esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results
### Quantitative Evaluation of Image Quality  
In addition to a visual comparison of the ISAL imaging results, we also evaluated the single-point ISAL imaging quality before and after compensation. For single-point ISAL imaging, the chirped pulse duration time \(\tau_{p}\) was equal to 35 \(\upmu\)s, as presented in Figure 8a. Here, the peak side lobe ratio (PSLR) was used to evaluate the ISAL image quality [33], which describes the ability of an ISAL system to eliminate the distortion caused by adjacent point targets and can be expressed as  
\[PSLR=20\cdot\log_{10}\frac{A_{S_{\text{max}}}}{A_{m}} \tag{17}\]  
where \(A_{S_{\text{max}}}\) and \(A_{m}\) represent the maximum value of the main lobe and the strongest side lobe maximum value outside of the main lobe, respectively. The smaller PSLR values indicate a smaller side lobe amplitude and higher image quality. Therefore, the PSLR value of single-point imaging before and after compensation at different atmospheric coherent times \(\tau_{c}\) is given in Figure 8b.  
The PSLR value after compensation was smaller than that before compensation, as shown in Figure 8b. In addition, with the longer atmospheric coherent times, the PSLR values became smaller, which indicates that the TMC algorithm can compensate for the phase error induced by atmospheric turbulence. On the other hand, the PSLR values for single-point imaging before and after compensation at different pulse duration times \(\tau_{p}\) and ISAL imaging times \(\tau_{T}\) are shown in Figure 8c,d. It was found that with the decreases in the pulse duration time \(\tau_{p}\), the PSLR value became smaller, which means that smaller \(\tau_{p}\) values could improve the compensation efficiency of the TMC algorithm. However, with the decreases in the ISAL imaging time \(\tau_{T}\), the PSLR value hardly changed, as shown in Figure 8d.  
Figure 7: ISAL image with different pulse duration times \(\tau_{p}\) and ISAL imaging times \(\tau_{T}\) (**upper** and **lower** plots: \(\tau_{c}=20\) ms). For the upper two plots, \(\tau_{T}=1.3\) s, and for the lower two plots, \(\tau_{p}=5.12\) ms.  
In summation, the relationship between the pulse duration time \(\tau_{p}\) and atmospheric coherent time \(\tau_{c}\) affected the ISAL image quality in the turbulence condition instead of the atmospheric coherent time \(\tau_{c}\) or the ISAL imaging time \(\tau_{T}\).
## 3 Materials and Methods
### Model Selection  
In order to select a single model for integration with AR-Sandbox, the validation was performed using the original dataSetOriginal and dataSetFilter, evaluating the loss validation coefficient, the accuracy validation coefficient, the validation coefficient of the mean squared error, and its respective variation between datasets. Table 3 shows each model with the values obtained with both datasets.  
