esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Meta-Classification Using Machine Learning Algorithms
#### 2.4.4 Naive Bayes (NB)  
Naive Bayes, a family of probabilistic classifiers based on [PERSON] theorem, assumes independence between features for simplicity [53]. Class probability is calculated by considering the probability of each feature occurring within that class and dividing it by the probability of the feature itself; see Equation (12). These classifiers are scalable, requiring parameters proportional to the dataset's number of variables, making them suitable for large datasets. NB is commonly used in text classification and NLP tasks, where strong independence assumptions are often met.  
Class prior and feature probabilities are computed from the training data using maximum likelihood estimation for NB predictions. [PERSON] theorem is then applied to find the posterior probabilities of each class for a given feature set, and the class with the highest posterior probability is chosen for prediction.  
An ensemble NB classifier combines the predictions of multiple NB classifiers to improve the performance and accuracy [54]. Ensemble methods use multiple learning algorithms utilizing techniques like bagging, boosting, and bootstrapping to improve prediction performance. The Ensemble NB classifier finds applications in various tasks, including text classification and NLP [55].  
\[P(class|feature)=\frac{P(feature|class)\times P(class)}{P(feature)} \tag{12}\]
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## Appendix A  
### Experimental Details for TSE and System Identification Using Loop Detectors  
#### a.1.1 Experimental Configurations  
The PUNN \(f_{\theta}(x,t)\) parameterized by \(\theta\) is designed as a fully connected feedforward neural network with 8 hidden layers and 20 hidden nodes in each hidden layer. The specific structure is: layers = [2, 20, 20, 20, 20, 20, 20, 20, 20, 1], meaning that PUNN inputs \((x,t)\) to estimate density \(\rho\). The hyperbolic tangent function (tanh) is used as the activation function for each hidden neuron in PUNN. In addition, the PICG part in the PIDL architecture is parameterized by the physics parameters \(\lambda\).  
We train the PUNN and identify \(\lambda\) through the PIDL architecture using the adaptive moment estimation (Adam) optimizer [126] for a rough training for about 1000 iterations. A follow-up fine-grained training is performed by the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimizer [127] for stabilizing the convergence, and the process terminates when the loss change of two consecutive steps is no larger than \(10^{-16}\). This training process converges to a local optimum \((\theta^{*},\lambda^{*})\) that minimizes the loss.  
For a fixed number of loop detectors, we use grid search for hyperparameter tuning by default. Specifically, since the Adam optimizer is scale invariant, we fix the hyperparameter \(\alpha\) to 100 and tune the other hyperparameters from [1, 10, 50, 100, 150, 200] with some follow-up fine tuning.  
The training details are presented in Algorithm A1.  
```
1 Initialization:
2 Initialized PUNN parameters \(\theta^{0}\); Initialized physics parameters \(\lambda^{0}\); Adam iterations Iter; Weights of loss functions \(\alpha\), \(\beta\), and \(\gamma\).
3 Input: The observation data \(\mathcal{O}=\{(x^{(i)},t^{(i)},\beta^{(i)})\}_{i=1}^{N_{0}}\); collocation points \(\mathcal{C}=\{(x^{(i)},t^{(i)})\}_{j=1}^{N_{0}}\); boundary collocation points \(\mathcal{C}_{B}\), e.g., \(\mathcal{C}_{B}=\{(0,t^{(i)})\}_{i=1}^{N_{0}}\cup\{(1,t^{(i)})\}_{i=1}^{N_{0}}\)
4:\(k\gets 0\)
5:\(\tilde{\theta}^{0}\leftarrow(\theta^{0},\lambda^{0})\)
6:while\(k<Iter\)do
7: Calculate Loss by Equation (10) using \((\alpha,\beta,\gamma)\) on (\(\mathcal{O}\),\(\mathcal{C}\),\(\mathcal{C}_{B}\))
8:\(\tilde{\theta}^{k+1}\leftarrow\tilde{\theta}^{k}-\text{Adam}(\tilde{\theta}^{k},\
abla_{\tilde{\theta}}Loss)\) // use Adam for pre-training
9:\(k\gets k+1\)
10:endwhile
11:while\(\tilde{\theta}^{k}\) not converged do
12: Calculate Loss by Equation (10) using \((\alpha,\beta,\gamma)\) on (\(\mathcal{O}\),\(\mathcal{C}\),\(\mathcal{C}_{B}\))
13:\(\tilde{\theta}^{k+1}\leftarrow\tilde{\theta}^{k}-\text{L-BFGS}(\tilde{\theta}^{k},\
abla_{\tilde{\theta}}Loss)\) // use L-BFGS for fine-grained training
14:\(k\gets k+1\)
15:endwhile
16:return\(\tilde{\theta}^{k}\)
