esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
The problem of message forgery has been noticed from the early days of V2X communication [10], to have dire safety consequences [11]. It is even considered the most serious security threat for its ease of implementation and execution [6]. Although the certifying authority (CA) is supposed to revoke the certificates on misbehaving devices as per the IEEE 1609.2 [1], there is an issue of how the authority can obtain and accumulate sufficient evidence that the devices are faulty or compromised [12]. Even if it could, it will take time during which the attacker has a time window of attacks to exploit. Especially if the attacker misbehaves in a discontinuous manner, namely evading detection intelligently, the task of identifying the misbehaving device and revoking its certificates will not be easy in the first place. So, the message correctness check is considered the primary focus of misbehavior detection [13].  
There have been mainly three approaches to solving the problem, i.e., behavior analysis [11; 14; 15], sensor-fusion [16; 17; 18] and communication-based constraint check [5; 12; 19]. These approaches are not mutually exclusive, so they can be used alone, or in combination. The first exploits physical constraints of vehicle movement dynamics. The second uses multiple sensor inputs to verify the position information in the received message. The third uses maximum communication range constraints.  
Below, we will briefly discuss these three that check the plausibility of the position information conveyed in a vehicle-to-vehicle (V2V) message. We note that because many of the existing approaches have been around for a long time, they do not reflect the recent standards development. It is one of the reasons that we need a new solution approach that is based on the standardized framework for the V2X communication.
## 1 Introduction
Since QSFR techniques have been proven capable of identifying isomers with similar structures and mass spectra [7; 8], our study aims to determine if this type of analysis will have good results in the case of some acetalized diastereomers of some monosaccharides. The QSFR technique presented here uses the well-known correlation between enthalpies and Gibbs energies, along with ionic currents (IC), as a tool for mining chemical structures from many calculated mass-energy profiles for the possible structures of the analyte. In the analytical context mentioned above, our study has the following main objectives: (i) the extension of the QSFR analytical technique based on mass-energy profiles to structural isomers and acetalized diastereomers of some monosaccharides; (ii) the simulation of structural analysis for five such isomers using the mining of experimental profiles obtained by mass spectrometry, in the set of quantum chemical (QC)-calculated mass-energy profiles for the candidate structures; (iii) the establishment of the analytical conditions that lead to the best matching scores between the experimental and calculated profiles, such that they indicate the true structures; (iv) the detailed presentation of the chemical structure mining algorithm and the relationship for the calculation of the profile matching score, in order to disseminate this QSFR technique; (v) the validation of QC-calculated mass energy profiles for such isomers, located in a database obtained in a previous work [11]. The main novelty of the work is that the mining of isomeric structures is conducted in parallel in two databases with mass-energy profiles, by calculating the matching score. One of the databases was quantum chemically calculated in a previous study [11]. The other was obtained in the present paper from the mass spectrometry analysis of the five isomeric chemical standards. Furthermore, the fulfillment of each of these objectives has an original conception and/or route. The optimization of the technique was achieved by group validation of analytes, a variant frequently used in chromatography because it can simultaneously cover a variety of structures [6; 8]. The optimization used four independent variables: the quantum chemical (QC) method, the descriptor of calculated energy, the impact energy (IE) of electrons, and the descriptor of experimental energy. If the mass-energy profiles, calculated and experimental, contain real structural information about the analytes, then the best matching score should correspond to its true positive structures.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
###### Abstract  
This paper proposes a gossip-based protocol that utilises a multi-factor weighting function (MFWF) that takes several parameters into account: residual energy, Chebyshev distances to neighbouring nodes and the sink node, node density, and message priority. The effects of these parameters were examined to guide the customization of the weight function to effectively disseminate data to three types of IoT applications: critical, bandwidth-intensive, and energy-efficient applications. The performances of the three resulting MFWFs were assessed in comparison with the performances of the traditional gossiping protocol and the Fair Efficient Location-based Gossiping (FELGossiping) protocol in terms of end-to-end delay, network lifetime, rebroadcast nodes, and saved rebroadcasts. The experimental results demonstrated the proposed protocol's ability to achieve a much shorter delay for critical IoT applications. For bandwidth-intensive IoT application, the proposed protocol was able to achieve a smaller percentage of rebroadcast nodes and an increased percentage of saved rebroadcasts, i.e., better bandwidth utilisation. The adapted MFWF for energy-efficient IoT application was able to improve the network lifetime compared to that of gossiping and FELGossiping. These results demonstrate the high level of flexibility of the proposed protocol with respect to network context and message priority.  
