esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Potential in Saudi Arabia for Flat-Plate Surfaces of Varying Tilt Tracking the Sun
## 3 Results
### Evaluation of the PV-GIS Tool  
There have been various validation studies for the solar radiation PV-GIS satellite-derived estimates in the literature (e.g., [24; 25; 26]). The reported differences between the estimated values and those derived by PV-GIS were found to vary between \(-14\%\) and \(+11\%\). Furthermore, [PERSON] et al. [1; 2] demonstrated such a comparison by taking monthly mean H\({}_{\rm g}\) values measured at the Actinometric Station of the National Observatory of Athens (ASNOA, 37.9\({}^{\circ}\) N, 23.72\({}^{\circ}\) E, 107 m above sea level) and corresponding values from the PV-GIS platform in the period 2005-2011. Figure 10 presents this comparison, which shows an excellent agreement (R\({}^{2}\) = 0.99). Although the PV-GIS-estimated values seemed to overestimate the measured H\({}_{\rm g}\) ones by \(+10\%\), this figure is within the acceptable range (\(-14\%\), \(+11\%\)). For this reason, the PV-GIS data were accepted for use in the present study.  
Figure 8: Seasonal variation of H\({}_{\rm g,t/\rho g}\) in Saudi Arabia. The black line represents the seasonal mean. The red line refers to the mean + 1\(\sigma\) curve, and the blue one to the mean \(-1\sigma\) curve, under all-sky conditions. All H\({}_{\rm g,t/\rho g}\) values were averaged over all 82 sites and over each season in the period 2005–2016. The green dotted line refers to the best-fit curve to the mean one. The numbers 1–4 in the \(x\)-axis refer to the seasons in the sequence spring to winter.  
Figure 9: Distribution of the annual \(\rm H_{g/\rho g}\) (kWhm\({}^{-2}\)year\({}^{-1}\)) sums across Saudi Arabia, averaged over the period 2005–2016, under all-sky conditions.
## 3 Front-End Challenges
### Pricing  
Since customers expect--beside increased productivity and quality--a reduction of operating costs, it is crucial for the supplier to offer appealing prices. Nevertheless, manufacturer would like to gain financial revenues from this serving activity as well, which is considered as one of the most critical challenges in servitization [19; 51; 87; 93]. However, according to [25; 94] most of services related to products are offered ultimately for free. The ratio of performance-based offerings is low (see Figure 5) [25].  
Offering selected basic services for free may be an appropriate means of acquisition (see [95]), but in general services should be subject of a well-considered pricing strategy on competitive markets,otherwise companies waive considerable revenue streams [25; 30; 96] and inherently suggest lower value of their offering [13; 97; 98].  
In fact, customers appreciate the value of services more if they are with reduced costs. Since the offer of servitization is based on ownerless consumption, the effective pricing is ultimately a very delicate issue for companies. In order to deal with that, one has to consider by all means the whole product/service life cycle, to achieve reliable revenues [51; 93; 99]. The authors of [93] identified the same behavior regarding customer perception, which is illustrated in Figure 6[93].  
Customers consider their own maintenance costs as well in order to reduce costs by saving on extensive maintenance obligations or intensive personnel training [80; 100; 101]. Figure 6 shows a typical life cycle of a product consisting of efforts generated before the purchase and therefore have to be taken over by the supplier, and costs occurring after the purchase of this product, which the customer has to bear. In fact, customer strive to reduce their after-sales cost, referring to installation, maintenance or dispose to a minimum. Accordingly, the challenge of suppliers is to provide a service offering that comprises the after-sales services, but still appears cheaper for the customer, than conducting the activity on its own. Customers will therefore profit from the service gain and likewise tied-in to the  
Figure 5: Revenue Modes of Service Provision amongst UK Manufacturers.  
Figure 6: Customer Benefit through Service Gain.  
supplier [93; 102; 103]. As a consequence, customers do not have to bear any fixed costs related to the ownership of machinery, and therefore profit from reduced risks since fixed costs turned into provisional variable costs [80; 104; 105]. Referring to this, Section 3.3 will elaborate on the shifts of risks and its implications, both for customers and suppliers.
