esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experimentation of Tensegrity Jumping Robots
## 3 Dynamic Modeling and Simulations Analysis
### Performance Analysis  
#### 3.2.1 Performance Analysis of Simultaneous Jumps  
Based on the known Lagrangian equations, we can derive the jump case using programming tools. With a stiffness of the elastic cable of \([0,\,40]\) N/mm and an initial tilt angle of the robot body of \([0,\,18]^{\circ}\), we obtain Figure 5.  
According to Figure 5, we can conclude that under the same conditions, the jump height of the robot increases significantly with the increase in stiffness. In particular, when the stiffness is 40 N/mm and the inclination angle is 0, the jump height of the robot is 679.42 mm. Meanwhile, the lateral jump distance of the robot increases with the increase in stiffness. When the stiffness is 40 N/mm and the inclination angle is 18\({}^{\circ}\), the jump distance of the robot is 59.75 mm. This is because the greater the stiffness of elastic cables of the robot, the greater the potential energy stored by the robot, which enables the robot a higher jumping performance. Also, as the tilt angle of the robot increases, the jump height of the robot decreases but the lateral jump distance increases. This is due to the fact that the larger the tilt angle, the more kinetic energy the robot shares in the transverse direction and the less kinetic energy in the longitudinal direction. In this case, we select the jumping schematic of the robot with a stiffness of 16 N/mm and a tilt angle of 18\({}^{\circ}\), as shown in the right section of Figure 5, where the robot's jumping height is 80.84 mm and the jumping distance is 3.83 mm. Therefore, to enhance the jumping performance of the robot effectively, it is advisable to moderately augment the stiffness of the elastic cables.  
Figure 5: Dual module simultaneous jump. (**a**) Vertical jump height. (**b**) Horizontal jump distance. (**c**) Schematic diagram of jumps at 8\({}^{\circ}\) and 16 N/mm.
# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
## 4 Conclusions  
This study introduces a novel YOLO-based BCD framework, designed for the accurate classification and detection of cancer tissues. To enhance the image clarity for more effective feature extraction by the CNN model, we adopted the CLAHE technique, which shows a 2.49 mAP improvement during evaluation. Data augmentation strategies were implemented to generate additional samples and mitigate overfitting risks. We proposed a parallel CNN architecture that utilizes multilevel and multiscale features. The model's effectiveness was rigorously evaluated using publicly available datasets, including IN-breast and CBIS-DDSM. The absence of multiscale and multilevel feature blocks resulted in lower mAP scores, highlighting their importance in capturing complex patterns. We employed an advanced activation function, namely, FTS, to prevent gradient vanishing and exploding issues in CNN training. The results reveal that the use of FTS instead of traditional LeakyReLU improved the mAP by 0.55. The scale-invariant loss function was utilized instead of the traditional YOLO loss function, improving the mAP by 1.21. Overall, it achieved an accuracy of 98.72% for breast cancer classification on the INbreast dataset and a mean Average Precision (mAP) of 91.15% for breast cancer detection on the CBIS-DDSM dataset. Notably, our proposed CNN architecture is computationally efficient, requiring  
Figure 6: Qualitative Results of Breast Cancer Detection on the CBIS-DDSM Dataset.  
only 11.33 million parameters for training. This optimization was achieved through careful model design, parameter reduction techniques, and the efficient use of the FTS activation function, ensuring that our system remains accessible for practical applications without compromising its effectiveness.  
Conceptualization, A.D.M. and D.E.; methodology, A.D.M.; software, A.D.M.; validation, A.D.M. and D.E.; formal analysis, A.D.M. and D.E.; investigation, A.D.M.; resources, A.D.M.; data curation, A.D.M.; writing--original draft preparation, A.D.M.; writing--review and editing, A.D.M.; visualization, A.D.M.; supervision, D.E.; project administration, A.D.M. and D.E.; funding acquisition, A.D.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data supporting the findings of this research are presented within the manuscript and appropriately cited. More Data will be made available upon request. Access to data will be in accordance with ethical and legal considerations.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Energy Efficiency Based on Loss of Mass  
To obtain the quality of combustion for final use apparatuses and the useful effect of the process, we recorded the temperature of the water in the recipients in each combustion chamber. Results are shown in Figure 14.  
