esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
## 5 Results and Discussion
### Discussion  
Unlike the original single-target detection method [12; 13] applied to terahertz images, which can only detect a single target, our method is also suitable for situations where there are multiple objects to be detected. Similar to [PERSON]'s experiments [14; 15], our experiments demonstrate that RetinaNet has better detection results than YOLOv3, YOLOv4, and FRCN-OHEM. We extend RetinaNet to better detect small targets by embedding low-level features.  
Because the number of negative samples in the training dataset is usually much larger than the number of positive samples, there is an imbalance problem in target detection. Aiming at solving the problem of unbalanced training samples, because the OHEM method [10] cannot be directly applied to one-stage target detectors, we proposed a new Hard Example Mining (HEM) approach for the multi-object detection. Our proposed HEM approach was successfully applied to the RetinaNet network and improved the detection rate. Through the above experiments, we demonstrate the role of hard example mining in the multi-object detection of terahertz images. In addition, the analysis of object positions in terahertz imaging shows that the position of hidden objects has a significant impact on the detection rate. Therefore, the use of multi-view detection is required in practical applications. We compare in detail the parameters that affect the image detection metrics and give a method for selecting the best threshold value. By discussing the various thresholds and the choice of parameters, the model can achieve the best detection results.  
Missing and wrong detection of dangerous goods bring serious harm to social security. The improvement of detection accuracy is of great significance in practical applications.
## 2 Materials and Methods
### Statistical Analysis  
The data set was first examined for erroneous values, outliers, and multicollinearity. It was confirmed that no incorrectly entered data were present. The analysis was conducted using SPSS version 25. Descriptive statistics were employed to analyze the demographic characteristics of the athletes, with results reported as arithmetic mean \(\pm\) standard deviation (x \(\pm\) ss). The normality of the distributions was assessed through skewness and kurtosis tests. Since the skewness and kurtosis coefficients fell within the range of \(-\)1.5 to +1.5, the data were deemed normally distributed [46]. Given that the assumptions for normal distribution were met, parametric tests were utilized. A repeated measures analysis of variance (ANOVA) was conducted to detect differences in the 10 m, 20 m, and 30 m sprints, CMJ, COD, and RSI parameters (3 groups \(\times\) 2 time points). For variables exhibiting significant group-time interactions, the Bonferroni test was applied to compare changes across groups and time. Greenhouse-Geisser corrections were implemented for F tests when [PERSON]'s test of sphericity was violated. Given the equality of group sizes and the homogeneity of variances, [PERSON]'s HSD test was employed for inter-group comparisons [47]. Additionally, partial eta squared (\(\eta_{\mathrm{p}}\)\({}^{2}\)) was calculated to assess effect size, categorized as large (\(\geq\)0.14), medium (\(\geq\)0.06), and small (<0.06) according to [PERSON] (2013) and [PERSON] et al. (2023) [48; 49]. Descriptive analysis, including relative and absolute frequencies, was conducted with a 95% confidence interval [50]. An independent \(t\)-test was employed for the RPE measurements, with [PERSON]'s d calculated to assess effect size. [PERSON]'s effect size classifications were 0-0.19 as insignificant, 0.20-0.49 as small, 0.50-0.79 as medium, and \(>\)0.80 as large [48; 51]. A significance level of \(p<\) 0.05 was established for this study.
