esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effect of Temporal Resolution and Input Variables  
The first experiment revealed that a higher temporal resolution of the input time series resulted in a more accurate classification. This is consistent with the results of crop classification studies by [19; 60], who also reported improved accuracy with higher temporal resolution, although with marginal improvements in [60]. The highest accuracies were achieved using multivariate daily time series consisting of NDVI, fAPAR, and FCover inputs. This suggests that incorporating a wide range of features can indeed be beneficial, contrary to the expected limitations of the Hughes phenomenon, where the addition of too many features beyond a certain point does not improve classification accuracy [61]. This added value of using more input features is observed at both daily and dekadal temporal resolution.  
In addition to the use of other engineered features, the use of (normalized) reflectance bands improved the performance of most of the RF and CNN models over using NDVI alone. Notably, the combination of CNN with normalized input features resulted in the best performance for the CNN model. This finding indicates a potential loss of information when overly relying on feature engineering in the case of CNN models [50]. Normalization of input features proved crucial when using reflectance band inputs in combination with the CNN model: the normalization not only increased the F-scores but also yielded more consistent predictions between model runs. This improvement can be attributed to the normalization process ensuring that the variation in data is not overly dominated by features with a larger dynamic range, thus balancing the influence of each feature on the model's output [17]. Experiment 2B showed that both extensively preprocessed datasets for higher temporal resolution and raw input time series can achieve similar accuracies when used with the appropriate model type.  
The experiments demonstrated that additional information could be extracted from the raw input by using biophysical variables in addition to the NDVI or by using a suitable model type in combination with the raw input itself. Future work could explore additional vegetation indices, similar to the approach taken by [59] in their catch-crop-type classification, especially since a number of alternative indices are aimed at addressing specific NDVI limitations. In addition, our study focused on the use of Sentinel-2, and although the daily NDVI, fAPAR, and FCover time series were derived from combining Sentinel-1 and Sentinel-2, a winter crop classification might benefit from a direct integration of radar-based (Sentinel-1) input features [8; 62; 63].
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 4 Results and Discussion
### Part 2--Isolated Noise Model  
To confirm the explanation in the previous subsection about the effect of the noise, we performed another test and moved the noise source to the filter's output and isolated it. As shown in Figure 6, we still obtained better results with the narrow RX filter; however, its advantage decreased and the penalty for using a wider filter dropped to 0.65 dB and 1.2 dB to DFE and FFE, respectively (compared to 1.6 dB and 1.8 dB).  
Figure 5: Penalty graph for desirable BER = 1 \(\times\) 10\({}^{-4}\), where 0 dBm OIP penalty corresponding to \(-\)14 dBm. **(a)** FFE with \(N_{1}=6\) **(b)** DFE with \(N_{1}=6\) and \(N_{2}=3\).  
On top of the negligible impact of the TX filter, the isolation of the noise resulted in an insignificant impact of the RX filer changing as well. From this part, we can understand that the main cause of the equalizer's reduced performances is the noise, and not only the ISI or the non-linear ISI.  
Compared to the results in the previous subsection, the required penalty is doubled and even tripled in some of cases; this emphasizes the filter's critical part as a noise reducer.
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
###### Abstract
With respect to existing fault diagnosis methods, the technique introduced in this paper has the advantage of recognizing faults quantitatively by using only three points on the \(I\)-\(V\) curve around the maximum power point (MPP), irradiance and panel temperature, and thus, does not need the whole \(I\)-\(V\) curve, which may not always be available during the normal operation of the PV system. Moreover, due to the fast elaboration of the ANN results, it can be easily implemented on an embedded system for online elaboration once the training phase is performed offline.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Change Detection Algorithms
#### 4.2.3 HDDMA and HDDMW  
Drift detection algorithms created according to the Hoeffding Drift Detection Method (HDDM) modify the original DDM using Hoeffding's inequality [14]. HDDMA is one of these algorithms and uses the moving average of the observed error rate to estimate the true error rate of the classifier. The algorithm calculates the average error rate over a sliding window of fixed size and compares it to [PERSON]'s bound. If the observed error rate exceeds the bound, the algorithm signals a drift.  
