esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Study
### Experimental Process  
The vibration signals of the training sets are shown in Figure 5a,b, and the RMS of the training sets are shown in Figure 5c,d. First was determining the SPT of the training sets, which is indicated by the red dashed line in Figure 4. It was observed that the SPT of the two bearings is 1427 and 824, respectively.  
After the SPT was determined, the data after SPT were processed. Each sample has 2560 points, which can be directly used as the time domain features to input the regression model. The training sets are transformed into time-frequency images by SWT. In order to illustrate the frequency energy change with regard to time, the vibration signals and time-frequency images of the first and last samples of bearing1_1 are shown in Figure 6. From Figure 6, we can see that the frequency fluctuations in the time-frequency image of the bearing in the initial degradation stage are not obvious. However, in the final stage, defective bearings appear periodic pulse phenomenon. These time-frequency images 320 \(\times\) 2560 are resized to 32 \(\times\) 32 to form a standard image size using principal component analysis (PCA), which is used as input of the regression model. The time-domain features and time-frequency images, and corresponding labels are used to train the regression model. After model training is finished, the RUL of test sets can be predicted.  
Figure 4: The PRONOSTIA platform.  
The vibration signals of the test sets are shown in Figure 7a,b, and the RMS of the test sets are shown in Figure 7c,d. First, the SPT of the test sets was determined, which is indicated by the red dashed line in Figure 7. The test samples after SPT were processed  
Figure 5: Vibration signals of the training sets and their RMS: (**a**) vibration signal of the bearing1_1, (**b**) vibration signal of the bearing1_2; (**c**) RMS of the bearing1_1; and (**d**) RMS of the bearing1_2.  
Figure 6: Bearing 1_1: (**a**) Waveform for the first sample; (**b**) time–frequency images for the first sample; (**c**) waveform for the last sample; and (**d**) time–frequency images last sample.  
in the same way as the training sets, and then input into the regression model for RUL prediction.  
The RUL prediction results of the test sets are shown in Figure 8. It can be seen from Figure 8 that the smoothed prediction curve is quite close to the actual RUL, which can prove that the proposed method is effective for detecting the bearing degradation trend and predicting RUL.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 2 Related Works and Background Information
### Safety and Security: Establishing the Difference  
The Industrial internet consortium established the difference between \"safety\" and \"security\" in a smart factory. In order to trust IIoT systems, five features were stipulated: security, safety, reliability, resilience, and privacy [20]. Security refers to the measures put in place to protect the system from unintended or unauthorized access, change, or destruction. On the other hand, safety can be described as the \"condition of the system operating without causing unacceptable risk of physical injury or damage to the **health of the people**, **either directly or indirectly**, as a result of damage to property or to the environment\" [20]. Two broad approaches in ensuring safety in the industries include: \"intrinsic\" and \"active\" measure [21]. In the intrinsic approach, workers and machines are separated by physically putting them in various locations, locked door, or through the use of specialized insulation. In the \"active\" measure approach, the system is designed to respond to triggered issues, such as detecting workers intruding or stepping into hazardous or out of band areas, as well as emergency situations [22]. An example of a functional safety standard put in place for the industry is the IEC 61508 [22]. The authors in Reference [23], developed a portable android platform that is designed to raise an alert on emergency cases regarding fall detection and a Bluetooth area positioning. It also incorporates a form of voice broadcast with warning messages to alert a fatigued worker in danger. On the other hand, the authors in Reference [24] designed a protective wearable gear to assist factory floor workers and en sure they put required safety measures in place. Such safety measures include appropriate safety equipment for specific machine type. Proffering solutions to the ethical issues which arise with the technologies that workers currently use or those they consider using in the the factory of the future, authors in Reference [25] believes that all or some of the issues that may arise with these technologies can be responded to and ameliorated by designing these technologies explicitly for human values. In this work, a practical testbed was developed to collect data from the smart factory shop floor which can help in monitoring the safety of smart factory shop floor workers by flagging emergency alert based on CNN detection and classification.
