esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Fit Modeling for Temperature Mitigation in
## 3 Results
### Advection  
The numerical fit model does not account for local horizontal advection. This was assumed to be the primary reason for the difference between the SCAM6 results and the current results.  
Warm air flowing into a mitigated area will slowly adjust to a lower temperature. If lesser solar radiation has been incident on the ground from calcite plumes, the ground will slowly cool the air. A similar process occurs whenever fronts pass into a new area, and it happens at night as the ground radiates heat into space and the surface air cools. Therefore, around the edges of a plume of calcite, the numerical fit model will have a smaller benefit than predicted in Figure 6. This section is to develop a process to account for it.  
[PERSON] et al. [35] analyzed the effects of warm continental air flowing over a cold sea. Air temperatures were simulated and compared to various aerial measurements. Heat exchange in marine environments is different than for continental areas, but the patterns are useful to understand. The simulation showed that an internal boundary layer forms immediately at the change of thermal properties. With a wind speed of 5 m/s, within 30 km, the surface wind had fully adjusted to the sea temperature. However, it took 120 km for the full height of the PBL to adjust to steady state temperatures. Higher wind speed caused the process to require longer distances.  
Turbulence is a factor in the spread of heat throughout the PBL. The surface roughness of cities causes high turbulence, so it is expected that this will shorten vertical heat transfer times.  
Measured temperatures in urban heat islands (UHI) at various wind speeds can be used to anticipate the effect of wind on cooled areas. [PERSON] et al. [36] correlated wind in New York City with the incremental temperatures from UHI. Below 5 m/s, the full UHI effect was observed. At twice that wind speed, the UHI effect was cut in half.  
Therefore, it is expected that the benefit of mitigation will vary with wind speed. There will be a buffer distance under the edges of plumes that will vary with the wind speed. However, in typical conditions, near the center of the city, the full effect of the cooling from the calcite should be felt at the surface. In contrast, SCAM6 is a single-column model, so it finds an average temperature over a cell which smooths out temperatures. On average, the  
Figure 6: Temperatures (K) in the base and mitigated cases at the 992 hPa level using numerically fitted temperatures for humid conditions. Dramatically reducing the solar radiation has a corresponding effect on air temperatures.  
temperatures below the plume may be more similar to those found by SCAM6, but where it matters in the densely populated areas, it will be much cooler such as from the numerical fit model.
# Adaptive Packet Coding for Reliable Underwater Acoustic Communications
###### Abstract  
This work investigates adaptive random linear packet coding (RLPC) for reliable underwater acoustic (UWA) communications. Our goal is to minimize the total transmission time of data blocks by adjusting the packet coding rate. We first consider the application of RLPC with the conventional automatic repeat request (ARQ) scheme. We dynamically adjust the coding rate to fit the time variations of UWA channels by choosing the optimal number of packets in each transmission. The optimal number of packets in each transmission is obtained based on a dynamic programming (DP) algorithm according to the feedback messages, which contain the number of successfully transmitted packets in the last transmission and the channel state information. Furthermore, considering the long propagation delay of UWA communications, we propose a modified juggling-like ARQ (-ARQ) for the RLPC scheme, for which the duration of each transmission can be adjusted based on the characteristics of RLPC. A two-step DP algorithm is proposed to find out the optimal solutions for this case. Simulation results show that the proposed schemes can improve the throughput efficiency and reduce the outage probability.  
underwater acoustic communications; random linear packet coding; dynamic programming; juggling-like ARQ 202214712[[https://doi.org/10.3390/rs14194712](https://doi.org/10.3390/rs14194712)]([https://doi.org/10.3390/rs14194712](https://doi.org/10.3390/rs14194712))
## 4 Discussion  
The scale-adjusted ROPs showed accuracy below 2 mm root means square error and 4 mm absolute maximum error, respectively that is enough to measure the instantaneous displacement for a bridge of large span showing large displacement.  
