esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The multi-pigment inversion model, namely MuPI, which semi-analytically obtains concentrations of multiple pigments from remote sensing reflectance, has been validated and applied to MERIS \(R_{\text{rs}}(\lambda)\) imagery to obtain not only [Chl-a], but also [Chl-b], [Chl-c], [PPC], [PSC] (and subsequently their ratiosto [Chl-a]) in the global oceans. The obtained pigment concentrations and the pigment ratios showed good agreement with in situ HPLC data, with the mean UAPD of 38%, 78%, 65%, 36%, and 47% respectively. Further, at the global scale, the MuPI obtained [Chl-a] from MERIS showed comparable results with those estimated from the widely used OC4E algorithm with mean UAPD of 48.8% and RMSE of 4.51 mg\(\cdot\)m\({}^{-3}\) for OC4E and mean UAPD of 49.3% and RMSE of 4.05 mg\(\cdot\)m\({}^{-3}\) for MuPI. However, unlike OC4E, MuPI as a semi-analytical model provided reasonable retrievals of several parameters {[Chl-a], [Chl-b], [Chl-c], [PFC], [PSC], \(a_{\text{dg}}(440)\) and \(b_{\text{bp}}(440)\)] simultaneously from satellite obtained remote sensing reflectance. The information of these accessory pigments would extend the application of satellite ocean color data in global biogeochemical studies that was previously limited due to [Chl-a] as the only available pigment.  
[PERSON] conceived and designed the analysis, collected the data and wrote the paper; [PERSON] and [PERSON] supervised the project, analyzed results and wrote the paper.  
This research was funded by National Aeronautics and Space Administration (NASA) Ocean Biology and Biogeochemistry and Water and Energy Cycle Programs, the National Oceanic and Atmospheric Administration (NOAA) JPSS VIIRS Ocean Color Cal/Val Project.  
We are grateful for the two anonymous reviewers who have provided constructive suggestions to improve the quality of this manuscript. We thank [PERSON] very much for the careful reading of the manuscript and all the helps he provided in improving the language. We thank [PERSON] and [PERSON] in helping to take in situ measurements of remote sensing reflectance, [PERSON]'s group for sharing the phytoplankton absorption coefficients, and National Aeronautics and Space Administration (NASA) for sharing the HPLC measurements from 2014-2015 VIIRS Ca1/Val cruises. We really appreciate those who shared the data from cruises of the Tara Oceans expedition and BIOSOPE and made it publicly accessible. We thank ESA for providing the MERIS imagery and NASA for SeaWiFS Bio-optical Archive and Storage System (SeaBASS) dataset, the Bermuda Institute of Ocean Science for the BATS dataset, and the International Ocean Color Coordinating Group for the IOCCG dataset. Support from the National Aeronautics and Space Administration (NASA) Ocean Biology and Biogeochemistry and Water and Energy Cycle Programs, the National Oceanic and Atmospheric Administration (NOAA) JPSS VIIRS Ocean Color Cal/Val Project and cruises are greatly appreciated.  
The authors declare no conflict of interest.
## 5 Application Related Issues
extensive performance evaluations [29, 57, 60, 64], still, they suffer from the high overhead caused by transmitting multiple copies of data packets and utilizing coding scheme. Due to the resource limitations of sensor nodes and low capacity of individual paths, recently, multipath routing approach is broadly utilized to increase network capacity under high traffic conditions (e.g., multimedia streaming [46, 56, 66, 71]). As it is observable from Figure 3 and Table 4, most of the recently proposed multipath routing protocols utilize concurrent multipath routing to support even traffic distribution (to balance resource utilization) and provide the required bandwidth of high-rate applications. On the other hand, when data is transmitted through multiple nearby paths, the overall capacity of each path reduces due to the interference caused by the other paths. From the MAC layer point of view, a node belonging to a path may sense the carrier busy while a nearby node in an adjacent path is transmitting. In addition, an ongoing transmission on a path may be affected by the interference induced from nearby paths. These issues cause higher medium access delay, increased packet loss and elevated end-to-end latency of the packets being transmitted to the sink node. Therefore, the use of concurrent multipath routing cannot necessarily satisfy the performance demands of high data rate applications. While the protocol designer should consider the required end-to-end latency and capacity of the underlying applications to establish a tradeoff between the inter-path distances and the length of each path, special MAC layer mechanisms may be required to schedule per-hop transmissions based on the experienced interference. Specifically, as cross-layer interactions of the network and MAC layer can result in higher performance [62, 63, 73], multipath routing can be significantly improved through utilizing cross-layer principles.
