esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Experimental Results at Baseline  
We first conducted experiments to evaluate the effects of pre-processing. Pre-processing reduced the training time and improved the model's performance significantly by increasing accuracy from 59.57% to 68.03%, which shows that the pre-processing method is effective.  
At the baseline, we conducted three sets of experiments: training models from scratch, transfer learning, and data augmentation. The results of testing models trained from scratch on the imbalanced training set were poor, with an accuracy of 50.0%, a TPR of 0, and a TNR of 100. The model could classify two categories without serious biases when training the model from scratch on the balanced training set. However, it was hard for the model to converge. We employed ResNet-50 pre-trained on the ImageNet dataset. The model was initialized with the pre-trained models' parameters and only trained with the modified fully connected layer; then, we fine-tuned the whole model. After transfer learning, the accuracy of the model improved to over 80% using either the imbalanced or balanced training set. Data augmentation was used to prevent overfitting observed during training. The models were improved further by data augmentation. The best choice is the use of the combination of transfer learning and data augmentation; thus, the results from this combination were regarded as the experimental baseline. The detailed results of three sets of experiments are reported in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Class**} & **DDR Test Set** & **Messidor-2 Test Set** \\ \cline{2-3}  & **Number** & **Number** \\ \hline
0 & 1690 & 1291 \\ \hline
1 & 1690 & 457 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test datasets were constructed from DDR and Messidor-2 data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Imbalanced Training Set**} & \multicolumn{2}{c}{**Balanced Training Set**} \\ \hline
**Metrics** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** & **ACC (\%)** & **TPR (\%)** & **TNR (\%)** \\ \hline Training from scratch & 50.0 & 0.0 & 100.0 & 68.03 & 65.89 & 70.17 \\ \hline Transfer learning & 81.78 & 68.42 & 95.14 & 80.27 & 75.02 & 85.52 \\ \hline Data augmentation & 82.12 & 68.03 & 96.12 & 81.53 & 74.05 & 89.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of the baseline.
## 3 Results and Discussion
### Optimization of the HS-SPME Methods
#### 3.1.2 Effect of Sample Volume  
Studies have shown that for higher sensitivity of HS-SPME and thus extraction yield of compounds, the sample headspace should be as small as possible [30], but there are few studies reporting optimizing SPME by adjusting the sample volume [26]. To prove the effect of volume on the extraction efficiency of trace compounds, six different sample volumes of 1, 2, 4, 5, 6, and 8 mL were used. Figure 3B shows that the response areas  
Figure 2: Analytical ion chromatogram contour plot for the SPME-GC\(\times\)GC–TOFMS analysis of the Guotai sample: (**A**) complete two-dimensional contour plot and (**B**) detailed portions of the contour plot.  
increased with the increase of sample volume. To the best of our knowledge, most studies chose half the volume of the headspace glass vial [31]. However, in actual analyses, 20 mL headspace vials containing up to 8 mL of liquid were used to prevent the SPME fiber from contacting the liquid, which is also the optimal filling volume within the achievable range. Therefore, the optimal volume of the sample placed in a 20 mL vial was 8 mL.
# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
## 2 Datasets
### Collected Dataset  
Our dataset is collected from February 2021 to December 2021 at the Specialized Scientific-Practical Medical center of Oncology and Radiology. It is important to note that collecting a balanced dataset across all BI-RADS categories is a difficult task since the vast majority of screening data belongs to BI-RADS 1 and 2. Therefore, after going through a screening process of all collected patients' data, 3134 mammograms (belonging to BI-RADS 2-5) are selected and annotated. Each image belongs to one of four types, such as R\({}_{\text{MLO}}\), R\({}_{\text{CC}}\), L\({}_{\text{MLO}}\), and L\({}_{\text{CC}}\), where R and L denote right and left breast, and MLO (mediolateral oblique) and CC (craniocaudal) denote two unique angles/views. The size of each mammogram is 5928 \(\times\) 4728 pixels. All mammograms have a corresponding XML file with annotated bounding boxes. To ensure that BI-RADS categories are assigned properly, two senior radiologists were asked to assess and annotate images independently.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BI-RADS Category** & **Assessment** & **Suspicion of Malignancy** & **Clinical Recommendation** \\ \hline
0 & Assessment incomplete & - & Need to review prior studies or complete additional screening \\
1 & Negative & \(\approx\)0 & Continue routine screening \\
2 & Benign cysts or other findings & \(\approx\)0 & Continue routine screening \\
3 & Probably benign finding & \(<\)2\% & Short-term follow-up screening \\  & & \(\geq\)2\% to \(<\)10\% (4a) & \\
4 & Suspicious abnormality & \(\geq\)10\% to \(<\)50\% (4b) & Perform needle biopsy \\  & & \(\geq\)50\% to \(<\)95\% (4c) & \\
5 & Highly suspicion of malignancy & \(\geq\)95\% & Biopsy and treatment \\  & & & Pending/ongoing treatment should be completed, surgical excision if appropriate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of BI-RADS categories.  
Next, conflicted annotations, in case of any, were validated by the third head radiologist. Radiologists were asked to annotate tumors found in the mammogram tightly (i.e., place a tight bounding box around the tumor) and label them with their corresponding class names.  
Collected data are then split into 50% training, 13% validation, and 37% test sets in such a way that each piece of training data contains at least one annotated breast tumor/cyst to train a detection model.  
Due to privacy issues, only 20% of anonymized data is open source. The dataset will be fully available for public by the end of the year (along with technical reports).
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 3 SatSINS
### Module 3: Analysis of Fine-Scale SAR Data and the Final Ship-Route Planning  
Subjective interpretation of SAR images was performed to identify sea ice types (with a proxy evaluation of ice thickness), concentration and surface deformation. Meteorological data were used in support of the visual images analysis. The images were then annotated with GPS (Global Positioning System) points of a proposed route and typically sent as 4-Mbyte data bundles to RV _Xuelong_. The suggested route was reviewed by the CHINARE team, taking into consideration the local conditions of the nearby sea ice as observed from the vessel. If the proposed sea ice route was deemed to be suitable after the review, RV _Xuelong_ would proceed along the proposed waypoints. Otherwise an improvedroute plan was requested by the ship-board party. Access to near-real-time remote sensing data (6-8 h as required by the operational ice-monitoring centers) was not a strict requirement for developing the proposed route as discussed in the next two sections.  
The processing chain to derive a full iteration of route planning and assessment takes about 2-3 h. Incorporation of SAR data is the slowest element in the process. It takes about half hour to download a single image, one to one and half hour to process the data and identify key ice conditions, and another half hour to transfer the proposed routes to RV _Xuelong_. This time lag needs be taken into account when developing the proposed route, especially if the MSLP and wind data confirm the possibility of rapid changes of sea ice conditions.
## 4 Results and Discussion
### Limitations and Future Work  
Although SIGNIFY resulted to be an effective tool both in terms of automatic hand gesture performance and application usability, there is still room for improvements. The hand sign dataset should be expanded by including images with different lighting conditions, varied and non-uniform backgrounds, and hand signs performed at different distances from the camera. This should be particularly significant, especially for the RGB-only database, since the RGB-D frames contains the 3D depth information useful to make the recognition more robust. Furthermore, the models are trained solely with signs made using the right hand. So, training the models with signs made using both hands will improve accessibility by ensuring the efficacy of recognition regardless of which hand is used. Another significant development will be the inclusion of dynamic letters [57] in the sign alphabet, such as \(g,j,s\), and \(z\), and also other signs that can be recognized only considering the dynamic nature of these gestures. This will likely require the inference to be performed on a brief video sequence instead of single frames.  
