esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Metric Description  
The following metrics were used to evaluate the model's performance:  
* Gray-level Dice coefficient (GDC): This metric measures the overlap between two gray-level, positive-definite images (such as the certainty maps analyzed in this work) and is an extension of the Dice coefficient (DC), which is commonly used to assess the quality of segmentations. As shown in Equation (1), the GDC is an enhanced version of the DC that can be applied to both binary and gray-level images, evaluating segmentation accuracy within the same range. Similar to the standard DC, the GDC value ranges from 0 to 1, where 0 indicates no overlap between the images, and 1 indicates perfect overlap. \[\text{GDC}=\frac{2\times\sum_{i=1}^{M}\sum_{j=1}^{N}\sqrt{Ref_{(i,j)}*Seg_{(i,j )}}}{\sum_{i=1}^{M}\sum_{j=1}^{N}Ref_{(i,j)}+\sum_{i=1}^{M}\sum_{j=1}^{N}Seg_{( i,j)}}\] (1)
* Normalized Root Mean Square Error (NRMSE): This metric measures the similarity between two images. As shown in Equation (2), the NRMSE is estimated by calculating the RMSE (Root Mean Square Error) divided by the Euclidean normalization for the reference image. In addition to the Euclidean, other normalizations can be used with the NRMSE, such as min_max and mean normalization. The expected score is between 0 and 1, and a lower value of NRMSE indicates better matching between the images. \[\text{NRMSE}=\frac{\sqrt{\text{MSE}}}{\parallel Ref\parallel}\] (2)
* Normalized Mutual Information (NMI): This metric measures the similarity between two images. As shown in Equation (3), the NMI is estimated by calculating the entropy \(I\) between the two images divided by the mutual information \(H\) of the same images. The score value is between 0 and 1 (In the scikit-image python package, the range is between 1 and 2), and a higher value of NMI indicates better matching between the images. \[\text{NMI}\ (S,T)=\frac{I(S;T)}{\sqrt{H(S)\cdot H(T)}}\] (3)
confidently extrapolate study results to each patient when prescribing a treatment in clinical practice, and (2) screening a patient's eligibility for ongoing clinical trials.  
In this work, we constructed a dataset using the clinical trial protocols published in the largest public registry available, and used it to train and validate a model that is able to predict whether short free-text statements (describing clinical information, like patients' medical history, concomitant medication, type and features of tumor, such as molecular profiles, cancer therapy, etc.) are considered as _Eligible_ or _Not Eligible_ criteria in these trials. This model is intended to inform clinicians whether the results obtained in the CTs--and, therefore, the recommendation in the standard guidelines--can be confidently applied to a particular patient. The ultimate goal of this work is to assess whether representation learning using deep neural networks could be successfully applied to extract the medical knowledge available on clinical trial protocols, thus paving the way toward more involved and complex projects.  
In the present work, the text was first preprocessed in order to construct training and validation sets. After extracting bigrams and word-embeddings (which are commonly used techniques used to generate semantic representations), we explored different state-of-the-art classification methods (FastText, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and k-Nearest Neighbors (kNN)). Finally, after validating and comparing the final classifiers, the model was further tested against an independent testing set.  
The main contributions of this work are as follows:  
* We propose a method to learn the eligibility for cancer clinical trials collected in last 18 years.
* Several classifiers (FastText, CNN, SVM, and kNN) are evaluated using word-embeddings for eligibility classification.
* Using learned deep representations, CNN and kNN (in this case, with average word-embeddings) obtain a similar accuracy, outperforming the other methods evaluated.
* Representation learning extracts medical knowledge in cancer clinical trials, and word-embeddings are suitable to detect tumor type and treatment analogies.
* In addition, word-embeddings are also able to cluster semantically related medical concepts.  
The remainder of this paper is organized as follows. Section 2 provides a brief review of the methods related to the proposed work. Section 3 describes the dataset constructed and the methodology used, including the details employed to train the embeddings and the text classifiers. The evaluation results are detailed in Section 4, along with an analysis of the word-embeddings that were learned. Finally, Section 5 addresses our conclusions and future work.
