esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs  
The placement of mirrors in a VLC system plays a pivotal role in enhancing signal propagation, especially in scenarios where LoS communication is obstructed. By strategically positioning planar or convex mirrors, reflected signals can be optimized to improve the overall SNR at ADRs. However, determining the optimal positions of these mirrors in a dynamic and complex indoor environment is a challenging task, particularly when considering the time-varying conditions of the channels, reflection properties, and signal combination schemes.  
To address this challenge, a recurrent neural network (RNN) is proposed as a dynamic optimization tool. The RNN captures temporal dependencies in the received signal powers at ADR photodiodes and iteratively adjusts mirror positions to maximize the combined SNR. The optimization framework supports both MRC and SBC schemes, ensuring adaptability to various system requirements.  
This section presents the mathematical formulation of the RNN-based optimization approach, including detailed models for input signals, hidden state updates, and outputs representing the optimized mirror positions. The loss function, designed to maximize the SNR while ensuring smooth position transitions, is also described. Finally, an algorithm is described to implement and train the RNN for effective mirror placement optimization in planar and convex configurations.
## 1 Introduction
The algorithm showed that the bigger the degree of a node and the fewer connections between neighboring nodes, the more important the target node. In this paper, we propose a novel method called NBF (based on node bridging feature) to identify the importance of nodes in the network. It is a challenging work to rank node importance in complex networks due to its large-scale size and frequently changed topology. We summarize the major contributions by the following four ingredients:  
* The paper presents a node bridging feature in complex networks, which refers to the fact that the greater the number of node pairs whose shortest paths pass through the target node and the less the number of shortest paths in its neighborhood, the more significant bridge function and structure importance the node has;
* We propose a novel node importance identification algorithm based on the node bridging feature, which just needs local information of the target nodes, making the algorithm applicable in large-scale networks;
* Through comprehensive experiments on real and synthetic datasets, the proposed algorithm is demonstrated to outperform a state-of-the-art model compared with five benchmark algorithms on evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter whether in static or dynamic attack strategies;
* The advantage of the proposed algorithm is more obvious when removing a small number of important nodes, which makes the algorithm applicable in the background of limited network attack cost. The outline of this paper is as follows. In Section 2, we present our method to identify node importance and introduce the benchmark algorithms, evaluation criteria and datasets. We make simulation analysis on real and synthetic networks under static and dynamic attacks in Section 3. Finally, a summary and some conclusions are stated in Section 4.
# Evaluation of Resilient Modulus and Rutting for Warm Asphalt Mixtures: A Local Study in Iraq
## 4 Conclusions  
This study compares WMA mixes to HMA mixes commonly used in Iraq and evaluates WMA utilizing two zeolite percentages (0.3% and 0.5%) by weight of the whole mix. The research's findings led to the following conclusions:  
* The optimum asphalt binder content decreases by using zeolite to produce the WMA mixtures. The reduction is more sensible when using 0.3% of zeolite; the reduction is  
Figure 7: Rut depth result values at 40 \({}^{\circ}\)C.  
0.4% compared to the HMA mixture. This reduction can be considered a benefit in the initial cost of the production of the WMA by using a zeolite additive.
* All mixtures satisfied the Iraqi specification limits of Marshall Stability and flow. HMA has a more Marshall Stability value than the WMA, whereas the flow value is lower than the WMA.
* The resilient modulus of the WMA shows lower values than the HMA.
* Statistically, there is no difference between HMA and 0.3% of zeolite WMA at low temperatures (20 and 25) \({}^{\circ}\)C, whereas the 0.5% zeolite WMA mixture has a significant difference from HMA and 0.3% zeolite WMA mixture.
* At high temperatures (30 and 40 \({}^{\circ}\)C), there is a substantial difference between the HMA combination and the WMA mixtures, but there is no significant difference between the two zeolite percentages.
* The rut depth for WMA is greater than the HMA for both zeolite percentages. Despite this result, the WMA mixtures show a good performance for both two percentages of zeolite. Putting resistance performance for the WMA mixture competes with the HMA mixture, indicating that the WMA mixture has good resistance to rutting.
* In all test findings, 0.3% content of zeolite performed better than 0.5% content of zeolite, so it is recommended to use the 0.3% zeolite for the production of WMA mixtures in Iraq.  
Conceptualization, M.Y.F.; methodology, M.M.H. and M.Y.F.; validation, M.M.H. and M.Y.F.; formal analysis, M.Y.F.; investigation, M.M.H.; resources, M.M.H.; data curation, M.M.H.; writing--original draft preparation, M.M.H.; writing--review and editing, M.M.H. and M.Y.F.; visualization, M.M.H.; supervision, M.Y.F.; project administration, M.M.H.; funding acquisition, Not applicable. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that have been used are confidential.  
