esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of \(e\)-\(Bmd_{g}\)(type)  
Based on the results shown in Tables 2 and 3 we found that \(e\)-\(BMD_{M}\)(_average_) provided both the highest correlation coefficient \(r\) for \(R_{M}\)Ca/ha(_average_) and the lowest RMSE and MAPE values. Figure 4 shows the Bland-Altman plot. A systematic bias of \(-\)0.0043 g/cm\({}^{2}\) for \(e\)-\(BMD_{M}\)(_average_) was detected, with a standard deviation of 0.05731 g/cm\({}^{2}\). The systematic bias in the \(e\)-\(BMD_{M}\)(_average_) measurement can be corrected using a linear fit model. Figure 5a shows the regression plot of \(R_{M}\)Ca/ha(_average_) _versus BMD\({}^{\prime}\)_, and Figure 5b shows the regression plot of \(e\)-\(BMD_{M}\)(_average_) _versus BMD\({}^{\prime}\)_. In summary, the experimental relationship between \(e\)-\(BMD_{M}\)(_average_) and \(BMD\)\({}^{\prime}\) was as follows:  
\[BMD\)\({}^{\prime}\)\(\approx\)\(e\)-\(BMD_{M}\)(_average_) \(=\) 1.3333 \(\cdot\)\(R_{M}\)Ca/ha(_average_) \(-\)0.3333  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Correlation Coefficients** & **Region \(D1\)** & **Region \(M\)** \\ \hline \(R\)Ca/ha(_radius_) & 0.823 & 0.815 \\ \(R\)Ca/ha(_ultra_) & 0.785 & 0.807 \\ \(R\)Ca/ha(_total_) & 0.835 & 0.827 \\ \(R\)Ca/ha(_average_) & 0.834 & 0.857 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficient \(r\) values according to the _type_ and the regions of \(M\) and \(D1\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Errors** & **RMSE (Root Mean Square Error)** & **MAPE (Mean Absolute Percentage Error)** \\ \hline Region (\(G\)) & \(D1\) & \(M\) & \(D1\) & \(M\) \\ \(R_{G}\)Ca/ha(_total_) & 0.0609 & 0.0615 & 4.7091 & 4.9510 \\ \(R_{G}\)Ca/ha(_average_) & 0.0609 & 0.0564 & 4.7175 & 4.5105 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Root mean square errors (RMSEs) and mean absolute percentage errors (MAPEs) between \(e\)-\(BMD_{G}\)(_type_) and \(BMD\)\({}^{\prime}\).  
The correlation coefficient \(r\) was 0.857 (\(r^{2}\) = 0.735). The RMSE and MAPE were 0.0564 g/cm\({}^{2}\) and 4.5105%, respectively. These results show that there is a strong correlation between the \(BMD^{\prime}\) from \(e\)-\(BMD_{M}\)(_average_) and that it is possible to predict the \(BMD^{\prime}\) on the basis of \(e\)-\(BMD_{M}\)(_average_) from \(R_{M}\)\({}^{\alpha\)/\(\alpha\)(_average_).
## 3 Methodology
### Hybrid Loss Function  
To solve the boundary blur problem but to preserve the overall information of the target organ, Render 3D U-Net adds deep supervision [32] with ground truth after every output block (\(X_{0..,i},i\in[1,2,3,4]\)) and connects these blocks directly to loss function for calculating the sum loss. Besides, we integrate two loss functions into a hybrid loss function, including dice loss and Hausdorff distance. Formulation of this _hybrid loss_ function is as follows:  
\[\textit{Hybrid Loss}=\frac{1}{4}\times\sum_{i=1}^{4}\left[1-\frac{2\times Y_{i }\times\overline{Y}+s}{Y_{i}^{2}i+\overline{Y}^{2}+s}+0.1\times\left(Y_{i}- \overline{Y}\right)^{2}\circ\left({HD_{Y_{i}}}^{2}+{HD_{\overline{Y}}}^{2} \right)\right], \tag{5}\]  
Figure 5: Illustration of the False Positives Classification (FPC) module.  
