esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Overview of Heat Reuse from Data Centers  
A standard and straightforward method of reusing heat from electronics involves integrating it into HVAC or hot water production systems. For instance, the temperature range of waste heat captured from air-cooled servers (35-45 \({}^{\circ}\)C) is high enough for repurposing in heating applications.  
A study [43] examines a scenario involving a data center with 100,000 servers, whose waste heat is exploited by a 175 MW coal-fired power plant for pre-heating purposes.  
Microevaporators at the chip level were set to an evaporation temperature of 60 \({}^{\circ}\)C, while the condensing temperatures for the two-phase liquid pumped and two-phase vapor compression cycles were assumed to be 60 \({}^{\circ}\)C and 90 \({}^{\circ}\)C, respectively. Simulations from this study suggested that using waste heat from data centers to pre-heat the boiler feed water in such a power plant could enhance its efficiency by up to 2.2%. The temperature of heat extracted from water-cooled data centers typically falls within the 60-70 \({}^{\circ}\)C range, which is suitable for pre-heating boiler feed water. Another interesting application of waste heat is in absorption cooling systems. Several studies [49; 50; 51] have calculated a 0.7 coefficient of performance (COP) by recovering 50.2 kW of waste heat at 88 \({}^{\circ}\)C.  
Other engineering solutions focus on recovering energy through ORC systems. The ORC process converts waste heat directly into electricity. The thermal efficiency of such cycles generally ranges between 5% and 20% [51; 52]. [PERSON] et al. [31] investigated an integrated ORC two-phase cooling system that recovers waste heat from data center electronics. In this setup, the cooling system serves as the heat source for the ORC plant. The overall energy efficiency of this integrated system was calculated to be between 5% and 10%, depending on the specific ORC and coolant fluids used.  
Regarding less-known technologies, the piezoelectric approach is particularly suitable for low-power applications [1; 53]. Research focusing on the Seebeck effect has theorized  
Figure 1: Air flow in a typical data center [42].  
efficiencies ranging from about 5% to 15-20% when applied to waste heat from electronics [54]. However, the literature review highlights that piezoelectric generators exhibit high costs, low conversion efficiencies, and low power output when applied to data centers [12].
## 3 Mirror Optimization Design  
### Fundamental Structure of a Mirror  
The choice of the diameter-thickness ratio of a mirror will affect not only the mirror deformation but also the lightweight rate. According to the relationship between the thickness ratio of a flat solid mirror with a circular edge and the self-weight deformation of the mirror, as shown in References [28; 29; 30], the diameter-to-thickness ratio of the mirror is preliminarily determined. The empirical equation is as follows:  
\[\delta=\frac{3\rho gR^{4}}{16 Et^{2}}=\frac{3\rho g\Delta^{2}D^{2}}{256E} \tag{4}\]  
where \(\delta\) (um) is the maximum deformation of the mirror, \(\rho\) (kg/m\({}^{3}\)) is the material density, \(R\) (m) is the half-diameter of the mirror aperture, \(E\) (GPa) is the elastic modulus of the material, \(t\) (m) is the mirror thickness, \(\Delta\) is the diameter-thickness ratio and \(g\) is the acceleration due to gravity. Aluminum alloy 6061 was selected as the material of the mirror in this study. The mirror aperture was 140 mm. For a value of \(\delta\) of 0.004 um, the diameter-thickness ratio \(\Delta\) is 6.72, and the mirror thickness \(t\) is 20.83 mm. The support form refers to the method mentioned in Reference [28]. Similar to a single-arch support form, this support form is simple and easy to implement for small-aperture mirrors, as shown in Figure 3. Here, the Z-axis direction was set as the direction of the optical axis, the direction of the Y-axis was set against gravity and the X-axis direction was determined by the rules of the Cartesian coordinate system.  
Figure 3: Fundamental structure of the mirror.
