esa-sceva/satcom-chunk-collection huggingface dataset:
# Light Transmission in Fog: The Influence of Wavelength on the Extinction Coefficient
## Theory and Definitions: Light and Fog
### Different Kinds of Fog  
In nature, the two most representative kinds of fog are defined according to the formation mechanism:  
* Radiation fog forms overnight or early in the morning. This type of fog generally dissipates in the morning. It frequently flows into valleys or low-lying areas. It may then also be qualified as \"continental fog\". Radiation fog is composed of small droplets, with diameters distributed around a mode between one and 10 microns.
* Advection fog forms when a moist warm air mass is pushed over a relatively cold surface. This type of fog can last all day. Advection fog may form over oceans and move to continents where the ground is colder. It may then also be qualified as \"maritime fog\". Advection fog is composed of large droplets, with diameters distributed around a mode between 10 and 20 microns.  
The DSD, denoted as \(n(r)\), characterizes the micro-structure of fog [11; 12; 13]. It gives the number of particles per unit of volume \(n(r)\,dr\), generally expressed in cm\({}^{-3}\), whose radius is in the radius class between \(r\) and \(r+dr\), where \(dr\) is the radius width of the class, generally expressed in \(\mu\)m. \(n(r)\) is generally expressed in \(\text{cm}^{-3}\cdot\text{$\upmu$m}^{-1}\). The DSD can be measured by a particle size analyzer (PSA), which is an optical particle counter.  
Figure 1 shows the models of the two most representative kinds of dense fog corresponding to heavy advection fog (Model 1) and heavy radiation fog (Model 3) of [11]. Models 2 and 4 of [11] were not used here because they correspond to moderate fogs. It is important to note that the measuring devices on which these models were calibrated (in 1979) could not detect the smallest drops (less than 1 \(\upmu\)m). These models therefore tended to underestimate the total number of drops, but also to overestimate the mean size of the drops. A land vehicle, particularly an autonomous vehicle, is likely to encounter these two types of natural fog if it moves within continental and seaside areas.
# Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors
## 7 Conclusions  
Under the impacts of long-term train load and the external environment, ballastless tracks have different degrees of damage and defectiveness, which poses a huge threat to the safety of railway operation. In this paper, according to the characteristics of the CRTSII slab, research on DAS-technology-based defect identification in ballastless tracks was carried out, and the following conclusions were obtained: DAS equipment was installed and debugged to collect the vibration signals of trains and events along the line. Each track vibration signal was extracted by identifying the running train track, denoising, framing and labeling to build a defect dataset. Time-frequency-domain statistical features, the wavelet packet energy spectrum and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. The XGBoost model was trained using the built dataset and reached an accuracy of 89.34%. This model's performance was better than that of other popular classifiers, such as RF, GBDT and SVM. A time-domain residual network that would expand the receptive field and test the accuracy obtained with convolution kernels of different sizes has been proposed. The best performance of this method can reach 94.82%, eliminating the manual process of feature extraction in traditional algorithms and realizing end-to-end information processing.  
Conceptualization, M.H. and W.Q.; methodology, M.H.; software, M.H.; investigation, M.H. and W.Q.; resources, W.Q.; data curation, W.Q.; writing--original draft preparation, M.H. and W.Q.; writing--review and editing, M.H., W.Q. and J.Q.; visualization, M.H.; supervision, J.Q.; project administration, M.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Key Scientific and Technological Project of Henan Province (No. 222102210006) and the Science and Technology Development Project of the China Railway Design Corporation (No. 2022A02538009).  
Not applicable.  
* Research data are not shared due to confidentiality reasons.  
The authors declare no conflict of interest regarding the publication of this research article.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Kernel Sizes** & **Accuracy** & **Loss** & **Parameters** & **Output RF** \\ \hline
129/2, 64 & 0.9157 & 0.3140 & 2.86M & 897 \\
65/2, 64 & 0.9482 & 0.2061 & 2.33M & 449 \\
33/2, 64 & 0.9414 & 0.2337 & 2.06M & 225 \\
17/2, 64 & 0.9249 & 0.2803 & 1.93M & 113 \\
11/2, 64 & 0.9068 & 0.3423 & 1.88M & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Various kernel sizes and impacts on classification.
