esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Comparative Analysis
### Energy Efficiency and Reliability Threshold  
ASC-RL was tested with another energy parameter related to the reliability threshold. It outperforms all three schemes because LSTM-RL is based on temporal learning through recurrent networks; AEM-RL works on adaptive energy distribution, and CAS-IIoT-RL is based on context-aware sensing, which delimits the balanced energy consumption and reliability, as shown in Figure 12. ASC-RL uses a multi-objective reward function that maximizes reliability and energy efficiency. It improves the energy usage, with the reliability factor of the other three schemes being 35% and marked at each point in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Latency Th (ms)** & **CAS-IIoT-RL** & **LSTM-RL** & **AEM-RL** & **ASC-RL** & **\% Increase** \\ \hline
100 & 0.65 & 0.64 & 0.63 & **0.69** & **6.25\%** \\
200 & 0.68 & 0.67 & 0.66 & **0.72** & **6.06\%** \\
300 & 0.71 & 0.70 & 0.69 & **0.75** & **5.71\%** \\
400 & 0.73 & 0.72 & 0.71 & **0.77** & **5.48\%** \\
500 & 0.75 & 0.74 & 0.73 & **0.79** & **5.17\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Transmission success probability with increasing latency threshold.  
Figure 11: False alarm probability comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reliability** & **CAS-IIoT-RL** & **LSTM-RL** & **AEM-RL** & **ASC-RL** & **\% Increase** \\
**Threshold** & **(bits/Hz)** & **(bits/Hz)** & **(bits/Hz)** & **(bits/Hz)** & **Over Avg** \\ \hline
0.600 & 0.7550 & 0.7217 & 0.7176 & 0.8833 & 20.77\% \\
0.644 & 0.6983 & 0.6844 & 0.6692 & 0.8328 & 21.76\% \\
0.688 & 0
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images  
[PERSON]  
1 Graduate School of Science and Engineering, Chiba University, 1-33 Yayoicho, Chiba 263-8522, Japan 1  
[PERSON]  
2 Graduate School of Engineering, Chiba University, 1-33 Yayoicho, Chiba 263-8522, Japan 21  
[PERSON]  
3 Department of Computer Science, Tokyo Metropolitan University, 6-6 Asahigao, Tokyo 191-0065, Japan 1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/article/pdf/10](https://www.mdpi.com/journals/article/pdf/10)]([https://www.mdpi.com/journals/article/pdf/10](https://www.mdpi.com/journals/article/pdf/10))  
system [34; 35; 36] for encryption. Thus, it can compress marked encrypted images without loss using image coding standards, such as JPEG-LS [37] and JPEG 2000 [38], without losing reversibility. Further, we can flexibly embed and extract payloads in plain and/or encrypted domains by using an RDH method based on histogram shifting (RDH-HS) [30]. This method, however, has an extremely low hiding capacity, i.e., about 0.1 bpp.  
RDH-EI methods can be classified into two types: reserving room before encryption (RRBE) and vacating room after encryption (VRAE). The former methods have a preprocessing step, so that the embeddable area can be ensured before encryption. A content owner encrypts the preprocessed image and sends the encrypted image to a third party. The third party hides some information into an embeddable area. In contrast, the latter methods do not have a preprocessing step. A content owner encrypts the original image directly and sends the encrypted image to a third party. The third party embeds the information into the encrypted image. Most of the state-of-the-art RDH-EI methods with high hiding capacity [29; 31; 32; 33] are classified into RRBE. In RRBE, however, a content owner should assume that a data-hiding step would be conducted to the encrypted image and reserve the embeddable area. This can be a problem that limits the range of practical applications.  
