esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Considerations
### PTR Method
As a result, a spatial temperature distribution \(T(x)\) arose in the coating. It was then the source of IR radiation. The mathematical formula for \(T(x)\) was derived using the thermal wave interference method. Significant differences in the characteristics of the PTR signals in these two models, described by Equations (1) and (4), can be observed in Figure 5, where the comparison of the two models is presented. The values of parameters taken for the simulation calculations were as follows: \(d_{\rm c}\) = 10\(\cdot\)10\({}^{-4}\) cm, \(\alpha_{\rm c}\) = 10.0 \(\times\) 10\({}^{-3}\) cm\({}^{2}\)/s, \(\lambda_{\rm c}\) = 10.0 \(\times\) 10\({}^{-3}\) W/cm\(\cdot\)K, \(\alpha_{\rm s}\) = 0.125 cm\({}^{2}\)/s, and \(\lambda_{\rm s}\) = 0.45 W/cm\(\cdot\)K. The values of the thermal diffusivity and thermal conductivity of the substrate (\(\alpha_{s}\) and \(\lambda_{s}\)) taken for the simulation correspond to the actual values of these parameters of the structural steel substrate on which the coatings, which were the object of research in this work, were deposited. Using the proposed model described by Equation (4), calculations of the PTR signal were performed and are presented in Figure 6. The goal of this calculation was to visualize the changes in the PTR amplitude and phase characteristics as a function of simultaneous changes in the values of the thermal diffusivity and thermal conductivity of the coating. The following thermal parameters of the coating were taken for computations: \(d_{\rm c}\) = 10\(\cdot\)10\({}^{-4}\) cm, \(\alpha_{\rm c}\) = 10.0 \(\times\) 10\({}^{-3}\) cm\({}^{2}\)/s, and \(\lambda_{\rm c}\) = 10.0 \(\times\) 10\({}^{-3}\) W/cm\(\cdot\)K.  
Figure 5: Theoretical PTR amplitude and phase frequency characteristics calculated in Model 1 and Model 2 of the PTR signal. The simulation investigations show that as thermal diffusivity and thermal conductivity increase, the characteristics shift to higher frequencies. Conversely, when these values decrease, the characteristics shift to lower frequencies. Notably, changes in the thermal parameters have a significantly greater effect on the phase characteristics of the PTR signal than on the amplitude characteristics.
# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 1 Introduction  
Urban agriculture, the practice of cultivating food in or around urban areas, is gaining momentum as a solution to various challenges including food security [1]. However, maximizing the potential of urban farms requires precise, efficient, and scalable methods for monitoring these complex systems. Here, computer vision and deep learning emerge as powerful tools [2]. By harnessing these technologies, urban agricultural systems can be equipped with predictive analysis capabilities, enabling real-time decision making and optimized resource utilization. The Internet of Things (IoT) in urban agriculture, particularly the use of environmental sensors for plant monitoring, marks a significant stride in the quest for sustainable and efficient urban food production. This research focuses on IoT plant environmental sensors and computer vision and machine learning (ML) technologies to forecast parameters related to plant well-being, a novel approach that promises to revolutionize agricultural practices in urban environments.  
Urban agriculture faces the challenge of optimizing plant growth conditions in varied and often constrained urban environments [3]. IoT environmental sensors, capable of monitoring factors like soil moisture, temperature, humidity, and light intensity, have become indispensable in addressing this challenge [4; 5]. The integration of computer visionadds a new dimension, enabling the analysis of visual cues from plants to predict their well-being, physiological traits, health, and growth patterns. This synergy is crucial for advancing urban agriculture, ensuring better resource management and enhanced crop yields in urban settings.  
The purpose of this research is to investigate the potential of combining IoT environmental sensors with computer vision and ML to accurately forecast plant well-being in urban agricultural systems. This approach is significant as it could lead to more proactive and precise farming practices, allowing for timely interventions to optimize plant health and productivity. It also holds the potential to minimize resource waste and improve the environmental footprint of urban farms.  
