esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methodology
#### Data Processing and Steps  
In this study, we aimed to detect false data injection attacks by analyzing the features **Gp3 Ph**. To simulate an attack, we artificially increased the values of these features by a factor of 2.0, resulting in **Gp3 Ph_a** and **Gc3 Ph_a**. After generating the labels based on the modified feature values, we performed feature selection using Recursive Feature Elimination with Cross-Validation (RFECV) [32, 33] to identify the most critical features for our model.  
To address the class imbalance in the dataset, we applied the SMOTEENN (Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors) [34, 35] method. SMOTEENN combines the over-sampling of the minority class with SMOTE and cleans the dataset with ENN, thereby effectively balancing the class distribution. In our code, after RFECV, we used SMOTEENN on the feature-selected data (**X_rfecv** and **y**) to produce a balanced dataset (**X_resampled** and **y_resampled**). This balanced dataset was then split into training and testing sets using **train_test_split**. Using SMOTEENN, we ensured that the decision tree classifier was trained on a dataset with an approximately equal representation of both classes. This helped prevent the model from being biased towards the majority class and improved its ability to detect anomalies effectively.  
The overall process for detecting FDI attacks using the ML algorithms mentioned above is shown in Figure 3. Every technique has to undergo the following steps: import the power dataset, simulate FDIA, carry out data preprocessing (which is described above in detail), and then train the model and execute it for attack detection. If the attack is detected, then the overall process is successful; otherwise, the model is fine-tuned persistently to obtain the correct result.
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
###### Abstract  
Soil thermal conductivity is a dominant parameter of an unsteady heat-transfer process, which further influences the stability and sustainability of engineering applications in permafrost regions. In this work, a laboratory test for massive specimens is performed to reveal the distribution characteristics and the parameter-influencing mechanisms of soil thermal conductivity along the Qinghai-Tibet Engineering Corridor (QTEC). Based on the measurement data of 638 unfrozen and 860 frozen soil specimens, binary fitting, radial basis function (RBF) neural network and ternary fitting (for frozen soils) prediction models of soil thermal conductivity have been developed and compared. The results demonstrate that, (1) particle size and intrinsic heat-conducting capacity of the soil skeleton have a significant influence on the soil thermal conductivity, and the typical specimens in the QTEC can be classified as three clusters according to their thermal conductivity probability distribution and water-holding capacity; (2) dry density as well as water content sometimes does not have a strong positive correlation with thermal conductivity of natural soil samples, especially for multiple soil types and complex compositions; (3) both the RBF neural network method and ternary fitting method have favorable prediction accuracy and a wide application range. The maximum determination coefficient (\(R^{2}\)) and quantitative proportion of relative error within \(\pm 10\%\) (\(P_{\pm 10\%}\)) of each prediction model reaches up to 0.82, 0.88, 81.4% and 74.5%, respectively. Furthermore, because the ternary fitting method can only be used for frozen soils, the RBF neural network method is considered the optimal approach among all three prediction methods. This study can contribute to the construction and maintenance of engineering applications in permafrost regions.  
Keywords:soil thermal conductivity; water holding capacity; RBF neural network; ternary fitting method; Qinghai-Tibet Engineering Corridor +
Footnote †: journal: Applied Sciences
# Internet of Things (IoT) and the Energy Sector
## 3 Enabling Technologies
### IoT Data and Computing
#### 3.4.2 Fog Computing  
Although cloud computing is one of the best computing paradigms for data processing for IoT applications. Due to the delay and bandwidth limitation of centralized resources that are used for data processing, more efficient ways are required. Fog computing is a distributed paradigm and an extension of the cloud, which moves the computing and analytic services near to the edge of the network. Fog computing is a paradigm that expands the cloud at a greater scale and can support larger workload [110]. In fog computing, any device with computing, storage, and network connection capability works as fog node. The examples of these devices include, but are not limited to, personal computers, industrial controllers, switches, routers, and embedded servers [111]. In this computing paradigm, fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. The advantages of this approach include enhanced secure services required for many IoT applications as well as reducing network traffic and latency [112]. Therefore, in contrast to the cloud computing, fog offers processing and computing services with faster response with higher security. This enables faster decision-making and taking appropriate actions.
## 4 Results and Analysis
### Results of Classifiers with Hyperparameter Optimization
#### 4.2.1 Hyperparameter Optimization of SVM  
For the SVM classifier, the complexity parameter (C) and the Kernel function were adjusted in this work. The complexity parameter (C) in SVM determines how often the classifier avoids making mistakes when classifying the training instances. The results of SVM hypermeter optimization are provided in Table 8.  
