esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Neural Network Models
#### 2.1.3 Configuration and Training  
The network configuration block diagram for the two neural network models is shown in Figure 2. The GABP model has the same input and output parameters as the BPNN model as they are only different in the learning algorithms, so they have the same network architecture. The network consists of a set of units that constitute an input layer with ten inputs, one or more hidden layers of computational nodes, and the output layer with one output.  
Generally, there are no hard and fast rules for choosing the number of hidden layers in training or the number of nodes. According to [PERSON]'s work, consensus favors the view that little is to be gained by having more than one hidden layer [16], which means it is not necessary to choose 2 or more hidden layers in most cases. After training with one and two hidden layers, respectively, by comparing the error difference between the observed and predicted values of foF2, we found that even one hidden layer can produce a good performance of forecasting foF2, eventually, we chose one hidden layer for the BPNN and GABP models in our work. The number of nodes within the hidden layer is chosen based on trial and error, fewer nodes produced a less effective network, while more nodes increased the training time without significantly improving the rms error and might result in overfitting problems. Based on the empirical principle that the number of hidden layer nodes should be less than twice the number of input nodes, we have trained many different combinations by training the BPNN and GABP from node 1 to 20 to obtain the relatively superior settings, measured by comparing the rms errors from testing data sets. Finally, the recommended network configurations are as below: the BPNN configuration is with one hidden layer and the number of nodes is 17; While the GABP configuration is with one hidden layer and the number of nodes is 18, the population size is 30 and the genetic algebra is 1000.
## 2 Architecture and Operation of Proposed RO PUF
### Power Consumption  
In our proposed RO PUF, the RO's frequency and the power consumption can be optimized by tuning \(V_{p}\) and \(V_{n}\) of the current starved inverters. Figure 9 presents the measured average power consumption versus the RO's frequency. It is indicated that the minimum measurable oscillation frequency is 100 KHz and the corresponding power consumption is only 5.16 \(\upmu\)W. This low power consumption feature is more suitable for the resource-constrained WSN devices working in the distributed area.
## 4 Results and Discussion
### Performance Metrics  
We used the four main performance criteria tested in the most state-of-the-art routing algorithms in UWSNs as discussed below:  
1. Energy consumption: the total amount of consumed energy by each sensor for the successfully forwarded packets.
2. Packet delivery ratio: the total ratio of the packet that reaches the distention/sink successfully to the number of transmitted packets by the source node.
3. Network lifetime: the overall lifetime of the networks calculated based on the first die node in the network.
4. End-to-End delay: the average delay between sensors used to forward the packets toward the destination.  
For experimental implementation clarification, our protocol is developed using a C++ programming language in the network layer in the Aqua-Sim package as a formal routing protocol for the ns2 network simulator. Furthermore, different underwater simulation scenarios have been developed using the TCL programming language. After executing the protocol with different underwater network scenarios, the results were extracted from trace files using a scripting language to save the results in a standard text file. The figures presented in the result section are a graphical representation of the text file data.
## 3 Model Building and Validation
### Model Validation  
There are a total of 138 surface rock movement GPS monitoring points in the West Second mining area, as shown in Figure 7. The rock shift monitoring updates the monitoring  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lithology** & **Densities/kg.m\({}^{-3}\)** & **Bulk** & **Shear** & **Internal Friction** & **Cohesion/MPa** & **Tensile** \\  & & **Modulus/GPa** & **Modulus/GPa** & **Angle/\({}^{\circ}\)** & **Angle/\({}^{\circ}\)** & **Strength/MPa** \\ \hline Surrounding rock & 3090 & 2.67 & 1.52 & 34 & 1.1 & 2.1 \\ \hline orebody & 3000 & 2.15 & 1.44 & 31 & 0.8 & 1 \\ \hline fault & 2300 & 0.38 & 0.18 & 21 & 0.05 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the rock mass.  
Figure 5: Numerical simulation model.  
Figure 6: Location of monitoring sites.  
point measurements in real time, and the rows involved are the 5-row and 7-row monitoring points. From 2012 to March 2016, when the collapse occurred, the cumulative vertical subsidence at the 5-11 row lines (Figure 7) for 3 years and 9 months was 1279 mm. The subsidence characteristics of the area clearly show that the subsidence of the measurement points near the fault area was larger than that of the points far from the fault area, and the vertical displacement of the points with large horizontal displacements was also large [34].  
