esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Improvements of MTL-LUE, MTG, and BTL in Simulating GPP over Mountainous Areas
#### 4.1.2 Improvement of MTG over TG  
TG is a VI-based model which uses the combination of EVI and LST to obtain GPP estimates [12]. In the model application, the LST input of TG is always provided by MODIS products at coarse spatial resolutions, which may lose a large portion of topographic characteristics over mountainous areas [51]. The MTG model incorporates an elevation-corrected factor and a radiation-corrected factor into the TG model to address the effect of elevation on temperature [52; 53] and characterize the contribution of the incoming solar radiation to the spatial variation in temperature [54; 52], respectively.  
At the watershed scale, the GPP estimates from MTG had a higher annual value than those from TG in shrub pixels, while they presented a lower annual value than those from TG in forest pixels and grass pixels. As for the effect of elevation on temperature, higher GPP estimates from MTG can occur in situations when (1) the elevation is low and the LST is smaller than the optimum photosynthesis temperature and (2) the elevation is high and the LST is higher than the optimum photosynthesis temperature. Lower GPP estimates from MTG can occur in situations when (1) the elevation is low and LST is greater than the optimum photosynthesis temperature and (2) the elevation is high and LST is lower than the optimum photosynthesis temperature. As for the contribution of the incoming solar radiation to the spatial variation in temperature, higher incoming solar radiation would lead to a higher GPP estimate from MTG.  
Results also indicated that the ratio between annual GPP estimates from MTG and TG decreased with increasing slope or decreasing SVF, possibly because a higher slope and a lower SVF would lead to lower incoming solar radiation. The ratio between annual GPP estimates from MTG and TG was observed to decrease and then increase with elevation varying from 2500 m to 4000 m, as incoming solar radiation decreased and increased due to the increase of slope in the elevation range of 2500 m to 3500 m, and the decrease of slope in the elevation range of 3500 m to 4000 m over Wanglang Reserve, respectively. The finding in this study that the ratio between annual GPP estimates from MTG and TG increased gradually and then decreased with aspects varying from 0\({}^{\circ}\) to 360\({}^{\circ}\) may be caused by the high incoming solar radiation in the sunlit terrain and low incoming solar radiation in the shaded terrain during the growing season.
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 3 Properties and Geomechanics of Bedded Evaporites
### Main Geomechanical Considerations in Bedded Evaporite Caverns
#### 3.3.4 Abandonment  
Salt caverns typically have a lifespan of 30-50 years, as suggested in [269]. The abandonment process commonly involves filling the cavern with brine and sealing the well with cement [270]. Pressure build-up following abandonment may cause fractures around the cavern and brine migrations. Key factors affecting pressure build-up include cavern compressibility, creep, equilibrium state, and brine thermal expansion [271]. The study in [272] proposed a model to forecast the behavior of abandoned salt caverns considering short-, medium-, and long-term implications. Another study in [146] assessed the suitability of abandoned caverns in China for hydrocarbon energy storage, aiming to mitigate safety and environmental risks. The stability of abandoned horizontal caverns for gas storage was studied in [273]. An 18-month test on a deep brine-filled cavern in [270] found that equilibrium pressure was significantly lower than geostatic pressure, reducing the risk of formation fracturing.  
Surface subsidence is another issue that may occur when a salt cavern is abandoned. According to [274], subsidence occurs when cavern pressure decreases below initial stress levels, causing volume shrinkage. Salt creep is a significant contributor to subsidence and displacement in this scenario. However, a study suggested that certain conditions, such as filling caverns with alkali waste, could minimize subsidence risks [269].
