esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result Analysis  
### Comparison of Performance  
The performances of four ML models were examined in this experiment. Three nature-based algorithms--FPA, PSO, and GA--were employed for feature selection. Performance was measured in terms of accuracy. The accuracy score was determined by dividing the number of accurate forecasts by the total number of predictions.  
In this study, a fivefold cross-validation process was used for training and testing. The final accuracy was determined by averaging the accuracy results from each fold. Results of the experiment with and without feature selection are displayed in Table 4:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **No Feature Selection** & \multicolumn{3}{c}{**With Feature Selection**} \\ \hline  & & **FPA** & **PSO** & **GA** \\ \hline LR & 69.9\% & **71.6\%** & 57.3\% & 69.0\% \\ \hline DT & 82.50\% & **84.8\%** & 81.8\% & 75.1\% \\ \hline RF & 90.9\% & **92.8\%** & 90.6\% & 92.4\% \\ \hline GB & 91.0\% & **91.1\%** & 83.9\% & 81.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the performance (accuracy) of different ML Models with and without feature selection.  
Figure 4: RF classification.  
Table 4 shows that accuracy of the ML models is relatively high when features are selected with the **FPA**. Therefore, this feature selection process can help physicians to make decisions while considering fewer features. Table 5 shows the features selected by different nature-based algorithms.  
Because the FPA outperformed all others method in this study in terms of accuracy, the features chosen by this algorithm can be considered important. Common features selected by at least two of the nature-based algorithms are age, BMI, hypertensive, atrial fibrillation, depression, COPD, heart rate, hematocrit, Blood Sodium, Urea nitrogen, systolic blood pressure, MCH, MCHC, and lactic acid.
## 3 Related Work
Scalability: OLSR can become inefficient and slow down as the network size increases. This is because OLSR floods topology information throughout the network, which can lead to excessive overhead and congestion. 2. Route Stability: OLSR's routing stability can be affected by link fluctuations and topology changes. If a link fails or becomes unstable, OLSR may take some time to re-calculate the new route, leading to packet loss. 3. Quality of Service (QoS): OLSR does not support QoS requirements. It cannot differentiate between different types of traffic, leading to possible congestion and delay. 4. Network Partitioning: OLSR cannot handle network partitioning effectively. If a network splits into two parts, OLSR may not be able to route packets between the two parts. 5. Multi-path Routing: OLSR does not provide efficient support for multi-path routing. It may not always be able to find and utilize multiple paths to a destination, which can limit its ability to provide reliable communication. 6. Mobility: OLSR's performance can be affected by the mobility of nodes in the network. As nodes move, the network topology changes, which can lead to route instability and overhead.
## 3 Experiment and Results  
### Specimen  
Figure 8 shows the shape and location of damages on a specimen. A total of four water-cooled tubes (SA210C) with inner and outer diameters of 47.3 to 51.3 mm and 63.5 mm, respectively, one-sided (t11-t14) and double-sided artificial damages (t41-t44) simulated for wears were produced on Tube-1 and Tube-4, respectively. In Tube-2, slit-type artificial damages (t21-t28) with the same width of 7.0 mm, depth of 0.9 mm, and lengths from 20 to 100 mm were produced. In Tube-3, slit-type artificial damages (t31-t38) having the same width  
