esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 2 Review of Related Studies
### Traffic Volume Prediction Based on Statistical Learning  
Traffic volume prediction typically utilizes a data-driven approach based on statistical techniques, which can be classified into parametric and nonparametric estimation methods [9]. The parametric techniques include traditional linear and nonlinear regression methods [10; 11; 12; 13], historical average algorithms, moving averages, smoothing techniques [14], and autoregressive linear processes [12; 13; 15]. The selection of the algorithm depends on the periodicity and variability of the traffic volume data, with the autoregressive moving average (ARMA) and ARIMA being the most commonly employed linear parametric models.  
In particular, the ARIMA model is notable for its application to differenced time series, addressing the non-stationarity and lack of mean reversion in the original series through differencing. This model involves detrending and deseasonalizing the data, applying the ARMA model, and subsequently reintegrating the trends and seasonality during theestimation phase. The ARIMA model is known for its effectiveness and is often improved by incorporating seasonal adjustments, as traffic volume data typically exhibit pronounced seasonal trends. This enhancement is captured in the seasonal ARIMA (SARIMA) model, which has been shown to provide more accurate predictions for traffic data with strong seasonal patterns [16].  
Nonlinear parametric models, such as the generalized linear mixed models, fuzzy logic, and genetic algorithms, require analysts to make subjective judgments regarding the form of nonlinearity, and their complex estimation processes can deter widespread usage. Spatial statistical methods, such as the Kriging interpolation, are used to predict traffic volumes by utilizing data from spatially related areas. However, these methods are typically suitable only for road segments with strong spatial interrelationships and are considered to be limited in their explanatory power for univariate analysis.  
Table 1 lists the studies conducted on traffic volume prediction based on statistical learning algorithms.
## 4 Methods for Manipulation Protection and Detection  
Manipulation detection methods not only enable the identification of tampering in an image but often also pinpoint the specific areas subjected to modification [35, 36]. Some advanced techniques can even reconstruct an approximate version of the original image prior to the manipulation [37]. In the years preceding the deep learning revolution, classical image processing algorithms were used to detect image manipulation [18, 38, 39]. However, these methods faced significant limitations in terms of effectiveness and generalizability. Today, the advancements in deep learning dominate both current research and practical applications in manipulation detection [19, 40]. Methods based on neural networks allow for more efficient and accurate detection of modifications, even in complex scenarios.  
Active methods require embedding additional information, such as digital watermarks or signatures, into the image. Digital watermarks involve hiding imperceptible data within the image, which can serve as redundant information for identifying manipulated areas or even partially reconstructing the original. Additionally, watermarks may include metadata such as author details, source information, rights data, or unique identifiers. In cases of suspected manipulation, this embedded information can be extracted and used to verify the authenticity of the image. Digital signatures, on the other hand, use cryptographic encryption techniques to generate unique identifiers for the image, similar to solutions used in telecommunication systems [41]. Any alteration to the image changes the signature, making unauthorized modifications easier to detect. Active methods are particularly effective when the entire process of image creation and distribution is under control. Passive methods, also known as blind methods, rely solely on analyzing the image itself without requiring any additional data or prior embedded information. These methods leverage the statistical and structural characteristics of the image, such as color histograms, textures, noise patterns, or compression artifacts. By analyzing these features, passive methods can detect inconsistencies and anomalies indicative of manipulation. For instance, they can identify discrepancies in lighting, shadows, or perspective, which are challenging to replicate accurately during editing. Thanks to deep learning techniques, passive methods have gained the ability to detect manipulations by extracting highly abstract and complex image features [42]. Neural networks with a large number of parameters can learn data representations at multiple levels, identifying subtle anomalies and patterns that are invisible to traditional methods.  
In the following subsections of this section, we will focus on the analysis of both active and passive image integrity assurance methods based on deep learning.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Data and Evidence Mapping
#### 4.4.2 A Data-Driven Method of Traffic Emissions Mapping with Land Use Random Forest Models [25]  
This study introduces a novel land use random forest (LURF) approach to mapping real-time, link-level vehicle emissions. The method integrates high-density traffic monitoring data with land use information to estimate emissions dynamically, providing a foundation for adaptive Low-Emission Zones (LEZs). Its key contributions are as follows:  
* High-Resolution Emission Mapping: Leveraging 272 predictors, including road features, population density, and land use variables, the model achieves a spatial generalization accuracy \(R^{2}>0.8\) for traffic volume and speed simulations.
