esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Cycled Uplink Resource Allocation over NB-IoT
## 2 System Model and Problem Formulation
### System Model
The scheduling delay determines how many subframes are needed to delay the delivery of data after a UE receives a DCI. The total delayed subframes for the uplink can be computed based on  
\[t_{i}^{c}=n_{p}^{c}+k_{0}^{i}+1. \tag{1}\]  
\(k_{0}^{i}\) is \(i\)-th delay value, and \(n_{p}^{c}\) is the last subframe in a series of NPDCCH subframes for delivering DCI \(c\) during the \(p\)-th NP [1]. Constant 1 is the changing time from DL to UL. For ease of presentation, subframe \(t_{i}^{c}\) is namely _NPUSCH start subframe_. Here we use Figure 2 as an example to explain how the unit type, resource assignment, and scheduling delay mechanisms work in uplink subframes of NB-IoT. We set the DCI repetition number for each UE as two such that \(n_{p}^{1}\) is 3. If the cell selects a scheduling delay \(k_{0}^{1}=4\) for a UE, the UE will transmit data from the NPUSCH subframe \(3+4+1=8\). Then, for the resource assignment, the cell allocates two resource units, six subcarriers with 2 ms, to the UE with a data repetition number of 3. As a result, six consecutive subframes are allocated to the UE. Because 24 subcarriers with 3.75 kHz in subframes 8-9 are allocated for an NPRACH, we can only select unit types with less than or equal to six subcarriers. The UE will transmit data in subframes 8-13 using six subcarriers. Because the uplink resource allocation should follow the NB-IoT protocol, as shown in Equation (1), we found that some subframes cannot be used by the current NP as shown in Figure 2(a). Specifically, the first applicable NPUSCH subframe in an NP is \(n_{p}^{1}+\min(k_{0}^{i})+1\), and \(n_{p}^{1}+\min(k_{0}^{i})\) subframes will be wasted in an NP, where \(\min(k_{0}^{i})\) represents the smallest scheduling delay value. The smaller the NP length, the higher is the waste percentage in an NP. To solve this problem, we propose the concept of cross-cycled resource allocation to increase resource utilization. As shown in Figure 2(b), a cell can allocate the NPUSCH subframes of the next NP, which cannot be used in the next NP, for a UE without causing wireless interference. If the cell assigns a UE to transmit data using the NPUSCH subframes of the next NP (i.e., the \((p+1)\)-th NP), the UE will stop monitoring its NPDCCH search space in the \((p+1)\)-th NP. Because NB-IoT UEs operate in the half-duplex mode, they cannot simultaneously transmit and receive data. Figure 3: \(Cont\).
## 2 Methods and Principles
### Response Deformation Method
#### 2.1.1 Lateral Response Deformation Method  
During the analysis, the correlation between the soil layer and the tunnel is approximately simulated by laying foundation soil springs around the lining segments. The beam-spring model can better achieve the splicing of the staggered joints of the shield tunnel's lining segments and consider the influence of the joints on the internal forces of the tunnel. The above is shown in Figure 1.  
In the calculation model, it is assumed that the calculated stratum is a single homogeneous layer (the laminate stratum can be simplified to a single layer via the weighted average method). The maximum response displacement of the stratum can then be obtained through the approximate treatment of Equation (1):  
\[u_{h}(z)=\frac{2}{\pi^{2}}S_{u}T_{s}\cos\Bigl{(}\frac{\pi z}{2H}\Bigr{)} \tag{1}\]  
In the above equation, \(S_{u}\) represents the velocity response spectrum of the stratum bedrock surface; \(T_{s}\) signifies the natural vibration period of the formation; \(Z\) stands for the variable of the downward coordinate system with the origin at the surface; \(H\) represents the soil layer thickness.  
