esa-sceva/satcom-chunk-collection huggingface dataset:
# Stress Analysis with Dimensions of Valence and Arousal in the Wild
## 2 Related Work
### LSTM Architecture  
[PERSON] and [PERSON] introduced Long Short-Term Memory (LSTM) in 1997 [14]--a type of recurrent neural network. This architecture is particularly well suited for sequential data such as video or audio. The crucial difference of LSTM compared to other recurrent neural networks is memory cell--a cell state to save long-term states, which allows LSTM to remember and connect previous information of data, that was introduced in detail in [18]. The LSTM unit structure is shown in Figure 1. Each LSTM unit comprises three gates: input gate, forget gate, and output gate. Each port is assigned a specific task. First, the input gate adds and controls the amount of information flow into the memory cell. Second, the forget gate defines the previous cell state that needs to be overlooked and keeps part of the data in the current moment. Finally, the output gate controls the amount of data flows in the rest of the networks.  
\[\begin{split} f_{t}&=\sigma\Big{(}W_{f}x_{t}+U_{f} h_{t-1}+b_{f}\Big{)}\\ i_{t}&=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i})\\ \widetilde{C}_{t}&=tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_ {c})\\ o_{t}&=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o})\\ C_{t}&=C_{t-1}\circ f_{t}+i_{t}\circ\widetilde{C}_{t }\\ h_{t}&=o_{t}\circ tanh(C_{t})\end{split} \tag{1}\]  
where \(x_{t}\) is input at the current time t; \(f_{t}\), \(i_{t},o_{t}\) are the vectors of the forget gate, input gate, and output gate, respectively. Character '\(\circ\)' denotes the Hadamard product; \(W_{f}\), \(W_{i}\), \(W_{c}\), \(W_{o}\) are weight matrices of the input \(x_{t}\) and \(U_{f}\), \(U_{i}\), \(U_{c}\), \(U_{o}\) are weight matrices of the previous hidden state \(h_{t-1}\) at the forget gate, input gate, cell state, and output gate, respectively; \(b_{f},b_{i}\), \(b_{c}\), \(b_{o}\) are the bias parameters. \(\sigma\), \(tanh\) are the sigmoid function and hyperbolic tangent function, respectively. \(\widetilde{C}_{t}\) refers to a \(tanh\) output. \(C_{t}\) and \(C_{t-1}\) denote the current and previous cell states, respectively. \(h_{t}\) and \(h_{t-1}\) denote the current and previous output, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Number of Videos** & **Length per Video Clip** & **Setting** & **Target Objects** \\ \hline SADVAW (proposed) & 1236 & 2–4 (s) & Movie & Stress, valence, arousal \\ WeSAD [15] & 15 & 36 (min) & Lab & Baseline, stress, amusement \\ MuSE [16] & 28 & 45 (min) & Lab & Activation, valence, stress \\ SWELL-KW & 25 & 2 (h) & Lab & Stress, valence, arousal, mental \\ [17] & 25 & 2 (h) & Lab & effort, frustration, task load \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparision of the SADVAW dataset with the existing stress datasets WeSAD [15], MuSE [16], and SWELL-KW [17].  
LSTM is widely used and yields good results in emotion recognition [19; 20; 21], video classification [22], and speech recognition [23; 24].
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 8 Conclusion  
This paper presents WSN-Maintain, a Cooja-based tool to maintain coverage requirements and evaluate network lifetime. As use cases for this tool, we implement greedy-maintain and local-maintain to configure the initial network, as well as to turn on redundant nodes. We evaluate our tool using the two redundant node algorithms with datasets that consist of the temperature sensor reading from five real deployments. Our simulation results with Cooja show that we can extend the network lifetime as many as \(9\times\) for the one-room one-hop TMON deployment and \(1.5\times\) for the multiple-room multiple-hop CIT-1 deployment.  
From the experiments, we learned that disconnected networks cause a shorter lifetime. Therefore, it is necessary to identify connectivity-critical nodes by taking message paths into account. This can later be utilised to deploy some additional nodes as backups. Therefore, when the critical nodes fail, the network is still connected. Our future work will include techniques to re-deploy sensor nodes to achieve better coverage and better lifetime, given a coverage threshold and an expected network lifetime. We will also investigate techniques to schedule, predict and defer the physical maintenance to reduce the maintenance cost.  
