esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks
## 1 Introduction
Therefore, only the user specific feedback channel is considered for LTE SC-PtM transmission [10]. To reduce feedback overhead by means other than NACK-based common feedback, we propose a new HARQ scheme for transmitting multicast packets. In a conventional HARQ scheme, the receiver sends back an ACK or NACK signal in accordance with packet reception. By contrast, our proposed HARQ scheme performs autonomous retransmission at the beginning of packet transmission. That is, it automatically retransmits a packet a predetermined number of times without ACK/NACK feedback, and then performs the HARQ operation in the conventional HARQ method. Since the proposed method does not use ACK channels during autonomous retransmission, the feedback overhead is reduced. We define a spectral efficiency metric considering both the HARQ gain and the feedback overhead, and determine the optimal number of autonomous retransmissions that maximizes its value. We then propose a practical algorithm that adjusts the number of autonomous retransmissions for application in real communication systems. By way of computer simulations, the proposed scheme is compared with the conventional HARQ scheme in terms of spectral efficiency. As the number of MSs in the multicast group increases, the resource saving of the proposed HARQ scheme relative to the conventional HARQ scheme also increases. Therefore, the proposed scheme is potentially a practically applicable multicast transmission scheme in WSNs that can enhance spectral efficiency while guaranteeing QoS. This paper is organized as follows. In Section 2, a brief introduction to the multicast transmission scheme using the conventional HARQ method is made. The proposed HARQ algorithm is explained in Section 3. In Section 4, we derive the optimal number of retransmissions for the proposed HARQ scheme based on a spectral efficiency metric in order to evaluate its performance as a component of multicast transmission. In Section 5, a practical algorithm adjusting the number of retransmissions is developed. Simulation results and conclusions are presented in Sections 6 and 7, respectively.
## 4 Experiments
### Cervical Cancer Classification  
#### 4.1.1 Datasets  
NCI dataset: In this study, the NCI dataset collected from the U.S. NCI was used for cervical cancer classification. The dataset was collected from approximately 10,000 women over 18 years old by the project Costa Rica: Proyecto epidemiologico guanacaste [91]. Eachpatient visited the hospital several times during the project and had several checkups per visit [86]. The total number of images is 45,009, consisting of 978 labeled images within one year of the inspection date and 44,031 unlabeled images. In addition, cervical intraepithelial neoplasia (CIN) grades (CIN 0, 1, 2, 3, and 4) are used to label each patient's cervical image with the ground truth. In Figure 6, CIN 2 or higher is classified as abnormal, and CIN0 and CIN1 are classified as normal. Therefore, in the experiment, the data were converted into a binary classification problem by dividing it into positive results (CIN 2+) and negative results (CIN0/1). The NCI dataset was used for the training and test sets.  
Intel and MobileODT Cervical Cancer Screening dataset: In addition to the NCI data, transfer learning was performed using similar cervical images for the \(T_{sim}\) experiments. The Intel and MobileODT Cervical Cancer Screening dataset is used to classify the transformation zone of the cervix [92]. Identifying the transformation zone can significantly improve the quality and efficiency of cervical cancer screening because, even if it is negative, additional screening may be required for the transformation zone. The transformation zone is classified into three types in Figure 7: Type 1, completely ectocervical, fully visible, and small or large; Type 2, endocervical component, fully visible, and may have an ectocervical component, which may be small or large; and Type 3, has an endocervical component, not fully visible, and may have an ectocervical component, which may be small or large. The dataset was used for the \(T_{sim}\) cervical cancer classification experiment because it could improve performance by first identifying the transformation zone and then classifying cancer with similar image training.
# Modeling of Geophysical Flows through GPUFOW
## 4 Results
### Computational Performance  
To evaluate the computational performance of the model, we ran the 2018 Etna simulation at three different resolutions (5 m, 10 m, 20 m) on two different computational devices: an NVIDIA GeForce RTX 3060 GPU and an AMD Ryzen 9 5950X processor. The NVIDIA GPU features 28 computational units with a total of 3584 streaming processing elements running at 1.3 GHz, resulting in a total of approximately 10 TFLOPS of single-precision processing power theoretical maximum. The AMD CPU features 16 cores (32 threads) with AVX2 support, running at 3.4 GHz with the possibility of a 4.9 GHz boost, for a total of approximately 1 TFLOPS of single-precision processing power theoretical maximum.  
