esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Removal of the Mixture of Different Classes of Pollutants in Actual Water
After 6 h, the almost complete mineralization was reached with zinc oxide-based materials, while more than 20% of the organic content was still present when using P25. This is in accordance with previous studies that evidenced for instance, the slow degradation of some intermediate compounds of diclofenac, with a 90% mineralization achieved after several hours of irradiation with UV-A light using P25 [39]. The pollutants removal was then assessed in real water sampled from an aquaculture plant of koi carps, using pristine ZnO, P25 and ZCC as photocatalysts. The calculated pseudo-first order kinetic constants are displayed in Figure 7, showing that the heterojunction between ZnO and CeO\({}_{2}\) and the impregnation with Cu\({}_{2}\)O led to a clear increase in the degradative capacity of the ZCC catalyst toward a complex mixture of pollutants in an actual matrix in respect to pristine ZnO and to a greater degradation efficiency in comparison to the benchmark P25. However, focusing on ZCC, the kinetic constants shown in Table 1, shows the significant role of the water matrix on the photocatalytic performance. Compared to Milli-Q water, the degradation rate of odorous molecules decreased significantly in the real water, with 30 min being required for their complete removal. As regards the abatement of CECs, a decrease in the kinetic constants was registered as well, especially for diclofenac. It is probable that the cause of the longer degradation times was due to the high organic load of the water used since we detected a NPOC value equal to 15 mg/L. Indeed, as the complexity of the matrix grew, the degradation rate decreased because of the presence of dissolved organic matter which could act as a scavenger of radical intermediates. By contrast, the photocatalysts maintained good performances for mercury(II) removal and their efficiency in this case was not affected by the matrix change. Figure 5: Hg(II) removal using ZCC in different working conditions. As Figure 8 shows, a large amount of the pollutants were also efficiently removed in a real scenario. Figure 6: Total Organic Carbon abatement for the mixture of pollutants in the presence of the different photocatalysts. Figure 7: Comparison between the kinetic constants obtained using ZCC, ZnO and P25 for the abatement of the mixture of Hg(II), CECs, MIB and geosmin in aquaculture water.
# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
## 4 Results and Discussion
### Dataset
This sequence served as the input for the hologram-generation process, where each perspective image contributes to the overall 3D perception of the final hologram. The number of perspective images directly influenced the angular resolution and perceived depth of the reconstructed hologram.
## 3 Proposed Approach
### MRHDC Module  
Segmenting numerous small, irregularly shaped bleeding lesion areas in the data presents the current task's most significant challenge. To address this, we devised a Multi-Rate Hybrid Dilated Convolution (MRHDC) structure to further capture the multi-scale background features of intracranial hemorrhage across various sizes. Inspired by Atrous Spatial Pyramid Pooling (ASPP) [37] and ResNet [38] structures, we combined dilated convolutions with varying dilation rates to augment the neural network's receptive field and obtain multi-scale information. The MRHDC module comprises several hybrid dilated convolution layers, max-pooling layers, and residual connections. The proposed module's framework consists  
Figure 6: CA network.  
of three parallel branches, each containing a dilated convolution with differing dilation rates to extract multi-scale feature information. Each branch includes a \(1\times 1\) convolution and a linear activation layer ReLU. Lastly, employing ResNet's residual concept, the three branches are fused via residual connections to optimize the network, and \(1\times 1\) convolution-based dimensionality reduction is employed to transfer multi-scale information to the subsequent network layer. The structure of the module is shown in Figure 7.  
As the network deepens, feature map resolution diminishes, and information loss increases. Thus, convolutions with larger receptive fields are necessary. Dilated convolutions can appropriately expand the receptive field without altering the feature map's size, significantly mitigating information loss. Nevertheless, utilizing dilated convolutions alone may not consistently yield favorable results due to large and small bleeding areas. Following the ideas in [24], we selected the dilation rate combination [1; 2; 4] for the MRHDC module's dilated convolutions and incorporated the structure thrice simultaneously. By integrating small and large receptive fields in shallow and deep layers, optimal segmentation results are attained, and small target information loss is minimized to the greatest extent possible.  
The addition of the MRHDC (Multi-Resolution and High-Dimensional Context) module has proven to be beneficial in preserving more valuable information while achieving a larger receptive field compared to other convolutional operations. Furthermore, it maintains the resolution of feature maps, effectively reducing spatial information loss, which contributes to improved accuracy in image segmentation. In the results section, a comparison is made between segmentation outcomes obtained with different groupings and varying dilation rates within the MRHDC module.
## 6 Future Research Directions
### Language-Level Understanding of Remote Sensing Image Scenes  
Regarded as a significant but rare task for AI, RS image captioning is more challenging [96], which is actually closer to RSISU itself. The words generated are preliminary comprehensions from the machine, which can be regarded as a bridge linking up computer vision and language.  
