esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art Internet of Things in Protected Agriculture
## 6 Conclusions  
In this survey, we present a comprehensive review of the state-of-the-art in IoT deployment for protected agriculture applications. First, a simple review of previous IoT structure was given. Secondly, the basic IoT architectures in protected agriculture were introduced. Next, discussions of sensor, data communication, cloud computing, edge computing, ML and other vital IoT technologies in protected agriculture are elaborated. Then, we highlight various IoT applications and their potential to solve various farming problems in protected agriculture. By classifying the research and deployment literature on IoT in protected agriculture, three important application fields were given: plant management, animal farming and agri-food supply chain traceability. Finally, a detailed analyses of IoT research challenges and future prospects were outlined.  
The survey of the existing works directs us concluding remarks. The future prospects of IoT in protected agriculture are expectant, but the challenges mentioned above must handle. In order to cope with its complex and changing agricultural environment, the hardware devices must be fully upgraded to further enhance their universality, reliability, expansibility, endurance and intelligence level, while reducing costs and operative difficulty. Secondly, the local network must be protected from interference from other networks. In addition, the interoperability, filtering and semantic annotation of data generated by the various devices of IoT must be realized to a certain extent. Only in this way can we optimize the Big Data decision support model by using a large amount of heterogeneous data. Security, anonymity and control over the access rights on the information is vital for such a system to be adopted. Finally, the impact of IoT on the agroecological environment and social economy should be considered in the application of protected agriculture, to realize the sustainable development of agricultural environment as quick as possible.  
The collection, classification and arrangement of references were done by [PERSON], [PERSON] The Writing and revising of this article were done by [PERSON] ([PERSON]), [PERSON], [PERSON] ([PERSON]), [PERSON] The Drawing of tables and pictures were done by [PERSON] The work presented in the paper was conceived within research projects led by [PERSON] ([PERSON]), [PERSON].  
This article was supported by National Natural Science Foundation of China (No. 31772068, 31701681, 31872909), Special Project of Independent Innovation of Shandong Province (2018 CXGC0214), Shandong Provincial Natural Science Foundation (ZR2016 CM29, ZR2017 BC001, ZR2018 ZC0126, ZR2018 BC055), Key Research and Invention of Shandong Province (2017 GNC10119), Key Innovative project for 2017 Major Agriculture Application Technology of Shandong Province.
## 5 Conclusions  
This study focused on investigating the feasibility and potential of both the GRNN and ANFIS models for modeling and forecasting daily carbon fluxes in three different forest ecosystems. Moreover, their predictive accuracy was compared with traditional ANN, SVM and MLR models. All the models were evaluated according to several performance indicators (\(R^{2}\), NSE, Bias and RMSE). It has been found that all the machine learning models proposed in this study, including ANN, GRNN, ANFIS and SVM, were capable of accounting for the most variance in each carbon flux at both daily and hourly time scales in the three stands, especially for both GPP and \(R\). These modern models consistently performed better than conventional MLR model for both daily and hourly carbon flux estimates in terms of \(R^{2}\), NSE, Bias and RMSE. Therefore, these advanced models were able to elucidate precisely non-linear processes of controlling the exchanges of the carbon fluxes between land and the atmosphere at the ecosystem level. Moreover, among all the applied models, the ANFIS and ANN models provided similar estimates of carbon fluxes and slightly outperformed the GRNN and SVM models. In practical terms, however, the ANFIS and GRNN models are more robust and flexible, and have less parameters needed for selection and optimization, when compared with two common ANN and SVM models. Accordingly, both the ANFIS and GRNN models are valuable tools for estimating forest carbon fluxes and interpolating the missing carbon flux data during the long-term EC measurements.  
Considering the findings above, our study has conclusively demonstrated for the first time that both the ANFIS and GRNN models can effectively reproduce carbon fluxes at the ecosystem level. Consequently, when upscaling the carbon and water fluxes from ecosystem to regional or global scale, these promising tools should be taken into consideration, which is comparatively important for the scientific community to determine the global carbon and water budgets and offer reliable information for policy makers responding to global climate change.  
**Acknowledgments:** The work was financially supported by the Natural Science Fund of China (No. 41672324, No. 41430317 and No. 41402291), the National Science and Technology Major Projects (No. 2016 ZX05044-002), and the Priority Academic Program Development of Jiangsu Higher Education Institutions. The present study used the EC measurements acquired and shared by the Fluxnet-Canada Research Network and FLUXNET community. The authors acknowledge all the project investigators and their staff and graduate students for providing the flux data. The authors also gratefully acknowledge three anonymous reviewers for their constructive comments.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, analyzed the data and wrote the manuscript; [PERSON] and [PERSON] provided important suggestions for the final manuscript.  
The authors declare no conflict of interest.
