esa-sceva/satcom-chunk-collection huggingface dataset:
# A Faster Algorithm for Reducing the Computational Complexity of Convolutional Neural Networks
## 1 Introduction  
Deep convolutional neural networks have achieved remarkable improvements in image and video processing [1; 2; 3]. However, the computational complexity of these networks has also increased significantly. Since the prediction process of the networks used in real-time applications requires very low latency, the heavy computational burden is a major problem with these systems. Detecting faces from video imagery is still a challenging task [4; 5]. The success of convolutional neural networks in these applications is limited by their heavy computational burden.  
There have been a number of studies on accelerating the efficiency of convolutional neural networks. [PERSON] et al. [6] indicate that there are significant redundancies in the parameterizations of neural networks. [PERSON] et al. [7] and [PERSON] et al. [8] use certain training strategies to compress these neural network models without significantly weakening their performance. Some researchers [9; 10; 11] have found that low-precision computation is sufficient for the networks. Binary/Ternary Net [12; 13] restricts the parameters to two or three values. [PERSON] et al. [14] used low-rank approximation to reconstruct the convolution matrix, which can reduce the complexity of convolution. These algorithms are effective in accelerating computation in the network, but they also cause a degradation in accuracy. Fast Fourier Transform (FFT) is also useful in reducing the computational complexity of convolutional neural networks without losing accuracy [15; 16], but it is only effective for networks with large kernels. However, convolutional neural networks tend to use small kernels because they achieve better accuracy than networks with larger kernels [1]. For these reasons, there is a demand for an algorithm that can accelerate the efficiency of networks with small kernels.  
In this paper, we present an algorithm based on [PERSON]'s minimal filtering algorithm which was proposed by [PERSON] [17] and [PERSON] [18] and generalized by [PERSON] [19]. The minimal filteringalgorithm can reduce the computational complexity of each convolution in the network without losing accuracy. However, the computational complexity is still large for real-time requirements. To reduce further the computational complexity of these networks, we utilize the Strassen algorithm to reduce the number of convolutions in the network simultaneously. Moreover, we evaluate our algorithm with the Visual Geometry Group (VGG) network. Experimental results show that it can save 75% of the time spent on computation when the batch size is 32.  
The rest of this paper is organized as follows. Section 2 reviews related work on convolutional neural networks, the Winograd algorithm and the Strassen algorithm. The proposed algorithm is presented in Section 3. Several simulations are included in Section 4, and the work is concluded in Section 5.
## 5 Cumulative Damage Model  
### Fatigue Evaluation of BRBs Device  
The fatigue life of BRBs is originated with the initiation of fatigue by the first yielding, and it grows slowly and gradually under each cyclic loadings until it reaches the failure thresholds. Thus, the fatigue life can be defined from the number of cycles \(N_{\!f}\) of the specific intensity that a BRB can sustain before the failure happens. For the earthquake and wind loads, which induce cyclic loads with variable intensities, the strain-based estimation of fatigue life is suitable. For this purpose, first, the random cyclic loads are sorted in bins of similar strain; then, for each of the bins, the cumulative damage is computed from the \(S\)-\(N\) curves, and finally, using the Miner's rule, the fatigue failure is evaluated.
## 4 Experiments and Results  
### Settings  
Sentiment analysis is a text classification task, where given written text as an input, positive, neutral, or negative class is returned as the output. Here we perform the binary (2-class, positive and negative) and 3-class (positive, negative, and neutral) sentiment classification.  
Our method was implemented using Tensorflow and Keras libraries with python programming language. Our experiments were executed with the datasets described in Section 4.2 and using the methods described in Sections 3.4 and 3.5. The results of the experiments are presented in Tables 4-9.
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 4 Experimental Setup And Results
### Lossless Compression of RASTER Maps  
RASTER maps are electronic maps embedded in navigation devices and comprises chiefly of redundant yet indispensable data. The data takes up a lot of storage space and being climacteric, its loss is highly pernicious. Lossless image compression is employed to decrease the data size while maintaining full quality of these images. Techniques proposed by [PERSON] et al. in [4] and [PERSON] et al. in [48] prove to be pivotal in this aspect. [PERSON] et al. scheme works on splitting the colour map into separate layers that are compressed singly.  
