esa-sceva/satcom-chunk-collection huggingface dataset:
# A Typology Model of Temporary Facility Constraints for Automated Construction Site Layout Planning
## 4 A Constraint Typology Model for CSLP
### Non-Overlap Constraint  
Non-overlap constraint prevents physical overlap between temporary facilities and other objects (ID: NO1). The minimum distance between the temporary facilities and other objects should be at least x(m) apart to prevent them from overlapping. To achieve this, the coordinates \((LLC_{i},URC_{i})\), \((LLC_{j},URC_{j})\) of different facilities \(i\) and \(j\) should satisfy the following equation as shown in Figure 6a:  
\[Max\ \Big{\{}[x_{li}-\ x_{rj}]\left[x_{ri}-\ x_{lj}\right],\ [y_{li}-\ y_{rj}] \left[y_{ri}-\ y_{lj}\right]\Big{\}}\geq 0 \tag{4}\]  
However, it is also possible to set the offset distance to maintain a safety distance (buffer zone) between the temporary facilities and other objects (Figure 6b). To do this, the following equation should be satisfied as shown below:  
\[Max\ \Big{\{}[x_{li}-\ x_{rj}-2d]\left[x_{ri}-\ x_{lj}+2d\right],\ [y_{li}-\ y_{rj}-2d]\left[y_{ri}-\ y_{lj}+2d\right]\Big{\}}\geq 0 \tag{5}\]  
This inequality is inspected only to check whether the safety distance is violated at the time of layout and is thus a necessary condition that must be satisfied before inter-facility distance constraints (explained in the following section) are checked by a computer.
## 3 Results and Discussion
### Multi-Objective Evaluation Procedure
This difference can be explained (i) by the higher electricity price in this work which is representative of the period of the present work and (ii) by the optimization procedure which is done in a proper way by taking into account various parameters. Another interesting comment is that the optimum collecting areas according to the LCC minimization are greater than the optimum collecting areas obtained by the multi-objective optimization procedure. This is an interesting result that shows that different optimization methods can lead to different results. However, both methodologies indicate that the advanced FPC is the best solar technology. The final selection of the solar field area is depended on the application and the investment type. In cases where the use of solar thermal will be exploited for many years and for the entire time span, then the LCC  
Figure 31: Multi-objective optimization for the Kastoria city—Zone D. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**City** & **Collector Type** & \begin{tabular}{c} **A\({}_{\text{col}}\)** \\ **(m\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} \(\beta_{\text{opt}}\)** \\ **(\({}^{\circ}\))** \\ \end{tabular} & \begin{tabular}{c} **F** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **LCC** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **SPP** \\ **(years)** \\ \end{tabular} &
\begin{tabular}{c} **CO\({}_{2}\) Avoidance** \\ **(kg/year)** \\ \end{tabular} \\ \hline Heraklion & Advanced FPC & 20 & 40 & 78.59 & 13,731 & 1.87 & 5854 \\ Athens & Advanced FPC & 25 & 45 & 80.25 & 15,257 & 2.03 & 6762 \\ Thessaloniki & Advanced FPC & 25 & 45 & 72.22 & 20,257 & 2.08 & 6573 \\ Kastoria & Advanced FPC & 25 & 45 & 70.67 & 22,193 & 1.99 & 6896 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Final summary of the optimum designs according to the multi-objective optimization. is the best criterion. On the other hand, when the utilization of the solar system regards a simple retrofitting application or a modification of the energy system which requires a relatively small budget and quick payback period, then the use of the optimum results from the multi-objective optimization work is recommended. In the future, there is a need for extending this analysis to more cities and for more collector types. Moreover, the results can be compared with results from other models based on dynamic simulations.
## 4 The PI-Based Task Offloading Algorithm  
This article develops a PITO algorithm for resolving delay-sensitive task offloading problems in SEC. The PITO performs on each satellite in parallel, and only relies on local communication via LISLs. We first introduce the distributed framework of PITO, followed by its two stages: _task inclusion_ and _consensus and task removal_. Additionally, we establish a re-offloading mechanism to handle the dynamic arriving delay-sensitive tasks.
