esa-sceva/satcom-chunk-collection huggingface dataset:
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 4 Discussion
### Limitations, Uncertainties, and Prospects  
This study primarily aims to reveal the differences among the conclusions drawn using SIF datasets with different spatial resolutions. However, the relatively low spatial resolution of the climate data we used may limit the ability to capture higher spatial heterogeneity, potentially restricting remote sensing's capacity to explore the mechanisms behind vegetation dynamics at various scales. In addition, the varying resolutions of SIF data used in this research may lead to variations in data quality and extracted phenological metrics due to differences in satellite platforms, sensors, and data-processing methods [91], introducing potential uncertainties.  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs17122059/s1](https://www.mdpi.com/article/10.3390/rs17122059/s1)]([https://www.mdpi.com/article/10.3390/rs17122059/s1](https://www.mdpi.com/article/10.3390/rs17122059/s1)): Table S1: Description of flux-tower-based GPP sites in this study; Table S2: Comparison of phenological metrics extracted from different scales of SIF and GPP; Figure S2: Spatiotemporal trends of various climate variables from 2008 to 2017: Panels S2a-m display results for the spring preseason, while panels S2n-z show results for the autumn preseason. Percentages indicate positive and negative growth, with values in parentheses representing significant trends (\(p<0.05\)); Table S3: List of abbreviations.  
**Author Contributions:** Conceptualization, X.C., Y.Y., and H.D.; methodology, X.C., T.X., and S.H.; software, X.C.; validation, X.C., Y.Y., and H.D.; formal analysis, X.C.; resources, Y.Y. and H.D.; data curation, T.X. and S.H.; writing--original draft preparation, X.C.; writing--review and editing, T.X. and H.D.; visualization, T.X.; supervision, Y.Y. and H.D.; project administration, H.D. and S.H.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (52079101, 42471445) and the Open Funding of Zhejiang Key Laboratory of Ecological and Environmental Big Data (EEBD-2024-02).  
**Data Availability Statement:** Data will be made available on request.  
**Acknowledgments:** The authors thank the European Space Agency for providing ERA5 reanalysis data and the MetOp--A platform for providing GOME-2 SIF data. We appreciate the developers of the Global OCO-2 SIF (GOSIF) dataset and the MODIS land cover team for the MCD12Q1 product.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## Appendix A Regression Output Interpretation  
The output of a regression is the estimation of which values of \(c,\beta_{1},\ldots,\beta_{n}\) provide the best prediction of Y. For example, consider the following regression on HighRisk:  
\[\texttt{HighRisk}=c+\beta_{1}\texttt{Employed}+\beta_{2}\texttt{ Gender}+\beta_{3}\texttt{Employed}\cdot\texttt{Gender}\]  
This model will generate an output of the type reported in Table 1 (also fictitious for the purpose of this explanation).  
This output indicates that unemployed (and male) subjects have a 22% (\(\exp(0.2)=1.22\)) (as the outcome variable of this example is binary, a logistic regression should be used. For a _logit_ regression, the outcome is the log odd ratio of the observation (\(\log(p(HighRisk)/(1-p(HighRisk))\); therefore, regression coefficients should be exponentiated to reveal the change in the odds ratio caused by a unit variation, or change in category in that variable.) higher probability of being assigned to the category HighRisk than to the category LowRisk. Being female (_and_ employed) decreases chances by 70% (\(\exp(-1.2)=0.3\)). The coefficient for \(\texttt{Employed}=N\wedge\texttt{Gender}=F\) tells us that, however, being female _and_ unemployed increases the baseline risk three times (\(\exp(1.1)=3.0\)). The statistical significance of each coefficient serves as an indication to the analyst that an estimation of at least that magnitude is unlikely to be generated if no real effect exists: the smaller the probability of observing an estimation at least that large (i.e., the infamous \(p\)-value [52]), the highest the confidence one can have that, given the data, the effect exists in reality and is unlikely to be explainable by chance alone. Generally, the threshold for significance is set at \(p\leq 0.05\), but this may vary considerably depending on the domain of application. In the example above, the \(p\)-values suggest that all coefficients are statistically significant, with the exception of the variable Gender for which no strong evidence of significance emerges (\(p=0.1\)). If one would set the significance level at 0.05, one would not reject the null hypothesis that the variable Gender has no effect on the outcome variable.
