esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Enzymatic Studies
#### 2.8.2 Inhibition of Tyrosine  
Tyrosinase in 67 mM phosphate buffer (pH 6.8) was prepared at a concentration of 242 units/mL. L-DOPA (L-3,4-dihydroxyphenylalanine) was prepared as a working solution at 5 mM. The experimental protocol involved adding 50 mL of the tested extract, 80 mL of phosphate buffer, and 40 mL of tyrosinase (final concentration of 46 units/mL) to a 96-well plate. After a 5-min incubation at 25 degC, 40 mL of L-DOPA (final concentration of 0.95 mM) or 40 mL of phosphate buffer (for the blank) was added. Absorbance measurements were taken on a multiplate reader (Tecan Infinite 200 Pro, TK Biotech, Warsaw, Poland) at 475 nm after a 30-min incubation period, with kojic acid (50-250 mg/mL) serving as the positive control for inhibition. The results are presented as the average of three independent measurements along with the standard deviation.  
The percentage inhibition of tyrosinase is calculated as follows (Equation (7)):  
\[\%\text{inhibition}=\left(\frac{\text{A}_{\text{slope}}-\text{A}_{\text{ test}}}{\text{A}_{\text{slope}}}\right)\times 100\% \tag{7}\]
###### Abstract  
With the growing demand for high-efficiency and secure information transmission, ultra-long-range optical communication has demonstrated significant potential. This paper proposes a method for establishing ground-based fixed-point ultra-long-range atmospheric optical communication links, aiming to overcome challenges such as atmospheric turbulence, transmission loss, and environmental interference. Through theoretical analysis and experimental validation, we developed a high-precision optical communication terminal installation model, determined the terminal's optical axis direction via stellar calibration, and established a coordinate transformation model from geodetic coordinates to initial pointing angles. By analyzing initial pointing errors, we designed a laser link scanning strategy to compensate for uncertainties in the initial pointing region. The feasibility of this approach was verified through near-field validation and a long-distance link acquisition experiment exceeding 100 km. Experimental results demonstrated successful 100 km/100 Gbps ultra-high-speed optical communication. This breakthrough study is expected to play a critical role in future space-localized optical communication networks.  
ultra-long-range optical communication; atmospheric turbulence; laser link acquisition Article
# Increasing Photovoltaic Systems Efficiency Through the Implementation of Statistical Methods
###### Abstract  
The article emphasises both the advantages and disadvantages of photovoltaic power plant deployment, assessing the current stage of development as well as the deficient characteristic criteria, such as the occupied specific surface area or the associated unpredictability. The authors consider that current technologies related to photovoltaic plants provide a maximum efficiency of approximately 28%. Consequently, management methods must be applied in order to improve efficiency and eliminate the reported deficiencies. When assessing a medium- to high-power PV plant, the initial investment, projected efficiency, and parameters of the desired plant are correlated, and sometimes, a cheaper and less efficient power plant can be recommended. Although solar trackers may represent a viable solution in certain scenarios, their effectiveness is strongly influenced by various factors, including panel orientation, climatic conditions, installed capacity, and the specific technologies. These variables can significantly affect such systems' overall efficiency and suitability. The present study proposes a statistical approach to assessing the economic efficiency of photovoltaic systems equipped with solar trackers, aiming to enhance energy production performance. The results are correlated and validated using field data obtained from existing literature studies to ensure the reliability and accuracy of the analysis. For a better analysis, the paper presents two methods, ANOVA and STEM, which are derived from quality control. The novelty aspect of this proposal consists of the combination of specific data obtained from the PVGIS platform with a new approach for optimisation of energy production in photovoltaic systems based on geographical coordinates. The STEM statistical method provides a high degree of novelty because, although it is a well-known method, it has not yet been applied to analyse the technical and economic efficiency of photovoltaic systems. One of the main advantages of this method is its ability to incorporate a wide range of technical and economic performance parameters. A case study is provided to evaluate the benefits of implementing the STEM method.  