## 3 Methodology  
The architecture of the multi-sensor fusion SLAM system presented in this paper is shown in Figure 1. It consists of three core modules: the segmentation module, tracking module, and Gaussian mapping module. The 3D segmentation module is employed to initially filter out laser point clouds corresponding to dynamic human bodies, while visual uncertainty segmentation is used to eliminate dynamic points at the edges, thereby enhancing segmentation robustness. The tracking module operates synchronously in a separate thread, continuously updating laser tracking results and extracting visual keyframes for high-fidelity reconstruction. The Gaussian mapping module utilizes laser point clouds for initialization and keyframes for mapping, incorporating a unique dynamic loss function in the mapping process. By seamlessly integrating these three modules, the system's operational efficiency is enhanced and its memory usage is reduced. An example result is provided to demonstrate the performance of the end-to-end system.
## 3 Results
### RQ2: Which Stimuli May Be Used to Elicit Emotions?
#### 3.2.2 Databases of Emotional Stimuli  
To elicit emotions, twenty-eight studies used public and pre-validated datasets [25; 28; 30; 31; 32; 33; 34; 41; 42; 44; 45; 47; 49; 50; 52; 53; 54; 55; 56; 60; 66]. The IAPS was the main source of stimuli [25; 28; 30; 31; 32; 34; 35; 36; 37; 38; 39; 41; 44; 49; 50; 52; 53; 54; 55; 56; 57; 60]. Some researchers complemented this dataset with some others. Particularly, [PERSON] et al. complemented IAPS with IADS, and [PERSON] et al. employed the Affective Norms for English Words (ANEW) [36; 52]. Only two studies preferred to use the NimStim face stimulus set [33; 38]. No other dataset was found to be repeated across reviewed studies.
## 5 Conclusions  
Considering the underlying soil, this study performed a systematic energy spectrum analysis for the SDOF element controlled by TVMD in order to reveal the energy dissipation enhancement mechanism of the TVMD compared with a single dashpot. Taking advantage of the revealed energy benefit, an optimal energy dissipation enhancement-based design strategy was proposed for optimization of the TVMD. The main conclusions of this study are as follows:  
1. The developed energy spectrum analysis framework effectively quantifies the degree of the energy dissipation enhancement effect of the TVMD. Compared with a single dashpot with the same damping coefficient, the TVMD exhibits improved efficiency for energy dissipation, with less energy required for the primary structure to dissipate.
2. Based on the revealed energy characteristic of the inerter system, the optimal design strategy can guarantee the target demand of displacement control, which optimizes the efficiency and quantity of the TVMD for energy dissipation, reducing the energy dissipation burden on the primary structure.
3. Taking the effect of the underlying soil into consideration, especially deep cover soft deposits, a fitted seismic input energy spectrum is provided for the inerter-based structure, which facilitates the optimal energy-based design of the TVMD.
4. For an inerter-based structure on a deep cover soft deposit, nonlinear ground motion propagation should be quantitatively considered for energy analysis and optimal design. After considering the underlying soil, the input energy and dissipated energy responses are dominant and large for long-period inerter-based structures (\(T_{g}=3.1\) s), owing to the effects of the underlying soil.  
The objective of this study was to develop an energy spectrum analysis framework for an SDOF-TVMD system by considering the soil underneath the inerter-based structure. In future studies, the nonlinear behavior of the interaction between soil and inerter-based structures should be incorporated into the numerical and experimental analysis for more complex structures, and its effect in the corresponding optimal design should be investigated.  
