esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 4 Performance Analysis of the Proposed SCbSC
### Cryptanalytic Analysis
#### 4.2.2 Statistical Analysis
To assess the correlation, we performed the following: first, we randomly selected 8000 pairs of two adjacent pixels from the image; then we calculated the correlation coefficients by using the following equation:  
\[\rho_{xy}=\frac{Cov(x,y)}{\sqrt{D(x)}\sqrt{D(y)}} \tag{30}\]  
where:  
\[Cov(x,y)=\frac{1}{N}\sum_{i=1}^{N}[x_{i}-E(x)][y_{i}-E(y)] \tag{31}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Chi-Square Test** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Boats** \\ \hline \(\chi_{ex}^{2}\) & 216.10 & 231.84 & 244.05 & 233.62 & 265.47 \\ \(\chi_{th}^{2}\) (255, 0.05) & 293.24 & 293.24 & 293.24 & 293.24 & 293.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Chi-square results on the histograms tested. \[E(x)=\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{32}\]  
\[D(x)=\frac{1}{N}\sum_{i=1}^{N}\left[x_{i}-E(x)\right]^{2} \tag{33}\]  
where \(x\) and \(y\) are the grayscale values of two adjacent pixels in the image. The obtained results are shown in Table 9.  
It appears from Table 9 that the correlation coefficients for the plain images are close to 1, which shows that the pixels are highly correlated, whereas for the encrypted images, the correlation coefficients are close to 0, which proves that there is no correlation between the plain and ciphered images. Therefore, there is no similarity between plain and encrypted images, proving the very good achieved confusion by the proposed SCbSC. According to all these results of the histogram, entropy, and correlation, the proposed stream cipher presents a good ability to resist statistical attacks.
# An Improved Opportunistic Routing Protocol Based on Context Information of Mobile Nodes
## 1 Introduction
This is because the mobility and traffic characteristics of nodes are not predictable and thus the collective characteristics of a node can be considered as a capability to deliver a message to a destination node. In the proposed protocol, average distance and average time are updated whenever a message is delivered to a destination node. Then, both average distance and average time as well as delivery predictability of PRoPHET protocol are used to decide message forwarding. Basically, a message is forwarded to a contact node, if the contact node has smaller average time and distance values, as well as a higher delivery predictability value. Then, the performance of the proposed protocol is compared with PRoPHET protocol with GRTR strategy and RPC [19] protocol, which is one of the latest protocols using contact history information of a mobile node. The idea of using both average distance and average time information of the proposed protocol is similar to our preliminary work presented as an extended abstract in [30], but the proposed work is significantly different from the previous work from the following aspects:  
* the proposed protocol extends the forwarding algorithm further by additionally considering the existence of both average distance and average time information firstly. Then, the hop count of a message is checked to choose an appropriate forwarding condition, which is extended from the preliminary work; and,  
. the performance of the proposed protocol is compared with RPC protocol additionally. Also, the performance of the proposed protocol is compared additionally from the aspect of delivery latency. Simulations are carried out extensively by additionally considering the change of number of nodes. . Related works are significantly extended in this paper. The remainder of this paper is organized as follows: In Section 2, a detailed algorithm of the proposed protocol is proposed and the proposed protocol is illustrated using examples. In Section 3, the performance of the proposed protocol is analyzed extensively using simulation. Finally, Section 4 summarizes this work and presents future work.
