esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The PSIM module can be substituted for PnET within the LandscapeDNDC framework to enable the modelling of separate vegetation types within an ecosystem, and thus simulate the main canopy trees separately from understorey trees. It can simulate different responses in canopy and understorey trees following changes, whether in climate or management, and compensatory mechanisms that can be triggered when the canopy is disturbed and the two vegetation types compete for light, water and nutrients. Such a linking of canopy and understorey models is deemed to be essential to predict the impact of global change on temperate forest composition and structure and hence the functioning of such forests in the future [25]. Data to evaluate biogeochemical models are provided by eddy covariance (EC) estimates of CO\({}_{2}\) fluxes calculated from continuous measurements at field and ecosystem scales. In Europe, there are relatively few such forest datasets longer than 10 years, most of which are from spruce (e.g., Hoeglwald, Germany; Norunda, Sweden), pine (e.g., Hyytiala, Finland; Lobobs, Netherlands) or beech forests (e.g., Hoglwald, Germany; Hesse, France; Soro, Denmark), set up as part of the EUROFLUX project [26; 27]. In the British Isles, the oak plantation of the Straits Inclosure, in Alice Holt Forest in south east England, is the only EC measurement site in a deciduous forest with more than 20 years of data. This study evaluates the LandscapeDNDC model with measured GHG flux data from eddy covariance and soil chamber studies in the Straits Inclosure where N deposition is relatively low at approximately 12 kg N ha\({}^{-1}\) year\({}^{-1}\)[28]. Some parameter changes were necessary and sensitivity analyses were carried out to identify which parameters and input values most influenced the resulting modelled GHG fluxes. The aims of this study were to determine the suitability of a widely used process-based ecosystem model to represent gas exchange fluxes of an oak stand with a substantial understorey by (i) quantifying differences between simulated and measured ecosystem and soil fluxes of CO\({}_{2}\) and N\({}_{2}\)O using two different vegetation modules, (ii) evaluating the sensitivity of the model to changes in key input values and parameters, and (iii) comparing the model's ability to simulate the ecosystem responses to thinning events using the two vegetation modules.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 3 Detailed Implementation Steps
### Implementation Steps for Embedding Watermark  
The embedding procedure mainly includes the following steps:  
1. Convert the watermark picture into binary stream with the length of \(L\), and then generate the binary chaotic sequence \(c(q)\) according to Formulas (3) and (4), a bipolar string \(w(q)\) is generated according to Formulas (5)-(7) ultimately.
2. Add a group of \"1111 1111\" at the beginning of the bipolar string as the start sign and add a group of \"-1-1-1-1-1-1-1\" at the end of the bipolar string as the end sign.
3. Divide \(A\) into \(M\) audio segments \(A_{l}\) with \(N\) sample points. \(M\geq L+16\).
4. Perform the \(r\)-level DWT on \(A_{l}\) to get \(De(r,n)\).
5. Separate \(De(r,n)\) into \(De_{1}(r,j)\) and \(De_{2}(r,j)\), and then implemented DCT on them to obtain \(C_{1}(r,j)\) and \(C_{2}(r,j)\).
6. Calculate \(M_{l}\), \(M_{c1}\) and \(M_{c2}\) according to Formulas (11)-(13).
7. Repeat Step 4 to Step 6. Calculate the average amplitudes of all audio segments to obtain \(Max\) and \(Min\).
8. Calculate the adaptive embedding depth of each audio segment according to Formula (31). Embed a 1 bit watermark into each audio segment according to the embedding rules in Formulas (14) and (15).
