esa-sceva/satcom-chunk-collection huggingface dataset:
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 6 Current Developments in Using Sentinel-2 for Phenological Research
### Gap Filling Techniques for Phenological Research Using Sentinel-2
Though the evolution of different gap-filling techniques has led to large improvements in time series analysis of vegetation phenology, they can only be used as a surrogate for real data. Even within the HLS data set, [PERSON] et al. 2020 [61] found missing high quality information for periods of up to four months. Similarly, issues are expected to be encountered when analysing time series data for areaswith extensive and frequent cloud cover [28]. Geostationary satellite sensors, such as the SEVIRI and Himawari-8, that provide multiple images at a sub-daily resolution, could help in further improving the identification of cloud-free data for accurate monitoring of land surface phenology [154; 155]. Studies [155; 156; 157; 158] evaluating the performance of geostationary datasets to construct time series of vegetation indices and estimate different LSP metrics have reported an increase of more than 50% cloud-free data in comparison to data from MODIS and VIIRS. Though improvements in SOS were marginal, the EOS estimated from the geostationary satellites were within days of the observed in situ dates, whereas MODIS-retrieved dates deviated by up to a month. The high temporal resolution of such geostationary satellites could thus be used in conjunction with Sentinel-2 sensors to model phenology effectively; however their low spatial resolution and limited viewing angle can limit their applicability for fragmented landscapes and at higher latitudes.
## 5 Conclusions  
To sum up, we have proposed an ultra-broadband linear polarizer. The experimental results show that the conversion from linear polarization to cross-polarization can be achieved in the range of 14.8-28.0 GHz with a relative bandwidth of 61.7% and a corresponding PCR greater than 95%. The working principles and physical mechanisms are analyzed by the electric field decomposition and surface currents. Moreover, based on the Pancharatnam-Berry phase theory, a one-dimensional gradient metasurface and 1-bit coded metasurface are proposed. The simulated results show that \(-23^{\circ}\) anomalous reflection can be realized at 15-27 GHz under normal incident left-handed circularly polarized waves. In addition, the polarizer achieves a 10 dB RCS reduction at 15.3-28.0 GHz with a relative bandwidth of 65%. The EM wave scattering characteristics of the 1-bit coded metasurface are more diverse than those of the PEC and checkerboard structures. The design presented in this paper has potential applications in stealth and antenna design, and other areas.  
Conceptualization, X.L.; methodology, X.L.; software, Y.W.; validation, Y.W.; formal analysis, X.L.; investigation, J.F.; data curation, J.F.; writing--original draft preparation, J.H.; writing--review and editing, X.H.; supervision, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Project of Science and Technology of Shaanxi (2021)M-395).  
All data are contained within the article.  
The authors declare no conflict of interest.
# Optimization of Airflow Organization in Bidirectional Air Supply Data Centers in China
## 2 Materials and Methods
### Thermal Metric  
Three thermal performance metrics are commonly used to evaluate bypass airflow and recirculation in data centers: the Return Temperature Index (RTI), Supply Heat Index (SHI), and Rack Cooling Index (RCI). Among these, SHI can be applied for both room-level and row-level assessments. RTI assessed CRAC airflow adequacy, SHI evaluated cooling air supply efficiency, and \(\mathrm{RCI}_{\mathrm{HI}}\) quantified compliance with ASHRAE thermal guidelines while reflecting rack cooling effectiveness and overall thermal health status. The mathematical definitions of these metrics are provided in Equations (9)-(11) [31; 32].  
