esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 2 Preliminaries
### Hyperchaotic Lorenz System  
In this section, hyperchaotic Lorenz systems are introduced. The method proposed by [PERSON] et al. [24] is superior in performance, which adds a nonlinear controller \(w\) to the Lorenz system to produce hyperchaotic behavior. The system is expressed as follows:  
\[\left\{\begin{array}{l}\dot{x}=a(y-x)+w,\\ \dot{y}=cx-y-xz,\\ \dot{z}=xy-bz,\\ \dot{w}=-yz+rw.\end{array}\right. \tag{5}\]  
where \(a\), \(b\), \(c\) and \(r\) are system parameters, and \(x\), \(y\), \(z\) and \(w\) are state variables. When \(a=10\), \(b=8/3\), \(c=28\), and \(-1.52\leq r\leq-0.06\), the system represents a hyper-chaos state. Hyperchaotic systems have a higher value in secure communication than ordinary chaotic systems.
## 2 Materials and Methods
### Data Preparation
#### 2.2.2 Meteorological Data  
Forests are sensitive to meteorological conditions. To account for the fact that the study area is an area with frequent floods and droughts. Therefore, we assume that the major meteorological disasters in the study area are floods and drought. To establish whether the appearance of IST-cubes is related to meteorological disasters, we acquired precipitation data from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), which provided daily global precipitation data from 1983 to the present, with a spatial resolution of 0.25\({}^{\circ}\)[48; 49]. According to the classification standard of flood and waterlogging weather in Hunan Province, which was developed by the local government [50], a flood is defined as precipitation during any 10 days of more than 200 mm.  
Whether drought and IST-cubes appear at the same time is also needs to be compared. Therefore, we introduced Palmer drought severity index (PDSI) as validation data to perform a quantitative evaluation on IST-cubes. PDSI is a widely used drought index to assess the degree of drought [51; 52]. PDSI data were acquired from Terra Climate, a global dataset of long-term monthly climate and climatic water balance, with a spatial resolution  
Figure 1: Location of the study area in Hunan Province, China.  
of 0.04\({}^{\circ}\)[53]. The averaged value of all the points of the two data sets in the study area was utilized as the basis for qualitative description of the research area.
# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
## 3 Materials and Methods
### Methodology Block Description
#### 3.1.2 Feature Extraction: MFCCs and PSFs  
MFCCs and custom-designed PSFs are used in this study. MFCCs are widely utilized features in speaker recognition applications due to their effectiveness. In this study, 15 MFCCs were extracted from each speech segment and used for classification. A set of 15 PSFs was extracted from each PS segment in voiced portions of the recordings. These features were explained in our previous work [56]. Table 1 provides the feature names along with the corresponding number of elements obtained from each feature. Since the speech samples underwent two distinct segmentation methods, MFCCs were separately extracted for each segmentation type. However, PSFs were exclusively extracted using the PS segmentation protocol. These features target cycle-to-cycle perturbations, which cannot be adequately captured by traditional measures like jitter and shimmer.  
Moreover, PSFs emphasize a wide range of attributes in both the time and frequency domains, enabling them to capture perturbations in both the vocal cords and the vocal tract. This comprehensive approach contributes to their effectiveness in the classification process.
## Materials and Methods  
### Reaction Set-Up  
The experiment was carried out in two different pilot scale reactors: flat-plate cascade reactor (FPCR) and tubular reactor with a compound parabolic collector (CPC), shown in Figure 1a,b. The FPCR (Figure 1a) consists of three cascades (\(500\times 250\) mm) made from resistant polycarbonate material. Model solution was continuously recirculated from the collecting tank over cascades using a Masterflex peristaltic pump; flow was set at 8.0 cm\({}^{3}\)/s. Total volume of model solution in recirculation was 1.7 dm\({}^{3}\). Water layer depth (h) over photocatalytic film was \(0.7\pm 0.1\) cm. The CPC reactor (Figure 1b) consists of two parallel quartz tubes (\(L=500\) mm, \(D_{\text{Outer}}=30\) mm, \(D_{\text{inner}}=27\) mm), placed in the middle of a compound parabolic mirror made of highly reflective alumina (JBL Solar Reflect 50). Tubes were connected by a PTFE U-tube of the same internal diameter to avoid changes in model solution flow rates. Experiments were performed in total recirculation (Q = 26.5 cm\({}^{3}\)/s), with a total volume of 1.5 dm\({}^{3}\) and effective reactor volume of 0.5 dm\({}^{3}\).  
