esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
There has been a great interest in the propagation and properties of optical vortices over the last few decades [1, 2]. With the prediction and subsequent demonstration that vortex beams carry orbital angular momentum [3], vortices have found a wide range of applications, such as optical trapping [4, 5], quantum information processing [6, 7], microscopy imaging [8, 9] and laser micromachining [10, 11]. Their generation has been proposed through interferometric setups [12], by using spiral phase plates [13] or in general, by using birefringent elements [1], where the spatial light modulator (SLM) is a popular choice as it offers dynamic control of vortex properties [14]. These advances allow for research under wide range of conditions, in this case, working with ultrashort laser pulses.  
Femtosecond light pulses allow for the generation of optical vortices with high energy and ultrashort temporal profiles [15], crucial to generating a supercontinuum and probing other nonlinear phenomena. Femtosecond vortices have found their main applications in subwavelength nonlinear microscopy [16] and femtosecond micro-nano manipulation [17], and their production has been recently studied by several groups [10, 18, 19]. Rapid technological improvements in supercontinuum generation (SCG), particularly with microstructured fiber, have sparked this development. With the unique ability to generate a wide spectral range of coherent light from a femtosecond laser of modest power (a mode-locked oscillator itself is sufficient), SCG fiber sources are opening the door to exciting new physics [20]. Combining a spatial light modulator with SCG sources is attractive, since it permits the production of vortex beams of varying color [21]. Vortex generation using polychromatic and SCG sources has been explored by [PERSON] [22, 23], [PERSON] et al. [24] and [PERSON] et al. [25]. In this paper, we create an optical system of unique versatility. In particular, we demonstrate that with the use of a large aperture SLM, multiple vortices may be produced and made to interfere in the focal plane of a lens. Indeed, each section of the SLM acts as an independent modulator, allowing one to precisely control the generation and propagation dynamics of each vortex beam. Bandpass filters may be employed in front of each segment, generating spectrally separate vortices of arbitrary charge. These engineered femtosecond white-light vortex beams are programmable, tunable, extremely high quality, yet still user-friendly. Patterns of exquisite complexity are produced and compared with numerical modeling based on the Huygens-Fresnel wavelet theory.  
In Section 2, we present the theoretical expressions, simulations and experimental setup for combining femtosecond vortices with SCG. In Section 3, the results from our experiments are presented, showing agreement with the simulations and leading to the discussion section and to our conclusions, presented in Sections 4 and 5, respectively.
## 1 Introduction
In this context, the architectural flexibility of neural networks offers a clear advantage in modelling these complex patterns, despite their higher computational demands, as they may yield higher accuracy [21; 22]. Balancing training efficiency and prediction accuracy is key when selecting an appropriate machine learning algorithm. Meanwhile, unlike traditional statistical models, machine learning models are often considered \"black boxes\" that do not provide clear interpretations of variable relationships, making the interpretability of machine learning models an important consideration. Currently, multiple ground-based SIF observation systems have been established for different vegetation communities, providing continuous real-time SIF observations [23]. This is also an important validation method for original satellite data and downscaled products. However, the time-reconstruction products mentioned above cannot be validated against original data outside the satellite data record, making spatial downscaling of long-term satellite products still highly valuable. For global-scale SIF product downscaling, machine learning methods are more efficient, and model interpretability can be enhanced using SHAP analysis. Comparing different algorithms to select the most suitable one for the target data is also a key focus of research. Therefore, this study is based on the GOME-2 SIF product and adopts a two-step downscaling framework. First, a model is constructed to represent the relationship between the explanatory variables and the SIF at a resolution of 0.5\({}^{\circ}\). Then, this model is applied at a resolution of 0.05\({}^{\circ}\). By comparing models, we selected the most suitable machine learning algorithm and achieved the following: (a) Using reflectance, NDVI, land surface temperature, MODIS photosynthetically active radiation, and land cover type data, the original 0.5\({}^{\circ}\) SIF data were improved to a resolution of 0.05\({}^{\circ}\). The resulting product outperforms the original in spatial coverage and detail while preserving the original SIF's spatiotemporal characteristics. (b) The results of this study were compared with ground-based SIF observations and other satellite SIF products, validating the feasibility and accuracy of the proposed approach. (c) Correlation analysis between the improved product and GPP data from FluxNET eddy covariance towers showed that the downscaled product had an improved correlation with GPP. Additionally, we quantified the contribution of different feature parameters to the model's predictions and emphasized the influence of categorical variables--particularly land cover data--on model performance.
