esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments
## 2 System Model
### Receiver Architecture Design  
We used power-splitting architecture in our system, as in Figure 3. The received radio frequency (RF) signals are divided for the information receiver and for the RF energy harvester with different power levels. When \(\mu_{i}\) is the fraction of RF signals for energy harvesting, \(\mu_{i}\in\ [0,\ 1]\) is defined as the power-splitting coefficient for receiver \(i\). This means that \(\eta\) is the portion of RF signals utilized for power gathering. Similarly, the following equation calculates the power of RF energy that is harvested at a receiver \(j\) (this receiver splits the power from the source):  
\[P_{i,j}=\eta P_{j}\big{|}h_{i,j}\big{|}^{2}\mu_{i}. \tag{9}\]
## 5 Novel Combined Extraction Techniques
### Cavitation and Microwave Assisted Extraction  
Two novel promising techniques, such as a microwave (MW) and ultrasound (US) can be combined for better extraction yield and energy efficiency. Microwaves can only be beneficial for selective materials corresponding to the presence of polar components, but the introduction of ultrasound can generate intense agitation and overall modification of the matrix surface.  
Both extraction techniques can be either operated as single reactor configuration or via two separately connected reactors, which can be operated simultaneously or one by one. The combined US-MW extraction of pectin from Pomelo peels [81], resulted in a 38% recovery within just 33.94 min as compared to 14.25% during only US extraction. Highest pectin yield was obtained for the sequential order of US-MW rather than MW-US treatment, and among the optimized parameters, pH played a significant role in deciding better yield. US-MW extraction has demonstrated to enhance the yield of oligosaccharides by 76.59%, 17.47% and 27.21%, respectively, as well as a substantial decline in the extraction time by 12.18, 8.92, and 1.16 times, respectively, compared to hot water extraction, UAE and MW [82]. [PERSON] et al. [83] reported ionic liquid-based US-MW extraction of anthraquinones from rhubarb, which demonstrated better efficiency as compared to employing individual techniques (ultrasound, microwave, and heat-reflux extraction). The combined US-MW approach exhibited higher efficiency (18.90-24.40%) and shorter extraction time (6 h to 2 min) than individually employing them. Table 3 lists UAE based combined methods for natural products extraction.  
Combined NPC-MW was used for the extraction of phenolic compounds (arbutin, hyperin, catechin, epicatechin, chimaphilin) from _Pyrola (P. callantha)_, following the selection of 1-butyl-3-methylimidazolium tetrafluoroborate as an appropriate extraction solvent [68]. The coupled technique showed higher extraction yield within a shorter duration than MW or NPC alone. Another report on NPC/MW coupled method for the extraction of hyperin and chimaphilin from _Pyrola (Pyrola incarnata Fisch.)_ demonstrated 1.43 and 1.28-folds higher extraction yield than NPC and MW methods respectively [66].
# Flexible Null Broadening Robust Beamforming Based on JADE
###### Abstract
Because this technique can separate each signal steering vector with array gain-phase errors from the observation data, this feature makes it suitable for solving the problem of performance degradation of the adaptive beamforming algorithm in the presence of gain-phase errors. The joint approximate diagonalization of eigen-matrices (JADE) is a blind separation technique with high separation performance, which is proposed by [PERSON] [19]. In addition, [PERSON] proposes a direction-of-arrival (DOA) estimation method based on JADE, which is robust to the array gain-phase errors [20]. In order to effectively suppress dynamic interference in the presence of gain-phase errors, this paper proposes a flexible null broadening robust beamforming algorithm. Firstly, we use the joint approximate diagonalization of eigen-matrices (JADE) algorithm to separate the signal steering vector; secondly, the correlation coefficient method is used to match the signal steering vector. Thus, we roughly estimate the interference angle and the actual interference steering vector with gain-phase errors. Then, the dynamic interference covariance matrix can be reconstructed easily. Finally, a novel CMT with the null deepening coefficient is used to taper the covariance matrix of the dynamic interference, which can flexibly control the width and depth of the null at the dynamic interference, resulting in the saving the DOFs and an enhanced anti-interference capability.
