esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Validations of ADRC Combined with Improved MPC  
As shown in Figure 23, when the load power increases from 80 W to 150 W, and the PI double closed-loop control strategy is utilized, the unbalanced power appears. At this moment, the supercapacitor current rapidly increases, suppresses the high-frequency power deficit, and then returns to its original state. The currents of batteries increase slowly to satisfy the increased power demand, and the transient time of HESS is 14 ms. The DC bus voltage amplitude decreases from 64 V to 62.5 V, the voltage drops by 1.5 V, and the output currents of Battery 1 and Battery 2 increase by 0.5 A and 0.7 A, respectively.  
In Figure 24, When the load power decreases from 150 W to 80 W, and the PI double closed-loop control strategy is used, the supercapacitor current amplitude rapidly increases and then restores to 0. The battery currents decrease slowly, and the smooth transient time is 13.2 ms. The DC bus voltage amplitude increases from 64 V to 65.4 V, the voltage increases by 1.4 V, and the output currents of Battery 1 and Battery 2 are simultaneously decreased by 1 A.  
In Figure 25, When the load power increases from 80 W to 150 W and ADRC combined with improved MPC is adopted, the battery currents decrease rapidly, and the smooth transient time is 8.8 ms, the DC bus voltage magnitude decreases from 64 V to 63.6 V, the voltage decreases by 0.4 V, and the output currents of Battery 1 and Battery 2 increase by 0.4 A and 0.6 A, respectively.  
In Figure 26, When the load power decreases from 150 W to 80 W and ADRC combined with improved MPC is utilized, the battery currents decrease rapidly, and the smooth transient time is 9.2 ms, the DC bus voltage amplitude increases from 64 V to 64.6 V, the voltage increases by 0.6 V, and the output currents of the battery 1 and the battery 2 are decreased by 0.5 A and 0.8 A.  
Figure 25: The DC bus voltage, the load power increase, the battery currents, and the supercapacitor current of HESS when ADRC combined with improved MPC used with voltage compensation. The non-English word “3745” in the figure means “average”.  
Compared to Figures 23 and 25, the voltage step decreases from 1.5 V to 0.4 V; the two battery current steps decrease by 0.1 A, and the system regulation time is reduced from 14 ms to 8.8 ms, and compared to Figures 24 and 26, the voltage step decreases from 1.4 V to 0.6 V; the two battery current steps decrease by 0.5 A and 0.2 A, respectively, and the system regulation time is reduced from 13.2 ms to 9.2 ms.  
Based on Figures 23-26, it is obvious that using ADRC combined with improved MPC increases the HESS discharging and charging response speed and reduces overshoots of DC bus voltage, compared with PI double closed-loop control.  
In summary, experimental results verify the effectiveness of the voltage compensation and the superiority of the proposed ADRC combined with improved MPC.  
Both experimental and simulation results show that ADRC, combined with an improved MPC control strategy, could achieve better bus voltage and accelerate the response speed of HESS.
# Assessment of Road Noise Pollution in Urban Residential Areas--A Case Study in Pitesti, Romania
## 3 Results
### Noise Indices Determination at Road Intersections in the Central Area of Pitesti
#### 3.2.2 The Podul Vilor Roundabout Intersection (Calea Bascovului Street-Calea Bucuresti Street)  
The measurements were made on a working day, around 4.30 pm in the Podul Vilor roundabout intersection, as shown in Figure 7.  
Proceeding in the same way as at the previous intersection, the LAF values distribution histogram was obtained (Figure 8).  
The three LAF percentiles and the extreme values are identified: Lmax = 84.8 dB; L10 = 70.4 dB; L50 = 66.4 dB; L90 = 64.0 dB; Lmin = 60.8 dB. Based on these, the values of the physical and psychophysical indices of the traffic road noise were determined and the psychophysical index, \(L_{eq}\) [dB] was calculated automatically, its value being: LAeq = 68.4 dB.  
Figure 8: Instantaneous noise level histogram (LAF)–Podul Vilor.  
Figure 7: Noise measurement at the Podul Vilor roundabout intersection: location of the sound level meter in the immediate vicinity of the traffic flow.  
3.2.3. The Rectorship Roundabout Intersection (Targul Din Vale Street-Gheorghe  
Sincai Street)  
The measurements were made on a working day, around 4.30 pm in the Rectorship roundabout intersection, as shown in Figure 9.  
The LAF values distribution histogram was made (Figure 10). In this case, too, a specific image is observed for the normal distribution, but also noise level values that exceed the normal distribution in the field of higher values.  
