esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
They are able to extract valuable insights from complex connectivity patterns in application domains, including social analysis ([PERSON] et al. [32]), materials science ([PERSON] et al. [35]), bioinformatics ([PERSON] et al. [36]), and computer vision ([PERSON] et al. [37]). Explainable artificial intelligence (XAI) was proposed by [PERSON] et al. [38] to deal with the lack of transparency and trust in machine learning models. [PERSON] et al. [39] presented the idea of the explanation model, which treats explanations of a model's prediction as a model itself. They proposed SHapley Additive exPlanations (SHAP) values as a unified measure of determining feature importance (see [PERSON] et al. [40]). These methods were evaluated through user studies. In our research, it is demonstrated that our approach outperforms the method proposed by [PERSON] et al. [28] with an accuracy of ten percent higher. In the context of different organizational cultures and industries, how can we leverage the power of graph convolutional networks (GCNs), explainable AI, and AutoML to effectively identify potential leaders within social networks? To answer this research question, we summarize the main contributions of this work as follows:  
* We propose to obtain sensible features by the PageRank algorithm to identify the importance of the nodes (project team roles) for each type of relationship. And we use word embedding with word2 vec to transform key phrases into meaningful features, to find the underlying meanings among phrases in corporate social networks. These features are integrated with our original feature sets. * We apply the feature importance method explainable AI (XAI) to identify the most significant features from the initial feature set, thereby enhancing the efficiency of the training process. We also compare the result of XAI with another feature selection method recursive feature elimination (RFE). We demonstrate the high accuracy of XAI. * We build models to spot potential leaders in social networks by statistical machine learning methods, graph convolutional networks (GCNs), and AutoML. The statistical machine learning methods include support vector machines (SVM) with different kernels, k-nearest neighbor (k-NN), and multilayer perceptron (MLP). * We apply and validate our models in the case study with six projects from different industries and organizational cultures. Various organizational cultures can enhance the richness and comprehensiveness of our study. The remainder of this research is structured as follows. Section 2 presents the models we build for identifying potential leaders. The models include XAI, statistical machine learning methods, GCN, and AutoML. Section 3 provides the case study of six project datasets applying to our model. These datasets are from different industries and organizational cultures. Section 4 displays the results of the experiments. Section 5 discusses the results and interprets the results from the perspective of previous studies. Section 6 explains the limitations and our future work. Section 7 concludes our research work.
# Low-Complexity GSM Detection Based on Maximum Ratio Combining
## 5 Simulation Results and Computational Complexity Analysis
### Simulation Result and Computational Complexity Analysis for M-PSK Constellation  
#### 5.2.1 Simulation Result  
Figure 8 shows the BER performance comparison of the proposed algorithms with the ML algorithm for a GSM system with \(N_{t}=16\), \(N_{r}=16\), \(M=16\), \(N_{p}=2\). From Figure 8, we can see clearly that the BER performance of the proposed two algorithms is almost the same as the ML detector.  
Figure 9 shows the average number of TACs detected by MRC-RS and MRC-MP when \(N_{t}=16\), \(N_{r}=16\), \(M=16\), \(N_{p}=2\). For the conventional GSM system, the total number of TACs is 64. As can be seen from Figure 9, the MRC-RS algorithm detects about 14 TACs  
Figure 8: BER performance comparison of the proposed detectors with ML detector for GSM system with \(N_{t}=16\), \(N_{r}=16\), \(M=16\) and \(N_{p}=2\).  
Figure 7: Comparison of the computational complexity of the proposed detectors and ML detectors for a GSM system with \(N_{t}=16\), \(N_{r}=8\), \(M=16\) and \(N_{p}=3\).  
under different SNR, which accounts for 21% of the total TACs. For the MRC-MP algorithm, the number of TACs detected by the receiver shows a downward trend with the increase of SNR. When SNR is 20 dB, the TACs detected by MRC-MP algorithm is 47, which accounts for 73% of the total TACs.
## 5 Evaluation of the Security Aspects of the Designed Protocol  
This section aims to confirm the security of our proposed method through an assessment of our proposed protocol's security, employing both formal and informal techniques. Through the utilization of the official AVISPA tool, we evaluated the security characteristics of the private variables belonging to the parties involved in communication within the protocol process and additionally proved the strength of the protocol's security robustness against active and passive attacks. Furthermore, in the informal analysis section, we outline various typical network attack types and assert the resilience of the designed protocol against such threats.
# FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks
## 2 Review of FM-UWB Transceivers
### FM-UWB Transmitters
Therefore, once calibrated, the VCO can operate in a free running manner until temperature or some other external factor causes a significant frequency shift. Since these external processes are usually slow, calibration only needs to be done once in a few hours or days, which makes the average power consumption of such an FLL practically negligible. The described approach led to the first sub-milliwatt FM-UWB transmitter [22]. The next step in reducing the VCO consumption was reducing the frequency of oscillation. Since an \(N\)-stage ring oscillator produces \(N\) equally spaced phases, these phases can be combined to produce a frequency that is \(N\) times higher [25]. It is then possible to use a ring oscillator that works at a frequency that is \(N\) times lower than the carrier center frequency. The approach was demonstrated in [25] and used for the FM-UWB transmitter in [10] to reduce the power consumption down to \(0.63\,\mathrm{mW}\). Even though the VCO cannot be neglected, the PA remains the most power-hungry block in the system. The key to further reducing the power consumption of an FM-UWB transmitter is an efficient power amplifier; however, the design of an integrated PA for such a low power and wide band poses a number of challenges. In standard narrow-band applications targeting relatively high output power, the most efficient approach is to use a switching PA such as class D or E. However, due to the high driving requirements, lack of voltage gain and difficulties in implementing a wideband matching network, switching PAs leads to sub-optimal solution. The linear power amplifiers, classes A, AB, B and C, generally do not achieve as high efficiency, but the driving requirement is also lower. Moving from class A to class C operation, the maximum attainable efficiency increases, but the power gain decreases and larger driving signal is necessary, thus shifting the burden from the PA to the driver. A good compromise is the class AB that attains decent efficiency and does not need a rail-to-rail input signal. In fact, all of the transmitters reported in [10; 22; 24], which achieve the lowest power consumption so far, use a complementary class AB power amplifier. Finally, by applying the current reuse technique, as demonstrated in [10], where the PA and the driver share the same current, efficiency can be further improved. This led to a record breaking low power FM-UWB transmitter as can be seen in Table 2.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 4 Simulation Assessment
### Simulation Scenario  
For the simulation, a communication scenario with a size of 450 m \(\times\) 10 m was considered. The communication mechanism was implemented based upon a set of fixed nodes forming a backbone. This backbone was organized according to an in-line cluster-tree topology, where nodes acting as cluster-heads are deployed in-line to cover a pre-established monitoring area. Typical examples can be found in outdoor environments such as tunnels, roads or field athletics tracks, or indoor environments like a hospital hallway. Figure 6 illustrates these types of communication scenarios.  
As shown in Figure 6, fifteen fixed nodes (CH0 to CH14) were positioned every 30 m simulating a typical athletics track, whose extension is 450 m. The CH0 node is the PAN coordinator, and node CH14 is the node positioned most deeply in the cluster-tree branch. The PAN coordinator is a special Full Function Device (FFD) acting as the root of the tree and as the sink node. The sink node acts as a gateway between the WSN and other networks (e.g., Internet). The deployment of CHs nodes guarantees that the coverage ranges of adjacent CHs are overlapped. Additionally, we consider that all CHs are connected to a power line supply and therefore, that they do not suffer from battery discharge problems. Finally, a set of mobile nodes (source-nodes) moving along the coverage area is also considered.  
The number of mobile nodes varies from 1 to 40, and they are activated at different time intervals (offset), depending on the speed at which they move on the track. An initial offset time of 150 s was established for the overall network formation. The activation of the mobile nodes was defined considering that an MN only activates after the previous MN has moved 30 m. For example, if the mobile node number 1 moves at a speed of 1.4 m/s, the mobile node 2 will only be activated in the network in 171.4 s, that is, 21.4 s after the mobile node 1 has been activated, and so on. Mobile nodes are activated near to the sink node (CH0), move in the network coverage area until the end of the lane (CH14), and return uninterruptedly, allowing the association with the other CHs. The simulation ends when all packets from the mobile nodes are sent or the simulation reaches a predefined time limit.  
The monitoring traffic is characterised by mobile nodes, and generate periodic messages at the same rate and send them to the PAN coordinator (sink) according to the communication rules defined by the IEEE 802.15.4 protocol. For the sake of simplicity, each mobile node generates a flow of 1000 messages to the sink node and the total simulation time was set to 50,000 s. For this simulation assessment, we defined two scenarios: mobile nodes moving at 1.4 m/s, emulating walking persons, and mobile nodes moving at 5 m/s, emulating running athletes. Table 1 summarizes the most important simulation configurations.  