Block-Line Separated Encoding (BIiSE) technique by [PERSON] et al. outperforms JPEG-LS, Portable Networks Graphic (PNG) and Graphics Interchange Format (GIF) compression. However, BBWCA  
Figure 5: University of Loughborough - UCID Outdoor Images.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**UCID Image No**} & \multirow{2}{*}{**Original Size BMP**} & \multicolumn{8}{c}{**Compression Scheme**} \\  & & **PNG** & **FP8** & **BMF** & **FUC** & **GRALIC** & **BBWCA (VUV)** & **BBWCA (YA6U\&VA6)** \\ \hline  
\hline Total & 14450 & 13573 & 8679 & 7585 & 7831 & 7440 & 7829 & 5519 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of compression sizes for UCID outdoor images.  
utilizing the YUV RCT gave higher compression results than BLiSE and different schemes used as benchmarks. The YA6 UA6 VA6 RCT BBWCA shows promising results in comparison to the YUV RCT based BBWCA. RASTER maps used to draw a comparative analysis of compression achieved with the benchmark schemes in comparison with the proposed BBWCA (YA6 UA6 VA6) algorithm is shown in Figure 6. Figure 7 demonstrates the results of the system for the respective RASTER maps.
# An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks
## 4 Energy Balanced and Lifetime Extended Protocol (EBLE)
### Detailed Design of EBLE  
In this section, we present our design of EBLE in detail. The EBLE protocol operates in two phases: candidate forwarding set selection phase and data transmission phase.
# Development of a Social DTN for Message Communication between SNS Group Members
## 5 Evaluation
### Route Establishment Time When There Are Terminals Belonging to Different Groups  
We made experiments to examine whether route establishment time changes if multiple terminals try to establish routes and if there are terminals nearby that have community tokens different from that of a particular terminal. The network structure used in this experiment is shown in Figure 11. The conditions for this experiment are shown in Table 10.  
The results of the experiment are shown in Figure 12. In the case of one hop, the route establishment time was shorter when all terminals belonged to the same group (homogeneous). However, in the case of two hops, the route establishment time was shorter when there were terminals that belonged to different groups (heterogeneous). This is because the time needed to start communication varies because of the specifications of Nearby Connections API [13], as mentioned in Section 5.2. However, terminals belonging to another group hardly affected the route establishment  
Figure 11: Network structure used in the experiment for a case where terminals belonging to different groups exist.  
time. The reason is that, in this system, a terminal decides whether to establish a communication with another terminal successively based on both community tokens and information in its routing table.
## 4 Computer Simulations  
Figure 7 shows a computer simulation model assuming a reverse two-path multipath condition. Two transmitting transducers TX1 and TX2, 3.25 m apart, were used to model the multipath environment. A receiving transducer RX moves 0.75 m from 0.5 m from TX1 in the direction of TX2 with a speed of 0.25 m/s. The OFDM signal length is about 10.0 s with 400 OFDM symbols. The calculated 1 st and 2 nd received wave delay time and the desired (1 st wave) to undesired (2 nd wave) power ratio (DUR) are shown in Figure 8. The applied carrier to noise ratio (CNR) is 40 (dB). Figure 9 shows the time dependences of the measured bit error rate (BER) and the real value of the 64 QAM constellation of the conventional receiver and the two-steps of the sequential ICI canceling mDDP receiver with NumCOL = 1, 5, 11, 21 (the number of taps in the multitap equalizer). The \"moving period\" in the graph of time dependence of bit error rate represents the interval during which the RX transducer is moving. Figure 10 shows 64 QAM constellations in the moving period for the same cases. As can be seen from these figures, in the two-steps of the sequential ICI canceling mDDP, the constellation disorder decreases and the BER value decreases dramatically as the value of NumCOL is increased.  
Figure 11 summarizes simulated BER with changes in NumCOL for conventional and iterative one- to three-step mDDP. Increasing from one- to two-steps dramatically reduces the BER value but increasing to three-steps has no effect. There is a dependence of BER reduction on increasing NumCOL values, especially in two-step. The two-step mDDP scheme reduces ICI dramatically.  
