esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 3 Results  
### Validation of the Revised Gash Model  
The canopy interception of the revised Gash model displayed a significantly correlation both with the PML_V2 dataset product (r = 0.8, \(p\)! 0.01, Figure 2a) and the in-situ measurements (r = 0.66, \(p\)! 0.01, Figure 2b). The points in Figure 2 mainly distributed around the 1:1 line and the NRMSE were both less than 15%, indicating that simulated results of the revised Gash model were relatively robust compared to those obtained from the existing remote sensing products and in-situ measurements.
## 2 Research Area and Data Sources
### Indicator Selection  
Annual datasets for the years 2000, 2010, and 2020 were selected for analysis to avoid errors in monthly remote sensing and meteorological data, which may be affected by precision limitations and extreme climatic conditions. The data include land use types, annual average temperature, vegetation index, and NPP, as well as total annual precipitation. NPP was calculated using the CASA (Carnegie-Ames-Stanford Approach) model.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### Support Vector Machine Method  
Support Vector Machine method (SVM) is a machine learning technique presently actively adopted in remote sensing [23; 51; 52; 53; 54]. SVMs are supervised learning models with associated learning algorithms that analyze data used for classification or regression. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier [55].  
SVMs are based on statistical learning theory and have the aim of determining the location of decision boundaries that produce the optimal separation of classes. [56]. In the case of a two-class pattern recognition problem with linearly separable classes, the SVM selects from among the infinite number of linear decision boundaries the one that minimizes the generalization error. Thus, the selected decision boundary will be the one that leaves the greatest margin between the two classes, where the margin is defined as the sum of the distances to the hyperplane from the closest points of the two classes [56]. The margin maximization is achieved using standard quadratic programming optimization techniques. The data points that are closest to the hyperplane are used to measure the margin and are referred to as support vectors.  
If the two classes are not linearly separable, the SVM tries to find the hyperplane that maximizes the margin while, at the same time, minimizing a quantity proportional to the number of misclassification errors. The trade-off between margin and misclassification error is controlled by a user-defined constant [55]. SVM can also be extended to handle nonlinear decision surfaces by projecting the input data onto a high-dimensional feature space using kernel functions [56]. Radial basis functions with accordingly selected parameters are a typical choice to serve as kernel functions [51; 52]. The gamma value varied here between 0.1 and 0.005 depending on the dataset.  
As SVMs are designed for binary classification, this method appears to be an ideal fit for outlier detection problems, i.e. separating damaged forest class against intact using temporal dynamics of SAR backscatter. However, for estimating severity of damage (evaluating \"change magnitude\"), the approach is less suitable.
## 6 3D Integration and Assembly
### RF Modulator Electrodes Design  
Prior to assembly, SiP wafers must be post-processed in order to receive bonding pads that are compatible with the solder reflow assembly process. Furthermore, this metallization will be the metallization of the electrodes of the modulators and should not lead to a deterioration of the performance of the devices. For this purpose, simulations of the RF performance have been conducted to carefully design the electrodes.  
The implementation of the L3 MATRIX technology requires the use of travelling wave (TW) electrodes, which are essential to achieve the high bandwidth requested by DCs. The downside of combining slow light with a travelling wave design is the mismatch between the microwave group index and the optical one [48]. For a lossless modulator with impedance matched load and generator, the 3 dB modulation bandwidth is given by:  
\[\mathrm{f_{3 dB}}=\frac{0.18}{l(n_{m}-n_{0})}\ (\mathrm{GHz}) \tag{1}\]  
where \(l\) is the electrode length, \(n_{m}\) is the microwave refractive index, and \(n_{0}\) is the group refractive index of the optical mode. In our case, taking a 0.5 mm long electrode, a microwave refractive index of 3.8 at 30 GHz and optical group indices of 10, 15 and 20 lead to a 3 dB bandwidth roll-off of 57, 32 and 22 GHz, respectively. Therefore, for a sufficiently short modulator, the velocity mismatch arising from the use of slow light is not impairing the high-speed performance of the modulator. A symmetrical travelling wave electrode based on a ground-signal-ground (GSG) coplanar strip waveguide (CPW) transmission line has been designed using the commercial finite element method solver for electromagnetic structures, ANSYS HFSS. Considering the metal stack present on the SiP wafers, S-parameters can be simulated and are shown in Figure 18.  