There were minor differences in the numerical results between the CPU and GPU execution due to different degrees of accuracy of the transcendental functions, which result in slightly different numbers of active cells and enveloping region. Therefore, rather than directly comparing the full simulation runtimes, we compared the average time needed for a full iteration for a single cell, by sampling the runtime of a full iteration and dividing by the number of cells in the active region.  
The results are shown in Figure 6. The GPU had a performance that was more than an order of magnitude higher than that of the 16-core/32-thread CPU in full use. The observed speed-up was consistent with the one expected from the theoretical peak TFLOPS.  
Figure 5: GPUFLOW simulations for the 24–27 December 2018 eruption of the Etna volcano. Lava flows were simulated using the temperature-dependent emissivity without (**a**) and with (**b**) the bias correction. Colors represent the lava flow thickness in meters. The actual lava flow field extracted by the Planetscope image (area of \(\sim\)1 km\({}^{2}\)) and the five emission points along the eruptive fissure is highlighted in black. The outline of the actual field was determined by adaptive local thresholding using the free and open-source GRASS GIS software.
# Numerical Quantification of Controllability in the Null Space for Redundant Manipulators
## 3 Simulation and Experiment
### Obstacle Avoidance  
The proposed method can be applied for obstacle avoidance. When obstacle avoidance takes action, it creates a repulsive force with respect to the vector between the robot and the obstacle. When the repulsive force vector points out to an infeasible direction, the manipulator is unable to avoid the obstacle. On the contrary, as the proposed method projects the force to a feasible (movable) direction, the manipulator is able to avoid the obstacle. Even though, the repulsive force points out to a nearby feasible direction, the proposed method can avoid the obstacle with smaller repulsive force with shorter duration.  
We have performed obstacle avoidance with the PANDA. The force was selected by a force inducing an artificial repulsion from the surface of the obstacle [25].  
\[F_{obs}=\begin{cases}\mu(\frac{1}{\rho}-\frac{1}{\rho_{0}})\frac{1}{\rho^{2}} \frac{\partial\rho}{\partial x}&\text{if }\rho\leq\rho_{0},\\ 0&\text{if }\rho>\rho_{0}\end{cases} \tag{19}\]  
The experiment was performed with and without the force projection. With the force projection, the manipulator avoids the obstacle faster with smaller input, as shown in Figure 5. Generally, the magnitude of torque may appear greater or lesser depending on the controllable direction, but when the proposed is used, the magnitude of torque becomes equivalent regardless of the controllable direction.
## 2 Materials and Methods
### System Modeling Process
#### 2.1.2 Digital Health System  
Figure 3 presents a context diagram for the DHS sub-system. Its main functions are as follows:  
* Patient authentication;
* Reporting/updating data;
* Classifying vital signs;
* Providing statistical information.  
The SSS is illustrated in detail in Figure 2, Figure 4, Figure 5. Figure 4 shows a use case diagram for the SSS that summarizes the goals and the scope of each actor/agent in the system and their interactions via explicit scenarios, while Figure 5 shows the activity diagram for the analytical process use case in the SSS. Its aim was to shape the functional aspect of the system by illustrating the flow from one activity to another. A functional view is presented in this diagram to explain the behavior of each actor throughout the process. The goal was therefore to model the flow of control and data flows. It graphically represents the behavior of the DHS use case. The communication between the patient, PDS, DA and doctor allowed both to process data and save it in the classification model. Between the two processes we applied support vector machine (SVM) to be able to prepare our learning  
Figure 3: Digital healthcare system context.  
Figure 2: Smart surveillance system context.  
model. Then came the validation phase of the obtained model. This process is therefore vital to the development of a reliable medical model that can be used later when an anomaly is detected.  
Taken together, these two sub-systems can collaborate to provide a solid platform where data examination is fluid. Four external agents collaborate in the use case diagram shown in Figure 6 and its analogous restricted activity diagram for the classification process in Figure 7. This figure illustrates the relationship between PDS and DA. Since the learning model was ready, it would be used for real-time data classification. If an anomaly was detected, it would be analyzed using the normal distribution. If our model considered the anomaly to be dangerous, then a broking process would be triggered to disseminate the patient's information and health status.  
Figure 4: Use case diagram for the SSS.  
Figure 5: Activity diagram for the analytical process use case (SSS).  
