esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire
## 4 Results and Discussion
### Numerical Simulation  
Numerical simulations were adopted to study the thermal radiation of the jet fires under higher pressure and larger leakage hole conditions. The simulation conditions with different leakage pressures and diameters are shown in Table 1. In the numerical model of the vertical jet fire in Figure 3, the combustion temperature and thermal radiation variable data in the \(y\)- and \(x\)-directions (\(y=2\) m plane) for the hydrogen jet fire under each simulation condition are extracted. The \(y\)-direction corresponds to the different flame height positions of the jet fire, and the \(x\)-direction corresponds to the radial position change from the flame axis.
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 5 Dual- and Triple-Frequency Multi-GNSS PPP Atmospheric Constraining with AR
### Triple-Frequency Analysis  
As shown in the dual-frequency scenario, similar characteristics were observed for the triple-frequency atmospheric constrained GREC PPP solutions. A look at the histogram, as shown in Figure 8, saw an average of 7% improvement over the unconstrained, in terms of percentages of stations converging under 20 cm of horizontal error.  
However, a significant level of improvement was observed by investigating the convergence of the time series of the atmospheric constrained solutions as depicted in Figure 9. Considering the 95 th percentile and convergence defined as 10 min for solutions to fall under 10 cm of horizontal positional error, the atmospheric (iono + tropo) constrained solutions achieved convergence in 2 min.  
Figure 8: Histogram showing percentage of 40 stations converging based on 24 hourly solutions in triple-frequency GREC processing mode. Results shown have a 20 cm horizontal error threshold.  
Figure 9: 95 th percentile time series showing horizontal positional error (hourly) based on 24 hourly solutions for 40 stations for triple-frequency GREC processing mode. Blue dotted line represents the 10 cm convergence threshold.  
This level of improvement is significant and relevant as it reveals not only the benefits of having more frequencies, but how the coupling of these extra frequencies with atmospheric constraining can enhance the solution quality of multi-GNSS PPP solutions. In comparison to the dual-frequency constrained scenario, the triple-frequency constrained solutions had a 67% improvement in terms of solution convergence.
# Communication Transport Protocol Strategies for Rail Applications
## 6 Results
### Critical Data Transfer  
For data transfer simulation, Iperf3 was used in the scenarios with a single bearer or two redundant bearers (either using TCP or UDP transport protocols for the tunnels). We tried several scenarios, including adding delay (from 0 to 400 ms) with/without additional packet loss  
Figure 16 reports the impact of induced packet loss on the bearers on the tunnel interfaces. Multi-path tunnels are consistently more resilient than single-path-based ones. In this configuration, a 5% packet loss rate was well-tolerated. As expected, the drop in performance (in terms of throughput) is less pronounced for multi-path tunnels. The TCP protocol proved to be more effective than UDP as the packet loss rate increases.  
Figure 17 reported the throughput as a function of latency in this case for one and two bearers and for TCP and UDP as transport protocols between OG and NG. The throughput progressively decreases as latency increases. In the presence of packet loss, this occurs in a more accentuated way (already for a packet loss equal to 1% the degradation is tangible).  
Figure 16: Data Transfer Throughput vs. Increasing Packet Loss on Bearer Interfaces.  
Figure 15: Selection of the transport protocol strategy based on the experienced couple \(<\)_packet loss_, \(delay>\).  
However, even in this case, the presence of two bearers produced a clear improvement (especially when using TCP as tunnel protocol).
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 6 Conclusions and Future Work  
The traditional boost converter, having an active clamp circuit along with the resonant inductor, is presented herein. Both the main switch and the auxiliary switch can be turned on with ZVS and the output diode can be turned off with ZCS. In addition, as the active clamp circuit is utilized, the voltage spike on the main switch can be suppressed to some extent, whereas because this structure, although the input inductor is designed in CCM, the output diode can operate with ZCS turn-off, leading to the resonant inductor operating in DCM, hence there is no reverse recovery current during the turn-off period of the output diode. Unlike the existing soft switching circuits, in order to improve the overall efficiency further, one look-up table is employed to adjust the cut-off time point of the auxiliary switch so that the current flowing through the output diode is reduced. By doing so, the maximum efficiency is 96.9%. In this paper, the cut-off time point of the auxiliary switch is adjusted only in the steady state. In the future, adjusting the cut-off time point for the auxiliary switch in the transient will be studied. In addition, the MOSFETs and Schottky diode used are all Si-based. For high switching frequency applications, SiC- or GaN-based semiconductors can be more attractive than Si-based ones. This will also be studied.  
