esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Based on GOME-2 \(SIF\) observations, we inspected variations in \(SIF_{yield}\) and its temperature sensitivity along the altitude gradient in Tibetan meadows. First, accompanied by lower air temperature and higher light intensity, \(SIF_{yield}\) was found to increase with altitude on summer days, and increased \(SIF_{yield}\) during seasonal cycles usually coincides with higher air temperatures. Second, no significant altitudinal differences were found in seasonal \(SIF_{yield}\) variations with DOY, whereas the increasing trend of its temperature sensitivity along the elevation gradient was fairly noticeable. Third, a relatively stable and highly negative correlation between \(SIF_{yield}\) and PAR was found at the interannual scale, which is consistent with intensified light stress reported in previous studies. Finally, both interannual and seasonal temperature sensitivity increased at high altitudes, the former faster than the latter along the altitude gradient, which even exceeded the seasonal temperature sensitivity above 4700 m. The sensitive response to the changing environment in high-altitude regions implies an urgent need to pay greater attention to alpine vegetation-climate interactions, and the patterns shown in this study may help to advance the understanding of photosynthesis-related physiological activities there.  
Conceptualization, L.L. and R.C.; methodology, L.L. and R.C.; software, R.C.; writing--original draft preparation, R.C.; writing--review and editing, L.L. and X.L.; visualization, R.C.; project administration, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (2017 YFA0603001), the National Natural Science Foundation of China (41825002), and the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2019-1)  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: The SIF dataset used in this study comes from the Global degradation corrected 0.05 degree GOME-2 SIF datasets (derived from PK datasets) [50] shared in Zenodo. [[http://doi.org/10.5281/zenodo.4050960](http://doi.org/10.5281/zenodo.4050960)]([http://doi.org/10.5281/zenodo.4050960](http://doi.org/10.5281/zenodo.4050960)) (accessed on 26 September 2020) 8-day instrument degradation corrected 0.05 degree GOME-2 SIF datasets on a global scale from 2010 to 2018. PK dataset from the spatially downscaled sun-induced fluorescence global product proposed by [PERSON] in 2020 is corrected based on a pseudo-invariant method and then masked. Mean value composite method is used to produce monthly data.
## 4 Analytical Results
### Whole-Rock Sr-Nd Isotopes  
Twenty representative samples (sixteen granitoids and four enclaves) were analyzed for Sr and Nd isotopic compositions in this study; they are listed in Table 2.  
Granitoids of the Dazeshan pluton show \({}^{87}\)Sr/\({}^{86}\)Sr ratios of 0.710638-0.710876 and \({}^{143}\)Nd/\({}^{144}\)Nd values of 0.511434-0.511578, whereas their enclaves have slightly higher \({}^{87}\)Sr/\({}^{86}\)Sr ratios of 0.711018-0.712482 and \({}^{143}\)Nd/\({}^{144}\)Nd ratios of 0.511439-0.511633.  
The initial \({}^{87}\)Sr/\({}^{86}\)Sr ratios (\({}^{87}\)Sr/\({}^{86}\)Sr)i and \(\varepsilon\)Nd(t) values were obtained at 120 Ma according to the zircon LA-ICP-MS U-Pb dating for both granitoids and their enclaves in this study. The data are plotted in the diagram of \(\varepsilon\)Nd(t) vs. (\({}^{87}\)Sr/\({}^{86}\)Sr)i (Figure 8). Granitoids show a narrow range of (\({}^{87}\)Sr/\({}^{86}\)Sr)i ratios of 0.709361-0.709858, and strongly negative \(\varepsilon\)Nd(t) values of -21.6 to -18.8, with the two-stage Nd model ages (TDM2) varying between 2449 and 2679 Ma, consistent with those of their enclaves, with (\({}^{87}\)Sr/\({}^{86}\)Sr)i = 0.709523-0.709726, \(\varepsilon\)Nd(t) = -21.8 to -17.8 and TDM2 = 2365-2691 Ma.  
