esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparison of Ensemble and Dimensionality Reduction DEA Models Based on Entropy Criterion
## 1 Introduction
By contrast, the ensemble DEA approach does not make any assumptions, and for small datasets, trying out different input and output combinations and aggregating efficiency scores provide more reliable efficiency estimates than variable selection models. Part of the reason for the stability of ensemble DEA efficiency scores is that, even for small datasets, some DEA models in an ensemble will always satisfy the heuristic given in Equation (1a), which will increase the reliability of the ensemble efficiency scores due to model averaging. This stability of ensemble efficiency scores is illustrated by comparing ensemble scores with the results of models from [PERSON] and [PERSON]'s [16] study and using the entropy criterion. To compare the results, the dataset from [PERSON] and [PERSON]'s [16] study is used. The dataset consists of three inputs and four outputs from an Iranian gas company. The inputs are budget (\(x_{1}\)), staff (\(x_{2}\)) and cost (\(x_{3}\)). The outputs are customers (\(y_{1}\)), the length of the gas network (\(y_{2}\)), the volume delivered (\(y_{3}\)) and gas sales (\(y_{4}\)). Table 2 lists these data. Table 3 lists the efficiency scores of the ensemble DEAwith the CCR and BCC models and models used by [PERSON] and [PERSON] (2016). Using formula (1b), a total of 105 unique DEA models were used to compute the DEA ensemble efficiency score. The entropies of the Ensemble CCR, Ensemble BCC, Non-Radial and Radial models were 2.616, 2.621, 2.615 and 2.599, respectively. The MEUB from Equation (2i) is 2.639. Comparing the Ensemble CCR with the Non-Radial and Radial CCR models shows that the Ensemble CCR model has a higher entropy. Only the VRS Ensemble BCC model has a higher entropy than the Ensemble CCR model. The standard deviations of the Ensemble BCC model are mostly lower than the CCR model's as well. More importantly, the Ensemble CCR model generates unique rankings for the DMUs, whereas the Non-Radial and Radial models generate a tie for three DMUs. The Ensemble BCC model also generates unique rankings, but the differences occur at the third decimal place. The Ensemble BCC efficiency scores for DMU 10, 12 and 13 were 0.960, 0.959 and 0.962, respectively. Figures 3 and 4 illustrate the numbers of models (out of 105 total models) where a DMU was fully efficient. These figures are useful for understanding to what extent the assumption \(\alpha_{1}\approx\alpha_{2}\approx\ldots\approx\alpha_{n}\) was satisfied for the theoretical normal distribution of the ensemble efficiency scores. For these parameters to be similar, the expectation is that a similar number of fully efficient DMUs should exist across all models. Clearly, some DMUs are never fully efficient under any of 105 models and the assumption of identical distributions is violated. While the assumption is violated, Figure 4 illustrates that some DMUs, e.g., 1, 8, 10, 12 and 13, have a somewhat similar number of fully efficient DMUs
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 4 Proposed Algorithm  
### Hybrid Iterative Algorithm Structure  
The SD algorithm based on matrix gradient search can have a good convergence direction at the beginning of the iteration [18], while the GS iterative algorithm has lower complexity. Therefore, using the above characteristics, this paper proposed a hybrid iteration of the joint SD and GS algorithm. The joint algorithm (called the SDGS algorithm) speeds up convergence of the iterative effect of the algorithm without increasing the complexity, and achieves error performance close to the MMSE ideal matrix inversion detection method. The steps are in SDGS Algorithm.  
**SDGS Algorithm**  
* **Step 1:** For the diagonal approximation's initial value setting, Equation (4) can be converted to \(W\hat{s}=\hat{y}\); \(W\) is a symmetric positive definite matrix and a diagonally dominant matrix, so \(W^{-1}\) is also a diagonally dominant matrix.
* **Step 2:** Determine the initial solution using \(D^{-1}\) instead of \(W^{-1}\):\[s^{(0)}=D^{-1}\;\hat{y}\] (15) Since \(D\) is a diagonal matrix, it is obvious that calculating \(D^{-1}\) requires only low complexity, and the initial value, \(s^{(0)}\), is set to the initial value of the SD algorithm according to Equation (15).
