esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Situ Monitoring and Analysis of the Fitting Corrosion of Carbon Steel by Acoustic Emission
## Materials and Methods  
### Material Preparation  
Q235 carbon steel was used for the measurements and the chemical composition of the specimens is listed in Table 1. The specimens were cut out from a cold rolled sheet of 2 mm thick steel before measurements and coated with a high-temperature resistant silicone gasket to keep the exposed surface as 15 mm \(\times\) 15 mm. The specimens were abraded gradually from 180 to 1000 grit silicon carbide paper. Then, they were rinsed with deionized water and acetone, and were dried and stored before use.
## 6 Conclusions and Outlook
Finally, the off-track results need to be further evaluated in future research based on more reliable independent dust detection products, for example, collocated ground-based AeroNET and/or lidar observations. In this study, we have achieved our main objective, i.e., exploring the feasibility of using the ML methods trained on the CALIOP track for off-track dust detection. The results are very encouraging and also inspiring. Many questions are raised during this study that warrant further investigations in the future. Here are a few examples. TheRF method ranked three shortwave infrared bands and one thermal radiation bands as the most important bands in terms of information content for dust detection. What are the underlying physics for this ranking? The very similar results based on the 0-D and 2D input data structures seem to suggest that the adjacent pixels provide little additional information for dust detection. Is this due to the nature of dust plume (i.e., a lack of spatial structure) or a result of inadequate spatial window (i.e., larger or smaller than 5 \(\times\) 5 pixels for 2D)? Conceptualization, J.L., Y.R.S., and C.C. ; methodology, J.L and Y.R.S. ; software, J.L. ; validation, J.L., Y.R.S., and P.C. ; formal analysis, J.L. and Y.R.S. ; investigation, J.L. and Y.R.S. ; resources, J.W. and Z.Z. ; writing--original draft preparation, J.L., Y.R.S., and Z.Z. ; writing--review and editing, J.L., Y.R.S., and Z.Z. ; visualization, J.L., Y.R.S. ; supervision, Z.Z. ; project administration, A.G. and Z.Z. ; funding acquisition, J.W. and Z.Z. All authors have read and agreed to the published version of the manuscript. This research is supported grants from NASA's CCST program (Grant No. 80 NSSC20K0130) managed by [PERSON] and the NSF Cybertraining program (Grant No. OAC-1730250). The computational resources are provided by the UMBC High Performance Computing Facility (HPCF) which was supported by the U.S. National Science Foundation through the MRI program (Grant Nos. CNS-0821258, CNS-1228778, and OAC-1726023) and the SCREMS program (Grant No. DMS-0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. All data used in this study including the Machine Learning model outputs and PHYS retrievals can be distributed upon request. We thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for inspiring discussions and suggestive comments. The authors declare no conflict of interest.
###### Abstract  
The omics disciplines remain largely distinct sciences due to the necessity of separating molecular classes for different assays. For example, water-soluble and lipid bilayer-bound proteins and metabolites are usually studied separately. Nonetheless, it is at the interface between these sciences where biology happens. That is, lipid-interacting proteins typically recognize and transduce signals and regulate the flow of metabolites in the cell. Technologies are emerging to converge the omics. It is now possible to separate intact membrane:protein assemblies (memteins) directly from intact cells or cell membranes. Such complexes mediate complete metabolon, receptor, channel, and transporter functions. The use of poly(styrene-_co_-maleic acid) (SMA) copolymers has allowed their separation in a single step without any exposure to synthetic deterg or artificial lipids. This is a critical development as these agents typically strip away biological lipids, signals, and metabolites from their physiologically-relevant positions on proteins. The resulting SMA lipid particles (SMALPs) represent native nanodiscs that are suitable for elucidation of structures and interactions that occur in vivo. Compatible tools for resolving the contained memteins include X-ray diffraction (XRD), cryo-electron microscopy (cryoEM), mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy. Recent progress shows that memteins are more representative than naked membrane proteins devoid of natural lipid and is driving the development of next generation polymers.  
