esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### PIR Signal Modeling for Time Period Estimation
Each PIR sensor operates as a linear system with a transfer function \(H_{i}(s)\) and impulse response \(h_{i}(t)\ (i=1,\ 2)\) (Figure 3). Consequently, output signals of the PIR sensors can be established in such a way that:  
\[x_{1}(t)=h_{1}(t)\bigotimes\phi(t)+n_{1}(t)x_{2}(t)=h_{2}(t)\bigotimes\phi(t- \tau_{0})+n_{2}(t-\tau_{0}) \tag{8}\]  
Using the Laplace transform, the system equations in the frequency domain can be written as:  
\[X_{1}(f) =H_{1}(f)\cdot\Phi(f)+N_{1}(f) \tag{9}\] \[X_{2}(f) =(H_{2}(f)\cdot\Phi(f)+N_{2}(f))e^{2\pi f\tau_{0}}\]  
For practical PIR sensor systems, the transfer function is often modeled as a second-order system [30; 31]:  
\[H_{i}(s)=\frac{A_{i}s}{(1+\tau_{th,i}s)(1+\tau_{c,i}s)}\ \ \ \ \ \ for\ i=1,2 \tag{10}\]  
where parameters \(\tau_{th,i}\), \(\tau_{c,i}\) are thermal time constant and electric time constant and \(A_{i}\) is the gain factor of each sensor \(i\ (i=1,2)\). Noise in PIR sensors primarily arises from thermal fluctuations in the sensing elements, electronic noise in the amplification and filtering stages, environmental interferences from ambient temperature variations, etc. [6]. Assuming the noise signals \(n_{1}\ (t),\ n_{2}\ (t)\) are white Gaussian noises within the working specific bandwidth [\(-B\), \(B\)] of the sensors, they satisfy the following Equations (11)-(15), according to references [14; 32; 33]. 1. Zero-mean:  
\[\mathbb{E}[n_{1}(t)]=\mathbb{E}[n_{2}(t)]=0;\ \mathbb{E}[N_{1}(f)]=\mathbb{E}[N_{ 2}(f)]=0 \tag{11}\]  
2. Independence:  
\[\mathbb{E}[N_{1}(f_{1})N_{2}(f_{2})]=0 \tag{12}\]  
3. Variance:  
Figure 1:0  
4. Power Spectral Densities:  
Figure 1:1  
Figure 1:2 PIR sensors perform as linear systems. 5. Correlated for identical frequencies:  
\[ \begin{split}&\mathbb{E}[Re\{N_{1}(f_{1})N_{1}(f_{2})\}]=\mathbb{E} [Im\{N_{1}(f_{1})N_{1}(f_{2})\}]=\frac{\sigma_{21}^{2}\cdot T}{2}\cdot(\delta( f_{1}-f_{2})+\delta(f_{1}+f_{2}))\\ &\mathbb{E}[Re\{N_{2}(f_{2})N_{2}(f_{2})\}]=\mathbb{E}[Im\{N_{2}( f_{1})N_{2}(f_{2})\}]=\frac{\sigma_{22}^{2}\cdot T}{2}\cdot(\delta(f_{1}-f_{2})+ \delta(f_{1}+f_{2}))\end{split}  \tag{15}\]  
where \(\delta\) is the notation of the Dirac delta function. These assumptions facilitate the derivation of analytical expressions for bias and variance in TPE. Further in this paper, it is the attempt to explore the effect of divergence between two responding systems of the PIR sensor modules on the biases and uncertainties of the estimations.
