esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Gross Primary Productivity (GPP)
## 1 Introduction
The objectives of this study were three-fold: (i) to understand which VI could be an ideal proxy of GPP phenology of SRC plantations, (ii) to identify optimal filtering and threshold methods for describing phenological metrics and (iii) to analyze the performance of each VI in capturing the seasonal variability of GPP. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Index** & **Formula** & **Use** & **Reference** \\ \hline Normalized Difference Vegetation & \(NDVI=\frac{\text{B08}-\text{B04}}{\text{B08}+\text{B04}}\) & Green biomass, canopy greenness & [68] \\ Index (NDVI) & \(NDVI=\frac{2.5\text{-}\text{B08}-\text{B04}}{\text{B08}+\text{B04}}\) & Green biomass, canopy greenness & [37] \\ Enhanced Vegetation Index (EVI) & \(EVI=\frac{2.5\text{-}\text{B08}-\text{B04}}{\text{B08}+\text{B02}-\text{B02 }+\text{B02}+\text{B02}+\text{B02}}\) & and phenology & [37] \\ MERIS Terrestrial Chlorophyll & \(MTCI=\frac{\text{B08}-\text{B09}}{\text{B08}+\text{B04}}\) & Canopy chlorophyll content, & [69] \\ Index (MTCI) & \(MTCI=\frac{\text{B08}-\text{B04}}{\text{B08}-\text{B04}}\) & Canopy chlorophyll content, & [49] \\ Chlorophyll Red Edge Index & \(ChlRadEdge=\frac{\text{B08}}{\text{B08}}-1\) & Canopy phenology and senescence & [49] \\ Modified Chlorophyll Absorption & \(MCARI=\frac{\text{C}}{\text{B08}}\) & Canopy chlorophyll content, & [57] \\ in Reflectance Index (MCARI) & \((B05-\text{B04})-0.2*(B05-B03))\times(\frac{\text{B05}}{\text{B04}}\) & Canopy phenology and senescence & [57] \\ Pigment Specific Sample Ratio & \(PSSR=\frac{\text{B08}}{\text{B04}}\) & Green biomass & [29] \\ Structure Insensitive Pigment & \(SIPI=\frac{\text{B08}-\text{B04}}{\text{B08}-\text{B04}}\) & Canopy and leaf carotenoids & [59] \\ Index (SIPI) & \(gNDVI=\frac{\text{B08}-\text{B03}}{\text{B08}+\text{B03}}\) & Green biomass, canopy greenness & [70] \\ Vegetation Index (gNDVI) & \(gNDVI=\frac{\text{B08}-\text{B03}}{\text{B08}+\text{B03}}\) & and phenology & [70] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Vegetation indices (VIs) derived from Sentinel-2. Abbreviations correspond with center wavelengths, in nm: B02 = reflectance at 490 nm; B03 = reflectance at 560 nm; B04 = reflectance at 665 nm; B05 = reflectance at 705 nm; B06 = reflectance at 740 nm; B08 = reflectance at 842 nm. B02, B04 and B08 have a 10 m spatial resolution, while B05 and B06 have a 20 m resolution.
# Review of Anomaly Detection Algorithms for Data Streams
## 8 Future Research Directions
### Future Research Challenges  
The significant demand for big data applications has driven the rapid development of the anomaly detection field; however, there are current difficulties in the field of data stream anomaly detection research:  
1. Difficulties based on global information settings and partial information settings. The data under the data stream are usually massive and infinite, and it is hard to store all the data in the practical application scenario, which we called global information. Some algorithms use staged data batch processing methods for data streams, or periodically retrain anomaly detection models in order to obtain global information about the input data; as a result, anomaly detection for streaming data could be reached. However, in practical applications where data streams are becoming increasingly massive, global information about the data is becoming increasingly unavailable. Therefore, it is expected that the anomaly detection of the model and the update of the model are performed simultaneously, which is very difficult in real streaming data scenarios.
2. On the basis of the difficulty of three parties balance including the speed of data stream, the speed of algorithm operation, and the accuracy. One of the characteristics of data streams is that they are fast. If anomaly detection is to be performed in real time on fast incoming data, it means that the anomaly detection algorithm must also be fast, but it always costs the accuracy of the algorithm. So, the way in which to strike the balance is also one of the difficulties of anomaly detection in data streams.