interactome; lipid; lipidomics; membrane structure; transmembrane protein; memtein; metabolomics; metabolon; proteome; native nanodisc; styrene maleic acid; SMALP
###### Abstract  
This article presents a new design for a dual-band circular polarization microstrip patch antenna that can be used in unmanned aerial vehicle (UAV) applications. The proposed antenna consists of an etched circular shape on the radiator side of the antenna with multiple slots and stubs. The bottom side comprises a partial ground plane with multiple horizonal, vertical and square slots. These shapes on the front and bottom sides of the antenna are used to keep the resonant frequencies, impedance bandwidth and axial ratio (AR) at the desired values. The antenna operation is within the WiFi frequency bands, achieving maximum gains of 5.01 and 5.27 dBi at 2.4 and 5 GHz, respectively. Circular polarization (CP) is effectively realized through the implementation of opposite truncated corners and intentionally located stubs. The 3 dB axial ratio bandwidth (ARBW) is significantly enhanced, while a defected ground structure (DGS) is utilized to further improve the bandwidth and gain. The optimized antenna has overall dimensions of 40 \(\times\) 40 \(\times\) 1.6 mm\({}^{3}\) and demonstrates a wide \(-\)10 dB reflection bandwidth of 5.38% (2.396-2.525 GHz) and 9.26% (4.91-5.38 GHz), along with a broad 3 dB axial ratio bandwidth (ARBW) of 380 MHz (2.29-2.67 GHz) and 80 MHz (5-5.08 GHz). The proposed antenna is fabricated using a low-cost FR-4 substrate with a dielectric constant of 4.4 and a loss tangent of 0.02. The fabricated antenna is experimentally characterized to verify the design concept as well as to validate the simulation results. It is found that the experimental measurements correlate very well with the simulation results. A comparison with comparable designs in the literature shows that the proposed antenna provides a higher gain with a relatively reduced size.  
m pacs: 1
## 3 Measurements
### B--Laboratory network:  
1. A pair of BPL modems and 50 m of extension cord, see Figure 5;
2. A pair of BPL modems and 50 m of extension cord, where a repeater was placed in the middle of the power line, see Figure 6.  
For examination of noise effects, in an isolated network, the isolation transformer and its filtration ability ensure the best possible communication channel for BPL modems.  
BPL modems in laboratory testing used a frequency range of 2-12 MHz with the Fast Fourier transform orthogonal frequency-division multiplexing (FFT OFDM) on the physical layer. The cable used in this set of measurements was CGSG 3 Gx2.5.  
Both ends of all presented topologies were terminated by network analyzers. For all four topologies, the RFC 6349 test was performed. Results can be seen in Figure 7.  
Figure 4: A2 topology—with repeater.  
Figure 5: B1 topology—direct connection.  
Figure 3: A1 topology—direct connection.  
It is obvious that the repeater caused a significant drop in throughput, especially for the isolated network topology. Throughput in topology (A2) with the repeater was reduced to 36.2% of the throughput in the direct connection (A1) topology.  
In the laboratory network, the repeater did not lead to such an extreme throughput drop. The throughput drop experienced in this topology (B2) was 56.1% of throughput of the direct connection topology (B1). It is important to remember that in the laboratory network topology, the distance between modems was reduced to half to compensate for the significant noise present on powerlines in such configuration.  
An isolated network, via usage of an isolation transformer, provides almost ideal conditions for PLC network communication in terms of noise level. On the other hand, noise present in topologies B1 and B2 represents a common level of noise, which is typical for powerlines in buildings. Devices connected into the powerline network closest to the tested PLC network were computers, displays and racks with telecommunication devices such as routers and switches. These devices introduce noise typical for switch-mode power supplies. Usage of any devices based on electric motors was avoided during tests of topologies B1 and B2.  
The main motivation for using both isolated and laboratory networks was to investigate differences between the performance of PLC network in almost ideal conditions and in conditions with regular noise present in powerlines. Isolated networks also enable the same tests to be repeated under the same conditions for all PLC technologies (replicable and repeatable conditions).  
Based on the measurements presented in this section, we expect the throughput drop caused by the repeater to be in the range from 35% to 60% of throughput of the direct connection without repeater.
