esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Results for the 33-Node AC MG Operating in Islanded Mode
#### 5.2.2 Statistical Analysis of the Objective Function \(FO_{2}\) (Energy Losses)  
Regarding loss minimization in islanded mode, all the analyzed techniques demonstrated outstanding performance, achieving an average minimum solution of 733.0458 kWh.  
An assessment of the average solutions for each technique revealed that the PGA provides the best performance (547.3763 kWh) when it comes to minimizing losses in islanded mode. The reported value is 58.3932%, 9.2595%, 19.7571%, 36.9182%, and 16.5941% higher than the results obtained with MC, PSO, JAYA, GNDO, and MVO. This improvement is clearly depicted in Figure 24.  
Figure 25 corresponds to a boxplot showing the 100 executions of each technique used for minimizing losses in the 33-node MG. In this case, PGA stands out once again for its effectiveness, showing a standard deviation of 0.1702%. Our proposal outperforms MC, PSO, JAYA, GNDO, and MVO by 1.1375%, 3.4178%, 6.6455%, 2.5100%, and 2.0800%, respectively.  
In this evaluation, JAYA once again demonstrated remarkable speed, finalizing its execution in 195.5680 s, the shortest time among all tested strategies. Close behind, the proposed PGA required 216.7334 s, outperforming most competitors and reinforcing its efficiency across diverse operational criteria. On the opposite end, MVO recorded the highest computation time (536.7649 s), more than doubling that of PGA. The overall average processing time for the objective function associated with energy losses was 331.6653 s, positioning PGA well below the benchmark.  
Figure 24: Loss reduction reported by PGA compared with other methodologies in the 33-node MG during islanded operation.  
These results confirm that PGA is not only competitive in terms of solution quality; it also boasts a high computational efficiency, which is essential for scenarios that require a continuous, real-time re-evaluation of operation plans.
## Project Introduction  
### Geological Conditions  
The Malan Coal Mine is located in Taiyuan City, Shanxi Province, China. Its yearly production capacity is designed to reach 4 million tons. It has a high gas content and is a high-gas mine. In the process of mining coal resources, various disasters and accidents will occur, such as gas explosions, coal dust explosions, coal and gas outbursts, etc. The \"U\"-type ventilation system is adopted in the working face, and when the absolute gas emission of the working face is greater than 5 m\({}^{3}\)/min, gas extraction must be carried out. The primary focus of the mine is on extracting coal from the #02 coal seam, which has an average thickness measuring 1.8 m and is located at a depth of approximately 400 m. As the #02 coal seam contains a significant amount of gas, it is necessary to extract the gas before mining, so a gas drainage roadway has been established within the #2 coal seam. After the drainage is completed, the gas drainage roadway will be used as the mining roadway of the #2 coal seam, fulfilling the goal of \"one roadway for multiple purposes\". The #2 coal seam has an average thickness of 1.3 m and is located approximately 7 m below the #02 coal seam [24].  
Mining work at the #02 coal seam working face will be carried out after gas extraction is completed. Due to the proximity of the #02 and #2 coal seams, the gas drainage roadway located at the #2 coal seam will then be affected by repeated mining, affecting the stability of the roadway and even endangering mine production and personnel safety, which could have a significant impact on the future utilization of the roadway. Therefore, it is necessary to analyze and summarize the damage characteristics of the roadway under the influence of mining, reveal the damage mechanisms of the roadway under dynamic pressure, and propose safe and efficient surrounding rock control technology accordingly.
## 4 Results
### Comparative Analysis with Traditional Methods
#### 4.8.3 Cost-Benefit Analysis  
A five-year economic evaluation revealed significant long-term advantages despite higher initial costs. The LSTM implementation required SGD 45,000 in setup costs compared to SGD 15,000 for queueing theory and SGD 5000 for building codes (see Table 14). However, the resulting benefits included construction cost savings of SGD 280,000, annual maintenance reductions of SGD 42,000, and space optimization of 45 m\({}^{2}\). Additional annual energy savings of SGD 12,000 further enhanced the economic advantages of the LSTM approach. Table 11 quantifies the operational benefits and cost savings achieved through LSTM implementation compared to traditional methods across construction, maintenance, space optimization, and energy usage.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **LSTM Model** & **Queueing Theory** & **Improvement** \\ \hline MAPE & 7.21\% & 11.87\% & 39.26\% \\ RMSE & 0.585 & 0.944 & 38.03\% \\ MAE & 0.463 & 0.752 & 38.43\% \\ R\({}^{2}\) & 0.938 & 0.837 & 12.07\% \\ Peak Prediction Error & 4.76\% & 19.05\% & 75.01\% \\ Response Time (ms) & 87 & 212 & 58.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Performance metrics comparison.