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 6 Results and Discussion
### Tracking of Zandvoort  
The Zandvoort track as shown in Figure 15a features a diverse range of corners, from tight hairpin turns to banked corners. This variety challenges the lateral control system of autonomous vehicles to adapt to different levels of curvature and adjust steering angles accordingly. This is crucial for testing the vehicle's ability to handle real-world driving scenarios with diverse cornering demands. In Figure 15 depicted below, the lateral controller's performance on the Zandvoort racetrack is showcased. Figure 15a illustrates the vehicle's tracked trajectory, closely mirroring the intended path, signifying the controller's precision. Figure 15b displays the cross-track error (CTE) over time, with the magnitude of deviations within a relatively narrow band \(\pm\)0.4 m, indicating consistent tracking. The fluctuations in CTE suggest the controller's continuous adjustments in response to the  
Figure 14: **(a)** Tracked speed vs. arc length. **(b)** Steering angle vs. time plot for the DLC trajectory tracking.  
Figure 13: (a) Tracked trajectory of a dual-lane-change (DLC) maneuver. **(b)** CTE vs. time plot.  
racetrack's complex turns and straights, demonstrating its capability to handle both high-speed maneuvers and maintain the racing line with competence.  
To gain insights into the controller's behavior during the Zandvoort racetrack simulation, we examine steering wheel angle versus time plots (Figure 16). These plots offer a dynamic view of the controller's responses to the track's twists and turns. The steering angle plot in Figure 16b exhibits a pattern of frequent adjustments, with some sharp spikes suggesting rapid steering corrections. These corrections correspond to the racetrack's complex turns, demonstrating the controller's active response to maintain the racing line. The degree of smoothness in the steering control output varies, with periods of gradually varying steering input interspersed with moments of aggressive steering to navigate the circuit.
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 3 Proposed Method
### Improved User Characteristics Model  
In real life, people living in the same area tend to have similar lifestyle and eating habits, while people in different areas may show greater differences. Similarly, if two people's characteristics are more similar, such as gender, age, and occupation, then their interests are more likely to be similar. For example, there will be more common topics between students, but students and teachers may have different interests due to their different work and social experiences. Therefore, it is reasonable to recommend a user preference item to other users similar to their characteristics when making recommendations. There are some improved collaborative filtering algorithms, which have used the user's characteristics information. However, there are still some problems in the existing method, for example, the similarity of age and occupation is calculated in a crude way, which makes the recommendation results have some limitations [29].  
To deal with these problems above, an improved user characteristics similarity model is set up in this paper, which is based on the fuzzy membership method. The proposed user characteristics similarity model can alleviate the cold start problem of the recommendation  
that the gender of user \(u\) is \(G_{u}\), and the gender of user \(v\) is \(G_{v}\), the gender similarity of users is calculated as:  
\[Sim_{\text{Gender}}(u,v)=\left\{\begin{array}{ll}0,&G_{u}\
eq G_{v}\\ \\ 1,&G_{u}=G_{v}\end{array}\right. \tag{8}\]  
(4) User characteristics similarity  
Combining the mentioned characteristics similarity of users in different dimensions based on the age, gender, and occupation, the final characteristics similarity of users is calculated as:  
\[Sim_{\text{Character}}(u,v)=\alpha Sim_{\text{Age}}(u,v)+\beta Sim_{\text{ Gender}}(u,v)+\delta Sim_{\text{Ocp}}(u,v) \tag{9}\]  
where \(\alpha+\beta+\delta=1\) and \(\alpha,\beta,\delta\in(0,1)\) are the similarity weights for the user's age, gender, and occupation. For different recommender systems, these weights can be adjusted dynamically to achieve the optimal recommender effect.
## 3 Results and Discussion
### Applications of SPME-IMS System
#### 3.2.1 Reduced Mobility Values of the CAWs  
In IMS, ion drift times are often reported as reduced mobility constants (K0) for identification purposes. This metric provides a basis for comparison of results by correcting for varying environmental and instrumental experimental conditions. In our work, the experimentally determined K0 values for each CWA which were obtained in positive mode for each method (including IMS, SPME-IMS and SPME-IMS in water analysis) were consistently matched. The typical product ions for the analytes were their protonated monomer (MH\({}^{+}\)) and dimer (M\({}_{2}\)H\({}^{+}\)) forms and the others were reactant ion peak responses. The K0 values of the typical product ions through SPME-IMS in water analysis are listed in Table 1 and the corresponding spectra are presented in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Analyytes**} & \multicolumn{2}{c}{**Typical Product Ions**} \\ \cline{2-3}  & **MH\({}^{+}\)** & **M\({}_{2}\)H\({}^{+}\)** \\ \hline TMP & 1.78 & 1.34 \\ TEP & 1.58 & 1.10 \\ TPP & 1.36 & 0.93 \\ Sarin & 1.68 & 1.28 \\ Soman & 1.81 & 1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The K0 values for each CWA obtained though SPME-IMS in water analysis.  
