esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Regionalized Error Similarity Compensation Method  
According to the character of error similarity, the positioning error of the robot in Cartesian space can be expressed as an unbiased optimal estimation model related to its joint configuration, and the error of the desired position is estimated depending on the error of the sampling points in the space. However, it can be seen from Section 2 that the TCP calibration error will significantly affect the accuracy of the error estimation. In addition, even if the error estimation method can compensate more than 90% of the positioning error, when the initial positioning error of the robot is too large due to the large orientation variation of the TCP frame, the residual error will still make its positioning accuracy difficult to meet the requirements of the aerospace manufacturing.  
For the purpose of solving the above problems, a regionalized error compensation method considering TCP calibration error is proposed. The specific steps are as follows.  
Step 1: divide the robot workspace into \(n\) regions denoted as \(\Omega_{1},\Omega_{2},\cdots,\Omega_{n}\) according to the orientation variation of desired TCP frames, as shown in Figure 6. And the center point of each region is selected as \(\{TDR_{1}\},\{TDR_{2}\},\cdots,\{TDR_{n}\}\).  
Step 2: use the method of Section 2.2 to calibrate the transformation matrix from \(\{WR\}\) to \(\{TR\}\) in each region and denoted as \({}^{\text{WR}}\)\(\mathbf{T}_{\text{TDR1}}\), \({}^{\text{WR}}\)\(\mathbf{T}_{\text{TDR2}}\), \(\cdots\)\({}^{\text{WR}}\)\(\mathbf{T}_{\text{TDR}n}\). Then measure the positioning error of the robot in each region.  
Step 3: for any desired TCP frame \(\{TDR_{x}\}\), select the corresponding region \(\Omega_{x}\) according to its orientation and solve the theoretical joint configuration \(\theta_{x}\) according to the kinematics parameters.  
Step 4: the method of Ref. [29] is used to estimate the error of the desired TCP frame.  
Step 5: because the estimated error is based on the robot error sampling result, that is, the positioning error when the transformation matrix from \(\{WR\}\) to \(\{TR\}\)\({}^{\text{WR}}\)\(\mathbf{T}_{\text{TDR}}\), which is calibrated in the corresponding region. Therefore, it is necessary to transform the calibrated desired TCP frame from \({}^{\text{WR}}\)\(\mathbf{T}_{\text{TDR}x}\) to the default \({}^{\text{W}}\)\(\mathbf{T}_{\text{T}}\) of the robot and finally get the desired TCP frame after calibration, which is denoted as \(\{TDA_{x}\}^{\prime}\).  
For any kind of robot and control system, the above compensation method can be represented by the flow shown in Figure 7. The robot control system (RCS) only needs to send the desired TCP frame to the position compensation system (PCS) and receive the compensated TCP frame from PCS. Obviously, the proposed compensation method has good versatility for any kind of robot and control system.  
Figure 6: Regionalized error similarity compensation method.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 3 Results and Discussion
### Self-Organizing Control of Mega Constellation
The results indicate that the GW-2 sub-constellation can provide continuous coverage within \(\pm\)78\({}^{\circ}\) along latitudes. Figure 28 shows the multiplicity of coverage of the GW-2 sub-constellation, where the blue curve denotes the initial condition and the green curve denotes the final condition. It can be found that the GW-2 constellation always provides continuous coverage within \(\pm\)78\({}^{\circ}\) latitude zones during the simulation time. Figure 27: Coverage performance of the GW-2 sub-constellation.
###### Abstract  
Recently, the JPEG working group (ISO/IEC JTC1 SC29 WG1) developed an international standard, JPEG 360, that specifies the metadata and functionalities for saving and sharing 360-degree images efficiently to create a more realistic environment in various virtual reality services. We surveyed the metadata formats of existing 360-degree images and compared them to the JPEG 360 metadata format. We found that existing omnidirectional cameras and stitching software packages use formats that are incompatible with the JPEG 360 standard to embed metadata in JPEG image files. This paper proposes an easy-to-use tool for embedding JPEG 360 standard metadata for 360-degree images in JPEG image files using a JPEG-defined box format: the JPEG universal metadata box format. The proposed implementation will help 360-degree cameras and software vendors provide immersive services to users in a standardized manner for various markets, such as entertainment, education, professional training, navigation, and virtual and augmented reality applications. We also propose and develop an economical JPEG 360 standard compatible panoramic image acquisition system from a single PTZ camera with a special-use case of a wide field of view image of a conference or meeting. A remote attendee of the conference/meeting can see the realistic and immersive environment through our PTZ panorama in virtual reality.  
