esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Reaction Mechanisms and Kinetics of Electrocatalytic Nitrate Reduction
### DFT Calculation as an Advanced Tool for eNO\({}_{3}\)R
#### 2.2.3 Nitrate to Nitrogen  
In the context of nitrogen formation, DFT computes the adsorption energy of nitric oxide on catalytic surfaces. Equally essential is the understanding of the intricate process of NO transfer from surface sites to the bulk solution, involving the assessment of energy profiles associated with desorption. Furthermore, the energetics of nitrogen-nitrogen (N-N) bond formation are of paramount significance [71]. The rational modification of catalyst structures can regulate the adsorption strengths between the active intermediates and the catalyst sites, suppressing competitive reactions and promoting the selectivity of the target product. [PERSON] et al. [63] developed an electrocatalytic material called Meso-Fe-N-C, consisting of atomically dispersed iron coordinated with nitrogen on a mesoporous carbon framework, which greatly improves the selectivity of eNO\({}_{3}\)R. They found that the generated *NO, which is a key intermediate for the formation of N\({}_{2}\) or NH\({}_{3}\)/NH\({}_{4}^{+}\), reacts with dissolved NO in solution to produce *N\({}_{2}\)O. Then, the latter can be easily reduced to *N\({}_{2}\) and desorbed from the active sites. They confirmed that the successive hydrogenation of *NO forms *NOH, *NH\({}_{2}\)OH, and finally *NH\({}_{3}\). Moreover, the study concluded that the Fe-N active site is more thermodynamically favorable than Fe-Fe for N\({}_{2}\) emergence. Furthermore, the confinement effect of mesoporous carbon is also a significant factor contributing to selective N\({}_{2}\) generation, mainly through two aspects: geometry distortion of surface Fe-N-C active sites and modified the distribution of NO concentration in mesopores [63].
## 4 A Case Study
The gap is calculated as \(Gap=(random\_scenario-\mathit{constructive\_scenario})/random\_scenario\), since the goal of CDP is to maximize the minimum distance between each pair of facilities while meeting the required capacity, a negative distance gap indicates that the algorithm performed better. Table 2 shows a generally better performance of a constructive scenario. Since both scenarios had almost the same demand to cover, the constructive procedure used less facilities to cover all required capacities and maximized the distances between each pair of facilities when compared to the random scenario. The greatest differences in the average distance are achieved in a very fluid \((-43.03\%)\) and fluid \((-26.22\%)\) state where the constructive scenario utilized 7 and 13 facilities to satisfy the required demand with the average distances of 3127.12 and 1521.98 m, respectively, while the random scenario used 10 and 19 facilities  
Figure 11: Very Dense, 34 Selected Facilities. Figure 10: Dense, 24 Selected Facilities. with the distance of 2186.30 and 1205.78 m. Additionally, the gap decreased to \(-19.12\%\) and \(-4.24\%\) when the traffic state shifted to the dense and very dense situation. When the traffic state changes from very fluid to very dense, a general decreasing trend in solution quality is identified, requiring a high capacity percentage to open more facilities. As a result, the constructive heuristic outperforms the random scenario in terms of meeting the required capacity with the fewest facilities while maximizing distances between them. Figure 12 depicts the differences between the random and constructive scenarios, demonstrating that the constructive version clearly outperforms the random version in terms of distance and selecting the number of facilities.
