esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition and Preprocessing  
EEG data was recorded during the speech-in-noise task at a rate of 4096 Hz using the BioSemi ActiveTwo system (BioSemi B.V., Amsterdam, The Netherlands). Electrodes with 64-channels were placed in line with the international 10-20 layout. The data from each electrode were bandpass filtered from 1 to 50 Hz, using a zero-phase finite-impulse-response filter with symmetric, non-causal response patterns [53], and re-referenced to the average amplitude from two reference electrodes at the mastoids. 3-s long epochs were extracted from 0.5 s before the stimulus onset to 1 s after the offset. Each epoch was baseline-corrected by subtracting the average amplitude of the pre-stimulus period from \(-\)200 ms to 0 ms relative to the stimulus onset and down-sampled to 256 Hz. Then, the Infomax algorithm ([PERSON] et al., 1996) implemented in EEGLAB [54] was used to decompose a set of epochs into independent components of multiple sources. The ocular artifact components were identified and removed from the data by visually examining the standard patterns (e.g., a robust frontal-channel activity or decreasing magnitude on the spectrum as a function of frequency) from the component scalp map, time course, and power spectrum. The reconstructed and cleaned epochs were averaged for each experimental condition, regardless of behavioral accuracy, to generate event-related potentials (ERPs) per condition at each electrode.  
The neural SNR was calculated using ERPs in the \"_NR-off_\" condition averaged across the front-central channels (FZ, FCz, FC1, FC2, and Cz). After ERP data were bandpass-filtered between 2 and 7 Hz again with zero-phase distortion, Hilbert transformation was applied, and then the absolute value was taken to derive the temporal envelopes. ERP temporal envelopes were used, as we evaluated the overall temporal dynamics of evoked potentials in the sensor space rather than focusing on a single ERP component. The peak amplitude of temporal envelopes obtained across 100 to 400 ms after the word onset was compared to that across 50 to 250 ms after the noise onset to compute the neural SNR in a dB scale (Figure 1 bottom panel). The concept of the neural SNR aligns well with the literature illustrating the enhanced neural representation of target-to-masker ratios due to efficient auditory scene analysis [32; 33]. Since its introduction by [PERSON], [PERSON] [30], the neural SNR has been used as an EEG measure of how individuals react differently to background noise and target words in multiple studies with varying hearing populations [31; 34; 35]. The original study [30] validated this cortical index by comparing two performance groups (good vs. poor); significantly different evoked responses occurred at the onset of task stimuli, whereas evoked responses to the neutral cue phrase \"check the word\" (not included in the current study) were not different between the two groups, indicating that individual differences in neural SNR were directly related to the speech-in-noise task.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review
### Challenges in Localization and Orientation
#### 3.1.2 Scarcity and Limitation of Maps  
[PERSON] et al. [1] reported that RSA agents primarily use Google Maps for outdoor spaces, and they perform Google searches to find maps for indoor places. RSA agents who participated in [PERSON] et al.'s study [1] reported that coarse or poor maps of malls or buildings limit their ability to assist users. They also stated that many public places either have no maps or have maps with insufficient details, which forces them to rely on another sighted individual in close proximity of the user for assistance. Sometimes, agents must orient users using their camera feeds only [1; 42], which makes the challenges worse. Navigating complex indoor layouts is a well-established challenge in pedestrian navigation, as reported by many researchers [13; 19; 33; 85].
## 3 Results and Discussions
### Intake Pressure
In addition, CA50 advances with increasing intake pressure. At the intake pressure of 12 kPa, Pmi increases with the delayed CA50, which indicates the combustion phase is too early and deviates from the thermodynamically optimal value. In Figure 11c, the increase in Pmi benefits from improved gas exchange rather than in-cylinder combustion. When the intake pressure is increased from 0 kPa to 12 kPa, the measured intake flow rate increases from 1.98 L/min to 2.23 L/min. Even though Pmi shows a visible increase with increasing intake pressure at the cylinder block temperature of 120 \({}^{\circ}\)C, the maximum Pmi is still obviously lower than that at the cylinder block temperature of 80 \({}^{\circ}\)C. For the gas exchange process using the cross-scavenged mode, increasing the intake flow by boosting the intake pressure is impractical because in-cylinder pressure at the end of the scavenging process is determined more by exhaust pressure than by intake pressure. At the cylinder block temperature of 80 \({}^{\circ}\)C, the intake and exhaust flow rates are also more extensive than those at the cylinder block temperature of 120 \({}^{\circ}\)C, resulting in a higher exhaust back pressure observed in the pressure curves in Figure 6a. So, the effect of boosting the intake pressure on the gas exchange is relatively small compared to that at the cylinder block temperature of 120 \({}^{\circ}\)C. At the cylinder block temperature of 80 \({}^{\circ}\)C, the intake flow rate was measured to increase from 2.19 L/min to 2.30 L/min as the intake pressure increased from 0 to 12 kPa. The increment is lower than that at the cylinder block temperature of 120 \({}^{\circ}\)C. Therefore, improving the operating load to compensate for increased friction, blow-by, and heat transfer losses remains challenging for downsizing reciprocating piston internal combustion engines. Figure 10: Pmi at different intake pressures (under cylinder block temperature of 80 \({}^{\circ}\)C).