9. Perform IDCT on \(C_{1}^{\prime}(r,j)\) and \(C_{2}^{\prime}(r,j)\) respectively to get \(De^{\prime}(r,n)\).
10. Perform IDWT on \(De^{\prime}(r,n)\) to reconstruct \(A_{l}^{\prime}\).
11. Repeat Step 8 to Step 10 until the end of the embedding process.
12. Recombine \(A_{l}^{\prime}\) to obtain the whole watermarked audio \(A^{\prime}\).
# Scene Text Detection Using Attention with Depthwise Separable Convolutions
###### Abstract  
In spite of significant research efforts, the existing scene text detection methods fall short of the challenges and requirements posed in real-life applications. In natural scenes, text segments exhibit a wide range of shape complexities, scale, and font property variations, and they appear mostly incidental. Furthermore, the computational requirement of the detector is an important factor for real-time operation. To address the aforementioned issues, the paper presents a novel scene text detector using a deep convolutional network which efficiently detects arbitrary oriented and complex-shaped text segments from natural scenes and predicts quadrilateral bounding boxes around text segments. The proposed network is designed in a U-shape architecture with the careful incorporation of skip connections to capture complex text attributes at multiple scales. For addressing the computational requirement of the input processing, the proposed scene text detector uses the MobileNet model as the backbone that is designed on depthwise separable convolutions. The network design is integrated with text attention blocks to enhance the learning ability of our detector, where the attention blocks are based on efficient channel attention. The network is trained in a multi-objective formulation supported by a novel text-aware non-maximal procedure to generate final text bounding box predictions. On extensive evaluations on _ICDAR2013_, _ICDAR2015_, _MSRA-TD500_, and _COCOText_ datasets, the paper reports detection F-scores of 0.910, 0.879, 0.830, and 0.617, respectively.  
scene text detection; MobileNets; convolutional network; text attention +
Footnote †: journal: Medical Imaging
## 1 Introduction  
### _1.1 IoE and Security: Surge in Security and Privacy Issues Driven by the Proliferation of IoE_  
The IoE represents an extended paradigm of the Internet of Things (IoT), establishing large-scale interconnections among devices, people, data, and processes [1, 2, 3]. IoE has rapidly evolved into a critical enabler across multiple domains, including smart cities,healthcare systems, industrial infrastructures, and intelligent transportation networks. However, the expansion of IoE has also significantly intensified security and privacy concerns [4; 5; 6].  
In IoE environments, millions of connected devices interact with each other and with decentralized infrastructures, generating critical security requirements such as authentication, data integrity, access control, and resilience against cyber-attacks [7; 8; 9]. Traditional security mechanisms, which typically rely on centralized systems for authentication and access management, are increasingly inadequate for large-scale, distributed IoE architectures [10; 11; 12].  
Risks such as cyber-attacks, data breaches, identity theft, and the misuse of autonomous devices pose substantial threats within the IoE ecosystem [13; 14]. Moreover, AI-driven attacks, vulnerabilities in edge computing infrastructures, and the potential of quantum computers to break classical encryption algorithms are projected to become major security challenges in the future landscape of IoE [15; 16; 17; 18].  
Considering the issues addressed above, the core security threats in IoE systems can be summarized as follows:  
* _Scalability Challenges_: The explosive growth of connected devices generates enormous volumes of data, challenging the capacity of centralized security architectures.
* _Data Privacy Risks_: IoE environments collect sensitive personal, financial, and medical data, often requiring privacy-preserving AI techniques to ensure confidentiality.
* _Cybersecurity Threats_: IoE is inherently vulnerable to Man-in-the-Middle (MITM) attacks, Distributed Denial of Service (DDoS) attacks, and adversarial AI-driven threats.
* _Quantum Computing Risks_: The rise in quantum computing threatens traditional cryptographic standards, making cryptographic agility and post-quantum security essential requirements.