## 3 Therapeutic Potential of Exosomes in Different Diseases
### Therapeutic Potential of Exosomes in Lung Diseases  
Bronchopulmonary dysplasia (BPD) is one of several conditions that could affect the lungs. BPD is a chronic disorder of the lungs in preterm infants [124]. Current therapeutic approaches using conventional therapy have only modest outcomes. The use of MDC-exosome therapeutics appears to be promising in clinical management, as shown in experimental models by alleviating neonatal lung injury [124]. Transplantation of various stem cell types--such as human amnion epithelial cells and endothelial colony-forming cells--appears to be promising with results from the preclinical models. MSC treatment of blunt hyperoxia-induced lung inflammation ameliorates vascular remodeling and improves exercise capacity with higher survival rates [46; 125]. Furthermore, using allogeneic human umbilical cord blood-derived MSC in phase 1 clinical trials, it was observed that MSC administration brought about a reduction in inflammatory markers and lowered BPD severity [126]. The mechanism for now remains unknown. Furthermore, using bone marrow stromal cells (BMSCs) in a murine model with neonatal chronic lung disease, the BMSCs release immunomodulatory factors which ameliorated parenchymal and vascular injury of BPD in vivo [46]. Additionally, intratracheal distribution of BMSCs in a murine model with BPD, improved survival and exercise tolerance by attenuating alveolar and lung vascular injury as well as pulmonary hypertension in vivo, while in vitro, the BMSC-derived conditioned medium prevented O2-induced AEC2 apoptosis, boosted endothelial cord formation, and enhanced AEC2 wound healing [125].
# An Insightful Overview of the Wiener Filter for System Identification
## 7 Simulations
### Wiener Filter with QEP Regularization  
Finally, the last experiment is dedicated to the QEP regularization from (60), which is explained in Section 6. The linearization methods from (54), (55), (57), and (58) are referred in Figure 11 as QEP 1 and QEP 2, respectively. The conventional Wiener filter using \(\delta=10^{-8}\) is considered for comparison.  
In Figure 11, different values of \(N\) and different SNRs are used. It can be inferred that the QEP regularization outperforms the regular \(\ell_{2}\)-norm regularization, especially for low values of \(N\). In heavy noise conditions (e.g., \(\mathrm{SNR}=0\) dB), the QEP approach leads to better results for all the values of \(N\), as indicated in Figure 11d. It can also be observed that the two linearization methods perform in a very similar way. It is interesting to compare the results from Figure 11 with the plots from Figure 3. In this context, it can be observed that the QEP regularization is close to the optimal \(\ell_{2}\)-norm regularization of the Wiener filter.
## 1 Introduction
Since the main users of SARs are older adults and patients who spend long periods indoors, need companionship, and require proper suggestions, a distinct advantage of SARs is their indoors installation. This approach can enhance user experience, as SARs provide suggestions with warm and encouraging words, motivating patients to participate more actively in their healthcare and self-management [32]. Therefore, if a SAR can make proper suggestions about air quality during their interactions, it might help patients manage their indoor environment more effectively. Bearing these principles in mind, we developed a system that integrates SARs and the IoT to utilize real-time detected information for interactive health management facilitated by SARs. The system operates as follows: (1) sensors detect air conditions, (2) the information collected from the sensors is sent to a server and converted into push data, (3) the push data are sent to the SAR, which then announces the indoor and outdoor air conditions and suggests ways to improve indoor air quality, (4) the COPD patient listens to the announcement and follows the instructions, and (5) the sensor detects the air condition again and provides feedback on the indoor air quality. This research aims to analyze changes in indoor air quality (IAQ) as an indicator of patients' responses to SAR notifications about air quality. Our research question investigates whether SAR-delivered notifications about IAQ can enhance the self-management skills of COPD patients in real-life settings. Additionally, this study aims to identify the perceived advantages and disadvantages experienced by COPD patients after using SARs.
## 3 Method
### Hardware Accelerator Structure Design of the Full-Connection Layer  
As mentioned earlier, the last two layers of the LeNet-5 network are fully connected layers. The hardware acceleration structure of the full connection layer is relatively simple, and its operation model can be expressed as:  
\[f_{out}=w_{in,out}*f_{in}+b \tag{5}\]  
where \(w_{in,out}\) is the weight of the full connection layer, corresponding to the \(in\)-th input feature graph and the \(out\)-th output feature graph; \(b\) is the offset of the \(out\)-th input feature graph.  
