esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Problem** & **Objective** & **Future Work** \\ \hline \begin{tabular}{l} Inefficient resource utilization in real environments. \\ \end{tabular} & \begin{tabular}{l} Minimize idle resources to enable \\ efficient resource use. \\ \end{tabular} & \begin{tabular}{l} Study and promote the use of \\ objectives such as minimizing idle \\ resources in real-world scenarios. \\ \end{tabular} \\ \hline \begin{tabular}{l} Inability to consider the size and \\ complexity of different projects with \\ current solution methods. \\ \end{tabular} & \begin{tabular}{l} Develop advanced methods tailored \\ to current project requirements. \\ \end{tabular} & \begin{tabular}{l} Investigate and expand the \\ application of multi-agent systems as \\ a promising approach for solving \\ multi-project scheduling problems. \\ \end{tabular} \\ \hline \begin{tabular}{l} Limited evaluation of proposed \\ algorithms in the RCMPSP domain. \\ \end{tabular} & \begin{tabular}{l} Enhance the reliability and \\ effectiveness of algorithms for \\ multi-project scheduling. \\ \end{tabular} & \begin{tabular}{l} Conduct thorough assessment and \\ comparison of algorithmic execution \\ to advance the field of RCMPSP \\ research. \\ \end{tabular} \\ \hline \begin{tabular}{l} Limited exploration of AI and hybrid \\ methods in dynamic multi-project \\ settings. \\ \end{tabular} & \begin{tabular}{l} Improve adaptability and efficiency \\ in complex, uncertain environments. \\ \end{tabular} & \begin{tabular}{l} Investigate hybrid metaheuristic and \\ AI models for stochastic multi-project \\ scheduling. \\ \end{tabular} \\ \hline \begin{tabular}{l} Inability to adapt schedules in real \\ time when project dynamics change. \\ \end{tabular} & \
\begin{tabular}{l} Increase responsiveness and \\ robustness of project plans. \\ \end{tabular} &
\begin{tabular}{l} Dynamic project environments: \\ investigate rescheduling strategies in \\ environments with dynamically \\ arriving and departing projects. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Problem, objective, and future work.
## 4 Simulation Results and Analysis
### Bit Error Rate Analysis  
Based on the parameters of the TianQin constellation, the BER of the simulation system was analyzed for different modulation indices. The results and their corresponding fitted curve are shown in Figure 18.  
From the fitted curve in Figure 18, it is evident that the BER is better than \(10^{-6}\) when the modulation index exceeds \(3.4\times 10^{-3}\). This indicates that the inter-satellite laser communication simulation system meets the requirements for space-based gravitational wave detection. Furthermore, all the measured BER data are based on the inter-satellite communication signal scheme designed in Figure 10, where the data rate for the transmission of the data code is 62.5 kbps. Therefore, the fitted curve corresponds to a data-transmission rate of 62.5 kbps.  
Figure 16: Comparison of the time–domain waveforms between the demodulated communication ranging code and the original communication ranging code.  
Figure 17: Comparison of the time–domain waveforms between the despread data code and the original data code.
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 3 Solution Method  
The BPR95 model is more complex in structure than the original BPR model and cannot be transformed into a linear model through appropriate transformations, making calibration more difficult. We propose three algorithms to solve this optimization problem.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 3 Types of LiDARs  
LiDAR instruments can be classified based on several criteria. A common classification was mentioned earlier in the introduction--based on the deployment platform. However, for the purposes of this paper, LiDAR systems are categorized on the basis of their light-target interaction phenomena. Based on this criterion, LiDAR instruments can be classified as follows [24, 69]:  
* Atmospheric backscattering LiDARs.
* Differential absorption LiDARs (DIALs).
* Doppler (wind) LiDARs.
* Ranging and altimeter LiDARs.
* Full-waveform LiDARs.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
###### Abstract  
Our study introduces a new approach, leveraging robotics technology and remote sensing for multifaceted applications in forest and wildfire management. Presented in this paper is PULSAR, an innovative UAV with reconfigurable capabilities, able of operating as a quadcopter, a co-axial quadcopter, and a standalone octocopter. Tailored to diverse operational requirements, PULSAR accommodates multiple payloads, showcasing its adaptability and versatility. This paper meticulously details material selection and design methods, encompassing both initial and detailed design, while the electronics design section seamlessly integrates essential avionic components. The 3D drone layout design, accomplished using SOLIDWORKS, enhances understanding by showcasing all three different configurations of PULSAR's structure. Serving a dual purpose, this study highlights UAV applications in forest and wildfire management, particularly in detailed forest mapping, edge computing, and cartographic product generation, as well as detection and tracking of elements, illustrating how a UAV can be a valuable tool. Following the analysis of applications, this paper presents the selection and integration of payloads onto the UAV. Simultaneously, each of the three distinct UAV configurations is matched with a specific forest application, ensuring optimal performance and efficiency. Lastly, computational validation of the UAV's main components' structural integrity is achieved through finite element analysis (FEA), affirming the absence of issues regarding stress and displacement. In conclusion, this research underscores the efficacy of PULSAR, marking a significant leap forward in applying robotics technology for wildfire science.  
