esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodological Background: LMI-Based Control and Observer Design
### Observer-Based Feedback Control in the Presence of Noise
\begin{bmatrix}\mathbf{x}_{v}^{T}&\mathbf{e}_{v}^{T}\end{bmatrix}  ^{T} \tag{35}\]  
consisting of system states and estimation errors for each extremal system representation from the polytope (14). The corresponding stochastic differential equations are given by  
\[\mathrm{d}\mathbf{z}_{v}=\boldsymbol{\mathcal{A}}_{v}\cdot\mathbf{z}_{v}\, \mathrm{d}t+\boldsymbol{\mathcal{S}}_{v}\cdot \begin{bmatrix}\mathrm{d} \mathbf{w}_{\mathrm{u}}\\ \mathrm{d}\mathbf{w}_{\mathrm{p}}\\ \mathrm{d}\mathbf{w}_{\mathrm{m}}\end{bmatrix}  \tag{36}\]  
with  
\[\boldsymbol{\mathcal{A}}_{v}= \begin{bmatrix}\mathbf{A}_{v}-\mathbf{B}_{v} \mathbf{K}_{\mathrm{o}}\mathbf{C}&\mathbf{B}_{v}\mathbf{K}_{\mathrm{o}} \mathbf{C}\\ \boldsymbol{\mathcal{A}}_{2,1,v}&\boldsymbol{\mathcal{A}}_{2,2,v}\end{bmatrix} , \tag{37}\]  
where the sub-matrices in the second row result in  
\[\boldsymbol{\mathcal{A}}_{2,1,v}=(\mathbf{A}_{v}-\mathbf{A}_{\mathrm{nom}})-( \mathbf{B}_{v}-\mathbf{B}_{\mathrm{nom}})\mathbf{K}_{\mathrm{o}}\mathbf{C} \tag{38}\]  
and  
\[\boldsymbol{\mathcal{A}}_{2,2,v}=(\mathbf{A}_{\mathrm{nom}}-\mathbf{H}\mathbf{ C}_{\mathrm{m}})-(\mathbf{B}_{\mathrm{nom}}-\mathbf{B}_{v})\mathbf{K}_{ \mathrm{o}}\mathbf{C}. \tag{39}\]  
Similarly, the augmented matrix of noise standard deviations turns into  
\[\boldsymbol{\mathcal{S}}_{v}= \begin{bmatrix}\mathbf{B}_{v}\mathbf{G}_{\mathrm{u },v}&\mathbf{G}_{\mathrm{p},v}&\mathbf{0}\\ \mathbf{B}_{v}\mathbf{G}_{\mathrm{u},v}&\mathbf{G}_{\mathrm{p},v}&-\mathbf{H} \mathbf{G}_{\mathrm{m}}\end{bmatrix} . \tag{40}\]  
Note that for the simplified case of [14] without parameter uncertainty, the sub-matrices in (38) and (39) turn into \(\boldsymbol{\mathcal{A}}_{2,1,v}=\mathbf{0}\) and \(\boldsymbol{\mathcal{A}}_{2,2,v}=\mathbf{A}_{\mathrm{nom}}-\mathbf{H}\mathbf{ C}_{\mathrm{m}}\) with \(\mathbf{A}_{\mathrm{nom}}\equiv\mathbf{A}_{v}\) and \(\mathbf{B}_{\mathrm{nom}}\equiv\mathbf{B}_{v}\). For this case, the separation principle holds and the observer and controller parameterization (hence also their optimization) can be performed independently. The following subsection introduces a cost function \(J\) that can be minimized in an iterative manner by applying state-of-the-art LMI solvers, such as SeDuMi[23] in combination with YALMIP [24]. Thereby, the controller and observer gains, as well as the Lyapunov function candidate, are computed in parallel.
# Research Progress on the Mechanisms and Control Methods of Rockbursts under Water-Rock Interactions
## 4 Consideration of Water's Impact on Rockburst Prediction and Prevention  
The impact of water on rock bursts is a complex issue. On the one hand, water can increase pore water pressure within the rock, thereby reducing its strength and stability, and increasing the risk of rock bursts. On the other hand, water can also slow the propagation of cracks by filling pores and reducing friction, thereby decreasing the risk of rock bursts. Therefore, when considering the impact of water on the development of rock bursts, it is essential to take into account multiple factors and analyze and assess the situation based on specific conditions [71; 72; 73].
