esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Pattern Recognition and Sex Differences  
In this study, accuracy and classification timelines for assessing the performance of a myoelectric pattern recognition control system were focused on. In total, four time-domain features from the EMG signal of six muscles for 2400 events were extracted, which have been widely used in the literature for motion classification. Considering the transmission delay and hysteresis prevalent in myoelectric control systems, the four time-domain feature values were filtered by dimensionality reduction based on correlation analysis. We selected only the iEMG values for pattern classification to reduce the computation time of the feature extraction phase and the complexity of the classification model. Previous studies have reported hand movements recognition using a combination of multiple time-domain feature values [30] and multiple time-frequency domains [41], special values with a high number of channels and data redundancy of EMG signals. The average prediction accuracy they obtained was only 90.3% and 97.23%. Furthermore, previous studies have presented and demonstrated sex-related differences in kinematics [34] and EMG in dynamic [35] manual manipulation tasks of the upper extremity. However, few studies have applied sex factors to gesture pattern recognition based on EMG signals.  
In this paper, considering sex differences in sEMG signals, the right upper limb muscles of the male and female were shown to be significantly different when performing the six gestures by statistical analysis (One-way ANOVA). Therefore, two types of machine learning algorithms that considered muscle sex differences have been proposed in the presented paper, the differentiation of sex datasets and the addition of a sex label. In particular, only the iEMG feature values of six channels per event were extracted for hand gesture recognition considering muscle sex differences. An average overall prediction  
Figure 6: Results of statistical analysis of the total prediction accuracy of three classifiers (kNN, SVM and ANN) with 5-fold cross validation, applied to a hand gesture recognition test set of two methods considering sex differences. (**A**) Differentiation of sex datasets. (**B**) Addition of a sex label. (*) represents \(p<0.05\) between the total prediction accuracy of the two datasets. Error bars represent standard errors.  
accuracy of 98.4% was obtained for the ANN classifier with the addition of a sex label. High prediction accuracy was obtained with fewer channels, fewer feature values and consideration of muscle sex differences. In future studies, it may be interesting to consider the EMG sex difference factor for the EMG control system design to achieve categorical control of prosthetic and robotic hands.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 3 Benefits and Uses of Argane  
### Ecological Interest of the Argane Stands  
The Argane stands play an irreplaceable role in maintaining ecological balance. Its powerful root system acts as a shield against both wind and water erosion, effectively capturing water, stabilizing the soil, and reducing erosion. This essential function helps mitigate the advance of desertification, as noted by [2]. Additionally, the tree counteracts rain-induced erosion, especially in mountainous regions, making it an excellent soil stabilizer for mountain soils. The deep and robust root system of the Argane stands facilitate rainwater infiltration, aiding groundwater. Furthermore, its shading and soil-enhancing properties foster the development of a diverse range of biological entities, encompassing fauna, flora, and microflora [1,21]. This rich biodiversity significantly enhances natural areas and contributes to vital microbial activities such as nitrogen mineralization and improved phosphorus availability, as observed in studies by [26]. The presence of the Argane stands are directly linked to the thriving of these living organisms, including plants, animals, and microflora, with researchers identifying approximately 100 plant species growing beneath their canopy [26].