wildfire management; unmanned aerial system; reconfigurable design; remote sensing; forest management; computational structural analysis +
Footnote †: journal:  
Article
# A Microfluidic Approach for Biosensing DNA within Forensics
## 3 Biosensing of DNA
### Next Generation Sequencing  
DNA sequencing is an essential technique within biotechnology, virology and medical diagnostics. In addition, for forensics these techniques have gained attention. In cases of a limited amount of DNA or highly degraded DNA the current NGS systems can help out.  
The power of sequencing is that standard STR-profiles can be generated, but also DNA repeats can be sequenced to look for polymorphisms. To acquire information on ancestry, paternity or phenotype additional single-nucleotide polymorphisms (SNPs) can be analyzed with NGS techniques. However, the NGS workflow takes much longer than the conventional STR-profiling workflow (typically days versus hours).  
There are several NGS machines on the market, such as the systems from Life Technologies, Illumina and PacBio. For forensic applications basically only the MiSeq/HiSeq (Illumina) and the Ion Torrent are used, with Nanopore as an upcoming sequencing system for forensics. A more elaborate review on NGS for forensics is given elsewhere [22].  
Figure 1: Examples of polymerase chain reaction (PCR) chip designs: (**A**) well-based (cross-section), (**B**) fixed-loop (top view), (**C**) oscillatory (top view) and (**D**) closed-loop systems (top view). The microfluidic amplification chamber (well-based) or channel (continuous-flow) is given in grey. The temperature zones (continuous-flow) are given in the various shades of red.
## 4 Internet Access for Women in Developing Countries
A higher GEM score (which indicates more gender equality), for example, is negatively associated with a gender gap in computer use and, inversely, gender inequality at the societal level is closely associated with a gender gap in computer use at home. This suggests that \"pre-existing measures of economic and social inequality are reasonable predictors of inequality in IT usage\" [9] (p. 1147). In a recent study undertaken by Intel [21], in which 2200 women and girls from India, Egypt, Mexico and Uganda took part, it was found that 25% of women in developing nations lacked Internet access and this figure was as high as 45% in Sub-Saharan Africa. The report highlights the stubborn gap in women's access to the Internet in Africa, but also in the Middle East and other developing parts of the world. According to [PERSON] [22], there are two critical factors that influence whether or not women can access the Internet: availability and affordability. As such, one of the recommendations outlined in the Intel report was for policymakers and technology companies to make the Internet more accessible and affordable on mobile phones [23]. It is estimated that, in developing nations, an Internet connection can cost up to 40% of annual per capita income, compared to just 1.7% of per capita GrossNet Income in wealthier nations. [PERSON] notes that, while these barriers apply to both genders, they hinder women more than men because other cultural obstacles compound the problem. Internet cafes, for example, are an easy way to access the Internet but they are often impractical for women who cannot leave home for religious and cultural reasons [22], and/or because they are intimidating for women due to low technological expertise and their belief that they are socially unwelcome [24]. Although there appears to be problems in disaggregating sex-specific information regarding access and use of the Internet in developing nations, men undoubtedly have greater access to the Internet than women. However, given that Internet access exceeds Internet use by women in developing countries, more research is required to identify the barriers women face to taking advantage of the online world.
## 3 Algorithm Design and Analysis
### TCP-SCTP Method
#### 3.3.2 Parameter Descriptions in TCP-SCTP Method  
The TCP-SCTP method predicted the network conditions according to the packet loss rate. In the method, some relevant parameters are shown in Table 4.  
In the TCP-SCTP method, the DetectSlot variable recorded the calculation interval of packet loss rate, initially set to 16 s. Each change in the DetectSlot was a multiple of two. The packet (segment) loss rate could be calculated by the sender. The Count variable recorded the consecutive increase times of packet loss rate. The LastRate and the Rate recorded the previous packet loss rate and the current packet loss rate. The Max array was used to store the maximum packet loss rate information on each path. The array subscript corresponded to the path label, and if the path was unreachable, it was set to 101 (which was corresponding to the Max array). The TCP-SCTP method defined the Min array to store the minimum packet loss rate information on each path. The elements in the Min array and in the Max array were initialized to zero.
