esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Caption Generation via Unified Retrieval and Generation-Based Method
## 2 Related Works
### Generation-Based Methods  
Inspired by the success of the encoder-decoder framework used in deep neural machine translation tasks [15; 16], the image captioning tasks adopted the structure to translate the source image into the target language [17; 18; 19]. [PERSON] et al. [20] first applied the framework into the image captioning task, which trained a convolutional neural network(CNN) as the encoder, used these extracted visual features as the input to a recurrent neural network(RNN) decoder, and generated the captions word by word until getting the end token of the sentence. These encoder-decoder-based models [21; 22] gained promising performance.  
Furthermore, the attention mechanism was introduced to the caption generation. [PERSON] et al. [5] used the deterministic soft attention and the stochastic hard attention to help the decoder focus on the most relevant image part when generating different words, and they promoted the sentence generation quality. [PERSON] et al. [23] further improved the attention module by the bottom-up and top-down mechanism. Attention mechanism bridged the gap between the vision domain and the language domain effectively. Thus, this mechanism has been widely adopted in the image captioning tasks [6]. [PERSON] et al. [7] proposed a _Transformer_ model which stacked the attention blocks entirely without convolution and recurrence. This model composes an encoder and a decoder. The encoder comprised a self-attention and a position-wise feed-forward block. The decoder contained a self-attention and a cross-attention layer. [PERSON] et al. [24] adapted their framework from the _Transformer_ architecture to perform image captioning. The difference lies in that they replace the encoder of the _Transformer_ by a CNN model. [PERSON] et al. [25] incorporates the information about the spatial relationships of the R-CNN detected object pairs to the attention block. [PERSON] et al. [26] proposed an \"Attention on Attention\" module to model the relationships of different objects of the image on encoder, and refining for the decoder with self-attention mechanism. [PERSON] et al. [27] propose a unified X-Linear attention block that fully leverage bilinear pooling to selectively capitalize on different visual information. [PERSON] et al. [28] propose a Meshed _Transformer_ with memory for image captioning. The framework uses a mesh-like connectivity at decoding stage to exploit low- and high-level features.  
Different with the above-mentioned methods, we design a novel image captioning model that incorporate the retrieval-based and the generation-based method which contains a visual encoder, a de-noising module and a decoder. The de-noising module does help to filter the retrieved similar sequences conditioned on the visual features. In addition, the decoder incorporates the visual features and the filtered textual features to decode into fluent descriptions.
## 4 Case Studies: Part I: Simulation Analysis  
### Experimental Environment and Data Preparation  
In modern power systems, the widespread integration of distributed sources and loads, as well as power electronic devices, has exacerbated the composite nature of PQDs. To more comprehensively cover the analysis scope of complex PQDs under real-world power grid operating conditions, according to IEEE Standard 1159-2019, a total of 31 types of composite disturbances are generated by superimposing nine basic disturbance mathematical models.  
Figure 5: POD classification framework based on LT and lightweight DenseNet. The red lines represent the LT corresponding to PQDs with waveform distortion, while the dotted lines indicate the LT corresponding to normal signal waveforms.  
Among these, the numbers of double, triple, and quadruple disturbances are 16, 10, and 5, respectively. The configuration parameters for the amplitude variation, duration, and other features of the one-dimensional PQD signal waveforms are shown in Table 1. The nominal system frequency is 50 Hz, with a sampling rate of 12.8 kHz, and each sampling process covers four cycles. For each disturbance type, 1000 samples with uniformly distributed amplitude and phase are generated under different signal-to-noise ratio (SNR) conditions (no noise, 60 dB, 40 dB, and 20 dB), ensuring coverage of most disturbance scenarios. Through the PQD data visualization process in Section 2.2, the samples are converted into two-dimensional feature trajectory images using Lissajous curves, resulting in a total of 41 types of the PQD LT dataset.  
This study constructs the PQD classification model based on the PyTorch 1.12.1 deep learning framework, with detailed experimental environment parameters as shown in Table 3. The LT dataset is divided into training, validation, and testing sets in a ratio of 60%, 20%, and 20%, respectively. During model training, we use the cross-validation method to obtain the optimal deep learning classifier based on classification accuracy and loss performance on the validation set.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 3 Design  
We present ECDedup, employing secret sharing in a decentralized server-aided encrypted deduplication storage system with the following design goals:  
* **Enabling decentralized server-aided encryption:** ECDedup first realizes coding-based secret sharing, which replaces the encryption key with a coding matrix to achieve the decentralized server-aided encryption, where the data chunks can be encoded into the coded chunks to achieve data confidentiality, while each key server only obtains a vector of the coding matrix so that it cannot restore the full matrix (Section 3.2).
