esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In this study, we assumed a realistic system disturbance and verified it using a simulation with noise. The results in Figure 8 indicate that the proposed control scheme based on the combined ESN and MPC can improve the control performance even if future disturbances change drastically under a large time margin. These findings indicate that by using the prediction obtained by the ESN, the proposed method can be applied to predictive control for future timing. However, EDT has been proposed as a method of compensating for the realistic dead-time at the current control timing. In this study, EDT-MPC was extended beyond the original compensation timing for comparison with the proposed method. Hence, the accuracy of compensation for the future timing in EDT-MPC is lower than that of the proposed method. It should be mentioned that the conditions were slightly unfair for comparison. Note that the main focus of this study is to demonstrate that the proposed method can be applied to predictive control for future timing. Moreover, the results of the computation time presented in Figure 9 demonstrate the feasibility of the proposed method. It is a reasonable requirement for the optimized calculation time and dead-time to be smaller than the time margin. The proposed method can be applied when the control performance can be satisfied with an appropriate time margin. As long as the disturbance can be reasonably measured and predicted, this scheme can be applied even if the optimization calculation is not completed within the control period. Moreover, the predictive model in the proposed method for the HEV system employs a linear model with state-space representation. The results shown in Figure 10 suggest that changing the engine inertia changes the torque balance, and appropriate torque limits can be established using the proposed method. In such examinations, physical-based linear modeling can help in conducting a design variable study for numerous cases. However, all models cannot be represented by state-space representations in real systems. Therefore, it is useful to utilize inference-based models using neural networks or similar ESNs.
# Secure Communication for Uplink Cellular Networks Assisted with Full-Duplex Device-to-Device User
## 3 Problem Formulation and Analysis
The derivative of \(F(\alpha)\) (18) with respect to \(\alpha\) can be derived as follows:  
\[\gamma_{1}=\big{|}f_{1}\big{|}^{2}/\sigma^{2} \tag{8}\]0  
We note that \(F(\alpha)\) is a monotonically decreasing function with respect to \(\alpha\), so according to the range of \(\alpha^{*}\) derived in (22), the optimal power allocation factor \(\alpha^{*}\) with respect to \(\beta\) that maximizes \(F(\alpha)\) can be obtained:  
\[\gamma_{1}=\big{|}f_{1}\big{|}^{2}/\sigma^{2} \tag{8}\]1  
Furthermore, in order to obtain the optimal value of power allocation factor \(\beta\) at CU that minimizes \(\alpha^{*}\), we calculate the derivative of \(\alpha^{*}\) with respect to \(\beta\) and set it to \(0\), as follows:  
\[\gamma_{1}=\big{|}f_{1}\big{|}^{2}/\sigma^{2} \tag{8}\]2  
where,  
\[ \begin{array}{l}a=(P_{C}\gamma_{C1}+\kappa P_{D}\gamma_{1}+1)^{2}(\eta-1) \\ b=(P_{C}\gamma_{C1}+\kappa P_{D}\gamma_{1}+1)P_{C}\gamma_{C1}\\ c=P_{C}^{2}\gamma_{C1}^{2}P_{D}\gamma_{B1}\\ d=P_{D}\gamma_{B1}(\kappa P_{D}\gamma_{1}+1)(\eta-1)(P_{C}\gamma_{C1}+\kappa P _{D}\gamma_{1}+1)\end{array}  \tag{26}\]  
It can be observed that the optimal value of \(\beta\) is obtained by solving the Equation (25). From (25), we derive that:  
\[bc\beta^{2}+2 ac\beta-(ac+bd)=0 \tag{27}\]  
According to the properties of the quadratic equation, we can calculate that the root discriminant \(\Delta=(2 ac)^{2}+4 bc(ac+bd)>0\) of the Equation (27). Since the coefficient \(bc\) of the quadratic term (27) is greater than zero, so there are two solutions to the Equation (27), denoted by \(\beta_{1}\) and \(\beta_{2}\), which can be obtained as follows:  
\[\beta_{1}=\frac{-2 ac-\sqrt{\Delta}}{2 bc} \tag{28}\]  
and  
\[\beta_{2}=\frac{-2 ac+\sqrt{\Delta}}{2 bc} \tag{29}\]  