From Table 8, a total of 21 trainings were performed by changing the C values from 1 to 7, and three kernels, namely PolyKernel, Pearson VII Universal Kernel (PUK) function, and Radial Basis Function (RBF), were used. The SVM with PUK and C = 7 produced the best accuracy of 93.1937% at the 14 th training, which accounted for an 8.55% improvement.
# Impacts of Climate Change on Blue Carbon Stocks and Fluxes in Mangrove Forests
## 3 Climate Change Impacts  
The carbon mass balance is only a work in progress, but instructive in identifying the main carbon pathways that, if affected by climate change, will significantly alter the future of the world's mangrove forests:  
1. Mangrove primary production and canopy respiration.
2. Soil CORG accumulation and burial.
3. Forest-coastal water exchange of dissolved carbon.
4. Carbon processes within the soil horizon.  
Coastal acidification is not considered here as mangroves are likely to be highly resilient due to the naturally highly variable salinity (range: 0.1-4.8), pH (range: 4-9), and \(p\)CO\({}_{2}\) (range: 4-32,763 uatm) of tidal waters that are a strong source of CO\({}_{2}\) emissions to the atmosphere due to \(p\)CO\({}_{2}\) and [CO\({}_{3}\)\({}^{2-}\)] oversaturation. The ordinarily low pH of mangrove soil (range: 4-7) as well as subsurface transport of groundwater derived from acidic soils also plays a role in maintaining acidic conditions [6]. Oversaturation and highly variable pH are the net result of high rates of bacterial respiration, eutrophication, and the influence of fluvial discharge, including export of alkalinity, organic matter and DIC, deposition of anthropogenic acids and bases, intense weathering, land-use change, and acid sulphate soil discharge. These chemical factors predispose mangroves to resilience to acidification. In fact, mangroves are apparent buffers of acidification in the tropical coastal zone as DIC and alkalinity exports create a measurable increase in pH indicating that mangroves partly counteract acidification in adjacent coastal waters.  
Similarly, increasing hypoxia in tropical coastal waters is unlikely to impact blue carbon in mangroves, considering that they are naturally subject to hypoxia and large tidal variations in dissolved oxygen levels for intermittent periods and have various mechanisms to cope with and mediate low dissolved oxygen levels. Some plants and animals can, to a considerable extent, counteract hypoxia.
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 5 Our Improved Chaff-Based CMIX Scheme
### Privacy Preservation: Pseudo-Identity Generation and Updating Authentication Key
#### 5.3.1 Pseudo-Identity Generation for Vehicles  
To preserve privacy and untraceability between vehicles, they need to use pseudo-identities rather than their real identities. As mentioned previously, \(PCA\)s are responsible for pseudo-identity generation. After \(VID_{j}\) is in the transmission range of \(RSU_{i}\), the vehicle generates pseudo-identity as follows:  
* \(VID_{j}\) computes the pseudo-identity of itself as \(PS_{VID_{j}}=(S,\Pi_{0},\Pi_{1})=(g_{1}^{s},H_{1}\)\((Cert_{VID_{j}},a_{j})X^{s},Y^{s}Z^{\theta_{s}})\), where \(s\in_{R}Z_{q}^{*}\) and \(\theta=H_{4}(g_{1}^{s},H_{1}(Cert_{VID_{j}},a_{j})X^{s})\) (note that the pseudo-identity of \(VID_{j}\) is equal to the encryption of \(H_{1}(Cert_{VID_{j}},a_{j})\) through the Cramer-Shoup encryption scheme [37], which is non-malleable and secure against adaptive chosen-ciphertext attack (CCA2)).
