esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Multi-Channel Traffic and Weather Scene Image
#### 3.1.2 Construction of Images
These are referred to as the flight volume, average altitude, and average speed channels, as shown in Figure 3. Figure 3a shows the flight volume channel, where a darker grid color indicates that more flights passed through the corresponding airspace during the time period. Figure 3b shows the average altitude channel, where darker colors represent a higher average flight altitude in that grid area. Subfigure (c) shows the average speed channel, where darker colors indicate a lower average flight speed in the corresponding airspace during the time period. (3) Visualization of convective weather features  
In addition, a convective weather channel is constructed to represent weather conditions within the terminal area. The original convective weather WAF data are a two-dimensional matrix consisting of 1250 \(\times\) 1250 data points. However, its resolution does not match that of the traffic-related channels and therefore needs to be reduced. To address this, following the concept of rasterized WSI, the WAF data are mapped onto a \(125\times 125\) terminal area grid. The Weather Severity Index (WSI) is then calculated for each grid cell, aligning the data dimensions while preserving both the intensity and spatial distribution of the convective weather. As shown in Figure 4, Figure 4a-c illustrate the distribution of weak, moderate, and strong convective weather within the terminal area, respectively. Figure 2: Example of terminal area rasterization.
## 5 Experiments
### Force and Displacement  
A linear encoder (MLS-12, Microtech Laboratory Inc., Sagamihara, Japan) was attached to the soft actuator to measure the displacement caused by the force. The resolution of the encoder was 0.04 mm.  
Figure 11 shows the result. The solid line and the dashed line represent the measurement value and theoretical value, respectively. When plotting the theoretical value, we set \(K_{c}\) and \(E\) in Equations (26) and (33) as \(K_{c}=36\) N/m and \(E=5.93\times 10^{5}\) Pa, respectively, according to the datasheet provided by the manufacturer. The natural length of the actuator was 35 mm. The theoretical value fit the experimental value well for low pressure. However, the experimental values differed from theory for high pressure.  
The magenta dashed line in Figure 11 shows the results obtained using the proposed method, combined with the nonlinear stress-strain relationship given by [PERSON] et al. [29]. The model is expressed as follows:  
\[\sigma=K_{m}N\mathcal{L}^{-1}\left[\frac{\lambda_{\mathrm{chain}}}{\sqrt{N}} \right]\frac{\varepsilon_{1}^{2}-\varepsilon_{2}^{2}}{\lambda_{\mathrm{chain}}}, \tag{34}\]  
where \(K_{m}=0.08\) and \(N=3.15\) are the constants depending on the material used and \(\lambda_{\mathrm{chain}}=1/\sqrt{3}(\varepsilon_{1}^{2}+\varepsilon_{2}^{2}+ \varepsilon_{3}^{2})^{1/2}\). The model gave a better description of the nonlinear behavior of the actuator.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & \(\mathbf{A_{c}+\eta A_{r}\) (mm\({}^{2}\)) \\ \hline
1 & 300 \\
2 & 291 \\
3 & 283 \\
4 & 294 \\
5 & 291 \\
6 & 283 \\ \hline average & \(290\pm 6.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-sectional area, 016 mm.  
Figure 10: Experimental result, 016 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Diameter** & \(\mathbf{A_{c}+\eta A_{r}\) (mm\({}^{2}\))} & \(\mathbf{\eta}\) \\ \hline \(\omega\)16 & 290 & 1.07 \\ \(\omega\)14 & 286 & 1.02 \\ \(\omega\)10 & 243 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results for various inner diameters.
## 2 Study Area and Data  
The Qingjiang River Basin (108 deg35'-111 deg35' E, 22 deg33'-30 deg50' N), situated in the south-western part of Hubei Province, China, flows from west to east through ten counties and cities and joins the Yangtze River at Yidu. The main channel stretches approximately 423 km, encompassing a controlled drainage area of about 16,700 square km. The basin experiences a mid-subropical monsoon climate, with an average annual temperature of about 14 degC and an annual precipitation of about 1400 mm, predominantly concentrated in the summer months. The terrain of the basin, primarily consisting of mountainous regions that account for over 80% of its area, slopes from west to east, forming a landscape of high mountains and deep valleys. This region, located in western Hubei, is known for its severe rainstorms. Influenced by the summer rainstorms, summer runoff accounts for about 40% of the annual runoff, with peak floods typically occurring in June and July. Given the significant impact of human activities, particularly dam construction, on the middle and lower reaches of the basin, this study focuses on the upper Qingjiang River Basin. This area is less affected by human interventions, with the Shuibuya Hydropower Station serving as the upstream catchment area (Figure 1).  