Conceptualization: Q.C.; methodology: Q.C., Y.W., and Z.Z.; software: Y.W. and Z.Z.; validation: Q.C., Z.Z., and Y.W.; formal analysis: Z.Z. and Y.W.; investigation: Q.C., Y.W., and Z.Z.; data curation: Y.W. and Z.Z.; writing--original draft preparation: Q.C., Y.W., and Z.Z.; writing--review and editing: Q.C., Y.W., Z.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (grant number 51778489), the National Science and Technology Pillar Program of China (grant no. 2015 BAK17B04), and the Basic Research Project of State Key Laboratory of Ministry of Science and Technology (grant number SLDRCE19A-02).  
Figure 19: Energy response results of designed SDOF elements. (**a**) Energy responses under the excitation of TAP077. (**b**) Energy responses under the excitation of CHY102.
## 3 Experimental Results
Figure 8: Plots of training and validation performance from three variation dataset on five pretrained architecture using transfer learning approach in deep CNN: (**1**) raw image dataset, (**2**) CLAHE image dataset, and (**3**) extraction image dataset. This result confirmed that the transformation of the training and validation datasets using the augmentation layer was performed effectively. From the five transfer learning models, the VGG16 model obtained the best results for the raw cut dataset with a validation accuracy of 95.37%, whereas GoogLeNet showed the best results for the CLAHE and extraction datasets with 96.32% and 93.17% validation accuracies, respectively. After analyzing the five transfer learning models, the ensemble model was trained using all of the models, and the majority voting classifier was trained for comparison. Table 3 shows the performance of each transfer learning model and comparisons with ensemble and majority voting models. From 150 maximum epochs, the average early stop training of each dataset was 115 epochs with 17.94 \(\pm\) 0.13 s/epoch for computation speed for the raw cut dataset, 109 epochs with 17.84 \(\pm\) 0.05 s/epoch for the CLAHE dataset, and 95 epochs with 17.84 \(\pm\) 0.07 s/epoch for the extraction dataset. The model accuracy was provided in three forms: testing, network, and weighting. The testing accuracy indicates the model performance in predicting the testing dataset, whereas the network accuracy and weighted accuracy indicate the average model performance for every defect class. Measuring the model accuracy in every class provides a better understanding of model performance. The weighted accuracy obtained higher results than the network accuracy, indicating that the classifier struggled to predict the testing model correctly in several defect classes. The F1 score clearly describes the uncertainty value in each class, and reflects the difficulties experienced by the classifier. After analyzing all of the models, the ensemble model consistently gave the highest testing results for each dataset compared with the average testing accuracy from the transfer learning and majority voting models. The ensemble model achieved the highest results on the CLAHE dataset with 98.21% testing accuracy, an area under the receiver operating characteristic curve of 99.08%, and a weighted F1 score of 0.982. In contrast, the lowest testing accuracy was achieved by the ResNet50 model on the extraction dataset, with 87.46% accuracy. Except for the majority voting model combining every transfer learning prediction, all models on each dataset performed with a relatively stable classification time, with an average of 2.57 \(\pm\) 0.02 s from 949 testing images. Figures 9-11 show the confusion matrices of the best and weakest models and their comparisons with the majority voting and ensemble models on each dataset.
## 4 Propagation Characteristics and Phenomena of LMS Communication  
The satellite system object of this study is represented by a LMS system where it is possible to individuate two types of channels:  
* Fixed channel: between NCC or gateway and satellite in the sky;
* Mobile channel: between mobile terminal and satellite.  
Both channels have distinct features that must be considered in the system design process. The mobile channel is the most crucial of the two links, since transmitter power, receiver gain, and satellite visibility are limited compared to the permanent link. The mobile terminal works in a dynamic and frequently hostile environment with continuously changing propagation characteristics. Then, in this scenario, it is fundamental to be aware of the propagation phenomena because, for high frequencies (typically over 10 GHz), atmospheric and geographical conditions can severely affect satellite communication.  
