esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Driving Direction of Traffic Accident Vehicles for Improving Traffic Safety
## 3 Proposed Method
### Extracting Accident Vehicle Destinations Using Geocoding  
This paper estimates the destination coordinates \((x_{dst},y_{dst})\) using the text-based destination information extracted from NER. This paper estimates the destination point through two stages of geocoding processes.  
In the first step, if the extracted destination from NER represents a specific place name such as 'Busan' or 'Seoul', the geocoding engine directly outputs the corresponding coordinates \((x_{dst},y_{dst})\). However, there are multiple locations with the same name, such as apartments or stores. In that case, obtaining specific coordinates from the geocoding engine becomes impossible. In such cases, it is reasonable to assume that the destination is located in the nearest area to the accident location.  
In the second step, geocoding is performed in a restricted area by providing additional information about the destination. The reverse-geocoding engine provides text address information for the accident location coordinates \((x_{acc},y_{acc})\). By adding the \"do\" and \"si\" information about the area to the destination and inputting it into the geocoding engine, reliable location information can be obtained. For example, assuming a traffic accident in \"358, Sagiso-dong, Dangjin-si, Chungcheongnam-do\", if the extracted destination from the NER results in \"Himart\", the geocoding engine cannot return any results. Therefore, by reverse-geocoding the accident information \((x_{acc},y_{acc})\), \"Chungcheongnam-do\" and \"Dangjin-si\"can be additionally obtained. A new query, \"Himart, Dangjin-si, Chungcheongnam-do\" is created. If multiple candidate destination points are returned, the geocoding engine sets the closest point to \((x_{acc},y_{acc})\) as the final \((x_{dst},y_{dst})\) using the second geocoding. Figure 3 illustrates the entire geocoding process.  
```
1:procedureNER(report)
2:\(BD\leftarrow\) [\"banghyang-eseo\", \"bangmyeon-eseo\", \"jlog-eseo\", \"eseo\"]
3:\(ED\leftarrow\) [\"banghyang-eulo\", \"bangmyeon-eulo\", \"jlog-eulo\", \"eulo\"]
4:entities\(\leftarrow\emptyset\)
5:for\(i\gets 1\) to len(report) do
6:for\(j\gets 1\) to len(\(BD\)) do
7:if\(report[i-\text{len}(BD[j])+1:i]=BD[j]\)then
8:for\(k\gets i+2\) to len(report) do
9:for\(l\gets 1\) to len(\(ED\)) do
10:if\(report[k:k+\text{len}(ED[l])-1]=ED[l]\)then
11:entities\(\leftarrow\)entities\(\cup\left\{(report[i+1:k-1])\right\}\)
12:endif
13:endfor
14:endfor
15:endif
16:endfor
17:endfor
18:destination\(\leftarrow\)entities\([0]\)
19:for\(i\gets 1\) to len(entities) do
20:if len(entities[i])\(<\) len(destination) then
21:destination\(\leftarrow\)entities\([i]\)
22:endif
23:endfor
24:returndestination
25:endprocedure
```  
**Algorithm 1** Rule-based Named Entity Recognition.  
Figure 3: Geocoding flowchart.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 2 Related Works
### Energy-Aware Routing Protocols for UWSNs
Simulation results show that the proposed NCRP enhances the network performance in terms of energy consumption, end-to-end delay, and the ratio of packet delivery, in comparison to other routing protocols. A Distributed Energy-Efficient and Balanced (DEEB) routing algorithm was proposed by [PERSON] et al. in [41]. It was designed for underwater wireless optical sensor networks. It is suitable for static and dynamic UWSNs. The protocol relies on setting an energy threshold so that the batteries of sensors in a network are not drained. The simulationresult shows that the DEEB algorithm outperforms the FBR and R-ERP2R algorithms in terms of energy consumption. A protocol called Underwater Modified LEACH (UMOD-LEACH) was proposed by [PERSON] et al. in [2]. It is an improvement of the Low Energy Algorithm Adaptive Clustering Hierarchy (LEACH) protocol and is based on TDMA scheduling. It aims to reduce energy consumption by using the localization concept. Simulation results indicated that UMOD-LEACH surpassed the LEACH protocol in terms of energy consumption by more than 30%. In [42], [PERSON], [PERSON] et al. proposed a Cluster-Based Adaptive Routing (CBAR) algorithm for UWSNs. Packet formatting is designed to be suitable for cluster networks. The CBAR algorithm depends on updating dynamic routes and saving energy consumed in the routing process. Simulation results show that the CBAR algorithm outperforms the FBR algorithm and DUCS in terms of energy consumption and the ratio of data delivery. In [43], [PERSON] and [PERSON] developed an Energy-Efficient Probabilistic Depth Based Routing called EEFDBR for UWSNs. The proposed EEFDBR considers vertical depth, residual energy, and the number of neighbors when calculating the probability of forwarding. Simulation results demonstrate that the EEFDBR algorithm achieves better performance results than other protocols in terms of energy consumption, packet delivery ratio, and time. [PERSON] et al. proposed a routing protocol called Geographic and Cooperative Opportunistic Routing Protocol (GCORP) in [44]. Determining the set of relay forwarding nodes is performed based on a depth fitness factor. A weight calculation scheme is performed to find the best relay node. Simulation results demonstrate that the GCORP algorithm outperforms other routing protocols in terms of average energy consumption, end-to-end delay, packet delivery ratio, and the lifetime of UWSNs.
