esa-sceva/satcom-chunk-collection huggingface dataset:
# Firewall Best Practices for Securing Smart Healthcare Environment: A Review
## 3 Firewalls Types for Smart Healthcare Environment
### Virtual Firewalls (VFs)  
The growing use of virtual environments applied to the enterprise network architecture paradigm have widely led to the demand for virtual firewall devices (Figure 5), a software acting as a hypervisor in VMs or kernel modules, instead of physical devices [30; 31], which is essentially addressed to those network systems more oriented to virtualized environments. Virtual firewalls were the first generation of devices no longer applicable at the hardware level, but directly within a virtualized environment, typically virtual machines (VMs), finally moving the same solution to cloud computing services. Next-generation firewalls (NGWF) are typically implemented in either PaaS or IaaS systems, while web application firewalls (WAF) are generally found in SaaS platforms and they are sometimes integrated into SaaS firewalls [32; 33]. These firewalls are described briefly in the following subsections.  
Unlike the other category of cloud firewalls, virtual firewalls apply transversally to all cloud platforms. Virtual firewalls can also protect a broader range of machines, from a single server to a group of virtual servers (this practice is better known as micro-segmentation), applying all firewall rules and security policies automatically to all devices added to the firewall application. In contrast, there are some potential downsides. Firstly, a virtual firewall might not be easy to implement in a Wide Area Network using wireless devices or software-based technology, which requires constant software updates and maintenance support; secondly, like in traditional IT network systems, there is not enough room for customization. Thirdly, the high quality of design standards required may expose  
Figure 5: Virtual Firewall Network Topology.  
cloud networks and security systems to additional (and sometimes unpredictable) design extra charges, and technically speaking, to potential vulnerabilities not easy to fix, especially when systems are hybrid cloud systems, a solution very often taken by those companies gradually moving to the cloud [34; 35]. However, virtual firewalls are still smart and portable solutions specifically for smart healthcare, showing very useful benefits, such as:  
* Better potential network performances in speed and connectivity terms due to more flexible positioning attitude and less resource consumption. A best practice can be to set a firewall machine behind each of the VMs, working as a managed kernel process for all running VMs separately [36]. This configuration should avoid the potential risk of malicious attacks to VMs when sharing network, storage, and computing resources.
* Virtual firewalls have additional functionalities provided by inter-VLAN (routed) firewalls and NIC-level firewalls, which are directly attached to VM NIC, able to filter ingoing and outgoing traffic through single VMs.
## 4 Evaluation Metrics
### Routing Protocols Metrics
#### 4.2.1 Packet Delivery Ratio  
The packet delivery ratio (PDR) is the ratio between the successfully-received packets and the total number of packets sent.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background
### Differences between TLS and DTLS  
TLS and DTLS follow the same protocol flow as depicted in Figure 2. However, there are some significant adjustments to DTLS due to the unreliability of the UDP transport layer.
## 2 Experimental Overview
### Testing Procedures
#### 2.2.2 Moisture Stability Testing  
Moisture stability is one of the crucial performance characteristics of road materials, typically defined as the material's ability to retain its original properties when saturated with water. It is assessed by observing indicators such as cracking and disintegration of the material when subjected to water saturation. However, the moisture stability of the material is typically quantified effectively using the moisture stability coefficient, denoted as '\(K\)'. This study explores the moisture stability of improved soils through UCS specimens at different curing periods, with calculations following Equation (1).  
\[K=\frac{R_{\mathrm{C}}}{R} \tag{1}\]  
where \(K\) represents the moisture stability coefficient; \(R_{\mathrm{C}}\) stands for the UCS of the sample soaked in water for 24 h (in MPa); and \(R\) represents the UCS of the sample without water immersion (in MPa).
