esa-sceva/satcom-chunk-collection huggingface dataset:
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 4 Strategies for Noise Reduction and Design Optimization  
### Sources of Noise  
For any sensing system, the noise and uncertainty in the obtained results need to be assessed to evaluate the performance of the sensor. High noise levels not only obscure the signal to be measured but also make the data reduction process difficult and time-consuming. In the following, we willfirst discuss the several major sources of noise in a LAS system, including laser noise, detector noise, analogue-to-digital conversion (ADC) noise, optical noise, and environmental noise [70; 71]. Then, different approaches for noise reduction and signal optimization will be reviewed to guide future design considerations.
## 2 Satellite Systems and Errors in Position Determination
### Satellite Systems Short Introduction  
Development of satellite systems began in the 1970s, and, basically, from the beginning of the 21 st century they have become widely used. Satellite navigation technologies are used everywhere [43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]: in archaeology, construction, geodesy, mining, transport, security services, or agriculture. Even devices such as hearing aids have built-in GPS receivers so that the user can find them at the application level. As technology advances at a dizzying pace, it is inevitable that future machines will be able to operate autonomously using satellite technology for location and navigation. The main satellite systems are listed as follows:  
* Global positioning system (GPS) arising from the American Navy Navigation Satellite System (NAVSAT), which was initially used for US Navy submarine positioning and was the first to allow satellite navigation;
* European satellite system--Galileo, a relatively young system, launched in 2016, developed in Europe beginning in the 1980s due to the fear of the indecessibility of the GPS and GLONASS systems;
* Russian positioning system--GLONASS;
* Chinese satellite system--BeiDou.  
What all four of them have in common is their method of operation, similar in all satellite systems. Several dozen satellites circulating around the Earth's orbit send satellite signals to on-earth receivers. Based on the knowledge of the electromagnetic wave propagation speed and the position of at least four satellites, receivers are able to determine their geographical location on the ground, which is the starting point for their \"guided movements\". This is where the first question arises--which of the available systems should be used in case of such an approach, based on position acquisition, and its implementation in autonomous machines operating outdoor? The answer is: all of them. Today's devices have various global navigation satellite systems (GNSS) modules built in. They can observe different circulating satellites from various satellite systems. Such a solution reduces the probability of the event that less than four satellites (the lowest limit number) will be visible to the receiver.  
The receiver used for measurements, analyzed in this paper, and described in Section 3, also has the advantage of observing satellites from different satellite systems.  
Satellite signal measurements may be carried out in various ways. Depending on the obtained data purpose, different methods are used. In this research, the absolute measurement method was used, although the authors are aware that this is not the most precise available method. However, it requires one GNSS receiver only, so it was decided to analyze such an option to verify the accuracy of positioning in case an autonomous machine is equipped with one receiver only (or at the moment that only one receiver works properly). Is it going to be a restraint with changing weather conditions or not?
## 3 Methodology
### Classification Methods
#### 3.3.4 Bidirectional Encoder Representations from Transformers (BERT)
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Classification Method** & **Advantage** & **Disadvantage** \\ \hline \multirow{8}{*}{RF} & It helps to enhance accuracy by mitigating overfitting in decision trees. & It requires a substantial amount of computational power and resources. \\  & It is versatile and can be used for both classification and regression problems. & It can take a long time to train, as it combines a large number of decision trees to determine the class. \\  & It is effective for working with both categorical and continuous values. & Due to the use of an ensemble of decision trees, RF also faces challenges with interpretability and struggles to determine the significance of each variable. \\ \hline \multirow{8}{*}{**SVM**} & SVM is particularly effective when there is a clear separation between classes. & The SVM algorithm is not suitable for processing large datasets due to its higher computational complexity and memory requirements. \\  & In high-dimensional spaces, SVM tends to be more effective. & SVM does not perform well when the dataset contains more noise. \\  & SVM is effective in scenarios where the number of dimensions exceeds the number of samples. & SVM does not perform well if the number of features per data point exceeds the number of training samples. \\ \cline{1-1}  & It is known for its relatively efficient memory usage. & Per data point exceeds the number of training samples. \\ \hline \multirow{8}{*}{**MLP**} & It is capable of addressing complex nonlinear problems. & The SVM algorithm is not suitable for processing large datasets due to its higher computational complexity and memory requirements. \\ \cline{1-1}  & After the training process, MLP can make quick predictions. & MLP includes too many parameters because it is fully connected. \\ \cline{1-1}  & MLP can achieve comparable levels of accuracy even with smaller sample sizes. & MLP includes too many parameters because it is fully connected. \\ \hline \multirow{2}{*}{BERT} & BERT is a technology for generating “contextualized” word embeddings/vectors, which is its biggest advantage. & It is very computationally intensive at inference time, meaning that if you want to use it in production at scale, it can become costly. \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages and disadvantages of classification methods. Figure 7: BERT architecture of pretraining and fine-tuning tasks [55].
