esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Critical Importance of Product Identity in Agricultural Supply Chains
### Systemic Deficiencies in Current Traceability Systems
#### 3.1.1 Infrastructure Limitations  
The sources consistently show that current traceability systems struggle to maintain a clear and reliable product identity throughout the supply chain [19]. This is largely due to the vulnerabilities of centralised databases, information asymmetry, and unreliable verification methods, all of which contribute to a lack of a robust way to establish, maintain, and verify a product's identity [22].  
* Centralised databases: Controlled by a single entity and vulnerable to manipulation. They represent single points of failure, which can compromise product data. This makes it hard to ensure the integrity of product information and thus a solid product identity [23].
* Information asymmetry: The fragmentation of data across different stakeholders results in a lack of a comprehensive and unified view of product information [24]. This lack of a holistic view makes it hard to maintain a cohesive product identity.
* Unreliable verification: The ease with which labels and certifications can be forged undermines the ability to verify the authenticity of a product, meaning there is no guarantee that a product is what it claims to be, and it further compromises the establishment of a clear product identity [25].
# Enhanced Linearity in Intracranial Pressure Monitoring System Through Sample Isolation Bridge ROIC
## 3 ROIC Implementation Details  
A basic circuit diagram for the suggested ROIC is presented in Figure 3. This diagram includes an IA utilizing a cross-connection approach along with a sampled-data isolation-based SAR ADC. This section will provide an in-depth discussion of the implementation of these components.
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 6 Conclusions  
This paper proposes a novel platoon vehicle priority-based resource allocation algorithm for 5G-V2X Mode 1. The proposed algorithm identifies resource requests for platoon maintenance based on the resource request messages transmitted by platoon vehicles. Consequently, the base station can indirectly infer that the destination of a packet from a platoon vehicle is the next vehicle in the platoon, enabling it to calculate the interference and SINR between vehicles reusing the same resources. The algorithm enforces a priority policy based on the identifiers of platoon vehicles to ensure reliable communication for the platoon. For platoon vehicles attempting to reuse resources, the algorithm calculates the impact of interference on SINR to determine whether resource reuse is feasible. After allocating resources to platoon vehicles, the remaining resources are allocated to general vehicles. Resource reuse for general vehicles is determined based on distance, while for platoon vehicles, reuse is determined by SINR, as previously described. The proposed algorithm was evaluated through simulations using the Simu5G simulator and demonstrated superior performance compared with existing algorithms in terms of Average Allocation Rate (AAR) and average delay. Future research will focus on enhancing the resource allocation performance for general vehicles by optimizing the resource reuse distance according to vehicle density. One promising approach is to apply machine learning techniques to dynamically optimize the reuse distance, taking into account factors such as vehicle density and communication conditions. In particular, one potential approach is to partition the entire road into clusters, allowing for the dynamic adjustment of resource reuse distances based on the vehicle density within each cluster, the presence of platoons, and the number of vehicles in each platoon. This optimization could further improve resource utilization and reduce delays for general vehicles in dense network scenarios.  
Conceptualization, T.-W.K., S.L. and K.-J.P.; Methodology, T.-W.K. and D.-H.L.; Validation, S.L. and D.-H.L.; Investigation, T.-W.K. and S.L.; Data curation, T.-W.K.; Writing--original draft, T.-W.K.; Writing--review & editing, T.-W.K., S.L., D.-H.L. and K.-J.P.; Supervision, K.-J.P.; Project administration, K.-J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2021-0-01277).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 3 Image Preprocessing
### Contrast Enhancement  
Radiographic images are usually low-contrast and short of detail due to the limitation of intensities range accommodated by the capture device. The objective of contrast enhancement is to adjust the contrast value for highlighting the important parts without the unimportant information lost [9,15]. [PERSON] used the contrast stretch and normalization algorithm to improve the image. The method first normalize the image with the low and high threshold values, then find the closest value to the minimum and maximum values, and finally do the contrast stretching according to determined range of contrast values [14]. [PERSON] applied the histogram stretch and the histogram equalization to obtain the optimum image before the segmentation process. The histogram stretch algorithm aims to increase the contrast of images. The objective of histogram equalization is to obtain an image with equally distributed brightness levels over the whole brightness scale [2]. [PERSON] improved both the contrast of welding seam area and background area with the method of sin function intensification. After that, the grayscales of background area and welding seam area was concentrated on high and low grayscale, and the curve was double-peak [16].
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 4 Double-Scale Channel State Prediction
### Large-Scale Channel State Prediction  
To predict large-scale channel states, a \(k\)-nearest neighbor algorithm with sliding window is designed based on subsequence matching. Furthermore, in order to reduce the computational complexity, the length of time series to be reserved is calculated according to the characteristics of channel fluctuation.
