esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Feature Selection and Decision
### GP Based on Bayesian Inference
The hyperparameters \(\mathbf{\Theta}=\{w,\eta,\sigma^{2}\}\) using the GP algorithm maximized the likelihood \(p(y|x)\) as a function of \(\mathbf{\Theta}\). \[\mathcal{L}(\widehat{\mathbf{\Theta}})=\arg\max_{\mathbf{\Theta}}\log(y|x,\mathbf{\Theta}) \tag{21}\]  
First, we determined \(\hat{w}(\eta,\sigma^{2})\) to predict hyperparameters that maximized the log-likelihood concerning \(w\) for a given \((\eta,\sigma^{2})\) as  
\[\hat{w}(\eta,\sigma^{2}) = \Big{\{}\mathbf{\Omega}^{\mathrm{T}}\big{[}\mathrm{k}(x,x^{{}^{ \prime}}|\eta)~{}+~{}\sigma^{2}\mathrm{I}\big{]}^{-1}\mathbf{\Omega}\Big{\}}^{-1} \mathbf{\Omega}^{\mathrm{T}}\big{[}\mathrm{k}(x,x^{{}^{\prime}}|\eta)~{}+~{} \sigma^{2}\mathrm{I}\big{]}^{-1}y \tag{22}\]  
Second, we needed a probability density function \(p(\mathbf{y}^{*}|\mathbf{y},\mathbf{x},\mathbf{x}^{*})\) for the probabilistic estimation of the Bayesian GP algorithm using known hyperparameters. However, we estimated a response variable \(y\) using a finite amount of new input data \(x^{*}\) and predicted the output of these data based on a multivariate Gaussian distribution with a kernel-generated covariance matrix. Thus, we denoted the conditional probability distribution, as follows. \[p(y^{*}|y,x,x^{*})=\frac{p(y^{*},y|x,x^{*})}{p(y|x,x^{*})} \tag{23}\]  
In order to acquire the joint density probability function in the numerator, as expressed in (23), the mapping functions \(f_{m}^{*}\) and \(f_{m}\) should be used, as follows. \[f_{m}(x)\backsim\mathbf{GP}(0,\mathrm{k}(x,x^{{}^{\prime}})) \tag{15}\]0  
The GP algorithm assumes that each response variable \(y_{i}\) depends only on the corresponding latent variable \(f_{m}(x_{i})\) and input vector \(x_{i}\). Given \(y,x\) and the hyperparameters \(\mathbf{\Theta}\), the expected value of the estimation is given as:  
\[ \begin{split}\mathbf{E}(y^{*}|y,x,x^{*},\mathbf{\Theta})& =\theta(\mathbf{x}^{*})^{\mathrm{T}}\mathbf{w}+\mathbf{c}(\mathbf{x},\mathbf{ x}^{{}^{\prime}}|\eta)\varphi\\ &=\beta(x^{*})^{\mathrm{T}}w+\sum_{i=1}^{l}\varphi_{i}\mathrm{k}( x^{*},x_{i}|\eta)\end{split}  \tag{25}\]  
where \(\varphi=[\mathrm{k}(x,x)+\sigma^{2}\mathrm{I}]^{-1}(y-\mathbf{\Omega}w)\). Practically, we determined an optimal point prediction \(\widehat{y}^{*}\) based on the loss function as  
\[\mathbf{E}_{\mathcal{L}}(\widehat{y}^{*}|x^{*})=\int\mathcal{L}(y^{*},\widehat {y}^{*})p(y^{*}|x^{*},\mathbf{\mathrm{D}})dy^{*} \tag{26}\]  
We obtained an predicted \(y^{*}\approx\widehat{y}^{*}\) and minimized the expected value of the loss function \(\mathcal{L}(y^{*},\widehat{y}^{*})\) by minimizing between \(y^{*}\) and \(\widehat{y}^{*}\) as  
\[\hat{g}_{\mathrm{opt}}|x^{*}=\arg\min_{\widehat{y}^{*}}\mathbf{E}_{\mathcal{L} }(\widehat{y}^{*}|x^{*}) \tag{27}\]  
In this study, we used the mean absolute error (MAE) as the loss function, given by: \(\mathcal{L}\). Generally, we can use the MAE metrics to evaluate the estimation accuracy.
