esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m\({}^{2}\) per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO\({}_{2}\)) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems.  
g rongs primary production (GPP); trend analysis; ARIMA; time series modeling; impacting factors +
Footnote †: journal: remote sensing  
0000-0001-6560-788 [PERSON] et al. Article
# Deepfake-Image Anti-Forensics with Adversarial Examples Attacks
## 4 Experiment and Discussion
### Attack Results
#### 4.3.3 Attack of [PERSON] et al.  
[PERSON] et al. [2017] used a Resnet34 pre-trained by ImageNet as the basic network, and regarded the GAN detection task as a simple binary classification problem. They trained different classifiers for comparison:  
**Img**: Trained with real images and fake images generated by CycleGAN.  
**A-Img**: Trained with real image and fake image generated by AutoGAN.  
**Spec**: The training data was the same as that of **Img**, but the classifier was trained with the spectrum input.  
**A-Spec**: The training data was the same as that of **A-Img**, but the classifier was trained with the spectrum input.  
The classifier trained based on fake images generated by the proposed AutoGAN (**A-Spec**) can achieve the best generalization effect. It achieved accuracy of 0.972 for the detection of CycleGAN. Therefore, to test our method on this detector, we applied the similar attack to this classifier (AutoGAN) for CycleGAN. We found that our method can also reduce its accuracy to almost 0 (0.052).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**f-ac**} & \multicolumn{2}{c}{**AP**} & \multicolumn{2}{c}{**AUC**} \\ \hline  & **Unperturbed** & **Perturbed** & **Unperturbed** & **Perturbed** & **Unperturbed** & **Perturbed** \\ \hline
**CRN** & 0.9987 & 0.0007 & 0.9823 & 0.3135 & 0.9877 & 0.0394 \\
**IMLE** & 0.9976 & 0.0393 & 0.9840 & 0.3069 & 0.9884 & 0.0024 \\
**SITD** & 0.8666 & 0.0611 & 0.9723 & 0.3068 & 0.9756 & 0.0156 \\
**StarGAN** & 0.8129 & 0.0065 & 0.9400 & 0.3126 & 0.9780 & 0.0417 \\
**CycleGAN** & 0.7887 & 0.0832 & 0.9246 & 0.3346 & 0.9384 & 0.0799 \\
**StyleGAN** & 0.7426 & 0.1330 & 0.9959 & 0.3100 & 0.9958 & 0.0354 \\
**GauGAN** & 0.6480 & 0.0180 & 0.8948 & 0.3077 & 0.9206 & 0.0130 \\
**BigGAN** & 0.4690 & 0.0465 & 0.8450 & 0.3087 & 0.8819 & 0.0160 \\
**DeepFake** & 0.0685 & 0.0111 & 0.8902 & 0.3063 & 0.8844 & 0.0000 \\
**SAN** & 0.0182 & 0.5844 & 0.7046 & 0.3400 & 0.7185 & 0.1049 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Generalization comparison of the detector before and after adding perturbation by PNDF.  
Figure 5: Comparison of histograms of f-ac (**a**), AP (**b**), and AUC (**c**) for the other ten generator models before and after adding perturbation. The blue histogram represents the indicator value before adding perturbations, and the orange histogram represents the indicator value after adding perturbations.
## 3 Methods
### Riemannian  
In the Riemannian framework, the distances are geodesics, the shortest distance in the form of a curve on the surface of a manifold between two points, as shown in Figure 1. In the context of EEG signals, the variable \(X\) will be referred to as an EEG signal in \(\mathcal{R}^{\mathrm{C}\times N}\) where \(\mathcal{C}\) denotes the number of channels, the electrodes placed on the subject's cap, and \(N\) denotes the number of samples (observations) recorded by the system. The covariance matrix is estimated by Equation (1) [16].  