The three LAF percentiles and the extreme values are identified: Lmax = 93.7 dB; L10 = 69.3 dB; L50 = 66.0 dB; L90 = 63.1 dB; Lmin = 60.9 dB. The value of psychophysical index, \(L_{eq}\) [dB] was calculated automatically: LAeq = 68.1 dB.  
Figure 10. Instantaneous noise level histogram (LAF)–Rectorship roundabout intersection.
## 2 Continuous Beam Steering MS Reflector Concept
By supplying the accumulated phase difference \(\Delta\varphi_{x}\) from left to right throughout the surface, between adjacent unit cells \(X_{j}\) and \(X_{j+1}\) in the x-axis, each reflected ray marked in dashed green arrows gets additional accumulated \(\Delta L\). Thus, it leads to the desired steering angle \(\theta\) in the \(XZ\) plane, as shown in \[Z_{S}=\frac{1}{1/R+j(\omega C-1/\omega L)} \tag{1}\]1 Using Equations (6) and (7), the connection between \(\Delta\varphi\) and \(\Delta X\) to the desired angle steering \(\theta\) is shown in Equation (8):  
\[Z_{S}=\frac{1}{1/R+j(\omega C-1/\omega L)} \tag{1}\]2  
The 2-D periodic structures can be simulated in three-dimensional electromagnetic simulation software, such as HFSS and CST [39]. Due to their importance, there are dedicated simulation solvers that simplify the process and save time. In addition, these solvers enable extracting the surface reflection properties and dispersion diagram from a single unit cell simulation [40]. To illustrate the resolution limit of digital coding metasurface, using the conceptual surface of 16 columns composed of 5 mm unit cells, with 2-bit coding, at an operating frequency of 10 GHz, and a planned beam-steering to \(\theta=1^{\circ}\) in \(XZ\) plane, as defined in Figure 2, is demonstrated. The four-phase state's phase range definition, the desired reflection phase-gradient, and the distribution of the final state are shown in \[Z_{S}=\frac{1}{1/R+j(\omega C-1/\omega L)} \tag{1}\]3  
Figure 3 shows the phase value required for the desired beam steering for each unit cell. According to Equation (8), \(\Delta\phi=1.05^{\circ}\), and the cumulative phase difference is \(1.05\times 15=15^{\circ}\), which is less than \(90^{\circ}\). Since all phase values are between 0 and \(90^{\circ}\), the entire unit cell surface is set to state '1,' and there is no manipulation of the beam reflection direction. For beam steering of \(\theta=5^{\circ}\), where \(\Delta\phi=5.23^{\circ}\) and the cumulative phase difference is \(5.23^{\circ}\times 15=78.45^{\circ}\), there is still no change in the unit cell state. Adding a constant phase to the entire MS that does not affect the phase gradient [13] leads to a maximum of two-phase states for use, as shown in \[Z_{S}=\frac{1}{1/R+j(\omega C-1/\omega L)} \tag{1}\]4  
As shown in Figure 4, the eight unit cells on the left are defined as phase state '1,' and the eight unit cells on the right are defined as phase state '2.' A change in the constant phase value leads to a change in the unit cells states for the same planed phase gradient; therefore, there is no single-valued par unit cell state for a given phase gradient. This  
\[Z_{S}=\frac{1}{1/R+j(\omega C-1/\omega L)} \tag{1}\]5 MS phase and state distribution using a constant phase. \[Z_{S}=\frac{1}{1/R+j(\omega C-1/\omega L)} \tag{1}\]6 MS phase and state distribution. inherent resolution difficulty of the digital coding of an MS reflector can be solved using unit cells, which provide a continuous reflection phase. The phase gradient is applied directly to the unit cells, as shown in Figure 4, line 2.
## 4 Prediction of Radiated Sound Power based on the DRSMs
### Prediction of Sound Radiation over the Entire Frequency Band  
According to Equation (12), the total radiated sound power of the cylindrical shell can be approximately calculated by superposing the radiated sound power of the DRSMs. The radiated sound power of each DRSM was obtained previously, and they are superimposed to obtain the final prediction result of the total radiated sound power. The prediction result is compared with  
Figure 8: Comparison of the radiated sound power calculated by different methods and the number of measuring points required by different methods (**a**) dominantly radiated structural mode (DRSM) (3,1), (**b**) DRSM (2,1), (**c**) DRSM (1,1).  
the actual radiated sound power of the same model calculated by the FEM, as shown in Figure 9. The radiation prediction of the DRSM (0,1) uses 220 (10 \(\times\) 22) measuring points, the DRSM (3,1) uses two measuring points, the DRSM (2,1) uses eight measuring points, and the DRSM (1,1) uses 20 measuring points. The prediction curve of the radiated sound power is in good agreement with the actual curve. In Figure 9, compared with the traditional BEM, the DRSM method can effectively reduce the number of required measuring points.
