esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Nerolidol on Renal Dysfunction following Bilateral Ureteral Obstruction
## 2 Materials and Methods
### Gene Expression Analysis
\begin{table}
\begin{tabular}{l l l} \hline \multirow{2}{*}{\begin{tabular}{l} KIM-1 \\ (NM\_173149.2) \\ \end{tabular} } & Forward & GCCTGGAATAACCACTGTAAG \\ \cline{2-3}  & Reverse & GCAACGGACATGCCAACATAG \\ \cline{2-3}  & Probe & d FAM-TCCCTTTGAGAAGCCGCAGA-BHQ-1 \\ \hline \multirow{2}{*}{\begin{tabular}{l} NGAL \\ (NM\_130741.1) \\ \end{tabular} } & Forward & CTGTCCCACCGAATGC \\ \cline{2-3}  & Reverse & CCACTGCACATCCCAGTCA \\ \cline{2-3}  & Probe & FAM-TGACAACTGAACAGCGTGAGCG-BHQ-1 \\ \hline \multirow{2}{*}{\begin{tabular}{l} TNF-\(\alpha\) \\ (NM\_012675.3) \\ \end{tabular} } & Forward & CTCACACTCAGATCATTTCTC \\ \cline{2-3}  & Reverse & CCGGTTGGTGTGTACT \\ \cline{2-3}  & Probe & FAM-CTCGAGTGAACACCGTGACC-BHQ-1 \\ \hline \multirow{2}{*}{\begin{tabular}{l} PAI-1 \\ (NM\_012620.1) \\ \end{tabular} } & Forward & GGCACAATCCAACAGAGACAA \\ \cline{2-3}  & Reverse & GGCTTCTCATCCACTCCAAG \\ \cline{2-3}  & Probe & FAM-CCTTTCATGGCCCAGCTG-BHQ-1 \\ \hline \multirow{2}{*}{\
\begin{tabular}{l} IL-6 \\ (NM\_012589.2) \\ \end{tabular} } & Forward & TCACAGAGAGAGAC \\ \cline{2-3}  & Reverse & CACAAGTCCGAGAGAGAC \\ \cline{2-3}  & Probe & FAM-TCAGAATTGCCATTGCCAACCTCT-BHQ-1 \\ \hline \multirow{2}{*}{
\begin{tabular}{l} IL-1\(\beta\) \\ (NM\_031512.2) \\ \end{tabular} } & Forward & ATGCCTCTGTCTGACC \\ \cline{2-3}  & Reverse & GCTCATGGAGAATACCACTTGTTG \\ \cline{2-3}  & Probe & FAM-AGCTGAAAAGCTCTCCACCTCAATGGA-BHQ-1 \\ \hline \end{tabular}
\end{table} 
Table 1: Forward and reverse primers and fluorogenic probe sequences used for real-time quantitative PCR analysis. KIM-1: kidney injury molecule-1; NGAL: neutrophil gelatinase-associated lipocalin; TNF-\(\alpha\): tumor necrosis factor-alpha; PAI-1: plasminogen activator inhibitor-1; IL-6: interleukin 6; IL-1ß: interleukin 1 beta; p53: pro-apoptotic gene \(p\)53; GSR: glutathione-disulfide reductase; GP-1: Glutathione peroxidase 1; PPIA: peptidylprolyl isomerase A (housekeeping gene).
###### Abstract  
In the Philippines, the use of non-native earthworm species in vermicomposting is popular. Given that the country is a vital geographical resource for earthworm diversity, the study of earthworm species to establish the potential of their vermicasts in agricultural applications is essential. In this study, the bacteria associated with the vermicasts of the recently described indigenous species, _Pheretima losbanosensis_, were investigated using next-generation sequencing, community-level physiological profiling, and NPK activity screening. The results showed diverse bacterial species belonging to the phyla Proteobacteria, Bacteroidetes, Acidobacteria, Actinobacteria, Chloroflexi, Firmicutes, Gemmatimonatedes, Nitrospirae, Planctomycetes, Spirochaetes, Thermodesulfobacteria, and Vermucomicrobia. Higher diversity and carbon substrate utilization (\(p<0.05\)) of amines and amides, phenolic compounds, polymers, and carboxylic and acetic acids were exhibited by the bacterial communities of _P. losbanosensis_ compared to those of _Eudrilus eugeniae_. Likewise, bacteria (n = 25) isolated from _P. losbanosensis_ vermicasts had higher nitrogen fixation and phosphate and potassium solubilization activities (\(p<0.05\)) than the bacteria (n = 20) isolated from _E. eugeniae_ vermicasts. Overall, our results indicate that the diverse bacterial communities inhabiting the vermicasts of _P. losbanosensis_ have nutrient mineralization and carbon substrate utilization activities that may have applications in sustainable agriculture as a potential organic input to promote plant growth and improve soil substrate.  
