esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
To evaluate the practical application of PANI/GF-based supercapacitors, the long-term cycling stability at a current density of 0.35 mA cm\({}^{-2}\) under continuous charge/discharge operation is shown in Figure 9a. Satisfactorily, PANI/GF exhibits a steadily continuous charge/discharge process for 2365 cycles without any noticeable degradation, which maintains a capacitance retention of 96%. In Figure 9b, surface (I-II) and cross-sectional (III-IV) SEM images of PANI/GF images are obtained after a long-term cycling test. The little gel-like electrolyte is found on the surface of PANI/GF. The SEM images exhibit approximate surface morphology and architecture at the microscale compared with PANI/GF before cycling measurement, which indicates excellent cycling stability of PANI/GF-based supercapacitors. Figure 7: CV profiles of (**a**) GF and (**b**) PANI/GF at various scan rates of 10, 20, 30, 40, and 50 mVs\({}^{-1}\); (**c**) CV curves of GF and PANI/GF at 10 mVs\({}^{-1}\); and (**d**) EIS analysis of fiber-shaped flexible supercapacitors. The fiber-shaped supercapacitors display good specific capacitance and excellent energy density. Many publications report pure graphene fibers or conducting polymer/graphene composite fibers as electrode materials for flexible supercapacitors, and these are summarized in Table 1. In brief, the fiber-shaped supercapacitors constructed by PANI/GF and gel-like electrolyte indicate good electrochemical properties because of the more internal electroactive sites, the designed wrinkling, and the stable architecture between PANI and graphene sheets in composite fibers. This work will provide a fabrication method and theory foundation for high-performance graphene composite fiber-based flexible supercapacitors for future wearable electronic devices. Figure 8: **(a)** GCD profiles and (**b**) specific capacitance of GF, PANI/GF1, PANI/GF2, and PANI/GF3 at a current density of 0.05 mA cm\({}^{-2}\); (**c**) galvanostatic charge/discharge (GCD) curves; and (**d**) specific capacitance of PANI/GF at various current densities of 0.05, 0.1, 0.2, 0.3, and 0.4 mA cm\({}^{-2}\). Figure 9: **(a)** Cyclic stability of PANI/GF at a current density of 0.35 mA cm\({}^{-2}\) under continuous charge/discharge operation. Inset: GCD curves from the 2355 th to 2365 th cycles. **(b)** Surface **(I,II)** and cross-sectional **(III,IV)** SEM images of PANI/GF.
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 4 Discussion
### Experimental Setup or Environment
#### 4.1.1 ExDark [49] Datasets  
The ExDark [49] (Extreme Darkness) dataset is a large-scale low-light object detection dataset. It encompasses 10 different low-light scenarios, ranging from extremely low light to twilight, including environments such as city roads, residential areas, and parking lots. The ExDark [49] dataset contains 7363 images with 12 object categories, including dog, motorbike, person, cat, chair, table, car, bicycle, bottle, bus, cup, and boat, totaling 18,103 annotated instances. To address the current lack of real nighttime datasets, the ExDark [49] dataset provides a rich experimental foundation for researching object detection algorithms in low-light environments. By offering a substantial number of low-light images, the ExDark [49] dataset helps improve the accuracy of object detection in low-light conditions. In this experiment, the dataset was divided into 80% for the training set and 20% for the validation set. No additional preprocessing was performed.
## 2 Evolution, Virulence and Pathogenicity of _E. coli_ O157:H7
### Epidemiology of _E. coli_ O157:H7  
According to [PERSON] and [PERSON] [67], _E. coli_ O157:H7 is responsible for approximately 73,000 illnesses, 2000 hospitalizations, and 50-60 deaths annually in the United States. Reports that evaluated _E. coli_ O157 outbreaks that occurred in the USA between 1982 and 2002 and were reported to the Centers for Disease Control and Prevention (CDC) showed 350 outbreaks, totalling 8598 cases and 1493 (17%) hospitalizations. A total of 354 (4%) presented with HUS and 40 (0.5%) died [68]. This highlights the significant public health potential of _E. coli_ and also provides an overview of its epidemiology in the region.  