## 3 Results and Discussion  
### Physical Properties of Mandarin Juice Powders  
#### 3.1.1 Colour Differences among Powder Samples  
The colour of the spray-dried powder is important, as it determines the quality and sensory attractiveness of the products [19]. Table 1 shows the colour attributes for the spray-dried mandarin juice powders prepared from the Afourer and Richard Special mandarin fruits (A1, A2, A3, RS1, RS2, and RS3, as described in Section 2.2). Generally, the L* value (lightness) for all of the samples ranged from 85.11 to 88.30, the a* value (redness) varied from 3.39 to 9.26, and the b* value (yellowness) ranged from 30.40 to 54.68 (Table 1). The results indicated that the mandarin juice powder samples showed higher differences in redness (a* value) and yellowness (b* value) than lightness (L* value). Furthermore, the chroma and the hue angle (representing the colour saturation and the colour perception, respectively) were calculated in order to better understand the colour differences among all of the samples. Powders that were made from the whole fruit samples had the highest colour saturation (chroma value of 52.71 for A3 and 54.28 for RS3), followed by the samples from the flavodo-removed fruits (A2 & RS2; 48.08 & 43.54), and those from the peel-removed fruits showed the least colour saturation (A1 & RS1; 29.54 & 37.95), as shown in Table 1. This suggested that the colour pigments in the mandarin peels have a significant contribution to the colour of the powders. The values of hue angle at 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) indicate the colour of red, yellow, green, and blue, respectively [23]. According to the results (Table 1), all of the samples were strongly characterized by yellow colour as their hue angle values are close to 90\({}^{\circ}\) (73.69\({}^{\circ}\) to 86.52\({}^{\circ}\)). The samples prepared from the peel-removed fruits (A1 & RS1) had the lightest yellowness (A1, 73.94\({}^{\circ}\); RS1, 73.69\({}^{\circ}\)), while the powders that were derived from the whole mandarin fruits (A3 & RS3, 86.50\({}^{\circ}\) & 84.69\({}^{\circ}\)) showed the most intensive yellow colour (_p_ < 0.05). The literature reported that inlet air temperature could affect the colour of the spray-dried powders due to the non-enzymatic browning reaction during processing [19]. Besides, the type of carrier or wall material added in the feed solution, together with their concentrations, could have an impact on the colour of the powder produced [14]. In this study, we have used the same wall material and spray drying conditions for all of the samples; therefore, the colour difference across the mandarin juice powders could be mainly attributed to the sample types (that were prepared with/without mandarin albedo and/or flavedo). The bright orange-yellow colour of mandarin is related with the levels of carotenoids, which normally present at a higher concentration in peel than in pulp
# Ultra-Compact Digital Metasurface Polarization Beam Splitter via Physics-Constrained Inverse Design
## 3 Results and Discussion
The simulation results under this circumstance indicate that the device is robust to small geometry changes, as shown in Figure 6a,b, while ERs of both the TE and TM mode are above 10 dB, and ILs remain smaller than 1.07 dB over the entire 80 nm bandwidth from 1510 nm to 1590 nm. Considering that the metasurface layer of the device needs to be formed on a standard SOI, there is a high possibility of deviation in the layer from the ideal position. We simulated the device performance when the additional layer deviates from the ideal position by 1 \(\upmu\)m toward the input port, as shown in Figure 6c,d. The results show that the separation function of the PBS device is still efficient, with low ILs (<1.36 dB for TM mode, and <0.79 dB for TE mode) over the entire 80 nm bandwidth and fairly low ERs (>9 dB for TM mode and >11.5 dB for TE mode) in the C-band. The simulation results indicate that the metasurface PBS is suitable for future WDM and PDM communication applications in SOI platform. Figure 5: (a) Device performance when suffering 20 nm over-etch for TE mode and (b) TM mode. (c) Device performance when suffering 20 nm under-etch for TE mode and (d) TM mode.
