esa-sceva/satcom-chunk-collection huggingface dataset:
# CubeSat Imaging Payload Design for Environmental Monitoring of Greenland
## 6 Conclusions  
This work aims to develop an imaging payload for a cube satellite; this project is part of the DISCO program, in which three Danish universities are involved. The imaging payload has three cameras (i.e., wide field, telephoto, and infrared) and redundant image processor units. Hardware and software requirements were defined to select the suitable cameras and image processor unit. Alvium cameras proved to be an adequate selection due to their power consumption, robustness, and software integration. System on module devices were found to be a good match with the selected cameras, the software requirements and development timeframes. Consequently, the PicoCore brand, a compact SOM with an ML accelerator core to speed up the inference process, is the target image processing unit. Furthermore, to save energy and create a redundancy system to interact with cameras and the PDU, a USB switch was designed and developed to select the SOM and the camera. The proposed methodology proved to be an excellent reference for structuring the design process and checking the requirements of the developed system; also, it is helpful to document the system correctly for further interactions or later production runs. As a future work, once the satellite is completed and launched, the next step is to develop a range of ML applications and models that leverage the hardware and software capabilities of the system, and compare those to other approaches. We look forward to the forthcoming launch of our satellite DISCO-2 and to presenting an evaluation of these design methodologies with empirical data from the test series and in orbit operations.  
P.D.R.-M.: original draft preparation, original draft preparation, investigation and software, methodology, formal analysis, writing--review and editing; J.C.P.P.: resources, project administration; supervision; writing--review and editing, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research received funding from Industries Fond.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** This work is part of the Danish Student CubeSat Project, which is a collaboration between Aarhus University, IT University of Copenhagen, University of Southern Denmark, and Industriens Fond.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Key Node Mining Method Based on K-Shell and Neighborhood Information
## 2 Related Works
### Global Indices  
Global index-based centrality methods measure the influence of a node according to the overall topological structure of the network. Examples include CC, BC, and the K-shell method. CC measures the average shortest path distance between a target node and other nodes, essentially being the inverse of the average shortest path distance from the node to others [24]. BC assesses a node's importance by counting the number of shortest paths that pass through it [25]. However, both CC and BC have significant drawbacks: they are computationally complex and not suitable for large networks [21]. In contrast, the K-shell method is a fast-computing and large-network-appropriate method for mining important nodes, proposed by [PERSON] et al. [26]. The K-shell method distinguishes the importance of nodes by stratifying them according to their degree; nodes with higher K-shell values are considered more important. The time complexity of the K-shell is relatively low at O(E), where E is the number of edges in the network [38].  
However, the KS method has a significant limitation; it cannot assess the difference in importance among nodes of the same layer. To address the monotonicity issue, researchers have proposed several improved methods, including CNC+, CN, KSIF, and MDD. The CNC+ method posits that the importance of a node should be determined by combining the KS values of neighboring nodes [27]. The CN method suggests that the order in which nodes are removed by the KS method affects the assessment of node importance and should be combined with its KS value to evaluate node significance [28]. The KSIF method assesses node importance through the iterative information of node removal [29]. The MDD method argues that assessing the importance of a node requires considering both the edges of the removed node and the edges of the remaining nodes [30].  
In addition to these methods, PageRank, a centrality method based on random walks, also falls into the category of global index-based centrality methods. Originally used for web page ranking, PageRank assesses the importance of a web page by considering both the number and quality of a web page [39]. LeaderRank, a variant of PageRank, is another classic centrality method based on random walks. However, these two methods perform poorly in undirected networks and are more commonly used in directed networks [21]. [PERSON] and colleagues have demonstrated through experiments that reachability is the best centrality indicator for measuring the importance of nodes in spatial networks with topological constraints [26].  
Global index-based centrality methods often achieve higher computational accuracy compared to local index-based centrality methods. This is because they consider the overall topological structure of the network, and using more comprehensive information leads to more precise results. However, this approach also has disadvantages: the computational complexity is typically higher and more time-consuming, making it difficult to apply these methods to large-scale networks.
