esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discussions  
The results demonstrate the effectiveness of the PPO algorithm in designing hairpin filters. The trained and learned PPO algorithm can modify the geometric dimensions of the filter to achieve the desired frequency response.  
In the design of microstrip filters, the topology design of the filter structure is relatively mature, and most structures can be found in various handbooks and literature. However, engineers often face challenges in adjusting the size parameters of the filter. In the past, this  
Figure 6: (**a**) The blue line represents the average reward during five training iterations, while the shaded area indicates the observed reward range (maximum and minimum values) throughout these five training processes. (**b**) Quantity of steps necessitated to attain the optimization objective.  
Figure 7: Comparison of frequency response curve variations during the PPO design process of hairpin filters. The red square scatter represents the predictions of the final design made by the ANN. (**a**) S11, (**b**) S21.  
process could only be manually completed by experienced engineers, which was expensive and time-consuming, lengthening the development cycle. In this work, by interacting with the design environment, the agent learns the filter design experience and replaces human engineers in executing this strategy. Additionally, we use a neural network model to replace the commonly used EM simulation by engineers, which speeds up the learning and design process, reducing computational costs. Table 2 summarizes the comparisons of different optimization targets, methods, and time costs in related fields in recent years. Among all the compared methods, our proposed method accomplishes the design task in the shortest CPU operation time.  
However, it is important to note that the neural network model is trained using EM simulation data, which means that the accuracy of the ANN model cannot surpass that of the EM simulation. This may result in the agent missing out on certain reasonable designs or providing designs that do not meet the specifications, which is one limitation of this method. This could potentially be addressed by improving the model and increasing the training dataset. In the majority of cases, using the ANN model as a replacement for the EM simulation is still reasonable.  
Furthermore, the discussions in this paper are limited to exploring fixed topology circuit structures. These circuit structures are reasonable but also restrict the exploration space of the agent. A potential research direction is to use artificial intelligence techniques to explore new topology structures, which may be irregular but potentially more reasonable.
# Seasonal Variations of Carbon Dioxide, Water Vapor and Energy Fluxes in Tropical Indian Mangroves
## 3 Flux Calculations and Corrections  
### Primary Raw Data Processing  
The initial flux processing was done from 1 April 2012 to 31 March 2013 using Eddypro post-processing software (version 5.0, LI-COR, USA). CO\({}_{2}\) and energy fluxes are calculated at a standard averaging interval of 30 min using the fast response measurements from the sonic anemometer (3D wind speed components and sonic temperature) and IRGA (concentrations of CO\({}_{2}\) and H\({}_{2}\)O).  
Figure 2: **(A)** Sundarbans flux tower; **(B)** Canopy walkway; **(C)** CO\({}_{2}\) and H\({}_{2}\)OIRGA (LI-7500A), Methane IRGA (LI-7700) and 3D sonic anemometer (CSAT-3) mounted on the flux tower.  
Every 30 min, raw data is corrected for standard flux corrections for the installed open path system to calculate CO\({}_{2}\) flux (Fc), termed as the mean covariance between deviations in instantaneous vertical wind speed (\(w^{\prime}\)) and density of CO\({}_{2}\) in air (\(c^{\prime}\)) [11].  
\[F_{c}=\overline{w^{\prime}c^{\prime}}\]  
The standard flux corrections include the removal of bad data in fast response measurements by de-spiking, de-trending and correcting for time delays [27]. The tilt corrections for sonic measurements are made by axis rotation such that mean vertical wind (\(w\)) is null [28]. Frequency response corrections are applied to compensate for flux losses at different frequencies (eddy sizes) [29; 30]. The density fluctuations of CO\({}_{2}\) and H\({}_{2}\)O due to temperature (thermal expansion) and water vapor (dilution) were compensated by Webb-Pearman-Leuing (WPL) Corrections [31]. The EC system measures the fastest and smallest eddies accurately when the terrain is flat and homogenous.
