esa-sceva/satcom-chunk-collection huggingface dataset:
# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 3 Trust-Based Reinforcement-Learning-Enabled MEC Server Selection  
The users evaluate the reliability (i.e., trust levels) of the MEC servers based on the quality of service that they provide for processing the offloaded computing tasks in order to ultimately identify the most dependable MEC server. The trust of the users to the MEC servers is assessed by considering both the direct trust \(dtr_{s,n}\) and the indirect trust \(itr_{s,n}\). The direct trust stems from the users' previous interactions with the MEC servers, while the indirect trust, or reputation and reliability, is built on the collective experiences of other users. This dual-trust approach improves the users' understanding of the service quality offered by the MEC servers, especially in the case where the direct interactions of the users with the MEC servers are infrequent.
# Investigation of a Possible Material-Saving Approach of
## 3 Results  
### Material Distribution and Recovering  
The first two depositions were carried out at the distance of 4.5 cm between the target and the SH in the top-down [13] position. In such a configuration, some amount of Cu material was able to escape and was deposited on the walls of the vacuum chamber. In order to overcome such an issue, in the next experiments performed, the working distance was reduced to 2 cm. As a result, no visible Cu deposits were discovered on the chamber walls. All calculations were carried out on the basis of the result of the deposition made with the distance of 2 cm.  
To continue with the weight-loss analysis, it was decided to weigh two main elements of magnetron: the central ring with its four screws and the recovering shield with its four screws. In each experiment, a new Cu target was used and weighted just before the assembling in the magnetron. In the calculation, the weight of the SH was considered as total weight of stick, base of the SH, cap of the SH, four screws with the nuts and four quartz samples.  
The choice of the sputtering deposition parameters was based upon the past research experience with copper deposition. Since the deposit quality was not a priority for this study, there were no additional stress tests to carry out. In the case of the DS process, the working pressure was chosen with the aim to work below 1 kV voltage. To achieve a desirable working pressure, the vacuum pumping frequency of the turbomolecular pump was decreased manually to 375 Hz, compared to 700 Hz for the stand-by regime in the MS deposition. The parameters of both deposition processes are shown in Table 1. All depositions were done in steps of 2-2.5 h.  
After the MS deposition, the target erosion depth was measured by a height measuring device (Digimar 816 CL) to evaluate the residual thickness. In the thinner part of the target, it was reduced from 5 to 0.78 mm.  
The weight of the sputtering target was evaluated before and after the deposition. The results shown in Table 2 point out that, in the case of the MS process, only 23,882 g out of 88,811 g of the target source mass, which corresponds to 26.9%, was used during the deposition process. The rest of the target material requires future recovering. According to calculation, only 9280 g was deposited on the sample holder, while 13,441 g was collected on the recovering shield.  
The target erosion depth after the DS process was not evaluated. In the ideal case, with the continued erosion for more than 20 days, the estimated usage of the target of the DS process would be 68.7%. Practically, only 1299 g of the target material was sputtered from the target, and only 21.6% has been trapped at the sample holder.  
According to the calculation, the MS seems more efficient than the DS, except for the target mass usage. In the case of the recovering shield utilization, the obtained losses were less than 5%.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 9 Summary and Outlook  
In this work, we presented a holistic framework for modeling, executing, monitoring and recording IoT-driven BPs. Following the Design Science Research Methodology, we identified a missing integrated support of these tasks. Then, we studied existing approaches to evaluate their suitability with respect to such an integrated lifecycle support for IoT-driven BPs. As drawbacks of the studied approaches, they do not support the intrinsic IoT characteristics, are described only theoretically, or focused on extending BPMN for modeling and do not support the execution, recording and monitoring of IoT-driven BPs. Based on these insights, we derived research questions, which we then refined by formulating several objectives to improve the lifecycle support of IoT-driven BPs. Regarding the modeling and monitoring phase, we extended the BPMN 2.0 metamodel with IoT-specific artifacts and events, resulting in BPMNE4 IoT. To be able to execute and monitor the BP created with BPMNE4 IoT, we defined an appropriate architecture and implemented an IoT-aware BPMS. Together, they enable a holistic framework for IoT-driven BPs. Moreover, we extended a common BPMN 2.0 modeler with BPMNE4 IoT and, thus, support process engineers with a modeling environment with which they are familiar. Based on the proof-of-concept evaluation and the application of the resulting prototype to the smart production and smart home, we could demonstrate the applicability of the framework and its concepts in different domains. Altogether, the prototype evaluation has demonstrated that we are able to model, execute, monitor and log IoT-driven BPs. The results of the user study confirmed that the use of BPMNE4 IoT has led to an increased usability and user experience (i.e., better task completion times, positive feelings and lower workload) compared the modeling of IoT-driven BPs with BPMN 2.0.  
