esa-sceva/satcom-chunk-collection huggingface dataset:
# Experiment Investigation of Bistable Vibration Energy Harvesting with Random Wave Environment
## 4 Discussion  
### Amplification Effect by Stochastic Resonance  
The standard deviation of the response displacement expressed by the following equation can be used to evaluate the magnitude of the response displacement of the vibrator including random vibration.  
\[S=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}(\pi_{i}- \overline{x})^{2}}. \tag{31}\]  
Here, \(x_{i}\) and \(\overline{x}\) are measured values of the response displacement, the average value, and \(N\) is the sample points of the measurement experiment. In order to evaluate the amplification effect expressed by the ratio of the output and the input of the vibration system, the amplification effect of the proposed bistable vibration model is evaluated using the ratio of the standard deviation of the response displacement of the mass block and the support point calculated in the following equation.  
\[S_{ratio}=\frac{S_{mass}}{S_{base}}. \tag{32}\]  
Reference numerals \(S_{mass}\) and \(S_{base}\) denote standard deviation values of the displacement of the mass block and the support point calculated by the Equation (31), respectively. For the measurement result of the vibration experiment shown in Figures 7-15 discussed in the previous section, \(S_{ratio}\) is calculated in Equation (32), and the result is summarized in Figure 16.  
Figure 16: Results of comparing Ratio of displacement standard deviation of mass block to support point. (The blue graph is the result of vibration with a periodic signal. The red graph is the result of vibration with random and periodic signals).  
In Figure 16, the blue graph shows \(S_{ratio}\) when the periodic signal is vibrated, and the red graph shows \(S_{ratio}\) when the random signal is combined with the periodic signal. As shown in Figure 16, in the case where the random signal is combined with the periodic signals of 0.4 Hz and 0.5 Hz, a stochastic resonance of bistable oscillation occurs, it is confirmed that the obtained amplification effect can be clearly higher than the other single stable motion states.  
For further comparison, the sum of an excited by random signal and periodic signal \(S_{ratio}\) is \(1.06+5.89=6.95\), and an excited by common signal \(S_{ratio}\) is 11.68, the difference is 68.06%. In the case of the periodic signal of 0.5 Hz, the sum of the obtained sum \(S_{ratio}\) was \(1.06+1.08=2.14\), and the joint vibration \(S_{ratio}\) was 9.43, and the difference between them was 340.65%. It was confirmed that both of them were higher than the sum obtained by excitation. This seems to be due to the stochastic resonance.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 2 Related Work
### Stereo Vision-Based Fire Localization System  
In [22], a fire location system, utilizing stereo vision, was designed to automatically determine the precise position of a fire and pinpoint the source for extinguishing it. The camera-captured image data underwent analysis via an image processing algorithm to instantly detect the flames. Subsequently, a calibrated stereo vision system was employed to establish the 3D coordinates of the fire's location or, more accurately, its relative position concerning the camera's perspective. Achieving an accurate fire location stands as a critical prerequisite for facilitating swift firefighting responses and precise water injection.  
Predominantly, a stereo vision-based system centered on generating a disparity map was introduced. By computing the camera's meticulous calibration data, the fire's 3D real-world coordinates could be derived. Consequently, an increasing amount of research has delved into fire positioning and its three-dimensional modeling using stereo vision sensors. For instance, the work described in [23] used a stereo vision sensor system for 3D fire location, successfully applying it within a coal chemical company setting. The authors in [24] harnessed a combination of a stereo infrared camera and laser radar to capture fire imagery in smoky environments. This fusion sensor achieved accurate fire positioning within clean and intricate settings, although its effective operational distance remained limited to under 10 m. Similar investigations were conducted in [25], constrained by the infrared camera's operational range and the stereo vision system's base distance. The system proved suitable only for identifying and locating fires at short distances. The authors in [26] established a stereo vision system with a 100 mm base distance for 3D fire modeling and geometric analysis. Outdoor experiments exhibited reliable fire localization when the depth distance was within 20 m. However, the stereo vision system for fire positioning encountered challenges, as discussed in [27; 28]. The calibration accuracy significantly influenced the light positioning outcomes, with the positioning precision declining as distance increased. Moreover, the system's adaptability to diverse light positioning distances remained limited. Thus, multiple techniques and solutions have been proposed and adopted to enhance the outcomes.