Figure 3: The typical spectra for each CWA obtained though SPME-IMS in water analysis.  
**Figure 3.**_Cont._
# The Local Integrity Approach for Urban Contexts:
## 4 Proof-of-Concept and Demonstration Results
### Obtained Results and Performance Assessment  
After the completion of the on-board integration, additional vehicular tests have been carried out with the prototype vehicle in order to validate the demonstrator. Figure 13 reports some results obtained during a data collection on the selected path (performed on 5 December 2014).  
In detail, Figure 13a shows the full selected path for live demonstration and the obtained LPL ellipses in each location. In this case, three colours have been used for denoting three possible conditions for the maximum value between the semi-major and semi-minor axes of each ellipse (_i.e._, ATPL and CTPL):  
* Green, if such LPL value is below 25 m,
* Yellow, if between 25 and 39 m, and
* Red, if greater than 39 m.  
Figure 13a clearly demonstrates that, during the test, most of LPL values were below 25 m (green ellipses). Only a few cases of LPL values above 25 m can be identified (yellow ellipses) and just one case above 39 m (one red ellipse), as shown by the magnification in Figure 13b. Figure 13c reports the LPL values _versus_ time. These results have been obtained parsing 900 positions logged during a vehicular data collection lasting 15 min, where the car reached a maximum speed equal to 46.6 km/h. The number of satellites (\(N_{sat}\)) used for computing the position and local PL information ranged from 8  
Figure 13: Results obtained on the selected demonstration path: (**a**) Estimated positions and LPL ellipses on the entire path; (**b**) Magnification of a small portion of the path; (**c**) LPL values computed along the entire path _versus_ UTC time.  
to 11. In detail, as also summarized in Table 1, the following maximum LPL values have been obtained: 72.1, 30.8 and 72.3 m for the ATPL, CTPL, and VPL, respectively, with 95% quantiles equal to 20.9, 24.7 and 36.3 m, respectively.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Application Scenario  
Smart clothing by incorporating multiple connected devices can communicate with cloud-based services to achieve improved human lifestyles [9]. Smart clothing, such as leggings, that can be in direct contact with a large area of the body can ensure comfort, usability, accuracy, product washability, and support real-time monitoring. The focus has been placed on introducing smart leggings for monitoring health, sports and athletics and fashion.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 3 Background: iSDVN System
### Resource Optimization  
Digital twins are being used to optimize resources through various applications that utilize digital twins' (DTs) capabilities to improve efficiency, productivity, and decision making. In addition to enabling the real-time monitoring, analysis, and simulation of their physical counterparts, digital twins allow the real-time analysis and monitoring of non-digital objects. Digital twins provide numerous benefits when applied to resource optimization by providing valuable insights and predictive capabilities. Modeling network traffic and resource utilization is made more accessible with DTs. Administrators can simulate and run \"what if\" scenarios to optimize resource allocation, provisioning, and routing decisions for improved network efficiency.
## 4 Results and Discussion
### Spatial Variation of Precipitation, Maximum Temperature, and Minimum Temperature
#### 4.3.3 Minimum Temperature  
Likewise, the results of the average daily minimum temperature show a similar pattern and change to the maximum temperature. The spatial distributions of the average daily minimum temperature over the study area for the uncorrected output GCM model are shown in Figure 11. The corrected datasets, after applying various bias correction methods, are compared against the observed minimum temperature. Figure 11a-e shows the spatial  
Figure 10: Spatial variation of average daily maximum temperature (\({}^{\circ}\)C) for (**a**) model-simulated raw data, (**b**) observed data, (**c**) LS-corrected, (**d**) VS-corrected, and (**e**) DM method.  
variation of the model-simulated raw minimum temperature data, observed minimum temperature data, LS-corrected, VS-corrected, and DM-corrected datasets, respectively.  
Similar to the raw precipitation and maximum temperature, the minimum temperature also shows a difference in the spatial patterns of the uncorrected data and observed data. In the case of the spatial variation of the minimum temperature uncorrected data (Figure 11a), this shows conspicuous high-temperature values ranging from 23 to 24 \({}^{\circ}\)C in the Western Ghat region. Since these are uncorrected raw model data, they may have some biases and, hence, they show such unacceptable results. After bias correction, the results of the corrected minimum temperature match the pattern of the spatial variation of the observed data. The overall spatial pattern of the minimum temperature distribution is the same for all three bias correction methods over the Upper Bhima sub-basin.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 4 Experimental Environment
### Implementation Aspects
#### 4.5.1 MQTT  
The MQTT experimentation environment has been developed in Python with the _paba_ library, and the Mosquito broker. The scripts created for MQTT ([[https://gitlab.com/iotlab_copelabs/mqtt](https://gitlab.com/iotlab_copelabs/mqtt)]([https://gitlab.com/iotlab_copelabs/mqtt](https://gitlab.com/iotlab_copelabs/mqtt)) (accessed on 15 May 2021)) are:  
* Subscriber: mqttsubs.py
* Publisher: mqttgen.py
* Report Generator: monitor.py  
The Subscriber script (_mqttsubs.py_) emulates a subscribre application which applies, in Mosquito, to a topic _\"sensors\"_.  