## 5 Numerical Results
\\ \cline{2-7}  & \(OP_{2}^{RAS}(1)\), (8) & \(F_{G_{2}}(\tau_{2})\), (10) & \(OP_{2}^{RAS}(k_{1}^{*})\), (8) & \(F_{G_{2}}(\tau_{2})\), (10) & \(OP_{2}^{RAS}(k_{2}^{*})\), (8) & \(F_{G_{2}}^{(k_{2}^{*})}(\tau_{2})\), (33) \\  & \(E_{x_{2}}^{RAS}(1)\), (18) & \(E_{x_{2}}^{RAS}(1)\), (21) & \(E_{x_{2}}^{RAS}(k_{1}^{*})\), (18) & \(E_{x_{2}}^{RAS}(1)\), (21) & \(E_{x_{2}}^{RAS}  
i.e., \(25\div 40\) dB, the proposed BAS-2 scheme for NOMA outperforms even the optimal performance of OMA, which approximates to our proposed RAS scheme. Figure 3: Outage performance of the proposed system, in which \(d_{1}=200\) m, \(d_{2}=100\) m, \(R_{1}=1\) (\(\rm{bps/Hz}\)) and \(R_{2}=3\) (\(\rm{bps/Hz}\)). Figure 2: Outage Probability at each user versus the average SIR (dB). Figure 4 plots ergodic capacity as varying SIR from 0 (dB) to 35 (dB). Ergodic capacity increases significantly at high SIR, as SIR = 35 (dB). It is important to indicate that the sum Ergodic Capacity is dominant by \(U_{2}\), the user with better normalized channel conditions; thus, should be focused on to optimize for obtaining optimal performance of user \(U_{2}\) rather than that of user \(U_{1}\). However, a slight improvement is observed at optimal ergodic capacity for user \(U_{2}\). In contrast with the previous figure, lower bound of ergodic capacity happens at RAS mode. A similar trend as observed in Figure 3 is that when the average transmit SIR is relatively high, our proposed TAS schemes provide better performance than OMA. Moreover, Figure 5 shows the ergodic capacity versus interference densities \(\lambda\) and optimal ergodic capacity is indicated while Figure 6 presents the impact of interference density to outage probability. It can be clearly seen that the ergodic capacity performance degrades more steeply with an increase of \(\lambda\) regardless of the increasing number of selected transmit antenna at the BS. It can be confirmed that higher ergodic capacity happens at higher number of transmit antennas, i.e., the better case as \(K=8\). The ergodic capacity can be decreased to very low by increasing \(\lambda\) close to \(-50\). Interestingly, the optimal ergodic capacity can be achieved at a slight improvement compared with the normal case. The lower bound of ergodic capacity exists at RAS scheme. This simulation result verifies the ergodic capacity declines significantly at high density of interference. Similar trend can be explained for case of outage performance as observed in Figure 6.  
Figure 4: Sum Ergodic Capacity. Figure 5: Sum Ergodic Capacity versus the interferer density, where \(\lambda=\lambda_{1}=\lambda_{2}\) and \(\bar{\gamma}=1\).
# Synthesis of Novel Chalcones as Acetylcholinesteraseerase Inhibitors
## 3 Results and Discussion
### Synthesis
#### 3.1.2 (E)-3-(2-chlorophenyl))-1-(4-((2-hydroxybenzyl)amino)phenyl)prop-2-ene-1-one (A2)  
Dark yellow crystals, very soluble in acetone, slightly soluble in ethanol and methanol, poorly soluble in water; mp: 204-206 \({}^{\circ}\)C; EIMS (\(m/z\)): 362.0930 [M \(-\) H]\({}^{-}\) (MW: 363.10); UV (\(\lambda_{\rm max}\) nm, EtOH): 309, 383; \({}^{1}\)H-NMR (500 MHz, DMSO-\(d_{6}\)): 8 ppm: 4.30 (d, 1H, CH\({}_{2}\)), 6.66 (d, 2H, \(J\) = 9 Hz, H3', H5'), 6.74 (t, 1H, \(J\) = 7.7 Hz NH), 6.84 (d, 1H, \(J\) = 8 Hz, H3\"), 7.08 (t, 1H, \(J\) = 7 Hz, H5\"), 7.14 (d, 1H, \(J\) = 15 Hz, Ha), 7.16 (t, 1H, \(J\) = 8 Hz, H4\"), 7.40-7.43 (m, 3H, H4, H5, H6); 7.54 (d, 1H, \(J\) = 2 Hz, \(J\) = 7.5 Hz, H6\"), 7.89 (d, 1H, \(J\) = 15.5 Hz, H\({}_{\beta}\)), 7.91 (d, 1H, \(J\) = 5 Hz, H6), 7.95 (d, 2H, \(J\) = 9 Hz, H2', H6), 8.14 (dd, \(J\) = 1.5 Hz, \(J\) = 8 Hz, H3), 9.61 (s, 1H, OH); \({}^{13}\)C-NMR (125 MHz, DMSO-\(d_{6}\)) 8 ppm: 56.03 (CH\({}_{2}\)), 114.9 (C3', C5'), 118.8 (C3\"), 124.6 (C6\"), 125.0 (C\({}_{\alpha}\)), 125.26 (C5\"), 127.6 (C1\"), 127.8 (C1'), 128.3 (C6), 128.3 (C4\"), 129.9 (C4), 131.1 (C3), 131.3 (C2', C6'), 132.8 (C1), 133.9 (C2), 136 (C\({}_{\beta}\)), 153.3 (C4'), 155.3 (C2\"), 185.5 (C=O).