## 3 Results
### Different Locations in the Same Space  
The second set of measurements aimed to examine the impact of different measuring positions in the same space. Comparison for octave band RT measurements between those obtained with the dodecahedron speaker and the proposed method for the same microphone and source positions are presented in Figure 8, for the points A, B and C in the amphitheater (Figure 1).  
Figure 9 presents comparison for octave band EDT, C\({}_{80}\) and D\({}_{50}\) measurements between those obtained with the dodecahedron speaker and the proposed method, while Figure 10 presents a comparison between the frequency responses.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Acoustic** & \multicolumn{3}{c}{**Small Space**} & \multicolumn{3}{c}{**Midsized Space**} & \multicolumn{3}{c}{**Large Space**} \\ \cline{2-10}
**Parameters** & **M.A.E.** & **S.D.** & **R.E.** & **M.A.E.** & **S.D.** & **R.E.** & **M.A.E.** & **S.D.** & **R.E.** \\ \hline RT (s) & 0.055 & 0.051 & 1.384 & 0.038 & 0.042 & 0.607 & 0.024 & 0.021 & 0.435 \\ EDT (s) & 0.091 & 0.080 & 2.644 & 0.175 & 0.140 & 1.963 & 0.089 & 0.091 & 2.202 \\ C\({}_{80}\) (dB) & 1.548 & 0.811 & 1.548 & 1.708 & 1.052 & 1.708 & 1.235 & 0.878 & 1.235 \\ D\({}_{50}\) (\%) & 6.300 & 2.892 & 2.063 & 7.325 & 3.970 & 5.705 & 7.423 & 6.346 & 2.302 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean Absolute Error (M.A.E.), Standard Deviation (S.D.) and Relative Error (R.E.) of RT, EDT, C\({}_{80}\) and D\({}_{50}\) for measurements with a directional speaker compared with measurements with the dodecahedron speaker for the small, midsized and large space.  
Figure 6: Reverberation Time (RT) for the small, midsized and large space (from left to right).  
Figure 7: Early Decay Time (EDT), Clarity (C\({}_{80}\)) and Definition (D\({}_{50}\)) for the small, midsized and large space.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 2 Establishment of the Electric-Power-Traffic Network Model  
The urban traffic road network serves as the foundation for EV travel, with the road network's structure and operational conditions impacting various aspects of EV journeys, such as destinations, travel routes, and travel times. Additionally, due to the dual load characteristics of electric power and traffic associated with EVs, the road network's structure and operational conditions also influence EV charging behaviors, including charge states and charging choices. Consequently, these factors have a certain impact on the operation of the power grid. In other words, the charging behavior of EVs at charging stations establishes power-traffic coupling network between the distribution grid, EVs, and the transportation network, as illustrated in Figure 1.  
On the basis of establishing a power-traffic coupling network, it is possible to facilitate information exchange between EVs, the power grid, and the road network. By considering the travel characteristics and charging habits of EV users in different areas of the road network, travel and charging paths can be planned to minimize user travel time costs while meeting their charging needs. This approach aims to reduce the impact of EVs on the power grid and alleviate traffic congestion near charging stations.
## 5 Results
### Pseudocode of Bi-Tuning SSO  
The bi-tuning SSO is a new SSO and can be used to tune both the parameters, i.e., \(C_{g}\), \(C_{p}\), and \(C_{w}\), and update the mechanism in the traditional SSO efficiently and easily. The pseudocode of the proposed bi-tuning SSO is designated for the budget-limited WSN sensing coverage problem by the following procedures:  
**STEP 0.**: Let \(i=1\).
**STEP 1.**: The parameters of SSO\(i\) are listed in Table 3 and let the best solution be \(G_{i}\).
**STEP 2.**: If \(i<8\), go to STEP 1.
**STEP 3.**: The best one among \(G_{1}\), \(G_{2}\),..., and \(G_{7}\), is the one we need.  
It is always a very critical task to determine the time complexity of any algorithms and the time complexity is always based on the worst time complexity, i.e., the O-notation. Additionally, the time complexity of the fitness calculation depends on the problems and was ignored in some studies. Hence, due to the simplicity of the SSO, the computational complexity of the proposed bi-tuning SSO is determined mainly by the update mechanism.  
The proposed bi-tuning SSO implementing the all-variable update mechanism needs to be run N\({}_{\text{var}}\) times for each solution in each generation. Hence, the time complexity of the proposed bi-tuning SSO is \(O(7\times\text{N}_{\text{gen}}\times\text{N}_{\text{sol}}\times\text{N}_{ \text{var}})\). Furthermore, the practical performance of the proposed bi-tuning SSO was tested for nine problems, as described in Section 5.