## 5 Results and Discussion
### Self-Localization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Examples**} & **Trial 1** & **Trial 2** & **Trial 3** & **Mean** \\ \cline{2-5}  & **Distance (m)/Time (s)** & **Distance (m)/Time (s)** & **Distance (m)/Time (s)** & **Distance (m)/Time (s)** \\ \hline
1 st round & 8.67 m/92 s & 9.79 m/98 s & 9.13 m/95 s & 9.2 m/95 s \\
2 nd round & 9.89 m/90 s & 9.13 m/87 s & 8.13 m/88 s & 9.0 m/88 s \\
3 rd round & 9.13 m/98 s & 9.05 m/89 s & 9.05 m/75 s & 9.0 m/87 s \\
4 th round & 8.44 m/87 s & 9.84 m/72 s & 9.94 m/61 s & 9.3 m/73 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental data for different trials. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Examples**} & **Trial 1** & **Trial 2** & **Trial 3** & **Mean** \\ \cline{3-5}  & & **Distance (m)/Time (s)** & **Distance (m)/Time (s)** & **Distance (m)/Time (s)** \\ \hline \multirow{2}{*}{A-star} & 1 st round & 8.85 m/91 s & 9.79 m/99 s & 9.15 m/95 s & 9.25 m/95 s \\ \cline{2-5}  & 2 nd round & 9.95 m/90 s & 9.15 m/87 s & 8.15 m/88 s & 9.10 m/88 s \\ \hline \multirow{2}{*}{Developed} & 1 st round & 8.15 m/84 s & 8.85 m/89 s & 7.75 m/85 s & 8.25 m/86 s \\ \cline{2-5}  & 2 nd round & 7.85 m/86 s & 7.55 m/87 s & 8.15 m/88 s & 7.85 m/87 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of A-star with the proposed approach.
# Use of Indoor Location Technologies in Healthcare Contexts: A Scoping Review
## 3 Results
### Technologies and Workflows  
Among the studies analysed, Radio Frequency Identification (RFID) was the most commonly used technology, appearing in 60 studies, followed by Bluetooth Low Energy (BLE), which was used in 9 articles. Over time, these technologies have maintained mainly  
Figure 2: Documents per year.  
RFID as the most popular choice, although new options are increasingly gaining traction. Of the 46 studies produced by 2014, 35 used RFID, and none employed BLE; only 4 studies used multiple technologies [31; 32; 33; 34]. However, after 2014, 25 of the 37 studies employed RFID, 9 used BLE, and 6 used more than one technology [35; 36; 37; 10; 27]. This trend is illustrated in Figure 3, which displays the number of publications by year and technology for the main identified technologies.  
In terms of workflows, the majority of publications analyse patient flows (45), followed by staff (25), materials (13), and other flows (6). Only 19 studies examined multiple flows, the most common combination being patient and staff flows. The primary variables measured were geospatial location and time, occasionally supplemented with additional information to enhance the analysis.  
Although many articles aim to improve workflows, most do not present the process before the technology implementation, making it difficult to understand the changes. The documents focus primarily on explaining the implementation methodology and the improvements in process metrics, such as waiting time.
# Synchrotron X-ray Radiation (SXR) in Medical Imaging: Current Status and Future Prospects
## 3 Synchrotron X-ray Radiation in Medical Imaging
### Imaging Lung Ventilation  
A recent study demonstrated that dual-energy KES CT imaging with SXR is capable of generating morphological and regional ventilation images along with quantitative regional maps of deposition of iodine-containing aerosol particles (after 5 and 10 minutes of aerosol administration) [22]. Acquisition of lung morphology, regional ventilation along with the quantitative information of regional aerosol particle deposition using single modality was never achieved before. The study was carried out on six rabbits with normal lung (n = 6) and six rabbits with asthma model (methacholine (MCH)-induced bronchoconstriction). The images are shown in Figure 6.  
Figure 4: SXR-based coronary angiography of rat hear. The images were acquired 72 h after ischemia-reperfusion injury. (**A**,**C**) represent single-frame images of iodine contrast during baseline condition and (**B**,**D**) during acetylcholine infusion. Reprinted with permission from [4].
# Design of Optical Tunnel Switching Networks for Big Data Applications
## 5 Network Performance and Its Applications
### Performance Evaluation
We can see that when the packet size is less than 1300 bytes, the cumulative percentage of bytes is only about 20%, but the cumulative percentage of packets is as high as 68%. The distribution of Ethernet packets of various packet sizes is shown in Figure 14. For example, a packet size group from 100 to 179 bytes is 50.35%, while a packet size group from 1300 to 1538 bytes is 33.21%. This 33.21% of packets has more traffic because these packets are large. The distribution of the traffic model is used to simulate the optical tunnel switching networks. We simulated the performance of the optical tunnel switching networks obtained from a computer. According to the Ethernet data packet distribution shown in Figure 14, the number of data packets transmitted by each server in the optical tunnel switching networks is \(10^{7}\) data packets. In our simulation results, the throughput of an optical tunnel switching network with 25 nodes using one plane and two planes under various loads is shown in Figure 15.  
