esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Improvements in the Development of Soft Grippers
## 3 Dexterous Manipulation
### Framework for Characterizing Dexterous Soft Grippers and Their Performance
#### 3.4.2 Performance Characterization
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Real Dexterity Evaluation** & **Potential Dexterity Evaluation** & **Other Evaluations** \\ \hline \multirow{4}{*}{[17]} & - & Opening of a bottle and handling of the bottle and its lid. Stabilization of the weight of the bottle with a horizontal grip. & Primitive movements that can be performed by hand & Measuring the blocked force during actuation \\ \hline \multirow{4}{*}{[16]} & - & Operating a syringe & \multirow{4}{*}{Realization of the primitives described in [56].} & \multirow{4}{*}{Maximum object pullout force test} \\  & - & Writing with a pen & & \\  & - & Opening a padlock & & \\  & - & Breaking an egg & & \\  & - & Assembling two Lego bricks & & \\ \hline \multirow{4}{*}{[12]} & - & Manipulation of objects from within the hand & \multirow{4}{*}{-} & \multirow{4}{*}{-} \\  & - & Rotation between fingers of a piece of wood, a brush, and a cutter & & \\ \hline \multirow{4}{*}{[13]} & \multirow{4}{*}{No tests} & - & Kapandji test & Grasping different objects of different masses \\  & & - & Feix test & Testing different force disturbances \\ \hline \multirow{4}{*}{[55]} & \multirow{4}{*}{No tests} & - & Grasping and twisting manipulation of different objects of different shapes and masses & Measurement of bending force and deflection of the fingertip \\  & & - & Primitive movements that can be performed by hand & — \\ \hline \multirow{4}{*}{[20]} & \multirow{4}{*}{No tests} & - & Primitive movements that can be performed by hand & Measurement of the bending angle and blocking force under actuation \\  & & - & & Workspace analysis \\ \hline \multirow{4}{*}{[21,88]} & \multirow{4}{*}{-} & \multirow{4}{*}{Rotation of a Rubik’s cube} & - & Possible gripping modes & Measuring the blocked force during actuation \\  & & - & Rotation and translation of a can in the 3 main axes & \multirow{4}{*}{-} \\  & - & Writing & - & Kapandji test \\  & - & Oscillation of a thin rectangular object & & \\  & - & Kapandji test & & \\  & - & Feix test & & \\ \hline \multirow{4}{*}{[22]} & \multirow{4}{*}{No tests} & - & Four possible gripping modes & Fingertip and joint forces measured under actuation \\  & & - & In-grasp manipulation & \\ \hline \end{tabular}
\end{table} 
Table 3: Performance characterization of dexterous soft grippers.
# A Model for Simulating the Upward Flow of a Viscous Fluid in a Fracture Network
###### Abstract  
Fluid migration in a fracture network plays an important role in the oil accumulation mechanism and hence is key to oil exploration. In this study, we build a model by combining one-dimensional (1D) Navier-Stokes equations, linear elastic equations, and energy equations, and validate the model by reproducing the thickness profile of a fluid-driven crack measured in an experiment. We employ this model to simulate the upward flow of viscous fluid in a single fracture during hydrocarbon migration. The simulation suggests that the parameters of both the fluid and the surrounding rock matrix, as well as the boundary condition imposed on the fracture outlet, affect the upward flow in the fracture. We then extend our model from the single fracture to the bifurcated fracture and the fracture network by maintaining homogeneous pressure and mass conservation at the connection of the channels. We find that the increase in network complexity leads to an increase in the inlet pressure gradient and inlet speed, and a decrease in the outlet pressure gradient and outlet speed. The effective area where the fluid is driven upward from the inlet to the outlet is offset toward the inlet. More importantly, the main novelty of our model is that it allows us to evaluate the effect of inconsistencies in individual branch parameters, such as matrix stiffness, permeability, temperature, and boundary conditions, on the overall upward flow of viscous fluid. Our results suggest that the heterogeneity enforces the greater impact on the closer branches.  
