esa-sceva/satcom-chunk-collection huggingface dataset:
# First Request First Service Entanglement Routing Scheme for Quantum Networks
## 3 Modeling for Entanglement Distribution Network
### Modeling of Nodes  
\(V\) is the set of \(k\) nodes, each node \(v_{i}\) is a bi-variate vector, defined as  
\[v_{i}\triangleq(W_{i},T_{i}), \tag{9}\]  
where \(1\leq i\leq k\). \(T_{i}\) is the supported wavelength channel vector, consisting of the channel indexes defined by the International Telecommunication Union (ITU) Grid C-Band (100 GHz  
Figure 1: Schematic diagram of the entanglement distribution network. The entangled photon source and WSS are located at the quantum server to provide entangled photons. The entangled photons are transmitted to the users through the wavelength routing network, which is mainly connected via DWDM, BS, OS and, WSS devices. Then the users establish communication with others by accessing the switching network. DWDM: Dense Wavelength Division Multiplexer, BS: Beam Splitter, OS: Optical Switch, WSS: Wavelength Selective Switch.  
Spacing) standard. The size of \(T_{i}\) shows the divided number of supported wavelength channels of \(v_{i}\). For different type of \(v_{i}\) considered in this article, \(T_{i}\) is defined as  
\[T_{i}=\begin{cases}[-3],&\text{Entanglement source}\\ [-2],&\text{User}\\ [-1],&\text{BS}\\ [0],&\text{OS or AOS}\\ \big{[}\text{ch}_{1},\text{ch}_{2},\dots,\text{ch}_{|T_{i}|}\big{]},&\text{ DWDM or WSS}\end{cases}. \tag{10}\]  
For the nodes implemented with entangled photon source, user, BS, and time-division multiplexing devices (such as OS and AOS), \(|T_{i}|=1\), which means all the wavelengths can be routed through the node \(v_{i}\). For the nodes implemented with DWDM and WSS (\(|T_{i}|\geq 2\)), the allowed wavelength channel index is varying from 1 to 72.  
Assume that \(m_{i}\) is the connection ports number of \(v_{i}\). \(W_{i}\) is defined as the pass-through loss matrix vector, consisting of \(|T_{i}|\) square matrices, shown as  
\[W_{i}=\big{[}W_{i}^{1},W_{i}^{2},\dots,W_{i}^{|T_{i}|}\big{]}, \tag{11}\]  
where \(W_{i}^{j}\) is the pass through matrix of \(j\)-th wavelength channel of \(v_{i}\), defined as  
\[W_{i}^{j}=\begin{bmatrix}w_{11}^{ij}&\cdots&w_{1m_{i}}^{ij}\\ \vdots&\ddots&\vdots\\ w_{m_{i}1}^{ij}&\cdots&w_{m_{i}m_{i}}^{ij}\end{bmatrix}, \tag{12}\]  
where \(1\leq j\leq|T_{i}|\). Each element \(w_{\alpha\beta}^{ij}\) is the insertion loss from port \(\alpha\) to port \(\beta\) of the \(j\)-th wavelength channel for node \(v_{i}\), \(1\leq\alpha,\beta\leq m_{i}\). If port \(\beta\) cannot be reached from port \(\alpha\), \(w_{\alpha\beta}^{ij}=\infty\).
## 3 Materials and Methods
### Methods  
#### 3.3.1 Spatial Attention Module  
Many studies have shown that introducing attention mechanisms into neural networks can improve the ability of network models to express features. The spatial attention module from CBAM (convolutional block attention module) [39] is a kind of effective attention mechanism which indicates the importance of the input values in the spatial dimension. It has been used in many applications due to its ease of integration. Figure 3 shows the details of the spatial attention module. The implementation of spatial attention is to perform MaxPool and AvgPool in the channel dimension to obtain two 2D spatial feature maps that capture cross-channel information. The feature map contains position-sensitive feature information. We use a 7 \(\times\) 7 convolution kernel to fuse the feature map information of MaxPool and AvgPool and use the sigmoid function to make the value of each point in the feature map between 0-1, which indicates the probability that the pixel is foreground or background. Finally, an element-wise dot product with the input feature map is conducted to suppress the feature of the background. The spatial attention module allows the model to locate and identify target regions more accurately. The computation is explained in Formulas (1) and (2).  
\[\sigma(x)=\frac{1}{1-e^{(-x)}} \tag{1}\]  
\[M_{s}(F)=F\bigotimes(\sigma(f^{7\times 7}([AvgPool(F);MaxPool(F)]))) \tag{2}\]  
\(F\) represents the input feature map and \(F^{\prime}\) represents the output feature map. \(M_{s}\) represents the feature map through the attention module. \(\bigotimes\) stands for element-wise multiplication. \(\sigma\) represents the sigmoid activation function. \(f^{n\times n}\) represents a convolutional layer with a convolution kernel of \(n\), with the size of the input and output feature maps unchanged. _Avgpool_ stands for average pooling, and _MaxPool_ stands for max pooling.  
