esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Off-Design Performance
The heat load of the generator, cooling capacity and COP of the absorption subsystem lowers by 14.28%, 17.24% and 3.45%, respectively, when the flow rate of chilled water comes down by 80%. In addition, the heat load of the condenser in the compression subsystem, compressor work, cooling out and COP of the hybrid system falls by 25.62%, 3.56%, 28.55% and 25.91%, respectively. The insignificant variation of the compressor work is attributed to the relatively smaller change of the pressure ratio caused by the simultaneous decline of the evaporator and condenser pressure. The off-design performance with the flow rate of circulation water in the subcooler is shown in Figure 5:1 The inlet and outlet temperature of the circulating water is strongly coupled with the corresponding flow rate. The less the flow rate is, the higher the temperature of the circulating water becomes. In addition, the multiplication of temperature difference and flow rate in the circulation water is nearly constant, i.e., the temperature difference goes up from 2.15 \({}^{\circ}\)C from 5.2 \({}^{\circ}\)C as the flow rate goes down by 40%. This trend means that the flow rate of the circulating water in the subcooler has a tiny effect on the performance of the absorption subsystem. Hence, the heat load of the component and COP in the hybrid system is thought to be independent of it as well. The heat load and COP for different compressor speeds are exhibited in Figure 5:2 For the compression subsystem, because the heat load and cooling output decrease with the decrease in the compressor speed, the condenser temperature decreases and the evaporator temperature increases. Accordingly, this change is adverse to the operation of the absorption subsystem but favorable for the performance of the compression subsystem. The heat load of the generator, cooling capacity and COP of the absorption subsystem goes down by 48.52%, 58.6% and 19.58%, respectively, when the speed of the compressor lowers by 80%. In addition, the heat load of the condenser in the compression subsystem, the work of the compressor and the cooling output of hybrid system drops by 78.46%, 82.04% and 74.7%, respectively. The COP of the hybrid system goes up by 40.8%. Figure 5:3 Performance with the flow rate of chilled water.
# Monitoring Safron Crops with UAVs
## 5 Discussion
### Future Directions  
Under this scope, the overall findings of the adopted methodology and of the pilot study performed for monitoring saffron cultivations can offer useful future directions to both researchers and cultivators.  
_Researchers_ are encouraged to engage in the following:
* Further investigate the object-based methodology applied in this type of cultivation so as to more effectively apply the segmentation algorithm in order to recognize the smallest objects of the image. In this direction, it is possible to collect much more information based on the morphology of the ground, or even investigate a more suitable parameterization of the segmentation algorithm. Additionally, in the future, researchers can experiment on a fusion approach of the pixel-based and object-based methods that might provide satisfactory results.
* Perform additional studies in safron-crop monitoring that will take into consideration the findings of the present study in order to achieve improved estimation accuracy. Researchers can change the parameters of the monitoring process and experiment in different safron fields, use different types of sensors to make more detailed annotations, etc.
* Compare data of UAV flights at different heights, so as to verify the accuracy of the two models and come up with the optimum height for the safron cultivation. In this study, we experimented with three flight heights: (a) 400 m, (b) 120 m, and (c) 12 m. The first height (400 m) in the case of safron cultivations cannot provide useful information, since the flower is very close to the ground and the accuracy of the image is too low to provide information. The second height is ideal for performing long-distance flights that can also provide images that can serve as input to the estimation models, while the third height (12 m) is ideal for acquiring images for training the estimation models. In the last case, the low height gives us access to high-accuracy images that can be precisely annotated by the experts. _Cultivators_ are encouraged to engage in the following:
* Adopt the presented monitoring process, as the average accuracy of the produced estimation models is overall promising. Since the recall rates of animal intrusion detection were not so high, cultivators are encouraged to collect more spectral information for cultivation to determine the presence of mammals, their extent, and the amount of damage they cause. This information can be thermal images, the use of vegetation indices for moisture, and the information of NIR (near infrared) or RE spectra (red edge).
