esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Motivation and Proposed Method Design  
### Motivation of the Study  
In most existing CDG studies, the network sensed data is considered to have a known and fixed sparsity. Therefore, according to the CS theory, once a recovery algorithm is picked, an upper bound of required measurements (\(O(Klog(N/K))\)) is determined. However, this assumption is not practical, and even problematic. Firstly, as depicted in Section 1, the sparsity of data signals observed in a real natural environment is changing, making it impractical to know the exact sparsity value before reconstruction. Secondly, to successfully reconstruct the network sensed data, the necessary number of measurements is continuously varying over a range, thus, taking a fixed upper bound as the number of measurements may inevitably cause excessive measurements. To show this point, we carried on an experiment to reconstruct an \(N\)-dimensional \(K\)-sparse signal from two typical measurement methods, namely a Gaussian measurement method and a Bernoulli measurement method (each entire measurement takes values of either \(\pm 1\) each with probability \(s/2\), or \(0\) with probability \(1-s\) (\(0<s<1\))). We randomly generated an original signal \(x\) (\(N=100\), \(K=10\), \(s=1/2\)), and to avoid the variations we repeated experiments of each measurement method for 600 times and employed the same algorithm (L1-formulation matching pursuit) on each reconstruction of \(x\).  
The experimental results are depicted in Figure 3. We can conclude that both measurement ensembles exhibit high variance (mean values are 32.9450 and 33.8167, respectively; standard deviations are 4.8468 and 4.9414, respectively) and the most minimum number of requiredmeasurements are lower than the upper bound (50). This explains that schemes relying on an upper bound can guarantee successful data recovery, but it often means that many unnecessary measurements are taken (in our experiment 17.5 and 16.18 of 50 on average). For CDG applications, as shown in Figure 2, the formation of each measurement result requires processing and forwarding through several intermediate nodes; thus, reducing the unnecessary measurements will definitely decrease nodes' energy consumption.  
Note that, comparing to the dense measurement method, we are particularly interested in the sparse measurement method. The reason is the sparse measurement method requires a very similar number of dense measurements (as shown in Figure 3), while for CDG applications, sparse measurement coefficient vector leads to a fewer interested nodes (interested nodes are the nodes whose corresponding coefficient of such measurement is non-zero) than that of dense method in the formation of each measurement, and thus other nodes can keep sleeping to save energy. For the sparse Bernoulli example in Figure 3, we experimented with \(s=1/2\), thus there were averagely 50% fewer nodes in the formation of each measurement than that of dense Gaussian method.
## 6 Functional Properties and Applications of Nanocomposites
### Lithium-ion batteries
#### 6.5.2 Polymer-nanocomposite electrolytes applying active ceramic nanofillers  
At the beginning of the nineties of the last century active micro- and nanosized fillers have been introduced to polymer electrolytes. Active fillers are either ceramic materials, which contribute directly to the lithium ion conductivity - in addition to the conducting salt - by e.g., adding lithium salts like LiAlO\({}_{2}\), or ceramics with large permittivity for better solvation of the conducting salt. [PERSON] and coworkers added fine LiAlO\({}_{2}\) to a PEO-LiClO\({}_{4}\)-complex [219]. The authors found, that an optimized filler concentration, which is lower as the maximum accessible filler load, allowed for the aspired crystallization suppression and an ionic conductivity increase (60 \({}^{\circ}\)C: 10\({}^{4}\) S/cm). [PERSON] and coworkers observed also a pronounced suppression of the polymer crystallization rate and a better ambient temperature ionic conductivity in the system PEO-Li[(CF\({}_{3}\)SO\({}_{2}\))\({}_{2}\)N] using submicron \(\gamma\)-LiAlO\({}_{2}\)[220]. The same filler was used for investigations on the two different polymer-conducting salt complexes PEO-LiCF\({}_{3}\)SO\({}_{3}\) and PEO-LiBF\({}_{4}\)[221,222]. An improved cycling efficiency around 99% was observed, also a good stability against the lithium metal anode even at elevated temperatures around 90 \({}^{\circ}\)C [221, 222, 223, 224]. A more complex polymer blend, consisting of polyethyleneglycoldiacrylate/PVDF/PMMA, a mixture of LiPF\({}_{6}\)/LiCF\({}_{3}\)SO\({}_{3}\) and LiAlO\({}_{2}\) or BaTiO\({}_{3}\) as nanosized fillers, showed an improved ionic conductivity, interfacial stability as well as better cycling performance [225]. [PERSON] and coworkers investigated the influence of micro- and nanosized BaTiO\({}_{3}\) in an electrolyte consisting of the amorphous copolymer PVDF-HFP and a room temperature ionic liquid [212]. They found a pronounced increase of the ionic conductivity with decreasing particle size from 1 \(\mathrm{\SIUnitSymbolMicro m}\) down to 60 \(\mathrm{nm}\) by a factor of almost 100. Quite recently, a high ionic conductivity of 8.1 \(\times\) 10\({}^{-3}\) S/cm using a polymer composite (PVDF-HFP/LiClO\({}_{4}\)) with 50 \(\mathrm{nm}\) LiAlO\({}_{2}\) could be measured by [PERSON] and [PERSON] [226].  