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 2 Use Cases and Requirements
### Industrial Requirements  
Table 1 shows the requirements of the selected use cases [18] defined in terms of end-to-end latency, communication service availability (CSA), typical message size, and the typical number of devices. The end-to-end latency is the time that takes to successfully transmit a packet from a source to a destination, measured at the communication interface, from the moment it is transmitted by the source to the moment it is successfully received at the destination. It is closely linked to the cycle time of control functions. The communication service availability is the percentage of time the communication service is considered available; the system is considered unavailable in case an expected message is not received within the required end-to-end latency. Table 1 shows that the end-to-end latency required can be below 1 ms for some use cases, and the communication service availability up to 99.999999% (i.e., \(1-10^{-8}\)). Other uses cases, however, require the transmission of video with less strict latency requirements. Especially the use of massive wireless sensors will require the deployment of a vast number of devices, in some cases requiring latencies up to 5 ms. As it can be observed, the requirements of the deployed devices will be very heterogeneous and simultaneous transmissions with very different requirements will need to coexist (often in the same link between the same nodes, but also in the same factory). This heterogeneity can also be observed in  
Figure 1, where the requirements of the six use cases analyzed in this paper are depicted in polar plots. Each sub-figure shows the requirements of one of the use cases in terms of latency, CSA, packet size, and number of devices. Since a range of values is provided for each use case (e.g., latencies between 4 and 8 ms), the requirements are plotted as areas. For some use cases that have 2 applications with very different requirements, 2 areas with different colors are depicted. This is the case of, for example, the massive WSN use case that has significantly different requirements for safety and non-safety monitoring. While condition safety monitoring requires between 5 and 10 ms latency, CSA up to \(1-10^{-8}\) and up to 1000 nodes, interval-based and event-based monitoring require between 50 ms and 1 s latency, CSA up to \(1-10^{-3}\) and up to 10,000 nodes.
## 3 Modified Hybrid Integration Processing
### Modified Hybrid Integration of OFDM IRCS Frequency Signal
#### 3.3.1 Intra-Frame Integration  
A linear phase shift is induced between each reflected subcarrier from a target at range \(R\). The high-order motion of the targets can be ignored because it does not result in Doppler crossing within one frame of OFDM IRCS. Thus, range information is discernible through the linear phase shift of the modulation symbols along the frequency axis. We can proceed directly to moving target detection (MTD) as follows:  
\[v_{frame}(k)=\sum_{n=0}^{N_{c}-1}\exp(j2\pi nT_{OFDM}f_{d})\bullet\exp\left(-j \frac{2\pi}{N_{s}}nk\right), \tag{21}\]  
where \(k\) is denoted by  
\[k=N_{s}T_{OFDM}f_{d}, \tag{22}\]  
which efficiently accumulates intra-frame energy within one subfragment. The workflow resembles Figure 2.
# Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks
## 3 Proposed Method
### Dataset Design  
The dataset is designed in three main steps: (1) image collection, (2) data labeling, and (3) data augmentation.  
Figure 4: The architecture of our network. (**a**) Generator. (**b**) Discriminator.  
First, the color stripe and multi-slit binary patterns are projected onto the objects, respectively. After the images are captured by the camera, they are classified into two groups: stripe images and multi-slit images.  
The process of data labeling is shown in Figure 5. The multi-slit images are first preprocessed with a Gaussian filter to reduce noise in the image. After the centerline images are obtained (Figure 5b), a morphological dilation operation is applied to obtain 3-pixel line images (Figure 5c). Finally, the resulting images are sequentially cut into patches with a size of 128 \(\times\) 128 pixels and selected as the labeled images.  
Our original dataset consisted of 40 pairs of original large images captured with the two patterns. Five of them were selected as test sets. After a bilateral filter was applied to reduce noise in the images of a color stripe pattern, these images with the corresponding 3-pixel line images were sequentially cut into patches with a size of 128 \(\times\) 128 pixels. As seen in Figure 6, each sample included an image patch and its label, and 20% of them were selected as the validation set. After applying some data augmentation techniques to all remaining samples, the training set was obtained with more than 1 million samples.  
In the color stripe edge detection method proposed in this paper, the design of the dataset plays a very important role. A color stripe pattern and a multi-slit binary pattern need to be designed accurately first, where the slits in the multi-slit binary pattern must be located at the positions corresponding to the left edges of the color stripes in the color stripe pattern. The multi-slit binary pattern images are processed to obtain the ground-truth images corresponding to the color stripe pattern images in the training and test sets. In many previous studies, data labeling has been done manually by experts. However, this requires a lot of time and effort, especially when the number of images is large. Moreover, the manual labeling is not objective, so the pixel location accuracy of the ground-truth  
Figure 5: Illustration of the data-labeling process. (**a**) Multi-slit image. (**b**) Centerline image. (**c**) Labeled image. (**d**) Three-pixel line image.  
images is not high. With our dataset, the ground-truth images are obtained directly from processing the multi-slit binary pattern images, so the pixel location accuracy is guaranteed. Thus, the proposed method can not only achieve ground-truth images with higher quality, but it also saves more time and effort than traditional manual labeling.
