esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 DIS-Trickle: A Mechanism for Accelerating Network Convergence
### DIS-Trickle Design
A DIO message transmission can take place at any point of the second half of the current interval, _i.e._, it can suffer a random delay between 4 ms and 8 ms (the latter is illustrated in Figure 14). Finally, the physical transmission of this DIO message (which takes 2.82 ms) starts, after the backoff period and the CCA, at time \(t_{5}\), and node A finishes receiving the DIO message at time \(t_{6}\). Considering the duration of all the periods included between \(t_{I}\) and \(t_{6}\), the DIS response time is a value between 14.16 and 52.08 ms. From the basis of this analysis, we tested different values for \(I_{\textit{DIS}}\), (see the results obtained and discussed later in Section 6.2), and based on these tests we finally set \(I_{\textit{DIS}}\) to 30 ms, since it yields good performance in terms of both network convergence time and number of DIO and DIS messages transmitted during network convergence. Table 3 shows the default values of the DIS-Trickle parameters used in our simulations.
# Modifications to ArduSub That Improve BlueROV SITL Accuracy and Design of Hybrid Autopilot
## 2 ArduSub Alterations
### Changes to ArduSub and Motivation
#### 2.2.4 Vehicle Simulation Model Update  
The original ArduSub vehicle model is inaccurate and produces unrealistic behavior. Appendix B introduces the full hydrodynamic model that the updates are based on as well as a method for determining control gains for a critically damped linear system approximation which is relevant to Section 4. As discussed in Section 2.2.1, the thruster allocation matrix used by SITL to calculate state updates was updated from Equation (7) to Equation (4). See the later Section 3 for a description of the experimental testing that was performed to determine the yaw hydrodynamic model of the BROV2. All 6 DOF drag models were updated to follow the generalized form of Equation 11. In order to generate simulation results that have practical usability, that yaw hydrodynamic hydrodynamic model updated yaw and pitch degrees of freedom identically, whereas coefficients in roll were scaled to around 86% of the former to match the proportionality suggested by [17]. Missing added mass parameters were introduced. Linear degree of freedom parameters or other untested parameters were updated to the values used in [16]. Vehicle mass was changed from \(10.5\,\mathrm{kg}\) to \(11.5\,\mathrm{kg}\) and the center of buoyancy with respect to the center of gravity was changed from \([0,0,0.15]\,\mathrm{m}\) to \([0,0,0.02]\,\mathrm{m}\). First-order linear drag coefficients were introduced \(\mathbf{B_{L,1:3}}=[4.03,6.22,5.18]\,\mathrm{Ns/m}\), as well as first-order angular drag coefficients \(\mathbf{B_{L,4:6}}=[0.0,0.0,0.0]\,\mathrm{Ns/rad}\). Second-order drag was incorporated with \(\mathbf{B_{NL,1:3}}=[18.18|u|,21.66|v|,36.99|w|]\,\mathrm{Ns^{2}/m}\) and second-order angular drag coefficients with \(\mathbf{B_{NL,4:6}}=[2.08|p|,2.42|q|,2.42|r|]\,\mathrm{Ns^{2}/rad^{2}}\). The linear and angular added mass vectors were also introduced, with values of \(\mathbf{M_{A,1:3}}=[5.5,12.7,14.57]\,\mathrm{kg}\,\mathrm{m^{2}}\) and \(\mathbf{M_{A,4:6}}=[0.10,0.12,0.12]\,\mathrm{kg}\,\mathrm{m^{2}/rad}\), respectively.  
Other minor changes included a correction of the application of the transformation matrix of the inertial-to-body frame \(\mathbf{J_{1}^{-1}}\) to \(\mathbf{F_{B}}\); previously, this was applied to \(\mathbf{r_{B}}\). Also, thruster force output calculations were updated to reference a lookup table, see Section 2 and Figure 6. In the next sections, the updates to the underlying hydrodynamic models of the autopilot and SITL produce accurate results useful for designing controllers that operate in real water settings.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation
### Experimental Analysis of Faulty Rolling Bearing
#### 4.2.1 Wavelets  
In this method, firstly, the observation signal is decomposed with six-layer Daubechies wavelet decomposition, and then the amplitude spectra of each component can be obtained with FFT. The result is shown in Figure 11. The frequency of 107 Hz, which is very close to \(f_{ic}\), is extracted in Figure 11b, but \(f_{oc}\) is not extracted. Therefore, wavelets cannot fully extract the fault feature of a rolling bearing.
