esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method and Mathematical Analysis  
### Overall Framework  
The framwork is divided into two main parts: the digital signature part and the encryption/decryption part. The digital signature part uses the ElGamal Digital Signature algorithm, and the encryption/decryption parts mainly apply the Arnold Translation and ElGamal Algorithm. Refer to Figure 1 for the specific process.  
Digital signatures are the most effective solution to the growing problem of image tampering. The ElGamal Digital Signature, which is based on the discrete logarithm problem, is a solution to the challenge of image tampering. Arnold Transform uses a 3D transform that scrambles the original image, resulting in better encryption. The Arnold Transform alone has the disadvantage that the key space is too small and vulnerable to exhaustive attacks, while the ElGamal Public-Key encryption, with the same encryption key, repeatedly encrypts different results and provides better security. The combination is a complete cryptographic verification algorithm that is lightweight and easy to use but guarantees the security and authenticity of the biometric data. The digital signature part is mainly used to verify that the biometric data has not been tampered with during transmission to ensure data integrity, and the encryption/decryption part is mainly used to encrypt the image to ensure the security of the image and prevent others from stealing it. The two parts have different divisions of labor and different roles, but both are ensuring that the security of the data is not threatened, and since the biometric data are singular in nature, it is important to ensure that the data have not been stolen. Because of the singular nature of privacy biometric data, it is important to ensure that the data are not tampered with. In order to make the implementation of the al  
Figure 1: The framework of the proposed method.  
the cryptographic algorithm and the digital signature in this paper are both based on the ElGamal algorithm, which has good randomness and resistance to cracking.
## 4 Algorithm Testing and Discussion
### Comparison of Classification of Unbalanced Samples with High Noise  
Comparing the MWMOTE-FRIS-INFFC algorithm with other methods on multiple data sets, the results show that MWMOTE-FRIS-INFFC has certain advantages. For further confirmation, we selected datasets with noise ratios above 20% for further comparison and analyzed the classification performance of MWMOTE-FRIS-INFFC on high noise and sample imbalance datasets. The basic parameter settings are the same as above. Classification lasts for 5 times. After each classification, the best classification algorithm  
Figure 3: F-measure values for different algorithms in different data sets.  
records the result and adds 1. Finally, 5 classification results are accumulated. The number of records of algorithms with the best performance is shown in Table 2.  
In practical applications, the presence of samples with high noise and significant imbalance is commonplace. Initially, severely imbalanced samples exacerbate the challenges posed by inter-class and intra-class imbalances, thereby necessitating a thorough evaluation of algorithmic capabilities. Based on the analysis of data results, it is evident that the MWMOTE-FRIS-INFFC classification approach exhibits superior performance [32]. Specifically, this algorithm demonstrates a superior capacity in balancing data samples, efficiently achieving the intended goal of expanding, interpolating, and increasing minority class samples to balance the sample ratio, thereby approximating data balance across classes. The MWMOTE algorithm effectively balances data during minority class synthesis and majority class weighting, particularly in cases of highly imbalanced datasets, exhibiting resilience against overfitting caused by a limited number of samples [33].  
However, during the process of data expansion and interpolation, the occurrence of boundary overlap and noise overlap in certain data can augment the difficulty in learning from minority class samples. Consequently, this study aimed to mitigate this issue by employing fuzzy rough set theory to eliminate inappropriate samples, thereby enhancing the membership degree of new samples and achieving the goal of balancing genuine samples. Ultimately, a combination of image classification and data classification was utilized, incorporating multiple iterative filtering techniques to address noise issues. This approach effectively mitigates noise in industrial datasets, particularly preventing excessive loss of genuine samples and minority samples during the processing of imbalanced samples by certain simpler classifiers. The retention of available instance data is maximized to the greatest extent possible.
## 3 Methodology
### Preprocessing  
Preprocessing is an important step because it cleans and refines the dataset in preparation for subsequent training. Our dataset includes Arabic consumer reviews written in various dialects. To address the issues with Arabic language comments, we used NLP techniques such as normalization, stemming, and stop word removal, as shown in Figure 1.  
