esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Thermodynamic Model  
Energy calculations were performed to impart the systems that provide the minimum electricity production cost considering the first law of thermodynamics. First, FG density and specific heat values are calculated using EES according to FG elemental content and fractions.  
Energy and mass balance equations are provided as in Equations (1) and (2) [17]:  
\[\sum\dot{m}_{\rm in}=\sum\dot{m}_{\rm out} \tag{1}\]  
\[\dot{Q}+\dot{W}=\sum\dot{m}_{\rm out}h_{\rm out}-\sum\dot{m}_{\rm in}h_{\rm in} \tag{2}\]  
\(\dot{Q}\) and \(\dot{W}\) refer to heat transfer rate and work leaving control volume [18], respectively. \(\dot{m}\) refers to mass flowrates of the flows, and the indices stand for the inlet and the outlets.  
\[\dot{W}_{\rm net}=\dot{W}_{\rm t}-\dot{W}_{\rm p} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Outlet temperature & 1093.15 & K \\ Volumetric flow rate & 40,000 & m\({}^{3}\)/h \\ Density & 0.3027 & kg/m\({}^{3}\) \\ Specific heat & 1.382 & kJ/kgK \\ Pressure & 1 & atm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flue gas thermophysical properties.  
Figure 1: General layout of the cascaded WHRS by the numbers of stations from 1–24 for the cycle components and by the letters from a–d for the stations of flue gas.  
where \(\dot{\mathrm{W}}_{\mathrm{net}}\) stands for each system's net power, \(\dot{\mathrm{W}}_{\mathrm{t}}\) indicates output power of turbine, and \(\dot{\mathrm{W}}_{\mathrm{P}}\) is the required pump power.  
The thermal efficiency is calculated using Equation (4) [19]:  
\[\eta_{\mathrm{th}}=\frac{\dot{\mathrm{W}}_{\mathrm{net}}}{\dot{\mathrm{Q}}_{ \mathrm{in}}} \tag{4}\]  
Turbine efficiency of SRC, ORC, and CO\({}_{2}\) is calculated by [11]:  
\[\eta_{\mathrm{th,src}}=\frac{\mathrm{h}_{3}-\mathrm{h}_{\mathrm{4a}}}{\mathrm{ h}_{3}-\mathrm{h}_{\mathrm{4s}}} \tag{5}\]  
Here, h indicates the enthalpy values of the flows, and subscript a indicates the actual values. Turbine efficiency of ORC is calculated by  
\[\eta_{\mathrm{th,orc}}=\frac{\mathrm{h}_{7}-\mathrm{h}_{\mathrm{8a}}}{\mathrm{ h}_{7}-\mathrm{h}_{\mathrm{8s}}} \tag{6}\]  
Turbine efficiency of CO\({}_{2}\) is calculated by  
\[\eta_{\mathrm{th,CO_{2}}}=\frac{\mathrm{h}_{11}-\mathrm{h}_{12a}}{\mathrm{h}_ {11}-\mathrm{h}_{12s}} \tag{7}\]  
where \(\dot{\mathrm{Q}}_{\mathrm{in}}\) is rate of thermal energy provided by FG. Table 2 represents energy balance equations for the components of each system.
# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
###### Abstract  
As a typical cyber-physical system, networked unmanned aerial vehicles (UAVs) have received much attention in recent years. Emerging communication technologies and high-performance control methods enable networked UAVs to operate as aerial sensor networks to collect more complete and consistent information with significantly improved mobility and flexibility than traditional sensing platforms. One of the main applications of networked UAVs is surveillance and monitoring, which constitute essential components of a well-functioning public safety system and many industrial applications. Although the existing literature on surveillance and monitoring UAVs is extensive, a comprehensive survey on this topic is lacking. This article classifies publications on networked UAVs for surveillance and monitoring using the targets of interest and analyzes several typical problems on this topic, including the control, navigation, and deployment optimization of UAVs. The related research gaps and future directions are also presented.  