## 3 Experimental Results and Simulation Analysis
### Experimental and Simulation Analysis
The left area includes points A and B, located on both sides of the top of the cable room and near the back area; the central area comprises measuring points C and D, located at the midpoint of the walls on both sides of the cable room; and the right area is located at the bottom of the cable room and near the cable room door, and includes measurement points D and E.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Layout 1**} & \multicolumn{4}{c}{**Layout 2**} & \multicolumn{4}{c}{**Layout 3**} & \multicolumn{4}{c}{**Layout 4**} \\ \cline{2-13} \multicolumn{1}{c}{\multirow{-2}{*}{**Points**}} & \multicolumn{4}{c}{**RH**} & \multicolumn{4}{c}{**RH**} & \multicolumn{4}{c}{**RH**} & \multicolumn{4}{c}{**RH**} \\ \cline{2-13} \cline{13-13} \cl  
Moving to Figure 7, we can observe that, overall, from the top of the cable room to the cable room door, and from the wall surface of the air supply and extraction outlet to the door, among the four layouts, the relative humidity and dew point temperature of Layout 2 have improved to some extent compared to the initial Layout 1. Layout 3 shows a certain degree of increase in relative humidity and dew point temperature compared to the original layout. Figure 7a,b show that Layout 4 is relatively low in relative humidity compared to the other layouts, with a maximum reduction of 5.6% and the lowest being 53%, and by comparing the dew point temperature Layout 4, it still has advantages in dehumidification, with a decrease of 2 \({}^{\circ}\)C in the experimental section and as low as 17.56 \({}^{\circ}\)C. Figure 8a,b display that Layout 4 is relatively low in relative humidity compared to other layouts, with a maximum reduction of 7% in the simulation section and the lowest being 50%, and by comparing the dew point temperature of Layout 4, it still has advantages in dehumidification, with a maximum reduction of 1.78 \({}^{\circ}\)C in the experimental section and as low as 17.15 \({}^{\circ}\)C.  
The primary reason why Layout 2 and Layout 4 yield better results may be attributed to their use of Pipe 1 and Pipe 4 as exhaust ducts, with the bottom of the cable room serving as the inlet for the humidity source. The exhaust duct is positioned closer to the bottom in both layouts, and a portion of the humid air is quickly extracted from the duct upon entering the cable room. Simultaneously, the ventilation system operation also ensures a more uniform gas flow inside the cable room. Layout 4 currently offers the most effective overall dehumidification compared to all other layouts.
## 2 Results and Discussions
### Total Polyphenolic Content of Pinus cembra L. Extracts in Relation to Altitude  
The results summarized in Table 1 show that the quantitative distribution of the total polyphenolic content (TPC) varies depending on the altitude and location of the sample from which the extracts have been obtained. Moreover, there is a significant difference between the TPC in hytidome extracts and periderm extracts, with the latter possessing a significantly lower concentration of polyphenols. Our findings suggest that altitude plays a decisive role in the difference in polyphenol content in the analyzed extracts, most evidently observed in the hytidome extracts where the samples collected from the altitude of 2429 m had a higher concentration of polyphenols, but not statistically significant compared to those collected from an altitude of 1620 m. According to Figure 2, a significant difference (\(p<0.0001\)) was observed in the content of polyphenols between the periderm and the hytidome within the species _Pinus cembra_ L. at the highest altitudes, more precisely at 2429 m. The hytidome extracts showed the highest content of polyphenols at this altitude, while the periderm extracts recorded the lowest content of polyphenols. These results suggest that altitude can influence the content of polyphenols in different parts of the plant and that there is a significant variation depending on the altitude and the parts analyzed. Figure 1: The histo-anatomical aspects of the _Pinus cembra_ L. stem: **(a)**_Pinus cembra_ L. stem cross-section; A—periderm, B—cortex, C—resin ducts, D—phloem, and E—secondary xylem; **(b)** central area of the _Pinus cembra_ L. stem cross-section; **(c)** perioderm of _Pinus cembra_ L. in detail. Quantitatively speaking, our results are also supported by the reports of other studies performed on the polyphenolic content in the bark of _Pinus cembra_ L; in a study performed
## 1 Introduction
### Review of State-of-the-Art Detection Methods
#### 1.2.1 FFT-Based Thresholding  
The most common approach involves applying Fast Fourier Transform (FFT) to the time-domain beat signal and selecting the index of the maximum spectral magnitude [3]. While this method is computationally efficient and suitable for real-time applications, it is highly sensitive to noise and performs poorly at low SNRs.
