esa-sceva/satcom-chunk-collection huggingface dataset:
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 4 Results
### Pilot Study  
Twelve PlaIMoS communication nodes were used to test the PlaIMoS platform (five in Cluster mode, six in Gateway mode and one in Sink mode). One PlaIMoS remote node was also used to form a distributed network in the University Regional Hospital. The 12 PlaIMoS communication nodes were distributed on the first floor of the hospital; of these, eight were installed in rooms and four in the main hallway. The test area measured 459 m\({}^{2}\) (27 m \(\times\) 17 m) and consisted of 16 patient rooms and two stairwells (Figure 17).  
The PlaIMoS remote node was used by a patient for the pilot study which included the transmission of temperature, heart rate, electrocardiogram and fall detection data. The PlaIMoS fixed measurement station was placed in one of the rooms (Room 1) to be used by the patient. This device was connected to the Internet via IEEE 802.11 to send measur  
Figure 17: First floor of the hospital (pilot study scenario).  
In order to validate our concept, actual hospital equipment was used to compare measurements obtained by PlaMoS platform. The equipment utilized included:  
1. Criticcare nGenuity [29] (to obtain electrocardiogram, heart rate, blood oxygen and respiration rate data)
2. Microlife MT3001 [30] (to determine body temperature)  
Participants in the pilot study included a PlaMoS engineer, a patient and a doctor.
## 2 Nonlinear-Trajectory SAR Deception Jamming Algorithm Based on HDE
### Deceptive Jamming Based on HDE  
In this section, the HDE deceptive jamming algorithm will be derived step by step. First, the SAR system-related filter is separated from the SAR JFR. Then, the mathematical derivation of the SAR system-related filter is described in the time-frequency hybrid domain. Finally, the mathematical expression of the HDE deceptive jamming algorithm is clarified.
## 5 Experiment and Result Analysis  
### Feature Data Selection  
#### 5.1.1 Data Preparation  
This study focuses on a production line of Pr/Nd element extraction and separation process at a rare earth company in Jiangxi. A total of 78 samples were collected from different Pr/Nd solution extraction tanks. Each sample solution was divided into two parts for processing. One portion of the sample was used for laboratory testing to obtain the element component content in the sample solution, with the component content of the Nd element ranging between 5% and 99.965%. The other portion of the sample was processed using an acquisition device developed by the laboratory to collect the image information of the sample solution and extract the R, G, and B color features of the image color space.
## 4 Algorithms Used
### Experimental Setup and Preprocessing  
Both datasets are unbalanced towards the \"malware\" class, and this has to be taken into account for all the following steps of the process. The solution is to leverage some sampling methods, which modify the starting data making their distribution more balanced. Tests were carried out in 10-fold stratified cross-validation, where, for each fold, the number of goodware and malware instances are randomly balanced (random undersampling). The values reported are the average values for the 10 folds.  
For the dataset _malware-analysis-datasets-api-call-sequences_, all features were already numerical. For the _APIMDS_ dataset, instead, all categorical variables were one-hot encoded, numerical variables were used as-is. In addition, only the first 114 features were kept into consideration for the _APIMDS_ dataset. This is because the following features were composed of almost all null values (>90% of null values), and thus it has been decided to remove them in order to decrease the complexity of the problem and better face the famous Bellman's \"curse of dimensionality\" problem. For using standard algorithms such as NODE, TabNet, and so on, datasets were standardized with z-score normalization prior to the training process.  
The stratified version ensures that the same proportion of the data is maintained in all training and test sets in the same way as the original data. This is so that no value is over (or under)--represented in the various sets to have a more accurate estimate of the results.  
The configuration of the algorithms is as follows: Random Forest and Extra Tree are configured to use 100 trees and the \"gini\" index. XGBost is configured as suggested by the algorithm's authors with the max depth pre-pruning parameter equal to 6, and the learning rate equal to 0.3. CatBoost is configured without any adjustment or selection of hyperparameters and default parameters. TabNet attention embedding amplitude and decision accuracy amplitude are equal to 8 as per the official publication. The default settings were used for NODE.
## 3 Background
### Intrusion Detection System
#### 3.1.1 Cloud-Based IDS Types  
IDSs in the cloud can be classified into four main types, as follows:  
1. Host IDS (HIDS): The HIDS operates by wiping and evaluating the records gathered from an individual computer system. The HIDS monitors system activities, such as system logs and CPU usage, to detect if the system has been attacked, and sends an alarm to the administrators [14].
2. Network IDS (NIDS): The NIDS observes and analyzes network flow and examines packet flow to detect suspicious activities, such as port scanning. NIDS efficiently detects network attacks, such as denial of service (DoS) [15].  
Figure 1: Classification of cloud-based IDSs.  