* \(PCA\) manages the generation of fake pseudo-identities \(Chaff_{PS_{j}}\) and fingerprints them \(FP(Chaff_{PS_{j}})\) and signs them \(Sign_{RSU_{i}}(FP(Chaff_{PS_{j}}))\) before inserting them into the ACFs. Then, \(RSU_{i}\) signs the whole ACF \(Sign_{RSU_{i}}(ACF_{i})\) to provide non-depuration and distributes it to the vehicles. \(RA\) can detect the real identity of the vehicle if it has malicious activities, as follows. Let \(VID_{j}\) be a malicious vehicle and its pseudo-identity be \(PS_{j}\). The \(RA\) obtains \((S,\Pi_{0},\Pi_{1})\) and computes \(\theta=H_{4}(S,\Pi_{0}),\Pi_{1}^{\prime}=S^{y+\theta_{2}}\). It then checks whether \(\Pi_{1}^{\prime}\overset{?}{=}\Pi\). The pseudo-identity is invalid if they are not equal. Otherwise, \(RA\) computes \(H=\Pi/S^{x}\). If \(\{Cert_{VID_{j}},H_{j},A_{j},r_{j},P_{j},T_{j}\}\) is valid in \(List_{priv}\) and \(H_{j}=H\), then \(RA\) attempts to find the \(Cert_{VID_{j}}\) from its database and learns the real identity of \(VID_{j}\). For privacy reasons, the real identity of the vehicle \(VID_{j}\) must be hidden from other \(RSU\)s and vehicles.
## 2 Proposed Inverse Solution for Risley Prisms
### Iterative Process with Geometric Method  
To improve the accuracy of reaching the target point, [PERSON] et al. developed an iterative method [7] consisting of a combination of the two-step method [9] and the Forward Vector Refraction Theorem. In this paper, the two-step method is replaced with the geometric method.  
In the first iteration, the exiting point of the system is assumed to be the centre \(N\) (\(0,\ 0,\ D1\)) of the second surface of the prism \(\Pi_{2}\). Given a target point \(P\) (\(x_{p},\ y_{p},z_{p}\)), it is possible to calculate the rotation angles (\(\theta_{1},\ \theta_{2}\)) of the prisms by the geometric method. Then, by substituting the rotation angles into the Forward Vector Refraction Theorem, the corresponding exit point \(N=(x_{n},y_{n},z_{n})\) and the tracking point \(P_{r}=\big{(}x_{rp},y_{rp},z_{rp}\big{)}\) is obtained. The tracking point \(P_{r}\) has a deviation (\(\varphi_{x},\ \varphi_{y}\)) from the target point \(\mathbb{P}\), with \(\varphi_{x}=x_{p}-x_{rp}\) and \(\varphi_{y}=y_{p}-y_{rp}\).  
In the second iteration, taking the point \(P_{i}=(x_{i},\ y_{i},z_{i})=\big{(}x_{p}+\varphi_{x},\ y_{p}+\varphi_{y},\ D2 \big{)}\) as the target point of the system, it is possible to calculate the rotation angles (\(\theta_{1},\ \theta_{2}\)) of the prisms by the geometric method. This process is iterated until the separation distance between the tracking point \(P_{r}\) and the target point \(P\) meets the given accuracy requirement \(\delta\).  
The steps to implement the geometric method in an iterative process are:  
**Step 1.** Given a target point \(P\left(x_{p},\ y_{p},z_{p}\right)\), assume that the initial point is \(N\left(x_{n},y_{n},z_{n}\right)=\left(0,\ 0,\ D1\right)\) on prism \(\Pi_{2}\) and that the deviation \(\left(\varphi_{x},\ \varphi_{y}\right)\) is zero.  
**Step 2.** Calculate the rotation angles \(\left(\theta_{1},\ \theta_{2}\right)\) of the two prisms using the geometric method by considering the initial point \(P_{i}=\left(x_{i},\ y_{i},z_{i}\right)=\left(x_{p}+\varphi_{x},\ y_{p}+ \varphi_{y},\ D2\right)\) as the point to be reached.  
**Step 3.** Update the current exit point \(N=\left(x_{n},y_{n},z_{n}\right)\) and the current target point \(P_{r}=\left(x_{rp},y_{rp},z_{rp}\right)\) substituting the rotation angles into the Forward Vector Refraction Theorem.  
**Step 4.** Calculate the error \(\Delta\) between the tracking point and the current target point on the receiving screen as: \(\Delta=\sqrt{\left(x_{rp}-x_{p}\right)^{2}+\left(y_{rp}-y_{p}\right)^{2}+ \left(z_{rp}-z_{p}\right)^{2}}\). If the error is less than the error threshold (\(\Delta<\delta\)) the rotation angles (\(\theta_{1},\ \theta_{2}\)) are considered to be determined. If not, update the deviation \(\varphi_{x}=x_{i}-x_{rp},\ \varphi_{y}=y_{i}-y_{rp}\) and go to step 2.
