esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiation-Balanced Lasers: History, Status, Potential
## 3 Theory of Radiation-Balanced Lasing
### Athermal Lasing
#### 3.2.1 Athermal Lasing in Ideal Systems
If the laser system is in a steady state, \(dN_{2}/dt=0\), Equation (5) takes the form  
\[W_{P}=W_{L}+\frac{N_{2}}{\tau_{R}} \tag{10}\]  
Let us find the relation between the pump and laser beam intensities \(I_{P}\) and \(I_{L}\) providing athermal operation of the laser, \(P_{heat}=0\). In this case, Equation (9) becomes  
\[\hbar\omega_{P}W_{P}=\hbar\omega_{L}W_{L}+\hbar\omega_{F}\frac{N_{2}}{\tau_{R}} \tag{11}\]  
The change in the laser signal intensity along the length of the laser medium can be described by the well-known equation  
\[\frac{dI_{L}}{dz}=\Big{[}\Big{(}\sigma_{a}^{L}+\sigma_{e}^{L}\Big{)}N_{2}- \sigma_{a}^{L}N_{T}\Big{]}I_{L}, \tag{12}\]  
where \(z\) is the coordinate along the length of the laser medium (see Insert in Figure 2). Substituting Equations (7)-(11) into Equation (12), one can obtain the equation, which describes the laser signal at any point, \(z\), along the length of the laser medium  
\[\frac{i_{L}(0)}{i_{L}(z)}exp(i_{L}(z)-i_{L}(0))=exp\Big{(}\sigma_{a}^{L}N_{T} z\Big{)}, \tag{13}\]  
where \(i_{L}(z)=I_{L}(z)/I_{L}^{sat}\), and \(I_{L}^{sat}=\frac{\hbar\omega_{I}}{\tau\big{(}\sigma_{a}^{P}+\sigma_{e}^{P} \big{)}}\Big{(}\frac{\omega_{F}-\omega_{p}}{\omega_{P}-\omega_{L}}\Big{)}\) is the saturation intensity of the laser signal. To keep the radiation balance at each point in the laser medium, the pump intensity must be distributed properly along the length of the laser medium following the relation obtained from Equations (10) and (11)  
\[N_{1}+N_{2}=N_{T}, \tag{6}\]0  
where \(i_{P}(z)=I_{P}(z)/I_{P}^{sat}\), and \(I_{P}^{sat}=\frac{\hbar\omega_{p}}{\tau\big{(}\sigma_{a}^{P}+\sigma_{e}^{P} \big{)}}\Big{(}\frac{\omega_{F}-\omega_{L}}{\omega_{P}-\omega_{L}}\Big{)}\) is the saturation intensity of the pump signal. As one can see in Equation (14), athermal laser operation requires careful control of the pump intensity distribution along the laser medium. Any deviation from this distribution will result to heating or cooling in some parts of the laser medium, which can be estimated with Equation (9). Since \(i_{P}(z)>0\), one can see in Equation (14) that there is a minimum value of laser signal intensity in the laser cavity that can undergo athermal amplification:  
\[N_{1}+N_{2}=N_{T}, \tag{6}\]1  
A comprehensive theory of the radiation-balanced (athermal) RE-doped bulk solid-state laser was developed in [14]. In [26], it was enhanced for the radiation-balanced (athermal) RE-doped fibre amplifiers.
## 3 Results
In addition, when the ambiguity search results of the three methods are confirmed, the R-ratio test is combined with the bootstrapping success rate index method. Bootstrapping success rate indicator is set to 99.5%, and the R-ratio test threshold is set to 3.0. The results of the FAR algorithm are shown in Figure 7, where it can be seen that due to the more complex environment around the mobile station, a large number of floating-point solutions are generated when the FAR algorithm is used for ambiguity fixation. The time series diagram of the number of available co-viewing satellites is given in Figure 8, from which it can be seen that the number of satellites changes more drastically in the first half of the data, indicating that the signals of some satellites may be interfered or blocked by obstacles such as surrounding trees and hillses during this period. Therefore, the signal quality is affected, causing frequent increases or decreases in the number of available satellites, which in turn affects the overall ambiguity fixation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Related Parameters** & **Numerical Value** \\ \hline Elevation Threshold & 20 \\ Consecutive epochs & 10 Epoch \\ Signal-to-noise ratio & 35 dBHz \\ GDOP & 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Combination optimization algorithm parameter setting. Figure 6: Experimental data collection site. The results of the conventional PAR algorithm are shown in Figure 8, from which it can be seen that the number of floating-point solutions is significantly reduced after adopting the partial ambiguity fixation algorithm, especially in the first half of the data. The calendar elements that appear as floating-point solutions in the FAR algorithm are almost all fixed in the PAR algorithm, which verifies the effectiveness of the partial ambiguity fixation algorithm. However, a large number of floating-point solutions still appear in the second half of the data, indicating that the conventional PAR algorithm does not eliminate all satellites that affect the ambiguity solution. The results of the new algorithm are illustrated in Figure 9, where almost all the solutions are fixed throughout the observation period. Compared to the conventional PAR algorithm, the ephemeris elements that appear as floating-point solutions in the conventional PAR algorithm in the latter half of the data are all fixed in this algorithm, indicating that the algorithm can further identify satellites that have an impact on the blur fixation and thus improve the blur fixation rate. Figure 8: 3D baseline results of conventional PAR algorithm. Figure 7: 3D baseline solution of the FAR algorithm.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 3 IberSpeech-RTVE 2022 Evaluation Tasks
### Text and Speech Alignment Challenge
#### 3.3.1 Alignment of Re-Speaking Generated Subtitles (TaSAC-ST)
Only those segments accepted by the system will be evaluated, so let \(S^{\prime}=\{s_{1},s_{2},\ldots,s_{N^{\prime}}\}\) (with \(N^{\prime}\leq N\)) be the sequence of accepted segments. Each accepted segment is then aligned with the ground truth, which produces a sequence of sub-segments, each of them aligned either with a ground truth segment or with a collar time segment. Sub-segments aligned with collar time are not evaluated and will not be considered hereafter. Let \(C=\{c^{(1)},c^{(2)},\ldots,c^{(M)}\}\) be the sequence of sub-segments obtained after aligning the accepted segments with the ground truth, excluding collar time. Each sub-segment is a 4-tuple:  
\[c^{(i)}=\Big{(}t^{(i)}_{bgs}t^{(i)}_{end^{\prime}}w^{(i)}_{a},w^{(i)}_{g}\Big{)}, \tag{10}\]  
where \(t^{(i)}_{bgs}\) is the starting time, \(t^{(i)}_{end}\) is the ending time, \(w^{(i)}_{a}\) is a word in the paired text and \(w^{(i)}_{g}\) is a word in the ground truth. The _performance metric_ is defined as follows:  
\[score(C)=\sum_{i=1}^{M}\delta(w^{(i)}_{a},w^{(i)}_{g})\cdot\Big{(}t^{(i)}_{ end}-t^{(i)}_{bgs}\Big{)}, \tag{11}\]  
where:  
\[\delta(w^{(i)}_{a},w^{(i)}_{g})=\left\{ \begin{array}{cl}1&\text{if }w^{(i)}_{a}=w^{(i)}_{g}\\ -1&\text{if }w^{(i)}_{a}\
eq w^{(i)}_{g}.\end{array} \right. \tag{12}\]  
The participants were encouraged to take into account that the higher the number of accepted segments, the higher the potential amount of correctly aligned speech, but also the higher the risk of having a large amount of wrongly transcribed speech. To find the optimal balance between both events, a suitable confidence score threshold should be applied to make decisions. The scoring script provided by the organization explores all the possible thresholds that can be applied to make decisions, and outputs the optimal score and threshold.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 5 Our Presented HM-ACOPSO Algorithm
### Anchor Nodes Merging  
The total traveling time contains two parts, including moving time and sojourn time. The total time the mobile agent uses to complete a whole travel path can be calculated using the following formula.  
\[T_{total}=\frac{L_{total}}{v}+N_{anchor}\cdot t_{sojourn} \tag{19}\]  
The sojourn time is related to the number of anchor nodes. More anchor nodes mean that the mobile agents have to spend more time data collecting. In this paper, the MU-MIMO technology is adopted to enable the mobile agent to collect the data from multiple sensor nodes. If any anchor node is in the transmission range of multiple CHs simultaneously, we can merge the relevant anchor nodes.  
Each anchor node is checked to determine whether it is in multiple CHs' transmission ranges utilizing the following equation:  
\[\sqrt{\left(x_{i}-x_{\triangle}\right)^{2}+\left(y_{i}-y_{\triangle}\right)} \leq R\&\&\sqrt{\left(x_{j}-x_{\triangle}\right)^{2}+\left(y_{j}-y_{ \triangle}\right)}\leq R \tag{20}\]  
where \((x_{i},y_{i})\),\((x_{j},y_{j})\) are the coordinates of any two CHs and \((x_{\triangle},y_{\triangle})\) is the coordinate of any anchor node. We only reserve the anchor nodes which are in a conjunct communication range and remove the other anchor nodes.
## 1 Introduction
The designed prototype allowed a real UAV to collect data from heterogeneous ground sensor devices using best-effort communication and send data back to the Sink using reliable communication. This paper also includes a simulation analysis that observes the bandwidth consumption in an environmental monitoring application scenario. The remaining parts of this paper are organized as follows. Section 2 presents and discusses some related works, including different schemes proposed for using mobile data collectors in WSNs. In Section 3, the proposed Dual-Stack Single-Radio Communication Architecture (DSSRCA) is explained, along with the hardware and software components, and some experimental results with a real UAV. Next, in Section 4, a detailed simulation study related to bandwidth consumption in an environmental monitoring application scenario is presented and discussed. Finally, conclusions and future work directions are addressed in Section 5.
## 5 Conclusions  
The results show that for the minimum load/balanced case, an average power injection of approximately 10 kW-13 kW per bus on the LVRN distribution grid would not push the voltage unbalance beyond the 2-3% limits. In a worst case scenario, where all of the PVs would be connected to the same phase and a maximum load would be applied, the power injection per bus may be up to 3.5 kW and 4.6 kW before reaching the same voltage unbalance limits. It should be emphasised that the reference network does not refer to any particular country or region. Nevertheless, the results are within the same order of magnitude as those found in the literature for several Member States. Three-phase connections should be encouraged whenever possible, allowing higher power injections. Only unbalances in the urban scenario were considered in the study. Semi-urban and rural networks could provide different outcomes for the study due to longer cable distances.  
Figure 7: Limit of power connection with voltage variation and voltage unbalance for a balanced PV distribution and minimum load scenario (phase 1).  
Congestions, amongst other factors, especially in urban areas where population density is higher, may be an important factor to consider as well. The improvement on LV network efficiency by reducing transformers' short circuit resistance and feeder impedance or the installation of tap changers in secondary substations, would mitigate voltage rise/drops. The curtailment of PV generation in order to charge local storage units for later use could be promoted, allowing higher power installations. It should also be highlighted that the cables considered in the simulation or in a given network are of extreme importance in allowing a higher penetration of DG. In reality, distribution networks should foresee a bidirectional flow of power, which increasingly tends to be the case in the future, especially in the smart grid context.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/8/1333/s1](http://www.mdpi.com/2076-3417/8/8/1333/s1)]([http://www.mdpi.com/2076-3417/8/8/1333/s1](http://www.mdpi.com/2076-3417/8/8/1333/s1)), Figure S1: Block diagram implementation of the single phase PV control system developed in RSCAD.  
This research received no external funding.  
The author declares no conflict of interest.
## 4 Discussion of the Results
### Effect of the Circumferential Joint
#### 4.2.2 Coefficient of Bending Moment Transmission
As shown in Figures 2 and 16, the F segment is the smallest segment, and the locations of JF9 and JF10 are close, which means that the area of the F segment is more flexible than other areas in the middle ring and comparatively large deformations may take place here. Besides, its neighboring segment (the segment between CJ9 and CJ10) is the largest segment. Hence, a large stiffness difference exists at the area near the F segment, and the coefficients of bending moment transmission are relatively large. The same explanation is also applicable for the larger coefficient (around 0.23) in JF5, whose neighboring segment (the segment between CJ5 and CJ6) is the neighboring T2 segment, which is well constrained not only by segments but also by the interior column. As can be seen from Table 6, the coefficients of bending moment transmission for different joints of a quasi-rectangular tunnel deviate a lot. These coefficients should be adopted properly based on the location of the joints, especially for JF5, JF9 and JF10. From the calculated coefficients of bending moment transmission, which consider constant rotational stiffnesses, it is obvious that the coefficients decrease with the increase of the constant stiffness values. This is because of the decrease of the stiffness difference between the joint and its corresponding neighboring segment section. Clearly, the coefficients derived from the constant stiffness method, and from the incremental method, deviate a lot. In conclusion, the evaluation of the bending moment transmission phenomenon could be unreliable when using the constant stiffness method, which could possibly result in an unsafe design in practice.
# Solution-Processed CdTe Thin-Film Solar Cells Using ZnSe Nanocrystal as a Buffer Layer
###### Abstract  
The CdTe nanocrystal (NC) is an outstanding, low-cost photovoltaic material for highly efficient solution-processed thin-film solar cells. Currently, most CdTe NC thin-film solar cells are based on CdSe, ZnO, or CdS buffer layers. In this study, a wide bandgap and Cd-free ZnSe NC is introduced for the first time as the buffer layer for all solution-processed CdTe/ZnSe NC hetero-junction thin-film solar cells with a configuration of ITO/ZnO/ZnSe/CdTe/MoOx/Au. The dependence of the thickness of the ZnSe NC film, the annealing temperature and the chemical treatment on the performance of NC solar cells are investigated and discussed in detail. We further develop a ligand-exchanging strategy that involves 1,2-ethanedithiol (EDT) during the fabrication of ZnSe NC film. An improved power conversion efficiency (PCE) of 3.58% is obtained, which is increased by 16.6% when compared to a device without the EDT treatment. We believe that using ZnSe NC as the buffer layer holds the potential for developing high-efficiency, low cost, and stable CdTe NC-based solar cells.  
solution processed; CdTe; ZnSe; nanocrystal; solar cells +
Footnote †: journal: Applied
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 6 Spatio-Temporal Oversampling of Photon Counting Pixels
### Single Photon Binary Quanta Imaging
The measured bit error rate is 0.0017 providing an equivalent DSERN of 0.168 e\({}^{-}\) Without CDS timing and increased column current, the field rate more than doubles [16]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **[41]** & **[42]** & **[16; 17]** \\ \hline
**Sensor Name** & QIS Pathfinder & SwissSPAD & SPC Imager \\ \hline
**Process Technology** & 180 nm CMOS & 0.35 \(\upmu\)m HV CMOS & 130 nm Imaging CMOS \\ \hline
**Array Size** & 1376 \(\times\) 768 & 512 \(\times\) 128 & 320 \(\times\) 240 \\ \hline
**Photo-detector** & “Pump-gate Jot” PD & SPAD & SPAD \\ \hline
**NMOS Pixel Transistors** & 3 & 11 & 9 \\ \hline
**Fill Factor (\%)** & 45 & 5 & 26.8 \\ \hline
**Pixel Pitch (\(\upmu\)m)** & 3.6 & 24 & 8 \\ \hline
**Microlensing** & N & Y (12\(\times\) concentration factor) & N \\ \hline
**Shuttering** & Rolling & Global & Global \\ \hline
**CDS** & True CDS & None & None \\ \hline
**Parallel Data Channels** & 32 & 128 & 16 \\ \hline
**Max. Field Rate (FPS)** & 1000 & 150,000 & 20,000 \\ \hline
**Sensor Data Rate** & 1 Gbps & 10.24 Gbps & 1.54 Gbps \\ \hline
**Pixel CVF Equivalent** & 120 \(\upmu\)V/e\({}^{-}\) & >1 V per SPAD Event & \textgreater{}1 V per SPAD Event \\ \hline
**Bit Error Rate** & Not Reported & Not Reported & 1.7 \(\times\) 10\({}^{-3}\) BER \\ \hline
**Read Noise (e\({}^{-}\)) or Equivalent** & Not Reported & Not Reported & 0.168 e\({}^{-}\) \\ \hline
**Power During Operation** & 20 mW & 1650 mW & 40.8 mW \\ \hline
**Power FOM†** & 2.5 pl/b (ADC only) & 168 pl/b & 104 pl/b \\  & 19 pl/b (Full Sensor) & (Full Sensor + SPADs) & (Full Sensor + SPADs) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Binary capture, oversampled output, quanta image sensor comparison table. FOM† = Sensor power/(No. of Pixel \(\times\) FPS \(\times\) N), where N = ADC resolution = 1b for these sensors.
###### Abstract  
Remediation of formerly used war zones requires knowledge of the depth of burial (DoB) of unexploded ordnances (UXOs). The DoB can vary greatly depending on soil and ballistic conditions, and their associated uncertainties. In this study, the well-known physics-based Poncelet equation is used to set a framework for stochastic prediction of the DoB of munitions in sandy, clayey sand, and clayey sediments using Monte Carlo simulations (MCSs). First, the coefficients of variation (COVs) of the empirical parameters affecting the model were computed, for the first time, from published experimental data. Second, the behavior of both normal and lognormal distributions was investigated and it was found that both distributions yielded comparable DoB predictions for COVs below 30%. However, a lognormal distribution was preferred, to avoid negative value sampling, since COVs of the studied parameters can easily exceed this threshold. Third, the performance of several MCS sampling techniques, including the Pseudorandom Generator (PRG), Latin Hypercube Sampling (LHS), and Gaussian Process Response Surface Method (GP_RSM), in predicting the DOB was explored. Different probabilistic sampling techniques produced similar DoB predictions for each soil type, but GP_RSM was the most computationally efficient method. Finally, a sensitivity analysis was conducted to determine the contribution of each random variable to the predicted DoB. Uncertainty of the density, drag coefficient, and bearing coefficient dominated the DoB in sandy soil, while uncertainty in the bearing coefficient controlled DoB in clayey sand soils. In clayey soil, all variables under various distribution conditions resulted in approximately identical predictions, with no single variable appearing to be dominant. It is recommended that Monte Carlo simulations using GP_RSM sampling from lognormally distributed effective variables be used for predicting DoB in soils with high COVs.  