# The Design of an Energy Harvesting Wireless Sensor Node for Tracking Pink Iguanas
## 3 Device Design
### Device Packaging and Animal Fixture  
To protect the device against environmental impacts (scratches, bumps, breakages, and pouring water), a PVC (PolyVinyl Chloride) package, shown in Figure 4 has been designed. Besides offering efficient protection to the hardware and to provide an easy and safe attachment on animal skin, the packaging should also have a null impact on sensors and on the energy harvester. For the accelerometer, a tight connection of the board to the package and then to the animal skin is required in order to avoid any interference due to the vibration of the board inside the packaging. Regarding to UV sensor and solar cells the obvious requirement is the transparency in the relevant wavelength ranges.  
The board packaging was molded in two separate parts from a 3 mm thickness transparent PVC. The lower base of the container is concave to facilitate the adhesion to the back of the animal, while the lid is shaped to adapt to the board profile. The two parts are tightly sealed through a thermoforming process. The packaged system has been mechanically tested to simulate stress due to the rubs occurring when iguanas digs their burrows and/or move inside them. It's worth to mention that the material used for the case is transparent furniture grade PVC treated to be UV and high impact resistant. This means that the box does not get brittle or brownish under prolonged sun exposure.  
Special attention has been devoted to the attachment of the node to the animal. Whereas the device could be surgically implanted, we preferred not to consider microsurgery because it may cause animal stress and requires a proper environment to be performed. Other methods based on fastened jackets were not considered as they also proved unsafe to iguanas in long term studies [17]. We instead performed several attachment tests by experimenting gluing a box (Figure 5) and gluing the board protected by a synthetic resin. In a pilot test, we showed that epoxy glue ensures that the device remains attached for at least eleven months. We are aware that shedding skin could in theory cause the detachment of the device. However we preferred to accept such a potential risk rather than increase the risk to harm iguanas by using more invasive methods.
# Improved ECG Watermarking Technique Using Curvelet Transform
## 1 Introduction
ECG signals are a mixture of curved lines and peak values. The curved lines play a very important role in diagnosing disease. The Shearlet transform is also an alternative to choose as an embedding approach. The Shearlet transform can deliver optimally sparse approximations for cartoon-like functions. This transform function was also used to hide the data. Some researchers [14; 15; 16; 17] also utilize the curvelet transform in ECG steganography. They hide the data into ECG by manipulating the curvelet coefficients. [PERSON] et al. [15] represent an ECG steganography technique by using the n x n sequence method and modified the curvelet coefficients by an adaptive technique. A novel technique for ECG steganography using quantization has proposed by [PERSON] et al. [16]. In this technique, the coefficients were selected based on the values equal to zero or close to zero. [PERSON] et al. [17] proposed the ECG-watermarking using a curvelet by evaluating the coefficients and embed binary information into ECG signals with the help of property of quantization and least significant bit (LSB). [PERSON] [18] analyzed the hybrid multi-biometric technique by exploiting the fast discrete curvelet transform. In this study, [18], the authors have investigated a technique of ECG-watermarking (using watermarking with compressive sensing theory) that hides the patient's information into the curvelet domain of ECG. To maintain the diagnosis process and the QRS complex, P and T wave attributes, the curvelet coefficient clustering approach is used. For the demonstration, the MIT-BIH arrhythmia database is used to collect the ECG signals [19]. The performance of the proposed technique has been measured by the PSNR, NC, KL, PRD, SSIM, MSE, and BER parameters to show the imperceptibility of the watermarked ECG signal. The clustering approach in curvelet coefficients provides the invisibility and robustness of embedded watermark as well as provides the good quality of extracted watermark even from image-processing attacks. The performance of the proposed technique considering different sizes of the watermark and different selection of clusters has also been presented.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 4 Analysis and Simulation
### The Simulator  
To build the simulator, we needed to understand how to manipulate the cropped images so they serve as a snapshot from a hovering drone in real time. The cropped size is influenced by the latitude (when altitude increases, more ground is covered) and the camera's field of view (FOV). We set the camera's FOV at 79\({}^{\circ}\). After a map from Google Earth has been selected, the values of the following parameters are used in the simulation: (1) _metersInLegend_--the scale in meters from the Google Earth legend; (2) _legendPixels_--represents the number of actual pixels, which appears in the Google Earth legend; (3) _photoWidth_--the photo's width in pixels; and (4) _photoHeight_--the photo's height in pixels. We uploaded the code to GitHub [6]. Figure 3 presents an example of the simulator screen.  