Obviously, the two-plane Torus topology optical switching network is sufficient to provide a throughput close to 100% of the average service throughput of the server. In our simulation results, the throughput of an optical tunnel switching network with 49 nodes in one plane, two planes and three planes under different loads is shown in Figure 16. Obviously, a three-plane Torus topology optical switching network is sufficient to provide a throughput close to 100% of the average service throughput of the server. Figure 12: Captured traffic of Ethernet packets. Figure 9:0 Distribution of Ethernet packets. Figure 9:1 Traffic analyses of Ethernet packets. Figure 9:2 Throughput of optical tunnel switching network with 25 nodes.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 1 Introduction
### Novelty and Structure  
As reported in the previous section, the state-of-the-art literature includes review/survey papers with references to vulnerabilities and countermeasures on different communication layers. Motivated by the scope of this paper, we reviewed the contents of the surveys [9; 34; 35; 36; 37; 38] regarding their presentation of physical layer security topics. As presented in Table 1, only works [9; 34; 37] delivered brief reviews on the relevant physical-layer attacks and mentioned some countermeasures. Nevertheless, none of these works treated these topics comprehensively and they also lacked discussion on wireless physical-layer techniques. Therefore, it is of our interest to bridge this gap by presenting a consistent review of the relevant physical-layer vulnerabilities and protection topics. For this review, we have included articles from the major publishers (e.g., IEEE/ACM/Elsevier/MDPI), which fulfill two main criteria: (1) the method/application involves LoRaWAN as a means of communication, and (2) the security topic of the paper is strictly connected to the physical layer (including both the device layer and wireless physical layer).  
The rest of the paper is organized as follows. After giving a brief introduction to the key components of LoRaWAN in Section 2, we move on to present the various physical layer vulnerabilities in Section 3. Subsequently, the protection topics are given in Section 4, after which we aim to deliver discussion points on LoRaWAN security and further research steps in Section 5. Ultimately, we conclude the paper in Section 6.
# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 5 Discussions
### Real-Time Ore-Production Prediction Using Optimum DNN Models
On 9 February, a difference was observed between the predicted ore production at the start of the haulage operation and cumulative ore production at the end of the said operation owing to changes in the number of the trucks dispatched during operation hours. If we analyze haulage-system conditions in real time by obtaining haulage data from mining sites in accordance with a specific operation period and provide them as input to DNN models, accurate ore-production predictions can be realized. Furthermore, task managers can make intuitive and effective decisions, such as dispatching additional trucks or changing the location and number of loading points to meet daily production targets by verifying the ore production predicted over a specific period.
## 4 VRIS Multidimensional Assessment
### Security Analysis  
#### 4.3.1 Evaluation Indicator Selection  
The Misclassification Rate is a key metric used to evaluate the performance of a classification model. It refers to the extent to which a model makes errors in the prediction process, which is defined as the ratio of the number of samples with classification errors to the total number of samples. In the image steganography task, the misclassification rate measures the misclassification effect of the adversarial sample when it deceives the target machine learning model. Specifically, it refers to the proportion of the total number of samples in an image dataset that successfully misleads a machine learning classifier to produce a misclassification in an image dataset containing steganography information. A high misclassification rate indicates that the image steganography model has a high technical level and innovation ability in the embedding and encoding of steganography information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Methods** & **PSNR** & **SSIM** \\ \hline \multirow{4}{*}{ImageNet} & VRIS & 37.82 & 0.99 \\  & ISGAN & 34.28 & 0.96 \\  & SGAN & 37.88 & 0.93 \\  & Ref. [38] & 32.9 & 0.96 \\ \hline \multirow{4}{*}{LFW} & VRIS & 42.51 & 0.99 \\  & ISGAN & 34.12 & 0.96 \\ \cline{1-1}  & SGAN & 37.59 & 0.93 \\ \cline{1-1}  & Ref. [38] & 39.9 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of image quality generated by different image steganography models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Noise Factor** & \multicolumn{2}{c}{**0.03**} & \multicolumn{2}{c}{**0.05**} & \multicolumn{2}{c}{**0.07**} & \multicolumn{2}{c}{**0.1**} \\ \hline
**Dataset** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline Tiny-ImageNet & 37.82 & 0.99 & 36.91 & 0.98 & 37.05 & 0.97 & 40.24 & 0.98 \\ Mini-ImageNet & 37.07 & 0.97 & 36.91 & 0.98 & 37.06 & 0.97 & 36.61 & 0.97 \\
256-ObjectCategories & 35.29 & 0.96 & 36.47 & 0.96 & 35.59 & 0.97 & 34.57 & 0.96 \\ LFW & 42.51 & 0.99 & 40.69 & 0.99 & 42.48 & 0.99 & 42.65 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Image PSNR and SSIM under different noise intensities.
# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 3 Definitions  
### Differential Privacy  
Differential privacy protection technology adds noise to the original data itself or its transformation in order to achieve the purpose of privacy protection. This method ensures that a record is inserted or deleted from the dataset without affecting the output of the query.  
**Definition 1** (Differential privacy [24]).: _Given two data \(D\) and \(D^{\prime}\), which are identical or differ by at most one record, given a random algorithm \(A\), range(\(A\)) represents the range of \(A\), and \(S\) is a subset of Range(\(A\)). If \(A\) satisfies (1), then Algorithm \(A\) satisfies \(\varepsilon\)-differential privacy,_  
\[P_{r}[A(D)\in S]\leq e^{\varepsilon}\times P_{r}[A(D^{\prime})\in S] \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Main Idea** & **Limitation** \\ \hline MDAV [7] & By micro-aggregating all attributes to achieve K anonymization, the amount of noise required can be effectively reduced. & Too much noise, poor utility, limited clustering effect, information loss. \\ \hline IDP\_KMENAS [22] & It uses a canopy to select the initial center point and uses the Laplace mechanism to realize the differential privacy protection. & Poor clustering results, poor utility, slow convergence. \\ \hline MDAV\_DP [23] & Adds noise to the micro-aggregated version ofthe original dataset, with the micro-aggregation dataset as our protection target. & Not suitable for complex data; value attribute utility is not considered. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contrast algorithms.  
_where probability \(P_{r}[\bullet]\) represents the probability of the algorithm, which is determined by algorithm \(A\); \(\epsilon\) is the privacy budget, which represents the degree of privacy protection against algorithm \(A\). The smaller the value of \(\epsilon\), the higher the degree of privacy protection for \(A\)._  
**Definition 2** (sensitivity [25]).: _Given the query function f: \(D{\rightarrow}\)Rd, the input data is \(D\), and the output is \(d\)-dimensional vector, then the sensitivity is defined as:_  
\[\Delta f=\max_{d(D,D^{\prime})=1}\lVert f(D)-f(D^{\prime})\rVert_{1} \tag{2}\]  
_where \(\lVert\cdot\rVert_{1}\)denotes the L1 norm._
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
###### Abstract
The error correction capability of an LDPC decoder is determined by the signal-to-noise power ratio per bit  
Figure 1: Tanner graph of \((6,2)\) code. \(E_{b}/N_{o}\) at the required bit error rate (BER), which is commonly stated in decibels. For the wireless personal area network (WPAN) [19] standard (672, 336) LDPC code, the suggested stochastic decoder in [18] has achieved a BER of \(10^{-6}\) at an \(E_{b}/N_{o}\) value of 3.5 dB. This is in comparison to the normalized min-sum (NMS)-based decoder, which has achieved the same BER at an \(E_{b}/N_{o}\) value of 3.4 dB. The number of node-interconnects (\(I\)) of a decoder is calculated from (1) to determine the routing complexity [20]. \[I=2\times N\times e_{l}\times w_{v} \tag{1}\]  
\(N\) is codeword length, \(e_{l}\) is extrinsic message length, and \(w_{v}\) is column weight. For instance, \(1/3\)-rate codeword length \(N=3808\) LDPC code having average column weight \(w_{v}=4.56\) is decoded using SPA and SD. The number of node-interconnects required for SPA and SD are 138,916 and 34,729, respectively. The extrinsic message length \(e_{l}=1\) for SD is responsible for the reduction of the number of node-interconnects in SD by a factor of four. On the other hand, the minimum value of \(e_{l}\) for fixed-point LDPC decoders that use SPA is determined to be 4. Our contributions:  
* This work's main contribution is to propose a new partially parallel decoder architecture for bit-wise stochastic decoding for 5G NR standard LDPC codes. This architecture was created for the LDPC code, which has a code word length of N = 3808 and code rates of \(R=1/3\), \(2/5\), \(1/2\), \(2/3\), \(3/4\), \(5/6\), and \(8/9\) for BGM1. * Our proposed automated design flow procedure enables runtime flexibility in the design. It creates an optimal FPGA-based stochastic LDPC decoder design for any selected code rate set. This approach helps to reduce the time needed to design hand-coded interconnections in hardware description language (HDL). This paper is structured as follows. Section 2 introduces the basic concepts of stochastic decoding and its algorithm. Section 3 focuses on constructing BGM of 5G NR standard QC LDPC codes suitable for stochastic decoders. Section 4 provides architecture details. Design flow is discussed in Section 5. Section 6 discusses simulation results. Finally, the conclusion is noted in Section 7.
