esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Inapproximability of MEB and MBB
### Putting Things Together  
We can now deduce inapproximability of MUCHB by simply picking appropriate parameters.  
Proof of Lemma 1.: The parameters are chosen as follows:  
* Let \(\beta=\varepsilon/30,\gamma=\varepsilon/6\), and \(k=\Omega(\log(1/\varepsilon))\) so that the term \(2^{-\Omega(k)}\) in Lemma 3 is \(\leqslant\varepsilon/4\).
* Let \(\varepsilon_{T}=O(\beta\varepsilon)\) so that the error term \(O(\varepsilon_{T}/\beta)\) in Lemma 3 is at most \(\varepsilon/4\).
* Let \(t=t(\varepsilon_{T},\gamma,\beta),\kappa=\kappa(\varepsilon_{T},\gamma,\beta)\) and \(d=d(\varepsilon_{T},\gamma,\beta)\) be as in Theorem 4.
* Let \(\zeta=\frac{\gamma\kappa^{2}}{4d^{4}\ell^{2}}\) be as in Lemma 8 and let \(\ell=\max\{100t,1000\log(1/\varepsilon)\}\).
* Let \(\varepsilon_{V}=O(\varepsilon\beta/\ell)\) where so that the error term \(O(\varepsilon_{V}\ell/\beta)\) in Lemma 3 is at most \(\varepsilon/4\).
* Let \(\eta=\min\{\frac{\zeta}{32k},O(\varepsilon\beta/\ell)\}\) so that the error term \(O(\eta\ell/\beta)\) in Lemma 3 is at most \(\varepsilon/4\).
* Let \(M=\max\{\frac{16k}{\beta\ell},\frac{3\beta}{\varepsilon_{V}}\}\).
* Finally, let \(R=\frac{k}{\beta\ell}\) where \(\delta=\delta(\eta,M)\) is the parameter from the SSEH (Conjecture 2).  
Let \(G=(V,E,w)\) be an instance of \(\operatorname{SSE}(\eta,\delta,M)\) and let \(H=(V_{H},G_{H})\) be the hypergraph resulted from our reduction. If there exists \(S\subseteq V\) of size \(\delta|V|\) of expansion at most \(\eta\), Lemma 3 implies that there is a bisection \((T_{0},T_{1})\) of \(V_{H}\) such that \(E_{H}(T_{0}),E_{H}(T_{1})\geqslant 1/2-\varepsilon\).  
As for the soundness, Lemma 8 with our choice of parameters implies that, if there exists a set \(T\subseteq V_{H}\) with \(\mu(T)\leqslant 1/2\) and \(E_{H}(T_{0})\geqslant\varepsilon\), there exists \(S\subseteq V\) with \(|S|\in\left[\frac{\delta|V|}{M},\delta|V|M\right]\) whose expansion is less than \(1-\eta\). The contrapositive of this yields the soundness property.
## 2 Materials and Methods
### Sensor Formula Optimization
#### 2.2.4 Loading of Dye in Sensor  
The optimum loading amount of the colorimetric indicator in the sol-gel sensor was evaluated to achieve the most sensitive response without diminishing colorimetric response. Four solutions were prepared for bromocresol green at 0.02% \(w/v\), 0.04% \(w/v\), 0.08% \(w/v\), and 0.12% \(w/v\) in NaOH 0.01 N and DI water. The vapochromic sensors were prepared at these corresponding concentrations and placed in the headspace of 100, 50, and 10 ppm NH4 OH dilutions.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 2 System Model
### Input Power Back-Off  
Figure 4 shows the AM-AM characteristics of the three PA models described above. It can be seen that in both the Saleh and polynomial models, when the input signal amplitude is less than \(r_{\text{max}}\), a smaller value of \(r/r_{\text{max}}\) leads to a better linear amplification property. The IBO is defined as the ratio of \(r_{\text{max}}^{2}\) to the average power [53], i.e.,  
\[\xi=\frac{r_{\text{max}}^{2}}{P_{\text{in}}} \tag{11}\]  
where \(P_{\text{in}}=E\big{[}r^{2}\big{]}=E\big{[}|x|^{2}\big{]}\) is the average power of the input signal and \(E[\cdot]\) denotes the expectation function.  