## 4 Proposed Method
### Object Uncertainty Measurement  
Unlike other uncertainty measurement methods [36; 40; 42] that require model modifications or extra parameter learning, we measure object geometry uncertainty using the entropy of the ray weight distribution. This entropy-based approach is efficient and fast, with no need for model changes.  
\[H(\mathbf{r})=-\sum_{i=1}^{N}[w_{i}\log(w_{i})+(1-w_{i})\log(1-w_{i})], \tag{8}\]  
Figure 2: Overall pipeline. We propose the See-Then-Grasp method for learning the full 3D geometry of an object in a two-stage reconstruction process. In each stage, the robot actively acquires data by receiving the optimal next view pose \(T_{\Theta}\) from the next best view selection module, as represented by the red and green dashed lines, moving to that position, and capturing images. The captured images undergo post-processing and are actively added to the training dataset as represented by the black dashed lines, ultimately enabling the model to learn the full 3D geometry of the object.  
where \(w_{i}\) is the ray weight defined in (4). Noisy ray weight distributions indicate inaccurate geometry, leading to high entropy. As geometry accuracy improves, the distribution converges to a single peak, reducing the entropy. Finally, the 2D pixel-wise uncertainty map \(\mathbf{U}\), corresponding to the single camera pose \(\mathcal{T}_{\text{cam}}\in\text{SE}(3)\), is calculated as the mean value of the entropy of all the rays:  
\[\mathbf{U}(\mathcal{T}_{\text{cam}})=\frac{1}{|\mathcal{R}_{\text{cam}}|}\sum _{\mathbf{r}\in\mathcal{R}_{\text{cam}}}H(\mathbf{r}), \tag{9}\]  
where \(\mathcal{R}_{\text{cam}}\) represents the batch of rays originating from single camera pose \(\mathcal{T}_{cam}\).  
While entropy-based uncertainty effectively represents object geometry uncertainty, applying it directly to the original NeRF model can cause overfitting, missing uncertainty in unseen views. To address this, we introduced an additional uncertain alpha voxel grid \(V^{(unc,\alpha)}\), inspired by [44]. Initialized to maximum uncertainty, this grid is updated during training with \(\alpha\) values from \(V^{(\text{SDF})}\), maintaining high uncertainty in unseen viewpoints and enabling the NBV agent to capture diverse views. As illustrated in Figure 3, our uncertainty measurement can capture both seen-view and unseen-view uncertainties.
## 2 Methods
### Vibration-Based Thermography  
The vibration-based thermography, also known as thermosonic testing or vibro-thermography uses, for example, an ultrasonic horn, an electrodynamic shaker or a vacuum attached piezoelectric shaker to excite a structure of interest by ultrasonic waves [19]. The subsequent vibration causes friction at boundaries of the damage (where crack faces are very close), and thus, converts mechanical energy into thermal energy. The subsequent local temperature increase can be evaluated for damage detection and identification. Recent investigations show high effectiveness for detection and identification of friction inducing damages like cracks and delaminations [14; 21]. Nevertheless, it should be noticed that there exist also other heat generation sources such as the plasticity-induced heat generation, the thermoelastic effect and the viscoelastic effect [22].
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 5 Limitations
[95] suggest the perceptibly of off light intensity is sufficient to maintain data rates of several Mb/s. While in order to obtain a high data transmission in lights off mode, a hybrid scheme of VLC and RF or IR is necessary. And how to achieve such a hybrid scheme with low complexity and cost is an important issue to deal with.
## 4 Rationale for Each TACE Technique
### Comparison of Therapeutic Effects between DEB-TACE and cTACE  
Table 1 shows the results of RCT studies regarding therapeutic effects between DEB-TACE and cTACE. Three RCTs conducted in Europe showed no significant differences in local tumor control and/or survival between the DEB-TACE and cTACE groups [62; 63; 64]. Supplementary analyses of the PRECISON V study showed that in 67% of patients with more advanced disease (Child-Pugh B class, Eastern Cooperative Oncology Group [ECOG] performance status 1, bilobar, or recurrent disease), the incidences of an objective response and disease control were significantly higher in the DEB-TACE than cTACE group (\(p\) = 0.038 and \(p\) = 0.026, respectively) [62]. Regarding the systemic side effects of doxorubicin, such as alopecia, skin discoloration, mucositis, and bone marrow suppression, there was a significant benefit of DEB-TACE over cTACE (\(p\) = 0.012) [62]. In the PRECISON Italia study, postprocedural pain was more frequent and severe after cTACE (\(p\) < 0.001) [64].  