# Improved PBFT Algorithm Based on Comprehensive Evaluation Model
## 2 Preliminaries  
### Protocol Overview of PBFT  
PBFT is a state machine replication algorithm, which mainly consists of three parts: The consistency protocol, view change protocol, and garbage collection. When the primary node fails, other nodes trigger the view change mechanism by timeout to ensure the activity of consensus. In addition to deleting expired logs, the garbage collection protocol is used to update the valid range of request sequence numbers. The consistency protocol is the core of the PBFT algorithm, which is mainly used to ensure the correctness and consistency of messages in the blockchain system. Assume that the total number of nodes in the consensus system is \(n\)= \(3f+1\), where \(f\) is the maximum number of Byzantine nodes that the system can tolerate. The consensus process of the PBFT algorithm can be divided into five phases: The request phase, pre-prepare phase, prepare phase, commit phase, and reply phase. The consensus process is shown in Figure 1. The algorithm process is divided into the following parts:  
* Request phase: The client sends a _request_ to the primary node.
* Pre-prepare phase: The primary node receives _requests_ from the client, then packages the transactions sequentially into blocks and broadcasts them to all replicas.
* Prepare phase: After receiving the _pre-prepare_ message, all replicas broadcast each other to verify the validity of the message.  
* Commit phase: Similar to the prepare phase, in the commit phase, each node will perform the all node broadcast mode to verify the validity of the message again.
* Reply phase: The committed node sends the consensus result to the client. If the client receives more than \(f\) valid _reply_ messages from all nodes, the system reaches a consensus.
## 2 Data Acquisition and Processing
### Study Area, Near Sukumba, Mali  
The study area is situated within the _terroir_ of the Sukumba community (commune of Koningue, Koutiala District, Sikasso Region) in the southeastern part of Mali located in the Soudano-Sahelian region (Figure 1) where rainfed cropping is the common practice. Forty-eight farmer fields were selected to host fertility treatments, sampling a topo-sequence from alluvial lowlands (altitude of 350 m above sea level) to the top of the plateau, with slopes characterized by breaks with locally steep inclines (total elevation difference of about 50 m). The catena ranges from deep, moderately fine textured and relatively well-drained soils in the lowlands (Plinthic/Ulic Haplustalfs, Oxic Haplustults) to moderately deep to shallow, gravelly and lateritic soils in the midslopes (Typic Cuirustalfs, Mollic Cuirortherns, Lithic Haplustalfs), and again to moderately deep, argillic soils on the plateaus [20]. Soil surface color markedly varies from reddish in sloping sections to light grey in flatter areas such as plains and plateaus (Figure 1).
## 1 Introduction
Therefore, the information provided by these characteristic parameters overlaps to some extent. Therefore, we need to use a kind of theoretical algorithm to reduce the dimension of the data and to decorrelate the input variables. Principal Component Analysis (PCA) is used to reduce the data dimension, which is used in various applications such as error recognition [21], pedestrian identification [22] and image tracking [23]. However, PCA is not used in the driving intention identification study under typical steering conditions. In essence, the driver's intention is a pattern recognition process. The cluster analysis is a typical method of pattern recognition. Compared with the traditional classification and identification of driver's intention using the neural network and fuzzy mathematics, the clustering algorithm only needs a small amount of data, which eliminates the need to construct the nonlinear recognizer and ensures that the accuracy is stable. In order to classify and identify driver's intention, three clustering analysis methods are studied: the Fuzzy C-Means algorithm (FCM), the Gustafson-Kessel algorithm (GK) and the Gath-Geva algorithm (GG). Due to its flexibility and robustness for ambiguity, the FCM algorithm is currently an active topic [24] and has been widely applied in the areas of pattern recognition [25], function approximation [26], image processing [27], machine learning [28], and so on. The GK algorithm can generate a fuzzy partition that provides the degree of membership of each data point to a given cluster [29]. The GG algorithm can make the parameters of the univariate membership functions be directly derived from the parameters of the clusters [30]. Therefore, this paper selected five driving school coaches of different driving experiences and genders as real vehicle test driver, and four typical car steering conditions' test data with four different vehicles were collected. Additionally, this paper used principal component analysis and clustering analysis to classify and identify the driver's intention. The paper analyzed the advantages and disadvantages of the three different clustering methods (FCM, GK and GG) in the direction of driver's steering intention. The paper is organized as follows. In the second section, five driving school coaches of different ages and genders are selected as the test drivers for the real vehicle test. In the third section, driver's characteristic steering parameters under different conditions are proposed. The fourth section uses principal component analysis and clustering analysis to classify and identify the driver's intention. In the fifth section, the clustering results and analysis are presented. Finally, the conclusions are drawn in the last section.