c cyber-physical systems; unmanned aerial vehicles (UAVs); drones; autonomous systems; navigation; deployment; control; surveillance; monitoring; wildlife monitoring +
Footnote †: journal:  
using a combination of various types of UAVs of different sizes and configurations can be used to conduct complex tasks. As a type of sensor-based, communication-enabled autonomous system, networked surveillance UAVs can be considered a typical cyber-physical system. In recent years, networked UAVs have become one of the most important topics in UAV research [9]. In particular, a large and growing body of literature has investigated UAVs for surveillance and monitoring tasks and has presented a variety of technologies and methods. Performing a search using keywords such as \"UAVs\", \"Drones\", \"Surveillance\", and \"Monitoring\" on the Web of Science Core Collection, we see that the research related to surveillance UAVs has increased rapidly since 2011, as indicated in Figure 2.
###### Abstract  
Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.  
software-defined wireless sensor network; intelligent routing control; deep reinforcement learning; graph convolutional network +
Footnote †: journal: Applied  
Article  
Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks
## 3 Simulated and Measured Results
### Electro-Mechanical Behavior  
As reported in Figure 3a, the simulated pull-in voltages of _Dev1_, _Dev2_, and _Dev3_ correspond to 7.75 V, 6.5 V, and 4.25 V, respectively. The measurements represented by Figure 3b were performed using a manual probe station equipped with micromanipulators and tips connected to two digital multimeters. The first multimeter, a Keithley 2430, provided the DC bias voltage and the ground reference to pads of the device under test (DUT), while the second one, a Keysight B2902A, maintained a limited voltage drop between the input and the output ends of the central RF signal line in order to measure the resistance of the line and the amount of flowing DC current. In fact, when the DC bias reaches the pull-in voltage, the collapse of the membrane will cause a sudden drop in the resistance (increase in DC current) measured along the RF line.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value [\(\upmu\)m]** & **Parameter** & **Value [\(\upmu\)m]** \\ \hline a & 50 & e & 25 \\ \hline b & 25 & g & 30 \\ \hline c & 35 & h & 55 \\ \hline m & 380 & o & 780 \\ \hline n & 170 & p & 270 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensional parameters of _Dev1_, _Dev2_, and _Dev3_ switches.  
Figure 2: (**a**) Description of beams employed in _Dev2_ switch in their main dimensional parameters. (**b**) Beams employed in _Dev3_ switch in their main dimensional parameters.  
Under the premise that the DC bias has been provided by 1 V steps, the values displayed in Figure 3b show a good agreement between the simulated and measured performances. In fact, while the measured pull-in voltages of _Dev3_ and _Dev1_ (9 V and 7 V) are slightly higher than the simulated ones (8 V and 5 V, respectively), the measured actuation of _Dev2_ coincides with the expected one. The limited mismatch characterizing _Dev3_ and _Dev1_ switches can be attributed to the increased stiffness of their movable membranes due to the residual tensile stress induced during the fabrication process. In fact, the initial measurements displayed pull-in voltages exceeding the ones in Figure 3b by 10-15 V. Most of the initial residual stress was released by means of a re-baking thermal treatment, during which the devices were kept at 200 \({}^{\circ}\)C for a couple of hours and then slowly cooled. The reported measurements were performed after.
