esa-sceva/satcom-chunk-collection huggingface dataset:
# Determining Factors of Fixed Offshore Platform Inspections in Indonesia
## 5 Discussion  
Compared to onshore structures, offshore structures are built by considering the more complex environmental loads in which they are located, such as wind, currents, waves, water level changes, sea ice, seabed erosion (scour), and earthquakes [48]. Therefore, the factor consists of X8, X10, X11, and X12, labeled as environmental conditions.  
In the research [24], the optimization of size and shape is considered when designing structures. This is adjusted to the type of platform based on its function [23]. [PERSON] et al. stated that modification is a change, such as a design code, reinforcement, or change in damage, which is different from the initial design [47]. Marine growth is considered to influence the nature of structures and the wave loads acting on them. It also adds mass to the support, such that it does not conform to the design [49]. Some of the considerations above mean that the factor consisting of X2, X3, X6, and X7, is labeled as structural design.  
During inspections, there shall be a system for the management of detected anomalies and defects. Any degradation in structural integrity, such as damage in service, corrosion, or cracking, must be identified during inspections [50]. Therefore, the factor consisting of X4 and X5 is labeled as anomalies and defects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Communalities**} & \multicolumn{3}{c}{**Varimax-Rotated**} \\  & & & & **Loading Factor** \\ \cline{3-5}  & & & **1** & **2** & **3** \\ \hline X8 & The magnitude of scour on the seabed & 0.753 & 0.866 & - & - \\ X10 & Ocean waves and currents & 0.796 & 0.860 & - & - \\ X11 & Weather and climate & 0.805 & 0.791 & - & - \\ X12 & The depth of the seawater & 0.514 & 0.651 & - & - \\ \hline X2 & The type of fixed offshore platform & 0.603 & - & 0.772 & - \\ X3 & Structural configuration & 0.734 & - & 0.768 & - \\ X6 & The number of modifications & 0.747 & - & 0.830 & - \\ X7 & Marine growth & 0.638 & - & 0.660 & - \\ \hline X4 & The number of instances of damage & 0.828 & - & - & 0.837 \\ X5 & The number of instances of corrosion & 0.781 & - & - & 0.843 \\ \hline Eigenvalues & - & - & 4.052 & 1.819 & 1.326 \\ \% variance & - & - & 40.522 & 18.194 & 13.259 \\ Cum \% variance & - & - & 40.522 & 58.716 & 71.976 \\ \hline \end{tabular}
\end{table}
Table 6: Factor analysis result.
## 4 Experiments and Analysis
### Multi-Model Fusion Learning Performance Analysis  
From the observations in Section 3, we can conclude that different machine learning models perform differently on different performance prediction tasks, i.e., no single algorithm performs the best on all task predictions. Based on this, this section proposes a multi-model fusion framework based on machine learning, which integrates multiple machine learning algorithms and selects the optimal machine learning algorithm by voting  
Figure 4: Comparison of model accuracy in grade prediction of different courses.  
on each task to predict student performance. We further apply this framework in the work of student education management work.  
The specific prediction process is shown in Figure 5 and is divided into five steps:  
Step 1: Data Preprocessing. Clean the data, such as handling missing values and outliers; normalize or standardize the data to ensure the data are on a uniform scale; analyze the relevance of course scores to ensure strongly relevant courses are selected.  
Step 2: Model Selection and Training. Select the regression prediction model and the classification prediction model suitable for student achievement prediction. In this paper, linear regression, ridge regression, lasso regression, and decision tree regression were used as candidates for the regression prediction models, and random forest, support vector machine, gradient boost tree, K-nearest neighbor, and adaptive boost were used as candidates for the classification prediction model.  
Step 3: Multi-Model Fusion and Model Evaluation. Each model is tested using the test set to calculate the accuracy. By comparing the accuracy of different models, the model with the highest accuracy is selected as the model for subsequent prediction.  
Step 4: Prediction and Student Classification. The students' grades are predicted using selected models or fused models. The students are then categorized based on these predicted grades. A threshold is employed to classify students into distinct categories, such as excellent, good, medium, passing, and failing grades.  