The sequential measurement based on the image matching showed results within almost one pixel, whereas in the \(Z\) direction of the camera depth, the result is approximately 2-3 times bigger due to the asynchronous time between stereo images and the uncertainty of the two cameras parameters for the calculation of the 3D spatial coordinates.  
The laser tracker is high precision, but it is costly and cannot represent the full-body twist of the span. The proposed method has the potential of being able to simultaneously  
Figure 10: Consecutive deformation patterns during the loading of the train measured by the proposed method. The 3D space (**top**) and X-\(Z\) plane (**bottom**) displacements are magnified by 10 and 100 times, respectively, and color represents time. (**a**) First train; (**b**) second train; (**c**) third train.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Train**} & \multicolumn{2}{c}{**\(X\) Direction**} & \multicolumn{2}{c}{**\(Y\) Direction**} & \multicolumn{2}{c}{**\(Z\) Direction**} \\ \cline{2-7}  & **RMSE** & **Abs. Max. Error** & **RMSE** & **Abs. Max. Error** & **RMSE** & **Abs. Max. Error** \\ \hline
1 st & 0.5 & 1.4 & 0.4 & 0.9 & 1.1 & 2.7 \\ \hline
2 nd & 0.5 & 0.8 & 0.4 & 0.7 & 2.3 & 3.5 \\ \hline
3 rd & 0.3 & 0.5 & 0.3 & 0.7 & 1.7 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Difference in displacement between the photogrammetry and the laser tracker (unit: mm).  
measure deflection and torsion over the entire span of a bridge despite the occurrence of millimeter-level errors. Therefore, it can be used to check the overall condition of the bridge before the detailed diagnosis using a precise measuring instrument such as electrical strain gages.  
We believe the proposed method can be used a compensation method to the laser tracker, especially for denser 3D displacement measurement over a girder, or for the areas where a laser tracker is not installed.
## 2 Background: Deep Neural Networks  
An Artificial Neural Network (ANN), henceforth called Neural Network (NN), is a mathematical model inspired by the biological neural networks. However, the NN model is too simple to replicate the behavior of its biological counterpart, faithfully. An NN is formed by interconnected nodes, as in a graph, that are organized in layers (see Figure 2). A layer of input nodes receives signals from the outside, which are then processed by some intermediate layers called hidden layers. The result is finally obtained by the last layer, also called output layer. An NN with more than three hidden layers is defined in literature as a Deep Neural Network (DNN) [27]. In short, the DNNs/NNs are mainly black-box model representation of a given function. That is, the model and its parameters are learned by finding the transfer function (composed of multiple layers of neurons, weights, and activation functions) from input to output through an extensive training process.  
The graphs of NNs are direct, i.e., the connections are oriented, and can be acyclic or cyclic. If the NN is acyclic, it is called feedforward, and the output depends only on the current input. If instead, the NN is cyclic, it is defined recurrent, and the output also depends on the previous inputs. Recurrent NNs are, therefore, models with state/memory.  
The nodes of NNs are the neurons, graphically and mathematically described in Figure 3 and Equation (1). A neuron receives \(n\) inputs \((x_{1},x_{2},\ldots,x_{n})\) and returns a scalar output \(y\). For a given neuron, the inputs are multiplied with the weights \((w_{1},w_{2},\ldots,w_{n})\) and summed together with a bias term \(b\). A non-linear function \(\sigma(\cdot)\), called activation function, is then applied to determine the output of the neuron. Common activation functions are Rectified Linear Unit (ReLU), Sigmoid or Hyperbolic tangent.  
\[y(\mathbf{x})=\sigma\bigg{(}\sum_{n=0}^{N-1}\mathbf{x}[n]\mathbf{w}[n]+b\bigg{)} \tag{1}\]  
Figure 2: An abstract example of a neural network.
