esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Actual Seismic Data Application  
The investigation area is located in the northern part of the Tarim Basin in northwestern China (Figure 4). It is a floodplain of the Tarim River. The terrain is relatively flat, and the surface elevation is 935-945 m. The underground structure is located in the Tabei Uplift-Northern Depression of the Tarim Basin. It is the slope part of the Lunnan Low Uplift that dips southward into the Northern Depression. Core slice analysis shows that the primary pores in the carbonate of the Yijianfang (TO\({}_{2y}\)) and Yingshan (TO\({}_{1-2y}\)) formations in the investigation area have almost disappeared. The secondary pores are chiefly secondary pores, accounting for more than 95% [46; 47], and the reservoir space are mainly pores, holes and cracks formed by secondary dissolution and structural fracture.  
The SSFCRs in the investigated area have undergone multiple faults and deep hydrothermal action, and the reservoir reformation is very strong, forming a complex karst fractured-cavity system with strong heterogeneity and complex and diverse reservoir types (Figure 5a,b). This kind of carbonate reservoir demonstrates a reflection type characterized by low frequency and weak-strong amplitude composed of trough-peak or trough-peak-rough on the seismic profile, which is the so-called \"beaded\" reflection (Figure 5c). Because karst is controlled by faults, the lateral distribution characteristics of the reservoir show the characteristics of strips spreading along the faults (Figure 5d).  
Figure 4: Distribution map of primary and secondary structural units and location map of three investigate areas in Tarim Basin.  
The application blocks of the actual data are selected in the SHB-YM, HD-YK and YGU blocks in the FMOF. The structural characteristics of the three blocks are different, including gentle structure, monoclinic and complex dome anticline. However, there is a common feature that a large number of strike-slip faults are developed, and high-quality reservoirs are concentrated near the faults. The three blocks are very suitable for carrying out the practical application and analysis of the method in this paper.
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 1 Introduction  
Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by difficulties in social interactions, language delay, and repetitive behaviors. The World Health Organization (WHO) reports that ASD affects 1 in 160 children worldwide [1]. Moreover, the severe deficits associated with ASD place a significant health and financial burden on the global community [2]. Given the increasing prevalence of ASD, it is important to further develop ASD diagnostic tools to reduce the impact of this burdens and to better manage ASD subjects. In the Diagnostic and Statistical Manual of Mental Disorders (DSM), the American Psychological Association (APA) has classified ASD into three subtypes based on impairment symptoms: classic autistic disorder (ASD), Asperger syndrome (APD) and pervasive developmental disorder--not otherwise specified (PDD-NOS) [3]. The different impairment ratings for ASD subtypes are based on repetitive behaviors, verbal skills, social interaction and communication. Diagnosis of autism is a difficult task because there is no standard medical test for accurate diagnosis [4]. Current clinical practice usesvarious questionnaires based on cognitive characteristics and behavioral observations for diagnosis of ASD [5; 6; 7; 8]. The disadvantages of these clinical assessments, which require direct interaction between the child and the clinician, are that they are time-consuming and costly. Because symptom-based diagnostic criteria depend on observational techniques and subjective decisions, autism researchers point out that diagnoses of ASD types vary widely from clinic to clinic, even when they are all based on the same standard tests [8]. Because clinical results are imprecise, there is a need to find accurate biological markers to automate the ASD diagnostic process [9]. Automated solutions based on artificial intelligence (AI) can enable rapid ASD classification, hence increasing the reliability and accuracy of diagnostic results [10; 11]. Experts have recently developed many AI algorithms for ASD diagnostic models based on functional magnetic resonance imaging (fMRI). Previous works have shown that resting-state functional magnetic resonance imaging (rs-fMRI) data play an essential role in diagnosing ASD. Because rs-fMRI has better spatial resolution, it enables the functional analysis of deep brain structures [11]. In particular, rs-fMRI is commonly used to study functional connectivity (FC) of the brain at rest by detecting fluctuations in blood oxygenation-dependent (BOLD) signals. Essentially, FC identifies the spatio-temporal correlations between brain regions based on BOLD signals [12]. The different patterns of FC in ASD are mainly used with AI algorithms to create ASD classifiers that distinguish ASD from normal cases (NC) [13; 14; 15].
# Determining Factors of Fixed Offshore Platform Inspections in Indonesia
## 5 Discussion
### Anomalies and Defects  
The actual conditions of platforms fixed at sea significantly affect the inspections that are to be carried out. The worse fixed offshore platforms are, the more rigorously they will be inspected. Their actual conditions can be seen from previous inspections, defects (corrosion), and damaged elements [16]. Fixed offshore platforms are inevitably exposed to interactive and aggressive environmental conditions for long periods, such as salt spray,temperature, precipitation, tides, and severe corrosion due to chemical and electrochemical reactions [55]. The structural corrosion of fixed offshore platforms significantly impacts the ultimate fatigue strength of the underlying structure [56]. Increased cracking, corrosion, and damage reduce the ultimate strength, leading to the risk of the collapse and failure of all offshore fixed platforms, leading to loss of life, property, and pollution [57].  