The irradiation source was a custom-made panel composed of three pairs of full-spectra lamps (JBL, Neuhofen, Germany, Solar Ultra linear fluorescent lamps, types: Color, Tropic and Natur; T5, 1450 mm, nominal power 80 W) placed in the reflective mirrors (JBL, Neuhofen, Germany, Solar Reflect 146) (Figure 1c). The distance between the FPCR cascade surface and the center of the lamps was 7 cm at the inlet (x\({}_{\text{max}}\)) and 2 cm at the outlet position (x\({}_{\text{min}}\)) to match the cascade length and the effective lamp length. In the case of CPC, both the panel and the tubes were inclined at 12\({}^{\circ}\). The distance between the lamps and the top of the quartz tubes was adjusted to achieve the same intensities as measured at the center of the FPCR cascade. The UVB and UVA intensities on the lamp wall (I\({}_{w}\)) were determined radiometrically using the UVX radiometer fitted with a corresponding midrange UV-B UVX-31 (range 280-340 nm) and longwave UV-A UVX-36 sensor (range 335-385 nm) with a sensor accuracy of \(\pm\) 5% (UVP Products, Analytik Jena US LLC, Upland, CA). The obtained average UVB and UVA intensities will be discussed later.  
In a typical experiment, model solution recirculated over the photocatalyst in the dark for 30 min before irradiation started to achieve sorption equilibrium. The photocatalytic process lasted 180 min. Control experiments were performed in the dark to estimate the adsorption of model compounds on the photocatalytic film or possible \"dark\" catalysis in the full length of 180 min. Additionally, model solutions were recirculated in empty reactors (without photocatalyst) under given irradiation conditions to exclude the effects of direct photolysis on overall degradation rates. All experiments were performed at least twice, and average values were reported.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 2 Diagnosability Architecture for Control Systems Based on Structural Analysis
### Diagnosability Architecture Based on Structural Analysis  
The diagnosability structural and functional design presupposes that the diagnosability needs of the equipment are met. The primary analysis method is the qualitative evaluation of diagnosability. Its primary technical means is the optimized configuration of the system structure and function. Its primary purpose is to improve system fault information content and to ensure that the faults are easily detected and isolated.  
Reconfigurability is the critical link of diagnosability design. Its object is to take the target state's structural controllability and observability. Its basis is to take the reconfigurability evaluation. Its constraint is to take the diagnosability demand. Its primary means is to take the structural and functional design based on reconfigurability. Its primary purpose is to take the improvement of the fault-tolerance control ability and the minimization of the fault reconfiguration cost.  
Comprehensive diagnosis is the critical technology of diagnosability. This topic is based on the principle of diagnosability integration design through a comprehensive analysis of the essential factors to design the complete diagnostic architecture. These factors include the selection of diagnostic resources, the fault diagnosis, performance, and the fault-tolerance performance of the comprehensive diagnostic architecture.  
Based on this, the control system diagnosability structure model is established, and its specific flow block diagram is shown in Figure 3.  
Figure 2: Fault diagnosis process based on structural analysis.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 3 Artifacts Correction by Deep Learning
### The Proposed Network  
The proposed network architecture for reflection artifacts correction is shown in Figure 2, which has following three parts:  
1. **Feature extraction** is to extract the feature representation from the input image \(I_{\text{RA}}\). This part contains 4 convolutional layers, each of them has 32 convolutional filters of size \(3\times 3\) with a stride of 1. Zero padding is not used for these four layers.
2. **Artifacts reduction** is to correct the artifacts and remove the noise from the feature-maps obtained above. We use a modified version of U-net coupling with a residual skip connection for this purpose. This part has 6 convolutional layers and 6 deconvolutional layers. Instead of down-sampling or up-sampling operation in original U-net [40], we use the convolution  
Figure 1: Numerical example to illustrate the effect of the acoustic reflections. (**a**): a \(128\times 128\) Shepp–Logan phantom as the initial pressure. (**b**): the recorded acoustic signal in the presence of sound hard reflectors. (**c**): reconstruction by conventional TR.  
with a stride of 2 to decrease the size of feature-maps at the 2 nd, 4 th and 5 th convolutional layers, and the deconvolution with a stride of 2 to increase the size of feature-maps at the 1 st, 3 rd, and 5 th deconvolutional layers. The stride of 1 is used at remaining layers. All layers have 32 (de)convolutional filters of size 5 \(\times\) 5 to capture the global structures. In addition to this, three conveying links [29; 40] copy the former feature-maps and reuse them as the input to a later layer that has the same size of feature-maps via a concatenation operation along channel dimension, which preserve high-resolution features. At last, a residual skip connection [41] enables the above U-net to infer the artifacts and noise from the input feature-maps. The summation between the input feature-maps and the outputs of the U-net are the corrected feature-maps, which is followed by an activation function and then serves as the input to the reconstruction part. Note that zero padding is used throughout this part.
3. **Reconstruction** is to recover the final output from the corrected feature-maps. This part has 4 deconvolutional layers, each of them has 32 filters of size 3 \(\times\) 3 with a stride of 1 except for the last layer that has only 1 filter. Zero padding is not used for these four layers.  