## 4 Discussion
In fact, in their follow-up study, they investigated the potential effects of the highest luminance level and found that the highest luminance levels played a key role and that the range of appropriate luminances varied according to the highest luminance levels [42]. More maximum levels of white luminance should be used in future studies during nighttime. To the best of our knowledge, the current study was the first to explore the fitted curve of optimum screen luminance under ambient illuminance using data on subjective ratings of visual comfort, visual fatigue, screen brightness and satisfaction during nighttime. The findings also supported the necessity of including multiple indicators of subjective feelings when determining the optimum screen luminance under different ambient illumination conditions. Some limitations should be noted in the current study. First, the current study only employed subjective ratings of visual comfort and visual fatigue; therefore, multiple objective indicators, including task performance and physiological activity (such as electroencephalogram (EEG) [56] and hemodynamic response [57]; see reviews [9; 58]) that could be involved in LCD screen use are required in future studies. Second, the order of ambient illuminance was fixed and increased from 0 lx to 100 lx, as the speed of light adaption is faster than that of dark adaption in humans. Would the findings be different if the opposite order of ambient illuminance manipulation was employed? The potential influence of the order effect would be an interesting question and deserves more attention in the future. Third, several previous studies have also demonstrated that the preference of screen luminance and ambient illuminance may differ for young and elderly populations [13]. Since the current study explored only young people, further research is required to test whether the current findings on optimal screen luminance levels under different ambient illuminance conditions are age dependent. With the widespread use of smartphones and tablets, future work would be more mobile and pose new challenges regarding the determination of lighting parameters and luminance levels. A recent review showed that the use of smartphones and tablets would cause the same ocular and visual discomfort caused by computers [59]. Hence, whether the current findings could transfer to the use of other types of media with LCD screens (such as smartphones and tablets) remains largely unknown and warrants further investigation. Finally, the obtained results are representative of a very specific environmental condition and are probably not transferrable to other spaces characterized by other luminance distributions; thus, the generalizability of the present findings warrants further testing.
## 3 Materials and Methods
### Image Preprocessing  
Misalignment between the five lenses of RedEdge-MX requires an additional data processing step prior to model training. A visual example of bandwidth misalignment is shown in Figure 3. Simply stacking the five bandwidths along the channel dimension results in counterfactual images such as Figure 3f. To cope with the misalignment and create RGB images visible to the human eye, we applied enhanced correlation coefficient (ECC) transformation [34; 35]. Next, with the help of agricultural experts, we visually examined the RGB images and generated pixel-wise ground truth labels of the cabbage fields, as shown in Figure 3h. We used four different combinations of input wavelengths to train the semantic segmentation models: RGB, RGB with RE, RGB with NIR, and RGB with both RE and NIR. We give comparisons with respect to the input types in Sections 4.3 and 5.2.
## 2 Materials and Methods
### Screening for Antibiotic Resistance, Efflux Pumps and Enterotoxin Genes by PCR
#### 2.3.3 Efflux Pumps Genes  
All isolates were screened for efflux pumps genes _qacA/B_ and _smr_ (_qacC/D_), responsible for conferring resistance to cationic antiseptic agents in _S. aureus_[25]. PCR was performed by mixing 2 \(\mu\)L (40-100 ng) of extracted DNA, 2.5 \(\mu\)L (400 nM) of each primer (Exxtend, Paulinia, Spain) listed in Table 1, 6 \(\mu\)L of dNTP's (200 pM), 0.75 \(\mu\)L (100 nM) of MgCl\({}_{2}\), 5 \(\mu\)L of _Taq_ buffer, 0.2 \(\mu\)L of _Taq_ polymerase, and ultrapure water (MilliQ, Kenilworth, NJ, USA) to complete 50 \(\mu\)L. Amplifications were comprised of initial denaturation at 94 \({}^{\circ}\)C for 2 min, secondary denaturation at 94 \({}^{\circ}\)C for 2 min, annealing at 55 \({}^{\circ}\)C for 1 min, 72 \({}^{\circ}\)C for 1 min, and extension at 72 \({}^{\circ}\)C for 5 min. The amplification products, and a 100 bp marker (Promega, Madrid Spain), were submitted to 2% agarose gel electrophoresis for 30 min/100 V, stained with SYBR Safe (Invitrogen, Waltham, MA, USA), and visualized using the Gel Doc XR\({}^{+}\) system (BioRad, Hercules, CA, USA). The positive controls were _Enterococcus faecalis_ ATCC 29212, for the _qacA/B_ gene, and _S. aureus_ CECT4465, for the _smr_ gene.