## 1 Introduction
Using these models, scholars have conducted in-depth studies on calculation accuracy, calculation speed, orientation, and boundary conditions, and concluded a series of results, which promoted the development and application of exploration of the noise field [19; 20; 21; 22]. [PERSON] et al. considered wind-induced waves as the noise source and used the transmission theory method instead of the traditional Monte Carlo method to construct a noise field model. They analyzed the spatial noise distribution and concluded that high-frequency noise had fluctuations in intensity at the offshore surface [23]. [PERSON], [PERSON] et al. considered the problem of carrier frequency offset estimation in OFDM underwater acoustic communication and analyzed the causes of changing environmental impacts [24]. [PERSON] et al. analyzed wave fluctuation on underwater acoustic communication using measured data collected with USV [25]. [PERSON] et al. used quantile-quantile (Q-Q) plots to analyze real marine environmental data, interpreting the impulsive property of ocean ambient noise in shallow waters [26]. [PERSON] et al. applied [PERSON]'s bistatic formula to SAS image processing, which provided a more accurate approximation of the spectrum compared to that based on phase center approximation [27]. [PERSON], [PERSON] et al. propose underwater acoustic (UWA) communications using a generalized sinusoidal frequency modulation (GSFM) waveform, which makes full use of the time and frequency variation laws of the marine environment in experimental data [28]. [PERSON], [PERSON] et al. proposed a deep-learning-based orthogonal frequency division multiplexing receiver for underwater acoustic communications to process marine environmental data through neural networks [29]. The stratum structure of the ocean space determines the multi-channel coherent structure characteristics of the ocean sound field. The motion of the transmitter end, the interface, and the receiving sensor affect the spatiotemporal fluctuation characteristics of the sound field, which shows that the channel response function is time-varying and space-varying. The feature of time-varying and space-varying channels is the key point to manage to achieve effective and stable UAC systems. In this study, the signal field and noise field were evaluated by establishing a model and obtaining data through experiments, and the spatial-temporal distribution of signals was summarized to provide theoretical support for the design of UAC systems.
## 4 Techno-Economic Analysis (TEA)  
A techno-economic analysis was performed based on the literature [60] to demonstrate the feasibility of xylitol production from BSG. It is known that BSG represents around 85% of the total by-products generated by the brewing industry and is commonly disposed in landfill. Several studies [54; 57; 59] have reported the valorization of BSG mainly with a focus on the production of health-promoting food products such as xylitol. In fact, xylitol represents a high-value product with large profit margins, even at a low production scale, with values >5 USD\(\cdot\)kg\({}^{-1}\) and a market with a compound annual growth rate (CAGR) >6%. Techno economic evaluations revealed that BSG valorization into xylitol presented the lowest production costs (<0.35 USD\(\cdot\)kg\({}^{-1}\)) when compared to other chemicals production such as lactic acid or polyhydroxybutyrate.  
The present study demonstrated that BSG has the potential to be used as a feedstock for the production of xylitol by _K. pastoris_. However, several improvements are required to make the process more attractive and economically feasible, namely optimizing the hydrolysis conditions to increase the xylose concentration in the acid hydrolysate, thereby maintaining the inhibitory compounds at low contents. In addition, the process scale-up is mandatory to investigate yield and productivity on a larger scale.
## 3 Results and Discussion  
### Transmission Coefficient  
Figure 6 displays the eight samples' transmission coefficients, as determined by Equation (2). Figure 6a-d demonstrates that adding WHA to the earth enhanced the microstructural acoustical properties like porosity, a result that was crucial for the construction of walls and allowed internal air to flow out through the walls and, thus, reduce the concentration of indoor air pollutants. The transmission coefficient was observed to increase with increase in the WHA content, as can be shown in Figure 6a-d. This is in agreement with a previous study by [PERSON] et al. [36], who also observed the increase in the transmission coefficient with an increase in the WHA content. However, SBA was found to reduce the transmission coefficient (Figure 6e-h). This is also in accordance with a previous study by [PERSON], [PERSON], and [PERSON] [33], which showed that SBA is very rich in silica content (64.88% silica), unlike WHA, which contains only 4.40% [26]. The silica (Cementous material) in the SBA binder reacted with portralide (calcium hydroxide) to form an additional calcium silicate hydrate gel. For Pozzolan, the sum of the composition of silica, alumina, and iron III oxide should be more than 70%, a condition that has been met by SBA in previous studies but has not been met by WHA. In fact, the study by [PERSON], [PERSON], and [PERSON] [33] found this value to be 73.91% for SBA. This explains why SBA-bound CEBs in this study showed stronger binding compared to the WHA-bound CEBs and thus, lower transmission coefficients. A lower transmission coefficient implies a lower intensity, which leads to a reduction in the sound between the inside and the outside of a house, a phenomenon that is ideal for occupants that live near noisy places such as those located close to airports.  
