esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Satellite Self-Organizing Network Simulation Platform Based on OMNeT++
### Module Domain  
In OMNeT++, a module is a functional entity that can be programmed to perform certain functions. Modules can communicate through artificially defined interfaces. Additionally, modules can be nested to form a hierarchical structure. In the satellite self-organizing network system implemented in OMNeT++, the module structure of the ground nodes and satellite nodes is consistent, both inheriting from the NodeBase module of the INET Framework. Based on the NodeBase module, network layer modules and Mac layer modules are added, as shown in Figure 3. The downward interface of the network layer module and the upward interface of the Mac layer module are bidirectionally connected, and the downward interface of the Mac is bidirectionally connected with the external interface of the node module. Although the structures of the two types of nodes are consistent, the functional design of their network layers and Mac layers is not the same, as shown in Figure 4.  
The functions of satellite nodes' network layer module include cluster maintenance, data generation, and routing. The Mac layer module of the satellite nodes is responsible for sending messages passed down from the network layer to other nodes, which includes end-to-end transmission and broadcasting functions.  
Figure 4: Functions of network layer and Mac layer of satellite and ground node.  
Figure 3: Structure of satellite and ground node.  
The network layer module of the ground nodes is responsible for cluster allocation to each node when the network is initially established, and the Mac layer module is responsible for allocating time slots for each node.  
The specific function is illustrated in the following part.
## 2 Materials and Methods
### LUE Model
#### 2.2.3 \(\varepsilon\) Estimation
This index was calculated for 150 granules of Sentinel-2 images, corresponding to all available images in the study area with less than 15% of cloud coverage for the whole study period of the atmospherically corrected bottom of the atmosphere product (Level 2A) available from the European Space Agency. LSWI was resampled to 10 m and extracted in the DBR area using Google Earth Engine. The resulting images were linearly interpolated pixel by pixel in order to obtain a daily image. Figure 2: Scheme of linear equations for the approach using meteorological variables. Once a daily image of LSWI was obtained, the index was standardized to values between 0 and 1 to obtain a scalar LSWI (LSWI_sc) using Equation (8), similar to [53], and it was used to estimate the final value of \(\varepsilon\) using Equation (9). \[\text{LSWI\_sc}=(1+\text{LSWI})/2 \tag{8}\]  
where LSWI_sc is the scalar land surface water index and LSWI is the land surface water index. \[\varepsilon=\varepsilon_{\text{max}}\times\text{LSWI\_sc} \tag{9}\]  
where \(\varepsilon\) (gC/MJ) is the light use efficiency parameter, \(\varepsilon_{\text{max}}\) (gC/MJ) is its maximum value defined in Section 2.2.3. above and LSWI_sc is the scalar land surface water index.
# Sustainable Removal of Cr(VI) by Line Peel and Pineapple Core Wastes
## 3 Results and Discussion
### PP Characterization  
Once PP was established as the best biosorbent, a deep physical-chemical characterization of this waste was accomplished.  
The morphology and structural aspects were determined by SEM-EDS. Related to the biosorbents composition, EDS analyses determined that the main constituents were C and O (Figure 2a). SEM images confirmed that this biosorbent has a heterogeneous surface with rough areas and micropores (inset Figure 2a) with an average of size ranging from 10 to 200 \(\upmu\)m. The chemical structure of the biosorbent was analyzed using FTIR spectroscopy with the purpose of determining the functional groups that exist in the surface of the sorbent (Figure 2c and Table 2). A considerable quantity of oxygen-functional groups (O-H, C=O, C-O) were observed. As it is shown in Table 2, the main functional groups identified into the biosorbents belonged to the polymers cellulose, hemicellulose, and lignin, which are present in the waste. This fact is in accordance with the composition of PP (hemicellulose 28.5%; cellulose 24.5%, lignin 5.8; pectin 1.6%) determined by [PERSON] et al. [18]. Furthermore, the presence of hydroxyl, amines, and carbonyl functional groups demonstrates that the adsorption and binding of heavy metals could be possible and the process occurs [21].  
Figure 2: Characterization of PP waste: EDS energy spectrum and SEM image (inset photos) (**a**) initially and (**b**) after Cr adsorption and (**c**) pineapple core FTIR spectra initially (1) and after the adsorption (2).  