## 3 Machining and Measurement Conditions  
### Materials, Tools, and Machining Station  
The Ni-based superalloy Inconel 718 was selected for machining tests. The TPL machining process was carried out using steel torus milling cutter body R300-016A20L-08L, dedicated to round cutting inserts R300-0828E-PL. The torus milling cutter as a tool assembly has a neutral geometry. The replaceable round cutting inserts are made of fine-grain carbide and coated with TiAlN anti-wear PVD technology. Round cutting inserts with a diameter of 8 mm were fitted into a tool body with a diameter of 16 mm at an overhang of 65 mm.  
The machining station with experimental set-up is shown in Figure 8. A 5-axis CNC vertical machining center, model DMU 100 MonoBLOCK, was used to carry out machinability tests in the multi-axis TPL milling process. The tool life was determined by the wear \(VB_{Bmax}\) located on the lateral surface of the cutting edge until the adopted maximum value of the wear criterion \(VB_{Blim}\) was reached. The machining process was interrupted at specific intervals to collect data for modeling and wear calibration. Measurements were carried out using a DinoLite 7000 CE workshop microscope. The above-mentioned experimental workstation is at the equipment of the Department of Manufacturing Technics and Automation, seated in Rzeszow.  
Figure 7: Cutting speed variation along the tool axis for torus milling cutter (tool diameter: 16 mm; round cutting insert radius: 4 mm; spindle speed: 2786 rpm; inclination angle: 10\({}^{\circ}\)).
## 4 Experimental Results and Analysis
### Dataset 3: CCTV Tower Parking Lot  
This experiment is based on the PAZ-1 dataset. The sequence of time-series images spans from 14 February 2023 to 31 August 2023 and contains a total of 12 images. Since this dataset reaches a resolution of 3 m \(\times\) 3 m, this experiment uses RTK (real-time kinematic) equipment to accurately record the coordinates of vehicles in the parking lot and the trajectory map when collecting data, and then it creates a vehicle distribution map by  
Figure 19: Logarithmic Background Subtraction method detection result.  
Figure 20: Pairwise comparison method detection result.  
Figure 18: Region of interest (BEJJING HYUNDAI AUTO Enterprise). (**a**) Optical image from Google Earth. (**b**) Generated binary mask for extracting the region of interest.  
parsing RTK data. Finally, the experimental results obtained by this method will be compared with the truth data to evaluate the accuracy of the algorithm again.  
As shown in Figure 22, the area marked by the red box is the parking lot area, and the image connected by the red line is the enlarged image of the parking lot and its corresponding optical image. As shown in the enlarged image, the size of the experimental processing area is 50 \(\times\) 50 pixels.  
The Logarithm Background Subtraction result of the image sequence is demonstrated in Figure 23. From left to right are the input image, the static background and the image that contains the changed part.  
After obtaining the image that contains the changed part through Logarithm Background Subtraction, it is necessary to perform target detection in the region of interest of the image. The CFAR detection result is shown in Figure 24. The left image is the binary image obtained after CFAR detection, in which the white point is the target, and the right image is the visualization result. The red point in the image corresponds to the white point in the left image, that is, the vehicle target.  
Figure 21: Curve of vehicle number. (**a**) Logarithm Background Subtraction method result. (**b**) Pairwise comparison method result.  
