esa-sceva/satcom-chunk-collection huggingface dataset:
# Technical Performance of an Inflatable Solar Dryer for Drying Amaranth Leaves in Kenya
## 2 Materials and Methods
### Computational Evaluation of Airflow inside the ISD  
The air velocity and the airflow distribution above the trays in the ISD could not be measured during the drying experiments. Frequent opening of the ISD for repeated measurements would greatly affect the drying performance [22]. Therefore, a numerical analysis using computational fluid dynamics (CFD) was applied to estimate and visualize the spatial air velocity distribution in the ISD, as suggested by [PERSON] et al. [8]. CFD simulation and 3D design of ISD were performed by software Solidworks (Solidworks 2017, Dassault Systemes, Velizy Villacoublay, France). The air flow was governed according to Navier-Stokes equations, with regard to mass, momentum, and energy conservation laws [23]. Geometry of the ISD, along with the air deflector, the trays, and the stands, was made according to the shape and size of the dryer during drying experiments. Three-dimensional body of the ISD was assumed to be an adiabatic wall. Cavities without flow conditions were excluded. Fluid volume inside ISD was 22.1 m\({}^{3}\). Automatic mesh generation was carried out, and total number of the cells was 67,671. As for the boundary conditions, environmental pressure of 101.3 kPa in the outlet and air mass flow of 0.75 kg/s in the inlet were set. The flow was assumed to be fully developed at steady-state condition, and the calculation of the flow was time-independent. The calculation  
Figure 2: Placement of trays on wooden frames inside the ISD.  
converged after 343 iterations. Air temperature at the inlet was considered to be similar to the average ambient temperature, which was set to 30 \({}^{\circ}\)C during the day. Because heat transfer and temperature distribution was excluded in the simulation, temperature rise along the ISD tunnel was neglected. The wire mesh bottom of the trays and the layer of drying material resting on it was assumed as a porous medium with a porosity of 0.6 [24]. The resulting resolution was set at level 8, which is the highest level.
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 1 Introduction
The authors of [22] proposed the co-estimation of state-of-charge (\(SOC\)) and state-of-health for Li-Ion batteries considering fractional-order calculus. In [23], a model for predicting the battery's remaining useful life was presented using the Particle Filtering framework. The authors of [24] studied a non-linear fractional-order estimator for \(SOC\) indication, providing a guarantee for stability and robustness of the error system under certain assumptions. Few works have proposed the operation of DC microgrids considering the introduction of EVs. In previous work, [25], the non-linear control of a DC microgrid considering EV charging was proposed. However, the presented model of the battery EV was simple. Thus, the aim of this paper is to assess the impact of accurate modeling of the EV's battery in a non-linear control model of a DC microgrid.
# An Improved YOLO Model for UAV Fuzzy Small Target Image Detection
## 3 Improvement of YOLOv5s Algorithm
### Transposed Convolution  
Transposed convolution is also a type of convolution operation. A large majority of transpose convolutions are used to achieve upsampling. Transposed convolution is an operation that maintains the correspondence between matrix elements, rather than being the inverse operation of convolution. Because it is upsampling, it is generally a one-to-many relationship. This fact implies that an element of the input matrix corresponds to multiple elements at the corresponding position in the output matrix. Transposed convolution can automatically learn parameters, enabling the network to learn the optimal upsampling method through training. In terms of form, transposed convolution is equivalent to backpropagation of the gradient computation of a convolutional layer. Here, unlike normal convolution, the weight matrix is first transposed and then left-multiplied with the input. The convolution process is illustrated in Figure 7.  
Assuming that the convolution kernel is \(C\) and that the input is a column vector n, the convolution and transposed convolution processes can be represented as follows:  
\[\begin{array}{l}CX=Y\\ X=C^{T}Y\end{array} \tag{9}\]  
In transposed convolution, although the receptive field of the points on the convolutional kernel remains the same, the convolution layer added after transposed convolution would expand the receptive field range in the network.  
In the YOLOv5s architecture, we replace the nearest-neighbor interpolation in the original network with transposed convolution to expand the receptive field [29] range in the neck structure. Doing so enables the network to capture more global information during the upsampling process, thereby reducing the loss of information and preventing problems such as low image resolution and detail loss at the end of the neck. The network architecture after the addition of the transposed convolution module to the YOLOv5s model is shown in Figure 8.  
Figure 7: Convolution process and transposed convolution process.