The values obtained for the temperature of the water during 45 min (10 repetitions, SD 0.62) of combustion reflect the performance of the fuels analyzed. During the first 10 min--setting out from an initial water temperature of 17 \({}^{\circ}\)C--the combustion results show that the briquette raised the water temperature to 36 \({}^{\circ}\)C, the pine firewood to 52 \({}^{\circ}\)C, and the oak firewood to 39 \({}^{\circ}\)C. At 25 min of combustion, the briquette raised the temperature of the water to 53 \({}^{\circ}\)C, the pine firewood to 58 \({}^{\circ}\)C,  
Figure 14: Temperature difference of the water heated by the three biofuels during combustion.  
Figure 13: (**a**) Integrated image of the three fuels; (**b**) emissions in the form of radiant bodies for the three fuels.  
and the oak firewood to 55 \({}^{\circ}\)C. Finally, at 40 min of combustion, the briquettes increased the temperature of the water to 89 \({}^{\circ}\)C to conclude this analysis.
# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 3 Methodology
### Loss Function  
Medical image classification tasks are of critical importance for the early diagnosis of diseases and treatment decisions, and the choice of loss function is crucial for model training and performance. The cross-entropy loss function is one of the most commonly used loss functions in deep learning classification tasks, and it is widely used in classification tasks. The cross-entropy loss function is a metric used to measure the difference between two probability distributions. In the medical image classification task, we want the output probability distribution of the model to be as close as possible to the probability distribution of the real label. The cross-entropy loss function evaluates the prediction accuracy of the model by comparing the probability distribution of the real label with the probability distribution of the model output. Meanwhile, the cross-entropy loss function has good mathematical properties and its gradient calculation is relatively simple. This is conducive to updating the model parameters efficiently using the backpropagation algorithm and accelerating the training speed of the model. In addition, medical image classification tasks usually involve multiple categories, such as identifying different lesion types or tissue structures. The cross-entropy loss function performs well in multi-category classification problems, which can effectively deal with the relationship between multiple categories  
Figure 5: Detailed structure of Rep-C module.  
and drive the model to learn the features that distinguish different categories. Therefore, cross entropy loss is used as the loss function in this paper.The computational equation of CrossEntropyLoss is as follows:  
\[\mathcal{L}_{\text{CrossEntropyLoss}}=-\sum_{x=1}^{N}p(x)\cdot\log(q(x)), \tag{6}\]  
where \(N\) represents the batch size, \(p(x)\) represents the true label, and \(q(x)\) is the predicted probability.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
### Performance Evaluation  
To validate the proposed model, we implemented the NG-EPON architecture using the OPNET simulator. All key components and protocols of NG-EPON, such as dynamic bandwidth allocation, cycle time, transmission capacity, guard time, etc., are fully modeled. The proposed system model consists of 32 ONUs and one OLT. The downstream and upstream channels between the OLT and ONU are configured to 1 Gbps. The distance from the OLT to the ONUs is uniformly distributed over 10 to 20 km. To generate Assured-Forwarding (AF), Best-Effort (BE), and Tactile-Internet (TI) traffic, we employ self-similarity  
Figure 9: The pseudocode of RDBA.  
and long-range dependence, generating highly bursty traffic with a Hurst parameter of 0.7 [17]. The packet size is uniformly distributed between 512 and 12,144 bits. The Expedited Forwarding (EF) traffic is modeled using a T1 circuit-emulated line with a constant frame rate (1 frame/125 \(\upmu\)s) and a fixed packet size of 560 bits, which occupies approximately 14% of the total upstream bandwidth. The remaining traffic is distributed as 50% AF, 20% BE, and 30% TI for scenario I, and 40% AF, 20% BE, and 40% TI for scenario II. To evaluate the proposed mechanism, we construct different scenarios: (1) no-fault, (2) one fault, and (3) three faults.  
