esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 2 Materials and Methods
### Transformer-Based Plant Disease Detection Algorithm  
CNN-based target detection algorithms (such as Faster RCNN, YoLo, FCOS [35], etc.) usually rely on a lot of manual design, such as the rules-based label matching mechanism, inspirational reprocessing processing, etc. An end-to-end concise target detection framework based on a transformer [36] was proposed, which has good detection performance.  
The transformer is a new neural network structure that mainly uses an attention mechanism to capture global contextual information and achieves long-range information fusion to extract more effective feature extraction. The transformer has had great success in natural language processing.  
[PERSON] et al. first proposed the transformer-based detection transformer (DETR) [37]. DETR treats target detection as a simple set prediction problem, removes the NMS and anchor design, has a concise pipeline, and introduces an attention mechanism to enhance feature representation, enabling simple and complete end-to-end target detection. This algorithm has high feature fusion capability and a high accuracy of detection, but the cost of training is significant.  
DETR extracts maize leaf image features using CNN networks and compresses the feature dimension into one dimension. The features are encoded at a fixed position before being fed into an encoder-decoder converter. The decoder uses a multi-head attention mechanism to decode N objects in parallel at each decoding layer to produce N outputs. Finally, the target class recognition and bounding box regression are performed by feedforward neural networks (FFNs) to achieve disease target detection.
## 3 Hsp60 in Carcinogenesis
### Hsp60 and miRNAs Correlations and Implications for Carcinogenesis
The level of miR-644a l was found low in hepatocellular carcinoma tissues and cell lines, and negatively correlated with tumor diameter and TNM (Tumor-Node-Metastases) stages [156]. In vitro, an increase in miR-644a promoted cancer cell apoptosis by inhibiting HSF1, Hsp90, Hsp60, Bcl-2, and Bcl-xl proteins while increasing the levels of BID, BAD, BIM, SMAC, Apaf-1, and cleaved caspases-3 and -9, which are all mediators of cell apoptosis [156]. In view of all these observations, miR-644a was suggested as potential prognostic biomarker and therapeutic target in HCC (Table 3) [156]. It is likely that Hsp60 typically displays a pro-tumorigenic activity that can be suppressed by its miRNA-mediated inhibition [157; 158; 159]. A further confirmation of the role of miRNAs in the regulation of Hsp60 level in cancer emerged from a study of the pro-tumorigenic activity of miR-17a in gastric lymphoma [160]. The expression of miR-17 was found significantly higher in gastric lymphoma than in normal tissues, which promoted tumor development, progression, and metastasization by regulating the Hsp60/TNF2 pathway [160]. Therefore, the latter pathway emerges as a potential target for the diagnosis and treatment of gastric lymphoma (Table 3).
## 2 Multi-Agent Hour-Ahead EM Modeling  
In this paper, we take the bilateral hour-ahead EM into consideration. In our proposed EM, for the sake of simplicity, some assumptions and descriptions are listed as follows:  
1. Every GenCO (NRGenCO and RPGenCO) has only one generation unit;
2. Similar to [PERSON] et al. (2018), the considered hour-ahead EM is a single period EM, hence each hour every NRGenCO and DisCO sends its bid curve for the next hour to the ISO. However, the proposed single-period EM modeling approach can be extended to a multi-period one such as a day-ahead EM;