## 3 Results and Discussion  
### Electric Field Intensity Study  
Using the ANSYS Maxwell software, the field analysis was performed (type Electric Transient). The electric field intensity (E) for the electrospinning machine is presented as vector distribution on plots from Figure 2. The needle length was set as 6 cm and was supplied by voltage 25 kV for two needle-to-collector distance cases: 50 and 100 mm. The arrows show the electric field direction; their length is proportional to the field intensity. The results are given using the logarithmic scale.  
Figure 2: Vector plot of electric field intensity for electrospinning machine for (**a**)—50 mm; (**b**)—100 mm needle-to-collector distance.  
Figure 3 contains the magnitude field distribution on the surface placed at the needle top and on the surface crossing the needle. A 6 cm length needle was supplied with a voltage of 25 kV for two needle-to-collector distance cases: 50 and 100 mm. The plots also use the logarithmic scale.  
The maximum electrical field intensity value is 6.73 \(\times\) 10\({}^{7}\) for the 50 mm needle-to-collector distance and 5.78 \(\times\) 10\({}^{7}\) for the 100 mm needle-to-collector distance. The highest values are concentrated close to the corners of the needle end, as presented in Figure 3 on the top view. Therefore, the next comparison was performed for the smaller distance value of 50 mm.  
The influence of the supplied voltage for the electric field intensity as a distance function, from the middle of the needle end to the collector (y-coordinate), is shown in Figure 4 for the case of 50 mm distance. The other conditions of the simulation remained unchanged.  
Analyzing the curves from Figure 4, the notable differences in electric field intensity one can see close to the needle end (0-2 mm). As the distance increases, these values approach each other and, after 10 mm, are about 590 kV/m, 506 kV/m, and 422 kV/m for 25 kV, 30 kV, and 35 kV, respectively. Close to the collector (after 50 mm), the electric field intensities for selected supply voltage values are as follows: 267 kV/m, 320 kV/m, and 373 kV/m.  
The maximum electric field intensity values for selected supplied voltages are presented in Table 1. As shown in Figure 3, the maximum field value concentrates close to the needle end corners.  
While changing the voltage value from 25 to 30 kV, the maximum field intensity value increases by 20.0%. Raising the voltage from 30 to 35 kV, this change is about 16.6%. As mentioned in [6], the knowledge of external electric field value may be used to calculate the electrospun fiber diameter using the Scaling law [38; 39; 40; 41; 42; 43; 44].  
Figure 3: Magnitude of the electric field intensity for the needle end and surroundings (front view and top view) for (**a**)—50 mm; (**b**)—100 mm needle-to-collector distance.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 3 Architectures and Protocols for IoT Communications via Satellites
### Lpwan
#### 3.2.3 Doppler Effect  
The Doppler effect is a physical phenomenon that causes the frequency of a signal to change when the source, such as a satellite, moves relative to the receiver or IoT node on the ground. This frequency shift can impact the quality and reliability of the communication [13], making it essential to implement compensation techniques at the receiver. This effect is crucial in configuring and parameterizing satellite-based IoT communications for LoRa and NB-IoT technologies.  
