esa-sceva/satcom-chunk-collection huggingface dataset:
# Coal-Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT
## 3 Experimental Results and Analysis
### Spectral Acquisition Experiment
#### 3.1.3 Dataset Description  
In this study, 120 coal and rock samples were collected, and the experimental samples were collected under ten simulated working conditions (H1\(\sim\)H9) and the ideal experimental environment (H0). The model of the spectrometer in the experiment is the Holland spectrometer, the collection wavelength range of this instrument is 1300-2500 nm, and the resolution is 8 nm. Each experimental sample was placed behind the test bench, and the reflectance spectrum data of the coal-rock sample were collected by the abovementioned experimental device. Each experimental environment collected 10 spectral data, and the average value was taken as the final experimental data. According to the properties of the selected near-infrared spectrometer, the dimension of data collected for each experimental sample is [2 \(\times\) 150], and we only keep the data of the dimension of reflectivity, that is, [1 \(\times\) 150]. After the data are preprocessed by the first-order differential and second-order differential methods, the dimensions of the data become [1 \(\times\) 149] and [1 \(\times\) 148], respectively, so the data dimension of the original RGB image after data conversion is [1 \(\times\) 148], which constitutes Dataset B. The dataset is described in Table 4. Dataset visualization is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Detection Distance** & **Dust Concentration** & **Share of Coal** & **Wind Speed** \\ \hline Level Factor 1 & 1.2 m & 200 mg/m\({}^{3}\) & All & 3 m/s \\ Level Factor 2 & 1.5 m & 500 mg/m\({}^{3}\) & 2/3 & 6 m/s \\ Level Factor 3 & 1.8 m & 800 mg/m\({}^{3}\) & 1/3 & 9 m/s \\ \hline \end{tabular}
\end{table}
Table 2: Influencing factors and level values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Detection Distance** & **Dust Concentration** & **Share of Coal** & **Wind Speed** \\ \hline H1 & 1.2 m & 200 mg/m\({}^{3}\) & All & 3 m/s \\ H2 & 1.2 m & 500 mg/m\({}^{3}\) & 2/3 & 6 m/s \\ H3 & 1.2 m & 800 mg/m\({}^{3}\) & 1/3 & 9 m/s \\ H4 & 1.5 m & 200 mg/m\({}^{3}\) & 2/3 & 9 m/s \\ H5 & 1.5 m & 500 mg/m\({}^{3}\) & 1/3 & 3 m/s \\ H6 & 1.5 m & 800 mg/m\({}^{3}\) & All & 6 m/s \\ H7 & 1.8 m & 200 mg/m\({}^{3}\) & 1/3 & 6 m/s \\ H8 & 1.8 m & 500 mg/m\({}^{3}\) & All & 9 m/s \\ H9 & 1.8 m & 800 mg/m\({}^{3}\) & 2/3 & 3 m/s \\ H0 & 1.5 m & 0 mg/m\({}^{3}\) & All & 0 m/s \\ \hline \end{tabular}
\end{table}
Table 3: Four-factor, three-level orthogonal test table.
## 3 System Design
### Acquisition in Normal-Sensitivity Mode  
When working in normal-sensitivity mode, the acquisition process detects GPS L1 C/A, GPS L5, Galileo E1b/c, and Galileo E5a signals received at nominal \(C/N_{0}\) levels. Additionally, the acquisition process can be configured to detect either the data component or the pilot component of the received signals. Much like the procedure in high-sensitivity mode, the acquisition process involves conducting a cross-correlation between the received signal and a local replica of the satellite PRN code. However, this correlation is performed using a short CI time, equal to the length of the PRN code (1 ms for GPS L1 C/A, GPS L5, and Galileo E5a; 4 ms for Galileo E1b+c) or a small multiple of this length. The results from several consecutive CAFs \(R_{k}(\tau,f_{D})\) can be effectively combined using the non-coherent Post Detection Integration (NPDI) technique, shown in Equation (8) [7], where \(Z_{\text{NPDI}}\) is the result of the NCI. Throughout this process, a CFAR detector is employed.  
\[Z_{\text{NPDI}}=\sum_{k=1}^{N_{\text{nr}}}|R_{k}(\tau,f_{D})|^{2} \tag{8}\]  
When operating in normal-sensitivity mode, the high-sensitivity acquisition hardware accelerator is responsible for capturing samples from the analog front-end. Subsequently, the embedded processor takes charge of all calculations, including Doppler wipe-off, the circular cross-correlation, and the computation and the non-linear combinations of consecutive CAFs. The rationale behind this arrangement lies in the current design of the high-sensitivity acquisition hardware accelerator in the FPGA. Specifically tailored for acquiring weak signals using long CI times, the current version lacks the flexibility required to compute the CAF in normal-sensitivity mode. Nonetheless, the Ultrascale+ XCZU9 EG MPSoC's embedded processor boasts ample computing power to execute the acquisition in normal-sensitivity mode in real time, manage the FPGA tracking multicorrelators, run the telemetry decoders, compute the observables, and determine the PVT.
