esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### The ARIADNE Prototype TPC  
Each of the four G-THGEMs/THGEM were characterised using the ARIADNE prototype detector. The ARIADNE prototype [34], shown in Figure 7, is an optical readout, 40 L cylindrical TPC, with both dual-phase cryogenic and gas capabilities. Characterisation of all THGEMs was undertaken in GAr conditions.  
The TPC field cage, comprised of 22 stainless steel rings, has a 178 mm diameter with a 20 cm drift length. The field cage is bounded by a cathode grid at the bottom of the TPC and capped at the top with the bottom electrode of the THGEM. The drift field is established between the cathode and bottom THGEM electrode. Outside the TPC active region, mounted on a rotatory feedthrough, is an Americium-241 alpha source, with an initial 30 kBq rate. This is collimated to around 500 Bq. The rotary feedthrough allows the source to be rotated in/out of the TPC volume during operation.  
A sheet of tetraphenyl butadiene (TPB)-coated wavelength shifting (WLS) acrylic is mounted directly above the THGEM. This WLS plate shifts the VUV S2 scintillation light from 128 nm [35] to 420 nm [36,37]. This wavelength is in the high quantum efficiency range of the externally mounted Andor iXon Ultra 888 EMCCD camera [38] (further discussed in [17]). The Andor iXon Ultra 888 EMCCD camera is positioned externally to the detector, at a distance of approximately 600 mm from the THGEM. The EMCCD is combined with a Spacecom VF50095M lens, with a speed of f/0.95 and a focal length of 50 mm, giving a field of view of approximately 160 \(\times\) 160 mm\({}^{2}\). The S2 scintillation light is imaged through a 90 mm diameter borosilicate glass viewport. The transparency of the viewport is >90% [39] at 420 nm (the peak of the TPB emission spectrum). The camera is mounted on an adjustable tripod frame which sits on the optical viewport. It was important to maintain consistent field of view and focusing between studies. This was achieved through reinforcement of the legs with locking nuts and retaining EMCCD orientation during reassembly.  
An 8-inch, TPB coated, Hamamatsu R5912-20-MOD Photomultipler Tube (PMT) is mounted below the TPC. The spectral response range of the PMT lies between 300 and 650 nm, with a quantum efficiency of approximately 30% at 420 nm [40]. Figure 8 shows the ARIADNE prototype TPC, with a Borofloat G-THGEM and a conventional FR4 THGEM mounted.  
Figure 8: (**a**) A Borofloat 33 G-THGEM and (**b**) conventional copper-coated FR4 PCB THGEM mounted on the ARIADNE prototype TPC.  
Figure 7: The ARIADNE Prototype: (**a**) operating principle and (**b**) TPC. As a single phase, optical readout GArTPC, a wavelength shifter (TPB) is used to shift the 128 nm VUV S2 scintillation light to 420 nm visible light. This is then imaged with an EMCCD.
## 4 Results and Discussion
### The Impact of HSR Network on Socio-Economic Growth
#### 4.1.5 JR Central Network (Japan)  
With respect to the nation's economy, during its early stage, the JR Central network seems to have influenced the labour sector. Numerous scholars studied the impact of the JR Central on migratory patterns, especially on the factors in the labour force. Some researchers discovered that the rate of rise was estimated to be roughly 1.8% in areas next to HSR stations, but just 1.3% in areas lacking the stations. Moreover, the employment growth in regions with Shinkansen services increased by 34% [24]. On the other hand, in the following years, the employment rate has decreased. [PERSON] and [PERSON] found that the participation rate for workers fell by 2.8%, compared to 3.6% in regions without HSR services [121]. In addition, the IT industry had increased by 22%. This indicates a very limited impact on the employment sector by the Shinkansen system. Businesses engaged, such as grocery retailers and the housing industry, expect a high level of station accessibility [25]. As observed in different industries and locations, the general advantage of the Shinkansen network to Japan's labour force had decreased. Likewise, in this research, a PCC result of 0.14 implies that the extension of the Shinkansen line was weakly favourable.  