3. The difficulty of the concept drift caused by data flow. Data stream anomaly detection algorithms all work in dynamic environments where data flow continuously. If the data distribution of a data stream is random, it would lead the target concept may change over time. However, most of the existing machine-learning-based work on data stream anomaly detection algorithms assume that the training samples are randomly generated according to some smooth probability distribution. The way in which to optimize the algorithm in the context of streaming data scenarios so that it has the ability to resist concept drift is necessary.
## 4 Simulation Verification
### Verification of PFR  
In order to confirm that PET has the ability to participate in PFR of the upper grid and meet the power requirements of each port, the following simulation is conducted:  
The power to the upper 35 kV AC grid is supplied by the synchronous generators. The power upper and lower boundaries of DC port at the output stage are \(P_{\text{2 max}}=\)3 MW and \(P_{\text{2 min}}=1\) MW, respectively; the initial settings for the active powers \(P_{1}\) and \(P_{2}\) are 3 MW and 2 MW, respectively. At \(t=2\) s, the load in the upper grid increases by 1 MW, and it increases by 4 MW at \(t=4\) s.  
The simulation results are present in Figure 16. Figure 16a illustrates the frequency response of the upper grid when PET participates in or opts out of the PFR. It can be observed that when the frequency changes, PET is able to offer a certain power support to the upper grid, participate in the PFR, and effectively reduce the frequency deviation. Figure 16a,b also shows that the descent rate of frequency accelerates significantly around \(t=4.2\) s. This is because \(P_{2}\) reaches its lower boundary and the low-voltage DC port keeps operating at the lower boundary, which makes PET no longer participate in the PFR. Meanwhile, since the AC port at the output stage does not participate in the PFR, \(P_{1}\) will consistently maintain its setpoint. Figure 16c demonstrates the response curve of the active power exchanged between the input stage and the upper grid. Due to the VSM control method, PET shows certain damping and inertia, and suppresses the frequency fluctuation of the upper grid.  
Figure 16: Cont.  
It can be learned from the above discussion that PET can give a certain power support to the upper grid and participate in the PFR by adjusting the power of the DC port at the output stage. Meanwhile, the output power of the AC port remains at its setpoint, which ensures the reliable and stable operation of PET.
## 3 CSI Measurement Environments
### CSI Measurement Tools and Deep Learning Model  
To capture the CSI from Wi-Fi signals, several CSI extraction tools have emerged, each with its unique capabilities. These include the Intel 5300 NIC (Network Interface Card) [34], Atheros CSI Tool [12], and Nexmon CSI Tool [35], all of which have been instrumental in developing practical sensing platforms. Intel 5300 NIC is the first and widely used tool. It captures 30 subcarriers at a 20 MHz Wi-Fi channel bandwidth. The Atheros CSI tool improves the resolution by recording 56 subcarriers at 20 MHz bandwidth and 114 subcarriers at 40 MHz bandwidth. The Nexmon CSI tool is the first to enable CSI recording on smartphones and Raspberry Pi. It can capture up to 256 subcarriers at 80 MHz bandwidth.  
For our study, we install the Nexmon CSI tool on a Raspberry Pi 4 with a single Wi-Fi receiving antenna to capture CSI [35]. We use an ESP8266 as the Wi-Fi transmitter to send Wi-Fi frames using only one transmitting antenna on the narrowest 20 MHz Wi-Fi channel. Specifically, we configure an ESP8266 to periodically send an UDP segment with a 1-byte payload every 10 milliseconds over a 20 MHz Wi-Fi channel in the 2.4 GHz band.  
Since the purpose of our study is to devise a novel CSI preprocessing method, we use CNN as the deep learning model for Wi-Fi sensing. This choice is motivated by the widespread adoption of CNNs in Wi-Fi sensing applications, owing to their effectiveness in processing spatial data and their ability to automatically learn hierarchical features. By using a well-established model, we can more clearly demonstrate the impact of our proposed preprocessing method, isolating its effects from the complexities of newer or less common deep learning architectures.
