esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Feedback Signaling on Osteogenesis and Bone Remodeling  
Due to the differentiation and activation of PBMCs and pre-osteoclasts, feedback signals are assumed. Compared to the NSO media, lower amounts of IL-4, IL-23, and TNF-\(\alpha\) were observed in the supernatants of pre-osteoclasts cultivated with ESO media. These proteins are known for their anti-osteoclastogenic properties, leading to reduced osteoblastic differentiation and activity [63; 64]. As electric stimulation indirectly reduced the amount of released IL-4, IL-23, and TNF-\(\alpha\), promoting feedback on osteoblasts can be expected. The increase in IL-6 concentration supports this assumption, as IL-6 is known to be important for osteogenesis [65; 66; 67]. Moreover, a rise in IL-6 can also improve the recruitment of new osteoclastic progenitors, as it can act as a proinflammatory signal [68; 69]. The analyzed BMP-2 concentration is in contrast to the previous hypotheses, as lower amounts of BMP-2, an inducer of osteoblast differentiation [70], were observed with ESO. Further studies on osteogenesis are necessary to observe the impact of the released cytokines. It can be expected that the indirect stimulation of PBMCs and pre-osteoclasts influences bone remodeling processes.  
Therefore, analyses of the interaction between progenitor cells, osteoclasts, and osteoblasts are necessary. Co-cultures such as trans-well systems or 3D scaffolds would allow the observation of feedback signals and the resulting changes in cell differentiation simultaneously [71]. Moreover, 3D scaffolds with a matrix that is similar to bone tissue would give a more physiological environment and allow the study of osteoblastic and osteoclastic cells together in one culture setup. Further, it has been described that exosomes and apoptotic bodies as osteoclast-derived extracellular vesicles can carry different miRNAs, which can influence osteogenesis [72]. Therefore, a protein screening of the supernatant as well as the determination of extracellular vesicles might give a more detailed insight into the signaling and interplay between the different cell types.
## 3 Using ML for Water Activities
### Advanced Techniques and Sustainability  
* **Optimizing agricultural irrigation**[263]: This study discussed the use of innovative technologies like UAVs, ML, and IoT to optimize irrigation in agriculture, improving water status monitoring and prediction.
* **Deep learning for agricultural water management**[264]: This study proposed a novel method using deep learning for feature extraction and classification in agricultural water management, achieving high accuracy and performance metrics.
* **Urban water demand forecasting with limited data**[265]: This study evaluated the effectiveness of extreme learning machine (ELM) models for daily urban water demand forecasting. The ELMW model achieved high accuracy, particularly in predicting peak demand.
* **Water demand forecasting in Kuwait**[266]: This study compared water demand forecasting methods in Kuwait, showing differences in accuracy between ARIMA and support vector linear regression models compared to actual consumption.
## 3 Results
For comparison, the base case with AR = 1, O/C = 0, and TA = 0 is also shown in Table 2. The obtained solution improves the maximum power coefficient by 32.4% in comparison with the base case. For comparison purposes, Figure 17 compares the power coefficient against the TSR corresponding to the base and optimum cases. It can be seen that the improvement is considerable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **AR** & **O/C** & **TA** & \(\mathbf{C_{pmax}}\) \\ \hline
**Base case** & \(1\) & \(0\) & \(0\) & \(0.2465\) \\ \hline
**Optimum** & \(7.5\) & \(0.1125\) & \(112\) & \(0.3263\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimum and base case solutions. Figure 16: Regression graphs of the ANN. Figure 17: Comparison between the base and the optimum cases.
## 2 Theoretical Framework for Assessing Virtual Globes
### Development of Virtual Globes to Date  
Compared to Geographic Information Systems (GIS), virtual globes only have basic GIS analytical functions such as measuring and identifying attributes [2]. Unlike popular 2D online mapping tools such as Google Maps, Bing Maps, and Open Street Map, virtual globes provide oblique views of detailed aerial photos and multiple uses of visual signs as overlays on top of the pictorial imagery, combined with 3D objects. Virtual globes include the open source NASA WorldWind, scientific prototypes under development in China and Australia, the commercial (although partly free to use) products Microsoft Bing Maps 3D, ESRI ArcGIS Explorer and ArcGlobe, Google Earth [8], and the open source Biosphere3D, which is designed specifically for landscape visualization ([[http://www.biosphere3d.org](http://www.biosphere3d.org)]([http://www.biosphere3d.org](http://www.biosphere3d.org))). According to [PERSON] _et al._[9], virtual globes are particularly successful for uses in education, scientific research and collaboration, and disaster response.  
