esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### eGA-DT Model for Factor Identification
#### 3.3.3 Fitness Evaluation Based on DT  
Feature vectors \(x=\{x_{1},x_{2},\ldots x_{i}\}\) and the target value \(y=\{y_{1},y_{2},\ldots y_{i}\}\) are selected as the inputs of the DT model to evaluate the fitness.  
1. Initial spatial division  
Feature vectors \(x\) are initially divided into two subspaces \(r=\{r_{1},r_{2}\}\) in space based on the selected feature \(x_{i}\) and cut-off point \(s\), which could be described as:  
\[r_{1}(i,s)=\{x|x_{i}<s\} \tag{3}\]  
\[r_{2}(i,s)=\{x|x_{i}\geq s\} \tag{4}\]  
2. Prediction in subspaces  
The value of prediction in each subspace is expressed as the average value of total vectors within the space:  
\[y_{r_{i}=}\frac{1}{n}\sum_{i\in r_{i}}y_{i} \tag{5}\]where \(y_{j}\) is the prediction of the feature vector \(x_{i}\) in range \(r_{i}\) and n is the total number of vectors in the range \(r_{i}\).
3. Spatial division based on error evaluation  
Feature vectors \(x\) are divided into several non-overlapping ranges \(r=\{r_{1},r_{2},\ldots r_{i}\}\) in space based on the feature \(x_{i}\) and cut-off point \(s\) to minimize the loss of regression. The evaluation criterion of the regression is described as:  
\[loss=\sum_{x\in r_{1}(i,s)}(y_{i}-y_{r_{i}})^{2}+\sum_{x\in r_{2}(i,s)}(y_{i} -y_{r_{2}})^{2} \tag{6}\]
4. Decision tree pruning  
If there is no restriction when branching, the model would be extremely complex and overfitting. Therefore, the pruning strategies are adapted to avoid overfitting. Construct the decision tree and delete or collapse the nodes with little information.
5. Fitness output  
After each iteration of GA, one chromosome represents a selection of features and the inputs of the DT model. The fitness of each chromosome is obtained by the DT model, which is an important basis for the following chromosome selection.
## 1 Introduction
They also compared the neural network's capabilities with that of a dental student, which underperformed both CNNs. In oral medicine, [PERSON] et al. [21] compared the diagnostic performance of a CNN with that of a group of oral surgeons and one of general dentists. They used 1603 orthopantomographs divided into four groups (dentigerous cyst, ameloblastoma, keratocyst, no lesion) based on the histopathological report of the lesion. The results obtained in this study showed little difference between the diagnostic performance of the algorithm and that of clinicians [22; 23]. In oral implantology, in a pilot study by [PERSON] et al. [24], the possibility of recognizing, from radiographic images,the model of the implant placed in the bone was evaluated using a CNN. A total of 1282 orthopantomographs were used, with implants of six different models. The ability of the DL algorithm to detect and discriminate between the implant and the radiograph was then evaluated. The results were accurate, but improvable by performing CNN training with a larger dataset of images and including other commercially available implant models; in dental traumatology, [PERSON] et al. [25] utilized a CNN for the assessment of vertical root fracture on OPTs; in endodontics, [PERSON] et al. [26] studied the performance of a DL system in detecting the presence, on orthopantomograms, of the distal second root in the lower first molar. Comparing the performance of the system to that of two experienced radiologists, they found that the software was more accurate in assessing the anatomical variant. In orthodontics and gnathology, [PERSON] et al. [27] applied a CNN to more easily detect the bony changes underlying osteoarthritis of the temporomandibular joint on CBCTs. Based on their results, one can look forward to the future use of this model as a diagnostic support tool. Finally, another study by [PERSON] et al. [28] employed a convolutional neural network to automatically identify cephalometric repere points on a latero lateral teleradiography to reduce inter-operator variability and standardize cephalometric tracings underlying orthodontic treatments. Several studies on artificial intelligence applications in periodonontology have also been published in this field in recent years, with different results depending on the tasks performed by the neural network and the type of neural network used. This literature review aims to understand the current artificial intelligence applications in periodonontology. Specifically:  
1. To identify which of these types of neural networks (CNN; GAN; hybrid NN; transformer NN) are most widely used to date. 2. To understand how these techniques can improve the early diagnosis of CAL and ABL in periodontal patients.
