esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 3 Results
### Permeability Coefficients  
Figure 7 shows the permeability coefficients in different reaction time periods. Some observations can be made from these results. They are as follows:  
The permeability coefficient curve showed a continuous decreasing trend until 60 h, after which the decreasing rate gradually slowed down. Among the three experimental groups, the fastest decreasing rate was in the process of T2, followed by T3, and finally, T1. The decreasing rate of T1 was similar to that of T2 and T3 in the initial period, which slowed down significantly after 48 h. The permeability coefficient curves of the three experimental groups all generally stabilized at and after 60 h, and the permeability coefficients remained almost constant.  
As can be seen by comparing Figures 6 and 7, the calcium carbonate precipitation rate at 60 h can reach approximately 98% of that at 96 h, and the permeability coefficients also varied very little from 60 h to 96 h; the arithmetic mean of the permeability coefficients of 15 samples in four time periods (60 h, 72 h, 84 h, and 96 h) was taken as the final permeability coefficients to reduce the error. The results were as follows: \(1.70\times 10^{-3}\) cm\(\cdot\)s\({}^{-1}\) (T1), \(1.56\times 10^{-3}\) cm\(\cdot\)s\({}^{-1}\) (T2), and \(1.59\times 10^{-3}\) cm\(\cdot\)s\({}^{-1}\) (T3). The final permeability coefficient of T2 decreased the most, with a decrease of 34.2%. T3 was similar to T2, with a decrease of 32.9%. The final permeability coefficient of T1 decreased the least, with only 28.3%.  
Figure 7: Permeability coefficient in different reaction periods: (**a**) variation of permeability coefficient in the process of T1 (red circle); (**b**) variation of permeability coefficient in the process of T2 (blue circle); (**c**) variation of permeability coefficient in the process of T3 (black circle); and (**d**) comparison of the permeability coefficient between T1, T2, and T3.
## 7 Simulation Parameters
### Analysis of Path Loss  
The connection between the two nodes is established when the path remains active; especially in IoUTs, this is a vital concern when the node deploys in a dense environment. Usually, in these types of environments, nodes are particularly far apart from each other and not an intimation of path link breakage. These links suddenly create a void hole for a long time as holes become a void region that isolates the network. Due to the abovementioned reasons, the path analysis between all schemes was experimented and is shown in Figure 12. The WDFAD-DBR and AVH-AHH VBF tackled the depth and coped early to handle the harsh environment; therefore, their path loss was not much increased, only 50 dB and 52 dB, respectively. Rest schemes like EBER\({}^{2}\), AEDG, and ASEDG lack such alternatives and, therefore, their path loss analysis satisfied 46 dB, 52 dB, and 53 dB, respectively. The TANVEER and ROBINA had path loss analysis around 200 s equally until it reached 400 s, at which point it increased to 50 dB and 51 dB, respectively. The 1 dB difference is ignorable after 5.5 min of simulation, by principle. Usually, this interval is considered good for out-breaking the result, especially when the critical parameter is under study. On the contrary, ROBINA started at 50 dB and reached the lowest path loss value even after 10 min equal to 600 s.
## 1 Introduction
Data transmission from the ceiling of the LED was performed by flicker-free FSOOK line coding modulation to minimize the bit error rate. The rolling shutter mechanism of the CMOS mobile image sensor was used to enhance the data rate of our proposed system. Several experiments were conducted in this study, and the results show that the proposed system can achieve centimeter-level positioning accuracy in large-area indoor environments. In summary, the significant contributions of this study are as follows:  
* Practical System: Designed and implemented an efficient VLP system using existing LED light infrastructure. The LED drivers were modified slightly to transmit the location information to the image sensor. * Test Area Accuracy: Implemented a multilateration positioning approach for this system, which utilized VLC and mobile CMOS image sensors for providing location-based service in a large indoor environment with many LEDs. The system ensures good accuracy and high precision within an area measuring 2.5 m \(\times\) 4.5 m.
* Sensor: Smartphone-embedded low-cost CMOS image sensor with a rolling shutter mechanism was used to scan every image pixel for increasing the data rate. However, no additional device was required at the receiver, affording a simple system. The remainder of this paper is organized as follows. Section 2 describes the system design. The proposed positioning method is provided in Section 3. The experimental environment and the outcome of this study are presented in Section 4. Finally, Section 5 presents the conclusions of the paper.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 2 Methodology
### Tree Construction:  
* For each tree \(t\):
* Draw a bootstrap sample \(\mathcal{D}_{t}\) by randomly selecting \(N_{t}\) instances from the training dataset \(\mathcal{D}\).