# Emerging Diagnostics in _Clostridioides difficile_ Infection
## 4 Established and New Immunological and Enzyme Assays
[101], in which a paper-based multiplex analytical device (mPAD) based on a lateral flow assay (LFA) was constructed with a unique configuration and architecture designed to overcome limitations in multiplexing, sensitivity, simplicity. Using signally enhanced paper-adhered gold nanoparticle-labeledantibodies, the researchers presented a multiplex device that permitted visual confirmation of GDH, toxin A, and toxin B in 32 positive and 8 negative fecal samples. For additional signal enhancement, gold ions were catalytically precipitated onto AuNPs accumulated on the test line, leading to particle size growth that allowed for better visualization of the test line and significant improvements in device performance. The device exhibited a sensitivity of 97%, specificity of 88%, accuracy of 95%, and LOD for GDH, tcd A, and tcd B of 0.16 ng mL\({}^{-1}\), 0.09 ng mL\({}^{-1}\), and 0.03 ng mL\({}^{-1}\), respectively. The inclusion of the catalytic step and its visual effect can be seen in Figure 3.  
NanoBiT immunoassays have reliably quantified several protein biomarkers and antibodies over concentrations spanning four orders of magnitude with low pM sensitivity and up to a 1000-fold signal-to-background ratio [102]. Recently, [PERSON] et al. utilized a POCT NanoBiT Split-Luciferase Assay to enhance GDH and Toxin B (TcdB) sensitivities [103]. NanoBiT GDH and TxB assays conducted in 3.33% (\(w/v\)) feces showed significant reductions in assay sensitivities (LOD = 4.5 and 2 pM, respectively) with overall performance values akin to the currently used point-of-care LFT, Quik Chek Complete. In addition to tcdB and A, another protein secreted by clostridial bacteria is PPEP-1, a unique zinc metalloproteinase. PPEP-1 belongs to a family of proteases that specifically cleave Pro-Pro bonds within proline-rich motifs. Its consensus sequence, distinct from other PPEP proteases, is conserved among _C. difficile_ strains and is essential for pathogen mobility and gut colonization [104; 105]. The unique cleavage activity of PPEP-1, exclusive to _C. difficile_, could serve as a specific and novel marker for CDI. Recent work by [PERSON] et al. coupled PPEP-1 activity with a \"turn-on\" Nluc bioluminescence sensor that could rapidly indicate CDI without requiring expensive equipment [106]. NanoLuc (Nluc), known for its exceptional brightness and stability, is commonly utilized in sensing platforms for various target biomolecules. These properties allowed for detecting PPEP-1 concentrations as low as 10 nM in fecal solution using a phone camera within minutes. For rapid diagnosis, the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) recommends using a two-step algorithm that begins with either NAA or GDH-EIA tests, as shown in Figure 4.  
Figure 3: Visual confirmation of the three targets (GDH, toxin A, and toxin B). Concentrations ranged from 0.1 ng/mL to 10 ug/mL. Photo columns (a) and (b) show the results obtained for each test without and with signal enhancement. The figure was modified with permission [90].