The outcome of the QoL's PCC is 0.08. In addition, the findings of PCC are \(-0.94\) and \(-0.24\) for age dependence ratios, young (PD2) and elderly (PD3). It can be implied that the JR Central network offers more QoL benefits to older groups than younger groups. However, younger groups obtain great benefits as education has a coefficient of 0.41, which is also higher than other nations. To our knowledge, no research has assessed the details of the educational benefits of the Shinkansen network. The interesting statistics of primary (ED1), secondary (ED2) and post-secondary (ED3) student attendance rates that have a PCC rating of 0.96, 0.92, and 0.69, respectively, are worth studying. The research's findings show a significant connection with the growth of the network.
# Influence of Roll Diameter on Material Deformation and Properties during Wire Flat Rolling
## 3 Results  
### Strain Distribution with Roll Diameter  
Figure 5a shows a comparison of the effective strains of the wire against the roll diameter. The trends in the strain distribution were similar regardless of the roll diameter. The center region had the maximum effective strain, and the free surface region exhibited the minimum strain. Additionally, the contact surface region exhibited a higher effective strain than the free surface region. MSBs were also observed in all the wires [10]. However, the density of strain distribution was different with the roll diameter. The strain distribution of the flattened wire was more inhomogeneous with the roll diameter, thus the shape of the MSBs was strengthened with the roll diameter.  
Figure 4: (**a**) Comparison of shape variation in flat-rolled wire with \(R_{\text{h}}\) based on FE analysis and (**b**) comparison of \(W\) and \(b\) values obtained from experiment and FE analysis with \(R_{\text{h}}\) at a roll diameter of 400 mm.  
To compare the strain distributions of the flattened wire with the roll diameter comprehensively, the effective strains were extracted based on the strain contour maps. Figure 5a,b compare the extracted effective strain along the horizontal and vertical directions of the wire against the roll diameter. The effective strains at the center region increased with the roll diameter, and those of the free and contact surface regions decreased with the roll diameter, resulting in a larger strain inhomogeneity of the wire with increasing roll diameter. For a better comparison of the inhomogeneity level, the strain inhomogeneity factor (_SIF_), defined as follows, was introduced:  
\[SIF=\frac{\varepsilon_{max}-\varepsilon_{min}}{\varepsilon_{ave}} \tag{7}\]  
where \(\varepsilon_{ave}\) and \(\varepsilon_{min}\) indicate the average and minimum effective strains, respectively. Figure 6a shows the \(\varepsilon_{max}\), \(\varepsilon_{min}\), and \(\varepsilon_{ave}\) of the wire against the roll diameter. Regardless of the roll diameter, all the wires had a similar \(\varepsilon_{ave}\) in both directions. The \(\varepsilon_{max}\) of the wires increased with the roll diameter, whereas the \(\varepsilon_{min}\) decreased along both the horizontal and vertical directions of the wire with the roll diameter. Figure 6b compares the strain inhomogeneity factor along both the horizontal direction (\(SIF_{h}\)) and vertical direction (\(SIF_{v}\)) against the roll diameter. Regardless of the roll diameter, \(SIF_{h}\) was higher than \(SIF_{v}\), which is consistent with previous results [1,4]. It is evident that the _SIF_ increased with the roll diameter.  
Figure 5: Comparison of effective strain (**a**) contours and profiles along the (**b**) horizontal and (**c**) vertical directions with the roll diameter.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of AP Number
#### 4.1.3 Performance of JMART  
Figure 7 shows the Fisher information and the corresponding error bounds by the JMART. From this figure, we can find that the larger number of APs results in the higher Fisher information and lower error bounds' indoor localization, which are in accordance with the results under the metrics of single and double measurement given above.  
Figure 5: Sum of diagonal elements in the FIM with respect to the metrics of (**a**) AOA/RSS, (**b**) TOF/AOA and (**c**) RSS/TOF.  
Figure 6: Error bounds achieved by the metrics of (**a**) AOA/RSS, (**b**) TOF/AOA and (**c**) RSS/TOF.  