## 5 Numerical Validation and Discussion  
To compare our proposed master-slave optimization approach based on the hybridization of the NMA with the successive approximation power flow method, we employed the GAMS software and the solver BONMIN to resolve the exact MINLP model (1)-(11) and a discrete-continuous version of the Chu and Beasley genetic algorithm (i.e., DCCBGA). The implementation of our proposed methods was in the MATLAB programming environment using own scripts. In the case of MATLAB implementations, its 2021\(b\) version was used on a PC with an AMD Ryzen 7 3700 2.3 GHz processor and 16.0 GB RAM, running on a 64-bit version of Microsoft Windows 10 single language. It is worth mentioning that to confirm that the GAMS and MATLAB models were complete equivalents, the benchmark cases were evaluated in both software, which provides the same operative costs when no PV sources are installed.  
Note that Figure 5 illustrates the general implementation of the proposed NMA combined with the successive approximation power flow method in the MATLAB programming environment.  
In the general algorithm implementation presented in Figure 5, it is worth mentioning that all the system data are previously charged with the information of the test feeders as well as the generation and demand curves since these are input data that are required in the power flow solution and the algorithm evolution though the solution space (as can be seen in flow diagram in Figure 1).
## 1 Introduction
### Related Work
To conclude, though these works use sparse measurement matrices similar to what we do, they only consider energy efficiency while choosing appropriate routs without considering the sensing quality. Furthermore, they do not consider the optimization of clustering. In this work, we introduce a two-tier PSO protocol for both clustering and routing. Specifically, our contributions are listed as below. 1. In the first tier, a PSO-based clustering protocol is proposed to find appropriate CHs with the comprehensive consideration of energy consumption efficiency and CH distribution uniformity. 2. In the second tier, a PSO-based routing protocol is introduced to find appropriate routs. A routing path consists of inner-cluster one-hop routing and outer-cluster multi-hop routing and each path corresponds to one cluster. Hence the number of paths equals to the number of clusters. It is worth mention that the multi-hop routing in our scheme is not restricted to consisting of CHs only. Note that these paths correspond to the rows of the measurement matrix for CS. In this way, we implement the combination of routing and nodes selection based on CS. Hence, the fitness function of PSO-based routing protocol comprises of several factors, including the criterions for energy efficiency, network balance, and data recovery qualities. To measure network balance, we divide the nodes in the 3-D model into different layers in accordance with their horizontal distance to the sink node. As we adopt Bayesian CS (BCS), Bayesian Cramer-Rao Bound (BCRB) is added in the fitness function to reflect the recovery performance. 3. With optimization in CH and routing chosen progress, the network could survive longer with relatively lower measurement error as demonstrated by the simulation results. What's more, taking energy-balancing into consideration contributes to form a more balanced energy-consuming network every round cycle, and also prolongs the network lifetime and reduces the sensing error. The remainder of this paper is organized as follows. In Section 2 we present preliminary knowledge. In Section 3, we introduce our system model for UWSN scenario. The detailed descriptions of the clustering and routing algorithms based on CS and PSO are given in Section 4. The simulation results are presented in Section 5. Finally, Section 6 concludes the paper, and introduces the future work. The notation used in this paper is according to the convention. Symbols for matrices and vectors are in boldface.
## 3 Results  
Figure 5 illustrates the results from stationary tests for the first segment of EMG signals with 100, 200, 500, and 1000 ms window sizes. The red and blue curves representing the measured value, \(A\), were obtained from RA and MRA tests, respectively. As observed, both red and blue curves laidbetween the upper and lower boundaries for 100 and 200 ms window sizes. Meanwhile, the curve of \(A\) was slightly outside the upper and lower boundaries for window sizes 500 and 1000 on both tests. Figure 6 presents the average percentage the stationary EMG signals for all subjects. If the threshold was set at 80% of stationary, a suitable window size should be 100 ms and 200 ms based on both tests. Using the RA test, the percentage of EMG signals determined to be stationary within 100 ms, 500 ms, and 1000 ms window sizes was recorded to be 88.21%, 50%, and 35.71%, respectively. The percentage of stationary EMG signals within 100 ms, 500 ms, and 1000 ms window sizes in the MRA test were 87.86%, 51.89%, and 39.29%, respectively. The 200 ms window size yielded similar results in both tests, with 88.57% being the highest value recorded.  
