esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 3 Mechanical Passive Constant Flow Regulators
### Devices with Flexible Membrane(s)
#### 3.1.1 Silicon Membrane(s)
An alternative Titanium device was made of a stack of laser-drilled and electrochemically micromachined Ti sheets. A 20 \(\upmu\)m spacer ring was used to form the gap between the membrane and the substrate. The laminated Ti sheets were finally welded together. Water flow regulation at 5 nL/s (0.432 mL/day) was demonstrated in the pressure range [100; 600] mbar. At higher pressure, the flow increased beyond the regulation limit [16]. [PERSON] et al. used a stack of three wafers: a top Pyrex wafer for fluidic connection, a central Si wafer for the membrane, and a bottom Si wafer for the microchannel substrate [30]. The membrane and the bottom channel are assembled, after structuration, by direct bonding, while the top wafer is anodically bonded to the membrane/substrate stack. After the determination of the contact radius of the membrane with the substrate as a function of the applied pressure, a 3D fluidic model was created and the program TASCflow was used to solve the incompressible Navier-Stokes equations. Figure 4: Cross-section of a triple-stack passive flow regulator, with a top Pyrex wafer (inlet port), a middle Si wafer (membrane), and a bottom Si or Pyrex wafer (substrate with a spiral microflow channel and an outlet port). As the pressure increases, the Si membrane deflects against the substrate and forces the fluid flow through the spiral channel. By design, the membrane covers a channel length that increases linearly with the applied pressure to maintain a constant flow rate (Courtesy of Debiotech SA, Switzerland). Three different designs were tested using water with nominal flow rates of 0.85, 1.30, and 0.54 mL/h. The \(Q(\Delta P)\) profile of the 0.85 mL/h design showed, after a flow rate overshoot at low pressure (up to 2.5 mL/h), a constant water flow rate of 0.85 mL/h \(\pm\) 5% in the pressure range \([180;\ 500]\) mbar. At higher pressure, in the shut-off range \([500;\ 800]\) mbar, the flow rate decreases progressively. Above 800 mbar, the channel is completely covered by the membrane and the flow is blocked. Long-term stability (26 days) and the ability to compensate for a sinusoidal pressure between 200 and 400 mbar were also demonstrated [30].
# Lightning-Ignited Wildfires beyond the Polar Circle
###### Abstract  
Warming-driven lightning frequency increases may influence the burning rate within the circumpolar Arctic and influence vegetation productivity (GPP). We considered wildfire occurrence within the different Arctic sectors (Russian, North American, and Scandinavian). We used satellite-derived (MODIS) data to document changes in the occurrence and geographic extent of wildfires and vegetation productivity. Correlation analysis was used to determine environmental variables (lightning occurrence, air temperature, precipitation, soil and terrestrial moisture content) associated with a change in wildfires. Within the Arctic, the majority (>75%) of wildfires occurred in Russia (and ca. 65% in Eastern Siberia). We found that lightning occurrence increase and moisture are primary factors that mediate the fire frequency in the Arctic. Throughout the Arctic, warming-driven lightning influences fire occurrence observed mainly in Eastern Siberia (>40% of explained variance). Similar values (ca. 40%) at the scale of Eurasia and the entire Arctic are attributed to Eastern Siberia input. Driving by increased lightning and warming, the fires' occurrence boundary is shifting northward and already reached the Arctic Ocean coast in Eastern Siberia. The boundary's extreme shifts synchronized with air temperature extremes (heat waves). Despite the increased burning rate, vegetation productivity rapidly (5-10 y) recovered to pre-fire levels within burns. Together with increasing GPP trends throughout the Arctic, that may offset fires-caused carbon release and maintain the status of the Arctic as a carbon sink.  
lightning fire ignition; arctic fires; northward fire migration; heat waves; arctic vegetation productivity; wildfire recovery +
Footnote †: journal:  
_Atmosphere_
## 5 Discussion
### Future Work and Practical Applications
#### 5.7.2 Practical Applications  
Practically, the findings support the deployment of predictive systems in schools to identify at-risk students early. Furthermore, incorporating fairness-aware algorithms can promote equitable outcomes. The study thus provides a blueprint for deploying interpretable, accurate, and ethical ML tools in educational settings.