\[\mathrm{RTI}=\frac{\Delta T_{\mathrm{CRAC}}}{\Delta T_{\mathrm{IT}}}\times 100\% \tag{9}\]  
\[\mathrm{SHI}=\frac{\delta Q}{\delta Q+Q}=\frac{\sum\limits_{j}\sum\limits_{i}m_ {i}c_{p}((T_{\mathrm{in}}^{\mathrm{s}})_{i,j}-T_{\mathrm{CRAC,supply}}))}{ \sum\limits_{j}\sum\limits_{i}m_{i}c_{p}((T_{\mathrm{out}}^{\mathrm{s}})_{i,j}- T_{\mathrm{CRAC,supply}}))} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **AA** & **AB** & **AC** & **AD** & **AE** & **AF** & **AG** & **AH** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: The measured power consumption of each rack (kW).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constants** & **Values** & **Type** \\ \hline CRAC supply air temperature & 19 \({}^{\circ}\)C & Velocity inlet \\ CRAC supply air rate & 40–60\% & Pressure outlet \\ CRAC return air & / & Volumetric heat source \\ Server heat dissipation & Actual measurements & Porous jump model \\ Perforated tile porosity & 36\% & Porous jump model \\ Rack door porosity & 86\% & Porous jump model \\ walls & / & Adiabatic wall \\ \hline \hline \end{tabular}
\end{table}
Table 3: Boundary conditions for the data center.  
\[\text{RCI}_{\text{HI}}=\left[1-\frac{\sum\left(T_{\text{in}}^{\text{s}}-T_{\text{r, max}}\right)_{T_{\text{in}}^{\text{s}}>T_{\text{r,max}}}}{\left(T_{\text{a,max}}-T_{ \text{r,max}}\right)n}\right]\times 100\% \tag{11}\]  
where \(\Delta T_{\text{CRAC}}\) is the average temperature difference between the supply and return air of the CRAC units, \({}^{\circ}\)C; \(\Delta T_{\text{IT}}\) is the average temperature difference between the inlet and exhaust air of server racks, \({}^{\circ}\)C; \(m_{i}\) is the mass flow rate of inlet air to server racks, kg/s; \(c_{p}\) is the specific heat capacity of air, J/(kg.\({}^{\circ}\)C); \(T_{\text{in}}^{\text{s}}\) is the inlet air temperature of server racks, \({}^{\circ}\)C; \(T_{\text{out}}^{\text{s}}\) is the outlet air temperature of server racks, \({}^{\circ}\)C; \(T_{\text{r,max}}\) is the recommended upper limit temperature for the data room, 27 \({}^{\circ}\)C; \(T_{\text{a,max}}\) is the allowable maximum temperature for the data rooms, 32 \({}^{\circ}\)C; and \(n\) is the number of server rack inlet openings.
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 4 Synthesis Methods and Applications of Flower-like Copper- and Iron-Based Nanostructures
### Copper-Based NFs
Firstly, its ability as a nanocarrier of DOX was investigated. Due to its high surface area, the nanomaterial showed a high drug loading capacity as well as time- and pH-dependent behavior in vivo. The amount of DOX released from loaded CuS NFs was at 80.2% at a pH of 4.5, similar to a tumor's microenvironment. Cell viability tests on human hepatocellular carcinoma (HepG2) showed that the synergetic action of CuS-DOX NFs and laser irradiation caused 96.8% cytotoxicity [123].
## 2 Background of Analysis
### Distance Estimation  
Distance estimation is one of the methods that measure the distance between the users to find the user's location. Location-based systems (LBS) are widely used to find and utilize a user's location. There are various data sources used for LBS such as LiDAR [21; 22], sonar [23; 24], and vision [25]. Additionally, wireless signals are popular methods to find location [26; 27; 28]. In [26], authors introduce the localization method using WiFi in an apartment environment. They use the RSS value of Wi-Fi and multiply it by a manually set weight to reduce the time variance of the RSS. In [27], authors use the BLE signal and fingerprinting approach. A denoising autoencoder is adopted to improve localization performance. Wi-Fi and BLE have the advantage that they can be used indoors as well as outdoors. In [28], BLE is better suited to indicate the location in more detail than Wi-Fi. The Kalman filter is used to remove noise in RSS. Even if LBS is a valuable method to find the user's location, LBS needs environments with devices that provide indirect information about location, such as Wi-Fi APs and BLE beacons. Also, the data for making LBS are location-dependent so that LBS is sensitive to the environment.  