In future work, we want to perform further experiments and studies to assess the completeness and domain independence of the BPMNE4 IoT framework. So far, we have compared BPMNE4 IoT with the de-facto process modeling standard BPMN 2.0, as there is no defined standard or tool for modeling, executing, monitoring and recording IoT-driven BPs. Nevertheless, in future work we want to compare BPMNE4 IoT with other approaches regarding modeling, usability, acceptance and user experience. Finally, we want to review how we can complement our framework with state-of-the-art IoT security mechanisms.  
Y.K. and F.G. designed the concept, conceived and wrote the paper; Y.K. designed the methodology; Y.K. and M.W. used static and mathematical techniques to analyze the study data; [PERSON] corrected and supervised this work. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 449721677.  
There is no relevant external data. All data is contained within the article.  
The authors declare no conflict of interest.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 5 Simulation and Analysis
### Simulation Results
#### 5.2.2 Average Power Consumption  
Another set of experiments is conducted to examine the average power consumption for all algorithms. Again, the same configuration used in the previous experiments is followed. Small-scale and large-scale networks are examined.  
For a small-scale set of experiments, it is considered more realistic to experiment with different sinkholes. This shows how the network is affected by the increasing number of sinkholes. The experiments are conducted for one, two, three, and five sinkholes and five sink nodes. Figure 12 shows the energy consumption results in all cases with a fixed number of packets sent through the network, 80,000 messages. Five hundred nodes randomly generate messages. The consumed energy is computed as a percentage out of the energy sum of the sensor nodes' initial energy. As can be seen in the figure, TBIDS gives the best energy consumption by almost 18% in all cases, while ABC and MBIDS consume, on average, almost 80%. However, MBIDS still overperforms ABC by 6% of the energy consumption; this percentage is increased to 9% when five sinkholes are considered. Regarding S-LEACH and MS-LEACH, as shown, they consume too much energy due to the cryptography algorithm that is used to authenticate the cluster heads.  
Figure 11: Detection accuracy for the large-scale network (8000 nodes) with 10, 20, and 30 sink nodes.  
Figure 12: Energy consumption for a small-scale network with 1, 2, 3, and 5 sinkholes.  
For a large-scale set of experiments, again, this section shows experiments with 15,000 nodes and five sink nodes. The number of messages to be sent by each sensor is assumed to be 100 messages. The results in Figure 13 show, on average, that TBIDS is the best among the five algorithms, while the worst is MS-LEACH. Besides, it turns out that ABC is much better than S-LEACH and MS-LEACH. However, MBIDS is still ahead of the ABC in terms of energy consumption by a slight difference. It is worth mentioning that after a deep investigation into the MBIDS, the overhead messages consume, on average, 5% of the overall network energy.  
To examine the energy consumption of the overall network with a large number of sink nodes, the same set of experiments introduced in the previous section with 8000 sensor nodes, five sinkholes, and variable sink nodes (10, 20, and 30) is conducted. The results of this set of experiments are shown in Figure 14. In this figure, the energy consumption percentage is displayed to clarify the difference between the algorithms. Again, energy consumption has been significantly reduced with the increasing number of sink nodes. One clear note is that TBIDS energy consumption is reduced from 65% to 58%, and MBIDS is reduced from 72% to 63%. At the same time, S-LEACH is also showing similar energy consumption degradation percentage with almost 5%, on average. Moreover, although MS-LEACH is considered the worst to consume energy, it has also been enhanced, on average, 4%, when the number of sink nodes increases to 30 nodes.