The varying aetiology of _E. coli_ O157:H7 infections explains why the pathogen has also been isolated in humans, animals, food products, and the environment, especially in the African region [69], where public health policies are not adhered to strictly. Examples of documented cases in Africa are as follows: human infection that was reported in Johannesburg, South Africa in 1990 [70]; the detection of the pathogen in individuals with haemorrhagic colitis that resulted to fatalities in Bangui, Central African Republic [71]; and the isolation of the pathogen in East African countries (Tanzania, Kenya, and Ethiopia) [72]. The prevalence rates of STEC O157:H7 reported in Africa were 7% in Morogoro, Tanzania, amongst patients suffering from diarrhoea [72], 2.3% isolated from raw cattle milk in Kwara, Nigeria [73], 1.9% amongst children with diarrhoea in Mozambique [74], and 8% in HIV infected individuals suffering from dysentery in Zimbabwe [75].
# Transmitter and Receiver Circuits for a High-Speed Polymer Fiber-Based PAM-4 Communication Link
## 2 Design of the RF-DAC-Based PAM-4 Modulator  
The topology uses emitter-coupled pairs (ECP), which function as a switch, to enable low power operation as well as fast switching (high data rate). An LO input is modulated to a PAM signal through different levels of amplification/attenuation applied to the signal. Furthermore, two ECPs are used for the PAM-4 signal. One pair is used for the most significant bit (MSB) and one for the least significant bit (LSB). The circuit is biased with negative voltage (V1 = \(-\)1.2 V, V2 = \(-\)2.8 V, V3 = \(-\)1 V, V4 = \(-\)1.7 V). A simplified schematic of the proposed PAM-4 modulator can be seen in Figure 2.  
Transistors Q2, Q4, Q6, and Q8 have a 12 \(\upmu\)m emitter length, while Q1, Q3, Q5, and Q7 have an 8 \(\upmu\)m emitter length. Q2 and Q6 are chosen to be relatively large to provide more gain for the RF output signal; Q4 and Q8 are chosen to be large for matching purposes. The LO input is fed at the base of transistors Q1 and Q5, which are both configured as emitter followers to provide a buffer between the LO input and the data input, while the data input  
Figure 1: Overview of the PMF link.  
is fed at the base of transistor Q4 for MSB, and Q8 for LSB. The voltage applied at V2 and V4 determines the amplitude change of the RF output of the corresponding ECP; thus, the MSB has a larger applied voltage at the resistor leading to the emitters of that pair. Both the LO input port and the RF output port are matched for D-band using shorted stubs.  
When a bit corresponding to '0' (0 V) is applied at the data input port, transistor Q4 or Q8 will be turned off, in turn also turning Q3 or Q7 off. The current will go through the Q2 and Q6 transistors, amplifying the LO input signal before delivering it to the RF output. When data '1' (0.5 V) are applied at the data input port, the current will be steered away from transistor Q2 or Q6, and the LO input signal will instead be attenuated before it is delivered to the RF output port.  
A photo of the RF-DAC-based PAM-4 modulator can be seen in Figure 3. The LO input signal is applied on the right side and the RF output signal is delivered at the left side of the chip. Dc bias is applied on the top, while the two data input ports are located at the bottom.  
The total size of the MMIC including pads is 0.55 mm\({}^{2}\), and 0.29 mm\({}^{2}\) without pads.  
Both the PAM-4 modulator and the PD were designed and simulated using the Keysight Advanced Design System (ADS), in the same InP DHBT process.  
Figure 3: A photo of the fabricated RF-DAC. The chip size including pads is 740 \(\upmu\)m by 740 \(\upmu\)m.  
Figure 2: A simplified schematic of the proposed RF-DAC-based PAM-4 modulator.  
