esa-sceva/satcom-chunk-collection huggingface dataset:
# Compensating Data Shortages in Manufacturing with Monotonicity Knowledge
## Appendix A Computing Monotonic Projections  
In order to validate our semi-infinite optimization approach to monotonic regression, it is compared, among other things, to the projection-based monotonization approach by [24]. As has been pointed out in Section 1, the projection method starts out from a purely data-based initial model \(\tilde{y}^{0}\) (a Gaussian process regressor in the case of [24]) and then replaces this initial model by the monotonic projection \(\tilde{y}\) of \(\tilde{y}^{0}\). I.e., \(\tilde{y}:X\to\mathbb{R}\) is the monotonic square-integrable function with monotonicity signature \(\sigma\) that is closest to \(\tilde{y}^{0}\) in the \(L^{2}\)-norm.  
In order to numerically compute this monotonic projection \(\tilde{y}\), the original procedure proposed in [24] is not used here, though. Instead, the conceptually and computationally simpler methodology from [48] is employed. In this methodology, the input space \(X\) is discretized with a fine rectangular grid \(G\). Then, the corresponding discrete monotonic projection \((\tilde{y}(\mathbf{x}))_{\mathbf{x}\in G}\), that is, the solution of the constrained optimization problem  
\[\begin{split}\min_{z\in\mathbb{R}^{G}}\sum_{\mathbf{x}\in G}\big{(} z(\mathbf{x})-\tilde{y}^{0}(\mathbf{x})\big{)}^{2}&\text{s.t.} \quad\sigma_{j}\cdot\big{(}z(\mathbf{x}+h_{j}\mathbf{e}_{j})-z(\mathbf{x})\big{)}\geq 0 \quad\text{for all }j\in J\\ &\text{and all }\mathbf{x}\in G\text{ for which }\mathbf{x}+h_{j}\mathbf{e}_{j}\in G \end{split} \tag{1}\]  
is computed. In the above relation, \(\mathbb{R}^{G}\) is the \(|G|\)-dimensional vector space of all \(\mathbb{R}\)-valued functions \(z=(z(\mathbf{x}))_{\mathbf{x}\in G}\) defined on the discrete set \(G\), \(h_{j}>0\) indicates the distance of adjacent grid points in the \(j\)th coordinate direction, and \(\mathbf{e}_{j}\in\mathbb{R}^{d}\) is the \(j\)th canonical unit vector. It is shown in [48] that the extension of \((\widehat{y}(\mathbf{x}))_{\mathbf{x}\in G}\) to a grid-constant function on the whole of \(X\) is a good approximation of the monotonic projection \(\widehat{y}\), if only the grid is fine enough and the initial model \(\widehat{y}^{0}\) is continuous, for instance. In contrast to [24], these approximation results from [48] also feature rates of convergence.  
Since both the objective function and the constraints of (A1) are convex with respect to \(z\), the problem (A1) is a convex program. We used cvxopt [49] to solve these problems because it offers a sparse matrix type to represent the large coefficient and constraint matrices for \(d>1\). Alternatively, the discrete monotonic projection problems can also be solved using any of the more sophisticated computational methods from ([50], Section 2.3), ([51], Section 4.1), or [52; 53; 54; 55; 56]. However, for the number of input dimensions considered here, our direct computational method is sufficient.
# Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise
## 2 System Model
### Linear Precoding Design  
The received SINR term in (6) relies on different statistics and the type of precoding scheme used by the BS, which is designed based on the DL channel estimation. In particular, from an information theoretic point of view, the optimum precoding design for multiuser MIMO systems can be obtained using a non-linear precoding, which is known as a dirty-paper coding (DPC) [37; 38]. However, due to its design complexity, DPC precoding is to date practically unfeasible [39]. Alternatively, linear precoding can be used to provide a sub-optimum performance with a reduced design complexity [40; 41]. Previous research in TDD MaMi systems has shown that the RZFBF precoder achieves a significant improvement in the sum rate performance over the beamforming(BF) precoder [10]. Therefore, this paper considers the RZFBF precoder. To this end, RZFBF precoding is given as  
\[\mathbf{W}=\mathbf{\hat{H}}^{\mathrm{H}}\big{(}\mathbf{\hat{H}}\mathbf{\hat{H}}^ {\mathrm{H}}+N\zeta\mathbf{I}_{\mathrm{K}}\big{)}^{-1}, \tag{7}\]  
where \(\zeta\) is a regularization coefficient of RZFBF precoding. In this paper, we follow [10] and consider \(\zeta\) as an inverse of the SNR. The term \(\mathbf{\hat{H}}\) is the estimate of the DL channel \(\mathbf{H}=[\mathbf{h}_{1},\mathbf{h}_{2},\ldots,\mathbf{h}_{K}]^{\mathrm{H}} \in\mathbb{C}^{K\times N}\). Further details about channel estimation based on the DTS are discussed in next section. It should be pointed out that the total average channel power is normalized as \(\mathbb{E}\big{[}\mathrm{tr}(\mathbf{H}^{\mathrm{H}}\mathbf{H})\big{]}=\sum_{k =1}^{K}\mathrm{tr}\big{(}\mathbb{E}\big{[}\mathbf{h}_{k}\mathbf{h}_{k}^{ \mathrm{H}}\big{]}\big{)}=NK\). The following section discusses the DL channel estimation process in an FDD MaMi system in the presence of colored noise.
## 3 The Proposed Method
### The Proposed Optimization Framework  
The optimum design of cable-stayed bridges concerning varieties of variables is typically a nonconvex problem. Gradient algorithms can often be trapped in local optima. Therefore, it is advisable to use heuristic algorithms with global search strategies to obtain the global optimum. Among the commonly used heuristic algorithms, Particle Swarm Algorithm (PSO) stands out due to its fast convergence, few parameters, and easy-to-implement formula. It is effective for high-dimensional optimization problems and converges quickly to the optimum solution. Thus, PSO is chosen as the optimization method to solve Formula (3) in Sp2. Based on the above considerations, the PSO-integrated overall procedure, presented in Figure 5, is as follows.  
1. **Problem Definition.** Basic information of the optimization problem is identified, including variables, objective, and constraints. In this work, the problem is defined as follows: * Objective function--the total theoretical material cost of the towers and the cables; * Design variables--several parameters determining the volume or the weight of the towers and the cables; * Constraints--variable boundaries to achieve practical design and strength, stiffness and stability verifications; * Load cases--dead load, vehicle load, wind load, as well as their combinations according to Chinese Design Code.
2. **Predictor Construction.** The cable forces predictor is constructed with the surrogate model method. Detailed construction steps can be found in Section 3.2.
3. **Design Optimization.** An optimization program is made based on PSO algorithm. Readers can refer to Appendix A for the basic theory of PSO and Appendix B for the detailed steps of the program. The program consists of three modules: * Initialization module--particles are generated randomly within the search area. Those that pass the constraint verification remain in the swarm. The Pbest, the Gbest, and their fitness are initialized. * Iteration module--velocity and position of each particle are updated in each iteration round. Given that the objective function (cost) can be calculated explicitly and directly by design variables, constraint verification is only performed for particles whose cost is lower than the previously lowest. The improvement leads to a reduction in verification times. If the updated particle passes the constraint verification, the Pbest, the Gbest, and their fitness are updated. * Structural analysis module--constraints relating to design code verifications are verified by calling the structural analysis module. In the analysis module, a finite element model is established, the desired completion state is obtained utilizing the cable forces predictor, the responses under various load combinations are analyzed, and finally, the constraints are verified according to the code.
## 3 Workflow and Preliminaries
### Measurement Infrastructure  
For our performance models (See Figure 1) we need to characterize the duration of the computation phase of each actor as well as the duration of the communication between actors. The measurement infrastructures used to characterize the computation and communication of our system are shown in Figure 2a (green). The _Computation Time Measurement_ (dark green) part is used to characterize the computation model, the _Communication Time Measurement_ (bright green) part is used to characterize the communication.  
For the computation phase, we measure the amount of clock cycles a compute phase needs to be executed. We use the measurement infrastructure presented in details in [27]. It has been designed in a way that starting or stopping a measurement does not cause any interference. The measurement infrastructure is connected to the tiles using a separate peripheral bus so that there will be no interference with the communication on the system bus. From each tile, a common clock cycle counter can be started and stopped which allows characterizing the compute phase of each actor on each different tile architecture. It is also possible to measure the whole iteration of the SDF graph from the begin of the read phase of the first actor to the end of the write phase of the last actor.  