## 4 Mechanism of Layered Grouting Reinforcement for Surrounding Rock of Roadway
### Mechanism of Roof Layered Grouting Reinforcement
The support resistance of the grouting layer is determined by the strength of the grouting body [39]:  
\[P=\frac{[\sigma]}{2}\bigg{[}1-\left(\frac{r}{R}\right)^{2}\bigg{]} \tag{2}\]  
where \([\sigma]\) is the uniaxial compressive strength of rock mass after grouting, \(r\) is the support radius of the roadway, and \(R\) is the support radius of the grouting layer. According to the empirical strength formula of the grouted rock mass [40], the strength of rock mass after grouting can be expressed as:  
\[[\sigma]=\frac{3.2543\times\sigma_{c}}{\big{[}2.45+\log_{10}\sigma_{c}-\log_ {10}q_{c}\big{]}^{2}} \tag{3}\]  
where: \(\sigma_{c}\) is the uniaxial compressive strength of rock mass before grouting, and \(q_{c}\) is the strength of grout stone in grouting. Therefore, the bending moment \(M_{1}\) of the \"shallow\" support body beam can be expressed as:  
\[M_{1}=\frac{qL^{2}}{2}-LP_{1} \tag{4}\]  
The bending moment \(M_{2}\) of the \"deep\" support body beam can be expressed as:  
\[M_{2}=\frac{qL^{2}}{2}-LP_{2} \tag{5}\]  
The bending moment formula of the beam can be obtained by substituting Equations (2) and (3) into Equations (4) and (5):  
\[M_{1}=\frac{qL^{2}}{2}-\frac{1.627\sigma_{c}L\times\big{(}2 ar+a^{2}\big{)}}{ \big{[}2.45+\log_{10}\sigma_{c}-\log_{10}q_{c}\big{]}^{2}\times\left(r+a\right) ^{2}} \tag{6}\]  
\[M_{2}=\frac{qL^{2}}{2}-\frac{1.627\sigma_{c}L\times\big{(}2 hr+h^{2}\big{)}}{ \big{[}2.45+\log_{10}\sigma_{c}-\log_{10}q_{c}\big{]}^{2}\times\left(r+h\right) ^{2}} \tag{7}\]  
According to Equations (6) and (7), the main factors affecting the bending moment are the strength of the grouting fluid stone body, the rock mass strength of each rock stratum, and the thickness of the grouting layer. According to the mechanical model analysis, after the roof is strengthened by layered grouting, the bending moments of the \"shallow\" and \"deep\" support body beams are reduced, and their abilities to resist bending deformation increase. At the same time, the normal bending stress in the beam of each support body  
Figure 9: Simply supported beam model of roof. is also reduced, indicating layered grouting can effectively control the deformation and failure of the roof. The strength parameters of grouting stones are obtained through laboratory mechanical experiments. The strength of \"shallow\" grouting material is 30.86 MPa and that of \"deep\" grouting material is 37.17 MPa. It substitutes the roadway parameters into Equations (6) and (7), and it can be obtained that the bending moment of the \"shallow\" support body beam decreases by 20.78 \(\times\) 10\({}^{6}\) N\(\cdot\)m, and the bending moment of the \"deep\" support body beam decreases by 26.50 \(\times\) 10\({}^{6}\) N\(\cdot\)m.
## 2 Materials and Methods
### Health Risk Assessment Model  
The health risk assessment model is a method used to estimate the nature and probability of adverse health effects in humans who may be exposed to toxic contamination in the environmental media, presently or in the future. There are several mechanisms of exposure to OCPs contamination, however, the most important one is oral exposure [37; 38]. In the present study, the US EPA health risk assessment model is applied for the approximation of carcinogenic and non-carcinogenic risks for adults and children consuming underground water as their drinking water source [39]. To estimate human exposure to contaminants, chronic daily intake (CDI) is used and can be calculated with the following formula [40].  
\[\text{CDI}=\frac{\text{C }\times\text{ IR }\times\text{ EF }\times\text{ ED}}{\text{BW }\times\text{AT}} \tag{1}\]  
Figure 2: A typical chromatogram for 20 organochlorine pesticides standard and peaks identification: (1) \(\alpha\)-BHC, (2) \(\beta\)-BHC, (3) \(\gamma\)-BHC, (4) \(\delta\)-BHC, (5) heptachlor, (6) aldrin, (7) heptachlor epoxide, (8) \(\gamma\)-chlordane, (9) \(\alpha\)-chlordane, (10) endosulfan I, (11) 4,4\({}^{\prime}\)-DDE, (12) dieldrin, (13) endrin, (14) endosulfan II, (15) 4,4\({}^{\prime}\)-DDD, (16) endrin aldehyde, (17) endrin ketone, (18) 4,4\({}^{\prime}\)-DDT, (19) endosulfan sulfate, (20) methoxychlor.  
where C = chemical concentration in water (mg L\({}^{-1}\)); IR = water ingestion rate (l day\({}^{-1}\)) (for children: IR = 1.0; for adults: IR = 2.0); EF = the exposure frequency (350 days year\({}^{-1}\)); ED = exposure duration (year) (for children: ED = 6; for adults: ED = 70); BW = bodyweight (kg) (for children: BW = 14; for adults: BW = 60); AT = average lifespan (days) (for children: AT = 2190; for adults: AT = 25,550).  
