esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste  
[PERSON]  
1 Institute of Applied Science, Malta College of Arts, Science and Technology, PLA 9032 Paola, Malta 1  
Publications examining olive mill waste began to appear around the 1960s, motivated by scientific curiosity and the possible agricultural benefits of the waste produced during the processing of olives. Various uses have been proposed, including use as a fertilizer [11; 12] or as a renewable energy source [13; 14; 15]. Olive waste has also been proposed to be used as a low-cost substrate for the production of ethanol [16], xanthan [17] and laccase [18; 19]. Furthermore, this waste is additionally used for the production of biological active compounds [20] such as phenolic antioxidants from olive mill waste constituents, which is a viable alternative for the valorising of this problematic waste.  
Olive waste is made up of olive mill wastewater and olive pomace. Olive pomace is a heterogenous solid waste of olive oil production which contains many inorganic and organic compounds, such as magnesium, sugars, calcium, potassium and different phenolic groups. These phenolic groups are bioactive compounds with antioxidants; hence, they provide nutritional properties [21]. A study conducted by [PERSON] et al. [22] on the cellular antioxidant activity of olive pomace extract states that olive pomace is more potent compared to an equivalent dose of hydroxytyrosol and tyrosol.  
For this study, three different extraction procedures were used, including maceration extraction, acid hydrolysis and alkaline hydrolysis. Although maceration extraction is one of the most common extractions used, it may not allow a complete recovery of phenolic compounds that are linked by a glycosidic or ester bond to matrix components, such as cell wall polysaccharides and lipophilic structures. Thus, to break these bonds and achieve higher recoveries, alkaline or acid hydrolysis is performed [23]. These two different hydrolysis procedures are often used in the extraction of phenolic acids from plants.  
The main aim for this study was to investigate the effect of hydrolysis on the antioxidant activity of olive mill waste. Thus, phenolic extracts derived from waste pomace of monocultivar and coupage (mixed-olive) olive oil from the Maltese island were analysed for total phenolic (TPC), flavonoid (TFC) and _ortho_-diphenolic content (TdOPC), ferric reducing antioxidant power (FRAP), cupric reducing antioxidant power (CUPRAC), hydrogen peroxide scavenging activity, 2,2-Diphenyl-1-picrylhydrazyl (_DPPH_) _Radical Scavenging_ activity, 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid) (_ABTS_) _Radical Scavenging_ stabilization, and lastly, a modified method for _Superoxide Radical Scavenging_ activity capable of working under reducing conditions.
# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 2 Materials and Methods
### Ground Data  
During the passage of the Sentinel-1 satellite, field work was carried out. 58 wheat fields were selected as the sample plots for measuring vegetation water content from 24 May to 26 May, 2017.  
Figure 1: Study area location.  
The rule of selecting sampling area in the study is that the wheat growing state is similar and the area is larger than 30 m \(\times\) 30 m. Sample points are located at the center of the sampling area. Soil moisture content and wheat crop water content were collected two times respectively in each sample point and averaged. A week before the beginning of sampling, the weather conditions in the study area were good and there were no large-scale farming activities. The condition of soil and vegetation in the study area was normal. Field data included global positioning system (GPS) coordinates of the sample locations, fresh weight of wheat, fresh weight of wheat ear, dry weight of wheat, dry weight of wheat ear, and soil water content. The GPS data was collected using a Trimble TM GeoXH GPS receiver. Fresh weight of wheat was the weight of the aboveground biomass. Fresh weight of wheat ear was the weight of the wheat ear. The collected samples were dried in an oven (30 min at 105 \({}^{\circ}\)C, then 85 \({}^{\circ}\)C until the sample reached a constant weight) and the dry weights of the samples were recorded. Soil water content was measured using a TDR-300 (Time Domain Reflectometry) instrument at a soil depth of 10-20 cm. The water content of the ear, stem and leaf, and crop (Including ear, stem, and leaf) were calculated based on the weight difference before and after drying (Formula (1)).  
\[\mathrm{cwc}=\frac{\mathrm{w_{f}-w_{d}}}{\mathrm{w_{f}}} \tag{1}\]  
where \(\mathrm{wc}\) is crop water content, \(\mathrm{w_{f}}\) is the fresh weight, \(\mathrm{w_{d}}\) is the dry weight.