For the communication phases, a measurement infrastructure is adopted to monitor and store the signals that are relevant to estimate the communication durations. It is first used to measure the durations of each elementary states (called the _elementary delays_) involved in communication. Second, it allows us to observe different contention situations at shared resources during the program execution. An analytical model is then created to compute the communication delays during these contentions. In the scope of this paper, we consider an IP, called System Integrated Logic Analyzer (SystemILA) provided by Xilinx to analyze a FCFS bus arbitration policy AXI4 LITE. SystemILA connects to the AXI4 LITE bus and observes the signals relating to the communication process. For the writing process, the write address valid signal (_AWVALID_) indicates that the channel is signaling valid write address and control information. The write response valid signal (_BVVALID_) indicates that the channel is signaling a valid write response. For the reading process, we observed the signals _ARVALID_ and _RVALID_. These signals are sent to the host computer via a JTAG cable. Further details about AXI4 protocol can be found in [28].
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)
### KDN vs. SDN vs. Conventional Network
#### 2.3.2 Network Programmability  
In traditional networks, each network device is required to be manually configured, which can become infeasible in large networks due to time consumption and the error-prone nature.  
Even though both SDN and KDN aim to improve network programmability, they differ in the approach by which they implement it. SDN uses application programming interfaces and software controllers to program network behavior, while KDN additionally involves knowledge-based systems such as AI and machine learning in addition to application of programming interfaces and software controllers to automate network control and management.
## 2 Related Work
### Pulse Rate Variability (PRV) for Sleep State Categorization  
The motivation for incorporating pulse rate variability (PRV) as an additional feature in behavioral state classification lies in its potential to reflect underlying autonomic nervous system (ANS) activity.  
Heart rate variability is regarded as a reliable reflection of the many physiological factors modulating the normal rhythm of the heart; for example, it provides a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems [12]. There are three frequency components of HRV associated with the autonomic nervous system: very low frequency (0.04 Hz, VLF), low frequency (0.04-0.15 Hz, LF), and high frequency (0.15-0.4 Hz, HF) components [13].  
Although the physiological interpretation of these components is not fully understood, HF power is frequently used to quantify parasympathetic activity [14]. LF power is considered as a function of both sympathetic and parasympathetic activity [15]. The ratio of LF to HF power is used as an indicator of sympathetic-parasympathetic balance [15]. This ratio has been used for sleep classification [16; 17; 18; 19], since there is a major difference in the autonomic nervous system's interplay during different stages of sleep [14]. During REM sleep, the cardiovascular system is unstable and greatly influenced by surges in sympathetic activity. It is also characterized by more irregular breathing and heart rate [14]. Whereas during NREM sleep, the cardiovascular system is stable, parasympathetic cardiac modulation is stronger [20].  
It is important to note that in this work, the output classes of the applied model are not entirely the same as the sleep stages (e.g., NREM, REM, etc.), although in the literature light and REM sleep, deep and NREM sleep are used interchangeably in certain contexts. There are also findings showing significantly higher HRV values during active sleep than during quiet sleep specifically [21].  