The following steps must be performed when using the simulator: (1) Obtain a map from Google Earth [25], and (2) insert the drone speed and altitude, and click on the \"Submit\" button. (3) Click on the image; the position clicked on represents the drone's starting position, and the black frame captures what the drone sees in a specific frame. (4) Use the arrows to move the black frame on the map--each movement will crop an image; the difference in the distance (in meters) between each frame is calculated, and the correlation between each frame is calculated; click the \"Enter\" button to stop.  
We then plot the correlation vs. distance on a graph. The graph starts at the (0, 100) point, which means that at the starting location the distance is zero. When moving along the map, the correlation between the images decreases as a function of the distance.
## 4 Experiments  
### Experiment Setup  
**Dataset.** For the AVE localization task, we conduct our experiments on the AVE dataset which is introduced in [6] and consists of 4143 videos, covering 28 event categories (e.g., human activities, instrument playing, vehicle sounds, etc.). All videos are temporally labeled with audio-visual boundaries on the segment level. We follow the default split where the train/validation/test sets are 3339/402/402 clips, respectively. All clips are divided into 10 segments, with each segment lasting for one second. Whether training AV-BT or TACMA, both networks only have access to the training set.  
**Evaluation.** We follow the routing in [7; 10; 11; 13; 19; 50] and consider global segment-wise classification accuracy as the evaluation metric. Specifically, for evaluating TACMA, the predictions made by the classification layer are used for evaluation. To evaluate the audio-visual feature learned by AV-BT, we train another three-layer MLP to directly produce predictions based on the fixed feature without any other late fusion modules. All of our results are reported on the test set.  
**Architecture.** During preprocessing before feature extraction, we sample 160 RGB frames at a resolution of 256 \(\times\) 256 from each 10 s video. Then for each 1 s long segment that contains 16 frames, we randomly pick one frame for each training epoch. For raw audio, we sample it at 16,000 Hz with an equal duration of 10 s for each video. After that, a short-time Fourier transform (STFT) is performed and the signal in the frequency domain is then transformed into Mel-scale filter banks with 64 bins. The resulting spectrogram is 96 \(\times\) 64 for every segment. For the random augmentations applied to the frames, we use the same settings as in [23].  
Considering the size of the AVE dataset is too small to train the feature encoder from scratch, ResNet-50 [53] (removing the final fully connected layer and retaining the 512-dimensional representation) pretrained on ImageNet [14], and VGGish [54] (outputting 128-dimensional representation) pretrained on AudioSet [15], are employed as CNN backbones for extracting spatial features. In AV-BT, the projector has three linear layers, and the output dimensions of each are 2048/4096/4096. For TACMA, the parameter \(\theta\) in Equation (4) is set to 0.03 to achieve the best performance.  
**Optimization.** For training AV-BT, we use the LARS optimizer [55] and train 100 epochs with a batch size of 128. The learning rates of weights and biases in the representation network are the same in [23] except that we multiply them by 2e-4. We reduce the learning rate by a factor of 10 using a cosine decay schedule after using 10 epochs of warm-up period [56]. In Equation (2), \(\lambda\) is set to 5e-3. For training TACMA, we use an initial learning rate of 1e-4 and multiply it by 0.98 after every 10 epochs, and the batch size is 128 using the Adam [57] optimizer.
## 4 Fast Automatic Adjustment Algorithm of Imaging Waveform
### Peak Intensity Linear Iterative Adjustment  
In order to further improve the convergence speed of peak intensity adjustment iteration, we proposed a linear iterative method, which simplified the exposure time and the peak intensity as a linear relationship, as shown in Figure 8. The relationship between the current waveform peak intensity and the ideal peak intensity was established, and the ideal peak intensity was approximated iteratively in the next frame.  
First, we judge whether the waveform peak intensity \(Pi\) of the current frame is in the ideal interval. If it is in the ideal interval, no adjustment is needed, otherwise, the peak intensity adjustment is required. According to the coordinate point (\(T_{i}\), \(P_{i}\)) consisting of the CMOS exposure time, \(T_{i}\), and the peak intensity, \(P_{i}\), a linear relationship is established, and the exposure time, \(T_{i+1}\), required for the next frame is calculated from Equation (8) for waveform adjustment. This cycle is iterated until the peak intensity is adjusted to the ideal interval.  
\[T_{i+1}=\frac{P_{i+1}}{P_{i}}T_{i} \tag{8}\]  
Figure 7: Analysis of centroid shift caused by different peak intensity. (**a**) Centroid difference curve; (**b**) Centroid difference in dynamic peak intervals; (**c**) Centroid difference in static peak intervals.  
