esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Defined Networks in Wireless Sensor Architectures
## 2 Background
### Network Function Virtualization  
As mentioned before, SDN and NFV are two important newness in network architectures. Although these technologies were developed individually, their combination opens up new network designs. Such combination is possible due to the common goals that they share that are complementary to each other. In this way, integrating both SDN and NFV is the target of many research efforts and industries in order to take advantage of their benefits. Nevertheless, this integration is becoming a big challenge due to the diverse elements implied in their interactions. Then, hypervisor and virtualization mechanisms have been used for supporting multiple virtual SDN networks. An example of this research approach is Flow Visor [3], which is previously explained.  
[PERSON] et al. [44] analyse the combination of these architectures providing a framework, which is categorized as SoftwareDefined Network Virtualization (SDNV). Equal to SDN, this framework is separated into data with control/management planes.  
It is divided into three layers as follows: Infrastructure Layer, Virtualization Layer and Service Layer. Both the Service Layer and the Infrastructure Layer have been separated in the data and control planes.  
Figure 2: OpenFlow architecture.  
The Infrastructure Layer (lowest layer) is composed of the network's physical resources and compute infrastructures. These sets of physical resources and compute infrastructures can be deployed as several autonomous domains (each domain with its proper controller).  
On the other hand, the Virtualization Layer (middle layer) provides abstraction in the connection between the Infrastructure Layer and the Service Layer. In addition, it provides a physical and virtual mapping for network resources.  
Finally, the Service Layer provides (upper layer) service-related functionalities. It receives the virtual resources provided by the Virtualization Layer to carry out the following functions: Virtual Service Functions (VSFs), Virtual Network Functions (VNFs) and Virtual Compute Functions (VCFs). Using these functions, the Service Layer selects and orchestrates the appropriate functions to build Virtual Networks (VNs) to satisfy users' requirements.
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 1 Introduction
Furthermore, a high-precision energy consumption prediction model was developed using response surface methodology, and this model was utilized to analyze the influence mechanism of main parameters and their interactions on system energy consumption, revealing its nonlinear characteristics. This paper aimed to provide a reliable prediction tool and theoretical guidance for the performance evaluation, energy consumption characteristics analysis, operation optimization, and the development of energy-saving strategies for ship central cooling systems.
## 5 Case Study and Experiments
### Simulation Results
Thus, the transaction time on the private chain is better than the Rinkeby and Ropsten on the test chain. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
As shown in Figure 13, we recorded the performance status when the inventory risk assessment function was executed five times, and we found that the CPU usage was high in the second and eleventh, which means that a large number of operations related to calculations and verifications are being written into the block. In order to test the CPU usage rate, we recorded the average value after the execution. As shown in Figure 14, we found that the average usage rate remained around 18%. On the private chain, the difficulty of mining affects CPU usage. Assuming that the mining difficulty is close to the difficulty of the test chain, the CPU usage rate will be greatly improved, and the verification time will be lengthened. In order to get closer to the mining difficulty on the test chain, we then adjusted the difficulty to 20 times the original private chain to perform the experiment. We once again deployed the smart contract on the private chain that has changed the difficulty for functional testing. Figure 15 shows the resulting time trend of each transaction. It can be found that the time spent is more than the last time, and the time is beginning to approach the verification time on the test chain. Figure 14: CPU usage average performance (the mining difficulty is not increased). Figure 13: CPU (Central Processing Unit) usage performance on the private chain. Next, after the mining difficulty changes, the CPU utilization rate is greatly improved. As shown in Figure 16, the utilization rate of the workstation in mining verification is close to 100%. As shown in Figure 17, we record the average value of the CPU usage after the execution. It can be found that when the difficulty is increased by 20 times each time, the average CPU usage rate has reached at least 50% or even 80%. Therefore, in the private chain, the difficulty level is set according to the computer's specifications, which can reduce the verification time and CPU usage and achieve the optimization of an entire system. Table 3 presents the cost for deploying and executing the designed smart contracts on the private chain. In terms of execution time, since the difficulty of the genesis block is not as difficult as the test environment of Rinkeby and Ropsten, the block generation speed on the Ethereum blockchain is several times faster than that on the test chain, but most of the cost is spent on the initial deployed contract. The result is much higher than that on the test chain because the private chain produces blocks faster than the test chain, so the cost will be slightly more, which is related to the consensus mechanism. Finally, the complete gas consumption value is added up and calculated on the day of 2020/8/10 (1 Ether converted to USD395.42). Figure 16: Illustration of CPU usage performance. Figure 15: The resulting time of each transaction on the private chain.
