esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mathematical Model for Reduction of Trim Loss in Cutting Reels at a Make-to-Order Paper Mill
## 4 Solution Procedure  
The solution procedure adopted is divided into several stages, as presented in Figure 2. First, to fill an order by setting knives to the desired widths, all feasible cutting patterns for all widths of master reels and stocked reels are generated. The cutting patterns must also be feasible in terms of operational and technological constraints. All evaluated cutting patterns are then used as columns in a linear programming model and the problem is solved using the simplex algorithm. Finally, the solution obtained is made integer if the solution algorithm results in one or more than one non-integer decision variables.  
As regards costs, it is always of interest to know how much savings one made due to the optimization effort as such inclusion of such information would have added value to the current study. However, once organizations or industries are approached for possible areas of improvement, while they readily share their problems, they are reluctant to share the finance related information. That is why the cost/savings information in monetary terms could not be included. However, the same information is included in terms of reels of paper saved through application of this study.  
To illustrate the solution procedure, a small real industrial example is solved which is concerned with production planning of grey back paper in master reels of widths 60 to 70 inches and cutting of limited stocked reels of widths 35, 30, 25, 22, and 18 inches, available in stock. The thickness of paper and grade are 1.0 mm and 350 g/m\({}^{2}\), respectively. The production requisition involved and stocked reels are described in Tables 2 and 3, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Production** & **Widths (in)** & **Paper Type** & \begin{tabular}{c} **Number of** \\ **Reels** \\ \end{tabular} & \
\begin{tabular}{c} **External** \\ **Diameter (in)** \\ \end{tabular} &
\begin{tabular}{c} **Internal** \\ **Diameter (in)** \\ \end{tabular} \\ \hline PR 881 & 17 & GB * & 30 & 60 & 6 \\ PR 882 & 21 & GB & 25 & 60 & 6 \\ PR 901 & 25 & GB & 40 & 60 & 6 \\ PR 909 & 27 & GB & 35 & 60 & 6 \\ PR 915 & 30 & GB & 35 & 60 & 6 \\ PR 916 & 34 & GB & 30 & 60 & 6 \\ \hline \hline \end{tabular}  
* Grey Back.  
\end{table}
Table 2: Data of a small industrial example.  
Figure 2: Optimal cutting algorithm.  
The operational constraints of identical paper type, internal and external diameters of reels have to be considered in making new master reels and in the selection of stocked reels to fill a given order.
## 4 Influence Parameter Analysis
### Ambient Pressure  
The bearing load capacity and the attitude angle of hydrogen gas bearings with ambient pressure are shown in Figure 9. The load capacity increases slightly with increasing ambient pressure. Therefore, it is feasible to improve the bearing load capacity by increasing the ambient pressure. At the same time, the bearing attitude angle decreases gradually.  
The friction torque of hydrogen gas bearings with ambient pressure is shown in Figure 10. The ambient pressure has a limited effect on the friction torque of the bearing. The friction loss is basically invariant with increasing ambient pressure.  
Figure 8: Critical masses of the bearings with rotational speed for the four different gases.  
Figure 9: (**a**) Bearing load capacity and (**b**) attitude angle of hydrogen gas bearings with ambient pressure.  
The critical mass of hydrogen gas bearings with ambient pressure is shown in Figure 11. Higher ambient pressure leads to increasing critical mass. Overall, increasing the ambient pressure is also an effective method of enhancing the performance of hydrogen gas bearings.
## 3 Results
### Data Analysis for Identified Parameter
#### 3.1.3 Flood Exposure Parameters  
There were three (3) identified parameters for flood exposure assessment. Figure 8 shows the population density data, land use map, and household numbers from the CLUP Odiongan and PSA Database.  
Figure 8a shows the Population Density in the Municipality of Odiongan. The map demonstrates that population density is low in the nearby Poblacion area or where the center of the municipality is located. Barangay Ligaya and Liwayway as the lowest population density followed by Tabin-Dagat, Dapawan, Budiong, Liwanag, Bangon, Tulay,  
Figure 8: Result maps for flood exposure parameters in using ArcMap: (**a**) Population Density; (**b**) Land Use Map; and (**c**) Household Number.  
Mayha, Batiano, Gabawan, and Libertad. However, Barangay Tumingad and Progresso Waste displayed a high population density.  