* Dynamic Temporal and Spatial Analysis: Evaluated hourly and daily CO, HC, NOx, PM2.5, and CO\({}_{2}\) emission patterns, revealing significant variations during peak and off-peak traffic periods.
* Drivers of Spatial Heterogeneity: Nonlinear relationships were identified between emissions and urban features, such as population density and proximity to logistical centers. For instance, freight activity predominantly influences NOx emissions, while CO\({}_{2}\) emissions are higher near Central Business Districts (CBDs).
* Scenario Evaluation for LEZ Policies: It conducted fine-grained assessments of emission reductions under two scenarios: (i) traffic demand management, exemplified by COVID-19 restrictions, achieving up to 66% CO\({}_{2}\) reductions; and (ii) fleet electrification, resulting in a 31% CO\({}_{2}\) reduction and significant reductions in NOx and PM2.5 emissions.
* Scalability and Policy Applications:The LURF model's computational efficiency and adaptability make it suitable for real-time traffic data integration, supporting LEZ strategies in global megacities.  
The study highlights how LURF-based mapping tools enable targeted LEZ policies by identifying emission hotspots and tailoring strategies to specific urban dynamics. The approach aligns with adaptive LEZ objectives, emphasizing precision and real-time adaptability for emission reduction.
# A Temperature Fiber Sensor Based on Tapered Fiber Bragg Grating Fabricated by Femtosecond Laser
## 2 Sensor Structure and Principle  
A FBG is a periodic and permanent modification of the core refractive index value along the optical fiber axis. The FBG inscription is defined by the Bragg phase matching condition [34]:  
\[m\lambda_{B}=2n_{eff}\Lambda \tag{1}\]  
where \(m\) is the order number, \(\lambda_{B}\) is the Bragg resonance wavelength, \(n_{eff}\) represents the effective index of the fiber core, and \(\Lambda\) is the grating pitch. Temperature affects the Bragg wavelength shift through the thermal expansion and the thermo-optical effect, which can be expressed by:  
\[S_{T}=\frac{d\lambda_{B}}{dT}=\lambda_{B}(\alpha+\beta) \tag{2}\]  
where \(\alpha=\frac{1}{\Lambda}\cdot\frac{d\Lambda}{dT}\sim 0.55\times 10^{-6}/^{ \circ}\)C is the thermal expansion coefficient; \(\beta=\frac{1}{n_{eff}}\cdot\frac{dn_{eff}}{dT}\sim 6\times 10^{-6}/^{ \circ}\)C is the thermo-optic coefficient of the optical fiber.  
The proposed tapered-FBG and its light propagation are illustrated in Figure 1. In principle, a tapered FBG is a special version of a standard fiber Bragg grating. The tapered fiber region contains two transition regions, where the fiber diameter gradually decreases and then increases. When the light propagates in the optical fiber, the energy inside the fiber core is constrained within by reflective boundary conditions. When the incident broadband light travels along the fiber core and arrives at the FBG, there will be a high back reflection only at the Bragg wavelength \(\lambda_{B}\).  
Meanwhile, the rest energy will be transmitted along the tapered region, namely an evanescent field. In a standard non-tapered fiber, modes are confined by the core/cladding interface. While in the tapered region, light is initially focused in the thinning core until core guidance becomes so weak that the mode expands, then the mode is guided by the cladding/air interface, and here the mode diameter is at its maximum. The mode is then confined by the cladding/air interface and reveals the maximum confinement, which makes the tapered structure more sensitive than common cylinder geometry.  
It has been demonstrated that the sensitivity of the tapered fiber is largely determined by its geometrical parameters, especially the taper lengths and diameters [3; 15; 33]. The tapered region is particularly important for sensing applications as it allows maximum interaction with the surroundings, and thus it has predictable compactness and sensitivity improvement.
## 4 Validation of the Numerical Model
### Previous Experimental Campaign by [PERSON] et al. [38]
#### 4.1.3 Boundary Conditions  
It was demonstrated that firebrand can transmit heat flux via radiation and convection on plane surfaces [46], therefore a surface convection and radiation boundary condition were used to simulate the flux transfer between the firebrands and the steel plates. This procedure is in accordance with standard fire safety design in structural codes [52], when a structure is subjected to the action of a nominal or performance base fire. Hence, the prescribed heat flux is given using  
\[q_{n}=-\overline{q} \tag{3}\]  
\[q_{n}=-h_{f}(T_{SUF}-T_{SINK})-\sigma\epsilon\Big{(}T_{SUF}^{4}-T_{SINK}^{4} \Big{)} \tag{4}\]  
where \(q_{n}\) is the heat flux in the \(n\) direction given using \(q_{n}=q_{i}n_{i}\), and \(\overline{q}\) is a heat flux in the normal direction. The heat flux can assume a positive (supplied heat) or negative (subtracted heat) value. On the surface, on which the firebrands were accumulated, a sink  
Figure 6: Geometry used in (**a**) the numerical model and (**b**) the experimental schematic setup.  