## 3 Results
### Numerical Results  
The structure was analyzed by using periodic boundary conditions and standard mesh through the full-wave simulation software CST Microwave Studio (Dassault Systemes, Velizy-Villacoublay, France).  
The project template workflow was as follows: Mw and RF and optical, periodic structures, FSS, metamaterial--unit cell, phase reflection diagram, solvers frequency domain.  
The study was carried out by assuming that a plane wave impinges on the top layer of the bandpass filter with different incidence angles (from \(0^{\circ}\) to \(60^{\circ}\)) and for TE polarization. Since the proposed structure is fully centrosymmetric, its response is the same at vertical incidence of TE and TM-polarized electromagnetic waves, and we will only discuss the case at TE-polarization in the subsequent analysis as well as in the experimental measurement section [40].  
The transmission coefficient resulting from this investigation is depicted in Figure 5. The \(-3\) dB fractional transmission bandwidth was evaluated equal to \(FBW_{-3\text{dB}}=108.7\%\) (corresponding to the condition in which \(S_{21}\geq-3\) dB, spanning from 2 GHz to 6.76 GHz) for \(\theta_{inc}=0^{\circ}\) and exceeded 90% for \(\theta_{inc}=40^{\circ}\). Performances were stable even up to \(\theta_{inc}=40^{\circ}\), with a bandpass ranging from 2.3 GHz to 5.5 GHz, resulting in a \(FBW_{-3\text{dB}}=85\%\). When the \(-1\) dB fractional transmission bandwidth \(FBW_{-1\text{dB}}\) was considered (corresponding to the condition in which \(S_{21}\geq-1\) dB), it exceeded 80% for \(\theta_{inc}=0^{\circ}\), \(\theta_{inc}=20^{\circ}\), \(\theta_{inc}=40^{\circ}\), spanning from 2.3 GHz and 5.4 GHz. The insertion loss level was approximately 0 dB (0.0007 dB) in correspondence with the central frequency (3.68 GHz).  
Therefore, the structure demonstrated a wide and flat bandpass stable behavior against the incidence angle up to \(40^{\circ}\), thus representing a very robust solution as an S-C band bandpass filter.
## 4 Experiments
### Performance Comparison  
#### 4.2.1 Performance Comparison with the Collaborative Filtering Model  
The ATKGRM-KG and three baseline collaborative filtering recommendation models are compared on MovieLens-1M and MovieLens-10M. Because these three models do not use knowledge graph information, we used ATKGRM-KG, which removed the process of aggregating knowledge graph information from ATKGRM, for a more accurate comparison. The comparison of the baseline models is shown in Table 3.  
Three baseline collaborative filtering recommendation models are introduced below:  
* CF-NADE [5] shows how to adapt NADE to CF tasks, encouraging the model to share parameters between different ratings;
* NCAE [6] can effectively capture the subtle hidden relationships between interactions via a nonlinear matrix factorization process;
* NESVD [7] proposes a general neural embedding initialization framework, where a low-complexity probabilistic autoencoder neural network initializes the features of the user and item.  