## 3 System Model
### Freshness of Collected Data  
We employ the AoI to measure the freshness of collected data, which is defined as the time that elapsed since the generation of the latest status update received by the UAV. The AoI of the \(n\)-th ground node at time slot \(t\) can be expressed by  
\[\Delta_{n}(t)=t-U_{n}(t), \tag{7}\]  
where \(U_{n}(t)\) denotes the time at which the latest status update of the \(n\)-th ground node, successfully received by the UAV, was generated.  
Figure 2 describes the characteristics of the AoI. It can be seen that when there is no data collection action, the AoI of the sensing device increases in a stepwise manner. When data is successfully collected, the AoI will decrease to the time it takes to generate the latest data. Moreover, the data freshness should be evaluated based on how valuable the fresh data is in the specific application. Therefore, data freshness can be characterized by a non-increasing utility function. Our optimization objective is the AoI-based system utility. The AoI-based system utility at the time slot \(t\) can be expressed as  
\[Q(t)=\frac{1}{N}\sum_{n=1}^{N}u_{n}(\Delta_{n}(t)), \tag{8}\]  
according to [35], we define the function \(u(\Delta)=a^{w\Delta}\), where \(0<a<1\) is a constant, and \(w\) is the time-sensitive weight.
## 4 Experiments
### Experimental Environments
#### 4.1.2 Open Datasets
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Device**} & \multirow{2}{*}{**Beam**} & \multirow{2}{*}{**S.R.**} & \multicolumn{2}{c}{**Resolution**} & \multicolumn{2}{c}{**FOV**} & \multirow{2}{*}{**Range (m)**} & \multicolumn{2}{c}{**Acceleration**} & \multicolumn{2}{c}{**Angular Rate**} \\ \cline{4-11}  & & & & **Horiz.** & & & **Vert.** & & **B.S.** & **S.E** & **Bias** & **S.F.** \\ \hline \multirow{2}{*}{Local} & Lidar & \(16\) & \(10\) Hz & \(0.2^{\circ}\) & \(2^{\circ}\) & \(360^{\circ}\) & \(-15^{\circ}\) to \(15^{\circ}\) & \(0.5\) to \(200\) & — & — & — & — \\  & IMU & — & \(100\) Hz & — & — & — & — & — & \(5\)\(\mu\)g & \(0.1\%\) & \(0.01^{\circ}\)/s & \(0.1\%\) \\ \hline \multirow{2}{*}{Open} & Lidar & \(32\) & \(20\) Hz & \(0.16^{\circ}\) & \(1.33^{\circ}\) & \(360^{\circ}\) & \(-30^{\circ}\) to \(10^{\circ}\) & \(1\) to \(70\) & — & — & — \\  & IMU & — & \(100\) Hz & — & — & — & — & — & \(20\)\(\mu\)g & \(0.06\%\) & \(0.2^{\circ}\)/s & \(0.05\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Lidar and IMU parameters between the local test vehicle and the open-dataset test vehicle. size threshold of the vehicle in x-y plane is set to 2 m \(\times\) 4 m. The comparison of device parameters between the local and open datasets used is presented in Table 3.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 4 Countermeasures against Covert Channel Attacks
### Runtime Detection of Covert Channels  
One of the most important countermeasures for covert channels is dynamically monitoring the host system in order to detect the presence of unauthorized data transmission. Typically, detection methods can be classified as threshold-based monitoring and machine learning-based methods. Threshold-based monitoring refers to the medium/signal of choice (instructions per cycle, power consumption, and data from thermal sensors) being observed during normal operation and a baseline being set. The system is monitored to see if this threshold is surpassed, which would indicate malicious behavior (the presence of the covert channel). Machine learning methods utilize the data from monitoring the system to train a neural network that can perform classification and, thus, determine the presence of a covert channel. These techniques are summarized in the following subsections
## 3 Experimental Testing  
### Metrological Characterization of the Optical Measurement Chain  
Two identical measurement referentials (with designations A and B) were subjected to dimensional testing, aiming at the determination of the reference targets world coordinates, which are visualized in the camera's field-of-view.  