photovoltaic power plant; efficiency; ANOVA; STEM; Kruskal-Wallis test +
Footnote †: journal: Applied sciences  
Article
## 5 Design Considerations
### Design of \(L_{r}\) and \(C_{r}\)  
The proposed soft switching structure is based on the turn-on of the auxiliary switch \(S_{u}\)/ prior to the turn-on of the main switch \(S_{1}\), so that the resonant inductor \(L_{r}\) resonates with the resonant capacitor \(C_{r}\) and the parasitic capacitor of \(S_{1}\), called \(C_{S1}\). As the current \(i_{L}\) is larger than the current \(I_{L1}\), the voltage across \(S_{1}\), called \(v_{S1}\), drops to zero abruptly, the parasitic diode of \(S_{1}\), called \(D_{S1}\), is switched on, and afterwards \(S_{1}\) is switched on with ZVT. To avoid the converter operating abnormally, the time interval, used to execute resonance, is not too long. Therefore, the turn-on time interval of the auxiliary switch \(S_{a}\) is chosen to be one tenth of the switching period \(T_{s}\), about 2 \(\mu\)s. Meanwhile, to reduce the effect of \(i_{L}\), on \(S_{1}\), the turn-on instant of \(S_{a}\) is prior to the turn-on instant of \(S_{1}\) by one fifth of the turn-on time interval of \(S_{a}\), namely 0.4 \(\mu\)s. Hence, to obtain the soft switching of \(S_{1}\), the time interval for \(v_{S1}\) to drop to zero is chosen to be 0.5 \(\mu\)s or less as shown in (11):  
\[(t_{1}-t_{0})+(t_{2}-t_{1})\leq 0.5\ \mu\text{s} \tag{11}\]  
where during the time interval between \(t_{1}\) and \(t_{0}\), the resonant current \(i_{L,r}\) rises from zero to the current \(I_{L1}\), whereas during the time interval between \(t_{2}\) and \(t_{1}\), the voltage across \(S_{1}\) drops from the output voltage \(V_{o}\) to zero.  
In Equation (11), the former time interval was quite significantly larger than the latter time interval, so the former time interval at a rated load is set at half of the time interval between the turn-on instant of \(S_{a}\) and the turn-on instant of \(S_{1}\), namely 0.2 \(\mu\)s, so as to make sure that \(S_{1}\) can operate under ZVT turn-on within the time interval of 0.5 \(\mu\)s. Therefore, the following equation can be obtained to be:  
\[(t_{1}-t_{0})=\frac{1}{\omega_{1}}\sin^{-1}\left[\frac{I_{L1}Z_{1}}{V_{o}} \right]=0.2\ \mu\text{s} \tag{12}\]  
To make sure that the resonant period of the auxiliary circuit, called \(T_{1}\), can make the ZVS turn-on of the main switches S\({}_{1}\) and S\({}_{2}\) happen within the switching period \(T_{s}\), \(T_{1}\) is set at 0.75 \(T_{s}\), equal to 7.5 \(\mu\)s, and afterwards, based on (12), the values of \(L_{r}\) and \(C_{r}\) can be determined to be 1.25 \(\mu\)H and 1.14 \(\mu\)F.
# Detecting Out-of-Distribution Samples in Complex IoT Traffic Based on Distance Loss
## 4 Experiments
### Hyperparameter Sensitivity Analysis
#### 4.4.2 Threshold Sensitivity to Group Number and Bandwidth  
We further study how threshold selection via KDE responds to variations in group number \(G\) and bandwidth \(h\). As shown in Figure 7, we evaluate the F1 score of the OOD decision results under varying group numbers (\(G\in[2,6]\)) and different KDE bandwidths (\(h\in\{\text{'scott'},\text{'silverman'},0.1,0.2,0.3,0.5,1.0\}\)). We find that changes in F1 score exhibit similar trends to AUROC with respect to \(G\), indicating that the thresholding closely tracks the underlying feature space quality. For the bandwidth \(h\), performance remains stable when \(h\in[0.2,0.3]\), while larger values such as \(h=0.5\) or \(1.0\) cause over-smoothed densities and imprecise thresholds.  
