esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 2 Related Work
### Filtering-Based Approach  
Filtering techniques to remove fog from images represent a pragmatic approach to enhancing visibility. These methods involve applying various filters to augment image clarity.  
[PERSON] et al. [3] introduce a median filter to restore visibility in individual images. This method offers the advantage of controlling image enhancement through a small set of parameters, facilitating rapid processing of both color and grayscale images. Nonetheless, the outcomes of fog removal may not consistently meet aesthetic expectations.  
[PERSON] [5] proposes a single-image dehazing technique that integrates sharpening, smoothing filters, contrast enhancement, and exposure fusion to enhance visibility. While effective, this technique is susceptible to generating halo artifacts and excessively darkening shadows, particularly in densely foggy images.  
Filtering techniques are crucial in mitigating the impact of fog and enhancing image visibility. However, it is equally imperative to acknowledge each method's inherent limitations and relevant application contexts.
## 2 Related Work
### Single Image Deraining  
Most of the state-of-the-art algorithms are based on convolutional neural networks (CNNs). Different modules can be used in deraining networks, for example:  
* Residual blocks are used in [43] and in [44; 45; 46; 47];
* Dilated convolution is used in [48; 49];
* Dense blocks are used in [50];
* Recurrent layers are used in [51; 52].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Image/ Video** & **Addressed Degradation** & **Method** & **Limitation** \\ \hline \multirow{3}{*}{[PERSON] et al. [31]} & \multirow{3}{*}{Video} & \multirow{3}{*}{Rain drops} & They used Hough transformation to detect raindrops in the context of lane scenes, and Sobel filter to detect raindrops in the context of a building scene & It requires the interaction of a human operator to process images \\ \cline{3-5}  & & & They exploited both spatial and temporal information. For dynamic scenes, they applied motion segmentation. Rain removal filters are applied after photometric and chromatic constraints are used for rain detection & Depends on temporal information \\ \hline \multirow{3}{*}{[PERSON] et al. [33]} & \multirow{3}{*}{Image} & \multirow{3}{*}{Rain streaks} & De-rainNet: They used image processing domain knowledge to improve deraining with a modestly sized CNN & \multirow{3}{*}{Low speed} \\ \cline{3-5}  & & & A rain-free frame is estimated from a single rain frame and then taken as a guidance along with previously recovered clean frames & & \multirow{3}{*}{Makes use of previously cleared frames to obtain a more accurate clean one} \\ \hline [PERSON] et al. [35] & Video & Rain streaks & Applied a multiscale convolutional sparse coding for rain removal & & \\ \hline [PERSON] et al. [36] & Image & Rain streaks & Frequency division, non-negative matrix factorization & Limited to rain streaks \\ \hline \multirow{3}{*}{[PERSON] et al. [37]} & \multirow{3}{*}{Video} & \multirow{3}{*}{Rain streaks} & They applied super pixel segmentation to perform scene decomposition into depth consistent units & \multirow{3}{*}{Limited to rain streaks.} \\ \cline{3-5}  & & & & \\ \cline{1-1} \cline{4-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rain detection and removal approaches.  
Some approaches used lightweight networks in either a cascaded or Laplacian pyramid manner aiming to enhance the computational efficiency [53; 54]; however, this degraded the deraining performance. On the other hand, deep networks are complicated and makes analyzing single modules harder.
## 5 Proposed Solution
### Experience Replay and Fed-DDPG  
Using experience replay and a target critic network, fed-DDPG enhances the stability and robustness of our proposed solution by minimizing data correlation in various TTIs. Policy exploration and action selection are further improved by adding Gaussian noise to the output of the policy network. In order to model the prior distribution of the UAV height as the UAV trajectory adapts to vehicles' spatial distribution and traffic demands, we use multivariate Gaussian distribution as per Equations (25) and (26):  
\[f_{i}(\mathcal{H}_{t})\sim\mathcal{G}(\mathbf{\mu}_{i}(\mathcal{H}_{t}),\mathbf{v}_{i} (\mathcal{H}_{t})) \tag{25}\]  
where \(\mathbf{\mu}_{i}(\mathcal{H}_{t})\) is the mean vector, and \(\mathbf{v}_{i}(\mathcal{H}_{t})\) is the covariance matrix for each sampling value on the trajectory point, without any prior information:  
\[f_{i}(\mathcal{H}_{t})|\mathcal{D}_{i}(t)\sim\mathcal{P}(\mathcal{D}_{i}(t)|f_ {i}(\mathcal{H}_{t}))\mathcal{P}(f_{i}(\mathcal{H}_{t})) \tag{26}\]  
At height \(\mathcal{H}_{t}\), given a maximum tolerable delay, the elements of the covariance matrix in Equation (27) are  
\[v_{\tau,\tau^{\prime}}(\mathcal{H}_{t})=\exp(-\frac{1}{2}||\mathbf{q}_{i}(\tau)- \mathbf{q}_{i}(\tau^{\prime})||^{2}) \tag{27}\]  
which implies a larger correlation when two trajectory points are closer to each other. To collect more data from the vehicles, the UAV selects the next trajectory point to maximize the expected value function (\(V(\mathbf{\psi})\)). The expected improvement in the value function as the UAV moves to the next set of coordinates in a time slot is based on the constraints C12 and C13 that limit the throughput of the vehicles' local models. As per Equation (28), the UAV selects an action to move to the next set of coordinates that maximizes the expected delay minimization:  
\[a_{i,t}(\mathbf{q}_{i})=\mathbb{E}[\max\{0,f_{i}(q_{i})-f_{i}^{*}(q_{i-1}(t))\}] \tag{28}\]  
where \(\mathbb{E}\) denotes the expected maximum value function when UAV visited the past coordinates. The next optimal trajectory coordinate in the next TTI is found by maximizing the expected improvement. The UAV periodically updates its location, buffer size, energy status, and channel conditions. The UAV status information adapts the vehicles' transmission strategies with the UAV trajectories and a vehicle successfully transmits its data to the UAV when the received SNR meets a minimum threshold.
## 4 Conclusions  
In this study, it was shown that our unique LIF-PIV setup can successfully measure the velocity of magnetic particles under the influence of an external magnetic field in two-phase flow.  
For the bubbly flow, the velocity near the wall was found to be similar to that of the flow at the center. The flow phenomenon differed depending on the size of the gap between the wall and the bubble. When this gap was less than 400 \(\upmu\)m, an increasingly higher velocity was measured for narrower gaps between the bubble and the wall. For gaps larger than 400 \(\upmu\)m, no such phenomenon was present. This shows that for narrow gaps, the velocity of the magnetic particles was higher than that of the bubble. The bubble played the role of an obstacle inside the pipe.  
For the slug flow, the flow near the wall was measured to be higher than the flow at the center. Furthermore, the velocity decreased for wider slug gaps, and it increased for narrower slug gaps. The reason for this phenomenon could be that the slug velocity had a greater effect on the magnetic particles than did the external magnetic force.  
To determine the degree of the nonrotational property, it was compared to theoretical flow and to single-phase nonmagnetic and magnetic particle vorticities. In the vorticity field, due to the effect of the external magnetic field, the vorticities of the bubbly flow and slug flow were lower than that of the single-phase nonmagnetic particle flow near the wall. At the center, the bubbly flow showed a lower vorticity than did the theoretical and single-phase nonmagnetic particle flows. However, the slug flow was higher than the theoretical flow. All of the experimental results were reduced by the presence of an external magnetic field when compared with the single-phase nonmagnetic particle results. These results mean that the ferrofluid was vertically aligned when it passed through the external magnet.  
In summary, the characteristics of two-phase flow under the magnetic field were studied using an experimental measurement. The experimental data contributed towards the development of a database that can be used to validate computational fluid dynamics simulations. The research methods proposed in this paper will contribute towards improvements in the design of the solenoid nozzle and the control of ferrofluid.  
Conceptualization, C.L.; Methodology, C.L.; Software, C.L.; Validation, C.L. and Y.-S.C.; Formal Analysis, C.L. and Y.-S.C.; Investigation, C.L.; Resources, C.L.; Data Curation, C.L.; Writing--Original Draft Preparation, C.L.; Writing--Review and Editing, Y.-S.C.; Visualization, C.L. and Y.-S.C.; Supervision, C.L.; Project Administration, C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 22: Uncertainty fields (**a**) near the wall, slug above; (**b**) near the wall, slug beside; (**c**) at the center, with a narrow slug gap; and (**d**) at the center, with a wide slug gap.