Figure 5: The result of stationarity tests with **(a)** 100 ms; **(b)** 200 ms; **(c)** 500 ms; and **(d)** 1000 ms window size. RA: reverse arrangement; MRA: modified reverse arrangement.  
Figure 6: Average percentage of stationary EMG signals obtained from RA and MRA tests at various windows size conditions.
## 4 Stochastic Differential Power Control Game
### Optimal Control Policy  
The utility function defined above depends on SINR performance as well as the value of the transmitting power selected by each MTC node. In the proposed design, each MTC device devises an optimal power control policy \(C_{k}{}^{*}(t)\) where \(\mathsf{t}\in[\mathrm{T}_{\mathrm{i}},\mathrm{T}_{\mathrm{f}}]\). A generic optimal control strategy and value function is usually defined as:  
\[C_{k}{}^{*}(t)=\underset{p_{k}(t)}{\mathrm{arg}min}\mathbb{E}\Bigg{[}\int_{T_ {i}}^{T_{f}}\theta_{k}(t)\;dt+\;\theta_{k}(T_{f})\Bigg{]}, \tag{21}\]  
\[V_{k}(t,\;S_{k}(t))=\underset{p_{k}(t)}{min}\mathbb{E}\Bigg{[}\int_{T_{i}}^{T _{f}}\theta_{k}(t)dt+\;\theta_{k}(T_{f},S_{k}(t))\;\Bigg{]}, \tag{22}\]  
where \({}_{k}(T_{f})\) and \({}_{k}(T_{f},S_{k}(t))\) are the values for the utility and value functions at the final state \(T_{f}\). Given the information about the optimal control problem, NE can exist for the above problem as shown later in this section. A power control profile \(C_{k}{}^{*}(t)=\;p_{k}{}^{*}(t)\), \(\forall\in\mathrm{K}\) is the Nash equilibrium of \(\bigcirc\)if and only if [25]:  
\[C_{k}{}^{*}(t)=\underset{p_{k}(t)}{\mathrm{arg}min}\mathbb{E}\Bigg{[}\int_{T_ {i}}^{T_{f}}\theta_{k}(p_{k}(t),p_{-k}{}^{*}(t))\;dt+\;\theta_{k}(T_{f})\Bigg{]}. \tag{23}\]  
subject to:  
\[\begin{array}{l}S_{k}(t)=[\;E_{k}(t),\;I_{k}(t)],\\ dE_{k}(t)=\;-p_{k}(t)dt,\\ dl_{k}(t)=\;\frac{\partial}{\partial t}I_{k}(t).\end{array}\]  
where \(p_{-k}{}^{*}(t)\) is the transmit power vector of all MTC devices except the \(k_{th}\) device. The optimal control policy ensures that no player can lower the utility further by adopting a power control policy other than the optimal policy.
# Dynamic Controllability of Processes without Surprises
## 6 Transforming Sudden Start into Sudden Termination
In particular, since \(wait_{S}.d_{min}=k(S)\leq wait_{S}.d\leq wait_{S};d_{max}\), we can say that \(\forall wait_{S}.e,w:wait_{S}.e-w\geq wait_{S}.d_{min},\ \forall C.s\ \exists C.d_{min}\leq C.d\leq C.d_{max}:\ \
eg\Phi^{\prime}_{P}\). In other words, \(\forall wait_{S}.s,C.s\ \forall wait_{S}.d_{min}\leq wait_{S}.d\leq wait_{S}.d_{max}\ \exists C.d_{min}\leq C.d\leq C.d_{max}: \ \
eg\Phi_{P^{\prime}}\) that is \(ST_{wait_{S},C}\) in \(P^{\prime}\), which corresponds to a sudden termination pattern for \(wait_{S}\) in \(P^{\prime}\) (\(ST_{wait_{S},C}\)). From Theorem 13 it follows that, if the waiting task in a transformed process \(P^{\prime}\) is not in a sudden termination pattern, then the associated activity is not in a sudden start pattern in the original process \(P\):  
**Corollary 1** (Absence of Sudden Start). : _Let \(P\) be a process model; let \(S,C\) be activities in \(P\) such that \(SSP_{S,C}\). Let \(P^{\prime}\) be the process model obtained by applying the transformation of Definition 13 to \(P\), with \(wait_{S}\) the waiting task associated to \(S\). Then \(\
eg STP_{wait_{S},C}\) in \(P^{\prime}\Rightarrow\
eg SSP(S,C)\) in \(P\)._  
Proof. : Follows directly from the proof of Theorem 13. Figure 10. Process model resulting from the transformation of the process of Figure 3.  
