esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement
### Comparison of the Stability  
The variance of throughputs of SCTP and TCP under different PERs was calculated to describe the throughput fluctuations of SCTP and TCP. A total of six simulations were performed. Figure 5 shows the comparison of throughput variance between SCTP and TCP under different PERs.  
Figure 5 shows that the SCTP throughput variance was greater than TCP in most cases as the PER changed. In the interval of data PER between 0% and 10%, the throughput variance of SCTP and TCP was on the rise. As the data PER increased, the network situation became worse. At this  
Figure 4: Comparison of the packet loss rate between SCTP and TCP.  
time, the throughput fluctuation was larger when data transmission was conducted. The data PER was between 10% and 50%, and the throughput variance curves of SCTP and TCP were both in the decline stage. When the PER reached 10%, the real-time throughput of both decreased significantly. While Figure 5 shows that the real-time throughput curve fluctuated less, the variance of real-time throughput decreased, and the results were consistent.  
The analysis above shows that, under the condition of the same PER, SCTP in the process of data transmission had larger real-time throughput and average throughput. However, from Figure 5, the SCTP throughput variance curve was basically above the TCP, which meant that TCP provided more stable and reliable data transmission than SCTP. Due to the fact that SCTP adopted a conservative congestion control scheme based on TCP, SCTP multipath transmission and multi-flow characteristics did not match with SCTP's congestion control strategy. Although SCTP provided the maximum throughput relative to TCP, TCP was more widely used in practical applications when considering the stability of data transmission. In this paper, a combined TCP and SCTP multi-path switching scheme TCP-SCTP was proposed to improve the throughput and stability of data transmission.
## 4 Design Architecture
### Control Logic  
While it is possible to address the design components directly from the host via an AXI4 slave interface, it is recommended to store input signatures and results in a dedicated DDR memory. The communication overhead with the DDR is much lower than with the host. Thus, the static signatures in the processing elements can be easily managed and exchanged, while the dynamic signatures can be restrained several times.  
In this work, the Xilinx XDMA IP-Core is used to communicate with the board and to access the attached DDR. A High-Level Synthesis (HLS) core, configured by software, has been developed to transmit the input signatures from the DDR memory to the calculation units. In addition, the core also defines basic configurations, such as the threshold size. When all dynamic signatures have been  
Figure 4: Connection of the priority arbiter.  
streamed through the processing elements, the core automatically sends new static signatures and restreams the dynamic signatures. This is repeated until all available static signatures are processed.  
Another HLS core was developed to handle the transfer of the results to the DDR. It reads the results from the result collector FIFO as soon as it contains data and transmits them to the connected DDR.
## 4 Solution Procedure
### Upper Bounds  
#### 4.1.1 LR1  
There are several Lagrangian relaxations for MMCMCLP. By using this procedure, a hard problem is converted into a relatively easy one by relaxing sets of complicated constraints. After testing possible combinations of constraints to be relaxed, in the first relaxation problem (LR1) the set of constraints (3)-(5) have been chosen to be relaxed. Let \(\mu_{jlt}\geq 0\ \forall\ j,l,l,\ A_{ilt}\ \geq 0\ \forall\ i,l,t,\ \gamma_{ilt}\ \geq 0\ \forall i,l,t,\) be the multipliers associated with constraint set (3)-(5), respectively. Then, the Lagrangian problem LR1 is defined as:  
\[\begin{array}{rl}Z_{LR1}=&\text{Max}\ \sum\limits_{i\in I}\sum\limits_{j \in I}\sum\limits_{l\in I}\sum\limits_{t\in T}\ \alpha_{i}g_{ij}a_{jlt}x_{ijlt}-\sum\limits_{i\in I}\sum\limits_{l\in L}\sum \limits_{k_{l}\in K_{l}}\sum\limits_{t\in T}h_{ilk_{l}t}y_{ilk_{l}t}\\ &+\sum\limits_{i\in I}\sum\limits_{j\in I}\sum\limits_{l\in T}\mu_{jlt}\left( 1-x_{ijlt}\right)+\sum\limits_{i\in I}\sum\limits_{t\in T}\sum\limits_{t\in T }\lambda_{ilt}(\sum\limits_{j\in I}a_{jit}\ x_{ijlt}-\sum\limits_{k_{l}\in K _{l}}b_{lk}y_{ilk_{l}t}\ )\\ &+\sum\limits_{i\in I}\sum\limits_{l\in I}\sum\limits_{t\in T}\gamma_{ilt}( \sum\limits_{k_{l}\in K_{l}}B_{lk}y_{ilk_{l}t}-\sum\limits_{j\in I}a_{jit}\ x_{ijlt})\end{array} \tag{9}\]  
Subject to (2), (6)-(8).  