## 4 Discussion
### Influence of Lateral Pressure Coefficient
#### 4.1.2 Stress Intensity Factors and Failure Modes  
Taking the crack half-length of a = 5 mm as an example, the stress intensity factor of the lateral pressure coefficient is analyzed from 0 to 1. The relationship curve between the stress intensity factor and the lateral pressure coefficient is shown in Figure 8.  
As the lateral pressure factor increases, the stress intensity factor at the crack tip decreases, indicating that crack propagation is impeded. The rate of decrease in the stress intensity factor is not the same at the left and right ends of the crack. Specifically, the rate of decrease at the left end of the crack is lower than that at the right end. This indicates that stress fluctuation is more pronounced away from the interface when ice is present.  
A numerical simulation was conducted using XFEM to study the propagation of a crack at an inclination angle of 30\({}^{\circ}\). The evolution of the crack is depicted in Figure 9.  
Figure 8: The variation curve of the stress intensity factor with the lateral pressure coefficient.  
From the figure, it is evident that when the lateral pressure is 0, the distribution of wing cracks at the crack tip is highly noticeable, with the upper and lower crack extensions symmetrically distributed. However, as the lateral pressure increases to 0.5, the crack extensions become smoother and the development of wing cracks slows down. Therefore, in practical engineering, increasing the lateral pressure coefficient can effectively inhibit the formation of tension cracks and help improve the load-bearing capacity of ice-rock.
# Case Study: Students' Code-Tracing Skills and Calibration of Questions for Computer Adaptive Tests
## 3 Research
### The Categories of the Questions.  
Apart from skills tracing, the authors were also keen to explore students' programming knowledge; therefore, the test contained six types of tasks (i.e., six categories). The task categories were the following:  
1. **Statements**. Through these questions, the authors aimed to explore the students' knowledge of various basic concepts of the C programming language. For example, students had to calculate an arithmetic problem with different operators, determine the correct names of variables, find syntax errors, etc. The \"statement\" category contained 12 questions.
2. **Conditional statements**. This category consisted of questions that used the _if, if-else, if-else-if:_ (ternary operator), and switch structures. The conditions that the students had to examine/conclude ranged from simple to complex. Altogether, there were six conditional statements in this category.
3. **Iterations.** With the help of these iteration questions, the authors studied how the students would solve questions with _for, while_, and _do-while_ cycles. The variation in task difficulty was achieved by adjusting the conditions of the cycle: they ranged from simple to complex. There were ten iteration-type questions in this category.  
4. **Structures**. This category of questions focused on the use of arrays and two-dimensional arrays with integers and characters, although there were no tasks of data sorting. This category contained six questions.
5. **Functions**. Questions in this category were related to the knowledge of function arguments (call by value, call by reference) and the use of local, static, and global variables. There was a total of six function-related questions in the category.
6. **Recursion**. Questions in this category were consciously created as \"difficult\", so that they could reveal whether senior students still knew the principles of recursion functioning or would use other methods. There were four recursion questions in the category, three of which were basic, while the fourth question had double recursion.  