The tubes were welded with a 6.0 mm thick membrane. There six slit-type artificial damages (w11, w12, w13, w31, w31, w33) on the two Membrane \(-1\) and \(-2\). The damages have the same width of 7.0 mm, different lengths from 20 to 80 mm, and different depths from 0.3 to 2.4 mm, as shown in Table 3. On the four welding lines (Welds 1, 2, 3, and 4), there are ten slit-type artificial damages (w11-w42) with the same width of 7.0 mm, different lengths from 30 to 100 mm, and different depths from 0.6 to 3.0 mm, as shown in Table 4. Totally, there are 40 artificial damages produced on the tubes, membranes, and welding lines. The picture of the specimen with damages is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & **Width** & **Length** & **Depth** & **Position** & **\#** & **Width** & **Length** & **Depth** & **Position** \\  & **[mm]** & **[mm]** & **[mm]** & **(mm)** & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** \\ \hline \hline \multicolumn{10}{c}{**Tube-1 (One-Side & **Taper-Type & **Wear**)**} \\ \hline t11 & 16 & 20 & 0.9 & 200 & t41 & 16 & 20 & 0.9 & 200 \\ \hline t12 & 20 & 40 & 1.5 & 400 & t42 & 20 & 40 & 1.5 & 400 \\ \hline t13 & 25 & 60 & 2.5 & 600 & t43 & 25 & 60 & 2.5 & 600 \\ \hline t14 & 28 & 80 & 3.1 & 800 & t44 & 28 & 80 & 3.1 & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of artificial taper-type wear on the Tube-1 and Tube-4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & **Width** & **Length** & **Depth** & **Position** & **\#** & **Width** & **Length** & **Depth** & **Position** \\  & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** & **[mm]** \\ \hline \hline \multicolumn{10}{c}{**Tube-2 (Same Width)**} \\ \hline t21 & 7 & 20 & 0.9 & 130 & t31 & 7 & 60 & 0.23 & 130 \\ \hline t22 & 7 & 30 & 0.9 & 230 & t32 & 7 & 60 & 0.50 & 245 \\ \hline t23 & 7 & 40 & 0.9 & 330 & t33 & 7 & 60 & 0.96 & 345 \\ \hline t24 & 7 & 50 & 0.9 & 430 & t34 &
# Preliminary Biomechanical Evaluation of a Novel Exoskeleton Robotic System to Assist Stair Climbing
## 3 Results
### sEMG of Knee Extensor Muscles  
The curves of the muscle activity of the knee extensor muscles (VAM and VAL) have a high degree of similarity in the contraction pattern as shown in Figure 10 for the tests with the eBUS (blue) and without the eBUS (red), which varies individually. The muscle activity varies greatly in individual cases with the eBUS, especially in Subject 1.  
The measured muscle activities of VAM and VAL are characterized by two peaks in Subjects 2 and 3 during both movements without using the eBUS. In Subject 1, only one peak occurs at the beginning of the stance phase. When using the eBUS, the curve of both muscle activities during ascent is characterized by only one peak at the beginning of the stance phase. During descent, this peak occurs in the phase of controlled lowering with the use of the eBUS. Quantitatively, for all the subjects the maximum of the measured muscle activity during the trials with the eBUS is up to 50% lower than the maximum during the trials without. It should also be noted that for Subject 2 during descent in the phase of controlled lowering, the maximum values of both muscle activities with the eBUS are higher than the maximum values of the activities without the eBUS. The integrals of each sEMG curve are shown in Table 4 to highlight differences in the net effort of both muscles by using the eBUS. It becomes obvious that the values of all the subjects are lower when using the system, both for ascent and descent.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Movement**} & \multicolumn{2}{c}{**Subject 1**} & \multicolumn{2}{c}{**Subject 2**} & \multicolumn{2}{c}{**Subject 3**} \\ \cline{2-7}  & **With** & **Without** & **With** & **Without** & **With** & **Without** \\  & **eBUS** & **eBUS** & **eBUS** & **eBUS** & **eBUS** & **eBUS** \\ \hline \hline \multicolumn{7}{c}{m. vastus medialis (VAM)} \\ \hline Ascent & 35.40 & 53.70 & 15.76 & 41.04 & 18.05 & 52.40 \\ \hline Descent & 27.52 & 46.99 & 33.91 & 42.22 & 19.38 & 52.32 \\ \hline \multicolumn{7}{c}{m. vastus lateralis (VAL)} \\ \hline Ascent & 20.08 & 39.07 & 17.77 & 31.69 & 17.54 & 31.12 \\ \hline Descent & 26.44 & 58.72 & 33.55 & 35.89 & 26.80 & 38.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Integrals of the normalized median sEMG curves of m. vastus medialis (VAM) and m. vastus lateralis (VAL) in time normalized stance phase with and without the exoskeleton per subject (S1, S2, S3) and movement (ascent, descent).  
Figure 9: Knee joint reaction force (proximal-distal) for ascent and descent without (red) and with (blue) the exoskeleton for Subject 1 (**left**), Subject 2 (**middle**), and Subject 3 (**right**); DSP = double stance phase, SSP = single stance phase, SP = swing phase.