Distributed IDS (DIDS): The DIDS contains various IDSs, for instance, HIDS and NIDS, that may communicate between themselves or with a centralized server for network monitoring. It may contain two main types of IDS methods. DIDS has the advantage of detecting known and unknown attacks [15].
* Hypervisor IDS: The hypervisor IDS was created to run a virtual machine (VM), which observes communication between the VM and hypervisors. This type has the advantage of providing state information and event monitoring [16].
## 5 Experimental Results
### Security and Privacy Analysis
#### 5.2.3. FexiChain  
The probability of the attacks to occurs in the blockchain paradigm is  
\[P(FC)=P(A)+P(B)+P(C) \tag{13}\]  
\(P(A)\) represents attack-1 and can be performed by a successful attack over all nodes \(\alpha\kappa\), acquiring nodes' credentials represented as \(\theta\kappa\), acquiring trusted attached hardware credentials, and Unique Identification (UID). The probability can be calculated by  
\[P(A)=\frac{1}{4}\prod_{\kappa=1}^{n}\alpha\kappa\times\frac{1}{4}\prod_{\kappa= 1}^{n}\theta\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n}\Phi\kappa \tag{14}\]  
\(P(B1)\) represents attack-2 and can be performed by a successful attack over all nodes \(\alpha\kappa\) and acquiring nodes' credentials represented as \(\theta\kappa\). There are \(\frac{n\times(n-1)}{2}=a\), which is the number of transmissions channels that can be created by pairs of nodes for n number of nodes [16]. The probability can be calculated by  
\[P(B1)=\frac{1}{4}\prod_{\kappa=1}^{a}\beta\kappa\times\frac{1}{4}\prod_{\kappa= 1}^{n}\theta\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n}\phi\kappa\times\frac{ 1}{4}\prod_{\kappa=1}^{n}\Phi\kappa \tag{15}\]  
The corresponding transmission channels are also considered in the formula which can be calculated similar to \(P(B1)\) and denoted as \(P(B2)\).  
\[P(B2)=\frac{1}{4}\prod_{\kappa=1}^{a}\beta\kappa\times\frac{1}{4}\prod_{\kappa =1}^{n}\theta\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n}\phi\kappa\times \frac{1}{4}\prod_{\kappa=1}^{n}\Phi\kappa \tag{16}\]  
Figure 11. Results for Three Categories of Attacks in the Blockchain-Based Scenario.  
\(P(C)\) represents attack-3 and can be performed by a successful attack over all nodes \(\rho\kappa\) and acquiring nodes' credentials represented as \(\theta\kappa\). There are \(\frac{n}{2}=v\), which is the number of mining nodes or validators (edges) that an attacker should control compromise the ledger. The probability can be calculated by  
\[P(C)=\frac{1}{4}\prod_{\kappa=1}^{v}\rho\kappa\times\frac{1}{4}\prod_{\kappa=1} ^{n}\theta\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n}\phi\kappa\times\frac{1} {4}\prod_{\kappa=1}^{n}\Phi\kappa \tag{17}\]  
\[P(FC)=\frac{1}{4}\prod_{\kappa=1}^{n}\alpha\kappa\times\frac{1}{4}\prod_{\kappa= 1}^{n}\theta\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n}\phi\kappa\times\frac{1 }{4}\prod_{\kappa=1}^{n}\Phi\kappa\]  
\[+\frac{1}{4}\prod_{\kappa=1}^{a}\beta\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n }\theta\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n}\phi\kappa\times\frac{1}{4} \prod_{\kappa=1}^{n}\Phi\kappa\]  
\[+\frac{1}{4}\prod_{\kappa=1}^{v}\rho\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n }\theta\kappa\times\frac{1}{4}\prod_{\kappa=1}^{n}\phi\kappa\times\frac{1}{4} \prod_{\kappa=1}^{n}\Phi\kappa\]  
This is shown as a function of the number of nodes in Figure 12 along with the parameters of an exponential fit of the data.
## 3 Materials and Methods
### Methods
#### 3.2.3 Causal Structure Learning  
Causal learning is a process of finding causal relationships in data structures. Reasoning about causal relationship between different time variables is easier than causal reasoning without a time structure [33]. In the causality mining part, we obtained the causal structure of three different components of the traffic data of each base station in the region. At present, the commonly used causality test algorithms mainly include Granger causality and transfer entropy. Granger causality is usually used in the context of linear structural equation. Transfer entropy is a nonlinear extension of Granger causality [34; 35].  
Due to the complexity of urban wireless network data, causal learning method based on transfer entropy was selected to process three components of each base station. Formula (11) gave the calculation method of transfer entropy, where \(l\) is lag.  
\[T_{C_{i}\to C_{j}}=H\Big{(}C_{i}(t)\mid C_{i_{t-1t-l}}\Big{)}-H\Big{(}C_{i}(t) \mid C_{i_{t-1t-l}},C_{i_{t-1t-l}}\Big{)} \tag{11}\]  
In which,  
\[H(C_{i}(t))=-\sum_{t}P(C_{i}(t)log_{2}P(C_{i}(t))). \tag{12}\]  
The definition of causality judgment standard was given by Formula (13), where true represents that time series \(C_{i}\) will affect the change of time series \(C_{j}\). False means that the historical data of \(C_{i}\) has no impact on time series \(C_{j}\).  
\[\frac{T_{C_{i}\to C_{j}}}{T_{C_{j}\to C_{i}}}=\Big{\{}\begin{array}{ll} true&\geq 1\\ false&<1\end{array} \tag{13}\]  
After the extraction in the previous step, three components of the traffic of each base station were obtained. Causal structure learning was used to obtain the spatial correlation of each component. As shown in Algorithm 2, spatial causal structure of three components can be found. For three components, their causality graph can be expressed as: \(D_{T}\), \(D_{p}\) and \(D_{E}\). The adjacency matrix based on causality graph can be expressed as: \(A_{T}\), \(A_{P}\) and \(A_{E}\).
# Attention Neural Network for Water Image Classification under IoT Environment
## 4 Results and Discussion
### Dataset and Measurement  
Essentially, there does not exist a proper benchmark dataset for water image classification task. We thus used a dataset obtained from [PERSON] et al. [33] with 1000 water images. As reported in their paper, they collected water images from either standard water related videos [8] or Internet sources including Google, Bing, and Baidu. With the same criterion for sub-categories of water images, their water images are labeled with ten subclasses, namely, Fountain, Lakes, Oceans, and Rivers for clean water and Algae, Dead animals, Fungus, Industrial pollution, Oils, and Rubbish for polluted water. After analyzing the image content, the considered dataset coincides with the real situation of water pollution monitoring.  
We evaluated classification with four standard measurements, namely precision, recall, f-score, and accuracy:  
\[\begin{split}\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP }}\\ \text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\\ \text{F-score}=\frac{2*\text{Precision}*\text{Recall}}{\text{ Precision}+\text{Recall}}\\ \text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN} +\text{FP}+\text{FN}}\end{split} \tag{10}\]  
where TP refers to the number of classified images in positive class, TN refers to the number of classified images in negative class, and FP and FN are numbers of incorrectly classified images in positive and negative classes, respectively.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
###### Abstract  
This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m\({}^{2}\) as well as in a large area of 10,000 m\({}^{2}\). Results show that the drone turnover rate is related to both users' sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14-16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.  
U  
A _Article_  
Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 2 Materials and Methods  
### Materials  
The banana pseudo-stem fiber from the _Musa acuminata_ species [26] with a diameter range from 500 \(\upmu\)m to 1 mm was supplied by Innovative Pultrusion Sdn Bhd, Seremban Negeri Sembilan, Malaysia. The EpoxAmite(tm) 100 epoxy resin and EpoxAmite(tm) 102 medium hardener [27] used as the base matrix were purchased from Kird Enterprise, Nilai Negeri Sembilan, Malaysia. This epoxy-resin system was mixed to a weight ratio of 10:2.9 g. The banana fibers were soaked in sodium hydroxide (NaOH) obtained from Orioner Hightech Sdn Bhd, Cyberjaya Selangor, Malaysia. In this study, a 2/2 twill weave carbon/Kevlar hybrid with a density of 210 g/m\({}^{2}\) was used for the skins. Carbon and Kevlar fiber tow was used as a comparison for the banana fiber. Those were purchased from EasyComposite Ltd., Stoke-on-Trent, UK [28]. Glass fibers were purchased from Alsey Kimia Sdn Bhd, Puchong Selangor, Malaysia. Detailed properties of all materials are given in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Banana** & **Kevlar** & **Carbon** & **Glass** & **Carbon Kevlar Twill** & **EpoxAmite(tm)-102 Hardener** \\ \cline{2-7}  & [26] & [28] & [28] & [28] & [28] & [27] \\ \hline Density (kg/m\({}^{3}\)) & 1350 & 1340 & 1780 & 2600 & & 1110 \\ Flexural Strength (MPa) & 52 & & & & & 84.25 \\ Tensile Strength (MPa) & 54 & 3260 & 4900 & 3450 & & 56.4 \\ Young’s Modulus (GFa) & 3.49 & 60–80 & 250 & 72–77 & & 3.1 \\ Elongation (\%) & & 4.4 & 2 & 4.7 & & 210 \\ Weight (g/m\({}^{2}\)) & & & & & 2(C)-1(K) & \\ Wert & & & & & 2(C)-1(K) & \\ Warp & & & & & 2(C)-1(K) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of fibers, skins, and EpoxAmite(tm)-102 hardener.
