esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition and Analysis
#### 2.4.1 Data Preprossesing  
The raw data were parsed from a text file and organized into a structured format (DataFrame) using the Python (version 3.10.12) Pandas library (version 2.2.3). Each entry included relevant fields, such as timestamp, event, valve state, sensor voltages, pressure, and EMG readings. The raw time data were converted to numeric values using Python's (version 3.10.12) Pandas library (version 2.2.3). Any rows with missing or non-numeric time values were subsequently removed to ensure a continuous and reliable time base. Data were filtered by each exercise for each condition (non-actuated, right hand actuation, and left hand actuation). In order to facilitate comparison across conditions, the time series were synchronized by aligning the onset of the phase to t = 0. Due to potential variability between sensor channels and across participants, each sensor signal was normalized using z-score normalization. Specifically, for each sensor channel, the mean was subtracted and the result was divided by the channel's standard deviation. For channels with zero variance, only the mean was subtracted. This normalization step ensured that differences in scale and sensor drift were minimized, thereby enabling a fair comparison of sensor responses across conditions.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Data Acquisition and Pre-Processing
#### 2.4.2 Time Series Construction  
The second pre-processing step was creating the time series for each life cycle of the organ tree (phenology). The remotely sensed spectral signature of organ trees varies over time due to several factors. A primary factor is the dependency of the Sentinel-2 imagery on sunlight; changes in sunlight intensity result in variations in spectral information. Additionally, alterations in the phenology contribute to spectral changes. To address this challenge, 24 image capture dates were selected for each year, representing various phenological stages in the organ tree's life cycle (Table 5). The inhabitants of the study area harvest organ fruits in the summer, during the months of July and August. We therefore considered that the cycle starts at the beginning of September and ends at the end of August. The dates were carefully and precisely selected based on specific criteria, such as choosing two images per month and ensuring that each image is ideal for the study, as shown in Table 5. The study years were selected because 2017 marks the first release of Sentinel-2 Level-2A data, while 2023 corresponds to the year in which the data used in this study were collected [102].
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 6 Experimental Results and Analysis
### Results of Cleaning Missing Data
The ARE values demonstrate a consistent stabilization below the threshold of 1.0%, which means that most repaired timestamps are very close to the corresponding original values. The above experimental results illustrate that the solution we proposed works well in missing data imputation on bus arrival and departure timestamps. Figure 10 also proves this conclusion. As shown in Figure 10, the \(x\)-axis represents a set of different stations belonging to a particular bus route, and the \(y\)-axis represents the timeline of the bus trip. The figure shows that the time difference between arrival and departure bus repairs is very small. The sum of time differences in arrivals is 241 s on this trip, and the sum of time differences in departures is 48 s. Among these, 66.67% of the time difference between the original and the repaired timestamps is less than 10 s, 91.67% of the time difference is less than 30 s, and 95.83% of the time difference is less than 60 s. In summary, the effectiveness of the multi-source data fusion cleaning method has been thoroughly demonstrated. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Metrics** & **MRR/\%** & **ARE/\%** & **R** \\ \hline Arrival time & 100 & 0.0690 & 0.9966 \\ Departure time & 100 & 0.0139 & 0.9984 \\ Arrival and departure time & 100 & 0.0418 & 0.9974 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of cleaning bus arrival and departure time data. Figure 10: Bus arrival and departure time fixed results.
# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 3 AI-Driven Blockchain Solutions
### AI-Driven Privacy Protection
#### 3.3.2 Federated-Learning-Based Privacy Protection for Blockchain
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Research ID** & **Research Title** & **Key Technologies** & **Application Scenario** & **Main Contributions** & **Limitations** \\ \hline \multirow{4}{*}{[PERSON] et al. (187)} & Blockchain-Enabled FL for Medical Data Sharing & Blockchain, FL & \multirow{4}{*}{Healthcare} & Improves privacy in medical data sharing, supports disease prediction & High latency in blockchain transactions \\  & Enhancing Security in Financial Transactions with Blockchain-Based FL & Blockchain, FL, Multiple ML Models & Finance & Detects fraudulent transactions, enhances privacy & Lack of real-world deployment and testing \\ \hline \multirow{4}{*}{[PERSON] et al. (189)} & SFedChain: Sure Data Sharing in Energy Storage Networks & Blockchain, FL, Computing & \multirow{4}{*}{Energy} & Protects distributed energy data privacy & Increased computational and storage demands \\ \hline \multirow{4}{*}{[PERSON] et al. (190)} & Enhancing IoT Supply Chain Security with Blockchain and FL & Blockchain, FL, Trusted Execution Environment & \multirow{4}{*}{Supply Chain} & Ensures secure computation and tamper-resistant data & Limited scalability due to TEE constraints \\ \hline \multirow{4}{*}{[PERSON] et al. (191)} & Blockchain-Based Multi-Task Privacy-Preserving FL Framework & Blockchain, FL, Data-Partitioned Blockchain & Industrial Applications & Supports multiple FL tasks, improves privacy & Complexity in multi-task model coordination \\ \hline \multirow{4}{*}{[PERSON] et al. (192)} & Practical and Privacy-Preserving FL with Verifiable Fairness & Blockchain, FL, Zero-Knowledge Proof & \multirow{4}{*}{General} & Ensures fairness in FL training, enhances privacy & High verification cost in real-world implementation \\ \hline \multirow{4}{*}{[PERSON] et al. (193)} & Optimizing Federated Person Re-ID in Smart UAV Delivery Systems & Blockchain, FL, Decentralized Mechanism & \multirow{4}{*}{UAV Delivery} & Prevents single points of failure, optimizes data privacy & Limited effectiveness in dynamic network environments \\ \hline \multirow{4}{*}{[PERSON] et al. (1944)} & Blockchain-Enhanced Reinforced FL for Scalable IoMT Privacy Protection & Blockchain, Reinforcement FL & \multirow{4}{*}{Healthcare IoMT} & Improves data security and communication efficiency & Needs further validation in large-scale IoMT systems \\ \hline \multirow{4}{*}{[PERSON] et al. (195)} & Blockchain-Integrated FL for Multi-Device Privacy Protection & Blockchain, FL, Reputation Mechanism & Multi-Device Communication & Enhances federated learning trust & Reputation mechanisms may introduce biases security & Reputation mechanisms may introduce biases \\ \hline \hline \end{tabular}
\end{table} 
Table 25: Blockchain-integrated federated learning for privacy protection.
## 5 Dynamic Pocket Feature Extraction
### Pocket Stability Calculation Based on Alpha Spheres
#### 5.2.2 Pocket Volume Sequence Extraction  
Because the pockets composed of alpha spheres have local overlap, it is relatively difficult to calculate the pocket volume directly through geometric operations. The Monte Carlo method provides an effective method, which uses statistical simulation to simplify the calculation (**R1**). The ratio of the number of simulation points in the pocket to the total number of simulation points multiplied by the volume of the bounding box is the volume of the pocket. Calculate the pocket volume by the Monte Carlo method,  
\[V_{p}=\frac{N_{p}V_{c}}{N_{c}}, \tag{3}\]  
where \(V_{p}\) is the value of the pocket volume, and \(N_{p}\) is the number of random points in the pocket. The \(V_{c}\) is the cube volume of package pocket, and \(N_{c}\) is the number of total random points.  
A major problem with the MD simulations method is that molecular jitter causes drastic data changes that cannot be directly observed on the whole timespan. Three schemes to assist the observation of pocket volume sequences are used in this paper: filtering, data compression, and data slicing. The Savitzky-Golay filter method [47] is a time-series data-filtering method based on a local polynomial least squares fit. The most important feature of this filter is to keep the original data shape and width unchanged. The volumes data compression method is also provided to compress the length of the volumes sequence by setting different compression rates. The filtering and data compression facilitate us to observe the trend of the full volume sequence, but it is difficult to observe the details of the data by these two methods. Therefore, the slicing operation of the sequence is provided to facilitate the establishment of an overall impression of the pocket, as well as to explore in-depth details.  