Figure 2: The architecture of the two designs. (**a**) The architecture of CloudRCNN (auxiliary loss); (**b**) The architecture of CloudRCNN (feature fusion).
# Numerical Simulation of KBKZ Integral Constitutive Equations in Hierarchical Grids
## 2 Governing Equations  
The governing equations for transient, isothermal and incompressible flows are the mass conservation and the equation of motion, which, in dimensionless form, can be written as follows (for details, see [PERSON] et al. [21]):  
\[\
abla\cdot\mathbf{v} = 0\, \tag{1}\] \[\frac{\partial\mathbf{v}}{\partial t}+\
abla\cdot(\mathbf{v} \mathbf{v}) = -\
abla p+\epsilon\
abla^{2}\mathbf{v}+\
abla\cdot\mathbf{S}+F. \tag{2}\]  
Using the EVSS transformation [25], the extra-stress tensor \(\boldsymbol{\tau}\) is written as  
\[\boldsymbol{\tau}=\mathbf{S}+\epsilon\ \dot{\boldsymbol{\gamma}}\,\ \text{ where }\dot{ \boldsymbol{\gamma}}=\
abla\mathbf{v}+(\
abla\mathbf{v})^{\intercal}\ \text{and}\ \epsilon=\frac{c}{Re};\ c>0,\]  
where \(\mathbf{S}\) is a non-Newtonian tensor, \(\mathbf{v}\) is the velocity field, \(p\) is the kinematic pressure and \(t\) is the time. In these equations, \(F\) represents the external forces, \(e\) is a stability parameter (as shown in [PERSON] et al. [26]), \(Re=\frac{\rho_{0}UL}{\eta_{0}}\) is the Reynolds number, \(\eta_{0}\) is the zero-shear-rate viscosity, \(\rho_{0}\) is the fluid density and \(U\) and \(L\) are the velocity and length scales, respectively.  
In this work, the rheological model that defines the behavior of fluid flow is the KBKZ-PSM [11] integral constitutive equation, which is shown below:  
\[\boldsymbol{\tau}(t)=\int_{-\infty}^{t}M(t-t^{\prime})H(I_{1},I_{2})\mathbf{ B}_{t^{\prime}}(t)dt^{\prime}\, \tag{3}\]  
where \(\mathbf{B}_{t^{\prime}}(t)\) is the Finger tensor and \(M\) is the memory function, which adopts the following form:  
\[M(t-t^{\prime})=\sum_{k=1}^{m_{1}}\frac{a_{k}}{\lambda_{k}De}e^{-\frac{t-t^{ \prime}}{\lambda_{k}De}} \tag{4}\]and \(H\) is the Papanastasiou-Scriven-Macosko [25] damping function, which is calculated using the following equation:  
\[H(I_{1},I_{2})=\frac{\alpha}{\alpha-3+\beta I_{1}+(1-\beta)I_{2}}. \tag{5}\]  
The parameters \(\lambda_{k},a_{k},k\) are relaxation times, relaxation moduli and the number of relaxation modes, respectively. The quantities \(I_{1}=tr[\mathbf{B}_{t^{\prime}}(t)]\) and \(I_{2}=\frac{1}{2}\big{(}(I_{1})^{2}-tr[\mathbf{B}_{t^{\prime}}^{2}(t)]\big{)}\) are the first and second invariants of \(\mathbf{B}_{t^{\prime}}(t)\), respectively. The parameters \(a_{k},\lambda_{k},\alpha,\beta\) are obtained from a curve fitting to the rheological properties of the fluid. \(De=\lambda_{ref}\frac{U}{L}\) is the Deborah number, \(\lambda_{ref}=\sum\frac{a_{k}\lambda_{k}^{2}}{a_{k}\lambda_{k}}\) is the average relaxation time and the zero-shear-rate viscosity is written as \(\eta_{0}=\sum a_{k}\lambda_{k}\).