Figure 7: Sensitivity analysis of key hyperparameters in the proposed OOD detection framework. Left: The effect of varying the number of groups on the F1 score under the KDE-based thresholding strategy. Results are obtained from the IoT dataset. Right histogram of OOD detection scores for in-distribution (ID, in blue) and out-of-distribution (OOD, in red) test samples. Overlaid Kernel density estimation (KDE) curves illustrate the effect of different bandwidth values (\(h\)) on score distribution smoothness. Larger bandwidths (e.g., \(h=0.5\) or \(h=1.0\)) lead to over-smoothing and reduced separability between ID and OOD samples.  
These results demonstrate that our method is relatively robust to hyperparameter settings. In particular, the KDE-based thresholding mechanism performs reliably under a broad range of bandwidths and group configurations, making it well-suited for practical deployment where exhaustive tuning may not be feasible.
## 4 Results
### Performance Experiment
#### 4.1.2 Rotating Classification Model  
This section presents testing of the performance of rotation-based classification models and comparison of the network performance of ResNet series. The performance of other types of classification models was also compared, and the results are shown in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification Methods** & _Accuracy_ & _Recall_ & **Specificity** & **F1** & **AUC** \\ \hline [PERSON] [25] & 0.7894 & 0.7546 & 0.8234 & 0.8601 & 0.8596 \\ [PERSON] [22] & 0.7920 & 0.7581 & 0.8380 & 0.8796 & 0.8576 \\ [PERSON] [9] & 0.8544 & 0.8345 & 0.8581 & 0.8601 & 0.9101 \\ [PERSON] [20] & 0.8544 & 0.8545 & 0.8581 & 0.8701 & 0.9032 \\ [PERSON] [19] & 0.8695 & 0.8645 & 0.8481 & 0.8801 & 0.9232 \\ ResNet50 & 0.8801 & 0.8753 & 0.8534 & 0.8706 & 0.9302 \\ [PERSON] & 0.8931 & 0.8865 & 0.8957 & 0.8942 & 0.9453 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results and comparisons of classification models for breast ultrasound reports.  
From the table, it is evident that as the depth of the ResNet model increases, it can extract more profound feature information from images. There is a significant improvement in the model's classification performance from ResNet18 to ResNet50. ResNet34 achieves the best results in terms of accuracy, recall rate and \(F1\) score. While ResNet50 attains the highest AUC score of 0.8862, it is slightly inferior to ResNet34 in other performance metrics. Although the performance of ResNet34 and ResNet50 are comparable, ResNet34 has a relatively simpler structure with fewer parameters. Considering this study's practical use case, we opt for ResNet34 as our rotation classification model.  
From the results, it can be seen that the ResNet models perform better than VGG, InceptionV3 and DenseNet121 in terms of accuracy, AUC and \(F1\) score. However, in terms of recall rate, DenseNet121 performs better than ResNet34. Additionally, from the table it can be seen that DenseNet121 and ResNet34 have similar and higher performance in all indicators compared with the other two models. This also reflects that by effectively utilizing features from previous layers to construct deeper and more accurate convolutional neural network models, image recognition and classification performance can be improved.
# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 4 Results and Discussion  
The results of two case studies are both qualitatively and quantitatively analyzed. The synoptic view of multi-temporal SAR data in the form of RGB combinations is widely used in the qualitative interpretation of land cover and surface dynamics [58; 59]. Different RGB combinations are adopted to give an intuition of flood extent in terms of intensity and coherence. For both cases, the results obtained from the fusion of intensity and coherence, and from intensity alone are quantitively analyzed. The overall accuracy (OA), kappa coefficient (\(\kappa\)), false-positive rate (FPR), precision (i.e., the correctly predicted positive patterns from the total predicted patterns in a positive class), recall (i.e., the fraction of positive patterns that are correctly classified), and F1 score (i.e., the harmonic mean between recall and precision) [60] are reported based on the flood reference derived from the aerial photographs mentioned in Section 3. In addition, the temporal variation of intensity and coherence as a result of flooding over different land cover types are also analyzed and discussed.