These tests were performed in a laboratorial environment (controlled temperature between 19 and 21 \({}^{\circ}\)C, and relative humidity below 65%), using an SI traceable three-dimensional optical measuring machine (brand Mitutoyo, model QV APEX 302 PRO, Kawasaki, Kanagawa, Japan), with a measurement interval of 300 mm \(\times\) 200 mm \(\times\) 200 mm, resolution equal to 0.1 \(\upmu\)m and an instrumental measurement accuracy of 5 \(\upmu\)m, as the reference equipment.  
Figure 8 shows the dimensional measurement of referential A in the mentioned optical measuring machine. Tables 1 and 2 show the estimates and sample experimental standard deviations of the  
reference targets' world coordinates for the measurement referentials A and B, respectively, considering the targets' identification and the coordinate system shown in Figure 8.  
The measurement samples of the reference targets show a dispersion of values comprised between 9 and 57 \(\upmu\)m, which can be considered as a major measurement uncertainty component, reflecting the influence of shape deviation of each target in the measurement of the center three-dimensional coordinates. Estimates of the \(Z\) coordinate show a variation of 0.1 mm order of magnitude close to the nominal value of 5 mm, which is considered suitable for the adopted camera model (weak perspective) and implemented observation distance.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## Inverse Models
### Temporal Inversion  
In order to model the dynamics of the magma intrusion as it propagated to the surface, GNSS data from the permanent network were inverted with the same boundary element program and the same inversion algorithm as for the static inversion. We chose to invert the whole time series running 29 independent inversions computing the cumulated displacement for progressively longer time periods as the displacement rate decreased. The start time was 17:00 and the end time varied from 20:40 on 25 May to 4:00 on 26 May. Because of the limited number of GNSS measurements (30 including the east, north and vertical directions), the parametrization of the source had to have less parameters than for the static inversions. Moreover, contrary to the static inversion, the source description could not rely on the eruptive fissure trace at the surface as the magma had not yet reached the surface. We tested three different methods to define the source. The Ellipse and the Projected Disk methods have been presented in greater detail in [PERSON] et al. (2016) while the Subgraph method is new. The Ellipse method does not use the a priori gained from the static inversion, while the Projected Disk and the Subgraph method relies on it.
## 2 Materials and Methods
### Meteorological Datasets  
Environmental factors, including the mean air temperature (\({}^{\circ}\)C), total precipitation (mm), daily shortwave solar radiation (MJ\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)) and atmospheric carbon dioxide concentration (CO\({}_{2}\), ppm), were considered as the abiotic controls of WUE in our analysis. For valid pixels, the seasonal temperature and precipitation from 2001 to 2014 were derived from the Centre for Environmental Data Analysis (CEDA) monthly temperature and precipitation dataset at 0.5\({}^{\circ}\) spatial resolution [37]. The seasonal solar radiation dataset at 0.5\({}^{\circ}\) spatial resolution was derived from the National Centers for Environmental Prediction (NECP) 4-times daily downward radiation data at 1.875\({}^{\circ}\) spatial resolution [38] by interpolation and aggregation. Due to limitations of data availability, the CO\({}_{2}\) concentration for each valid pixel was replaced by that measured daily at Mauna Loa (Hawaii) [39] during 2001-2014, and then was aggregated to seasonal mean data for each year.
