esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Continuous Speech Analysis/Synthesis System
### Maximum Voiced Frequency Estimation  
In voiced sounds, MVF is used as the spectral boundary separating a low-frequency periodic and high-frequency aperiodic components. It has been used in numerous speech models, such as [23; 48; 55], which yields sufficiently better quality in the synthesized speech.  
The preliminary version of our vocoder followed the [PERSON] and Stylianou [30] approach, which exploits both amplitude and phase spectra. Although this approach tends to relatively reduce the acoustic buzziness of the reconstructed signals, it cannot distinguish between a production noise and a background noise. This means that MVF might be underestimated if the speech is recorded in a pseudo noisy environment. Moreover, we found that the estimation-based MVF [30] lacks the ability to capture some components of the sound that lie in the region of the higher frequencies (especially for the females). For this reason, higher MVF is required in this work to yield more natural synthetic speech.  
Figure 4: Example of an HNR parameter for the clean speech signal. Sentence: “They die out of spite.”  
Over the last few years, several attempts, with varying results, have already been made to analyze the MVF parameter. In this paper, similar to [23], we used a sinusoidal likeness measure (SLM) [56]-based approach to extract the MVF. A representative block diagram is shown in Figure 5 using five main functional steps:  
1. Consecutive frames of the input signal \(x[n]\) are obtained by using a 3-period-long Hanning window \(w[n]\).
2. \(N\)-point fast Fourier transform (FFT) of every analysis frame \(m\) is computed \(X^{  
This observation suggests that the baseline often underestimates some of the voicing frequency in the higher frequency regions of the spectrogram.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Mathematical Foundations of DDPM
#### 3.2.5 Diffusion Noise Schedule  
The diffusion noise schedule determines how noise is added during each step of the denoising diffusion process. It plays a crucial role in balancing the expressiveness of the model and the computational efficiency of training. The schedule is often chosen to be annealed, starting with higher noise levels and gradually decreasing. The choice of the noise schedule is a critical parameter that influences the model's performance.
###### Abstract  
This paper presents an innovative approach that integrates a fixed-time control (FTC) algorithm with an improved sparrow search algorithm (ISSA) to enhance the trajectory tracking accuracy of a two-degree-of-freedom (two-DOF) robotic arm. The FTC algorithm, which incorporates barrier Lyapunov function (BLF) and adaptive neural network strategies, ensures rapid convergence, effective vibration suppression, and the robust handling of system uncertainties and input saturation. The ISSA, inspired by the foraging behavior of sparrows, improves search efficiency through dynamic weight adjustments and chaotic mapping, balancing global and local search capabilities. By optimizing control parameters, ISSA minimizes tracking errors. Simulation results demonstrate that the combined FTC and ISSA approach significantly reduces tracking errors and improves response speed compared to the use of FTC alone, underscoring its potential for achieving high-precision control in robotic arms and offering a promising direction for precise robotic control applications.  
fixed-time control; sparrow search algorithm; Barrier Lyapunov; input saturation; robotic arm +
Footnote †: journal: applied sciences
## 2 Materials and Methods
### Satellite Data
The AOI is not completely included within a single tile of S2; for this reason, two tiles (T32 TMR and T32 TNR) for each image have been acquired to create a mosaic (Figure 3). The implemented methodology is shown in Figure 4 and explained in detail in the following paragraphs. Pre-processing and processing steps for the satellite data were carried out to build an automatized model to obtain the results directly. This model is fundamental for the realization of an automatized processing chain. Figure 4: Data pre-processing and processing chain. Figure 3: Satellite dataset. Study area and spatial extent of satellite data.
## 4 Implementation
### Simulation Model Structure  
Mathematical modeling is a reasonable way to test the feasibility of this proposed roll gap misalignment compensation method. To that end, the authors developed a model that simulates automatic control of hydraulic SD positions (DS and OS) and is adjusted for how these positions are linked by the metal strip. Figure (a)a shows the flowchart of the model. It corresponds to the structure of SD positioning as shown in Figure 5. Figure (b)b shows the flowchart that explains how the AGCS is linked to the SD position controls. The models are implemented in Matlab Simulink.  