# Calibration and Analysis of Seeding Parameters of Soaked _Cyperus esculentus_ L. Seeds
## 1 Introduction
The above results demonstrate that using EDEM parameter calibration, the simulation test after the calibration of parameters closely matches the real test population distribution, with no significant differences between the two. When conducting precision seeding simulation experiments, it is necessary to set the intrinsic parameters of the seeds (density, Poisson's ratio, shear modulus), as well as the contact parameters between seeds, and between the seeds and seeder materials (static friction coefficient, rolling friction coefficient, restitution coefficient). According to the local standard DB15/T 3454-2024 of the Inner Mongolia Autonomous Region in China, \"Technical Regulations for Intercropping of _C. esculentus_ and Soybean\", _C. esculentus_ seeds can be soaked in water at 40 \({}^{\circ}\)C to 50 \({}^{\circ}\)C or in cold water before sowing [12]. Preliminary experiments have shown that soaking _C. esculentus_ seeds in cold water for 48 h until they are fully swollen results in a very high germination rate and germination potential. After soaking, the seed coat softens, and the granular filling, as well as the geometric characteristics and surface features of the seeds, change significantly. This makes it difficult to achieve the same results in simulation experiments as in actual experiments, necessitating the re-measurement and optimization of the intrinsic characteristics and contact parameters [13-17]. The intrinsic and contact parameters of _C. esculentus_ seeds have been measured by several researchers. These studies solved and calibrated the inter-grain static friction coefficient and inter-grain rolling friction coefficient. The results were verified through seeding simulation experiments, seeder test bench experiments, and field sowing experiments, showing no significant differences between the simulation and real experiments [18-20]. However, current research is primarily focused on dry _C. esculentus_ seeds for parameter calibration, without addressing the intrinsic and contact parameters of soaked _C. esculentus_ seeds. Therefore, this study takes soaked _C. esculentus_ seeds as the research object, measures their intrinsic and contact parameters, and constructs a filling model in EDEM. A steel cylinder is used to simulate the angle of accumulation of the seed population. A Plackett-Burman experiment was used to identify the inter-grain rolling friction coefficient, inter-grain static friction coefficient, and the rolling friction coefficient between _C. esculentus_ seeds and the steel plate as significant influencing factors. A steepest ascent experiment was employed to determine the optimal range for these significant factors. Finally, a Box-Behnken response surface experiment was conducted with the goal of minimizing the difference from the actual angle of accumulation of _C. esculentus_ seeds to find the best parameter combination. The results were verified by comparing the simulation and physical experiments, providing parameter support for the analysis of the precision seeding of soaked _C. esculentus_ seeds.
# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 5 Methodology
### Monitoring and Evaluating the Performance  
The performance of Micro-FL is assessed from two perspectives: (1) Federated learning performance, which encompasses the efficiency of the global model and execution time of the experiments, and (2) Software system performance, which includes CPU utilization and metrics pertinent to the message broker.
## 2 First Step of One-Bounce Scattering Signal Identification Algorithm  
### System Model  
The system model assumes that direct signal is either obstructed or interfered with by multipath components, signals only propagate through the NLOS signals. Although the exact scattering environments should involve multiple-bounce scattering signals, one-bounce scattering is assumed because multiple-bounce scattering signals can be ignored as they suffer from severe attenuation and are almost negligible [32], which is well-fitted for modeling multipath propagation channels, especially in urban or indoor environments, this assumption is also made in the work of [33].  
