esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Furthermore, the surface result indicates that air temperature has a moderate effect on fuel outlet temperature, but the changes are smoother than those observed for  
Figure 5: Main and collective effects on the reformer fuel outlet temperature: Relative influence of individual parameters (**a**); interaction of flow rates (**b**); interaction of fuel flow rate and air temperature (**c**); air temperature-air mass flow rate interaction (**d**). air flow. This suggests that, while air temperature influences the thermal behavior of the reformer, its effect is less drastic compared to air flow contribution. The increasing fuel flow also raises the outlet temperature, but the overall gradient is less dominant than in the previous surface. This implies that while fuel flow contributes to temperature control, it does so more gradually, making it a parameter that can be adjusted without causing large, abrupt changes in component's behavior. The 3D response surface given in Figure 5d shows the combined effect of air flow and air temperature on the fuel outlet temperature. The color gradient illustrates how changes in these variables affect the outlet temperature. The plot again highlights that air flow has a dominant effect on temperature, as indicated by the steep gradient along the air flow axis. Even at lower air temperatures, increasing the air flow leads to a significant rise in temperature. Interestingly, the combination of high air temperature and high air flow results in the highest outlet temperatures, confirming the superimposed effect of these parameters. This suggests that both factors should be optimized simultaneously to avoid excessive heating that could reduce system efficiency; thus, synergistic effects between parameters need to be accounted for.
## 4 Results and Discussions
### Scribing Experimental Results  
The scribing results with different laser pulse energies are demonstrated in Figure 10. The same water pressure and laser parameters were selected corresponding to Figure 9. Moreover, the scribing velocity of LWJ was set as 10 mm/s with a 5 mm scribing length. With a uniform processing time of 2 min, the scribing number on the same path of every single groove is 240. As a result, the depth of grooves from low to high was 541 \(\upmu\)m, 615 \(\upmu\)m, 718 \(\upmu\)m, and 1385 \(\upmu\)m corresponding to a laser pulse energy of 0.4 mJ, 0.6 mJ, 0.8 mJ and 1 mJ, respectively. According to the scribing results, the proportion of removal depths with 0.4 mJ, 0.6 mJ, 0.8 mJ, and 1 mJ was obtained, demonstrated as 1:1.137:1.327:2.560. These scribing results presented a weaker machining capacity compared to the numerical simulation. According to the analysis above, an increasingly complicated inner structure might serve as the main reason for this deviation, as explained in step (4). However, the numerical and experimental results share a similar variation trend, as shown in Figure 10e.  
Figure 10: _Cont._  
A further scribing experiment was developed to investigate the ultimate machining capacity of LWJ. With series scribing times of 2 min, 4 min, 6 min, 8 min, and 10 min, as well as a pulse energy of 1 mJ and water pressure of 20 Mpa, the scribing results can be observed in Figure 11. The groove depths of different scribing times were 1385 \(\upmu\)m, 1944 \(\upmu\)m, 2287 \(\upmu\)m, 2398 \(\upmu\)m, and 2461 \(\upmu\)m, respectively. It can be clearly seen in Figure 11 that the increase in groove depth stagnated after 6 min. This indicates that there is a limited value for the groove depth for single-row scribing. Combining the numerical calculation and experimental results, it states that an environment with expedited drainage space is crucial for LWJ machining. As the further development of the groove, a deep and narrow \"V\" shape arose with a tapering bottom. Such a feature could weaken the efficacy of LWJ directly, and only a small amount of laser energy could be delivered onto the material before the water jet broke completely. Although CMCs material removal could also be realized in this situation, a limit value was finally reached, as shown in Figure 11b.