It is clear that the biosorbent surface charge is a fundamental parameter regarding the disposal of ionic forms. The pH\({}_{\text{PZC}}\) of the biosorbent was around 3.5 and determined the affinity of this biosorbent. Thus, the surface of the biosorbent is positively charged at pH < pH\({}_{\text{PZC}}\) which lead to an electrostatic attraction for Cr(VI) as oxyanion. On the other hand, the charge of the biosorbent surface is negative at pH > pH\({}_{\text{PZC}}\) and this circumstance generates electrostatic repulsion of Cr(VI), however, it favors Cr(III) adsorption. Therefore, pH has proven to be a crucial factor in the adsorption process and ought to be considered to optimize the adsorption process.
# Deep-Learning-Based Approach for IoT Attack and Malware Detection
## 8 Conclusions  
This study presents an optimized 1D convolutional neural network (1D CNN) model for classifying Internet of Things (IoT) security data, characterized by its low computational overhead and high efficiency. The proposed model was rigorously tested on three comprehensive datasets: CIC IoT 2023, CIC-MalMem-2022, and CIC-IDS2017. The results indicate that the model demonstrates superior performance compared to existing methods, achieving high accuracy, precision, recall, and F1-scores. On the CIC IoT 2023 dataset, the model achieved an accuracy of 98.36%, precision of 100%, recall of 99.96%, and F1-score of 99.95%. These metrics underscore the model's capability to effectively identify and classify large-scale attacks within IoT environments. Similarly, on the CIC-MalMem-2022 dataset, the model attained an accuracy of 99.90%, precision of 99.98%, recall of 99.97%, and F1-score of 99.96%, highlighting its proficiency in malware detection. The CIC-IDS2017 dataset results, with an accuracy of 99.99%, precision of 99.99%, recall of 99.98%, and F1-score of 99.98%, further confirm the model's robustness in detecting network intrusions. This research validates the applicability and effectiveness of deep-learning techniques in enhancing IoT security. The developed model, with its low computational demands and high performance, presents a valuable tool for real-world IoT operations, facilitating the detection and mitigation of large-scale attacks. Its suitability for resource-constrained devices and real-time applications is particularly noteworthy.  
This research received no external funding.  
Not applicable.  
In this paper, the dataset is publicly available.  
The author declares no conflicts of interest.
## 2 State-of-the-Art Review
Various methodologies have been explored here, but the gap lies in further refining and expanding the application of deep learning techniques to address specific challenges in error detection, diagnosis, and prediction in quay container crane data analytics. This would ultimately improve safety, efficiency, and automation in port operations. The essential part is early failure diagnosis, which comes with the initial error identification and prediction step. The proposed methodology aims to do just that. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Description** & **Reference** \\ \hline Fault Detection and Diagnosis (FDD) & These methodologies detect emerging irregularities and predict their consequences for the system’s behaviour. & [55] \\ Data-Driven Fault Diagnosis & This approach uses machine learning algorithms for mechanical fault detection or fault prognosis in manufacturing equipment. & [24] \\ An array of ML, DL, and DHL Algorithms & These algorithms can potentially perform early fault detection that would lead to future machine failure. & [56] \\ Automated Machine Learning Approach & This approach is used for real-time fault detection and diagnosis in discrete manufacturing machines. & [57] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine learning methodologies.
## 3 The Proposed Method
### Reversible Adversarial Example Generation
#### 3.2.1 Auxiliary Information Encoding  
Each pixel can be represented as a vector \((x,y,R,G,B)\), \((x,y)\) represents the coordinates. If the image size is \(32\times 32\), \(x,y\in[0,31]\). \(R,G,B\) represent the values in the red, green, and blue channels, respectively. \(R,G,B\in[0,255]\).  
As shown in Figure 2, after one-pixel AE generation, the perturbed pixel is represented as a vector \((x^{\prime},y^{\prime},R^{\prime},G^{\prime},B^{\prime})\). To restore the original image, we have to record and save the pixel values at the same position in the original image, i.e., \((R_{0},G_{0},B_{0})\), where \((x_{0},y_{0})=(x^{\prime},y^{\prime})\). \((R_{0},G_{0},B_{0})\) is converted from decimal to a fixed-length bit string. Three 8-bit codes are for \(R_{0},G_{0},B_{0}\), respectively, and a 24-bit code is for them totally.