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 3 Scenario and Problem Statement  
### Scenario  
Let us consider a smart apartment which is referred as SensiHome. SensiHome can include controllable lightning fixtures, infra-red sensors for movement detection, temperature sensors, ambient light sensors, a number of cameras to monitor the entrance and other locations, and controllable window shades. SensiHome includes a local home controller, which binds the functions of the sensors and actuators together to form a functional system. The sensors and actuators use the Constrained Application Protocol (CoAP) to communicate with the local controller and/or other network entities. Let us assume that SensiHome is a partner with a mobile operator called FutureTel. The operator connects all controllers to SensiHome's network through FutureTel's network so that SensiHome could allow its customers to control their smart apartments using their mobile phones and a FutureTel subscription. In the following,we discuss the potential challenges of this scenario and what is the potential of our solution in tackling of these challenges.
## 2 Materials and Method
### Method
#### 2.2.3 Rotation Speed Calculation Method
\begin{bmatrix}\frac{2}{n}&0&0\\ 0&\frac{2}{n}&0\\ 0&0&\frac{1}{n}\end{bmatrix}\begin{bmatrix}\sum\limits_{k=0}^{n-1}x(k)\cos(k \delta)\\ \sum\limits_{k=0}^{n-1}x(k)\sin(k\delta)\\ \sum\limits_{k=0}^{n-1}x(k)\end{bmatrix}  \tag{5}\]where \(n\) is the total amount of fitted data. After obtaining \(a\) and \(b\), the amplitude of the sinusoidal signal \(A\) and the initial phase \(\varphi_{0}\) can be calculated using the following formula, and \(atan2(a\),\(b)\) is a four-quadrant inverse tangent. \[\left\{ \begin{array}{l}A=\sqrt{a^{2}+b^{2}}\\ \varphi_{0}=atan2(a,b)\end{array} \right. \tag{6}\]  
[PERSON] et al. [38] proposed a phase difference correction method to provide a theoretical basis for this algorithm. The main unknown here is the length of the fitting period \(n\), which is determined by the following method: an FFT operation is performed on the sampled signal within \(t=1\) s, and the main frequency \(f_{\mathrm{FFT}}\) is calculated. Then, according to the relationship between period length \(n\), total sampling number \(m\), and main frequency \(f_{\mathrm{FFT}}\), the estimated fitting period length is expressed as follows:  
\[n=\left[\frac{f_{s}}{t\cdot f_{FFT}}\right] \tag{7}\]  
where [] is an integer symbol. Through the above fitting, the initial phase and amplitude information of a single period can be obtained, and then the phase information of each point on the sampled signal can be obtained via piecewise fitting. The fitted phase is constrained between \((-\pi,+\pi)\) and is called the wrapped phase. However, it is necessary to unwrap the phase to calculate the phase difference, so the wrapping phase needs to be unwrapped. The Process of Calculating the Main Frequency Using the Phase Difference  
The specific steps for calculating frequency using the phase difference method are as follows:  
Step 1: The calculation of continuous phase. Through the wrapping phase, the solution formula is expressed as follows:  
\[\left\{ \begin{array}{l}\theta_{0}=\varphi_{0}\\ \theta_{k}=\theta_{k-1}+W_{k}\end{array} \right. \tag{8}\]  
where \(W_{k}\) is the unwrap operator, defined as follows:  
\[W_{i}=\left\{\begin{array}{l}\varphi_{k}-\varphi_{k-1}-2\pi\ \ \ \ \ \ \ \ \ \ \,\ \
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Ihs  
A color image can be represented by a three-primary red-green-blue (RGB) color system. However, this system does not conform to the human understanding of color. Another method of understanding color utilizes hue H, saturation S and intensity I. The hue H is determined by the dominant wavelength of the spectrum, saturation S characterizes the portion of the dominant wavelength of the spectrum and intensity I represents the brightness of the spectrum. In RGB space, three-spectral coordinate (R, G and B) coupling is strong, and changes in any component of the spectral information will alter the entire spectrum. As a result, image processing in RGB space is difficult. On the other hand, in IHS space, main spectral information is reflected in hue and saturation, while changes in intensity have a very limited effect on the spectral information, and are easy to process. The main objective in processing high resolution and multispectral images is to add details of high-resolution images while retaining spectral information. Consequently, it is easier to conduct image fusion in IHS space. Examples of transformation from RGB values to IHS values are shown in Equations (1)-(3). In the medical field, IHS is useful for the fusion of Pseudocolor image processing and fusion. Pseudocolor image converts the grayscale values to the RGB values. Its aim is to better present the details on the medical image in order to obtain a clear visualization of the images. A good example of such an image is the Pseudocolor-PET images, which images are widely used for liver lesion detection [31,32]. The fusion of Pseudocolor-PET and MRI medical image, which utilize fusion methods, such as IHS-PCA [31], IHS-wavelet transformation-based method [33,34] and IHS-salient features extraction [35], has shown to be useful for both human visualization and the objective evaluation of lesions.  
