esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Clearance Influences on Cage Motion and Wear in Ball Bearings
## 3 Effect of Cage Guiding Clearance
### Wear of the Bearing Cage  
The wear of the cage-guiding surfaces from the disassembled test bearing after the same operating with the three different guiding clearances cycles is shown in Figure 6. It can be seen that the degrees of cage wear are significantly different through observation of wear width and color on the guiding surfaces. The smaller the guiding clearance is, the more serious the wear of the guiding surface, as shown in Figure 6.  
Compared with the cage motion spectra and trajectories in Figure 5, it can be found that when the cage motion irregularly, especially when \(f_{i}\) takes a high proportion because of the frequently and greater impact transmitted from the inner ring to the cage due to higher contact forces with small clearances, the wear of the guiding surface is serious, where the wear width is 3.5 mm in Figure 6a,b.  
When the guiding clearance is large enough, the regular cage motion with the main component \(f_{c}\) will appear, because larger guiding clearance will weaken the impact between the cage and the inner ring, and the cage unbalance forces will play a more and more important role. Accordingly, it is nothing serious for the cage guiding surface with larger clearance in which the wear width is 2 mm in Figure 6c.  
Figure 7 shows the wear of cage pockets with different guiding clearance. It is obvious that the wear of the cage pocket in Figure 7b is the most serious with obvious black band marks, while the wear is not obvious in Figure 7a,c.  
By comparing with the cage motion spectrum in Figure 5, it can be found that \(2f_{c}\) is the main frequency in cage motion when \(C_{g}=0.2\) mm, because the intermittent and recurring rub-impact between the cage pocket and balls is severe, which aggravates the wear.  
Figure 6: Wear of cage guiding surface with different guiding clearances. (**a**) \(C_{g}=0.1\) mm, (**b**) \(C_{g}=0.2\) mm, (**c**) \(C_{g}=0.4\) mm.
## 2 Methodology and Scope  
Both modified and un-modified binders, including base binder, Styreneic Block Copolymers (SBS) modified binder, rubber modified binder, and warm mix binders (Sasobit, Evotherm M1 and Evotherm 3G), were included. The contents of Sasobit and rubber were 1%, 2%, and 3%, and 5%, 10%, and 15% by weight of asphalt binder, respectively. The ratios of SBS, Evotherm M1m and Evotherm 3G were 4.5%, 0.6%, and 0.6% by weight of asphalt binder, respectively. The aggregate consisting of three stockpiles with basalt was used as the major aggregate source. To better control aggregate gradation, three aggregate stockpiles were further sieved in a laboratory based on each individual sieve size. Mix design was validated in the laboratory to determine the optimum asphalt content using the Superpave method.  
Several fatigue tests for both asphalt binders and asphalt mixtures were conducted, consisting of binder MSCR, a mixture semi-circular bending- Illinois flexibility index test (SCB-IFIT), and a mixture indirect tensile test (IDT). Other binder testing, including performance grading, viscosity, and binder monotonic fracture tests were also performed as the reference. Table 1 shows a summary of the all the laboratory mixture and binder tests included in this paper.  
The sensitivity of the MSCR test to the binder type and additive content was evaluated. The binder with the best fatigue resistance and the control base asphalt were further chosen to conduct the asphalt mixture fracture test. A ranking between the MSCR test and mixture fatigue test was further compared to evaluate if the MSCR test was well correlated with mixture performance. Short- and long-term aging were applied on both binders and mixtures to address the effect of aging. For all the asphalt binder and mixture tests, three repetitions were used, except for the bending beam rheometer (BBR) test, which includes two test replicates as specified in the AASHTO specification.