This manuscript is organized into five sections which are \"Related Work and Research Gaps\", and it provides an overview of the existing studies about monitoring in precision agriculture and identifies the current gaps that this research aims to fill. The \"Methodology\" section explains the methodology used in the research. The \"Model Development and Training\" section describes how the deep neural network was trained for forecasting sensor measurements from thermal images. The \"Results Evaluation and Discussion\" section presents the results of the research, evaluating the accuracy of the CNN model in forecasting relative air humidity, soil moisture, and light intensity. The \"Conclusions\" section summarizes the key findings of the research and outlines directions for future research.
# Research on Bidirectional Reservation Method for Anti-Permeation Geomembrane Slack
## 3 Analysis and Comparison of Different Methods
### Cross-Notch-Type Centralized Laying Relaxation Amount Method Analysis
#### 3.1.2 Right Angle Program  
The corners of the cross-recesses in Program I were not treated, and the cross-recesses were surrounded by right-angle edges. The results of the laying test are shown in Figure 4. From Figure 4a,b, with the original grooves, although the left and right banks of each do not show obvious folds, the membrane produces large folds at the cross-intersection, affecting the safety of geomembrane impermeability.  
A theoretical analysis was conducted on the causes of folds to eliminate those that occur at the cross-intersection of cross-groove geomembranes. Folds occur when the geomembrane transforms from flat into recessed into the groove. After this transformation, the excess membrane does not fit the surface of the groove, and it is reflected in the form of folds. Figure 5 below shows a model of the principle of excess geomembrane generation when geomembranes are laid in a bidirectional manner in a cross-rectangular recess. As shown in Figure 5, when the geomembrane in a flat state plunges into the recess, the geomembrane in four directions (south, east, north, and west) moves toward the cross-intersection. According to the geometric relationship, the shaded part in Figure 5 represents the excess membrane area generated during the bidirectional laying of the cross-groove geomembrane. Therefore, the excess membrane area generated during the bidirectional  
Figure 4: Test results of Program I. (**a**) cross-groove, (**b**) laying test membrane.  
Figure 3: Preparation scheme of the bidirectional, concentrated, cross-groove-shaped slack-laying method for testing.  
laying of the cross-groove geomembrane is calculated as follows, where \(S_{m}\) is the area of the geomembrane forming the fold, \(h\) is the depth of the groove, and \(b\) is the width of the groove.  
\[S_{m}=\left(2h+b\right)^{2}-b^{2}=4h(h+b)\]
## 3 Results
### Relationship between FD and MCW  
Subsequently, the FD variable was correlated with the MCW variable, both in the total group and in the presence/absence of OI subgroups.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{\begin{tabular}{c} **Total Sample** \\ **(n = 41)** \\ \end{tabular} } & \multicolumn{3}{c}{**Group**} & \multicolumn{2}{c}{**Contrast Test**} & \multirow{2}{*}{\begin{tabular}{c} **Effect Size** \\ **(n = 21)** \\ \end{tabular} } \\ \cline{3-4}  & & \begin{tabular}{c} **O.I.** \\ **(n = 21)** \\ \end{tabular} & \begin{tabular}{c} **Control** \\ **(n = 20)** \\ \end{tabular} & \begin{tabular}{c} **Value** \\ \end{tabular} & \begin{tabular}{c} **\(p\)** \\ \end{tabular} & \begin{tabular}{c} **Effect Size** \\ **R\({}^{2}\)** \\ \end{tabular} \\ \hline SVE & & & & 0.20 & 0.655 & 0.005 \\ Thin & 73.2\% (30) & 76.2\% (16) & 70.0\% (14) & & & \\ Not thin & 26.8\% (11) & 23.8\% (5) & 30.0\% (6) & & & \\ \hline MCI & & & & 16.35 & 0.000 & 0.398 \\ C1 & 34.1\% (14) & 9.5\% (2) & 60.0\% (12) & & & \\ C2 & 43.9\% (18) & 47.6\% (10) & 40.0\% (8) & & & \\ C3 & 22.0\% (9) & 42.9\% (9) & 0.0\% (0) & & & \\ \hline MCW & & & & & & \\ Median (SD) & \begin{tabular}{c} 2.8911 \\ (0.6076) \\ \end{tabular} & \begin{tabular}{c} 2.7361 \\ (0.7319) \\ \end{tabular} & \
\begin{tabular}{c} 3.0255 \\ (0.4197) \\ \end{tabular} &
\begin{tabular}{c} \(-1.40\) \\ \end{tabular} & 0.170 & 0.048 \\ Rank & 1.43/4.16 & 1.43/4.16 & 2.42/3.86 & & & \\ \hline \end{tabular}  
* SD: Standard deviation.  