## 2 System Model and Problem Formulation  
### System Model  
We consider a square grid world composed of \(U\times U\) cells with a UAV collecting data from \(K\) SNs, as shown in Figure 1. The side length of each cell is \(l\). The UAV only hovers over the center of each cell, transmits broadcast signals, and collects data during the hovering time. The position of the \(k\)-th SN at the \(t\)-th time slot can be described as  
\[u_{k}(t)=[x_{k}(t),y_{k}(t),0]^{T}, \tag{1}\]  
where \(k\in[1,\cdots,K]\), and \(t\in[1,\cdots,T]\). In actual scenarios, precise position information may not be easy to obtain and cannot even be reliably predicted through explicit models. Thus, we assume that the UAV is not aware of the precise information about which cell the SN is located in. It is supposed that the UAV flies at a constant altitude, \(H\), so the UAV's position can be expressed as  
\[p(t)=[x(t),y(t),H]^{T}. \tag{2}\]The mission time slots are small enough so that the speed of the UAV, \(v(t)\), should be regarded as constant in any time slot. The UAV has two states: hovering or flying at a fixed speed, \(V\), i.e., \(v(t)\in\{0,V\}\) for any \(t\in[1,\cdots,T]\). Each time the UAV moves, it will move to the center of its adjacent cell which is specified as  
\[V\times(t-(t-1))=V=l. \tag{3}\]  
During the mission time, the UAV needs to collect data from all devices. \(p_{k,t}\) represents the uplink transmit power for the \(k\)-th SN, \(i_{k}\), when it wakes up to communicate. We assume that only when the UAV wakes up the SNs can they start to upload information or transmit signals in response to the broadcast signal from the UAV, and the device will no longer respond to the UAV after the data are collected. Therefore, \(p_{k,t}\) can be expressed as  
\[p_{k,t}=\begin{cases}p_{k,upl},&\text{uploading data}\\ p_{k,res},&\text{responsing the UAV}\\ 0,&\text{otherwise}.\end{cases} \tag{4}\]  
The channel capacity between the UAV and SN, \(i_{k}\), at the \(t\)-th slot, measured in bits per second (bps), can be expressed as  
\[R_{k}(t)=B\log_{2}(1+\frac{p_{k,t}\beta_{0}}{\sigma^{2}\parallel p(t)-u_{k}(t) \parallel^{2}}), \tag{5}\]  
where \(p(t)\) denotes the UAV's position, \(B\) is the channel bandwidth, \(\beta_{0}\) is the reference channel gain, and \(\sigma^{2}\) is the channel noise power.
## 6 Discussion  
Despite of the change in wind speed, irradiation and load, the results show that PV current, PMSG current, battery current, DC-Bus voltage and the speed turbine reach  
Figure 31: Wind power, PV power, battery power and SOC.  
Figure 30: Wind power, PV power, battery power and SOC.  
their references rapidly, Figures 26 and 27. In spite of the wind speed change, the power coefficient remained constant with maximal value (0.515). The turbine speed reached its optimal value, so it takes the same profile as the wind speed, although it still tracks its reference. The results confirm the good tracking of the proposed control and the system operates in optimal conditions even under wind, irradiation and load variations (Figures 28 and 29).  
The DC-Bus voltage tracks its reference thanks to the battery because it stores the excess energy and restores it if there is an energy deficit; see Figures 30 and 31. The battery absorbs the excess power and ensures the power balance of the wind and PV hybrid system. Even if the system operates at variable conditions (wind speed, irradiations, load), the DC-Bus voltage remains stable, as shown in Figures 26 and 27. This result confirms the robustness of the proposed control method. Figures 30 and 31 illustrate the power flow of each system component (solar, wind, battery and load profile).  