After the activating function ReLU, Equations (2) and (5) are combined to obtain the operation model of the full-connection layer as follows:  
\[f_{fc\ layer}^{out}=ReLU\Big{(}Full\text{-}Connection\Big{(}x,w_{fc},b\Big{)} \Big{)} \tag{6}\]  
According to Equation (6), when the _Full-Connection_ module is realized, the output result of the _Full-Connection_ module can be obtained through the _ReLU_ activation function after the weighted operation of the feature graph and weight. The most basic hardware accelerator structure of a _Full-Connection_ module is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **No Optimization** & **Pipelined** \\ \hline Latency \& Interval (clock cycles) & 7,709,158 & 353,263 \\ \hline  & BRAM\_18K & 79 & 83 \\ Utilization Estimates & DSP48E & 9 & 244 \\  & FF & 29,061 & 53,049 \\  & LUT & 24,651 & 41,842 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of resource usage of convolutional layers before and after optimization.  
For the fully connected layer, the input \(f_{in}\) and output \(f_{out}\) are one-dimensional arrays with the weight \(w_{in,out}\) being two-dimensional arrays. The model of the full-connection layer can be represented as follows (Algorithm 3).  
```
OUT: Output IN: Input W: Weight CHin: In-Channel size CHout: Out-Channel size 1. for(chi = 0; chi < Chin; chi++)
2 for(cho = 0; cho < CHout; cho++)
3.  OUT[cho] = W[cho][chi] * IN[chi];
```  
**Algorithm 3**Full-Connection  
Using the Unroll command, users can transform a fully connected layer software model represented by a loop into an efficient hardware accelerator structure. As shown in Table 3, adding the Unroll command at line 2 of the algorithm given above reduces the latency and interval of the full-connection layer hardware accelerator from 128,856 to 45,696 clock cycles. Obviously, the throughput has improved by about three times. Again, this is to increase the overall utilization of logical resources to improve the data-processing capacity of the whole connection layer.
# Numerical and Experimental Investigation of Guided Wave Propagation in a Multi-Wire Cable
## 4 Experimental Analysis  
### Experimental Setup  
Experimental verification of the proposed methods was performed on three types of samples. The first sample was a single 7-mm-diameter steel wire: the main component of the multi-wire bridge cable. The second sample was a pair of mechanically coupled 7-mm-diameter steel wires in contact. The third sample was an LGJ-400/35 ACSR cable (Tianhong Electric Power Fitting Co. Ltd., Zhejiang, China) comprised of two layers of seven steel wires and three layers of 48 aluminum wires, which were arranged helically in five layers (1-6-10-16-22). The diameter of the individual steel wire was 2.5 mm and the diameter of the aluminum wire was 3.2 mm. All these samples were the same length: 1.6 m.  
A piezoelectric (PZT) UGW experimental inspection system was employed, as shown in Figure 11a. The pitch-catch method was used with a broadband excitation PZT transducer pasted to one end of the cable and the same reception transducer pasted to another cable end. The coupling radius of the transducer was 10 mm and the height of the transducer was 22 mm, as shown in Figure 11b. It had a broadband characteristic (frequency range of 30-150 kHz). The directivity of the transducer was perpendicular to the contact surface. Epoxy resin glue was used to paste the transducer to the wire surface.  
Figure 11: The experimental setup for guided wave excitation and reception in three types of samples. (**a**) The schematic of the experiment. Photos of (**b**) the piezoelectric (PZT) transducer, (**c**) the single wire experiment case, and (**d**) the two contact wires.  
For excitation, the transducer was driven by a personal computer (PC)-controlled preamplifier (RAM-5000, Ritec Inc., Warwick, RI, USA) with a Hann-windowed, five-cycle, sinusoidal tone burst. The excitation signal was applied to the transducer with an amplitude of 100 \(V_{pp}\) (peak-to-peak value). The detected voltage signals were filtered by a bandpass filter with a bandwidth of 150 kHz and a center frequency of 80 kHz, and amplified by about 41 dB. Then, the signal acquisition was conducted to the filtered signals with a sampling frequency of 2000 kHz. This routine was repeated 50 times with a pulse repetition frequency (PRF) of 20 Hz to remove random noise and achieve a higher signal-to-noise ratio (SNR). The signal series were averaged and processed using Matlab.
