esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 5 Conclusions  
In this paper, the influences of in situ stress on the energy generation and attenuation of blast-induced seismic waves were investigated by using theoretical analysis, numerical modeling, and a case study. The following conclusions are obtained:  
1. In situ stress has an important effect on the initial seismic energy generated in deep rock blasting. With the increase in the in situ stress level, the explosive energy consumed in the rock fracture is reduced, and more explosive energy is converted into seismic energy.
2. The blasting seismic energy attenuation, specifically its viscoelastic attenuation, is affected by the in situ stress level. The increasing in situ stress causes the seismic \(Q\) to first increase and then decrease, and thereby the attenuation rate of the seismic energy first decreases and then increases. The blasting seismic energy attenuation under in situ stress conditions is slower than that of the case without in situ stress. Compared to shallow rock blasting, the seismic waves generated in deep rock blasting are more likely to reach and exceed the PPV limits specified in the blasting vibration standards.
3. Under non-hydrostatic in situ stress, the blasting seismic energy generation and attenuation are anisotropic. The anisotropy becomes more significant with an increase in the lateral coefficient that is greater than 1.0. The seismic energy density generated in the minimum principal stress orientation is the highest. The attenuation rate of the seismic energy density is mainly affected by the stress that aligns with the wave propagation orientation.  
Conceptualization, J.Y.; methodology, J.S. and Y.J.; software, Y.Y.; investigation, J.Y. and J.S.; data curation, J.Y.; writing-original draft preparation, J.Y.; writing-review and editing, J.S. and Y.J.; supervision, Y.J.; project administration, Y.Y.; funding acquisition, J.S., Y.J. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant nos. 51969015 and 52179102), the Open Research Program of Hubei Key Laboratory of Blasting Engineering (Jianghan University) (grant no. HKLBF202007), and the Natural Science Foundation of Jiangxi Province (grant nos. 20204 BCJ23002 and 20204 BCJ24004).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
**Acknowledgments:** Special thanks for organizing and conducting the case study to the researchers in the State Key Laboratory of Precision Blasting, Jianghan University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 2 Fundamental Characteristics  
### High Resolving Power  
An echelle is a kind of blazed grating, and the grating equation is:  
\[\sin i+\sin\theta=m\lambda/d \tag{1}\]  
where \(i\) is the incidence angle, \(\theta\) is the diffraction angle, \(\lambda\) is the wavelength, \(d\) is the groove spacing, and \(m\) is the diffraction order.  
At a certain wavelength, when \(i\) and \(\theta\) satisfy the following condition, the light at this wavelength is blazed, and the diffraction efficiency is maximum.  
\[i+\theta=2\theta_{B} \tag{2}\]  
where \(\theta_{B}\) is the blaze angle of the grating. The angular dispersion can be obtained from the grating equation:  
\[\frac{d\theta}{d\lambda}=\frac{m}{d\cos\theta}=\frac{\sin i+\sin\theta}{ \lambda\cos\theta} \tag{3}\]  
and the theoretical resolving power:  
\[R=\frac{\lambda}{\delta\lambda}=mN=\frac{W}{\lambda}(\sin i+\sin\theta) \tag{4}\]  
where \(N\) is the total number of grooves illuminated on the grating surface, and \(W\) is the illuminated width of the grating. Equation (3) shows that the angular dispersion is only relevant to the incidence angle \(i\) and the diffraction angle \(\theta\). The larger \(i\) and \(\theta\) are, the higher the angular dispersion is. Equation (4) shows that the resolving power also increases with the increase in \(i\) and \(\theta\). Replacing \(\theta\) in Equation (4) with Equation (2), the resolving power at the blazed wavelength \(\lambda_{b}\) is expressed as:  
\[R_{b}=\frac{\lambda_{b}}{\delta\lambda_{b}}=\frac{W}{\lambda_{b}}(\sin i+\sin(2 \theta_{B}-i)) \tag{5}\]  
In differentiating Equation (5), we obtain the condition of the maximal resolution at the blazed wavelength:  
\[\frac{dR_{b}}{di}=\frac{W}{\lambda_{b}}(\cos i-\cos(2\theta_{B}-i))=0 \tag{6}\]  
When \(i=\theta_{B}\), the resolving power at the blazed wavelength is maximum. This type of layout is called the Littrow mode [25; 26]. At this time, the angular dispersion is:  
\[\frac{d\theta}{d\lambda}=\frac{2\tan\theta_{B}}{\lambda} \tag{7}\]  
and the resolving power is:  
\[R=\frac{\lambda}{\delta\lambda}=\frac{2W}{\lambda}\sin\theta_{B} \tag{8}\]  
Equations (7) and (8) show that the angular dispersion and the resolving power at the blazed wavelength directly depend on the blaze angle of grating, which is normally 50-80\({}^{\circ}\) in an echelle. Assume that there is an echelle with a groove spacing of 79 gr/mm and a blaze angle of 76\({}^{\circ}\). There is a common grating with a groove spacing of 1200 gr/mm and a blaze angle of 22\({}^{\circ}\). The illuminated width of both gratings is 50 mm. Under the condition of \(\lambda=632.8\) nm, the resolution of the common grating is ~60,000, and the resolution of the echelle is ~155,000. So, the echelles have a higher intrinsic resolving power than that of common blazed gratings.
