esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware-Based Architecture for DNN Wireless Communication Models
## 3 Background
### Conventional Detector
#### 3.2.1 ZF Detector  
Zero-forcing is a method used to recover the received signal. In this case, we assume that no noise occurs during the data transmission phase. In the ZF algorithm, the transmit ted signal \(S(n)\) is recovered by multiplying the received signal \(Y(n)\) by the pseudo-inverse of the channel matrix \(W_{ZF}=(H^{H}H)^{-1}H^{H}\), which can be described as follows  
\[\begin{split} Y(n)&=HS(n)+W(n)\\ W_{ZF}Y(n)&=W_{ZF}HS(n)+W_{ZF}W(n)\\ W_{ZF}Y(n)&=S(n)+W_{ZF}W(n)\\ =>Y_{ZF}(n)&=W_{ZF}Y(n)\end{split} \tag{6}\]  
where, the superscript H denotes the conjugate transpose operation. Then, the component-wise detection is performed on received signals \(Y(n)\) to recover the transmitted signals. The advantage of the ZF method is that the implementation is quite simple, and the computational complexity is very low (The noise is assumed to be non-existent). Hence, the limitation of the ZF method is that the signal detection efficiency is not high. Then, MMSE and MLD methods are used to remove the existence of noise to improve the performance of the ZF method further.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 4 Application to GPS and Galileo Signals
### Comparison of GPS L1 C/A and L5 Signals Acquisition
#### 4.2.3 Determination of Ratio of Complexity for Multiple Frequency Bins
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Parallel without Assistance**} & \multicolumn{4}{c}{**Serial with Assistance**} \\ \cline{2-9}
**Sensitivity** & \multicolumn{2}{c}{**Non-Coherent**} & \multicolumn{2}{c}{**Coherent Only**} & \multicolumn{2}{c}{**Non-Coherent**} & \multicolumn{2}{c}{**Coherent Only**} \\ \cline{2-9}  & **Average** & **Worst** & **Average** & **Worst** & **Average** & **Worst** & **Average** & **Worst** \\ \hline \(-140\) dBm & 0.8 & 0.8 & 0.6 & 1.0 & 0.8 & 0.8 & 0.5 & 0.8 \\ \(-145\) dBm & 0.8 & 0.8 & 1.8 & 3.1 & 0.8 & 0.8 & 1.4 & 2.5 \\ \(-150\) dBm & 0.8 & 0.8 & 5.5 & 9.7 & 0.8 & 0.8 & 4.5 & 7.9 \\ \(-155\) dBm & 0.8 & 0.8 & 17.3 & 30.5 & 0.8 & 0.8 & 14.1 & 24.9 \\ \(-160\) dBm & 0.8 & 0.8 & 54.8 & 96.5 & 0.8 & 0.8 & 44.7 & 78.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Ratio of number of frequency bins between the L5 and L1 C/A signals.
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 4 CS-FL Method
### Training Phase: Hybrid Encryption and SCI Incentive Method
#### 4.3.2 SCI Incentive Mechanism  
In CS-FL, selected EN's behaviors and contributions to FL both influence their SCI value through rewards or penalties of proposed SCI incentive mechanism (SIM), which is the core reason why CS-FL is effective to model attacks and cheated attacks. To minimize the impact of non-responsive ENs, ZSs penalize selected ENs whose communicational latency, defined as \(t^{\prime}_{n}=t^{com_{4}}_{n}-t^{com_{3}}_{n}\), exceeds a specified threshold \(T_{m}\) by imposing a penalty \(P_{t}\) through setting \(\alpha\) to \(-1\) in the current round. Additionally, any actions compromising blockchain integrity, such as identity forgery during ID\({}_{n}\) verification, result in a penalty \(P_{b}\) through setting \(\beta\) to \(-1\). Here, the default values for \(\alpha\) and \(\beta\) are set to 0. To quantify the contributions of the local model \(w_{n,i}\), cosine similarity is used to measure the alignment between the vectors of \(\bar{w}_{i}\) and \(w_{n,i}\), as in Equation (14). In this case, ZSs can get \(w_{n,i}\) using the local gradient \(\bigtriangledown g_{n,i}\) and the global model from the previous round \(\bar{w}_{i-1}\):  
\[Sim_{n,i}(w_{n,i},\bar{w}_{i})=\frac{w_{n,i}\cdot\bar{w}_{i}}{\|w_{n,i}\|\times \|\bar{w}_{i}\|} \tag{14}\]  
The SIM method incorporates outlier detection to identify anomalous values within model contributions. For a given set of cosine similarity values \(Sim_{i}\), \(L_{min}\) is defined as the lower outlier boundary for \(Sim_{i}\) and \(L_{max}\) is defined as the upper outlier boundary. Should \(Sim_{n,i}\) fall outside the interval [\(L_{min}\), \(L_{max}\)], it suggests potential issues, such as suboptimal model or model poisoned attacks. To punish ENs with anomalous \(Sim_{n,i}\)  
Figure 4: Detailed flow of the hybrid encryption algorithm. \(\