We propose a novel VRAE-based method to simultaneously achieve a high hiding capacity and high compression efficiency. The proposed method introduces the method of [PERSON] et al. [39] with which conventional EEC systems [34; 35] are extended to enhance security. Consequently, the proposed method can produce marked encrypted images with high compression efficiency by using lossless image coding standards. The interpixel correlation in each block is not transformed before or after the ETC process; thus, we can obtain prediction values with high accuracy even after encryption. Accordingly, we achieved high hiding capacity using prediction-error expansion and histogram shifting. The proposed method can also derive marked images by decryption without data extraction. Through our experiments, we confirm the effectiveness of the proposed method in terms of data-hiding capacity, marked-image quality, and lossless compression performance using JPEG-LS and JPEG 2000.
## 3 Case Studies
### Case Study 1: Coupling of Continuous Substructures
#### 3.1.1 Determining the Components of Subsystems Acceptance
The boundary conditions when an excitation couple applies at \(x=0\) are the following:  
\[EI\frac{d^{2}y_{(x=0)}}{d^{2}x}=-\mathrm{M}_{2}^{B}e^{i\omega t},\ EI\frac{d^{3}y_{( x=0)}}{d^{3}x}=EI\frac{d^{2}y_{(x=L)}}{d^{2}x}=EI\frac{d^{3}y_{(x=L)}}{d^{3}x}=0. \tag{25}\]  
Recceptance elements for subsystem B with the same length L are as follows:  
\[H_{y_{2}\theta_{2}}^{B}=\frac{y_{2}^{B}}{M_{2}^{B}}=\frac{-S_{2}}{EI\lambda^{2} S_{7}},H_{y_{1}\theta_{2}}^{B}=\frac{y_{1}^{B}}{M_{2}^{B}}=\frac{-S_{4}}{EI \lambda^{2}S_{7}},H_{\theta_{2}\theta_{2}}^{B}=\frac{\theta_{2}^{B}}{M_{2}^{B} }=\frac{S_{5}}{EI\lambda S_{7}}. \tag{26}\]  
The boundary conditions when an excitation force applies at \(x=0\) can be written as follows:  
\[EI\frac{d^{2}y_{(x=0)}}{d^{2}x}=\mathrm{F}_{2}^{B}e^{i\omega t},\ EI\frac{d^{3}y_{( x=0)}}{d^{3}x}=EI\frac{d^{2}y_{(x=L)}}{d^{2}x}=EI\frac{d^{3}y_{(x=L)}}{d^{3}x}=0. \tag{27}\]  
The corresponding receptance functions are given by the following:  
\[H_{y_{2}y_{2}}^{B}=\frac{y_{2}^{B}}{F_{2}^{B}}=\frac{-S_{1}}{EI\lambda^{3}S_{ 2}}.H_{\theta_{2}y_{2}}^{B}=\frac{\theta_{2}^{B}}{F_{2}^{B}}=\frac{-S_{2}}{EI \lambda^{2}S_{7}}=H_{y_{2}y_{2}}^{B},\ H_{y_{1}y_{2}}^{B}=\frac{S_{3}}{EI \lambda^{3}S_{7}}=H_{y_{2}y_{1}}^{B},H_{\theta_{1}\theta_{2}}^{B}=\frac{ \theta_{1}^{B}}{M_{2}^{B}}=\frac{S_{6}}{EI\lambda S_{7}}. \tag{28}\]  
Due to symmetry in the free-free beam, it follows that \(H_{\theta_{2}\theta_{2}}^{B}=H_{\theta_{1}\theta_{1}}^{B}\), and \(H_{y_{2}y_{2}}^{B}=H_{y_{1}y_{1}}^{B}\). The terms S1 to S8 used in Equations (24)-(28) are defined as follows:  
\[ \begin{array}{l}S_{1}=\cos(\lambda\ L)\sin h(\lambda\ L)-\sin(\lambda\ L) \cos h(\lambda\ L),\\ S_{2}=\sin(\lambda\ L)\sin h(\lambda\ L),\ S_{3}=\sin(\lambda\ L)-\sin h( \lambda\ L),\\ S_{4}=\cos(\lambda\ L)-\cos h(\lambda\ L),\\ S_{5}=\cos(\lambda\ L)\sin h(\lambda\ L)+\sin(\lambda\ L)\cos h(\lambda\ L), \\ S_{6}=\sin(\lambda\ L)+\sin h(\lambda\ L),\\ S_{7}=\cos(\lambda\ L)\cos h(\lambda\ L)-1,\\ S_{8}=\cos(\lambda\ L)\cos h(\lambda\ L)+1.\end{array}  \tag{29}\]  
The parameters S1 to S8 defined in Equation (29) represent the components of the system's displacement and rotation functions in the context of the boundary conditions and receptance functions [5; 31]. These parameters are derived from the solutions to the beam equations under various loading and boundary conditions, and they account for the interaction between the beam's deflections and rotational displacements. Figure 4: Excitation force and moment on substructures B for case study 1.