The third case study, Platform Z in Indonesia, has safe structural conditions and no physical damage, but there is some corrosion on the bracing and a decrease in cathodic protection up to 10%. With such anomalous conditions and defects, the platform is considered to have a medium risk and is set to carry out underwater inspections at intervals of 7 years.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### The Worst Case TEST Conditions  
For the component-level tests, worst case configuration is well defined and described in standards [26; 27] and literature [28; 29]--both in terms of component configuration (such as applied bias) and in terms of environmental conditions (such as temperature or dose rate). In the case of system tests, the definition/choice of the worst-case conditions is more complex and might be the primary issue when considering qualification by only system-level tests.
# A Data-Driven Situational Awareness System for Enhanced Air Cargo Operations Emergency Control
## 4 Communication Link  
The ULD is designed to take measurements from sensors independently using settings ordered by the HMI wirelessly via Message Queuing Telemetry Transport (MQTT) protocol for when and how often to report that information. The ULD-internal timing of settings is supervised by RTOS mechanisms. The connectivity main frame of ULD can be seen in Figure 7.  
It is a procedure that is fully automatic, with only one care of the installer agent: that the ULD and HMI are close enough for the Wi-Fi connection when the power is on. When the device (ULD) is given power, internal communications are ensured. If that is not ensured, the device resets. Otherwise, the device scans for Wi-Fi with Service Set Identifier (SSID) beginning with \"INTELLICONT\", which the HMI broadcasts, and connects to the one with the strongest signal. This means that there can be many installations with \"INTELLICONT\" HMI nearby, but the ULD will connect to the closest one. One should consider that this will happen when a ULD is powered on. Again, if no networks are found, or if the Wi-Fi connection cannot be established, the device resets. That functionality can be power consuming, thus it is recommended that the matter of when to power the ULD be cared for. Then, a connection to HMI's Message Queuing Telemetry Transport (MQTT) server with pre-configured static settings is tried a maximum number of times before a reset. If successfully connected to the MQTT server, the MQTT Ack - encryption  
Figure 7: ULD connectivity block diagram.  
key exchange happens between the ULD and HMI, as seen in the sequential diagram of Figure 8. After that, having secured the connection, the ULD awaits orders from the HMI to serve each of the HMI-ULD needed functionalities.
## 3 Results and Discussion
### Validation of Calculated C-NMR Chemical Shifts of Flavone  
The \({}^{13}\)C NMR chemical shifts of unsubstituted flavone (0-HF) and 19 mono-substituted flavone derivatives have been experimentally measured in dimethyl sulfoxide-d\({}_{6}\) (DMSO) by [PERSON] et al. [7]. To validate the reliability of our computational approach, Table 2 presents a comparison between the DFT-calculated \({}^{13}\)C chemical shifts for 0-HF (at the B3 PW91/cc-pVTZ level in DMSO) and the experimental values reported by both [PERSON] et al. [6] and [PERSON] et al. [7] in the same solvent. The calculated shifts exhibited a good overall agreement with the experimental data, with a root mean square deviation (RMSD) of 0.85 ppm relative to [PERSON] et al. and 0.93 ppm relative to [PERSON] et al., indicating the robustness of the DFT method in reproducing experimental chemical shifts in polar solvents.  
The largest deviation was observed at the C2 carbon, where the calculated value differed by 2.21 ppm from the measurement reported by [PERSON] et al., and by 2.41 ppm from [PERSON] et al.'s data [6,7]. Such deviations at specific sites--especially those adjacent to the carbonyl and aromatic regions--may arise from subtle solvation effects, conformational flexibility, or limitations in the treatment of solvent-solute interactions within the implicit solvation model used. Nonetheless, the overall consistency affirms the suitability of this computational protocol for extending chemical shift predictions to substituted flavone systems, including the structurally and electronically complex 3-hydroxyflavone.
