esa-sceva/satcom-chunk-collection huggingface dataset:
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 7 Results
### Throughput  
Prior to its analysis in the performed tests, a theoretical computation of the maximum expected throughput (\(T_{\text{max}}\)) for the network configuration of Section 7.2 is provided below. Firstly, time between two consecutive _primary beacons_ is forced to be minimum (\(T_{p_{\text{min}}}\)), as a result of not considering secondary beacons (i.e., \(k_{s}=0\)) and only summing the time for the _STA association mechanism_ and that reserved to data transmissions:  
\[T_{p_{\text{min}}}=(a_{s}\cdot T_{a}+T_{g})\cdot a_{t}+w\cdot R\cdot T_{r}. \tag{5}\]  
Then, considering that all network STAs (\(N_{\text{STA}}\)) follow the same beacon scheduling from Section 7.2 (i.e., 1 _statistics packet_ every nine consecutive _application packets_) and all transmitted packets are received by the GW, \(T_{\text{max}}\) is obtained as:  
\[T_{\text{max}}=\frac{N_{\text{STA}}\cdot\left[(\frac{9}{10})\cdot L_{\text{ap} }+(\frac{1}{10})\cdot L_{\text{sp}}\right]}{T_{p_{\text{min}}}}=\frac{N_{ \text{STA}}\cdot\left[(\frac{9}{10})\cdot L_{\text{ap}}+(\frac{1}{10})\cdot L _{\text{sp}}\right]}{(a_{s}\cdot T_{a}+T_{g})\cdot a_{t}+w\cdot R\cdot T_{r}}, \tag{6}\]  
where \(L_{\text{ap}}\) and \(L_{\text{sp}}\) are, respectively, the size of the net information contained in an _application packet_ and in a _statistics packet_. Lastly, assuming \(N_{\text{STA}}=12\), \(L_{\text{ap}}=10\) bytes, \(L_{\text{sp}}=20\) bytes, and setting GW association parameters to those of the _STA association mechanism_ from Table 8, computed \(T_{p_{\text{min}}}\) and \(T_{\text{max}}\) for different \(R\) values are compiled in Table 10.  
Figure 19: Average end-to-end delay when \(T_{r}=5\) s.  
The highest throughput value \(T_{\text{max}}=25.76\) bits/s corresponds to the single-hop case (\(R=1\)), when time between _primary beacons_ is \(T_{p_{\text{min}}}=41\) s. For higher \(R\) values, the growth of \(T_{p_{\text{min}}}\) is inversely proportional to the decrease of \(T_{\text{max}}\), with expected values below 10 bits/s.  
Figure 20 reflects the measured throughput in the different network configurations of Section 7.2, where \(T_{p}\) was set to 3 min. With a throughput of 5.87 bits/s, this value is far from those of Table 10. In our experiments, the use of a more conservative \(T_{p}\) value, which deliberately leaves the channel empty for certain time periods, results in less use of available resources.  
While both employed RDC sublayers behave almost equally (achieving their maximum throughput values in low error-prone configurations), X-MAC responds better than NullRDC in \(E_{20/10}\) and \(E_{30/15}\) due to its lesser exposure to packet losses. As for network topologies, and again being consistent with reliability results, single-hop cases perform slightly better than multi-hop ones in high error-prone scenarios.
# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 3 The Proposed Method and Its Applications on Downstream Tasks
### The Sentiment-Enhanced Word Embedding Method  
The S-EWE method aims to establish the mapping relationship between words in sentiment lexicons and their sentiment orientations to obtain trained sentiment-enhanced word embeddings for downstream tasks. When we convert one word to a word embedding, the dimensions of the word embedding are much larger than those of the sentiment orientation, and they belong to two different vector spaces. Therefore, we assign a sentiment mapping vector to the word embedding. This sentiment mapping vector can help word embedding find the mapping relationship between it and its sentiment orientation.  