The objective function of the problem LR1, \(Z_{LR1}\), provides an upper bound on the objective function of the MMCMCLP for \(\mu_{jlt}\geq 0\ \forall j,l,t,\ \lambda_{ilt}\geq 0\ \forall i,l,t,\ \gamma_{ilt}\geq 0\ \forall i,l,t\).
## 4 Data Silos and Fragmentation:
### Proposed PoS  
Decentralized Storage: In the proposed framework, each participating healthcare institution maintains its own local storage of electronic medical records (EMRs). These EMRs contain valuable patient data, including medical history, lab results, treatment records, and other relevant health information. The EMRs remain decentralized, meaning that they are stored locally within each institution's secure servers or databases [31].  
Data Privacy and Security: To protect patient privacy and ensure data security, the actual patient data are not stored directly on the blockchain. Instead, the blockchain contains hashed references or pointers to the respective EMRs in each institution. Hashing ensures that patient data remains confidential and tamper-resistant. As a result, sensitive patient information is not exposed on the publicly accessible blockchain, mitigating the risk of unauthorized access or data breaches.  
Immutable Audit Trail: The blockchain, being an immutable and distributed ledger, maintains an auditable record of all data access and model training activities. Each update to the federated learning model or access to specific EMRs is recorded as a transaction on the blockchain. This transparent and tamper-proof audit trail enhances transparency and accountability, promoting trust among the participating institutions and patients [32].  
Smart Contracts for Data Access Control: The framework utilizes smart contracts to manage data access and sharing permissions. These self-executing agreements enforce predefined rules and conditions for data sharing. Healthcare institutions can define access policies through the smart contracts, specifying which data can be shared and with whom. This fine-grained control ensures that only authorized parties can access specific datasets for collaborative model training [33].  
Interoperability: The blockchain's role in the framework extends beyond data storage and access control. It also facilitates interoperability among different EMR systems used by various healthcare institutions. By using standardized data formats and communication protocols, the blockchain enables seamless data exchange and aggregation during federated learning without compromising data integrity. Overall, the dataset storage in the proposed framework strikes a delicate balance between data sharing and privacy. By leveraging the inherent strengths of blockchain technology, the framework ensures that healthcare institutions can collaboratively train machine learning models to gain revolutionary insights in precision medicine while upholding the utmost data privacy and security for patients' sensitive information.
## 2 Model Description
The corresponding form of laser intensity at the surface (\(z=0\)) in this case is  
\[I_{abs}\left(0,t\right)=\left(1-R\left(T_{a}\right)\right)\sqrt{\frac{\xi}{ \pi}}\frac{\Phi_{inc}}{t_{p}}\exp\left(-\varsigma\left[\left(t-3t_{p}\right) \left/t_{p}\right]^{2}\right), \tag{14}\]  
where \(\Phi_{inc}\) is the incident fluence, \(\varsigma=4\ln 2\), and \(R(T_{a})\) is the reflectivity function (see Table 1). In the present calculations, the center of Gaussian pulse is shifted in time from the initial time, \(t=0\), to 3 pulse duration times, \(3\,t_{p}\), which in turn is defined as the pulse width at the half of maximum. The dependence of the laser pulse intensity, \(I_{abs}\), on depth can be found upon the solution of differential equation of the attenuation process:  
\[\frac{dI_{abs}}{dz}=-\alpha I_{abs}\left(z,t\right)-\beta I_{abs}^{2}\left(z, t\right)-\Theta nI_{abs}\left(z,t\right), \tag{15}\]  
where \(z\) is the depth into sample; the terms on the right side are responsible for one- and two-photon absorption, and for the free-carrier absorption processes. Thus, from the system of Equations (7) to (9), we can fully determine the dynamics of \(n\), \(T_{e}\), and \(T_{a}\) in 1D using the following initial and boundary conditions, suitable for a free standing film:  
\[ \begin{split} T_{a}\left(z,0\right)&=T_{e}\left(z,0 \right)=300\,\mathrm{K},\\ n\left(z,0\right)&=n_{eq}=1\times 10^{16}\,\mathrm{m}^{-3},\ \mathrm{ref.}\@@cite[cite]{[\@@bibref{}{K}{}{}]},\\ J\left(0,t\right)&=J\left(L,t\right)=0,\\ W\left(0,t\right)&=W\left(L,t\right)=0,\\ k_{a}\frac{\partial T_{a}}{\partial z}\left(0,t\right)& =k_{a}\frac{\partial T_{a}}{\partial z}\left(L,t\right)=0,\end{split}  \tag{16}\]where \(L\) is the thickness of the sample. Owing to its similarity with an ordinary well-known TTM model [16], but with an additional equation for free carriers density \(n\), we refer to the described approach as \(n\)TTM model, as it was suggested in [24].