JPEG systems; JPEG 360; metadata; 360-degree image; PTZ camera; panorama  
1
# The Odyssey of Entropy: Cryptography
## 3 Entropy and Encryption
### Analysis and Evaluation
#### 3.2.3 Applications in Adversarial Analysis
* **Key Negotiation Attack:** It was shown by [PERSON] et al. [186] and [PERSON] et al. [187] that an attacker can manipulate the key entropy negotiation protocols used by Bluetooth and Bluetooth low energy and notably reduce the encryption key space. * **Backdoor Attack:** As an example of the applications of entropy in backdoor attacks, we can mention the research reported by [PERSON] and [PERSON] [188]. They argued that some backdoor attacks, such as _Monkey_, require the attacker to obtain a large number of ciphertext blocks all encrypted by the same symmetric key, each containing one known plaintext bit. They proposed a new backdoor that eliminates the need for known plaintext while leaking a bound on the plaintext entropy to the reverse engineer. * **Dictionary Attack:** Some researchers have worked on the role of entropy in dictionary attacks. For example, it was shown by [PERSON] et al. [189] that low-entropy keys make some PAKE (password-authenticated key exchange) protocols, such as the one presented by [PERSON] and [PERSON] [190], vulnerable to dictionary attacks. * **Algebraic Attack:** In addition to dictionary attacks, low-entropy keys make cryptosystems vulnerable to algebraic attacks. For example, the complexity of finding low-entropy keys using SAT (Boolean satisfactory problem) solvers was studied by [PERSON] et al. [191]. * **Collision Attack:** It was demonstrated by [PERSON] [192] that replacing random permutations by random functions for the update of a stream cipher causes entropy loss, which makes the cipher vulnerable to collision attacks. * **Correlation Attack:** [PERSON] and [PERSON] [193] argued that the _correlation-enhanced power analysis collision attack_ against AES proposed by [PERSON] et al. [194] usually yields a set of keys (instead of one) due to noise-related problems. To alleviate this problem, they proposed a practical search algorithm based on a theoretical analysis on how to quantify the remaining entropy.
## 2 Method
### Easy and Effective Control Algorithm  
We developed a practical control algorithm to enable upper limb amputees to use the developed hand easily and effectively. This algorithm uses the output of the CC, OS, and CS signals from the two EMG sensors to make the hand perform four hand gesture motions and four grip motions. Figure 6a shows the state transition diagram of the developed algorithm. The hand has a grip motion and gesture motion state, and the normal motion state is a grip motion. When the CC signal is inputted in the grip motion state, the hand switches to the gesture motion state, and when the CC signal is inputted again, the hand switches back to the grip motion state. When the grip motion state or gesture motion state is determined by the CC signal, the adduction or abduction state of the thumb is determined by the value of the HS mounted in the palm of the hand. If HS1 is high and HS2 is low in the grip motion state, an abduction state is applied. At this time, when the OS signal is applied, a hook grip with the thumb extension state is performed; when the CS signal is applied, a lateral grip with the thumb flexion state is performed. In the grip motion state, when HS1 is low and HS2 is high, an abduction state is applied. At this time, when the OS signal is applied, a power grip with the thumb extension state is performed, and when the CS signal is applied, a precision grip with the thumb flexion state is performed. When the CC signal is applied in the grip motion state (i.e., normal state), the hand changes to the gesture motion, and when HS1 is high and HS2 is low, an abduction state is applied. At this time, when the OS signal is applied, the thumb up gesture with the thumb extension state is performed, and when the CS signal is applied, the direction gesture with the thumb flexion state is performed. In the gesture motion state, if HS1 is low and HS2 is high, an adduction state is applied. At this time, when the OS signal is applied, the OK gesture with the thumb extension state is performed, and when the CS signal is applied, the victory gesture with the thumb flexion state is performed.  