# Calibration Method of Projectional Geometry for X-ray C-arm Fluoroscopy Using Sinogram Data
## 2 Materials Furthermore, Methods  
### Phantom Design Furthermore, Fabrication  
The calibration phantom used a 2-layered model designed with 3D CAD software, SolidWorks (Dassault Systems SolidWorks Corp., Velizy-Villacoublay, France), and was built with plastic material, Vero (Stratasys, Rehovot, Israel), using a 3D printer, Objet30 Prime (Stratasys, Rehovot, Israel). Figure 1 shows the CAD model of the calibration phantom and the manufactured phantom, both of which have fiducial markers to establish precise 3D locations. Figure 2 depicts the layouts of the two layers. The size of the phantom is 170 mm \(\times\) 170 mm \(\times\) 103.54 mm, and lead ball bearings, SL-20 and SL-40 (Suremark, Simi Valley, CA, USA) lead, were used as fiducial markers. The upper and lower layers are the same size but differ in height. On the upper layer, 16 markers with a diameter of 2 mm are arranged in a rectangular pattern at 20 mm intervals toward the X-ray source. On the lower layer, there are two sizes of markers--eight 4 mm diameter markers arranged in small rectangular shapes at 20 mm intervals and twenty-four 4 mm diameter markers arranged in large rectangular shapes at 20 mm intervals and located toward the X-ray detector. Only the height differs between the horizontal and vertical centers of the three squares and the center of the phantom.
# A Multichannel, High-Bandwidth Wirelane Receiver for D2D Interconnects
## 1 Introduction  
With the development of data exchange rates, the amount of data transmission has grown exponentially, which is very important for hyperscale data centers, high-performance central processing units (CPU), graphics processing units (GPU), and artificial intelligence (AI). Therefore the development of system-on-chip (SoC) will face unprecedented challenges. With the slowdown of Moore's Law, improvements in chip performance and power consumption are increasingly uneconomical. Heterogeneous integration (chiplet) technology provides a new design solution to this problem [1, 2]. Multiple smaller chips in a multichip module (MCM) are linked by D2D interconnects that have extremely low power consumption and high BW at the edge of each chip. In high-performance computing (HPC) and AI applications, a large SoC is divided into two or more homogeneous chips. As shown in Figure 1, the I/O and network cores are divided into separate chips in networking applications. D2D interconnects in such SoC must not affect overall system performance [3], so D2D interconnects focus more on lower bandwidth density (bandwidth per area/layer), lower power consumption (power consumption per data rate) and lower BER [4].  
Traditional D2D interconnects often adopt single-end and low-speed parallel transmission [5, 6] to increase bandwidth density, but with the rapid increase of the amount of exchanged information, this method cannot meet the demand for D2D interconnects. To solve the problem of low bandwidth, Pulse amplitude modulation (PAM) was used in D2D interconnects, but it brings a larger area and power consumption problems [7], resulting in high power consumption. At the same time, PAM4 has lower signal-to-noise ratio (SNR) than NRZ, so BER will higher than the systems using NRZ. With the application of SerDes between chips, D2D interconnects have gradually turned to single-ended and high-speed transmission to further increase bandwidth density. However, with thecontinuous increase of speed, the marginalization of signals is intensified, and it is easily affected by external factors. So single-ended signals (SES) are more dependent on advanced packaging technology [8], but not in standard packages. In order to be suitable for standard packaging and higher rates, differential signals (DS) transmission is often used. But due to circuit characteristics of DS, the theoretical power consumption for D2D interconnection is twice that of SES transmission, and the bandwidth density is half of that. Therefore, it is necessary to improve the bandwidth density and reduce the power consumption of DS transmission in order to be suitable for standard packaging under high speed transmission.  