## 3 UAV Power Supply and Charging Mechanisms
### Battery Powered UAVs
#### 3.1.2 Laser Beam Charging  
Another technique for battery recharge is laser beam in-flight recharging [48]. This technique involves recharging the UAV battery during the flight, without making it land for the battery recharging or replacement, as is the case in the battery swapping technique. To recharge a UAV's battery using the laser beam technique, an external energy source feeds the laser beam at a particular frequency and wavelength. The laser is directed towards a photo-voltaic (PV) cell on the UAV that converts the laser beam into electrical energy required to recharge the UAV's batteries. To ensure maximum output from laser beam charging technique, a power tracking device is used to identify the maximum energy point in the laser beam. In order to maintain power transfer from the laser transmitter, the UAV must operate at low heights and occasionally in a restricted area while using the laser-beam in-flight recharging strategy [48]. Furthermore, as each UAV requires its own dedicated laser source, the number of UAVs must be reduced in order to maintain a fair operational cost [46] The laser beam is an effective method for charging UAVs, and it can be applied to both rotary-, and fixed-wing UAVs. However, the use of laser beam recharging methodsrestricts the operation of the UAV to a limited area in order to ensure that the recharging link is active.
## 2 Engineering Background  
The deep shaft under study is located in the Wutongzhuang mine in Handan, China. The depth of the inlet tunnel of the mining area is about 739 m. To reduce the loss of cold transmission, the distance of cold transmission should be the shortest, which can be achieved by setting a refrigeration station on the ground in the vertical direction of the underground heat exchange station. However, the ground in this location is the protected farmland, which is unsuitable for setting the refrigeration station, while 450 m to the northwest is an industrial sub-plaza of the mine, where a ground refrigeration station can be set up, and the correspondence relation in the vertical direction is shown in Figure 1a. The chilled water supply pipe and its installation borehole between the surface cooling station and the underground high and low-pressure heat exchange station are constructed with an oblique borehole passing through the strata. The vertical depth and the length of the chilled water supply pipe through the strata are approximately 630 m and 700 m.  
Figure 1: Cont.  
Except when affected by the heat transfer from the stratum with a certain temperature distribution, the cold pipe must have a certain mechanical strength to withstand various stress impacts and damages. Therefore, the cold transmission pipe needs to be designed to meet both the adiabatic insulation needs of the heat transfer and cold loss in the cold transfer process and the compressive strength requirements of the stress damage. The preliminary design of the composite insulation pipe structure is shown in Figure 1b. The layer from the inside to the outside is cold steel pipe, solid pipe filling cement slurry layer, cold pipe protection casing, solid pipe plugging cement slurry layer, and drilling, respectively. The cold transmission pipe is divided into the water supply and return pipe. As the cooling load is greater, the diameter of the cold transmission pipe is larger, which will inevitably cause the casing and borehole diameter to be larger. The larger diameter of cold transmission pipe and borehole brings huge difficulty and risk to drilling slant holes through deep strata while increasing the construction cycle. Therefore, the cold transmission system is composed of four cold transmission pipes: two are water supply pipes and two are return pipes, as shown in Figure 1. The heat transfer of the cold pipe through the deep strata directly affects the size of the loss in the process of cold-water transmission, and if the temperature rise is too large, it directly affects the cooling effect downhole.
## 2 Methodology of the Study
### Data Partition and Pre-Processing  
The data partition is necessary to avoid overfitting the model, a situation in which the model performs well with the training data but may not be accurate with the new data. Hence, the data were divided among a training set (70%) and a test set (30%) to check the performance of the ML models. In data pre-processing, the data were transformed and scaled to improve the performance of the ML models. The data transformation can be performed based on the identified distribution of the input features, and it can be implemented to improve the model performance. Furthermore, feature scaling helps to avoid the dominance of high-range features over lower-range features. Particularly, feature scaling will improve classification techniques which are based on the similarities and distances between data, such as K-Nearest Neighbours, Support Vector Machine, and Neural Networks [6].
###### Abstract  
Physical-layer key generation technology requires information reconciliation to correct channel estimation errors between two legitimate users. However, sending the reconciliation signals over the public channel increases the communication overhead and the risk of information leakage. Aiming at the problem, integrated secure communication schemes using non-reconciled keys have attracted extensive attention. These schemes exploit channel coding to correct both inconsistent keys and transmission error bits. Meanwhile, more redundant code bits must be added to correct errors, which results in a lower secure transmission rate. To address the problem, we analyze the merit of channel correlation between non-reconciled key generation and secure transmission. Inspired by this, we propose a non-reconciled physical-layer keys-assisted secure communication scheme based on channel correlation. First of all, the signal frame is designed to make use of channel correlation between non-reconciled key generation and secure transmission. Based on the channel correlation, non-reconciled keys are then generated from the wireless channel to encrypt transmitted data. Moreover, an adaptive coding algorithm based on the equivalent channel is presented to encode the data bits before encryption, to guarantee reliable transmission. Finally, theoretical analysis and simulations demonstrate the significant performance of the proposed scheme in terms of low bit error ratio and high secure transmission rate.  