## 3 Results and Discussion
### TL Sensitivity  
TL sensitivity is represented as the TL signal per unit dose per unit mass (uC g\({}^{-1}\) Gy\({}^{-1}\)), Figure 3 revealing present results from use of \({}^{60}\)Co gamma irradiation. Sample sensitivity with dose is subject to various influencing factors including choice of TLD reader [22] and the means of sample handling. The TL sensitivity graph depicts that all studied samples demonstrate a pronounced low-dose sensitivity relative to high-dose exposure. This is because, as the dose is increased, the concentration of recombination centres may become less than that of the electron traps, thus leads in the reduction of TL sensitivity. Honda windshield glass is observed to produce somewhat better sensitivity throughout the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Windshield Glass** & **TL Peak Position,** & **Geometric Factor,** & **Activation Energy** & **Frequency Factor** \\
**from Car Brand** & **TM (K)** & \(\mathbf{\mu_{g}}\) & **(eV)** & **(s\({}^{-1}\))** \\ \hline Honda & 627.15 & 0.51 & 1.18 & \(2.97\times 10^{10}\) \\ Toyota & 646.15 & 0.49 & 0.83 & \(1.86\times 10^{7}\) \\ Proton & 662.15 & 0.48 & 1.29 & \(5.93\times 10^{10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimation of the trapping parameters of windshield glass for different car brands subjected to the dose of 10 Gy with corresponding maximum temperatures calculated from Chen formula.  
Figure 2: Typical TL glow curve for glass samples from Honda, Toyota and Proton windshields, \({}^{60}\)Co irradiated to a fixed dose of 10 Gy.  
studied dose ranges from 0.9 x to 1.3 x of that of the Toyota samples, while the Proton windshield glass is seen to exhibit the least sensitivity. The differences are dominated by relative differences in elemental makeup (Table 2). As instances, the Honda windshield glass shows relatively higher concentrations of fractional Ca, at 8.88% versus 7.27% for Toyota and 5.16% for Proton windshield glass. Variations such as these play significant roles in trap/luminescence centre formation and hence TL sensitivity, in particular the presence of charge states and abundance acting as activators.
## 3 Results and Discussion
### The Influence of Various Factors on the Dynamic Performance of RFR  
According to the previous analysis, it can be seen that the preload, the contact interface, the coordinate deformation of the tie rods, and the parallel relationship between the tie rods and the rotor will all affect the total stiffness matrix K of the rotor. Therefore, based on the numerical method to solve the free vibration equation of the rotor system, the influence of each influencing factor on the dynamic characteristics of the RFR will be investigated in detail. For ease of description and comparison, five different situations are listed in Table 1.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 6 A Case Study: Remote Patient Monitoring of a Heart Patient in the IoMT Networks
### A Single-Point-of-Failure Scenario of the IoMT Networks  
A detailed overview of IoMT networks with its vulnerability of single-point-of-failure (SPoF) is shown in Figure 5. It represents the interconnection between patients and an IoMT-equipped health center. Patients are connected through different sensors at home. The home router collects the data and connects through an ISP where the SDN controller is located centrally to manage and optimize the data traffic route between the patient and the hospital. These ISPs are further connected to the central public networks. Eventually, they are connected to a data server of the hospital through a cloud service provider. The entire remote patient monitoring is fully dependent on this network infrastructure. The critical health status of the patient is transmitted through this path.  
The SDN controller in this setup is useful for continuous monitoring, security, traffic, and resource management in normal situations. However, in some unforeseen circumstances due to some faults, if the connection with the controller is broken and it is no longer in operation, the functionalities associated with this controller will also stop working during the fault. This situation can cause a complete breakdown until the controller comes back into operation. This scenario is considered as a single point of failure in the IoMT networks.  
As mentioned in the AHA guidelines in the background section, a patient with a cardiac arrest needs appropriate interventions administered by a medical team within 4-6 min. Otherwise, this IoMT information setup will lose its usefulness if the information is received at the receiver's end after this duration. Hence, the vulnerability of a single point of failure in the IoMT networks breaches the resilience requirement and it needs to be mitigated.