According to the data presented in Table 3, model number 7 was chosen, given that it has the highest accuracy coefficient demarcating the fourth largest variation of this metric; in addition, it has the lowest coefficient of categorical_crossentropy, chosen as a function of loss, despite not having the most significant variation in this hyperparameter; finally, the value of the mean squared error coefficient, taken as a second metric, is an intermediate value within this set of models. All of this was calculated through validation with the dataset segmented by color-space. Figure 6 presents a representation of the architecture of the convolutional neural network model, in which the layers with their corresponding types are shown, denoting the characteristics and hyperparameters used.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key** & **List of Values** \\ \hline Filter\_1 & 16, 32 \\ Filter\_2 & 32, 64 \\ Filter\_3 & 64, 128 \\ Activation\_1 & Relu, LeakyReLU \\ Rate\_1 & 0.25, 0.5, 0.75 \\ Rate\_2 & 0.25, 0.5, 0.75 \\ Rate\_3 & 0.25, 0.5, 0.75 \\ Rate\_4 & 0.25, 0.5, 0.75 \\ Units\_1 & 64, 128, 256, 512 \\ Batch\_size & 2, 4, 8, 16, 32 \\ Optimizer & Adam, RMSProp, Nadam, Adadelta \\ \hline
**Model’s ID** & **Accuracy** & **Mean Squared Error** & **Loss** \\ \hline
1 & 0.9871 & 0.0634 & 0.0088 \\
2 & 0.9615 & 0.1021 & 0.0194 \\
3 & 0.9615 & 0.0729 & 0.0138 \\
4 & 0.9923 & 0.1537 & 0.0185 \\
5 & 0.941 & 0.1448 & 0.0279 \\
6 & 0.9333 & 0.1771 & 0.032 \\
7 & 0.9641 & 0.2625 & 0.0251 \\
8 & 0.9743 & 0.0958 & 0.0158 \\
9 & 0.9820 & 0.1619 & 0.0129 \\
10 & 0.8820 & 0.2886 & 0.0551 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Preselected models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Measure** & **Dataset** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multirow{4}{*}{Validation\_accuracy} & Original & 0.7333 & 0.7111 & 0.7111 & 0.6444 & 0.5333 & 0.6111 & 0.7000 & 0.6555 & 0.7111 & 0.5888 \\  & Filter & 0.7777 & 0.8999 & 0.7666 & 0.8333 & 0.7555 & 0.7444 & 0.8700 & 0.7666 & 0.8666 & 0.7222 \\  & Difference & 0.0444 & 0.1888 & 0.0555 & 0.1889 & 0.2222 & 0.1333 & 0.1700 & 0.1111 & 0.1558 & 0.1334 \\  & Original & 1.0977 & 1.0986 & 1.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
###### Abstract  
The new era of IoT brings the necessity of smart synergy for diverse communication and computation entities. The two extremes are, on the one hand, the 5G Ultra-Reliable Low-Latency Communications (URLLC) required for Industrial IoT (IIoT) and vehicle Communications (V2V, V2I, V2X). While on the other hand, the Ultra-Low Power, Wide-Range, Low Bit-rate Communications, such as Sigfox, LoRa/LoRaWAN, NB-IoT, Cat-M1, etc.; used for smart metering, smart logistics, monitoring, alarms, tracking applications. This extreme variety and diversity must work in synergy, all inter-operating/inter-working with the Internet. The communication solutions must mutually cooperate, but there must be a synergy in a broader sense that includes the various communication solutions and all the processing and storage capabilities from the edge cloud to the deep-cloud. In this paper, we consider a non-orthogonal multiple access (NOMA)-based device to device (D2D) communication system coexisting with a cellular network and utilize Greedy Asynchronous Distributed Interference Avoidance Algorithm (GADIA) for dynamic frequency allocation strategy. We analyze a max-min fairness optimization problem with energy budget constraints to provide a reasonable boundary rate for the downlink to all devices and cellular users in the network for a given total transmit power. A comprehensive simulation and numerical evaluation is performed. Further, we compare the performance of maximum achievable rate and energy efficiency (EE) at a given spectral efficiency (SE) while employing NOMA and orthogonal frequency-division multiple access (OFDMA).  
IoT; 5G; eMBB; mMTC; URLLC; GADIA; NOMA; OFDMA +
Footnote †: journal: Information
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Challenges for Network-Wide Scheduling
The authors suggested some solutions for cycle and bandwidth mismatch but still have open issues related to reliability by frame replication and elimination and sporadic traffic handling. - A fourth challenge is related to the interface between the different domains. If domains involved in the forwarding of the frame are using different technologies, frames crossing the border between them need to be modified. Different technologies use different features and traffic formatting. For example, the priority of an Ethernet frame is encoded in the PCP field of the header, but for wireless communication, the packet header has a different structure (e.g., in the 5G system, the priority of a packet is encoded in the 5 QI field). Therefore, a translator is required that rewrites packet headers or encapsulates packets and translates the time synchronization traffic without precision degradation. Ref. [115] suggested a hybrid architecture for a TSN device that allows the integration of wire-line and wireless TSN. In this regard, one challenge for distributed cross-domain scheduling is to consider the latency added by the translation operations at the translator. If the schedule is calculated centrally, then the centralized controller should have the information about the translator. However, in a distributed mode, every domain controller considers only the ports belonging to it, but the translator partially belongs to different domains. This brings additional complexity to the network-wide scheduling.