Figure 8 summarizes how medical data are transferred from the patient after being sensed using a WBAN device, through the double classification process mentioned in the next section, and finally to an agent broker, which is responsible for generating an alert of a given severity. An interaction diagram was used for this purpose.  
Figure 6: Use case diagram for the digital health system (DHS).  
Figure 7: Activity diagram for the classification process use case (DHS).
# Sensitivity Analysis for Microscopic Crowd Simulation
## 2 Materials and Methods
### Choice of Sensitivity Analysis Methods
#### 2.3.2 Activity Scores (Derivative-Based Method)  
The main idea of active subspaces [19] is to identify important parameter directions, which can be any linear combination of the input parameters. While they were originally designed for model reduction, [PERSON] and [PERSON] [26] suggest a way to use the interim results of this process to perform a global sensitivity analysis. The method described by [PERSON] et al. [19] is based on gradients. Central to the method is the calculation of the matrix  
\[C=\int\
abla f(x)\
abla f(x)^{T}\rho dx \tag{8}\]  
which is described as \"(uncentered) covariance matrix of the gradient\" ([51], p. 22) with input parameter density \(\rho\).  
In practice, these high-dimensional integrals can neither be computed analytically nor by numerical quadrature and, therefore, \(C\) is typically approximated with a Monte Carlo approach while using \(M\) samples \(x_{i}\) which are drawn \(\rho\):  
\[C\approx\hat{C}=\sum_{i=1}^{M}\
abla f(x_{i})\
abla f(x_{i})^{T}=\hat{W}\hat{ \Lambda}\hat{W}. \tag{9}\]  
We refer to the approximations by adding a hat (\(\hat{\cdot}\)) to the variable names. From now on, we describe the computation of the active subspaces and activity scores while using \(\hat{C}\) instead of \(C\)The steps are identical for the true matrix \(C\). We approximate the gradients \(\
abla f(x_{i})\) by central finite difference quotients. Entry \(j,j=1,\ldots,n\) of the gradient is then calculated as  
\[\
abla f(x_{i})_{j}\approx\frac{f(x_{i}+h)-f(x_{i}-h)}{2h} \tag{10}\]  
with step size \(h\). In the next step, the eigenvalues of \(\hat{C}\) are analyzed using a spectral decomposition of the matrix:  
\[\hat{C}=\hat{W}\hat{\Lambda}\hat{W}^{T}=[\hat{w}_{1}\cdots\hat{w}_{n}]\;\text{ diag}(\hat{\lambda}_{1},\ldots,\hat{\lambda}_{n})[\hat{w}_{1}\cdots\hat{w}_{n}]^{T}. \tag{11}\]  
A spectral gap in the eigenvalues \(\hat{\lambda}_{i}\) identifies an active subspace: the largest gap separates the eigenvalues in two groups. We use the largest gap among the logarithmic representation of the eigenvalues. The eigenvectors \(\hat{w}_{i},i=1,\ldots,k\) associated with the first \(k\) eigenvalues above the eigenvalue gap form the active subspace \(\hat{W}_{1}\). The remaining eigenvectors form the inactive subspace \(\hat{W}_{2}\). If \(C\) is approximated by \(\hat{C}\), [PERSON] shows that the distance between true and estimated subspace depends on the size of the spectral gap ([51], p. 54).  
The \(i\)-th activity score [26] is calculated by summing over the \(k\) active eigenvalues multiplied with the squared entries of the \(i\)-th eigenvector:  
\[\hat{a}_{i}=\hat{a}_{i}(k)=\sum_{j=1}^{k}\hat{\lambda}_{j}\hat{w}_{i,j}^{2},i =1,\ldots,n \tag{12}\]  
Consequently, the activity scores \(\hat{a}_{i}\) depend on the user's choice where to settle the eigenvalue gap therefore on the resulting size of the subspace \(k\). While the largest gap in the eigenvalues should be used in theory, there might not always be a clear gap or only a certain size of subspace may be feasible.
