esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 3 System Model and Problem Formulation
### Transmission Model and Data Queue  
In RSN, different user satellites have different types of data acquisition missions. Considering the differentiated data amount of missions, we denote \(\varphi_{i}(t)\) as the data collection rate of user satellite \(i\) in slot \(t\). The upper bound of \(\varphi_{i}(t)\) is denoted by \(\varphi_{\max}\), i.e.,  
\[0\leq\varphi_{i}(t)\leq\varphi_{\max}. \tag{3}\]  
The spatial data collected by the user satellite at each time slot is first stored in the buffer and then transmitted in a carry-forward mode. We define \(\mathcal{D}_{i}(t)\) as the buffered data  
Figure 1: RSNs architecture.  
queue length at the beginning of slot \(t\). We also let \(\xi_{ij}(t)\) be the link capacity from user satellite \(i\) to relay satellite \(j\)[38], expressed by  
\[\xi_{i}(t)=\frac{P_{u}G_{t}G_{r}L_{s}(t)L_{f}}{KT_{s}\cdot(E_{b}/N_{0})_{\text{ req}}\cdot M_{a}}, \tag{4}\]  
where \(P_{u}\) is the ISL transmission power (in W). \(G_{t}\) is the transmitting antenna gain of the user satellite. \(G_{r}\) is the receiving antenna gain of the relay satellite. \(L_{s}(t)\) and \(L_{f}\) are the free space loss and the total line loss, respectively. \(k\) is the Boltzmann's constant (in JK\({}^{-1}\)), and \(M_{a}\) is the link margin. Furthermore, \(T_{s}\) is the temperature of system noise (in K), and \((E_{b}/N_{0})_{\text{req}}\) is the ratio of received energy per bit to noise density. We define \(\xi_{\text{max}}\) as the upper bound of link capacity, i.e., \(\xi_{i}(t)\leq\xi_{\text{max}}\).  
In addition, we define \(\gamma_{i}(t)\) as the inter-satellite transmission rate of satellite \(i\) through ISL in slot \(t\), which is limited by the link capacity. Therefore, we have  
\[0\leq\gamma_{i}(t)\leq\xi_{i}(t). \tag{5}\]  
We formulate the data queue evolution of user satellite \(i\) by input process and delivery process, which corresponds to data acquisition rate \(\varphi_{i}(t)\) and data transfer rate \(\gamma_{i}(t)\), i.e.,  
\[\mathcal{D}_{i}(t+1)=[\mathcal{D}_{i}(t)-\tau\sum_{j=1}^{N}\delta_{ij}(t)\gamma _{i}(t)]^{+}+\tau\varphi_{i}(t), \tag{6}\]  
where \(\tau\varphi_{i}(t)\) is the data flow entering data buffer in slot \(t\), and \(\tau\sum_{j=1}^{N}\delta_{ij}(t)\gamma_{i}(t)\) is the data flow leaving data queue in time slot \(t\), respectively. To keep the stability of the data queue, we impose the following constraint that the total collected data is less than the transferred data over the infinite time horizon, i.e.,  
\[\lim_{T\rightarrow+\infty}\frac{1}{T}\sum_{t=0}^{T-1}\sum_{i=1}^{M}\varphi_{i }(t)\leq\lim_{T\rightarrow+\infty}\frac{1}{T}\sum_{t=0}^{T-1}\sum_{i=1}^{M} \sum_{j=1}^{N}\delta_{ij}(t)\gamma_{i}(t). \tag{7}\]
## 5 Sensitivity Analysis of Thermal Conductivity
### Effects of Steel Fiber  
In some studies [30; 31; 32], steel fibers with high thermal conductivity were added to asphalt concrete to enhance the thermal conductivity. In this section, the effect of steel fibers on the thermal conductivity of asphalt concrete was evaluated and verified with the existing experimental data [30].  