Figure 22: Parking lot area for experiment. (**a**) SAR image acquired on 31 August 2023 by PAZ-1 satellite. (**b**) Optical image of parking lot area from Google Earth. (**c**) SAR image of parking lot area acquired on 31 August 2023 by PAZ-1 satellite.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 3 Description of the INSPIRE-SAT 7 Mission and Mission Operations
### The Space Segment
#### 3.2.2 The TSIS and UVS Sensors (Standard and New Generation)  
To measure with accuracy TSI and SSI, it is fundamental to develop instruments that are calibrated to the International standards. These instruments must be accurate in-orbit, and require in-orbit calibration with internal (lamps) and/or external sources (stars). Solar space-based instruments exposed to the space environment usually degrade [17; 18] due to the harsh environment in which the instruments are expected to operate. Indeed, the space environment is considered hazardous to spacecraft. It results in materials degradation. An understanding of the degradation phenomena of space-based instruments is fundamental to achieve the expected scientific objectives resulting from these instruments. CubeSat missions offer possibilities for testing new technologies. In INSPIRE-SAT 7, several sensors  
Figure 2: INSPIRE-SAT 7 during integration phase in November 2021. View of ERS location.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Type** & **Location** & **N** & **Sensitivity** & **Range** & **Resolution** & **Noise** \\ \hline ERS\({}_{\rm{cn1}}\) & +X\({}_{\rm{}}\), \(-\)X\({}_{\rm{}}\) +Y\({}_{\rm{}}\), \(-\)Y & 4 & 1.5 \(\mu\)V/Wm\({}^{-2}\) & [\(-\)4500 \(\ldots\) 4500 \(\mu\)V] & 34.3 nV/bit & 150 nV rms \\ ERS\({}_{\rm{sr1}}\) & +X\({}_{\rm{}}\), \(-\)X\({}_{\rm{}}\) +Y\({}_{\rm{}}\), \(-\)Y & 4 & 1.5 \(\mu\)V/Wm\({}^{-2}\) & [\(-\)4500 \(\ldots\) 4500 \(\mu\)V] & 34.3 nV/bit & 150 nV rms \\ ERS\({}_{\rm{sr2}}\) & +Z\({}_{\rm{}}\), \(-\)Z & 2 & 0.2 \(\mu\)V/Wm\({}^{-2}\) & [\(-\)750 \(\ldots\) 750 \(\mu\)V] & 5.7 nV/bit & 25 nV rms \\ ERS\({}_{\rm{sr2}}\) & +Z\({}_{\rm{}}\), \(-\)Z & 2 & 0.2 \(\mu\)V/Wm\({}^{-2}\) & [\(-\)750 \(\ldots\) 750 \(\mu\)V] & 5.7 nV/bit & 25 nV rms \\ T\({}_{\rm{ERS}}\) & 2 per side & 12 & 1.0 \(\mu\)A K\({}^{-1}\) & [\(-\)70 \(\ldots\) 70 \(\mu\)A] & 0.5 nA/bit & 100 nA rms \\ \hline \hline \end{tabular}
\end{table}
Table 3: ERS and temperature sensors technical requirements. N is the number of parts.  
will be used to observe the Sun and the Earth, and to validate in-orbit technologies. The INSPIRE-SAT 7 mission will use four TSIS sensors (200-1100 nm, transmission of 0.1%) based on S1226-44 BQ silicon photodiode (TO-5 package). The principle of this technology is shown in Figure 3. The INSPIRE-SAT 7 mission will also use four UVS\({}_{NG}\) sensors of new generation (215-220 nm, bandwidth of 20-30 nm) for the direct measurement of the Herzberg continuum, three UVS\({}_{2}\) sensors (308 nm, bandwidth of 10 nm, transmission of 15%), and three UVS\({}_{3}\) sensors (340 nm, bandwidth of 5 nm, transmission of 25%). Table 4 provides the technical expected performance of the TSIS and UVS sensors to observe the Sun and the Earth's atmosphere.
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 4 Reliability  
There are several references which have provided several indicators for calculating the reliability of systems, including indicators such as loss of load expectation (\(LOLE\)), loss of energy expectation (\(LOEE\)), or expected energy not supplied (\(EENS\)), loss of power supply probability (\(LPSP\)), equivalent loss factor (\(ELF\)), and so on. The above indicators are defined by the following relationships. In this paper, Markov chain method is used to calculate the reliability parameters. Other required parameters are also derived from [28; 29; 30].  
