esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Privacy-Preserving Capability Evaluation  
The decentralized training strategy of the proposed Federated-Learning (FL) architecture protects privacy by processing data locally on client devices without sending it to a central server. This distributed strategy lowers data breaches, protecting critical data on individual devices. This capacity must be validated by quantifying privacy protection using theoretical and empirical assessment measures. Differential privacy is a common FL privacy validation metric. This approach hides data point details by adding controlled noise to model updates before aggregation. Privacy protection is measured by parameters \(\epsilon\) (privacy budget) and \(\delta\) (breach probability). Differential privacy in FL may improve privacy but reduce model accuracy.  
Another relevant statistic is gradient leakage resistance. FL training gradients may be used by attackers to infer private data. The framework's resilience may be tested using adversarial approaches like gradient inversion and model inversion. By preventing data reconstruction or inversion during communication cycles, our tests protected privacy against gradient-based assaults. Communication efficiency reduces client-central server data interchange, protecting privacy. During the simulation, clients only sent model weight updates, resulting in an average communication overhead of 13.5 s (see to Table 10). This effective communication architecture avoids transmission interception and data leakage.  
Furthermore, privacy effect simulation may generate situations where hostile clients steal data from global model updates. In these studies, our approach showed that federated aggregation concealed individual contributions, preventing access to client-specific information. This shows that the federated architecture protects privacy by not reconstructing raw data from aggregated updates. The FL architecture strikes a good compromise between privacy and detection. To offer verifiable security assurances in high-stakes cyber-physical IoT contexts, privacy validation will be improved using Secure Multi-Party Computation (SMPC) and homomorphic encryption.
## 2 Materials and Methods
### Ultra-Wideband PCW with PBG  
In theory, the PCs' PBG characteristic decides the spectrum of the signal which can be propagated in the PCW. The section view of the MO isolator is shown in Figure 2, in which the TLPCs' lattice constant \(a\) is 3.5 mm. The Al\({}_{2}\)O\({}_{3}\) posts' dielectric constant and radius \(r_{0}\) are 9.2 and 1 mm, respectively. The width of the PCW is marked by \(w\), which is the distance of the two TLPCs. The four ports marked Port 1, 2, 3, 4, respectively, are transmission ports and reflection ports.  
The PBG of the TLPCs is calculated to be 22.89 to 31.20 GHz like that in [21] and not repeated here. Theoretically, a signal within that frequency range can propagate stably in the PCW. In our previous work [21], the transmission efficiency of the PCW rose when its width changed from 6.25 to 10.70 mm in the three-dimensional numerical simulation. The optimal PCW structure of \(8.64\times 4.32\) mm\({}^{2}\) was chosen to investigate its transmission characteristics.  
The transmission efficiency's variation of the PCW with frequency is calculated by FEM, and the numerical results show that there is an ultra-wide bandwidth of the PCW from 21.60 to 31.90 GHz, in which the transmission efficiency keeps above \(-3\) dB (about 50%). In order to validate the aforementioned numerical results, the experiments were performed at the 5G millimeter wave band. The experimental verification of the PCW is described in detail in [21] and not repeated here. The variation of the PCW's transmission efficiency with frequency is shown in Figure 3, which has subtracted the loss of the cables and connectors.  
In the experiments, the peak of transmission efficiency could reach 90.78% (\(-0.42\) dB) and the operating bandwidth of the PCW was about 7.8 GHz. The transmission efficiency kept above \(-3\) dB between 23.45 to 31.25 GHz, while the relative bandwidth was about 28.71%. This experiment not only verifies the high transmission efficiency and ultra-wideband characteristics of PCW but also provides a basis for developing a novel MO isolator.  
Figure 3: The variation of the transmission efficiency of the PCW with frequency.  
Figure 2: The sectional view of the MO isolators.