In actual applications, the IBO is usually defined with respect to the 1 dB compression point instead of the maximum limit. This is called effective IBO, which is defined as [53]  
\[\xi_{\text{eff}}=\frac{r_{\text{1 dB}}^{2}}{P_{\text{in}}}=k^{2}\xi \tag{12}\]  
where \(k\) is the relative 1 dB compression point, and \(r_{\text{1 dB}}=kr_{\text{max}}\). The 1 dB compression point is defined by the following equation:  
\[G_{\text{dB}}(r_{\text{1 dB}})=10\lg\left|\frac{g(r)}{Ar}\right|^{2}=-1. \tag{13}\]  
Based on the above equation, the value of \(r_{\text{1 dB}}\) can be calculated from the PA's AM-AM characteristic. It follows that the 1 dB compression coefficient \(k\) can be obtained as \(k=r_{\text{1 dB}}/r_{\text{max}}\). For the Saleh model and polynomial model in Figure 4, \(k\) is given by 0.6986 [53] and 0.7817, respectively. In the rest of this paper, the term IBO refers to effective IBO.
## 2 System Model of GCI-DCSK SWIPT
### Receiver  
The signal received by the receiver is given by  
\[r(t)=\frac{1}{\sqrt{d_{tr}^{m}}}\sum_{l=1}^{L}\alpha_{l}\delta(t-\tau_{l}) \otimes s(t)+n_{p}(t), \tag{2}\]  
where \(d_{tr}\) is the distance between transmitter and receiver, \(m\) is the path loss exponent, \(n_{p}(t)\) represents the additive white Gaussian noise (AWGN) with zero mean and variance \(\frac{N_{0,p}}{2}\), \(\tau_{l}\) and \(\alpha_{l}\) denote the path delay and the channel fading coefficient of the \(l^{th}\) path between transmitter and receiver, and \(\otimes\) is convolution operator.  
At the receiver, the received RF band signal \(r(t)\) is first converted to a baseband signal and then divided into two intervals by switchers, where the frame-divided factor is \(\beta_{1}\). In addition, the noise introduced by the RF band signal to baseband signal conversion is denoted by \(n_{c}(t)\) with zero mean and variance \(\frac{N_{0,p}}{2}\).  
In the first \(\beta_{1}\) chips, the signal is fed into an analog-to-digital converter (ADC) for sampling and digitization. Then, the sampled signals for \(n\) branches labeled from \(1\) to \(n\) are correlated with those of the reference branch through the correlator, respectively. By comparing the absolute values of correlator outputs, \(p_{2}\) carriers with larger values are judged as active carriers, the remained carriers are judged as inactive carriers. By the permutation of active carriers, the index bits are recovered. Meanwhile, a control bits sequence is generated for \(n\) branches. By the control bits, the active branches will be connected to the matched filter and the inactive branches will be connected to the energy harvester.  
In the last \(\beta-\beta_{1}\) chips, the energy on the inactive branches will be collected. Meanwhile, the signal on each active branch is filtered by a match filter and sampled at every \(kT_{c}\) instant. Then, the sampled discrete signals on reference branch and active branches are stored to  
Figure 1: Block diagram of the GCI-DCSK SWIPT scheme.  
two matrices \(\mathbf{A}_{1\times(\beta-\beta_{1})}\) and \(\mathbf{B}_{p_{2}\times(\beta-\beta_{1})}\), respectively, and correlated. Finally, the correlated results are stored in matrix \(\mathbf{Z}_{1\times p_{2}}\). When the value of \(z\) is larger than 0, the modulated bit will be demodulated as 1; otherwise it will be demodulated as 0.
# Corrosion Behavior and Conductivity of TiNb and TiNb Coated Steel for Metallic Bipolar Plates
## 3 Results and Discussion
### Interfacial Contact Resistance  
The conductivity of bipolar plates has great impact on the PEMFC's working efficiency [46]. Normally, the bulk resistance of metallic bipolar plates can be ignored since the ICR between the bipolar plate and the carbon paper is much greater. In Figure 10, the ICR of the material with a carbon paper is plotted as a function of the compression force.  