* **Step 3:** The iterative results of the first two GS algorithms are represented by the SD algorithm, and the second GS iteration result can be expressed as: \[\begin{split} s^{(2)}&=\left(D+L\right)^{-1}\Big{(} \;\hat{y}-L^{H}s^{(1)}\Big{)}=\left(D+L\right)^{-1}\Big{[}\big{(}(D+L)-W)s^{(1) }+\hat{y}\Big{]}\\ &\quad=s^{(1)}+\left(D+L\right)^{-1}\Big{(}\;\hat{y}-Ws^{(1)} \Big{)}=s^{(1)}+\left(D+L\right)^{-1}r^{(1)}\end{split}\] (16) where \[r^{(1)}=\hat{y}-W\Big{(}s^{(0)}+ur^{(0)}\Big{)}=\hat{y}-Ws^{(0)}-uWr^{(0)}=r^{ (0)}-up^{(0)};\;u=\frac{\Big{(}r^{(0)}\Big{)}^{H}r^{(0)}}{\big{(}p^{(0)}\big{)} ^{H}r^{(0)}}\] (17)
* **Step 4:** Combine single SD and GS iterations into one hybrid iteration by substituting Equation (17) and \[s^{(1)}=s^{(0)}+ur^{(0)}\to\;s^{(2)}=s^{(0)}+ur^{(0)}+\left(D+L\right)^{-1} \Big{(}\;r^{(0)}-up^{(0)}\Big{)}\] (18) This represents the first two GS iterations as Equation (18); update the mixed iteration value \(\hat{s}^{(1)}=s^{(2)}\), and then perform the next GS iteration.
* **Step 5:** Using the \((i-1)\)th GS iteration using Equation (14), ideal estimated value \(\hat{s}^{(i)}\) of the transmitted signal vector \(s\) can be obtained by setting the appropriate number of iterations, \(i\): \[\hat{s}^{(i)}=\left(D+L\right)^{-1}\Big{(}\hat{y}-L^{H}\hat{s}^{(i-1)}\Big{)}\] (19) Then, \(\hat{s}^{(i)}\) is related to the complex domain for the next soft decision, so the hybrid iterative algorithm can converge very quickly after a small number of iteration.
## 3 Validation of the Proposed Method  
### Regional Engineering Geology of Test Points  
The proposed RSR method were verified by conducting the in field tests in the deep roadway of the PDS No. 1 and No. 11 coal mines. The PDS coalfield, which is one of the three largest coal production areas in China, is located in the north of Pingdingshan City, Henan Province. As shown in Figure 6, the PDS coalfield has a length of 38 km and width of 20 km, approximately. The general geological structure is a series of folds in NW direction with limb angles in the range of 8-12\({}^{\circ}\). Faults in this area can be divided into two types: parallel to the direction of the fold axis, which mainly included the Guodishan normal fault and the Baishigou reverse fault, and the other type is perpendicular to the direction of fold axis, which mainly includes the Luogang and Xiaxian normal faults. The Guodishan normal fault, located to the southeast of the No. 11 coal mine and the nearby Guodishan Mountain, has great influence on the in situ stress distribution of the PDS coalfield.  
The test location of the PDS No. 1 coal mine was located in the inclined roadway of the third level with the burying depth of 877 m, approximately (Figure 7). The excavated geology and borehole data show the Zhangjia fault, which strikes in the NW direction is located to the southwest of the mining area. The test location of the PDS No. 11 coal mine was located in the inclined return airway of the second level with the burying depth of 780 m (Figure 8). In order to investigate the basic mechanical properties of surrounding rock in the test sites, the rock specimens were collected and uniaxial compression tests were carried out. The test results are listed in Table 1.  
Figure 6: Regional geological structure and mining distributions of the PDS coalfield (1. Qiannie anticline; 2. Renzhuang syncline; 3. Jiaozan syncline; 4. Guozhuang anticline; 5. Niuzhuang syncline; 6. Haotang; 7. Zhangwan fault; 8. Xindian fault; 9. Renzhuang fault; 10. No. 12 coal mine fault; 11. No. 11 coal mine fault; 12. Niuzhuang fault; 13. Zhuyuan fault; 14. Zhangjia fault; 15. G hole fault; 16. Inclined shaft fault; 17. Haotang No. 1 fault; 18. Zhangjiazhuang fault.).
