esa-sceva/satcom-chunk-collection huggingface dataset:
# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
## 3 Automatic Noise Detection and Removal
### Coding Samples to Replace Noisy Samples  
We have focused on the method for locating every sample containing speckle noise. We then use a recursive LP model-based interpolator to replace the signals distorted by the noise, one by one. The basic idea of an LP model-based interpolator for noise removal can be expressed as follows: \"The present value of a speech sample can be approximated by a weighted linear combination of past values of several speech samples\" [27]. Samples irrevocably distorted by speckle noise are discarded and the gap to the left or the right is interpolated. Firstly, the available (clean) samples in the past of a noise pulse are used to estimate the linear predictor coefficients for the linear prediction model of the signal. Afterwards, the estimated model parameters and the samples on the left of the gap are used to interpolate the polluted sample.  
For quasiperiodic signals, such as voiced speech, there are two types of correlation structures that can be utilized for an interpolation:  
(1) the short-term correlation, which is the correlation of each sample with the \(P\) immediate past samples \(x(m-1)\), \(x(m-2),\ldots,x(m-P)\).  
(2) the long-term correlation, which is the correlation of a sample \(x(m)\) with \(2\text{Q}+1\) similar samples, a pitch period \(T\) away \(x(m-T+\text{Q}),\ldots,x(m-T-\text{Q})\).  
Due to the disturbances of speckle noise that usually contaminates a relatively small fraction \(\alpha\) of all the samples, the length of the samples to be interpolated is short, and the purpose is to remove the influence of speckle noise. Therefore, we do not use the periodic structure for interpolation, but only the interpolation of short-time correlation. That is the linear prediction of a sample \(\hat{x}(m)\), based on \(P\) past samples. This can be defined as:  
\[\hat{x}(m)=\sum_{k=1}^{P}a_{P}(k)\cdot\hat{x}(m-k) \tag{11}\]  
where \(\hat{x}(m)\) is the encoded sample at the location of the speckle noise, \(P\) is the coding order and \(a_{P}(k)\) are predictor coefficients calculated using the Levinson-Durbin algorithm.  
Figure 6: Laser captured speech signal, with the average short-time energy and kurtosis for (**a**) the raw-detected signal and (**b**) the extracted-signal by decorrelation.  
Note that each sample involved in the interpolation is the latest one after the code replacement. The advantage of using an LP model-based interpolator to replace contaminated samples is that it avoids truncation of the signal and keeps the signal consistent. It is not only effective in predicting the content of the signal, but also significantly improves the auditory character of the speech.
### Contribution Preview  
In this paper, we considered that the BS has no energy limits; the size of the unfolded area is unknown; low initial energy nodes are located close to the BS, and high powered sensor nodes are located distant. With this model in mind, we have proposed novel algorithm well recognized as the EZ-SEP protocol. In EZ-SEP, normal nodes will communicate with the base station directly, while cluster members in advanced nodes will pass the sensed data to the cluster head, and aggregated records will be sent to BS by the cluster head. Furthermore, cluster head election at advanced nodes will depend on a threshold value based on the node's residual energy. This model affects the reduction of the cluster head power consumption and the increase in the sensor network service life.  
In the first round, each node, as in LEACH, selects a random number in the range from 0 to 1. If the selected number is less than the threshold, then this sensor node is elected as the cluster head for this contemporary round. In the end of the 1 st round, the remaining residual energy of each node is revised. If a node has higher remaining residual energy, then this sensor node will have a better chance of becoming a cluster head for the next round. This method increases the lifetime of the first node to its death, which is an important factor in the stability of the network. Moreover, the behavior of EZ-SEP will be measured with the previous parent algorithm taking into consideration such evaluative cases as (1) change in the position of the base station (2) change in density of nodes in the field and (3) change in the initial energy of the nodes. Simulation results show that the proposed EZ-SEP algorithm improves the network stability and also extends the lifespan over existing cluster-based heterogeneous parent protocols.
