esa-sceva/satcom-chunk-collection huggingface dataset:
# Apparatus and Method of Defect Detection for Resin Films
## 4 Discussion
### Implementation Issues  
Camera data issue: 2K resolution does not have sufficient quality for the plastic resin film; the nonbackground object is still considerably blurry under the camera and sometimes it is difficult to distinguish between a bubble and a defect. Many defects were less than 5 pixels across and the CNN could not be trained to detect them. Raising the platform and lowering the FOV, the defects increased to 10 to 15 pixels and exhibited a superior result in classification. However, three images were required to compose one plastic resin film. This leads to other problems such as localization of the defects in one combined image and ensuring every part of the plastic resin film is captured in the three images. Therefore, we did not raise the height of the platform, and although classification was not better and the nonbackground objects were not clearer, it was considerably efficient during the inspection process.  
Data labeling: Considerable time was required for training the data, as every object was cropped to be classified as the right class, most of the time microscope was required to classify the object into the correct class. Furthermore, expanded area of 5, 10, and 15 pixels exhibited different data. This is  
Figure 15: Data difference in depicting the difference between expanding the bounding box area or not.  
time consuming and careful scrutiny is required at this stage because this strongly affects the efficiency of our classification model.  
During the classification of the camera data, unstable results were obtained for the white background when divided into three categories, and bubbles and scratch would be categorized as defects as shown in Table 7a and Figure 16a. Both models were trained on the same data with the same hyperparameters. Therefore, the bubble and the scratch classes were combined, which exhibited superior results as depicted in Table 7b and Figure 16b.  
However, while white-background plastic has better results while dividing into two categories, the transparent-background plastic does not. As shown in Table 8 and Figure 17, the recall of the defect class is higher when dividing into three categories. Therefore, during training the transparent-background plastic data, we choose to divide the data into three categories: defect, bubble and scratches.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 4 Experimental Results and Discussion
### Performance in Various Error Environments  
Figures 10 and 11 show the amounts of data collected by a sink node and the blackout times, respectively, according to a varying BER. As can be seen from Figure 10, RS(68, 32) yields a constant amount of data irrespective of the BER, because data recovery is possible even if the channel condition is poor, owing to its high error correction rate. However, as can be seen from Figure 11, RS(68, 32) exhibits the greatest amount of blackout time at all BERs, which degrades the data collection of the sink.  
Figure 8: Received data according to time, for 200 nodes and 0.15 BER.  
Figure 9: Received data of sink for varying number of nodes (BER 0.15).
## 3 Method
### Feature Interaction Layer  
Based on all features of the nodes, the key issue in high-order combined feature modeling is to determine which combinations of features form high-value, high-order features. Through iterative training, users learn preference representations for different feature fusions and obtain the semantic information from the fused high-order features. Therefore, depending on the obtained user, item, and feature vectors with the same dimension, our feature extraction approach employs a multi-head attention mechanism to integrate different features of items and users to discover which combinations are more consistent with user preferences. As an illustration, let us consider feature \(m\) as a specific instance. The association between feature \(m\) and \(k\) is calculated. The multiple valuable high-order features of feature \(m\) are achieved within head \(h\).  
\[\begin{split}&\alpha_{m,k}^{h}=\frac{\exp{(\varphi^{(h)}(e_{m},e_{k}))}}{ \sum_{l=1}^{M}{(\varphi^{(h)}(e_{m},e_{k}))}}\\ &\varphi^{(h)}(e_{m},e_{k})=<W_{q}^{(l)}e_{m},W_{k}^{(h)}e_{k}> \\ & e_{m}^{(h)}=\sum_{k=1}^{M}{\alpha_{m,k}^{h}(W_{v}^{(h)}e_{k})} \end{split} \tag{3}\]  
where \(l\) is a variable that ranges from 1 to \(m\), \(e_{m}^{(h)}\) is updated by combining all relevant features with coefficient \(\alpha_{m,k}^{h}\) of head \(h\), and \(\varphi^{(h)}(e_{m},e_{k})\) represents the attention function, which computes the similarity between features \(m\) and \(k\). The transformation matrices \(W_{q}^{(h)}\), \(W_{k}^{(h)}\), and \(W_{v}^{(h)}\) are used to transform the original embedding space into the new space.  
