esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Applying MARS Model  
The optimal MARS model included 28 terms, and the GCV was 0.157. MARS model provides the optimal model only by selecting the necessary parameters. In this research, nine GEVs including lithology, distance from road, distance from river, drainage density, elevation, aspect, convergence index, slope, and NDVI were used to construct the optimal model from the 12 GEVs. The GESM by the MARS model was implemented in ArcGIS 10.5 using Equation (12). According to Equation (12), distance from roads, elevation, and lithology were the most important variables. Values of GESM by MARS model varies from \(-\)9.8 to 7.3. At first, GESM classified using quantile, equal interval, natural break, and geometrical interval classification techniques, then, by comparatively analyses of the distribution of training and validation gullies in high and very high classes, the natural break classification technique was most accurate. As a result, GESM by MARS were classified into very low (\(-\)9.86--6.24), low (\(-\)6.24--2.31), moderate (\(-\)2.3-0.04), high (0.04-0.38), and very high (0.38-7.32) gully erosion susceptibility zones by natural break classification technique (Figure 5c). The results indicate that 0.02 km\({}^{2}\) (2.10%) of GE in the study area are located in the very low susceptibility class, with 339.01 km\({}^{2}\) (39.98% of total study area) and 0.58 km\({}^{2}\) (79.16%) located in the very high susceptibility class with 105.50 km\({}^{2}\) (0.58%) (Table 6). In general, the results indicate that for all three models with increasing susceptibility (from very low to very high), the area of the respective classes decreased, while in contrast the areas of GE increased. These results is in line with [PERSON] et al. (2015).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**Standard Error**} & \multirow{2}{*}{\
\begin{tabular}{c} **Asymptotic** \\ **Significant** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Asymptotic 95\% Confidence** \\ **Interval** \\ \end{tabular} } \\ \cline{5-6}  & & & & **Lower Bound** & **Upper Bound** \\ \hline RF & 0.927 & 0.007 & 0.000 & 0.914 & 0.941 \\ MARS & 0.911 & 0.008 & 0.000 & 0.896 & 0.926 \\ BRT & 0.919 & 0.007 & 0.000 & 0.905 & 0.933 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Area under the curve (AUC) values of RF, MARS, and BRT data mining models.  
Figure 6: Relative influence of effective conditioning factors in boosted regression tree (BRT) model.
## 4 Discussions and Results  
### Consumption Analysis  
The annual consumption graph (Figure 3) indicates that energy usage peaks during the winter and summer, with January and July showing the highest values, respectively. In contrast, spring and autumn exhibit more moderate consumption levels. The analysis of monthly consumption patterns (Figure 4) shows that daily energy usage remains relatively stable each month, with no significant variations between weekdays and weekends. This observation highlights the consistent nature of residential energy behavior, irrespective of the day of the week. Furthermore, the daily consumption graph for January (Figure 5)reveals two distinct peaks: one around midday and another in the evening. These peaks reflect typical residential routines and are critical for identifying the hours of maximum energy demand, which is a key factor in flexibility evaluation.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 3 Experiments
### Comparisons With Canny Algorithm  
Traditional edge detection is based on gray image, which detects the discontinuous gray scale of the image. Where the gray value of a pixel transforms is the edge of the gray image. The Canny algorithm and its variants are classical edge detection algorithms in the traditional machine learning field. We carry out the experiments with the Canny algorithm and compare them with the results of the proposed method. As shown in Figure 7a, all the edges in the image are detected with the Canny algorithm. The covered part of the conveyor belt cannot be automatically repaired. However, it can be clearly seen from Figure 7b, and the detection result of the proposed method has almost no breakpoint and avoids interference from other objects on the conveyor belt.
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Ship Arrivals and Departures
#### 4.4.7 Implementation  
Starting in 2001, ships and ports started to install automatic identification system (AIS) equipment. AIS transmitters on board the ships automatically report the arrival and departure times to the port authorities. This technology is primarily used to avoid collisions and increase port security. At the present time, AIS devices have not been installed in all ships and ports, but most major ports and the largest ships are included. AIS provides a means for ships to electronically send data, including vessel identification, position, speed, and course. AIS uses global positioning systems (GPS) in conjunction with shipboard sensors and digital VHF radio communication equipment to automatically exchange navigation information electronically and to monitor vessel location and movement primarily for traffic management, collision avoidance, and other safety applications [55; 56; 57].