abla Sim_{n,i}\) represents the deviation of similarity from the outlier boundaries, defined as \(\
abla Sim_{n,i}=L_{min}-Sim_{n,i}\) when \(Sim_{n,i}<L_{min}\), or \(\
abla Sim_{n,i}=Sim_{n,i}-L_{max}\) when \(Sim_{n,i}>L_{max}\). The comprehensive contribution values of \(n\in S_{i}\) within SIM is calculated as follows:  
\[Con_{n,i}=\left\{ \begin{aligned} &\alpha P_{t}+\beta P_{b}+\frac{ Sim_{n,i}}{4},\ Sim_{n,i}\in[L_{min},L_{max}]\\ &\alpha P_{t}+\beta P_{b}-\frac{\
abla Sim_{n,i}}{2},\ Sim_{n,i} \
otin[L_{min},L_{max}]\end{aligned} \right. \tag{15}\]  
The ZS leader updates SCI values of ENs after calculating contributions by:  
\[SCI^{\prime}_{n,i}=SCI_{n,i}+Con_{n,i} \tag{16}\]  
Algorithm 2 describes the complete workflow of CS-FL.
## 4 Discussion
### Limitation of the Developed CET  
Despite the stated advantages, the developed CET has a few limitations, which should be addressed in future research and development. The main shortcoming of CET is the pronounced dependence of coastline extraction accuracy on the input data quality. While CET managed to resolve errors caused by the presence of shadows in multispectral imagery, its extraction accuracy is still, in some cases, affected by spectral information. The presence of artificial objects (e.g., small fishing boats) close to the coast can lead to the overestimation of the extracted coastline. This is especially present within harbors, where anchored boats are regarded by the CET as part of the coast (Figure 15). However, if the whole length of the coastline extracted within the study area is considered, such areas are covering very small portions of the total extracted coastline (<1%), and thus, those areas can be easily manually corrected if necessary.  
The second limitation is related to the cloud cover, as CET performance is highly affected by artefacts in DSM or WV-2 MS, which are introduced by the presence of clouds in original high-resolution imagery. Those artefacts could limit the applicability of CET for the extraction of coastlines within the obstructed areas. Therefore, before the application of CET, users should ensure that their whole study area is free of any cloud cover. As our whole study area was without any cloud cover, such problems with the CET application have not been presented in the results.
## 6 Performance Evaluation
### Evaluation Results
#### 6.3.3 Total Number of Packets Processed  
The total number of packets processed as a function of the number of Interest packets is shown in Figure 9. The results show that the CIDF-WSN processed a far smaller number of packets--both Interest and Data packets--compared with GIF and NDN. It is taken into consideration that the proposed NFIB's maintained cost and the robust Interest processing mechanism have a significant contribution in ensuring the less number of retransmissions.  
The former procedure helps in the selection of the optimal next-hop forwarder, whereas  
Figure 8: Interest satisfaction delay as a function of Interest.  
the latter enables the neighbor nodes to continue the communication process when the nominated forwarder fails to perform further transmission. This process assures the seamless end-to-end Interest and Data delivery process with the minimum number of transmissions, thus significantly minimizing the packets to be processed by each node in the network. In contrast, in the GIF, when the relay node fails to further transmit the packet, the forwarder restarts the whole process without considering the potential neighboring nodes to resume the communication process. Moreover, if there is no information of sender in the forwarder's neighbor table, the forwarder re-initiates the neighbor table maintenance procedure. With the lack of immediate neighbor collaboration in packets transmissions and frequent neighbor table maintenance procedure, redundant packet transmissions increase the number of packets required to be processed, which, as a result, also affects the Interest satisfaction delays.  
On the other hand, in NDN, each node rebroadcasts the received packet which significantly increases the overall transmissions in the network which in turn results in the wastage of network resources, congestion, and eventually increases the number of processed packets in the network.
