esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setups and Methodologies
### Performance Measurement of Stand Fan  
According to two measurement standards of the fan, a closed region enclosing the stand fan is constructed to avoid flow disturbance from the environmental disturbances. In addition, the measuring plane is set in front of the stand fan with a distance of three times that of the impeller diameter. Therefore, the measurement points are located at 1.05 m away from the fan's outlet. Moreover, the flow channel is designed with a square cross-section and the length of three-time-rotor-diameter to avoid the reverse flow caused by the wall. Also, this measuring plane is divided into several square annular zones, which start from the center of the monitoring plane outwards and are separated every five centimeters. That implies that the width of each square annular area is 5 cm.  
In addition, the hot wire anemometer is utilized to measure the air speed at the center points of each annual area with a 30-s recording time for each test. Hence, the mean flow speed of each annular region is obtained by averaging the measurement values attained around that annual area. Furthermore, the flow rate through that area equals to the product of this average velocity and its area. Clearly, the summation of the flow rate for each annual area yields the total flow rate generated by this stand fan as long as the measuring plane is large enough to cover the air stream. Note that the minimum effective velocity measurement allowed by this hot wire anemometer is 0.1 m/s. Thus, the velocity data less than that minimum are truncated.
# Performance-Driven Yield Optimization of High-Frequency Structures by Kriging Surrogates
## 4 Demonstration Case Studies  
This section provides the results obtained using the algorithms of Sections 3.1 and 3.2 using a ring-slot antenna, and a miniaturized rat-race coupler as verification structures. The procedure is benchmarked against the surrogate-assisted approaches of Section 2. In order to verify the reliability of the considered methodology, a Monte Carlo analysis has been executed at the nominal design and the yield maximizing design.  
Figure 5: Surrogate domain definition using the reference designs. Microwave couplers case.  
Figure 6: Yield optimization of microwave couplers by means of performance-driven surrogates: (**a**) scattering parameters of an exemplary coupler at the nominal design \(\mathbf{x}^{(0)}\), design \(\mathbf{x}^{(1)}\) (spoiled power split), and design \(\mathbf{x}^{(2)}\) (improved –20 dB bandwidth); for clarity, only the selected S-parameters are shown for \(\mathbf{x}^{(1)}\) ( \(S_{21}\ |\,\
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 1 Introduction
In addition, there is no consensus when it comes to including inorganic carbon, different levels of rock content [19], and the effects of natural or anthropogenic phenomena (such as flooding, erosion, fire, soil fertilization, and plowing [20]) in carbon stock assessments. If we are to move forward in understanding and managing SOC for the benefit of humanity as a whole, effective and efficient methodologies for continuous monitoring of SOC on a global scale are needed. Unfortunately, traditional methodologies employed in monitoring SOC tend to be labor-intensive, costly, and impractical [21]. These procedures entail comprehensive soil sampling campaigns, subsequent laboratory testing, and extensive data processing [22] to obtain what can truly be labeled as \"ground truth\" data. In this study, we aimed to determine the soil organic content (SOC) of agricultural fields using Sentinel-2 satellite imagery, which provides spatially continuous and cost effective data about the state of the Earth's surface. To achieve this, we used machine learning techniques, which require a dataset of satellite images and corresponding SOC measurements collected through field sampling, on which to train and validate our models.
## 1 Introduction  
Partial discharge (PD) may occur when high voltages are applied across insulators with defects, which has been widely used for online monitoring [1, 2]. However, due to the complex and harsh field environment, the PD signal detected online is often accompanied by strong electromagnetic interference [3]. It is difficult to extract pure PD signals. Therefore, it is very important to effectively suppress the noise in the PD signal.