## 2 Results and Discussion
### Determination of AA Sequence  
The AA sequences of eight isolated peptides (F7-P1 to F7-P8) were analyzed using a Protein/Peptide Sequencer (Table 1), and their sequences were determined to be Gly-Pro-Tyr (GPY, F7-P1), Gly-Pro-Thr-Gly-Glu (GPTGE, F7-P2), Pro-Phe-Gly-Pro-Asp (PFGPD, F7-P3), Gly-Pro-Thr-Gly-Ala-Lys (GPTGAKG, F7-P4), Pro-Tyr-Gly-Ala-Lys-Gly (PYGAKG, F7-P5), Gly-Ala-Thr-Gly-Pro-Gln-Gly (GATGPQG, F7-P6), Gly-Pro-Phe-Gly-Pro-Met (GPFGPM, F7-P7), and Tyr-Gly-Pro-Met (YGPM, F7-P8), respectively. Using electrospray ionization-mass spectrometer (ESI-MS), molecular masses of eight isolated peptides (F7-P1 to F7-P) were determined as 335.31 Da, 459.47 Da, 531.52 Da, 586.65 Da, 591.69 Da, 586.61, 604.73 Da, and 466.50 Da, respectively, which agreed well with their theoretical masses of 335.36, 459.45 Da, 531.56 Da, 586.64 Da, 591.66 Da, 586.60 Da, 604.72 Da, and 466.55 Da, respectively.
# Use of Social Media Data in Disaster Management: A Survey
## 6 Information Dimension
### Temporal
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multicolumn{3}{c}{**Temporal**} & \multirow{2}{*}{**Pros and Cons**} \\ \cline{2-3} \cline{5-5}  & **Pre-Event** & **Real-Time** & **Post-Event** & \\ \hline  & & & & & The spatiotemporal \\  & & & & & visualisation can \\  & & & & & bring a significant \\  & & & & & benefit for decision- \\  & & & & & making. However, \\  & & & & distribution of tweets & obtaining spatial \\  & & & & for Hurricane Sandy & information from \\  & & & & micro-blogs is a big \\  & & & & challenge that \\  & & & & is not discussed in \\  & & & & these papers. \\ \hline  & & & & & The idea of using \\  & & & & social media for \\  & & & & wildfire hazards \\  & & & & analysis is very \\  & & & & interesting, but \\  & & & & the analysis method \\  & & & & and evaluation are \\  & & & & too simple. \\ \hline  & & & & This is the first \\  & & & & study to utilise \\  & & & & Chinese social media \\  & & & & to uncover emergency \\  & & & & events in an urban city. \\  & & & & This paper introduced \\  & & & & a useful data-cleaning \\  & & & & method for Chinese texts, \\  & & & & which can be used for many \\  & & & & purposes. However, the \\  & & & & analysis the use of \\  & & & & the extracted spatial \\  & & & & information is too \\  & & & & simple. \\ \hline  & & & & This paper makes a \\  & & & & very detailed and \\  & & & & complete categorisation \\  & & & & for social media in \\  & & & & disaster management. \\  & & & & This expert knowledge \\  & & & Hurricane Sandy & can be reused by \\  & & & & other researchers. \\  & & & & However, the data \\  & & & & analysis is too simple. \\ \hline  & & & & This paper provides a \\  & & & & & solution to processing \\  & & & & social media data in \\  & & & & real time. However, the \\
41 & - & & using the burst detection & - \\  & & & & sufficient to prove \\  & & & & that the proposed \\  & & & & method is practical \\  & & & & and applicable to \\  & & & real-world applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Temporal. The classification of these social media messages into different contextual categories and their analysis over time help to identify the transition between various phases of disaster management and support effective decision-making for disaster preparedness, response, and recovery. The authors of [44] present a classifier based on logistic regression that automatically classifies the gathered social media data into various topic categories during various disaster phases and classifies the temporal trends of these topic categories in different phases. The experimentation using tweets related to Hurricane Sandy revealed that: (i) Tweets regarding preparedness reached their peak on the day before the event when the emergency declaration was issued, (ii) a large proportion of tweets related to impact were observed within a few days of the event's occurrence, and (iii) the largest peak of tweets related to disaster recovery was observed five days after the event.
## 4 Implementation Results  
To verify that the proposed STFT processor architecture is efficient in terms of hardware complexity, we implemented STFT processors using R2 SDF, R4 MDC, and the proposed architectures.  
Figure 8: Hardware architecture of the proposed WM.  
Figure 9: Hardware architecture of the proposed data reordering module (DRM).  
A 12-bit word for the real and imaginary data paths was selected to satisfy the requirement for a signal-to-quantization-noise ratio (SQNR) of 40 dB. Three STFT processors were designed using hardware description language (HDL) and synthesized for an operating frequency of 200 MHz with a 65 nm CMOS standard cell library using Synopsys Design Compiler tool [27]. Table 1 summarizes the comparison results in terms of the number of logic gates. As shown in the Table, the proposed STFT processor architecture reduces the number of logic gates by 54% compared with the conventional R4 MDC-based STFT processor and by 63% compared with the R2 SDF-based STFT processor.  