The characteristics of the satellite channel are different for different types of satellites, which can be geostationary or orbit on elliptical orbits at different heights. The fundamental parameter to characterize the satellite channel is the elevation angle under which the satellite is seen; in fact, with the satellite very high on the horizon, it is quite unlikely that the signal will be blocked by obstacles on the Earth's surface. When the orbit is low, or when the satellite is not geostationary, there is channel variability due to the fact that the satellite is seen moving on different elevation angles. In addition to what has just been said, the satellite channel is often also time-varying, due both to the fact that the Earth station and the satellite can move, and that atmospheric conditions can change [19].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Band** & **Frequency Range** \\ \hline P & 0.2–1 GHz \\ L & 1–2 GHz \\ S & 2–4 GHz \\ C & 4–8 GHz \\ X & 8–12 GHz \\ Ku & 12–18 GHz \\ K & 18–26 GHz \\ Ka & 26–40 GHz \\ Q & 33–50 GHz \\ V & 40–75 GHz \\ W & 75–110 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Satellite spectrum bands.  
In this section, the satellite propagation characteristics and propagation phenomena will be provided in order to show the main issues to be considered in a satellite system, other than propagation due to a direct path [20; 21]. Moreover, in order to make the mathematical expressions in the section easier to follow, we provide a table listing the used mathematical symbols; see Table 3.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 2 UAV Tracking System Overview
### Steering System  
The steering system is significant in the overall functionality of the UAV tracking system. The primary task of this unit is to rotate the antenna in azimuth and elevation look  
Figure 2: A UAV tracking antenna system.  
angles. At present, there are two types of UAV steering systems, namely (i) mechanical system; and (ii) electrical (or electronic) steering.
## 2 Materials and Methods
### Antioxidant Properties Measurements
#### 2.11.2 Reducing Power  
Potential antioxidant activity can be measured by a compound's reductant capability [43]. For this method, 250 \(\upmu\)L of 1% potassium ferricanide and 250 \(\upmu\)L of 0.2 mM phosphate buffer (pH 6.6) were mixed with 100 \(\upmu\)L of a sample. The mixture was incubated at 50\({}^{\circ}\) for 20 min, before 250 \(\upmu\)L of 10% trichloroacetic acid was added to the mixture, then centrifuged at 3000 rpm for 10 min. Afterward, in a 96-well plate, 50 \(\upmu\)L of the upper layer of the mixture solution was mixed with 50 \(\upmu\)L of distilled water and 10 \(\upmu\)L of 0.1% ferric chloride solution. The absorbance was measured at 700 nm in a UV spectrometer. Gallic acid was used as a standard. Moreover, the results were expressed in \(\upmu\)g gallic acid equivalent per gram (\(\upmu\)g GAE/mg extract) of sample.
## 3 Results and Discussion
### Chemical Analysis  
In this study, mid-infrared spectroscopy chemical analysis was performed on four types of samples. As shown in Figure 5, the chemical characteristics of the four types of tea tree seeds differ significantly and have clear physicochemical directivity:  
Dry and healthy tea tree seeds (HDSC): the O-H stretching vibration peak at 3379.8 cm\({}^{-1}\) indicates the presence of hydroxyl-related substances in the seeds. The C-H asymmetric/symmetric stretching vibration peaks at 2856.2 cm\({}^{-1}\) and 2926.1 cm\({}^{-1}\) suggest the presence of lipids and long-chain alkanes. Additionally, the C-H bending vibration peak at 1380.6 cm\({}^{-1}\), the potential ester C-O or amide III band characteristic peak at 1246.2 cm\({}^{-1}\), and the polysaccharide C-O-C skeleton vibration peak at 1042.8 cm\({}^{-1}\) collectively demonstrate the stable chemical composition and good structural properties of the seed components.  
Soaked healthy tea tree seeds (SHCS): Compared to HDSC, the O-H stretching vibration peak at 3452.3 cm\({}^{-1}\) exhibits a red shift, implying that the soaking environment promotes the synthesis of hydrophilic substances. The enhanced C-O-C asymmetric stretching vibration peak at 1162.8 cm\({}^{-1}\) (originating from hemicellulose synthesis) and the sharp peak of long-chain lipid ordered structures at 722.1 cm\({}^{-1}\) indicate that the soaking treatment facilitates hemicellulose synthesis while maintaining lipid structural stability.  