Regarding the communication between Python and Unity 3D [58], the current application uses a text file to read and utilizes the information derived from the prediction of the model used in Python. However, this method of communication between the two channels is not optimal from a security point of view, and is the cause of minor delays if the available hardware is underperforming. A client-server architecture could mitigate this issue.  
In addition, based on the results obtained from the questionnaires administered to potential users of the application, some criticalities emerged regarding the graphical component of the animations of the signs to be reproduced, which were deemed unrealistic. A possible development would be to use a more realistic hand model based on the 3D capture to make the hand movements more easily understandable to the end user in the tutorial section.  
Finally, to reach a broader audience, developing a mobile version of the application will be a key goal. As already mentioned, both RGB and RGB-D methodologies could be  
Figure 11: Questionnaire results. Allowed answers were: no, not too much, little, quite a lot, yes.  
adopted, thanks to the integration of 3D depth acquisition cameras in the cutting-edge personal devices.
## 5 Translational Temperature Determination in HAI-Trap
### Energy Distribution of Trapped Ions  
A 3D model of the trap is imported into the COMSOL environment to obtain the potential landscape created by the static voltages of the various electrodes. Subsequently, in the Monte-Carlo simulation, this static potential is then combined with the potential created by the rf field to simulate the energy distribution, as shown in Figure 5. In case of OH\({}^{-}\) thermalizing with helium buffer gas, the temperature obtained from the distribution is 373 K, and is a few Kelvin higher than that of the infinitely long wires (no endcaps). This stems from the fact that the static voltages push the ions into the rf field near the endcaps. While this relative effect is minimal at room temperatures, it is shown to become more pronounced and critical for cryogenic traps, where collisions occur in Kelvin regime [37]. While the dynamics of the ions in the low-to-mid energy range are well-represented by a Maxwell distribution, the high-energy tail of the distribution can be described with a Tsallis distribution [38].  
Figure 4: Simulated energy distribution of ions after collisions with helium buffer gas at 293 K for different trap configurations are shown. For traps with hyperbolic rods, cylindrical rods, and wires, the temperature obtained from the distributions are all 366 K, using Equation (3). On comparison with an analytical Maxwell-Boltzmann distribution at 366 K (black dashed line), it can be seen that the distribution is not perfectly thermal.
## 5 Discussion  
These experiments shed light on the effectiveness of pretraining methods using X-ray images, especially in the context of detecting defects in manufacturing products. Our experiments show that models pretrained with X-ray images generally surpass those pretrained with ImageNet in discerning relevant features for defect detection. This superiority of domain-specific pretraining highlights the importance of aligning the pretraining phase with the unique characteristics of the task-specific images.  
In comparing different backbones, the Swin Transformer models demonstrates superior performance over CNNs when large, labeled data are available [37]. However, the scenario was different with the GDXray dataset, which has fewer labeled images; here, the CNN backbones yielded better results. This suggests that, while Transformers outperform in data-rich environments, CNNs might be more effective with limited data.  
A significant aspect of our research is the marked improvement in detecting critical defects following pretraining with X-ray images. This improvement is especially pertinent in industrial settings, where accurately identifying such defects is crucial for safety and reliability. This enhancement in defect detection, particularly for critical defects, could have substantial implications for industrial applications. Moreover, the pretraining on X-ray images demonstrated improved results even with the smaller GDXray dataset, indicating the adaptability of the models to different data volumes. This adaptability is a crucial trait for practical deployment, where data availability can be variable.  
While our system has not been implemented in a real production environment, our findings suggest that it holds promise for future applications. The execution time of approximately 0.126 s per image positions our method well within the operational limits set by industry standards, which allows up to a maximum of 1 s for image analysis. This performance metric shows the viability of our method for production line integration. It highlights the potential for real-world deployment, thereby enhancing the feasibility of using deep learning to detect defects in manufacturing settings.