## 4 Modeling of AV-Mixed Traffic Information Services
### Derivation of Services
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **(_TrInfoToType_)** & **O** & **T** & **L** & **C** \\ \hline Traffic Flow & \(\{\)_detector1D_\(\}\) & \(\{\)_crTime_\(\}\) & \(\{\)_link_, & \(\{\)_link_/_Velocity_,_link_/_Volume_,_link_/_OccupancyRate_,_link_/_TravelTime_,_ \\ \((TrInfoToView)\) & \(\{\)_detector1D_\(\}\) & \(\{\)_crTime_\(\}\) & \(\lambda(link)\)\(\}\) & \(<\)_luneVelocity_,_lane_Volume_,_lane_OccupancyRate_,_lane_TravelTime_\(\}\)\(\}\) \\ \hline Traffic Incident & \(\{\}\) & \(\{\)_strtTime_, & \(\{\)_link_, & \(\{\)_link_/_Incident_Code_, _link_Progress_Status_, \\ \((TrInfoIn^{\textit{t}indirect})\) & \(\{\}\) & \(\{\)_end_Time_, & \(\lambda(link)\), & \(\lambda(link)\), & \(\begin{array}{l}linkIncidentTitle, \textit{link}IncidentIn formation, \\ \textit{<}\textit{lnetIncidentCode, laneProgressStatus, }\\ \textit{laneIncidentTitle, laneIncidentInformation>}\}\) \\ \hline Traffic Facility & \(\{\)_facility1D_, & \(\{\)_crTime_, & \(\{\)_link_, & \(\)_link_, & \(\{\)_facilityStatus_Code_\}\}\) \\ \((TrInfoF^{\textit{facility}})\) & \(\{\)_facilityType_\(\}\) & \(\{\)_crTime_\(\}\) & \(\{\)_covard_\(\}\) & & \\ \hline Weather & \(\{\)_weather_\(\}\) & \(\{\)_watherStation1D_\(\}\) & \(\{\)_crTime_, & \(\{\)_coord_\(\}\) & \(\{\)_temperature_, _humidity_,_air_V_Volume_, \\ \((TrInfoWather)\) & \(\{\)_wath, & \(\}\)_crTime_\(\}\) & \(\{\)_windSpeed_,_rain_fall_,_ snow_fall_\(\}\) \\ \hline Probe Vehicle & \(\{\)_vehicle1D_, & \(\{\)_crTime_, & \(\}\)\(\{\)_link_, & \(\}\)\(\lambda(link)\), & \(\{\)_accuracy_, _transmission_, _speed_, _heading_, _angle_, _acceleration_Longtitude_, _acceleration_Latitude_, _acceleration_Latitude_, _acceleration_Attitude_, _wheel_Brake_, _traction_, _antiLockBrake_, _brakeBoost_, _auxiliaryBrake_\} \\ \hline Traffic Signal & \(\{\)_signalGroup1D_\(\}\) & \(\{\)_strtTime_, & \(\}\)\(\{\)_end_Time_, & \(\}\)\(\{\)_node_\(\}\) & \(\{\)_descriptiveName_, _signalStatus_, _movementName_, _movement_Phase_Status_\} \\ \hline Autonomous & \(\{\)_autoVehicle1D_, & \(\{\)_crTime_, & \(\}\)\(\{\)_link_, & \(\}\)\(\lambda(link)\), & \(\{\)_accuracy_, _transmission_, _speed_, _heading_, _angle_, _accelerationLongtitude_, _acceleration_Latitude_, _acceleration_Attitude_, _acceleration_Altitude_, _wheel_Brake_, _traction_, _antiLockBrake_, _brakeBoost_, _auxiliaryBrake_\} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result of AV-mixed Traffic Information Modeling.