The authors declare no conflict of interest.
# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 5 Discussion
Analysing the contents of Tables 3 and 4, it can be seen that although the values of selected objective metrics show a strong correlation with the results of subjective research, the coefficients of determination for models based on these metrics are lower. For example, the highest correlation coefficients were obtained for the metrics SSIM (r = 0.98) and VQM (r = 0.99), and the quality models built based on these metrics correctly predicted 89% (COD = 0.89) and 91% (COD = 0.91) of the results, respectively. The results of the video quality prediction obtained thanks to the machine learning model implemented by the authors are characterised not only by a very high correlation coefficient with the subjective evaluation of the users (r = 0.99) but also by a high coefficient of determination at the level of 97% (COD = 0.97).
## 5 Discussion
### Advantages of the Proposed Approach

Table 6: _Cont._by simulating various scenarios, leading to better resource allocation and increased productivity. Industry 5.0-oriented DTs emphasize human-AI collaboration, enhancing worker safety and productivity by providing AI-assisted decision support. The implementation of cyber-physical security measures ensures data integrity and protection against cyber threats, making DTs more reliable. AI-based DTs facilitate mass customization by dynamically adjusting production parameters to meet individualized consumer demands. The scalability of these approaches allows for integration across multiple industries, including automotive, aerospace, pharmaceuticals, and energy sectors. Multi-agent DT systems enable interconnected decision making, improving supply chain management and logistics. The ability to simulate and optimize energy consumption in industrial processes contributes to sustainability and cost savings. AI-enhanced DTs streamline compliance with industry regulations by automatically monitoring and adjusting processes to meet safety and environmental standards. The combination of reinforcement learning and generative AI enables the creation of highly adaptive DTs capable of responding to unforeseen disruptions. Finally, the integration of quantum computing in AI-based DTs has the potential to accelerate complex simulations, unlocking new possibilities for industrial process innovation [82; 83; 84; 85; 86]. These findings bridge the gap between theoretical innovation and practical implementation, accelerating the responsible evolution of digital twins in real-world environments. These findings have significant practical implications, offering practical implications for implementing AI-based digital twins across various industries. By improving predictive accuracy and making real-time decisions, these systems can optimize operations in manufacturing, energy, healthcare, and logistics. Integrating AI enables digital twins to dynamically adapt, reducing downtime, improving maintenance strategies, and minimizing operating costs. Policymakers can use these findings to establish standards and guidelines that ensure the safe, transparent, and ethical use of digital twins. In industrial applications, the findings support the development of more resilient supply chains and streamlined production lines through improved simulation and scenario testing. For researchers, the results highlight key areas where AI models require further refinement to better represent complex physical systems. The work also highlights the importance of data quality and interoperability, which are key to scalable and portable digital twin applications. Future AI-based DTs could incorporate the study's recommendations to improve personalization and generalization and reduce bias, supporting broader adoption across various sectors. Furthermore, these findings could inform workforce training and retraining initiatives, ensuring operators can effectively interact with AI-based systems.
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 5 Experiment and Results
### Data Collection and Blockchain Integration  
Data collection and blockchain integration were central to the experiment. For instance, a humidity sensor in factory B recorded 65% at 12:00:05 p.m., highlighting the importance of continuous data collection for proactive monitoring and the early detection of potential issues. Each data packet was assigned a unique SHA256 hash at the edge nodes, ensuring tamper-proof identification. Any alteration in the data would change the hash, exposing fraud, while AES-256 encryption safeguarded the data from unauthorized access. The processed data was then stored on the blockchain, with each transaction encapsulating details such as the device ID, sensor data, timestamp, and data hash. Table 3 provides an example of a typical transaction structure. In contrast, Table 4 expands on the blockchain transaction structure by providing additional examples of how data packets are stored.  
\begin{table}
\begin{tabular}{l} \hline \{ \\ “device\_id”: “device\_001”, “sensor\_data”: [“sensor\_type”: “temperature”, “value”: 25.4], “timestamp”: \\ “2023-04-08T12:00:00”, “data\_hash”: “a1b2c3d4\_...” \\ \hline \end{tabular}
\end{table}
Table 3: Translation structure.  
Figure 6: Distributed IIoT architecture with decentralized edge processing and blockchain integration.
