esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Synthesis
### Sol-gel Method  
The sol-gel method is relatively simple, and the prepared nanomaterials have high purity and uniform particle size. Generally, the sol is prepared by uniformly mixing the sol and the carbon materials, followed by ageing to form a gel and calcination at a higher temperature. However, the main disadvantage of this method is the shape of the material is not well controlled.  
Recently, [PERSON] et al. [28] fabricated BC(coconut shell)-TiO\({}_{2}\) compound by sol-gel method. First, the coconut shell was pyrolyzed under nitrogen, followed by washing and rinsing with HCl acid. Secondly, the acid-treated biochar was ageing, washed, dried overnight, and sieved through the mesh. Then, the sol-gel process involved the following sequences. (1) Mixed ethanol with butyl titanate to form solution A; (2) solution B was obtained through mixing ethanol, deionized water, acetic acid with polyethylene glycol; (3) dropwise added solution B into solution A and aged for 24 h. Then, the as-prepared BC was mixed with the sol-gel. Finally, the above mixture was further treated by ageing, filtration, drying, and calcination to form the BCPs. The scheme of the BCPs synthetic process was illustrated in Figure 2A.  
For comparison, [PERSON] et al. [29] impregnated TiO\({}_{2}\) in the Salvinia molesta derived carbonaceous matrices via the sol-gel process and mechanical mixing route. The results showed that the type of TiO\({}_{2}\) precursor plays a much more critical role than the impregnation method on the photocatalytic  
Figure 2: Schematic image of the preparation methods of BCPs.  
efficiency of the composites. Compared with the sol-gel way, the BCPs fabricated by mechanical mixing exhibited higher photocatalytic performance.
## 5 Improved Performance of Harmonic Injection by the Phase Perturbation Method
### Islanding Decision (Section D)  
According to all previous sections of the algorithm (A-C), at this point, it is possible to assume that the impedance at the PCC has suffered a change. However, the last step of the algorithm is presented in Figure 16 to provide a more stable decision to the islanding episode. Thus, a potential true/false grid failure flag is generated.  
This final step takes the \(\delta\) value from the output of Section C and contrasts it with a \(\delta_{lim}\). The \(\delta_{lim}\) is tuned considering the perturbation frequency and the expected DFBW. When \(\delta\) exceeds \(\delta_{lim}\), a counter allows shifting in time the decision to ensure a real detection.  
Using an IM anti-islanding strategy, different from a PF one, the detection time is not directly proportional to the injected perturbation. The bigger the perturbation is, the more reliable the impedance measurement becomes, not the faster. In fact, the detection time is only fixed by the gap needed for the DFT calculation and the arranged delay by the decision.  
Figure 14: Impedance measurement diagram.  
Figure 15: Trip generation method (\(\delta\) pulse). (**a**) Generation of the \(\delta\) signal. (**b**) \(\delta\) pulse generation.