## 4 Test Results and Analysis
### XRD Analysis of Hardened Paste  
XRD spectra of cement mortar absorbing 2.20% CO\({}_{2}\) and the cement mortar without CO\({}_{2}\) absorption under two stirring methods are shown in Figure 11. The XRD spectra and phase analysis results of cement paste without CO\({}_{2}\) absorption under mechanical agitation, cement paste with 2.20% of CO\({}_{2}\) absorption under mechanical agitation, and cement paste with 2.20% of CO\({}_{2}\) absorption under ultrasonic vibration agitation stirring are shown in Figure 11a-c, respectively.  
(1) Pure cement paste  
It can be seen from Figure 11a that there is a diffraction peak of Ca(OH)\({}_{2}\) (Portlandite) in the XRD spectra of pure cement paste, indicating that this specimen contains abundant Ca(OH)\({}_{2}\) crystals in hydration products at 28 d. The diffraction peaks of CaCO\({}_{3}\) (Calcite) and 3 CaO\(\cdot\)Al\({}_{2}\)O\({}_{3}\cdot\)3 CaSO\({}_{4}\cdot\)32H\({}_{2}\)O (Ettringite) are relatively low [29; 30].  
Figure 11: Diffraction results of cement slurry sample. (**a**) Unabsorbed CO\({}_{2}\) cement paste; (**b**) 2.20% CO\({}_{2}\) (mechanical agitation); (**c**) 2.20% CO\({}_{2}\) (ultrasonic vibration agitation).
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 8 Conclusions  
Congestion in radio frequency spectrums has motivated researchers to develop new technologies and approaches. One such technology is LiFi, a cutting-edge network design that uses VLC with a nearly unlimited spectrum to provide high data rates with unopposed security due to the natural physical property of light. Companies such as pureLiFi, Oledcomm, and Signify have made commercially available kits for standalone LiFi implementation. However, due to constraints in LiFi transmission such as frequent handovers due to channel blockages from external light sources or opaque objects, which can cause frequent handovers or total loss of connection, in recent years, researchers have focused on hybrid network technologies as merging of LiFi with WiFi technology can mitigate these problems. Thus, introducing HWLNets can provide an improved network performance and reliability while maintaining high data rates and throughput. Moreover, data demanding applications and IoT would benefit from hybrid network implementations. Therefore, ongoing research focuses on the optimization and mathematical modelling of user mobility that immensely constrain HWLNets. Future directions could include significant hardware integration upgrades to develop a hybrid LiFi-WiFi AP, improve cell deployment optimization, and involve machine learning to enable intelligent network management.  
This paper provided an overview of HWLNets framework design with special attention on cell deployment of LiFi APs, modulation schemes, and the design of backhaul devices. The performance metrics such as SINR, spectral efficiency, as well as the achieved QoS are discussed in regard to HWLNets and with comparison with standalone networks. User behaviour modelling was introduced with regard to device orientation, movement, and blockages. An overview of the handover models and load balancing is given with a focus on user mobility and algorithms addressing key issues such as computational complexity and long processing time. Finally, possible implementations of HWLNets in IoT and various applications are given with latest advancements in TCP technology that will likely solve VHO issues.  
Conceptualization, T.B. and K.F.; investigation, T.B.; writing--original draft preparation, T.B.; writing--review and editing, T.B., K.F., V.L. and I.Z.; supervision, K.F. and V.L.; and project administration, T.B. and I.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 4 Content Protection with Ternary Unclonable Functions  
One way to protect digital files is to encrypt them with a cryptographic key, such as the session key **Sk** presented in Section 3. The same session key can be used to either encrypt or decrypt multiple digital files. In this section, we present methods for delivering and protecting digital files, each of which is protected by its own set of keys. Rather than using on-demand challenge-response pairs (CRPs) to protect session keys, we suggest using a different CRP for each digital file. The random number **T** and the individual digital access parameter _IDAccess_ of each CRP generates the response **K** protecting a particular digital file **M**.  
Figure 4: Block diagram of the recovery of the session key **Sk** by the client device. The challenges (**T**, _IDAccess_) generate **K**\({}^{\prime}\) from the PUF. The search engine finds **K**\({}^{\prime}\) through an iterative process from **K**\({}^{\prime}\) and **H**(**K**\({}^{\prime}\)). The ciphertext is decrypted with **K**\({}^{\prime}\) to recover **Sk**.