## 2 Methods
### Evaluation  
#### 2.3.1 Performance Measures  
The classification accuracy (CA), the true-positive rate (TPR), and the true-negative rate (TNR) were calculated from the confusion matrix (Table 2):  
\[\text{CA}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+\text{TN}}, \tag{4}\]  
Figure 7: Point cloud from an initial viewpoint of a sweet pepper indicating the five boxes that have a corresponding next-viewpoint. The number corresponds to the next-viewpoint number.  
\[\text{TPR} =\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{5}\] \[\text{TNR} =\frac{\text{TN}}{\text{FN}+\text{TN}}, \tag{6}\]  
where TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives, and FN is the number of false negatives.  
The total classification costs, \(c_{total}\), is the sum of the costs associated with misclassification and sensing:  
\[c_{total}=(\text{FN}+\text{FP})\cdot c_{pepper}+\sum_{i=1}^{5}n_{i}\cdot t_{i }\cdot c_{robot}, \tag{7}\]  
where \(n_{i}\) is the number of classifications that used the additional viewpoint \(i\) and \(t_{i}\) the corresponding sensing time.  
As a measure for repeatability, the consistent classification factor (CCF) is introduced. The CCF is defined as the percentage of the peppers where all three repetitions yield identical classification. The higher the CCF, the more consistent a classification is and the more independent the algorithm is on the pepper's orientation on the plant. Ideally, the CCF would be 1, implying consistent classification independent of the side of the pepper facing the row. The relation between the consistency and the CA is derived by the majority classification accuracy (MCA), defined as the CA where the most common classification from the three repetitions is derived.
# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
###### Abstract
The main contributions of this paper are as follows:  
* A vehicle-UAV collaboration methodology is proposed for the large-area efficient coverage path planning problem. Figure 1: Schematic diagram of UAV coverage path planning based on multiple flights for a large area. * A dynamically adjusted region segmentation algorithm is proposed based on the construction of regular hexagons and a Voronoi diagram. * An ant colony optimization algorithm is adopted to generate the vehicle paths from the supply points for connecting each sub-area, and a zigzag coverage path method is utilized to ensure the UAV paths to fill the contours of each sub-area. * Simulation experiments are provided to verify the effectiveness and feasibility of the proposed vehicle-UAV collaboration method, and two comparative experiments are constructed to demonstrate that the proposed method excels at large-scale coverage path planning scenarios, and achieves a significant improvement in coverage efficiency.
## 1 Introduction
### Research Gap and Originality Highlights
showed that an average of 3.5 kWh of peak load reduction per day could be achieved through adjusting the residential tariffs in peak times from 5 to 8 pm, using TOU pricing. Another research conducted by [PERSON] and [PERSON] emphasized the potential for energy saving in the Jordanian residential sector through energy efficiency measures, where they reported a saving of 15% in energy consumption and 22% in cost could be achieved through the use of efficient lighting and solar heaters by applying for an hourly analysis program (HAP) software [38]. The insight gained from these studies highlights further the potential of DR in giving more incentives for residential and commercial users to pursue energy-efficient electrical appliances and usage. However, this potential has not yet been exploited because there is no understanding of the factors that amplify this potential. This is mainly due to the lack of a conceptual framework and methodological approach that can be applied to a robust data set to quantify the main implications of using DR applications in the Jordanian power sector. This research aims to fill the gap by presenting a detailed day-ahead price-based demand response model for the residential sector in Jordan with the following contributions:  
1. A well-defined and optimized deep learning model for accurate day-ahead hourly short-term load forecasting (STLF) is trained on four-years of Jordan's hourly electrical demand from 2016 to 2019. The model's architecture and input features follow state-of-the-art feature engineering based on recent research discussed in detail. Up to date, there are very few Jordanian case studies that examined daily hourly STLF rather than day-ahead hourly STLF, such as [39], where they used only one year of electrical demand data. This research proposes a new set of time series features that are novel to previous works on Jordan's electrical demand forecasting. 2. A comprehensive demand response model for the Jordanian power sector is introduced, considering the interaction between the power generators (PGs), GOs, and service providers (SPs), which uses the estimated day-ahead hourly demand STLF, considering the detailed data on generation capacities and costs of Jordan's power suppliers as well as bulk consumers' peak load and bulk supply prices. A precise PEMD estimation was implemented for Jordan's residential sector based on recent research on the short-term price elasticity of Jordan's residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys and studies present. To the best of our knowledge, this is the first study in Jordan's electricity market to estimate the DR impact on the residential sector and find the potential implications in peak demand reduction and generation savings.