# A Case Study on Distresses of Concrete Pavements Supported on a Retaining Wall
## 3 Evaluation of the Performance of the Subject Section
### Light Detection and Ranging (LiDAR) Survey  
To obtain quantifiable displacement data, a LiDAR survey was carried out across the entire 600 ft (183 m) section of the pavement surface, as well as the MSE wall. Since there is no survey data generated at the time of construction, the design details from the plan set drawings served as a reference condition in this study. Figure 9 shows the cross-section of the subject section at STA 1025+00 created using the LiDAR measurements (presented as a white line) overlaid on top of the cross-section from the plan set drawing (presented as a green line). As mentioned previously, pavement structures on the eastbound (EB) side did not show any major distress, and the cross-section of EB at STA 1025+00 created using the LiDAR measurements is in very good agreement with that derived from the plan set drawings, showing a difference of less than 0.01 ft (0.3 cm) between them; this proves the validity of the LiDAR measurements. On the other hand, the cross-section of the westbound (WB) side shows a notable difference from the design cross-section, showing a difference as large as 1.02 ft (31.1 cm).  
Details of the cross-section of the westbound side at STA 1025+00 are presented in Figure 10. Lane settlements were observed from the edge of the MSE wall to 36.9 ft (11.2 m) toward the inside lane, roughly corresponding to the longitudinal construction joint between the middle lane and the inside lane. The recorded settlement was 0.42 ft (12.8 cm) at the center of the middle lane, whereas the settlement at the edge of the outside shoulder was 0.29 ft (8.8 cm). Additionally, lateral movements of 0.6 ft (18.3 cm) and 0.94 ft (28.6 cm) were observed at the bottom and top of the MSE wall, respectively; the outside face of the end barrier (i.e., traffic rail) showed a lateral movement of 1.02 ft (31.1 cm). These observations indicate that the MSE wall has experienced both translation and rotation. The LiDAR survey data suggest that the tilting degree of the MSE wall is about 1.4\({}^{\circ}\).  
Figure 10: Details of displacement data of the subject section at STA 1025+00 measured using a LiDAR survey.  
Figure 9: Comparison of cross section of the subject section at STA 1025+00 created using LiDAR measurements (in white) with that from design cross section (in green).  
The settlement and tilting of the MSE wall were also observed from the visual surveys. Figure 11 shows the riprap at the bottom of the MSE wall of the subject section and that on the east side of the bridge. The plan set drawing, presented as an inset drawing in Figure 11, shows that the riprap should be constructed with a 4:1 slope, having a higher elevation at the wall facing. The riprap on the eastside of the bridge indeed showed a 4:1 slope as designed, but the riprap at the subjected section showed a reversed slope due to the settlement and rotation of the MSE wall.
## 5 Experiments
### Experiment Setup  
We used the Xilinx ZC702 Evaluation Board as platform for our evaluation. On the Zynq, FPGA we instantiated a hardware platform as described in Section 3.1. In Figure 1(a), we present our heterogeneous multiprocessor system that was used for all the experiments. On the upper part of the figure, the platform in purple contains 7 tiles that are connected to a shared memory via a shared interconnect. Three PEs are equipped with a hardware multiplier, and three with a floating point unit. The individual tiles use a MicroBlaze soft core that was equipped with private instruction and data memories. The measurement infrastructure is presented in green on the lower part of Figure 1(a). This platform is used to characterize software following the computation model described in Section 3.2. We then simulate and analyze different mappings and compare the results with the observed behavior on the real platform.  
In Section 3, we argued that communication and computation do not interfere and they can be considered as independent.  
We characterized the compute phase of each actor using the measurement infrastructure described in Section 3.3. For the characterization, we measured the execution time of  
Figure 5: Architecture of our SystemC simulation showing how our models are integrated.  
the compute phase of each actor 1,000,000 times with different characteristic input data. These measurements have been done once each different kind of processing element.  