Let the \(\mathcal{V}\) be a set of all words in a sentiment lexicon, and \(\mathcal{Y}\) be a set of sentiment orientations of all words in the sentiment lexicon. Given a word \(w_{i}\in\mathcal{V}\), its word embedding \(\mathbf{w}_{i}\), and its sentiment orientation \(y_{i}\in\mathcal{Y}\), we assign a sentiment mapping vector \(\mathbf{\
abla}_{i}\) to the word embedding \(\mathbf{w}_{i}\). Then, the goal of the S-EWE method is to solve the mapping relationship function among \(w_{i},\mathbf{w}_{i}\) and \(\mathbf{\
abla}_{i}\), denoted by \(f(y_{i}|w_{i},\mathbf{\
abla}_{i})\). Figure 3 shows the running  
Figure 2: The workflow of S-EWE method and its applications on the downstream tasks. The orange dashed box indicates the stage in which we train the S-EWE embeddings, and the blue dashed box represents the stage in which we apply the embeddings to downstream tasks. The red texts are the inputs, and the purple texts are the outputs.  
process of the S-EWE method. The method implements \(f(y_{i}|w_{i},\mathbf{\
abla}_{i})\) by four modules, including the embedding layer, addition layer, MLP layer, and loss function as follows.
## 5 Proposed Alternative Conceptual Model
### Processes Occurring at the Metal Oxides-Water Interface
In these latter surfaces, temperature-programmed desorption reveals that the production of H\({}_{2}\) is thought to be due to an oxygen vacancy at the surface [62]. The different types of ionizing radiation in spent fuel, because of the different stopping powers or LET, also play an important role in determining the energy distribution and resulting species formed within the UO\({}_{2}\) matrix, at the water-spent fuel interface and in bulk water. When a gamma ray, a beta particle, or an alpha particle is released by the radionuclides embedded in spent fuel, part of the energy that is absorbed by the solid forms either defects or excited species that can interact with the water at the interface and beyond. The fraction of the ionizing radiation that is not used in direct effects in the solid causes ionizing events in water within a range that depends on its energy on leaving the surface and LET. Typically, gamma and beta radiation are able to cause ionizing events at a far greater distance than alpha radiation due to their lower LET. When modelling the effect of the intrinsic radiation on water radiolysis, the fraction of the dose absorbed by the UO\({}_{2}\) itself should be assessed by taking into account the electron density and the solid/water ratio in the system because it is possible that most of the ionizing radiation energy is absorbed by the solid itself. One of the more common methods for determination of the energy dissipation through a metal-water interface is the measurement of the yield of the main reducing molecular radiolytic species, H\({}_{2}\). The H\({}_{2}\) yield has been determined for a wide variety of compounds [42; 43; 50; 53; 63; 64], including UO\({}_{2}\)[43]. In quite a few cases, the production of H\({}_{2}\) far exceeds that expected from energy deposited in the water layer alone. Later studies of UO\({}_{2}\) found that the radiolytic yield of H\({}_{2}\) was about 40 molecules 100 eV\({}^{-1}\) energy directly absorbed by a few adsorbed water layers, compared to the value of 0.45 molecules/100 eV for bulk water [42]. Clearly, there is a transport of energy or charge from the bulk of UO\({}_{2}\) to the interfacial water. The exact precursor to this excess H\({}_{2}\) formation is not known but is believed to be due to the transport of excitons [43]. In the next section, we provide a possible mechanism for the production of H\({}_{2}\) at the spent fuel-water interface.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Feature Extraction from Quad-Polarimetric SAR Data  
#### 2.2.1 Single Polarimetric Intensity  
The intensity of co-polarized channel is largely used in single polarimetric SAR-based oil spill detection algorithms. In this study, \(S_{VV}^{2}\) is considered as one of the features for its higher SNRs compared to \(S_{HH}^{2}\) on the sea surface. The hydrocarbons on the sea surface damp the short gravity and capillary waves of the sea surface, and hence, they are usually observed as very low backscatter areas. However, very similar dark areas can also be observed from SAR images when other kinds of oil are present, such as biogenic slicks.
