esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Host Mobility
* _Overlay approaches using tunnelling._ This is where one protocol is encapsulated--tunnelled--within another so that the underlying network infrastructure is unaware of the protocol internal to the tunnel. Tunnelling is typically used as an encapsulation mechanism, where the underlying network infrastructure will not support a protocol directly. A tunnel must be created between tunnel end points, which are new (logical or physical) entities in a network. This approach has the advantage that it can be deployed 'over' an existing network technology. Also, it can use a mixture of approaches as suits a particular application, e.g., one or more of the approaches listed above could be incorporated, including the use of proxies, or special in-network support. The main drawbacks here are the requirement that are typical for a new control plane protocol for the OAM functions of the tunnelling mechanism. This requires increased OAM overhead for deployment at scale. If proxies or special in-network support are used, then the drawbacks of those approaches also apply.
# Framework for Behavioral Analysis of Mobile Networks
## 1 Introduction
Some works are only focused on specific aspects of the network such as a certain type of traffic. Other works are limited to the analysis of historical data but do not include a real-time stage for network monitoring. Finally, many of them are based on only one ML method, not taking advantage of the benefits of combining unsupervised and supervised algorithms. In this context, this work provides the following contributions:  
* The proposed framework is designed to analyze the overall performance in a mobile network, since it allows the inclusion of indicators related to different aspects of the network (e.g., traffic, radio conditions, quality). In addition, it can be set up for the characterization of the network focusing on a specific aspect such as type of traffic or a certain management task. * The use of an unsupervised pre-stage allows the system to automatically tag an untagged real dataset for use as input to a subsequent supervised stage. This aspect enables the benefits of unsupervised and supervised algorithms to be exploited in the same system. * The framework consists of two phases: a first training stage intended to acquire knowledge as cell patterns from the data and a second part that monitors the network to detect possible pattern change, not necessarily related to a network failure. For this reason, the proposed framework is intended to be used not only for information extraction, but also for network monitoring. * The system has been tested with a live LTE dataset since it is currently the predominant technology. However, the methodology followed for the framework definition and implementation can be adapted to any other mobile network technology, since the SOM algorithm is based on finding patterns in the set of network indicators used as inputs regardless of the network technology. This amount of data is expected to be larger in the new 5G networks; hence, the use of the propose framework will benefit operators in the management tasks. The rest of the paper is structured as follows. Section 2 reviews related work. Section 3 presents the proposed framework with definition and implementation details. Section 4 explains the experiments and results of a live LTE network. Finally, Section 5 describes the main conclusions of the work.
## 4 Evolution of the Smart Grid Infrastructure: SDN-Enabled Smart Grid
### Candidate SDN Application for the Smart Grid
The performance result shows that SDN APP can provide not only the DiffServ QoS of the smart grid flows, but also the dynamic QoS configuration of the flows in the network. * Security: The smart grid system autonomously collects massive amounts of data and transfers them to utility companies, consumers and service providers [35]. These data include the private information of customers, such as activities, devices being used and times at which the home is vacant. Abnormal user access and control packets of the SDN are protected by secure socket layer (SSL) and digital mark-based packet-inspection algorithms. The traditional packet-inspection system that is used as a firewall may be degraded because of the large network scale of the smart grid. In addition, the network installation cost will increase when the number of firewalls in the network is increased. However, SDN can provide packet inspection without specialized hardware, which allows dynamic protection rule updates by a system administrator. The ForNox is a role-based access control system for SDN [39]. In this scheme, the SDN controller inserts the validated digital sign to all control packets, and it is encapsulated by using a pre-shared key. Further, the SDN control channels are protected by the SSL protocol [29]. [PERSON] et al. presented an SDN security analysis model for information disclosure, denial of service and tampering attack detection [40]. In this algorithm, the SDN controller randomly chooses the sample packets and data flow table from the network devices. The collected sample data are analyzed using Microsoft STRIDE [40] and attack tree modeling methods, and experimental results show that the SDN effectively isolates and detects the abnormal flows without the need for an additional network monitoring system.
# Complete Subhedge Projection for Stepwise Hedge Automata +
## 7 Congruence Projection
### Ideas
#### 7.1.2 Least Difference Relations  
For any binary relation \(R\subseteq\mathcal{Q}\times\mathcal{Q}\), let \(ldr^{\Delta}(R)\) be the least difference relation on states that contains \(R\).  