## 2 Materials and Methods
### CNN-Based Feature Extraction and Fusion  
Machine learning-based image analysis often relies on manually extracted features. However, this method requires domain knowledge and is difficult to apply especially forcomplex data patterns. DL methods, on the other hand, eliminate the need for manual feature engineering by learning features automatically. In this study, the best-performing three models among 14 different CNN models are identified and the features extracted from these models over the Global Average Pooling (GAP) layer are fused. The feature fusion method aims to create a more comprehensive feature set that can discriminate between classes by combining the features learned by different models. The feature vectors extracted from the three different CNN models are combined using the mathematical formula in Equation (1):  
\[F1=GAP(f_{CNN1}(X)),\;F2=GAP(f_{CNN2}(X)),\;F3=GAP(f_{CNN3}(X)) \tag{1}\]  
\[F_{fusion}=[F1\;\parallel\;F2\;\parallel\;F3] \tag{2}\]  
In Equation (1), \(X\) is the input image, \(\text{f}_{CNNi}(.)\) is the feature extraction function of the corresponding CNN model, \(GAP(\cdot)\) is the Global Average Pooling process and \(Fi\) is the feature vector extracted from the GAP layer. In Equation (2), the extracted feature vectors are combined. The operator \(\parallel\) denotes the merging of the feature vectors on the horizontal axis. \(F_{fusion}\) is classified with the ML algorithms mentioned in Section 2.4. Figure 5 illustrates the feature fusion process, where deep features extracted from three CNN models are concatenated to create a more comprehensive feature representation for hypertensive retinopathy classification.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 5 Parametric Study
### Influence of Perfobond Thickness  
As shown in Figure 16, the perfobond thickness had a great influence on the pull-out behavior of notched perfobond shear connectors. When the thickness of the perfobond rib was increased from 12 mm to 16 mm, 20 mm, 25 mm, and 30 mm, the pull-out resistance of notched perfobond shear connectors varied by \(-1\%\), \(13\%\), \(17\%\), and \(24\%\), respectively. It was revealed that the increase of the perfobond thickness would lead to an increase in the pull-out resistance of the notched perfobond shear connector. The main reason for this was that increasing the thickness of the perfobond rib increased the contact area of the hole edge, which reduced the contact stress between the perfobond rib and the concrete dowel in the hole. As a result, the pull-out resistance of the notched perfobond shear connector could be increased by using a thicker perfobond rib.
Micromachines_**2014**, \(5\), 1373-1415; doi:10.3390/mi5041373  
_micromachines_  
**ISSN 2072-666X**  
www.mdpi.com/journal/micromachines  
_Review_  
**Advances in Silicon Based Millimeter-Wave Monolithic Integrated Circuits**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2,* * 1**Academia Sinica Institute of Astronomy and Astrophysics (ASIAA), No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan; E-Mails: [EMAIL_ADDRESS] (H.-C.Y.); [EMAIL_ADDRESS] (C.-C.C.); [EMAIL_ADDRESS] (M.-T.C.)**  
Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +886-3366-3637.  
External Editor: [PERSON]  
_Received: 13 March 2014; in revised form: 15 September 2014 / Accepted: 5 November 2014 / Published: 10 December 2014_  
**Abstract:** In this paper, the advances of the silicon-based millimeter-wave (MMW) monolithic integrated circuits (MMICs) are reported. The silicon-based technologies for MMW MMICs are briefly introduced. In addition, the current status of the MMW MMICs is surveyed and novel circuit topologies are summarized. Some representative MMW MMICs are illustrated as design examples in the categories of their functions in a MMW system. Finally, there is a conclusion and description of the future trend of the development of the MMW ICs.  
**Keywords:** SiGe; CMOS; MMW IC
## 3 Results
### The Role of BCM  
Focusing on BCM, we first investigated its association with performance in ADL measured by BI T0. The linear regression analysis showed a significant association between BI T0 and BCM, even adjusting for sex and age as confounders (Table 4).  