Figure 6: Communication scenario.
## 4 Case Study of a Suspension Bridge
### Traffic Load Modeling Using Weigh-in-Motion Data  
Weigh-in-motion (WIM) data collected from a highway bridge in China were selected for the probabilistic modeling of traffic loads. The WIM system is illustrated in Figure 7, and more detailed information can be found in [PERSON] et al. (2018). The traffic parameters utilized in the present study were vehicle weights, axle weights, driving lanes, and vehicle spacing. The proportion of trucks and the ratio of truck overloading were assumed as 12% and 21%, respectively.  
All vehicles were classified into six types, based on vehicle configurations and axle characteristics. The occupancy of the different vehicle types and their distributions in each lane are displayed in Figure 8.  
As it can be seen in Figure 8a, light cars (V1) had the highest proportion of 35.64%, while two-axle (V2) and six-axle trucks (V6) had a higher proportion compared to other types of trucks. As it can be observed in Figure 8b, there is a high probability that the light cars are driving in the fast lane, while heavy trucks more mostly likely to drive in the slow lane. This phenomenon is in accordance with the practical situation.  
The V6 trucks were selected as a prototype to investigate the probability of vehicle loads. Figure 9a shows histograms of the distribution of monitored empirical gross vehicle weights (GVWs). They were  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} distribution \\ distribution \\ \end{tabular} & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical distributions of the random variables.  
Figure 8: Proportion of vehicles (**a**) Vehicle types; (**b**) driving lanes.  
Figure 7: Weigh-in-motion system of a highway bridge: (**a**) site photos; (**b**) plane view; (**c**) elevation view unit: mm.  
fitted using a Gaussian mixture model (GMM) that is a combination of several Gaussian distributions. The two peaks of the PDF in Figure 9 represent the normal and overloaded trucks. It can be observed that the GVW followed a multimodal distribution, where the truck overloading effect was captured by the probabilistic model. Figure 9 plots the relationship between axle weight (AW) and GVW, where the star, circle, and plus symbols represent the weights of the first, second, and fifth axle, respectively, and \(AW_{ij}\) represents the AW of the \(j\)-th axle of the \(i\)-th type of vehicles. It is observed that the AW and GVW were almost linearly dependent.  
Based on the estimated probability distribution models, a stochastic traffic load model was developed using MCS. Figure 10 plots the simulated dense traffic flow model, where each vehicle was simulated as a label. The stochastic traffic load model contained the parameters of vehicle type, GVW, driving lane, and vehicle spacing.
# Biophotonics in Dentistry
## 3 Applications in Dentistry
### Interferometry Techniques
#### 3.2.2 Moire Interferometry  
Moire interferometry is an alternative optical method for strain analysis, with applications in many fields, including biomechanics, fracture mechanics and the study of composite materials [104]. The high sensitivity associated with moire interferometry makes it a valuable technique for performing real-time study of the biomechanics of dental structures. The technique involves two superimposed light arrays, and the resultant interference patterns, termed moire fringes, are analysed to provide information about mechanical behaviour. Understanding the biomechanical behaviour of dentin structures is essential in order to correctly restore any loss of mechanical integrity through dental treatments [105].  
[PERSON] and [PERSON] (1998) studied strain distributions in different structures of human teeth using the moire interferometry technique [106]. Figure 10 [106] shows the resultant moire fringe patterns. Results showed strain in enamel to be significantly lower compared to strain in dentin, with a subsequently higher elastic modulus. The study confirmed the hypothesis that dentin contains structural adaptations to withstand and transfer stress.  
In 2001, tooth deformation caused by moisture change was mapped by [PERSON] et al. using moire interferometry [107]. Dentin and enamel have different water contents; therefore, changes in humidity can be used to change their moisture levels and thus produce  
Figure 10: Canine tooth slice showing (**a**) anatomical terminology and (**b–d**) moiré fringe patterns obtained with a load of 500 N. Strain–structure relations in human teeth using moiré fringes. Adapted with permission from Ref. [106]. 2019, Elsevier.  
a mechanical load across the dentin-enamel junctions. Moire interferometry was used to measure magnitudes of strain. Wide variations of strain were seen between the two samples. The study was significant in showing that the dentin-enamel junction is a unique interface that must be studied. [PERSON] et al. stated that the full-field and high-resolution results obtained proved that moire interferometry is a powerful tool to study deformation of materials.  