The following list contains a few examples of categories, with the boxes for their level of difficulty that had to be ticked:  
* Conditional statements what is the output of the following code? Hard Medium Easy  void main(){ int i = 0; if((13 + 1)/2) { if(13 > 5) i+=5; else i++; } else i+=2; cout<<i; }
* Iteration statements what is the output of the following code? Hard Medium Easy  main(){ int i = 0; int s = 9; for (; i < 5; i++) s -= i; cout<<s; }
* Data structures what is the output of the following code? Hard Medium Easy  void main(){ int j, i; int ar[] = {1, 2, 3, 4, 5, 6, 11}; for(j = 5, i = 0 ; i < j ; j-.,i++) ar[i] = ar[j];  i = ar[i]; cout<<i; }
* Functions what is the output of the following code? Hard Medium Easy  void swap(int8, int8); int main(void) { int a = 10, b=20; swap (a, b); cout<<a<<\"\t\"<<b<<\"\
\";  return 0; } void swap(int8 x, int8 y) { x+=2; y+=3;
) a. 14, 24 b. 11, 21 c. 10, 20 d. Nothing, there is an error in the code e. 12, 23
## 2 Material and Methods
### Models Design and Evaluation
#### 2.2.2 MPMR Model  
The MPMR theory was presented and applied in [11; 26; 27; 28]. In general, MPMR is a kernel regression algorithm can be expressed as follows to estimate the desired value (\(y\)).  
\[y=\sum_{i=1}^{n}\beta_{i}K(x_{i},x)+b \tag{6}\]  
where, \(K(.)\) is kernel function; \(x\) and \(y\) are the input and output variables; \(\beta_{i}\) are unknown coefficients (or weight) and \(b\) is a bias, and \(n\) is the dataset number.  
MPMR recreates the \(x\) and \(y\) datasets using a distribution of both datasets with a given mean and covariance of them as follows:  
\[u_{i}=\left(y_{i+1},x_{i1},x_{i2},\ldots,x_{in}\right) \tag{7}\]  
\[v_{i}=\left(y_{i-1},x_{i1},x_{i2},\ldots,x_{in}\right) \tag{8}\]  
The statistical-classification boundary between \(u_{i}\) and \(v_{i}\) can be classified as a regression surface [27]. Based on the training dataset (\(nx\),\(ny\)) of input and output variables, the coefficients in Equation (6) can be calculated based on a kernel width of the kernel function and insensitive error zone required. In the current study, the Radial basis function (RBF) was applied (\(K(x_{i},x)=exp[-\frac{(x_{i}-x)(x_{i}-x)^{T}}{2\sigma^{2}}]\)) where \(\sigma\) is the kernel width. For developed MPMR model for S of SCC, the design values of \(\sigma\) and error insensitive zone were 0.6 and 0.003 respectively; whereas, that for CS of SCC were 1.7 and 0.003, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Parameters** & **Nn** & **Ln** & \(\alpha\) \\ \hline S & 15 & 5 & 0.3 \\ \hline CS & 10 & 4 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: GMDH model parameters for S and CS of CSS.
## 3 Results and Discussion  
### Supercritical Debinding  
Figure 3 shows the weight fraction of the PEG extracted in four hours after being debound by supercritical carbon dioxide at 40 MPa, and the temperatures from 383.15 to 483.15 K. The results indicate that the discrepancies between the three repetitive tests for all of the temperatures were approximately 1.5%.  
It is observed that the evolution of the PEG binder removal is proportional to the temperature. When the temperature increases, the PEG removal increases. The temperature is an influential parameter on the debinding. The plasticizing effect of the supercritical carbon dioxide probably affects the melting point of the binder system [22]. The effect of the optimal supercritical debinding parameters (\(T\) = 423.15 K and \(P\) = 40 MPa) on the components' deformation after the debinding tests was lower than 1%. Moreover, the PEG was entirely extracted with 423.15 K and 40 MPa after four hours. The parameter identification of [PERSON]'s model will be carried out with this experimental database.  
Figure 2: (**a**) Diagram of the supercritical debinding equipment [10]; (**b**) Carbon dioxide pressure-temperature phase diagram, showing the triple point (TP) and critical point (CP) [32].
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
###### Abstract  
The aim of this study is to establish an analysis of certification indicators for the Pavement Condition Index (PCI) to address previously unaccredited issues. In the management of road infrastructure, the PCI serves as a crucial indicator for assessing road pavement conditions. However, the lack of standardized detection and accreditation mechanisms in the past may have led to variations in the reliability of detection results among different personnel. This study observes the accuracy and precision of the data obtained by different inspectors. The analysis is based on repeated manual inspections conducted by four inspectors across 12 road units on Zhongda Road, using the ASTM D6433-23 (2023) standard. To ensure the reliability of the inspection results, the Coefficient of Variation (CV) is used to measure repeatability, while the Mean Relative Error (MRE) is used to evaluate accuracy. Training was introduced between inspection phases to assess performance improvement. The results showed that the average CV decreased from 27.2% to 7.5% and the average MRE reduced from 45.3% to 5.9% after training. These findings demonstrate that targeted training significantly enhances the repeatability and accuracy of manual PCI assessments. This study concludes with recommendations to set a CV threshold of \(\leq\)0.1 and an MRE threshold of \(\leq\)10% as practical benchmarks for manual PCI Quality Control. These standards can serve as validation criteria for future automated inspection systems.  