# Study on Vibration Effects and Optimal Delay Time for Tunnel Cut-Blasting Beneath Existing Railways
###### Abstract  
With the development of underground space in urban areas, the demand for tunneling through existing railways is increasing. The adverse effects of cut-blasting during the construction of tunnels under crossing existing railways are investigated. Combined with the principle of blasting seismic wave superposition, LS-DYNA numerical simulation is used to analyze the seismic wave superposition law under different superposition methods. This study also investigates the vibration reduction effect of millisecond blasting for cut-blasting under the different classes of surrounding rocks. The results show that the vibration reduction forms of millisecond blasting can be divided into separation and interference of waveform. Based on the principle of superposition of blasting seismic waves, vibration reduction through wave interference is further divided. At the same time, a new vibration reduction mode is proposed. This vibration reduction mode can significantly improve construction efficiency while improving damping efficiency. The new vibration reduction mode can increase the vibration reduction to 80% while improving construction efficiency. Additionally, there is a significant difference in the damping effect of different classes of surrounding rock on the blasting seismic wave. Poor-quality surrounding rock enhances the attenuation of seismic wave velocity and peak stress in the surrounding rock. In the Zhongliangshan Tunnel, a tunnel cut-blasting construction at a depth of 42 m, the best vibration reduction plan of Class III is 3 ms millisecond blasting, in which the surface points achieve separation vibration reduction. The best vibration reduction plan of Class V is 1 ms millisecond blasting, in which the surface points achieve a new vibration reduction mode. During the tunnel blasting construction process, electronic detonators are used for millisecond blasting of the cut-blasting. This method can reduce the vibration effects generated by blasting. The stability of the existing railway is ultimately guaranteed. This can improve construction efficiency while ensuring construction safety. This study can provide significant guidance for the blasting construction of the tunnel through the railway.  
t  
t
# Low-Complexity QRD-\(M\) with Path Eliminations in MIMO-OFDM Systems
## 5 Simulation Results
In Figure 9, the average number of metric operations for the proposed QRD-\(M\) is lower than the modified tree structure QRD-\(M\) in [11] at all SNRs because the modified tree structure QRD-\(M\) uses a fixed value of threshold and an accuracy is low when the number of transmit antennas is large. In addition, the modified tree structure QRD-\(M\) eliminates unnecessary paths only from the top layer to the \(\lceil N_{t}/O\rceil\)-th layer, and the conventional QRD-\(M\) is applied under the \(\lceil N_{t}/O\rceil\)-th layer. In Figures 10 and 11, the number of complex multiplications for the conventional and the proposed QRD-\(M\) using 16-QAM and 64-QAM modulation is shown with respect to the number of transmit antennas, respectively. For comparison, the number of complex multiplications for the E-DFE, which has poor error performance compared to proposed QRD-\(M\) in [16], is also shown. In this simulation, it is assumed that the multiplication of two complex numbers requires four real multiplications. To show the complexity improvements with respect to SNR, the number of complex multiplications for the proposed QRD-\(M\) is shown with SNR values of 10, 20, and 30. The complex multiplications for the proposed QRD-\(M\) (10 dB) are slightly lower than the conventional QRD-\(M\). However, complex multiplications for the proposed QRD-\(M\) (20 dB) are much lower than the conventional QRD-\(M\) because many unnecessary paths are eliminated due to relatively small noise power. Finally, complex multiplications for the proposed QRD-  
Figure 11: The number of complex multiplications for conventional and proposed QRD-\(M\) using 64-QAM modulation. Figure 10: The number of complex multiplications for conventional and proposed QRD-\(M\) using 16-QAM modulation. For further comparisons, Table 3 represents the comparisons for the complexity ratio using 16-QAM modulation in \(4\times 4\) system. In Table 3, a simplified QRD-\(M\)[6], combined QRD-\(M\) and Kalman filter [7], a pseudo-inverse-based QRD-\(M\)[8], and an advanced QRD-\(M\)[9] are compared with the conventional QRD-\(M\) and the proposed QRD-\(M\). The error performances for the conventional QRD-\(M\), existing algorithms, and the proposed QRD-\(M\) are almost the same. Therefore, only the complexity is compared. The main advantages for the proposed QRD-\(M\) compared to existing QRD-\(M\) algorithms are summarized with two factors. First, the number of complex multiplications for the proposed QRD-\(M\) is much lower than existing QRD-\(M\) algorithms. For example, the complexity for the pseudo-inverse-based QRD-\(M\) is about 1.26 times and 1.34 times higher than the proposed QRD-\(M\) at 10 dB and 20 dB, respectively. Second, the complexity for the proposed QRD-\(M\) is variable with respect to the SNR, while existing QRD-\(M\) algorithms have fixed or almost unchanging complexity with respect to the SNR.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 3 Problem Solution