This research is fully funded by the Irish Research Council and United Technologies Research Center Ireland Ltd. under the Enterprise Partnership Scheme. The authors thank the NIMBUS Centre at Cork Institute of Technology (CIT) Ireland, the Laboratorio Tempo Sistemas de Tempo Real e Embarcados at Universidade Federal Fluminense (UFF) Brasil and the Data Sensing Lab for the datasets used in this paper. The work reported in this paper was done by [PERSON] when she was a post-doctoral researcher at University College Cork Ireland under the supervision of [PERSON] and [PERSON]. The authors declare no conflict of interest.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Gateway Advertisement Process  
After the MGW is selected, the MGW starts to broadcast itself as the gateway using a gateway advertisement (GWadv) message within its IEEE 802.11p coverage zone. The GWadv is broadcast using the TTL value set to one. Other vehicles that need to send the DT packets need to check the existing MGW before initiating the gateway selection process (refer to Algorithm 3). If the MGW is found, the IV sends the packets to the existing MGW. If the MWG is not found, the IV needs to reinitiate the gateway discovery and selection process (Algorithm 2).
## 3 Results and Discussion
### Effects of Radius Ratio on Skin Friction Coefficient and Flow Characteristics
Further, it can be seen that the vorticity in the whole gap increased with \(Re\) for all cases, indicating that the dissipated energy due to the vortexes was greater at higher \(Re\). Figure 14 shows the pressure contours in the tangential direction and velocity distribution in the radial direction when \(Re\) was \(10^{4}\). It is noted that the pressure \(P\) in the legends is 7.38 MPa, which is critical CO\({}_{2}\) pressure. \begin{table}
\begin{tabular}{c c c} \hline \(Re\) & \(Ra/\)mm & \(C_{\text{f}}\) \\ \hline \multirow{4}{*}{\(10^{2}\)} & 0.056 & \(2.70\times 10^{-2}\) \\  & 0.113 & \(4.93\times 10^{-2}\) \\  & 0.169 & \(8.47\times 10^{-2}\) \\  & 0.225 & \(1.49\times 10^{-1}\) \\ \hline \multirow{4}{*}{\(10^{3}\)} & 0.056 & \(7.96\times 10^{-3}\) \\  & 0.113 & \(9.11\times 10^{-3}\) \\  & 0.169 & \(1.09\times 10^{-2}\) \\  & 0.225 & \(1.56\times 10^{-2}\) \\ \hline \multirow{4}{*}{\(10^{4}\)} & 0.056 & \(3.68\times 10^{-3}\) \\  & 0.113 & \(4.55\times 10^{-3}\) \\  & 0.169 & \(5.16\times 10^{-3}\) \\  & 0.225 & \(5.49\times 10^{-3}\) \\ \hline \multirow{4}{*}{\(10^{5}\)} & 0.056 & \(1.39\times 10^{-3}\) \\  & 0.113 & \(2.35\times 10^{-3}\) \\  & 0.169 & \(2.54\times 10^{-3}\) \\  & 0.225 & \(2.68\times 10^{-3}\) \\ \hline \multirow{4}{*}{\(10^{6}\)} & 0.056 & \(9.44\times 10^{-4}\) \\  & 0.113 & \(1.19\times 10^{-3}\) \\  & 0.169 & \(1.50\times 10^{-3}\) \\  & 0.225 & \(1.71\times 10^{-3}\) \\ \hline \multirow{4}{*}{\(10^{7}\)} & 0.056 & \(9.11\times 10^{-4}\) \\  & 0.113 & \(9.51\times 10^{-4}\) \\  & 0.169 & \(1.29\times 10^{-3}\) \\  & 0.225 & \(1.49\times 10^{-3}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Values of \(C_{\text{f}}\) versus \(Re\) for \(\eta\) ranging from 0.056 to 0.225 at \(Ra=0.8\) μm. Figure 12: Relative deviations versus \(Re\) under \(\eta\) = 0.056\(\sim\)-0.225 conditions. Figure 13: Streamlines and vorticity contours at meridional plane with \(Re\) of \(10^{2}\), \(10^{4}\), and \(10^{7}\) at different \(\eta\) = 0.056–0.225. It was found that the pressure gradually increased from the rotational to stationary walls according to the law of energy conservation, which corresponded to the velocity distribution shown in Figure 9, but it decreased with increasing \(\eta\) as a whole. Additionally, there were periodic low-pressure and high-pressure regions near the walls as a result of Taylor vortexes in the gap, and the periodic high-pressure regions near the stationary walls decreased at larger \(\eta\). This was because when \(\eta\) increased, the size of the Taylor vortexes increased but fewer vortexes occurred; hence the periodic fluctuation of pressure was less, resulting in fewer high-pressure regions. Moreover, it was noted that the velocity distribution was similar to Figure 10 for different cases, which could also be divided into three regions, but the velocity gradient near the stationary walls for Case IV was smaller than that of other Cases due to the significantly larger \(\eta\).