```  
**Algorithm A1**PIDL training for deterministic TSE.
## 3 Methodology
### Registration Accuracy Verification of the Two DSM Data  
In order to eliminate the calculation error of vertical variation caused by horizontal and height errors, the matching accuracy of both DSMs must be verified and evaluated before data processing. In this paper, visual recognition and coordinate comparisons are used, and landmark buildings are employed as checkpoints to verify the registration accuracy of the two DSMs and the Landsat 7 ETM+ images.  
A total of 100 landmark buildings were selected as checkpoints (Figure 3). Then, each landmark building was checked to see if it was visually from different DSMs (AW3D30 DSM and SRTM DSM) to ensure that the location of the buildings in different DSMs was accurately matched. The result of the inspection was that the horizontal accuracy of the two datasets met the requirements. There was no need to perform a registration. This conclusion also provides convenience for others to follow up on this method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bands** & **Wavelength (Micrometers)** & **Resolution (Meters)** \\ \hline Band 1—Blue & 0.45–0.52 & 30 \\ Band 2—Green & 0.52–0.60 & 30 \\ Band 3—Red & 0.63–0.69 & 30 \\ Band 4—Near Infrared (NIR) & 0.77–0.90 & 30 \\ Band 5—Shortwave Infrared & 1.55–1.75 & 30 \\ Band 6—Thermal & 10.40–12.50 & 60 (30) \\ Band 7—Shortwave Infrared & 2.09–2.35 & 30 \\ (SWIR) 2 & 0.52–0.90 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Landsat 7 Enhanced Thematic Mapper Plus (ETM+).  
Figure 2: Methodology flowchart.
# Deep-Feature-Based Approach to Marine Debris Classification
## 1 Introduction
The VGG16 network pre-trained on the ImageNet dataset is used as a fixed feature extractor for plastic marine waste classification in [25; 26]. In [24], object-detection-based transfer learning is applied on the proposed RetinaNet model with ResNet50 backbone to detect waste items in aquatic surroundings. Successful object detection networks, YOLOv2, Tiny-YOLO, Faster RCNN, and Single Shot MultiBox Detector (SSD), are fine-tuned in [27] for underwater detection of three classes of objects, including plastic marine debris. As a backbone for their Mask R-CNN architecture for seafloor litter detection, [PERSON] et al. [28] use MobileNetV1 architecture pre-trained on the COCO detection dataset. The main goals of this study were to: (1) develop the model for autonomous marine debris identification and classification of different types of marine debris; (2) compare the performance of prominent deep convolutional architectures, including VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNet121, and MobileNetV2, on the task of marine debris classification; (3) investigate different schemes to utilize transfer learning for marine debris classification: fixed extraction of features, fine-tuning, and combination of both; (4) compare the performance of conventional machine learning classifiers trained on feature vectors extracted by deep convolutional architectures. The rest of the paper is structured as follows. Section 2 describes materials and methods used in this study: dataset (Section 2.1), implemented deep convolutional architectures (Section 2.2), and machine learning algorithms (Section 2.3). Section 3 gives implementation details and experimental settings. Experimental results are presented in Section 4 and discussed in Section 5. In Section 6, concluding remarks and directions for future work are given.
# Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
## 6 Experimental Evaluation  
Considering the goals described in Section 4, we aim at answering the following research questions:  
* Can we mine specifications that describe the input unlabeled trajectories?
* Are the mined specifications readable and interpretable for a human?  
We consider to formula describe the dataset well if it tightly fits the pool of trajectories. To this end, we verify whether the fitness \(f\) of the learned formula is as close as possible to 0.0. We say that a formula is readable and interpretable for a human if it is parsimonious; to this end, we verify whether the size of a formula (number of nodes of the derivation tree), \(|\varphi|\), is reasonable. Moreover, we also verify whether a formula is easily understandable by a human by manually inspecting and reporting it.  