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
###### Abstract  
This research addresses a variant of the traveling salesman problem in drone-based delivery systems known as the TSP-D. The TSP-D is a combinatorial optimization problem in which a truck and a drone collaborate to deliver parcels to customers, with the objective of minimizing the total delivery time. Determining the optimal solution is NP-hard; thus, the size of the problems that can be solved optimally is limited. Therefore, metaheuristics are used to solve the problem. Metaheuristics are adaptive and intelligent algorithms that have proved their success in many similar problems. In this study, a solution to the TSP-D problem using the greedy, randomized adaptive search procedure with two local search alternatives and a self-adaptive neighborhood selection scheme is presented. The proposed approach was tested on 200 instances with different properties from the publicly available \"Instances of TSP with Drone\" benchmark. Results were evaluated against state-of-the-art algorithms. Non-parametric statistical tests concluded that the proposed approach has comparable performance to the rival algorithms (\(p=0.074\)) in terms of tour duration. The proposed approach has better or similar performance in instances where the drone and truck have the same speed (\(\alpha=1\)).  
GRASP; TSP-D; optimization; routing; metaheuristics; drone +
Footnote †: journal: Applied Sciences
# Study on Elastoplastic Damage Coupling of Reservoir Mudstone Considering Permeability Change
## 2 Conventional Triaxial Test of Mudstone
### Damage Evolution  
The damage variable can quantitatively describe the deterioration degree of rock material properties [21]. Based on the existing damage variable calculation method [22], the damage variable of mudstone is obtained using a stress-strain curve (Figure 4). According to the inflection point E after the peak stress, the damage variable calculation is divided into  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Confining Pressure** & **Elastic Modulus** & \multirow{2}{*}{**Poisson Ratio**} & **Peak Strength** & **Residual Strength** \\
**/MPa** & **/GPa** & & **/MPa** & **/MPa** \\ \hline
10 & 12.46 & 0.281 & 69.11 & 61.48 \\
20 & 13.78 & 0.383 & 75.49 & 73.12 \\
30 & 15.83 & 0.374 & 87.28 & 81.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of mudstone under triaxial compression.  
Figure 3: Stress–strain cures of mudstone under triaxial compression test.  
two parts: before the inflection point E, the elastic modulus without damage is obtained based on the straight line 'OK', and thus, the damage variable at point I is as follows:  
\[\varepsilon_{e}^{\rm I}=\frac{\sigma^{\rm I}}{E} \tag{1}\]  
\[\omega^{\rm I}=1-\frac{\varepsilon_{e}^{\rm I}}{\varepsilon^{\rm I}} \tag{2}\]  
where \(\omega\) is the damage variable, \(\varepsilon\) is the total strain, \(\varepsilon_{e}\) is the elastic strain, and \(\sigma\) is the axial stress.  
However, this method has some limitations. The calculated damage variable rapidly increases after the peak strength and exponentially increases in the residual strength stage. However, acoustic emission technology is used to monitor the whole process of the triaxial test [23], and it was found that an acoustic emission signal is not active after rock failure. Therefore, the above method is improved, and the damage variable is calculated according to the above method before the inflection point E in the post-peak stage. After the inflection point E, the damage variable of point F is as follows:  
\[\omega^{\rm F}=\omega^{\rm E}+\left(1-\frac{\sigma^{\rm F}}{\sigma^{\rm E}}\right) \tag{3}\]  
The damage evolution variation of mudstone under different confining pressures is obtained (Figure 5):  
(1) Elastic damage stage 'OA' (corresponding to the initial straight section of the stress-strain curve in Figure 4): mudstone is in the elastic stage, showing small damage and slow development;  
(2) Plastic-dominated elastoplastic damage coupling stage 'AB' (corresponding to the curve section before point E of the stress-strain curve in Figure 4): mudstone presents plastic deformation characteristics, and damage occurs and rapidly accumulates;  
(3) Damage-dominated elastoplastic damage coupling stage 'BC' (corresponding to the curve segment after point E of the stress-strain curve in Figure 4): the damage accumulates and controls, and the stress gradually decreases.  