# Real-Time Guitar Amplifier Emulation with Deep Learning +
###### Abstract  
This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Streamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.  
a Article Real-Time Guitar Amplifier Emulation with Deep Learning  
[PERSON] _et al._ (2020), 10, 766; doi:10.3390/app10030766
## 3 Results  
### Effect of Temperature on the Decomposition Process  
3.1.1. Decomposition Rate of PMVE/N\({}_{2}\) Gas Mixture  
To investigate the decomposition process of the PMVE/N\({}_{2}\) mixture at different temperatures, the reaction kinetics of the PMVE/N\({}_{2}\) mixture with 60% N\({}_{2}\) content were simulated over  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key Parameter** & **PMVE/N\({}_{2}\)** \\ \hline N\({}_{2}\) content (mix by mole ratio) & 40\% \\ Number of PMVE & 100 \\ Number of N\({}_{2}\) & 400 \\ Density (g/cm\({}^{3}\)) & 0.00227 \\ Box length (Å) & 273.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters and simulation conditions of PMVE/N\({}_{2}\) system.  
a temperature range of 1600-2600 K. Figure 2 illustrates the decomposition behavior of PMVE and N\({}_{2}\) within this temperature range. It is evident that the thermal decomposition rate of PMVE increases gradually with temperature. At 1600 K, the decomposition of PMVE was slow, with only 11 PMVE molecules decomposed within the limited simulation time. However, when the temperature reached 2600 K, 88 PMVE molecules had decomposed. In contrast, the thermal decomposition rate of N\({}_{2}\) remained relatively consistent across the temperature range. N\({}_{2}\) decomposed rapidly between 0 and 200 ps, and after 300 ps, the decomposition process of N\({}_{2}\) molecules reached saturation, with this quantity remaining stable.  
Figure 3 shows the time evolution of the total potential energy of the PMVE/N\({}_{2}\) system in the temperature range of 1600 K to 2600 K, thus revealing the energy change characteristics during the reaction. It can be seen from the figure that the total potential energy of the system increases with time, indicating that the decomposition process of the PMVE/N\({}_{2}\) gas mixture is an endothermic reaction. This is consistent with the actual situation. When the temperature is lower than 2000 K, the change in the system's potential energy is not obvious, which indicates that the decomposition degree of PMVE/N\({}_{2}\) is low, and the decomposition process is not violent. When the temperature rises to 2200 K, the potential energy of the system increases significantly, indicating that the increase in temperature accelerates the decomposition of the gas mixture. In summary, the significant decomposition of the PMVE/N\({}_{2}\) mixture mainly occurs in the temperature range above 2000 K.
# RFID and Drones: The Next Generation of Plant Inventory
## 2 Materials and Methods
### Field Experiment
#### 2.6.1 Vertically Fixed (VF)  
The first field experiment was designed to evaluate the scan total of 6 tag designs when the aircraft would fly back and forth at a fixed height over tags on the ground (VF). Two factors were evaluated: altitude and tag orientation (Table 5). The experiment was designed so data for all 7 treatments could be collected during the same flight (single battery). The three altitudes evaluated were: 4.5 m, 6.0 m, and 7.6 m. Moreover, four different tag orientations were used for each altitude: tag facing upward (UP), downward (DN), Sideways Left (SL), and Sideways Right (SR). To ensure that a complete experimental run could be completed using a single aircraft battery, duplicate copies of some tag designs (5B = 5A = 59; BF = 9F; ZF = 80 = 39) were used in this experiment. Tags used were: 5A, BF, 5B, 2F, 59, 12, 81 and 6F. Tag 81 was included since it yielded a high scan total in laboratory experiment when orientated sideways to the reader. These eight RFID tags were taped to a 5 cm \(\times\) 10 cm wood board in four different orientations. The tags designated in the upward (UP) orientation were: 5A and BF, while for the downward (DN) orientation were: 5B and 2F. Furthermore, the tags designated for Sideways Left (SL) were: 59 and 12. Tag 81 and 6F was designated at Sideways Right (SR). Wood boards were placed 3 m apart from each other in a parallel configuration.  
Figure 8 shows the experimental layout. Tags which were attached to a 5 cm \(\times\) 10 cm board were placed in the middle of road. Four meters on either side of the center of the road and in line with the boards were two markers. Markers were used to help the pilot stay centered while flying over the tags and served as visual markers for the outer boundary of aircraft flight. As an example, the aircraft would start a run outside the southern marker, fly in a straight line over the tags and continue beyond the northern most marker. One flight path is equivalent to one complete trip from south to north and vice versa. This process was repeated 5 times for each of the 3 altitudes at a forward flight speed of approximately 6 km per hour. Tags were equally scanned through the number of flight paths.  