## 7 Discussion
This is the first study to combine both approaches and effectively rank mobile learning platforms with the use of Fuzzy AHP from the failure mode effect analysis perspective. While this study focused on five mobile learning platforms (MLPs) in a specific educational context, the methodology--an integrated approach combining Failure Mode and Effects Analysis (FMEA), Fuzzy AHP (FAHP), and Grey relational analysis (GRA)--is designed to be scalable and adaptable to various settings. The strength of this method lies in its ability to handle multi-criteria decision-making (MCDM) problems by weighing qualitative and quantitative data. This makes it applicable to a wide range of platforms and educational environments. The proposed method can easily handle a larger number of platforms by scaling the FMEA and FAHP processes. The ranking procedure, based on GRA -FAHP and the calculated risk priority numbers (RPNs), is not limited to a specific number of platforms. Increasing the number of platforms would primarily require more expert input for the FAHP process, but the methodology itself remains robust and can handle the added complexity. The methodology is not only limited to the evaluation of mobile learning platforms. The evaluation framework can be easily applied to assess a variety of educational contexts using software. Risk factors, such as time behavior, fault tolerance, and operability, can be tailored to match the critical features of different applications as well. The FAHP process provides the adjustment of criteria and their relative importance depending on the specific needs of the educational context. FMEA assessment ensures that platform-specific risks are identified and assessed based on the context. The proposed methodology provides flexible and adaptable features beyond the scope of mobile learning platforms such as e-learning systems, training platforms, or hybrid learning solutions.
## 3 Research Methodology and Model Development
### Safety Analysis  
The safety analysis of the selected corridor was prepared based on statistical data on the number of accidents in the last three years (from January 2021 to July 2024). The data were obtained by the Zagreb Police Department and contain data on the number and type of traffic accidents. Figure 7 represents the number of accidents for every intersection of the selected corridor for the period of the last three years.  
According to Figure 7, the most unsafe intersection is (b) Vukovar Street-Nova Cesta Street, with 15 recorded accidents, while at the signalized pedestrian crossing, there was only one accident in the past three years. Due to the relatively large number of accidents in the selected corridor, it is notable that there is a possibility of improvement of safety parameters through the implementation of DLA. The following graphs represent the structure (type) of the accidents at every intersection along the corridor.  
Figure 6: Vehicle count at the intersection (c) Vukovar Street–Florjana Andraseca Street on the heaviest load day.  
Figure 7: Presentation of accidents at the intersections of the selected corridor.  
As shown in previous Figures 8-10, the accidents along the selected corridors are divided into accidents with injured people and accidents with material damage (with no fatalities), which points to the need for better horizontal markings of pedestrian crossings and closer considerations of traffic safety aspects, especially for pedestrians. According to papers [39; 40; 41], DLA implementation can significantly impact the traffic safety of a part of the network. Firstly, the traffic flow can be harmonized, reducing congestion and vehicle interactions (with the minimization of stop-and-go actions). Secondly, DLA reduces the number of lane-switching needs and actions that directly reduce the possibility of sideswipes and merging accidents. Even though drivers can be confused during the first period after DLA implementation, on a longer scale with proper signalization, it improves driver awareness and minimizes hesitation and erratic driver behavior [42].
## 3 Results
### Impact of Opening Sizes
#### 3.1.2 Results for Building Models with Varying Leeward Opening Sizes
In models with wider outlet openings, lower back-pressure is generated, allowing the inlet jet to start at higher speeds. Therefore, the airflow is shaped not only by the local effect of the opening configuration but also interactively throughout the entire volume. As moving towards x/D = 0.5 (middle region) along the building, the normalized velocity value in LO1, which has the narrowest outlet, remains almost negligible (U/U\({}_{\text{ref}}\) = 0.011056), indicating that the flow forms a dispersion and recirculation zone in the early stage due to high back pressure. This situation is also seen in the streamline distribution in Figure 17. The U/U\({}_{\text{ref}}\) value reaches 0.482278 at LO5. This reveals that the pressure gradient in the internal volume is directly related to the outlet opening ratio, thus indicating that ventilation efficiency can be directly improved by increasing the air velocity in the middle zone. Such an effect is particularly important in natural ventilation scenarios for reducing dead zones within the volume and homogenizing indoor air quality. The trend is reversed in the outlet region (x/D = 1). LO1 reaches the highest normalized velocity value of 0.96 at the outlet, while LO5 drops to 0.330001. This indicates that narrow outlet openings create a jet-like nozzle effect, accelerating the airflow at the outlet point. In models with wide openings, however, the resistance in the exit region is very low, allowing the airflow to be discharged at a lower speed but in a more homogeneous manner.