* At each node within the tree, randomly select a subset of mm\(m\) features from the total set of \(m\) features.  
* Determine the optimal split \(j\) at the node using a specific criterion, such as Gini impurity for classification tasks or Mean Squared Error for regression problems.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### Theoretical Foundations of IECPS
#### 5.1.1 Refined Modeling of IECPS  
The modeling technology of IECPS is of great significance in understanding the system's operational mechanisms, analyzing deliberate attack behaviors, and formulating effective defense strategies. By meticulously modeling the infrastructure, energy conversion devices, and communication controls of each system, it captures the complex coupling relationships between cyber and physical networks, thereby accurately characterizing the dynamic properties between cyber flow and energy flow [190]. Based on the system's dynamic characteristics, the impact of deliberate attacks and their propagation process can be accurately simulated, while also allowing for the evaluation of the defense effectiveness of corresponding response strategies. The main distinction between IECPS and integrated energy systems lies in the former's focus on the interaction mechanisms between cyber and physical systems. In modeling, it requires separate modeling of the abnormal states of each  
Figure 15: Key defense technologies against deliberate attacks in IECPS.  
cyber-physical coupling component. In contrast, the latter diminishes the role of cyber systems in operational processes and is unable to display the real impact of cyberattacks on physical systems.  
In addition to the detailed modeling of the system's devices, it is also crucial to develop a precise attack model. By hypothesizing the behavior of attackers based on existing case studies and deducing their actions using the IECPS operational model, the attacker's path and its impacts can be simulated. When hypothesizing attacker behaviors, it is important to not only consider traditional single attack methods but also combine them to design diversified attack models involving multiple types of cyberattacks and cyber-physical coordinated attacks, thereby creating a diverse attack model library to guide the research of defense strategies.  
Moreover, the focus is on all the defense resources within IECPS that can be used to resist or mitigate deliberate attacks. By combining the operational model and attack model, the response of these defense resources is analyzed, and a corresponding defense resource response model is established. Besides depicting the response model of defense resources, a cost model for utilizing these resources must also be established. Given a limited budget, the optimal allocation of defense resources is studied to maximize the effectiveness of the defense strategy [191]. During the model-building process, to ensure that the model can be solved within a limited time, it is necessary to balance model accuracy with computational speed, thereby enabling online application of the model. This allows for quick adjustments to the system's operational state and a preemptive response to deliberate attacks.
## 6 Discussion and Future Considerations
### Channel Models  
Modeling an A2G wireless channel is important in trajectory optimization since performance degradation may be observed with inappropriate channel models compared to actual environments [62]. Obtaining realistic and reliable results for AoI minimization in UAV-assisted WSNs/IoT requires using accurate channel models. In this case, a thorough evaluation of existing channel models is important for identifying and understanding the effect of channel parameters in several use cases [108], especially those targeted at time-sensitive applications. Additionally, a map-based LOS channel model can be used to capture the actual environmental characteristics [62]. The aforementioned recommendations have the potential to reduce power consumption and properly evaluate the network performance. Therefore, realistic channel models are essential.
# Development of the Virtual Reality Application:
## 2 Methodology
### Polygon Mesh Optimization
#### 2.2.2 Retopology  
Retopology is \"the act of recreating an existing surface with more optimal geometry\" and it is used to modify a high-resolution 3D model to its optimal form, to be utilized for animation or texturing [25]. The reason retopology was used in \"The Ships of Navarino\" was twofold. Firstly, since retopology is one of the most popular methodologies that 3D designers use to reduce polygon count, it was utilized for optimization purposes. Secondly, a low polygon model, with clean topology that provides edge flow, significantly reduces the UV mapping complexity, a process necessary for later texturing (Figures 7 and 8).  
Figure 6: Result of quad-remeshing the NURBS surfaces of Figure 5.  
There are various options for retopologization:  
* Manually, in a 3D-modeling software such as Blender. Given the built-in features of Blender, such as surface snapping and the shrink-wrap modifier, a designer can attempt to rebuild the high poly model polygon-by-polygon.