On the other hand, the latest RCT conducted in Japan proved significant differences in CR rates between cTACE and DEB-TACE groups (84.2 vs. 35.7% at 1 month and 75.2 vs. 27.6% at 3 months, respectively [both \(p<0.0001\)]), although the rates of grade 3-4 of elevation of serum aspartate aminotransferase and alanine aminotransferase were significantly higher in the cTACE group (48.5 vs. 5.1% and 38.7 vs. 3.1%, respectively [both \(p<0.0001\)]) [65]. This study indicates that cTACE can achieve complete tumor necrosis by sacrificing the surrounding liver, and the discrepancy of outcomes of cTACE between Europe and Japan might be caused by different devices and techniques because superselective cTACE using a smaller (1.5- to 2-F tip) microcatheter under intraprocedural monitoring by CT or cone-beam CT (CBCT) is a standard technique in Japan [13]. This study did not evaluate the survival benefit of superselective cTACE over DEB-TACE because of a short follow-up period; however, it is expected the extension of the patient's lifespan in the cTACE group, because a CR after the initial TACE is the most significant prognostic factor in HCC patients [20].  
Theoretically and clinically, liver toxicity of DEB-TACE is mild compared with cTACE [62,64] because of a lower dose of active chemotherapeutics and proximal embolization for liver and tumors, although sudden death caused by systemic embolization infrequently develops [31,32] and the rate of biliary complication is significantly higher compared with that of cTACE (36.8 vs. 14.4%, respectively, \(p<0.001\)) [28]. Therefore, DEB-TACE is favorable for patients with an advanced age, large and/or bilobar tumors, or a poor liver function. On the other hand, cTACE should be indicated for patients with localized tumors and a preserved liver function. Adverse effects of bland embolization with microspheres are milder than those of DEB-TACE [47]; therefore, it is suitable for elderly patients with large HCCs, Child-Pugh score 9 patients, or patients with ruptured HCC.
# Recent Spatiotemporal Changes of Subhourly Extreme Rainfall Events in Seoul
## 3 Methodology
### Moran's I Index and Its Statistical Test for Spatial Dependence  
Moran's I is a statistical measure developed to detect spatial autocorrelation, which indicates the degree to which a given variable is similar to itself in nearby locations [27]. It is widely used in spatial statistics to analyze and quantify the similarity or dissimilarity of a variable across space. Essentially, [PERSON]'s \(I\) assesses whether the observed pattern is clustered, dispersed, or random. This method is particularly valuable in fields such as geography, environmental science, and water resources, where understanding spatial relationships is crucial for interpreting patterns and trends in data [28; 29; 30; 31]. [PERSON]'s \(I\), therefore, indicates the spatial clustering pattern. It is calculated using the following formula:  
\[I=\frac{N}{W}\frac{\sum_{i=1}^{N}\sum_{j=1}^{N}w_{ij}(x_{i}-\overline{x})\big{(} x_{j}-\overline{x}\big{)}}{\sum_{i=1}^{N}(x_{i}-\overline{x})} \tag{4}\]  
where \(N\) represents the number of observations; \(w_{ij}\) denotes the weight between locations \(i\) and \(j\); \(x_{i}\) and \(x_{j}\) are the values at locations \(i\) and \(j\), respectively; \(\overline{x}\) signifies the mean of the variable; and \(W\) is the sum of all weights (\(\sum_{i=1}^{N}\sum_{j=1}^{N}w_{ij}\)). In this study, \(w_{ij}\) is given by the following equation:  
\[w_{ij}=\frac{1}{d_{ij}} \tag{5}\]  
where \(d_{ij}\) is distance (km) between location locations \(i\) and \(j\).  
[PERSON]'s I ranges from \(-1\) to \(1\). Values approaching \(1\) indicate a strong positive spatial autocorrelation, suggesting that similar values cluster spatially. Conversely, values nearing \(-1\) indicate strong negative spatial autocorrelation, implying that dissimilar values are located adjacently. Values close to \(0\) suggest the absence of spatial autocorrelation. To assess the significance of [PERSON]'s \(I\), the z-value is computed using the expected value and variance of the statistic. The expected value is given by \(E(I)=-\frac{1}{N-1}\), while the variance is derived using a more intricate formula. Typically, the significance is evaluated through Monte Carlo simulations or randomization tests, which offer robust assessments of the spatial patterns in the data. In this study, [PERSON]'s \(I\) and its hypothesis test were computed using the \"Spatial Dependence: Weighting Schemes, Statistics\" package in R 4.3.1 [32].
