esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Application
### Application: Fiber OPO Pumping
The idler pulse duration, measured with an autocorrelator (Femtochrome FR-103 XL), was found to be 2.5 times shorter than the pump pulse around 14 ps and its peak power was as high as 140 W. The peak emission at 1530 nm represents the amplified spontaneous emission (ASE) in erbium, and the emission around 1600 nm represents the wave resulting from the interaction by FWM of the pump with the ASE at 1530 nm. Figure 10: Experimental (blue dots) and calculated (solid lines) parametric sidebands as a function of pump wavelength (red dots). When the pump pulse at 1566 nm and the idler pulse were overlapped, the adjustment of the optical delay line in a range of 7 mm allowed for a fine tuning of the idler wave between 1620 nm and 1690 nm, as can be seen from Figure 12. For this pumping wavelength, the nanojoule level was reached in a 30 nm-bandwidth with wavelengths between 1655 nm and 1685 nm. In addition, we show in Figure 13 the FOPO tuning as a function of both the pump wavelength and ODL adjustment. With these combined techniques, the idler wavelength could be tuned between 1619 nm and 1790 nm (Figure 13) when pump pulses and idler pulses were synchronized. This limitation was caused by the rapid walk-off between pump and idler pulses. Another important process that participates in the enhancement of the idler energy around this window close to 1660 nm was the overlapping of the Raman and FWM gain bandwidths. This Raman assistance to the parametric process can be seen as an energy transfer from the up-shifted parametric component (i.e., signal) to the down-shifted component (i.e., idler) [33]. For the pump and signal pulses inside the FOPO cavity while pumping the DSF in its normal dispersion. We thus show the FOPO spectra for the two pump wavelengths 1546 nm and 1548 nm in Figure 14. The conversion efficiencies were relatively low compared to those in the band around 1700 nm. When the pump was set at 1548 nm, the signal and idler were generated at wavelengths of 1322 nm and 1864 nm with conversion efficiencies of 5.93% and 2.46%, corresponding to energies of 0.23 nJ and 0.1 nJ, respectively. The extreme experimental signal and idler sidebands were centered at wavelengths of 1319 nm and 1876 nm with lower conversion efficiencies of 1.15% and 0.6%, respectively, for a pump wavelength of 1546 nm (Figure 14). Figure 11: Typical FOPO output spectrum for a pump wavelength of 1566 nm with a pump-to-idler conversion efficiency of \(\approx\)20%. The low-intensity peak around 1530 nm is attributed to erbium amplified spontaneous emission (ASE). Figure 8:0 Idler tenability, in linear scale, while tuning the optical delay line. Figure 8:1 Idler tenability, in linear scale, while tuning the pump wavelength and the optical delay line.
# Heat Capacity of Drained Peat Soils
## 2 Materials and Methods
### Volumetric _Heat Capacity_  
The volumetric heat capacity (\(C_{\text{p}}\) data) of the moorsh (muck) layers were measured on undisturbed samples to characterize the functional relationships between \(C_{\text{p}}\) and moisture content (\(\theta_{\text{p}}\)). A portable thermal properties analyzer KD2-Pro Decagon with a SH-1 dual-needle probe was used [43]. The principles of this measurement system rely on heat conduction analysis from a transient line heat source [13; 21; 22]. In the measurement system heat is applied to one of the needles (30 mm long) for a set time while in the second needle, which is placed in parallel at a 6 mm distance, the temperature response is monitored. Based on the set of temperature measurements taken at 1 s intervals during a 30 s heating period and a 30 s cooling period, and using the solution for radial conduction from an infinite line source [22; 46] the KD2 Pro, with SH-1 dual-needle probe, determined the soil thermal properties (i.e., volumetric heat capacity (\(Cv\)), thermal conductivity (\(\lambda\)), and thermal diffusivity (\(D\)). The basic theory of the KD2 Pro system [43] is described in the study by [PERSON] et al. (2017) [47]. Similar commercial measurement systems have been used for the determination of the thermal properties of ombrothropic peat soils [39] as well as for moss peaks [38].  