These calculated values show that electrodes compatible with the targeted performance and solder reflow processes can be fabricated for the L3 MATRIX devices.
# Photo-Fermentative Bacteria Used for Hydrogen Production
## 2 Photo-Fertanative Bacteria for Hydrogen Production
### Rhodopseudomonas
#### 2.2.2 Rhodopseudomonas capsulata  
R. capsulata can produce hydrogen by converting light into chemical energy. This microbe is mainly found in aquatic environments and is known for its ability to perform anoxygenic photosynthesis, as it does not produce oxygen as a by-product. Bacteriochlorophyll and carotenoids are the essential pigments for photosynthesis in these bacteria.  
In a study conducted by [PERSON] et al., the maximum hydrogen productivity was achieved at a temperature of 30 \({}^{\circ}\)C and an irradiance of 31.6 W/m\({}^{2}\)[49]. In addition, a study carried out in 1971 to elucidate the photophosphorylation system of this microbe found that sonication of chromatophores in the presence of EDTA produced non-photophosphorylating particles and protein factors that helped to restore photophosphorylation [50].  
It was observed that the metabolism of _R. capsulata_ was affected by intermittent illumination, suggesting that the light conversion and biosynthesis are closely linked in these bacteria. Thus, the analysis of _R. capsulata_ could help to gain an insight into the phototrophy of the bacteria and its ability to produce hydrogen, and to identify the relationship between the energy conversion and biosynthesis. _R. capsulata_ has been identified for its ability to degrade toxic contaminants along with hydrogen. Benzoate is one of the identified inhibitory compounds that can prevent hydrogen production during anaerobic metabolism in the absence of light and electron acceptors, but under phototrophic conditions, _R. capsulata_ can extract hydrogen and use benzoate as a carbon source [51]. Table 2 shows a list of substrates used by these bacteria hydrogen production.  
The studies showed that a mixture of acetate, propionate, and butyrate is an optimal carbon source for _R. capsulata_ strains to produce hydrogen in the PBRs. However, the microbe has not been analyzed in many studies but has shown a great potential for photobiological hydrogen production, paving the way for the production of renewable energy sources in the future.
# Confidence Measures for Deep Learning in Domain Adaptation
## 3 Confidence Measures
### Entropy and Max Scaled Softmax Output  
The majority of current neural networks, differently from older ones, are poorly calibrated to be representative of the true output distribution. One of the most effective technique to calibrate the output of a model is _temperature scaling_[54]. Given the vector of the network logits \(\mathbf{z}\) and the softmax output \(\sigma_{SM}(\mathbf{z})\), to better represent the true posterior probability of the model, a scaled version of \(\sigma_{SM}(\mathbf{z})\) can be defined as:  
\[\mathbf{p}=\sigma_{SM}(\frac{\mathbf{z}}{T}), \tag{1}\]  
with  
\[\sigma_{SM}(h_{i})=\frac{h_{i}}{\sum_{j=1}^{c}(e^{h_{j}})},\quad\text{ \ \ \ where \ \ \ }h_{i}=\frac{z_{i}}{T} \tag{2}\]  
being \(c\) the number of classes. \(T\) is a scalar parameter, called _temperature_, optimized on the validation set, which aims at \"softening\" the softmax (i.e., it raises the output entropy). The network output \(y=argmax_{c}(\mathbf{z})\) does not change after temperature scaling. Two types of confidence measures have been considered in this case:  
* Entropy \[e=-\sum_{i=1}^{c}(p_{i}\log(p_{i}));\] (3)
* Max Scaled Softmax Output \[s=\max_{c}(p_{i}).\] (4)
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 5 Simulation Analyses
### Impact of k-Value on ICNS  
We let \(\kappa=2\) in (17), in order to conduct simulations in Section 5.1, which meant those CLCs that occurred more than two times in selected CCLCs needed to be added to the blacklist. The impact of \(\kappa\)-values on ICNS is studied in this section. As Figure 5 shows, only the first method of Algorithm 2 was adopted to select the CCLC in simulation group 1, which did not need the blacklist; thus, we focused on simulation group 3.  