3. Each hour, every RPGenCO submits only its own predicted production with bidding price 0 ($/MW) for the next hour to ISO because of its low marginal cost and the role of \"price taker\" [2; 33; 35], and the only strategic players are NRGenCOs [2] and DisCOs. Therefore, each NRGenCO and DisCO can be considered an agent that adaptively adjusts the bidding strategy in order to maximize profit. After receiving all agents' supply and demand bid curves and all RPGenCOs' predicted production submission in each hour, ISO performs the process of congestion management and sends the market clearing results, including power schedules and prices, to all market participants (NRGenCOs, RPGenCOs, and DisCOs). The pricing mechanism in the market clearing model is locational marginal price (LMP), which is popular in most developed countries. A flowchart for describing how the considered bilateral hour-ahead EM works is shown in Figure 1:  
For the next hour \(t\), the supply bid curve submitted by NRGenCO \(i\) (\(i\) = 1, 2,..., \(N_{g1}\)) to ISO in hour \(t\)-1 can be formulated as [13]:  
\[SF_{i,t}(P_{g;t,t},k_{g;t,t})=k_{g;t,t}(a_{i}P_{g;t,t}+b_{i}),\quad P_{g;t}\in \left[P_{g;\min},P_{g;\max}\right]\, \tag{1}\]  
where, \(P_{g;t,t},k_{g;t,t}\) is the power production (MW) and bidding strategy ratio of NRGenCO \(i\) for the next hour \(t\), respectively. NRGenCO \(i\) can change its bid curve by adjusting its parameter \(k_{g;t,t}\). The marginal cost function of NRGenCO \(i\) is:  
\[MC_{i}(P_{g;t,t})=a_{i}P_{g;t}+b_{i}, \tag{2}\]  
where, \(a_{i}\), \(b_{i}\) represent the slope and intercept parameters, respectively. For the next hour \(t\), the demand bid curve submitted by DisCO \(j\) (\(j\) = 1, 2,..., \(N_{d}\)) to ISO in hour \(t\)-1 can be formulated as [13]:  
\[DF_{j,t}(P_{dj,t},k_{dj,t})=k_{dj,t}(-c_{j}P_{dj,t}+d_{j}),\quad P_{dj,t}\in \left[P_{dj,\min},P_{dj,\max}\right]\, \tag{3}\]  
where, \(P_{dj,t},k_{dj,t}\) is the power demand (MW) and bidding strategy ratio of DisCO \(j\) for the next hour \(t\), respectively. DisCO \(j\) can change its bid curve by adjusting its parameter \(k_{dj,t}\). The marginal revenue function of DisCO \(j\) is:  
\[MD_{j}(P_{dj,t})=-c_{j}P_{dj,t}+d_{j}, \tag{4}\]  
where \(-c_{j}\) and \(d_{j}\) represent the slope and intercept parameters, respectively. Figure 1: Flowchart of the considered bilateral hour-ahead EM in each hour.
## 2 System Model of NGSO Satellites Equipped with Multi-Beam Antennas
### Interference Evaluation Index and Antenna Radiation Patterns
\tag{10}\]  
where \(\psi\) is the off-axis angle, \(G_{m}\) simplifies maximum gain in the main lobe, \(\psi_{b}\) represents one half the 3 dB beamwidth in the plane of interest at the largest off-axis angle, \(L_{s}\) is main beam and near-in side-lobe mask cross point below peak gain, \(L_{F}\) is the far-out side-lobe level, equals to 0 for ideal patterns, and \(Y\) and \(Z\) are calculated by \(Y=\psi_{b}\sqrt{(-L_{s}/3)}\) and \(Z=Y\cdot 10^{0.04(G_{m}+L_{s}-L_{F})}\), respectively. Typically for a LEO satellite, \(L_{s}=-6.75\) and \(Y=1.5\psi_{b}\). According to ITU-R S.672-4 [33], the reference radiation pattern suitable for GSO satellite antenna is given by  
\[G(\psi)=\left\{ \begin{array}{ll}G_{m}&\text{dBi}&0\leq\psi<\psi_{b}\\ G_{m}-3(\psi/\psi_{b})^{\alpha}&\text{dBi}&\psi_{b}\leq\psi<a\psi_{b}\\ G_{m}+L_{N}+20\text{log}(z)&\text{dBi}&a\psi_{b}\leq\psi<0.5b\psi_{b}\\ G_{m}+L_{N}&\text{dBi}&0.5b\psi_{b}\leq\psi<b\psi_{b}\\ X-25\text{log}(\psi)&\text{dBi}&b\psi_{b}\leq\psi<Y\\ L_{F}&\text{dBi}&Y\leq\psi\leq 90^{\circ}\end{array} \right. \tag{11}\]  
where \(L_{N}\), expressed in dB, is the near-in-side-lobe level relative to the peak gain required by the system design. \(z\) is the ratio of major axis to minor axis for the radiated beam, and the values of \(a\), \(b\), and \(\alpha\) are determined by the value of \(L_{N}\). The ratio is 0 dB expected, and in most cases it is devised not to exceed 3 dB. For \(L_{N}=-20\,\text{dB}\) and \(-25\,\text{dB}\), \(b\) is always 6.32 and \(\alpha\) is always 2, and \(a\) is evaluated by \(2.58\sqrt{1-\text{log}(z)}\) and \(2.58\sqrt{1-0.8\text{log}(z)}\), respectively. According to ITU-AP8-10 [34], the reference radiation pattern for the ground station antenna with antenna aperture diameter to wavelength ratio \((D/\lambda)<100\) is given by  