The focus of the simulation is to evaluate the system's performance after faults are detected. Fault scenarios with one fault and three faults were introduced, and the system's performance was measured in terms of key metrics, such as mean packet delay, system throughput, packet drop rate, and bandwidth waste. These measurements help validate the resilience of the architecture in ensuring performance guarantees, particularly in terms of low-latency requirements for real-time traffic such as Tactile Internet (TI). While the optical network's physical characteristics (e.g., power levels, impairments) were not the focus of this simulation, the system response to fault scenarios was crucial in demonstrating the architecture's ability to maintain service continuity and minimize disruption. To further validate the system, we compared the performance of the proposed RDBA mechanism against a traditional DBA approach, which does not incorporate fault-tolerant features. In the baseline DBA approach, bandwidth is allocated without any resilience mechanisms to manage fault scenarios. The simulation parameters are summarized in Table 3.
# Surface Application of Different Insecticides Against Two Coleopteran Pests of Stored Products
## 3 Results
### Mortality of Lasioderna serricorne
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Days**} & \multicolumn{1}{c}{**Surface Type**} & \multicolumn{1}{c}{**Control**} & \multicolumn{1}{c}{**Alpha-**} & \multicolumn{1}{c}{**Primiphos-**} & \multicolumn{1}{c}{**Stinosed**} \\  & & & **Cypermethrin** & **Methyl** & **Spinosad** \\ \hline \multirow{4}{*}{3} & Concrete & 6.6 \(\pm\) 3.0 & 100.0 \(\pm\) 0.0 & 92.5 \(\pm\) 1.1 a & 98.3 \(\pm\) 1.6 b \\  & Metallic & 11.6 \(\pm\) 4.2 & 100.0 \(\pm\) 0.0 & 97.5 \(\pm\) 1.7 b & 76.6 \(\pm\) 4.0 a \\  & Plastic & 1.6 \(\pm\) 1.6 & 100.0 \(\pm\) 0.0 & 81.6 \(\pm\) 4.5 a & 82.5 \(\pm\) 6.0 a \\  & Ceramic & 4.1 \(\pm\) 2.0 & 100.0 \(\pm\) 0.0 & 100.0 \(\pm\) 0.0 b & 90.0 \(\pm\) 2.8 ab \\ \hline \hline \end{tabular}  
* Within each exposure interval and insecticide, means followed by the same letter do not differ significantly according to the Kruskal-Wallis _H_-Test at \(p\)\(<\)0.05. Where no letters exist, no significant differences were noted.  
\end{table} 
Table 3: Mean mortality (% \(\pm\) SE) of _Lasioderna serricorne_ adults after exposure to three different insecticides (alpha-cypermethrin, pyrimiphos-methyl, and spinosad) and four surfaces (concrete, metallic, plastic, and ceramic) for 3, 7, 14, and 21 days (total degrees of freedom = 3), n = 24.
## 3 Analysis of Contrast Extraction Algorithms
### Contrast Extraction Procedure
The scattering in each measurement condition therefore consists of a broad distribution of count rates as shown in Figure 2a,c. Note here that droplet-based algorithms including LSF and GG have a precise count of photons, and they therefore share the same count rate distribution. On the other hand, the PPC method, as an algorithm based on rounding of pixel readout, has a tendency to underestimate the total number of photons. This led to the difference between their scattering intensity histograms. In the count rate range from 0.02 to 0.2 photons/pixel, the GG algorithm showed consistency despite the intensity fluctuations, with the contrasts from individual intensity groups following the horizontal dashed lines denoting the overall effective contrasts. On the other hand, PPC and LSF, respectively, give an increasing and decreasing trend with increasing count rates. This discrepancy combined with varying pulse intensity distributions will lead to additional systematic errors. For example, though the individual contrasts from 250 \(\upmu\)m slit beam and beam with wide opened slits show a difference for the PPC method, the extracted overall contrasts \(\beta_{e}\) cannot distinguish between the two conditions as displayed in Table 1. The reduced signal to noise ratio comes from their deviations in the count rate distribution as displayed in Figure 2a, while the weights for extracting the overall contrasts only consider photon counting statistics. To improve the sensitivity, contrasts need to be extracted from a subset of the data within a much narrower intensity range, which in turn reduces usable data fraction and increases the time needed to reach sufficient photon statistics. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & \(\beta_{e}\)**(250 \(\upmu\)m)** & \(\beta_{e}\)**(Unfocused \& No Slit/\(\beta_{b}\)**)** \\ \hline PPC & \(-\)0.19 (0.02) & \(-\)0.19 (0.02) \\ GG & \(-\)0.156 (0.002) & \(-\)0.161 (0.002) \\ LSF & 0.143 (0.009) & 0.137 (0.007) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Extracted contrasts by the the three algorithms. The error bar is included in the parenthesis.
