esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### MC Analysis
As a result, the frequency of collisions and friction between the molecules grows, which leads to heating of the sample and heat transmission from the inside out. These phenomena can be accompanied by cell breakage and a higher yield of antioxidant extractives [49; 50]. [PERSON] et al. reported that higher microwave power affected the porosity of green coffee beans and the fragmentation of structure [30]. In this work, the parameters of MWR treatment were constant; thus, the microstructural changes in the sample primarily depend on its dielectric properties, which increase with growing MC [51; 52]. As it was concluded in [30; 49; 50; 51; 52], beans with the highest MC (green coffee) are most susceptible to microstructural changes and thermal heating as a result of MWR treatment. At the same time, MWR treatment of green coffee was accompanied by the release of volatile substances and the change in the color to yellow-brown (Appendix A). A drop in MC in green coffee beans after MWR treatment from 9.33% to 5.95% (\(p<0.01\)) significantly facilitated their grinding. In the process of MWR treatment of roasted coffee beans, no release of volatile substances was observed because, firstly, the bulk of volatile substances had left the beans during roasting and, secondly, an oily film on the roasted bean surface can block these processes. After combined SF + MWR and FF + MWR treatments, MC decreased only in green coffee (from 9.33% to 6.28-6.44%, \(p<0.01\)), while no significant changes in MC in roasted coffee were observed. The obtained data can be explained as follows: after freezing treatment, MC grows in roasted coffee beans, but these changes are insignificant in green coffee beans (Table 1). As a result of subsequent MWR treatment, moisture evaporates. This process occurs faster in green coffee beans because roasted coffee beans are covered with an oily film that blocks the escape of moisture. Together, these processes result in lower MC in green coffee and its preservation in roasted coffee relative to the initial samples. CAP-treatment did not show any significant changes in the bean surface temperature. MC fluctuations in coffee beans treated with CAP were also insignificant.
# Optimization of Composite Material Repair Patch Shape Based on Strength Analysis
## 5 Tensile Test of Composite Laminate Panels Repaired with Different Patch Shapes
### Comparison of Analysis Results for Different Patch Shapes  
According to the results obtained from Abaqus, the tensile failure force of the healthy specimen is 94,414 N, corresponding to an ultimate strength of 486 MPa.  
The average parameters obtained from the tensile experiments of circular and square patch repaired specimens are listed in Table 7. The average elastic modulus of the square patch repaired plates with rounded corners is smaller than that of the circular patch repaired plates. This is related to the lesser amount of base material removed by the circular patch and the quality of patch repair. Therefore, when considering permanent repairs for damage sizes ranging from 1 inch to 8 inches, the choice of patch shape should be studied simultaneously with the opening shape to reflect the engineering significance of this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimens** & **Parameters** & **Elastic Modulus** & **Ultimate Failure Strength** \\ \hline  & **Circular Patch Specimens** & 12,034 & 404.311 \\  & **Square Patch Specimens** & 11,779 & 338.239 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Material parameters of circular patches obtained from tensile test/MPa.  
Figure 51: Average displacement of the entire board and the patch in the z-direction of sample R6.  
For repairs under the same conditions, it is believed that the stability of the square patch repairs is higher than that of circular patch repairs, so this factor should be considered when choosing patch shapes.  
Finally, the average tensile ultimate strength of the square patch repaired plates is 338.239 MPa, while that of the circular patch repaired plates is 404.311 MPa. These strength values reach 69.5% and 83% of the healthy specimen, respectively, demonstrating the advantages of the circular patch repair method.  
The pattern of patch shape selection provided in this paper can be found in Figure 52.  
Clearly, when selecting repair patches, it is necessary to consider the opening shape simultaneously. So, the shape of the damage needs to be considered as well to ensure minimal removal of the base material, thereby reducing the loss of strength and stiffness of the base plate.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 3 3 GPP Release 16: First 5G Upgrade
### Enhancements to Existing Features  
MIMO, a cornerstone of 5G's ability to deliver high data rates and spectral efficiency, received substantial upgrades in Release 16 [34; 35]. Release-15 MIMO faced challenges such as a high uplink overhead due to the Type-II CSI reporting mechanism. Release 16 addressed these limitations by introducing compression techniques in both spatial and frequency domains for CSI reporting. These enhancements reduced reporting payloads significantly while preserving spectral efficiency. The introduction of default spatial relation configurations simplified beam management [36]. Additionally, support for multi-transmission-reception-point (multi-TRP) scenarios enhanced both downlink reliability and data rates. Additionally, Release 16 introduced new designs for the demodulation reference signal (DMRS) to reduce peak-to-average power ratio.  