## 4 Simulation Results
To investigate the advantage of the current ripple cancellation in the interleaved PFC converters over the conventional PFC converters, the input current ripple for both converters is measured at the same switching period from 1.999925 to 1.999985. Figure 20 shows the waveforms of the boost converter input current and inductors currents for the conventional and the interleaved PFC converters. The total input ripples current peak to peak over this switching period is measured for both topologies, which is about 0.6901 A for the conventional PFC converter and is reduced to about 0.3091 A for the interleaved PFC converter due to the ripple current cancellation of the interleaved converters, reducing of the input current ripples leads to reducing the current total harmonic distortions and increasing the input supply power factor. Figure 19: Output voltage dynamic performance with different input voltage for the interleaved CCM PFC. Figure 18: Output voltage dynamic performance with different input voltage for the conventional CCM PFC. Figures 21 and 22 shows the Fourier analysis for the input current at the full load condition of the conventional and the interleaved CCM PFC converters, respectively. It is observed that the third harmonic is the dominant harmonic in the current spectrum and it is about 0.578 A for the conventional PFC, which reduced to about 0.220 A with the interleaved PFC converter. Furthermore, the total harmonic distortion (THD) of about 4.67% in the conventional PFC converter and reduced to about 2.65% with the interleaved PFC converter. It can observe that the designed PFC converters offer harmonic level follows the harmonic standard specification IEEE 519-20142 [35] and IEC 61000-3-2 [36] for the design of the telecom power supply.
## 2 Photovoltaic Modeling
### MDDM Representation  
When irradiation exists, the P-N junction uses the energy of photons to produce electron-hole pairs [15]. A potential difference across the junction arose and carriers starts to flow in the circuit, so the elementary description of an illuminated PV cell is a current source (\(I_{PV}\)) in parallel with a P-N junction diode (\(D1\)). Another diode (\(D2\)) is added to consider the effect of recombination current loss in the depletion region in the model of a PV cell. \(R_{GB}\) is a series resistance connected to the second diode (\(D2\)) to take into consideration the resistivity in the vicinity of grain boundaries as it is higher than the resistance within the crystallites [16]. \(\mathrm{R_{S}}\) is a series resistance that represents the effect of the contact resistance between silicon and electrode surfaces, the resistance of electrodes, and the current flow resistance. \(\mathrm{R_{P}}\) is a parallel resistance that should be added to consider the effect of the leakage current of P-N junction. The above-described model is shown in Figure 1.  
Figure 1: Modified double diode model.  
The mathematical equations of this approach are formulated as follows:  
\[I_{D1}\ =I_{s1}\bigg{[}\exp\bigg{(}\frac{V+IR_{s}}{\eta_{1}V_{T}}\bigg{)}-1 \bigg{]} \tag{1}\]  
in which \(I_{S1}\) is the reverse saturation current, \(\eta_{1}\) is the ideality factor of (\(D1\)), \(I\) and \(V_{T}\) are given by:  
\[I=I_{PV}\ -\ I_{D1}\ -I_{D2}\ -\ I_{P} \tag{2}\]  
and:  
\[V_{T}=K_{B}T/q \tag{3}\]  
where, \(K_{B}\) is Boltzmann's constant, \(T\) is the absolute temperature, and \(q\) is the electron's charge. The currents \(I_{D2}\) and \(I_{P}\) are calculated by:  
\[I_{D2}\ =I_{S2}[\exp\bigg{(}\frac{V+IR_{s}-I_{D2}\ R_{GB}}{\eta_{2}V_{T}} \bigg{)}-1] \tag{4}\]  
and:  
\[I_{P}\ =\ \frac{V+IR_{s}}{R_{P}} \tag{5}\]  
where \(I_{S2}\) is the reverse saturation current, \(\eta_{2}\) is the ideality factor of (\(D2\)), and \(V\) is the cell output voltage. The \(I_{PV}\), \(I_{S1}\), \(\eta_{1}\), \(I_{S2}\), \(\eta_{2}\), \(R_{GB}\), \(R_{P}\), and \(R_{s}\) are the eight parameters that would be estimated.