## 4 Empirical Insights from Sectorial and Regional Studies
### Digital Readiness as a Foundation for AI Adoption in Emerging Market SMEs in India  
[PERSON] et al. [5] provide a multi-study, mixed-method investigation into the digital readiness of SMEs operating in emerging markets, with a focus on India. Although the study does not address AI adoption directly, it offers foundational insights highly relevant to understanding the preconditions for effective AI integration in resource-constrained contexts. The authors conceptualize \"digital readiness\" as a higher-order construct composed of three dimensions: technological sensemaking, agility, and implementation capacity. These dimensions align well with the TOE framework's technological and organizational pillars, while also reflecting DOI principles such as trialability, complexity, and compatibility.  
Using qualitative interviews with 50 SME leaders and follow-up surveys from 324 SMEs across two empirical studies, the authors develop a validated 20-item scale for assessing digital readiness. Quantitative findings reveal strong predictive power of digital readiness on three critical performance outcomes: new product development (\(R^{2}=0.42\)), operational efficiency (\(R^{2}=0.18\)), and overall business performance (\(R^{2}=0.17\)). Notably, leadership style (e.g., millennial leadership), strategic focus (e.g., operational efficiency, rural outreach), and external market dynamics (e.g., platform availability and institutional support) emerged as major enablers.  
These insights are highly consistent with the challenge-solution framework of this paper. They reinforce the need for foundational digital maturity as a prerequisite for advanced AI adoption and provide empirical grounding for the role of contextual factors, such as infrastructure, market structure, and institutional trust, in shaping adoption trajectories. The study's use of agility and technological sensemaking also supports the inclusion of DOI-oriented attributes such as observability and reinvention in strategic readiness assessments.  
Overall, [PERSON] et al.'s [5] framework enhances the external validity of our proposed TOE-DOI model by emphasizing how strategic alignment, digital agility, and environmental enablers can shape AI adoption in SMEs, particularly in volatile or under-resourced economies.
## 3 Technical Background
To that end, the distance between the wireless charging station and the target node should be estimated and compared with a threshold distance known as the cross over distance (\(d_{crossover}\)) [33], which will be calculated first using Equation (7), and according to Equation (8), the suitable path loss model is utilized for a certain signal frequency \(f\):  
\[d_{\text{crossover}}=\frac{4\pi\sqrt{f}h_{t}h_{r}}{\lambda} \tag{7}\]  
\[P_{r}=\left\{ \begin{array}{c}P_{r-f_{8}},d<d_{\text{crossover}}\\ P_{r-nf_{8}},d\geq d_{\text{crossover}}.\end{array} \right. \tag{8}\]  
In our work, \(d_{crossover}=923\) meters, where \(f=915\) MHz and \(h_{t}=h_{r}=0.05\) meters. Since the distances used in the conducted tests were much lower than the value of \(d_{crossover}\), we adopted the free space wireless channel model for the theoretical analysis, showing a close match with the obtained practical results.
# Low Cost AIP Design in 5G Flexible Antenna Phase Array System Application
## 3 Array Antenna Design
The simulation results of the beam steering tilted angle at 28 GHz are shown in Figures 11 and 12. The maximum gain was 14.4 dBi for both X cut and Y cut. The beam steering tilted angle was \(\pm 34^{\circ}\) in the X cut while the beam steering tilted angle was \(\pm 26^{\circ}\) in the Y cut. Figure 11: Simulation results of the beam steering pattern at 28 GHz (X-cut). Figure 10: Antenna peak gain.