## 1 Introduction
The results showed an adequate prediction accuracy for both Chl-a (R\({}^{2}\) = 0.85, RMSE\({}_{\text{p}}\) = 48.57) and TSS (R\({}^{2}\)= 0.80, RMSE\({}_{\text{p}}\) = 19.55). [PERSON] et al. [29] conducted a study aimed at providing the capability of remote sensing technology in mapping water quality parameters (suspended sediments (turbidity), chlorophyll and temperature). As a result, in situ measurements have been used to assess water quality, and empirical relationships between spectral properties and water quality parameters have been established. Another study was carried out by [PERSON] et al. [30] in the USA at three lakes in the Great Salt Lake surface water system (namely the Great Salt Lake, Farmington Bay and Utah Lake), the objective of which was to improve techniques for the development of algal mapping models through the use of field sampling methods. This study has shown that Landsat, SENTINEL-2 and MODIS sensors are suitable for monitoring water quality in the lake system. In some cases, temporal variability may be an obstacle to detecting short-term events, but it may be sufficient in other areas where short-term variability is lower. In Morocco and in another context, [PERSON] et al. [34] conducted a study in the Tafilalet plain aiming at modeling and mapping soil salinity through Landsat Oli images. The results showed a strong fitting of this technique with R\({}^{2}\) of models ranging from 0.53 to 0.75 and root mean square error of 0.62 to 0.82 dS/m. [PERSON] et al. [3] carried out a study aiming at estimating and mapping the water quality parameters in the Bin El Ouidane reservoir through better understanding the relationship between the latter and digital data. The correlation results showed that all the studied parameters have an R\({}^{2}\) greater than 0.52 and that they can be transformed into predictive models by stepwise regression. This work carried out at the Bin El Ouidane reservoir is of considerable importance for the water resource managers of the Oum Er-Riba Hydraulic Agency. Thus, the present study was carried out at the level of the Hassan Addakhil dam, in collaboration with the Guir-Ziz-Rheris Hydraulic Basin Agency (HBAGZR). It aims at the validation of this method and the strengthening of its results by comparing them with other studies in the same context. The objective of this study is to conduct modeling and spatiotemporal mapping of water quality of the Hassan Addakhil reservoir by combining the high spatial resolution data (Sentinel-2) and field measurements. In fact, 20 samples were collected on 14 March 2021, at the same Sentinel-2 satellite transit time. Measurements of nitrate, dissolved oxygen and Chl-a were carried out. Next, a statistical study was performed to select the bands correlated with the quality measurements, and a stepwise regression analysis was elaborated to model each parameter. Finally, a spatiotemporal mapping was made for water quality.
# Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases
## 2 Methods
### Experimental Validation  
To validate the stress performance (max Von Mises stress) of the optimized bike crank arm result, an experimental study is conducted. A three-dimensional printed model is used to create the crank arm model obtained from topology optimization precisely. Three-dimensional printing is used in this research since it is the most suitable and efficient method to validate such a topology optimization model, as previously done by [PERSON] et al. [28] and [PERSON] et al. [29]. The material for 3D printing is the polylactic acid (PLA) plastic which is well-known and widely used in the 3D printing field. It is also known that the PLA is not a material used to fabricate a crank arm in a real application. However, it is used in this paper since it offers an avenue to conduct a preliminary validation of the topology optimization result.  
Rosette strain gauges are assembled on the crank to measure the strain after the loading. In the loading process, two different loads are applied, i.e., static and dynamic loads. The static load simply means an application of only one single load to the crank arm. This was done merely to verify the measurement method and validate the static finite element stress result. Next, the dynamic load is applied using real cycling load from a cyclist to the crank arm which has been installed on a bike. The result from the dynamic load test is a time-series data so that the maximum and minimum load during cycling can be observed and recorded.  
The signal from strain gauges is processed using Arduino Nano and sent to the computer or mobile device via Bluetooth connection. Then, the signal is post-processed in the computer using Microsoft Excel to calculate the stress. The employment of Arduino Nano and Bluetooth connection is purposely to neglect the complexities of using too many and long cables during measurement that might give some analog noise to the result which can be seen from the previous study [24]. The stress data from the experiment is then compared with the simulation result from ANSYS with PLA material. Figure 6 below shows the experimental setup of the measurement.  
Figure 6: Experimental setup.  
The obtained strain from the rosette strain gauge \(\varepsilon_{1}\),\(\varepsilon_{2}\),\(\varepsilon_{3}\) are used to calculate the maximum shear stress and Von Mises stress using:  
\[\tau_{\text{max}}=\frac{E}{2(1+v)}\sqrt{2\big{|}(\varepsilon_{1}-\varepsilon_{ 3})^{2}+(\varepsilon_{2}-\varepsilon_{3})^{2}\big{|}} \tag{7}\]  
\[\sigma_{VM}=\sqrt{\sigma_{\text{max}}^{2}+\sigma_{\text{min}}^{2}-(\sigma_{ \text{max}}\sigma_{\text{min}})} \tag{8}\]
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 1 Introduction
### Recent Surveys on AMI and Diseases  
Table 1 outlines previous surveys conducted on the applications of AMI in healthcare, highlighting their main objectives. The authors of [5] addressed the different sensors that can capture varied sensory information, including depth sensors, thermal sensors, radio sensors, acoustic sensors, etc., all of which can be deployed in hospital or daily spaces for monitoring human activities. However, they did not cover methods for identifying abnormal behavior, challenges in dataset acquisition, and the creation of artificial datasets when needed. On the other hand, [6] accentuated case studies through abstract experimentation without highlighting the technical information associated with data processing or model deployment. The impact of AIoT was evaluated in [7] for the healthcare domain. The authors performed a detailed exploration of various facets of IoT advancements specifically tailored to the medical field; however, their approach lacked specificity in addressing specific diseases, and did not emphasize how AMI could handle emergency cases. Moreover, the authors of [8] provided a detailed and comprehensive review of the techniques used to profile activities of daily living (ADL) and detect abnormal behavior for healthcare purposes. They defined abnormal behaviors and provided examples of abnormal behaviors/activities in the case of elderly people. In addition, they described different ambient sensor types and deep learning (DL)-based methods for abnormal behavior detection.  