In one-bounce scattering environments, a signal is transmitted from the transmitter (MS) to the receiver (BS) via scatterer. For a simple scenario with one MS, two BSs and three scatterers, the geometric position relationship of BS, scatterer and MS, as well as one-bounce scattering signals are depicted in Figure 1. MS is located at T, S1, S2 and S3 are all one-bounce scatterers. A and B are BS. Suppose that BS A detects two one-bounce scattering signals from S1 and S2, path 2 and path 1, respectively. BS B detects two one-bounce scattering signals from S1 and S3, path 3 and path 4, respectively. In Figure 1, two one-bounce scattering signals from the same scatterer S1, path 2 and path 3, are detected by BS A and B, respectively. The scatterers like S1, whose scattering signals are detected by multiple BSs, will be located with our proposed algorithm and used as a virtual station for MS localization.  
Figure 1: Geometry of one-bounce scattering system model.
# Optimal Control of an Ultraviolet Water Disinfection System
## 3 Disinfection System's Optimal Control
### Statement of Optimal Control Problem  
This section addresses the optimal control of the UVDS, tackling simultaneously the two optimal criteria presented in the previous section. Hence, a bi-criteria optimization problem is stated in the sequel; roughly speaking, the optimization algorithm should determine the values \(I_{0}\) and \(Q_{0}\) such that:  
* the final value of bacterial abatement should be \(A_{0}\) (an imposed value);
* the control inputs minimize the energy consumed for irradiation.  
Hereafter, we present the constitutive elements of this optimal control problem denoted by UV-OCP1 (UV-Optimal Control Problem 1).  
(a) The dynamic model of the UVDS  
This relates to the simplified model presented in Section 2.2, that is \(I(t)=I_{0}\) and \(Q(t)=Q_{0}\) have constant values according to Remark 2. This simplified model is composed of Equations (2)-(4), (9), and (10) and completed with functions \(A(I_{0},Q_{0},t),\)\(k_{1}(I_{0},Q_{0}),\) and \(k_{2}(I_{0},Q_{0}).\)  
(b) Constraints  
Time horizon:  
\[t\in[0,\ t_{f}] \tag{15}\]  
\[t_{f}\ =\ \frac{V}{Q_{0}}\ =\ t_{\rm exp}\]  
The value \(t_{f}\) denotes the final time (the water passage time through the irradiation room). Bound constraints:  
\[I_{\rm min}\leq I(t)\leq\ I_{\rm max};\ Q_{\rm min}\leq Q(t)\leq\ Q_{\rm max}\]  
(c) Performance index  
\[\min_{I_{0},Q_{0}}\!\!J(I_{0},Q_{0})\ =\ w_{1}\cdot D(I_{0},Q_{0},t_{f})\ +\ w_{2}\cdot\Big{|}A(I_{0},Q_{0},t_{f})\ -\ A_{0}\Big{|}, \tag{16}\]  
\[w_{1},\ w_{2}\ >\ 0\]  
Expression (16) of the objective function \(J(I_{0},Q_{0})\) is a weighted combination of the two criteria and transforms our problem into a standard optimization problem with a single criterium. The numerical weights \(w_{1},\ w_{2}\) specify the relative importance we attach to the two criteria and, at the same time, allowing the ranges of both values to be compared. We can consider that \(w_{1}\) also includes the equivalent surface \(S.\) Hence, the first term corresponds to energy consumption. The second term indicates that the final abatement should approach the value \(A_{0}\) as much as possible.  
Instead of using an objective function with two numerical weights, we can use an equivalent expression as below:  
\[J(I_{0},Q_{0})\ =\ D(I_{0},Q_{0},t_{f})\ +\ \alpha\cdot\Big{|}A(I_{0},Q_{0},t_{f})\ -\ A_{0}\Big{|}, \tag{17}\]  
where \(\alpha\ =\ w_{2}/w_{1}\). The choice of value \(\alpha\) needs careful, practical analysis.