## 2 Challenges and State of the Art
### Cost and Time  
Frameworks widely used for ICT security certification, such as CC, OCTAVE, or CAP UL [17], are based on formal, documentation-intensive processes, specially for high Evaluation Assurance Levels (EALs). While effective in establishing security baselines, CC can be inflexible and resource-intensive, making it unsuitable for fast-evolving systems requiring frequent updates [18; 19]. Recertification under these schemes can be time-consuming and costly, posing challenges for continuous security assurance in dynamic environments. Although EUCC and EUCS offer important foundations for security certification, their complex processes do not always adapt quickly to emerging technologies or recertifications [20; 21], and no products have been certified under these evolving schemes.  
Automated testing methodologies, such as Model-Based Testing (MBT) and fuzzing, improve the efficiency of security evaluations by automatically generating test cases or random input data to detect vulnerabilities. However, these tools are often implemented in isolation and are not fully integrated with broader certification frameworks, creating a gapbetween empirical testing and formal certification. Our methodology integrates automated testing tools and methods to accelerate the assessment process. It is flexible enough to be instantiated through other tools and mechanisms, and it allows for composition by reusing the previous results of individual components to assess more complex systems.  
One critical aspect often overlooked in security evaluation is value management. This involves balancing the trade-offs between security investments, operational costs, and the overall benefit to the organization. The proposed methodology ensures that resources are allocated efficiently by aligning risk assessment and treatment efforts with the evaluator priorities, which could be mapped to the organization priorities. By integrating continuous feedback mechanisms and cost-effective automated tools, the methodology minimizes unnecessary costs while maintaining robust security levels.
## 5 Conclusions  
The investigated techniques potentially can be used to detect and monitor EIFs in the Arctic. The proven methods can be based on single and/or stereo VHR electro-optical and infrared (EOIR) data, multispectral Landsat 8/9 imagery and VHR and high-resolution SAR data, including single-pass InSAR. The demonstrated single-image-based technique such as height from shadow can provide useful elevation information for EIF identification and characterization. These results are very important for multiple practical applications but especially in the offshore industry. Future work will be focused on automating these satellite-based techniques with machine learning, including deep learning algorithms. The data fusion of different bands can be investigated to improve detection confidence.  
Conceptualization, I.Z., P.B. and D.P.; methodology, I.Z.; software, I.Z. and M.H.; validation, I.Z. and M.H.; writing--original draft preparation, I.Z. and P.B.; writing--review and editing, I.Z., P.B. and D.P.; project administration, S.W.; funding acquisition, D.P. and P.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Canadian Space Agency (CSA) under Earth Observation Application Development Program (EOADP) and LOOKNorth (C-CORE's Center of Excellence).  
The authors thank the German Aerospace Center (DLR) for the TerraSAR-X/TanDEM-X scientific data.  
The authors declare no conflict of interest.
# Space Weather Effects on Satellites
## 3 Notable Satellite Failures  
### Historical Overview  
Since the satellite malfunctions are not subject to public disclosure, the academic investigation of direct or indirect SW effects on satellites is hindered to a large degree. An incomplete list of known satellite failures is provided in Table 1 (see e.g., [[https://swe.ssa.esa.int/TECEES/spweather/workshops/esww/proc/brekke_stoa5.pdf](https://swe.ssa.esa.int/TECEES/spweather/workshops/esww/proc/brekke_stoa5.pdf)]([https://swe.ssa.esa.int/TECEES/spweather/workshops/esww/proc/brekke_stoa5.pdf](https://swe.ssa.esa.int/TECEES/spweather/workshops/esww/proc/brekke_stoa5.pdf)), [[https://ntrs.nasa.gov/api/citations/19810005468/downloads/19810005468.pdf](https://ntrs.nasa.gov/api/citations/19810005468/downloads/19810005468.pdf)]([https://ntrs.nasa.gov/api/citations/19810005468/downloads/19810005468.pdf](https://ntrs.nasa.gov/api/citations/19810005468/downloads/19810005468.pdf)), accessed on 17 August 2023). We add significant radio blackouts reported by [8] and some major SW-driven failures on ground due to GICs, as outlined by [45].