## 2 Study Area and Data Collection  
### Study Area  
In this study, we focused on the Haihe River Basin (HRB), one of China's nine primary river basins, as our study area for estimating water and carbon fluxes. Based on ground-observed data, our study period primarily spanned from 2003 to 2020. Located in northern China, the HRB covers an area of 3.18 \(\times\) 105 km\({}^{2}\), encompassing Beijing, Tianjin, as well as portions of Hebei, Shanxi, Shandong, Henan, Inner Mongolia, and Liaoning provinces (refer to Figure 1). The basin's terrain slopes down from northwest to southeast, featuring three distinct geomorphic types: plateau, mountain, and plain. The HRB is typically characterized by a temperate East Asian monsoon climate, with an average annual temperature ranging from 1.5 \({}^{\circ}\)C to 14 \({}^{\circ}\)C. The annual average relative humidity lies between 50% and 70%. With an annual precipitation average of 539 mm, land surface evaporation averaging 470 mm, and water surface evaporation at 1100 mm, the region is classified as semi-humid to semi-arid. Notably, cropland dominates the land use in the HRB, covering a vast area of 1.47 \(\times\) 10\({}^{5}\) km\({}^{2}\), making it a significant source of food production in China. Consequently, gaining insights into the water and carbon fluxes of these croplands is crucial for effective agricultural water management and production strategies in the HRB.
## 3 Fault Localization Methods  
### Introduction  
Unexpected outages caused by faults or short circuits due to equipment malfunction, animal contact, weather conditions or even poor vegetation management are a frequent occurrence in EPSs and are the cause of up to 80% of all interruptions in DNs. The ability to swiftly and accurately locate a fault in an EPS can expedite the repair of faulted components, speed up power restoration and thus enhance the system's reliability and availability. Practices such as investigating a line with a repair crew or locating a fault based on customer calls are time-consuming, outdated and inadequate for today's demanding needs of power utilities.  
Fault detection and localization is a theoretically well-known and developed field, with a variety of applicable methods [15]. Some of them have recently gained momentum due to the significant advancements made in time-precise measurements and communication infrastructure, enabling real-time processing at the edge and performing comprehensive analytics in the cloud. These methods can be roughly divided into three groups:  
* impedance-based methods;
* travelling wave methods;
* pattern recognition methods.  
Impedance-based methods are characterized by low complexity and offer a fairly accurate performance in the case of grounded systems. Their main disadvantage is the poor performance in isolated and Petersen-coil grounded systems. Additionally, they are highly sensitive to fault resistance and can yield multiple solutions in highly branched networks. Comparative studies of some representative methods are provided in [16; 17].  
Traveling wave methods offer relatively good accuracy and are not significantly affected by loads, current transformer saturation, fault type and fault resistances. However, they require measurement equipment with a high sampling frequency (in the order of MHz) along with time-precise measurements in order to detect the wave-fronts. Furthermore, most of those methods were developed for the purposes of TNs, which are usually significantly less branched and more homogenous in terms of line properties. In contrast, the heterogeneousness of DNs can manifests in the complex behaviour of the recorded traveling waves, which disables the proper localization of the events. Some representative traveling wave techniques and their application are provided in [18; 19; 20; 21; 22].  
Pattern recognition methods require large training databases that contain reference fault cases for a given network. On the one hand, these methods do not need complicated formulation, but, on the other hand, very high-quality measurements and long training periods are required. Furthermore, in the case of any modification to the existing system, the entire training process and high computational burden has to be repeated [23; 24; 25; 26; 27; 28].