## 4 Results
### Overlap Comparison
The RMS residuals are approximately 10 cm and 20 cm in the radial and along components for BeiDou-2 IGSO satellites, respectively. For BeiDou-2 MEO satellites, the RMS residuals are approximately 5 cm and 15 cm in the radial and along components, respectively. \begin{table}
\begin{tabular}{c c c c} \hline
**PRN of BeiDou Satellites** & **Radial (cm)** & **Along(cm)** & **Cross-track (cm)** \\ \hline BeiDou-3 C31 (IGSO) & 12.51 & 24.95 & 23.82 \\ BeiDou-3 C32 (IGSO) & 11.88 & 25.81 & 24.32 \\ BeiDou-3 C33 (MEO) & 40.75 & 58.67 & 51.82 \\ BeiDou-3 C34 (MEO) & 41.16 & 59.88 & 59.11 \\ BeiDou-2 C01 (GEO) & 27.39 & 58.72 & 36.49 \\ BeiDou-2 C02 (GEO) & 25.72 & 38.79 & 29.09 \\ BeiDou-2 C03 (GEO) & 27.32 & 30.87 & 36.78 \\ BeiDou-2 C04 (GEO) & 41.55 & 69.19 & 47.12 \\ BeiDou-2 C05 (GEO) & 33.58 & 41.25 & 34.81 \\ BeiDou-2 C06 (IGSO) & 12.35 & 20.05 & 10.77 \\ BeiDou-2 C07 (IGSO) & 11.46 & 16.12 & 10.57 \\ BeiDou-2 C08 (ICSO) & 8.49 & 20.71 & 15.03 \\ BeiDou-2 C09 (ICSO) & 11.12 & 16.67 & 13.17 \\ BeiDou-2 C10 (ICSO) & 11.23 & 20.65 & 14.65 \\ BeiDou-2 C15 (ICSO) & 9.26 & 15.63 & 12.21 \\ BeiDou-2 C11 (MEO) & 5.69 & 12.45 & 6.37 \\ BeiDou-2 C12 (MEO) & 4.46 & 13.46 & 7.31 \\ BeiDou-2 C14 (MEO) & 5.22 & 14.41 & 12.19 \\ \hline \end{tabular}
\end{table} 
Table 4: Averaged daily root mean square (RMS) values of 24-h orbit overlap comparison. Figure 2: Overlap comparison of BeiDou-3 and BeiDou-2 orbit determination. Figure 4: BeiDou C32 satellite overlap comparison in the radial, along, and cross-track components. Figure 5: BeiDou C33 satellite overlap comparison in the radial, along, and cross-track components. Figure 3: BeiDou C31 satellite overlap comparison in the radial, along, and cross-track components.
## 2 Materials and Methods
### The US Salinity Laboratory Staff Diagram  
The US Salinity Laboratory Staff diagram [35] is used for evaluating the suitability of groundwater for agriculture purposes. According to this diagram, the groundwater can be classified on its salinity and sodium hazard. The sodium hazard (S) is a function of the Sodium Absorption Ratio (SAR) which measures the degree to which sodium (Na\({}^{+}\)) in irrigation water replaces the adsorbed calcium (Ca\({}^{2+}\)) and magnesium (Mg\({}^{2+}\)). The SAR is defined by the following equation:  
\[\text{SAR}=\frac{\text{Na}^{+}}{\sqrt{(\text{Ca}^{2+}+\text{Mg}^{2+})/2}}\]  
The resulting four categories of sodium hazard are: S1 is less than 10, considered to be low sodium hazard water. S2 is between 10 and 18, considered to be medium sodium hazard water. S3 is between 18 and 26, considered to be high sodium hazard water. S4 is more than 26, considered to be very high sodium hazard water.  