###### Abstract
This manuscript then presents a comprehensive positioning performance assessment of individual GNSS constellation for four different scenarios: (i) nominal maritime navigation without the presence of jamming, (ii) maritime navigation under the influence of jamming on GNSS L1/E1/B1 signal, (iii) maritime navigation under the influence of jamming on GNSS L5/E5a/E5b signal, (iv) maritime navigation under the influence of jamming on lower and upper L-bands. Finally, the authors proposed a resilient MFMC receiver based on the implemented jamming event detector. The findings imply that, in the event of single-frequency jamming, the resilient MFMC receiver can work seamlessly by switching to the jamming-free GNSS signals using a jamming event detector. In the case of multi-frequency jamming, the proposed resilient MFMC receiver falls back to a traditional receiver with whatever GNSS signals it can track and tries to offer position solutions with those measurements. The jamming test campaign was carried out on a cruise ship named \"MS Megastar\" from Tallink in the Gulf of Finland, which is the easternmost arm of the Baltic Sea. An in-house software receiver known as \"FGI-GSRx\" [14; 15; 16; 17] is used to process and analyze the raw samples recorded by the radio front-end. In addition to FGI-GSRx, separate MATLAB scripts are utilized to analyze the results and generate appropriate figures. The other novelty of this work is the analysis of positioning performance for GPS-L5-only, Galileo-E5a-only, or Galileo-E5b-only signals in a maritime operational environment. To the authors' knowledge, there is little analysis present in the literature for comparing the performance of each signal on the L5/E5 band along with the legacy L-band signals (i.e., L1/E1/B1) for maritime navigation. The remainder of this manuscript is organized as follows: Section 2 describes the processing software, definition for jamming event detection, hardware setup for data collection, and the associated front-end configuration. Section 3 provides an overview and concept illustration of the proposed resilient MFMC GNSS receiver. Section 4 demonstrates four different test scenarios that were stated earlier in this section. Section 5 discusses the performance of several GNSS signals in four different test scenarios. Finally, Section 6 concludes the manuscript with the findings of the experimental analysis and some guidelines for future work.
We particularly summarize the progress in the utilization of different molecular biomarkers for RT toxicities and highlight a need for improving the relative knowledge. These research efforts are expected to be significantly facilitated by big data and artificial intelligence in the future and eventually allow the identification of accurate and molecular biomarkers of radiotoxicity toward personalizing RT therapeutic regimens. The main goal of this paper is to provide a comprehensive review of the existing data on the utilization of molecular biomarkers for predicting radiosensitivity in human patients. Therefore, we have excluded genomic-based studies reporting on the possible use of gene signatures for predicting tumor radiosensitivity and in general response to radiation therapy (for an extensive review please see [12]) or markers on the response of tumor microenvironment [13]. We retrieved relevant literature from PubMed and Google Scholar. As our focus was the clinical relevance of molecular biomarkers for predicting radiosensitivity and toxicity in RT, we used the 'clinical trial' filter on PubMed, while using the following related keywords: 'patients radiosensitivity', 'patients radiotoxicity', 'radiation induced damage', 'radiosensitivity molecular markers', 'radiosensitivity biomarkers', and 'radiosensitivity in radiotherapy' to discover the Journal articles that fulfill these requirements. According to our literature research, five studies have examined the use of DNA Damage Response (DDR)-related biomarkers in the last five years to predict radiosensitivity/radiotoxicity using patient samples and only one has examined apoptotic markers. Seven articles were found for biomarkers involving secreted soluble proteins, whereas nine studies looked at biomarkers involving cell-free DNA (cfDNA) and two referred to machine-learning algorithms employed for patient radiosensitivity predictions.