Land use and land cover type are vital factors responsible for flood incidence. The land cover represents the physical (water, bare ground, and artificial structures) and biological (grass and trees) land cover. In contrast, land use describes how men utilized the land to improve their state of living [58,59]. The occurrence of flooding was inversely related to vegetation density. The study area's land cover classes were prepared from the municipal's CLUP. The land cover map was reassigned by categorizing the land-use types into seven (7) general categories. The map shown in Figure 8b indicates that most of the municipality's land area was cultivated. Brushland is observed in the center part of the map, while the scattered site of tree plantation and perennial were seen on the map. The built-up zone is located in the town proper of the municipality.  
Evacuation decision before (preemptive) or during (forced) a disaster indicates the choice of households to evacuate or stay in the area at risk of impending hazard [43]. The household number was considered one of the parameters in assessing exposure. The study obtained the data from PSA's last 2020 census. Presented in Figure 8c is the range of the number of households for every barangay in the municipality. It was recorded that a high number of households in Baranagy, Panique, Pato-o, Gabawan, Dapawan, and Tabin-Dagat were exposed to flood events. Between 172 and 250 households, which was the least number, were observed in Liwanag, Liwayway, Barangay Malilico, and Progresso Waste. However, Liwanag and Liwayway have small land areas that cater only to some houses.
## 3 NDN Enabled Communication and Transport Research in V2X
### Security, Authenticity and Integrity
In this work we want to discuss another case which we think is important for V2X scenarios. Imagine, a vehicle is approaching an area of interest, e.g., a critical (highway) passage, or an intersection. However, due to circumstances, such as high distance, weather conditions, or obstacles, it is not yet able to analyze (sense) this area of interest. This vehicle requests information, e.g., in form of sensor data or traffic information, from all vehicles approaching or leaving (\(n\in\mathbb{N}\)) this area of interest. Assume, that the vehicle has to make certain decisions based on the gathered information. The previously discussed mechanisms can help integrity but may not be sufficient. One more vehicles (\(f\in\mathbb{N}:f\leq n\)) could act faulty (overtaken by an adversary) and will send incorrect information. The sent messages, however, are secured and authentic. In this context we want to introduce the term **byzantine vehicles**. This example raises the following question:  
* How can the requesting vehicle retrieve consistent information? A possible answer can be found in literature of distributed systems implementing byzantine fault tolerance: practical byzantine fault tolerance (PBFT) [36] and improvements thereof, such as Q/U [37], HQ [38], Zyzzyva [39] and Aardvark [40]. Some of the named improvements to PBFT address its performance (Q/U, HQ, Zyzzyva) whereas others (Aardvark) deal with its robustness. However, the protocol to mitigate byzantine faults needs at least \(3f+1\) non-byzantine vehicles or conversely if we have \(n\) vehicles we are able to tolerate at most \(f\leq\lfloor\frac{n-1}{3}\rfloor\) faulty vehicles. However, these protocols generate a lot of messages leading to unnecessary delays. Furthermore, they assume that each vehicle is holding the same information. For the V2X scenario, this assumption does not apply due to different sensors, angle of arrival/departure, or other circumstances. Therefore, before checking consistency, a preprocessing step has to be introduced which transforms the information to a comparable representation. How this is done in detail is subject to future research and will highly depend on the available information. Thus, solutions that aim on allowing byzantine vehicles or even byzantine infrastructures will have to focus on performance (mainly low latency), robustness (vehicles may be out of range after a short period of time), and caching support (some vehicles may have cached copies of other vehicles' information).
# Study on Share Rate of Support Structure for Super-Large Span Twin Tunnels with Small Interval
## 2 Methodology
### Finite-Element Model  
In this paper, to find out the share rates of support structure, numerical simulation was used for matching the results of field measurement. ANSYS, a powerful finite-element software, was used to build simulation models of primary-support and secondary-lining models of Daling Tunnel, which were both built by beam elements (Beam 3). This is a kind of 2-D element, which has 3-degrees of freedom on each node and can endure axial tension or compression. The parameters of primary support and secondary lining are shown in Table 2.  
As for the boundary conditions, the springs were built by link elements (Link 10) and used to simulate the effect of the surrounding rock. Link 10 is a kind of 3-D two-node element, which has 3-degrees of freedom on each node. Considering the mechanics characteristics of the surrounding rock, these springs were set to only be compressed. Furthermore, the bases of springs were completely fixed. According to the parameters of surrounding rocks in Table 1, the length and elastic module of springs are 0.2 m and 200 MPa, respectively.  