# Finding the Best 3-OPT Move in Subcubic Time
## 2 Selections, Schemes and Moves
### Reinsertion Schemes and Moves  
Let \(S=(i_{1},i_{2},i_{3})\) be a complete selection. When the edges \(R(S)\) are removed from a tour, the tour gets broken into three consecutive segments which we can label by \(\{1,2,3\}\) (segment \(j\) ends at node \(i_{j}\)). Since the selection is pure, each segment is indeed a path of at least one edge. A reinsertion set patches back the segments into a new tour. If we adopt the convention to start always a tour with segment 1 traversed clockwise, the reinsertion set: (i) determines a new ordering in which the segments are visited along the tour and (ii) may cause some segments to be traversed counterclockwise. In order to represent this fact, we use a notation called a reinsertion scheme. A reinsertion scheme is a signed permutation of \(\{2,3\}\). The permutation specifies the order in which the segments \(2,3\) are visited after the move. The signing \(-s\) tells that segment \(s\) is traversed counterclockwise, while \(+s\) tells that it is traversed clockwise. For example, the third reinsertion set depicted in Figure 1 is represented by the reinsertion scheme \(<+3,-2>\) since, from the end of segment 1, we jump to the beginning of segment 3 and traverse it forward. We then move to the last element of segment 2 and proceed backward to its first element. Finally, we close the tour by going back to the first element of segment 1.  
There are potentially \(2^{2}\times 2!=8\) reinsertion schemes, but for some of these the corresponding reinsertion sets are degenerate. A scheme for a pure reinsertion must not start with \"\(+2\)\", nor end with \"\(+3\)\", nor be \(<-3,-2>\). This leaves only 4 possible schemes, let them be \(r_{1},\ldots,r_{4}\), depicted in Figure 1.  
Given a selection \(S\), the application of a reinsertion scheme \(r\) univocally determines reinsertion set \(I(r,S)\) and clearly for every reinsertion set \(I\) there is an \(r\) such that \(I=I(r,S)\). Therefore, a 3-OPT move if fully specified by a pair \((S,r)\) where \(S\) is a complete selection and \(r\) is a reinsertion scheme. Let us denote the set of all true 3-OPT moves by  
\[\mathcal{M}=\big{\{}((i_{1},i_{2},i_{3}),r):(i_{1},i_{2},i_{3})\in\mathcal{S}, r\in\{r_{1},r_{2},r_{3},r_{4}\}\big{\}}.\]  
The enumeration of all moves can be done as follows: (i) we consider, in turn, each reinsertion scheme \(r=r_{1},\ldots,r_{4}\); (ii) given \(r\), we consider all complete selections \(S=(i_{1},i_{2},i_{3})\), obtaining the moves \((S,r)\). Since step (ii) is done by complete enumeration, the cost of this procedure is \(\Theta(n^{3})\). In the remainder of the paper we will focus on a method for lowering significantly its complexity.