# Automated Assessment Tool for 3D Computer-Aided Design Models
## 2 State of the Art and Related Works  
### CAD Model Comparison: Application Domains  
CAD model comparison has a wide range of applications in a variety of industries and fields. The following are a few notable areas where CAD model comparisons are often used:  
* Engineering and manufacturing: To ensure the accuracy and consistency of design models, CAD model comparison is widely used in the engineering and manufacturing industries. It helps identify inconsistencies between different versions of a CAD model, assists in design verification, and provides assurance of compliance with design standards and constraints. It can be applied in the automotive and aerospace fields, industrial equipment, and many more areas.
* Architecture and civil engineering [9]: In the architecture domain, CAD models are compared to detect changes or discrepancies between design iterations, to track changes made during construction, and to verify the accuracy of existing models against original design drawings. This helps with quality control and documentation, facilitates clash detection, and improves coordination between project stakeholders.
* Digital prototypes and simulations [10]: Comparing CAD models is an essential part of creating digital prototypes and simulations. This allows engineers and designers to compare model iterations to evaluate design changes, analyze the effects on performance parameters, and optimize the design to be both functional and efficient. This domain includes areas such as product development, virtual testing, and computer-aided engineering (CAE).
* Reverse engineering and 3D printing [11; 12]: CAD model comparison is used in the reverse engineering process, which involves the digital recreation of existing physical objects or parts. Any discrepancies or deviations can be identified by comparing the CAD model created from scanned data with the original object. This facilitates the creation of accurate and functional models.
* Quality assurance and testing: In QA and Inspection processes, CAD model comparison is used to validate that manufactured parts match the original design. Deviations or defects can be detected and corrective action taken to maintain product quality.
* Education and training [11]: Comparing CAD models is also used for education purposes, teaching students about design principles, tolerance analysis, and model verification. Educators can also assess student learning, provide feedback, and improve students' understanding of design concepts by comparing different versions of CAD models or evaluating student designs against predefined criteria.
In Kazakhstan, only a few researchers have dealt with BOF slag reuse. BOF slags from JSC \"AraclorMittal Temirtau\" (Temirtau, Kazakhstan) with a pH of 12.07, conductivity of 1087 \(\upmu\)s/cm, and a specific gravity of 2.09 are used to produce BOF-enhanced cement mortar [17]. In addition, an ordinary asphalt concrete mix must contain coarse aggregate, fine aggregate, filler, and bitumen (asphalt binder). Several studies have been considered where BOF slags are used as coarse aggregates, fine aggregates, and filler. However, it must be noted that the substitution of natural aggregates (conventional materials, such as basalt, limestone, granite, dolerite, and others) with BOF slags in asphalt mixes should be carried out for either the fine aggregate fraction only or the coarse aggregate fraction only [18]. The authors highlighted that \"if slag is used to replace both the coarse and fine aggregate components in HMA, the mix is likely to have a lot of air voids, necessitating the use of high quantities of bitumen; this could lead to bulking and flushing problems in a road pavement\". This refers to the development of mix designs with steel slags, such as BOF slags. However, the application of only BOF slags as both coarse and fine aggregates in asphalt mixtures is not a good decision. Asphalt with such an aggregate skeleton will exhibit poor workability, as BOF slags have high angularity and significantly rough texture. Therefore, it would be rational to use BOF slags in combination with other conventional aggregates [19; 20]. For example, using a combination of BOF slag and limestone as coarse aggregates makes asphalt mixture more resistant to fatigue failure and plastic deformation [21]. In general, many Kazakhstan studies have claimed that the problem with the utilization of basic oxygen furnace slags has not been resolved yet in the whole country. Most of the studies have performed only a preliminary characterization analysis on basic oxygen furnace slags and no further analysis has been carried out. Thus, in this research, the engineering properties of BOF slag aggregates were identified, and the feasibility of BOF slag aggregates in mortar application was investigated. Several types of BOF slags were considered: stockpiled BOF, fresh BOF, CO\({}_{2}\)-exposed BOF. BOF aggregates were analyzed in comparison with an ordinary river sand material. Moreover, in this research, mineral filler made with combinations of stockpiled BOF and MP1 (traditional filler) with the proportions of 100-0%, 75-25%, 50-50%, and 25-75% was used in asphalt concrete mixes and tested.