## 5 Validation and Results
### Case Studies  
This section visually demonstrates the evaluation effect of the method by comparing three urban intersection scenarios, which vary significantly in complexity. These scenarios were generated using the urban complex intersection scenario simulation model built into the PanoSim simulation software.  
Figure 9a-c display key frame screenshots for three respective cases. Figure 9a illustrates case 1, where fewer vehicles at the intersection lead to simpler interaction characteristics. Conversely, Figure 9b,c show cases 2 and 3, where increased vehicle density leads to more complex interactions. The complexity evaluation results of 0.10, 0.47, and 0.69 for the corresponding cases demonstrate that this evaluation method accurately reflects reality and can finely differentiate between scenarios of varying complexity.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 4 Experiments and Results
### Experiment 1-BiSGRU-GA  
#### 4.1.1 Data Set and Data Preprocessing  
The data set selected in this experiment is from the operation log of the all-business Unified Data Center of the Shandong Electric Power company of China, with a total of 26,433 log messages. Each operation log information includes time (year, month, day, hour, minute, and second), source IP, data size, and other fields, and has a unique data block ID. The power operation and maintenance expert divides the operation log into 5647 event samples according to the data block ID and log information. Among them, about 0.8% of 45 samples were marked as abnormal. Therefore, we take the samples being labeled abnormal or not as the basis for the evaluation of detection accuracy. Several rounds of comparative tests are carried out to evaluate the performance of the model in this paper.
## 3 Results
### GSD Accuracy  
Figure 11 illustrates part of the images used to analyze the GSD. Table 6 displays the designed value, mean horizontal and vertical GSD, arithmetic mean of the GSD in both directions, errors for each value, and standard deviations for each value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Horizontal Direction** & **Vertical Direction** &
\begin{tabular}{c} **Arithmetic Mean of** \\ **Both Directions** \\ \end{tabular} \\ \hline Designed value (mm) & & 2 & \\ \hline Estimated value (mm) & 2.1519 & 2.5500 & 2.3510 \\ \hline Error (mm) & 0.1519 & 0.5500 & 0.3510 \\ \hline Error rate & 0.0760 & 0.2750 & 0.1755 \\ \hline \hline \end{tabular}
\end{table}
Table 6: GSD accuracy of the simulator.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline CMOS & \(1^{\prime\prime}\) \\ \hline Pixel Size & \(5472\times 3078\) \\ \hline Focal Length & 8.8 mm \\ \hline ISO & 100–3200 (Automatic) \\  & 100–12,800 (Manual) \\ \hline Shutter Speed & \(1/2000\)–8 s (Mechanical) \\  & \(1/8000\)–8 s (Electrical) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specification of the digital camera used in the test from [38].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline GSD & 2 mm \\ \hline Overlap & Forward overlap & 80\% \\ \cline{2-3}  & Side Overlap & 60\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: First test scenario for the validation of the simulator.
## 2 Theory
### The Magnifications \(M_{L}\) and \(M_{T}\) of the Images in the In-Line Interferometer
The DSB filter does not impact either the transverse magnification or the longitudinal magnification as it is solely influenced by the focal length of the lens and the position of the CCD camera with respect to this lens. Finally, the CCD records the corresponding intensity images that are later digitally processed on the computer to retrieve the magnitude and phase information (hologram) of the magnified wavefront in the plane \((x_{2},y_{2})\). In the computer, the digital hologram is numerically propagated by a distance \(\Delta z_{r}\) until the arbitrary plane \((x_{r},y_{r})\), where the magnified and focused image is reconstructed. The longitudinal magnification \(\mathrm{M_{L,0-r}}\) is determined by the relationship between the propagation distance of the diffracted object, \(\Delta z_{o}\), and the reconstruction distance of the digital focused image, \(\Delta z_{r}\), as follows:  
\[\mathrm{M_{L}}\ =\ \mathrm{M_{L,0-r}}, \tag{3}\]  
\[\frac{dz_{i}}{dz_{o}}\ =\ \frac{\Delta z_{i}}{\Delta z_{r}}. \tag{4}\]  
Considering that the longitudinal magnification remains constant between object and image regions, and in accordance with Equation (2), the transverse magnification associated with the reconstructed image and the shifted object corresponds to the transverse magnification between the planes \((x_{2},y_{2})\) and \((x_{1},y_{1})\):  
\[\mathrm{M_{T}}\ =\ \mathrm{M_{T,0-r}}, \tag{5}\]  
\[\frac{x_{r}}{x_{0}}\ =\ \frac{x_{2}}{x_{1}},\ \ \frac{y_{r}}{y_{0}}\ =\ \frac{y_{2}}{y_{1}}. \tag{6}\]  