# Sensory Panel Performance Evaluation--Comprehensive Review of Practical Approaches
## 4 Methods, Models and Frameworks in Sensory Panel Evaluation
### Structure and Models of Sensory Data
Each cell encapsulates the signals and the color codes for discrimination, agreement, repeatability and scaling [55]. There are some difficulties, contradictions, and problems with the MAM-CAP method. Due to the different degrees of freedom (assessment/panel), a situation may arise where there is no discrimination and consensus at the level of the individual, but exists at the level of the panel. In the MAM-CAP table, the repeatability of the assessor can be evaluated only in relation to the repeatability of the panel, and there is no test related to the repeatability of the panel. A solution to this could be to compare the distribution of root mean square error (RMSE) extracted by the meta-analysis of the database. The CAP method should be used instead of the MAM-CAP method if the data set is unbalanced and/or contains only one or two products. The overall utility of an extended model such as MAM may depend on the profiling approach. For example, if using an absolute scale-based system, it may not be appropriate to remove the scaling differences from the error term in the analysis [6]. In further research, descriptor-specific and overall coefficients were created by multiplicative decomposition of the scaling effect. The overall scaling coefficient is independent of the descriptors and expresses the psychological trend of the panelist in the case of a scoring task. The corrected scaling coefficient for each descriptor expresses specific sensitivity of the panelist to the descriptor [54]. [PERSON] and his co-workers created a method to determine the temporal dominance of sensations (TDS) to measure the performance of sensory assessors and the panel [56]. Several statistical methods were used to evaluate the performance of the panels. These methods have been implemented in various programming environments. The present comparison supports the choice of the performance parameter and its related statistical procedures. Available software platforms, their accessibility (open-source status) and their functions are analyzed in detail concerning panelist or whole panel evaluation. For the convenience of the audience, the statuses of software platforms were indexed according to their accessibility (open source status). Practical applications and case studies are also displayed in the last column of the table, to support the panel leaders', panel analysts' and sensory scientists' work (Table 1).
# Practical Implementation of a Blockchain-Enabled SDN for Large-Scale Infrastructure Networks
## 2 Related Work
Different metrics were taken into consideration, and from the simulation results, the utilization of Ryu was considered to be the one that is more favorable. The study in [21] uses the Mininet-Wifi network emulator with the provided Ryu controller. The objective was to simulate an SDN environment to assess the efficacy of multipath routing in enhancing QoS. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Blockchain** & **SDN** & **ISP** \\ \hline
[2] & cross-blockchain smart & - & - \\  & contract synchronization & - & - \\ \hline
[3] & blockchain and federated learning & - & - \\ \hline
[4] & consortium blockchain and IPFS & - & - \\ \hline
[5] & consensus algorithm for a & - & - \\  & lightweight blockchain & - & - \\ \hline
[6] & - & - & inter-ISP traffic control \\ \hline
[7,8] & - & - & game theory, ISP interaction and QoS \\ \hline
[9] & - & - & supervised machine learning, ISP and QoS \\ \hline
[10] & blockchain and IoT & SDN network & - \\ \hline
[11] & blockchain for flow rule table & SDN and IoT network & - \\ \hline
[13] & blockchain-enabled secure & SDN and SD-WAN & - \\  & coordination among controllers & & \\ \hline
[14] & blockchain-agnostic & SDN network & multi-stakeholder SDN \\ \hline
[15] & energy trading schemesupported by & SDN network & energy internet \\  & blockchain technology & & \\ \hline
[18] & blockchain integrated into the SDN & SDN & \\  & controller nodes & & \\ \hline
[proposed solution] & blockchain integrated into the SDN & SDN network & \\  & controller nodes & & ISP federation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work and proposed approach summary.