model; fracture network; upward flow; viscous fluid; heterogeneity +
Footnote †: journal: Applied sciences
## 4 Results and Performance Evaluation
### Simulation Results  
The simulation was performed with a maximum of 20,000 data packets that could be exchanged during the experiment, and the quantity of the data packets received during various simulation rounds was recorded. The device speed and direction of the communication device decided the mobility. The simulation was carried out based on the parameters given in Table 1. The constraint of the transmit amplifier was important so that even weak signals could be captured with low power transmission. The heterogeneous sensors were used by providing the amount of initial energy for the sensor nodes, and the cluster head selection in every round was directly proportional to the initial energy of the node. The packet size was the most significant characteristic for reliability and transmission delay. The super nodes had and consumed a high amount of energy compared with the normal and advanced nodes, which would balance the consumption of energy by all the nodes and increase the lifetime of the network.  
Figure 6 shows the comparison of the suggested TIGSO-EDS technique with the EEHC, EECT, and S-EECP methods based on the calculations and displays. TIGSO-EDS  
Figure 4: Comparison of alive nodes (base station located at \(50\times 50\)).  
Figure 5: Comparison of alive nodes (base station located at \(100\times 250\)).  
improved the data packet delivery ratio by 5.66%, 3.84%, and 2.50% with EEHC, EECT, and S-EECP, respectively. When the base station was located at 100 \(\times\) 250, the success data was evaluated, and it was observed to have worse performance than the previous scenario. The same was observed with the other models, and the comparison results are presented in Figure 7.  
The energy consumption per round for the proposed TIGSO-EDS method and other different methods were calculated, and they are shown below in Figure 8. A comparison of the energy consumption with TIGSO-EDS with the base station located at 50 \(\times\) 50 is shown in Figure 8. From the analysis, it is shown in Figure 9 that the proposed TIGSO-EDS method improved the energy consumption per round by 19.96%, 16.85%, and 12.71% when compared with EEHC, EECT, and S-EECP, respectively.  
Figure 6: Comparison of data packets received at the base station (BS) located at 50 \(\times\) 50).  
Figure 7: Comparison of data packets received at the base station (BS) located at 100 \(\times\) 250).  
The comparison of energy consumption per round for the proposed method TIGSO-ADC method with EEHC, EECT, and S-EECP was calculated with a base station located at 100 \(\times\) 250, as shown in Figure 8. From the analysis, it is shown in Figure 9 that the proposed TIGSO-EDS method improved the energy consumption per round by 18.86%, 15.99%, and 12.16% when compared with EEHC, EECT, and S-EECP, respectively.
## 2 Materials and Methods
### Literature Review and Significance of Research
#### 2.1.4 Review on Carrier Phase Multipath Estimation  
Details and characteristics of carrier-phase multipath errors can be found in [14; 15; 36]. Similar to the pseudorange multipath estimation, there are triple-frequency approaches for carrier-phase multipath estimation. The equation applied by [PERSON] and [PERSON] (2017) is similar to the one in the pseudorange multipath (i.e., Equation (1)):  
\[M_{\Phi_{ijk}}=\lambda_{k}^{2}\big{(}\Phi_{i}-\Phi_{j}\big{)}+\lambda_{j}^{2} \big{(}\Phi_{k}-\Phi_{i}\big{)}+\lambda_{i}^{2}\big{(}\Phi_{j}-\Phi_{k}\big{)} \tag{6}\]  
where the subscripts \(i,j,\) and \(k\) denote the three frequencies of \(L_{i}\), \(L_{j}\), and \(L_{k}\). \(M_{\Phi_{ijk}}\) denotes the _ijk_ triple-frequency combined carrier-phase multipath error, \(\Phi\) denotes the carrier-phase measurement at a frequency, and \(\lambda\) denotes the wavelength of a frequency. The authors of [37] used two ionosphere-free linear combinations (IF) from three frequencies to assess BDS carrier-phase errors:  
\[DIF(\varphi_{1},\varphi_{2},\varphi_{3})=IF(\varphi_{1},\varphi_{2})-IF( \varphi_{1},\varphi_{3}) \tag{7}\]  
This triple-carrier combination is geometry-free and ionosphere-free. The determined carrier-phase error contains the carrier-phase multipath error, measurement noise, and the linear combined carrier-phase ambiguity constant. The error determined by this approach is very \"noisy\" because it contains three carrier-phase measurements and three linear combinations (i.e., two iono-free combinations and one differencing). Moreover, the availability of triple-frequency is not common at CORS stations. This approach is not suitable for the work of this paper because carrier-phase multipath errors and measurement noise of individual frequencies at a CORS station cannot be estimated (i.e., the data quality information determined using the triple-carrier combination method is not suitable for single- or dual-frequency GNSS users).  
Signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR) are the ratios of the signal/carrier strength to the noise level. SNR/CNR are affected by the multipath effect and atmospheric and hardware noise [15; 36]. Therefore, SNR/CNR can be used to estimate/mitigate carrier-phase multipath errors [36; 38]. Raw SNR/CNR data and measurement noise change with the change in satellite elevation angle. Special treatment is needed to make the raw SNR/CNR data represent the multipath-contaminated component. Special treatments include, for example, the use of elevation-dependent nominal SNR/CNR [36] and wavelet analysis [39]. Therefore, raw SNR/CNR data cannot be used to represent the data quality of carrier-phase measurements directly.
# A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter
## 3 Proposed Algorithm Based on the Maximum-Crentropy UKF
### Principle of Maximum Correntropy  
The maximum entropy principle, also known as the maximum information principle, is a criterion for selecting the statistical characteristics of random variables that are the most consistent with an objective situation. The probability distribution of a random quantity is difficult to measure, and generally, only its various mean values, such as mathematical expectation and variance, or the values known under certain conditions. The distribution of these values can be varied or even infinite. Generally, one distribution will have the largest entropy. We select the distribution with the maximum entropy as the distribution of the random variable is an effective method and criterion [19].  
Given two random variables, \(\mathbf{X}\) and \(\mathbf{Y}\), the correlation entropy between them is defined as [20; 21]  
\[V(\  
Because only a limited part of the data in the actual system is known and it is difficult to accurately obtain joint probability density, the average value of the sample is usually used to calculate the estimated value of the correlation entropy:  
\[V(\mathbf{X},\mathbf{Y})=\frac{1}{N}\sum_{i=1}^{N}\kappa(x_{i},y_{i}) \tag{24}\]  
Through substitution of Equation (23) into Equation (24) and expanding of the Gaussian kernel function with the Taylor expansion, we derived  
\[V(\mathbf{X},\mathbf{Y})=\sum_{n=0}^{\infty}\frac{(-1)^{n}}{2^{n}\sigma^{2n}n!}E\Big{[} (\mathbf{X}-\mathbf{Y})^{2n}\Big{]} \tag{25}\]  
The above formula shows that electron entropy is obtained from the weighted sum of all even-order moments of the random variable \((\mathbf{X}-\mathbf{Y})\). Therefore, Formula (25) contains high-order moment information. At the same time, the correlation entropy, \(V(\mathbf{X},\mathbf{Y})\), receives the maximum value of 1 only when \(\mathbf{X}=\mathbf{Y}\). With selection of the right \(\sigma\), higher-order moments can be incorporated into the signal-processing algorithm to more accurately describe the higher-order characteristics of the non Gaussian distribution. This is the main advantage of correlation entropy in processing non Gaussian noise.