## 4 System Components
### Networking System
#### 4.1.3 Networking Advances
For instance, an investigation of propagation channel models for drones at flight heights of 0 to 100 m showed that the environment, heights of the adjoining obstacles, which causes reflections of the earth's surface, together with the drone system uncertainties, all at varying heights, have adverse effects on the wireless propagation channels [62]. There are suitable control solutions from the literature to strike a balance for drone system uncertainties, actuator failures, misalignment and external disturbances. For instance, a finite-time fault-tolerant adaptive robust control for a class of uncertain nonlinear systems with saturation constraints using integral back-stepping approach [63], was proposed to achieve robust control and high-precision tracking performance for nonlinear systems such as robots, spacecraft and aircraft under actuator faults, external disturbance and system uncertainties. Another example is using distributed consensus tracking control for nonlinear multi-agent systems with state constraints [64] to control against multiple actuator faults, parametric uncertainties and external disturbance in quadrotors.
## 4 Discussion
### Cytotoxicity and Anticancer Activity of C. sativa Leaf Extracts  
The obtained results confirmed the remarkable anticancer activity of the _C. sativa_ extracts against different carcinoma cell lines (AGS, A375 and A549). This agreed well with previous works that studied the anticancer activity of _C. sativa_ on different cell lines such as melanoma [20], ovarian cancer [21], prostate cancer [22], and breast and pancreatic cancer [16], among others. The results are also in agreement with the biological activities based on both the chemotype and the extraction taken from the leaves of the plant. [PERSON] et al. [86] demonstrated that the ethanolic extract of the leaves and seeds of the _C. sativa_ plant chemotype III, exhibited cytotoxicity activity against B16F10 melanoma cells in a concentration dependent manner (cytotoxicity of 46% at 1 mg/mL and total inhibition at 10 mg/mL). Additionally, both leaf and seed extracts demonstrated negligible toxicity against human skin fibroblast (viability above 80% for concentration below 0.5 mg/mL) confirming high biocompatibility.  
The notable activity against melanoma cells combined with the negligible impact on healthy human skin cells confirms the great pharmacological potential that makes them suitable candidates for the development of new-generation topical treatments with reduced side effects, especially for melanoma, the most common and aggressive type of skin cancer. These findings have been confirmed in several works presenting promising results, both in vitro [87] and in vivo [20].  
On the other hand, the potential selective toxicity of _C. sativa_ leaf extracts has been widely studied in order to develop novel therapies with reduced negative side effects. [PERSON] and colleagues [15] evaluated selectivity by comparing the toxicity of six different genotypes of medical cannabis against three cancer cell lines (Ht-29, Caco-2, and Hep-G2) and two healthy cell lines (FHs 74 Int: healthy intestinal cells and MRC-5: healthy lung fibroblast). They demonstrated that the compound content of the different genotypes strongly affects selectivity. Highlighting specific compounds such as myrcene, \(\beta\)-elemene, \(\beta\)-selinene, and \(\alpha\)-bisabolol oxid as enhancers of selectivity and \(\beta\)-ocimene and \(\beta\)-caryophyllene oxide as cytotoxicity-associated molecules. Selectivity is therefore determined by the plant genotype (chemical profile and content) and by the specific cell line.  
In consequence, these findings can explain the selectivity differences between all the different evaluated cell lines, especially, the significant increase of cytotoxicity observed in Vero cells. Furthermore, the obtained toxicity profiles against Vero cells agree strongly with previously reported articles. For example, [PERSON] and coworkers [88] showed that the toxicity of cannabis extracts against Vero cells is highly influenced by compound content, reporting high toxicity in the crude and CBN extracts (IC50 of 13.4 and 10.6 \(\mu\)g/mL, respectively) and lower toxicity in the CBG, CBD, and THC (IC50 699.7, 39.77 and 67.2 \(\mu\)g/mL, respectively).
# Parameter Optimization for Ship Antiroll Gyros
## 2 Dynamical Model of Ship's Antiroll Gyro  
### Mathematical Model of Random Waves  
The interference moment of random waves was mainly related to the wave slope angle \(\alpha(t)\), and the essence of \(\alpha(t)\) was to convert the spectrum of waves into that of \(\alpha(t)\). Where \(\alpha(t)\) is the maximum inclination of the wave surface on a vertical section orthogonal to the crest, and the wave slope angle \(\alpha(t)\)[19] was defined as follows:  
\[\alpha(t)=\left(\sum_{i=1}^{N}\sqrt{2\int_{\omega_{ei-1}}^{\omega_{ei}}S_{ \alpha}(\omega_{e})\mathrm{d}\omega_{e}\cos(\omega_{ei}+\varepsilon_{i})} \right)\sin(\chi),i=1,2,\cdots \tag{1}\]  
where \(\omega_{e}\) is the encounter frequency, \(\varepsilon_{i}\) is the random phase angle uniformly distributed between 0 and \(2\pi\), \(N\) is the number of selected harmonics, and \(\chi\) is the course angle. \(S_{\alpha}(\omega_{e})\) is the spectrum function of wave slope angle, which could be obtained with the density of the wave energy spectrum \(S_{\zeta}(\omega)\):  
\[S_{\alpha}(\omega_{e})=\frac{\frac{\omega^{4}}{g^{2}}S_{\zeta}(\omega)}{1+ \frac{2\omega}{g}u\cos\chi} \tag{2}\]  
where \(g\) is the gravitational acceleration, \(u\) is the ship sailing speed, and \(\omega\) is the harmonic angular frequency.  