## 4 Results and Discussion
### Classification Methodology  
The results of all SCPTu tests were compiled and are graphically represented in Figure 7, following the classification methodology proposed by [PERSON], [PERSON] and [PERSON] [28; 29; 30]. The plot illustrates that iron ore tailings exhibit a heterogeneous nature, as the data points are widely distributed on both the horizontal and vertical axes. This distribution spans both plastic and non-plastic behavior areas, ranging from non-plastic sensitive soils to silts and sands. However, it should be noted that approximately 97% of the data fall within the non-plastic region, indicating a predominantly granular behavior in the tailings. Consequently, the assessment of the susceptibility to liquefaction is performed for the entire material studied.  
While the correlation derived from SCPTu tests (I\({}_{\text{c}}\)) suggests a heterogeneous composition of the tailings, with layers containing clays and organic materials, the classification methodology illustrated above categorizes the tailings as a non-plastic material, with many tests falling within the sands and silts categories (regions 3 and 4). This classification is corroborated by the particle size distribution curves (Figure 5a-c), which confirms a sandy/silty granulometry and is further supported by additional laboratory tests such as Atterberg limits and triaxial tests.  
Figure 6: Mohr circles and the failure envelope derived from a representative triaxial test result. The plot includes the Mohr-Coulomb derived parameters: cohesion (0) and friction angle (37\({}^{\circ}\)).
## 5 Discussion
### Performance Comparison of Traditional and Proposed Method
#### 5.1.2 Dataset 3: CCTV Tower Parking Lot  
We take the image dated 31 August 2023 as an example to analyze the experimental result. The vehicle distribution map of the parking lot recorded by RTK equipment is shown in Figure 27a, and Figure 27b contains the enlarged image of CFAR detection result.  
The true value is 53 vehicles, and the detection result is 57 vehicles. From the experimental result, it is not difficult to see that compared with Dataset 1, Dataset 3 has higher resolution and more accurate detection results. However, there is only one set of true values in this experiment. In the future, more truth data will be collected to verify the detection effect of the proposed method on high-resolution spaceborne SAR images.
# A Survey of Secure Time Synchronization
## 3 Current Time Synchronization Methods
### Tiny-Sync and Mini-Sync Protocols  
[PERSON] and [PERSON] proposed two lightweight synchronization algorithms, i.e., tiny-sync and mini-sync [20]. To obtain the offset and rate difference between two nodes, both the tiny-sync algorithm and the mini-sync algorithm use a multiple round-trip time measurement technique and line fitting technique. Multiple round-trips are performed to obtain data points for line fitting, as shown in Figure 6, where Node A is the client and Node B is the reference [46].  
Figure 5: Hierarchical structure.  
Figure 6: Data points calculation.  
In [20], the authors assumed that Node A timestamps a message with \(t_{0}\) and sends it to Node B. Node B timestamps the message with \(t_{b}\) as soon as it receives it and sends it back to the Node A which receives the message and timestamps it with \(t_{r}\), as shown in Figure 6 below [20,46].  
Each round trip measurement results in a data point (\(t_{b}\), [\(t_{0}\), \(t_{r}\)]) which effectively limits the possible values of the parameters \(a_{12}\) and \(b_{12}\). Since \(t_{0}\) happened before \(t_{b}\), and \(t_{b}\) happened before \(t_{r}\), the following dissimilarities should hold there:  
\[t_{0}(t) <a_{12}t_{b}(t)+b_{12} \tag{8}\] \[t_{r}(t) >a_{12}t_{b}(t)+b_{12} \tag{9}\]  
The calculation described above is repeated several times and all time data points are stored [46]. The two lines with minimum and maximum slope are determined using the line-fitting technique, which provides us with the bounds for the relative offset and rate difference of the two nodes by using their slope and axis. The line which has average slope and intercept is used as the offset and rate difference between those two nodes.  
