esa-sceva/satcom-chunk-collection huggingface dataset:
For example, microalgae carotenoids are widely used in the food industry, and fish feed since they improve the appearance of fish flesh, as well as in cosmetics and pharmaceuticals for their antioxidant capacity [2; 8]. Since the 1950s, various microalgae have been increasingly used in large-scale applications. Aquaculture is one of the major sectors that utilize microalgae, as these are essential for the diet of zooplankton, which is used in the larvae feed [9; 10]. Additionally, they can be incorporated into fish feed to replace part of fishmeal or to supply the highly unsaturated fatty acids (HUFA), especially n-3 eicosapentaenoic acid (EPA, [4; 5; 8; 11; 14; 17; 14; 17; 18; 19; 20; 21; 22; 23;  
6500 K, cells from green colonies were transferred to new Petri dishes containing solidified mASW, following the streak plate method. This procedure was repeated consecutively (i.e., up to 10 times) until mono-algal but non-axenic strain isolation. It is noted that the use of antibiotics (i.e., ampicillin and streptomycin sulfate, both at concentration 0.05 g/L) was quickly discontinued since it turned out to defective in the elimination of the diverse bacterial populations, while microalgae seemed to be stressed after their extended use. The extermination of rotifers, when present, was accomplished by adding 0.08 g/L urea in the medium, and diatoms' growth was inhibited through the addition of 0.01 g/L GeO\({}_{2}\). During the time of treatment (i.e., approximately 3 days) with these chemicals, microalgal growth did not seem to be affected. Molecular identification of the microalgal isolates was performed by PCR amplification of the 18S rRNA and ITS (internal transcribed spacer) gene markers. Both isolation and molecular characterization processes are described in detail in [21]. A paddle wheel, open raceway pond (ORWP) photobioreactor was used as an open-type pilot-scale reactor for the mono-algal but non-axenic cultures of the aforementioned microalgal strains under non-aseptic conditions (Figure 1). The photobioreactor had the following dimensions: an outside length of 110.5 cm and an inside length of 80 cm, with a width of 61 cm. Its total depth was 20 cm, while the culture depth specifically measured 7.5 cm. The photobioreactor had a capacity of 100 L and a working volume (V\({}_{\mathrm{w}}\)) of 40 L. This reactor simulated an open pond with mechanical agitation through the rotation of a double 4-bladed paddlewheel driven by an electric motor rotating at 35 rpm. Inside the reactor, there was a horizontal partition 80 cm long, creating two hollow partitions and two circulation channels (Figure 1). This photobioreactor was made of stainless steel, which is resistant to corrosion from salt water. Pre-cultures were performed at a scaled level, initially from 0.1 L (in 0.25 L Erlenmeyer flasks) to aquarium-type tanks V\({}_{\mathrm{w}}\) = 6 L prior to inoculation of the cultures at 40 L with initial culture density of \(1.5\cdot 10^{6}\) cells/mL (i.e., 0.01-0.03 g/L of dry biomass, depending on the microalgal strain). The growth medium was modified Artificial Sea Water (mASW),
## 2 Related Work
### State-of-the-Art Deraining Networks
#### 2.2.4 Hinet  
In 2021, HINet was designed by [PERSON] et al. [56] as a simple and powerful multi-stage network. It consists of two subnetworks. Each subnetwork is a U-Net [57]. They introduced a novel block called the half instance normalization block (HIN). The developed network surpasses SOTA on multiple image restoration tasks, such as denoising, deblurring, and deraining.  
SOTA deraining networks succeeded in achieving deraining of single images; however, their performance was not evaluated on the performance of object-detection process. Some of them can process different image restoration tasks, and not limited to deraining. Moreover, the running time was not the main concern for the reviewed SOTA networks.  
To summarize, many deraining networks and approaches have been developed. However, reviewed approaches suffered from different limitations, such as slow running time, limited to a single rain degradation, or the requirement of a human operator. These limitations disable their employment on AVs. Therefore, the main objective of this proposed research is to develop and evaluate a lightweight deraining network that can mitigate different rain degradations in real-time and enhancing the following object-detection process.
# Comparative Analysis of the Methods for Fiber Bragg Structures Spectrum Modeling
###### Abstract  
The work is dedicated to a comparative analysis of the following methods for fiber Bragg grating (FBG) spectral response modeling. The Layer Sweep (LS) method, which is similar to the common layer peeling algorithm, is based on the reflectance and transmittance determination for the plane waves propagating through layered structures, which results in the solution of a system of linear equations for the transmittance and reflectance of each layer using the sweep method. Another considered method is based on the determination of transfer matrices (TM) for the FBG as a whole. Firstly, a homogeneous FBG was modeled using both methods, and the resulting reflectance spectra were compared to the one obtained via a specialized commercial software package. Secondly, modeling results of a \(\pi\)-phase-shifted FBG were presented and discussed. For both FBG models, the influence of the partition interval of the LS method on the simulated spectrum was studied. Based on the analysis of the simulation data, additional required modeling conditions for phase-shifted FBGs were established, which enhanced the modeling performance of the LS method.  
fiber Bragg grating; phase-shifted fiber Bragg grating; fiber-optic sensor; transfer matrix method Article
# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
## 3 Results
### Contrast Test  
The grape leaf disease identification model based on the Fourier domain adaptation was compared with the method without Fourier domain adaptation, using the same identification network. This experiment uses the same number of source domain datasets and target domain datasets. The network selects Vgg13 and other parameters are shown in Table 7. From Table 7, we can see that the prediction results of our model are better.  
In order to further analyze the recognition effect of the Fourier domain adaptation method proposed in this chapter on grape leaf diseases, this experiment uses the confusion matrix to compare and analyze the recognition effect of the grape leaf disease recognition model with Fourier domain adaptation and the grape leaf disease recognition model without Fourier domain adaptation on grape leaf diseases, mainly analyzing the recognition accuracy of the three diseases, when \(\beta\) = 0.09, learning rate = 0.01, batch_size = 4, and epoch = 100.  
According to Figures 5 and 6, the number of correct recognitions of black measles fungus, black rot fungus, and leaf blight fungus by the recognition model without Fourier domain adaptation is 184, 185, and 187, respectively. After adding Fourier domain adaptation, the number of correct recognitions is 195, 193, and 194. It can be seen from the confusion matrix that the recognition accuracy of various grape leaf diseases based on the adaptive recognition method in the Fourier domain adaptation has been significantly improved and the recognition accuracy of black measles fungus has been improved the most, indicating that the domain adaptive effect of black measles fungus is the best.
## 4 MSCT Topology
### Indoor Propagation Models  
Since our proposed algorithm for topology control is intended for ZigBee networks, the deployment environment is considered to be inside buildings. The indoor networks have some particularities to take into consideration, especially the presence of walls.  
We take into consideration a model treating the placement of walls that dictates the signal path inside a building. Generally, in an indoor environment, the average path loss \(PL(d)\) for a transmitter and receiver over a distance dis given in [26] by:  
\[PL(db)=PL(d_{0})+10 nlog(d/d_{0})+X_{\sigma} \tag{1}\]  
where \(PL(d_{0})\) is the path loss for 1 m, nthe path loss exponent, and \(X_{\sigma}\) is a zero-mean Gaussian distributed random variable with standard deviation \(\sigma\).  
This model has been generalized in [27] to a multi-wall environment. This consists of adding a further attenuation term due to losses introduced by the walls penetrated by the direct path between the receiver and transmitter:  
\[L(db)=PL(db)+M_{\omega} \tag{2}\]  
where \(M_{\omega}\) represent the multi-wall component expressed as:  
\[M_{\omega}=l_{c}+\sum_{i=1}^{l}k_{wi}l_{i} \tag{3}\]  
where \(k_{wi}\) is the number of penetrated walls, \(l_{c}\) is constant and \(l_{i}\) depends on wall size as: \(l_{1}\) for a light wall (0-20 cm), \(l_{2}\) for a medium wall (20-40 cm) and \(l_{d}\) for a door.  
In this paper, we consider that the cost of the transmission of a packet is based on the distance between transmitter and receiver. This cost can be considered as the weight of connection between the two nodes. In the case of free space transmission, the weight would simply be the Euclidean distance between the nodes. However, in the indoor environment, the presence of obstacles and path loss must be taken into consideration. Thus, based on the model above, we conclude this equation that calculates the weight of the connection between two nodes in indoor conditions:  
\[Weight=D_{E}*(1-L(db)\times\frac{P_{r}}{P}) \tag{4}\]  
where \(D_{E}\) represents the Euclidean distance between the transmitter and the receiver and \(P_{r}\) and \(P\) represent respectively the received power at the destination and the power from the source.