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 3 Experiments, Results, and Analysis
### Mining Hotspots in Residents' Travel Space
#### 3.3.1 Hotspot Area Identification
Drop-off points are frequently entertainment venues (Wanda Plaza, Jinli Ancient Street), dining areas (Longsenyuan Hotpot), and residential communities (Chengdu Garden). This phenomenon occurs because, during this time period, the majority of residents have typically completed their work or school commitments and have the option to participate in leisure or dining activities or return to their residential areas independently. The distribution pattern on the heat map also corresponds to the evening travel characteristics of residents.
## 2 Materials and Methods
### Ground-Based Measurements  
Ground-based measurements were compiled over the period 2014-2022 from the 20 pastures included in the Collaborative Adaptive Rangeland Management (CARM) experiment, which contrasts the effects of traditional versus adaptive grazing management. Each pair of CARM/TRM pastures (i.e., block) contained a similar distribution of ecological sites, with 4 composed entirely of Loamy Plains, one entirely of Sandy Plains, and 5 composed of a mixture of Loamy Plains, Sandy Plains, and/or Salt Flats (see map in [PERSON] et al., 2020 [31]). In pastures with only Loamy and/or Sandy Plains, we established 4 vegetation monitoring plots located on the ecological sites in proportion to their extent. In pastures that additionally contained Salt Flats (3 blocks), we established 6 vegetation monitoring plots, with 4 plots on Sandy and/or Loamy Plains and 2 plots on Salt Flats, giving a total of 92 plots in the study. Each plot contained a systematic grid of four 30-m transects spaced 106 m apart, where we measured various vegetation metrics. Of the four transects per plot, two are used to measure ANHP by clipping biomass in one 0.18 m\({}^{2}\) quadrat inside a movable grazing cage located \(\sim\)10 m and \(\sim\)20 m along each transect. Cages are moved to a new, random location in the vicinity of the transect prior to the start of the growing season each year and are clipped in early August (as recommended by [PERSON] and [PERSON] 2008 [26]). This provides an estimate of aboveground production of vegetation that was not grazed in the current year but was subjected to that pasture's grazing management in prior years.  
For this study, if a CARM pasture was not grazed before the date of ANHP data collection, it was recategorized as 'ungrazed'. Thus, for a given year, each of the 20 pastures was classified into one of three possible management categories--season-long grazing, pulse grazing, or ungrazed. The season-long grazing management always occurred on the same 10 pastures, while the other 10 pastures fell into either the pulse-grazed or ungrazed category, depending on the year. For 2019-2022, we also included ANHP data collected using the same methods from the three heavily stocked pastures.  
Vegetation from each clipped quadrat was separated into current-year production by functional groups (C4 perennial grasses, C3 perennial graminoids, annual grasses, frobs, and subshrubs) plus standing dead biomass from the prior growing season. The ANHP data were aggregated at the plot level, with mean and variance calculated for each set of quadrats (N = 4). Since subshrubs are a minor component of ANPP, they were excluded from our estimates of aboveground net herbaceous production (ANHP). A meteorological station located at CPER measured hourly incoming photosynthetically active radiation (PAR). PAR measurements were aggregated at a daily time step and subsequently used in the calculation of the absorbed photosynthetically active radiation (APAR) time series.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 6 Conclusions  
1. By selecting appropriate p-y curves from lab or field tests, calculating iteratively through finite difference program, the numerical frame introduced in this paper is feasible for the displacement analysis of large diameter monopole, and the accuracy and reliability of this method are satisfisfactory.
2. The scour characteristics were concluded by field data from Dafeng Offshore Wind Farm. The range of scour is mainly concentrated 17 m from the center radius of monopile foundations, about 3D. The maximum scour depth is 9.18 m (1.67D), and the average is 5.67 m (1.03D). Instead of regular scour pit, scour for large diameter monopile shows local erosion and deposition.
3. A new method applicable to large diameter monopile to take account of scour effect was proposed. In this study, the erosion or deposition volume and stress history were used as \"memories\" for the scour effect. Compared with the traditional method of simply removing a certain depth of soil, the errors of maximum value of shear force, bending moment, and horizontal displacement are 16.2%, 10.6%, and 46.4%, respectively.
