esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Methodology of the Research
### Hyperparameter Optimization
#### 2.3.1 Parameter Space  
The following hyperparameters and associated search distributions were defined (see Figure 44). Each hyperparameter was sampled from a specific probability distribution, ensuring a flexible search across a broad range of values:  
* Iterations (parameter iterations): Fixed at 2000. A high iteration count was chosen, relying on early stopping in CatBoost to prevent overfitting.
* Learning rate (parameter learning_rate): Sampled from a log-uniform distribution over \([10^{-3},\,10^{-1}]\). Formally, if \[x\sim\text{Uniform}\left(\log(10^{-3}),\,\log(10^{-1})\right),\] then \[\texttt{learning\_rate}=\exp(x).\] This distribution is appropriate for the learning rates, which often span multiple orders of magnitude.
* Depth (parameter depth): Sampled from a quantized uniform distribution over \(\{4,5,6,7,8,9,10\}\): \[\texttt{depth}\sim\text{QUUniform}(4,\,10,\,1).\]
* L2 leaf regularization (parameter 12_leaf_reg): Sampled from a log-uniform distribution \([10^{-5},\,10]\). This controls the strength of the \(L_{2}\) regularization in leaf values.
* Random strength (parameter random_strength) and random subspace method (rsm): Sampled from a uniform distribution over \([0,\,1]\) and \([0.5,\,1.0]\), respectively.
* Grow pole (parameter grow_policy): Chosen via parameter hp.choice among the following options: 1. SymmetricTree (no minimum data in leaf constraint); 2. Depthwise, requiring min_data_in_leaf_depthwise sampled from a quantized uniform distribution over \(\{1,\dots,10\}\); 3. Lossguide, requiring min_data_in_leaf_lossguide similarly sampled. This nested choice approach allowed us to conditionally sample a min_data_in_leaf parameter for the selected grow policy.
* Bootstrap type (parameter bootstrap_type): Chosen via hp.choice among the following options:1. Bayesian, which also samples bagging_temperature from a uniform distribution [0, 1];
2. Bernoulli, which also samples subsample_bernoulli from a uniform distribution [0.5, 1.0];
3. No, which disables bootstrapping altogether. This conditional structure ensured that bagging_temperature or subsample were only relevant if the chosen bootstrap_type supported them.  
In the CatBoost method there are four bootstrap options; however, we opted not to include Poisson bootstrap, due to its limitations (supported for GPU only) and to not use MVS because, according to the CatBoost documentation, \"MVS may not be the best choice for regularization, since sampled examples and their weights are similar for close iterations\" [46]. Thus, in our hyperparameter search, we focused on the most commonly used sampling methods--Bayesian and Bernoulli--preventing us from further complicating the search space without clear benefits for our particular data and objectives.  
Figure 14 shows the precise Python definition in code.
## 5 Numerical Results and Discussion
### Synchronous Networks
#### 5.2.1 Single-Slot Networks
It is not difficult to find that, based on the inflection points of the SPEB curves in Figure 9b,c, in the sensing-resource-abundant network, 44% of  
Figure 9: SPEB comparison between the step-by-step scheme and the integrated scheme in four single-slot synchronous networks. (**a**) SPEB comparison in a single-slot synchronous network (Figure 7a). (**b**) SPEB comparison in a single-slot synchronous network (Figure 7b). (**c**) SPEB comparison in a single-slot synchronous network (Figure 7c). (**d**) SPEB comparison in a single-slot synchronous network (Figure 7d). the total energy is further allocated to step 1 for radar positioning, leading to a smaller sensing SPEB in the step-by-step scheme than in the integrated scheme. Additionally, the optimization results of step 1 (radar positioning) in the step-by-step scheme can serve as prior information for step 2. Therefore, the improvement of radar positioning accuracy has a positive promoting effect on the improvement of target sensing accuracy. However, in the integrated optimization scheme, due to simultaneous radar positioning and target sensing and the optimization of the sensing SPEB, poor radar positioning accuracy will be obtained by the integrated scheme, which is detrimental to the accuracy of target sensing. Therefore, the step-by-step scheme is more suitable for sensing-resource-abundant networks. For sensing-resource-limited networks, as radar positioning is easier than target sensing, the optimized power allocation results for target sensing in the integrated optimization scheme can meet the needs of radar positioning in the networks. In this situation, the accuracy of radar positioning obtained by the integrated scheme will have no adverse impact on its sensing accuracy. However, in the step-by-step scheme, in order to ensure that the accuracy of radar positioning does not have an adverse impact on sensing accuracy, a portion of energy needs to be allocated to step 1 for radar positioning, resulting in a reduction in the energy allocated to step 2 for target sensing. Due to the fact that target sensing is harder than radar positioning in sensing-resource-limited networks, the reduction in the energy allocated to step 2 is not conducive to the accuracy of target sensing. Therefore, the integrated scheme is more suitable for sensing-resource-limited networks. Finally, due to the spatial cooperation between radar positioning and target sensing, the accuracy of radar positioning can affect the accuracy of target sensing, resulting in the trade-off mentioned above.