bacteria; diversity; physiological profile; nutrient mineralization; vermicasts; earthworm; _Pheretima losbanosensis_; _Eudrilus eugeniae_
## 3 Methodology
### The Data Processing Strategy  
The data processing strategy includes selecting the most optimal \(\chi^{2}\) value through a preliminary clustering of all candidate tracks based on the proximity of their corresponding \(\chi^{2}\) values. The most populated cluster then chosen to estimate the \(\chi^{2}\) value for tracks in that cluster is the most significant.  
To extend this strategy to three levels of data contaminated by fake measurements, three stages of data processing were used. In the first stage, which did not involve any fake hits, all tracks in the dataset were clustered using \(\chi^{2}\) as a measure of track proximity. X-means clustering [12] was applied to these data, with the clustering conducted in two phases. In the first phase, initial clustering centers were obtained using the KMeans++ algorithm, which provides a close estimate to the optimal centers due to its probabilistic  
Figure 6: Track with fake hits, a sample of fitted trajectory with no noise.  
Figure 7: Track with fake hits, the same track sample fitted with 10 additional noisy hits.  
approach. The remaining clustering relied on the X-means algorithm, which refines clustering decisions by computing Bayesian Information Criterion at each step of the algorithm. The goal was to achieve a stable approach for determining false tracks, i.e., tracks that do not correspond to a correct particle trajectory. The results of the first stage are presented in Table 1.  
A total of 30 clusters were obtained, and the results of the clustering analysis for the first 10 clusters with the lowest chi-squared values are shown in Table 1. Analysis of the clusters based on mean and number of elements revealed that 83% of the tracks belonged to cluster 1, which had the lowest mean and standard deviation values for \(\chi^{2}\). The other clusters showed noticeably higher errors in the helical fitting procedure for the tracks. A visual comparison of the tracks also revealed the differences between the clusters. The \"good\" tracks in cluster 1 showed no significant discontinuities or spirals. This strong correlation between track properties and the value of \(\chi^{2}\), which represents the quality of the helix line fitting for each track, led to the idea of using \(\chi^{2}\) to filter out false tracks that can occur during neural network reconstruction.  
The first stage of our work involved developing a parallelization approach to speed up the procedure of false track rejection using neural network tracking.
## 3 Results and Discussion
Therefore, the inference time is significantly less than the system's acquisition rate and this method can be used for real-time catheter tracking. Compared to other well-known networks, the results of average accuracy, average training time, and average inference time of some models are shown in Table 1. Our method, which is the two-stage and VGG-based model, outperforms all the mentioned models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Mean Error**} & \multicolumn{2}{c}{**Standard**} & \multirow{2}{*}{**Training Time**} & \multirow{2}{*}{**Inference Time**} & \multirow{2}{*}{**Output Type**} \\  & & & & & & \\ \cline{3-6}  & & & & & & \\ \hline Mobile Net [73] & \(5\times 5\) & 19.29 & 9.77 & 3.8 s & 3.6 ms & Landmark \\ ResNet [74] & \(5\times 5\) & 3.35 & 6.23 & 4.9 s & 4.4 ms & Landmark \\ Dense Net [75] & \(5\times 5\) & 3.86 & 7.29 & 21.5 s & 11.2 ms & Landmark \\ U-Net [17] & \(1\) & 1.00 & 6.13 & 0.2 s & 10 ms & Mask \\ VGG [71] & \(1\) & 7.36 & 4.90 & 2.1 s & 1.7 ms & Landmark \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of proposed method, in terms of accuracy, training time, and inference time, with other methods. Figure 8: Average percentage error for different regions.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 2 Wave Energy Generation  
Nearshorve wave energy converters are installed in waters of moderate depth and only a few hundred meters from the coastline, thus offering the advantages of lower construction and maintenance costs than deep-water devices. INGINE's new system takes advantage of the nearshore location and adopts an innovative method for generating power using the whole range of wave movements, even in shallow waters [6].  