## 2 Materials and Methods
### Anti-Inflammation Test  
To determine the anti-inflammation ability of R-PE, interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF\(\alpha\)) were detected in this study following the protocols as described in [27; 28]. RAW264.7 cells were seeded in a 24-well plate (5 \(\times\) 10\({}^{5}\) cells/well) (Corning, New York, NY, USA) and left to settle overnight at 37\({}^{\circ}\)C. On the next day, cells were co-treated with lipopolysaccharideaccharides (LPS, 1 ug/mL, Sigma-Aldrich, St. Louis, MO, USA) and increasing concentrations of R-PE (0 (negative control), 1.25, 2.5, 5, 10, and 20 ug/mL). Concurrently, an additional cell group that was co-treated with LPS and the p38 MAPK inhibitor SB203580 (3 uM, Sigma-Aldrich, St. Louis, MO, USA) was established as a positive control group. After 24 h, the supernatant for each group was collected for the detection of the produced IL-6 and TNF-\(\alpha\). Supernatant samples were applied to the Quantikine(r) Colorimetric Sandwich ELISA Kits (R&D Systems, Minneapolis, MN, USA). Percentage of inhibition (%) = 100 \(\times\) (sample/control). In addition, the cells were subjected to an MTT assay to evaluate cell viability after treatments.
## 2 Assessment of the SPWM Angles  
As shown in Figure 1, the unipolar sinusoidal pulse width modulation (SPWM) technique involves sampling a sinusoidal signal using one positive triangular signal for the positive semi-cycle and one negative triangular signal for the negative semi-cycle of the sinusoidal signal. The sinusoidal signal acts as a reference to obtain the desired sine wave signal, which in many cases is a 60 Hz network signal. The triangular signal, which operates at a considerably higher frequency than the original sine signal, compares and generates pulses that determine the necessary cuts (\(\beta\) and \(\beta_{l}\)) in the inverter signal. This process is performed by comparing amplitudes between the sine and triangular signals: when the sine signal exceeds the triangular signal, a pulse representing an \(\alpha\) angle is generated, indicating the cutoff points in the inverter signal. These cutoffs (\(\beta\) and \(\beta_{l}\)), calculated by this comparison, allow the resulting square waveform to resemble a sine signal in key areas. In this way, the unipolar SPWM effectively modulates the signal using the high-frequency triangular signals' reference.
# Photovoltaics on Landmark Buildings with Distinctive Geometries
## 4 Results and Discussions
### PV System as Architectural and Environmental Value  
The clients are very often keen to use architectural elements that have an impact on society. This could demonstrate power, richness, social status, professional orientation, aesthetical understanding, etc. By applying PV systems, the impact on the society is manifold, but always oriented towards environmental responsibility and sustainability. For example, the PV systems on the roof of Maracana Stadium, the Theme Pavilion and the Kaohsiung National Stadium reflect the green energy orientation of the events for which the buildings were reconstructed or built. The Copenhagen International School and the Umwelt Arena bring a pedagogic message related to environmental responsibility. The PV installations of the Apple Headquarters, the Powerhouse Bratterkaia and the Novartis Campus demonstrate the technical leadership of the companies in terms of sustainability. The PV visibility at the Paris Courthouse and La Seine Musicale guide the green regeneration of their neighbourhoods. It has to be noted that, although photovoltaic systems can currently be considered as \"clean\" and have a relative low environmental impact, depending on their installation location and local electricity mix, this might not always be the case, as many researchers have pointed out [2; 14; 56] and the same applies for their economic viability.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 6 Reducing Errors and Enhancing the Sensory Information Channel Capacity
### Temperature Variation Techniques
#### 6.2.1 Sampling-and-Hold Measurement Technique  
To get around the difficulty of the interference of the moving gas close to the sensor, sampling-and-hold (\"frozen-smell\") FES was introduced for heated sensors [47; 91] resulting in not only higher reproducibility but also in higher specificity [92; 93]. During this measurement, the sensor is first heated for a short time to let the agent diffuse into the molecular lattice of the sensor film. Then the heating is switched off and the FES measurements are done on the cold sensor. The agent does not even have to be present in the ambient atmosphere because the information is stored in the molecular lattice.
