esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 4 Future Challenges
### Power Demand Forecasting for Different Airspace  
The accuracy of the predicted working conditions is critical for determining the performance of HEVs. However, predicting such conditions is challenging due to their dynamic and uncertain nature, which includes factors such as the state of traffic signals, traffic flow, and road surface conditions, all of which are subject to change. Therefore, forecasting must consider the impact of complex traffic scenarios in future spatial and temporal domains on energy management, as well as the marginal benefits of such forecasts. Achieving accurate prediction information is a demanding task that requires careful consideration of multiple factors, including the cost of collecting data from various sources and the computational resources required to process this data, all while ensuring the accuracy and real-time performance of the forecast results.
# Networked Roadside Perception Units for Autonomous Driving
## 5 Evaluation
### Simulation Analysis
#### 5.3.1 PDR Evaluation  
Figure 11 illustrates the simulation of Tokyo Map's PDR with densities being 25, 50, and 75 vehicles/km. Blue and red lines denote the RSPUs scenarios with and without the distance priority algorithm, respectively, also reflective of the average PDRs. The colored zones below/above show the standard deviations. In line with expectations, the two RSPUs schemes were able to transmit messages across greater distances as compared to the lone RSPU scenario wherein the packet was not delivered further than the range of wireless under any scenarios of vehicle density.  
Figure 11: Evaluation of packet delivery ratio (PDR). (**a**) Tokyo scenarios, (**b**) Paris scenario.  
The RSPUs scenario was able to maintain a PDR of 100% across all distances when the vehicle's density was 25 vehicles/km. On the contrary, those with distance priority algorithm scenario were able to replicate the maintenance of PDR till a distance of 900 m, beyond which a gradual decline to 1000 m was observed since the algorithm disregarded information provided by distant transmitters for maintaining space for further information from closer proximity.  
The RSPUs scenario was able to maintain a PDR of 70% when the vehicle's density was 50 vehicles/k. However, big standard deviations were seen for these reasons. The packets that arrived initially at the stated RSPU did reach the receiving node. However, the subsequent ones risked a decline in packets in accordance with the ratio of queue occupancy. With the simulation reflecting the randomness of vehicle detection (in terms of timing), the packet-drop targeted packets from more than one distance, thus increasing the standard deviation. The distance priority algorithm's application led to a 100% PDR, with the distance being 740 m. Subsequently, a gradual decline in PDR was observed until a distance of 990 m; the detection timing's randomness also led to a comparatively big standard deviation.  
Nearly 50% of the messages got lost across the distances when the vehicle's density was 75 vehicles/km. However, the aforementioned algorithm's application ensured that all messages were delivered across a 450 m range. Thereafter, there was a gradual decline of PDR up to 800 m.  
The Paris map's PDR is shown in Figure 11b. When vehicle densities were 25 and 50 vehicles/km, there was 100% PDR under the non-priority situation across all covered distances due to fewer RSPUs being deployed in the intersections. The algorithm drops messages from far away transmitters (850 and 900 m) for ensuring the information's reservation in closer transmitters. When the density was 75 vehicles/km, a 100% PDR was maintained by this distance priority algorithm within a range of 650 m, with the non-priority situation being unstable across all distances.  
As indicated by the above findings, the networked RSPUs system not only broadens the range of message transmission but also allow the algorithm to maintain a message delivery rate of 100% for information obtained from RSPUs.
## 3 Energy Characteristics of SHIR Specimens with Different \(\alpha\)  
### Principles and Calculation Methods of Energy  
In materials, fracture damage and eventual failure are driven by energy. According to thermodynamics, energy conversion is an inherent characteristic of physical processes, and material failure is an unstable phenomenon driven by energy [26]. In this experiment, the energy evolution of SHIR specimens is illustrated in Figure 3. It is generally understood that heat exchange between rocks and the surrounding environment during deformation and failure processes can be neglected, hence externally input energy primarily converts into elastic energy and dissipation energy [27]:  
\[U=U^{e}+U^{d} \tag{1}\]  
In the equation above, \(U\) represents total strain energy input from surroundings, \(U^{e}\) denotes recoverable elastic strain energy, and \(U^{d}\) stands for dissipation energy.  
Figure 2: Experimental system and specimen: (**a**) loading system; (**b**) light source and CCD camera; (**c**) specimen.  