The HDDMW algorithm is similar to the HDDMA algorithm but uses a moving weighted average of the observed error rate instead. The algorithm assigns a weight to each data point based on its recency, with more recent data points having higher weights. The weighted average is then used to estimate the true error rate of the classifier and then compared to [PERSON]'s bound. HDDMW is effective in detecting both abrupt and gradual drifts, and it outperforms DDM and HDDMA in some scenarios.
## 3 Data Collections
### Satellite Measurements
#### 3.1.3 MISR-HR FAPAR Product for Skukuza  
This MISR-HR FAPAR product has been extensively evaluated [53; 54; 55; 56] and serves as a benchmark in various contexts, as noted earlier. It also offers three advantages over the traditional NDVI used in many phenological studies: (1) FAPAR is much better grounded in radiation transfer theory and has been identified as an \"Essential Climate Variable\" by the GCOS, which is not the case for NDVI, (2) this particular product is derived using the JRC-TIP algorithm, a state of the art inversion procedure that has been extensively validated (see, e.g., [[https://fapar.jrc.ec.europa.eu/Home.php](https://fapar.jrc.ec.europa.eu/Home.php)]([https://fapar.jrc.ec.europa.eu/Home.php](https://fapar.jrc.ec.europa.eu/Home.php)), last visited on 21 November 2020), and routinely used by the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and the European Commission Quality Assurance for Essential Climate Variables (QA4 ECV) project, for instance, and (3) this product is made available at a higher spatial resolution (275 m) than any of the traditional global vegetation index products (500 m at best, and often 1 km or more) available over the last 20 years.  
The Skukuza flux tower site falls within Block 110 of MISR Paths 168 and 169 (Figure 1, panel (a)). The repeat period of this instrument for a single Path is exactly 16 days, but the study area is located within the overlap of those Paths, giving a staggered revisit period of seven and nine days between those two Paths. A total of 859 combined Orbits for both Paths (i.e., 429 and 430, respectively) were acquired between 15 March 2000 and 30 December 2018; 17 of them were irretrievable from NASA's Atmospheric Science Data Center (see [[https://asdc.larc.nasa.gov/project/MISR/](https://asdc.larc.nasa.gov/project/MISR/)]([https://asdc.larc.nasa.gov/project/MISR/](https://asdc.larc.nasa.gov/project/MISR/)), last accessed on 14 October 2020.), due to technical issues with the platform. As a result, the longest period between successive acquisitions is 32 days. However, cloudiness further reduces the availability of usable surface observations, so the time gaps between successive observations is typically 7 or 9 days during the dry season, but may reach up to 100 days or more during the wet season.  
The MISR-HR FAPAR product is made available on the Space Oblique Mercator (SOM) map projection, like all standard MISR data products. This projection minimizes shape distortion and scale errors throughout the length of the Orbit near the satellite ground track. However, each Path possesses its own unique SOM map projection parameters, so assembling data sets involving two or more Paths imply re-projecting the data onto a common grid, in this case, the geographic spatial reference system, i.e., European Petroleum Survey Group (EPSG) code: 4326, as explained below in Section 4.2.2.
## 3 Results and Discussion
### Adsorption/Desorption Isotherms of Activated Dry Mortars (Mix 2, Mix 3 et Mix 4)
The increase of 1% CaCl2 leads to a decrease in the volume of adsorbed N2 from 28 to 17 cm3/g. The volume of hysteresis seems to be little impacted. On the other hand, the intensity of the adsorbed N2 volume is six times higher than the volumes measured on the raw materials. These two curves have a very low adsorption/desorption hysteresis loop compared to the samples activated by the two previous activators. Their specific surface areas are also low according to the data in Table 8.  
