esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Suggestions for Remote Sensing SIF Applications in LUE-Based Modeling  
Previous studies have demonstrated that remotely sensed SIF retrievals can not only offer another approach to explore the seasonal variations in the photosynthesis, but can also restrict the parameters in process-based models to improve GPP simulations [77]. On the one hand, the Soil-Canopy Observation, Photosynthesis and Energy balance (SCOPE) model provides a theoretical method for modeling canopy SIF and photosynthesis by constraining the estimates of the maximum carboxylation rate (Vcmax) by coupling the soil-vegetation-atmosphere-transfer (SVAT) model with radiation transmission models [77]. On the other hand, the SCOPE model can also combine the SIF data derived from satellites to infer GPP from the land surface process model or carbon cycle data assimilation system [78; 79].  
Figure 8: Scatterplots of the solar-induced chlorophyll fluorescence (W m\({}^{-2}\) μm\({}^{-1}\) sr\({}^{-1}\)) and sunlit/shaded/total leaf area index (LAI\({}_{\text{su}}\), LAI\({}_{\text{sh}}\), LAI, m\({}^{2}\)/m\({}^{2}\)) on the mid-day, daily, and monthly timescales, respectively.  
In addition to the advantages of SIF in process-based models, it is also beneficial to the development of LUE-based models. [PERSON] et al. (2019) report that the correlation between the observed and simulated LUE is improved slightly when the normalized PRI and SIF yield are combined as predictive factors in the framework of multiple linear regression. Many studies have also shown that SIF comprises not just the light absorption information, but also the LUE information (SIF). Moreover, we found that the values of \(\varepsilon/SIF_{\text{yield}}\) and \(\varepsilon_{\text{max}}\) tend to converge across time in this study, which may provide a basis for combining the satellite products of PRI and SIF to track ecosystem LUE. Additionally, remote sensing SIF retrievals tend to represent the top layer of the vegetation canopy, primarily composed of sunlit leaves (SIF; 2017), which may provide information for further studies to determine the contribution of the sunlit leaves and the structural sensitivity of SIF signals escaping the canopy to refine SIF-based models. We also can assume that SIF observations can be used to constrain the maximum rates of sunlit leaves in the two-leaf LUE model to improve the robustness of their estimates in future studies.
# Comparison of Functional Connectivity Analysis Methods in Alzheimer's Disease
## 3 Functional Connectivity Analysis Methods
### Data-Driven Methods
#### 3.2.4 Machine Learning-Based Methods  
Recently, the efficacy of brain FC network-based classification has been proven a great help in diagnosing and detecting brain disorders [42]. FC characterizes the brain's functional network structure and can be conveniently extracted from fMRI data classifications. In addition, the FC between pairs of ROIs is one of the most common feature representations of fMRI data under the machine learning framework [43]. Machine learning approaches, which are generally data-driven, give an explicit characterization of rs-fMRI [44]. Unsupervised learning approaches in rs-fMRI are primarily concerned with understanding the functional organization and dynamics of the healthy brain [45]. Methods such as matrix decomposition or clustering, for example, can disclose numerous functional networks inside the brain while also revealing the underlying structure of dynamic FC [46; 47].  
On the other hand, supervised learning approaches may use resting-state FC (RSFC) to produce individual-level predictions [48; 49]. Substantial effort has been devoted to using rs-fMRI to classify patients versus controls or to predict disease prognosis and guide treatments [50; 51]. The detailed conceptual explanation of ML methods and their limitations are shown in Table 2.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{227.6 pt}} \hline \hline
**ML-Based Method** & **Conceptual Explanation and Limitations** \\ \hline \hline
**Supervised Learning:** & \(\bullet\) & All algorithms have advantages and disadvantages. The algorithm selection should be guided by various criteria such as the prediction objective, sample size, and type of input data. \\ \hline \hline
**Supervised Learning:** & \(\bullet\) & Experiment based on the brain  
Many advanced approaches for rs-fMRI analysis are based on machine learning. Both unsupervised and supervised learning approaches have significantly expanded the application fields of rs-fMRI. With the acquisition of vast amounts of neuroimaging data and advancements in learning algorithms, an even more significant impact is predicted in the future. Despite machine learning's practical advantages, it is essential to understand the challenges faced in its present application to rs-fMRI.