\[G(\psi)=\left\{ \begin{array}{ll}G_{m}-2.5\cdot 10^{-3}(D/\lambda\cdot\psi)& \text{dBi}&0\leq\psi<\psi_{m}\\ G_{1}&\text{dBi}&\psi_{m}\leq\psi<\psi_{r}\\ 52-10\text{log}(D/\lambda)-25\text{log}(\psi)&\text{dBi}&\psi_{r}\leq\psi< \psi_{b}\\ 10-10\text{log}(D/\lambda)&\text{dBi}&\psi_{b}\leq\psi\leq 180^{\circ}\end{array} \right. \tag{12}\]  
where \(G_{m}\), \(G_{1}\), and \(\psi_{m}\) can be evaluated by equations \(G_{m}=20\text{log}(D/\lambda)+7.7\), \(G_{1}=2+15\text{log}(D/\lambda)\), and \(\psi_{m}=20\lambda/D\sqrt{G_{m}-G_{1}}\), respectively. \(\psi_{b}\) is \(48^{\circ}\), and \(\psi_{r}=100\lambda/D\) for antenna parameters satisfying \(D/\lambda<100\). The related parameters are listed in Table 1, and the relationship between the antenna gain and the off-axis angle are depicted in Figure 3. It is easy to see that when the absolute values of the off-axis angles increase, the antenna gains decrease rapidly. The gains remain unchanged when the off-axis angles are very large.
## 2 Results and Discussion
### Evaluation of the Effect of Independent Variables on the Extraction of VOCs
#### 2.1.2 Extraction Time  
Extraction time is an essential parameter for the effective use of the HS-SPME technique. It must be sufficient to release the analytes from the analyzed matrix until they settle in equilibrium in the headspace vial and are adsorbed or absorbed by the fiber coating [23; 33; 34]. In the experiments carried out, ranging from 10 to 30 min, the time did not significantly affect the volatile extraction yield for the studied fibers.  
Observing the DVB/CAR/PDMS fiber response surface graph (Figure 4), it is possible to confirm no influence of weather conditions on the extracted volatile area. The most significant areas were obtained from 24 min of fiber exposure. It was observed that there was no increase in the efficiency of extracting volatile compounds from the sample, even in tests with more extended time values at a constant sample quantity.  
Figure 3: Effects of time and temperature of extraction and quantity of pulp variables on the extraction of volatiles using different fiber coatings for HS-SPME: (**a**) PDMS/DVB, (**b**) PA, and (**c**) CAR/PDMS/DVB.  
On the other hand, the volatile areas extracted by PDMS/DVB were greater when using time values greater than 20 min, even when associated with a smaller amount of sample, and intrinsically, a lower concentration of VOCs. In regions with sample amounts greater than 2.0 g, it is also possible to obtain higher extraction yields. However, they were not superior to those obtained with 0.5 g of _E. Klotzschiana_ pulp at a time longer than 20 min. The optimal extraction time calculated by the model was 19.71 min, confirming what was demonstrated by the response surface.  
Figure 4: Three-dimensional response surface (RSM) graphs of the variables time and temperature of extraction and amount of pulp in the extraction of volatiles using different fiber coatings for HS-SPME: (**a**) DVB/CAR/PDMS extraction temperature vs. sample weight, (**b**) DVB/CAR/PDMS extraction time vs. sample weight, (**c**) PDMS/DVB extraction time vs. sample weight, (**d**) PDMS/DVB extraction temperature vs. sample weight, (**e**) PA extraction temperature vs. sample weight, and (**f**) PA extraction time vs. sample weight.  
As for the PA fiber, it was observed that the highest extraction yield values were detected in a time greater than 20 min. However, similar values were not observed when larger amounts of sample were used, as occurred in the PDMS/DVB fiber. A possible explanation is that the fiber sorbent reached its maximum adsorption capacity of the analytes to which it was exposed [35].
