esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Previously published work from our lab assessed migratory signals produced by artificial wound models [12]. From a panel of several key cytokines (IL-1b, IL-2, TNF\(\alpha\), IL-12p70, and IL-6), only IL-6 was identified as being able to induce MSC migration from the spheroid models [12]. Well known for its roles within inflammation, immune response, and skeletal maintenance, IL-6 is also a key  
Figure 1: Schematic of three-dimensional spheroid culture. Following its formation, the spheroid is levitated in a type I collagen gel where it can be maintained for several weeks. regulator in cell differentiation, as it is heavily associated with haematopoiesis and differentiation of both osteoblasts and osteoclasts [23; 24; 25; 26]. Understanding MSC migration will help inform on potential delivery routes in cancer therapeutics and within the field of regenerative medicine. Cell migration involves the breakdown of extracellular matrix (ECM), which requires the action of proteolytic enzymes such as matrix metalloproteinases (MMPs). There are several classes of MMPs depending on their substrate specificity: (1) collagenases (MP-1, -8, -13, and -18), which cleave fibrillar collagens type I, II, and III, (2) gelatinases (MMP-2 and -9), which cleave gelatin, but can also degrade other extracellular matrix proteins including collagen and laminin, (3) stromelyins (MMP-3, -10, and -11), which degrade several non-collogenous proteins, and (4) membrane-type MMPs (MMP-14, -15, -16, -17, -24, and -25) [27; 28]. MMP activity is regulated by tissue-specific inhibitors of MMPs (TIMPs). The balance between MMPs and TIMPs is critical in ECM remodelling, cell migration, cell differentiation, and the regeneration of any tissue [29]. Therefore, the involvement of these enzymes in MSC migration could provide valuable information with regard to tissue regeneration. In this study, we aim to identify which MMPs are employed in IL-6-induced migration in both 2D and 3D MSC culture by analysing media extracted from monolayer and spheroid cultures. The presence of a panel of seven human matrix metalloproteinases (MMP-1, MMP-2, MMP-3, MMP-8, MMP-9, MMP-10, and MMP-13) was quantified following IL-6 addition into the microenvironment. A panel of three tissue inhibitors of metalloproteinases (TIMPs; TIMP-1, TIMP-2, and TIMP-4) was also assessed, to analyse the relationship between protein and inhibitor following IL-6 sensing by cells. TIMPs directly inhibit MMP protein via binding of the active site [30]. To complement this analysis, monolayer cells were also observed on an Ibidi cell migration plate assay (using time lapse microscopy) over a 24 h period in the presence of IL-6, whilst spheroids were imaged over a series of days both in the presence and absence of IL-6, allowing visualisation of both 2D and 3D migratory response leading to a greater understanding of the migration and giving further insight into the role IL-6 plays within these model systems.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 5 Related Works
Then, a central controller tunes the value of \(CWratio\) (the ratio between \(CW\) and \(M\)) periodically, searching for the optimal \(CWratio\). This value is sent to the network and then each station sets its contention window size according to its \(M\) value, i.e., \(CW=M\times CWratio\). In [34] authors analytically derive the CW value that maximizes the throughput under both saturated and non-saturated conditions. Then, they propose a distributed algorithm to be executed each time there is a packet on the buffer of a station to be transmitted or when a collision occurs. Refs. [35; 36; 37] propose mechanisms to tune the CW by observing the channel status. Ref. [38] proposes to dynamically adjust the CW size considering channel bit error rate (BER). However, these methods have to estimate the number of active stations. Finally, ref. [6] proposes a modification of CSMA/CA contention protocol to mitigate inter-packet latency (also known as tail latency) in IEEE 802.11-based networks. Here, it is guaranteed that every competing node transmits a packet in a round (virtual time slot). The rounds are implemented in a distributed way by the definition of two non-overlapped contending ranges, one of them \([0,\alpha)\) and the other \([\alpha,W)\). As nodes success in their transmissions, they change the contending range from the first range to the second one. However, this solution is still focused on providing QoS in scenarios where real-time services are dominant.
# Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
###### Abstract
The results showed that artificial neural network-based approaches can be a solution to hydrological problems, such as river flow prediction [16]. [PERSON] [17] evaluated the ability of two different types of models, the hydrological simulation program (HSPF) model and the artificial neural network (Ann) model, to simulate runoff and showed that the runoff simulated by the artificial neural network is closer to the observed values than the HSPF prediction and reduces the complexity of modeling the system. Currently, the application of wavelet analysis to neural networks can combine the learning ability of the neural network model and the localization ability of wavelet transform to form a new neural network model with fast approximation and strong fault tolerance, which can obtain faster convergence speed and more accurate prediction results. The application of the wavelet neural network model to oil field production prediction in petroleum exploration has resulted in high prediction accuracy, simple operation and low error due to the model's better convergence and ability to handle complex situations [18]. [PERSON] et al. [19] quantified the spatial correlation of eight factors, such as bulk density, clay content and topography on potassium with the wavelet coherence model, which was very important for hydrological simulation and irrigation management in arid areas of Loess Plateau in China. [PERSON] et al. [20] discussed the influence of input variable uncertainty and wavelet decomposition on the performance of hydrological and water resources probabilistic prediction model, and establishes a prediction model driven by probabilistic wavelet data. The flow prediction problem with precipitation as an input was based on the hydrological model of ANN, which shows that the wavelet derived value can provide relevant flow path and hydrological information. The wavelet analysis (WA) method has the ability to perform time-frequency integrated analysis of non-stationary time series and is suitable for studying complex hydrological and water resources systems with multi-timescale variability. This paper analyzes the precipitation and runoff changes of the Lancang River system based on wavelet transform, and uses the first main cycle of rainfall and runoff as a training set to provide relevant flow path and hydrological information for predicted sediment in the basin, which helps to understand the evolution of the river to improve water conservancy construction and ecological civilization in inland and coastal areas, and provides a reference basis for water resources utilization.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 6 Testbed and Performance Evaluation
### Performance Evaluation  
We evaluated our solution in terms of scalability and flexibility. Firstly, we present a scalability analysis where we compared our SRv6 solution against a non-SRv6 one in terms of the configuration times and number of rules to be installed. Subsequently, we show the performance of our solution, comparing it with a non-SRv6 one in terms of the round-trip time in static conditions. Finally, we illustrate the results of the analysis in dynamic conditions, i.e., when the MEO triggers container migration.  
As stated in the testbed description, for our evaluation, we used different network topologies. All of them are leaf-spine topologies, which differ in terms of the number of core layers. Specifically, we used three different networks. The smallest one is composed of one core layer, for a total of one core node. Another network is composed of two core layers, with three nodes each. The biggest network is composed of four core layers, for a total of twelve core nodes.
## 4 Discussion
### Limitations  
However, this study is not without its limitations. One notable limitation is the reliance on data from a statistics website. Although the data are considered reliable, any inherent biases or inaccuracies in the source data could potentially affect this study's outcomes. Additionally, the analysis is based on data from a single season of one country (2022-2023, Ligue 1), which may limit the generalizability of the findings across different seasons or other leagues. Furthermore, football's inherent complexity, characterized by numerous unpredictable factors, such as injuries, weather conditions, and player morale, presents another limitation. These factors are not captured in the dataset, which may constrain the predictive accuracy of the models. Finally, this study was conducted using the static method rather than the dynamic method, meaning that it did not account for changes that occurred during or between matches (e.g., match status, players' transfers) [59]. However, the static method is widely used in performance analysis in sports, offering valuable information to stakeholders [60]. In addition, from the perspective of the ML, the selected models have some limitations that we need to point out. In particular, XGBoost is computationally intensive and prone to overfitting on small, noisy datasets; SVM struggles with scalability and interpretability, while LR is limited by its linear assumption and sensitivity to multicollinearity.  
Future research should consider a longitudinal approach by analyzing data from multiple seasons and different European leagues to enhance the generalizability of our findings. Additionally, incorporating dynamic in-game factors, such as match status or player substitutions, can provide a deeper understanding of how tactical adjustments impact team performance. Finally, future studies can focus on individual player contributions within the team context, using our findings to guide recruitment and development strategies that align with the identified key performance factors.