## 2 Experimental Section  
### Preparation of CdS/g-C\({}_{3}\)N\({}_{4}\)  
CdS/g-C\({}_{3}\)N\({}_{4}\) heterostructures were prepared using an in situ co-precipitation approach. Initially, 50 mg of porous g-C\({}_{3}\)N\({}_{4}\) was added to a 150 mL beaker with 100 mL of deionized water, followed by ultrasonication at 100 W for 20 min. Subsequently, 3.46 mL of Cd(NO\({}_{3}\))\({}_{2}\) (0.1 mol/L) was added and stirred at room temperature for one hour. Next, 4.15 mL of Na\({}_{2}\)S solution (0.1 mol/L) was gradually infused while maintaining sonication and stirring for another three hours. After centrifugation at 5000 r/min, the sediment at the bottom was collected and washed; this operation was repeated three times. Vacuum drying at 40 \({}^{\circ}\)C for 12 h yielded a transition-metal semiconductor consisting of CdS and g-C\({}_{3}\)N\({}_{4}\) in a 1:1 proportion. The resulting sample was labeled CdS/ g-C\({}_{3}\)N\({}_{4}\)-1; the same procedure was applied to prepare CdS/g-C\({}_{3}\)N\({}_{4}\)-2 (2:1) and CdS/g-C\({}_{3}\)N\({}_{4}\)-3 (3:1).
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 3 Experiments and Result Analysis
### Performance Analysis of IPSO-AR
#### 3.2.4 Recommended Parameter Settings for IPSO-AR  
Through the above analysis, we can find that the performance of IPSO-AR is closely related to the property of DD ambiguity float resolution (dimension and precision). When the dimension reaches 12, the efficiency of IPSO-AR will decrease (more than 1 s). Thus, we will not further discuss our observations for the case of increased dimensions. Meanwhile, low precision (such as ADOP > 3) may result from the inappropriate processing of error sources in the GNSS model or from the inappropriate external observation environment. In this case, searching for the correct integer ambiguity will be difficult, regardless of the AR method. Thus, this case will not be discussed further.  
Parameter setting is crucial for the performance of IPSO-AR. We synthetically consider the efficiency and correct rate of IPSO-AR on the basis of the above analysis of the three schemes. Table 4 provides the recommended parameter settings for IPSO-AR under various dimensions and precisions of the DD ambiguity float resolution. Except for parameters \(n\) and \(s\), which are presented in Table 4, the other parameters are presented in Section 3.1. We refer to Table 4 to adjust the parameters of IPSO-AR adaptively within the program through the procedure of parameter setting introduced in Section 2.2.3.
## 5 Performance Evaluation
### Simulation Results
#### 5.3.2 Interruption Rate  
Figure 10 shows the interruption rates of different schemes as the number of flows changes. Since there are no tools to directly measure the interruption rate of a flow, we use a combination of iperf3, Wireshark, and Linux system's \"netstat\" and \"ss\" commands to judge whether a flow is interrupted, and thus estimate the interruption rate of a flow. Specifically, after the link failure is recovered, we let all the flows continue to transmit for a few seconds, and then stop and count statistical data. Note that when switching forwarding paths, packets loss may also occur, and we only count the number of lost packets after the link failure has been recovered. For TCP flows, if retransmissions occur, then these flows are considered to be interrupted. The \"netstat\" and \"ss\" commands are used to monitor TCP retransmissions. For UDP flows, if there are packet losses or excessive delays, these flows are considered to be interrupted. We use \(\mathit{iperf3}\) to monitor UDP packet loss and Wireshark to assist in monitoring abnormal delays in UDP packets.  