As can be seen from Figure 8, after mining the ore body, there was an obvious subsidence center on the surface, located near line 7 of the exploration line, and the subsidence range was between line 5 and line 7 of the exploration line, which can be seen to be consistent with the actual range of rock movement on the surface and the mining range of the ore body. The shape of the subsidence contour was a concentric ellipse, like the actual shape in reality, and the long axis of the ellipse pointed to the strike direction of the ore body; furthermore, with the mining of the ore body, the subsidence area expands, the maximum amount of subsidence also expands, and the subsidence area gradually expands in the direction of the fault, which is consistent with the deformation and damage area observed on the surface. A numerical simulation could be used to calculate that the maximum vertical displacement was about 1.3 m, and the settlement center was near rows 5 and 6.
# Otolaryngology Conditions and Diseases in Migrants: The Experience of the PROTECT Project
###### Abstract  
Introduction: The number of migrants living in Europe is growing rapidly. The PROTECT project is a national study conducted among refugees and migrants in the Lazio region, Italy from February 2018 to September 2021 to evaluate their otolaryngological, dental, and ophthalmic health status. This article reports the results of this study with a special focus on otolaryngological conditions and diseases. Materials and methods: A total of 3023 participants were included in the project. Data on the participants' demographic factors, migration status, and otolaryngological history were collected using a questionnaire. Each patient underwent clinical ear, nose, and throat examination, and the main otolaryngological conditions and diseases were noted. Results: nearly two-thirds of participants were males (68.1%). The mean age was 31.6 \(\pm\) 13.1. Most of the participants were born in Nigeria, followed by Bangladesh, Pakistan, Somalia, Mali, and Gambia. The prevalence of chronic noise exposure was 52%. Unilateral hearing loss was reported by 6.5% of the subjects, and bilateral hearing loss by 3.6%. The most frequent symptoms reported in the questionnaire were snoring (10.4%), nasal obstruction (5.9%), vertigo (5.0%), otalgia (4.5%), and tinnitus (4.2%). At the clinical examination, the most frequent findings were nasal septum deviation (25.2%), ear wax (6.5%), hypertrophic palatine tonsils (5.3%), and tympan membrane perforation (1.3%). Conclusions: the PROTECT project allowed for the evaluation of otolaryngological, dental, and ophthalmological conditions in over 3000 migrants, giving them the possibility to access specialist care.  
migrants; vulnerability; hospitality; otolaryngology; screening; inclusion
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### WiFi-Based Experiment  
We performed another experiment in a real WiFi testbed with settings similar to those of Scenario 5, but using two WiFi network interfaces. For both paths, the bandwidths were 54 Mbps and the delays were 8 ms. Packet losses were determined empirically based on the characteristics of the WiFi channel. We again used five MPTCP connections and measured the average throughput of the individual connections. The results are shown in Figure 11.  
In this experiment, both the delay and the packet loss probability were very low, so the throughput advantages of RSF-DB MPTCP were small (but still measurable) compared to the other MPTCP algorithms. Note that our RSF MPTCP coincided with LIA under deterministic path conditions and zero packet loss. However, these properties seldom hold on real wireless networks. Furthermore, we observed that the target loss and delay were often always met with RSF MPTCP where attainable (aggregate achievable loss/delay \(<\) target).
