esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Object Detection
#### 2.2.1 Convolutional Neural Network (CNN)  
CNN is one of the most widely used image classification methods, object detection, and semantic image segmentation because of its robust feature learning and classification capabilities [16; 17]. Their primary purpose is to reduce the images into a more straightforward process without losing features relevant for the right prediction. In other words, the objective is to extract the high-level features from the input image.  
A typical CNN consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. The convolutional layer contains feature detectors, best known as kernels or filters, followed by a normalization layer and a non-linear activation function to extract feature maps of the input image. These feature detectors swiftly through the input image and filter the critical parts to scan images and categorize them more accurately. These feature maps are then down-sampled by pooling layers, removing redundant features, and reducing their dimensions from their previous layers to improve the statistical efficiency and model generalization. Finally, fully connected layers are applied to classify the extracted features and obtain an input-based probability distribution. After that, the network's output is a fixed-sized vector [18; 19]. Figure 3 shows the classical CNN architecture example.  
In terms of accuracy, the CNN is superior to traditional image classification methods, such as Directed Acyclic Graph-Support Vector Machine (DAG-SVM) [20] or Constrained Linear Discriminant Analysis (CLDA) [21], and are the most developed networks for image classification tasks nowadays [17; 18]. The structure of early CNNs was relatively simple, such as the classic LeNet-5 model [22] (shown in Figure 4), which was mainly used in handwritten character recognition and image classification. However, with the continuous deepening of research and the need for more complex and extended applications, CNN's structure is continuously optimized. For example, the Convolutional Deep Belief Network (CDBN) [23] is an unsupervised generative model resulting from combining a CNN and a Deep Belief Network (DBN). Successful applications in the field of object detection  
Figure 3: Convolutional Neural Network (CNN) architecture with an input, convolutional, subsample, fully connected, and output layers (image under creative commons license).  
(discussed in detail in the following section) were presented in [24] with the intent of performing region feature extraction based on a CNN.
## 5 Results and Discussion  
The experimental tests were carried out using the artificial agent in an environment of size \(100\times 100\) cm\({}^{2}\) for validating the proposed quadrant model. Here, the step size of the movement of the agent was fixed, but it could move in all directions randomly. The different grid patterns of different scales and orientations generated during different walks are shown in Figure 18.The trajectory of the agent is shown using black color and the red color showing the firing locations of a grid neuron. The observed firing locations in the figure constituted the perfectly periodic hexagonal grid pattern as expected.  
Similarly, in the place neuron results, shown in Figure 19, the activation of a place neuron is perfect, and they have shown excellent silence in all over the field except the activation field of the place neuron (no false positive).  
Figure 19: Place selective activation patterns of two individual place cells inside and outside the place field.  
Figure 18: Output grid patterns of the quadrant model: (**a**) grid spacing of 15 units taken with the grid ring radius of 3 units; (**b**) grid spacing of 35 units with the grid ring radius of 10 units; (**c**) spacing of 25 units, radius 3 units and orientation of 30 degrees; (**d**) spacing of 25 units, grid ring radius is of 3 units and orientation of 10 degrees; (**e**) spacing of 55 units, and radius of 10 units; (**f**) Interference of two different grid patterns.  
The experimental detail of different grid patterns shown in Figure 18 are as follows: (a) grid pattern generated corresponding to the 2000 steps of the agent, where the grid spacing is of 15 units and the radius of the grid circle is of 3 units; (b) representing the grid pattern corresponding to the grid spacing of 35 unit, where the radius of the grid circle is of 10 units; (c) spacing of 25 units, radius 3 units and orientation of 30 degrees; (d) spacing of 25, radius 3 unit and orientation of 10 degrees; (e) spacing of 55 and radius of 10 unit, the orientation of 0 degrees; (f) representing the overlapping of firing patterns of two different grid neurons.  
The result for the grid code is shown in Figure 20, where, the activations of 20 different grid neurons are shown using the heat map corresponds to a random location, whose coordinates are mentioned in the figure.  
The performance of the proposed place and grid neuron interaction system is assessed in the task of object recognition and navigation. The results of the proposed model with regard to the different model parameters are given in the below subsections.