More recently, digital moire interferometric analysis was used to study the effect of bioactive biopolymeric nanoparticle surface conditioning on mechanical deformation in dentin. Specimens were loaded, moire interferometry showed distinct patterns of deformation and the experiment successfully investigated a method of improving mechanical integrity of dentin [105].
###### Abstract  
This study aims to investigate a new shear reinforcement method which utilizes thin mild steel (TMS) plates as shear reinforcement in deep beams to replace conventional reinforcement. Thirteen reinforced concrete deep beam specimens with three different plate thicknesses and four varying perforated hole arrangements on the TMS plates were experimentally tested to determine the load-carrying capacity and crack pattern. The experimental results indicate that the 2.0 mm thick TMS plate has the highest load-carrying capacity. Among the four different hole arrangements on the TMS plates, the perforated plates with a three-column hole arrangement show the best performance in terms of load-carrying capacity, with a 2.9% increment against the control beam specimen. The specimens also demonstrated compatible elastic stiffness with the control beam that used conventional shear links. This shows that TMS plates have the potential to replace conventional shear links in deep beams. This proposed method also changed the failure mode from conventional diagonal shear tension failure to a combination of flexural failure and shear deformation. A numerical model was developed and was found to have a good correlation with the experimental results, demonstrating potential for use in future parametric investigations on deep beams and cost reduction in future experimental work.  
reinforced concrete; deep beam; shear reinforcement; mild steel plate; perforated steel plate +
Footnote †: journal: Applied Sciences  
Article  
Experimental and Numerical Study of the Strength Performance of Deep Beams with Perforated Thin Mild Steel Plates as Shear Reinforcement
## 4 Discussion  
The issues of power consumption, easy maintenance, and periodical calibration were taken into consideration in the development stages. First of all, the operating current values of the BME680, PMS7005, SCD30, and BH1750 are 714 \(\mu\)A (maximum for pressure measurement), 100 mA, 19 mA, and 190 \(\mu\)A, respectively. Therefore, the total current and power consumption requirement and power consumption of the sensors are 120 mA and 600 mW, respectively. The built-in power IC in the TIGO ESP32 Wi-Fi module can sufficiently provide 5/3.3 V\({}_{\rm DC}\) operation voltage, current, and power consumption for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{4}{c}{**Accuracy Analysis**} \\ \cline{2-5}  & **Difference Range** & **MAE** & **MAPE (\%)** & **RMSE** \\ \hline AQI value & \(-\)0.14396–0.1002 & 0.06 & 0.31\% & 0.07 \\ AQI level & 0 & 0 & 0\% & 0 \\ PM\({}_{1.0}\) & \(-\)0.1157–0.1183 \(\mu\)g/m\({}^{3}\) & 0.06 \(\mu\)g/m\({}^{3}\) & 0.37\% & 0.07 \(\mu\)g/m\({}^{3}\) \\ PM\({}_{2.5}\) & \(-\)0.1182–0.11201 \(\mu\)g/m\({}^{3}\) & 0.06 \(\mu\)g/m\({}^{3}\) & 0.50\% & 0.07 \(\mu\)g/m\({}^{3}\) \\ CO\({}_{2}\) & \(-\)3–4 ppm & 1.48 ppm & 0.35\% & 1.72 ppm \\ Brightness & \(-\)15.7741–17.3249 Lux & 1.85 Lux & 0.25\% & 3.70 lux \\ Temperature & \(-\)0.1758–0.2280 \({}^{\circ}\)C & 0.10 \({}^{\circ}\)C & 0.36\% & 0.12 \({}^{\circ}\)C \\ Humidity & \(-\)0.3894–0.2767\% & 0.16\% & 0.31\% & 0.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Close-agreement analysis of proposed DYU Air Box and measurement instruments.  
itself and the additional sensors. On the other hand, the well-designed 4-pin dip header and socket of 2.54 mm for standard series communication interfaces including the I\({}^{2}\)C and UART interfaces provide the customized wireless multi-sensor module much convenience in installation and maintenance of multiple heterogeneous sensors. Based on the PCB design for the standard I\({}^{2}\)C extension, additional heterogeneous sensors with a standard I\({}^{2}\)C interface can be easily aggregated in the proposed wireless multi-sensor modules. Due to the oxidation/reduction on its sensitive metal-oxide-based layer, the BME680 gas sensor detects and measures AQI for the concentration level of ethanol and b-VOCs, and the periodical calibration of BME680 with an instrument is necessary.  