## 1 Introduction
In contrast to previous approaches, deep learning enables us to extract features from low- to high-level images using deeper architectures that boost the capacity to learn more complicated features than shallow architectures. The deep learning image features are more representative than the handmade features [37]. Consequently, numerous academics are shifting their focus to object detection using deep learning. In general, object detectors based on deep learning can be classified as either single-stage or two-stage. Based on past research, it is clear that researchers selected a reliable two-stage detector to detect objects. The You Only Look Once (YOLO) framework is a popular and often used method for identifying things in real time [38, 39]. In this study, we picked YOLO version 4 as the foundational model for addressing the fashion category detection problem in the intelligent business domain. We will concentrate on the Shopee E-commerce platform, which ranks highly among the most powerful E-commerce platforms in the consumer base. Even when the system recommends products for the category in the Shopee seller portal, sellers continue to make category errors, especially the fashion product [6]. Therefore, we will use FC-YOLOv4 to automatically recognize the product category, assisting merchants in making the best selection which is a crucial aspect of E-commerce business intelligence. This study aimed (goal) to discover and categorize clothing trends. The main contributions of this paper are as follows:  
* We present a recently acquired, tiny dataset from the Shopee E-commerce (Thailand) website. Skirt, Dress, Pants, Hoodie, and Jacket, with a total of 4116 photographs, were compiled under the category \"women.\" Images of various resolutions, sizes, and positions are gathered in accordance with the website. * We use the semi-supervised learning approach for automatically annotating data to save manual tasks and increase the number of images by using image augmentation to improve object detection accuracy. * We propose an accurate image-based classification model utilizing FC-YOLOv4 that can classify Shopee E-commerce products' images and identify their types. * To bring network performance and feature improvements, we increase the quantity of short-circuiting and stacking. * To assess and evaluate the applicability and efficiency of the proposed method in fashion category recognition, we evaluated a model with a photo with a cluttered background and compared our FC-YOLOv4 model to YOLOv4 and YOLOv3 with detection time and accuracy as a benchmark. The content of this article is organized in the following manner. The related work is discussed in the second part. The third section discusses the distribution of our dataset, as well as the structure and implementation of the FC-YOLOv4 detection algorithm. Part four gives a summary of the experimental data as well as a comparison. The fifth section concludes with a strategic approach.
## 3 Results
### Eeg  
#### 3.2.1 Alpha PSD  
There were no significant differences in the alpha frequency band between the two protocols in the central, frontal, and parietal scalp zones, nor for TP (all \(p>0.05\)) (Table 2). However, a significant main effect of the time was observed in the parietal scalp zone (\(F=1.71\), \(p=0.043\)), indicating a time-related increase in alpha power in this region. No significant effects were found for the condition (\(F=0.24\), \(p=0.623\)) or the time x condition interaction (\(F=0.54\), \(p=0.898\)). Additionally, in the central scalp zone, there was a trend towards a time-related effect, but it did not reach significance (\(F=1.65\), \(p=0.053\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **IET** & **SPV** & \(p\) \\ \hline Rf (1/min) & \(61.1\pm 5.1\) & \(61.7\pm 8.1\) & \(0.844\) \\ Vt (L(bpts)) & \(2.9\pm 0.6\) & \(2.8\pm 0.5\) & \(0.131\) \\ VE (L/min) & \(147.8\pm 27.4\) & \(144.7\pm 31.4\) & \(0.844\) \\ RER & \(1.2\pm 0.1\) & \(1.1\pm 0.04\) & \(0.674\) \\ Relative VO\({}_{2}\) (mL-kg\({}^{-1}\cdot\)min\({}^{-1}\)) & \(54.3\pm 8.2\) & \(53.7\pm 7.7\) & \(0.313\) \\ Absolute VO\({}_{2}\) (mL-min\({}^{-1}\)) & \(3832.0\pm 648.3\) & \(3792.1\pm 630.3\) & \(0.313\) \\ Relative VO\({}_{2}\) (mL-kg\({}^{-1}\cdot\)min\({}^{-1}\)) & \(58.0\pm 9.6\) & \(57.2\pm 8.6\) & \(0.438\) \\ Absolute VO\({}_{2}\) (mL-min\({}^{-1}\)) & \(4088.3\pm 721.9\) & \(4030.2\pm 673.0\) & \(0.438\) \\ HR (bpm) & \(171.8\pm 14.7\) & \(173.0\pm 15.5\) & \(0.498\) \\ SV (mL) & \(145.1\pm 8.4\) & \(145.8\pm 11.8\) & \(0.813\) \\ CO (L/min) & \(24.4\pm 22.2\) & \(24.4\pm 3.7\) & \(0.813\) \\ MAP (W) & \(326.5\pm 67.3\) & \(331.7\pm 67.1\) & \(0.563\) \\ Pmax (W) & \(358.3\pm 74.3\) & \(374.8\pm 66.3\) & \(0.343\) \\ Cadence (rpm) & \(89.16\pm 4.5\) & \(96.65\pm 4.4\) & \(\mathbf{0.031}\) \\ \hline \hline \end{tabular} Note: the value in bold indicates a statistically significant difference between the two tests (\(p<0.05\)). Abbreviations: IET = incremental exercise test, SPV = self-paced VO\({}_{2}\)max test, Rf = respiratory frequency, Vt = tidal volume, VE = ventilatory flow, RER = respiratory exchange ratio, VO\({}_{2}\) = oxygen uptake, VO\({}_{2}\) = carbon dioxide output, HR = heart rate, SV = stroke volume, CO = cardiac output, MAP = maximal aerobic power, Pmax = maximal power output, rpm = rotations per minute.  