# Quantum Computing for Healthcare: A Review
## 7 Open Issues and Future Research Directions
### Quantum Web and Cloud Services  
Bringing quantum computing services to commodity hardware is a critical challenge to reap the benefits of the extended functionalities provided by quantum computing. Due to the large number of resources required for quantum computing implementations, it becomes challenging to access quantum computing for general-purpose problem-solving. The USD 500 million under-development Cleveland Clinic-IBM Discovery Accelerator [198] is an example of a project that attempts to overcome the quantum healthcare services on commodity hardware challenges. The project's goal is to provide a cloud-based platform, dubbed \"RXN\", that uses AI models to predict chemical reactions for optimized synthesis methods and automated generation of chemical procedures for remotely accessible laboratory operations. Amazon web services provide an example of an implementation scenario that can be used to implement quantum web services. Amazon Braket [199] is one example of implementing quantum web services. It provides an efficient platform for researchers and experts to analyze and evaluate quantum computing models in a real-time testing environment. Amazon Braket provides an experimental environment to design, test, and evaluate quantum computing algorithms on a simulated quantum environment and runs them on quantum hardware. It uses D-wave's quantum annealing and gate-based hardware under the hood. These gate-based quantum computers include ion-trap devices from IonQ, as well as systems built on superconducting qubits from Rigetti [200]. Apart from the Amazon web services environment, other quantum computing solutions are required to provide quantum web services to the users. Software development kits (SDK) could be implemented, which can be used to simulate the developed quantum computing algorithm.
## 2 Materials and Methods
### Sample Characterization
#### 2.2.2 TEM  
The structure of the samples was analyzed via Carl Zeiss Libra 200 FE (Carl Zeiss Microscopy, Jena, Germany) equipped with a highly efficient field emission emitter and an OMEGA energy filter in the research park of St. Petersburg State University (Interdisciplinary Center for Nanotechnology, Russia). TEM images were registered at an accelerating voltage of 200 kV with various magnifications from 20 kV to 400 kV.
## 4 Experimentation
### Evaluation Metrics  
In this study, the effectiveness of our proposed framework, DMRMs, was evaluated using a receiver operating characteristics curve (ROC-Curve), which is a trade-off plot between False Positive Rate (FPR) and True Positive Rate (TPR), and the area under the curve (AUC) metric. This metric is a quantitative measure for the ROC-Curve, which has been used in similar previous studies [8; 13; 14] for evaluating the anomalous event detection task. Moreover, FPR and TPR can be calculated as follows:  
\[TPR\;or\;Recall=\frac{\sum TP}{\sum(TP+FN)}\]  
\[FPR=\frac{\sum FP}{\sum(TP+FN)} \tag{18}\]  
where \(TP\), \(FN\), and \(FP\) stand for True Positive, False Negative, and False Positive, respectively. Additionally, the performance of the anomalous event recognition task is evaluated using an accuracy metric.
# A Deadbeat Current and Flux Vector Control for IPMSM Drive with High Dynamic Performance
## 2 IPMSM Fundamental Model
### Study on Motor Parameter Variation  
The motor parameter variation can significantly influence the accuracy and performance of the control method. In this work, several simple estimation methods of parameter variations are conducted to secure the robustness and reliability of the proposed drive system.  
When IPMSMs are in operation, stator inductances and resistance usually vary with various working conditions while PM flux linkage variation rarely occurs under normal operations. Hence, the PM flux linkage is assumed to be constant in this work. Regarding stator resistance variation, it only affects the flux linkage estimation in the proposed DB-CFVC scheme; however, this problem has been resolved by the proposed MO-FOB.  