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 2 Related Work
### IEEE802.15.4g Performance Evaluation  
Some related work evaluates the performance of IEEE802.15.4g PHYs (without the 6 TiSCH stack).  
[PERSON] et al. [25] examine the impact of interference between MR-FSK mode 2 and MR-OFDM option 4 MCS 3. Authors deploy IEEE802.15.4e MAC with multihop capability on top of each PHY layer and measure the impact of the interference between two networks, each running one PHY. Results are reported in terms of the degradation of throughput of each network in the presence of the other interfering network. Authors demonstrates that since the OFDM modulation scheme uses multiple sub-carriers, it performs better than FSK in the presence of frequency selective interference.  
[PERSON] et al. [1] run an experimental campaign to compare the performance of IEEE802.15.4 O-QPSK 2.4 GHz at 250 kbps, and IEEE802.15.4g OFDM. They show a higher robustness of OFDM, even though it operates as a higher bit rate (800 kbps). They show that, although a radio draws less current when running O-QPSK 2.4 GHz, using OFDM 868 MHz leads to an overall lower power budget as transmission happens much faster.  
The same authors also evaluate the performance of all IEEE802.15.4g PHYs [3]. They conduct a complete range-testing campaigns for the 31 PHYs on the four scenarios they consider the most prevalent in outdoor applications: line of sight, smart agriculture, urban canyon, smart metering. Results of the range-tests are reported in terms of PDR measurements, throughput, and electric charge consumption. They demonstrate the longer range of FSK and O-QPSK in the sub-GHz band compared to OFDM options due to their higher receiver sensitivity.  
They provide interesting results as to which radio could be better in certain scenarios. This paper goes one step further to address the end-to-end performance of a full 6 TiSCH stack using these PHY layers.
## 1 Introduction
### Literature Review
The radiating structure is fed with a microstripe line. The lower side consists of a meandered ground plane, which not only improves the impedance bandwidth but also acts as a balun choke. An extended parallel strap, which acts as an impedance-tuning strap, is used. In [33], a dual-band miniature antenna for LPWAN, covering 433 MHz and 868 MHz bands, is designed with total dimensions of 90 mm \(\times\) 30 mm \(\times\) 1.6 mm. The radiating structure is loaded with lumped components (an inductor and capacitor) to control the resonant frequencies. This technique is interesting, and can be employed to compensate the frequency shift due to plastic enclosures where the antenna will be housed. All of the designs (in [29; 30; 31; 32; 33]) practically use the same concept of design, a meandered structure sized to resonate at interesting frequencies, and a short to the ground to improve antenna matching. In this paper, we present a solution for rapid development of an internal reconfigurable PCB antenna, inside enclosure, for connected devices for IoT applications using hybrid connectivity LPWAN and UHF satellite communication. Simulation tools today are powerful; if the simulation setup is correctly set and the 3D model is well modeled, simulation results will be coherent with the measurements. Accurate measurements of \(\varepsilon_{\mathrm{r}}\) and tan \(\delta\) of the plastic materials (enclosure) need time and add an extra cost. The difference between using an approximate and an accurate value of these two parameters results in a frequency shift of the resonant frequencies. The aim of the proposed solution is to reduce costs, save time (respect time to the market), and accelerate the development of new connected devices. Thus, the concept consists of modeling all the parts of the device and proposes an adequate radiating structure loaded with lumped components, which help to compensate not only the frequency shift due to the device enclosure, but also to control the resonant frequencies. This makes the definition of the material properties (\(\varepsilon_{\mathrm{r}}\), tan \(\delta\)) of the plastic enclosures that are available on the market not critical, so approximate values are sufficient to perform simulations. By performing parametric studies on the geometric parameters, and the values of the loaded lumped components, the best values ensuring the target resonant frequencies will be identified and the frequency response of the input reflection coefficient will be understood. Understanding the antenna response enables the designer to adjust the resonant frequencies of the antenna in a few steps, if a frequency shift is found in measurements compared with the simulation results.
