esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Model Testing
Additionally, the variation of apparent resistivity in the \(y\)-direction has clear traps at the center of the anomalous bodies. Figure 9.0 The imaging results for the model I: (**a**) the apparent resistivity calculated from magnetic field, (**b**) the apparent resistivity variation predicted from magnetic field gradient \(H_{z}^{\Delta x}\) along the \(x\)-direction survey line, (**c**) the apparent resistivity calculated from magnetic field, (**d**) the apparent resistivity variation predicted from magnetic field gradient \(H_{z}^{\Delta y}\) along the \(y\)-direction survey line. The white box indicates the outline of the conductive abnormal body. Figure 9.1 The imaging results for model II: (**a**) the apparent resistivity calculated from magnetic field, (**b**) the apparent resistivity variation predicted from magnetic field gradient \(H_{z}^{\rm{Ax}}\). The white boxes indicate the outlines of the conductive abnormal bodies. Figure 9.2 Schematic diagram of model III with two conductive objects positioned along the \(y\)-axis: (**a**) XOY plane, (**b**) YOZ plane. Figure 9.3 Schematic diagram of the model II with two conductive objects positioned along the \(x\)-axis: (**a**) XOY plane, (**b**) XOZ plane. Figure 9.4 The imaging results for model III: (**a**) the apparent resistivity calculated from magnetic field, (**b**) the apparent resistivity variation predicted from magnetic field gradient \(H_{z}^{\rm{Ay}}\). The white boxes indicate the outlines of the conductive abnormal bodies. The calculated results of the apparent resistivity obtained by the traditional iteration-matching procedure are shown in Figures 9a,c, 11a and 13a. As the magnetic field primarily reflects changes in subsurface electrical properties, the distribution of apparent resistivity derived from the magnetic field exhibits significant changes at anomaly boundaries, thereby limiting its ability to indicate the center of anomalies. In contrast, Figures 9b,d, 11b and 13b show the predicted results of the apparent resistivity variation by the NGO-LSSVM algorithm, which indicate the anomaly location clearly and demonstrate the feasibility of such predictions. Notably, since diffusion causes apparent resistivity to reach extreme values at boundaries, the largest variation in apparent resistivity occurs precisely at the center of anomalies. Consequently, utilizing magnetic field gradients to predict distributions of apparent resistivity variations enables more accurate identification of anomaly locations.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 3 Modeling Approaches and Frameworks in the Field of Industrial IoT
### MDE-to-IoT Approaches in a Nutshell
#### 3.1.2 General MDE Mechanisms for IoT  
Aside from the fully-fledged MDE tools and frameworks mentioned above, there are emerging general MDE mechanisms for IoT in the literature. UML [42] and SysML [43] are widely regarded as de facto standards for software and systems development, respectively. Both UML and SysML have IoT extensions. UML4 IoT [44, 45] is a UML profile [26], which is a specialization of the UML's meta-model with IoT-specific constructs.  
SysML4 IoT [46] is a SysML profile adaptation that addresses the heterogeneity of hardware devices and software components in IoT system modeling. SysML4 IoT includes abstractions for describing various types of hardware devices and software services. Another abstract SysML profile for Arrowhead System of Sytems (SoS) design is proposed in [41]. This SysML profile is intended to create abstract Arrowhead SoS instances.
## 4 Implementation
### Modeling Normal Traffic  
In order to conduct the simulation, traffic regarding normal operation of IoT devices had to be simulated. To formalize this simulation, four generic profiles of IoT devices have been employed.  
* **Single server devices:** These are devices which communicate with a single host to upload data. This happens periodically for most of the devices and the volume of data is usually small. Such devices communicate to a specific host \(h\in H\) and the corresponding traffic packets are modelled according to a normal distribution. The mean \(\mu\) and the variance \(\sigma^{2}\) of the distribution are fixed throughout the simulation, for each different device.