In terms of load, the form and direction of load is the same as the theoretical load (shown in Figure 3). Furthermore, the value of load is a portion (10%, 20%,... ) of the theoretical load. Moreover, the gravity is 9.8 m/s\({}^{2}\) downward and applied to all elements. The model is shown in Figure 4; (Since the models of the primary support and secondary lining are similar, only one model is shown as an example.)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Part** & **[PERSON]’s Model** & **Poisson’s** & **Unit Weight** & **Thickness** \\  & **/GPa** & **Ratio** & **/kN/m\({}^{3}\)** & **/m** \\ \hline Primary Support & 25.3 & 0.2 & 25 & 0.3 \\ Secondary lining & 28 & 0.2 & 25 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of primary support and secondary lining.  
Figure 3: Theoretical load of Daling Tunnel (unit: kPa). The process of calculation will be mentioned in Section 3.
# Screen-Based Sports Simulation Using Acoustic Source Localization +
## 3 Three-Dimensional Impulsive Acoustic Source Localization  
As shown in Figure 2, the 3D impulsive acoustic source position is estimated from the closest point of the two vectors obtained from Section 2. Two identical DSBF models are used to estimate the direction of the impulsive acoustic source. Each model has its own unit vector \(\overrightarrow{v_{1}}\) and \(\overrightarrow{v_{2}}\). \(P_{S1}\) and are the center positions of each microphone array. The parametric form of the line equation for the two vectors is expressed as follows:  
\[\left\{\begin{array}{l}l_{1}=P_{S1}+h_{1}\vv{v_{1}}\\ l_{2}=P_{S2}+h_{2}\vv{v_{2}}\end{array}\right., \tag{9}\]  
where, \(h_{1}\) and \(h_{2}\) are the parameters to find \(P_{E1}\) and \(P_{E2}\), respectively. They are the closest points between two vectors on each line. The shortest distance between those two straight lines is the length of the vector, \(\vv{w}\), which is perpendicular to both lines. Thus, \(\vv{w}\) is expressed as follows:  
\[\vv{w}=l_{2}-l_{1}=P_{S2}+h_{2}\vv{v_{2}}-\left(P_{S1}+h_{1}\vv{v_{1}}\right). \tag{10}\]  
Since \(l_{1}\) and \(l_{2}\) are perpendicular to \(\vv{w}\), the dot product of the direction vector of each line and \(\vv{w}\) is expressed as follows:  
\[\left\{\begin{array}{l}\vv{w}\vv{v_{1}}=0\\ \vv{w}\vv{v_{2}}=0\end{array}\right.. \tag{11}\]  
By substituting Equation (10) into (11), Equation (11) is rewritten as follows:  
\[\left\{\begin{array}{l}P_{S2}+h_{2}\vv{v_{2}}-\left(P_{S1}+h_{1}\vv{v_{1}} \right)\vv{v_{1}}=0\\ P_{S2}+h_{2}\vv{v_{2}}-\left(P_{S1}+h_{1}\vv{v_{1}}\right)\vv{v_{2}}=0\end{array}\right.. \tag{12}\]  
Thus, the parameter \(h_{1}\) and \(h_{2}\) is obtained as follows:  
\[\left\{\begin{array}{l}h_{1}=\frac{\left(P_{S2}-P_{S1}\right)\vv{v_{1}}+h_{2 }\vv{v_{1}}\vv{v_{2}}}{\vv{v_{1}}\vv{v_{1}}}\\ h_{2}=\frac{-\left(P_{S2}-P_{S1}\right)\vv{v_{2}}+h_{1}\vv{v_{1}}\vv{v_{2}}}{ \vv{v_{2}}\vv{v_{2}}}\end{array}\right., \tag{13}\]  
By solving Equation (13), \(P_{E1}\) and \(P_{E2}\) are calculated as follows:  
\[\left\{\begin{array}{l}P_{E1}(h_{1})=P_{S1}+h_{1}\vv{v_{1}}\\ P_{E2}(h_{2})=P_{S2}+h_{2}\vv{v_{2}}\end{array}\right.. \tag{14}\]  
Finally, the position of impulsive acoustic source is obtained as follows:  
\[P_{C}=P_{E1}+\frac{P_{E2}-P_{E1}}{2}. \tag{15}\]  
Figure 2: Geometric model to find the closest point of two vectors in a three-dimensional (3D) space.
