esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Neural Radiance Fields for Fast Training  
Since the NeRF was proposed, it has inspired a large amount of research work. These studies aim to expand the capabilities of the NeRF from multiple aspects, including improving the quality of visual synthesis [20,21], speeding up rendering [17,22,23], reducing the number of input views required [24, 25, 26], improving generalization to different scenes [27], reconstructing large-scale urban scenes [28, 29, 30, 31], and implementing scene editing [32]. A review of related work can be found in the literature [33]. Here, we will focus on those research efforts aimed at speeding up NeRF training.  
Figure 1: The task of 3D reconstruction of satellites using neural radiance fields involves processing a set of satellite images taken from multiple viewpoints along with their associated poses. An MLP (multi-layer perceptron) network is employed to simulate the scene’s color and density attributes. The outcome is the generation of a rendered scene in the form of RGB images, digital surface models (DSM), point clouds, or mesh representations.  
Traditional NeRF methods are generally slow in terms of training speed, which has motivated a series of studies, such as DVGO [34], Plenoxels [35], TensoRF [36], and Instant-NGP [17], which aim to accelerate the training process of the NeRF. DVGO represents the scene using an optimized dense mesh approach, which consists of a density mesh for scene geometry and a feature mesh to capture view-dependent appearance details. Impressively, DVGO's training time on the Synthetic-NeRF dataset is only 15 min, which is a significant speedup over the time required by the traditional NeRF. Plenoxels represent the scene as a sparse 3D mesh with spherical harmonics without any neural network, and the optimization speed is two orders of magnitude faster than the NeRF. TensoRF also adopts an MLP-free strategy by decomposing the 3D scene, storing the scalar density and vector color features in a three-dimensional voxel grid, and expressing them as tensors. By further decomposing these tensors into more compact vector and matrix factors, TensoRF is able to represent the scene in a more compact form and speed up the training process.  
Instant-NGP [17] proposes a multi-resolution hash position encoding technology and utilizes small but efficient MLP to represent the scene. This method achieves an extremely fast training speed. Furthermore, Instant-NGP can even complete the reconstruction task of small-scale scenes within 1 min by adopting an empty space-skipping strategy and efficient CUDA implementation. Most importantly, an Instant-NGP framework is designed to be more flexible, allowing it to be easily expanded to different application tasks. In particular, for large-scene reconstruction, Instant-NGP demonstrates its unique advantages and can effectively handle the details and complexity of large-scale scenes while solving the time and resource consumption problems commonly encountered in processing large-scale scenes.
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
### Motivation for Pronunciation Classification  
In a typical CA learning institute, a single teacher may be responsible for listening to and correcting the mistakes of several dozen learners on a single day. This is quite a laborious job leading to fatigue and is prone to diminished error correction by the instructor over time. Moreover, each student has to wait for their turn to speak or read the learned parts. If this job can be automated by letting the students' work be evaluated through an automated mechanism, it will improve the efficiency of the learning process and the overall productivity of the students and instructors. Moreover, this will open new ways of distant and home-based learning with little reliance on the presence of an instructor. As learning can take place anytime and anywhere, it is required to develop automatic approaches that can detect pronunciation errors and give feedback to the learner.  
It is worth mentioning that in the Arabic language, the correct pronunciation is vital and requires a lot of practice by the learner of the language. Currently, the students not only rely on in-class participation but also practice using digital technology. In this regard, we have evidence of the development of many applications supporting the students' learning process. There is a need for research in developing systems and applications for improving Arabic reading and speaking knowledge. In various parts of the world when there is an unavailability of real instructors, such applications play an important role in supporting the student's learning process. The improved model can easily be integrated with existing applications to increase the accuracy of Arabic short vowels classification in the learning process. The Arabic-speaking countries are a recent place of interest for many tourists around the world. Therefore, many people are interested to learn this language for their survival in this part of the world. Any applications developed using better speech recognition models will be beneficial for people who want to learn Arabic using the available online resources.