# Time Domain NMR in Polymer Science: From the Laboratory to the Industry
## 2 Sequences
### \({}^{1}h\) T\({}_{1}\) Relaxation  
It is the characteristic time that describes the recovery of magnetization along the static field direction axis, or longitudinal relaxation. Since it involves a variation of the total energy of the system, it implies an energy exchange between the spin system and the environment. This is allowed if the energy gap between the high-energy and low-energy spin states overlaps with the vibrational and rotational energies associated with motional states of the atoms in the surrounding environment. Analysis of T\({}_{1}\) relaxation gives back information about faster segmental dynamics, involving motion of side groups or rotations in the backbone.  
It is usually measured with saturation recovery (SATREC) [17] (Figure 2d) or inversion recovery (INVREC) [18] (Figure 2c) experiments. The main difference is the initial flipping of magnetization in the INVREC, substituted in the SATREC by a train of \(\pi\)/2 pulses, to set the magnetization in the transverse plane to zero. Inversion provides better quality fittings, but it is unsuitable in highly coupled systems where quantitative inversion is not easily achieved.  
Ideally, signals acquired with these sequences are fitted by a sum of exponential decay functions, corresponding to different phases within the sample. Operatively single exponential decay is usually prevalent, even for multiphase materials, because of spin diffusion [3,4]. In fact, T\({}_{1}\) measurements require a longer time than T\({}_{2}\) measurements, since T\({}_{1}\) is longer than T\({}_{2}\), and consequently the magnetization has the time to transfer from one domain to another during the measurement, averaging the magnetization all over the samples. That makes it difficult to distinguish between different phases, especially at low-field where the longitudinal relaxation occurs on the same timescale as the spin-diffusion process.
## 6 Conclusions  
This study investigated the influence of superhydrophobic nano-SiO\({}_{2}\) on hydraulic conductivity and PSD of expansive soil and then analysed the mechanism of modification between superhydrophobic nano-SiO\({}_{2}\) and expansive soil from a microscopic view. Based on the investigation, the following conclusions can be summarised:  
1. The hydraulic conductivity of soil samples decreased with increasing superhydrophobic nano-SiO\({}_{2}\) content.
2. The PSD of expansive soil was changed by adding superhydrophobic nano-SiO\({}_{2}\). After the hydraulic-conductivity test, the pore radius of micropore, medium pore, and  
Figure 16: The permeable pore volume of soil samples with different SiO\({}_{2}\) contents.  
Figure 17: The relationship between permeable pore volume and hydraulic conductivity.  
macropore of modified soil was smaller than that of unmodified soil. This finding indicated that the pore volume of soil samples decreased with increasing nano-SiO\({}_{2}\) content in the range of 0.0-1.0%.
* FTIR results indicated that the silanol on the nano-SiO\({}_{2}\) surface reacted with the silanol on the surface of quartz to form new siloxane (=Si-O-Si=) after adding nano-SiO\({}_{2}\), causing the nano-SiO\({}_{2}\) to adhere onto the surface of the soil particles and thus reducing the hydrophilicity of expansive soil. After adding superhydrophobic nano-SiO\({}_{2}\), the contact angle of samples increased from 15\({}^{\circ}\) to 67\({}^{\circ}\). And TGA results revealed that the content of interlayer water and adsorbed water in montmorillonite decreased after adding superhydrophobic nano-SiO\({}_{2}\).
* Based on the above test results, it can be stated that superhydrophobic nano-SiO\({}_{2}\) could reduce the hydraulic conductivity by changing the PSD of expansive soil. It had an excellent linear relationship with the hydraulic conductivity and permeable pore volume of samples containing different nano-SiO\({}_{2}\) contents.  
The paper was written by [PERSON] under the guidance of [PERSON]. The formal analysis and pre-literature research were carried out by [PERSON] and [PERSON]. The laboratory tests were carried out by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the financial support of the National Key R&D Program of China (Grant No. 2019 YFC1509901) and the National Natural Science Foundation of China (Grant No. 41772339).  
All data generated or analysed during this study are included in this published article.  
The authors appreciate to anonymous reviewers whose constructive comments were helpful for the improvement of this paper.  
The authors declare no conflict of interest.
