esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization-Based Strategies  
Theoretically, the local optimization is to find the minimum value of the objective function in a limited space, while the global optimization is to find the minimum value of the objective function in the whole space. If the information for the entire driving cycle is acquired in advance, the optimal control consequence at each moment can be obtained by minimizing the objective function of the entire driving cycle, which means that the global optimal fuel economy can be obtained. The corresponding control strategy is implemented based on the known global driving information, which is defined as global information-driven optimization. If only the current state of the vehicle is available in the entire drive cycle, the optimal control sequence at each moment is obtained by minimizing the objective function of the current moment. Relative to the entire drive cycle, the local optimal fuel economy is obtained. The corresponding control strategy is implemented based on the current driving information, which is defined as instantaneous optimization.  
Therefore, the optimization-based control strategy contains instantaneous optimization and global information driven optimization. Equivalent consumption minimization strategy (ECMS) and model predictive control (MPC) strategy belong to instantaneous optimization. When the information for the entire driving cycle is acquired in advance, the deterministic dynamic programming (DDP) and Pontryagin's minimum principle (PMP) strategies are designed to achieve theoretical global optimal fuel economy over the entire driving cycle. Equivalent consumption minimization strategies (ECMS) expresses electrical energy as an equivalent fuel quantity by introducing an equivalence factor, which can achieve online control due to requiring less computational time compared with DDP. Meanwhile, due to the prior knowledge of the driving cycle and the uncertainty of the optimal equivalent factor, real-vehicle application of this strategy is limited.  
The deterministic dynamic programming strategy can obtain global optimal fuel economy; however, it suffers from the \"curse of dimensionality\", and can only be implemented offline, owing to its time consumption and requirement of a tremendous amount of memory. Additionally, the entire driving cycle information needs to be acquired in advance when applying the DDP or PMP algorithms, which is extremely difficult in real applications for energy management in HEVs. To overcome these issues, the stochastic dynamic programming (SDP) strategy was designed to optimize power distribution between different energy sources based on the prediction of driving condition.  
Since the driving information is updated in real time, the data-driven control strategy is adopted to achieve real-time optimization of energy management for HEVs, which includes the neural network-dynamic programming (NN-DP) strategy, the reinforcement learning (RL) strategy, and the adaptive dynamic programming (ADP) strategy.
# Forecast Analysis of Pollutant Emissions of Cruise Ship Routes in Western Mediterranean
## 4 Results  
### Cruise Routes in the Western Mediterranean  
The sample of ports analysed led to 4081 connections. Moreover, the cruise port network analysed is composed of 292 cruise routes (links) connecting 98 ports (nodes) associated with 47 cruise lines and 158 vessels. The routes registered in the Western Mediterranean during 2019 follow the Pareto 80/20 rule; namely, a small number of routes contain most of the connections. Specifically, 28.8% (84 routes) of all the routes registered a number of connections equal to or greater than 10, and cumulatively, they represented 85.7% (3497 connections) of all connections. Next, the emission inventory is estimated by considering all the routes identified to achieve the most accurate results.  
Figure 4 shows the graphical results of routes (links) by pair of ports (nodes) with equal or higher than 10 connections of the cruise shipping network of the Western Mediterranean, which are obtained through network analysis. In addition, the ports (nodes) of the routes with less than 10 connections are represented.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Machinery Type** & \begin{tabular}{c} **Slow-Speed** \\ **Engine** \\ \end{tabular} & \begin{tabular}{c} **Medium-Speed** \\ **Engine** \\ \end{tabular} & \
\begin{tabular}{c} **High-Speed** \\ **Engine** \\ \end{tabular} &
\begin{tabular}{c} **Gas Turbine** \\ **Gas Turbine** \\ \end{tabular} \\ \hline Specific fuel consumption (kg/kW\(\cdot\)h) & 0.195 & 0.215 & 0.230 & 0.290 \\ SO\({}_{2}\) (fuel of 0.5\% of max. sulfur content) (kg/kg\({}_{\text{fuel}}\)) & 10 & 10 & 10 \\ NO\({}_{x}\) emission factor (kg/kg\({}_{\text{fuel}}\)) & 87 & 57 & 57 & 7 \\ CO\({}_{2}\) emission factor (kg/kg\({}_{\text{fuel}}\)) & 3170 & 3170 & 3170 & 3170 \\ \hline \end{tabular} Source: Adapted from [45].  