# Photon Enhanced Interaction and Entanglement in Semiconductor Position-Based Qubits
## 3 Position-Based Semiconductor Qubits in the Frame of Semiconductor Photon Communication
### Description of the System of Coupled Position-Based Qubit with a Cavity  
In this section, we will expand the system of semiconductor position-based qubit by including a Jaynes-Cummings-Hubbard formalism for the full description of the system of a coupled qubit with a cavity. We will additionally assume four energy levels, and a square double-well potential energy approximation for the potential energy profile of the double quantum dot, as they are depicted in Figure 3. All the simulations in this study were performed assuming the potential energy profile andenergy levels of Figure 3. In this case, the four bound states of the system can be expressed in the position basis \(\left\{\left|0_{g}\right\rangle\left|n\right\rangle,\left|1_{g}\right\rangle \left|n\right\rangle,\left|0_{e}\right\rangle\left|n+1\right\rangle,\left|1_{ e}\right\rangle\left|n+1\right\rangle\right\}\):  
\[\left|0_{g}\right\rangle\left|n\right\rangle=\frac{1}{\sqrt{2}} \Big{(}\left|\psi_{0}\right\rangle\left|n\right\rangle+\left|\psi_{1}\right\rangle \left|n\right\rangle\Big{)},\quad\left|1_{g}\right\rangle\left|n\right\rangle= \frac{1}{\sqrt{2}}\Big{(}\left|\psi_{0}\right\rangle\left|n\right\rangle- \left|\psi_{1}\right\rangle\left|n\right\rangle\Big{)}\] \[\left|0_{e}\right\rangle\left|n+1\right\rangle=\frac{1}{\sqrt{2}} \Big{(}\left|\psi_{2}\right\rangle\left|n+1\right\rangle+\left|\psi_{3}\right\rangle \left|n+1\right\rangle\Big{)},\quad\left|1_{e}\right\rangle\left|n+1\right\rangle =\frac{1}{\sqrt{2}}\Big{(}\left|\psi_{2}\right\rangle\left|n+1\right\rangle- \left|\psi_{3}\right\rangle\left|n+1\right\rangle\Big{)} \tag{13}\]  
where the localized \(\left|0,1\right\rangle_{g,e}\) states are expressed as a superposition of \(\left|\psi_{i}\right\rangle,i=0,\ldots,3\) eigen-states, and \(n\in\mathbb{N}\) denotes the number of photons. In this basis, the Hamiltonian of the system in spectral representation can be written as:  
\[\hat{\mathbf{H
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 3 Dedicated Short-Range Communication (DSRC)
### Multiuser Channel Access  
Multiuser channel access in a vehicular network is more challenging due to the high-speed mobility of the vehicles, the frequent topology variations, and the inevitable network resource sharing. In an IoEV in particular, channel access techniques need to guarantee high reliability, collision avoidance, and low computational complexity. The majority of the proposed channel access techniques for vehicular ad hoc networks (VANET) adopted contention-based medium access control (MAC), in which a node tries to access the channel using carrier-sense multiple access (CSMA). Collisions may occur if another node senses the channel and decides to transmit a packet at the same time [59]. To avoid this issue, CSMA with collision avoidance is recommended by IEEE 802.11p standard for DSRC physical layer [60]. In contrast, contention-free channel access is considered by assigning the channel to only one node at any given time [61] using time division multiple access (TDMA), frequency division multiple access (FDMA), and code division multiple access (CDMA). The authors in [61] claim that TDMA is the most appropriate technique compared to other contention-free protocols. Several works discussed hybrid channel access that makes use of the characteristics of the two mechanisms to improve the performance and minimize the collision rate [62; 63; 64]. A comparison between contention-based and contention-free MAC protocols is presented in Table 2.
# An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
## 3 Allocating Resources in Heterogeneous Cloud Infrastructures
### Tier 1: Node Allocation in the Cloud Using Genetic Programming
#### 3.1.2 Problem Formulation  
Variables  
Table 1 summarizes the variables used in our formulation.  
Figure 2: Mapping 32 virtual cores to eight physical cores on two NUMA domains.  
Constraints  
A virtual core must be mapped only to one single physical core:  
\[\forall i\in V,\forall j\in P\sum_{j=1}^{N_{p}}m_{ij}=1 \tag{3}\]  
The maximum utilization of a physical core is 1:  
\[\forall i\in V,\forall j\in P\sum_{j=1}^{N_{c}}u_{ij}m_{ij}\leq 1 \tag{4}\]  
Objective Function  
We consider two objective functions: one that seeks to maximize the utilization without considering penalties for mappings that do not take advantage of the NUMA architecture in an efficient way, and another that takes into account these penalties. We describe both of these functions below.  
Objective function without a penalty for NUMA mapping:  
\[f=max\forall j\in P\sum_{j=1}^{N_{p}}U_{j} \tag{5}\]  
where \(U_{j}>0\).  