## 3 Results and Discussion
### Mass Transfer Coefficient
#### 3.2.1 Influence of the Flow Rates of the Fluids on the Mass Transfer Coefficient
In other words, the gas pressure limitation of the membrane contactor was exceeded. Consequently, the mass transfer occurred but the efficiency decreased because the operating limitations were not respected. Therefore, this result is discussed with special caution. The effect of the fluids also revealed that all the mass transfer coefficients addressed in Figure 4 share the same order of magnitude, although the exchange area in the membrane contactor is two times lower than in the column and the volume is also smaller. This verifies the idea that much lower mass transfer coefficients would be expected in the packed column at identical experimental conditions in both kind of absorbers (for example, liquid and gas streams of 1 L min\({}^{-1}\) and 6.5 L min\({}^{-1}\), respectively), and, therefore, the process intensification would be directly identified. Nevertheless, it is needed to go further and to analyze the influence of the composition of the fluids, also in the efficiency on the elimination of the contaminant.
## 4 Discussion
### Accuracy of AIRS Irradiances
The results presented in Sections 3.1 and 3.2 show that the irradiance tilt response is sufficiently linear at least in the tested cases. In AIRS, the photodiodes are installed at a 10\({}^{\circ}\) tilt, as this was considered to be the maximum UAV tilt angle that needs to be well covered. If for other applications, the photodiode angle was to be increased, it is possible that the linearity of the sensor tilt response might not be as good anymore, especially in all atmospheric conditions and with all solar elevations. In such a case, selection of a more advanced interpolation method using empirical or modeled tilt response shapes should be considered. The flight test also revealed some imperfections in the FGI AIRS calibration. Firstly, the tilt corrected irradiance data showed (Figure 6a) up to 3% outliers from trend occurring during fast flight maneuvers, where the UAV changed orientation rapidly. This could indicate some issues in accurate synchronization and handling of the almost instantaneous IMU measurements and up to 100 ms photodiode exposures. The tilt corrected data also showed still approximately \(\pm\)2% shifts relative to the on-ground reference occurring at the times where the attitude of the UAV changed. This indicates that the relative radiometric calibration of the photodiodes should still be improved. However, even with these issues identified in the system calibration, the method was shown to significantly improve the stability of the on-board measurement. In future studies, in order to facilitate accurate direct reflectance measurements of the full spectra, we will perform further calibration and cross-calibration of the imaging and irradiance sensors.
# Image Error Concealment Based on Deep Neural Network
## 4 Experiments
### Objective and Subjective Performance Comparison  
In order to verify the performance of the proposed method, eight other state-of-the-art EC methods--POCS [4], MRF [27], nonnormative SEC for H.264 (AVC) [31], content adaptive technique (CAD) [32], OAI [6], VC [7], sparse linear prediction (SLP) [9], and KMMSE [10]--are compared with our method. The source code of all the above methods are based on a third-party implementation [34]. The results of the EC performance comparison of the nine competing methods are given in Tables 2-4. As can be seen from the tables, our method is superior to the other eight methods in average PSNR and SSIM under three loss modes. Table 2 illustrates the reconstruction performance of the compared methods on \(16\,\times 16\) isolate block losses. As can be observed from Table 2, the proposed method outperformed all of the other methods in average PSNR by a considerable margin. Compared with the very recent image EC method KMMSE, the average PSNR gain was 0.59 dB. Compared with the well-known OAI method, our method achieved up to 0.59 dB higher PSNR and 0.0069 higher SSIM. When compared with the POCS, VC, SLP, and MRF methods, our method obtained gains of 3.73 dB, 0.98 dB, 1.42 dB, and 1.46 dB in terms of PSNR and gains of 0.0550, 0.0143, 0.0062, and 0.0285 in terms of SSIM, respectively. Table 3 shows the quantitative comparison of \(16\times 16\) regular consecutive block losses. Under this loss mode, the proposed method performed better than the remaining eight methods on all of the test images in terms of both PSNR and SSIM. The average performance over the second-best method