Figure 1: Methodological Framework for Sentiment Analysis in Arabic Texts.  
* Text Cleaning: The first step is to clear the text of any extraneous elements. This includes removing special symbols, punctuation marks, diacritics, non-Arabic letters, and numbers. Additionally, embedded URLs are detected and removed. This cleansing phase is critical in ridding the text of unnecessary information, thereby reducing the complexity inherent in Arabic script.
* Emoji Removal: Because of the nature of our analysis, emojis are considered non-essential. As a result, a dedicated process is used to identify and remove these characters, ensuring that the focus is solely on the textual content.
* Normalization: Normalization is an essential step in processing Arabic text. This entails standardizing various forms of the same letter into a consistent model. Such normalization is critical in addressing the Arabic language's morphological richness, thereby improving dataset uniformity and comparability.
* Tokenization: Tokenization is a critical step in text analysis, particularly for reducing typographical variations in words. It significantly improves feature extraction processes such as Bag of Words (BOW) [32] and is helpful for n-gram, co-occurrence analysis, and stemming. Tokenization typically uses delimiters such as spaces, tabs, and newlines. Punctuation, such as commas, question marks, and exclamation points, can also serve as delimiters in informal Arabic on social media due to their unconventional use. Tokenization makes it easier to accurately tally word frequencies, which is important for understanding text content and structure in NLP.
* Stop Word Removal: During this phase, stop words in Arabic are filtered out. Stop words are frequently used words that are excluded from NLP due to their minimal semantic contribution.
* Stemming: Stemming is the final step in the preprocessing pipeline. We used the IS-RI Stemmer to reduce words to their base or root form, which is especially useful in linguistic analyses because it combines multiple word forms into a single representation.
## 6 Discussion
### Other Factors Influencing SIF Retrievals  
Regarding the up-scaling issues: BRDF effects, footprint variability, and scale mismatch are still factors that are difficult to characterize in linking and up-scaling remotely sensed SIF and E-C data. In this context, notable advances in UAV technology currently provide an opportunity to face these challenges ([PERSON] et al., 2016). While optical systems mounted on towers can monitor the vegetation canopy of interest from a fixed height (5\(\sim\)50 m), UAVs can attain heights of hundreds of meters, and are flexible enough to acquire the same target from different heights. However, most of the atmospheric approximations assumed for tower-basis cannot be met for UAVs. For instance, while in this work it was demonstrated that only compensating for oxygen absorption effects was enough to properly estimate SIF from a few meters distance; conversely, aerosols effects should also be compensated when processing data from UAVs. Certainly, the impact of aerosols on the estimated SIF from UAVs will definitively depend on the atmospheric conditions and the sample-sensor distance. Overall, the application of a full atmospheric correction strategy, like those applied for airborne sensors, will avoid errors in the estimated SIF caused by ignoring atmospheric effects. Apart from the distinct impact of atmospheric effects and the contrasting spatial scale, when aiming to validate satellite-derived SIF by making use of proximal sensing measurements, the different instrument's specifications such as the spectral resolution, the signal-to-noise ratio (SNR), the level of stray-light contamination or the instrument polarization sensitivity, must also be taken into account. In terms of the SNR, since it is expected that areas selected for satellite validation will be homogeneous enough to be representative of the satellite pixels provided at a lower spatial resolution, the aggregation of multiple proximal sensing measurements can increase the effective SNR of the proximal sensing instrument, which is generally lower than the SNR of instruments on board satellites. The up-scaling process (and aspects to be taken into account) is certainly an essential field of study with strong implications on satellite validation strategies and protocols. To further investigate this issue, the Fluorescence Across Space and Time: FAST field campaign ([PERSON] et al., 2016) has been recently dedicated to measuring SIF from leaf level to tower, UAV and satellite scale.