The tiny-sync and mini-sync protocols use the estimation method to provide the solution of relative skew and relative offset which reduces the complexity and energy consumption [32,34]; however, it requires higher computational resources and memory to generate accurate results. Computational resources and memory depend on the number of data points. A lower number of data points provides a suboptimal solution, whereas a higher number of data points provides an optimal solution, but takes a large amount of memory and computational resources. Miniaturized modern technologies have less memory and fewer available computational resources which makes this algorithm unsuitable for them.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Energy-Harvesting Techniques
#### 3.2.5 Water Flow Energy  
Multi-energy sources (water, wind, and solar) platform is designed for precision agricultural applications by [PERSON] et al. [128]. The authors explored water flow, wind speed and solar radiation feasible energy sources to encounter the requirements of ZigBee router node in WSN. Thus, several powered solutions for WSN is presented. Design considerations regarding water flow energy, the water flow in the pipes of crops irrigation system are employed to generate the energy for ZigBee router node. This idea can be applied in different agricultural applications such as greenhouses, aquaculture, and hydroponic systems, where the water recirculation in pipes is continuous. Like to bulky hydroelectric generation utilities, a water flow in the pipes, derived from the main water source, can be employed to run a turbine joined to a small direct current (DC) generator. The experimental results revealed that the three energy sources combined together can generate an energy of 58 mAh, more than the requirement of the ZigBee router node (39 mAh).
###### Abstract  
A methane (CH\({}_{4}\)) sensor based on off-axis integrated cavity output spectroscopy (OA-ICOS) was developed, equipped with two measurement schemes: direct absorption spectroscopy (DAS) and wavelength modulation spectroscopy (WMS). The sensor used an optical resonant cavity composed of two high reflection mirrors (reflectivity \(>\) 99%). With a cavity length of 7 cm, an effective optical path length of 10.8 m and a cavity volume of 8.9 mL were achieved. A distributed feedback laser was used to precisely target the CH\({}_{4}\) absorption line near 1.6537 \(\upmu\)m. Compared with the original system, the cavity mode noise of the CH\({}_{4}\) sensor was further reduced by adding white noise perturbations. The white noise perturbations were generated by the broadband random noise from the signal generator. The special customized narrowband RF noise source was not required. The system complexity and cost could be reduced. In DAS mode, the signal-to-noise ratio (SNR) of the OA-ICOS was 16.2 and the minimum detection limit (MDL) was 2.2 ppm at 117 s. In WMS mode, the SNR of the OA-ICOS was 113.9 and the MDL was 1.2 ppm at 106 s. Compared with the results obtained from the WMS mode and DAS mode, the SNR and MDL was improved 7.0 times and 1.8 times, respectively. The proposed sensor system not only enabled high-accuracy trace gas measurement, but also demonstrated strong potential for applications due to its compact design and low cost.  
off-axis integrated cavity output spectroscopy; direct absorption spectroscopy; wavelength modulation spectroscopy; white noise perturbation; methane concentration
## 2 Background
### Smart Healthcare  
The evolution of technologies contributes to the high quality of services in the healthcare sector as patients receive faster and more personalized services [17]. Smart healthcare, or healthcare 4.0, is an intelligent healthcare asset that uses sensing devices to gather medical data, network devices to transmit data, and an advanced infrastructure to process, store, and display that data for enhancing healthcare services. In summary, smart healthcare involves the use of cutting-edge technologies to increase the effectiveness of medical assistance and, where possible, to decrease healthcare costs. Smart healthcare provides significant capabilities, such as continuous interaction between all the relevant parties in healthcare, helping the healthcare providers make knowledgeable decisions, and supporting the dynamic allocation of healthcare resources. In short, today's healthcare services need to be personalized and available anytime, anywhere, and for everyone. This goal is met through smart healthcare [1].