esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Micro-Settlement Control Techniques
### Method for Determining the Appropriate Pipe Roof Stiffness  
As noted in Reference [17], settlement trends observed in experiments using rectangular and gate-shaped pipe roofs were generally consistent. However, surface settlement in the gate-shaped pipe roof tests was 50.8% greater than that in the rectangular pipe roof tests. Therefore, selecting a rectangular cross-section design is essential for controlling surface micro-settlement during large-section tunnel construction.  
The pipe roof stiffness is one of the primary factors influencing surface settlement caused by tunnel excavation. To better understand surface settlement patterns, the concept of the load-to-pipe roof stiffness ratio (\(\beta\) = load/stiffness) is introduced. According to Equation (9), the relationship curve between the load-to-pipe roof stiffness ratio \(\beta\) and the longitudinal surface settlement u1 is depicted in Figure 5. Based on specific settlement control standards and load conditions, the pipe roof stiffness is preliminarily determined, and Figure 6 depicts the relationship curve between surface settlement and pipe roof stiffness. The curve AB represents the sharp descent segment, indicating that an increase in pipe roof stiffness significantly reduces the surface settlement. The curve BC represents the transition segment between a steep descent and a gentle slope, where the pipe roof stiffness is both effective in controlling settlement and cost-efficient. Therefore, the pipe roof stiffness should be designed to fall within this range. The curve CD represents the gentle slope segment, indicating that an increase in pipe roof stiffness has minimal effect on surface settlement control. If the preliminarily determined pipe roof stiffness falls within the BC range, it is deemed reasonable; otherwise, adjustments should be made to bring it into this range.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 3 System Model
### IRS System Structure
Again, the BS channel \(\mathbf{H}\) to the IRS channel can be expressed as follows:  
\[\mathbf{H}=\sqrt{\frac{LN}{T_{1}}}\sum_{t_{1}=1}^{T_{1}}\eta_{t_{1}}\mathbf{b} (\rho_{t_{1}}^{H_{r}},\phi_{t_{1}}^{H_{r}})\mathbf{a}^{H}(\rho_{t_{1}}^{H_{r}},\phi_{t_{1}}^{H_{r}})^{T}, \tag{7}\]  
where \(T_{1}\) represents the number of paths between the BS and IRS, \(\eta_{1}\) stands for the complex gain of the paths available from the BS to IRS, \(\mathbf{b}(\rho_{t_{1}}^{H_{r}},\phi_{t_{1}}^{H_{r}})\) is the steering vector related to IRS elements, and \(\mathbf{a}^{H}(\rho_{t_{1}}^{H_{r}},\phi_{t_{1}}^{H_{r}})\) is the steering vector connected to the BS for the \(t_{1}\)-th path. To define the channel between the IRS and the user, the channel matrix can be described as follows:  
\[\mathbf{H}_{z,i}^{H}=\sqrt{\frac{N}{T_{2}}}\sum_{t_{2}=1}^{T_{2}}\eta_{t_{2}} \mathbf{a}^{H}(\rho_{t_{2}}^{H_{r}},\phi_{t_{2}}^{H_{r}}), \tag{8}\]  
where the complex gain of paths is represented as \(\eta_{t_{1}}\); \(T_{2}\) is the number of paths between the IRS and the user, \(\rho_{t_{1}}^{H_{l}}\) is the departure angle in the azimuth direction, and \(\rho_{t_{1}}^{H_{l}}\) is the  
Figure 4: IRS-based communication system. elevation angle. The array response vector is represented as \(\mathbf{a}^{H}(\rho_{t_{1}}^{H_{t}},\phi_{t_{1}}^{H_{t}})\). Thus, the total channel can be written as follows:  
\[y_{i}=\mathbf{H}_{z,i}^{H}\Phi\mathbf{H}\mathbf{x}+n_{i}, \tag{1}\]0
## 3 Problem Statement and Input Data Representation Methods
### Problem Statement  
Traffic prediction involves using historical traffic data to forecast future traffic conditions accurately. In the context of traffic data analysis, predictions are typically categorized into short-term, mid-term, and long-term predictions based on the forecast horizon. Short-term prediction involves forecasting traffic conditions over a horizon ranging from a few minutes to a few hours into the future. This type of prediction is crucial for real-time traffic management and control systems. Mid-term prediction refers to forecasting traffic conditions over a horizon extending from a few hours to several days. This type of prediction is used for planning and operational strategies that require an understanding of traffic patterns beyond immediate real-time needs. Long-term prediction involves forecasting traffic conditions over a horizon extending from several days to several months or even years. This type of prediction is important for strategic planning, infrastructure development, and policymaking [67]. In this review, short-term traffic prediction problems are our focus. Due to the diversity and complexity of traffic data, the traffic prediction tasks also vary. Therefore, a sufficiently generalized problem definition is needed to represent the short-term traffic prediction problem clearly.  