Long straight steel fibers with an average length of 35 mm and equivalent diameter of 1 mm were used in this paper. The steel fibers had a density of 7.8 g/cm\({}^{3}\) and a thermal conductivity of 46.4 Wm\({}^{-1}\)K\({}^{-1}\). The corresponding heterogeneous meso-structure model of steel fiber asphalt concrete was established using four different contents of steel fiber (0%, 0.5%, 1%, and 1.5%) by weight. The thermal conductivity of the steel fiber asphalt concrete three-dimensional model was calculated, and the results are shown in Figure 15.  
Figure 14: Effect of air void distribution on asphalt concrete thermal conductivity.  
Figure 15: Effect of steel fiber content.  
After adding steel fiber with different contents, the difference between the experimental value and the simulated value of effective thermal conductivity of asphalt concrete was 3.2%-9.6%. In general, the effective thermal conductivity increases with the steel fiber percentage.  
Three sets of cube asphalt concrete models with varied steel fiber orientations were used to assess the influence of steel fiber orientation on effective thermal conductivity. The above virtual models were denoted as F1-F3, respectively. The steel fibers in F1 were randomly distributed in the asphalt concrete model. The steel fibers in F2 and F3 were parallel to and perpendicular to the direction of thermal flux, respectively. Four different steel fiber contents (0%, 0.5%, 1%, and 1.5%) were considered for each group specimen to calculate the corresponding effective thermal conductivity. As illustrated in Figure 16, the changes in effective thermal conductivity of asphalt concrete caused by different steel fiber orientations were very minor, and the variable ranges of thermal conductivity were less than 0.01 percent. This shows that the steel fiber orientation has a small effect on the effective thermal conductivity, which is negligible. The main reason may be that the steel fiber content needed in the steel fiber asphalt concrete is small, so the orientation has little influence on the effective thermal conductivity.
## 3 Results
### Substituted Benzenes
#### 3.3.4 Methoxybenzenes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Methodybenzenes** \\ \end{tabular} & \begin{tabular}{c} **Exp.** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ \end{tabular} & \begin{tabular}{c} **Model** \end{tabular} & \begin{tabular}{c} **Model** \end{tabular} & \begin{tabular}{c} **- Exp.** \\ \end{tabular} & \begin{tabular}{c} **ABS** \\ \end{tabular} & \begin{tabular}{c} **(Model \(-\) Exp.)** \\ \end{tabular} & \begin{tabular}{c} **ABS** \\ \end{tabular} & \
\begin{tabular}{c} **(Exp. \(-\) G4)** \\ \end{tabular} &
\begin{tabular}{c} **G4** \\ \end{tabular} \\ \hline methoxybenzene (anisole) & \(-\)67.9 & \(-\)67.5 & 0.4 & 0.4 & 0.4 & 3.7 & \(-\)71.6 \\ \hline
1,2-dimethoxybenzene & \(-\)206.0 & \(-\)219.5 & \(-\)13.5 & 13.5 & 2.5 & 3.4 & \(-\)209.4 \\ \hline
1,3-dimethoxybenzene & \(-\)223.6 & \(-\)219.5 & 4.1 & 4.1 & 0.0 & 0.6 & \(-\)224.2 \\ \hline
1,4-dimethoxybenzene & \(-\)216.2 & \(-\)219.5 & \(-\)3.3 & 3.3 & 3.3 & 0.2 & \(-\)216.0 \\ \hline
1-methoxy-3-methylbenzene & \(-\)104.1 & \(-\)103.9 & 0.2 & 0.2 & 0.2 & & \\ \hline averaged absolute difference & & & & 4.3 & 1.3 & 2.0 & \\ \hline
1,2,3-trimethoxybenzene & \(-\)346.0 & \(-\)371.5 & \(-\)25.5 & 25.5 & 0.6 & 5.8 & \(-\)351.8 \\ \hline
1,2,4-trimethoxybenzene & \(-\)360.6 & \(-\)371.5 & \(-\)10.9 & 10.9 & 0.1 & 0.1 & \(-\)360.7 \\ \hline
1,3-trimethoxybenzene & \(-\)381.6 & \(-\)371.5 & 10.1 & 10.1 & 1.9 & 0.4 & \(-\)382.0 \\ \hline