Figure 6: General degree distribution.  
Figure 7: Dynamic-connect durations in the simulation group 1.  
Firstly, we constructed ICNSs through Algorithm 2 with different \(\kappa\)-values. Secondly, we conducted simulations on the dynamic connectivity of these ICNSs, as detailed in Section 5.2. Finally, we analyzed the simulation results. The range of \(\kappa\)-values was set to \([0,5]\).  
We did not demonstrate the constructed ICNSs, as shown in Figure 4, due to limited space, while we adopted structural entropies to show the corresponding CCLCs, as shown in Figure 9. We can draw the following three conclusions:  
The result of \(\kappa=0\) was the worst. No new CCLC was obtained by Algorithm 2 from slot 25 onwards (CCLCs from slot 25 to slot 40 were all the same as the CCLC for slot 24 in Figure 9), which means that the ICNS cannot be dynamically connected from slot 25. Moreover, \(\kappa=0\) means that any two satellites have to establish an ISL only once. A satellite will have no available satellite to establish an ISL with if the number of the slot is not smaller than the number of its potential link objects.  
If \(\kappa\geq 2\), the CCLCs for the same slot are identical, even though they are under different \(\kappa\)-values. Therefore, different \(\kappa\)-values may not lead to different ICNSs and it is pointless to set a greater \(\kappa\)-value.  
If \(\kappa\geq 1\), the CCLCs for each one of the first 31 slots under different \(\kappa\)-values are identical. Thus, not all CCLCs that are selected using the second method of Algorithm 2 are affected by \(\kappa\)-values.  
Then, we compared and analyzed several properties with respect to the dynamic-connection of ICNSs for \(\kappa=1\) and \(\kappa=2\).  
Figure 8: Durations of dynamic connectivity in simulation group 3.  
Figure 9: Structural entropies of CCLC under different \(\kappa\)-values.  
Figure 10 shows the general degree of all satellites under these two \(\kappa\)-values. There were 25 satellites whose general degree equaled the number of potential link objects if \(\kappa=1\), which was less than 28 if \(\kappa=2\). \(\oplus\)  
Table 2 illustrates other dynamic connectivity properties. Since ICNS could not remain dynamically connected during the whole state period 2, we only introduced the results of state period 1. Obviously, ICNS under \(\kappa=2\) had better dynamic connectivity properties than ICNS under \(\kappa=1\), and that is why we adopted \(\kappa=2\) in Section 5.1.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 4 Theoretical Analysis
### Impact of \(\gamma\) on BECPB  
From Equation (19), it can be shown that its first part increases with \(\gamma\), so is the second part, which means that \(E_{\text{bat,I}}\) is an increasing function of \(\gamma\). Similar to Equation (19), \(E_{\text{bat,M}}\) increases with \(\gamma\) as well.  
To sum up, the BECPBs for both types of batteries are increasing function of \(\gamma\). This is due to the fact that higher \(\gamma\) leads to smaller BUF and thus more energy waste during battery discharge process.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 4 Numerical Simulation  
This section simulates over the AWGN channel for Sections 4.1-4.4. Section 4.5 is simulated over a Rayleigh channel. The SNR is expressed as \(E_{s}/N_{0}\), where \(E_{s}\) is the energy per symbol and \(N_{0}\) is the single-sided noise power spectral density. We used Keras as the front end of the Tensorflow2 platform with an NVIDIA 3080 graphics card and Intel(R) Core(TM) i9 CPU to run the NN model, with the configuration given in Table 1. NNNs in Sections 4.2-4.5 have the code length \(N_{S}\)= 16. The polar code was constructed at 2.5 dB unless otherwise stated. Since the decoding scheme in this paper is proposed for the receiving end, the decoding performance is focused on the BER.