Figure 7: Sum of diagonal elements in the (**a**) FIM and (**b**) error bounds by the Joint Metric of AOA/RSS/TOF (JMART).
## 3 Results and Discussion
### Zoned Washcot
#### 3.2.1 Average Partition  
While keeping the other conditions of the model unchanged, we uniformly distribute the lengths of two segments of the porous medium (\(X\) = 0.5). The segment close to the inlet surface is designated as the first part, while the remaining one is referred to as the second. Five values are equally selected, with the porosity of the front part ranging from 0.5 to 0.505, while the corresponding porosity of the rear part varies between 0.5 and 0.495. The change in catalyst coating amount corresponds to approximately 1.76 g\(\cdot\)L\({}^{-1}\)[28] and 0.148% pore volume filled with washcoat [44] for every 0.001 variation in porosity. The simulation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Diameter (mm) & 330.2 \\ Length (mm) & 177.8 \\ Thickness (mm) & 0.31 \\ Inlet volume flow rate (m\({}^{3}\cdot\)s\({}^{-1}\)) & 0.2–0.8 \\ Cell density (cpsi) & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model validation condition.  
Figure 3: Verification of pressure drop under different engine torques with experimental data.  
results are depicted in Figure 4. The axial position, defined as the location along the central axis of the porous medium, is normalized such that the minimum value of 0 corresponds to the beginning segment and the maximum value of 1.0 corresponds to the end segment. In comparison, the values of the inlet channel rate and permeation rate exhibit a similar order of magnitude as reported in the reference [45], while the axial rate distribution also demonstrates certain similarities.  
The deviation in porosity from the average value in two parts of the porous medium leads to an increase in the permeation velocity of the first part, and a decrease is observed in the second part, with a similar quantity of change occurring throughout the whole porous medium, as shown in Figure 4a. An increase in porosity within the low coated zone expands its internal space, which consequently leads to increased resistance for gas flow, accompanied by a corresponding reduction in space within the high coated zone [46]. Furthermore, as porosity increases within the first part, there is an enhanced tendency for gas passage through this place, gradually widening the variation range across the two parts.  
Figure 4: (**a**) Contour and curve of axial permeation velocity distribution, (**b**) velocity variance corresponding to different porosity when \(X=0.5\).  
The velocity variances in the porous medium are calculated for different porosity conditions, and the corresponding results are presented in Figure 4b. As the porosity gradually deviates from the average value, the variance initially decreases and then increases. The minimum variance of 3.8 \(\times\) 10\({}^{-6}\) occurs when \(\epsilon_{rear}\) varies in the range of [0.503, 0.504]. With a further increase in porosity, a higher variance of 5.2 \(\times\) 10\({}^{-6}\) is observed. This indicates that excessively large differences in porosity between two parts can counter-productively improve uniformity.
## 4 Case Study
### Simulation Analysis of Restorative Measures
Figure 13: Network efficiency recovery diagram under passenger and freight flow fluctuation simulation. Finally, the failure nodes in the network were removed to simulate the disturbances of natural disasters and technical malfunctions. The number of removed failure nodes was set at 100, and the results are shown in Figure 14.  