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 4 Modulation/Demodulation Scheme
### Frequency Multiplier's Selection  
As was shown before, for calculating the adequate symbols' codification frequencies, the frequency multiplier (\(\alpha\)) must be determined. The (\(\alpha\)) value depends on several system characteristics: the distance range, the selected camera's vertical resolution, the vertical FOV, and the selected emitter's dimensions. This frequency multiplier is an integer parameter that multiplies the camera's frame rate (\(f_{\text{CAM}}\)) to obtain the coupled frequency, defined as \(f_{\text{SW}}=\alpha f_{\text{CAM}}\). Using this relation, Equation (3) can be modified to calculate the band's size in terms of the camera's resolution and the frequency multiplier, as shown in Equation (\(\mathcal{7}\)).  
\[\begin{split} N_{\text{BAND}}&=\frac{N_{y}f_{\text{ CAM}}}{2\ \alpha f_{\text{CAM}}}\\ N_{\text{BAND}}&=\frac{N_{y}}{2\ \alpha}\end{split} \tag{7}\]  
As has been stated previously, the PCC requires that each image contain at least two strips. Therefore, the projected vertical size of the light source, \(y_{\text{Source}}\), must be greater than the double of the strip's vertical size. Additionally, in order to locate the transmitter in the frame, recover the corresponding ROI, and adequately process the cropped image, the band's vertical size should be greater than five pixels. The theoretical value is three pixels for assuring the four possible PCC values. However, the five pixel requirement was determined from the execution of several experiments related with the data extraction process for OCC. Therefore, the boundaries of the number of light bands are expressed as:  
\[\begin{split} y_{\text{Source}}>vN_{\text{BAND}}\ \ /N_{\text{BAND}}\geq 5, \forall v\geq 2\\ \frac{2}{\phi_{y}}\left[\arctan\left(\frac{d_{y}}{2D}\right) \right]>v\left(\frac{N_{Y}}{2\ \alpha}\right)>5v\\ \frac{N_{y}}{10}>\alpha>\frac{\phi_{y}N_{y}}{2\arctan\left(\frac {d_{y}}{2D}\right)}\frac{v}{2}\end{split} \tag{8}\]  
Figure 9: Image acquisition example. In this case, \(D\) is the distance between the camera (receiver) and the object (transmitter), \(d_{x}\) and \(d_{y}\) are the horizontal and vertical distances in the real world.  
Then, the frequency multiplier \(\alpha\) can be obtained based on the camera's characteristics (\(\phi_{y}\), \(N_{y}\), FoV\({}_{y}\)), the distance between the emitter and the receiver (\(D\)), and the light source's size (\(d_{y}\)) using Equation (9)  
\[\frac{N_{y}}{10}>\alpha>\frac{v\text{FoV}_{y}}{4\left[\arctan\left(\frac{d_{y}} {2D}\right)\right]} \tag{9}\]  
As previously stated, this frequency multiplier is the base for selecting the switching frequencies under specific conditions. The selection process is presented in the following subsection.
# Influence of the Diatomite Specie on the Peak and Residual Shear Strength of the Fine-Grained Soil
## 2 Materials and Methods  
### Materials Characterization and Experimental Designs  
In this research, an untreated commercial kaolin available in Bogota (Colombia) with a specific gravity (GS) of 2.69 was employed as the fine-grained soil matrix, and two distinct types of diatom microfossils commonly used as pesticide in the field of agriculture were selected to be studied. The first type of diatom microfossil corresponds to the _Alucoseira Granulata_ species (GS = 2.55), extracted from the lacustrine soil of Tunja in Colombia (Colombian diatomic species), and the second is the _Coscondiscus Centralis_ (GS = 2.34) imported directly from Mexico City in Mexico (Mexican diatomic species). Scanning electron micrographs (SEM) were taken of the three materials, as shown in Figure 1. According to the SEM images, the kaolin presents a set of many laminar layers overlapping together and without pores within the internal structure. Concerning diatom microfossils, the Colombian species has a cylindrical shape with pores along its walls. Similarly, the Mexican species has a shape of discs with pores, too, along its surface. In both cases, the pores sizes range from 200 to 900 nm, approximately.  