Figure 4: Amino acids lining the dynamic pocket.
# Change Control Design in Product Supply Chain System Based on Radial Basis Function Neural Network
## 3 Product and Supply Chain System Controller Design
### Optimal LQR Control for Nominal Systems  
LQR control, as a manifestation of optimal control theory, achieves optimal system control by minimizing the cost function. The cost function is typically a quadratic function of the system state and control inputs. Its goal is to achieve the desired control effect with minimal control effort while ensuring system stability. In product supply chain management, LQR control enables the product supply chain system to quickly reach an optimal state, thereby improving both response efficiency and stability.  
**Remark 3**.: _In a product supply chain system, only the nominal system is considered when adopting LQR control because LQR control cannot effectively suppress the influence of system changes._  
First, the cost function in optimal control is  
\[J=\frac{1}{2}\int_{0}^{\infty}(x^{T}Qx+u^{T}Ru)dt \tag{6}\]  
where \(Q\) and \(R\) are positive definite weight matrices that determine the relative importance of the state and control inputs in the cost function.  
The optimal LQR control law is  
\[u_{lqr}(t)=-Kx(t) \tag{7}\]  
where \(K\) is the controller gain matrix, and  
\[K=-R^{-1}B^{T}Px(t) \tag{8}\]  
which can be obtained by solving the algebraic Riccati equation,  
\[A^{T}P+PA+Q-PBR^{-1}B^{T}P=0 \tag{9}\]  
where \(P\) is a positive definite solution to the Riccati equation.  
The above methods and controllers ensure the asymptotic stability of the nominal system. In this process, the cost function represents the trade-off between adjustment error and control inputs, while the algebraic Riccati equation provides a means to determine the optimal feedback gain matrix, leading to the optimal control law. This approach improves both the stability and efficiency of the nominal system in the product supply chain.
## 3 Materials and Methods
### Instrumental Conditions  
High resolution imaging was carried out using a Hitachi SU-70 field emission scanning electron microscope (FESEM) (Hitachi, Maidenhead, UK) with Image J software for image processing. All samples were gold sputtered using a BaITec SCD 050 sputter coater (Ladd Research, 83 Holly Ct, Williston, VT, USA) prior to imaging at 1.5 kV. Surface areas were characterised by the nitrogen adsorption/desorption method using a Micromerities Gemini TriStar surface area analyser (Micromerities, Norcross, GA, USA). The balance used was a Sartorius Extend (Sartorius, Goettingen, Germany) and the sonication bath used was from Branson Ultrasonic Corporation (Danbury, CT, USA). FTIR analysis was carried out using a Bruker IFS 66 infrared spectrometer. An Ultimate 3000 capillary chromatography system (Thermo Fisher Scientific, Sunnyvale, CA, USA) was used to acquire all chromatographic data. The dimensions of polyHIPE columns used in silicosteel tubing were 229 mm \(\times\) 200 \(\upmu\)m i.d. The RP-HPLC separations were performed under the following conditions: the mobile phase was 50:50 acetonitrile/water, the flow rate was 5 \(\upmu\)L\(\cdot\)min\({}^{-1}\) with an injection volume of 1 \(\upmu\)L, column temperature was 25 \({}^{\circ}\)C and ultraviolet (UV) detection was at 214 nm.