Internet of Things (IoT); wireless sensor network (WSN); gossiping protocol; context-aware; content-aware; routing protocol +
Footnote †: journal: Sensors
## 3 Results  
### 22 June 2015 Geomagnetic Storm  
The storm is related to active region (AR) 12,371 on the Sun according to the SOHO LASCO CME catalog ([[https://cdaw.gsfc.nasa.gov/CME_list/](https://cdaw.gsfc.nasa.gov/CME_list/)]([https://cdaw.gsfc.nasa.gov/CME_list/](https://cdaw.gsfc.nasa.gov/CME_list/)); SOHO--the Solar and Heliospheric Observatory, LASCO--the Large Angle and Spectrometric Coronagraph, CME--Coronal Mass Ejection). The AR produced several CMEs. The 22 June 2015 magnetic storm is mainly due to \(\sim\)1350 km/s Halo CME that appeared at 2:36. The CME with kinetic energy of \(\sim\)9.0\(\cdot\)10\({}^{31}\) erg ([[http://cdaw.gsfc.nasa.gov/CME_list/](http://cdaw.gsfc.nasa.gov/CME_list/)]([http://cdaw.gsfc.nasa.gov/CME_list/](http://cdaw.gsfc.nasa.gov/CME_list/))) is associated with an M2.7 flare with maximum at 02:34 UT. On 22 June, a cluster of shocks passed the Advanced Composition Explorer (ACE) spacecraft at 17:58 UT [22]. Figure 2 presents time series of the data to help tracking storm evolution. We see a sharp increase in the proton density (Figure 2c) and a sudden storm commencement (SSC) (Figure 2a) at \(\sim\)05:45 and \(\sim\)18:30 UT on 22 June, when the shocks hit the Earth's magnetosphere.  
The data presented in Figure 2 (Bz component of the interplanetary magnetic field (IMF), H-SYM index, AE-index, and proton density) were retrieved from the OMNI database ([[ftp://spdf.gsfc.nasa.gov](ftp://spdf.gsfc.nasa.gov)]([ftp://spdf.gsfc.nasa.gov](ftp://spdf.gsfc.nasa.gov))) [50]. The spread-F (Figure 2c, red line) was measured based on the Irkutsk ionosonde data. As the shock hits the 22 June 2015 magnetosphere at 18:30 UT, we see the storm onset as a SSC, and the H-SYM index starts decreasing. The proton density changed from 12 cm\({}^{-3}\) (before the shock) to 52 cm\({}^{-3}\) (after the shock). The H-SYM had a local minimum (135 nT) at 20:15 UT. After 20:15 UT, the magnetosphere was recovering. The recovery of the magnetosphere lasted until 00:50 UT, when Bz turned south again, and the proton density decreased to \(\sim\)9 cm\({}^{-3}\). The minimal H-SYM was observed at 04:25 UT with the value of \(-\)207 nT. We should note that the substorm occurred before the storm. The AE-index shows an increase after 14 UT on 22 June. H-SYM dropped to \(-\)50 nT at \(\sim\)16:15 UT.