Instead of simply predicting individual tags, it generates a complete sentence that is comprehensible. This not only means classifying each image scene as scene classification does, but also mining the dependencies between classes. It means adding contextual information between scenes to RSISR. Furthermore, it not only means recognizing objects and analyzing each image scene at a multi-scale, but also finding out the spatial relationship between the objects. Such descriptive sentences are closer to RSISU. By using deep learning and FC networks, an RS image capture framework was constructed in this article. The descriptions produced show wonderful performance. For instance, the lowest accuracy of the scenes in Google Earth images achieved 79%. This inspires us in at least two ways:  
* Image Retrieval: Rather than keyword searching, users can further describe their needs and improve the approachability of gathering useful images.
* Military Intelligence Generation: Battlefield images can immediately be converted to text messages by machine. These messages can be delivered to soldiers and help them to fight.
# The Spatial Structure of Passively Simulated Atmospheric Boundary Layer Turbulence
## 2 Theoretical Considerations
When \(\gamma=1/2\), [PERSON] [33] proposes the following coherence mode:  
\[Coh_{i}(\kappa_{1},r)=(1-\frac{1}{2}\kappa_{1}r)\cdot\exp(-\kappa_{1}r) \tag{11}\]  
When \(\gamma=6/5\), ESDU 86010 [34] proposes the following simplified expression:  
\[Coh_{u}(k_{1},r)=\exp(-1.15\cdot\varphi^{1.5}) \tag{12}\]  
\[[PERSON]_{i}(k_{1},r)=\exp(-0.65\cdot\varphi^{1.3}) \tag{13}\]  
where \(\varphi\) is a dimensionless coefficient, \(\varphi=\left[(0.747\theta)^{2}+\left(2\pi ck_{1}r\right)^{2}\right]^{1/2}\), \(\vartheta=r/L\), \(c=1.6\theta^{0.12}/\delta^{b}\) or \(1\), whichever is greater, \(b=0.35\theta^{0.2}\), and \(\delta=\left[(0.747\theta)^{2}+\left(2\pi ck_{1}r\right)^{2}\right]^{1/2}\). In addition, there is a classic exponential coherence function model proposed by [PERSON] [4]. In the [PERSON] model, the decay factor of the boundary layer turbulence is taken as a constant, \(c_{i}^{j}=10\). \[Coh_{u}(k_{1},\Delta y)=\frac{S_{u}(k_{1},\Delta y)}{S_{u}(k_{1})}=1-\Delta y^{2 }\cdot\int_{0}^{\infty}\frac{kS_{u}(k)}{S_{u}(k_{1})}\cdot G(\Delta y\cdot\sqrt {k^{2}-k_{1}^{2}})dk \tag{5}\]0  
Although strictly isotropic turbulence does not exist in the natural atmospheric boundary layer, under a series of simplifications and assumptions, it is still an effective theory to describe the characteristics of atmospheric turbulence [3; 4; 18; 29; 32]. However, it cannot be ignored that when the low frequency and the distance are large, the theoretical model may overestimate the correlation of wind fluctuations. According to the isotropic turbulence theory, the cross-spectrum of different fluctuating turbulent components is only nonzero on the diagonal and fully conforms to the assumption of spherical symmetry on the horizontal plane, but the cross-power spectrum of the actual along-wind and vertical components is nonzero, i.e., \(S_{uw}(k_{1},\Delta y,\Delta z)\
eq 0\). In the vertical direction, the phase angle \(\varphi(k_{1},0,\Delta z)=0\) is also different from the actual situation [35]. Due to the influence of the earth's rotation, gravity, temperature, and pressure difference, the isotropic turbulence will become nonisotropic over time. As a result, this study investigated the spatial structure of large-scale boundary layer turbulence that was passively stimulated by a wind tunnel, and based on the experimental results, improved coherence function models are proposed through comparative analysis with isotropic turbulence theory.
###### Abstract  
Traditional structural damage detection relies on multi-sensor arrays (e.g., total stations, accelerometers, and GNSS). However, these sensors have some inherent limitations such as high cost, limited accuracy, and environmental sensitivity. Advances in computer vision technology have driven the research on vision-based structural vibration analysis and damage identification. In this study, an optimized Lucas-Kanade optical flow algorithm is proposed, and it integrates feature point trajectory analysis with an adaptive thresholding mechanism, and improves the accuracy of the measurements through an innovative error vector filtering strategy. Comprehensive experimental validation demonstrates the performance of the algorithm in a variety of test scenarios. The method tracked MTS vibrations with 97% accuracy in a laboratory environment, and the robustness of the environment was confirmed by successful noise reduction using a dedicated noise-suppression algorithm under camera-induced interference conditions. UAV field tests show that it effectively compensates for UAV-induced motion artifacts and maintains over 90% measurement accuracy in both indoor and outdoor environments. Comparative analyses show that the proposed UAV-based method has significantly improved accuracy compared to the traditional optical flow method, providing a highly robust visual monitoring solution for structural durability assessment in complex environments.  
optical flow; image processing; structural vibrations; UAV +
Footnote †: journal: Applied sciences  
0000-0001-5821-389X
# An AC/DC LED Driver with Unity Power Factor and Soft Switching
## 4 Parameters Design and Experimental Results  
### Parameters Design  
A prototype circuit of 60-W was designed and built to drive sixty 1-W high brightness LEDs connected in series. The rated voltage and current of each LED is 3.25 V and 0.308 A, respectively. The specification of the proposed LED driver is shown in Table 1.  