Another method to measure the distance is a proximity-based system (PBS) that measures and utilizes the proximity between the users. Wireless signals such as Wi-Fi [29; 30] and BLE [31; 32; 33; 34] are often used to measure proximity between users. In [29], a method to check whether two mobile devices that can use Wi-Fi are close to each other, within 2 m, is proposed. In [30], the authors use Wi-Fi and BLE signals to estimate the proximity between two users. In [31], proximity is used to select the beacon that is nearest to the scanner. They demonstrate that the density of beacons can affect the accuracy of proximity detection. In [32], Bayesian filtering is used to improve proximity accuracy. Beacons are used in their experiments. However, using a beacon is not suitable for interaction between people. This is because people rarely own beacons in their daily lives. In [33], the authors use the Kalman filter to reduce signal fluctuation. Then, proximity detection is performed using pre-processed RSSI data.  
The aforementioned PBS provides distance between the beacon and the user. However, most studies use static beaconing that the position of the beacons is fixed. Because the position of the beacon is fixed, deployment of the system is not flexible. Also, there is no consideration for obstacles that interrupt physical interaction between users. It indicates that the users should be in physical contact to use the system. So, we propose a novel proximity detection that includes dynamic beaconing and consideration of obstacles that prevent physical contact. In Table 2, the comparison of recent studies and our system is represented.
## 3 SES/MB Framework  
The SES/MB framework generates simulation models of modular and hierarchical systems through the SES ontology and the classical workflow of modeling [20]. The SES/MB framework is used for the multifaceted modeling simulation of a standalone system. The framework uses a transformation process to synthesize entity structures and DEVS models to generate a simulation model, as follows:  
\[SES\to Pruning\to PES\to Composition\;Tree+MB\to Simulation\;Model \tag{1}\]  
In Equation (1), the SES is represented by a tree structure containing alternative edges starting at decision nodes. The SES has to be pruned to select a specific design through an algorithm and to build a specific model variant. This pruning process constructs a Pruned Entity Structure (PES) that defines one model variant. A composition tree derived from the PES includes all the necessary information to build a modular and hierarchical simulation model using predefined basic components from the MB.  
Figure 2 shows the main transformation process in the SES/MB framework. A simulation model is built through trees in the SES and PES bases (Figure 1(a),b) and basic models in a model base (Figure 1(c)) according to the process. The SES trees portray the structural knowledge of a system and the model base stores the real models that represent the behavioral knowledge of the system. In this Section, the SES and PES bases are discussed in Section 3.1, and the model base is introduced in Section 3.2.
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.2.4 Livox Mid-40 Data Acquisition  
For the acquisition of 3D point clouds of large-scale plantations, the loop path planning alone cannot cover the whole area fully, with missing point clouds inside the plantation. Therefore, when planning the path of large-scale plantations, a combination of straight and loop paths is adopted. The ZEB-HORIZON scanner cannot acquire data under straight-line path planning (as shown in Figure 9a). In viewing the above problems, this paper has modified the Livox Mid-40 and constructed 3D point clouds through SLAM, in order to reduce the cost of data acquisition and obtain 3D point clouds of the target forest quickly. Livox Mid-40 LIDAR can be widely used in unmanned environment sensing, robot navigation, and high precision mapping, etc. It adopts non-repetitive scanning technology to obtain higher density point clouds, with a maximum range of 260 m. In this paper, the handheld data acquisition was carried out by adding a handheld grip to the lower part of the LIDAR and modifying the connecting battery to be carried on the shoulder, as shown in Figure 10a after modification. Using straight-line path planning, the LIDAR was first connected to the Ubuntu system and debugged, and then connected to the Ros system for handheld data acquisition, as shown in Figure 10b. The plantation of the experimental area was recorded in a data set in the form of a Rosbag through the Ros system, so as to facilitate the subsequent processing of the data set.  