Assume a spatial network with a set of \(n\) sensors deployed, where each sensor records \(f\) attributes (such as traffic flow, traffic speed, traffic occupancy, etc.) over \(t\) timestamps. Thus, the observations of all sensors across all time points can be represented as a three-dimensional tensor, \(\mathbf{X}\in\mathbb{R}^{n\times f\times t}\), and it can be represented as \(\mathbf{X}=\left\{X_{1},X_{2},\ldots,X_{t}\right\}\), where \(X_{t}\in\mathbb{R}^{n\times f}\) represents the attribute data of all sensors at timestamp \(t\). \(\mathbf{E}\) represents the exogenous factors that influence future traffic conditions, such as information on weather, holidays, big events, etc. Assume that the traffic prediction task requires predicting the traffic attributes for the next \(u\) time steps based on the traffic attributes from the past \(d\) time steps and exogenous factors \(\mathbf{E}\); the traffic prediction task can be defined as follows:  
\[\left\{\hat{X}_{t+1},\hat{X}_{t+2},\ldots,\hat{X}_{t+u}\right\}=F\{(X_{t-d+1}, \ldots,X_{t-1},X_{t}),\mathbf{E},\phi\} \tag{1}\]  
where \(F(\cdot)\) is the method used for prediction. \(\phi\) represents the model's parameters, including all weights and biases in the network. \(\hat{X}_{t}\) is the prediction at time \(t\). This problem definition provides a clear and structured approach to utilizing various methods for traffic prediction, ensuring coverage of all relevant variables.
## 1 Introduction
All of these detectors use nitrogen, helium and hydrogen as the carrier gas. However, an FID is widely used as a detector for detecting FAs. FIDs offer more sensitivity and a higher resolution than other detectors, and they are also reliable and relatively easy to use. In addition, various compositions can be detected using FIDs. These methods have been optimized for the analysis of specific foods, such as VOs [17; 20; 21; 22; 23]. Most studies regarding the use of GC for the analysis of polyunsaturated FAs, such as linoleic and linolenic acids and their isomers present in VOs, also recommend a high polarity column of the CP Sil 88 type. The main advantage is that this column can separate the isomers of linoleic and linolenic acids well [24; 25; 26]. A method for validating the determination of FAs is the procedure for the derivatization of FAs after the extraction of lipids from food products. Methods of FA derivatization  
Figure 1: Share of VO types produced in Kazakhstan by year: (**a**) 2015 and (**b**) 2020. All data were obtained from the Bureau of National Statistics [14]. include acid or alkaline catalysis. Such methods of derivatization simplify the volatility of FAs, and also increase the separation and sensitivity of GC detectors [27; 28]. Usually, the most common way to determine FAs is through lipid extraction, followed by the transformation of the FAs into methyl esters (methylation). In GC analysis, the identification of FAs is based on changing the FAs to the corresponding fatty acid methyl esters (FAMEs), which have a higher volatility [6; 24]. The most commonly used base reagents for rapidly changing FAs into FAMEs are NaOH or KOH in methanol [29; 30]. Other methods of methylation used benzyl alcohol, diazomethane in a mixture of ether, trichloride boron and sodium thermoxide in methanol [31; 32; 33; 34]. The purpose of the present work was to examine the authenticity of the main seven types of VOs produced by Kazakhstan's oilseed producers by identifying the indicators (FA composition) using gas chromatography with flame ionization detection. The main VOs established the accurate data of FAs that are available for scientists, as well as the food and energy industries. A multivariate statistical analysis was used to differentiate each of the vegetable oils.