* **Multiple clients devices:** These are devices which provide access to data they collect to multiple clients. Such devices communicate with more than one host and these communications are not as uniform as those of the previous profile. The traffic packets for such devices are produced by normal distributions. A different distribution is used for each different counterpart host \(h\in H\). The mean \(\mu\) and the variance \(\sigma^{2}\) of the distribution for the communication of a specific device with a specific host are fixed throughout the simulation.
* **Streaming devices:** These devices are practically the equivalent of IP cameras. They stream high loads of traffic for continuous time windows to more than one host. The model for producing the packet flows for such devices is similar to the one of the previous profile. The difference is that the mean \(\mu\) of the distribution is significantly higher due to higher traffic rates of such devices. Additionally, the packets produced are not scattered in time. The model produces continuous flows of packets for longer time periods, in comparison to previous models.
* **Management traffic:** Devices that do not send data but store them only locally. These devices may communicate with other hosts on the internet for management reasons, such as time syncing or DNS resolution. The model of this profile produces short occasional packet flows, that correspond to communication with a few standard hosts. The mean \(\mu\) and the variance \(\sigma^{2}\) of the distribution are both relatively small, to simulate the exchange of few packets in fixed time intervals.  
The devices \(d\in D\), used in the simulation, were picked randomly out of these four profiles, in order to create a realistic set of devices, with respect to real world IoT landscape.
## 1 Introduction
Training of the model with the L2-norm layer removed makes it possible to generate an appropriate embedding vector with the embedding vectors beyond the surface of the sphere and distributed in an infinite vector space. To verify the proposed methodology, the same architecture was trained with the existing learning method and the proposed learning method, and then the accuracy, one-shot identification performance, and embedding vector distribution of each model were compared. The evaluation was performed with images of dogs that have not been trained. The change in the distribution of the embedding vector according to the learning method was visualized as a 2-dimensional image in a toy experiment. In addition, to compare the distribution of multidimensional embedding vectors, the distance and the length of the embedding vector were calculated for each class. The purpose of this study is to improve the performance of the dog face identification model, which is biometrics for dogs. In this paper, we proposed the learning method of the dog face identification model with improved performance compared to the previous training method for the return of lost dogs. We proposed the novel loss function and a two-stage learning method to improve the performance of the dog face identification model. As a result, we demonstrated that the model trained with the proposed learning method performs dog face identification with embedding vectors distributed in the vector space. In addition, it was demonstrated that the dog face identification performance of the model trained with the proposed learning method was improved compared to the model trained with the existing learning method. The proposed loss function and learning method can be used with various metric learning methods, and then the performance is improved. In addition, the study results proved that metric learning is possible to utilize infinite vector space beyond the surface of a sphere. The study results are expected to improve the performance of the dog face identification model and to help in finding lost dogs. Studies on human and animal face identification are reviewed in Section 2. The novel loss function and learning method for utilizing a vector space are described in Section 3. The models are implemented based on the existing methodology and the proposed methodology in Section 4. The accuracy, one-shot identification performance,and embedding vector distribution are evaluated to compare the performance of the proposed methodology with that of the existing methodology in Section 5.
applied sciences Article  
A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry  
[PERSON],*(c) and [PERSON] Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Wongsawang, Bangsue, Bangkok 10800, Thailand Department of Production Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bang Mod, Thung Khru, Bangkok 10140, Thailand; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]; Tel: +66-87-106-6119  
This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO\({}_{2}\) equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the \(\varepsilon\)-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.  
green ship routing and scheduling problem; set covering model; berth time-window constraints; \(\varepsilon\)-constraint method
# Plasma-Sprayed Flexible Strain Sensor and Its Applications in Boxing Glove
## 2 The Controllable Plasma Spraying Method for Conducting Nanoparticles
### Mechanism of the Low-Temperature-Plasma Treatment  
The sensing mechanism of the flexible strain sensor was as follows: when the stretchable base material generated a small strain, the CNT conductive material structure slipped, destroying part of the conductive path and changing the resistance of the sensing layer. In the spraying process proposed here, the use of low-temperature plasma to treat the sprayed material broke some of the carbon-carbon bonds, which reduced the length of the CNTs, and resulted in shorter and more conductive paths. In addition, the adhesion of CNTs to the silica gel substrates was improved. Therefore, under the same strain, the plasma-treated conductive path was easier to break than untreated path, which resulted in more stable changes in the resistance and an improvement in the sensitivity.