Step 5: Results Output. The performance of the model can be further analyzed to provide a reference for future academic interventions. From the perspective of application results, the machine learning algorithm based on multi-model fusion can integrate the advantages of a single algorithm, providing the integrated model with stronger generalization ability than the single algorithm model.  
Figure 5: Multi-model fusion performance analysis flow chart.
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
###### Abstract
This differs from existing algorithms, which typically change the bitrate by one level at a time, regardless of how high or low the CBR is, as our proposed algorithm changes the bitrate to whichever value it estimates to be the most appropriate based on the current CBR, even if it is multiple levels away from the current bitrate in use. This results in the channel congestion changing more quickly, so it will converge to the desired level much faster. This higher convergence speed also makes the vehicles more efficient at sending packets, which leads to fewer lost beacons and a higher packet delivery ratio. The results of our simulations demonstrate that our proposed method can outperform existing bitrate-based approaches in both successful packet delivery rate and overall channel congestion. The rest of the paper is organized as follows. In Section 2, we review some existing VANET congestion control approaches, and we present our proposed congestion control algorithm in Section 3. We discuss the results of our simulations in Section 4, and in Section 5, we present our conclusions and give some directions for future work.
## 3 Materials and Methods
### Experimental Setup and Implementation
#### 3.3.2 Definition of Responses  
In our study, we selected the following performance metrics as the responses: throughput (TH), packet loss ratio (PLR), average end-to-end delay (AE2 ED) and routing overhead (RO), computed using the equations below.  
Throughput is the total number of bits successfully received during simulation time. It is expressed in kilobits per second (Kbps).  
\[\mathrm{TH}(\mathrm{Kbps})=\frac{\mathrm{totalRxBytes}*\mathrm{PacketSize}*8}{ \mathrm{T}*1000} \tag{1}\]  
where totalRxBytes and PacketSize represent the total received bytes and the packet size, respectively. T is the difference between the time when the last packet is received and the time when the first packet is sent.  
Packet loss ratio represents the ratio of the number of lost packets to the total number of transmitted packets.  
\[\mathrm{PLR}\left(\%\right)=100*\frac{\mathrm{totalTxPackets}-\mathrm{ totalRxPackets}}{\mathrm{totalTxPackets}} \tag{2}\]  
where totalTxPackets and totalRxPackets refer to the total amount of sent and received packets, respectively.  
Average end-to-end delay is the average time needed to transmit data from the source to destination, and it is measured in milliseconds (ms).  
\[\mathrm{AE2 ED}(\mathrm{ms})=\frac{\mathrm{totalDelays}}{\mathrm{totalRxPackets}} \tag{3}\]  
where totalDelays represents the summation of all delays of received packets, which corresponds to the difference between the transmission time and reception time.  
Routing overhead (RO) is the ratio between the total number of control packets generated by the AODV routing protocol during route discovery and route maintenance to the total number of transmitted packets.  
\[\mathrm{RO}=\frac{\mathrm{totalControlPackets}}{\mathrm{totalTxPackets}} \tag{4}\]  
where totalControlPackets and totalTxPackets refer to the total number of control packets generated by AODV and the total amount of sent packets, respectively.
## 4 Security Threats
### Negotiation
#### 4.2.2 Fake Spectrum Requests Injection  
All entities taking part in the negotiation process have to send their requests, as well as other details concerning their operation, to the spectrum sharing mechanism of a DSA network [42]. Obviously, each single node may send one or no such requests, depending on its current needs. One problem that arises here is that an attacker could inject fake requests (and information) [28] into the DSA network aiming at either confusing the sharing mechanism, or misleading it to assign the available opportunities to the wrong nodes. In the latter case, the fake requests might seem to be sent from an existing and valid entity of the network (a victim in this case), while in the former case this is not a requirement. In any case, since the input of the sharing mechanisms would not be representative of the nodes' needs, the output would be--with high probability--suboptimal, unfair and possibly highly disturbing. Efficient authentication techniques, as well as detailed lists of all valid participants in the negotiation processes, could prove beneficial and act as a countermeasure to such an attack.