###### Abstract  
We analyzed the distribution of chlorophyll-a (Chla) in the Bohai Sea area based on data from the geosynchronous orbit optical satellite Gaofen-4 (GF-4), which was launched in 2015, carrying a panchromatic multispectral sensor (PMS). This is the first time the geosynchronous orbit optical satellite GF-4 remote-sensing data has been used in China to detect the Chla change details in the Bohai Sea. A new GF-4 retrieved model was established based on the relationship between in situ Chla value and the reflectance combination of 2 and 4 bands, with the R\({}^{2}\) of 0.9685 and the total average relative error of 37.42%. Twenty PMS images obtained from 2017 to 2019 were applied to analyze Chla in Bohai sea. The results show that: (1) the new built Chla inversion model PMS-1 for the GF-4 PMS sensor can extract Chla distribution details in the Bohai Sea well. The high Chla content in the Bohai Sea is mainly located in coastal areas, such as the top of Laizhou Bay, Bohai Bay and Liaodong Bay, with the value being around 13 \(\upmu\)g/L. The concentration of Chla in the Bohai Strait and northern Yellow Sea is relatively low with the value being around 5 \(\upmu\)g/L. (2). Taking full advantage of the continuous observation of geostationary orbit satellite, GF-4 with a high-resolution sensor PMS of 50 m can effectively detect short-term change (changes within 10 min) in Chla concentration. The changes mainly appear at the southwest and northeast costal area as well as in the center of Bohai Sea with the change value of around 3 \(\upmu\)g/L. (3) The change of Chla concentration in the Bohai sea is related to the environmental factors such as seawater temperature, salinity, illumination and nutrient salts, as well as the dynamic factors such as wind, flow field and tidal current.  
GF-4; chlorophyll-a; Bohai Sea 2020, 20, 5471; doi:10.3390/s20195471
# Studying Semi-TCP and Its Application in Marine Internet
## 2 Overview of Related Algorithms
### Semi-TCP Congestion Control Algorithm Based on RTS/CTS
#### 2.1.1 Overview of the RTS/CTS Handshake Protocol  
The RTS/CTS handshake protocol is an optional scheme on top of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol [14] for a four-way handshake mode (RTS-CTS-DATA-ACK) [15]. It is proposed to solve the hidden terminal [11] problem, which refers to a node that is outside the coverage of the sending node but in the coverage of the receiving node, as illustrated in Figure 1.  
In Figure 1, node A sends data to node B, and node C also sends data to node D. Node C is a hidden terminal. Without RTS/CTS handshaking, node C is listening to the channel, and sends data to node D when the channel is idle. However, node B is in the receiving range of nodes A and C at the same time, which will cause collision at node B when receiving data. With RTS/CTS handshaking, a node can send data to node B only after it receives a CTS frame from node B, otherwise, it has to back off to avoid the hidden-terminal problem. The Semi-TCP to be discussed below is based on such a handshaking protocol.