# Analysis of Seaweeds from South West England as a Biorefinery Feedstock
## 3 Results and Discussion
### Lipid and High-Value Omega-3 Oil Content  
The FAME analysis was used to assess the total lipid content of each seaweed species (Table 2). The total concentration of lipids in the seaweeds are small in comparison to most land-based plant species [16] and varies with seasonality, generally peaking in late summer, decreasing over the autumn and winter and increasing again in spring [17]. Our data reflects this, with lipid content ranging from 0.8% of dry biomass in _Colpomenia peregrina_ to 2.9% in _Fucus serratus_. Whilst the overall lipid content was low, the proportion of commercially important long-chain polyunsaturated fatty acids (PUFAs) such as eicosapentaenoic acid (EPA) and arachidonic acid (ARA) was high, suggesting some seaweeds may prove viable sources of high-value nutraceuticalals, in a global market worth in the region of $400 billion annually [18]. All of the seaweeds tested with the exception of _Ullva lactuca_ showed high concentrations of ARA (C20:4) and/or EPA (C20:5). Fish-derived oils (themselves a bioaccumulation of algae synthesised PUFAs) are currently the major source of omega-3 and omega-6 PUFAs [19; 16]. Seaweed could provide a viable alternative source where, for example, in _Palmaria palmata_, despite having a lipid content of just 2.3% at the time of sampling, 50% (11.6 mg/g biomass) of this lipid was in the form of EPA (20:5). Given that the EPA content of commercially available fish oil supplements range between \(\sim\)40-500 mg/g [20], it is clear that seaweeds could make a big impact as a substitute feed stock for the production of high-value omega-3 animal feed supplements, nutraceuticalals, pharmaceuticals and cosmetics. Indeed, the recommended dietary ratio of \(\omega\)-6: \(\omega\)-3 PUFA is less than 10 [16] and with all of the seaweeds analysed here having a ratio of between 0.15 and 2, so offering a significant advantage to the food and feed industries. _Ulva lactuca_ (a common bloom forming species in the South West and globally) was the only seaweed tested which did not contain any EPA or ARA, although it did, however, contain a relatively high abundance of the essential fatty acid linoleic acid (18:2). The majority of the fatty acid content was palmitic acid (C16:0), which is used at high concentrations in products such as soaps and industrial
# Enhancing Friction Pendulum Isolation Systems Using Passive and Semi-Active Dampers
## 5 Results and Analysis  
### Performance under Several Earthquakes  
The isolator configurations studied in the former section (Table 2) are now numerically tested under eight selected earthquakes. As an example, the input ground acceleration, the structure acceleration, the bearing plate displacement and the inter-story drift responses for one of the earthquakes, the Northridge earthquake (17 January 1999, magnitude of 6.7 Mw) are plotted in Figure 15. All configurations are effectively working for the range of maximum ground acceleration.  
Figure 14: CDF of bearing plate displacement and drift under the serviceability earthquake.  
When the earthquake intensity is reduced, the high-friction-coefficient FPS tends to behave as the non-isolated structure, that is, it is unable to filter out the input ground motion. Moreover, it can be observed that both semi-active strategies perform in a similar way. Indeed, the CDFs for the bearing plate displacement and the inter-story drift are computed in Figure 16. Similar conclusions as for Figure 15 can be stated from this figure.  
Figure 15: Time history (TH) to Northridge earthquake for the five isolator configurations studied.  
Table 4 shows the PIs achieved for the eight selected earthquakes. They are located in the USA (North American plate), Japan (Pacific plate), New Zealand (Indo-Australian plate), and finally Italy and Greece (Eurasian plate) [27]. An important point to highlight is that the influence of the TH record and the frequency content in earthquakes, with similar intensity, may change drastically the system response. As an example, if one compares Corinth and Imperial Valley events, the last one is shorter, with broader frequency content, lower PGA, and high RMS value than the first one. The results of this table are summarized in terms of the performance function \(\phi_{2}\) in Figure 17.
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### Constitutive Model Under THMC Coupling with Governing Equations
#### 3.1.5 Coupling Effects in THMC  
The THMC model requires coupling these fields to understand rock behavior holistically. The mechanical field is influenced by temperature changes (thermal expansion), pore pressure (effective stress), and chemical changes (alteration of rock matrix). The governing equations for these fields are interdependent, and solving them simultaneously is necessary for accurate predictions.  
Full THMC-Coupled Governing Equations
* _Mechanical equilibrium equation_: \[\
abla.\sigma+\rho g=0\] (12) where \(\sigma\) is the stress tensor, \(\rho\) is density, and \(g\) is the gravitational acceleration in Equation (12).
* _Heat conduction and convection equation:_ \[\rho c\frac{\partial T}{\partial t}=\
abla.(k_{T}\
abla T)+QT-\rho cv.\
abla T\] (13)
* _Darcy's Law for fluid flow_: \[q=-k\
abla\rho,\hskip 14.226378 pt\
abla.q=S_{s}\frac{\partial p}{\partial t}\] (14) where \(S_{s}\) is the specific storage in Equation (14).