For t < 13 s, the load is mainly powered by the hybrid system (PV-Wind) and the available power is greater than the power demand of the load. We notice that the battery power is negative, which signified the charging of the battery. After 13 s, the available hybrid power (PV-Wind) is less than the load power. The battery power becomes positive, which means the battery discharges.  
This effect can also be observed in the battery state of charge (SOC). The battery guarantees the stability and energy balance of the system. Additionally, it can be seen from the figure that the total SOC is proportional to the battery power as shown in the figure. In case there is a surplus of energy, the system charges the battery with the energy generated from photovoltaic energy and wind turbines, where the SOC value increases. In the case of a lack of power for the loads, the battery performs a discharge, and the SOC value decreases.  
We can confirm that the control is robust and ensures the good tracking of all currents to their reference under parameter variations; Figures 26 and 27. The results prove the effectiveness of the proposed control.
## 5 Simulation Results
Moreover, as the pointing error level increases, the BER performance is degraded, as expected. Figure 5: EC versus \(\mu\) with different turbulence conditions. Figure 6: Average BER versus \(\mu\) with different turbulence conditions.
# Widely-Tunable Ring-Resonator Semiconductor Lasers
## 2 Basic Principle of Wide-Tunability Utilizing Ring Resonators
The \(A\) term, corresponding to the linewidth reduction from reduced longitudinal mode confinement, is often denoted as the ratio of the external (passive section) cavity path length to the gain section path length. As the effective length of a ring resonator is maximized at resonance, the \(A\) factor is maximized when the ring is placed exactly at resonance. The \(B\) term corresponds to the reduction in linewidth from the negative feedback effect, where a decrease in wavelength increases reflectivity (increasing photon density in the cavity), and hence decreases carrier density, which in turn causes the wavelength to increase, due to the carrier plasma effect. We plot some key parameters in Figure 3, where we assume a 3 mm long Fabry-Perot cavity and use identical SWF, as plotted in Figure 1b. The first takeaway from Figure 3 is that the effective cavity length increases at resonance, and it is well known that longer cavities generally correlate with lower linewidth. A longer \"effective\" cavity, at the same time, reduces the longitudinal mode spacing, so SWF has to be designed properly to provide sufficient passive side-mode suppression ratio (SMSR) for the laser to be single mode. The effective cavity length is actually a derivative of phase with respect to frequency, and, as expected, the phase changes faster around the resonance. Finally, we plot the \(A\), \(B\), and \(F\) factors in Figure 3d. As the \(B\) factor is antisymmetric around the resonance, and has a maximum when slightly detuned from the resonance, the result is that the total effect \(F\) is actually maximized off resonance. The phase section in the cavity can be used for a slight detuning of the laser oscillation with respect to the minimum cavity loss condition (resonator resonance) for best linewidth performance.
## 3 Results and Discussion
### Thermogravimetric Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**T\({}_{\text{g0\%}}\)**, (\({}^{\circ}\)C)**} & \multirow{2}{*}{**DTGA (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Mass of Fillers (phr)**} \\ \cline{3-5}  & & & **Loaded** & **Measured** \\ \hline PLA/PCL/0-0 & 360.1 & 360.3 & 0 & 0 \\ PLA/PCL/0-10 & 362.6 & 358.3 & 10 & 9.86 \\ PLA/PCL/10-10 & 371.6 & 365.5 & 20 & 19.62 \\ PLA/PCL/10-0 & 374.2 & 374.7 & 10 & 9.58 \\ PLA/PCL/0-20 & 375.2 & 370.4 & 20 & 17.92 \\ PLA/PCL/20-20 & 382.1 & 366.1 & 40 & 39.51 \\ PLA/PCL/20-0 & 371.8 & 368.9 & 20 & 19.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: TGA and DTGA data of pure and composite filaments. Figure 3: TGA (**a,b**) and DTGA (**c,d**) graphs of neat and composite filaments. the mass difference between the same composition specimens at 550 \({}^{\circ}\)C was less than 1.2%. TGA results using the same method to prepare PLA/HAp filaments exhibited a deviation of about 1% to the loading concentration [42]. Other methods include the solvent-free method in which HAp is mixed with melted PLA, next cooled and shaped in pellets, and then the resulting pellet is cut into pieces and fed the extruder [43].