\end{table}
Table 6: Emission factor and specific fuel consumption factor by propulsion machinery type.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Number and Arrangement of Cylinders** & **Number of Ships** \\ \hline V12 & 46 \\ V16 & 17 \\ V12 and L8 & 15 \\ L8 & 13 \\ L6 & 12 \\ L9 & 10 \\ L7 & 7 \\ L9 and L6 & 5 \\ V12 and V16 & 4 \\ V14 & 4 \\ V16 and V12 & 4 \\ L9 and V12 & 4 \\ V12 and V14 & 3 \\ L6 and L8 & 3 \\ L7 and L8 & 2 \\ L10 & 1 \\ V16 and V14 & 1 \\ L5 & 1 \\ L6 and V12 & 1 \\ L8 and L9 & 1 \\ \hline \end{tabular} Note: V and L denote V-type and in-line cylinder arrangement, respectively. The number after L or V indicates the number of cylinders of the engine. Source: Authors’ elaboration.  
\end{table}
Table 5: Distribution of the cruise fleet analysed by number and arrangement of the cylinders of the engines installed.  
Figure 4: Cruise routes with 10 or more connections during 2019 in the cruise port network of the Western Mediterranean. Note: The thickness of the lines indicates the traffic intensity (no. of connections) in each route. Source: Authors’ elaboration.
## 4 Conclusions  
With the help of DIC technology and based on a uniaxial test, and by taking loading rate, particle size and prefabricated fracture angle as variables, this paper carried out a study on the mechanical properties and strain field evolution laws of sandstone under uniaxial compression, which revealed the deformation and failure mechanism of sandstone. The main conclusions are as follows:  
1. With the increase in loading rate, the time for the growth of cracks inside the sandstone is no longer enough; thus, the peak strength and elastic modulus of the rock under uniaxial compression will gradually increase.
2. The increase in grain size will result in the decrease of homogeneity and uniformity of sandstone, which will further lead to the decrease in its peak strength, elastic modulus and density. Under the same stress conditions, fine-grained sandstone, compared with coarse-grained sandstone, will undergo a longer elastic deformation stage.
3. Although sandstone with prefabricated fracture subjected to uniaxial compression will undergo shear failure, tensile strain can also be observed near the crack tip. This means, essentially, that the failure of rock is tensile, and that shear is caused by the displacement of the tensile failure parts of the rock.  
Conceptualization, C.X. and Y.Y.; methodology, C.X. and M.Y.; validation, Y.S.; formal analysis, C.X.; data curation, P.L.; software, H.S.; writing--original draft preparation, C.X.; writing--review and editing, C.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51774167, and also by Provincial Natural Science Research Project of Colleges and Universities of Anhui Province, grant number KJ2019A0741.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methods
### Photonic Component
This could be used for expanding the photonic system to measure several joints in a human body using just one fiber and the same setup, as presented in Figure 1. The acquired data can be stored for subsequent analysis and investigation, thereby facilitating temporal comparisons. Furthermore, the developed system was engineered to achieve maneuverability, thereby enabling it to be compatible with a wide range of body kinematic movements. In Figure 2 the block diagram of the FBG's measuring system is represented. The gait monitoring system, the Fiber Bragg Grating (FBG) sensor, and the wireless inertial modules function as complementary components, each capturing distinct yet correlated aspects of knee joint biomechanics. The FBG sensor measures localized strain on the surface of the knee, providing high-resolution data on deformation caused by flexion and extension during walking. Simultaneously, the wireless modules comprising accelerometers and magnetometers track the angular orientation of the femur and tibia, capturing dynamic joint movement throughout the gait cycle. While these two sensing modalities operate in parallel, they do not require real-time mutual calibration. Instead, during system development, an initial correlation phase is performed where the FBG wavelength shifts are experimentally mapped to the angular displacements recorded by the inertial sensors. This one-time regression mapping allows the strain data from the FBG sensor to be interpreted in terms of joint angles, thereby enabling synchronized and accurate motion analysis. Once calibrated, the sensors can operate independently in practice, provided they are placed consistently. This dual-sensor approach enhances the  