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data/Model** & **Annual Data** & **Monthly Data** \\ \hline Exponential & y = 937.95e\({}^{-0.019\times}\) & y = 900.21e\({}^{22}\)–06x \\ \cline{2-3}  & R\({}^{2}\) = 0.6083 & R\({}^{2}\) = 0.0005 \\ \hline Linear & y = \(-\)13.829x + 920.97 & y = 0.0027x + 862.02 \\ \cline{2-3}  & R\({}^{2}\) = 0.5766 & R\({}^{2}\) = 0.0013 \\ \hline Logarithmic & y = \(-\)73.48 In(x) + 927.65 & y = 106.88 ln(x) \(-\) 161.11 \\ \cline{2-3}  & R\({}^{2}\) = 0.2827 & R\({}^{2}\) = 0.0011 \\ \hline Polynomial of 2 nd degree & y = \(-\)1.6013x\({}^{2}\) + 21.401x + 785.93 & y = 7E-06x\({}^{2}\) - 0.5564x + 12532 \\ \cline{2-3}  & R\({}^{2}\) = 0.8019 & R\({}^{2}\) = 0.0163 \\ \hline Polynomial of 3 rd degree & y = 0.1435x\({}^{3}\) - 6.3358x\({}^{2}\) + 64.039x + 698.81 & y = \(-\)1E-08x\({}^{3}\) + 0.0017x\({}^{2}\) - 69.977x + 978278 \\ \cline{2-3}  & R\({}^{2}\) = 0.8521 & R\({}^{2}\) = 0.1304 \\ \hline Polynomial of 4 th degree & y = 0.0061x\({}^{4}\) - 0.1246x\({}^{3}\) - 2.4829x\({}^{2}\) + 44.145x + 725.24 & y = \(-\)5E-12x\({}^{4}\) + 8E-07x\({}^{3}\) - 0.0477x\({}^{2}\) + 1303.7x - 1E+07 \\ \cline{2-3}  & R\({}^{2}\) = 0.8546 & R\({}^{2}\) = 0.1579 \\ \hline Polynomial of 5 th degree & y = \(-\)0.0067x\({}^{5}\) + 0.3758x\({}^{4}\) - 7.4473x\({}^{3}\) + 60.24x\({}^{2}\) - & y = 4E-15x\({}^{5}\) - 9E-10x\({}^{4}\) + 8E-05x\({}^{3}\) - 3.3252x\({}^{2}\) + 69716x \\ \cline{2-3}  & 172.54x + 935.78 & \(-\) 6E+08 \\ \cline{2-3}  & R\({}^{2}\) = 0.9327 & R\({}^{2}\) = 0.2052 \\ \hline Polynomial of 6 th degree & y = \(-\)0.0003x\({}^{6}\) + 0.0147x\({}^{5}\) - 0.1649x\({}^{4}\) - 0.9136x\({}^{3}\) + & y = 4E-18x\({}^{6}\) - 1E-12x\({}^{5}\) + 1E-07x\({}^{4}\) - 0.0056x\({}^{3}\) + 174.52x\({}^{2}\) \\ \cline{2-3}  & 21.908x\({}^{2}\) - 75.253x + 861.02 & \(-\) 3E+06x + 2E+10 \\ \hline \multirow{2}{*}{Potentiometric} & R\({}^{2}\) = 0.9373 & R\({}^{2}\) = 0.2721 \\ \cline{2-3}  & y = 949.27x\({}^{-0.102}\) & y = 481.83x\({}^{0.0656}\) \\ \cline{2-3}  & R\({}^{2}\) = 0.3059 & R\({}^{2}\) = 0.0004 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Trend models for Lubuskie province.
# Experimental Study on the Workability and Stability of Steel Slag Self-Compacting Concrete
## 3 Experimental Results and Workability Evaluation
### Basic Workability of Steel slag Self-Compacting Concrete (SSCC) with Different VPS  
The basic workability experimental results of SSCC with different VPS are shown in Figure 4.  
The test results show that when VPS = 20%, due to the small amount of steel slag sand, it has little effect on the fluidity and filling performance of the fresh mixture, and the performance of S\({}_{20}\)SCC in the test group is similar to that of SCC in the reference group. However, about 40%, 60% and 80% replacement ratios of steel slag sand, the total sand amount used in SSCC mixture is 634.2 kg/m\({}^{3}\), and then by adjusting the amount of water reducing agent, SSCC with ideal workability and meeting the specification requirements can be trial prepared. In particular, when the VPS is 60% and 40%, the filling and fluidity of fresh SSCC are relatively high, which meet the specification requirements. The minimum value of SF is 690 mm, which belongs to the first-level standard. The maximum value of PA is 30 mm, which can meet the requirements of relevant standards and specifications. \(\Delta\)h meets the requirements of experience summary less than 25 mm. When VPS is 80% and 100% respectively, although it can meet the expected requirements, due to its large flow time of T500, the maximum value is about 18.2 s, which will have a certain hidden danger to the filling and passing ability of concrete mixture. The analysis shows that when the replacement ratio of steel slag sand is large, the ground friction resistance between coarse aggregate and fine aggregate increases, which results in a large flow  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Slump Flow**} & \multirow{2}{*}{**T500**} & \multicolumn{2}{c}{**J-Shaped Ring**} & \multirow{2}{*}{**Stratification**} & \multirow{2}{*}{**Bleeding Condition**} \\ \cline{3-3} \cline{5-6}  & & \(\Delta\)h & & & \\ \hline
700 mm & 8.2 s & 14 mm & 30 mm & Fine & No \\ \hline \hline \end{tabular}
\end{table}
Table 12: Basic workability of the reference group (S\({}_{0}\)SCC).  