## 3 Results and Discussions
### The Morphology of the Machined Surfaces Investigation  
The machined surfaces were examined under a microscope to study the behavior of CFRP during the piercing process, focusing on aspects such as delamination, uncut fibers, and abrasive particle embedment.  
The microscope images of the specimen shown in Figure 6a highlight the general form of composite material delamination. This specimen was machined during experimental run number 3 using 250 MPa water pressure, a 5.5 mm stand-off distance, and a 5 mm material thickness, without abrasive start synchronization. The observed delamination extend (Dext) was 17.5 mm wide. The delamination appeared throughout the entire thickness of the composite material, not just as a single crack.  
Figure 6b shows the morphology of a hole created by AWJ piercing without delamination. It was machined during experimental run number 38 using 100 MPa water pressure, a 5.5 mm stand-off distance, and a 5 mm material thickness, and the abrasive start was synchronized with the water start. The hole's dimensions were determined by measuring the hole dimensions at the top (O1.2 mm) and at the bottom (O1.1 mm). The filled radius in the bottom area was 0.18 mm. Carbon fiber pull-out was also observed on a few of the machined surfaces.
# An Empirical Recommendation Framework to Support Location-Based Services +
## 2 Related Work and Comparative Analysis
### Place Recommendation System  
[PERSON] et al. [14] introduced the tensor factorization for a recommendation system to improve the accuracy of user recommendation. First, to extract the user's POI information they used the Latent Dirichlet Allocation (LDA) model and created probability distribution for each kind of information. Then the extracted data are divided into different groups for each user which defines the preferences of a user. For generating recommendation finally a singular value decomposition (SVD) algorithm is used to find the preferences. [PERSON] et al. [15] worked on a system that can help the tourism services. They proposed a system using the user-location vector to identify the relationship between POIs and user interest. [PERSON] et al. [16] proposed a semantic approach to find the recommendation. They identify the similarity between the user's previous activities using user-based collaborative filtering.  
[PERSON] et al. [17] presented a machine learning (Probabilistic Neural Network) method for recommendations. They extracted the data from Location-Based Social Network (LBSN). The system generates the suggestion according to the user's previous activities. [PERSON] et al. [18] introduced a model for Online Social Network services (OSN) using the matrix factorization. the similarity of users can be calculated with the interest and location of the user. [PERSON] et al. [19] used a semantic approach to measure the similarity using the location. [PERSON] et al. [20] proposed a hybrid method combining three machine-learning algorithms to form a recommendation system. At first, they used the _K-nearest neighbor_\((KNN)\) clustering for a collaborative filtering module. Then a decision tree is applied for demographic filtering (DF). Finally, a switching and weighted technique is used to find the optimal solution. [PERSON] et al. [21] Worked on a system based on the user's previous activities to provide a nearby suggestion.  
Tourists use to visit different locations at different times. Sometimes it is difficult to find a preferable place due to a lack of tourist guides. To overcome such problems, [PERSON] et al. [22] developed a mobile application using the triangulation method for android devices to identify nearby tourism places as well as to provide weather forecasts for visitors. [PERSON] et al. [23] also worked on a similar android application to provide a place RS. [PERSON] et al. (2018) introduced a recommendation system based on two modules. The first one is the offline data processing module and the second one is online recommendation generation. In the first module. they used weighted category hierarchy (WCH) and for the second they considered local preferences. [PERSON] et al. (2019) surveyed some RS systems to provide some additional features for future development.