## 7 Benchmark Problems and Case Study
### Bonded Support  
The problem described in Figure 4 was selected to provide a three-dimensional case study that is more representative of the complex geometries that can be found in engineering design problems.  
Figure 2: Dimensions and boundary conditions of the Cantilever beam numerical model.  
The design space was modeled using an irregular mesh with an average element size of 1.25 mm and hexahedral C3D10, leading to an approximate total of 370.000 elements. A unit load was applied at a reference point attached to the connection region of the structure, while the surface of the adhesive joint was considered fixed. To minimize the computational cost, two planes of symmetry were considered along the XY and YZ planes, reducing the total number of elements. The problem was solved using an implicit analysis considering a material with: a 70.000 MPa Young's modulus, a 0.33 Poisson's ratio, and a material density of \(2.7\times 10^{-9}\) ton/mm\({}^{3}\). The problem also considered a simplified representation of an adhesive bond, accounting only for the elastic properties (a 1.500 MPa Young's modulus and a 0.3 Poisson's ratio). Due to the complexity of the design, the interested reader is referred to the Mendeley Dataset [94] for the complete description of the geometry considered in this case study and shown in Figure 4.  
The material elements representing the adhesive bond are identified as \"frozen elements\". This excludes the possibility of editing the material distribution in this region, which would influence the resistance of the joint due to the reduction of the bonded area and excludes the contribution of these elements during the use of the blurring filter, which would be physically unreasonable as the adhesive and substrate are made of different materials. On the other hand, the first layer of elements of the support in contact with the adhesive was identified as a \"neighboring region\", which does not allow changes in the material distribution of these elements, but allows the consideration of their influence in the determination of the gradients. However, unlike the previous case, the elements in this region were considered during the filtering process, as the material is the same as the one assigned to the editable region of the topology optimization process. The \"frozen elements\" and \"neighboring region\" are shown in Figure 4 in red and blue, respectively.  
The ABAQUS(r) models necessary to reproduce all the results described in this work are available in the Dataset [94].  
Figure 3: Dimensions and boundary conditions of the L-bracket numerical model.
## 2 Related Works
Figure 1 shows an overview of recent studies on EMG based pattern recognition. The number of electrodes used in the study is plotted against the number of classes. As can be seen, the number of electrode is greater than or equal to the number of classes in nearly all of these studies. Also, almost all of the current works only deals with small number of classes (less than 10) [11; 17; 18; 19; 20; 23; 29; 30]. Only an extremely small number of studies have reported classification results for more than 10 classes but they require large number of electrodes [21; 31; 32; 33]. However, this option may not be possible with an amputee. This is because using large number of electrodes requires a large surface on the forearm, which may not be available in the case of amputees. Furthermore, having large number of electrodes will increase the cost and complexity of the required hardware as well as increase the computation load which slows down the processing speed of the system [34]. As such, it is desirable to reduce the number of electrode but at the same time have the ability to classify large number of movements. It is also worth noting that most existing studies involve sophisticated clinical grade sensor equipment that are expensive, making practical social use difficult or prohibitive. For the purpose of developing a low-cost EMG control system that can classify large numbers of movements, this study presents a signal processing technique to classify 17 wrist and finger movements (with a rest state included) using 6 consumer grade EMG sensors. This study will tackle the following research questions:1. How well can the state-of-the-art classification scheme classify 17 types of movements? 2. Can a consumer grade EMG sensor serve as a modality in myoelectric prosthetic control? 3. How long can the user control the prosthetic hands until the accuracy drops significantly? To answer the first question, evaluation of a classification scheme using spectral features and multilayer perceptron as the classifier will be performed and discussed. To answer the second question, a consumer grade acquisition device (sampling frequency of 200 Hz) will be used in the experiment. The answer to these two questions will give us an idea of the extent of hand movement recognition capabilities with limited hardware. The answer to the third question will give us a hint of when classifiers may need to be recalibrate.