The network efficiency of the service network when it is not disturbed is 0.68524, and it drops to 0.6784 when disturbed by natural disasters and technical malfunctions. These random disruptions have an uneven impact on the network, as the removal of nodes may occasionally affect critical nodes and at other times less significant ones. After the simulations of random disturbances such as natural disasters and technical malfunctions, all three lines exhibit significant efficiency leaps at certain specific points. These sharp increases suggest that the reintegration of certain key nodes during the recovery process has a disproportionately positive effect on network efficiency. These nodes may be the bridge nodes that connect multiple network clusters or key pathways. The efficiency curves of the betweenness centrality recovery strategy and the degree value recovery strategy are very close, indicating that there is not a significant difference in the improvement of recovery efficiency by prioritizing the recovery of nodes with higher degree values or higher betweenness centrality. This is because in the simulations of random disturbances such as natural disasters and technical malfunctions, both strategies can effectively recover some critical nodes. In contrast, the random recovery strategy (represented by the red line) exhibits greater volatility, characterized by a relatively slow and steady increase in efficiency without prominent surges. This reflects the instability and lower effectiveness of the random strategy. This strategy does not consider the strategic positions or roles of nodes in the network; therefore, its effect is not as good as that of other strategies. This is because the random selection of nodes for recovery does not target the recovery of the most critical nodes, resulting in a temporary improvement in efficiency but an unstable overall trend. In conclusion, different perturbation events of the railway network have a significant impact on the effectiveness of recovery strategies. The betweenness centrality recovery strategy has demonstrated a superior recovery efficiency in most cases, especially when the perturbation targets the \"bridging\" nodes in the network. Meanwhile, the random recovery strategy, due to its inherent uncertainty, has exhibited volatility in recovery efficiency in all circumstances.
## 3 Confidence Interval of Damage: Stationary Case
### Case M: Multiple Stress-Time Histories  
Assume that \(N\) stress-time histories \(x_{i}(t)\), \(i=1,\ 2,\ldots N\) of same duration \(T\) are available, e.g., from measurements replicated under the same conditions. The damage values \(D_{i}(T)\), \(i=1,\ 2,\ldots N\) of each \(x_{i}(t)\) are characterized by the sample mean and sample variance [27]:  
\[\overline{D}(T)=\frac{1}{N}\sum_{i=1}^{N}D_{i}(T)\,\delta_{D}^{2}=\frac{1}{N-1} \sum_{i=1}^{N}\bigl{[}D_{i}(T)-\overline{D}(T)\bigr{]}^{2} \tag{2}\]  
These sample estimates represent the unbiased estimators of the (unknown) expected value \(E[D(T)]\) and variance of the damage \(\sigma_{D}^{2}\), respectively. In the hypothesis of normally distributed damage, the above sample values allow the \(100(1-\alpha)\)% confidence interval on the expected damage (with variance unknown) to be constructed as [27]  
\[\overline{D}(T)-t_{\alpha/2,N-1}\frac{\vartheta_{D}}{\sqrt{N}}\ \leq E[D(T)]\leq\ \overline{D}(T)+t_{\alpha/2,N-1}\frac{ \vartheta_{D}}{\sqrt{N}} \tag{3}\]  
in which \(t_{\alpha/2,N-1}\) is the upper \(\alpha/2\) percentage point of \(t\) distribution with \(N-1\) degrees of freedom. For large \(N\) (for example, \(N>30\)), the \(t\) distribution approaches a standard normal distribution, \(t_{\alpha/2,N-1}\ \cong z_{\alpha/2}\). In Equation (3), the only unknown is \(E[D(T)]\).  
Predictably, the confidence interval becomes increasingly narrower if \(N\) increases. If the number of load records \(N\) were infinite, no statistical uncertainty would be present; \(\overline{D}(T)\) would be equal to \(E[D(T)]\), and the confidence interval would have a zero width (zero prediction error).  