## 3 Ground Motion Selection Techniques
### Selection Based on Spectral Values
In these algorithms, simulated realizations and target spectra are generated randomly, and records are then selected based on the difference between the means and variances of the simulated realizations and the target spectra. CMS can also be referred to as conditional spectra (CS) when both the mean and variance of the target spectra are matched. Recent studies by [PERSON] et al. [82] have extended the CS approach to include both vertical and horizontal records and to select records based on multiple components. There is a major limitation when using CS, due to the fact that the response spectrum does not accurately represent the seismic response of structures. Several IMs were proposed that are associated with both structural and seismological parameters, as discussed in Section 2. This led [PERSON] [83,84] to develop the generalized conditional intensity measure (GCIM), a more holistic approach to include any IM in conditional distribution calculations. The selection can be based on a scalar or a vector of intensity measures. To be consistent with current practices in seismic hazard analysis, [PERSON] et al. [85] further modified the CS algorithm by allowing different GMMs to be used to calculate \(\epsilon\). Highlighting the importance of the spectral correlation structure with periods, [PERSON] and [PERSON] [86] developed a methodology considering correlation structure in the selection process along with target spectra. This is very important when selecting simulated ground motions that do not necessarily follow the same correlation structure as recorded ground motions [87]. [PERSON] et al. [88] developed a compound target spectrum that incorporates multiple target spectra at multiple IM levels into a single spectrum. [PERSON] et al. [89] presented a newer CMS to use average spectral acceleration over a period range (\(AVG_{\text{s4}}\)) instead of \(Sa(T_{\rho})\) as the conditioning IM.
# Valkyrie--Design and Development of Gaits for Quadruped Robot Using Particle Swarm Optimization
## 2 Kinematic Modeling of the Quadruped Robot
### Forward Kinematics  
Forward Kinematics is widely used in robotics to determine the position of the end effector by providing inputs like link lengths and joint angles. The joint variables are \(\theta_{1}\) which is the angle between link 1 and the body of the robot and \(\theta_{2}\) is the angle between link 1 and link 2. \(a_{1}\) is the length of link 1 and \(a_{2}\) is the length of link 2. The Z-axis is assigned along the axis of rotation, and the X-axis is placed perpendicular to Z-axis as seen in Figure 1b. The Denavit-Hartenberg method was used to determine the forward kinematics equations. The homogeneous transformation matrix, \(A_{i}\) can be written as  
\[A_{i}=\left[\begin{array}{cccc}c\theta_{i}&-s\theta_{i}c\alpha_{i}&s\theta_ {i}s\alpha_{i}&a_{i}c\theta_{i}\\ s\theta_{i}&c\theta_{i}c\alpha_{i}&-c\theta_{i}s\alpha_{i}&a_{i}s\theta_{i}\\ 0&s\alpha_{i}&c\alpha_{i}&d_{i}\\ 0&0&0&1\end{array}\right] \tag{1}\]  
Using the parameters in the DH Table (Table 2), we could get the transformation matrix as,  
\[A_{i}=A_{1}\cdot A_{2}=\left[\begin{array}{cccc}c_{1}&-s_{1}&0&a_{1}c_{1}\\ s_{1}&c_{1}&0&a_{1}s_{1}\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]*\left[\begin{array}{cccc}c_{2}&-s_{2}&0&a_{2}c_{2 }\\ s_{2}&c_{2}&0&a_{2}s_{2}\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]=\left[\begin{array}{cccc}c_{12}&-s_{12}&0&a_{1}c _{1}+a_{2}c_{12}\\ s_{12}&c_{12}&0&a_{1}s_{1}+a_{2}s_{12}\\ 0&0&1&0\\ 0&0&0&1\end{array}\right] \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Specifications** \\ \hline Robot body dimension & 150 mm \(\times\) 90 mm \(\times\) 30 mm \\ Link length (\(a_{1}\) and \(a_{2}\)) & 70 mm \\ DoF & 2/Leg \\ Weight & 1.5 kg \\ Material & Polyethylene terephthalate (PET) and acrylic \\ Joint constraints & 0 \(\leq\)\(\theta_{1}\)\(\leq\)\(+\)20 \\ \(0\)\(\leq\)\(\theta_{2}\)\(\leq\)\(+\)20 \\ \hline \end{tabular}
\end{table}
Table 1: Robot Parameters.  