\end{table}
Table 2: Intergroup analysis. Differences in radiological parameter variables, depending on the presence of OI.  
Figure 3: Fractal dimension (FD) in basal cortical bone (n = 41).  
In Figure 4, the dot plot suggests the absence of a relationship between these variables. Along the same lines, the value of the correlation coefficient is very low (0.098) and is far from reaching statistical significance (\(p\)-value = 0.542).
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 4 Experimental Evaluation
### Proof of Concept 2  
We performed the PoC-2 with a set of image file exams (**\"DICOM) generated with MRI/CT on a patient at the Intervention Center of the InRad of HCFMUSP, as illustrated in Figure 17. The MRI/CT (Magnetic Resonance Imaging/Computed Tomography) from Siemens Healthcare Scanner Room with Softwares sVC (syngo Virtual Cockpit Modality and Steering Clients type VA16A) remote access is a multimodality reading solution built on a client-server platform. In legacy solutions, the machine technician is usually located in a room adjacent to the MRI/CT. sVC allows an operator to remotely command the CT machine from a different location. With this, hospitals can benefit from operational costs and specialized doctors can be more readily available.  
All MRI/CT image files were sent and obtained via PACS (Picture Archiving and Communication System) via sVC software (syngo Virtual Cockpit Modality and Steering clients) to facilitate handling and visualization via 2D and 3D in the 5G private network.  
In PoC-2-1, we monitor, manipulate and visualize images from the MRI/CT unit by analyzing the latency of packet times from one point to another in the network and the bandwidth that defines the speed and capacity of the 5G private network, as shown in Figure 18.  
Figure 19 presents the architecture of the 5G network for Modality/sVC simulation.  
Figure 17: MRI/CT scanner room.  
Figure 18: Manipulation and visualization of images from the tomograph via 5G network.
## 4 Evaluation
### Experiments and Results
#### 4.4.1 Experiment 1: Detecting Mutants of Existing Attacks
For deep learning, we employed a multi-layer perceptron (MLP), while for the classical ML models we employed K-nearest neighbor (KNN), decision tree, support vector machine (SVM) and random forest. All the baseline models were trained using the full dataset. Figures 4-6 depict the results of such comparisons. As can be seen from results, there was not much of a difference in terms of performance between the DL models, ML models, and our model. It is well known that classical ML models perform well on small and medium datasets. The highest DR score was 100% on the Neptune DoS mutant (Figure 4), which was achieved by all baselines, while our model scored 99.6% (despite having been trained with only 10 examples), which is quite on par with the baselines results. The lowest DR score was for the back attack mutant, where our model's DR score as 0.0%. However, the lowest DR score from the baselines was achieved by SVM, which scored 40.4% on the tear drop attack. Figure 3: Result of few-shot detection on DoS attack mutant for CIC-IDS2017 dataset. Figure 4: Detection rate (DR) comparison of various classification methods on DoS subclasses for NSL-KDD datasets. Figure 5: Detection rate (DR) comparison of various classification methods on probe attack subclasses for NSL-KDD datasets. In addition, for the probe attack class, the result followed similar trend, the highest DR score achieved was 100%, which was achieved by the baseline models on the port sweep attack mutant (Figure 5), while our model was slightly worse with a DR score of 99.8% on the same attack class. Our model also recorded the lowest DR score, 70.7%, on the Satan attack mutant, while the lowest DR score achieved by the baselines was 79.5%, which was scored by SVM on the Nmap attack mutant. Overall, there was no significant difference in performance with our model; our model was either slightly worse or on par with the baselines. Similarly, Figure 6 presents the results of the comparison with baselines models for the CIC-IDS2017 dataset. The highest performing model was random forest, which achieved a DR of 100% for the DoS golden eye attack mutant. The lowest DR score was 48.5% which was achieved by the decision tree model for the DoS http test attack mutant. Similar to the NSL-KDD dataset, our model performed competitively with the baseline models, where it achieved DRs of 99.6%, 99.8%, 80.0%, and 70.7% on IP sweep, port sweep, Nmap and Satan, respectively.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 6 Results  
This work resulted in a novel network authentication protocol based on FIDO2 authentication: FIDO2 CAP. The protocol includes a new architecture based on RFC 8952 [8], and a message flow design for authentication and registration. Our proposed architecture adds a WebAuthn Authentication Web Application (WAWA), a FIDO2 Authenticator, and other elements to a captive portal. Also, we present a prototype implementation of FIDO2 CAP in an environment modeling a hotel Wi-Fi network.  
