esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Intruder Reconnaissance Behavior through State Diagrams to Support Defensive Deception
## 3 Background
Specifically, for each port scanning method, the following details can be mentioned:  
* Full TCP Connection Scans: This type of port scan uses the connect() system call provided by the operating system to connect to a remote host. Specifically, the TCP connect() uses a three-way handshake and will succeed when the port being scanned is listening. When a client wants to connect with a server, it first sends a TCP packet with the SYN flag set. Subsequently, if the port is open, the server sends back a TCP packet with the SYN and ACK flags set and the client computer then sends an ACK packet back to the server. Otherwise, if the port is closed, an RST packet is sent to the client. This method's advantage is its ease of implementation, as default system calls can be used, while the main disadvantage is that it is easily detectable. * TCP SYN or half-open scanning: During this type of scan, the port scanning terminates before completing the three-way handshake process. Specifically, only the first stage of the TCP three-way handshake, a segment with the TCP SYN flag set, is sent to the server. If a port is open, the server responds with a TCP SYN/ACK packet. Subsequently, the intruder sends a TCP RST packet, destroying the connection. Such scans are logged less frequently, but non-standard system calls need to be used during execution and special privileges may be required. * TCP stealth scan: This type of port scan can be performed using an FIN scan, a Null scan or an Xmas tree scan. As regards the FIN scan, the source host sends a packet with just the FIN flag set to the destination host. In the case of a Null scan, a packet is sent with no flags, and in the case of an Xmas tree scan, a packet is sent with a three header flag set (FIN, URG, and PSH). In all three cases, if a port is open, the host ignores the packet and nothing is returned to the intruder. On the other hand, if a port is closed, the host sends back a packet with the RST flag set. The port is filtered if an ICMP unreachable error (type 3, code 0, 1, 2, 3, 9, 10, or 13) is received. * TCP ACK Scan: During TCP ACK scans, the intruder sends TCP ACK packets to multiple ports on a target and analyzes the TCP RST responses. Responses from open ports may deviate in header values such as TTL and window size on operating systems with BSD-derived TCP/IP stack implementations, while this type of port scanning does not work against hosts with a Windows operating system. * In UDP port scanning, probing the status of a UDP port is mainly executed by transmitting an empty UDP datagram. If the port is open, it may either respond with an error datagram or ignore the prob. If the target port is closed, an ICMP Port unreachable (Type 3 Code 3) packet should be sent back to the intruder. However, if a datagram is lost or filtered, no response will be received.
## 4 Sources of Error  
Printed appliances show very high precision, good fit, and low error rates. Nonetheless, digital workflows and clinical implementation are complex and require specific operational knowledge and exact working methods.  
Depending on the chosen workflow and appliance installation mode, there are possible sources of error to be considered. In case that all steps have been accurately performed, guided insertion enables accurate insertion and appliance fits. However, minor errors may accumulate, and in this case implants may show a certain degree of variance from their planned position, resulting in poor congruence and thus requiring an adjustment of the device's connectors before installation. Position variance may occur due to the plastic guide's flexibility, if too much pressure forces the drills into a false angulation, or if the time period between the intraoral scan and the appointment of appliance insertion has been too long. In the latter case, the insertion template and the appliance itself can show variation due to a patient's natural growth or a change in the stage of eruption and tooth position. If the inconsistency between the implants and the connector is too big, tension will be placed on the connection, resulting in patient discomfort, implant overload, and potential implant failure. Nonetheless, the connectors can be minimally widened by using a milling  
Figure 6: Post-processing of a 3D-printed appliance. (**A**) Unprocessed printed parts, (**B**) surface milled and polished, Jack screw and tubes welded to the appliance, (**C**) completed appliance for molar distalization and simultaneous face mask wear (hooks).  
cutter. Additionally, the rigid printing alloys also allow a certain degree of bending, leading to the possibility of readjusting and correcting minor inaccuracies. According to clinical experience, implants inserted into the patient's palate before the scan without a stabilizing appliance may trigger the patients' tongues to press against the implants, resulting in manipulation and a possible loss of the implants.  
_Direct Screw_-borne appliances that are fixed onto the teeth prior to the implant insertion usually do not bear the risk of screw overloading or improper fit, as minor angular deviations can be compensated by the appliance. Nonetheless, insertion of the screws through the incorporated guides can generate moments and forces on the whole appliance when locking the screws into the thread of the connector, especially in case of angular discrepancies. This could stress implants and adhesive connections. As a result, the implants may loosen, or the adhesive bonds may weaken. Therefore, maintaining a correct insertion angle is strongly recommended. Similar to conventional appliances, printed metal devices can be subject to fracture or loss of adhesive connections to the teeth. Whereas screw-retained devices can be removed temporarily for repair, fracture of the direct screw-borne appliances may necessitate exchange of the implants whenever no intraoral repair is possible.