## 2 Materials and Methods
### Cell Viability Assessment: MTT  
Primary osteoblasts were seeded at a concentration of 50,000 cells/mL/well in a 24-well plate. Biomaterial granules were re-suspended in complete DMEM at a concentration of 3 mg/mL and diluted to the desired concentration (3, 1, 0.3, 0.1 mg/mL). One ml of granule suspension was then added to the wells 24 h after seeding, and cells were grown in complete medium for 3 or 7 days. The medium was replaced with fresh solution three times a week. An MTT colorimetric assay, based on the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzoolium bromide dye, was performed according to [PERSON] [19]. Briefly, 100 \(\upmu\)L of MTT substrate at the concentration of 5 mg/mL was added to each well,and the plate was incubated for 4 h at 37 \({}^{\circ}\)C. A solubilization solution was prepared by dissolving a 10% solution of 100X Triton (Sigma Aldrich) in 0.1 N HCl acid isopropanol. One ml solubilization solution was added to each well and incubated for 15 minutes on orbital shaker. Absorbance was then read at 570 nm by a spectrophotometer Infinite F200 microplate reader (Tecan Group Ltd., Mannendorf, Switzerland).
# Calculation of the Rearfoot Angle Representing Flatfoot from Comparison to the Cavanagh Arch Index
## 5 Conclusions  
A study to analyze flatfoot using Rear Foot Angle (RFA) measurement has been presented. This present study has produced an equation that correlates between AI and RFA as presented in Equation (2), which can be quantified as the minimum RFA. The flatfoot is identified if RFA \(\geq\) 4.3\({}^{\circ}\). The correlation equation is very important for estimating the reduction in the degree of flatfoot when using orthotic shoes from RFA measurements, which cannot be determined by the AI value from typical footprint measurements.  
The method of determining flatfoot based on RFA measurement as an alternative method has not been obviously observed. Determination of conventional flatfete using the arc index measurement and the Cavanagh method requires a few measurement steps. One of the measurements is the foot area after the footprint or foot scanning technique. The RFA method is simpler because it only measures the angle of the back of the foot when someone is standing upright. The finding of this study is evidently essential as RFA measurement has a significant correlation with AI and offers a more effective method for flatfoot detection. Conducting diverse works on this study with a larger number of participants and more valid methods of RFA measurement is highly recommended in the future as RFA is acknowledged as a more effective diagnosis measurement method for flatfoot.  
Conceptualization, D.B.W. and A.S.; methodology, Y.U. and D.B.W.; software, Y.U. and A.S.; validation, D.B.W., A.S., and W.C.; formal analysis, Y.U. and D.B.W.; investigation, Y.U. and D.B.W.; resources, D.B.W. and A.S.; data curation, Y.U. and D.B.W.; writing--original draft preparation, Y.U. and D.B.W.; writing--review and editing, A.S., W.C., and K.A.P.; visualization, Y.U.; supervision, D.B.W. and W.C.; project administration, A.S.; funding acquisition, Y.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research was collaborated and supervised by [PERSON] from the Department of Orthopedic, RSUD Tugurejo Hospital, via medical-related discussion, data acquisition, and data processing. The authors thank [PERSON] for their support during the study. The authors also thank the subjects who were involved and participated in this study.  
The authors declare no conflict of interest.
## 3 Methodology
### Model Performance Evaluation  
In this study, coefficient of determination (\(R^{2}\)), root mean square error (\(RMSE\)), and mean absolute percentage error (\(MAPE\)) were used as indicators to assess the model performance [45]. The equations are written as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-o_{i}\right)^{2}}{\sum_{i=1}^{n}\left( y_{i}-\bar{y}\right)^{2}} \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(o_{i}-y_{i}\right)^{2}} \tag{8}\]  
Figure 3: (**a**) A research framework of BO-LSTM neural network model for county-level winter wheat yield estimation; (**b**) Bayesian optimization of LSTM hyperparameters process.  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-o_{i}}{y_{i}}\right| \tag{9}\]  
where \(n\) is the number of samples, and \(y_{i}\) and \(o_{i}\) are the measured and predicted values of winter wheat yield, respectively. \(R^{2}\) measures how well a model explains the variance in the dependent variable, with higher values indicating a better fit. _RMSE_ is used to assess the deviation between measured and predicted values. The smaller the value, the smaller the deviation is between the measured and predicted values of the model; the higher \(R^{2}\), the smaller _RMSE_ is and the better the model. For the _MAPE_ range [0, +\(\infty\)), the smaller the _MAPE_, the better the model simulation results are. Compared to _RMSE_, _MAPE_ is equivalent to normalizing the error at each point, reducing the effect of absolute error from individual outliers.