As \(\beta\in(-\infty,\beta_{1})\) and \(\beta\in(\beta_{2},+\infty)\), we obtain that the derivative \(\frac{\partial\alpha^{*}}{\partial\beta}\) is greater than \(0\) according to the properties of the quadratic equation, then \(\alpha^{*}\) is a monotonically increasing function of \(\beta\). The derivative \(\frac{\partial\alpha^{*}}{\partial\beta}\) is less than \(0\) as \(\beta\in(\beta_{1},\beta_{2})\), that is, \(\alpha^{*}\) is a monotonic decreasing function of \(\beta\). Obviously, \(\beta_{2}\) is the minimum point. Moreover, since \(\beta_{1}<0\) and \(\beta_{2}>0\), \(\beta_{2}\) is the global minimum point. Therefore, the optimal power allocation factor \(\beta^{*}\) at CU can be expressed as:  
\[\beta^{*}=\beta_{2}=\frac{-2 ac+\sqrt{\Delta}}{2 bc}=\frac{(P_{C}\gamma_{C1}+ \kappa P_{D}\gamma_{1}+1)\left[\sqrt{\eta(\eta-1)}-(\eta-1)\right]}{P_{C}\gamma _{C1}} \tag{30}\]  
Thus, the optimization object \(R_{D}\) in Equation (19) can be obtained by the optimal power allocation factor \(\alpha^{*}\) of CU and the optimal power allocation factor \(\beta^{*}\) of D1.
# New Developments and Progress in Absorption Chillers for Solar Cooling Applications
###### Abstract  
At present, novel, small-to-large capacity absorption chillers with unique technical features have emerged on the global market, and laboratory and pre-industrial prototypes have also been developed. These chillers have been designed for the efficient use of low-grade heat sources; some are air-cooled, small capacity systems; compact water/LiBr chillers; or solar-gas-fired single/double-effect chillers. Also, some advanced commercial absorption chillers have an extensive temperature glide in the driving heat stream (>30 K) which extracts approximately twice as much heat (\(\sim\)200%) as the single-effect chiller. This large temperature glide means that the chillers are well suited to solar thermal collector installations and district heating networks, and the extra driving heat increases cold production. Moreover, recent advances in R718 turbo compressor technologies have helped to solve the problems water/LiBr absorption chillers have in adapting to extreme operating conditions (e.g., high ambient temperature, >35 \({}^{\circ}\)C) by using a compressor-boosted absorption chiller configuration. This review paper presents and discusses the developments and progress in these absorption chiller technologies. In summary, the new absorption chillers may be useful for developing efficient, cost-effective, and robust solar cooling solutions that are needed to mitigate the unsustainable impact of the rising global demand for space cooling.  
absorption chillers; air-cooled; double-lift; driving heat temperature glide; plate heat exchangers; solar cooling +
Footnote †: journal: Applied Physics Letters
# DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching
## 3 Experiments and Discussion
### Runtime Analysis  
In this section, we report the runtimes of our ontology matching algorithm for different matching scenarios. Since DAEOM consists of three major steps, we present the time devoted to each of them  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ground truth** & **Concept from O\({}_{\text{s}}\)** & **Concept from O\({}_{\text{t}}\)** & \begin{tabular}{c} **LSTM** \\ **Similarity** \\ \end{tabular} & \begin{tabular}{c} **DAEOM** \\ **Similarity** \\ \end{tabular} \\ \hline True & \begin{tabular}{c} internal carotid artery \\ lamina propria bronchus \\ vitreous chamber eyeball \\ ligamentum teres liver \\ cerebellomedullary cistern \\ photoreceptor layer retina \\ root tooth \\ eotaxin 2 \\ fourth lumbar vertebra \\ \end{tabular} & \
\begin{tabular}{c} internal carotid artery \\ bronchus lamina propria \\ vitreous chamber eyeball \\ round ligament liver \\ cistern magna \\ layer red cone \\ radix dentis \\ small inducible cytokine a24 \\ l4 vertebra \\ \end{tabular} &
\begin{tabular}{c} 0.000 \\ 0.094 \\ 0.012 \\ 0.162 \\ 0.101 \\ 0.101 \\ 0.113 \\ 0.0114 \\ 0.012 \\ 0.013 \\ 0.014 \\ 0.015 \\ 0.016 \\ 0.017 \\ 0.018 \\ 0.019 \\ 0.017 \\  
in Figure 5a. In brief, the steps of our algorithm are the following: The training of TBERT (Step 1), the training of SGAT (Step 2), and the mapping selection (Step 3).  