# Dynamic Characteristics of the Bouc-Wen Nonlinear Isolation System
## 4 Analysis of the Influence Factors of the Nonlinear Isolation System
### Influence of the Damping Ratio of the Isolation Layer  
When other control parameters are kept constant, the influence of the damping ratio on the isolation system can not be underestimated. As shown in Figure 6, when the damping ratio of the isolation layer is relatively small, a large amplitude jump occurs in the range of the first-order resonance zone of each particle. With the increase in the damping ratio, the unstable solutions of each particle gradually disappear, and the system response gradually presents a stable state. As can be seen from Figure 6a-c, the response of the system does not decrease with the damping ratio, which gradually increases when it is larger than the critical value. Therefore, the reasonable selection and placement of damping materials and damping elements can make the material properties work better, and it is necessary to consider the optimal selection of different damping ratios for the target model in the actual design.  
Figure 6: _Cont._
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 2 Materials and Methods
### Path-Tracking Model Based on 4 WIS-4 WID Structure and Fuzzy Control
#### 2.2.3 Discussion of the Yaw Response  
Both lateral and heading deviations are amended by wheel steering. It is obvious that to achieve fast steering correction under the condition of significant variations, large steering angles of the wheels need to be adopted. As shown in Figure 5, the variables are defined in the same way as previously. The test prototype vehicle with 4 WIS-4 WID structure has all-wheel steering and therefore has more flexibility in the form of motion, such as crabbing, diagonal travel, and in situ steering. Values of deviation \(\mu\) of the prototype are composed of the lateral deviation \(d\) and heading deviation \(\theta\). We can see from the works of literature that no formula was given to represent the deviation of the prototype currently. Thus, in this paper, we adopt the \(\Delta\theta\), the angle between the longitudinal vehicle direction \(OB\), and the look-ahead straight-line \(OS\) to represent the deviation of the test prototype.  
\[\Delta\Theta=f(d,\theta)=\theta+\arctan(\frac{d}{LD}) \tag{3}\]  
In Equation (3), \(d\) and \(\theta\) denote the lateral and heading deviation of the prototype vehicle in meters and degrees, respectively, and \(LD\) indicates the look-ahead distance in meters.  
We can see that the larger the \(\Delta\theta\), that is, the greater the angle between the longitudinal direction and the aiming straight line, the greater the deviation of the prototype, the closer  
Figure 5: Schematic diagram of vehicle deviation based on the pure pursuit model.  
\(\alpha\) is to the longitudinal direction, and the value of \(\alpha\) is close to \(0^{\circ}\) or \(180^{\circ}\) according to the conclusion (1) in Section 2.2.2. Conversely, the smaller the \(\Delta\theta\) is, the closer the \(\alpha\) is to the axle direction; the angle is approximately \(90^{\circ}\).