Figure 7: (**a**) Specific heat (\(C_{p}\)) and (**b**) thermal conductivity (\(k\)) used [54].  
temperature equal to the top thermocouple in the top steel plate \(T_{SINK}=T_{Firebrands}\) was adopted (Figure 8). All other surfaces were simulated with convection and radiation at an ambient temperature (\(T_{SINK}=T_{amb}\)), using Equation (3), but two sides had adiabatic surfaces due to the symmetrical axes (Figure 9). The interaction between the steel plates was simulated using gap radiation (\(\epsilon=1.0\))--functionality in ABAQUS for modelling radiation between surfaces across a narrow gap--because there is a small gap between the steel plates that must be taken into account. Hence, the heat flux per unit surface area crossing the gap between steel plates is given using  
\[q_{n}=C\Big{(}T_{SINF.A}^{4}-T_{SINF.B}^{4}\Big{)} \tag{5}\]  
\[C=\frac{F\sigma}{\frac{1}{\frac{1}{\varepsilon_  
Finally, the temperature distribution in a medium is obtained through conduction thermal analysis, and the heat transfer equation is formulated by applying the principle of conservation of the energy to a three-dimensional differential control volume. The heat transfer equation in this problem, for isotropic material, is given by  
\[\left[\frac{\partial}{\partial x}\left(k\frac{\partial T}{\partial x}\right)+ \frac{\partial}{\partial y}\left(k\frac{\partial T}{\partial y}\right)+\frac{ \partial}{\partial z}\left(k\frac{\partial T}{\partial z}\right)\right]=\rho c _{p}\frac{\partial T}{\partial t} \tag{7}\]  
where \(\partial T/\partial_{x}\) is the temperature gradient in the \(x\)-direction, same for the \(y\)- and \(z\)-direction, and \(\rho c_{p}(\partial T/\partial_{t})\) is the time rate of change of the thermal energy of the medium per unit volume. It is worth pointing out that for all models analysed in this paper, internal heat generation was not considered, and the material is not experiencing a change in phase.
# Transfer Incremental Learning Using Data Augmentation
## 3 Proposed Method
### Details of the Proposed Method
#### 3.2.3 Aggregation of Subspaces Weak Classifiers  
At the prediction stage, consider an input signal \(\mathbf{s}\) and the associated feature vector \(\mathbf{x}\). We split \(\mathbf{x}\) into the corresponding \(P\) parts and obtain \(\left(\mathbf{x}_{p}\right)_{1\leq p\leq p}\). We compute Euclidean distances between each \(\mathbf{x}_{p}\) and all anchor subvectors \(\mathbf{y}_{c,p,i}\) for which the counter is not 0. Note that there are at most \(kC\) such distances, where \(C\) is the number of classes seen so far. The class of the closest average anchor subvector is considered as the decision for the \(p\)-th subspace. Finally, we apply a majority vote over all subspaces to achieve an aggregate decision (see Algorithm 2). Note that more elaborate strategies can result in higher accuracy but may require more computation during the learning phase as well as memorisation of previously seen examples.  