* Using specialized retopology Blender add-ons that attempt to reduce complexity and accelerate the retopologizing process.
* Using software outside Blender that offers more retopology tools.  
Using some Blender retopology add-ons that allowed face snapping was the best option for this project.  
After remeshing, retopologizing, and refining the meshes, the most optimal form of the ship models was achieved. The final models counted significantly less polygons and were lighter and easier to manipulate.  
Converting a high poly to a low poly benefits not only the modeling process but also the quality of the final model, because details of the high poly can be maintained without causing performance penalties (Figure 9).  
By converting the high poly model into normal, occlusion, and other maps, details that demand a large number of polygons can be rendered into them and used as textures afterwards.
## 4 Experiment Result
### Answer to RQ2--The Effective of FLFR  
The experimental results are shown in Table 5. We can see that the effect of FLFR on fault localization is the most obvious, followed by GA, and the effect of RT ranks third. In addition, we also found that except for the Texas dataset, which did not improve in the three methods, the other datasets improved in the three methods. This is because the TCAS dataset has only 173 lines of code, the complexity of the program is low, even if more test cases are generated, and the spectral coverage ratio of the program lines will not cause changes, so the test case generation will not affect the fault localization results.  
We analyzed the results of RT and GA. For the RT algorithm, because it uses a random strategy to generate test cases, there is no guarantee that the test cases will cover as many statements containing failures as possible. In contrast to RT, FLFR employs an overlay-based strategy that generates test cases that are helpful for fault location. Therefore, in terms of the effect of software fault location, FLFR is better than RT.  
Compared with the genetic algorithm GA, FLFR not only generates test cases by simulating population evolution, but also improves individual generation strategies. Therefore, FLFR generates test cases through multiple iterations of software fault location results, so that the generated test cases achieve better results in software fault location than GA.  
Therefore, we can conclude that the FLFR method improves the software fault localization effect.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject** & **MIES (\%)** & **MOES (\%)** & **MOCC (\%)** \\ \hline Kulcyznskil & 0 & 15.2 & 18.2 \\ \hline Tarantula & 0.2 & 9.5 & 17.0 \\ \hline Jaccard & 11.0 & 16.4 & 16.5 \\ \hline Ochiai & 1.3 & 6.2 & 11.8 \\ \hline Ochiai2 & 10.5 & 15.1 & 14.9 \\ \hline \(D^{2}Star\) & 5.4 & 7.0 & 10.8 \\ \hline \(D^{3}Star\) & 5.4 & 6.9 & 8.5 \\ \hline Op2 & 8.1 & 3.4 & 7.7 \\ \hline Gp02 & 8.1 & 7.3 & 7.2 \\ \hline Gp19 & 0.4 & 0.8 & 8.0 \\ \hline BPNN & 51.1 & 65.5 & 61.1 \\ \hline RBFNN & 41.0 & 45.5 & 61.5 \\ \hline CNNFL & 63.6 & 74.8 & 65.2 \\ \hline Average & 15.85 & 21.05 & 23.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance on different failure types.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Subject** & \multicolumn{3}{c}{\(\boldsymbol{Ochiai(\%)}\)} & \multicolumn{3}{c}{\(\boldsymbol{Ochiai2(\%)}\)} & \multicolumn{3}{c}{\(\boldsymbol{D^{3}Star(\%)}\)} \\  & **FLFR** & **GA** & **RT** & **FLFR** & **GA** & **RT** & **FLFR** & **GA** & **RT** \\ \hline Print-token & 23.1 & 16.6 & 18.1 & 5.7 & 0 & 0 & 19.7 & 20.4 & 0 \\ \hline Print-token2 & 38.7 & 12.3 & 0 & 5.7 & 3.70 & 1.8 & 6.2 & 8.8 & 2.9 \\ \hline Tot-info & 22.4 & 19.6 & 0 & 10.5 & 7.92 & 1