Figure 2: The block diagram of the proposed measuring system for the FBG sensor. system's overall robustness and accuracy by combining high-resolution strain detection with three-dimensional angular tracking.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.3 Field Data  
FLUXNET GPP data: FLUXNET is a global network of micrometeorological tower sites used to measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. This global network includes more than eight hundred active and historic flux sites, which are dispersed across most of the world's climate space and across representative biomes. FLUXNET data are most commonly used in validation and calibration of ecosystem models, parameter estimation and optimization procedures in data assimilation systems [39; 40]. In our study, daily GPP derived by the daytime partitioning method from 59 FLUXNET sites (See Table S1 in Supplementary Materials) was firstly collected, and then eight-Day GPP were obtained by averaging the daily GPP to validate our GPP estimates and MODIS GPP product [41]. Vegetation types in these FLUXNET sites include deciduous broadleaved forest (DBF), evergreen broadleaved forest (EBF), evergreen needle-leaf forest (ENF), cropland (CRO), grassland (GRA), and mixed forest (MF). The locations of FLUXNET sites used in this study, and the IGBP landcover classes are shown in Figure 2.  
BigFoot NPP data: The overall objective of BigFoot was to provide ground validation for MODIS land cover, LAI, FPAR and NPP products [42]. Nine BigFoot sites were established from Alaska to Brazil. The BigFoot NPP product was generated using the Biome-BGC model [21], based on Landsat ETM+ derived land cover and LAI, tower measured meteorological data and eco-physical parameters. Each BigFoot NPP product covered a 7 \(\times\) 7 km area with a temporal resolution of one year and a spatial resolution of 25 m. In our study, BigFoot NPP data were used to validate NPP estimates and the MODIS NPP product. Basic information on BigFoot sites is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site Name** & **Latitude** & **Longitude** & **Biome Type** & **Year** \\ \hline NOBS & \(55.8853\) & \(-98.4773\) & \(\phantom{-}\) & \(\phantom{-}\) \\ AGRO & \(40.0067\) & \(-88.2915\) & \(\phantom{-}\) & \(\phantom{-}\) \\ HARV & \(42.5285\) & \(-72.1729\) & \(\phantom{-}\) & \(\phantom{-}\) \\ CHEQ & \(45.9453\) & \(-90.2731\) & \(\phantom{-}\) & \(\phantom{-}\) \\ METL & \(44.4508\) & \(-121.5733\) & \(\phantom{-}\) & \(\phantom{-}\) \\ TAPA & \(-2.86957\) & \(-54.9497\) & \(\phantom{-}\) & \(\phantom{-}\) \\ KONZ & \(39.0890\) & \(-96.5714\) & \(\phantom{-}\) & \(\phantom{-}\) \\ SEVI & \(34.3509\) & \(-106.6900\) & \(\phantom{-}\) & \(\phantom{-}\) \\ TUND & \(71.2719\) & \(-156.6130\) & \(\phantom{-}\) & \(\phantom{-}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of BigFoot sites.
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 3 Methodology
### Data Collection and Preprocessing
#### 3.1.1 Description of the Dataset
* **Imbalanced Data:** As with many real-world intrusion detection datasets, UNSW-NB15 exhibits class imbalance, where the number of normal records is significantly larger than the attack records. This imbalance presents a challenge for machine learning models and underscores the need for specialized techniques to handle skewed data distributions.
## 1 Introduction
### Outline of the Paper  
This paper is organized as follows: Section II describes the system model and the relevant assumptions. In Section III, we present the source to relay transmission. To this end, we propose the design of an integer forcing precoder. We also present the corresponding decoding process at the relay. Section IV describes the relay-to-destination transmission modeled as a point-to-point transmission. The theoretical performance analysis of our proposed scheme is presented in Section V. The numerical results are presented in Section VI, followed by conclusions in Section VII.
# Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data
## 2 River Flow Velocity Inversion Model
### Inversion Method
Figure 6 is the result of the FFT of the unwrapped residual phase (Figure 4) of the GPS PRN 9 and BDS IGSO 10 after unwrapping. Figure 5(a) is the spectrum of GPS PRN 9, the maximum value of the low-frequency component appears at 3.249 Hz and 3.047 Hz, which can be represented as the Doppler frequency \(f_{flow}\) generated by river flow. Considering the geometric relations in Figure 5, the \(V_{flow}\) could be deduced as follows [30]:  
\[V_{flow}=f_{flow}\cdot\frac{c}{\cos(\theta)\cdot f_{GNSS}} \tag{12}\]  
where \(c\) is the light speed, \(\theta\) is the elevation angle of satellite, \(f_{flow}\) is the low-frequency Doppler component caused by river flow, and \(f_{GNSS}\) is the carrier frequency of the satellite signal. According to the result of the maximum value of the low-frequency component in Figure 6 (upper panel 3.249 Hz and bottom panel 3.047 Hz), the flow velocity of the river can be calculated as 1.0217 m/s using GPS and 0.9758 m/s using BDS. Considering a larger river flow velocity condition, in the case of \(V_{flow}\approx 4\) m/s and elevation \(\theta\approx 50\) degrees, \(f_{flow}\) will be about 12 Hz according to Equation (12), which can be extracted from the residual phase using FFT. Therefore, this method can also be used under conditions of large river flow rates in theory, which is of great significance for water flow observation. Figure 6: Spectrum analysis of GPS PRN 9 (**a**) and BDS IGSO PRN 10 (**b**) in 2 min (10:40 a.m.–10:42 a.m. local time).
## 2 Materials and Methods
### Data Processing  
The data from various samples were analyzed and categorized using Microsoft Excel 2016. Area stacked plots, histograms, Sankey plots, and radar plots were generated using Origin 2022 software v9.900225. Thermograms and Wayne plots were created using TBtools software v2.119. Additionally, principal component analysis (PCA) was performed using a software cloud platform. Hierarchical cluster analysis (HCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed using SIMCA 14.1 software (V16.0.2, Sartorius Stedim Data Analytics AB, Umea, Sweden). An independent samples \(t\)-test was performed with SPSS 26.0 to determine the level of significant difference. Variable importance in projection (VIP) scores was obtained according to OPLS-DA, combined with an independent samples \(t\)-test at \(p<0.05\). Screening for differential aroma substances was conducted with VIP \(>\) 1, \(p<0.05\), and three measurements were obtained in parallel for each sample.
## 3 Materials and Methods
### Laboratory Analyses
#### 3.2.1 Magnetic Susceptibility Measurement  
Magnetic susceptibility is a well-established geophysical technique used to evaluate the magnetic properties of rocks and sediments. It quantifies the extent to which a material becomes magnetized in response to an external magnetic field, providing valuable insights into the mineralogical composition of geological materials, particularly the presence and concentration of magnetic minerals such as magnetite. For instance, rocks enriched in ferromagnetic minerals like magnetite typically exhibit high magnetic susceptibility, while those with low iron oxide content tend to show much lower values. In this study, magnetic susceptibility is expressed in SI units (m\({}^{3}\)/kg). These measurements offer a cost-effective, non-destructive method for characterizing the magnetic and compositional properties of subsurface materials, making them particularly useful in geological and geothermal investigations [46].  