* Give more attention to the annotation of the images so as to avoid classifying the different types of field attributes into wrong classes. As domain experts, they need to carefully interpret the images and provide the related knowledge to the annotation tools that will be used to train the estimation models.
# Gain and Pain in Graph Partitioning: Finding Accurate Communities in Complex Networks +
## 2 Methodology
### Heuristic but Fast
The procedure updates communities and influential vertices after each iteration, and it continues until either no further local search improvements are possible or a certain number of iterations have been performed. This heuristic two-stage community detection algorithm stands out as a powerful approach to identifying well-separated communities within complex networks concerning minimizing the introduced quality measure. Evidently, depending on the quality of initial communities obtained in the first step, one can effectively reduce the need for reforms in the revising stage. This is particularly significant when time constraints are critical, resulting in fewer executions of the revising stage. This is while, according to [21], the first stage indeed returns high-quality communities, enabling us to confidently decrease the number of iterations in the second phase or even consider skipping it. In the theoretical explorations below, we state some mathematical foundations of this algorithm, aiming to unveil essential insights into its efficiency and solution quality. Particularly, since the algorithm's first phase aims to establish initial communities by determining the best set of influential nodes \(P_{\alpha}\), to maintain efficiency, \(|P_{\alpha}|\) should be manageable. **Lemma 1**. : _Consider a graph \(G=(V,E)\) and let \(\alpha\) be an integer such that \(2\leq\alpha\leq\text{diameter}(G)\). Let \(P_{\alpha}\) be the set of corresponding influential nodes, meaning the set of nodes arranged in descending order according to their degrees, provided they are all at least \(\alpha\) steps away from each other. Then, the time complexity of building \(P_{\alpha}\) is of \(O(n(n+m))\)._  
Proof. : Let \(n\) be the number of nodes in \(G\). To analyze the time complexity of building \(P_{\alpha}\), we need to consider the steps involved in constructing this set:  
1. Sorting nodes by degree: sorting \(n\) nodes based on their degrees takes \(O(n\log(n))\) time. 2. Constructing \(P_{\alpha}\): we iterate through the \(n\) nodes. For each node \(v\), we need to check its distance to all nodes in \(P_{\alpha}\). In other words, for each node \(v\), we should verify that it is at least \(\alpha\) steps away from all nodes in \(P_{\alpha}\). To do this, we can use _Breadth-First Search_ (BFS) starting from \(v\). BFS runs in \(O(n+m)\) time, where \(m\) is the number of edges in Graph \(G\). Thus, the total time complexity for constructing \(P_{\alpha}\) is dominated by the distance verification step, giving us \(O(n(n+m))\). ```
Input: Graph \(G\), set of initial communities \(\mathbf{C}\), set of influential vertices \(P\), integer \(k\). Output: Set of revised communities \(\mathbf{C}\).
## 3 Results and Discussion  
### Effect of NaSt on the Compressive Strength and Water Absorption Rate of AASRSS Mortars  
#### 3.1.1 Compressive Strength  
Figure 2 shows the results of the compressive strength for each group of mortar specimens with different ages. As a whole, the compressive strength of all the groups of specimens increased gradually with age, and the compressive strength of the AASRSS control exceeded 60 MPa at the age of 28 days. After adding NaSt, the compressive strength of the AASRSS mortar specimens decreased slightly for a given age, and the more NaSt added, the greater the strength decreased. The addition of 0.5 wt.% and 1.5 wt.% NaSt resulted in approximately a 10% and 16% reduction in the 28-day compressive strength, respectively.  
[PERSON] et al. [24] investigated the effect of NaSt on OPC. In their study, the addition of 0.5% NaSt (mass ratio) to OPC concrete with a W/B ratio of 0.5 resulted in a decrease in the compressive strength of about 50% at 28 days. This is related to the strong hydrophobic effect of NaSt, which inhibits the hydration of ordinary silicate cement [17; 25]. According to the results obtained in this study, NaSt has a similar effect on the development of the compressive strength of AASRSS, but the effect on AASRSS is much lower than that on OPC. It seems that NaSt is more applicable to alkali-activated systems.  
