esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Work
### System Architecture  
Figure 1 illustrates the system architecture of the proposed DCB-NS scheme. The proposed architecture divides the 5G network model into access, transport, and core networks. The main access point for users is through the access network. Next, the transport nodes forward service requests to the core network. Then, all service requests in the form of NSRs are processed in the core network, where the DCB-NS agent provisions appropriate network slices.  
The proposed NS architecture consists of a set of physical nodes such as base stations, network switches, edge servers, and core computing servers \(n=\{1,\,2,\,\ldots,\,N\}\), physical links \(l=\{1,\,2,\ldots,\,L\}\) which are either wired or wireless, and user equipments \(u=\{1,\,2,\ldots,\,U\}\). The network provides each node with a CPU computing capacity \(C\). On the other hand, the network links are provided with link capacity \(B\), measured in the allotted bandwidth per link. Lastly, the user equipments represent the devices (IoT devices, smartphones, and autonomous vehicles) that access and send NSRs to the 5G network.
## 2 Bridge Dynamics in a VBI System and the Data Reconstruction Algorithm  
### Dynamic Responses of a Bridge in a Simple VBI System  
When a vehicle traverses a bridge, it engages in an interaction with the bridge structure, collectively constituting a VBI system, as depicted in Figure 1[19]. The dynamic responses of this system can be ascertained by solving the corresponding dynamic equations. In the case of a simply supported beam, and by modeling the vehicle as a single-degree-of-freedom sprung mass, the dynamic equations governing the VBI system are articulated as follows [18].  
For the vehicle:  
\[m_{v}\ddot{u}_{v}(t)+k_{v}[u_{v}(t)-u_{w}(t)]=0 \tag{1}\]  
\[u_{w}(t)=\left.u_{b}(x,t)\right|_{x=vt}+\left.R(x)\right|_{x=vt} \tag{2}\]  
where the \(u_{v}(t)\) denotes the vertical displacement of the sprung mass, \(\ddot{u}_{v}(t)\) is the vehicle's vertical acceleration, \(u_{w}(t)\) represents the vertical displacement of the contact point, \(m_{v}\) and \(k_{v}\) are the vehicle mass and the spring stiffness, respectively, \(\left.R(x)\right|_{x=vt}\) denotes the road roughness at location \(x\), and \(v\) is the constant vehicle speed.  
For the bridge, assuming that the bridge has a constant cross-section [23]:  
\[\overline{m}\ddot{u}_{b}(x,t)+E{I}u_{b}^{\prime\prime\prime\prime}(x,t)=\{k _{v}[u_{v}(t)-\left(u_{b}(x,t)\right|_{x=vt}+\left.R(x)\right|_{x=vt})]-m_{v}g \}\delta(x-vt) \tag{3}\]  
Figure 1: A simple VBI system [19].  
where \(\overline{m}\) is the unit mass, \(E\) is the elastic modulus, \(I\) is the moment of inertia, \(g\) is the acceleration of gravity, and the prime denotes differentiation to coordinate \(x\) of the beam. Moreover, by assuming that the vehicle mass is much smaller than the bridge mass [18] and neglecting the influence of road roughness, Equation (3) can be solved as follows [18]:  
\[u_{b}(x,t)=\sum_{n}\frac{\Delta_{st,n}}{1-S_{n}^{2}}\{sin\frac{n\pi x}{L}[sin \frac{n\pi vt}{L}-S_{n}sin\omega_{b,n}t]\} \tag{4}\]  
where \(L\) represents the beam length, and:  
\[\Delta_{st,n}=\frac{-2m_{v}gL^{3}}{n^{4}\pi^{4}EI} \tag{5}\]  
\[S_{n}=\frac{n\pi v}{L\omega_{b,n}} \tag{6}\]  
\[\omega_{b,n}=\frac{n^{2}\pi^{2}}{L^{2}}\sqrt{\frac{EI}{\overline{m}}} \tag{7}\]  
For a stationary monitoring sensor, the variable \(x\) represents a constant in Equation (4). The bridge responses delineated by Equation (4) comprise two distinct components: the responses attributable to the driving force \(u_{b,1}(x,t)\) as presented in Equation (8), and the free vibration \(u_{b,2}(x,t)\) as illustrated in Equation (9).  