## 4 Special Cases and Applications  
**Application 4.1**. : _The results obtained in earlier studies such as [5, 6, 7, 14] require that the operator \(F\) (i.e., \(G\)) is Frechet differentiable. This assumption limits the applicability of the earlier results. In the present study we only require that \(F\) is a continuous operator. Hence, we have extended the applicability of Newton-type methods to classes of operators that are only continuous. Moreover, as we will show next, by specializing \(F\) to be a Frechet differentiable operator (i.e., \(F^{\prime}\left(x_{n}\right)=A\left(x_{n}\right)\)), our Theorem 3.1 improves earlier results. Indeed, first of all, notice that the Newton-type method defined by Equation (3.1) reduces to [PERSON]'s method:_  
\[x_{n+1}=x_{n}+y_{n},\text{ }F^{\prime}\left(x_{n}\right)y_{n}+F \left(x_{n}\right)=0 \tag{4.1}\] \[\Leftrightarrow y_{n}=T_{n}\left(y_{n}\right)=\left(I-F^{\prime} \left(x_{n}\right)\right)y_{n}-F\left(x_{n}\right)\]  
Next, we present Theorem 2.1 from [14] and the specialization of our Theorem 3.1 so that we can compare them. **Theorem 4.2**. : _Let \(F:D\to X\) be a Frechet differentiable operator and \(x_{0}\in D\). Suppose that the following conditions hold:_  
_(\(\overline{H}_{1}\)) There exists an operator \(M_{0}\in B\left(I-F^{\prime}\left(x_{0}\right)\right)\)._  
_(\(\overline{H}_{2}\)) There exists an operator \(N_{1}\in L_{+}\left(E^{2},E\right)\) satisfying for_  
\[x,y\in D,z\in X:\left/\left(F^{\prime}\left(x\right)-F^{\prime}\left(y\right) \right)z\right/\leq 2N_{1}\left(/x-y/\,,/z/\right)\]_(\(\overline{H}_{3}\)) There exists a solution \(c\in K\) of the inequality_  
\[\overline{R_{0}}\left(c\right):=M_{0}c+N_{1}c^{2}+/F\left(x_{0}\right)/\leq c\]  
_(\(\overline{H}_{4}\)) \(U\left(x_{0},c\right)\subseteq D\)._  
_(\(\overline{H}_{5})\left(M_{0}+2N_{1}c\right)^{k}c\to 0\) as \(k\rightarrow\infty\)._  
_Then, the following hold_  
_(\(\overline{C}_{1}\)) The sequence \(\left\{x_{n}\right\}\) generated by Equation (4.1) is well-defined and converges to a unique zero of \(F\) in \(U\left(x_{0},c\right)\)._  
_(\(\overline{C}_{2}\)) An a priori bound is given by the null-sequence \(\left\{c_{n}\right\}\) defined by_  
\[c_{0}=c,\ c_{n}:=\overline{P}_{n}^{\infty}\left(0\right),\] \[\overline{P}_{n}\left(t\right) : =M_{0}t+2N_{1}\left(c-c_{n-1}\right)t+N_{1}c_{n-1}^{2}\]  
_(\(\overline{C}_{3}\)) An a posteriori bound is given by the sequence \(\left\{d_{n}\right\}\) defined by_  
\[d_{n}=\overline{R}_{n}^{\infty}\left(0\right)\text{, \ }\overline{R}_{n} \left(t\right):=M_{0}t+2N_{1}b_{n}t+N_{1}t^{2}+N_{1}a_{n-1}^{2}\]  
_where sequences \(\left\{a_{n}\right\}\) and \(\left\{b_{n}\right\}\) are as defined previously._  
**Theorem 4.3**. : _Let \(F:D\to X\) be a Frechet differentiable operator and \(x_{0}\in D\).
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
###### Abstract  
Exposure to radiofrequency (RF) electromagnetic fields (EMFs) in indoor environments depends on both outdoor sources such as radio, television and mobile phone antennas and indoor sources, such as mobile phones and wireless communications applications. Establishing the levels of exposure could be challenging due to differences in the approaches used in different studies. The goal of this study is to present an overview of the last ten years research efforts about RF EMF exposure in indoor environments, considering different RF-EMF sources found to cause exposure in indoor environments, different indoor environments and different approaches used to assess the exposure. The highest maximum mean levels of the exposure considering the whole RF-EMF frequency band was found in offices (1.14 V/m) and in public transports (0.97 V/m), while the lowest levels of exposure were observed in homes and apartments, with mean values in the range 0.13-0.43 V/m. The contribution of different RF-EMF sources to the total level of exposure was found to show slightly different patterns among the indoor environments, but this finding has to be considered as a time-dependent picture of the continuous evolving exposure to RF-EMF.  