\[\Sigma=\frac{1}{N-1}XX^{T} \tag{1}\]  
Covariance matrices defined on a manifold are SPD matrices in \(\mathcal{R}^{\mathrm{C}\times\mathrm{C}}\) that satisfy symmetry; all eigenvalues are non-negative, given \(\Sigma=\Sigma^{T}\)[10]. The mean of the EEG signals represented by covariance matrices is computed using Frechet mean, a method for calculating the mean of data on a manifold [10]. It is also known as the geometric or Riemannian mean and is calculated using the geodesic distance on a manifold. The Frechet mean is used to reduce the influence of outliers and provides a more representative estimate of the central tendency of the data on the manifold [40]. Considering the trials of an experiment of a specific class, as a set of covariance matrices denoted as \(\{\Sigma_{i}\}_{i}=1\ldots I\), the mean or center of the class is expressed as follows:  
\[\hat{\Sigma}=\arg\min_{\Sigma\in\mu}\sum_{i=1}^{I}d(\Sigma_{i},\Sigma)^{2}. \tag{2}\]  
When estimating the mean of SPD matrices, the choice of the distance metric is crucial to the resultant mean. In this work, the AIRM is used to measure the distance between covariance matrices, and is then used to compute the Frechet mean.  
\[d_{AIR}=\|Log(\Sigma_{1}^{-1/2}\Sigma_{2}\Sigma_{1}^{-1/2})\|_{F} \tag{3}\]
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 7 Recent Advances in Deep Learning-Based SLAM  
Underwater SLAM has traditionally relied on geometrical methods for accurate pose estimation and mapping. However, underwater environments' complex and dynamic nature presents significant challenges to traditional approaches, including limited visibility, sensor noise, and environmental distortions. DL offers a data-driven alternative, providing robust solutions to these challenges by leveraging large datasets and powerful computational models. This section explores the application of DL in underwater SLAM, focusing on key areas such as feature extraction, pose estimation, loop closure detection, and mapping. We also discuss the challenges unique to underwater environments and how DL techniques address them. DL offers a data-driven alternative to conventional localization and mapping methods.  
Comprehensive surveys have explored the effectiveness of deep learning-based VO on global relocalization and SLAM, synthesizing research from robotics, computer vision, and ML to guide future directions. These surveys conclude that the capability of deep learning models to draw from previous experiences and effectively harness new data allows these models to self-learn and adapt to changing environments. [113, 114]. This is particularly important in underwater settings, where the scenery is continuously altered and distorted by varying light conditions and other environmental factors in a dynamic aquatic environment.  
DL-based underwater SLAM methods, summarized in Table 8, illustrate the substantial impact of DL on improving SLAM techniques, especially in challenging underwater environments. The table presents various methods and findings, highlighting how DL approaches enhance feature extraction, robustness, and real-time performance in SLAM systems.  
**Key Trends in DL-Based Underwater SLAM:** The surveyed deep learning approaches reveal several important developments in underwater SLAM. First, architectural evolution is evident, progressing from basic CNNs to more sophisticated designs like Siamese networks and variational autoencoders, yielding accuracy improvements of 30-40% in feature matching and loop closure tasks. Second, we observe a clear shift from supervised methods requiring labeled datasets to unsupervised [115] and self-supervised approaches, addressing the scarcity of annotated underwater data. Third, while early work focused on single modalities (visual or sonar), recent studies demonstrate improved robustness through multi-modal fusion, with Wang's 2022 VAE achieving 92.31% recall in challenging conditions. However, three persistent limitations include scalability issues with large-scale environments, limited real-world validation, and modality-specific constraints where visual methods struggle with turbidity and sonar approaches lack precision. The most promising direction appears in hybrid systems combining the precision of visual SLAM with the reliability of acoustic sensing through learned fusion mechanisms.
## 1 Introduction
### Contributions  
In this work, we consider a buffer-aided multi-hop relay integrated cognitive radio network (CRN) model characterized by the interference constraints imposed by the primary network, as well as the interference caused by users of the secondary network, i.e., the CRN. This model also takes into account the channel fading that results in fluctuating signal-to-noise ratios (SNRs) in each hop, making the average data rate, outage, and delay performance of the multi-hop cognitive network very different from that of non-cognitive networks. Under these constraints, the performance of this underlay cognitive buffer-aided relay network, operating in virtual duplex mode, is analyzed using a Markov chain-based approach. Therefore, the main contributions of this work are as follows:  
1. A buffer-aided multi-hop relay selection (BAMR) technique is proposed that integrates a virtual duplex multi-hop mechanism with the simple buffer-aided relaying in a CRN; this enables overcoming the possible limitation of incurring additional delays associated with simple buffer-aided relaying. The incorporation of virtual duplexing, a delay-reducing technique, primarily augments the relay selection mechanism, which in turn increases the throughput and reduces end-to-end delay.