The elementary delays of the communication phases are characterized using the Xilinx System Integrated Logic Analyzer (SystemILA). We simply built a simple workload model containing WriteTokens and ReadTokens functions of the communication phase. We observed specific signals of the communication process to measure the elementary delays. For each function (WriteTokens, ReadTokens), we measured the elementary delays of 20 different iterations. The measured elementary delays are constant in all iterations. Since the characterization is independent from application, we can use the elementary delays to build communication model of different mappings and applications. The communication characterization phase is not included in the simulation overhead of our proposed approach.  
For the analysis results, we simulated 1,000,000 iterations. We considered parallel simulation by splitting our simulations into 16 processes. Each process simulated 62,500 iterations.  
We provide a git repository (_Git repository:_[[https://zenodo.org/record/4876805](https://zenodo.org/record/4876805)]([https://zenodo.org/record/4876805](https://zenodo.org/record/4876805)), accessed on 31 May 2021) for all characterization data and results as well as the source code of the simulation and evaluation tools.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
###### Abstract  
Zero-liquid discharge wastewater treatment driven by sunlight shows potential to minimize its environmental impact by producing solid-only waste from solar energy. To overcome the key barrier of solar absorber contamination, solar-driven contactless evaporation (SCE) has been proposed. However, only a small-scale laboratory device has been studied, which cannot support its scalable application. To analyze the potential of SCE, it is essential to understand the conjugated heat and mass transfer under a scalable application scenario. In this study, a comprehensive model of SCE is developed, which is validated by the laboratory evaporation test and applied to scalable evaporation scenario. Results showed that the scalable evaporation (0.313 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\)) could obtain higher evaporation rate than the laboratory evaporation (0.139 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\)) due to suppressed heat losses from the sidewalls. If the design parameters are finely tuned and thermal insulation are properly applied, the evaporation rate could be further enhanced to 0.797 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\), indicating a 473.3% performance enhancement than the laboratory SCE. The modelling framework and understanding are expected to pave a way for the further improvement and scalable application of SCE.  
solar evaporation; zero-liquid discharge; contactless evaporation; heat and mass transfer; scalable application
## 3 Results
### Comparison of the Estimation Ability of Vegetation Index for fPAR at the Entire Crop Season

Table 7: Statistical parameters of linear, exponential, and logarithmic estimation models between \(f\)PAR and vegetation indices across the entire crop season. Among them, MNDWI, NDBI, and NDWI values are less than 0, so there is no logarithmic correlation with \(f\)PAR. The fitting results show that different vegetation indices have significant differences in fitting \(f\)PAR, and the accuracy of linear, exponential, and logarithmic fits also varies. OSAVI shows better estimation ability in discriminating observation dates, but throughout the critical growth period, its coefficient of determination R\({}^{2}\) with \(f\)PAR is highest at 0.5131. This indicates that OSAVI is strongly influenced by lighting conditions and the correlation model established using it lacks stability, making it difficult to estimate \(f\)PAR for the entire critical growth period. When a single vegetation index is used for fitting, MNDVI, NDVI, NDPI, RVI, and DVI show good fitting accuracy. Among them, MNDVI maintains a high correlation both throughout the critical growth period and at different dates, indicating its stability in different phenological stages, different solar weather conditions, and solar zenith angles. Therefore, MNDVI has great potential for the estimation of \(f\)PAR.
## 2 Optical Uplink Channel Model
### Closed-Form Expression for the Channel Model
 \right]\] \[\quad+\delta(h)\cdot\exp\left(-\frac{\Omega_{F}^{2}}{2\sigma_{ \theta}^{2}}\right), \tag{17}\]  
where \(\delta(\cdot)\) denotes the Dirac delta function. In addition, background noise constituting an additive noise component in the received optical power, it is fundamentally governed by the spectral radiance density \(N_{b}(\lambda)\) through the radiometric relationship shown in (10) and (11). This spectral radiance is intrinsically related to environmental radiation sources, with three dominant scenarios considered in this work: direct solar radiation, Earth-reflected sunlight, and thermal emission from the Earth's night side, corresponding to maximum, moderate, and minimum noise conditions, respectively [27]. At an operational wavelength of \(\lambda=1550\) nm, under blackbody temperature for the Sun (5776 K) and Earth (288 K) with \(R_{f}=1\), \(N_{b}(\lambda)\) exhibits extreme variability, ranging from \(6.4\times 10^{-20}\) W/Hz in the solar-dominated worst-case scenario to \(2.5\times 10^{-33}\) W/Hz under optimal Earth nighttime conditions. This thirteen-order-of-magnitude disparity highlights the critical role of orbital positioning and receiver pointing accuracy in mitigating background noise, particularly when operating near solar illumination zones, where adaptive filtering strategies become essential for maintaining communication fidelity.