# Optimal Opacity-Enforcing Supervisory Control of Discrete Event Systems on Choosing Cost
## 2 Preliminary  
### Supervisory Control Theory  
We consider a DES modeled by a determined finite transition system, \(G=(Q,\Sigma,\delta,q_{0})\), where \(Q\) is a finite set of states, \(\Sigma\) is a finite set of events labeled in the transition, partial function \(\delta:\Sigma\times Q\to Q\) is the transition function and \(q_{0}\in Q\) is the initial state. A run of \(G\) is a finite non-empty sequence, \(q_{0}\sigma_{1}q_{1}\sigma_{2}\cdots q_{n-1}\sigma_{n}\), where \(q_{i}\in Q,\sigma_{i+1}\in\Sigma\) and \(q_{i+1}=\delta(q_{i},\sigma_{i})\) for \(i=1\cdots n-1\). The trace \(tr(\rho)\) of run \(\rho\) is \(\sigma_{1}\sigma_{2}\cdots\sigma_{n}\). The languages of are the set of all traces of runs of \(G\), and they are denoted \(L(G)=\{s\in\Sigma^{*}|\delta(s,q_{0})!\}\). Event set \(\Sigma\) is assumed to be partitioned into controllable event set \(\Sigma_{c}\) and uncontrollable event set \(\Sigma_{u}\), where \(\Sigma=\Sigma_{c}\dot{\cup}\Sigma_{u}\). Each subset of events is a control pattern, and the set of all control patterns is denoted by \(\Gamma=\{\gamma|\Sigma_{u}\subseteq\gamma\subseteq\Sigma\}\). A supervisor for \(G\) is any map \(f\): \(L(G)\to\Gamma\). For system \(G\), we denote \(\Gamma^{L(G)}\) as the set of supervisors. The closed behavior of \(f/G\), i.e., \(G\) under the supervision of \(f\), is defined to be language \(L(f/G)\subseteq L(G)\) described as follows:  
* \(\epsilon\in L(f/G)\);
* \(s\sigma\in L(f/G)\Leftrightarrow s\in L(f/G),s\sigma\in L(G),\sigma\in f(s)\).  
For language \(K\subseteq L(G)\), notation \(\overline{K}\) is said to be the prefix(-closure) of \(K\). \(K\) is said to be (prefix-)closed if \(\overline{K}=K\).  
Definition 1 ([33]): We consider non-empty language \(K\). Then, \(K\) is controllable if \(\overline{K}\Sigma_{u}\cap L(G)\subseteq\overline{K}\).  
A necessary and sufficient condition for the existence of a supervisor is given as follows:  
Theorem 1 ([33]): We consider non-empty language \(K\subseteq L(G)\). Then, there exists supervisor \(f\) such that \(L(f/G)=K\) if and only if \(K\) is a controllable and closed language.
## 4 Discussion
### Limitations  
This study has several limitations that warrant consideration. First, the narrow demographic and metabolic scope of our sample restricts the generalisability of the findings. While our cohort primarily consisted of subjects with suspected MASLD, the absence of detailed metabolic profiles, such as insulin resistance or lipid metabolism data, limits the ability to fully characterise the relationship between pancreatic and liver fat. Future studies should include diverse populations with varying metabolic profiles to enhance generalisability.  
Second, the study relied on imaging data acquired exclusively using a 1.5T MRI scanner. Variations in imaging results due to differences in MRI field strengths (e.g., 3T) or scanner manufacturers were not explored, which may impact the generalisability of our results to other field strengths. Future studies will investigate the performance of DL models like NLV-Net across different imaging platforms, including field strengths and vendors.  
Third, our analysis revealed a narrow distribution of pancreatic PDFF values in the study population, which may have reduced the statistical power to detect stronger correlations with liver fat or MASLD. The inclusion of subjects with higher pancreatic fat content could provide a more comprehensive understanding of these relationships.  
Fourth, while CSE-MRI provides a reliable and non-invasive method for fat quantification, it lacks the definitive diagnostic accuracy of histological examination. The absence of biopsy data, considered the gold standard, limits our ability to validate MRI-derived PDFF measurements fully. However, the invasive nature of biopsy presents challenges for routine use in research and clinical practice.  
Fifth, our study provides a cross-sectional assessment of fat content in the pancreas and liver. The lack of longitudinal data prevents us from evaluating temporal changes in fat accumulation or their potential clinical implications over time. Future studies should aim to address this limitation by incorporating repeated measures to understand the progression or regression of steatosis in both organs.  
Lastly, the clinical relevance of pancreatic steatosis remains underexplored in this cohort. Without direct clinical outcomes or data on associated risk factors, such as diabetes or pancreatitis, the broader implications of pancreatic fat accumulation in MASLD patients remain uncertain but warrant further exploration to clarify their clinical significance.  
Despite these limitations, this study highlights the potential of advanced DL models like NLV-Net to provide accurate, non-invasive measurements of pancreatic fat, offering a promising step toward improved understanding and management of metabolic diseases.