### Hardware Implementation  
We implemented and validated a CHP2 prototype on a COTS evaluation board, which we discuss in greater detail later in this manuscript. This implementation includes the Xilinx Zynq UltraScale+ MPSoC ZCU102 evaluation kit and an ADI FMCOMMS5 RF evaluation board. This prototype has a reasonably small form factor (\(\sim\)0.01 m\({}^{3}\)) and moderately low weight (\(<\)1 kg), and supports a 10 MHz signal bandwidth operating at carrier frequencies of 915 MHz in the U.S. and 783 MHz in Europe. We support this hardware with a custom transmit-receive switching and amplifier board and custom printed antennae; while these additions are no longer within the scope of \"COTS\", they are not fundamental to the design, as we previously demonstrated a working prototype with simple dipole antennas and separate transmit-receive paths on the same computational hardware [12]. The details of this implementation and the processing architecture are discussed in greater detail later in this manuscript. This prototype is depicted in Figure 2.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 6 Conclusions and Recommendations  
Design, fabrication, and flight testing of the first-ever turboelectric VTOL unmanned aircraft provides insights for future manned and unmanned hybrid air vehicles. An experimental aircraft was constructed and flown to create recommendations for future vehicles. Time-resolved experimental flight test data provided new understanding of aircraft performance across a flight profile for field testing design decisions. A key insight from the study was the maximum electric current draw of 390 A experienced during a dive-pull-out utilizing the vertical lift motors. The maximum current draw was 48% larger than on takeoff, which was the expected maximum load case. Another insight was the need for dedicated active cooling for high-power IGBTs for flight safety. Aircraft ground testing showed that passively cooled heat sinks were inadequate to overcome elevated ambient temperatures, even with oversized IGBTs.
## 4 Scheduling
### Modified Scheduling Scheme Based on B-MAC  
There are two kinds of scheduling: (1) Integrated scheduling with MAC layer, such as B-MAC [6], S-MAC [14]; (2) Independent scheduling based on routing topology, such as FPS [15], DB-TDMA [16], and DCS [17]. Independent scheduling protocols need time synchronization and energy consuming.  
Figure 6: Parent node informs its children to trigger routing maintenance.  
Figure 7: When a new node F joins network, routing maintenance is triggered.  
CENet adopts B-MAC which has friendly interfaces that allow users to flexibly control the operation of scheduling. It has better performance than S-MAC.  
For hierarchical architecture of cabinet monitoring, a master node can't forecast when to receive data packets from its children. In B-MAC, to ensure sensed data being forwarded to the BS reliably, master nodes must detect data when each of its neighbors transmits a packet, regardless of the packet destination. In CENet, mode 4 of B-MAC is selected as the scheduling of master node, which is the optimum mode of B-MAC. Master nodes wake up every 100 ms to detect channel activity. On the other hand, slave nodes, which do not forward data, can decide when to send data. Based on B-MAC, we have developed a scheduling scheme for slave nodes.  
The proposed scheduling must support beacon and command transmission. Figure 8 describes the pseudo code of the scheduling. Each node has two states: sleep and active. When a slave node needs to communicate with its master, it enters active state. Because communication nodes must be in the same mode in B-MAC, the scheduling for active state is mode 4 of B-MAC. A slave node wakes up at  
Figure 8: The pseudo code of the proposed scheduling.  
predetermined time to sense the environment and sends data to its master. Meanwhile, it piggybacks the parameter version number on data packet to check whether the parameters are the latest. If its master has a new version of parameters, it sends updated parameters to its slave immediately. If the number of data packet retransmissions or drop rate is over a threshold, the slave node starts to look for a new master. Otherwise the slave node goes to sleep.
# Model-Guided Manufacturing of Transducer Arrays Based on Single-Fibre Piezocomposites
## 2 Materials and Methods
### Transducer Model  
To simulate the transducer response, we used the KLM equivalent circuit approach [16]. It allows rapid performance prediction based on lumped parameters [15; 22]. Each assembly step can be addressed by changing or adding elements to the circuit. The model covers only linear, 1D oscillations. Hence, the obtainable results lack effects coming from other dimensions, anisotropy and non-linearities.  