# Clinical Assessment of Customized 3D-Printed Wrist Orthoses
## 3 Phase II Trials  
The control group had nine subjects (6 males, 3 females; average age 65.2) with wrist fractures and were given fiberglass cast treatment without surgery. The experimental group consisted of twenty-four subjects (4 men, 20 women; average age 49.2) with wrist fractures and were given customized wrist braces with a fine layer of mesh linear underneath 10 days after surgery. Some of the wrist fracture cases include distal radius or a combination of ulnar fractures. Subjects in both groups were asked to perform daily tasks. They quantitatively rated their disability status using both the DASH (Disability of Arm, Shoulder, and Head) and the MAM (Manual Ability Measure) surveys at 2, 4, 8, 12, 26, and 52 weeks after medical treatment. As shown in Figure 4, the 3D brace scores were much lower than the plaster cast on both the DASH and MAM by two-way ANOVA test. There are statistically significant differences denoted in \(p\) < 0.05 (*), \(p\) < 0.01 (**), and \(p\) < 0.001 (***). The vertical axis in Figure 4 denotes the disability level; it depicts the 3D brace was clearly better than the plaster cast in terms of post-immobilization perceived disabilities. The data also showed that, regardless of the type of immobilization, i.e., cast or customized wrist brace, longer wearing time resulted in lower disability scores.
## 5 Field Trial and Validation
### Experimental Setup  
As WQM is used here to show the performance of the proposed LDAP algorithm, we used different types of sensors with the SNs mainly to collect various water quality parameters such as pH, turbidity, and temperature, and air parameters such as temperature, pressure, and humidity. In Figure 9a, a Google map view of one field trial is shown, where three SNs (red dots) were installed with one static DS (red triangle) and one mobile DS; lines of different color show the LoRa links. In the first phase, we collected sensor data using the static DS1 from SN1, SN2, and SN3 directly, and then we collected data from SN1 via the SN3 as the RN using DS2 (red lines). SN1 has no line of sight (LOS) with DS2 (the green line shows the link with no line of sight). In the second phase, we collected data from SN1 using a mobile DS. Figure 9b shows the map view of some positions of the mobile DS. We also measured the energy performance of the SNs using an energy monitoring and logging setup in the laboratory.
# Functional Safety Networks and Protocols in the Industrial Internet of Things Era
## 3 Functional Safety Protocol over Wireless Networks
Thus, the reaction time of an FSN implemented on top of a given industrial wireless network is strictly related to the polling time of a device connected to that network. If we suppose that the adopted safety protocol is a pure master-slave, \(T_{c}\) is the cycle time (i.e., the time necessary to execute the polling of all the slaves), \(N\) is the number of slaves, and \(T_{pi}\) is the polling time of the \(i\)-th slave, then trivially, the reaction time, \(T_{r}\), becomes  
\[T_{r}=2T_{c}=2\sum_{i=1}^{N}T_{pi} \tag{1}\]  
since one cycle time is the maximum time necessary to detect the fault, and another cycle time, in the worst case, is necessary to notify the command to all the slaves. As an example, Table 2 reports the indicative values of the polling times for the networks mentioned so far. They have been derived from [20] for WISA, from [21] for Wireless HART and from [22] for WIA-FA. As it is evident, the networks have different performances. However, it has to be stressed that several applications, for example, those concerned with process automation, often do not require very fast reaction times, even if they have to be upper bounded [23; 24]. Thus, even slower networks may be profitably used, provided that their behaviors ensure bounded reaction times. Finally, it has to be pointed out that the addressed networks represent meaningful examples, but further ones may be considered, since the black channel approach does not introduce any specific limitation.