## 5 Experiments with Other Known Kernels
### Rezantisym Kernel  
This is a composite function defined as [87; 88; 89] (singular at \(t_{0+}\))  
\[M_{R}(\alpha,t;\lambda)=\frac{t^{\alpha-1}}{\Gamma(\alpha)}\exp(-\lambda t), \quad 0<\alpha<1 \tag{118}\]  
which is a product of a particular case, the Sonine kernel \(\frac{t^{\alpha-1}}{\Gamma(\alpha)}\) (known as _the kernel of fractional integration_ or _Gel'fand-Shilov distribution_) [90; 91] and the exponential decaying function.  
It can be presented as a product of two particular versions of the Prabhakar kernel (see (44))  
\[M_{R}(\alpha,t;\lambda)=e^{\gamma}_{\alpha,\beta}(\lambda=0,t)\times e^{1}_{1,1}(-\lambda t) \tag{119}\]  
or in terms of the two-parameter Mittag-Leffler function as  
\[M_{R}=\frac{t^{\alpha-1}}{\Gamma(\alpha)}E^{1}_{1,1}(-\lambda t) \tag{120}\]  
The Laplace transform of \(M_{R}\) is (see Equation (25) for the special case \(\alpha=\beta=1\))  
\[\mathcal{L}[M_{R}(\alpha,\lambda t)]=\frac{1}{\Gamma(\alpha)}\frac{1}{s+\lambda} \tag{121}\]  
The derivative \(\frac{d}{dt}M_{R}(\alpha,t;\lambda)\) is (recall the definition (25) with \(\alpha=\beta=1\)) is \[\frac{d}{dt}[M_{R}(\alpha,t;\lambda)]=\frac{t^{-1}}{\Gamma(\alpha)}E_{1,0}(\alpha, t;\lambda) \tag{122}\]  
which is singular at \(t_{0+}\) with a Laplace transform (see (121))  
\[\mathcal{L}\bigg{[}\frac{d}{dt}M_{R}(\alpha,\lambda,t)\bigg{]}=\frac{1}{\Gamma (\alpha)}\frac{s}{s+\lambda} \tag{123}\]  
Assuming \(\lambda=\frac{\alpha}{\Gamma-\alpha}\), we get  
\[M_{R}(\alpha,t)=\frac{t^{\alpha-1}}{\Gamma(\alpha)}E_{1,1}^{1}\bigg{(}-\frac{ \alpha}{1-\alpha}t\bigg{)},\quad 0<\alpha<1 \tag{124}\]  
\[\frac{d}{dt}[M_{R}(\alpha,t)]=\frac{t^{-1}}{\Gamma(\alpha)}E_{1,0}\bigg{(} \alpha,t;\frac{-\alpha}{1-\alpha}\bigg{)},\quad 0<\alpha<1 \tag{125}\]  
Now, we may write the heat flux constitutive equation as  
\[q_{R}(x,t)=-k_{0}\frac{\partial T(x,t)}{\partial x}-k_{2}\int\limits_{0}^{t} \frac{(t-\tau)^{-1}}{\Gamma(\alpha)}E_{1,0}\bigg{(}\alpha,t;\frac{-\alpha}{1- \alpha}\bigg{)}\frac{\partial T(x,t)}{\partial x}d\tau \tag{126}\]  
Then, what is _mutatis mutandis_, we get  
\[q_{R}(x,t)=-k_{0}\frac{\partial T(x,t)}{\partial x}-k_{2}\int\limits_{0}^{t} M_{R}\bigg{(}\alpha,t;\frac{-\alpha}{1-\alpha}\bigg{)}\frac{d}{d\tau} \bigg{[}\frac{\partial T(x,\tau)}{\partial x}\bigg{]}d\tau \tag{127}\]  
and a heat diffusion equation  
\[\frac{\partial T(x,t)}{\partial t}=a_{0}\frac{\partial T(x,t)}{\partial x}+a_{ 2}(1-\alpha)\int\limits_{0}^{t}M_{R}\bigg{(}\alpha,t;\frac{-\alpha}{1-\alpha} \bigg{)}\frac{d}{d\tau}\bigg{[}\frac{\partial T(x,\tau)}{\partial x}\bigg{]}d\tau \tag{128}\]  
With the Rzhanitsyn kernel, we, therefore, defined a fractional derivative of the Caputo type while assuming a normalization function \(N(\alpha)=1-\alpha\), namely  
\[{}^{RC}D_{t}^{\alpha}f(t)=\frac{1}{1-\alpha}\int\limits_{0}^{t}M_{R}\bigg{(} \alpha,t;\frac{-\alpha}{1-\alpha}\bigg{)}\frac{d}{d\tau}[f(\tau)]d\tau \tag{129}\]
## 5 Discussion
### Use of Quantitative Remote Sensing Products
The vegetation coverage of grassland is similar to the coverage of forest land, shrubs and crops, but in GPP it may be lower than these three categories, so the importance of GPP will be higher. Because of its high albedo, the feature importance of glacier is higher in albedo. Cities are more affected by ET and albedo. GPP and NDVI are of high feature importance to the desert due to their extremely low GPP values and lower NDVI values. These results confirm to some extent the conclusion that quantitative remote sensing parameters are helpful for land cover classification. Figure 10: Characteristic importance of each category. Figure 9: Differences in time series between six quantitative remote sensing products in 10 categories. By extracting the annual characteristic curves of six quantitative remote sensing products as the input of the model, the model does not just obtain the characteristic parameter information of various surfaces but can also obtain the time change information. The time performance of different surface parameters in the same feature parameter is also different. This time information is also provided to the classification model, which has a very positive effect on the classification of different categories.