# High-Rise Social Housing in Hot-Humid Climates: Towards an 'Airhouse' Standard for Comfort
## 3 Results
### Computer Modelling and Comparison  
The simulation modelling using IES Virtual Environment (IESVE) software confirmed that the actual climatic conditions in Kuala Lumpur are not able to create the right comfort by natural means in indoor and outdoor spaces as required by the established standards. In a year, approximately 70% of the time, the operative temperature in indoor spaces in the PPR second generation housing is above 28 \({}^{\circ}\)C, and 85% of the time the relative humidity is above 65% RH. The simulation has also confirmed that the RH in inward-facing rooms and lightwells is high, especially at levels 1 and 20. There is a weaker stack effect in place inside the light-wells. The average operative temperature and relative humidity in PPR second generation housing is in accordance to the simulation, namely, 29.1 \({}^{\circ}\)C and 66.4% RH, respectively (Table 6). The temperature is similar to the results from the fieldwork and still surpasses the ASHRAE and CIBSE requirements. The RH is slightly lower, but still in the higher range of the requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PPR Second-Generation** & **Mean OT (\({}^{\circ}\)C)** & **Mean RH (\%RH)** \\ \hline Ranges & 24.0–28.4 \({}^{1}\) & 40.0–70.0 \({}^{2}\) \\ \hline Fieldwork Studies (1 and 2) & 29.3 & 70.1 \\ IES Simulation 1 (Full-year) & 29.1 & 66.4 \\ \hline \hline \end{tabular} Note: i. \({}^{1}\)ASHRAE 55, \({}^{2}\) CIBSE Guide A.  
\end{table}
Table 6: Fieldwork studies and IES 1 results for the PPR second generation housing.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Case Studies** & **Mean PM10 (\(\upmu\)g/m\({}^{3}\))** & **Mean PM2.5 (\(\upmu\)g/m\({}^{3}\))** & **Mean CO\({}_{2}\) (ppm)** \\ \cline{2-4}  & **Indoor (Outdoor)** & **Indoor (Outdoor)** & **Indoor (Outdoor)** \\ \hline PPR 1 st Generation & 25.6 (31.5) & 29.2 (36.3) & 590 (498.4) \\ PPR 2 nd Generation & 53.5 (49.9) & 44.2 (42.3) & 600 (529.9) \\ \hline WHO Limits (24 h) & 50.0 & 25.0 & 1000 \({}^{*}\) \\ \hline \hline \end{tabular}  
\({}^{*}\) Department of Occupational Safety and Health (DOSH) Malaysia [16].  
\end{table}
Table 5: Findings of the second fieldwork study.  
The fieldwork studies and simulation identified the four issues that need to be addressed in achieving indoor comfort in high-rise social housing buildings in Kuala Lumpur: (1) Reducing the air temperature, (2) reducing humidity, (3) filtering airborne particles, and (4) providing adequate and constant air movement.
###### Abstract  
The energy consumption of a constructed facility is a primary concern as a result of its impact on the total energy expenditure. It has been found that up to 70% of the power consumption in Saudi Arabia are caused by building structures and air conditioning (AC). Energy consumption in government-constructed buildings constitutes a considerable \(\approx\)13% of the consumption of the total energy in Saudi Arabia. Therefore, the government of Saudi Arabia initiated the Saudi Energy Efficiency Program (SEEP) that goals to lower the domestic energy severity by roughly 30% by 2030. This paper introduces a study carried out in Eastern Province in Saudi Arabia to identify factors influencing the consumption of energy in school facilities (which are built of concrete in hot and humid climate zones), investigate the correlation between those factors and their impacts on the consumption of energy in school facilities, and finally, develop a prediction model for the energy consumption of school facilities. The study was based on the utilization of 352 real-world datasets of energy consumption of operating schools across Eastern Province in Saudi Arabia. The developed energy prediction model considers eleven identified factors that influence the consumption of energy of constructed schools. The identified factors were utilized as input variables to build the model. A systematic search among different neural network (NN) design architectures was conducted to identify the optimal network model. Validation of the developed model on eight real-world cases demonstrated that the accuracy of the developed model was about 87.5%. Moreover, the findings of this study indicate that the weakest correlation between the input variables was recorded as \(-\)0.015 between \"type of school\" and \"AC capacity,\" while the strongest correlation was recorded as 0.95 between the variables of \"number of classrooms\" and \"total air-conditioned area (sqm),\" followed by \"total air-conditioned area (sqm)\" and \"number of students,\" which was recorded as 0.90. It is worth noting that \"AC capacity\" was the most significant predictor, which increased exponentially for high values of energy consumption, followed by \"total school roof area.\" The study also found that the age of the schools had a very small impact on energy consumption, although the age of the schools varied from 11 to 51 years. This was probably due to a good maintenance system applied by the Ministry of Education. The implication of the developed prediction model was that the model can be used by the Ministry of Education to predict the energy consumption and its associated cost for public school buildings for the purpose of budget allocation. The model may be utilized as a stand-alone application, or it can be integrated with an existing building information module (BIM)-based system.  