Figure 8: (\({}^{87}\)Sr/\({}^{86}\)Sr)i vs. \(\varepsilon\)Nd(t) diagram for Dazeshan granitoids and their enclaves. Data sources: MORB [68]; Mafic dikes derived from lithospheric mantle (132–120 Ma) from [28; 47; 69]; Mafic rocks derived from asthenospheric mantle (107–78 Ma) from [34; 70]; Neoarchaean-Neoproterozoico: basement rocks in the Jiaobei Terrane from [71]; Neoproterozoic basement rocks in the Sulu Terrane from [38]; Triassic Shidao alkaline complex from [72]. CHUR: chondritic uniform reservoir; MORB: middle ocean ridge basalt; DM: depleted mantle.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The SAC Algorithm  
Our proposed routing algorithm operates in five steps to elect the best cluster head. Each step is then repeated after a round of \(T\) seconds. The five steps are described below.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 4 Discussions and Conclusions  
The solid attitude and divert propulsion system (SADPS) is characterized by its time-variant, strongly coupled, highly nonlinear, and complex nature. To tackle these challenges, we integrated reinforcement learning (RL) for control system design, adaptive parameter adjustment, and continuous data-driven learning. This study provides several key findings and insights into the effective management of pressure and thrust through a unified RL control architecture.
## 3 Results
### Qualitative Synthesis
#### 3.1.4 Other Conditions  
The assessment of other pathological conditions is also reported on the literature, yet with less broadening. [PERSON] et al. diagnosed carpal tunnel syndrome with artificial neural network classifiers and IR data with an ACC of 82.2% [60]. The authors identified relevant segments of the hand for improved classification, having tested this finding in a larger data set further along. Nonetheless, the classifier performance decreased, making the authors recommend their methodology only for the screening of severe cases [61]. [PERSON] et al. focused their research on dry eye disease, first for treatment monitoring (k-NN: ACC = 99.88%; SN = 99.7%; SP = 100%) and then for diagnosis (k-NN: ACC = 99.8%; SN = 99.8%; SP = 99.8%) [62; 63]. The presence of rheumatoid arthritis disease and particular areas of interest for improved diagnosis were identified by [PERSON] et al. (Decision Trees: SN = 96%; SP = 92%) [64], while [PERSON] et al. used k-means methods to segment hands, aiding in the evaluation of this health problem [7]. Other applications of infrared thermography and ML algorithms include the evaluation of whiplash injury extent [65], back pain [66], thyroid tumors [67], breathing [68], drunkenness state [69], exercise-induced fatigue [70], hypertension [71], schizophrenia [72], burn wounds [73], facial recognition [74], cardiovascular disease [75], hemodynamic shock [76] and stress recognition [77], with the preferred classifiers being based on ANN [65; 68; 70; 71; 74; 77], SVM [66; 69; 70; 72], Decision Trees [67] and Naive-Bayes [75].
# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
## 4 The Arrowhead Framework
Apart for the aforementioned protocols, the Arrowhead Framework comprises adapters to interoperate with a number of other frameworks such as Bassissystem Industrie 4.0 (BaSys) [32], which is an SOA middleware for the dynamic management and reconfiguration for industrial-scale production facilities, FIWARE [33], widely adopted in Europe to build smart solutions, and IEC61499 [34], which is an international standard to define function blocks for industrial process measurement and control systems. Thus, by choosing to integrate a new system into an Arrowhead local cloud, it is possible to consume and produce services from other platforms. The Arrowhead approach can provide solid benefits to maintenance applications since it can provide seamless security, good support for dynamic use cases, and for remediation, in which contexts where the involved systems can evolve over short time frames, and cannot always be designed prior to creation. With regard to security, in an Arrowhead platform, a system can be authenticated and authorized to interact only with required systems and services and for the required time span. This allows for more stringent security when for example a system is from a third-party stakeholder, or when a system is executed on a mobile device that was physically part of less secure environments, or when a system pertains to a different OSA-CBM layer of the one it targets, and then it must be granted a short-term authorization to perform its functions. Dynamicity and churning can be a problem in realistic scenarios since maintenance subsystems can be replaced, and then they must be able to register themselves, discover and be discovered, and in general, substitute previous systems. The SOA tenants that are the base of the Arrowhead approach allow developing systems independently from each other, with the reasonable confidence that they will interoperate even when being shipped by different stakeholders. Remotization is a novelty for modern maintenance solutions, but it became a requirement for most advanced solutions. In fact, it is hardly possible to co-locate a computational cloud with factories, and thus it is important to have access to a secure inter-cloud to connect different OSA-CBM layers and deployment tiers. Arrowhead local clouds are inherently autonomous but can create secure tunnels between each other to transport data and provide capabilities between different tiers such as factory, platform, and business [12]. Another advantage of adhering to the Arrowhead approach is related to its open source nature, which guarantees a lower entrance barrier for SMEs that want to employ its technologies in their solutions. Finally, Arrowhead is part of the Eclipse project, which ensures a proper level of stability and maturity.