## 3 Results and Discussion
### Biological Potential of WOLE-S vs. Pure Oleuppein
The antiproliferative activity of oleupreein and olive leaf extract is documented in many studies [28; 29; 30; 31; 32]. [PERSON] et al. [44] showed the antiproliferative and pro-apoptotic activity of olive leaf extract in the A549 cell line, while other authors observed that oleupreein decreases the cell viability in T24 cancer cells [45; 46]. [PERSON] et al. [47] showed that the microencapsulation of olive leaf extract with calcium alginate and chitosan enhanced its antiproliferative activity against A549 cells. Figure 2 shows the expression of genes encoding the inflammatory biomarkers, such as interleukin 1 (IL-6), IL-8, and IL-10 involved in the inflammatory process in A549 cells. The results show that WOLE-S significantly downregulates the expression of the anti-inflammatory cytokine IL-6 in A549 cells, indicating a possible anti-inflammatory effect in lung cancer cells. Given the importance of IL-6 in initiating and sustaining inflammatory response [48], reduced levels of IL-6 mRNA expression after WOLE-S treatment of A549 cells is evidence of WOLE-S anti-inflammatory potential. The expression of IL-8 was slightly reduced by treating the cancer cell line with WOLE-S, but the reduction was not statistically significant. Considering that TNF\(\alpha\) treatment did not increase IL-8 expression as much as IL-6 expression in A549 cells, it can be concluded that IL-6 expression is a more sensitive indicator of the inflammatory process at a 2-h timepoint. The expression of IL-10 was not altered by treatment with WOLE-S or oleupreein. According to other in vitro studies, oleupreein from olive oil and olive leaf extract modulates the inflammatory process in the tissue [49; 50]. [PERSON] et al. [51] demonstrated a change in the pattern of cytokine expression (IL-19, IL-6, IL-10, TNF-\(\alpha\), and IL-8) of secoriridoids from olive leaf extract in an in vivo study. Interestingly, our results have shown that the anti-inflammatory activity of WOLE-S was not caused exclusively by oleupreein. Considering the different anti-inflammatory, as well as anti-proliferative, activities of WOLE-S and pure oleupreein demonstrated by these results, it is apparent that oleupreein is not the only biologically active component of the extract. Specifically, hydroxytyrosol has previously shown anti-inflammatory activity [52], and its presence in WOLE-S could be the cause for different biological activity compared to oleupreein alone. However, the downregulation of inflammatory cytokines might not be a desirable property for potential anticarcinogens, considering the importance of the immune system in cancer suppression. Future studies should explore both antiproliferative and anti-inflammatory properties of WOLE-S to determine which holds greater potential.
###### Abstract  
The present study compared the adsorption properties of two plant materials and the waste products after their essential oil extraction for removing Cu(II) ions from contaminated water. Methods like SEM, XRD, nitrogen adsorption, DTA, TGA, FTIR, and XPS were used for characterization of the materials. All materials showed similar porosity and structure, favoring Cu(II) biosorption. The effects of contact time, pH, temperature, sample amount, and initial metal concentration on Cu(II) removal were examined. Optimal pH was 4, with equilibrium reached in less than 10 min. Temperature and sample amount do not significantly influence the biosorption. The experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich isotherm models, and maximum adsorption capacities were calculated. The four plant materials proved to be effective biosorbents for removing copper ions from contaminated water. Desorption experiments using 1 M HNO\({}_{3}\) and 0.1 M EDTA showed 100% recovery. The reusability of the most effective biosorbent was confirmed through four adsorption/desorption cycles with EDTA. This material was also used to study the possibilities of purifying a real sample of contaminated water.  
biosorption; Melissa officialis L.; Lavandula angustifolia L.; waste materials; copper ions +
Footnote †: journal: Applied sciences  
Article
## 2 HSI Procedure
### Preprocessing
#### 2.2.1 Multiplicative Scatter Correction (MSC)  
It was stated that MSC could correct for multiplicative effects such as baseline shifts, light scattering, and instrumental variations in order to make the true spectral characteristics of samples clearer [39; 41]. MSC is typically carried out by regressing the measured spectrum against a reference spectrum and applying corrections according to the regression model. An appropriate benchmark spectrum is used for effective MSC. Predefined weight vectors have been used in several studies to arrive at an optimum correction performance. MSCremoves an estimate of the scattering component of the spectral data and can therefore increase the intensity of absorption bands, which is related to the composition of the sample [9; 41]. When combined with other preprocessing techniques, such as moving average (MA), Savitzky-Golay (SG) smoothing, and regression coefficients, MSC has been shown to improve model calibration, including partial least squares regression (PLSR) [39]. This has proven effective for monitoring moisture content in food samples.  