# Synergistic Effects of CuO and ZnO Nanoadditives on Friction and Wear in Automotive Base Oil +
## 3 Results  
The tribological performance of the Group III base oil containing various concentrations of CuO and ZnO nanoparticles was evaluated by analyzing frictional behavior and wear characteristics. The assessment involved measuring the dynamic and static coefficients of friction and the wear volumes of both tribological counterparts. Specifically, ball wear volume (BWV), disk wear volume (DWV), and total wear volume (TWV) were determined using 3D confocal microscopy.  
The experimental results demonstrate apparent friction reduction and material loss trends, with notable synergistic interactions observed between CuO and ZnO nanoadditives. These interactions affected not only the overall wear resistance of the tribosystem but also the distribution of wear between the contact surfaces. The combination of these two oxides showed a more pronounced effect than either additive alone, suggesting beneficial mechanisms such as complementary tribofilm formation, particle rolling, or protective layer stabilization. Detailed quantitative results are presented in the following sections, accompanied by comparative analysis and graphical representations of the measured parameters.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 3 System Architecture  
The system consists of several ESP32-CAM edge devices and a central server hosted on a personal computer (PC). Each ESP32-CAM is equipped with a 240 MHz dual-core Xtensa processor, integrated Wi-Fi, and a built-in camera, making it suitable for low-power edge computing tasks such as local training and data collection [45]. The server is a standard desktop PC running Windows 10, equipped with an 11 th Gen Intel Core i7-1165G7 CPU at 2.80 GHz and 12 GB of RAM, and it is responsible for global coordination of the federated learning process. Since the server does not perform intensive training but only coordination and model aggregation, it does not require a high-end GPU or multicore processing.  
The architecture follows a star network topology where all ESP32-CAM devices communicate directly with the server over Wi-Fi. The server acts as the central node in the network, while ESP32 devices operate as clients. Figure 1 illustrates both communication phases: uplink (blue arrows), where ESP32-CAM devices transmit data to the server, and downlink (orange arrows), where the server sends data back to the devices.  
The ESP32-CAM devices perform three key roles:  
* Data collection: Capture sensor or image data.
* On-device training: Train lightweight ML models locally.
* Federated participation: Exchange model parameters with the server during the FL.  
The server coordinates the system and manages communication by
* Training configuration: Sending configuration parameters to devices, such as the number of epochs, learning rate, and model structure.
* Model aggregation: Receiving local model updates from devices, aggregating them into a global model, and distributing the updated model.
* Model synchronization: Providing the latest global model to devices that rejoin the training process after disconnection or power loss.  
Communication between ESP32-CAM devices and the server is handled over Wi-Fi using a lightweight HTTP protocol, with data formatted in JSON for simplicity and compatibility. A basic request-response synchronization is used, ensuring that devices wait for updated configurations or model responses before proceeding to the next step. In case of temporary power loss or connectivity issues, devices can rejoin the network and request the current global model without disrupting or restarting the training process.
## 2 Theory  
### Physical Model of the SLM  
As mentioned in previous sections, the accurate description of the nematic cell vector distribution of a liquid crystal in the presence of an electric potential involves the minimisation of the so-called Frank-Oseen free energy, which captures the elastic, electric, and surface contributions and is given by the following expression [8; 9]:\[F=\frac{1}{2}\int f\,dV \tag{1}\]  
where  
\[f=\frac{1}{2}K_{11}(\
abla\cdot\mathbf{n})^{2}+\frac{1}{2}K_{22}(\mathbf{n}\cdot( \
abla\times\mathbf{n}))^{2}+\frac{1}{2}K_{33}(\mathbf{n}\times(\
abla\times \mathbf{n}))^{2}-\frac{1}{2}\varepsilon\mathbf{E}\cdot\mathbf{E}. \tag{2}\]  
and \(\mathbf{n}\) is the unit director field, \(K_{11}\), \(K_{22}\), \(K_{33}\) are the splay, twist, and bend constants, and \(\mathbf{E}\) is the electric field. \(\varepsilon\) is the dielectric tensor, which can be expressed as:  
\[\varepsilon_{ij}=\perp\delta_{ij}+(\varepsilon_{\parallel}-\varepsilon_{ \perp})\hat{n}_{i}\hat{n}_{j}, \tag{3}\]  
where \(\varepsilon_{\parallel}\) and \(\varepsilon_{\perp}\) are the relative permeability for liquid crystals aligned parallel and perpendicular to the electric field direction [8]. The interaction with the external electric field is obtained considering its relationship with the electrostatic potential \(\mathbf{E}=-\
abla\Phi\) and the application of Gauss's Law (\(\
abla\cdot(\varepsilon\mathbf{E})=-\
abla\cdot(\varepsilon\
abla\Phi)\)) (2). Gauss's Law solution for the electric potential \(\Phi\) of each 3D space unit, considering the influence of the dielectric tensor \(\varepsilon\) in each point, involves derivatives of the dielectric tensor and the electric potential. For computational efficiency, we approximate these derivatives using neighbouring differences. The interested reader can find the explicit closed expressions for this part of the algorithm in [31].  