Figure 1 shows the schematic diagram of the system. The system is composed of two main building units: the energy absorbing unit (EAU) and the power generating unit (PGU). The EAU is a floating buoy, located approximately 80 m from the shoreline, anchored to the pulley located at the seabed depth of 4 m, which is connected to the onshore PGU by three ropes, the tension of which is balanced by counterweights. The buoy is able to move within a limited range in any direction in response to wave motion. When the buoy rises, at least one of the connecting ropes pulls to the water side, and the kinetic energy from the motion is transferred to the PGU [13]. The opposite ends of the ropes connecting the buoy to the power converter are attached to a drum, where the linear reciprocating motion is converted into rotary motion through the process of winding and unwinding the rope on the drum. A one-way clutch installed inside the drum ensures that the shaft connected to the generator always rotates in one direction. The wave energy recovered by the EAU is thus transferred to the PGU through the reciprocating motion of the ropes, which is further converted into the one-directional rotary motion of a generator turbine. In this system, energy transfer from the water side to the land side occurs only when the buoy rises; consequently, the WEC generates power only when the ropes are pulled by the rising buoy. On the other hand, when the buoy descends, the counterweight attached to the other end of the rope maintains the tension on the rope and ensures that the buoy stays in its designated position. The direction of movement of the buoy and the rope is indicate by the arrows in Figure 2.  
This WEC increases the power generating efficiency by storing the energy transferred from the water side. According to [PERSON], [PERSON] and [PERSON] [6], the PGU uses a portion of the transferred energy to rotate the output shaft of the generators and a portion as stored potential energy. Thus, the system can compensate for fluctuations in wave strength by using the stored energy. As Figure 2 illustrates, the power-transmitting ropes are wound and unwound as the buoy moves up and down, respectively. As a result, the PGU generates electric power continuously, and structural stability is maintained regardless of any external force exerted by the waves.
# Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints
## 4 Numerical Calculation Verification
### Comparative Analysis of Numerical Models  
The stabilities of the wellbore structure before and after the optimization of construction measures are compared by numerical calculation techniques in ABAQUS 2020. The two finite element models are consistent in the definition of material parameters, section characteristics, analysis steps and load structures, but the constraint conditions at the bottom of the wellbore have changed. The constraint comparison of the wellbore models before and after optimization is shown in Table 5.  
Figure 7: Finite element model of the entire wellbore and the types of elements used. (**a**) Numerical model of the complete wellbore. (**b**) Element type of S3R. (**c**) Element type of S4R.  
The boundary conditions of the finite element model of the drilling wellbore established under the two constraints in Table 5 are shown in Figure 8.  
The cloud diagram obtained after the model was submitted for analysis is shown in Figure 9.  
As can be seen from Figure 9, the stress conditions of wellbore structures before and after optimization are similar. The maximum displacement values for both cases are 1 m. However, due to the different locations of maximum deformation, the eigenvalue of the wellbore structure under the traditional method is 0.9978, which is less than 1, indicating that the wellbore structure is unstable. In contrast, the eigenvalue of the optimized wellbore structure is 2.7471, which is more than 1, indicating that the structure is stable. This is in agreement with the theoretical calculations of the wellbore structure's stability. Therefore, the optimization measure can significantly enhance the stability of wellbore structures.  
Figure 8: Comparative boundary condition analysis of wellbore finite element models before and after optimization. (**a**) Boundary condition of wellbore before optimization. (**b**) Boundary condition of wellbore after optimization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Construction Techniques** & **Constraints on BOTTOM** & **Constraints on TOP** \\ \hline Traditional construction technology & U1, U2, U3 & U1, U3, UR2 \\ Optimized construction technology & U1, U2, U3, UR1, UR2, UR3 & U1, U3, UR2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of constraints of wellbore models before and after optimization.  
Figure 9: The displacement cloud diagram of the finite element model for wellbore structure. (**a**) Under conventional construction method. (**b**) Under optimized construction method.