## 4 Discussion
### Classification of the Tested Joints  
According to EC3 Part 1-8 [26], joints can be classified as rigid joints, nominal pinned joints, and semi-rigid joints in terms of their rotational stiffness. The detailed classifications are as follows: for a rigid connection, \(K_{i}\geq k_{\mathrm{B}}E_{\mathrm{b}}\mathrm{I}_{\mathrm{b}}/L_{\mathrm{b}}\); for a pinned connection, \(K_{i}\leq 0.5E_{\mathrm{b}}\mathrm{I}_{\mathrm{b}}/L_{\mathrm{b}}\); and for a semirigid connection, \(0.5E_{\mathrm{b}}\mathrm{I}_{\mathrm{b}}/L_{\mathrm{b}}<K_{i}<k_{\mathrm{b}}E _{\mathrm{b}}\mathrm{I}_{\mathrm{b}}/L_{\mathrm{b}}\). Note that in these classifications, \(E_{\mathrm{b}}\) is the elastic modulus of the steel beam, \(I_{\mathrm{b}}\) is the second moment of area for the steel beam, \(L_{\mathrm{b}}\) is the beam span, and \(k_{\mathrm{b}}=8\) (for a braced frame) or 25 (for a non-braced frame). According to the flexural resistance, joints can be divided into full-strength joints, nominal pinned joints, and partial-strength joints. The detailed classifications are as follows: for a full-strength joint, \(M_{u}\geq M_{\mathrm{bp}}\); for a pinned joint, \(M_{u}\leq 0.25M_{\mathrm{bp}}\); and for a partial-strength joint, \(0.25M_{\mathrm{bp}}<M_{u}<M_{\mathrm{bp}}\). Note that in these classifications, \(M_{u}\) is the flexural resistance of the joint and \(M_{\mathrm{bp}}\) is the designed plastic moment resistance of the steel beam. The classification of the tested joints is shown in Figure 16. The results show that the anchored blind-bolted extended end-plate joints to CFST columns with a strong beam are classified as semi-rigid and partial-strength joints, whereas those with a weak beam are in the range of semi-rigid and full-strength.
# Workspace and Productivity: Guidelines for Virtual Reality Workplace Design and Optimization
## 3 Results
### Study 2a  
#### 3.2.1 Remote Working  
Study 2 explored immediate environment, broader environment, and movement and posture when executing teamwork and task work activities. Each knowledge worker is here represented by a mock name.  
The immediate environment here displayed unique features in relation to desk material and shape; objects and artifacts found on each desk; the number of monitors used; and the type of space created for \"availability\" (i.e., being available for co-workers). Overall, most participants worked from a desk that was wooden, rectangular, and roughly 1 m long and 0.6-0.8 m wide (cf. Figure 1a). If the employee did not utilize their standard workspace, they were usually on the go. Items that employees frequently needed and could be easily stored were often found on desks, including electronics (cf. Figure 1b), writing materials and beverages, as well as photos and art to create a personal and pleasant environment (cf. Figure 1c).  
Figure 1: (**a**). Examples of the immediate environmental features of knowledge workers: use of tables and desks. (**b**). Examples of the immediate environmental features of knowledge workers: use of monitors and electronic devices. (**c**). Examples of the immediate environmental features of knowledge workers: customization of workspace.  
Broader Environment  
In this study, remote workers spent most of their time at home office desks, designed for predominantly sedentary contexts. Windows were often positioned nearby, but room brightness varied. Pets, children, and other people often entered workspaces. Interactions with them were initiated visually, aurally, or mediated through messages. While most work was carried out at desks, other spaces of interest included the balcony, kitchen counter, sofa/chairs, and an exercise bike (cf. Figure 2a-c).  