## 3 Filter-Level Pruning Based on RL
### The FPRL Algorithm  
The framework of the proposed FPRL method is shown in Figure 1, while the implementation of the filter-level DCNN pruning method is summarized in Algorithm 1, which is composed of two stages: (1) The training of DDPG to offer an optimized layer-wise filter pruning rate and (2) the pruning of a DCNN given the layer-wise filter pruning rate and network retraining for accuracy reconstruction. The main steps in Algorithm 1 are further explained as follows:  
1. Observe the network state. This step obtains the characteristics of the current network layer, the compression rate, and the accuracy of the network as the state information of DDPG.
2. Network pruning. This step prunes less important filters given the filter pruning rate.
3. Fine-tuning. The pruned network is fine-tuned for several epochs to restore accuracy.
4. DDPG update. Use the rewards to update the actor and critic networks in DDPG.  
```
1:Stage 1: Train DDPG agent
2: Initialize DDPG model
3:episodes \(\leftarrow\) 0
4:While episodes \(\leq max\_episodes\)do
5:network \(\leftarrow\) load original network
6:For each layer \(t\) in the networkdo
7: Observe network state \(s_{t}\);
8: Select action \(a_{t}\) according to the current policy in DDPG;
9: Determine the number of filters to be pruned given \(a_{t}\);
10: Sort the filters of layer \(t\) based on Taylor expansion;
11: Prune the less important filters of layer \(t\);
12: Fine-tune network;
13: Calculate reward \(r_{t}\) and new state \(s_{t+1}\);
14: Update actor network and critic network in DDPG;
15:episodes \(\leftarrow\) episodes + 1
16:Stage 2: Prune and retrain network
17:network \(\leftarrow\) load original network
18: Prune network using the optimized pruning rate from well-trained DDPG based on Taylor expansion
19:Retrain pruned network
```  
**Algorithm 1** The FPRL method
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 4 Fundamentals of Smart Polymeric Materials: Focus on Magneto-Active Elastomers and Hydrogels
### Magnetorheological Elastomers (MREs)
#### 4.2.3 Magnetorheological Elastomers as Dynamic Cell Culture Substrata  
Mechanical cues are transformed into intracellular signals in a process known as mechanotransduction. These mechanical cues, such as ECM strain and elasticity have an important influence on different processes, such as cellular proliferation and migration. Altered tissue biomechanics appear to play a role in several diseases and disorders, such as cancer or chronic ulcers.  
Many attempts have been made to replicate the substrate's different mechanical conditions and how it affects cell behaviour. However, most of these attempts involve the fabrication of static substrates with a known stiffness (by mixing different ratios of PDMS and curing agent, for example [127] or the fabrication of substrates with stiffness gradients [128]. Other attempts have been made at the fabrication of dynamic substrates, such as thermoresponsive polymers [129], electro-active monolayers or piezo-controlled substrates [130]. However, these methods are either applied in situ or have a direct effect on cell culture. In this regard, magnetoactive elastomers pose as an interesting candidate for emulating these varying mechanical cues. They allow for remotely tuning the mechanical properties of a material by the application of an external magnetic field, which is almost undetected by the biological tissue or culture due to their low magnetic permeability. In this line of work, [131] developed a cell culture substrate based on ultra-soft PDMS embedded with carbonyl iron powder with tuneable mechanical properties, which can alter cell functions and induce surface translocations. The authors of [115] developed another kind of magnetically controlled substrate for cell culture, based on magnetic micro-pillars embedded in a polymeric matrix for the study of mechanotransduction mechanisms in single cells. In a recent article, [PERSON] et al. [132] discuss the potential application of MREs as smart substrate for epithelial cellular systems.