Finally, we conducted compatibility tests and a usability experiment with 15 real users. We found that more than 69% of them successfully completed the connection, but they had different problems: almost two errors on average per task. They performed much better on the second try. Most of them found the system easy or very easy to use.  
In this section, we summarise the specific results, namely, (1) the FIDO2 CAP protocol; (2) a prototype of the FIDO2 CAP in a mock scenario; and (3) the results of the compatibility tests and the usability experiment.
## 5 Experimental Results
### Baselines
CUTE-ST: under the assumption of perfect knowledge of the **R**-matrix and \(\mathcal{H}\), this baseline reutilizes observations and takes the error covariance of \(\mathbf{x}^{b}\) and \(\mathbf{y}^{o}\)--that is, \(\text{Cov}(\mathbf{e_{\mathbf{x}^{b}}},\mathbf{e_{\mathbf{y}^{o}}})\)--into account within the iterative process of the covariance refinement. This algorithm enhances the posterior state estimation while simultaneously improving the structure of the comprehensive **B**-matrix. Although [PERSON] et al. [39] also introduced the PUB method, CUTE is adopted as the control experiment in this study due to its superior assimilation performance when the initial **B**-matrix is arbitrarily specified under sufficient observational conditions.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 3 Establishing Group Keys
### Summing Strategies
#### 3.1.2 Optimizing the Summing Strategy
The main question studied with regard to covering designs is to construct a \((v,k,t)\)-covering design with a small number of blocks. We write \(C(v,k,t)\) to denote the smallest possible number of blocks in a \((v,k,t)\)-covering design. The number \(\gamma_{5}\) corresponds to a dual problem in which \(2\)-element blocks have to be contained in \(5\)-element sets. However, note that a \(t\)-element set \(A\) is contained in a \(k\)-element set \(B\) if and only if the \((n-t)\)-element set \(\mathcal{N}\setminus A\) is a superset of the \((n-k)\)-element set \(\mathcal{N}\setminus B\). Thus, we may conclude the following:  
\(\gamma_{5}(n)=C(n,n-2,n-5)\). Let us consider an example case of \(n=16\). Then, per Corollary 2, \(\gamma_{5}=C(16,14,11)\). A comprehensive base of known values of \(C\) can be found at [[https://ljcr.dmgordon.org/cover/table.html](https://ljcr.dmgordon.org/cover/table.html)]([https://ljcr.dmgordon.org/cover/table.html](https://ljcr.dmgordon.org/cover/table.html)) (accessed on 1 September 2024). Unfortunately, it does not contain the value of \(C(16,14,11)\). To obtain a rough approximation, we use the Schonheim inequality [14]:  
\[C(t,k,v)\geq\lceil t/k\cdot C(t-1,k-1,v-1)\rceil\]and the known value \(C(13,11,8)=15\) (see [[https://ljcr.dmgordon.org/cover/show_cover.php?v=13&k=11&t=8](https://ljcr.dmgordon.org/cover/show_cover.php?v=13&k=11&t=8)]([https://ljcr.dmgordon.org/cover/show_cover.php?v=13&k=11&t=8](https://ljcr.dmgordon.org/cover/show_cover.php?v=13&k=11&t=8)), accessed on 1 September 2024):  
\[C(16,14,11)\geq\left\lceil\frac{16}{14}\cdot\left\lceil\frac{15}{13}\cdot\left \lceil\frac{14}{12}\cdot\left\lceil\frac{13}{11}\cdot C(13,11,8)\right\rceil \right\rceil\right\rceil=29.\]  
Hence, \(\gamma_{5}\geq 29\), and we need at least 29 subsets of \(\mathcal{N}\) in \(\mathcal{P}\) with cardinality at least 2. Together with 16 singleton sets (corresponding to link keys responsible for bilateral communication with the receivers), the sender has to preload at least \(29+16=45\) keys. Moreover, what we have done is only a rough estimate of the minimal cardinality of \(\mathcal{P}\). On the other hand, recall that the simple strategy from Section 3.1.1 requires 60 keys. Therefore, we may conclude the following. **Conclusion 1**. : _Achieving a significant improvement over the method presented in Section 3.1.1 might be impossible for the small values of \(n\) that we study here; moreover, finding optimal solutions by exhaustive search seems infeasible given the lack of results for the seemingly simpler problem of constructing minimal covering designs._
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 7 Performance Evaluation  
In this section, we evaluate the performance of the proposed scheme in terms of communication overhead and computation complexity.
# Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets
## 5 Validation  
We used well-known methods in the literature [28] for the validation of the PN. We applies the matrix equation approach, reachability tree method, and invariant analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Place** & **Description** \\ \hline P1 & A new packet comes from the Internet (network) to the router, which is ready. \\ P2 & The packet is received. \\ P3 & The AQM gets the average queue length (AQL) at the router. \\ P4 & The packet is accepted because the AQL is below the min. threshold. \\ P5 & The AQM gets the probability \(p_{max}\)\(I_{2}\) (\(\alpha,\beta\)). \\ P6 & The packet is rejected because the AQL is over the max. threshold. \\ P7 & The packet is accepted (not discard the result of \(p_{max}\)\(I_{2}\) (\(\alpha,\beta\))). \\ P8 & The packet is rejected (discard the result of \(p_{max}\)\(I_{2}\) (\(\alpha,\beta\))). \\ P9 & The packet is put into the buffer queue to be transmitted to the destination. \\ P10 & The AQM ready for receiving the next packet. \\ \hline
**Transition** & **Description** \\ \hline T1 & Input a new packet; the router is ready. \\ T2 & Start the transmission of the packet once received. \\ T3 & The AQL is below the min. threshold. Check guard G1. \\ T4 & The AQL is between thresholds. Check guard G2. \\ T5 & The AQL is over the max. threshold. Check guard G3. \\ T6 & Fill the buffer with the received packet. \\ T7 & Drop the packet; the AQL is over the max. threshold. \\ T8 & Do not discard the packet. Check guard G4. \\ T9 & Discard the packet. Check guard G5. \\ T10 & Input the packet into the buffer. \\ T11 & Reject the packet. \\ T12 & Leave the buffer. The packet is transmitted. \\ \hline
**Guard** & **Description** \\ \hline G1 & \((q_{min}<AQL)=True\). Guard associated to transition T3 \\ G2 & \((q_{min}\leq AQL\leq q_{max})=True\). Guard associated to transition T4 \\ G3 & \((AQL>q_{max})=True\). Guard associated to transition T5 \\ G4 & _Not Discardi = True_. Guard associated to transition T8 \\ G5 & _Discardi = True_. Guard associated to transition T9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Places, transitions, and guards of AQM.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 3 Related Works
### Electronic Health Records and FHIR  
EHR systems are essential for modern healthcare, and the FHIR standard plays a crucial role in ensuring interoperability. Studies have shown that EHRs improve patient care by enhancing communication between clinicians, streamlining clinical activities, and providing comprehensive patient information at the point of care. A study by [PERSON] et al. (2018) showed that EHR-based sign-outs reduced medical errors by 30% compared to verbal hand-offs between hospital providers, while a review found that EHR enhanced communication between providers reduced hospital readmissions by 20-30% ([PERSON] et al., 2018).  
The FHIR standard has emerged as a critical component in achieving interoperability between different healthcare systems. [PERSON] and [PERSON] (2018) introduced FHIR as a solution to address the limitations of previous standards, highlighting its potential to facilitate seamless data exchange. [PERSON] et al. (2018) further demonstrated FHIR's effectiveness in enabling third-party apps to integrate with EHRs, enhancing functionality and data accessibility. Implementation of EHR systems with FHIR capabilities has shown promising results in improving healthcare delivery ([PERSON] et al., 2018). For instance, [PERSON] et al. (2018) developed a data analytic framework that supported clinical statistics and analysis by leveraging FHIR, showcasing the practical applications and benefits of FHIR in real-world healthcare settings. These studies collectively emphasize the crucial role of EHR systems and the FHIR standard in modernizing healthcare delivery, improving patient outcomes, and ensuring efficient, interoperable health information exchange.