where \(Y_{i}\) is the output of block \(X_{0,i}\) and \(\overline{Y}\) is the ground truth, \(s\) is the smooth soft dice coefficient part, and \(\circ\) is the operation of the Hadamard product, \(HD\) denotes the Hausdorff distance between the boundaries of predicted result and annotated result. The Hausdorff distance (\(HD\)) is defined as follows and the diagram of Hausdorff distance is shown in Figure 6:  
\[HD_{Y_{i}}=\max_{Y_{i}(x_{1},y_{1})=1}\left\{\min_{\overline{Y}(x_{2},y_{2})=1} Distance\left[(x_{1},y_{1}),(x_{2},y_{2})\right]\right\}, \tag{6}\]  
\[HD_{\overline{Y}}=\max_{\overline{Y}(x_{2},y_{2})=1}\left\{\min_{Y_{i}(x_{1}, y_{1})=1}Distance\left[(x_{1},y_{1}),(x_{2},y_{2})\right]\right\}, \tag{7}\]  
where \(Distance\) is the Euclidean distance and \(Distance\left[(x_{1},y_{1}),(x_{2},y_{2})\right]=\sqrt{(x1-x2)^{2}+(y1-y2)^{2}}\).  
As we can see, \(\frac{2\times Y_{i}\times\overline{Y}+s}{Y_{i}^{2}+\overline{Y}^{2}+s}\) is the soft Dice coefficient. This index represents the area of overlap between the model output and the annotated result. This index can help the model pay attention to the overall information of the target organ, but it cannot effectively supervise the boundary contour. As a consequence, we referred to the research by [35] and introduced the Hausdorff distance for strengthening the learning of boundaries and to solve the boundary blur problem. \(\left(Y_{i}-\overline{Y}\right)^{2}\circ\left(HD_{\overline{Y}_{i}}^{2}+HD_{ \overline{Y}}^{2}\right)\) measures the segmentation performance on boundary by calculating the Hausdorff distance between the model output boundary and the ground truth boundary.
## 2 Study Area  
The study area is located in the middle part of Kazakhstan between 48\({}^{\circ}\)20' and 49\({}^{\circ}\)30' northern latitude and 72\({}^{\circ}\) and 74\({}^{\circ}\)10' eastern longitude and encompasses the southern margin of the Kazakh Hills. It comprises the northern area of the Shestky _raion_ (district) in Karaganda _oblast'_ (province). The climate of the region is dry, cold and high continental. Average annual precipitation is about 250-300 mm. The most part of precipitation falls during warm period from March to October. Interannual rainfall variation has a coefficient of variation of 20-35%. The region is often affected by drought hazard. The temperature amplitude is relative high, average January temperature is below - 12\({}^{\circ}\)C and average July temperature is about 26-28 \({}^{\circ}\)C. The growing season starts in April and continues till October.  
In the vegetation cover, xerophylous bunch grasses are dominant in the northern part of the study area and dwarf shrubs in the south. The steppe grassland is dominated by the genera _Festuca_ and _Stipa_. Few euryxerophilous forbs occur; the co-dominants are dwarf shrubs of the genus _Artemisia_ and sometimes of other genera, particularly _Anabasis_ and _Salsola_. Species diversity is about 12-15 species per square metre. The height of the canopy decreases from 30-40 in the north to 15-20 in the south,while vegetation cover decreases from 50-70% to 20-30%, and even less. The vegetation growth in the study area is strongly dependent on precipitation dynamics. Grasses and shrubs in the vegetation cover grow during the whole vegetative period, but the vegetation growth is most rapid during May and early June (the period of greatest precipitation) in the southern part, and during June in the northern part of the study area. During droughty months in summer (July and early August) their rate of development is hindered. This period of semi-dormancy occurs throughout the study region [34, 35].  
Despite of the fact that the study area shows a wide spectrum of vegetation communities due to variations in topography and soils, there are only two main vegetation classes, short grassland and steppe grassland (Figure 1). The land cover data in the study area were taken from the digital land-cover map derived from Moderate Resolution Imaging Spectroradiometer (MODIS) by [36] that has been accessible in the United States Geological Survey (USGS) archive centre. According to this map, short grassland covers 74.22% of the whole territory, while steppe grassland covers 25.77%.
# Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites
## 1 Introduction
In this study, EMI shielding anisotropy of unidirectional CFRP composites was investigated in the frequency range of 5-15 GHz, which is often used in wireless computer networks, radar and satellite communication. Continuous carbon fiber-reinforced polymer composites have been widely used as structural materials for lightweight structures and EMI shielding applications. However, most researches have focused on the improvement of EMI shielding performance by adjusting the fiber lay-up arrangement or structure [36; 38]. [PERSON] et al. [39] reported that the EMI shielding effectiveness (SE) of PVB/Ni-Gr/SCF films exhibited different performances along and perpendicular to the casting direction, where the short carbon fiber direction was changed. [PERSON] et al. [40] indicated that the orientation of fillers could play an important role in determining EMI SE in the polymer-based composites with magnetically responsive aligned Fe\({}_{3}\)O\({}_{4}\) decorated reduced graphene oxide. The previous works [41; 39] revealed that the distribution state of fillers plays an important role in the shielding performance of composites, but the shielding anisotropy was not analyzed further. The carbon fiber orientation has a significant influence on the EMI shielding performance of CFRP composites. However, the EMI shielding anisotropy of unidirectional CFRP, especially its shielding anisotropy mechanisms and SE prediction, has not been systematically investigated and reported. This paper aims to provide a theoretical analysis of the shielding anisotropy of CFRP composites and propose formulas to predict electrical conductivity and EMI SE of CFRP at different orientation angles. This study mainly discussed the EMI shielding anisotropy of unidirectional CFRP composites. A specially designed free-space measurement was set up in a one direction vibration of the incident EM wave, which can measure the EMI shielding performance in any specified direction. The EMI shielding anisotropy of unidirectional CFRP composites was discussed, and the theoretical formula was used to predict the EMI SE of CFRP at various carbon fiber orientations. The shielding results of the free-space measurement and coaxial transmission line method were compared, and the influence of the EMI shielding anisotropy on the test results with different measurements was discussed.
# A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning
## 3 Prototype Development
### Shading System Development  
This paper describes a shading system comprising a smart node (Raspberry Pi), a set of shutters, a servo, a temperature and humidity sensor, and an illuminance sensor. Due to the expense and the fact that blinds are generally linked devices, the blinds in this system are also linked devices, meaning that only one control device is required to rotate a group of blinds. The temperature and humidity sensor and the illuminance sensor are used to measure the indoor environment, and the fuzzy controller outputs the blinds' tilt angle. Using a drive servo, intelligent nodes then regulate the amount of natural light entering a room by controlling the shutters. Figure 7 illustrates how blinds regulate the quantity of natural light by adjusting the leaf rotation angle. In Figure 7a, when the light level in the room is too high or the temperature is too high, the leaves of the blinds are tilted downward externally, reflecting back the sunlight that hits the window and preventing it from entering the room directly, thereby decreasing the amount of natural light radiation in this systemfor blinds tilted at an angle greater than 90 degrees. In Figure 7b, when there is insufficient light level or low temperature in the room, the exterior of the leaves are tilted upwards to reflect the sunlight shining onto the window into the room, thereby increasing the amount of natural light radiated into the room for the case where the tilt angle of the blinds is less than 90 degrees.  
This paper's selected drive unit is the MG996R servo, which has a rotation angle between 0\({}^{\circ}\) and 180\({}^{\circ}\) and whose rotation angle can be controlled directly. Power (red), ground (brown), and signal (yellow) are the three leads. The MG996R is compatible with input voltages between 4.8 V and 7.2 V and has a no-load operating current of 120 mA and a blocking operating current of 1450 mA.  
The shading subsystem collects environmental data with the help of the temperature and humidity sensor DHT11 and the illuminance sensor BH1750. The DHT11 is a sensor module that transmits data using a single-bus data format and has the advantages of long-term stability, quick response, and high interference resistance. The microcontroller can simultaneously acquire temperature and humidity data through a single I/O port. It is suitable for indoor environmental testing due to its temperature and humidity measurement ranges of \(-20\)\({}^{\circ}\)C to 60 \({}^{\circ}\)C and 5% to 95% RH, respectively. As the BH1750 FV chip has a built-in ADC converter, Raspberry Pi can easily obtain illumination data by communicating with the BH1750 module using the IIC protocol. In addition, it has a high resolution (minimum 0.11 k) and can detect a broad spectrum of light intensities (up to 100,000 lx).
# Application of PolyJet 3D Printing in Production of Flexographic Printing Plates
## 3 Results and Discussions
### Summary of the Results and Future Research Directions  
This comparative study revealed that 3D-printed plates made using PolyJet technology exhibit different physical properties compared to conventional plates. The resin used in this study demonstrates low resistance to most solvents commonly employed in printing houses, limiting its application to water-based and UV printing inks and unfortunately excludes solvent-based inks, which constitute the majority of flexographic printing inks. Given the variety of resins available in the market, exploring other rubber-like materials in future research would be beneficial.  