Strip-mediated SD links are modeled by the non-linear blocks \(G(F)\). They compute the elastic modulus of the stand as a function of the rolling force. The blocks are part of the DS/OS SD positioning channels as shown in Figure (b)b. Moreover, the structure includes the blocks that simulate \(F(G)\) the feed stiffness. Both dependencies were derived experimentally and are stored in table format in the controller memory; they are adjusted when calibrating (setting the gap of) the rolls upon their replacement.  
The flowchart in Figure (b)b includes the RAC controller. It takes the difference in rolling force between DS and OS as the input and generates additional control signals fed to the SD positioning circuits as the output. These circuits receive the main signal from the gauge controller output. The model shows an integral gauge controller even though the AGCS uses a non-linear one [15; 37]. This simplification is acceptable, as the RAC controller uses small movements to align misaligned gaps. This is why the non-linearity of the control object does not have a noticeable impact on the tested processes.  
Figure 9: Cont.  
Figure 10: _Cont_.  
Figure 9: Structural diagram (**a**) and flowchart (**b**) of the model of linked screw-down positioning systems (with a RAC controller).  
The presented simulation model is based on the known structures of electric and hydraulic drives, which are detailed in literature. Some can be found in Matlab libraries. A detailed description of the mathematical model of the object is left out for space considerations; another paper could be written solely on such a model. Such a model should incorporate mathematical models of: automatic electric drives installed in stands; hydraulic screwdowns; automatic parameter controls systems that implement the ROLL-GAP CONTROL concept. They also need to be adjusted for how the strip connects different components. The authors' papers [38; 39; 40] detail parts of the model.
# Application Layer Protocol Identification Method Based on ResNet
## 2 Related Work
### Protocol Format
#### 2.4.2 SMB Protocol  
SMB is a protocol used for sharing files and printers in a network, commonly found in Windows systems. It allows applications to share files, printers, and other resources between computers. The SMB protocol message consists of requests and responses. The request message typically includes operation codes (such as file read, write, query, etc.), while the response message returns the operation results. The SMB protocol allows remote procedure calls (RPCs) between different computers, enabling communication through mailslots and named pipes.
## 6 Scenario and Methodology
### Performance Metrics
#### 6.1.3 Cumulative Reward  
A DRL algorithm is judged by the quality of the policy it finds and the received reward. Rewards are the values in number an agent receives when it acts under certain environmental conditions. This value can be negative or positive, depending on the agent's actions. In RL, the goal is to maximize the cumulative reward rather than the reward received from the current state. The cumulative reward is the sum of all the rewards received so far. It shows the performance of a RL algorithm by plotting the cumulative reward as a function of a number of time frames.  
\[CR=r_{t+1}+\gamma r_{t+2}+\gamma^{2}r_{t+3}+\ldots\ldots=\sum_{k=0}^{\infty} \gamma^{k}r_{t+k+1} \tag{3}\]  
In Equation (3), cumulative reward at every time step \(t\) is given by \(r_{t+1}\) and \(\gamma\) is the discount factor. \(r_{t+1}\) is the reward agent received at time step \(t\) when performing an action to go from one state to another state and the discount factor defines the importance of immediate and future rewards and must satisfy 0\(\leq\gamma\geq\)1. The agent selects the actions in order to maximize the expected (discounted) return. Reward discount \(\gamma\) is set to refine the agent goal. A value close to 0 emphasizes the importance of immediate rewards, and a value close to 1 means that future rewards are more important.