Figure 8: Cloud map of artificial forest points in three paths. (**(a)** shows straight-line path; **(b)** shows S-line path; **(c)** shows loop path).  
Figure 9: Laser odometer diagram of three paths. (**(a)** shows straight-line path error; (**b**) shows S-type line path error; (c) shows loop path).
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 4 Experiment and Results
### Quantitative Analysis
Peripheral biosignals, such as GSR and ECG, tend to provide faster and more direct autonomic nervous system responses to acute pain stimuli [3], whereas cortical signals captured by fNIRS reflect slower hemodynamic changes that are often more susceptible to noise and motion artifacts [4]. Despite these challenges, fNIRS remains valuable in clinical contexts where direct brain activity monitoring is essential. Therefore, while certain biosignals may outperform others depending on the dataset characteristics and response types, each modality offers distinct advantages depending on the application scenario. Moreover, the observed gap between validation and test accuracy could be partially attributed to overfitting. Given the limited training set size, the model may have captured validation-specific patterns that do not generalize well to the test set. This overfitting risk is further amplified by the inherent class imbalance across both splits, suggesting that larger and more diverse datasets would be beneficial for improving generalization performance. In addition to accuracy and F1 analysis, we also evaluated model performance using ROC curves and AUC scores (Figure 6). On the BioVid dataset, the model achieved a strong AUC of 0.88, whereas on the AI4 Pain dataset, AUC values ranged from 0.54 to 0.61, reflecting the challenges posed by subtle signal differences and class imbalance. Figure 5: Vizualiation of confusion matrix MMAA (Video + fNIRS). Figure 6: ROC curves for BioVid (**left**) and AI4 Pain (**right**). The high AUC on BioVid (0.88) demonstrates strong separability, whereas there are lower AUCs on AI4 Pain (0.54–0.61).
# Background Knowledge Based Multi-Stream Neural Network for Text Classification
## 4 Experiments and Discussion
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unit: \%**} & \multicolumn{2}{c}{**Macro**} & \multirow{2}{*}{**Acc**} \\ \cline{3-4} \cline{6-6}  & \multicolumn{1}{c}{**P**} & \multicolumn{1}{c}{**R**} & \multicolumn{1}{c}{**F1**} & \\ \hline \multirow{3}{*}{Single stream:} & _W (Baseline)_ & **64.09** & 63.95 & **64.02** & **93.30** \\ \cline{2-6}  & Key & 73.14 & 67.64 & 70.28 & 90.46 \\ \cline{2-6}  & Cow & 68.97 & 63.89 & 66.33 & 91.04 \\ \cline{2-6}  & KeyCow & 63.80 & 54.92 & 59.03 & 90.62 \\ \hline \multirow{3}{*}{Early fusion:} & W + Key & 74.90 & 68.32 & 71.46 & 93.50 \\ \cline{2-6}  & W + Co & 73.66 & 65.60 & 69.40 & 93.61 \\ \cline{1-1} \cline{2-6}  & W + KeyCow & 73.31 & 65.84 & 69.38 & 93.69 \\ \cline{1-1} \cline{2-6}  & W + Key + Cow & **77.28** & 68.40 & 72.57 & 94.00 \\ \hline \multicolumn{6}{c}{Average pooling} \\ \cline{2-6}  & W + Key & **78.10** & **69.87** & **73.76** & **94.04** \\ \cline{1-1} \cline{2-6}  & W + Co & 70.19 & 68.94 & 69.56 & 93.93 \\ \cline{1-1} \cline{2-6}  & W + KeyCow & 71.74 & 65.25 & 68.34 & **94.04** \\ \cline{1-1} \cline{2-6}  & W + Key +Cow & 76.36 & **70.30** & **73.20** & **94.35** \\ \hline \multicolumn{6}{c}{Soft voting} \\ \cline{1-1} \cline{2-6}  & W + Key & 76.89 & 67.82 & 72.07 & 93.03 \\ \cline{1-1} \cline{2-6}  & W + Co & 72.27 & 66.64 & 69.34 & 92.91 \\ \cline{1-1} \cline{2-6}  & W + KeyCow & 71.87 & 60.44 & 65.66 & 92.99 \\ \cline{1-1} \cline{2-6}  & W + Key + Cow & **81.96** & **71.06** & **76.12** & 93.