Figure 8: Illustration of aircraft flight path with tags in the center.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Altitude** & **Tag Orientation** \\ \hline
4.5 m & Facing Upwards \\
6 m & Facing downwards \\
7.6 m & Sideways Left \\  & Sideways Rights \\ \hline \hline \end{tabular}
\end{table}
Table 5: Factors evaluated in the field experiment.
# Teleseismic Tomography for Imaging the Upper Mantle Beneth Northeast China
## 3 Northeast China
### Picking Up the Relative Travel Time Residuals  
The travel time of the seismic wave is the observation data of tomography. In teleseismic tomographic imaging, in order to eliminate the source error and the error outside the model in the study area, it is necessary to reduce the observation travel time by the theoretical travel time to obtain the travel time residual. Then, the final \"relative travel time residuals\" can be obtained by subtracting the mean value of travel time residuals (Figure 5).  
Relative travel time residuals correspond to velocity anomalies, and then we use relative travel time residuals for inversion [35]. It will be a very time-consuming work to pick up each waveform data manually. Therefore, seismologists have proposed many accurate and efficient methods to pick up travel time residuals based on waveform correlation [36]. This paper also uses this method to pick up the waveform. The two cross-correlation methods are integrated into a graphical interface, which greatly improves the efficiency of picking up travel time residuals. Because the deployment time of each array selected in this study is scattered, the array data of each uniform sampling rate is picked up separately, as shown in Figure 6. Before picking up the data, bandpass filtering is carried out. The frequency range is 0.01-1 Hz, which avoids filtering out the relative high-frequency information of distant earthquakes, and keeps the first arrival of the P-wave to a high degree. Then, an adaptive stacking code is used to process the waveform and delete the waveform with a low signal-to-noise ratio.  
Figure 5: Flow chart of picking up relative travel time residuals.
# IRONEDGE: Stream Processing Architecture for Edge Applications
## 5 Results and Comparison
### Resource Usage  
Figure 6 depicts the averaged values for the memory and CPU utilization. Regarding the memory, the average utilization varied between 71.2% and 71.8% for K0-WC, corresponding to a range of 0.6%. For K1-NC, there was a wider variation, between 67.3% and 70.7%, for a range of 3.4%. Related to the CPU, the average utilization showed variations between 58.1% and 63.4% for K0-WC and 50.8% and 55% for K1-NC. This corresponds to variation ranges of 5.3% and 4.2%.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Value** & **Default** \\ \hline \multicolumn{3}{c}{Apache Storm} \\ \hline topology.stats.sample.rate & 0.001 & 1 \\ topology.produser.batch.size & 5000 & 1 \\ topology.executor.receive.buffer.size & 32,000 & 1 \\ \hline \multicolumn{3}{c}{Mosquito} \\ \hline listener & 1883 & - \\ allow\_anonymous & true & false \\ \hline \multicolumn{3}{c}{Apache Kafka} \\ \hline offsets.topic.replication.factor & 1 & 3 \\ transaction.state.log.replication.factor & 1 & 3 \\ transaction.state.log.min.isr & 1 & 2 \\ inter.broker.protocol.version & 3.1 & N.A \\ \hline \multicolumn{3}{c}{Kafka Mirrormaker v2} \\ \hline offset.flush.timeout.ms & 20,000 & 5000 \\ producer.buffer.memory & 2,194,304 & 33,554,432 \\ max.request.size & 2,194,304 & 1,048,576 \\ batch.size & 524,288 & 16,384 \\ max.poll.records & 20,000 & 500 \\ config.masterage.replication.factor & \(-1\) & 3 \\ offset.storage.replication.factor & \(-1\) & 3 \\ status.storage.replication.factor & \(-1\) & 3 \\ \hline \multicolumn{3}{c}{Kafka Connect} \\ \hline config.masterage.replication.factor & \(-1\) & 3 \\ offset.storage.replication.factor & \(-1\) & 3 \\ status.storage.replication.factor & \(-1\) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Non-default configurations used for the components.  
It is clear that, comparing K0-WC and K1-NC, the introduction of Kafka Connect, as expected, increased the overall resource usage. The increase was minimal for memory utilization, corresponding to an increase of, at most, 4.4%. For CPU utilization, this increase was more significant, reaching upwards of 12.5%. Additionally, for both implementations, there was no significant increase in resource usage resulting from the increased rates of Data Reports. On the contrary, there was an overall decrease in CPU utilization for both implementations.
# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
## 4 DQN-Based Load-Balancing Routing Algorithm
### DQN-LLRA Framework and Mechanisms
The estimated network updated the network weights according to the gradient descent method, and the target network updated the network weights from the estimated network every \(C\) steps. Upon reaching the destination node, the route ended, and the cumulative reward value was computed using the cumulative reward function. Repeated random generation of source and destination node pairs was used for training. The pseudo-code for the DQN-LLRA algorithm is as follows in Algorithm 1. ```
1:Source node and destination node, satellite topology
2:Route path
3:for episode = 1 to M do
4: Initialize state \(s_{t}\);
5: Move the agent to the source node;
6:for step = 1 to k do
7: Select \(a_{t}\) based on dynamic greed strategy;
8: Add the current node to the path;
9: Execute \(a_{t}\), observe reward \(r_{t}\) and next state \(s_{t+1}\);
10: Store transition \((s_{t},a_{t},r_{t},s_{t+1},done)\) into \(D\);
11:if next node is destination then
12:\(y_{t}=r_{t}\)
13:else
14:\(y_{t}=r_{t}+\gamma\max\limits_{a_{t+1}}Q^{\prime}(s_{t+1},a_{t+1}\Big{|}\theta^ {Q^{\prime}})\)
15:endif
16:if\(D\). size \(>\) R then
17: Sample random minibatch of transition \((s_{t},a_{t},r_{t},s_{t+1},done)\) from \(D\);
18: Perform gradient descent step about \(\theta^{Q}\) on \(\frac{1}{N_{b}}\sum_{t}\left(y_{t}-Q(s_{t+1},a_{t+1}\big{|}\theta^{Q})\right)^ {2}\);
19: Update the target Q network every C steps, let \(\theta^{Q}=\theta^{Q^{\prime}}\)
20:endif
21:endfor
```  
**Algorithm 1** DQN-based load-balancing routing algorithm for LEO satellite networks  
In the decision stage of the DQN-LLRA algorithm, on the basis of the above-trained decision model, firstly, the source node, destination node, and network topology were input to initialize the routing path. The agent selected the next hop node according to its surrounding environment. Then, the selected node was added to the routing path. The agent moved to the next node. The algorithm checked whether the next node was the destination node. If it was, the decision-making process was completed, and the path is output. Otherwise, the routing decision continued. Figure 7 illustrates the decision-process flowchart of the DQN-based load-balancing routing algorithm. ```
1:Source node and destination node, satellite topology
2:Route path
3:Initialize path
4:current node = source node
5:next node = None
6:while current node is not destination do
7: Agent obtains the state from the environment
8: Agent chooses the next node
9: Add the next node to the path
10: current node = next node
```  
**Algorithm 2** The decision process of the DQN-based load-balancing routing algorithm  
Algorithm 2 presents the pseudo-code for the decision process of the DQN-based load-balancing routing algorithm.
# Delayed Benefits from Spaced Training When Learning a Precision Throwing Task
## 1 Introduction  
Learning a new motor skill requires a number of repetitions to implement an optimal adaptation and a stable performance. Additional gain in performance may be achieved when the practice is spaced by intervals of rest [1; 2; 3]. This benefit from spaced training--termed offline gain--is associated with the process of memory consolidation developed during the intervals between training sessions [4; 5; 6]. In fact, after practice has ended, memory consolidation may be corrupted by interferences induced, for example, by other tasks [7] or molecular synthesis inhibition [8]. Regarding motor skills, between-session intervals of 4-6 h prevent interferences and the memory will be properly consolidated [4; 9; 10].  
As the simple passage of time is an important factor for memory consolidation, the amount of practice engaged before the pause may be crucial to trigger memory processing and the associated offline enhancements. In fact, the consolidation of a memory trace depends on the number of repetitions needed to obtain a relatively stable skill representation. This view is supported byHauptmann, [PERSON], [PERSON], & [PERSON] [11] who used the repetition priming effects to demonstrate that offline gain may be induced when the amount of training was sufficient to achieve a stabilization of the performance.  
The majority of studies conducted in the area of spaced learning adopt training paradigms where the performance stabilizes after a relatively low number of repetitions. Thus, the most common outcome in the relevant literature is that the offline enhancement appears early, after the first session of training. However, many everyday motor skills and sport activities require extensive practice, with multiple training sessions before the performance stabilizes. In these cases, the benefits from spaced training may be delayed over the course of the learning period.  
In the current paper, we tested the presence of this delay using a precision throwing task that required an elevated number of repetitions to acquire a stable performance. This choice was based on the results reported in our previous work [12] where changes in performance of a precision underarm throwing task were studied over two sessions of practice, followed by a retention test. We showed that the rate of performance change was constant during the first session and stabilized during the second, after a pause of 40 min. Here, we adopted the same paradigm but four training sessions and two groups with practice spaced by 40 min and 1 day were compared. Considering the findings of our previous work [12], if the offline gain was triggered by the amount of practice associated with the performance stabilization, the benefits from spaced learning should be observed starting from the third session and for the group with longer intervals.