The graphs of SNR vs. BER for the conventional method and iterative multistep mDDP with a NumCOL value of 21 are shown in Figure 12. When CNR is large, the effect of the iterative two-step mDDP can be seen clearly; when CNR is less than 20 dB, the difference between one-step mDDP and iterative two-step mDDP is small, but some BER improvement can be expected. As for the iterative three-step mDDP, the difference between it and the two-step mDDP is very small in the CNR vs. BER graph.  
Figure 8: Delay time and DUR in simulation.  
Figure 7: Computer simulation model assuming reverse two-path multipath condition.  
Figure 9: Result of computer simulation assuming time dependencies of measured BER and constellation (real part of complex).  
Figure 10: Result of computer simulation assuming constellations in moving period.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 4 Case Study
### Parameter Effect  
In this section, we will investigate the influence of different quantities of set-based individuals on the search effectiveness of the SetGSO algorithm. To illustrate this, we will employ a modified version of the IEEE 30-bus system, keeping all parameters constant except for the number of set-based individuals, which will vary from 2 to 8. This study focuses on optimizing fuel cost, reducing power loss, and minimizing voltage deviation as the three main objectives. The research utilizes SetGSO to identify optimal solutions and records the average imprecision and hyper-volume of each group. Figure 4 illustrates the hyper-volume and average imprecision curves, demonstrating the impact of increasing the number of set-based individuals.  
Figure 5 illustrates the relationship between the hyper-volume generated by SetGSO and the number of set-based individuals. It is observed that, as the number of set-based individuals increases, the search members also increase, leading to the discovery of more Pareto-front solutions. However, the average imprecision remains relatively stable, suggesting that it is not significantly affected by this shift. Consequently, there is a conflict between the hyper-volume and average imprecision, resulting in instances where the hyper-volume is smaller when a high number of sets is present compared to when the number of sets is small. Ultimately, the computational time will experience a substantial increase as more set-based individuals are incorporated. To summarize, based on the research objectives outlined in this study, it is determined that employing four set-based individuals is satisfactory for the forthcoming experiment.  
Figure 4: Network topology diagram of the IEEE 30-bus system.
## 3 Experimental Results
### Results of IEEE 69-Bus System
#### 3.2.1 Voltage Profile  
The voltage profiles because of the optimal allocation of DGs and/or CBs in different scenarios are demonstrated in Figure 9. The best voltage profile is obtained in scenario IV; here, both active and reactive power are supplied to the radial system, and the optimum sitting, sizing, and power factor of the DGs are determined.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Bus No.** & **DG Size (MW)** & **OPF** & **Power Loss (kW)** & **Loss Reduction (\%)** \\ \hline \multirow{3}{*}{WOA [38]} & 61 & 1.8076 & 0.85 & & \\  & 4 & 1.9887 & 0.85 & 7.89 & 96.49 \\  & 17 & 0.53472 & 0.85 & & \\ \hline \multirow{3}{*}{DA [38]} & 11 & 0.49634 & 0.85 & & \\  & 61 & 1.7481 & 0.85 & 5.01 & 97.77 \\  & 17 & 0.39501 & 0.85 & & \\ \hline \multirow{3}{*}{MFO [38]} & 11 & 0.51573 & 0.85 & & \\  & 61 & 1.7456 & 0.85 & 5.0 & 97.77 \\  & 18 & 0.38617 & 0.85 & & \\ \hline \multirow{3}{*}{IA [35]} & 17 & 0.510 & 0.82 & & \\  & 5 & 0.6798 & 0.82 & 4.95 & 97.74 \\  & 61 & 1.6999 & 0.82 & & \\ \hline \multirow{3}{*}{EA [36]} & 11 & 0.548 & 0.82 & & \\  & 18 & 0.380 & 0.83 & 4.48 & 98.00 \\  & 61 & 1.733 & 0.82 & & \\ \hline \multirow{3}{*}{AOA} & 11 & 0.453 & 0.7071 & & \\  & 21 & 0.349 & 0.8342 & 4.44 & 98.03 \\ \cline{1-1}  & 61 & 1.7064 & 0.8290 & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: The results of optimum DGs allocation at optimal p.f for IEEE 69-bus system.  
Figure 9: Voltage profile of the IEEE 69-bus system in different scenarios.