The PZT fibre is modelled as an ideal transformer coupled to the centre of a transmission line, as shown in Figure 8. \(C_{0}\) is the blocking capacitance, while \(C^{\prime}\) and \(\Phi\) are the reactance and the transforming ratio of the PZT fibre, both dependent on the excitation frequency. To account for dielectric and elastic losses of the PZT fibre, the loss tangent \(\tan(\delta)\) and material attenuation \(\alpha_{0}\) were included according to [PERSON] et al. [23]. \(C_{\text{par}}\) accounts for parasitic capacitances of the supply lines and the PCB. The transducer radiates to the front and back into a medium with acoustic impedance \(Z_{\text{F}}\) and \(Z_{\text{B}}\).  
Each of the added assembly layers are modelled with a separate transmission line. \(Z\) is thereby the acoustic impedance, \(d\) the thickness, \(\beta\) the wave number and \(\alpha\) the attenuation of the respective layer. To simplify calculations, we divided the model into transmission matrices as proposed by [PERSON] and [PERSON] [24]. The additional layers were then added by multiplying two-ports to the PZT fibre model. All formula and more detailed descriptions can be found in Appendix A.  
The initial parameters of the PZT fibre were taken from the datasheet. The acoustic parameters \(Z\), \(\alpha\) and \(v\) of the added layers were taken, if possible, from literature [25; 26]. Missing parameters were estimated. The thickness dimensions were measured with a micrometer gauge and the average layer thicknesses of \(n=15\) samples were used for the model. All parameters are listed in Appendix B.
## 3 Disturbance Reference  
This paper purposes to obtain the optimal 2 DOF PI-P controller parameter for PMSM speed control, where the speed response follows the ideal response for step response and disturbance response. The step reference has been designed for the step response, as shown in Equation (11). In this section, the disturbance reference is designed as a reference for the disturbance response. To obtain the disturbance reference, a disturbance is applied to the system. The closed loop response to a step input disturbance (\(d(k)=1\) and \(r(k)=0\)) is considered. Figure 3 shows the closed loop control system when the disturbance is applied to the system. The disturbance reference model \(M_{2}(z)\) is the transfer function from disturbance \(d(k)\) to the controlled output \(y(k)\), and the disturbance reference model \(M_{2}(z)\) is given based on the reference model \(G_{r}(z)\), which is the transfer function from reference \(r(k)\) to the output \(y(k)\), as shown in Figure 1. From Figure 1, the transfer function of \(G_{r}(z)\) (\(r(k)=1\) and \(d(k)=0\)) can be calculated by:  
\[y\left(k\right) =G\left(z\right)\left(C_{1}\left(q_{1},z\right)\,\left(r\left(k \right)-y\left(k\right)\right)-C_{2}\left(q_{2}\right)y\left(k\right)\right) \tag{14}\] \[y\left(k\right) =G\left(z\right)\left(C_{1}\left(q_{1},z\right)r\left(k\right)- \left(C_{1}\left(q_{1},z\right)+C_{2}\left(q_{2}\right)\right)y\left(k\right)\right)\] (15) \[\frac{y\left(k\right)}{r\left(k\right)} =\frac{G\left(z\right)C_{1}\left(q_{1},z\right)}{1+\left(C_{1} \left(q_{1},z\right)+C_{2}\left(q_{2}\right)\right)G\left(z\right)}\] (16) \[G_{r}\left(z\right) =\frac{G\left(z\right)C_{1}\left(q_{1},z\right)}{1+\left(C_{1} \left(q_{1},z\right)+C_{2}\left(q_{2}\right)\right)G\left(z\right)} \tag{17}\]The transfer function from disturbance \(d(k)\) to controlled output \(y(k)\) is shown in Equation (19), where the closed loop response to a step input disturbance (\(d(k)=1\) and \(r(k)=0\)) is considered. \[y\left(k\right)=G\left(z\right)\left(d\left(k\right)-\left(C_{1}\left(q_{1},z \right)+C_{2}\left(q_{2}\right)\right)y\left(k\right)\right) \tag{18}\]  
\[\frac{y\left(k\right)}{d\left(k\right)}=\frac{G\left(z\right)}{1+\left(C_{1} \left(q_{1},z\right)+C_{2}\left(q_{2}\right)\right)G\left(z\right)} \tag{19}\]  
From Figure 3, the disturbance reference model is shown in Equation (22):  
\[y_{dr}\left(k\right)=M_{2}\left(z\right)d\left(k\right) \tag{20}\]  
\[y_{dr}\left(k\right)=\left(\frac{G\left(z\right)}{1+\left(C_{1}\left(q_{1},z \right)+C_{2}\left(q_{2}\right)\right)G\left(z\right)}\right)d\left(k\right) \tag{21}\]  
\[M_{2}\left(z\right)=\frac{G\left(z\right)}{1+\left(C_{1}\left(q_{1},z\right)+ C_{2}\left(q_{2}\right)\right)G\left(z\right)} \tag{22}\]  
The disturbance reference model \(M_{2}(z)\) (22) is the same as the transfer function from disturbance \(d(k)\) to the controlled output \(y(k)\) (19).
## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Conventional Algorithms (CA)
#### 7.1.1 Heuristic Algorithms  
Exact algorithms are developed in such a way that the optimal solution can be achieved in a limited time. However, for some complicated optimization problems (e.g., NP-hard or global optimization), this limited amount of time may expand exponentially in relation to the problem sizes. Heuristics lack this guarantee and, as a result, often provide solutions that are less than optimal or approximate solutions. Heuristic algorithms frequently find acceptable solutions in a reasonable amount of time. In addition, they are often problem-dependent, that is, they are designed for a specific problem such as energy optimization in cellular networks, UAV routing problems, etc. There are two heuristic approaches to solving hard optimization problems. One of such approaches is the constructive heuristics which develops solutions via iterations. It is called a constructive heuristic because it begins with an empty solution and continues to expand on it until a complete solution is discovered [125]. The other heuristic method is called improvement or local search heuristics. Improvement heuristics start with a complete solution and then strive to improve on the existing solution further by local searches. Examples of heuristic algorithms include block coordinate descent (BCD), Dinkelbacks method, successive convex approximation (SCA), circle parking theory, etc.
## 3 Results
### Influence of Geometric Parameters
#### 3.3.2 Influence of the Scale Factor of the Footing (Parameter b/a)  
In this case, the quality parameters of the rock mass and the intact rock are fixed at UCS = 10 MPa, GSI = 50, m\({}_{\rm i}\) = 17, b/a = 0.50  
The two following cases have been studied, varying the eccentricity E/a from zero until its influence is negligible:  
\begin{tabular}{l l l l l l} IE-1: & 2a = 8 m & 2b = 4 m = 0.5a & H = 4 m = 2b & B = 6 m = 1.5a \\ IE-2: & 2a = 4 m & 2b = 2 m = 0.5a & H = 4 m = 4b & B = 6 m = 3a \\ \end{tabular}  
The failure mechanisms obtained for each case studied are the same as those shown in Section 3.1, following the same pattern based on the eccentricity E/a.  
Figure 15 only represents the influence of the position of the load (with respect to the cavern crown, E/A) on the relative (dimensionless) value of the ultimate bearing capacity (q\({}_{\rm u}\)/q\({}_{\rm uH\&I}\)).  
\begin{tabular}{l l l l l l} Case IE-1: & 2a = 8 m & 2b = 4 m = 0.5a & H = 4 m = 2b & B = 6 m = 1.5a \\ Case IE-2: & 2a = 4 m & 2b = 2 m = 0.5a & H = 4 m = 4b & B = 6 m = 3a \\ \end{tabular}  
By analysing the curves of the above figure, the following conclusions can be drawn:  
For eccentricity values E > 2a, the variation in the relative depth H/b, the cavern size (a,b)and the relative width of the load b/a exert little influence on the ultimate bearing capacity (q\({}_{\rm u}\)/q\({}_{\rm uH\&I}\)).  
The lower the eccentricity values, the higher the reduction in the ultimate bearing capacity for footings smaller than the cavern width (case IE-1). However, a lower value may be reached, even with higher eccentricities, if the footing is greater than the cavern width (case IE-2).  
Figure 15: The load eccentricity on the ultimate bearing capacity value for different geometries with respect to the footing width.