* _Reactive transport equation:_ \[\frac{\partial T}{\partial t}+\
abla.(-D\
abla c+vc)=R\] (15)  
The constitutive model of rocks under THMC coupling integrates the mechanical, hydraulic, thermal, and chemical processes that occur in deep rock formations. The governing equations for each of these fields are coupled, reflecting the interdependence of stress, temperature, fluid flow, and chemical reactions. This holistic approach allows for better predictions of rock behavior in complex geological environments such as those encountered in geothermal energy extraction, nuclear waste disposal, or deep mining. Solving these equations requires numerical simulations, as analytical solutions are not feasible due to the nonlinear and coupled nature of the problem.
## 4 Simulation Modeling Results
### BER Performance in AWGN Channel  
First, the BER performance of quadrature signals with optimal function \(a(t)\) without offset (QPSK) was analyzed. Figure 16 shows the error probabilities from the signal-to-noise ratio when using the coherent element-by-element detection algorithm for optimal FTN signals with pulse duration \(T_{s}=4T\) and \(T_{s}=8T\). In this case, it was assumed that there was no Doppler frequency shift. When determining the SNR, the energy of the optimal FTN signal was determined by taking into account (9). Therefore, when the oscillations in the preamplifier (Figure 12) were amplified, the signal energy increased according to (15).  
Considering the dependencies presented in Figure 16, the following conclusions can be drawn. First, by increasing the average power of the transmitted signal packet without exceeding the clipping level, it is possible to obtain a certain energy gain. At the bit error probability of \(p=10^{-4}\), these gains for signals with a pulse duration of \(T_{s}=4T\) were about 2 dB. For the signals with a duration of \(T_{s}=8T\), these gains were also significant; for \(N_{p}=8\), the gain was 5 dB for \(p=10^{-4}\).  
The BER performance for the optimal FTN signals with QQPSK is shown in Figure 17. The signal parameters were the same as those in Figure 16. As displayed in Figure 17, an  
Figure 16: BER performance of the optimal FTN signals with QPSK in the AWGN channel and pulse duration of: (**a**) \(T_{s}=4T\); and (**b**) \(T_{s}=8T\).  
Figure 15: PAPR values vs. the constraint on maximum PAPR of signal packet for BPSK.  
upward trend in energy gain for QPSK modulation was not very obvious. The energy gains did not exceed 1 dB for all the cases of the optimal FTN signals under study. However, as shown in Figures 10 and 11, the absolute PAPR values of the signals with OQPSK were significantly lower than those of the signals with QPSK modulation.
###### Abstract  
Shadows in very high-resolution multispectral remote sensing images hinder many applications, such as change detection, target recognition, and image classification. Though a wide variety of significant research has explored shadow detection, shadow pixels are still more or less omitted and are wrongly confused with vegetation pixels in some cases. In this study, to further manage the problems of shadow omission and vegetation misclassification, a mixed property-based shadow index is developed for detecting shadows in very high-resolution multispectral remote sensing images based on the difference of the hue component and the intensity component between shadows and nonshadows, and the difference of the reflectivity of the red band and the near infrared band between shadows and vegetation cover in nonshadows. Then, the final shadow mask is achieved, with an optimal threshold automatically obtained from the index image histogram. To validate the effectiveness of our approach for shadow detection, three test images are selected from the multispectral WorldView-3 images of Rio de Janeiro, Brazil, and are tested with our method. When compared with other investigated standard shadow detection methods, the resulting images produced by our method deliver a higher average overall accuracy (95.02%) and a better visual sense. The highly accurate data show the efficacy and stability of the proposed approach in appropriately detecting shadows and correctly classifying shadow pixels against the vegetation pixels for very high-resolution multispectral remote sensing images.  
invariant color space; multispectral images; shadow detection; shadow omission; threshold; vegetation misclassification; very high-resolution +
Footnote †: journal: Applied Sciences