## 2 Governing Equations  
The complete set of our flow governing equations can be written as:  
\[\begin{split}&\mathbf{\
abla}\cdot\mathbf{u}=0,\\ &\frac{\partial\mathbf{u}}{\partial t}+\mathbf{\
abla}\cdot(\mathbf{u }\otimes\mathbf{u})+\mathbf{\
abla}p-\mathbf{\
abla}\cdot\Big{(}(\
u+\
u_{t})\Big{(} \mathbf{\
abla}\mathbf{u}+\mathbf{\
abla}\mathbf{u}^{T}\Big{)}\Big{)}=\mathbf{0},\\ &\frac{\partial\tilde{\
u}}{\partial t}+\mathbf{\
abla}\cdot(\mathbf{ u}\tilde{\
u})-\text{P}_{\tilde{\
u}}+\text{D}_{\tilde{\
u}}-\frac{c_{\tilde{ \
u}2}}{\sigma}\mathbf{\
abla}\tilde{\
u}\cdot\mathbf{\
abla}\tilde{\
u}-\frac{1}{ \sigma}\mathbf{\
abla}\cdot\big{(}(\
u+\tilde{\
u})\mathbf{\
abla}\tilde{\
u}\big{)}= 0,\end{split} \tag{1}\]  
where \(\mathbf{u}\) is the velocity vector, \(p=P/\rho\) is the pressure divided by the density, \(d\) is the distance form the nearest wall, and \(\
u\) is the kinematic viscosity. The turbulent viscosity, \(\
u_{t}\), is computed according to the \(\tilde{\
u}\) variable as:  
\[\
u_{t}=f_{\tilde{\
u}1}\tilde{\
u}, \tag{2}\]the following closure functions are introduced to close the \(\tilde{v}\) equation (Equation (1)):  
\[f_{v1} =\frac{\chi^{3}}{\big{(}\chi^{3}+c_{v1}^{3}\big{)}}, f_{v2} =1-\frac{\chi}{(1+\chi f_{v1})}, \tag{3}\] \[g =r+c_{w2}\Big{(}r^{6}-r\Big{)}, f_{w} =g\Bigg{[}\frac{1+c_{w3}^{6}}{s^{6}+c_{w3}^{6}}\Bigg{]}^{\frac{1}{6}},\] \[\tilde{S} =[\Omega+\min(0,S-\Omega)]+\frac{\tilde{v}}{k^{2}d^{2}}f_{v2}, r =\left\{\begin{array}{ll}r_{max}&\frac{\tilde{v}}{5k^{2}d^{2}}<0\\ \min\Big{(}\frac{\tilde{v}}{5k^{2}d^{2}},r_{max}\Big{)}&\frac{\tilde{v}}{5k^{2 }d^{2}}\geq 0\end{array}\right.,\]  
where \(\chi=\tilde{v}/\
u\) is the dimensionless turbulent variable, \(\Omega=\sqrt{2\mathbf{W}:\mathbf{W}}\) is the vorticity tensor module, \(S=\sqrt{2\mathbf{D}:\mathbf{D}}\) is the strain rate tensor module, and \(\tilde{S}\) is a function of both the vorticity magnitude \(\Omega\) and \(\tilde{v}\). Finally, to complete the SA model, the following (standard) closure constants are adopted:  
\[c_{b1} =0.1355, c_{b2} =0.622, c_{v1} =7.1, \tag{4}\] \[\sigma =2/3, c_{w1} =\frac{c_{b1}}{k^{2}}+\frac{(1+c_{b2})}{\sigma},\] (5) \[c_{w2} =0.3, c_{w3} =2, k =0.41. \tag{6}\]
# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 2 Materials
### Labeling for Semantic Segmentation  
A total of 16 PlanetScope images from eight marine oil spill incidents were obtained through a literature review and by referencing relevant articles (Table 2). The Magic Wand Tool in Adobe Photoshop was used to create labels indicating the presence of oil spills. This tool is capable of grouping pixels that exhibit similar spectral characteristics. In Figure 1, row (a) shows the original true color satellite image, where oil spills are not clearly discernible. To improve visibility, brightness was adjusted using gamma correction, and contrast was enhanced through histogram adjustment, making it easier to distinguish oil spills from other pixels. After applying gamma correction and histogram adjustment, as shown in row (b), the visibility of oil spills improved significantly. These enhanced images were then used to create labeled images, with the resulting labels displayed in row (c). Pixels corresponding to oil spills were assigned a value of 1, while background pixels were assigned a value of 0.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite** & **PlanetScope (DOVE)** \\ \hline Orbit Altitude & 450–580 km \\ \hline Field of View & 3.0\({}^{\circ}\) (swath) \\  & 1.0\({}^{\circ}\) (scene length) \\ \hline \multirow{2}{*}{Sensor Type} & Four-band frame imager with VIS + NIR filter (Planet Labs Inc., \\  & San Francisco, CA, USA) \\ \hline \multirow{4}{*}{Spectral bands} & Blue: 455–515 nm \\  & Green: 500–590 nm \\  & Red: 590–670 nm \\  & NIR: 780–860 nm \\ \hline Ground Sample Distance & 3.0–4.1 m \\ \hline Image Capture Capacity & 200 million sq km/day \\ \hline Revisit Time & Daily at nadir \\ \hline Imagery Bit Depth & 12-bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the PlanetScope satellite [22].  
