esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Space-Based Visible Detection for Conical Space Targets
## 4 Determination of Threshold Caliber with Signal-to-Noise Ratio as Evaluation Criteria
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Average wavelength (\(\overline{\lambda}\)) & 632.8 nm \\ \hline The average quantum efficiency (\(\eta\)) & 60\% \\ \hline Pixel fill factor (\(\epsilon\)) & 40\% \\ \hline System optical transmittance (\(\tau_{0}\)) & 70\% \\ \hline Minimum integration time (\(t_{0}\)) & 100 ms \\ \hline Number of electrons of dark current noise (\(D_{c}\)) & 18e\({}^{-}\) \\ \hline Number of electrons of readout noise (\(R_{e}\)) & 6e\({}^{-}\) \\ \hline Threshold SNR (\(SNR_{T}\)) & 5.6 \\ \hline Limit detection distance (\(L\)) & 35,000 km \\ \hline Pixels imaged by target (\(n\)) & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values of parameters of the SBV sensor. Figure 5: The relationship between signal-to-noise ratio and detection probability and the false alarm rate. We set the radiation intensity value of the space target that the detector can receive as 266.6 W/sr. We may obtain a threshold caliber value of 144.4 mm to fulfill the system detection criteria by plugging the precise values of each parameter into the Formula (12). The actual caliber of the SBV sensor is 150 mm, which proves that the method for determining the caliber of the detection system given in this paper is correct and has reference value.
## 4 Experimental Results and Discussion
### Performance of Proposed Power Control Strategies
#### 4.1.4 Comparison of the Power Control Methods  
In order to compare the performances of the proposed power control methods previously mentioned, the voltage perturbation step size of 4 V is selected. Figure 15a,b depict the effectiveness and the system response for LPP1 and LPP2. It can be observed that the response timeliness is identical whatever the algorithm method used. In contrast, the oscillation state differs according to the power control method. The FL method's power control has better dynamic performances with more steady oscillations compared to the fixed step size and [PERSON]'s methods.  
Figure 16a,b represent the descriptive statistical analysis (called a boxplot) of these three selected methods. The boxplot is a standardized way of displaying the dataset based on a five-number summary i.e., the minimum, the maximum, the sample median, the first quartile (25 th percentile) and the third quartile (75 th percentile). The results clearly show a huge power variation of the fixed step size and [PERSON]'s methods compared to the FL technique, despite having roughly the same mean and median (around 49-50 W) (Table 2). The variability could be calculated by measuring the interquartile range (IQR) which means the difference between the third and first quartiles (the distance covering 50% of the data). Regarding LPP1 (Figure 16a), the IQR values are 29, 11 and 3 W for the fixed step size, [PERSON]'s and FL methods, respectively, whereas for LPP2 (Figure 16b), the IQR values are 32, 12 and 5 W for the fixed step size, [PERSON]'s and FL methods, respectively. These values reflect the power oscillations' variations for each technique. In addition, when comparing boxplot results between LPP1 and LPP2 for the same method, it is obvious that the power oscillations around LPP2 are larger than those around LPP1. However, if there is a possibility to work in both LPP1 and LPP2, the priority should be given to LPP2. Indeed, as losses are mainly related to the current, LPP2 is placed in the high voltage and low current side, which insures less power loss. Yet, sometimes the system should operate in the low voltage and high current side (LPP1) to respect the equipment's physical limits (to avoid reaching the maximal generator speed and minimize the mechanical stress).  
Summarizing all presented results, it can be concluded that all proposed LPPT control strategies are able to work properly, reach operating points and identify the direction of the next perturbation. However, the performances of the FL method are better, despite its complexity when implemented. FL enhances the overall efficiency and eradicates the limitations of the fixed step size and [PERSON]'s methods.  
Figure 15: Comparison of experimental results: (**a**) evolution of \(p_{WT}\) at LPP1; (**b**) evolution of \(p_{WT}\) at LPP2.
## 4 Semi-Supervised Learning Based on Mean Teacher Model
### Data Augmentation
#### 4.2.2 Random Amplitude Scaling  
As RSS-based fingerprint positioning systems are commonly used, their fundamental limitation is their inability to capture multipath effects [9]. To fully characterize each path, the wireless communication propagation channel is modeled as a time-linear filter called CIR. CIR is similar to the RSS sequence, but it has a finer frequency resolution and equally higher time resolution to distinguish multipath components. Therefore, we can reasonably infer that CIR has a high power feature for effective positioning, and augmentation can be effectively performed by perturbing the power characteristics of CIR.  
The received power measured at a fixed frequency is proportional to the amplitude of the channel frequency response (CFR) [9]. Similarly, in CIR, we infer that the amplitude information is highly relevant to the received power, and different amplitude represents different LOS/NLOS environment distributions. Thus, we propose a random amplitude scaling in training steps to perturb partial LOS/NLOS distribution in the indoor environment, aiming to provide accurate predictions outside the training data. The amplitude of the CIR received by antenna \(u\) of UE \(n\) from transmitter antenna \(s\) can be expressed as:  
\[Amp^{n}=\sqrt{Re\{H_{u,s}^{n}(\tau,t)\}^{2}+Im\{H_{u,s}^{n}(\tau,t)\}^{2}}. \tag{11}\]  
The scaling size is represented as a positive random number \(\mu\), where \(\mu\sim U(0,a)\), with \(a\) being the maximum scaling size. Then, the random scaling amplitude can be expressed as \(A_{scale}^{n}=\mu Amp^{n}\). Note that the same scaling on the time series of CIR should be performed to ensure that the shape of the amplitude remains unchanged and to avoid destroying effective positioning features.
## 3 Results and Discussion
### Data Preparation
Subsequently, with a minimized number of features, a total correlation analysis is carried out with respect to the features of all sensors. In the last step, another univariate analysis with respect to the target variable is performed including all the selected features, as well as the process parameters such as laser power, powder mass flow and scan speed. Based on this last univariate analysis, the final input data set is generated. The described feature extraction was performed for each geometry individually.
## 2 Materials and Methods
### Composite Characterization  
#### 2.2.1 Mechanical Testing  
Tensile tests were performed using a universal testing machine type InstronTM 1122 (IDM Test, San Sebastian, Spain) fitted with a load cell of 5 kN and operating at 2 mm/min. For tensile testing, specimens were cut to \(65\times 6\times 1.5\) mm for all compositions at different orientation (\(0^{\circ}\), \(45^{\circ}\) and \(90^{\circ}\)) b). Flexural testing samples were prepared according to the standard ASTM D790 and tensile modulus samples were prepared according to ASTM D638 by using an extensometer (MFA2-Velbert, Germany). All the tests were conducted at room temperature condition (\(23\pm 2\)\({}^{\circ}\)C) and relative humidity (\(50\pm 5\%\)) for dry samples. The same was repeated for 48-h conditioned specimens using a Dycometal (Barcelona, Spain) climatic chamber at 23 \({}^{\circ}\)C and 50\(\%\) relative humidity for 48 h, according to ASTM D618 13 standard, for all composition. A minimum of five samples were tested for each formulation.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value/s** \\ \hline Temperature profile & 210, 215, 220, 225, and 230 \({}^{\circ}\)C \\ Mold temperature & 60 \({}^{\circ}\)C \\ Injection pressure & 575–650 bars \\ \hline \end{tabular}
\end{table}
Table 1: Injection molding parameters for pulp composites.  
Figure 4: (a) Square sample with dimension \(70\times 70\times 1.5\) mm, and (b) Square samples from which oriented specimens are laser cut, (from left to right) parallel (\(0^{\circ}\)), perpendicular (\(90^{\circ}\)) and \(45^{\circ}\) oriented cut.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 4 Simulation Results
### Simulation Setting  
In the simulation, the data symbols were drawn from the QPSK constellation. The number of transmitting antennas was \(M=2\) or \(4\). The number of users was \(K=2\), \(4\), \(8\) or \(16\). The number of subcarriers ranged from \(N=64\) to \(256\).  
An \(8\)-tap time-delay-line channel model was used for the channel from the \(m\)-th transmitting antenna to the \(k\)-th user. The time domain channel response was:  
\[\mathbf{h}_{k,m}=\left[h_{k,m}^{0},h_{k,m}^{1},\cdots,h_{k,m}^{7}\right]^{\text{ T}}, \tag{34}\]  
for all \(k=1,2,\cdots,K\) and \(m=1,2,\cdots,M\). All \(\{h_{k,m}^{l}\}\) were independently drawn from the complex Gaussian distribution \(\mathcal{CN}(0,1)\) and then each \(\mathbf{h}_{k,m}\) was normalized, such that \(\|\mathbf{h}_{k,m}\|^{2}=1\). The frequency domain channel coefficients were obtained by performing FFT on \(\mathbf{h}_{k,m}\), i.e.,  
\[\mathbf{H}_{k,m}=\sqrt{N}\mathbf{F}\,\text{vect}(\mathbf{h}_{k,m},\mathbf{0}_{N-8}), \tag{35}\]  
where \(\mathbf{0}_{N-8}\) is an all zero vector of length \(N-8\).  
The synchronization error (including propagation delay) \(\tau_{k}\) for the \(k\)-th user was uniformly distributed in the sampling interval. Specifically, \(\tau_{k}\) was uniformly distributed in \(\left[-\frac{T}{2N},\frac{T}{2N}\right]\) for the OFDM system in which the sampling interval was \(T/N\) and it was uniformly distributed in \(\left[-\frac{T}{2V},\frac{T}{2V}\right]\) for the proposed system in which the sampling interval was \(T/V\).
## 1 Introduction
The use of muzzle patterns for livestock identification can also improve breeding programs by enabling the accurate identification and tracking of breeding stock. This can help to improve the quality and efficiency of breeding programs, as well as the overall genetic diversity and health of the livestock population. In addition to traceability and breeding programs, accurate livestock identification using muzzle patterns can improve the overall management and organization of livestock. It can help to ensure that animals are properly cared for, housed, and managed, as well as to track their production efficiency and welfare. This study proposes a system using the biometrics of livestock animals, such as their muzzle patterns, for the automated, instant identification of livestock, utilizing deep transfer learning to detect the face and the muzzle point of the animal. The system comprises three steps that include the detection of the animal's face in the image to ensure that the animal is present, and the face is again processed to detect the nose or muzzle point of the animal; both of these detections are achieved using the state-of-the-art you only look once (Yolo v7) [8] object detection model. Any noise or image with motion blur is filtered out in consideration of any movement by the animal, and only clear images of the nose are captured, which are then used to extract features using SIFT (scale-invariant feature transform) [9] and then stored in the database. Later on, the initial steps are the same when recognizing but, after the features are extracted, the system uses an approximate nearest neighbor search by applying FLANN (fast library for approximate nearest neighbor) matcher [10; 11] to match SIFT-extracted features. The THDD dataset [12; 13] was applied to train the Yolo object detector in this study as an example of the proposed approach, due to the lack of available data for all livestock animals. The unique features of livestock animals such as cattle, goats, sheep, and horses/equines in their noses or muzzles can be used for identification purposes [2] by assigning an ID and other relevant information to their extracted features and retrieving any associated information upon recognition. In summary, the use of muzzle patterns for livestock identification has the potential to improve traceability, disease control, breeding programs, and the overall management and organization of livestock. It is also important for the monitoring of animal welfare and production efficiency. The use of biometric identification technologies, such as image scanning and automated detection and feature extraction algorithms, can enhance the accuracy and efficiency of this process. Accurate identification of livestock is essential for the success of modern livestock industries and the proper functioning of related systems. The demand for various pattern recognition applications has drastically increased during the last couple of decades. Considering the importance and benefits of biometric identification, livestock farmers are more eager to deploy the most efficient and cost-effective system.
# Fusing Appearance and Prior Cues for Road Detection
## 2 Proposed Algorithm
### Construction of the Geometric Prior Model  
Most monocular road detection algorithms usually fail to make good use of the geometric prior information which can be very helpful for reducing ambiguity and improving accuracy. As we mentioned above, road probability maps computed using appearance cues can depict the potential of a pixel being road surface. Unfortunately, it will fail when there are objects which are made of similar material and have a similar appearance to road surface in the image, for example, sidewalks, buildings, and overpasses.  
In order to improve the robustness and reliability of the algorithm, we propose the novel idea to construct a geometric prior model offline. Considering that the camera mounted on the ego-vehicle is usually fixed to the windshield, as a consequence pixels of the road surface will be concentrated in a specific part of the images. In other words, nearly all the road pixels occur towards the bottom region of the image; conversely, there are never road pixels appearing at the top of the image. In view of this, by aggregating and averaging all the ground truth images provided by the KITTI road benchmark in the training set, we can obtain probability maps as illustrated in Figure 4 which indicate how frequently the road pixels occur in the corresponding position. We regard this probability map as the empirical geometric prior model \(P_{r}\) and it will be fused with the appearance model to boost the performance of the algorithm.
# Dependence of PV Module Temperature on Incident Time-Dependent Solar Spectrum
## 2 NOCT (Nominal Operating Cell Temperature)-Based Module Temperature  
The module operating temperature that is based on normal operating cell temperature (NOCT) is commonly used [9; 10].  
The NOCT Equation is given by:  
\[T_{c}=T_{a}+(NOCT-20)\times\frac{P_{nd}}{800} \tag{1}\]  
where \(T_{c}\) is the module temperature in \((^{\circ}C)\) according to the NOCT model, \(T_{a}\) is the ambient temperature, and \(P_{nd}\) is the solar irradiance in \(W/m^{2}\). The NOCT value is defined by the global G in-plane irradiance 800 \(W/m^{2}\), air mass AM1.5G, ambient temperature \(T_{a}=20\)\({}^{\circ}C\), and wind speed \(V_{wind}=1\)\(m/s\) at the PV module height. It should be mentioned that NOCT represents a steady condition at open circuit temperature of the PV module, a condition not met in real module operation.  
In the beginning, the use of the energy balance Equation [11] was attempted to define the module operating temperature, however it was difficult to measure a term in the thermal losses of the PV module to the surroundings (see Section 3) in the energy balance Equation and, therefore, PV manufacturers adopted the NOCT expression. This simple method generally gives satisfactory results for ground mounted PV systems, but not for roof- integrated systems [9]. The effect of the wind speed on cell temperature is given by [10; 12]:  
\[T_{c}(^{\circ}C)=T_{a}+\frac{0.32}{8.91+2\cdot V_{wind}}\cdot G \tag{2}\]  
where G in-plane irradiance in \(W/m^{2}\).  
An expression for the module temperature by a linear model including wind speed is in [13], and Ref. [14] deals with a comparative study of NOCT-based temperature by comparing three NOCT models.
# A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform
## 6 Performance Evaluation
This indicates that when the GNSS interference intensity increases for any type of Hough transform-based interference detection method, most of the Hough-transformed energy is more concentrated in a specific local intersection area within the parameter space, and the residual Hough-transformed energy distribution is better suppressed to zero in the other areas. Figure 8:0 The smoothed pseudo-Wigner–Hough transform (SPWHT) of the GPS L1-C/A signal disturbed by the sweep interference; (**a**) SPWHT; (**b**) SPWHT’s contour. From Figure 16, it is easy to observe that the zero Hough-transformed energy percentage result obtained using the proposed smoothed pseudo-Wigner-Hough transform method is much better than the other techniques such as Wigner-Hough transform and pseudo-Wigner-Hough transform. For example, the zero Hough-transformed energy percentage result of the smoothed pseudo-Wigner-Hough transform method is at least double that of the traditional Wigner-Hough transform approach; in particular, when the JNR of the interfered GPS L1-C/A signal is lower than or equal to \(-4\) dB, the zero Hough-transformed energy percentage of the smoothed pseudo-Wigner-Hough transform method is at least 20% higher than that of the pseudo-Wigner-Hough transform technique; in addition, when the JNR of the interfered GPS L1-C/A signal is higher than \(-4\) dB, the zero Hough-transformed energy percentage of the smoothed pseudo-Wigner-Hough transform method is at least 42% better than that of the pseudo-Wigner-Hough transform approach. Therefore, the proposed smoothed pseudo-Wigner-Hough transform method presents superior properties of energy aggregation and cross-term suppression in the detection of GNSS interfering signal, indicating that the smoothed pseudo-Wigner-Hough transform method can provide significantly improved GNSS interference detection performance over the other Wigner-Hough transform and pseudo-Wigner-Hough transform approaches.