Conceptualization, Y.-T.Y.; methodology, K.-I.H.; software, Y.-K.T.; validation, Y.-T.Y.; formal analysis, Y.-T.Y.; investigation, Y.-K.T.; resources, Y.-T.Y.; data curation, Y.-K.T.; writing--original draft preparation, K.-I.H.; writing--review and editing, K.-I.H.; visualization, Y.-K.T.; supervision, K.-I.H.; project administration, K.-I.H.; funding acquisition, Y.-T.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, under the Grant Number: MOST 109-2222-E-167-003-MY3.  
Not applicable.  
Not new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 3 Results
### Space State: Blood Spot Coordinates in the Camera View
#### 3.2.5 Performance  
Training the agent under these principles was carried out by selecting random initial camera view frames. First results suggested the system would get stuck if the random initial camera view frames were far away from any blood sample. Chaotic movements were observed under the null case state, but the system learned to reach the target if the initial frame captured any blood spot. This is likely an exploration issue where the agent is unable to find the exit the first time. The reformulation of the problem by a possible combination of this AI approach with the automatic scan mode of the HORUS scanner was considered at this stage.  
An example of training of 100,000 episodes (starting from random initial frames capturing any blood pixel) is shown in Figure 8. Exploration, learning rate and discount factor were the values for Agent 8 in previous experiments. A and B images represent the result for the first definition of rewards for ZI and ZO states, respectively, in contrast to C (ZI) and D (ZO) that correspond to the second reward shaping. The action with highest Q-Value is represented by a different color and those actions are the Cartesian movements of the camera image views respect to the whole image framework. No invalid joint space actions are observed in the images.  
Figure 8: Example of greedy behaviour after 100,000 epochs for training a camera agent to center a blood spot through Q-Learning. The state is a different camera view of the blood trace and the ten possible actions (represented by colors) are movements of the camera respect to the whole image framework. (**A**) (ZI) and (**B**) (ZO) images corresponds to Agent 8 (Reward Shaping I) and (**C**) (ZI) and (**D**) (ZO) images are for Agent 8 (Reward Shaping II).  
Figure 7: Total reward on episode averaged over 5 different runs for eight different agents (**left**) and the total number of steps averaged over these runs (**right**) using Reward Shaping II. The training of each agent comprised 100,000 episodes and curves are smoothed with a window of 1000 epochs.  
In order to quantify how reliable the indicated greedy actions are, Figures 9 and 10 represent the Q-Values of greedy actions, a measure of goodness for a iteratively updated state-action pair [27], and a counter of how many times the agent has come through each state during the whole training, respectively.  
High Q-values (Figure 7) in highly visited states (Figure 8) represent best and more reliable actions to reach our goal. Otherwise, seldom frequented states and small Q-Values result into unsafe or suboptimal actions.
## 3 Results
### Antioxidant Activity
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sample**} & **Total Glucan** & **\(\alpha\)-Glucan** & **\(\beta\)-Glucan** \\  & **(g/100 g)** & **(g/100 g)** & **(g/100 g)** \\ \hline C & 0.26 \(\pm\) 0.02 \({}^{\mathrm{a}}\) & 0.05 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 0.21 \(\pm\) 0.02 \({}^{\mathrm{ac}}\) \\ E2.5 & 0.28 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 0.09 \(\pm\) 0.01 \({}^{\mathrm{e}}\) & 0.18 \(\pm\) 0.01 \({}^{\mathrm{ab}}\) \\ E5 & 0.25 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 0.07 \(\pm\) 0.01 \({}^{\mathrm{d}}\) & 0.18 \(\pm\) 0.01 \({}^{\mathrm{ab}}\) \\ E7.5 & 0.24 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 0.07 \(\pm\) 0.01 \({}^{\mathrm{cd}}\) & 0.16 \(\pm\) 0.02 \({}^{\mathrm{b}}\) \\ E10 & 0.25 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 0.06 \(\pm\) 0.01 \({}^{\mathrm{bcd}}\) & 0.19 \(\pm\) 0.01 \({}^{\mathrm{abc}}\) \\ M2.5 & 0.25 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 0.06 \(\pm\) 0.01 \({}^{\mathrm{abc}}\) & 0.19 \(\pm\) 0.01 \({}^{\mathrm{abc}}\) \\ M5 & 0.26 \(\pm\) 0.02 \({}^{\mathrm{a}}\) & 0.06 \(\pm\) 0.01 \({}^{\mathrm{abc}}\) & 0.20 \(\pm\) 0.02 \({}^{\mathrm{abc}}\) \\ M7.5 & 0.28 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 0.06 \(\pm\) 0.01 \({}^{\mathrm{ab}}\) & 0.22 \(\pm\) 0.01 \({}^{\mathrm{c}}\) \\ M10 & 0.26 \(\pm\) 0.02 \({}^{\mathrm{a}}\) & 0.05 \(\pm\) 0.01 \({}^{\mathrm{ab}}\) & 0.21 \(\pm\) 0.02 \({}^{\mathrm{ac}}\) \\ _p-value_ & 0.0516 & 0.000025 & 0.000312 \\ \hline \end{tabular} \(C\)—control (pasta from semolina); E2.5–10–pasta from semolina flour fortified with 2.5–10% of Enoki mushroom powder, M2.5–10—pasta from semolina flour fortified with 2.5–10% of Maitake mushroom powder. Samples with different superscripts within a column are significantly different at \(p\leq 0.05\).  