\[\begin{split}& e_{m}^{H}=e_{m}^{1}\bigoplus e_{m}^{2}\bigoplus \cdots\bigoplus e_{m}^{h}\\ & e_{m}^{Res}=(e_{m}^{H}+W_{Res}e_{m})\end{split} \tag{4}\]  
where \(e_{m}^{H}\) is the concatenation of each head's output, \(W_{Res}\) is the projection matrix in the case of mismatched feature dimensions, and \(e_{m}^{Res}\) is the updated representation of high-order combination features. To preserve the original features, standard residual connections are added in the neural network topology architecture, which means that \(e_{m}\) is added to the learned feature representation \(e_{m}^{H}\).  
The use of a multi-head attention mechanism is motivated by the need to integrate different features of items and users in a way that reflects user preferences. This approach is supported by previous works, which have demonstrated the effectiveness of attention mechanisms in capturing the importance of various features in node representations. By employing multi-head attention, our model can learn multiple preference representations, enhancing its ability to identify and prioritize relevant features.
## 3 Results and Discussion
### Formatting of Mathematical Components
#### 3.4.1 Keyword Network and Research Topics
\"MERRA-2\" (Modern-Era Retrospective Analysis for Research and Applications) is an atmospheric reanalysis dataset [67]. \"CALIPSO\" is a joint initiative by NASA and the French space agency aimed at observing clouds and aerosols, providing insights into atmospheric structure and dynamics [68]. Figure 6: (**a**) Keyword network with grouped clusters, with the node size correlated positively with the frequency of keyword occurrence, and (**b**) research trends in topics over time. Each blue sphere represents a topic, corresponding to an author keyword, with its size proportional to the keyword’s frequency of occurrence. The placement of each sphere corresponds to the median frequency distribution of the keyword’s occurrences, and the grey bars represent the first and third quartiles of the frequency distribution. Cluster 5 centers on the theme of \"optical properties\", featuring key terms including \"mixing state\", \"PM\({}_{2}\)5\", \"morphology\", \"absorption enhancement\", \"organic carbon\", \"aging\", and \"transport\". Research within this category primarily explores changes in the microscopic physical and chemical properties of particles during aging and transport processes, particularly investigating how changes in mixing state and morphology affect their optical properties [17; 69; 70]. Cluster 6 centers on the core keywords \"biochar\" and includes key terms such as \"charcoal\", \"carbon sequestration\", \"soil organic matter\", \"pyrolysis\", \"soil\", \"carbon cycle\", \"humic acid\", \"stable isotope\", \"drought\", \"wildfire\", \"forest fire\", and \"Holocene\". Research within this theme focuses on the impact of black carbon, biochar, and charcoal, which are generated from wildfires and forest fires, on soil and the climate, as well as their role in carbon sequestration [11; 12; 71]. Additionally, it includes studies utilizing stable isotope-based methodologies to explore environmental changes, climate evolution, and ecosystem dynamics during the Holocene period in paleoclimatology [72; 73]. It is noteworthy that Cluster 6 demonstrates relatively fewer connections with the previous five clusters, indicating a more independent research direction within the BCL dataset. The keyword classification using CiteSpace (Figure S1) generally corresponds to the findings obtained from Figure 6a, although differences exist due to varying algorithms and criteria. Nevertheless, through keyword clustering analysis, we can extract the main research directions from the extensive information, offering vital insights for enhancing our macroscopic understanding of the entire research domain.
