esa-sceva/satcom-chunk-collection huggingface dataset:
### Goals and Contributions  
The main goal of this study is to develop an end-to-end machine learning pipeline for the automated detection and classification of adrenal lesions in MRI images. Specifically, the pipeline focuses on identifying adrenal adenomas and distinguishing them from other lesion types or the absence of lesions within the dataset.  
During the course of this investigation, multiple approaches were explored, including an end-to-end deep learning model for both detection and classification in a single stage. However, this approach yielded lower-than-expected validation performance, likely due to the limited dataset size and class imbalance, which posed challenges for deep learning optimization. Given these findings, we concluded that the most effective strategy is the two-stage pipeline presented here.  
This framework integrates a DL model for region-of-interest (ROI) detection on MRI slices and a traditional machine learning (ML) model to classify the detected regions in a binary setting--adenoma versus non-adenoma. By combining the strengths of DL for feature extraction and traditional ML for robust classification, this study aims to address key challenges in adrenal lesion analysis, such as overlapping imaging features, imbalanced datasets, and the need for automation in diagnostic workflows.  
By employing our pipeline, manual lesion segmentation during inference is no longer required, significantly streamlining the diagnostic process. Additionally, the pipeline filters out non-lesion slices, which are the vast majority in routine medical imaging, allowing for focused lesion analysis. Furthermore, training on multiple MRI sequences enhances robustness to variations in imaging protocols, making the method more adaptable to real-world clinical settings. This automated approach reduces the reliance on expert annotations, minimizes human error, and accelerates the analysis, making it a practical and efficient solution for clinical applications. Moreover, the usage of a second stage with a traditional ML model helps mitigate the challenges posed by small and imbalanced datasets, where deep learning alone struggles. Additionally, it allows for greater interpretability, as feature-based models provide insights into the classification decision-making process, which could be valuable for future clinical applications.  
The contributions of our developed ML pipeline are as follows:  
* The pipeline filters out non-lesion slices, which are the majority in medical imaging, allowing for a more efficient and targeted lesion analysis.
* The pipeline was trained using multiple MRI sequences, enhancing robustness against variations in imaging protocols.
* The two-stage approach mitigates deep learning challenges by using traditional ML for classification, which is more effective on small and imbalanced datasets.
* Unlike end-to-end deep learning, using an ensemble ML classifier allows for greater interpretability, making it easier to understand decision-making processes.
* The pipeline eliminates the need for manual segmentation, which is common in radiomics-based studies, improving clinical usability and efficiency.
## 2 Experimental Setup and Procedure
### Test Engine and Operating Procedure  
The test and data collection in this study were carried out on an engine test system, which is shown in Figure 1. The test engine was a Hyundai in-line four-cylinder CRDI diesel engine whose specifications are shown in Table 2. The engine speed and load were controlled at 1600 rpm and 40 Nm, respectively, by an eddy current dynamometer (Hwanwoong Mechatronics Co., Ltd., Gyeongnam, Korea). The relevant combustion pressure was measured by a piezoelectric pressure sensor (Type 6056A, Kistler Korea Co., Ltd., Seongnam-si, Korea) and recorded using a National Instruments PCI-6040E (National Instruments, Austin, TX, USA) data acquisition (DAQ) board. The fuel consumption of the engine was processed from the data obtained from a high-precision digital electronic weighing balance (GP-100K, A&D Co. Ltd., Tokyo, Japan). The GreenLine MK2 exhaust gas analyzer (Eurotron (Korea) Ltd., Seoul, Korea) and OP160 non-spectrophotometer (EPLUST Co., Ltd., Seongnam-si, Korea) were used for the measurement of NOx and smoke opacity, respectively. The pilot and main injection timings were separately set at 21 CA BTDC and 6 CA BTDC. The main difference between the two test conditions was the presence of EGR. When the EGR valve was closed, the fuel injection pressure was set to 300, 350, 400, and 450 bar in sequence, while when the EGR valve was open, 0%, 10%, 20% of EGR was introduced, under the injection pressures of 300 and 450 bar, representing low and high injection pressures. The engine was warmed up at an idling speed until the cooling water temperature stabilized at 85 \({}^{\circ}\)C, when a smooth operating condition was reached. The test conditions are reported in Table 3, and the EGR rate involved can be calculated by the following:  