# Simulink Model of a Thermoelectric Generator for Vehicle Waste Heat Recovery
## 1 Introduction
Their model considered the coverage and placement of the thermoelectric modules on the heat exchanger surface. The primary outcome was that increasing the number of modules may eventually saturate the total power output. [PERSON] et al. [15] investigated the design of a cooling channel for a thermoelectric module using a CFD software program (Fluent). Their results showed that in general, the output power is higher when using liquid as cooling fluid compared to air. The analysis of this literature shows that there is a wide variety of numerical models for TEG simulation with different types of sophistication. However, most models are usually only briefly presented, with little detailing of the underlying equations and of the implementation. Therefore, the aim of this study is to develop and validate a respectively simple steady-state Simulink model that can be utilized to estimate and optimize TEG system performance. The model is intended to aid in the design and development of thermal energy recovery systems for ICEs, but it can be applied in other areas as well. Employing the described model early in the design process can facilitate whether thermal energy recovery is feasible for the TEG system under specific operating conditions. Furthermore the proposed Simulink model can be integrated into existing software solutions for the analysis of different energy sources and powertrains.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 4 Experiments
### Visualization of Attention Layer  
Figure 5 presents the average intra-segment self-attention maps for two types of audio segments: those containing speaker changes (Figure 5a) and those without speaker changes (Figure 5b). Each attention matrix is of size \(50\times 50\), reflecting attention weights between 50 temporal frames (approximately one frame every 20 milliseconds in a 1 s segment).  
Notably, the attention map for speaker change segments exhibits more diffuse and scattered patterns. The attention weights are distributed more broadly across off-diagonal regions, indicating that the model attends to a wider temporal context when a speaker transition is occurring. This suggests increased uncertainty or contextual reevaluation during such transitions.  
In contrast, normal segments show strongly diagonal attention patterns, meaning each frame primarily attends to itself and its immediate neighbors. This reflects temporal stability and high model confidence when no speaker change is detected.  
These patterns align with our entropy-based analysis, which showed statistically higher attention entropy in speaker change segments. As shown in Figure 6, the attention entropy is significantly higher in speaker change segments compared to normal ones. The result shows that speaker change segments tend to have significantly higher attention  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Previous Work** & **P** & **R** & **F1** & **FAR** & **MDR** \\ \hline [PERSON] et al [26] (2019) & - & - & - & 70.46 & 19.62 \\ \hline [PERSON] et al. [23] (2023) & 66.73 & 80.49 & 73.61 & - & - \\ \hline [PERSON] et al [21] (2023) & 79.40 & 68.1 & 73.30 & - & - \\ \hline
**eMD\({}_{att}\)** (audio-only) & 61.14 & **89.08** & 72.39 & 25.69 & 10.92 \\ \hline
**eMD\({}_{att}\)** (multi-modal) & 64.30 & 85.85 & 73.39 & 21.78 & 14.15 \\ \hline
**eMD\({}_{att}^{*}\)** (multi-modal) & **69.61** & **79.23** & **74.01** & 15.88 & 20.77 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with the previous work on multi-modal approaches. The asterisk (*) denotes the model optimized with focal loss parameters. Bold values indicate the best performance among all models for each metric.  
entropy, suggesting that the model distributes attention more broadly when detecting transitions. This further supports the hypothesis that the model attends more diffusely under speaker transition conditions.  
Figure 5: Comparison of attention distribution between speaker change and normal segments in the **eMD4t** model: (**a**) Average attention heatmap for speaker change segments. (**b**) Average attention heatmap for normal segments.
## 3 Results and Discussion
### Mineralogical Properties of Dust and Soil  
The mineralogical composition of the collected soil and dust samples was analyzed using X-ray diffraction (XRD, MAXima_X XRD-7000, Shimadzu, Japan). The XRD analysis of the soil samples revealed that quartz (with peaks at 3.17 A-3.34 A) and calcite (with peaks at 1.34 A-2.70 A) were the dominant minerals present. This mineral composition suggests a strong influence of dust deposition on the soil surface, alongside natural weathering of rocks and surface soils in the vicinity of the study sites [PERSON] and [PERSON] (2018). The accumulation of quartz and calcite can be attributed to these processes, with quartz serving as a key indicator of dust accumulation, as highlighted by [PERSON] et al. [PERSON] et al. (2018). These findings align with the notion that dust deposition plays a crucial role in shaping the mineralogical composition of soils in arid regions.  
Similarly, the mineralogical analysis of the collected dust samples, also performed using XRD, confirmed that quartz and calcite were the most prevalent minerals. The XRD patterns, illustrated in Figure 6, displayed peaks at 3.33-3.71 A for quartz and at 2.94 A for calcite, further supporting the dominance of these minerals in the dust samples. The high concentration of quartz in both dust and soil samples aligns with the findings of [PERSON] et al. [PERSON] et al. (2018), emphasizing quartz as a reliable marker for dust deposition.  