As seen in Figure 5a, the majority of the time is allotted to the training of TBERT and SGAT. Specifically, we would like to note that training time depends on the number of epoch. Besides, it can be observed that running time gradually increases as the scale of ontology. We also experimented to analyze the running time and performance (F-measure) of our improved Stable Marriage (SM) matching across the different matching scenarios. The experimental results are shown in Figure 5b,c. With the increasing scale of ontology, the running time of the unimproved matching presents exponential growth. However, the running time of our improved matching is linear growth. In addition, we can see that the running time of the improved matching is shorter than unimproved matching across different matching scenarios. This can be explained as follows: The unimproved solution requires each entity of ontology \(O_{\text{s}}\) to be compared across all possible entities of ontology \(O_{\text{t}}\), and has worst-case time complexity \(\Theta\left(n^{2}\log n\right)\). Our improved matching only finds the highest correspondences in the semantic similarity matrix as candidate alignments. The time complexity is \(\Theta\left(n\right)\). In Figure 5c, we can see that the improved and unimproved matching methods obtain similar or even better performances in all different matching tasks. Experimental results show that only finding the highest correspondences can reduce the running time without affecting the matching performance.
## 3 Materials and Methods  
### Data Sources  
All original raw data utilized in this study (excluding Landsat images) were procured from the Changjiang Water Resources Commission (CWRC; [[http://www.cjw.gov.cn/](http://www.cjw.gov.cn/)]([http://www.cjw.gov.cn/](http://www.cjw.gov.cn/)), accessed on 1 March 2023), encompassing hydrological and sediment data (e.g., daily mean discharge, water levels, and sediment concentrations at multiple hydrometric stations; Figure 1), as well as topographical data (i.e., annual one-dimensional cross-sectional profiles at 450 specific locations, measured using a multi-beam echo-sounder). Prior to public release, these datasets undergo stringent verification and uncertainty analysis in accordance with governmental protocols, such as the protocol for liquid flow measurement in open channels, the protocol for measurements of suspended sediment in open channels, and the protocol for port and waterway engineering survey, as published by the Ministry of Water Resources, China [9] (Table 3).  
The satellite data employed in this research were Landsat 5, 7, and 8 surface reflectance products, which were obtained from Google Earth Engine (GEE, [[https://developers.google.com/earth-engine/datasets/catalog/landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat)]([https://developers.google.com/earth-engine/datasets/catalog/landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat)), accessed on 1 March 2023), with the aim of monitoring vegetation dynamics for 16 bars spanning the period of 2003 to 2020 (Table 3). Additionally, GEE is a cloud computing platform that provides access to an enormous amount of geospatial data and computing resources. It allows users to process and analyze petabytes of satellite imagery and other geospatial data, making it possible to perform large-scale analyses that were previously impossible due to computational constraints. In this study, we downloaded satellite images from GEE and performed some processes on them (please refer to Section 3.2.1).
###### Abstract  
TLS can quickly and accurately capture object surface coordinates. However, TLS point clouds cannot cover the entire surface of the target object, due to block of view and limitation of measurement condition. Thus, using it to monitor deformation of slope reduces the detection accuracy of slope surface deformation. To overcome the drawbacks, a method to improve TLS point clouds by UAV photogrammetric point clouds is proposed. The two kinds of point clouds are registered as the new multi-view point clouds by PCA and ICP. The locations of monitoring points are extracted based on HSL color space recognition method from the new multi-view point clouds to analyze the surface displacement. At present, the proposed method has applied in a highway slope in Yunnan Province, and complete point clouds were successfully constructed. A RTK survey was used to compare and verify the proposed method. The verification result demonstrate that the difference of displacement between two measurement methods is less than 10 mm. Comprehensive experiments demonstrate that the proposed method is reliable and meets the slope displacement monitoring standard.  