## 2 Methods
### Image Reconstruction  
Compressive Sensing (CS) is a mathematical technique that has been widely adopted for intelligent and efficient sampling and storage, enabling sparse representations. Initially proposed to overcome the limitations of conventional signal processing that relied on Nyquist sampling requirements, CS is based on the concept of obtaining observations by computing the inner product between the signal and an observation function, and then recovering the signal from linear observations using a highly nonlinear optimization problem. Unlike traditional imaging methods, CS imaging can acquire high-quality images from a lower sampling rate than the Nyquist sampling rate, thereby reducing the number of sensors and hardware costs [29]. However, solving the inverse problem in CS requires iterative linear programming solvers to optimize the problem. It can be written as:  
\[y=\Phi\Psi\theta, \tag{1}\]  
where \(x=\Psi\theta\) delineates N-dimensional real signals and the retrieval of \(x\) from \(y\) presents a conundrum of solving a system of linear equations. In this context, the expansion factor  
\begin{table}
\begin{tabular}{c c} \hline
**Aperture/Focal Length** & **Imaging** \\ \hline
105 mm/600 mm & \\ \hline
200 mm/2000 mm & \\ \hline \end{tabular}
\end{table}
Table 1: A comparison of different camera lenses.  
\(\theta\) may be presumed to exhibit \(k\)-sparsity, thereby signifying that the quantity of nonzero coefficients, denoted as \(k\), markedly diminishes in comparison to the dimensionality \(N\) of the signals. Furthermore, the introduction of an observation matrix \(\Phi\), conventionally a Gaussian matrix, assumes prominence. Remarkably, \(\Phi\) operates autonomously from the orthogonal base dictionary \(\Psi\), with its interaction confined solely to the coefficients. This enables the capture of a fraction of the signal's information. In scenarios where the coefficients \(\theta\) manifest sparsity, thereby substantially curtailing the number of unknowns, the reconstruction of the signal is viable even at sampling rates lower than the Nyquist sampling rate. Table 2 depicts the imaging results of compressive sensing correlation imaging at various sampling rates. The imaging target is a tower crane at a construction site located 4000 m away, under imaging conditions of 5:00 PM in the afternoon with clear weather, showcasing the resilience of CS in image reconstruction even at remarkably low sampling rates, such as 5% and 10% (original sampling rate of 3000). However, lower sampling rates result in the loss of some fine details. In the context of compressive sensing algorithms, it has been observed that at a sampling rate of 20%, the basic outlines of the signal can still be discerned. Here, we focus solely on compressive sensing without further elaboration. Sampling rates above 20% ensure the acquisition of essential signal features, satisfying the requirements of imaging detection. This significantly reduces the time required for imaging detection.
# Investigation of the Scattering Noise in Underwater Optical Wireless Communications
## 6 Conclusions  
In this paper, we introduced a new source of noise caused by the number of photons experiencing scattering along the propagation path. In dirtier waters or over longer transmission link spans, this noise (i.e., the scattering noise) is more or less the same as the signal-dependent shot noise. We determined the IR based on MC for three types of waters, which is affected by both the scattering and absorption. We introduced the \(\kappa\) coefficient and show that it is a function of the transmission distance for the clear, coastal, and harbor waters, where for longer link distances and dirtier waters, it approached the unity. For the clear, coastal and harbor waters and beyond the transmission spans of 90, 40 and 6.5 m, respectively, \(\kappa\) also approached one. The BER as a function of the transmission distance was compared for the links with and without the scattering noise as well as the shot noise, thermal noise, dark current noise, and background noise. The results demonstrated that for three types of waters, the scattering noise is not negligible; however, its effect is more prominent in the coastal and harbor waters due to higher \(\kappa\) coefficient. The variance of the shot noise and the scattering noise for three types of waters was also calculated, where the dirtier waters or longer link ranges both showed the same variances.  