Figure 2: Compressive strength of AASRSS with/without NaSt.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Response Time and QoS-Node Content Store Size
#### 5.4.2 Response Time and QoS-Node Content Store Size for Overall Traffic  
Response times corresponding to QoS-node content store size with interest packet rate for \(|M|\) = 500 and \(|M|\) = 1000, i.e., \(\delta=50\) and \(\delta=100\), respectively, are evaluated, and the results are plotted in Figure 8. QLPCC outperforms EQPR in the range of 49% to 72%, the PRR scheme in the range of 43% to 69%, the probability cache scheme in the range of 45% to 74%, the LFF scheme in the range of 55% to 79%, and the LFU scheme in the range of 60% to 80%. It is observed that the results are consistent when the network is scaled, proving the suitability of the scheme for network scaling. The QoS-node content store size ranges from 50 MB to 250 MB, as shown in the X axis of the graph. A graphical presentation of the results is shown in Figure 8.  
Figure 8: Response time vs. QoS-node content store size for overall traffic. (**a**) Response time vs. QoS-node content store size (overall traffic) for \(|M|\) = 500. (**b**) Response time vs. QoS-node content store size (overall traffic) for \(|M|\) = 1000.
# An Automatic Detection Method for Cutting Path of Chips in Wafer
## 3 Results
### Accuracy and Stability Evaluation of Cutting Path  
This section verifies that the fitting accuracy of the improved RANSAC is better than that of the LSM through experiments. The higher the fitting accuracy is, the more accurate the position of the interlayer is, and the closer the final positioning cutting path is to the ideal position. The experiment used 100 images. We use the Mean Absolute Error (_MAE_) [27] to evaluate the fitting accuracy of cut paths in images.  
\[MAE=\frac{\sum_{i=1}^{m}\left|YReal-YPred\right|}{m} \tag{22}\]  
where \(m\) is the number of discrete points, \(YReal\) is the real coordinate value, and \(YPred\) is the predicted coordinate value. The smaller the calculated MAE value, the higher the fitting accuracy. The average value of the MAE of the fitting line in the image is taken as the fitting accuracy of this image. As shown in Figure 12, different categories of images use RANSAC and the LSM to fit the precision comparison of discrete points.  
Figure 11: Error evaluation for template matching.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Error** & **Cat.1** & **Cat.2** & **Cat.3** & **Cat.4** \\ \hline Mean error & 2.82 & 3.36 & 2.52 & 2.10 \\ \(\leq\)2 & 1 & 0 & 7 & 7 \\ \(2<\sigma\leq 3\) & 20 & 26 & 19 & 2 \\ \(3<\sigma\leq 4\) & 7 & 10 & 4 & 1 \\ \(4<\sigma\leq 5\) & 2 & 2 & 0 & 0 \\ \(\sigma>5\) & 0 & 2 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of image chip matching error after brightness calibration (unit: Pixel).  
It can be seen from Figure 12 that the fitting accuracy of the improved RANSAC is higher than that of LSM, and the fitting accuracy of RANSAC is stable. The MAE value evaluated fluctuates within 0.5-0.9 pixels, with little fluctuation. LSM fluctuates greatly due to the influence of abnormal points. When processing the Cat4 image, an exception occurs. The deviation distance is up to 20.18 pixels, which will locate the wrong cutting path. When the distance between the discrete point and the fitting straight line is less than 1 pixel, it indicates that the influence of the abnormal discrete point is removed, the reserved discrete point can be accurately positioned to the interlayer, and the fitting straight line is tangential to the interlayer. The cutting path located by the fitting straight line will be distanced from the chip and located in the center of the street, ensuring that the chip will not be damaged during the cutting process.
## 2 Literature Review
### Resolution Algorithms  
The scheduling of operations in the wine industry has been tackled using various resolution methods including heuristics, metaheuristics, and exact approaches, each offering distinct trade-offs. Heuristic methods, like those proposed by [PERSON] [27], provide fast, feasible solutions to reduce downtime but often lack precision and scalability in environments with high variability and frequent format changes. Metaheuristics (e.g., genetic algorithms as applied by [PERSON] [24]) can reduce transition times but may fall short when exact solutions are required. In contrast, mixed-integer programming (MIP) models, such as those developed by [PERSON] [29], deliver detailed optimization by incorporating sequence-dependent setup times; however, they struggle with simultaneously allocating multiple batches across different production lines.  