###### Abstract  
_Background:_ There is a lack of specific genetic studies regarding injuries in women's football. However, different genetic factors have been associated with tendon/ligament injuries in women football players. The aim of the study was to examine the genotypic frequencies of genes associated with injury risk and epidemiology in women's amateur football players and the aetiology of injuries. _Methods_: In total, 168 women's amateur football players from football clubs in the Spanish second division league and Caucasian descent were enrolled in this prospective observational cross-sectional study. _AMPD1_ (rs17602729), _ACE_ (rs4646994), _ACTN3_ (rs18187539), _CKM_ (rs8111989) and _MLCK_ (rs2849757 and rs2700352) polymorphisms were genotyped. The characteristics of 169 non-contact injuries during the 2022/2023 season were classified following the International Olympic Committee (IOC) Consensus Statement for reporting injuries as follows: musculoskeletal, tendon/ligament, injury setting; and severity. The disequilibria of polymorphisms were estimated using the Hardy-Weinberg Equilibrium (HWE). The characteristics of the injuries were recorded, and the genotype characteristics were analysed. The genotype frequencies of all polymorphisms were compared between non-injured and injured football players and injury aetiologies. _Results_: The _AMPD1_ genotype distribution differed between tendon/ligament injured and non-injured (\(p=0.003\)) with a higher frequency in the TT genotype and T allele. The genotype distribution was different for the _CKM_ and _MLCK_ c.37885C-A polymorphisms in training and match injuries (\(p=0.038\) and \(p=0.031\), respectively). In the _ACTN3_ and _AMPD1_ polymorphisms, the distribution of the TT genotype in both genes showed a higher frequency in severe injuries (all \(p<0.001\)). _Conclusions_: Tendon/ligament injury epidemiology in women's amateur football players was associated especially with the TT genotype of the _AMPD1_ gene. The TT genotype of the _AMPD1_ and _ACTN3_ genes was also associated with severity, and the _CKM_ and _MLCK_ polymorphisms were associated with injury settings.  
g 2024 1 2024 1 2024 1
# Improved Application of Hyperspectral Analysis to Rock Art Panels from El Castillo Cave (Spain)
## 2 Materials and Methods
### Data Acquisition and Pre-Processing  
#### 2.4.1 Hyperspectral Imaging System  
The spectral camera is the combination of the Specim V10E spectrograph with a monochrome sCMOS camera (CL-30). This generates a hyperspectral image that allows solving colorimetric applications in both scientific and industrial applications (Figure 2).  
Specim V10E is a spectrographic system that, combined with a monochrome camera, becomes a hyperspectral camera covering a spectral range of 400-1000 nm. It reads a line from the sample and decomposes the spectrum information of that line into a 2D image, composed of spectral information on the \(Y\)-axis and spatial information on the \(X\)-axis. 214 spectral bands were recorded and the spectral resolution was 5.6 nm.
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 5 Results
### Experimental Coverage Analysis
Concerning the experimental CDF of the RSSI measurements, Figure 13 shows that the urban environment had high values, which would be a benefit for our work, but it should take into account that if the RSSI decreases, the bidirectional interconnection is lost, and therefore, the system would be useless. The rural and natural environments were again similar, and the rural environment had a large percentage of its measurements very close to the RSSI limit, where the operability of the system would be lost. On the other hand, looking at the experimental CDF plot for the SNR, Figure 14 shows that in the rural case, half of our measurements were negative, as previously mentioned. As for the SNR, the natural environment showed adequate behavior, as did the urban case. Considering all the results, LoRa modulation configuration and the chosen scenarios, our solution offered a sufficient coverage range for the interconnection of emergency equipment even without cellular coverage. This statement was due to the fact that the signal strength decayed rapidly with this technology in the considered environments. Furthermore, in comparison with the theoretical results presented in [47], this configuration allowed coverage ranges up to 3 km for radio (i.e., about three times more than the range used in this study). Finally, it is known that the latency will depend on the slowest technology. In our case, this will be LoRaWAN because Bluetooth is intended to operate in real time, and Wi-Fi and 4G have latencies of milliseconds. The latency in LoRaWAN depends on the \(SF\) and can take a few seconds, as shown in [48]. Therefore, it was considered that the total latency in end-to-end forwarding should be a few seconds. This time is high compared with LANs, but it is an acceptable delay for messaging services in environments without coverage or in cases of emergency. Figure 12: Comparison of experimental SNRs as a function of distance.
## 2 Methods  
### Multi-Sensor Travelling Wave Based Localization Problem  
In the case of online partial discharge location, the multi-sensor travelling wave measurement method is employed to locate the PD source by comparing the difference of pulse arrival time between the different sensors installed alone the cable, and it can effectively solve the wave velocity uncertainty problem encountered by the traditional single/double-end travelling wave method. As shown in Figure 1, three sensors A, B, and C are installed on the cable body, where A and B are located at both ends of the measurement interval with the distance L, and C is located at the midpoint of the AB segment. As the length of the cable increases, the sensor number can be appropriately increased to avoid the problem that the PD pulse cannot be detected due to the excessive propagation of the signal along the cable.  