3,4-trimethoxytoluene & \(-\)382.2 & \(-\)407.9 & \(-\)25.7 & 25.7 & 3.7 & 1.2 & \(-\)383.4 \\ \hline averaged absolute difference & & & & 18.0 & 1.6 & 1.9 & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Experimental, present GC model, and G4 model values for the heat of formation for methoxybenzenes. All values in kJ/mol. Experimental and G4 quantum data for the upper part of Table are from [PERSON] c.s. [44] except for the experimental values for methoxybenzene and 1-methoxy-3-methylbenzene (Pedley [34]) and the G4 value for methoxybenzene [19]. Experimental and G4 results in the lower part of the table were taken from [PERSON] c.s. [47]. The sixth column contains our GC model results with methoxy–methoxy neighbour interactions accounted for (see text for further explanation). values +11 kJ/mol and \(-\)4.1 kJ/mol were determined by considering simultaneously the methoxybenzenes, the methoxyphenols, and the methoxybenzaldehydes so as to obtain suitable numerical values applying to all three classes. For the methoxybenzenes, now all GC model values agree with the corresponding experimental value within chemical accuracy, with an averaged absolute deviation of 1.6 kJ/mol. The available sufficient data for the methoxy-benzenes made this possible.
## 4 Characteristic Test and Analysis of the FBG Dynamometry Bol  
### Tensile Test of the Dynamometry Bol  
In order to verify the correctness of the above formula (13), two groups of bolt tensile tests were designed. The electro-hydraulic servo testing machine is used to perform tensile loading on the bolt sample. The size of the FBG dynamometry bolt sample is \(\Phi\)20 mm\(\times\)840 mm. In test I, two measuring positions were arranged on the bolt body. The 2 measuring positions were 200 mm apart, and each of them was 320 mm away from the end of the bolt sample, respectively. In test II, only one measuring position was arranged and it was located in the middle of the bolt sample. A resistance strain gauge sensor and a FBG sensor were pasted at each measuring position. Using the strain measured by the strain gauge as a reference, the center wavelength shift (\(\Delta\lambda_{B}\)) of the reflected light measured by the FBG sensor was substituted into formula (13) to obtain the measured strain. Additionally, the measured strain of the resistance strain gauge sensor and the measured strain of the FBG sensor were compared and analyzed to study the rule of the sensing characteristics for the FBG dynamometry bolt. In addition, the adhesive creeps under the long-term loads, which affects the stress state of the fiber grating, so it is very important to study the stability of the FBG dynamometry bolt under the long-term tensile loads.  
The diagram of the test device and the schematic diagram of the connection of the test equipment are shown in Figures 8 and 9.  
Figure 8: The diagram of the test device. (**a**) Electro-hydraulic servo testing machine; (**b**) FBG sensing demodulator and static resistance strain demodulator; and (**c**) the sample of the FBG dynamometry bolt.
# Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
## 3 Emotional State Monitoring Methodology  
### Sensor Description  
Physiological data can provide valuable information about subtle changes in the activation of the autonomic nervous system caused by an emotional stimulus [18]. For this reason, we used a medical wearable sensor as described in this section.  
We collected physiological data from participants using the wearable device Empatica E4 [12], which is a non-obtrusive medical-certified device in the form of a watch that is worn on the wrist (Figure 2). Empatica E4 provides the following sensors and corresponding signals:  
* Photoplethysmogram (PPG) sensor. This sensor measures the blood volume pulse (BVP), i.e., the variation of volume of arterial blood under the skin resulting from the heart cycle, which is sampled at 64 Hz. This signal is used to derive the heart rate variability (HRV) which is computed by a built-in internal function within the wearable device. The sampling rate corresponding to the HRV signal is 1 Hz.