## 4 Experimental Modal Parameters Analysis
### Measured Modal Frequency under Different Wind Speeds
#### 4.2.1 Identification Method of Modal Parameters
The above analysis shows that wind load has great influences on the fundamental frequencies of high-rise buildings, while codes in many countries (American UBC 1997 [30], European codes [31], Japanese Building Standards Law [32], and Load Codes of China [33]) have not taken the influences of wind-induced response on the fundamental frequencies into consideration, thus the fundamental frequencies calculation formulas of high-rise buildings considering the influences of wind speed were put forward in this paper:  
\[\frac{1}{T_{(l,s,t)}(\overline{V})}=\frac{1}{T_{0(l,s,t)}}-\alpha_{(l,s,t)} \overline{V} \tag{15}\]  
In Equation (15), \(T_{l}\), \(T_{s}\), \(T_{l}\) represent the fundamental natural periods along the X axis, Y axis, and torsional direction, respectively, when the basic time duration of the mean wind speed(\(\overline{V}\) m/s) is 10 min. \(T_{0l}\), \(T_{0s}\), \(T_{0l}\) represent the fundamental natural periods along the X axis, Y axis, and torsional direction under static wind condition. \(\overline{V}\) is the mean wind speed within 0-20 m/s when the basic time duration is 10 min; H is the total height of the building. \(\alpha_{(l,s,t)}\) is the wind load sensitivity coefficient of fundamental frequency, and the corresponding \(\alpha_{(l,s,t)}\) of the measured high-rise building along the X axis, Y axis, and the torsional direction are 0.0027, 0.0019, and 0.0019 respectively. Figure 18: The relationships between the first three-order modal frequencies and mean wind speeds along the X axis, Y axis, and torsional direction: (**a**) The first-order modal frequency along the X axis; (**b**) The second-order modal damping ratio along the X axis; (**c**) The third-order modal damping ratio along the X axis; (**d**) The first-order modal frequency along the Y axis; (**e**) The second-order modal frequency along the Y axis; (**f**) The third-order modal frequency along the Y axis; (**g**) The first-order modal frequency along the torsional direction; (**h**) The second-order modal frequency along the torsional direction; (**i**) The third-order modal frequency along the torsional direction.
# Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks
## 2 Models and Signals
### Wave-Spectral Scaling Relation  
In practice, the Fourier scaling relation can be equivalently replaced by the wave-spectrum scaling relation. The wave-spectral analysis can be applied to self-affine fractal structure of temporal and spatial signals [20]. Traditionally, the wave-spectral function is defined as follows  
\[W(k)=\frac{|F(k)|^{2}}{N}=\frac{F(k)\overline{F(k)}}{N}=\frac{a^{2}+b^{2}}{N} \tag{9}\]  
where \(W(k)\) denotes wave-spectrum density. For the self-affine fractal series, there is a scaling relation between wave-spectrum density and wave number, that is  
\[W(k)=W_{0}k^{-\beta} \tag{10}\]  
where \(\beta\) is spectral exponent, and \(W_{0}\) refers to the proportionality coefficient. It can be proved that \(\beta=2\alpha\). Thus the self-affine record dimension can be expressed as [12; 13; 14; 19]  
\[D_{s}=\frac{5-\beta}{2}=\frac{5}{2}-\alpha=2-H \tag{11}\]  
where \(H\) denotes Hurst exponent [12; 13; 22; 30]. Accordingly, the self-similar trail dimension can be calculated by [19; 22]  
\[D_{f}=\frac{7}{2}-D_{s}=\frac{\beta}{2}+1=\alpha+1 \tag{12}\]  
That is to say, a self-affine fractal can be approximately described with self-similar fractal dimension. The self-similar process and self-affine process are related to and different from one another. In fractal theory, self-similarity is a special case of self-affinity. The former means isotropic growth, and the latter means anisotropic growth. However, for a given direction, a self-affine process can be treated as self-similar process.  
The fractal dimension is a spatial characteristic quantity, and different types of fractal parameters indicate different spatial properties. In the geographical world, there are corresponding relationships between temporal processes, hierarchical structure, and spatial patterns [21; 31]. Time series analysis can be turned into spatial series analysis, and vice versa [32]. In fact, for geographical systems, different types of signals correspond to different types of geographical space (Table 1). Time series reflect phase space, spatial series represents real space, and hierarchal series reflect order space [33]. Different spaces have different fractal dimensions. Real space can be described with box dimension and radial dimension, phase space can be described with correlation dimension, and order space can be described with similarity dimension. The fractal-based spatial signal analysis is associated with radial dimension, which can be measured and calculated with the cluster growing method, or the radius-number scaling method [26]. The spatial signals are extracted by means of concentric circles. For urban form and transportation networks, concentric circles can be drawn with the center of a city as the center.
## 4 Conclusions  
This paper systematically investigates the relationship between the composition of a series of PSA emulsions, the performance parameters of the latex film, the compression-fold ratio, the fracture energy, and the fracture toughness of their modified mortars. The conclusions drawn are as follows:  
1. With an increase in the relative content of St in the PSA emulsion, the fracture energy of the formulated modified mortar first increases and then decreases, consistent with the pattern of change in the damping properties of the latex film. Among them, the fracture energy of modified mortar SA4M 15 is the highest, reaching 211.2 N/m, which is approximately 2.4 times that of the blank mortar control group.