# Placement of IoT Microservices in Fog Computing Systems: A Comparison of Heuristics
## 2 Background and Problem Definition
### Reference Scenario
In this paper, we focus on SLAs based on the average service time of the microservices composing the chain; however, more complex SLAs could be included in the model and evaluated in future works. In the scientific literature, several studies have proposed mechanisms for service placement over the geographically distributed nodes of a fog infrastructure starting from the simplifying assumption that an IoT application consists of only a single microservice [17; 18; 19]. A lower number of studies have considered IoT applications modeled as microservice chains to be placed over the fog nodes. An example of such proposed solutions are those based on completely distributed approaches [20; 21]. The study in [20] sought to optimize energy consumption and communication costs by exploiting a game-theoretic approximation approach. In [21], a cooperative scheme allowed fog nodes to identify the best amount of requests to be forwarded and processed by each other to improve the response time. On the other hand, a centralized approach was presented in [22], where the authors formulated a mixed-integer nonlinear programming problem with the objective of minimizing the completion time of applications by jointly considering task placement and scheduling. In this paper, we focus on centralized approaches relying on heuristics able to cope with the nonlinear nature of the optimization problem used to minimize the response time of the service chains. One of the presented heuristics, based on Genetic Algorithms, was presented by the authors in our previous paper [5], while the other two heuristics are novel proposals to address the microservice placement problem in fog computing contexts. The proposed heuristics are described in detail in Section 3.
## 5 Discussion
### Comparison with Previous Studies  
From the prediction results, it can be seen that the LSTM model only trended in the prediction results, but the predicted values were significantly larger than the observed values. Upon comparison with two other articles in Table 3, the results were inferior to those reported in [37]. However, after conducting replication experiments, we discovered that this disparity could be attributed to differences in the input features used in those two studies. Using the same input approach as in [37], a similar performance was obtained, as shown in Figures 14 and 15.  
Figure 14: Retrospective test of prediction of the maximum magnitude earthquake using the similar input with [37]. The blue, green, and purple curves represent training, validation, and test period of the observations, respectively. The yellow, red, and brown curves represent training, validation, and test period of the predictions, respectively.  
Figure 15: Comparison of the predicted and observed maximum magnitudes using the same input with [37]. The black dots, red triangles, and blue crosses represent test, validation, and training data set, respectively.  
Meanwhile, in Table 3, a comparative analysis of the results with those presented in [36] is conducted. The authors of this paper employ generalized linear models (GLM), random forest (RF), and deep neural network (DNN) to predict the maximum magnitude of future earthquakes. Unlike this study, their work benefits from sufficient training data. Remarkably, their predicted magnitudes are smaller than the observed values. Notably, the LSTM model outperforms the predictions reported in [36], underscoring the effectiveness of the model construction.
## 2 Background
[38; 39] performed experimental research to evaluate the thermal behavior of small-scale test cells incorporating PCM-enhanced mortars with different binders, observing a reduction of extreme temperatures ranging from 2 to 5 \({}^{\circ}\)C, coupled with a significant time lag delay when subjected to different Portuguese climate conditions. [PERSON] et al. [29] investigated the mechanical and thermal properties of different formulations of expanded clay mortar with the incorporation of PCM, attaining a reduction of about 20% of mechanical strength (flexural and compressive), an increase of 17% in the specific heat capacity and a reduction of 24% in the thermal conductivity for the resulting PCM-containing mortars. Embedding PCM into a mortar matrix must guarantee that the resulting PCM-containing mortar is suitable for construction applications and behaves within satisfactory levels of performance regarding mechanical strength and workability [40]. Additionally, it is paramount to avoid chemical incompatibilities between the PCM and the mortar matrix, as well as leakage issues during the material's liquid state [8]. [PERSON] et al. [41] studied different mortar compositions with various binders and different quantities of embedded PCM, thus concluding that a good compromise between thermal and mechanical performance of the resulting mortar is achieved for an amount of 20 wt.% of incorporated microencapsulated PCM. [PERSON] et al. reached a similar ratio of mass fraction for incorporated PCM of 20 wt.% of the mortar's global mass [40; 42]. Higher amounts of incorporated PCM into the mortar matrix do not necessarily imply an increase in latent heat transfer due to the role of internal porosity since nanopores significantly reduce the heat transfer capability, even for increased contents of embedded PCM [40]. Since incorporating PCM into mortars for indoor wall finishing in lightweight buildings (new constructions and retrofit scenarios) is a stimulating strategy for passive thermal regulation and reduction of energy demand for heating and cooling purposes, two thermally-enhanced mortars with incorporated microencapsulated PCMs were developed and characterized in previous research stages. The developed mortars are intended to tackle overheating risk through the reduction of indoor thermal fluctuations. An experimental campaign for monitoring the thermal behavior and energy consumption profile of a real-scale application comprising two lightweight test cells located in Valladolid (Spain) is presented in this work. Thus, previous knowledge from laboratory tests and materials research is used to take a step forward towards real-scale demonstration of the PCM-containing mortars applied to indoor wall coatings at the targeted test cells.