During the process of deformation and failure, rocks continuously exchange energy with their surroundings. Energy conversion mainly involves four stages: energy input, energy accumulation, energy dissipation, and energy release [14]. During the compaction stage, part of the externally input energy accumulates in the rock as \(U^{e}\), while another part is consumed by the closure of original microcracks and microfissures. In the elastic stage, \(U^{e}\) stored in the rock continues to increase. In the plastic stage, cracks begin to initiate and propagate; the proportion of \(U^{e}\) decreases gradually, and \(U^{d}\) begins to increase. In the post-peak stage, cracks further propagate and interconnect, resulting in macroscopic rock failure, and most of the previously stored \(U^{e}\) is released. The formulas for calculating the various strain energies are as follows [28]:  
\[U=\int_{0}^{\varepsilon_{1}}\sigma_{1}d\varepsilon_{1}+\int_{0}^{\varepsilon _{2}}\sigma_{2}d\varepsilon_{2}+\int_{0}^{\varepsilon_{3}}\sigma_{3}d \varepsilon_{3} \tag{2}\]  
\[U^{e}=\frac{1}{2}\sigma_{1}\varepsilon_{1}^{e}+\frac{1}{2}\sigma_{2} \varepsilon_{2}^{e}+\frac{1}{2}\sigma_{3}\varepsilon_{3}^{e} \tag{3}\]  
\[U^{d}=U-U^{e} \tag{4}\]  
In the equations above, \(\sigma_{i}\) (\(I=1\), \(2\), \(3\)) represents principal stresses, and \(\varepsilon_{i}\) and \(\varepsilon_{i}^{e}\) (\(i=1\), \(2\), \(3\)) denote strains and elastic strain energy in the principal stress directions. According to Hooke's Law, Equations (2) and (3) under uniaxial compression conditions can be rewritten as [29]  
\[U=\int_{0}^{\varepsilon_{1}}\sigma_{1}d\varepsilon_{1} \tag{5}\]  
\[U^{e}=\frac{\sigma_{1}^{2}}{2E_{0}} \tag{6}\]  
In the equations above, \(E_{0}\) represents the initial elastic modulus during the loading process of the specimen.
## 3 Transmitter and Receiver Structure
### Receiver Design  
In the previous section, the transmitter side was described, where the use of a precoder removes all MUI from the receiver side. This means that only SU detection needs to be implemented at the receivers. Due to the use of CRMs, each GSM symbol comprising a PT-GSFIM block is dispersed across numerous subcarriers. As a consequence, the received signal can be written as  
\[\mathbf{y}_{g}^{u} = \overset{\mathbf{A}_{g}^{u}}{\underset{\sim u}{\mathbf{H}_{g}^{ u}}}\widetilde{\mathbf{s}}_{g}^{u}+\mathbf{n}_{g}^{u}\] \[= \overset{\mathbf{A}_{g}^{u}}{\underset{\sim u}{\mathbf{H}_{g}^{ u}}}\mathbf{s}_{g}^{u}+\mathbf{n}_{g}^{u}\]where \(\widetilde{\mathbf{H}}_{\mathcal{B}}^{u}=\widetilde{\mathbf{H}}_{\mathcal{B}}^{u} \left(A_{N_{f}}\otimes\mathbf{I}_{N_{s}}\right)\) is the overall equivalent channel that the user \(u\) \"perceives\" for the \(g^{th}\)PT-GSFIM symbol. To design the detector, first, we formulate the maximum likelihood detection (MLD) problem as  
\[\min_{\mathbf{s}_{g}^{u}}f(\mathbf{s})\triangleq\left\|\mathbf{y}_{g}^{u}- \widetilde{\mathbf{H}}_{\mathcal{B}}^{u}\mathbf{s}_{g}^{u}\right\|_{2}^{2} \tag{13}\]  
subject to \(\mathbf{s}_{g}^{u}\in\mathcal{A}_{0}\prescript{N_{s}N_{f}}{}\) (14)  
\[\text{supp}\left(\mathbf{s}_{g}^{u,i}\right)\in\mathbb{S}_{0},i=1,\ldots,N_{f} \tag{15}\]  
\[\text{supp}\left(\mathbf{s}_{g}^{u}\right)\in\mathbb{J} \tag{16}\]  
where \(\mathcal{A}_{0}\) represents the set with the complex-valued symbols from the adopted APM constellation, including symbol \(0\). \(\mathbb{S}_{0}\) denotes the set of valid supports of \(\mathbf{s}_{g}^{u,i}\), according to the possible GSM symbols. \(\mathbb{J}\) represents the set of valid supports of \(\mathbf{s}_{g}^{u,i}\) according to all possible active subcarrier combinations. Applying the alternating direction method of multipliers (ADMM), following the approach described in [43], results in the algorithm in Table 1 In the algorithm, \(\overline{I}\) designates the complement of the support set \(I\) and \(Q\) is the number maximum of iterations.