Figure 4: Schematic diagram of damage variable calculation.
## 3 Experiments and Results
### Experimental Setup
#### 3.1.3 Evaluation Indicators  
Given the scarcity of anomalous samples in practical industrial inspection scenarios (where normal samples account for over 75% of instances), this study adopts a multi-dimensional evaluation framework: Area Under the ROC Curve (AUC) evaluates the model's overall discriminative capability under class imbalance; Accuracy (ACC) provides baseline recognition performance for quality control documentation; Recall ensures sensitivity to critical safety hazards (e.g., bolt missing); and F1-score (F1) integrates precision and recall through harmonic mean, mitigating the misleading nature of accuracy in imbalanced classification. The formulas for each indicator are defined in Equations (18)-(20):  
\[\text{ACC}= \frac{\text{N}_{\text{TP}}+\text{N}_{\text{TN}}}{\text{N}_{\text{TP}} +\text{N}_{\text{FP}}+\text{N}_{\text{FN}}+\text{N}_{\text{TN}}} \tag{18}\] \[\text{Recall}= \frac{\text{N}_{\text{TP}}}{\text{N}_{\text{TP}}+\text{N}_{ \text{FN}}}\] (19) \[\text{F1}= \frac{2\times\text{N}_{\text{TP}}}{2\times\text{N}_{\text{TP}} +\text{N}_{\text{FP}}+\text{N}_{\text{FN}}} \tag{20}\]  
where N\({}_{\text{TP}}\) is the number of anomalies correctly detected by the network, N\({}_{\text{FP}}\) is the number of samples with no actual anomalies incorrectly detected as having an anomaly by the network, N\({}_{\text{FN}}\) is the number of anomalies not correctly detected by the network, and N\({}_{\text{TN}}\) is the number of samples with no anomalies correctly detected as having no anomalies by the network.
## 4 Sentinel-3A SRAL and MWR Cal/Val Results  
The latest calibration results for the radar altimeter and for the microwave radiometer of Sentinel-3A, are given in this section, along with their uncertainties.
## 4 Design of Controller
Then:  
\[\mathbf{\chi}-\tilde{W}_{\text{C}}^{\text{T}}\hat{\mathbf{\psi}}=\mathbf{\Xi}_{\chi}+W_{ \text{S}}^{\text{T}}\tilde{\mathbf{\psi}}+\tilde{W}_{\text{S}}^{\text{T}}\hat\mathbf{{\psi}} \tag{26}\]  
where \(\tilde{W}_{\text{S}}=W_{\text{S}}^{*}-\hat{W}_{\text{S}}\), \(\mathbf{\Xi}_{\chi}=\mathbf{\chi}-W_{\text{S}}^{\text{T}}\mathbf{\psi}^{*}\). Combined with Equation (26), the Equation (20) can be rewrite:  
\[M_{\text{h}}\dot{\mathbf{r}}=-(\mathbf{K}_{\text{r}}+\mathbf{H}_{\text{h}}+\mathbf{D}_{\text{ Lh}})\mathbf{r}+\mathbf{\Xi}_{\chi}+W_{\text{S}}^{\text{T}}\tilde{\mathbf{\psi}}+\tilde{W}_{ \text{S}}^{\text{T}}\hat{\mathbf{\psi}}-\mathbf{\tau}_{\text{H}} \tag{27}\]  
The adaptive rate of ASN and ACN are designed as follows:  
\[\dot{\hat{\mathbf{w}}}_{\text{Sk}}=k_{\text{nsck}}(\hat{r}_{\text{sk}}+\hat{r}_{ \  