Figure 1: Examples of image processing steps: (**a**) original satellite images, (**b**) images after gamma correction and histogram adjustment, and (**c**) labeled images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Region** & **File Name** & **Width** & **Height** \\ \hline \multirow{3}{*}{11 August 2017} & \multirow{3}{*}{Persian Gulf} & 20170811\_064051\_1001\_3B\_AnalyticMS & 1784 & 3305 \\  & & 20170811\_064052\_1001\_3B\_AnalyticMS & 3139 & 1986 \\  & & 20170811\_064053\_1001\_3B\_AnalyticMS & 1790 & 2621 \\ \hline
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 2 Traditional Adaptive Algorithms  
### NLMS Algorithm  
Considering the application in the SI, we give the assumptions that an input signal vector is \(\mathbf{x}(n)=[x(n),x(n-1),\cdots,\cdots,x(n-L+1)]^{T}\), and a CIR unknown system denotes \(\mathbf{h}(n)=[h_{0}(n),h_{1}(n),\cdots,h_{L-1}(n)]^{T}\), where \(n\) represents the time index and \(L\) denotes the total elements in the discrete CIR system. Thus, the expected signal \(d(n)\) is [1]  
\[d(n)=\mathbf{x}^{T}(n)\mathbf{h}(n)+r(n) \tag{1}\]  
where \(r(n)\) is an additive noise that is an IN. \(\mathbf{\hat{h}}(n)\) is considered as the estimation of the CIR, and then, the estimated error is described as [1]  
\[e(n)=d(n)-\mathbf{x}^{T}(n)\mathbf{\hat{h}}(n). \tag{2}\]  
Within the AF framework of SI, our purpose is to minimize the statistical measure of error signal. Two statistical measures, namely, squared error and fourth error, are employed to construct the cost function of the classical LMS and LMF [1; 6], which are  
\[\begin{array}{l}E[e^{2}(n)]=E[(d(n)-\mathbf{x}^{T}(n)\mathbf{\hat{h}}(n))^{ 2}]\\ E[e^{4}(n)]=E[(d(n)-\mathbf{x}^{T}(n)\mathbf{\hat{h}}(n))^{4}].\end{array} \tag{3}\]Based on the two measurements, many AF algorithms have been derived to implement the SI. For example, the well-known LMS algorithm aims to minimize the squared error to form the cost function (CF) \(J_{\text{LMS}}(n)=\frac{1}{2}E[e^{2}(n)]\). Considering the commonly used stochastic gradient method (SGM), the LMS's updating equation is  
\[\mathbf{\hat{h}}(n+1)=\mathbf{\hat{h}}(n)+\mu_{\text{LMS}}e(n)\mathbf{x}(n) \tag{4}\]  
where \(\mu_{\text{LMS}}\) denotes the overall step-size. Considering the normalized AF theory, the NLMS's updating equation is given by  
\[\mathbf{\hat{h}}(n+1)=\mathbf{\hat{h}}(n)+\frac{\mu_{\text{NLMS}}}{\varepsilon _{\text{NLMS}}+\left\|\mathbf{x}(n)\right\|^{2}}e(n)\mathbf{x}(n) \tag{5}\]  
where \(\varepsilon_{\text{NLMS}}>0\) is to prevent dividing from zero, \(\mu_{\text{NLMS}}\) is a step-size, and \(\left\|\cdot\right\|\) means the Euclidean norm. Then, the LMF is reported by using \(\frac{1}{4}E[e^{4}(n)]\) instead of \(\frac{1}{2}E[e^{2}(n)]\), and its updating equation is  
\[\mathbf{\hat{h}}(n+1)=\mathbf{\hat{h}}(n)+\mu_{\text{LMF}}e^{3}(n)\mathbf{x}( n). \tag{6}\]
## 4 Technical Challenges and Considerations of LLMs
### Countering Malicious Attacks  
LLMs are vulnerable to adversarial attacks [205], such that even small but targeted disruptions in the input data can lead to misinterpretations by the model. Addressing this challenge is essential to ensure the reliability and trustworthiness of LLMs, and achieving stable performance in the face of such threats requires reducing vulnerability to adversarial manipulations. To combat these threats, a range of methods can be used, including input preprocessing and transformation, adversarial training, robust optimization techniques, adversarial sample detection, defensive distillation, model ensembles, adaptive training against adversarial attacks, attack transferability analysis, data augmentation considering attack scenarios, guaranteed robustness, explainable hardening mechanisms, and specific evaluation metrics for adversarial attacks [206]. With input preprocessing and transformation, techniques like noise cleaning or data-based defense transformations are applied to reduce adversarial disturbances. In adversarial training, models are trained with manipulated samples to become resistant to attacks; meanwhile, in robust optimization, training objective functions are modified so that the model performs more stably against disruptions.  