Equation (3) relies on the hypothesis of normally distributed damage \(D(T)\), which can be assumed under the validity of the central limit theorem in the limit \(T\to\infty\). Under this hypothesis, the Palmgren-Miner damage is, in fact, the sum of a countless number of damage values from individual cycles. The hypothesis of central limit theorem has been supported by many authors [6; 7; 8; 9; 28]. In [11], it was concluded that the departure from the normal distribution for the damage is only marginal, especially for low values of the CoV of damage, which in turn indicates that in practical situations, \(T\) is usually long enough for the central limit theorem to apply. Recent studies [29] have nevertheless discovered that, for larger values of both CoV and S-N slope (i.e., less steep S-N line), the damage distribution tends to be skewed and non-normal, even though this conclusion has been drawn based on a very small number of cycles (3200) that, while used for accentuating the skewness of damage distribution, seem unrealistically too small for common engineering applications. Even in the case (discussed in Section 3.2) of block subdivision, which yields a block length \(T_{B}<T\), the number of blocks is small so that \(T_{B}\) remains long enough to assume that \(D_{B}(T_{B})\) is normally distributed.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 7 Conclusions  
In this article, we propose an MEC and WPT integrated system that considers multiple users and partial task-offloading in multiple time slots. The multi-antenna AP provides energy for multiple MDs at the same time, and the energy harvested by the MDs provides energy for task-offloading and local computing. By jointing optimization of task-offloading size, energy harvesting time, task-offloading time and CPU frequency, it is ensured that the energy harvested by each MD in each time block meets the energy consumption requirements of various tasks. At the same time, it can extend the battery duration of MDs and improve the computing power of MDs. The optimal solution is obtained by using the Lagrange method, KKT condition and augmented Lagrangian method. The numerical results show that the proposed joint optimization scheme is superior to the traditional benchmark scheme. The integrated system of MEC and WPT is becoming the hotpot research because of its practicability. Our work provides a new idea for related research. Next, the development of MDs will have greater applicability. In the following research, we will pay more attention to the nonlinear energy transmission model, and apply more task offloading communication protocols such as Orthogonal Frequency Division Multiple Access to make the system model more in line with technological development.  
Conceptualization, L.L. and Y.L.; methodology, L.L. and G.X.; software, L.L. and Y.L.; validation, L.L. and Y.L.; investigation, L.L. and P.L.; resources, Y.L.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, L.L., P.L., Y.L. and J.G.; visualization, L.L.; supervision, G.X.; project administration, G.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key R&D Plan of China under Grant 2017 YFA0604500, and in part by the Jilin Province Science and Technology Development Plan Project under Grant 20190201180 JC, 20200401076 GX, in part by the Jilin Province Education Department '13 th Five-Year' Science and Technology Project under Grant JJKH20191310 KJ.  
The authors declare no conflict of interest.  
Figure 6: Task size vs. failure ratio.
# A Perspective on Information Optimality in a Neural Circuit and Other Biological Systems
## 1 Introduction  
### The Logic Gate Model  
[PERSON] and [PERSON] introduced the concept of a logic gate model for explaining the processing of information in an animal neuronal system, based on the concept that a neuron is in a resting or active state, and the synaptic connections exhibit specific non-dynamic behaviors [1]. The logic gate design is an efficient model of Boolean algebraic operations, including the basic operators AND, OR, and NOT (Figure 1) [2; 3; 4; 5; 6]. However, others consider the biological design of a neuronal network inconsistent with the assumptions of this model [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Even though the logic gate model is not expected to predict the behavior of a neuron at the cellular level, it does offer a measurement of expected computational efficiency for a neural network. Given knowledge of a neural system and its layer of nodes of inputs, its layer of nodes of outputs, and an assumption that a neuron is at a resting or active state, it is possible to estimate the optimal number of logic gates to model information optimality, and potentially the behavior of the system. This metric of optimality is also applicable for making comparisons between neural circuits, and potentially as a source of intuition on network structure and function. The assumption where a neuron as active or at rest can be represented by the binary values one and zero.  
One problem with modeling neuronal communication is the availability of reliable data on the activity of a neural system. This activity is dependent on external effects, such as the environment, the interactions between the organism and the environment, and its nonlinear dynamics at the spatial and temporal scales. The complexity of the system is expected to grow exponentially with variation in the environmental conditions, and likewise with organism complexity, such as in the physiological states. This phenomenon is observed in the sociality of ants [18].  
Evolution can lead to adaptation for efficient communication in a neural system. One kind of adaptation is modularity, which can enhance the efficiency of informationprocessing, such as that observed in the social behavior of honeybees [19]. Another strategy is where multiple functions are handled by neurons in neural circuits, otherwise separate neural circuits would be required for changes in the surrounding environment. This is a confounding factor for disentangling the workings of a neural circuit. However, in the case of a simple example, along with a controlled environment, then it should be possible to gain insight on information processing by mapping a neural circuit to a simple model [20], and therefore avoid the effects of the increasingly complex dynamics that often occur in Nature.