# Investigation of Load Environment and Bending Load Capacities of Aged Prestressed Concrete Sleepers
## 2 Investigation of Load Environment of PC Sleepers
### Investigation Methods
#### 2.1.3. Analysis Method  
Figure 4 shows the outline of the 3D numerical analysis model used in this work. The simulation program Dynamic Interaction Analysis for Shinkansen Train and Railway Structures was used for numerical analysis [20; 21; 22].  
Figure 4a shows the dynamic model of the vehicle, which was generated assuming that the body, bogies, and wheelsets were rigid. These rigid three-dimensional elements were linked by springs \(K_{N}\) and dampers \(C_{N}\) (\(N\) is a subscript number in Figure 4a) according to their characteristics. Each train car had 31 degrees of freedom. Each train consisted of multiple vehicle models linked together by springs \(K_{\rm C}\) and dampers \(C_{\rm C}\) attached to the ends of the vehicle models. The vehicles were given the specifications of a vehicle passing through the measurement point (a 56.6 kN static wheel load). The vehicle occupancy rate was set at 50%.  
Figure 1: Outline of PC Sleeper (classified as 3T in JIS E 1201).  
Figure 3: Arrangement of wheel load gauge and concrete gauge.  
Figure 2: Field measurement points.  
Figure 4b shows the dynamic model of the track, which was created using the finite element method. The track was modeled as a 160 m unit. The rails were modeled as beam elements, with each rail fastener divided into four segments by nodes. Each PC sleeper was modeled as a beam element divided into 10 segments by nodes. The track pads were modeled as spring elements. The ballast and roadbed were modeled as an integrated spring element. In total, the analysis models had 4634 nodes and 8468 elements. Since the measurement point was on a viaduct, the concrete member of the viaduct was assumed to be sufficiently rigid, and the boundary condition at the lower end of the spring element simulating the ballast was fixed. Figure 4c shows the rail surface roughness of the test section, which was statically measured using a surface roughness measuring device with a length of 1 m. The measured rail surface roughness was used to reproduce the exciting force generated between the wheel and the rail.  
The equations of motion concerning the vehicle and the track were modally converted for numerical analysis efficiency. The resulting equations of motion on the modal coordinate system of the vehicle and the track were progressively solved in time increments of \(\Delta t\) using the Newmark method. For modal analysis, mode orders that can reproduce vibrations of up to approximately 10 kHz were used, and the analysis was executed at 0.1 msec intervals. An attenuation constant of 0.1 was used to ensure consistency with the time history response analysis [23].  
Figure 4: Outline of 3D numerical analysis model. (**a**) Dynamic model of vehicle; (**b**) dynamic model of track; (**c**) rail surface roughness.
# Prediction System for Overhead Cranes Based on Digital Twin Technology
## 3 Development of the Digital Twin System for Overhead Cranes
### Verification of Serial Communication Function of the Overhead Crane  
In the serial communication of the overhead-crane project, the com terminal was plugged into the microcomputer system to forward and verify the cyclic redundancy  
Figure 5: The development of the stress module.  
Figure 6: The prompt effect of an abnormal message.  
code CRC16. During this experiment, eight instructions needed to be issued to the crane, including front, back, left, right, rise, decline, start, and stop. The work instructions of the overhead crane were generally composed of eight bytes. For a specific instruction, CRC16 calculation was first conducted through a software program to obtain a two-byte check code. Then, the eight bytes of the original instruction and the two-byte check code were spliced into a new data stream of ten bytes, which were sent to the Com terminal through serial communication. After the Com terminal confirmed the verification, the corresponding work instruction was finally sent to the overhead crane.  
Corresponding instruction values could be obtained from the equipment operation manual of the overhead crane. Meanwhile, the eight instruction values required for the final experiment were calculated and obtained according to the CRC16 check. Each instruction had ten bytes, and the last two bits are the check code.  
Start instruction: 01 10 06 04 02 00 00 00 60 18  
Stop instruction: 01 10 06 04 01 00 00 00 60 5C  
Cart forward instruction: 01 10 06 04 20 00 00 06 60  
Cart vehicle backward instruction: 01 10 06 04 10 00 00 06 56 60  
Trolley left-turning instruction: 01 10 06 04 04 00 00 00 60 90  
Trolley right-turning instruction: 01 10 06 04 08 00 00 00 63 C0  
Rise instruction: 01 10 06 04 80 00 00 00 48 60  
Decline instruction: 01 10 06 04 40 00 00 00 74 60  
The realization of the digital twin target of the overhead crane was developed based on the C# language in Unity3d. Then, the C# language and the Winform framework were exploited to assist in the development of the joint debugging serial communication function and encapsulate it into a module tool class. According to the requirements of this experiment, the serial communication tool class should have included encapsulation of basic functions such as serial port opening, serial port closing, instruction sending and receiving, etc.  