## 3 Methodology
### Data Collection  
To test these predictions, we conducted our survey in the Sina micro-blog. The Sina micro-blogging service is one of the largest and most popular social network-based brand communities in China. Users who are followers or fans of a brand community (such as Dell, Lenovo and P & G) were recruited to participate in this survey by providing opportunities to win monetary awards after the survey. We provided a hyperlink to the survey web page. We removed surveys that proved invalid after scrutinising the responses. As a result, we collected 273 valid responses. The respondents were from 135 brand communities. Approximately 54.6% of the respondents were male, whereas 45.4% were female. Most respondents were between the ages of 21-30. A summary demographic profile of the samples is depicted in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline  & **Options** & **Participants Number** & **Percentage (\%)** \\ \hline \multirow{2}{*}{**Gender**} & Male & 149 & 54.6 \\  & Female & 124 & 45.4 \\ \hline \multirow{4}{*}{**Age**} & 12–20 & 10 & 3.7 \\  & 21–30 & 235 & 86.1 \\  & 31–40 & 27 & 9.9 \\  & \textgreater{}41 & 1 & 0.3 \\ \hline \multirow{4}{*}{**Education**} & High school or below & 5 & 1.8 \\  & Two-year college & 7 & 2.6 \\  & Four-year college & 120 & 44.0 \\  & Graduate school or above & 141 & 51.6 \\ \hline \multirow{4}{*}{**Brand community type**} & Cosmetics brands & 32 & 11.7 \\  & Clothing brands & 46 & 16.8 \\  & Consumer electronics brands & 35 & 12.8 \\  & Car brands & 21 & 7.7 \\  & Fashion accessory brands & 43 & 15.8 \\  & Food product brands & 31 & 11.4 \\  & Personal care brands & 32 & 11.7 \\  & Retailer brands & 23 & 8.4 \\  & Others & 10 & 3.7 \\ \hline \multirow{4}{*}{**Length of follow**} & \textless{}1 month & 59 & 21.6 \\  & \textgreater{}1 month and \textless{}3 months & 60 & 22.0 \\  & \textgreater{}3 months and \textless{}6 months & 71 & 26.0 \\  & \textgreater{}6 months and \textless{}12 months & 60 & 22.0 \\  & \textgreater{}12 months & 23 & 8.4 \\ \hline \multirow{4}{*}{**Average frequency of use**} & At least 1 time per day & 175 & 64.1 \\  & Every few days & 68 & 24.9 \\  & 1 time per week & 19 & 7.0 \\  & 1 time per months & 6 & 2.2 \\  & Every few months & 5 & 1.8 \\ \hline \multirow{4}{*}{**Average time of use per day**} & Less than 15 min & 30 & 11.0 \\  & 15–29 min & 106 & 38.8 \\ \cline{1-1}  & 30–59 min & 67 & 24.5 \\ \cline{1-1}  & 60–119 min & 34 & 12.5 \\ \cline{1-1}  & More than 120 min & 36 & 13.2 \\ \hline \end{tabular}
\end{table}
Table 1: Of the research sample.