physical-layer security; key generation; channel correlation; information reconciliation 202241167. [[https://doi.org/10.3390/e24081167](https://doi.org/10.3390/e24081167)]([https://doi.org/10.3390/e24081167](https://doi.org/10.3390/e24081167))
## 3 Numerical Simulation
### Prediction of the Influence of \(A\), \(S\), \(L\) on \(\Phi\) Value
#### 3.2.1 Study of the Functional Relationship between \(A\) and \(\Phi\)  
To study the functional relationship between \(A\) and \(\Phi\), with settings \(S=2\) mm and \(L=4\) mm, \(A\) contained three parameters: \(d\), \(D\), and \(N\). Changing these three parameters separately gave the results displayed in Figure 6. It can be seen that as long as \(A\) was consistent, the relationship between \(A\) and \(\Phi\) was linear and consistent. The relationship was given by:  
\[\Phi=0.33A-0.025 \tag{4}\]  
When changing \(N\) and \(L\), the relationship between \(A\) and \(\Phi\) was also linear. As \(N\) increased, the slope increased, and the variation of \(S\) and \(L\) only affected the parameter term of the linear relationship. With increasing \(A\) and \(N\), the non-uniformity of the flow distribution in each channel increased.  
Therefore, when \(A\) was small, a relatively uniform flow distribution could be expected because a certain amount of momentum always required a corresponding friction to balance. For larger \(A\), momentum could not balance the friction effect, resulting in non-uniform flow distribution. Thus, selecting the appropriate \(A\) could improve the uniformity of the pressure drop in the parallel channels. This was consistent with the conclusion in refs. [18; 23].  
Figure 6: Study of the relationship between \(A\) and \(\Phi\). (**a**): Change \(D\) in parameter \(A\); (**b**): Change \(d\) in parameter \(A\); (**c**): Change \(D\) and \(d\) in parameter \(A\); (**d**): Change \(N\) in parameter \(A\).
## 3 Results  
In this section, we first test the robustness of MPM and discuss the classification results of two machine learning algorithms on the synthetic data set. First, to test the robustness of the MPM in the presence of noise, we introduce system and measurement noise with different variances. Figure 4 shows an estimate of poles for various values of delay and gains and the effect of noise with different variances on the estimation of poles, indicating that the estimates from MPM are robust to noise and disturbances. For all the data generation, we assume that system and measurement noises have the same variances.
### Relevance of the Study  
The current methods of transmission line protection employed in most countries, including South Africa, either use costly communication links or backup protection systems to operate reliably. This study aims to provide further information and reassurance on current research suggesting that boundary protection using non-unit information is a viable replacement of unit protection and an improvement on non-unit protection systems. One of the biggest benefits of a theoretical study before practical implementation is that most scenarios can be tested without the possibility of causing damage to expensive switching and protection equipment if the system were to fail in its operation.
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### Method
#### 2.2.2. FPN Network Module for Extracting Multi-Meteorological-Element Fusion Features  
In the feature extraction module, to enhance the performance of small object detection, the model adopts the feature pyramid network (FPN) [24], a feature fusion mechanism that combines the multi-resolution scale prediction of the SSD network with the multi-resolution feature fusion architecture of Unet. Its operational mechanism is illustrated in Part A of Figure 3 and consists of three components: bottom-up convolution, top-down upsampling, and lateral feature fusion.  