# Evaluation of Methodologies and Measures on the Usability of Social Robots: A Systematic Review
## 3 Results
### Evaluation Methods
#### 3.2.1 Type of Stimuli
In addition, early studies which used videos proved the advantage of higher degrees of freedom in experimental control: for example, when overlaying the same behavior on the appearance of different gender [15] or neutralizing cultural effects by using standardized animation prototypes [16]. There were four studies which conducted the social robot evaluation through showing a video which contains the interaction between users and robots [9; 10; 12; 16]. One of these studies [16] chose a video stimulus for conducting their survey via online. A video stimulus through an online survey could be an efficient substitution of interaction between subjects and a robot in a real-world setting. Similarly, in a study which provided VR modalities as stimuli to subjects which conveyed various posed robots, it was found that there is a significant effect between a physically present robot and a VR robot on both valence and arousal ratings. However, it depends on which phase of development the robot is in. If the robot can't guarantee the best condition to interact with subject freely, we can use a video stimulus in place of a physically present robot to avoid usability challenges [10]. A video type can depict the context of use more accurately and the movement or facial expression of the robot more vividly than a static image. Live interaction. 31 studies we reviewed provide a physically present robot to subjects for their research. Previous research found that physically present robots had been found to be more persuasive, arousing, and to be received more positively when compared to robots depicted in the video, images, or as virtual agents [12]. There were studies that used commercialized robots (e.g., Pepper, iCat, Karotz, Aibo, RoboVie) for their research [17; 18; 19; 20; 21; 22; 23; 24]. When fully commercialized robots can be provided, test participants can interact with the robot freely. Especially [23] provided Karotz to participants, and they tried to use Karotz willingly in their home for a long time. This research was well designed to understand how subjects' beliefs on robots can be built. However, when researchers provided prototype robots as stimuli, 22.6% of studies chose a Wizard of Oz method for their interaction [9; 20; 22; 25; 26; 27; 28]. Even though a participant considers he or she communicates with the robot in real-time, a hidden experimenter controls the robot after a test participant commands something to the robot. This method can prevent unexpected errors of robots and make up for specific functionality which is not implemented yet.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 7 Conclusions and Future Work  
In this work, we have introduced a novel Reconfigurable Vector-Processed Interleaving Algorithm for the DVB-RCS2 Turbo Encoder, designed specifically to exploit the SIMD and VLIW features of modern DSP architectures. By restructuring the interleaving stage around Load-Execute-Store phases and eliminating costly per-bit modulo operations through vector-coalesced indexing, our approach outperforms the traditional serial implementation in every tested scenario.  
The vector-wise algorithm implementation outperforms the serial implementation for all tested cases. Speedups were achieved even though the DSP hardware required padding and unpadding of the original data and the final result, respectively. A hardware that is better fit for the parallel algorithm, enabling the Transpose, Concatenation, and Ordering phases to be executed on bits rather than words, will improve the superiority of the parallel algorithm over the serial implementation.  
Figure 11: Speedup results of read, write, and total cycles by vector size.  
Comprehensive profiling on the CEVA-XC4500 platform, using the CEVA-Toolbox profiler, demonstrated consistent speedups across eleven vector sizes: up to 7.3\(\times\) read, 4.8\(\times\) write, and 3.4\(\times\) total cycle reductions (see Table 2 and Figure 11). These gains were achieved despite the overheads of data padding and alignment required by the DSP hardware, underscoring the robustness of the vector-wise method even on non-ideal architectures [44].  