# Depletion-of-Battery Attack: Specificity, Modelling and Analysis
## 5 DoB Detection
Generally, it needs to minimize the difference \(T_{alarm}-T_{attack}\) expecting that \(T_{alarm}>T_{attack}\). If \(T_{alarm}<T_{attack}\) then the false alarm takes place. Due to the stochastic character of observed energy consumption, the false alarm can be generated many times. The reaction for false alarms can essentially reduce the network performance. It needs to reduce the false alarm rate as much as possible. A conventional change-point detection approach is based on the cumulative sum:  
\[S_{0}=0,S_{i}=\max(0,S_{i-1}+\Delta_{i}).\]Here, \(\Delta_{i}\) is a function of observations \(\xi_{i}\). For example, in some approaches, which have been offered for DDoS attacks detecting, the log-likelihood ratio is taken as the function \(\Delta_{i}\). It can be applied for DoB detection as well. The alarm time moment is defined as follows:  
\[T_{alarm}=\inf\left\{i\geq 1:S_{i}>h\right\},\]  
where \(h\) is some threshold value. This scheme has the disadvantage that the proper threshold \(h\) is hard to evaluate. Generally, it is recommended to define \(h\) by simulation to get the tradeoff between efficient detection and false alarm rate. If the battery capacity is large enough, then the fast discharge of sensor battery within a reasonable time is not a significant detriment. Therefore, if the attack is detected during this time, it is possible to avoid a considerable harm. In this case, the efficient method of point-of-change detection with given lag can be applied [58]. Let the admissible lag equals \(T_{lag}\) time units. If the alarm signal is declared at any time after \(T_{alarm}\) and before \(T_{attack}+T_{lag}\), then the attack is considered to be successfully detected. If \(T_{alarm}>T_{attack}+T_{lag}\), then the intrusion is successful. Under the condition of fixed false alarm rate, the attack detection probability has to be maximized, while under the fixed probability of attack detection, the probability of false alarm has to be minimized. The provided formulation of the change-point detection problem addresses the case where the cumulative distribution function for \(\xi_{j}\) convolution can be derived. In this case, the optimal threshold value can be calculated. Let \(\widetilde{\alpha}\) be the required false alarm rate, \(w\) be the number of observations within \(T_{lag}\), \(F_{\xi,w}\) is CDF of the convolution \(\xi_{1}*\xi_{2}*\ldots*\xi_{w}\):  
Figure 4:0  
Therefore, the optimal threshold is as follows:  
Figure 4:1  
where \(F_{\xi,w}^{-1}\) is the inverse function of \(F_{\xi,w}\). If the alarm time moment is defined as follows:  
Figure 4:2  
then the provided threshold maximizes the probability of attack detection. The choice of a particular discord detection procedure depends largely on practical applications and WSNs environment.
# Method for Resonant Frequency Attenuation in Dynamic Audio Equalizer
## 2 Materials and Methods  
Figure 1 illustrates the proposed method, which comprises four main steps: determine a representative value for each frequency band, design a threshold curve, create a filter bank, and filter the input signal. A frequency domain analysis of the input signal is carried out via the Fast Fourier Transform (FFT). The input signal is analyzed in the frequency domain using the FFT algorithm, which allows the spectral behavior of the signal to be determined and the attenuation required for each band to be calculated. This information is then used to design an audio filter bank for each frequency band. Finally, filters are applied to the input signal to attenuate the resonant frequencies.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Informal Analysis
#### 7.2.3 Stolen Mobile Device Attacks  
In this attack, adversary \(A\) can steal a legitimate user's mobile device and obtain its secret credentials \(\{PID_{D},K_{M}^{*},G_{M},r_{M},h(\cdot)\}\) by performing a power analysis attack. However, all the values in the user's mobile device are masked with the user's identity (\(ID_{M}\)) and password (\(PW_{M}\)) and a random number \(r_{M}\). Hence, it is computationally infeasible for \(A\) to simultaneously guess \(ID_{M}\), \(PW_{M}\), and \(r_{M}\). Therefore, the proposed protocol is resilient to stolen mobile device attacks.
## 2 Methodologies of Developing Fragility Curves  
In modern seismic engineering, fragility curves are employed to assess the vulnerability of a structure due to seismic events. Fragility curves describe the probability an engineering demand parameter (EDP) to exceed the capacity level of \(\hat{\mathsf{C}}\) for a given IM, and can be calculated as:  
\[\mathsf{P}[\;\text{EDP}|\text{IM}\geq\mathsf{C}|\text{IM}]=\Phi\bigg{(}\frac {\ln\mu-\ln\hat{\mathsf{C}}}{\beta}\bigg{)} \tag{1}\]  
The fragility assessment of the bounding effect is examined considering the following five methods:1. Empirical cumulative distribution function (CDF),
2. Moment method (MM),
3. Maximum likelihood estimation (MLE) method,
4. 16%, 50%, 84% IM--percentiles, and
5. Probabilistic seismic demand model (PSDM).  
The empirical CDF method provides discrete probability data points at a given value of IM. For a continuous representation of the fragility curve, the median (\(\mu\)) and the dispersion (\(\beta\)) of the responses have to be defined utilizing one of the other four methodologies. All methods are presented and discussed below. Fragilities based on the empirical CDF method are considered as the reference probability data.