For example, when an upper limb amputee wants to perform a victory motion, in the grip motion state, the CC signal is applied to switch to the gesture motion state; the thumb is put into the adduction state, and the CS signal is applied to perform the victory gesture. Further, when an upper limb amputee wants to perform a lateral grip motion in the victory motion, the CC signal is applied to switch to the grip motions state; the thumb is put into the abduction state, and the OS signal is applied to perform the hook grip motion (Figure 6b). Thus, the upper limb amputees using the easy and effective control algorithm can perform various hand motions with two (CC, CS or CC, OS) control commands.
## 3 Results  
### Baseline Characteristics of Participants  
Thirteen participants were initially selected to take part in our study, and three participants were excluded. We included 10 individuals who fulfilled the inclusion criteria, and these individuals were assigned to an untrained group and a trained group (\(n=5\) for each group). Baseline characteristics of all participants, including age, body weight (BW), height, BMI, % body fat, lean body mass (kg), habitual activity/week (h), and training load for each exercise are shown in Table 1. The two groups did not differ significantly in age, BW, height, BMI, or % body fat. However, the training load at 60% 1 RM differed between the two groups and the exercise type because of training experience. None of the participants in either group experienced any adverse events during or after the three training sessions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Untrained (\(n=5\))** & **Trained (\(n=5\))** \\ \hline Age (years) & 24.80 \(\pm\) 2.40 & 28.40 \(\pm\) 4.63 \\ \hline Weight (kg) & 78.66 \(\pm\) 15.52 & 80.72 \(\pm\) 12.32 \\ \hline Height (cm) & 174.60 \(\pm\) 2.80 & 173.60 \(\pm\) 3.61 \\ \hline Body mass index (kg/m\({}^{2}\)) & 25.72 \(\pm\) 4.59 & 26.78 \(\pm\) 4.21 \\ \hline Lean body mass (kg) & 63.16 \(\pm\) 11.80 & 65.76 \(\pm\) 7.44 \\ \hline \% Body fat & 22.54 \(\pm\) 9.44 & 22.74 \(\pm\) 4.46 \\ \hline Habitual activity/week (h) & 3.14 \(\pm\) 0.92 & 5.52 \(\pm\) 0.96 \\ \hline RM 60\% of Bent-Over Row (kg) & 28.50 \(\pm\) 8.43 & 43.00 \(\pm\) 11.14 \\ \hline RM 60\% of Deadlift (kg) & 31.50 \(\pm\) 10.00 & 56.00 \(\pm\) 12.08 \\ \hline RM 60\% of Lunge (kg) & 19.35 \(\pm\) 6.26 & 31.00 \(\pm\) 4.85 \\ \hline \hline \end{tabular} Values are expressed as the means \(\pm\) standard deviation. RM, repetition maximum. Training loads are presented for dumbbell exercises for both sides (left and right) in kilograms (kg).  
\end{table}
Table 1: Baseline Characteristics of Study Participants (\(n=10\)).  
Figure 2: Metabolic Equivalent (MET) of weight training exercises. (\(n=10\)).
## 3 Dielectric-Based Passivation Layers
### Light Management Techniques at Back Contact  
Light management in photovoltaic devices means giving the light another chance to be absorbed inside the solar cell, i.e., increasing the optical path length of the light. As mentioned in the previous section, using NPs is a convenient solution to manage the path of the light inside the solar cell by changing the direction of the light by reflection. The effect of the light management can clearly be seen in external quantum efficiency (EQE) and/or absorption measurements of the devices, which will be discussed in section v.  
Another way to control the incoming light at the rear surface is to use an additional silver (Ag) back mirror or Ag NPs at the rear surface under the passivation layer. In [19], Sn:In\({}_{2}\)O\({}_{3}\), indium tin oxide (ITO), was used as a back contact instead of Mo. ITO allows the use of a mirror on the glass substrate, different than Mo rear contact. In [19], SiO\({}_{2}\) NPs were used as a passivation layer and an Ag mirror was used at the rear surface (ITO); hence, significant enhancements in \(J_{sc}\) and \(V_{oc}\) values were measured. The improvement in \(J_{sc}\) values can also be clearly seen in EQE curves [19].  