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Reuters-21578 R8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unit: \%**} & \multicolumn{2}{c}{**Macro**} & \multirow{2}{*}{**Acc**} \\ \cline{3-4} \cline{6-6}  & \multicolumn{1}{c}{**P**} & \multicolumn{1}{c}{**R**} & \multicolumn{1}{c}{**F1**} & \\ \hline \multirow{3}{*}{Single stream:} & _W (Baseline)_ & _84.71_ & _85.37_ & _85.04_ & **96.12** \\ \cline{2-6}  & Key & 90.41 & 92.16 & 91.28 & 96.30 \\ \cline{2-6}  & Cow & 91.35 & 89.84 & 90.59 & 95.39 \\ \cline{2-6}  & KeyCow & 91.44 & 88.81 & 90.11 & 95.75 \\ \hline \multirow{3}{*}{Early fusion:}  
The comparison results between above optimal model and basal model show that the macro indicators have significantly improved while the overall accuracy growth is slight. The reason can be that in these three imbalance corpus, there are some categories with less data, and their classification results effect slightly on overall accuracy results because of the data distribution. However, during the model training, accuracy is often used as evaluation indicator and the categories with less data often
## 2 Materials
### Data  
With the purpose of meticulously monitoring mining-induced surface subsidence in the study area, this paper uses Sentinel-1A data to derive information related to phase deformation in the Wanli mining area and employs the assistance of Precise Orbit Determination (POD) ephemeris data and the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) to eliminate orbit deviation and remove the interferometric terrain phase, respectively.  
In this study, we collected Sentinel-1A data from the Alaska Satellite Facility (ASF) [33] by setting several specific search filters in order to capture, in interferometric wide (IW) swath mode, Sentinel-1A single look complex (SLC) images during its ascending orbit. Benefiting from a revisit cycle of 12 days, we selected 64 Sentinel-1A images in every month  
\begin{table}
\begin{tabular}{c c c c} \hline
**Zone** & **Description** & **Area (km\({}^{2}\))** & **Capacity (M/a)** \\ \hline R1 & Qijapan small coal mine reconstruction area & 57.80 & 1.50 \\ R2 & Zhaoyoufang small coal mine reconstruction area & 45.90 & 1.50 \\ R3 & Suancigou small coal mine reconstruction area & 51.33 & 1.50 \\ R4 & Chanoanoaliang small coal mine reconstruction area & 115.20 & 3.00 \\ C1 & Wanli mining field & 75.76 & 8.00 \\ C2 & Fanjucian mining field & 10.03 & 1.20 \\ C3 & Niampanaling mining field & 11.81 & 1.20 \\ C4 & Yangiaucun mining field & 37.20 & 5.00 \\ C5 & Talahao mining field & 81.90 & 6.00 \\ C6 & Lijiahao mining field & 56.05 & 6.00 \\ C7 & Wangjia mining field & 60.11 & 5.00 \\ C8 & Gaojialiang mining field & 88.50 & 6.00 \\ \hline \end{tabular}
\end{table}
Table 1: Detailed information of the twelve zones studied in the Wanli mining area.  
Figure 1: Geographical location of the Wanli mining area in Ordos (Inner Mongolia, China). (**a**) Digital Elevation Model of Ordos; (**b**) Landsat 9 image of the Wanli mining area on 6 December 2023.  
from 2018 to 2023, ensuring the precision and credibility of deformation results, which are detailed in Table 2.