## 3 Results and Discussion
### Effect of the Volume Fraction of the Second Phase \(\alpha_{nf}\) on the Thermal Characteristics
The effect of the second phase \(\alpha_{nf}\) on the hydraulic characteristics is consistent over the Reynolds number range 92 to 455 and Figure 10 enables the appreciation this consistency by providing a larger spread among the friction factor curves at the low end of the Reynolds number range compared to Figure 9. The friction factor prediction that follows most closely the experimental results is that from the two-phase mixture model with \(\left(\alpha_{nf}=4\%,\Phi=25\%\right)\) that under-predicts the measured friction factor by 4%. The single-phase model under-predicts the measured friction factor by 16%, and the two-phase model with \(\left(\alpha_{nf}=1\%,\Phi=100\%\right)\) under-predicts the measured friction factor by 20%. Effect of the Diameter Size of the Second Phase Nanofluid Bubbles on Thermal and Hydraulic Characteristics  
Figure 11 presents a parametric study of the minichannel average bottom wall temperature and of its pressure drop using the two-phase mixture model with \(\left(\alpha_{nf}=20\%,\Phi=5\%\right)\) and \(\left(\alpha_{nf}=4\%,\Phi=25\%\right)\), in which the second phase nanofluid bubble diameter is varied  
Figure 10: Friction factor along the minchannel predicted by the single-phase approach and by the two-phase mixture model at \(92\leq Re\leq 455\). over the range 85 nm \(\leq\ d_{nf}\ \leq\ 150\) nm. The average bottom wall temperature and the pressure drop are shown to be substantially independent from \(d_{nf}\) over the range 85 nm to 150 nm. The two-phase mixture model with \(\left(\alpha_{nf}=4\%,\Phi=25\%\right)\) did not meet the residuals convergence threshold of \(1\ \times 10^{-6}\) when using a second phase nanofluid diameter in the range 137 nm \(\leq\ d_{nf}\ \leq\ 150\) nm. These less converged results are shown in Figure 11 by the open symbols. While not meeting the \(10^{-6}\) residuals convergence criterion, these less converged results support the substantial independence of the two-phase mixture model predictions from the second phase bubble diameter \(d_{nf}\), over the range 85 nm \(\leq\ d_{nf}\ \leq\ 150\) nm. All other two-phase mixture model simulations presented in this paper use a fixed value of \(d_{nf}=98\) nm, which is within this range.
## 3 Results and Discussion
### Physiological Characterization
#### 3.2.4 Properties of Sulfur Dioxide Tolerance of the Selected Yeasts  
The sulfur dioxide tolerance of the tested strains was evaluated under SO\({}_{2}\) concentrations ranging from 100 mg/L to 500 mg/L. The results are shown in Figure 2D. Within this concentration range, both _Pichia kudriavzevii_ GAAS-JG-1 and the reference strains demonstrated strong sulfur dioxide tolerance, yet significant differences were observed among the strains. At a lower SO\({}_{2}\) concentration (100 mg/L), CICC 31838 exhibited the highest OD\({}_{600}\) value (1.97 \(\pm\) 0.02), while the OD\({}_{600}\) value of _Pichia kudriavzevii_ GAAS-JG-1 was 1.89 \(\pm\) 0.02, slightly lower than CICC 31838 but significantly higher than CICC 1004 (1.74 \(\pm\) 0.01) and CICC 31399 (1.68 \(\pm\) 0.01) (_p_ < 0.05). As the SO\({}_{2}\) concentration increased to 200 mg/L and 300 mg/L, _Pichia kudriavzevii_ GAAS-JG-1 maintained a strong growth performance, with OD\({}_{600}\) values of 1.76 \(\pm\) 0.01 and 1.86 \(\pm\) 0.01, respectively. These values were significantly higher than those of CICC 1004 (1.76 \(\pm\) 0.02 and 1.73 \(\pm\) 0.02) and CICC 31399 (1.55 \(\pm\) 0.01 and 1.41 \(\pm\) 0.01) (_p_ < 0.05). At a high SO\({}_{2}\) concentration of 500 mg/L, _Pichia kudriavzevii_ GAAS-JG-1 achieved an OD\({}_{600}\) value of 1.84 \(\pm\) 0.01, comparable to that of CICC 31838 (1.86 \(\pm\) 0.01) (_p_ > 0.05), yet significantly higher than those of CICC 1004 (1.67 \(\pm\) 0.01) and CICC 31399 (1.30 \(\pm\) 0.02) (_p_ < 0.05). Its strong sulfur dioxide tolerance underlines the competitive advantage of _Pichia kudriavzevii_ GAAS-JG-1 in SO\({}_{2}\)-rich fermentation environments. Moreover, this strong sulfur dioxide tolerance reflects the strain's physiological advantage in coping with fermentation stress, as also reported for _P. kudriavzevii_ and other non-_Saccharomyces_ yeasts used in winemaking [14; 15].