* **Preserving deduplication efficiency:** ECDedup designs a content-based coding matrix to generate an identical coding matrix for the identical data chunk, so as to generate an identical coded chunk and preserve the deduplication capability (Section 3.3).
* **Efficient and scalable matrix generation:** We design an element-based matrix generation pipeline to reduce the computation and bandwidth overhead of the matrix generation to improve generation efficiency. We further design a rolling-based matrix generation scheme to enhance the scalability of the matrix (Section 3.4).
# Smart Grid Testing Management Platform (SGTMP) +
## 4 SGTMP Deployment Scenario--SG Component Stress Testing
### Other SGTMP Usage Scenarios  
SGTMP is not only limited to the testing of real/virtualized devices. It can be used as a standalone platform to train and then verify machine learning (ML) models for SG components. One example is the development of an SM enhancement to work with variable energy prices (determined by current energy availability) throughout the day. One application is heating water inside a boiler with hot water storage for use throughout the day, optimizing for maximum instant hot water availability or hot water availability at a given time with the lowest price possible. For houses with PV panels, another variable is whether the excess energy should be sold to the grid or used for other planned household tasks. The SG topology in SGTMP for training consists of a power grid providing current energy prices (to which power from PV panel units can be sold), a single SM, an electric water heater with water storage tank, and an optional PV panel unit. The advantage is that, in this setup, there are no hardware components and it is possible to run faster than real-time, allowing quicker data generation. The generated data can be used to train the ML model either off-line, using the data captured in SGTMP or training can be done during an SGTMP test run.  
Figure 14: Single burst response time of an SMDC in milliseconds as the average of all requests under varying number of simultaneously sent requests. Five trials performed.  
Figure 15: Response time of an SMDC in milliseconds as the average of all requests under increasing number of requests per second. Requests are uniformly distributed. Five trials performed.  
The result of training an ML model using a single SM is shown in Figure 16, which shows how each generation progressed in optimizing the cost function. The model was trained to optimize the cost function, which was the total amount of currency used to heat the water and amount of water above the required minimum for use in other household requirements. Models not meeting the minimum level of water needed at the required time in the day cycle were penalized. The training data was based on taking the prices of electricity in a fixed region over a one-week period with one-hour resolution and cycling this data for the duration of the supervised learning period. Once the learning phase was completed, the resulting model was fixed and placed into a development model of an SM. SGTMP was then used to verify the new SM algorithm for controlling linked smart home appliances--in this case, the water heater based on the state of the energy grid. In the future, we could expand this model to add more input variables to the model and control multiple smart appliances at the same time. This scenario demonstrates how SGTMP can be used for the initial SM prototype development inside an SG, using SGTMP in the first phase to gather data and in the second phase to verify the application of the prototype inside the created SG topology.
# Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
## 3 DL-Based OTFS Channel Estimation
The process of DnCNN-based OTFS channel estimation is shown in Algorithm 2. ```
1:Training process
2:Input: The observation vector \(y\); perception signal \(x\);
3:Output: \(\hat{H}_{DL}\);
4:Initialization: the residuals \(r_{0}=y\), initialize DnCNN weights and bias randomly;
5:for each \(Epoch\in[1,e]\)do
6:for each \(Iterations\in[1,i]\)do
7: Using OMP algorithm to obtain raw channel information \(\hat{H}_{OMP}=(D_{l}^{T}D_{l})^{-1}D_{l}^{T}y\)
8:\(\hat{H}_{OMP}\) is input into the DnCNN network for training;
9: Calculate loss function, and return the gradient;
10: Update network parameters with certain learning rate and optimizer. 11:endfor
12:endfor
13: Save the trained DnCNN with optimal weights and biases. 14:Testing process
15: Load the trained DnCNN;
16: The current estimated channel information \(\hat{H}_{OMP}\) is input to the loaded DnCNN model;
17: The channel estimation result is obtained according to Eq. (7). ```  