## 8 Conclusions  
We have developed, deployed and demonstrated a complete, scalable, end-to-end image-based sensor application for environmental monitoring to unobtrusively observe biological phenomena. Our deployment has shown that our system design has met most of the application requirements: sufficient image transport rate for our biological study, scalability exceeding that of wired cameras, ease of use andflexibility of deployment. Tenet software made it easy to monitor, modify and reconfigure the application behavior from a remote location without re-programming the motes. Rate-controlled reliable transport along with the tiered architecture resulted in an image retrieval rate of approximately 3\(\sim\)4 images per node per hour. Future deployments can further improve this by using a better compression algorithm or newer-generation radios. Finally, our system proved to be scalable. Multihop tiered routing of our sensor network system enabled a larger network with greater spatial reach.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 3 Materials and Methods
### ETC Gantry Data Completion Method Based on Improved Tensor Decomposition
#### 3.2.7. Solution to the Objective Function  
1. Initial Solution:  
Taking into account the properties of adjacent tensor blocks, the initial solution for the objective function is estimated using the tensor core and factor matrices, represented as \(G_{0},U_{0}^{(1)},U_{0}^{(2)},\text{ and }U_{0}^{(3)}\).  
2. Optimization of Solutions:  
The solution is refined through iterative computation of the approximate gradients for each variable. This iterative process ensures the optimal update of the objective function, guided by the following update rule:  
\[\begin{array}{ll}G_{k}=&argmin\bigg{\langle}\
abla_{G}f\bigg{(}G_{k-1}, \widetilde{U}\bigg{)},G-G_{k-1}\bigg{\rangle}+\frac{L_{G}}{2}\|G-G_{k-1}\|_{F}^ {2}+\lambda_{i}\|G\|_{1}\\ &\quad=max\bigg{|}0,G_{k-1}-\frac{1}{L_{G}}\
abla_{G}f\bigg{(}G_{k-1}, \widetilde{U}\bigg{)}-\frac{\lambda_{G}}{L_{G}}\bigg{|}\\ \\ U_{k}^{(i)}=&argmin\bigg{\langle}\
abla_{U^{(i)}}f\bigg{(}\widetilde{G}, \widetilde{U}^{(j\
eq i)},U^{(i)}\bigg{)},U-U_{k-1}^{(i)}\bigg{\rangle}+\frac{ L_{U^{(i)}}}{2}\bigg{\|}U-U_{k-1}^{(i)}\bigg{\|}_{F}^{2}\\ &\quad\quad+\lambda_{i}\bigg{\|}U^{(i)}\bigg{\|}_{1}=max\bigg{|}0,U_{k-1}^{(i)} -\frac{1}{L_{U^{(i)}}}\
abla_{U^{(i)}}f\bigg{(}\widetilde{G},\widetilde{U}^{( j\
eq i)},U^{(i)}\bigg{)}-\frac{\lambda_{i}}{L_{G}}\bigg{|}\end{array} \tag{23}\]  
In the above equations, \(\
abla_{G}f\bigg{(}G_{k-1},\widetilde{U}\bigg{)}\) and \(\
abla_{U^{(i)}}f\bigg{(}\widetilde{G},\widetilde{U}^{(j\
eq i)},U^{(i)}\bigg{)}\) are the gradients of \(f\Big{(}G,U^{(1)},U^{(2)},U^{(3)}\Big{)}\) to \(G\) and \(U^{(i)}\), respectively, and \(L_{G}\) and \(L_{U^{(i)}}\) are the Lipschitzconstants of the corresponding gradient function, respectively, satisfying the Lipschitz conditions.  
\[\left\|f\left(G_{k-1}(1),\widetilde{U}\right)-f\left(G_{k-1}(2), \widetilde{U}\right)\right\|\leq L_{G}\|G(1)-G(2)\|_{F},\forall G(1),G(2) \tag{25}\] \[\left\|f\left(\widetilde{G},\widetilde{U}^{(j\
eq i)},U_{k-1}^{ (i1)}\right)-f\left(\widetilde{\widetilde{G}},\widetilde{U}^{(j\
eq i)},U_{k- 1}^{(i2)}\right)\right\|\leq L_{U^{(i)}}\left\|U^{(i1)}-U^{(i2)}\right\|_{F}, \forall U^{(i1)},U^{(i2)} \tag{26}\]  
The flow of the ETC gantry data completion calculation, based on tensor decomposition, is presented in Table 4.
# Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
###### Abstract  
Wireless networks are vulnerable to jamming attacks. Jamming in wireless communication becomes a major research problem due to ease in Unmanned Aerial Vehicle (UAV) launching and blocking of communication channels. Jamming is a subset of Denial of Service Attack (DoS) and an intentional interference where the malicious node disrupts the wireless communication by increasing the noise at the receiver node through transmission interference signal towards the target channel. In this work, the considered jammer is a UAV hovering around the target area to block the communication channel between two transceivers. We proposed a three-dimensional (3-D) UAV jamming localization scheme to track and detect the jammer position at each time step by employing a single boundary node observer. For this purpose, we developed two distributed Extended Kalman Filter (EKF) based schemes: (1) the Distributed EKF (DEKF) scheme using the information of the received power from the jammer at a single nearby boundary node only and (2) Distance Ratio aided Distributed EKF (DEKF-DR) based scheme utilizing an edge node in addition to a single boundary node. Extensive simulations are conducted in order to evaluate the performance of the proposed distributed algorithms for a 3-D trajectory and compared with that of the conventional Centralized EKF (EKF-Centr) based method (which is also modified for the 3-D scenario). The results show the clear supremacy of the proposed distributed algorithms with much lesser complexity in contrast to the conventional EKF-Centr technique.  
Keywords:jamming attacks; Unmanned Aerial Vehicle (UAV); localization; Extended Kalman Filter (EKF) +
Footnote †: journal: Sensors
# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 2 Experimental Materials and Methods  
### Sample Preparation  
Soil was collected from the Jilin University Experimental Farmland (at 43\({}^{\circ}\)51\({}^{\prime}\)41\({}^{\prime\prime}\) N and 125\({}^{\circ}\)20\({}^{\prime}\)27\({}^{\prime\prime}\) E) and was confirmed to be unpolluted before sampling. Gas chromatography-mass spectrometry was used to determine that the collected samples were not contaminated with pesticides. Twenty sampling points were randomly selected in the sampling field, stones and weeds were removed from the soil surface, and the soil was collected from a depth of 0-20 cm. All soils were placed at room temperature (24 \({}^{\circ}\)C) for natural air drying, were evenly mixed, sieved through a 1 mm sieve, and evenly mixed again for later use. Taking the representativeness and severity of the pesticide pollution to be the criteria, we comprehensively considered the types of pesticides, the amount of pesticides used, the types of pesticide applications, and the pollution hazards. Six pesticides were selected, namely Dithane (65%, Zhongxun, Jiangxi China), Mancozeb (80%, Guoguang, Sichuang China), Chloryrifos (40%, Xinnong, Zhejiang China), Cyfluthrin (5.7%, Weierda, Zhejiang China), Glyphosate (41%, Weiyuan Tiansheng, Zhejiang China), and Deltamethrin (25 g/L, Bayer, Leverkusen Germany). The six selected pesticides involved organophosphorus, organosulfur, and pyrethroid, which were widely used and severely harmful pesticides. And, they contain herbicides, insecticides, and fungicides in their type of application, and vary in their remediation measures. The pesticide solution was diluted according to the recommended dosage in the pesticide user manual (Figure 1a).  
In order to obtain a sufficient number of soil samples contaminated with various types of pesticides, it is necessary to manually prepare the soil to be tested. The use of leaching experiments to prepare soil pollution samples is more in line with the diffusion law of pesticides in natural soil. The preparation of 7 soil-leaching devices (pollution-free, Dithane, Mancozeb, Chloryrifos, Flumethrin, Glyphosate, and Deltamethrin) was conducted. A layer of glass wool and quartz sand were placed on the bottom of the leaching column, 40 cm of soil was poured into the leaching column, and compacted and wet (Figure 1c). One mL of six diluted pesticide solutions were and evenly dropped onto the soil surface of the leaching column. The distilled water leaching column was used as a pollution-free control (Figure 1b). Then, the leaching of soil by rainwater was simulated using an 800 mL  
Figure 1: Experimental soil preparation of the (**a**) pesticide dilution, (**b**) drip irrigation pesticides, and (**c**) soil leaching.  