Objective function using penalties for inefficient NUMA mapping:  
\[f_{p}=f+\sum_{i=o}^{z}\theta_{i}\varphi_{i} \tag{6}\]  
where \(\varphi_{i}=1\) if a penalization is considered, and \(\varphi_{i}=0\) otherwise. The value chosen for \(\theta_{i}\) depends on how critical the penalty is. We consider two penalty levels: (i) a penalty for each pair of virtual cores with a high degree of shared memory mapped to different NUMA domains, and (ii) a penalty for each pair of virtual cores with a low degree of shared memory mapped to the same NUMA domain.
## 3 Results  
### Playing Positions  
There were significant differences with moderate effect sizes between playing positions in match RPE (\(p\) < 0.01, \(\eta p^{2}\) = 0.089) (Figure 2a), fatigue (\(p\) = 0.003, \(\eta p^{2}\) = 0.114), muscle soreness (\(p\) = 0.002, \(\eta p^{2}\) = 0.121), and the Hooper index (\(p\) = 0.004, \(\eta p^{2}\) = 0.108 (Figure 3). More specifically, backs registered moderately more match RPE (6.01 \(\pm\) 2.34 a.u.) compared to pivots (4.12 \(\pm\) 2.47 a.u.) (\(p\) < 0.05, ES = 0.84). In relation to well-being responses, wings experienced moderately more fatigue in MD+1 (3.65 \(\pm\) 1.12 a.u.) compared to backs (2.77 \(\pm\) 1.45 a.u.) (\(p\) = 0.009, ES = 0.66). Also, wings registered moderately more fatigue in MD+2 (2.96 \(\pm\) 0.87 a.u.) than backs (2.03 \(\pm\) 0.99 a.u.) and pivots (2.17 \(\pm\) 1.13 a.u.) (\(p\) < 0.001, ES = 0.93; \(p\) < 0.05, ES = 0.79, respectively). Likewise, wings reported moderately more muscle soreness in MD+1 (3.15 \(\pm\) 0.96 a.u.) and MD+2 (2.96 \(\pm\) 0.95 a.u.) than backs (2.27 \(\pm\) 1.32 a.u., \(p\) = 0.004, ES = 0.75; 2.15 \(\pm\) 1.12 a.u.; \(p\) = 0.004, ES = 0.77, respectively) and pivots (2.23 \(\pm\) 0.83 a.u., \(p\) < 0.05, ES = 0.78; 2.00 \(\pm\) 0.86 a.u.; \(p\) = 0.006, ES = 0.91, respectively).  
Wings reported moderately greater Hooper index scores in MD+1 (10.34 \(\pm\) 2.66 a.u.) and MD+2 (9.00 \(\pm\) 2.28 a.u.) than backs (8.20 \(\pm\) 3.19 a.u., 7.00 \(\pm\) 2.74 a.u., respectively) (\(p\) = 0.006, ES = 0.73; \(p\) = 0.004, ES = 0.77, respectively).  
Figure 2: Differences according to playing positions and player roles on match RPE. Significance level is indicated by the number of symbols: one symbol for \(p\) < 0.05, two for \(p\) < 0.01, and three for \(p\) < 0.001.  
Additionally, there were significant differences with moderate-to-large effect sizes between match day in fatigue (\(p<0.001\), \(\eta p^{2}=0.358\)) and the Hooper index (\(p<0.001\), \(\eta p^{2}=0.254\)). Particularly, all playing positions reported moderately less fatigue and lower Hooper index scores in MD+2 compared to MD+1 (\(p<0.05\), ES = 0.66-0.84; \(p<0.001\), ES \(>1.20\), respectively). Furthermore, no significant interaction effect (playing positions vs. match day) was found in any well-being responses (\(p>0.05\)).
## 2 DPS Modeling of LEO-SIN
### Channel Model and DPS Model  
The propagation paths of signals in LEO-SIN are composed of air propagation paths and earth-surface propagation paths, which are shown in Figure 3. The air propagation paths mainly suffer large-scale fading. The path loss is the major fading. The earth-surface propagation paths mainly suffer small-scale fading. The multi-path fading is the major fading, which is caused by the signal's refractions and reflections by obstacles in the earth. Due to the high-speed movement of LEO satellites, based on the shelter by obstacles, the total fading after the arrival of the signal of the surface would periodically change with the elevation change between satellites and UEs.  