# Design and Applications of GLANCE: GLanceable Alarm Notification for a User Centered Experience
## 5 Discussion
_As to the first project_, the GLANCE workshop organized by Technolabs people was a unique briefing and testing opportunity. To our surprise company managers showed great interest in not only in the problem-solving side of the project but also in the research side. Furthermore, company people reactions to the proof-of-concept demo provided additional evaluation elements, with a clear confirmation of the notifier suitability at provoking reactions correctly depending on the alarm severity. _As to the application project_, results of the evaluation sessions at Broadcast Solutions were very positive with all users having paid attention to high severity alarms and a significant role was played by auditory stimuli since operators had to leave their desktops frequently to attend other ongoing working tasks; all operators easily used the alarms tree, discovered HW problems related to alarms, and detected HW/alarm correlations, in particular from a \"service in alarm view\"; all users easily interacted with the interface both at service-oriented level and at device-oriented level; as to subjective judgment, all users considered the interface usable, considered the information organization \"well arranged\", and judged the display intuitive as to the visualization of alarm data at the glance on both levels; minor concern was expressed only for background color and severity indicators of service components, a flaw easily surmountable by indicating only the total number of alarms associated to a service component. Overall, the proposed service-oriented GLANCE-based fault management notification was hence considered successful. It has to be observed that in both cases the objective was the definition and evaluation of proof-of-concept prototypes and that the implementation of operative releases of the notifiers was beyond the scope of the projects and left to the company.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Proof of Concept
#### 6.8.2 Implementation Choices and Considerations  
As for the underlying crypto library, we mainly investigated two lightweight crypto libraries: wolfSSL (previously known as CyaSSL) [55] and mbed SSL (formerly known as PolarSSL) [56]. Both of them are lightweight embedded SSL libraries, providing cryptographic and SSL/TLS/DTLS capabilities for IoT devices with minimal coding overhead. We implemented two suites of crypto library wrappers, one for wolfSSL and one for mbed SSL. At the point of implementing the protocol prototype, to the best of our knowledge, mbed SSL is more stable and easier to use with respect to the cryptographic algorithms used in our protocol. In the final protocol prototype, we adopted mbed SSL as the underlying crypto library and its wrapper as crypto/networking APIs to facilitate the crypto and networking operations.  
In the protocol specifications, authenticated encryption is used to protect two tokens carrying the secret key material \(k\) as well as the session information. The prototype adopts AES Galois/Counter Mode (GCM) [57] as the authenticated encryption algorithm, which is a block cipher with both encryption and authentication functionalities. With AES-GCM, the key material \(k\) is first encrypted and then authenticated together with the session information to generate an authentication tag. As a result, the token \(\alpha\) and \(\beta\) consist of three parts: a plaintext of the session information, a ciphertext of the key material and an authentication tag.  
The session manager module has a responsibility of monitoring and checking the session information. For example, in a new session, when a party receives the fresh session information, namely relevant identifiers and nonces, from an incoming message, it should first validate the freshness and the correctness of the session information. Specifically, it is important to verify whether the nonce has appeared before. Therefore, creating and maintaining a local database to store and retrieve all the past session information is necessary. Currently, we simply use a database file to store all the previous session data. The session manager looks up and updates the database file upon receiving the session data from a fresh protocol session.  
Major implementation choices for the cryptography algorithms is listed in Table 2 and parameter settings are listed in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Cryptography Operation** & **Algorithm Choice** \\ \hline hash & SHA-256 \\ pseudo random function (prf) & HMAC-SHA256 \\ Diffie-Hellman Groups & 2048-bit MODP Group [58] \\ asymmetric crypto & 2048 bits RSA \\ Message Authentication Code (MAC) & HMAC-SHA256 \\ Certificate standard & X.509 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cryptography Algorithm Choices.  
Figure 10: Modularized prototype framework design: the framework includes three major modules (instance manager, protocol manager and utility functions) for three protocol participants.
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 2 Proposed Methodology
### Preprocessing  
Noise reduction is used as a preprocessing step in the proposed methodology. It deals with suppressing the background noise available in the noisy speech signal. The input noisy speech \(s_{k}\left(t\right)\) is obtained by corrupting the clean speech \(x_{k}\left(t\right)\) by the additive noise \(v_{k}\left(t\right)\), as in (1),  
\[s_{k}\left(t\right)=x_{k}\left(t\right)+v_{k}\left(t\right) \tag{1}\]  
To reduce the effect of background noise from the noisy speech signal, spectral subtraction proposed by [PERSON] (1965) is used, because the statistical and structural properties of the speech signal gets corrupted when the SNR tends to go lower than \(-5\) or \(-10\) dB, or with complex audible events. To enhance the spectrum of the speech signal, spectral subtraction is considered. The spectrum of noise \(V_{k}\left(f\right)\) was estimated during speech inactive periods and subtracted from the spectrum of the current frame \(S_{k}\left(f\right)\) resulting in an estimate of the spectrum \(X_{k}\left(f\right)\) of the noise reduced speech as in (2),  
\[\left|X_{k}\left(f\right)\right|=\left|S_{k}\left(f\right)\right|-\left|V_{k} \left(f\right)\right| \tag{2}\]  
In this scenario, the primary interest of this paper is in evaluation of the proposed VAD classifier under conditions of noise-cancellation, and while a ground truth measurement is used to identify speech and non-speech frames, this is to allow for sufficient evaluation, using a standard approach to noise-cancellation. The impact of noise on the speech is explained in Figure 2. The clean speech signal is corrupted by additive white noise for SNRs 0 and 5 dB. As seen in Figure 2, clean speech's histogram shows leptokurtic and heavy-tailed distributions, while lowering the SNR cause the histogram to become mesokurtic with medium-tailed distributions.  