Figure 1: The Qingjiang River Basin.  
This study incorporates meteorological and hydrological observation data, as well as EPFs from NWP centers. The observation dataset includes daily temperature, precipitation, potential evaporation, and runoff data spanning from 2014 to 2020. Meteorological data were sourced from 175 automatic meteorological stations distributed across the study area, and hydrological data were collected from the Shuibuya Hydropower Station (Figure 1). The EPFs utilized in this research were obtained from two highly regarded NWP centers within the TIGGE database, specifically ECMWF and NCEP. Detailed information about these models is provided in Table 1.
## 3 Methods
### Improvements to the RT DETR Algorithm
#### 3.2.3 Improving the Upsampling in the CCFM Module of the RT DETR Algorithm  
In the RT DETR algorithm, the CCFM upsampling module plays a crucial role by effectively restoring the spatial resolution of feature maps, significantly enhancing the accuracy of object detection tasks. This module's upsampling process ensures consistency in spatial dimensions between low-resolution and high-resolution feature maps, promoting effective integration of multi-scale features.  
DySample represents a significant advancement in dynamic upsampling technology [40]. By avoiding the complexity of dynamic convolution and adopting a point sampling strategy, it achieves efficient resource utilization. This method considerably reduces the computational burden compared to traditional dynamic upsampling techniques while demonstrating better performance in object detection tasks. Its mathematical representation can be summarized in the following steps:  
1. **Feature map representation:** let the input low-resolution feature be \(F_{\text{low}}\in\mathbb{R}^{H\times W\times C}\), where \(H\), \(W\), and \(C\) represent height, width, and channels, respectively.
2. **Sampling point generation:** The sampling point generator in DySample dynamically selects sampling points based on the input \(F_{\text{low}}\). Let the generated set of sampling points be \(S=\{(x_{i},y_{i})\}_{i=1}^{N}\), where \(N\) is the number of sampling points, and \((x_{i},y_{i})\) is the coordinate of the \(i\)th sampling point.
3. **Upsampling operation:** through the sampling points \(S\) and corresponding interpolation algorithms like bilinear interpolation, a high-resolution feature map \(F_{\text{high}}\in\mathbb{R}^{H^{\prime}\times W^{\prime}\times C}\) is generated from \(F_{\text{low}}\), where \(H^{\prime}\) and \(W^{\prime}\) are the target high-resolution dimensions.  
DySample's upsampling technology significantly improves computational efficiency and optimizes the recovery of high-resolution feature maps through fine sampling point selection, effectively enhancing object detection accuracy. Moreover, DySample technology does not rely on specific hardware acceleration, increasing its practicality and flexibility in student classroom scenarios.
## 2 Extraction Techniques
### Solid-Phase Microextraction (SPME)
#### 2.1.1.3 Extraction Time and Temperature  
Extraction time and temperature are two of the most important parameters affecting the SPME extraction efficiency. The extraction time is dependent on the partition coefficient of the analyte between the fiber coating and the sample matrix and also on the sample stirring, this being generally shorter for extractions from the headspace. SPME has a maximum sensitivity at the equilibrium point, defined as the time after which the level of analyte extracted remains constant, corresponding to the limit of experimental error to the amount extracted after infinite time. At equilibrium, small extraction time variations do not affect the level of analyte extracted by the fiber. Moreover, full equilibration is not necessary for accurate and precise analysis by SPME, due to the linear relationship between the amount of analyte adsorbed by the SPME fiber and its initial concentration in the sample matrix in non-equilibrium conditions (reviewed in [22, 30]).  
