esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Power Generation Performance  
The power generation performance of the BIPV system was analyzed using measured data. The analysis period was a total of 366 days from 1 January 2012 to 31 December 2012. Statistical analysis of the measured data of a total of 307 days was performed; data with measurement errors due to power failure and system malfunction during the period were excluded.  
The power generation performance was analyzed using the performance ratio of International Electrotechnical Commission (IEC) 61724. The PR (performance ratio), which is defined as the ratio of the final yield (\(Y_{F}\)) during actual operation to the reference yield (\(Y_{R}\)) under STC, can be differentiated into subcategories of system loss (\(L_{S}\)) and capture loss (\(L_{C}\)). The main reason behind the system loss is the DC-AC conversion loss of the inverter. Capture loss, on the other hand, is lost for a variety of reasons, such as module temperature, wiring, string diode, low irradiation loss, partial shading loss, dust and containments loss, snowing loss, irregular irradiation condition, solar cell mismatch, MPPT failures loss, and inefficient system configuration. The analysis procedures on the measured power yield data were based on IEC guideline 61724 [25] to calculate the power yield performance. The power generation performance was analyzed using Equations (1)-(6).  
\[\mathrm{PR}=Y_{F}/Y_{R} \tag{1}\]  
\[Y_{R}=H_{A,d}/G_{I,ref} \tag{2}\]  
\[Y_{A}=E_{A,d}/P_{O} \tag{3}\]  
\[Y_{F}=E_{S,d}/P_{O} \tag{4}\]\[L_{C} =Y_{R}-Y_{A} \tag{5}\] \[L_{S} =Y_{A}-Y_{S} \tag{6}\]  
where PR is the performance ratio (-), \(Y_{R}\) is the reference yield (h/day), \(Y_{F}\) is the final yield (h/day), \(Y_{A}\) is the array yield (h/day), \(H_{A,d}\) is the daily accumulated irradiation of the plan of array (POA) (kWh/m\({}^{2}\)), \(G_{I,ref}\) is the irradiation of STC (1 kWh/m\({}^{2}\)), \(E_{A,d}\) is the daily accumulated DC power (kWh), \(E_{S,d}\) is the daily accumulated AC power (kWh), and \(P_{O}\) is the installation capacity (kWp).
## 4 Results
### Determination of the Overestimation Threshold  
The positive correlation between overestimated SCA and increased SZA described above occurs extensively within the Tarim River Basin. From our results, it can be assumed that the erroneous expansion of snow-covered pixels would occur at a certain threshold value of SZA along a scan line between its nadir and its terminus. Snow pixel data obtained at SZA values larger than this threshold might therefore be untrustworthy. To delimit this threshold, we extracted data from a region within the Tarim River Basin with a nadir SZA (\(0^{\circ}\) to \(5^{\circ}\)), and marked the imagery from that date as having been taken on \"Day 1\". Owing to the 16-day cyclicity in SZA from MOD10A1, the following 16 days were also examined for comparative purposes. The highest, lowest, and average SZA values were calculated over this 17-day period (Figure 6a) by averaging values obtained from 20 different test periods in the same region. We found that the SZA fluctuated for 16 days, and then returned to its original minimum value on the 17 th day. Such a distinctive pattern existed across the entire Tarim River Basin.  
Because of this cyclicity, we treated the SCA on Day 1 (SZA of \(0^{\circ}\) to \(5^{\circ}\)) as a benchmark that did not contain unwanted effects attributable to SZA; thus, all snow-covered areas determined during the following 16-day period would inevitably be considered influenced by the SZA effect. In theory, any minimum SZA value recorded during this 16-day period during snow melting that produced an SCA greater than that on the first day could be considered a possible threshold; therefore, in this work, we selected the third day for comparison with the first. This was done because the SZA value on the second day was nearly \(60^{\circ}\), which was too high to determine a sensitive angular threshold. The third day had an appropriately low SZA value of between \(16^{\circ}\) and \(30^{\circ}\), and it constituted only a short period from the first day. All other days were dismissed because of extensive shrinkage of the snowpack.  
In order to find the lowest SZA that induced an overestimation, all pixels with emerging snow cover on the third day were recorded and plotted on a histogram with their corresponding SZA value (Figure 6b). Over 750,000 pixels were collected for one test period, which produced a minimum SZA value of \(23.08^{\circ}\) for the emergence of new snow cover. This statistical test was conducted for 20 test periods and the calculated SZA minimum values ranged from \(21.08^{\circ}\) to \(23.26^{\circ}\). We calculated a final optimal threshold of \(22.37^{\circ}\), which represents the average of all 20 calculated minima, and we used this value to apply modifications to the snow cover data within the Tarim River Basin.