# Fungal Pigments and Their Roles Associated with Human Health
## 5 Medical Relevance of Fungal Pigments
### Medical Roles of Other Pigments
#### 5.2.4 Cholesterol-Lowering and/or Anti-Atherosclerotic Agents  
In view of the association of obesity with an elevated risk of developing diabetes as well as cardiovascular disorders, the search for the cholesterol-lowering compounds has attracted much attention from chemists, biologists, pharmacists and medical practitioners. Monascin and anakfalavin, the two yellow pigments from _Monascus_ spp., are found to have a remarkable antiobesity activity in a 3T3-L1 preadipocyte model of rat [71]. Their hypolipidemic effects were attributed to: (i) the inhibition of the differentiation and lipogenesis of preadipocytes by downregulating CCAT/enhancer-binding protein \(\beta\) (C/EBP\(\beta\)) expression and its downstream peroxisome proliferator-activated receptor \(\gamma\) (PPAR\(\gamma\)) and CCAT/enhancer-binding protein \(\alpha\) (C/EBP\(\alpha\)) expressions, and (ii) the inhibition of lipogenesis by increasing lipase activity and decreasing heparin releasable lipoprotein lipase (HR-LPL) activity [72]. Notably, the actions of monascin and anakfalavin do not resemble that of monacolin K (viz. lovastatin), a well-known cholesterol-lowering drug, which elevates creatine phosphokinase (CPK) activity, known to be a rhabdomylysis marker [72].  
Epidemiological studies have revealed a potential role of carotenoids in preventing cardiovascular disease (CVD) [8]. Reductions of low density lipoprotein (LDL) oxidation and of oxidative stress during plaque formation were thought to account for their effects. An association has been found between lowserum lycopene level and an increased risk of atherosclerotic vascular events in middle-aged men [188]. Recent studies have demonstrated that fungal species such as _Blakeslee trispona_ and _Rlodottorula_ can produce lycopene [189], thereby indicating their potential roles in the production of pharmaceuticals, food additives, or nutritional supplements that could be beneficial for patients with CVD.
## 6 Conclusions  
Applying SVM in the interference monitoring of GNSS signals satisfies the requirement of objectivity and accuracy. However, the training speed of standard SVM cannot satisfy the requirement of being in real-time for interference monitoring. TWSVM, which is faster than the standard SVM in training speed, is introduced in this paper. The TWSVM model is solved by a modified algorithm, LSTWSVM. LSTWSVM is much faster than the standard SVM, and it can be applied in the field of interference monitoring of GNSS signals. The experimental results indicate that the training speed of TWSVM is on millisecond (ms) level and the classification speed of TWSVM is on microsecond (us) level, which can be used in practice.  
This research work is supported by the National Natural Science Foundation of China (No. 61202078).  