# Deformation and Stress Analysis of Pile-Supported Immersed Tunnels under Seismic Loads
## 5 Conclusions  
In this paper, the stresses and deformations of pile-supported immersed tunnels under seismic loads are investigated by using a numerical model developed in ABAQUS. The numerical model results are verified by the physical model experiment of the SIS system. The mechanical behaviors of the immersed tunnel and pile-soil interaction under different loads and SCP arrangement were parametrically analyzed. The conclusions are as follows:  
1. Increased seawater pressure suppresses the vertical frequency of the tunnel. The suppression is enhanced by the deformation of the tunnel's \"H-shaped\" structures and stress transfer in the tunnel wall when the pile replacement rate reaches 50%.  
Figure 13: Horizontal displacements of foundations at 0.8 m, 1.6 m, and 2.4 m to the left and right of the center pile: (**a**) plum blossom piles, (**b**) diagonal piles, (**c**) rectangular piles.  
2. The vertical frequency at the far end of the immersed tunnel from the source is similar to the seismic excitation frequency along the width of the tunnel, resulting in resonance. The seismic injury at the distal end is amplified.
3. The soil densification is higher at the end of the pile when the seawater pressure increases and the lateral earth pressure increases, resulting in a large change in the slope of the \(p\)-\(y\) curve at the tip of the pile. The increase in pile slenderness ratio (\(\gamma\)) will increase the axial force of the pile to produce an additional bending moment, resulting in large changes in the slope of the \(p\)-\(y\) curve at the end of the pile with larger axial force. When the slenderness ratio is in the range of 9.25 to 15, it can reduce the injury at the connection between pileed foundations and tunnels.  
Conceptualization, H.F. and Y.Z.; methodology, H.F.; software, S.H.; validation, H.F., Y.Z. and Z.C.; formal analysis, S.H.; investigation, Z.C.; resources, Y.Z.; data curation, H.F.; writing--original draft preparation, H.F.; writing--review and editing, S.H.; visualization, Z.C.; supervision, S.H.; project administration, Z.C.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
The financial support of the National Natural Science Foundation for General Program of China (Grant No. 52178316), and the National Science Foundation for Excellent Young Scholars of China (Grant No. 51922029) is acknowledged.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 4 Architecture and Procedures for MEC-Based Mobile Services
### SRv6-Based Path Allocation
#### 4.2.2 Path Allocation Procedure in Enhanced Mode with Unchanged gNB Behavior  
The Enhanced Mode with Unchanged gNB Behavior characterizes a 5G network configuration wherein the gNB lacks SRv6 capabilities and instead supports only the GTP-U encapsulation of IPv6 packets. The 5 GC network can manage SRv6 tunneling. The Enhanced Mode with Unchanged gNB Behavior, as stated in [13], provides a mechanism for the interworking between a 5G legacy gNB and 5G SRv6-enabled core network.  
As shown by Figure 6, gNB1 encapsulates the uplink packets into GTP-U. Therefore, SR Endpoint1 is required to substitute the GTP-U encapsulation with the SRv6 one, by applying the End.M.GTP6.D behavior.  
Figure 6 shows the data plane of the path allocation procedure. The UE sends a packet to its service instance running on MEC Host 1. Since gNB1 is not SRv6 capable, it tunnels the packet through GTP-U encapsulation. gNB1 is configured by the AMF to enter, as the destination address, B:0:0:1:G000:1::, so that SR Endpoint1 applies the correspondent policy. SR Endpoint1, upon receiving a packet whose destination address corresponds to B:0:0:1:G000:1::, which is a BSD, applies the correspondent SR policy, consisting of the End.M.GTP6.D behavior with a preinstalled list of segments. The BSID is an address that identifies, for a node, an SRv6 tunnel between itself and another endpoint. The SR policies can be preconfigured or installed during the path allocation procedure by the SDN controller. Again, since the list would be composed of just one segment, it is not required to add the SRH; placing the only segment in the destination field of the outer IPv6 header is enough. SR Endpoint1 sets as the source address of the outer IPv6 header its locator (B:0:0:1::). SR Endpoint3 applies the End behavior with the USD, removing the outer IPv6 header; then, it forwards the packet to the destination container. A description of the control plane procedures required to configure both the gNB and the SR Endpoints can be found in Appendix A.  
Figure 5: Data plane in Enhanced Mode path allocation.  
Figure 6: Data plane in Enhanced Mode with Unchanged gNB path allocation.