# Stability Index of Surrounding Rock during Deep Rock Excavation Considering Energy Release Speed
## 2 Local Energy Release Effectiveness
### Effect of the Energy Release Speed on the Energy Release Effectiveness  
In the local energy release rate index described above, rock failure occurs when the accumulated energy in the surrounding rock exceeds its energy storage limit, and the released energy is considered the energy source for the rock mass nearby. The stability of a rock mass with high energy will be destroyed by an energy fluctuation, and the interlocking failure process can lead to rockburst. However, the energy release of rock alone is not enough to reflect the effectiveness of the energy release of the surrounding rock.  
From a modern ballistics perspective, the effectiveness of a kinetic energy projectile on the target is determined by several factors: (1) the kinetic energy when the projectile hits the target, (2) the ability of the projectile to transmit energy to the target, (3) the area of energy transfer, (4) the speed of energy transfer, and (5) the effect of the shock wave induced by the projectile upon impact. While the first factor emphasizes the energy of the projectile, the second to fourth factors emphasize the process of energy transfer from the projectile to the target. It is clear that the kinetic energy of the projectile is a necessary but not sufficient condition for killing effectiveness. Assuming that the energy transferability of energy from the projectile to the target is poor, and it is difficult to ensure the effective transmission of the projectile energy to the target, the killing effectiveness of the projectile will be reduced or even ineffective. Therefore, in the design process, it is necessary to consider both the energy of the projectile itself and the process of its energy transfer to the target [24].  
Explosions provide another example. From the perspective of energy, industrial explosives have relatively low energy density compared with common combustibles, such as coal (Table 1). The reaction heat of TNT, known as the \"king of explosives\", is less than half of that of coal, and the reaction heat of black powder, used for gunpowder, is less than one-third of that of coal. The reaction heat of fulminating mercury and lead azide, which is still used for initiating explosives, is less than one-quarter of that of coal. However, coal cannot be used as an explosive because the reaction of general combustibles, such as coal, is very slow during the combustion process, and the energy release speed is very slow, whereas explosive reactions are rapid, and the energy release speed is very high. These examples illustrate that the energy release speed has a significant impact on the effectiveness of energy release.
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 2 Materials and Methods
### EEG Data
#### 2.6.2 Real EEG Data  
In order to prove the applicability of our proposed methods, we performed a dipole source localization using real EEG data corresponding to the activation produced by steady-state visual-evoked potentials (SSVEP). The data we used is freely available in [27].  
The stimulus of the experiment was a violet box, presented on the center of the monitor, flickering in five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz). The experimental setup is shown in Figure 5. The experiment was divided into five identical sessions, and each one was initiated with 100 s of resting period where the participant could look at the black screen of the monitor without being involved in any activity. A 100 s adaptation period followed, which consisted in the presentation of the five frequencies in a random way. The following 30 s interval was left for the subject to rest and be prepared for the next trial, which consisted in the presentation of one frequency for three times before another 30 s break. Every frequency is presented sequentially for three times and with a resting period of 30 s between each trial. Each session eventually includes 23 trials, with eight of them being part of the adaptation [28].  
Figure 4: Tessellated model of the brain (top view). Red points indicate the \(L=3\) real sources’ positions. Other candidate positions that are part of the ROI are shown as blue points.  
Figure 5: Experimental setup for the acquisition of EEG SSVEP data. Image taken from [[https://youtu.be/8](https://youtu.be/8) lGBVvCX5d8]([https://youtu.be/8](https://youtu.be/8) lGBVvCX5d8) (accessed on 2 February 2023).