## 3 System Design
### Functional Architecture Design of the Platform  
In the field of software development, application systems with C/S architectures possess strong logical transaction processing and data processing capabilities, strong interactivity, fast response, and are able to perform complex business processes. Systems with B/S architectures possess strong distribution and sharing, as well as convenient maintenance processes, and are able to realize cross-platform and cross-application data sharing. The software design of the cross-regional operation and maintenance platform for combine harvesters adopted a design mode using a three-tier C/S architecture, which is able to intelligently and comprehensively manage and monitor the long-distance O&M of the combine harvesters. The operation and maintenance platform makes decisions regarding the analysis and output of the data of each harvester module, and develops a complete historical data report (including operating parameters, curve charts, and alarm records). When a problem arises with an online combine harvester, the platform is able to quickly send the fault information to the driver via the app, while at the same time pushing maintenance and service plans to the driver. The platform is able not only to collect real-time information using the harvester's monitoring module, it is also able to query and access data information by means of service platforms and mobile terminal apps for agricultural machinery operators and maintenance personnel, while at the same time, it is also able to push relevant technical parameters to the relevant personnel through text messages and telephones. The functional architecture of the platform for the cross-regional operation and maintenance service for combine harvesters is shown in Figure 4.
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
## 3 Results
### Determination of 2-FAL in a White Wine Sample  
The sample considered was a white wine in a tetra pack container (WW) purchased in a local supermarket, in which a concentration of 2-FAL of 0.1 ppm was found by an HPLC method. The SPR spectra were obtained as reported in the experimental part, using water as the liquid in which the spectra were finally recorded (bulk liquid). This method had to be applied since the exact RI of the white wine is unknown. Figure 7 shows the transmission spectra recorded in water of the considered WW sample, and with some standard additions of 2-FAL.  
Figure 6: (**a**) SPR spectra of the SPR-MIP sensor at different 2-furaldheide (2-FAL) concentrations in water; (**b**) SPR wavelength shift vs. concentration of 2-FAL in a water solution and the calculated continuous curve; (**c**) SPR spectra of the SPR-MIP sensor at different 2-FAL concentrations in wine-mimicking solution; (**d**) SPR wavelength shift vs. concentration of 2-FAL in wine-mimicking solution and the calculated continuous curve. Vertical bars in (**b,d**) correspond to the standard deviations (error bars).  
The variation of \(\lambda_{\rm{ris}}\) from the blank (water) to the WW (2.8 nm) is due to the adsorption of 2-FAL on the MIP during the incubation with the sample. There is only a very slight variation of \(\lambda_{\rm{ris}}\) in response to the standard additions of 2-FAL to the sample. This behavior indicates that the imprinted sites are almost saturated by the 2-FAL originally present in the WW considered. In fact, the concentration calculated from Equation (1), using the parameters evaluated in wine-mimicking solution, is 0.107 mg L\({}^{-1}\), near to the approximate saturation concentration, in acceptable agreement with the value found by the HPLC method (0.1 mg L\({}^{-1}\)).
## 3 Material and Methods
### Method Verification
#### 3.4.1 Automating Eye-Tracking Measurement
In the first step, a video frame is taken, on which the detection of switches is made. Based on the coordinates of the found objects, the switches' mutual position is determined and numbered, starting with the found object lying closest to the upper left corner of the image. Depending on which classifier detected a particular switch, theswitch's state (lever position) is recorded. The subsequent image frame is taken if no switches are found in the image. In the next step, the coordinates of the point at which the gaze was directed are determined. The image from the eye-tracking camera contains this information in the form of a red point. Based on the knowledge of the color and size of the point, its localization is performed. The thresholding and contour detection tools cv.findContours from the OpenCV library were used for this purpose. If the coordinates of the point are contained in any of the areas where switches were found, this is noted in memory. This process is repeated until the end of the recorded material. In this way, an array is created containing frame-by-frame information about the state of each switch and the gaze directed to the switch. These data are then analyzed according to the assumed two scenarios as follows:1. The observed person looked at one or more turnout switches and switched the last one he looked at;
2. The observed person looked at one or more turnout switches, then turned his or her gaze again to the switches he or she had already seen and only then made a switch. In addition, each such event is determined as follows:
1. Whether the number of observed switches was more significant than three;
2. Whether the observed person looked alternately at the mock-up and the switches more than once. Based on a time-lapse analysis of the image from the eye-tracking camera and tests of the subsequent performance, the number of frames of the image where the point of gaze is within the switch was selected. With this number set at three, the optimal performance of the program was obtained, that is, the elimination of situations in which the gaze was accidentally directed briefly to the switch and, simultaneously, the actual observations of the switches were not omitted.
