esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Board Decentralized Observation Planning for LEO Satellite Constellations
###### Abstract
ICBBA is an innovation based on CBBA in MSOOP, which is reflected in the following aspects: (1) aiming at other coupled constraints such as the storage constraint, the energy constraint and the temporary constraints in MSOOP, a constraint check and a mask recovery are introduced into bundle construction and consensus check to overcome the shortcoming that the original algorithm cannot solve the coupled constraints; (2) according to the characteristics of different scenes, the fitness function is adjusted to adapt different dominant constraints. To validate the effectiveness of ICBBA, we carry out experiments on several groups of instances with different satellite and task scales. The experimental results show that ICBBA can not only reduce the communication cost compared with other decentralized algorithms but also have a good optimization performance. ICBBA can achieve the global consensus in an acceptable number of iterations and the re-convergence in the face of dynamic task arrival when it is running. The remainder of the paper is organized as follows. Section 2 gives the description of MSOOP. ICBBA including bundle construction and consensus check is introduced in Section 3. Section 4 presents the design of test instances and the experimental results of ICBBA on various scenes. Concluding remarks are drawn in Section 5.
## 3 Results and Discussion
### Colour Characteristics, pH, and Mould and Yeast Count in Edible Mushrooms
Thermal treatment could also lead to the loss of desirable food compounds, including colour pigments [31]. Thermal pre-treatment of mushrooms enhances the value of the red colour (a*) as non-enzymatic browning [32]. In our study, this was also observed with thermally treated _B. edulis_ samples. The production of organic acids by LAB causes a reduction in pH and reflects the progress of fermentation. It is known that fermented product with a pH of 4.0-4.1 is durable [6]. In the present study, the pH of most fermented samples was within this range. Moreover, a lower number of yeasts and moulds were detected in pre-treated or/and fermented mushrooms, compared to fresh ones. This could be explained by the thermal pre-treatment and ultrasonication contributing to the partial reduction in microflora [33]. Moreover, organic acids generated during fermentation are a major safeguard against food spoilage microorganisms, since their undissociated forms exhibit strong microbial antagonistic effects. Comparing the antimicrobial potential of different organic acids, acetic acid inhibits yeasts and moulds more than lactic acid [12; 34; 35; 36]. Since antifungal activity is mainly related to the production of acetic rather than lactic acid (because of acetic acid's higher dissociation constant), heteroformentative LAB display a wider spectrum of antifungal activity in comparison with homofermentative ones [37; 38].
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Hybrid Protocols
#### 3.3.3 Synchronized MAC  
A multi-transceiver protocol is Synchronized MAC (SYN-MAC), [81], and it works for multi-hop CRN. SYNC-MAC operation does not require the existence of CCC. Meanwhile, a dedicated radio is assigned for control signal interchange and one radio for information transmission. Time is partitioned into N slots and every slot allocated to a different data channel. One slot is used particularly for control signal exchange, while for the data channel, a given node pair should assign a suitable data channel among the existing ones.  
During the network initialization state, the CR users tune their committed control radios to the channel indicated by the protocol, and nodes that desire to start an information exchange broadcast a beacon in all available channels. Neighboring nodes with interest respond with their available channel list. The transmitting node waits for the time slot corresponding to the channel that has been chosen. The given node pair then begins a settlement procedure like the IEEE 802.11 DCF [82]. Nodes that are not included in transmission process listen to channel to get flagging data or detect any possible PU activity and avoid the hidden terminal problem.  
Though SYN-MAC is not using dedicated CCC and it avoids the hidden terminal problem, it fails to utilize the available channel efficiently. Moreover, since the arrival of a PU is notified to neighbors in a specific time slot, the chance of interference between PU and CR users is high.