In the low-force region, as compaction force raises, the contact area between carbon paper and sample increases leading to the decrease of ICR, and when the force is high enough, the ICR is dominated by the surface composition and nature [46; 47]. At the same force, the contact resistance of 316L SS, TiNb/316L SS, and TiNbN/316L SS decreases in turn, and their conductivity increases sequentially. I.e., both the TiNb and the TiNbN coatings provide lower ICR than 316L SS's, but the effect of TiNbN is more remarkable. At 276 N\(\cdot\)cm\({}^{-2}\), TiNbN coating's ICR is approximately 48 m\(\Omega\)\(\cdot\)cm\({}^{2}\), and the ICR value of TiNb/316L SS is nearly 1/5 of that of 316L SS. One of the reasons behind this is that both coatings could reduce the roughness of 316L SS substrate leading to an increased contact area. For the TiNbN coating, the dopant of TiN and NbN in its surface layer improves conductivity. In comparison with TiO\({}_{2}\) and Nb\({}_{2}\)O\({}_{5}\), TiN [48] and NbN have higher conductivity. Overall, the TiNbN coating is of relatively high electrical conductivity, and the TiNb film reduces the substrate's ICR. However, considering the moderate corrosion resistance of TiNbN, TiNb/316L SS is more promising for use as bipolar plate. Further studies are relevant assessing a combined film with good conductivity from TiNbN and great corrosion resistance from TiNb.  
Figure 10: Interfacial contact resistances (ICR) between 316L SS, TiNb/316L SS, TiNbN/316L SS and carbon paper as a function of compaction force.  
Figure 9: Potentiostatic polarization curves of 316L SS, TiNb/316L SS, and TiNbN/316L SS.
# Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section
###### Abstract  
The correlation between the kind of cesarean section and post-traumatic stress disorder (PTSD) in Greek women after a traumatic birth experience has been recognized in previous studies along with other risk factors, such as perinatal conditions and traumatic life events. Data from early studies have suggested some possible links between some vulnerable factors and the potential development of postpartum PTSD. The classification of each case in three possible states (PTSD, profile PTSD, and free of symptoms) is typically performed using the guidelines and the metrics of the version V of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) which requires the completion of several questionnaires during the postpartum period. The motivation in the present work is the need for a model that can detect possible PTSD cases using a minimum amount of information and produce an early diagnosis. The early PTSD diagnosis is critical since it allows the medical personnel to take the proper measures as soon as possible. Our sample consists of 469 women who underwent emergent or elective cesarean delivery in a university hospital in Greece. The methodology which is followed is the application of random decision forests (RDF) to detect the most suitable and easily accessible information which is then used by an artificial neural network (ANN) for the classification. As is demonstrated from the results, the derived decision model can reach high levels of accuracy even when only partial and quickly available information is provided.  
artificial neural networks; random decision forests; posttraumatic stress disorder; DSM-V; emergency cesarean section; elective cesarean section; postpartum period +
Footnote †: journal: Applied Sciences
# Simulation-Based Design for Inlet Nozzle of Vortex Tube to Enhance Energy Separation Effect
## 1 Introduction
Other crucial parameters include orifice diameter (D\({}_{\text{O}}\)), L/D ratio (the ratio of length (L) to cold tube diameter (D), and hot tube area. [PERSON] et al. [20] emphasized the importance of nozzle design over orifice design, noting that very small L/D ratios lead to poor performance due to mixing of cold and hot gases, while very large L/D ratios between 45 to 55 showed no significant effect on temperature separation. [PERSON] et al. [21] experimentally investigated the effect of varying the number of inlet nozzles (from 2 to 6) and inlet pressure (200-300 kPa) and found that four nozzles yielded the highest energy separation. Additionally, while energy separation initially increased with inlet pressure, a pressure surge led to smothering effects and a downward trend in separation efficiency. [PERSON] (2009) [22] conducted an experimental study on the impact of the cold outlet diameter on separation efficiency, while [PERSON] et al. [23] examined valve angles of 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 75\({}^{\circ}\), finding higher efficiency at 30\({}^{\circ}\) and 60\({}^{\circ}\). Figure 1: Schematic diagram of a vortex tube. Table 2 summarizes numerical studies of vortex tubes using Computational Fluid Dynamics (CFD) techniques. [PERSON] et al. (2018) performed numerical simulations using a simplified axisymmetric model and compared the results with experiments. Although significant discrepancies were observed