Figure 6: Transmission coefficients of the 8 samples of the earth blocks (SG1 to SG8): **(a–d)** CEBs bound using WHA, and **(e–h)** CEBs bound using SBA.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 5 Analysis of the Simulation and Test  
Both a simulation and field experiment were carried out, in order to evaluate the performance of the proposed time synchronization protocol. The feasibility was tested on a simulation platform of NS 2.29. In addition, the time precision and topology adaptation of the proposed protocol were evaluated on the embedded Linux platform.
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 1 Introduction
methods are adopted: (1) high-gain antennas and SIMO radar configuration are designed to increase the transmitting power (Pt) of the link; (2) a pair of circularly polarized antenna is used to reduce the interference of meteorological particles to the radar effectively. The paper is arranged as follows: the radar architecture design is presented in Section 2. Section 3 describes the simulation and measurement results of the circularly polarized antenna. The radar waveform configurations are discussed in Section 4. Section 5 offers a comprehensive analysis of the radar's performance in measurements. Finally, the conclusions are presented in Section 6.
## 3 Multi-Label Hierarchical Classification  
### General Concept  
The main idea is transformation of a multi-label classification task with a large set of labels \(L\) into a tree-shaped hierarchy of simpler multi-label classification tasks, each one dealing with a small number \(k\) of labels: \(k<<|L|\) (sometimes \(k<|L|\)). Below follows the explanation of the general concept of the multi-label hierarchical classification [4].  
Each node \(n\) of this tree contains a set of labels \(L_{n}\subseteq L\). Figure 1 shows 6 leaves and 3 internal nodes. There are \(|L|\) leaves, each containing a singleton (a single element set) \(\{\lambda_{j}\}\) with a different label \(j\) of \(L\). Each internal node \(n\) contains a union of labelsets of its children: \(L_{n}=\cup L_{c},\,c\in children(n)\). The root accommodates all the labels: \(L_{root}=L\).  
Meta-label \(\mu_{n}\) of node \(n\) is defined as conjunction of the labels associated with that node: \(\mu_{n}=\cup\lambda_{j},\,\lambda_{j}\in L_{n}\). Meta-labels have the following semantics: a document is considered annotated with the meta-label \(\mu_{n}\) if it is annotated with at least one of the labels in \(L_{n}\). Each internal node \(n\) of a hierarchy also accommodates a multi-label classifier \(h_{n}\). The task of \(h_{n}\) is to predict one or more meta-labels of its children. Therefore, the set of labels for \(h_{n}\) is \(M_{n}=\{\mu_{c},\,c\in children(n)\}\).  
Figure 1 shows a sample hierarchy produced for a multi-label classification task with \(6\) labels.  
Figure 1: Hierarchical multi-label classification work flow.  
For multi-label classification of a new document, the classifier starts with \(h_{root}\) and then forwards it to the multi-label classifier \(h_{c}\) of the child node \(c\) only if \(\mu_{c}\) is among the predictions of \(h_{parent(c)}\). The main issue in building hierarchies is how to distribute the labels of \(L_{n}\) among the \(k\) children. One can distribute \(k\) subsets in such a way that the labels belonging to the same subset remain similar. In [15], the number \(k\) of labels is a set given for each \(L_{n}\).