## 2 Materials and Methods
### Optical Scheme for Multiple Customer Assignments Using Optical Combs  
To assign the spectrum in this solution, we propose using a passive optical network that employs wavelength division multiplexing to transmit digital information across multiple channels. In this network topology, we utilize a programmable line-by-line pulse shaper to assign the spectrum generated by the optical microresonators and to obtain the wavelengths used in modulation [43; 44]. A pulse shaper is an optical device that controls the phase and amplitude of a light wave. This device consists of three key elements. First, there is a diffraction grating whose primary function is to separate the light pulse into its different constituent wavelengths. The second component is an optical modulator, critical in allowing programmable adjustments of each spectral component's amplitude, phase, and polarization. In a more detailed context, known as \"line-by-line\", this modulator can finely tune each spectral line in a specific manner. As the final component, an inverse diffraction grating is employed to reconstruct the spectral components that have been modified, thus forming the final light pulse. It can work with one or multiple light waves at its input, which makes it adaptable to various needs requiring waves with different spectral properties. Additionally, this device can have one or multiple outputs, allowing for the light wave to be redirected toward different directions or to separate different portions of the spectrum of a light wave for various communication channels. Moreover, it can divide the input power into several outputs, which is helpful in applications requiring precise and efficient wave distribution. This device is electronically controlled, enabling the real-time adjustment of assignments algorithmically.  
For this study, the pulse shaper selects from four optical combs with separations of 12.5 GHz, 50 GHz, 100 GHz, and 200 GHz. The pulse shaper assigns the spectrum of each comb according to instructions from the RMLSA algorithm. Each wavelength is then fed into a QAM multi-level modulator, and the modulation level is determined using the RMLSA method based on the required distance and requested bandwidth. Subsequently, the modulated waves are combined using an interleaver for transmission over a single fiber  
Figure 5: The control scheme for deterministic single soliton generation.  
channel. In the reception stage, another pulse shaper separates the spectrum again according to the previous assignment and performs the demodulation process. Figure 6 shows the network topology. Notably, the pulse shaper can assign either a single wavelength from the comb or a range of wavelengths in the case of a super-channel, thus enabling the generation of a flexible grid, which is fundamental in our methodology.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
###### Abstract  
Understanding the response of vegetation to drought is of great significance to the biodiversity protection of terrestrial ecosystem. Based on the MOD13A2 NDVI, GOSIF, and SPEI data of the Yellow River Basin from 2001 to 2020, this paper used the methods of Theil-Sen median trend analysis, Mann-Kendall significance test, and Pearson correlation analysis to analyze whether the vegetation change trends monitored by MODIS and GOSIF are consistent and their sensitivity to meteorological drought. The results showed that NDVI and SIF increased significantly (\(p<0.001\)) at the rate of 0.496 \(\times\) 10\({}^{-2}\) and 0.345 \(\times\) 10\({}^{-2}\), respectively. The significant improvement area of SIF (66.49%, \(p<0.05\)) is higher than NDVI (50.7%, \(p<0.05\)), and the spatial distribution trend of vegetation growth monitored by NDVI and SIF is consistent. The negative value of SPEI-12 accounts for 65.83%, with obvious periodic changes. The significant positive correlation areas of SIF-SPEI in spring, summer, and autumn (R \(>\) 0, \(p<0.05\)) were 7.00%, 28.49%, and 2.28% respectively, which were higher than the significant positive correlation areas of NDVI-SPEI (spring: 1.79%; summer: 20.72%; autumn: 1.13%). SIF responded more strongly to SPEI in summer, and farmland SIF was significantly correlated with SPEI (0.3424, \(p<0.01\)). The results indicate that SIF is more responsive to drought than NDVI. Analyzing the response of vegetation to meteorological drought can provide constructive reference for ecological protection.  
NDVI; SIF; meteorological drought; correlation analysis; Yellow River Basin