\[u_{b,1}(x,t)=\sum_{n}\frac{\Delta_{st,n}}{1-S_{n}^{2}}sin\frac{n\pi x}{L}sin \frac{n\pi vt}{L} \tag{8}\]  
\[u_{b,2}(x,t)=-\sum_{n}\frac{\Delta_{st,n}S_{n}}{1-S_{n}^{2}}sin\frac{n\pi x}{L }sin\omega_{b,n}t \tag{9}\]  
Assume that the total time for the vehicle to pass the bridge is \(T_{0}\):  
\[T_{0}=L/v \tag{10}\]  
As Equation (8) indicates, the driving-force frequencies are:  
\[\Omega_{b,1,n}=\frac{n\pi v}{L}=\frac{n\pi}{T_{0}} \tag{11}\]
## 2 Experiments and Methods
### Characterization Method  
A JEOL 7800F Schottky field SEM (scanning electron microscope) was used to conduct a systematic research on the influence of different laser parameters, such as average power, pitch spacing and scanning speed on surface geometrical morphology of laser processed Mo samples. The X-ray photoelectron spectroscopy (XPS) surveys were performed using an AXIS ULtrabld spectrometer with non-monochromatized Al Ka X-ray source operated at 150 W. The SEY testing equipment is introduced in detail in Reference [18]. With a primary electron (PE) dose of \(7.6\times 10^{-6}\) C \(\cdot\)mm\({}^{-2}\) and a PE current of 10 nA, the SEY of laser processed Mo metal samples was characterized using a dedicated SEY measurement set-up at the incident angles of \(90^{\circ},80^{\circ}\) and \(60^{\circ}\). Here, the incidence angle, \(\theta\), acted as the angle between the sample surface and the incident PEs.
## 3 Theoretical Formulation and Numerical Modeling
### Numerical Method  
The detailed solution of the governing equations can be found in the study of [PERSON] and [PERSON] [51]. For two-dimensional incompressible flow, only the momentum equations in one direction are considered. The time derivative term in the governing equation is given by the following equation:  
\[\frac{d}{dt}\int\limits_{v}\rho udU=\frac{(\rho uU)^{n+1}-(\rho uU)^{n}}{ \Delta t} \tag{4}\]  
where the superscripts \(n\) and \(n+1\) represent the current time step and the next time step, respectively. The control volume, \(U^{n+1}\), for the time step of \(n+1\) is given by Equation (5):  
\[U^{n+1}=U^{n}+\frac{dU}{dt}\Delta t \tag{5}\]  
where \(\frac{dU}{dt}\) is the derivative of the control volume. In order to satisfy the conservation law of the mesh, the derivative can be given by Equation (6):  
\[\frac{dU}{dt}=\int\limits_{\partial V}u_{b}dA=\sum\limits_{j}^{n_{f}}u_{b,j} \cdot A_{j} \tag{6}\]  
where \(n_{f}\) is the number of surfaces on the control volume, and \(A_{j}\) is the area vector of \(j\). The dot product \(u_{b,j}\cdot A_{j}\) of the surface of each control volume can be given by Equation (7):  
\[u_{b,j}\cdot A_{j}=\frac{\delta U_{j}}{\Delta t} \tag{7}\]  
where \(\delta U_{j}\) is the volume that the surface, \(j\), on the control volume sweeps out in time with a step of \(\Delta t\). The commercial software ANSYS FLUENT was used to perform the numerical analysis in this paper. For the other terms in Equation (2), the second order upwind scheme [52] is used for the convective fluxes, and the central difference scheme with second order accurate is applied for the diffusion fluxes. The source term is discretized by the SIMPLE algorithm [53] to describe the coupling of pressure and velocity. Moreover, the Large Eddy Simulation method was adopted as the numerical simulation method for turbulent flow. Subgrid-scale stress is modeled by the Dynamic SGS Model. A suitable amount of dissipation is given by the SGS model due to its main role for turbulent wave dissipation. There is no additional dissipation for the central difference format, so it is well suited for LES to discretize the diffusion flux. The main reason for 2D LES was chosen in this paper instead of 3D LES is that the large computing resources were required in 3D simulation [54; 55; 56]. Many other researchers [57; 58; 55; 59] have also conducted many studies based on 2D LES, and the accuracy of 2D LES had also been reported in the literature.
# A Reinforcement Learning Approach to Speech Coding
## 5 Learning (Parameter Adaptation)
### AR and MA Parameter Adaptation  
The autoregressive predictor coefficient adaptation is based on a lattice structure as shown in Figure 2.  