# Changes in Oil Quality and Peroxidase Activity during Germination of Rape Seeds and Mustard Seeds
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
Principal component analysis (PCA) was applied to observe possible clusters in oil samples obtained from rape and white mustard seeds. The first two principal factors accounted for 81.5% (Dim1 = 55.9% and Dim2 = 25.6%) of the total variation. The PCA analysis showed differences between the samples of oil obtained from rape seed and  
Figure 7: Protein content [%] in rapeseeds and mustard seeds during germination (days 0, 1, 2, 3).  
mustard seed but also within the individual oils (Figure 9). Factor 1 was mainly correlated with the content of plastochromanol-8 (PC-8) (r = 0.990), PUFA (r = 0.969) and total sterols (r = 0.893). It was also negatively correlated with the MUFA (r = \(-\)0.975), SFA (r = \(-\)0.946) and total tocopherools (r = \(-\)0.823). Factor 2 was mainly correlated with peroxide value (r = 0.880) and negatively correlated with peroxidase activity (r = \(-\)0.854). The data shown in the score plot divided samples of oil into two groups. On the left side of the Y axis, there are oil samples obtained from rape seeds. On the right side of the axis, there are oil samples obtained from mustard seeds. Additionally, the observed distances between individual samples presented in the score plot are bigger in the case of rapeseed oil. This is especially true for the oil obtained from seeds after the 1 st and 3 rd day of germination. In the score plot, the samples of oil from non-sprouted seeds (R0, M0) are above the x-axis, but in two different quarters of the diagram. This is mainly due to differences in the total content of phytosterols, tocopherools, and PC-8 as well as the peroxide and acid numbers. Rapeseed oil was characterized by a lower content of sterols and PC-8 and a higher content of toochromanols compared to mustard oil. In rapeseed oil, the highest peroxide value and the lowest acid value were observed. The germination process changed the composition of the obtained oil and the seeds used. The shape of the surface area of all samples is similar in both cases (rape and mustard seeds). However, the distance between the non-germinated sample and after three days of germination is shorter for the mustard seed and its oil (Figure 9). After three days of germination, the mustard seeds were characterized by lower peroxidase activity (0.162 and 0.213 \(\Delta\)OD/min/ug protein for mustard and rapeseed oil, respectively) and protein content (8.40% and 9.36% for mustard and rapeseed oil, respectively). The mustard seed oil was characterized by higher sterols and PC-8 content, higher level of peroxide value, and lower acid value, compared to rapeseed oil.  
Figure 8: Peroxidase activity [\(\Delta\)OD/min/ug proteins] in rapeseeds and mustard seeds during germination (days 0, 1, 2, 3) (values are means \(\pm\) SD from three experiments.).
## 1 Introduction
For optimizing the coupled phase-shift variables, we utilize the existing Penalized Dyadic Decomposition (PDD) framework to update them alternately in closed form. For the IP scheme, the non-convex phase shift constraints are approximated as tractable convex constraints using the Concave-Convex Procedure (CCCP) method. Numerical results compare the performance of the proposed algorithms, which are validated against four benchmark schemes for both coupled and independent phase-shift models. To the best of our knowledge, similar algorithms in this specific area have not been examined in this study. This research provides new ideas and methods to enhance the security of MISO PLS systems and promote their development and application. We organize the remainder of this paper in the following order: Section 2 presents the STAR-RIS-assisted MISO system model and formulates the optimization problem for various phase shift schemes. Section 3 shows the analysis of the proposed algorithms. Section 4 presents the simulation results of the algorithms and compares the performance of different schemes. Section 5 summarizes the thesis work. Notations: Bold letters represent vectors. \(\mathbb{C}\) represents the set of complex numbers. \(\mathbb{E}\{x\}\) represents the mathematical expectation about \(x\). \(\mathcal{CN}(\mu,\sigma^{2})\) stands for the complex Gaussian distribution with mean \(\mu\) and variance \(\sigma^{2}\). \(Diag(\cdot)\) is for constructing a triangular matrix. \(\mathfrak{R}\{\cdot\}\) and \(\mathfrak{I}\{\cdot\}\) denote the real and imaginary parts of the complex variable, respectively. \(\left|\kern-1.075 pt\left|\kern-1.075 pt\left|\kern-1.075 pt\left|\kern-1.075 pt \left|\kern-1.075 pt\left|\left|\left|\left|\left|\left|\left|\left|\left| \left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left| \left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left| \left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\left|\right| \right|\right|\right\|\right|\right|\right|\right|\right|\right|\right|\right| \right|\right|\right|\right|\right|\right|\right|\right|\right|}\right)\right)\right.\right.\) denotes the Euclidean norm of a vector or the Frobenius of a matrix. \(Tr(\mathbf{A})\) represents the trace of matrix \(\mathbf{A}\).