CaCl\({}_{2}\) solution of 0.01 mol/L. After leaching, the soil was removed from the leaching column and stored in a plastic bag [28].
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 2 Algorithms of Pedestrian Detection from Images  
To analyze groups of pedestrians, it is first necessary to have a means of gathering the required data. Data can be represented in many forms, but the most basic and efficient abstraction may involve representing people as points with their locations and time of detection. Modern devices can provide accurate time readings. The remaining issue involves determining the precise locations of pedestrians at any given moment. To detect the positions of people in a video frame, we employ methods of computer vision, namely object detection. A suitable algorithm, in our case YOLO, is used to perform detection on an image frame. The algorithm then proceeds to mark rectangular areas containing each of the detected objects. Furthermore, these objects can be filtered based on their class, which makes it possible to display people and disregard all of the remaining objects.
## 2 Results and Discussion
### Photocatalytic Performance of TiO\({}_{2}\), TC and PPTC
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Photocatalyst** & **Organic Pollutant** & **Adsorption Rate/\% a** & **Degradation Rate/\% b** \\ \hline
1 & TiO\({}_{2}\) & DMF & 6.5 & 52.8 \\
2 & TC & DMF & 21.2 & 65.4 \\
3 & PPTC & DMF & 26.0 & 73.7 \\
4 & TiO\({}_{2}\) & Methyl orange & 3.6 & 55.5 \\
5 & TC & Methyl orange & 29.7 & 91.6 \\
6 & PPTC & Methyl orange & 53.9 & 97.8 \\
7 & TiO\({}_{2}\) & Methylene blue & 40.0 & 90.9 \\
8 & TC & Methylene blue & 66.3 & 94.3 \\
9 & PPTC & Methylene blue & 94.9 & 100 \\
10 & TiO\({}_{2}\) & Phenol & 32.1 & 83.6 \\
11 & TC & Phenol & 46.3 & 90.7 \\
12 & PPTC & Phenol & 58.7 & 98.6 \\ \hline \end{tabular} Note: “a” represents the adsorption of the organic compounds by photocatalysts in the dark environment; “b” represents the photocatalytic degradation rate of the organic compounds catalyzed by the photocatalysts under the irradiation of Xe lamp light.  
\end{table} 
Table 3: Photocatalytic degradation rates of several organic pollutants catalyzed by TiO\({}_{2}\), TC, and PPTC in the dark and under the irradiation of Xe lamp light. Figure 10: H\({}_{2}\)-temperature-programmed reduction (TPR) curves of TiO\({}_{2}\), TC, and PPTC. Figure 9: ESR spectra of (**A**) superoxide free radical (O\({}_{2}\)), (**B**) singlet oxygen (\({}^{1}\)O\({}_{2}\)), and (**C**) hydroxyl radical (-OH) in TiO\({}_{2}\), TC, and PPTC.
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 5 Conclusions  
In this study, we intensively analysed the performance of SC-FDMA, FBMC, and LP-FBMC via field test using SDR devices in the real indoor wireless environments. As for indoor wireless channel, we tested the various sub-3.5 GHz bands when considering the different indoor wireless band allocations in the different countries. Basically, the benefits of LP-FBMC over FBMC and SC-FDMA, i.e., low PAPR, and the robustness against inter-user synchronization errors were confirmed in our field test. For instance, LP-FBMC achieves a BER of less than 1/10 of that of the FBMC in a practical indoor distance between the BS and user. In addition, no matter how large inter-user timing/frequency offset ranges are given in the considered region, LP-FBMC achieves the identical BER performance to that of zero offset case, whereas SC-FDMA severely degrades, even with slight offsets. Summing up the major observations of our field test, the BER performance gain of LP-FBMC over SC-FDMA is more increased for lower carrier frequency, larger subcarrier spacing, and larger timing/frequency offset among the users. In light of these results, we expect that LP-FBMC is a promising waveform solution for beyond 5G uplink waveforms in indoor sub-3.5 GHz Bands.  
Writing--review and editing, K.C.; writing--original draft preparation, D.N.; Implementation and field test, S.J.; project administration, W.-G.S.; resources, W.-G.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (I Information Technology Research Center) support program (IITP-2020-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation. This research was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1010370).  
The authors declare no conflict of interest.