The undisturbed soil samples were collected into vertically oriented standard cores in triplicate (100 cm\({}^{3}\) volume) from the depths 5 cm, 7.5 cm, 12.5 cm, and 22.5 cm. Different depths of soil collection was chosen to ensure soil moisture gradients. The samples were taken out from the soil profiles at sites 1, 2, and 4 during one field campaign on 17 June 2016 whereas the soil profile at site 3 was sampled on 17 April 2015. Additional samples at sites 3 and 5 were taken in late autumn 2015. In total, sampling was done 22 times in triplicate for the distinct depths (66 soil cores). SH-1 dual-needle probe, of the KD2 Pro system, was installed vertically in each soil sample from the top surface and then thermal properties were measured. The bottom end of the core was covered. According to the Decagon Devices Inc manual, no specific calibration of the measurement system was required. Nevertheless, thermal readings were performed in a Darlin block and compared with the range included in the quality assurance Certificate provided by the manufacturer [43]. The thermal measurements using the SH-1 probe were done at one-hour intervals to avoid overheating of the probe. For each soil sample, the volumetric moisture content and soil bulk density was determined as described in the section on basic soil properties.  
Figure 1: Example of the MDSC results of the specific heat capacity measurements.
# Immunohistochemical Expression of ABCB5 as a Potential Prognostic Factor in Uveal Melanoma
## 3 Results
### Clinico-Pathological Characteristic of Uveal Melanomas
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Sex (m-f)** & **Age (yrs)** & **Location** & **Thickness** & **Largest diameter** & **Cell type** & **Extrascalcal extension** & **Pathological Tagg (months)** & **DPS** & **Follow-up (months)** & **ABCB5** \\ \hline  & & & & & & & & & pT1 Ta: 7 & & & & \\  & & & & & & & & pT1 Tb: 1 & & & & \\  & & & & & & & pT2 Ta: 21 & & & & & \\ All & 67 & Ch 40 & 9.7 & 14.1 & Epittle: 15 & Nov: 54 & pT2 Tb: 8 & 42 & 60 & 4 \\ (m=55) & 28-27 & (29-85) & Ch/ CB 15 & (3-2:16.3) & (6-4:20.8) & Spindle: 12 & Yes: 1 & pT2 Ta: 8 & (0-138) & (8-138) & (0-12) \\  & & & & & & & pT3 Ta: 5 & & & \\  & & & & & & pT4 Ta: 1 & & & & \\ Metastasis free & & & & & & pT4 Ta: 3 & & & & \\ (m=32) & 17-15 & 64 & Ch 24 & 9.5 & 11.9 & Epittle: 6 & & pT1 Tb: 1 & & & \\  & & & & & & & pT2 Ta: 12 & 81 & 81 & 3 \\ (m=32) & 17-15 & (29-84) & Ch/ CB 8 & (3-2:16.3) & (6-4:20.8) & Mixed: 16 & & pT3 Ta: 4 & & & \\  & & & & & & & pT4 Tb: 3 & & & \\  & & & & & & & pT1 Ta: 1 & & & \\ Metastasis & 11-12 & 72 & Ch 16 & 9.7 & 15.6 & Epittle: 9 & Nov: 22 & pT2 Ta: 2 & 25 & 42 & 9 \\ (m=23) & 11-12 & (50-85) & Ch/ CB 7 & (3-5:15.5) & (6-5:20) & Spindle: 2 & Yes: 1 & pT2 Ta: 1 & (0-63) & 13 death & (0-12) \\  & & & & & & & pT3 Ta: 5 & & & \\ \(p\) (_instatiss free_ & & & & & & pT4 Ta: 1 & & & \\ vs metastasis) & & _0.400\({}^{*}\)_ & _0.762\({}^{*}\)_ & _0.911\({}^{*}\)_ & _0.007\({}^{*}\)_ & _0.400\({}^{*}\)_ & _0.418\({}^{*}\)_ & _0.560\({}^{*}\)_ & _\(<\)0.001\({}^{*}\)_ & _0.001\({}^{*}\)_ & _0.030\({}^{*}\)_ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Median (range) of demographics, tumour parameters, disease free time, follow-up, ABCB5 expression in primary uveal melanoma without and with systemic metastasis.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 3 Linguistic Analysis of Uyghur, Kazakh, and Kyrgyz
### Individualities of Each Language
#### 3.2.3 Vowel Harmony  
As mentioned above, vowel harmony exists in all three languages, but the degree of strictness is different. For Kyrgyz, the vowel harmony rule is the most strict. For Kazakh, the harmony rule may be broken into words involving the vowel \(\alpha\), perhaps because it is a new pronunciation in the language. For Uyghur, \(e\) and \(i\) are relatively neutral, and so can be combined with either front vowels or back vowels [110].