Figure 2: System model of Low Earth Orbit Satellite Internet Network (LEO-SIN).  
The received signals can be considered as one signal with the Light of Sight (LOS) superposing on \(N\) Non-light of Sight (NLOS) paths around the LOS paths. At the moment of \(t\), the received signals can be expressed as,  
\[s(t)=s_{LOS}(t)+s_{NLOS}(t)=h_{LOS}(t)x(t)+\sum_{l=1}^{N}h_{l}(t)x(t) \tag{1}\]  
where, \(s_{LOS}(t)\) denotes the LOS component of signals, \(s_{NLOS}(t)\) denotes the NLOS or multi-path component of signals, \(x(t)\) denotes transmitted signals, \(h_{LOS}(t)\) denotes channel impulse responses of LOS component, \(h_{l}(t)\) denotes the channel impulse responses \(l\)th multi-path component, and \(N\) denotes the number of the multi-path signal.  
The channel impulse response of the LOS component can be expressed as:  
\[h_{LOS}(t)=\sqrt{P_{0}(t)}\cdot e^{i2\pi f_{d}(t)\cdot t} \tag{2}\]  
where, \(P_{0}\) is the power of the LOS component, and \(f_{d}(t)\) is the value of DFO. The major \(f_{d}(t)\) is caused by the high-speed relative movement between satellites and UEs, which can be calculated according to ephemeris [22].  
The channel impulse response of multi-path component can be expressed as:  
\[h_{l}(t)=\sqrt{P_{l}(t)}\cdot e^{(-j2\pi f_{d_{l}}(t)\cdot t+\phi_{l}^{0})} \tag{3}\]  
where, \(P_{l}(t)\), \(\phi_{l}^{0}\), and \(f_{d_{l}}\) are the power, initial phase, and Doppler frequency offset of the \(l\)th multi-path signal. Here, \(P_{1}(t)=\sum_{l=1}^{N}P_{l}(t)\), \(P_{1}\) obeys Rayleigh distribution. In our assumptions for further study, \(\phi_{l}^{0}\) obeys the uniform distribution range on \([0,2\pi]\). The multi-path signals only need to consider one-time reflection by the nearby scatterers. The power of signal caused by twice or more reflections by far scatterers can be ignored [23].  
Then the ACF of the signal at time \(t\) and \(t+\tau\) can be expressed as:  
\[R(\tau)=R(t,t+\tau)=\left\langle h_{LOS}(t)(t)\cdot h_{LOS}(t)^{*}(t+\tau) \right\rangle_{t}+\left\langle h_{l}(t)\cdot h_{l}{}^{*}(t+\tau)\right\rangle_ {t}. \tag{4}\]  
DPS can be obtained by its Fourier transform,  
\[S(f_{d})=\int_{-\infty}^{\infty}R(\tau)\cdot e^{(-j2\pi\cdot f_{d_{l}}(\tau) \cdot\tau)}d\tau=S_{LOS}(f)+S_{NLOS}(f)=P_{0}\cdot\delta(f-f_{d})+\frac{P_{1} }{2\pi f_{max}\sqrt{1-\left(\frac{f}{f_{max}}\right)^{2}}}\,. \tag{5}\]  
Figure 3: Signal model and DPS model of LEO-SIN.
## 3 Problem Formulation  
### Scenario Description  
This paper studies the MCMUAVCTAP in agricultural contexts. The problem is described as follows: In a rice planting area, n UAVs are tasked with monitoring m disease and insect pest monitoring points. All UAVs must perform these tasks. The UAVs depart simultaneously from the agricultural command center, visit and complete their assigned monitoring points in sequence, and finally return to the command center. Each monitoring task requires a different execution time. The objective is for all UAVs to complete the monitoring tasks as quickly as possible and return to the command center, while adhering to practical constraints, that is, in the optimal UAV allocation plan, the time it takes for the latest UAV to arrive at the starting point. The schematic diagram is shown in Figure 1.  