This paper proposes a solution for the problem of DS transmission for D2D interconnects. MINI-PLL and CDA circuit replaces CDR in traditional receiver, and a delay matching circuit is adopted to combat PVT variation and lane skew, which reduce power consumption and area.
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 2 Materials and Methods
### Data Recording in Movement  
#### 2.2.1 Effects of Sensor Movement on Measurement Data  
The relative movement between recording camera and observed measurement object leads to a shift of the pixels representing a given object point. When this shift exceeds one or two pixels per sequence recording, it leads to smearing of the image points comparable to the application of an average filter operator. Therefore, it leads to errors in 3D measurement.  
If these errors are to be corrected, the typical application scenarios of the sensor must be considered. Let us consider the case of a linear sensor motion with constant velocity \(v\), image sequence length \(n\), and 2D image frame rate \(f\). For simplicity and due to the lack of exact a priori information concerning the object distance of a mapped point, we assume a constant object distance, \(d\). Additionally, the virtual principal distance, \(c\), the pixel size, \(ps\), and the binning factor, \(b\), are influencing parameters. The pixel shift, \(\Delta s\) (in camera pixels), is:  
\[\Delta s=v\cdot c\cdot n/(f\cdot d\cdot b\cdot ps). \tag{1}\]  
Let us consider the case of fast sensor motion and high 2D image frame rate \(f\). Let \(v\) = 1 m/s, \(c\) = 17 mm, \(ps\) = 0.009 mm (binning mode), \(d\) = 2 m, and \(f\) = 900 Hz. Additionally, we assume a motion direction mainly in the Y direction of the camera coordinate system (perpendicular to the optical axis of the system). Let us consider a ten-image (\(n\) = 10) sequence recording to obtain one 3D scan. The image recording time is approximately 10/900 Hz \(\approx\) 11 ms. The actual movement, \(\Delta S\), of the sensor in this time is 11 mm. An object point at a 2 m distance \"moves\" in the image plane \(\Delta s\approx\) 10.4 px per sequence, i.e., approximately one pixel per image in the sequence. Instead, using \(f\) = 300 Hz frame rate, we already have a 31-pixel shift per sequence and a three-pixel shift per image.
## 5 Experimental Validation and Benchmarks
### Self-Organizing Networks (SONs)
#### 5.3.2 Fault Management and Performance Optimization  
AI models in self-organizing networks (SONs) play a crucial role in fault management and performance optimization. By leveraging machine learning algorithms, SONs can predict potential network faults, identify underperforming or malfunctioning components, and isolate issues before they impact overall network performance. These predictive capabilities are powered by the continuous monitoring of network health, which allows AI to recognize early warning signs of failures, such as latency spikes, signal degradation, or resource overloading. Early fault detection ensures that corrective measures are applied swiftly, minimizing network downtime and preventing service disruptions [125].  
Figure 9: Impact of AI-driven autonomous configuration on overall network performance in SONs, focusing on improvements in throughput, latency, and resource utilization.  
Moreover, AI-driven fault management in SONs extends beyond just detection. The algorithms can automatically initiate remediation actions, such as rerouting traffic, adjusting bandwidth allocation, or deploying backup systems, without requiring human intervention. This proactive approach to fault resolution enhances network resilience, enabling SONs to self-heal and maintain consistent service quality even in the face of hardware failures or unexpected traffic surges [125].  
In terms of performance optimization, AI models continuously assess the performance of network components, adjusting parameters in real-time to ensure that resources are used efficiently [126]. By analyzing data such as traffic flow, congestion points, and resource utilization, machine learning algorithms can dynamically allocate resources, prioritize traffic, and optimize routing paths [127]. This not only helps in reducing network bottlenecks but also improves overall Quality of Service (QoS) by ensuring that critical applications or services receive the necessary bandwidth and low latency.  
The ability of AI to learn from past network conditions allows SONs to evolve, optimizing their operations based on historical data and current performance trends. This learning capability ensures that the network continually adapts to changing demands, offering the highest possible performance while minimizing operational costs [128].