Equation (1) takes the derivative of \(Z\) and multiplies the formation shear modulus to obtain the shear of the natural stratum corresponding to Equation (2):  
\[\tau_{h}(z)=\frac{G_{d}}{\pi H}S_{u}T_{s}\sin\Bigl{(}\frac{\pi z}{2H}\Bigr{)} \tag{2}\]  
In the above equation, \(G_{d}\) denotes the dynamic shear elastic modulus; \(S_{u}\) represents the velocity response spectrum of the stratum bedrock surface; \(T_{s}\) signifies the natural vibration period of the formation; \(Z\) stands for the variable of the downward coordinate system with the origin at the surface; \(H\) represents the soil layer thickness.
## 4 Experimental Results and Discussions
### Datasets and Evaluation Metric  
#### 4.1.1 Datasets Description  
The NWPU VHR-10 dataset is a 10-class geospatial object detection dataset. The dataset contains 3775 object instances, including 757 airplanes, 302 ships, 655 storage tanks, 390 baseball diamonds, 524 tennis courts, 159 basketball courts, 163 ground track fields, 224 harbors, 124 bridges, and 477 vehicles. These object instances are manually marked with horizontal borders. It contains a total of 800 images, and we only used 650 of them with annotated images in this experiment.  
The Pascal VOC [38] dataset is a standardized dataset with good image quality and complete labels. It is widely used to evaluate object detection and instance segmentation algorithms. It contains 20 common objects, namely an airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV Monitor. Each image in the dataset has a one-to-one correspondence with its annotation file (class label and bounding box). At the same time, there may be multiple different object categories in an image. The training set and test set were divided according to a ratio of approximately 1:1. Pascal VOC 2007 contains 9936 labeled images with 24,640 objects, of which about 5011 were used for training and validating, and the remaining 4952 images were used for testing. Pascal VOC 2012 contains 11,540 labeled images with 27,450 objects. In this experiment, all the labeled images in Pascal VOC 2012 were used for training and validating [39].
## 4 Equalizer Design Techniques
### Frequency Domain Deconvolution  
Another technique used for the equalizer design is based on frequency domain deconvolution. As initially proposed in [80], the equalizer can be directly designed in the discrete Fourier transform (DFT) domain by considering the reciprocal of the room response. In [80], the technique is applied to the DFT of a windowed impulse response in order to correct only the early reflections of the room response that affect the perception of timbre and to obtain a short equalizer response. In general, this technique can be applied both for minimum-phase and mixed-phase equalization (adding an appropriate delay), but the room response must be properly pre-processed. In particular, the depth of the notches of the room response should be suitably limited to avoid excessive gains and long impulse responses of the equalizer, which could result in tonal artifacts [52]. In [50], the equalizer is designed by dividing the complex spectrum of a target response with the complex spectrum of the measured room response. To avoid the problem of notches, a positive bias is added to the measured room response. This technique is known as _\"deconvolution with regularization\"_, with the bias called _\"regularization parameter\"_. The concept was formalized by [PERSON] and colleagues in [51; 81]by considering a least-squares optimization criterion with a _\"penalty effort\"_. It is also known as the _\"Kirkeby algorithm\"_. In [52], the technique was applied to RRE. The regularization parameter controls the longest time constant of the inverse filters [81] in practice. In order to ensure that the time constant is neither too long nor too short, the regularization parameter must be set appropriately [51]. In [82], the authors show how the poles of the deconvolution solution are influenced by the regularization parameter. In particular, for each zero close to the unit circle, a triplet of two poles and one zero is generated, with one of the poles outside the unit circle. This pole is responsible for an acausal response, and thus modeling delay should be introduced. In [43], an analysis of RRE based on the Kirkeby algorithm on the basis of psychoacoustic criteria is provided. In the considered conditions, it was shown that the \"errors in the dereverberation process manifested themselves as extremely audible and annoying resonances. These arose from the presence of deep spectral notches in the transfer functions of loudspeaker-room combinations, which created tonal artifacts that occurred long before and after the direct-arrival sounds. Furthermore, an extreme sensitivity to changes in position was found, which prevented the optimization of dereverberation over practically sized listening areas. The quality of the dereverberation was found to degrade even further for larger acoustic spaces.\" Despite these limitations, deconvolution with regularization approaches has been successfully applied in combination with other techniques used to avoid perceivable distortions. For example, it has been combined with frequency warping [83; 84], or used in wave field synthesis [85].