Equations (1)-(3) and (5) indicate that the transverse and longitudinal magnifications remain unchanged by the displacement \(\Delta z_{o}\) of the object as long as the object is illuminated with a collimated beam and the distance \(s_{i}\) between the lens and the CCD camera remains constant. The distance at which the digitally reconstructed focused image is located is:  
\[\Delta z_{r}\ =\ \Delta z_{o}\mathrm{M_{L}}. \tag{7}\]  
The transverse coordinates of the digitally reconstructed image concerning the object are:  
\[x_{r}\ =\ x_{0}\mathrm{M_{T}}, \tag{8}\]  
\[y_{r}\ =\ y_{0}\mathrm{M_{T}}. \tag{9}\]
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 3 Pedestrian Dead Reckoning
### Step Detection  
Step detection is one of the three basic modules of PDR system. Only when determining the occurrence of one step does the system use the stride length estimation and heading calculation modules. Therefore, the step detection module plays an important role in PDR system. Usually, we determine whether a step occurs or not by detecting the periodicity of two-peaks oscillation of accelerations in the process of walking [29]. There are some common step detection methods: zero-crossing detection [30], peak detection [31] and autocorrelation [32]. To gain high robustness, we choose improved zero-crossing detection, which uses the amplitude of accelerations to make the judgement, as our step detection method. The amplitude of accelerations can be calculated by triaxial outputs of the accelerometer:  
\[a_{m}=\sqrt{{a_{x}}^{2}+{a_{y}}^{2}+{a_{z}}^{2}} \tag{2}\]  
Then, we decide whether the step occurs according to the values of the interval between acceleration amplitude and the threshold and the time interval of two undetermined steps. The specific criteria are obtained by analyzing the waveform of accelerations, as shown in Figure 2.  
* The peak value of acceleration amplitude is greater than the threshold, and the valley value is less than the threshold during the period of step \(k\).
* The difference between the peak value of acceleration amplitude and the threshold in step \(k\) is within the restricted range. In addition, the difference between the threshold and the valley value of acceleration amplitude in step \(k\) is within the restricted range.
* The duration of step \(k\) is within the restricted range, which can be determined by the walking frequency.  
Figure 1: The workflow of PDR.  
With the restricted conditions above, the step detect module can eliminate the error steps introduced by perturbation of acceleration and gain a high accuracy in step counter.
# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
###### Abstract  
The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.  
CNNs; autoimmune diseases; IIF test; HEp-2; deep learning; fine-tuning; features extractors +
Footnote †: journal: Applied Physics Letters
## 4 Conclusions
### Limitations and Future Work  
While the closed-form expression showed strong alignment with experimental data, the study was limited to specific fiber types and wavelengths. Future work should extend the model's validation to diverse fiber structures and broader wavelength ranges. Additionally, further research on integrating optical amplifiers within the leaky feeder system could help mitigate overall attenuation and extend operational range. Also, multimode fiber should be considered, since we believe it will introduce a mode interference that is hard to control.  
Conceptualization and methodology, H.F. and J.S.R.; software, J.S.R.; validation, D.I.A., I.M., A.K.A. and X.F.; writing--original draft preparation, H.F. and J.S.R.; writing--review  
Figure 7: BER vs. SNR (dB) for OLF MIMO System.  
and editing, H.F. and J.S.R.; funding acquisition, H.F. and X.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Optiwave Systems Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
## 2 Experimental Program  
### Specim Preparation  
The primary focus of this study is on four typical types of rocks: granite, red sandstone, gray sandstone, and shale. Before conducting triaxial compression experiments, uniaxial compression tests were first performed on these four rock types to obtain their peak strengths and average uniaxial compressive strengths.  
For the uniaxial compression experiments, the surface of the rock samples and the instrument joints were cleaned. The rock samples were then encased in FEP heat shrink tubing. Due to its high toughness, temperature resistance, and immunity to chemicals such as oil, this material can effectively prevent hydraulic silicone oil from infiltrating the rock samples during the experiment when heated with a heat gun, as shown in Figure 1a. After placing the rock samples, circumferential and axial strain gauges were sequentially installed on the samples, and loading was applied, as illustrated in Figure 1b.  