# Hydrothermal Manufacture of Zeolitic Lightweight Aggregates from Clay and Marine Plastic Litter
## 4 Conclusions  
A study is presented to sustainably develop innovative zeolitic lightweight aggregates (ZLAs), based on the protocol recently published by [PERSON] and [PERSON] [21]. The main conclusions that can be drawn can be summarized as follows:  
* The incorporation of MPF in proportions not exceeding 10 wt.% would be positive since its combustion during firing favors the formation of open porosity, which makes the subsequent alkaline hydrothermal treatment more effective. Therefore, adjusting the percentage of MPF in the mixture could help control the final properties of the material obtained. Thus, although the mechanical strength decreases with the MPF, the lightness and degree of zeolitization improve. * The type of clay used in manufacturing is a critical aspect. Kaolin is very prone to zeolitization, being a suitable raw material for the manufacture of ZLAs. On the contrary, the rejected Mg-clay investigated (rich in magnesium smectite and sepiolite) seems to negatively affect zeolitization when it is added in high proportions. However, in a moderate content (33% rejected Mg-clay over 67% kaolin), it can give rise to materials with properties similar, or even superior (e.g., mechanical strength), to those obtained with kaolin alone. This invites further research into this clay with different formulation and manufacturing conditions, including the application of acid pretreatments for Mg removal. * The hydrothermal zeolitization method allows working at significantly lower firing temperatures (600-900 \({}^{\circ}\)C in this work) than those usual for lightweight aggregates (>1000 \({}^{\circ}\)C). This can provide an important economic and environmental advantage by reducing energy consumption during manufacturing. In any case, it will be necessary to investigate these aspects in more detail in the future (for example, through life cycle assessment), especially to check if the emissions linked to the combustion of the MPF are compensated by the energy savings during hydrothermal manufacturing. * The high zeolite content in the new lightweight aggregates could give them advanced properties compared to conventional ones. In addition to a significant increase in mechanical strength (even >50 fold compared to the starting fired material), it is well known the adsorbent capacity of zeolites, thus paving the way to investigate the decontamination capacity of the new ZLAs. * Despite the potential environmental advantages indicated above, the impact generated by the burning of plastic waste in the manufacturing process is an aspect to be evaluated (pending for another study). However, it should be noted that the most widely used way of disposing of plastic waste in Europe is energy recovery by incin
## 2 Materials and Methods
### Methods Used
#### 2.2.6 Accuracy Validation  
Accuracy of the proposed and applied models was validated by the coefficient of determination (R) and the root mean square error (RMSE) [27; 28; 29; 30]. RMSE is the average squared difference between outputs and targets [31]. It is always positive and its units match the units of the response. Lower RMSE values are better [32; 33]. The R values measure the correlation between outputs and targets [34; 35; 36; 37]. While R can be more easily interpreted, RMSE can be know the amount of predictions deviate, on average, from the actual values in the dataset [31]. The formulas were as follow [38; 39; 40; 41]:  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(pr_{i}-y_{i}\right)^{2}}{n}} \tag{10}\]  
\[R=\frac{\sum_{i=1}^{n}(pr_{i}-\overline{pr})\left(y_{i}-\overline{y}\right)}{ \sqrt{\sum_{i=1}^{n}(pr_{i}-\overline{pr})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}- \overline{y})}} \tag{11}\]  
where \(pr_{i}\) and \(\overline{pr}\) are inferred as output values from the models, \(y_{i}\) and \(\overline{y}\) are the actual and mean values of IRI, respectively, and n is defined as the number of input data.  
In addition to above validation criteria, a non-parametric test named Wilcoxon sign-ranked test was used in this study to evaluate that whether the difference of the accuracy among the models are statically significant [42]. This test was based on a predominant null hypothesis that there was no difference between the models at the significant level of \(\alpha\) = 0.05, and that statistical values such as the \(z\)-value and \(p\)-value were calculated [42]. If \(z\)-value and \(p\)-value were out of the range from \(-\)1.96 and \(+\)1.96 and smaller than 0.05, respectively, the null hypothesis was rejected, which means the difference between the models was statically significant and vice versa [43].
## 2 Materials and Methods  
### Finite Element Analysis  
#### 2.1.1 Model Construction  
With respect for the density of life and compliance with research ethics guidelines, the tibia specimen (Figure 1a) used in this study was obtained from a 6-month-old commercially farmed pig procured from a licensed slaughterhouse (all procedures adhered to approved protocols), confirming that the animals were not slaughtered for experimental purposes but for commercial use. First, the porcine tibia was scanned using a computed tomography (CT) scanner (GE Ltd., Boston, MA, USA) with a slice thickness of 0.6 mm. The DICOM-format CT images (Figure 1b) were then imported into Mimics Research 21.0 software (Materialise, Leuven, Belgium) for three-dimensional reconstruction of the porcine tibia. To enhance the reliability of finite element simulations, cancellous and cortical bone regions were differentiated via grayscale thresholding, while preserving the hollow medullary cavity within the tibia. After optimizing the model into solid components using Geomagic Wrap 2021 (Geomagic Inc., RTP, NC, USA), medial tibial osteotomy was performed in SolidWorks 2022 (Dassault Systemes SolidWorks Corp., Waltham, MA, USA) following HTO surgical specifications, and the resulting tibial defect model is illustrated in Figure 1e,f.  