## 3 Results
### WRF Simulation of Wind Speeds in the East China Sea
#### 3.1.3 Horizontal Distribution of Wind Speeds at Each Height in the Yellow Sea Simulated by WRF  
The horizontal wind field distribution at heights of 50 m, 70 m, 90 m, 150 m, 220 m, and 300 m was obtained through vertical linear interpolation (Figure 5). It was evident that the horizontal wind direction remained nearly constant across various heights. The northern part of the Yellow Sea was primarily characterized by a northerly wind, while the southern part experienced prevailing winds from the northeast. Moreover, the wind speeds over the ocean were generally higher than those over land. Along the coastal region of the Yellow Sea in China, the land wind speeds exceeded those over the Korean Peninsula. In the marine area between the Shandong Peninsula and the Korean Peninsula, where the mountainous terrain obstructs the wind flow, the wind speeds were relatively lower, ranging from 6 to 8 m/s. However, in the marine area between the southwest of Jeju Island and the Yangtze River Delta, where there is no land obstruction from the northeast due to the presence of the northwest Pacific Ocean, the wind speeds were significantly higher than in the northern part of the Yellow Sea, reaching above 9 m/s. Additionally, the wind speeds over the ocean increased with increasing height, while the wind speeds over land exhibited a more pronounced increase with height, particularly when compared to the ocean.  
Figure 4: Time-series distribution of wind speeds at each height over the Yellow Sea.
# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
###### Abstract  
Monitoring the training load in football is an important strategy to improve athletic performance and an effective training periodization. The aim of this study was two-fold: (1) to quantify the weekly training load and recovery status variations performed by under-15, under-17 and under-19 sub-elite young football players; and (2) to analyze the influence of age, training day, weekly microcycle, training and playing position on the training load and recovery status. Twenty under-15, twenty under-17 and twenty under-19 players were monitored over a 2-week period during the first month of the 2019-2020 competitive season. Global positioning system technology (GPS) was used to collect external training loads: total distance covered, average speed, maximal running speed, relative high-speed running distance, high metabolic load distance, sprinting distance, dynamic stress load, accelerations and decelerations. Internal training load was monitored using ratings of perceived exertion (RPE) and session rating of perceived exertion (sRPE). Recovery status was obtained using the total quality recovery (TQR) scale. The results show an age-related influence for external training load (\(p\leq 0.001\); \(d=0.29\)-0.86; moderate to strong effect), internal training load (\(p\leq 0.001\), \(d=0.12\)-0.69; minimum to strong effect) and recovery status (\(p\leq 0.001\), \(d=0.59\); strong effect). The external training load presented differences between training days (\(p<0.05\), \(d=0.26\)-0.95; moderate to strong effect). The playing position had a minimum effect on the weekly training load (\(p<0.05\); \(d=0.06\)-0.18). The weekly microcycle had a moderate effect in the TD (\(p<0.05\), \(d=0.39\)), RPE (\(p<0.05\); \(d=0.35\)) and sRPE (\(p<0.05\), \(d=0.35\)). Interaction effects were found between the four factors analyzed for deceleration (\(F=2.819\), \(p=0.017\)) and between inter-day, inter-week and age for total covered distance (\(F=8.342\), \(p=0.008\)). This study provided specific insights about sub-elite youth football training load and recovery status to monitor training environments and load variations. Future research should include a longer monitoring period to assess training load and recovery variations across different season phases.  