## 1 Introduction
The proposed research addresses these issues by integrating incremental dynamics and an NDO, which reduces model dependency and enhances robustness by estimating disturbances without requiring direct state derivative measurements. The advantages of this approach include improved control performance in the presence of uncertainties, reduced reliance on detailed aerodynamic models, and practical applicability to aircraft systems where state derivative information is often unavailable. The contribution of the proposed controller based on INDI and NDO are as follows: The proposed method uses an incremental dynamics-based nonlinear controller to attain robust properties. Second, the controller implements NDO to estimate uncertainties arising from incremental dynamics for compensation. Moreover, the NDO assumes no prior model information except the control direction of actuators. The simulation result shows the performance increase in the proposed method compared to INDI without NDO. The flight controller is tested on the generic transport model (GTM) developed by NASA in the simulation. The remaining sections of this paper are organized as follows: Section 2 defines the problem statement. Section 3 provides the equation of motion of an aircraft. The proposed control method is explained in detail in Section 4. Section 5 describes stability analysis issues, and Section 6 shows the simulation results. Finally, Section 7 presents the conclusions.
## 2 Circuit Modeling of the BPF and the WG ZOR, and Its Geometry
### Formation of the Passband by Cascading the ZORs with Coupling Elements  
The resonators are placed in series through coupling elements as quantified in Figure 2 and Table 2. The coupling elements are denoted as \(length_{i}\) for the eighth order of filtering. Prior to the high order filter, the second-order BPF is built to see the basic characteristics of the coupling element suggested together with the ZORs.  
Figure 5a is drawn with the geometrical parameters in Table 5. S\({}_{21}\) in Figure 5b reveals the slope of the skirt has been made rapid compared to the 1-pole case in Figure 3b. The gap length of the coupling element between the resonators is obtained by finding the value which generates the desirable S\({}_{11}\) and S\({}_{21}\) performances, as it is varied as in Figure 5b. The gap length of 15 mm is proper for the second-order case because of the impedance matching as S\({}_{11}\) becomes worse, and the bandwidth increases for gap length 13 mm and the bandwidth decreases for gap length 17 mm. Now the attenuation level in the stopband is around 10 dB. To have higher attenuation at the stopband, the structure is extended to the eighth-order filter.  
Figure 4: Observing the ZOR of the resonator. (**a**) E\(-\)field resonant and confined to a thin plane. (**b**) Dispersion diagram.  
Beyond the 2-pole BPF, for assuring a high level of noise suppression at the stopband, 8 ZORs are put in order, and adjacent resonators are coupled through \(length_{ij}\). Figure 6a is an open structure before assembly and Figure 6b is the complete shape. The geometric values in Table 5 change to those in Table 6 due to changing the 1 resonator as a narrow band to form the required passband by cascading eight resonators through coupling sections. The coupling required by adjacent resonators causes a change in the phase and geometric values. Giving the values to the variables as in Table 6, the full-wave EM simulation provides the designer with the frequency response of Figure 6c. Excellent impedance matching is seen through S\({}_{11}\) of \(-\)20 dB and the insertion loss of S\({}_{21}\) of \(-\)0.9 dB in the passband. This is also checked with Figure 6d as the magnified version. Among other things, the attenuation has been improved by a large margin with the steeper skirt. It is 40 dB.  
Figure 5: Combining two ZORs through the gap for coupling. (**a**) Structure. (**b**) S\({}_{11}\) and S\({}_{21}\) vs. _gap_.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Value [mm]** & **Variable** & **Value [mm]** \\ \hline \(L1\) & 8 & \(W2\) & 1 \\ \(L2\) & 5.7 & \(W3\) & 1 \\ \(L3\) & 2.9 & _gap_ & 15 \\ \(W1\) & 1 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The values of the physical dimensions of the second-order filter.  
Figure 6: Combining eight ZORs through the gaps for coupling. (**a**) and (**b**), structure. (**c**) S\({}_{11}\) and S\({}_{21}\). (**d**) S\({}_{11}\) and S\({}_{21}\) zoomed\(-\)in.