## 2 Materials and Methods
### Research Method
#### 2.3.1 Comparison of Zr-EPRC Preparation Methods
In each flask, 3 g of fly ash was weighed, and zirconium oxychloride octahydrate was added in accordance with various mass ratios of zirconium oxide octahydrate (m\({}_{1}\)) to fly ash (m\({}_{2}\)) at values of 0.2, 0.4, 0.6, 0.8, and 1.0. The mixture was kept in suspension until complete dissolution of \"ZrOCI2-8H2O\". The suspension was then adjusted to a pH level of 10 using a 1 mol/L NaOH solution. Subsequently, the conical flasks were placed within a constant temperature oscillator set at 25 \({}^{\circ}\)C and oscillated at a speed of 150 rpm for a duration of 10 h. Following the completion of the reaction, solid-liquid separation was achieved via centrifugation. The solid powder obtained was thoroughly rinsed with deionized water until the cleaning solution reached a neutral pH. Finally, the solid powder was subjected to drying in an oven at 105 \({}^{\circ}\)C, and the zirconium-modified fly ash was subsequently crushed and ground to yield Zr-FA, which was recorded as such. Static adsorption experiments were conducted on the modified fly ash. In a similar fashion, zirconium-modified steel slag was prepared by employing zirconium oxychloride octahydrate (m\({}_{1}\)) and steel slag (m\({}_{2}\)) with mass ratios of 0.2, 0.4, 0.6, 0.8, and 1.0, respectively. These modified steel slag samples were designated as Zr-SS. Static adsorption experiments were conducted on the modified steel slag powder. * Determination of Optimal Loading pH  
Building upon the optimal loading conditions established in investigation of optimal modification conditions experiment, the pH of the suspension was adjusted to values of 3, 5, 7, 10, and 12 by introducing 1 mol/L HCl and 1 mol/L NaOH solutions as necessary. Subsequently, loading was conducted at a constant temperature for a duration of 12 h. Once the loading process was finalized, the materials were washed until they reached a neutral pH, followed by drying and milling. This process led to the formation of zirconium-loaded steel slag (Zr-SS) and zirconium-loaded fly ash (Zr-FA) at different loading pH levels. Static adsorption experiments were then performed on these adsorbent materials. * Determination of Optimal Loading Time  
After introducing steel slag and fly ash based on the optimal loading conditions determined in investigation of optimal modification conditions experiment, the suspension was adjusted to a pH of 10 and underwent loading for varying durations of 2 h, 4 h, 6 h, 10 h, and 12 h. Following the loading process, the materials were washed until they reached a neutral pH, and subsequently, they were dried and ground. This process resulted in the production of zirconium-loaded steel slag (Zr-SS) and zirconium-loaded fly ash (Zr-FA) samples with different loading modification times. These materials were then subjected to static adsorption experiments.
## 1 Introduction  
The lung tissue is composed by alveolus, bronchial tubes and blood vessels. Because the densities of air in alveolus and bronchial tubes are lower and cannot pass through by X-ray, the Hounsfield unit (HU) is also lower in CT images as shown for the gray regions [1,2]. Otherwise, owing to the high density of blood in vessels, the HU is higher as shown for the white regions in Figure 1a. However, when we change the CT image to gray-level image, the lobe fissure is not very obvious as shown for the yellow arrow in Figure 1a. It may cause the segmentation faults for the lobes separation in clinical experiments.  
About the researches for lobe segmentation, all of them were first to improve the lobe fissure by different methods to facilitate the following analyses [3, 4, 5, 6]. [PERSON] _et al._[4] used the proposed transformation called \"ridge map\" to enhance the appearance of fissure. [PERSON] _et al._[5] used a pre-clustering method based on the distributions of bronchial tubes and blood vessels, and segmented the lobe fissure by the watershed transform. In the above researches of lobe fissure, most of them were focused on the axial view in CT images. However, the imaging structure of axial view cannot display the whole information of lung lobe, as shown in Figure 1a; it will increase the difficulty of lobe fissure tracking. In this paper, we focused on the sagittal view in CT slices. Although the lobe fissures are still ambiguous on sagittal view, as shown in Figure 1b; the whole information of lung lobe is more suitable than axial views or coronal view.  