\end{table} 
Table 3: Content of glucans in pasta fortified with dried Enoki and Maitake mushrooms.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 2 Neurotransmitter Detection Methods
### Optical Sensing Techniques
#### 2.2.5 Optical Fiber Biosensing  
The principle of optical fiber biosensors is based on the optical transduction induced by the binding of the target molecule to an immobilized receptor on optical fibers acting as transduction elements [151]. The optical fiber core is doped with germanium for a higher refractive index than the surrounding cladding made of silica, which then induces light propagation through total internal reflection [152]. When light passes through the optical fiber, the generated electromagnetic field or evanescent wave is related to the distance-dependent, rapid light intensity decay that occurs between the interface and the medium of smaller refractive index. In the case of molecular recognition, the evanescent light excites a fluorophore and the resulting fluorescence emission is propagated through the fiber towards an optical sensor [153]. The optical fiber-based sensing technique was first applied to glutamate sensing by immobilizing glutamate dehydrogenase at the probe (core), which catalyzes the production of reduced nicotinamide-adenine dinucleotide (NADH) upon oxidation with glutamate. The obtained NADH produces fluorescent light that is proportional to the concentration of glutamate [154]. This technique is advantageous in monitoring glutamate release and continuous monitoring of neurophysiological responses, and an LOD of 0.22 uM and absolute mass detection limit of 44 fg were reported [155].  
Fiber optic aptasensors have been used for dopamine detection around the physiological concentration (LOD: 37 nM) [156]. The sensor is composed of a recognition element made of a 57-mer dopamine binding aptamer and a probe made of a nonadiabatic tapered optical fiber. The aptamer has a specific affinity to dopamine and, in the presence of dopamine, it changes its structure from a random conformation to a rigid pocket-like tertiary conformation as shown in Figure 2. This change of tertiary conformation induces a variation of the refractive index around the tapered fiber, hence indirectly permitting the detection of dopamine.
###### Abstract  
In order to improve the downlink communication performance of the traditional LoRa wide area network (LoRaWAN), a LoRaWAN downlink routing control strategy based on the software defined networks (SDN) framework and the improved auto-regressive integrated moving average (ARIMA) model is proposed. The SDN architecture is used to monitor the network traffic, and the link bandwidth occupancy rate is calculated based on the monitored downlink traffic. Taking into account the impact of data volatility on the accuracy of the prediction results, the Savitzky-Golay (S-G) smoothing filter and the sliding window method are introduced for data pre-processing. Stationarity processing is carried out for the time series data in the window, and the ARIMA model is developed to predict the downlink bandwidth occupancy rate. The triangle module operator is then used to incorporate multiple path parameters to finally calculate the selectivity of different paths, and the optimal path for LoRaWAN downlink communication is then provided. Simulation and experimental results show that the root mean square error of the improved ARIMA prediction model is reduced by 87% compared with the standard ARIMA model. The proposed routing control strategy effectively reduces the service transmission delay and packet loss rate. In the LoRaWAN test environment, as the downlink load rate increases, the average link bandwidth occupancy rate of this solution increases by 12% compared with the traditional method.  