# Towards Deep Integration of Electronics and Photonics
## 3 Building Blocks of Hybrid Integrated Circuits
The question of conversion efficiency in such devices becomes crucial for large circuits, where optical losses introduced by each device are summed up. The construction of large plasmonic circuits using the developed structural elements is still at the initial phase [19]. Chip-to-chip data transfer using SPP is demonstrated experimentally [76]. Four-channel array of plasmonic waveguides made of 2.5 cm long golden strips with a polymer cladding is used. A 10 Gbps transmission rate is reached (2.5 Gbps for each channel) at the wavelength of 1.3 \(\upmu\)m. A plasmon circuit for optical signal processing is experimentally demonstrated by [77]. The proposed structure represents a 200 nm large ensemble of metallic nanorods supporting evanescently coupled LSP. The device mixes two optical signals and provides a measure of their phase difference at the output. It is claimed that similar circuits can perform other mathematical operations on multiple signals. Figure 3: Various models of electro–optical modulators available in literature: (**a**) Silicon field effect plasmostor. Reprinted with permission from [64]; 2009 American Chemical Society. (**b**) Surface plasmon polariton absorption modulator. Reproduced from [57] with the permission. (**c**) Nanoscale conducting oxide plasmostor. Reprinted with permission from [66]; 2014 American Chemical Society. (**d**) Ultra-compact silicon nanophotonic modulator. Reproduced from [68] with the permission. (**e**) Ultra-compact electroabsorption modulator based on tunable ENZ waveguides. Reproduced from [PERSON] et al. [69] with the permission. (**f**) TCO electro–optical modulator on silicon platform. Reproduced from [60], with the permission of AIP Publishing. In all cases the direction of optical modes propagation is orthogonal to the plane of the figure. The construction of large plasmonic circuits using the developed structural elements is still in the initial phase [19]. Chip-to-chip data transfer using SPPs has been demonstrated experimentally [76]. A 4-channel array of plasmonic waveguides made of 2.5 cm long golden strips with a polymer cladding is used. 10 Gbps transmission rate is reached (2.5 Gbps for each channel) at the wavelength of 1.3 \(\upmu\)m. A plasmonic circuit for optical signal processing was experimentally demonstrated by [77]. The proposed structure represents an ensemble of metallic nanorods 200 nm in size supporting evanescently coupled LSPs. The device mixes two optical signals and provides a measure of their phase difference at the output.
# Optimizing the Scheduling of Teaching Activities in a Faculty
## 1 Introduction
### Expected Impact  
The anticipated benefits of implementing an automated timetable generation system within the faculty are manifold and significant. Automation will reduce the time required by administrative staff and faculty, allowing them to focus on more strategic activities. Optimizing timetables can increase efficiency in resource utilization, reduce scheduling conflicts, and streamline administrative processes. The system will efficiently allocate courses, faculty members, and rooms, mitigating inefficiencies and minimizing underutilization or overbooking.  
The implementation of an automated timetable generation system will also enhance the academic experience by promoting a better work-life balance for faculty members. This will be achieved by aligning their schedules with their preferences and workload constraints, preventing scheduling conflicts. The system is designed to adapt to growing faculties and new courses, accommodating evolving academic requirements while ensuring scalability.  
Additionally, the system will manage various scheduling formats, including semester-based or modular systems, and handle a range of academic activities such as lectures, seminars, laboratory sessions, and exams (with a focus on weekly scheduling). This flexibility ensures the system's applicability across different faculties within the institution.
## Discussion
### Relative Humidity as the Criterion for Assessing Drought  
Soil moisture supply and atmospheric water demand are the dominant factors restricting ecosystem productivity [61]. The atmospheric water vapor content controls vegetation transpiration by regulating stomatal conductance, and it also directly affects the photosynthetic process [62]. Since the amount of rainfall in Hainan Island is sufficient in the rainy season even if the rainfall is much lower than the climatic average, resulting in a meteorological drought, it does not generally develop into an ecological drought. The evapotranspiration in Hainan Island is high throughout the year; the atmospheric water demand directly determines the transpiration rate of plants, which is the most direct cause of plants' water loss and stomatal closure, leading to carbon starvation [63]. In addition, in the recent scientific debate on whether soil moisture or atmospheric water demand dominates terrestrial ecosystem drought, all agree that herbaceous functional types are more sensitive to soil moisture than woody plants, and low-latitude, wet regions are mainly affected by atmospheric water demand [44; 45; 46]. Therefore, for the present study, SRHI was selected as the index to measure drought events, and the EC-LUE, including VPD as the water-limiting factor, was selected as the research tool in order to draw more reliable conclusions than when using soil moisture or precipitation indexes. At the same time, SRHI has the same advantages as SPI which is widely used in drought monitoring because of its recognized advantages of simple calculation, flexible time scales [64]. In addition, drought assessment indexes utilizing rainfall, such as SPEI and SPI, may have some limitations in describing the detailed spatiotemporal distribution of drought due to the large temporal variability and spatial heterogeneity of precipitation data at ground-based observations [35]; while relative humidity has better performance in spatial continuity and temporal stability compared with precipitation, and earlier drought detection capacity [65].