in inlet pressure, reducing the nozzle area brought the simulations closer to the experimental results, with an error margin of 20% in the predicted separation effect. [PERSON] et al. (2018) used Large Eddy Simulation (LES) techniques and found that LES models predicted the final temperature difference more accurately than Reynolds-Averaged Navier-Stokes (RANS) simulations, though they still struggled to predict the separation effect precisely. [PERSON] et al. (2018) demonstrated that streamlined nozzles improved rotational velocity at the inlet, leading to more efficient gas flow. In a recent study, [PERSON] and [PERSON] (2018) concluded CFD simulations using a simplified geometry excluding the hot outlet device, yielding results qualitatively similar to experimental data but with a 14% average error in the final temperature difference. In this study, we present a CFD-based investigation aimed at gaining a deeper understanding of turbulent thermal flows in vortex tubes and enhancing the accuracy of separation performance predictions. The actual geometry of the experimental device was modeled as closely as possible to reflect real-world conditions, rather than relying on the simplified geometries used in prior CFD studies. Additionally, simulations were performed by varying the grid systems and turbulence models to improve the predictive capabilities. Finally, modifications to the inlet nozzle design were explored to assess their influence on the energy separation effect. The simulations were conducted using a commercial CFD software, Siemens STAR-CCM+ v16.04.
## 2 Fracture Propagation Model of CBM Reservoirs with Different Lithologies  
### Node Connection System of CBM Reservoirs  
Unlike the traditional division of reservoirs into grid systems, this study uses a node connection system to perform an equivalent characterization of coal rock minerals, as shown in Figure 1. First, based on the fine geological model of the traditional grid system, each grid center is regarded as a node (node in Figure 1), dividing the reservoir into a series of evenly distributed node systems. The node density can be uniformly refined or coarsened according to the simulation accuracy. Secondly, for complex lithological coal rock, when special media, such as natural fractures, are present in the reservoir, new nodes can be added to describe these unique lithological media. When considering bedding or interlayer distribution, specific nodes can be deployed for precise characterization. This study focuses on the impact of mineral composition of coal rock on fracture propagation. Therefore, three different mineral components are considered in node settings. In Figure 1, nodes of different colors represent distinct mineral nodes, each with unique physical properties interpolated from the actual geological model. A connection exists between adjacent nodes to enable material exchange. Based on the node connection system, flexible grid-free nodes can replace the traditional grid topology, and one-dimensional connection channels between nodes can be constructed, creating richer connection relationships. Consequently, the node connection system facilitates hydraulic fracture propagation simulation.  
Figure 1: Node connection system of CBM reservoirs.
# A Portable Non-Motorized Smart IoT Weather Station Platform for Urban Thermal Comfort Studies
## 4 Efficiency and Performance of the Proposed Weather Station Platform
### Test 1: GPS Module--Accuracy and Precision  
The GPS sensor location readings (latitude and longitude) were analyzed by importing them into'my Google Maps' and were overlapped with the actual location points. The distance between the actual location points and the GPS sensor values was measured to assess accuracy.  
Figure 6 shows the result of the 'On the Move' scenario. Each red dot corresponds to a specific latitude and longitude value. Comparing it with the actual routes, Route A and Route B, the GPS sensor of the developed portable device accurately identified both Routes, irrespective of their built characteristics. However, the yellow dots (GPS sensor measurements) do not overlap with the red dot (actual location); see Figures 7 and 8, especially in all the commercial neighborhood locations of the 'Stop-and-Go' scenario. Figures 7 and 8 illustrate the typical behavior of the sensor readings in all the 'Stop-and-Go' locations.  
Figure 6: Location points are calculated using the latitude and longitude values provided by the GPS sensor. (**Top**) image—Route A and (**Bottom**) two images—Route B.  
Although the GPS sensor could capture the exact route of the 'On the Move' scenario, it struggled to capture the precise location within the street. Figure 9 shows the sensor's inaccuracy within one of the routes of the 'On the Move' scenario. This might be due to building density, as some precise and accurate readings were noted in locations that have more openness. Another reason might be due to the Kalman filter or similar filters that are built within the sensor.  