## 5 Tests and Results
### Datasets Description
Nodes contain a categorical label stating the type of secondary structure (\(\alpha\)-helix, \(\beta\)-sheet or turn) and a real-valued vector containing 18 physical and chemical measurements. Conversely, edges are unlabeled. The prediction task is used to classify the protein structures into one of the six Enzyme Commission classes [51]. Each dataset has been split into three disjoint sets: training, validation and test. For the six IAM datasets, we used the very same training/validation/test splits provided in the repository, whereas for the four TUDatasets, we had to perform our own splits with the following ratio: training set (50%), validation set (25%) and test set (25%). The splitting procedure has been performed in a stratified manner in order to preserve labels' distribution across the three sets. For Letter-L, Letter-M, Letter-H, GREC and AIDS, details about the dissimilarity measures on nodes and edges can be found in ([32] Appendix B). We anticipate that GREC is the only dataset with parametric dissimilarity measures on nodes and edges, i.e., for which \(\Pi_{\text{edge}}\
eq\emptyset\) and \(\Pi_{\text{node}}\
eq\emptyset\). For MUTAG and Mutagenicity, since in both datasets nodes and edges are equipped with categorical labels, the dissimilarity between nodes and the dissimilarity between edges are set as the plain discrete distance between labels (i.e., their distance is 1 if they are not equal and 0 otherwise) ([61] Chapter 1). For DD and NCII, since in both datasets nodes are equipped with categorical labels and edges are unlabeled:  
* The dissimilarity between nodes reads as the plain discrete distance between labels;
* The dissimilarity between edges reads as a constant value. Finally, for ENZYMES:
* The dissimilarity between nodes reads as the (normalized) Euclidean distance between their respective 18-length attribute vectors if and only if the two nodes refer to the same secondary structure (i.e., both are \(\alpha\)-helices, \(\beta\)-sheets or turns); otherwise, their dissimilarity is 1;
* The dissimilarity between edges (that are unlabeled) reads as a constant value.
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 2 Background
### The Observation Model and Likelihood Function of Radar
\begin{array}{ll}\frac{1}{2}\exp\left(- \frac{z^{i}+z^{i}}{2\sigma_{w_{n}}^{2}}\right)I_{0}\left(\frac{\sqrt{z^{i}+z^{ i}}}{\sigma_{w_{n}}^{2}}\right),&z^{i}>0\\ 0,&z^{i}<0.\end{array} \right. \tag{11}\]  
where \(I_{0}\left(u\right)\) is the first kind modified Bessel function with 0 order. When \(\hat{z}_{A}^{i}=0\) ( only with noise), \(z^{i}/\sigma_{w_{n}}^{2}\) subjects to a central \(\chi^{2}\) distribution with 2 degrees of freedom. The probability density of \(z^{i}\) is  
\[P\left(z^{i}|\mathbf{X}\right)=\left\{ \begin{array}{ll}\frac{1}{2}\exp\left( -\frac{z^{i}}{2\sigma_{w_{n}}^{2}}\right),&z^{i}>0\\ 0,&z^{i}<0.\end{array} \right. \tag{12}\]  
Dividing Equation (12) by Equation (11), we can obtain that the likelihood ratio function of cell \(i\) is  
\[l\left(z^{i}|\mathbf{X}\right)=\left\{ \begin{array}{ll}\exp\left(-\frac{z^{ i}}{2\sigma_{w_{n}}^{2}}\right)I_{0}\left(\frac{\sqrt{z^{i}+z^{i}}}{\sigma_{w_{n}}^ {2}}\right),&z^{i}>0\\ 0,&z^{i}<0.\end{array} \right. \tag{13}\]  
The likelihood function of the measurement vector \(\mathrm{Y}\) can be written as  
\[g\left(\mathrm{Y}|\mathbf{X}\right)=f\left(\mathrm{Y}\right)\prod_{i\in\left( \cup_{\mathbf{x}\in\mathbf{T}\left(\mathbf{x}\right)}\right)}l\left(z^{i}| \mathbf{X}\right) \tag{14}\]  
where \(f\left(\mathrm{Y}\right)=\prod_{1\leq i\leq m_{z}}0.5\exp\left(-0.5\big{(} \sigma_{w_{n}}^{2}\big{)}^{-1}z^{i}\right)\) denotes the likelihood function only with noise. \(g\left(\mathrm{Y}|\mathbf{X}\right)\) cannot be denoted as a product form of each target likelihood function so it is called the inseparable likelihood. If we assume that the measurements generated by different targets do not overlap and any measurement is most affected by one target. Therefore, there is at most one item in \(\hat{z}^{i}\),  
\[\hat{z}^{i}=\left|\hat{z}_{A}^{i}\right|^{2}=\left|1_{T\left(\mathbf{x} \right)}\left(i\right)\bar{A}h_{A}^{i}\left(\mathbf{x}\right)\right|^{2}. \tag{15}\]  
Further, the second term of Equation (14) can be simplified to  
\[\prod_{i\in\left(\cup_{\mathbf{x}\in\mathbf{T}\left(\mathbf{x}\right)}\right) }l\left(z^{i}|\mathbf{X}\right)=\prod_{\mathbf{x}\in\mathbf{X}}\left(\prod_{i \in T\left(\mathbf{x}\right)}l\left(z^{i}|\mathbf{x}\right)\right). \tag{16}\]  