# Effects of Simulated Nitrogen Deposition on the Bacterial Community of Urban Green Spaces
###### Abstract  
Continuing nitrogen (N) deposition has a wide-ranging impact on terrestrial ecosystems. To test the hypothesis that, under N deposition, bacterial communities could suffer a negative impact, and in a relatively short timeframe, an experiment was carried out for a year in an urban area featuring a cover of Bermuda grass (_Cynodon lactylon_) and simulating environmental N deposition. NH\({}_{4}\)NO\({}_{3}\) was added as external N source, with four dosages (N0 = 0 kg N ha\({}^{-2}\) y\({}^{-1}\), N1 = 50 kg N ha\({}^{-2}\) y\({}^{-1}\), N2 = 100 kg N ha\({}^{-2}\) y\({}^{-1}\), N3 = 150 kg N ha\({}^{-2}\) y\({}^{-1}\)). We analyzed the bacterial community composition after soil DNA extraction through the pyrosequencing of the 165 rRNA gene amplicons. N deposition resulted in soil bacterial community changes at a clear dosage-dependent rate. Soil bacterial diversity and evenness showed a clear trend of time-dependent decline under repeated N application. Ammonium nitrogen enrichment, either directly or in relation to pH decrease, resulted in the main environmental factor related to the shift of taxa proportions within the urban green space soil bacterial community and qualified as a putative important driver of bacterial diversity abatement. Such an impact on soil life induced by N deposition may pose a serious threat to urban soil ecosystem stability and surrounding areas.  
Nitrogen deposition; bacteria; soil biodiversity; urban; 165 rRNA +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Defect Localization and Error Discussion  
Once the decorrelation coefficients and kernel function have been calculated in the defined time domain, the localization is found to be in [270 mm, 265 mm] based on Equations (4)-(7), as shown in Figure 7. A further comparison is made among different time windows in Figure 8. The localization error varies from 13.5% to 6.7%. It decreases initially due to the increasing interaction between the diffuse wave and the defect. The direct reflection wave is estimated to arrive at the defect position at \(t_{\mathit{reflection}}=\frac{255+(255-80)}{8000}=53\) us. Subsequently, the localization error increases because of the lowering sensitivity of the waveform to the defect shown in Figure 5, which may be caused by the reflection and attenuation. Although they suffer a lot from attenuation, which limits the inspection domain, a meaningful signal can still be extracted.  
Figure 8: Localization error varied with time window.  
Figure 7: Localization image when \(t=45\) us.  
In addition to the severe signal-to-noise ratio degradation due to viscoelasticity, the moderate defect localization deviation mainly comes from two aspects. Theoretically, wave diffusivity and dissipation parameters are assumed constant in the kernel calculation model, which varies according to the directional angles. This approximation contributes to the error in the kernel calculation and grid search procedure in Equation (6). Experimentally, the signal obtained from each pair is the averaged information from the sensor's field of view. Localization sensitivity may be lost to some extent so that error is introduced as well. The sensor diameter and distribution could be optimized in the future between the direction sensitivity and inspection efficiency in order to obtain high defect localization precision.
## 4 Proposed Framework
### Layer 4--Anomaly Detection
```
1:Elasticsearch connection parameters definition (index, es)
2:INPUT:\(df\_benign\_data\gets read\_from\_elastic(index,es)\)
3:\(df\_train\_data\gets 80\%\) of \(df\_benign\_data\)
4:\(df\_test\_data\gets 20\%\) of \(df\_benign\_data\)
5:\(window\_size\gets 6\) (defined arbitrarily)
6:\(n\_features\leftarrow\) number of columns of \(df\_benign\_data\)
7:\(train\_data\leftarrow MinMaxScaler(df\_train\_data)\)
8:\(train\_data\_wz\gets sliding\_window(train\_data,window\_size)\)
9:\(ds\_train\_full\leftarrow\) Generate tensorflow dataset from \((train\_data\_wz)\)
10:\(ds\_train\gets 95\%\) of \(ds\_train\_full\)
11:\(ds\_validation\leftarrow\) 5% of \(ds\_train\_full\)
12:\(model\leftarrow\) Model definition
13:\(training\_parameters\leftarrow\) Training parameters
14:While training_parameters are satisfied Do:
15:\(model.fit(ds\_train,ds\_validation,training\_parameters)\)
16:EndWhile
17:Save trained model
18:\(test\_data\gets MinMaxScaler(df\_test\_data)\)
19:\(test\_data\_wz\gets sliding\_window(test\_data,window\_size)\)
20:\(ds\_test\leftarrow\) Generate tensorflow dataset from \(test\_data\_wz\)
21:\(df\_pred\gets model.predict(ds\_test)\)
22:Calculate value difference (error) between \(df\_pred\) and \(ds\_test\)
23:OUTPUT: Save error threshold for the confidence intervals (1, 0.995, 0.99, 0.98, 0.97)
```  
**Algorithm 1** Machine learning module processing (training mode)  
The preprocessing of each dataframe involves operations such as data normalization and the creation of sliding windows containing sequences of system calls. The system call windows consist of a data structure that stores sequences of system calls of multiple sizes and is a common approach for system call analysis [33]. The choice of the size of the system call windows is a factor that directly influences the detection results, with the literature recommending window sizes in the range of 6 to 10 [13; 15; 43; 44]. At the end of the processing performed in the machine learning module, the result of anomaly detection for each model prediction is recorded in an index created in Elasticsearch (OUTPUT line in Algorithm 2), where it can be graphically viewed and analyzed by the SOC team through the Kibana tool or through Python notebooks [33].
# SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment
## 1 Introduction
### Smart Homes  
Smart homes are getting popular due to two factors. First, sensing and actuation techniques as well as wireless sensor networks, have dramatically advanced. Second, nowadays, individuals rely on technology to answer their worries about their quality of life and home security. Intelligent and automated services are provided via a range of IoT-based sensors in smart homes, which help individuals who forget to automate daily duties and maintain routines and can save energy by automatically shutting off lights and electrical gadgets. Motion sensors are employed for this purpose and security can also be achieved. Sensors collect data from the environment by conserving energy (light, temperature, humidity, gas, fire events).  
The data from the heterogeneous sensor is given to the context aggregator, which then transmits it to the context recognition service engine. This engine chooses services depending on their context. When the humidity rises, for example, an application can automatically switch on the air conditioner. If there is a gas leak, you can also switch off all of the lights. Smart home applications are highly beneficial to the elderly and the disabled. Health can be monitored and professionals can be notified immediately in the event of an emergency. The floor is outfitted with pressure sensors, which aid in tracking a person's activity in a smart home and detecting falls. CCTV cameras may be used in smart homes to record interesting occurrences. There are countless challenges and questions about smart home applications [8]. Security and privacy are of paramount importance, as all data about what is happening at home is recorded [9]. An intruder can attack the system and cause it to behave maliciously if its security and dependability are not ensured. When such abnormalities are noticed, smart home systems are meant to warn the owner.  
In continuation to smart homes, there are IoT-based applications that govern with smart homes. The role of the most relevant applications to the proposed systems are discussed as follows.
# Optimized Architecture for Efficient OFDMA Network Design
## 6 Conclusions  
This research presents a significant step forward in the validation and robustness assessment of analytical models tailored for WiMAX systems. By focusing on diverse scheduling algorithms, our approach allows for precise calculation of performance metrics essential for  
Figure 14: The average instantaneous throughput of active users during an ON period for the different channel models in the Throttling regime (\(N=10\), \(x_{\alpha n}=3\) Mbits, \(t_{\alpha\beta}=3\) s, and MSTR = 2048 Kbps).  
efficient network dimensioning. Through the development and implementation of a custom simulator, we successfully validated the analytical model under controlled conditions and extended its robustness testing with more realistic, dynamic network scenarios.  
The comparison between the simulated performance curves and the analytical model reveals an impressive alignment, highlighting both the accuracy and computational efficiency of the model. This not only reduces the simulation time but also confirms the model's adaptability across various channel conditions, traffic distributions, and network loads. Our results validate the model's capability to consistently deliver reliable outcomes in increasingly complex and heterogeneous environments.  
However, this work represents just the beginning of broader possibilities. Expanding the model to integrate real-time traffic alongside elastic traffic would open new avenues for understanding the nuanced interactions between different service types, enabling a more holistic view of system behavior. Incorporating parameters like delay and jitter would further enhance the model's applicability to latency-sensitive applications, addressing critical requirements for modern networks.  
Looking ahead, an exciting avenue for future exploration is adapting this model to LTE and next-generation systems. Investigating the interactions within multicellular networks, particularly how adjacent cells influence each other under varying load conditions, would provide deeper insights into resource management at scale. By expanding the analytical framework to embrace these additional complexities, we can continue to push the boundaries of what's possible in optimizing wireless networks for future technological advancements.  
Conceptualization, S.B.B. and R.B.; methodology, S.B.B.; software, S.B.B.; validation, S.B.B., A.Z. and S.D.; formal analysis, A.Z.; investigation, S.D.; resources, A.Z.; data curation, A.Z.; writing--original draft preparation, S.B.B.; writing--review and editing, S.D.; visualization, S.D.; supervision, R.B.; project administration, R.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data availability Statement: Data is contained within the article.  
We acknowledge the Deanship of Scientific Research at Shaqra University and the InnovCom laboratory for supporting this work. The authors declare no conflicts of interest.