The rectified linear unit (ReLU) is used throughout this network [42], which is defined as \(f(x)=x^{+}=\max(0,x)\). The input to the network \(I_{\text{RA}}\) is the output of the first iteration of averaged time reversal (ATR), which is denoted as ATR\({}^{*}\).
## 4 Discussion
The improvement in marginal fit as the spacing increases is attributed to a number of factors including a decrease in the hydrostatic forces generated while the crown is cemented, an improvement in the cement flow and a decrease of friction between the crown and the abutment during the cementation. [PERSON] et al. suggested that increased spacing allows a better flow of excess cement and thus lowers the chance of cement particles accumulating at the margins. In addition, [PERSON] et al. argued that contact between irregular areas on the inside surface of the crown and the abutment may cause improper seating of the crown and may lead to increased vertical marginal fit; due to this they suggested performing spacing to avoid these contacts [17; 21; 22]. It should be emphasized that although it has been shown from previous studies that an increase in the thickness of the radial spacer improves the marginal fit, a thickness of more than 120 microns may lower the resistance to fracture in porcelain crowns without significantly improving the marginal fit. Therefore, the optimum thickness of spacer should give us the best marginal fit without harming the mechanical properties of the material and give us the maximum retention [23; 24]. In our study the marginal fit was the same for both 90 microns and 120 microns radial spacers, therefore 90 microns should be the spacer of choice for CELTRA(r) DUO without decreasing the retention and the mechanical properties of the material. In this study there are several limitations: it is an in-vitro study, only one type of intra oral scanner was used, only one type of material was used, the crowns were not sintered which may affect the accuracy of the marginal fit, the occlusal spacer was set on 160 microns and we did not check if by changing this parameter it would affect the marginal gap, a non-standard occlusal reduction for Celtra Duo was used and we observed only 12 points for each unit.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 5 MIMONet: A DL-Based MIMO Detector  
The proposed MIMONet is a DL-driven MIMO detector designed to achieve optimal detection performance with low computational complexity. The MIMONet detector utilizes a state-of-the-art DL-based customized FFNN model structured specifically for MIMO signal detection, which operates effectively across a range of SNR. By leveraging a customized FFNN architecture, the MIMONet detector efficiently performs signal detection, ensuring both high detection accuracy and low resource usage. The functionality for designing and implementing the MIMONet detector can be broken down into two primary phases: network architecture design and training/testing of the detector. Figure 1 presents the systematic architecture of the MIMONet detector. The following sub-section presents a detailed explanation of each phase.
## 5 Experiments
### Experimental Results
#### 5.4.2 Comparison Experiments with Noisy Data  
To evaluate the robustness of different algorithms under noisy conditions, Gaussian noise with a mean of 0 and a standard deviation of 0.05 was added to each point in the graph-structured data with a 60% overlap rate. The other experimental conditions remained consistent with the previous experiments. The comparison results are shown in Table 6.  
We observed that the introduction of moderate noise led to a slight decrease in the overall rotation matrix error (MSE(R)) and translation vector error (MSE(t)), with minimal impact on the network's running speed on the test set, demonstrating a certain level of interference resistance. Moreover, our algorithm was able to more effectively adapt to the registration task under high-noise conditions. Specifically, the NIGCN algorithm struggled to complete registration quickly and accurately under high-noise conditions. This is because noise points further disrupted the global consistency it relies on, failing to meet the registration process's demands for global consistency and smoothness. In contrast, our deep learning-based registration algorithm could extract deeper-level features, effectively addressing this issue. In terms of running time, our algorithm and the GCN algorithm both achieved times around 18 s, showing high efficiency and significantly outperforming the GAT algorithm. However, the error of GCN's method is only achieved with the support of other modules in our method, and it is still larger than ours. Therefore, compared with other registration algorithms, our algorithm demonstrated advantages in both high-noise conditions and running speed.
## 4 Performance Evaluation and Result Analysis
### Speech Quality Analysis
In comparison to SNR and SSNR, the proposed algorithm showed different behavior for PESQ and achieved the three highest improvements for white noise at 0 dB, 5 dB, and 10 dB SNR, which were 27.48%, 20.53%, and 20.31%, respectively. The fourth-highest improvement of 18.64% was achieved for f-16 noise, followed by 17.01% for babble noise, both at 0 dB SNR. For STOI, the only significant improvement of 8.72% was achieved for white noise at 0 dB SNR, followed by 2.73% for f-16 at 0 dB for SNR. It is also worth mentioning that for every noise, the proposed algorithm always achieved the best SNR, SSNR, and PESQ, except for the babble noise at 0 dB SNR, where the K-SVDCS achieved the best SSNR. To further validate the performance of the proposed algorithm, a comparison with non-OMP (non-CS) algorithms for PESQ and STOI measures is presented in Tables 4 and 5, respectively. These results show that the proposed technique performed better at all SNRs except 18 dB, where DNN-Quality-Net improved by 3.96%. However, the proposed algorithm achieved the highest PESQ improvement of 20.32% at \(-\)6 dB. For STOI, the BLSTM (MSE) performed better for 18 dB, 12 dB, and 6 dB, while for 0 dB and \(-\)6 dB, the proposed technique performed better and achieved a maximum improvement of 8.29% at \(-\)6 dB SNR.