According to the US EPA exposure factors handbook, carcinogenic risk (R) can be calculated as follows [41];  
\[\text{R}=\text{CDI}\times\text{SF} \tag{2}\]  
where CDI is the chronic daily intake from the oral exposure route (mg kg\({}^{-1}\) per day), SF is the slope factor of the contaminant via the oral exposure route [(mg kg\({}^{-1}\) per day)\({}^{-1}\)]. Non-carcinogenic risk estimated by hazard quotient (HQ) is calculated using the following formula;  
\[\text{HQ}=\text{CDI}/\text{R}_{\text{f}}\text{D} \tag{3}\]  
where R\({}_{\text{f}}\)D (mg kg\({}^{-1}\) per day) is the reference dose of the contaminant via oral exposure route. The values of slope factor and reference dose (Table 1) for OCPs are obtained from the US EPA integrated risk information System [42].
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
###### Abstract
[29] showed that optimized SVMs can detect and locate damage accurately with autoregressive (AR) coefficients and residual errors as features. [PERSON] et al. [30] used wavelet packet transform with least square SVM to detect damage. [PERSON] et al. [31] demonstrated successful use of SVM models with wavelet transform, Hilbert-Huang transform (HHT), and Teager-Huang transform (THT) on a numerical cable stayed bridge model. In most of these studies, ambient vibrations were used as the input excitation. Earthquake vibrations caused by strong ground motion have rarely been utilized with ML classifiers. Moreover, most of the current ML aided SHM methodologies require high-dimensional features. As a result, the issue of the _curse of dimensionality_ arises, i.e., the size of the training dataset needs to be large for such methodologies. This is a challenge for the earthquake damage assessment problem as the data from recorded earthquakes are usually limited in size. Low dimensional features are crucial for successful implementation of ML algorithms for such a problem. Besides, low-dimensional and computationally efficient models allow using ML for _edge devices_ and enable real-time damage assessment. Therefore, the study presented in this paper proposes a novel approach that uses low-dimensional damage features in ML classification models on strong motion data of instrumented structures. Figure 1 shows the overview of the approach where CAV based features are extracted from the acceleration response of a structure following an earthquake. Subsequently, ML models are developed to detect and assess the condition of the structure.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 5 Implementation Issue
### SIC-Error Propagation  
SIC is often assumed to be successful with perfect decoding. However, for systems with actual modulation and coding, decoding error inevitably occurs, causing error propagation and remarkable performance degradation. As shown in the simulation results, there are performance differences with and without SIC errors. Ultimately, well-designed system for the proposed scheme which avoids error propagation and decoding error should be considered for optimum performance.
## 4 Experiments
### Low-Bit Quantization  
Since LWAS and DSP both focus on designing model architectures, it is orthogonal to the general acceleration techniques, e.g., low-bit quantization. We further employ quantization methods to reduce model sizes without removing parameters. As shown in Table 5, we introduce additional quantization methods into the LWMD platform. With an acceptable performance loss, LWMD can achieve 4.27\(\times\) parameter reduction with the float16 precision and 8.55\(\times\) parameter reduction with the int8 precision. In this way, an extreme compression result can be achieved by the LWMD platform.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pruning Methods** & **\#Params (M)** & **FLOPs (G)** & **Dev CER** & **Test CER** \\ \hline w/o pruning & 17.7 & 2.81 & 4.7 & 5.2 \\ random pruning & 13.2 & 2.13 & 5.2 & 5.8 \\ structured \(l_{1}\)-pruning & 13.2 & 2.13 & 5.0 & 5.6 \\ structured \(l_{2}\)-pruning & 13.2 & 2.15 & 5.0 & 5.5 \\ DSP w/o Attention-similarity Loss & 13.2 & 2.13 & 4.8 & 5.4 \\ DSP with Attention-similarity Loss (Default) & 13.2 & 2.13 & 4.7 & 5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ablation studies of pruning algorithms on Aishell-1. For random pruning, structured \(l_{1}\)-pruning and structured \(l_{2}\)-pruning, we manually set the pruning ratio to ensure they have close pruning ratios for a fair comparison.  