2. A Markov chain-based model of the BAMR for an underlay cognitive radio network operating in virtual duplex mode is developed. This model simultaneously takes into account the inter-relay interference and the power constraints imposed by the primary network. The interference from the primary user to the secondary user (at both the receiving relay and the destination) is also considered in the model.
3. Considering finite buffers at the source and the relay cluster, the outage probability, delay, and target rate/capacity are analyzed using the developed Markov modeling framework. For the delay performance, end-to-end delay rather than the delay only at the relay nodes is considered in this work. When compared with contemporary schemes, like max-max [7] and max-link [8], the proposed scheme shows significant improvement in key performance measures that include outage probability, throughput, and average packet delay.  
The rest of the paper is organized as follows. In Section 2, related work on buffer-aided schemes is discussed. Section 3 discusses the general system model and benchmark schemes in the context of the underlay CRN. Outage probability, throughput, delay, and target rate are discussed in Section 4, and results are analyzed in Section 5. We discuss various challenges that must be addressed to apply buffer-aided (BA) relay selection efficiently in 5G networks in Section 6, and finally, conclusions are given in Section 7.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 4 Application of UAV-Aided Non-Terrestrial Networks in UAIS
### Connected and Automated Vehicles in UAV-Aided NTNs
#### 4.2.1 Vehicle-to-Everything Communication via NTN
This method aimed to enable efficient data caching in V2X networks.
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 4 Experiments
### Training Procedures  
To evaluate the performance and to verify the efficiency of the proposed model, it needed to be trained using the dataset. Therefore, we conducted several experiments with two different training approaches. In the first type of experiment, we applied the k-fold cross-validation approach to split our data for training and testing. We chose to set the k-fold number to 5. This means that the dataset will be split into five parts in each iteration, four parts used for training and one for testing alternately in each iteration. In the second type of experiment, we used the train/test split approach at three different sizes. Among the whole dataset, we took a subset of data equal to 60%, 70%, or 80% as the size for the training set. Then, the remaining subset of data, that is, 40%, 30%, or 20%, was used for testing.
# Low Dose Ionising Radiation-Induced Hormesis: Therapeutic Implications to Human Health
## 4 The Phenomena of LDIR
### Low-Dose Hyper-Radiosensitivity and Increased Radioresistance
In recent decades, efforts have sought to deal with the increased radioresistance of cancerous cells, which poses a major challenge to radiotherapy as DNA damage-repair and survival signalling mechanisms are activated, revealing the presence of a protective mechanism to alleviate radiation-induced cytotoxic effects [119]. Moreover, evidence has indicated that the radioresistancet cells proliferate and survive with a more aggressive phenotype than their parental cells due to genetic mutation [120,121]. Experimental studies of colorectal cancer stem cells have indicated that the JAK2/STAT3/CCND2 signalling pathway is responsible for the radioresistance, while knocking down the long non-coding RNA TINCR was found to decrease radioresistance in colorectal cancer cells [121,122]. Note though that tumour stem cells exhibit the intrinsic characteristics of radioresistance, whereby the damaged DNA can be immediately repaired by the alteration of the DNA repair pathway involving DNA replication in the S phase. Thus, the genomic stability of the entire tumour population is sustainable and resistant against low radiation dose via an increase in the DNA repairing efficiency, with this in turn via the homologous recombination-mediated DNA repair process [123]. Other pathways, including the AKT/cyclin D1, A20/NF-kB, ERK, JNK, ROS, and p53, have been reported to contribute to the radioresistance in tumour cells, therefore targeting these signalling pathways could act as an effective therapeutic strategy to overcome some of the poor clinical outcomes of radiotherapy [64,124,125].