# A Review of Recent Literature on Audio-Based Pseudo-Haptics
## 2 The Review
### Visual-Auditory-Based Pseudo-Haptics
When they blurred the virtual object's edge to either 0.4 cm or 0.6 cm and accompanied it with sound at a specific frequency of 265 Hz or 966 Hz, participants perceived a vibrating object. [PERSON] et al. [65] examined improving immersion, attractiveness, and intuitiveness of VR, AR, and the metaverse using cross-modal (audio and visual)-driven pseudo-haptics for fundamental tasks such as clicking, scrolling, object manipulation, and zooming in and out. Ten participants took part in a study consisting of conventional (visual and auditory) and multimodal (visual, audio, and haptic) feedback when conducting five virtual activities (mental, physical, temporal, performance, effort, and frustration) in AR. It was found that cross-modal-driven pseudo-haptic interaction resulted in higher performance on all the parameters, i.e., intuitiveness, attractiveness, and immersion.
## 4 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Approaches** &
\begin{tabular}{c} **Imaging** \\ **Modalities** \\ \end{tabular} & **Datasets** & **Results** \\ \hline
[12] & CNN-LSTM & Histopathology & BreakHis & Acc = 91\% \\  & & & Prec = 96\% \\ \hline
[13] & U-Net CNN & Mammography &
\begin{tabular}{c} CBIS-DDSM mass images \\ CBIS-DDSM microcalcification images \\ \end{tabular} & Acc = 94.31\% \\ \hline
[14] & CNN & Histopathology & BreakHis & Acc = 91.4\% \\ \hline
[15] & CNN & Mammography & DDSM & Acc = 93.5\% \\  & & & AUC = 0.92315 \\ \hline
[16] & CNN-RNN & Mammography & Mammogram Image dataset & Acc = 90.59\% \\  & & & Sn = 92.42\% \\  & & & Sp = 89.88\% \\ \hline
[17] & \
\begin{tabular}{c} Bayes Network \\ Naïve SWS \\ Decision Tree J48 \\ MLP \\ RF \\ RT \\ ELM \\ \end{tabular} & Thermal &
\begin{tabular}{c} University Hospital of the Federal \\ University of Pernambuco \\ \end{tabular} & Acc = 76.01\% \\ \hline
[18] & \begin{tabular}{c} SVM \\ KNN \\ DT \\ ANN \\ \end{tabular} & Thermal & \begin{tabular}{c} DBT-TU-JU \\ DMR-IR \\ \end{tabular} & Acc = 84.29\% \\  & \
\begin{tabular}{c} DT \\ ANN \\ \end{tabular} & Thermal &
\begin{tabular}{c} DMR-IR \\ \end{tabular} & Acc = 87.50\% \\ \hline
[19] & \
\begin{tabular}{c} CNNs-Bayes \\ optimization \\ algorithm \\ \end{tabular} & Thermal &
\begin{tabular}{c} DMI \\ \end{tabular} & Acc : 98.95\% \\ \hline
[20] & DCNNs & Thermal & \
\begin{tabular}{c} DMR-IR \\ \end{tabular} & Acc = 95.8\% \\  &
\begin{tabular}{c} CN = 99.40\% \\ Sp = 76.3\% \\ \end{tabular} \\ \hline
[21] & DNN & Thermal & DMR-IR &
\begin{tabular}{c} Conf (Sick) = 78\% \\ Conf (Health) = 94\% \\ \end{tabular} \\ \hline
[22] & \
\begin{tabular}{c} DT \\ Fuzzy Sugeno \\ Naïve Bayes \\ K Nearest Neighbor \\ Gaussian Mixture \\ Model \\ Probabilistic Neural \\ Network \\ \end{tabular} & Thermal &
\begin{tabular}{c} Singapore General Hospital \\ NEC-Avio Thermo TVS2000 \\ MLIST System \\ \end{tabular} & Acc = 93.30\% \\
[23] & \begin{tabular}{c} CNN-GVF \\ \end{tabular} & Thermal & \
\begin{tabular}{c} NEC-Avio Thermo TVS2000 \\ MLIST System \\ \end{tabular} &
\begin{tabular}{c} Sn = 86.70\% \\ Sp = 100\% \\ AUC = 0.99 \\ \end{tabular} \\ \hline
[24] & multi-input CNN & Thermal & \begin{tabular}{c} DMR-IR \\ \end{tabular} & Acc = 97\% \\  & & & \
\begin{tabular}{c} NEC-Avio Thermo TVS2000 \\ MLIST System \\ \end{tabular} &
\begin{tabular}{c} Sn = 83\% \\ Sp = 100\% \\ AUC = 0.99 \\ \end{tabular} \\ \hline
[25] & CNNs & Thermal & DMR-IR & Acc(color) = 98\% \\  & & & Acc(grayscale) = 95\% \\  & & & Acc(color) = 95\% \\  & & & Acc(grayscale) = 92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Breast cancer detection and classification studies.
