esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results
### Establish of Forest Stand Age Inversion Model in Loess Plateau
#### 3.3.2 Establishment of Forest Stand Age Inversion Model Using MLR Model  
In this study, a multiple nonlinear regression model was selected to establish a remote sensing inversion model of forest stand age in the Loess Plateau. About 70% of the data (400 pairs of data) were selected as regression samples, and the remaining samples were used for dataset validation. The model structure of Age = a \(\times\)\(SR\) + b \(\times\)\(NDVI\) + c \(\times\)\(B_{5}\) + d (a, b, c and d were unknown parameters) was constructed using MATLAB 2023a, and the optimal solution of its parameters was obtained by using the partial least square method. The results are shown in Table 3. This model has high accuracy, and its \(R^{2}\) is 0.69.
# Control-Based 4D Printing: Adaptive 4D-Printed Systems
## 3 Integration of 3D-printed Sensors into 4D Printing
### Environmental Measurements  
Temperature and humidity are two essential properties to measure in 4D-printed systems interacting with the external environment. Functional polymers, due to their variable mechanical and electrical response to external stimuli such as humidity, temperature, pH and stress, are often used in 4D printing. In an adaptive 4D-printed system, sensing can be done in spatial layers using integrated sensors [88]. Electrically conductive extrudates, which contain conductive nano particles, have frequently been used in capacitive-based sensor 3D printing for measuring humidity and temperature [89].  
Chemical sensors operating in polyelectrolyte solutions were 3D-printed, which demonstrated reversible bending through the polarity changes of the electrode [48]. Three-dimensional printing enabled the calibration through the monitoring of electrolyte ion concentrations. There are also some studies on FDM-printed graphene-based PLA electrodes for electrochemical sensing purposes [90; 91].  
A conductive composite of PVDF and the multi-walled carbon nanotube (MWCNT) was prepared as the filament feed for the 3D printing of a chemoresistive sensor responsive to volatile organic compounds [92]. The 3D-printed sensor could have applications in adaptive 4D-printed systems in rescue and mine missions. A biological tissue-compatible pH sensor was 3D-printed for wet medium soft robotics applications. The sensor was demonstrated as operational under cyclic torsional and bending stresses and was made from hydrophilic polyurethane and poly(3,4-ethylene dioxythiophene) [93]. Biocompatible hydrogel sensors were also successfully 3D-printed for drug release and medical diagnostic applications [94; 95; 96; 97]. The material properties, geometry and dimensions of the print played a significant role in the performance of the sensors in the adaptive 4D-printed systems to produce a detectable response (Table 1). Each approach of embedding sensors has advantages and disadvantages. These are discussed in detail in earlier papers [4; 98].
## 4 Experimental Results and Discussion
### Experimental Settings  
#### 4.2.1 Experimental Environment and Baselines  
In subsequent experiments, a Windows personal computer with AMD Ryzen 7 5800H CPU and 16G of memory will be used.  
We compared the performance of the ISOD algorithm with eight state-of-the-art outlier detection algorithms. These eight outlier detection algorithms--k Nearest Neighbor (KNN) [42], Local Outlier Factor(LOF) [43], Isolation Forest (IForest) [33], Clustering-Based Local Outlier Factor (CBLOF) [44], Locally Selective Combination in Parallel Outlier Ensembles(LSCP) [45], One-Class Support Vector Machines (OCSVMs) [46], Deep Isolation Forest (DIF) [47], and GANomaly [48].
## 1 Introduction
### Development of OSC System
Therefore, researchers suggested a remarkable signal processing technique for high-performance and least EVMs, i.e., DSP, compensating for the effects of phase mismatching, PWB, Kerr's effects, and other noises using different algorithms. Most OSC systems now incorporate PDM and multilevel modulations for high-speed and long-distance transmissions.
