esa-sceva/satcom-chunk-collection huggingface dataset:
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 2 Mission and Payload Operations
### Operations Workflow
The image data are automatically downlinked at the next ground station pass by the MCS. Downlink of a single capture may require more than one ground station pass, as an example in Table 3 shows. After being downlinked, the data are extracted from the MCS database and processed. ing off non-essential subsystems on the satellite to avoid damage caused by radiation from a solar flare), news about algal blooms, and requests made by researchers or scientists. During the first months of operations, 5-6 captures per day were typically planned for nominal operations because they fit within the downlink capacity limit for compressed raw data. More captures can be performed per day for a short period, but a backlog for downlink accumulates. The backlog must be downlinked at a later stage, which limits the downlinking time which can be dedicated to new captures. Assuming undisturbed operations, around 450 MB can be downlinked per day [29], equivalent to 5-6 captures when using a single ground station (Longyearbyen, Svalbard or NTNU Trondheim). Imaging targets geographically close to the ground station reduce the time that can be dedicated to downlinking data because the ground station communication then overlaps with an observation. Effective downlinking of data requires pointing the satellite antenna toward the ground station, which is not possible while pointing the camera to the targeted area, as the antenna and camera is fixed to the spacecraft. Only the ground stations in Norway and at Svalbard are currently in use, causing the largest downlink capacity reduction for imaging operations over the Arctic, Norwegian areas and other parts of northern Europe. To mitigate this, more ground stations that support S-band radio communication spread out at other locations on the earth could be included in the ground segment.
## 3 Results
### Classification Features Choice
Due to the fact that impulse noise has a broadband spectrum, the kurtosis value remains high from 5 to 15 in the entire frequency range; the presence of a signal leads to the appearance of an additional extremum in the frequency band of 50-100 kHz. Figure 17b shows the same data for the stationary noise of the loading device. Figure 16: Time realization of stationary noise (**a**), time manifestation of AE signal against the background of impulse noise (**b**), wavelet spectrogram of noise (**c**), and wavelet spectrogram of AE signal against the background of impulse noise (**d**). Figure 18 shows the dependences of the wavelet frequency kurtosis on time. Figure 18a corresponds to the case of impulse noise, and Figure 18b to the case of stationary noise. Since the impulse components of noise are extended in the frequency domain and localized in time, and the continuous components of the stationary noise, on the contrary, are extended in the time domain and localized in the frequency domain, the frequency kurtosis for stationary noise is higher than for impulse noise. The frequency range of the useful AE signal is broader than that of stationary noise but narrower than that of impulse noise, so the presence of a useful signal increases the kurtosis of impulse noise and decreases it for continuous stationary noise. The full list of features selected for classification is given in Table 7. For classification, it is proposed to use wavelet coefficients averaged over time and frequency, as well as time and frequency wavelet kurtosis. Compression is used to reduce the dimensionality of the selected parameters. Compression is performed using the discrete wavelet transform (DWT) with the Daubechies wavelet 'db6'. The parameters depending on the frequency are compressed by 4 times (up to the A2 approximation), and those depending on the time are compressed by 64 times (up to the A6 approximation). Figure 17: The time wavelet kurtosis vs. frequency for impulse noise and for the AE signal against the background of impulse noise (**a**) and the same data for the stationary noise of the loading device (**b**). Figure 18: The frequency wavelet kurtosis vs. time for impulse noise and for the AE signal against the background of impulse noise (**a**), for the stationary noise (**b**).