Mobility management also saw notable improvements in Release 16. Conditional handover (CHO) and dual active protocol stack handover were introduced to address the limitations of traditional handover mechanisms [37]. These features reduced connection interruptions and improved handover reliability, ensuring seamless transitions for UEs between cells. This was particularly important for high-mobility scenarios, such as vehicles and trains, where maintaining uninterrupted connectivity is a significant challenge.  
Energy saving was another area where Release 16 delivered meaningful advancements, particularly for UEs [38; 39]. In both connected and idle states, UEs benefited from enhanced techniques such as discontinuous reception (DRX) adaptation, cross-slot scheduling, and reduced RRM measurement requirements. These improvements allowed UEs to communicate their power-saving preferences dynamically, optimizing energy consumption while maintaining network performance.  
DC/CA, which are essential for maximizing spectrum utilization and ensuring robust network performance, were further improved in Release 16 [40]. Enhancements included asynchronous NR-DC support, direct secondary cell group (SCG)/secondary cell (SCell) configurations, and the introduction of NR SCell dormancy. These changes reduced setup times, enabling better resource allocation and enhancing the overall network experience.  
Another area of improvement was interference management, an issue that becomes particularly challenging in dynamic environments and dense deployments. Cross-link interference (CLI) mitigation emerged as an important issue for time-division duplexing (TDD) systems, reducing interference between uplink and downlink transmissions [41]. Remote interference management (RIM) tackled issues caused by atmospheric ducts in wide-area TDD deployments, enabling the automated resolution of interference scenarios [42].
# An Enhanced Message Distribution Mechanism for
## 5 Simulation and Evaluation
### Comprehensive Network Performance  
The cbench test method that was used in the previous chapter can only verify the performance under idle (latency experiment) or full load (throughput experiment) conditions. The actual SDN network is complex and difficult to predict; [27] points out that there are key values of parameters in which the target function has first-generation breaks. When considering the comprehensive evaluation and practicality of the SDN network, more network parameters are needed, such as delay, delay variation (jitter), bandwidth, and packet loss parameters on a network [28]. Therefore, this section uses mininet to construct a virtual network to further test the effectiveness of the EMD.  
In the experiment, the tested network is a pod-4 fat-tree network constructed by mininet, running on a ubuntu18.04 virtual machine with a 4-core processor and 4 GB of memory; the tested controller is a Ryu controller running on another machine with a 2-core processor and 4 GB of memory. Furthermore, _shortest_path_ is an app that is based on the shortest-path-first algorithm of the number of hops, while _Hedera_ is a routing algorithm that is based on the network status, the other example labels in figure are the same as in Section 4.1.  
Figure 7: Latency & Throughput Test of Ryu under Various App Combinations.  
The experiment shown in Figure 8 is a bandwidth test performed on a switch in a virtual network built by mininet, and the test software is iperf. The blue part is the bandwidth test between hosts under the same aggregation switch, it can be seen that the algorithm that is based on the network status can slightly increase the bandwidth of the network by approximately 5.4% (from 2871.56 Kbps to 3027.63 Kbps). However, after adding redundant apps, due to the increase in single response time, the network bandwidth also drops from 3027.63 Kbps to a minimum of 1201.49 Kbps, a decrease of 60.3%. When the EMD is selected, the network bandwidth is 2887.68 Kbps, a drop of only 4.6%, which is even higher than shortest_path app. Similarly, in the orange part, the core switch test can also draw the same conclusion. The gray part is a random test between switches. We use stacked bars to make the disparity more obvious.  
Furthermore, we use the tcp mode in iperf to test the time that it takes to transmit 1 MB of data on the network, and the conclusions are very similar (as shown in Figure 9). At the same time, other network parameters of using EMD also have a little advantage, such as jitter and packet loss rate. It can be concluded that the EMD is effective for the improvement of the SDN network's performance.  