In order to approach the ideal peak intensity, \(P_{i+1}\), Equation (8) is replaced by the ideal peak intensity, \(P_{c}\), as shown in Equation (9):  
\[T_{i+1}=\frac{P_{c}}{P_{i}}T_{i} \tag{9}\]  
where \(P_{c}\) is a constant, determined to be 520 by the analysis of the actual imaging waveform. On the basis of the above peak intensity linear iterative approximation method, the peak could be quickly adjusted to an ideal interval, and therefore improve the response performance of the sensor.
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 4 Proposed Indoor Positioning Method
### Double Deep Q-Network
#### 4.3.3 Reward  
Compensation evaluates the action performed by the agent. If the agent moves the prediction point closer to the UE, the behavior is reinforced with a positive reward. Conversely, if the agent moves the prediction point further away, the behavior is weakened with a negative reward. Here, the Euclidean distance between the RSS value of the UE and the fingerprinting database was used to calculate the compensation. The Euclidean distance between the previous state and the current state was calculated. The model's behavior was reinforced with a positive reward when the Euclidean distance decreased and a negative reward when the Euclidean distance increased. The reward \(r\) can be calculated as (5):  
\[r=\sqrt{\left(X_{c}-X_{p}\right)^{2}-\left(Y_{c}-Y_{p}\right)^{2}} \tag{5}\]  
where \(X_{c}\) and \(X_{p}\) represent the x-coordinates of the current and previous states, respectively, and \(Y_{c}\) and \(Y_{p}\) represent the y-coordinates of the current and previous states, respectively.
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 2 Related Work  
### Auto-Encoder Network  
Auto-encoder is an unsupervised neural network model, which consists of two modules: the encoder and decoder. It is widely used for many tasks such as image reconstruction, clustering, and machine translation, dimensionality reduction, document retrieval, classification, and anomaly detection [23; 24; 25]. The auto-encoder aims to learn an efficient representation of the input data by compressing them into a lower-dimensional space and then reconstructing them back to their original form. During the encoding phase, the input data are processed by the encoder, which encodes and compresses them into hidden layer features. These hidden layer features represent the extracted features in the mapping process. Subsequently, the decoder decodes the hidden layer features to reconstruct the original data. By performing non-linear dimensionality reduction, the auto-encoder learns a mapping function to extract implicit features from the input data, effectively reducing noise in the original data. [PERSON] et al. [26] proposed a hyperspectral anomaly detection algorithm based on the auto-encoder, which first represents hyperspectral backgrounds of different complexity with the auto-encoder. Then, the anomalies are detected by calculating the reconstruction error between the original image and the decoded image.
# Nonlinear Nonsingular Fast Terminal Sliding Mode Control Using Deep Deterministic Policy Gradient
## 3 DDPG-NNFTSMC Control Design
### Design of the NNFTSMC  
In this part, we design a new NNFTSMC sliding surface for the manipulator with uncertain dynamic model based on traditional NTSMC, and then solve the reaching law and design the controller based on the sliding surface.  
According to [33], the function \(\text{sig}(\text{x})^{\text{a}}\) is introduced, when \(\text{a}>0\), \(\forall\text{x}\in\text{R}\), \(\text{sig}(\text{x})^{\text{a}}\) monotonically increasing, and always returns a real number.  