## 3 NDN Architecture  
The most distinctive feature of NDN is the use of names to identify data rather than locations. While IP names the network location of the host to send the message to, NDN names the content of the message itself, without specifying any location. In the NDN architecture, every data chunk has a name, and the network layer uses data names to communicate. NDN communication consists of a pull-based model, i.e., the receiver sends an _Interest_ packet that contains the name of the requested data chunk, and fetches one _Data_ packet back from either the original data producer, or from a node cache. A clear advantage of this architecture is that any node that has the requested data packet can reply to the interest, without needing an overlay solution that changes the addresses of the remote hosts, like in TCP/IP.  
With respect to the forwarding of NDN packets, each NDN node maintains three data structures: the forwarding information base (FIB), the pending interest table (PIT) and thecontent store (CS). Thus, once an interest packet is received at an interface of an NDN node, the following procedure is executed:  
1. The NDN node checks the CS for a copy of the requested data. If there are matching data in the CS, they are sent following the reverse path of the interest packet.
2. Otherwise, the NDN node searches the PIT to find any prior interest with the same name from a different input interface. If there is a matching PIT entry, the incoming interface is added to the list of incoming interfaces for that entry.
3. In the case of no match, neither in the CS nor in the PIT, the NDN node inserts a new entry for the name of the interest packet into the PIT, and the interest forwarding strategy decides whether, when and where to forward the incoming interest, among all the possible outgoing interfaces, according to the FIB for a given name or, alternatively, using a _forwarding hint_. (When a name cannot be announced globally, the interest packet can carry the ISP that hosts the producer. That is referred to as a _forwarding hint_. So, when an NDN node does not find a matching prefix for the name in an interest packet, it uses the forwarding hint to forward it.)  
Data packets follow the reverse path of their corresponding interest packets, hop by hop, to reach all the requesters. When a data packet arrives at an NDN node, it checks the PIT to get the incoming interfaces for that name, and sends a copy through every one of them. Then, that PIT entry is removed, and the NDN node optionally saves a copy of the data in its CS. The CS is optional and works like a cache, that is, it is used to store the most popular data chunks traversing the node. If no matching entry exists in the PIT, the data packet is dropped.  
NDN networks can run routing protocols to announce the reachability of data names, like IP does with IP addresses. The FIB also works just like the traditional IP forwarding table, but using NDN names instead of IP prefixes. Routing protocols are usually responsible for populating the FIB with entries relating names to output interfaces.
## 2 Forward Modeling Method  
### Borehole-to-Surface Electromagnetic Monitoring System  
The borehole-to-surface electromagnetic method (BSEM) (frequency domain) primarily achieves power supply through the metal casing of the target well, transmitting low-frequency pulse signals (square waves or sine waves) into the well and conducting multi-component observations of electromagnetic responses via surface survey networks. Changes in the frequency-domain BSEM field responses observed on the surface reflect alterations caused by borehole hydraulic fracturing operations. Through inversion calculations, three-dimensional imaging of fracture anomalies induced by fracturing is performed to evaluate fracturing effectiveness. The observation system for the frequency-domain borehole-to-surface electromagnetic method is illustrated in Figure 2. During observations, multi-frequency signal transmissions are typically produced, and two electric field components (\(Ex,\,Ey\)) and three magnetic field components (\(Hx,\,Hy,\,Hz\)) are received on the surface, which yield better monitoring results for application scenarios such as fracturing fracture imaging. This study focuses on discussing the lateral dynamic changes of fracturing, thus primarily addressing \(Ex,\,Ey,\,Hx,\) and \(Hy\).  
The positive pole (A) is connected to the wellhead, while the negative pole (B) is positioned on the surface or connected to another well, typically 2-3 km away from the wellhead. The red line in Figure 2 represents a cased well, which is often simplified as a line source in simulations, decomposed into vertical and horizontal line-source components. The surface survey network is usually deployed along the ground-projected plane of the wellbore trajectory, typically configured as a square grid covering the target fracturing interval to be monitored.
## 3 Results
### Absolute Differences  
MT significantly decreased when moving to the four-quarter format from two halves (\(p<0.001\), \(\eta^{2}=0.522\)). On average, MT is 5.2 min less in 2019 compared with 2018. In 2019, the average penalty corner time was 41.4 \(\pm\) 8.3 s, with an average of 10.5 \(\pm\) 4.1 penalty corners per game.  
Overall, the four-quarter format has more substitutions than two halves (\(p\) = 0.0133, \(\eta^{2}=0.075\)), with an average of six more substitutions occurring per game. Midfielders are substituted more compared with forwards, who are substituted more than defenders (\(p<0.001\), \(\eta^{2}=0.890\)). The interaction effect (\(p<0.001\), \(\eta^{2}=0.029\)) shows defenders subbed more often in 2019 compared with 2018, with midfielders and forwards having no differences between years. The number of substitutions across positions is consistent with the typical rotation for the team, where seven players commonly rotated among four midfield positions, five players commonly rotated among three forward positions, and defenders were rotated sparingly, with three to four players typically being rotated among three positions.  
There is no significant main effect for the year (\(p\) = 0.288, \(\eta^{2}=0.016\)) on TT. There is a main effect of positions where defenders played significantly longer than forwards and midfielders (\(p<0.001\), \(\eta^{2}=0.151\)). There is no interaction effect for TT (\(p\) = 0.606, \(\eta^{2}=0.029\)).  
For TD, there is no significant main effect for the year (\(p\) = 0.288, \(\eta^{2}=0.011\)). There is a significant main effect of position as defenders had a higher TD traveled compared with forwards and midfielders (\(p<0.001\), \(\eta^{2}=0.017\)). There is no interaction effect for TD (\(p=0.229\), \(\eta^{2}=0.010\)).  
There is no main effect of year for LSR (\(p\) = 0.405, \(\eta^{2}=0.012\)) or HSR (\(p\) = 0.233, \(\eta^{2}=0.003\)). There is a main effect of position as defenders covered more distance in LSR compared with both forwards and midfielders (\(p<0.001\), \(\eta^{2}=0.141\)). There is no positional difference in HSR (\(p\) = 0.054, \(\eta^{2}=0.114\)). There is no interaction effect in LSR (\(p\) = 0.706, \(\eta^{2}=0.023\)). There is an interaction effect in HSR for defenders in 2019 compared with defenders in 2018 (\(p<0.001\), \(\eta^{2}=0.000\)).  
There is no significant main effect for the year (\(p\) = 0.472, \(\eta^{2}=0.000\)) or position (\(p\) = 0.481, \(\eta^{2}=0.013\)), nor an interaction (\(p\) = 0.270, \(\eta^{2}=0.004\)) for MV. There is no main effect of year for the number of surges (\(p\) = 0.243, \(\eta^{2}=0.020\)). There is a main effect of position for the number of surges (\(p\) = 0.010, \(\eta^{2}=0.072\)), with forwards completing more surges per game than midfielders. There is an interaction effect (\(p\) = 0.011, \(\eta^{2}=0.004\)), with forwards in 2018 having more surges than defenders across both years; however, the focus of the current study is comparing positions within a year or comparing the same positions across years.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation
### Simulation
#### 5.1.3 Moving Speed  
This section evaluates the impact of the moving speed of a mobile node. The moving speed varies from 0.2 to 1.4 m/s. The report interval, the neighbor missing rate and the elevator waiting time are set to 10 s, 0% and 0 s, respectively. Figures 5b and 6b show the results. The figures clearly show that the success rate decreases when the moving speed is increasing. These results indicate that several nodes are classified incorrectly as failed nodes within the monitoring area because they reported their last values within the area. Some nodes do not satisfy the condition \(Q_{w}<T_{nc}\); others do not satisfy the condition \(R_{w}>T_{r}\). However, the proposed method correctly classifies all failed nodes and all left nodes when the moving speed is less than or equal to 0.5 m/s. In this case, the moving distance of a mobile node between two consecutive reports is less than the communication range. This means that the left nodes can report their last values from the outside of the monitoring area at least once before they are disconnected from the sensor network. Therefore, all left nodes are classified correctly.