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System
### Texture Analysis and Classification
#### 2.3.7. Alarm Activation  
To determine whether the fire alarm must be activated, the upward motion and the growth of a specific region (\(F_{t}^{k}\)) must satisfy:  
\[F_{t}^{k}=\left\{\begin{array}{ll}1&\text{if}\quad R_{t}^{\prime k}\wedge E_{t }^{\prime k}\\ 0&\text{otherwise}\end{array}\right. \tag{32}\]  
where \(\wedge\) denotes the logical operator AND, during a time interval given in advance. The alarm is activated if Equation (33) is satisfied during monitoring time \(\varepsilon\), given by \(\varepsilon=r\times\tau\)  
\[FSA=\bigcup_{t=1}^{\varepsilon}F_{t}^{k} \tag{33}\]  
where FSA means fire smoke alarm, \(\tau\) is the previous frame number, and \(r\) is a factor that determines the monitoring time. If \(r\) is small, the alarm will be more sensitive prone to false alarms, while, if it is very large, it will take longer to be activated.
## 3 Results and Discussion
### Antioxidant Capacities of Porcine Organ Hydrolyzates  
#### 3.3.1 DPPH Radical Scavenging Activity  
DPPH radical scavenging activity revealed a dose-dependent increase in inhibitory effects (Figure 3a-e). In most instances, the porcine hydrolyzates achieved greater radical scavenging than the non-hydrolyzed control. In Figure 3f, with the exception of the porcine lung, the Alcalase-mediated hydrolyzates exhibited lower IC\({}_{50}\) values for DPPH radical scavenging activity; only Alcalase-mediated porcine heart (4.93 mg/mL) and liver (4.69 mg/mL) hydrolyzates had significantly lower values compared to their non-hydrolyzed counterparts. Previously, [PERSON] et al. [8] reported that the DPPH IC\({}_{50}\) of the non-hydrolyzed porcine liver was 4.73 mg/mL, but the IC\({}_{50}\) value decreased to 5.42 mg/mL after autolysis at pH 4.8. [PERSON] et al. [9] observed that the DPPH radical scavenging activity of porcine liver hydrolyzates noticeably increased after 2 h of enzymatic hydrolysis, indicating that 2 h of enzymatic hydrolysis might be sufficient to generate antioxidant peptides in this study. However, according to the authors of [8], trypsin was more effective than Alcalase when it came to enhancing the DPPH radical scavenging activity of porcine liver hydrolyzates, which contradicted the findings of the current study.
# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
###### Abstract  
Detecting and monitoring changes in open-pit mines is crucial for efficient mining operations. Indeed, these changes comprise a broad spectrum of activities that can often lead to significant environmental impacts such as surface damage, air pollution, soil erosion, and ecosystem degradation. Conventional optical sensors face limitations due to cloud cover, hindering accurate observation of the mining area. To overcome this challenge, synthetic aperture radar (SAR) images have emerged as a powerful solution, due to their unique ability to penetrate clouds and provide a clear view of the ground. The open-pit mine change detection task presents significant challenges, justifying the need for a model trained for this specific task. First, different mining areas frequently include various features, resulting in a diverse range of land cover types within a single scene. This heterogeneity complicates the detection and distinction of changes within open-pit mines. Second, pseudo changes, e.g., equipment movements or humidity fluctuations, which show statistically reliable reflectivity changes, lead to false positives, as they do not directly correspond to the actual changes of interest, i.e., blasting, collapsing, or waste pile operations. In this paper, to the best of our knowledge, we present the first deep learning model in the literature that can accurately detect changes within open-pit mines using SAR images (TerraSAR-X). We showcase the fundamental role of data augmentations and a coherence layer as a critical component in enhancing the model's performance, which initially relied solely on amplitude information. In addition, we demonstrate how, in the presence of a few labels, a pseudo-labeling pipeline can improve the model robustness, without degrading the performance by introducing misclassification points related to pseudo changes. The F1-Score results show that our deep learning approach is a reliable and effective method for SAR change detection in the open-pit mining sector.  