Figure 4: Visualized attention weights. (**a**) is the original attention weight without any pruning, (**b**) is the attention weight pruned with the attention-similarity loss, and (**c**) is the attention weight pruned without the attention-similarity loss.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pruning Methods** & **\#Params (M)** & **Dev CER** & **Test CER** \\ \hline Conformer Baseline & 32.5 & 4.7 & 5.2 \\ LWMD (float32) & 13.2 & 4.7 & 5.2 \\ LWMD + float16 & 7.6 & 4.9 & 5.3 \\ LWMD + int8 & 3.8 & 5.2 & 5.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Exploration results of low bit quantization on the LWMD platform. We try to introduce the float16 precision and the int8 precision into LWMD.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 4 The Proposed Scheme
### The Re-Encryption Processes
#### 4.3.6 Data Retrieval by a Third Party  
After the third party receives the requested pair \((\mathcal{C},\ \mathcal{E})\) via the process presented in Section 4.3.5, \(\mathcal{E}\) is first decrypted to obtain the symmetric key \(K\) with secret key \(\mathtt{sk}_{t}=(x_{t,1},\ x_{t,2})\). Subsequently, the third party decrypts \(\mathcal{C}\) with \(K\), and verifies the integrity using the verification algorithm of the digital signature. The processes involved are as follows:  
1. The third party calculates \(W\oplus H_{2}(V^{\frac{1}{n_{2}}})\). The following result can be obtained if no problem arises: \[W\oplus H_{2}(V^{\frac{1}{n_{2}}})=H_{2}(g^{\
u})\oplus(h\|\pi)\oplus H_{2}( g^{x_{t,2}\cdot\cdot\cdot\frac{1}{n_{2}}})=(h\|\pi).\]
2. Using the result of Step 1, the third party derives \(K\) by calculating \(F\oplus H_{2}(E^{\prime\frac{1}{h}})\). If no problem arises, the following result can be obtained: \[F\oplus H_{2}(E^{\prime\frac{1}{h}})=H_{2}(g^{\prime})\oplus(K\|\omega)\oplus H _{2}\Bigg{(}\bigg{(}pk_{M,1}^{H_{4}(p\{k_{M,2}\}}\cdot pk_{M,2}\bigg{)}^{r \times k_{M\to t}^{<1>}\cdot\frac{1}{h}}\Bigg{)}\] \[=H_{2}(g^{\prime})\oplus(K\|\omega)\oplus H_{2}\Bigg{(}g^{\prime}(x_{M,1} \cdot H_{4}(p\{k_{M,2}\}+x_{M,2})\cdot\frac{r\cdot b}{(x_{M,1}\cdot H_{4}(p\{ k_{M,2}\}+x_{M,2})})\cdot\frac{1}{h}\Bigg{)}\] \[=H_{2}(g^{\prime})\oplus(K\|\omega)\oplus H_{2}(g^{\prime})=(K\|\omega);\]
3. The third party verifies whether \(V=pk_{t,2}^{H_{1}(h,\pi)}\) and \(E^{\prime}=g^{H(K,\omega)\cdot h}\) hold. If they do not hold, then the third party rejects the received data and requests them again from the server;
4. Using the decrypted symmetric key \(K\), the third party decrypts \(\mathcal{C}\) using the AES decryption scheme. Subsequently, \((M\|S)\) is obtained, where \(M\) is the plaintext of the requested EHR, and \(S\) is its signature;Finally, the third party verifies the integrity of \(M\) via the process to verify the digital signature using \(S\). If the verification is successful, then the third party accepts \(M\) and terminates the process.
## 3 Materials and Methods
### Network Model  
Wireless channels are interchangeable, and both sending and receiving sides are multidirectional. The transmitter's energy consumption is calculated by Equation (1):  
\[E_{tx}(j,d)=\left\{\begin{array}{ll}E_{tx-elec}\times j+\varepsilon_{fs}\times j \times d^{2},&d<d_{0}\\ E_{tx-elec}\times j+\varepsilon_{amp}\times j\times d^{4},&d\geq d_{0}\end{array}\right. \tag{1}\]  
The energy consumed by the transmission circuit when transmitting 1 bit of data is denoted by \(E_{tx-elec}\). The energy amplification parameters \(\varepsilon_{fs}\) and \(\varepsilon_{amp}\) are linked to the chosen transmission-energy- consumption approach. \(\varepsilon_{fs}\) represents free-space transmission, whereas \(\varepsilon_{amp}\) multipath loss represents attenuated transmission.  
The boundary condition that distinguishes the two models is \(d_{0}\), \(d_{0}=\sqrt{\varepsilon_{fs}}\). When the transmission distance, d, is more significant than d_0, as transmission distance increases, so does the transmitter's energy consumption. The energy consumed by the receiving side when receiving 1 bit data at the receiving end is as follows in Equation (2):  
\[E_{rx}(j,d)=E_{tx-dec}\times j \tag{2}\]