## 3 Helicopter Emergency Medical Services and Air-MSU
### Air-MSU Concept  
To date, most MSUs have been developed for urban and metropolitan areas. Stroke physicians have seen the limitations and potential applications for patients living in rural and remote areas. The concept of Air-MSU for delivering acute stoke treatment to rural  
Figure 1: Exterior of Melbourne MSU (left); interior of Melbourne MSU (right) [11; 14]. The image has been reproduced under a creative commons licence (Creative Commons Attribution 4.0 International Licence) from [14].  
patients has been proposed [21]. Although the concepts of both road and Air-MSU are fundamentally the same, they have different technical challenges and limitations [22]. For example, to align with the rotorcraft flight manual (RFM) as approved by the European Union Aviation Safety Agency (EASA) under the Airworthiness Directive (AD), the optimal weight limit and balance must be achieved within a defined center-of-gravity range, as they affect the flight performance of the corresponding aircraft [23], and the materials used for the interior of an aircraft must comply with the applicable flammability requirements for that aircraft type [24]. In order to maintain the performance of the medical devices (and in this specific case, the CT scanner) installed in an aircraft, they must show resilience to mechanical vibrations, sudden pressure changes, and any electromagnetic interferences. Depending on the catchment of rural and remote areas, the Air-MSU needs to demonstrate resilience to extreme temperature changes as well as dust that might affect the performance of the CT scanner. Current commercially available CT scanners are traditionally designed for hospitals; thus, to be operated in an aircraft or rotorcraft that may be subjected to vibration, dust, and temperature variations, modification is required. Therefore, fixations between the CT scanner and the interior of the aircraft must be designed with a much higher safety factor than its road MSU counterpart to account for the events of an emergency landing [21]; this will undoubtedly influence the structural design and material selection process of the CT scanner. The current literature and application regarding Air-MSU is still very limited, where the Norwegian Air Ambulance Foundation has announced their collaboration with Semcon to work on developing a lightweight and small pre-hospital CT scanner which can be fitted into a helicopter, and similar work was proposed from the collaboration between Micro-X and Royal Flying Doctor Service.
## 2 The Intelligent Fault Diagnosis System Model
### Computing Model of Intelligent Fault Diagnosis System  
The task \(C_{k}(t)\) received at intelligent terminal \(k\) at time \(t\) need to be processed during time interval \(t\). Denote the task splitting ratio as \(\alpha_{k}\in[0,1]\), which indicates that at time interval \(t\), \(\alpha_{k}C_{k}(t)\) bits are executed at the intelligent terminal device and the remaining \((1-\alpha_{k})C_{k}(t)\) bits are offloaded to and processed by the MEC server.  
(1) _Local computing:_ In local computation, the CPU of the intelligent terminal device is the primary engine, which adopts the dynamic frequency and voltage scaling (DVFS) technique and the performance of the CPU is controlled by the CPU-cycle frequency \(\kappa_{u}\). Let \(p_{k}^{l}(t)\) denote the local processing power at intelligent terminal \(k\), then the intelligent terminal's computing speed (cycles per second) \(f_{k}^{l}(t)\) at \(t\)-th slot is given by  
\[f_{k}^{l}(t)=\sqrt[3]{\frac{p_{k}^{l}(t)}{\kappa_{u}}}. \tag{3}\]  
Let \(D_{k}\) denote the number of CPU cycles required for intelligent terminal \(k\) to accomplish one task bit. Then the local computation rate for intelligent terminal \(k\) at time slot \(t\) is given by  
\[r_{k}^{l}=\frac{f_{k}^{l}}{D_{k}}=\frac{\sqrt[3]{\frac{p_{k}^{l}}{\kappa_{u}} }}{D_{k}}. \tag{4}\]  
(2) _Mobile Edge Computation Offloading:_ The task model for mobile edge computation offloading is the data-partition model, where the task-input bits are bit-wise and can be arbitrarily divided into different groups. At the beginning of the time slot, the intelligent terminal chooses which MEC server to connect to according to the channel state. Assume that the processed power which is allocated to the intelligent terminal \(k\) by the MEC server \(m\) is \(p_{m,k}^{c}\), then the computation rate \(r_{m,k}^{c}\) at MEC server \(m\) for intelligent terminal \(k\) is:  
\[r_{m,k}^{c}=\frac{\sqrt[3]{\frac{p_{m,k}^{c}}{\kappa_{m}}}}{D_{m}}, \tag{5}\]  
where \(D_{m}\) is the number of CPU cycles required for the MEC server to accomplish one task bit, and \(\kappa_{m}\) denotes the CPU-cycle frequency at the MEC server. It is noted that the MEC server can simultaneously process tasks from multiple intelligent terminals. We assume multiple applications can be executed parallel with a negligible processing latency. The feedback time from the MEC to the intelligent terminal is ignored due to the small-sized computational output.