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Unreliable Communication Channel
#### 2.2.3 Ambient Noise  
Ambient noise is another factor that severely influences the communication in underwater acoustic channel. The ambient noises include: the radiated noises and self-noises from vessels, noises caused by waves and other surface motions; turbulence noise, thermal noise, and the noises that come from marine animals. These ambient noises can be classified into four types that affect acoustic communication at different frequency band, including turbulence noise (\(N_{t}\), less than 10 Hz), shipping noise (\(N_{s}\), 10-100 Hz), wind noise (\(N_{w}\), 100 Hz-100 kHz), and thermal noise (\(N_{th}\), over 100 kHz) [10]. These ambient noises can be calculated by the following empirical equations in dB, which is a function of frequency:  
\[10\log N_{t}(f)=17-30\log f \tag{7}\]  
\[10\log N_{s}(f)=40+20(S-0.5)+26\log f-60\log\left(f+0.03\right) \tag{8}\]  
\[10\log N_{w}(f)=50+7.5w^{\frac{1}{2}}+20\log f-40\log\left(f+0.4\right) \tag{9}\]  
\[10\log N_{th}(f)=-15+20\log f \tag{10}\]  
where \(w\) is the speed of sea-surface wind in m/s. Based on these four ambient noises, the total noise can be calculated with the following equation:  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f) \tag{11}\]  
The ambient noises may cause critical effects upon sonar performance and large fluctuations upon a change in time, location or depth, which may influence the availability of the network.
## 1 Introduction
### Main Features and Parameters of Operational Polar Orbiting IR Sensors
The LTAN and LTDN of the piggyback launched BIRD, TET-1 and BIROS initially were all close to the LTAN and LTDN of the main satellites of these launches. The LTDN of BIRD and the LTAN of TET-1 drifted as indicated in Table 2. They could not be corrected because they had no propulsion system to correct their local times of equator crossing. In the right column of Table 2, \"grouping\" numbers for comparing the utilization of IR data registered nearly simultaneously by operational satellite systems, on one hand, and data of BIRD or FireBIRD, on the other, are presented. Table 3 shows an overview of selected scientific papers and scientific documents, called \"References\", structured by the \"grouping\" numbers presented in the right column of Table 2.  
Comment: Many scientific papers describe the BIRD, TET-1 or BIROS system function and the utilization of BIRD or FireBIRD data without comparison to other data sources, but this chapter contains selected application examples based on data registered nearly simultaneously by operational satellite systems and by BIRD or FireBIRD. The following examples for the combined utilization of data registered nearly simultaneously by operational satellite systems and by BIRD or FireBIRD were selected:  
* MODIS \"Terr\"-BIRD data comparison in a fire scene in Siberia close to Lake Baikal--see [9], according to \"grouping\" N\({}^{\circ}\) 1 (in Table 3);
* MODIS \"Aqua\"-TET-1 data comparison in low-temperature peat fire scenes in Borneo--see ([PERSON] et al., 2016), according to \"grouping N\({}^{\circ}\) 2\";
* TET-1-BIROS-VIIRS \"Suomi NPP\" data comparison for the devastating \"Paradise fire\" in California/the US--see [5], according to \"grouping N\({}^{\circ}\)3\". \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Satellite** & **Launch Date** & **LTDN** & **LTAN** & **Comment** & **“Grouping” N\({}^{\circ}\)** \\ \hline Terra & & & & & \\ (MODIS) & 1999 & 10:30 h & & & \\ BIRD & 2001 & 10:30 h (2001) & & & LTDN & 1 \\  & & & & & changed in 2 \\  & & & & years by \(\sim\)1 h & \\ \hline Aqua & & & & & \\ (MODIS) & 2002 & 13:30 h & & & \\ \hline TET-1 & 2012 & & 11:30 h (2012) & & & \\  & & & & & changed in 3 \\ \hline Suomi NPP & 2011 & 10:30 h & & & \\ (VIIRS) & 2016 & 10:00 h & & & \\ \hline Sentinel-3A & & & & & \\ (SLSTR) & 2016 & 10:30 h (2016) & & & \\  & & & & & LTDN & 3 \\ BIROS & 2016 & 10:30 h (2016) & & & & propulsion \\  & & & & system & \\ \hline NOAA-20 & & & & & & \\ (VIIRS) & 2017 & & 13:30 h & & 2 \\ \hline Sentinel-3B & & & & & & Phased by \\ (SLSTR) & 2018 & 10:00 h & & & 140\({}^{\circ}\) to & 3 \\  & & & & Sentinel-3A & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The LTAN and LTDN of the operational satellites carrying the IR sensors MODIS, VIIRS and SLSTR, as well as the LTAN and LTDN of BIRD, TET-1 and BIROS.
