esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Diagnostic Model and Diagnostic Process  
### Diagnostic Model  
The overall model of this article mainly consists of three parts: an image feature extraction model, an attention model, and a classification model. The image feature extraction module is the ResNet model, and the attention module and classification module together form a complete neural network structure, as shown in Figure 3.  
The ResNet model mainly extracts features from GAF images to obtain features from different types of fault images. The attention module further focuses on the key information in the image, using a multi-head attention mechanism to enhance the image features in the flattened features, and finally inputs them into the classification module composed of a fully connected network for transformer fault classification.  
ResNet has multiple variant structures, including ResNet18, ResNet34, and ResNet50. Considering the relatively small dataset used in this study, to avoid overfitting caused by too many model parameters, ResNet18 (with fewer layers) was chosen as the base model. The structure of the multi-head attention mechanism is shown in Figure 4, with 32 linear transformation parameters, 8 heads, and 256 linear transformation parameters after concatenation. The classification layer adds a ReLU activation layer after a linear transformation with 256 parameters, fully connects with 16 neurons, and finally activates through the Softmax function. The Adam optimizer is used, with an initial learning rate set to 0.001 and a loss function of cross entropy loss.  
Figure 3: The GAF-ResNet-MA diagnostic model.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication
### WDM OWC Receiver with a Single Surface Grating Coupler
#### 3.4.3 Results  
Firstly, the losses of the surface grating coupler were characterized by probing on both sides of the structure with a cleaved SMF. The relation of the SGC loss with the angle of incidence and the optical frequency in C-band is shown in Figure 12a. The measured sample has an increasing loss for increasing frequency in the C-band. The approximate FoV of a  
Figure 11: **Left**: Diagram of the experimental setup used for WDM transmission where the surface grating coupler is used as part of the receiver. **Right**: Photograph of the part of the setup with the lens, the IMOS SGC, and the SMF. Due to high losses, the wireless path is limited to 10 cm.  
surface grating coupler is around 10\({}^{\circ}\), however here we show only four discrete angles due to the limitation in the setup. For the angles between 8\({}^{\circ}\) and 11\({}^{\circ}\) the loss can vary up to 3 dB. The frequencies used in the WDM transmission experienced an 8 dB loss. The addition of the 10 cm wireless path, collimator and lens increased the optical path loss by 3 dB. Figure 12b shows the measured spectra of the modulated WDM signal after splitting by AWGR and amplification by EDFA. The adjecent channel isolation of the AWGR is >30 dB and non-adjacent channel isolation >40 dB. The power of each wavelength was equal at the beginning of the transmission link. However, channels experienced slightly different losses because of the AWGR and the wavelength-dependent loss of the SGC. The peak powers are within 2 dB margin and OSNR levels are 17 dB for all channels. Figure 12c shows BER curves of each channel and back-to-back for comparison. An error-free 25 Gbps NRZ OOK per channel is achievable for power levels above -9.5 dBm, which is 1 dB higher than back-to-back. The eye diagrams of the error-free signals are shown as inset in Figure 12c.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
###### Abstract  
Expanded anchor cables have been widely used in the rapid development of underground engineering. However, there are still some deficiencies in the computation of the ultimate bearing capacity of expanded anchor cables. Based on the upper-bound theorem of limit analysis, the upper-bound solution of the ultimate bearing capacity of an expanded anchor cable was derived. For this calculation, the instability mechanism of the soil at the front surface of the anchorage segment of the expanded anchor cable was assumed to satisfy the logarithmic spiral failure model, its 3D velocity discontinuity surface was generated using the spatial discretization technique, and the optimal solution was determined through the particle swarm optimization (PSO) algorithm. The breakage mechanism of the anchorage side surface was assumed to appear at the interface between the anchorage body and the soil, and its velocity field satisfied the requirements of the associated flow rule. The accuracy of the proposed analytical solution was well-verified through a comparison with three-dimensional numerical simulations, and its superiority was also well-demonstrated in comparison to the existing theoretical calculation method. Subsequently, the influence of the key parameters of the anchor cable on the ultimate lateral resistance, end resistance, and total pullout capacity was discussed. The results showed that: the anchorage segment diameter, anchorage segment length, and buried depth of the expanded anchor cable had a great influence on the ultimate lateral resistance, ultimate end resistance, and total ultimate pullout capacity; however, the anchorage segment length had little influence on the ultimate end resistance, and the inclination angle of the anchor cable had relatively little influence on the resistance.  