Figure 1: Analysis of body composition parameters at admission and rehabilitation recovery. Comparison of BIA parameters at baseline between patients with better recovery (\(\Delta\)BI \(>\) 10) or with worse recovery, (\(\Delta\)BI \(\leq\) 10). FM, fat mass; FMI, fat mass index; FFM, fat-free mass; FMI, fat-free mass; AKMM, appendicular skeletal muscle mass; SMI, skeletal muscle Index; BCM, body cell mass; BCMI, body cell mass index; PhA, phase angle. Mean bars and 95% of CI are reported. * refers to the statistically significant difference (\(p\) < 0.05), according to the Mann–Whitney U test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Barthel Index T0** & \\ \hline  & **Spearman’s Rho** & \(p\)**-Value** \\ \hline \multicolumn{3}{c}{_Body Fat composition_} \\ FM T0 & 0.155 & 0.356 \\ FMI T0 & 0.076 & 0.543 \\ \hline \multicolumn{3}{c}{_Body lean mass composition_} \\ FFM T0 & **0.375 ** & 0.002 \\ FMI T0 & **0.366 ** & 0.003 \\ ASMM T0 & **0.395 ** & 0.001 \\ SMI T0 & **0.399 *** & \textless{}0.001 \\ BCM T0 & **0.414 *** & \textless{}0.001 \\ BCMI T0 & **0.386 *** & 0.001 \\ \hline \multicolumn{3}{c}{_Muscle quality_} \\ PhA T0 & **0.354 ** & 0.004 \\ \hline \hline \end{tabular} FM, fat mass; FMI, fat mass index; FFM, fat mass index; FFM, fat-free mass index; ASMM, appendicular skeletal muscle mass; SMI, skeletal muscle Index; BCM, body cell mass; BCMI, body cell mass index; PhA, phase angle. **\(p\)-value < 0.01; ***\(p\)-value < 0.001.  
\end{table}
Table 3: Correlations between Barthel Index and body composition parameters measured by bioelectrical impedance analysis (BIA) at admission (T0).  
Finally, from the logistic regression analysis (Table 5), we found that BCM at T0 was positively associated with a better recovery, regardless of BI at T0, sex, and the presence of paresis on the dominant arm.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### Baseline Schemes  
Joint Power Control and Link Selection Algorithm (JPLA): The JPCALSA only considers the current system state, and calculates the maximum rewards of relay and direct link, respectively. Then, the optimal access link is selected by comparing the rewards.  
Power Control Algorithm (PCA): The PCA is to maximize the reward in single slot by adjusting the power of the relay, and link selection scheme is not taken into account [11].  
When the energy of relay is sufficient, the system will be in the ideal state. In order to compare the ideal results with our results in different situations, we propose JPLA-F and BIABoE-F, which are JPLA and BIABoE with enough renewable energy.
###### Abstract  
Wireless Body Area Network (WBAN) is usually composed of nodes for contacting the body and coordinator for collecting the body data from the nodes. In this setup, the nodes are under constraint of the energy resource while the coordinator can be recharged and has relatively larger energy resource than the nodes. Therefore, the architecture mechanism of the networks must not allow the nodes to consume much energy. Primarily, Medium Access Control (MAC) protocols should be carefully designed to consider this issue, because the MAC layer has the key of the energy efficiency phenomenon (e.g., idle listening). Under these characteristics, we propose a new MAC protocol to satisfy the higher energy efficiency of nodes than coordinator by designing the asymmetrically energy-balanced model between nodes and coordinator. The proposed scheme loads the unavoidable energy consumption into the coordinator instead of the nodes to extend their lifetime. Additionally, the scheme also provides prioritization for the emergency data transmission with differentiated Quality of Service (QoS). For the evaluations, IEEE 802.15.6 was used for comparison.  
wireless body area networks; medium access control; energy-balanced model; energy efficiency; Quality of Service +
Footnote †: journal: Applied  
Article
# Temperature Estimation Adaptive to Variables over Distance Using Infrared-LiDAR
## 2 Background
### Planck's Law  
Before Planck's Law in 1900, blackbody radiation consisted of two theories [25]. First, in 1879, with [PERSON]'s experimental [26] discovery of the relationship between radiant energy and temperature, then in 1884, [PERSON] theoretically summarized [PERSON]'s experiment [27]. The Stefan-Boltzmann law states that the sum of the energy of all wavelengths emitted per unit time in a unit area of a blackbody at temperature \(T\) is proportional to the absolute temperature to the fourth power. Equation (2) is the Stefan-Boltzmann law. \(\sigma\) is a Stefan-Boltzmann constant, \(\sigma=5.6696\times 10^{-8}\) W/m\({}^{2}\cdot\)K\({}^{4}\).  