All isotherms of Mix 2, Mix 3 and Mix 4 exhibit hysteresis loops at P/P0 > 0.5; this is related to the capillary condensation in the interparticle voids. The linear section corresponding to P/P0 = 0.05-0.2 is due to the sorption of nitrogen on the surface of the mesopores and the external surface of particles, mesopores will be filled at around P/P0 = 0.3-0.4. Then, the second linear section at P/P0 = 0.4-0.05 is due to the sorption of nitrogen on the external surface of particles. In summary, when 5% to 10% Na2 CO3 is added, a decrease in the volume of adsorbed N2 is observed. For the similar formulation and implementation, the increase in the w/b ratio from 0.5 to 0.6 leads to a significant increase in the volume of N2 adsorbed. The increase is 12 cm3/g for 5% and 10 cm3/g for 10% Na2 CO3. The volume of hysteresis seems to be little impacted by the addition. When 6% Na2O3 Si is added, for the mixture of WSA+GGBBS and a w/b ratio = 0.5 to 0.6, then, an increase in adsorbed volume is observed with very little impact on the evolution of hysteresis. For the same ratio w/b:0.5 or 0.6, increasing the concentration of Na2O3 Si leads to a significant decrease in the volume of hysteresis and the volume of N2 \begin{table}
\begin{tabular}{c c} \hline
**Mix 1** & **Specific Surface Area BET (m2/g)** \\ \hline Mix 2 (5\% Na2O3), w/b = 0.5 & 6.91 \\ Mix 2 (10\% Na2 CO3), w/b = 0.5 & 5.63 \\ Mix 2 (5\% Na2 CO3), w/b = 0.6 & 9.41 \\ Mix 2 (10\% Na2 CO3), w/b = 0.6 & 8.75 \\ \hline Mix 3 (3\% Na2O3 Si), w/b = 0.5 & 9.01 \\ Mix 3 (6\% Na2O3 Si), w/b = 0.5 & 5.88 \\ Mix 3 (3\% Na2O3 Si), w/b = 0.6 & 10.64 \\ Mix 3 (6\% Na2O3 Si), w/b = 0.6 & 6.43 \\ \hline Mix 4 (2\% CaCl2), w/b = 0.5 & 4.73 \\ Mix 4 (3\% CaCl2), w/b = 0.5 & 3.33 \\ \hline \end{tabular}
\end{table} 
Table 8: Specific surface area BET for Mix 2, Mix 3 and Mix 4 (activated samples). adsorbed. Increasing 1% CaCl\({}_{2}\) results in a decrease in the volume of adsorbed N\({}_{2}\) from 28 to 17 cm\({}^{3}\)/g. Hysteresis seems to be little impacted.
## 3 Design Approach and Methodology
### Target Audience and Application Output  
The _Anat_Hub_ application seeks to assist undergraduate health sciences students at the University of the Western Cape (UWC) to learn and understand the basic musculoskeletal anatomy of the upper and lower extremities, by allowing them to visualize and interact with 3D models in an immersive and non-immersive manner. The MVP and main outcome of the application consist of core features: AR mode, 3D mode, Glossary, and Quiz. The \"Clinical Anatomy by Regions\" ninth edition by [PERSON] was used as a source to add anatomy content to the muscle information panels in the AR and 3D modes, glossary, and quiz. This was done so that we could provide users with information that aligns with the standard anatomy curriculum.  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline  & \multicolumn{1}{c}{**Software**} \\ \multirow{4}{*}{**Unity**} & This cross-platform game engine was used to develop the _Anat_Hub_ application. Unity allows for the implementation of markerless AR Functionality of the _Anat_Hub_ application UI Using Unity’s On Click events, sound was incorporated into the application to enhance user experience and provide users with a feedback response for UI interaction \\ \multirow{4}{*}{**Unity Visual Studio IDE**} & Used for scripting and debugging and to improve the functionality of the application. Various touch inputs to enhance UX: tap to interact with icons or models, double-tap to zoom in and out of models, and swipe up or down to scroll Background music and sounds were manipulated in Visual Studio to enhance user experience and provide users with a feedback response for UI interaction \\ \multirow{4}{*}{**3 ds Max**} & Used to design, construct, modify, rig, and animate the assets (upper and lower limb) needed in the application with the modifier and motion tools. \\ \multirow{4}{*}{**Mockup**} & Mockup was used to create a storyboard (style guide) for icons, fonts, colour palette, aspect ratio, and orientation of the application. Lucid chart and Framer were used to depict and create the prototype layout (UI and UX) and general flow of the application \\ \multirow{4}{*}{**Monday.com and Hack ‘n plan**} & These tools were used for the project management and communication between the teams and main stakeholders of the application \\ \multirow{4}{*}{**Guogle Mons**} & These software tools were used to create the UI icons and design of the _Anat_Hub_ application. \\ \multirow{4}{*}{**Kita, Photopea, Adobe Studio**} & \multicolumn{1}{c}{**Hardware**} \\ \multirow{4}{*}{**Computer system**} & Functions as input, processing (internal storage, computation and control), output, secondary storage (for data and programs), and communication for our application development. \\ \multirow{4}{*}{**Mobile (android) device**} & Functions as a platform to test application builds. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Functions of the various software and hardware used for the _Anat_Hub_ application development.