Google also provides free tools such as Sketchup to generate 3D content for virtual globes. Over the last few months, Google has launched additional geospatial tools, particularly the new Google Earth version 6, which supports 3D tree models [10], Google Earth Builder, an online GIS for geodata management, Google Earth Engine, a future environmental monitoring platform that adds more complex analytical functions, and the Open Data Kit, a set of tools for mobile data collection.
## 3 Application and Results  
In this section, the measured data of neutron and gamma waveforms are utilized to implement the application of software training, FPGA platform inference, and training set extension of neutron and gamma identification models through a convolutional neural network particle identification platform.
## 5 Discussion
### Engineering Example Analysis  
In the engineering example, we sought to further validate the capabilities of the improved modular vector method. Our findings confirm that this method outperforms genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms in three critical aspects: accuracy, stability, and dependence on the quantity of measured data for parameter inversion results. Furthermore, there is a degree of consistency observed between the inversion values and the initially selected exploration values. However, it is important to acknowledge that the absence of reference values for the expected parameters limits our ability to conduct a comprehensive analysis of relative errors in the inversion results.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 7 Edge Compression and Proactive Computation in Cache-Empowered RANs
### When Caching Meets MEC  
Both caching and RSs rely on request predictions, resulting in much computational load. Mobile edge computing holds the promise of providing the required computational power. MEC is expected to merge with caching in the era of 6G [3] in order to predict user requests, make caching decisions, price caching services, and realize cache-friendly recommendation and preference-aware compression, while assuring low latency and privacy protection. Similar to RANs, peak-time overload and congestion exist in MEC. Therefore users may suffer from a large latency when a MEC unit is heavily loaded [46].  
To reduce the computational workload during peak times, we borrow the idea of caching based on an observation that the computational tasks required by users are also  
Figure 7: Edge Compression with User Preference Awareness in Cache-Empowered RANs [44, 45].  
predictable. By predicting users' most common computation tasks in the future and based on the pro-actively cached data, off-peak-time computational resources can be fully exploited to implement these tasks before user requests and hence reduce the service delay. For instance, once a content item becomes popular over the Internet or on social networks, joint caching and recommendation can be triggered to foresee if the content is worth recommending or pushing to a user. In this case, the computational task is implemented without the user's order to reduce the MEC's peak load.  
Proactive MEC is made feasible with cached data. Such an idea can be further borrowed by other MEC applications, e.g., mobile navigation or autonomous driving, in which route planning between a user's favorite locations can be determined based on the traffic information pushed. The concept of recommendation can also be generalized given increasing MEC capabilities. Not only what to read in cyberspace, but also how to enhance the physical layer performance, can be suggested. For instance, an RS can suggest that a user moves to a nearby mmWave BS if the user asks for a large file that has not been cached. If the user agrees to do so, navigation software will guide it to the selected mmWave-covered cell. To realize the above beyond-pipeline functions, computation task prediction and joint scheduling of communication, caching, and computation emerge as open issues for making MEC more proactive.
## 3 Methodology
### Energy Consumption Model for Sustainable Energy Approach  
We developed a network model to calculate a more sustainable energy approach, representing our problem. Let us assume that \(G=(N,L)\) is a directed multi-network with \(N\) representing several nodes and \(L\) representing the set of edges in the network. In this network, edges represent the flow of objects from \(i\) to node \(j\) where \(i,j\in N\). Edges \((i,j)\in L\) have a capacity, and the associated energy consumption \(C_{ij,j},S\in N\) depicts the sender node and transmits to vertex \(x\in N\). Bandwidth, defined as \(u_{i,j}\), is also based on network link capacity. We consider demand and the source \(B_{i}\) as follows: \(B_{i}<0\) indicates a relay node with the demand \(B_{i}=0\), and \(B_{i}>0\) indicates a demand destination. For the prototype, an energy consumption model for both wireless and wired cases is considered. In this model, during the radio transmission generation, the transmitter and power amplifier consume power, and the receiver consumes energy to receive and process these radio transmissions.  