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 4 Revisit the Regulatory Mechanisms of tRNA and tsRNA
### tRNA: Focus on Both Main and Side Businesses
#### 4.1.2 Binding to DNA/RNA  
tRNA can regulate gene expression by binding to DNA through a sequence base-pairing strategy. During the development of zebrafish embryos, full-length tRNA can form a stable tRNA:DNA hybrid at the tRNA gene loci, thus hindering the transcription process of tRNA genes [23]. However, the 5'tsRNAs, such as 5'tsRNAGly(CCC) and 5'tsRNAGlu(CTC), could compete with full-length tRNA to bind with the template strand of tRNA genes, which forms an unstable 5'tsRNA:DNA hybrid compared to the tRNA:DNA hybrid, thus facilitating the transcription of tRNA genes [23]. In addition, Bacteriophage could utilize _E. coli_ tRNA as a primer to initiate lagging strand DNA synthesis [138]. It has been observed that the growth defect that is caused by the absence of DNA primase in Bacteriophage can be rescued by mutations in T7 gene 5.5 [138]. These mutations on gene 5.5 directly reduce the abundance of gp5.5, which hinders the formation of the gp5.5-tRNA-H-NS complex [138]. As a result, the additional free tRNA can function as a substitute primer for Bacteriophage DNA replication [138]. This strategy allows Bacteriophage to flexibly use host tRNAs for survival and amplification [138]. In addition to the above-mentioned role of tRNA as a retrotransposon primer, tRNA can also interact with mRNA to fulfill functions. For example, research has demonstrated that initiator-tRNAMet (tRNAMet) can regulate pre-mRNA splicing through base-pairing with the start codon of targeted mRNA, which is independent of its aminoacylation status [139]. Additionally, another study found that uncharged tRNAAsp can adopt an atypical structure to interact with Alu elements in the 3\({}^{\prime}\) Untranslated Region (3\({}^{\prime}\)UTR), influencing the selection of polyadenylation sites [140]. These functions of tRNA, involving sequence pairing with DNA/RNA or conformational changes, shift the focus away from the anticodon sequence, emphasizing the functional significance of the entire tRNA sequence and the secondary or tertiary structures.
## 4 Measurement and Discussion
### Radiation Pattern and FBR  
Figure 12 shows the LHCP and RHCP realized the gain pattern of the LPCPA at 2.3 GHz and 2.4 GHz for both the free space and the on-phantom scenarios. The LHCPs are 10 dB larger than the RHCPs, which means the main polarization of the antenna is left-handed circular polarization. In free space (Figure 12a,b), the flat antenna has a measured realized gain of 8.9 dBic and 7.1 dBic at 2.3 GHz and 2.4 GHz, respectively. Correspondingly, the estimated proximate efficiencies are 52.96% and 44.12% based on the half-power beamwidth [41]. Compared to the literature in Table 1, the designed low-profile antenna has achieved the highest peak gain even with lossy textile materials rather than the less-lossy PCB substrates that are used in [13; 14]. Due to the bending, a decrease in the LHCP gain is observed. The measured realized gain is 8.7 dBic (2.3 GHz) and 5.4 dBic (2.4 GHz) when the antenna is bent with a curvature radius of 5 cm. As for the on-phantom scenario, see Figure 12c,d, the measured realized gains at 2.3 GHz are 8.6 dBic for on-chest and 8.4 dBic for on-arm, respectively, and at 2.4 GHz the corresponding ones are 7.1 dBic  
Figure 11: Simulated and measured AR in (**a**) free-space and (**b**) on-phantom scenarios.  
Figure 10: Simulated and measured reflection coefficient in (**a**) free-space and (**b**) on-body scenarios.  
and 5.9 dBic. According to the patterns, we can also obtain the FBR values. The evaluated FBR values are all larger than 18 dB, and the peak one is 35 dB for the flat antenna in free space, which shows good radiation shielding due to the presence of the ground. As for positions having limited space or with drastic bending, for example, the wrist, the ground should be decreased accordingly for good wearing comfortability. Alternatively, the materials could be changed to more flexible and stretchable ones.