# An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks
## 4 The Protocol Details  
The whole operation is divided into periodic rounds. Each round consists of the cluster head election and data transmission phases. Cluster members sense the information from the environment and send the collected data to their cluster head. A cluster head receives and aggregates the data from its member nodes and sends the data towards the BS based on the constructed routing path. Data transmission phase should be longer than cluster-head election phase to reduce the overhead of the protocol and to prolong the network lifetime. Several control and data messages are used. The description of the messages is given in Table 2. A flowchart of ECCR operation has been given in Figure 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Message** & **Description** \\ \hline _Hello\_Msg_ & Tuple (selfid, clusterid), a control message of nodes’ initial information. \\ \hline _Node\_Msg_ & Tuple (selfid, selfrank, selfenergy), a control message of members’ information. \\ \hline _Handover\_Msg_ & Tuple (selfid, clusterid, headid), a control message of hand over the role of cluster head to a prospective cluster head in a cluster. \\ \hline _Schatule\_Msg_ & Tuple (schedule, order), a control message for assign the time slot for a member node to send local data to an associate cluster head. \\ \hline _Routr\_Msg_ & Tuple (selfid, selfrank, selfenergy, disttoBS), a control message to collect neighbor cluster heads’ information. \\ \hline _D\_Msg_ & Tuple (selfid, clusterid, selfrank, selfenergy, ‘_local data_’), a local data message from a member node to associate cluster head. \\ \hline _AD\_Msg_ & Tuple (selfid, clusterid, newchopid, ‘_fused data_’), an aggregated data message from a cluster head to a next hop or BS. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the control and data messages.
# Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery
## 2 Materials and Methods
### Augmented Reality Environment Integration  
AR enables the overlay of holograms into a real environment, creating an augmented experience. This technology is particularly useful in robotic surgery, where the surgeon's information is limited to images from the endoscopic camera. In the context of surgical simulators, AR provides many advantages such as better integration with real surgical environments, due to its partially immersive nature which allows for more realistic simulations of the actual operating room, improved spatial awareness, and depth perception for a better understanding of the relationship between toll pathways and tissues. It also enables enhanced hand-eye coordination, preserving natural coordination, and an easy transition to real surgeries [19].  
HoloLens 2 was used for this study as it has shown the most promising results across all parameters and improved performance measures in surgical trainees [20].  
The simulator was developed using Unity 3D, considering aspects such as AR framework, physics engines, and high-quality graphics. Due to the PhysX 5 physics engine, included in Unity, this platform is particularly useful for physical simulations of soft bodies such as organs [9].  
An accurate representation of the anatomical structures and surgical tools within the virtual environment was essential to achieve a realistic simulation.