## 2 Materials and Methods  
### Materials  
ECG Database  
The MIT-BIH arrhythmia database [53] is used to evaluate the underlying research. It comprises 48 records, each lasting 30 min, obtained over a 10 mV range with an 11-bit resolution and a sampling frequency of 360 Hz. Each file represents a distinct patient's pathology. In this study, we excluded four files with paced beats and solely analyzed 44 records.  
The AAMI EC57 standard [54] recommends organizing heartbeat classes into five distinct categories, as specified in Table 1, namely the N, V, S, F, and Q categories. This study focuses on the classification of three categories: supraventricular (S), normal (N), and ventricular ectopic (V). Figure 2 illustrates the manifestation of four heartbeat categories in one patient ECG signal (record 208).  
Each heartbeat is represented by a segment of \(D=280\) samples (\(X\in\mathbb{R}^{D}\)) taken from the raw single lead ECG signal in the time domain. The ECG in each record has been normalized to have a zero mean and a standard deviation equal to one. To ensure accurate heartbeat segmentation, we used precisely annotated QRS complex occurrences from the MIT-BIH database, which effectively eliminates biases introduced by imperfect QRS detection algorithms. This will enable a concise and objective classification performance assessment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Class** \\ \hline \multirow{2}{*}{N} & Left and right bundle branch block beats (L, R), Normal beat (N), \\  & Nodal (junctional) escape beat (j), Atrial escape beat (c) \\ \hline \multirow{2}{*}{S} & Aberrated atrial premature beat (a), Atrial premature beat (A), \\  & Supra-ventricular premature beat (S), Nodal (junctional ) premature beat (J) \\ \hline \multirow{2}{*}{V} & Ventricular escape beat (E), Premature ventricular contraction (V) \\  & \\ \end{tabular}
\end{table}
Table 1: Classes of heartbeat for each category.
## 3 Results and Discussion
The F1 score was calculated, providing an evaluation metric that harmonizes the average between Precision and Recall. The F1 score is particularly interesting in scenarios where there is an imbalance in the sample sizes among different classes, as it accounts for both the Precision (the ratio of true positive instances to the sum of true positives and false positives) and the Recall (the ratio of true positive instances to the sum of true positives and false negatives). By combining these two metrics, the F1 score offers a balanced measure that reflects the model's ability to identify relevant instances while also minimizing the occurrence of false positives and negatives. Figure 12 presents the F1-Confidence Curve, which is an indicator of the optimal Confidence threshold to use for minimizing errors. This curve illustrates how the F1 score varies with different Confidence levels, guiding the selection of a threshold that balances Precision and Recall effectively. By identifying the appropriate Confidence level, the model can reduce both false positive and negative occurrences, enhancing overall prediction accuracy. This approach ensures that the model performs robustly, even when trained on an unbalanced dataset, thereby providing reliable and precise classifications across diverse classes. Figure 11: Precision–Recall Curve.
# Application of Machine Learning to Express Measurement Uncertainty
## 2 Methods
### Empirical Loss Function  
Expressing utility in machine learning is standard using a loss function \(L\). The loss function is defined as the amount lost utility by predicting \(h(x)=\hat{y}\) when correct is the answer \(f(x)=y\)[1]:  
\[L(y,\hat{y})=Utility(y)-Utility(\hat{y}). \tag{4}\]  
Figure 1: The probability distribution function of uniform distribution.  