As shown in Figure 10, when the number of flows increases, the flow interruption rates of the three schemes increase as well. However, under the same experimental conditions, the flow interruption rate of the FAPR scheme is always lower than the other two schemes. Among these three schemes, the proactive scheme corresponds to the highest flow interruption rate. This can be explained by the fact that the proactive scheme deploys flow rules in advance in switches, and does not consider the real-time network view. When link failure occurs, flows may forward along multiple backup paths that contain the same link, leading to congestion or interruption. Compared with the proactive scheme, the reactive scheme considers the real-time view of the network, so it performs better. Unlike the other two schemes, the FAPR scheme has the lowest interruption rate by classifying different types of flows and assigning the most suitable path to each flow, thus avoiding over-concentration of flows.  
In addition to the interruption rate, the FAPR scheme can also improve the quality of service in the network, because it assigns the most suitable backup paths to flows with different characteristics. However, we cannot quantify this indicator well in a simulation environment, because it is difficult for us to simulate multiple different applications and design network indicators corresponding to different types of applications. Nevertheless, it is not difficult to infer that, in a real complex network with various types of traffic, assigning adaptive backup paths to flows with different needs after a link failure can indeed improve indicators of the network.  
Figure 9: Stability of recovery time using three schemes.
## 3 System Implementation for Serialized Track and Trace
### Target Process  
As shown in Figure 2, the entire supply chain comprises three sites: a supplier plant, a consolidation center, and an OEM plant. The target parts are rear axles, and they are supplied to the OEM after sequencing at the consolidation center, which requires attaching of an RFID metal tag at the supplier plant. Visual marks are printed on RFID metal tags so that the types of rear axles can be identified by sequencing workers. The first site, i.e., the supplier plant has three RFID read points: production, 7-rack loading, and shipping. In the 7-rack loading stage, seven rear axles are loaded onto a single 7-rack. At the second site, i.e., the consolidation center, the receiving, stocking, picking, and sequencing stages are conducted. Excluding the stocking stage, the RFID's are read in the other three stages. In the sequencing stage, five parts are loaded on a dolly, which is a portable container. The sequencing workers visually check the marks of RFID metal tags attached at the supplier plant and sequentially load the rear axles on the dollies based on the sequence information provided by the OEM about an hour in advance. Because of the importance of sequencing accuracy, currently, manual inspections are conducted to detect sequencing errors by reading five rear axles on a pertinent dolly with a barcode reader after completing the sequencing; in this study, a system was developed to check the sequencing errors automatically by using an RFID reader. Lastly, at the OEM, four processes, namely, receiving, dolly waiting, dolly towing, and assembly-line feeding, are conducted, and the RFID's are read in the receiving and assembly-line feeding stages only.  
Figure 2: The overall supply chain process for rear axles.
# On Wind-Induced Fatigue of Curtain Wall Supporting Structure of a High-Rise Building
## 2 Research Methodology
### Joint Distribution Model of Wind Velocity and Direction  
According to the generalized extreme-value distribution model, this study fitted a probability density distribution of wind velocity under different wind directions, finding that extreme value type II (Frechet) distribution had a better fitting degree, and its formula is as follows:  
\[p_{1}(v)=\left(\frac{1}{\sigma}\right)\exp\left(-\left(1+k\frac{(v-\mu)}{ \sigma}\right)^{-\frac{1}{k}}\right)\left(1+k\frac{(v-\mu)}{\sigma}\right)^{-1 -\frac{1}{k}} \tag{2}\]  
where \(\mu\), \(\sigma\), and \(k\) represent location, scale, and shape parameters, respectively, and \(v\) is the wind velocity. Parameters for the extreme value type II distribution are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wind Direction** & \(\mu\) **(Location Parameter)** & \(\sigma\) **(Scale Parameter)** & \(k\) **(Shape Parameter)** \\ \hline
1 & \(-0.102\) & 1.198 & 2.107 \\ \hline
2 & \(-0.032\) & 1.107 & 2.083 \\ \hline
3 & \(-0.090\) & 1.107 & 2.006 \\ \hline
4 & \(-0.147\) & 0.686 & 1.475 \\ \hline
5 & 0.017 & 0.675 & 1.361 \\ \hline
6 & \(-0.095\) & 0.674 & 1.311 \\ \hline
7 & \(-0.011\) & 0.695 & 1.338 \\ \hline
8 & \(-0.038\) & 0.694 & 1.346 \\ \hline
9 & \(-0.076\) & 0.830 & 1.704 \\ \hline
10 & \(-0.085\) & 0.882 & 1.749 \\ \hline
11 & \(-0.133\) & 1.037 & 2.104 \\ \hline
12 & \(-0.159\) & 1.163 & 2.190 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Probability density function parameters of wind velocity at all azimuth angles.  