In summary the addition of active micro- and nanosized fillers, mainly LiAlO\({}_{2}\), causes in case of PEO-based electrolytes an improvement of the ionic conductivity by the suppression of the PEO's crystallization below 60 \(\mathrm{\SIUnitSymbolOhm}\). An enhancement of the cycling behavior due to an improved interfacial stability was also observed. Gels on the basis of PVDF-HFP copolymers show higher ionic conductivity due to nanosized ceramic filler also. The addition of micron sized filler causes negligible effects. This can be explained by the large specific surface area of the nanofillers enabling longer conducting paths for the lithium-ions.
# A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x
## 1 Introduction
[PERSON], [PERSON] [19] proposed to use a lightweight scalable attention module based on dilated convolution to maintain important feature channels and a multiplexed connection residual block to construct a lightweight network for pedestrian detection. [PERSON] [20] proposed a lightweight detection model based on YOLOv5, which combines the MD-SILBP operator and the five-frame differential method to enhance the contour feature extraction capability and uses Distance-IoU non-maximum suppression to reduce the missed detection rate in detection. [PERSON], [PERSON] [21] proposed the PVformer algorithm for vehicle and pedestrian detection in rainy scenarios, based on the Swin transformer. They introduced a local enhancement perception block and a deraining module to improve the accuracy of detection in rainy scenes. In this paper, we investigate a robust pedestrian detection network applied to complex scenarios. Using YOLOv5-x as the baseline, the focus is on the network lightweighting and the improvement of the accuracy of pedestrian detection in complex conditions. The contributions of this study can be summarized as follows:  
* A RainDet3000 dataset is proposed to fill the gap that the pedestrian detection dataset does not target rainy days, providing a more realistic detection scenario for network training. * The bottleneck layer structure of GhostNet has been optimized using a compact bilinear pooling algorithm to enhance the network's feature learning capability while maintaining its lightweight architecture. The resulting CBP-GNet is then utilized as the backbone network for RSTDet-Lite. * The proposed Simple-BiFPN is an extension of BiFPN, which delivers superior computational efficiency compared with YOLOv5's PANet feature fusion network by eliminating redundant computational overhead. In addition, an attention mechanism module called CBAM is incorporated between the backbone network and the feature fusion network to optimize the assignment of feature weights, thus enabling the network to learn more effective features. * The REP structure is a novel approach to enhancing the capacity of neural networks through structural reparameterization. During training, the REP structure increases the number of trainable parameters, allowing the network to capture richer and more abstract semantic concepts without increasing inference time. In other words, the REP structure provides a way to improve the network's performance without compromising its efficiency in real-time applications.
## 4 Results and Discussion  
### Discriminant Factor for Defects  
Commercial software, \"Disperse Version 2.0\" (Imperial College NDT Lab, London, UK), was used to obtain the dispersion curve for the 3 mm thick 304 SS plate, as shown in Figure 3. Because group velocities of S and A modes show constant values above 2.25 MHz, as the optimal frequency of the ultrasonic GW for the 3 mm thick 304 SS plate, a pair of 2.25 MHz ultrasonic transducers was selected.  
