esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
## 4 Results
### General Concept  
The stiffness of the slab is much lower than that of the bearing wall, so that the wall in the wall-slab joint can be relatively considered as a rigid body. Thus, the stiffness under a lateral load of the entire joint is mainly determined by the flat slab component. Based on these considerations, a relationship between the effective beam width that is expressed by \(b_{e}\) and other parameters of the wall-slab joints have been established as the calculation flow chart showed in Figure 6 (details for each step are presented in the following sections).  
The actual initial stiffness of the entire wall-slab joint is represented by \(K\). Considering the relationship of rotational behaviors of the bearing wall and the effective beam, the rotational stiffness of the effective beam \(K_{s}\) can be obtained from \(K\). On the other hand, \(K_{s}\) is as well equal to the analytical rotational stiffness \(K_{b}\) which can be expressed as a common beam in general frame structures and calculated using the beam width \(b_{e}\). Thus, \(K_{s}\) and \(K_{b}\) are both values representing the rotational stiffness of the effective beam so that they should be equal. By equaling \(K_{s}\) and \(K_{b}\), the effective beam width can be expressed by other parameters related to the joint features, such as configurations and material characters.
## 2 Experimental
### Experimental Setup  
The schematic diagram in Figure 1 illustrates the experimental setup for the physical simulation of three-layer combined production in tight gas reservoirs. It is worth noting that the number of simulated layers corresponds to the number of long coreholders utilized in these experiments. This study involved simulations ranging from one to four layers of combined production, indicating simultaneous utilization of one to four long coreholders.  
The experimental setup consisted of three subsystems, i.e., the injection subsystem, production subsystem, and data acquisition and control subsystem. In the injection subsystem, a high-pressure gas cylinder (WMA219-40-15, 40 L) was utilized to supply gas to the transfer cylinder. A high-pressure syringe pump (100 DX, ISCO Inc., Lincoln, NE, USA) was employed for injecting gas from the transfer cylinder into the core plugs contained in the coreholders (DCD-80b, Hai'an County Petroleum Scientific Research Instruments Co., Ltd., Nantong, China) in the production subsystem at various pressures. These long coreholders, within which the cylindrical cores at each layer were horizontally assembled, were capable of accommodating a core plug with a length of up to 80 cm. To provide confining pressure for the core plugs, a high-pressure manual pump (JB-II, Hai'an, China) was used with an additional 2.00 MPa above the injection pressure setting. Initially, a vacuum pump (FY-4c-N, Nantong, China) was employed to evacuate the cores and remove the impurities. Each long coreholder was equipped with three digital pressure gauges (3051 TG, Rosemount, Chanhassen, MN, USA) at its inlet, middle, and outlet, each of which had a pressure range of 0-69 MPa and an accuracy of 0.025% full-scale. Also, a flow controller was employed to regulate the total gas flow rate during the depletion of one or multiple long core plugs. A back pressure regulator (BPR) (HY-2, Nantong, China) primarily controlled the ultimate outlet pressure, while several mass flowmeters (DMF-1-1, Beijing Shouke Shihua Automation Equipment Co., Ltd., Beijing, China) with a pressure range of 0-40 MPa  
Figure 1: Schematic diagram of the experimental setup used for commingled multilayer production.  
and an accuracy of \(\pm\)(0.8% reading + 0.2% full-scale) were utilized for real-time monitoring and measuring the flow rates of each individual layer. The data acquisition and control subsystem enabled most data collection and experimental operations to be continuously monitored and measured via a desktop computer, thereby minimizing experimental errors associated with manual operations.