expanded anchor cable; ultimate pullout capacity; upper-bound theorem of limit analysis; particle swarm optimization algorithm; numerical simulation +
Footnote †: journal: Applied Sciences
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 6 Simulation--Basic Topologies  
There are many simulation results of the influence of power line topology parameters on PLC systems. The power line topology parameters, e.g., load impedance, direct line length, and branch lengths, are considered and the influence of these parameters on channel transfer function were published in previous studies [31; 51], but the influence of these power line topology parameters on throughput is missing.  
Based on two-port PLC model and computation of the channel capacity, simple experimental topologies are used for an analysis of:* The influence of background noise on channel capacity.
* The influence of direct line length from transmitter to receiver on throughput.
* The influence of number of branches on throughput.
* The influence of branch (tap) length on throughput.
* The effect of branch length, number of branches, terminal load impedances, and time frequency varying behaviour on channel capacity.
## 2 Material and Methods
### Determining Uncertainty and Quality Assurance  
Table 2 presents the stages and procedures for determining the uncertainty and quality assurance of the proposed device. Determining the uncertainty and quality assurance of the proposed device involves assessing the potential variations and errors in its measurements.  
The following considerations are taken into account:  
* The standard method of measurement involves a spectrophotometer with a cuvette and the standard methodology for the determination of chlorophyll content;
* The proposed device is presented in this work as a new development;
* The compared device is a CCM-200 Plus that Trakia University, Bulgaria, owns, and it is used in the areas of ecology and agriculture sciences.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Determining Uncertainty** & **Quality Assurance** \\ \hline Calibration & Compare device readings against known references & Regular calibration and maintenance \\ \hline Reproducibility & Measure multiple samples; calculate variability & Standard operating procedures (SOPs) \\ \hline Inter-laboratory comparisons & Compare device measurements with reference methods & Quality-control samples \\ \hline Error analysis & Identify and quantify potential sources of errors & Documentation and traceability \\ \hline Uncertainty calculation & Combine uncertainties from different sources & User feedback and support \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stages of determining the uncertainty and quality assurance of the proposed device.
## 3 Signal Processing
### Supervised Classification  
For the supervised classification method a so-called long short term memory (LSTM) network was used, which can be found in Figure 5.  
The network was chosen because it is not only suitable for the extraction of stationary features, but also considers the temporal dynamics of tracked objects. As can be seen, each LSTM layer is divided into three different gates: the forget, input, and output gates. The forget gate has the function to erase irrelevant information from the input data. Since LSTM networks are a special type of recurrent neural network, the input data of the network consists of the actual input signal \(x_{k}\) and the previous cell states \(h_{k-1}\). The sigmoid function \(\sigma\) is then used in order to decide which information is to be forgotten. Forgetting the irrelevant information takes place via multiplication with the cell state \(C_{k-1}\) of the previous LSTM layer. In contrast to the forget gate, the input gate subsequently determines which part of the input data should be used to modify the memory of the cell state. The update of the new cell state is then obtained via the addition of the forget gate and the input gate.  
Figure 5: Block diagram of an LSTM network [23; 24].  