In Figure 4, a simulation of the output power while sweeping the data input voltages between \(-0.1\) and \(0.6\) V can be seen. Four different LO power levels were used (\(-10\) dBm, \(-5\) dBm, \(0\) dBm, and \(5\) dBm).
# Investigations into Frost Flower Physical Characteristics and the C-Band Scattering Response
###### Abstract  
A dedicated study on the physical characteristics and C-band scattering response of frost-flower-covered sea ice was performed in an artificial sea ice mesocosm over a 36-h period in January 2017. Meteorological conditions were observed and recorded automatically at the facility when the sea ice grew and frost flowers formed while the C-band scattering measurements were conducted continuously over a range of incidence angles. Surface roughness was characterized using a LiDAR. During the experiment, frost flowers did not initially form on the extremely smooth ice surface even though suitable meteorological conditions prevailed during their development (low air temperature, low near-surface wind speed, and high near-surface relative humidity). This provides evidence that both the presence of (i) liquid brine at the surface and (ii) raised nodules as nucleation points are required to enable frost flower initiation. As the ice thickened, we observed that raised nodules gradually appeared, frost flowers formed, and flowers subsequently spread to cover the surface over a six-hour period. In contrast to previous experiments, the frost flower layer did not become visibly saturated with liquid brine. The C-band scattering measurements exhibited increases as high as 14.8 dB (vertical polarization) in response to the frost flower formation with low incidence angles (i.e., 25\({}^{\circ}\)) showing the largest dynamic range. Co-polarization ratios responded to the physical and thermodynamic changes associated with the frost flower formation process. Our results indicate that brine expulsion at the sea ice surface and frost flower salination can have substantial temporal variability, which can be detected by scatterometer time-series measurements. This work contributes towards the operational satellite image interpretation for Arctic waters by improving our understanding of the highly variable C-band microwave scattering properties of young sea ice types.  
radar; microwave; scatterometer; LiDAR; Arctic; sea ice; frost flowers; surface roughness; NRCS +
Footnote †: journal: Remote Sens. 10, 991
## 2 Methodology of Multi-Scale Subset and Subvoxel Shifting in DVSP
### Theory of Multi-Scale Subset and Subvoxel Shifting in DVSP
After that, the subset with the size (\(2^{n-1}\times 2^{n-1}\times 2^{n-1}\)) is applied, the corresponding subset in the deformed volumetric image is re-selected with reference to the first integer voxel predicted displacement (\(u_{0},v_{0},w_{0}\)), and the displacement value (\(u_{1},v_{1},w_{1}\)) of each subset is then calculated by the DVSP method. The subset size is gradually reduced according to above mentioned steps. When the subset size reaches a predetermined value and the obtained displacement values \(\Delta u\), \(\Delta v\), \(\Delta w\) are no more than 1 voxel in size, then surrounding an integral voxel of the crest a cubic subset with the size of 3 \(\times\) 3 \(\times\) 3 voxels is selected and a cubic spline interpolation is used to assess the sub-voxel value \(\delta u\), \(\delta v\) and \(\delta w\). The reference subset can be moved with an amount \(\delta u\), \(\delta v\) and \(\delta w\) by using the shifting property of Fourier transform. To weaken the edge effects and get good localization properties of Fourier transform, the shifted reference subset and the deformed subset are narrowed by a Kaiser window. Then, the subvoxel translation is calculated again. Some errors due to the interpolation require to reiterate by considering the \"reference\" subset with a new subvoxel shifting until a convergence criterion is matched. The final displacement value (\(u,v,w\)) of each subset can be obtained from the sum of the displacement values in the above steps. The flowchart of the MS-DVSP algorithm is shown in Figure 1. The procedures are programmed in MATLAB codes. Figure 1: Flowchart of the (Multi-scale and Subvoxel shifting Digital Volumetric Speckle Photography) MS-DVSP algorithm.