# On the Redundancy in the Rank of Neural Network Parameters and Its Controllability
## 5 Experiments
### Results
Again, we measure the average of 20 training runs. Overall, we find that increasing its value leads to a model with fewer trainable parameters as expected. However, this method is less sensitive and \(\lambda_{1}\) needs to be changed in an exponential scale to have a noticeable effect. Another thing to note is that a large portion of the reduction in the rank is concentrated on the early phase of training as shown in Figure 4a. We hypothesize that as the training progresses, more parameters become necessary to the model's objective which makes the rank reduction more difficult. Intuitively, a model's performance should decrease as the number of trainable parameters decreases if the model is utilizing all of its weights. However, we find that this is not the case. As can be seen in Figure 3a, the model does not suffer serious degradation in accuracy to the point where 34.22% of the parameters are removed (\(\lambda_{1}=40.0\)). On the other hand, forcing the model to use even fewer parameters leads to a dramatic drop in performance. This can be observed more clearly when the accuracy is plotted as a function of compression rate in Figure 3b. Merely removing 6.91% more parameters from this point results in a model less accurate than the baseline. In addition, the model with 10.41% (\(\lambda_{1}=1.0\)) compression performs _vorse_ than the models with 27.22% (\(\lambda_{1}=10.0\)) and 30.65% (\(\lambda_{1}=20.0\)) compression. These results indicate that redundancy in the rank of neural network's weight can negatively impact the model, and our proposed method is effective at reducing it. To analyze the amount of redundancy present in each parameter, we summarize the feature, input, and output dimensions after training in Figure 4b-d and Table 2. Overall, all parameters' shapes become close to a square, due to the reduction process. However, the dimensions of each layer differ while the weighting term in (16) is the same for layers 2 to 5. Similar results were reported in the network pruning literature [36,52] and this suggests that our method is able to effectively identify and remove redundant part of the model. Further, even though the weighting term in (16) of layer 6 is the smallest, its input dimension is reduced most aggressively. Given that this parameter has the highest value of \(|n-m|\), this supports our hypothesis that the redundancy is high when the matrix is rank deficient. The dimensionality of the feature space lies in between the initial value and the layer's output dimension, which is in line with our claim that the feature space can have higher dimension than the output space due to the nonlinear activation function but still has redundancy which is possible to be removed. Figure 2: The effect of \(\tau_{cos}\) and \(\tau_{euc}\) on model training and compression. (a) Accuracy (%) on the test set. (b) Compression rate (%) at the end of the training process.
## 2 Preliminaries on the 3 GPP NR Physical Layer Specification
### Use Cases and Their Requirements  
Reliability and user-plane latency are the new physical layer requirements in URLLC systems. The reliability requirements are defined to ensure that a 32-byte URLLC packet is successfully transmitted with a success probability of \(1-10^{-5}(99.999\%)\) with a user-plane latency of 1 ms [9]. The user-plane latency includes scheduling delay, queuing delay, transmission delay, receiver-side processing, and decoding delay, as well as multiple round-trip times for hybrid automatic repeat request (HARQ).  
Before supporting a variety of URLLC use cases in the future releases, prior use cases such as AR/VR, factory automation, transport industry, and power distribution were specified in Release 16 [10]. Moreover, command and control communication for UAVs is another important use case. As individual use cases have their own requirements, physical layer enhancement has been specified to improve URLLC performance. Table 1 illustrates the generally considered requirements for the evaluation of individual use cases, including one for command and control in the UAV network. The following subsection shows the physical layer aspects specified for supporting URLLC traffic while allowing for efficient means of coexistence between URLLC and eMBB traffic in various use cases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Scenario** & **Reliability** & **Latency** \\ \hline AR/VR & \(1-10^{-5}\) & 1 to 4 ms (user-plane) \\ Factory Automation & \(1-10^{-6}\) & 1 ms (user-plane) \\ Transport Industry & \(1-10^{-5}\) & 3 or 7 ms (user-plane) \\ Power Distribution & \(1-10^{-6}\) & 3 or 6 ms (user-plane) \\ Command and Control of UAV Network & \(1-10^{-3}\) & 10, 40, or 140 ms (end-to-end) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Use Cases and Their Performance Requirements for URLLC.