# Mutual Influence of Users Credibility and News Spreading in Online Social Networks
## 3 Trust Based News Spreading Model
### OSN Layer  
Here we introduce the model adopted to describe how news propagate in the OSN layer. In particular, we applied a modified version of Susceptible-Infected-Recovered (SIR) model [18; 68], usually adopted in epidemic spreading processes, where:  
* susceptible status corresponds to ignorant status, i.e., OSNs users who belong to this category are not aware of the news;
* infected status corresponds to spreader status, i.e., OSNs users that are aware of news and try to spread it through sharing;
* recovered status corresponds to stifle status, i.e., OSNs users aware of the news, but are not interested in propagating it. Considering that from the OSNs data analysis it is not easy to distinguish who viewed news without sharing it or who was not connected at news' publishing time, we believe that this category should include users who have at least one neighbor who shared the news and did not re-share it.  
We tailor to our context the transmissibility formula of [PERSON]'s epidemic spreading model [19]:  
\[T_{ij}=1-e^{-\beta_{ij}\tau_{i}} \tag{1}\]  
Figure 1: A two-layers representation of our model: online social network (OSN) and credibility network (CN) layers.  
where \(T_{ij}\) is the transmission probability, i.e., the probability that individual \(i\) infects his neighbor \(j\), \(\beta_{ij}\in[0,1]\) is the contact rate between \(i\) and \(j\) and \(\tau_{i}\) is the infection time, that is the time during which \(i\) can infect his neighborhood.  
In OSNs context, we reinterpret \(T_{ij}\) as the probability that \(i\) spreads news and \(j\) receives and propagates it, whereas \(\beta_{ij}\) becomes the fraction of time during which \(i\) and \(j\) are simultaneously connected to the same OSN and finally \(\tau_{i}\) is viewed as the time for which the news propagated by \(i\) is visible in the foreground on OSN home page (this actually can depend on several factors and can also change for different kinds of OSN home pages).  
Note that, while in real physical diseases \(i\) is naturally contagious, in the news spreading scenario being infected means that \(j\) believes the news received by \(i\) is reliable and therefore is also willing to transmit it over the network (via its neighborhood).  
In our proposal, we also introduce the credibility \(C_{ji}\) to [PERSON]'s Equation (1) as follows:  
\[T_{ij}=1-e^{-\beta_{ij}\tau_{i}C_{ji}} \tag{2}\]  
\(C_{ji}\) represents the direct credibility, i.e., how much \(j\) trusts \(i\); this value is the weight of the direct link connecting node \(j\) with node \(i\) in the CN layer. The rationale behind the Equation (2) is that trust in an individual may depend on past interactions with him, but also on the reputation that he/she has in neighborhood and it is important because it can influence the attention people paid when he/she posts news.  
In other terms, Equation (2) models the news diffusion process in the OSN layer as a function of CN layer (direct coupling between OSN and CN layers).
# Smart System to Detect Painting Defects in Shipyards: Vision AI and a Deep-Learning Approach
## 3 Deep-Learning Model for Detecting Painting Defects on Steel Plates
### Discussion  
In the experiments, the hyper-parameter was changed in various ways for each model, and filters were added or modified to the existing model. The performance of the basic network model was mostly high, and the accuracy varied from 85% to 90% for YOLO and 72% to 82% for SSD. In addition, at the beginning of data acquisition, fewer than 500 images of data were acquired for each line. Individual models showed higher accuracy than common models. However, building one model worked well on various defect patterns after more than 1000 images were acquired.  
Figure 12: YOLO-v3 mAP and loss graph (200,000 iterations) of bottom images.  
The strength of the system built using deep learning is that it can detect untrained defects. For example, during the actual development, a rusty steel plate was produced due to the omission of the blasting process. The DL model correctly judged it to be defective although this case was not in the training set. Although the patterns of defects trained in the actual field are similar, the rusty steel is a different type of defect. This detection of untrained defects is a strength of this system. Figure 13 shows the actual detection of the rusty steel plate as defective.  
Reliability is the most important factor in building an AI for field applications. To maintain this reliability, it is necessary to have a strong detection ability against unexpected, untrained errors.  