## 4 RMSSA Problem Formulation in Spatial Channel Networks
### Constraints
#### 4.5.4 Spectrum Block Contiguity  
Equation (12) shows that when multiple SBs are assigned to traffic request \(t\), it is necessary to ensure that the indexes of multiple SBs on the same SDM link are contiguous.  
\[\sum_{i=1}^{\zeta_{e}^{t}}\sigma_{e}^{t,l,f}=\zeta_{e}^{t}\;\forall t\in T, \;\forall\theta^{o(t)}\in\Theta^{o(t)},\;\forall e\in\theta,\;\text{if}\; \omega_{p,\rho,\theta}^{t,l,f}=1 \tag{12}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Condition** & **Value** \\ \hline \multirow{8}{*}{\(m_{p,\rho,c\in\theta}^{t}\)} & \(0<q_{\theta,\theta}^{t}\leq 125\) km & \(0\rightarrow\) (DP-32 QAM) \\ \cline{2-3}  & \(125\) km \(<q_{\theta,\theta}^{t}\leq 250\) km & \(1\rightarrow\) (DP-16 QAM) \\ \cline{2-3}  & \(250\) km \(<q_{\theta,\theta}^{t}\leq 500\) km & \(2\rightarrow\) (DP-8 QAM) \\ \cline{2-3}  & \(500\) km \(<q_{\theta,\theta}^{t}\leq 1000\) km & \(3\rightarrow\) (DP-QPSK) \\ \cline{2-3}  & \(1000\) km \(<q_{\theta,\theta}^{t}\leq 2000\) km & \(4\rightarrow\) (QPSK) \\ \cline{2-3}  & \(2000\) km \(<q_{\theta,\theta}^{t}\leq 4000\) km & \(5\rightarrow\) (BPSK) \\ \hline \multirow{2}{*}{\(\sigma_{e}^{t,l}\)} & SL \(l\) on SDM link \(e\) is assigned to \(t\) & \(1\) \\ \cline{2-3}  & Otherwise & \(0\) \\ \hline \multirow{2}{*}{\(\sigma_{e}^{t,l,f}\)} & SB \(f\) on SL \(l\) on SDM link \(e\) is assigned to \(t\) & \(1\) \\ \cline{2-3}  & Otherwise & \(0\) \\ \hline \multirow{4}{*}{\(\sigma_{e}^{t,l,f}\)} & \(\overline{\mu}_{e}^{t,l,f}>\mu_{e}^{t,l,f}\). \(\mu_{e}^{t,l,f}\) is the index of the & \\  & starting SB on SDM link \(e\), \(\overline{\mu}_{e}^{t,l,f}\) is the index & \(1\) \\ \cline{2-3}  & of the ending SB on SDM link \(e\) & \\ \cline{2-3}  & \(\overline{\mu}_{e}^{t,l,f}<\mu_{e}^{t,l,f}\) (\(\overline{\mu}_{e}^{t,l,f}\leq 32\)) & \(0\) \\ \hline \multirow{2}{*}{\(\zeta_{e}^{t}\)} & Number of SBs allocated to \(t\) on SDM link \(e\) & \(\overline{\mu}_{e}^{t,l,f}-\mu_{e}^{t,l,f}+1\) \\ \hline \multirow{2}{*}{\(\zeta_{e}^{t}\)} & Number of SLs allocated to \(t\) on SDM link \(e\). & \(\max\left(\sigma_{e}^{t,l}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 2 Methods  
### The Turbofan Engine RUL Prediction Problem and the C-MAPSS Datasets  
To verify our transfer learning-based algorithm, we selected the Turbofan Engine RUL prediction problem as the benchmark. The corresponding C-MAPSS datasets (Turbofan Engine Degradation Simulation Datasets) are widely used for RUL estimation [33]. These datasets are provided by the NASA Ames Prognostics Data Repository [34], which contains 4 sub-datasets as given in Table 1. Each sub-dataset consists of multiple multivariate time series, which are further divided into training and testing sets. In the training set, the fault grows in magnitude until the system fails. In the testing set, the time series ends some time prior to system failures.  