## 3 Results: The Theoretical Geometrical Model and Its Application for Budapest
### Urban Structures  
To model the city logistics network, it is necessary to know the urban road network and the structure of the examined city. Examined from a morphological point of view, cities are usually composed of several different structures. For the current research, three main structural groups can be identified:  
* irregular structure,
* grid-based structure, and
* radial structure.  
In the case of an irregular urban structure, irregularity can mostly be found in the whole urban structure; the road network and the arrangement of the building are irregular as well. If the given city or given urban area has resulted from a conscious engineering design, there is a grid-based structure. In the case of radial urban structure, radially converging main roads and rings give the backbone of the urban structure [50], such as in the case of Budapest with \"Kiskorut\" and \"Nagykorut\" or in Vienna with the \"Ring\". The available road network provides a reasonable basis for the organization of the city logistics systems; the road network itself can also establish the logistics network, as the example of Budapest presented it previously. In the current case, the radial urban structure provides the basis of the geometrical modeling since the model that was examined earlier came from the radial structure of Budapest. In the previously examined model, we started from the benefits of the radial structure, taking advantage of the fact that the intersections of the rings and rays designate high-traffic nodes, and the rings and rays themselves define the main urban road network.  
In general, there is a tendency for the CSDLs to be located in the main junctions (as they are transport junctions as well, this is ideal for customer traffic), and in the case of a radial structure, these junctions are provided by the intersections of rings and rays. The theoretical geometrical model, which is presented in the next sections, is based on the radial structure, but naturally, it can be adapted to any other structure type if a central ring and rays can also be designated on the road network, as can be seen in Figure 10. Later in the research, it will be an important task to develop the automatic, algorithm-based designation of the central ring and the rays (for example, by use of a genetic algorithm); in this paper, intuitive identification was used.
###### Abstract  
The integrated energy system (IES) has the characteristic of energy system integrated/multi-energy coupling that involves heat, cooling, electricity, natural gas, and various other energy forms, which can maximize the synergistic effects and complementary benefits among various energy forms and their comprehensive utilization. In this paper, based on energy hub (EH), the day-ahead hierarchical steady state optimal operation for IES is discussed, in which the coupling natural gas system, electricity transmission system, and district heating system are all considered. Firstly, the model architecture of EH with diverse storage devices, renewable energy, and different energy conversion equipment is proposed and the steady state mathematical model of different energy networks in IES is developed, respectively. Secondly, the day-ahead operating cost of EH is minimized by an optimizing strategy to maximize the benefits of all kinds of energy demand users, where different types of energy power input into EH can be obtained. Then, the day-ahead optimal operation mode for IES considering minimization of operating fuel cost index is proposed via an energy management system, which provides various energy power data that are uploaded from EH. Finally, numerical results are presented to verify the effectiveness and usefulness of the day-ahead hierarchical optimal operation and steady state calculation analysis for IES, which could further illustrate that the proposed optimal operation can meet the requirements of practical engineering applications.  