\[I_{ck}=\left\{ \begin{array}{ll}0&\text{if }(\|\hat{\mathbf{w}}_{\text{CK}}\|<<b_{ \text{wck}})\text{ or }\Big{(}\|\hat{\mathbf{w}}_{\text{CK}}\|=b_{\text{wck}},\,\,\hat{r}_{sk}(\hat{\mathbf{w}}_ {\text{SR}}^{\text{T}}\hat{\mathbf{y}})\hat{\mathbf{w}}_{\text{CK}}^{\text{T}}((\hat{ \mathbf{y}}-\alpha^{\text{T}}\hat{\mathbf{\sigma}}-\beta^{\text{T}}l)\Big{)}\geq 0 )\\ 1&\text{if }\Big{(}\|\hat{\mathbf{w}}_{\text{SR}}\|=b_{\text{wck}},\hat{r}_{sk}(\hat{ \mathbf{w}}_{\text{SR}}^{\text{T}}\hat{\mathbf{y}})\hat{\mathbf{w}}_{\text{CK}}^{\text{T}} ((\hat{\mathbf{y}}-\alpha^{\text{T}}\hat{\mathbf{\sigma}}-\beta^{\text{T}}l)\Big{)}<< 0)\\ \end{array} \right. \tag{33}\]  
\[I_{\omega}=\left\{ \begin{array}{ll}0&\text{if }(\|\hat{\mathbf{\sigma}}\|<<b_{ \text{\varpi}})\text{ or }\Big{(}\|\hat{\mathbf{\sigma}}\|=b_{\text{\varpi}},\hat{\mathbf{ \sigma}}^{\text{T}}\Big{(}A\hat{\mathbf{W}}_{\text{C}}\Gamma\hat{\mathbf{W}}_{\text{S }}^{\text{T}}\hat{\mathbf{y}}\Big{)}\geq 0)\\ 1&\text{if }\|\hat{\mathbf{\sigma}}\|=b_{\text{\varpi}},\hat{\mathbf{\sigma}}^{\text{T}} \Big{(}A\hat{\mathbf{W}}_{\text{C}}\Gamma\hat{\mathbf{W}}_{\text{S}}^{\text{T}}\hat{ \mathbf{y}}\Big{)}<<0)\\ \end{array} \right. \tag{34}\]  
\[I_{l}=\left\{ \begin{array}{ll}0&\text{if }\big{(}\|\hat{\mathbf{l}}\|<<b_{l} \big{)}\text{ or }\Big{(}\|\hat{\mathbf{l}}\|=b_{l},\hat{I}^{\text{T}}\Big{(}B\hat{\mathbf{W}}_{\text{C}} \Gamma\hat{\mathbf{W}}_{\text{S}}^{\text{T}}\hat{\mathbf{y}}\Big{)}\geq 0)\\ 1&\text{if }\big{\|}\|=b_{l},\hat{I}^{\text{T}}\Big{(}B\hat{\mathbf{W}}_{\text{C}} \Gamma\hat{\mathbf{W}}_{\text{S}}^{\text{T}}\hat{\mathbf{y}}\Big{)}<<0)\\ \end{array} \right. \tag{35}\]  
**Theorem 1. **_For the hybrid system dynamic mode of the Equation (13), supposing that Assumptions 1 to 3 hold and adopting the error evaluation signal shown in Equation (15), the control signal shown in Equation (19), the reinforcement signal shown in Equations (16) and (21), and the update rate of fuzzy wavelet neural network shown in Equations (28)-(31) can ensure that the trajectory tracking error \(\mathbf{e}\) converges to zero asymptotically._  
**Proof of Theorem 1.
# Multivariate Time Series Data Prediction Based on ATT-LSTM Network
## 4 Experiment
### Dataset and Evaluation Indicators  
#### 4.1.1 Dataset  
The data sets are two real multivariate time series data sets, the Nasdaq 100 stock data set and the Beijing PM2.5 data set. The following is a detailed description of the two data sets, and the relevant information of the two data sets is shown in Table 1:  
1. Nasdaq 100 stock data set: This data set consists of the stock prices of 104 companies under the Nasdaq 100 and the index value of the Nasdaq 100. The data collection frequency was 1 min. The data set contains 191 days of closing data from 26 July 2016 to 28 April 2017, and includes 390 data points per day. We take the Nasdaq 100 index as the target sequence and the stock prices of other 104 companies as the correlation sequence.