Table 2 shows the comparison results between the proposed STFT processor and the STFT processor described in [18]. To make a fair comparison, we normalized the area as  
\[A_{norm}=\frac{Area\times 10^{3}}{\left(Tech/65 nm\right)^{2}\times\log_{2}\left(N \right)\times\alpha} \tag{2}\]  
where \(N\), \(\alpha\), and \(Tech\) are the window length, maximum overlap ratio, and the process technology in nanometers, respectively. Even though the design of [18] can support a higher overlap ratio than the proposed STFT processor, it only supports a window length of 32. It also requires an approximately 2.3 times larger area than the proposed STFT processor.
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles
### COSMIC-2 Excess Phase Calculation
#### 2.4.4 Extra Excess Phase Error Corrections  
The transmitted signal can have a phase difference from the actual path transmitted due to the relative rotation between the GNSS transmitter and the LEO receiver antenna and the nature of the GNSS microwave signals' polarization. This phase change with the rotation angle cannot be attributed to the ambiguity. [PERSON] et al. [45] calculated this phase difference for receivers on the ground stations. We must find the instrument coordinate system's antenna dipole for the LEO satellite receivers. For COSMIC-2, the boresight vectors can be used for this. However, depending on the clock removal method, the phase windup caused by the receiver side dipole rotation is not included for the single differencing method. The phase variation has been largely absorbed into the receiver clock bias and can be removed through single differencing. This term is small and negligible for contributions from different GNSS satellites since the relative rotation between the GNSS and LEO satellites is approximately 2 h.  
The multi-path from the receiver ambient is not corrected. A recent study shows that COSMIC-2 multi-path effects from spacecraft ambient environment are minor [46]. This multi-path is different from the atmospheric-caused multi-path, which can be partially resolved using wave optics methods in bending angle conversion. There is no bending angle derivation in the lower atmosphere because SNR values are too low to resolve that.
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 5 Simulation Results and Analysis
When the distance between UAVs was large, the communication channel quality was relatively low, so the communication noise  
It can be seen in Figure 12 that when the number of UAVs increased, the distance between the UAVs decreased. This is because when different communication models were used, different observation configurations are obtained, which causes different average distances. When the distance between UAVs was small, there was low communication noise, which explained why these three methods had a very close estimate error in Figure 11 when the number of UAVs was very large. Figure 11: Estimate error with different methods (number of UAVs was 8). Figure 12: The impact of number of UAVs.
# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 1 Introduction
Integration of AI and Industry 4.0 in the IIoT: The paper highlights that the IIoT represents a forward-thinking industrial technology that combines two digitization strategies--Artificial Intelligence (AI) and Industry 4.0. This integration of data analysis and high-level automation significantly enhances the industrial environment and operations. 3. Challenges in IIoT Implementation: The introduction acknowledges the challenges associated with the IIoT, particularly in terms of security. The complex and heterogeneous nature of IIoT systems makes them vulnerable to sophisticated security attacks at various levels of networking and communication architecture. These challenges can lead to mistrust in network operations, privacy breaches, and the loss of critical data. 4. Advantages and Disadvantages of IIoT Devices: The introduction presents a clear and concise list outlining the advantages and disadvantages of IIoT devices in Table 1. The benefits include enhanced efficiency, improved data collection, cost savings, and remote monitoring. On the other hand, the disadvantages include security risks, compatibility issues, lack of standardization, and high implementation costs. 5. Focus on Industrial Privacy in IIoT Ecosystems: The research aims to address the crucial aspect of industrial privacy in an IIoT ecosystem. It defines industrial privacy as the protection of sensitive information and personal data within industrial settings, such as manufacturing plants and supply chains. The research emphasizes the need to implement security measures, policies, and practices to safeguard confidential information from unauthorized access and misuse. Identification of Privacy Dimensions: The study delves into the identification and definition of privacy dimensions within the IIoT ecosystem. These dimensions represent different aspects or components of privacy that are considered when addressing privacy concerns. By understanding the multifaceted nature of privacy, the research aims to guide the development of privacy frameworks and practices. * Techniques and Best Practices for Data Privacy and Security: The paper explores contemporary techniques, technologies, and best practices used to ensure data privacy and security in IIoT systems. It includes an analysis of the latest methodologies and tools implemented to maintain data confidentiality, integrity, and availability in industrial environments. * Establishing a Safe and Private IIoT Ecosystem: The research aims to identify how organizations can establish an ideal, safe, and private IIoT ecosystem within the industrial domain. It delves into various considerations and factors that need to be taken into account to create a secure environment. Additionally, the study offers recommendations on implementing privacy-enhancing measures and adhering to industry standards to effectively manage privacy risks and regulatory compliance.