The compressive strength of rock exhibits dispersion and follows a normal distribution. Therefore, based on the relevant theories of material mechanics, the standard compressive strength values for the four types of rocks were calculated. In order to ensure that the rock samples are within the elastic phase during the loading process under different working conditions, 70% of the standard compressive strength value was chosen as the stress limit for this experiment. The results of the uniaxial compression tests and the selected stress limits for the four types of rocks are shown in Table 1.  
Figure 1: Installation of conventional uniaxial test rock samples and sensors: (**a**) rock sample encased in FEP heat shrink tubing; (**b**) installation of circumferential and axial strain gauges on the rock sample.
# Adaptive Dynamic Search for Multi-Task Learning
## 3 The Proposed Method
### Optimization
The last relation approximates the computation of all possible models in the search space using a set of model structures \(\widehat{\mathcal{A}}\) randomly sampled from \(S(x_{t};\theta^{s})\)[39]. This learning approach resembles a single-step Markov decision process (MDP) by predicting all actions at once via \(\mathcal{A}^{k}\) based on a reward \(\mathcal{M}^{k}\)[28]. In addition, we apply the epsilon-greedy method [40] on the output \(S(x_{t};\theta^{s})\) when approximating the gradient to allow for a more dynamic search of the network models. The proposed method, which we named _ADM_, is summarized in Algorithm 1. In the algorithm, we adopt an alternating optimization approach to learn the sets of parameters\(\theta^{b}\) and \(\theta^{s}\), where we update each set of parameters until convergence in every learning step. We update the parameters in \(\theta^{b}\) with respect to the discovered network model for each instance. After finishing the learning procedure, we have a trained backbone architecture containing a pool of candidate models and a trained global search network that has knowledge of multiple tasks. ```
1:Input: \(\mathcal{D}\), \(\lambda_{l}\) (\(l\) = 1, 2,..., \(c\)), \(c\), learning steps \(n_{s}\)
2:Initialize: \(\theta^{b}\), \(\theta^{s}\)
3:for\(i\) = 1 to \(n_{s}\)do
4:repeat
5: Choose \(c\) model structures \(\mathcal{A}^{l}\) (\(l\) = 1, 2,..., \(c\)) from \(S\)
6: Update \(\theta^{b}\) from (6)
7:until convergence
8:repeat
9: Update \(\theta^{s}\) from (8)
10:until convergence
11:endfor
12:Output: Learned sets of parameters \(\theta^{b}\) and \(\theta^{s}\)
```  
**Algorithm 1** ADM: Adaptive Dynamic Search for MTL
# Potential Study of Solar Thermal Cooling in Sub-Mediterranean Climate
## 3 GJU System Description
### Meteorological Data  
The Photovoltaic Geographical Information System (PVGIS) tool was used to generate Typical Meteorological Year (TMY) data of solar radiation, temperature, and other meteorological data [25]. The data was provided by the typical meteorological year (TMY2) format containing hourly global and beam radiation data in addition to ambient temperature and sunshine duration values. The considered period for the data that was exported was from 1991-2010. Figures 3 and 4 show the metrological data (daily ambient temperature and daily average global and diffuse radiation in June 2015) in the site over a period of a year.  
The air temperature data for year 2015 in Madaba is presented in Figure 3. Madaba has an inland climate with large air temperature fluctuations across the seasons. In typical summer months (May-September), the highest air temperature normally occurs in June and between 12:00 and 14:00 where the air dry bulb temperature could reach 40 \({}^{\circ}\)C. The solar radiation normally reaches its peak value of around 1030 W/m\({}^{2}\) between 9:00 and 15:00, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Irbid Chamber** & **Royal Culture** & **German Jordanian** & **Petra Guest** \\  & **of Commerce** & **Centre** & **University** & **House** \\ \hline
**Location** & Irbid & Amman & Madaba & Petra \\
**Nominal cooling capacity, kW** & 50 & 160 & 160 & 160 \\
**Gross solar collector area, m\({}^{2}\)** & 140 & 449 & 480 & 388 \\
**Operating hours per day** & 6–12 & 8–16 & 6–10 & 24 \\
**Chilled water supply temperature, \({}^{\circ}\)C** & 8–10 & 8–10 & 6–8 & 9–11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: An overview of the installed absorption chillers.