The two-parameter spectrum proposed by the International Towing Tank Conference (ITTC) was used as the density of the wave energy spectrum \(S_{\zeta}(\omega)\)[20]:  
\[S_{\zeta}(\omega)=\frac{173h_{1/3}^{2}}{\omega^{5}T_{1}^{4}}\mathrm{e}^{\frac {-\omega 01}{4^{4}T_{1}^{4}}} \tag{3}\]  
where \(h_{1/3}\) is the significant wave height and \(T_{1}\) is the mean period of the wave.
# SNR Enhancement of an Electrically Small Antenna Using a Non-Foster Matching Circuit
## 2 Non-Foster Circuit Design  
This section first introduces the ESA used in this study. This is important since the NFC design strongly depends on the impedance of the ESA connected with it. Next, the proposed NFC design is discussed together with the stability validation methods and their simulation results.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System
### Application in an Industrial Robot System
#### 4.1.1 The CHIMERA Robot  
The robot used in our use-case is a mobile manipulator called CHIMERA (shown in Figure 5). CHIMERA is a mobile manipulator consisting of the mobile industrial platform MiR100 and the industrial robot arm UR10. It is intended for applications like intralogistics, machine tending or location-independent manipulation. Both robots are individual sub-systems that are integrated using dedicated computing hardware and combined in a shared software environment (see below). However, in hard- and software, we designed CHIMERA for the base and the arm to be exchangeable with other robots. To integrate the two robots both physically and logically we employ additional network hardware, an industrial PC as well as PLCs. Apart from that, a separate battery system ensures the endurance for complex industrial applications.  
The software stack--as described below--runs on the industrial PC communicating with MiR and UR via the internal network. From the software perspective on application layer, CHIMERA provides workflows for moving the arm and for changing the robots location using the mobile base via the API. We are going to use these workflows in order to assemble a workflow queue for the upcoming evaluation part.
# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
## 2 Related Work  
### Classic Registration Algorithms  
Classic registration algorithms for point clouds mainly include the iterative closest point (ICP) algorithm [11; 12], variants of ICP [13; 14; 15; 16; 17; 18; 19], and geometry-based registration algorithms [20; 21; 22; 23; 24].  
The ICP algorithm [11; 12] represents a major milestone in point cloud registration and is extensively applied in various ways. The essence of ICP is to minimize the sum of the distances between correspondence points of pairwise point clouds using iterative calculations, thereby optimizing the relative pose of the pairwise point clouds. When the relative pose deviation of the pairwise point clouds is small, the algorithm is guaranteed convergence and can obtain an excellent registration result. Scholars have made various improvements to the ICP algorithm to enhance registration efficiency [16] and accuracy [17; 18; 19]. However, all ICP-style algorithms still rely on the direct calculation of the closest point correspondences; moreover, they cannot dynamically adjust according to the number of points and easily fall into local minima.  
The workflow of geometry-based registration algorithms is as follows: calculate the geometric feature descriptors between pairwise point clouds [25], determine the correspondence relationship of the pairwise point clouds according to the similarity of the descriptors, and calculate the optimal matrix for rigid body transformation between the pairwise point clouds. Random-sample-consistency-based registration algorithms [20], such as the sample consensus initial alignment (SAC-IA) [24], are the most widely used geometry-based registration algorithms. In SAC-IA, the correspondence points are established by calculating the local fast point feature histograms (FPFH) of the pairwise point clouds, and registration is subsequently accomplished by minimizing the distance between correspondence points. The algorithm can achieve invariance to the initial pose, and a satisfactory registration result can still be obtained when the overlap in the pairwise point clouds is low.  