\[LOLE=\sum_{t=1}^{N}E[LOL(t)] \tag{11}\]  
In the above relation, \(E[LOL(t)]\) is the mathematical expectation of the time offset in time \(t\), which can be defined by  
\[E[LOL]=\sum_{s\in S}T_{s}\times P_{s} \tag{12}\]  
In this case, \(P_{s}\) is the probability of being in the state of \(s\) and \(T_{s}\) in the case of being in this situation. \(S\) is the total set of possible situations for the system. Therefore\[LOEE=EENS=\sum_{t=1}^{N}E[LOE(t)] \tag{13}\]  
Here \(E[LOE(t)]\) is the mathematical expectation of the amount of lost energy in the time interval \(t\) that can be defined by  
\[E[LOE]=\sum_{s\in S}Q_{s}\times P_{s} \tag{14}\]  
\(Q_{s}\) is the amount of lost load (kWh) if positioned in \(s\). The probability of loss of source (LPSP) is obtained according to (15).  
\[LPSP=\frac{LOEE}{\sum\limits_{t=1}^{N}D(t)} \tag{15}\]  
In the above relation, it is assumed that \(D(t)\) is equal to the load demand (kWh) in time \(t\). Finally, the equivalent load offset coefficient can be defined as (16)  
\[ELF=\frac{1}{N}\sum_{t=1}^{N}\frac{Q(t)}{D(t)} \tag{16}\]  
Since the \(ELF\) contains more information, both the number of determinations \(N\) (and the values of \(Q(t)\)), this paper is used as the main criterion of reliability. The maximum allowed for \(ELF\) in developed countries is equal to 0.0001, however, for an independent system to the network, this limit is considered to be 0.01.
###### Abstract  
The development of smart vehicles has increased the demand for high-definition road maps. However, traditional road maps for vehicle navigation systems are not sufficient to meet the requirements of intelligent vehicle systems (e.g., autonomous driving). The present work comes up with a method of generating high-definition map models based on the geographic information system (GIS). A systematic map construction framework including the road layer, intersection connection layer, and lane layer is proposed based on the GIS database. Specifically, the constrained Delaunay triangular network method is applied to extract road layer network models, which are then used as linear reference networks to construct lane-level road maps. To further examine the feasibility of the proposed framework, a field experiment is then conducted to build a high-definition road map. Furthermore, a lane-level map matching test is conducted in the constructed road map using the trajectory data collected from a probe vehicle. The results show that the proposed method provides an efficient way of extracting lane-level information from urban road networks and can be applied for lane-level map matching with good performance.  
high-definition map model; GIS; constrained Delaunay triangulation; lane-level map matching Article
# Current Perceptions of Virtual Reality Technology
## 3 Results and Discussion
### Who Uses VR?  
The first research question aimed to determine who from the population uses VR. Here, a Barnard's exact test was used to determine whether the proportion of participants who had tried VR differed across groups. Gender differences, as well as the effect of motion sickness and sensory impairments, were assessed, both of which might be expected to reduce engagement with VR.  
The proportion of male participants (78%) who had tried VR was higher than the proportion of female participants (61%, \(p\) = 0.0228).  
There were 46 participants (32%) who experienced travel-induced motion sickness, consistent with the expected incidence of between 25% and 41% [108]. In addition, 22 (15%) experienced technology-induced motion sickness. Visual impairments (not including wearing glasses or contact lenses) were experienced by 11 individuals (8%), and 7 (8%) experienced auditory impairments.  
The proportion of participants trying VR was not affected by visual impairment (73% with visual impairments who had used VR, versus 67% without, \(p\) = 0.652), auditory impairment (71% versus 69%, \(p\) = 0.810), or technology-induced motion sickness (64% versus 68%, \(p\) = 0.586). However, fewer people who experience travel sickness had tried VR than those who do not (57% versus 72%, \(p\) = 0.038).  
For those who had tried VR, whether the enjoyment ratings varied across these groups was also inspected. Enjoyment of VR was not affected by visual (_p_ = 0.554) or auditory (_p_ = 0.277) impairment, or by motion sickness induced by technology (_p_ = 0.0647) or travel (_p_ = 0.498). There was also no gender difference in the enjoyment of VR (_p_ = 0.766).  
The majority of the participants reported enjoying entertainment such as the theatre (_n_ = 131, 91%), watching television (_n_ = 141, 98%), and playing board games (_n_ = 120, 83%). Other activities that were enjoyed by the majority were online (_n_ = 98, 68%) and offline (_n_ = 106, 74%) gaming. By assessing how engagement with these activities was associated with experience and enjoyment of VR, it was revealed that people who enjoy online games were more likely to have tried VR (_p_ = 0.0376). Experience of VR did not vary with enjoyment of any of the other activities (board games, \(p\) = 0.498; offline games, \(p\) = 0.131; television, \(p\) = 0.900; theatre, \(p\) = 0.178). Enjoyment of VR was higher in those who enjoy board games (_p_ = 0.015) and offline games (_p_ = 0.0064), but did not vary with any of the other activities (online games, \(p\) = 0.207; television, \(p\) = 0.093; theatre, \(p\) = 0.065).  