## 4 Experiments Results and Discussion
### Results and Discussion
#### 4.3.2 Results of the Single Detonation Shock Tunnel Test  
For the validation of the proposed NTC sensors, the flat plate with the two types of NTC sensors was tested in the single detonation shock tunnel, where the test time is much longer than that of the detonation shock tunnel. The detonation section was filled with a mixture of 2H\({}_{2}\) + O\({}_{2}\) + 1.25N\({}_{2}\) at a pressure of 0.12 MPa. The total pressure was about 0.725 MPa, and the temperature was about 2862 K. The stagnation pressure history is shown in Figure 18 for a test time of about 30 ms. Six experimental runs were conducted in the single detonation operation mode, with three test runs for plates 1 and 2, respectively. The sensor installations were the same as those in the detonation shock tunnel experiments. The free-stream parameters are not displayed here since theoretical methods are not available to obtain the heat flux on the flat plate for the combustion products of 2H\({}_{2}\) + O\({}_{2}\) + 1.25N\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor No.** & **C04** & **C05** & **C06** & **C07** & **C08** \\ \hline \(a\) & 0.038 & 0.035 & 0.030 & 0.040 & 0.041 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effusivity calibration results of the NTC-2.5 sensors.  
Figure 17: Heat flux curves of C02 (NTC-10) and C05 (NTC-2.5) with/without corrections; the data of C05 before the correction is the average of three test runs.  
Figure 18: Stagnation pressure histories of the single detonation shock tunnel.  
Figure 19 shows the heat flux curves of NTC-10 and NTC-2.5. Both sensors underwent three test runs. The repeatability of both sensors is good. The heat flux curve of NTC-10 shows a downward trend with time, which is attributed to the continuous decrease in stagnation pressure, as shown in Figure 18, rather than a sensor problem. Besides, the heat flux obtained from the NTC-2.5 is smaller than that of the NTC-10 and shows the same regularity, as shown in Figure 16, in the detonation shock tunnel experiments. This result is attributed to the much greater heat loss from the CVD diamond to the epoxy than to the air. Since the test time is much longer in the single detonation shock tunnel, it is obvious to find that the difference between these two sensors increases over time, which also corresponds with the simulated results shown in Figure 6.  
Subsequently, the heat flux curve of C05 in Figure 19 is corrected by Equation (14), and the results are shown in Figure 20. It should be noted here that the value of \(a\) is the value obtained from the results in Section 4.3.1. The corrected/uncorrected results of C05 are shown in Figure 20. The corrected results of C05 agree well with the heat flux of C02, which has a small deviation from the real heat flux loading, as discussed in Section 4.3.1. Thus, the dynamic correction method for the NTC sensors shows good performance, even for a test duration as long as 30 ms.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 3 Experiment Setup and Results
### Experiment Setup  
In order to explore different performances while using different parameters of interest, the following configuration choices were made, and the snapshot data set was processed using all combinations of these settings:  
* Integration time (ms): \(\{40,60,80,100\}\);
* Signal bandwidth (MHz): \(\{6.36,7.95,10.6,12.72,15.9,21.2,25.44,31.8\}\);
* Baseline distance (km): \(\{0,5,10,15,20,25,30,35,40,45,50\}\).  
As mentioned before, the integration time was controlled by cropping the original collected signal to the desired duration and the signal bandwidth was adjusted in the front-end by low-pass filtering and then resampling to the Nyquist frequency. The baseline distance change was implemented by feeding different VRS data files to the RTK filter.  
Figure 6: Antenna location for snapshot data collection.  
The RTK positioning engine used for this research is RTKLIB, [30; 38]. The RTK fix rate was finally calculated with the LRF threshold set to two.
## 4 Results II--Review of Research Clusters
### Cluster 1: Risk/Accident Analysis
#### 4.2.2 Sub-Cluster 2: Risk Analysis
The results showed that this methodology achieved a 24.78% cost reduction compared to conventional maintenance strategies, demonstrating its contribution to improving operational efficiency for fishing vessels. The proposed approach effectively incorporates uncertainty to optimize maintenance planning and provides reliable analysis by considering interactions between risk factors. However, there is a limitation in that the computational burden increases significantly when applied to large-scale systems. This issue is similar to the challenges highlighted by [PERSON] et al. (2017) [14]. To address this, the study suggests that simplifying the model and developing automated tools are necessary to improve analytical efficiency.