Unfortunately, the registration results of the geometry-based registration algorithms mainly depend on the calculation accuracy of the geometric feature descriptors between pairwise point clouds. It is necessary to manually adjust the calculation parameters involved in the geometric feature descriptor, such as the neighborhood radius of the FPFH descriptor, to minimize calculation errors and improve registration accuracy. This method consumes a significant amount of time, and it is not easy to determine its optimal parameters.
# Composite Foundation Settlement Prediction Based on LSTM-Transformer Model for CFG
## 4 Engineering Example
### Analysis of Prediction Results  
The results obtained in this experiment were achieved using the PyTorch deep learning framework, implementing a LSTM-Transformer encoder-decoder model. The implementation was carried out in the Python programming language, on a Windows 10 operating system, using the PyCharm development environment. The experimental setup included an NVIDIA GeForce RTX 3090 Ti graphics card, an Intel(R) i7-13700k CPU, and 64 GB of memory. The LSTM-Transformer model was applied to predict and analyze four cross-sections: DK23 + 200, DK23 + 250, DK23 + 300, and DK23 + 350. The network training utilized the Adam optimization algorithm, with mean squared error loss (MSELoss) as the loss function. The learning rate was set to 0.001, and parameter tuning was performed using grid search. Specific parameter settings are detailed in the Table 3. The prediction results obtained after 200 iterations are illustrated in Figures 10 and 11.  
Figure 10: LSTM–Transformer model settlement prediction results.  
The LSTM model, with its unique gating mechanisms like forget gate, input gate, and output gate, has strong memory capability and can adaptively mine historical information about foundation settlement and various influencing factors. Its prediction results in terms of MAE, RMSE, and MAPE are improved by 13.3%, 24.7%, and 12.6%, respectively, compared to the Transformer model, thus better predicting foundation settlement. In the LSTM\(-\)Transformer model, the LSTM layer extracts historical state features for global learning and weight distribution, while the Transformer layer's multi-head self-attention mechanism performs feature extraction and fusion at multiple levels. The diverse features extracted pay special attention to and learn from key factors like filling height, enhancing the capture of the early-stage filling height's effect on the physical and mechanical structure changes of the geotechnical body. As seen from the predictive effects of the four sections in Figure 10, the predictions closely fit the actual values, with each point's prediction being consistent with the actual deformation trend. As known from Figure 11, the LSTM\(-\)Transformer model has the lowest average absolute error (MAE), average absolute percentage error (MAPE), and root mean square error (RMSE) of 0.224, 0.563%, and 0.274, respectively, among the four models, making it the most accurate in prediction and the best in feature matching.
## 3 Sample Environments
### Pressure
This makes it possible to perform such experiments with a reasonably high time resolution using the 'cyclic' procedure mentioned in the section on temperature jumps. An example is the phase transition from swollen chains to polymer mesoglobules of an aqueous solution of poly(N-isopropylacrylamide) with pressure jumps across the coexistence line [35]. This was investigated with kinetic small-angle neutron scattering with a 50 ms time resolution. The pressure jumps were on the order of \(\Delta\)p \(\approx\) 200 bar. The interesting variation here is that the data acquisition time per frame was not constant but starting with a time frame length of 0.05 s that was elongated by a factor of \(1.1\times\) for each frame, thus anticipating that events evolved fastest immediately after the pressure jump. Obviously, this has some consequences regarding the statistical quality of the data, but the experiment only had to be repeated five times. This is another area where 'event mode' data collection will allow the experimenter more freedom to postmortem decide how to time-slice the data set. A similar development was used to study the volume phase transition kinetics of N-n-propylamide microgels [13]. A jump sequence of \(200\to 40\to 200\) bar was performed using a time frame length of 5 ms over a total time period of 350 ms. However, in this case, the experiment had to be repeated 5400 times before satisfactory data sets could be obtained. It should be noted that pressure jump experiments, as those described above, are not only interesting for fundamental research purposes, but also understanding industrial  
Figure 4: The effects of hydrostatic pressure on a micellar SDS solution as observed in a SANS experiment. At increased pressures, there is a continuous transformation from spherical to elongated micelles as evidenced by the gradual peak shift to lower angle. Figure provided by author [32]. processes. However, to do so, the apparatus should be capable of jumping both pressure and temperature simultaneously, preferably with the application of a shear force [36; 37]. This type of experiment has been implemented for X-ray scattering, but it is probably still beyond the limits of what is feasible for neutron scattering due to beam size limitation and neutron flux.