## 3 Materials and Methods
### SAR Images
This relation can be expressed as  
\[\mathrm{w}_{S1}<\mathrm{w}_{\mathrm{fit}}-\mathrm{err}_{max},\]  
where  
\[\mathrm{w}_{\mathrm{fit}}=1.002\ \mathrm{w}_{ECMWF}-0.3825\]  
is obtained by Gaussian fit as shown in Figure 2a and  
\[\mathrm{err}_{max}=\max\left\{\frac{\mathrm{w}_{\mathrm{fit}}-\mathrm{w}_{S1 }}{\mathrm{w}_{\mathrm{fit}}}\ \ \mathrm{s.\ t.\ w_{S1}<\mathrm{w}_{\mathrm{fit}}}\right\}.\]  
Notice that condition \(\mathrm{w}_{S1}<\mathrm{w}_{\mathrm{fit}}\) refines the range as only damping effects needs to be considered. For the data-set in Figure 2a, \(\mathrm{err}_{max}=0.8993\). Let us define the ratio  
\[\mathrm{WR}=\frac{\mathrm{w}_{S1}}{\mathrm{w}_{ECMWF}} \tag{3}\]  
to monitor phytoplankton bloom from SAR, where \(\mathrm{w}_{S1}\) is the wind speed estimated from Sentinel 1 and \(\mathrm{w}_{ECMWF}\) indicates the a-priori ECMWF wind speed. As mentioned before, there are different factors able to significantly damp the backscatter but the knowledge of the lagoon's characteristics and  
Figure 2: **(a)** Scatter-density plot between the a-priori European Centre for Medium-Range Weather Forecasts (ECMWF) wind velocity, \(\mathrm{w}_{ECMWF}\) and the wind velocity estimated from Sentinel 1, \(\mathrm{w}_{S1}\), from L2 OCN products of internal points of the Curonian lagoon. Wind data showed are from 1 of May 2019 and 30 of June 2019, when no scum events were observed. The condition \(\mathrm{WR}<0.1\), which sets empirically a “scum” alert, is colored in red (\(R^{2}=0.72\), rmse = 1.7589); **(b)** Attenuation of the backscattering for \(\mathrm{WR}=0.1\) as a function of the incidence angle \(\theta\). For data showed in panel **(a)**, \(\theta\in[30^{\circ},46^{\circ}]\). Effect of wind direction \(\phi\) is also showed, where \(\phi=0^{\circ}\) corresponds to wind blowing towards the radar. the seasonality of the events allow us to attribute the smallness of WR most likely to the presence of cyanobacteria's bloom at the water surface. Defining an index that does not come directly from the amplitude of the SAR signal, but rather from a derived product such as the retrieved wind, is due to the strong dependence of the NRCS on the incidence angle \(\theta\). In Figure 2b it is shown how the condition \(\text{WR}=0.1\) is read in term of \(\sigma^{0}\) for incidence angle values characteristic of S1 images for the Curonian Lagoon (\(\theta\in[30^{\circ},46^{\circ}]\)) and different wind blowing direction. The use of an ocean-backscatter model such as CMOD allows to simply remove the incidence-angle dependence of the \(\sigma^{0}\) and therefore to enable the estimation of phytoplankton bloom statistics in SAR images. The _a priori_ wind velocity also sets the applicability limits of this approach: for ECMWF wind speed below 2 m/s a weak backscattering area is likely due to a lack of wind than to the presence of floating substance on water surface. Therefore, this method can not be applied when w\({}_{\text{ECMWF}}\) falls in this range.
## 5 Discussion
The use of probabilistic maps could allow the flood risk managers to improve the security of population evacuation routes, as well as to delimit the higher hazard areas so that people cannot access them when the occurrence of a flood event is foreseen, or for the evacuation of the population from those areas (thus preventing them from going out into the street). One of the main problems associated with this hazard management is related to the disparity (or variability) of the results obtained, depending on the model used and its complexity. The two models (Figure 5) used in this analysis can be regarded as being of medium complexity, and they show significant differences in the results, which could be increased if lower [67] or higher [68] complexity models are used. Probably, as is pointed out by [PERSON] et al. [68], the analysis of the human loss of stability (which include such factors as slipping, toppling and drowning) is a complicated task, due to the multiple factors involved (surface slope, water density, suspended load, such as tree branches, etc. ), but the use of very complicated models may not be absolutely necessary for flood risk management tasks, such as land use limitations. Finally, regarding hazards for vehicle stability and damage, there are some models too, although the literature here is less mature than that for damage curves for buildings; there are only three studies focused on damages to vehicles in contact with floodwater [69]. According to [PERSON] et al. [69; 70], the model proposed by USACE [45] and [PERSON] et al. [71] are the more robust and testing. Damage to vehicles can include different processes, such as sliding, toppling, and floating, which could be the origin of vehicle impact against other structures and their resulting damage, although vehicle damage does not require these movements and can be caused only by flooding and the deterioration of the materials of which it is composed. The same holds for human stability (manoeuvrability, abilities, or psychological aspects); vehicle characteristics (weight, shape, or design) determine their stability level [70], and all of them introduce a certain degree of uncertainty and complexity into this type of analysis. Similar to the scenario about human loss of stability, non-consideration of uncertainty in flood hazard by flood risk managers may increase flood damages (as can be derived from the Zamora city results, Figure 7). Once again, the availability and use of probabilistic flood maps could improve the flood damage assessment and management by means of the limitation of vehicle parking areas, both temporary and permanent in time.