# Metrics for Polyphonic Sound Event Detection
## 3 Intermediate Statistics and Averaging Options
### Segment-Based Metrics  
Segment-based metrics compare system output and reference in short time segments. Active/inactive state for each event class is determined in a fixed length interval that represents a segment. Based on the activity representation, the following intermediate statistics are defined:  
* true positive: the reference and system output both indicate an event to be active in that segment;
* false positive: the reference indicates an event to be inactive in that segment, but the system output indicates it as active;
* false negative: the reference indicates an event to be active in that segment, but the system output indicates it as inactive.  
Some metrics also count true negatives: when the reference and system output both indicate an event to be inactive. Total counts of true positives, false positives, false negatives and true negatives are denoted as _TP, FP, FN_, and _TN_, respectively.  
The size of the time interval can be chosen based on the desired resolution needed for the application. For example, in [31] a segment of 100 ms was proposed, and the metric was referred to as frame-based. In [26] a length of one second was suggested, for calculating the metrics on longer time segments than the typical frame length used in analysis. By using a longer segment for evaluation, the activity indicator covers a longer chunk of audio, allowing a degree of misalignment between the reference and the system output. This alleviates issues related to annotator subjectivity in marking onset and offset of sound events.
## 4 Numerical Investigation
### Material Model  
Figure 14a indicates the stress-strain relationship for precast concrete material. For the compressive properties of precast concrete, the stress-strain curve including three segments proposed by [PERSON] [16] was adopted. The first segment of the stress-strain curve is assumed to be in the linear elastic region to 0.3(\(f_{c}\)') [17]. The initial Young's modulus (\(E_{c}\)) is computed using Equation (12). The second segment of the stress-strain curve is the nonlinear parabolic part. The variation of this segment can be determined by Equation (13). The third segment of the stress-strain curve is the linear decreasing segment, and varies according to Equation (14).  
\[E_{c}=8500\times(f_{c}^{\prime})^{1/3} \tag{12}\]  
Figure 13: Loading and boundary conditions.  
For the precast concrete in tension, before the concrete cracks, the tensile stress is assumed to increase linearly, but then decreases linearly. The maximum tensile strength of concrete (\(f_{t}\)) is assumed to be 5 % of the compressive strength of concrete (\(f_{c}\)) [18].  
To model the precast concrete material, the Concrete Damage Plasticity model available in the ABAQUS material library was used. This model assumes a non-associated potential plastic flow, where recommendation values (default) of the ordinary concrete material in the ABAQUS are taken: \(\psi=30^{\circ}\), \(K=0.667\), \(\varepsilon=0.1\), \(f_{b0}/f_{c0}=1.16\).  
For the compressive properties of grout, the stress-strain relationship proposed by [PERSON] et al. [19] was adopted. [PERSON] et al. [19] found that the strain of grout at the maximum compressive strength (\(\varepsilon_{0}\)) is taken as (0.010 to 0.013), and its ultimate strain (\(\varepsilon_{cu}\)) is taken as (0.017 to 0.019). The elastic modulus of grout (\(E_{g}\)) is calculated using Equation (15). Thus, in this analysis, ultimate strain (\(\varepsilon_{cu}\)) and the strain at the maximum compressive strength (\(\varepsilon_{0}\)) of grout are assumed to be (0.018 and 0.012), respectively. The ultimate stress and limit stress of the linear elastic range are assumed to be (80 and 30) % of the compressive strength of grout, respectively. A tri-linear model was used. For the tensile properties of grout, the variation rule of tensile stress of grout is assumed to be similar to that of precast concrete. Figure 14b indicates the stress-strain relationship of the grout material. To model the grout material, the Concrete Damage Plasticity model was also used.  
\[E_{g}=200\times(f_{c}^{\prime}) \tag{15}\]  
The elastic-perfectly plastic curve was applied to the reinforcement and shear connector steel. The real stress-strain curve was applied to structural steel HSB380.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 7 Proposal for Security Incident Simulation Method
### Procedure for Threat Analysis and Countermeasure Development  
PASTA (Process for Attack Simulation and Threat Analysis) is a threat modeling framework developed in 2012. As shown in Figure 15, it consists of seven stages, each involving different activities. This paper focuses on stage 4, which deals with threat analysis, and stage 6, which covers attack modeling.  