# Modeling and Control of an Underactuated System for Dynamic Body Weight Support
## 2 Methodology and Research Object  
The purpose of this paper is to present the methodology used for the stability analysis of a control system for a dynamic body weight support system. A PD controller with compensation was proposed to control the drive system. As an example of a device for the re-education of gait, a body weight support system coupled with a treadmill was used [19]. The main components of the electromechanical part of the rehabilitation system are shown in Figure 1.  
The structure of the body support system was analyzed in detail in [34]. Although the body support system is fitted with two independent drive systems, the presented analyses assumed that the dynamic compensation system operated only when gait was measured on a treadmill due to the little vertical movement of the patient during gait, with values falling within the range of SEA drive parameters.  
For maintenance of a constant body support load, one sensor that controls the drive operation with a feedback loop according to a signal indicating load offset is sufficient. The method proposed for the verification of the control system stability requires all generalized coordinates to be measured. For this purpose, additional adapters were developed for spring deflection measurement of the SEA system and determination of the drive's passive component position. Measurements were performed with a linear variable differential transformer sensor. Figure 2 presents the crucial components of the dynamic compensation system.  
Control was measured in real time using MATLAB/Simulink branded software. Transmission of the control and measurement signals was done with two RT-DAC4 cards provided by the Spartan II series Field-Programmable Gate Array (FPGA) system. Communication with the system was done with system Peripheral Component Interconnect (PCI) bus and external input/output accelerator (PLX 9030). FPGA along with programming languages enabled us to transfer operations related to complicated digital processing of  
Figure 1: Mechatronic device for gait re-education.  
measurement data and control signal modulation onto hardware. An interface for analogue signal conditioning was an additional part of the measuring system. Drive control signals generated by the control application were sent to particular servodrives in the form of voltage at an appropriate level. The servomotors were controlled in speed mode. The signal sent from the computer was proportional to the set rotational speed and the servoinverter controlled the motor torque [35].  
To demonstrate the stability of the control system, it was necessary to formulate a set of equations regarding the movement of mechanical parts, develop a control algorithm, determine relevant errors, and define the control method. Next, determination of the Lapunov function was necessary. These steps are described in the following chapters of this paper.
# Underwater Object Detection Method Based on Improved Faster RCNN
## 4 Experiments
### Comparison Experiments  
In order to show the detection effect of the improved Faster RCNN model, other different object detection algorithms are also trained, tested and validated in this paper. Observe the performance comparison between the improved algorithm model in this paper and the other algorithms in the tasks of underwater object detection.  
This paper selects the Fast RCNN, SSD, YOLOV3, the unimproved Faster RCNN and the improved Faster RCNN for comparison. According to the data enhancement pre-processing described in the previous section, the 3500 underwater environment images are divided into training data samples, verification data samples and test data samples, with a ratio of 7:2:1. After 500 epochs of iterative training and learning, the learning rate drops to 0.0001 and the parameters of the underwater object detection model converge. The experimental results of the Fast RCNN, SSD, YOLOV3, the Faster RCNN and the improved Faster RCNN are shown in Table 2.  
As can be known from Table 2, in terms of underwater image detection the detection results of various algorithm models are compared. The improved Faster RCNN underwater object detection algorithm has relatively good _AP_, _mAP_ and \(F1\) scores, and these three indicators are higher than the original Faster RCNN algorithm; [EMAIL_ADDRESS](%) reaches 71.7%, which is 3.3% higher than the original algorithm, and the \(F1\) score reaches 55.3%, which is 2.5% higher than the original algorithm.  
As can be known from Figure 5, the improved Faster RCNN-based underwater object detection method has better performance than the Fast RCNN, the unimproved Faster RCNN, YOLOV3 and SSD. The improved method in this paper has higher accuracy when detecting multiple categories at the same time, and can reduce the occurrence of the missed detections and the false detections well. As can also be known from Figure 5, the method in this paper has also made progress in the detection of small underwater objects, and the small scallops in Figure 5 can also be accurately detected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Avg-P(\%)** & **[EMAIL_ADDRESS](\%)** \\ \hline Faster RCNN & 38.3 & 62.1 & 45.6 \\ Faster RCNN & 41.0 & 68.4 & 52.8 \\ YOLOV3 & 42.1 & 70.1 & 54.7 \\ SSD & 42.0 & 70.4 & 54.5 \\ Ours & **43** & **71.7** & **55.3** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of different models in underwater object detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Avg-P(\%)** & **[EMAIL_ADDRESS](\%)** \\ \hline Faster RCNN (VGG16) & 41.0 & 68.4 \\ Faster RCNN (ResNet50) & 41.2 & 68.1 \\ Faster RCNN (ResNet101) & 41.1 & 68.7 \\ Faster RCNN (Res2 Net101) & **41.5** & **69.1** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of different feature extraction modules in the underwater object detection.