TLS; UAV photogrammetric point clouds; registration; slope surface displacement Article  
An Improved Point Clouds Model for Displacement Assessment of Slope Surface by Combining TLS and UAV Photogrammetry  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], S. An Improved Point Clouds Model for Displacement Assessment of Slope Surface by Combining TLS and UAV Photogrammetry. _Appl. Sci._**2022**, 12, 4320. [[https://doi.org/10.3390/appl2094320](https://doi.org/10.3390/appl2094320)]([https://doi.org/10.3390/appl2094320](https://doi.org/10.3390/appl2094320))
# Noninvasive Suspicious Liquid Detection Using Wireless Signals
## 4 Evaluation and Analyses
### Detection of Suspicious and Nonsuspicious Liquids  
In this section, we first analyze the detection of suspicious and nonsuspicious liquids.  
Figure 4 shows the raw WCI amplitude information on 30 subcarriers when using C-band electromagnetic wave signals. We can see the difference of amplitude change between suspicious liquids and nonsuspicious liquid intuitively.  
The conclusion of the S-band was the same, as shown in Figure 5.  
Figure 4: Amplitude information of 30 subcarriers of Step 1 at the C-band. (**a**) Using the paper cup; (**b**) Using the plastic bottle; (**c**) Using the glass bottle.  
Figure 5: Amplitude information of 30 subcarriers of Step 1 at the S-band. (**a**) Using the paper cup; (**b**) Using the plastic bottle; (**c**) Using the glass bottle.  
Figure 6: The KNN algorithm Classification results of Step 1 at the C-band and the S-band.  
The KNN classification algorithm was used to further detect suspicious and nonsuspicious liquids. The classification results of the KNN algorithm for two bands of data are shown in Figure 6. Blue, orange, and grey represent the paper cup, the plastic bottle, and the glass bottle, respectively. In the C-band environment, the detection accuracy of Step 1 was over 98%, and that for S-band was 99%.
## Nomenclature  
Roman symbols:  
\(a,b\)  
weight on bit exponent and rotate speed exponent, respectively  
\(A\)  
cross-section area of drill-string, m\({}^{2}\)  
\(c\)  
drilling fluid drag, N s/m\({}^{2}\)  
\(D\)  
inner diameter of drill-string, m  
\(E\)  
Elastic (Young's) modulus of drill-string, Pa  
\(\overset{\rightarrow}{e}_{\text{g}}\)  
vector of submerged drill-string weight  
\(\overset{\rightarrow}{e}_{\text{l}}\overset{\rightarrow}{e}_{\text{l}} \overset{\rightarrow}{e}_{\text{l}}\overset{\rightarrow}{e}_{\text{l}} \overset{\rightarrow}{e}_{\text{l}}\)  
unit base vectors in natural curvilinear system  
\(f\)  
frequency of vibration, Hz  
\(F,F_{\text{m}},F_{\text{b}}\)  
normal contact force and its components in \(\overset{\rightarrow}{e}_{\text{n}}\) and \(\overset{\rightarrow}{e}_{\text{b}}\) direction, N  
\(F_{c}\)  
Coulomb friction force, N  
\(F_{\text{a}}\)  
amplitude of exciting force, N  
\(F_{f}\)  
axial friction force, N  
\(F_{f}\)  
total static friction of drill-string, N  
\(F_{\text{m}p}\)  
hook load, N  
\(g_{s}\)  
linear buoyant weight of drill-string, N/m  
\(g(x)\)  
constraint condition  
\(G_{\text{t}}\)  
axial component of gravity of the whole drill-string, N  
\(h\)  
space step, m  
\(i\)  
parameter determines the shape of strain-stress curve  
\(k_{x}\)  
rate of change of deviation angle, and, respectively, rad/m  
\(k_{y}\)  
rate of change of azimuth angle, rad/m  