2. Beijing PM2.5 concentration dataset: This dataset is a PM2.5 concentration dataset sampled by the US Embassy in Beijing. The frequency of data collection was 1 h. The data set includes the PM2.5 concentration from 1 January 2020 to 31 December 2015 and other related factors (temperature, humidity, wind direction, wind force, rainfall, etc.). We take PM2.5 concentration as the target sequence and other related factors as the correlation sequence.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **NASDAQ 100** & **PM2.5 of Beijing** \\ \hline Target series & NDX & PM2.5 \\ Related series & 104 & 8 \\ Time & 2016/07/26-2017/04/28 & 2020/01/01–2015/12/31 \\ Time Intervals & 1 min & 1 h \\ The amount of data & 74,501 & 52,584 \\ Train/Validation/Test & 56875/7626/10000 & 39438/3146/10000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Details.
## 1 Introduction
As a result, energy-efficient protocols and advanced power transfer methods are necessary to enhance the sustainability of WSN-based applications, including smart irrigation systems. Another major issue in WSNs is the hotspot problem, which arises due to the uneven energy consumption among sensor nodes [15; 16; 17; 18]. In multi-hop communication networks, nodes closer to the base station (BS) are burdened with relaying data from distant nodes, leading to accelerated energy depletion. This creates energy imbalances, where certain nodes die prematurely, resulting in network fragmentation and reduced data transmission reliability. Traditional clustering and routing protocols often fail to mitigate this issue effectively as they do not dynamically adapt to energy levels across the network. Therefore, innovative clustering techniques, adaptive data collection mechanisms, and wireless power transfer (WPT) solutions are required to address the hotspot problem and ensure a prolonged and stable network operation. This paper introduces a new clustering scheme designed to partition the designated area into two distinct regions. The initial region is situated at the network's center and is delineated by a circular area, housing a category of nodes referred to as standard nodes. The secondary region encompasses both sides of the circle and is further subdivided into four clusters, two on each side, comprising advanced nodes. In this proposed scheme, unmanned aerial vehicles (UAVs) function as mobile sinks, traversing the network to collect data from optimized clusters. Simultaneously, WPT is employed to wirelessly charge both fixed sink and helper nodes, with adaptive data collection rates based on available energy levels. This innovative approach mitigates the hotspot issue, leading to enhanced overall network connectivity and performance.
###### Abstract  
**Objective:** The resistive knee orthosis, as a novel rehabilitation device, is designed to provide resistance to joint movement during continuous walking, thereby enhancing the postoperative recovery effect. This study aims to explore the impact of such orthoses on the joint coordination patterns of martial artists after anterior cruciate ligament (ACL) reconstruction. **Methods:** A total of 44 martial artists who underwent ACL reconstruction were recruited and divided into an experimental group (EG, n = 22, using resistive braces) and a control group (CG, n = 22, using conventional braces). Assessments were conducted preoperatively (T0) and at 15 days (T1), 30 days (T2), and 60 days (T3) postoperatively. The changes in joint coordination patterns during the gait cycle were analyzed, and the ACL force was estimated using a musculoskeletal model. **Results:** At T2 and T3, compared with the CG, the EG exhibited a significantly larger peak knee flexion angle (\(p<0.05\)). At T3, the EG showed higher hip-ankle in-phase coordination (\(p<0.05\)), increased proximal hip-knee coordination (\(p<0.05\)), and decreased knee-ankle anti-phase coordination (\(p<0.05\)). In addition, the ACL force in the EG was significantly lower. **Conclusions:** The resistive knee orthosis can effectively improve the joint coordination of martial artists after ACL reconstruction and reduce the ACL force.  
**Keywords:** ACL reconstruction; functional rehabilitation knee brace; resistance; joint coordination; ACL force +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of CNT Types and CNT Concentrations on Suspension Polymerization of Styrene and nBA
There are four primary points shown in Figure 7: first, the toner particles remained within the micron range at all concentrations; second, the particle sizes obtained via SEM are consistent with the DLS measurements discussed in Table 9; third, the particle uniformity  
Figure 7: SEM micrographs for suspension polymerization of St and nBA in the presence of CNT-D and different concentrations of MEP-51: (**a**) SP-CNT-D-MEP-51-5; (**b**) SP-CNT-D- MEP-51-4; (**c**) SP-CNT-D- MEP-51-3; (**d**) SP-CNT-D- MEP-51-2; and (**e**) SP-CNT-D- MEP-51-1. The magnifications are 500\(\times\) (scale bar = 50 \(\upmu\)m) and 100\(\times\) (scale bar = 100 \(\upmu\)m). decreased with greater MEP-51 concentrations; and fourth, CNT particles were attached to the polymerized particles. Using CNTs as pigment in toner applications suggests that they behave similarly to CB. The advantages of CNTs over CB include high conversion and particle sizes in the micron range, which avoids the potential harm caused by particles in the nanometer range. In toner formulations, MEP-51 provides excellent control over the toner particles' charge, ensuring that they are appropriately charged for efficient printing. As a result, the toner has better flowability, which is essential for even distribution during printing. In addition, MEP-51 improves toner adhesion to paper, producing sharp, high-quality prints.
