esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 3 Case Analysis
### The Prediction of the NAR Model  
#### 3.2.1 Series Normalization  
To eliminate the difference in the order of magnitude of the data, the data need to be normalized in advance, so the prediction of traffic flow can be more scientific and reliable [46]. In this paper, the Max-Min normalization method suitable for the neural network was chosen, which is also called dispersion standardization. The main process is to carry out the linear transformation to the original series and adjust the series data to the range of [0, 1]. The normalized conversion formula is as follows:  
\[x^{*}=\frac{x-x_{\min}}{x_{\max}-x_{\min}} \tag{12}\]  
Figure 4: Autocorrelation function and partial autocorrelation function. (**a**) ACF (\(d=1\), \(D=1\)), (**b**) ACF (\(d=0\), \(D=1\)), (**c**) PACF (\(d=1\), \(D=1\)), (**d**) PACF (\(d=0\), \(D=1\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SARIMA (\(p\), \(1\), \(q\))** (\(P\), \(1\), \(Q_{12}\)** & \multicolumn{2}{c}{**SARIMA (\(p\), \(0\), \(q\)) (\(P\), \(1\), \(Q_{12}\)**} \\
**Model** & **BIC** & **Model** & **BIC** \\ \hline SARIMA (0,1,2) (0,1,2)\({}_{12}\) & 22.119 & SARIMA (1,0,0) (0,1,0)\({}_{12}\) & 22.132 \\ SARIMA (2,1,2) (2,1,1)\({}_{12}\) & 22.122 & SARIMA (1,0,0) (1,1,0)\({}_{12}\) & 22.141 \\ SARIMA (1,1,1) (0,1,2)\({}_{12}\) & 22.124 & SARIMA (1,0,1) (2,1,0)\({}_{12}\) & 22.143 \\ SARIMA (1,1,1) (2,1,0)\({}_{12}\) & 22.136 & SARIMA (2,0,0) (2,1,0)\({}_{12}\) & 22.143 \\ SARIMA (0,1,0) (2,1,1)\({}_{12}\) & 22.140 & SARIMA (0,0) (2,1,0)\({}_{12}\) & 22.162 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ SARIMA (1,1,1) (1,1)\({}_{12}\) & 22.401 & SARIMA (2,0) (1,1,2)\({}_{12}\) & 22.542 \\ SARIMA (1,1,2) (1,1,2)\({}_{12}\) & 22.408 & SARIMA (0,0,2) (1,1,2)\({}_{12}\) & 22.568 \\ SARIMA (0,1,0) (1,1,2)\({}_{12}\) & 22.413 & SARIMA (2,0,2) (1,1,1)\({}_{12}\) & 22.581 \\ SARIMA (2,1,2) (1,1,2)\({}_{12}\) & 22.421 & SARIMA (2,0,1) (1,1,2)\({}_{12}\) & 22.650 \\ SARIMA (1,1,0) (1,1,2)\({}_{12}\) & 22.487 & SARIMA (2,0,2) (1,1,2)\({}_{12}\) & 22.717 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of BIC values.  
where \(x^{*}\) is the normalized series value, \(x\) is the original series, and \(x_{\text{max}}\) and \(x_{\text{min}}\) are the maximum and minimum values of the original series, respectively.  
We normalized the original traffic flow time series and the residual sequence of the SARIMA model. Then, we took the normalized result of the residual sequence of the SARIMA model as an example, as shown in Figure 5.
## 1 Introduction
Furthermore, we integrate the Dynamic Head's scale and spatial focus, employing adaptable convolutions to concentrate on target shapes, thereby tackling local occlusion and the disappearance of 119 object characteristics, plus the disappearance of characteristics of objects. 3. We select the NWD as the loss function for regression analysis at the edges of smaller objects. Furthermore, we evaluate the impact of employing the IoU, GIoU, CIoU, and NWD as loss functions for localization in infrared small target bounding box regression. The findings show that the NWD loss mechanism markedly diminishes incorrect alerts and overlooked detections stemming from difficulties in pinpointingand identifying small infrared targets in congested environments. The enhancement bolsters the detection model's overall resilience. 4. Utilizing a long-wave infrared camera, we capture images of drones in various situations and over various distances (100-1200 m), resulting in the formation of a comprehensive infrared target dataset called IRMT-UAV. This dataset covers a wide variety of application scenarios, including those with highly cluttered backgrounds and local occlusion between objects and their surroundings. The targets in the dataset vary from bright, medium-sized objects at close range to dim, small objects at extended distances. The varied nature of this dataset bolsters its resilience and flexibility, rendering it ideal for the training and assessment of object detection algorithms.
