LIME explains the model by trying to minimize the fidelity function and the complexity so naive humans can easily interpret the model.