## 3 Materials and Methods
### Similarity Measures  
In this section, the significant impact of similarity measures (also called distance metrics) used to produce an accurate and compact model is demonstrated. Since there is a lack of distance metrics for CARs, developing such metrics will be an extremely important contribution to the field. After the strong association rules have been generated, as described in Section 3.1, they are then clustered. Since hierarchical agglomerative clustering will be used, the similarity (how far apart the two rules are) between CARs needs to be measured. Research studies have suggested a few indirect distance metrics for association rules, such as Absolute Market-Basket Difference (AMBD) [39], Conditional Market-Basket Probability (CMBP) and Conditional Market-Basket Log-Likelihood (CMBL) [40], as well as Tightness [41].
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 2 Principle
### The Composition of the Rotating Optical Measurement System  
The established rotating optical measurement system and the calibration unit include a checkerboard target, a camera, servo control units, computer processing units, etc. The schematic diagram of the measurement system is shown in Figure 1a.  
In Figure 1a, the camera is placed on the servo control units composing of a rotating platform and two translation platforms perpendicular to each other. The two translation platforms are fixed on the turntable to move the camera for multiple calibrations and final alignment. \(O_{r}\) is the rotation axis of the turntable. \(O_{c}\)-\(X_{c}Y_{c}Z_{c}\) is the camera coordinate system, and \(O_{w}\)-\(X_{w}Y_{w}Z_{w}\) is the fixed world coordinate system. We will give the explanations about these coordinate systems in Section 2.2. The checkerboard target is fixed in front of the system for camera calibration. A sequence of the images, which include the whole checkerboard or a part of the checkerboard, will be collected while the camera is rotated by the turntable.
## 5 Challenges of CV in Smart Agriculture
### Trustworthiness and Explainability  
The integration of AI-based CV technologies into agriculture introduces not only technical challenges but also socio-ethical concerns, particularly regarding transparency, interpretability, and trustworthiness. Agricultural stakeholders, including farmers, agronomists, and policymakers, often rely on these systems for high-stakes decisions--such as pesticide application, irrigation scheduling, and yield forecasting--making it critical that AI systems offer clear justifications for their outputs [307].  
Traditional deep learning models, especially CNNs and generative architectures, are often perceived as black boxes, lacking interpretability and traceability. This lack of transparency can undermine a user's confidence and limit the adoption of AI tools, especially in rural regions where farmers may have less familiarity with digital technology. For instance, a farmer may question why a disease detection system flagged a healthy plant or failed to identify a visibly infected crop, particularly when such decisions lead to economic loss [294]. Moreover, automated decisions that lack context-specific justification may conflict with indigenous farming knowledge or experience, creating a barrier between traditional and digital practices [296].  
XAI methods such as Grad-CAM, LIME, and SHAP have been adopted to visualise model attention and feature importance, enabling partial insights into decision mechanisms. However, these techniques are mostly post hoc and provide only localised explanations that may not be consistent across different inputs or tasks [300]. Additionally, many XAI tools have yet to be optimised for the multimodal and temporal nature of agricultural CV data. There is a pressing need for native interpretability within model architectures, allowing for transparency without compromising performance.  
Trust also intersects with model fairness and accountability. Models trained on skewed datasets may propagate biases--for example, under-detecting diseases on underrepresented crop types or soil textures--leading to unequal treatment or decision errors [301]. Furthermore, in scenarios involving automated robots or drones, liability in cases of system malfunction or misjudgement remains legally ambiguous. These risks underscore the importance of building AI systems that are not only technically sound but also socially responsible and ethically aligned [298].  
To this end, collaborative human-in-the-loop systems are gaining traction. These systems keep the human decision-maker involved during critical inference steps, thus facilitating better oversight, correction, and learning. Additionally, regulatory frameworks and auditing mechanisms must be developed in tandem with technical innovation to ensure responsible deployment. Ultimately, the trustworthiness of AI systems in agriculture will depend not only on their predictive accuracy but also on their transparency, user-friendliness, and alignment with human values.