## 2 Materials and Methods
### Modelling of Equilibrium and Adsorption Data  
For equilibrium modelling, the pseudo-first order model (Equation (1)) and the pseudo-second order model (Equation (2)) were employed, as they were the most popular and were able to give reliable results [3; 4; 10; 14; 18; 22]:  
\[\log(q_{e}-q_{t})=\log q_{e}-\frac{k_{1}}{2.303}t \tag{1}\]  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{2}\]  
For adsorption data modeling, the [PERSON] (Equation (3)) and [PERSON] (Equation (4)) isotherm models were adopted [3; 22]:  
\[\frac{C_{e}}{q_{e}}=\frac{1}{bC_{e}}+\frac{C_{e}}{Q} \tag{3}\]  
\[\log q_{e}=\log k+\frac{1}{n}\log C_{e} \tag{4}\]  
A linear solver was applied to determine the isotherm constants.  
The adsorption efficiency was determined by Equation (5):  
\[\% Ionremoval=\frac{C_{0}-C_{e}}{C_{0}}\times 100 \tag{5}\]  
The amount of each metal adsorbed per adsorbent mass unit (uptake) \(q\) (mg/g) was calculated by Equation (6):  
\[q=\frac{(C_{0}-C)V}{m} \tag{6}\]where \(C_{0}\) and \(C\) are the initial and final metal ion concentrations (mg/L) in the solution, \(V\) is the solution volume (L) and \(m\) is the mass of adsorbent used (g).
## 2 Related Works
### Hessian Regularized Non-Negative Matrix Factorization  
Hessian energy is motivated by Eells-energy for mapping between manifolds [25]. Given a smooth manifold \(M\subset R^{n}\) and a map function \(f:M\to R^{r}\), the Eells-energy of \(f\) can be written as [10]:  
\[S_{Eells}(f)=\int_{M}\ ||\bigtriangledown_{a}\bigtriangledown_{b}f||_{T_{x}M \otimes T_{x}M}^{2}dV(x) \tag{5}\]  
where \(\bigtriangledown_{a}\bigtriangledown_{b}f\) is the second covariant derivation of \(f\), \(T_{x}M\) is the tangent space at point \(x\in M\) and \(dV(x)\) is the natural volume element. Using normal coordinate, \(\int_{M}\ ||\bigtriangledown_{a}\bigtriangledown_{b}f||_{T_{x}M \otimes T_{x}M}^{2}\) can be written as:  
\[\int_{M}\ ||\bigtriangledown_{a}\bigtriangledown_{b}f||_{T_{x}M\otimes T_{x}M }^{2}\ =\ \sum_{r,s=1}^{d}\ (\frac{\partial^{2}f}{\partial C_{r}\partial C_{s}})^{2} \tag{6}\]  
where \(C_{r}\) and \(C_{s}\) are normal coordinates. So given point \(x_{i}\), the norm of the second covariant derivative is just the Frobenius norm of the Hessian of \(f\) in standard coordinate. Thus the resulting functional is called Hessian regularizer \(S_{Hess}(f)\):  
\[S_{Hess}(f)=\sum_{i=1}^{n}\ \sum_{r,s=1}^{d}\ \|\frac{\partial^{2}f}{\partial C _{r}\partial C_{s}}|x_{i}\|^{2} \tag{7}\]  
Let \(N_{k}(X_{i})\) represent the set of \(k\) nearest neighbors of \(X_{i}\), the Hessian of \(f(X_{i})\) on \(N_{k}(X_{i})\) can be approximated as follows:  
\[\frac{\partial^{2}f}{\partial C_{r}\partial C_{s}}|X_{i}\approx\sum_{j=1}^{k} \ H_{rsj}^{(i)}f(X_{j}) \tag{8}\]  
The operator \(H\) can be computed by fitting a second-order polynomial \(p(X)\) in normal coordinates to \(\left\{f(X_{j})\right\}_{j=1}^{k}\). Let \(V_{ki}=f_{k}(X_{i})\) and \(X_{k}=(X_{k1},\dots,X_{kp})\), the estimate of the Frobenius norm of the Hessian of \(f\) at \(x_{i}\) is thus given by  
\[||\bigtriangledown_{a}\bigtriangledown_{b}f||^{2}\approx\sum_{r,s=1}^{m}\ (\sum_{a=1}^{k}\ H_{rss}^{(i)}f(\alpha))^{2}=\sum_{a,\beta=1}^{k}\ f(\alpha)f( \beta)B_{\alpha\beta}^{(i)} \tag{9}\]where \(B_{\alpha\beta}^{(i)}=\sum_{r,s=1}^{m}\ H_{r\kappa}^{(i)}H_{r\beta}^{(i)}\) and the total estimated Hessian energy \(\hat{S}_{Hess}(f)\) is the sum over all data points as follows:  