**Author Contributions:** [PERSON] contributed to writing much of this paper and performed the whole experiment in Section 5.2. [PERSON] and [PERSON] provided valuable feedback, advice, and mentoring during the manuscript modification. [PERSON] provided some suggestions to the design of the experiments and especially the method on the choice of the penalty parameters in Sections 3.1 and 5.2. [PERSON] contributed to the data analyses in Section 5.1 and edited the manuscript. [PERSON] contributed to several sections of this paper and also gathered sources from the literature.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract
Additionally, we conducted an experimental campaign to examine the scalability and throughput of the system. Our findings indicate that our solution scales well with the number of clients. Moreover, the results reveal that the reduction in throughput is influenced by the integrity check operations. However, since the frequency of these checks can be adjusted as needed, we can configure it to ensure that the throughput reduction remains negligible. Finally, we conducted a detailed analysis of the costs of executing smart contracts on the Ethereum blockchain. Our analysis shows that the overall costs of this solution are affordable. Furthermore, our analysis demonstrates that as the number of subscribers in the system increases, the cost per client decreases. This further confirms that our solution does not encounter any scalability issues. Overall, our results show that it is feasible to implement our solution in the real world to ensure accountability and data integrity in MQTT communications. The rest of the document is structured as follows. Section 2 provides background information on MQTT, blockchain, and IPFS technologies. Section 4 describes the motivations and potential use cases for our approach. Section 5 presents our approach. This is followed by an analysis of the costs of smart contract execution in Section 6 and an analysis of the performance of our solution in Section 7. In Section 8 we analyze the security of the proposed approach. Finally, Section 9 concludes the paper.
## 3 Results and Comparative Experiments
### Model Robustness Analysis  
By deriving the model, it can be seen that the depth of the sea area affects the coverage width and overlap rate of both PMOM and CASLOM, thereby influencing the measurement effect. Therefore, we conducted a robustness analysis on the two measurement models by changing the original dataset's seabed depth to verify the models' sensitivity and adaptability to changes in environmental variables.  
For our robustness analysis experiment, we meticulously selected the second dataset as the control data. We then systematically adjusted the entire seabed elevation data within this sampling area by \(-\)300 m, \(-\)150 m, \(+\)150 m, and \(+\)300 m, creating four distinct sets of experimental data. This process allowed us to observe the models' responses to varying environmental conditions. The results of this experiment are presented in Table 3.  
We observed that the model demonstrates the highest coverage efficiency, lowest overlap rate, and best data integrity within the moderate water depth range (elevation from \(-\)550 m to \(-\)250 m). Although the overlap rate increases and data integrity slightly decreases under extreme water depth conditions (elevation of \(-\)700 m), the model maintains efficient and reliable performance across most water depths.
## 4 Results
### Parameter Estimation Results  
The results for parameter estimation are displayed in Table 5 for the urban crash model and Table 6 for the rural road model. Comparing the goodness of fit to the Fixed Parameter Logit model (FPL) and the Random Parameter Logit Model with Unobserved Heterogeneity in Means and Variances (RPLMV) for urban roads, the McFadden \(\rho^{2}\) for FPL and RPLMV were 0.1013 and 0.1046, respectively. Meanwhile, for crash models on rural roads, the values were 0.1021 and 0.1063, respectively. The McFadden \(\rho^{2}\) of RPLMV was greater for both models, indicating its better suitability. The YAM crash model on urban roads revealed the following three random parameters: crashes involving foreign riders, those involving local drivers, and those resulting from exceeding the speed limit. Two pairs of variables with unobserved heterogeneity in means were FOREIGN: RAINING and EXCESS_SPEED_LIMIT: RAINING. For unobserved heterogeneity in variance, crashes on wet roads increased the variance of two variables, LOCAL_ADDRESS and EXCESS_SPEED_LIMIT. The predominant fixed parameter, UNIL-CENSE, had a positive direction, indicating that unlicensed YAMs had a higher chance of experiencing crashes resulting in fatality. The YAM crash model on rural roads identified three random parameters: crashes on curves, crashes during rainy weather, and crashes occurring in the daytime. Unobserved heterogeneity in means comprised four pairs: RAINING: HELMET; RAINING: NIGHT_LIGHT; MORNING: ILLEGAL_OVERTAKING; and MORNING: NIGHT_LIGHT. Unobserved heterogeneity in variances consisted of two pairs, and traffic violations affected the variance of CURVE and MORNING. The predominant fixed parameter, EXCESS_SPEED_LIMIT, was positively significant, suggesting that when YAM crashes occur due to exceeding the speed limit, the chance of a fatal crash increases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(m_{1}\) = Rural & \(m_{21}\) = Urban & \(\chi^{2}\) & \(Df\) & \(p\)-Value & Level of Confident \\ \hline \(-4006.86425\) & \(-4023.35934\) & \(32.99018\) & \(15\) & \(0.00470858\) & \(99.53\%\) \\ \hline \(m_{1}\) = Urban & \(m_{21}\) = Rural & \(\chi^{2}\) & \(Df\) & \(p\)-Value & Level of Confident \\ \hline \(-2431.00996\) & \(-2441.8613\) & \(21.70268\) & \(19\) & \(0.29930232\) & \(70.07\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Like hood ratio Test (Pairwise Test). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LL(B) Rural** & **LL(B) Urban** & **LL(B) Total** & \(\chi^{2}\) \\ \hline  & \(-4006.86425\) & \(-2431.00996\) & \(-6503.81093\) & \(131.87344\) \\ \hline Degree of Freedom & \(19\) & \(15\) & \(16\) & \(18\) \\ \hline \hline \end{tabular} Note: \(p\)-value < 0.000 (Level of Confident \(\Rightarrow\) 99.99\%).  