\[E_{T_{x}}(k,d)=E_{T_{x}-dec}(k)+E_{T_{x}-amp}(k,d) \tag{9}\]  
\[E_{T_{x}}(k,d)=\left\{\begin{aligned} & k\cdot E_{dec}+K\cdot \varepsilon_{f_{x}}\cdot d^{2},d<d_{0}\\ & k\cdot E_{dec}+K\cdot\varepsilon_{ap}\cdot d^{4},d>d_{0}\end{aligned}\right. \tag{10}\]  
\[E_{T_{x}}(k,d)=E_{R_{x}-dec}(k)=k\cdot E_{R_{x}-dec} \tag{11}\]  
Taking radio transmission as an example, power control can be employed to mitigate signal propagation loss by appropriately adjusting the power amplifier. For instance, if the transmission distance is less than the threshold \(d_{0}\), the free space prorogation model with the attenuation parameter \(\varepsilon_{f_{x}}\) is used. Otherwise, the multi-path propagation model with the attenuation parameter \(\varepsilon_{mp}\) is utilized. The energy consumption model for transmitting k-bits of data to a receiver at a distance of \(d\) can be calculated using Equation (9). The transmitter's consumed energy, denoted by \(E_{dec}\), depends on several factors, including digital coding, modulation, and filtering signal processing procedures. Additionally, the energy consumed by the amplifier is influenced by the distance to the receiver and the acceptable bit-error rate. The energy consumption for the data received can be calculated using Equation (11). The volume of data \(k\) and the transmission distance \(d\) are critical factors in the energy consumption model. These factors significantly affect the overall consumption compared to the energy consumption of other electronic components. Transmission distance plays a crucial role in energy consumption; greater transmission distance results in higher energy consumption. It is essential to shorten the transmission distance to reduce energy consumption. In this context, we will explain the utilization of the minimum-cost flow problem for the different data volumes and assess energy consumption by transferring data between two locations and nodes.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Summary of Presented Techniques  
This survey aims to provide a comprehensive literature review of the various techniques involved in Arabic OCR and to provide valuable insights into the current state-of-the-art in Arabic OCR. To achieve this goal, we analyzed a range of research papers and articles that focused on different Arabic OCR techniques and methods. Our findings are summarized in Table 5, which presents a concise overview of the methods employed in the reviewed papers. The table outlines the different OCR techniques, including preprocessing, segmentation, recognition, and postprocessing, along with their performance evaluations in terms of accuracy using various types of printed, scanned, and handwritten datasets. This survey is useful for researchers and practitioners who are interested in Arabic OCR systems. By comparing and contrasting the different techniques in the complete pipeline of the Arabic OCR, they can choose the most appropriate one for their specific task. To further aid researchers and practitioners, Table 6 presents various methods commonly employed in OCR systems and their respective advantages and disadvantages.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**OCR Techniques**} & \multicolumn{6}{c}{**OCR Tasks**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-7}  & **Preprocessing** & **Segmentation** & **Recognition** & **Postprocessing** & **Evaluation** \\ \hline [PERSON] et al. [63] & ✓ & ✓ & ✓ & & 99.3\% (Scanned) \\ [PERSON] et al. [7] & ✓ & ✓ & ✓ & & 90\% (Scanned) \\ [PERSON] et al. [59] & ✓ & ✓ & ✓ & ✓ & 87.4\% (Handwritten), 90\% (Scanned) \\ [PERSON] et al. [67] & ✓ & ✓ & ✓ & & ✓ & 94.1\% (Printed) \\ [PERSON] et al. [70] & ✓ & ✓ & & & ✓ & 11\% (Segmentation) \\ [PERSON] et al. [77] & ✓ & ✓ & ✓ & & ✓ & 98.36\% (Scanned) \\ [PERSON] [78] & & ✓ & ✓ & & 93.65\% (Handwritten), 86.14\% (Scanned) \\ [PERSON] and [PERSON] [82] & ✓ & ✓ & & & No Recognition \\ [PERSON] et al. [83] & ✓ & & ✓ & & ✓ & 99.88\% (Handwritten) \\ [PERSON] et al. [86] & ✓ & & ✓ & & ✓ & 87\% (Scanned) \\ [PERSON] et al. [98] & & & & ✓ & ✓ & No Recognition \\ [PERSON] et al. [100] & & & & ✓ & ✓ & No Recognition \\ [PERSON] et al. [105] & & & & ✓ & No Recognition \\ [PERSON] et al. [108] & ✓ & & ✓ & & ✓ & 83.5\% (High-quality), 58.4\%(Low-quality) \\ \hline \hline \end{tabular}
\end{table}