## 3 Standardization ISO 9283--Path Accuracy
### Implementation and Evaluation of Experimental Measuring  
The object of path-accuracy measurement was the robotic arm of a collaborative robot by Fanuc designated as CRX-10 iA. Overall, six types of measurements were performed. These were the measurements prescribed by the standard under a 100% nominal load and at 100, 50, and 10% speed. Furthermore, measurements under a 50% and 10% load, respectively, and the velocities of 100, 50%, and 10% were performed. The number of cycles for each measurement was 10. Figure 13 shows the measuring plane, along which measurements took place from points C2 to C8. A total of 10 points, subject to deviation detection, lay on this programmed path [32].  
The measurements were performed according to the relevant standard. The measurement involved a weight installed on the robot's end flange with the attached GEPARD device.  
Figure 14 shows the measuring setup used in the measuring cycle. The testing cycle was bidirectional, i.e., the measuring started at point C2, from which the robot passed over to C8 and then returned back to point C2. This cycle was repeated 10 times under a 100% load at three velocities of 100, 50 and 10%, respectively. The initial point was outside the measuring cube, as prescribed by the standard.  
Figure 12: First and last measuring point in the ISO cube.  
Figure 15 represents the cycles of measurements. Each measurement involved every joint of the robot.  
Figure 14: Measuring setup.  
Figure 13: Location of the measuring cube, plane, and the path measured.
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 6 Security Objectives of WSN-IoT
### Accountability  
Accountability is one of the WSN-IoT framework's basic properties, but it cannot preempt network attack risks and WSN-IoT vulnerabilities. However, rationing and supporting other security criteria such as data integrity and privacy are imperative. They are utilized to follow any node (device) that sends and receives information to notice and distinguish any obscure activities by providing guidelines for the device, clients, and their actions.
# A Variable Step Size Normalized Least-Mean-Square Algorithm Based on Data Reuse
## 1 Introduction
The most reliable solution to this problem is the use of an adaptive filter that generates at its output a replica of the echo, which is further subtracted from the microphone signal [4; 5; 6]. Consequently, the adaptive filter has to model an unknown system, i.e., the acoustic echo path between the loudspeaker and microphone. Even if the problem formulation is straightforward, the specific features of AEC represent a challenge for any adaptive algorithm. First, the acoustic echo paths have excessive lengths in time (up to hundreds of milliseconds), due to the slow speed of sound in the air, together with multiple reflections caused by the environment; therefore, long-length adaptive filters are required (hundreds or even thousands of coefficients), influencing the convergence rate of the algorithm. Second, the acoustic echo paths are time-variant systems (depending on the temperature, pressure, humidity, and movement of objects or bodies), requiring good tracking capabilities for the echo canceler. Third, the input of the adaptive filter (i.e., the far-end signal) is mainly speech, which is a nonstationary and highly correlated signal that can influence the overall performance of the adaptive algorithm. Since the conventional NLMS algorithm uses a constant step size parameter to control its performances, a compromise should be made when choosing this value. It is known that a large value implies a fast convergence rate and tracking, while a small value leads to low misadjustment and good robustness features. Since in AEC, there is a need for all these performance criteria, the step size should be controlled; therefore, a VSS version represents a more reliable choice. In addition, for real-world AEC applications, it is highly desirable to use nonparametric algorithms, in the sense that no information about the acoustic environment is required. Nevertheless, we should note that the applications of the VSS-NLMS algorithms are not limited only to the framework of AEC. Different other application areas can be explored in the field of communication systems, such as the carrier phase recovery, e.g., see [26; 27] and the references therein. Following this Introduction, in Section 2, we present the data reuse NLMS algorithm, but using a computationally efficient method to exploit the data reuse process, thus keeping almost the same complexity as the conventional version. Then, in Section 3, we develop a VSS-NLMS algorithm based on the data reuse approach. Simulation results in the context of AEC are provided in Section 4 in order to support the performance of the proposed VSS-NLMS algorithm. Finally, several conclusions are summarized in Section 5. The acronyms used in this paper are provided in Table A1 in Appendix A, while the notation and symbols are summarized in Table A2 from the same section.