## 6 Performance Evaluation
These mechanisms reduce radio-on time and improve duty-cycle performance, thereby extending the overall network lifetime. Experimental results demonstrate that AAS achieves lower energy consumption compared to other schemes like ALICE and A\({}^{3}\). Figure 8: PDR versus packet interval. Figure 7: Contention probability versus slotframe length for different packet intervals. Figure 9 illustrates the network lifetime for different slotframe lengths. The result shows that when a packet interval is set to 1 min and the slotframe of 101 timeslots, the network lifetime in AAS is 10% higher than A\({}^{3}\), 23% higher than ALICE, and 64% higher than Orchestra. Node's energy consumption is higher for a shorter slotframe, as nodes wake up more frequently. It can be minimized by increasing the slot frame size. However, it is at the cost of higher end-to-end latency. For a smaller slotframe with higher traffic conditions, AAS consumes more energy, as it has more active cells for a short period than in the Orchestra. For simulation, we considered 60 nodes that represent a mid-scale industrial scenario, balancing complexity and real-world density. To evaluate the scalability of the proposed AAS mechanism, we conducted simulations using 40 node count, arranged in grid topologies of \(6\times 5\) (600 m \(\times\) 500 m). The performance in terms of latency, PDR, and contention remains comparable, with AAS demonstrating its advantage over existing schedulers.
## 2 Materials and Methods
### Methods  
The methodology used in this study is based on 5 phases (Figure 2): (1) Data extraction, (2) Data processing, (3) Data analysis and (4) Energy poverty mapping. Data processing and analysis are performed using Python 3.12 and QGIS 3.34.11 'Prizren'. Python is used for statistical analysis, while QGIS facilitates the visual representation of data on cartographic maps [33].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**District ID** & **District** & \begin{tabular}{c} **Number of** \\ **Census Tracts** \\ \end{tabular} & \begin{tabular}{c} **No.** \\ **Inhabitants** \\ \end{tabular} & **Area (km2)** & \
\begin{tabular}{c} **Population** \\ **Density** \\ **(Inhabitants/km2)** \\ \end{tabular} &
\begin{tabular}{c} **Average Annual** \\ **Income per** \\ **Household (€)** \\ \end{tabular} \\ \hline
1 & Casco Antiguo & 47 & 57,068 & 4.23 & 13,491 & 39,934 \\ \hline
2 & Macarena & 61 & 73,682 & 3.22 & 22,883 & 26,814 \\ \hline
3 & Nervión & 41 & 51,084 & 3.19 & 16,014 & 46,398 \\ \hline
4 & Cerro-Amate & 69 & 89,593 & 7.45 & 12,026 & 23,045 \\ \hline
5 & Sur & 53 & 70,274 & 7.53 & 9333 & 35,113 \\ \hline
6 & Triana & 43 & 46,926 & 9.25 & 5073 & 35,837 \\ \hline
7 & Norte & 51 & 71,235 & 38.45 & 1853 & 27,892 \\ \hline
8 &
\begin{tabular}{c} San Pablo-Santa \\ Justa \\ \end{tabular} & 50 & 59,068 & 5.63 & 10,492 & 34,803 \\ \hline
9 & Este & 69 & 106,490 & 30.09 & 3539 & 31,856 \\ \hline
10 &
\begin{tabular}{c} Bellavista- La \\ Palmera \\ \end{tabular} & 29 & 42,284 & 16.29 & 2596 & 39,820 \\ \hline
11 & Los Remedios & 19 & 25,525 & 15.19 & 1680 & 49,483 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the districts of Seville [28; 31; 32].  
Figure 1: Districts and census tracts of Seville. The numbers indicate the district ID.