* Electrodermal activity (EDA) sensor. This sensor measures the variations in electrical conductance of the skin as a response to sweat secretion, which is a potential response of the autonomic nervous system to a stimulus. Data are sampled at a fixed frequency of 4 Hz. This signal is filtered using a 3 rd order low-pass Butterworth filter with a cut-off frequency of 1.5 Hz. In addition, the signal is decomposed into two signals: the skin conductance level (SCL), and the skin conductance response (SCR) [38].
* Infrared thermophilic sensor. This sensor measures the peripheral skin temperature variations. Data are sampled at a frequency of 4 Hz.
* 3-axis accelerometer. This sensor records wrist motion data in the carrier's \(x\), \(y\), and \(z\) axes, which are sampled at 32 Hz. Additionally, we calculate the Euclidean norm of the three collected axes. Finally, we apply a Butterworth filter with cut-off frequencies of 0.2 Hz and 410 Hz [39].  
Raw signals were anonymously uploaded to a controlled cloud environment by the participants. After the experiment period, they were downloaded for processing, feature extraction, and classification (all data are collected, stored and processed in accordance with the GDPR regulations).
## 2 Literature Review
[PERSON] et al [35] implemented GPS trajectory information to forecast the formation, distribution, and dissipation of traffic congestion [36]. To take more exogenous dependencies into account, the deep fusion network [37] and the Deep Spatial-Temporal Multi-View Network (DMVST-Net) [38] were developed to determine the demands of individuals for online taxi-hailing applications, which outperformed ARIMA and multi-layer perceptron. The authors of [39] presented the diffusion of a convolutionary recurrent neural network for traffic flow forecasting and attained a 12-15% rise in the forecast's performance [40]. In 2008, RNN technology was implemented to analyze wireless communication network traffic, which was the first attempt to use deep learning-based knowledge for network destination projection [41]. RNN performed better than the Gravity method when forecasting destination problem, demonstrating the vast potential of RNN in the network analytics. Much research has been performed on the current stage of the taxi destination flow forecast, and various traffic data have been used in this research. [PERSON], et al. [42] utilized LSTM to collect data flow of metro travelers in order to predict complex destination flows among metro network locations, which enhanced performance compared to conventional VAR and Calendar methods [43]. Nevertheless, this technique cannot fully forecast the dynamic taxi destination flows because the metro lines are comparatively fixed, the roads or highways have a small scale compared to the metropolitan road traffic network, and the time between the metro stations is static. In particular, as traffic data have grown more widely, non-parameterized forecast models have become the more accurate traffic forecasting models. They have started to play a role in nonlinear, static, and dynamic processes and spatial-temporal analyses. In addition, these kinds of models can be coupled with the environmental influences for further enhancing prediction accuracy [44]. To address the limitations of the methods discussed above, we propose the novel DWSTTNs model. In our framework, the encoder and decoder are the primary units of the transformer. The Location Based Social Networks (LBSN) application helps; we encode the geographical information bases od visited semantic locations. In particular, we trained our model for the exact longitude and latitude coordinates to predict the next destination.
###### Abstract  
With the developing of high integrations in large scale systems, such as aircraft and other industrial systems, there are new challenges in safety analysis due to the complexity of the mission process and the more complicated coupling characteristic of multi-factors. Aiming at the evaluation of coupled factors as well as the risk of the mission, this paper proposes a combined technology based on the Decision Making Trial and Evaluation Laboratory (DEMATEL) model and the Bayesian network (BN). After identifying and classifying the risk factors from the perspectives of humans, machines, the environment, and management, the DEMATEL technique is adopted to assess their direct and/or indirect coupling relationships to determine the importance and causality of each factor; moreover, the relationship matrix in the DEMATEL model is used to generate the BN model, including its parameterization. The inverse reasoning theory is then implemented to derive the probability, and the risk of the coupled factors is evaluated by an assessment model integrating the probability and severity. Furthermore, the key risk factors are identified based on the risk radar diagram and the Pareto rule to support the preventive measurements. Finally, an application of the take-off process of aircraft is provided to demonstrate the proposed method.  