Assuming that the PD happens at time \(t_{0}\), and the PD pulse reaches the sensors A, B, and C at time \(t_{\rm A}\), \(t_{\rm B}\), and \(t_{\rm C}\), respectively, the distance between PD source and A can be deduced as:  
\[X_{\rm A}=\frac{((t_{\rm A}-t_{0})-(t_{\rm B}-t_{0}))v+L}{2}=\frac{(t_{\rm A} -t_{\rm B})v+L}{2} \tag{1}\]  
where \(v\) is the propagation speed of PD signal in the cable, which can be calculated by recording \(t_{\rm A}\), \(t_{\rm B}\), and \(t_{\rm C}\), the process is as follows:  
If \(\left|(t_{\rm A}-t_{0})-(t_{\rm C}-t_{0})\right|<\left|(t_{\rm B}-t_{0})-(t_{ \rm C}-t_{0})\right|\), the PD source is located between the AC segment, and the wave velocity \(v\) is:  
\[v=\frac{L}{2((t_{\rm B}-t_{0})-(t_{\rm C}-t_{0}))}=\frac{L}{2(t_{\rm B}-t_{\rm C })} \tag{2}\]  
If \(\left|(t_{\rm A}-t_{0})-(t_{\rm C}-t_{0})\right|>\left|(t_{\rm B}-t_{0})-(t_{ \rm C}-t_{0})\right|\), the PD source is located between the BC segment, and the wave velocity \(v\) is:  
\[v=\frac{L}{2((t_{\rm A}-t_{0})-(t_{\rm C}-t_{0}))}=\frac{L}{2(t_{\rm A}-t_{ \rm C})} \tag{3}\]  
Figure 1: Multi-sensor installation. Three sensors A, B, and C are installed on the cable body, where A and B are located at both ends of the measurement interval, and C is located at the midpoint of the AB segment. HFCT: high-frequency current sensor.  
From the above analysis, it can be found that the multi-sensor localization method does not need to measure the travelling wave velocity or the empirical wave velocity in advance, so it solves the problem of wave velocity uncertainty caused by factors such as cable structure and signal characteristics. However, the localization accuracy of this method depends on accurate estimation of arrival time of real PD pulse heavily. The effective and robust extraction of real PD pulse from input noisy signal with low signal to noise ratio (SNR) is the key of this method.
## 4 Experimental Results and Discussion
### Performance of Proposed Power Control Strategies
#### 4.1.2 P&O with Variable Step Sizes Based on Newton's Algorithm  
Figures 11 and 12 present the experimental results of P&O with three different variable step sizes, which are limited to 2, 4 and 6 V, in order to target LPP1 and LPP2 for a wind velocity of 6.5 m/s. The power generated (\(p_{WT}\)), for different varied step sizes, follows the user recommendation well. It oscillates at around 50 W for both LPP1 and LPP2, whereas the voltage (\(v_{WT}\)) varies close to 15 V and 60 V for LPP1 and LPP2, respectively. In addition, concerning the current, it varies from 3.5 A to 1.5 A for LPP1 and LPP2, respectively.  
As for the P&O with a fixed step size method, the performances differ according to the selected step size. A larger step size leads to a faster transient response with more power oscillations around the \(P_{\text{lim}}\) and, hence, less efficiency. Contrariwise, a smaller step size enhances efficiency, but reduces the convergence speed. These results mean that with the variable step size method, a compromise must be chosen between the state of dynamic transient response and the oscillation state. In addition, working in LPP2 point leads to more power oscillations around \(P_{\text{lim}}\) for all variable step sizes compared to LPP1 point.  
Figure 10: Fixed step-size P&O for LPP2: (**a**) evolution of \(\Delta v\); (**b**) evolution of \(p_{WT}\); (**c**) evolution of \(v_{WT}\); (**d**) evolution of \(i_{WT}\).  
Figure 11: [PERSON]’s variable step-size P&O for LPP1: (**a**) evolution of \(\Delta v\); (**b**) evolution of \(p_{WT}\); (**c**) evolution of \(v_{WT}\); (**d**) evolution of \(i_{WT}\).  
Figure 12: _Cont._