In this study, we try to use a high-performance tracking framework to obtain the vague lobe fissure on the sagittal view in CT slices. In order to provide accuracy, we modified an algorithm from ant movements in the natural world called the ant colony algorithm (ACA) [7, 8, 9, 10, 11, 12, 13]. The ant colony algorithm (ACA) is a method to process the optimized problem. It also can be applied to this study to assist the tracking framework of lobe fissure. We also add several evaluation rules to increase the number of walking by the ants on the lobe fissure. Let the pheromone on the lobe fissure become more and more to make the ants follow the candidate paths. Finally, all the ants may follow one optimal path, which has the highest pheromone level. The lobe fissure will be obtained correctly to assist the corresponding studies.  
Figure 1: The lung CT image for (**a**) axial slice (whole lung) and (**b**) sagittal slice (right lung).  
Whole information of lung lobe is more suitable than axial views or coronal view.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 4 Experiments
### Ablation Studies
#### 4.5.3 The Weight Factor for Cycle Consistency  
To investigate the effect of the cycle consistency loss weight \(\lambda\), we performed a grid search over \(\lambda\in\{0.01,0.05,0.1,0.5\}\), balancing it against the supervised multi-scale flow loss. As summarized in Table 5, a very small value (e.g., Table 5 (a)) had a minimal regularizing impact, while overly large values (e.g., Table 5 (c)) over-smoothed the deformation field, reducing the DSC. The optimal value \(\lambda=0.1\) provided the best trade-off--enforcing inverse consistency while maintaining local flexibility--and consistently yielded the best TRE and DSC.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 5 Use Cases and Applications  
Leveraging the power of current and upcoming wireless networks, 6G holds immense potential to enable a wide range of applications, particularly for energy-efficient devices reliant on symbiotic communication. This is especially pertinent in regions densely populated with passive sensors, offering effective harnessing of their capabilities. Additionally, 6G's role extends to serve various sectors such as building and factory automation, manufacturing, e-health, intelligent transport systems (ITS), smart and precision agriculture, surveillance, and the smart grid. These applications are vital components of Industry 4.0, anticipated to drive profound paradigm shifts.
# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 3 Deep Learning-Based Blockchain Transaction Privacy Protection Model
### Transaction Legitimacy Verification  
In a blockchain, transactions are recorded via a consensus mechanism. During the consensus process, miners verify transactions' legitimacy, which primarily includes the verification of participant identity and transaction amount legitimacy.  
Identity legitimacy verification involves verifying the legitimacy of both the sender and receiver's identities. Within the UTXO model, the sender uses \(Sk_{s}\) to unlock UTXO inputs. Therefore, miners only need to use the sender's anonymous identity public key (called \(AID_{s}\)) to verify the legitimacy of the unlocking script signature.  
Verifying the receiver's address is crucial to prevent fraudulent transactions and potential asset loss. Our scheme uses the receiver's anonymous identity public key (denoted as \(AID_{r}\)) as the receiver's address.  
\[AID_{r}=AID_{1}\|AID_{2} \tag{16}\]  
\[AID_{1}=BG.Enc(Pk_{BG},ID_{u}) \tag{17}\]  
\[AID_{2}=IBC.Sign(Sk_{a},AID_{1}) \tag{18}\]  
\(AID_{2}\) represents the signature performed by the regulatory authority using its private key \(Sk_{a}\) on \(AID_{1}\). Thus, to validate the unlocking script's signature, miners just have to utilize the sender's anonymized public identity key (designated as \(AID_{2}\)).  
Transaction amount legitimacy also requires two aspects of verification: the equality of input and output amounts and the validity of the range of output amounts.  
For the transaction \(M_{Tx}\) and \(Tx\), miners need to calculate the transaction public key:  
\[Pk_{Tx}=(P_{in1}+P_{in2})-\left(P_{out1}+P_{out2}+v_{fee}G\right) \tag{19}\]Using \(Pk_{Tx}\) to verify the legitimacy of the transaction signature \(Sig_{Tx}\), fulfilling the requirement of input and output amount equality. To ensure the output amount is within a valid range, the existing Bulletproofs zero-knowledge proof technology is used to verify \(P_{range}\), as referenced in [15].