## 3 Key Techniques
### FE Simulation Data Mapping Method
#### 3.3.1 Displacement Mapping Method  
The keyframe method of Blender [43] is used to achieve the displacement mapping. A keyframe is a location on a timeline that marks the beginning or end of a transition. For example, the displacement keyframe defines the beginning and endpoints of the displacement. Meanwhile, a kinematic keyframe defines the information about the kinematic type. There are two kinematic types in Blender: (1) the animation system, in which the motion of the object is controlled by the displacement keyframe, and (2) the physics engine system, in which the motion of the object is controlled by the simulation results obtained using the physics engine. Displacement mapping requires only the displacement keyframe. Before the initial moment of collapse \(T_{\text{c}}\), the motions of objects in Blender are controlled by the displacement keyframes derived from the FE results. This means that only Blender is used to map the FE results; the Bullet physics engine is not involved.  
Specifically, the displacement mapping method is divided into three steps, as shown in Figure 4.  
* Step 1. Extract the FE model data. After the FE simulation, the displacement data of each vertex at each time step are extracted, and then the deformed coordinates of each vertex are calculated from the displacement data and the initial coordinates. The deformed centroid coordinates of each object at each time step can be calculated from the deformed vertex coordinates of each object.  
Figure 4: Flowchart of the displacement mapping method.  
* Step 2. Insert the displacement keyframe. Initially, the keyframe that corresponds to the first time step in the FE model on the Blender timeline is selected. The deformed centroid coordinates obtained from the FE results for one object are then calculated. Subsequently, the deformed centroid coordinates are inserted as the displacement keyframe. When all the objects in the structure have been processed, the simulation moves to the next time step, and the aforementioned procedure is repeated until the initial moment of collapse \(T_{\mathrm{c}}\).
* Step 3: Change the kinematic type. After Step 2, the kinematic type of each object needs to be converted to the animation system, otherwise the object motions will be controlled by the physics engine simulation, rather than by the FE result.
# Vegetation Health in China Is Severely Compromised by Drought, Wet and Heat Stress Events
## 3 Results
### PSR Models Most Accurately Reflect Vegetation Health
#### 3.1.2 Evaluation of the Vegetation Health Assessment System Construction Effectiveness
It is measured by detecting the faint fluorescence emitted by vegetation leaves when exposed to sunlight. This fluorescence is closely related to the photosynthetic activity of vegetation; healthy vegetation typically exhibits higher photosynthetic levels and stronger fluorescence  
Figure 4: (**A**) Fitting graph of VHI and LAI. (**B**) Fitting graph of vegetation health and NPP (units: g C/m\({}^{2}\)). (**C**) Fitting graph of vegetation health and SIF. signals. Compared to traditional indices such as NDVI and EVI (Enhanced Vegetation Index), which rely on reflected light, SIF is more sensitive to photosynthetic activity, particularly under high-stress conditions, where it can detect subtle changes [43]. Thus, SIF serves as a powerful tool for evaluating vegetation health and vitality. Fitting results between SIF and the VHI reveal that for the CIM system, the correlation and explanatory power between VHI and SIF increase annually: \(R^{2}\) = 0.47, \(r\) = 0.61 in 2001, \(R^{2}\) = 0.57, \(r\) = 0.69 in 2010, and \(R^{2}\) = 0.60, \(r\) = 0.71 in 2020, indicating that CIM is increasingly capable of accurately capturing the key factors affecting SIF (Figure 4C(a-c)). The TOPSIS method also shows a clear annual improvement trend, with correlation and explanatory power rising from \(R^{2}\) = 0.44, \(r\) = 0.58 in 2001 to \(R^{2}\) = 0.55, \(r\) = 0.67 in 2020, further demonstrating the enhanced effectiveness of TOPSIS in explaining the relationship between SIF and vegetation health (Figure 4C(d-f)). Meanwhile, the PSR model consistently demonstrates the best fitting performance across all years with correlation and explanatory power evolving from \(R^{2}\) = 0.74 in 2001 to \(R^{2}\) = 0.70 in 2010 (Figure 4C(g,h)). The PSR method provides consistent explanatory power and shows significant statistical correlation and high goodness of fit. Its stable performance indicates that the PSR model effectively balances environmental pressures, states, and response factors influencing SIF, thereby reliably predicting SIF trends. In summary, three models show an increasing trend over time to varying degrees. The VHI constructed using the PSR model demonstrates higher \(R^{2}\) values and stronger correlations with the three indices reflecting vegetation health compared to CIM and TOPSIS. Over time, the PSR model may prove more suitable for applications in vegetation ecological carbon monitoring. Therefore, this study explores the response of the VHI to stress events based on calculations from the PSR model.