artificial neural network; consumption; energy prediction; factors; school buildings +
Footnote †: journal: Applied Soft
## 5 Results
### Influence of Wind Fluctuating Effect
#### 5.2.1 Fragility Analysis  
The nonlinear static and dynamic analyses of 30 groups of uncertain models under combined ice and wind loads are carried out, and the corresponding fragility surfaces are obtained, as shown in Figure 14. Figure 15 presents the fragility curves of the tower-line system under different ice thicknesses. It can be seen that the fragility surface considering the fluctuating effect is located above the fragility surface of the static analysis. The maximum exceedance probabilities of static analysis and dynamic analysis are 77.58% and 88.23%, respectively, and the difference reaches 12.06%, which indicates that the fragility of the tower-line system increases significantly after considering the fluctuating effect.  
Table 7 compares the maximum and minimum exceedance probabilities of the transmission tower-line system under both static and dynamic loads. When the ice thickness is 5 mm, the differences between the maximum and minimum exceedance probabilities at the three wind speeds are 0.70%, 7.39%, and 24.83%, respectively. And under the same wind speed, the difference gradually increases with the increase in ice thickness. Additionally, the difference between the exceedance probabilities obtained from the static and the dynamic analyses is more evident than the influence of uncertainty parameters. This phenomenon indicates that the fluctuating effect has a more significant effect on the fragility of the tower-line system.  
Figure 14: The comparison of fragility surfaces.  
Figure 15: The comparison of fragility curves.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 4 Baseline Clustering Strategies under Jamming
### Cooperation Domain
#### 4.2.1 Cooperation (CP)  
In the cooperation case, the nodes can retransmit their data using relay nodes until reaching the CH. However, they use a fixed power. The nodes evaluate the best cooperation route using the Minimum Receiver Sensitivity (MRS) metric to build the retransmission chain. Several nodes can cooperate in this scheme, but the selection between the different routes is based on the \(SINR\) value of the weakest link of each route. Once the link with the lowest \(SINR\) in each route has been identified, the \(SINR\) values of these worst links in each route are compared and the route corresponding to the one with the highest \(SINR\) is chosen, i.e., the route with the best worst link. Since in this scheme it is assumed that all nodes use the same transmit power, the smaller (greater) the Euclidean distance between nodes, the greater (smaller) the \(SINR\), as long as the distances from the jammer to the potential relays are similar.  
Summarizing, for the cooperation scheme, we have the following considerations and assumptions  
* All the nodes can assume the role of a relay node in the routing chain.  
* The Euclidean distance is used to find the optimum route considering the distance and hops between the isolated nodes and the CHs.
* If two cooperating nodes have the same Euclidean distance, the first node found by the algorithm is chosen.  
In this strategy, the nodes that by direct connection with the CH meet the _SINR_ threshold (i.e., those that can directly transmit their messages) are considered associated. Then, each isolated node evaluates if the closest associated neighbor serves as a relay, according to the _SINR_ of that link. As new nodes become associated, those isolated ones evaluate new associations that imply more hops. This action is repeated until all nodes can associate or at least all nodes that can associate according to the preset transmit power.