## 5 Simulation Experiment
### Effectiveness Verification of Improvement Strategies  
In order to verify the effectiveness of the proposed improvement strategy for the MISSA algorithm, three incomplete variants of the MISSA algorithm were designed, namely: 1 MISSA_1, which introduces global and local search strategies on the basis of the original SSA, without including population initialization strategy. 2 MISSA_2 introduces population initialization strategy and local search strategy, without including global crossover strategy 3 MISSA_3 includes population initialization strategy and global crossover strategy, but does not include local search strategy. For all cases, the algorithm is repeated ten times to eliminate the influence of policy randomness. To calculate the index values of the set solved by the algorithm, the solution sets obtained by the MISS A algorithm and its variants on the case are merged, and then sorted by non-dominated relationships to obtain the solution of the first dominated frontier. This first dominated frontier solution is then used as the optimal Pareto frontier solution set for the case. The performance of the algorithm was compared using three evaluation indicators, GD, IGD, and \(\Delta\). The comparison results are shown in Table 6 and Figure 8. Table 7 lists the average values of ten solution results for MISS A and its variants on this test case, while Figure 8 shows the indicator value box plot of the ten solution results obtained by the algorithm. From Table 6 and Figure 8, it can be seen that compared with the algorithm variant, the GD index values obtained by the MISSA algorithm are superior to those of the algorithm variant in 9 cases, and have significant advantages in some cases such as MK01, MK02, MK03, and MK09. In terms of IGD indicators, MISSA also outperforms the comparison algorithm in 8 cases. In terms of diversity index \(\Delta\) value, the performance of MISSA and MISSA2 algorithms is superior to the other two algorithms. The GD and IGD indicators obtained by the three algorithms MISSA_1, MISSA_2, and MISSA_3 have their own advantages in different cases, proving that each improvement strategy proposed in this paper can improve the overall performance of the improved algorithm. In terms of algorithm stability, the MISSA algorithm outperforms the three variant algorithms in terms of GD and IGD indicators of the solution results.  
Based on the above analysis, each improvement item effectively improves the performance of MISSA in solving FJSP-RPM problems, and the effect is significant.
## 4 Experimental Results
### Implementation Details  
All the models are trained using ISIC 2018 [18] training data, and their performance is assessed using testing data. A dermoscopic image (input), its matching ground-truth segmentation mask and an edge (contour) image (outputs) are required for training the proposed model. By using a contour detection technique, the ground truth of the edge picture may be automatically extracted from the ground truth of the segmentation mask. We trained all the models with stochastic gradient descent (SGD) with a learning rate of 0.001, momentum = 0.9 and decay = 0.0005. The batch size is set to 1 and the models were trained for 100 to 200 epochs with early stopping criteria. The models were trained with binary cross-entropy loss.  
The methods were implemented in Keras with the TensorFlow backend. All the experiments were carried out on Nvidia Tesla V100 GPUs with 32 GB memory.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 3 Sensors  
Engagement, as a perceived variable state, can be complicated to measure precisely. Sensors play a critical role in relaying the physiological and cognitive states to a digitized signal to be analyzed. We pay attention to _behavioural_, _emotional_, and _cognitive_ cues and features that each type of sensing technique is good at acquiring, as well as strengths and weaknesses of different sensing techniques in different application domains. Specifically, we investigate data logging, eye trackers, cameras for visual surveillance, laser sensors, physiological measurement devices, and microphones.
## 1 Introduction
An objective method to measure alertness is through reaction time (RT) to an unpredictable incoming stimulus. Higher alertness levels are expected to result in shorter RTs, and vice versa. One of the tests used for this measurement is the Go/No-go association task [10; 11]. This test is designed to assess selective RT, where subjects must respond as quickly as possible to a specific stimulus and inhibit their response when a different stimulus appears. The literature suggests that RT may be influenced by various variables, such as age, sex, handedness, or education [12; 13]. There is a consensus that RT tends to increase with age [14]. The effects of light, particularly light with a wavelength of approximately 480-500 nm, on alertness have gained importance with the discovery of a new type of photoreceptor [15] known as intrinsically photosensitive retinal ganglion cells (ipRGCs). ipRGCs are highly sensitive to blue light and play a crucial role in the suppression of melatonin, a hormone that regulates numerous processes related to circadian rhythm and sleep [9; 16]. Exposure to blue light, especially in the evening and at night, can interfere with the natural circadian rhythm by inhibiting melatonin production through ipRGC activation [17]. On the other hand, red represents significant psychological connotations related to danger, caution, or alertness. Studies suggest that red light can also affect mood and visual well-being or modify alertness states [18; 19; 20; 21]. Cognitive performance is defined as the brain's capacity to process information and carry out tasks such as perception, thinking, and reasoning [22]. This work focuses on attention and memory because of their relevance to the processing of visual stimuli. Attention is the mechanism involved in the selective processing of psychological activity [23], enabling a response to a single stimulus among various distractors and reducing the response time to expected events [23]. If an individual anticipates what will happen, they can prepare to act accordingly and achieve a shorter RT through preparatory action. Memory refers to the brain functions that allow us to organize, store, and retrieve information [23]. The Atkinson and Shiffrin model [24] divides memory into three types: sensory (a few seconds),short-term (a few minutes), and long-term. Information is registered by sensory memory, transferred to short-term memory, and can either generate an associated response or be stored in long-term memory. The main objective of this study was to design three computerized tests to assess visual function, alertness, and visuocognitive integration under three different lighting conditions (white, red, and blue) in healthy young subjects. Specifically, this study aimed to evaluate visual function under these lighting conditions, measure ocular changes via aberrometry after the tests compared with a baseline measurement, and analyze cognitive processing and alertness by quantifying accuracy and reaction time under different lighting conditions.