**Algorithm 2** DnCNN-Based OTFS Channel Estimation  
After continuous testing, the DnCNN network for UWA OTFS channel estimation is optimized. Table 2 lists the parameters of the DnCNN network for UWA OTFS channel estimation. Figure 2: DnCNN-based OTFS channel estimation. Both the first layer and the middle layers have 32 convolutional kernels, and the last layer has 2 convolutional kernels. All the convolutional kernel sizes in the DnCNN are set to \(3\times 3\). The convolutional kernel size of \(3\times 3\) has good performance, which is the size used in the paper that proposed DnCNN [27]. The setting is validated based on [28]. The same padding is used in the online DnCNN to ensure that the size of the convolution remains constant after convolution. The learning rate is set to 0.001, the batch size is 50, and a total of 12 training rounds are performed. The parameters of the DnCNN are updated using the Root Mean Square propagation (RMSprop), which employs an exponentially weighted moving average to adjust the gradient accumulation, effectively discarding distant historical gradient information [29]. For DnCNN-based channel estimation, \(30,000\) samples are generated under the DD channel. The data samples are divided into a training set, validation set, and test set in the ratio of 2:1:1. We can see that the model-driven DL-based method only takes a small number of data samples for efficiency learning.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Lessons Learned
#### 6.1.5 Self-Healing and Robustness  
While performing the mission in a complex dynamic environment, UAVs might lose connection with their neighbors and could be disconnected from the swarm owing to limited energy, hardware failure, inter-UAV collision, or collision with other obstacles. This problem can be solved effectively by designing a self-healing flocking algorithm [10]. The flocking control algorithm generates the optimal mobility and formation for each UAV in a swarm by interacting with its local neighbors, GUs, and dynamic obstacles with the help of their onboard sensors [20]. Thus, joint mobility control and routing can be an interesting research idea to improve both communication performance and mission performance.
## 4 Definition of System Output and Optimization Goal
### The Structural Compliance
#### 4.3.4 MIMO Case  
This is the most general case. Each individual component \(F_{i}\) of the input vector \(\mathbf{F}\) is assumed to act independently, resulting in \(N\) distinct load cases. On the output side, each corresponding compliance term \(\mathcal{C}_{i}=F_{i}u_{i}\) forms a component of the output vector \(\mathbf{z}\), thereby enabling a fully decoupled multi-input, multi-output (MIMO) representation.
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
## 6 Example Analysis and Results
### Operating Result
#### 6.2.4 Real-Time Analysis Results of DTs  
Under the premise of meeting the accuracy requirements, the calculation time of the two models is compared. The solution time is defined as the computational duration required to calculate deformation values for all nodes of the model using either the random forest (RF) method or the finite element method under a specific working condition. For the finite element method, the solution time does not include the pre-processing time. Table 4 shows the comparison of the average time of a single solution case (calculated as the mean value of 50 repeated simulations). It can be seen that the RF model improves the calculation efficiency by about 98.3% compared with the finite element model.  
At the same time, the Unity Profiler performance analysis tool is used to analyze the resource occupancy during the operation of the DTs. Figure 18 is the analysis result. It can be seen that based on the local test environment, the maximum time for the CPU to process data is 270.4 ms, and the average time is 60 ms, which can meet the real-time requirements of data-driven DTs.
# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
###### Abstract  
Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R\({}^{2}\) value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R\({}^{2}\) from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R\({}^{2}\) value that is 81.39% higher than regression model.  
field measurement; laboratory measurement; multilinear regression analysis; artificial neural networks +
Footnote †: journal: Applied Sciences  
Accepted: 12 December 2022  
Published: 13 December 2022
## 3 Polynomial Fitting Model Based on Least Squares Method  
Make the actual detected value be \(y(z)\), by order polynomial of RPM variable:  
\[y(t)=a_{1}t^{n}+a_{2}t^{n-1}+\cdots+a_{n}t+a_{n+1} \tag{1}\]  
It is assumed that there are currently \(N+1\) detection data with equal time intervals, which are \(y_{k},y_{k+1},\cdots,y_{k+N}\), and the interval is \(T\), assuming that the corresponding time  
Figure 1: Process of missing flight data fitting and correction.  
point of the initial measurement point is \(t_{k}=0\), then the corresponding time point of the \(y_{k+l}\) measurement points is \(t_{k+lT}=lT\), it can be derived from Equation (1).  