The minimisation of (2) can be done through its derived Euler-Lagrange equations using central finite differences and updating \(\Phi\) and \(\mathbf{n}\) iteratively until convergence. This procedure is fully defined in the literature, and interested readers can read more about this in [9] and Section 2.3 in [8]. Here, this method has been implemented, and it is considered a reference for comparing the GA method proposed in this work to overcome the implementation of the demanding formalism derived from the application of the Euler-Lagrange equations.
# An Evaluation of the Demineralizing Effects of Various Acidic Solutions
## 3 Results and Discussion
### X-ray  
Analyzing the X-rays, no noticeable radio-translucency was found on the surface of the dental enamel of the teeth treated for 30 min and 1 h with different acid solutions. Radiologically, one can observe a major demineralization of the dentine and the complete  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Initial** & **30 Min** & **1 Hour** & **24 Hours** & **96 Hours** \\ \hline Hydrochloric gel & 2.46 & 3.90 & 3.68 & 19.70 & 27.31 \\ Orthophosphoric acid gel & 3.44 & 3.51 & 5.71 & 23.30 & 29.95 \\ Lactic acid & 2.50 & 2.77 & 2.69 & 5.94 & 18.86 \\ Orthophosphoric acid solution & 4.50 & 5.12 & 5.03 & 28.33 & 32.20 \\ Formic acid & 2.31 & 2.09 & 3.48 & 10.21 & 23.04 \\ CaCl\({}_{2}\), NaH\({}_{2}\)PO\({}_{4}\) and acetic acid solution & 2.07 & 2.13 & 3.41 & 3.19 & 4.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean results from the DIAGNODent(tm) pen before and after the application of acid solutions on the enamel surface.  
Figure 9: The mean values measured with the DIAGNODent(tm) pen.  
absence of dental enamel on the teeth immersed for 24 and 96 h (mostly on those treated with liquid 37% orthophosphoric acid). This is shown in Figure 10b.  
Nowadays, the quantity and frequency of acidic food and beverage consumption have both increased [21]. As a result, examining the etiology, prevalence, prevention and treatment of dental erosion has become greatly important. There are many factors that can cause enamel erosion: diet, oral hygiene routine, drugs and professional environment. To prevent its progression, the causative factors should be identified as soon as possible so that protective measures can be implemented [23].  
Studies show that beverages with low pH cause dental erosion of different degrees [32; 33; 34]. Case reports and other studies suggest that dietary acid erosion is playing a significant role in tooth wear [35; 36]. How demineralizing a solution is depends on the type of acid it contains, its pH value, its acid concentration and its temperature [37; 38; 39; 40; 41; 42; 43]. The results of this in vitro study show that acids in liquid state are more harmful to the dental hard tissues than gels; therefore, beverages and acid reflux represent a greater concern. In our research, different demineralizing periods were studied, and it has been shown that prolonged exposure (exposure for more than 24 h) to strong acidic solutions causes total dissolution of the dental enamel. However, an acid solution that contains calcium chloride, sodium phosphate and acetic acid with a pH of 4.5 did not demineralize the teeth, even after 96 h of exposure.  