\(k_{y}\)  
total bending curvature, rad/m  
\(K_{t}\)  
tangential stiffness of contact surfaces, N/m  
\(L\)  
length of drill-string, m  
\(n\)  
rotate speed of drill-string, 2\(\pi\) rad/s  
\(ROP\)  
rate of penetration, m/s  
\(s\)  
well depth, m  
\(t\)  
time, s  
\(T_{max}\)  
strength of drill-string, N  
\(T,T_{t},T_{w},T_{b}\)  
internal tension force and its components in \(\overset{\rightarrow}{e}_{\text{l}},\overset{\rightarrow}{e}_{\text{l}} \overset{\rightarrow}{e}_{\text{u}},\overset{\rightarrow}{e}_{\text{b}}\) direction, N  
\(u\)  
axial displacement of drill-string, m  
\(v_{r}\)  
relative velocity of drill-string, m/s  
\(WOB\)  
weight on bit, N  
\(z(\text{t})\)  
offset displacement of asperities at time \(t\), m
# Equivalence between LC-CRF and HMM, and Discriminative Computing of HMM-Based MPM and MAP
## 4 Discriminative Classifiers in Generative HMMs
### Discriminative Computing of HMM-Based Map: Discriminative Viterbi  
Let \(X_{1}\),...,\(X_{N}\), \(Y_{1}\),...,\(Y_{N}\) be an HMM (Equation (1)). The Bayesian MAP classifier (Equation (5)) based on such HMM is computed with the following Viterbi algorithm [22]. For each \(n=1\),..., \(N\), and each \(x_{n}\), let \(x_{1:n-1}^{max}(x_{n})=\big{(}x_{1}^{max},\ldots,x_{n-1}^{max}\big{)}(x_{n})\) be the path \(x_{1}^{max}\),..., \(x_{n-1}^{max}\) verifying  
\[p(x_{1:n-1}^{max}(x_{n}),x_{n},y_{1:n})=\underset{x_{1:n-1}}{sup}p(x_{1:n-1}, x_{n},y_{1:n}) \tag{34}\]  
We see that \(x_{1:n-1}^{max}(x_{n})\) is a path maximizing \(p(x_{1:n-1},x_{n}|y_{1:n})\) over all paths ending in \(x_{n}\). Then having the paths \(x_{1:n-1}^{max}(x_{n})\) and the probabilities \(p\big{(}x_{1:n-1}^{max}(x_{n}),x_{n},y_{1:n})\) for each \(x_{n}\), one determines, for each \(x_{n+1}\), the paths \(x_{1:n}^{max}(x_{n+1})\) and the probabilities \(p\big{(}x_{1:n}^{max}(x_{n+1}),x_{n+1},y_{1:n+1})=p\big{(}x_{1:n-1}^{max}(x_{ n}^{max}),x_{n}^{max},x_{n+1},y_{1:n+1})\), searching \(x_{n}^{max}\) with  
\[p\big{(}x_{1:n-1}^{max}(x_{n}^{max}),x_{n}^{max},x_{n+1},y_{1:n+1} \big{)}\big{)}=\] \[\underset{x_{n}}{\sup}\big{[}p\big{(}x_{1:n-1}^{max}(x_{n}),x_{n}, y_{1:n}\big{)}p(x_{n+1}|x_{n})p(y_{n+1}|x_{n+1})\big{)}\big{]} \tag{35}\]  
Setting in Equation (35) \(p(y_{n+1}|x_{n+1})=\frac{p(y_{n+1})p(x_{n+1}|y_{n+1})}{p(x_{n+1})}\), we see that \(x_{n}^{max}\) which verifies Equation (35) is the same that \(x_{n}^{max}\) which maximizes \(p\big{(}x_{1:n-1}^{max}(x_{n}),x_{n},y_{1:n}\big{)}p(x_{n+1}|x_{n})\frac{p(x_{ n+1}|y_{n+1})}{p(x_{n+1})}\), so that we can suppress \(p(y_{n+1})\). In other words, we can replace Equation (35) with  
\[p\big{(}x_{1:n-1}^{max}(x_{n}^{max}),x_{n}^{max},x_{n+1},y_{1:n+1} \big{)}\big{)}=\] \[\underset{x_{n}}{\sup}[p(x_{1:n-1}^{max}(x_{n}),x_{n},y_{1:n})p(x_ {n+1}|x_{n})\frac{p(x_{n+1}|y_{n+1})}{p(x_{n+1})}] \tag{36}\]  
Finally, we propose the following discriminative version of the Viterbi algorithm:  
* Set \(x_{1}^{max}=\underset{x_{n}}{argmax}[p(x_{1}|y_{1})]\);
* For each \(n=1\),..., \(N-1\), and each \(x_{n+1}\), apply (3.17) to find a path \(x_{1:n}^{max}(x_{n+1})\) from the paths \(x_{1:n-1}^{max}(x_{n})\) (for all \(x_{n}\)), and the probabilities \(p\big{(}x_{1:n}^{max}(x_{n+1}),x_{n+1},y_{1:n+1}\big{)}\) (for all \(x_{n+1}\));
* End setting \(x_{1:N}^{max}=\underset{x_{N}}{argmax}\big{[}p\big{(}x_{1:N-1}^{max}(x_{N}),x_{ N},y_{1:N}\big{)}\big{]}\).  