## 2 Mathematical Model of Vortex Compressor Profile
### Performance Analysis of the Working Chamber
It can be inferred that the shorter the leakage line length, the fewer the number of radial engagement points, thus reducing the volumetric loss caused by gas leakage through the radial gap. The simplified radial clearance leakage model is shown in Figure 2. The radial leakage height is scroll-tooth height H, and the width at the clearance is changing state, that is \(\overline{d}r\), \(d_{\text{r1}}\), \(d_{\text{r2}}\). Assuming that the average value of the radial clearance is \(d_{\text{r}}\). The gas flow at the radial gap conforms to the one-dimensional steady flow. The energy equation is shown in Equation (8). \[dh+\frac{1}{2}dv^{2}=0 \tag{8}\]  
The tangential leakage velocity gas from neighboring compression cavities is shown in Equation (9) as follows. \[V_{i}=\sqrt{2(h_{i}-h_{i+1})} \tag{9}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Leakage Line** & **Stroke** & **Radial Engagement Points** \\ \cline{2-4}  & **Length/mm** & **Volume** & **Radial Engagement Points** \\ \hline Numerical value(D) & 1010.65 & 236,756.6 & 6 \\ Numerical value(B) & 757.99 & 236,756.6 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Geometric properties related parameters. The tangential gas leakage per unit rotary angle is shown in Equation (10). \[\frac{dm_{lri}}{d\theta}=\frac{a_{\tau}\rho_{i}(\theta)\overline{d}_{\tau}H}{w} \sqrt{2[h_{2}(\theta)-h_{i+1}(\theta)]} \tag{10}\]  
where \(\rho_{i}(\theta)\) is the instantaneous density of the gas in the working chamber, \(w\) is the rotational speed of the main shaft, \(\alpha_{\tau}\) is flow coefficient, \(H\) is leakage length.
# An Integrated Capacity Allocation and Dynamic Pricing Model Designed for Air Cargo Transportation
## 2 Materials, Methods, and Models  
The solution to this optimization problem comprises three primary components: capacity allocation, demand forecasting, and dynamic pricing. In order to determine the model inputs, the existing literature was extensively reviewed, and expert opinions were sought through interviews with the employees of one of the leading air cargo companies in the sector. The interrelation of these three components is illustrated in Figure 2. The model commences with the capacity allocation component, which aims to allocate resources in an efficient manner. The model employed in this stage is derived from the capacity allocation model for the air cargo industry, as published in 2022 [7]. The capacity obtained in this stage serves as the input for both demand forecasting and dynamic pricing. In the subsequent stage, the output of the demand forecasting model serves as the input for pricing. The third stage of the problem resolution is the dynamic pricing of the capacity. The price obtained from the pricing model serves as the input for the capacity allocation section in subsequent periods. Consequently, the objective was to furnish dynamic price recommendations with an integrated structure and an iterative process.
## 3 Dynamic Object Identification and Tracking  
Radar sensors are growing in popularity across various industries, including automotive vehicles, robotics, and industrial applications. The radar sensor used in this work is the IWR6843 ISK (Texas Instruments, Dallas, TX, USA), a radar technology that utilizes short-wavelength electromagnetic waves to transmit and receive reflections when they encounter an object. This capability enables the radar to determine the range, velocity, and angle between the radar and the object. One of the key advantages of this radar is its ability to measure in millimeter ranges, allowing for the detection of movements in small fractions of millimeters. As a result, it can identify human actions swiftly and make faster decisions to reach the goal.  
Figure 1 presents a flow diagram of the object recognition and tracking system, designed to allow the robot to navigate safely. The system comprises four main stages: the first stage involves obtaining measurements from the radar and publishing the data on a topic. The second stage focuses on object recognition using clustering algorithms and filtering techniques to minimize radar errors. The third stage tracks the previously identified objects through predictive methods, such as the Kalman filter. Finally, the objects are visualized and distinguished with different colors for better representation of objects in the bird's-eye view.
## 4 Conclusions  
This paper presents an automated preliminary design approach for radial inflow turbines, as the first part of the integrated design and optimization approach. The GA is introduced to explore the design space defined by loading coefficients, flow coefficients, and rotational speeds. The aim of this design approach is to obtain the best design scheme with high aerodynamic performance under specified constraints and reduce the dependency on human experiences for designing a radial inflow turbine. The following conclusions can be drawn from this study.  