According to the analysis in [33; 34; 35], it can be realized that the effect of \(d\)-axis inductance error, \(\Delta L_{\text{d}}\), on driving performance has been insignificant compared to that of the \(q\)-axis stator inductances, \(\Delta L_{\text{q}}\). Thus, only the error \(\Delta L_{\text{q}}\) is considered in this study. By the approximate calculation based on produced electromagnetic torque proposed in [36], the \(q\)-axis stator inductance can be estimated by:  
\[\hat{L}_{\text{q}}(k)=\frac{L_{\text{q}}}{\left(1+K_{T}\frac{\hat{T}_{\text{ e}}(k)}{T_{\text{e,rated}}}\right)} \tag{48}\]  
where \(\hat{L}_{\text{q}}(k)\) is the estimated \(q\)-axis stator inductance, \(\hat{T}_{\text{e}}(k)\) and \(T_{\text{e,rated}}\) are the estimated and rated torques, respectively, and \(K_{T}\) is torque constant. The estimated parameter (i.e., \(\Delta L_{\text{q}}\)) at the present instant will be updated in all the related calculations at the next control period.
# Numerical Analysis on Self-Propulsion of a Waterjet-Propelled Ship with Different Propulsion Models
## 3 Results and Discussions
### Computations of Self-Propulsion
#### 3.2.4 Transient Sliding Mesh Model  
Taking the results of the MRF model as the initial condition, the sliding mesh model is applied to simulate the transient flow under \(Fr=0.542\) (design speed). The time step corresponds to \(1^{\circ}\) rotation of the impeller. Figure 19 exhibits the net force of the waterjet-propelled ship and volume flow rate at station 6 with the MRF model, body-force model and sliding mesh model. In the sliding mesh model, the net force of the waterjet-propelled ship fluctuates within a small range, and the mean value is zero (resistance/thrust balance). The volume flow rate with the sliding mesh model is oscillating within the range of \(\pm\) 0.42%. However, the net force and volume flow rate computed with the MRF and body-force models are almost constant at the self-propulsion point. This indicates that the sliding mesh model is capable of capturing the unsteady periodic characteristics of the actual flow, while the MRF and body-force models cannot.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(Fr\)} & **MRF** & **BFM** & \(1-t_{r}\) & **MRF** & \(1-t_{j}\) & **BFM** & \(1-t_{j}\) & **MRF** & **BFM** & \(1-t\) **Error** \\  & \(1-t_{r}\) & \(1-t_{r}\) & **Error** & \(1-t_{j}\) & \(1-t_{j}\) & **Error** & \(1-t\) & \(1-t\) & \(1-t\) **Error** \\ \hline
0.300 & 0.914 & 0.917 & 0.33\% & 1.111 & 1.119 & 0.72\% & 1.016 & 1.027 & 1.08\% \\ \hline
0.400 & 0.946 & 0.941 & 0.53\% & 1.034 & 1.053 & 1.84\% & 0.978 & 0.991 & 1.33\% \\ \hline
0.500 & 0.961 & 0.969 & 0.87\% & 0.990 & 0.998 & 0.81\% & 0.951 & 0.967 & 1.68\% \\ \hline
0.542 & 0.983 & 0.980 & \(-0.31\%\) & 0.978 & 0.979 & 0.10\% & 0.962 & 0.959 & \(-0.31\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of \(1-t_{r}\), \(1-t_{j}\) and \(1-t\) with the MRF model and body-force model (BFM).  
Figure 19: Net force of waterjet-propelled ship and volume flow rate at station 6 (VFR-6) with three different models.  
Figure 20 shows the non-dimensional axial velocity distribution at station 3 and 6 at a certain time with the sliding mesh model, which shows more uneven axial velocity distribution in the upper half disk of station 3, compared with the MRF and body-force models. Table 8 lists the results of nozzle velocity ratio \(NVR\) (\(NVR=\overline{U_{6}}/U_{0}\)), flow rate of waterjet \(Q_{I}\) and momentum non-uniformity factor \(\beta_{M6}\) at station 6 with the MRF model, body-force model and sliding mesh model. The nozzle velocity ratio and flow rate of waterjet with the sliding mesh model are larger than those of the MRF and body-force models. Compared with the body-force model, the momentum non-uniformity factor at station 6 with the sliding mesh model is slightly greater.