In contrast, those participants who reported not having tried VR were asked why. Shown in Figure 3 is a word cloud for the free-text responses. The word that appeared most frequently was _'opportunity'_. There is thus still not widespread accessibility of VR.
# Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain
## 3 The Advanced Solution: On-Chain/Off-Chain Load Balancing
### Conventional Smart Contract Workflow  
Tezos is a Smart contract platform, featuring formal verification for Smart contracts: a mathematical proof that all deployed Smart contracts are designed and executed according to their design is thus provided.  
While the formal verification is a precious tool to help Smart contract programmers, the underlying Smart contract deployment is still a complex process that requires several steps, as illustrated in Figure 2 and explained here-after:  
1. The Programming step writes a Michelson program that converts a logical contract (if... then series) into a Tezos script.
2. The Formal Verification step executes the Tezos command \"typecheck script\" (with the proper parameters); this command does not apply any action on the Blockchain, yet its output informs the developer about the Smart contract correctness.
3. The Deployment step executes the Tezos command \"originate\" with the proper parameters (Smart contract name, owner, transaction cost,...). The Smart contract is added to the new block (the Smart contract keys are generated) and is ready to be invoked.
4. The Call/Execute step executes the Tezos command \"transfer\" with its parameters (Smart contract ID, Smart contract monetizing conditions, Smart contract parameters,... ); the action is added to the Blockchain, the Smart contract is executed, and the results are stored in the Blockchain.  
While this basic Smart contract deployment workflow perfectly caters to the needs of legacy, financial-related operations (a simple transfer from one account to another, assuming a given condition is met), their limitations appear when dealing with more complex-use cases.  
For instance, the Tezos' Michelson language is bound to process integer values and cannot be extended (while keeping backward compatibility) so as to process floating point values.  
Moreover, as an example of applicative limitation, the Blockchain operation cost steadily increases with the size and complexity of the Smart contract. In order to overcome these limitations, two solutions can be considered.  
First, the mapping of the operations from floating point to integers can be a solution for this fundamental limitation but it increases both the size and the computational complexity of the Smart contracts.  
Consequently, we designed an architecture for ensuring an on-chain/off-chain code balancing, as described in the next section.
# Applications of Tumor Cells in an _In Vitro_ 3D Environment
## 5 Co-Culture Systems  
Over the past ten years, the field has made progress in incorporating multiple elements of the TME by using co-culturing models [39]. Moreover, CAFs have a prominent role in cancer development, from initiation to primary and metastatic progression, and in drug resistance [40]. There has also been a growing appreciation of the ability of cancer-associated fibroblasts (CAFs) to modulate the immune system [41]. CAFs modulate cancer metastasis through the synthesis and remodeling of the ECM, the production of growth factors, and their influence on angiogenesis, tumor mechanics, drug access, and therapy responses. Targeting CAFs by altering their numbers, subtypes, or functionality is a very intriguing potential approach to improving cancer therapies. CAFs are perhaps the most effective cells within the TME in depositing on and remodeling the ECM [41]. The alterations in matrix production and tumor mechanics largely result from the action of CAFs and have complex consequences for tumors. In a recent review, [PERSON] et al. describe that most CAF subpopulations usually exhibit cancer-promoting effects, while the discovery of cancer-restraining CAFs (rCAFs) was reported to instead exert inhibitory effects on tumor progression [42]. Many studies have now shown that interactions between CAFs and immune cells, or other immune components, are capable of inhibiting the anti-tumor immune response. [PERSON] et al. showed that CAFs are an important IL-6 source and that anti-IL-6 receptor antibodies suppressed angiogenesis and inhibited tumor-stroma interactions. Furthermore, it was demonstrated that CAFs contribute to drug resistance acquisition in cancer cells [43].  
Moreover, 3D multicellular tumor spheroid (MCTS) models were developed by combining three different cell types: epithelial colon cancer cells (HCT116), human intestinal fibroblasts, and monocytes [44]. These CRC models mimicked ECM production, spatial organization, and the formation of a necrotic core and proved to be adequate for drug screening.