Figure 1: Block diagram for the proposed fuzzy entropy and support vector machine based voice activity detection (VAD).
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Acoustic-Based Detection  
Audio-based UAV detection is another commonly used technical solution for UAV detection. While sound waves and electromagnetic waves are different, audio-based detection and radio-frequency detection share some similarities due to their wave nature. Acoustic-based detection can be broadly categorized into two main types, i.e., traditional acoustic-based detection and acoustic-based detection using deep learning.
# The Electromagnetic Field Analytical Solution Analysis to Downhole Current Injection [PERSON]
## 2 Signal Model and Electromagnetic Field Distribution Analysis
### The Primary Electric Potential in Active Region Analysis  
According to [PERSON]'s equation, the primary electric potential in the active region satisfies [PERSON]'s equation [32], as follows:  
\[\
abla^{2}V_{1}=-\frac{I}{\sigma_{1}}\delta(\mathbf{r}), \tag{1}\]  
where \(\
abla^{2}\) is the Laplace operator; \(V_{1}\) denotes the primary electric potential; \(I\delta(\mathbf{r}_{0})\) represents the point current source; \(\delta(\bullet)\) is the Dirac function; \(\mathbf{r}_{0}=(\rho_{0},\varphi_{0},z_{0})\) is the position of the point current source; and \(\sigma_{1}\) denotes the conductivity in the active region. The solution to this equation is as follows:  
\[V_{1}=\int_{0}^{+\infty}f(\lambda)K_{0}(\lambda\rho)cos(\lambda z)\;d\lambda, \tag{2}\]  
where \(f(\lambda)=\frac{I}{2\pi^{2}\sigma_{1}}\), \(K_{\rm i}(\bullet)\) denotes the \(i\)-order modified Bessel functions of type 2 [33]. By employing the cylindrical coordinate system, when the point source is located at \((\rho_{0},\varphi_{0},z_{0})\), the primary electric potential can be represented as follows:  
\[V_{1}=\frac{I}{4\pi^{2}\sigma_{1}}\int_{-\infty}^{+\infty}K_{0}(\lambda|\rho- \rho_{0}|)e^{i\lambda(z-z_{0})}\;d\lambda. \tag{3}\]  
In addition, according to the addition formula for the modified Bessel function, we have  
\[K_{0}(\lambda|\rho-\rho_{0}|)=\left\{\begin{aligned} \sum\limits_{m=-\infty}^{\infty}I_{m}(\lambda\rho_{0})K_{m}(\lambda\rho)e^{im( \varphi-\varphi_{0})},(\rho>\rho_{0})\\ \sum\limits_{m=-\infty}^{\infty}K_{m}(\lambda\rho_{0})I_{m}(\lambda \rho)e^{im(\varphi-\varphi_{0})},(\rho<\rho_{0}).\end{aligned}\right. \tag{4}\]  
Figure 2: Cylindrical two-layer signal model for current injection system.  