Although EMD can only directly affect the efficiency of northbound communication, when the impact of each packet in-out accumulates, the impact on the performance of the data plane also becomes important. It can be concluded that EMD indirectly improves the performance of the data plane.  
Figure 8: Bandwidth Test of A Fat-tree Data Center.  
Figure 9: Transmission Time Test of A Fat-tree Data Center.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 5 Results  
The channel model implemented in Python was employed to characterize the received power of the four indoor scenarios presented in [17]. The results obtained with the Python package were compared with the results presented in [17] to validate the model. Additionally, the SER of a CSK modulation with sixteen symbols was estimated based on the cross-talk matrix, spectral responses of the LEDs, detectors, and reflectance of the walls over an AWGN channel.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Evaluation Summary  
Based on the above experimental results, we compare the technical characteristics of the CCLF and PCON methods for congestion control, as shown in Table 4.  
Following the PCON method, consumers adjust the sending rate of Interests to limit Data packet traffic, thereby alleviating congestion. However, there is a response delay between detecting congestion and reducing the Data forwarding rate. Especially during sudden traffic surges, PCON struggles to alleviate congestion promptly, leading to frequent packet loss. This receiver-driven approach resolves congestion by indirectly limiting Data traffic but results in lower bandwidth utilization and network throughput.  
In contrast, the proposed CCLF method is a router-driven solution where each router directly schedules the forwarding of Data packets, thereby effectively preventing congestion. Moreover, CCLF achieves both rapid forwarding of delay-sensitive Data and balanced scheduling of regular Data. In the case of burst traffic, Data packets can be stored in the CS and subsequently scheduled and forwarded based on priority. This ensures full bandwidth utilization without packet loss while reducing flow completion time.
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 1 Introduction
Consequently, nonlinear feedback controllers are proposed to imitate the natural oscillation of the body, which results in a prescribed average velocity. The work in [18] introduced a particle swarm optimization (PSO) algorithm to optimize the characteristic parameters of the CPG model to obtain the maximum average speed and improve efficiency. In addition, research [19] has been conducted on the control of pectoral fins to improve propulsion efficiency. The problem of high-efficient propulsion has been widely studied. However, the efficiency of the robotic fish is still far from the expectations of the application. Why is the propulsive efficiency of the robotic fish much lower than that of the fish in nature? When a fish swims, power is generated by the myotome muscle on either side of the body to overcome the drag [20]. A wave of muscle activation/contraction passes alternately down each side of the body from head to tail in an oscillation cycle. A wave of curvature also travels down the body due to the combined effects of muscle activities, the physical properties of the skeletal elements, and the interaction between the fish's body and the reactive forces from the water in which it moves [21; 22]. The muscles of the fish contract to generate the power for the swimming thrust. In contrast, the motors in the joints of the robotic fish generate the power for the oscillation to achieve forward propulsion. Therefore, applying the control mechanism of the fish muscle onto the motors in the joint space may be a feasible way to improve the efficiency of robotic fish propulsion [23]. In this paper, we aim to derive the high-efficient methodology from the fish in nature and design a novel closed-loop virtual musculoskeletal control method based on the dynamic model of the robotic fish and the musculoskeletal model of the biotic system. The main contributions of this paper are as follows: (1) a virtual musculoskeletal control method is the first used to raise the propulsion efficiency of robotic fish; (2) a closed-loop control framework, which improves the propulsive efficiency, is constructed by implementing the virtual musculoskeletal mechanism on the dynamic model of the robotic fish; and (3) a series of experiments is conducted to validate the proposed method in various swimming velocities and different parameters of the algorithm and hydrodynamic environment. The rest of the paper is organized as follows. The mathematical model of a two-joint robotic fish and the problem formulation is stated in Section 2. The model of the musculoskeletal system, the control structure, and the designed strategy are presented in Section 3. Experimental results are presented in Section 4, and the paper ends with the conclusion in Section 5.