\end{table}
Table 1: Maximal values for physiological and power variables during the incremental exercise test and the self-paced VO\({}_{2}\)max test.
## 2 Materials and Methods
### Data Used  
This study employed multispectral data from the European Copernicus Program's Sentinel-2 satellite constellation ([[https://sentinel.esa.int/web/sentinel](https://sentinel.esa.int/web/sentinel)]([https://sentinel.esa.int/web/sentinel](https://sentinel.esa.int/web/sentinel)), accessed on 1 June 2021). Sentinel-2 captures optical images at a spectral range of 0.47 to 0.6 um with a high spatial resolution of 10 m above land and coastal areas, which ensures capture with a high revisit frequency of 5 days. Currently, the mission comprises two satellites, Sentinel-2A and Sentinel-2B. Sentinel-2 satellite delivers high-resolution (optical bands at 10 m) images over a wide swath (290 km) in the optical, near-infrared (NIR), and short-wave infrared (SWIR) electromagnetic spectrum. Monthly maximum NDVI with cloud screening for June to November 2021 were collected using data from Sentinel-2 bands 4 (red wavelength) and 8 (NIR wavelength), a spatial resolution of 10 m (Table 1).  
Figure 1: Selected study villages in Karimnagar district of the Telangana with location of the study area.  
Figure 2: Flow diagram representing the methodology for integrating the crop simulation model with remote sensing.  
Using Sentinel-2, data stacking was performed. Spectral signatures represent crop behavior over time [43,44]. Based on the NDVI spectral signatures, a rice cropland mask was generated, which was used to integrate the LAI for spatial rice yield estimation.
# A Survey of Secure Time Synchronization
## 6 Conclusions  
Problems associated with time synchronization are not new. In the past, many time synchronization protocols have been proposed, and some of the popular protocols are discussed in this paper. Promising applications of wireless sensor networks are emerging and increasing demand for their use have made it vital to have an error-free and secure time synchronization protocol. However, almost all the current time synchronization protocols have time synchronization errors. To conclude, we have analyzed the current time synchronization protocols and methods for providing secure time synchronization. In addition, we emphasize the importance of secure time synchronization in Industry 4.0 and Healthcare 4.0. The integration of secure time synchronization with blockchain techniques is a promising future research direction. This survey may help researchers in the wired and wireless sensor network community to better understand time synchronization and secure time synchronization.  
Conceptualization, Y.W.; methodology, Y.W.; formal analysis, Y.W. and Y.Z.; investigation, Y.W. and Y.Z.; writing--original draft preparation, Y.W. and Y.Z.; writing--review and editing, Y.W. and Y.Z.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ningbo Major Science & Technology Project 2022Z126 and the Ningbo Commonwealth Project 2019C50052.  
Not applicable.  
The simulation work was supported by [PERSON] from Northeastern University, China.  
The authors declare no conflict of interest.
# Coherence Enhancement Based on Recursive Anisotropic Scale-Space with Adaptive Kernels
## 1 Introduction
The resultant desirable anisotropic properties improve the characterization of local features while suppressing insignificant details. In summary, our proposed algorithm is developed based on the scale-space theory; however our algorithm proceeds to apply anisotropic diffusion process based on the adaptive kernels that are alternatively designed to take into consideration of local geometrical features whereas the conventional anisotropic diffusion approaches are developed using static smoothing kernel for computing structure tensor in the analysis eigen-system. We present preliminaries in Section 2 followed by the proposed algorithm in Section 3. Section 4 describes detection of curvilinear features based on the recursive anisotropic scale-space. The robustness and effectiveness of the proposed method are numerically demonstrated in Section 5. The conclusion follows in Section 6.