safety; risk assessment; coupled risk factors; DEMATEL; Bayesian network +
Footnote †: journal: Applied Sciences
# Toward Explainable AutoEncoder-Based Diagnosis of Dynamical Systems
## 3 Preliminaries  
### LTI System  
We focus on discrete-time linear time-invariant (LTI) dynamical systems in this article. We can define an LTI system [28] in state space form as  
\[x_{k+1} = Ax_{k}+Bu_{k}+w_{k};x(0)=x_{0}; \tag{1}\] \[y_{k} = Cx_{k}+Du_{k}, \tag{2}\]  
where \(x\in\mathbb{R}^{n}\) is the state vector, \(x_{0}\) is the initial condition of the system, \(w_{k}\sim N(0,Q)\) is the uncorrelated zero-mean Gaussian measurement noise and standard deviation \(Q\), and \(u\) and \(y\) are the plant input and output vectors, respectively. Matrices \(A,\ B,\ C,\ D\) are real constant matrices of appropriate dimensions.  
We can extend the nominal system (Equations (1) and (2)) to incorporate faults. Faults may be additive or multiplicative, and we consider faults to both actuators and sensors. If we define multiplicative fault parameter matrices \(\Gamma_{a},\ \Gamma_{s}\) to both actuators and sensors, respectively, we obtain  
\[x_{k+1} = Ax_{k}+B\Gamma_{a}u_{k}+w_{k};x(0)=x_{0}; \tag{3}\] \[y_{k} = \Gamma_{s}(Cx_{k}+Du_{k}), \tag{4}\]  
The fault matrices are diagonal matrices with the \([0,1]\) parameters along the diagonal denoting the fault extent, with \(0\) denoting totally faulty and \(1\) normal. For example, we may have actuator loss of effectiveness encoded by this approach. We may define additive faults in an analogous manner.  
We refer to a _mode_ as an operating condition of an LTI system, as characterized by the system parameters \((A,B,C,D,\Gamma_{a},\Gamma_{s})\).
## 5 Analysis and Discussion
### Impacts of Digitalization Improvement on EPC Projects' Cost Performance  
One of the aims of this research was to estimate the benefit that would be achieved in terms of cost performance by utilizing new digital technologies in real-life EPC projects. To achieve this, three indicators were developed, described in this section, namely (1) the Cost Achievement Ratio (car); (2) the Potential Cost Saving Index (pcsi); and (3) the Actual Cost Saving Index (acsi).  
As mentioned previously, the realized cost benefits resulting from the adoption of new-generation digital technology can be measured by a comparison of the potential cost saving predicted during the design stage and the actual cost saved during construction compared to the contract sum. The former is measured by the pcsi and the latter is measured by the acsi. The ratio of acsi to pcsi is a measure of how well the use of digital technologies enhances the cost performance; therefore, the Cost Achievement Ratio is defined as:  
\[\text{Cost Achievement Ratio (car) = acsi/pcsi \times 100\%} \tag{5}\]  
where  
\[\text{Potential Cost Saving Index (pcsi) = PCS/(contract cost per m\textsuperscript{2}) \times 10,000} \tag{6}\]  
\[\text{Actual Cost Saving Index (acsi) = ACS/(contract cost per m\textsuperscript{2}) \times 10,000} \tag{7}\]  
The car, pcsi, and acsi of the sampled projects are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Technology Utilization** \\ \hline
**Technology Name** & **Frequency** \\ \hline
3D modeling & 9 \\
3D Laser scanning & 8 \\ VR & 6 \\ Oblique photography & 6 \\ IoT & 6 \\
4D Assembling & 5 \\ Lofting robot & 5 \\ Solid Model(include 3D printing) & 4 \\ GIS & 4 \\
4D SCHEDULING & 4 \\
5D BIM & 4 \\ Collaboration platform & 4 \\ AR & 3 \\ CDE & 3 \\ AI & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Technology utilization.