# Antimicrobial Peptides--Mechanisms of Action, Antimicrobial Effects and Clinical Applications
## 3 Antimicrobial Effects of AMPs  
### Antibacterial Activity of AMPs  
As the primary goal in researching AMPs was the fight against (increasingly resistant) bacteria, some scientists emphasize this need and call them antibacterial peptides (ABP) [34; 35]. They can destroy bacteria by breaking the cell wall and membrane of the bacteria, intracellular action, through a combination of dual destruction mechanisms, and by acting on the bacterial biofilm [36; 37]. By binding to lipid II, which is part of the peptidoglycan can molecule and an essential factor in cell wall synthesis, ABPs prevent cell wall formation. In addition to controlling its assembly, it can destroy the already-formed cell wall [37; 38; 39; 40]. Acting on the membrane is possible in several ways. The barrel-shaped and toroidal pore models can be defined as transmembrane pore models. Vertical insertion of the peptide into the membrane while promoting peptide-peptide lateral interaction is typical of the barrel-shaped pore model [37; 41; 42]. In the toroidal pore model, peptides are also vertically embedded in the membrane. However, here the pores are created by both peptide and lipid groups. The most crucial difference between these two models is that the barrel model's hydrophobic and hydrophilic bilayers remained intact [37; 42; 43]. The model that does not create pores through the membrane and in which specific peptide-peptide interactions are not necessary belongs to the so-called carpet model. It is characterized by adherence of ABP along the bacterial membrane and its adsorption until a particular concentration is reached. After crossing the tolerance threshold, a detergent-like model appears, and a loss of integrity and opening of the bacterial membrane happens [13; 37; 43; 44; 45]. Many peptides work by destroying intracellular functions, and in that way, they kill bacteria. Some important ones inhibit DNA, RNA, and protein synthesis [42; 46; 47; 48]. The development of research and the discovery of peptides with antimicrobial effects show that specific peptides do not have only one impact model (Figure 5). On the contrary, most of them share a combination of mechanisms [37; 42; 43; 49; 50].  
AMPs can act on biofilm at all stages of its development. They can inhibit biofilm formation by disrupting the signaling pathway of bacteria cells. By encouraging bacteria to produce guanosine tetraphosphate (ppGpp) and pentaphosphate (ppGpp) with limiting nutrients in the biofilm, the synthesis of nucleic acids is inhibited. Another effect is a reduction in the expression of binding protein transport genes necessary for forming a bacterial biofilm. Additionally, ABP can destroy the already-formed biofilm by acting on the membrane potential of bacteria [37; 43; 51; 52].  
Figure 4: Carpet model.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## Appendix A The ISODATA  
The ISODATA is an unsupervised clustering algorithm that separates groups of objects in a scene. A typical scene consists of regular and/or irregular regions arranged in a patchwork manner, each containing one class of surface cover type (e.g., flooded areas, croplands, forests, manmade structures, etc.); these homogeneous regions are the objects in the scene. Objects within each cluster should be as close to each other as possible and as far from other objects in other clusters as possible. The ISODATA begins with arbitrary cluster means and then clusters the pixels according to the minimum spectral distance technique. It is an iterative procedure, in which each iteration recalculates means and reclassifies pixels with respect to the new means. The process ends when a maximum number of iterations has been performed, or a maximum percentage of unchanged pixels between two iterations has been reached. The ISODATA does not keep a fixed number of clusters. In particular, clusters having a very large number of members are split, while if two cluster means are very close in terms of spectral distance (e.g., they represent an unnecessary or an injudicious division of the data), they are merged [38]. Clusters that contain so few points as to be meaningless (e.g., that they would not give acceptable statistics estimates if used in training a maximum likelihood classifier) are discarded and their members are reassigned to other clusters.  
Since the ISODATA splits and merges clusters, it just requires specifying a range for the number of clusters. The optimum number of clusters is not known, so it is generally chosen to be conservatively high [38].