2. The compressive-flexural ratios of the series of PSA emulsion-modified mortars are associated with the polymer emulsion dosage; they decrease as the P/C ratio increases, with a minimum value of 3.44. However, there is no obvious correlation between the compressive-flexural ratio and the composition of the PSA emulsions themselves, as well as the performance parameters of the latex films.
3. The fracture toughness of the series of PSA emulsion-modified mortars is also associated with polymer emulsion doping. The maximum load of the fracture test decreases significantly with an increase in P/C ratio, and the fracture toughness also decreases.  
Figure 12: The microscopic morphology of mortar: (**a**) M 0; (**b**) SA4M 15.  
The lowest value is approximately 0.4 MPa-m\({}^{1/2}\), which is nearly half that of the control group. At the same dosage, the fracture toughness increases with an increase in the proportion of St monomer in the PSA emulsion, although there is no obvious relationship with the damping performance parameters of the latex film.  
G.M.: Writing--original draft, investigation, resources; P.J.: writing--original draft and investigation; W.G.: writing--review and editing, and validation; W.M.: writing--review and editing, and methodology; G.C.: writing--review and editing, and funding acquisition; X.L.: validation and resources; Y.G.: writing--review and editing, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 52478290) and by the Fund of China Academy of Railway Sciences (2022Y129).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
We extend our sincere gratitude to [PERSON] and [PERSON] for their invaluable technical guidance.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Railway Engineering Research Institute, China Academy of Railway Sciences Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Techniques Used in Academia to Improve Performance Metrics
### Frequency Diversity
#### 4.1.4 Network Coding  
The broadcasting nature of wireless networks offers opportunities to address WSNs problems, such as lack of reliability and low throughput. This characteristic provides a large overlap in the information available in the network. While two nodes are communicating, the same information is being delivered to the nodes within the transmitter's communication range. This data redundancy in the network can be used to improve the robustness of the lossy wireless networks, as well as the throughput, and also to save bandwidth. In addition, application of network coding in the WSNs requires a simple, easily implemented protocol.  
Robustness enhancement on data transmission can be achieved by applying network coding in wireless networks. Network error correcting codes and network erasure correcting codes are considered as two possible techniques which use spatial redundancy concepts to improve robustness [40]. Network coding can help to enhance the robustness in data transfer by playing a similar role in traditional FEC, _i.e._, source-based coding schemes. In [41], [PERSON] and [PERSON] apply the network erasure correcting codes in wireless sensor and wireless mesh networks to provide proactive protection against link failures. In [42], [PERSON] _et al._ show that network coding performs better than probabilistic routing in terms of reliability and robustness.  
Network coding can be applied to infer internal network characteristics, called network tomography. Loss tomography is referred to as the study of link loss rate inference through network coding. In [43], authors study the loss inference problem in sensor networks and present a passive loss network tomography scheme using network coding. However, in [44], authors study the identifiability problem and precisely compute the loss rate of links, rather than merely categorizing the links as either good or bad, as done in [43].
###### Abstract  
We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors.  
support vector machine one class; burned area; active fire; Cerrado; PROBA-V; VIIRS
## 5 Object Depth Modelling
### Object Depth Estimation from Images  
A full depth map of an image is used to arrange a vector data array, \(\mathbf{v}_{0}\), of all depth values. Then, a new vector, \(\mathbf{v}_{1}\), is obtained by sorting the elements of \(\mathbf{v}_{0}\). The histogram of \(\mathbf{v}_{1}\) is calculated and used as a feature vector, \(\mathbf{v}_{\mathbf{Feature}}\), which represents the relative estimated depth of the objects in the whole image.
# A Power-Gated 8-Transistor Physically Unclonable Function Accelerates Evaluation Speeds
## 4 8-Transistor PUF Performance Measurements
### Randomness  
Firstly, the Applied Auto-correlation Function (ACF) was used to analyse the spatial correlations among 64 groups of 2048 8T PUFs. The result is 0.0315, which is close to zero, within the 95% confidence bound. This indicates a low spatial correlation of the 8T PUFs based on the physical extraction from the layout design. Then, the randomness of 64 groups of 2048 8T PUFs was assessed with the NIST SP 800-22 [63] statistical test suite. The NIST results are listed in Table 3. The simulation results passed most of the tests. However, the randomness generated by the simulator has limitations, especially for the large amount of data which is required by both the ACF and NIST test suite. Moreover, this practical simulation method is time and resource consuming. A test chip for facilitating on-chip statistical experiments is under development to improve the evaluation efficiency and verify the PUF behaviour in the real world.