This paper provides a comprehensive exploration of various sensors along with their usage, benefits and challenges. The contemporary literature addressing the accuracy limitations of these sensors is thoroughly reviewed. Additionally, it includes a brief overview of different communication protocols for the IoMT. A detailed explanation of various datasets is provided, along with an investigation of contemporary research on the fundamental AI principles of classification, segmentation, and detection within the context of AMI applications in healthcare. The primary aim is to offer a comprehensive overview that can facilitate broader integration between AI, AMI, and the IoMT.
## 3 Materials and Methods
### Body Temperature Detection  
Our proposed IoT-based SSDWG has multiple features, and it is divided into two modules. The most prominent part of SSDWG measures the temperature of the human body in real-time in a contact-free manner, and it stores the person's body temperature, along with a picture of a suspected person in our system, as shown in Figure 7, and we can track the most vulnerable persons through recorded temperature data because [6] some of the most common symptoms of COVID-19 are temperature or fever.  
Temperature measuring sensor MLX90614 is used in our proposed SSDWG [33]. It is a non-contact wireless infrared thermometer that is used for human body temperature measurement, and its micro-controller is capable of wireless communications. We proposed contact-free temperature checking mechanisms because it is not violating the social distance SOP's. An image processing module is used to capture a suspected photo whose body temperature is not average. In our proposed model, we placed our temperature measure sensor and image capturing module on the front side of SSDWG. Whenever any person wants to pass from SSDWG, he/she must stand in front of the temperature sensor for 3 s; the temperature sensor detects the human body temperature and contact-free sensor shown in Figure 8. If the human body temperature ia detected at 100.4 Fahrenheit or 38.0 Celsius, or more than this, the temperature sensor passes the indication to the alarm, and the body temperature and photo captured from CCTV are stored in the database [34]; the authors proposed a complete system to auto-detect routine examinations on a teleophthalmology network. They used pathological pattern mining with lesion detection methods to obtain the information from the image.  
Figure 6: Smart Screening and Disinfection Walkthrough Gate (SSDWG).  
Figure 7: Temperature measuring and mask detection module.
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
## 4 Refinement Methods
### Current Limitation Method  
The first method tested was the current limitation method, where the maximum fault current generated by the power system's transformers is limited to a certain value through employing the current limitation applications discussed in Section 2: Literature Review. For this study, the resistive neutral earthing method was employed using neutral earth resistors (NERs), where the NERs limit the fault current passing through the transformer's neutral conductor to the ground using resistive elements. The power system model designed in Section 3: Earth Grid Design Criteria was updated from having a solid neutral to the substation earth grid to having a neutral earth resistor connected between the neutral point and the substation earth grid. Figure 7 shows the supply transformer's secondary winding settings, with the grounding configuration changed to resistor, and the desired value to which the fault current was reduced to is specified as 400 A. This value was then varied between 500 A, 800 A, and 1000 A based on the commonly used NER values in the industry. Figure 8 shows the updated power system model with the NER on the neutral of the transformers.  
Figure 7: ETAP transformer and NER settings.  
The short circuit study was then simulated on the power system for each of the NER values, and the results were then imported to the ground grid design interface, where the earth grid study was conducted for each fault level to assess its impact on the earth grid's performance. Figure 9 shows the steps and results of importing the short circuit results to the substation earth grid model. This process was then repeated for all the predetermined fault currents, and the imported results are shown in Figure 10.  
Figure 8: ETAP power system model—NER application.  
Figure 9: Updated fault current.  
The simulation was then conducted for each fault level resulting from the variation in the NER size, and the simulation results are tabulated in Table 3, showing the impact of limiting the fault current through the NER and its impact on the safety limits of touch voltage, step voltage, and ground potential rise.  