From Corollary 1 we conclude that for treating a process with sudden start, we can transform the process with the transformation of Definition 13, and apply sudden termination avoidance techniques to the new process model. Given a process \(P\), if the process \(P^{\prime}\) obtained with the transformation of Definition 13 is semi-dynamically controllable, then no activity in the process is required, at run time, to suddenly start or suddenly terminate. Thus, we only need to focus on the sudden termination problems, to automatically solve also any sudden start problem.
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 5 Software and Protocol Stack
### Software Tools  
A hardware platform needs to be accompanied by a set of development tools that facilitate its use, specially in the prototyping space where adopters need to quickly startup the platform. The I3 Mote software ecosystem is built through a set of license-free tools including:  
* Toolchain: A license-free toolchain is offered through the Code Composer Studio (since version 7), an integrated development environment (IDE) based in Eclipse and open-sourced by TL. The toolchain comes with different configurations supporting the GNU ARM GCC compiler and an optimized version referred as TI ARM Compiler. The IDE provides debugging capabilities through different JTAG/SWD devices and supports all TI embedded microcontrollers, in particular the CC26 XX and the MSP43X families.
* Real Time Operating System (RTOS): the provided tools include the TI-RTOS, a real-time operating system specially tailored to TI low-power microcontrollers. TI-RTOS provides task abstractions and a priority-based preemptive scheduler which enables real-time operation and ensures extremely low-power operation of the platform, thanks to its tight integration with the TI device abstraction layers. In addition, I3 Mote has been ported to other real time operating systems such as FreeRTOS and non-preemptive operating systems such as Contiki (only the CC2650).
* Gateway: The mesh network built using the TI 6 TiSCH stack is rooted at a gateway device, acting as the 6 LoWPAN Low Power Border Router (6 LBR) of the network. Its functionalities range from 6 LoWPAN to IPv6 packet inflation to the possibility to run a centralized scheduling mechanism forthe network. The gateway software runs on a BeagleBone Black [14] device attached to an I3 Mote that acts as the L2 network interface of the mesh network. The I3 Mote uses a serial interface to bridge packets to the 6 LBR. From the 6 LBR IPv6 packets are forwarded to the Internet.
* Backend Demo: A backend demo graphical user interface (GUI) developed with Node.js is also part of the open tools provided with the board. This back-end software can be executed in any cloud instance or server and used to monitor the status of the nodes in the network as well as to gather sensor information through IETF CoAP [15].
## 5 Discussion of Measurement Results: Model of Charged Defects Motion Near a Waveguide  
The following facts were established during temperature tests on a large number of samples of Y-splitters with an active pyroelectric effect:  
1. The disappearance of the waveguide is observed only when the sample is heated.
2. The output power varies differently on the two arms of the modulator.
3. These phenomena disappear when the edges of the sample are closed with a conductive paste.
4. The observed power output does not correspond to a complete laser shutdown, but to the input of radiation into the crystal without a waveguide.
5. In some samples, accompanying phenomena were observed without the disappearance of the channel. These samples differed from other samples by the width of their waveguides and the mode of their creation.
6. When the temperature is stabilized, the power in the arms of the Y-splitter is restored to the initial values.  
The given pieces of evidence are interpreted from the point of view of the model of a channel waveguide surrounded by mobile charged defects. Let us consider experimental facts in detail.
## 2 The Mechanism of the HES
### The Expected Failure Mode of the HES  
The low-yield-point steel plates (LY 100) the yield stress of which is 100 MPa, are utilized in the ED zone. In order to ensure the bearing capacity and shear stiffness of the SL zone, Q345B is utilized the yield stress of which is 345 MPa. The threshold control displacement should be reasonably established to ensure that the ED zone and the SL zone could perform well in sequence. Furthermore, the threshold displacement could be adjusted according to the requirement of the expected structural performance, by changing the hole diameter in the shear stiffness lifting plate. In addition, the shear stiffness and strength of the shear stiffness lifting plate could be effectively enhanced by adding the flange plate on both sides. Simultaneously, the functional bolts are designed to come to failure in the second stage and the other parts of the SL zone are in elastic. When the ED zone and the functional bolts are damaged in the earthquake, they could be fast and easily replaced, largely enhancing the structural resilient performance. Therefore, the ED zone is expected to dissipate the input seismic energy firstly and the functional bolts are expected to fail with high ductility.  
In order to clarify the mechanical mechanism of the HES, simplified models are used in finite element modelling analysis. The lateral connection of the HES to the wall is considered to be rigid. The bolted connection of the steel plates in the ED zone is also considered to be a rigid connection. The threshold control displacement of the typical specimen of HES is set to be 3 mm. On the basis of the simplification, this expected failure mode would be validated by numerical analysis.