## 3 Multi-Objective Optimization (MOO) Algorithm for RRBCS of R-EEV
### Optimization Decision Based on the Comprehensive Regenerative Braking Performance (CRBP)  
A well-distributed Pareto optimal solution set is found through the optimization algorithm. In engineering practice, only one final solution is needed. So, just finding the Pareto optimal solution set does not completely solve the MOO problem. The multi-objective normalization optimization discussed in this paper adopts the linear normalization method, which is simple, practical, and efficient to deal with the frontier of non-convex problems. Additionally, it can reflect the weights of optimization goals clearly. Since the above four optimization variables represent different physical meanings, it is necessary to unify the cost functions in each dimension by normalizing the normalization method. After transformation, it becomes a dimensionless expression.  
Normalization of linear functions (Min-Max scaling) converts the original data linearization method to the range of [0, 1]. The normalization formula is as follows:  
\[X^{(nor)}=\frac{X_{p}-X_{p}^{(\min)}}{X_{p}^{(\max)}-X_{p}^{(\min)}} \tag{16}\]  
where \(X^{(nor)}\) is the normalized data; \(X_{p}\) is the original data in Pareto optimal solution sets; and \(X_{p}^{(max)}\) and \(X_{p}^{(min)}\) are the maximum and minimum values of the original data sets respectively.  
The evaluation indicator of comprehensive regenerative braking performance \(I_{crbp\_cyc}\) is calculated as follows:  
\[I_{crbp\_cyc}=\lambda_{b}I_{bp\_cyc}^{(nor)}+\lambda_{r}I_{rble\_cyc}^{(nor)}+ \lambda_{l}I_{bclr\_cyc}^{(nor)} \tag{17}\]  
where \(\lambda\) is the weighting coefficients of normalized data.  
Different weighting results are obtained when different weighting coefficients are selected. Designers can choose a reasonable weight coefficient allocation based on design performance requirements. In this paper, the weight parameter selection is as follows: \([\lambda_{b},\lambda_{r},\lambda_{l}]^{\text{T}}=[0.4,0.3,0.3]^{\text{T}}\).
# Battery Management System Hardware Concepts: An Overview
## 6 Safety and Reliability
Figure 7 illustrates the issue: due to the low-pass filter capacitances (Section 4 in Figure 7), a disconnected line (Section 1) or a blown fuse (Section 2) does not lead to an immediate voltage drop. The voltage at the measurement hardware (BMS AFE) decays rather slowly over the course of several minutes or even longer, depending on the magnitude of the quiescent current. This can be a critical safety issue, as the cell voltage of at least two neighbouring cells cannot be supervised any longer. By using the cell balancing system or current source circuits, such a fault can be detected [73; 74]. This is also illustrated in Figure 7: enabling the balancing circuit (Section 3) leads to a voltage change at the adjacent cells, whose magnitude is much larger than normal and can easily be detected by the AFE. Other types of faults, for example defective sensors, can be detected by appropriate diagnostic algorithms [75]. Knowledge of the electrical behaviour of batteries can also be used to check sensor signals for validity. For example, a sudden increase of the discharge current should lead to a certain voltage drop for all battery cells in a serially connected string. An increasing or constant voltage value of individual cells is therefore not plausible and could be a sign of a sensor malfunction in that situation. For all safety measures that are implemented, it has to be considered that the implementation of all the aforementioned (and other) safety functions can, by itself, be the cause of further hazards. Take for example the case of an opening of the main contactors by the BMS during drive operation of a battery electric vehicle: depending on the momentary driving situation, the following sudden loss of power can be more hazardous than the actual cause of the contactor opening action (for example, a short crossing of an undervoltage limit during acceleration). Similar considerations apply to aircraft or other applications, where a sudden loss of power is not tolerable. A counter-example is the case of a photovoltaic battery storage system, which does not feed power to critical systems. In this case, protection of the battery should be the higher priority. The two optimization goals of safety and reliability have to be balanced against each other for the specific applications.
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 4 Experimental Results
### Experimental Results and Analysis
#### 4.3.3 Effectiveness of Backbone Network with the Proposed Method  
This section details the performance comparison of structural changes in the embedding network. The detector was YOLOv3. The experimental results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Detector** & **Algorithm** & **Recall\(\uparrow\)** & **Precision\(\uparrow\)** & **Frag\(\downarrow\)** & **MOTP\(\uparrow\)** & **IDs\(\downarrow\)** & **MOTA\(\downarrow\)** \\ \hline FcRCNN & SORT [8] & 0.67 & 0.58 & 81 & 0.53 & 151 & 0.58 \\ YOLOv3 & GoogLeNet + FC [20] & 0.88 & 0.83 & 33 & 0.65 & 46 & 0.72 \\ FcRCNN & DeepSORT [9] & 0.75 & 0.67 & 42 & 0.58 & 51 & 0.66 \\ YOLOv3 & Triplet (ours) & 0.9 & 0.85 & 25 & 0.68 & 28 & 0.73 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed method with other models. The arrows indicate better performance for each metric. (i.e., down arrow—lower is better).  