[PERSON] et al. [30] have further contributed by employing a decomposition algorithm for large-scale wine bottling, achieving cost reductions of 15-30% through effective handling of sequence-dependent setups across parallel lines--though their approach can be computationally intensive and is often tied to specific operational data under the assumption of two setup types. In light of these limitations, our work proposes an exact and continuous scheduling approach that avoids time discretization, enabling a more precise modeling of processing and setup times. This method thoroughly evaluates all possible batch combinations for filtration and bottling, supports daily operational decision making in environments with a finite number of lines, and aligns with [PERSON]'s observations on the critical role of sequence-dependent setups in optimizing production flow. Overall, our framework offers robust, scalable, and adaptable support for precise and efficient daily planning in the wine-bottling industry.
## 2 Submillimeter-Level Microwave Ranging
### MWR Payload Development
#### 2.3.1 Antenna Scheme Design  
The MWR antenna is composed of a K/Ka dual-frequency corrugated horn and a dual polarization quadrature coupler, as shown in Figure 3 below. The antenna was specially designed using a horn configuration, which has the advantages of small volume, compact connection with feed source, high-precision installation accuracy, and small thermal deformation (smaller thermal deformation of horn antennas made of invar steel). The disadvantage is that it is difficult to achieve high gain performance. Fortunately, this was finally solved by choosing the appropriate size of the corrugated horn, with a huge number of simulations and calculations.  
Figure 3: Illustration of the MWR transceiving antenna. (**a**) Three-dimensional antenna structure design including corrugated horn and K/Ka dual-polarization quadrature coupler. (**b**) Test results of the antenna pattern (radiation angle deviation from line-of-sight (LOS) direction vs. antenna gain) for both K and Ka bands.  
Figure 2: Block schematic of the microwave ranging (MWR) system, mainly including a K/Ka horn transceiving antenna with quadrature coupler, frequency reference unit, K/Ka-band microwave channel, and digital signal processing platform.
# Study of Fast and Reliable Time Transfer Methods Using Low Earth Orbit Enhancement
## 4 Example Analysis
### Analysis of LEO-Augmented GPS Time Transfer Convergence Performance

Table 5: Convergence time and percentage of improvement in time transfer links (min).
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 2 Proposed EWS-16T SRAM Cell
### Design and Operation of the EWS-16T Cell
#### 2.1.2 Read Operation  
During the read operation, the bit lines BL and BLB are precharged to VDD. WL is set to VDD to activate access transistors N7 and N8, while WWL is also set to VDD to deactivate access transistors P7 and P8. In this configuration, the voltage of BL remains unchanged, but BLB can be discharged through transistors N3 and N8. When the voltage difference between BL and BLB exceeds approximately 50 mV, the sense amplifier can accurately determine the stored logic value.
## 7 Simulation Results
### Communication Cost  
Figure 4 shows the communication costs for all four protocols grow as \(h\) grows. This is because the times of transmission for packets during the \(h\)-directed routing phase increases as \(h\) increases. The communication costs for phantom single-path, PRLA and our SLP are very close. The communication cost of SLP-E is higher than that of SLP. This is because bypassing the unsafe area during the shortest path routing process incurs extra transmissions. Specifically, compared with phantom single-path, which has the smallest communication cost, the communication for SLP-E increases by 8.26% on average and increases by 14.27% when \(h=30\). Figure 4 also shows that the communication cost for SLP-E increases merely by 3.91% on average when compared with SLP. Thus, the packet will be transmitted for four more times in SLP-E than SLP. Theory analysis in Section 6 indicates that \(f_{avg}=3\) if \(r/H=1/10\). In conclusion, the simulation results is consistent with our theory analysis, which show that the increase amount of communication cost for SLP-E is acceptably low.  