## 5 Experiments and Analysis
### Recursive Feature Elimination (RFE)  
This study also employed the RFE technique to further refine the selection of features for predicting the \"final_result\" target class. The RFE analysis ranked the importance of each feature based on its ability to improve the accuracy of the prediction model. The top-ranked features identified by the RFE analysis included \"code_module\", \"date_registration\", \"module_presentation_length\", \"region\", \"imd_band\", \"studied_credits\", and \"total_click\", all of which had a ranking score of 1. This indicates that these features were the most critical in predicting a student's final result in a course. The RFE analysis further strengthened the importance of \"total_click\" as the most important feature in predicting the \"final_result\" target class, as it was also identified as the most significant feature in the RFI analysis.  
In general, the outcomes of the RFI and RFE analyses offer significant perspectives on the characteristics that hold the most significance in forecasting a student's ultimate outcome in a given course. The aforementioned discoveries can facilitate the creation of precise prognostic models and enable educators to promptly recognize students at risk of academic failure, thereby furnishing them with the requisite assistance to excel in their coursework. The amalgamation of RFI and RFE methodologies is a proficient strategy for selecting features in classification predicaments.
## 4 Results and Discussion
### Results of Random Component Analysis of the Models
#### 4.5.5 Results of the Hypothesis of Homoskedasticity of the Random Components of the Residuals  
The results of the Breusch-Pagan test are shown in Table 15.  
Analyzing Table 15, it should be noted that only the linear model meets the criterion set. The computational value of the \(\chi^{2}\) statistic is less than the critical value of \(\chi^{2}\alpha\). Therefore, there is no basis for rejecting hypothesis H\({}_{0}\) about the constancy of the variance of the model's residuals. For the power model, the value of \(\chi^{2}\) is greater than the critical value of \(\chi^{2}\alpha\). Therefore, there are grounds to reject the hypothesis of constancy of variance in favor of hypothesis H\({}_{1}\). Therefore, the power model was rejected.  
The verification shows that only the linear model described by Equation (21) is characterized by high cognitive quality.  
The linear model shows that the explanatory variable X5 has the greatest impact on brake pad wear. The coefficient for X\({}_{5}\) is 5 times greater than the coefficient for X\({}_{2}\). The correct interpretation of the regression model requires compliance with the ceteris paribus condition, i.e., changing only one explanatory variable. The presented model shows that each braking of the X\({}_{2}\) translates into 44 km of brake pad durability. Each increase in kilometers traveled <20% GW increases the durability of the brake pads by a further 234 km.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 1 Introduction
### Tower Companies
#### 1.1.1 RAN-Sharing Types and Operator Differentiation  
One way of classifying RAN sharing could be as active and passive. Under passive sharing, operators share the costs associated with deploying the physical infrastructure, such as the costs of masts, towers, and the space to deploy this infrastructure. It results in fewer sites relative to multiple standalone networks. On the other hand, active sharing involves sharing the electronic equipment such as transmitters and receivers. Enabling active sharing can improve energy savings as the active components contribute to around 60 percent of a site's energy consumption (see Figure 2). Beyond reducing the OPEX and CAPEX, it would enable an operator to focus on its core business and further specialize in service provisioning to end users.  
While both active and passive sharing have clear benefits in terms of CAPEX and OPEX for the network operators, an assessment of the likely effects on competition is often necessary as competition authorities often raise concerns about the extent to which such sharing could impair an operator's ability to differentiate its network quality and services. Of the two, passive sharing has a limited impact on competition as only the physical location of each site is shared [11] while the active sharing of the infrastructure leads to operator differentiation issues due to the deeper level of network sharing involving active equipment used for transmitting, receiving, and processing signals. This link between physical equipment and service differentiation makes it difficult for operators to actively participate in a RAN-sharing arrangement.  
For a successful implementation of such a system, operators need _(i)_ to be able to differentiate their RAN from competitors' RANs as much as possible, _(ii)_ lower the cost associated with sharing resources, and _(iii)_ be as independent as possible from the other MNOs using the same shared infrastructure. It calls for an underlying architecture that provides orchestration services across different RAN instances belonging to various operators. We call this system ClusterRAN--a system capable of providing control of RAN instances so that operators can maintain independent operations wherever possible while leveraging cost savings from shared resources.