\[y(t)=a_{1}t^{n}+a_{2}t^{n-1}+\cdots+a_{n}t+a_{n+1} \tag{2}\]  
\[a_{1}^{\prime}=a_{1}T^{n},a_{2}^{\prime}=a_{2}T^{n-1},a_{3}^{\prime}=a_{3}T^{n -2},\cdots,a_{n}^{\prime}=a_{n}T \tag{3}\]  
We can attain:  
\[\left[\begin{array}{c}y_{k}\\ y_{k+1}\\ \vdots\\ y_{k+n}\end{array}\right]=\left[\begin{array}{ccccc}0&0&\cdots&0&1\\ 1&1&\cdots&1&1\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ N^{n}&N^{n-1}&\cdots&N&1\end{array}\right]\left[\begin{array}{c}a_{1}\\ a_{2}\\ \vdots\\ a_{n+1}\end{array}\right] \tag{4}\]  
Let:  
\[V=\left[\begin{array}{ccccc}0&0&\cdots&0&1\\ 1&1&\cdots&1&1\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ N^{n}&N^{n-1}&\cdots&N\end{array}\right] \tag{5}\]  
\[Y=\left[y_{k},y_{k+1},\cdots,y_{k+N}\right]^{T} \tag{6}\]  
\[X=\left[a_{1}^{\prime},a_{2}^{\prime},a_{3}^{\prime}\cdots,a_{n+1}^{\prime}\right] \tag{7}\]  
Substitute in Equation (4), so we attain:  
\[Y=VK \tag{8}\]  
It can be seen that this is a contradiction equation and solved by the least squares method.  
\[\hat{X}=\left[V^{T}V\right]^{-1}\left[V^{T}Y\right] \tag{9}\]  
And so that:  
\[\hat{Y}=\left[V^{T}V\right]^{-1}\left[V^{T}Y\right] \tag{10}\]  
From the above equations, it can derive the equations for the \(N+1\) points, and the \(n\) order polynomial roughly derives the data estimate \(y_{k+l}^{\ast}=(l=0,1,2,\cdots,N)\) at time \(l\):  
\[y_{k+l}^{\ast}=\left[\begin{array}{ccccc}1&l&l^{2}&\cdots&l^{n}\end{array} \right]\left\{\left[V^{T}V\right]^{-1}\left[V^{T}Y\right]\right\} \tag{11}\]  
When \(l>N\), Equation (11) is changed to estimate the fitted data without the detected amount with the current value data. When \(l=N+1,N+2,N+3\), it is the first, second, and third more successive estimation point. The data point at the point in time. Since the predicted deviation gradually increases away from the current data segment, the estimated deviation is gradually increased, so usually seeking out the most suitable fitting polynomial order through the numerical calculation of the fitting equation of the polynomial and the minimum deviation analysis.
## 1 Introduction  
Landslides are among the most diffuse natural hazards, and each year, they lead to significant human, economic and societal losses [1; 2; 3; 4; 5]. In recent decades, remote sensing techniques have become key tools that can provide qualitative and quantitative information suitable for landslide investigations and monitoring, even in emergency situations [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Ground displacement measurements, for example, are among the most useful data for assessments and characterizations of slope instabilities. Certain remote sensing technologies, including terrestrial Synthetic Aperture Radar(SAR) interferometry (TInSAR), Satellite InSAR and multi-temporal terrestrial laser scanning (TLS), are becoming standard methods to analyse ground deformations [18; 19; 20; 21; 22; 23].  
Recently, Digital Image Correlation (DIC) has been recognized as a powerful instrument to measure landslide displacements because it offers a synoptic overview of slope instability that can be repeated and collected at different time intervals [8] and at various scales (from single slopes to regional scales). Image correlation techniques have been used to measure ground deformation [24], volcanic slope spreading [25], glacier-flow tracking [26; 27; 28] and earthquake-induced displacement [29; 30], and for landslide monitoring [31; 32; 33; 34].  
According to the author of [35], automatic matching and correlation algorithms can theoretically provide a sub-pixel precision of approximately \(1/50\) of a pixel, although issues such as image orientation, co-registration, topographic distortion, instrumental and atmospheric noise, temporal and spatial decorrelations and co-registration errors still represent limitations; thus, higher resolution input data are required.  
Sub-pixel image correlation techniques have been used as complements to satellite SAR interferometry (InSAR) to allow for measurements of 2D in-plane displacement information, thereby completing line of sight (LOS) displacement measurements derived from satellite InSAR [36; 37; 38; 39; 40].  
The aim of this paper is to provide a comprehensive overview of the potential use of the DIC technique in landslide analyses. Therefore, several analyses have been conducted on a single well-constrained landslide for which several image datasets are available; i.e., the landslide that occurred on 3 December 2013 in Montescalioso (Basilicata, southern Italy) [38; 41; 42; 43]. Specifically, LANDSAT 8 OLI-TIRS images, high-resolution airborne optical photos, digital terrain models (DTMs) and COSMO-SkyMed SAR images have been used to derive and validate the landslide displacement field.  
Sensitivity analyses that exploit statistics-based processing approaches have been performed to retrieve information on the potential use of the DIC methodology for landslide investigations.