\[\text{EGR rate}=\frac{V_{1}}{V_{1}+V_{2}} \tag{1}\]  
\(V_{1}\) represents the volume of exhaust gas introduced into the cylinder, and \(V_{2}\) represents the volume of fresh air entering the cylinder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties (Units)** & **Crude Palm Oil** & **Diesel** \\ \hline Density (kg/m\({}^{3}\) at 15 \({}^{\circ}\)C) & 903.8 & 836.8 \\ Calorific value (MJ/kg) & 39.34 & 43.96 \\ Viscosity (mm\({}^{2}\)/s at 40 \({}^{\circ}\)C) & 42.21 & 2.719 \\ \(\text{\centering}\)\(\text{\centering}\)\(\text{\centering}\
## 4 Numerical Results and Discussion
### Stability Analysis of Small Clear-Distance Tunnels under Different Excavation Schemes
#### 4.2.2 Stress Analysis of Supporting Structure  
Figure 15 presents the maximum principal stress distribution of the main line tunnel and ramp support structure under different lithology grades, and the positive values in the figure represent tension. The main line and ramp vault and invert were tensile areas. A comparison of the maximum and minimum peak principal stresses in different lithological grades showed that the peak tensile stress of grade III surrounding rock was the largest, followed by grade IV surrounding rock and grade V surrounding rock, which had the smallest value. The peak compressive stress of grade III surrounding rock was the smallest and that of grade V surrounding rock was the largest. Given the weak ability of concrete to bear tensile stress, the lining should maintain a certain thickness even under grade III surrounding rock to improve safety; the lining thickness should also be increased under grade V surrounding rock to bear high pressure loads.  
Figure 14: Variation curves of hance horizontal displacement under different lithology grades: (**a**) Main line tunnel; (**b**) Ramp.  
Overall, the vertical displacement of the ground and vault was positively correlated with the elastic modulus of the rock mass, which was consistent with the conclusion obtained by [PERSON] et al. [37] that with the increase of surrounding rock grade, the deformation of the tunnel vault became smaller. The horizontal displacement of the main line tunnel was smaller than that of the ramp under the surrounding rock grades III and IV. The area from the hance to the arch foot in the grade V surrounding rock was prone to large deformation. Attention should be paid to the tension zone at the bottom of the inverted arch under the grade III surrounding rock.  
Analysis of the Construction Behaviour of Small Clear-Distance Tunnels under Different Footage Lengths  
Footage length is an important factor to weigh construction progress and construction safety. When the surrounding rocks are relatively stable and the section is small, increasing the footage can increase the construction speed and is conducive to the economic efficiency of the project. When the section is special, the footage length needs to be controlled to ensure construction safety. The footage of the small clear-distance tunnel in this study was set to 3, 4, and 5 m. The tunnel was excavated by the step method; the main line tunnel was excavated first. The main line and ramp were staggered by 15 m, and the step face was staggered by 15 m. The surrounding rocks all belonged to grade III. The section 20 m away from the main line tunnel portal was selected as the target section for analysis.
## 3 The Stereo System and Its Underwater Calibration  
### Description of the Stereo-Catadioptric System  
Many techniques and devices developed for the aim of 3D mapping applications were evaluated in robotics and computer vision research works. Omnidirectional cameras are frequently used in many new technological applications such as underwater robotics, marine science and underwater archaeology since they offer a wide field of view (FOV) and thus a comprehensive view of the scene.  
In our work, we go towards an observation of the entire robot's environment with the use of minimum cameras to avoid occlusion and dead angles. We propose a stereo catadioptric system (Figure 1) mounted with a belt of twelve equidistant laser pointing devices (Apinex.Com Inc., Montreal, Canada). The catadioptric system consists on a Point Grey's Flea2 camera (Point Grey Research Inc., Richmond, BC, Canada) pointing at a hyperbolic mirror (V-Stone Co., Ltd., Osaka, Japan). The laser pointer is a typical pen-shape of 532 nm wavelength laser pointer. In order to ensure the sealing of the cameras, we insert the catadioptric systems and the lasers into a tight waterproof system.  
Figure 1: Architecture of the omnidirectional system composed of stereo catadioptric system and the laser belt.