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 6 Discussion  
In this study, we used the DyGPP model to conduct an in-depth analysis of passenger travel behavior patterns. The results showed that DyGPP outperformed all existing methods in predicting the next station for passengers. This outcome indicates that comprehensively considering the historical interaction information between passengers and stations is crucial in PP tasks. The DyGPP model significantly improved the prediction accuracy of PP tasks, while maintaining a relatively simple architecture and achieving efficient predictions in a short time. This lays a solid foundation for the model's application in real-world production.  
Experimental analysis revealed that the neighbor co-occurrence encoder significantly impacted the model's prediction accuracy, with the cross-attention part playing a critical role. Since the model uses first-order neighbors as the basis for sequence construction, and the first-order neighbors of a station are typically passengers, a small amount of non-repetitive passenger information may be insufficient to fully represent the characteristics of a station when the sequence length is short. Additionally, although there are often spatial and logical relationships between stations, this study treated stations as independent nodes for prediction and judgment. This simplification may have affected the prediction accuracy.  
Given the limitations of the experiments, future research should focus on two main aspects: first, for station feature extraction, future studies should consider using second-order neighbors to represent stations. Since the second-order neighbors of a station are still stations, this approach can better capture the logical similarities between stations. Second, the integration of spatial location information (including the geographical location of stations and the public transportation routes passing through them) should be considered to represent station features. By doing so, the model can better learn the implicit relationships between stations during training, further improving the prediction accuracy.  
The model proposed in this study not only addresses the current issue of traffic management departments relying solely on station flow rate and volume for predictions but also enhances data utilization efficiency by incorporating individual passenger characteristics to more accurately predict future station passenger flow. This method can identify differences among various passenger groups, which can help improve a station's early warning and risk response capabilities in emergencies or special incidents.
# DAB Signal Preprocessing for Passive Coherent Location
## 3 Results
### Substitution of the Null Symbol by Selected MSC Symbol  
The simplified method of copying just one MSC symbol from the previous frame in place of the Null symbol gives the same improvement (depicted in Figure 20) as in the previous case. The periodical artifacts are removed from the bistatic \(v/R\) plane, and the echo from the moving object is still visible at the low-level background noise.
# Self-Similar Markovian Sources
## 2 Markov Chains and the Markov Traffic Sources
### Special Semi-Markov Process (SSMP)  
The SSMP process was first introduced as a new type of a stochastic process by [PERSON] and [PERSON] [37]. These processes are a generalization of the discrete and continuous Markov processes with a countable number of states. Here, we follow a proposition of a discrete time process which has only two values, 0 and 1, established at the beginning of each time-slot and determined by a discrete time Markov chain [38] (modulator) with \(n\) states. If it currently is in state 1, the value of the process is 1, and it is 0 for any other state of the modulator. State 1 means an arrival in this time slot and state 0 means that there is no arrival. The transition matrix \(\mathbf{A}=[p_{ij}]\), where \(p_{ij}\) is the probability of going from state \(i\) to state \(j\) of the modulator, should be chosen to match the process to the arrival pattern observed in real traffic. To simplify this task, in [38], the authors suggest representing \(p_{ij}\) by only three parameters, \(a\), \(q\) (auxiliary parameters that can be determined based on the observed data) and \(n\) (the number of states in a Markov chain), in the following way:  
\[\mathbf{A}=\left[\begin{array}{c|c|c|c|c}1-1/a-1/a^{2}-\dots-1/a^{n-1}&1/a&1/ a^{2}&\dots&1/a^{n-1}\\ ineq/a&1-a/a&0&\dots&0\\ ineq/a^{2}&0&1-(q/a)^{2}&\dots&0\\ ineq\dots&\dots&\dots&\dots\\ ineq(q/a)^{n-1}&0&0&\dots&1-(q/a)^{n-1}\end{array}\right]\]  
The expected value of such a flow is  
\[E[X]=\frac{1-(1/q)}{1-(1/q)^{n}} \tag{8}\]  
and from this, we can determine the value of \(q\). With the increase in the number of states in the Markov chain, we should also observe an increase in the number of time-scales over which the process exhibits a self-similar behavior as \(\lim_{n\to\infty}E[X]=1-(1/q),1/q<1\). The parameter \(a\) is determined by the Hurst parameter value as the slope of the plot \(\log_{10}\frac{var(X^{(n)})}{\sigma_{2}}\) versus \(\log_{10}(m)\). We find this value using an iterative algorithm; e.g., the Newton-Raphson equation.  