## 4 Simulation Results and Analyses
### Results and Analyses
#### 4.2.3 Simulation Results and Analyses for 8:00 to 17:00
In Figure 15, the SC is recharging in three periods, Period 1, Period 2, Period 3, in case 4. In Period 1, the SC starts recharging at 9:33. The SC cannot stop recharging when the \(t_{SC\_CH}\) is more than \(T_{SC\_MIN}\), because the \(soc_{SC}\) is always less than the \(SOC_{SC\_MAX\_MIN}\) while the \(\Delta p\) is positive in case 4. The SC stops recharging at the end of the Period 1 in case 6. In Period 2, the SC starts recharging at 9:41 and stops recharging at 9:44 when \(t_{SC\_CH}\) reaches \(T_{SC\_MIN}\), because the \(soc_{SC}\) reaches the \(SOC_{SC\_MAX\_MIN}\) while the \(\Delta p\) is positive in case 4. In Period 3, the SC starts recharging at 9:46 and stops recharging at 9:49 in case 4. For an easy reading of Figure 15a,b, the same points are marked in both figures and the given numerical values only reserve the integer part. The marked values show that the DC bus voltage is stable, the PV is working in MPPT mode, the load works at its load demand power, the SC is being charged by the \(\Delta p\), and the BS, the DG, and the public grid are not working (case 4). During the period 8:00-17:00, the SC is also recharged at 13:41, 14:57, and 16:12 and shows the same results as those depicted above at 9:33, except for the condition in which the SC stops recharging when \(soc_{SC}\) reaches the \(SOC_{SC\_MAX\_MAX}\). Figure 15: Curves of common DC bus voltage and SOC of storage (a) and curves of power (b) during SC recharging.
# Advances in Organic and Organic-Inorganic Hybrid Polymeric Supports for Catalytic Applications
## 2 Organocatalysed Non-Asymmetric Reactions
### Bronsted Acid Catalysed Reactions
#### 2.1.1. Esterification Reactions
In comparison to reported solid catalysts [9; 10], the esterification of glycerol with acetic acid catalysed by **5a-c**, was demonstrated to be superior, considering the acetic acid molar ratio, the reaction time, and the catalyst preparation. Application of **5a-c** in the acetalization of glycerol also gave excellent results in terms of activity and reusability [6]. Recently, carbon nanotube-based solid sulfonic acids were reported as catalysts for production of fatty acid methyl esters via transesterification and esterification [11]. The investigated materials were obtained by covalent grafting of multi-walled carbon nanotubes (CNT) with sulfonic acid-functionalized polymers. In detail, polyelectrolyte brush-modified solid acid **7a** and **7b** were prepared by sulfonation of poly(1-vinylimidazole)-grafted multi-walled CNT **6a** and poly(4-vinylpyridine)-grafted multi-walled CNT **6b**, respectively, with chlorosulfonic acid (Scheme 4). The precursors **6a** and **6b** were synthesized by an in situ radical polymerization of 1-vinylimidazole and 4-vinylpyridine monomer, respectively, in the presence of multi-walled CNTs. The polyelectrolyte brush-modified solid acid **7c** was obtained by acidification of poly(sodium 4-styrenesulfonate)-grafted CNT **6c**, prepared by an in situ polymerization of sodium _p_-styrenesulfonate monomer in the presence of multi-walled CNTs. At first, the resulting catalytic materials were tested in transesterification of glyceryl triacetate and glyceryl tributyrate with methanol. All of the solid sulfonic acids **7a-c** showed excellent catalytic performance giving high conversions after 10 and 5 h. The methanolysis of glyceryl triacetate was proved to be much faster than the methanolysis of glyceryl tributyrate. In both reactions, the activity of catalysts **7a-c** decreased in the following order **7a** > **7b** > **7c**. In addition to the transesterification of triglycerides, catalysts **7a-c** were also effective for esterification of C18 mono-unsaturated oleic acid with methanol giving excellent yields of methyl oleate. The reusability of catalyst **7a** was investigated by transesterification of glyceryl tributyrate with methanol at a low glyceryl tributyrate conversion level. The conversion of glyceryl tributyrate decreased from 52.4% to 40.9% after six cycles. The elemental analysis of recovered catalyst revealed a decrease of -SO3H concentration compared to fresh catalyst. Therefore, the loss of activity of catalyst was presumably due to partial cleavage of polymeric chains covalently linked on the CNTs, which resulted in a partial loss of catalytic active sites (-SO3H groups). **Scheme 4. Carbon nanotube-based solid sulfonic acids **7a-c** as catalysts for synthesis of methyl esters via transesterification and esterification.