## 4 Online Control Strategy
### Neural Network Training  
In this study, a BP neural network was used. The input parameters of the network should contain sufficient information to reflect output parameters. The current time, speed, torque requirement, battery SOC, total mileage, residual mileage, average speed, maximum speed, minimum speed, maximum acceleration, and minimum acceleration were chosen as the input parameters, according to a large number of previous studies [13; 27; 28; 29; 30]. The time intervals used to calculate the average speed, the maximum and minimum speeds, and the maximum and minimum accelerations were all 60 s. The target output parameter takes the median of the upper and lower limits of the optimal equivalent factor. Figure 7a shows the network structure. To compare it with the traditional neural network method, this study used the same neural network to fit the CVT speed ratio and motor torque obtained by the dynamic programming algorithm, which is shown in Figure 7b.  
Through the BP neural network fitting in Figure 7a, time-varying equivalent factors with real-time operating conditions and the vehicle state could be obtained. Figure 8 shows the change in the equivalent factor with regard to the battery SOC under different remaining driving ranges when the vehicle speed is 60 km/h and the demanding torque is 30 Nm. When the remaining driving range is relatively small, the equivalent factor rapidly increases as the battery SOC decreases. The energy distribution of the vehicle changes from being driven mainly by electricity to being driven jointly by the engine and motor to maintain the SOC balance during the entire cycle. When the remaining driving range is large, to ensure that the battery can provide sufficient electricity in the future, the equivalent factor is always maintained at a high level, and does not change significantly with the decrease of SOC.  
Figure 7: Neural network structure diagram, **(a)** Based on DP-A-ECMS; **(b)** Based on DP.  
The trained BP neural network can be used to establish a real-time control strategy. Figure 9 shows a flowchart of the online controller. According to the required torque signal given by the driver, the current battery SOC signal, and the real-time vehicle speed, the remaining mileage is calculated at the same time, and combined with the historical working condition data, the neural network module can calculate the currently optimal equivalent factor within a 60-s period. The equivalent factor is the input into the A-ECMS energy distribution module to minimize the equivalent fuel consumption, and the optimal engine and motor power output at this moment can be obtained.
# Microwave Photonic Signal Processing and Sensing Based on Optical Filtering
## 5 Conclusions  
Microwave photonics based on the use of optical filtering techniques offer the intrinsic advantages of electromagnetic immunity, wideband operation bandwidth, high-resolution for both signal processing and sensing applications. Recent progress in microwave photonics based optical filtering approaches has been described with reference to optical single sideband modulation schemes, single passband RF signal processors and microwave photonic sensing structures. Particular attention has been focused on describing the novel techniques reported to overcome the major limitations of conventional systems. These techniques will provide new capabilities to pave the development roadmap towards the evolution of high-performance optical filtering assisted microwave photonic signal processing and sensing.  
Figure 4: Schematic diagram and measurement results. (**a**) Schematic diagram and operational principle of the sensing scheme. (**b**) Measured RF response of the high sensitive temperature sensor. (**c**) Measured narrowband notch location changes of the microwave photonic filter corresponding to the applied temperature variations.  
**Author Contributions:** This review article was jointly written by and proof-read by all authors. All authors contributed in various degrees to the research concept, theory and the experimental study. [PERSON], [PERSON] and [PERSON] performed a great part of the analysis and experimental study. [PERSON], [PERSON], [PERSON] and [PERSON] worked on the design and structures. [PERSON], [PERSON] and [PERSON] contributed to the research concept. All authors participated in revising the article critically for important intellectual content.  