deep learning; change detection; synthetic aperture radar (SAR); mining 2023
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 3 Downlink Transmitter PHY for CS
Similarly, SSS is also a 127-length BPSK modulated sequence, and one of the 336 candidate sequences is chosen using the input PCI, \(N_{ID}^{Cell}\), as shown in Figure 4. In SSS generation, two reference 127-lengths sequences, \(\text{SS}_{ref0}\) and \(\text{SS}_{ref1}\), are stored in the memory, and they are calculated as  
Figure 3: Architecture for generating the 3 GPP PSS. \[\text{SSS}_{ref0}(i+7)=[\text{SSS}_{ref0}(i+4)+\text{SSS}_{ref0}(i)]^{\%}2\quad i \in\{0,1,\dots,126\} \tag{3}\]  
Figure 1.0  
Here, \(\text{SSS}_{ref0}(6:0)=\text{SSS}_{ref0}(6:0)=\) {0,0,0,0,0,1}. These reference sequences are read in a particular order followed by element-wise multiplication to obtain SSS. Mathematically, SSS is calculated as [16],  
Figure 1.1  
where  
Figure 1.2  
Figure 1.3  
The DMRS is 144-length Quadrature Phase Shift Keying (QPSK) modulated sequence generated using the PCI, \(N_{ID}^{Cell}\) and SS index, \(SS_{i}\) as shown in Figure 1.4 Due to dependence on the value of \(SS_{i}\), the DMRS is different for each SS in a SS burst. The DMRS generation involves pseudo-random binary sequence (PRBS) generation of length 288 which is then QPSK modulated to obtain the final DMRS. The PRBS generation is based on length-31 Gold sequence and it needs the initial seed, \(PRBS_{init}\), which is calculated using the PCI, \(N_{ID}^{Cell}\) and SS index, \(SS_{i}\)[16]. Figure 1.5  
Using the \(PRBS_{init}\), \(PRBS_{seq}\) of length 288 is generated using length-31 gold sequences [16]. The detailed steps and algorithm for PRBS generation have been discussed in [16] and the corresponding proposed hardware architecture is given in Figure 1.6 The final step in SS generation is the PBCH. Since it is not needed for the first phase of CS, we have randomly generated 432 complex symbols that are placed on the resources allocated for PBCH. Figure 1.7 Architecture for generating the 3 GPP SSS. The generated SS block, i.e., four OFDM symbols each of 240 complex symbols, is then stored in the memory. The resource mapper block generates the stream of OFDM symbols of size 4096 SCs with an embedded SS at the desired time and frequency location. The frequency location is controlled by the GSCN configured by the MAC layer while the time location is as per the 3 GPP specifications discussed in Section 2: Figure 1.8 Next, the design of the downlink or UE receiver PHY is presented.
## 4 Case Study for Signal Processing of Pulsed Thermography
### Advances of TSR  
[PERSON] et al. described the dependence of extended interface detection on the ratio of layer thermal effectivities [69] and added the aspect ratio dependence for discrete defects [70]. Like PPT and PCT, TSR is also an algorithm widely used recently, providing improvements in noise reduction and detecting defects. The acquisition procedure required to apply this post-processing should be a PT. Generally, TSR consists of using a low-order polynomial function to fit the profile of the cooling process belonging to the temperature evolution of each pixel of the thermal image tested over a specific period. A change in effective thermal diffusivity characterizes defect detection with conventional TSR. Recently, [PERSON] et al. [71] studied an extended TSR technique that can automatically segment defects into defects in the sound domain and generate defect maps by utilizing the signal characteristics of defects. They showed that the defect map provides an estimate of the depth of the defect and is inexpensive to compute. [PERSON] et al. [72] introduced a new approach to TSR. Instead of analyzing the time-derivate images, they proposed to utilize the coefficient images generated from each term of the differential equation to detect all defects.  