## 2 Material and Methods
### Test Specim Generation  
Considering deflection principles under the remote force acting on the structure, the bending-dominated nature of the structure's constituents, and the impact of relative density in a function of struts number, there is a need for a better understanding of mechanical behavior inside the lattice. Typical Voronoi tessellation ensures the randomized arrangement of cells within certain volume boundaries. This randomized cell arrangement results in a nonuniform open-cell lattice named a stochastic structure. The curiosity is that the user has no control over the stochastic cell arrangement except for defining input parameters such as cell count and seed. The second sub-hypothesis proposes establishing a predefined geometric routine to arrange the cells, introducing a semi-controlled mechanism over points' distribution in tessellation. It means that stochastic lattices were designed using Rhinoceros 7 software by filling a cuboidal volume of \(50\times 50\times 50\) mm\({}^{3}\) with pseudorandomly distributed points [8; 14] along a predefined geometric pattern. Cell arrangments like this aim to predict deformation and make the structure's mechanical response more uniform and reliable, unlike in stochastic cases, which do not ensure it. This means defining a parabolic pattern as the geometric base for the randomized points distribution of the Voronoi tessellation (Figure 5).  
The parabola's height (\(h_{p}\)) is a geometric entity positioned inside predefined volume boundaries (any volume dimensions) determined in four dimensions: \(0.5h_{p}\), \(0.7h_{p}\), \(0.9h_{p}\), and \(1.0h_{p}\) (Figure 6).  
Figure 5: The parabolic pattern of geometry for point distribution in Voronoi tessellation.  
Therefore, the proposed design model ensures the routine of randomized point arrangements along the parabolic surface, including its varying heights, which can impact a structure's mechanical response. The proposed design model aims to establish a semi-controlled mechanism over tessellating an uncontrolled number of struts, thus significantly affecting the lattice's density change.  
Following the proposed design model, lattice structures for experimental testing were modeled in 125 cm\({}^{3}\) (50 \(\times\) 50 \(\times\) 50) volume using a Rhinoceros Grasshopper parametric module. Experimental testing aims to show and assess the mechanical behavior of lattice structures made following the proposed design model guidelines, starting from typical Voronoi tessellation (randomly distributed) and shifting to the topological and geometric predefined routine for Voronoi tessellation (semi-controlled). The experimental testing plan includes printing each lattice in five specimens per selected seed for each design model (DM) and repeating all series for another seed position (Table 1).  
Regarding the population density and the number of points that must be populated inside the volume, ten points in total were used for distribution for all model designs. This means that ten points in total were arranged randomly in both designs, typical Voronoi tessellation and along the parabolic surface tessellation.
## 3 Heat Transfer Enhancement
### Experimental Results
However, they noted that this enhancement was not solely responsible for the overall heat transfer performance of the car radiator. The benefits of dispersing \(Al_{2}O_{3}\) nanoparticles into a 50:50 EG/W mixture as a car radiator coolant was experimentally investigated by [PERSON] et al. [PERSON] et al. (2018) under laminar flow conditions for different nanoparticle volume fractions up to 0.8% and temperatures between 65 and 85 \({}^{\circ}\)C. The results revealed that the heat transfer performance of the radiator was enhanced by using nanofluids compared to the base HTF. They noted that the heat transfer rate increased by increasing the nanoparticle concentration. For example, at a given flow rate, the Nusselt number was enhanced from 3.89%-28.47% when the \(Al_{2}O_{3}\) volume fraction increased from 0.2-0.8 vol.%. They stated that the use of nanofluid makes possible the design of compact car radiators, which could reduce the system weight, diminish the drag, and so, save fuel consumption. [PERSON] et al. [PERSON] et al. (2018) analyzed experimentally the heat transfer characteristics of a car radiator using PG/W-based \(TiO_{2}\) nanofluid as a coolant. The nanofluids were prepared by dispersing 0.1 and 0.3 vol.% of \(TiO_{2}\) nanoparticles into a 30:70 PG/W under different operating temperatures within the range [50-80 \({}^{\circ}\)C]. They observed that the Nusselt number increased by increasing both the inlet temperature and the flow rate. They reported that at low inlet coolant temperatures, the PG/W mixture exhibited higher heat transfer rates compared to those obtained with the nanofluids. However, at higher flow rates and higher operating temperatures, the latter showed better heat transfer performances. For example, adding 0.3 vol.% of \(TiO_{2}\) nanoparticles to EG/W increased the Nusselt number by up to 8.5% compared to the base fluid at \(T=80\)\({}^{\circ}\)C. Finally, they concluded from their results that the nanofluid coolants were suitable for heavy-duty engines. This experimental study was unfortunately one of the few using propylene-glycol in a realistic application.