## 5 Future Work
### Attempts to Estimate Rainfall Using Cellphone Signals  
As fiber optic communications are increasingly used in cellular communication networks, CMLs will become less available in certain areas. In this case, the large number of links between user terminals and cell towers (base stations) may be an alternative. These non-line-of-sight links typically operate at frequencies below 6 GHz and are, thus, theoretically insensitive to rainfall in the propagation path [209]. However, during rainfall, the wetting effect of obstacles in the path [210; 211], reflective surfaces (walls, grounds, etc.) [212; 213; 214] and the transmitting antenna [215; 216] may significantly affect the signal quality. [PERSON] et al. [217] constructed a probabilistic neural network to distinguish rainfall classes using statistical features of 4G/Long Term Evolution (LTE) signals and achieved a classification accuracy of 96.7%. Subsequently, [PERSON] et al. [218] built a 2 GHz experimental link and used the C4.5 algorithm to implement the classification of dry and wet periods based on cellphone signals. In order to estimate the distance using low-frequency radio signals, [PERSON] et al. [219] developed a path loss model considering rainfall attenuation effects, which achieved a satisfactory performance. Therefore, with distances between the receiving terminal and the cell tower known, the model should be able to provide accurate rainfall estimation. Recently, the literature [220] also reported cases of estimating rainfall using cellphone signals based on deep learning algorithms. Although preliminary studies on the use of cellphone signals to monitor rainfall have been conducted, the law of rainfall-induced comprehensive effects on signal propagation is still unclear and the generalization ability of the models proposed in the above studies is yet to be verified. Therefore, future explorations can attempt to model low-frequency non-line-of-sight signal transmissions to address the limitations in current studies of rainfall measurement by cellphone signals, especially in regions where the number of CMLs is decreasing.
## 4 Experiment I--DSM Quality Evaluation
### Statistical Evaluation for Different Land Cover Types
One reason could be that in low-density forests, LiDAR data tend to measure terrain height rather than canopy height. Matching the optical image is also more challenging in low-density forest regions, as explained in [4], which influences the quality of the photogrammetrically derived DSMs.
## 2 Models and Methods
Therefore, the mutual inductance can be estimated by measuring the rectifier output voltage \(U_{\rm f}\). Based on the mutual inductance \(M\) estimation result, system transmission efficiency can be improved by adjusting the inverter phase shift angle. Considering the expression of \(R_{\rm e}\) shown in (2) and the optimal load resistance shown in (7), and then setting \(R_{\rm e}=R_{\rm e\_nmax}\), the inverter optimal phase shift angle can be obtained from (11) to achieve maximum transmission efficiency. \[\varphi_{\rm opt}=2\arccos\left(\sqrt{\frac{\pi^{2}U_{\rm out}R_{\rm e\_nmax }L_{\rm p1}^{2}}{8M^{2}U_{\rm dc}^{2}}}\right) \tag{11}\]  
Finally, on the basis of the above analysis, the proposed coordinated control method is presented step by step as follows:  
Step 1: Measure the rectifier output voltage \(U_{\rm f}\), the Buck-Boost converter output voltage \(U_{\rm out}\), and the output current \(I_{\rm out}\). Step 2: Calculate the load resistance \(R_{\rm L}\) according to the measured output voltage \(U_{\rm out}\) and output current \(I_{\rm out}\); then get the equivalent resistance \(R_{\rm e}\) based on (2). Step 3: According to the obtained result of \(R_{\rm e}\), get the real-time estimated value of the mutual inductance \(M\) based on (10). Step 4: Obtain the optimal phase shift angle \(\varphi_{\rm opt}\) based on (11), and then the inverter phase shift angle is adjusted to achieve maximum transmission efficiency tracking. Step 5: Adjust the duty cycle of the Buck-Boost converter to achieve the constant voltage output. According to the above steps, the proposed WPT coordinated control method can be implemented, which can improve WPT system transmission efficiency while maintaining the constant output voltage.