## 5 SoSOO-Based Architectural Steps and Application Example
### Step 5: Capture Behavioral Flows among Activities Based on the Synthesis Method  
This step addresses question 3 by describing the behavior of operational performers based on the _OperationalActivityEdge_. To achieve this, we propose a synthesis approach that involves the following steps:  
1. Transforming performers from the Op-Sr view into the heads of partitions within the SysML activity diagram (ACT) to group all actions (usage of activities) performed by the same performers.  
Figure 17: Operational Sequences (Op-Sq) of operational performers.  
Figure 18: Operational Processes (Op-Pr) of operational activities.  
2 Transforming activities from the Op-Pr view into actions contained in the partitions of the ACT.
3. Representing operational connectors from the Op-Cn view by object flows (full lines) between the partitions in the ACT. Control flows (dashed lines) within each partition should be informed by domain knowledge.  
The Operational Processes Flow (Op-Pr-Fl) view identifies performers based on the Op-Sr view, describes operational activities based on the Op-Pr view, and determines input/output flows between activities based on the Op-Cn view. Figure 19 illustrates the execution of the search activity, involving four performers engaged in a series of activities, from discovering victims to their placement.  
To capture the state-based behavior of operational performers, we propose using the Operational States (Op-St) view of the UAF implemented by the SysML state machine diagram (STM), as described in the SoSOO. This view enables us to model the operational states of _OperationalPerformer_, including the behaviors that occur in these states, the _Transitions_ between states, and the events and guards that cause these transitions. Figure 20 provides an example of the Op-St view, depicting the operational states of the \"Searcher ph1\" in the Op-Pr-Fl view. In this example, the search node waits for a distress signal, and once received, the assignment is validated, and the searcher node transitions to searching for the victim.  
Figure 19: Operational Processes Flow (Op-Pr-Fl) of operational activities.
Lately, considerable research has been dedicated to studying changes in the choroid related to systemic and ocular diseases, especially focusing on choroidal thickness (CT). At present, there is no consensus on CT variation in patients treated with intravitreal dexamethasone [13; 14; 15; 16; 17], possibly due to the poor reliability of the parameter, which is closely dependent on confounding factors such as patient age, gender, refractive error, and the presence of concurrent systemic and ocular pathologies [18; 19]. The choroidal vascularity index (CVI) is a relatively novel value recently introduced into the study of choroidal vascularization. This parameter is obtained from the ratio between the choroidal luminal area (LA) and the total area (TA). The CVI has been shown to be more reliable and less dependent on external factors [20; 21]. Several authors have studied the behavior of the CVI in diabetic patients and patients with DR, and there is a general consensus on the reduction of the parameter in affected patients. [PERSON] et al. [22] and [PERSON] et al. [23] conducted studies in patients with type 1 DM with no ophthalmological alterations and reported that the CVI was significantly reduced in affected patients versus a control group. [PERSON] et al. hypothesized that these results may be justified by vascular narrowing secondary to choroidal hypoxia [23]. [PERSON] et al. reported that the CVI was reduced in patients presenting with type 2 DM, with values related to the progression of DR [17]. Thus, it has been established that the CVI is lower in patients with diabetes (with or without DR) compared to age-matched healthy individuals. However, the impact of intravitreal dexamethasone therapy on these parameters is still being debated and could be used as a biomarker to assess the anatomical and functional response to treatment. The principal objective of our investigation was to evaluate the CVI in patients with DME before and after receiving the intravitreal dexamethasone injection, in accordance with international guidelines. We also evaluated alterations in LA, stromal area (SA), and TA to understand how dexamethasone affected diabetes-induced alteration of the choroid. Additionally, we sought to correlate these parameters with treatment response, as indicated by central macular thickness (CMT).
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Forms of Telemedicine
#### 3.4.3 Synchronous Telemedicine  
This method, sometimes known as interactive telemedicine services, consists of several contacts between patient and doctor, including phone calls, online diagnosis, and communications. It involves the collection and transmission of physiological and clinical data as well as medical images to a specialist for real-time online diagnosis [43]. Interactive telemedicine can be used to conduct physical examinations as well as psychiatric and ophthalmological assessments [81].