\begin{table}
\begin{tabular}{c c c} \hline  & **Proposed Network** & **Without Attention Module** \\ \hline Total No. of parameters & 13,562,160 & 13,549,856 \\ Input size (MB) & 0.57 & 0.57 \\ Forward/backward size (MB) & 179.66 & 146.24 \\ Param size (MB) & 51.74 & 51.69 \\ Estimated total size (MB) & 231.97 & 198.50 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of additional computational cost of attention blocks.  
We experimented with a network consisting of 128 features as the outputs by adding FCL to GoogLeNet. Table 3 shows the performance of the proposed network with and without the attention module. The experimental results showed that the proposed model with the attention module was the most-effective model.
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
## 3 Group IV Semiconductors
### Ge
In 2018, a PDs based on Ge QDs fabricated on Ge substrates has been demonstrated with a broadband photoresponse spectral range (\(\lambda\) = 400-1550 nm) [70]. Its responsivity at room temperature was up to 1.12 A/W, the obtained internal quantum efficiency was IQE = 313%, higher than conventional Si and Ge photodiodes, specific detectivity at room-temperature was D* \(\approx\) 210\({}^{10}\) cm Hz\({}^{1/2}\) W\({}^{-1}\) both at visible \(\lambda\) = 640 nm and telecom \(\lambda\) = 1550 nm wavelengths. When the operating temperature and incident power were decreased, sharply improved performance were obtained, indeed at T = 100K for an incident power of 10 nW at \(\lambda\) = 1550 nm, the device showed D* = 1.110\({}^{12}\) cm Hz\({}^{1/2}\) W\({}^{-1}\) and IQE = 1000% [70]. In a very recent work, MSM NIR photodiodes are fabricated on the epitaxial uniform Ge film with a grain size of up to 12 \(\upmu\)m, with two diverse electrodes and two different surface passivation interlayers [71]. When electrodes were made of amorphous-Ge (30 nm)/Al (100 nm) and TiO\({}_{2}\) (5 nm)/Al (100 nm), the device showed a 1000% increase in the energy loss of the device. The device showed a 1000% increase in the energy loss of the device. The device showed a 1000% increase in the energy loss of the device. The device showed a 1000% increase in energy loss of the device. Au (80 nm), MSM devices show an average spectral responsivity of \(0.50\pm 0.16\) A/Wand \(0.35\pm 0.09\) A/W at 1550 nm and voltage bias of \(-3\)V, respectively. The maximum spectral responsivity reported was of 0.78 A/W and 0.48 A/W, respectively. Considering the recent encouraging reported studies and results, together with a well-tested compatibility with the conventional CMOS technology, Ge can be considered a material on which to continue investigating.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 4 Methods
### Data Preprocessing  
The fault features used in this study had different units and properties, making it difficult to perform the analysis directly. Considering these differences, the data were preprocessed before PCA was applied to them. During preprocessing, the mean was set as zero through mean centering. Then, owing to the changes in data by the fault conditions associated with each fault feature, scaling was conducted so that the symptoms of faults could be highlighted. As a last step, to reduce the effect of the data having different units, the values were normalized to a range between 0 and 1.  
In the preprocessing, the degree of scaling was set as a parameter, such that when the results after PCA were visualized, the symptoms of faults could be visually distinguished. The details are explained with figures in Section 5. The scaling parameter values were derived through a process of trial and error.
## 3 Results and Discussion
### Substrate Scope Investigation  
Lastly, to achieve the aim of verifying the generality of our protocol, we tested a variety of \(\beta\)-nitroacrylates and arylhydrazines under the optimized reaction conditions (Table 3). In all cases, the products were isolated from satisfactory to good overall yields (38-55%), even in the presence of versatile and reactive functionalities. In particular, it was possible to prepare indices functionalized on the C-3 chain that were suitable for further manipulations, such as **5e-g** and **5j**, which contained a phenyl ring, a double and triple bond, and an ester group, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Entry** & **Amberlyst 15 (g/mmol)** & **Temp. (\({}^{\circ}\)C)** & **Time (h)** & **Yield (\%) 1 of 5a** \\ \hline \(a\) & 1 & reflux & 8 & 30 \\ \(b\) & 1.5 & reflux & 8 & 34 \\ \(c\) & 2 & reflux & 8 & 38 \\ \(d\) & 2.5 & reflux & 8 & 39 \\ \(e\) & 2 & 60 & 8 & 15 \\ \(f\) & 2 & 100 2 & 8 & 40 \\ \(g\) & 2 & reflux & 16 & 45 \\ \(h\) & 2 & reflux & 24 & 53 \\ \(i\) & 2 & reflux & 32 & 51 \\ \(j\) & 2 & 80 3 & 4 & 42 \\ \(k\) & 2 & 90 3 & 4 & 54 \\ \(l\) & 2 & 100 3 & 4 & 49 \\ \hline \end{tabular} \({}^{1}\) Overall yield of the pure isolated product. 2 Reaction performed in a sealed vessel. 3 Reaction performed in a sealed vessel under microwave irradiation.  