integrated energy system (IES); hierarchical optimization; steady state; energy hub (EH); heat/electricity/natural gas +
Footnote †: journal:  
Article Day-Ahead Hierarchical Steady State Optimal Operation for Integrated Energy System Based on Energy Hub
# Flexible Coated Conductive Textiles as Ohmic Heaters in Car Seats
## 1 Introduction
Printed heaters have used tiny carbon heating elements between silver conductors to get around the large sheet resistance of carbon sheets [13]. GNP-only inks are ideal for wearable heating applications, and rigid substrates have been employed in printed heating systems because of their high resistance [14]. The effectiveness of the heat pumps during tensile extensions had not traditionally been evaluated, notwithstanding flex testing. Moreover, it has been demonstrated that commercially available carbon-inks are not sufficiently serviceable against wrinkling and electro-thermal effects [15]. Polymer composites compounds that make up nanofillers have benefits including being lightweight,  
Figure 1: Wiring pattern in a carrier with sinusoidal loops. Reproduced with permission from [[PERSON]], [Flexible plane heater: Graphite and carbon nanotube hybrid nanocomposite]; published by [Elsevier], [2015][9]. resistance to corrosion, simple processing methods, degradability, chemical stability, tuneable electrical properties, and lower production costs for electric heater applications [16]. Graphene-based heaters frequently exhibit fast heating rates and uniform temperature delivery [17]. However, because graphene-based heaters generally possess a high sheet resistance, they demand substantial voltage levels for applications which require extreme heat, which could pose an injury risk. As a result, a low resistance heater that operates at lower voltages is preferred [17]. As a result of their minimal sheet resistance, metal nanostructure heating systems could be operated at lower driving voltage levels. The incorporation of electro-conductive features into the fabric structure results in a flexible textile which does not compromise the level of comfort, maintenance, and breathability of the clothing. Electrical heating items, in general, increase the temperature through integrated heating elements. Metal-coated fibers or fabrics are frequently utilized to heat textiles for heat control using various techniques [18]. The current review focuses on the use of various textile-based ohmic heating systems, their fabrication techniques, heating range, and reliability. Furthermore, the review focuses on the mechanical efficiency and comfort features of the described fabrics (used in car seats). This study is useful for future trends in reducing energy consumption, which is an effective strategy for lowering the air pollution and carbon emissions caused by vehicles.
# Predictors of Engagement in Virtual Reality Storytelling Environments about Migration
## 3 Hypotheses Development
### Usability  
Some studies regarding VR scenarios have assessed the usability variable. For instance, [PERSON] et al. [62] tested the usability of a vocational training system based on a VR storytelling scenario. That study was conducted with 30 participants who were exposed to three different training methods (a VR environment, a 2D video, and a text manual). The authors found that usability in the VR environment was rated higher than in the other training methods. In another study, the usability was tested with 50 participants who interacted with a non-game application and a game-based VR environment [63]. As a result, it was identified that the VR environment presented more usability problems for novice gamers because of the complexity of the components used to make the VR game more attractive to users.  
There are some studies that evaluate both concepts as independent constructs. For instance, [PERSON] et al. [64] evaluated both usability and engagement, usability in terms of how easy it was to use the user interface of the immersive environment and engagement in terms of how immersed the users were while carrying out activities. They found that users had good immersion; however, the usability was marginally acceptable. Despite these results, authors from different studies recommend improving interactions between the user and the graphical interface and considering user-centered designs [64; 65].  
**Hypothesis (H4).**_The usability in the virtual reality storytelling environment has a positive and significant effect on engagement._
# Robotics in Gastrointestinal Endoscopy
## 2 Review of Robot Assisted Endoscopic Devices (Robot Assisted ESD System)
### Electromechanical Control Device for Conventional Endoscope
#### 2.4.2 Endoscopic Operation Robot (EOR; Kyushu Institute of Technology, Kitakyushu, Japan)  
Endoscopic Operation Robot (EOR) was developed to replace an additional endoscopist who controls the endoscope itself in a robotic platform other than the console surgeon in the robotic endoscopic multitasking platform. The EOR is a system mounted on a conventional scope capable of manipulating it through a joystick with one hand (Figure 12) [36; 37]. The master unit of the EOR Ver.3 consists of a torque sensor, a rotary motor, a rotating handle and a mini joystick. The master unit can move the flexible endoscope to four axes with one hand, enabling intuitive operation. An operating block with a rotating handle was installed on a linear motor. By operating the linear motor, the endoscope can be inserted or retracted, and the scope can be rotated with the rotating handle. It can provide complete remote control of flexible endoscopy for use in clinical practice.  
Figure 11: Robotic steering and automated lumen centralization (RS-ALC, Enschede, The Netherlands) [35].  
Recently, the same research team developed a master-slave system that allows remote manipulation of endoscopic instruments and combined it with EOR Ver.3 to create a novel endoscopic treatment robot system: the endoscopic therapeutic robot system (ETRS) [38]. Ex vivo porcine stomach ESD experiments were successfully conducted using ETRS. CE or FDA approval was not obtained.