# Audio Feedback with the Use of a Smartphone in Sailing Training among Windsurfers
## 2 Methods
### Outcomes  
The primary outcome was the usefulness of the augmented audio feedback provided in real-time with the use of a smartphone application, based on the opinions of windsurfers. Opinions were collected with the use of custom-designed questionnaire. Immediately after the last training session, the participants were asked eight questions (Q1-Q8) in order to gauge their thoughts on the implementation of the SoniSailing application and how it influenced their training:  
1. Was the speech generator audible enough?
2. Did the application not disturb you while sailing?
3. Was it easy to focus on the information provided by the application?
4. Did the application reliably reflect the deviation of the board course from the set angle?
5. Did the performance of the application strengthen your orientation towards the tack section?
6. Was a real-time information about the deviation from the set course helpful?
7. Was the app helpful in deciding to change tack due to changed wind direction?
8. Was the app helpful in deciding to change tack (\"Layline\" sound message) in order to make your last turn and sail to the top mark?  
Questions 1-3 were related to the technical aspects of the functions of the application. The others were connected to the usefulness for windsurfers during upwind sailing races.  
The answers were set in the five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
# MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
## 2 Related Work
### Fake News Detection  
Fake news detection methods generally fall into the following four categories:  
* Content-based approaches: These approaches rely on text to check the veracity of news articles, which usually contain long text. A variety of text characteristics are investigated for supervised learning such as TF-IDF, document topic [20], language styles [22], writing styles and consistency [48], and social emotions [49]. In addition, Ref. [23] found that the inquiry phrases from user responses are useful, and [21] used recurrent neural networks to learn superior representations of user responses.
* User-based approaches: These approaches model the traits and credibility of users who retweet the source tweet. The authors of [27] extracted account-based features, such as the account's verification state, gender, location, and the number of followers. Ref. [26] showed that user profiles between fake and real news are significantly different. The CRNN proposed in [25] devised a joint recurrent and convolutional network model to better represent the retweeter's profile. Session-based heterogeneous graph embedding [24] was proposed to learn the traits of users so that they can be identified in shared accounts.
* Network-based approaches: These approaches leverage the propagation structure in social networks or cascade networks to detect fake news. Ref. [30] leveraged the implicit information, i.e., hashtags and URLs, to connect conversations in which users do not have social links. In addition, finding such implicit information can improve the performance of rumor classification. The authors of [50] created a kernel-based method that captures high-order patterns that differentiate various types of rumors. Ref. [29] developed tree-structured recursive neural networks to learn the embedding of rumor propagation structure. Multi-relational graph embedding methods [28; 31] can effectively learn how different types of entities (related to source news articles) interact with each other in a heterogeneous information network for classification tasks.  
Hybrid-based approaches: These approaches consider and integrate multi-modal context information regarding the source tweets. CSI [32] learned the sequential retweet features by incorporating response text and user profiles, and generated suspicious scores of users based on their social interactions. Ref. [34] developed an event adversarial neural network to learn transferable features by removing the event-specific features, along with convolutional neural networks to extract textual and visual features. dEFEND proposed in [33] jointly learned the sequential effect of responding comments and the correlation between news content and comments and uses an attention mechanism to provide explainability. GCAN proposed in [36] further enhanced the model explainability and models short text user comments of fake news articles on social media platforms.
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 2 Previous Voice Transformation Research
The work [2] is an early effort using NN to transform the source speaker formants to target speaker formants using a feedforward NN. In [30], the authors proposed a method for VT using NN with three layers. This method is based on LPC spectral features using radial basis function NN. In [31] the authors compared using a NN and GMM for VT. GMMs capture the joint distribution of the source features and the target feature, while the work in [31] directly maps the spectral source feature information onto the spectral target feature information. Additionally, with GMM they use maximum likelihood parameters generation (MLPG) to obtain a smooth trajectory of spectral features, while the mapping with NN provide best transformation results without using MLPG. Long short-term memory (LSTM) NNs have been used for VT [32]. The joint use of deep bidirectional (DB) LSTM and identify vectors (i-vectors) [33], Kullback-Leibler divergence and deep NN-based approach [34], and DBLSTM based Recurrent NNs [35, 36], have been recently used for VT. More recently, Generative Adversarial Networks (GAN) such as VAW-GAN [37], CycleGAN [38, 39], and many-to-many mapping with StarGAN [40] further advance the state-of-the-art. Previous methods generally work by extracting a parametric acoustic parameter (such as pitch, MFCC, etc.) and transforming this parameter from one speaker to another based on estimated joint densities. This introduces complexities associated with this initial parameter estimation. The approach taken here avoids the need to find parametric acoustic parameters, or to model the joint density, and instead deals directly with spectral information. (That is, this is analogous to spectral estimation, where a parametric model might be used, such as AR or ARMA, or the spectrum may be estimated directly using the DFT.) NNs are used to learn the transformation which maps the spectral information of one speaker to another speaker. We have found that this basic idea yields improvements when the spectral information from the preceding and following speech segments are used to provide context. The transformation is achieved using a two-level DW. Based on the two-level DW it is straightforward to map the source speech to target speech when both are available.
## 2 Materials and Methods  
### Patient Population, Scanner, and Mesh Generation Software  
A total of 5 AIS patients (2 left thoracolumbar and 3 right thoracolumbar curves), who had been prescribed TLSO Boston brace treatment at the Sri Lanka School of Prosthetics and Orthotics, Rheumatology and Rehabilitation Hospital Ragama, Gampaha, Sri Lanka, were recruited for this study. Posteroanterior (PA) X-rays of the patients were taken as part as the normal clinical process and not for the specific purpose of this study. The mean age of the patients was 13.7 \(\pm\) 1.8 years (mean \(\pm\) standard deviation), ranging from 11-16 years old. The mean Risser sign was 1.8 \(\pm\) 1.6, with values from 0-4 and an average Cobb angle of 31.2\({}^{\circ}\)\(\pm\) 11.2\({}^{\circ}\), varying from 19\({}^{\circ}\)-43\({}^{\circ}\). The apical vertebrae were located from T12-L2. Approval from the SIDCER ethics review committee from the Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka, FWA00013225 (REF. P/08/02/2020) was obtained. The study was initiated after obtaining written consent from the patients' parents/guardians.  
A low-cost three-dimensional scanner Mark II (Occipital Inc., Boulder, CO, USA) was used in combination with an iPad Mini 5 th Gen (Apple Inc., Cupertino, CA, USA). The mesh generation software used was the Scanner application (Occipital Inc. Boulder, CO, USA). The selected scanner and mesh generation software demonstrated suitable accuracy and repeatability with maximum mean deviations within 1.7 mm \(\pm\) 3.6 mm on the torso of a standing subject. The maximum mean deviations were determined in a preliminary study testing different mesh generation software from a set of three torso scans.
## 6 Experiment and Analysis
### Comparative Experimental Results and Analysis
The number of SFCs exceeding the maximum delay limit is shown in Figure 11. In the deployment results of DRL-G, the number of SFCs exceeding the maximum allowable delay is less than ten, which is far lower than that of other algorithms, indicating that the proposed hybrid algorithm has a stable delay and can better meet the delay requirements of SFCs. Figure 12 shows the number of SFCs that do not meet resource constraints after deploying requests by different algorithms. Compared with other algorithms, DRL-G can better meet environmental resource constraints. For this, the reason is that other algorithms tend to use fewer hosts to save energy, but this approach easily fails to meet the resources required by the service, resulting in a decrease in the success rate of deployment. Although DRL-G increases the cost of deploying SFC, it can greatly improve the request acceptance ratio. Figure 11: The number of SFCs exceeded the delay constraint with the different algorithms. Figure 10: Comparison of energy consumption of different algorithms.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 4 Classifiers
### Cnn  
CNN [9], like all neural networks, is inspired by the brain's ability to detect and process text, images, and videos. It is made up of neurons with learnable weights and biases. It has an input layer, an output layer, and various hidden layers.  
Each neuron receives several inputs; it takes a weighted sum over them, passes it through the activation function, and responds with an output. There are five steps of CNN, which are as follows:* In the convolutional step, there are three important parts to mention: the input, the feature detector, and the feature map. The main objective of this step is to reduce the size of the input, which would eventually make the process faster and easier.
* The rectified linear unit is the rectifier function (activation function) that increases the non-linearity in CNN.
* Pooling enables CNN to detect features in various images, irrespective of the difference in lighting or their variant angles.
* Once the pooled feature map is obtained, the next step is to flatten it. Flattening means converting the entire pooled feature map image into a single column, which is then fed to the neural network for further processing.
* The last layer is made up of an input layer, a fully-connected layer, and an output layer. The output layer generates the predicted class. The information is passed through the network, and the error of prediction is calculated. The error is then transmitted back to the network for improvement in prediction.