## 3 Mathematical Formulation
### Individual Bidding Strategies for a DR Aggregator  
The problem of the optimal bidding strategy of a DR aggregator is formulated as follows:  
\[\max\,\,\,PF_{DR}^{s}+\beta\Bigg{(}\theta_{w}-\frac{1}{1-\alpha}\sum_{s}^{N_{s }}\pi_{s}\eta_{w}^{s}\Bigg{)} \tag{17}\]  
\[PF_{DR}=\sum_{s\in N_{s}}\sum_{t}^{T}\pi_{s}\Big{(}P_{DR,t}^{s}\lambda_{DR,t} +\lambda_{reg,t}^{cap}C_{DR,t}^{reg,s}S_{t}+\lambda_{reg,t}^{per,s}C_{DR,t}^{ reg,s}R_{t}^{mil}S_{t}-E_{t}\lambda_{t}\Big{)} \tag{18}\]  
\[C_{DR,t}^{sp,s}=P_{0,t}-P_{t}^{s} \tag{19}\]  
\[C_{DR,t}^{reg,s}=C_{DR,t}^{up,s}+C_{DR,t}^{dn,s} \tag{20}\]  
\[P_{DR,t}^{s}+C_{DR,t}^{up,s}\leq C_{DR,t}^{s} \tag{21}\]  
\[P_{DR,t}^{s}-C_{DR,t}^{dn,s}\geq 0 \tag{22}\]  
\[P_{DR,t}^{s},C_{DR,t}^{up,s},C_{DR,t}^{dn,s}\leq C_{DR,t}^{s} \tag{23}\]  
\[PF_{DR}+\eta_{w}-\theta_{w}\geq 0 \tag{24}\]  
The aggregator aims to maximize the total profit \(PF_{DR}^{s}\) from demand response involvement and regulation market participation minus the cost of energy purchased from markets. \(P_{DR,t}^{s}\) and \(C_{DR,t}^{reg,s}\) refer to the amount of responsive load that participates in the energy market and regulation capacity bid. \(\lambda_{DR,t}\) signifies the price or incentive offered to customers for their participation in DR. \(E_{t}\) is the increased consumption for participating in down-regulation and \(\lambda_{t}\) is the corresponding price that customers pay for the market. Equation (19) expresses the available DR capacity \(C_{DR,t}^{s}\) for market bids. The up- and down-capacity limits for regulation bidding are shown in Equations (20)-(22). \(C_{DR,t}^{up,s}\) and \(C_{DR,t}^{dn,s}\) represent the up-regulation and down-regulation bids. Equation (22) enforcesthat the difference between the energy and the up-regulation should be larger than 0. In Equation (23), the market bid of the DR aggregator is restricted by the forecast of available DR capacity. Equation (24) denotes the constraint associated with CVaR.
## 3 Experiments and Analysis
### Performance Evaluation
The results show that the positioning accuracy across the five devices is very consistent, with accuracy rates around 95%. Therefore, we can conclude that device differences have minimal impact on positioning accuracy with the proposed algorithm. Thus, it is device-independent, making it scalable and compatible. Table 2 further compares the performance of the proposed algorithm with other localization algorithms in terms of various localization errors. In the table, the total error  
Figure 5: Cumulative distribution function of the number of positioned APs under different RSS thresholds used in the proposed PONE AP selection. Figure 6: Positioning accuracy across five different WiFi-enabled devices using the proposed method with an RSS threshold of \(-90\) dBm. The numbers displayed above the bar graph represent accuracy as a percentage. consists of 3D, 2D, building, and stair errors. The results indicate that Adaboost, like other strong classifiers such as SVM, has no 3D localization error. Compared to other machine learning-based localization algorithms, Adaboost achieves a lower 2D localization error. It is also evident that the primary source of localization errors for Adaboost occurs in the staircase area. According to the experimental data, the number of access points (APs) detected in the staircases is five, whereas in another area it is thirteen. Consequently, localization errors in the staircases contribute significantly to the overall error. In Figure 7, we compare the 2D error distance of the proposed Adaboost-based localization system with other algorithms and examine the 2D localization errors at various RSS thresholds. The 2D error distance in Figure 7 is projected by assuming that the devices are all at the center of each grid. The results show that the proposed method is more accurate than other machine learning-based localization algorithms, with a maximum 2D error of 2.3 m and a minimum error of 0.25 m. Similar to the total error, the optimal threshold value of \(-90\) dBm effectively filters out noise from fingerprint data, leading to a reduced localization error.