Figure 1: (**a**) Pig tibia sample; (**b**) tibial CT image; (**c**) tibial FE model; (**d**) tibial defect sample; (**e**) tibial defect model; (**f**) tibial defect FE model.  
A contour-adapted personalized tibial implant for HTO was designed based on the morphology of the bone defect area. The implant incorporates a porous structure design, with its composite pore configuration developed according to previous research findings as an artificially designed internal topological structure--a metamaterial honeycomb system. Both square and circular pores represent conventional design approaches. All porous tibial implants with different structures were parameter-adjusted to achieve four porosity levels, and the specific parameters are detailed in Table 1. The implants were geometrically positioned within the osteotomy gap of the tibial model to establish postoperative HTO tibial models containing three distinct structural configurations with four porosity gradients (Figure 2a). All models were imported into HyperMesh 2022 (Altair Engineering Inc., Michigan, USA) for meshing into C3D4 tetrahedral elements suitable for finite element analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Structure Parameters**} & \multicolumn{4}{c}{**Porosity**} \\ \cline{2-5}  & **53\%** & **63\%** & **73\%** & **83\%** \\ \hline Hybrid hole strut size & 0.9 mm & 0.7 mm & 0.5 mm & 0.3 mm \\ Square hole diameter & 4.4 mm & 4.8 mm & 5.1 mm & 5.5 mm \\ Circular hole diameter & 5.4 mm & 5.8 mm & 6.3 mm & 6.7 mm \\ \hline \end{tabular}
\end{table}
Table 1: Implant porosity parameter adjustment table.  
Figure 2: (**a**) The establishment process of the postoperative tibial finite element model; (**b**) tibial implants with different porous structures and porosities.
# Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence +
## 3 Methodology
### Formulation of RFA  
As discussed in Section 2, graph embedding has already been proven to be a general framework for dimensionality reduction. However, there are two shortages of graph embedding. First, graph embedding does not obtain and preserve the relational information between data. Second, the graph embedding framework needs to be solved by generalized eigenvalue decomposition, which is only an approximate approach. Inspired by graph embedding, we propose a new dimensionality reduction framework called RFA, which integrates relationships among data into the dimensionality reduction model and can alleviate the two problems mentioned above. Formulation of the proposed RFA is described as follows.  
We use \(\mathbf{R}\in\Re^{N\times N}\) to denote the relational matrix. The dimensionality reduction framework RFA is modeled as  
\[\mathcal{L}=\min_{\mathbf{W}}\frac{\text{tr}(\mathbf{W}^{T}\mathbf{X}\mathbf{ L}_{j}\mathbf{X}^{T}\mathbf{W})}{\text{tr}(\mathbf{W}^{T}\mathbf{X}\mathbf{L}_{P} \mathbf{X}^{T}\mathbf{W})}+\lambda\text{tr}(\mathbf{W}^{T}\mathbf{X}\mathbf{ RX}^{T}\mathbf{W}), \tag{3}\]  
where \(\mathbf{L}_{I}\) and \(\mathbf{L}_{P}\) define the intrinsic and penalty graphs, respectively, and \(\lambda\geqslant 0\) is a hyperparameter. Specifically, we only consider undirected graph and assume that \(\mathbf{L}_{I}\), \(\mathbf{L}_{P}\) and \(\mathbf{R}\) are symmetric and psd. Based on this formulation and these assumptions, the generality of RFA can be explained from the following two points:  
(1) If \(\lambda=0\), our algorithm can be simplified to a basic graph embedding model, so that some commonly used dimensionality reduction algorithms can be regarded as special cases of RFA;  
(2) Otherwise, if \(\mathcal{L}\) only contains relational information, RFA can be considered to use relational learning to reduce the dimensionality of data. For instance, the MDS algorithm is a special RFA algorithm under this condition.