The adaptation of the parameters in this lattice predictor is accomplished using the Recursive Least Squares (RLS) lattice algorithm, which operates by first defining the initial conditions for \(n=0\) as [22; 23; 27]  
\[f_{0}^{f}(n)=f_{0}^{b}(n)=x_{r}(n) \tag{5}\]  
\[\zeta(n)=0 \tag{6}\]  
\[E_{1}^{f}(n)=E_{1}^{b}(n)=\lambda E_{1}^{f}(n-1)+[f_{0}^{f}(n)]^{2} \tag{7}\]  
Then, we perform the following recursions in order for \(k=1,2,\,\dots,\,N\),  
\[\Gamma_{k}(n)=\lambda\Gamma_{k}(n-1)+\frac{f_{k-1}^{f}(n)f_{k-1}^{b}(n-1)}{1- \zeta_{k-1}(n)} \tag{8}\]  
\[\Gamma_{k}^{f}(n)=\frac{\Gamma_{k}(n)}{E_{k}^{f}(n)} \tag{9}\]  
\[\Gamma_{k}^{b}(n)=\frac{\Gamma_{k}(n)}{E_{k}^{b}(n-1)} \tag{10}\]  
\[E_{k+1}^{f}(n)=\frac{E_{k}^{f}(n)-\Gamma_{k}^{b}(n)\Gamma_{k}(n)}{v^{2}} \tag{11}\]  
\[E_{k+1}^{b}(n)=\frac{E_{k}^{b}(n-1)-\Gamma_{k}^{f}(n)\Gamma_{k}(n)}{v^{2}} \tag{12}\]  
\[\zeta_{k}(n)=\zeta_{k-1}(n)+\frac{[f_{k-1}^{b}(n-1)]^{2}}{E_{k}^{b}(n-1)} \tag{13}\]  
Figure 2: Lattice Predictor.  
where the forward and backward prediction errors are updated as  
\[f_{k}^{f}(n) =f_{k-1}^{f}(n)-\Gamma_{k}^{b}(n)f_{k-1}^{b}(n-1) \tag{14}\] \[f_{k}^{b}(n) =f_{k-1}^{b}(n-1)-\Gamma_{k}^{f}(n)f_{k-1}^{f}(n) \tag{15}\]  
The \(0<\lambda<1\) and \(0<v<1\) parameters are selected to keep the algorithm from reaching an undesirable steady state as the number of samples processed becomes larger, and thus make sure that the algorithm continues to adapt and track the rather rapidly changing input speech signal.  
Advantages of the RLS lattice algorithm are that the recursions are identical for each stage, all of the variables are scalars, and it exhibits fast convergence, so it allows the coder to respond quickly to changes in the speech signal [22; 23]. [PERSON] and [PERSON] [22] provide extensive derivations and discussions of least squares lattice algorithms.  
The \(N_{b}\) MA coefficients (\(\widehat{b}_{i}\)) of the predictor are adapted as [24; 25]  
\[\widehat{b}_{i}(k)=(1-2^{-8})\widehat{b}_{i}(k-1)+2^{-7}\text{sgn}[u(k)] \text{sgn}[u(k-i)] \tag{16}\]  
for \(i=1,2,\ldots,N_{b}\). This algorithm is obviously extremely simple but plays an important role by aiding in the overall adaptation performance, as discussed in Section 4.1, while avoiding any stability issues.
## 4 Results and Discussions  
### Static Analysis  
The developed code is first cross-checked with OrcaFlex in the static condition before dynamic simulations. Because static displacements of the tunnel are only affected by weight, buoyancy, and stiffness components of tunnel and mooring lines, direct comparison can be made after initial modeling of the entire SFT system. Figure 5 shows the vertical displacements of tunnel and mooring tension in the static condition. The results produced by the developed program coincide well with OrcaFlex's results. The reference dashed line in the tension figure indicates the allowable tension (minimum break load divided by safety factor).  
Figure 4: Time histories of real seismic excitations in longitudinal = x (**a**), transverse = y (**b**), and vertical = z (**c**) directions and its corresponding spectra.