## 2 Materials: System Description and Experimental Set-Up
### Overview of the Experimental Set-Up: Test Cell  
To measure the performance of the radiant system, sensors for measuring air temperature, surface temperature, and water temperature were placed at the points indicated in Table 1. The thermocouples used were type T and were connected to a National Instruments Compaq DAQ data acquisition system with an accuracy of 0.8 \({}^{\circ}\)C. All thermocouples used were calibrated using a thermocouple calibration furnace to guarantee the measurements' quality.  
Figure 4 (left) shows the thermocouples corresponding to the air temperature of the test cell. Each red circle represents a thermocouple, with a total of fifteen distributed in the cell. This distribution allowed for monitoring and knowledge of the distribution of temperatures inside. Likewise, Figure 4 (right) represents the thermocouples corresponding to the surface temperature measurement. A total of ten sensors were distributed, as shown in the figure (yellow circles). These were placed by the different surfaces of the test cell in the central position of each face corresponding to walls (glass and wood), radiant solution, and floor.  
Finally, Figure 5 shows the distribution of thermocouples (circles in orange) to measure water temperature. Also, four thermocouples were placed inside the tank at different levels and the water flows were measured at the inlet and outlet. Finally, a flow meter (grey diamond in Figure 5) was used to evaluate water energy variation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable to Be Recorded** & **Type of Sensors** & **Measurement Points** \\ \hline Interior surface temperature & & 10 (one for each face glass surface, one for each wooden surface face, the ceiling, and the floor) \\ (see Figure 5—right) & Type T thermocouples & 15 (5 for each height) \\ (see Figure 5—left) & & 22 (4 in the water tank, 1 inertial tank outlet, 1 manifold inlet, 2 module inlets, 4 module inlets, 4 module outlets, 2 collector returns, 1 collector outlet, 1 inertial tank inlet and outlet–heat pump) \\ \hline Water temperature & & 22 (4 in the water tank, 1 inertial tank outlet, 1 manifold inlet, 2 module inlets, 4 module inlets, 4 module outlets, 2 collector returns, 1 collector outlet, 1 inertial tank inlet and outlet–heat pump) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of sensors.
# Peptides That Block RAS-p21 Protein-Induced Cell Transformation
## 2 Anti-Onogenic RAS-p21 Peptides from RAS-p21 Itself
### Three RAS-p21 Peptides Selectively Block Oncogenic RAS-p21
#### 2.1.1 Site of Action of PNC-7 (RAS-p21 Residues 35-47)  
Since PNC-7 is from the switch 1 domain of RAS-p21 and known to interact with RAF as a downstream target, to localize its site of action, full length c-RAF was injected into oocytes and found to induce maturation that was blocked by PNC-7. In contrast, an oncogenic RAF form, RAF BXB, that has its amino terminal regulatory domain deleted, induced oocyte maturation that was not inhibited by PNC-7 [15]. These results suggest that the site of inhibition of PNC-7 was the amino terminal regulatory domain of RAF. In view of the finding that microinjection into oocytes of a construct expressing dominant negative RAF (dn-RAF) inhibited both oncogenic RAS-p21- and insulin-induced oocyte maturation, there are further suggestions that oncogenic and wild-type RAS-p21 interact with RAF differently and may activate oocyte maturation by different pathways [15].  
Evidence has accumulated that supports this conclusion. In oocytes injected with oncogenic RAS-p21, there was a dramatic increase in the level of phosphorylated (activated) MAPK (ERK) and jun kinase (JNK) [15; 21], presumably stimulated by activated RAF in contrast to levels expressed in oocytes induced to mature with insulin which were significantly lower than in the oncogenic RAS-p21- matured oocytes, despite the fact that the expression of both downstream proteins was the same in both sets of oocytes. These studies concluded that the binding of oncogenic RAS-p21 to RAF resulted in high levels of phosphorylated MAPK (MAPK-P), and that activated wild-type RAS-p21, while it utilized the RAF-MEK-MAPK pathway to some extent, did not require this pathway since there are other RAF targets that were activated by the wild-type RAS-p21-RAF complex [21].