Beyond the raw throughput, the vector-wise algorithm offers strong scalability: its parameter-driven design directly maps onto different DVB-RCS2 block lengths and puncturing rates without code restructuring. This reconfigurability makes it a prime candidate for integration into next-generation satellite modems and multicore SoCs, where both flexibility and high performance are critical.  
Future work will involve a detailed power consumption analysis of the algorithm across different hardware configurations, with an emphasis on low-power satellite and IoT systems. We also plan to explore the scalability of the algorithm under broader parallel processing architectures, such as multicore vector DSPs or SIMD-capable FPGAs. Additional optimizations will investigate adaptive parameter tuning to maximize throughput and energy efficiency depending on varying vector sizes and system constraints.  
Conceptualization, M.B., O.B. and S.G.; methodology, M.B., O.B. and S.G.; software, M.B. and O.B.; validation, M.B. and O.B.; formal analysis, M.B. and O.B.; writing--original draft preparation, M.B., O.B., S.G. and Y.B.-S.; writing--review and editing, M.B., E.M., S.G. and Y.B.-S.; visualization, M.B., O.B. and Y.B.-S.; supervision, S.G. and Y.B.-S.; project administration, S.G. and Y.B.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Sensitive Analysis of Parameters
#### 3.2.2 Effect of Ovality  
The pressure versus displacement curves when the value of \(q\) is changed are shown in Figure 9. Similar to the effect caused by changing the value of \(DR\) on the buckling behavior, as the value of \(q\) increases, the critical buckling pressure of the liner decreases and the displacement of buckling lobe increases. Unlike the effect caused by the changing value of \(DR\), the curves of Group C-6 in Figure 9a and Group D-6 in Figure 9b have distinct peaks compared to the other curves and the increasing rate of pressure in the pre-buckling equilibrium path and the decreasing rate of pressure in the post-buckling equilibrium path are significantly greater than the other groups. As the value of \(q\) increases, the curve peak is gradually less obvious and the effect of changing the liner cross-sectional shape from circular to elliptical on the buckling equilibrium path is greater than the effect of continuing to increase the value of \(q\) for elliptical cross-sections. The decreasing of the value of \(a/R\) has the greatest effect on the case where the liner cross-section is circular (\(q\) = 0); the critical buckling pressure decreases and the displacement of the buckling lobe increases, the increasing rate of pressure in the pre-buckling equilibrium path and the decreasing rate of pressure in the post- buckling equilibrium path decrease, but there are still obvious peaks in the curves.  
Figure 8: Comparing the influence of increasing \(DR\) for critical buckling pressure under different \(a/R\).  
Figure 9: The pressure–displacement curves of FIPP liner with different \(q\) under (**a**) \(a/R\) = 0.99; (**b**) \(a/R\) = 0.98.  
The comparison of the critical buckling pressure between the numerical result and calculation results when the value of \(q\) is changed under a different value of \(a/R\) are shown in Figure 10. The critical buckling pressure of the simulation results are significantly greater than the calculation results. The greatest discrepancies are 22.92 kPa and 7.68 kPa, which occur at \(q=0\) when the \(a/R=0.99\) and \(a/R=0.98\). The reduction of the value of \(a/R\) diminishes the reduction effect caused by the changed value of \(q\) on the critical buckling pressure; the decreased rate of critical buckling pressure gradually decreases as the value of \(q\) is increasing. The critical buckling pressure of Group C-7 decreased 51.45% compared to Group C-6, while Group D-7 decreased 33.72% compared to Group D-6; the decreasing of the value of \(a/R\) will cause the reduction in the decreasing rate of critical buckling pressure under the same increment of the value of \(q\).