# Time Series Visualization and Forecasting from Australian Building and Construction Statistics
## 3 Methodology
### Data Processing
#### 3.1.2 Data Preprocessing  
ABS provides both state- and city-level statistics. For most of the city-level statistics, it only has capital city's data and does not have data for the rest of the state. We consider this as incomplete data, as it only represents very limited cities. Instead, our project considers state-level data that either represents the statistics of the whole state or the whole of Australia. Identifying the data sheets we want can be done by parsing the title of the data sheets, which follows a naming convention that contains three parts: chart title, data type, and region. For example, a data sheet is named \"Value of Building Work Done, By Sector, Original, Australia\", for which the chart title is \"Value of Building Work Done By Sector\", the data type is \"Original\" and region is \"Australia\", indicating it is Australia-wide data.  
Each of the kept data sheets contains three types of data, i.e., original, seasonal, and trend time series. The original time series is raw time series data that has not been modified with any of the data analysis techniques. In our application, we only consider this unchanged type of data. The seasonal time series is the original data, with seasonal effects removed. We do not consider this type of data in our application. The trend time series shows general trends of the statistics. It is the data calculated by the data analysts from ABS. As it is significantly incomplete, we ignore this type of data.  
The first nine rows of each data sheet describe the attributes of the columns. Among them are three rows, i.e, unit, series type, and frequency, which contain important information for our preprocessing. _Unit_ is used to check whether the data are in the same unit, so that different data can be put on the chart after changing the unit. The unit is denoted with a \"$\" sign, trailed by \"000\", which means the data are recorded in the thousands. _Series Type_ is used for filtering out only the original data, and only the original data are kept in our application, as aforementioned. The series type is recorded as a string, so we only look for the string \"original\" to identify original time series data. _Frequency_ ensures the time interval between each data point is identical. In our application, we need to make sure that the frequency is four, so that the data entries are recorded quarterly. Due to the data availability, some data sheets contain data that are dated back to 1983, while other data sheets provide more recent data only. Therefore, the number of data available for each section, under each category, varies. The longest time coverage is provided by _total number of dwelling units of New South Wales_ data, which contains 457 row entries. Additionally, the shortest time coverage is provided by _residential property price indexes_ data, which contains 78 row entries. We keep the full length for visualization, but keep consistent length when the statistics are considered features (i.e., the economic time series).
# Construction of a Numerical Model for Flow Flash Evaporation with Non-Condensable Gas
## 2 Construction of the Numerical Model
### Model Construction and Governing Equations
#### 2.3.5 Phase-Change Lee Equations  
Based on the above model, the values of the source terms \(\dot{m}_{\rm lv}\) and \(\dot{S}_{\rm lv}\) in Equations (3) and (7) were further varied to simulate the phase-change process.  
The transfer of water from the liquid phase to the gas phase during the phase-change process is described by \(\dot{m}_{\rm IV}\) in the volume-fraction equation, which ultimately leads to a decrease in \(\alpha_{\rm I}\) and an increase in \(\alpha_{\rm V}\). The decrease in sensible heat owing to evaporation is controlled by the energy source term \(\dot{S}_{\rm IV}\) in the energy equation.  
In this case, the values of the source terms were obtained from the modified Lee model, as shown in Equations (16) and (17).  
\[\dot{m}_{\rm IV}=\begin{cases}c\times\frac{\alpha_{\rm I}\rho_{\rm I}\left(T-31.35p_{\rm w}\,^{0.1526}+191.1\right)}{31.35p_{\rm w}\,^{0.1526}+191.1}&\left(T_ {\rm I}>31.35p_{\rm w}\,^{0.1526}+191.1\right)\\ 0&\left(T_{\rm I}\leq 31.35p_{\rm w}\,^{0.1526}+191.1\right)\end{cases} \tag{16}\]  
\[\dot{S}_{\rm IV}=-\dot{m}_{\rm IV}r \tag{17}\]  
where \(c\) represents the time relaxation parameter, \(p_{\rm w}\) is the partial pressure of local water, and \(r\) is the latent heat of evaporation.  