# Optimization of Phase-Only Computer-Generated Holograms Based on the Gradient Descent Method
###### Abstract  
The Gerchberg-Saxton (GS) algorithm is a Fourier iterative algorithm that can effectively optimize phase-only computer-generated holograms (CGHs). This study proposes a new optimization technique for phase-only CGHs based on the gradient descent method. The proposed technique evaluates the intensity distributions of reconstructed images to directly obtain the phase distributions of the CGHs, whereas the GS algorithm equivalently evaluates the amplitude distributions of reconstructed images and extracts phase distributions from complex-amplitude distributions of the holograms using a constant-amplitude constraint. The proposed technique can reduce the errors in the reconstructed images with fewer iterations than the GS algorithm.  
holography; computer-generated hologram; phase-only hologram; gradient descent algorithm; high efficiency Article
# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 2 Research Area and Data Pre-Processing  
### Characteristics of the Research  
At the end of July 2023, due to the impact of Typhoon \"Dussuri\", significant rainfall occurred in the Beijing area, leading to floods. The severely affected Fangshan area (39 deg30 deg-39 deg55' N, 115 deg2'-116 deg15' E) is considered as the research area in this article, which is located in the southwest of Beijing. It is adjacent to Mentougou District to the north and Fengtai District to the northeast, faces Daxing District across the Yongding River to the east, and borders Zhuozhou City, Laishui County, and Yixian County in Hebei Province to the south and west, respectively. The terrain of this area is high in the northwest and low in the southeast, with mountainous regions (54% of total area) in the northwest and alluvial plains, depressions, and floodplains in the southeast. The elevation range is 30 m (southeastern plains, e.g., Doudian Town) to 2017 m (northwestern mountainous areas, e.g., Baihua Mountain) above sea level. Land use in 2023 comprised 60% forest/cropland (primarily mountainous oak/pine forests), 23% other areas (bare rock, grasslands), 15% urban/built-up zones (concentrated in Liangxiang and Chengguan subdistricts), and 2% water bodies, notably the Dashi River and reservoirs. Fangshan District is located in a warm temperate semi-humid continental monsoon climate zone, with complex landforms and significant differences in relative height between mountainous areas and plains, resulting in significant climatic differences. The annual precipitation in 2022 amounted to 589.4 mm, but in 29-31 July 2023, extreme rainfall triggered by Typhoon Doksuri reached 744.8 mm at Hebei Town Station (maximum hourly intensity: 111.8 mm/h), the highest recorded in Fangshansince 1951. In 2022, the region had a resident population of 1.31 million (Beijing Statistical Yearbook 2023) and an urbanization rate of 78.6%, below the Beijing municipal average of 87.6%. The elevation map of Fangshan District is shown in Figure 1.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 4 Multiple Feature Extraction Methods for Target Identification
### Hu Invariant Matrix Feature Extraction Method  
The Hu invariant matrix is a set of characteristic quantities composed of seven invariant matrices. In 1962, it was proven that these characteristic quantities are invariant to image scaling, translation and rotation [48]. Matrix invariants can well represent the structural features of images. The advantage of Hu matrix invariants for image feature extraction is that the calculation speed is fast, but the disadvantage is that the accuracy of image recognition is low, especially for images with rich texture. Especially, Hu invariant matrices are not sensitive to texture-rich images, and generally can only recognize large objects in the image. As the ship is a large object, so the texture is relatively simple. Therefore, Hu matrix invariants are used as auxiliary features to further enhance the robustness of ship identification in combination with other visual angle features. In particular, after being processed by the Hu invariant matrix feature method, it is a vector composed of seven scalars, rather than a processed ship image.
# Research on Applying the \"Shift\" Concept to Deep Attention Matching
## 3 Improved DAM  
### Deep Attention Matching Model  
DAM consists of three main components: representation, matching, and aggregation. In the multi-round response selection problem, capturing the different granularities of matching information between response and utterance is the key to solving this problem. Before DAM, most models considered shallow textual and extract features using RNN. DAM replaces RNN with the attention component of the transformer and improves by removing the multi-headed part of the component. The component is called an attentive module in DAM, its input includes query, key, and value, which are denoted by \(Q\), \(K\), \(V\):  
\[Q=[e_{i}]_{i=0}^{n_{Q}-1},K=[e_{i}]_{i=0}^{n_{k}-1},V=[e_{i}]_{i=0}^{n_{v}-1} \tag{1}\]  
where \(e_{i}\) denotes the dimensional of the words with a value of 200. As with the encoding stage of the transformer, the first step is to do the Scaled Dot-Product Attention operation, then the weighting operation. The formulae are assumed as follows [21]  
\[Att(Q,K)=[soft\max(\frac{Q[i]\cdot K^{T}}{\sqrt{d}})]_{i=0}^{n_{Q}-1} \tag{2}\]  
\[V_{att}=Att(Q,K)\cdot V \tag{3}\]  
Next is to calculate the summation of \(V_{att}\) and \(Q\), followed by a layer of normalization [46] operations to prevent the gradient from exploding, and process by a forward feedback layer, and then do another summation and normalization operation to get the final output. _Attentive Module_ (\(Q\), \(K\), \(V\)) will be used to denote the operation of the module.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 4 Security Technical Framework  
Cryptography technology is one of the most effective, reliable and economical key technologies to protect the secure sharing and circulation of data assets. Based on the protection model covering the main security risks and overall requirements of data assets in  
Figure 4: Life cycle security protection model of data asset circulation sharing.  