Figure 1: (a) Robot prototype; (b) frame assignment; (c) kinematic model of a leg.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Link** & **0** & \(d\) & \(a\) & \(A\) \\ \hline
1 & \(\theta_{1}^{*}\) & 0 & \(a_{1}\) & 0 \\
2 & \(\theta_{2}^{*}\) & 0 & \(a_{2}\) & 0 \\ \hline \end{tabular}
\end{table}
Table 2: DH parameters Table.  
From the \(A_{i}\) matrix, the position, and the orientation can be determined easily. The position is given as  
\[x_{2}=a_{1}cos_{1}+a_{2}cos_{12},\;z_{2}=a_{1}sin_{1}+a_{2}sin_{12} \tag{3}\]
# Impact of Music Selection on Motivation and Performance during Cardiopulmonary Exercise Testing
###### Abstract  
Background: The aim of this study was to examine the effect of applying synchronized music and appropriate music selection on motivation for exercise and achieving better results in individuals of different fitness levels. Methods: The study included a total of 20 female participants, who for certain analyses were divided into two groups with slightly different levels of aerobic fitness: students from the Faculty of Sport and Physical Education (n = 10, age 23.0 \(\pm\) 2.8), and middle-aged adult women exercising recreationally (n = 10, age 38.3 \(\pm\) 11.6). Cardiopulmonary exercise testing (CPET) was conducted using a treadmill and gas analysis equipment, and motivational music qualities were assessed using the BMRI-2 questionnaire. The procedure included an initial maximal CPET test, echocardiography, and spirometric tests, followed by an interview to select preferred music tracks. A second CPET test was then performed with the chosen motivational music. The Borg Rating of Perceived Exertion (RPE) scale was used in both tests. Results: The internal consistency of the questionnaire was confirmed with a Cronbach's Alpha of 0.982. The synchronized motivational music significantly improved cardiopulmonary parameters such as peak oxygen consumption (peak VO\({}_{2}\)), oxygen consumption (VO\({}_{2}\)) at the second ventilatory threshold (VT2), peak heart rate (peak HR), test duration, and reduced perceived exertion (RPE) at the beginning of the test and at the intensity level corresponding to the VT2. Negligible differences were noted between students and recreational athletes, so it can be assumed that music had an equal impact on these two groups of subjects. Conclusions: The study concluded that synchronous motivational music significantly enhances cardiopulmonary performance and reduces perceived fatigue during physical exertion by serving as a key motivational element and facilitating more economical movement.  
synchronized music; aerobic fitness; peak VO\({}_{2}\); endurance training; exercise performance; perceived fatigue +
Footnote †: journal: _sciences_
## 3 Large-Scale Triaxial Test on the Shear Failure Characteristics of a Soil-Rock Mixture
### Analysis of the Stress-Strain Relationship Curve  
Figure 7 displays the stress-axial strain curves and volumetric strain-axial strain curves of the soil-rock mixture at various confining pressures during a triaxial test. According to the Standard for Geotechnical Test Methods (GB/T 50123-2019) [25], the stress corresponding to the axial strain of 15-20% is usually taken as the failure stress in the hardening curve. The axial strain corresponding to the failure stress is 15% in this test. As the dry density remains constant, the failure stress increases with increasing confining pressure. Moreover, as the dry density increases, the failure stress tends to increase as well. During the initial phase of the test, the specimen undergoes shrinkage followed by dilatancy, resulting in an increase in the volume as the dry density increases. As the confining pressure increases, the specimen's dilatancy exhibits hysteresis, transitioning from shear dilatation to shear shrinkage, ultimately leading to a decrease in the volume variable. [PERSON] et al. [26] suggested that this behavior can be explained by the state-dependent dilatancy characteristics of bulk particles, specifically the phase transition stress ratio \(M_{pt}=q/p\) (where \(q\) represents the critical dilatancy stress and \(p\) is the average primary stress). Figure 8  
Figure 6: Triaxial testing machine.  
displays the phase transition stress for various dry densities at varying confining pressures. It is evident from Figure 8 that the phase transition stress ratio increases with increasing dry density under a specific confining pressure, resulting in an increase in the volume. Conversely, as the confining pressure increases, the phase transition stress ratio gradually decreases at a constant dry density, indicating a steady decrease in the volume variable. This finding corroborates the previously stated conclusion.  
Figure 7: Stress–strain relations of different types of samples in the triaxial tests: (**a**) dry density of 1.76 g/cm\({}^{3}\), (**b**) dry density of 1.82 g/cm\({}^{3}\), (**c**) dry density of 2.00 g/cm\({}^{3}\), (**d**) dry density of 2.15 g/cm\({}^{3}\), and (**e**) dry density of 2.30 g/cm\({}^{3}\).
# Proposed New Analytical Method of Tower Load in Large-Span Arch Bridge Cable Lifting Construction
## 2 Proposed Calculation Method
### The Acting Force of Lifting Cable to Tower  
The lifting winch is arranged at the rear of the tower, and the moving head of the lifting cable enters into the lifting winch after passing a guide wheel at the tower top and anchor, and the fixed head is anchored on the fixed sliding trolley, so the lifting cable only produces force on the tower and anchor at the left bank. Figure 4 shows the calculation diagram of the acting force of the lifting cable on the tower.  
Figure 4: Calculation diagram of the acting force of lifting cable on tower.  
The horizontal and vertical forces on a tower top from the lifting cable are \(H_{3}\) and \(V_{3}\), respectively, which can be formulated as:  
\[H_{3}=\eta^{2}F(\cos\gamma_{1}+\cos\gamma_{2})-2\eta F\cos\theta \tag{23}\]  
\[V_{3}=\eta^{2}F(\sin\gamma_{1}+\sin\gamma_{2})+2\eta F\sin\theta \tag{24}\]  
where \(F\) represents the running head traction force; \(\eta\) represents the efficiency coefficient of the lifting pulley; \(\gamma_{1}\) and \(\gamma_{2}\) respectively represent the forward tilt angle of the lifting running head before and after the lifting point, and its calculation formulae are Equations (23) and (24).  
\[\gamma_{1}=tan^{-1}\frac{f_{1}-L_{1}\cdot\Delta H/L}{L_{1}} \tag{25}\]  
\[\gamma_{2}=tan^{-1}\frac{f_{2}-(L_{1}+L_{2})\cdot\Delta H/L}{L_{1}+L_{2}} \tag{26}\]
## 3 Results and Discussion
### Validation of the Multi-Mycotoxin Determination Method in Vegetable Drinks
#### 3.1.4 Matrix Effect
As there is a matrix effect, the need to compensate for this matrix effect is established by using extracts of mycotoxin-negative vegetable drinks to construct the calibration curves and perform quantitative determination in the sample.
# LSRM: A New Method for Turkish Text Classification
###### Abstract  
The text classification method is one of the most frequently used approaches in text mining studies. Text classification requires a model generation using a predefined dataset, and this model aims to assign uncategorized data to a correct category. In line with this purpose, this study used machine learning algorithms, deep learning algorithms, word embedding algorithms, and transfer-learning algorithms to classify Turkish texts using three diverse datasets, one of which is new, to analyze text classification performances for the Turkish language. The preparation process of the newly added dataset involved the variations in Turkish word usage patterns over the years, since it consisted of timestamp-enabled data. The study also developed a novel method named LSRM to increase the text classification performance for agglutinative languages such as Turkish. After testing the new method on datasets, the statistical ANOVA method revealed that applying the proposed LSRM method increased the classification performance.  
text classification; text categorization; machine learning; deep learning; CNN; LSTM; GRU; NLP; LSRM
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 5 Experimental Evaluation
### Bag of Words  
Finally, we evaluated the use of the bag of words method explained in Section 4.2.2. Results are shown in Table 7.  
The random forest classifier also achieved the best results when using these features. However, the results are not positive. Hence, when used in isolation, BoW features are not effective in determining the privacy level of images.  
Considering More Features at Once  
As shown in previous results, all features have a correlation with the ground truth. Hence, we tried different a combination of features to improve the recognition rates. In these experiments, we used the Random Forest classifier, since it achieved the best results in all the previous experiments.  
Results are shown in Table 8. We report those combinations obtaining large recognition rates. The best results were achieved using both hue-saturation features and face detection ones. Results improved significantly with respect to the use of the two kinds of features separately. Indeed, the achieved F\({}_{1}\) score is 0.727 (vs. 0.67 achieved by face detection features alone), and the MCC value is 0.473 (vs. 0.409 achieved by face detection features alone). In particular, with the combined features, we noticed a remarkable increase in recall: i.e., recall is 0.731 with the combined features, while it is 0.686 using face detection features alone. These results show that the combination of different feature types, both considering the presence of people faces and low-level characteristics of the image, are needed to improve the recognition rates. Overall, we observed that the combination of different features improves the recognition rates with respect to the use of single types of features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F\({}_{1}\)** & **MCC** \\ \hline SVM & 0.589 & 0.584 & 0.577 & 0.173 \\ Random Forest & 0.615 & 0.614 & 0.613 & 0.229 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of image privacy level prediction using bag of visual words features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F\({}_{1}\)** & **MCC** \\ \hline Random Forest & 0.600 & 0.593 & 0.586 & 0.192 \\ SVM & 0.573 & 0.573 & 0.573 & 0.167 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of image privacy level prediction using bag of words features.  
Finally, we experimented the weighted graph method explained in Section 4.2.1. In this experiment, we used only those images having some associated tags, resulting in a dataset of 9300 images well balanced between public and private ones. Results are shown in Table 9.  
In general, the results achieved by the weighted graph method were inferior to the ones obtained using numerical features. Of course, the effectiveness of this method depends on the accuracy of tags. With more accurate tags and a larger knowledge base, we expect achieving better results.
## 2 Materials and Methods
### Data Contraction Detection Methods  
As mentioned earlier, the chosen methodology for detecting contractions was to use the associated energy bursts as a feature. These intervals of increased energy have a large variability that should be reduced in order to obtain a smoothed version over which the contraction detection and delineation could be performed. Five different methods were applied: wavelet energy, Teager energy, RMS, squared RMS, and Hilbert envelope, for which a brief description is provided below. For all of these methods except the wavelet energy, a smoothing window length of 70 s was selected upon on the inspection of a substantial number of application cases and evaluating its compliance with the values referred to in the relevant bibliographic references [14; 34; 35; 36]. Knowing that a contraction duration typically spans from half a minute to several minutes, the selected window length represents an adequate resolution compromise.
## 3 Experimental Results
### PID Controller: Yaw and x-Direction  
After tuning the two PID controllers for the yaw angle and translation in x-direction by means of the AMIGO method (with some further fine-tuning), we determined the gains as K\({}_{\text{P}}\) = 1.0, K\({}_{\text{i}}\) = 0.10, K\({}_{\text{d}}\) = 0.045 and K\({}_{\text{P}}\) = 5.10, K\({}_{\text{i}}\) = 5.40, K\({}_{\text{d}}\) = 0.62. respectively. Based on that, an experiment was conducted where the quadcopter was placed in a position approximately 30 cm away from the marker in x-direction (\(x_{cmd}\)) with a 35\({}^{\circ}\) relative angle (\(\
abla_{cmd}\)). The experimental results are shown in Figure 7. Based on the experimental results, the performance is considered to be quite accurate with a deviation around the set point of only about \(\pm\)5 degrees. The x-direction controller shows significant improvement over using a simple P-controller but still displays a minor oscillation around the set point. However, the results are very much sufficient for the scope of this project as it shows that the MAV is able to orient and position itself relative to the target marker with only a small amount of oscillations and overshoot in indoor conditions.  
Figure 6: Actual distance between the camera and the marker versus the distance calculated by the marker detection and pose estimation function.