## 1 Introduction
Along with the launch of the Orbiting Carbon Observatory 2 (OCO-2) in 2014, a new SIF product is now available to estimate GPP at a finer spatial resolution (\(1.3\times 2.25\) km), comparing with previous products from GOME-2 (\(40\times 40\) km) and GOSAT (10 km diameter) [22]. The main task of OCO-2 is to map the global atmospheric CO\({}_{2}\) concentration by recording the sunlight reflected from the Earth's surface. The main sensor of OCO-2 consists of three high-resolution spectrometers that make coincident measurements of reflected sunlight in the 1.61 (CO\({}_{2}\) weak absorption), 2.06 (CO\({}_{2}\) strong absorption) and 0.76 \(\upmu\)m (O\({}_{2}\) A-Band) bands. The SIF product can be retrieved using IMAP-DOAS algorithm of NASA. The general principle of retrieving SIF is based on the filling of Fraunhofer solar lines. Measuring the partial depth of Fraunhofer spectral lines, which decreases with thepresence of surface SIF radiation. Previous studies have shown that there is a strong linear/nonlinear relationship between SIF products of OCO-2 and GPP [25; 26]. However, the SIF signal can be affected by solar and viewing zenith angles [27]. Moreover, the atmospheric CO\({}_{2}\) concentration is another important factor affecting vegetation photosynthesis and GPP [28]. Existing studies have confirmed that the increasing atmospheric CO\({}_{2}\) concentration promotes the rate of photosynthesis of tomato plants on the scale of individual plants [29]. On the scale of larger vegetation distribution, previous studies also demonstrated that CO\({}_{2}\) has a significant impact on the total primary productivity of vegetation [30; 31; 32]. However, current studies on estimating GPP using satellite-derived SIF products ignored the possible effect of atmospheric CO\({}_{2}\) concentrations on modeling GPP. The SIF-GPP model could be further improved by the introduction of a new parameter, namely, the atmospheric CO\({}_{2}\) concentration. Therefore, we propose to investigate the effect of atmospheric CO\({}_{2}\) concentrations on estimating GPP using SIF products. Because OCO-2 provides of column-averaged CO\({}_{2}\) dry air mole fraction, abbreviated as XCO\({}_{2}\) hereafter, and SIF simultaneously. Which will be very easy to take the atmospheric CO\({}_{2}\) concentration into the new model of GPP. The rest of this work is organized as follows. Section 2 introduces the study area, datasets and methods. The results are demonstrated in Section 3. Discussions are provided in Section 4. Finally, we conclude the whole study in Section 5.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 3 Experimental Results and Analysis
### Datasets  
The dataset used in this study comes from the National Data Buoy Center and consists of observations recorded at Station 42040 (LLNR 245)--LUKE OFSHORE TEST PLATFORM, located 63 nautical miles south of Dauphin Island, Alabama (29.207\({}^{\circ}\) N, 88.237\({}^{\circ}\) W). This particular station was chosen because it has well-documented instrumentation that provides high-resolution measurements of ocean dynamics under pressure. The key variables extracted from the dataset are the significant wave height (column 9: the 9 th column of the data is the wave height data measured at this station).  
In this study, 16,427 wave height observation data points were converted into natural logarithms to construct a logarithmic wave height dataset. The distribution of wave height data is shown in Figure 4.  