The presence of these minerals, particularly quartz, underscores the significant impact of dust deposition on soil mineralogy in the study area. This is consistent with the observed mineralogical patterns, where dust deposition, combined with natural weathering processes, has led to the accumulation of quartz and calcite in the soil. For instance, at site S11, which recorded the highest dust deposition, quartz levels were notably elevated, illustrating the direct relationship between dust accumulation and soil composition. Similarly, sites S6 and S9, which also experienced considerable dust deposition, displayed mineralogical patterns consistent with the accumulation of both quartz and calcite, driven by natural weathering processes. The findings from the XRD analysis provide clear evidence of the mineralogy alterations in the soil due to dust deposition, highlighting the interplay between dust accumulation and soil mineral composition.
## 5 Experimental Results
### Effectiveness of Auxiliary Learning  
Our algorithm performs a second 2D object detection while estimating the initial 3D box; this is to add constraints to the loss function, aiding in the learning of 3D parameters by virtue of the high precision of the 2D detection. The algorithm uses \(\alpha\) to adjust the proportion of the 2D detection loss to the total loss function. In order to verify the effectiveness of the auxiliary learning, a set of ablation experiments is set up. The experimental results are shown in Table 5.
## 2 Methods
### Channel Selection Block  
The channel selection block is at the core of this mechanism, playing a pivotal role in the process. During the forward propagation phase, it dynamically evaluates the significance of each input channel by computing gating signals denoted as \(g\). These gating signals are generated based on the input data features, as seen in the following equation:  
\[g=\sigma(Wg\times x+bg) \tag{3}\]  
where \(g\) represents the gating signal, \(\sigma\) is the sigmoid activation function, and \(Wg\) and \(bg\) stand for the weight and bias of the gate convolution, respectively.  
The gating signals are instrumental in determining which channel features should progress to the subsequent processing layer, selectively allowing certain feature channels to pass through while deliberately suppressing others. This selective activation is described by the following equation:  
\[x^{\prime}=x\times g \tag{4}\]  
Equation (4) demonstrates that the input features (\(x\)) are selectively modified by the gating signals (\(g\)) to produce \(x^{\prime}\), which represents the channel features after activation or suppression.  
The convolution module complements this process by handling feature processing. It employs a convolutional layer specialized in processing input data features. Notably, it differs from standard convolutional layers in that the output of the convolution module is selectively influenced by the gating signals. This means that only those channel features deemed essential by the model are permitted to pass through the convolution module, while others are intentionally suppressed. This selective processing can be expressed as follows:  
\[x^{{}^{\prime\prime}}=Conv(x^{\prime}) \tag{5}\]  
where \(x^{{}^{\prime\prime}}\) represents the channel features after selective convolution processing.  
Moreover, the unique strength of the channel selection mechanism lies in its adaptability. By autonomously learning and assigning importance to individual input channels, the model becomes adept at dynamically enabling or inhibiting specific channels as needed. This adaptability proves invaluable in effectively managing a wide range of tasks, as the relevance of different channels can fluctuate depending on the specific requirements of each task. In models that incorporate the channel selection mechanism, input data undergo processing, resulting in an output tensor. The output tensor exclusively comprises features from channels the model deems crucial, with features from less important channels consciously suppressed. This reduction in computational redundancy significantly simplifies the model, making it a robust and efficient solution for various gesture recognition tasks. The channel selection mechanism's adaptability and efficiency make it a valuable addition to the field of sEMG signal processing, where streamlined models capable of accommodating diverse gestures and tasks are in high demand.