# Context-Based Model for Browsing the Web Through Voice
## 5 Model Functionalities for Voice-Based Web Browsing
### Web Page Context Processing  
Web page context is composed of data extracted from the content of the web page to determine its usefulness and relevance, which makes it possible to decide what content to present to the user according to their own user context. The availability of data varies between web pages due to the decisions and approaches taken by developers when designing them. Each developer is free to determine what information to include and how to organize it, resulting in differences in the data available for contextual analysis. For example, some pages may include the author's name and publication date, while others may not.  
The model focuses on retrieving the following data items: title, author, publication date, subtitles, content keywords, and multimedia indicator. These data items give an overview of the topic, focus, and key aspects of the page while also indicating whether the web page is updated and whether it contains multimedia elements.  
As mentioned above, a list of ten URLs generated from the query-based search is sent to the Web Page Context Analyzer to extract the context of each web page. This process is outlined in the following three steps:  
1. Content collection: the HTTP request is sent to the server to obtain the HTML of the web page, while possible errors are handled;
2. Metadata analysis: the objective of this process is to identify and search the content of HTML tags, such as titles, descriptions, introductions, keywords defined by the page developer, author's name, and publication date;
3. Data extraction: natural language processing techniques such as sentence segmentation, tokenization, part-of-speech tagging, dependency parsing, and named entity recognition are applied to understand the content, identify relevant entities, and finally extract the keywords that describe the context of the web page. To achieve the above, the following steps are performed:  
* Separate the text into sentences;
* Remove unwanted elements such as symbols, tags, and punctuation marks;
* Separate each sentence into individual words (tokenizing);
* Label words according to their grammatical category;
* Obtain dependency relations between words;
* Select the words that have the dependency relationships shown in Table 7;
* Detect named entities and select those belonging to the following groups:
* Persons: names of individuals, such as \"[PERSON]\";
* Geographic locations: place names, such as \"Mexico\";
* Organizations: names of companies, institutions or organizations, such as \"Google\" or \"Cinvestav\";
* Dates: expressions of time or dates, such as \"today\" or \"12 July 2023\".
A set of selected words is obtained from which repeated words are removed, thus ensuring that each word is represented only once in the final set of descriptor terms describing the context of the web page content. Finally, a row is added to the Web Page Context table with the URL and keywords of the analyzed page.
# Assessment of Areal Methane Yields from Energy Crops in Ukraine, Best Practices
## 3 Results and Discussion
### Miscantlus
The highest SMY (0.29 \(\pm\) 0.02 m\({}^{3}\) kg\({}^{-1}\)VS) and the highest AMY (7404.55 \(\pm\) 199.00 m\({}^{3}\) ha\({}^{-1}\)) were measured for \"Giganteus\" from the 8 th vegetation year harvested at H1 (BBCH-code 36). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cultivaz, Vegetation Year**} & **Harvesting Time** & **DM\({}_{\text{FM}}\)\({}^{\star}\), [\%]** & **VS\({}_{\text{DM}}\)\({}^{\star\star}\), [\%]** & **DMY \({}^{\star\star}\), [\%]** & **Crop N Uptake, [\({}_{\text{IPQM}}\) ha\({}^{-1}\)]** & **TopSN \({}_{\text{BM}}\) ha\({}^{-1}\)]** \\ \hline Giganteus, 3 rd year & H1 & 44.4 (2.00) & 93.8 (4.22) & 20.5 (0.92) & 115.0 (5.18) \\ Giganteus, 8 th year & H1 & 45.6 (2.05) & 92.9 (4.18) & 27.5 (1.24) & 110.1 (4.96) \\ S sinensis, 3 rd year & H1 & 36.6 (1.65) & 91.0 (4.10) & 11.0 (0.50) & 55.2 (2.48) \\ Giganteus, 3 rd year & H2 & 45.6 (0.91) & 97.5 (1.95) & 21.1 (0.42) & 118.0 (2.36) \\ Giganteus, 8 th year & H2 & 46.9 (0.94) & 96.8 (1.94) & 28.3 (0.57) & 113.3 (2.27) \\ S sinensis, 3 rd year & H2 & 38.6 (0.77) & 94.4 (1.89) & 11.6 (0.23) & 58.2 (1.16) \\ Giganteus, 3 rd year & H3 & 48.9 (1.37) & 95.5 (2.67) & 22.6 (0.63) & 126.7 (3.55) \\ Giganteus, 3 rd year & H3 & 50.4 (1.41) & 96.8 (2.71) & 30.4 (0.85) & 121.7 (3.41) \\ S sinensis, 3 rd year & H3 & 44.1 (1.23) & 93.1 (2.61) & 13.3 (0.37) & 66.5 (1.86) \\ \hline \hline \end{tabular} \({}^{\star}\) The dry matter content related to fresh matter, ** Volatile solids content related to dry matter, *** Dry matter yield.  