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Informal Analysis
#### 7.2.5 Offline Password Guessing Attacks  
This work assumes that an attacker (\(A\)) extracts the values of \(\{PID_{D},K_{M}^{*},G_{M},r_{M}\}\) stored in the mobile device. Adversary \(A\) can attempt an offline password guessing attack to guess the real password (\(PW_{M}\)) of the mobile user, which is included in \(F_{M}=h(h(HID_{M}\parallel r_{M})\oplus h(HPPV_{M}\parallel r_{M}))\). However, it is computationally infeasible for \(A\) to guess the mobile user's identity, password, and random number simultaneously. Therefore, the proposed protocol is resistant to offline password guessing attacks.
# Energy-Efficient Device Discovery Mechanism for Device-to-Device Communications in 5G Networks +
###### Abstract  
The conventional direct device discovery scheme which uses the random access protocol and encounters contentions or collisions is highly energy and time-consuming. To reduce the energy consumption of user equipments (UEs), this work proposes a two-phase hybrid device discovery mechanism for device-to-device (D2D) communications. In the first phase, the evolved packet core (EPC) or base station (BS) uses the location information of UEs to judge whether two UEs are able to establish a D2D link. In the second phase, UEs use Wi-Fi Direct to discover their target UEs. The BS directly assigns UEs' states and allocates appropriate Wi-Fi channels to UEs. UEs no longer have to search or listen to all channels, thus reducing the discovery delay. The proposed mechanism saves cellular spectrum resources because it uses unlicensed bands for D2D discovery and communications. The performance of the proposed hybrid D2D discovery mechanism is also theoretically analyzed in this paper. Evaluation results show that the proposed D2D discovery mechanism has better performance in terms of energy consumption, discovery delay and discovery success rate, compared with the conventional direct D2D discovery scheme, especially in the network scenarios with smaller cells such as 5G networks. Additionally, the analytic results coincide with simulation results, demonstrating that our theoretic analysis is accurate.  
device-to-device (D2D) communications; device discovery; Internet of Things (IoTs); Wi-Fi Direct; 5G networks +
Footnote †: This paper is an extended version of our paper published in 2018 IEEE Tenth International Conference on Ubiquitous and Future Networks (ICUFN 2018), Prague, Czech Republic, 3-6 July 2018; pp. 241-243.
# Hyperlipidemic Rabbit Models for Anti-Atherosclerotic Drug Development
## 3 Hyperlipidemic Rabbit Models for the Development of Drugs
### Statins  
Statins, 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase inhibitors, have been widely used by hyperlipidemic patients. Statins have a similar structure to HMG-CoA, a precursor of cholesterol, and inhibit HMG-CoA reductase, the rate-limiting enzyme of cholesterol biosynthesis, which is the major pathway for its lipid-lowering functions [57]. In addition, statins indirectly up-regulate LDLR activity and reduce the secretion of VLDL into circulation [57]. The lipid-lowering and atheoprotective effects of statins have been verified by many studies using different hypercholesterolic rabbits (Table 1).  
Regardless of different types of statins, doses, and durations, all studies showed that statins can reduce plasma TC levels in hypercholesterolic rabbits, while some studies failed to demonstrate anti-atherogenic effects. [PERSON] et al. reported that atorvastatin showed anti-atherogenic effects in an iliac-femoral artery microscopic lesion but did not reach a significant difference in the thoracic aorta gross lesion [58]. Pravastatin significantly reduced plasma TC levels and coronary atherosclerotic lesions in WHHL rabbits but had no effects on aortic atherosclerotic lesions [59]. These studies indicate that the efficacy of statins on atherosclerosis is more complicated than their lipid-lowering effect, possibly due to the pluripotent effect of statins.