## 3 Design of a General Relay-Node Selection on Intersection
### Exponent-Based Partition on General Intersection
#### 3.3.2 Branch Phase  
In the branch phase, the selection of the node farthest from Relay\({}_{1}\) means that the node nearest to the junction of each branch and the circle for the communication range of Relay\({}_{1}\). When Relay\({}_{\rm B}\) is at the junction, the message progress in the branch phase will be the maximum. We term the junction on the \(n\)-th branch as the optimal point, P\({}_{\rm opt}^{n}\). Since the node nearer to the optimal point than Relay\({}_{1}\) is expected, the partition range \(R_{\rm P}\) is determined as the minimum range among the range from Relay\({}_{1}\) to the optimal point and the range from C to the optimal point. Consequently, when Relay\({}_{1}\) in the \(m\)-th branch, \(R_{\rm P}\) on the \(n\)-th branch can be defined as  
\[R_{\rm P}=\begin{cases}D_{\overline{\rm Relay_{1}P_{\rm opt}}},&\text{when $n=m$}\\ D_{\overline{\rm CJ_{\rm opt}}},&\text{when $n\
ot\
eq m$}\end{cases} \tag{4}\]  
where \(D_{\overline{\rm A,B}}\) represents the range between the points of A and B. Corresponding \(R_{\rm P}\) and P\({}_{\rm opt}^{n}\) to \"Communication Range\" and \"Border\" in Figure 1, the partition procedure proceeds. After CTB contention, Relay in each branch is selected. The general relay-node selection on intersection is completed, then the message is delivered to every direction. The mini-Black-Burst-Assisted mechanism in Ref. [14] is also applied in our proposal to alleviate the high partition latency in the adverse scenario [14], in which numerous nodes distribute only in the final segment near the sender, and in the sparse network.
## 3 Results and Discussion
### Regulatory Strategies Involved in the Transmission of ARGs
Pb concentration and pH fluctuations should be closely monitored during remediation, as well as ARGs that are particularly relevant to human health. For example, certain \(\beta\)-lactam resistance genes are strongly associated with public health concerns [50]. Co-selection of these genes can enhance the proliferation of antibiotic-resistant bacteria, leading to resistant pathogens that complicate treatment and increase morbidity and mortality rates. Human exposure occurs through contaminated food, water, and soil, with resistant bacteria accumulating in crops and water sources. This situation places a strain on healthcare systems, as patients with antibiotic-resistant infections often face longer hospital stays and more complex treatments, raising healthcare costs. Enhanced public health surveillance is critical for tracking resistance trends and ensuring timely interventions. Vulnerable populations, such as children and the elderly, are particularly at risk, highlighting the need for public health initiatives that promote safe agricultural practices and environmental remediation. Overall, the implications of ARGs and LRGs in Pb-contaminated soils underscore the urgent need for integrated strategies to mitigate their impact on community health. Therefore, both ecological and human health risks should be considered, with targeted monitoring of ARGs that pose the greatest risks during the remediation of Pb-contaminated soils.
# Comparison of ECG Between Gameplay and Seated Rest: Machine Learning-Based Classification
## 2 Materials and Methods
### Experimental Protocol  
The ECG recordings were conducted in a seated posture to ensure consistency in physiological measurements. The total measurement duration varied between 40 min and 120 min per participant, depending on individual engagement and tolerance levels. The experiment was conducted under two distinct conditions:  
Gameplay Condition: Participants in the gaming group played an interactive video game. The choice of the game was unrestricted, allowing players to engage with a game of their preference to maintain natural gaming behavior.  
Seated-Rest Condition: The control group remained seated in a relaxed state without engaging in any external stimuli, such as reading, watching videos, or using smartphones.  
Both conditions were conducted in a quiet environment with controlled room temperature (24 \(\pm\) 2 \({}^{\circ}\)C) to minimize external influences on heart-rate variability (HRV). The seated posture was chosen to avoid additional physiological variations caused by movement or changes in body position. To ensure the reliability of physiological measurements and the validity of group comparisons, strict exclusion criteria were applied. Participants with any history of cardiovascular disease, autonomic dysfunction, or neurological disorders were excluded, as these conditions could significantly influence heart-rate variability (HRV) and confound the results. Additionally, individuals currently taking medications that affect autonomic function, such as beta blockers or antidepressants, were not included in the study. Given that this research focuses on physiological differences between individuals with and without gaming addiction, only healthy participants without pre-existing medical conditions were recruited. Furthermore, individuals with irregular sleep patterns, excessive caffeine or alcohol consumption prior to the experiment, or high levels of daily physical activity were excluded to minimize confounding factors. These strict criteria ensured that the observed physiological differences were attributable to gaming addiction rather than other underlying health conditions.