The script assumes a timeout of 5 seconds for message reception. If the timeout occurs, the script stops and generates a log file named _\"receiver.txt\"_ and another file named by default _\"ttc_file.txt\"_, containing the timestamps of each message in the format: _\"t1-12\"_, where _t1_ corresponds to the timestamp registered by the publisher at the instant when the message is sent, and _t2_ corresponds to the timestamp registered by a subscriber, once a message is received. These timestamps are then used in the experiments to compute time-to-completion.  
The Publisher script (_mqttgen.py_) generates and sends a specific number of messages to the broker to the topic _\"sensors\"_, each separated by a specific interarrival time interval. These attributes are specified in the code. By default, the message size has been set to 7 KB. Any other number can be provided as argument to the script. The payload of each of these messages contains the timestamp taken at the instant the messages have been sent, so that once messages are received, the subscribers can rely on such timestamp to locally compute the time it took for completion. Once all messages are sent, the publisher script generates a log file named _\"sender.txt\"_, containing only the number of messages sent.  
Both publisher and subscriber scripts have been configured to connect to the same broker identified in our testbed by a private IP: \"12.0.0.170\".  
A third script has been created, to provide a report based on the publisher and subscriber logs. This report consists of, per presented order: number of messages sent by the publisher; number of messages received by the subscriber; _time to completion (TTC)_; packet loss.
## 3 Results and Discussion
### Literature Revision of Materials Applied for Cool Surfaces
#### 3.3.1 Pigments
The photocatalysts active white inorganic pigment TiO\({}_{2}\) represents the most widely used nontoxic pigment and is characterized by strong scattering, weak absorption, and good stability [63]. As a result, most studies have been compared to its reflective performance [64]. The use of developed pigments was recently the most processed strategy. When incorporated into glazes, fillers, and engobes, synthesized pigments are used in coatings in order to enhance the optical and thermal performances of cool materials, as well as to respect the aesthetic requirements of a design. Several oxides were demonstrated to be able to enhance the NIR reflectance of glazes for concrete cement substrates, steel substrates, metal panels, and clay tiles [33; 65; 66]. For traditional buildings, using pigments based on sodium silicate for the tile coating improved the solar reflection in the NIR spectrum by 13% without affecting the visual appearance [64]. The integration of such pigments lowers roof overheating and reduces the energy required for cooling. Cool pigments have now become a growing trend that is taking over cool surface technology, as nanoparticles are integrated into material structures to fulfil a specific design criterion and to enhance the reflective performance. In addition to their chemical stabilities, their effective thermal and optical performances have been proven in numerous studies. However, the acquisition, the production process, the synthesis methods, the high cost of the rare-earth elements used in their composition, and the hazardous environmental effect of the heavy metals may have a negative environmental impact [67; 68]. On the other hand, the development of new compositions of reflective pigments continues to increase, reaching more potential evaluations. In addition to the high reflectivity, the research field has also been evaluating the functional and long-term performances against surface contamination [69], thermal insulation [70; 71], and the synthesis methods in terms of the energy and raw material costs, which were directed for the sol-gel method and rare-earth compounds [72; 73; 74; 75; 76; 77].
## 2 Materials and Methods
### Data Processing and Bioinformatics Analysis  
#### 2.5.1 16S and 18S Amplicon Meta-Barcoding  
For prokaryotes, raw reads (_fastq_ files) were quality-trimmed using Trim Galore with default settings [29]. Further analysis was conducted using Quantitative Insights into Microbial Ecology (QIIME) pipeline [30]. In brief, paired-end reads were joined using the fastq-join method (join_paired_ends.py) and demultiplexing of libraries was performed with split_libraries_fastq.py script. OTU picking was executed with pick_open_reference_otus.py script with the default clustering algorithm UCLUST [31]. Sequences were clustered into operational taxonomic units (OTUs) with 99% sequence similarity and aligned to SILVA 128 release for QIIME.  
Eukaryotes were analyzed using the SOP provided by [PERSON] et al. [32] for microbiota processing in mothur [33]. Briefly, paired-end reads were joined and trimmed from adaptors and indices. The remaining sequences were checked for chimeric sequences using the chimera.uchime command. Sequences outside the range of 150-450 bp were removed and the remaining were aligned to SILVA database (release 123) for taxonomic assignment into OTUs, with 99% sequence identity.