## 1 Introduction
By comparing EVI and the photochemical reflectivity index, [PERSON] et al. (2018) demonstrated that chlorophyll fluorescence derived from GOME-2 more accurately captures seasonal and interannual variations of gross primary productivity across dryland ecosystems of southwestern North America [30]. Through evaluating the SIF time series derived from GOME-2 with tower-based GPP in Australia, [PERSON] et al. (2016) found that natural biome types, such as savanna and open woodland, showed weaker correlations relative to agricultural biomes [31]. Taking into account the sparse spatial resolution of the existing spaceborne SIF product, as well as the varying mixtures of trees, shrubs, and grasses in Australia's dryland ecosystems, a high-resolution contiguous SIF product (SIF\({}_{\text{occ2}}\)\({}_{\text{,005}}\)) was utilized to explore the phenology dynamics of dryland vegetation along the North Australian Tropical Transect [23]. They found that these up-scaled SIF data at a 0.05\({}^{\circ}\) spatial resolution outperformed EVI for characterizing seasonal onset and senescence of dryland vegetation and suggested it had the potential for large-scale mapping of phenology dynamics as opposed to traditional reflectance-based vegetation indices. Nevertheless,SIF\({}_{\rm 00c02\_005}\) was generated by the OCO-2 native SIF along with MODIS reflectance using machine learning [32], and it therefore showed an enhanced accuracy in determining the phenological transition of GPP derived from flux tower measurement as a consequence of it containing both structural and physiological information. However, whether the original SIF observation can accurately capture the phenology dynamics of Australia's dryland vegetation under climate variability remains unclear. In arid and semiarid ecosystems, rainfall strongly affects the strength and occurrence of photosynthetic and respiratory activities and is the dominant driver for vegetation phenology and productivity [33; 34; 35]. The seasonality of Acacia-dominated woodlands (as measured by EVI) was found to show substantial responsiveness to hydroclimatic variability [4]. Additionally, approximately 80% of the variations in the length of the growing season of major dryland biomes along the NATT could be attributed to the variability of annual precipitation [4]. Nevertheless, there is a knowledge gap regarding how and to what extent major environmental and climatic drivers determine the phenological dynamics of fluorescence (as a surrogate of photosynthesis). The objectives of this study were: (1) to evaluate the spatial patterns and seasonal variations of dryland vegetation phenology across NATT under a dynamic climate; (2) to contrast the differences in phenological metrics derived from satellite-observed SIF and EVI; (3) to determine the dominant factor that drives the interannual and seasonal variability of each vegetation variable among major biome types.
# Analysis of Spatiotemporal Gait Variables before and after Unilateral Total Knee Arthroplasty
## 2 Materials and Methods
### Data Collection Procedures  
The demographic parameters included age, body mass (Inbody 230, InBody Co., Ltd., Seoul, Korea), height (Seca 206, Seca GmbH, Hamburg, Germany), lower limb dominance (by handedness laterality), the number of falls in the last month, the laterality of surgery, pain in the last week in both knees as assessed by the visual analogue scale, and the radiological severity of knee arthroplasty for both knees (assessed by a researcher with practice in knee arthroplasty radiology) using the Kellgren-Lawrence scale. The functional parameters included the measurement of the active and passive range of motion (ROM) with a handheld goniometer (Enraf-Nonius, Rotterdam, The Netherlands), and knee extension force, measured with a MicroFET2 (Hogan Health Industries, Draper, UT, USA) handheld dynamometer. These tests were performed for both knees by an experienced clinician. Additionally, the functional section of the Knee Society Score (2011 KSS) [23; 24; 25] and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) [26; 27; 28], were used for the characterization of the function and quality of life, respectively. For a more detailed description of the clinical assessment, see [PERSON] et al. [29].  
After the aforementioned evaluations, all participants underwent a gait analysis, after a brief familiarization period. A 12-camera motion capture system, composed of Oqus cameras (Qualisys AB, Goteborg, Sweden) operating at 200 Hz sampling frequency, was used for the kinematic gait analysis. The camera system was calibrated with an error below 0.6 mm on the central 6 m of a 10 m walkway. Retroreflective markers, with a 12 mm diameter, were placed on the participants according to the IOR lower body marker set [30], as well as 4-marker clusters on the thigh and shank of both lower limbs. Participants were instructed to walk at a comfortable, self-selected speed along the 10 m walkway, with at least ten valid attempts being recorded in each assessment session [31; 32]. Kinematic data was recorded using Qualisys Track Manager 2022.2 build 7710 (Qualisys AB, Goteborg, Sweden).