the whole life cycle, this paper designs a security technical framework that runs through the production, storage, exchange, processing, return and destruction of data assets, as shown in Figure 5. The security technical framework takes cryptography technology as the core, considering four aspects: cryptography support, cryptography service, security execution and security application. It realizes the protection coverage of transaction circulation, outsourcing storage and cross-border transmission of data assets, and ensures the security of data assets in the process of dynamic sharing and circulation in uncontrolled environments.
## 1 Introduction
Secondly, we propose and study a system that conveniently combines the surveyed techniques and methods to be capable of processing multiple sources of images in real-time and detect potential events of interest. Specific computational intelligence methods (Hungarian algorithm, Kalman filters, classification) are applied to handle the particularities of pedestrian movements. Finally, to demonstrate the practical applicability of the proposed scheme and study its properties, we conduct experiments over several benchmark videos from PETS09-S2L1 [5] and the comparative analysis of results with state-of-the-art methods from the Multiple Object Tracking (MOT) Challenge competition. The insights from our study contribute to understanding the design methodology, the state-of-the-art of related works, architecture, and components of the proposed system. We proposed to develop a system based on simple image processing and computational intelligence techniques, to provide an efficient solution to be applied in real-time and realistic scenarios. Thus, we focused on the utilization of free software libraries and standard methods for pattern detection, which have been properly extended, configured, and integrated into the proposed system. The rest of the article is structured as follows. Section 2 contains a brief theoretical introduction to image processing and pattern detection. A review of related work on recognition and pattern detection and tracking on surveillance systems is presented in Section 3. Section 4 presents the general architecture and design of the system. The main implementation details of the proposed system are described in Section 5. Sample results from the evaluation are presented in Section 6. Finally, Section 7 presents the conclusions and the main directions for future work.
## 3 Proposition Development
### Organizational Barriers and Enablers  
Besides examining contextual factors (i.e., RQ1), the study will subsequently focus on municipalities that scored rather low for green policymaking initiatives (i.e., RQ2). Therefore, this section presents potential barriers and enablers for successful GPP implementation. The conducted analysis for RQ2 (see Section 6) did not require the development of hypotheses. Instead, the literature presented here provides a background for the analyzed variables.  
First, a lack of information and lack of procedures are widely acknowledged as barriers to GPP establishment [12; 52; 53; 54; 55]. It is argued that the inclusion of environmental criteria in public tenders requires technical expertise, which is sometimes lacking in government procurement staff [54]. Moreover, it might also be the case that the staff themselves are lacking. [PERSON] et al. [55] found that many small public organizations clearly lack the human resources for GPP establishment. Interestingly, [PERSON] [56] presented financial pressure as the most salient barrier to GPP implementation. Several studies support this finding, by showing that there is a common perception among public procurers that it costs more and takes longer to carry out purchasing when environmental requirements are included [7; 10; 32; 57; 58]. Finally, it was also shown that procurers are not only concerned about the costs involved, they might also doubt the effectiveness of GPP [7].  
As enablers, several studies support the development of tools to assist local authorities [59]. Manuals with procedures or example forms with specifications can help municipalities in establishing GPP [12; 52; 53; 54]. Other enablers might be expert assistance or a consultation desk. [PERSON] et al. [33] argued that external assistance might be valuable to identify the juridical boundaries of the tender, to choose the correct and most appropriate modalities, and to articulate the corresponding technical criteria. Additional external knowledge might also come from other local governments having a successful GPP strategy [31]. Finally, GPP briefing sessions for local government officials might be another enabler, as these can produce similar benefits to those of training sessions [31; 46; 47; 48; 50].