```
Input: input signal \(\mathbf{s}\)  Compute the feature vector \(\mathbf{x}\) associated with \(\mathbf{s}\)  Initialize the vote vector \(\mathbf{v}\) as the \(\mathbf{0}\) vector with dimension \(C\) for\(p:=1\) to \(P\)do \(\mathbf{v}_{p}=\underset{c}{\text{arg min }}\underset{i}{\text{min }}\left[\underset{i}{\text{min }}\|\mathbf{x}_{p}-\mathbf{y}_{c,p,i}\|_{2}\right]\) \(\mathbf{v}_{p_{p}}=\mathbf{v}_{p_{p}}+1\) endfor \(\tilde{C}=\underset{c}{\text{arg max }}(\mathbf{v}_{c})\)
```  
**Output:** class \(\tilde{C}\) attributed to \(\mathbf{s}\)  
**Algorithm 2** Predicting the Class of a Test Input Signal
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
## 4 Numerical Test Cases
### 2-D Problems
#### 4.2.4 Spurious Currents in a Static Bubble  
A classic test-case [1; 34; 51; 53] to validate surface tension schemes is that of a static bubble. It is employed here to validate the accuracy of both HLLC and CICSAM VoF methods in conjunction with height function and convolution curvature calculation. An ideal gas bubble with radius \(R_{0}=0.4\) is initialised in a slightly compressible liquid at the centre of a unit domain. The material properties are set as per [PERSON] et al. [12]:  
\[(\rho_{1},\gamma_{1},p_{\infty_{1}})=(1.0,1.4,0.0)\text{and}(\rho_{2},\gamma_{ 2},p_{\infty_{2}})=(1.0,7.14,300),\]  
with \(\sigma=1\) and where the grid is such that \(\frac{R_{0}}{\Delta x}=12.4\) (equispaced structured). A uniform pressure field is initialised. The simulation is run for 15 s (\(\approx\)179,229 steps at \(\Delta t\approx 8\text{e}^{-5}\)) to allow the system to reach steady state. The infinity norm of the maximum pressure jump, \(\|\varepsilon_{\Delta p}\|_{\infty}\), is computed using  
\[\|\varepsilon_{\Delta p}\|_{\infty}=\frac{|\Delta p_{nu}-\Delta p_{an}|}{ \Delta p_{an}},\]  
where \(\Delta p\) is the difference between the maximum and minimum pressure in the domain, with \(p_{nu}\) and \(p_{an}\) the numerical and analytical pressure, respectively. Further, the velocity is non-dimensionalised with respect to the characteristic inviscid velocity \(U_{\sigma}\), which is computed as per [PERSON] et al. [50]:  
\[U_{\sigma}=\sqrt{\frac{\sigma}{2\rho R_{0}}}.\]  
The quantitative results are summarized in Table 1 showing the clear superiority of CICSAM with height functions if used on a Cartesian mesh. Figure 14 illustrates the evolution of the velocity currents over time. The maximum velocity recorded with CICSAM and height functions is of the order of \(10^{-8}\) while that with HLLC is divergent. This shows that the proposed scheme is well-balanced as well as the importance of accurate curvature calculation. The errors when using convolution to compute a pressure jump are significantly higher due to a larger error in curvature similar to [54].
# Compatibility Analysis Between RedCap Non-Public Networks and 5G NR in TDD FR1 and FR2 Bands
## 4 State of the Art  
At present, there are no studies that show how the deployment of RedCap may affect the 5G NR networks. There are some studies that show how 5G NR, including IoT, may affect these. Several studies show how 5G NR, including IoT or a low-power wide-area network (LPWAN), may affect other radiocommunication services; for example, [11] studies the impact of 5G NR IoT on the microwave stations operating in the fixed services. In [12], there is an analysis of how IoT of LPWAN may affect radiolocation services2. In [13], the compatibility between 5G NR that uses active antenna systems and LTE, including IoT, is studied, and [14] analyzes the compatibility between 5G networks in a cross-border scenario when TDD is not synchronized, thus partly exploring a similar problem. Thus, while similar studies were conducted, no study has shown how RedCap will affect the current 5G NR public networks.
## 1 Introduction
### The Quest for ORAMs with Optimal Complexity Brought by Position Map  
For \(N\) blocks, the size of position map is \(O(NlogN)\), which is prohibitively large to be stored at the client side. One solution of the previous write-only schemes to address it is the recursive position-based technique, such as HiVE. Concretely, a series of ORAMs such as ORAM\({}_{0}\), ORAM\({}_{1}\),..., ORAM\({}_{X}\) are constructed where ORAM\({}_{0}\) stores the data blocks and the position map of ORAM\({}_{i}\) is stored in ORAM\({}_{i+1}\). The client stores the position map of ORAM\({}_{X}\). If the block size in these position map ORAMs is \(\chi\)_-logN_ for some constant \(\chi\geq 2\), \(O(logN)\) recursive levels are constructed until the final ORAM is small enough to fit for the client storage. Although HiVE has a better asymptotic communication complexity than the traditional ORAMs in a non-recursive setting, the costs of position map in the two kinds of ORAM schemes are identical. In the tree-based schemes, both the read operation and write operation must occur for every recursive level, which incurs \(O(log^{3}N)\) communication [22; 23]. In Hive, the write operation also occurs for every recursive level. Moreover, when writing at a certain level, all lower recursive levels must be read. Therefore, the overhead of the communication is also \(O(log^{3}N)\)[19].  