## 2 Algorithm for Uncertainty Analysis  
### The Aleatory Uncertainty Quantification  
During the process of quantifying aleatory uncertainties, it is important to choose the suitable probability distribution. The European Maritime Safety Agency assumes that the occurrence of vessel traffic accidents obeys the Poisson distribution according to expert experience, historical statistical data and theoretical analysis [18]. According to the a priori knowledge of the European Maritime Safety Agency, the Poisson distribution is chosen to quantify the aleatory uncertainty of vessel traffic accidents in this study. With a given confidence level, the confidence interval of Poisson-distributed data can be calculated by [25]:  
\[\begin{array}{c}\lambda_{U}=\frac{\chi_{1-\alpha_{U}/2}^{2}(2n+2)}{2}\\ \lambda_{L}=\frac{\chi_{\alpha_{U}/2}^{2}(2n)}{2}\end{array} \tag{1}\]  
where \(\lambda_{U}\) and \(\lambda_{L}\) are defined as the upper and lower limits of the confidence interval of \(n\), which is the number of times an event occurs in a certain time window. \(a\) denotes the significance level and \((1-a)\) denotes the confidence level. \(\chi_{1-a/2}^{2}(2n+2)\) is the \((1-\alpha/2)\)th quantile of the chi-squared distribution with \((2n+2)\) degrees of freedom; \(\chi_{\alpha/2}^{2}(2n)\) is the \((\alpha/2)\)th quantile of the chi-squared distribution with \((2n)\) degrees of freedom.  
It should be noted that there is epistemic uncertainty when selecting the probability distribution due to referring to the a priori knowledge of the European Maritime Safety Agency. In other words, other probability distributions can also be selected to quantify aleatory uncertainty, such as the Normal distribution, the Log-normal distribution and the Beta distribution. However, the information available in this study is insufficient to model the Normal distribution, the Log-normal distribution and the Beta distribution. In order to make the epistemic uncertainty as small as possible, the Poisson distribution is selected in this study. When there is more information, it is possible to select other probability distributions to quantify the aleatory uncertainty of vessel traffic accidents. For the sake of simplicity, the possible effects of the epistemic uncertainty of selecting the Poisson distribution are not considered in this study, which can be studied further in the future.
## 2 Proposed Method
### Security Analysis  
For security analysis, ABE is used, which is the best encryption algorithm, as plain text cannot be accessed. At the most, the access policy is verified using a bloom filter, which makes it difficult for the hacker to determine the attributes for access. This improves the confidentiality of the data.  
During break-glass access, the auxiliary message can only be viewed by either the CSP or the HC. The BGK is utilized for the retrieval of data from the CSP. The original data can be decrypted only by using an encapsulated password on the destination side. This mechanism protects the integrity of the data in emergency cases as well. Hence, inthis approach, in both the access schemes, the confidential data cannot be viewed, which provides the safe healthcare IoT with emergency access to save the patient's life.
## 3 Results and Discussion
### Classifier Performance Based on ROC, Sensitivity, Specificity and Accuracy  
Two common and useful ways to analyze the performance of classifiers are the receiver operating characteristic (ROC) curve and the criteria of sensitivity, specificity, and accuracy [37]. Figure 4 depicts the ROC curves of the proposed system for the four defined classes. On the other hand, Table 7 shows the performance results of the hybrid ANN-GA classifier using the three above-mentioned criteria and the area under the ROC curve (AUC) for each class.  
The maximum value of sensitivity is reached in the ripe class, with 98.41%. Table 6 proves this fact, since only 12 samples of this class were misclassified in other classes. The maximum value of specificity of the classifier is given in Class 4, with 99.02%, as only nine samples of Class 3 are misclassified as Class 4. To conclude, this table shows that the maximum accuracy of the classifier is also given in the overripe class with a 99.16% accuracy, although it has the lowest sensitivity. In general, all the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Sensitivity (\%)** & **Accuracy (\%)** & **Specificity (\%)** & **AUC** \\ \hline
1 & 97.69 & 98.78 & 98.68 & 0.9941 \\
2 & 98.09 & 98.73 & 96.46 & 0.9983 \\
3 & 98.41 & 99.06 & 96.86 & 0.9923 \\
4 & 97.54 & 99.16 & 99.02 & 0.9889 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance results of the hybrid ANN-GA classifier using different criteria: sensitivity, accuracy, and specificity, and area under the ROC curve (AUC). Classes: 1. unripe; 2. half-ripe; 3. ripe; 4. overripe.  
Figure 4: ROC curves of the proposed hybrid ANN-GA classifier.  
classes achieve very similar results, so the system will be able to work correctly throughout the process of growing of the apples. The values of the AUC also indicate very good results, near or above 0.99. This proves that the obtained classifier is not only very accurate, but also very robust in relation to its parameters. Only Class 4 presents a lower AUC value, indicating a slight confusion with Class 3.  