\[\begin{split}&\hat{S}_{Hess}(f)=\sum\limits_{i=1}^{n}\ \sum\limits_{r,s=1}^{m}\ \left(\frac{\hat{\sigma}^{2}f}{\delta C_{r}\delta c_{s}}|x_{i} \right)^{2}\\ &=\sum\limits_{i=1}^{n}\ \sum\limits_{\alpha\in N_{k}(X_{i})}\ \sum \
olimits_{\beta\in N_{k}(X_{j})}\ f_{\alpha}f_{\beta}B_{\alpha\beta}^{(i)}\ =\ <f,Bf>\end{split} \tag{10}\]  
where \(B\) is denoted as the Hessian regularization matrix, and is the accumulated matrix summing up all the matrices \(B^{(i)}\).  
Applying Hessian energy as the regularization term in NMF to estimate the local manifold structure, the Hessian regularized NMF (HNMF) can be formulated as:  
\[\begin{split}\min_{\mathbf{U},\mathbf{V}}\mathbf{1}||\mathbf{X}- \mathbf{U}\mathbf{V}||_{F}^{2}+\lambda tr(\mathbf{V}\mathbf{BV}^{\mathrm{T}}) \\ & U_{ik}\geq 0,V_{kj}\geq 0,\ \forall i,j,k.\end{split}, \tag{11}\]  
where \(\lambda\) is the regularization parameter.
## 2 Research Methodology
### Misalignment
Secondly, the model does not consider thermal effects in the lubricating layer, which can affect its viscosity and pressure distribution, especially at high rotation speeds. Thirdly, checking the results is limited only to computer numerical simulations, and there is no experimental testing on physical test stands, which can decrease the reliability of the resultant conclusions. Finally, the approach considers only laminar flow regimes in the bearing, while in practical applications, turbulent effects are possible, requiring additional consideration and elaboration of the model. A comparative analysis of the methods for axes misalignment detection is summarized in Figure 12:0 \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** & **Domain * \\ \hline \multirow{4}{*}{Finite element method (FEM)} & Ability to consider nonlinear contacts and imbalances [48; 50]. & Sensitivity to model parameters and accuracy of the input data [49]. & \\ \cline{2-4}  & Suitable for modeling both flexible and rigid rotors [27; 48]. & Difficulty in choosing suitable element partitioning and boundary conditions [50]. & N/A partitioning and boundary conditions [50]. \\ \hline \multirow{4}{*}{Finite difference method (FDM) [50]} & More straightforward to implement and calculate in comparison with FEM. & Poor accuracy in coarse mesh partitioning. & Poor accuracy in coarse mesh partitioning. \\ \cline{2-4}  & High computational speed for few-dimensional problems. & Problems with solution stability for rigid systems. & \\ \cline{2-4}  & Convenient for solving problems of hydrodynamic lubrication and analysis of film bearings. & It is difficult to use for complex geometries due to the need for a regular mesh. & \\ \hline \multirow{4}{*}{Orbit analysis} & It allows the analysis of nonlinear effects and motion instabilities [50]. & Requires high-precision sensors for data collection [48; 50]. & \\ \cline{2-4}  & An effective method for detecting rotor imbalance and misalignment [48; 49]. & Limited efficiency at low rotor speeds [49]. & TD/FD \\ \cline{2-4}  & Convenient for diagnosing the condition of bearings and contact zones [49]. & It is challenging to interpret orbits with complex defects [50]. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of methods for axes misalignment detection. Figure 15: The lubrication performance difference vs. time in aligned and misaligned conditions. Adopted from [50].