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
Both VPM GPP dataset and CMFD data were aggregated to the spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and the temporal resolution of 8 days. We first evaluated the spatio-temporal variations of GPP in the YRB, analyzing the changes of GPP among different water resource regions and vegetation types. The spatio-temporal variations of precipitation, temperature, and shortwave radiation were also explored. Considering the possible correlations among climate factors, we applied Partial Least Squares Regression (PLSR) to analyze the response of GPP to variations in precipitation, temperature, and shortwave radiation. PLSR is a statistical modeling method based on Principal Component Analysis (PCA) and multiple linear regression techniques. The PLSR method was commonly used in studies when the predictors are highly correlated [47; 48]. We quantified the relative contributions of precipitation, temperature, and shortwave radiation to the variations of GPP.  
\[\xi_{p}=100\%\times\frac{\left|\eta_{p}\right|}{\left|\eta_{p}\right|+\left| \eta_{t}\right|+\left|\eta_{s}\right|} \tag{1}\]  
\[\xi_{t}=100\%\times\frac{\left|\eta_{t}\right|}{\left|\eta_{p}\right|+\left| \eta_{t}\right|+\left|\eta_{s}\right|} \tag{2}\]  
\[\xi_{s}=100\%\times\frac{\left|\eta_{s}\right|}{\left|\eta_{p}\right|+\left| \eta_{t}\right|+\left|\eta_{s}\right|} \tag{3}\]  
\(\xi_{p}\)(%), \(\xi_{t}\)(%) and \(\xi_{s}\)(%) represented the relative contributions of precipitation, temperature, and shortwave radiation to the variation of GPP in the YRB, respectively. \(\eta_{p}\), \(\eta_{t}\), and \(\eta_{s}\) represented the standardized sensitivity coefficients of GPP to the changes in precipitation, temperature, and shortwave radiation, respectively. Before applying the PLSR method, all variables were standardized using the z-score method. The above statistical analysis was conducted using RStudio (Version 2023.06.1+524).
# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Model-Based Techniques
#### 4.5.1 Parametric Deformable Models  
Parametric deformable modeling, also called snakes or active contours, are parametrized curves that depend inherently on particular parameters to be created. The major goal of active contour modeling is to segment objects in retinal images by fitting the curve to objects' boundaries in the image. It is called dynamic contour modeling since it initialized at a place in the neighborhood of target object, then the model can evolve dynamically to fit the shape of object by an iterative adaption. The major idea of snakes is to represent a curve via parametric curve; however, since it depends on a parameter to control the movement of curves (when slithering as snakes) during fitting process, it is rigid topologically, namely, it does not have the flexibility required to represent objects composed of a variable number of independent parts [108]. Moreover, another widely-recognized issue associated with snake-based segmentation technique is the incapability of snakes to converge to the correct vessel edges in the presence of high level noise or if the vessels were \"empty\" or have relatively low contrast levels [74].  
A novel segmentation algorithm built on snake contours was developed by [PERSON] et al. [74]. The proposed technique consists of three major steps: (1) First, parameter initialization technique based on Hessian feature boundaries, where the Hessian feature was used to extract all darker linear structures in retinal image, and then, the retinal image was divided into (N) segmentation regions (R), based on the seeds of extracted linear structure; (2) Second, each segmentation region R was represented as image through utilizing pixels' average intensity influence, and then, the snake energy function was constructed on this image representation to realize the snake's locations from the neighborhood of vessel edges to real ones; (3) Finally, as all model-based methodologies end, a region growing technique was used r to get the final vessels' area, and then, the grown area was post-processed via context feature. The proposed methodology, validated on DRIVE dataset, reported a competitive performance, reaching up to 95.21% (accuracy), 75.08% (sensitivity), and 96.56% (specificity).  
An efficient and effective infinite perimeter active contour model with hybrid region terms for vessel segmentation was proposed by [PERSON] et al. [31]. The proposed model used hybrid region information of the retinal image, such as the combination of intensity information and local phase based enhancement map. For its superiority, the local phase based enhancement map was used to preserve vessel edges, whereas the given information of image intensity guaranteed a correct feature segmentation. The proposed method was applied to DRIVE and STARE datasets where the methodology achieved sensitivity, specificity and accuracy of 0.780, 0.978 and 0.956, respectively, on STARE, whereas, for DRIVE, the measures are reported to be 0.7420, 0.9820 and 0.9540, respectively.