## 4 Assessment and Analysis of LeGNSS Precise Positioning
### Satellite Visibility  
In order to make a comprehensive comparison, the datasets are processed in six different constellation combinations, i.e., GPS-only, BDS-only, LEO-only, GPS/BDS, GPS/LEO, and GPS/BDS/LEO. The satellite elevation mask angle is set to 7\({}^{\circ}\). The coverage of the calculation is global, and the space resolution is 5\({}^{\circ}\) by 5\({}^{\circ}\). The sampling interval is 5 min. Figure 2 shows the average number of visible satellites over the entire analysis period for six constellation combinations.  
Figure 2: Average number of visible satellites for six different system combinations.  
As shown in Figure 2, GPS, BDS, and LEO satellites provide different coverage. There are always more than four visible GPS satellites everywhere in the world. For the BDS-only case, the number of visible satellites in the Asia-Pacific area creates a large elliptical zone, since the BDS GEO, and IGSO satellites operate in this area so far. Users can observe nine to fourteen BDS satellites in BeiDou service areas while only about one to three BDS satellites in other areas. It is interesting to see the number of visible LEO satellites on a global scale, for which one can observe nearly eight to ten LEO satellites in polar areas and only five or even fewer satellites in other areas. This is easy to understand since the LEO satellites have near-polar circular orbits. However, due to their low Earth orbit, the footprint of LEO satellite is much smaller than that of other GNSS satellites. The diameter of the footprint is around 3000 km for Iridium satellites, while being about 12,000 km for GPS satellites [29]. As a result, much fewer visible LEO satellites can be observed in medium and low latitude regions. It is clear to see that the different constellation combination provides coverage improvement. The LeGNSS (GPS + BDS + LEO) exhibits the best coverage of satellite visibility on a global scale, and the number of visible satellites is thirteen to twenty-five.
# 1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction +
## 4 Experimental Verification
### Radiation Performance  
The simulated and measured gain of the designed low-RCS 1-bit RTA prototype at different frequencies is illustrated in Figure 13. It is seen that loading the AFST on top of the 1-bit RTA will result in a gain loss of about 1 dB. The measured realized gain at 7.5 GHz is approximately 20.1 dBi, with a corresponding aperture efficiency of 12.7%, and the 3 dB gain bandwidth is about 8.7%. Moreover, the radiation efficiency of the design RTA is around 65\(\sim\)70%. Compared to the simulated results, the measured gain is reduced by approximately 0.6 dB, and the gain bandwidth is also slightly reduced. In taking into account machining and measured errors, the slight warping of the AFST layer, and the parasitic effect of the pin diode, the results are considered acceptable.  
The simulated broadside radiation patterns at different frequencies are presented in Figure 14. It is seen that the designed antenna has a relatively stable co-polarized pattern in the operating band, while the cross-polarized level gradually deteriorates as the frequency approaches the edge of the band. In addition, the sidelobe levels of the pattern in the H-plane are relatively stable compared with those of the E-plane.  
Figure 12: Measured setups for (**a**) radiation and (**b**) RCS reduction performance.  