# Supporting Privacy of Computations in Mobile Big Data Systems +
## Complexity  
### Circuit Size of One Garbled Table Entry  
We analyze the size of the Boolean circuit (\(B^{\prime}\)) for computing one specific entry (\(A_{ab}\)) in the garbled table. Assume that each gate produces one output bit from two input bits. As we have shown in Section 4.1.1,\(A_{ab}=\gamma_{[((\lambda_{x};\alpha\oplus a)\otimes(\lambda_{y}\oplus b))\oplus \lambda_{2}]}\oplus[G_{b}(\alpha_{a1})\oplus G_{b}(\alpha_{a2})\oplus\ldots \oplus G_{b}(\alpha_{am})]\oplus[G_{a}(\beta_{b1})\oplus G_{a}(\beta_{b2})\oplus \ldots\oplus G_{a}(\beta_{bm})]\); here \(\otimes\in\{XOR,AND\}\) denotes the binary operation of gate \(A\).  
Let \(s\) denote the expression: \(((\lambda_{x}\oplus a)\otimes(\lambda_{y}\oplus b))\oplus\lambda_{2}\). Since \(\lambda_{x}=\bigoplus_{i=1}^{n}\lambda_{xi}\), \(\lambda_{y}=\bigoplus_{i=1}^{n}\lambda_{yi}\), \(\lambda_{z}=\bigoplus_{i=1}^{n}\lambda_{zi}\), where \(\lambda_{xi},\lambda_{yi},\lambda_{zi}\) are shares of server \(p_{i},(1\leq i\leq n)\), computing \(s\) requires a total of \(3+3(n-1)=3n\)\(XOR\) gates and \(1\otimes\) gate.  
Boolean circuit \(B^{\prime}\) includes a multiplexer that chooses \(\gamma_{s}\), where \(\gamma_{s}=((\gamma_{0}\oplus\gamma_{1}).s)\oplus\gamma_{0}\), which is composed of \(2\)\(XOR\) gates and \(1\)\(AND\) gate. The multiplexing is performed on the most significant \(nk\) bits since \(|\gamma_{0}|=|\gamma_{1}|=nk+1\), and \(LSB(\gamma_{s})=s\). This multiplexer is composed of a total of \(2 nk\)\(XOR\) gates and \(nk\)\(AND\) gates.  
Now, consider the expression: \(\gamma_{s}\oplus[G_{b}(\alpha_{a1})\oplus G_{b}(\alpha_{a2})\oplus\ldots \oplus G_{b}(\alpha_{an})]\oplus[G_{a}(\beta_{b1})\oplus G_{a}(\beta_{b2}) \oplus\ldots\oplus G_{a}(\beta_{bm})]\); it has \((2n+1)\) terms, which are combined using \(2n\)\(XOR\) operations. Computing this expression requires a total of \(2n(nk+1)\)\(XOR\) gates, since each term has a length of \((nk+1)\) bits.  
Therefore, the Boolean circuit (\(B^{\prime}\)) that computes one specific garbled table entry (\(A_{ab}\)) has a total of \((3n+2 nk+2n(nk+1))\)\(XOR\) gates, \(nk\)\(AND\) gates, and \(1\otimes\) gate.  
Figure 3 shows the total number of gates in the circuit that computes \(A_{ab}\), when \(A\) is an \(AND\) gate, as a function of \(n\) for a fixed value of \(k=128\) bits. Notice the relatively small number of \(AND\) gates in the circuit. For example, when \(n=6\), the circuit that computes \(A_{ab}\) has a total of \(10782\)\(XOR\) and \(769\)\(AND\) gates. To summarize, while the number of \(XOR\) gates increases quadratically with \(n\), the number of \(AND\) gates increases only linearly with \(n\).  
Let \(B\) denote the Boolean circuit that corresponds to the desired computation such as addition of two numbers. Then, in order to create the garbled circuit \(GC\) for \(B\), the \(n\) parties use the Boolean circuit \(B^{\prime}\) for the protocol of Goldreich to compute each one of the four garbled table entries of the form \(A_{ab}\) for each gate \(A\) in the circuit \(B\).