As shown in the two equations above, this model first determines whether the cell is undergoing a phase transition, and it then calculates the source terms of the phase-transition cell using the local temperature and static pressure. This approach does not require a distinction between nucleation boiling and interfacial mass transfer and uses the same operation to deal with the boiling phenomenon that occurs in both the phase-interface cell and the cells filled with liquid, which leads to a simple and effective simulation of the full-scale flow boiling phenomenon.  
Compared to the original Lee model, the main change in the previously described model is that the driving force of the source term is provided by the local pressure and temperature. This modification is important because a small local pressure change at low ambient pressures can have a significant impact on the saturation temperature in that the distribution of water pressure due to gravity can lead to a non-uniform distribution of the flash vapor rate.
## 4 Numerical Simulation
### System Parameters
The position vector of point 1 in the coordinate system \([\theta_{w}\_{x}\_{w}\_{w}\_{w}\_{w}]\) is \(\mathbf{p}_{n1}=[n_{x1},\ n_{y1},\ n_{z1}]^{T}\), where \(n_{x1}\) = 0.12 m, \(n_{y1}\) = 0 m and \(n_{z1}\) = 0 m. The position vectors of the rest points can be calculated along the path. The parameters for the cutter tool are described below. The teeth number and helical angle of the milling cutter are 2 and \(\pi/3\) in this simulation. The diameter of the tool is 0.024 m, and the material of the tool and workpiece are uncoated tungsten carbide and aluminum alloy, respectively. The axial \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Link** & **Masses (kg)** & **Position Vector of CG (m)** & **Inertia Tensors (kg/m2)** \\ \hline
1 & 80 & \((0,0,0.1)^{T}\) & diag (0.9, 1, 1) \\
2 & 40 & \((0.4,\ 0,0)^{T}\) & diag (0.6, 6.5, 6.5) \\
3 & 55 & \((0.4,\ 0,0)^{T}\) & diag (0.8, 9.5, 9.5) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The inertial properties of the links. Figure 5: The reference robotic milling system in the simulation: (**a**) The reference coordinate systems of the robot and work table and (**b**) the model of the workpiece and the milling groove. cutting depth and radial cutting depth are 0.002 m and 0.004 m, respectively, and the feed per tooth \(f_{t}\) is 0.00012 m. The coefficient parameters \(k_{t1}\) and \(k_{t2}\) for calculating the cutting force (see Appendix A) are experimentally determined as 387 and 0.0018, respectively. Similarly, parameters \(b_{1}\) and \(b_{2}\) are determined as \(-0.327\) and \(-0.224\), respectively [43]. The readers are referred to the Appendix A for more information about these cutting force parameters.
## 3 Results and Discussion
### Inconsistency in Sensor View Imposed Significant Variability RapidEye VI Model Performance
Given the increasing amount and complexity of remote sensing data at the disposal of terrestrial scientists, multi-resolution and time series data assimilation approaches to developing temporally and spatially resolved remote sensing products will need to address the confounding effects of view angle on apparent vegetation cover, especially in sparse canopy systems.
## 4 Results
Figure 13: Behavior of biohydrogen purity in the PSA process controlled by the NN controller. Figure 16 shows the behavior of the NN controller at the Aspen model and the H-W model when a disturbance occurred at time 3000 s in the output of the system. We can observe an overshoot at this time because of the disturbance applied. Figure 16: Behavior of the NN controller when a disturbance in the output occurred at 3000 s.  