This in vitro research was limited due to the small number of extracted teeth that were treated, a shortcoming that will be overcome with a new, larger study that will use a protocol that includes remineralizing cycles and weaker acids.
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 2 Antenna Pattern Characteristics
### Antenna Pattern Characteristics Analysis  
According to the models given above, this section analyzes the antenna pattern characteristics of a squint steering beam. Equations (7) and (10) indicate three characteristics. First, the azimuth beamwidth is \(k_{\phi}\) times the width of the physical beam, due to the beam steering; second, the AAP becomes asymmetric, especially in the high squint case; last, the EAP varies two-dimensionally, not only in elevation but also in azimuth.  
The AAP from Equation (10) is almost symmetric in the low-squint case. However, as the squint angle increases, the AAP shape becomes asymmetric. Figure 3 shows the AAP of a squint steering beam. The width of the physical beam is narrow. As the antenna beam rotates, it is broadened in a ground point target view, scaled approximately by factor \(k_{\phi}\). Meanwhile, the pattern shape becomes asymmetric, so its centroid shifts from the beam center.  
Figure 3: Comparison of AAPs. The solid red is the squint beam-steering pattern, the solid black is the physical beam pattern, and the dashed blue is the \(k_{\phi}\)-scaled physical beam pattern.  
The following focuses on the azimuth dependence of EAP. The EAP from Equation (7) is  
\[G_{el}(\theta,\phi)=\text{sinc}^{2}\bigg{[}\pi\frac{D_{\epsilon}}{\lambda}(\sin \theta-\sin\theta_{h})\cos\phi\bigg{]}\,. \tag{11}\]  
It varies mainly in elevation and slightly in azimuth. When SAR observes broadside, \(\cos\phi\simeq 1\), the azimuth dependence of EAP is negligible. At the swath centerline, \(\theta=\theta_{h}\) results in \(G_{el}=1\), i.e., independent of the squint angle. However, as the elevation angle deviates from boresight, a squint dependence arises in the EAP and increases to the maximum at the swath edge. Figure 4 shows the azimuth variation of the EAP at the swath edge. It is approximately a linear model, so a convenient expression is  
\[G_{el}(\theta,\phi)\approx G_{el}(\theta,\phi_{c})+(\phi-\phi_{c})\frac{ \partial G_{el}}{\partial\phi}\,. \tag{12}\]
## 3 Results and Discussion
### The Implication for EDB Groundwater Resources Management  
Sustainable management of groundwater is critical to achieving access to potable water supply, food security, ecosystem/environmental protection and economic growth in sub-Saharan Africa countries [51]. Effective groundwater resources management relies on sound hydrogeological knowledge and ultimately on the quality of available data and information necessary for accurate judgement and planning.  
In light of this, our study has advanced the available data, information and knowledge required to understand better the hydrogeology of the eastern Dahomey Basin, Nigeria. A combination of isotopes, geology, and hydrogeochemical approaches have revealed the groundwater is at an early stage of geochemical evolution with short residence time. However, the basin has a large volume of water with variable aquifer hydraulic characteristics. It is therefore pertinent to emphasise the threat to its water quality as short residence time, and rapid recharge will allow rapid ingress of contamination. The shallow aquifers of this basin are, thus, described as having a low resilience and high vulnerability to contamination and pollution from surface water, leakage from septic tanks, waste sites, industrial pipes, and leachates from agricultural waste. Groundwater quality degradation has been identified in part of this basin, especially the urban and agricultural areas by [21; 52]. It is, therefore, necessary to ensure enforcement of a proper waste disposal management strategy that will protect groundwater within this basin. Policies and urban planning that provide best practice from industries and agricultural industries are necessary to protect these vital resources. A review of the existing Integrated Water Resource Management (IWRM) system is needed to include threats imposed on groundwater resources through technological development in the modern economy.