## 3 Results
### Hyperparameter Tuning Results
\begin{table}
\begin{tabular}{l r r r} \hline \hline
**Parameter** & **Default** & **Tuned** & **Importance** \\ \hline grace\_period & 200 & 12 & 0 \\ max\_depth & 20 & 20 & 0.00 \\ delta & \(1\times 10^{-7}\) & \(1\times 10^{-6}\) & 0.00 \\ tau & 0.05 & 0.01 & 0.00 \\ leaf\_prediction & mean & adaptive & 88.66 \\ leaf\_model & linear regression & linear regression & 100.00 \\ model\_selector\_decay & 0.95 & 0.99 & 0.00 \\ splitter & EBSTS-Splitter & EBSTS-Splitter & 0.17 \\ min\_samples\_split & 5 & 8 & 0.00 \\ binary\_split & 0 & 1 & 0.00 \\ max\_size & 500 & 987.96 & 0.00 \\ memory\_estimate\_period & \(1\times 10^{6}\) & \(2.6\times 10^{5}\) & 0.00 \\ stop\_mem\_management & 0 & 0 & 0.00 \\ remove\_poor\_attrs & 0 & 0 & 0.00 \\ merit\_preprune & 0 & 0 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of default and tuned hyperparameters for improved performance of the HTR. The column on the far right illustrates the determined importance of the individual parameters during the tuning process. Figure 8: Evaluation results of the tuned and untuned HTR models for mean absolute error, computation time, and memory consumption. The X-axes are related to the evaluation steps. The tuning was performed with a focus on improving the performance (MAE). This behavior can be elucidated by examining Table 4. It is apparent that the grace period is nearing the upper boundary established in the tuning process. Consequently, the splitting process of the leafs was not executed as frequently as for the untuned model, thereby conserving computational resources. Moreover, the maximum depth of the tree at a value of four was markedly lesser than the default setting of the algorithm, which stood at 20. This further contributed to the reduction in computational effort and memory usage.
# Strongly Focused Circularly Polarized Optical Vortices Regulated by a Fractal Conical Lens
## 3 Numerical Simulations and Analysis
Figure 7 depicts evolution of the \(z\)-polarized component intensity Iz (upper row) and the total intensity Is (lower row) of strongly focused RHC Gaussian beams (\(\sigma_{x}=+1\), \(m=0\)) regulated by a FCL of stage \(S=3\). It is clearly seen from Figure 7 that, in the presence of a FCL, both the \(z\)-polarized component and the total intensity exhibit a single major focus and several subsidiary focal points surrounding it, which leads to neither the \(z\)-polarized component nor the total intensity maintaining symmetry about the focal point. When the incident optical field is a RHC polarized optical vortex \(\text{LG}_{0}^{m}\) with \(\sigma_{x}=+1\) and \(m=+1\), the high NA optical system is regulated by a FCL of stage \(S=3\), as shown in Figure 8. Because of FCL's self-similarity diffractive properties, in the focal region, several subsidiary focal points are distributed  
Figure 10: Evolution of the total intensity Is/Is\({}_{\text{max}}\) (2D (**a**,**c**,**e**,**g**), and 1D (**b**,**d**,**f**,**h**)) of strongly focused RHC (\(\sigma_{x}=+1\)) polarized optical vortex \(\text{LG}_{0}^{m}\) (\(m=0\)) regulated by a FCL of stage \(S=0\), 1, 2, and 3, respectively. Figure 3.0 Evolution of the \(z\)-polarized component intensity Iz/Is\({}_{\text{max}}\) (**a**,**b**) and the total intensity Is/Is\({}_{\text{max}}\) (**c**,**d**) (2D (**a**,**c**), and 1D (**b**,**d**)) of strongly focused LHC polarized (\(\sigma_{x}=-1\)) optical vortex \(\text{LG}_{0}^{m}\) (\(m=+1\)) regulated by a FCL of stage \(S=3\). surrounding the major focus along the \(z\)-axis; additionally, both the \(z\)-polarized component and the total intensity maintain on-axis null along the \(z\)-axis (see Figure 8). If the incident beam is a LHC polarized optical vortex \(\text{LG}_{0}^{m}\) with \(\sigma_{x}=-1\) and \(m=+1\), the high NA optical system is regulated by a FCL of stage \(S=3\), as shown in Figure 3.1 In this case, the helical phase and the circular polarization have opposite directions, therefore, the photon has no OAM, which causes both the \(z\)-polarized component and the total intensity distributions to have a principle maximum in the focal point and several sub maximums located nearby along the \(z\)-axis. Figure 10 studies the influence of FCL's stage \(S\) on the evolution of total intensity of strongly focused RHC polarized optical vortex \(\text{LG}_{0}^{m}\) with \(\sigma_{x}=+1\) and \(m=0\), where FCL's stage \(S\) is chosen as 0, 1, 2, and 3, respectively. As can be found from Figure 10, in the presence of a FCL the total intensity distributions will not maintain symmetry about the focal plane any more. Furthermore, more subsidiary focal points emerge when increasing FCL's stage \(S\).