In this study, magnetic susceptibility measurements were performed using the MS2B sensor of the Bartington Instruments system. Rock samples were first crushed and ground to a grain size of approximately 75 \(\upmu\)m using a tungsten carbide vibratory grinder to ensure homogeneity and measurement consistency. For measurement purposes, samples were shaped into cylindrical forms with a diameter of approximately 10 mm and a length ranging between 10-20 mm. For measurement, the powdered samples were formed into cylindrical shapes with a diameter of approximately 10 mm and a length ranging from 10 to 20 mm. Prior to measurement, all samples were carefully cleaned and oven-dried to remove any moisture or contaminants that could potentially affect the results [46]. The prepared samples were then placed into 10 cc plastic pots and inserted into the sensor's measurement chamber. Readings were recorded at both low (0.47 kHz) and high (4.7 kHz) frequencies, with a sensitivity of 0.1 SI units, to allow for detection of frequency-dependent susceptibility. To calculate the mass-specific magnetic susceptibility, the measured magnetic susceptibility values (\(\chi\)) were normalized by the corresponding sample densities (\(\rho\)), following the methodology described by [PERSON] et al. [46]:  
\[\chi_{LF} =\chi/\rho, \tag{1}\] \[\chi_{HF} =\chi/\rho\]  
The percent frequency-dependent magnetic susceptibility, \(\chi_{FD}\%\), is derived from the values obtained from Equation (1) through  
\[\chi_{FD}\%=\left[(\chi_{LF}-\chi_{HF})/\chi_{LF}\right]\times 100 \tag{2}\]  
The mass-specific and frequency-dependent magnetic susceptibility, \(\chi_{FD}\), is calculated using the values obtained from Equation (1), along with the density:  
\[\chi_{FD}=(\chi_{LF}-\chi_{HF})/\rho. \tag{3}\]
# Enhancing the Flavor Profile of Summer Green Tea via Fermentation with _Aspergillus niger_ RAF106
## 2 Materials and Methods
### Non-Volatile Extraction and Analysis
#### 2.5.7 Analysis of Soluble Sugars via Ultraviolet Spectrophotometry  
Tea extracts were prepared according to the method described in the China National Institute of Standardization (CNIS) GB/T 8314-2013 [15] for extraction. The quantitative analysis of soluble sugars was conducted using the anthrone reagent [16]. The extraction method was the same as in Section 2.5.2. In 1.5 mL centrifugal tubes, 100 \(\upmu\)L of a glucose standard assay solution and the sample extract were separately taken, with tertiary water used as a blank control. In an ice-water bath, 900 \(\upmu\)L of anthrone-sulfuric acid solution was slowly added and shaken well. The mixture was then heated in a boiling water bath for 9 min, cooled, and left for 30 min before measuring the absorbance at 625 nm. The content was calculated based on the standard curve of soluble sugar (y = 0.0078x + 0.0421, R\({}^{2}\) = 0.9996).
## 5 Conclusions  
This study highlights the benefits of using machine learning models for intelligent bandwidth allocation in UAV-GS communication systems. By analyzing key QoS metrics--throughput, the PDR, and delay--under both circular and random UAV movement patterns, we revealed important trade-offs between coverage and QoS stability. While random movement offers broader area coverage and better represents real operational variability, circular movement provides more stable connectivity, making it suitable for targeted monitoring scenarios such as disaster response and surveillance. The findings consistently show that the Bayesian Regularized FFNN outperforms LSGB, achieving a higher throughput and PDR while reducing delay. This improved performance can be attributed to the BRFFNN's ability to capture complex, non-linear interactions within the  
Figure 14: Standard deviation for throughput (UAV random movement).  
Figure 13: Standard deviation for delay (UAV random movement).  
data, leading to more accurate bandwidth predictions even under highly dynamic conditions. From an academic perspective, this work expands the growing body of research on intelligent bandwidth management in aerial networks. From a practical viewpoint, it offers a basis for adaptive bandwidth allocation strategies that can be applied to critical applications like emergency response, remote monitoring, and security operations.  
Nonetheless, this work has limitations. It is constrained to a single UAV and GS setup, which simplifies the network dynamics but does not reflect the complexity of multi-UAV coordination. Additionally, the models were trained and evaluated under simulation-specific conditions, meaning that their generalizability to real-world deployments with channel interference, diverse traffic patterns, and mobility challenges may be limited.  
Future work will concentrate on applying adaptive modulation schemes designed for low-latency, high-reliability 5G and beyond, implementing online learning and increasing this framework to multi-UAV coordination. We also intend to narrow the gap between simulation and deployment by implementing hardware-in-the-loop (HIL) validation or field testing and integrating real-world datasets to enhance real-world applicability. Furthermore, investigating multi-modal communication frameworks could increase robustness and make the models more appropriate for mission-critical, large-scale UAV operations.  
M.A.A.Writing--original draft, Methodology, Investigation, Conceptualization, Formal analysis, and Data curation. S.S.T.: Writing--review and editing, Validation, Formal analysis, and Supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.