The following points need to be considered for determining the limitations and maintenance of field systems that use the Vision AI.First, Ambient light should be controlled as much as possible, and the location of the camera and lighting affects the reliability of the system.Second, Since the location and angle of view of the camera may be changed due to the layout of the facility or an equipment change, it should be configured so that the trained model can operate universally by acquiring data from various angles during development.
## 4 Optimization Scheduling Model
### Constraints  
#### 4.2.1 Power Balance Constraint  
Considering the five types of loads in the system (electricity, gas, cooling, heating, and hydrogen), maintaining a balance between supply and demand is crucial. The power balance constraint ensures that the total power supplied equals the total power consumed. This constraint is expressed as:  
\[\left\{\begin{aligned} P_{\text{load}}(t)=P_{\text{echp}}(t)+P_{ \text{wind}}(t)+P_{1}(t)+P_{\text{buy}}(t)-P_{\text{ec}}(t)-P_{\text{P2G}}(t)- P_{\text{ccs}}(t)\\ H_{\text{load}}(t)=H_{\text{gb}}(t)+H_{\text{echp}}(t)+P_{2}(t)\\ C_{\text{load}}(t)=C_{\text{ccbp}}(t)+C_{\text{ec}}(t)+P_{3}(t)\\ G_{\text{load}}(t)=G_{\text{buy}}(t)+G_{\text{MR}}(t)-G_{\text{GT}}(t)- G_{\text{gb}}(t)\\ Q_{\text{load}}(t)=Q_{\text{buy}}(t)+Q_{n}(t)+G_{\text{echp}}(t)\end{aligned}\right. \tag{19}\]where \(P_{\rm load}(t)\), \(H_{\rm load}(t)\), \(C_{\rm load}(t)\), \(G_{\rm load}(t)\) and \(Q_{\rm load}(t)\) represent the electricity, heat, gas, cooling, and hydrogen load demand at time interval \(t\); \(P_{i}(t)\) (i = 1, 2, 3) represents the discharging or charging power of energy storage, heating storage, and cooling storage devices at time \(t\); \(P_{\rm H_{2}}(t)\) represents the mass of hydrogen storage or release at time \(t\); \(P_{\rm cchp}(t)\), \(P_{\rm wind}(t)\) represent the power generation of the gas turbine and wind power devices at time \(t\); \(P_{\rm cc}(t)\), \(P_{\rm 2C}(t)\) and \(P_{\rm ccs}(t)\) represent the power consumption of the electric refrigeration, electric boiler, and carbon capture system at time \(t\); \(G_{\rm MR}(t)\) represents the gas production power of the methane reactor at time \(t\); \(G_{\rm cchp}(t)\), \(G_{\rm gb}(t)\) represent the gas consumption power of the gas turbine and gas boiler at time \(t\).
## 3 Case Study: Evaluation of a Passive Back-Support Exoskeleton
### Stage I: Pre-Experimental Planning
#### 3.1.2 Preliminary Study  
Two preliminary studies were conducted to investigate the impact of the exoskeleton [27; 36]. These studies had two main objectives: (1) to provide an initial assessment of the analyzed parameters and evaluate their variation due to the exoskeleton technology; (2) to highlight any limitations and defects in the testing procedure. Specifically, the first preliminary study involved six workers performing two MMH tasks at two different companies: a chemical company and a textile company [27]. The MMH task at the chemical company involved lifting twenty bags, each weighing 25 kg, and moving them from a conveyor to a pallet (task A). The MMH task at the textile company involved moving sixteen horizontal rolls of fabric from a basket positioned on a forklift's forks to a pallet on the ground, where the rolls were placed vertically (task B). During the handling process, the workers rotated each roll from a horizontal position on the forks to a vertical position on the pallet.  
The second preliminary study involved six users performing a palletizing task in a laboratory environment [36]. Each user was required to handle twelve boxes, each weighing 15 kg, and place each box on a pallet to create four different deposit levels. Each subject performed the task twice, i.e., the first time without and the second time with exoskeleton support. The panel was divided into two equal groups: one group performed the task first without and after with exoskeleton aid, and the other group performed the task first with and after without exoskeleton aid. The data collected during each trial were the pressures of the feet on the ground, the heart rate, the blood oxygen saturation, and the user perceptions about comfort and usability. The findings of both the preliminary studies revealed distinct variations in the distribution patterns of the pressure of the feet on the ground and the heart rate when the tasks were performed with and without the exoskeleton [27; 36].