esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
The scenario of how the proposed intelligent camera receiver works is depicted in Figure 1. Based on our scheme, we have developed a new data decoding algorithm for FSOOK based OCC. Our designed receiver first detects every LED region employing CNN. From each region, features are extracted to train the SVM classifier. Using the trained classifier model, the camera successfully separates accurate regions for data decoding purposes. As a result, unwanted LED regions are removed which will result in low computational complexity and mitigation of neighboring interference. The remainder of this paper is structured as follows: Section 2 describes the SVM classifier. The overall OCC system including the modulation technique and algorithm for decoding data intelligently from the recognized region has been illustrated in Section 3. We have demonstrated how the data set has been prepared using extracted features in Section 4. In Section 5, we have elaborately discussed the experimental results to justify our scheme. Finally, we concluded our article in Section 6.
###### Abstract  
In this article, we propose a reliable and low-latency Long Range Wide Area Network (LoRaWAN) solution for environmental monitoring in factories at major accident risk (FMAR). In particular, a low power wearable device for sensing the toxic inflammable gases inside an industrial plant is designed with the purpose of avoiding peculiar risks and unwanted accidents to occur. Moreover, the detected data have to be urgently and reliably delivered to remote server to trigger preventive immediate actions so as to improve the machine operation. In these settings, LoRaWAN has been identified as the most proper communications technology to the needs owing to the availability of off the shelf devices and software. Hence, we assess the technological limits of LoRaWAN in terms of latency and reliability and we propose a fully LoRaWAN compliant solution to overcome these limits. The proposed solution envisages coordinated end device (ED) transmissions through the use of Downlink Control Packets (DCPs). Experimental results validate the proposed method in terms of service requirements for the considered FMAR scenario.  
LoRaWAN; reliability; downlink; safety; IoT +
Footnote †: journal: Sensors
## 3 Signal Analysis
### Bispectrum Analysis
#### 3.2.3. Bispectrum Features of Arc Faults
(**a**) Normal contour; (**b**) Normal amplitude; (**c**) Normal phase angle; (**d**) Normal 1\(\
icefrac{{\
u}}{{2}}\)-D slices; (**e**) Arc fault contour; (**f**) Arc fault amplitude; (**g**) Arc fault phase angle; (**h**) Arc fault 1\(\
icefrac{{\
u}}{{2}}\)-D slices. Figure 6: AR bispectra of electrical drill. (**a**) Normal contour; (**b**) Normal amplitude; (**c**) Normal phase angle; (**d**) Normal 1/2-D slices; (**e**) Arc fault contour; (**f**) Arc fault amplitude; (**g**) Arc fault phase angle; (**h**) Arc fault 1/2-D slices. Compared with traditional classification algorithms, such as a neural network algorithm, SVM can better solve the difficult problems listed as follows: dimension disaster, over learning, local minima [40,41], _etc_. It has several merits consisting of convergence to global optimum, good generalization ability and adaptation for some small samples and high dimension data [42,43]. To better discriminate arc fault states from load states, the frequencies \(\omega_{i}\) and \(\omega_{2}\) are selected as the input vector. Based on the features including distribution regularities of bispectrum peaks, SVM is introduced to classify series arc faults and the normal states in the following section.
## 3 P-MAC Design Integrated with Routing
### Scalability Discussion  
P-MAC is a cross-layer duty cycle MAC protocol seamlessly integrated with routing function. It not only reduces the protocol overhead, but also helps enhance scalability when there are changes in network topology:  
1. If a new node is added to the network, it will identify which grade it should join and the corresponding schedule it should choose after listening to its neighbors for some time. Subsequently, it can join the network for data transmission.
2. It is relatively easy for P-MAC to support node mobility. A mobile node needs to redefine its grade and schedule if it moves out of its original grade area. Like a newly added node, it can rejoin the network later, unless it moves out the network and thus becomes isolated.
3. In a large-scale WSN, multiple sinks are needed to increase the network manageability and balance energy dissipation. P-MAC can partition the whole network into several sub-networks using its grade division mechanism. Each sink serves one sub-network independently. Figure 4 shows a two-sink case. For a data-gathering network, all sinks are expected to be connected to an outside system. Hence, it is irrelevant which exact sink receives the data information.  
No further elaboration on these issues is presented in this paper, but we believe these issues are reasonable and provide a guideline for future exploration.