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 2 Recent Advances on Catalysts for Syngas Production
### Transition Metals-Based Catalysts
#### 2.2.1 Nickel Catalysts
The authors showed that a low fuel/oxidant ratio for the combustion-based synthesis resulted in catalysts with high surface area (S\({}_{\rm BET}\) = 266 m\({}^{2}\) g\({}^{-1}\)) and high reducibility (degree of reduction = 93%). Moreover, the microwave-synthesized 10 wt% Ni/Al\({}_{2}\)O\({}_{3}\) catalysts presented an H\({}_{2}\) yield of 58% with low coke deposition (1.2 wt%) and H\({}_{2}\)/CO ratio close to 1.0 over 20 h of TOS. The same research group has also reported microwave-assisted synthesis of alumina supports for DRM [36]. The synthesized alumina presented a high surface area (221.2 m\({}^{2}\)/g) and was used as support for the deposition of 10 wt% Ni via incipient wetness impregnation for DRM evaluation. The catalyst presented significant DRM activity, with over 87% CO\({}_{2}\) conversion (T = 700 \({}^{\circ}\)C, GHSV = 72 L g\({}^{-1}\) h\({}^{-1}\)) at a 20 h test. The authors also report the production of a syngas with H\({}_{2}\)/CO = 1.09 and 13.7 wt% carbon deposition. [PERSON] et al. [72] recently investigated the influence of several promoters (Fe, La, Zr, Ce and Ca) on the catalytic performance of 10 wt% Ni/MgAl\({}_{2}\)O\({}_{4}\) for DRM and reported that La, Zr, Ce and Ca were able to improve Ni dispersion over the catalyst, which could increase catalytic activity. Furthermore, Ce was found to be the promoter which better increased CH\({}_{4}\) conversion (X\({}_{\text{CH}_{4}}\) = 48%), at an optimal loading of 3 wt% Ce. Finally, the authors evaluated that the overall H\({}_{2}\)/CO ratio could be tuned as a function of CH\({}_{4}\)/CO\({}_{2}\) feed composition, and the highest H\({}_{2}\)/CO = 1.4 was obtained at CH\({}_{4}\)/CO\({}_{2}\) = 2.0.
###### Abstract  
Exploring resistance training methods is crucial for optimizing performance programs. Isometric muscle actions have gained popularity in athletic training, but their impact on dynamic performance is uncertain. Isolated isometric actions also lack ecological validity. We compared the effects of 8-week combined isometric and plyometric (COMB) training and contrast strength training (CST) programs on junior male handball players. Thirty-six male first national division players (17.6 \(\pm\) 1.0 years) were enrolled and randomly assigned to COMB, CST, or control (CONT) groups (all n = 12). Sprinting, change of direction, ball throwing velocity, jumping, and strength were assessed pre- and post-intervention. A significant group \(\times\) time interaction was observed between the COMB and CONT groups for 20 and 30 m sprints (\(p\leq 0.002\)) and between the COMB and CST groups (\(p\leq 0.042\)). The COMB group had the largest improvements in change of direction and the modified T-test, with significant group \(\times\) time interactions between the COMB and CONT groups (\(p\leq 0.021\)). Significant group \(\times\) time interactions were observed between the COMB and CST groups and between the COMB and CONT groups for 3 step running throw (\(p=0.003\); \(p<0.001\)), running throw (\(p=0.02\); \(p=0.031\)), and jumping throw (\(p=0.001\); \(p<0.001\)). Countermovement jump showed a significant group \(\times\) time interaction (\(p=0.014\)), with the COMB group outperforming the other groups. Generally, COMB yielded larger improvements than CST. Coaches should consider incorporating a combination of isometric and plyometric exercises for in-season strength training.  
agility; vertical jump; sprinting; change of direction; throwing; strength; youth athletes