esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The first and second derivatives of the more generalized double logistic function, including two additional parameters, have been more commonly used [8; 29]. [PERSON], et al. [30] compared different fitting functions to describe vegetation dynamics at high latitudes and reported that double logistic function was more suitable than the Fourier series and the asymmetric Gaussian functions. Similar fitting function has also been proved effective in evergreen forests at mid-to-high latitudes [19]. However, little information was available on the VIs based phenological metrics extracted from double logistic function across different landscapes in pan-Arctic regions. Pan-Arctic ecosystems are of interest due to their important role in the global ecosystem, as well as their sensitivity to climate change. Boreal and Arctic systems have been estimated to store 15% of the world's carbon [31]. However, despite their important role, it remains unclear how Arctic biomes have responded to and interacted with the warming climate [32]. Although the biological cycles of photosynthesis are thus crucial and sensitive indicators. A lack of in situ observations and models for different biomes in this area have led to high levels of uncertainty regarding the estimates of carbon, water, and energy fluxes. Improving our ability to describe the timing of phenological transitions accurately and associating them with climate/environmental changes would therefore advance our understanding of climate-ecosystem linkages and feedback under both current and future climatic conditions [17]. Models and tipping point analyses have suggested that there could be disproportionately large carbon-climate feedback effects in the Arctic/Boreal Zone [33]. However, despite efforts to improve our estimates, satellite-observed phenological metrics remained largely uncertain and rarely validated, due to the short growing seasons and the presence of snow cover and soil moisture during the transition dates. We used MODIS reflectance products to analyze the performances of PI and PPI compared with NDVI and EVI for estimating SOS and EOS in pan-Arctic regions. Satellite-based SOS and EOS were compared against GPP-extracted SOS and EOS from a network of EC flux-tower observations in pan-Arctic areas. SIF derived from GOME-2 instruments was tested in evergreen forest ecosystem where optical satellite observations were limited to monitor changes. We aimed to focus on the comparison of different VIs to identify a more accurate approach to quantifying SOS and EOS across heterogeneous landscapes and to highlight the uncertainties in pan-Arctic regions.
## 3 Results and Discussion
### Morphology and EDX Analysis  
The surface morphology of the HfO\({}_{2}\), graphene, and HfO\({}_{2}\)-graphene nanocomposites were characterized by SEM. Figure 3a-d shows that the HfO\({}_{2}\) nanoparticles are densely attached to the surface of graphene sheets. The surface morphology of the HfO\({}_{2}\), graphene, and HfO\({}_{2}\)-graphene nanocomposites was characterized by SEM and displayed in Figure 3a-d. Figure 3a shows HfO\({}_{2}\) particle aggregation due to the high surface interaction between nanoparticles with a large specific area and high surface energy [42]. Figure 3c,d shows that the graphene sheets are covered densely with the HfO\({}_{2}\) layer.  
To further characterize the morphology of the HfO\({}_{2}\)-graphene composites, HR-TEM was observed. The HR-TEM images confirm the present structure of the composites that contain graphene sheets covered with HfO\({}_{2}\). The HR-TEM images (Figure 4a,d) also revealed that the HfO\({}_{2}\) nanoparticles were densely bound on the surface of the graphene sheets. Similar results were observed by [PERSON] et al., 2022 [55]. It can be seen from HR-TEM images the observed average size of the HfO\({}_{2}\) nanoparticles was around 10 nm. The HR-TEM image size also matched with XRD measurements. The EDX spectrum of the HfO\({}_{2}\)-Graphene nanocomposite is shown in Figure 5. The results further confirm the presence of carbon (C), oxygen (O), and hafnium (Hf) elements in the HfO\({}_{2}\)-graphene nanocomposite.  
Figure 3: SEM images of (**a**) HfO\({}_{2}\) nanoparticles, (**b**) graphene, (**c**) HfO\({}_{2}\)-graphene (10 wt%) nanocomposite, and (**d**) HfO\({}_{2}\)-graphene (50 wt%) nanocomposites.