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
## 3 Results and Discussion
### Hydrophobic Modification
#### 3.1.3 FTIR Analysis  
To further determine the changes of groups contained in the CGC before and after modification, infrared spectroscopy was conducted in this experiment, as depicted in Figure 6.  
The infrared spectrum reveals that the absorption peak at 1059 cm\({}^{-1}\) stems from the antisymmetric stretching vibration of Si-O-Si [37]. The peak around 847 cm\({}^{-1}\) corresponds to the bending vibration absorption of Si-OH, while the peak at 786 cm\({}^{-1}\) is the symmetric stretching vibration of the Si-O bond. Additionally, the absorption band near 1617 cm\({}^{-1}\) shows the tensile vibration peak of the -OH group. As the concentration of the modifying solution increases, the peak shape of the -OH group moves to lower wavenumbers, likely due to the accelerated consumption of -OH bonds with higher KH-560 content [38].  
Figure 5: Aggregate water absorption rate.  
Figure 6: FTIR spectrum of modified aggregate.  
Furthermore, a new absorption peak emerges at 2951 cm\({}^{-1}\), attributed to the stretching vibration peak of the C-H bond in the propyl group [39], and all these indicate that CGC was successfully modified and hydrophobic groups were introduced.  
SCA KH-560 is a low-molecular-weight organosilicon compound with a unique structure. During the coupling process, the alkoxy group at one end of KH-560 undergoes hydrolysis to form a silicon hydroxyl group, which can undergo a condensation reaction with the hydroxyl group on the surface of CGC, establishing strong hydrogen bonds and Si-O-Si covalent bonds, thus increasing the interaction between the SCA and the aggregate and allowing the SCA to be firmly adsorbed onto the ceramsite aggregate surface [40]. Meanwhile, the other end of the KH-560 molecule contains hydrophobic epoxy groups (-OR), which can make SCA, when adsorbed onto the surface of inorganic materials, to form a hydrophobic layer, thus reducing the contact area between the surface of inorganic materials as well as water and improving the hydrophobicity of the surface. The hydrolytic condensation reaction equation is presented as follows (6):  
\[\begin{split}\text{OC}_{2}\text{H}_{5}\\ \text{RO
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 3 Transmission Acceleration Using BoostTCP
### Improved Congestion Judging and Handling Mechanism
#### 3.1.1 Prevent Excessive Conservative Transmission  
Because the current TCP protocol stack has difficulty determining the cause of a packet loss (caused by network congestion) and the actual bandwidth available on the connection path following the packet loss, restoration has been typically performed to reduce the transmission rate significantly. This mechanism results in an idle path bandwidth, which is one of the primary reasons for TCP's inefficient transmission performance.  
The ultra-long-distance optical fiber transmission network with a special line often has sufficient bandwidth but a relatively long delay. In such a case, both the initial sending window and the current TCP protocol stack's sending window increase at a relatively conservative rate. BoostTCP begins sending data with a large initial sending window and rapidly increases the sending window size to reach the upper limit of available bandwidth in the shortest time.  
In an ultra-long-distance optical fiber transmission network with a special line, the BoostTCP congestion judge algorithm considers network characteristics, determining whether packet loss results from network congestion or not. As packet loss occurs as a result of random errors in an optical fiber network, and the transmission rate increases and is maintained at a higher rate, the rate is adjusted instantly when real congestion occurs. Namely, the bandwidth closest to the available bandwidth on the current path is used to perform transmission, and a slightly lower transmission rate is used to clear the queue on the path, which contributes to the recovery of nodes in a congested network. Moreover, transmission behavior is maintained to be consistent with and related to the network state. The judge algorithm eliminates idle bandwidth, resulting in a faster, more consistent transmission rate.
# New Perspectives in the Development of the Artificial Sport Trainer
## 3 Illustrative Example
### Specifics of a cycling training  
The power meter has brought a revolution in the process of cycling sport training. It is well known that preparing for a competition demands physical, as well as mental, changes of the athlete in the sport training. Indeed, the power meter takes care of making both changes simultaneously: On the one hand, the power meter ensures an athlete knows and increases his/her physical limitations, while, on the other hand, the athlete can prepare for the race mentally by knowing its demands, and, thus, performs sport training sessions that match these precisely.  