# Thermal Stability Design of Asymmetric Support Structure for an Off-Axis Space Camera
## 3 Design of Support Structure with Low Expansion Coefficient
### Design of The Support Frames  
The front and rear frames in the support structure are directly in contact with the reflector assembly. According to the layout of the optical elements and the supporting structure, the thermal expansion of the frame structure has a decisive effect on the thermal displacement of the mirror assembly in the Y direction. Therefore, we need to design theply angle to minimize the thermal expansion coefficient of the front and rear frames in the Y direction.  
In Section 2.3, we have already obtained the zero-expansion paving scheme. Based on the calculation in Table 2, we selected \(\pm 20^{\circ}/0^{\circ}/90^{\circ}\) laying method, winding the front and rear frames, where \(0^{\circ}\) is in the same direction as that of +Y, as shown in Figure 6.  
Figure 5: Support structure made of carbon-fiber-reinforced plastic.  
Figure 6: Definition of ply angle in support frames.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 3 Exhaust Manifold Analysis
### Boundary Condition  
Conducting a comparative study of different turbulence models with consistent boundary conditions is crucial for a meaningful analysis to investigate how each turbulence model responds to different flow conditions. Some models may perform better in certain regimes (e.g., attached boundary layers or separated flows) than others.  
Table 2 provides a comprehensive overview of the boundary conditions employed in our comparative study. These conditions were consistently applied across all turbulence models to facilitate a comprehensive evaluation. The specified boundary conditions encompass a velocity magnitude of 20 m/s for the three inlets of the exhaust manifold. Additionally, distinct inlet temperatures of 790 K,  
Figure 2: (**a**) Top view of the exhaust manifold. (**b**) Bottom view of the exhaust manifold. (**c**) Isometrical view of the exhaust manifold.  
800 K, and 810 K were designated for inlet 1, inlet 2, and inlet 3, respectively. Notably, all specified zones exhibit a hydraulic diameter of 0.06 m. This standardized methodology affords a meticulous assessment of turbulence model performance across a spectrum of flow scenarios. Convective heat transfer has been employed to represent the thermal interaction between the exhaust gas and the external environment. For this, a convective boundary condition is set ([PERSON] et al. [34]), using [PERSON]'s law of cooling:  
\[-K\frac{\partial T}{\partial n}=h(T_{wall}-T_{\infty}) \tag{20}\]  
where:  
\(h\) is the heat transfer coefficient of 40 W/m\({}^{2}\)K.  
\(T_{\infty}\) is the ambient temperature of 400 K.  
The approach ensures that the simulation accurately depicts the heat exchange between the exhaust manifold walls and the surrounding air.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Features of TD-Light Propagation Including Penetration Depth, Optical Pathlength, etc.
#### 5.1.3 Use of Null SD Distance  
[PERSON] et al. [130] studied \(<\!\!l_{i}(t)\!\!>\) and penetration depth, \(<\!\!z(\rho,t)\!\!>\), for semi-infinite two-layered media using the analytical solution of the TD-DE. Based on this study, [PERSON] et al. [131] proposed the usage of null SD distance for improving contrast and resolution of diffuse optical imaging from TR reflectance measurements. They concluded that the null SD distance for TR reflectance measurement provides four advantages over the conventional SD distances of a few centimeters, i.e., (i) stronger optical signals, (ii) deeper penetration depths, (iii) larger contrast for absorbing inclusions, and (iv)higher localization of the inclusions, but also discussed a problem of too strong signals of early photons. They argued several ways for solving the problem: (i) gating the detector to measure photons arriving at 200 ps and later, (ii) using SPADs which are not damaged by the burst of initial photons, and (iii) the use of non-null but small SD distance, while keeping the advantages of a null SD distance. The method of the null SD distance for TR reflectance measurements was further studied for two-layered media by [PERSON] et al. [132] using MC simulations and the TD-DE. Later, proof-of principle tests of the null SD distance technique were reported using fast-gated SPAD as well as non-contact probes showing good agreements of the depth sensitivity and spatial resolution between the phantom experiments and MC simulations [133]. These results indicated the feasibility and potentiality of the null SD distance technique for applications to non-contact and high-density diffuse TR reflectance measurements.
## 3 Methodology
### Orthogonal Frequency Division Multiplexing Symbol TX/RX  
The OFDM symbol TX refers to transmitting symbols in an OFDM (orthogonal frequency division multiplexing) communication system. Figure 4 presents a basic diagram with the necessary steps for creating OFDM symbols. The first step is to convert binary information into complex symbols generated from modulation schemes such as PSK (Phase Shift Keying) or QAM (Quadrature Amplitude Modulation). Next, the Serial-Parallel (S/P) block will divide the transmitted data symbols serially into subgroups. These subgroups will be modulated onto subcarriers.  