p _et al._ 2025, 15, 5600
## 5 Artificial Neural Networks Analysis
### Database Used in the Development of the ANN Method  
The dataset used in the ANN analysis consisted of \(n=88\) cases of results of index properties tests (organic content and void ratio) and results of dilatometer tests and stress history indicators (\(oc\) or \(nc\)) as independent variables and as dependent variables of normalized undrained shear strength from field vane tests shown in Table 7. The dataset was described by five independent variables: \(X_{1}=\) organic content \(I_{om}\in\) [8-85%]; \(X_{2}=\) void ratio \(e\in\{1.9\)-7.4\(\}\); \(X_{3}=\) normalized net value of corrected first pressure reading \((p_{o}-u_{o})/\sigma^{\prime}_{v}\in\{1.41\)-20.0\(\}\); \(X_{4}=\) normalized net value of corrected second pressure reading \((p_{1}-u_{o})/\sigma^{\prime}{}_{v}\in\{1.87\)-\(27.35\}\); \(X_{5}\) = code of overconsolidated state (\(oc\)) or normally consolidated state (\(nc\)), respectively \(\in\{0;1\}\), and one dependent variable \(Y=\tau_{fu}/\sigma^{\prime}{}_{v}\in\{0.26\)-\(5.00\}\). Variable \(X_{3}\) is the same as the horizontal stress index \(K_{D}\). Variable \(X_{5}\) was treated as a quantitative variable since it was a zero-one variable [42]. Sensitivity analysis of the Neural Network was performed based on the network error quotient, with one of the input variables for the basic error of the network calculated for the neural network having all input variables. A larger ratio of errors in the absence of a given input variable means that a larger error is created in the neural network devoid of this input variable. This indicates how the variable is important in the model. In the proposed neural network, the relevant importance of input variables is: \(X_{5}=oc\) or \(nc\) (with Ratio = 9.73), \(X_{3}=(p_{o}-u_{o})/\sigma^{\prime}{}_{v}\), (with Ratio = 9.62), \(X_{4}=(p_{1}-u_{o})/\sigma^{\prime}{}_{v}\), (with Ratio = 5.04), \(X_{2}=e\) (with Ratio = 3.29), and \(X_{1}=I_{om}\) (with Ratio = 2.28).
## 2 System Description
### Cell Search Procedure  
At the beginning of communication, the MTC UE has to search for a best serving eNodeB by retrieving the CID and accurate initial synchronization [9; 10; 11; 12], which is known as initial cell search. Due to the vulnerability of OFDM to time-frequency misalignments, symbol timing offset (STO) and carrier frequency offset (CFO) are usually estimated during the cell search procedure. To assist these operations, the PSS and SSS sequences are transmitted periodically from the eNodeB. Figure 2 shows the initial cell search procedure. The initial cell search procedure can be typically divided into three phases. Firstly, initial STO and fractional CFO (FFO) are estimated in the time domain by using redundant information present in the CP of the OFDM symbol [9; 10]. Once the FFO is compensated and the CP type is identified, the DFT unit is used to transform the received time-domain samples into the frequency domain. Upon the successful completion of this step, the IFO detection can be performed together with the search process of PSS, which may be accomplished either in the time or frequency domain [11]. Finally, the detection of the GID and the acquisition of frame boundary are performed by analyzing the received SSS [12; 13], thus resolving 10 ms radio frame timing. Once these operations have been finished, the MTC UE tries to detect broadcast data channel information checking the successful completion of initial cell search. If the CID is erroneously identified, it is useless to  
Figure 1: Long term evolution frequency division duplex downlink frame structure.  
decode the MIB and SIB. Therefore, the CID detection is one of the most challenging tasks during overall cell search procedure.