Figure 8: Commercial neighborhood locations. The images from left to right are locations 1, 7, and 14. The red dot on the image is the actual location of the device, the small yellow dots are the GPS sensor measurements, and the blue dot is the average of the yellow dots.  
Figure 7: Residential neighborhood locations. The images from left to right are locations 3, 6, and 11. The red dot on the image is the actual location of the device, the small yellow dots are the GPS sensor measurements, and the blue dot is the average of the yellow dots.  
Figure 9: Part of ‘On the Move’ Route A—with actual route (Red) and sensor values (yellow dots).
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Uncooperative Localization Approach  
The device localization methods are essential work in wireless security systems and radio management systems. Therefore, a few studies have focused on these localization systems and the difficulty of a transmitter localization of radio frequency energy. One approach introduced a three-dimensional algorithm to obtain the accurate localization of an unknown emitter in an indoor environment [263]. This system was constructed with received signal strength difference (RSSD) information and factor graph (FG), which is good for LoS and NLoS situation. The proposed method considered the stochastic properties and the Gaussian assumption of measurement errors. In this system, the positioning performance has a higher KNN and least squares algorithm. In addition, it shows the mean error below 1.15 m.  
On the other hand, there are several works that have been presented using RSS calibration measurements between anchor nodes for indoor localization. However, some works have considered directly measuring the transmitter location from the RSS by dropping this requirement. For instance, the uncooperative emitter localization system was developed in [264; 265], which used a listen only uncalibrated receiver. In this approach, the authors developed the bias effects such as additive random variables for an individual receiver in the path loss model. The unknown bias and noise variance parameters were estimated by the variance least squares. Then, the NLS and the Gaussian particle filter (GPF) algorithms were used to handle these bias effects. Another non-cooperative emitter localization focused on enhancing wireless security with only a single receiver [266]. In this system, 3D signal characteristics have extracted for room-based transmitter localization by the development of a vector sensor and compared with traditional methods. The machine learning method utilized for room localization using wavelet transform and the short-time Fourier transform. The results of the room localization performance were higher than TOA, DOA, and RSS. The accuracy achieved above 90% for wideband and narrowband wireless communication signals.
# Multi-Objective Optimization of LCC-S-Compensated IPT System for Improving Misalignment Tolerance
## 5 Conclusions  
In this paper, a MOPSO-based compensation parameter optimization design scheme for CCO- and CVO-type LCC-S-compensated IPT systems is proposed to improve misalignment tolerance. The multi-objective functions (including output fluctuation, current stresses and reactive power requirement) and physical constraints (including size of components, voltage and current withstanding capability) are identified in detail. Based on the Pareto front of the optimization results, the proposed methods enhance the robustness of the power transfer response to both coupling variations and loads. Moreover, the ZVS operation can be realized for the primary power switches under full coupling range operations. The misalignment region of the wireless coupler exceeds the vertical distance of two coils by about 125 mm. When implementing the optimized parameters, the CCO-type LCC-S-compensated system was demonstrated to be able to keep the current fluctuation under 8.8%, within 100% variation in both coupling and load. The experimental results show that the voltage fluctuation was not more than 10.4% with a coupling range of 0.25-0.5 and a narrow load range. The CVO-type IPT system cannot meet the stable output demand in the scenario of large misalignment and wide load simultaneously. Therefore, if the load changes over a wide range, extra impedance matching circuits may need to be added between the rectifier and the actual load. The compensation optimization method in this paper can be integrated with the outstanding magnetic couplers and excellent control strategies to further improve the anti-misalignment performance and extend dynamic wireless charging.  
Conceptualization, Q.T.; methodology, R.L. and X.Y.; software, J.Y. and R.L.; validation, R.L. and J.Y.; investigation, Q.L.; writing--original draft preparation, J.Y., A.Y. and R.L.; writing--review and editing, Q.T.; funding acquisition, A.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Cangzhou Plan Guide Project for Key Research, grant number 204103003.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.  
Figure 14: Measurement efficiencies of two new designed parameters when the \(k\) changes due to misalignment.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.1 HJ-1 Data Preprocessing
After removing the atmospheric effect, radiometric calibration of the data was carried out to convert the DN value into the surface reflectivity, based on the spectral response function of the HJ and MODIS data. The four-band spectral response functions of HJ-1A CCD1 and MODIS are shown in Figure 2b.