Since every newborn is constantly monitored with a pulse oximeter, but not with an ECG, we decided to use the pulse signal and elaborate on PRV metrics, even though it is mentionable that there is not a clear settlement on the substitutability of HRV and PRV values [22]. However, the results speak in favor of sufficient accuracy when subjects are at rest in a resting position [23]. Thus, we used these metrics as a feature for the sleep stage categorization to investigate its effectiveness.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 2 Polypyrrole-Based Electrodes
### 3D Nanostructures  
The detection capabilities of biosensor electrodes can be improved by maximizing the number of attached receptors per unit volume. This is achieved using 3D nanostructures possessing maximized surface areas as the biological receptor templates. Researchers have been actively involved in manufacturing 3D nanostructures using PPy for subsequent applications as biosensors. As seen in one-dimensional structures, multidimensional PPy nanotubes with vertically coated bumps were manufactured using polyvinyl alcohol fibers as the mold via vapor deposition polymerization of the pyrrole monomer (Figure 3a) [68; 90]. The 3D PPy nanotubes are ideal for monitoring cortisol hormone levels, thereby ensuring accurate regulation of blood pressure, glucose levels, and carbohydrate metabolism in response to environmental factors and the circadian rhythm. Relative to 2D nanofilms, 3D PPy films are manufactured via vapor deposition polymerization on the surface of the Co\({}_{3}\)O\({}_{4}\) nanoplate attached perpendicular to the graphene electrode. The resulting thin film can be used to detect platelet-derived growth factor BB (PDGF BB), a bioindicator of blood cancer [91]. [PERSON] et al. modified the electrochemical deposition technique to synthesize 3D PPy nanofilms with vertically coated nanorods on the electrode [92], thereby enabling the rapid transfer of electrical signals for sensing purposes. These nanofilms were ideal sensor electrodes for detecting HBsAgAg, a hepatitis B factor (Figure 3b). Relative to the 0D nanostructures discussed previously, urchin-like 3D PPy nanoparticles were fabricated using a combination of electrospray and vapor deposition polymerization of pyrrole (Figure 3c) [93]. Next, binding aptamers were introduced onto the particle's surface to detect the environmental hormone, bisphenol A, as these aptamer-functionalized 3D nanoparticles were extremely sensitive environmental hormone sensors. The sensing performance of PPy-based biosensors are suggested in Table 1.  
Figure 3: (**a**) Multidimensional PPy nanotubes based on electrospinning and vapor deposition polymerization techniques [68]. Copyright 2012, Royal Society of Chemistry. (**b**) 3D PPy-based conductive nanofilms for use as serum hepatitis B surface antigen biosensors [92]. Copyright 2018, American Chemical Society. (**c**) Urchin-like CPPy nanoparticle aptasensors for bisphenol A detection [93]. Copyright 2014, Wiley.
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 3 Methodology
### Revocation Phase  
Once the multiple autonomous devices authenticate each other and, if due to location/communication lag, the mutual session key is terminated. In the case of re-initiating the authentication, the autonomous protocol should be succeeded in the next two attempts; otherwise it will be blocked for one day. Every device maintains its own list of blocked devices which is cleared every day. The purpose of the key revocation phase is to keep log records for security audit at a regular interval.
# Research on an Evaluation Method for the Adaptability of TBM Tunnelling
###### Abstract  
When a TBM carries out tunnelling in complex strata, there is often low tunnelling efficiency and an increase in tunnelling costs due to the improper selection of tunnelling parameters, the wrong estimation of geological conditions, or adverse geology, so it is necessary to evaluate the tunnelling adaptability of TBM construction. In this paper, based on hydraulic engineering in Xinjiang, 11 evaluation indexes of TBM tunnelling adaptability are determined by comprehensively considering the influence of tunnelling parameters, geological conditions, and adverse geological factors on TBM tunnelling adaptability. After that, the membership function of each evaluation index is determined by referring to the existing research results and fuzzy mathematics method, and the weight of each evaluation index is determined and adjusted by the analytic hierarchy process (AHP)-entropy weight (EW) method. Finally, the adaptability evaluation method and evaluation model of TBM tunnelling are put forward. The TBM tunnelling adaptability evaluation model proposed in this paper is verified by relying on the actual situation of three interval tunnels in the project, and good effects are obtained. This study can provide a reference for the evaluation of TBM tunnelling adaptability in similar strata.  
TBM; tunnelling adaptability; AHP-EW method; evaluation method; evaluation model +
Footnote †: journal: Applied Sciences
## 3 Methods
### Resilience Evaluation and Network Repair  
#### 3.3.1 Network Resilience Assessment Model  
The metro network performance under disruptions is shown in Figure 5. It can be divided into four stages: the normal stage (before \(t_{1}\)), in which \(t_{1}\) is the moment of node failures; the disturbed stage (from \(t_{1}\) to \(t_{2}\)), in which \(t_{2}\) is the moment when the network service efficiency decreases to the minimum and also the repair plan is initiated; the restoration stage (from \(t_{2}\) to \(t_{3}\)), in which \(t_{3}\) is the moment when the network service efficiency returns to normal after nodes are repaired; and the return-to-normal stage (after \(t_{3}\)). The area enclosed by points A, B, and C is the performance loss of the metro network under repair strategy.  