The ultrasonic GW signals generated using the ultrasonic transducers, and then received from test specimens with and without defects, were compared. The STFT algorithm was applied to all the measured data with a window length of 50. To consider the feasibility of the STFT for detecting defects, the signals measured from specimens with and without defects were compared. The results are shown in Figure 4a,b, which show the received ultrasonic GW signals for the specimens without and with defects in the time domain, respectively. It was difficult to recognize the significant differences between sound and defective specimens with the naked eye because of the small defect size. Figure 4c,d show the results of the STFT for the ultrasonic GW signals received from specimens with and without defects, respectively. The window length for the STFT was 500. In Figure 4c, the symmetric (red part in the upper left, 50) and asymmetric (red part in the upper right, A0) modes appear, and both modes are mixed in Figure 4d. Nevertheless, it was difficult to distinguish the two conditions from these results.  
Figure 2: (a) Photograph of the experimental setup and (b) measuring angles between each ultrasonic transducer and a defect.  
To find a factor for discriminating defects, the number of pixels in the area of each mode was counted in the resulting image after the STFT. Given that the area of modes (S0  
Figure 4: (**a**) Received ultrasonic GW signals from specimens without and (**b**) with defects in the time domain, and (**c**) STFT results for specimens without and (**d**) with defects.  
Figure 3: Calculation result of dispersion curve for 3 mm thick 304 SS plate.  
and A0) in a specimen without defects is larger than that of a specimen with defects, as a discriminant factor, the differences in the number of pixels (\(R\)) of mode areas from the resulting images of STFT were defined by Equation (4):  
\[R(i,j)=|B(i,j)-S(i,j)| \tag{4}\]  
where \(R(i,j)\) is the difference in the number of pixels of the mode area in the resulting image of the STFT between that without defects (\(B(i,j)\)) and that with defects (\(S(i,j)\)). Figure 5 shows the results of applying Equation (4) to the specimens with and without defects, and some differences in the number of pixels between specimens with and without defects were found. If Equation (4) was applied to the same specimens, the differences in the number of pixels would be zero or very small.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Data Decoding from CH to Cloud Server  
Figure 10 illustrates how data is decoded when it is received straight from the CH to the cloud end. In this case, the data is thoroughly processed after being decrypted using the decryption key. Additionally, it can be seen that when the data is decrypted, the original content of the data is preserved, which serves as the primary protection against outsiders accessing the data's original values.
## 2 Materials and Methods
### Experimental Protocol  
Following their arrival, female hockey players filled out a questionnaire which included personal information such as body mass, height, medical history, training frequency and experience (practice hours per week and playing experience in field hockey). Testing (i.e., ROM, isometric strength, and countermovement jump) was performed in the clinical area of each field hockey club. Testing was conducted by two sports physiotherapists: a senior physiotherapist with over nineteen years of experience and a junior physiotherapist with two years of experience, to ensure participants' positioning during measurements. Considering recommendations by [PERSON], [PERSON] and [PERSON] [19], the testing order of the players and the selection of the leg tested were randomly chosen prior to the pre-match test. Pre-match testing was performed 60 min prior to match-play, and the post-match re-testingwas performed immediately after the match. At the beginning of the pre-match testing, participants carried out a standardized warm-up that consisted of 5 min of jogging at 10 km\(\cdot\)h\({}^{-1}\) and 5 min of static stretches and joint mobility exercises [27]. Subsequently, participants played the simulated field hockey match according to the International Hockey Federation rules on a rectangular surface, 91.40 m long and 55.00 m wide. The external load of the simulated matches were estimated using a global positioning system (GPS) (Wimu ProTM, RealTrack Systems, Spain) placed in specific vests worn by the players, these devices operated at a sampling frequency of 10 Hz and its validity and reliability have been reported previously [28]. In addition, subjective internal load of the game was obtained using the modified RPE scale (i.e., 0-10 points) within 30 min of match termination [29]. The following variables were used to assess the external load during match-play, total distance covered per minute at different velocities ranges during a 60 min match as previously reported [3]. To reduce the interference of uncontrolled variables, all subjects were instructed to maintain their habitual lifestyle and normal dietary intake before and during the study, and refrain from caffeine ingestion 24 h before the experiment [30].