## 5 Future Perspectives
### Expansion of Application Fields
#### 5.2.2 New Energy and Clean Energy Technologies  
In the field of new energy and clean energy, SiC materials exhibit broad application potential due to their high thermal conductivity, high temperature resistance, and excellent oxidation resistance. Especially under high-temperature conditions, SiC materials can effectively improve the efficiency of thermal management systems and reduce the energy consumption of equipment [204]. Through SiC 3D printing technology, complex structures such as heat sinks, heat exchangers, and core components for energy conversion devices can be manufactured, further enhancing the overall performance of new energy systems. Additionally, SiC's chemical stability and structural durability make it highly promising for future applications in clean energy fields, such as solar cell and fuel cell components.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 5 Conclusions and Future Works
In the ConvLSTM, which considers spatial relationships, this can adversely affect accuracy. Therefore, there is an additional need for a method to manage the influence of land on the input data in the future. Ocean weather has significant features for each region. Therefore, in order to build good learning models, it is necessary to divide it into areas with similar characteristics, such as the North Sea, the Atlantic Ocean, and the Pacific Ocean. Probably considering these characteristics, it is expected that we can build models that are superior to the error rate suggested in this study. In addition, we plan to carry out a sensitivity test for shifting prediction and to verify the proposed model with the climatology. Therefore, we plan to improve the models by reflecting the point of view in the future. Conceptualization, K.-S.K., J.-B.L., and M.-I.-R.; methodology, K.-S.K., J.-B.L., and M.-I.-R.; software, K.-S.K. and J.-B.L. ; validation, K.-S.K., J.-B.L., and M.-I.R. ; formal analysis, K.-S.K., J.-B.L., and M.-I.R. ; investigation, K.-S.K., J.-B.L., and M.-I.-R.; resources, M.-I.R. ; data curation K.-S.K. and [PERSON]-B.L. ; writing--original draft preparation, K.-S.K. ; writing--review and editing, M.-I.R. ; visualization, K.-S.K. ; supervision, M.-I.R. ; project administration, M.-I.R. ; funding acquisition, M.-I.R., K.-M.H., and G.-H.L. All authors have read and agreed to the published version of the manuscript. This work was partially supported by (a) Korean Register, Republic of Korea, (b) HRD Program for Green & Smart Ship funded by the Ministry of Trade, Industry and Energy, Republic of Korea (No. P0001968), (c) Research Institute of Marine Systems Engineering of Seoul National University, Republic of Korea, and (d) Institute of Engineering Research of Seoul National University, Republic of Korea. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# FDA Approvals of Biologics in 2022
## 2 Analysis  
Several factors, among them industry failures and/or political issues, can explain a drop in the number of authorizations. For example, 2016 registered a decrease in submissions and also an increase in the rejection rate of drugs by the FDA. Of note, 2016 was also an election year in the United States, with [PERSON] taking office in January 2017 [11; 12]. The following election year in that country was 2020, with [PERSON] taking over in January 2021, and there was a completely different scenario in terms of the total number of drug approvals, as that year registered the second highest figure (53) in the period 2015-2022. Importantly, the World Health Organization (WHO) declared the COVID-19 pandemic in 2020. In response, the FDA, along with other organizations, devoted efforts to research, and accelerated approval and review processes, resulting in many Emergency Use Authorizations issued for vaccines and other drugs. This scenario also affected the authorization process of specific drugs such as Veklury(tm) (Rendesivir-2020), for example, indicated to treat COVID-19 [5; 13].  
Although 2016 registered the lowest number of drug authorizations in that period, it witnessed the approval of two key antibacterial mAbs, namely Anthim(tm) (oblitoxaximab), indicated to treat inhalation of _Bacillus anthracis_ (anthrax), and Zinplava(tm) (bezotoxumab), indicated to reduce recurrent infections of _Clostridium difficile_[9]. Oblitoxaximab and bezotoxumab were the only antibacterial mAbs approved in the period 2015-2022, and this fact gains relevance in the context of increasing antibacterial/ antimicrobial resistance [12; 14; 15].  
As seen in Figure 1, in 2022, the number of approvals of the three classes of Biologics hardly varied from the figures of previous years. Similarly, mAbs for cancer continued to be the therapeutic indication receiving the most approvals, with six Biologics, followed by autoimmune conditions, with four. The other conditions and diseases targeted by the Biologics authorized this year included eschar removal from thermal burns, aesthetic purposes, chemotherapy-induced neutropenia (CIN), eye disorders, and acid sphingomyelinase deficiency (ASMD).  