Figure 3: The basic workability test of S\({}_{0}\)SCC. (**a**) Slump flow test (SF = 700 mm); (**b**) J-ring test (SF\({}_{\text{J}}\) = 670 mm).  
time of T500 and general filling performance of fresh SSCC. Therefore, when testing SCC with steel slag sand instead of natural sand, it is reasonable to suggest that VPS should not be more than 60% under the same conditions.
## 4 Conclusions  
Distinguishing of eight different human cells based on Raman spectroscopy was attempted in this work. The PCA method was first attempted to visualize and extract the useful information from multivariate spectral data to examine the qualitative differences among all types of samples. Two discrimination models (LDA and QDA) were developed comparatively in this work. The results indicated that the human cell detection based on Raman spectroscopy was feasible, and the QDA method performed much better in contrast to the LDA method, resulting in the unambiguous identifications of all eight cells. It could be concluded that it is a promising method using Raman spectroscopy technique combined with appropriate discrimination models to distinguish different cancerous human cells. Furthermore, it will be a very interesting topic to study the Raman detection of cancerous cells mixed with the corresponding normal cells (lung, breast, etc.) in our future research.  
The work was financial supported by National Natural Science Foundation of China (21407145), and National Key Research and Development Program of China (2016 YFC0101301). [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] designed and conceived this study. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] wrote this paper. All authors approved this manuscript.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PCs**} & \multicolumn{4}{c}{**Classification Rates (\%)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**LDA**} & \multicolumn{2}{c}{**QDA**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} \\ \hline
3 & 53.72 & 51.39 & 57.18 & 56.00 \\
4 & 57.83 & 56.15 & 68.43 & 67.77 \\
5 & 64.23 & 62.42 & 83.11 & 81.35 \\
6 & 68.78 & 69.21 & 88.03 & 87.17 \\
7 & 71.25 & 70.68 & 92.03 & 92.47 \\
8 & 75.69 & 74.20 & 95.42 & 94.98 \\
9 & 78.12 & 76.85 & 97.47 & 96.30 \\
10 & 83.97 & 84.47 & 98.79 & 97.85 \\
11 & 90.29 & 88.26 & 100.00 & 99.07 \\
12 & 93.48 & 91.21 & 100.00 & 100.00 \\
13 & 96.26 & 95.75 & 100.00 & 100.00 \\
14 & 98.10 & 96.35 & 100.00 & 100.00 \\
15 & 100.00 & 98.35 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification rates from LDA model and QDA model with the number of PCs.  
Figure 6: Classification rates of LDA model and QDA model with the number of PCs in the prediction set.
## 2 Experimental Test Overview
### Experimental Setup
#### 2.2.2 Input Seismic Motion  
The required response spectrum (RRS) for the seismic simulation test is presented in Figure 5. Based on the final safety analysis report of a Korean-type NPP and considering the elevation of the field survey location conducted in this study, the floor response spectra (FRS) at 12.192 m and 21.336 m of the auxiliary building were used to generate the RRS with a damping ratio of 5%. The zero period acceleration (ZPA) was set to 0.2 g in the horizontal directions (X and Y) and 0.14 g in the vertical direction (Z). The acceleration time history was prepared to satisfy the performance capabilities of the shaking table and the requirements of IEEE 344 [47]. It was constructed with a frequency range of 1-50 Hz for 30 s, a strong motion duration of 20 s, and a cross-correlation coefficient not exceeding 0.3, as indicated in Figure 5. In the time history plot (Figure 5), the solid line indicates the acceleration time history generated in accordance with IEEE 344 to match the RRS. In the response spectrum plot, the dashed line indicates the target RRS, while the solid line represents the test response spectrum (TRS) derived from the generated acceleration time history. The shaking table test was conducted to ensure that the TRS adequately enveloped the RRS.