Figure 6: Node analysis.  
The relationship between the azimuth angle and probability can be expressed by the following formula:  
\[p_{2}(\theta)=(a+b\times\theta)/(1+c\times\theta+d\times\theta^{2}) \tag{3}\]  
where \(p_{2}\) is the probability; \(\theta\) is the azimuth angle that will be radiated during regression; \(a\), \(b\), \(c\), \(d\) are model parameters, as shown in Table 4.  
\(P\) indicates the joint distribution probability of any working condition of curtain wall supporting structure nodes and can be calculated through the following formula:  
\[P(v,\theta)=p_{1}(v)\times p_{2}(\theta) \tag{4}\]  
where \(P\) is the probability of any working condition; \(v\) and \(\theta\) are wind velocity and azimuth angle of working condition, respectively.
###### Abstract  
The International Association of Classification Societies (IACS) has introduced the concept of the Equivalent Design Wave (EDW) for the calculation of dynamic loads for structural analysis in the Common Structural Rule (CSR) for the design of bulk carriers and oil tankers. An EDW is a set of loads representing a design wave whose response is equivalent to the required design value at a given probability level. Classification societies are applying the EDW concept by implementing design loading equations for LNG carriers, bulk carriers, and oil tankers. According to the EDW loads, dozens of load cases are generated using the design load formula, and the hull girder loads calculated from them are adjusted to the target values. In this short communication, the hull girder load is adjusted according to the method described in the DNV rule for a cargo hold model of an LNG carrier. The adjusted load is distributed as a nodal force by shear flow and simple beam theory. An alternative method is introduced by comparing existing methods with regard to adjustment methods and load distribution. After discussing existing methods, a method for adjusting all hull girder load components in three steps and a load distribution method using the stress field were derived. The limitation of the current technique and insights summarised in this short communication may support readers in understanding the rule-based hull girder load adjustment technique. In addition, this limitation may clearly motivate the demand for alternative technology development for hull girder load adjustment.  
Keywords:load calculation; hull girder load; load adjustment; boundary condition; model extent +
Footnote †: journal: Applied sciences
# Design of Unmanned Road Widths in Open-Pit Mines Based on Offset Reaction Times
## 2 Methodology  
### Open-Pit Mine Road Width Analysis  
As shown in Figure 1, an open-pit mine roadway consists of a traveled way, a retaining wall, a protective area, and a safety width [10].  
1. Travel lanes: The two-lane design includes two mine truck haul roads, which can ensure that two haul trucks pass at the same time, while the single-lane design has only one mine truck haul road.
2. Retaining walls: A mound of loose stripped material with a profile pattern that is roughly an isosceles trapezoidal shape with a natural buildup.
3. Protected area: An area that is artificially delineated and located near the inside of a hauling pan to prevent falling rocks from impacting personnel and equipment and causing safety accidents, which is generally closed to traffic.
4. Lateral safety distance: The main categories are the safe width for trucks meeting on two-lane type transport roads, the safe width between trucks and retaining walls and the width of the protection zone.  
In the establishment of road widths for open-pit mines, one must consider the maximum widths of the diverse types of mining trucks utilized in the mine, the minimum lateral safety distance when two mining trucks intersect, and the safe width of the mining trucks in relation to the road's edge. Equation (1) displays the conventional road width [11]:  
\[W=2l+d_{c}+2d_{r} \tag{1}\]  
Figure 1: Open-pit end wall road: (**a**) two-lane road; (**b**) single-lane road.  
where \(W\) is the width of the road in m, \(l\) is the width of the largest type of mine truck used in the open-pit mine in m, \(d_{c}\) indicates the meeting distance of two mine trucks in m, and \(d_{r}\) indicates the distance between the trucks and the edge of the road, which shall not be less than 5 m for the non-working slope and shall not be less than 4 m for the working slope.