\[\left[\begin{array}{c}I\\ \
u_{1}\\ \
u_{2}\end{array}\right]=\left[\begin{array}{ccc}1/3&1/3&1/3\\ -\sqrt{2}/6&-\sqrt{2}/6&2\sqrt{2}/6\\ 1/\sqrt{2}&-1/\sqrt{2}&0\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right], \tag{1}\]  
\[\mathrm{H}=tan^{-1}\left(\frac{\
u_{2}}{\
u_{1}}\right), \tag{2}\]  
\[\mathrm{S}=\sqrt{{\
u_{1}}^{2}+{\
u_{2}}^{2}}, \tag{3}\]  
here, \(\
u_{1}\) and \(\
u_{2}\) are the translation values.
## 3 Proposed PAPMCC Algorithm  
The PAP can provide amazing convergence performance in Gaussian noise environments, but the performance will degrade under the impulsive noise environments. To take full use of the sparse characteristics of the CIRs, a robust PAP algorithm is realized by combining the proportionate idea with the basic AP and MCC together to construct the PAPMCC algorithm. As a result, the proposed PAPMCC algorithm solves the minimization problem given by  
\[\left\|\hat{\mathbf{w}}(m+1)-\hat{\mathbf{w}}(m)\right\|_{\mathbf{G}^{-1}(m)} ^{2}\text{ subject to }\widetilde{\mathbf{e}}(m)=\left[\mathbf{1}_{M}-\xi \exp\left(-  
Solving Equations (16) and (17), the Lagrange multiplier vector is given by  
\[\mathbf{\lambda}^{T}=2\xi_{\mathrm{g}}^{\mathrm{z}}[\mathbf{\mathrm{X}}^{T}(m)\mathbf{ \mathrm{G}}(m)\mathbf{\mathrm{X}}(m)]^{-1}\exp\left(-\frac{\mathbf{\mathrm{e}}(m)\odot \mathbf{\mathrm{e}}(m)}{2\sigma^{2}}\right)\odot\mathbf{\mathrm{e}}(m). \tag{18}\]  
Substituting Equation (18) into Equation (16), the iteration of the PAPMCC is expressed as  
\[\mathbf{\hat{w}}(m+1)=\mathbf{\hat{w}}(m)+\xi\mathbf{\mathrm{G}}(m)\mathbf{\mathrm{X}}(m)[\bm {\mathrm{X}}^{T}(m)\mathbf{\mathrm{G}}(m)\mathbf{\mathrm{X}}(m)]^{-1}\exp\left(-\frac{ \mathbf{\mathrm{e}}(m)\odot\mathbf{\mathrm{e}}(m)}{2\sigma^{2}}\right)\odot\mathbf{ \mathrm{e}}(m). \tag{19}\]  
In practice, Equation (19) can be corrected to  
\[\mathbf{\hat{w}}(m+1)=\mathbf{\hat{w}}(m)+\mu\mathbf{\mathrm{G}}(m)\mathbf{\mathrm{X}}(m)[\bm {\mathrm{X}}^{T}(m)\mathbf{\mathrm{G}}(m)\mathbf{\mathrm{X}}(m)+\mathbf{\mathrm{I}}_{M} \delta_{\mathrm{PAPMCC}}]^{-1}\exp\left(-\frac{\mathbf{\mathrm{e}}(m)\odot\mathbf{ \mathrm{e}}(m)}{2\sigma^{2}}\right)\odot\mathbf{\mathrm{e}}(m), \tag{20}\]  
where \(\mu=\xi\) acts as the step size, \(\delta_{\mathrm{PAPMCC}}\) denotes the regularization factor, and \(\mathbf{\mathrm{G}}(m)\) is the weight assignment matrix that is defined in Equations (10)-(12).  
The computation complexity of the devised PAPMCC algorithm is compared with the AP, ZA-AP, RZA-AP and PAP algorithms with respect to the total number of additions, multiplications, and divisions in each iteration. The comparison is presented in Table 1. It is clear to see that the computational complexity of the proposed PAPMCC algorithm is comparable to that of the PAP algorithm.