# Design of Adaptive Kalman Consensus Filters (a-KCF)
###### Abstract  
This paper addresses the problem of designing an adaptive Kalman consensus filter (a-KCF) which embedded in multiple mobile agents that are distributed in a 2D domain. The role of such filters is to provide adaptive estimation of the states of a dynamic linear system through communication over a wireless sensor network. It is assumed that each sensing device (embedded in each agent) provides partial state measurements and transmits the information to its instant neighbors in the communication topology. An adaptive consensus algorithm is then adopted to enforce the agreement on the state estimates among all connected agents. The basis of a-KCF design is derived from the classic Kalman filtering theorem; the adaptation of the consensus gain for each local filter in the disagreement terms improves the convergence of the associated difference between the estimation and the actual states of the dynamic linear system, reducing it to zero with appropriate norms. Simulation results testing the performance of a-KCF confirm the validation of our design.  
adaptive consensus filters; Kalman filters; distributed system; communication topology 1
## 2 Methodology
### Step 1: Estimation of the Individual Modes
#### 2.1.2 Mode Extraction  
Once the free-decay vibration of measured signal, \(\ddot{R}_{xy}(\tau)\), is obtained using the NExT method, the SWT is employed to decompose it in its individual mono-components, \(x_{i}(t)\), which correspond to the excited modes of the analyzed structure. SWT is capable of analyzing nonlinear, transient, and non-stationary signals. Further, it has been proven to be a reliable tool to detect components in highly noisy environments without compromising the quality of the retrieved individual modes [11; 41].  
In general, the SWT of a time signal is obtained in four steps [42]. Firstly, the continuous WT (\(W_{f}\)(\(a\),\(b\))) of the signal is calculated; secondly, using the obtained wavelet coefficients, the phase transform, \(\omega_{f}\), is employed to estimate the instantaneous frequency of the in-test signal by means of:  
\[\omega_{f}(a,b)=\left\{\begin{array}{ll}\frac{-j}{W_{f}(a,b)}\frac{\partial \left[W_{f}(a,b)\right]}{\partial b}&\left|W_{f}(a,b)\right|>0\\ \infty&\left|W_{f}(a,b)\right|=0\end{array}\right. \tag{3}\]  
where \(W_{f}\), \(a\), and \(b\) are the wavelet coefficient, scale and time offset, respectively. Later, the calculated wavelet coefficients \(W_{f}\)(\(a\),\(b\)) are reassigned to the time-frequency domain. Hence, every point (\(a\),\(b\)) is converted to another point (\(\omega_{f}\)(\(a\),\(b\)),\(b\)) using an operator named synchrosqueezed (\(T_{f}\)). This operation allows enhancing the frequency location and is defined as:  
\[T_{f}(\omega_{l},b)=\Delta\omega^{-1}\sum_{a_{k}:\omega_{f}(a,b)\in[\omega_{l }-\Delta\omega/2,\omega_{l}+\Delta\omega/2]}\;\;W_{f}(a_{k},b)a_{k}^{-3/2}( \Delta a)_{k} \tag{4}\]  
where \(\Delta\omega\) = \(\omega_{l}-\omega_{l-1}\) and \(\Delta a\) = \(a_{k}-a_{k-1}\).  
Finally, each individual mono-component, \(x_{i}(t)\), of the original signal, detected in the previous step, is recovered as follows:  
\[x_{i}(t)=2R_{\psi}^{-1}\text{Re}\left(\sum_{l}T_{f}\right) \tag{5}\]where \(R_{\psi}^{-1}\) is a constant calculated as:  
\[R_{\psi}=\int_{0}^{\infty}\frac{\dot{\psi}(\xi)}{\xi}d\xi, \tag{6}\]  
where \(\psi\) is the wavelet mother selected and \(\hat{\ }\) denotes the Fourier transform of \(\psi\). An in-depth explanation of SWT can be found in [42]. It should be pointed out that if the frequency value of the mode is desired, tools like the Fourier or Hilbert Transforms must be used [11].
# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 1 Introduction
Section 3 describes the proposed method, a 3D texture feature extraction technique for the analysis of volumetric images. Section 4 describes the experimental setup, Section 5 presents the obtained results, while the conclusions are given in Section 6. The proposed approach fuses two complementary feature vectors: a feature vector derived from the 3D version of the Block Matching and 3D Filtering Extended Local Binary Patterns (BM3 DELBP_3D) operator and a feature vector generated by the Improved 3D Gray-Level Co-occurrence Matrix (IGLCM_3D). BM3 DELBP_3D provides features that are robust to Gaussian noise and have good invariance properties. The extracted features are based on the sign of the difference between pixels, but the magnitude of the difference is not taken into consideration. This type of information is generated by the complementary feature vector given by the Improved 3D Gray-Level Co-occurrence Matrix. This method computes co-occurrence matrices based both on image intensity information and on the gradient image.
## 2 Materials and Methods
### Radiative Transfer Simulation  
The Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) is used to simulate satellite observations and related weighting functions regarding parameters retrieved for multi-angle viewing geometries. For UNL-VRTM to simulate TOA reflectance and polarization, the sensor setup, atmospheric profile, aerosol characteristics, and surface reflectance are also required as inputs. The vertical profiles of atmospheric temperature, pressure, and trace gases are presumed to be based on the mid-latitude summer atmospheric profiles from Optical Properties of the Atmosphere, Third Edition, AFCRL-72-0497. The 49-layer standard atmospheric profile is mapped to the GEOS-5 vertical grids with 47 hybrid pressure-sigma layers to increase the vertical resolution [30]. Meanwhile, the MT CKD model created by [PERSON], [PERSON], and [PERSON] is used to determine the continuum absorption of water vapor [31]. The UNL-VRTM is based on the VLIDORT model for calculating the Stokes vector \(\left[I,Q,U,V\right]^{T}\), and details about each module in UNL-VRTM can be found in [32], where \(I\) represents the total radiant intensity, \(Q\) represents the difference between the linearly polarized components parallel to and perpendicular to the reference plane, \(U\) describes the linearly polarized irradiance on the polarization plane at an angle of \(\pm 45^{\circ}\) from the reference plane, and \(V\) describes the circular polarization. We consider an observation vector comprising \(I\) and the degree of linear polarization (\(DoLP\)) which can eliminate the influence of absolute calibration, where \(DoLP\) is defined by  
\[DoLP=\frac{\sqrt{Q^{2}+U^{2}}}{I} \tag{6}\]  
Furthermore, the Jacobian matrix of the \(DoLP\) for each component of the state vector can be described as  
\[\frac{\partial DoLP}{\partial x}=-\frac{DoLP\frac{\partial I}{\partial x_{i}} }{I}+\frac{Q\frac{\partial Q}{\partial x_{i}}+U\frac{\partial II}{\partial x_ {i}}}{I\sqrt{Q^{2}+U^{2}}} \tag{7}\]  
Using the superscripts \(v_{1}\)-\(v_{\text{M}}\) to note each viewing in multiple observations, the measurement vector \(y\) can be described as  
\[y=\left[I_{\text{A}}^{v_{1}},\ldots,I_{\text{A}}^{v_{\text{M}}},DOLP_{\text{A }}^{v_{1}},\ldots,DOLP_{\text{A}}^{v_{\text{M}}}\right]^{T} \tag{8}\]  
The Jacobian matrix calculation of \(y\) to \(x\) is performed by the UNL-VRTM. Combined with the simulated propagation of errors and the posterior errors, DFS and a posterior error of ALH under different aerosol types and observed geometry can be derived from Equations (4) and (5). According to the results, we can carry out the sensitivity analysis.