esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 4 Results and Analysis
### Projected Distance During Communication Delays (DR4)  
This requirement ensures that drones estimate their projected travel distance between communication updates to maintain safe separation. It relies on a key environmental assumption (A15), which must be validated to prevent miscalculations that could compromise operational safety.  
Assumption A15 states that each drone is aware of its physical dimensions and communicates this information at startup. If this assumption is missing or incorrect, drones may lack accurate awareness of their own size, leading to miscalculations in separation distances. This can result in drones flying closer than intended, thereby increasing the risk of mid-air collisions. To mitigate this, developers may need to implement periodic broadcasting of size information or design automated validation routines to ensure that each drone has access to reliable size data throughout the mission.  
Additionally, the requirement implicitly assumes predictable communication latency. In real-world deployments, however, communication delays can vary significantly due to network interference, congestion, or hardware limitations. If the system assumes minimal and constant latency, drones may base their projections on outdated positional data, potentially violating safe separation thresholds. In such cases, the requirement must be revised to incorporate latency bounds and adaptive error correction mechanisms that adjust projected travel distances dynamically.  
**RTD:** If these assumptions are not validated or regularly reassessed, the requirement may become based on flawed operational premises, resulting in misalignment with actual drone behavior. This type of debt is classified as _misconstrained assumption debt_, as the requirement imposes fixed constraints that do not hold under variable real-world conditions such as communication lag or missing dimension data. This can lead to late-stage reengineering to correct unsafe behavior or redefine system boundaries.
# Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics
## 3 Methods
### Implementation
#### 3.2.2 Installation  
At the initial download of the SA app on either an Android or iOS device, the RN library \"react-native-RSA-native\" [21] is used to generate a 2048-bit RSA key pair. In addition, the \"crypto-es\" library [22] is also used to generate a 256-bit symmetric key. Further, a device notification ID (DN-ID) is generated using functionality provided by the RN library [23]; DN-ID is used to identify a specific device during authentication requests. To overcome the failings of shared secrets in authentication systems, the keys are storedsecurely on the user's device. React Native provides the app with an encrypted storage area on the device, such that the cryptographic keys cannot be extracted [24,25].  
As shown in Figure 2, public keys are exchanged in the first connection of the app to the BE, as well as the DN-ID; the app's public key and DN-ID is saved to the database as the unique ID for that app, while the server-generated public key is saved to local storage on the app. Private keys are never exchanged during this process. All requests from the app to the server are identified using the app's public key. The server also generates a unique ID to be associated with the user's typing pattern (TP), encrypts it with the app's public key, and sends it to the app.
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 3 Proposed Point-Image Fusion Network (PIFNet)
### Two Stream Network Architecture
#### 3.1.2 Point Cloud Feature Extraction Stream  
The point cloud feature extraction stream takes LiDAR point clouds as input and generates the 3D proposals. Considering the tradeoff between the performance and computation time, the geometric stream uses four pairs of Set Abstraction (SA) layers [11] and Feature Propagation (FP) layers [11], for point-wise feature extraction. For the convenience of description, the outputs of SA and FP layers are denoted as \(S_{i}\) and \(P_{i}\) (I = 1,2,3,4), respectively. As shown in Figure 1, the point feature map Si is combined with the semantic image feature \(F_{i}\) in our fusion module. In addition, the point feature \(P_{4}\) is further enriched by the multi-scale image feature functional units to obtain a compact and discriminative  
Figure 2: Encoder-Decoder Fusion (EDF) module architecture.  
feature representation, which is then fed to the detection modules for foreground point segmentation and 3D proposal generation.
## 4 Conclusions  
In the present study, numerical simulations were carried out to study the effect of shock waves on the loss and breakdown characteristics of the tip-leakage vortex. By altering the backpressure of the turbine rotor, a series of results with different degrees of interaction were obtained. Based on the obtained result and performed analyses, the main conclusions of the present work are as follows:  
(1) Shock wave has a serious influence on the tip-leakage vortex loss. The induced vortex breakdown by shock wave would enhance the mixing loss in the leakage vortex. As the outlet Mach number increases from 0.67 to 1.15, the loss in the leakage vortex grows exponentially, the loss increases by about 10 times and the corresponding proportion in the total loss increases sharply to 30.2%.  
(2) The stagnation position of leakage vortex breakdown is nearly behind the shock wave interaction in supersonic flow. It may fluctuate as the back-pressure changes, moving forward and backward. The change distance in the axial direction is up to 0.13 C\({}_{\rm x}\). Therefore, it infers that the breakdown of leakage vortex core may be influenced by the downstream blade passing and the induced pressure fluctuation may result in additional vibration to the rotor blade.  
(3) The shock wave intensity has an obvious influence on the interaction of leakage vortex breakdown. With the interference of strong shock waves, the centrifugal breakdown of the leakage vortex core occurs rapidly and it influences both the expansion and instability of the vortex core and the shear layer flow on the surface of the leakage vortex.  
Figure 17: Schematic diagram of the interference between the shock wave and the tip-leakage vortex.  
(4) The tip-leakage vortex can be considered as a swirling vortex whose structure is forms by the vortex core with low-velocity and the high shear flow wrapped around the core. It is unstable and may turn into a flow with a breakdown bubble in core region. The interaction of shock wave is the main trigger cause of leakage vortex breakdown, but the internal cause of vortex core instability is the centrifugal instability of vortex.  
Conceptualization, Z.W.; methodology, Z.W. and W.C.; software, Z.W.; validation, Z.W. and W.C.; formal analysis, Z.W.; investigation, Z.W.; resources, Z.W.; data curation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W., G.R., M.N. and X.G.; project administration, G.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic and Applied Basic Research Foundation of Guangdong Province, China (Grant No. 2019A1515110450) and Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. JCYJ20200109141403840).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors would like to thank [PERSON] for the guidance and kind advice and the fellow apprentices in the research group for their help.  
The authors
## 2 Proposed PV-Battery Islanded DC Microgrid  
The schematic of the PV-battery islanded dc microgrid configuration, as a studied network, is shown in Figure 1. This dc microgrid consists of a PV system, a dc load, a battery energy storage system (BESS), and a capacitor bank. In this dc microgrid, a PV system is connected via a dc-dc converter to the dc bus. In addition, a battery and capacitor bank are connected to the dc bus through a dc-dc converter for operating the islanding mode of the system, and a dc variable resistive load is connected directly to the bus bar to model the dc system load. During the operation of the microgrid, the PV power generation changes alternately throughout the day. The management plan of the dc microgrid in the islanding mode is such that if the PV system's power generation is greater than the power consumption, the battery and capacitor bank begin to charge and the additional power is used to balance the system. Otherwise, if the PV system's power generation is less than power consumption, the energy storage is discharged and, as a result, the microgrid voltage remains constant [19].  
The proposed dc microgrid is designed to withstand heavy disturbances. In addition, at every moment of operation, the capacity of the dc microgrid should be such that it feeds the load. For these reasons, in the design of the dc microgrid, two energy storage devices such as BESS and capacitor bank have been employed. The details of dc microgrid modeling are shown in Figure 2, and their parameters and mathematical relations are presented as follows.
# Natural Photosensitizers in Clinical Trials
## 4 Natural Photosensitizers Pending Clinical Trials
### Chlorophylllin  
Chlorophyllin is a derivative of chlorophyll, and its source is cyanobacteria. This compound is readily soluble in water, has low toxicity, and is rapidly removed from the body [110; 111; 112; 113; 114]. It has favorable optical properties, with an absorption maximum in the 600-670 nm range [24]. ROS generated with PDT in target cells damage mitochondria, which activates a cascade of apoptotic proteins such as caspase-8 and caspase-9. This induces the release of cytochrome C from mitochondria, leading to the activation of apoptosis. Chlorophyllin has been shown to decrease the expression of the anti-apoptotic protein BCL-2 and increase the level of the pro-apoptotic protein Bax, which alters the BCL-2/Bax ratio, promoting apoptosis of cancer cells [115]. Previous work has demonstrated the potential of chlorophyllin as a photosensitizer in PDT of cervical cancer and bladder cancer [111; 112; 113; 114].