## 6 Simulation Results  
In this section, the results of a Monte Carlo simulation performed to demonstrate the effectiveness of the proposed sum-rate expressions are shown. The theoretical and simulated sum-rate performances with different cooperative user numbers, quantization bits and feedback strategies were compared. During each snapshot, the channel matrices were randomly generated; then, the instantaneous achievable rate was determined according to the quantized CSI and DI. The final average achievable rate is the mean of all instantaneous rates from all snapshots. The simulation IA scenarios are simply symmetric \(K\)-user ICs with the small-scale Rayleigh fading channel model, denoted by \((M\times N,d)K\), where \(M\), \(N\), \(d\), and \(K\) separately represent the numbers of transmitter antennas, receiver antennas, data streams, and cooperative users.
## 2 Related Work
### End-to-End Network Slicing
The paper formulates it as an integer programming optimization problem; it also provides a sub-channel allocation scheme to ensure slice isolation [33]. For solving this complex optimization problem, two RL algorithms have been proposed and implemented, covered by the fog and traditional UEs requirements. The major contribution of this work is to generate an optimal policy for mode selection according to the reward provided by the RL models and apply that reward to the environment. The simulation results generated with this system can validate the performance in terms of optimal queue delay and power consumption [34]. This work [35] proposes a RAN slicing system for 5G networks named iRSS (intelligent resource scheduling strategy) that works based on deep learning (DL) algorithms with the combination of the RL. In addition, large time scale resource allocation has performed using DL and small time scale dynamics, such as online resource scheduling, inaccurate prediction, and unexpected errors related to network states have performed by the RL. The DL algorithm is trained on historical traffic data and it predicts according to the amount of training data, while iRSS system compares the prediction results with RL online decision engine and adjusts the values flexibly. Hence the combined iRSS system can assist the RAN in deciding the resource scheduling. Compared with other algorithms, the proposed iRSS system iRSS shows better performance for online resource scheduling and increases resource utilization. It can also ensure the complete isolation among RAN slices. In this study [36], the authors have designed a novel framework for the fog RAN slicing domain which can orchestrate the network slices. For the case study, two slices were created and realized for hotspot and vehicle to infrastructure use cases. Additionally, this system considered RAN slicing as an optimization problem to deal with mode selection and content caching. The diverse user requirements and constrained resources cause this optimization problem much complexity so traditional approaches fail to solve it [37]. To solve this difficulty, a reinforcement learning (RL)-based algorithm has been proposed in which the cloud server is responsible to decide on mode selection and content caching. It can boost the reward by checking the status of the channel and cache dynamically. Hence, results show that this system can significantly improve the performance in terms of hit ratio and transmission rate. The comparative analysis of the proposed system with existing studies is presented in Table 1. This comparison is done on the basis of literature review with important parameters for network slicing systems.
# The Principle and Downhole Testing of Water Injection Exploration in Depleted Reservoirs
## 4 Conclusions  
Through theoretical analysis and on-site water injection experiments, this study investigated the disturbance and stress changes around goat areas (old galleries) and the feasibility of using water injection to explore these areas. The following conclusions were drawn:  
1. Theoretical analysis suggests that when the equivalent normal stress caused by groundwater in fractured rock masses is greater than zero, the permeability of the fractures will significantly increase. The presence of disturbance and stress reduction zones around goat areas (old galleries) provides a theoretical basis for using water injection to detect these areas.
2. On-site water injection experiments showed that regardless of whether the boreholes were directed towards the goat or the old galleries, the closer they were to these areas, the lower the water injection pressure and the higher the permeability.
3. Based on the water injection pressure and permeability data from the experiments, and referencing previous theories, the goat areas (old galleries) were divided into three zones: the original intact zone, the stress relief zone, and the disturbance zone.
4. The experiments demonstrated that the significant reduction in pressure and increase in permeability during water injection were easily observable, indicating practical significance.  
Conceptualization, J.X.; methodology, H.Z.; software, H.Z.; validation, Y.L. and C.W.; formal analysis, H.Z.; investigation, B.D.; resources, B.D.; data curation, Y.L. and C.W.; writing--original draft preparation, H.Z.; writing--review and editing, J.X.; visualization, B.D.; supervision, H.Z.; project administration, J.X.; funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Program of the National Natural Science Foundation of China (NO. 52274243).  
The data used to support the findings of this study are included within the manuscript.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Anhui Wanbei Coal Power Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
In this paper the cyclic lateral response of reinforced concrete (RC) beam-column joints with composite carbon fiber-reinforced polymer (CFRP) bars as a longitudinal reinforcement in the beam is simulated with finite element (FE) modeling using software Abaqus. An experimental project of two full-scale joint specimens subjected to cyclic loading with supplementary accompanying pull-out tests of CFRP bars is also included in this study. These test results are used to calibrate the developed FE model, the constitutive laws of the materials and the bond response between CFRP bars and concrete. Comparisons between test data and numerical results indicate that the calibrated model accurately predicts the cyclic response of RC beam-column joint specimens with CFRP longitudinal bars as the beam's tensional reinforcement. A parametric analysis is also performed to provide useful concluding remarks concerning the design of concrete joints with composite bars and the ability of CFRP bars to substitute for conventional steel bars in RC structural members under seismic excitations.  