In this example, three UAVs--UAV1, UAV2, and UAV3--are required to complete pest monitoring tasks for Task 1 to Task 6, aiming to find the optimal task allocation scheme while satisfying practical constraints. UAV1, UAV2, and UAV3 start simultaneously from the same agricultural command center, perform their assigned tasks, and return to the command center. The total time for the allocation scheme is determined by the UAV that takes the longest to return. Therefore, the optimal scheme minimizes this return time.
## 4 Models and Problem Definition
### Competitive Live-Edge (\(\mathsf{CLE}\)) Model  
We follow the method in [22] to construct a _live-edge_ model and prove this model is equivalent to \(\mathsf{TCLT}\) model. This property is used for estimating the objective function as well as the designing of our algorithm in the next sections.  
From original graph \(G=(V,E)\) under \(\mathsf{TCLT}\) model, we construct a _sample_ graph (or _realization_) \(g\) from \(G\) as follows. For each \(v\in V\), we randomly select one in-edge \((u,v)\) with probability \(w_{A}(u,v)\), and do not select any in-edge with probability \(1-\sum_{u\in V}w_{A}(u,v)\). The selected edge is called _\(A\)-live edge_. On the other hand, we also randomly select one in-edge \((u,v)\) (called _\(B\)-live edge_) with probability \(w_{B}(u,v)\), and do not select any in-edge with probability \(1-\sum_{u\in V}w_{B}(u,v)\). Let \(g_{A}\) and \(g_{B}\) be the sub-graph including only \(A\)-live edges and \(B\)-live edges, respectively. Finally, we return \(g\) as union of \(g_{A}\) and \(g_{B}\).  
In graph \(g\), we denote \(A^{\prime}_{t}\) and \(B^{\prime}_{t}\) as sets of \(A\)-active nodes and \(B\)-active nodes on \(g\) at step \(t\), respectively. we denote \(d_{A}(A_{t},u)\) (\(d_{B}(B_{t},u)\)) was the minimum distance from \(A_{t}\) (\(B_{t}\)) on \(g_{A}\) (\(g_{B}\)) to node \(u\). The distribution of \(A\)-active and \(B\)-active nodes in \(g\) happens in discrete steps \(t\) as follows:  
* At step \(t=0\), \(A^{\prime}_{t}=S_{A}\) and \(B^{\prime}_{t}=S_{B}\).
* At step \(t\geq 1\), first set \(A_{t}=A_{t-1}\) and \(B_{t}=B_{t-1}\). A node \(v\
otin A^{\prime}_{t-1}\cup B^{\prime}_{t-1}\) becomes _\(A\)-active_ if \(v\) is reachable from \(A^{\prime}_{t-1}\) in one step in \(g_{A}\) (i.e., \(d_{A}(A^{\prime}_{t-1},v)=1\)) but not reachable from \(B^{\prime}_{t-1}\) in one step in \(g_{B}\) (i.e., \(d_{B}(B^{\prime}_{t-1},v)>1\)), then \(v\) is in \(A^{\prime}_{t}\). Symmetrically, if \(v\) is reachable from \(B^{\prime}_{t-1}\) in one step in \(g_{B}\) but not reachable from \(A^{\prime}_{t-1}\) in one step in \(g_{A}\), then \(v\) is in \(B^{\prime}_{t}\).
* If at step \(t\geq 1\), \(v\) is reachable from \(A^{\prime}_{t-1}\) in one step in \(g_{A}\) and reachable from \(B^{\prime}_{t-1}\) in one step in \(g_{B}\), \(v\) is \(A\)-activated with probability \[p_{A}(v|A^{\prime}_{t-1},B^{\prime}_{t-1})=\frac{\sum_{u\in N_{-}(v)\cap A^{ \prime}_{t-1}}w_{A}(u,v)}{\sum_{u\in N_{-}(v)\cap A^{\prime}_{t-1}}w_{A}(u,v)+ \sum_{u\in N_{-}(v)\cap B^{\prime}_{t-1}}w_{B}(u,v)}\] (7)  
Figure 2: A counter example.  