monitoring; workload; perceived exertion; soccer; periodization +
Footnote †: journal: _Sci._**2021**, _11_, 4871. [[https://doi.org/10.3390/app1114871](https://doi.org/10.3390/app1114871)]([https://doi.org/10.3390/app1114871](https://doi.org/10.3390/app1114871))
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 3 System Diagram
### Non-Line-of-Sight (NLoS) Link  
Multipath channels cause stochastic and time-varying signal distortion in Radio Frequency (RF) communications, causing microwave channels to be modelled as random. The multipath channel in the VLC, on the other hand, is deterministic because \(A_{\text{R}}\gg\lambda^{2}\). In other words, the PD captures the optical signal over an area that is millions of times larger than a square wavelength. The indoor VLC channel is time-invariant as long as the objects in the room are fixed. Nonetheless, multipath propagation can cause intersymbol interference in VLC systems at high data rates, according to [PERSON] [71]. To obtain \(h_{\text{NLoS}}(t)\), it is easiest to begin with a single reflector. This reflector serves as a virtual light source (VLS). Because most reflections are diffuse, the angle of irradiance \(\theta_{2}\) is not always the same as the angle of incidence \(\phi_{1}\). Furthermore, Lambertian reflections are commonly used. By using \(r_{1}\) to represent the distance between LS and VLS and \(r_{2}\) to represent the distance between VLS and PD, \(G_{\text{conc}}G_{\text{filter}}\;=1\), and Equation (3) can be extended to [71]:  
\[P_{\text{R,opt}}=P_{\text{T,opt}}f(\theta_{1},\theta_{1/2})\frac{A_{\text{ref {eff}}}}{\pi r_{1}^{2}}\cdot\rho(\lambda)f(\theta_{2},60^{\circ})\frac{A_{ \text{R,eff}}}{\pi r_{2}^{2}} \tag{3}\]
## 4 Prediction of Side Wall Rock Mass Mechanical Parameters
### Estimation of Rock Mass Parameters
#### 4.2.6 The Relationship between Wave Velocity and Rock Density  
Previous studies have shown that there is a polynomial relationship between rock density and rock compressional wave velocity \(V_{\rm pr}\). By using Equation (1) to calculate the rock compressional wave velocity \(V_{\rm pr}\) and substituting it into the following expression, the density of the rock can be estimated. The expression is [24]:  
\[\rho=mV_{\rm pr}{}^{2}+nV_{\rm pr}+l \tag{15}\]  
In the equation: \(\rho\) represents the estimated rock density in g/cm\({}^{3}\); \(V_{\rm pr}\) represents the rock compressional wave velocity in km/s; \(m\), \(n\), and \(l\) are all constants that can be obtained from Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lithology** & \(m\) & \(n\) & \(l\) \\ \hline Shale & \(-0.0261\) & \(0.373\) & \(1.458\) \\ Limestone & \(-0.0296\) & \(0.461\) & \(0.963\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Partial polynomial constants.  
4.2.7. The Relationship between [PERSON]'s Modulus and Poisson's Ratio  
According to extensive field tests and engineering experience, there is a certain relationship between [PERSON]'s modulus and [PERSON]'s ratio [25]. The expression is as follows:  
\[\mu=0.4-0.05\lg\frac{E_{\text{m}}}{2} \tag{16}\]  
where \(\mu\) represents the Poisson's ratio of the rock mass, and \(E_{m}\) represents the deformation modulus of the rock mass in MPa.  
Due to the complex geological conditions in the vicinity of the ventilation tunnel, there is a significant variation in wave velocity between adjacent blast holes. Therefore, in this study, the areas with higher and lower wave velocities are divided into different calculation segments. The average wave velocity within each calculation segment is used for the calculations. The results of the segmented calculations are shown in Table 4.
## 3 Methodology
### Distance Estimation Using Size Information  
[PERSON] et al. [19] utilized the size information (the height or width) of a car for estimating the distance using perspective. Since the width of an unknown vehicle could vary differently, the accuracy of estimation using size information was only about 30% in their research. The estimation from the size information could only be used as a sanity check. The technique of object detection is implemented so NFD can identify the class of the object in the image. Because the size of the public object in each class is standard, distance estimation using size information is now feasible. The solution prefers to use the height of the target object instead of the width since the projection width of the object in the image varies along with the oblique angle to the camera axis. Meanwhile, the height is nearly unchanged assuming that the lens distortion is negligible. Let \(h\) be the height of the target object in the image in pixel and \(H\) is the height of the target object in real-world coordinates. When the target object is at distance \(D\), \(D\) can be estimated by the Equation (11).  
\[D=\ \frac{fH}{h} \tag{11}\]  
where \(f\) is the focal length of the camera (pixel/meter) and depends on the camera's hardware.