**Lemma 8**.: \((p_{1},p_{2})\in ldr^{\Delta}(R)\Leftrightarrow\exists(q_{1},q_{2})\in R\)_. \(\exists h\in\mathcal{H}_{\Sigma}\), \(p_{1}\xrightarrow{h}q_{1}\text{ wrt }\Delta\wedge p_{2}\xrightarrow{h}q_{2}\text{ wrt }\Delta\)._  
Figure 14: The congruence projection \(\text{dSha}^{\bot}\)\(A^{\text{Gyr}}[\text{{\tt(dec)}}]\) for the counter example of the completeness of safe-no-change projection, i.e., the dSha\(A\) for the filter \([\text{{\tt child::item}}]\) in Figure 12 with the schema final states \(F_{\mathbf{S}}=\{0,5,6\}\). Note that state \(\{\}\{\}\{\}\)D3 that is reached over the prefix \(\langle\texttt{list}\cdot\langle\texttt{item}\rangle\) and the state \(\{\}\{\}\{\}\)D3 that is reached over the prefix \(\langle\texttt{item}\cdot\langle\texttt{item}\rangle\) are subhedge irrelevant, and thus, subhedge projection states.  
Proof.: The set \(\{(p_{1},p_{2})\in\mathcal{Q}^{2}\mid\exists(q_{1},q_{2})\in R\). \(\exists h\in\mathcal{H}_{\Sigma}\). \(p_{1}\xrightarrow{h}q_{1}\) wrt \(\Delta\wedge p_{2}\xrightarrow{h}q_{2}\) wrt \(\Delta\}\) clearly is a difference relation that contains \(R\), and thus, contains the least such difference relation \(\mathit{ldr}^{\Delta}(R)\). Conversely, each pair in the above set must be contained in any difference relation containing \(R\), and thus, in \(\mathit{ldr}^{\Delta}(R)\).  
For any \(R\subseteq\mathcal{Q}\times\mathcal{Q}\), the difference relation \(\mathit{ldr}^{\Delta}(R)\) is the value of predicate \(D\) in the least fixed point of the ground datalog program generated by the following inference rules:  
\[\frac{p,q\in\mathcal{Q}}{D(p,q):-\ R(p,q)}\qquad\frac{p_{1} \xrightarrow{a}p_{2}\text{ wrt }\Delta\qquad q_{1}\xrightarrow{a}q_{2}\text{ wrt }\Delta}{D(p_{1},q_{1}):-\ D(p_{2},q_{2}).}\]  
Proof.: The first inference rule guarantees that \(R\subseteq D\). The three later inference rules characterize difference relations \(D\in\mathcal{D}^{\Delta}\). The second and third rules state that differences in \(D\) are propagated backwards over hedges \(h\). This is done recursively by treating letter hedges \(h=a\) by the second rule and tree hedges \(h=\langle h^{\prime}\rangle\) by the third rule. The fourth rules expresses the symmetry of difference relations \(D\). So, the least fixed point of the datalog program generated by the above rules contains \(R\) and is the least relation that satisfies all axioms of difference relations, so it is \(\mathit{ldr}^{\Delta}(R)\).  
For any \(R\subseteq\mathcal{Q}\times\mathcal{Q}\), the least difference relation \(\mathit{ldr}^{\Delta}(R)\) can be computed in time \(O(|A|^{2})\).  
Proof.: The size of the ground datalog program computing \(\mathit{ldr}^{\Delta}(R)\) from Lemma 3.1 is at most quadratic in the size of \(A\), so its least fixed point can be computed in time \(O(|A|^{2})\).
## 3 Co-Optimization Model of EVs and PVs
### Battery Life Loss Model  
The aging mechanism of the battery itself is mainly the formation of a Solid Electrolyte Interphase (SEI) on the contact surface of the battery's negative electrode and the electrolyte, which consumes the active material and reduces the battery capacity. Generally, when the battery capacity decays to 80% of the initial capacity, it is considered that the battery life has reached the end of its battery life cycle. Additionally, the battery cathode will also structurally age under the action of various mechanisms. In addition to its own physical characteristics, the aging process of the battery is affected by environmental factors such as state of charge, depth of discharge, temperature and humidity.  
The battery life model is mainly divided into the mechanism model and the external characteristic model. The mechanism model is modeled according to the physical and chemical reactions inside the battery. It has high accuracy but is complex, which is not conducive to the calculation of the simulation model. The external characteristic model is based on a data-driven method, using experimental data to model the battery life by observing the capacity fade and internal resistance growth exhibited during cycling [29; 30].  