This model may be used together with any discrete time Markov server model; see [39].
# Design of a New Non-Coherent Cross-QAM-Based \(M\)-ary DCSK Communication System
## 2 Non-Coherent XQAM-_M_DCSK System
In addition, because the proposed framework can obtain the channel compensation value every symbol duration for demodulation, it can well adapt to the varying channel. However, in every frame including pilot symbols and data, the SQAM-_M_DCSK and star QAM-_M_DCSK systems first estimate the CSI by using pilot symbols, then the CSI is used for demodulation of the data. Hence, the existing systems cannot adapt well to the varying channel. Figure 1: The proposed non-coherent XQAM-MDCSK system.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Lead Sulfide Films  
The PbS films were deposited on soda lime glass in the presence of the TEA solution [81]. SEM and XRD studies showed rough morphology and the strongest diffraction peak corresponding to the (200) plane. The growth of the films could be observed on silicon (100) and silicon (111) substrates at room temperature [82]. The XRD analysis confirmed the cubic phase and polycrystalline (in nature). The microscope glass slide was used as the substrate during the formation of thin films [83]. The thickness and crystalline size were 480 and 18.6 nm, respectively. Other results showed the band gap was 1.77 eV and the films were cubic structure. [PERSON] and co-workers [84] highlighted that the extinction coefficient, refractive index, and imaginary dielectric constants varied when the immersion cycle increased during the experiment. The XRD studies exhibited the cubic structure, the strongest peak changed from the (111) to the (200) plane with the increasing dipping cycles. The influence of pH on the properties of the films was reported [85]. According to XRD data, with the average crystallite sizes in the range of 16-23 nm, improvement in the crystalline could be observed with increasing the pH value. Room temperature resistivity (1.2 \(\times\) 10\({}^{7}\) to 3.5 \(\times\) 10\({}^{7}\)\(\Omega\cdot\)cm) and direct band gap values (0.99 to 1.84 eV) were also concluded. [PERSON] and co-workers [86] proposed that PbS could be used as a gas sensor. The XPS analysis confirmed the formation of a stoichiometric compound. FESEM showed clearly visible compact grains (Figure 4) for the films deposited by using a 0.25 M precursor concentration when compared to other concentrations. The thickness and grain size were 232.9 and 100 nm, respectively. These films exhibited the highest sensitivities because of the highest rough surface (i.e., the total number of sites for the adsorption of gas was increased).
## 4 Result and Discussion
### Practical Contributions  
This new metric for measuring overall vehicle effectiveness provided a performance breakdown of all MTOVE factors during the hauling process, as described in Figure 9. It was possible to identify the factors that affected the overall performance; some of them were attributed to controllable factors, but others were noted to be uncontrollable by the company because of environmental conditions such as rain and slippery roads. After viewing this result and assessing the benefits, management decided to make MTOVE one of the performance measurement metrics of the company. This metric was adopted by the company and used to evaluate performance in a weekly operation review meeting. Several improvements were identified at the organizational level, starting with information from the use of utilization data (UA); the company decided to improve road maintenance quality to improve the UA. Improving road maintenance will lead to cycle time improvement, thus impacting productivity and, in the end, increasing the MTOVE score.  
The MTOVE building structure allows the company to create a culture of data-driven management. After several meetings with the management team, we defined the ideal MTOVE range for the company in the hauling process as between 40% and 45% by considering the average MTOVE weekly score. We want to highlight that this range recognizes that MTOVE results are influenced by controllable and uncontrollable factors. Some factors were not directly controlled by the company because of environmental impact and could not be compared with the OEE ranges obtained in production. Several studies in manufacturing plants estimated the benchmark value as an average of 85% [26; 28; 29]. Studies of OEE in manufacturing plants estimated an average value between 30% and 80% [59]. Preview studies on TOVE application in the road transportation sector set the value at 48% [14]. We believe that this proposed MTOVE can be replicated in other companies that perform hauling services in the mining domain. This study can also be a baseline for current Industry 4.0 applications in the coal mining transportation process, which allow real-time data processing of information collected from the mining operations and subsequently  
Figure 8: Weekly average of OEE vs. MTOVE.  
evaluate its effectiveness [60; 61; 62]. MTOVE also establishes a chain of causality, hierarchical dependencies, and metrics of interest that can be analyzed from different perspectives to provide additional insights into mining transportation operations. This concept provides a tailored metric to be applied in mining transportation. Through MTOVE, mining industry data are significantly more accessible with the Internet of Things (IoT) [63]. MTOVE combined with the global positioning system (GPS) and other technologies will help to improve transportation effectiveness [64].