esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison of PEH/EMEH Assuming Typical Parameter Values  
Systems in the regime, \(Q\approx Q_{crit}\), \(\xi_{E}\ll 1\) and \(\eta\ll 1\), are uncommon according to the small survey performed in [8]. This survey shows the typical case for an EMEH to be \(Q\gg Q_{crit}\) and \(\xi_{E}\gg 1\), and for a PEH it is \(Q<Q_{crit}\) and \(\xi_{E}\ll 1\). Even though \(Q\gg Q_{crit}\) for an EMEH, it is still likely less than \(k_{crit}Q_{crit}\), as \(k_{crit}\) increases exponentially with \(\xi_{E}\) (see Figure 3). This leads to both systems typically having only one power optimum, \(\gamma_{opt\_R}\), corresponding to the resonant state. The same source, [8], states that \(\eta\) is typically small for a PEH (also mentioned in [1]) compared to an EMEH. Assuming \(\eta\ll 1\) (which implies \(Q_{crit}\gg 1\)) for the PEH, we can conclude that the typical case is that both systems have \(\gamma_{opt\_R}\approx 1\).  
Using the typical values for \(Q\) and \(\xi_{E}\) for an EMEH and PEH, respectively, we can state that power generated from an EMEH can be found in the upper right region of Figure 7 (left), while for the PEH it is found in the lower left region of Figure 7 (right). These numerical results indicate that, for a specific value of \(\overline{P}\), the dependance between \(k\) and \(\xi_{E}\) has a constant linear slope in the logarithmic space, if \(k>1\), with varying offset depending on the value of \(\overline{P}\). We find that, regardless of \(\eta\), the linear slope in this region is approximately 1. The dependance between the ratio \(k/\xi_{E}\) and value of \(\overline{P}\) to be traced thus follows a logarithmic slope. Figure 15 shows \(k/\xi_{E}\) as a function of \(\overline{P}\) at \(\eta=\{5,1,0.1\}\). We can see that the sensitivity of \(k\) to \(\xi_{E}\) increases dramatically near the theoretical power maximum. The quality factor and/or effective coupling coefficient required to achieve a power output close to the theoretical maximum can thus become large for energy harvesters with a large loss coefficient, such as for a typical EMEH.  
In the region of \(k<1\) and \(\xi_{E}\ll 1\), the sensitivity of \(k\) to \(\xi_{E}\) is small. The power is in this region mainly a function of \(k\) and \(\eta\). The goal for both types of energy harvesters should be to achieve a load power as close to the theoretical maximum as possible, without requiring a large quality factor, as the latter is equivalent to a small bandwidth. In both cases, \(\eta\) should be as large as possible, while \(k\) should be close to its critical value (see Figure 3). The loss factor, \(\xi_{E}\), typically only relevant for an EMEH, should be minimized.  
The same arguments regarding load power can be applied to efficiency if load and excitation frequency remain optimized by load power.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 2 The Plasma Sheath and the THz Array Antenna
### The Plasma Sheath and THz Array Antenna Coupling Models  
The antenna performance can be obtained by solving the plasma antenna coupling model. The electromagnetic field \(\overrightarrow{\mathrm{E}}\) of the active antenna can be solved with the inhomogeneous Helmholtz wave equation for plane waves:  
\[\
abla\times\
abla\times\overrightarrow{\mathrm{E}}-\omega^{2}\varepsilon_{0} \mu_{0}\varepsilon_{p}\cdot\overrightarrow{\mathrm{E}}=i\omega\mu_{0}J \tag{4}\]  
where \(\omega\) is the angular frequency, \(\varepsilon_{0}\) is the vacuum permittivity, \(\mu_{0}\) is the vacuum permeability, \(\varepsilon_{p}\) is the relative permittivity of plasma, and \(J\) is the exciting current. When the equation is compared with the wave equation for propagation in vacuum, it is notable that all of the plasma-physical response is bound up in \(\varepsilon_{p}\)[22]. With the above-written equation, the near field is solved with the finite element method (FEM) [23].  