## 4 Conclusions  
The effect of calcination temperature plays an essential role on particle size, phase transition, crystallinity, and crystallite size of synthesized TiO\({}_{2}\) via the WBMS method. Narrow size distribution of TiO\({}_{2}\) nanoparticles could be obtained. A mixture of brookite and anatase phases was found at calcination temperature less than 400 \({}^{\circ}\)C. The rutile phase was found above 500 \({}^{\circ}\)C. The mixture of anatase, rutile, and brookite phases was obtained at calcination temperature between 500 \({}^{\circ}\)C  
Figure 6: (**a**) Photocatalytic activity of TiO\({}_{2}\) presenting the kinetic constant at of TiO\({}_{2}\) as a function of calcination temperatures from 300 \({}^{\circ}\)C to 800 \({}^{\circ}\)C. Proposed mechanisms of different conduction and valence band alignments of (**b**) anatase-brookite, (**c**) anatase-rutile-brookite, (**d**) anatase-rutile, and (**e**) rutile phase. Striped and black arrows show electron transfer in conduction band and hole transfer in valence band, respectively. Checkerboard arrow represents a recombination of electrons and holes.  
and 600 \({}^{\circ}\)C. Anatase and brookite phases were completely transferred to the rutile phase at 800 \({}^{\circ}\)C. Phase composition and crystallite size are important factors for high photocatalytic activity. The high percentage weight of the anatase phase had a strong effect on the photocatalytic activity between 300 \({}^{\circ}\)C and 600 \({}^{\circ}\)C while the crystallite and particle size of TiO\({}_{2}\) had a dominant influence on the photocatalytic activity from 600 \({}^{\circ}\)C to 700 \({}^{\circ}\)C. The highest photocatalytic activity with three phases of TiO\({}_{2}\)-600 was achieved with kinetic constant of \(9.46\times 10^{-2}\) h\({}^{-1}\). The highest photocatalytic activity in this study can be applied for environmental application such as water decontamination or pollutants removal from water.  
Conceptualization, T.E. and C.S.; methodology, T.E. and C.S.; validation, S.P., T.W. and P.K.; investigation, S.P., T.W. and P.K.; formal analysis, S.P., C.S. and T.E.; resources, P.K.; writing--original draft preparation, S.P.; writing--review and editing, T.E. and C.S.; supervision, T.E. and C.S.; funding acquisition, T.E. and C.S. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA) (grant number P1850142). This study was also funded by Thailand Graduate Institute of Science and Technology (TGIST) (grant number SCA-CO-2560-4582-TH). The authors declare no conflict of interest.
## 4 Results and Discussion
### Statistical Calculation
#### 4.3.2 Calculation of Shear and Tensile Stress  
The shear stress \(\sigma_{c}\) (the support crush stress) is found numerically by dividing the force applied to the support surface of the system by the support area. The shear stress \(\sigma_{c}\) is given in Equation (11). The support plate is assumed to be rigid because it is fixed to the concrete floor of the building. In the following equations, \(f_{g}\) is the static force, \(A_{support}\) is the support plate area, \(D\) is the outer diameter of the refuse chute, and \(d_{si}\) is the thickness of the refuse chute.  
\[\sigma_{c}=\frac{f_{g}}{A_{support}}\rightarrow\sigma_{c}=\frac{f_{g}}{\frac{ \pi D^{2}}{4}-\frac{\pi\left(D-2\sigma_{d_{si}}\right)^{2}}{4}} \tag{11}\]  
where \(D=\mathcal{O}\) 500 mm, \(d_{si}=1.5\) mm, and \(f_{g}=3.924\) N, so the result is \(\sigma_{c}=1.67\) N/mm\({}^{2}\).  
The tensile stress \(\sigma_{t}\) is found numerically by dividing the force applied to the support surface of the system by the chute area and is given in Equation (11). In the following equations, \(f_{g}\) is the static force, \(D\) is the outer diameter of the refuse chute, \(\text{d}\text{s}\)i is the thickness of the refuse chute, \(A_{clute}\) is the area of the chute, and \(w_{t}\) is the width-length.  
\[\sigma_{t}=\frac{f_{g}}{A_{clute}}\rightarrow\sigma_{t}=\frac{f_{g}}{{W_{t}} ^{2}-\frac{\pi D^{2}}{4}} \tag{12}\]  
where \(D=\mathcal{O}\) 500 mm, \(W_{t}=700\) mm (square section), and \(f_{g}=3.924\) N, so the result is \(\sigma_{t}=0.133\) N/mm\({}^{2}\).