###### Abstract  
The aim of this clinical study was to determine the impact of the consumption of chicken eggs enriched with n-3 polyunsaturated fatty acids, selenium, vitamin E, and lutein on micro- and macrovascular endothelium-dependent dilation, inflammation biomarkers, and oxidative stress levels in participants with chronic coronary syndrome (CCS). This was a double-blind, placebo-controlled clinical study that included 30 CCS participants (9 women, 21 men) randomized into the control group (N = 15), who ate ordinary chicken eggs (three per day), and the Nutri4 group (N = 15), who ate enriched eggs (three per day) for 21 days. Microvascular and macrovascular endothelium-dependent vasodilation was evaluated by measuring forearm skin post-occlusive reactive hypereemia (PORH) and acetylcholine-induced dilation (AChID) and the flow-mediated dilation (FMD) of the brachial artery, respectively. The serum lipid profile, anti- and proinflammatory cytokine levels, serum concentration of nitric oxide synthase (NOS) isoforms, and oxidative stress biomarkers were measured before and after the diet protocols. Enriched, but not regular, chicken eggs significantly improved microvascular PORH and AChID and macrovascular FMD, increased the serum concentration of inducible NOS, decreased serum triglyceride levels, and decreased proinflammatory cytokine IL-17A and TGF-18 levels compared to initial measurements. Patients with CCS can benefit from the consumption of enriched chicken eggs due to improved lipid biomarkers, a more favorable anti-inflammatory milieu, and improved vascular relaxation at micro- and macrovascular levels.  
chicen eggs; functional food; chronic coronary syndrome; endothelium; IL-17A; inflammation; oxidative stress
## 5 Results and Discussion
### Spatial Autocorrelation Test  
The dependent variable and independent variable data are imported into ArcMap10.8, and the Moran's index of each variable is obtained through the \"spatial autocorrelation\" function, and the spatial autocorrelation test is performed. The results are shown in Table 3.  
According to the analysis of Table 3, the \(Z\) value of the dependent variable and the independent variable is greater than 1.96. The Moran's index is greater than zero, and the \(p\) value is less than 0.05, indicating that there is a significant spatial positive correlation between the dependent variable and the independent variable. It shows that variable data can be used to analyze the GWR model for explaining spatial heterogeneity.
# Wireless Epidermal Six-Axis Inertial Measurement Units for Real-Time Joint Angle Estimation
## 3 Results  
### Implementation Results  
The implementation results and mechanical characteristics of the epidermal six-axis IMU are shown in Figure 4. The overall size of the proposed system was approximately \(40\times 37\) mm\({}^{2}\), with the thickness being less than 100 \(\upmu\)m (excluding the coin cell battery and ICs). In addition, the current consumption was measured to be 46 mA for full-function operation. The stress applied on the Cu interconnects as a result of strain and various deformations can be minimized by the serpentine structures, thus, the elongation limit of the implemented device depends on the stiffness of the stretchable substrate. Figure 4b shows the experimental waterproof results of the implemented device. The Bluetooth signal was continuously acquired, and data were transmitted despite water reaching the device. Owing to the semipermeable characteristics of the Tegaderm, the inner electronic circuitry could be perfectly protected from external intrusion during operation while attached to the skin.  
Figure 3: Manufacturing procedure. (**a**) Cu foil preparation; (**b**) Cu foil lamination on thermal release tape (TRT); (**c**) patterning using cutting tool; (**d**) residual Cu removal; (**e**) adhesion on water-soluble tape (WST), fixation on glass substrate using Kapton film, and TRT removal; (**f**) device soldered on Cu-patterned interconnects; (**g**) Kapton film removal and device release from glass substrate; (**h**) top-layer encapsulation; (**i**) WST removal; (**j**) bottom-layer encapsulation.  
Figure 4c,d presents the flexibility and stretchability experiments on the implemented epidermal device. Even after fully wrapping the device around a 20-mm-radius cylindrical tube or stretching  
Figure 4: Implementation results. (**a**) Wireless epidermal six-axis IMU; (**b**) waterproof experimental results; (**c**) flexibility test results; (**d**) stretchability test results with 0% to 30% tensile strain.  
it up to 30%, the acceleration and angular velocity signals sent through Bluetooth communication were maintained at steady levels, indicating that the serpentine-structured interconnects are robust to mechanical deformations. The flexibility and stretchability of the implemented device allow it to be utilized on the human body, including on the wrists, ankles, neck, and abdominal region, which are common sites for monitoring motion for clinical diagnosis.