## 3 Abiotic Environmental Stresses and Crop Quality  
Abiotic stresses, which include environmental contaminants [44], have been identified as the key factors responsible for most of the reduction in agrifood production (Figure 6); they also play a major role in determining the nutritional value/quality of fruits and vegetables during their growth, harvesting, handling, storage, and transport to end users [45]. The impact made by these abiotic stresses have been found to depend on the (1) the part (tissue or organ) of the plants involved [45; 46]; (2) crop species; (3) duration; as well as (4) intensity of the stress [47] and could cause morphological, physiological, biochemical, and molecular alterations within the affected plants [45]. Retardation of plant growth may also occur as the plants make efforts to conserve and reallocate resources that can become limited under extreme stress conditions [48].  
[PERSON] and [PERSON] [47] indicated that all quality traits of agricultural produce can be influenced by abiotic environmental stress factors which can be linked with the different physiological reactions/responses in the affected plants as a result of changes in gene expression and enzyme  
Figure 5: Growth of Micro-Tom tomatoes in pots containing crude oil at 0 mg/kg TPH (left plant) and 5000 mg/kg TPH (right plant), before fruiting (**A**) and during ripening (**B**). Source: [PERSON] [5].  
Figure 6: Agricultural yield losses due to abiotic and biotic stress. Adapted from: [PERSON] and [PERSON] [49].  
activity, oxidative stress arising from the accumulation of reactive oxygen species (ROS), accelerated senescence, which shortens the crop maturity period and alters the nutrient distribution processes within the stressed plants, reduced water content which leads to increase in nutrient content, changes in mineral uptake and translocation, as well as reduced biomass/yield which is the most obvious and identified effect of abiotic environmental stress on agricultural produce. Although some crops respond differently, in most cases, environmental stresses cause (1) a decrease in starch concentration, lipids {particularly the polyunsaturated fatty acids (PUFA)}, feed value, and physical/sensory (P/S) traits; (2) an increase in protein and antioxidant contents; and (3) no clear trend in sugar and mineral contents [47].  
On the other hand, one of the common mechanisms by which plants adapt to abiotic stresses encountered in the environment involves the accumulation of compatible solutes which are highly soluble and low molecular weight compounds with no toxic effect at high concentrations [50]. The different forms of these compounds accumulated may be species-specific and include amino acids (like proline), glycine betaine, sugars (such as sucrose, and trehalose), and sugar alcohols (like sorbitol and mannitol) [50; 51; 52]. These compatible solutes do not interfere with normal cellular metabolism [52; 53].
## 3 Results and Discussion  
### SSF of PFPT, Sample Collection, and Variations in Chemical Compounds  
To better understand the natural SSF of traditional Chinese PFPT, laboratory fermentation was developed and repeated twice. Every seven days, tea leaf samples were collected and subjected to sensory evaluation (Figure 1). After SSF, the tea leaves became dark, the tea liquid became reddish-brownish, and the fermented tea leaves became softer than those of the raw material. The contents of chemical compounds of tea leaves are listed in Table 1. After SSF, the levels of TPs, FAA, EGC, EC, EGCG, GG, ECG, GA, TF, and TR decreased significantly (\(p<0.05\)), and contents of TB increased significantly (\(p<0.05\)). These changes in the chemical compounds during the SSF of PFPT were in accord with the previous reports of [PERSON] [15], [PERSON] [16], and [PERSON] [17]. It was hypothesized that microbiota led to these chemical changes. However, the influence of microbial populations on the changes of tea chemical components during SSF of PFPT is not established. So we developed the further research to survey the diversity and dynamic of microbiota and change of tea compounds.