## 2 Dynamic Time History Analysis of the Structure  
The reasons for damping are extremely complicated, and mathematical and physical characteristic languages are employed to describe the damping characteristics. The damping force \(F_{c}\) is commonly described as the product of the damping coefficient and the particle velocity:  
\[F_{c}=cv \tag{1}\]  
where \(c\) denotes the damping coefficient, and \(v\) represents the relative velocity of the particle.  
The damping ratio is a key parameter in characterizing the behaviour of oscillating systems:  
\[\xi=c/c_{cr} \tag{2}\]  
\[c_{cr}=2m\omega_{0}=\sqrt{2 km} \tag{3}\]  
where \(c_{cr}\) denotes the critical damping coefficient; \(m\) denotes the mass; \(\omega_{0}\) represents the natural frequency; and \(k\) represents the stiffness coefficient.  
For the bridge pier structure system, considering the impact of viscous damping, the motion equation of the free vibration of the viscous damping system is [22]:  
\[[M]\{a\}+[C]\{v_{d}\}+[K]\{x\}=\{F_{e}\} \tag{4}\]  
where \([M]\) denotes the mass matrix of the structure; \([C]\) denotes the damping matrix of the structure; \([K]\) denotes the stiffness matrix of the structure; \(\{x\}\) denotes the displacement of the structure; \(\{v_{d}\}\) denotes the velocity of the structure; \(\{a\}\) denotes the acceleration of the structure; and \(\{F_{e}\}\) represents the external force of the structure.  
In this study, the structural dynamic analysis adopts the Rayleigh damping method [16]:  
\[[C]=\alpha[M]+\beta[K] \tag{5}\]  
where \(\alpha\) denotes the mass damping coefficient, and \(\beta\) denotes the stiffness damping coefficient. The calculation formula is as follows:  
\[\alpha=\frac{2\xi f_{m}f_{n}}{f_{m}+f_{n}} \tag{6}\] \[\beta=\frac{2\xi}{f_{m}+f_{n}}\]  
where \(\xi\) represents the damping ratio of the structural material, and \(f_{m}\) and \(f_{n}\) are the \(m\) and \(n\)-order natural circular frequencies, respectively. It is recommended to take the first and fifth orders.  
The error calculation formula is as follows:  
\[E_{M}=\frac{R_{2}-R_{1}}{R_{1}}\times 100\% \tag{7}\]  
where \(R_{2}\) and \(R_{1}\) are the dynamic response values under different conditions.
## 3 Actuation and Control
### Control  
The arm is entirely passive, so that the control system is located on the MA. A microcontroller, specifically an Arduino Nano, controls the MA's movement, joint rotation, and gripper. The angular displacement of the joint rotation mechanism is measured using a magnetic encoder that provides 24 counts per motor revolution. Given the large transmission ratio, a change of one degree between a pair of links requires counting approximately 400 encoder steps, enabling precise control.  