Figure 5 shows a sample result after segmentation and classification. In this case, all the 18 existing apple samples were correctly detected and classified. More sample results of the segmentation and classification algorithm are available in the indicated link. It can be observed that the system is able to work under very different conditions. In some cases, when the apples are overlapped, only one object is detected for two or more apples. This would be problematic for a fruit counting algorithm, although for the purpose of estimating the state of maturation it is not irrelevant. In most cases, both apples have the same ripeness stage, and the system will produce an average estimation.
## 4 Experimental Results and Comparisons
### Experiments on a Real-World Case  
The proposed method was applied to solve a real-world steel-surface defects-detection case. We assessed a Chinese real-world cold-rolling steel workshop which produces electro-galvanized steel strips for vehicles. The flowchart of the production is shown in Figure 8. Statistically, the qualified rate of this production is only 78.6%, and more than 95.4% of defects occur on the surface. An automatic surface inspection (ASI) system was set up, and defect images were collected. In the early stages, the workshop had already carried out some degree of intelligent defect detection. Support vector machine (SVM) algorithms were applied to classify the defects, but the recognition accuracy was less than 70%, and at least 85% was required in this workshop.  
In order to improve the recognition accuracy, 25,000 defect images with six typical defects were provided, and 3000 of these images were manually labeled, i.e., rolling marks, sliver, edge defect, scratches, and hole. Examples of collected images are shown in Figure 9. For a better comparison, we also adopted Inception_v2 for the experiments. This paper demonstrates the noise immunity of the progressive learning method to noisy labels. In this case, we only compared the recognition accuracy and FP rate, which are the two most important indicators for the workshop.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Classification Accuracy with 50\% Noisy Labels (\%)** \\ \hline CleanNet & 81.33 \\ MentorNet & 82.24 \\ Proposed method & 87.14 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparisons with recent state-of-the-art label noise learning methods.  
Figure 8: The flowchart of production in a real-world case.  
Figure 9: Examples of the collected images.  
In addition to accuracy, the FP rate reflects the ability of the classification model to correctly predict the purity of positive samples in order to reduce the number of negative samples predicted as positive, i.e., the proportion of negative samples predicted as positive to the total number of negative samples. The FP rate is calculated as follows:  
\[\text{FP rate}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{10}\]  
The comparison results are presented in Table 7. From the table, it is clear that the proposed method outperforms the standard Inception_v2 in terms of accuracy and FP rate, especially for the rolling marks, hole, and edge defects. For the proposed method, the classification accuracy is slightly lower, and the FP rate is slightly higher than other defects in cracks and scratches. A possible explanation for this is the similarity in the features of cracks and scratches, which are sometimes difficult to distinguish.  
In practical applications, the number of noisy labels is uncertain and unevenly distributed. In this case, the results also demonstrate that the proposed method can improve the accuracy of network model training to some extent, thus improving the effectiveness of defect classification.
# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 2 Relevant Theories
### Construction of Dense Point Cloud Maps  
Based on the rotation vectors and translation vectors obtained by estimating the camera position, the RGB-D data are converted into point cloud data, and then multiple point cloud data are stitched together to generate a point cloud map.  
The stitching of the point cloud is essentially a process of transforming the point cloud. This transformation process can be described by the transformation matrix as shown in Equation (9):  
\[T=\begin{bmatrix}R_{3\times 3}&t_{3\times 1}\\ O_{1\times 3}&1\end{bmatrix}\in\mathbb{R}^{4\times 4} \tag{9}\]  
where \(R_{3\times 3}\) is the \(3\times 3\) dimensional rotation matrix, \(t_{3\times 1}\) is the \(3\times 1\) dimensional displacement vector, and \(O_{1\times 3}\) is the \(1\times 3\) dimensional scaling vector, which is usually taken as 0 in SLAM because scaling does not exist in real environments. These matrices can be chi-square transformed into other matrices as in Equation (10):  
\[\begin{bmatrix}y_{1}\\ y_{2}\\ y_{3}\\ 1\end{bmatrix}=T\cdot\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\\ 1\end{bmatrix} \tag{10}\]  
The point cloud used for SLAM mapping includes the coordinates, color, and depth information of spatial points, which are synthesized from the image frames captured by the camera. To obtain the color and depth images of the environment, an RGB-D camera can be used for acquisition and then the point cloud is stitched together according to the camera's position information, and finally, the whole point cloud is obtained.