Since the particle size is less than 0.075 mm remaining in the fine range, the particle size distribution for the three materials was obtained through the hydrometer, as ian be seen in Figure 2. From the graph, it can be observed that the three materials fit in the size of the silt. Similar gradation curve was also noticed between Colombian species and kaolin with a more regular size distribution, while in the Mexican species, the slope of the curve is steeper.  
Figure 1: SEM images of: (**a**) kaolin; (**b**) Colombian diatom species; (**c**) Mexican diatom species.  
For the experimental designs, artificial mixtures of kaolin and the two diatom microfossils were manufactured to investigate the influence of the diatomic species in the shear strength of the soil. The mass ratio expressed in percentage for all the mixture designs is depicted in Table 1. For the specimen's conditioning before the annular shear strength test, the diatomic-kaolin mixtures were initially blended in dry conditions and then were added to distilled water so that the mixture reached the moisture content corresponding to the liquid limit (\(LL\)). Later, the mixes were homogenized and kept wrapped in plastic bags for 24 h so that all the mixtures were completely humidified.
## 3 Uncertainty Modeling Techniques
### Probabilistic Methods  
Probabilistic methods are fundamental to uncertainty modeling in power systems, using probability theory to evaluate the likelihood of various outcomes under uncertain conditions. These methods can be divided into analytical, numerical, and sampling approaches. Analytical methods compute probabilities directly using predefined distributions, suitable for systems with well-defined parameters. In complex power systems where analytical solutions are difficult, numerical methods, such as Monte Carlo simulations, are employed. These simulations use random sampling to estimate outcome probabilities, addressing both aleatory and epistemic uncertainties by evaluating a wide range of scenarios.
## 3 Results and Discussion
### Mechanical Properties
#### 3.2.2 Flexural Tests
On the contrary, the same tests performed on ZX samples led to the premature failure of samples at low value of strain; this provided evidence of their brittle behavior. The arrangement of interfaces among stacked layers, which are perpendicular with respect to the sample axis, and therefore parallel to the load application can be considered the main cause of the different flexural and deformation features of ZX samples. The experimental results conclusively showed that the building direction has a significant effect on both the tensile and flexural properties. Samples with XZ orientation showed the best performances, while slightly lower mechanical properties were observed for XY specimens. On the contrary, the arrangement of stacked layers in ZX samples represents the main weakness of these samples when they are subjected to both tensile and flexural loads.
## 3 Finite Element Model of Ballastless Track-Bridge Structural System
### Verification of Calculation Method  
In the work by the authors of [33], ABAQUS software was used to establish a numerical model of a simply supported concrete beam, the predict results were compared to the experimental results in the literature [34], and the validity of the equivalent static analysis method was verified. Considering the issues (such as large test site, expensive test cost and long test time) related to the full-scale test of the ballastless track-bridge structural system under the train fatigue load, a \(\frac{1}{4}\)-scale indoor model test of a 3-span 32 m CRTS II slab ballastless track-bridge was carried out [35], and the predicted result of  
Figure 5: Flow chart of equivalent static analysis for structural system under fatigue loading.  
the finite element model was compared to the test results in this paper. The validity of the equivalent static analysis method was further verified.  
For the sake of brevity, only the comparison between the test and the finite element model results of the mid-span load-deflection curve of the intermediate beam under static load and that of its top surface reinforcing steel strain under fatigue load is presented in this paper, as shown in Figures 6 and 7. Figure 6 shows that the simulated mid-span load-deflection results of the beam are more significant than the experimental results. However, the maximum error is less than 10%. Figure 7 shows that, although the strain of the beam top surface steel is not apparent with the increase in the fatigue loading cycle, the downward trend is approximately the same, and the error is controlled within 5%. Therefore, this analysis method can be used to analyse the mechanical fatigue properties of the ballastless track-bridge structural system.  