Each convolutional layer in the FPN module comprises three operations: convolution, max pooling, and ReLU activation. In all convolutional operations, the kernel size is 3, with a padding of 1 and a stride of 1. All pooling layers use a kernel size of 2, with a padding of 1 and a stride of 1.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Stream Name** & **BIOM** & **O\({}_{2}\)-I** & **O\({}_{2}\)-II** & **CHAR** & **GAS-1** & **GAS-2** \\ \hline Temp (\({}^{\circ}\)C) & 25 & 250 & 250 & 800 & 1224 & 50 \\ Pres (bar) & 5 & 5 & 4 & 4 & 3 & 3 \\ Total flow rate (kg/h) & 1055 & 300 & 420 & 145 & 1624 & 1128 \\ Partial flow rates (kg/h) & & & & & & \\ BIOM & 1000 & & & & & \\ ORGANICS & & & & & & \\ CHARORG & & & & 145 & & \\ Water & 50 & 100 & 140 & & 518.8 & 22.23 \\ Oxygen & & 200 & 280 & & & 35.36 & 35.36 \\ Hydrogen & & & & & 541.7 & 541.7 \\ Carbon monoxide & & & & & 527.5 & 527.5 \\ Carbon dioxide & & & & & 0.009 & 0.0004 \\ Carbon oxysulfide & & & & & 0.1004 & 0.005 \\ Sulfur dioxide & & & & & 0.0004 & 0 \\ Ammonia & & & & & 0.0006 & 0 \\ Methane & & & & & 0.0004 & 0.0004 \\ Ethylene & & & & & & \\ Ethane & & & & & & \\ Nitrogen & & & & & & \\ Nitrogen oxide & & & & & & \\ Nitrogen dioxide & & & & & & \\ Inorganics & 5 & & & & & \\ Carbon & & & & & & \\ \hline
**Stream Name** & **H\({}_{2}\)** & **SYNGAS** & **W-HRB** & **STEAM-HRB** \\ \hline Temp (\({}^{\circ}\)C) & 137 & 91 & 50 & 207 \\ Pres (bar) & 4 & 3 & 5 & 5 \\ Total flow rate (kg/h) & 167 & 1295 & 1200 & 1200 \\ Partial flow rates (kg/h) & & & & & \\ BIOM & & & & & \\ ORGANICS & & & & & \\ CHARORG & & & & & \\ Water & 51.9 & 74.09 & 1200 & 1200 \\ Oxygen & & & & & \\ Hydrogen & 115.2 & 150.5 & & & \\ Carbon monoxide & & & 541.7 & & \\ Carbon dioxide & & & & & \\ Carbon oxysulfide & & & & & \\ Hydrogen sulfide & & & & & \\ Sulfur dioxide & & & & & \\ Ammonia & & & & & \\ Methane & & & & & \\ Ethylene & & & & & \\ Ethane & & & & & \\ Nitrogen & & & & & \\ Nitrogen dioxide & & & & & \\ Inorganic material & & & & & \\ Carbon & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Stream compositions, as calculated. for these production plants means shorter methanol transportation distances, along with smaller CO2 quantities generated during this transportation.
###### Abstract  
This study investigated the applicability of algae (_Hizikia fusiformis_, _Green gracilaria_, and _Codium fragile_) for removing heavy metals (Cd\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\)) from aqueous solutions. Among the algae, _H. fusiformis_ was chosen as a bioadsorbent and modified with NaOH and HCl. The results showed that the biosorption capacity of _H. fusiformis_ improved significantly after treatment with NaOH; however, _H. fusiformis_ modified with HCl did not achieve the expected value. The NaOH treatment enhanced the biosorption of metals on the treated _H. fusiformis_ because of the hydrolysis reaction producing carboxylic (-COOH) and hydroxyl groups (-OH). The kinetics for Cd\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\) biosorption well fitted to pseudo-first-order, pseudo-second-order, and Elovich models, with \(R^{2}\) of \(>\)0.994. The Freundlich model provided a good fit for the equilibrium biosorption of Cd\({}^{2+}\), Cu\({}^{2+}\), and Ni\({}^{2+}\) by both algae and the Langmuir model for Pb\({}^{2+}\). The maximum biosorption of metals was in the order Pb\({}^{2+}\)\(>>\) Cu\({}^{2+}\)\(\approx\) Ni\({}^{2+}\)\(>\) Cd\({}^{2+}\), with \(q_{\text{max}}\) of 167.73, 45.09, 44.38, and 42.08 mg/g, respectively. With an increase in the solution pH, metal biosorption was enhanced, and considerable enhancement was observed in the pH range of 2-4. Thus, _H. fusiformis_ is expected to be considered a superior candidate for metal biosorption.  