# Endophytic Colonization of Pepper (_Capsicum annum_) Controls Aphids (_Myzus persicae_ Sulzer)
## 4 Discussion
maculipennis_[24] have been reported as mycosed by endophytic _B. bassiana_, but not in all tested strains. Mycosis was not determined on _Bennicia tabaci_ (Homoptera: Aleyrodidae) following exposure to endophytic fungal entomopathogens (_B. bassiana_ strain _NATURALIS_ and _Metrahizium brunneum_ strain _BIPESCO5_) [38]. In our experiment, mycosis of aphids was found at 7 DAE for _B. bassiana_ and at 14 DAE for _M. anisopliae_ and _I. fumosorosea_. Established endophytes should not normally influence the physiology and growth of the plant [40]. Occasionally, they may enhance host resistance to stressful environmental conditions [7], such as drought and lack of nutrients [6], or strengthen host defense against biotic threats [4; 11; 23; 25]. Endophytic entomopathogenic fungi may enhance plant growth in a way which renders plants more resistant to insect herbivory and ensuing loss of biomass [8]. [PERSON] and [PERSON] [30] found that the presence of entomopathogenic fungi had a positive impact on all growth parameters of coffee seedlings, whereas [PERSON] et al. [41], [PERSON] et al. [42], and [PERSON] et al. [21] observed that _B. bassiana_ in tomato and cotton plants contributed to a substantial increase in the height of these crops. [PERSON] and [PERSON] [43] noted that inoculation with _B. bassiana_ and _Purpurecillium lilacinum_ ([PERSON] (Hypocreales: Ophiocordcyipitaceae) produced an enhancement of certain growth parameters in cotton plants, such as dry weight and size of the reproductive structures. [PERSON] et al. [44] observed increased growth of cassava plants, post-inoculation with _B. bassiana_ and _M. anisopliae_. [PERSON] et al. [45], who inoculated two different strains of _B. bassiana_ in tomato plants, noted that one strain favored plant growth while the other delayed plant growth and development and caused a reduction in the size of the fruits. _B. bassiana_ benefited all growth parameters of _Glycine max_ (L.[PERSON] [PERSON]. [40]. Our results indicate that the three fungal isolates used in this study had no role in suppressing or promoting the growth of _C. annum_ under the conditions tested. However, further research is required to discover the mechanisms by which these fungi manage to regulate aphid populations. Such studies are crucial in evaluating the potential of non-chemical alternatives for pest management in peppers, as this information has previously been unavailable. Moreover, as the use of endophytes is a pioneering research field, more studies need to be pursued on the applicability of spraying fungal conidia on pepper plants, especially in field conditions.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 5 Conclusions  
Wireless positioning systems play an instrumental role in people's lives today and will continue to do so in the years to come. In this review, we provided a comprehensive taxonomy of the related research over the last 30 years, with respect to the localization technique and the wireless technology employed. First, with respect to the localization/positioning algorithms and techniques, the reviewed articles were classified according to the signal measurements employed (i.e., time-based, angle-based, and channel-based), the localization method (i.e., range-based, range-free, ML/AI), and the tracking method (i.e., DR, Filters, SLAM). Next, numerous wireless technologies were presented and discussed, with respect to localization, including satellite, cellular, RADAR, FM, LoRa, and SigFox, Wi-Fi, Bluetooth, UWB, geomagnetism, vision technology, VLC, RFID, acoustic, ultrasound, and infrared radiation. Finally, a statistical analysis of the results revealed gaps in the existing research and identified directions for future research.  
In particular, although intensive research efforts have been made, significant challenges for designing future localization systems still remain, with respect to availability, cost, energy-efficiency, privacy issues, position accuracy, fault-tolerance, latency, and scalability. To date, none of the existing techniques can be considered as a one-solution-fits-all for the different indoors/outdoors environments, since the effectiveness of each method depends on the availability of the required data and technology, level of complexity, and the properties of the specific environment or the setup considered. Almost all considered approaches achieved only some of the performance criteria at the expense of others. Some technologies and techniques appear promising in simulations or controlled experiments, but without real-world testing to back up the claims made in the lab or during simulations. A promising future direction is hybrid positioning systems that can fuse different methods, measurements, and/or technologies for improving the existing localization systems. A good possibility in this direction, would be the implementation of intelligent handover systems, able to seamlessly transfer the localization responsibility from one wireless technology to another, with the overall aim of achieving continuous and transparent positioning for the end user in both indoor and outdoor environments.  
Conceptualization, C.I. and M.P.M.; methodology, C.I. and M.P.M.; formal analysis, C.I.; investigation, C.I.; resources, C.I.; data curation, C.I.; writing--original draft preparation, C.I.; writing--review and editing, M.P.M.; visualization, C.I.; supervision, M.P.M. 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.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking  
The robust video watermark is an important branch of digital video watermarks, which uses the temporal redundancy and spatial redundancy of video content to embed watermark information to achieve video copyright protection. General robust video watermarking algorithms include three components: watermark generation, watermark embedding, and watermark extraction or detection. According to the application of watermark technology, the emphasis of the algorithm in these three parts will also change accordingly. A framework of a general robust video watermarking algorithm is shown in Figure 3.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
###### Abstract  
In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite-ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks.  
satellite data transmission; genetic algorithm; constraint satisfaction model; tabu search algorithm; heuristic rules +
Footnote †: journal: Applied  
0000-0001-999-2233-2233][PERSON]  
0000-0001-9993-3330][PERSON] and [PERSON]
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 3 GARSU: Automatic RSU Deployment
### Crossover or Re-Combination  
The crossover, also known as re-combination, operator merges two parents to generate a third new individual. GARSUD uses the default re-combination operator in genetic algorithms, i.e., the one-point crossover [25] depicted in Figure 2.  