As with MPM above, we see that we can find \(x_{1:N}^{max}\) with the only use of \(p(x_{1})\),..., \(p(x_{N})\), \(p(x_{2}|x_{1})\),..., \(p(x_{N}|x_{N-1})\), and \(p(x_{1}|y_{1})\),..., \(p(x_{N}|y_{N})\), exactly as in CRF case. As above, it appears that dropping HMMs in some NLP tasks on the grounds that MAP is a \"generative\" classifier, is not justified. In particular, in supervised stationary framework, distributions \(p(x_{n})\), \(p(x_{n+1}|x_{n})\), and \(p(x_{n}|y_{n})\) can be estimated in the same way as in LC-CRFs case.
## 2 Materials and Methods
First, to evaluate the shielding performance of direct rays, the range of X-ray energy used in the diagnosis area was tested based on human body imaging. The following conditions were used in the X-ray energy shielding test--tube voltage: 60-120 kVp, tube current: 200 mA, and irradiation time: 0.1 s.  
Because X-ray energy is not a single energy source, the effective energy must be determined. The effective energy of the braking radiation X-rays can be determined using the half-value layer (HVL) measurement method [22]. The HVL is the thickness of a material that attenuates the incident radiation by half. Although there is a difference between materials, the effective energy can be calculated using the following set of equations, after the material for measuring the HVL is determined [23; 24]. When the incident radiation intensity \(I_{0}\) interacts with the shielding material while passing through the shield, the intensity \(I\) of the transmitted radiation can be expressed by Equation (1):  
\[I=I_{\varphi^{(-\mu)}} \tag{1}\]  
Here, \(\mu\) is the linear attenuation coefficient and \(t\) is the thickness of the shield. The HVL is the thickness of the material at which the intensity of the incident radiation is halved (\(\frac{\mu}{2}\)). Thus, after the expression \(I=\frac{I_{0}}{2}\) is substituted in Equation (1) with \(t=\text{ HVL}\), it becomes \(\frac{I_{0}}{2}=I_{0^{-\mu\text{HVL}}}\):  
\[\mu=\frac{\text{In2}}{\text{HVL}} \tag{2}\]  
Figure 4: Tungsten nanopowder dispersion method. Therefore, the effective energy of the incident X-rays can be determined using the linear attenuation coefficient \(\mu\) calculated from Equation (2) and [PERSON]'s mass attenuation coefficient [25]. The medical radiation-shielding performance was evaluated for each calculated effective energy. For the experimental method for testing the shielding performance of the two shielding fibers presented in this study, the geometric conditions were set as shown in Figure 5. The shielding rate calculation of the shielding sheet was \(\left(1-\frac{W}{W_{0}}\right)\times 100\)[26]. Here, \(W\) is the irradiation dose measured when there is a shielding fiber between the X-ray tube (E7239, 150 kV-500 mA, Toshiba, Tokyo, Japan) and the dosimeter (Dosimax plus 1, IBA Dosimetry Corp., Schwarzenbruck, Germany) \(W_{0}\) is the irradiation dose measured when there is no shielding fiber between the X-ray tube and the dosimeter. In this study, in order to verify the effectiveness of low-dose radiation shielding in a general imaging room of a medical institution using the fabricated shielding fiber, the irradiation dose was measured from the X-ray generator referenced to the horizontal plane of the patient examination table [27]. The measuring points were set 360\({}^{\circ}\) from the radiation generator position and at distances up to 200 cm in 50 cm increments. The spatial dose was measured using a spatial dosimeter (Raysafe 452, Billdal, Sweden).