The likelihood function \(g\left(\mathrm{Y}|\mathbf{X}\right)\) can be denoted as the product of the likelihood function of each target \(\mathbf{x}\) so it is called the separable likelihood. The expression can be written as  
\[g\left(\mathrm{Y}|\mathbf{X}\right)=f\left(\mathrm{Y}\right)\prod_{\mathbf{x} \in\mathbf{X}}g_{\mathrm{Y}}\left(\mathbf{x}\right), \tag{17}\]  
\[g_{\mathrm{Y}}\left(\mathbf{x}\right)=\prod_{i\in T\left(\mathbf{x}\right)}l \left(z^{i}|\mathbf{x}\right). \tag{18}\]
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 5 Proposed Classification
### Classification of RL-Based Routing Protocols Based on Routing Algorithm  
In the proposed classification, reinforcement learning-based routing methods can be examined based on the routing algorithm in four aspects:  
* Routing path;
* Network topology;
* Data delivery method;
* Routing process.
# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 3 Methods and Design
### Object Detection of the Car
#### 3.1.2 Car Object Detection Model Structure  
We used Darkflow [18], which is the github open source code. It is written in the Python background and designed as the YOLO version two. We applied a simpler version of the YOLOv2 model structure called Tiny YOLO VOC, which is a kind of object detection model, and we did not change any of the model's parameters [18]. Figure 11 shows the structure of the Tiny YOLO VOC model and the essential parameters of Tiny YOLO VOC are shown in Table 1. The white rectangles are the layers, and the blue rectangles and red triangles are the sign of convolution process.  
Figure 11: The structure of the tiny-YOLO-VOC model.  
Figure 10: Our road images data set includes four types of images, divided into the training data and test data.  
The model was designed as the YOLOv2 form, so there were no fully connected layers in the structure. The model contains nine convolutional layers. The input is 416 \(\times\) 416 \(\times\) 3 and the output is 13 \(\times\) 13 \(\times\) 30. When the road images are input, it goes through nine convolutional layers. Eight of them have the same parameters, including kernel_size of 3, stride of 1, Batch_normalize of 1, and activation_function of Leaky Relu. The parameters of the last convolutional layer include kernel_size as 1, stride as 1, and activation_function as Leaky Relu. The filters from the first convolutional layer to the last convolutional layer are 16, 32, 64, 128, 256, 512, 1024, 1024, and 30. For the meaning of the parameters in each convolutional layer, the filters mean the number of output filters in the convolution, kernel_size is the 2-D convolution window that slides the whole image, stride means the strides of the convolution along the height and width, Batch_normalize means the batch normalization, and activation is the activation function to use. At last, we set the filter number of the output layer at 30 due to there being only one class. The 30 count is obtained by adding one to five and then multiplying by five. One is class number, the first five refers to the values that the bounding box contains, while the latter five refers to the number of anchor boxes. This is our car detection model.
## 4 Methodology
### Feature Selection Using XGBoost  
The first phase of the analysis involves the application of the XGBoost algorithm to identify the most significant predictors of business failure. XGBoost is a machine-learning technique that employs gradient boosting to improve predictive accuracy through an ensemble of decision trees [47]. The process proceeds as follows:  
* Variable Ranking: XGBoost is applied to the full set of 25 variables, generating importance scores for each predictor.
* Selection Criteria: Variables are ranked based on their contribution to model performance, and those with the highest scores are retained. In this study, the top financial predictors are selected (see Figure 1), and the TRUCAM variable is added, given its central role in representing environmental financial exposure.
* Model Parsimony: This feature selection step not only enhances the predictive capability of the model but also reduces the complexity of subsequent analyses, ensuring that only the most relevant variables are carried forward.  
This initial screening provides a robust set of predictors that form the basis for further causal analysis using fsQCA.  