\end{table} 
Table 3: Like hood ratio Test (Global Test).
# Preparation and Properties of Poly(imide-siloxane)
## 3 Results and Discussion  
### Preparation of PIs and Their Composite Films  
The preparation of poly(imide-siloxane) copolymers (PIs) is illustrated in Scheme 1. The PIs were synthesized according to a conventional two-step procedure. During typical conventional polyimide synthesis, aprotic polar solvents such as NMP, dimethylacetamide (DMAc), or dimethylformamide (DMF) are usually used. However, the siloxane group underwent microphase separation when aprotic solvents were used during the copolymerization. To overcome this problem, the preparation of PIs required the use of a co-solvent system or THF [41; 42].  
In this work, 6 FDA, MDA, and PDMS were copolymerized using THF as a solvent (Table 1), except for PAA-1 which was prepared without the PDMS moiety and used NMP as a solvent. 6 FDA and mixed diamines (MDA and PDMS) were first polymerized to prepare poly(amic acid-siloxane)s (PAA-1-PAA-7). The diamine molar feed ratio was controlled as summarized in Table 1. The PDMS content of PAA copolymers was determined by 1H NMR (Table 1 and Figure S1). The mole percent of PDMS was calculated from the ratio of the integration values of the methyl groups in PDMS and the methylene groups of MDA. It was found that the determined values agreed well with those corresponding to PDMS contents in the feeds. The PAA solutions were drop-cast onto slide glasses and then PAAs were converted to PI-1 PI-7 films by thermal imidization. The PAA and PI powders were also prepared for characterization via FT-IR spectroscopy and GPC. Table 1 lists the molecular weights and polydispersity indexes (PDIs) of the PAAs measured by GPC. In addition, PI/Al2O3 composites were prepared by adding various amount of Al2O3 powder to PAA solutions. The resulting suspensions were drop-cast onto galss slides and subsequent thermal imidization was carried out to obtain PI/Al2O3 composite films.
## 4 Results and Discussion  
### Performance Evaluation  
#### 4.1.1 Estimation Performance  
We used the NIST canopy PV dataset as a training and validation set, in 2015. For the evaluation metrics, we used MAE and SMAPE. Figure 10 shows the training and validation errors of estimating power generation. We saved an estimation model at each epoch, and utilized the model that showed the best performance in the validation set. We tested the model using the NIST canopy PV dataset, in 2016. For the power generation estimation, the training, validation, and test errors were 4.99, 5.48 and 8.26, in terms of MAE, and they were 4.34, 6.32 and 8.30, in terms of SMAPE, respectively. Figure 11 shows a comparison between the measured and estimated profiles, while (a), (b) and (c) show the results of the AC power, current, and voltage estimations, respectively. Table 1 describes the performance details of each model.  
Figure 11: Profile comparison between the estimation and measurement values.  
Figure 10: Loss curve in power generation estimation.