Adversarial sample detection is carried out using methods such as input reconstruction, uncertainty measurement, and anomaly detection. Techniques like defensive distillation and model ensembles, by combining the outputs of multiple models, reduce the impact of adversarial attacks, especially when diverse models are integrated. In adaptive training, adversarial samples are dynamically generated during training to enhance the model's resistance, and attack transferability analysis provides insights for designing more general defenses. In data augmentation considering attack scenarios, data are developed in accordance with threat scenarios, while guaranteed robustness provides formal guarantees to specify the model's performance against certain attacks. Using explainable hardening mechanisms also allows for examining how the model counters attacks; finally, employing specific evaluation metrics for adversarial attacks enables the comparison and assessment of different methods and models [207]. These techniques, rooted in traditional machine learning, require localization and adaptation to the unique features of LLMs, and their development calls for further research to design defenses tailored to the structure and behavior of LLMs.
## 3 Measurement of Thunderstorm Winds  
### Field Measurement  
As shown in Figure 4, a 325-m-high cable-stayed meteorological tower with an equilateral triangle section was utilized [26]. It is located in the north of Beijing between the Third Ring Road and the Fourth Ring Road. The surrounding site can be regarded as class C land form according to the National Load Code of China (GB50009-2012) [27]. The meteorological tower is equipped with multiple mechanical anemometers and ultrasonic anemometers at different heights. Therefore, it was considered the best observation station for exploring the urban boundary layer and urban strong storms in Beijing.  
For the purposes of this paper, high-precision measured data were mainly used, obtained by nine ultrasonic anemometers with a sampling frequency of 10 Hz. The ultrasonic anemometers were tri-axial, and installed at different heights, ranging from 8 m to 20 m, on the north side of the meteorological tower. Thus, three instantaneous wind speed  
Figure 4: Beijing 325-m-high meteorological tower.  
Figure 3: Decomposition of thunderstorm wind speed based on the directional signal decomposition method.  
components, \(V_{x}\)\(V_{y}\) and \(V_{z}\), could be acquired. Their positive directions were defined as south to north, east to west, and directed upwards, respectively. Before further analysis, the statistical data obtained from the ultrasonic anemometers were calculated and compared with those obtained from the mechanical anemometers on the tower, so that the accuracy of the data could be verified.
## 5 Discussion
### Limitations and Way Forward
Future research should focus on evaluating the performance of these thresholds in additional regions and years within the country. Additionally, more efforts are needed to collect more crop phenology data for robust validation and evaluation. This will support our approach of producing more integrated and comprehensive validation results and enhance our study's performance, applicability, and transferability.