esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of a Manual Methodology for Measuring Roundness and Cylindicity Tolerances
## 2 Materials and Methods
### Theoretical and Analytical Fundamentals and Development
#### 2.1.2 Cylindricity Tolerance
\tag{10}\]  
The objective is to minimize the sum of squared distances, \(\varepsilon\), between the straight line describing the cylinder axis and each of the centers, \(C_{i}\), of each section, Equation (11). \[\varepsilon=\sum\limits_{i=1}^{n}d_{i}(C_{i},r)^{2}=\sum\limits_{i=1}^{n} \Bigl{[}\left(x_{i}-\overline{x}+u(z-\overline{z})\right)^{2}+\left(y_{i}- \overline{y}+v(z-\overline{z})\right)^{2}\Bigr{]} \tag{11}\]  
By setting the partial derivatives of \(\varepsilon\) with respect to the coordinates of the vector, \(u\) and \(v\), equal to zero, Equations (12) and (13) are obtained. It can be appreciated that they include the values that make \(\varepsilon\) minimized. \[u=\ \frac{\sum\limits_{i=1}^{n}\left(x_{i}-\overline{x}\right)(z_{i}- \overline{z})}{\sum\limits_{i=1}^{n}(z_{i}-\overline{z})^{2}} \tag{12}\]  
\[v=\ \frac{\sum\limits_{i=1}^{n}\left(y_{i}-\overline{y}\right)(z_{i}- \overline{z})}{\sum\limits_{i=1}^{n}(z_{i}-\overline{z})^{2}} \tag{13}\]  
Note that the covariance between \(x\) and \(z\) and between \(y\) and \(z\), as well as the variance of \(z\), are obtained from Equations (14)-(16). \[\sigma_{xz}=\frac{\sum\limits_{i=1}^{n}\left(x_{i}-\overline{x}\right)(z_{i}- \overline{z})}{n} \tag{14}\]  
\[\sigma_{yz}=\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\overline{y}\right)(z_{i}- \overline{z})}{n} \tag{15}\]  
\[\sigma_{z}^{2}=\frac{\sum\limits_{i=1}^{n}(z_{i}-\overline{z})^{2}}{n} \tag{16}\]  
Taking into account Equations (14)-(16) and the solution obtained for the \(u\) and \(v\) components, the director vector of the axis of the least squares cylinder can be expressed according to Equation (17). \[\overset{\rightarrow}{V}=\left(\frac{\sigma_{xz}}{\sigma_{z}^{2}},\frac{ \sigma_{yz}}{\sigma_{z}^{2}},1\right) \tag{17}\]  
Once the equation of the axis of the cylinder is known, the distance from each point on the surface of the measured part with respect to the axis is calculated according to Equation (18). \[d_{i}(P_{i},r)=\frac{\left|\overrightarrow{M\!P_{i}}\times\overrightarrow{V} \right|}{\left|\overrightarrow{V}\right|}=\frac{\sqrt{\left(x_{i}-\overline{x} \right)^{2}+\left(y_{i}-\overline{y}\right)^{2}+\left(z_{i}-\overline{z} \right)^{2}-\left[u(x_{i}-\overline{x})+v(y_{i}-\overline{y})+\left(z_{i}- \overline{z}\right)\right]^{2}}}{\sqrt{u^{2}+v^{2}+1}} \tag{18}\]  
The cylindricity tolerance, \(T_{cylindricity}\), is finally obtained as it has been mentioned before, i.e., as the difference between the largest and smallest distance between the points on the surface and the cylinder axis, Equation (19). \[T_{cylindricity}=d_{\text{max}}-d_{\text{min}} \tag{19}\]  
This analytical procedure for cylindricity tolerance evaluation can be easily implemented in an Excel spreadsheet.
# Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration
## 2 Materials and Methods
### Methodology Design
#### 2.3.2 Input Variables  
In our in-field calibration problem, input variables from the sensor taken into consideration for the calibration model include PM concentration, relative humidity and temperature, since we find the association between each variable and the error to be calibrated at an hourly scale. We define the 'error to be calibrated' as the PM concentration deviation between sensor and reference station in co-location on an average hourly scale.  