Every trajectory is an engine's cycle records data. Every cycle record is a snapshot of data taken during a single operational cycle. A single-cycle datum in the C-MAPSS dataset is a 24-dimensional feature vector consisting of 3 operational settings and 21 sensor values. The operating condition settings are altitude, Mach number, and throttle resolver angle respectively, which determine different flight conditions of the aero-engine. In sub-dataset FD001, the engine suffered a failure of a high-pressure compressor with a single operation condition. In sub-dataset FD002, engine suffered a failure of a high-pressure compressor with six operation condition. In sub-dataset FD003, engine suffered a failure of a high-pressure compressor and fan with a single operation condition. In sub-dataset FD004, engine suffered a failure of a high-pressure compressor and fan with six operation condition.  
Figure 1 illustrates the standardized operational setting values in every sub-dataset. The operational setting 3 values are stable in FD001 with a single operation condition, just as in FD003. The change and distribution of operational setting values are different between a single operation condition and six operation conditions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **FD001** & **FD002** & **FD003** & **FD004** \\ \hline Dataset & FD001 & FD002 & FD003 & FD004 \\ Train trajectories & 100 & 260 & 100 & 249 \\ Test trajectories & 100 & 259 & 100 & 248 \\ Maximum life span (Cycles) & 362 & 378 & 525 & 543 \\ Average life span (Cycles) & 206 & 206 & 247 & 245 \\ Minimum life span (Cycles) & 128 & 128 & 145 & 128 \\ \hline Operating Conditions & 1 & 6 & 1 & 6 \\ Fault conditions & 1 & 1 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: C-MAPSS Datasets.
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 2 System Model
### Problem Formulation  
This paper aims to enable LEO satellites to transmit as much data as possible within a limited time window. Thus, we investigate a sum-rate maximization problem by jointly optimizing the precoding matrix at the LEO satellite and reflect beamforming at the IRS, which can be given as:  
\[\begin{array}{c}\mathrm{P1:}\underset{w,\mathbf{\phi}}{\max}\sum\limits_{k=1}^ {K}R_{k}\\ \mathrm{s.t.}\ \ \theta_{n}\in[0,2\pi],\ \forall n\in\{1,2,\cdots,N\},\\ \sum\limits_{k=1}^{K}\|w_{k}\|^{2}\leq P_{L}.\end{array} \tag{9}\]  
where the constraints of (9) are the phase shift of IRS and the maximum transmit power of the LEO satellite, respectively. Due to the objective function (9) is nonconvex [14], and the traditional optimization algorithms usually require many iterations and are not suitable for high-speed scenarios. To solve the problem with low complexity, we propose an RBF-GAT to establish a direct mapping from the received pilots and network topology to the satellite and IRS beamforming.
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 4 Experimental Setup
### Network Architecture  
U-Net is chosen as the network in this paper, which has been widely adopted for the speech separation task [35]. As shown in Figure 1, the network consists of the convolutional encoder and decoder, both of which are comprised of five convolutional blocks where the 2-D convolution layer is adopted, followed by batch normalization (BN) [36] and exponential linear unit (ELU) [37]. Within each convolutional block, the kernel size is set to \((2,3)\) along the temporal and frequency axis. Skip connections are introduced to compensate for the information loss during the features compression process. Note that the mapping target is the gain function and the sigmoid function is adopted to make sure that the output ranges from 0 to 1. A causal mechanism is used to achieve real-time processing, where only the past frames are involved in the convolution calculation. The tensor output size of each layer is given with \((Channels,TimeStep,Feat)\) format, which is shown in Figure 1. A more detailed description of the network can refer to Table 1. The total number of trainable parameters of the network is 0.59 M.  
Figure 1: The network architecture adopted in this study. Input is the noisy magnitude spectra and output is the estimated gain functions. T refers to the timestep length of the utterances within a minibatch.