# Risk-Based Selection of Inspection Method for External Post-Tensioning System of Bridges
## 2 Literature Review
### Defining and Classifying Applicable Inspection Methods
#### 2.2.9 Sensors  
Time domain structural health monitoring is often done using sensors. In the case of the selection of the inspection method, by default, there are methods in which the inspection time is limited to the days of the presence of inspection agents, while in the inspection by time domain methods, the entire data of inspection interval is monitored. A variety of sensors have been named in the literature; however, fiber optic sensors, accelerometers, and strain gauges are more common within them. Fiber optic sensors are lightweight, unobtrusive, and insensitive to electromagnetic interference [61]. Accelerometers are used to measure the stiffness, define the modal shapes, and find the force within post-tensioning elements along time continuously. The use of strain gauges data and corresponding time domain records for analysis of section loss and corrosion is sophisticated, since all environmental parameters and load patterns influence the strain. Nevertheless, comparing the structural behavior pattern over time can be considered a sign of deterioration [62; 63]. Figure 10[9] shows the strain gauge on the post-tensioning strand.
## 4 Simulation Experiments
### The Simulation and Analyzing for Anomaly Detection and Recognition Method  
In the above, the simulation experiments are conducted to verify the ability of each indicator to separate different anomalies. This part is to test the diagnosis performance of the proposed method. To conduct the Monte Carlo simulation test, the isolated outliers, outlier patches, transient faults, noise faults, constant drift faults, and multiplicative faults are injected into the original observation data 500 times for each type. In each simulation experiment, only one anomaly is injected into the normal signal, and the injection moment is random. Other simulation conditions and parameters are the same as in Section 4.1.  
The probability of correct detection (PCD) and the probability of correct recognition (PCR) are counted out and shown in Table 7. From Table 7, the PCD and PCR of the new method can both reach a high level. We can conclude that the proposed method is capable of anomaly diagnosis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Data Anomalies Types**} & \multicolumn{2}{c}{**Distribution**} \\ \cline{2-3}  & **\textless{}37.16\%** & **37.16\%\(\leq\)** \\ \hline Constant Drift Fault & 100 & 0 \\ Complete Fault & 0 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The distribution of the mean-value-crossing rate of constant drift faults and complete faults.  
Figure 12: The distribution of the mean-value-crossing rate of constant drift faults and complete faults.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 6 Discussion and Conclusions
### Abilities and Characteristics Need to Be Carried Forward  
In fact, all the abilities or characteristics of these state-of-the-art TNGAPMSs need to be carried forward and some of them can be improved in future systems.  
_Mobility of Carriers:_ The mobility of the carriers enables one sensor node to cover a sufficient large geographic area within a short period of time. The number of sensor nodes required is reduced and the system cost and maintenance are relaxed. In fact, the multi-rotors (There are regulations for drones in some areas and we put aside this issue for a moment in this paper.) will not be trapped into traffic-jams or stop by unreachable areas as the carriers in VSN systems. The multi-rotors have much higher mobility than the carriers in VSN systems.  
_Feasibility of Maintenance:_ The system's feasibility on maintenance need to be carried forward and enhanced because it affects the data quality and cost efficiency of the system. If the fully controlled UAVs were utilized in the system, professional maintenance on large number of sensor nodes can be performed simultaneously by driving all UAVs to a specific location. In this case, the quality of the sensed data is guaranteed while the time and manpower for maintenance are saved.  
_Add-on Sensor Ability:_ We note that all the sensor nodes in the existing TNGAPMSs are with no add-on ability. Reconfigurations on the hardware and software of the sensor nodes are needed whenever the sensing species are modified. In real-life large-scale applications, there could be hundreds or even thousands of sensor nodes in the system. Sensor nodes with add-on (the sensor node is able to identify the type of sensor mounted and chooses the suitable program to handle the sensing data) ability are essential in this case. Properties like modifiable sensing and transmitting intervals, remote programmable ability, cost and energy efficiencies and failure check feature are also essential.  
Last but not least, all the existing state-of-the-art TNGAPMSs claim that they have a better spatio-temporal resolution than the conventional air pollution monitoring systems (which is obvious). However, none of them has ever considered how good they are, not to mention the comparisons among the SSN, CSN and VSN systems, with respect to real-time performance, spatio-temporal resolution and QoS. And this will be a major direction of our future works.