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 2 Quantification of Total MC by the MMPB Method
### Lemieux Oxidation Step
#### 2.2.3 Animal Matrices  
The oxidation of animal matrices contaminated with MCs is a more complex task, as shown by the lower and more variable yields (from 6 to 114% yield, Table 1) and the very different concentrations of oxidants according to the studies (KMnO\({}_{4}\): from 334 uM to 0.1 M [28; 71]; NaIO\({}_{4}\): from 1.4 mM to 0.1 M [28; 60]. Matrix effects can strongly influence the oxidation and extraction efficiencies, and it would be difficult to propose a harmonized protocol suitable for all animal matrices. However, a few protocols have been applied to very different matrices and could therefore serve as a good reference protocol for the analysis of the total forms in animal samples. For example, the protocol proposed by [PERSON] and [PERSON] [72] has been applied to fresh human sera, fresh human liver, fresh human blood, and lyophilized terrapin tissue (_Envs orbicularis_ and _Mauremys leproas_) [73; 74; 75; 76]. In this protocol, 1 g of fresh rat or chicken liver was oxidized with 0.02 M of oxidants for 3 h at pH 9. Then in the 2010s, two other protocols seemed to be reused in several studies. The first one is the Neffling and Lance protocol [28; 49] on freeze-dried gastropods (_L stagnalis_) using 0.1 M of both oxidation reagents, which was reused by [PERSON] et al. [29] on rainbow trout (_Oncorhynchus mykiss_) (lyophilized muscles, liver, intestines, and gills), by [PERSON] et al. [77] on tilapia fishes (_Oreochromis niloticus_) ( lyophilized muscles, liver, and eggs), by [PERSON] et al. [78] on a lyophilized sample of coastal bottlenose dolphins (_Tursiops truncatus_), by [PERSON] et al. [59] on lyophilized samples of mallard ducks (_Anas platyhynchos_), by [PERSON] et al. [5] on fresh samples of Nile tilapia (_Oreochromis niloticus_) fish muscles, and by [PERSON] et al. [70] on lyophilized clams, with varying degrees of extraction efficiency (from 5 to 55%). The second protocol that stands out was proposed by [PERSON] et al. [66], using 0.05 M of the two oxidant reagents. Although this protocol has not been applied to as many matrices as the previous one, it has been developed on river water and then used on frozen homogenized fish tissues (_Osmerus mordax; Mugil cephalus_; _Salvelinus funfinalis_; _Perca flavescens_; _Sander vitreus_; _Ameirus nebulosus_; _Coregonus clupeiformis_) [79] and algal dietary supplements [80] with satisfactory extraction yields (91%, 54-72%, and 67-74%, respectively). There seems to be disagreement on the concentration of oxidants (0.1 M versus 0.05 M for both reagents for [PERSON] and [PERSON]'s and [PERSON]'s protocols, respectively) to be used, which depends on both the matrix and the number of toxins to be oxidized. The reaction time for cyanobacterial matrices has to be under 3 h, but [PERSON] and [PERSON] recommend a time reaction of 3 h in animal matrices against 2 h for Roy-Lachapelle. Both protocols indicate that an alkaline pH between 8 and 9 allows for better results.