In addition to the recursively storage, the pointer-based technique is another solution for the storage of position map, in which an oblivious data structure (ODS) in the form of Trie [25] is used to store the position map, such as the DetWoORAM [24]. Every node except the leaves in the Trie stores a pointer to its child node. Instead of a logical address, the physical address of the child is stored in the pointer. Therefore, it is no longer necessary to translate a logical address to a physical address within the Trie itself. Hence, the position map for the Trie itself is eliminated. A path in the Trie is supposed to be traversed when reading the data block or its corresponding physical address. Although the cost of position map is reduced to \(O(log^{2}N)\), it incurs \(O(Blog^{2}N)\) cost to read the data blocks. Moreover, the DetWoORAM requires \(O(logN)\) rounds to get the physical address of a block as a node in Trie can only be read from the server after its parent has been read already.  
We propose a write-only scheme to improve the efficiency of communication complexity and communication rounds. The construction of position map follows the idea proposed by [PERSON] et al. [26]. In their scheme, a static position map is used for a two-server-setting ORAM. After initialization, the position map does not change anymore. The privacy of read operation in their scheme is protected by private information retrieval (PIR) [27; 28]. In this case, both servers cannot locate the desired data block. The remaining security requirement is identical with that in write-only ORAM. Therefore, the static position map can be employed to our write-only scheme, which brings an overhead of \(O(B)\) communication if \(B=\Omega(log^{2}N)\) and \(O(1)\) rounds.
# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 2 Biochemical Features of RAS
### RAS Mutations Trigger Signaling Dysfunction  
KRAS G12 mutations are predominant (81%) in human cancers, followed by G13 (14%) and Q61 (2%) (Figure 2a) [1]. Glycine at codon 12 or 13 mutated to an amino acid other than proline results in steric hindrances that reduce the van der Waals forces between RAS and the GAPs and prevent the arginine finger of GAP from entering the GTPase site [27]. KRASG12C could prompt the exposure of the effector binding site and the nucleotide binding site, which can increase the intrinsic GTPase activity and thus upregulate signal transduction [28; 29]. As an actuator of GTP hydrolysis, mutations of glutamine 61 disrupt GAP-mediated intrinsic GTP hydrolysis [27]. Other mutations such as A146V [30], T158A [31], and R164Q [28] cause the rapid dissociation of GDP (Figure 2b). Overall, most RAS mutations can disrupt the balance of the GTPase switch function and continuously activate downstream signaling pathways, leading to the uncontrolled proliferation of tumor cells.  
Three main strategies have been proposed to treat RAS-related cancers: (I) targeting the signaling pathway upstream of RAS, such as vascular endothelial growth factor (VEGF), epidermal growth factor receptor (EGFR), or the phosphorylation of upstream regulatory kinases [32]; (II) targeting RAS downstream signaling, such as RAF [33], MEK [34], ERK [35], PIJK [36], AKT [37], or mTOR [38]; (III) directly targeting RAS itself. However, in the absence of a deep pocket for binding small compounds with high affinity, RAS has long been considered undruggable [39; 40]. In recent decades, it has become apparent that advanced CADD technology can transform RAS into a promising druggable target. Inhibitors that directly target RAS can be divided into two categories: (i) targeting the plasma membrane localization fragment of RAS: farnesyltransferase (FTase) inhibitors disrupt the post-translational modifications of the CAAX tetrapeptide chain [41] and phosphodieserase \(\delta\) (PDE\(\delta\)) inhibitors prevent PDE\(\delta\) from binding farnes  
Figure 2: Mutations on KRAS. (**a**) Schematic diagram showing the positions of KRAS mutations; (**b**) stick representation showing six residue mutations mapped on the cartoon representation of the crystal structure of KRAS.  
RAS into the interior of cells [42]; (ii) targeting the activation domains of RAS: drugs binding to the \(\alpha\)4-\(\alpha\)5 region of RAS can disrupt RAS-effector interactions [43], and binding to the Switch II pocket (in GDP-bound inactive conformation) can block the interaction between RAS and SOS [44; 45], whereas BI-2852 and BAY-293 have been shown to inhibit the interaction between KRAS and SOS1 with effective antitumor potency [46].