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 2 Related Work
### Network and System Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(CN\) & Controller Node \\ \(C_{SN/RN}\) & Cost function for the CN to select relay and source nodes \\ \(DN\) & Destination node \\ \(E_{0}\) & Initial UAV energy \\ \(E_{fly}^{SN,RN}\) & Energy required to fly to an event or ideal location \\ \(E_{fly}(d,s_{i})\) & Energy required to move a certain distance \(d\) with speed \(s_{i}\) \\ \(E_{min}\) & Energy required to trigger the battery replacement operation \\ \(E_{replacement}\) & Energy required to fly back to the control center for battery replacement \\ \(E_{rx}\) & Energy spent for receiving a packet with length \(n\) bits and with a bit rate of \(r\) bps \\ \(E_{t}\) & Current energy \\ \(E_{tx}^{RN}\) & Energy required to transmit packets for a given video \\ \(E_{tx}\) & Energy required to transmit a packet of length \(n\) bits and with a bit rate of \(r\) bps \\ \(E_{video}\) & Energy required to retrieve video frames \\ \(L(X,Y)\) & Ideal relay node location, where \(X\) and \(Y\) are the ideal coordinates \\ \(L(X_{i},Y_{i})\) & Current UAV Location, where \(X_{i}\) and \(Y_{i}\) are the UAV coordinates \\ \(L(X_{r},Y_{r})\) & Location for the battery replacement, where \(X_{r}\) and \(Y_{r}\) are the battery replacement coordinates \\ \(R_{max}\) & Radio Range \\ \(rotate_{RSN,DN}\) & route between source and destination nodes via2 multiple relay nodes \\ \(RE_{i}\) & Remaining energy of each UAV \\ \(RN_{i}\) & Relay node for a path between the source \(SN\) and destination \(DN\) \\ \(s_{i}\) & UAV speed \\ \(S_{max}\) & Maximum speed limit \\ \(SN\) & Source node \\ \(t_{move}\) & Time needed to fly from the current location to the event location \\ \(traj_{i}\) & UAV mobility trajectory, which can be Stay-At, Way-point, Eight, Scan, and Oval movements \\ \(v_{i}\) & A given UAV with an individual node identity \(i\), where \(1<i<n\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of main symbol used for VOEI description.
## 4 Application
### Clinical Application
#### 4.1.4 Arrhythmia Detection  
An abnormal heart rhythm is called an arrhythmia. [PERSON] et al. [127] applied the iPPG technique to detect arrhythmia by using a monochrome camera based on a signal fusion technique. The signal fusion technique was used to extract the blood pulse signal using prior information. They formulated the problem based on a Bayesian classification and modeled a novel probabilistic pulsatility model which combined spectral and spatial priors derived from physiology of the blood pulse signal.
###### Abstract  
In this paper, we present a new objective quality metric designed for immersive video applications--IV-SSIM. The proposed IV-SSIM metric is an evolution of our previous work--IV-PSNR (immersive video peak signal-to-noise ratio)--which became a commonly used metric in research and ISO/IEC MPEG standardization activities on immersive video. IV-SSIM combines the advantages of IV-PSNR and metrics based on the structural similarity of images, being able to properly mimic the subjective quality perception of immersive video with its characteristic distortions induced by the reprojection of pixels between multiple views. The effectiveness of IV-SSIM was compared with 16 state-of-the-art quality metrics (including other metrics designed for immersive video). Tested metrics were evaluated in an immersive video coding scenario and against a commonly used image quality database--TID2013--showing their performance in both immersive and typical, non-immersive use cases. As presented, the proposed IV-SSIM metric clearly outperforms other metrics in immersive video applications, while also being highly competitive for 2D image quality assessment. The authors of this paper have provided a publicly accessible, efficient implementation of the proposed IV-SSIM metric, which is used by ISO/IEC MPEG video coding experts in the development of the forthcoming second edition of the MPEG immersive video (MIV) coding standard.  
* [leftmargin=*] *  
is a tedious and time-consuming process, which is highly inconvenient and expensive in practical systems. Therefore, a natural alternative to performing exhaustive subjective tests is objectively assessing the quality.  
Significant challenges in immersive video are the rendering-induced distortions that are unique to this medium. Traditional video quality metrics may not adequately capture these distortions, as they are tailored to conventional two-dimensional video content. This necessitates the development of a specialized objective quality metric that can address the specific issues associated with immersive video, such as the slight shift of reprojected pixels and variations in color characteristics between input views. Development of such a specialized metric is crucial for ensuring that the virtual scenes appear natural and are free from visual discomfort, thereby maintaining the overall immersive experience.
# Eyelid Dynamics Characterization with 120 GHz mmW Radar
## 2 Theoretical Background
### Assessment of the Radar's Safety  
The IEEE, in [17], defines exposure limits for electric, magnetic, and electromagnetic fields within 0 Hz to 300 GHz. It is important to respect these safety limits to minimize health effects and risks during measurements and interference with other radio frequency-based equipment. In this study, the radar specifications and lens characteristics allow us to estimate a radiated power of \(-4.6\) dBm (0.35 mW) and an illuminated spot diameter of 3.5 cm with a \(-3\) dB contour at a typical operation range of 1 m. The resulting illumination power density is 36 \(\upmu\)W/cm\({}^{2}\), well below the 2.3 mW/cm\({}^{2}\) ERL of local exposure limits at 120 GHz recommended in [17] for people in unrestricted environments, which is the most stringent limit. For this reason, the proposed technique can be considered to be of minimal health risk, with exposure limits well below the exposure limit recommendations.