## 4 Road Sensing
### Detecting and Monitoring Moving Vehicles
#### Acoustic Sensors  
They can be used to detect vehicles by capturing the engine noise with a microphone. Their main advantage is the long range of detection, much longer than the range of any other sensor presented. It can therefore be used to detect vehicles from further distances as well as in multiple lanes. In addition, the acoustic signature of vehicles can be used to classify them by means of neural networks [62]. However, it has important drawbacks. The high spectral amplitude of the monitored signal requires a very high sampling rate, thus making a duty-cycle scheme unachievable. In addition, acoustic sensors can be interfered by noise originated by different weather conditions such as wind or rain, making them prone to fail and report false positives as well as requiring that the signal be filtered. Finally, they are not appropriate for detecting slow moving vehicles or for high dense traffic situations.  
In spite of their shortcomings, they can be used to provide unreliable detections, possibly at low sampling rates, which can be used to wake up other sensors performing more precise detection methods [63]. This is a really attractive option if the acoustic detector is an independent subsystem of the sensor node which, rather than providing measurements upon CPU requests, work autonomously letting the rest of the hardware enter the power saving mode until a vehicle arrives [64].
# Genesis and Magmatic Evolution of the Gejiu Complex in Southeastern Yunnan, China
## 5 Discussion
### Genesis of the Gejiu Complex
#### 5.2.2 Genesis of Gabbro  
The Gejiu gabbro exhibited high total rare earth element (\(\Sigma\)REE) contents and was strongly enriched in light rare earth elements (LREEs). Its Nb/Ta ratios (average: 21.77) were close to those of primitive mantle and chondrites, indicating a mantle-derived source for the magmas. The La/Nb (0.64-12.75, average: 5.19) and La/Ta (16.85-148.94, average: 118.94) ratios were consistent with basaltic magmas (La/Nb > 1.5, La/Ta > 22), ruling out the possibility of crustal partial melting [57]. [PERSON] et al. [58] emphasized that the formation of intermediate-mafic rocks and alkaline rocks is closely related to mantle-derived materials. Furthermore, the high content of hydrous minerals (e.g., amphibole and biotite) in the Gejiu gabbro (Figure 3c) and its trace element characteristics, including enrichment in large-ion lithophile elements (LLEEs) and LREEs and depletion in high-field-strength elements (HFSEs, e.g., Nb, Ta, Ti, P) (Figure 5), further suggest that the gabbro originated from an enriched lithospheric mantle rather than the asthenospheric mantle. Therefore, this study concludes that the Gejiu gabbro is a product of fractional crystallization of basaltic parental magmas derived from the lithospheric mantle.  
[PERSON] [5] demonstrated that the gabbro exhibits heterogeneous Sr-Nd isotopic characteristics (initial \({}^{87}\)Sr/\({}^{86}\)Sr = 0.7096-0.7111, \(\varepsilon\)Nd = \(-7.9\) to \(-5.3\)), indicating crustal contamination during magma evolution. Combined with the gabbro's high aluminum saturation index (ASI), it is inferred that the mantle-derived magmas were contaminated by crustal materials during their ascent, leading to an increase in aluminum content. Thus, the Gejiu gabbro experienced crustal contamination during its evolution.