## 3 Model Structure  
### BIBC Model  
The BIBC model is essentially a multi-neural network fusion model, which consists of four main components, namely the BERT layer, IDCNN layer, BiLSTM layer, and CRF layer. The CRF layer is computed to obtain the final labeling result. Convolutional and recurrent neural networks extract text information at different granularities and integrate the features extracted by these two neural networks, which can extract features more adequately for entity recognition compared to a single neural network model.  
The Iterated Dilated Convolutional Neural Network (IDCNN) allows more local information to be obtained in a single convolutional operation. DCNN adds a dilation width after the filter of a standard CNN. When the input data are represented by a matrix, the filter skips all the intermediate input data of the expansion width and keeps its size constant so that the data information of a larger range of input matrices can be obtained. Compared to CNN, IDCNN can learn more contextual information faster.  
As shown in Figure 1, the output of the BERT layer is a feature representation of each word of the medical text. After the output of the BERT layer is passed to the IDCNN layer, the convolutional kernel of IDCNN quickly scans the whole sequence by sliding and covers the whole sequence quickly. The output data from the BERT layer is passed through four DCNN layers, learning the features of the input data at each layer, and then the learned features from these four layers are output. These features mainly include local features and global features of the input text. These extracted features are then fed into the BiLSTM layer for further feature extraction to obtain more comprehensive feature information. Finally, the model outputs the best-labeled sequence through the CRF layer.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 3 System Model
### Problem Formulation
Two important challenges to solving this problem are the difficulty of the problem itself, and the prediction of future network status, video content, and other information. Since edge nodes typically run for months or years, in order to deal with the problem of unpredictable future information, it is necessary to relax the constraints in each time slot of the objective function to the average over a long period of time. In addition, the optimization problem is a mixed integer nonlinear program which is hard to resolve even if the future information is known. To address these two challenges, we need to design an algorithm that provides the best offloading and resource allocation for video streaming without being able to foresee future information.
## 5 Discussion  
As has been reviewed in the previous section, there is a substantial effort in the literature on detecting the sentiment polarity of people that create different kinds of content, using different sources of data. Between the data used to perform sentiment analysis, we can find extensive literature featuring approaches using text, audio, images, and in less quantity writing patterns. Some approaches only perform detection of sentiment on one data source, while others do it on multiple, which is the case of multi-modal sentiment analysis. CBR technology has also been applied to performing sentiment analysis. Additionally, stress level detection has been performed on text data and writing patterns. Moreover, as has been commented in Section 1, the emotional state affects the decision making process, and there are risks that can arise from social interaction in online social environments such as SNSs. User state detection, as has been revised on this survey can be effectively used together with MAS technologies to guide and recommend users, and prevent potential risks or issues. Moreover, using different data modalities, which can be implemented in the MAS architecture, can improve the capacity of the system to detect a risk.  
In the literature, there are works reviewing different previous approaches for sentiment analysis. One the one hand, there are surveys specialized in single data modality. Aspect-based sentiment analysis works on text data have been reviewed in Reference [32], while works on sentiment analysis applied to images have been reviewed in Reference [33]. On the other hand, in Reference [9] is a survey on works about sentiment analysis applied to different data modalities and also multi-modal sentiment analysis. Although there are works that review sentiment analysis using one or various data modalities, to the best of our knowledge, there is a lack of a review of works in user risk prevention when navigating online social environments, reviewing different strategies of sentiment and stress analysis, which is the case of the present survey. We also focus on works that use MAS-based techniques for recommending and guiding users, which is a technology that can be used together with SNSs as it fits the social network architecture by using agents to represent entities in the network, and guide or interact with users.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 5 Efficient Depth-Based MAC Protocol
### Initial Phase  
At the deployment time, the start time of the initial phase for each node is set. During the initial process, every node randomly broadcasts its beacon packet to its neighbourhood. The beacon packet includes the ID and depth of the sender. Each node exploits a pressure gauge, which is embedded inside the sensor, to obtain its depth [48]. The pressure gauge may give some variance in the depth reading; however, considering such an error in depth reading is out of scope of this work.  
The purpose of this phase is to exchange the ID and depth between neighbouring nodes which can be used at each node to populate neighbouring table, \(N_{t}\). Upon receiving a beacon packet, every node immediately updates its \(N_{t}\). The length of this phase, \(T_{b}\), is set to a predefined fixed value for all nodes. It is also a function of the transmission range of each node, \(R_{tr}\), and the maximum number of nodes per neighbourhood, \(N_{max}\), which should be long enough to let them create their own one-hop neighbouring tables with accurate information. However, the length of this phase is very short compared to that of the third phase.