## 6 Simulations Results and Discussions
### Average Network Throughput  
The performance of different routing protocols in terms of average throughput is demonstrated in Figure 17, where the average throughput is defined as the average number of bits collected from the FoI in each round. As depicted in the figure, the proposed EEUCH protocol outperforms the other benchmark protocols significantly due to the prolonged life of SNs. In contrast, the benchmark protocols exhibit early SN death, resulting in lower throughput compared to the EEUCH protocol. It is worth mentioning that the EEUCH protocol efficiently utilizes the energy of each SN, which leads to a longer network lifetime. The longer SN lifespan ultimately leads to a more reliable and efficient data collection process, thus improving the overall performance of the proposed protocol.  
Figure 16: Number of rounds versus percentage of node deaths.  
Figure 17: Average throughput.
## 4 Penetration of L-Band Signal and Its Influencing Factors
### Penetration of L-Band Signal
The simulation of [PERSON] et al. showed the vegetation optical depth increased linearly with the decrease in LAI, while the results were similar to those from the satellite-based L-band, C-band and X-band. Their sensitivity tests indicated that polarization dependence become more pronounced at higher frequencies [58]. [PERSON] et al., through unsupervised classification analysis on a global scale, found that the L-band is suitable for monitoring dense canopies, while X-band and LCX vegetation optical depths are more suitable for sparse tree canopy, savannas and grasslands [59]. [PERSON] et al. analyzed the relationship between live fuel coverage and vegetation optical depth through random forest regression, using multi-band datasets and soil-moisture--marine-salinity sensors, providing important guidance for selecting suitable wavelengths for specific applications and algorithm development [60]. [PERSON] et al. studied the attenuation characteristics of L-, C- and X-bands with respect to the conditions of different land-cover types, and concluded that shrub had a high transient peak value, while forest canopy had a low value [61]. In the areas with strong seasonal rainfall, the seasonal amplitude was greater in the C-band than in the L-band. The penetration characteristics of the L-band in various conditions and its comparison with the C-band and X-band are summarized in Table 2.
# TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space
## 5 Lessons Learned and Discussion
Several items in the system, including the servos and TLE data cause slight misalignments when pointing to the satellites. The servo accuracy could be improved by replacing the hobby servo motors with stepper motors, which can move with higher precision. Any issues with the TLE data being inaccurate could be addressed with the use of the previously mentioned computer vision systems, allowing for a tuneable feedback loop. Figure 11: Moon computer vision tracking. Contours in the image are highlighted in red, with the largest one enclosed by a green rectangle. The centre of this rectangle can then be further used as the centre of the Moon.
# First Request First Service Entanglement Routing Scheme for Quantum Networks
Therefore, the entanglement routing with active wavelength multiplexing schemes is desiderated for constructing entanglement distribution networks with various topologies [42; 43; 44; 45]. In the meantime, existing quantum routing schemes [2; 46; 47; 48; 49; 50; 51; 52] are mainly focused on trusted node networks and quantum memory-assisted networks, and cannot be applied to quantum entanglement distribution networks, where time division multiplexing (TDM) and wavelength division multiplexing (WDM) techniques need to be adopted to forward entangled photons. In this article, the entanglement distribution network is abstracted into a directed graph firstly, where the nodes represent the entangled photon source, the users and the wavelength routing devices, the edges stand for the quantum links among nodes. Especially, the pass-through loss matrix among all ports within a node is defined for each supported wavelength channel. Afterwards, we propose a novel first request first service (FRFS) entanglement routing scheme for quantum networks, which performs the modified Dijkstra algorithm to find the lowest loss path from the entangled photon source to each paired users in order. Finally, the evaluation results show that the proposed FRFS entanglement routing scheme can be applied to the large-scale and dynamic topology quantum networks.