## 3 Results and Discussion
### Total Phenolic Content (TPC) and Radical Scavenging Activity (ABTS, DPPH)  
Burgers with wine lees showed significantly higher values of TPC (mg GAE/gDM) than burgers with sodium ascorbate (Figure 2) due to the phenolic composition of the lees (Table 1). Despite the highest phenolic content detected in lees from Palomino variety, only significant differences were found in burgers skyped with the greatest lees concentration. An increase in TPC was also reported in pork burgers with oak wood extracts added as a natural antioxidant from 0.49 mg GAE/gDM in control with sodium ascorbate to 1.75 mg GAE/gDM in burgers with 1% oak wood extract at 12 days of storage [16].  
After 12 days of storage, burgers with 5% wine lees (Lvb and Lpb) showed the highest values of radical scavenging activity, tested by both methods, but ABTS was more efficient to highlight the differences. Other authors have also observed a greater antioxidant activity using natural preservatives instead of ascorbic acid [29].
# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 2 Materials and Methods
### Methodology
#### 2.2.3 Improvement of the Loss Function  
The loss function for bounding box regression plays a crucial role in object detection. In YOLOv8, the loss function adopted for bounding box regression is CIoU [26] Loss. The calculation formula for CIoU is as follows:  
\[\mathcal{L}_{\text{CIoU}}=1-IoU+\frac{\rho^{2}\Big{(}b_{gt},b_{pred}\Big{)}}{C ^{2}}+\alpha\
u \tag{2}\]  
\[IoU=\frac{b_{gt}\cap b_{pred}}{b_{gt}\cup b_{pred}} \tag{3}\]  
\[v=\frac{4}{\pi^{2}}\Bigg{(}arctan\frac{w_{gt}}{h_{gt}}-arctan\frac{w_{pred}} {h_{pred}}\Bigg{)}^{2} \tag{4}\]  
\[\alpha=\frac{\
u}{(1-IoU)+\
u} \tag{5}\]  
Here, \(b_{gt}\) represents the ground truth bounding box, while \(b_{pred}\) represents the predicted bounding box. \(\rho^{2}\Big{(}b_{gt},b_{pred}\Big{)}\) represents the Euclidean distance between the centers of the predicted bounding box and the ground truth bounding box. \(C\) represents the diagonal distance of the smallest enclosing rectangle that can contain both the predicted bounding box and the ground truth bounding box. \(v\) is used to measure the consistency of the relative proportions of the two rectangular boxes, and \(\alpha\) is a balancing parameter.  
CIoU comprehensively considers the aspect ratio, center point distance, and overlap area between the predicted bounding box and the ground truth bounding box. However, training data inevitably contain low-quality annotations of signature marks, and geometric factors such as distance and aspect ratio can aggravate the penalty for these low-quality annotations, potentially degrading the generalization performance of the model. To mitigate this issue, [PERSON] et al. [27] designed WIoU, which weakens the penalty of geometric factors when the anchor box coincides well with the target box. This reduced intervention during training allows the model to achieve better generalization ability. The formula for WIoU is as follows:  
\[\mathcal{L}_{WIoU}=\mathcal{R}_{WIoU}*\mathcal{L}_{IoU} \tag{6}\]  
\[\mathcal{R}_{WIoU}=\exp\Bigg{(}\frac{\left(x-x_{gt}\right)^{2}+\left(y-y_{gt} \right)^{2}}{\left(W_{g}^{2}+H_{g}^{2}\right)^{*}}\Bigg{)} \tag{7}\]  
Figure 9: High-stage gather-and-distribute module structure diagram.  
\(\mathcal{L}_{IoU}=1-IoU\), which belongs to the interval [0, 1]. When the anchor box aligns well with the target box, it significantly reduces the \(\mathcal{R}_{WIoU}\) of the high-quality anchor box and diminishes its emphasis on the distance between central points. Conversely, \(\mathcal{R}_{WIoU}\in[1,e)\), which notably amplifies the \(\mathcal{L}_{IoU}\) of the ordinary-quality anchor box.