Hence, Equation (3) can be modified as follows:  
\[V_{1}=\begin{cases}\dfrac{I}{4\pi^{2}\sigma_{1}}\int_{-\infty}^{+\infty}I_{0}( \lambda\rho_{0})K_{0}(\lambda\rho)e^{i\lambda(z-z_{0})}\;d\lambda_{,}(\rho>\rho_ {0})\\ \dfrac{I}{4\pi^{2}\sigma_{1}}\int_{-\infty}^{+\infty}I_{0}(\lambda\rho_{0})K_{ 0}(\lambda\rho)e^{i\lambda(z-z_{0})}\;d\lambda_{,}(\rho<\rho_{0}).\end{cases} \tag{5}\]  
According to the model, the point current source is located in the outermost layer, so the scenario \(\rho<\rho_{0}\) needs to be considered and the primary electric potential in the active region is expressed as follows:  
\[V_{1}=\dfrac{I}{4\pi^{2}\sigma_{1}}\int_{-\infty}^{+\infty}I_{0}(\lambda\rho_ {0})K_{0}(\lambda\rho)\;e^{i\lambda(z-z_{0})}\;d\lambda. \tag{6}\]
# Sustainable Modularity Approach to Facilities Development Based on Geothermal Energy Potential
## 2 Materials and Methods
### Geothermal Potential--Calculation Methodology  
Available thermal power from the geothermal resource is calculated based on geothermal conditions at the sites along with the cascade method of usage. The geothermal resource is considered as a source of energy for providing heat for facilities and balenological purposes. This principle led to the three limiting parameters for the system:  
* temperature stability,
* available resource quantity,
* and chemical composition  
The possibility of geothermal energy application depends on fluid temperature. The two selected locations covering the whole range of geothermal heating purposes. The Ljuba site comes with hydrogeothermal resources accumulated in the groundwater of a temperature scope to 30 \({}^{\circ}\)C which is conditioned by the application of geothermal heat pumps [41], while Veliko Banatsko Selo comes with hydrogeothermal resources accumulated in the groundwater of temperature above 30 \({}^{\circ}\)C, which means direct use is possible. Therefore, the heating operating system on the Ljuba site is considered to be an open loop GHP system contrary to the passive geothermal system with heat exchanger at the Banatsko Veliko Selo site.  
The thermal power availability for the both geothermal sites can be calculated as in Equation (1)  
\[\text{E}=\text{H}\times\text{Q}\times\Delta\text{T} \tag{1}\]  
where E is the nominal available power quantity (kW); H the specific heat of water (constant, 4.2 kJ/kg/\({}^{\circ}\)C); Q the yield of the source (kg/s, for water the same as l/s); and \(\Delta\text{T}\) denotes the temperature difference between the entering water and the leaving water.  
Geothermal systems on the both sites perform heating operations and must be able to cover the maximum thermal load demands. The physical and chemical characteristics have been analyzed simultaneously to define the method for balneological use (drinking, swimming, inhalation). Furthermore, the concept of the geothermal heating system is conditioned by the method of balneological application. For example, drinking does not consider temperature, but swimming does, which means a projected geothermal system must satisfy these conditions.  
In order to provide relevant data for calculations, a geological/hydrogeological reconnaissance was carried out, followed by groundwater parameters regime monitoring (temperature, chemical composition, flow capacity). For the purpose of geothermal water and reservoir characterization, the D'Amore method and diagram was applied [42]. The method is based on the determination of six main genetic parameters, mainly based on cation-anion ratios. Calculated genetic parameters are in line with geothermal reservoir geology and are applied for both sites, Ljuba and Banatsko Veliko Selo. Geothermal investigations and monitoring last for 12 months.
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 5 Experiment Results and Analysis
### Experiment
#### 5.3.1 Storage Space of Vector Maps  
This experiment aims to assess the space-saving effectiveness of unique identification compared to full map data in blockchain storage, ensuring the practical applicability of the model. Four map files are selected to ensure applicability across different maps, from which corresponding unique identifications are constructed. Initially, following [PERSON]'s comparative algorithm, full map data are deployed onto the blockchain. Subsequently, according to the proposed BCPM-UI model, unique identifications of vector maps are constructed and deployed onto the blockchain, recording the respective storage space requirements. A comparison of the storage space utilization between the two methods is conducted, and the experimental results are presented in Figure 9. Figure 9 shows that unique identifications with small data volumes exhibit significant space-saving effects compared to the use of full map data in blockchain storage. Using this method, for smaller-scale datasets, approximately 200 times more storage space can be saved, while for larger-scale datasets, around 5000 times more storage space can be conserved. The experiments demonstrate that the proposed BCPM-UI model effectively reduces the storage of map data in the blockchain.