OFDM symbol RX refers to the reception process of OFDM symbols in a communication system. After dividing the received signal into individual subcarriers through FFT, channel estimation and equalization occur, using the characteristics of the channel to equalize the received signal. Next, symbols on each subcarrier are demodulated using appropriate modulation schemes. Subsequently, the demodulated symbols are mapped back to their original bit sequences.
###### Abstract  
This study explores poly(methyl methacrylate) (PMMA)-based composites as potential alternatives to conventional TiO\({}_{2}\)-based photocatalysts. Specifically, it examines PMMA composites enriched with oxo-titanium(IV) complexes, [Ti\({}_{8}\)O\({}_{2}\)(O'Pr)\({}_{20}\)(man)\({}_{4}\)] (**I**), [Ti\({}_{4}\)O(O'Pr)\({}_{10}\)(O\({}_{3}\)C\({}_{14}\)H\({}_{8}\))\({}_{2}\)] (**2**), and [Ti\({}_{6}\)O([PERSON])\({}_{2}\)(O\({}_{3}\)C\({}_{14}\)H\({}_{8}\))\({}_{4}\)(O\({}_{2}\)C\({}_{6}\))] (**3**), alongside ruthenium(III) complexes, K[Ru(Hedta)Cl]-2H\({}_{2}\)O (**4**) and [Ru(pic)\({}_{3}\)]-H\({}_{2}\)O (**5**). We assessed the physicochemical, adsorption, and photocatalytic properties of these composites with structural analyses (Raman spectroscopy, X-ray absorption (XAS), and SEM-EDX), confirming the stability of complexes within the PMMA matrix. Composites containing titanium(IV) compounds demonstrated notably higher photocatalytic efficiency than those with ruthenium(III) complexes. Based on activity profiles, composites were categorized into three types: (i) UV-light active (complexes (**1**) and (**2**)), (ii) visible-light active (complexes (**4**) and (**5**)), and (iii) dual-range active (complex (**3**)). The results highlight the strong potential of titanium(IV)-PMMA composites for UV-driven photocatalysis. Moreover, their activity can be extended to the visible range after structural modifications. Ruthenium(III)-PMMA composites, in turn, showed superior performance under visible light. Overall, PMMA composites with titanium(IV) or ruthenium(III) complexes demonstrate promising photocatalytic properties for applications using both UV and visible light ranges.  
titanium(IV)-oxo complexes; ruthenium(III) complexes; PMMA-based composites; photocatalytic activity; adsorptive properties; physicochemical properties
## 2 Description of the Proposed Method
### Frequency-Power Droop Coefficient Determination Method
#### 2.2.2 Graphical Analysis to Determine Droop Coefficients  
In this section, we derive the droop coefficients of LCC and VSC HVDCs by graphical analysis. Figure 1 shows a concept of the proposed method of droop coefficient determination. Red points are operating points of LCC and VSC HVDCs determined by the optimization problem. Grey dots represent optimization results of the Monte Carlo samples. The points are categorized by active power reference and grid frequency, so that the frequency-power droop can be represented by the slope of the blue line, to minimize the root mean square error between the points above the blue line and the grey dots. The slopes of the blue lines are presented as \(-R_{VSC,0}\) for VSC HVDC and \(-R_{LCC,0}\) for the LCC HVDC. Note that the determination method is called the \"graphic method\", in that curve-fitting is exploited for coefficient determination.  
We can derive droop coefficients of LCC and VSC HVDCs from Figure 1, but the coefficients may not satisfy Equation (14), because the power loss of the system is not considered in (12). We can determine final values of the coefficients by the additional correction method. As active power sharing ratio of HVDCs depend on the ration of droop coefficients, final droop coefficients are corrected by multiplying the same constant to \(R_{LCC,0}\) and \(R_{VSC,0}\). Thus final coefficients are determined as \[R_{VSC}(k)=\left(\sum\limits_{n=1}^{N}\frac{R_{\text{sq}}}{R_{VSC,0}(n)}+\sum \limits_{n=1}^{N}\frac{R_{\text{sq}}}{R_{LCC,0}(n)}\right)R_{VSC,0}(k)\,\&\,R_{LCC}(k)= \left(\sum\limits_{n=1}^{N}\frac{R_{\text{sq}}}{R_{VSC,0}(n)}+\sum\limits_{n=1} ^{N}\frac{R_{\text{sq}}}{R_{LCC,0}(n)}\right)R_{LCC,0}(k), \tag{15}\]  
where \(R_{VSC,0}(n)\) and \(R_{LCC,0}(n)\) are the coefficients of the HVDCs connected to \(n\)-th bus, derived from the graphical analysis.