vertical train load adopts the China Passenger Transport (ZK) standard live load value [36], which acts on the intermediate beam rail as a larger concentrated load, resulting in more substantial deformation of the spring of the simulated fastener. The relative longitudinal displacement of the beam and the base under static is shown in Figure 9. Due to the \"dual textile-one membrane\" geotextile sliding layer connection, there is a slip between the beam and the base along the length of the bridge, and the maximum slip occurs at the end of the beam with a magnitude of 1.27 mm.  
and the maximum longitudinal compressive stress at the shear groove near the side beam is 1.535 MPa. The longitudinal stress of the CA mortar layer top and bottom surfaces substantially coincides, and the beam mid-span section longitudinal compressive stress is substantially stable at 0-0.15 MPa. The slab top surface longitudinal stress abruptly changes at the beam end, and the maximum longitudinal tensile stress near the shear groove of the middle beam reaches 2.047 MP. From the stress analysis of the structural system, the fatigue life of the beam can be seen to be controlled by compression, while the fatigue life of the track system layers is controlled by the top surface tensile stress of the layer at the beam end.
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
## 5 Conclusions  
This paper investigates the allocation of downlink resources in a multi-user NOMA system based on base station power transmission and minimum user rate constraints, intending to maximize energy efficiency. The NOMA system transmits signals from multiple users superimposed on the same subchannel, so the user grouping scheme will significantly impact the system performance. We consider that the optimal user grouping scheme obtained by exhaustive enumeration has high algorithmic complexity and is unsuitable for practical applications. In this paper, a low-complexity greedy algorithm based on the principle of worst-case subchannel priority matching is proposed to solve the subchannel assignment problem. In addition, we propose a power allocation strategy based on a modified particle swarm optimization algorithm that combines subchannel power allocation and inter-user power allocation for a joint optimization solution. Meanwhile, we add a beta distribution to the inertia weights to address the problem that particle swarm optimization tends to fall into local optimization and poor search accuracy. This improvement allows the algorithm to be dynamically tuned and gives the algorithm good global convergence capability. In addition, we use variation and crossover operations in the differential evolution algorithm to update the particles' positions, increasing the population's diversity. Finally, the simulation results show that our proposed scheme not only significantly improves the energy efficiency of the NOMA system, but also ensures the high fairness of the fire usage.  
Conceptualization, Y.C. and P.L.; methodology, Y.C.; software, P.L.; validation, Y.Z. and W.D.; formal analysis, Y.C.; investigation, Y.C.; resources, P.L.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, P.L.; visualization, Y.C.; supervision, P.L.; project administration, P.L.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Science and technology projects of State Grid Corporation (No.1400-202157214A-0-0-00).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Energy efficiency versus minimum data transmission rate requirements.
## 3 Results and Discussion
### _Satchard Model Analysis of Molecularly Imprinted Stirring Bar_  
The Scatchard model is a graphical method proposed by [PERSON] in 1949 for analyzing the binding relationship between ions, drugs, and other molecules with proteins (including receptors). In this study, the model is used to analyze the binding relationship between the template molecule (BPA) and molecularly imprinted sites, in order to determine the maximum apparent binding capacity (Q\({}_{\rm m}\)) of the molecularly imprinted sites for BPA.  
In the Scatchard equation, the relationship between q\({}_{\rm e}\)/C\({}_{\rm e}\) and q\({}_{\rm e}\) is linear. By performing a linear fit, one can determine the equilibrium dissociation constant (K\({}_{\rm d}\)) of the binding sites and the maximum apparent binding capacity (Q\({}_{\rm m}\)) of the binding sites. With K\({}_{\rm d}\) and Q\({}_{\rm m}\), the theoretical cavity content generated by the imprinting adsorbent can be determined. The Scatchard equation fitting of the magnetic molecularly imprinted stir bar  
Figure 4: BET analysis of the coating of the bisphenol A molecular imprinting stirring rod.  
for bisphenol A (BPA-MIPs-SBSE) is shown in Figure 5. As determined from Scatchard Equation, Qm = 33.79 umol/g, and Kd = 1.828 umol/L.  
This indicates that in the preparation of BPA-MIPs-SBSE, the appropriate amounts of template molecule BPA, functional monomer MAA, and cross-linker EGDMA were used. MAA effectively binds BPA and forms functional imprinting sites. At the same time, BPA was completely removed during the elution process, allowing the imprinting cavities to form, thus successfully binding BPA in water samples.