## 2 Materials and Methods
### Matrix Metalloproteinase (MMP-1) Collagen Inhibition Assay  
Matrix metalloproteinase-1 (MMP-1) is an interstitial collagenase or fibroblast collagenase. These enzymes play a significant role in targeting collagen, gelatine, entactin, pro-TNF-\(\alpha\), and the chemokine SDF-11-4. The MMP-1 Colorimetric Drug Discovery kit was purchased from Enzo Life Sciences (BML-AK404). This kit is a complete assay system designed to screen MMP-1 inhibitors using a thiopeptide as a chromogenic substrate (Ac-PLG-(2-mercapto-4-methyl-pentanoyl)-LG-OC\({}_{2}\)H\({}_{5}\)). During this assay, the MMP cleavage site peptide bond is replaced by a thioester bond in the thiopeptide (substrate), which produces a sulfhydryl group that reacts with DTNB (5,5\({}^{\prime}\)-dithiobis (2-nitrobenzoic acid)). The final product was detected at a wavelength of 412 nm. During experimentation, the kit and reagent were thawed to room temperature from \(-80\)\({}^{\circ}\)C and were kept in a cool environment. This is to ensure that maximal enzymatic activity is retained. Initially, in a separate vial, 1.0 \(\mu\)L of inhibitor (MMP, N-isobutyl-N-(4-Methoxyphenylsulfonyl) glycyl hydroxamic acid) was diluted with 199.0 \(\mu\)L of assay buffer (500.0 mM HEPES, 10.0 mM CaCl\({}_{2}\), 0.05% Brij-35, and 1.0 mM DTNB; pH 7.5). Subsequently, a substrate solution was prepared by diluting 6.4 \(\mu\)L of the substrate (thiopeptide, Ac-PLG-(2-mercapto-4-methyl-pentanoyl)-LG-OC\({}_{2}\)H\({}_{5}\); 100.0 mM) with 153.6 \(\mu\)L of assay buffer in a separate tube. Next, an enzyme solution was prepared by diluting 1.0 \(\mu\)L of the MMP-1 enzyme (human recombinant) with 40.0 \(\mu\)L of assay buffer. To ensure collagenase inhibition activity, 20.0 \(\mu\)L of the _Melastoma_ extract mixture was prepared and diluted with 65.0 \(\mu\)L of buffer solution in a 96-well plate. Next, 20.0 \(\mu\)L of the enzyme was added to each respective well containing an active extract, followed by incubation for 30 min at a reaction temperature of 37 \({}^{\circ}\)C to allow interaction of the inhibitor and enzyme. Then, 10.0 \(\mu\)L of the substrate solution was added to each well, and the absorbance 412 nm was monitored for 10 min. The blank, 90.0 \(\mu\)L of the assay buffer, and 10.0 \(\mu\)L of the substrate were added to the well. For the positive and negative control, 20.0 \(\mu\)L of the test inhibitor was added to 10.0 \(\mu\)L of the enzyme and 10.0 \(\mu\)L of the substrate, except for the negative control; the test inhibitor was excluded from the well.
## 4 Simulation and Results
### Incorporation of Uncertainty in the Model
This increase conveys a condition that the demand is at a high level and the solar radiation is at a low level. Therefore, the worst conditions must be contemplated in the design of the microgrid. However, as it is evident, the cost is drastically \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bus** & **Location** & **PV Size** & **Diesel Size** & **Number of Batteries** & **Cost (€)** \\ \hline
33 & 2 & 395 & 215 & 42 & 70,342 \\
69 & 6 & 400 & 214 & 39 & 69,214 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of optimal sizing of test grids incorporating uncertainties of demand and solar radiation and the consideration of the maximum cost of stochastic scenarios as the objective function. Figure 14: The upper bound, the lower bound, and the mean value of daily solar irradiance in the probabilistic study. increased by 50% in comparison with the case of the deterministic model (regardless of uncertainties). Thus, it should be noted that the overall cost of the microgrid scheme may be enhanced extensively for uncertain problems. It is important to pay attention to a critical question. The cost of supply has increased by 50% in order to include uncertainties. It conveys a high level of consumption and a low level of generation. The occurrence of such generation and consumption levels for this system at the targeted time is not a definitive and certain event. The question is whether it is sensible to increase the cost of the system by 50% for a forecast that has an occurrence probability of 10%. In the procedure of seeking the optimum solution of uncertain parameters, for each suggested answer provided by the optimization program, all possible scenarios are taken into account, and the highest cost incurred to the system, among all scenarios, is regarded as the cost of the suggested answer. If the suggested answer does not satisfy even one or more constraints, that answer should be dismissed. Such a procedure profoundly mounts the system cost. In order to tackle this problem, no