## 2 Theoretical Analysis  
### Problem Definition  
The problem addressed in this study is schematically described in Figure 1. The slightly inclined pile is embedded into a slope-ground multilayered soil deposit. It is divided into three characteristic segments by the slope surface and potential sliding surface. The pile length above the slope surface is \(L_{f}\), above and beneath the potential sliding surface is \(L_{p}\) and \(L_{a}\), respectively. The segment of the pile is divided into free, passive and active segments by the similarities and differences between the lateral displacement of pile and soil and the loaded main body. Taking into account the difference in the subjected horizontal loads over those segments, corresponding local coordinate systems were developed by treating the vertexes of those characteristic segments as the origins, respectively.\(z_{f}\), \(z_{p}\) and \(z_{a}\) denote the distances between certain points to the vertexes for the free, passive and the active segment, respectively; \(y_{f}\), \(y_{p}\) and \(y_{a}\) are the horizontal deformation of pile shaft for the free, passive and the active segment, respectively; \(\varphi_{IN}\) is the initial inclination of the pile shaft. This pile is assumed to have a horizontal force \(Q_{t}\), a moment \(M_{t}\), and an axial load \(N_{t}\) acting on the pile head. The distributed load \(q_{f}\big{(}z_{f}\big{)}\) acting on the pile above the slope surface is also considered to simulate the extra external loadings, such as wind and wave. The distributed load \(q_{p}\big{(}z_{p}\big{)}\) acting on the pile above the potential sliding surface is also considered to simulate the extra external loadings, such as soil lateral pressure caused by the landslide.  
In this study, the interaction between soil and pile is simulated as a linear spring in series with a slider distributed along the pile shaft. The sign conventions for shear force, moment, horizontal deformation, and rotation angle of pile shaft follows: the positive directions for the horizontal deformation and the rotation angle are assumed as the right direction; for that of the bending moment, it is presumed that the left side of the pile shaft is subjected to tension; and for the shear force, the clockwise direction is assumed as the positive direction.  
Figure 1: Simplified model for the slightly inclined pile in sloping ground.
## 4 Numerical Example
The maintenance plan for the machining center subsystems within a limited amount of time is shown in Table 8 and Figure 7. The numbers in the table indicate the maintenance method: 0 indicates no maintenance activities, 1 indicates preventive maintenance activities, 2 indicates preventive replacement activities, 3 indicates opportunistic maintenance activities, and 4 indicates opportunistic replacement activities. A total of 16 shutdowns are carried out; meanwhile, the total preventive downtime is 49 h.  
When the machining center adopts a strategy of not considering opportunistic maintenance, the cost incurred in a finite time domain is 108,257. A total of 26 preventive shutdown activities are conducted, including 21 preventive maintenance and 5 preventive replacements. The total preventive downtime is 69 h. Adopting opportunistic maintenance can reduce downtime by 10 times, reduce costs by 7%, and reduce preventive downtime by 29% in this example. The comparison between the two policies is shown in Table 9. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time/h** & **H** & **A** & **F** & **NC** & **T** & **L** \\ \hline
851 & 0 & 0 & 1 & 0 & 0 & 0 \\
1417 & 3 & 0 & 0 & 3 & 0 & 1 \\
1579 & 0 & 0 & 1 & 0 & 0 & 0 \\
2113 & 0 & 0 & 3 & 0 & 1 & 0 \\
2621 & 0 & 0 & 2 & 3 & 0 & 3 \\
2655 & 1 & 0 & 0 & 0 & 0 & 0 \\
3479 & 0 & 0 & 1 & 0 & 0 & 3 \\
3647 & 0 & 0 & 0 & 1 & 0 & 0 \\
3706 & 1 & 0 & 0 & 0 & 0 & 0 \\
3909 & 0 & 0 & 0 & 0 & 1 & 0 \\
4208 & 0 & 0 & 1 & 0 & 0 & 4 \\
4497 & 0 & 3 & 0 & 1 & 0 & 0 \\
4605 & 2 & 0 & 0 & 0 & 0 & 0 \\
4820 & 0 & 0 & 1 & 0 & 0 & 0 \\
5207 & 0 & 0 & 4 & 2 & 0 & 0 \\
5428 & 0 & 0 & 0 & 0 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Machining center maintenance schedule. Figure 7: Maintenance schedule. The opportunity maintenance strategy described in this article can generate a clear maintenance schedule and determine which maintenance actions to take at specific time points, including preventive maintenance, preventive replacement, opportunistic maintenance, and opportunistic replacement. Due to the opportunistic maintenance strategy utilizing downtime to repair other subsystems that are about to require preventive maintenance, it can significantly reduce the number of preventive shutdowns, decrease downtime, and achieve the goal of reducing costs and improving system reliability.