## 3 Description of the Maritime Data Transfer Protocol
### Serialized Data  
The individual PDUs exchanged both in overwater communications and underwater communications for each of the messages, as classified in the taxonomy proposed in the present paper. Each of the PDUs described is based on the format proposed in Figure 5.  
1. _Overwater communications (IP channel)_. As there will not be any bandwidth limitation in this case, the middleware will take advantage of this situation to exchange data with vehicles whose size is expected to be large, like mission plans, that after being defined by the operators of the missions will be transmitted from the middleware to the AUVs before launching them to sea (e.g., if they are on a ship). Table 4 has been used to include all the pieces of information mentioned. In addition to that, the PDUs that are involved in the communications have also been included. They follow the structure that was described, which consists of a vehicle identifier (_VID_), the type of PDU that is used, its subtype (it is required because there are several different kinds of requests, reports or events that can be triggered in a mission), its sequence operation (included as _SeqOp_) and the data used to make the task possible.
2. _Underwater communications (acoustic channel)_. Will make use of PDUs with smaller amounts of data. As it was done in Table 4, the PDUs involved in the communications have also been included. The main reason for MDTP to have been designed is this kind of communication; if only Over-the-air data transmissions existed, it is likely that a different solution would have been studied. However, as described in the open issues, none of the existing options seemed to completely satisfy the required capabilities for data transmissions.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 2 Motion and Distortion Induced Artefacts
### Involuntary Eye Motion
#### 2.2.2 Drifts  
Drifts are slow retinal movements that can move the image of a fixated object on the retina across a dozen photoreceptors. [PERSON] [27] was one of the first to report a drift mean speed, 6's\({}^{-1}\), and a maximum speed of 30s\({}^{-1}\). Years later, [PERSON] et al. [28] reported a mean drift speed of 24.6's\({}^{-1}\). In both studies, it was observed that drifts occur simultaneously with tremors and between microsaccades.  
Figure 1: Planes Coronal, Slow, Fast, and 3D view of a raw unregistered OCT volume acquired with a custom made device at Medical University of Vienna. Scanning angles are 45\({}^{\circ}\) on the \(x\)-direction and 25\({}^{\circ}\) on the \(y\)-direction, where 1\({}^{\circ}\) corresponds approximately to 300 \(\upmu\)m. The depth extension is 471 pixels (3.3 \(\upmu\)m pixel\({}^{-1}\)). Only the fast plane is visually artefact free, since the acquisition rate was 200k A-scans s\({}^{-1}\) resulting in 200 B-scans per second.  
However, their amplitude (between 0.8\({}^{\prime}\) and 31.4\({}^{\prime}\)[12; 23]) is much larger than in tremors, and therefore drifts are likely to affect 3D-OCT data.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 5 Channel Aware Adversarial Sample Generation Method Based on Frequency Domain Transformation
Specifically, if the target attack disturbance \(\delta^{target}\) is multiplied by the conjugate \({h_{ar}}^{*}\) of channel \(h_{ar}\), the received power can be maximized along the channel direction. After being transmitted through the channel, the disturbance power at the receiving end becomes \(\left\|h_{ar}\right\|_{2}^{2}\delta^{target}\). Through this operation, the adversarial attack not only maintains the consistency of the perturbation direction with the channel but also maximizes the transmission efficiency of the perturbation energy through the channel gain. Ultimately, the attacker needs to generate targeted perturbations for all possible modulation types and calculate the scaling factor to meet the power constraints of the opponent. The calculation of the scaling factor \(\beta\) has been obtained from reference [33] and will not be elaborated here. Based on the above derivation and combined with the methods mentioned in the previous section, the adversarial sensing adversarial perturbation generation algorithm based on frequency-domain transformation can be obtained. The specific details are given in Algorithm 1.