## 3 Research Methods
### Steps of BasicVSR++ Restoration
#### 3.1.1 Propagation  
The propagation module determines the way that the algorithm uses the video sequence information. Current propagation methods are mainly local propagation, one-way propagation, and two-way propagation [19]. BasicVSR++ is based on two-way propagation, which can realize multiple information propagation optimizations and improve the information aggregation ability. However, the algorithm has difficulty distinguishing between unwanted shadows and beneficial details in the case of unknown degradation types. Therefore, a pre-cleaning module is needed to process the degradation information before information propagation. The residual network is used to process the input and output of the pre-cleaning module. The variable \(x_{i}\) represents the \(i\)-th frame image of the input sequence, and C represents the image cleaning module.  
\[\widetilde{x_{i}}=C(x_{i}) \tag{1}\]  
Figure 2: Research process and structure.  
The cleaned sequence information is input into the BasicVSR model for quality reconstruction, as shown in Equation (2), where \(S\) represents the VSR algorithm.  
\[\{y_{i}\}=S(\left\{\overset{\sim}{x}_{i}\right\}) \tag{2}\]  
To constrain the output of the image pre-cleaning module, the cleaning process loss, \(L_{clean}\), and fidelity loss, \(L_{out}\), are introduced as shown in Equation (3), as follows:  
\[\begin{array}{l}L_{clean}=\sum\limits_{i}\rho(\overset{\sim}{x}_{i}-d(z_{i}) )\\ L_{out}=\sum\limits_{i}\rho(y_{i}-z_{i})\end{array} \tag{3}\]  
where \(z_{i}\) represents the high-resolution image, \(d\) represents the down-sampling operation, and \(\rho\) represents the Charbonnier loss [30], as follows:  
\[\rho=\sqrt{x^{2}+\varepsilon^{2}} \tag{4}\]  
where \(x\) represents the difference between the predicted value and the true value, and \(\varepsilon\) represents a constant, usually \(10^{-3}\).
# Improvement of Fast Simplified Successive-Cancellation Decoder for Polar Codes
## 3 Proposed Method
### Performance Analysis
This theorem has been proven in [10; 11] using mathematical induction. (e) For an R-other node that does not satisfy the FC, the bit-decision SC decoding is performed at the leaf node. If a constituent code is activated, the \(M_{i}\) corresponds to the number of all leaf nodes of this constituent code. Due to the control of the Algorithm 2 node state initialization( ), \(M_{i}\geq 2^{1}\) for the R-0, R-1, or REP code, and \(M_{i}\geq 2^{2}\) for the SPC or the R-other node. When an R-other node does not satisfy the FC and its child node is not a constituent code, and the leaf node of the R-other node is activated, \(M_{i}=2^{0}\) corresponds to the bit process. The distribution and number of constituent codes are affected by the code length and code rate of the polar code. When the polar codes are constructed with the method in [15], the R-0 code is more likely to be the first segment, and \(M_{0}\geq 2^{1}\). Summarizing the above five cases, we can conclude that \(\mathrm{P_{Fast-ISS}}\left({{{u_{0}^{M_{0}-1}}\
eq\ u_{0}^{M_{0}-1}}}\right)\) of Fast-ISSS decoding is no worse than \(\mathrm{P_{SC}}\left({{{u_{0}^{M_{0}-1}}\
eq\ u_{0}^{M_{0}-1}}}\right)\) of the bit-decision SC decoding for the first segment. Therefore, we have  
\[p\left({{C_{0}}}\right)=p\left({{C_{0}^{\prime}}}\right) \tag{10}\]  
For \(1\leq i<K\), the segment error probability \(\mathrm{P}\left({{{\hat{u}}_{M_{i}}^{M_{i}+M_{i+1}-1}}\
eq\ u_{M_{i}}^{M_{i}+M_{i+1}-1}} \middle|{{{\hat{u}}_{0}^{M_{i}-1}}=u_{0}^{M_{i}-1}}\right)\) can be analyzed in a similar way as for Equation (10); we also have  
\[p\left({{C_{i}}}\right)=p\left({{C_{i}^{\prime}}}\right)\ for\ 1\leq i<K. \tag{11}\]  
According to Equations (10) and (11), we have  
\[\mathrm{P_{M+B}}\left({{{\hat{u}}_{0}^{N-1}}\
eq\ u_{0}^{N-1}}\right)=\mathrm{ P_{B}}\left({{{\hat{u}}_{0}^{N-1}}\
eq\ u_{0}^{N-1}}\right) \tag{12}\]  
\(\square\)  
Therefore, the FER of the Fast-ISSS algorithm is not degraded compared to the original SC algorithm.