Figure 1: Representation of individuals in the genetic algorithm for roadside unit (RSU) deployment (GARSUD). Each possible location for RSUs appear as blue circles on the map layout.
## 6 Evaluation of Results
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**AWS Cloud Metrics (N. Virginia Datacenter)—Python** & \multicolumn{6}{l}{**AWS Cloud Metrics (N. Virginia Datacenter)—Python**} \\
**Script—IoT-Fog–Cloud Architecture without** & & & & & & & & & & & \\
**Blockchain-Based Certificate Model** & & & & & & & & & & & & \\ \hline IoT devices & 1 & & & & & & & & & & \\ Subscribe and publish requests & 2 & & & & & & & & & & \\ AWS cloud metrics (minutes) & 0.5 & 1 & 5 & 15 & 60 & AWS cloud metrics (minutes) & 0.5 & 1 & 5 & 15 & 60 \\ connect.success & 2 & 2 & 2 & 2 & 2 & connect.success & 2 & 2 & 2 & 2 & 2 \\ ping.success & 2 & 2 & 8 & 29 & 120 & ping.success & 2 & 2 & 8 & 29 & 120 \\ publishin.success & 44 & 44 & 206 & 586 & 2360 & publishin.success & 44 & 44 & 206 & 586 & 2360 \\ subscribe.success & 2 & 2 & 2 & 2 & 2 & subscribe.success & 2 & 2 & 2 & 2 & 2 \\ unsubscribe.success & 2 & 2 & 2 & 2 & 2 & 2 & unsubscribe.success & 2 & 2 & 2 & 2 \\ IoT devices & & & & & & & & & & & & \\ Subscribe and publish requests & & & & & & & & & & & & \\ AWS cloud metrics (minutes) & 0.5 & 1 & 5 & 15 & 60 & AWS cloud metrics (minutes) & 0.5 & 1 & 5 & 15 & 60 \\ connect.success & 4 & 4 & 4 & 4 & 4 & connect.success & 4 & 4 & 4 & 4 & 4 \\ ping.success & 2 & 2 & 8 & 29 & 120 & ping.success & 2 & 2 & 8 & 29 & 120 \\ publishing.success & 70 & 70 & 350 & 1110 & 4590 & publishing.success & 70 & 70 & 350 & 1110 & 4590 \\ publishout.success & 70 & 70 & 350 & 1110 & 4590 & publishout.success & 70 & 70 & 350 & 1110 & 4590 \\ subscribe.success & 4 & 4 & 4 & 4 & 4 & subscribe.success & 4 & 4 & 4 & 4 & 4 \\ unsubscribe.success & 4 & 4 & 4 & 4 & 4 & unsubscribe.success & 4 & 4 & 4 & 4 & 4 \\ IoT devices & & & & & & & & & & & \\ Subscribe and publish requests & & & 6 & & & & & & & & & \\ AWS cloud metrics (minutes) & 0.5 & 1 & 5 & 15 & 60 & AWS cloud metrics (minutes) & 0.5 & 1 & 5 & 15 & 60 \\ connect.success & 6 & 6 & 6 & 6 & 6 & connect.success & 6 & 6 & 6 & 6 & 6 \\ ping.success & 2 & 2 & 9 & 29 & 119 & ping.success & 2 & 2 & 9 & 29 & 119 \\ publishing.success & 115 & 115 & 548 & 1660 & 6510 & publishin.success & 115 & 115 & 548 & 1660 & 6510 \\ publishout.success & 115 & 115 & 548 & 1660 & 6510 & publishout.success & 115 & 115 & 548 & 1660 & 6510 \\ subscribe.success & 6 & 6 & 6 & 6 & 6 & subscribe.success & 6 & 6 & 6 & 6 & 6 \\ unsubscribe.success & 6 & 6 & 6 & 6 & 6 & 6 & unsubscribe.success & 6 & 6 & 6 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AWS cloud metric results on N. Virginia datacenter for the IoT–Fog–Cloud architecture with the blockchain-based certificate model versus without the blockchain-based certificate model.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 1 Introduction
Flattened butterfly networks used as a basic module also reduces the congestion and increases the throughput within the basic module because its many short length links increase the connectivity within the basic module and yield good performance [27; 28]. The novelty and superiority of the proposed TFBN are assessed by static analysis of static network performance and static time-cost effective analysis. The preliminary version of a study on TFBN is presented in a conference--some static network performance parameters of TFBN for the Level-2 only consisting of only 256 nodes [29]. The main objectives of this study are the versatile study of the static performance of networks by evaluating these static network performance parameters for the higher-level network, static cost-effectiveness analysis, and static cost-performance trade-off analysis. The practical acceptance of any interconnection network topology by the industry community depends on the detailed evaluation and analysis of static network performance, cost-effectiveness analysis, dynamic communication performance, fault tolerance performance, on-chip, and off-chip power consumption, computational intensive problem mapping in the network topology, and so forth. In this first step of detail study, we have considered static analysis of network performance, cost-effectiveness as well as cost-performance trade-off. After the Introduction, the remainder of this article is organized as follows: Sections 2 and 3 discuss the network architecture of the proposed TFBN and the routing of the message within it, respectively. The detailed study of the static network performance of a TFBN is discussed in detail for both the lower level as well as a higher level in Section 4. The cost-effectiveness as well as the cost performance trade-off factor of this TFBN is analyzed in Section 5. Finally, the concluding remarks and the future works of this study are presented in Section 7.