(1) The mean line design method for radial inflow turbines is presented for getting the aerodynamic performance under given structure parameters. In addition, the radial inflow turbine T-100 published in the literature is used to verify this method. It is indicated that the verification result shows good agreement with the T-100 original data, which proves the correctness and accuracy of the present mean line design method.  
(2) The automated preliminary method is developed by combining the mean line design with GA to explore the design space for obtaining the best design scheme of radial inflow turbines. The T-100 turbine is redesigned by using the automated preliminary method to check the effectiveness. It shows that the total-to-static efficiency of the T-100 re turbine increases by 1.0% and the rotor weight decreases by 0.35 kg (26.7%) compared with the T-100 original design. Therefore this method can not only reduce the dependency on a designer's expertise and guarantee the optimal design at the preliminary design stage under specified constraints but also make a good foundation for the later 3-D numerical optimization.  
Figure 12: Comparison of Mach number contour at the mid-span section.  
(3) The CFD results of T-100 and T-100 re turbines are examined and compared to the T-100 turbine experimental data. It shows that the total-to-static efficiency is a little higher than that of the experiment results due to ignorance of the windage loss and the flow non-uniformity at the volute outlet in the CFD simulations, but the trend of both results are in good agreement. In addition, the performance of T-100 re turbine is better than that of T-100 turbine under most conditions.  
Conceptualization, Q.D.; Methodology, Q.D. and S.S.; Software, L.F. and H.L.; Writing-Original Draft Preparation, S.S.; Writing-Review & Editing, Q.D. and Z.F.  
This study is partially supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20130201130005).  
The authors declare no conflict of interest.
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
###### Abstract  
Soil stabilization using Portland cement is a widely adopted technique. Previous research has demonstrated that molasses, which contains sugars, enhances the reaction between cement and aggregates. This study investigates the impact of adding molasses to soil stabilized with Ordinary Portland Cement (OPC) on geotechnical properties. Expansive clay soil samples from Taru Jabba, District Nowshera, Pakistan, were treated with various combinations of molasses and cement. The concentrations of each stabilizer were varied at 0%, 4%, 8%, and 12% by dry weight of the soil. Additionally, the soil was treated with constant molasses contents of 4%, 8%, and 12%, while varying the cement content at 4%, 8%, and 12% by dry weight. Geotechnical tests, including Proctor compaction, Atterberg limits, Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR), and swelling potential, were conducted to assess the effects of the stabilizers. The results indicated that the addition of molasses improved soil strength, mitigated shrinkage cracks, and reduced brittleness. Specifically, the CBR value increased from 3.2% in the native soil to 12.3% with 12% molasses and 12% cement. The Plasticity Index (PI) decreased from 14.23% to 8.12%, and the CBR swell value reduced from 9.66% to 3.82%. Furthermore, the UCS of the stabilized soil increased by 64.7% compared to the untreated soil after a 7 day curing period.  