# Synergistic Effect and Phase Behavior of SCG-CAPB-H\({}_{2}\)O
## 2 Materials and Methods
### Small-Angle X-ray Scattering (SAXS)  
Small-Angle X-ray Scattering (SAXS) is employed to determine the structure of liquid crystal phases. SAXS experiments were conducted on the SAXSpace small-angle X-ray scattering instrument from [PERSON] (Graz, Austria). The instrument operated at a voltage of 40 kV, utilizing Cu K\(\alpha\) radiation with a wavelength (\(\lambda\)) of 0.15406 nm. SAXS patterns within the 1-3\({}^{\circ}\) scanning range were collected at a scanning rate of 0.5 degrees per minute using a tube current of 100 mA. The sample-to-detector distance was set to 317 mm, and the acquisition time for all samples was 600 s. Throughout the entire testing period, the experimental temperature was maintained at 25.0 \(\pm\) 0.1 \({}^{\circ}\)C.  
In the spectrum, a plot of scattering intensity versus scattering vector (q) was generated, where q = 4\(\pi\)sin\(\theta\)/\(\lambda\), with \(\lambda\) representing the wavelength and \(\theta\) denoting the scattering angle. The type of liquid crystal phase was determined by the ratio of scattering vectors at the SAXS peak [43; 45]. For instance, the scattering vector ratio for the hexagonal phase is 1:\(\surd\)3:2:\(\surd\)7, for the lamellar phase is 1:2:3:4, and for the cubic phase is 1:\(\surd\)2:\(\surd\)4:\(\surd\)6:\(\surd\)8. Based on the scattering vector q corresponding to the first scattering peak, the lattice parameter of the liquid crystal phase can be calculated using the formula d = 2\(\pi\)/q, where d represents the interlayer spacing between layers in the layered phase and the lattice parameter in the hexagonal phase.
## 4 Hazard Analysis Methods
### Conditioning Factors
#### 4.1.4 Engineering Geology Units (UGIs)  
The engineering geology map was elaborated by interpreting homogeneous surface geological units (GSUs). Super-surface geological units were defined as a set of materials that make up the ground surface to a specific depth related to the scale of work; these units were classified as rocks or soils, taking into account their genesis in four large groups: rocks, residual soils and saprolites, transported soils, and anthropic soils [13]. The UGS map at the 1:5000 scale was elaborated from a compilation of secondary information, primarily taking into account the geological plates at the 1:100,000 scale from the Colombian Geological Survey (SGC), the deposits mapped in the geomorphological map at the 1:25,000 scale, the geological map of the seismogeotechnical zonation, photointerpretation of aerial photographs, and inputs obtained from processing the digital terrain model (DEM). For the evaluation of the UGS, field exploration was carried out at sites identified as critical according to the hazard map elaborated in phase I (1:25,000). The evaluation followed the Generalized UGS Field Guide [1] and included descriptions and analyses of drill holes, apiques, laboratory tests (liquid limit, plastic limit, granulometry, and hydrometry), and analysis and interpretation of SEV, MASW, and refraction seismic data available in the study area. The global exploration campaigns include four boreholes executed in 2017, one in 2021, and three in 2023, for which the aforementioned tests were performed. A seismic refraction line and vertical electrical soundings were also executed in each year of analysis.  
Each superficial geologic unit was subdivided according to the dominant geotechnical composition in each of the differentiated geologic formations, which allowed the separation of each superficial geologic unit into one or several homogeneous units.  
With the information from these homogeneous zones, it was possible to develop a map of engineering geological units (UGIs), which formed the base input to generate the engineering geology map essential for the calculation of hazards at a scale of 1:5000. The EGU map was defined based on the analysis and correlation of the geotechnical parameters of cohesion, friction angle, and wet unit weight, obtained from the direct shear and unit weight laboratory tests, respectively.