## 2 DC Fault Current Analysis
### DC Fault Analysis of the Test System  
In the monopolar MMC-HVDC system, the DC line is typically composed of cables because the fault possibility is considered much lower in cables than in overhead lines [35]. However, an overhead line is typically adopted in the bipolar system, especially in long-distance power transmission with a high capacity. Given that an overhead line is likely to be affected by a short circuit, grounded fault, and nonpermanent fault, analyzing the fault characteristics for protection relaying is necessary. The DC line faults are mainly classified into a single pole-to-ground fault, pole-to-pole short-circuit fault (PPF), and line disconnection. The PPF is regarded as a serious fault considering an extremely high fault current within a few milliseconds after fault occurrence. In Figure 2, the MMC-HVDC scheme indicates that the single pole-to-ground fault in the bipolar system can be equivalent to the PPF given bipolar system's metallic return is connected to ground. Therefore, the PPF is selected to be investigated in this study, especially in terms of fault current. Fault current propagation can be segmented into SM capacitor discharging before blocking IGBT, decayed freewheeling current, and AC gird in-feed current after blocking IGBT. Before blocking IGBT, the discharging capacitor in inserted SMs leads to surge current, which provides a high electrical pressure on semiconductor devices. Thus, from a protection perspective, IGBT is blocked with a control signal when the arm current reaches the threshold value.
## 2 Overview of the Case Study Mining Site  
### Basic Situation of Main Roadway  
The main roadways in the fourth district of a certain mine are arranged in the 2-1 coal seam. On the east side of the main roadway is the second district, and the main roadway is parallel to the working face in the second district. On the west side of the main roadway is the fourth district, where there are 12 longwall faces (LW) including 21405 and 21406 arranged perpendicular to the main roadway. Among them, LW21405 is the initial mining face in the fourth district. As shown in Figure 1, the main roadway group in the fourth district is designed with three main roadways: the main headgate, auxiliary main headgate, and main tailgate. Among them, the main headgate and auxiliary main headgate are newly excavated tunnels, while the main tailgate utilizes the original auxiliary headgate from LW21205. The overall structure of the 2-1 coal seam in the fourth district is simple, with an average thickness of 2.95 m, a dip angle of 3 degrees, and an average burial depth of 650 m. The main roof strata of the coal seam include the Zhiluo Formation, Anding Formation, and Zhidan Group's sandstone strata. Within the upper 400 m above the roof, there are no exceptionally thick sandstone strata, and most sandstone strata have a thickness of less than 20 m. The interlayer between the sandstone strata is mostly composed of sandy mudstone strata. The laboratory identification indicates that the 2-1 coal seam has a strong tendency for bumps, with the roof and floor showing a weak tendency for bumps. In the main roadway area of the fourth district, various widths of chain pillars are left: the goat of LW21205 has coal pillars of 30 m, the main tailgate and the main headgate have coal pillars of 35 m, and the auxiliary main headgate and the retreat line of the fourth-district working face have chain pillars of 360 m.  
The cross-section of the main roadway is rectangular, with a design dimension of width \(\times\) height = 5.4 m \(\times\) 3.2 m, adopting \"bolting and shotcreting with wire mesh\", with a length of 2.5 m for the bolt and 7.2 m for anchor cable support. Some roadways undergo concrete spraying, with a spraying thickness of 0.1 m on the top and around the roadway, and 0.2 m on the bottom of the roadway. The overall support strength of the main tunnels is high. Affected by the asymmetric goat, the deformation and damage of the main roadways in the fourth district are severe. However, both the roof and sidewalls of the roadways have strong support, except for the points of impact accidents. The deformation and damage of the roadways are mainly manifested in floor damage. As shown in Figure 2, after the formation of the goat in LW21205, the deformation and damage of the roadways are evident: the main tailgate has severe deformation and damage; in the main headgate, there are obvious floor heating and roof fall in some areas, and the sidewall deformation is significant; the auxiliary main headgate has a small overall deformation, with localized areas showing roof and floor damage.