Table 5: A brief tabular outline of the described papers with the proposed OCR techniques and their performance evaluations.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 4 Micro-Supercapacitor Electrolytes
### Solid-State-Electrolytes  
Solid-state electrolytes have the advantages of being leak-proof, high performance, light weight, and a simple packaging and fabrication process. Synthesis of solid-state electrolytes should aim for high ionic conductivity, high chemical, electrochemical, and thermal stability, and sufficient mechanical strength. A main drawback of solid-state electrolytes is the limited mobility of ions, which negatively impacts the power density [122]. Therefore, recent research focused on the development of thin solid-state electrolytes with higher ion mobility and more electrochemically active electrodes [123]. For example, proton-conducting polymers have been used, but they have a narrow potential window, and maintaining a high degree of hydration to maintain high performance can be a challenge [122].  
Barium titanate films with incorporated protons by RF magnetron sputtering were fabricated to act as a solid-state electrolyte for micro-supercapacitors, which exhibit a large potential window (3.2 V), wide operational temperature (about 350 \({}^{\circ}\)C), and fast charge and discharge cycles (about 3 s) [124].
# Evaluation Method for Rock Burst Hazards in Strip Filling of Working Faces in Deep Coal Mines
## 3 Impact Hazard Evaluation Method
### Indicators and Membership Functions Determination
#### 3.1.1 Stress State of Coal
The bearing capacity of the strip coal pillar can be expressed by the following equations, in which the ratio of the abutment pressure to the bearing capacity is denoted as \(l_{\rm c2}\), which is  
\[\overline{\sigma}=\frac{2l_{\rm d}\sigma_{\rm d}(x)+\lambda l_{\rm e}[\sigma_{ \rm c}]}{2l_{\rm d}+l_{\rm e}} \tag{8}\]  
\[l_{\rm c2}=\frac{\sigma_{z}}{\overline{\sigma}} \tag{9}\]  
where \(\overline{\sigma}\) is the overall bearing capacity of the coal in the working face; \(\lambda\) is the ratio of the bearing capability of the elastic area to the uniaxial compressive strength of the coal seam, with a value of \(\lambda\) = 3-5; and \(\sigma_{\rm z}\) is the total stress of the coal after the dynamic and static loads on the working face are superimposed. According to engineering practice experience, when the total stress acting on the strip coal pillar is greater than the overall bearing capacity of the working face, there is a possibility of overall impact risk [33]. If the total stress on the strip coal pillar is between 1.0 and 1.5 times the overall bearing capacity of the coal body in the working face, the working face has a weak overall impact risk; when the total stress on the strip coal pillar is between 1.5 and 2.0 times the overall bearing capacity of the coal body in the working face, it has a medium overall impact risk; if the total stress on the strip coal pillar is 2.0 times the overall bearing capacity of the working face coal body, then the working face has a strong overall impact risk. The membership function of the coal pillar stress ratio \(l_{\rm c2}\) to the overall impact risk is \(\varphi_{l_{\rm c2}}\), as shown in Equation (10):  
\[\varphi_{l_{\rm c2}}=\left\{ \begin{array}{cc}0.25l_{\rm c2}&l_{\rm c2}<1.0 \\ 0.5l_{\rm c2}-0.25&1.0\leq l_{\rm c2}<2.0\\ 1&l_{\rm c2}\geq 2.0\end{array} \right. \tag{10}\]
# Anomaly Detection in IoT Communication Network Based on Spectral Analysis and Hurst Exponent
## 3 Self-Similarity and Multifractal Spectrum Dependences
### Self-Similarity Statistical Factor  
Stationary processes are characterized by a probability distribution that, for these processes, has a constant value. However, such a term is not entirely accurate and does not provide a proper description of the stochastic process. Most stochastic processes are characterized by the fact that the values of these processes are dependent on one another in time. This means that the value of the process at some point in time depends on the process that precedes it. The time dependencies described in the stochastic process can be described through the autocorrelation function in Equation (1) [23; 24]:  
\[R_{X}(s,t)=E(X(s)X(t)) \tag{1}\]  
for which:  
* \(X\) is the stochastic time process analyzed in a specified time scale \(s\), \(t\),
* \(R\) the autocorrelation function.  