The plates prepared using PolyJet technology enable printing with water-based inks, however, the quality of these prints is relatively low and does not meet current expectations for flexographic printing. A satisfactory reproduction quality is only achievable for solid printed areas (apla) and lines, while significant challenges arise with the reproduction of raster fields. It has been observed that a correct raster grid cannot be obtained with the PolyJet technology and the material used. To address this issue, it may be worthwhile to explore the possibility of creating a 3D-printed plate using a different resin. Despite the high precision of the PolyJet technology, it would be worthwhile to check the possibility of  
Figure 8: Average measured values of percentage coverage of raster fields of prints made on different substrates (P1–P6) using multiple printing plates (F1–F5) with various raster cylinder rulings of (**a**) 160 lpc field 75%, (**b**) 200 lpc field 75%, (**c**) 160 lpc field 50%, (**d**) 200 lpc field 50%, (**e**) 160 lpc field 10%, and (**f**) 200 lpc field 10%.  
reproducing raster points using other AM technologies or to optimize the parameters of the PolyJet printing process.  
In summary, the challenges associated with the reproduction of the raster points may stem from the inadequate manufacturing quality of the raster structures on the printing plate, the hardness of the 3D-printed plate due to the properties of the resin, which affects ink transfer, or the design of the printing form itself. Further research seems valuable and would allow us to explore the use of PolyJet technology to produce flexographic plates using different parameters of the 3D-printing process and alternative resins. Additionally, it may be worthwhile to consider the design of new printing plates with modified raster dot shapes, such as a different cone angle, vertex shape, or relief depth. However, material type appears to be a crucial factor, as the 3D-printed plates transferred less ink to the printing substrate, resulting in test prints characterized by a lower optical density and higher lightness. Furthermore, the quality of the prints is significantly affected by the print substrate; better results were obtained on coated substrates compared to uncoated ones. Given the widespread use of films in flexographic printing, it would be worthwhile to incorporate such a substrate in future studies.
# Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
## 5 Result and Discussion
### Summary of Experiments  
Through experimentation, the proposed anomaly detection algorithm based on time windows achieves a good performance with multidimensional data. The algorithm can filter out multiple models that are more suitable for the dataset from multiple anomaly basic detectors and merge the results through two strategies. The algorithm can also obtain optimal or suboptimal performance results. Although its performance is mediocre on one-dimensional data, the algorithm is shown to achieve excellent performance with software fault diagnosis experimentally.
# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 2 Materials and Methods
### Superpixel-Based Processing Framework  
The proposed processing framework is divided into six steps (Figure 4). First, homogenous spectral areas of the input HS cube (Figure 4A) are extracted by using a superpixel segmentation algorithm (Figure 4B). The main motivation on the use of a superpixel segmentation approach prior to classification is to alleviate the amount of data to be processed, without losing relevant information from the HS image. After superpixel segmentation, superpixels belonging to the background light of the microscope are removed, while superpixels corresponding to tissue are stored (Figure 4C). Since the annotation of the images was performed at a macroscopic level, all the superpixels extracted from a single image are annotated as non-tumor or tumor depending on the annotation of the current image (Figure 4D). Finally, the spectral data from all patients in a fold are used to train, validate and optimize, and test a supervised classifier (Figure 4E). In the test stage, the prediction about the diagnosis of each superpixel (non-tumor or tumor) is performed using the classifier trained and optimized in the training/validation stage. Finally, the classification performance is quantitatively measured, and a representation of the classification is provided as heat and classification maps (Figure 4F).  
Figure 4: Proposed superpixel-based hyperspectral image processing framework.  
Figure 3: Data partition among the different folds (patients with only tumor samples are marked with \(\ddagger\)).
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 2 Staged Softening Process of Water-Rich Soft Surrounding Rock Based on Tunneling
### Applicability of Damage Evolution Equation
#### 3.5.1 Consideration of Unloading Softening  
This method of damage variables' calculation applies to the tunnel excavation in weak surrounding rocks that are not influenced by groundwater or where the influence of groundwater is not significant. The corresponding equations and change curves of the unloading damage variables are shown in Equations (34) and (35) and Figure 8.  