## 2 Analysis of Previous Studies
### Spatial Identification  
When combined with quantitative data analysis, the use of spatial data can result in synergistic effects to derive results on various topics. An analysis of similar studies using geographic information system (GIS) technology revealed the usefulness of GIS-based analysis in the identification of assessed project sites, related routes, and marine accidents.  
[PERSON] et al. investigated the spatiotemporal patterns of car accidents over several years in Jordan [9]. They used a GIS-assisted technique based on statistical and clustering approaches to identify areas with car crash points.  
[PERSON] et al. conducted research to identify safety measures to prevent traffic accidents in Lisbon [10]. They applied meteorological data, historical traffic status data, and location datasets to ArcGIS Pro software.  
[PERSON] et al. conducted research on methods for monitoring vessels potentially at high risk of oil spills in marine pollution accidents using an electronic platform [11]. The authors proposed the construction of an AI-based monitoring system within a GIS environment.  
[PERSON] et al. used GIS to develop a \"Marine Accidents Density Map\" for the Singapore Strait by reviewing areas where marine accidents are concentrated by performing the Kernel Density Analysis method [12].  
Studies have also combined GIS and electronic charts (e-charts) to analyze the relationship between marine accident locations and specific causes. [PERSON] et al. used GIS to integrate spatiotemporal information, such as marine accident locations and timings, with environmental factors such as wind, waves, tides, and currents, after which the data were combined on e-charts [13].
## 3 Mathematical Model and Numerical Calculation
### Equation of Motion  
In this paper, the vibration system of two staggered cylinders can be simplified as a mass-spring-damping (M-C-K) system with only one degree of freedom considered. The equation of motion of the oscillator is as follows:  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Cylinder** \\ \hline Structural damping per unit length: \(C\) (N-s/m) & 1.36 \\ Spring stiffness per unit length: \(K\) (N/m) & 80.38 \\ System quality per unit length: \(m_{\text{osc}}\) (kg/m) & 2.76 \\ Feature size: \(D\) (m) & 0.04 \\ Cylinder length: \(L\) (m) & 0.5 \\ Density of water at 15 \({}^{\circ}\)C: \(\rho\) (kg/m\({}^{3}\)) & 999.10 \\ Kinematic viscosity of water at 15 \({}^{\circ}\)C: \(\
u\) (m\({}^{2}\)/s) & \(1.14\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of two cylinders with roughness strips oscillatory system.  
Figure 1: The physical model of two staggered circular cylinders with elastic support.  
\[m\ddot{y}+C\dot{y}+Ky\ =\ F_{fluid,y} \tag{4}\]  
where \(m\) is the total mass of the vibration system, consisting of the mass of the vibration cylinder and one third of the mass of the spring; \(\ddot{y}\), \(\dot{y}\), and \(y\) represent the acceleration, velocity, and displacement of the oscillator perpendicular to the flow direction (\(y\) direction), respectively; \(F_{fluid,y}\) is the fluid force acting on the surface of the cylinder in the y direction, which can be obtained by integrating the pressure on the surface of the cylinder and the viscous friction force; \(C_{system}\dot{y}\) is the sum of all kinds of drags acting on the cylinder in the flow field; and \(Ky\) is the elastic force of spring acting on the cylinder. It should be noted that the linear viscous damping and linear stiffness was considered for the two cylinders FIV model.  