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 2 Waveform Models for SC-FDMA, FBMC, and LP-FBMC
### Fbmc  
Figure 2 shows the structure of a conventional FBMC transmitter for the \(k\)-th uplink user's, where \(R\{\cdot\}\) and \(I\{\cdot\}\) are the real and imaginary parts of the input, and \(h(t)\) represents the impulse response of a prototype filter [20]. The \(m\)-th complex data symbol transmitted to the \(n\)-th subcarrier of the \(k\)-th user denoted by \(x_{n,m}^{(k)}\) is expressed, as follows:  
\[x_{n,m}^{(k)}=a_{n,m}^{(k)}+jb_{n,m}^{(k)},0\leq n\leq N-1,\,0\leq m\leq M-1, \tag{2}\]  
where \(a_{n,m}^{(k)}\) and \(b_{n,m}^{(k)}\) are the real and imaginary parts of the \(x_{n,m}^{(k)}\). Subsequently, the FBMC signal of \(k\)-th user \(s^{(k)}(t)\) is expressed, as follows [8]:  
\[s^{(k)}(t)=\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}\left\{\begin{array}{ll}a_{n,m}^ {(k)}h(t-mT)&+jb_{n,m}^{(k)}h(t-mT-\frac{T}{2})\\ 0&\text{elsewhere}\end{array}\right.\Big{\}}e^{j\frac{2\pi n(n)}{T}t}e^{jn \frac{\pi}{2}}, \tag{3}\]  
Figure 1: Structure of the \(k\)-th user’s single carrier frequency division multiple access (SC-FDMA) transmitter.  
where \(\alpha(n)\) is expressed in the same way as in SC-FDMA.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 6 Conclusions  
This paper proposes a sparse trajectory restoration method for vessels based on feature correlation. The method demonstrates significant effectiveness and advantages in the trajectory repair task. Through comparative experiments, this study compares the restoration effect of the baseline network with different Seq2 Seq structure models and further explores the influence of different modules on the restoration effect and the sensitivity of different step sizes on the restoration effect in depth through ablation experiments.  
In the current trajectory restoration methods, anomalous data are often simply eliminated, ignoring their effects on trajectory integrity. Most existing models seldom consider the correlation between trajectory features in trajectory restoration, and inputting redundant data increases the computational complexity of the model. The method used in this study can effectively detect abnormal data and accurately mark the missing locations so that the interpolation method is used to preliminarily repair the trajectory to ensure the overall trend of the trajectory in the preliminary restoration method. Then, a composite indicator of features correlation is constructed to screen high correlation features as model inputs, and a Seq2 Seq structure model is constructed for the vessel trajectory restoration model, which enables the model to carry out vessel trajectory restoration more efficiently and accurately.  
It is worth noting that this study found that the MAE and RMSE evaluation metrics for longitude are skewed larger than those for latitude. This difference may be attributed to the characteristics of the data itself, and the latitude data may have more nonlinear relationships or noise which increases the difficulty of model restoration. This study did not validate the robustness of the model in a more complex navigational environment. Therefore, our next research paper will focus on validating the model in more diverse and complex navigation environments to comprehensively assess the performance and robustness of the model. This will provide a deeper understanding and is expected to promote the further development of vessel trajectory repair technology in practical applications.  
Conceptualization, L.Y., X.C. and H.L.; methodology, L.Y. and X.C.; validation, J.L.; investigation, L.Y.; resources, C.L. and Y.Z.; data curation, Y.Z.; writing--original draft preparation, L.Y.; writing--review and editing, X.C. and H.L.; visualization, R.Z.; supervision, X.C. and H.L.; project administration, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (42371438).  
Not applicable.  
The data supporting the findings of this study can be found at [[https://www.marinecadastre.gov/ais/](https://www.marinecadastre.gov/ais/)]([https://www.marinecadastre.gov/ais/](https://www.marinecadastre.gov/ais/)) (accessed on 18 January 2024).  
The authors declare no conflicts of interest.
## 3 Numerical Analysis Calculation Mode
### Boundary and Initial Conditions  
The model had a free boundary on the top surface and a fixed boundary on the bottom surface, with no displacement in any direction. The four sides of the model had no horizontal displacement but allowed for vertical displacement due to the constraints of the surrounding soil. All sides of the hexahedron model were considered permeable boundaries.  
The groundwater level was assumed to be flush with the ground, and the static pore water pressure at the ground node was set to 0, which increased linearly along the depth with a gradient of 10 kPa. The initial stress state was set as the gravity field. The density of each group was assigned, and the gravity acceleration was set to calculate the initial stress distribution for each group. After the calculation reached equilibrium, the deformation and the rate of the entire model node were assigned a value of 0.  