\[L(T)=\sigma T^{4} \tag{2}\]  
Next, in 1893, [PERSON] summarized that absolute temperature is inversely proportional to the wavelength at which the energy density is at its maximum [28]. [PERSON] law is Equation (3). From the [PERSON] curve in Figure 2a, it can be seen that the lower the temperature, the longer the wavelength at the maximum energy. \(b\) is a Wien's constant, \(b=2.898\times 10^{-3}\) m\(\cdot\)K.  
\[\lambda_{max}=\frac{b}{T} \tag{3}\]  
In 1900, [PERSON] organized the two previous theories [22; 23; 24] as [PERSON]'s law. The density of radiant energy emitted at a specific temperature and specific wavelength of an ideal blackbody can be obtained through [PERSON]'s law Equation (4).  
\[L(\lambda,T)=\frac{2 hc^{2}}{\lambda^{5}}\frac{1}{e^{\frac{hc}{kT}}-1} \tag{4}\]  
\(T\) is the absolute temperature of the blackbody, \(k\) is the [PERSON] constant, \(k=1.38\times 10^{-16}\) erg/K; \(h\) is the [PERSON] constant, \(h=6.625\times 10^{-27}\) erg-sec; \(c\) is the speed of light, \(c=3\times 10^{8}\) m/s. Radiation measured at a specific temperature is obtained by integrating the wavelength band that can be measured with an infrared camera. If the infrared camera can measure from \(\lambda_{1}\) to \(\lambda_{2}\), you can obtain the radiation corresponding to the temperature by Equation (5).  
\[L(T)=\int_{\lambda_{1}}^{\lambda_{2}}\frac{2 hc^{2}}{\lambda^{5}}\frac{1}{e^{ \frac{hc}{kT}}-1}d\lambda \tag{5}\]  
Furthermore, from Figure 2b, it can be seen that the wavelength at the maximum energy at room temperature is actually located in the long-wave infrared band (8-14 \(\upmu\)m). Therefore, it can be seen that the long-wave infrared camera is suitable for room temperature measurement.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 4 L2-CalSat Description
### L2-CalSat Design  
To obtain a first design of the calibration satellite from the requirements shown previously, a session has been carried out in the IDR-UPM's Concurrent Design Facility (CDF). Figure 7 shows the exterior view (a) and exploded view (b) of the resulting 6U cubesat.  
From the subsystems shown in the previous figure, those that represent critical technologies have been identified as the following:  
1. For the attitude control and determination, Fine Sun Sensors by GomSpace [45] and star trackers by CubeSpace CubeStar [46] can be used.
2. For metrology (relative position determination) and communications, RF ranging, a flight-proven technology [47] able to provide the required accuracy. Tethers Unlimited has developed an integrated system for cubesats [48] although it is also possible to build the system with COTS [49] (antennas, On-Board Computer (OBC) and software-defined radio).
3. For the propulsion of the CubeSat, cold gas thrusters to provide small and precise impulses with low consumption, like GOMSPACE NanoProp [50], can also be used.
## 4 Discussion
### Bedload Transport Rate Related to Bedforms  
The objective of this study was to enhance the understanding of bedload transport theories, specifically focusing on transport properties over dune bedforms. A comparison of the classical bedload transport theories--including [PERSON] [47] drag force theory and the formulas by [PERSON] [26], [PERSON] [27], and [PERSON] [29]--revealed that the theoretical values calculated using these models deviated from the measured values when dunes were present. This discrepancy aligns with the findings from previous studies [55, 56, 57], suggesting that dune bedforms may significantly influence the deviation in the estimated bedload transport rates. Consequently, the existing equations may require adjustments to accommodate the effects of dune bedforms, which is crucial for accurate flood forecasting and river management.  
Figure 11: Relationship between bedload transport intensity and bed resistance.  