SPME extraction is an exothermic equilibration process and, therefore, the increase in the extraction temperature causes an increase in the extraction rate, and simultaneously, the distribution constant decreases [28]. On the other hand, the headspace-analyte partition coefficient increases with higher sampling temperature, resulting in a higher analyte concentration in the headspace and a consequent shorter extraction time [22]. In the case of natural product extraction, less aggressive conditions should be applied, such as moderate temperature and protection from light and oxygen, to prevent the degradation of some thermosensitive compounds.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Deep Learning Methods for BC Detection and Classification
#### 2.2.1 Transfer Learning and Data Augmentation
This work is the first to completely classify mammograms into all eight BI-RADS categories. Another study that involved the BI-RADS categories was presented by [PERSON] et al. [19] and it involved 314 mammograms. Twelve radiologists interpreted the examinations in two sessions: the first one, without AI, and the second one, with the help of AI. In the second session, the radiologists improved their ability to assign mammograms to the correct BI-RADS category without slowing down the interpretation time. Transfer learning and data augmentation are crucial techniques for improving the performance of deep learning models. They are effective in preventing overfitting, enhancing the model's generalization ability, and achieving better performance on smaller datasets with limited computational resources. However, using transfer learning without the proper cautions, may lead to overfitting of the new data if the new data are significantly different from the original dataset on which the model was trained. Additionally, data augmentation techniques may require generating additional training data, which can be time-consuming and computationally intensive; it may also generate irrelevant or unrealistic data.
## 3 Methods
### Governing Equations
#### 3.2.3 Conditioned Spaces  
For the determination of the temperatures in the conditioned spaces, the convective gains from the surfaces (\(q_{Surf}\)), infiltration gains (\(q_{Inf}\)), solar radiation entering the spaces through windows (\(q_{Solar}\)), absorbed solar radiation on internal shading devices in the spaces (\(q_{ISD}\)), indoor gains (\(q_{Gain}\), listed in Table 3), and cooling by hybrid desiccant cooling (\(q_{Coul}\)) should be considered (Equation (5)). For moisture balance, the mass balance in the spaces associated with infiltration and supply air was considered (Equations (6)-(8)):  
\[\rho_{S}c_{p,\;S}V_{S}\frac{dT_{S}}{dt}=\;q_{Surf}+q_{Inf}+q_{Solar}+q_{ISD}+q_ {Gain}-q_{Coul}, \tag{5}\]  
\[q_{Inf}=\dot{m}_{Inf}c_{p,\;air}(T_{Amb}-T_{S}), \tag{6}\]  
\[q_{Coul}=\dot{m}_{SA}c_{p,\;air}(T_{SA}-T_{S}), \tag{7}\]  
\[m_{S}\frac{\mathrm{d}\omega_{S}}{\mathrm{d}t}=\dot{m}_{Inf}(\omega_{Amb}- \omega_{S})+\dot{m}_{SA}(\omega_{SA}\;-\;\omega_{S}). \tag{8}\]
## 5 Results and Discussion
### Usage of Pre-Drilling Technology in the New Pile Penetration  
Pre-drilling technology is a typical approach for reducing the squeezing impact of a static-pressure pile in geotechnical engineering. Accordingly, the pre-drilling method can also be used to decrease the effect of new pile penetration on an adjacent pile. To analyze the effect of pre-drilling and drilling depth, Equation (5) should be improved in this section. Assuming that the penetration depth of the new pile is \(L\), the pre-drilling depth is \(L^{\prime}\) and the diameter of the pre-drilling hole is \(d_{r}\), the equivalent pile diameter of the new pile within the pre-drilling depth can be written as:  
\[d^{\prime}_{0}=\sqrt{d_{0}^{2}-d_{r}^{2}} \tag{47}\]  
Considering pre-drilling, the lateral displacement of the existing pile under new pile penetration can be described as follows:  
\[u_{r}=\int_{0}^{L-L^{\prime}}\frac{d_{0}^{2}}{16}\cdot\Big{(}\frac{r}{R_{1}^{ 2}}+(3-4\
u)\frac{r}{R_{1}^{2}}-\frac{6\pi z(2+h)}{R_{2}^{2}}\Big{)}\text{d}h+ \int_{L-L^{\prime}}\frac{d_{0}^{2}}{16}\cdot\Big{(}\frac{r}{R_{1}^{2}}+(3-4 \
u)\frac{r}{R_{1}^{2}}-\frac{6\pi z(2+h)}{R_{2}^{2}}\Big{)}\text{d}h \tag{48}\]  
The squeezing displacement calculated by Equation (48) can be used to analyze the adjacent pile response under new pile penetration. In this section, the length and diameter of the existing pile are 10 m and 0.5 m, respectively. The depth of new pile penetration is 10 m, with a diameter of 0.5 m. Other parameters are the same as in the previous section. The lateral displacement and bending moment of the existing pile are calculated, including the condition without pre-drilling and those with pre-drilling depths of 2.5 m, 5 m and 7.5 m.  