## 1 Introduction  
In order to capture gentle tectonic strain caused by volcanic activity [1], fault failure process [2; 3; 4; 5; 6; 7; 8; 9], etc., high-sensitivity borehole tensor strainmeters, such as RZB strainmeter [5], YRY strainmeter [10], and GTSM (Gladwin Tensor Strainmeter) strainmeter [11; 12], have been developed and installed in Plate Boundary Observatory (PBO) [13; 14; 15; 16; 17] and the China Borehole Strainmeter Network [18; 19]. The expansive grout cements the strainmeter to the borehole, which changes the strain that is in the rock to the diameter change of the stainless steel instrument casing. These diameter changes measured by four gauges of different orientation give the instrumental strains. The coupled parameters between the strain in the rock and instrumental strains must be calibrated in situ, which is usually done by comparison between instrumental strains and the calculated strain from theoretical earth tide [14; 16]. Theoretical earth tide, though, is influenced by the deep structure of the Earth, the model of the ocean load both near and far from the coast, and topographic and geologic effects, which will induce the evident error in the calibration [14; 17; 20; 21]. In order to increase the precision of in situ calibration for borehole tensor strainmeters, the combination of measured particle velocity from collocated borehole seismometers and local phase velocity yields the referenced strain-wave signal for high-precision, in situ calibration of borehole tensor strainmeters [8; 22; 23; 24; 25; 26]. The dynamic couple relation between the referenced strain wave from the seismometer and the measured instrumental strain wave from the borehole tensor strainmeter must be provided for this attractive in situ calibration of the borehole tensor strainmeter, which has not been investigated yet. [27]  
The scattering model of incident seismic waves by an empty borehole has been presented to investigate the theoretical frequency response and the corresponding bandwidth of borehole tensor strainmeters [27]. Inner-diameter changes of the stainless steel instrument casing are measured by horizontal extensometers (gauges), after the borehole tensor strainmeter is installed at a certain depth of the borehole and boned by expansive grout with the surrounding rocks. There is, however, no answer for the difference between this case and the empty case. Concerning the uncontrollability of the mechanical parameters of the expansive grout, it is necessary to discuss the influence of these mechanical parameters of the expansive grout in guaranteeing accuracy in determining bandwidth.  
In this paper, the model of the incident elastic P/S waves scattering by a two-ring borehole is introduced to investigate the theoretical frequency response between the referenced strain wave and the measured instrumental strain wave, and the corresponding bandwidth of borehole tensor strainmeters for measuring strain waves quantitatively. The influence of the mechanical parameters of the expansive grout on the frequency response and bandwidth of borehole tensor strainmeters for the gauge combinations are discussed in detail.
## 4 Discussion
The results indicate that, for helicopter shot animals, most of the carcasses had bullet particles displaced between 30 and 225 mm from the point of impact/entry on the carcass, whilst 17% of carcasses had bullet particles in excess of 226-337 mm from the point of entry (Figure 5b). Though different from the helicopter killings, the majority of the dispersion in rifle thorax shot carcasses was between 30 mm and 332 mm from the point of entry (Figure 5a). The results of this study confirm findings [58] that, depending on the placement shot and the type of bullet used, a radius of up to 340 mm should be examined and may need to be trimmed to remove any bullet particles from a thoracic killing shot. This could be difficult to achieve with the current meat inspection program in South Africa and Namibia, especially at game animal slaughter facilities, where the detection of metals during the secondary dressing of game animals generally depends only on meat inspection [50; 51; 52]. Trimming will not only lead to a loss of money due to the significant volume of meat that will be removed from each carcass suspected of being contaminated, but it will also reduce the general appearance and value of the carcasses. In addition, meat inspectors and slaughter operators should be trained to ensure that they are sufficiently skilled in using the available technologies that could be implemented for the purpose of identifying physical hazards during slaughter [60]. To aid meat inspectors and slaughter operators, metal detectors, near-infrared spectroscopy (NIS) and imaging X-ray equipment can be used [52; 61]. However, the use of these methods on a routine basis in abattoirs is neither practical nor economically viable, especially in rural abattoirs that are far from the main national or provincial facilities that have these technologies.
## 3 Results
### Statistical Comparison of Method Performance
#### 3.3.2 Segmentation Performance Analysis  
We compared the DSC for segmentation tasks across methodological approaches. Table 3 presents the mean DSC values for different brain structures and statistical comparison results.  