# Designing Audio Equalization Filters by Deep Neural Networks
## 4 Baseline Methods
### Steepest Descent Method  
FIR filters can be obtained by applying the Steepest Descent algorithm to audio equalization [24; 25]. The first step of the algorithm consists in defining a target impulse response:  
\[d=[\underbrace{0\quad\ldots\quad 0\quad 1\quad 0\quad\cdots\quad 0}_{L+\mathcal{T}-1} ]^{T}, \tag{9}\]  
where \(L\) is the length of the impulse response, \({}^{T}\) denotes the transpose operator, and \(\mathcal{T}\) is the number of taps of the FIR filters. The filters are adapted to match the target impulse response:  
\[y_{m}=h_{m,1}*g_{1}+h_{m,2}*g_{2}+\cdots+h_{m,\mathcal{S}}*g\mathcal{S}=\sum_ {s=1}^{\mathcal{S}}h_{m,s}*g_{s}\approx d, \tag{10}\]  
where \(g_{s}\) are the FIR filters, \(h_{m,s}\) are the impulse responses and \(y_{m}\) is the output at the \(m\)-th microphone.  
The optimization goal is achieved by minimizing the cost function:  
\[J=||d_{\mathcal{M}}-y||_{2}, \tag{11}\]  
where \(y\) is the vector containing the output impulse response \(y=[y_{1},y_{2},\ldots,y_{\mathcal{M}}]\) and \(d_{\mathcal{M}}\) is the vector containing \(\mathcal{M}\) times the target impulse response. The inverse system \(g\) can be obtained by:  
\[g=H^{+}d_{\mathcal{M}}, \tag{12}\]  
where \(H^{+}\) is the pseudo inverse of the system matrix \(H=\begin{bmatrix}H_{1,1}&H_{1,2}&\cdots&H_{1,\mathcal{S}}\\ \vdots&\vdots&\vdots&\vdots\\ H_{\mathcal{M},1}&H_{\mathcal{M},2}&\cdots&H_{\mathcal{M},\mathcal{S}}\end{bmatrix}\) and its elements \(H_{m,s}\) are \((L+\mathcal{T}-1)\times\mathcal{T}\) circular matrices composed by the impulse responses \(h_{m,s}\)[24]:  
\[H_{m,s}=\begin{bmatrix}h_{m,s}(0)&0&\cdots&0\\ h_{m,s}(1)&h_{m,s}(0)&\cdots&0\\ \vdots&\ddots&\ddots&\vdots\\ h_{m,s}(L-1)&\cdots&\vdots&\vdots\\ 0&h_{m,s}(L-1)&\ddots&\vdots\\ 0&\cdots&0&h_{m,s}(L-1).\end{bmatrix} \tag{13}\]  
If \(H\) is full rank, then \(H^{+}=H^{-1}\).  
The FIR filters are calculated adaptively: the gradient of the cost function \(\
abla J\) is given by:  
\[\
abla J=-2H^{T}d_{\mathcal{M}}+2H^{T}Hg \tag{14}\]  
and the inverse system can be obtained by:  
\[G(k+1)=G(k)-\mu\
abla J, \tag{15}\]  
where \(\mu\) is the step-size.