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 2 Technical Background
### SafeCOP Use Cases
#### Vehicular Applications
This system will represent one of the SafeCOP demonstrators. It is composed of on-board (OBU) and road side (RSU) units, and a server-based Control Center. Communications between the parts of the demonstrator system are performed through radio frequency front-ends which transmit and receive on-the-air, or through attenuators and noise generators for testing purposes. The OBU integrates radio communication and inertial sensors, allowing additional information on vehicle behavior to be received by the RSU. The RSU acquires video from the camera and performs the necessary initial elaboration to reduce communication times, aggregates information from vehicles in its operating range, and transmits the information over a wired connection to the remote Control Center. Figure 5: Typical scenario for a V2I cooperation system for traffic management.
# Semantic Multigranularity Feature Learning for
## 4 Experiments  
### Dataset  
In the experiments, the proposed SMGFL-Net and baselines are evaluated on the NWPU-RESISC45 [25] and AID datasets.  
* NWPU-RESISC45 is an open-source RS image scene dataset created by Northwestern Polytechnical University. This dataset contains 31,500 images, covering 45 scene classes. Each category consists of 700 images measuring 256 \(\times\) 256 pixels. For most scene categories, the spatial resolution varies from 0.2 m to 30 m per pixel. Exceptions are the categories of island, lake, mountain, and snowbank, which are with lower spatial resolutions. NWPU-RESISC45 is divided into 45 scene categories as follows: Airplane, Airport, Baseball diamond, Basketball court, Beach, Bridge, Chaparral, Church, Circular farmland, Cloud, Commercial area, Dense residential, Desert, Forest, Freeway, Golf course, Ground track field, Harbor, Industrial area, Intersection, Island, Lake, Meadow, Medium residential, Mobile home park Mountain, Overpass, Palace, Parking lot, Railway, Railway Station, Rectangular farmland, River, Roundabout, Runway, Sea ice, Ship, Snowbank, Sparse residential, Stadium, Storage tank, Tennis court, Terrace, Thermal power station, and Wetland. In Figure 4, we display two scene samples per category for all categories. In these categories, there are a number of fine-grained scenes with small intervariance and large intravariance. Additionally, both global and local features are necessary for the recognition of certain categories. For example, as shown in Figure 1, images from Palace and Medium residential have similar global shapes, and there is a big difference between images in Palace category. The Medium residential and Density residential samples have local similarities, but differ in global characteristics such as spatial layout.
* As a dataset for aerial scene classification, AID is made up of the following 30 classes: airport, bare land, baseball field, beach, bridge, center, church, commercial, dense residential, desert, farmland, forest, industrial, meadow, medium residential, mountain, park, parking, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks, and viaduct. The number of sample images varies with different classes--from 220 up to 420. In all, there are 10,000 images within 30 classes in AID. Images in AID are from different remote imaging sensors. Moreover, all the sample images per class are carefully chosen from different regions around the world. AID is a challenging dataset due to the higher intraclass variations and smaller interclass dissimilarity. The pixel resolution in AID changes from about 8 m to about half a meter, and the size of each image in AID is fixed to be 600 \(\times\) 600.
## 2 Methodology
### Building and Thermal Zoning  
Building D' of the Aristotle University of Thessaloniki's Faculty of Engineering, which is located between Egnatia and 3 rd September streets, is the building under the investigation in this study. Building D', which has ten floors (including the ground floor), houses the secretariats of the various departments as well as the offices of the faculty's teaching and research staff. Some laboratories are also housed in the basement of the building.  
The building's overall volume and surface area are 44,671,197 m\({}^{3}\) and 10,595,451 m\({}^{2}\), respectively, of which 29,017,462 m\({}^{3}\) and 8,167,691 m\({}^{2}\) are heated spaces. The structure has not undergone renovation since it was built, and it is thermally insulated in accordance with the 1979 Greek law on thermal insulation [25]. For the building under consideration, the entire building's external surface that is exposed to air is A = 8714.90 m\({}^{2}\). The window-to-wall ratio is roughly 16%, and the overall area of glazing is 1147.78 m\({}^{2}\).  
To achieve a more accurate energy analysis, the building under study was divided into 12 thermal zones. Each of the 10 floors comprised a different conditioned thermal zone and the basement and roof were unconditioned thermal zones. On floors 1-9, most of the surface (70%) is occupied by offices, while auxiliary spaces and hallways occupy the remaining area. The zone that corresponds to the ground level of the building is made up of 70% ancillary spaces and 30% offices and secretariats.
## 4 Results and Discussion
### Quantitative Results
Furthermore, if the target object is present in the previous (\(t-1\))th frame and it is not present in \(t\)th frame, then the bounding box pair will not be generated in the future frame, ultimately blocking either of the previous nodes (\(t-1\)) or (\(t-2\)) resulting in the disappearance of the frame. Therefore, the chaining operation used in the proposed method is challenging to parameterize, and consequently, regressions do not guarantee optimization in frame label congruency.