**Acknowledgments:** The authors would like to thank the funding support from Australian Department of Defence and Australian Research Council.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Pointing Accuracy of an Operational Polarimetric Weather Radar
## 5 Pointing Accuracy and Beam Squint from Boxscans
However, the values of elevation bias from different days (Figure 8, lower panel) show a small spread in the morning and evening, which implies that the diurnal variation of the refractive index profile is small. Especially the fact that the morning and evening biases have a similar spread, this suggests a weak sensitivity on the assumptions of a refractive index profile. From a typical diurnal boundary layer cycle we would expect a larger variation of the elevation bias in the morning due to the larger gradients in the refractive index depending on the evolution of the nocturnal boundary layer. The magnitude of the gradient depends on the associated predominant processes of a nocturnal boundary layer which determine the vertical profiles of temperature and humidity [12]. In the evening, it is a fair assumption, that the boundary layer is well mixed in temperature and humidity, so that the gradients in the profile of refractive index are expected to be small. The weak sensitivity on the profile of refractive index may be explained by the location of the radar. The Hohenpeissenberg site is a mountain station situated in the relatively flat Alpine foreland (the height difference is about 400 m). Therefore, this site is often above possibly strong inversion layers so that anomalous radar beam propagation is less likely. To conclude, it is argued that the estimate of the azimuth bias here is not sensitive to the assumption related to the refractive index profile so that the variation of the azimuth bias as a function of azimuth angle can be attributed to the mechanics of the gear drive assembly (Figure 8, upper panel). Figure 7: Beams quint based on the elevation and azimuth estimate for 3 and 15 June 2018.
## 2 Materials and Methods
### Data and Processing  
The meteorological and GPP data utilized in this study are comprehensively presented in Table 1. The daily maximum temperature, minimum temperature, and precipitation data acquired are the basis for calculating the extreme climate indices.  
The reliability of ERA5 meteorological data was systematically evaluated through comprehensive validation against in situ observations from three representative meteorological stations within the desert meadow ecosystem of the NFYS region: Darhan (Station ID: 53352), Erenhot (Station ID: 53068), and Mandula (Station ID: 53149). The validation metrics revealed strong consistency between the ERA5 data and ground-based measurements, with Nash-Sutcliffe Efficiency (NSE) values consistently surpassing 0.98 for temperature parameters (including maximum and minimum temperatures) and maintaining values above 0.63 for precipitation data (Figure S1 of supporting document). For future climate trend analysis, this study utilizes CMIP6 meteorological data under two representative emission scenarios (SSP2-RCP4.5 and SSP5-RCP8.5), which were obtained from the National Tibetan Plateau Data Center [33].  
Figure 1: Overview of the study area (**a**) geographical location (**b**) elevation and meteorological stations (**c**) annual average temperature and multi-year monthly temperatures (**d**) annual average precipitation and multi-year monthly precipitation.  
The GOSIF-GPP dataset spanning from 2001 to 2023 was obtained from the global high-resolution Gross Primary Productivity (GPP) product developed through the Orbiting Carbon Observatory-2 (OCO-2) satellite observations [34]. This advanced dataset incorporates eight distinct SIF-GPP conversion algorithms, which have been rigorously validated at both site-specific and pixel levels, demonstrating exceptional performance in global GPP estimation. Furthermore, the accuracy of the GOSIF-GPP dataset was systematically evaluated using eddy covariance flux measurements from the Darhan Maominghan Joint Banner experimental site [35]. The validation results, as illustrated in Figure S2 of the supplementary document, reveal strong consistency between the datasets, with a Pearson correlation coefficient of 0.82, confirming the reliability of the GOSIF-GPP data in representing actual carbon flux dynamics.
## 4 Experiments and Results
### Experiment 4--Search Performance of MPUSMs-IMFECOA
In addition, the visible/hidden layer units of the RBM are the same as those of the Dual-RBM, and  
Figure 11: Population evolution of RBM-IGA based on the RBM surrogate model. Figure 12: Population evolution of MPUSMs-IMFEAO based on the RBM surrogate model. Figure 9: Population evolution of DRBM-Social-IGA based on the DRBM-Social surrogate model. probability models are set for them. The dataset is the same as Experiment 1. The true-train in the legend is the real historical items, and the true-pool denotes the items in the item pool, and the predict indicates the evolving individuals. As can be seen from Figures 9 and 10, the MPUSMs-IMFECOA based on the DRBM-Social surrogate model is more likely to keep the population distribution similar to the actual item distribution, ensuring that the produced individuals are valid. From Figures 11 and 12, the evolved population in Figure 12 has more unduplicated individuals, indicating that the IMFEOA is more likely to maintain population diversity. Comparing Figures 10 and 12 shows that they differ only in the surrogate model, but both have a richly diverse population. It further indicates that IMFEOA can effectively enhance population diversity. However, MPUSMs-IMFECOA based on the RBM surrogate model is insufficient to maintain the correct true distribution of populations, suggesting that the performance of MPUSMs-IMFECOA is also highly dependent on the surrogate model.