s +
Footnote †: journal: Applied sciences  
supar cane molasses; soil stabilization; geotechnical properties; expansive clay soil; stabilization mechanism
# Predictor Selection for Bacterial Vaginosis Diagnosis Using Decision Tree and Relief Algorithms
###### Abstract
Only a few investigations have been carried out regarding machine learning methods applied in the field of bacterial vaginosis. Some of the works that motivated this research are described below. The purpose of [PERSON] et al. 's [8] work was to discover the most significant features of BV and to apply some classification algorithms to diagnose the BV. Twenty feature selection algorithms, in combination with nine classification algorithms, were applied using Weka. The precision (proportion of correctly classified instances), the recall (proportion of true positive and false negative), and the number of attributes reduced were some of the metrics they considered. They determined that the functional tree (FT) and WrapperSubSetEval algorithms produced the best combination. [PERSON] and [PERSON] [7] applied random forests (RFs) and LR, in combination with Relief, to diagnose BV. To rank the features, they considered the purity increase in the node as a measure in the RFs. For LR, the features were ranked according to their mean coefficient magnitude in all cross-validation datasets divided by their standard deviation. Relief was implemented to calculate a third feature ranking. A table with the top 15 important features ranked by classification accuracy was obtained. Features like _Aeroccocus_, _Atopobium_, _Dialister_, _Eggerthella_, and _Gardurella_ were categorized as the most important. Their results and the results of this research were compared. [PERSON] [9] evaluated CFS (correlated-based feature selection) with three machine learning algorithms--C4.5 (decision trees), IB1 (an instance-based learner), and naive Bayes--on artificial and natural datasets to test the hypothesis that algorithms based on a correlation between attributes improved the performance of the classifiers. The accuracy, CPU time, and the number of features reduced were used as the performance measures. The experiments on artificial domains showed that the methods were able to identify interactions between features. They concluded that a superior algorithm does not exist for all machine learning problems, but in many cases, the CFS algorithm can enhance the performance of classification algorithms, while at the same time achieve a reduction in the features used. This work is organized as follows: Section 1 provides the introduction and the current state of the art; Section 2 explains the details regarding the used dataset, methods, and techniques; in Section 3, the results obtained by all phases of the experimental design are provided; and finally, the conclusions are provided in Section 4.
## 3 Proposed System
### System Configuration  
The proposed system comprises a supporter device equipped with stretch sensors and a wireless module, and a PC. The system configuration is shown in Figure 2. The system wirelessly transmits data from stretch sensors on the supporter to the PC. For calibration, users perform predetermined knee flexion and valgus movements at the start of system use. Subsequently, users train normally, and the system continuously estimates knee flexion and valgus angles using machine learning from the sensor data. The system evaluates ACL injury risk based on the estimated angles and notifies users performing high-risk movements, encouraging training to reduce ACL injury risk.  
Figure 2: Proposed system.  
The supporter device is shown in Figure 3. The device uses three C-STRETCH MEASU-SURE stretch sensors from Bando Chemical Industries, Ltd., Kobe, Japan, equipped with wireless modules [28]. Sensor data are transmitted wirelessly to the PC in millimeters. The supporter, a ProFits knee supporter (PS271) from PIP Co., Ltd., Osaka-shi, Japan [29], does not overly restrict knee movement. Based on knowledge from rehabilitation and sports science experts, the stretch sensors are positioned to capture knee flexion and valgus states. Three stretch sensors are placed on the back of the supporter, with one on the patella side for flexion movement, and one each on the inner and outer sides of the knee for valgus movement.
## 3 Preprocessing
### Dimensionality Reduction
#### 3.2.2 High Correlation Filter  
A high correlation score between two features indicates that they carry related information. This high correlation between two independent variables is termed multicollinearity. Multicollinearity can drastically damage the performance and accuracy of some regression models such as linear and logistic regression models. A high correlation is defined as an r-value greater than 0.6; if the correlation between a pair of feature columns is greater than 0.6, one of the two features should be dropped. The exception to this rule is that feature columns with a high correlation to a target feature should be retained. When the high correlation filter was applied to the _baseline gene dataset_, \(31,893\) feature column pairs were flagged as having multicollinearity. Dropping the features with the lowest correlation to the tumor target features from each pair and accounting for duplicate gene features yielded a _high-correlation-filtered dataset_ of 46,688 gene features for classification modeling.  
Figure 3: Histogram—gene variance distribution.  
Figure 2: Dimensionality reduction techniques.  
Three variations of the high correlation filter were also used to produce additional reduced feature datasets. For the first variation, the low variance filter was applied to the results of the high correlation filter, yielding a reduced dataset of \(26,963\) gene features. The second variation involved retaining only gene feature columns with moderate to high correlations (\(|r|\geq 0.5\)) to the 18 tumor target features. This resulted in a reduced dataset of 3928 gene features. The third variation entailed compiling the top 250 correlated gene column features for each of the 18 tumor target features. This yielded a dataset of 4082 gene features.