Unfortunately, measuring intensity during a sport training session is difficult task. Typically, in practice, it is measured by the following measures [36]: Rating of Perceived Exertion (RPE), Heart Rate (HR), and PoWeR (PWR). Thus, the RPE estimates the intensity of the workout subjectively, because the athlete rates his/her perceived exertion in the numerical scale on a hunch. The HR is a more precise measure of effort, and measures how intensively an athlete is working, but the sport trainer does not gain any direct information about the effectiveness of the performed workout. The more accurate measure of intensity is PWR, which can be applied in cycling, and gauges the power that the cyclist places on the pedal directly as a product of torque by cadence [37].  
The intensity of training load in cycling is prescribed by sport trainers using power zones. These present a simple training tool similar to the HR zones in running, where the training load is specified by HR intensity. The starting point for determining the personal power zones is the so-called Functional Threshold Power (FTP). This is unique for each cyclist, and indicates the maximum intensity that can be produced by the cyclist in one hour. In line with this, [PERSON] [37] proposed the so-called 1-hour test, where the road cyclist must overcome a 1-hour criterium at maximum intensity (i.e., power). If a shorter duration is considered, the measured FTP value needs to be decreased by an appropriate percent (e.g., 5 % in the case of a 30-minute test).  
The personal training zones are then constructed according to the hints illustrated in Table 1, from which it can be seen that there are seven power zones. These differ among each other regarding the percentage of the FTP, starting from less than 55 % of the FTP by the active recovery PWR zone, to greater than 150 $ of FTP by the sprint power PWR zone.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### Conceptual Design
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Equipment**} & \multirow{2}{*}{**Specifications and Features**} & **Price (USD)** & \multirow{2}{*}{**Amount**} & **Total (USD)** \\ \hline \multicolumn{5}{c}{Antenna System: Terrestrial and LEO Satellite} \\ \hline
1 & UHF antenna & 400–438 MHz crossed Yagi–Uda, boom 2.5 m 21 elements \(\times\) 2 & 189.50 & 1 & 189.50 \\ \hline
2 & Filter & UHF 400–438 MHz Band pass & 27 & 1 & 27 \\ \hline
3 & LNA & RF LNA 433 MHz, 22 dB gain & 29.70 & 1 & 29.70 \\ \hline
4 & VHF antenna & 136–150 MHz crossed Yagi–Uda boom 5 m. 10 elements \(\times\) 2 & 189.50 & 1 & 189.50 \\ \hline
5 & Filter & VHF 136–150 MHz band pass filter & 27 & 1 & 27 \\ \hline
6 & LNA & Nooelee SAWbird NOAA, 137 MHz center, 20 dB gain & 40 & 1 & 40 \\ \hline
7 & Discone antenna & Omni-directional antenna D300N diamond antenna supports & 135.30 & 1 & 135.30 \\ \hline
8 & LNA & 0.01–4 GHz LNA 40 dB gain & 57.34 & 2 & 114.68 \\ \hline
9 & Parabolic grid antenna & Size 60 cm \(\times\) 90 cm & 43.30 & 1 & 43.30 \\ \hline
10 & LPDA feed antenna & Support frequency 600–6500 MHz & 81.20 & 1 & 81.20 \\ \hline
11 & Filter & HPF 600 MHz for LPDA & 27 & 1 & 27 \\ \hline
12 & Rotator & Yaesu G-5500 with rotation of azimuth 450\({}^{\circ}\) and elevation 180\({}^{\circ}\) & 950 & 1 & 950 \\ \hline
13 & Antenna tower & Tower 6 m and installation service & 138/3 m & 2 & 276 \\ \hline \multicolumn{5}{c}{Total cost of ground station–terrestrial and LEO satellite} \\ \hline \multicolumn{5}{c}{Antenna system: L-band for GEO satellites} \\ \hline
14 & Dish antenna & Aluminum mesh dish antenna size 1.9 m, prime feed & 54 & 1 & 54 \\ \hline
15 & Helical feed antenna & 1500–1700 MHz 2.5 turns LHCP & 5.4 & 1 & 5.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BOM of a satellite ground station. Figure 6: System diagram from antennas to SDR server (numbers in figure refer to BOM in Table 4).