# A Vector Sensor-Based Acoustic Characterization System for Marine Renewable Energy
## 1 Introduction
These systems, with vector sensors spaced several kilometers apart [5] or that contain a single vector sensor [9] are not fit-for-purpose systems built to monitor and measure MRE noise. They lack real-time onboard processing and data transmission capabilities, and they do not take advantage of advances in low-cost vector sensor technology. NoiseSpotter is a newly developed monitoring technology to support the evaluation of potential environmental effects of MRE devices. The technology seeks to improve upon traditional acoustic sensing techniques through integration of a compact array of acoustic vector sensors with custom data dissemination technologies to characterize, classify, and provide accurate location information, in near real time, for anthropogenic and natural sounds. Traditional acoustic sensing techniques typically involve the use of hydrophones that measure scalar acoustic pressure. Consequently, acoustic source localization typically requires the use of large arrays consisting of multiple hydrophones [10]. The large size and footprint of hydrophone arrays can, therefore, make it difficult to deploy using a small vessel in energetic environments near operational MRE devices. An attractive alternative to the use of large hydrophone arrays is the use of compact arrays of acoustic vector sensors. A vector sensor measures three-dimensional (3D) acoustic particle velocity in addition to acoustic pressure on a single sensor, which inherently provides directional information (acoustic bearing) to a source of sound. A vector sensor array (VSA) can, therefore, triangulate individual measured bearings to provide sound source localization, thereby helping to characterize sounds specific to a source. MRE devices are expected to emit low-intensity operational sounds on the order of 110-130 decibels (dB) referenced to 1 microPascal (re 1 \(\upmu\)Pa) at 1 m [11]. These levels are considerably lower than sounds from other anthropogenic activities such as shipping or pile-driving that are known to exceed 180 dB re 1 \(\upmu\)Pa. Therefore, in order to characterize MRE sounds, it is important to be able to distinguish it from other sources of sound such as boats, marine mammals, and fish choruses. Furthermore, given the increasing interest on acoustic particle velocity effects on fishes and invertebrates [12], the particle motion measurements afforded by a VSA can play an important role in further understanding this aspect of acoustic propagation. The objectives of this paper are to (1) describe technological aspects of the 'a newly developed system called the NoiseSpotter including results from in-water field measurements and (2) demonstrate the utility and feasibility of vector sensors to locate a known, controlled acoustic source. These objectives help address key knowledge gaps regarding noise levels of MRE devices. These knowledge gaps, once addressed, can help potentially mitigate concerns about the acoustic effects of MRE on marine animals, paving the way for a more sustainable and equitable utilization of MRE resources.
# Evaluation of the Prospects for the Use of Microalgae in Functional Break Production
## 3 Results
### Polyphenol Content and Antioxidant Activity of Bread with microalgae  
The content of polyphenols and antioxidant activity of bread with microalgae are presented in Table 9.  
The samples of bread with microalgae content of 3% and 5% had the highest antioxidant activity (Table 9). These samples contained approximately the same amount of polyphenols (92.4 mg/100 g dry weight and 93.4 mg/100 g dry weight). The lowest amount of polyphenols and the lowest antioxidant activity were observed for bread samples without added microalgae. This fact indicates a significant contribution of microalgae polyphenols to the antioxidant activity of bread. Polyphenols retained their effectiveness after heat treatment.
## 4 Experimental Results
### Irregular Defect Shape Mapping  
In this sub-section, we describe defect 4, which had an irregular shape. The C-scan contour image is shown in Figure 15. In the experiment, the scanning speed \(v_{\mathrm{x}}\) was approximately 0.015 m/s. As shown in Figure 15, the time difference between the ends of the longer length \(\Delta t_{1}\) was approximately 2600 ms and the time difference between the ends of the shorter length \(\Delta t_{2}\) was approximately 1300 ms. The length of the defects was calculated using the estimation formula for defect length. The estimated longer length \(L_{1}\) of defect 4 was 39 mm, and the estimated shorter length \(L_{2}\) was 19.5 mm. The estimated length was a little smaller than the actual length of defect 4 (40 mm and 20 mm). According to the scanning path, the estimated width \(W\) of defect 4 was 15 mm. The estimated width was much bigger than the actual width of defects, at 12 mm. Even with this, the shape of defect 4 could be imagined.  
Figure 14: C-scan contour images of subsurface defect 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scanning Direction** & **Defect No.** & **Defect 1** & **Defect 2** & **Defect 3** \\ \hline \multirow{2}{*}{Magnetic induction flux} & Max. amplitude (mV) & 1337.5 & 1301.1 & 1156.7 \\  & Min. amplitude (mV) & 180.7149 & 194.8317 & 151.1605 \\ \hline \multirow{2}{*}{Exciting current} & Max. amplitude (mV) & 1089.8 & 738.4175 & 783.0181 \\  & Min. amplitude (mV) & 227.4575 & 483.8115 & 654.0376 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Amplitude of surface defects in two directions.
###### Abstract  
Distributed fountain coding plays an important role in rateless code research. The reliability and effectiveness of these coding schemes are increasingly challenged with the growing applications. In this paper, a novel multiple-access network with cooperative relay is presented, and a novel enhanced distributed fountain coding scheme for this network is proposed. The overall degree distributions are derived, and the asymptotic decoding performance is analyzed theoretically by employing the And-Or tree method. On this basis, a design method using joint iterative optimization algorithms is proposed to optimize the degree distributions of the sources and relays. Simulation results show that the proposed enhanced distributed fountain codes outperform the existing generalized distributed fountain codes (GDFC) and have a good performance on both lossless and lossy channels. It reveals that the proposed codes can provide unequal error protection (UEP) property for different sources by introducing the extra cooperative relay. The performance improvement is not restricted to the sources connected to the cooperative relay but applies to all sources. With the additional relay, the proposed codes are able to overcome the effects of bad channel conditions caused by terrain, obstacles, and so on, to avoid communication interruptions and improve the reliability of the network.  
fountain codes; degree distributions; asymptotic performance analysis; multiple access networks; cooperative relay +
Footnote †: journal: Computer Science
# Three-Band Spectral Camera Structure Design Based on the Topology Optimization Method
## 4 Performance Analysis  
### Modal Analysis  
The optimized model underwent modal analysis using ANSYS. The first three mode shape cloud diagrams for the housing and support structure are shown in Figures 10 and 11. After optimization, the first-order mode of the housing is 126.01 Hz, the second-order mode is 192.7 Hz, and the third-order mode is 297.22 Hz. For the support structure, the first-order mode is 217.89 Hz, the second-order mode is 270.28 Hz, and the third-order mode is 292.81 Hz. Both the housing and the support structure meet the requirement of a first-order mode greater than 100 Hz after optimization.  
Figure 10: _Cont._  
Figure 9: Final structure: (**a**) final optimization structure; (**b**) three-band optical system structure.  
Figure 11: Mode shapes for support: (**a**) support 1 st-order modal; (**b**) support 2 nd-order modal; (**c**) support 3 rd-order modal.  
Figure 10: Mode shapes for housing: (**a**) housing 1 st-order modal; (**b**) housing 2 nd-order modal; (**c**) housing 3 rd-order modal.
# A Study of EEG Feature Complexity in Epileptic Seizure Prediction
## 2 Materials and Methods
### Statistical Analysis  
Following the complexity metrics assessment, we conduct a statistical test to certify that the analysis results are significant. We performed the t-test for each retained feature, distinguishing the inter-ictal and pre-ictal classes, to assess how significant the difference between the categories. For the between-patient variability study, since classifying the pre-ictal samples by patients is a multi-class problem, we applied the one-way ANOVA test to verify that the promising discriminant features are significantly different between subjects. The classes are said to differ significantly if the p-value of the statistical test is smaller than a two-sided significance level of 0.05.
## 3 Results and Discussion
### Classification Performance of the Proposed Model
#### 3.1.2 Network Structure  
In general, in order to extract features comprehensively, the depth of network structure needs to be sufficient. However, take actual application scenario into account, we hold the view that the complex structure of the network is unnecessary. The reason is that samples are two-dimensional which indicates that features in the time-frequency map are not as rich as the real pictures. The experimental results confirm our conjecture, that applying deep network structure does not improve performance, but brings more time overhead. Based on the above considerations and experimental verification, we choose a network structure containing three convolutional layers. In detail, for convolutional layer 1, we adopt the kernel size \(3\times 3\) with 3 channels inputs and 32 channels outputs; in terms of convolutional layer 2, we adopt the kernel size \(3\times 3\) with 32 channels inputs and 64 channels outputs; as for convolutional layer 3, we adopt the kernel size \(3\times 3\) with 64 channels inputs and 64 channels outputs; moreover, the activation function and pooling layer function are Rectified Linear Units (ReLU) [42] and maximizing function respectively; what's more, the fully connected layer is N \(\times\) 64 (N is determined by the window size) to 64 for features mapping.