## 2 Materials and Methods
### Model Architecture  
#### 2.2.1 GAN  
GAN (generative adversarial network) is a type of deep learning model whose core idea is to train two models simultaneously: a generative model (Generator, denoted as G) and a discriminative model (discriminator, denoted as D) [44]. The task of the generative model is to generate fake data samples. The generative model typically uses the following loss function:  
\[\mathrm{L_{G}}=-\mathrm{E_{Z\sim p_{z}(z)}}[\log(1-\mathrm{D}(\mathrm{G}(z)))] \tag{1}\]  
Here, z represents random noise, \(\mathrm{p_{z}(z)}\) is the noise distribution, \(\mathrm{G(z)}\) is the data generated by the generative model, and \(\mathrm{D}(\mathrm{G(z)})\) indicates the discriminative model's judgment of the generated data. The task of the discriminative model is to distinguish whether the received data sample is a real image from the training data or a fake image from the generative model. The discriminative model will provide a probability value indicating whether an input sample is a real image. Its loss function can be expressed as follows:  
\[\mathrm{L_{D}}=-\mathrm{E_{x\sim p_{t}(x)}}[\log(\mathrm{D}(x))]-\mathrm{E_{Z \sim p_{z}(z)}}[\log(1-\mathrm{D}(\mathrm{G(z)}))] \tag{2}\]  
x represents the actual data, \(\mathrm{p_{t}(x)}\) represents the distribution of the actual data, and \(\mathrm{D(x)}\) represents the judgment of the discriminative model on the actual data.  
During the training process, the generative model will continuously learn to deceive the discriminative model, and the discriminative model will also continuously learn to distinguish between real and fake data. The ultimate goal of the generative adversarial network (GAN) is to generate new data samples that are similar to the actual data. This paper accomplishes the task of generating sandstone sample two-dimensional CT grayscale images by using the adaptive enhancement of the generative adversarial network (StyleGAN2-ADA).
## 3 Datasets  
### Experiment 1: Static Antenna  
The first experiment was carried out with one month of GNSS data gathered from a Leica GR10 receiver and a Leica AR25.R3 antenna during July 2018 (GPS Day of Year 185-216). This equipment is operated by the German Federal Institute of Hydrology (Bundesanstalt fur Gewasserkunde (BfG)), and the antenna is installed on a pile atop the tide gauge station TGW2 (Figure 3), which belongs to the German Federal Waterways and Shipping Administration (Wasserstrassen- und Schifffahrtsverwaltung des Bundes (WSV)). The tide gauge is located 1.7 km north of the coast of Wangerooge island. The sea level is recorded with a 1 min sample rate and a 1 cm resolution. The height offset between tide gauge zero and the antenna reference point is known from BfG information. Depending on the tide, the reflector height above the water surface varies between 10.1 m and 14.4 m. For this experiment, BfG increased the sample frequency of the GNSS receiver to 1 Hz. The antenna's surroundings are free of static obstacles, and due to the installation structure, negative near-field effects could be assumed to be ruled out. Hence, no azimuth dependent masks were used.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 2 Underwater Communication Technology Overview
### Magnetic Induction (MI)  
This way of communication is mainly used below the sea floor. The communication distance is approximately up to 10 m. The speed of communication is \(3\times 10^{8}\) m/s. The data rate is in kbps.
###### Abstract  
The falling weight deflectometer (FWD) is a widely used nondestructive test (NDT) device in pavement infrastructure. A FWD test measures the surface deflections subjected to an applied impact loading and the modulus of pavement layers can be determined by back-calculating the measured deflections. However, the modulus of asphalt layers is significantly influenced by temperature; hence, the temperature correction must be considered in back-calculation to evaluate the moduli of asphalt layers at a reference temperature. In addition, the in situ temperature at various pavement depths is difficult to measure. A model for evaluating the temperature at various depths must be established to estimate the in situ temperature of asphalt layers. This study collected the temperature data from a FWD test site to establish a temperature-evaluation model for various depths. The cored specimens from the test site were obtained to conduct dynamic modulus tests for asphalt layers. The FWD tests were applied at the FWD test site and the back-calculation was performed with temperature correction using the frequency-temperature superposition principle. The back-calculated moduli of asphalt layers were compared with the master curve of dynamic modulus to verify the application of the frequency-temperature superposition principle for FWD back-calculation. The results show that the proposed temperature-evaluation model can effectively evaluate the temperature at various depths of pavement. Moreover, the frequency-temperature superposition principle can be effectively employed to conduct temperature correction for FWD back-calculation.  