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 4 Building a QKD Emulation System to Test Protocols
### PUF Implementation  
Our ReRAM PUF shield allows for an addressable PUF driven by CrossBar Inc., Santa Clara, CA, USA ReRAM chip(s). Multiple protocols for key extraction are supported, and using this shield, we are able to extract the PUF ternary and quaternary message for the QKD protocols in Section 2.2 using the methods from Section 3.  
The ReRAM PUF was implemented on a low-power client device using a custom-built printed circuit board (PCB) and various integrated circuit (IC) devices. The low-power client device we used is the commercially available developer kit, the Chipkit Wi-FIRE (Figure 8).  
The Chipkit Wi-FIRE was chosen because it operates at low power and allows for rapid development. It has 43 available general purpose input/output (GPIO) pins, 12 analog input pins, a 12-bit ADC, a 3.3 V and 5 V power supply, 2 MB of flash, and 512 K of RAM. It has a PIC32 microcontroller that operates at 200 MHz and 3.3 V logic. The Wi-FIRE's primary limitations are its operating frequency, its small number of I/O and analog pins, and its fixed operating voltage.  
While the developer kit gave us easy access to pins and other additional circuitry, a custom PCB with various IC components was needed to communicate with the ReRAM PUF (Figure 9). This PCB required buffers, logic shifters, GPIO expanders, and a comparator to execute the required protocols in this paper.  
Figure 8: Chipkit Wi-FIRE developer kit.  
Figure 9: Custom PCB for Wi-FIRE.  
Additionally, the ReRAM PUF used in this implementation is the CrossBar Inc.'s 1B chip. The 1B is a pre-formed ReRAM PUF that is not commercially available and is made specifically for use as a PUF. It contains 4096 pre-formed resistors capable of creating a response by applying a voltage or current through the cells. These can be applied to the bottom or top pads of the cell for forward or reverse reading. The applied voltages range from 0.15 V to 1.5 V, and the currents range from 79 nA to 15 nA.  
To generate a stream from the PUF, we select a current and generate random pairs of addresses. We read the resistances at each address of the pair and compare. If the first resistance is greater than the second, the output is 1; otherwise, the output is 0. We actively filter out pairs whose resistances are close or equal. We then apply the methods from Section 3 to achieve higher base output.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Discussion and Analysis
#### 8.6.2 Performance Analysis in the Context of Global Digital Registries  
For global digital registries, which may need to handle millions of entries and frequent updates, our system's performance is promising. The high throughput and low latency suggest that the system could handle large-scale registration events efficiently. Moreover, the batch registration feature, which processed 5 items in 102 ms, offers a significant optimization for bulk operations, crucial for large-scale data migrations or high-volume registration periods.