# Prediction of Friction Resistance for Slurry Pipe Jacking
## 1 Introduction  
In many parts of the world, the numerous constructions of municipal tunnels are creating unforeseen problems, such as blocking of roads, existing pipelines failure, and buildings subsidence. This has motivated attempts at the development of trenchless construction technology, such as pipe jacking, especially in metropolitan cities [1,2]. Pipe jacking is defined as a trenchless excavation technique, which employs hydraulic jacks to thrust specially made pipes through the ground behind a jacking machine, from a drive shaft to a reception shaft, as illustrated in Figure 1. It has manytechnical merits, such as a short time limit, high security, low environmental effect, and little traffic disturbance [3; 4; 5; 6]. Because of that, pipe jacking has been widely used in the construction of infrastructure for traffic and transportation systems in cities [7; 8].  
In pipe jacking, the jacking force is a critical factor that determines the thickness of pipe and reaction wall, selection or design of jacking machine and lubricant requirements [9]. The accuracy of prediction of jacking force is directly related to the structural safety and construction cost.  
The main component of the jacking force is due to frictional resistance. Application of a lubricant such as bentonite slurry in pipe jacking (so-called'slurry pipe jacking') is essential to reduce the friction resistance and, therefore, the jacking force [3; 10; 11; 12; 13]. However, the use of slurry makes it more complex to calculate or predict the friction resistance because of the change in contact conditions between the pipe and soil and lubricant slurry. The new contact state, which is due to the pipe-soil-slurry interaction, is affected by factors such as pipe diameter, soil properties, overcut [3; 9], lubrication efficiency [9], pipeline misalignments [10; 14; 15; 16], and stoppages [9; 14; 17; 18; 19]. The existing prediction models have not fully taken these factors into consideration, leading to an overestimation or underestimation of the friction resistance [9; 20; 21; 22; 23]. It is therefore obvious that a new prediction approach or model is imperatively needed to be established to solve the problem in slurry pipe jacking [24].
## 4 Results
### Material Strain Analysis  
To investigate the strain variation regularity of the gypsum-concrete dense-column composite board with external thermal insulation system, Strain gauges with materials corresponding to typical positions in the specimen were selected as the research object, i.e., rebar strain gauge A2, concrete strain gauge B2, gypsum board strain gauge on the side without thermal insulation system C4, and external gypsum board strain gauge B2. All material strain gauges were placed at the same height. The lateral distances between B2, A2, C4, and D2 were all 250 mm. Hence, their positions could be approximated as the same. Based on the test results, the most lateral rebars and concrete yielded or fractured in the fifth loading stage. Hence, in this paper, only the load-strain curves of different materials before strain gauge failure in the first five loading stages were demonstrated, as shown in Figure 8.  
In the initial loading and deformation stage, the specimen stayed in the elastic deformation region due to the limited external load. According to the load-strain curves of rebar, concrete, and gypsum in the facade and backface, the variation and development of different materials strains were synchronized with a limited difference. When the typical positions were stretched or compressed, the material strains were all between \(-50\) and \(+50\). With the load increased to the concrete fracture limit, the concrete cracked. Most of the load previously born by the concrete transferred to rebars and gypsum boards nearby also shared some load. The gypsum board failed after the concrete, indicating that glass fibers in the gypsum board enhanced the tensile strength in the early stage of cracking.  
Figure 7: Hysteresis and skeleton curves.  
Figure 8: Load-strain curves.  
According to the overall variation tendency of the load-strain curves of concrete and gypsum, the strain variation was large under tensile forces and experienced sharp increase. Meanwhile, under the compressive load, the strain varied little without an abrupt increase. Thus, it is implied that brittle materials such as concrete and gypsum entered the elastoplastic deformation easier under tensile stress, than under compressive stress. Accordingly, cracks also occurred earlier.  