Differentiating from the battery loss model that only combines the depth of discharge, a half-empirical and half-mechanical dynamic battery life decay model that combines the internal resistance growth model with the capacity decay model was used, as shown in Equation (5), which takes into account environmental factors such as the depth of discharge, battery \(SOC\) and temperature [31].  
\[M^{i}(k)=\frac{Q_{site}}{c_{ref}T_{acc}}\left(\frac{D^{i}(k)}{D_{ref}}\right) ^{-\beta}e^{-\frac{\alpha E}{K}\left(SOC^{i}(k)-SOC_{ref}\right)} \tag{5}\]  
The depth of discharge \(D\) is defined as the difference between the load state at the beginning of the discharge and the load state at the end of the discharge. The formalization of the depth of discharge of the \(i\)th EV is shown in Equation (6); the initial state of \(SOC^{i}\) of the \(i\)th EV is formalized, as shown in Equation (7).  
\[D^{i}(k)=\frac{\left|\sum_{k_{discharge.start}^{i}}^{k_{discharge.end}}P^{i}_{ex.discharge}(k)\Delta t \right|}{E_{max}} \tag{6}\]  
\[SOC^{i}(k)=\frac{E^{i}(k)}{E_{max}} \tag{7}\]  
\[E^{i}(k)=E^{i}_{start}+\sum_{k_{start}^{i}}^{k}P^{i}_{ev}(k)\Delta t \tag{8}\]  
In Equations (5)-(8), \(Q_{site}\) is the capacity decay rate; \(T_{acc}\) is the temperature acceleration coefficient; \(D_{ref}\) is the standard depth of discharge; \(SOC_{ref}\) is the standard value of the state of charge at the beginning of the discharge cycle; \(F\) is the Faraday constant; \(R\) is the molar gas constant; \(\alpha\), \(\beta\), and \(c_{ref}\) are fitting constants, which can be obtained by fitting the experimental data of the battery; \(E_{max}\) is the battery capacity; \(E^{i}\) is the current capacity of the \(i\)th EV battery; \(k_{discharge.start}^{i}\) is the start time of a period of discharge; and \(k_{discharge.end}^{i}\) is the end time of a period of discharge.
# An Optical Encoder Chip with Area Compensation
## 3 Theoretical Analysis of Light Intensity and Compensation Design
### Design of Photodiode Area Compensation
So the relationship between the incident light power \(P\) and the position and area of the photodiode is as follows:  
\[P=\int_{x_{1}}^{x_{2}}\int_{y_{1}}^{y_{2}}E(x,y,z)dxdy=\int_{x_{1}}^{x_{2}} \int_{y_{1}}^{y_{2}}\frac{z^{m+1}I_{0}}{\left[\left(x-X\right)^{2}+\left(y-Y \right)^{2}+z^{2}\right]^{\left(m+3\right)/2}dxdy} \tag{9}\]  
In actual use, the height of the light source from the code disk is determined, so \(z\) in Equation (9) is a constant. According to Equations (7) and (9), the photocurrent generated by photodiode B6 can be calculated. Then, based on this current, the photodiodes in other positions are compensated with different degrees of area to make their photocurrents equal to this reference current. The specific compensation height value is calculated by MATLAB software according to Equation (9). After the compensation height is obtained, the lengths of the upper and lower sides of the new compensated photodiodes can be calculated according to the imaging principle. The shape of the photodiodes can be approximated as a trapezoid, and the new area information of the photodiodes after compensation can be calculated according to the area formula of the trapezoid. The size information of the photodiode at each position after compensation is shown in Table 1, and the schematic diagram of the photodiode arrays after compensation is shown as the solid lines in Figure 5.
## 3 Simulation of Bridges on Lie Groups  
In this section, we consider the task of simulating (6) conditioned to hit \(v\in G\), at time \(T>0\). The potentially intractable transition density for the solution of (6) inhibits simulation directly from the bridge SDE (9). Instead, we propose to add a guiding term mimicking that of [PERSON] and [PERSON] (2015), i.e., the guiding term becomes the gradient of the distance to \(v\) divided by the time to arrival. The SDE for the guided diffusion becomes  
\[dY_{t}=-\frac{1}{2}V_{0}(Y_{t})dt+V_{i}(Y_{t})\circ\left(dB_{t}^{i}-\frac{ \left(\
abla_{y|_{y-Y_{t}}}d(y,v)^{2}\right)^{i}}{2(T-t)}dt\right),\qquad Y_{0 }=e, \tag{13}\]  
where \(d(\cdot,v)\) denotes the Riemannian distance to \(v\). Note that we can always, for convenience, take the initial value to be the identity \(e\). Equation (13) can equivalently be written as  
\[dY_{t}=-\frac{1}{2}V_{0}(Y_{t})dt+V_{i}(Y_{t})\circ\left(dB_{t}^{i}-\frac{ \operatorname{Log}_{Y_{t}}(v)^{i}}{T-t}dt\right),\qquad Y_{0}=e,\]  
where \(\operatorname{Log}_{p}\) is the inverse of the Riemannian exponential map \(\operatorname{Exp}_{p}\). Figure 2 illustrates one sample path of the simulation scheme in (13) on the Lie group \(\operatorname{SO}(3)\). The corresponding axis-angle representation is visualized in Figure 3.  