Finally, the output gate passes the updated cell state \(C_{k}\) and the output state \(h_{k}\) to the next layer. The algorithm used to train this network was the stochastical gradient descent with momentum (SGDM) method, which can be formulated as follows [25]  
\[\hat{w}(n+1)=\hat{w}(n)-\eta_{1}\
abla\varepsilon(\hat{w}(n))+\gamma(\hat{w}(n)- \hat{w}(n-1)),\ \ 0\leq\gamma\leq 1. \tag{15}\]  
Here \(\hat{w}\) represents the parameter vector which is updated with the gradient of the error function \(\varepsilon\). Moreover, \(\eta_{1}\) is the learning rate and \(\gamma\) determines the influence of how much the previous gradient step is considered in the current iteration. In this method, so-called mini-batches are used in order to train the network. These mini-batches are simply a subset of the entire training data. The use of mini-batches leads to noisy estimations of the optimal parameter vector changes, which determines the training updates to be stochastic. Since the error function \(\varepsilon\) has to be minimized, the noisy estimation of the parameter vector offers the advantage that local minima can be avoided more efficiently in order to reach a global minimum. Furthermore, the momentum term \(\gamma\) reduces oscillations along the path of steepest descent towards the optimum and favors an acceleration of the training, allowing a faster convergence.  
Even though the features are extracted within a black box, they can be classified by a higher level of abstraction, depending on the number of layers. In addition, compared to the unsupervised method, the entire frequency spectrum of \(x(t)\) is considered and not only the feature space in which the notches are located.
# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 2 Materials and Methods
### Evaluation of Model Performance  
In this study, \(\mathrm{GPP_{MOD}}\) was evaluated against \(\mathrm{GPP_{EC}}\). First, according to the climate characteristics of the grassland sites, we merged the 15 grassland sites into three grassland biomes, i.e., temperate, tropical, and alpine grasslands. Second, we evaluated the model performance by comparing the seasonal dynamics of \(\mathrm{GPP_{MOD}}\) and \(\mathrm{GPP_{EC}}\) by using the linear regression model (\(\mathrm{GPP_{EC}}\) = \(a\)\(\times\)\(\mathrm{GPP_{MOD}}\) + \(b\)). During the evaluation, modeled data (\(\mathrm{GPP_{MOD}}\)) are plotted on the x-axis and observation data (\(\mathrm{GPP_{EC}}\)) on the y-axis. This avoids estimating spurious biases in the representation [40]. The coefficient of determination (\(R^{2}\)) was used to evaluate the models' explanatory abilities for variances in GPP. Third, the root mean squared errors (\(RMSE\)) were calculated to quantify the agreement between \(\mathrm{GPP_{EC}}\) and \(\mathrm{GPP_{MOD}}\) during the plant-growing season (\(\mathrm{GPP}\) > 1 g C m\({}^{-2}\) day\({}^{-1}\)).  
\[RMSE=\sqrt{\frac{1}{n}\times\sum_{n=1}^{n}(X_{i}-Y_{i})^{2}} \tag{5}\]  
where \(n\) is the total number of sample points and \(X_{i}\) and \(Y_{i}\) represent the observed and simulated values, respectively.  
We used the bootstrapping method to provide confidence intervals necessary to determine whether these indicators (_slopes_, \(RMSE\), and \(R^{2}\)) are different or not and repeated sampling for 4000 times in the bootstrapping analysis.
## 4 Results and Validation
### Full-Scale Testing
#### 4.2.2 Railway Bridge Monitoring
Distances larger than approximately 20 m between a measuring node and the base station interrupted the permanent wireless Internet connection and continuous remote communication. To effectively establish the wireless connection and remote access on measurement channels 1 and 2, the Wi-Fi router was relocated to the closest position to both spots to verify the functioning of the measuring nodes by a smartphone login.
# Embedded Deep Learning for Ship Detection and Recognition  
[PERSON]  
1 College of Computer and Communication Engineering, China University of Petroleum (UPC), Qingdao 266580, China; [EMAIL_ADDRESS] (H.Z.); hys_upc@163.  
and also the cost of using 4G for transmission of video streams is an important factor to design possible recognition solutions.  
To address these challenges, we propose an embedded deep learning approach called ESDR-DL (Embedded Ship Detection and Recognition using Deep Learning), in order to conduct ship recognition on the fly by connecting the embedded device to a camera directly. In ESDR-DL, we propose a neural network named DCNet (composed with DNet and Cnet as detailed later) which conducts ship recognition as a classification problem by detecting and identifying key parts of a ship (the bow, the cabin, and the stern), and classifies the ship's identity based on these key parts. These classification results are then voted for the decision of the ship's identity. In order to boost performance, ESDR-DL is designed to handle multi-channel video at the same time. We conduct comprehensive evaluations for the embedded system of ESDR-DL, including the performance of accuracy and efficiency.  