Fifteen Biologics were approved in 2022 and mAbs continued to account for the majority of FDA approvals among them. The authorization of mAbs in 2022 was slightly higher than in 2021 (nine vs. eight, respectively), the same applied for proteins and enzymes, while there was one less ADC approval than in 2021. The authorization of a new mAb for Alzheimer's Disease (AD) was expected in 2022, but it was finally approved in January 2023.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Total Drugs Approved**} & \multirow{2}{*}{**Biologics Approved**} & \multirow{2}{*}{**References**} \\  & **(Biologics and NCEs)** & & \\ \hline
2022 & 37 & 15 (40.5\%) & [3] \\
2021 & 50 & 14 (28\%) & [4] \\
2020 & 53 & 15 (28.3\%) & [5] \\
2019 & 48 & 12 (25\%) & [6] \\
2018 & 59 & 17 (28.8\%) & [7] \\
2017 & 46 & 13 (28.2\%) & [8] \\
2016 & 22 & 7 (31.8\%) & [9] \\
2015 & 45 & 13
# IoT-Driven Intelligent Scheduling Solution for Industrial Sewing Based on Real-RCPSP Model
## 5 The Real-RCPSP Problem Applying in Automated Production Planning
### The Real-RCPSP Problem  
This article proposes using the Real-RCPSP problem for production planning, which is very suitable for this purpose because it considers resource, worker level, and real-time constraints. The Real-RCPSP problem is described through specific mathematical constraints, presented in detail in Formulas (1) to (10) below. Table 4 presents the list of notations used to define the Real-RCPSP problem. The objective function of the Real-RCPSP problem is as follows:  
\[f(P)\to min \tag{1}\]  
where:  
\[f(P)=\underset{W_{i}\in W}{max}\{E_{i}\}-\underset{W_{i}\in W}{min}\{B_{k}\} \tag{2}\]  
The objective function of the problem aims to minimize the project completion time (referred to as makespan). Makespan is determined based on the completion time of all tasks assigned to the resource that finishes the work last. Subject to:  
\[S^{k}\
eq\emptyset\hskip 42.679134 pt\forall\;L_{k}\in L \tag{3}\]  
\[t_{jk}\geq 0\hskip 42.679134 pt\forall\;W_{j}\in W,\hskip 42.679134 pt\forall\;L_{k}\in L \tag{4}\]  
\[E_{j}\geq 0\hskip 42.679134 pt\forall\;W_{j}\in W \tag{5}\]  
\[E_{i}\leq E_{j}-t_{j}\;\forall\;W_{j}\in W,j\
eq 1,W_{i}\in C_{j} \tag{6}\]  
\[\forall W_{i}\in W^{k}\exists S_{q}\in S^{k}:S_{S_{q}}=g_{r_{i}}\text{and}\;h_{ S_{q}}\geq h_{r_{i}} \tag{7}\]  
\[\forall L_{k}\in L,\forall q\in m:\sum_{i=1}^{n}A_{i,k}^{q}\leq 1 \tag{8}\]\[\forall W_{j}\in W\exists tq\in[0,m],tL_{k}\in L:A_{j,k}^{q}=1;\text{ where }A_{j,k}^{q}\in\{0;1\} \tag{9}\] \[t_{ik}\leq t_{il}\,\
u\delta i\;h_{k}\leq h_{l}\,\forall\;(r^{k},r^{l})\in \{S^{k}\times S^{l}\} \tag{10}\]  
The specific constraints are as follows:  
* Constraint (3): Each resource must have at least one skill. * Constraints (4) and (5): The execution time for any task must be at least 0. (In practice, the execution time for any real task is always > 0; the 0 case illustrates two dummy tasks representing the project's start and end times). * Constraint (6): A parent task (task \(i\)) must finish before its child task (task \(j\)) begins. The end time of task \(i\) is denoted as \(E_{i}\), and the start time of task \(j\) is \(E_{j}-t_{j}\) (end time minus execution time). * Constraint (7): For every task \(i\in W^{k}\) (the set of tasks that resource \(k\) can perform), there must exist a skill \(S\in S^{k}\) (the set of skills of resource \(k\)) such that \(g_{S_{q}}=g_{r_{i}}\) (the skill type of \(S\) matches the skill type required by task \(i\)) and \(h_{Sq}\geq h_{rl}\) h (the skill level of the resource performing the task is greater than or equal to the skill level required for the task). * Constraint (8): At any given time (\(q\)), each resource can perform at most one task. If \(\sum_{i=1}^{n}A_{i,k}^{q}=\)0, the resource \(k\) is not assigned to any task. If \(\sum_{i=1}^{n}A_{i,k}^{q}=\)1, the resource \(k\) is assigned to exactly one task. * Constraint (9): Each task must be assigned to exactly one resource and can only be executed by that single resource.