### Digital and Analog Precoder Design [21]
#### 3.2.2 Digital Precoder Design
For a fully connected structure, the number of phase shifters is equal to the product of the number of transmitting antennas and the number of RF chains, which is \(N_{t}N_{\text{RF}}\). For all-digital precoders, the energy efficiency can be written as  
\[EE_{d}=\frac{R}{P_{c}+N_{t}P_{\text{RFC}}+P_{0}}, \tag{35}\]  
Figure 4: Relationship between the number of RF chains and the maximum achievable rate. Figure 3: Relationship between the transmit power and the maximum achievable rate. where \(P_{c}\) represents the transmit power of the base station, \(P_{\text{RFC}}\) represents the power consumption for the RF chains and amplifier, and \(P_{0}\) represents the power consumption for other hardware. Figure 5 compares the energy efficiency of the all-digital precoder with the fully connected hybrid precoder and the subarray-connected hybrid precoder, where \(P_{\text{RFC}}=100\) mW, \(P_{\text{PS}}~{}=~{}10\) mW, and \(P_{0}=200\) mW [24,25]. As the number of RF chains increased, the energy efficiency of the hybrid precoder decreased, while the energy efficiency of the all digital precoder remained unchanged. This is because the number of RF links in the all-digital precoder is constant, and the average rate tended to stabilize even with other parameters unchanged, ensuring that the energy efficiency of the all-digital precoder remained unchanged. For the subarray-connected structure, due to the single user, the increase in the RF chains had little significant impact on the achievable rate, while the total power consumption of the system gradually increased, resulting in a downward trend in energy consumption. For fully connected structures, the increase in the achievable rate was far less than the increase in the energy consumption, resulting in a decreasing energy efficiency. Comparing the fully connected structure with the subarray-connected structure, the energy efficiency of the subarray-connected structures was significantly higher than that of the fully digital structures, indicating that using subarray-connected structures is a relatively better choice in terms of energy efficiency.
## 2 Materials and Methods
### Detectors  
The PTW microSilicon is a new unshielded p-type silicon diode detector, recommended for measurements in small electron and photon beams due to its very small sizes (radius = 0.75 mm, thickness = 0.18 \(\upmu\)m). The disk-shaped sensitive volume is 0.03 mm\({}^{3}\), and the reference point position is on the chamber axis, 0.9 mm from its tip, which also corresponds to the effective point of measurement for photons. The effective point of measurement for electron beams is located at 0.3 mm from the chamber tip. No bias voltage needs to be applied.  
The PTW microDiamond is a synthetic single crystal diamond Schottky diode, with a disk-shaped sensitive volume of 2.2 mm in diameter and about 1 \(\upmu\)m thick. The sensitive volume is located below the detector surface at a water equivalent depth of 1 mm. The device operates with no external bias voltage applied. Pre-irradiation with a dose equal to 5 Gy was performed.  
The PTW diode E is a previous generation silicon diode detector, characterized by a circular sensitive volume of a 1 mm\({}^{2}\) circular and 30 \(\upmu\)m thickness. The effective point of measurement is located 0.8 mm below the detector front surface, and the water-equivalent window is 1.33 mm thick. No bias voltage was applied.  
The depth ionization distributions, measured by the PTW Advanced Markus, were converted to depth dose distributions by multiplying the ionization charge at each measurement depth by the stopping-power ratio swair at that depth, according to the IAEA TRS-398 protocol [14]. The variation of ion recombination and polarity effects with depth was also considered.
## 5 Adaptive Scheduling Mechanism Based on Self-Similarity
### The LMMSE Predictor  
It is supposed that \(f(i)\) is the \(i\)th inter-contact interval of the past visited \(m\times n\) inter-contact intervals. There are \(n\) continuous measurement periods, each measurement period has the length of time \(\tau\), and \(m\) is the number of samplings in each measurement period. The predictor keeps track of the aggregate series samples, measured in the past \(n\) measurement period. It is supposed that \(f^{m}(k),1\leq k\leq n\), is the aggregate series sample of inter-contact interval samples, which is taken in the \((n+1-k)\)th most recent measurement period, as is given in Equation (1).  
\[f^{m}(k)=\frac{1}{m}\sum_{i=(k-1)m+1}^{km}f(i) \tag{1}\]  
Based on the past \(m\times n\) contact intervals, we predict the aggregate series sample in the next interval as a weighted sum of the past \(n\) average samples, which is denoted as \(f^{m}(n+1)\) shown in Equation (2).  