Figure 5 shows that the communication cost for all the four protocols grow as \(H\) grows and are very close. This is because as \(H\) grows, the distance from the source to the base station grows and consequently the number of transmission times needed for a packet to be transmitted from the source to the base station increases. Specifically, compared with phantom single-path and SLP, the communication cost for SLP-E merely increases by 6.50% and 4.98%, respectively.  
Figure 4: Communication cost _vs._ random directed hops.
## 3 Results and Discussion
### Characterization of Biochar
#### 3.4.3 FTIR Analysis  
The chemical functional groups of oak char are obtained via FTIR analysis, as shown in Figure 9. The oak char exhibits distinct peaks at various wavenumbers, indicating specific vibrational intervals. These include 2980-2800 cm\({}^{-1}\), corresponding to the stretching vibrations of C-H bonds in functional groups such as -CH\({}_{3}\) and -CH\({}_{2}\)-. Additionally, stretching vibrations of carbonyl groups are observed at 1750-1500 cm\({}^{-1}\)[61]. The vibrational intervals of 1500-1200 cm\({}^{-1}\) indicate the presence of C-O-C and C-H in-plane vibrations. Furthermore, the stretching vibrations of -OH groups are detected at 1200-1000 cm\({}^{-1}\). Finally, the vibrations related to the bending of the C-H bonds out of the plane are detected  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **BET (m\({}^{2}\) g\({}^{-1}\))** & **Total Pure Volume (cm\({}^{3}\) g\({}^{-1}\))** & **Average Pore Diameter (nm)** \\ \hline
**Samples** & **Oak Chars** & **Corn Stalk Chars** & **Oak Chars** & **Corn Stalk Chars** & **Oak Chars** & **Corn Stalk Chars** \\ \hline MG (\({}^{\circ}\)C) & & & & & \\
600 & 289.54 \(\pm\) 9.22 & 452.25 \(\pm\) 10.25 & 0.32 \(\pm\) 0.06 & 0.32 \(\pm\) 0.04 & 3.52 \(\pm\) 0.19 & 3.92 \(\pm\) 0.21 \\
700 & 355.52 \(\pm\) 7.21 &  
within the range of 1000-650 cm\({}^{-1}\). The FTIR curves of the same biomass are consistent across different heating methods, indicating that the heating methods do not impact the surface functional groups of biomass charcoal. As the gasification temperature increases, the strengths of the characteristic peaks of the functional groups on the surface of the two samples gradually diminish. This observation indicates that the high temperature led to the breakdown of functional groups on the surface of the biomass chars.  
As shown in Figure 10, the characteristic peaks in corn stalks have distinct locations from those in oak char. Specifically, the absorption characteristic peaks are identified at 1600-1800 cm\({}^{-1}\), corresponding to the C=C vibrational intervals of aromatic or thick aromatic rings. Also, 1200-1000 cm\({}^{-1}\) peaks represent the telescopic vibrational intervals of -OH groups. Furthermore, two peaks are detected at 1000-450 cm\({}^{-1}\), specifically at 797 and 469 cm\({}^{-1}\), corresponding to the C-H vibrational intervals of different aliphatic groups in corn stalk char. The absorption peaks of each functional group of biomass char from MG exhibit a lower intensity than that from EG. The gap between the two heating methods gradually decreases as the temperature increases. The evidence suggests that MG is more beneficial than EG at lower temperatures.  
Figure 10: FTIR characterization of corn stalk chars derived from MG (**a**) and EG (**b**).  
Figure 9: FTIR characterization of oak chars derived from MG (**a**) and EG (**b**).
## 7 Application Layer
### Summary  
The application layer is open to adaptation opportunities at the service level. Through the deployment of adaptive middleware, the interoperability requirement of _IoT_ is met in the form of service integration. The behavior of applications can also adapt to the specific context with the help of contextual knowledge deduction and retrieval of the middleware. Although many studies have been dedicated to this area, no one has yet provided an ideal solution that completely satisfies _IoT_ requirements [124]. As a result, _IoT_ middleware will remain an open and active research domain.