[PERSON] et al. [73] proposed a hybrid of TSR and Automatic Seeded Region Growing (ASRG) algorithm for thermal image signal processing. The proposed approach significantly reduced the uneven illuminance and improved the detectability. They showed improved detection resolution and were useful for the automatic selection of regions suspected of being defective. In addition, the ASRG algorithm reliably extracts the internal defect information and improves the contrast between the defect area and the sound area. [PERSON] et al. [74] proposed combining TSR and Canny shape-reconstruction algorithms for defect characterization. According to the TSR technique, the approach consists of fitting the raw thermographic images to a low-degree polynomial in the log-log representation of the time axis and then applying the Canny algorithm to reconstruct the original signals. The proposed juxtaposition of the two methods benefits from the inherent signal denoising and compression of the TSR algorithm, thus enhancing the performance of the overall reconstruction process.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 3 Two-Stage Model
### The Stay Cable Icing Recognition Framework  
In this study, the U-Net network and ResNet50 network were cascaded to identify the ice layer and icicles on the cable's surface on the cable's ice image, as shown in Figure 12. In the first stage, the original image was input into the U-Net semantic segmentation network, and the semantic segmentation was used to locate the icing area. This identified the background, icicle, and icing. In the second stage, the two-channel mask generated by semantic segmentation was input into the ResNet50 image classification network, and the output was the current icing level. The final output of this method consists of two parts: the position of the cable icicles and the surface ice, and the current icing level.
# The Local Integrity Approach for Urban Contexts:
## 5 Conclusions  
A proof-of-concept of the local integrity approach has been presented in this paper, including the theoretical aspects, an experimental validation, and a prototype implementation. Results from a successful live demonstration (carried out during the final event of GLOVE project [12]) have also been reported, showing the achievable performance in a real urban scenario. The proposed approach differs from other state-of-the-art GNSS integrity solutions (e.g., SBAS, GBAS, RAIM) because it performs a collaborative spatial and temporal characterization of local signal degradations. In this way, it does not rely on conventional analytical models for predicting the pseudorange error variances (well assessed in the aviation context only).  
The obtained results from this work are promising and worth of deeper investigation and exploitation, especially in the road transport domain. Further research and development activities are needed in order to build a complete integrity framework for vehicular applications. In detail, it is necessary to refine the statistical characterization of different error sources in nominal conditions (_i.e._, by means of cumulative distribution functions computed on larger data sets and different scenarios) and to adapt/calibrate the local integrity algorithm to different application requirements and operative conditions (e.g., different speed of each vehicle, multiple GNSS signals, and different receiver classes).  
Other necessary steps may include the definition of a proper integrity checking strategy, in order to detect and possibly mitigate non-nominal error conditions. In addition, regulatory and standardization aspects are important for the identification and definition of integrity requirements for different road applications, the adoption of a collaborative architecture for sharing GNSS data (e.g., based on VANET), and the standardization of proper communication interfaces.  
This article presents some findings and results of GLOVE (joint GaliLeo Optimization and VANET Enhancement) [4; 20], a project co-funded under the European 7 th Framework Program, in the context of \"Exploiting The Full Potential--GALILEO-2011-GSA-1\", Grant Agreement n. 287175. The activities here presented have been carried out by the authors in collaboration with other GLOVE's partners. The article reflects the views only of the authors and the European Commission cannot be held responsible for any use that may be made of the information contained therein. The two pictures in Figure 12 are reproduced with permission from [12]. Copyright © European GNSS Agency. Parts of this article are reproduced with permission from [13]. Copyright © IEEE. All rights reserved. Personal use of this material is permitted. However, permission to reuse this material for any other purpose must be obtained from the IEEE.  
[PERSON] and [PERSON] contributed equally to this work.  