Multifractal processes are defined by a scaling law for moments (\(E\)) of the processes' increments over finite time intervals. Stochastic processes characterized by infinite time are called long-rangedependence (LRD). The autocorrelation function of a long-term process is called a slowly disappearing function. The basic concept of a self-similar process is the autocorrelation function. The next necessary concept is the stochastic process itself (defined by the letter \(X\)) in the time scale (defined by the letter \(m\)), and this process is defined by the Riemann integral, as given in Equation (2):  
\[X^{(m)}(t)=\frac{1}{m}\int\limits_{t}^{m(t+1)}X(s)\ ds \tag{2}\]  
The above equation allows us to define a general formula for the process of statistical similarity that can be described as Equation (3):  
\[R_{X^{(m)}}(k)=m^{-\beta}R_{X^{(1)}}(k) \tag{3}\]  
where \(X^{(m)}\) is the \(X\) process in the \(m\) time scale and \(\beta\) denotes a constant.  
The \(\beta\) parameter, which determines the rate of change of the autocorrelation function during the time change, is significant. For short-term processes (SRD), this parameter is close to a value of 1. According to Equation (4) and the application of discrete processes in the Riemann integral and conversion to sum, the value of \(\beta\) parameter can be in the range of 0-1 [21].  
\[X^{(m)}_{t}=\frac{1}{m}\sum_{k=0}^{m-1}X^{(1)}_{k+mt}=\frac{1}{m}\sum_{k=0}^{ m-1}X_{k+mt} \tag{4}\]  
The main advantage of using models of self-similar patterns of a time series is that the degree of self-similarity of the series is only expressed by one parameter. The parameter expresses the speed of decay series autocorrelation function. For historical reasons, the parameter used is the Hurst parameter \(H=1-\beta/2\). For self-similar series, 1/2 \(<H<1\). As \(H\to 1\), the degree of self-similarity increases. Thus, the main criterion for assessing self-similarity is the question: Is \(H\) exponent significantly different from 0.5?
# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
Based on the above observations, our main research objectives are to investigate that for alpine meadow ecosystem, (1) whether SIF at red and far-red bands could track variations of GPP over diurnal and seasonal scales; (2) what is the effect of environmental factors upon the SIF-GPP relationship for different bands; and (3) whether the SIF-GPP relationship and its environmental effects are wavelength-dependent and which band is better for GPP estimation.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
###### Abstract  
With the development of intelligent waterborne transportation, mining collision avoidance patterns based on spatiotemporal and motion data of ships are crucial for the autonomous navigation of intelligent ships, which requires accurate collision avoidance information under various encounter scenarios. Addressing the existing issues of low precision and false detection in data mining algorithms, this paper proposes a collision avoidance behavior mining model considering encounter scenarios. The model is based on the Automatic Identification System (AIS) and the International Regulations for Preventing Collisions at Sea (COLREGs); it firstly identifies ship collision avoidance turning points by analyzing trajectory curvature with turning and recovering factors. Then, by combining AIS data and the specific navigational environment, it matches the ship encounter pairs and determines the encounter scenarios. Comparative experiments show that the model demonstrates superior accuracy in various scenarios compared to traditional algorithm. Finally, the model was applied to AIS data east of the Yangtze River Estuary, recognizing a total of 827 instances of ship collision avoidance behavior under different encounter scenarios. The case study shows that the model can precisely mine collision avoidance information, laying a solid foundation for future research on autonomous collision avoidance decision making for intelligent ships.  
ship behavior; data mining; encounter scenarios; collision avoidance +
Footnote †: journal: Applied sciences  
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