## 3 Results
### Accuracy of Modeled SIF at Different Ecosystems  
The OCO-2 SIF points as observed values and GOSIF and HRGCSIF as modeled values were utilized to quantify simulation accuracy in different ecosystems. At the footprint scale, we randomly selected 100 OCO-2 points for each ecosystem within the appropriate timeframe and extracted the modelled SIF values. From Figures 8 and 9, it can be observed that R\({}^{2}\) values differ between ecosystems at the footprint level. In the case of both GOSIF and HRGCSIF products, the R\({}^{2}\) (\(p<0.001\)) values encountered in cropland were higher than those calculated for forest and grassland areas. This pattern was also true for the values of RMSE and slope. At the regional scale, all three ecosystems yielded R\({}^{2}\) values higher than 0.8. The value calculated for cropland was particularly high, exceeding 0.9.  
We can state with some statistical certainty that the comparisons in Figures 8 and 9 support the following claims: (i) Numerical simulations achieved a greater approximation of reality at a regional scale than at the footprint level, with the simulation effect differing among the three ecosystems. Accuracy was highest in cropland, followed by forest, and finally grassland areas; (ii) Simulations under-estimated SIF values at both a regional and at a footprint scale; (iii) Better simulation results can be obtained if we use individual models for each ecosystem.  
Figure 8: The accuracy of GOSIF and HRGCSIF for cropland, forest, and grassland at footprint scale (\(p<0.001\)).
## 5 RTIoT System Design Methodology  
The implementation of the functional model of the real-time system proposed in Section 2 requires the implementation of selected elements included in the IoT solution architecture. Typical IoT systems primarily consist of sensors providing data to the system, end devices (usually also equipped with sensors), a server application, and a client application [5]. The sensors can be directly connected to the server via internet links or can be an integral part of the end device. Indirectly, there may also be edge devices whose job is to pre-process data. Communication is usually carried out via low-energy technologies such as BLE (Bluetooth Low Energy) [108], ZigBee [109], and LoRaWAN (Long Range Wide Area Network) [110]. End devices using sensors communicate with an IoT system via internet links. They can also include actuators and controllers, so an IoT system can remotely control such devices. Server applications are usually installed in cloud computing or on a dedicated server, while the client applications used to operate the system can come in the form of mobile, desktop or web applications. All of the aforementioned components communicate using the relevant protocols.  
In the case of real-time systems, there is a time limit on obtaining a response, which means that once a request is sent, a response is expected to be obtained within an assumed maximum time (deadline). Exceeding this time can be treated as an error (systems with hard constraints) or as a reduction in service quality (systems with soft constraints) [111]. Requests can be sent by either end devices or server or client applications. In order to reduce the occurrence of the aforementioned errors or to maintain the required level of quality of service in IoT systems, one can use the solutions described earlier, such as edge computing, fog computing, SDN, NFV, real-time databases, scheduling methods, and relevant communication protocols. These elements are described in Sections 3 and 4 of this article, and based on them, an illustrative architecture for a real-time IoT system can be proposed, as shown in Figure 2. In the architecture, the following computing layers that can perform various tasks of the IoT system can be distinguished:  
* Sensor and actuator layer (SL): the layer consisting the interface between an IoT system and its environment that enables the collection of data from the environment and the control of the elements of the environment.
* Edge layer (EL) (optional): an intermediate layer that enables the distributed processing of data without the need to send them to a central system server.
* Cloud layer (CL): the layer that contains the system's servers and databases and that usually controls the operation of the entire system.
* User layer (UL): the layer that uses user applications (mobile, web or desktop). These applications allow a user to interact with the rest of the IoT system.  
Figure 2: General architecture of a real-time IoT system.  
There are communication links between different layers that enable data transfer.