Conceptualization, B.M.; methodology, B.M.; software, B.M.; validation, B.M., A.G.; formal analysis, B.M.; investigation, B.M.; resources, B.M.; data curation, B.M.; writing--original draft preparation, B.M.; writing--review and editing, A.G.and Z.G.; supervision, A.G.and Z.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Principle and System Structure
### Structure of GVD Compensation  
Typically, \(d\) is constant and \(G\) is an inconvenience adjustment. The slight non-parallelism introduces a spatial chirp, which is detrimental to subsequent coupling. The most appropriate method to regulate compensation dispersion is to adjust the two-dimensional orientation of the plane mirror to alter the diffraction angle. However, manual adjustment is not sufficiently accurate nor efficient, and cannot be applied using adaptive feedback. Hence, an electric adjustment is required for precise tuning and active closed-loop feedback compensation. A schematic of the active closed-loop feedback compensation scheme is presented in Figure 2. The pulse with phase modulation attains FM-AM after transmission, which can be reduced after compensation. The oscilloscope then collects the time -domain signal and transmits it to the computer to calculate the corresponding FM-AM depth. If the value of the depth increases owing to external interference or changes in the input light parameters, according to the recorded data, a control signal is generated by a computer program and fed back to the mirror motor control, and then automatically fine-tuned to improve the compensation results.
## 3 Results and Discussion
### The Coherence Volume of \(D\) = 3 Dimensional Random Lasers
The scatterers’ radius is \(r_{scat}=423.0\) nm, the particles refractive index is \(n\,=\,2.4\), the real part of permittivity is \(\text{Re}\,\epsilon_{scat}\,=\,5.76\), which is comparable to diamond (C). The transport wavelength for this result is chosen as \(\lambda\,=\,385\) nm, the samples filling fraction is chosen as 50% volume filling. The sample is of finite size in the z-direction, its extension in z-direction is \(d\,=\,80\,r_{scat}\,=\,33.84\,\upmu\)m. The strongly scattering medium is thus open, and the translation invariance is broken in the z-direction, whereas the sample is infinite in the x-y plane. We show results for the excitation rates of \(P\,=\,0.2\,\gamma_{21}\) (red), \(0.5\,\gamma_{21}\) (blue), \(1.0\,\gamma_{21}\) (green), \(9.0\,\gamma_{21}\) (yellow) in units of the stimulated emission rate \([\gamma_{21}]\), see Figure 1b. The stationary state coherence volume for each value of the excitation power assumes in stationary state the form of a three dimensional extended disk, which is symmetrical in plane of the slab. The radius of the disk for each excitation power is noted in the figure. The disk is a spatial measure for the random laser threshold of the specific random laser arrangement. At the samples boundaries, the coherence length assumes a finite value which depends on the pump strength. It shall be noted that in cases for \(P\,<\,1.0\,\gamma_{21}\) the external excitation strength is smaller than the threshold value for bulk matter. Thus in this regime the multiple scattering procedures between Mie resonators are the dominating physical concept for the lasing transition of the random scatterer arrangement. in a random laser. The coherence volume is thus a measure of the _correlation_ of multiple scattering events between otherwise independent scatterers. At the samples boundaries the coherence length \(\xi\) becomes always finite. The coherence length \(\xi\) is derived in the stationary state, which means that it is the quantitative length scale which is the measure for the system's random laser threshold. We find no crossover of these length scales \(\xi\) for varying pump rates anywhere in the sample with otherwise identical parameters.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.3 Rule-Based Formalisms  
Rule-based systems provide a structured and explicit representation of knowledge, leveraging if-then rules to offer a clear and formal language to express domain-specific information. These systems stand out for their accuracy in reflecting domain-specific knowledge and rules, ensuring that the conclusions drawn align well with the established rule set. Additionally, they offer a high degree of precision by offering detailed descriptions and constraints via explicit rules, enabling fine-grained control and specific reasoning within the domain.  
While these systems comprehensively cover the knowledge explicitly represented in rules, they might encounter challenges in handling missing or incomplete information not explicitly encoded within the rule set, generalizing beyond the defined rule set. The reasoning process is based on explicit rules, allowing for traceability and justifications for conclusions. This clarity in the reasoning process makes them highly explainable and suitable for presenting transparent decision-making pathways.  