## 4 Experiments  
### Experimental Setup  
The experimental scheme for the relative acoustic nonlinearity measurements using the fully non-contact surface acoustic wave method is shown in Figure 2. A pulsed Nd:YAG laser beam of 1064 nm (SL280, Spectron Laser Systems, Warwickshire, UK) was used to excite surface acoustic waves. The pulse duration of the pulsed laser is 10 ns, the repetition rate is 10 Hz, and the maximum energy is 300 mJ. A line-arrayed slit mask was designed to create surface acoustic waves with a wavelength of 2.92 mm, which corresponds to the fundamental frequency of 1 MHz in aluminum and 0.75 MHz in copper. The duty ratio of the slit mask was 50%, which theoretically does not generate a second harmonic component [12].  
Figure 1: Specimens.  
The wave propagated on the specimens was received at the other point on the same side of the specimen by a photorefractive interferometer (TWM, Tecnar, Saint-Bruno-de-Montarville, QC, Canada). In general, when detecting the displacement of the surface wave, the diameter of the measuring laser beam should be smaller than the wavelength to avoid a decrease in signal sensitivity. The beam diameter of the interferometer used in the experiment was 0.8 mm, which is sufficiently small compared to the wavelength of the excited surface acoustic wave. The propagation distance was 65 mm. If the propagation distance is too long, the effects of attenuation and diffraction cannot be ignored. The received surface acoustic wave signal was captured by a digital oscilloscope (Lecroy HDO4034A, Teledyne LeCroy, Chestnut Ridge, NY, USA).  
The Hanning window was applied to minimize the effect of the side lobe, and the fast Fourier transform was used to obtain the amplitudes of the fundamental frequency component \(A_{1}\)\({}^{\prime}\) and the second harmonic frequency component \(A_{2}\)\({}^{\prime}\). The Hanning window size is the same as the data length. Sampling frequency was 10 GHz, and the number of data points was 10,000. As a result, the frequency resolution in FFT (Fast fourier transform) spectrum is 0.001 MHz. In order to obtain the magnitudes at the fundamental and second harmonic frequencies, we searched for peaks within the \(\pm\)0.1 MHz range at each frequency. The frequency of the peak detected in the experimental results was within the range of \(\pm\)0.02 MHz from the predicted frequency. The measurements were repeated by increasing the laser energy from 95 to 145 mJ in seven steps. The relative nonlinearity parameter \(\beta^{\prime}{}_{SAW}\) was determined from the linearity slope of between \(A_{1}\)\({}^{\prime}\)\({}^{2}\) and \(A_{2}\)\({}^{\prime}\).
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 3 Componential Classification of Blockchain Privacy Protecting Techniques
### Effect of Blockchain's Primary Cryptography on Privacy  
Cryptography is a technique of data storing and transmission in a certain form such that it is only interpretable by the intended user [43]. Besides safeguarding data from theft and alteration, cryptography may also be used for user authentication purposes [44]. Blockchain networks highly rely on cryptography for network integrity and data sharing. Cryptography is enforced in blockchains to accomplish the three basic information security tasks i.e., confidentiality (or data privacy), integrity or authentication (user privacy) and non-repudiation [45]. While it is deemed as an impeccable solution for online information security, cryptography does not guarantee complete protection of assets. However, it is an efficient method of shielding the data which minimizes the impact of unauthorized penetration if it does occur.  
Fundamentally, two kinds of cryptographic algorithms are used in blockchains. The first one is known as asymmetric or public-key cryptography [46], and the second one is called hashing [47] both of which are elaborated further in this section.