In [25], instead of Ag mirror, Ag NPs were used together with Al\({}_{2}\)O\({}_{3}\) passivation layer. It is concluded in this study that using Ag NPs at rear surface enhanced the optical quality of the solar cell, and this can be seen through improved \(J_{sc}\) values and EQE curves. It is also concluded that this approach needs optimization because there should be a more homogeneous Ag NPs layer to help optical improvements.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Future Directions
#### 5.2.2 Advanced Security Mechanism  
Water supply systems are critical infrastructure and must be protected from any kind of cyber-physical attack. Due to the growing computing power that leads to the growth of many decoding algorithms, current approaches to solving data security are not sufficient to create a substantial improvement. This creates an urgent need for new studies to develop a novel and robust security mechanism for cyber-physical water systems. Advanced security mechanisms for CPSs are therefore considered a hot research area.
# Scale-BOSS-MR: Scalable Time Series Classification Using Multiple Symbolic Representations +
## 4 Evaluation
### Comparison with the State-of-the-Art Algorithms  
From the state-of-the-art algorithms, WEASEL 2.0 and MR-SQM exploit symbolic representations and are thus closer to our method. Table 9 and Figure 6 report on state-of-the-art algorithms:  
1. WEASEL V2.0 achieves the best accuracy of 0.904 with 339 s of mean total execution time compared to 309 of Rocket and 18.7 s of Mini-Rocket;
2. MR-SQM with five SFA representations achieves a 0.889 accuracy with 418 s of mean total execution time. With single SFA representation and single SAX representation, it achieves 0.878 with 176 s of exec time. Using only a single SFA, it achieves an accuracy of 0.861 with 117 s of mean total execution time.  
Given the above results, we can conclude the following:  
1. SBMR-RidgeCV-W8-trend-D6-BG achieves an almost state-of-the-art accuracy, but with a significant increase in total execution time compared with the state-of-the-art methods. SBMR-RidgeCV-W8-trend-D6-BG is only 1% less accurate than WEASEL 2.0, Rocket, and MiniRocket. This suggests that the added representations help achieve a state-of-the-art accuracy;
2. SBMR-RidgeCV-W11-trend-D6-BG achieves an accuracy of 0.881, very close to the accuracy achieved by state-of-the-art algorithms, and with 129 s of mean total execution time, which is significantly faster than state-of-the-art algorithms exploiting symbolic representations;
3. SBMR-RidgeCV-W15-trend-D6-BG achieves an accuracy of 0.871, which is still close to the state of the art with 79 s of mean total execution time, being even faster than SBMR-RidgeCV-W11-trend-D6-BG.  
These results show that the SCALE-BOSS-MR can be configured to achieve high accuracy, with low total execution time, compared to state-of-the-art methods exploiting symbolic representations.  
Figure 6: Average total execution time (in seconds) and accuracy for the state-of-the-art representations.
# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
## 4 Results and Discussion  
### Performance of the Proposed DAE  
At training, the loss of the model started as 0.0019, but it is saturated at the 45 th epoch to 0.0005 and it is early stopped at the end of the 50 th epoch. After the training, 1000 mixed ultrasonic A-scan signals were used to test the performance of the DAE. Figure 10 shows some samples of the testing signals and the flaw signals extracted from the mixed signal via DAE. The upper signals of Figure 10 are the mixed A-scan signals that are named the original signals, and the bottom signals are the flaw signals that are extracted from the original signals via the DAE used in this study.  
The flaw signals extracted from the original signals share some similarities. The amplitudes of the reconstructed flaw signals are reduced compared to those of the original signals, and some of the extracted flaw signals still contain remnants of the background signals beside the flaw signal.  
To evaluate the extracted signal, a point-by-point comparison method is suggested. The validation dataset is zero-padded according to the pre-obtained gates by ultrasonic testing experts, which indicate the location of the flaw. The evaluation method, or point-by-point comparison method, involves subtracting all the data points in two different datasets and averaging the outcome by the number of data points. This helps evaluate the performance of the extracted signal dataset, where the better the performance, the smaller the value will be. Table 3 shows the point-by-point comparison of the extracted flaw signal and the flaw signal zero-padded by the pre-obtained gates.  
Figure 10: The examples of the performance testing of each flaw types: (**a**) A-scan mixed crack signal (original signal) and extracted crack flaw signal from the original signal; (**b**) A-scan mixed lack of fusion signal (original signal) and extracted lack of fusion flaw signal from the original signal; (**c**) A-scan mixed slag inclusion signal (original signal) and extracted slag inclusion flaw signal from the original signal; (**d**) A-scan mixed porosity signal (original signal) and extracted porosity flaw signal from the original signal; (**e**) A-scan mixed incomplete penetration signal (original signal) and extracted incomplete penetration flaw signal from the original signal.  