# Blockage Effects in Wind Tunnel Tests for Tall Buildings with Surrounding Buildings
## 4 Blockage Effect of Grouped Models
### When the Height of Surrounding Models Is Equal to That of the Target Pressure Model  
In this section, the height of the surrounding models is equal to that of the target pressure model. The mean pressure coefficient is shown in Figure 17, and the relative error is shown in Figure 18. As shown, the pressure coefficient at different heights is approximately equal when the height of the surrounding models is equal to that of the target pressure model compared to the effect when the surrounding models are half the height of the target pressure model. For example, the relative error of the pressure coefficients of 1-4 layers in Figure 9 increased gradually when the surrounding models were half the height of the target pressure model, while that of 1-4 layers in Figure 17b were approximately equal when the surrounding models were equal to the height of the target pressure model. The other data analysis of Figures 17 and 18 are examined comprehensively from the perspective of drag coefficient in the following sections of this paper.  
Figure 16: Wind tunnel test results of test condition 8. (**a**) Layer pressure coefficient; (**b**) relative error of layer pressure coefficient.  
Figure 15: Wind tunnel test results of test condition 7. (**a**) Layer pressure coefficient; (**b**) relative error of layer pressure coefficient.  
Figure 17: The mean pressure coefficient of the layers. (**a**) test condition 1; (**b**) test condition 2; (**c**) test condition 3; (**d**) test condition 4; (**e**) test condition 5; (**f**) test condition 6; (**g**) test condition 7; (**h**) test condition 8.  
Figure 18: The relative error of the mean pressure coefficient of layers. (**a**) test condition 1; (**b**) test condition 2; (**c**) test condition 3; (**d**) test condition 4; (**e**) test condition 5; (**f**) test condition 6; (**g**) test condition 7; (**h**) test condition 8.  
It should be pointed out that the interference effects of the three scaled models of each test condition were the same, therefore, all of the above relative errors of the wind pressure are not related to the interference effect.
## 4 Experimental Results
### Estimation of the Energy Consumption  
The network energy was determined using the sum of the energy for the various nodes. Figure 5 compares the recommended system's overall energy use to the existing practices. The novel method used 0.30 nJ less energy per 500 nodes than the previous methods. The graph shows how the new strategy performed better than the alternatives in terms of expressiveness. As demonstrated in Table 3, the frequency of the sensor node upsurges influenced the amount of energy used.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Area & 1000 m\({}^{2}\) \\ Nodes count & 100 \\ Initial energy & 0.1 J \\ Bandwidth & 20 kbps \\ Size of packets & 500 bytes \\ Distribution of nodes & Random \\ Total nodes count & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental Setup.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node Counts** & **LEACH** & **EECRP** & **FEEC-IIR** & **CL-IoT** & **SFO-CORP** \\ \hline
100 & 86 & 89 & 93 & 95 & 99 \\
200 & 83 & 87 & 90 & 92 & 94 \\
300 & 80 & 84 & 88 & 89 & 93 \\
400 & 77 & 80 & 85 & 87 & 90 \\
500 & 73 & 77 & 81 & 85 & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of PDR (%).  
Figure 4: Comparison of PDR (%) for the various nodes.  
Figure 5: Node counts vs. energy consumption (n)).
## 6 Discussions on Future Research Directions and Challenges
### The Joint Design of Classic Cryptographic and PHY Securities  
Another promising and futuristic research topic, which has not yet been investigated deeply in the literature, is the joint design of PLS and classic cryptographic techniques. The upcoming B5G networks require high security to ensure the most secure service applications. High-layer cryptographic techniques, such as privacy-preserving, authentication and encryption, will still be the main safeguarding mechanisms in data privacy and network security for B5G. Such techniques are usually implemented at the higher layers of data processing stacks. However, the advancements in wireless networks and the limitations of energy consumption in the mobile devices, hardware and processing capability in B5G networks require lower cost and more efficient data privacy and network security solutions. Dynamic and robust security solutions, such as secure service-oriented and group-based authentication systems [185; 186], will be the major future directions for classic cryptographic security and privacy. In addition to the upper layer cryptographic techniques, the PLS is required to offer another layer for safeguarding data confidentiality in B5G networks [186; 187; 188]. PLS is emerging as a promising technique for managing the high costs of computational complexity and the resource consumption problems. Nowadays, PLS techniques have been widely adopted to provide secure transmission in emerging technologies, such as massive MIMO, advanced channel coding, terahertz (THz), millimeter wave (mmWave), etc. However, employing only PLS techniques may not be sufficient to ensure efficient and robust security in future 5G and 6G networks. PLS can be integrated into classic cryptographic techniques to improve the requirements of future wireless communication systems. Therefore, the basic issue is the joint utilization of the benefits of PLS and classic cryptographic security techniques.  