In the realm of LoRa, it is essential to account for the Doppler effects, particularly in LEO satellite connections. Variations in satellite speed relative to terrestrial IoT devices can introduce Doppler shift and rate challenges [63]. Study [63] suggests that a frequency of 433 MHz is particularly effective, offering enhanced stability and reliability and achieving a 100% packet delivery rate in specific scenarios. In contrast, higher frequencies, such as 868 MHz and 2.1 GHz, are more prone to the Doppler effect, potentially reducing communication reliability. This is especially true under certain spreading factors and bandwidth conditions. For example, a smaller spreading factor and frame length improve the transceiver's immunity to the Doppler effect when using COTS LoRa chips in LEO satellites [64]. Operators of LoRa Satellites need to consider the Doppler effect carefully, optimize for a Low Data Rate Operation (LDRO), and strategically select critical parameters, including bandwidth, carrier frequency, and satellite orbital height. These steps are vital for ensuring robust and reliable communication in LoRa satellite networks, particularly in the dynamic environment of LEO satellites.  
Likewise, the Doppler effect is critical for NB-IoT, especially in LEO satellites with lower orbits and higher relative velocities [65]. This effect significantly influences signal quality, requiring precise tuning of crucial communication parameters. The study in [64] highlights specific parameters for direct connectivity to LEO satellites, such as a 2.2 GHz carrier frequency and the adoption of Orthogonal Frequency Division Multiplexing (OFDM) in the downlink and Single-Carrier Frequency Division Multiple Access (SC-FDMA) in the uplink, using a narrow 180 kHz spectrum. This research underscores the significant impact of the Doppler effect on NB-IoT connectivity in lower orbits. It emphasizes the need for adaptations in receiver architecture, management of propagation losses, and power adjustments based on satellite-terminal distances.
## 4 Dynamic Characterization
### Modulation Response of the PIC  
We start by measuring the small-signal frequency response of the slave laser using the setup in Figure 6(dotted and continuous red lines). Two measurement scenarios are implemented: with and without optical injection from the master laser. We referred back to our original static characterization and chose two optimum operating points, \"bias points 1 and 2\", in Table 5 below. We turned the master laser off to realize the first scenario (without injection) and simply turned the master laser on with the bias values stated in Table 5 to realize the second scenario (with injection).  
Figure 6: (**a**) Schematic setup diagram of frequency response measurements (red wire connections), short-reach transmission system (black wire connections); solid lines: optical connections, dashed lines: electrical connections; (**b**) frequency response of the slave laser two bias points of the PIC listed in Table 5 without (blue, green) and with (red, orange) injection; (**c**) BER results for back to back (B2B) (blue) and 25 km SSMF transmission distances (black) with and without injection; (**d**) optical spectra of the PIC under modulation with injection (blue) and without injection (red); inset: obtained eye diagrams after 25 km SSMF transmission and 0.5 dBm received optical power.  
For this measurement, a 50 GHz network analyzer (HP 8517B) is used to directly modulate the SG section of the device. The frequency of the modulated RF signal is swept from 0.05 to 20 GHz. The optical output of the PIC is split using a 90:10 coupler, where the 90% branch is detected by a 20 GHz photodiode (DSC30S) and then sent back to port 2 of the network analyzer. The other 10% branch is used to monitor the optical spectrum (ensuring single-mode operation) of the device. The measured \(S_{21}\) parameter (i.e., the frequency response) of the slave laser is shown in Figure 6b.  
The measurement is first carried out for the free-running slave laser biased at \"bias point 1\" in Table 5 without injection (MG,MR = 0) (blue trace) and subsequently for the optical injection case (red trace). The figure shows that by employing OIL, the 3 dB modulation bandwidth of the slave can be increased from 9.5 to 14 GHz (45% enhancement). The same measurement is conducted for \"bias point 2\" in Table 5. Slave laser without injection (green trace) shows a 3 dB modulation bandwidth of 9.2 GHz. Injection from the master laser improves the modulation bandwidth to 12.1 GHz. The improvement as a result of injection from the master laser enables higher direct modulation rates of the device as well as improves the linearity of the slave laser frequency response (much flatter frequency response over the given data bandwidth).
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 2 Development and Application of Multimodal Interaction Technology in Remote Education
### Applications of Multimodal Interaction Technology in Enhancing Educational Models
Moreover, VR technology has been employed to develop interactive remote teaching systems, thereby improving teacher-student engagement and enhancing learning outcomes in remote education [31]. * Remote User Attention Assessment Methods: Sensor-based learning analytics approaches, including smartphone-embedded sensor technologies for the collection of data on student behavior and learning, have been integrated with embedded hardware-software systems and backend data processing frameworks to facilitate real-time dynamic assessments in remote education [31, 32, 33]. * Data Fusion and Visualization: Data fusion methodologies employ tools such as \"physiological heat maps\" to integrate eye-tracking and physiological data, dynamically visualizing learners' emotional and cognitive states [34]. Additionally, machine learning algorithms have been utilized to analyze students' visual attention trajectories, enabling the prediction of learning outcomes and the refinement of instructional design strategies [35].