## 5 Experiment and Performance
### Experiment on Interconnection between the WSN and the CMS  
The interconnection testing was carried out by interconnecting the designed WSN with the KJ95N cable monitoring system which was developed by the Changzhou Institute of Automation which belongs to the China Coal Research Institute (Beijing, China). The sink node in the WSN was connected to the KJF16B monitoring station via a RS232/RS485 conversion device; and the KJF16B was connected to the KJ95N monitoring and dispatch center via a RS485 interface. KJF16B is an embedded monitoring station based on an ARM7 core processor. The environmental information of the underground coal mine is transmitted to the GMDC through the KJF16B, and the information can be displayed on its LCD. During the test, the working parameters of KJF16B were configured by the KJ95N. Three types of environmental parameters were added, including gas, temperature, and smoke. The baud rate of serial data transfer was set to 1,200 bits per second (bps).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID of Sensor Node** & **Number of Periods** & **Number of Received Data** \\ \hline
21 & 12 & 11 \\
23 & 12 & 10 \\
24 & 12 & 10 \\ \hline \hline \multicolumn{3}{c}{**(a)**} \\ \hline
**ID of Sensor Node** & **Number of Periods** & **Number of Received Data** \\ \hline
21 & 18 & 17 \\
23 & 18 & 17 \\
24 & 18 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Transmission results of field test. **(a)** Statistics of WSN_1; **(b)** Statistics of WSN_2.  
Figure 7: _Cont._  
The test data is as shown in Figure 8. The data from the WSNs is displayed on the LCD of the KJF16B monitoring station, including temperature, gas and smoke. The test results prove that the WSNs and cable monitoring system can be well connected by using the proposed interconnection scheme.
## 4 Experimental Validation and Discussion
### The Drain Current Deviations Tolerance Capability of CMUDPD  
The PA operation modes in the actual antenna array system may involved several complicated situations, e.g., the PA is not a standard class AB or class A and has inconsistent operation modes. Therefore, an ADS simulation was conducted by manually introducing the drain current deviation to verify the tolerance capability of CMUDPD. The drain current deviation was caused by increasing the bias current by 10% or decreasing the bias current by 10%. The three different operation modes' PSDs without and with CMUDPD are shown in Figure 17 (i.e., a1 W/O DPD, a2 W/O DPD, a3 W/O DPD, a1 W CMUDPD2, a2 W CMUDPD2, and a3 W CMUDPD2). For comparison, the light-colored lines represent the PSDs of the pre-distortion outputs with the drain current deviations (i.e., a1 W I\({}_{\text{DI}}\) * 1.1, a2 W I\({}_{\text{DI}}\) * 1.1, a3 W I\({}_{\text{DI}}\) * 1.1, a1 W I\({}_{\text{DI}}\) * 0.9, a2 W I\({}_{\text{DE}}\) * 0.9, a3 W I\({}_{\text{DI}}\) * 0.9). It can be seen that the CMUDPD can still reduce the PSD to below \(-45\) dB. Even though the \(\pm 10\%\) drain current deviation was introduced, the proposed CMUDPD still exhibited satisfactory results.  
Figure 16: Simulation results of PSDs under an experimentally generated 30 MHz bandwidth signal with (W) and without (W/O) CMUDPD. (**a**) Class AB W and W/O CMUDPD. (**b**) Class A W and W/O CMUDPD.