## 3 Modelling
### Rider Model  
In contrast to cars, the rider's weight strongly influences the total motorcycle weight in riding conditions. Moreover, the rider has some relative motions with respect to the motorcycle. One can therefore expect the rider to have a great influence on motorcycle stability. This is indeed the case, as shown by both simulation [7,40] and experimental [41] results. In this work we only focus on the rider's passive motions. This is reasonable, as the eigenfrequencies of wobble and weave are too high for the rider to actively counteract them; therefore, it can be assumed that the rider behaves as a passive body when the motorcycle experiences wobble or weave. Under this assumption, different rider models can be developed. The most simple model has been adopted by several works [12, 26, 27, 28] and includes only one DoF: the rider's upper body rotation about an axis parallel to the frame x axis. A different one-DoF model [42] reproduces the rider's rotation about the vertical axis (parallel to the frame z axis); moreover, the connection between the upper body and handlebar is included with a parallel rotational spring-damper element. A 2-DoF model can then be obtained by combining the two previously described models [40]. Finally, the lateral displacement (frame y axis) of the rider's hip can be added, thereby obtaining a 3-DoF rider model [7]. An interesting observation is made by [PERSON] [13] (p. 326) about the influence of the rider's parameters: he reports that the rider's upper body parameters mainly influence the weave mode, while the lower body parameters have a greater effect on wobble. The literature also offers several examples of more complex rider models containing up to 28 DoF [43]. In this case, the rider model was used to faithfully evaluate the motorcycle race performances, so that the whole rider motion was needed, including his control action on the handlebars and his lean-in strategy. As introduced at the beginning of this paragraph, the current work pursues a very different objective, i.e., the analysis of the motorcycle eigenmodes, so that such a complex rider model is not necessary and would only complicate the interpretation of the results.  
The rider model used in the present work is based on that presented in [7]. The rider is composed by two masses, representing the upper and lower body. The DoF are:  
* \(\alpha_{\rm ri}\): the rider's upper body lean.
* \(\gamma_{\rm ri}\): the rider's upper body yaw.
* \(y_{\rm ri}\): the rider's hip (lower body) lateral motion.  
As previously said, the connection with the handlebar is modelled by a rotational spring-damper element which reacts to the relative rotation between the upper body and handlebar, and applies equal and opposite moments for lower body and upper body, and frame and steering head, respectively.
## 2 Commercial and Recreational Complex Building Projects (CRCBPs)  
The increase of the urban population in large metropolises, followed by the increase in demand for better building infrastructure on a global scale, paints a positive outlook for improving the construction market's situation in developing countries [18]. Over the past two decades, the construction of CRCBPs, to develop the welfare of citizens and increase socio-economic development indicators and subsequent sustainable development in Iran, has been greatly increased. Stemming from that, realizing projects faster and cheaper has been prioritized by policymakers and, as a consequence, the amount of construction has exceeded its quality [19].  
Projects are divided into different types depending on the purpose and type of expected operation. In the field of construction projects, we are faced with different types that the 2015 United Nations' product classification (see Section 5) categorizes as Constructions Buildings and Civil Engineering Works [20]. A subclass of Construction Buildings is Commercial Buildings and among them there are Commercial and Recreational Complex Buildings. CRCBPs comprise a series of interconnected sidewalk shops designed and constructed in conjunction with entertainment, residential, office, hotel, restaurant, and cinema spaces [5]. In order to achieve economic growth, developing countries are forced to increase investment in infrastructures [21]. In fact, development of an infrastructure, such as construction of CRCBPs, can positively affect economic development [22]. Someexamples of CRCBPs include Siam Paragon in Bangkok, Berjaya Times in Kuala Lumpur, Jewelry in Istanbul, West Edmonton in Edmonton, Canada, Dubai Mall in the UAE, Aventra Mall in Miami, and Harrods in London.  
However, some CRCBPs construction projects have been unsuccessful due to various marketing, financial and investment issues. A clear example of such projects is the CRCBP of Arge Jahannam in Isfahan, which has failed due to a lack of detailed market studies and non-compliance with social rules and conditions [23]. In general, the existence of risk and uncertainty in the project reduces the accuracy and proper estimation of objectives and the efficiency of the project itself; in sum, the need to recognize and manage the risk of full CRCBPs is being increasingly perceived.