FWD; frequency-temperature superposition; dynamic modulus test; asphalt pavement and temperature evaluation model +
Footnote †: journal: Applied Physics Letters
## 4 Experiment
### The Processing of Data  
To verify the performance of the proposed prediction model, this paper adopts one-minute interval k-line fitting price spread data as the experimental data. Each term of the price spread data is composed of eight features, including the opening price spread, highest price spread, lowest price spread, closing price spread, MACD, DEA, DIF, and price spread fluctuation. In the experiment, we allocate 70% of the dataset as the training set for the comparative model, with the remaining 30% serving as the test set. For the PSO Deep-ConvLSTM model, the first 70% of the dataset is utilized for the optimization algorithm to search for optimal parameters and train the model, with the final 30% deployed as the test set to assess the model's generalization error. Herein, the data are processed as follows: Prior to feeding the feature data into the artificial neural network, the data  
Figure 5: Flowchart of PSO Deep-ConvLSTM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & **Python** & \multirow{2}{*}{**Memory**} & **GPU** & **System** & **CUDA** \\  & **Version** & & & & \\ \hline Parameter & Python3.8 & 32 GB & Tesla V100 & Centos7 & CUDA11.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental environment.  
undergo normalization, being effectively converted into a [0, 1] range. This approach not only minimizes the impact of noise, enhancing the predictive accuracy of the model, but also expedites model convergence, ensuring efficient parameter updates within the neural network. Equation (7) provides the formula for normalization:  
\[\overline{x}_{i}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{7}\]  
where \(x_{\text{min}}\) and \(x_{\text{max}}\), respectively, represent the minimum and maximum values of the entire training set.  
As the data are subject to normalization during the model training phase, the output of the test set can be reverse normalized using Equation (8).  
\[x_{i}=x_{t}^{\prime}\cdot(x_{\text{max}}-x_{\text{min}})+x_{\text{min}} \tag{8}\]  
where \(x_{t}^{\prime}\) is the output value of the forecasting model.
## 3 Results and Discussion
### Data Processing using Machine Learning Methods  
The following sequence of characterization methods was used to achieve robust results during the engineering process: data preparation, choosing of the base characterization method, convolutional neural networks (CNN) based autoencoding, convolutional autoencoding, feature engineering, and final comparison.
# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 3 Results  
To the observations from different payloads onboard CSES-01, we conducted a comprehensive and retrospective examination for the abnormal phenomena during the global strong shallow EQs. Since its launch in February 2018, by the end of February 2023, CSES-01 completed its five-year lifespan design and is still stably operating in orbit. During its 5-year operation, CSES-01 collected observations, including 66 global strong EQs with a magnitude of Ms \(\geq\) 7 ([[https://news.ceic.ac.cn/speedsearch.html](https://news.ceic.ac.cn/speedsearch.html)]([https://news.ceic.ac.cn/speedsearch.html](https://news.ceic.ac.cn/speedsearch.html)), accessed on 1 August 2024). According to previous pioneering studies [6; 15; 36; 37], the propagation capability of seismic anomalies from the lithosphere to the ionosphere depends on the magnitude and depth of the EQ. Given that there are rare reports of seismic ionospheric disturbance induced by EQs with a depth \(\geq\) 100 km, this study only focuses on the shallow and strong EQs with a depth \(\leq\) 100 km and Ms \(\geq\) 7 that occurred during CSES-01's 5-year operation. Figure 3 shows the distribution of the 47 EQs involved in this study.