Particularly, MSC has been proven to play a significant role in enhancing predictive models, especially the performance of models on complex tasks. For instance, the inclusion of MSC in the SVM + PCA + MSC + SPA model developed high accuracy for the detection and location of ADFM in soybean protein meat semiproducts [42]. This preprocessing step therefore improved the accuracy of the detection of the contaminants to enhance the quality of the production and the food safety. Similarly, MSC was applied in the MSC-CARS-CNN model in the classification of beef origin to select 29 critical feature wavelengths while achieving a high classification accuracy [43]. As a result, further studies can be focused on the quality assessment of different meat types using MSC preprocessing. Also, detecting the origin of meat products, especially those that grow in different areas, is a field in which it is possible that MSC can assist in achieving a high-accuracy model.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 3 Proposed Hardware Architecture
### Clock Cycles Information  
The clock cycle information in the proposed cryptographic accelerator design can be calculated using Equation (6). The equation takes into account the initialization part, the point multiplication computation (considering two-stage pipelining), and the quad block Itoh-Tsujii computation. For the non-pipelined architecture, the term \"16\" in Equation (6) is replaced with \"13\". Overall, Equation (6) provides a concise representation of the clock cycle calculation for both the pipelined and non-pipelined architectures:  
\[clockcycles=initial\_states+16\times(m-1)+inversion\_states \tag{6}\]  
Table 4 summarizes the results of the cryptographic accelerator design in terms of clock cycles. It includes three columns: the name of the selected parameter, the clock cycle results for the non-pipelined architecture, and the clock cycle results for the two-stage pipelined architecture. The first row specifies that the initial states for Algorithm 1 are 6 for the non-pipelined architecture and 12 for the two-stage pipelined architecture. The key length is mentioned as 233 in the second row (for both cases). The next two rows provide the clock cycles required for point multiplication (PM) computation in both architectures. Row five presents the cost of inversion. Finally, the last row of the table shows the total number of clock cycles required to execute Algorithm 1 for both cases (pipelining and without pipelining).  
Figure 3: Control unit of proposed architecture.
# Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study
## 2 Methods
### Multi-Scale Feature Pyramid  
The main types of objects detected in this paper include electric poles, surveillance poles, traffic signal poles, traffic cones, road pile poles, street lamp poles, and traffic sign poles. These pole-shaped obstacles vary in size, ranging from small traffic cones to large surveillance poles. Due to the significant variation in the scales of their features, this paper proposes an optimized multi-scale feature fusion network architecture (MFFNA).  
The core idea is to introduce a multi-scale feature pyramid. However, traditional feature pyramid structures, such as FPN and PAN, have issues such as high computational costs and inadequate information propagation. Therefore, an efficient feature pyramid called Reap-GFPN [26] was introduced in this paper. Although Rep-GFPN addresses the issue of multi-scale features, it still has some limitations in terms of performance, including a high computational cost and large memory consumption.  
First, this network model replaces the ConvBNAct and ConvWrapper operators with convolution (Conv) layers and Cross Stage Partial Network (CSPStage) to simplify the model structure, improve the training speed, and reduce the model complexity. Furthermore, the complexity of the model is reduced and the training speed is improved by replacing RepC3 with a lightweight convolution layer called C3. Finally, a hybrid attention mechanism called the Convolutional Block Attention Module (CBAM) [27] was introduced into the neck network of the Rep-GFPN to enhance attention towards important features and suppress the extraction of unimportant features. This improvement not only improves the accuracy and robustness of the model but also reduces the computational cost and memory consumption, and its network architecture is shown in Figure 3.  
Figure 2: Cascade Hybrid Attention Mechanism.  
According to the network architecture diagram, it can be seen that in the feature fusion module, this paper first utilizes the improved CSPStage [28] for convolutional fusion. Then, it proceeds with structural reparameterization--specifically, the fusion of Conv2d and BN. Therefore, the expression of BN in the channel \(i\) of the feature map is as follows.  
\[y_{i}=\frac{x_{i}-\mu_{i}}{\sqrt{\sigma_{i}^{2}+\epsilon}}\gamma_{i}+\beta_{i} \tag{7}\]  
where \(\mu\) denotes the mean, \(\sigma^{2}\) denotes the variance, \(\gamma\) denotes the weights, \(\beta\) denotes the bias, and \(\epsilon\) is a very small constant used to prevent the denominator in the formula from being zero.