Figure 2: One sample path of the guided bridge process (13) on \(\operatorname{SO}(3)\) visualized by its action on the basis vectors (red, blue, green) of \(\mathbb{R}^{3}\). The bridge is conditioned on the rotation indicated by the black arrows.  
The guiding term in (13) is identical to the guiding term described in [4; 7]. In [7], the guided processes used the frame bundle of \(M\). In the Lie group setting, since Lie groups are parallelizable, the use of the frame bundle is not needed: the invariant vector fields \(V_{i}\) provide a frame of reference at all points of \(G\).  
Numerical computations of the Lie group exponential map are often computationally efficient; see [23] and the references therein for efficient algorithms. Therefore, by a change of measures argument, the equation above can be expressed in terms of the inverse of the Lie group exponential:  
\[dY_{t}=-\frac{1}{2}V_{0}(Y_{t})dt+V_{i}(Y_{t})\circ\left(d\tilde{B}_{t}^{i}- \frac{\log_{Y_{t}}(v)^{i}}{T-t}dt\right) \tag{14}\]  
\(Y_{0}=e\), where \(\tilde{B}\) is a Brownian motion under a new measure, say \(\mathbb{P}\). The measure \(\mathbb{P}\) can explicitly be expressed as  
\[\frac{d\mathbb{P}}{d\mathbb{P}}|_{\mathcal{F}_{t}}=\exp\bigg{[}-\int_{0}^{t}H _{v}(s,Y_{s})-\frac{1}{2}\int_{0}^{t}\frac{\|\log_{Y_{s}}(v)-\log_{Y_{s}}(v)\| _{Y_{s}}^{2}}{(T-s)^{2}}ds\bigg{]},\]  
where \(\mathbb{P}\) denotes the law of the SDE in (13) and  
\[H_{v}(t,Y_{t})=\bigg{\langle}\frac{\left(\log_{Y_{t}}(v)-\log_{Y_{t}}(v) \right)}{T-t},V(Y_{t})dB_{t}\bigg{\rangle}_{Y_{t}}.\]  
Note that when the metric is bi-invariant, the group logarithm and the Riemannian logarithm coincide.
## 5 Data Analysis
### Development of Riyadh Pavement Performance Model  
In developing the pavement maintenance performance model for Riyadh roadways, the following methodology was adopted. All the available data were sorted and classified based on the following:  
* Traffic levels,
* Maintenance strategy type,
* Maintenance age,
* Initial PCI value (at the start of maintenance application).
* Data in each homogeneous group were plotted using a scatter plot. The data were then analyzed, the outliers were eliminated, and further analysis was conducted.
* For each group, a regression model was developed, as the drop in the PCI value for each traffic level and PCI initial value, while the other factors were used as the predictors for each group of homogeneous sections.
* The regression curves of the same maintenance and traffic group were compared to combine them into one model if possible.
* The regression coefficients in the final models were used in the expert system for easy application.  
Figures 5 and 6 show the regression performance models for the no M&R strategy for a low- and high-traffic case, respectively. Performance models for routine maintenance are shown in Figures 7-10 show the overlay strategy for low and high traffic, respectively. Finally, Figures 11 and 12 show regression performance models for the reconstruction strategy for low and high traffic, respectively.  
Figure 8: Effect of routine maintenance on the deterioration of PCI for high traffic.  
Figure 6: Effects of the no maintenance strategy on the deterioration of PCI for high traffic.  
Figure 7: Effect of routine maintenance on the deterioration of PCI for low traffic.  
Figure 11: Effect of reconstruction on the deterioration of PCI for low traffic.  
Figure 10: Effect of the overlay on the deterioration of PCI for high traffic.  
Figure 9: Effect of the overlay on the deterioration of PCI for low traffic.
## 4 Experiments  
In this section, to evaluate our proposed RFEPPO method, we use it to optimize the hyperparameters of XGBoost and CNN. Among them, XGBoost is a common algorithm for handling tabular data, while CNN is a typical model for processing image data. We first introduce the experimental setup, present the datasets, the conventional approaches, and the evaluation method. Then we conduct comparison experiments on tabular and image datasets to compare our method with conventional methods. Finally, to verify the effect of the components in our method on the HPO results, we perform ablation experiments.  