To answer the research questions according to these definitions, we ran an experimental campaign on real-world data of road traffic. We performed 10 evolutionary runs with different random seeds. We used the same parameter values for all the runs and set \(n_{\text{pop}}=500\), \(n_{\text{gen}}=50\), \(n_{\text{tour}}=5\), \(p_{\text{xover}}=0.8\), \(n_{\text{atts}}=100\), \(d_{\text{min}}=1\), and \(d_{\text{max}}=12\).  
We executed the experiments on an HPC cluster with nodes equipped with \(2\times 18\) cores based on 2.30 GHz Intel Xeon E5-2697 v4 (Broadwell) and with 128 GB RAM. Fitness evaluations were parallelized across cores; evolutionary runs were parallelized across nodes. We implemented the software for the experiments in the Java programming language and made it publicly available at [[https://github.com/pigozzif/STLRulesEvolutionaryInferenceNoClass](https://github.com/pigozzif/STLRulesEvolutionaryInferenceNoClass)]([https://github.com/pigozzif/STLRulesEvolutionaryInferenceNoClass](https://github.com/pigozzif/STLRulesEvolutionaryInferenceNoClass)) (accessed on 1 November 2021). The project employs the monitoring tool MoonLight ([[https://github.com/MoonLightSuite/MoonLight](https://github.com/MoonLightSuite/MoonLight)]([https://github.com/MoonLightSuite/MoonLight](https://github.com/MoonLightSuite/MoonLight)), accessed on 1 November 2021) [28] and the evolutionary framework JGEA ([[https://github.com/ericmedvet/jgea](https://github.com/ericmedvet/jgea)]([https://github.com/ericmedvet/jgea](https://github.com/ericmedvet/jgea)), accessed on 1 November 2021).
## 4 Security in Time-Critical CPSs
### Security as a Prerequisite for Functional Safety  
Safety and security are interconnected and can influence each other; there is a recognised need to consider them jointly [40]. A connected safety-critical CPS, cannot be argued as being acceptably safe unless security impact on safety is considered [41]. Security breaches may lead to safety hazards and in this way change the probability of their occurrence, which leads to a necessity to re-assess risks. The term security informed safety can be applied in the case when safety is the overall goal of an effort and security is considered as a supportive property.  
Safety as well as security is not composable, thus an impact of CPS security on CPS safety has to be considered on the system level. There are many research works focusing on safety and security co-analysis [42] and its different phases such as requirements engineering or Hazard Analysis and Risk Assessment (HARA) [43] and Threat Assessment and Remediation Analysis (TARA) [44]. However, one of the main remaining gaps in the direction of safety and security co-analysis, is run-time support Looking at the example of an autonomous quarry the navigation and collision hazard can be triggered by security causes such as Denial of Service or forgery attacks [17], i.e., by an attacker making a communication media unavailable or modifying control messages. Incorporating such security causes into safety analyses implies providing safety arguments that cover security considerations and performing a joint safety-security risk assessment. In an ideal case, safety and security process are joint to fully capture all interconnections and make work on the dependencies more efficient [45].
## 3 The Proposed Protocol
### Registration Phase  
The registration phase comprises the following: In the terminal of the GWN, the user and BASN need to complete the registration of identity information and receive authentication parameters in order to be ready for future identity authentication and key agreement. Specifically, the registration phase includes the registration of the BASN and users.
# Free-Space QKD with Modulating Retroreflectors Based on the B92 Protocol
## 3 Results
In RM2018, \(e^{\prime}_{detect}\) is related not only to the extinction ratio, but also the optical misalignment error \(e_{detect}\). As shown in Figure 4, we studied the relationship between the SKR and transmission loss of both our system and RM2018 under different \(e_{detect}\) values. The dotted line represents RM2018, and the solid line represents our system. When the extinction ratio is fixed, it is clear that the SKR of the two systems decreases as \(e_{detect}\) increases. We found that the robustness of our scheme countering the increase in \(e_{detect}\) was better than that of RM2018. When \(e_{detect}=1\%\) and \(3\%\), the SKR of RM2018 was slightly higher than that of our system in most cases, mainly because the efficiency of the BB84 protocol was twice that of the B92 protocol. However, when \(e_{detect}=5\%\), the performance of our system was much greater than that of RM2018. This result shows that the higher the \(e_{detect}\), the greater the advantage of our system is over the RM2018. This occurs because the SKR decreases faster as \(e_{detect}\) increases. Moreover, the \(e^{\prime}_{detect}\) of RM2018 is always larger than the \(e_{detect}\) of our system. We study the intrinsic maximal tolerance of misalignment error \(e^{U}_{detect}\) at which the SKR becomes zero. The relationship between the extinction ratio and \(e^{U}_{detect}\) of two systems is compared in the condition of transmission probability of 100%. The result is shown in Figure 5. We can see that \(e^{U}_{detect}\) in our system is not sensitive to the change of the extinction ratio. The change of it is mainly originated in the change of decoy state intensity. When the extinction ratio is lower than about 13.5 dB, RM2018 cannot operate. This is because the error rate caused by noise leakage has exceeded the threshold. In other cases, \(e^{U}_{detect}\) climbs up with the increase of the extinction ratio and the difference between our system and RM2018 gets smaller. When the extinction ratio is greater than 40 dB, they almost have similar performance \(e^{U}_{detect}\approx 7.65\%\). Figure 4: The relationship between the SKR and the transmission loss of our system and RM2018 under different \(e_{detect}\). The dotted line represents RM2018, and the solid line represents our system.