XGBoost is robust to issues related to endogeneity and multicollinearity, which are common concerns in traditional econometric analyses. Unlike parametric models that rely on strict assumptions about variable independence and linear relationships, XGBoost is a non-parametric machine learning algorithm that iteratively builds decision trees to optimize predictive accuracy [48]. Its use of regularization techniques, such as L1 and L2 penalties, helps prevent overfitting and minimizes the influence of spurious correlations [30]. Additionally, XGBoost's ability to handle complex and nonlinear interactions among variables ensures that the presence of correlated predictors does not undermine its predictive power. This makes it particularly well-suited for analyzing complex datasets, where endogeneity and multicollinearity might otherwise create significant challenges. As XGBoost prioritizes prediction rather than causal inference, it reduces the risk of biased estimates arising from endogeneity concerns.  
An important characteristic of XGBoost is its ability to perform feature selection. The algorithm can identify the most relevant subset of features that contribute the most to the prediction accuracy of the model. This is carried out by assigning a score to each feature based on its importance in the prediction problem. The higher the score, the more important the feature [31].
## 3 Time-Slotted Channel Hopping  
The aim of this section is to describe the major steps and challenges when implementing TSCH. The time synchronized nature of the protocol and its adaptation to commercial off-the-shelf transceivers impose some restrictions and considerations that are outlined in the following subsections.
## 2 Development of a New Analytical Model Based on the Virtual Solid Flame Concept
### Evaluation of the Fire Geometry and Its Position in the Compartment  
The following symbols are used (referring to Figure 1):  
* \(\mathrm{d_{x}}=\) width of each band;
* \(\mathrm{n_{dx}}=\) quantity of bands;
* 1).d\({}_{x}\) to x = i.d\({}_{x}\), etc.;
* At time t = 0, fire starts in band (1) only and will thus spread towards increasing values of x, i.e., to band (2), and then band (3), etc.
* The following inputs have to be given to describe the problem:
* Width b, length L, and floor to ceiling distance h of the compartment [m];
* Width of the bands \(\mathrm{d_{x}}\) [m], the total number of bands \(\mathrm{nd_{x}}\) is therefore evaluated by rounding up the ratio L/d\({}_{x}\), and \(\mathrm{d_{x}}\) is then re-evaluated as \(\mathrm{d_{x}}=\mathrm{L/nd_{x}}\);
* The design value of the fire load density \(\mathrm{q_{f,d}}\) [M]/m\({}^{2}\)];
* The rate of heat release density \(\mathrm{RHR_{f}}\) [kW/m\({}^{2}\)];
* The height of the fire load basis \(\mathrm{h_{base}}\) (to allow for elevated combustible, the floor height being situated at 0 m height) [m];
* The fire spread rate v [mm/s];
* The combustion factor m (fixed to 0.8 in EN1991-1-2 [13]) [-];
* The effective heat of combustion of the fire load H [M]/kg];
* The total area of vertical openings A\({}_{v}\) (as the openings are considered as fully open, this area should correspond to the area of broken or open windows and doors) [m\({}^{2}\)];
* The equivalent height of the openings \(\mathrm{h_{eq}}\) [m];
* The time-step dt [s].  
The ratio \(\mathrm{q_{f,d}}\)/\(\mathrm{RHR_{f}}\) corresponds to the time required to consume all the fire load present within a band, and the ratio \(\mathrm{d_{x}}\)/v corresponds to the time needed for the combustion front to cross a band. The band width \(\mathrm{d_{x}}\) should be defined to be lower than the ratio (v. \(\mathrm{q_{f,d}}\)/\(\mathrm{RHR_{f}}\)), otherwise the fire will extinguish in one band before spreading to  
Figure 1: Simplified representation of the compartment to apply the analytical model.  
the adjacent one. The procedure considers the dimensions of the vertical openings (thus the amount of oxygen present to sustain combustion) but not their locations within the compartment. This implies that any local effect resulting from fire passing from a ventilated zone to an under-ventilated zone--and vice versa--(implying, amongst other, acceleration or deceleration of the fire) is disregarded. The parameter hbase allows the consideration of situations in which the fire load does not lay on the floor (i.e., at z = 0), but at a given height above the floor (i.e., at z = hbase).
# Feasibility Study and Prospects of Rock Fragmentation Using Ultrasonic Vibration Excitation
## 3 The Theoretical Feasibility of Ultrasonic Rock Breakage
### Ultrasonic Vibration Excitation Mechanisms for Rock Breakage
The competent rock normally has a natural frequency from 15 kHz to 40 kHz, while the ultrasonic frequency is above 20 kHz; therefore, the ultrasonic frequency can reach most of the natural frequencies of the rock, making the rock resonate, to promote crack propagation within the rock and to improve the rock crushing efficiency. This is an important feature of ultrasonic rock breakage, compared to the general rock breakage methods that use high frequency vibration excitation.