\[\hat{f}^{m}(n+1)=\left[\begin{array}{cccc}a_{1}&a_{2}&\cdots&a_{n}\\ \end{array}\right]\left[\begin{array}{c}f^{m}(1)\\ f^{m}(2)\\ \cdots\\ f^{m}(n)\\ \end{array}\right] \tag{2}\]  
where \(a_{1}\)\(a_{2}\)\(\cdots\)\(a_{n}\) are the _LMMSE_ coefficients and can be expressed as  
\[\begin{array}{cccc}[&a_{1}&a_{2}&\cdots&a_{n}\\ \end{array}]=\left[\begin{array}{cccc}R(n-1)&R(n-2)&\cdots R(0)\\ \end{array}\right]\\ \times\left[\begin{array}{cccc}R(0)&R(1)&\cdots&R(n-1)\\ R(1)&R(0)&\cdots&R(n-2)\\ \cdots&\cdots&\cdots&\cdots\\ R(n-1)&R(n-2)&\cdots&R(0)\\ \end{array}\right]^{-1}\end{array} \tag{3}\]  
where \(R(n)\) is the covariance function of the time series, and can be estimated (due to the property of asymptotically second-order self-similarity) in practice as  
\[R(i)=\frac{1}{n}\sum_{t=i+1}^{n}f^{m}(t)f^{m}(t-i),0\leq i\leq n-1 \tag{4}\]  
where \(n\) is the number of aggregate series samples kept. Both \(m\) and \(n\) are tunable parameters. Note that the Hurst parameter H that characterizes the LRD property has been implicitly calculated in the covariance function R(i). Then each node can predict the future inter-contact interval of the node pair with the \(m\times n\) inter-contact intervals recorded in the node buffer at the beginning time of each period. More information about the _LMMSE_ predictor can be found in [18].
# Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert
## 3 Results and Discussion
### Microbiota Contained in the Soiling
The taxonomic composition results revealed that at the UA, the phyla represented were Proteobacteria, Bacteroidetes, Deinococcus-Thermus, and _Actinobacteria_ (Figure 6A). Figure 5: The phylogenetic tree constructed from the 16S rRNA gene sequences of the 34 bacterial isolates obtained from soiling samples collected from the surfaces of PV modules at the UA and PSDA, which resulted in 7 bacterial species. The species sampled from the UA are highlighted in black, while those from the PSDA are highlighted in red. The genus _Arthobacter_, which included species from both the UA and PSDA, is represented with a dark green bar, the genus _Kocuria_ with a blue bar, and the genus _Dietzia_ with a light green bar.3.2. Taxonomic Analysis. Three orders, _Cytophagles_, _Burkholderiales_, and _Micrococcales_, stood out for their abundances (Figure 6B). The dominant genera, in decreasing order of abundance, were primarily _Pontibacter_, _Noviherbspirillum_, _Massilia_, _Arthrobacter_, _Hymenobacter_, and _Deinococcus_ (Figure 6C). According to [PERSON] et al. [9], the abundance and diversity of microorganisms on solar panels are explained by wind-driven deposition or the presence of an active ecological community in the area. These factors may explain the high diversity of bacteria found at the UA in the frame of the harsh conditions of soiling in desert environments (Figure 6). The bacterial taxa identified through massive 16S rRNA gene sequencing at the UA indicated the presence of a diverse microbial community, which was consistent with the bacterial populations described by [10; 40; 41; 46] for microbial dirt samples in environments other than the Atacama Desert. Two of the six most representative genera identified in our study were shared with other genera described in research conducted in different regions worldwide [8; 9; 10; 40] (Table 1). The detection of the genera _Deinococcus_ and _Hymenobacter_ in our results aligned with their presence in soiling samples deposited on the surfaces of solar panels in other settings [40]. These genera were isolated from warm, high-solar-radiation environments, such as arid soil crusts or hyperarid desert soils. The presence of _Deinococcus_ and _Hymenobacter_ in the soiling across all studies conducted on PV modules (Table 1) suggests that environmental factors, such as solar radiation, dryness, temperature, or nutrient availability, create a local arid environment on PV modules that promotes the presence of both genera [46]. Furthermore, _Deinococcus_ and _Hymenobacter_ were proposed as biomarkers for airborne bacteria in desert environments [47; 48], which is consistent with their presence in our results. Figure 6: Taxonomic diversity of soiling samples deposited on the surfaces of PV modules (UA) analyzed by 16S rRNA gene sequencing (Nanopore platform) and visualized using histograms of relative abundance: (**A**) phylum level, (**B**) order, and (**C**) genus.