Based on Lemma 1, we design upper and lower submodular functions, which are cores of our proposed algorithms in next sections.
## 4 Experimental Results
### Event-Related (De)Synchronizations, ERDs/ERSs  
When analyzing the amplitude envelopes evolution over time for different frequency intervals within 3-40 Hz, we notice the highest differences between classes (0.5-1 \(\upmu\)V) within the \(\alpha\) (8-14 Hz) and \(\beta\) (20-28 Hz) frequency bands, shown in Figure 7. The envelopes are similar until the 300 ms time point, corresponding to the same type of processing the external information (visual perception). First a synchronization event (ERS) > 0 \(\upmu\)V starts  
Figure 5: Grand Average (GA) Event Related Potential (ERP) and scalp topographies over all blocks for channel P3. The scalp topographies relate to the temporal intervals shaded in grey.  
Figure 6: GA signed \(r^{2}\) over all blocks for: (**a**) Nat–Uni and (**b**) Frac–Nat discriminations at P3 channel.  
at 200 ms, more pronounced for the parietal sites (P3 and P4) within 8-14 Hz (Figure 7a), followed by desynchronization, deeper at 500 ms, expressed by decreased amplitudes (<0 mV), which corresponds to a preparation of a higher complexity task (evaluating images complexity in case of Nat and Frac classes). Further, an amplitude increase follows from 600 ms, producing synchronization at 1000 ms within the \(\beta\) band. More pronounced desynchronization is modulated by more complex cognitive processing, for Nat and Frac perceptions, compared to Uni image perceptions where no cognition is involved, highest for the parietal sites. While between Nat and Frac image perceptions, the envelopes evolutions tend to be similar before 600 ms, and differ in synchronization, higher for Frac perception (see P4 within \(\alpha\) band at 1000 ms and P4, T4, T3 within \(\beta\) band, even from 400 ms onwards). The variations in amplitude at 500 ms and 1000 ms time points correspond to the ERP peaks.  
Details on the spatial activity of the neural modulations in the \(\alpha\) (8-14 Hz) and \(\beta\) (20-28 Hz) bands can be seen in Figure A3 in Appendix F.
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 3 Proposed Solution
### Hardware Architecture  
Examining Figure 1, presenting the hardware architecture of the device, we can consider it to be composed of three main sections: the power supply, the data conditioning and acquisition, and the data processing and user interaction circuits.  
The first section ensures the device's power supply from both the electricity grid network and a backup battery in case of power outages or grid faults. The switching between the two power sources, the _AC/DC Power Supply (5V)_ and the _Backup Battery_, is performed automatically by a specialized circuit without interrupting the device's functionality. Regardless of the primary voltage source used, its output voltage must be adapted to the acceptable range for the rest of the circuit components. Responsible for this task is a buck-boost converter, the _Main Power Supply (3.5V)_ block.  
The second section of the architecture contains the _Voltage sensing circuit_ and _Current sensing circuit_, circuits responsible for transforming the two physical quantities of interest into a voltage signal that can be interpreted by the next. The _Signal conditioning circuit_ blocks adapt the output of the sensing circuits using a set of operational amplifiers (Op-Amp) and then feed their output signal to the data acquisition block, the _ADC_ block, which was implemented using an analogue-to-digital converter, as the name states. Both ADC and Op-Amps require specific operating voltages generated by the _Positive Voltage Supply (3 V)_ and the _Negative Voltage Supply (\(-\)1.2 V)_ blocks, powered from the buck-boost converter presented in the previous section.  
The digital information generated by the ADC is transmitted to the microcontroller through the SPI interface. Then, it makes them available to the user using multiple communication protocols: over the included Wi-Fi interface or the _RS485 interface_. The _User interface elements_ block acts as a minimal Human-to-Machine (HMI) interface, including a set of LEDs and buttons physically accessible to the user who can use them to interact with  
Figure 1: Device hardware block diagram.  
the logic of the device. An _SD card_ slot was added to allow for the logging of the acquired signal samples for an external, device-independent analysis.