## 3 Results
### Results for Poland
Such a system would not only enhance domestic connectivity but also strengthen Poland's integration into the broader European transport infrastructure, boosting its competitiveness and strategic relevance. To reduce the negative impact of weather conditions on the functioning and efficiency of transport systems, the following measures should be applied:  
1. Preventive measures: - Improvement of weather forecasting systems and timely communication of forecasts to transport services, companies, and passengers for appropriate planning and the implementation of precautionary measures; - Strengthening the resilience of transport infrastructure by using more durable construction materials, building drainage systems, reinforcing embankments, and applying other engineering solutions aimed at enhancing infrastructure resistance to extreme weather events;  
Figure 4: Elasticity of transport service volume growth in Poland by industry (2020). Constructed by the authors based on [30; 31]. Training personnel of transport services and companies in actions under extreme weather conditions;
2. Active protection measures, which include the implementation of modern traffic safety technologies such as systems for monitoring road surface conditions, vehicle movement control systems, and navigation systems;
3. Emergency response measures, which involve the preventive development and implementation of action plans in cases of hazardous weather conditions and the availability of specialized equipment for eliminating the consequences of natural disasters. Thus, the practical significance of this study lies in the quantitative assessment of the mutually conditioned impact of a country's economic growth on the development of the transport sector (and vice versa), the state stimulation of transport sector development, and the spread of the return effect throughout the economy. The strength of this effect may vary depending on the established structure of intersectoral linkages, as demonstrated by the examples of Ukraine and Poland. On the other hand, the developed methodology does not take into account the use of the transit potential of the transport sector, which could be the subject of future research.
###### Abstract  
An efficient and all-inclusive health management encompassing condition-based maintenance (CBM) environment plays a pivotal role in enhancing the useful life of mission-critical systems. Leveraging high fidelity digital modelling and simulation, scalable to digital twin (DT) representation, quadruples their performance prediction and health management regime. The work presented in this paper does exactly the same for an electric braking system (EBS) of a more-electric aircraft (MEA) by developing a highly representative digital model of its electro-mechanical actuator (EMA) and integrating it with the digital model of anti-skid braking system (ABS). We have shown how, when supported with more-realistic simulation and the application of a qualitative validation approach, various fault modes (such as open circuit, circuit intermittence, and jamming) are implemented in an EMA digital model, followed by their impact assessment. Substantial performance degradation of an electric braking system is observed along with associated hazards as different fault mode scenarios are introduced into the model. With the subsequent qualitative validation of an EMA digital model, a complete performance as well as reliability profile of an EMA can be built to enable its wider deployment and safe integration with a larger number of aircraft systems to achieve environmentally friendly objectives of the aircraft industry. Most significantly, the qualitative validation provides an efficient method of identifying various fault modes in an EMA through rapid monitoring of associated sensor signals and their comparative analysis. It is envisaged that when applied as an add-on in digital twin environment, it would help enhance its CBM capability and improve the overall health management regime of electric braking systems in more-electric aircraft.  