To this end, cross-layer security design approaches aim to integrate higher layer security and PLS to provide secure strategies for each layer of data processing protocol. This can be performed by utilizing the mechanisms and functionalities of the two layers for security purposes. In order to achieve this challenging task, there are still many issues that are expected to be solved in future research. For example, secure coding techniques, network protocols, adaptive modulation, hybrid encryption algorithms and other techniques can be adopted as joint mechanisms for security. However, the structures of B5G networks are typically decentralized and transmission nodes are expanded with different characteristics. This requires a joint security scheme to adopt the same variety of nodes and heterogeneous architecture of B5G networks. In order to satisfy the requirementsof service-related and user-related elements in B5G networks, dynamic, robust and efficient secure joint security techniques need to be developed [186].
## 4 Conclusions  
In our work, we consider the introduction of coherent transmission in a PON infrastructure. Based on recent research, it appears that beyond 200 Gbps, the use of coherent technology for front-haul or industrial applications is a reasonable possibility. In this scenario, it may be cost-effective for telecom operators to merge the metro and access networks into a single domain. We demonstrated the feasibility of converging these segments by designing a joint path for downstream transmission.  
To achieve this, we implemented two test-beds and characterized both coherent transmission with offline processing and two pre-commercial transceivers. After presenting the transceivers' characterization results in terms of BER for different OSNR values and received optical power levels, we analyzed the access optical power budget versus the Optical Signal-to-Noise Ratio (OSNR at the metro segment output) versus the optical power budget of the metro path. These results showed that merging the two networks is feasible, and we provided a design for this integration. Specifically, an OSNR in the metro path higher than 25 dB produces an optical power budget of 29 dB in the access network, compliant to the PON N1 class.  
This result was achieved with coherent transmission and offline processing, as well as with pre-commercial transceivers that already exhibit a similar optical power budget. Our final comment is that, if an even higher optical power budget is required in the future  
Figure 7: Design of the metro path and access path comparing the performance of the three transceivers at \(@BER=10^{-2}\). **(Left)**: Results shown as the optical power budget (OPB) of the access network versus the OSNR of the metro (\(OSNR_{METRO}\)). **(Right)**: Sensitivity at \(@BER=10^{-2}\) of the receivers versus the OSNR of the metro network.  
(i.e., compliant to the PON E2 class), it can likely also be achieved with commercial transceivers by simply increasing their transmitted optical power.  
Conceptualization, M.C., G.R., A.P., V.F. and R.G.; methodology, M.C. and G.R.; validation, M.C. and G.R.; resources, A.P., E.R. and R.G.; data curation, M.C., A.P. and G.R.; writing--original draft preparation, M.C.; writing--review and editing, V.F.; visualization, M.C. and V.F.; supervision, V.F. and R.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the EU Horizon Europe framework under project ALLEGRO No. 101092766. This work was carried out over an in-field TIM infrastructure and in the PhotoNext Center facilities at Politecnico di Torino (www.photonext.polito.it, accessed on 21 June 2024) under a research collaboration with Telecom Italia (TIM). [PERSON]'s PhD is sponsored by TIM.  
Data are contained within the article.  
The authors declare no conflicts of interest.
# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 4 Results and Analysis
### Simulation Results
Table 7 shows the effect of the proposed TVH on the performance of the two state-of-the-art (SOTA) text analysis methods. In the simulation we trained the text analysis model using clean training, and validation data. We have checked and cleaned the training, and validation data manually. We normalized the noisy test data using the proposed TVH, and then passed the normalized test data from the text classifier. We observed significant improvement in the performance of these existing method when integrated with our proposed TVH. From the experiments it was found that the average accuracy of our system in text normalization is about 91%, and the average improvement in the accuracy of our proposed text analysis pipeline is about 6% for LSTM and 4% in the case of BERT. The improvement in the case of BERT was lower than the LSTM-based text classification method; however, the overall performance of the BERT-based text classification was far better than the LSTM-based text classification method. The overall result depends on the performance of the spell-checker as well as the type of text. The maximum improvement in the accuracy was up to 12% when the proposed TVH was applied to the very noisy text. The performance of the proposed system is improved in terms of accuracy; however, the combination of normalization with sentiment analysis introduced a processing time overhead. However, this integration is inevitable for more accurate prediction, without losing useful information and process automation (noise detection and correction). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Without TVH**} & \multicolumn{4}{c}{**With TVH**} \\ \hline  & **Textual Variations Level** & **Precision** & **Recall** & **F-Score** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Accuracy** & **Improvement** \\ \hline LSTM & Less Variations (Spam) & 74.30 & 80.67 & 77.35 & 91.62 & 86.39 & 93.46 & 89.79 & 95.55 & 3.93\% \\  & High Variations (Tweets) & 54.76 & 52.48 & 53.59 & 64.04 & 74.19 & 70.63 & 72.37 & 77.40 & 13.36\% \\ \hline BERT & Less Variations (Spam) & 96.40 & 97.66 & 97.03 & 98.68 & 94.59 & 96.39 & 95.48 & 97.96 & \(-\)0.72\% \\  & High Variations (Tweets) & 61.83 & 56.94 & 59.28 & 68.94 & _77.82_ & 74.91 & 76.34 & 80.87 & 11.93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effects of the proposed model with respect to textual variations.
# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
###### Abstract  
In the era of widespread digital image sharing on social media platforms, deep-learning-based watermarking has shown great potential in copyright protection. To address the fundamental trade-off between the visual quality of the watermark image and the robustness of watermark extraction, we explore the role of structural features and propose a novel edge-aware watermarking framework. Our primary innovation lies in the edge-aware secret hiding module (EASHM), which achieves adaptive watermark embedding by aligning watermarks with image structural features. To realize this, the EASHM leverages knowledge distillation from an edge detection teacher and employs a dual-task encoder that simultaneously performs edge detection and watermark embedding through maximal parameter sharing. The framework is further equipped with a social network noise simulator (SNNS) and a secret recovery module (SRM) to enhance robustness against common image noise attacks. Extensive experiments on three public datasets demonstrate that our framework achieves superior watermark imperceptibility, with PSNR and SSIM values exceeding 40.82 dB and 0.9867, respectively, while maintaining an over 99% decoding accuracy under various noise attacks, outperforming existing methods by significant margins.  
image watermarking; edge-aware optimization; multitask learning; knowledge distillation; social network noise; copyright protection Article
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
## 5 Design of the Fast Charging Robust Controller
### Analysis of the Control Loop Performance  
The fast charging controller previously designed was applied to the model (nonlinear) of the battery described in Section 2.3. As previously mentioned, this model has a large sensitivity to aging and temperature, and the closed loop system sensitivity and robustness to these parameters must be verified. It is achieved by studying the impacts of battery parameters variations on the closed loop system response. As an example, Figure 9 shows the optimized side reaction profile tracking (profile computed as in Section 3) for the cell at BOL and for a temperature of 10 \({}^{\circ}\)C. The simulation is performed with a model whose parameters defining the cell aging, the initial SEI layer thickness (nm), the cell high frequency resistance (\(\Omega\)), the cell capacity (Ah) and the specific surface area of the anode (m\({}^{-1}\)) were changed by \(\sim\pm 20\%\). The \(J_{sr}\) diagram in the figure shows a tracking error close to 2 (\(\mu\)A\(\cdot\)m\({}^{-3}\)). In spite of plant uncertainties, the controller tracking performance is thus guaranteed.  
Figure 7: Nichols chart of the open loop for the uncertainty domains (**green**) associated with the nominal plant (**blue**) and Nichols abacus (**red**).