## 3 Results and Discussion
### SEY Results  
The SEY properties of laser processed OFHC, Ti film, and Ti-Zr-V-Hf film coatings with OFHC and laser processed OFHC substrates are shown in Figure 5. When the primary electron energy varied between 100 and 3000 eV, the maximum SEYs (\(\delta_{max}\)) of Ti film coating and Ti-Zr-V-Hf film coatings with OFHC substrates were 1.49 and 1.37, respectively, with the corresponding primary energy (\(E_{max}\)) of 200 and 300 eV, respectively. The \(\delta_{max}\) of Ti and Ti-Zr-V-Hf film coatings with laser processed OFHC substrates were 1.34 and 1.18, respectively. The \(\delta_{\max}\) of as-received OFHC samples were 1.6-2.0 [19; 21; 22] with different polish states and SEY test parameters. It can be seen that the \(\delta_{\max}\) of Ti and Ti-Zr-V-Hf film coatings with laser processed OFHC substrates are lower than the ones of as-received OFHC samples.  
Compared to the \(\delta_{\max}\) of laser processed OFHC, the ones with Ti film coating and Ti-Zr-V-Hf film coating with laser processed OFHC increased from 0.99 to 1.34 and 1.18, respectively. The SEY increase of Ti film coating and Ti-Zr-V-Hf film coating may relate to the sizes of the particles and grains on the surface, and the existence of nano-size floccules formed by laser processing, etc. In other words, the reduction of floccules-like structures formed by laser ablation and the increase of grain size after film deposition may have contributed to the SEY increase of both films. When the substrate was bare OFHC, the \(\delta_{max}\) of Ti film coating and Ti-Zr-V-Hf film coating were 1.49 and 1.37, respectively. These results indicate that the \(\delta_{max}\) of Ti-Zr-V-Hf quaternary alloy film coatings with bare OFHC is slightly lower than the one of Ti film coating with the same substrate. This can possibly be explained because the lower \(\delta_{max}\) property of Ti-Zr-V-Hf film may be determined by the surface morphologies and physical properties of film coatings, etc.
## 2 Materials and Methods
### HS-SPME-GC-MS Analysis Methods  
Sample pretreatment: different tea samples (1.0 g) were placed into a 20 mL SPME vial, then combined with 10 mL at 80 \({}^{\circ}\)C of ultrapure water and equilibrated at 80 \({}^{\circ}\)C for 20 min. Then the SPME DVB/CAR/PDMS-coated SPME fiber (2 cm) was inserted into the balanced sample vial, with a constant-temperature water bath at 80 \({}^{\circ}\)C for 40 min; the SPME fiber was thermally desorbed at 250 \({}^{\circ}\)C for 5 min in a splitless injection port of the GC for analysis.  
GC-MS conditions were according to the procedure described by [PERSON] et al. (2018), with modifications; an Agilent 7890B-7000D model GC-MS system (Agilent, USA) with a fused-silica capillary column (DB-35 MS, 30 mm \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m; Agilent, USA) was used for the analysis of tea volatiles. The initial temperature was 50 \({}^{\circ}\)C, retained for 2 min, rising to 90 \({}^{\circ}\)C at the rate of 4 \({}^{\circ}\)C/min and held for 3 min, then heated to 150 \({}^{\circ}\)C at the rate of 3 \({}^{\circ}\)C/min and held for 5 min, finally heated to 250 \({}^{\circ}\)C at the rate of 5 \({}^{\circ}\)C/min and held for 5 min. Helium was used as the carrier gas, at a flow rate of 1.0 mL/min with the splitless GC inlet mode. The MS fragmentation was performed by electronic impact (EI) mode (ionization energy, 70 eV; source temperature, 230 \({}^{\circ}\)C). The quadrupole temperature was 150 \({}^{\circ}\)C. The acquisition was in full-scan mode with a mass acquisition range of 35-550 \(m/z\).  
The obtained chromatograms were analyzed by determining the peak areas, retention times, spectra and base peaks on the chromatograms, and then each peak was examined by referring to the characteristic mass spectra of the compounds listed on the National Institute of Standards and Technology (NIST). The relative contents of each compound in the volatile compounds of tea samples were obtained by the area normalization method. If the similarity index is greater than 80, it is considered that the compound exists in the sample.
## 4 Static Network Performance of a TFBN
### Cost Parameters
#### 4.2.2 Wiring Complexity  
In an interconnection network, the wiring complexity implies the combination of all the interconnected links in the network. The cost of the entire system rises with the increase in wiring complexity. The TFBN consists of numerous BMs in the form of a 2D-Torus network. The wrap links in these BMs result in a large number of communication links, which incur high level of wiring complexity. The wiring complexity of a TFBN is given by Equation (1), where \(L\) is the level number and \(q\) is the inter-level connectivity.  
\[\textit{Wiring complexity}=\#\text{ of links in a BM}\times k^{2(L-1)}+\sum_{q=2}^{L}2(2^{q})\times k^{2(L-1)}. \tag{1}\]  
The wire complexity of the TFBN is larger than that of torus, mesh, TESH, and TTN networks, as seen in Table 1. The BM has a smaller diameter and average distance due to the extra-short length wrap-around links.
## 3 Experimental Results
### Tensile Tests Results  
The yield and ultimate strengths evaluated from the tensile tests are available for SAC305 and SACX0807 in Tables 3 and 4, respectively. The dependency of both quantities on the strain rate is shown in Figure 4. The strain rate was determined as the slope of the tangent created in the time history of axial strain at the ultimate strengths. As visible from the comparison of regression line parameters of both solder alloys (Figure 4a,b), the SACX0807 shows slightly higher sensitivity to the strain rate.
# Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints
## 3 The Proposed Approach  
### Basic Concept  
Let \(Y\) denote the continuous response variable, \(X\) be the predictor in isotonic regression, and \(\mathbf{Z}=\{Z_{s},\ 1\leq s\leq l\}\) be a set of covariates. Here, \(X\) is used specifically for regression, while the set of covariates \(\mathbf{Z}\) are exclusively used for splitting the data. This study will construct an unbiased isotonic regression tree (UIRT) using the covariates to detect hidden subgroups with different monotonically increasing patterns between \(X\) and \(Y\).  
Similarly to a regression tree, UIRT is built iteratively by partitioning each node into multiple subgroups in each iteration. In this study, UIRT adhered to the binary splitting approach (i.e., creating two subnodes), which is commonly adopted in the regression tree framework. There are two possible scenarios: (i) When a global model is appropriate to explain the observations at a given node \(R\), there is no need to partition the current node \(R\) using a set of covariates \(\mathbf{Z}\). In this situation, a single monotonic increasing/decreasing pattern is good enough to model the observations. (ii) If a global model is inadequate, it is preferable to split the current node \(R\) into two subgroups:  
\[R_{1}=\{\mathbf{Z}|Z_{s}\leq c\},\ \ R_{2}=\{\mathbf{Z}|Z_{s}>c\}\]using a cutpoint \(c\) and then fitting separate isotonic regression for each subgroup. From all possible split variables and cutpoints, UIRT aims to identify the optimal split variable and cutpoint that optimizes the objective function in isotonic regression at each subgroup.  
The proposed unbiased isotonic regression tree addresses the partitioning problem shown in Figure 2 through a two-step process: First, select the optimal split variable \(Z^{*}\) and then determine the optimal cutpoint \(c^{*}\) of the selected variable \(Z^{*}\). Similarly to the methodology employed in GUIDE, the variable selection step is designed to mitigate selection bias in the identification of the split variable. During the learning process, covariates with more potential cutpoints are favored since they offer broader and finer solution spaces. To achieve the unbiased selection of split variables, this study proposes a chi-square test to find the optimal split variable by following the idea of split variable selection in GUIDE. The effectiveness of the proposed method is validated through simulation in Section 4. After selecting the optimal variable, an exhaustive search was conducted to find the best cutpoint \(c^{*}\), such that the loss function of isotonic regression for two distinct subgroups was minimized.
# Field Experience for Determination of Formaldehyde in Stack Emissionss
## 3 Results and Discussion
### Sampling  
Water contents and H\(-\)CHO concentrations measured by RSE in the first campaign are shown in Table 6, accompanied by the analyzes carried out with the HPLC method (Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling** & \
\begin{tabular}{c} **Humidity** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **H\(-\)CHO** \\ **(mg/m3)** \\ \end{tabular} \\ \hline
1 & 12.7 & 33.4 \\
2 & 6.6 & 1.2 \\
3 & 14.4 & 47.4 \\
4 & 5.0 & 3.1 \\
5 & 16.5 & 14.5 \\
6 & 11.2 & 3.0 \\
7 & 11.2 & 2.9 \\
8 & 11.5 & 2.9 \\
9 & 10.6 & 10.6 \\
10 & 12.0 & 8.7 \\
11 & 12.2 & 7.5 \\
12 & 10.9 & 3.0 \\
13 & 10.9 & 3.4 \\
14 & 10.3 & 12.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Humidity and H\(-\)CHO obtained by Lab. RSE during First Campaign.  