# A 200-MS/s 10-Bit SAR ADC Applied in WLAN Systems
## 6 Conclusions  
This paper employs an improved high-linearity bootstrap to achieve high linearity sampling. The 4-reference voltage technique is used to increase the unit capacitance and reduce capacitor mismatch while maintaining the total capacitance. A comparator with a kickback cancellation mechanism is used to mitigate the impact of kickback noise. In terms of speed, a binary-scaled recombination redundancy design and an open-loop output stage RVB are used to accelerate the establishment of the reference voltage on the top plate of the CDAC. Additionally, a window-opening strategy is employed in the SAR logic to reduce the delay in the data path. The experimental results reveal that the proposed ADC achieves an impressive SNDR of 55.3 dB and a SFDR of 66.6 dB at a sampling rate of 200 MHz with Nyquist frequency input. It operates with a power consumption of 2.8 mW at a 1.2 V power supply, resulting in a remarkable FoM value of 29 f/conversion step. The core area of the proposed ADC is only 0.008 mm\({}^{2}\). With its impressive area efficiency and high-performance, low-power characteristics, the proposed ADC emerges as a promising solution for analog-to-digital conversion in WLAN applications.  
Conceptualization, Y.Z.; Methodology, Y.Z., T.M. and T.Y.; Software, Y.Z., Y.P. and B.W.; Validation, Y.Z.; Formal analysis, Y.Z. and Y.P.; Investigation, Y.Z.; Resources, Y.Z. and B.W.; Data curation, Y.Z.; Writing--original draft, Y.Z.; Writing--review & editing, Y.Z.; Visualization, Y.Z., Y.P. and B.W.; Supervision, Y.Z., T.M. and T.Y.; Project administration, Y.Z. and Y.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data in this paper are all from simulation and test results of real circuits, and there is no data plagiarism or falsification. Due to project confidentiality requirements, we regret that we cannot release more details of the data at this time.  
The authors declare no conflict of interest.
# Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation
## 6 Sensitivity Analysis
### Effects of Green Phase Percentage and Signal Cycle on Average Vehicle Delay  
In this subsection, we analyze the delay reduction in the modified MUT intersection under different green phase percentages and signal cycle durations. In typical traffic environments, major roads usually experience higher traffic volumes. Consequently, the settings of the green phase percentage for the major road significantly affect the overall performance of intersections. The delay reduction degree can be observed directly by varying the green phase percentage. Therefore, the percentage of the green phase of the major road is set to 0.2, 0.25, 0.3, 0.35, and 0.4. The traffic signal cycle durations range from  
Figure 12: Vehicle delay reduction for different vehicle free-flow speeds and numbers of left-turn vehicles.  
60 s to 150 s. The proportion of left-turn vehicles is fixed at 1:3. The vehicle lane change rate is set at 0.7, and the probability of vehicle random slowing is 0.5. The free flow speed is set to 8 m/s. All other parameters of the cellular automata models remain unchanged. The delay reduction results are shown in Figure 13.  
As shown in Figure 13, when the signal cycle is certain, the degree of delay reduction increases with the increase in the green timing percentage. This is because a higher green phase percentage allows more left-turn and straight-through vehicles on the major road to pass through the intersection, which helps dissipate queuing vehicles more quickly and reduces vehicle queuing time. As a result, the degree of delay reduction will be greater. When the green phase percentage of the major road is certain, the degree of delay reduction increases continuously from 60 s to 120 s. However, the delay reduction gradually decreases from 130 s to 150 s. This indicates that appropriately increasing the signal cycle and green phase duration within a specific range is beneficial for improving the traffic efficiency of MUT intersections. However, as the signal cycle and green phase duration continue to increase, the number of vehicles passing through the intersection during the corresponding time will also rise. This may cause drivers of left-turn and right-turn vehicles on the minor road to make more cautious decisions when weaving with the vehicles from the major road. Additionally, excessive vehicles on the major road may form a sufficiently long queue outside the median U-turn. These situations can lead to an increase in delay times. The delay reduction will decrease during a specific signal cycle and duration of the green phase time.