However, the FEM is not suitable for the far field calculation since the thickness of the plasma sheath in Figure 1 is several times larger than the wavelength of 0.14 THz signals. To reduce the computation, the far field can be solved from the near field \(\overrightarrow{\mathrm{E}}\) with the integration method. The Stratton-Chu formula [24] is used:  
\[\overrightarrow{\mathrm{E}_{p}}=\frac{jk}{4\pi}\overrightarrow{r_{0}}\times \int\limits_{S}\left[\vec{n}\times\overrightarrow{\mathrm{E}}-\eta\sqrt{ \frac{\mu_{p}}{\varepsilon_{p}}\overrightarrow{r_{0}}}\times(\vec{n}\times \vec{H})\right]\!e^{(j\vec{k}\vec{r}\cdot\overrightarrow{r_{0}})}dS \tag{5}\]  
where \(\vec{\mathrm{E}}_{p}\) is the calculated far field in the direction from the origin towards point \(p\). \(\vec{r_{0}}\) is the unit vector pointing from the origin to the field point \(p\). \(\vec{r}\) is the radius vector of the surface \(S\). \(n\) is the unit normal to the surface \(S\).
# New Results for the Error Rate Performance of LoRa Systems over Fading Channels
## 3 Main Results
### Symbol Error Probability for Rice Fading Channels  
Under Rice fading, the RV \(h^{2}\) follows a non-central chi-square distribution with PDF given as [28]  
\[f_{h^{2}}(y)=\frac{1+K}{\exp(K)}\exp[-(1+K)y]I_{0}\bigg{[}2\sqrt{K(1+K)y}\bigg{]}, \tag{24}\]  
where \(K\) is the Rice factor. For \(K=0\), (24) reduces to the exponential distribution, i.e., Rayleigh fading.  
An exact analytical expression for \(P_{s}\) is given in the following proposition.  
**Proposition 4**.: _The exact symbol error probability of LoRa systems under Rice fading in terms of a single integral is given as_  
\[P_{s}^{\rm Rice} =\frac{(1+K)\exp(-K)}{2(1+K+N\gamma)}\int_{0}^{\infty}e^{-x/2}[1- (1-e^{-x/2})^{L}]\] \[\times e^{\frac{2K+2K^{2}+\gamma Nz}{2+2K+2N}}I_{0}\bigg{[}\frac{ \sqrt{2 NK(1+K)\gamma x}}{1+K+N\gamma}\bigg{]}\mathrm{d}x \tag{25}\]  
Proof.: The proof can be concluded by following a similar line of arguments as in the proof of Proposition 1. Specifically, by substituting (24) into (9) and changing the order of integration, \(P_{s}\) can be expressed as  
\[P_{s}=\frac{1+K}{2\exp(K)}\int_{0}^{\infty}e^{-x/2}[1-(1-e^{-x/2 })^{L}]\] \[\times\bigg{[}\int_{0}^{\infty}e^{-(N\gamma+K+1)y}I_{0}\bigg{[}2 \sqrt{K(1+K)y}\bigg{]}\] \[\times I_{0}\Big{(}\sqrt{2 Nxy}\Big{)}\mathrm{d}y\bigg{]}\mathrm{d}x. \tag{26}\]  
The inner integral, i.e., with respect to \(y\), can be evaluated in closed form by employing [29] (Equation (3.15.17/1)), yielding (25), thus completing the proof.  
Again, (25) converges rapidly due to its exponentially decaying kernel and can be evaluated numerically in an efficient manner. In what follows, an accurate approximation for \(P_{s}\) will be derived. The following result holds.  
**Proposition 5**.: _Under Rice fading, an accurate closed-form approximation for \(P_{s}\) can be deduced as_  
\[P_{s}^{\rm Rice}\approx 1-Q_{1}\bigg{[}\sqrt{\frac{2N\gamma K}{1+K+N\gamma}} \sqrt{\frac{\tilde{x}_{N}(1+K)}{1+K+N\gamma}}\bigg{]} \tag{27}\]  
Proof.: Using (18) and (24), \(P_{s}\) can be approximated as  
\[P_{s}\approx 1-\frac{1+K}{\exp(K)}\int_{0}^{\infty}e^{-(1+K)y}I_{0}\bigg{[}2 \sqrt{K(1+K)y}\bigg{]}\] \[\times Q_{1}\Big{(}\sqrt{2N\gamma y},\sqrt{\tilde{x}_{N}}\Big{)} \mathrm{d}y \tag{28}\]  
By employing [35] (Equation (15)), the resulting integral can be evaluated in closed-form yielding (27), thus completing the proof.
# Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning
## 2 Proposed Adaptive Clustering
### Spectral Clustering  
Spectral clustering is an algorithm that evolved from graph theory and has been widely used in different fields such as speech recognition, document analysis, and image segmentation. Implementation of the spectral clustering model includes construction of a similarity matrix and clustering partition. The detailed process is as follows:  
* Give the face representations \((X_{1},X_{2},\ldots,X_{n})\), and calculate the similarity matrix \(S\) based on the full connection method.
* Construct the adjacency matrix \(W\) based on \(S\), and then calculate the degree matrix \(D\).
* Calculate the Laplacian matrix \(L\): \(L=D-W\)
* Construct the normalized Laplacian matrix \(L^{*}\): \(L^{*}=D^{-1/2}LD^{-1/2}\)
* Create a set of eigenvectors by computing the eigenvectors of \(L^{*}\) and use dim eigenvectors corresponding to the largest dim eigenvalues. The dim is an adjustable parameter, which must be less than the dimension of the representation \(X\).
* Create a new face representation matrix \(F\), which uses these dim eigenvectors as columns; these eigenvectors are also normalized by L2.
* With the new representation matrix \(F\), the final clustering result is generated by using K-means++.  
In the above steps, we use the Gaussian Dadial Basis Function to construct \(S\) and \(W\) as:  
\[s_{ij}=s_{ji}=\sum_{i=1,j=1}^{n}\exp(-\frac{\left\|X_{i}-X_{j}\right\|^{2}}{2 \sigma^{2}}),(i,j\in 1,2,\ldots,n) \tag{1}\]  
where \(\sigma\) is the radial width parameter, \(w_{ij}\) is the adjoin metric and \(s_{ij}\) is the similarity metric of \(X_{i}\) and \(X_{j}\), respectively.
# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 4 Experiments
### Evaluation Metrics  
The experiment employed a diverse set of evaluation metrics to comprehensively assess the performance of the proposed model in tomato ripeness detection. Specifically, we analyzed mAP and mAP at IoU thresholds of 0.5 and 0.75 (designated as mAP\({}_{0.5}\) and mAP\({}_{0.75}\)) along with precision, recall, model parameter count (Parameters), and floating point operations (FLOPs). These combined metrics enable systematic evaluation of detection accuracy, computational efficiency, and model complexity.  
In this study, mAP is used as the primary metric to evaluate object detection model performance. The mAP measures detection performance across all categories by computing the average precision for each class. Researchers commonly use the mAP\({}_{0.5}\) metric, which applies a 0.5 IoU threshold. Under this criterion, we consider a predicted bounding box to be a correct detection if its IoU with the ground truth exceeds 0.5. To evaluate the model under stricter precision requirements, we also introduce the mAP\({}_{0.75}\) metric, which assesses performance at a higher IoU threshold. The following sections detail the specific calculation methods for these metrics:  
\[\text{mAP}=\frac{1}{N}\sum_{i=1}^{N}\frac{1}{M}\sum_{j=1}^{M}\text{AP}_{ij} \cdot I(\text{match}) \tag{14}\]  
Figure 5: Examples from our tomato maturity detection dataset.  
where \(N\) is the number of categories, \(M\) is the number of samples in each category, AP\({}_{ij}\) is the precision of the \(i\)-th class and \(j\)-th sample, and \(I(\text{match})\) is an indicator function for matching.  
In addition to mAP, precision and recall are critical metrics for evaluating the performance of the detection model.  
Precision: This metric measures the proportion of true positive detections among all predicted positive detections. It is computed as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{15}\]  
where \(TP\) denotes the number of true positive detections (correctly predicted tomatoes) and \(FP\) represents the number of false positives (incorrectly predicted tomatoes). Precision helps to evaluate how accurately the model detects tomatoes, with a higher precision indicating fewer false positive predictions.  
Recall: This metric measures the proportion of true positive detections among all actual positive samples in the dataset. It is computed as follows:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{16}\]  
where \(FN\) denotes the number of false negatives (missed tomato detections). Recall is important for evaluating how well the model detects all instances of tomatoes, with higher recall indicating fewer false negatives and better ability to detect all true positives.  
Together, precision and recall provide a more complete understanding of the model's performance, with precision focusing on correctness and recall focusing on completeness. Both metrics are essential for balancing false positives and false negatives, particularly in real-world agricultural environments where both detection accuracy and recall are crucial for effective ripeness assessment.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 1 Introduction
### What Is the Domain Gap Problem?
All the factors above collectively contribute to nontrivial differences in the data distributions. See Section 8.1 for concrete examples. Note that the domain gap problem exists regardless of what machine learning model or architecture is being used.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 2 Background on Weighted Least-Squares Adjustment and Dilution of Precision (DOP)
### Measurement Geometry and DOP  
In Equation (5), \(\mathbf{Q_{P}}\) is weighted by the cofactor matrix of measurements (\(\mathbf{Q_{R}}\)) and is a function of \(\mathbf{H}\), which itself is a function of the receiver-satellite geometry. Because the diagonal elements of \(\mathbf{Q_{P}}\) are typically greater than one, the precision is diluted. Therefore, these diagonal elements are used to constitute quality metrics for measurement geometry called (weighted) Dilution of Precision (DOP) [1]. Assuming the position errors are estimated in east, north and vertical (up) local coordinate frame, the corresponding East DOP (EDOP), North DOP (NDOP) and Vertical DOP (VDOP) values can be computed based on the square root of the diagonal elements of \(\mathbf{Q_{P}}\)[1]:  
\[\mathrm{EDOP} =\sqrt{\left(\mathbf{Q_{P}}\right)_{\Delta E}}, \tag{6a}\] \[\mathrm{NDOP} =\sqrt{\left(\mathbf{Q_{P}}\right)_{\Delta N^{\prime}}}\] (6b) \[\mathrm{VDOP} =\sqrt{\left(\mathbf{Q_{P}}\right)_{\Delta V}}. \tag{6c}\]  
The same approach is also considered for time estimation and corresponding Time DOP (TDOP) value. The Position DOP (PDOP) which relates to the three-dimensional (3D) position solution, is also computed as  
\[\mathrm{PDOP}=\sqrt{\left(\mathrm{EDOP}\right)^{2}+\left(\mathrm{NDOP}\right) ^{2}+\left(\mathrm{VDOP}\right)^{2}}. \tag{7}\]  
The same methodology can be applied for geometry DOP which also involves TDOP values in its definition. The PDOP values presented here will be used in Section 4.2 to develop the proposed geometry-based exclusion and de-weighting algorithms.
## 2 Literature Review
### Overview of PV Power Prediction
#### 2.1.3 Hybrid Methods
In a study where various forecast models were used, it was stated that the ANN model based on the genetic algorithm created the most accurate PV generation model in three different climatic condition tests [68]. [PERSON] et al. applied a Particle Swarm Optimization-based XGBoost model (PSO-XGBoost) for PV power prediction. They introduced the PSO algorithm to optimize the hyperparameters in the XGBoost model. They demonstrated that the prediction accuracy and stability of PV power prediction by the PSO-XGBoost model, based on meteorological data, are higher compared to the XGBoost model [69]. Similarly, a wide range of optimizers has been used to solve models or tune hyperparameters in various studies [70; 71; 72]. [PERSON] et al. proposed a new methodology to forecast the power production of a 48 kWp PV system located at Campus Feuchtwangen in 2025. Their proposed methodology incorporates hybrid time series techniques, including state-space models, supported by artificial intelligence tools to generate forecasts. Their results show an accuracy of about 3% for the forecast in nRMSE [73]. [PERSON] et al. presented a hybrid Attention-Temporal Convolutional Network (TCN)-LSTM model for short-term PV power forecasting. They utilized the PSO algorithm to determine the optimal hyperparameters, ensuring that these parameters did not impact the forecasting results. They obtained an RMSE value of 0.363, an MAE value of 0.161, and an R\({}^{2}\) value of 0.98 with their proposed algorithm [74].
# Effect of Injecting Epoxy Resin Adhesive into Cement Mortar on Tile Adhesion Performance
## 3 Results and Discussion  
### Permeability of Epoxy Resin into Mortar  
Figure 6 shows the viscosity and setting time of the epoxy resin adhesive at various solvent mixing ratios. The viscosity was drastically reduced to 2820 cps when the solvent was mixed at 5 wt%, whereas the highest fluidity (170 cps) was achieved when the solvent was mixed at 15 wt%. However, the workability was presumably low at a solvent mixing ratio of 15 wt% during the injection of the epoxy resin adhesive because the viscosity was similar to that of water. The setting times with no solvent (91.5 and 110.3 min) differed by 18.8 min when the solvent ratio was 15 wt%. Within the range of mixing ratios used in this study, the effect of the solvent on the setting time was smaller than that on the viscosity.  
Figure 7 depicts the penetration depth of the epoxy resin adhesive in relation to the solvent mixing ratio. The penetration depth was approximately 1.31 mm in the absence of solvent; however, it increased drastically to 5.3 mm when the solvent ratio was 5 wt%. A further increase in the penetration depth to 6.59 mm at a 15 wt% solvent mixing ratio confirmed that the penetration depth of the epoxy resin adhesive increased with increasing solvent mixing ratio.  
Figure 6: Viscosity and setting time of epoxy resin based on the solvent mixing ratio.  