The authors declare no conflict of interest.
## 3 TF Transforms and Results
### TF Analysis
It refers to a local oscillation between the main components of signals, which may take a value two times greater than the value of the main signal [32]. Thus, on the fundamentals of WVD, the Pseudo Wigner-Ville Distribution (PWVD) was created. The transformation was introduced by adding a windowing operation to the analyzed signal over the time domain [30; 33]. This allows one to overcome parasitic interferences that hinder the interpretation of the result of transformation (what leads to averaged representation). As a result of the operation of this transformation, one obtains the real values--not the complex ones as in the case of the STFT. Further modification of the WVD was made by adding two smoothing windows, both in the time domain and in frequency domain as well. The modified transformation is named the Smoothed Pseudo Wigner-Ville Distribution (SPWVD). The SPWVD is given by formula [31]:  
\[SPWVD(t,f)=\int\limits_{t_{1}}\int\limits_{f_{1}}g(t-t_{1})H(f-f_{1})W(t_{1}f _{1})dt_{1}df_{1} \tag{4}\]  
\[W(t,f)=\int_{-\infty}^{\infty}x\bigg{(}t+\frac{\tau}{2}\bigg{)}x^{*}\bigg{(}t -\frac{\tau}{2}\bigg{)}e^{-j2\pi f\tau}d\tau \tag{5}\]where \(g(t)\)--smoothing window in time domain, \(H(f)\)--smoothing window in frequency domain, \(\tau\)--is a lag, and \(x(t)\)--an input signal. In this paper, two configurations of the SPWVD transform have been compared. The first one (ascribed as SPWVDv1) was limited, so that the resolutions in the frequency and the time domains corresponded to the ones obtained for the STFT. In this case, the resolutions were equal to \(\Delta f\) = 456 Hz and \(\Delta t\) = 5310 \(\upmu\)s. In the second case (ascribed as SPWVDv2), the boundary conditions were released, which allowed one to obtain resolutions of \(\Delta f\) = 125 Hz and \(\Delta t\) = 50 \(\upmu\)s.
## 3 Results and Discussion
### Experimental Analysis  
To ensure accurate numerical simulation results, an experimental platform for the two Nautilus VAWTs was constructed for experimental comparisons. The experimental setup for the Nautilus VAWT in this study was built based on the testing platform constructed by [PERSON] et al. [34]. The experimental setup used in this study is shown in Figure 15, and it mainly consists of a Nautilus wind rotor, a middle shaft, a wind speed monitor, a speed detector, a bearing, and a series of components. Two wind rotors are designed and manufactured using 3D printing, and the wind speed monitor uses an anemometer (range 0.3-30 m/s, measurement error \(\pm\) 3%rdg \(\pm\) 0.1), while the speed detector uses a combination device of a timer and a counter. Anemometers were placed around the wind turbine, and the average value was recorded. The magnitude of the wind speed was controlled by adjusting the distance between the wind turbine and the blower. The farther the distance from the blower, the lower the wind speed. The timer is set to its default value of 60 s, and the counter works through a Hall effect switch. The number of wind rotor rotations in one minute is recorded by the counter to calculate the rotor speed. The same operating conditions were set in CFD, and the rotor speed was monitored during the calculation. The experimental results were compared with CFD results, as shown in Figure 16. Each line represents the average speed of two wind turbines. The CFD data is represented by solid lines, while the experimental data is represented by dashed lines. By using different spacing ratios, which are also the focus of this study, it can be observed that the experimental and CFD speed curves exhibit similar trend patterns. The experimentally measured overall average speed is 8.1% lower than the simulated CFD speed. This is due to the presence of support frames during the platform setup, frictional forces between structures [35], instrument accuracy, and other factors [36], which inevitably lead to some errors that are acceptable. It is acceptable that the CFD results have guiding significance for the calculation model.  
Figure 16: Comparison of speed between experiment and CFD.  
Figure 15: Experimental prototype model.