Movement and Posture  
Although half of the participants had rollable chairs, movement was minimal; a wide range of postures was observed, including twisting, and leaning forwards and backwards. Focused work sessions could be as short as a few minutes, 15 or 20 min, and reached up to 45 min to slightly over an hour. Any longer sessions were extremely rare. Meetings were different, as they often lasted at least an hour, while breaks were frequent and often involved leaving workspaces (cf. Figure 3a-c).  
Figure 2: (**a**). Examples of observed visual, aural, or mediated interactions. (**b**). Examples of window setup in offices. (**c**). Examples of additional, non-traditional, workspaces utilized.
## 3 Methodology
### Model Performance Evaluation Criteria  
In regression, the model error is the difference between the actual sample and the predicted values. The following evaluation criteria were used to determine the model's performance:  
1. Mean absolute error (MAE): this is the arithmetic mean of the absolute values of the errors, representing the deviation from actual values. The computational equation is given as follows: \[MAE=\frac{\sum_{i=1}^{n}|y_{i}-y_{i}|}{n}\] (3)
2. Root mean squared error (RMSE): a popular performance evaluation metric for models. It can be interpreted as the standard deviation of the forecast errors.  
3. Mean absolute percentage error (MAPE): the total of each period's individual absolute prediction error divided by the actual values. The RMSE and MAPE are computed as follows, respectively: \[RMSE=\sqrt{\left(\frac{1}{n}\right)\sum_{i=1}^{n}\left(y_{i}-y_{i}\right)^{2}}\] (4) \[MAPE=\frac{\sum_{i=1}^{n}\left|y_{i}-y_{i}\right|}{n}\times 100\] (5)  
Execution time: the time it takes for the model to learn and predict based on the given input variables, where \(\hat{y}\) is the predictive value at time point \(i\) and \(y_{i}\) is the actual value. The execution time in this paper is the computational time when SHAP is applied to the trained models. Lower MAE, RMSE, and MAPE values indicate better performance of the model in predicting energy demand and ambient temperature.
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Comparative Analysis of MAC Layer-Based UWAC Full-Duplex Protocols
#### 3.4.4 Transmission Range
This will save resources while also reducing network end-to-end latency and increasing throughput.
# Application of Deep Learning to Construct Breast Cancer Diagnosis Model
###### Abstract  
(1) Background: According to Taiwan's ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SCDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients.  
Keywords: breast cancer; neural network; support vector machine; deep learning; convolutional neural network +
Footnote †: journal: Medical Imaging
# Measurement of Head-Related Transfer Functions: A Review
## 6 Post-Processing of Measured HRTFs  
The raw HRTFs should be post-processed to remove the reflections, extend the low-frequency components, and compensate for influences of measurement systems. Note that the order of post-processing processes varies in the literature, and some processes may be performed several times. Moreover, some studies proposed to remove perceptually irrelevant components in HRTFs [106], and correct measurement errors caused by the equipment misalignment [137] and temperature fluctuations [174]. The important post-processing steps/methods are described below.