Figure 2: Relation between measured humidity and H\(-\)CHO concentrations obtained by Lab. RSE during First Campaign.  
The expected humidity of the flue gases was 10-12%, the value obtained in sampling from 6 onwards. Values from 1 to 5 are not compatible with expected values and must be related to a malfunction of the sampling procedure; it was verified that, due to a malfunction of the heated line, there was a cold (unheated) point in which condensation occurred accumulating liquid droplets that, from time to time, ended up flowing into the trap.  
The most important effect is that the formaldehyde content found in the laboratory has the same behavior, as this compound is very soluble and was trapped in the cold point of the line and ended up flowing with the condensate.  
It should be noted that such verification can be used as a quality assurance indicator. The first 5 samplings can be considered invalid for any legal purpose of the measurement, as they would significantly alter the results obtained.  
The malfunction of the heated line during the second campaign has been resolved, as demonstrated by the values obtained (Table 7; Figure 3).
## 3 Results
### Comparison Between Machine Learning Techniques and Traditional Methods
#### 3.4.1 Quantitative Comparison  
Several studies have benchmarked ML and DL approaches against traditional empirical methods. Table 3 presents a comparative analysis of different magnitude estimation methods based on their MAE and standard deviation, highlighting the enhanced accuracy of ML and DL techniques compared to traditional approaches.  
As shown in Table 3, ML and DL models have demonstrated lower MAE and standard deviation compared to traditional approaches. The \(\tau_{c}\) method presents the highest error among the methods considered, while modern Transformer-based architectures tend to exhibit lower uncertainty, suggesting that attention mechanisms may contribute to improving magnitude estimation.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 5 Experiment
### Visual Interpretability  
#### 5.2.1 ASVspoof Dataset  
In Figure 3, all XAI methods for the CNN-based model highlight formants 1, 2, and 3, respectively, around areas F1, F2, and F3 of each figure. In acoustics, a formant is the local maximum or broad peak, similar to contours of objects as well as the background for visual content. For human speech, these first three formants are used to identify the vowel that is the fundamental component of the spoken language. In Figure 3a,d, [PERSON] highlights the general formants 1, 2, and 3 while distinguishing unvoiced speeches by highlighting the corresponding frames at all frequencies around the area \(U\).  
Figure 4 shows the result for each XAI method applied to the CNN-LSTM-based model. Only in Figure 4b,e, do the integrated gradients highlight the formants similar to the CNN-based model in Figure 3. In Figure 4a,d, [PERSON] does not highlight the formants and unvoiced speeches. Instead, certain frequency bands are highlighted regardless of energy distribution from 0 to 500 Hz, and 1000 Hz to 2000 Hz, respectively, around areas \(A\) and \(B\). In Figure 4c,f (LRP), though, the attribution score is no longer formant-dependent, unlike Deep Taylor, and it becomes less recognizable at the human cognitive level.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Dataset**} \\ \cline{2-6}
**Model** & **ASV** & **ASV** & **ASV** & **ASV** & **ASV** \\  & **Single TTS** & **Multiple TTS** & **Single VC** & **Multiple VC** & **LJSpeech** \\ \hline CNN & 97.32\% & 77.78\% & 77.93\% & 83.61\% & 98.88\% \\ CNN-LSTM & 98.91\% & 88.74\% & 86.90\% & 84.81\% & 99.92\% \\ CNN-LSTM-perm & 99.85\% & 99.97\% & 94.19\% & 88.29\% & 99.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model Performance.  
Figure 3: ASVspoof attribution score with the CNN model. (**a**) [PERSON] deepfake voice. (**b**) Integrated gradients deepfake voice. (**c**) LRP deepfake voice. (**d**) [PERSON] real voice. (**e**) Integrated gradients real voice. (**f**) LRP real voice.  
To see the frequency-wise series data characteristic of the model, CNN-LSTM-perm was used. Only [PERSON] Taylor showed a difference--showing that less significant frequency band centered scoring was weakened. For integrated gradients and LRP, no recognizable difference was observed.