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 2 Methods
### The Proposed Methods  
Inspired by the application of the Swin transformer and CNN in computer vision (CV), this paper designs a three-stream pan-sharpening model for fusing hyperspectral and panchromatic images. A Swin transformer module, a multi-scale residual and dense block (MRDB), and a residual feature fusion module make up the proposed network, which is shown in Figure 1. For ease of description, we use P for Pan images; HS for multi-spectral images; HS\(\uparrow\) for multi-spectral images after up-sampling; HRMS to represent the reconstructed image; and L for images of HS\(\uparrow\) and Pan concat. This paper uses the bicubic algorithm for up-sampling HS images, with this algorithm producing the best and most accurate interpolated graphics. In the feature extraction stage, the global features of L are obtained using the Swin transformer feature extraction branch. Furthermore, we utilize the multi-scale feature extraction branch of the CNN architecture to extract local features from both HS and P images. In the fusion stage, we choose the residual fusion block to fuse the features extracted from the three branches, a process discussed in detail below for the Swin-MRDB method.
# Development of a Mouthwash Using Freeze-Drying Technique: An Optimization Study
## 3 Results and Discussion
### Development and Evaluation of the Cetraria islandica Extract Mouthwash
#### 3.2.1 Experimental Design  
In the complex composition of cosmetics, all the ingredients can exert an important impact on the characteristics of the final products. In this study, DoE was used as a strategy to assess the effects of the key ingredients on the quality attributes of the products. DoE allows knowledge and understanding not only of individual effects but can also show how the interaction between the compounds influences their characteristics. In the case of the freeze-dried mouthwashes, the filler needed to reinforce the structures could have an important influence on the mechanical profile, on the dissolution speed and the taste. Therefore, the type of filler (X1) was evaluated, with two options, mannitol and sorbitol. The first quantitative variable, the filler ratio (X2), varied between 3 and 7%. As a hydrophilic polymer is needed to ensure the three-dimensional structure and integrity of a freeze-dried product, methylcellulose ratio (X3) was chosen as a quantitative variable and varied between 0 and 0.5%. After the preparation and the complete characterization of the 11 formulations generated through DoE, the data analysis aimed at regression equations (model equations) for each of the responses as functions of the input variables X1, X2 and X3. Several steps were taken to study the impact of variables on the results. The Modde software was used to perform data processing, then fitting the model, refining the coefficients of the results, and finally optimizing the input variables to reach an ideal formulation. Ten responses (Y1-Y10) were analyzed; the results of all the tests performed on the 11 formulations, that were used in the DoE analysis, are presented in Table 3.  
Each of the responses (Y1-Y10) corresponding to a certain quality attribute was expressed mathematically as an equation that reveals its correlation to the formulation factors (X1-X3):  
\[ \begin{split}\text{Y}=\text{b}_{0}+\text{b}_{1}\text{X1}+\text{b} _{2}\text{X2}+\text{b}_{3}\text{X3}+\text{b}_{4}\text{X1}^{2}+\text{b}_{5} \text{X2}^{2}+\text{b}_{6}\text{X3}^{2}+\text{b}_{7}\text{X1}\text{X2}+\text{ b}_{8}\text{X2}\text{X3}+\\ \text{b}_{9}\text{X1}\text{X3}+\text{b}_{10}\text{X1}\text{X2}\text{X3} \end{split}  \tag{1}\]  
where Y is the response, b\({}_{0}\) is the mean response of the 11 formulations and b\({}_{i}\) are the regression coefficients. X1-X3 represent the individual effects, X12-X32 are quadratic effects that indicate a non-linear correlation to the response, while X1X2, X3X2, X1X3, and X1X2X3 are interaction effects which show the variations of the responses when two or more factors are changed simultaneously [23].