Table 3 shows the results of implementing the current limitation method using the neutral earthing resistor as the power system's supply transformer secondary grounding method of connection to the earth grid as opposed to the traditional method of solid grounding, where the neutral of the transformer is connected directly to the earth grid. From the simulation results, it is observed that the fault current injected into the earth grid is significantly reduced; however, for the application of NERs, care must be taken to ensure that the specified value of the NERs does not affect and influence the operation and sensitivity of the power system's earth fault protection relays. The protection settings of the relays must be set in such a manner that the relay will still be able to detect and clear undesired earth fault currents within the power system.
## 3 Design  
We propose a distributed file-encryption scheme that integrates Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and AES-CTR-based encryption for cloud systems composed of a master node and multiple slave nodes. The scheme enables quantum-resilient key distribution and scalable encryption performance through offset-aware partitioning and parallel encryption processing. The design is structured as a layered cryptographic stack with clearly separated roles and interfaces among PQC, QKD, and AES, which avoids redundant coverage and enables modular composition of secure components.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 5 Discussion and Issues
Another issue relates to the object transformation. In multimedia objects, each file has different formats, which may result in watermarked data being lost during configuration change. Therefore, the effect of object transformations on watermarked data needs further analysis to understand their behaviours. In terms of the performance metrics, most of the watermarking techniques for which their medium type represents an image are evaluated by the MSE, PSNR, BER, and SNR metrics. Meanwhile, other types of media use the energy consumption and computational cost for the evaluation. Furthermore, most of the reviewed studies focus on satisfying one type of the watermarking requirements, such as security, robust or capacity. Hence, finding or developing a new watermark algorithm that unifies these requirements is considered a potential challenge. Another challenge related to attack types can be taken into account by improving the existing watermarking technique for supporting all possible attacks on a single object.
## 2 Related Work on Diacritics Restoration
### Classical Approaches
#### 2.1.2 Statistics-Based Approaches
The third method, which is the hybrid of both, is able to overcome the limitations of the Bayesian (which performed poorly on rare words) and the HMM (which relied on the imperfect morphological analysis) models to demonstrate the best performance. The decision-list approach combines word-form frequencies, morphological information, and collocational information to restore omitted diacritics for Spanish and French languages [19]. First of all, it identifies ambiguity with the help of lexical resources (dictionaries), then it collects the context of \(\pm\)\(k\) words around the target word. Afterward, it measures collocational distributions (containing the target word) to select the most useful representatives. When the log-likelihood values of these collocations are calculated, the algorithm sorts them into decision lists, performs pruning and interpolation. The prepared decision lists are later used to restore diacritics. The diacritics restoration system for the Croatian language presented in [20] successfully combines the statistical bigram language model with the dictionary (of 750 000 entries) look-up method. The diacritization process contains three stages. During the first stage, substitution schemes are applied to the raw text result for generating the diacritized candidates; then, the validity of each candidate is determined via a comparison with dictionary forms; and finally, correct forms are selected with the language model. The authors demonstrated the effectiveness of their method not only on the artificial data (newspaper articles that were undiacritized, namely for experiments) but also on the real data (forum posts). The statistical language model can be created not only on the word level but on the character level, as in [21]. During the first stage, for recognized words, it uses a statistical \(n\)-gram language model with \(n=[1,4]\) that works on the word level; during the second stage, it processes the out-of-vocabulary words with the statistical \(n\)-gram character-basedmodel that works on the character level. The authors proved that their offered approach led to the better diacritization accuracy of the Arabic dialectal texts.
## 7 Results and Discussion
### Discussion of Computational Modeling Applications  
When evaluating multiple models or cases and dealing with multiple evaluation indexes, assessing the merits of different options through a single index becomes challenging. In such situations, it is necessary to consider multiple indexes comprehensively and normalize them for comparison. Particularly when there are numerous evaluation indexes, the method proposed in this paper can simplify the data and provide more meaningful interpretations. The FAEWM is suitable for the following situations: when there are a large number of evaluation indexes or dimensions, when there are complex relationships among the data, and when there is a high correlation between the evaluation indexes.  
However, when there are few evaluation indexes, the data do not meet the basic assumptions of the FAM, or there is no correlation among the evaluation indexes, the FAEWM cannot be used.  
The FAEWM can be implemented in practical applications through the following steps:Firstly, the FAM can be used to uncover common factors or latent structures among variables, reducing the complexity of the data. By employing the FAM, original variables can be transformed into fewer and more meaningful factors, aiding in understanding the structure and patterns of the data.  
Next, the EWM can be utilized to determine the weights of factors or criteria and evaluate their relative superiority or inferiority. The EWM calculates weights based on the information entropy of the criteria, considering the importance and distinctiveness of different factors or criteria. This allows for quantifying their relative significance in decision-making or evaluation processes.