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 4 Threat Models and Attack Scenarios
### RTL-SDR Attack  
Aviation sectors such as UAVs are vulnerable to SDR Attacks. The newest threat to aviation, RTL-SDR software, is installed on a system, and it has hardware used to connect to the tuned frequency and can listen to message exchanges between the devices. The common RTL-SDR frequency is 25 mhz-1750 mhz. It is used to receive and decode radio signals cheaply using a personal computer device. RTL-SDR explores the vulnerability of aviation. By the RTL-SDR, we can easily listen to all the information shared between the ground station (monitor or remote) and UAVs [102]. RTL-SDR is a software-hardware device installed in pc, and hardware is connected to a pc via USB or cable. The attacker tunes the frequency at which the drone flies  
Figure 4: SkyGrabber software attack in Beyond Line Of Sight (BLOS) scenario.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **Frequency** & **Usage** \\ \hline L1 & 1575.42 MHz & L1 Civilian (L1C) \\ L2 & 1227.60 MHz & L2 Civilian (L2C) \\ L3 & 1381.05 MHz & Nuclear/Research \\ L4 & 1379.913 MHz & Research \\ L5 & 1176.45 MHz & Safety-of-life(SOL) Data \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequencies used by GPS channels in UAVs [25].  
with the help of RTL-SDR. As tuning is comparatively easy in RTL-SDR because it knows the range of frequency the drone and other aviation devices are working. After tuning, the attacker eavesdrops on the information shared between the drone and GCS [103]. Hack-RF is a similar device used to work with radio frequency. The basic hack-RF 1 has a receiver and transmitter. The attacker can tune to an RF frequency by using it and can even transmit messages [104]. A hack-RF device can receive and transmit between 1 Hz and 6 GHz, which is better in terms of range than RTL-SDR.  
As the connection shown in Figure 5, the pc, RTL-SDR dongle, and antenna are the leading equipment used in the link. Firstly the RTL-SDR dongle is connected to the pc; the SDR antenna is further connected to the SDR dongle. The SDR sharp software running on the system is simulated with the SDR dongle. These attacks violate the drone's confidentiality and integrity. When the communication between the drone and drone device control is encrypted, the attack is not easy to execute. A possible method to restrict this attack is to employ frequency hopping, employ message confidentiality, and user authentication.
## 4 Monitoring System Framework
### Components of the Multi-Source Monitoring System
#### 4.1.2 Roof and Floor Proximity Monitoring  
FBG displacement sensors like that shown in Figure 6 are used to detect the roof and floor proximity of the self-made roadway and its specific parameter models are shown in the Table 3.  
The FBG displacement sensor uses an isosceles cantilever beam structure. Two FBGs with different central wavelengths are arranged on the strain beam to monitor the relative displacement of the top and bottom plates. The internal structure of the sensor is shown in Figure 7. When the distance  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter Type** & \multicolumn{6}{c}{**Parameter Values**} \\ \hline Range (kN) & 200 & 300 & 500 & 1000 & 2000 & 3000 & 5000 \\ Resolution accuracy & & & & 1\%\(\times\) F.S. & & & \\ Grating number & & & & 3 & & & \\ Central wavelength of grating (mm) & & & & 1510–1590 & & & \\ Reflectivity & & & & \(\geq\)90\% & & & \\ Height (mm) & & & & 100 & & & \\ Internal diameter (mm) & 20 & 202 & 40 & 60 & 90 & 200 & 300 \\ External diameter (mm) & 70 & 80 & 95 & 130 & 175 & 270 & 650 \\ Weight (kg) & 1.9 & 3 & 3.7 & 6.5 & 11.8 & 17 & 197 \\ Connection mode & & & & Welding or FC/APC splicing & & \\ Installation mode & & & & & Piercing jacket & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of FBG anchor cable force sensor.  
Figure 5: FBG anchor cable force sensor.  
Figure 6: FBG displacement sensor.  
between the fixing ring of the wire rope and the main body of the sensor increases, the wire rope causes the winding wheel to rotate; the winding wheel causes the precision screw to rotate, energy is stored in the scroll spring, the precision screw moves in the direction of the strain beam and the flexural strain of the strain beam increases. The measured displacement is obtained by a calibration formula relating displacement to flexural strain. When the distance between the fixing ring of the wire rope and the main body of the sensor becomes small, the winding wheel rotates under the elastic force of the scroll spring, the wire rope is coiled onto the winding wheel, the precision screw moves to the far side of the strain beam and the flexural strain of the strain beam decreases. The measured displacement is again obtained by the displacement-flexural strain calibration formula. The displacement will be measured once a day.