The first step of serial communication is to open the serial port. The overhead crane adopted COM3 as the serial number, and the baud rate was 19,200. The key code to open the serial port link is listed below.  
To facilitate the observation and demonstration, the UI of the verification program was established using the Winform framework. The final running effect of the software is demonstrated in Figure 7.  
Figure 7: The final running effect of the software.
# Online Efficiency Optimization and Speed Sensorless Control of Single-Phase Induction Motors
## 5 Control Scheme Implementation  
The controller consists of two main parts of frequency and voltage control. Figure 4 illustrates the block diagram of the controller. This system, which is void of any mechanical sensors, utilizes STCR for motor speed estimation. For this purpose, the main and auxiliary winding currents are measured via current sensors. After removing noise and high frequency components using a Low Pass Filter (LPF), the rms values are calculated and later, by dividing the rms values of main and auxiliary winding currents, the STCR will be obtained.  
As previously stated in Section 3, the NN estimates the motor slip utilizing STCR and frequency. The evaluated slip is further used to calculate the speed (\(\omega_{\text{m,est}}\)), which  
Figure 4: Block diagram of the proposed control scheme.  
acts like a mechanical speed sensor and is used as the speed feedback to the motor speed controller. The speed controller consists of a PI controller commanding the reference frequency of inverter.  
In addition to speed control, the presented system is capable of optimizing the motor efficiency. To carry out this, the motor voltage should be adopted according to the value commanded by the voltage controller. The voltage controller maximizes the efficiency by adapting the input voltage amplitude. To do so, \({K_{\mathrm{s}}}^{*}\) is initially extracted from the lookup table according to the set frequency and further multiplied by the corrective factor (\(K\)), which is determined by the optimization module, to give the final optimal value of STCR. The voltage controller later attempts to drive the STCR to its optimal designated value through reducing the voltage amplitude. In this process, firstly the optimum value of stator main winding current (\({I_{m}}^{*}\)) is calculated via multiplying the optimal SCTR by the measured auxiliary winding current. A closed-loop PI controller regulates the main winding current to its reference value (\({I_{m}}^{*}\)). The time constant of this controller is larger compared to that of speed controller in such a way that the voltage amplitude variations have almost no bearing on the frequency transients. The optimization algorithm requires the power loss measurement. However, as it is not practical to measure the power loss directly, the input power is used instead, as previously described in Section 4.  
In spite of providing the two objectives of speed regulation and efficiency optimization, the drive utilizes merely two current sensors as feedbacks. The control algorithm is rather simple and does not demand a fast-processing technology for implementation. Therefore, the controller and SPWM signal generator can both be implemented in an inexpensive microcontroller. The controller's simplicity, on the one hand, and speed sensor-free feature, on the other hand, makes the proposed drive quite suitable for low-power and less expensive applications.
## 2 Generator Dynamic State Estimation Model
### Error Analysis  
Equations (4) to (9) show that the imprecision of the model parameters \(T_{j}\), \(D\) and the errors in the measurement of \(P_{e}\) and \(P_{m}\) will lead to the imprecision of the model. The obtained value of \(T_{j}\) is generally accurate. In general, the value of \(D\) is very small, indicating the influence of mechanical friction and wind resistance on the operation of generators. Therefore, the measurement errors of \(P_{e}\) and \(P_{m}\) are the main components of generator process noise. Actually, it is difficult to obtain precise \(P_{m}\) value, assuming that \(P_{m}\) value is constant when there is only governor but no shut-off and fast shut-off valve; the change of \(P_{m}\) caused by governor action is treated as the process noise of the system.  
According to [16; 17], the measurement error is generally between 1% and 2%. Considering the influence, the process noise variance matrix is set as  
\[Q=diag(0,0.0004P_{e0}+0.0001) \tag{10}\]  
where \(P_{e0}\) is the standard unit of output electromagnetic power in steady state.  