The open source computational fluid dynamics (CFD) tool OpenFOAM-extend (Wikki Ltd., London, United Kingdom) was used to simulate the flow past two cylinders in the present study. A second-order Gauss integration scheme with a linear interpolation for the face-centered value of the unknown was used for the divergence, gradient, and Laplacian terms in the governing equations. The second-order backward Euler method was adopted for time integration. Thus, the numerical discretization scheme gave second order accuracy in space and time. A pressure implicit with the splitting of operators (PISO) algorithm was used for solving the momentum and continuity equations together in a segregated way. The coupling between flow equations and motion equation was strongly coupled. The equation of motion for the circular cylinder was solved using a second-order mixed implicit and explicit time integration scheme.
# Design, Synthesis, and Cell Lines Studies of Oleanolic Acid--Hydrogen Sulfide Donor Hybrids
## 2 Materials and Methods
### General Procedure for the Synthesis of Targeted Compounds
#### 2.1.4 General Procedure for the Preparation of **12a**, **12b**
: 22.9-224.5 \({}^{\circ}\)C: 1\({}^{\circ}\)H NMR (CDCl3, 300 MHz) 8 ppm 7.66 (d, 2H, \(J\) = 8.8 Hz, Ar-H), 7.39 (s, 1H, 8\({}^{\prime\prime}\)-H), 7.16 (d, 2H, \(J\) = 8.8 Hz, Ar-H), 6.29 (t, 1H, \(J\) = 5.6 Hz, NH), 5.36 (t, 1H, \(J\) = 3.2 Hz, 12-H), 4.36 (d, 1H, \(J\) = 7.2 Hz, 1\({}^{\prime}\)-Ha), 4.32 (d, 1H, \(J\) = 7.2 Hz, 1\({}^{\prime}\)-Hb), 4.32 (d, 1H, \(J\) = 7.2 Hz, 1\({}^{\prime}\)-Hb), 3.24 (m, 1H, 3-H), 2.97 (dd, 1H, \(J\) = 10.2, 3.0 Hz, 18-H), 1.13-2.20 (m, 22H, methylene and methine protons), 1.12 (s, 3H, 29-H), 0.98 (s, 3H, 30-H), 0.95 (s, 3H, 26-H), 0.93 (s, 3H, 23-H), 0.90 (s, 3H, 25-H), 0.80 (s, 3H, 27-H), 0.76 (s, 3H, 24-H), OH shows no signal; \({}^{13}\)C NMR (CDCl\({}_{3}\), 75 MHz) \(\delta\) ppm 215.5, 178.5, 171.6, 171.2, 153.5, 143.0, 136.0, 129.2, 128.2, 123.1, 122.9, 78.9, 55.2, 47.5, 47.4, 46.9, 45.7, 41.8, 41.4, 39.5, 38.8, 38.5, 37.0, 33.8, 33.1, 32.8, 32.3, 30.7, 28.1, 27.8, 27.2, 25.8, 23.6, 23.4, 23.0, 18.3, 17.4, 15.6, 15.4; HRMS (ESI) Calcd for C\({}_{41}\)H\({}_{56}\)NO\({}_{4}\)S\({}_{3}\) [M + H]\({}^{+}\) 722.3366, found: 722.3359. _Oleanolic acid 28-[4-(5-thioxo-5H-1,2-dithiol-3-yl)phenyl] carboxamido propanoate_ **(12b)**. Yield: 24%. Brick red solid, m.p. : 129.4-131.2 \({}^{\circ}\)C: \({}^{1}\)H NMR (CDCl\({}_{3}\), 400 MHz) \(\delta\) ppm 7.56 (d, 2H, \(J\) = 8.4 Hz, Ar-H), 7.13 (s, 1H, 8''-H), 7.11 (d, 2H, \(J\) = 8.4 Hz, Ar-H), 6.28 (t, 1H, \(J\) = 5.6 Hz, NH), 5.32 (brs, 1H, 12-H), 3.62 (m, 1H, 1''-Ha), 3.31 (m, 1H, 1''-Hb), 3.20 (m, 1H, 3-H), 2.74 (m, 1H, 18-H), 2.70 (m, 2H, 2''-H), 1.13-2.20 (m, 2H, methylene and methine protons), 1.12 (s, 3H, 29-H), 0.98 (s, 3H, 30-H), 0.95 (s, 3H, 26-H), 0.93 (s, 3H, 23-H), 0.90 (s, 3H, 27-H), 0.80 (s, 3H, 24-H), OH shows no signal; \({}^{13}\)C NMR (CDCl\({}_{3}\), 100 MHz) \(\delta\) ppm 215.5, 178.5, 171.6, 171.2, 153.5, 143.0, 136.0, 129.2, 128.2, 123.1, 122.9, 78.9, 55.2, 47.5, 47.4, 46.6, 45.7, 41.8, 41.4, 39.5, 38.8, 38.5, 37.0, 33.8, 33.1, 32.8, 32.3, 30.7, 28.1, 27.8, 27.2, 25.8, 24.9, 23.6, 23.4, 23.0, 18.3, 17.4, 15.6, 15.4; HRMS (ESI) Calcd for C\({}_{42}\)H\({}_{58}\)NO\({}_{4}\)S\({}_{3}\) [M + H]\({}^{+}\) 736.3522, found: 736.3519.
## 2 Forward Model Development
### Including Inelastic Water Raman Effects  
The molecular water Raman scattering contribution to \(r_{s}\) can be included in Equation (12) as an additive term, resulting in the expression:  
\[\begin{array}{ll}r_{rs}(\theta_{s},\theta_{v},\phi,V,a,b_{b})\cong r_{rs,Raman }(\theta_{s}^{\prime},a,b_{b})+\frac{1}{\overline{\mu}_{d}\big{(}\theta_{s} ^{\prime},V\frac{b}{a},\eta_{bb}\big{)}}\frac{\beta(\psi)}{b_{b}}\big{/}\\ \Big{[}\frac{a}{b_{b}}\Big{(}1-\cos(\theta_{v})\Psi_{K_{L}u}(\psi)\overline{ \mu}_{\infty}\Big{(}\frac{b_{b}}{a},\eta_{bb}\Big{)}^{-1}\Big{)}+f_{L}( \theta_{s},\theta_{v},\phi)\big{(}1-\widetilde{b}_{b}{}^{-1}\Big{)}+ \widetilde{b}_{b}{}^{-1}\Big{]}.\end{array} \tag{18}\]Full dependencies for all parameters in the model are shown except for \(\lambda\); all parameters exhibit dependence on \(\lambda\) in the model except for \(\Psi_{K_{Lx}}(\psi)\). We note a similar approach has been taken in adding water Raman effects in other reflectance models (e.g., [59]; reviewed in [13]). The term \(r_{rs,Raman}\) can be derived according to [PERSON] et al. ([60]; see their Equation (7)) with inputs of above water (z = 0\({}^{+}\)) downwelling irradiance \(E_{d}\)(0\({}^{+}\),\(\theta_{s}\)'), \(a\), and \(b_{b}\). The NASA Generalized IOP (GIOP) inversion model implementation [16] also currently uses this Raman formulation. Terms for other inelastic effects, such as fluorescence from dissolved organic matter and pigments may also be added if representative models are available.
## 3 Results and Discussion
### Applications of SPME-IMS System
#### 3.2.2 Effect of Ionic Strength  
Previous studies have shown that ionic strength can improve the extraction efficiency, particularly for compounds with lower solubility. The ionic strength was modified by adding various amount of NaCl to 5 mL of deionized water. Figure 4 shows that the extraction of TMP increased with the  
Figure 3: _Cont._increase of NaCl content, then approached a plateau or even decreased. Therefore, 0.4 g NaCl was added to 5 mL deionized water in the subsequent experiments.
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Wi-Fi RSSI Characteristics  
This section examines characteristics of Wi-Fi RSSIs and discovers the necessity of the feature extraction for Wi-Fi RSSI-based indoor localization.