Here, service resilience is defined as the ratio of the accumulated performance retained by the network from the moment of node failure to the moment of returning to normal, to the accumulated performance of the network over an equivalent duration under normal conditions. That is, service resilience is the ratio of the green area to the sum of the blue and green areas in Figure 5.  
\[\psi_{e}=\frac{\int_{t_{2}}^{t_{3}}R_{N}(t)dt}{R_{N}(t_{1})(t_{3}-t_{2})}, \tag{6}\]  
where \(\psi_{s}\) represents the service resilience of the metro network; \(R_{N}(t)\) denotes the network service efficiency at time \(t\); and \(R_{N}(t_{1})\) represents the network service efficiency under normal conditions.
# Enhancing the Photocatalytic Activity of Immobilized TiO2 Using Laser-Micropatterned Surfaces
## 2 Materials and Methods
### Laser Microstructuring of Silicon Substrates  
N-type silicon wafers with a (1 0 0) orientation were precisely cut into small dies (1.5 cm \(\times\) 1.5 cm). Prior to laser microstructuring, a thorough cleaning process was employed to eliminate any residues from the sample surfaces. The wafers underwent ultrasonic bath treatment with acetone and methanol for 10 min each. The laser-structuring setup included a pulsed Q-switched Nd:YAG laser system, operating at a wavelength of 532 nm, with a pulse duration of 5 ns (FWHM), and a repetition rate of 10 Hz. The silicon samples were placed in a vacuum chamber, which underwent evacuation to a base pressure of 10\({}^{-2}\) mbar before being filled with 600 mbar of SF\({}_{6}\). A lens with a focal length of 20 cm was utilized to focus the laser beam onto the sample surface, achieving an incident fluence of \(\sim\)1.1 J cm\({}^{-2}\), which surpasses the threshold for monocrystalline silicon modification in the multi-pulse laser irradiation mode [38; 51]. The distance between the lens and the sample surface was maintained at 16 cm. A computer-controlled set of \(x\)-\(y\) translation stages was used in order to raster-scan the sample relative to the laser beam for large-area patterning. The scanning speed was set to 62.5 \(\upmu\)m s\({}^{-1}\), resulting in each spot on the silicon surface receiving approximately 100 laser pulses during the irradiation process.
## 5 Automation and Teleoperation  
### Technology Selection  
In the context of automation and teleoperation, the need for low latency and high data rates (as discussed in Section 3) limits the feasible network technologies to those in the GHz spectrum, such as 5G, LTE (Long-Term Evolution) standard/4G, as well as Wi-Fi 2.4/5/6 GHz. As they require (near) line-of-sight (LOS) conditions to maintain signal strength in the face of high attenuation in that frequency spectrum, feasible areas of usage would be repetitively used areas where infrastructure can be installed and maintained. These areas must cover (in a relatively small area) full process usage cycles of machines that then can be monitored or teleoperated (e.g., a full haulage cycle).
# A LiDAR-Based Backfill Monitoring System
## 3 Methods
### Slurry Points Determination and Generation
#### 3.2.1 Slurry Points Determination  
Assume the plane equation of slurry surface is ax + by + cz + d = 0 and a point P (A, B, C) is not on the surface. The signed distance from point P to the surface is the result of substituting the coordinate of P to the plane equation. A positive value indicates that point P is on one side of the plane, while a negative value signifies it is on the opposite side. Therefore, points under the backfill slurry surface can be determined as follows:  
In the two three-dimensional spaces divided by the slurry surface, it is crucial to select a point that consistently lies on the opposite side of the slurry points. The product of the signed distance from this point to the slurry surface and the signed distance from slurry points to the slurry surface must be negative. Since the Livox Mid-40 LiDAR always maintains itself as the origin, and the scanning space is a conical space in the positive direction of x-, y-, and z-axes, thus the point (\(-\)1, 100, 100) on the upper space above the slurry surface is selected. The procedure is illustrated in Figure 4.