heavy metals; biosorption; modification; algae; _Hizikia fusiformis_
# Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
### ML with Low Quality Datasets: State-of-the-Art and Recent Progress
#### 1.1.2 Classical Approaches to ML with Incomplete Data  
The classical approach to supervised learning with missing or noisy data is to preprocess the available data in order to infer missing/corrupted values such that standard ML algorithms can be used on the corrected dataset [18; 19]. This imputation aproach can be based on statistical principles, such as computing the mean of available samples for missing features or more sophisticated estimators like the regression imputation, which has the advantage that it can take into consideration the correlations among various features. Other imputation methods are based on machine learning ideas by estimating missing entries through the \(k\)-nearest neighbor [20], Self Organization Maps (SOM) [21], multilayer or recurrent neural networks [22; 23], and others.  
A different approach is to avoid direct imputation of lost inputs and rely on a probabilistic model of input data, based for example on the Gaussian Mixture Model (GMM) and learning model parameters through the Expectation Maximization (EM) algorithm and building a Bayesian classification. The advantage of this approach is that class labels of input data is fully exploited which helps for a correct imputation of missing entries [19; 24]. However, the latter \"model-based\" approach has the disadvantage that it requires a good probabilistic data model, which is usually not available, especially for real-world applications such as those involving natural images.  
Recently, some approaches based on the low-rank property of the features data matrix were investigated and algorithms for data completion were proposed incorporating the label information [25; 26; 27]. Since the rank estimation of a matrix is a computationally expensive task, usually based on the Singular Value Decomposition (SVD), the obtained algorithms are prohibitive to solve modern machine learning problems with large datasets. Additionally, as in the case of the probabilistic generative models, none of these methods considered complex classifications functions. To overcome this drawback, more recently, a framework based on neural network architectures such as autoencoders, multilayer perceptrons and Radial Basis Function Networks (RBFNs), was proposed for handling missing input data by setting a probabilistic model, e.g., a GMM, for every missing value, which is trained together with the NN weights [28]. This method combined the great capability of NNs to approximate complex decision functions with the nice formulation of the GMM to model missing data. However, it inherited the drawbacks of GMMs, e.g., they are not well suited to higher-dimensional datasets.
## 2 System Architecture
### K-Nearest Neighbor Algorithm  
Although the packet rate is low (a few packets per minute), the transmission period should be as short as possible because multiple sensors are operating and collecting data simultaneously. Despite its large size, the sampling data do not cause congestion or exert a burden on the server. The network connection's status needs to be confirmed to ensure that the sensors are operating without power loss at the interface. If the transmitted data are lost, the sensor data must be repaired to compensate for the missing data. The k-nearest neighbor (KNN) algorithm is a simple method that can be applied to datasets requiring high precision, as it achieves highly accurate predictions. During the training phase, the entire dataset is utilized. Whenever missing values need to be predicted, the algorithm searches for the k most similar data points within the dataset. It then calculates the selected k-nearest training data points as the neighbors of the missing data. Finally, the data point  
Figure 3: Three QoS types for the MQTT protocol.  
that occurs most frequently among these k neighbors is used as the predicted value for the missing data. The k-nearest neighbor (KNN) algorithm was used to restore missing data [10]. Figure 4 depicts a schematic of the KNN algorithm with Manhattan distance.  
As shown in Figure 4, a new test sample to be classified is represented by a green solid circle placed at the center of two concentric circles. For the scenario where k = 1, the inner circle encloses only one blue square sample, representing Class 1. Therefore, the KNN algorithm classifies the green test sample (\(\bullet\)) into the same class as the blue square sample (\(\blacksquare\)), namely Class 1. For the scenario where k = 3, the outer circle encloses two red triangle samples, representing Class 2, and one blue square sample, representing Class 1. Therefore, the KNN algorithm classifies the green test sample (\(\bullet\)) into the same class as the red triangle sample (\(\blacktriangle\)), namely Class 2. When k = 3, the nearest neighbor is the red triangle sample because there are two red triangle samples and only one blue square sample within the outer circle (k = 3). Numerous unrelated samples will be included in the analysis if the selected value of k is too large. According to the example illustrated in Figure 4, four blue square samples and four red triangle samples will be considered if k > 3. The optimal k value to be used can be determined by comparing the training set with the test set.