## 2 Materials and Methods
### Estimation of the AOD  
As illustrated in Figure 1, the AOD has influence on the atmospheric transmittance, especially for the O\({}_{2}\)-B band. However, it needs to be noted that, although AOD has influence on the atmospheric transmittance, the effect is spectrally smooth, which is different from the effect by absorption. On the basis of Fraunhofer Line Depth principle, the influence on absorption depth is the main factor influencing the errors in SIF retrieval. Thus, compared to absorption, the scattering effect is less important in atmospheric correction for tower-based SIF measurement. As synchronous measurements of the AOD are not available for all flux sites, in this study, a method for estimating the AOD based on the downwelling irradiance spectra is proposed.  
The amplitude of atmospheric scattering effects is spectrally variable and stronger at shorter wavelengths. Therefore, we analyzed the relationship between AOD\({}_{550}\) and the ratio of the irradiance at 790 nm to that at 660 nm (E\({}_{790}\)/E\({}_{660}\)) based on the MODTRAN simulations. As shown in Figure 2, for a specific SZA, the value of E\({}_{790}\)/E\({}_{660}\) increases with AOD\({}_{550}\), and the relationship is close to linear. For a specific AOD\({}_{550}\), the value of E\({}_{790}\)/E\({}_{660}\) increases with the SZA. Accordingly, the AOD\({}_{550}\) can be estimated using a look-up table that has E\({}_{790}\)/E\({}_{660}\) and SZA as inputs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Value/Range** & **Unit** \\ \hline SZA & Solar zenith angle & 0–70, in steps of 0.5 & degree \\ VZA & View zenith angle & 0–70, in steps of 0.5 & degree \\ Sensor height & Sensor height & 25 & m \\ Altitude & Surface elevation & 1.5 & km \\ Atmospheric profile & Atmospheric profile & Mid-latitude summer & - \\ AOD550 & Aerosol optical depth at 550 nm & 0.1–1.0, in steps of 0.1 & - \\ Aerosol model & Aerosol model & Rural & - \\ Reflectance & Surface reflectance & 0.5 & - \\ Band model & Band model algorithm & MODTRAN® & - \\ RT mode & Radiation transport mode of execution & Thermal plus Radiance & - \\ Multi-scattering & Program executes with multiple & True & - \\  & DISORT discrete ordinate multiple & True & - \\  & scattering algorithm & True & - \\ Number of Streams & Number of streams to be used by DISORT & 8 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main input parameters for MODTRAN 5 simulations.
# Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
## 2 Theoretical Background
### Malware Detection Techniques  
Malware detection strategies can be divided into three categories based on signatures, heuristics, and requirements. These strategies describe and track malware, as well as taking countermeasures against it, to safeguard information systems, data, and resources from being harmed by it [27]:  
1. Signature-based detection technique: When malware is created, it has a signature, which may determine which malware family it belongs to. The majority of antivirus solutions use a signature-based identifying mechanism. The antivirus software decodes the corrupted file code and looks for malware-related sequences. Signatures of malware are stored in a database and are compared later during the detection process. This type of identification technique is also known as scanning or matching string or pattern. It could be static, dynamic, or hybrid in nature.
2. Heuristic-based detection technique: Heuristic-based identification detects or distinguishes between a system's normal and abnormal activities, allowing known and suspected malware threats to be identified and resolved. The heuristic-based detection technique is divided into two parts. In the first stage, the behavior of the mechanism is observed in the absence of an attack, and a record of essential details is kept that may be confirmed and tested in the event of an assault. This disparity is noted in the second phase to detect malware from a certain family. The action detector in the heuristic-based methodology is made up of the three basic components listed below. 1. Data collection: Under this component, the data collection is performed with either static or dynamic techniques. 2. Interpretation: This component turns the data obtained by the data collection component into the intermediate form after interpretation. 3. Matching algorithm: This component is used to match the transformed information to the behavior signature in the interpretation component.
3. Specification-based detection techniques: Applications are managed according to their parameters and controls for normal and abnormal behavior in specification-based detection approaches. This methodology is derived from a heuristic technique. The key difference is that heuristic detection techniques use machine learning and AI to identify a legitimate program's valid and invalid behavior. In contrast, specification-based detection techniques study the actions defined in the system specification. This method is more of a manual comparison of the system's typical operations. It solves the flaws of heuristic-based techniques by lowering the number of false positives and increasing the number of erroneous negatives. Each malware detection technique might be static, dynamic, or hybrid. Figure 1 shows an overview of the different malware detection techniques.