Table 3 shows the point-by-point comparison of the extracted flaw signals and the zero-padded flaw signals in both training and testing. The training dataset demonstrated better performance, as the average difference value was lower. However, for the testing dataset, the average difference value was not significantly high. The average difference indicates that, on average, the data points have that amount of difference from the original flaw. If there were distortions in the data, the average difference would be much larger, considering the average of the maximum points.
## 3 Results
### Sibari  
We analyzed the surface movements occurring in the Sibari Plain (hereinafter SP). The SP is located in the northeastern part of the Calabria Region (Southern Italy) and extends over an area of -500 km\({}^{2}\)[30]. The SP has an important economic factor thanks to its agriculture, and there is also a cultural enticement due to the presence of the ancient Sybaris, which was a famous Greek colony during the Magna Grecia period. The studied site is also interesting due to its flooding hazard, even if it is reduced by a drainage channels network. Subsidence phenomena have been known in the Sybaris coast and its archaeological area since the 1950s. More than 20 cm were observed between 2003-2010, which was ascribed to groundwater exploitation and primary terrain consolidation.  
Figure 5: Campi Flegrei 2011–2015 uplift highlighted by COSMO-SkyMed interferometric synthetic aperture radar (InSAR) data. Deformation trend estimated along the ascending (**a**) and descending (**b**) orbits. In the right panels, the time series of four points are shown.  
We adopted the SBAS approach to a SAR image dataset acquired from the ESA ENVISAT satellite along the ascending track. This dataset (track 86, frame 789) contained 38 images in the interval 2003-2010, resulting in 135 pairs. In the processing chain, temporally decorrelated atmospheric artifacts were estimated using double-pass filtering in the temporal and spatial domain, respectively. The SRTM DEM (90 m) was selected to subtract the topography contribution. Precise orbit positions were considered to remove orbital residuals, and ground control points (GCPs) on stable areas were chosen mainly along the frame borders to remove residual ramps.  
The surface deformation field was typified by common subsidence with a rate of up to \(\sim\)15 mm/year (Figure 6). Anthropic activities can be considered an additional factor for coastal erosion and subsidence phenomena. Indeed, higher velocities were observed in the more urbanized areas, where urban-induced loading magnified soil compaction. Some areas were characterized by extreme water withdrawals (e.g., Sybaris archaeological site); water table changes may increase the mean displacement velocity, which could be the reason for the set back consolidation process in soft materials.
## 3 Materials
### Study Area  
In this study, to evaluate the classification capabilities for different machine learning based classifiers, an image of 636 \(\times\) 954 pixels for an urban area in Beijing, China (see Figure 1) is selected. A summary of the geographic location, number of spectral bands, imagery pixels, and cloud cover is displayed in Table 2. In particular, all satellite images of Sentinel series could be freely downloaded from Sentinel Open Hub ([[https://sciubub.copernicus.eu/](https://sciubub.copernicus.eu/)]([https://sciubub.copernicus.eu/](https://sciubub.copernicus.eu/))). The officially customized software Sentinel Application Platform (SNAP) is utilized to import all the sensor information and export tailored data for follow-up analysis in comparison to other geo-software such as ENVI [25; 26]. The selected field is a typical area composed of five main classes: buildings (such as universities, factories and companies), trees, roads, water, and crop fields.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Band No.** & **Characteristic** & **Wavelength (\(\upmu\)m)** & **Resolution (m)** \\ \hline
1 & Coastal Aerosol & 0.443 & 60 \\
2 & Blue & 0.490 & 10 \\
3 & Green & 0.560 & 10 \\
4 & Red & 0.665 & 10 \\
5 & Near Infrared & 0.705 & 20 \\
6 & Near Infrared & 0.740 & 20 \\
7 & Near Infrared & 0.783 & 20 \\
8 & Near Infrared & 0.842 & 10 \\
8A & Near Infrared & 0.865 & 20 \\
9 & Water Vapour & 0.945 & 60 \\
10 & Cirrus & 1.375 & 60 \\
11 & Shortwave Infrared & 1.610 & 20 \\
12 & Shortwave Infrared & 2.190 & 20 \\ \hline \end{tabular}
\end{table}
Table 1: Band information of Sentinel-2A/B.