In general, minor deviations are better than larger ones. The two functions that follow the previously mentioned logic are the absolute value of the difference (\(L_{1}\) loss) and the square of the difference (\(L_{2}\) loss) [1]:  
\[L_{1}(y,\hat{y})=|y-\hat{y}|,\ \ \ L_{2}(y,\hat{y})=(y-\hat{y})^{2}. \tag{5}\]  
Theoretically, an agent can maximize its expected utility by choosing the hypothesis that minimizes the expected loss of overall input-output pairs it considers. An agent can estimate the generalization loss for a hypothesis \(h\) (to a loss function \(L\)) with an empirical loss on a set \(E\) of \(N\) examples (average loss) [1]:  
\[EmpLoss_{L,E}(h)=\frac{1}{N}\sum\limits_{(x,y)\in E}L(y,\hat{y}). \tag{6}\]  
Instead of average loss, the maximal loss can be used [35]:  
\[EmpLoss_{L,E}(h)=\max\limits_{(x,y)\in E}L(y,\hat{y}). \tag{7}\]  
In the given cases, the estimated best hypothesis from the set of hypotheses \(H\) is the one with the minimum empirical loss [1]:  
\[\hat{h}^{*}=\operatorname*{arg\,min}_{h\in H}EmpLoss_{L,E}(h). \tag{8}\]  
Minimizing average loss is a more popular approach. In some situations, researchers recommend minimizing maximal loss instead [35].  
Empirical average loss with \(L_{2}\) function is usually called Mean Square Error (\(MSE\)) [36]:  
\[MSE=EmpLoss_{L_{2},E}(h)=\frac{1}{N}\sum\limits_{(x,y)\in E}(y-\hat{y})^{2}. \tag{9}\]  
If the \(MSE\) is observed as the variance of the function \(y\), then the \(MSE\) represents the variance of hypothesis \(h\).  
Another way of evaluating the generalization of the hypothesis mentioned in the literature is quantifying the progress achieved by learning (\(h\)) to some trivial predictive method that is available even without training. One such trivial approach is to take the mean value of the output on some set of examples and use that value for prediction (generalization). The given method is simple and does not consider the input value when predicting in contrast to the hypothesis obtained by training [37].  
The coefficient of determination, or \(R^{2}\), compares the mean squared errors of the hypothesis \(h\) and the mean value of the output (variance) for the observed \(N\) examples [37]:  
\[R^{2}=1-\frac{MSE}{\operatorname{var}[y]}=1-\frac{\sum\limits_{i=1}^{N}{(y- \hat{y})}^{2}}{\sum\limits_{i=1}^{N}{(y-\overline{y})}^{2}}. \tag{10}\]
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Structure of Dataset
#### 4.1.2 Lab Results Dataset  
This dataset encompasses measurements of biomarkers and biochemical parameters obtained from blood samples, including D-dimer, hemoglobin, and troponin levels. It offers valuable insights into patients' physiological profiles and provides markers for potential clotting abnormalities and cardiac injury associated with AAS.  
Table 4 provides data on troponin levels in the lab results dataset for both the non-AAS (148,578 individuals) and AAS (129 individuals) groups. Troponin is a protein associated with heart muscle contraction, and its levels in the blood can indicate cardiac injury. The unit for troponin measurement is nanograms per liter (ng/L).  
Figure 4: Age distribution of patients with AAS.  
Figure 5: Age distribution of patients with AAS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Demographics**} & \multicolumn{2}{c}{**Non-AAS (n = 148,578)**} & \multicolumn{2}{c}{**AAS (n = 129)**} \\ \cline{2-5}  & **Count** & **Percentage** & **Count** & **Percentage** \\ \hline Age & & & & \\ \(<\)18 & 4848 & 3.3\% & 0 & 0\% \\ 18–40 & 50,328 & 33.9\% & 11 & 8.5\% \\ 40–60 & 50,796 & 34.2\% & 40 & 31\% \\ 60–80 & 32,691 & 22\% & 60 & 46.5\% \\ 80+ & 7322 & 4.9\% & 18 & 14\% \\ Missing data & 2593 & 1.7\% & 0 & 0\% \\ Gender & & & & \\ Male & 66,960 & 45.1\% & 84 & 65.1\% \\ Female & 79,025 & 53.2\% & 45 & 34.9\% \\ Missing data & 2593 & 1.7\% & 0 & 0\% \\ \hline \end{tabular}
\end{table}
Table 3: Demographics dataset details.  
Figure 3: Age frequency distribution of patients with AAS.  