## 2 Materials and Methods
### Economic Model  
The total capital cost of the ORC system is mainly determined from the cost of the four components (evaporator, turbine, condenser, and pump). The bare module equipment cost (US dollars in the year 1996) of each component can be calculated by:  
\[C_{\text{BM}}=C_{\text{b}}F_{\text{bm}}=C_{\text{b}}(B_{1}+B_{2}F_{\text{m}}F_{ \text{p}}). \tag{7}\]  
\[\text{lg}C_{\text{b}}=K_{1}+K_{2}\text{lg}Z+K_{3}(\text{lg}Z)^{2}. \tag{8}\]  
\[\text{lg}F_{\text{p}}=C_{1}+C_{2}\text{lg}P+C_{3}(\text{lg}P)^{2}. \tag{9}\]  
where \(C_{\text{b}}\) is the basic cost of each equipment. \(Z\) is the related parameters of each ORC component; for evaporator and condenser, it refers to heat transfer area; for the pump, it means the power consumption; for the turbine, it refers the power generation. \(F_{\text{bm}}\), \(B_{1}\), \(B_{2}\), \(K_{1}\), \(K_{2}\), \(K_{3}\), \(C_{1}\), \(C_{2}\), \(C_{3}\) are the constants for different ORC components [22]. \(F_{\text{m}}\) and \(F_{\text{p}}\) represent the material factors and pressure factor for different components.  
The total capital cost of the ORC system is the sum of the cost of each component, which can be expressed as:  
\[C_{1996}=C_{\text{BM,eva}}+C_{\text{BM,con}}+C_{\text{BM,pump}}+C_{\text{BM, tur}}. \tag{10}\]  
Considering the value of money at different periods, the total capital cost of the ORC system is adjusted by using a chemical engineering plant cost index (CEPCI):  
\[C_{2014}=C_{1996}\times CEPCI_{2014}/CEPCI_{1996}. \tag{11}\]  
where \(CEPCI_{1996}\) and \(CEPCI_{2014}\) are the chemical engineering plant cost index in 1996 and 2014 respectively.  
The specific invest cost (\(SIC\)) is the system total cost of per unit net power output:  
\[SIC=\frac{C_{2014}}{W_{\text{net}}}. \tag{12}\]
## 4 Results and Discussion
### Overall Assessment  
Based on the assessment, a total of 22 application domains are identified where the decision support system actively assists in decision-making problems. These domains are diverse and they show the potential of a decision support system using a multi-criteria approach for solving problems. These problems vary from the computing field to agriculture, the medical domain, personal safety such as police departments, decision-making in business and management activities, employee satisfaction, waste management, and so on. It has been observed that the role of decision support systems in solving the problems associated with a certain domain such as the medical domain, computer field, transportation, energy sector, and agriculture is major. These are the application areas where decision support systems provide an intelligent, efficient, robust solution regarding decision-making in simple to complex problems. The application of the decision support system in various application areas is shown in Figure 9. Based on its effectiveness in the different application domains, a decision support system based on the multi-criteria approach is an active area of research that aims to solve more complex problems by incorporating machine learning techniques and the power of artificial intelligence. \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline
**Quality Assessment** & **Description** & **Formula** \\ \hline QA1 (Impact) & The impact of a paper is considered as high if a working system is developed for solving decision-making problems as compared to the framework/model proposed or the review or survey conducted. & Review or survey = 0.5 Framework or model = 0.7 Decision support system = 1 \\ \hline QA2 (Scope) & This measures the support for use of a decision support system in the application domain in solving decision-making problems. The application domain is considered to have higher priority if more applications from that domain are supported by decision support systems. & \(Freq(Domain_{i})\) is the no of papers belonging to the same area of application \(i\in Domain_{i}\)_where \(i=1,\ 2,\ \dots,\ 22\) \\ \hline QA3 (Accumulative) & To score the papers by the inclusion of paper impact as well as the importance of the application domain & \(Impact_{j}*Scope_{j}\) \\ \hline Quality criteria & Based on the scoring criteria, papers are measured in terms of overall quality & If value (QA3) \(>=0.5\) (Good Quality) If value (QA3) \(<0.5\) (Acceptable Quality) \\ \hline \end{tabular}
\end{table} 
Table 7: Criteria for assessment of the selected papers. Table 8 shows the details of the application domain, the number of studies addressing decision support based on multi-criteria support for solving decision-making problems, and the references of the papers. Based on the statistics, one can see that most of the active application areas for decision support systems are the computing field, business sector, transportation, and medical domain.