The measurement errors of \(\delta\) and \(\omega\) are mainly caused by PMU measurement errors. Ideally, the measurement error variance of \(\delta\) is \(2^{2}\), \(\omega\) is \(10^{-6}\), and the measurement noise variance matrix of the system is set as  
\[R=diag(2^{2},10^{-6}) \tag{11}\]
## 4 Temperature Finite Element Model and Test Comparison
### Distribution of Temperature Sensors  
To validate the finite element model of the U-shaped girder bridge, 55 temperature sensors were installed in the midspan cross section, uniformly distributed across the surfaces and interiors of both side webs and the bottom slab. Specifically, 31 temperature sensors were arranged inside, while 24 sensors were positioned on the surface. The top of the webs is affected by daytime sunlight, resulting in significant temperature gradient changes. Therefore, two rows of temperature sensors were arranged longitudinally at the top of the left and right webs, with three in each line and 3 cm between sensors (internal sensors WD1-WD6, WD26-WD31; and surface sensors BT1-BT2, BT23-BT24). The temperature difference in the bottom slab of the U-shaped girder bridges is also very significant, and hence both internal and surface temperature sensors were installed on the bottom slab, namely WD14-WD18 and BT10-BT15, respectively. In addition, these temperature data were collected every 10 min, allowing for 24-h continuous monitoring, with automatic data storage and transmission. The specific arrangement of the temperature sensors is shown in Figure 10.
# A High Resolution Vernier Digital-to-Time Converter Implemented with 65 nm FPGA
## 4 Discussion  
The comparison of the proposed DTC and previous DTCs is shown in Table 3. The resolutions of the proposed DTC implemented with 90 nm FPGA and 65 nm FPGA are measured by increasing 992 and 1000 counts, respectively. The measured INL with an input value equal to 2000 is about 3.5 LSBs on Stratix III FPGA. Obviously, the proposed 3D Vernier DTC implemented on Stratix III FPGA DTC has a much higher resolution than all the previous works. With adding an extra dimension, the resolution of the proposed 3D Vernier DTC can be improved by almost two orders of magnitude implemented with the same FPGAs. The dynamic range of proposed DTC can be easily extended by a greater input bit width. Although the nonlinearity of proposed DTC is a little worse than previous FPGA-DTCs with quantization of LSB, the proposed DTC has almost the best absolute nonlinearity quantized by seconds. It is because the proposed 3D Vernier DTC is relying on PLLs to synthesize delays, instead of delay lines or logic gates. Moreover, the proposed DTC can also obtain good resolution implemented with Cyclone IV E FPGAs, which are almost the lowest cost and lowest power consumption FPGAs  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cyclone IV E DTC** & **Stratix III** \\ \hline Process & 90 nm & 65 nm \\ Input Frequency & 50 MHz & 50 MHz \\ Output Fast Frequency & 160 MHz & 640 MHz \\ Output Medium Frequency & 155 MHz & 635 MHz \\ Output Slow Frequency & 150 MHz & 630 MHz \\ Input Bits & 32 & 48 \\ Theoretical Resolution & 13.44 ps & 195 fs \\ PLLs & 3 & 3 \\ ALUTs & 344 & 448 \\ Dedicated Logic Registers & 201 & 237 \\ Power Consumption & 160 mW & 210 mW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications for Cyclone IV and Stratix III FPGA DTCs.  
Figure 12: The post-simulated INL of the proposed DTC implemented with Stratix III FPGA.  
during all series FPGAs nowadays. Therefore, the proposed FPGA-DTC can be widely used in many applications whether it needs low cost or high resolution.
## 4 Numerical Modeling
### PLAXIS3D Model
The meaning of the above parameters is the following: \(E^{ref}_{50}\) and \(E^{ref}_{od}\) are secant stiffness in standard drained triaxial test and tangent stiffness for primary oedometer loading; \(E^{ref}_{ur}\) and \(E^{\prime}\) are the unloading/reloading stiffness from drained triaxial test and the Young Modulus; \(G^{\prime}_{0}\) and \(\gamma_{0.7}\) are the shear modulus at very small strains and the threshold shear strain at which \(G^{\prime}_{sec}=0.722G^{ref}_{0}\); and, finally, \(v^{\prime}\) is the soil Poisson coefficient [40]. A set of numerical analyses was performed with PLAXIS-3D, to model the system response when considering the SSI, both in the case of free vibrations and when subjected to an earthquake (Section 3). First, the free vibration of the structure, after the application of a 1000 kN at the top of the pier, was evaluated, to numerically assess the structural period at small oscillations. Secondly, the seismic inputs described in Section 3 were applied at the bottom boundary of the FE model (with different F\({}_{a}\) = 1.5, 1.0, 0.75, and 0.4). Results are shown in the following Section 5. Figure 6: Three-dimensional view of the model, with the evidence of the caisson, modeled pier, and applied loads.