esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 2 Methods
### Problem Statement and Proposed Solution Framework
#### 2.1.2 Source Separation  
Stage two, the source separation task, concerns inverting the (generative) sound mixing model in (1), meaning that we are interested in recovering the constituent source signals \(s_{n}^{1:K}\) from a received mixture signal \(x_{1:n}\). Using the inferred source models from the first stage the constituent sources are separated from the mixture. This procedure is sometimes called \"informed source separation\" in the literature [6]. This approach contrasts to _blind_ source separation methods where very little prior information about the underlying sources is available. In the proposed framework, informed source separation is performed through probabilistic inference for \(p(s_{n}\ |\ x_{1:n},\ \hat{s})\) on the specified generative model, see Figure 2 for a graphical overview. Source separation by inference can then be worked out to  
\[p(s_{n}\ |\ x_{1:n},\ \hat{s})\propto\int p(z_{0})\ \prod_{i=1}^{n}\ p(x_{i}\ |\ s_{i})\ \prod_{k=1}^{K}\ p(s_{i}^{k},\ z_{i}^{k}\ |\ z_{i-1}^{k},\ \hat{s}^{k})\ \mathrm{d}s_{1:n-1}\ \mathrm{d}z_{0:n}, \tag{3}\]  
where  
\[p(s_{i}^{k},\ z_{i}^{k}\ |\ z_{i-1}^{k},\ \hat{s}^{k})=\int p(\theta^{k}\ |\ \hat{s}^{k})p(s_{i}^{k},\ z_{i}^{k}\ |\ z_{i-1}^{k},\ \theta^{k})\ \mathrm{d}\theta^{k}. \tag{4}\]  
Again, note that all factors in (3) and (4) are already specified as factors in the generative model (1) or a result from the source modeling task of (2). Therefore, (3) and (4) are computable. Technically, (3) is a Bayesian filtering (state estimation) task that can be efficiently realized by (generalized) Kalman filtering [31; 32]. We will automate this Kalman filtering task through message passing in a factor graph representation of the generative model.  
Figure 1: An overview of the source modeling stage. The user records short fragments of the observed signals and infers the corresponding model parameters.
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 3 Impact Evaluation of Missing Data Imputation on PV Forecasting  
In order to verify the effect of missing data imputation on PV forecasting, we used four error indices, root mean square error (RMSE), mean relative error (MRE), root mean square deviation (RMSD), and mean relative deviation (MRD), defined, respectively, as follows:\[\begin{array}{l}\text{RMSE}\;=\;\sqrt{\sum\limits_{i=1}^{N}\frac{1}{N}\left( \hat{P_{i}}-\;P_{a,i}\right)^{2}}\\ \text{MRE}\;=\;\frac{1}{N}\sum\limits_{i=1}^{N}\frac{\left|\hat{P_{i}}-\;P_{a, i}\right|}{P_{total}}\;\times 100(\%)\\ \text{RMSD}\;=\;\sqrt{\sum\limits_{i=1}^{N}\frac{1}{N}\left(\hat{P_{i}}-\;P_{ i}\right)^{2}}\\ \text{MRD}\;=\;\frac{1}{N}\sum\limits_{i=1}^{N}\frac{\left|\hat{P_{i}}-\;P_{ i}\right|}{P_{total}}\;\times 100(\%)\end{array} \tag{4}\]  
RMSE and MRE are performance errors of prediction models used in previous studies. RMSD and MRD are indicators that quantify the effect of the selected missing data imputation method on the PV power prediction model. Here, \(P_{i}\) denotes the predicted PV power data without using missing data imputation. In other words, \(P_{i}\) is the output predicted by the model trained with the initial weather data. We set \(P_{i}\) as the predicted value and not the actual value in order to identify only the effect of missing data imputation. These indices are very different from the error metrics used in previous forecasting studies [8; 9; 10; 23]. If \(P_{i}\) is set as the actual output, the prediction error can be changed according to the forecast model and not the imputation method. Further, in the above equations, \(\hat{P_{i}}\) denotes the predicted value obtained with the application of missing data imputation, \(P_{total}\) the PV power capacity, N the number of forecast PV power outputs, and \(P_{a,i}\) the actual PV power. In this study, \(P_{total}=196\;\text{kW}_{\text{p}}\). The RMSE and MRE can be calculated from these values. According to [9], RMSE indicates the average size of the error. A larger error corresponds to a larger weight being added to RMSE. The special importance of RMSE is that it indicates that the occurrence of a sudden large error is more problematic than multiple small errors occurring multiple times. Further, we use the MRE instead of the MAPE because a small real value can cause a large error. For example, in the case of the PV model, there is almost no solar power generation at 06:00, and the MAPE value is only measured for actual solar power generation. We therefore choose the error index of MRE.
## 5 Experimental Procedure and Results
### Comparison and Evaluation of Models
DeBERTa, a variant of BERT, achieved an accuracy rate of 88.6% in category classification and 90.3% in sentiment classification, surpassing both BERT and RoBERTa in sentiment analysis. Its processing time was 7.28 s, making it the fastest among the transformer models tested. GPT-2's results lagged behind the aforementioned models, with a category classification accuracy of only 55.2% and sentiment classification accuracy of 60.8%, signaling its limitations in this specific domain. Regarding speed, it registered an 8.07-second completion time, making it the slowest among the transformer models tested. ChatGPT in the zero-shot setting reached a 58.2% accuracy for category classification and a significantly higher accuracy of 84.5% for sentiment classification, showcasing its strength in grasping the subtleties of human emotion in text. Nevertheless, its processing time was considerably longer, at 335.45 s, which may pose constraints for applications requiring prompt responses. When utilizing a few-shot learning approach, ChatGPT's accuracy in category classification improved to 70.3%, and sentiment classification maintained a strong 78.0%. However, this strategy resulted in an even longer processing time of 557.66 s, further highlighting the trade-offs between the depth of language understanding and operational practicality. GPT-4, in a zero-shot setting, achieved an impressive 81.0% accuracy in category classification and 89.4% in sentiment classification, outperforming ChatGPT. However, its processing time was even longer, at 545.57 s. With few-shot learning, GPT-4's performance improved, reaching an accuracy of 84.0% in category classification and 87.9% in sentiment classification, while the processing time slightly decreased to 507.02 s.  
In light of these findings, DeBERTa stood out for its high accuracy in sentiment classification and speed, while RoBERTa also demonstrated strong performance across both tasks. BERT showed robust performance with a good balance of accuracy and speed. ChatGPT and GPT-4 showed enhanced performance in sentiment analysis but at the cost of significantly longer processing times. These results are instrumental in guiding the choice of an appropriate model for specific use cases, considering the trade-offs between accuracy and processing efficiency.
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
###### Abstract  
In this study, the interferometric coherence calculated from 12-day Sentinel-1 image pairs was analysed in relation to moving events on agricultural grasslands. Results showed that after a moving event, median VH (vertical transmit, horizontal receive) and VV (vertical transmit, vertical receive) polarisation coherence values were statistically significantly higher than those from before the event. The shorter the time interval after the moving event and the first interferometric acquisition, the higher the coherence. The coherence tended to stay higher, even 24 to 36 days after a moving event. Precipitation caused the coherence to decrease, impeding the detection of a moving event. Given the three analysed acquisition geometries, it was concluded that afternoon acquisitions and steeper incidence angles were more useful in the context of this study. In the case of morning acquisitions, dew might have caused a decrease of coherence for moved and unmowed grasslands. Additionally, an increase of coherence after a moving event was not evident during the rapid growth phase, due to the 12-day separation between the interferometric acquisitions. In future studies, six-day pairs utilising Sentinel-1A and 1B acquisitions should be considered.  
synthetic aperture radar; SAR, moving detection; grasslands; agriculture; interferometric coherence; weather radar; precipitation estimates +
Footnote †: journal: Remote Sensing, 802  
1
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 3 Material and Methods
### Overview of the Proposed Learning Pipeline  
Figure 3 shows the proposed SentiBERT-BiLSTM-CNN learning pipeline, which consists of three sequential modules:  
1. SentiBERT is utilized to transform word tokens from the raw Tweet messages to contextual word embeddings. Compared to BERT, SentiBERT is better at understanding and encoding sentiment information.
2. BiLSTM is adopted to capture the order information as well as the long-dependency relation in a word sequence.
3. CNN acts as a feature extractor that strives to mine textual patterns from the embeddings generated by the BiLSTM module.  
The output of the CNN is fed to a detection layer to generate the final prediction result, i.e., disaster or not.
# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
## 3 Results and Discussions
### Best Sensor Configuration
Sensitivity enhancement (Figure 2d), measured as the percentage increase in SPR response relative to Sys\({}_{0}\) in water, provides a baseline measure for evaluating the impact of each layer. Sys\({}_{0}\) was selected as the baseline because it represents the simplest configuration in water, devoid of additional layers and reflective of the least complex SPR setup. The configurations using PBS, specifically Sys\({}_{2}\) through Sys\({}_{9}\), exhibit varying sensitivity enhancements depending on the number and type of layers included. Among these, Sys\({}_{3}\), Sys\({}_{8}\), and Sys\({}_{9}\) show the highest sensitivity enhancements, with values of 5.19%, 5.66%, and 5.69%, respectively (Table S3). While Sys\({}_{9}\) demonstrates the highest sensitivity enhancement, practical considerations, such as the complexity of manufacturing this configuration, led to the selection of Sys\({}_{8}\) as the best configuration. To remark, the observed drop in sensitivity for Sys\({}_{4}\) and Sys\({}_{5}\) likely results from the absence of a silicon nitride layer, which plays a critical role in stabilizing the graphene and enhancing the refractive index contrast needed for optimal SPR performance. When silicon nitride is introduced, as in Sys\({}_{6}\) through Sys\({}_{9}\), the system achieves more effective resonance conditions, resulting in higher sensitivity. Then, based on percentage attenuation, FWHM, and percentage sensitivity enhancement metrics, Sys\({}_{8}\) was selected as the optimal configuration for future analyte testing. This configuration, comprising prism/silver/silicon nitride/graphene/ssDNA layers with PBS as the medium, offers high sensitivity and stability while avoiding the manufacturing complexities associated with Sys\({}_{9}\)* Graphene directly on silver and topped with Si\({}_{3}\)N\({}_{4}\) is unconventional, posing adhesion issues. This unusual stacking order requires precise control to avoid layer instability. * Binding ssDNA to graphene is less straightforward than to silicon nitride, often needing intermediary molecules or additional treatments, which increases fabrication steps and variability. * Differences in thermal expansion among silver, graphene, and Si\({}_{3}\)N\({}_{4}\) can lead to strain or delamination, especially under handling or environmental changes. * The precise deposition and functionalization required for each layer make it challenging to scale Sys\({}_{8}\) for commercial production, as it requires stringent quality control to maintain sensor performance. These factors suggest Sys\({}_{8}\) is more practical for scalable, reproducible manufacturing while still offering high sensitivity.
# An NDN Cache Management for MEC
## 4 A Limited Domain Cache Management Policy
### Hierarchical Named Node  
NDN originally used a hierarchical data name. Recently, although NDN additionally utilizes a flat data name like other ICNs, NDN does not utilize a name resolution server that resolves the flat name of the content to a host identity caching the content. Therefore, to forward _Interest_ for _Data_ to a content provider which generated the _Data_, NDN still needs a hierarchical data name for the _Interest_.  
Taking advantage of the characteristics of a layered data name, is an efficient approach to transmit the _Interest_ by referring to the prefixes of the data name of the Interest sequentially from a higher layer prefix. Therefore, the LdC rationally assumes that there are hierarchical representative nodes to which _Interests_ having the same name prefixes are commonly forwarded. That is, these nodes are the domain gateway nodes of a publisher-side network domain. The nodes have a hierarchical node identity which publishers use as the prefix of the data name. So, every _Interest_ having a data name in which the prefix of the data name is the same as the node identity should be transmitted to one of these nodes. For example, _Interest_ having a data name \"/n1/n2/n3/text.txt/s1/239\" is first forwarded to a node having the identity \"/n1\". Then the node forwards the _Interest_ to an internal node having the identity \"/n1/n2\". The LdC calls such nodes the representative nodes of the name prefix \"/n1\". A node can have several different identities. LdC assumes that the data name hierarchy of NDN consists of two parts, as shown in Figure 4.  
* A named domain hierarchy (NDH): NDH is used to forward _Interest_ toward a data publisher. In Figure 4, \"/suv/research/security/david\" is the NDH. A name prefix with 1-layer is \"/suv\". Then, one of the representative nodes having identity \"/suv\" handles the _Interest_ with the data name prefix \"/suv\". In addition, a name prefix with a 2-layer is \"/suv/research\". One of the representative nodes having identity \"/suv/research\" handles the _Interest_ with the data name prefix \"/suv/research\".
* Object File Information (OFI): This is the information of a segment of data. It is formatted as \"/doc/report.txt/s2/239\". The \"/doc\" denotes the file path of the segment. The \"/report.txt\" is the file name of the segment. The \"s2\" is the segment number and the \"239\" is the time stamp.  
Figure 4: Data name hierarchy.
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
## 4 Proposed Methodology  
### Our Consideration on Cost of Data Transfer Delay  
In our study, we assume that the routes are not susceptible to failures or issues that could result in the cancellation of the data flow. So, the value of \(MF_{k}\) is zero and the model (see Equation (1)) can be modified as presented by Equation (6):  
\[M_{k}=MD_{k}+MB_{k}\quad\forall k\in\{1,\ldots,|R|\} \tag{6}\]  
Furthermore, among the bandwidths of the links on route \(r_{k}\), the value of the useful bandwidth (the smallest bandwidth on the chosen route) to allow the use of a service in our model is the minimum value on all the links of the chosen route Furthermore, we introduce a concept using the MIP proxy, which is the mobility anchor. Based on [20], this allows the improvement of node mobility management and the data transfer delay to be reduced. We summarize these principles into three points to outline this method.  
* An anchor is a domain located on the data route between the mobile node (MN), which is also called the source, and its corresponding node (CN), which is also called the destination.
* An anchor is a domain located between the most connected domain and the one closest to the destination.
* An anchor must be unique.  
In the context of our study, the variable \(MD_{k}\) is translated as the sum of the number of hops between the mobile node \(N\) and the anchor \(A\), denoted as \(MD_{NA}\), and between the anchor and the corresponding node \(C\), denoted as \(MD_{AC}\). Based on [18], for the closest anchor node to the destination, the route between the anchor and the destination becomes a mandatory path, so only one is needed, \(MD_{NA}\), which is most likely to change. Therefore, we obtain  
\[MD_{k}=MD_{NA},\quad\forall k\in\{1,\ldots,|R|\} \tag{7}\]  
So, the model can be summarized as follows:  
\[M_{k}=MD_{NA}+B_{k}\quad\forall k\in\{1,\ldots,|R|\} \tag{8}\]  
with  
\[B_{k}=min\{B_{e}\},\quad e\in E_{k}, \tag{9}\]
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 5 Channel Signal Generation
### Exfiltration (Creating Power Line Current Fluctuations through the CPU)  
The computer's electrical hardware modules primarily consist of the CPU, motherboard, GPU, and hard drive. While CPUs and GPUs possess the highest power consumption, not all computers are equipped with discrete graphics cards [41]. Instead, many utilize internal display modules, which allow for leveraging the CPU to generate current fluctuations in order to enhance compatibility across systems since all computers are equipped with CPUs. By employing a simple dead loop within a single-core CPU, nearly maximum power utilization can be achieved; whereas in multi-core CPUs, engaging multiple threads within the dead loop can fully load all cores of the CPU. Henceforth, this principle offers an effortless means to construct signals exhibiting power fluctuations. Figure 18 illustrates the comparison of low and high CPU loads on the total power consumption of several brands of laptops.  
Universality: The operating system of a computer grants each process the CPU utilization privilege without requiring separate permission requests, thereby minimizing the detection or termination of malicious code responsible for signal transmission by conventional security software.  
To assess and evaluate the risks associated with this air-gap channel, we have developed a simulation program that leverages CPU power fluctuations for signal transmission. The program initially reads the relevant data and converts them into the communication encoding format (2 ASK), such as the binary format. Subsequently, it is modulated to align with CPU power consumption levels. A low load condition, indicating reduced power consumption over a duration of T, represents 1; whereas a high load condition, indicating increased power consumption over a duration of T, represents 0. At the receiving end (smart plug), the signal can be decoded using a protocol based on received power consumption signals. Algorithm 1 outlines the fundamental process of transmitting signals based on CPU power fluctuations.  
```
1. Input: Number of threads, Payload, payloadSize, Cycle
2. binBuff[]=payload
3. i = 0
4. Do
5. If (binBuff[i++] == 1)
6. HighCPUload(Cycle);
7. else
8. LowCPUload(Cycle)
9. While (i < payloadSize)
```  
**Algorithm 1** CPU Power consumption signal modulation algorithm  
Figure 18: Comparison chart of power consumption among 5 popular laptop brands.
## 2 Materials and Methods
### Signal Segmentation
#### 2.6.3 Data Cleaning  
The signals and location of fiducial points were resampled back to the signal's 350 Hz. To ensure that the time-series could be compared, all of them were cut from the first detected work cycle to the last. Like this, the jumps and end of acquisition information were deleted, leaving the recordings with 40 min.
# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 6 Conclusions  
This paper proposes a personalized learning resource recommendation model, LKGA, which naturally integrates learning resource knowledge graphs with recommendation systems for learning resources. The proposed LKGA model extracts collaborative signals from learners and concatenates the learning resources clicked by learners who have clicked the same resource as latent information with the original learning resources to form the initial entity representation of the learner. In entity aggregation, an attention mechanism is utilized to focus on multiple important pieces of information. By adding residual connections in each hop of the learner's aggregation process, the information from the first hop is added in each hop to enhance the information representation. By distinguishing the contributions of different neighbors and enhancing the learner's representation capability, the model's performance is improved. The experimental results demonstrate that the proposed model significantly outperforms baseline models across multiple evaluation metrics. This research is an important part of the research findings in the important domain where artificial intelligence and education empower each other. For future research, there are two areas that can be further explored: firstly, leveraging large language model technology to enrich the information in knowledge graphs from multiple aspects, such as image information, video information, and audio information, thereby enhancing the representation capabilities of learners and learning resources, and secondly, further optimizing the sampling strategy to better represent learners' latent interests and improve model performance.  
C.D.: writing--original draft, review, and editing, conceptualization, funding acquisition; Q.C.: formal analysis, methodology, validation, writing--original draft; J.Y.: review and editing; W.Z.: review and editing; X.W.: visualization; M.Z.: supervision, project administration. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (62177024, 62207027), the Ministry of Education of the People's Republic of China (20 YJC880024), the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province (jykf22029), the Zhejiang Province educational science and planning research project (2023 SCG369), and the University-Industry Collaborative Education Program (220906424035704). No additional external funding was received for this study.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare that there are no conflicts of interest regarding the publication of this paper.