RF electromagnetic fields; exposure assessment; indoor environments +
Footnote †: journal: Journal of Environmental Research and Public Health
## 3 The Proposed Method
### ROI Recognition  
Because some land regions with low scattering intensity are also interfered with by surrounding buildings, there may be some false alarms in the extracted ROIs. The difference between the false alarm and the true target is that the double-bounce scattering power of the land in ports is much higher than that of the false alarm. The reason for this is that there are usually some metal structures with a strong scattering in ports. The area of the strong scattering region of the true target is also larger than that of the false alarms. Thus, the false alarms can be removed by the thresholding segmentation of the double-bounce scattering power and by calculating the area ratio of the strong double-bounce scattering region in the land of the extracted ROI. However, it is difficult to obtain the threshold because the sampling region is not available. Since the volume scattering power of the strong double-bounce scattering region is usually low, the PRDV parameter used for the interference water extraction can also be used for the extraction of the strong double-bounce scattering region in the land. Because the volume scattering power of the interference water is much smaller than that of the land region, the threshold \(th_{PRDV}\) obtained in the interference water extraction can also be used for this extraction.  
For a candidate ROI, the land regions in the ROI are first extracted based on the water and land segmentation results. The strong double-bounce scattering region of the land in the ROI is then extracted by the thresholding segmentation result of PRDV using \(th_{PRDV}\). If the area ratio of the strong scattering region to the land region in the ROI \(R_{\text{ROI}}\) is smaller  
Figure 6: The multi-scale thresholding segmentation for the extraction of the interference water, where ‘thres’ denotes the thresholding process, ‘mx down’ denotes m times downsampling, and ‘mx up’ denotes m times upsampling.  
than the threshold, the ROI is recognized as a false alarm; otherwise, it is recognized as a detected port.  
The extension of the interference water in a port may be far away from the land. To make the detected port closer to the true port, the minimum external quadrilateral of the land region in the ROI is finally recognized as the detected port.
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 5 System Overview
### System Architecture
#### 5.2.4 Certificate Revocation  
Certificates require revocation upon expiration, yet certain circumstances may demand invalidation before their expiry, such as private key compromise, compromised introducer-provided certificates, or domain-name changes. To ensure timely and accurate revocation, our system meticulously examines all revocation requests, engaging multiple ring members in the verification process. With sufficient evidence, the certificate is revoked by the ring members. Authorized members, including certificate holders, existing ring members, regular users, and government entities, may request certificate revocation for various reasons.  
Revocation requests may stem from the following sources:  
* **Certificate Owners:** They may request revocation in the event of server attacks or upon suspicion of compromised, stolen, or lost private keys.
* **Authenticated Ring Members:** These members possess the authority to request revocation for diverse reasons.
* **Other Entities:** This category encompasses users who have evidence of certificate misuse, deception, or inappropriate behavior, as well as authorized government members who can apply for certificate revocation.  
When submitting a revocation request, the requester must provide a valid reason and supporting identity authentication documents, such as a signed X.509 digital certificate and public key. This signature ensures the authenticity of the request, while the public key serves as evidence of possession of the private key and other pertinent information.  
When a revocation request is received within the blockchain network, introducers are given priority to initiate the request verification process. The system automatically conducts signature validation to confirm the requester's authenticity using the provided public key. Introducers are required to validate additional information, such as the reason for revocation, which may necessitate manual validation and subsequent signing. The system employs a process akin to the X.509 certificate validation process and assesses a mathematical equation (Equation (1)) to determine whether to invalidate the certificate. Subsequently, the ring owner confirms certificate revocation by responding to the requester and also updates the Online Certificate Status Protocol (OCSP) [42] server to reflect the changes in the revocation list. Browsers can verify the revocation status of a certificate by utilizing the Certificate Revocation List (CRL) or processing the OCSP response.  
Additionally, the proposed system stores revocation information in the blockchain network, thereby ensuring public accessibility to this information. This approach allows anyone to promptly access the revocation details. The key-ring network, as proposed, is established with multiple trusted introducers, thereby enhancing the likelihood of having available introducers for prompt validation of revocation requests. The involvement of multiple introducers concurrently enhances the system's efficiency and security.