Dry and moldy tea tree seeds (DMCS): in addition to the C-H stretching vibration peaks shared with HDSC, an ester carbonyl C=O peak appears at 1744.8 cm\({}^{-1}\), signaling lipid peroxidation. The CH\({}_{2}\)/CH\({}_{3}\) bending vibration peak at 1461.1 cm\({}^{-1}\), ester C-O stretching vibration peak at 1242.5 cm\({}^{-1}\), residual C-O-C glycosidic bond breakage peak at 1160.8 cm\({}^{-1}\), and -(CH\({}_{2}\))\({}_{n}\)-out-of-plane rocking vibration peak at 722.1 cm\({}^{-1}\) reveal degradation of lipid side chains and disruption of polysaccharide structures.  
Soaked moldy tea tree seeds (SMCS): The O-H stretching vibration peak at 3385.8 cm\({}^{-1}\) indicates the presence of H-bonded hydroxyl groups from polysaccharide hydrolysis products and fungal metabolites. The shift in the amide I band peak at 1655.2 cm\({}^{-1}\) may lead to changes in the secondary structure of proteins. The characteristic peaks at 862.9 cm\({}^{-1}\) and 719.5 cm\({}^{-1}\), potentially associated with carbohydrate structures (e.g., cellulose) and lipid crystallization, respectively, reflect the damage to the structure and properties of multiple seed components caused by soaking and mildew.  
Differences in the characteristic peak position, intensity, and shape (e.g., amide I band shift, lipid peak sharpness) serve as qualitative indicators, providing a basis for constructing deep learning-based seed quality classification models.
## 10 Conclusions  
High-resolution ultrasound (US) remains the gold standard for diagnosing flexor tendon pulley injuries due to its reliability, cost-effectiveness, and practicality. In case of doubts, artifacts, or measurement issues, magnetic resonance imaging (MRI) can be used as an equally precise but more expensive and less practical alternative. The diagnostic criterion for such injuries involves measuring the tendon-to-bone distance (TBD) during finger flexion against resistance, simulating the crimp position. The threshold TBD value indicative of injury is 2 mm for both A2 and A4 pulleys. For partial A2 pulley injuries, the minimum TBD is 1.6 mm. A classification scheme for managing partial A2 pulley injuries or strains exists, but its empirical nature necessitates primary studies to establish its validity. Additional studies are required for A3 pulley injuries, as current ones have shown poor diagnostic sensitivity with ultrasound. Multiple A2, A3, and A4 pulley injuries can be diagnosed using the clinical bowstringing sign. Further research is needed to verify the reliability of the bowstringing sign in cases of isolated A2 or A3 pulley injuries.  
Clinical signs, ultrasound TBD measurements, and grip strength should be considered together for an optimal diagnosis of flexor unit finger injuries. Grip strength is an excellent complement to ultrasound in diagnosing and following up on A2 and A4 pulley injuries. A 41.4% reduction in grip strength during the single-finger crimp position corresponds to pulley injury, and this measurement proves valuable in rehabilitation and return to sports. Before beginning any activity, always perform an adequate warm-up and stretching routine for the fingers. Maintain a regular conditioning regimen to strengthen the tendons and pulleys. Ensure proper technique during climbing or other activities to minimize stress on the fingers.  
This research received no external funding.  
The authors declare no conflict of interest.
# Western Mediterranean Precipitation Extremes, the Result of Quasi-Resonant Sea-Atmosphere Feedbacks
Here, because precipitation is used as an indicator of remote connection with the torrential rainfall event, the study of the coherence and the phase of the precipitations resulting from convective systems while being distant is made possible thanks to the GPM (Global Precipitation Measurement) satellite constellation using microwave sensors. This equipment provides a reliable and quantitative description of the precipitation (instantaneous and on the daily scale) throughout the evolution of the precipitation systems in the Mediterranean region [21]. As such, the study presented here could not have been conducted with the same representativeness without the inclusion of GPM.