Nonetheless, these systems may encounter limitations in handling certainty and consistency, especially in dealing with uncertainty or encoding probabilistic beliefs as effectively as probabilistic models. While they follow a structured order of execution (forward or backward chaining) to resolve conflicts or inconsistencies in a deterministic manner, they lack mechanisms to prevent contradictory rules. Furthermore, while they allow for the incorporation of new rules or modifications to existing ones, expanding to entirely new knowledge domains might necessitate significant effort and expertise. Managing a large number of rules might lead to scalability issues, impacting performance and complexity as the rule base grows.  
In summary, rule-based systems offer structured and explicit representations with high accuracy and precision. They excel in explainability and expandability, but might face challenges in dealing with uncertainty, scaling to large rule bases, and generalizing to new domains.
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 2 Methodology
### Basic Principle of ANN  
The capability of an ANN model to optimize, determine, and predict a given situation depends heavily on the reliability of the database it draws from as well as the amount of data it has processed. This is because an ANN system derives its output from patterns recognized during the pre-learning stage so that a more reliable outcome can be acquired from a model that has learned from a more extensive variety of situations.  
Figure 3 shows a model of the artificial neuron or processing element (PE) forming the basis of the ANN structure. This layered structure is the most common structure in ANNs and is usually called the fully connected feedforward or acyclic network. The starting point of the ANN structure is a layer of input units that allows entrance of information into the network. Normalization is the process of equalizing the signal range of different inputs. Normalization ensures that changes in the signals of different inputs have the same effect on the network's behavior regardless of their magnitude [19].  
Figure 2: Study process.  
Learning from examples is the main operation of any ANN. In this case, 'learning' means the ability of an ANN to improve its performance through an interactive process of adjusting its free parameters. The adjustment of an ANN's free parameters is stimulated by a set of examples presented to the network during the application of a set of well-defined rules, called a learning algorithm, for improving its performance. There are many different learning algorithms for ANNs, each with different ways of adjusting the synaptic weights of PEs and different ways of formalizing the measurement of the ANN's performance [19].  
In general, there are three major learning algorithms: supervised learning, unsupervised learning, and reinforcement learning [34]. In supervised learning, the network is trained by providing it with input and matching output patterns. These input output pairs can be provided by an external teacher or by the system that contains the neural network (self-supervised). This study adopted supervised learning. Furthermore, there are two different approaches: error back-propagation algorithms, a gradient method, and a genetic algorithm, which is a stochastic search method [35]. The back-propagation training algorithm is the most frequently used neural network method [36; 37; 38]. It is trained using a set of examples of associated input and output values.  
After the ANN reaches the required performance by learning examples, it can be used to predict the outcome of situations given specific input parameters. It merely behaves as a series of functions that produce an output for a particular input.
# Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5
## 1 Introduction  
### Motivation  
Currently, China's total highway mileage has reached 5.28 million kilometers, with over 970,000 highway bridges, playing a significant role in the national comprehensive transportation system. More than 95% of these highway bridges are constructed using concrete, and cracks serve as a key indicator in the mechanical testing of concrete structures, as well as an important aspect of bridge health assessment. Cracks provide a visual understanding of the extent of structural damage and can reveal early signs of most types of defects. Cracks can lead to the detachment of protective layers, resulting in the corrosion of reinforcement bars and diminishing structural strength, durability, and stability. Severe through-cracks can even pose substantial threats to structural safety [1]. Therefore, conducting regular inspections of bridge cracks and evaluating their residual performance, identifying potential issues, and implementing appropriate maintenance measures not only ensure smooth traffic flow and enhance transportation efficiency but also play a pivotal rolein promoting regional economic development. However, traditional bridge crack detection heavily relies on manual operations, resulting in low efficiency, unstable outcomes, and difficulties in quantification [2]. There is a significant safety risk when conducting high-altitude crack inspections manually, as shown in Figure 1. This necessitates a substantial additional investment to ensure the safety of inspection personnel. Influenced by factors such as lighting conditions and height, the data are difficult to present intuitively. In terms of economy, efficiency, accuracy, and data management, traditional methods struggle to meet the demands of extensive bridge crack detection [3].