answer should be dismissed, and the system cost must be obtained for all answers in as scenarios, even when they were unable to meet some constraints. Finally, the average cost of all scenarios is dedicated to the answer. Hence, if the suggested answer does not meet the constraints in some scenarios, the answer will not be dismissed. It is obvious that, for the problems with a few scenarios, the inclusion of scenarios, which used to be disregarded, does not have a remarkable impact on the solution of the problem. With consideration of the proposed condition, the optimal results of the simulation can be expressed in Table 5. This assumption has been led to an increase of 26% in the system cost for the incorporation of uncertainties in the model. It is just over 20% fewer than the normal approach. Thus, the uncertainties can be included in the design by incorporation of the average cost of the system. As can be seen, the system cost has increased by 26% in comparison with the deterministic approach.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 5 Testing
### Various Testing Results  
After testing the tool's performance at different times, different results have been obtained, as shown in Table 2. In Table 2, different plain text has been taken, and each line text's length has been identified. The length of the plain text has been represented with \"P\". The algorithms have been then implemented on the plain text, and various encrypted texts have been obtained. The user-defined key has been implemented on the encrypted text, and various ciphertexts have been obtained. The length of the cipher text has been represented with \"X\".  
Figure 46: Plain Text.  
Figure 47: Testing 1.  
After obtaining the ciphertext, the length difference between the ciphertext and the plain text is determined. When an attacker tries to decrypt the data, the attacker tries to decrypt each character, but when the ciphertext length is more than the plain text, the attacker will obtain the data derived in parallel for each character, which will be incorrect.
## 7 Challenges of Edge AI in Early Health Prediction and Future Directions
### Privacy and Security
#### 7.1.2 Security  
Here are some key aspects to consider:  
* **Security challenges:**
* **Data breaches:** Edge AI systems store and process sensitive health data, making them potential targets for cyberattacks. Robust security measures must be in place to prevent unauthorized access and data breaches.
* **Secure communication:** Edge AI systems often rely on transmitting data between devices and cloud servers. Ensuring secure communication channels and encryption protocols is vital to protect data during transmission.
* **Adversarial attacks:** Edge AI models can be vulnerable to adversarial attacks, where malicious actors manipulate input data to deceive the system. Developing robust defenses against such attacks is crucial to maintaining the integrity of early health prediction.  
Future research directions to address the security challenges in Edge AI for early health prediction include:* **Robust authentication:** Exploring advanced authentication mechanisms, such as multi-factor authentication and biometrics, to enhance the security of Edge AI systems and prevent unauthorized access.
* **Secure hardware and firmware:** Enhancing the security of edge devices, such as wearables and IoT devices, by implementing secure hardware components and regularly updating the firmware to mitigate potential vulnerabilities.
* **Intrusion detection and prevention:** Developing sophisticated intrusion detection and prevention systems specifically designed for Edge AI in early health prediction. This helps detect and mitigate potential security breaches.
* **Resilient AI models:** Designing AI models that are resilient to adversarial attacks and can identify and reject manipulated input data, ensuring the accuracy and trustworthiness of early health predictions.
## 3 Proposed Scheme
### Trusted Data Identifications
```
1 Input:
2 Number of nodes \(K\)
3 Total number of channels \(M\)
4 Threshold Value \(V_{th}\)
5 SNR between all possible links \(SNR_{1}\), \(SNR_{2}\), \(SNR_{3},\ldots SNR_{M}\),
6 Trust value calculated by node itself \(T_{1}^{lf},T_{2}^{lf},T_{3}^{lf},\ldots,T_{K}^{lf}\)
7 Trust value computed by neighboring nodes \(T_{nx}^{lf}\)
8 for\(i=1\) to \(K\)do
9 Calculate \(\sigma S_{i}\)
10 if\(\sigma S_{i}\leq V_{th}\)then
11 Malicious node
12 end if
13 else
14 Legitimate node
15 end if
16\(i++\)
17
18 end for
```  
**Algorithm 1**Trust-evaluating algorithm. Figure 4: Trust management procedure to determine legitimate nodes in the proposed scheme.