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 2 GNSS Satellite Constellation Determination
### Optimization Algorithms
#### 2.3.3 Genetic Algorithm (GA)  
GA is an algorithm developed in the 1970s by [PERSON] et al. [44]. The algorithm mimics [PERSON]'s concept of evolution and natural selection. GA generates a set of possible solutions. These possible solutions are improved over generations of iterations through selection, crossover, and mutation procedures, as shown in Figure 3. Using the roulette wheel selection technique, the fittest individuals are more likely to be chosen to establish new solutions through crossovers and mutations. GA is a flexible and robust algorithm [45], but it is complex [45; 46], sensitive to the value of parameters [45], and dependent on the initial population [46; 47].  
Figure 3: Representation of the GA processing steps.  
Figure 2: Schematic representation of the solution building process by ants in ACO.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 3 Results and Discussion
### Biocompatibility of Hydrogels  
The cytotoxicity and biocompatibility of the polysaccharide hydrogel were evaluated using the CCK-8 assay. L929 mouse fibroblasts were cultured with varying concentrations of CAA-CS gel extract for specific time intervals, and cell growth and viability were monitored and quantified. In Figure 8a, under an optical microscope, it was observed that the L929 cells cultured with the extract exhibited robust growth. Cell viability tests at designated time points revealed no significant differences in cell viability among the groups compared to the blank control group as the concentration of the extract increased as shown in Figure 8b. The cell viability across all groups remained comparable to that of the blank control, indicating that the gel extract did not adversely affect cell proliferation. Over time, there were no significant differences in OD values between the experimental groups and the blank control, further confirming that cell viability was maintained. After three days of culture, the results indicated that the polysaccharide hydrogel demonstrated excellent biocompatibility with L929 fibroblasts, with cell viability remaining above 85% (Figure 8c,d). These findings suggest that the CAA-CS system possesses good biocompatibility and is suitable for biomedical applications. For instance, these injectable hydrogels can serve as carriers for delivering precursor substances to target sites via injection.
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 3 Results
### Multi-Frequency LEO-Only PPP AR  
To verify the capability of LEO constellation for precise positioning independently, we conducted the LEO-only PPP test in dual-frequency and triple-frequency modes based on the observation of 288-LEO constellation. Figure 11 shows LEO-only PPP float and fixed solutions at stations CEBR, AREG, and DYNG from 20:00 to 20:05 on 1 January 2017. According to Figure 11, the LEO-only PPP can converge to a centimeter level in all three components within 5 min and the ambiguity resolution can be achieved in a very short period, which is about 2 min. Compared to the dual-frequency LEO-only fixed solutions, the triple-frequency PPP fixed solutions present a better performance in terms of convergence and reliability.  
Figure 12 presents the average TTFF of the LEO-only PPP AR for all user stations. For dual-frequency solutions, the TTFF is less than 100 s, except for the station AREG, and the average TTFF is 71.8 s, which is obviously less than the TTFF of the GREC PPP AR of about 7.1 min, which is benefited by the rapid change of spatial geometry of LEO satellites. The TTFF of the triple-frequency  
Figure 11: LEO-only PPP float and fixed solutions at stations AREG, CEBR, and RGDG from 20:00 to 20:05 on 1 January 2017.  
Figure 10: Average TTFF of the 288-LEO augmented GNSS PPP AR at dual-frequency and triple-frequency modes.  
PPP AR is less than 80 s for all user stations and the average TTFF is 55.2 s, with an improvement of 23.1% compared to the dual-frequency solutions.  
The positioning errors of 1-min static LEO-only float and fixed solutions are given in Figure 13. The positioning accuracy of LEO-only solutions can converge to 6 cm in all three components within 1 min. With the ambiguities fixed, the 1-min positioning accuracy can be significantly improved. The positioning error of triple-frequency PPP AR at 1 min is (1.69, 0.75, 4.16) cm in the east, north, and up components, respectively, with an improvement of 63.2%, 67.2%, and 20.7% compared to the PPP float solutions, and 50.2%, 56.8%, and 17.2% compared to the dual-frequency PPP fixed solutions. It also demonstrates the benefit of the third frequency for PPP ambiguity resolution.
# Towards Explainable Deep Neural Networks for the Automatic Detection of Diabetic Retinopathy
## 2 Materials and Methods
### Performance Evaluation Metrics  
In this work, five evaluation metrics were employed to provide complete coverage and unbiased analysis of the results. This includes the following:  
* Accuracy: calculated as the percentage of the correctly classified images by: \[\text{Accuracy}=\frac{TP+TN}{N}\] (3) where \(N\) is the total number of images in the evaluated set, \(TP\) is the true positive, i.e., detected abnormal cases, and \(TN\) is the true negative, i.e., normal cases not detected as abnormal.