The contributions for this paper include:  
* To decrease model parameters, we design a tiny network DNet for ship detection, and share the base convolutional layers with CNet.
* To address challenges of variations of ship license plate locations and text types, we propose a classification network CNet to recognize ships.
* We run the DCNet on embedded devices, which has good scalability and can handle a large number of video streams at the same time.  
The remainder of the paper is organized as follows: Section 2 discusses the related work. Section 4 presents the architecture design of ESDR-DL. Section 3 discusses the implementation and training steps of DCNet. Section 5 presents comprehensive evaluations of the deployed solution. Section 6 concludes the paper.
## 1 Introduction
Here, the PAM4 signals at data rates from 160 Gbit/s to 240 Gbit/s (i.e., 80 Gbaud to 120 Gbaud) were experimentally generated using a silicon microring modulator (SiMRM) [29]. It is also worth mentioning that the PAM4 signal can be generated by other schemes, such as injection-locked vertical-cavity surface-emitting lasers (VCSELs) [30; 31]. As the SiMRM has a 3-dB modulation bandwidth of ~67 GHz, the expected PAM4 data rate is ~134 Gbit/s (i.e., 2 bit/symbol \(\times\) 67 Gbaud). When the data rate is operated at ~200 Gbit/s, the generated PAM4 optical signal suffers from severe ISI. After the utilization of the proposed MZI-based ONN, the result shows that soft-decision (SD) forward-error-correction (FEC) requirement (i.e., bit error rate, BER < 2.4 \(\times\) 10\({}^{-2}\)) can be achieved at 200 Gbit/s transmission, and the proposed ONN has nearly the same performance with the artificial neural network (ANN) implemented using computer software.
## 4 Results and Discussion
### Comparison of Our Proposed Identification System and Similar Existing Studies
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Authors** & **Data Types** & **Study Types** & **Samples** & **Methods** & **Language** & **Characters** & **Writers** & **ACC (\%)** \\ \hline [PERSON] et al. [9] & Online & Text-independent & 1230 & \begin{tabular}{l} Based on \\ Distance \\ \end{tabular} & Kanji & 4 & 41 & 96.5 \\ [PERSON] and Arai [24] & Offline & Text-dependent & 50,000 & \
\begin{tabular}{l} Based on \\ Distance \\ \end{tabular} & Kanji & 100 & 100 & 95.2 \\ [PERSON] et al. [11] & Offline & Text-dependent & 50,000 &
\begin{tabular}{l} Majority \\ Voting \\ \end{tabular} & Kanji & 100 & 100 & 99.0 \\ [PERSON] et al. [17] & Offline & Text-independent & 2965 & CNN & Kanji & 100 & 400 & 93.8 \\ Our proposed & Online & Text-independent & 47,520 & SVM & Kanji & 72 & 33 & 99.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of our proposed system against similar existing studies. obtained an identification accuracy of 99.9% for 200 characters and 100 writers; 92.8% identification accuracy for 50 characters and 100 writers; and 93.8% identification accuracy for 100 characters and 400 writers. Although the authors achieved good identification accuracy, it required writing 200, 50, or 100 characters to identify a person, which is very time-consuming and laborious. As mentioned, the above studies were conducted for writer identification using online or offline handwritten Kanji characters based on only text-independent [9,17] or text-dependent methods [11,24] ([PERSON] et al., 2013; [PERSON] et al., 2014). Moreover, [PERSON] et al. [9] conducted their study on only four online Kanji characters. As shown in Table 7, we used both text-dependent and text-independent writer identification methods in this work. Our experimental results demonstrated that SVM achieved the highest identification accuracy of 99.6% for only the three connected characters text-independent and 99.0% identification accuracy for only the top five characters text-dependent, which are shown in Table 7.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 2 Related Work  