Figure 9:0 Control signal of the NN controller. Figure 9:1 Temperature to control the purity of biohydrogen. Figure 17 presents the NN controller's performance in response to a disturbance at 3000 s in the output of the system; remember that first the NN controller is applied without the inverse blocks. The temperature to control the heat in the reboiler to maintain purity when using the inverse blocks is presented in Figure 9:2 In this figure, we can see the disturbance that occurs at 3000 s, but the purity of the product is not affected, maintaining the margin; this is because the controller sends a signal that keeps it cyclically stable in the reference (see Figure 16). Furthermore, Table A1 presents an analysis of the flows and energy consumed in the adsorption process in the 44 th cycle. Also, Table 7 shows the productivity report for the 35 th cycle. Analyzing the table, we concluded that we had an efficient separation process with minimal reverse flow. In addition, we obtained high hydrogen purity, about 0.99 kmol/kmol, in most of the streams. Carbon monoxide maintains a steady presence at around 11%, while methane remains at about 28%, with negligible reverse amounts. The total forward material is consistently distributed across the different streams, ensuring a smooth process operation. The energy efficiency and recovery of H\({}_{2}\) are reported in Table 8 where we can find that most of the energy is held in the waste stream. The system successfully removes impurities. Figure 9:3 Behavior of the NN controller when a disturbance in the output occurred at time 3000 s.  
Figure 9:4 Temperature to control the purity of biohydrogen when a disturbance in the output occurred at time 3000 s.  
Table 7 shows the productivity achieved in each section of the process using NN control. Significant results are observed since the productivity is higher than that obtained in the open loop (without control), generating a productivity of 0.0024 kmol and \(6.14333\times 10^{-4}\) kmol/s. Table 8 shows the results of energy recovery and efficiency. Biohydrogen recovery is above 50% using NN control, generating an energy efficiency of 1.67%. These results have great scientific contribution since little energy is used to reach a purity of 99% with less energy and greater recovery. In addition, it mitigates the disturbances that exist in this complex technology.
# Analysis and Synthesis of Architectures for Automotive Battery Management Systems
## 4 Analysis of Centralized BMS Architectures
### Wireless Communication  
A few manufacturers manage the communication traffic between the master and the slave units wirelessly, which benefits in less cables between each unit and an easier malfunction detection of the single modules. However, this design requires further transmitter modules and needs to be safe against the electromagnetic interference of third players in the EV environment as well as against access from the outside by persons or malware. None the less, through a certain point, wireless communication inside an BMS could lead to many benefits. A wireless communication enables the ability to share parameters to systems beyond the vehicle, such as mobile devices, charging stations, solar roofs and smart home systems. In addition, the BMS and the home storage systems demand energy and work either as an rechargeable energy storage system (RESS) or as a buffer in times where less electrical energy is used than produced. Communication between the battery and the charging station can enable new efficient or battery life-saving charging strategies. Moreover, third party systems, such as security systems, can take advantage of a wireless transmission of BMS data.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Multi-Scale Feature Adaptive Enhancement (MS-FAE) Module
#### 3.2.1 Multi-Scale Feature Pyramid Fusion  
To fully utilize the complementary information from feature maps at different levels, we design a top-down feature fusion pathway. Specifically, the \(P2\) (1/8), \(P3\) (1/16), and \(P4\) (1/32) feature maps extracted from the backbone network are fused as follows: First, the \(P4\) layer is upsampled to the size of \(P3\) using transpose convolution and then added to the \(P3\) feature map as follows:  
\[C3=\text{ConvTrans}(P4)+P3 \tag{1}\]  
Next, C3 is further upsampled to the size of \(P2\) and fused with the \(P2\) feature map as follows:  
\[C2=\text{ConvTrans}(C3)+P2 \tag{2}\]  
To further enhance the multi-scalereceptive field, we introduce an Atrous Spatial Pyramid Pooling (ASPP) module at the C2 layer, which extracts multi-scale contextual information using convolution kernels with different dilation rates (1, 3, 5) as follows:  
\[F_{\text{context}}=\text{Conv}_{1\times 1}(\text{Concat}[C2,D_{rate=1},D_{rate =3},D_{rate=5}]) \tag{3}\]  
Here, \(\text{Conv}_{1\times 1}\) is used to reduce the channel count back to the original dimension. This approach preserves the spatial details of high-resolution feature maps while incorporating the semantic information from higher-level features.