## 2 Modeling and Optimization Methodology
### System Modeling
#### 2.2.2 Optimization Strategy  
The optimization strategy aims to minimize the LCC and is applied to the system model, assuming operation at design (full load) conditions throughout the annual load profile and the total system lifetime [24]. Specifically, the objective function of the optimization problem is defined as follows:  
\[\min(LCC)=\min\left(C_{tvac}+C_{prop}+C_{cmi}+C_{tcf}-\left(D_{dep}+D_{crad}+D_ {salv}\right)\right) \tag{21}\]  
The decision variables for the optimization are chosen based on realistic ranges, where they can be varied to optimize the objective function [25; 26]. Four parameters have been identified as the decision variables of the optimization problem: the temperature at anode inlet (\(T_{8}\)), the temperature at cathode inlet (\(T_{13}\)), the temperature at combustor exit (\(T_{17}\)), and the steam-to-carbon ratio (\(SC\)). The minimum values for the bounds of \(T_{8}\) and \(T_{13}\) are fixed at 700 \({}^{\circ}\)C, because the temperature difference between the fuel cell and the air/fuel inlet must not exceed 100 \({}^{\circ}\)C, to avoid thermal stresses in the SOFC [21]. The steam-to-carbon ratio can vary between 2.5-4.0, which is a realistic operating range for SOFC systems [27]. The minimum, maximum, and initial values of the decision variables for the optimization process in the current study are tabulated in Table 2.
# A Division-of-Labour Approach to Traffic Light Scheduling
## 4 Empirical Procedure
* Scenario Eight: Traffic intensity was scaled by \(\sin x\), where \(x=\frac{t\pi}{21,600}\) and \(t\) was the elapsed time since the simulation started in seconds. This scenario evaluated the ability of an algorithm to handle varying levels of traffic that models a \"rush hour\" in an irregular intersection layout. Figure 1: A visualisation of the first intersection layout. Red dots represent traffic lights. A saturation flow rate of 0.38 was assumed for all simulations. The yellow light duration was set to two seconds, and the duration for which all traffic lights are set to red when changing phases was set to three seconds. The Webster, max pressure, and developed division-of-labour algorithms use a minimum green light duration of seven seconds to prevent extremely short green light durations. A maximum green light duration of 60 s is enforced on the Webster and division-of-labour algorithms to prevent scenarios where traffic scenarios with low traffic demand are never given time. Figure 3: A visualisation of the second intersection layout. Red dots represent traffic lights, the white dot represents a round-about, and the blue dot represents the internal cul-de-sac vehicles were allowed to enter and leave the layout from, in addition to the outer edges. Figure 2: A complex four-way intersection in the first intersection layout; green indicates a green traffic light for traffic that may go, and red indicates a red light for traffic that has to stop. With respect to the Webster algorithm, the cycle duration has a minimum of 38 s and a maximum of 250 s, and timing is recomputed every 450 s. The static uniform algorithm has green light durations that depend on the scenario to which it is applied. The durations for a scenario are calculated by applying the Webster algorithm to the point in the scenario with the highest traffic volume. An algorithm was applied to every scenario 30 times, and the average vehicle delay and travel time caused by the algorithm were recorded over the 30 independent runs. A value for the \(c\) parameter of the division-of-labour-based traffic scheduling algorithm, which sets how sensitive the steepness is to the total traffic volume around the intersection, was found through a grid search before any algorithms were applied to simulations. It was found that values for \(c\) smaller than one lead to poor performance, and values much larger than 50 lead to poor performance as well. For those reasons, the average vehicle delay was taken across 30 independent runs after applying the division-of-labour algorithm to Scenario Four, with values of \(c\) between and including 1 and 50. Scenario Four was chosen for the variety of traffic intensities that arise.