# Pipeline Damage Identification Based on Additional Virtual Masses
## 2 Damage Identification Method Based on Additional Virtual Masses  
### Additional Virtual Mass  
After adding masses on a structure, frequency response can be calculated using the excitation and acceleration responses of the original structure. Hence, a new virtual structure can be created. The theoretical derivation process is as follows [16].  
Assume that \(\mathbf{H}_{lp}(\mathbf{\omega})\) is the unit excitation applied to the \(p\)-degree of freedom (DOF) on the original structure and \(l\) is the acceleration frequency response function. If a certain mass is added and in the same direction as that of the \(p\) DOF, excitation is applied, then acceleration frequency response is \(\widetilde{\mathbf{H}}_{pp}(\mathbf{\omega},\mathbf{m})\). Hence, additional mass \(\mathbf{m}\) only produces inertial force in the direction of \(p\) DOF, as \(-m\mathbf{H}_{pp}(\mathbf{\omega},\mathbf{m})\). According to the basic concept of VDM, frequency response \(\widetilde{\mathbf{H}}_{pp}(\mathbf{\omega},\mathbf{m})\) with additional mass \(m\) in the \(p\) DOF can be expressed as the sum of frequency response \(\mathbf{H}_{lp}(\mathbf{\omega})\) of the original structure and the response generated by mass inertia force \(-m\widetilde{\mathbf{H}}_{pp}(\mathbf{\omega},\mathbf{m})\).  
\[\widetilde{\mathbf{H}}_{pp}(\mathbf{\omega},\mathbf{m})=\mathbf{H}_{lp}(\mathbf{\omega})-m \widetilde{\mathbf{H}}_{pp}(\mathbf{\omega},\mathbf{m})\mathbf{H}_{pp}(\mathbf{\omega}) \tag{1}\]  
\[\widetilde{\mathbf{H}}_{pp}(\mathbf{\omega},\mathbf{m})=\frac{\mathbf{H}_{lp}(\mathbf{\omega})}{ \mathbf{1}+m\mathbf{H}_{lp}(\mathbf{\omega})} \tag{2}\]In the equations above,  
\[\mathbf{H}_{lp}(\mathbf{\omega})=\frac{\mathbf{Y}(\mathbf{\omega})}{\mathbf{F}(\mathbf{\omega})}=\frac{ \mathbf{\mathcal{F}}(\mathbf{y}(I))}{\mathbf{\mathcal{F}}(\mathbf{f}(I))} \tag{3}\]  
The variable \(\mathbf{f}(I)\) is the excitation time in the \(p\) DOF, \(\mathbf{y}(I)\) is the acceleration response, and \(\mathbf{\mathcal{F}}\) represents the Fourier transform. Substituting Equation (3) into Equation (2) gives  
\[\widetilde{\mathbf{H}}_{pp}(\mathbf{\omega},\mathbf{m})=\frac{\mathbf{Y}(\mathbf{\omega})}{\mathbf{F}( \mathbf{\omega})+\mathbf{m}\mathbf{Y}(\mathbf{\omega})}, \tag{4}\]  
Equation (4) shows that only by measuring excitation \(\mathbf{f}(I)\) and acceleration response \(\mathbf{y}(I)\) can we calculate the acceleration frequency response \(\widetilde{\mathbf{H}}_{pp}(\mathbf{\omega},\mathbf{m})\) of structures with any added virtual mass \(\mathbf{m}\). In other words, where the acceleration sensor is arranged, virtual masses can be added. The method is easy to operate and the calculation is simple.