To address this issue, this study employed a nonlinear least squares fitting method to derive a new bedload transport formula (Equation (5)) based on the measured data of bedload transport rates and flow intensities in the experiments. This proposed formula accounts for the relationship between bedload transport intensity and flow intensity, incorporating the Shields parameter and bed shear stress as the key variables. The accuracy of this formula was confirmed by calculating the Mean Square Error (MSE, Equation (6)), which was low at 0.016, indicating strong predictive capability. However, it is acknowledged that any formula has limitations in its applicability; thus, the behavior of the proposed formula under different hydraulic and sediment conditions remains to be verified.  
Given the complexities of bedload transport, which is influenced by various factors [39; 58; 59], future research should explore bedload transport patterns across different grain sizes and flow conditions, as well as the mechanisms by which bedforms affect bedload transport. Additionally, integrating numerical simulations with field observations [24; 60; 61] could yield a more comprehensive understanding of bedload transport complexities, providing a more robust scientific foundation for river engineering and sediment management.
# An Analysis of the Factors Influencing Dual Separation Zones on a Plate
## 4 Result and Discussion
### Effect of Expansion Waves  
It is well known that pressure rise is a crucial factor determining the scale of separation zones, further influencing the state of dual-separation-zone flow fields. As observed in Figure 1, the expansion wave \(\xi_{0}\) at the trailing edge of the shock generator impinges between the dual separation zones, which may directly influence their interaction. To investigate the effects of the expansion wave itself and its impingement position on the merging and re-separating process of dual separation zones, three shock generators with different lengths were selected for numerical analysis. The length of the shock generator is denoted as \(l\). Based on the data from Section 4.1, the shock generator lengths \(l_{1}\) to \(l_{3}\) were set as 90 mm, 95 mm, and 105 mm, respectively. The ICR of each model was set to be larger than 111 to prevent the evolutionary laws of the dual-separation-zone flow field from being coupled with the inlet's starting characteristics due to the increase in shock wave generator length.  
Figure 7 presents the Mach number contours of the dual-separation-zone flow field with the same shock-generating point under varying incident positions of expansion waves. As \(\xi_{0}\) progressively moves away from the first separation zone, the subsonic region of  
Figure 6: X component wall shear curves under different ICRs.  
dual separation zones continues to expand. In the figure, \(d_{sz}\) denotes the distance between the dual separation zones, which is calculated by subtracting the x-coordinate of the reattachment point R\({}_{1}\) of the first separation zone from the separation point S\({}_{2}\) of the second separation zone.  
When maintaining a fixed incident shock wave position, the first separation zone gradually expands and the distance between zones continuously decreases as the expansion waves move downstream. By comparing Figure 7a,c, significant differences in flow field morphology can be observed. For the case of \(l_{1}\), the dual separation zones in the flow field are distinctly separated. In contrast, for the configuration of \(l_{3}\), the dual separation zones have completely merged. This clearly demonstrates that the position of the expansion waves exerts a substantial influence on the merging process of the dual separation zones.
## 4 Discussion
### Comparison between the OLCI-Derived and MODIS-Derived \(K_{\mathrm{d}}\)(490)
Figure 11: **(a,b)** Validation of the algorithm for estimating \(K_{\rm d}\)(490) in Lake Chaohu using the OLCI-derived \(R_{\rm rs}\) with the default parameterization and local parameterization; **(c,d)** OLCI RGB and OLCI-derived \(K_{\rm d}\)(490) distributions in Lake Chaohu on 26 February 2017. However, most of the pixels were distributed along the 1:1 line. These results further demonstrate that the dual band ratio algorithm and OLCI images are suitable for \(K_{\text{d}}\)(490) estimation. Figure 12: Distribution of the RMSE (**a**) and pixel-by-pixel scatter plot (**b**) between all of the \(K_{\text{d}}\)(490) products from the OLCI and MODIS. A total of 18 OLCI-MODIS image matchups were used, and six matchups with large uncertainties in the MODIS atmospheric correction were excluded. Note that bad pixels were removed. Figure 13: (**a,b**) OLCI and MODIS RGB images, (**c,d**) OLCI- and MODIS-derived \(K_{\text{d}}\)(490) values, (**e,f**) absolute relative differences (ARE) and pixel-by-pixel scatter plots between the \(K_{\text{d}}\)(490) products from the OLCI and MODIS on 14 March 2017, and 29 April 2017. Note that bad pixels were removed. Overall, there is good consistency between the OLCI-derived and MODIS-derived \(K_{\rm d}\)(490) values. However, because the OLCI has higher spatial and radiometric resolutions and a higher ANR, the OLCI provides more detailed information for future monitoring of the water quality of inland waters.