## 6 Spontaneous Four-Wave Mixing (SFWM)
Here, \(d\) is fixed at 650 nm. (**c**) Phase-matching curves of the signal and idler as a function of the pump wavelength at 900 nm (red), 925 nm (purple), and 950 nm (orange) in diameter. Average pump power is 0.5 mW. where \(\chi^{(3)}\) is a non-linear susceptibility that characterizes the non-linear process, and the effective length of the crystal is \(L\). The pump beam can be expressed with plane wave, \(\hat{E}_{p}^{(+)}(z,t)=\int E_{p}(\omega_{p})\text{e}^{i(k_{p}z-\omega_{p}t)}d \omega_{p}\). The signal and idler photons are quantized as  
\[\hat{E}_{s,i}^{(+)}(z,t)=\int_{0}^{\infty}d\omega_{s,i}\sqrt{\frac{\hbar\tilde {\omega}_{s,i}}{2\epsilon_{0}A}}\hat{a}(\omega_{s,i})\text{e}^{i(k_{s}z- \omega_{s,i}t)}, \tag{21}\]  
where \(\omega_{j}(j=s,i)\) and \(k_{j}(j=s,i)\) represent the frequencies and wave vectors of signal (\(s\)) and idler (\(i\)), respectively. \(A\) is the single-mode cross-section area. \(\hat{a}_{s}^{\dagger}\) and \(\hat{a}_{i}^{\dagger}\) are the creation operators for signal and idler photons. The Hamiltonian becomes  
\[ \begin{split}\hat{H}_{int}(t)=&\frac{\chi^{(3)} \hbar\sqrt{\omega_{s}\omega_{i}}L}{2A}\int_{0}^{L/2}dz\int d\omega_{p_{1}}\int d \omega_{p_{2}}\int d\omega_{s}\int d\omega_{i}E_{p_{1}}(\omega_{p_{1}})E_{p_{2 }}(\omega_{p_{2}})\\ &\times\text{e}^{-i\frac{\hbar\omega t}{2}}\text{Sinc}\left( \frac{\Delta kL}{2}\right)\text{a}_{s}^{\dagger}(\omega_{s})\text{a}_{i}^{ \dagger}(\omega_{i})+\text{H.C.},\end{split}  \tag{22}\]  
where \(\Delta k=k_{p_{1}}(\omega_{p_{1}})+k_{p_{1}}(\omega_{p_{2}})-k_{s}(\omega_{s} )-k_{i}(\omega_{i})\) is the phase-mismatching condition. Hence we can obtain the two-photon state as  
\[ \begin{split}\left|\psi\right\rangle&=-\frac{i}{ \hbar}\int dt\hat{H}_{int}(t)\left|0\right\rangle,\\ &=\frac{\chi^{(3)}\sqrt{\omega_{s}\omega_{i}}L}{2 iA}\int\int\int \int d\omega_{p_{1}}d\omega_{p_{2}}d\omega_{s}d\omega_{i}E_{p_{1}}(\omega_{p_ {1}})E_{p_{2}}(\omega_{p_{2}})\text{e}^{-i\frac{\Delta kL}{2}}\text{Sinc} \left(\frac{\Delta kL}{2}\right)\text{a}_{s}^{\dagger}(\omega_{s})\text{a}_{i }^{\dagger}(\omega_{i})\left|0\right\rangle,\\ &=\int\int d\omega_{s}d\omega_{i}F(\omega_{s},\omega_{i})\text{a} _{s}^{\dagger}(\omega_{s})\text{a}_{i}^{\dagger}(\omega_{i})\left|0\right\rangle, \end{split}  \tag{23}\]  
where \(F(\omega_{s},\omega_{i})\) refers to the joint spectral amplitude (JPA) that consists of the pump envelope function and the phase-matching function.
# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 4 Experiments  
### Datasets  
We used VoxCeleb2 [49] as the source-domain dataset and CN-Celeb1 as the target-domain dataset [50]. VoxCeleb2 is a large-scale corpus containing over 1 million utterances from 5994 speakers. It was collected from YouTube, with most of the speech data being in English. CN-Celeb1 is a multi-genre Chinese corpus comprising over 125 k utterances from 997 speakers, spanning 11 distinct genres. The number of utterances, speakers, and hours in these 11 genres all vary, representing natural and complex domain variations. The entire dataset is divided into two parts: CNC1.dev, which contains 797 speakers for domain adaptation, and CNC1.eval, which consists of 200 speakers for performance evaluation. More detailed information can be found in Table 1. We highlight that CN-Celeb1 is highly complex and their genres can be regarded as different domains, so our task is essentially a multi-domain adaptation, a scenario seldom explored before. Note that the two datasets were well constructed and silence was well purged, so no voice activity detection (VAD) was employed in training and testing.