# Methods for Aggregation and Remuneration of Distributed Energy Resources
## 2 Proposed Methodology  
In the present section the implemented methodology is defined. We consider demand response programs and distributed generators, in a mixed-integer nonlinear problem, involving an aggregator managing the technical conditions of a region. In this way, the aggregator assumes a role of operator and resource manager. This perspective is also applicable to a Balance Responsible Party (BRP) [40; 41]. The need for several definitions of an aggregator arises as it also can assume multiple roles in the energy system's operation. Figure 1 shows the implemented concept.  
In the scheduling of resources, the authors propose the introduction of three demand response programs: load reduction, curtailment, and shifting. Thus, a multi-period optimization is considered and performed simultaneously for all the periods considered. Regarding generation-side resources, the main grid suppliers and distributed generators are considered, in order to satisfy consumption in any given period, allied with the use of the demand response program flexibility. For the aggregation and remuneration, based on the aggregator for the contribution of its resources, the authors propose distinct methods that allow different outcomes for the aggregator's cost and consumer/producer's benefit. Depending on the specific actual scenario, the aggregator may choose different methods for the aggregation and remuneration of its resources.  
The aggregation variables, i.e., the features upon which the aggregation will be made, are the energy scheduled for each resource and the costs of this schedule. The aggregation is made separately for each type of resource, producer and consumer. In this way, the costs considered for the first are the linear costs of each producer, while for the latter, one considers the costs of each demand response program.  
Table 1 shows the aggregation and remuneration methods that will be applied after the resource scheduling. These methods will be discussed in the results section. The consideration and comparison of several aggregation and remuneration methods is also an improvement from previous published work, in [14]. Thus, the influence of different data in the aggregation and remuneration process is analyzed rather than just chosen and presented.  
The methodology is fully developed in MATLAB, based on the use of its optimization tool, TOMSYM programming. The aggregation is performed using the clustering algorithm K-Means, available through an embedded MATLAB function. The use of matrix-based software in these kinds of applications allows a clearer and easier definition of the problem, since the issues addressed here, often involve multiple dimensions and variables.  
Figure 1: Possible aggregator roles in the energy system.
# Control of Ribosome Synthesis During the Cell Division Cycles of _E. coli_ and _Synechococcus_
## 2 Control of r-protein synthesis in _E. coli_
### Control of rRNA synthesis in _E.coli_ during steady state growth
It turned out, however, that ppGpp was not required for the growth rate-dependent regulation of rRNA synthesis ([PERSON] and [PERSON], 1990). The role of iNTP in the growth rate-dependent regulation of rRNA has been a controversial issue as indicated below. In an earlier report ATP and GTP pools were found to increase substantially with increases in growth rates ([PERSON] et al, 1997). On the other hand, [PERSON] and [PERSON] (2000) found that RNA precursor nucleotide concentrations were increased in a narrow range while total RNA accumulation increased as growth rates were increased. The discrepancies in the results reported above were resolved in a recent paper ([PERSON] and [PERSON], 2004). A new luciferase bioassay for ATP was developed and used to measure NTP concentration at increasing growth rates. These results are now in agreement; _i.e._, nucleotide triphosphate pools are not involved in the growth rate-dependent regulation of rRNA synthesis  
Two other control mechanisms in the regulation of rRNA synthesis have been proposed: the rRNA transcription antitermination model ([PERSON] _et. al._, 1984), and the passive model ([PERSON] 1969). The rRNA antitermination model was proposed as an independent mechanism that coordinates the r-protein and rRNA syntheses. In this model, the Rho dependent transcription terminators (nut sites) located downstream of \(m\) P2 can prevent RNAP from transcribing \(m\) operons. The antitermination complexes, composed of r-proteins S10 (=NusE), S4, L3, L4 L13 and Nus factors (A, B, G) are able to permit read-through of nut sites ([PERSON] et al., 2001; [PERSON] et al., 2002;[PERSON] et al., 2004). In addition, r-proteins, S4 and L4, can act as regulatory elements in repressing mRNA translation by means of translational feedback, and they play key roles in restoring the stoichiometry of r-protein and rRNA. The passive model of ribosome production is based on the relative strength of mRNA promoters over rRNA promoters, and on the premise that the concentration of RNAP in cells is limited. Serious arguments against the passive model have come from the observations that promoters of \(m\) are the strongest promoters in cells, and that RNAP concentration is not a limiting factor (see reviews by [PERSON] et al., 1995; [PERSON] et al., 1996; [PERSON] and [PERSON], 1996).