## 2 Digital Twins for Next-Generation Networks
### Digital Twins for Access Networks
#### 2.1.1 Radio Networks  
In 6G THz networks, Line-Of-Sight signal establishment is required to enable high bandwidth and low latency; however, obstacles in the path may absorb THz signals, which makes Line-Of-Sight signal establishment impossible. [PERSON] et al. (2016) tackle the problem of signal reflection to avoid obstacles for the signals between a Base station (BS) and a user in indoor amenities, using a digital twin to model and control the signal propagation. The DT uses a set of cameras to stream images of the indoor space. The described system includes modules to perform calculations, e.g., ray tracing, path loss prediction, reflector, and mobile endpoint alignment, allowing the DT estimation of the THz Potential Field (THzPF) and use of this estimation to redirect the signal in real time. The authors present simulation results to show the effectiveness of the system.  
In a similar context for outdoor applications, [PERSON] and [PERSON] (2017) address the adjustment of narrow beams in large-scale MIMO systems. This traditionally requires a large amount of data acquisition/beam sweeping, which scales with the system size/number of antennas. The authors propose to (i) construct a 3D digital replica, i.e., a digital twin, of the real-world communication system, and (ii) use ray tracing algorithms to simulate the signal's propagation. The authors demonstrate that the DT can be used to pre-train Machine learning (ML) models, thus reducing the data acquisition overhead as an important part of the data collection can be simulated. The expected mismatches between the real world and the DT _\"can be calibrated by a small amount of real-world data\"_.
# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
###### Abstract  
Addressing the challenges in online multi-object tracking algorithms under complex scenarios, where the independence among feature extraction, object detection, and data association modules leads to both error accumulation and the difficulty of maintaining visual consistency for occluded objects, we have proposed an end-to-end multi-object tracking method based on hypergraph matching (JDTHM). Initially, a feature extraction and object detection module is introduced to achieve preliminary localization and description of the objects. Subsequently, a deep feature aggregation module is designed to extract temporal information from historical tracklets, amalgamating features from object detection and feature extraction to enhance the consistency between the current frame features and the tracklet features, thus preventing identity swaps and tracklet breaks caused by object detection loss or distortion. Finally, a data association module based on hypergraph matching is constructed, integrating with object detection and feature extraction into a unified network, transforming the data association problem into a hypergraph matching problem between the tracklet hypergraph and the detection hypergraph, thereby achieving end-to-end model optimization. The experimental results demonstrate that this method has yielded favorable qualitative and quantitative analysis results on three multi-object tracking datasets, thereby validating its effectiveness in enhancing the robustness and accuracy of multi-object tracking tasks.  
multi-object tracking; object detection; hypergraph; hypergraph matching; data association +
Footnote †: journal: Applied sciences
## 3 Results
### Variability of NDVI, EVI, and SIF in Driving Factors
#### 3.2.3 Impact of Socio-Economic Factors on NDVI, EVI, and SIF  
As shown in Table 3, the \(p\)-values of the NDVI, EVI, and SIF correlations with population size and GDP are less than 0.01, indicating that the two factors significantly correlate with the NDVI, EVI, and SIF. Population size (=0.926, 0.95, and 0.941) and GDP (=0.574, 0.595, and 0.501) were significantly and positively correlated with the NDVI, EVI, and SIF, and the correlation of population size was more significant than that of GDP.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Cropland** & **Forest** & **Shrub** & **Grassland** & **Water** & **Snow/Ice** & **Barren** & **Impervious** & **Wetland** \\ \hline NDVI & \(-\)0.89 & 0.88 & \(-\)0.85 & \(-\)0.63 & 0.93 & 0.28 & \(-\)0.28 & 0.90 & \(-\)0.85 \\ EVI & \(-\)0.78 & 0.84 & \(-\)0.94 & \(-\)0.84 & 0.83 & 0.03 & \(-\)0.39 & 0.95 & \(-\)0.69 \\ SIF & \(-\)0.76 & 0.85 & \(-\)0.95 & \(-\)0.90 & 0.78 & 0.03 & \(-\)0.60 & 0.96 & \(-\)0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation between NDVI, EVI, and SIF and land use type in China from 2000 to 2019.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Population Size** & **GDP** \\ \hline NDVI & 0.926 ** \\ EVI & 0.950 ** \\ \hline \hline \end{tabular} \({}^{\textbf{w}}\) is at the 0.01 level (two-tailed), with a significant correlation.  
\end{table}
Table 3: Correlation between NDVI, EVI, and SIF and human factors in China from 2000 to 2019.  
Figure 9: Interannual timeline change in land use type. **(A)**: Time variation of cropland; **(B)**: Time variation of forest; **(C)**: Time variation of shrub; **(D)**: Time variation of grassland; **(E)**: Time variation of water; **(F)**: Time variation of snow/ice; **(H)**: Time variation of barren; **(I)**: Time variation of impervious; **(G)**: Time variation of wetland.