The results can be seen in Table 3. It is worth noting that the underscore represents no corresponding data. The ATKGRM-KG model has superior performance. Specifically, on the MovieLens-1M dataset, compared with the optimal baseline collaborative filtering recommendation model NESVD and CF-NADE, the RMSE of ATKGRM-KG is reduced by 38.1% and 38.4%, respectively. On the MovieLens-10M dataset, compared with NESVD, CF-NADE, and NCAE, the RMSE of ATKGRM-KG is reduced by 42.1%, 42.8%, and 42.5%. ATKGRM-KG is better than NESVD, CF-NADE, and NCAE, which can show the advantages of adversarial training. The purpose of GAN is to conduct an adversarial battle between the generator and the discriminator and it is an effective way to alleviate data noise.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 1 Introduction
Additionally, VQ-Font [3] requires the calculation of a component-based dictionary map, which introduces an additional layer of complexity in its implementation. To overcome the above issues, we use a patch-based attention encoding scheme to focus on the local styles of font images, instead of using time-consuming component-based generation or attention methods with multiple stages that are hard to compute. We use a multitask encoder to separate global and local styles from reference images. This lets us synthesize images that look like the font family as a whole and are also consistent in properties within the style, like the number of serifs, the shapes of the strokes, and the spaces between the parts. To learn global style representation, we pass the entire image through the encoder, obtain the global style code, and divide the same image into 5 \(\times\) 5 overlapping patches of size 32 \(\times\) 32 pixels. We then pass each patch through the encoder to obtain local style codes. We then applied patch-based attention when calculating the relevance of each patch based on their similarity to focus on the most important patches for the final style representation that contributes to local styles. Later, by combining the global and local style codes, the generated font images not only exhibit the overall style of the font family but also maintain consistency in specific style attributes, such as stroke width and curvature. This approach not only improves the efficiency of the generation process but also ensures the quality and fidelity of the generated font images. In this work, we propose a patch-based attention approach, called Patch-Font, which balances simplicity, speed, and generation quality. This method forgoes the need for manual component labeling by learning local style features via patch embeddings. Our main contributions are  
* A single-stage multitask style encoder that extracts both global font characteristics and local style details from overlapping patches, avoiding multi-stage or dictionary-based procedures. * A triplet margin loss framework with hard positives and hard negatives to disentangle style from content effectively, along with a style fidelity loss that enforces consistent local stroke patterns. * Empirical demonstration on Korean (printed and handwritten) and Chinese fonts, showing improvements over existing baselines in style accuracy, perceptual metrics, and training speed. These contributions enable high-fidelity font generation with minimal references, broadening real-world applicability for rapidly customizing or prototyping fonts in various industries.
## 3 Archimedean Spiral Antenna
### Prototype  
To test the correct performance of the antenna, a prototype of the Archimedean spiral is manufactured with a LPKF ProtoMat S100 milling machine, as shown in Figure 7. As discussed before, both arms are extended with an impedance step to the end of the FR4 substrate to improve the bandwidth of the antenna. The reflection coefficient is then measured in the laboratory and compared with the simulated one. Both results are depicted in Figure 5. With a bandwidth exceeding 15 GHz, measurements prove that the antenna covers most frequency bands of interest. It is perfectly matched from 0.35 GHz to 16 GHz, showing an ultrawideband performance.  
The antenna is sensitive to small variations in the metallic strip width \(w\), in the separation between two adjacent strips \(s\), and in the inner radius \(r_{1}\). Thus, although the spiral has an outstanding bandwidth, there exist differences between the simulated and the measured curves, mainly caused during the manufacturing process. Beyond that, the upper and lower cutoff frequencies (16 GHz and 350 MHz, respectively) coincide perfectly in both graphs of Figure 5.
# Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
## 3 Methodology
### Air Quality Assessment
#### 3.3.4 Stability Classification and Dispersion Parameters  
The stability classification, determined using the wind direction-fluctuation method recommended by the CPCB, India [37, 38], allowed for the quantification of the atmospheric stability through the standard deviation of wind-direction fluctuation (\(\sigma_{a}\)), as shown in Equation (2).  
\[\sigma_{a}=\frac{W_{dr}}{6} \tag{2}\]  
where \(\sigma_{a}\) is the standard deviation of wind-direction fluctuation, and \(W_{dr}\) is the overall wind-direction fluctuation or width of the wind direction in degrees.  
The classification significantly influenced dispersion parameters and predicted pollutant concentrations, with stability classes ranging from A (very unstable) to F (very stable). Unstable conditions (A, B, and C) lead to greater mixing and dispersion, while stable conditions (E and F) restrict dispersion, leading to higher concentrations near the source. Different stability classes have significant implications for dispersion parameters and ultimately influence the predicted pollutant concentrations.  