For segmentation tasks, deep learning methods demonstrated statistically significant advantages over traditional ML approaches for all brain structures, with the most pronounced difference observed for subarachnoid space segmentation (DSC difference: 0.32, \(p=0.004\)). This finding is particularly important given the critical role of subarachnoid space assessment in diagnosing DESH, a key radiological marker of NPH. To account for potential publication bias favoring positive results, we conducted a non-parametric analysis using the Mann-Whitney U test, which confirmed the significant performance advantage  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Metric** & \begin{tabular}{c} **Traditional ML Methods** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} & \
\begin{tabular}{c} **Deep Learning Methods** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} &
\begin{tabular}{c} **Hybrid Methods** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} & \(p\)**-Value (ML vs. DL)** \\ \hline Accuracy (\%) & \(85.7\pm 7.8\) & \(94.3\pm 4.2\) & \(96.8\pm 2.1\) & 0.032 * \\ \hline Sensitivity (\%) & \(82.9\pm 12.1\) & \(92.1\pm 5.3\) & \(94.6\pm 3.7\) & 0.041 * \\ \hline Specificity (\%) & \(83.4\pm 9.8\) & \(90.8\pm 7.2\) & \(97.3\pm 4.6\) & 0.046 * \\ \hline ROC-AUC & \(0.90\pm 0.06\) & \(0.95\pm 0.03\) & \(0.96\pm 0.02\) & 0.027 * \\ \hline \hline \end{tabular}  
* Statistically significant difference (\(p<0.05\)); \(p\)-values calculated using independent samples \(t\)-test.  
\end{table}
Table 2: Summary of advantages and disadvantages of AI approaches for the detection of NPH.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Brain Structure** & \begin{tabular}{c} **Traditional ML Methods** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} & \
\begin{tabular}{c} **Deep Learning Methods** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} &
\begin{tabular}{c} **Hybrid Methods** \\ **(Mean \(\pm\) SD)** \\ \end{tabular} & \(p\)**-Value (ML vs. DL)** \\ \hline Ventricles & \(0.69\pm 0.15\) & \(0.88\pm 0.09\) & \(0.89\pm 0.04\) & 0.019 * \\ \hline Gray-white matter & \(0.84\pm 0.08\) & \(0.91\pm 0.06\) & \(0.93\pm 0.03\) & 0.048 * \\ \hline Subarachnoid space & \(0.35\pm 0.14\) & \(0.67\pm 0.11\) & \(0.71\pm 0.06\) & 0.004 * \\ \hline Overall & \(0.79\pm 0.14\) & \(0.87\pm 0.08\) & \(0.90\pm 0.04\) & 0.068 \\ \hline \hline \end{tabular}  
* Statistically significant difference (\(p<0.05\)); \(p\)-values calculated using independent samples \(t\)-test.  
\end{table}
Table 3: Statistical comparison of segmentation performance (Dice similarity coefficients).  
of DL methods for ventricle segmentation (\(p\) = 0.023) and subarachnoid space segmentation (\(p\) = 0.008) but found marginal significance for gray-white matter segmentation (\(p\) = 0.061).
## 4 Engine Test Results and Discussions
### Engine Combustion Characteristics
#### 4.2.1 Effects on the Combustion of the Pre-Injected Fuel  
It is known that the ignition delay of fuel is related to its cetane number. A higher CN of fuel means a lower autoignition temperature or a shorter time of ignition delay at the same temperature. In this study, n-heptane and n-dodecane have higher CN values of 56 and 82.5; their mixtures also have higher CN. On the contrary, tetralin and decalin have lower CN values of 8.9 and 46.5, which reduces the CN of mixtures compared with diesel.  
The sampled injection strategy of the engine is shown when it runs on diesel in Table 6. In the experiments, the fuel injection strategy consisted of pre-injection and main injection. Under the full load conditions, all the fuels utilize the same injection strategy, that is, the same timing and duration, while under the partial load conditions, there are few adjustments according to the opening of the peddle to achieve the same power/torque due to the difference of the blended fuels. Of course, the injection timings and the duration of the split injection will change a little, accordingly.  
From Figure 8, it can be seen that the starts of the combustion of the preinjected fuels generally follow the CN rule; that is, the higher the fuels' CN, the earlier the start of the combustion. With increased engine load, it becomes irregular, but the differences are within 2-3 \({}^{\circ}\)CA.  
The combustion process of the pre-injected fuels cannot be simply divided into pre-mixed and diffusion, either. There is no typical dual peak heat release rate. Due to the relatively longer ignition delay, the higher injection pressure, and the higher temperature of the air in the cylinder, the combustion of the pre-injected fuels is all premixed combustion.  