# Biometric Authentication and Verification for Medical Cyber Physical Systems
## 3 Security  
Security is a fundamental requirement for WBANs as a result of the constant transmission of medical data. Since WBANs are transmitting important information, a security method is necessary to ensure communications remain private and to prevent security threats. Authentication, integrity authorization, availability, non-repudiation, and confidentiality must be implemented. Fraudulent instructions sent to actuators can have potentially fatal outcomes [17]. Patient data must not be  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Data Rate** & **Bandwidth** & **Accuracy** \\ \hline ECG (12 leads) & 288 kbps & 1–1000 Hz & 12 bits \\ ECG (6 leads) & 72 kbps & 1–500 Hz & 12 bits \\ EMG & 320 kbps & 0–10,000 Hz & 16 bits \\ EEG (12 leads) & 43.2 kbps & 0–150 Hz & 12 bits \\ Motion Sensor & 35 kbps & 0–500 Hz & 12 bits \\ Temperature & 120 bps & 0–1 Hz & 8 bits \\ Blood Saturation & 16 bps & 0–1 Hz & 8 bits \\ Glucose Monitor & 1600 bps & 0–50 Hz & 16 bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data transmission rates for several Wireless Body Area Network (WBAN) devices, adapted from [7].  
obtained by an adversary who could then use it to authenticate themselves as the wearer [15]. One of the marked challenges also is to answer the user's basic security questions. For instance, how does a user know if their critical health information is secure or not? How consistent or accurate is the data going to be? Sensor validations are subject to inherent communication and hardware constraints. Some of these constraints are experience by the majority of the sensors, but are more apparent for medical sensors.  
There are several protocols designed with the purpose to improve the energy efficiency of WBAN, and that means a longer life of the WBAN sensors [15; 23]. The data access security requirements would need to have access control, accountability and non-repudiation. One of the biggest questions we ask with the WBAN technology is going to be regarding the authentication. How do we know if it is the right person using the WBAN? Confidentiality, integrity, and dependability are absolutely necessary for WBAN networks. We will discuss several studies about the verification techniques of the WBAN and their respective effectivenesses and accuracies.
## 4 Results and Discussion
### Results and Analysis
#### 4.3.3 Ablation Study of Key Modules in the Proposed Method
\begin{tabular}{c} **R2** \\ **Ratio** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Sharpe** \\ **Drawdown** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Max** \\ **Drawdown** \\ \end{tabular}  } \\ \hline XGBoost [61] & 0.12 & 3.31 & 0.94 & 0.60 & 0.63 & 0.44 \\ BERT [63] & 0.16 & 3.62 & 0.87 & 0.67 & 0.67 & 0.39 \\ LSTM-based [65] & 0.22 & 3.91 & 0.78 & 0.74 & 0.74 & 0.33 \\ Transformer-based [64] & 0.25 & 4.13 & 0.72 & 0.78 & 0.78 & 0.30 \\ DQN [62] & 0.29 & 4.36 & 0.60 & 0.83 & 0. In the absence of the perturbation module, the model's adaptability to dynamic behavior changes declined significantly. When the profit-backtracking module was removed, the ability to capture long-term rewards was diminished, leading to reduced prediction accuracy and lower cumulative returns. Disabling the strategy stabilizer resulted in a notable increase in policy variance, compromising the consistency of execution. The full model consistently outperformed all ablated configurations, confirming the compounded benefit of combining all modules. From a theoretical perspective, although the backbone structure possessed basic temporal awareness and action-mapping capabilities, it lacked the mechanisms required to effectively address data sparsity and delayed feedback. The perturbation module introduced Gaussian and adversarial noise mechanisms, thereby constructing a localized generalization neighborhood within the input space to enhance policy robustness. The profit-backtracking module employs a multi-step bootstrapped return formulation defined as  
\[R_{t}^{(n)}=\sum_{i=0}^{n-1}\gamma^{i}r_{t+i}+\gamma^{n}\max_{a^{\prime}}Q(s_{t +n},a^{\prime}) \tag{33}\]  
where \(n\) is the lookahead step (set to \(n=5\) in our experiments), \(\gamma\) is the discount factor, and \(Q(s,a)\) is the estimated action-value function. This approach allowed the model to improve value-function accuracy, enabling it to optimize policy trajectories under long-term incentive structures. The strategy stabilizer reweighted historical action distributions to impose structural constraints in the output space, reducing volatility and enhancing policy coherence. Together, these modules jointly optimized the convergence trajectory from the input perturbation to value estimation and action generation, achieving the dual objectives of improved stability and increased return from a mathematical standpoint.