To ensure that the MA stops precisely at the correct position to engage the gears and rotate the joints, small magnets are inserted into the centers of the joints, and a Hall effect sensor (A1302) is mounted on the MA. Inputted into the control algorithm is the initial configuration of the arm and a series of \(m\) actions, where each action specifies the \(i\)-th joint number and the required angle for that joint, \(\theta_{i}\). By using the encoder and Hall sensor, the MA can reliably bring the arm to the desired configuration.  
The series of actions for the motors is derived from the motion planning algorithm, as presented in Figure 7. This setup allows for precise and reliable control of the robot's movements by ensuring the accurate positioning and actuation of the joints.
# Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting
## 3 Experimental Results and Analysis
### Analysis of Pre-Crack Effect
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Degree of Effect**} \\ \cline{5-6}  & & & **Severe** & **Moderate** & **Mild** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Geological conditions \\ \end{tabular} } & \multicolumn{3}{c}{Distance between blasting point and structures} & \(\Circle\) & & \\  & \multicolumn{3}{c}{Overburden} & & & \\  & Uncontrollable & \multicolumn{3}{c}{Topography and stratum} & & & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Blasting conditions \\ \end{tabular} } & \multicolumn{3}{c}{Type of explosive} & \(\Circle\) & & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{Charge per delay} & \(\Circle\) & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{Delay time of detonation} & \(\Circle\) & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & & \\ \cline{1-1} \cline{3-5}  & Controllable & \multicolumn{3}{c}{} & \multicolumn{3}{c}{Drilling angle} & & \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & & \\ \cline{1-1}  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{Burden and hole spacing} & & \\ \cline{1-1}  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & Stemming conditions & & \\ \cline{1-1}  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{Primer position} & & \\ \cline{1-1}  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{Charge weight} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Group of factors affecting blast-induced vibration. Figure 8: Blasting vibration analysis by area. (**a**) General blasting; (**b**) combined blasting. resulting in relatively lower vibration levels [37]. Furthermore, reducing the minimum burden helps decrease vibration levels [38].
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 3 Preliminaries
### Bloom Filter Plan within LBS  
The user, LBS server, and SNS should have the following properties for their protocols:  
1. User/server: The user and SNS can send a particular query if there is something in the LBS server's range.
2. Query list: The original server can locate one bit of data from the query list, while SNS can locate none.
3. Server range: Set of objects in the range of the server will need to be itemized.
4. Malicious LBS server: A malicious LBS server can be able to detect with probability q.
5. Probability: Probability q can be increased close to 1 by increasing the queries.  
The BF facilitates the testing of a secure membership and never discloses more than a bit of data concerning this set. As depicted in Figure 3, if A is an element that presumes a set S, then the BF generates a probabilistic system that can be used in determining if \"q is not a component of S\" and H(q) = 0, which means no false negatives occurred. Thus, the assertion is accurate. When all k values in the hash functions are 1, but the item q is not in S, false positives occur. B denotes bloom filter; H denotes hash function [58].  
LBS embraces BF, which can use data about a location set as bit vectors. A user most often creates q pairs (assertion, query), which are then transmitted to the LBS, which then creates a vector of q qualities. If the \(i\)th points of this established vector are zero, then the resulting Qi and Ai are unpredictable. However, if the \(i\)th point is 1, it follows that Qi and Ai are steady [33].  
Figure 2: Zero Knowledge protocol example [50].  
The assessments can be correct even if the server is benign. Conversely, a malicious server results in erroneous assessments. As previously established, the client can build Q and A pairs with an identified state. It follows that the server can run tests against recognized values. False positives, which most likely point to an erroneous server, can be decreased using N reiterations of q series. Therefore, a server molded to have a malicious event can be identified by the property (\(1-\mathrm{p^{N}}\)), where p refers to a genuine false positive. Apart from a negligible probability caused by the inverse HK, malicious servers cannot receive information from any exchange.