A coordinate system was established for pore water pressure and sand drain height, with the center of the sand drain bottom as the origin, as shown in Figure 4.  
In the fluid-solid coupling numerical analysis, the pore water pressure at the top node of the sand drain was assumed to be \(-80\) kPa, as the vacuum degree under the membrane in the field exceeded 80 kPa within 8 h. The influence range of vacuum action was set at the bottom of the sand drain, where there was hydrostatic pressure but no negative excess pore pressure caused by vacuum, i.e., 0 kPa. Therefore, four models of pore water pressure distribution were assumed along the axial direction of the sand drain, as shown in Figure 3:  
1. \"Concave\" parabolic distribution: According to the standard parabolic equation (\(x^{2}=-2 py(p>0)\)) and coordinate points (18, \(-80\)), \(p=2.025\), so \(y=-x^{2}/4.05\). Because the distribution was concave relative to the vertical axis of the sand well, it is referred to as a \"concave\" parabola.
2. \"Convex\" parabolic distribution: \(y=\left(x-18\right)^{2}/4.05-80\) was calculated according to the translation transformation of the parabolic equation obtained in (1). Because the distribution was convex relative to the vertical axis of the sand drain, it is referred to as a \"convex\" parabola.
3. Linear distribution: The linear equation across the origin was \(y=-40x/9\).
4. Uniform distribution: The equation was \(y=-80\).  
Figure 3: Computational model.  
The subcycle command flow was compiled to realize the application of a vacuum load under the above four working conditions. The unbalanced force ratio was set to \(10^{-4}\). The master-slave program method was adopted to solve the problem. The number of mechanical substeps was subordinate to the number of seepage substeps, and the seepage time was set as 4.8384 \(\times\) 10\({}^{6}\) s.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
###### Abstract  
In the context of dam deformation monitoring, the prediction task is essentially a time series prediction problem that involves non-stationarity and complex influencing factors. To enhance the accuracy of predictions and address the challenges posed by high randomness and parameter selection in LSTM models, a novel approach called sparrow search algorithm-long short-term memory (SSA-LSTM) has been proposed for predicting the deformation of concrete dams. SSA-LSTM combines the SSA optimization algorithm with LSTM to automatically optimize the model's parameters, thereby enhancing the prediction performance. Firstly, a concrete dam was used as an example to preprocess the historical monitoring data by cleaning, normalizing, and denoising, and due to the specificity of the data structure, multi-level denoising of abnormal data was performed. Second, some of the data were used to train the model, and the hyperparameters of the long and short-term memory neural network model (LSTM) were optimized by the SSA algorithm to better match the input data with the network structure. Finally, high-precision prediction of concrete dam deformation was carried out. The proposed model in this study significantly improves the prediction accuracy in dam deformation forecasting and demonstrates effectiveness in long-term time series deformation prediction. The model provides a reliable and efficient approach for evaluating the long-term stability of dam structures, offering valuable insights for engineering practices and decision-making.  
dam deformation prediction; sparrow search algorithm (SSA); long short-term memory (LSTM); denoising +
Footnote †: journal: Applied sciences
## 2 Technical Enablers
### Deploying Intelligence on the Network Edge
#### 2.1.1 Multi-Access Edge Computing  
Multi-access Edge Computing (MEC) is an architectural paradigm that aims to deliver some of the capabilities of cloud computing bundled with an IT service environment, within the Radio Access Network (RAN). This approach introduces a processing environment closer to service subscribers, characterized by ultra-low latency, high bandwidth, real-time access to radio network and context information, location awareness, efficient network operation and enhanced service delivery. The elevated Quality of Experience (QoE) levels [48] for all end-users create additional value through novel application delivery, thus introducing many advantages for all stakeholders.  
The design philosophy behind MEC renders it flexible enough to play numerous roles in use cases related to edge applications with infrastructure similar to commodity servers and intelligent networking appliances. The European Telecommunications Standards Institute (ETSI), an independent, not-for-profit, standardization organization in the telecommunications industry, recently issued such a scenario in which MEC servers deployed at an LTE base station can be utilized for delivering IoT applications, essentially acting as a low latency aggregation point for managing communication protocol enforcement, rapid message distribution and analytics processing (see Figure 1). Through the proposed blueprint, MEC enables the aggregation and distribution of IoT services for real-time responses inside a highly distributed environment. Data round trip time is reduced for the core network, and the cloud applications are abstracted in a new single layer.