# Object-Level Semantic Map Construction for Dynamic Scenes
## 3 Methodology
### Notation and Preliminaries  
We define the image space domain as \(\Omega\subset\) N2 and define the 3D back-projection of point \(u\ \in\ \Omega\) given depth map D as \(p(u,D)=K^{-1}ud(u)\), where \(K\) is the intrinsic camera matrix and \(u\) is the homogeneous pixel coordinate form. We also specify the perspective projection of 3D point \(p=[x,y,z]^{T}\) as \(u=\pi(Kp)\), where \(\pi(p)=[x/z,y/z]^{T}\) denotes the dehomogenization operation. The intensity value of pixel \(u\ \in\ \Omega\) given color image C with color \(c(u)=[c_{1},c_{2},c_{3}]^{T}\) is defined as \(I(u,C)=(c_{1}+c_{2}+c_{3})/3\). For each input frame at time \(t\), we estimate the global pose of the camera Pt (with respect to a global frame \(F\)) by registering the current live depth map and color image captured by the camera with the surfel-splatted predicted depth map and color image of the active static model from the pose estimate corresponding to the last frame. All camera poses are represented by a transformation matrix:  
\[T_{cur}=\left[\begin{array}{ccc}&R_{t}&&t_{t}\\ 0&0&0&1\end{array}\right]\in SE_{3}, \tag{1}\]  
where every object model is stored in surfels \(M_{m}^{s}\), which have attribute values \((p\in R^{3},n\in R^{3},c\in R^{3},w\in R,r\in R,t\in R^{2})\ \forall s<|M_{m}|,\text{position}\ p\in R^{3}, \text{normal}\ n\in R^{3}\), color \(c\ \in\ N^{3}\), weight \(w\ \in\ R\), radius \(r\ \in\ R\), initialization timestamp \(t_{0}\), and last updated timestamp \(t\). The radius \(r\) of each surfel is intended to represent the local surface area around a given point while minimizing the visible holes, computed using the method of [PERSON] et al. (2016). Additionally, each model is associated with a class ID \(C_{m}\in\ \{0\dots 80\}\) to represent semantic information and an object index ID \(I_{m}\ =\ m\ \forall m\ \in\ \{0\dots N\}\), static indicators \(st_{m}\in\ 0,\ 1\), and a rigid pose \(T_{m}\in\ SE_{3}\). We define the object model pose \(T_{m}\) as the product of the inverse of the camera pose corresponding to the frame in which the object is observed times the current frame camera pose:  
\[T_{m}=T_{obs}^{-1}T_{cur} \tag{2}\]  
where \(T_{obs}\) is the precise camera pose corresponding to the frame in which the object is observed and \(T_{cur}\) is the accurate camera pose of the current frame. Therefore, the defined object pose is equivalent to the relative conversion matrix of the camera pose between the two frames.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 4 Hybrid Training  
In our approach, agents start with decentralized learning, where they individually interact with the environment to learn. They then share this information with a central network, thus forming a hybrid training process. Decentralized learning lets V2X agents adapt independently based on local observations, but it may not always optimize collaboration.  
To enhance collaboration, we use QMix, which is a centralized technique. QMix's central mixer network processes individual Q values from agents, thereby considering relationships and dependencies, and generates final Q values [27]. This incentivizes agents to consider team success, thus fostering efficient and collaborative decision making in V2X communication. Our deployment integrates both decentralized and centralized learning for optimal outcomes. The learning and deployment procedures are summarized in Algorithm 1 and Algorithm 2, respectively.  
```
1:Start environment simulator, generating vehicles and links
2:Initialize Q networks for all V2V agents randomly
3:for each episode do
4: Update vehicle locations and large-scale fading
5: Reset parameters for all V2V agents
6:for each step \(t\)do
7:for each V2V agent \(k\)do
8: Observe the environment at time \(t\)
9: Choose an action using a policy
10: Share Q values with QMix
11:endfor
12: Update channel small-scale fading
13: All agents take actions and receive rewards
14:for each V2V agent \(k\)do
15: Observe the environment at time \(t+1\)