###### Abstract  
Specific leaf area (SLA) is a key leaf functional trait associated with the ability to acquire light. Substantial variations in SLA have not been well described in the community land model (CLM) and similar terrestrial biosphere models. How these SLA variations influence the simulation of gross primary productivity (GPP) remains unclear. Here, we evaluated the mismatch in SLA between the CLM4.5 and observed data collected from China and quantified the impacts of SLA variation calculated from both observations and the default values across seven terrestrial biosphere models on modeled GPP using CLM4.5. The results showed that CLM4.5 tended to overestimate SLA values at the top and gradient of the canopy. The higher default SLA values could cause an underestimation of the modeled GPP by 5-161 g C m\({}^{-2}\) yr\({}^{-1}\) (1%-7%) for temperate needleleaf evergreen tree (NET), temperate broadleaf deciduous tree (BDT), and C3 grass and an overestimation by 50 g C m\({}^{-2}\) yr\({}^{-1}\) (2%) for temperate broadleaf evergreen tree (BET). Moreover, the observed SLA variation among species ranged from 21% to 59% for 14 plant functional types (PFTs), which was similar to the variation in default SLA values across models (9%-60%). These SLA variations would lead to greater changes in modeled GPP by 7%-19% for temperate NET and temperate BET than temperate BDT and C3 grass (2%-9%). Our study suggested that the interspecific variation in SLA and its responses to environmental factors should be involved in terrestrial biosphere models; otherwise, it would cause substantial bias in the prediction of ecosystem productivity.  
22
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 2 Materials and Methods
### Methods
#### 2.2.2 Deep Transfer Learning Methods
A list of thirty-nine models used for transfer learning in this study:  
* DenseNet121, DenseNet169, and DenseNet201 [40];
* EfficientNetB0-EfficientNetB7 [41];
* EfficientNetV2B0-EfficientNetV2B3 [42];
* InceptionV3 [30];
* MobileNetV1 [43] and MobileNetV2 [44];
* NASNetMobile [54];
* RegNetX002, RegNetX004, RegNetX006, RegNetX008, and RegNetX032 [45];
* ResNet50, ResNet101, and ResNet152 [29];
* ResNetR550, ResNetRS101, ResNetRS152, ResNetRS200, ResNetRS270, ResNetRS350, and ResNetRS420 [50];
* ResNet50V2, ResNet101V2, and ResNet152V2 [55];
* VGG16 and VGG19 [28]. The workflow utilized to train the 39 pre-trained models is summarized in Figure 2. In each instance, we removed the top layer of our pre-trained models, kept the underlying layers frozen, and then trained the added layers, which will be discussed in Section 2.2.3, using the train set. These added layers were fine-tuned using the validation set.
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 5 Simulation Results
### PMEPR and PSLR Performance of IRC Waveform  
For a fixed weight \(\alpha=5\), an IRC waveform is obtained by joint optimization of the PMEPR and PSLR, and then the PMEPR, objective function of (21) and PSLR are calculated with respect to the number of iterations and are shown in Figure 4. It can be observed from Figure 4a that the objective function of (21) shown on the right axis is monotonically decreasing, and the PMEPR on the left axis and the PSLR in Figure 4b generally show decreasing and increasing trends, respectively. Another observation is that the obtained IRC waveform has the desired PMEPR and PSLR performances, reaching 2.3 dB and 29.6 dB, respectively.  
Figures 5 and 6 show the CCDF curves of the PMEPR and PSLR for the IRC waveforms with \(\alpha=5\) processed by our proposed algorithm, and give the results obtained by [23] (i.e., only the PMEPR is optimized in (20)) and [31] for comparison. The PMEPR and PSLR performance are evaluated at CCDF \(=10^{-4}\). It is found from Figures 5 and 6 that the proposed CGIA-JOPP algorithm achieves 10 dB PMEPR reduction, which is 1.1 dB worse than only optimizing PMEPR, but improves the PSLR by 10.4 dB, which is a significant improvement effect. Compared with [31], the PMEPR reduction and PSLR improvement obtained by our proposed algorithm reach 2.6 dB and 14.8 dB, respectively. This shows that the proposed algorithm has obvious advantages in both the PMEPR and PSLR, which fully demonstrates its effectiveness. In addition, the input data modulated by 16 QAM are also used to generate the IRC waveform, and the obtained PME  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Baseband bandwidth & 128 MHz \\ Sampling frequency & 512 MHz \\ Number of subcarriers & 512 \\ Modulation & 4 QAM/16 QAM \\ Single OFDM symbol duration & 4 μs \\ Cyclic prefix duration & 10 μs \\ Number of OFDM symbols & 3 \\ Pulse width & 22 μs \\ Tone reservation ratio & 50\% \\ \(p\) in \(L_{p}\)-norm & 10 \\ \(\alpha\) & \([0.2,100]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: PMEPR, objective function, and PSLR versus the iteration numbers. (**a**) PMEPR and objective function. (**b**) PSLR.  