Based on the PASTA threat analysis model in Figure 16, as well as the strategies and techniques from the MITRE ATT&CK framework, the threat analysis procedure can be defined as follows.  
Figure 13: Classification by security incident type and identification of incident type.  
Figure 14: List of security incident scenarios classified by type.  
Figure 15: PASTA (Process for Attack Simulation and Threat Analysis) threat analysis model.  
Based on the scenario generation procedure, the security incident prediction and management web system should be enhanced to assess the likelihood of various types of security incidents occurring at the present time, using both collected and input information from within the organization. By improving the system to predict and monitor potential future incidents, several key reporting functions are required to facilitate effective response.  
First, the system must enable security incident prediction and monitoring, as well as provide comprehensive reporting capabilities. This includes proposing countermeasures for new types of security incidents through integration with cloud-based systems and identifying the causes and appropriate responses to major security incidents both domestically and internationally [39].  
Additionally, the system should be capable of generating prediction lists for different incident types, tailored to the specific organizational context. Ensuring the security of collected information through encryption across all communication channels is crucial, along with the implementation of a notification system (such as SMS or email) to help prevent incidents outside regular working hours.  
Furthermore, it is essential to automate the generation of security audit reports. This would involve the automated collection of logs from security products, including SIEM and ESM, ensuring seamless integration with existing security infrastructures.  
A structured classification system for security incident types is also necessary. This classification should be based on industry-specific characteristics--such as finance, healthcare, and manufacturing--while organizing security incidents into broader categories such as external, internal, and supply chain threats. Systematizing incident types like hacking, data leakage, and supply chain tampering would provide a clear framework for incident management.  
Finally, the system should incorporate AI-driven capabilities to predict the likelihood of specific types of security incidents. A comprehensive dashboard and management tools should be provided, enabling administrators to effectively monitor and respond to these predicted incidents [40].
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
## 2 Experimental Datasets and Methods
### Experimental Methodology
#### 2.2.4 Bayesian Optimization  
Bayesian optimization (BO) is an efficient global optimization method suitable for optimizing objective functions that are computationally expensive or difficult to analyze [39; 40]. Unlike traditional optimization methods, BO constructs a surrogate model of the objective function and incrementally explores the input space to find the global optimum within a limited number of evaluations.  
Bayesian optimization is widely used in hyperparameter tuning for machine learning. Hyperparameters are crucial for model performance, but their large and complex search spaces make efficient exploration difficult with conventional methods like grid or random search. Bayesian optimization uses a surrogate model to guide the search process, reducing the number of evaluations needed and improving efficiency. In the XGBoost, SVM and RF models, Bayesian optimization improves the model performance by tuning several hyperparameters and their value ranges, including: learning rate (0.01 to 0.05), number of trees (integer between 150 and 200), maximum depth of the tree (integer between 3 and 8), minimum loss reduction (0 to 5), sub-sampling ratio (0.5 to 0.8), column sampling ratio (0.5 to 0.8), and column sampling ratio (0.5 to 0.8). This process identifies the optimal model configuration.
# POL-MUX System for Noncoherent Optical Networks
###### Abstract  
This paper is focused on applying a polarization multiplex to passive optical networks to double their transmission bandwidth without significant changes in the distribution network. Although polarization multiplexes are already employed for high-speed optical transport networks with digital signal processing and coherent detection, we propose a system that could be used in existing older optical networks using a dynamic polarization controller in combination with a wavelength division multiplex.  
WDM; polarization; PDM; POL-MUX; PON; crosstalk; BER; DPC
## 5 Water Pool Experiment
### Analysis of Experimental Results  
#### 5.2.1 Direct Spread-Spectrum Coding System  
The BER performance of the DSSS communication system under different coding methods is shown in Figure 17. Analysis of Figure 17 indicates that the LDPC coding/decoding method using CCSDS codes can achieve a better coding gain than other channel coding algorithms, such as Turbo code, convolutional code and BCH code. The performance of (2, 1, 7) convolutional codes is related to the quantization accuracy, which is 4-bit quantization. The performance of Turbo codes is similar to that of LDPC codes at a lower SNR, and as the SNR increases, the method proposed in this paper has the best performance when the BER is \(1\times 10^{-4}\), where it has a BER performance improvement of 0.3 dB compared to Turbo codes.