## 5 Data Acquisition via V2V Communications
### Incentive Strategy for Supplementary Storage  
Although from the standpoint of the whole system, the supplementary storage improves the access ratio by exploring available storage capacity and communication opportunity, it indeed consumes the storage and communication resources of the assistant vehicles. Thus, vehicular nodes may not volunteer to participate in the supplementary storage. We put forward an incentive strategy to do with this conflict based on a metric, named the assistant score, which implies the contribution of a vehicular node in the supplementary storage. The assistant score of \(v_{i}\) is denoted by \(S(i)\). The two core aspects of this strategy are discussed below.  
1. How to gain the assistant score. A vehicular node gains its assistant score when it takes part in the supplementary storage. Specifically, when a vehicular node \(v_{i}\) obtains assistant content from a roadside unit and carries it for a carry-recording time \(t_{Sc}\), \(v_{i}\) gains a carrying score \(Sc\) and updates  
Figure 4: An instance of the forwarding of assistant contents.  
its assistant score as \(S(i)=S(i)+Sc\); when \(v_{i}\) forwards an assistant content to its neighbor or delivers it to its request sender, \(v_{i}\) gets a transmitting score \(St\) and updates as \(S(i)=S(i)+St\). In the simulation experiments, we set \(t_{Sc}=10\) s, \(Sc=1\), \(St=5\).
2. How to use the assistant score. As analyzed above, when several vehicles ask a roadside unit for different contents simultaneously, the roadside unit with limited bandwidth needs to send out these contents in some order. In our incentive strategy, this order is set based on the assistant scores of the request senders. In detail, the roadside unit computes the sum of the assistant scores of those vehicles requesting the same content, takes this value as the sending priority of the content, and sends these contents in descending order of their priorities until the communication chance passes by. For instance, four vehicular nodes, i.e., \(v_{1}\), \(v_{2}\), \(v_{3}\) and \(v_{4}\), send content requests to a roadside unit \(u\). Their assistant scores are 5, 6, 4 and 8, respectively. \(v_{1}\) and \(v_{2}\) would like the content \(c_{1}\), while \(v_{3}\) and \(v_{4}\) request \(c_{2}\). We get the sending priority of \(c_{1}\) is \(S_{1}+S_{2}=11\) and that of \(c_{2}\) is \(S_{3}+S_{4}=12\). Since \(c_{2}\) has a higher sending priority than \(c_{1}\), \(u\) sends \(c_{2}\) to \(v_{3}\) and \(v_{4}\) first. Then, if the communication chance still exists, \(u\) sends \(c_{1}\) to \(v_{1}\) and \(v_{2}\).  
To sum up, the supplementary storage at vehicular nodes makes better use of the storage space and the communication bandwidth, achieves more fair transmission scheduling at roadside units via the incentive strategy, and therefore further improves the access ratio in cooperative vehicle-infrastructure systems.
## 3 Dynamic Resource Allocation and ACB Scheme for Delay-Sensitive Devices
### Clustured Structure  
Delay-sensitive devices utilize the preamble resources occasionally due to the lower incidence of such services, which results in smaller traffic loads compared with delay-tolerant ones. Considering this, we classify those devices that attempting to access the network into two clusters according to their delay requirements. As depicted in Figure 2, we divide the available preambles which are reserved for contention-based random access procedure into two pools (marked as Pool 1 and Pool 2). The preambles in Pool 1 are dedicated for DS devices while preambles in Pool 2 serve the delay-tolerant ones. The number of preambles in each pool is not fixed and it can be adjusted by eNodeB dynamically with the change of network overload. It is worth noting that the total amount of the preambles in the two pools remains unchanged. Considering the distinct communication requirements of DS and DT devices, we set different ACB factors for the devices in different clusters. As general delay-tolerant devices can tolerate high latency, we give priority to DS devices and focus on ACB scheme and resource allocation from the perspective of this type of devices. In addition, in order to avoid all RA resources are occupied by DS devices, we set an upper bound which is denoted as \(L_{avail\_DS}\) for the number of preambles in Pool 1. Furthermore, we define \(L_{total}\) as the total number of available preambles for DS and DT devices.
###### Abstract  
In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices--Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18\({}^{o}\)-30\({}^{o}\)N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIFT serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI's capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIFT data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughs. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes.  
solar-induced chlorophyll fluorescence; vegetation indices; water stress; lightGBM-Shapley; MODIS; GOSIF; Southern China 2024