LoRaWAN; SDN framework; ARIMA model; downlink routing control 1  
1 2022
## 2 Methodology
### Analysis of Observation Space
#### 2.4.3 Pairwise Observation Distribution Comparison  
To quantify the similarity between observation distributions of pairs of agents, the following metrics were computed. The cluster definitions and the individual agent occupancy histograms (e.g., \(p_{i}(c)\) and \(p_{j}(c)\)) used for these pairwise comparisons are established and computed according to the procedures detailed in Section 2.4.2.  
* **Pairwise Total Variation (TV) Distance**: For each pair of agents \((i,j)\), the TV distance between their cluster occupancy histograms \(p_{i}(c)\) and \(p_{j}(c)\) was calculated. This ranges from 0 (identical) to 1 (disjoint). \[\text{TV}(i,j)\ =\ \frac{1}{2}\sum_{c}\big{|}\,p_{i}(c)-p_{j}(c)\,\big{|}.\] (5)
* **Pairwise Maximum Mean Discrepancy (MMD)**: MMD was computed between the PCA-reduced feature sets of each pair of agents using a Gaussian kernel [13]. The kernel bandwidth sigma was set using the median heuristic. MMD is a statistical test for determining whether two samples are drawn from the same distribution, and its magnitude provides a measure of their dissimilarity. It operates directly on the feature representations rather than relying on the explicit cluster histograms. \[\text{MMD}(i,j)=\sqrt{\mathbb{E}_{x,x^{\prime}\sim P_{i}}\big{[}K(x,x^{\prime })\big{]}\ -\ 2\,\mathbb{E}_{x\sim P_{i},y\sim P_{j}}\big{[}K(x,y)\big{]}\ +\ \mathbb{E}_{y,y^{\prime}\sim P_{j}}\big{[}K(y,y^{\prime})\big{]}},\] (6) where \(K\) is a positive-definite kernel. We choose a Gaussian kernel \(K(x,y)=\exp(-|x-y|^{2}/2\sigma^{2})\). The kernel bandwidth \(\sigma\) is set to the median pairwise distance among all states in the combined dataset (median heuristic), which provides a reasonable scale. MMD is essentially a distance in reproducing kernel Hilbert space and is 0 iff the two distributions are identical (for characteristic kernels).
# Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
## 4 Results and Discussion
### The Influence of Adsorbed Gas on the Permeability  
Gas adsorption capacity is measured through gasses at identical pressures but of different types, thereby ensuring the comparability of the permeability results. The adsorption capacity was measured for the following four gases, in ascending order: He, N\({}_{2}\), CH\({}_{4}\), and CO\({}_{2}\). The variation in permeability with the capacity of gas adsorption is illustrated in Figure 6. It is evident that there is an inverse correlation between the permeability of the coal body and the gas adsorption capacity. As the capacity for gas adsorption increases, the permeability of the coal samples is 0.0186 \(\upmu\)m\({}^{2}\), 0.0172 \(\upmu\)m\({}^{2}\), 0.0144 \(\upmu\)m\({}^{2}\), and 0.0132 \(\upmu\)m\({}^{2}\), respectively. Taking the permeability of the non-adsorptive inert gas He as the baseline, the reductions in permeability for the other three gasses are 8%, 23%, and 29.2%, respectively. Thus, it is evident that gas adsorption exerts an inhibitory effect on the permeability of the coal body. The reason is that, after the coal body adsorbs gas, it undergoes adsorption-induced swelling and deformation. However, constrained by the confining pressure, deformation can only occur inwardly, thereby compressing the internal seepage pathways of the coal body and consequently reducing its permeability. As the  
Figure 5: Variation law of permeability with the static load stage of the cyclic dynamic load.  
amount of adsorption increases, so does the degree of adsorption-induced swelling and deformation, resulting in a more significant reduction in permeability.
## 3 Implementation
### Neural Network Model  
For the target application described in this paper, a relatively simple SNN has been developed to run on SpiNNaker, since the aim is to demonstrate our hardware platform. Development of a general SNN for the task of predicting the end position of a ball based on its initial motion (the 'goalkeeper' task) is a non-trivial problem and it is beyond the scope of this paper. A generalised methodology for developing an SNN to run on a DVS-SpiNNaker platform, using either supervised or unsupervised training algorithm, is described in Appendix A. Here the developed SNN is described to demonstrate our platform, and the model is shown and explained in Figure 6(a).  