## 4 Maneuvering Policy Generation Algorithm Designing in 1-vs-1 WVR Air Combat Based on MADDPG
### Maneuvering Strategy Generation Algorithm Outline
```
Input:\(M,\mathcal{S}_{base_{0}}\) Output:\(\boldsymbol{\mu_{a_{1}},\mu_{a_{2}}}\)
1 Initialize online network \(Q_{i}\) with random parameters \(\boldsymbol{\theta}_{i}\);
2 Initialize target network \(Q^{\prime}_{i}\) with random parameters \(\boldsymbol{\theta}^{\prime}_{i}\leftarrow\boldsymbol{\theta}_{i}\);
3 Initialize prioritized replay buffer \(\mathcal{D}\);
4 forepisode = 1 to Mdo
5 Initialize a random process N for action exploration, and receive the initial state information of two UCAVs \(\mathcal{S}_{base_{0}}\);
6 for\(t=1\) to max-episode-length do do
7 for\(i\in[\text{UCAV}_{1},\text{UCAV}_{2}]\)do
8\(\boldsymbol{a}_{i}\leftarrow\boldsymbol{\mu_{a_{i}}}(\boldsymbol{s})+\mathcal{N }_{t}\) ;
9 Execute actions \(\boldsymbol{a_{1}}\) and \(\boldsymbol{a_{2}}\), then observe reward \(\mathcal{R}_{i}\) and new state information \(\mathcal{S^{\prime}}_{base}\);
10 Calculate \(\mathcal{S}^{\prime}_{relative}\) using Equation (6) and Equation (7) ;
11 Estimate prediction interval \(T_{pi}\) using Equation (13);
12 Calculate \(\mathcal{S}^{\prime}_{predict}\) using Algorithm 1;
13\(\mathcal{S}^{\prime}\leftarrow\mathcal{S}^{\prime}_{base}\oplus\mathcal{S}^{ \prime}_{relative}\oplus\mathcal{S}^{\prime}_{predict}\);
14 Calculate \(\mathcal{R}_{rs}\) using reward shaping from Section 4.2;
15 Store \((\mathcal{S},a_{1},a_{2},\mathcal{R}_{rs},\mathcal{S}^{\prime})\) in prioritized replay buffer \(\mathcal{D}\);
16 Set \(\mathcal{S}\leftarrow\mathcal{S}^{\prime}\);
17 Sample a random minibatch of \(S\) samples \((\mathcal{S}^{k},a^{k}_{1},a^{k}_{2},\mathcal{R}^{k}_{rs},\mathcal{S}^{\prime k})\)from \(\mathcal{D}\);
18\(y^{k}\leftarrow\mathcal{R}^{k}_{rs}+\gamma Q_{i}(\mathcal{S}^{\prime},a_{1},a _{2})\big{|}_{a_{j}=\boldsymbol{\mu}_{j}(s^{k})}\) ;
19 Update critic by minimizing the loss \(\mathcal{L}(\theta_{i})=\frac{1}{K}\sum_{k}(y^{k}-Q_{i}(\mathcal{S}^{k},a^{k}_{ 1},a^{k}_{2}))^{2}\);
20 Update actor using the sampled policy gradient defined in Equation (16): \[\
abla_{\theta_{i}}J_{i}(\theta_{i})=\frac{1}{K}\sum_{k}\
abla_{\theta_{i}} \boldsymbol{\mu}_{i}(\mathcal{S}^{k})\
abla_{\boldsymbol{a}_{i}}Q^{ \boldsymbol{\mu}_{i}}_{i}(\mathcal{S}^{k},\boldsymbol{a}_{i},\boldsymbol{a}^ {k}_{other})|_{\boldsymbol{a}_{i}=\boldsymbol{\mu}_{i}(\mathcal{S}^{k})}\] (16) Update target network parameters for each agent \(i\): \(\theta^{\prime}_{i}\leftarrow\tau\theta_{i}+(1-\tau)\theta^{\prime}_{i}\);
21 end for
22
23 end for
24
25 end for
```  
**Algorithm 2**Multi-agent deep deterministic policy gradient (MADDPG) Training process for \(1\)-vs.-\(1\) within visual range (WVR) air combat engagement
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 3 Results
### Denoising Performance in Hyperspectral Image  
The previous sections showed the effectiveness of the SR method in simulated and ASD-measured \(R_{\text{rs}}\) spectra. In this section, we will show the performance of the SR denoising method on PRISMA hyperspectral data. The lower SNR level of PRISMA (compared with ASD) leads to its stronger vibrations over all of the spectral bands (Figure 8). We can observe two kinds of noises from the spectra. In the wavelength range that is shorter than 550 nm, the noises are relatively wavelength-independent. The PRISMA \(R_{\text{rs}}\) curves changed randomly along the wavelength. Another kind of noise showed more common characteristics, such as the sharp small peak at around 775 nm. According to the simulated (Figure 5) and field-measured \(R_{\text{rs}}\) curves (Figure 7), this peak is a systematic error caused by the sensor or the atmosphere. Under this complex error condition, the SR-denoised spectra (Figure 8) look smooth at the random-error region and flatten the small peaks around 775 nm.  