* Precision: calculated as the number of \(TP\) divided by the sum of \(TP\) and false positives, normal cases detected as abnormal. \[\text{Precision}\;=\frac{TP}{TP+FP}\] (4)
* Sensitivity/Recall: calculated as the number of, divided by the sum of \(TP\) and false negatives \(FN\), abnormal cases detected as normal: \[\text{Recall}\;=\frac{TP}{TP+FN}\] (5)
* F1-Score: defined as the harmonic mean of precision and recall: \[\text{F1}-\text{Score}\;=2*\frac{\text{Precision}*\text{Recall}}{\text{ Precision}+\text{Recall}}\] (6)
* Confusion matrix: A confusion matrix is a table used for summarizing a classifier's performance. The number of correctly and incorrectly classified samples are summarized with count values and broken down by each category.
# DEM Simulation of the Load Transfer Mechanism of a GRPS Embankment with a Fixed Geogrid Technique
## 2 Establishment and Calibration of DEM Model  
Two-dimensional DEM software, namely, the particle flow code (PFC2D), was used to build the numerical model in this study. The establishment of the DEM model was based on the two-dimensional laboratory model tests conducted by [PERSON] et al. (2019). In the laboratory model tests, analogical soil (steel rods) was used as embankment fill, and foam material was used as the subsoil between the piles. Well-calibrated micromechanical parameters were the basis for the numerical model to reproduce the mechanical response of the test material.  
The contact constitutive model in the PFC involved in this study included a linear model and a linear parallel bond model. Among them, the linear model was used to simulate the linear elastic friction behavior of grain particles. The linear contact force acts on a vanishingly small area, so only one force is transmitted. The linear parallel bond contact provides the mechanical behavior of the finite-size cement-like material deposited between the two contact pieces and establishes an interaction between the two particles. A linear parallel bond can transfer forces and moments between particles. When the parallel bond fails (the maximum stress or moment exceeds its failure strength), the linear parallel bond model degenerates into a linear model. In this study, the linear model was used to simulate the contact between the embankment fill and the subsoil between the piles, and the linear parallel bonded model was used to reproduce the mechanical response of the reinforcement. The micromechanical properties of the test materials were back-calculated through simulation tests with clear boundary conditions. The size and macrophysical properties of the test materials in the DEM models were consistent with the laboratory models. In addition, based on the model tests, the geogrid was adopted in the numerical model to compare the load transfer mechanisms of GRPS embankments with a fixed geogrid and an unfixed geogrid in this study.
## 4 System Model  
A communication network in Mobile Ad-hoc network environment can be represented as a graph \(G=(V,\ L)\) wherein V is the set of nodes and L is the link between the nodes. The link between the source node \(s\) and destination node \(d\) is denoted by \((s,\ d)\) i.e., \(s,\ d\in V\).Whenever node \(s\) is the neighbor of node \(d\), both of which can directly communicate with each other. In a centralized network, the network topology information is maintained by a network controller. In a decentralized network, network topology information is maintained by the information exchange between the node and its neighbors. The proposed routing scheme is for a decentralized network, in which all mobile nodes work as hosts as well as a router for other nodes to build network infrastructure. In the proposed scheme, the route selection and data transmission is based on the collaboration among network nodes for topological information exchange among each other. Due to the dynamic movement of nodes, network topology continuously changes in an unpredictable manner. In this scenario, if two nodes do not share a direct link with each other, they will utilize routing protocols to establish a connection, using intermediate nodes between them to transfer data. The proposed MRLAM routing scheme utilizes some key factors, such as the energy consumption, link quality of channel, and mobility of nodes in the route computation, to enhance the energy efficiency and QoS of communicating nodes as discussed below:
## 2 Description of the Site  
### Dams in Jordan  
Jordan is a naturally water scarce country. Its climate ranges from semi-arid in the north-western part of the country to arid desert in its eastern and southern parts. Jordan is subjected to periodic droughs; water supply from surface sources has declined substantially over recent years leading the country's development plans to prioritize dam construction throughout the country.  
Multiple large-scale dams are in the northern and southern parts of Jordan. Fortunately, most of the existing dams are surrounded by mountains and hills with significant heights that are promising for implementing the pumped-hydro energy storage system proposed in this paper. Dams in Jordan are generally being used for irrigation mainly in addition to hydropower generation, ground water recharge, drinking water production, and industrial purposes.