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 2 Proposed Methods
### Feature Matching  
The bidirectional matching method is utilized to obtain the stable match result [38]. According to the bidirectional matching method, a match is considered valid only if the same pair of feature points in both directions is obtained. Because visible images usually have more content than infrared images, visible images can have more feature points. When searching for similar features of feature points of the visible image in the infrared image, the feature points generated by the extra content of the visible image will only produce the wrong matches. To avoid this situation, the bidirectional matching method searches for similar features of feature points within the infrared image in the visible image. Then similar features of the matched feature points of the visible image are searched in the infrared image.  
When determining feature correspondences, the nearest neighbor ratio method is used to select matching points for feature points. The nearest neighbor ratio method is introduced in Reference [13] to improve the matching robustness. When the ratio of the distance of the nearest neighbor over the  
Figure 3: Sobel filters of five orientations at 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) and no orientation.  
distance of the second nearest neighbor is less than the specified threshold, the nearest neighbor is regarded as the correct match. The nearest neighbor ratio method is defined by the equation below.  
\[\left\|f_{a}-f_{b}\right\|/\left\|f_{a}-f_{c}\right\|<r, \tag{7}\]  
where \(f_{b}\) and \(f_{c}\) are the nearest and second nearest neighbors of \(f_{a}\), respectively. Parameter \(r\) is set as 0.8 [13]. The distance between two feature descriptions is calculated by the equation below.  
\[\left\|f_{a}-f_{b}\right\|=\sqrt{\sum_{i}\left(h_{a}^{i}-h_{b}^{i}\right)^{2}}, \tag{8}\]  
where \(h^{i}\) is the histogram bin of the feature description \(f\).
## 4 Antennas and Antenna Related Technologies for Future Internet
### Antennas for Distributed Solutions Based on Network Structure
#### 4.4.3 Antennas for Edge-Computing Solutions  
It is common practice, when latency is a key factor, to employ edge computing solutions. Various relevant reviews exist [320; 321; 322]. Notable antenna designs include edge computing antennas for rail [323], 5G [324], and MIMO systems [325]. Nevertheless, particularly noteworthy examples include movable and fluid antenna solutions [326; 327]. While the massive MIMO UAV-aided approach in [326] emphasizes energy optimization for vehicular networks, the fluid antenna concept in [327] offers adaptability and improved connectivity in dynamic environments. Figure 21 illustrates the concept of a fluid antenna.  
This novel antenna design dynamically adjusts its configuration to optimize signal quality and coverage.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 5 Practical Sources of Errors
### Ambient Air, Unknown Agents, and Humidity  
The FES spectral patterns of various gas compositions are learned by a classifier (a pattern recognizer such as a neural network) that will display the result of the analysis. However, strictly speaking, this approach only works for agents used in the training of the classifier, typically in a lab environment. When the FES system is used in the field, extra agents can be present. If they influence the FES spectra, they become a problem. Humidity can strongly vary and it typically has a major influence on the spectra. In this case, humidity must also be treated as an agent. Note that the human nose is using not only FES (via random neural signals) but also a large quantity of different types of olfactory neurons to compensate for these situations. Possibly, future chip technology can help with this problem by imitating biological smelling. Such a \"visionary chip\" would include a large number of diverse type and independent FES sensors integrated together with their signal processing units. The output signals of these units would be evaluated in a \"spatio-temporal\" fashion similarly to dog noses. Thus, not only the spectral features of the sensor signals would be evaluated but also transient, delay, and spatial and time correlation effects between the statistical responses of sensors. This would enrich the sensory information by surface and air diffusion responses similarly to biological noses.