## Nomenclature and Symbols  
\begin{tabular}{l l} BP & Brake power, \\ Q\({}_{\text{esh}}\), & Exhaust gas losses \\ YGME & Yellow Grese methyl ester \\ FAME & Fatty ester methyl easter \\ HRR & Heat Release rate \\ ANN & Artificial neural network \\ DICI & Direct ignition combustion engine \\ PDSO & Pithecellobium dulce seed oil \\ CO\({}_{2}\) & Carbon dioxide \\ NOx & Nitrogen oxide \\ ASTM & American Society for Testing Materials \\ RPM & Revolution per minute \\ DAQ & Data acquisition systems \\ \(\text{SBF}_{20}\) & 20\% of spirulina microalgae biodiesel and 80\% diesel \\ \(\text{SBF}_{40}\) & 40\% of spirulina microalgae biodiesel and 60\% diesel \\ \(\text{SBF}_{60}\) & 60\% of spirulina microalgae biodiesel and 40\% diesel \\ \(\text{SBF}_{80}\) & 80\% of spirulina microalgae biodiesel and 20\% diesel \\ \(\text{SBF}_{100}\) & 100\% of spirulina microalgae biodiesel \\ \(\text{Q}_{\text{w}}\) & Cooling water losses \\ Q\({}_{\text{un}}\) & Unaccounted losses \\ TP & Transesterification process \\ GHGs & Greenhouse gases \\ CP & Cylinder pressure \\ RSM & Response surface method \\ SME & Soyabean methyl ester \\ FFA & Free fatty acid \\ HC & hydrocarbon \\ CR & Compression ratio \\ LHV & Lower heating value \\ ANOVA & Analysis of variance \\ \end{tabular}
# Computational Dynamics of Multi-Rigid-Body System in Screw Coordinate
## 2 Kinematics of a Multi-Rigid-Body System
### Relative Displacement, Velocity and Acceleration of Each Joint
\begin{pmatrix}0\mathbf{V}_{n}^{p}\\ 0\end{pmatrix} ^{T}\right]_{6p\times 1}^{T} \tag{12}\]  
in which \({}_{0}\mathbf{V}_{n}^{p}\) is the vector of the absolute velocity screw of the \(n\)th joint in the single kinematic chain \(loop(p)\). Equation (10) is the screw matrix of the multi-rigid-body system. Equation (11) is a velocity vector composed of the relative angular speeds of each generalized joint with respect to its previous link. Equation (12) is the velocity vector of each end joint in the absolute coordinate frame. With Equation (6), when the kinematics of the terminal link \(j_{n}\) is specified, its inverse kinematics of the multi-rigid-body system can be derived,  
\[\mathbf{S}^{T}\mathbf{S}\mathbf{\omega}=\mathbf{S}^{T}\mathbf{V} \tag{13}\]  
When \(\left|\mathbf{S}^{T}\mathbf{S}\right|\!\!=0\), the multi-rigid-body system is either redundantly actuated or in singularity configuration. Otherwise, we gain the angular speeds of all joints from Equation (13):  
\[\mathbf{\omega}=\left[\mathbf{S}^{T}\mathbf{S}\right]^{-1}\mathbf{S}^{T}\mathbf{V} \tag{14}\]  
where \(\left[\mathbf{S}^{T}\mathbf{S}\right]^{-1}\mathbf{S}^{T}\) is called the pseudo inverse of screw matrix \(\mathbf{S}\). Equation (14) represents the inverse velocity of the multi-rigid-body system. Based on Equation (14), we get the steps to calculate the inverse kinematics of a multi-rigid-body system below:  
1. With the initial condition consisting of position angles \({}_{n-1}\mathbf{\theta}_{n}^{p}(t=0)\) and structure parameters \(R\), \(r\), and \(l_{n}^{p}\), the unit screw matrix \(\mathbf{S}(0)\) of the multi-rigid-body system at time \(t=0\) can be gained first. 2. Substituting \(\mathbf{S}(0)\) into Equation (14) and with the twist matrix \(\mathbf{\xi}_{n}^{p}\) of the end joint \(j_{n}^{p}\) in Equation (7) being known, the relative angular velocity of each joint \(\mathbf{\omega}(0)\) at time \(t=0\) can be derived. 3. With the initial conditions consisting of position and velocity, the parameters of \(\mathbf{\xi}_{n}^{p}(k)\) from Equation (10) and \(\mathbf{\omega}(k)\) from Equation (14) could be updated by steps 1 and 2 where \(t=0\) is replaced by \(t=k\Delta t\): \[{}_{n-1}\mathbf{\theta}_{n}^{p}((k+1)\Delta t)={}_{n-1}\mathbf{\theta}_{n}^{p}(k \Delta t)+\Delta t\mathbf{\omega}(k)\] (15) where \(k\!\!=1,2,\cdots\). With the unit screw matrix \(\mathbf{S}(k)\), the position vector, \({}_{0}\mathbf{r}_{n}^{p}\), and orientation vector, \(\mathbf{e}_{n}^{p}\), of each joint can be deduced from Equation (14).