The project's rural setting with flat terrain uses open country-dispersion parameters recommended by the CPCB, India [39, 40]. These parameters, crucial for simulating pollutant plume behavior, vary with downwind distance and depend on the assigned stability class. Table 3 outlines these coefficients specifically for rural conditions, where \(\sigma_{y}\) represents lateral dispersion, and \(\sigma_{z}\) represents vertical dispersion. These parameters are used for modeling atmospheric dispersion in flat terrain and rural areas.  
The lateral dispersion (\(\sigma_{y}\)) and vertical dispersion (\(\sigma_{z}\)) parameters vary with downwind distance (\(x\)) and are determined by different empirical formulas depending on the stability class [41, 42], thus ensuring accurate modeling of pollutant dispersion under different atmospheric stability conditions, as is crucial for Environmental Impact Assessments and air quality management in rural settings.  
Based on the model assumption, the mixing height of particulate matter during the day (Figure 5a), standard deviation of the wind-direction fluctuations along with frequency of occurrence by stability classes A to F (Figure 5b), and coefficients of the lateral and vertical dispersion in different stability classes (Figure 5c) were evaluated and recorded.  
Figure 4: Flow diagram of the air pollution modeling with ISCST3.
## 4 Results
### Overall Vocal Tract Dynamics  
The assessment of the methods' capabilities began with an investigation of the evolution in time of the tract. For this, GAMMS were applied to the EP five nasal vowels and oral counterparts. The results obtained for the first prosodic condition (see Section 3.2) are presented in Figure 3 as 'areagrams' (spectrogram-like image representations, representing vocal tract areas as a function of tract position and time). In the 'areagrams', the vowel appears at the center of each plot (from 0.33 to 0.66 normalized time), with context consonants (\([\mathrm{p}]\) and \([\mathrm{t}]\)) represented at the beginning and end. Each row of the figure presents the 'areagram' for an oral vowel, its nasal congener, and the difference. As no major differences were observed between the two bilabial contexts, only vowels in the context of \([\mathrm{p}]\) are shown in the figure. Figure 4 shows the difference 'areagrams' for all the oral/nasal vowel pairs in the second prosodic condition. These can be compared with the rightmost column in Figure 3, and they mostly exhibit the same patterns, that is, the differences observed between the oral vowel and its nasal congener show the same pattern for both prosodic conditions. Therefore, from now on, we will only refer to results for the first prosodic condition.  
The leftmost panels for [a] and [i] replicate the results of [5] for [a:, i:] analyzed from 38 speakers of German. In both studies, the palate is expanded and the pharynx constricted for [a], while the palate is constricted and the pharynx expanded for [i]. In our data, nasal \([\bar{\mathrm{u}}]\) shows less expansion along the palate and less constriction throughout the pharynx, meaning that this nasal vowel presents a more raised and advanced tongue position than its oral congener.  
The dominant grayish color of the difference diagram for \([\mathrm{u}]/[\bar{\mathrm{u}}]\) and \([\mathrm{i}]/[\bar{\mathrm{i}}]\) are clear indications of the very small overall differences for these two pairs of vowels.  
For the other three vowel pairs, differences are more noticeable. Differences are negative in the pharynx region, hinting on a more open passage for the nasal vowel in this region. These data hint on a more backed configuration for the orals when compared with the nasal congeners. On the contrary, differences are positive in the alveolo-palatal region, with a maximum at the middle of vowel production, pointing to a higher degree of closure for nasal counterparts. For \([\mathrm{s}]/[\bar{\mathrm{o}}]\), these differences are of lower magnitude and more concentrated at the middle of production.  
Overall, the GAMMs presented here were successful in deriving representations from vocal tract area functions, which provide interesting and useful insights regarding oral configurations. However, even with the modifications introduced concerning velar and labial apertures (see Section 3.3), nasality differences between oral and nasal vowels, particularly regarding the velar region, were not evident in the representations.