While the resulting four categories of salinity are: C1 is less than 250 uS\(\cdot\)cm\({}^{-1}\) and considered low salinity water. C2 is between 250 and 750 uS\(\cdot\)cm\({}^{-1}\) and is considered medium salinity water. C3 is between 750 and 2250 uS\(\cdot\)cm\({}^{-1}\) and is considered high salinity water. C4 is more than 2250 uS\(\cdot\)cm\({}^{-1}\) and is considered very high salinity water.  
In addition, the suitability of groundwater for irrigation purposes was estimated by using the diagram proposed by [PERSON] [47]. The water classes are determined by the quantity of mineral substances as indicated by the specific conductance (or meq\(\cdot\)L\({}^{-1}\)) and the percentage of sodium. In general, the water takes a progressively lower classification as the mineralization and percentage of sodium increase.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 4 Optimization of the Skeleton Cross-Section  
In this section, the optimization of the shape of the cross-section of Rogowski coils is discussed. The goal is to minimize the perimeter of the cross-section under constant magnetic flux, so that the stray capacitance \(C_{AB}\) is reduced without compromising other performance indicators of Rogowski coils, e.g., the mutual inductance \(M\).
## 2 Related Work  
With a variety of sensors mounted on smartphones, several researchers have studied intelligent notification systems [4; 5; 6; 7; 8; 9; 18; 19]. [PERSON] et al. and [PERSON] et al. unobtrusively captured user contexts and system configurations regarding the preference of notifications [18; 19]. To improve the performance of interruptibility models, [PERSON] et al. and [PERSON] et al. employed notification content as a feature to build a machine-learning model [4; 5]. [PERSON] et al. [6] and [PERSON] [7] proposed a system to predict breakpoints in which users can receive notifications with low interruptions. Furthermore, with the trained models, an intelligent notification coordinator was proposed [8; 9].  
Several studies have investigated smartwatch notifications [10; 11; 12]. [PERSON] et al. [10] performed a quantitative analysis using a large number of notifications (2.8 million) collected from in the wild. [PERSON] et al. [11] conducted qualitative user interviews. Such studies revealed that users are willing to optimize notification configurations based on the sender, topic, or even location. In addition to a single personal device, interruptibility works have been extended to multi-device environments [12; 13; 14; 15; 16]. These studies considered the emerging situation in which many people carry multiple mobile devices daily. In this emerging circumstance, _PASS_ tackled the problem of reducing notification delivery to a smartwatch from a smartphone to reduce user distraction. However, there is still a lack of longitudinal studies that investigate user behavior changes over time and evaluate the quality of models on an in the wild long-term dataset. Most existing studies experience accuracy drops caused by extreme changes in a newly collected dataset.  
Online learning has been used in the following domains to adapt to changing or new behavior from new data: human activity recognition [20; 21; 22], speech recognition [20; 21], and intrusion detection [23]. However, previous studies did not consider multi-device environments.  
There have been long-term studies on in the wild data analysis [24; 25; 26]. [PERSON] et al. [24] conducted a longitudinal study on smartwatch wearing habits. [PERSON] et al. [25] performed an analysis on a large number of notifications from the production-level service, Yahoo JAPAN. [PERSON] et al. [26] conducted a study on the usage style transformation for the long-term usage of smartwatches. However, prior studies did not consider adaptation to new data or multi-device environments. Therefore, there is scope to study users' behavioral changes over long-term periods. To the best of our knowledge, our study is the first to investigate the accuracy drop of prediction models induced by newly available data in multi-device environments.
## 4 Signal Preprocessing
### Doppler Fast Fourier Transform  
A slight change in the distance to the target resulted in a significant shift in the IF signal phase. To determine the target velocity, chirps separated by two or more times in duration \(T_{chirp}\) are required. Subsequently, a Doppler-FFT was applied across the phases received from these chirps. Therefore, the target radial velocity can be estimated by comparing the phase differences between two received signals. If the target is moving, the phase difference \(\omega\) can be calculated as:  
\[\omega=\frac{4\pi\ T_{chirp}\ v}{\lambda}, \tag{3}\]  
This approach can discriminate between targets moving at various velocities and at the same distance. The target velocity \(v\) for each moving target can be calculated as:  
\[v=\frac{\lambda\omega}{4\pi\ T_{chirp}}, \tag{4}\]  
where \(\lambda\) is the wavelength.  