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 6 Simulation Results and Discussion
The performance gain of the proposed scheme is higher than that of the conventional schemes by detecting the channel variations within the packet and appropriately selecting the better technique on a symbol-by-symbol basis between DD-TT and TRFI. The second discussion is related to the trade-off between performance and complexity according to the algorithm's selection period within a packet. If the computational complexity is large, the implementation feasibility in hardware is very poor. In Tables 3 and 4, the proposed SLS scheme has a linear time complexity and the performance reduction is not significant even if the selection period of the proposed scheme is increased as shown in Figure 6. Therefore, it is feasible to switch the two techniques in terms of hardware implementation, and the proposed scheme can cope with rapid channel variations. The third discussion is related to the effective communication range of the proposed SLS scheme in the live network. Performance analysis in the live network over a link budget is important in determining whether the proposed SLS scheme satisfies the required performance on a given communication link. The link budget analysis shows that the proposed SLS scheme provides the best performance in the range between 35 and 135 m compared with when using the other schemes alone.
## 3 System Model  
In this paper, we consider vehicles moving along a linear highway segment with multiple lanes. The transmission power and the Received Signal Strength (RSS) threshold of On-Board Units' (OBUs) equipment are the same for all vehicles. Each OBU contains a Global Position System (GPS) unit and IEEE 802.11p radio equipment. For a given vehicle, its neighboring vehicles should lie in the radio range \(R\). Here, \(R\) is assumed to be the range within which messages can be received with a high probability of success, while reception over a distance bigger than \(R\) is deemed to be unreliable. This notion can take into account margins for shadowing [50; 51]. The radio range can be estimated by all vehicles, e.g., by measuring the packet delivery ratio from neighbor nodes [52; 53; 54; 55; 29; 56; 57]. In the following design and analysis, the radio range \(R\) is assumed to be known in advance. To keep a relatively low control overhead, the beacon-less routing strategy is used in this paper. For road traffic monitoring applications or video-based entertainment applications, suppose that one content SUbscribingVehicle (SUV) needs a chunk of data. The volume of the target content is large enough so that it is worth downloading it from neighboring vehicles through the VANET, rather than using the cellular network. Therefore, a SOurce Vehicle (SOV) that owns the required multimedia content should be selected as the provider instead. The chosen SOV will initialize a transmission path to the SUV and transmit the target data flow continuously. To build a routing path from the SOV to the SUV, some intermediate vehicles, denoted as the Backbone Vehicles (BVs), would be selected to forward the target content. It should be noted that a successful multi-hop transmission depends on the vehicle density of the road. The case when at least one route exists from SOV to SUV is referred to as the connected case, while the disconnected caserepresents the opposite event. In the disconnected case, SUV can only send its content subscribing request to the RSUs or the cellular base stations. While in the connected case, a multi-hop transmission protocol should be designed to choose suitable vehicles as relays, which is proposed in the following (Table 1). Because of the dynamic network topology, the large volume content transmission can hardly finish with an unchanged routing path. Therefore, the whole content's transmission might need more than one routing path. Each stage with one constructed routing path, defined as a transmission phase, can deliver some part of the target contents, which includes a control sub-phase and a data transmission sub-phase, as shown in Figure 1. Let \(T_{i}^{c}\) and \(T_{i}^{d}\) denote the consumed time for the \(i\)-th control sub-phase and data transmission sub-phase, respectively. Moreover, let \(T_{\rm e2e}\) denote the overall