## 2 Autonomous Driving in Adverse Weather Conditions
### Autonomous Driving's Dataset Analysis for Adverse Weather Challenges
#### 2.5.4 Implications for Model Selection  
Selecting the appropriate combination of datasets and algorithms is critical for optimizing autonomous driving systems under varying conditions. The findings indicate that datasets like nuScenes and Waymo Open are ideal for models that require detailed scene understanding and robust object tracking capabilities [55; 56]. These datasets provide rich multimodal data, making them suitable for complex tasks such as sensor fusion.  
On the other hand, smaller datasets such as DAWN and WEDGE are particularly effective for refining models that need to handle rare or extreme weather conditions [53; 54]. These datasets, although limited in scale, offer high-quality annotations that are invaluable for training models focused on specific challenges like low-visibility detection.  
The results provide a roadmap for researchers and engineers to select the most suitable datasets and models for different autonomous driving challenges. For real-time applications, lightweight models like YOLOv8 are recommended, while scenarios that require deeper environmental analysis may benefit from self-supervised learning methods or Vision Transformers. The strategic use of GAN-generated data can further enhance the adaptability of these models in unpredictable environments.
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 2 Project Overview  
This research relies on the Tianyong Expressway project, which is located in Qingyang City, eastern Gansu Province. The lengths of the left and right lines are 1900 m and 1985 m, respectively, and the maximum buried depth is 112.35 m. The net distance between the left and right holes is 21-38 m.  
The lithology of the strata in the tunnel site area is divided into upper and lower layers. The upper part is mainly the Quaternary Upper Pleistocene (Q3 eol) Malan loess and the lower part is mainly the Middle Pleistocene (Q2 eol) Lishi loess sandwiched with multiple layers of ancient soil. Where there is groundwater, the surrounding rock conditions are poor, belonging to V-level surrounding rocks.  
The excavation of the tunnel adopted the excavation method of three steps and seven steps of flowing water. The tunnel adopted composite lining, and the initial support adopted C20 shotcrete with a thickness of 26 cm and an I2 web I-shaped steel arch frame, without system anchors. The secondary lining was made of C35 molded concrete, and the thickness of the inverted arch and side wall was 50 cm. Geotextile and waterproof boards were laid between the primary support and secondary lining.  
At the same time, Yulinzi Tunnel encountered many difficulties during the construction process. These difficulties mainly include the following three aspects:  
1. The tunnel was surrounded by rock with low strength, classified as Class V rock. Due to the large amount of seepage, the self-bearing capacity of the surrounding rock was reduced. Tests showed that the soil moisture content was between 21% and 28%, with an average of 23.8%;
2. The deformation of the surrounding rock of the tunnel was large. The average settlement of the left line of the tunnel was 361.04 mm, and the maximum settlement could reach 904.6 mm; the average settlement of the right line was 235.15 mm, and the maximum settlement could reach 473.1 mm. The reserved deformation value of the tunnel design was exceeded, which caused serious damage to the primary support structure of the tunnel, distortion of the steel frame, cracking of the shotcrete, and severe violation of the primary support;
3. Through on-the-spot investigation, cracks in different degrees were observed in the secondary lining structure of the Yulinzi Tunnel within 1-2 years, as shown in Figure 1. Field statistics found that there were many circumferential cracks at the side walls and vaults of the tunnel. The width of the cracks at the tunnel arch and arch foot was usually less than 0.5 mm, and a few cracks exceed 1 mm in width The pictured crack was a longitudinal crack; the width of the crack at the inverted arch could reach up to 3 mm, and the crack length can reach up to 48 m.
## 5 Insertion Loss Reduction Techniques (ILRTs)  
The primary sources of DSA insertion loss are twofold: the transistor on-resistance and reflection between multiple attenuation bits. Consequently, this paper proposes ILRTs to minimize circuit insertion loss and enhance matching performance.