By comparing the load-strain curves of gypsum on two sides of the specimen, the micro strain range of gypsum board was [-60, +350] in the facade while [-50, +75] in the backface. According to the micro strain range, it can be found that the load applying on the facade gypsum board was much greater than that on the backface. Thus, under reciprocated loading, the load was mainly born by the core load-bearing components, while the load applying on the nonstructural component thermal insulation system was limited.
# Safety Verification of Driving Resource Occupancy Rules Based on Functional Language
## 1 Introduction
Due to its accuracy and verifiability, formal methods are widely used in safety-critical scenarios such as autonomous driving. Process algebra is a formal method used to solve the communication problems of concurrent systems. It can describe the concurrent, asynchronous and non-deterministic behaviors of the system. It is very suitable for the modeling of automatic driving environment model and the analysis of resource security occupation. Communication sequential process (CSP) was proposed by [PERSON] [16] in 1978. CSP can perform model checking on concurrent systems. CSP\({}_{\text{M}}\) is an inert functional programming language based on CSP, which can be executed by machines. Failure Divergence Refinement (FDR) is an analysis program tool based on CSP\({}_{\text{M}}\) description [17]. This paper combines the autopilot model with the functional language CSP\({}_{\text{M}}\) to verify the resource occupation rules in the autopilot scene. We abstract the scene into multiple segments, and abstract the typical collision, overtaking, lane change and other scenarios of automatic driving into the occupation and operation of the resources of the segment. Firstly, the environment of autonomous driving is modeled, and the environment model is abstracted (Abstract Environment, AE). Secondly, the typical single-loop environment model in autonomous driving is abstracted, and the mapping method from automatic driving model to CSP is described in detail for the automatic driving environment and the typical collision, overtaking, lane change and other scenes involved. Then, the model checking tool FDR is used to test the CSP\({}_{\text{M}}\) model and analyze its state space diagram to determine whether it will produce a collision unsafe situation. Finally, the results of CSP\({}_{\text{M}}\) model checking are used to modify the autopilot model, which can improve the reliability of the autopilot model to a great extent. We abstract the scene into simpler segments, simplify the driving of the vehicle into segment operations, verify the whole process, and analyze the safety of the vehicle in a typical roundabout scenario. Therefore, it has certain guiding significance for the research on the scene safety of autonomous driving. Section 2 of this paper compares related work; Section 3 proposes a CSP\({}_{\text{M}}\)-based modelling and verification framework for automated driving models; Section 4 gives the process algebraic definition of CSP\({}_{\text{M}}\); Section 5 abstracts the environment model; Section 6 describes the mapping method from automatic driving environment to CSP\({}_{\text{M}}\) in detail; Section 7 summarizes the full text and prospects the future work.
# QPSK MMW Wireless Communication System Based On p-i-n InGaAs Photomixer
## 3 Experimental Setup
### MMW Generator and Transmitter: InGaAs p-i-n Photomixer
The two LAT115 are plano-convex Polytetrafluoroethylene (PTFE) lenses with 76.2 mm and 115 mm of diameter and focal length, respectively, at 500 GHz. The carrier wave can be focused on a longer distance by placing the lenses in a different configuration. The 90 GHz CW received at a fixed distance of 0.5 m between the photomixer and the horn antenna. The received signal was further downconverted to IF signal using the high harmonic mixer as done in the valuation of the photomixer. The downconverted signal was further analyzed using KSA and VSA, where the demodulation results, i.e., the constellation, eye pattern, and Bit Error Rate (BER) calculation, were achieved.