## 3 Datasets and Methods
### High Resolution Pleiades Images  
Standard orthorectified Pleiades images, acquired on five dates (25 April 2013, 3 August 2016, 6 October 2016, 24 Mai 2017 and 7 October 2017--Table 1), were used to determine the vegetation type in the study area. The Pleiades satellite constellation (Pleiades-1A was launched on 17/12/2011 and Pleiades-1B on 02/12/2012) acquired PAN (wavelength in the 470-830 nm range) images, with a ground sampling distance of 0.7 m in the nadir direction, which were resampled at 0.5 m and in four MS bands--blue (B: 430-550 nm), green (G: 500-620 nm), red (R: 590-710 nm), and Near-Infrared (NIR: 790-940 nm)--at a spatial resolution of 2 m and over a swath width of 20 km at nadir.
## 4 Numerical Results
### Algorithm Performance and Robustness Analysis
#### 4.2.4 Sensitivity Analysis of Objective Function to Reward Weights  
The analysis of Figure 12 reveals that even with different settings of weight coefficients (\(\lambda_{E},\lambda_{Q},\lambda_{C},\lambda_{F}\)), the overall trend of the objective function value (\(R(t)\)) with respect to the number of vehicles (\(N\)) remains largely consistent. The objective function value gradually increases when the number of vehicles is small, reaches a peak in the intermediate range (approximately 80-120 vehicles), and then may slightly decrease or level off due to resource saturation. This demonstrates the robustness of the multi-agent deep reinforcement learning scheme proposed in this paper to changes in system scale under different weight configurations.  
Differences in \(\lambda\) group values lead to slight variations in the absolute magnitude of the objective function value: when the weight coefficients are generally higher (e.g., the group with \(\lambda_{E}=0.6,\lambda_{Q}=0.2,\lambda_{C}=0.2,\lambda_{F}=0.9\)), the total objective function value is typically slightly higher, as it places greater emphasis on various reward components, incentivizing agents to achieve higher overall performance. Conversely, when the weight coefficients are generally lower (e.g., the group with \(\lambda_{E}=0.4,\lambda_{Q}=0.05,\lambda_{C}=0.05,\lambda_{F}=0.7\)), the total objective function value is typically slightly lower. This consistent trend supports the rationality of the default \(\lambda\) parameter configuration in this paper, indicating that the overall behavior pattern of system performance remains stable even under different performance trade-offs, and validating the model's effectiveness in complex dynamic environments.
This causes crosstalk distortion, where the separated sources contain spectral content from more than one source at the corresponding TF. Considering this situation, a collaborative blind source separation method is proposed by using pair location-informed coincident microphone arrays in [20]. This method can jointly separate simultaneous speech sources based on TF source localization estimates from each microphone recording. The musical and crosstalk distortion is effectively reduced by the combination of the microphone pair and vector decomposition. However, if there are three or more speech sources, the vector decomposition will get more difficult and the computation will increase exponentially. In previous work [21], we achieved an effective multiple sound source localization method by applying \"single source zone detection\". The method proposed in [21] provides the possibility and necessary parameters for further separation of the corresponding sound sources, including the source number and the corresponding DOA estimations. In this paper, in contrast to existing methods, only a B-format microphone with four channels is used to separate the TF components of the signals. We firstly measure the proportion of overlapped components among multiple sources and find that there exist many overlapped TF components with increasing source number. Thus, a multiple speech source separation method by using inter-channel correlation and half-K assumption is proposed. Specifically, considering the relaxed sparsity of speech sources, we propose a dynamic threshold-based separation approach of sparse components where the threshold is determined by the inter-channel correlation among the recording signals. Thereafter, after conducting a statistical analysis of the number of the active sources at each TF instant, it is concluded that no more than half of the sources are active in a certain TF bin among simultaneously occurring speech sources. By applying the assumption, the non-sparse components are recovered by using the extracted sparse components combined with vector decomposition and matrix factorization. Eventually, the final TF coefficients of each source will be recovered by the synthesis of sparse and non-sparse components. The remainder of the paper is organized as follows: Section 1 presents the signal model and the limitations of the W-DO assumption. Section 2 introduces the proposed separation method. Experimental results are presented in Section 3, while conclusions are drawn in Section 4.