Figure 7: Typical ASD measured and their denoised spectra.  
In three different datasets, the proposed SR denoising method showed its encouraging performance. This method can handle both random noise at varied levels (Figure 6), unevenly distributed noise (Figure 7), and systematic noise (Figure 8).
# The Role of Sensory Cues in Collective Dynamics: A Study of Three-Dimensional Vicsek Models
## 2 Methods
### Sensing modalities  
We employ the same update protocol (Equations (1) and (2)) described above, but modify the sensing regions to implement visual, auditory, and composite modalities. The primary difference between visual and auditory modalities lies in the index set of neighbors, \(\Lambda_{i}(k)\). Figure 1 illustrates the schematics of the three sensing modes. For visual sensing, the field of vision of each particle is modeled as a spherical cone with radius \(R\), symmetric in its current direction of movement. The cone represents _field of vision_ with an opening angle of \(2\phi\), where \(\phi\) can range from 0 to \(\pi\). When \(\phi=\pi\), this model simplifies to the original 3D Vicsek model, where the interaction neighborhood of a particle forms a sphere. Visual neighbors contain particles that are in the field of view of an individual at each time step.  
For auditory sensing, an _acoustic beam_ for each particle is modeled as a spherical cone, mimicking the directivity pattern of ultrasonic beams observed in biological systems [32]. We assume that the emitted acoustic beam is symmetric about the particle's current heading direction. Similar to the visual field, the acoustic beam has an opening angle of \(2\phi\), with \(\phi\) varying from 0 to \(\pi\). The auditory neighbors of a particle are those from whom it can 'hear'. Thus, the auditory neighbors for a particle include all the particles whose acoustic beams intersect with it.  
In order to implement _composite sensing_, we first construct the auditory and visual sensing modes independently, then combine them to determine the neighbors of an individual. Consequently, the index set of neighbors for the \(i\)-th particle at time step \(k\) in composite sensing is given by  
\[{}^{C}\Lambda_{i}(k)={}^{V}\Lambda_{i}(k)\cup{}^{A}\Lambda_{i}(k),\]  
where the left superscripts \(C\), \(V\), and \(A\) represent composite, visual, and auditory modalities, respectively. Therefore, in composite sensing, particles that an individual can _see_ as well as _hear_ are its neighbors.  
Figure 1: Schematic explaining the implementation of sensing modalities, where both the field of vision and the acoustic beam are modeled as spherical cones in three dimensions. (**a**) In the auditory mode, the orange has the blue particle as its neighbor, but not the yellow, since orange resides within the acoustic coverage of the blue and thus can hear it. (**b**) In the visual mode, the orange has the yellow particle as its neighbor, but not the blue, since the yellow resides within its field of vision. (**c**) In the composite mode, the orange particle can ‘see’ the yellow and ‘hear’ the blue, making both yellow and blue its neighbors.
## 4 Results and Discussion
### First and Second Step Results and Discussion
The statistical parameter used to assess the model error rate on unknown samples is the RANGEPERR%@95%, allowing a clear interpretation of its meaning, being related to LODP%@95% and to the statistical concept of confidence interval. Figure 6b shows the relationship existing between RANGEPERR%@95% and RPD. The correlation is hyperbolic; RPD is inversely correlated with RANGEPERR%@95%, i.e., their product is equal to a constant, and its value is 98.39 (+/ -0.16). The constant depends on the product under analysis. For example, from previous works, for kaki fruit, the mean value of the constant correlating RPD and RANGEPERR%@95% was equal to 36.39 (\(\pm\)3.70) [43], while for olive pomace the mean value of the constant was 45.60 (\(\pm\)1.78) [44]. With regard to donkey milk [45], the mean value of the constant was 45.95 (\(\pm\)0.52) for protein content, 70.35 (\(\pm\)1.32) for lactose content, and 73.84 (\(\pm\)0.82) for dry-matter content.