## 4 Simulation and Experimental Results
### Simulations  
The simulations were performed to evaluate the performance of the proposed controller. The load torque was 0.5 Nm. The position and velocity tracking errors of the PI controller and the proposed method were shown in Figures 4 and 5, respectively. The position and velocity tracking errors of the PI controller were relatively larger than those of the proposed method. Furthermore, a large peak appeared in the velocity of the PI controller. This resulted in the large peak of the position of the PI controller. On the other hand, the position and velocity tracking errors converged to zeros rapidly. Furthermore, the position and velocity tracking errors of the proposed method were smaller than those of the PI controller in the steady-state responses. We conclude that the position and velocity tracking performances were improved by the proposed method. Figure 6 shows the load torque estimation performance of the nonlinear observer. The estimated load torque satisfactorily followed the actual load torque.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Para.** & **Value** & **Para.** & **Value** \\ \hline \(L\) & 11 mH & \(R\) & 0.12 \(\Omega\) \\ \(\Phi\) & 0.18 V\(\cdot\)s/ rad & \(J\) & 0.006 Kg\(\cdot\)m\({}^{2}\) \\ \(p\) & 3 & B & 0.001 N\(\cdot\)m s/rad \\ \hline \hline \end{tabular}
\end{table}
Table 1: PMSM Parameters.  
Figure 4: Position and velocity tracking errors of the PI controller.
###### Abstract  
Vietnam's 3260 km coastline is densely populated, experiences rapid urban and economic growth, and faces at the same time a high risk of coastal hazards. Satellite archives provide a free and powerful opportunity for long-term area-wide monitoring of the coastal zone. This paper presents an automated analysis of coastline dynamics from 1986 to 2021 for Vietnam's entire coastal zone using the Landsat archive. The proposed method is implemented within the cloud-computing platform Google Earth Engine to only involve publicly and globally available datasets and tools. We generated annual coastline composites representing the mean-high water level and extracted sub-pixel coastlines. We further quantified coastline change rates along shore-perpendicular transects, revealing that half of Vietnam's coast did not experience significant change, while the remaining half is classified as erosional (27.7%) and recreational (27.1%). A hotspot analysis shows that coastal segments with the highest change rates are concentrated in the low-lying deltas of the Mekong River in the south and the Red River in the north. Hotspots with the highest accretion rates of up to +47 m/year are mainly associated with the construction of artificial coastlines, while hotspots with the highest erosion rates of \(-28\) m/year may be related to natural sediment redistribution and human activity.  
osatellite dynamics; Landsat archive; sub-pixel coastline extraction; time series; hotspot analysis; Google Earth Engine 2022142476[[https://doi.org/10.3390/rs14102476](https://doi.org/10.3390/rs14102476)]([https://doi.org/10.3390/rs14102476](https://doi.org/10.3390/rs14102476))
# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 5 Experimental Results: Performance Evaluation of the Proposed Method
### Evaluation of the Proposed Method in Terms of Provider's Profit  
Considering conflicts between virtual machines, heterogeneity in resources, and energy consumption costs helps the broker reduce the operational costs of the cloud provider and increase the success rate in performing users' requests. As mentioned in the earlier section, the broker considers these parameters while creating the game tree and determining the players' utility for every potential action. By carefully considering different factors while allocating users' requests to virtual machines and setting a price for those virtual machines, the cloud provider can not only increase its revenue but also its profit. The total revenue of the provider is calculated using the following formula:  
\[Revenue_{cp}=\sum_{i=1}^{n}\left(\pi_{cp}(VM_{i}^{*},P_{i}^{*})-Penalty_{i}\right) \tag{11}\]  
where \(n\) indicates the total number of requests submitted to the system and fulfilled during the simulation time. The cloud provider's utility (\(\pi_{cp}\)) is calculated based on Equation (1) and \(Penalty_{i}\) represents the financial penalty paid by the cloud for the \(i^{th}\) request if it fails to perform successfully. It is assumed that in the case of violating SLA, the provider must refund the total funds received to the user. Since the operating costs of the system can be changed with different policies for mapping and executing requests, the provider's profit is also calculated by reducing the expenses from revenue (Equation (12)).  
\[Profit_{cp}=\sum_{i=1}^{n}\left(\pi_{cp}(VM_{i}^{*},P_{i}^{*})-Penalty_{i}-E_{ i}\right) \tag{12}\]  
The parameter \"\(E_{i}\)\" denotes the expenses that correspond to the energy consumption required for executing the request. These expenses are calculated using Equation (7). The performance of the proposed approach, in comparison to other methods, is displayed in Figures 5 and 6. These figures showcase the provider's total revenue and profit from different perspectives.  
As shown in Figure 6, the proposed method is more successful in performing users' requests with lower energy consumption. This conclusion is based on the fact that the results shown in Figure 6 are obtained according to Equation (12) by deducting the costs related to energy consumption from the cloud revenue (i.e., the results shown in Figure 5). The mapping obtained from the Nash equilibrium point results in reduced energy consumption and sometimes a reduced response time to user requests. This leads to more profit for the cloud provider.