\end{table} 
Table 5: Miscanthus: DM and VS- content at harvest time, dry matter yield per hectare and N-uptake. Units are given in square brackets. Values are given as mean; standard deviation is given in round brackets. For Germany, the specific methane yields for miscanthus between 0.179 and 0.280 m\({}^{3}\) kg\({}^{-1}\)VS are reported [10,15]. The areal methane yields for German miscanthus varied between 2300 and 6400 m\({}^{3}\) ha\({}^{-1}\)[15]. The results of these investigations are in the middle range of the values reported in the literature, except for miscanthus \"Giganteus\" from the 8 th vegetation year with the higher SMY and AMY values.
## 6 Automatic Active and Sleep (AAS) Model
Thus, \(CS_{\alpha}\) is greater than or equal to the amount of energy consumed in the active and idle states minus the energy consumed during the total \"OFF\" time. The above model indicates that the energy consumption due to idle listening can be avoided. In BN-MAC, each node remains in the sleep state until the next data-sending schedule begins. The BN also announces its schedule; therefore, there is no probability of consuming energy. There is also no hidden terminal in BN-MAC. BN-MAC requires 832 \(\upmu\)s to send a 14-byte BNIS message that produces a 0.3% duty cycle; other hybrid MAC protocols require an average of 1,209-1,532 \(\upmu\)s to send each BNIS message that produces an average of 0.52-0.78 duty cycles, as shown in Figure 13. Thus, BN-MAC reduces the overhead by using fewer BNIS control messages and a synchronization message.
## 3 Results and Discussion
### Solar Cell Characterization
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Oxide** & \begin{tabular}{c} _PCE_ \\ **\%** \\ \end{tabular} & \begin{tabular}{c} _Voc_ \\ **mV** \\ \end{tabular} & \begin{tabular}{c} _J\({}_{SC}\)_ \\ **mA/cm\({}^{2}\)** \\ \end{tabular} & \
\begin{tabular}{c} _FF_ \\ **\%** \\ \end{tabular} &
\begin{tabular}{c} _R\({}_{S}\)_ \\ **\(\Omega\)cm\({}^{2}\)** \\ \end{tabular} \\ \hline V2O5 & 12.7 & 593 & 29.6 & 72.4 & 0.9 \\ MoO3 & 12.6 & 581 & 29.6 & 73.1 & 0.8 \\ WO3 & 11.8 & 570 & 29.1 & 71.0 & 1.0 \\ ReO3 & 3.1 & 245 & 28.9 & 44.4 & 1.1 \\ a-Si:H & 13.1 & 617 & 29.1 & 73.0 & 0.55 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance parameters of the fabricated oxide/n-Si solar cells and the a-Si:H reference device. Figure 6: External quantum efficiency and total reflectance of the fabricated oxide/n-Si solar cells, including an a-Si:H reference device. its \(V_{OC}\) indeed recovered to 614 mV. For the MoO\({}_{3}\) cell, its efficiency dropped to 11.0% due to \(V_{OC}\) (560 mV) and \(J_{SC}\) (27.6%) deterioration. The WO\({}_{3}\) cell was the only material that showed a positive improvement in efficiency (12.6%), increasing its \(V_{OC}\) and \(J_{SC}\) to 583 mV and 30 mA/cm\({}^{2}\). Overall, the results suggest that the V\({}_{2}\)O\({}_{5}\) and MoO\({}_{3}\) films are strongly sensitive to temperature, although the threshold at which their optical and electric properties degrade still needs to be determined.