Tensile damage:  
\[D_{t}=\begin{cases}0&\overline{\epsilon}\leq\epsilon_{t}\\ 1-\frac{\sigma_{tr}}{E_{0}^{2}}&\epsilon_{t}<\overline{\epsilon}<\epsilon_{tu} \\ 1&\epsilon_{tu}\leq\overline{\epsilon}\end{cases} \tag{34}\]  
Compression shear damage:  
\[D_{c}=\begin{cases}D_{m}\frac{\overline{\epsilon}^{p}}{\overline{\epsilon}^{ p}_{r}}&0\leq\overline{\epsilon}^{p}<\overline{\epsilon}^{p}\\ D_{m}&\overline{\epsilon}^{p}\geq\overline{\epsilon}_{r}{}^{p}\end{cases} \tag{35}\]  
where, \(D_{t}\) and \(D_{c}\) are the damage variables in tensile damage and compression shear damage, respectively; \(\sigma_{tr}\) is the residual tensile strength of rock; \(\epsilon_{t}\) is the maximum elastic tensile strain; \(\epsilon_{tu}\) is the ultimate tensile strain; \(\overline{\epsilon}\) is the equivalent strain; \(E_{0}\) is the initial elastic modulus of rock; \(\overline{\epsilon}^{p}\) is the equivalent plastic strain; \(\overline{\epsilon}^{p}\) is the equivalent plastic strain when rock reaches residual strength; \(D_{m}\) is the damage variable when rock reaches the residual stage.  
Figure 8: Schematic diagram of the evolution of unloading softening damage variables: (**a**) Tensile damage variable, (**b**) shear damage variable.
## 4 Results and Discussion
### State-of-the-Art Comparison  
Table 4 presents a comparison of various object detection [3; 4; 8; 48] models based on their performance across different datasets sizes, specifically focusing on how the inclusion of synthetic data affects their results. The mAP and AP\({}^{50}\) results presented in the table are partially derived from the original publications. In the section of Table 4 detailing the full AGAR dataset performance, we observe that YOLOv8, which was trained in the same way as described in Section 4.2, achieved commendable results, with an AP\({}^{50}\) of 93.7 and an mAP of 64.9, despite having a relatively small parameter count of 3.2 million. This indicates that YOLOv8 is both efficient and  
Figure 5: Comparison of different sizes of fine-tuning datasets of YOLOv8-Nano [48] on the AGAR dateset [4]. (**a**) Mean Average Precision (mAP). (**b**) Average Precision at an IoU-threshold at 0.5 (AP\({}^{50}\)). effective in detecting colonies. However, AttnPAFPN [8] outperformed all others on this dataset, achieving an impressive AP\({}^{50}\) of 96.3 and an mAP of 68.2, suggesting that self-attention mechanisms significantly enhance detection accuracy. However, the parameter count and complexity is much higher compared to YOLOv8. When evaluating the performance with approximately 500 real images, AttnPAFPN consistently outperformed, achieving an AP\({}^{50}\) of 92.3 and an mAP of 62.9. In comparison, YOLOv8 combined with our synthetic data generation strategy attained an AP\({}^{50}\) of 89.9 and an mAP of 57.5, while YOLOv8 without synthetic data only reached an AP\({}^{50}\) of 84.7 and an mAP of 53.0. Notably, incorporating synthetic data improved the AP\({}^{50}\) by +5.2 and the mAP by +4.5 for YOLOv8, demonstrating that synthetic data augmentation significantly enhances performance, even with a limited set of real images. In the scenario with only 100 real images of the AGAR dataset, the advantages of synthetic data became even more pronounced. YOLOv8 with our augmentation method achieved an AP\({}^{50}\) of 84.9 and an mAP of 52.6, significantly outperforming models like the Faster-RCNN and Cascade-RCNN, which achieved mAPs of only 40.1 and 41.6, respectively. This highlights the effectiveness of the synthetic data generation approach, particularly in low-data situations. It also highlights the importance of fine-tuning on real data, as [PERSON] et al. [3] utilize the 100 real images to generate the synthetic data without any fine-tuning. Finally, when the dataset was reduced to only 50 real images, all models exhibited decreased performance, but the synthetic data still provided a critical advantage. YOLOv8 with our method for data generation led this group with an AP\({}^{50}\) of 79.2 and an mAP of 48.1, while the performance of the Faster-RCNN, AttnPAFPN, and YOLOv8, which did not utilize synthetic data, dropped to an mAP of 41.2, 42.8, and 36.0, respectively.