## 3 Results  
### Patients  
The imaging archive revision identified 245 patients with brain tumors and resting-state data acquired with the same protocol between January 2016 and September 2021. Following our inclusion criteria, 66 patients were excluded due to right-sided tumors, 65 patients were excluded due to prior surgery or treatment, 30 patients were excluded due to pathologic diagnosis other than glioma (21 metastases, 7 lymphoma, 2 radiation necrosis) and 10 patients were excluded due to motion artifacts or the presence of hemorrhagic tumor components, leading to drop-out artifacts on fMRI. Seventy-four patients with gliomas in the left hemisphere (32 LGG; 42 HGG) were selected based on our inclusion criteria. Four patients were excluded due to poor fMRI quality during the data processing phase (interpreted as excessive noise from motion or other artifacts). Ten patients were excluded due to technical failure of image co-registration to the standard space (Figure 1).  
Sixty patients with gliomas in the left hemisphere (30 LGG, mean age 40 years, 21 males; 30 HGG, mean age 62 years, 22 males) were therefore recruited for the study. Additionally, 20 HC (mean age 48 years) were included for comparison. Tumor location labeling demonstrated the involvement of the left frontal lobe 20/60 (5 HGG, 15 LGG), left temporal lobe 29/60 (16 HGG, 13 LGG), left parietal lobe 15/60 (11 HGG, 4 LGG) and left insula 17/60 (5 HGG, 12 LGG) (Figure 2).  
No patient showed involvement of the occipital lobe. The Boston Naming Test displayed significantly lower scores in HGG vs. LGG patients (\(p=0.04\)). Particularly, HGG patients displayed BNT values between 24 and 60 points (average 46), while LGG values ranged between 51 and 60 points (average 56). The presence of aphasia was different in HGG vs. LGG patients (aphasia in 10/30 HGG vs. 0/30 LGG) in the preoperative setting and after surgery (aphasia in 12/29 HGG vs. 6/27 LGG). Missing datapoints in the presurgical cognitive testing (Boston Naming Test) were related to patient compliance and clinical condition. Missing datapoints in the neurologic/neurosurgical evaluation were related to conservative treatment (no surgery performed) or loss of patient at follow-up. Patient demographics and clinical data regarding language function are available in Supplementary Tables S2 and S3. A statistical description of patients' age and tumor size is presented in Table S4 and Figure S2.
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods/Model** & **Dataset Used** & **Accuracy (\%)** & **Precision** & **Recall** & **F1 Score** \\ \hline Random Forest [38] & LIDC-IDRI & 85.00 & 0.7962 & 0.7810 & 0.7918 \\ \hline Deep RNN [39] & LIDC-IDRI/Luna 16 & 85.71 & 0.879 & 0.892 & 0.882 \\ \hline SVM [16] & LIDC-IDRI & 93.0 & 0.87 & 0.86 & 0.84 \\ \hline Res U-Net [36] & LIDC-IDRI & 90.70\% & 0.916 & 0.902 & 0.911 \\ \hline Proposed Modified Gradient Boosting Classifier & LIDC-IDRI & 95.67 & 0.957 & 0.91 & 0.941 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the proposed modified gradient boosting classifier with existing models.
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 2 Background  
### Polar Code  
A polar code of length \(N\) with \(K\) information bits can be expressed as (\(N\), \(K\)), where \(K\) channels are used to transmit information bits, and \(N\)-\(K\) channels are used to transmit frozen bits. The encoding process of polar codes can be described as follows:  
\[x_{1}^{N}=u_{1}^{N}G_{N}, \tag{1}\]  
where \(x_{1}^{N}=\{x_{1},x_{2},\ldots,x_{n}\}\) represents the codeword bits, \(u_{1}^{N}\) represents the source bits, \(G_{N}=B_{N}F^{\otimes n}\) is the generator matrix. \(B_{N}\) is the bit-reversal permutation matrix, and \(F^{\otimes n}\) denotes the \(n\)-th Kronecker power, with  
\[F=\begin{bmatrix}1&0\\ 1&1\end{bmatrix}. \tag{2}\]