Keywords:qualitative validation; electro-mechanical actuator; electric braking system; more-electric aircraft; digital model; digital twin +
Footnote †: journal: Applied Sciences
# Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications
###### Abstract  
Bluetooth low energy (BLE)-based location service technology has become one of the fastest growing applications for Bluetooth. Received signal strength (RSS) is often used in localization techniques for ranging or location fingerprinting. However, RSS-based localization solutions have poor performance in multipath environments. In this paper, we present a measurement system designed using multiple ESP32 BLE modules and the Bluetooth mesh networking technology, which is capable of exploiting the space, time, and frequency diversities in measurements. To enable channel-aware multi-device RSS measurements, we also designed a communication protocol to associate channel ID information to advertising messages. Based on channel measurement and analysis, we demonstrate that with a six-receiver configuration and space-time-frequency diversity combining, we can significantly reduce the residual linear regression fitting errors in path loss models. Such a reduction leads to more accurately correlating RSS measurements to the distance between the transmitter and receiver devices and thus to achieving improved performance with the RSS-based localization techniques. More importantly, the reduction in the fitting errors is achieved without differentiating the three advertising channels, making it possible to conveniently implement the proposed six-receiver configuration using commercially available BLE devices and the standard Bluetooth mesh networking protocol stack.  
bluetooth; BLE; indoor positioning; localization; mesh networking; received signal strength +
Footnote †: journal: _Article_  
**Citation:** [PERSON]; [PERSON]; [PERSON] Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications. _Network_**2021**, \(1\), 315-334. [[https://doi.org/10.3390/network1030018](https://doi.org/10.3390/network1030018)]([https://doi.org/10.3390/network1030018](https://doi.org/10.3390/network1030018))  
Academic Editors: [PERSON] and [PERSON]  
Received: 29 August 2021  
Accepted: 30 November 2021  
Published: 2 December 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Cognition:** 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Specimens and Experiment Setup  
### Materials and Specimens  
The structure of the pump housing was fabricated from acrylic, due to its low cost and ease of shaping. Two rectangular bimorphs were attached to this structure under one-edge-fixed boundary conditions. The piezoceramic bimorph used in the experiments was a triple-layer structure with the following dimensions: 63.5 mm \(\times\) 31.8 mm \(\times\) 0.48 mm (Q220-A4-503 YB, Piezo System Inc., Cambridge, MA, USA), as detailed in Figure 1. After it was clamped into the pumping element structure, the effective length of the bimorph was reduced to 55 mm, as shown in Figure 2a-d. Table 1 lists the material properties of the piezoceramic plate (PSI-5A4E, Piezo System Inc., MA, USA) bonded to the outer layer of the bimorph, and those of the brass in the middle layer. Figure 3 presents images of the pumping unit.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Properties** & **Structural Plate (Brass)** & **Piezoelectric Plate (PZT)** \\ \hline Model Name & Bronze C86100 & PSI 5A4E \\ \hline Density & 8830 kg/m\({}^{3}\) & 7800 kg/m\({}^{3}\) \\ \hline Young\({}^{\prime}\)s Modulus & \(1.03\times 10^{11}\) Pa & \begin{tabular}{c} \(x\)-direction- 6.6 \(\times\)\(10^{10}\) Pa \\ \(y\)-direction- 6.6 \(\times\)\(10^{10}\) Pa \\ \(z\)-direction- 5.2 \(\times\)\(10^{10}\) Pa \\ \end{tabular} \\ \hline Poisson\({}^{\prime}\)s Ratio & 0.34 & \begin{tabular}{c} \(x\)-direction- 0.31 \\ \(y\)-direction- 0.242 \\ \(z\)-direction- 0.242 \\ \end{tabular} \\ \hline Piezolectric Constants & - & \
\begin{tabular}{c} \(d_{15}=550\) pm/V \\ \(d_{31}=\) -190 pm/V \\ \(d_{33}=\) 390 pm/V \\ \end{tabular} \\ \hline Dielectric Constants & - &
\begin{tabular}{c} \(\varepsilon^{5}_{11}=11.12\) nF/m \\ \(\varepsilon^{5}_{33}=\) 14.02 nF/m \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of piezoelectric bimorph.  
Figure 1: Schematic illustration showing the geometric dimensions of the piezoceramic bimorph in a parallel connection configuration.