According to Figure 18, pre-drilling can significantly reduce the lateral displacement of the existing pile caused by the squeezing impact from the new pile penetration. Meanwhile, with the increasing length of pre-drilling, the effect on the lateral displacement of the upper pile body becomes smaller. It is worth noting that the pre-drilling technology greatly reduces the lateral displacement of the lower pile, which indicates that pre-drilling causes a significant reduction in the squeezing effect.  
Figure 19 shows the bending moment of the existing pile with the condition of pre-drilling. It can be found that the bending moment is less affected by pre-drilling than is the  
Figure 18: Influence of the pre-drilling depths of the new pile on the lateral displacement of the used pile.  
lateral displacement. Generally speaking, the greater the depth of pre-drilling, the smaller the pile bending moment.
## 3 A Fast Prediction Model for the Coupling Coefficient
### Interference Analysis of LTE-R System  
The result of the BLER varies with the SNR (Signal-to-Noise Ratio) and is shown in Figure 11. The result of throughput varies with SNR and is shown in Figure 12.  
\[\text{SNR}=E_{b}/N_{0}, \tag{7}\]  
where \(E_{b}\) is the signal energy per bit and \(N_{0}\) is the white Gaussian noise power of the channel.  
As shown in Figure 11, the BLER of the LTE-R system with added noise increases significantly when the SNR is in the range of 0 dB-17 dB. After adding the single arcing pulse signal, the BLER is less than \(10^{-1}\) when the SNR is greater than 0 dB. However, after adding the multi-arcing pulse signal, the BLER is less than \(10^{-1}\) when the SNR is greater than 6 dB. It indicates that the LTE-R system is affected more by the multi-arcing pulse signal. Moreover, as the SNR increases, the BLER after adding noise eventually decreases to the order of \(10^{-3}\). In other words, in the presence of poor SNR conditions, pantograph arcing radiation noise can lead to an increase in the BLER, resulting in an increase in the probability of system disruption.  
According to the performance requirements of the 3 GPP (3 rd Generation Partnership Project) for LTE systems, when the BLER is less than 10 percent, the system can meet the service reliability requirements. The LTE system can improve the decoding accuracy rate through the HARQ (Hybrid Automatic Repeat Request) retransmission process, so that the BLER is gradually lower than \(10^{-1}\). However, an increase in the number of retransmissions leads to a decrease in the system throughput and decoding gain.  
Figure 11: BLER varies with SNR.  
Figure 12: Throughput varies with SNR.  
As shown in Figure 12, compared to the noiseless case, the throughput is reduced by an average of 8% in the SNR range below 18 dB and by an average of 2.9% in the SNR range above 18 dB and below 25 dB after the addition of a single arcing pulse signal. After adding the multi-arcing pulse signal, the throughput is reduced by an average of 20% in the SNR range below 16 dB and by an average of 9% in the SNR range above 16 dB and below 25 dB. Combining this with Figure 11, it can be concluded that when the BLER is high, the system will increase the retransmission, leading to a decrease in the effective throughput. However, as the SNR gradually increases, the effect of noise on the throughput gradually decreases, but the effect of the multi-arcing pulse is greater than that of a single-arcing pulse.  
Therefore, pantograph arcing can affect LTE-R communication quality at 400 MHz and affect transmission reliability when the SNR is poor. It generally leads to a reduction in throughput and a reduction in service data transmission. In general, this will result in a reduction in throughput and data transmission for the service.
## 4 Conclusions  
In this study, optimal liquefaction and saccharification combinations of three important growth parameters, temperature, enzyme dose, dry weight for liquefaction and temperature-enzyme dose, and time for saccharification were generated using RSM with Box-Behnken design. Optimum combinations were 95 \({}^{\circ}\)C, 1 mL of \(\alpha\)-amylase, and 4.04 g dry weight of waste potato mach per 100 mL of deionized water and 60 \({}^{\circ}\)C, 0.8 mL of amyloglucidase, and 72 h for liquefaction and saccharification, respectively. The final glucose conversion under optimum medium was 34.9 g/L. Furthermore, higher glucose conversion was obtained by increasing amounts of dry-weight and enzyme by keeping ratio constant. Maximum glucose (137 g/L) was obtained from 24.24 dry-weight/100 mL DIW, however 16.16 g dry-weight/100 mL DIW was promising for ethanol fermentation process when agitation is of concern.  
Overall, ethanol fermentation from waste potato mash was found to perform better at pH of 5.5, and 3% inoculum size. The maximum production rate was obtained at pH 5.5, 30 \({}^{\circ}\)C, 400 rpm agitation, and 3% inoculum size with a 30.99 g/L ethanol production. By combining optimum conditions, 35 g/L ethanol was produced at pH 5.5 with an inoculum size of 3% when the nitrogen source was poultry meal. However, kinetic parameters of this fermentation were relatively low in comparison with yeast extract and feather meal. Feather meal yielded 32.9 g/L ethanol with 0.28 log CFU/mL/h growth rate and 3.59 g/L/h production rate, which are comparable kinetic parameters to yeast extract. It is clearly indicated that waste potato mash can be an effective fermentation medium for production of ethanol under conditions of controlled pH at 5.5, inoculum size of 3% and supplementation of the nitrogen source. In conclusion, waste potato mash was found promising carbon source for ethanol fermentation with alternate nitrogen sources.  
The work was supported in-part by Pennsylvania Agricultural Experiment Station and Turkish Ministry of Education by providing scholarship to [PERSON]. The authors gratefully acknowledge Keystone Potato Products (Hegins, PA, USA) and Griffin Industries, Inc. (Butler, KY, USA) for supplying waste potato mash and alternative nitrogen sources, respectively.
## 4 Implementation and Operations
### Dynamic Service Extension at Runtime  
Undoubtedly, the Strategy design pattern enables TinyONet to extend its services without disturbing other parts of the system, but the pattern is effective only when the system is recompiled at the source code level. That is, the adoption of the Strategy pattern is not sufficient to guarantee the dynamic addition of new services at runtime, therefore other technical methods should be considered to support the dynamic service extension.  
Figure 9: Process of _Slice_ creation.  
Usually, it is difficult to dynamically extend the compiled C\(++\) code, because a developer cannot know the name of class to be made after the development phase. For example, if an 'average' action is needed, a developer may want to write codes as like this:  
\[\text{Action *pAction}=\text{new AverageAction( )};\]  
However, this coding is nearly impossible because the developer cannot know the name of the target class, 'AverageAction', in advance. Needless to say, the AverageAction class will be developed after the code is released.  
We overcame the issue by combining the Abstract Factory pattern [20] with the C\(++\) dynamic loading [25]. The C\(++\) dynamic loading enables the compiled code to use the classes in Dynamic Linked Library (DLL). However, this binary level extension also has the same weakness as described above. In the case of using the C\(++\) dynamic loading, a developer still has to know the target class name in advance. The Abstract Factory pattern can be a missing part of the solution to the problem. As shown in Figure 10, each Factory class is dynamically loaded from DLLs and it is stored into the Map data structure with its action's name as the key. When an action is requested, the corresponding Factory class is retrieved from the Map and then the Factory class creates the instance of requested action. Using this combining method, the dynamic service extension at runtime is possible without indicating class names in the source code.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 8 Evaluation
### Storage Overhead  
After a round ends, the Process Tree is archived in the secondary storage of the logger. The primary storage usage in the logger consists of log entries arranged as leaf elements of the Process Tree. Storing the interior elements is optional because they can be recomputed from the leaf elements if needed. This presents a trade-off between computation time and storage efficiency. Retaining the interior elements can speed up the dispute process by reducing the need for immediate calculations. In contrast, not storing these elements saves space but requires more computing power during audits. Additionally, the logger maintains a version map that links each version number to the position of the corresponding leaf element to help interpret \(\mathbb{V}\) in the log entries.  
Figure 8: Memory overhead.  
Figure 9 illustrates the storage usage of the logger when the number of collector nodes is 100, assuming only the log entries are stored. We analyze the storage overhead for different aggregation factors, denoted as \(d\) and representing the average number of children for each node. With the same number of collector nodes, a higher \(d\) results in fewer aggregator nodes and subsequently lowers the height of the Report Tree.  
When the size of a version number, \(|R|\), and \(|C|\) are 2, 64, and 34 Bytes, respectively, the total storage occupied by the log entries is 17.2 KBytes when \(d=6\) and increases to 20 KBytes when \(d=15\). Furthermore, we compare the storage overhead when group collapsing is applied to the aggregator nodes. In such cases, log entries for the collapsed group are removed from storage. However, the interior elements required to compute the same commitment should be retained to facilitate future validations of the other parts. With the leaf-grouping feature, the removed parts are replaced with the minimum-necessary interior elements. Consequently, as depicted in Figure 9, storage usage decreases by 46.1%, 47.3%, 48.6%, and 48.8% when \(d\) is 6, 9, 12, and 15, respectively.