4. P-y curves of large diameter monopile considered scour effect were obtained by ABAQUS. The deformation characteristics of large diameter monopile under scour were studied with the proposed numerical frame. With the increase of scour depth, the horizontal displacement of pile head increases nonlinearly. Under test conditions, the pile head displacement after scour can reach 1.4-3.6 times of that before scour. The depth of rotation point moves down with the increase of scour depth. At a high load level, scour has less influence on the rotation point depth; instead, scour has a greater influence at a low load level.
5. The relationships between pile head displacement, pile parameters, and scour depth are discussed. For different pile parameters, not only should the displacements be compared, the susceptibility to scour should also be evaluated. The analysis indicates that increasing the pile length is a more reasonable way than pile diameter and wall thickness to limit the scour effect on displacement of a large diameter pile. The definition of scour sensitivity is proposed, which may provide ideas for the design and analysis of monopile under scour.  
[PERSON] conceived the study. Model test and field data of local scour were collected by [PERSON] ([PERSON]), and [PERSON] ([PERSON]) carried out tri-axial CU test. P-y curves and case study were analyzed by [PERSON] [PERSON] gave some suggestions for manuscript writing. [PERSON] and [PERSON] helped with plotting and page layout. [PERSON]. ([PERSON]) supervised the whole study. All authors have read and agree to the published version of the manuscript.  
This study was supported financially by the National Natural Science Foundation of China (grant no. 41572247).  
Special thanks to China Power Investment Group and Huadian Heavy Industry Co., Ltd. for their generosity and support for scour monitoring.  
The authors declare no conflict of interest.
## 4 Heterogeneous Systems: Ozonation  
Microreactors have great potential for the investigation of heterogeneous gas-liquid reactions, i.e., large contact area between the phases, liquid recirculation in slugs, and effective diffusion, resulting in higher and uniform concentrations. However, no papers haveinvestigated the intrinsic reaction kinetics of AOPs in the presence of ozone. Two recent studies [64; 65] describe interesting devices, but only apparent first-order kinetics have been evaluated: in the first case, an ozone-permeable photocatalytic membrane covered with a thin layer of TiO\({}_{2}\) is placed between a gas chamber, containing flowing ozone, and a liquid chamber, irradiated by the incident light. The second is a micro-packed bed reactor, with wastewater and ozone flowing through zirconia/alumina microparticles. The promising preliminary results of these studies show optimal ozone saturation and consequent apparent kinetic constants, three orders of magnitude larger than the ones obtained in traditional lab-scale systems, where mass transfer limitations can be the controlling mechanism. Based on these results, future studies will have to address the fast and accurate determination of intrinsic kinetic parameters and the use of this devices to decouple mass transfer and intrinsic reaction for better understanding and modeling at scale.  
5 Homogeneous Systems: Direct Photolysis, UVC/H\({}_{2}\)O\({}_{2}\), and Photo-Fenton Processes  
Oxidation in the presence of hydrogen peroxide and ultraviolet C radiation (UVC/H\({}_{2}\)O\({}_{2}\)) is a commonly proposed AOP for wastewater treatment. The main advantage is the adoption of a relatively cheap reagent, hydrogen peroxide, for homogenous treatment with the generation of non-selective hydroxyl radicals. These efficiently mineralize a wide range of recalcitrant pollutants and their oxidized by-products. The main drawback is the adoption of UVC lamps and the necessity to ensure full degradation of hydrogen peroxide or its removal before wastewater discharge.  
Intrinsic kinetic studies for the UVC/H\({}_{2}\)O\({}_{2}\) system in a photo-micro reactor have been carried out by [PERSON] et al. in a series of papers published over the last five years [11; 23; 51; 52; 53]. In this case, the reactor is substantially different from the one used for studies with immobilized solid photocatalysts. It consists of a polymeric film consisting of ten circular microcapillaries in parallel, with an average diameter of 200 \(\upmu\)m and a length of 2.2 m. The polymeric material has a refractive index close to the one of water. In the experimental runs, the polymeric film was coiled around a cylindrical germicidal lamp emitting at 254 nm.
# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 2 Materials and Methods
### Evaluation Metrics  
Different evaluation metrics are reported in the literature for measuring the performance of morph detection systems. The evaluation metric may lead to ignoring a few important aspects and emphasizing the rest. DEER (detection equal error rate), _APCER_ (attack presentation classification error rate), _BPCER_ (bonafide presentation classification error rate), and _ACER_ (average classification error rate) are the most commonly used metrics for this problem domain.  
_APCER_ can be defined as the percentage of morphing attacks predicted as bonafide. Mathematically,  
\[APCER=1-\left(\frac{1}{|MA|}\right)\sum_{w=1}^{|MA|}R_{w}\]  
here \(|MA|\) is the total number of morph attacks and \(R_{w}\) has a value equal to one if an attack is classified correctly as an attack, zero otherwise.  
_BPCER_ can be expressed as the percentage of legitimate occurrences erroneously predicted as morph attacks.  
\[BPCER=\frac{\sum_{w=1}^{|BF|}R_{w}}{|BF|}\]  
Here \(|BF|\) is the total occurrences of bonafide samples. \(R_{w}\) is one for bonafide image marked as bonafide, and zero otherwise.  
_ACER_ is the mean of both the quantities mentioned above.  
\[ACER=\frac{APCER+BPCER}{2}\]  
DEER is the detection trade-off curve point where _APCER_ and _BPCER_ are equal.
## 2 Materials and Methods
### Measuring Instrument  
The Kinvent Move (K-Move) V3 system (Kinvent, Montpellier, France) was utilized for the experiment, comprising an electronic sensor and a fixation strap. The electronic sensor weighs 18 g, with dimensions of 12.5 \(\times\) 34.5 \(\times\) 44.5 mm. It features a wireless transmission range of up to 50 m, a maximum acceleration capacity of \(\pm\)16 G, an accuracy within 2\({}^{\circ}\) of the reading, and a data acquisition frequency of up to 400 Hz. The transmission operates in a 2.4 GHz frequency band. A study by [PERSON] and associates revealed that both the inter-rater reliability and test-retest reliability of the K-move for all ROM values in the lower limbs were excellent (ICC > 0.80) [20]. Moreover, the K-move has been utilized in previous clinical studies, which demonstrates its high reliability and validity [21].  
Data were collected via Bluetooth using the KINVENT PHYSIO application and stored on an Apple iPad for subsequent analysis when testing with the K-Move V3 system. Additionally, a digital goniometer (Suncosmo, Tokyo, Japan) was used to determine the starting position before each movement.
# Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles
## 2 Test Methods
### Image Acquisition  
There are multiple methods of photogrammetric image acquisition that are capable of achieving various levels of image scene reconstruction. Both the number of measurable dimensions of an imaged object, and the requirements for scaling the object in the imaged scene are dependent on the number of cameras used and their relative positioning (i.e., imaging configuration). Imaging configurations include single-, stereo-, and multi-image photogrammetry methods [12]. Stereo-image and multi-image methods provide three-dimensional scene reconstruction and require either the use of multiple cameras or the controlled movement of a single camera. Single-image photogrammetry requires just a single camera in a fixed position, but only provides a two-dimensional planar view of the imaged object. This shortcoming can be overcome, however, by enlisting additional prior knowledge of the imaged object. Three-dimensional measurements can be achieved with just a single camera if geometric relations, measurements, or symmetries of the object are linked to the image.  
The scale of an object in an image must be known to acquire its photogrammetric measurements. Scale is defined as the ratio of the size of an object in the imaged world (i.e., in pixels) to its size in the physical world. The scale of an object in an image depends on five things: (a) the object's distance and position with respect to the camera, (b) the lens used by the camera, (c) the distance of the lens from the image sensor plane, (d) optical distortion due to inherent imperfections in the lens, (e) perspective distortion (i.e., radial distortion) which is dependent on the angle-of-view of the object in the image [11]. These camera state values can be used for scaling the imaged object, but they can also be arduous and time consuming for the layman to obtain. A simpler method of scaling employs the placement of an object with known dimensions into the image scene to calibrate the scaling of the object. This can be achieved by placing a ruler adjacent to the object of interest in the image scene prior to testing, as is done in this research. It is important to note that measurement accuracy is highly dependent on the image resolution provided by the camera used for image acquisition, as well as the position of the camera with respect to the measured object.