## 4 Materials and Methods
### Transcriptomic Analysis
#### 4.3.3 Illumina Library Preparation and Sequencing  
After RNA from each leaf sample had been pooled in equimolar amounts, they were used for Illumina library preparation. First, mRNA was purified using oligo (dT) magnetic beads, after which it was then purified and fragmented into small pieces with a fragmentation buffer. Random hexamer primers were then used for first-strand cDNA synthesis, followed by second-strand cDNA synthesis. Samples then underwent index adapter ligation, poly-adenylation, and end repair. The resultant cDNA fragments were subjected to PCR amplification and purified with Ampure XP Beads dissolved in EB solution. An Agilent Technologies 2100 Bioanalyzer was used to ensure the quality of the resultant product, and double-stranded PCR products from the prior step were heated, denatured, and circularized with the splint oligo sequence to yield a final library, which was amplified with phi29 to generate DNA nanoballs (DNBs) containing over 300 copies of a given molecule. These DNBs were then loaded into a patterned nanoarray, and single-end 50 bp reads were obtained using a BGIseq500 instrument (BGI-Shenzhen, Shenzhen, China). The transcriptome sequencing reads were deposited in NCBI SRA with accession number SUB10986546.
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 2 Quantum Linear Systems and the Implementation of the Solver
### Overview of the Harrow-Hassidim-Lloyd (HHL) Algorithm
#### 2.1.3 State Evolution in HHL  
In general, the evolution of states in the HHL circuit is \[\ket{0}\ket{0}\otimes_{n_{c}}\ket{0^{n_{d}}} \xrightarrow{\text{Data loading}}\ket{0}\ket{0}\otimes_{n_{c}}\ket{b}\] \[\xrightarrow{\text{QPE}}\sum_{j=0}^{2^{n_{d}}-1}b_{j}\ket{0}\ket{ \tilde{\lambda}_{j}}\ket{v_{j}}\] \[\xrightarrow{\text{Eigenvalue}}\] \[\xrightarrow{\text{Inversion}}\sum_{j=0}^{2^{n_{d}}-1}b_{j}\left( \sqrt{1-\frac{C^{2}}{\tilde{\lambda}_{j}^{2}}}\ket{0}+\frac{C}{\tilde{\lambda }_{j}}\ket{1}\right)\ket{\tilde{\lambda}_{j}}\ket{v_{j}}\] \[\xrightarrow{\text{Measure the ancillary}}\] \[\xrightarrow{\text{only keep }\ket{1}}D\sum_{j=0}^{2^{n_{d}}-1} \frac{b_{j}}{\tilde{\lambda}_{j}}\ket{1}\ket{\tilde{\lambda}_{j}}\ket{v_{j}}\] \[\xrightarrow{\text{QPE}^{\pm}}D\ket{1}\ket{0^{n_{c}}}\sum_{j=0}^ {2^{n_{d}}-1}\frac{b_{j}}{\tilde{\lambda}_{j}}\ket{v_{j}}\approx D\ket{1}\ket{ 0^{n_{c}}}\ket{x}\]  
where \(\tilde{\lambda}_{j}\approx\lambda_{j}\) is the eigenvalue of \(A\) with a finite accuracy estimated in QPE, and \(C\) and \(D\) are both constant normalization factors  
\[D=\frac{C}{\sqrt{\sum_{j=0}^{2^{n_{d}}-1}C^{2}\frac{\ket{b_{j}} ^{2}}{\tilde{\lambda}_{j}^{2}}}}\approx\frac{1}{\|A^{-1}\ket{b}\|_{2}}.\]  
Thus, the norm \(\|A^{-1}\ket{b}\|_{2}\) can be estimated by the probability of measuring the ancillary qubit in state \(\ket{1}\), i.e., \(\|A^{-1}\ket{b}\|_{2}\approx\sqrt{\text{Pr}(\text{Measure }1\text{ in ancillary})}\). This value can be obtained without extra cost as we need to run the circuit multiple times to get \(\ket{x}\) or \(\bra{x}M\ket{x}\) for some observable \(M\). The overall runtime complexity of HHL algorithm is \(\tilde{O}(\log(N)s^{2}\kappa^{2}/\epsilon)\), where \(s\) is the sparsity of \(A\), \(\kappa=\|A\|\|A^{-1}\|\) is the condition number of \(A\), and \(\epsilon\) is the final additive error of the solution defined by the ideal state \(\ket{x}\) and the result from HHL \(\ket{x_{HHL}}\) through \(\|\ket{x}-\ket{x_{HHL}}\|\leq\epsilon\)[3].