## 3 The Ionospheric Scintillation Compensation-Based Modified BP Imaging Method  
The configuration of separate transmitter and receiver increases the flexibility and application, enabling the GNSS-BSAR system to provide richer spatial and geometric information in a variety of environments. However, the unique configuration also causes the target echo to have different characteristics from traditional monostatic SAR. The Back Projection (BP) algorithm can accurately process echo across different time and spatial locations and is suitable for bistatic SAR systems. This section first introduces the application of the traditional BP algorithm in the dual-channel receiving GNSS-BSAR system and analyzes the limitations of the current process. Then, the ionospheric scintillation phase error extraction technology based on reference channel processing is introduced, and error separation is performed through polynomial fitting. Finally, the modified BP algorithm is proposed, which divides the azimuth compensation phase of the imaging channel into difference phase and reference phase (including ionospheric scintillation error and other Doppler phase errors). This approach facilitates high-precision azimuth focusing in the GNSS-BSAR system through two-step azimuth phase compensation process.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 3 Methodology  
In this section, we propose a deep learning network structure--named the dual-encoder-condensed convolution (DECC) method--to improve positioning accuracy. An overview of the DECC network is illustrated in Figure 2. The proposed structure is composed of three modules: the Conv-for-Origin module, the Dual-Encoder module, and the Conv-for-Sum module. The Conv-for-Origin module is responsible for resizing the original CSI matrix and embedding the position information. The Dual-Encoder module is responsible for feature interaction of the CSI matrix, and enriches the features through the Conv-for-Origin module. The Conv-for-Sum module works for the convergence of feature information processed by different convolution kernels.
# Pruning Adapters with Lottery Ticket
## 3 Results
### Comparisons of Pruning Strategies  
We insert adapters of size 128 into each layer of transformers in the BERT-Based model. Depending on the text classification task, we put a task-specific classifier at the end of the model. Only the parameters in the adapters and task-specific classifier are fine-tuned, and the rest of the parameters in the model are untouched.  
We iteratively prune the parameters in the adapter by 20% per iteration. We perform 15 iterations for weight pruning and neuron pruning and 11 iterations for layer pruning, since there will be less than one adapter left after the 11 th iteration.  
The results of the GLUE test sets are presented in Table 1. For each pruning strategy, we select the best result in all iterations of pruning. The best model (the winning ticket found) is chosen by the metric of the corresponding task and evaluated using the GLUE testing server. From the test results, we can see a 0.2 percentage performance gap between the original adapter model and the fully fine-tuned one. Our pruning schemes help us spot the pruned adapters that outperforms the original ones, implying that the winning ticket has been found successfully. Particularly, models after pruning even outperform the fine-tuned ones in datasets CoLA, QQP, QNLI, MNLI\({}_{\text{mm}}\), and RTE, and particularly CoLA, which has been improved by more than 1%.  
In general, models pruned with weights and neurons can outperform the adapter model of size 128 on average and have almost the same performance as the fully fine-tuned ones, as shown in Figure 2. It indicates that the pruning strategies can enable us to identify a better subnetwork of adapters and thus boost the model's performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **CoLA** & **SST-2** & **MRPC** & **STSB** & **QQP** & **MNLI m/mm** & **QNLI** & **RTE** & **AVG** \\ \hline Full & 52.10 & 93.50 & 88.90 & 85.80 & 71.20 & 84.60 & 83.40 & 90.50 & 66.40 & 79.60 \\ Adapter & 51.70 & 93.10 & 88.50 & 85.60 & 71.50 & 83.40 & 83.20 & 90.50 & 67.30 & 79.42 \\ \hline Weight & 54.00 & 92.80 & 88.10 & 84.60 & 71.80 & 84.30 & 83.60 & 91.10 & 65.80 & 79.57 \\ Neuron & 52.10 & 93.00 & 88.50 & 84.60 & 71.90 & 83.80 & 83.30 & 90.60 & 68.40 & 79.58 \\ layer & 49.50 & 92.60 & 88.00 & 83.50 & 71.50 & 84.10 & 83.10 & 90.80 & 70.60 & 79.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results on GLUE test sets using the GLUE server. CoLA is evaluated using [PERSON]’s correlation. STS-B is evaluated using [PERSON]’s correlation coefficient. MRPC and QQP are evaluated using F1 score. The rest of the tasks are evaluated by accuracy. For three pruning schemes, the best model on the validation set is tested.