Figure 8 shows the result of the mockup test in which the epoxy resin adhesive was injected at the tile joint at the top of a tile to evaluate the permeability of the adhesive as a function of the solvent mixing ratio. Contrary to the results of the test conducted using mortar bars, the mockup test confirmed that the optimum and lowest permeabilities were achieved when the adhesive contained 5 and 15 wt% solvent, respectively. The adhesive containing 5 wt% solvent was more viscous than those containing 10 and 15 wt%; therefore, the former took longer to move from the top to the bottom of the mortar, with more gradual penetration. In the case of the adhesive containing 15 wt% solvent, the reinforcement injected at the top traveled rapidly towards the bottom, resulting in insufficient time for penetration to occur. Hence, the adhesive was absorbed faster by the urethane foam than by the mortar at the bottom, thereby reducing the amount of the reinforcement penetrating the mortar. This result suggested that, for the injection method, the permeability of the epoxy resin adhesive should be carefully considered and that the solvent mixing ratio of 5 wt% provided the most effective penetration of the epoxy resin adhesive.  
Figure 7: Penetration depth of epoxy resin based on the solvent mixing ratio.
## 3 Results and Discussion
### Raw BSG Characterization and Pretreatment
The decrease of protein content might have resulted from the interaction of Ca\({}^{2+}\) with proteins, in a reaction pH dependent, and adsorbed on the calcium sulfate precipitate. The phosphate content also decreased with the addition of Ca(OH)\({}_{2}\), which can be explained by the reaction between the Ca\({}^{2+}\) and the anion PO\({}_{4}\)\({}^{3-}\) resulting in calcium phosphate precipitate formation [27]. PO\({}_{4}\)\({}^{3-}\) is an essential nutrient for the fermentation step. Though PO\({}_{4}\)\({}^{-}\) content was significantly reduced after neutralization, the ratio C:N:P of the hydrolysate was 100:33:2, which is still in the range of the reference values for anaerobic digestion [28; 29]. Furfural and 5-HMF concentrations also decreased with Ca(OH)\({}_{2}\) addition (Table 3), probably due to its conversion into other less toxic compounds and/or to its oxidation under alkaline conditions [30; 31]. [PERSON] et al. [31] reported the reduction of furfural and 5-HMF content more than half after hydrolysate treatment with Ca(OH)\({}_{2}\), whereas HAc concentration remained the same, which was also observed in the present study. According to the literature, the inhibitory concentrations were above 1 g L\({}^{-1}\) and 5 g L\({}^{-1}\), for furfural and HAc, respectively [32; 33]. 5-HMF was not considered to be inhibitory due to its low concentration. Then, although the decrease in furfural and 5-HMF concentrations with neutralization was desirable, because it reduces the negative impact on fermentation, the concentrations achieved were not inhibitory for the fermentation process. Results showed that acid hydrolysis is an efficient method for sugars extraction from BSG's fractions achieving a hydrolysis efficiency of 84.5% (Table 2). Therefore, the BSG hydrolysate rich in sugars represents an excellent carbon source for several biotechnological purposes, namely, for VFAs production through acidogenic fermentation.
## 1 Introduction
They also discovered that a higher P/F ratio and a longer pressure equalization time are conducive to the enhancement of hydrogen purity. Therefore, it is necessary to provide guidelines for optimizing the actual PSA performance through simulation, improving the efficiency and economy of the entire process. Activated carbon is a widely utilized adsorbent material for hydrogen purification through PSA technology. Coal-derived activated carbon (AC) is the most primitive activated carbon, with a wide range of sources and affordable prices. Zeolites, particularly valued for their high CO\({}_{2}\) adsorption capacity, are also extensively studied and commonly used in layered bed configurations with activated carbons for improving PSA hydrogen purification efficiency [23]. In this study, an adsorption dynamic model is developed for a lean hydrogen mixture (N\({}_{2}\)/CO\({}_{2}\)/H\({}_{2}\)/CO = 44.6/35.4/19.9/0.1 mol%) on a coal-derived activated carbon/zeolite 13X layered bed using adsorption isotherm experimental data. Additionally, the simulation results of breakthrough curves for both single and layered beds are compared with published data, validating the model's accuracy. This study also examines the effects of the feed pressure, feed flow rate, and AC/zeolite 13X ratio on the dynamic separation behavior on the layered bed. Based on these results, a dual-bed, six-step pressure swing adsorption cycle model is established. The accuracy of this cycle model is validated using purity and recovery as indicators of hydrogen purification performance. Finally, the impacts of various parameters, the feed pressure, feed flow rate, adsorption time, P/F ratio and AC/zeolite 13X ratio, on hydrogen purification performance are investigated.