In the non-AAS group, the average troponin level is 2 ng/L, while in the AAS group, it slightly rises to 4 ng/L. The examination of troponin level distribution across various ranges reveals differences between the two groups. For example, in the 0-1 ng/L range, a higher percentage of individuals is observed in the AAS group (41.86%) compared to the non-AAS group (37.13%). Additionally, the proportion of individuals with troponin levels in the 15-25 ng/L range is higher in the AAS group (1.55%) than in the non-AAS group (0.42%).  
The table also underscores the presence of missing data, with 55% of individuals in the non-AAS group and 45% in the AAS group lacking recorded troponin values.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 3 Experimental Setup
### The Glider Simulator  
A 4D (latitude, longitude, depth, and time) C-Language glider kinematic simulator was implemented, trying to reproduce, as close as possible, the real vehicle behaviour, but without introducing unnecessary computational cost that could lead to unacceptable optimization times. The simulator takes as inputs the glider displacement velocities, data about the trajectory profile, and mission parameters (start and target points, maximum and minimum depths, and number of yo-yo's per stint, to mention some), a list of control headings to be sequentially selected after every surfacing, and the forecasted ocean currents.  
This simulator has been adapted from a Matlab version that was used in previous works [13; 14; 15]. It has been redesigned to be able to respond to the much higher computational demands the present work requires; however, exhaustive testing has been performed in order to guarantee that both simulators produce the same results under the same input conditions.  
The glider control configuration was set to include 10 stints composed by two yo-yo's each, defining the inflection range of a maximum depth of 1060 m and a minimum of 10 m. The vehicle was commanded to stay at the surface for 20 min before the next dive. The nominal velocities were configured to model the performance of the Slocum G2 glider, also incorporating some manoeuvrability restrictions. Figure 1 shows an example of 3D glider trajectory, illustrating the vehicle inflections, surface drifting, and tidal-current effects.
# Yield and Quality of Essential Oils in Hemp Varieties in Different Environments
## 3 Results and discussion
### Phytocannabinoids Determination in Inflorescences and EOs  
Phytocannabinoids analysis was performed in the three environments in 2017: Aviano, Camapoformido and Maiano. The concentrations of the main phytocannabinoids THC, CBD and CBN, in the inflorescences and in the EOs obtained by hydrodistillation were determined through the GC-MS analysis (Figure 4). The THC concentration detected in each inflorescence was significantly lower than the limit of 0.2% (data not shown) that the EU regulation states for cannabis plants for industrial uses. ANOVA analysis indicates the absence of the environments and interaction effect on CBD content in the inflorescences. Conversely, the variety effect, as mean across environments, was significant. In particular, Futura registered similar and higher levels in all environments than the other varieties, with an average value of 61.1 mg 100 g\({}^{-1}\) of dry matter, followed by Fedora (mean 49.0 mg) and Ferimon; this latter showed the lowest CBD concentration (mean 40.3 mg) (Figure 4). The CBD concentration obtained in Fedora (0.49%) was comparable to that obtained by [PERSON] et al. (1998) (0.32%), but higher than those reported by [PERSON] and [PERSON] (2009) (0.19%) and by [PERSON] et al. (1998) (0.1%). Similarly, CBD concentration in Futura (0.61%) was almost double what was observed by [PERSON] et al. (1998). The 10-based logarithm analysis of the relationship between CBD and THC (1998), confirmed that all three varieties have a value between 0.95 and 1.51, and therefore belong to chemotype III, representing non-psychotropic varieties that are normally used for industrial purposes.  
In all the EOs analyzed, THC and CBN content was lower than the instrument's detection limit (1 ppb). Conversely, the CBD content in EOs, expressed as mg 100 g\({}^{-1}\) of inflorescence as dry matter, showed the highest values in Futura in all environments (values ranging from 0.055-0.068 mg), with a similar trend to that reported for inflorescences, but with values about 1000 times lower. This result means that the steam during the hydrodistillation is not able to remove the phytocannabinoids from the tissues and convey them into the EO. If, as appears from the first evaluations, CBD is not found in the aromatic distillation waters (personal communication), it is very likely that these compounds remain concentrated in the biomass and that the latter could be usefully exploited instead of being considered a waste product.