The preinjected fuel combustion on the cylinder pressure shows a weak influence. For an injection duration of about 0.2 ms, there is some nonlinearity affecting the injected fuel quantity, and the total quantity is rather small.  
Figure 7: Al50 at 1900 r/min.  
Figure 8: _Cont_.  
Figure 8: The curves of in-cylinder and HRR at 1900 rpm.  
Figure 11: The cumulative heat release versus crank angles at 1900 rpm.  
Figure 10: The peak values of HRR versus engine loads.  
Figure 9: The peak values of in-cylinder pressure versus engine loads.
## 3 Dynamic Characteristics and Test Cases
### Input Ground Motions  
In this paper, two near fault (NF) ground motions, recorded at stations within 20 km of the fault, are selected as input motions. The first one is EL-Centro wave, recorded in El Centro Array #9 station with epicentral distance of 6.09 km during the Imperial Valley earthquake in 1940. The ratio of PGV/PGA is 0.11. The other motion is recorded at station TCU052 with an epicentral distance of 1.84 km during the 1999 CHICHI earthquake. The PGV/PGA ratio is 0.43.  
The algorithm proposed by [PERSON] and [PERSON] [22] can identify pulses at arbitrary orientations. The procedure has been widely used to classify ground motions in the Next Generation Attenuation-West2 database [23]. Based on the above method, TCU052 is identified by an obvious velocity pulse, while the El-Centro motion has no velocity pulse.  
During the shake table test, the PGA of both recorders are adjusted to 2.08 m/s\({}^{2}\). According to the similitude ratio of time in Table 2, the recorder motions are scaled in time domain. The adjusted El-Centro wave and TCU052 wave are renamed as ScEL and ScTCU respectively.  
Figure 9a shows the acceleration time history of two motions. Figure 9b shows the acceleration Fourier amplitudes spectrum. It can be seen that the ScEL has more energy in the frequency range of [10 Hz, 20 Hz], while the ScTCU has higher energy in the low frequency band of \(f<10\) Hz. For comparison, the identified frequencies of the first six modes are marked in Figure 9b. It is obvious that the frequencies of the first two modes are close to the predominant frequency of the ScTCU.  
Figure 9c,d show the velocity and displacement time histories of two motions. It shows the ScTCU has significant higher velocity than ScEL (2.89 cm/s). Besides, the maximum displacement value of ScTCU is much larger than ScEL, shown in Figure 9d. In conclusion, the ScTCU is stand for the PNF ground motion, while the ScEL can be seen as NNF ground motion.
# Magnonic Metamaterials for Spin-Wave Control with Inhomogeneous Dzyaloshinskii-Moriya Interactions
## 2 Analytical Model
### Magnonic Snell's Law
\(\theta_{i,t,r}\) denote their angles with respect to the interface normal. The red double-headed arrow shows the Gaussian distribution AC Magnetic field \(\mathbf{h}(t)\) exciting the spin-wave. Based on translation symmetry considerations, the refraction angle obeys the generalized Snell's law, which guarantees continuity of the tangential components of the \(\mathbf{k}\) vector across the DMI step interface along the \(\vartheta\) axis, such that \(\mathbf{k}_{i,y}=\mathbf{k}_{i,y}\)[19; 20; 37]. Consequently, the generalized magnonic Snell's law based on modifying the dispersion relation with inhomogeneous DMI can be rewritten in the following form:  
\[\mathbf{k}_{g,i}\sin\theta_{i}+\delta_{i}=\mathbf{k}_{g,t}\sin\theta_{t}+ \delta_{t}, \tag{4}\]  
where \(\mathbf{k}_{g,n}=\sqrt{\left(\omega/\gamma\mu_{0}-K^{*}\right)/A^{*}+\delta_{ n}^{2}}\) is the value of \(\mathbf{k}_{g,n}\). Here, the generalized [PERSON]'s law shown in Equation (4) is derived for an interface between two spin-wave media with different material parameters (interfacial DMI), which can be viewed as graded-index magnonic metamaterials. However, this is different from [PERSON]'s laws based on the interface inside magnetic textures, such as chiral domain walls (the interface formed by two opposite magnetic domains) [37].