\[\text{sig}(\text{x})^{\text{a}}=|\text{x}|^{\text{a}}\text{sgn}(\text{x}) \tag{9}\]  
It can be seen from [5] that the traditional ntsmc sliding surface and control rate are as follows:  
\[\text{s}=\text{x}_{1}+\frac{1}{\beta}\text{x}_{2}\eta \tag{10}\]  
where: \(\beta>0\), \(2>\eta>1\), because the exponential term of \(\text{e}_{2}\) is greater than 0, the singular problem is avoided, but in the region far away from the equilibrium point, the state derivative of the system  
The Lyapunov function is selected as:  
\[\mathrm{V=}\frac{1}{2}s^{2} \tag{14}\]  
Equations (11) and (13) are substituted into Equation (14) and their derivatives are obtained:  
\[\begin{array}{ll}\dot{\mathrm{V}}&=\mathrm{s\,\dot{s}}\\ &=\mathrm{s}\left(\frac{\eta}{\beta}|\mathrm{e}_{2}|^{\eta-1}[-(\Lambda+ \varepsilon)\mathrm{sgn(s)+D}]\right)\\ &=\frac{\eta}{\beta}|\mathrm{e}_{2}|^{\eta-1}[\mathrm{Ds\,-\,(\Lambda+ \varepsilon)|s|}]\end{array} \tag{15}\]  
Due to \(1<\eta<2\), \(0<\eta-1<1\), and \(\beta>0\), when \(\mathrm{e}_{2}\
eq 0\), \(\frac{\eta}{\beta}|\mathrm{e}_{2}|^{\eta-1}>0\) is established. Equation (15) combined with Equation (8) can be obtained as follows: (16)  
\[\begin{array}{ll}\dot{\mathrm{V}}&\leq\frac{\eta}{\beta}|\mathrm{e}_{2}|^{ \eta-1}[\mathrm{D}||\mathrm{s\,-\,(\Lambda+\varepsilon)|s|}]\\ &\leq\frac{\eta}{\beta}|\mathrm{e}_{2}|^{\eta-1}[\mathrm{\Lambda}|\mathrm{s\, -\,(\Lambda+\varepsilon)|s|}]\\ &\leq\frac{\eta}{\beta}|\mathrm{e}_{2}|^{\eta-1}(-\varepsilon|s|)\\ &\leq 0\end{array} \tag{16}\]  
Therefore, when \(\mathrm{e}_{2}\
eq 0\) is applied, the controller satisfies Lyapunov stability condition [34] and has good stability and robustness. The system will arrive at sliding mode surface in finite time. The time for sliding mode surface \(\mathrm{s(0)\,\,\
eq\,\,0}\) to \(\mathrm{s\,=\,\,0}\) is \(\mathrm{t_{r}}\), when \(\mathrm{t\,=\,\,t_{r},\,s=\,\,0}\), that is \(\mathrm{s(t_{r})}=0\), we can get:  
\[\dot{\mathrm{
# Polymer@gold Nanoparticles Prepared via RAFT Polymerization for Opto-Biodetection
## 4 Au NPs in Opto-Biodetection and Beyond
Subsequently, doxorubicin (DOX), which was employed as a model anticancer drug, was linked using cysteine (Cys) and a crosslinker. This latter linkage, hydrazone linkage, is sensitive to pH. First, drug release experiments were carried out in PBS at pH values of 7.4 and 5.3, simulating the healthy and cancerous cellular environments, respectively, and then in cell cultures. These Au NP-PMMA-Cys-DOX nanostructures seem to be very promising platforms for theranostic treatments, combining chemotherapy/radiotherapy and fluorescence imaging. A similar work was performed by the same group but here the authors synthesized by RAFT polymerization, not only MAA but also OEGMA and/or mannose-containing monomer. The incorporation of these carbohydrates allows the guiding of the nanocomposites to the cancer cells, via biorecognition processes, enhancing the target exposure to the drug [156]. The thermoresponsiveness of certain polymers can be combined with the photothermal behavior of Au NPs in phototherapy, eventually combined with chemotherapy. These plasmonic photothermal properties result from light excitation matching the LSPR that leads to local heating, thus can be used to increase the local temperature in tumors, provoking protein denaturation (>42 \({}^{\circ}\)C) and consequently death of the cancer cells. Moreover these metal NPs are less invasive than conventional photoabsorbing dyes because they present a higher absorption cross section, so are more effective using lower excitation energies. In the specific case of Au NRs, the shift of the longitudinal mode to the therapeutic NIR window provides an optimal region to use for application in tissues and aqueous environments, due to minimal absorption. Thus Au NRs and plasmonic nanostructures with a LSPR band in the NIR region (e.g., nanoshells) are excellent candidates for photothermal therapy [157]. For instance, the copolymer poly(ethylene glycol)\({}_{114}\)-_b_-poly(\(N\)-vinylcaprolactone)\({}_{237}\) (PEG-_b_-PNVCL) with a LCST at 39 \({}^{\circ}\)C was synthesized via RAFT polymerization and then grafted onto Au NRs surface. The GNR@PEG-_b_-PNVCL was loaded with rhodamine B, as hydrophilic drug model, and drug release tests were performed by using a NIR laser (\(\lambda=802\) nm). These smart drug delivery systems have shown biocompatibility and are promising nanomedicines for chemotherapeutic and phototherapeutic functions [158]. All these in vivo applications must address human safety issues, biocompatibility and cytotoxicity. There are several works that show that these \"smart\" nanomaterials are promising to be used in biomedicine [155; 158; 159; 160; 161].