The simulated and measured broadside radiation patterns at 7.5 GHz are shown in Figure 15. It is found that the simulated and measured results are in good agreement. The measured half-power beamwidths (HPBWs) are \(10.4^{\circ}\) and \(10.8^{\circ}\) in the E- and H-planes, respectively. Additionally, the measured cross-polarized level is less than \(-20\) dB in both principal planes. The simulated and measured beam scanning performances at 7.5 GHz in both principal planes are shown in Figure 16. It is evident that the beam accurately reaches the expected direction within the range of \(60^{\circ}\), and the scanning gain loss is about 5 dB in both principal planes, which is mainly affected by the phase compensation errors. This can be addressed using either two-bit phase compensation or smaller array elements.  
Figure 14: Simulated broadside radiation patterns at different frequencies. (**a**) E-plane and (**b**) H-plane.  
Figure 13: Simulated and measured gain of the designed low-RCS 1-bit RTA and its corresponding aperture efficiency.
# Soft Contrastive Cross-Modal Retrieval
## 3 Methodology
### Optimization  
We conduct the model in the process of learning the optimal function by minimizing the soft contrastive loss \(\mathcal{L}_{scl}\) and smooth label cross-entropy loss \(\mathcal{L}_{scc}\) as obtained in Equations (4) and (5), where \(\hat{\theta}_{I}\) and \(\hat{\theta}_{T}\) are the optimized parameters of visual network \(f_{I}\) and textual network \(g_{T}\), mathematically. As the optimization process of two networks is carried out simultaneously, the process runs as:  
\[(\hat{\theta}_{I},\hat{\theta_{T}})=\arg\min_{\theta_{I},\theta_{T}}\alpha \mathcal{L}_{scl}+\beta\mathcal{L}_{scc} \tag{7}\]  
In addition, the visual network \(f_{I}\) is optimized by Adaptive Moment Estimation (Adam) in the following as Equation (8):  
\[\theta_{I}\longleftarrow\theta_{I}-\lambda\cdot\
abla_{\theta_{I}}\frac{1}{m} (\alpha\mathcal{L}_{scl}+\beta\mathcal{L}_{scc}) \tag{8}\]  
Homosplastically, the textual network \(g_{T}\) is optimized by Adam as follows in Equation (9), respectively:  
\[\theta_{T}\longleftarrow\theta_{T}-\lambda\cdot\
abla_{\theta_{T}}\frac{1}{m} (\alpha\mathcal{L}_{scl}+\beta\mathcal{L}_{scc}) \tag{9}\]  
Figure 4: General idea of soft contrastive learning to improve sharp edges between classes.  
Here, \(\theta_{I}\) and \(\theta_{T}\) are the parameters of visual network \(f_{I}\) and textual network \(g_{T}\), \(\lambda\) denotes the learning rate, and \(m\) is the number of samples in mini-batch for each modality. Therefore, the complete optimization process is shown in Algorithm. 1.  
```
1:Input: a training set \(\mathcal{D}=\{\langle\mathbf{I}_{i},\mathbf{T}_{i},\mathbf{L}_{i}\rangle\}_{i=1}^{n}\), \(m\) samples in each mini-batch, the number of model training steps \(k\), learning rate \(\lambda\), hyperparameters \(\alpha\) and \(\beta\).
2:Initialize randomly \(\theta_{I}\), \(\theta_{T}\)
3:repeat until convergence:
4:for\(k\) steps do
5: Randomly select \(m\) image-text pairs from multimodal dataset \(\mathcal{D}\) to construct a mini-batch\(\{I_{i},T_{i}\}\).
6: Compute the representations \(f_{I}(\mathbf{I})\) and \(g_{T}(\mathbf{T})\) for the instance by forward propagation.
7: Compute the soft contrastive loss \(\mathcal{L}_{scl}\) and smooth label cross-entropy loss\(\mathcal{L}_{sce}\) by Equations (4) and (5).
8: Update the parameters \(\theta_{I}\) of visual network \(f_{I}\) by Equation (8);
9: Update the parameters \(\theta_{T}\) of textual network \(g_{T}\) by Equation (9);
10:endfor
11:Output: the optimized SCCMR model and learned representations in common subspace: \(f_{I}(\mathbf{I})\) and \(g_{T}(\mathbf{T})\)
```  
**Algorithm 1** Pseudocode of optimizing our SCCMR