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 3 Proposed Research Methodology
### Decision Tree  
Commonly employed for both classification and regression applications, decision trees are a type of machine-learning technique. Using the training set of data, the algorithm creates a tree-like model of decisions and their outcomes [24]. The tree structure helps to visualize and interpret the decision-making process.  
The key features of a Decision Tree algorithm include:  
* Simple to comprehend and interpret since they provide a visualizable, tree-like framework for the decision-making process.
* Their ability to handle both categorical and numerical data makes them useful for a wide range of applications.
* Non-parametric meaning they do not make any assumptions about the distribution of the data.
* Can handle missing values in the data by using different techniques such as surrogate splits or imputation.  
Decision trees provide a tree-like model that is easy to interpret, offering insights into feature importance. The tree-based approach is suitable for capturing non-linear relationships. In the context of IDS, decision trees have been used as part of several IDS constructions (see, e.g., [25; 26]), particularly on finding relevant features for classification.
###### Abstract  
The aim of this study was to evaluate the clinical, volumetric, radiographic, and histologic aspects of autogenous demineralized dentin matrix (DDM) combined with recombinant human bone morphogenetic protein-2 (rhBMP-2) used for ridge preservation, compared to those of deproteinized bovine bone with collagen (DBBC). Following atraumatic extraction, the socket was filled with DBBC, DDM, or rhBMP-2/DDM. Scanned images of dental casts and cone beam computed tomographs (CBCT) were superimposed for the calculation of soft and hard tissue volume alteration. Preoperative and postoperative measurements of the height and width of the alveolar ridge were compared using CBCT images. After 4 months, bone specimens were harvested for histomorphometric assessment. Loss of hard and soft tissue volume occurred at 4 months after extraction and ridge preservation in all groups. No volumetric differences were detected among the three groups before and 4 months after ridge preservation. The reduction in the horizontal width at 5 mm was higher in the DBBC compared to the DDM. Histologically, approximately 40% newly formed bone was founded in rhBMP-2/DDM group. The autogenous dentin matrix used to fill the socket was as beneficial for ridge preservation as conventional xenografts. The combination of rhBMP-2 with dentin matrix also demonstrated appreciable volumetric stability and higher new bone formation compared to DDM alone and DBBC.  
autografts; bone regeneration; bone morphogenetic proteins; bone substitutes; cone-beam computed tomography; tooth extraction +
Footnote †: journal: Medical Imaging
## 3 Fuzzy Logic Q-Learning Routing Scheme with Asymmetric Link Aware
### Proposed Scheme  
The design of the FQ-AGO uses a distributed multi-hop ad hoc wireless communication without relying on any network infrastructure units. The proposed scheme is an opportunistic routing that employs a fuzzy logic-based approach (Figure 2) to evaluate each direct wireless link of neighbors in the candidate set. Each node evaluates its candidate set members based on their distance to destination, transmission power, link quality, and available throughput. These metrics are jointly evaluated, utilizing a fuzzy logic analysis system. The wireless links evaluation values of the candidate set nodes are stored in the neighbor table (Figure 2) and these evaluation values are kept up-to-date. Whenever a forwarder node has a packet for forwarding, based on the destination's current location, the forwarder applies a Q-learning algorithm to the candidate set, and the candidate that reveals the highest Q-learning value is considered the next-hop forwarder.  
For each packet that the forwarder node has to forward, the candidate forwarding node with the maximum Q-learning value is selected as a next-hop forwarder. The membership functions and fuzzy  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Packet Sequence Number & 2 bytes \\ Originator ID & 4 bytes \\ Originator Position & 16 bytes \\ Transmission Range & 1 byte \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hello packet format.  
rules used by the fuzzy logic system are easy to modify and tune to make the protocol more suitable for a particular network environment and condition. Therefore, the proposed protocol can provide a practical and intelligent solution for routing in varieties of ad hoc networks, such as VANETs, and Wireless Sensor Network (WSN). In FQ-AGO, if a local minima problem is detected, the forwarder node will select a new next-hop forwarder, and a re-transmission of the packet is required to progress the transmission packet operation.