# Addressing ZSM Security Issues with Blockchain Technology
## 7 Assessment of Blockchain and DAG Platforms to Be Used for the ZSM-Solution Implementation
### Assessment of Blockchain/DAG Platforms
The development of a private/consortium and permissionless DAG network could be the most suitable solution, bearing in mind the main characteristics of the presented DAG-based solutions.
## 5 Results
### Scalability Simulation Analysis
The decentralised Fog computing results clearly show more than a 34% reduction in network bandwidth usage compared to the centralised Cloud computing results. In summary, this study used real-world IoT device data and real-world infrastructure data with multiple Raspberry Pi 4 model B parameter values representing Cloud data centres, proxy servers, and Fog nodes in a smart building's Cloud-Fog network architecture. According to the simulation results, the management and computation of data processed at both the centralised Cloud data centre location and the decentralised Fog node clusters closer to the network's edge, are highlighted and compared. The Cloud and Fog computing comparison also highlights the difference in energy consumption, latency, and network bandwidth usage output for Cloud and Fog computing data processing. Overall, the data processed at the Cloud layer had a relatively higher energy consumption, latency, and network bandwidth output. Conversely, the results from data processing at the Fog computing layer demonstrated the optimisation benefits of lower energy consumption, improved latency, and less congested network bandwidth usage.
## 2 Theory and Method for Fatigue Crack Propagation
### Calculation of Crack Propagation  
Both the maximum circumferential stress and the maximum energy release rate criteria serve as the fundamental basis for the crack propagation evaluation. In this study, the maximum circumferential stress criterion is adopted to determine the crack propagation direction. The expressions for the stress components at the crack tip are as follows [2]:  
\[\begin{array}{l}\sigma_{r}=\frac{1}{2\sqrt{2\pi r}}\left[K_{I}(3-cos\varphi )cos\frac{\varphi}{2}+K_{II}(3 cos\varphi-1)sin\frac{\varphi}{2}\right]\\ \sigma_{\varphi}=\frac{1}{2\sqrt{2\pi r}}cos\frac{\varphi}{2}\left[K_{I}(1+ cos\varphi)-3K_{II}sin\varphi\right]\\ \tau_{r\varphi}=\frac{1}{2\sqrt{2\pi r}}cos\frac{\varphi}{2}\left[K_{I}sin \varphi+K_{II}(3 cos\varphi-1)\right]\end{array} \tag{5}\]where \(\sigma_{r}\), \(\sigma_{\varphi}\), \(\tau_{r\varphi}\) represent the radial, circumferential, and tangential stress, respectively, and \(\varphi\) represents the crack inclination angle, whose expression is as follows [23]:  
\[\varphi=2 arctan\left(\frac{1}{4}\frac{K_{I}}{K_{II}}\pm\frac{1}{4}\sqrt{\left( \frac{K_{I}}{K_{II}}\right)^{2}+8}\right) \tag{6}\]  
The stress-strain in linearly elastic materials satisfies [PERSON]'s law:  
\[\epsilon_{r}=\frac{1}{E}\big{(}\sigma_{r}-\vartheta\sigma_{\varphi}\big{)}; \epsilon_{\varphi}=\frac{1}{E}\big{(}\sigma_{\varphi}-\vartheta\sigma_{r} \big{)};\gamma_{r\varphi}=\frac{2(1+\vartheta)}{E}\tau_{r\varphi} \tag{7}\]  
where \(\epsilon_{r}\), \(\epsilon_{\varphi}\), and \(\gamma_{r\varphi}\) represent the radial, circumferential, and tangential strains, respectively. Meanwhile, the crack propagation direction fulfills these conditions [2]:  
\[\begin{array}{c}\frac{\partial\epsilon_{\varphi}}{\partial\varphi}=0;\frac {\partial^{2}\epsilon_{\varphi}}{\partial\varphi^{2}}\leq 0\\ K_{I}sin\frac{\varphi}{2}\big{[}2\theta-3(1-\vartheta)cos^{2}\frac{\varphi}{2} \big{]}+K_{II}cos\frac{\varphi}{2}\big{[}9(1-\vartheta)sin^{2}\frac{\varphi}{ 2}-(3+\vartheta)\big{]}=0\end{array} \tag{8}\]  
In this study, Paris Law is used to evaluate the crack propagation rate, and expressed as follows:  
\[\frac{da}{dN}=C(\Delta K)^{m} \tag{9}\]  
Here, \(a\) is the crack propagation length, and \(N\) is the number of fatigue load cycles. \(C\) and \(m\) are the material coefficients. \(\Delta K=K_{max}-K_{min}\) is the stress intensity factor range.