## 3 Results and Discussion
### Optimum Operational Parameters of CSR
#### 3.2.2 Roasting Efficiency of CSR  
The experimental results for damaged and roasted groundnut kernel at different roasting conditions are shown in Figure 6. It was found that the average roasting performance (Equation (2.7)) of CSR was \(91.7\pm 1.0\%\), \(90.1\pm 1.0\%\), and \(88.6\pm 1.0\%\) for light, medium, and dark roasting conditions respectively which implies that the percentage of damaged kernels (by weight) was increased as the roasting time was increased. It might be due to the fact that, as roasting time increases, more moisture was drawn from the kernels and they became more brittle and brittleness makes the kernels more susceptible to breakage during roasting and conveyance [13].
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
###### Abstract  
As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021-2025), it is expected that Sentinel will become the satellite mission with the greatest influence.  
Earth observation satellite; scientometric analysis; meta-analysis 2022141863390/rs14081863
## 3 Results
### IFN Pathway Inhibitors Enhanced RABV Production in MRC-5 Cells  
Two small molecules, nuclear factor kappa-B kinase-2 inhibitor, TPCA-1, acting on IFN-\(\beta\) promoter [33], and Janus kinase-1 inhibitor, ruxolitinib, acting on the signal transmission of interferon receptor [34], were used to inhibit IFN responses. These inhibitors were incubated with the cells 2 h before viral infection, and then endogenous IFN-\(\alpha\), IFN-\(\beta\), STAT1, IRF7, and OAS1 mRNA expression were examined by qRT-PCR analysis at 24 hpi (Figure 2A). The MRC-5 cells treated with TPCA-1 or ruxolitinib showed a significant reduction in the mRNA expression of IFN-\(\beta\), STAT1, IRF7 and OAS1 compared with the untreated cells (Figure 2A), suggesting the effectiveness of the inhibitor. Subsequently, the viral RNA, antigen G concentration, and RABV titer in the culture supernatant were measured by qRT-PCR, ELISA, and FFU assays at 96, 168, and 240 hpi [35], respectively (Figure 2B-D). The results showed a significant enhancement in viral RNA, G concentration, and titer at 168 and 240 hpi compared with the untreated cells, indicating an increase in RABV production. There was no significant difference in RABV production between MRC-5- and Vero-infected cells. These results confirm that IFN response is an important limiting factor of RABV production. Inhibiting the activation of IFN response can enhance RABV production in MRC-5 cells.  
To explore the conditions of optimum IFN inhibitor usage for further improving RABV yields, yields at different inhibitor concentrations, MOI values, and virus harvest times were subjected to orthogonal testing (Table 3). We observed that virus production reached its maximum at an MOI of 0.1 and inhibitor concentrations of 1 uM at 240 hpi, and at an MOI of 0.05 and inhibitor concentrations of 4 uM at 168 hpi. These data provide potential guidance for applying IFN inhibitors for the production of RABV-inactivated vaccines.  
Figure 1: RABV infection activated IFN signaling pathway-related gene expression. **(A,B)** MRC-5 cells were infected with the PM RABV strain at MOI of 0.05, 1, and 10. Vero cells were infected with the aG RABV strain at an MOI of 0.0001, 0.001, and 0.05. RABV RNA expression and titer in culture supernatant were detected by qRT-PCR and FFU assays at 168 hpi. **(C)** MRC-5 cells were infected with the PM RABV strain at an MOI of 0.05. Vero cells were infected with the aG RABV strain at an MOI of 0.001. The total RNA from cells was extracted at 8, 12, 24, 48, 60, 72, and 106 hpi, and the mRNA expression of IFN-\(\alpha\), IFN-\(\beta\), STAT1, IRF7, and OAS1 was detected by qRT-PCR. Data are shown as fold change in expression of mRNA for each IFN-\(\alpha\), IFN-\(\beta\), STAT1, IRF7, and OAS1 compared with that of uninfected MRC-5 or Vero cells, respectively, at the indicated time point and normalized to the values for 18S rRNA. *: RABV infection; * \(p\) < 0.05; *** \(p\) < 0.001; *** \(p\) < 0.0001; ns \(p\)\(\geq\) 0.05 compared with Vero\({}^{+}\) at the indicated time point.