The network has three layers as shown in Figure 6(b). The first (input) layer of the SNN is connected to processed DVS output, which has a resolution of \(16\times 16\) after downsampling (explained in Figure 3). The second (hidden) layer has eight neurons, and each neuron is connected to two columns of the DVS layer, and hence has 32 input synapses. The third (output) layer also has eight neurons, which correspond to eight possible positions of the goalkeeper. The neurons in the layers two and  
Figure 5: Synchronization of events, spikes and commands at the assumed maximum rate of 2000 packets/s for the SpiNNaker input spikes.  
Figure 6: (**a**) Setup of the neuromorphic robotic goalkeeper. (**b**) Connections between the modules. One input/output port is used on SpiNNaker (SpiNN-3) board (shown left) and set in SpiNNakerLink class. Two chips on the protoboard are level shifters (1.8 V to/from 3.3 V), which are used between the microcontroller and SpiNNaker. The microcontroller (Arduino Due) is at the right end of the picture.  
three are connected using one-to-one connectivity. The total number of synapses in the SNN is \(32\times 8+8=264\). The SNN has been developed using PyNN [29] with sPyNNeker extension [30] and run on the SpiNNaker. The neuron model used in the SNN is standard leaky integrate-and-fire (LIF).  
For the input real-time captured events form DVS has been used, which was recording a ball rolling (pushed by a hand towards the goal from the opposite side of the table). The weights of all synapses were adjusted manually, without using any training algorithms, based on the model explained in Figure 7.  
As shown in Figure 7b, lateral inhibition is not implemented in the output layer. This means the output neurons can spike simultaneously, and in that case the resulting output could not guide the robot arm to a unique location. Therefore, a voting mechanism (Figure 4) has been introduced. It is setting a threshold of 10 spikes as a criterion for deciding which neuron will determine the goalkeeper's position. Additionally, SpiNNaker will not send any packets unless a neuron in the output population is spiking. Furthermore, this threshold increases the robustness of the network to random spikes in the input layer representing the DVS pixels.
# Sources of Extraterrestrial Rare Earth Elements: To the Moon and Beyond
###### Abstract  
The resource budget of Earth is limited. Rare-earth elements (REEs) are used across the world by society on a daily basis yet several of these elements have <2500 years of reserves left, based on current demand, mining operations, and technologies. With an increasing population, exploration of potential extraterrestrial REE resources is inevitable, with the Earth's Moon being a logical first target. Following lunar differentiation at ~4.50-4.45 Ga, a late-stage (after ~99% solidification) residual liquid enriched in Potassium (K), Rare-earth elements (REE), and Phosphorus (P), (or \"KREEP\") formed. Today, the KREEP-rich region underlies the Oceanus Procellarum and Imbrium Basin region on the lunar near-side (the Procellarum KREEP Terrain, PKT) and has been tentatively estimated at preserving 2.2 \(\times\) 10\({}^{8}\) km\({}^{3}\) of KREEP-rich lithologies. The majority of lunar samples (Apollo, Luna, or meteoritic samples) contain REE-bearing minerals as trace phases, e.g., apatite and/or merrillite, with merrillite potentially contributing up to 3% of the PKT. Other lunar REE-bearing lunar phases include monazite, yititrobetafite (up to 94,500 ppm yttrium), and tranquillityite (up to 4.6 wt % yttrium, up to 0.25 wt % neodymium), however, lunar sample REE abundances are low compared to terrestrial ones. At present, there is no geological, mineralogical, or chemical evidence to support REEs being present on the Moon in concentrations that would permit their classification as ones. However, the PKT region has not yet been mapped at high resolution, and certainly has the potential to yield higher REE concentrations at local scales (<10s of kms). Future lunar exploration and mapping efforts may therefore reveal new REE deposits. Beyond the Moon, Mars and other extraterrestrial materials are host to REEs in apatite, chevkinite-perrierite, merrillite, whitlockite, and xenoting. These phases are relatively minor components of the meteorites studied to date, constituting <0.6% of the total sample. Nonetheless, they dominate a samples REE budget with their abundances typically 1-2 orders of magnitude enriched relative to their host rock. As with the Moon, though phases which host REEs have been identified, no extraterrestrial REE resource, or ore, has been identified yet. At present extraterrestrial materials are therefore not suitable REE-mining targets. However, they are host to other resources that will likely be fundamental to the future of space exploration and support the development of in situ resource utilization, for example: metals (Fe, Al, Mg, PGEs) and water.  
rare-earth elements; Moon; extraterrestrial; space; Mars; meteorites +
Footnote †: journal: