esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.5.2 Curve Fitting of the Adsorption Breakthrough  
The breakthrough obtained with the RSSCs for every GAC profile as function of the BV was modelled via curve fitting. The breakthrough was defined following [PERSON] [36] definition, as the time between C/C\({}_{0}\) equals to 0.05 and 0.95. The BV is a dimensionless parameter defined as in Equation (3) [36]. The modelling was performed according to two different approaches: under the hypothesis that the adsorption of the model compound follows a first-order kinetic [37] and under the hypothesis of a second-order kinetic [38]. The best-fitting model will allow a proper comparison between the materials.  
\[\mathrm{BV}\ =\ \frac{\mathrm{V_{Feed}}}{\mathrm{V_{R}}}\ =\ \frac{\mathrm{t}}{ \mathrm{EBCT}} \tag{3}\]  
where V\({}_{\mathrm{Feed}}\) is the volume fed (mL), V\({}_{\mathrm{R}}\) is the adsorbed volume (mL), t is time (min) and EBCT is the empty bed contact time (min).  
The fitting with a first-order kinetic was carried out by the model described in Equation (4):  
\[\mathrm{G}\ =\ \frac{\mathrm{q\ K\ BV}}{1+\mathrm{K\ BV}} \tag{4}\]  
where G is the amount of the model compound per gram of GAC, q and K are adsorption parameters and BV is the BV at which the effluent concentration is measured.  
For the second-order kinetics, the proposed model is stated in Equation (5):  
\[\frac{\mathrm{C}}{\mathrm{C}_{0}}\ =\ \frac{1}{1+\mathrm{e}^{(\mathrm{A-B\ t} )}} \tag{5}\]  
where C is the concentration at time t, C\({}_{0}\) is the initial concentration, A and B are adsorption parameters and t is time.  
The viability of the model is evaluated by the regression coefficient (square of the Pearson Product-Moment Correlation Coefficient) R\({}^{2}\). Values closer to 1 mean more representative data.
# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 4 Simulation Methodology
Our project considers scenarios where SNs are placed over a large area with no direct communication between them, so we considered a single UAV as a mobile sink to gather data. In these scenarios, the SN has it own timeslots to become active (as considered by other authors), but we propose that the SN timeslots can be shared or dedicated. As the UAV must fly over them to find out which are their active timeslots, along with the Prohibition List, we propose a new strategy ensuring the UAV path length reduction, associated with a reduction in the SN active time after SNs are properly scheduled. In addition, the UAV decisions strategy is based on always consulting the ProhibitionList database. Our focus was not to evaluate the quality of data transferred between UAV and SNs, or study energy constraints of the communication because these aspects were already explored by other authors, as cited before. Our focus in this work is on the dynamics of UAV flight to understand which scenario presents better results based on each functionality of the system.
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 4 Discussion  
The Android Raw GNSS Measurements API and the progress made by smartphone and GNSS chipset manufacturers allowed us to evaluate the possibility of using this new generation of GNSS receivers for scientific applications in the field of geosciences. This study represents a feasibility study for real-time detection of fast and periodic movements and ionospheric monitoring using GNSS measurements from a Xiaomi Mi 8. We used two pieces of software that were developed internally at Sapienza University of Rome that have already been used for several published studies: VADASE to estimate 3D velocity with a stand-alone receiver and VARION to compute sTEC time variation.  
Considering the position/velocity domain, the noise level obtained in static scenario (i.e., few mm/s for the horizontal components and around 1 cm/s for the vertical component) underlines the possibility of detecting fast movements with horizontal velocity higher than 1 cm/s. In fact, using a vibrating table, a simulated 1D periodic movement was retrieved with RMSEs of 0.011 m/s and 0.02 m for the residual velocity and displacements with respect to the reference solution obtained with a geodetic receiver. The acquisition rate, fixed to 1 Hz from the Raw GNSS Measurements API, could represent the main constraining factor for the detection of fast movements. However, considering, for example, the effects of earthquakes on smartphones, the acquisition rate should ensure the detection of most of the effects, since the typical period of seismic waves is between 6 and 7 s.  
The preliminary ionospheric analysis carried out in this paper shows that the measurement noise could prevent the possibility of using raw GNSS data from smartphones for ionosphere monitoring: RMSEs for both GPS and Galileo were 5 to 6 times higher then the results obtained on the M0 SE station. However, using the moving median technique it was possible to reduce--in real-time--the noise to levels comparable to what is achievable with geodetic class receivers, increasing the capabilities of raw Android GNSS measurements. Nonetheless, it is important to highlight that all these considerations are extremely dependent on the desired object of detection in the ionosphere: not all the traveling ionospheric disturbances are characterized by the same frequency and period.  
The results obtained in this paper encourage further tests to evaluate the performance of Android smartphones in more realistic environments. In fact, smartphones are typically used in urban environments where GNSS signal is poor, and in kinematic conditions. This and further feasibility studies will contribute to define the benefits coming from an integration of GNSS measurements from smartphones in the existing monitoring systems for geophysics phenomena using a Big Data analysis approach in order to increase the knowledge of our World.
## 5 Conclusions  
In this study, the time histories of radial and axial expansive pressures for the entire hydration process of SCA in cylinders with various diameters were first obtained based on the \"axial-output method\". The chemical reaction and expansion process of SCA was then simulated by thermal expansion. Taking the product of the normalized axial expansive pressure and the amplitude coefficient as the load input, a finite element model for the physical tests and the mathematical model for variation of elastic modulus with the major principal stress were established. The following conclusions can be drawn:  
(1) Under the volume constraint condition, the hydrated SCA is compacted with increasing expansive pressures. As a result, the elastic modulus increases first and then tends to be stable at a constant value. When the expansion pressure is greater than 20 Mpa, the elastic modulus is stable at about 36 Gpa.  
(2) When the elastic modulus remains unchanged, both the radial and axial expansive pressures of SCA increase linearly with increasing Poisson's ratio. When the radial pressure remains unchanged, the Poisson's ratio and the amplitude coefficient are inversely proportional and have no impact on the ratio between axial and radial pressures.  
(3) A mathematical model for variation of elastic modulus with the major principal stress is established based on the variation laws of elastic modulus and Poisson's ratio of the SCA model. The modeling results are in good agreement with the measured data. Hence, this model is feasible for investigating the expansion process of SCA in a cylinder with constant volume and providing practical mechanical parameters for engineering application of SCA.  
This paper is a result of the collaboration of all co-authors. [PERSON] conceived and designed the study; [PERSON] and [PERSON] conducted the experimental; [PERSON] drafted the manuscript; [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Data fitting for the relationship between normalized stress and elastic modulus.  
Figure 13: Radial and axial pressures for models with varying cylinder diameters.  
Funding: This research was funded by the State Key Research Development Program of China (Grant No. 2017/YFC06029002). The fourth author also wish to express his thanks to China Scholarship council (Grant No. 202006080082) for the support. Conflicts of Interest: The authors declare no conflict of interest. List of Symbols
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 5 Analysis and Discussion  
This section provides a detailed analysis of the performance and internal behavior of the proposed watermarking method, based on the experimental results presented earlier. Beyond raw performance metrics, it explores how various design components interact to influence robustness, imperceptibility, and computational efficiency. The discussion is structured into four subsections: analysis of error patterns and recovery behavior, the synergy between redundancy and adaptive modulation, computational cost breakdown of the proposed method, and a comparative evaluation against other state-of-the-art watermarking techniques.
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 2 Materials and Methods
### Data
#### 2.2.2 Forest Attributes and Disturbance  
We used forest age [43; 44], forest composition [43; 45], and forest land cover [42] (Table A1). Details on the development and validation of these datasets have been described elsewhere [42; 44; 45; 46]. With respect to disturbance data, we used forest fires and harvest [46] as well as Canada Landsat Disturbance (CanLaD, [47]) and [PERSON] et al.'s [48] Global Forest Change (GFC) 2000-2022 product (version 1.10). Furthermore, we used the forest insect damage event database (polygon-based) provided by the Government of Ontario [49].
## 4 Results and Discussions
### Verification of Optical Method Using Ultra-Sound Measurements
Using ImageJ, the frozen tissue boundaries were measured, and the results are shown in Figure 11, where the depth of frozen tissue is shown as a function of time. It is important to note, that as can be seen from this graph the maximum depth measured by ultrasound was also about 1.6 mm. To correlate the ultrasound measurements with the backscattering optical intensity, the fiber optic sensor on side-A was placed in contact with the frozen tissue, and the synchronized intensity timeline for both measurements were recorded. In Figure 12a, the backscattering optical intensity timeline for the fibers array sensor is shown, while Figure 12b shows the synchronized timelines for both the backscattering intensity and the depth of frozen tissue measured by ultrasound. Figure 11: Measurement of frozen depth (mm) with ultrasound as a function of thawing time (s) measured from side-A. Figure 12: (a) Intensity timeline; (b) optical intensity and frozen tissue depth using ultrasound. In Figure 12b, the synchronized timelines begin at 60 s which was the time the fiber array sensor was placed in contact with the frozen skin. Furthermore, no changes in intensity were recorded in the central fibers (3 and 4), until 90 s, corresponding to the time required for the frozen depth, under the fiber array sensor, to be reduced to about 1.6 mm which is the maximum detectible frozen depth. Moreover, the ultrasound frozen skin depth measurements exhibited a similar timeline which was linearly correlated (\(R^{2}\) = 0.98) to the optical intensity measured by fiber sensor (Figure 13). The results shown in Figure 13 are in agreement with the previous calibrations experiments in Figure 8a, and effectively verify, by an independent method, that the backscattered intensity from the frozen tissue can be used to accurately measure the depth of freezing.
# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 1 Introduction
### FREEDM Architecture with Multiple Levels
#### 2.1.3 Intelligent Energy Management (IEM) Level  
The IEM level coordinates the SST and PnP level in order to manage DRERs, DESDs, and loads effectively. The typical functions of the IEM level include control commands to the SST, the management of ER, control of DESDs for charge/discharge, and so on [46].  
IEM also communicates with the intelligent fault management (IFM) system of the respected local area for fault isolation. The revenue metering and instrumentation systems are also managed by IEM for its participation in the demand side management. According to varying information about load-demand and network topology, the effective correctness of the unit commitment and tariff scheduling is updated by IEM in coordination with higher levels of the FREEDM system.
# A Review on Methods and Systems for Remote Collaboration
## 3 Electronic Devices Used in Remote Assistance
The system records the local space and sends images to the remote user, who annotates them and sends them back to be visualized in AR by the local user. The following table (Table 1) presents the repartition of the aforementioned articles and the system components used for the proposed solutions. Interested readers can easily and quickly identify the works that address certain areas/tasks and the methods/equipment on which the solution of remote assistance/collaboration is based. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Application** & \multicolumn{6}{c}{**Devices for Capturing and Visualization**} \\ \cline{2-7}
**(Task Type)** & **Video** & **HMC 1** & **HMD** & **Smart Device 2** & **Robotic Device** & **Projector** & **Display** \\ \hline Surgery & [1,2,4,7,8] & & [2,7,8] & & [1,2,4] & & [1,2,4] \\ \hline Crime Investigation & [26] & & [26] & & & [26] \\ \hline Building Wired Circuit & & [27] & [27] & & & [27] \\ \hline Lego Assembly & [28,35] & [35] & [28,42] & [42] & & & [28,35,42] \\ \hline Education & [29] & & & & [29] & [29] \\ \hline Physical Task—Industry & [30] & & & [30] & [30] & [30] \\ \hline Medical Consultation & & [31] & & & [31] \\ \hline Aviation & & & [32] & & [32] \\ \hline Car Maintenance & [33] & & & [33] & & \\ \hline Wiring tasks & [34] & & & [34] & [34] \\ \hline Object Manipulation & [36,37,53] & & [38] & [36,54] & [37] & [36, 37, 38,54] \\ \hline Object Pointing & [39] & & & [39] & [39] \\ \hline Maintenance—Industry & [41] & [40] & & [60] & [41] & [40,41] \\ \hline Object Identification & [55] & [54,58] & [43, 44, 45,54,58,59] & [46,55] & [43,46] & [45,46,59] & [45,46,54,55] \\ \hline Cooking & & [47] & & & & \\ \hline Assembly—Furniture & [48] & & [48] & & [48] & [48] \\ \hline Assembly Task & [57] & [49, 50, 51] & [57] & [52] & [57] & [51,57] & [51] \\ \hline Sport—Education & & & [53] & & [53] \\ \hline Manufacturing & [16] & & & [16] & \\ \hline \hline \end{tabular}  
* Head-mounted camera; 2 Mobile phone/table.  
\end{table} 
Table 1: Devices and components for remote collaboration systems.
## 1 Introduction
[PERSON] et al. [18] have studied the influence of atmospheric water content, temperature, and aerosol optical depth on downward longwave radiation in arid conditions. [PERSON] and [PERSON] [12] proposed a cloud fraction for all-sky conditions, which was defined as the ratio of the measured solar radiation to the clear-sky radiation (\(k_{c}\)). Other authors used the clearness index (\(k_{l}\)), which is defined as the ratio of the measured solar radiation to the extraterrestrial solar radiation [7,19]. Therefore, the cloud modification factor (\(clf^{\prime}\) or \(clf\)) can be estimated as follows:  
\[clf^{\prime}=1-k_{c}\text{ or }clf=1-k_{l} \tag{3}\]  
The determination of the daytime downward longwave radiation is estimated in all cases under clear-sky and all-sky conditions [4,11,20,21,22,23]. However, [PERSON] and [PERSON] [7] have proposed a downward longwave model based on air temperature and relative humidity under all-sky conditions which are classified into four cases: All-day, nighttime, and daytime with and without considering the cloud modification factor. A lot of publications appeared in the literature concerning the shortwave radiation components measured at Athalassa (2016, 2017, 2018, 2019, 2018), and [PERSON] et al. (2019) have presented the \"Typical Meteorological Year\" for Nicosia (Cyprus). More recently, [PERSON] et al. (2019) presented an assessment of the solar radiation climate at two sites in Cyprus one at Athalassa an inland location and the second one at Larnaca a coastal location, based on measurements of about two years. Furthermore, [PERSON] et al. (2019) have analyzed the direct, diffuse, and global short-wave irradiation using three years of data. The characteristics of the longwave downward and upward irradiance at two sites in Cyprus were also presented by [PERSON] et al. (2019). The paper has also evaluated various empirical models under clear-sky and all-sky conditions. The current study covers two years of measurements (2016-2017) of various short and longwave radiation components and has four objectives. Firstly, the longwave data were undergone an extensive quality control process based on tests proposed by the Baseline Surface Radiation Network (BSRN). The tests include globally physical possible, extremely rare and climatological configurable limits as well as global cross-comparisons for the surface longwave radiation measurements ([PERSON] et al., 2019). Furthermore, consistency and persistency tests were applied to improve the validity of the data. Secondly, the data were statistically analyzed to reveal the characteristics of longwave radiation on a monthly and hourly basis. Furthermore, we implement simple empirical models which were developed in a previous study under clear and all-sky conditions using local calibrated coefficients (2019). Finally, the net longwave radiation is calculated from the difference of downward and upward longwave irradiances and estimated from other models based on air temperature, relative humidity and global solar radiation.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Additional Considerations
#### 3.6.3 Trusting the Controller  
One question that may remain is how the Smart Object can trust the Controller. Basically this is explained and discussed in the EAP KMF (Sections 2.3 and 3.1 Part b) [14]. In short, the trust is based on the fact that the controller receives and uses (proof of possession) the MSK (in reality a key derived from the MSK such as COAP_PSK or DTLS_PSK) to establish a security association after a successful authentication. Since the IdP's AAA server provides this MSK to the Controller and the Smart Object trusts the IdP's AAA server, if the AUTH option is verified correctly it means the controller obtained the MSK from a trusted entity (the IdP's AAA server) and derived the COAP_PSK. Alternatively, if the DTLS handshake is used and finishes correctly, it means the Controller was able to derive the DTLS_PSK from the MSK.  
Additionally, the Controller needs to be trusted by the AAA infrastructure. This is typically done by the SP administrator configuring the Controller with a credential (e.g., a shared secret) that can be verified by the SP's AAA server. Moreover, the SP's AAA server is configured to register the Controller as a trusted entity. In the same way, SP's AAA server is trusted by the IdP's AAA server. Thus, the Controller is trusted by the AAA infrastructure for a principle of trust transitivity, which is the common model in AAA infrastructures [11].
## 1 Introduction
Quantum dots (QDs) are semiconductor nanocrystals with broad absorption spectra, narrow and tunable emission, and high photoluminescence [29]. Combining the unique optical ability of QDs and specific recognition of MIPs, the common applications of imprinted polymers in optical sensors have been reported for the detection of different molecules. For example, [PERSON] et al. developed molecularly imprinted silica layers coated with QDs for diethylslibestrol [30]. [PERSON] et al. obtained molecularly imprinted sensor-coated QDs to determine tetrabromobisphenol-A (TBBPA) by sol-gel method [31]. [PERSON] et al. have described a novel fluorescent sensing platform by employing inorganic perovskite quantum dots as a fluorescence signal for the detection of omotheate [32]. However, there have been no fluorescence-imprinted sensor studies used to detect _Listeria monocytogenes_ in food samples. The objective of this study was, therefore, to fabricate stable, rapid, low-cost, and convenient fluorescence-imprinted polymers for the visual qualitative identification of _L. monocytogenes_ via a fluorescence microscope. The fluorescence MIPs were prepared by Pickering emulsion polymerization, in which whole _L. monocytogenes_ were directly used as the template, and the bacteria-chitosan-QDs network stabilized the particles. In addition, a novel MIP-based sensor based on the response of fluorescence intensity was developed for the qualitative detection of _L. monocytogenes_.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### Experiments
#### 4.2.2 Performance Evaluation for the Mobility Scenario  
We evaluated GCS-routing performance for the mobility scenario shown in Figure 10(a), where Drone 4 generating 1 Mbps UDP traffic to GCS moves along the arrow at 2 m/s. GCS-routing updated drone routes based on the mobile drone's location, as shown in Figure 12. GCS-routing redistributed routing tables only twice (at 31.5 and 46 s), and network disconnection time due to the route update was only approximately 1 s for each redistribution. Thus, GCS-routing reacted to topology changes immediately and would be suitable for real-time applications. In contrast, after Drone 4 lost connection with Drone 1, DSDV with 4 s HELLO interval (DSDV-4s) could not reconfigure a routing table, because Drone 4's velocity was too fast for DSDV-4s to configure a valid routing table before the next topology change. For example, when a route passing through Drone 3 was configured, Drone 4 was already out of communication range of Drone 3, so the new routing table was no longer valid. Throughput was still unstable when HELLO interval was reduced to 2 s. Network disconnection time was significantly longer than that of GCS-routing.  
On the other hand, OLSR provided relatively stable throughput compared with DSDV. OLSR updated routing tables four times, at 8.5, 44.5, 55, and 67.5 s, but the first and the third updates were unnecessary. Thus, OLSR wasted more than 9 s due to frequent and unnecessary route updates. Table 1 shows that GCS-routing achieved higher average throughput and superior stability compared with both DSDV and OLSR.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Throughput (Kbps)** & **Standard Deviation** \\ \hline GCS-routing & 974.5 & 219.7 \\ DSDV-2s & 756.4 & 441.5 \\ OLSR & 798.7 & 417.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Throughput for the mobility scenario.  
Figure 10: Route update time and throughput of DSDV and GCS-routing.