# Breakthrough and Challenging Application: Mixed Reality-Assisted Intracardiac Surgery
###### Abstract  
Background: While several studies investigate the utility and clinical value of 3D printing in aiding diagnosis, medical education, preoperative planning, and intraoperative guidance of surgical interventions, there is a scarcity of literature regarding concrete applications of mixed reality in the cardiovascular domain due to its nascent stage of study and expansion. This study goes beyond a mere three-dimensional visualization of the cardiac district, aiming to visualize the intracardiac structures within the scope of preoperative planning for cardiac surgery. Methods: The segmentation of the heart was performed through an open-source and a professional software and by applying different procedures. Each anatomical component of the heart, including the aortic valve, was accurately segmented and a 3D model was built to represent the entire heart. Results: Beyond the three-dimensional visualization of the cardiac region, the intracardiac structures were also segmented. A mixed-reality app was implemented with the possibility of exploding the model, interacting with it, and freely sectioning it with a plane. Conclusions: The proposed segmentation methodology allows a segmentation of the valve and the intracardiac structures. Furthermore, the mixed-reality app has confirmed the potential of this technology in diagnostic and preoperative planning, although some limitations should still be overcome.  
mixed reality; surgical planning; segmentation; cardiovascular surgery
# A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs +
## 4 Experiments
### Methodology
#### 4.3.2 Listening Tests  
For the listening tests, all stimuli were downsampled to 32 kHz, which is the lowest common denominator between the different systems.  
* **Mean Opinion Score (MOS)**: For the systems trained on natural singing, we conducted a MUSHRA [42] style listening test. The 40 participants, of which 8 indicated native or good knowledge of Japanese, were asked to rate different versions of the same audio excerpt compared to a reference. The test consisted to 2 short excerpts (<10 s) for each of the 3 validation set songs, in 7 versions (reference, hidden reference, anchor and 4 systems), for a total of 42 stimuli. The scale used as 0-100, divided into 5 segments corresponding to a 5-scale MOS test. The anchor consistedof a distorted version of the NPSS synthesis, applying the following transformations: 2D Gaussian smoothing (\(\sigma=10\)) of harmonic, aperiodic and F0 parameters, linearly expanding the spectral envelope by 5.2%, random pitch offset (\(\pm 100\) cents every 250 ms, interpolated by cubic spline), and randomly \"flipping\" 2% of the voiced/unvoiced decisions. We excluded 59 of the total 240 tests performed, as these had a hidden reference rated below 80 (ideally the rating should be 100). We speculate that these cases could be due to the relative difficulty of the listening test for untrained listeners.
* **Preference Test**: For the systems trained on pseudo singing, we conducted an AB preference test. The 18 participants were asked for their preference between two different stimuli, or indicate no preference. The stimuli consisted of two short excerpts (<10 s) of one song per voice/language. Versions with and without background music were presented. We perform pairwise comparisons between our system and two other systems, resulting in a total of 24 stimuli.
## 3 Semi-Physical Experiment and Analysis
### Results of Semi-Physical Experiments
#### 3.2.1 Coarse Registration after mpp-MLS Processing  
Point cloud coarse alignment aims to approximately align two frames of point clouds that are initially far apart, with the objective of establishing a reliable initial estimate for subsequent fine alignment. This study explored the impact of resolution and noise criteria on point cloud alignment algorithms and employed the Random Sample Consistency (RANSAC) alignment algorithm to enhance the precision of coarse alignment.  
In order to assess the robustness of the mpp-MLS algorithm under varying levels of noise, this study employed the RANSAC algorithm to conduct a comparative experiment on coarse alignment with noise both before and after processing, as illustrated in Figure 9.  
In order to verify the robustness of the features extracted by the algorithm to the point cloud noise, in the experiments of this paper, we superimposed the Gaussian noise with the mean value \(\mu=0\) and standard deviation \(\sigma=0.01,\ 0.02,\ 0.03,\ 0.04,\ 0.05,\ 0.07\) in the \(x,y,z\) directions of the original point cloud of the satellite to simulate the measurement noise of the 3D sensor. Coarse alignment was achieved by changing the noise standard of the point cloud and using the RANSAC algorithm. The obtained rotation error and translation error are shown in Figure 10. The results show that the alignment results using the point cloud processed by the algorithm proposed in this paper were better than those using the point cloud without processing, both in terms of point cloud resolution and point  
Figure 8: Point cloud before and after optimization using the mpp-MLS algorithm.  
Figure 9: Coarse registration results (model point cloud in blue, field point cloud after alignment in red). (**a**) Coarse registration without mpp-MLS processing; (**b**) coarse registration after mpp-MLS processing.  
cloud noise criterion. Moreover, the error stability was also better than before in the case of low noise.