The data distribution span is calculated by the difference between the maximum value of 2.1126 and the minimum value of \(-\)2.3026, which is as high as 4.415 logarithmic units, which directly shows the breadth of the data value range. From the perspective of central tendency, the mean is \(-\)0.2608, slightly higher than the median of \(-\)0.2744, which initially suggests that the data distribution has a right-skewed tendency. Dispersion is a key indicator for assessing data stability. The standard deviation was calculated to be 0.6288, reflecting the degree to which the data points deviate from the mean. In addition, the interquartile range (IQR) was derived from the first quartile (\(Q1=-\)0.7340) and the third quartile (Q3 = 0.1906), which was 0.9246. Compared with the theoretical IQR under the normal distribution, the observed IQR is significantly larger. This significant deviation indicates a higher dispersion and suggests that the data distribution has a fat tail phenomenon, that is, the probability of extreme values appearing is greater than that of the normal distribution.
## 4 Backgrounds and Methods
### Backgrounds
#### 4.1.3. Handover Performance Indicator  
HPI serves to monitor the performance of the HO procedures by each cell. The total HPI is a summation of the handover failures (HOF), handover ping-pong (HPP) and RLF indicators (Equation (2)).  
\[HPI=HPI_{HOF}+HPI_{HPP}+HPI_{RLF} \tag{2}\]  
In [18], the following definitions is defined:  
1. \(HPI_{HOF}\): If the HO procedure fails when the UE connects to the target cell, the UE must re-establish the connection to the serving cell and begins a new HO procedure. If UE moves out of coverage of the serving cell, the re-establishment process also fails. It is added to the RLFs. The failed HPI is the ratio of the number of the HO failures (\(HO_{failed}\)) to the total number of the HOs (\(HO_{total}\)). \(HO_{total}\) signifies the total number of the HO attempts. \[HPI_{HOF}=\frac{HO_{failed}}{HO_{total}}\] (3)
2. \(HPI_{HPP}\): The ping-pong HPI is the ratio of the number of ping-pong HOs (\(HO_{ping-pong}\)) to the total number of HOs: \[HPI_{HPP}=\frac{HO_{ping-pong}}{HO_{total}}\] (4)
3. \(HPI_{RLF}\) is the number of the HO calls that were dropped (by the network) before the HO procedure was finished, if the signal quality was not enough for the data communication requirement, and the UE moves out of coverage of the serving cell. In this scenario, the UE drops the HO and sends a new connection request to the nearest available cell (based on measurement). \(HPI_{RLF}\) is the ratio of \(HO_{dropped-network}\) to the number of calls that were accepted in the target cell: \[HPI_{RLF}=\frac{HO_{dropped-network}}{HO_{accepted}}\] (5)
# Internet of Things and Long-Range-Based Smart Lampposts for Illuminating Smart Cities
## 2 Related Works and Overview of IoT
### Related Works
The system detects the movement of the pedestrian and illuminates his path till the next streetlight and shuts off when a pedestrian walks out of the lamp range [34]. The most recent evolution in solid-state lighting based on leading technology accelerated the phase-out of inefficient lighting systems [35]. Adding IoT connectivity to streetlights adds the benefits of sustainable development [36]. Smart lighting helps save energy, reduces the cost of power, reduce maintenance, and saves tax payer's money; along with automation and network control, the system becomes even more efficient. A cloud-based system is proposed by integrating ZigBee and NodeMCU for monitoring the environmental parameter from any remote location [37]. An IoT-based pollution monitoring system is implemented with the assistance global system for mobile communication (GSM) module and distinct gas sensors are utilized for sensing the gases [38]. Real-time air quality monitoring is designed and developed for detecting pollutant levels by employing distinct gas sensors [39]. From the above literature, it is realized they are need of implementing fog- and edge-assisted IoT server architecture for a smart lamppost to support the smart infrastructure that is required in smart cities. From the above review, it is concluded that the distinct wireless communication and sensor technologies are implemented in the lighting system for enhancing energy efficiency. However, with respect to smart cities, the resource management and establishment of hybrid framework-based system are lacking as it delivers the multitude applications with single system. Multitude applications are possible with smart lamppost as it provides the smart infrastructure and amenities to the citizens. Before addressing the proposed architecture, we will discuss the overview of IoT and enabling technologies that supports the implementation of multitude applications with smart lamppost.