The phase difference between the two chirps at the range-FFT peak is proportional to the radial velocity of the detected target. Applying the Doppler-FFT to the signal range spectrum yielded 2D range-Doppler localizations.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 3 Methodology  
Our proposed scheme addresses these issues by employing a layered trust assessment within dynamic behavior-homogeneous clusters. It dynamically segments clusters based on behavioral similarity, enabling localized and scalable trust evaluations that adapt to the high mobility of VANETs. The scheme aggregates trust values from diverse information sources and adjusts them using nodes' interrelationships, enhancing trust assessments' credibility and reliability. By combining interrelationships with node interactions, our approach mitigates the impact of false or manipulated data, improving robustness against malicious behavior. Additionally, the hybrid trust model effectively identifies misbehavior in complex networks, achieving a balance between accuracy and efficiency and overcoming the limitations of traditional node-centric detection methods.
# Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on \(L_{p}\) Decomposition
## 3 Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on \(L_{p}\) Decomposition
### \(L_{p}\) Decomposition Used in dMOEA/D-\(L_{p}\)  
In this paper, the \(L_{p}\) decomposition method is introduced and used in dMOEA/D-\(L_{p}\). Its detailed description can be presented as follows:  
\[\left\{\begin{array}{l}\min_{x\in\Omega}g^{L_{p}}(x,t\,|\lambda,z^{*}(t)\,)= \sum_{i=1}^{m}\{\lambda_{i}(|f_{i}(x,t)-z_{i}^{*}(t)|^{p})\}^{1/p},\\ \text{s.\ t.\ }g_{i}(x,t)\leq 0,i=1,2,...,s;h_{j}(x,t)=0,j=1,2,...,q,\end{array}\right. \tag{6}\]  
where \(g^{L_{p}}(x,t\,|\lambda,z^{*}(t)\,)\) is the scalar objective function. \(m\) is the number of objectives, \(\lambda=(\lambda_{1},...,\lambda_{m})^{T}\), for all \(i=1,...,m,\lambda_{i}\geq 0\) and \(\sum_{i=1}^{m}\lambda_{i}=1\). \(p\in(0,\infty]\), when \(p=1\), \(g^{L_{p}}\) becomes the weighted sum method, and when \(p=\infty\), \(g^{L_{p}}\) becomes the standard Tchebycheff function. The purpose of formula \(\left|f_{i}(x,t)-z_{i}^{*}(t)\right|\) is to minimize the maximum deviation. We have drawn the contour lines of the improved function with different values in Figure 1, showing that with the increase of \(p\), the search ability of \(L_{p}\) function decreases. All the \(g^{L_{p}}\) functions suffer from the geometry issue except for the Tchebycheff function. Especially, a \(g^{L_{p}}\) function cannot identify the whole Pareto optimal front of an MOP when the curvature of the Pareto optimal front is larger than the curvature of the contour line of the chosen \(g^{L_{p}}\) function. Since the curvature of the Tchebycheff function is \(\infty\), it is able to identify Pareto optimal solutions for any type of geometry.  
Overall, for a \(g^{L_{p}}\) scalar function, its search ability and its robustness on problem geometries is a trade-off. The higher the search ability, the lower the robustness. If we can estimate the curvature of the Pareto optimal front in advance, then we can easily select a suitable \(g^{L_{p}}\) function based on its search ability. For example, if the curvature of the Pareto optimal front is quadratic, the most suitable \(g^{L_{p}}\) function should be one using \(p=2\). Alternatively, for a new problem or a problem having a complex geometry, the use of the Tchebycheff function is a good choice.