For fairness, all search algorithms ran on Intel Core i9-11900K CPU. The XGBoost algorithm also ran on the CPU. To save time, we used a single NVIDIA GeForce RTX 3090 GPU when training the CNN model.
# Stressing State Analysis on a Single Tube CFST Arch Under Spatial Loads
## 5 Stressing State Analysis Based on the Interpolation of Strains
### Interpolation of Strain/Stress Fields by TSI Method  
First of all, the software ANSYS was used to construct the FE model of arch's cross section and meshed the section model with element Shell 181, as shown in Figure 8a. The radius of the cross section (R) is 60.5 mm and the thickness of steel tube (d) is 4.5 mm. In the coordinate system (_x,y_), built in the FE model, the coordinate (_x,y_) corresponds to _j_-th element node. As known quantities, the deflections of controlled points 1-4 are correspondent with strains \(x\)1-_x_4. Similar to the calculation of common shape functions [31; 32; 33], the discrete weighting functions \(\mathbf{N}_{1}\)-\(\mathbf{N}_{4}\) are obtained based on four controlled points. Take \(\mathbf{N}_{1}\) for example, applying a unit displacement at point 1 while points 2-4 are fixed to restrict rigid displacements, so \(\mathbf{N}_{1}\) is formed by static calculation and extracting the function values from each element node, as shown in Figure 8b. \(\mathbf{N}_{3}\) is also depicted in Figure 8c. Due to the central symmetry of the section shape, \(\mathbf{N}_{1}\) is in rotational symmetry with \(\mathbf{N}_{3}\).  
Figure 8: The finite element (FE) model of the cross-section and contour maps of numerical shape functions: (**a**) FE model; (**b**) discrete weighting function \(\mathbf{N}_{1}\); (**c**) discrete weighting function \(\mathbf{N}_{3}\).  
Figure 7: The curves of generalized energy densities for axial force and bending moments.  
The discrete weighting functions \(\mathbf{N}_{1}\)\(\sim\)\(\mathbf{N}_{4}\) can be explained as the aggregations by all function values at element nodes, they are  
\[\mathbf{N}_{i}=\Big{[}N_{i}(x_{1},y_{1}),\ N_{i}(x_{2},y_{2}),\ \cdots\ N_{i}(x_{j},y_{j}),\ \cdots\ N_{i}(x_{n},y_{n})\Big{]}, \tag{14}\]  
where \(\mathbf{N}_{i}\) is the discrete weighting function acquired on the basis of \(i\)-th controlled point, \(N_{i}(x_{j},y_{j})\) is the function value at element node (\(x_{j}\),\(y_{j}\)), \(n\) is the total number of element nodes. Without considering large deformation or elastoplasticity, the interpolation field constructed by [PERSON]'s theorem is independent of loading paths, and linear superposition can be carried out for the simulative results with explicit physical meanings. Therefore, according to the deflection of points 1\(\sim\)4 and the corresponding discrete weighting functions \(\mathbf{N}_{1}\sim\mathbf{N}_{4}\), the deflection field \(\mathbf{D}\) of the whole cross section is assembled, that is  
\[\mathbf{D}=\sum\varepsilon_{i}\mathbf{N}_{i}. \tag{15}\]  
In other words, \(\mathbf{D}\) can be perceived as the aggregation of strains at all element nodes.  
According to the constitutive relation of the materials, the corresponding stress filed is calculated. The constitutive models of concrete in compression and tensile behavior are the uniaxial stress-strain curve [34] and [PERSON]'s model [35], respectively. As for steel, the five-stage stress-strain curve [36] is applied.
# Vertical Seismic-Profile Data Local Full-Waveform Inversion Based on Marchenko Redatuming
## 4 Discussion
The common VSP virtual source gather is shown in Figure 14. Compared with Figure 9a, the retrieval accuracy of the laterally propagating waves receives a larger impact with the reduction of the aperture. The main reason for the current limitation of local full waveform inversion mainly depends on the accuracy of the redatumed wavefield, and more exploration in this direction is needed in the future. In addition, considering the viscoelastic behavior of subsurface in petroleum exploration is crucial because viscoelastic inversion is highly sensitive to fluid content due to the energy losses caused by their viscosity [30; 31]. This sensitivity is explained by viscous fluid flow during the passage of the waves, which causes frictional movement at the grain surfaces [32; 33]. The physical complexity of FWI models can be overcome by incorporating machine-learning models [34]. Therefore, incorporating the viscoelasticity and machine learning models in local FWI may significantly enhance seismic imaging and interpretation in reservoir zones, which is the direction of our future research. Figure 13: Gradient comparison of VSP-driven local FWI term (a) and surface data local FWI term (b) at 30 Hz frequency.