16: Store experience for learning
17:endfor
18:endfor
19:for each V2V agent \(k\)do
20: Sample mini-batches for learning
21: Share learned Q values with QMix
22: Optimize Q network and QMix using gradient descent
23:endfor
24:endfor
```  
**Algorithm 1** Collaborative spectrum sharing with multi-agent RL and QMix integration.
## 3 Results and Discussion
#### 3.1.3 Model Validation
_aureus_ indicated a slight overestimation (\(B_{f}>1\); fail-safe model), whereas for \(E\). _coli_, some underestimation was observed (\(B_{f}<1\); fail-dangerous model) of independent experimental data sets. The accuracy factors for both bacteria grown in milk indicate that, on average, predictions are approximately 1.2 factors of difference with respect to the observations. Validation indices \(B_{f}\) and \(A_{f}\) close to one generally confirm the acceptable growth prediction of microorganisms and an acceptable bias factor value for a predictive model is 0.75-1.25 [33]. From this, it follows that the HM1-5 one-step approach tertiary model exhibits considerable potential as a useful and efficient tool to improve growth predictions of the single cultures under study and can be employed as a more accurate and robust alternative method to the traditionally used two-step kinetic data analysis.
## 5 Smart Radio Environment (SRE) with IRS  
Smart radio environment is an emerging paradigm shift from limitation of uncontrollable propagation of radio signals in the existing radio environment. Presently, the optimization of wireless networks is performed at the communication endpoints, i.e., transmitters and receivers. Until now, several advanced techniques such as modulation/encoding schemes and multiple antenna processing at the transceivers, efficient transmission and retransmission protocols, and robust demodulation and decoding methods at the receiver have been proposed to improve the performance of wireless networks [78]. The wireless environment, on the other hand, has remained largely uncontrollable, making communication engineers model transceivers to adapt to the propagation environment.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 2 Theoretical Review
Finally, the coherent component of each Stokes parameter can be computed using:  
\[P_{c_{p}}(\theta)=\Gamma_{p}(\theta)\cdot\frac{\lambda^{2}P_{t}G_{t}G_{r}}{(4 \pi)^{2}(R_{t}+R_{r})^{2}} \tag{2}\]5  
for \(x\) = {0,1,2,3} for each of the DDM pixels. Finally, the normalized Stokes parameters \(\bar{(S_{x})}\) are computed as the surface integral of all pixels in a given Delay-Doppler window of 4 C/A chips and \(\pm\)1.8 kHz, as shown in Equation (9) from [18]. This Doppler and delay box give us \(\sim\)30 km of coverage. Hence, detecting as much scattering as possible within the antenna footprint.
## 4 Discussion  
### Key Factors of the Overall Void Fraction  
From Formulas (3) and (9), it could be seen that the key parameters of the calculation model were the mining height, face length, number of overlying rock layers, and bulking coefficient of the crushed direct roof. Under fixed geological conditions, the influencing factors of face length and mining height were analyzed. The analysis was intended to obtain the distribution of the void fracture in the stope's strike and dip directions. We adjusted the working face length and the mining height, i.e., the \(l_{y}\) and \(H\) values, and analyzed their impacts on the void ratio of the stope, as shown in Figures 8 and 9. We set \(l_{y}\) as 60 m, 80 m, 100 m, 120 m, and 150 m and set \(H\) as 3 m, 4 m, 5 m, and 6 m.  
Figure 8: Void distribution of the stope under the influence of the face length. (**a**) Void distribution of the roof overburden. (**b**) Overall void distribution.  
The face length impacted the void ratio in the middle strike of the goat, and the maximum and minimum values of the void ratio in the middle gradually decreased with the increase in the face length from 46.7% and 42.3% to 44.4% and 40.9%, respectively. The turning point of the porosity in the middle remained unchanged at 20-23 m. The face length impacted the distribution of the void ratio in the goat towards the middle. With the increase in the face length, the minimum value of the void ratio in the middle and the minimum value at the boundary gradually decreased from 30% and 46.7% to 29.2% and 44.5%, respectively.  
According to the analysis in Figure 9, the mining height affected the strike porosity of the goat. With the increase in mining height, the maximum porosities of the middle and the boundary increased from 37.5% and 75.0% to 54.6% and 100%, respectively, and the minimum porosities of the middle and the boundary were not affected. The mining height affected the porosity tendency of the goat, and the mining height increased. The maximum porosities of the middle and the boundary increased from 33.7% and 75.0% to  
Figure 9: Distribution of the void ratio in the stope under the influence of the mining height. (**a**) Void distribution of the roof overburden. (**b**) Overall void distribution.  
34.3% and 100%, respectively. The minimum values of the void fractions in the middle and the boundary increased from 26.6% and 37.5% to 30.3% and 54.6%, respectively.