are also analyzed in Figures 5 and 6; the results show that the proposed algorithm is also applicable to higher order modulations.
## 4 Results and Discussion
### Rotor Optimization Results
#### 4.2.2. Blade Angle Optimization Results  
The blade angle (beta-angle) distribution does not affect the rotor weight too much, but does affect the aerodynamic performance and the stress level. In this study, the blade angle distributions at three sections (hub, middle, and tip) are optimized. The last beta-angle (\(\beta_{te}\)) on each section indicates the blade turning, which correlates to the aerodynamic performance. Figure 22 shows the total-to-static efficiency with respect to the last trailing edge beta-angle at the hub, middle, and tip sections. It can be seen that promising designs tend to have a bigger blade turning at each section for both T-100 and T-100 re rotor, and the trend is more obvious at the middle and tip sections of T-100 re rotor. The label of the horizontal axis in Figure 22 indicates the variation range specified for \(\beta_{te}\). It can be seen that the range specified for \(\beta_{te}\) at each section is sufficient because no concentration happens on two sides.
## 3 Results
### Experimental Testing of the Prosthetic System for LLD Correction
#### 3.4.2 Experimental Testing of the Prosthetic System
Upon initiation of the recording, a significant peak in acceleration is observed, indicative of the system's startup effect. This is followed by relatively stable oscillation. Throughout the entire measured period, the acceleration oscillates frequently between positive and negative values, corresponding to the rotation of the threaded axis driven by the actuator. The acceleration values remain relatively modest, falling below 0.4 mm/s\({}^{2}\), suggesting precise control over the movement. The oscillations are not perfectly uniform, with both their frequency and amplitude varying over time. This phenomenon can be attributed to the active adaptation of the system. As illustrated in Figure 18, the displacement speed of P1 mounted on the EPM is represented. The recording was performed during a programmed stroke of the actuator, highlighting two distinct stages: first, the advance (extension of the prosthesis), followed by retraction (shortening of the prosthesis). As illustrated by the initial segment of the graph, there is a progressive increase in speed, which ultimately attains a stable oscillatory regime at approximately 0.3 mm per second. These oscillations are attributed to the rotation of the threaded axis driven by the actuator, thereby reflecting the controlled movement of the prosthesis. At approximately 75 s, a sudden transition is observed, marked by a rapid decrease in speed, followed by a reversal of the direction of displacement. Figure 16: Variation of displacement along the X-, Y-, and Z-axes for P1 during a programmed stroke with return to the initial position. In the subsequent segment of the graph, the velocity assumes a negative value, denoting the initiation of the retraction phase of the prosthesis. The oscillations persist, though their amplitude differs from that of the initial phase, suggesting potential variations in the forces exerted on the system during this phase. As the graph approaches its conclusion, a renewed increase in speed is observed, indicating the prosthesis's cessation of movement. This, in turn, signifies the return of the EPM to its original position. This increase is attributed to the progressive deceleration of the mechanism prior to the complete stop, a standard occurrence in the operation of electrically driven devices. The prosthesis follows a controlled motion cycle; however, the speed oscillations during a full stroke could affect the accuracy and stability of the system.