The cost of the proposed wireless multi-sensor module is about USD 58, including TTGO ESP32 Wi-Fi device: USD 20, BH1750: USD 5, BME680: USD 8, PMS7003T: USD 20, and PCB: USD 5. The cost for the commercially available Enviro+ Raspberry Pi HAT is over USD 70 even without taking both the dust sensor and Raspberry Pi platform into account. Furthermore, the price of the Verkada SV11-HW is over USD 1500 online, and an extra fee is necessary for the application platform. Therefore, the proposed wireless DYU Air Box is much more cost-effective as compared to the above two commercialized devices.
## 2 Materials and Methods
### Specific Energy Consumption (SEC)  
_SEC_ refers to the energy transferred by waves as heat which can lead to the drying of the product. The energy consumption of various pretreatments was first calculated. Then the SEC of each dryer was estimated.  
_SEC_ for _MW_, _US_, and _BL_ pretreatments can be determined by Equations (6)-(8), respectively [14]:  
\[\mathrm{SEC_{MW}=P\cdot t/M_{w}}, \tag{6}\]  
\[\mathrm{SEC_{US}=UP\cdot t/M_{w}}, \tag{7}\]  
\[\mathrm{SEC_{BL}=Q\cdot t/M_{w}}, \tag{8}\]  
In which:  
\[\mathrm{UP=UIcos\Phi} \tag{9}\]  
\[\mathrm{Q=m\cdot C_{a}\cdot\Delta t}, \tag{10}\]  
where _SEC\({}_{MW}\)_, _SEC\({}_{US}\)_, and _SEC\({}_{BL}\)_ are the specific energy consumption for microwave (MJ/kg), ultrasonic and blanching drying methods, respectively (kJ/kg), \(t\) is the total drying time (min), _M\({}_{w}\)_ represents the weight loss (kg), _UP_ is the US power (W), while U and I are the applied voltage (V) and current (A) of the US generator, respectively. cos\(\Phi\) is the power factor and equals 0.8. K is the lamp power (W), \(\Delta T\) is the temperature difference (\({}^{\circ}\)C), \(Q\) is the volume flow rate of air into the drying chamber (m\({}^{3}\)/min), \(C_{a}\) represents the specific heat (kJ/kg\({}^{\circ}\)C), m is the mass of water (kg).
# An Evaluation of the Efficiency of Compartmented
## Materials and Methods
### Asphalt Mix Healing Programme  
In order to investigate effect of the fibres on the mechanical properties of the asphalt mix and evaluate the healing efficiency of the compartmented fibres encapsulating the rejuvenator, a special testing programme was designed, including:  
Figure 7: (**a**) 4 PB fatigue test system; (**b**) Schematic illustration of the 4 PB Fatigue test beam set up (dimensions are given in mm) and (**c**) Continuous sine-shaped deformation in the four-point bending fatigue test.  
* two mixtures: control mix and mix containing fibres (5%),
* following test methods: ITS, SCB, and 4 PB Fatigue,
* test temperature of 20 \(\pm\) 3 \({}^{\circ}\)C,
* healing temperature of 20 \(\pm\) 3 \({}^{\circ}\)C,
* healing time of 20 h and 40 h after the initial test.  
For the 4 PB Fatigue test, only a single healing stage was performed.  
A parallel testing programme was conducted on samples with and without the healing system, where the mixture without the healing system was used as a reference guide. In this way, one can investigate whether the mix design is suitable for standard testing; for example, it is well-known, via international standards, what the expected stiffness performance of a particular standard mix is. Three tests were used in the investigation, namely, ITS, SCB, and 4 PB Fatigue test methods.  
The purpose of the ITS test was to evaluate the crack healing efficiency and strength recovery through experimental load-displacement histories. More specifically, the maximum load for all tests was determined and compared. In terms of the SCB tests, two parameters for both mixtures were monitored and compared: the crack speed and fracture toughness. Finally, the healing efficiency was also investigated using 4 PB Fatigue tests via flexural strength vs. number of cycles histories. For all test methods, special attention was given to the repeatability of the results.