However, the size of the input signal feature map of the model is single, especially when removing non-stationary, high-energy co-band noise without a fixed waveform, and the signal feature that the model can learn is limited, resulting in waveform distortion of the denoised seismic signals. In summary, to mitigate the waveform distortion of seismic signals after denoising, further improve the SNR of seismic signals, and improve the quality of seismic signals, this paper takes the U-Net as the basic framework, combines atrous convolution and residual dense blocks, and designs a seismic co-band denoising model ARDU. The ARDU model connects atrous convolution and residual dense blocks to form a feature extraction unit of the model encoder. This feature extraction unit can deepen the network's depth, expand the convolution receptive field, and obtain more abundant signal features, thereby reducing waveform distortion, protecting effective signals, and improving the quality of seismic signals. The model was trained and tested on the Stanford Global Seismic dataset, and the results showed that the ARDU model can effectively remove seismic noise in the co-frequency band, protect effective signals, improve the SNR of seismic signals, and enhance the quality of seismic signals. Compared with wavelet threshold denoising, the U-Net model, and the DnRDB model, the ARDU model has the best overall denoising effect and the least waveform distortion of seismic signals.
# Speeding up Statistical Tolerance Analysis to Real Time
## 5 Methods: Detailing the Approach to Speeding Up Tolerance Simulations
### Procedure for Statistical Tolerance Synthesis  
The purpose of statistical tolerance synthesis is to allocate the maximum tolerance of the functional key characteristic to the characteristics of the parts of the product [9]. It is thus the inverse or inversion of statistical tolerance analysis [9]. The goal of statistical tolerance synthesis is to identify the best compromise between (usually two) divergent requirements for a product and thus for tolerating the individual part. In the case of a cost-driven statical tolerance synthesis, this compromise is the tolerance that is associated with the lowest manufacturing costs, but at the same time ensures that a defined rejection rate of the final product is not exceeded [9]. If this compromise is identified by means of numerical optimization, the phrase statistical tolerance optimization has become established. Pseudocode for the implementation of a cost-driven statistical tolerance optimization algorithm is provided in Figure 5. Let \(T\) be a selected set of tolerances, which is chosen by a global optimization solver. The cost function is denoted by \(C\). The total production cost is the sum of the production costs of each tolerance \(C(T)\) on the basis of the tolerance cost model.  
\[C(T)=C_{fix}+C_{par}=C_{fix}+\frac{C_{ind}\cdot e^{-m\cdot T}}{T^{k}} \tag{5}\]  
and is usually specified by the coefficients \(C_{ind}\), \(m\), and \(k\) to approximate the dependencies between the costs and tolerances [33; 34]. The manufacturing costs consist of the following components: The variable costs \(C_{par}\) and the fixed manufacturing costs \(C_{fix}\) for each tolerance \(T\). Let \(S\) be the system model of the corresponding tolerance problem and \(D\) be the probability distributions of the considered tolerances \(T\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Backend** & **Storage Layout** & **Float Format** \\ \hline CuPy Float32 C-Order & Python+CuPy & Row-major & Single-precision \\ CuPy Float64 C-Order & Python+CuPy & Row-major & Double-precision \\ CuPy Float32 F-Order & Python+CuPy & Column-major & Single-precision \\ CuPy Float64 F-Order & Python+CuPy & Column-major & Double-precision \\ MATLAB Float32 GPU & MATLAB & Column-major & Single-precision \\ MATLAB Float64 GPU & MATLAB & Column-major & Double-precision \\ \hline \hline \end{tabular}
\end{table}
Table 4: Implementation of the statistical tolerance analysis implementations using a GPU.
## 4 Results and Discussion
### Sink Energy Consumption  
Figure 10 shows the sink/base station energy consumption. In comparison to other protocols, it can be seen that the EQPD-MAC delivers a considerable decrease in energy consumption of up to 27.4%. The sink node uses adaptive active/sleep time with the help of TA, which helps preserve more energy. RMP-MAC, on the other hand, automatically changes the duty cycle based on the energy level of the nodes and includes an adaptive backoff mechanism to maximize energy savings in the network. When there are fewer sensor nodes, the protocol reduces the energy consumption. However, as the number of sensor nodes increases, it begins to lose packets, resulting in increased energy consumption. This is because when the sender nodes' buffer limit is surpassed, the incoming data packets are dropped. In MPQ-MAC and PMME-MAC, the sink node does not provide any method of conserving energy and the sink nodes rarely sleep, causing its remaining energy to decline rapidly.