\end{table}
Table 2: Optimization studies concerning the indolization step.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Entry** & **R2** & **R3** & **G** & **Solvent** & **Yield (\%) 1 of 5** \\ \hline \(a\) & Et & Et & H & EtOH & **5a** & 54 \\ \(b\) & CH\({}_{3}\)(CH\({}_{2}\))\({}_{4}\) & Et & H & EtOH & **5b** & 52 \\ \(c\) & CH\({}_{3}\)(CH\({}_{2}\))\({}_{8}\) & Et & H & EtOH & **5c** & 50 \\ \(d\) & Et & Et & 7-Me & EtOH & **5d** & 55 \\ \(e\) & Ph(CH\({}_{2}\))\({}_{2}\) & Et & H & EtOH & **5e** & 43 \\ \(f\) & CH\({}_{2}\)=CH(CH\({}_{2}\))\({}_{2}\) & Et & 7-Me & EtOH & **5f** & 43 \\ \(g\) & CH=C(CH\({}_{2}\))\({}_{3}\) & Et & H & EtOH & **5g** & 41 \\ \(h\) & Et & Me & H & MeOH & **5h** & 42 \\ \(i\) & Et & Me & 5-Br & MeOH & **5i** & 41 \\ \(j\) & MeOOC(CH\({}_{2}\))\({}_{4}\) & Me & H & MeOH & **5j** & 36 \\ \(k\) & Et & Pr & H & PrOH & **5k** & 46 \\ \(l\) & Et & Pr & 7-Me & PrOH & **5l** & 45 \\ \(m\) & CH\({}_{3}\)(CH\({}_{2}\))\({}_{6}\) & Pr & H & PrOH & **5m** & 38 \\ \hline \end{tabular}
\end{table}
Table 3: Synthesis of indoles 5a-m.  
It is important to highlight that, in order to prevent the transesterification reaction at C-2, the indolization was conducted using the same alcohol (R\({}^{3}\)OH) of the ester moiety. Detailed spectroscopic data for compounds 5a-m are included in the Supplementary Materials.
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## 4 Experimental Results and Comparison Analysis
### Performance of Tamper Detection and Self-Recovery
#### 4.2.2 Copy-Move Attack  
In Figure 7, a sky region from another image is copied and pasted into watermarked Goldhill image. If the attackers choose tampered region premeditatedly, it will be difficult for the receiver to find the trace of tampering. From the detection results shown in Figure 7d-f, it can be observed that the detection results of references [13; 25] have serious missed detection problem, while the proposed  
Figure 6: Tamper detection and recovery performance for text addition attack: (**a**) Original Lena image; (**b**) watermarked Lena image; (**c**) attacked image; (**d**) tamper detection result using the method in [13]; (**e**) tamper detection result using the method in [25]; (**f**) tamper detection result of the proposed method; (**g**) image recovery result using the method in [13]; (**h**) image recovery result using the method in [25]; (**i**) image recovery result of the proposed method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **FPR** & **FNR** & **PSNR (dB) of Recovered Image** \\ \hline [PERSON] et al. [13] & 0.41\% & 5.41\% & 35.01 \\ [PERSON] et al. [25] & 0.66\% & 0.93\% & 40.85 \\ The proposed method & 0.67\% & 0 & 45.52 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparisons for text addition attack using Lena image.  
method can locate the tampered region precisely. These image blocks that are falsely detected in the former two algorithms make the restored images having a serious trace of tampering, which are shown in Figure 7g,h, respectively. Table 4 gives the performance comparisons of these three methods in FPR, FNR, and PSNR of recovered images. The FNR of the proposed method is much lower than the other two methods, which is only about 0.4%. From the restored image shown in Figure 7i and its PSNR value given in Table 4, it can be proved that the recovered image has a good image quality subjectively and objectively.