reinforced concrete; beam-column joints; carbon fiber-reinforced polymer longitudinal bars; seismic performance; finite element analysis; tests; bond; cyclic loading Article
# MIDOM--A DICOM-Based Medical Image Communication System
## 5 Conclusions
### Summary  
In this paper, we proposed MIDOM, an e-health platform whose main aim is to increase the availability of medical care in underdeveloped parts of the world and, generally, in places with limited access to medical professionals. It does so mainly by increasing interoperability by acting as a man in the middle between different medical actors, devices, medical professionals, and inherently standards in electronic health. MIDOM enables the increase in communication reliability, confidence, and confidentiality by providing secure communication packed with a lossless compression method, which significantly reduces sizes, implying a reduction in network latencies and data savings. Implicitly, it provides mitigation mechanisms between various standards in electronic health systems, which are heavily limited in terms of interoperability.  
In addition to MIDOM's implementation and integration with broader systems like PACS server, one of the main focuses of this paper is integration of the aforementioned custom compression method (CBPC), and its additional implementation (CBPC2) which features run-length coding enhancement. Higher compression rates with lossless compression methods are crucial in achieving the availability of electronic health systems, which is especially important for low throughput networks. The latter kind of network is still widely used as failover networks in developed parts of the world and as main networks in underdeveloped parts.  
We demonstrate significant data reductions through experiments by using our custom compression methods, which implied latency reductions in the tested network. We performed five benchmarks on three different networks, namely Wi-Fi, LTE, and 3G, demonstrating that our compression methods pay off in nearly every testing scenario by lowering the network latency to at least 60% of the original latency measured when transferring equivalent uncompressed dataset. The best result is achieved by lowering  
Figure 10: Latency reduction for MR-2 benchmark.  
network latency to mere 25% of uncompressed transfer latency for MR-1 benchmark on Wi-Fi and LTE networks and X-ray benchmark on the Wi-Fi network environment.
## 4 Discussion
### Relations between Parameters
#### 4.1.2 Relationships between Gas Species
Methane emission to the atmosphere can also occur as a purely diffuse mechanism [68] to form the so-called CH\({}_{4}\) micro-seepage, defined as the slow and continual loss and upward flux of CH\({}_{4}\) and light alkanes from depths of 2-5 km in sedimentary basins [69]. Methanotrophic oxidation processes in soil consume CH\({}_{4}\) and lead to non-existent or negative CH\({}_{4}\) flux at the soil/atmosphere interface - this is congruent with our observations (no CH\({}_{4}\) flux). The occurrence of such seepage is yet unproven and seems hypothetical in view of other information. Reactional processes can consume methane, but this is unlikely for helium. The Lacq-Rousse reservoir is \({}^{4}\)He-rich (65 ppm; [67]), and if micro-seepage occurs then \({}^{4}\)He will also diffuse and, as a chemically very stable gas, should be detected in the soil. No \({}^{4}\)He enrichment is reported in soil gas (unpublished data) neither in a local aquifer sampled near injection well RSE-1 [31]. This aquifer has also very low concentrations in dissolved CH\({}_{4}\) (10\({}^{-8}\) mol\(\cdot\)L\({}^{-1}\) when measurable; unpublished data) and the supply of CH\({}_{4}\) by the aquifer is highly unlikely. Natural micro-seepage from the reservoir is thus not certain and it must be assumed that the presence of CH\({}_{4}\) is rather linked to surface biological processes in non-anthropogenic soils. The fact is that soil CO\({}_{2}\) seems to be dependent from production through CH\({}_{4}\) oxidation in winter during periods of lower biological activity in soil. This effect vanishes--or is non-existent--during period of higher CO\({}_{2}\) production from biological processes in soil during summer. The relation between CO\({}_{2}\) as soil gas and CO\({}_{2}\) as soil flux is presented in Figure 4. Baseline data did not highlight a relation between concentration and flux, only a slight positive tendency for \"high season\" acquisitions [35]. The situation did not change during injection and post-injection monitoring. \"Low season\" measurements are strictly uncorrelated (R\({}^{2}\) = 0.006; 458 data) and \"high season\" ones are barely correlated (R\({}^{2}\) = 0.018; 252 data), but this may be only related to the number of data that is smaller for summer acquisitions. There is no correlation between CO\({}_{2}\) flux and CO\({}_{2}\) soil-gas concentrations, suggesting a near-surface origin of the CO\({}_{2}\) gas (no CO\({}_{2}\) flux from the storage reservoir and only CO\({}_{2}\) production in the soil). In addition, this highlights the poor vertical connectivity of the soil, probably due to high clay-mineral content.