## 4 Conclusions  
Si micropillars with various sizes were successfully fabricated from Si waste powder via nanosecond-pulsed laser irradiation for fabricating lithium-ion battery anodes. The pillar size was controllable by tuning the thickness of waste Si slurry layer. The smaller the thickness of the slurry layer, the smaller the size of the Si micropillars. The rate capability and capacity were dependent on the pillar size: the smaller the pillars, the better the electrochemical properties. The morphological changes of Si micropillars during the charge/discharge process caused the capacity decay in the later period of cycling. During the electrochemical cycling, Si micropillars expanded and formed an island-like structure. After 100 cycles, the Si layer was a flat and thick film with microsized cracks. The number of cracks could be reduced greatly by using a thinner Si slurry layer for micropillar generation. However, even though there was crack generation on the anode surface, the Si layer was firmly attached to the Cu foil after 100 cycles, which strongly demonstrated that the pillar structure had significantly absorbed the volume expansion and prevented the destructive delamination of the Si layer from the Cu foil.  
This study has demonstrated the possibility of an efficient and environmentally friendly method of fabricating Si-based LIB anodes from waste Si materials. Future research will be focused on improving the cycling performance in the later period by optimizing the Si micropillar generation and controlling SEI formation.  
[PERSON] and [PERSON] designed the experiments. [PERSON] and [PERSON] performed and analyzed the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the manuscript. [PERSON] supervised the project.  
This study has been financially supported by the Japan Society for the Promotion of Science, Grant-in-Aid for Exploratory Research, 17K18833 (2017-2019), and the Leverhulme Trust (UK), Grant Reference Number VP1-2018-022.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Surface Properties of Crosslinked CHT  
#### 3.2.1 X-ray Photoelectron Spectroscopy (XPS)  
XPS survey spectra of chitosan films with 15% and 30% of HNa are shown in Figure 5. Because hyaluronic acid and chitosan are polysaccharides with a similar molecular structure, C1s, N1s, and O1s, were detected. The presence of Ca was also recorded, which may come from the exoskeleton of the source, and Si considered as contaminant (Table 4). The only difference between CHT and HNa lies in the carboxyl group in HNa and the amino groups in chitosan and therefore Na1s was also detected for the corresponding salt [30]. When HNa was added to CHT there was an increase in the concentration of Na and increased from 0.27 at.% for films with 15% HNa to 1.26 at.% for films with 30% of HNa. Although it was expected an increase in oxygen with HNa addition due to the COO-groups, this was only observed for CHT/HNa 15% films and a reduction for CHT/HNa 30% uncrosslinked films. The latter behavior implied that oxygen is no longer in the surface but forming a complex in the bulk. This was also corroborated by the nitrogen content reduction.  
PEGDE crosslinked films showed an increased in oxygen content as the crosslinker agent increased i.e., from 24 at.% (CHT/HNa 15%) up to 27 at.% (CHT/HNa 15% PEGDE3 crosslinked crosslinked). A clearer behavior was observed for CHT/HNa 30% PEGDE crosslinked samples as it varied from 18 at.% to 22 at.%.  
GA crosslinked sample showed a reduction in oxygen and nitrogen content for 15% HNa and an increase in carbon content with respect to the CHT/HNa uncrosslinked sample. However only an increase in oxygen content was detected for 30% HNa films. However, the contained nitrogen decreased from 5% to 3% (Supplementary data S4 showed the corresponding XPS scans for C, N and O).