### Prior-Based Image Dehazing Methods  
Prior-based dehazing approaches generally depend on the atmospheric scattering model [1]. [PERSON] et al. [2] proposed the Dark Channel Prior (DCP), which assumes that in haze-free images, at least one color channel in a local region exhibits low intensity. Using this assumption, the dark channel is employed to estimate the haze density, from which the transmission map and atmospheric light are computed to reconstruct a clear image. [PERSON] et al. [3] introduced the Gradient Profile Prior (GPP) to estimate the depth of a scene from a hazy image. They further refined the transmission map using an edge-preserving filtering method and a learning-based image filter applied in several steps. Their final reconstruction model helps reduce common problems like overly bright pixels and unnatural colors in the output image. Similarly, [PERSON] et al. [4] proposed a method based on physical principlesthat separates a hazy image into two parts: reflectance and incoming light. They added regularization terms to help stabilize the solution and used an optimization technique called ADMM to estimate both parts together. To avoid overly bright or unnatural results in areas with heavy haze, they also included a transmission map and a correction term. Likewise, the Saturation Line Prior (SLP) [5] estimates transmission by leveraging a linear correlation between color saturation and the inverse of brightness. While such methods perform well under ideal conditions, their effectiveness diminishes in real-world scenarios where these priors often break down.
# Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation
## 5 Simulation Results
### UAV Group Control  
Next, we consider the control problem for a group of four autonomous UAVs. The formation is configured by offsets (49) and (50) relative to the reference trajectory (70):  
\[\delta_{x}=[0,-25,-25,-50]^{\mathrm{T}},\quad\delta_{y}=[0,25,-25,0]^{\mathrm{T} },\quad\delta_{h}=[0,0,0,0]^{\mathrm{T}}. \tag{71}\]  
The consensus algorithm in form (62) was used to implement the group control. Data exchange between agents was carried out according to the connections graph shown in Figure 1.  
The consensus algorithm parameters are as follows: \(k_{xc}=k_{yc}=k_{bc}=350\), \(\rho_{x}=\rho_{y}=\rho_{h}=250\).  
The following common initial conditions for all UAVs of the group were used: initial speed \(V_{g}(0)=50\) m/s; initial attitudes \(\gamma(0)=0\) rad, \(\psi(0)=\pi/2\) rad.  
Figure 5: Coordinate errors (**a**), estimated mass value (**b**), controller gains (**c**), and thrust force (**d**) for scenario of jumplike UAV mass changing.  
For the first UAV, the initial uncertain mass was \(m=500\) kg, and the coordinates were \(x(0)=50\) m, \(y(0)=0\) m, \(h(0)=75\) m. For the second UAV, the initial parameters were \(m=450\) kg and the initial coordinates \(x(0)=100\) m, \(y(0)=30\) m, \(h(0)=75\) m. For the third UAV, the initial uncertain mass was taken as \(m=400\) kg and the initial coordinates \(x(0)=0\) m, \(y(0)=0\) m, \(h(0)=75\) m. For the fourth UAV, the initial uncertain mass was taken as \(m=350\) kg and the initial coordinates \(x(0)=75\) m, \(y(0)=0\) m, \(h(0)=75\) m.  
The masses of the agents change linearly with the same slope of \(-0.22\) kg/s; in addition, each UAV loses 50 kg of mass once at time instants 100 s, 150 s, 200 s, and 250 s.  
The simulation results are shown in Figures 6-9. Figure 6 shows the errors in coordinates for each UAV in the group, Figure 7 demonstrates the mass estimates, Figure 8 shows the controller gains time histories, and Figure 9 shows the thrust forces time histories.  
Figure 7: Estimated mass values when controlling a group of UAVs of variable mass with initial mass values of 500 kg (**a**), 450 kg (**b**), 400 kg (**c**), and 350 kg (**d**).  
Figure 8: Coefficients of regulators when controlling a group of UAVs of variable mass with initial mass values of 500 kg (**a**), 450 kg (**b**), 400 kg (**c**), and 350 kg (**d**).