p 2025153259
## 3 Results and Discussion
### Starch Particle Size Analysis  
As shown in Table 2, following the ultrasonic treatment, the average particle size of the broken rice starch expanded from 131.5 nm to 314.53 nm. The increase in the average particle size of the starch granules after the ultrasonic treatment might be attributed to the cavitation and mechanical effects of the ultrasonic process. Such cavitation could lead to the breakage of intramolecular hydrogen bonds within the starch structure, generating more pores and fissures on the surface. In turn, this may have enlarged the structure of the starch granules by inducing changes in their physical geometry, thereby resulting in a significant increase in particle size [57]. Upon compounding with quercetin after the ultrasonic treatment, the average particle size decreased from 314.53 nm to 242.53 nm. This could potentially be because quercetin penetrated through the pores into the depths of the starch granules and attached to them, forming a complex and undergoing re-association, consequently leading to a reduction in particle size.
###### Abstract  
Fracture toughness is used to characterize rock resistance to fracturing and it is important in theoretical research and engineering applications. Mode-I fracture toughness can be predicted on the basis of an empirical relationship between fracture toughness (\(K_{IC}\)) and tensile strength (\(\sigma_{t}\)). In underground engineering, rocks are often subjected to different temperatures. Therefore, this paper explores the effect of temperature on the relationship between mode-I fracture toughness and tensile strength. The results show that the change trends in the \(K_{IC}\) and \(\sigma_{t}\) values of rocks at temperatures from 20 \({}^{\circ}\)C to 600 \({}^{\circ}\)C are broadly consistent with each other. For rocks heat-treated to the same temperature, the \(K_{IC}\) of the rock increases with an increase in \(\sigma_{t}\). This positive correlation between \(K_{IC}\) and \(\sigma_{t}\) is different in rocks heat-treated to different temperatures. Critical crack propagation radius (\(r_{IC}\)) is an important factor in the relationship between \(K_{IC}\) and \(\sigma_{t}\) and is related to the type of rock and the conditions under which it is tested. For the same rock, \(r_{IC}\) is quite different after it has been exposed to different temperatures. The positive correlation between \(K_{IC}\) and \(\sigma_{t}\) results from a similarity in the fracture morphology and properties of failure when rock is destroyed in fracture and tensile tests.  
**Keywords:** rock; fracture toughness; tensile strength; temperature; crack propagation radius +
Footnote †: journal: Applied Mechanics and Physics
# Enhancement of PV Panel Power Production by Passive Cooling Using Heat Sinks with Perforated Fins
## 4 Validation of the Model
### Heat Sink Validation  
An experimental study on heat sinks attached to a PV panel [34] were used in order to compare the results with the present model (Figure 7b).  
A reasonable variation of the simulated PV panel average temperature compared to the literature for low and high ambient temperatures was recorded, in addition to different levels of radiation (Figure 7). The differences recorded were between \(-5.18\%\) and \(+4.03\%\). According to the literature [49], this variation was acceptable so the study was considered consistent and the next step consisted of modelling the heat sinks with perforated fins (Cases 2-6) in the same conditions as those validated above.
## 4 Discussion
### Transition to Future Work  
Aspect-based sentiment analysis (ABSA) has demonstrated its effectiveness in extracting fine-grained sentiment tied to specific aspects. However, as datasets become increasingly complex and large, future research could explore the integration of knowledge graphs to enhance the interpretability and depth of ABSA models. Knowledge graphs provide a structured representation of entities and their relationships, enabling richer contextual understanding [36]. In the context of higher education, a knowledge graph could map relationships between key aspects such as teaching quality, curriculum relevance, infrastructure adequacy, and administrative services.  
Future research can also focus on developing hybrid models that combine pre-trained language models such as BERT or IndoBERT with knowledge graph embeddings. Such integration could improve the model's ability to interpret complex sentiment structures while maintaining accuracy. Additionally, dynamic knowledge graphs, which evolve as new data are added, could enable real-time updates to reflect changing sentiment trends. For instance, continuous feedback from students can be incorporated into the knowledge graph, allowing institutions to respond promptly to emerging issues.