# On the Performance of Interleavers for Quantum Turbo Codes
## 4 Conclusions  
We have adapted three interleaving design methods used for classical turbo codes to the quantum domain, demonstrating the importance of interleaver construction in quantum turbo codes. By using Monte Carlo simulations, we have shown that these constructions do enhance the error correction capability of quantum codes in the error floor region, leading to error rate improvements of up to two orders of magnitude. We have also shown that memory requirements can be lowered by using specific interleaver designs, while the performance in the error floor region is comparable or even better than that of the original EXIT optimized QTCs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Parameters** & **Storage Requirements** \\ \hline Random [17] & \(\pi\) & \(N\) \\ \hline \(S\)-random & \(\pi\) & \(N\) \\ \hline Welch-Costas & \(N,\,\alpha\) & 2 \\ \hline JPL & \(N,\,k_{1},\,p_{q}\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Memory requirements.  
Figure 2: Word error rate (WER) performance curves for the 1/9-QTCs in Table 1 when different interleaving methods are used.  
Author Contributions: Funding acquisition, P.M.C. and J.E.M.; Investigation, J.E.M.; Software, J.E.M.; Supervision, P.M.C. and J.G.-F.; Writing--original draft, J.E.M. and P.M.C.; Writing--review & editing, J.G.-F.  
This work was supported by the Spanish Ministry of Economy and Competitiveness through the CARMEN project (TEC2016-75067-C4-3-R). [PERSON] is funded by the Basque Government predoctoral research grant.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
QECC Quantum Error Correction Code  
QCC Quantum Convolutional Code  
QTC Quantum Turbo Code  
QIRCC Quantum IrRegular Convolutional Code  
QURC Quantum Unity Rate Code  
EXIT EXtrinsic Information Transfer  
EPR Einstein-Podoslky-Rosen  
SISO Soft-Input Soft-Output  
JPL Jet Propulsion Laboratory  
CCSDS Consultative Committee for Space Data Systems  
WER Word Error Rate  
EA Entanglement-Assisted
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 4 Proactive Service Caching Method  
In Figure 3, we show the overall procedure of our proactive service caching scheme. Our method is composed of two main modules. The first module is responsible for collectively representing the popularity distribution of each service within the MEC system. A controller periodically collects \(\sum_{u\in U_{m}(t)}b_{m,u,k}(t)\) from each MECS \(m\in\mathcal{M}\) and builds a \(M\times K\) heatmap image to collectively represent the service popularity of each service in each MECS. The second module collectively determines the services to be cached by each MECS for the next time slot with the ConvLSTM model. In the second module, a controller takes the recent \(w_{a}\) consecutive heatmap images and predicts the next heatmap image. By using the estimated service popularity of each service in each MECS containedin the predicted heatmap image, a controller simultaneously determines the services to be cached by each MECS for the next time slot. We will detail the operation of each module in Sections 4.1 and 4.2.
## 3 Materials and Method
### Proposed Method
#### 3.3.4 Unified Optimization Objective for Multi-Module Coordination  
To further strengthen the coordination among modules, a unified optimization objective is introduced into the training framework. Instead of relying solely on feature concatenation and attention mechanisms, the proposed method jointly trains the structural, temporal, and macroeconomic modules under a shared loss function. Specifically, the total objective combines three components: the classification loss \(\mathcal{L}cls\), the regression loss \(\mathcal{L}reg\), and a semantic alignment loss \(\mathcal{L}align\) that ensures consistency between macroeconomic representations and temporal embeddings. The overall formulation is as follows:  
\[\mathcal{L}_{\text{total}}=\mathcal{L}_{\text{cls}}+\alpha\cdot\mathcal{L}_{ \text{reg}}+\beta\cdot\mathcal{L}_{\text{align}} \tag{23}\]  
where \(\alpha\) and \(\beta\) are trade-off hyperparameters. This joint training strategy enables cross-module gradient propagation and collaborative representation learning. The empirical results presented in Section 4.9 demonstrate that the unified objective improves not only the convergence stability but also forecasting accuracy, confirming the effectiveness of the proposed integration enhancement.