As shown in Figure 2, sensory PM\({}_{2.5}\) concentration samples share a strong association with the error. That is because the sensor's PM concentration reading shares a close trend with that of reference station on hourly average scale. When it comes to variable relative humidity and temperature, although their respective association with error is not as remarkable as concentration in Figures 3 and 4, we also find that error's upper bound may bear association with relative humidity or temperature. With regard to PM\({}_{10}\), we also find a similar association. So based on these three input variables, the calibration model has potential to compensate for sensory data errors.  
Figure 2: Samples: Sensory PM\({}_{2.5}\) concentration vs. error to be calibrated
## 3 Results and Discussion
### Limitations of General Applications  
The 51 geospatial layers were selected and scaled with the goal of distinguishing different acoustic environments [26]. However, it is likely that there are acoustic sources and propagation effects which are not well-represented in the geospatial data. Additionally, it is possible that some acoustic effects are not well-represented in clusters due to trends in a larger number of geospatial layers, which dominate clustering assignments. We also note that the spatial resolution (270 m) makes cluster maps ineffective at investigating local (i.e., over small spatial regions; e.g., \(<\)1 km) cluster assignments or patterns.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subcluster Number** & **Rank 1** & **Rank 2** & **Rank 3** \\ \hline
1a & Water (0.70) & DistCoast (\(-\)0.58) & DistRoadsAll (0.34) \\
1b & Water (0.60) & DistRoadsAll (0.14) & TMaxWinter (\(-\)0.08) \\
2a & Evergreen (0.78) & Slope (0.38) & TMinSummer (\(-\)0.34) \\
2b & Elevation (0.31) & Evergreen (0.30) & TMinSummer (\(-\)0.29) \\
3a & TdewAvgWinter (0.43) & TMaxWinter (0.37) & TMinWinter (0.37) \\
3b & Wetlands (0.79) & PPTSummer (0.27) & TdewAvgWinter (0.23) \\
4a & Herbaceous (0.57) & DistMilitary (\(-\)0.18) & TMaxSummer (0.15) \\
4b & Herbaceous (0.63) & DistAirPHigh (0.31) & TMinWinter (\(-\)0.27) \\
5a & Deciduous (0.78) & PPTSummer (0.25) & FlightFreq\_25 km (0.25) \\
5b & MixedForest (0.39) & Deciduous (0.29) & TMaxWinter (\(-\)0.27) \\
6a & Cultivated (0.65) & DistAirPHeli (\(-\)0.28) & Elevation (\(-\)0.27) \\
6b & Cultivated (0.53) & TMinWinter (\(-\)0.31) & TMaxWinter (\(-\)0.27) \\
7a & Developed (0.44) & RddAll (0.31) & RddMajor (0.30) \\
7b & RddMajor (0.74) & Developed (0.74) & RddAll (0.67) \\
8a & Shrubland (0.59) & TMaxSummer (0.41) & TMaxWinter (0.36) \\
8b & Shrubland (0.62) & TdewAvgSummer (\(-\)0.48) & Elevation (0.45) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top three distinct/unique correlated geospatial variables ranked by magnitude for each subcluster.
## 4 Results
### Optimized Two-Dimensional Object Assignment  
For the cluster diameter \(D\), the range is set to [20, 100]. The cluster circle positioning distance \(\Delta d\) spans a range of [0, 50]. The weights \(w_{1}\), \(w_{2}\), \(w_{3}\), and \(w_{4}\) are set to 1, 0.5, 0.5, and 0.5, respectively. The optimization procedure and its outcomes are shown in Figure 5 and Table 2:  
\begin{table}
\begin{tabular}{c c c c c} \hline
**[\(D\), \(\Delta d\)]** & \(N\) & **Mean[\(L_{i}\)]** & **Max[\(L_{i}\)]** & \(\sigma_{1}\) \\ \hline
[30, 0] & 18 & 70.50 & 116.53 & 23.58 \\ \hline
[40, 5] & 13 & 86.55 & 141.87 & 33.97 \\ \hline \end{tabular}
\end{table}
Table 1: Relevant data of UAVs\({}^{\prime}\) path plan before optimization.  
Figure 5: _Cont._  
The trend analysis shows that both the PSO and CSO methods can achieve convergence swiftly, with the PSO algorithm necessitating fewer iterations to reach convergence. When comparing the fitness, quantity, average path length, most extended path length, and standard deviation of each machine's path length, both methods produce identical results, indicating that the original problem has already converged.  
For the initial optimization task, 23 unmanned aerial vehicles can accomplish a multi- objective search in the shortest approximate time. Using both optimization methods, the paths obtained for each machine are the same, proving that the solution to the original problem obtained using this algorithm is highly stable and is minimally affected by the algorithm itself. In addition, in the optimization results obtained from both methods, the values corresponding to the optimal cluster diameter are nearly identical, and the disparities in the cluster migration distances are inconsequential, resulting in no significant differences in the final outcomes, indicating that the original algorithm has a certain tolerance for the excessively large cluster migration distance.
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 3 Experimental Results
### Comparative Studies
[72], [PERSON] et al. [73], [PERSON] et al. [37], [PERSON] et al. [39], [PERSON] et al. [74] and [PERSON] et al. [12]. The comparison results using ORIGA, HRF, and DR HAGIS datasets are reported in Table 9. For glaucoma detection, the comparative methods have applied different sets of performance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **SP** & **SE** & **Acc** & **AUC** \\ \hline [PERSON] et al. [28] & 0.635 & 0.77 & - & 0.94 \\ [PERSON] et al. [57] & 0.91 & - & 0.913 & 0.96 \\ [PERSON] et al. [58] & 0.863 & 0.995 & - & - \\ [PERSON] et al. [59] & 0.96 & 0.88 & 0.93 & - \\ [PERSON] et al. [61] & 0.818 & 0.81 & 0.93 & - \\ [PERSON] et al. [60] & 0.94 & 0.92 & - & 0.92 \\
**Proposed** & 0.965 & 0.961 & 0.95 & 0.967 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison against DR detection methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SE** & **SP** & **Acc** \\ \hline [PERSON] et al. [62] & 0.70 & 0.76 & 0.912 \\ [PERSON] et al. [63] & 0.95 & 0.90 & - \\ [PERSON] et al. [64] & 0.95 & 0.95 & - \\ [PERSON] et al. [2] & - & - & 0.921 \\ [PERSON] et al. [65] & 0.80 & 0.90 & - \\ [PERSON] et al. [66] & 0.85 & 0.55 & - \\ [PERSON] et al. [67] & 0.96 & 0.95 & 0.935 \\ [PERSON] et al. [68] & 0.85 & 0.80 & - \\ [PERSON] et al. [69] & 0.959 & 0.97 & - \\
**Proposed** & 0.96 & 0.958 & 0.958 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison against DME detection methods over MESSIDOR dataset. evaluation measures. For fair comparison, we have reported the performance of our method using all the performance evaluation measures i.e., SE, SP, AUC, dice score (D\({}_{\text{c}}\)), and computation test time over all the three datasets. From the results we can observe that, our method has acquired the highest SE and AUC rates i.e., 0.945, and 0.947, respectively, that signifies the reliability of our approach. The proposed method achieved the specificity of 0.96 which is slightly lower (only for the HRF dataset) than the methods presented in [39] and [74], but both methods showed lower sensitivity rates, which clearly shows their inability to detect the glaucoma signs. In terms of SE over HRF dataset our method showed an approximately 10% performance gain over the work of [PERSON] et al. [39], and approximately 24% performance gain over the work of [PERSON] et al. [74]. In terms of AUC, and SE our method showed significant performance gain than all the comparative approaches. [PERSON] et al. [70] also reported the performance of their method using D\({}_{\text{c}}\) measure, where our method also outperformed their work. Moreover, our technique can easily run on CPU or GPU machines and each image test time is 0.9 s which is faster than the work of [PERSON] et al. [39], which take 1.49 s. Hence, based on the result it can be concluded that our method is also equally reliable for the glaucoma detection.
# Quantum Dot Solar Cells: Small Beginnings Have Large Impacts
## 2 Timescales of Excited State Processes
### Cooling  
One pathway the electron and/or hole can take to decay to their ground state energy first involves thermalizing or \"cooling\" to the band edges emitting excess energy as phonons. Energy lost through cooling is the largest loss-mechanism for lower-bandgap solar cells with unabsorbed photons being the largest loss for higher-bandgap materials. There are several strategies to overcome this limitation including MEG (discussed above), tandem solar cells [95; 96; 97; 98], and hot-carrier extraction [99; 41; 91]. Due to quantum confinement, the available energy states of electrons and holes are discrete and energetically separated by an amount of energy larger than the energy of a single optical phonon. Simultaneously emitting multiple phonons is an unfavorable process; therefore, carrier cooling should be considerably slowed leading to what is known as a phonon bottleneck [100; 101]. However, experiments show far less than the predicted slow carrier cooling time, indicating other energy-loss processes are occurring, possibly through coupling to the vibrational modes of the surface ligands [102; 103]. C-O, C-H, and O-H vibrations are at energies comparable to the energy level spacing in QDs (e.g., 10 s to 100 s of meV). A systematic exploration of the carrier cooling rates as a function of ligand moieties with different vibrational spectra has not been done but would shed light on the absence of this interesting phenomenon.  
While the quantization of energy levels in QDs is expected to reduce the electron-phonon coupling due to significant differences in electron and phonon energies, recent studies have shown otherwise. [PERSON] et al. [104] quantitatively show the existence of higher energy phonon states that arise due to the mechanical softness of the QD surface where atoms vibrate with higher amplitudes than the core atoms. These results show the existence of a higher electron-phonon coupling that provides a quick path for hot electron cooling as opposed to the initial hypothesis of the existence of a phonon bottleneck. Recently the same group showed theoretically that such soft surface phonon modes indeed enhance electron and hole cooling in PbS QDs [105].  
Another process known as Auger cooling allows for the transfer of energy between the excited electron and hole which can cool more efficiently due to the closer energy spacing of the valence band states in many QD materials [106; 107; 108]. Now that these processes are better understood, work can be done in designing ways to overcome the challenges. Perhaps incasing the QDs in a solid matrix to minimize the QD surface vibration and, if the matrix is conductive, transport the charge carriers away from each other before multi-exciton processes can occur--as discussed next.
## 2 Materials and Methods
### Formulation of the Reduced-Order Model
#### 2.1.2 Modified Bernoulli Equation  
[PERSON] et al. [3] did an early work by adding viscous terms in the Bernoulli equation to predict the pressure pattern inside the glottis. Concretely, in the contraction part, based on the measurements of [PERSON] et al. [28], a loss factor of 0.37 was included to account for the abrupt contraction; in the glottis part, a viscous loss term was added to modify the pressure drop in the Bernoulli equation; in the expansion part, the pressure recovery was estimated with [PERSON]'s law. Motivated by their work, we applied a viscous loss term to each section of the glottis and combine them with the Bernoulli equation to obtain the pressure pattern inside the glottis.  
Refer to Figure 1, for the contraction and glottis parts, assuming that the vocal folds are discretized with \(n+1\) uniformly distributed cross sections along the airflow direction, then similar to the method in Reference [3], the modified Bernoulli equation which includes the viscous loss can be expressed in the following form:  
\[P_{s}+\frac{\rho}{2}(\frac{Q}{A_{s}})^{2}=P_{n}+\frac{\rho}{2}(\frac{Q}{A_{n}} )^{2}+\frac{\rho}{2}Q^{2}\sum_{i=1}^{n}\frac{f_{r_{i}}}{A_{i}^{2}}, \tag{1}\]  
where \(\rho\) is the air density, \(A_{i}\) is the glottal area at the \(i\)th cross section, \(f_{r}\) is the flow resistance coefficient of each section of the glottis whose value is not known a priori and needs to be predicted by the DNN model. Details about the implementation of the DNN model will be discussed in Section 2.2.  
For the expansion part, similar to the treatment in Reference [3], we have:  
\[P_{n}-P_{c}=-\frac{\rho}{2}(\frac{Q}{A_{n}})^{2}\cdot 2\frac{A_{n}}{A_{c}}(1- \frac{A_{n}}{A_{c}}). \tag{2}\]  
Therefore, the pressure at each cross section of the glottis can be written as follows:  
\[P_{i}=P_{s}+\frac{\rho}{2}(\frac{Q}{A_{s}})^{2}-\frac{\rho}{2}(\frac{Q}{A_{i}} )^{2}-\frac{\rho Q^{2}}{2}\sum_{j=1}^{i}\frac{f_{r_{j}}}{A_{j}^{2}}, \tag{3}\]  
where \(1\leq i\leq n\).
## 4 Experimental Results
### Datasets
#### 4.1.2 RAF-DB Dataset  
The Real-World Affective Faces Database (RAF-DB) is an FER dataset (Figure 7) that includes 29,672 facial images that have been annotated by 40 different taggers describing simple and complex expressions. Facial photos can indicate seven basic emotions and eleven complex emotions. The subject's age, gender, ethnicity, and head posture, as well  
Figure 6: FER2013 dataset samples.  
as the lighting, occlusions, and post-processing techniques, are all highly variable in the photographs in the RAF-DB.
# Sugarcane Bagasse Torrefraction for Fluidized Bed Gasification
## 3 Materials and Methods
### Experimental Setup  
Torrefaction experiments were performed in a lab-scale batch installation. Figure 1 shows a simplified diagram of the experimental setup. A flow regulator (1) fixes a nitrogen flow at 1 L/min to guarantee an inert atmosphere inside the reactor (4), loaded with 50 g of raw material by test. The reactor is heated through a 3-resistance furnace (3) with a heating rate of 5 \({}^{\circ}\)C/min in all tests. The controller (2) manages the furnace temperaturesand heating rate. Volatiles leave the reactor through a thermally insulated line. Condensed gases are collected in the condenser (5), coupled to a water-cooling bath, recirculating water at 7 \({}^{\circ}\)C (6). Finally, non-condensable volatiles are evacuated from the installation after passing by a water trap (7). The datalogger (8) registers the reactor temperatures (T\({}_{1}\) and T\({}_{2}\)) and the gas temperature at the reactor exit (T\({}_{3}\)).  
The experimental design evaluated the effect of the final process temperature (T\({}_{\rm torr}\)) and residence time (t\({}_{\rm R}\)) on the process products and the torreified samples' physico-chemical properties. The process heating rate was the same for all the experimental runs.  
An experimental design setup of two factors--T\({}_{\rm torr}\) and t\({}_{\rm R}\), and two levels for each factor (namely 2\({}^{\rm k}\)) was used. For the T\({}_{\rm torr}\) factor, the levels assessed were 230 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, and for the t\({}_{\rm R}\), the levels were 30 and 45 min. In addition, three experimental repetitions for each process condition were performed, totaling 12 experimental runs. The main output experimental variables observed in the experiments were the proximate and elemental composition and calorific value of torreified bagasse.  
In order to assess the impact of T\({}_{\rm torr}\), temperatures of 230 \({}^{\circ}\)C and 280 \({}^{\circ}\)C were selected considering previously reported experiences with similar feedstocks [25; 39; 40; 44; 45] and the TGA analysis results, which are presented in the next section.
# Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping
###### Abstract  
Finding outliers in functional infinite-dimensional vector spaces is widely present in the industry for data that may originate from physical measurements or numerical simulations. An automatic and unsupervised process of outlier identification can help ensure the quality of a dataset (trimming), validate the results of industrial simulation codes, or detect specific phenomena or anomalies. This paper focuses on data originating from expensive simulation codes to take into account the realistic case where only a limited quantity of information about the studied process is available. A detection methodology based on different features, such as h-mode depth or the dynamic time warping, is proposed to evaluate the outlyingness both in the magnitude and shape senses. Theoretical examples are used to identify pertinent feature combinations and showcase the quality of the detection method with respect to state-of-the-art methodologies of detection. Finally, we show the practical interest of the method in an industrial context thanks to a nuclear thermal-hydraulic use case and how it can serve as a tool to perform sensitivity analysis on functional data.  
functional data analysis; functional outlier detection; probabilistic modeling; computer experiments; sensitivity analysis +
Footnote †: journal: Medical  
==WARNING: Truncated because of repetitions==
\(p_{ak}=f_{k}(\mathbf{u}_{\alpha}|\mathbf{\mu}_{k},\mathbf{\Sigma}_{k})\) such that \(\mathbb{P}(\mathbf{u}_{\alpha}|g_{k}=1)\geq\alpha\). The set of data points less likely than \(\mathbf{u}_{\alpha}\) is composed by points \(\mathbf{u}\in\mathcal{S}\subset\mathbb{R}^{R}\) which verify:  
\[(\mathbf{u}-\mathbf{\mu}_{k})^{T}\mathbf{\Sigma}^{-1}(\mathbf{u}-\mathbf{\mu}_{k})\geq(\mathbf{u}_{ \alpha}-\mathbf{\mu}_{k})^{T}\mathbf{\Sigma}^{-1}(\mathbf{u}_{\alpha}-\mathbf{\mu}_{k}).\] (4)  
And therefore, \(\mathbb{P}[(\mathbf{u}-\mathbf{\mu}_{k})^{T}\mathbf{\Sigma}^{-1}(\mathbf{u}-\mathbf{\
+++  
performing this test over all the points considered in the feature space, we obtain a unique criterion for outlier detection, such that the outlying points will be the ones presenting \(p\)-values under a certain threshold \(\alpha,\forall f_{k}\).
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
###### Abstract  
The rapid growth of internet usage in daily life has led to a significant increase in cyber threats, with malicious URLs serving as a common cybercrime. Traditional detection methods often suffer from high false alarm rates and struggle to keep pace with evolving threats due to outdated feature extraction techniques and datasets. To address these limitations, we propose a deep learning-based approach aimed at developing an effective model for detecting malicious URLs. Our proposed method, the Char2B model, leverages a fusion of BERT and CharBiGRU embedding, further enhanced by a Conv1D layer with a kernel size of three and unit-sized stride and padding. After combining the embedding, we used the BERT model as a baseline for comparison. The study involved collecting a dataset of 87,216 URLs, comprising both benign and malicious samples sourced from the open project directory (DMOZ), PhishTank, and Any.Run. Models were trained using the training set and evaluated on the test set using standard metrics, including accuracy, precision, recall, and F1-score. Through iterative refinement, we optimized the model's performance to maximize its effectiveness. As a result, our proposed model achieved 98.50% accuracy, 98.27% precision, 98.69% recall, and a 98.48% F1-score, outperforming the baseline BERT model. Additionally, the false positive rate of our model was 0.017 better than the baseline model's 0.018. By effectively extracting and utilizing informative features, the model accurately classified URLs into benign and malicious categories, thereby improving detection capabilities. This study highlights the significance of our deep learning approach in strengthening cybersecurity by integrating advanced algorithms that enhance detection accuracy, bolster defense mechanisms, and contribute to a safer digital environment.  
malicious URLs; BERT; CharBiGRU; deep learning; char2B Article
# Short-Range Transportation Using Unmanned Aerial Vehicles (UAVs) during Disasters in Japan
## 2 Materials and Methods  
### UAV and Test Sites  
We used a Japanese UAV, the M1000 (Mazex Co. Ltd., Osaka, Japan), which has a maximum take-off weight of 32 kg and a maximum speed of 58 km/h. UAV-assisted transportation experiments were conducted in Tomi (1060-1100 m above sea level) (Nagano Prefecture) and Fujisawa (35 m above sea level) (Kanagawa Prefecture) in Japan. Both are notably hilly regions. In Tomi, some land is too swampry and weedy to walk on; therefore, small hazards exist. We created restrictions on flights by situating routes across earthen obstacles and over streams.