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 7 Conclusions  
This study provides a comprehensive empirical assessment of TLS-secured MQTT communication on resource-constrained IoT hardware, with a focus on V2X-relevant message workloads. Our experimental results yield several key findings. First, we demonstrate that constrained devices such as Raspberry Pi 4\(B\) boards can effectively support TLS 1.3 in MQTT-based deployment, but the introduction of security comes with measurable resource overheads: we observe up to 37% higher CPU utilization, 23.5% longer execution times, and 23.7% increased energy consumption during the handshake phase. Second, our results reveal that the selection of message packaging strategies significantly influences both performance and efficiency; e.g., batching payloads into fewer, larger messages consistently reduces latency and energy cost per byte while minimizing protocol overhead. Third, the choice of the cipher suite is shown to be critical: ChaCha20_Poly1305 outperforms AES_256_GCM in all examined scenarios, especially in wireless settings, making it better suited for time-sensitive and energy-constrained V2X applications.  
Despite these findings, the study has certain limitations. Our evaluation is restricted to Cortex-A72 ARMv8 boards and does not yet cover more constrained microcontrollers, such as ESP32 or ARM Cortex-M, which may face additional challenges. In addition, while our wireless experiments employed realistic WiFi conditions, they did not fully explore the impact of real-world wireless conditions, e.g., jitter and mobility. These aspects may further influence protocol performance and robustness in real-world deployment.  
Future work should address these open issues. A promising direction is to systematically evaluate all MQTT Quality of Service (QoS) levels under various network loads and mobility patterns to understand the trade-offs in reliability, latency, and resource usage. Additionally, we plan to conduct a rigorous comparison between MQTT over TLS and DTLS-secured CoAP in V2X settings, assessing protocol suitability in terms of latency, bandwidth, energy consumption, and resilience.  
Conceptualization, N.O.G., S.T.H. and S.P.; methodology, N.O.G., S.T.H. and S.P.; software, N.O.G.; validation, N.O.G., S.T.H. and S.P.; formal analysis, N.O.G., S.T.H. and S.P.; investigation, N.O.G., S.T.H. and S.P.; resources, N.O.G., S.T.H. and S.P.; writing--original draft preparation, N.O.G., S.T.H. and S.P.; writing--review and editing, N.O.G., S.T.H. and S.P.; visualization, N.O.G.; supervision, S.P.; project administration, S.P. All authors have read and agreed to the published version of the manuscript.  
This research study received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 5 Conclusions  
In this paper, we propose a novel scene perception model that leverages multi-source data from smartphones to explore the relationship between scene environments and information features from three perspectives: time, space, and mathematical statistical models. This model deep mines five information sources--satellite signals, cellular networks, Wi-Fi, IMU, and light intensity--and integrates a whale optimization algorithm (WOA)-optimized dual-scale CNN network with a BiLSTM network.  
First, we conduct ablation experiments using a dataset collected from multiple cities, devices, and scenes. The proposed algorithm achieves an accuracy of 98.83%, demonstrating both a strong performance and generalization ability on the test set. Finally, comparisons with other existing advanced algorithms reveal that our proposed model exhibits a higher accuracy rate and optimal performance across four scenarios. This enhanced accuracy can better support scene perception and recognition for smartphones, providing a robust environment-aware foundation for plug-and-play multi-source fusion intelligent localization.  
Conceptualization, [PERSON] ([PERSON]) and Z.D. ([PERSON]); methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), E.H. and B.L.; formal analysis, [PERSON] ([PERSON]) and Z.Z.; investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]) and M.M.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]); supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (grant number: 2022 YFB3904702 and 2022 YFB3904603).  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.