###### Abstract  
The transition zone between a shallow-water delta and tidal flat is characterized by a high degree of mixed siliciclastic-carbonate sedimentation. There are frequent lateral and vertical variations in sandstone, dolostone, limestone, and mixed siliciclastic-carbonate rock (MSR); however, their influence on reservoir quality remains uncertain. Member B of the Asmari Formation (Asmari B) in Iraq's C Oilfield was deposited in a remnant ocean basin formed by the closure of the Neo-Tethys Ocean. During the Oligocene-Mocene, frequent exposure of the Arabian Shield provided intermittent sediment sources to the study area. Under shallow water and relatively arid conditions, widespread mixed sedimentation of siliciclastic sand and dolomitic components occurred. Taking Asmari B as a case study, this research employs core and thin-section observations, trace element analyses, and quantitative mineralogical interpretations of logging data to investigate the characteristics of mixed sedimentation and to evaluate its impact on reservoir quality. Four key aspects were identified: (1) Four main types of mixed lithofacies developed in Member B of the Asmari Formation, namely sandstone-bearing dolomite, dolomitic sandstone, dolostone-bearing sand, and sandy dolostone. These lithofacies were deposited in the transition zone between distributary channels and intertidal zone with different water depths. As the terigenous input decreased, the water depth for sand-bearing facies increased. In particular, sandy dolostone was predominantly formed in subtidal settings under the influence of storm events. (2) MSRs are categorized based on the proportion of the minor component into high and low mixing degrees. Based on mineral compositions interpreted from well logging data, the mixing degree of MSRs was characterized by the thickness ratio, using the thickness of high- and low-mixing-degree MSRs relative to the total thickness of the formation. The MSRs mainly developed in the B1, B2, B3-1, B3-2, and B4 sublayers, where moderate provenance supply facilitated the high mixing of terrigerious clastic and carbonate components. (3) The pore and throat patterns of MSR reservoirs change with the mixing degree index. When the dolomite content in sandstone exceeds 25%, the pore-throat structure changes significantly. A small amount of sand in dolostone has little effect on the pore and throat. Sandy dolostone exhibits the poorest reservoir quality. (4) Mixed sandstone reservoirs are distributed on both sides of the distributary channels and mouth bar. The dolostone-bearing sand reservoirs are distributed in the transition zone between the sandy flat and dolomite flat. Sandy dolostone is mainly thin and isolated due to the influence of storm events. This study provides guidance for understanding the development patternsof MSR reservoirs under similar geological settings, facilitating the next step of oil and gas exploration in these special reservoirs.  
mixed siliciclastic-carbonate rocks; petrography; mixing degree index; distribution of mixing sedimentation
# Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
## 2 Traditional Central Positioning Method
### Gray Centroid Method  
The gray centroid method is a sub-pixel algorithm commonly used for high-precision localization of regular or irregular targets in images. In optical measurement systems, the gray centroid method is commonly used to calculate the laser spot center position. Suppose that the size of the spot image is \(m\times n\), and the gray value of each pixel point is \(G(i,j)\). The centroid of the laser spot is calculated using the following formula:  
\[x_{c}=\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}i\times G(i,j)}{\sum \limits_{i=1}^{m}\sum\limits_{j=1}^{n}G(i,j)},\ y_{c}=\frac{\sum\limits_{i=1}^{m }\sum\limits_{j=1}^{n}j\times G(i,j)}{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{ n}G(i,j)}. \tag{4}\]  
The processing of the gray centroid method is simple and requires only a small amount of calculation. However, it is susceptible to the influence of noise, leading to large errors. The gray centroid algorithm can produce high localization accuracy for images with uniform distribution and a high signal-to-noise ratio.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 3 Proposed Problem
### Optimization Problem  
The proposed likelihood probability, \(\hat{p}_{r,k}[n]\), is formulated as a linear regression of the channel-based likelihood probability in (12) and the sample-based likelihood probability in (16). This approach is used because the channel-based likelihood probability captures the change in channel statistics, whereas the sample-based likelihood probability trains the true likelihood probability from the quantized received signal and re-detected symbol index. The model for the proposed likelihood is expressed as follows:  
\[\hat{p}_{r,k}[n](\Theta_{n})=\theta_{n}^{0}+\theta_{n}^{1}p_{r,k}^{\text{ channel}}[n]+\theta_{n}^{2}p_{r,k}^{\text{sample}}[n], \tag{17}\]  
where \(\Theta_{n}=[\theta_{n}^{0},\theta_{n}^{1},\theta_{n}^{2}]^{T}\) is the model parameter. Using this model, the optimization problem is defined as follows:  
\[\Theta_{n}^{\star}=\underset{\Theta_{n}}{\text{argmin}}\sum\limits_{r\in \mathcal{R}}\sum\limits_{k\in\mathcal{K}}\|p_{r,k}[n]-\hat{p}_{r,k}[n](\Theta _{n})\|^{2}, \tag{18}\]  
where \(\Theta_{n}^{\star}=[(\theta_{n}^{0})^{\star},(\theta_{n}^{1})^{\star},(\theta_ {n}^{2})^{\star}]^{T}\).  
Directly solving the optimization problem in (18) is difficult due to the nonlinear nature of the function \(\Phi(\cdot)\), and the fact that sample-based likelihood probability cannot be readily represented in a mathematically convenient form. To address these issues, linear regression is used as an alternative approach to determine the model parameter \(\Theta_{n}\).