By substituting the values of \(v_{in}\), \(V_{o}\), and \(D\) into Equations (13) and (17), the constraint of \(V_{dc}\) can be obtained. In this illustrative example, \(V_{dc}\) is designed to be 350 V. Assuming 93% circuit efficiency, the buck-boost inductor can be calculated from Equation (16).  
\[L_{p}=\frac{\eta{V_{m}}^{2}D^{2}}{4P_{o}f_{s}}=\frac{0.93\times\left(110\sqrt{ 2}\right)^{2}\times 0.5^{2}}{4\times 60\times 50\times 10^{3}}=0.47\text{ mH}\]  
The buck inductor can be calculated from Equation (19).  
\[L_{b}=\frac{D^{2}(V_{dc}-V_{o})V_{dc}}{2P_{o}f_{s}}=\frac{0.5^{2}\times(350-19 5)\times 350}{2\times 60\times 50\times 10^{3}}=2.26\text{ mH}\]  
In order to achieve a sinusoidal input current, a low-pass filter is required to cascade in front of the rectifier bridge to filter out the high-frequency current induced from the buck-boost converter. \(L_{m}\) and \(C_{m}\) are designed to perform the ow-pass filter. By rule of thumb, the corner frequency of a low-pass filter should be lower than one eighth of the switching frequency. Here, the corner frequency is designed to be about 5 kHz, and \(L_{m}\) and \(C_{m}\) are determined to be \(L_{m}\) = 2.0 mH, \(C_{m}\) = 0.47 uF.  
The circuit parameters of this prototype circuit are listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline Input voltage \(v_{in}\) & 110 V \(\pm\) 10\% (rms), 60 Hz \\ \hline Output power \(P_{o}\) & 60 W \\ \hline Output voltage \(V_{o}\) & 3.25 V \(\times\) 60 = 195 V \\ \hline Output Current \(I_{o}\) & 0.308 A \\ \hline Equivalent LED Resistance \(R_{LED}\) & 633 \(\Omega\) \\ \hline Switching frequency \(f_{s}\) & 50 kHz \\ \hline Duty ratio \(D\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the proposed light-emitting diode (LED) driver.
## 1 Introduction  
Standard GNSS positioning was previously limited to open areas that guaranteed the unobstructed reception of GNSS satellites signals. With the emergence of so-called high sensitivity GPS receivers (HS GPS), the practical application of GNSS has expanded to include areas with poor signal reception. Challenging environments, such as urban canyons, forested areas and indoors are often labelled as \"indoor\" environments [1]. Such environments cause signal attenuation and signal reflection from nearby reflective surfaces, resulting in multipath errors. Signal attenuation and multipath can cause large errors in the position estimation.  
Upon its arrival on Earth, the GPS signal is already weak. Regular GNSS receivers are able to track signals attenuated by the atmosphere only. If a signal passes through a physical substance, the signal becomes too weak to be tracked by a regular GNSS receiver. HS GPS receivers are able to track exceptionally weak signals by incorporating a large number of correlators and employing an extended integration period. Regular GNSS receivers incorporate up to 50 correlators [2], whereas the latest HS GPS chips incorporate more than one million correlators, e.g., u-blox 5 and u-blox 6.The typical integration time for regular GNSS receivers is a few ms [1], while HS GPS receivers perform integration over a longer interval. Integration can be extended to several hundred ms [1].  
According to [3], the tracking performance does not entirely depend on the signal strength but also on the signal-to-noise ratio (SNR) and more specifically on the SNR in a 1 Hz bandwidth, _i.e._, the carrier-to-noise density ratio (C/N\({}_{0}\)).Regular GPS receivers are able to acquire and track signals with a C/N\({}_{0}\) above 33-35 dBHz. The HS GPS receivers in our experiments were able to track signals with a C/N\({}_{0}\) as low as 10 dBHz.  
HS GPS receivers track signals from GPS satellites on the L\({}_{1}\) frequency. The results of the signal tracking are usually code pseudoranges, carrier-phase and Doppler observables. A SNR value is also typically available. Most of the HS GPS receivers available on the open market determine the current position internally and output their results via NMEA 0183 messages. Only a few of them can be setup to output raw observation data.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 4 Analysis on Eavesdropping Probability
### Analysis of Fri-Jam Schemes
#### 4.2.1 Case I: FJ-Reg Scheme  
We first analyze the case of FJ-Reg, in which all the jammers are regularly placed in grid manner as shown in Figure 1. We denote the expectation of the cumulative interference generated by jammers by \(E[I_{j}]\), which is given by Lemma 1.  
**Lemma 1**: _The expectation of the cumulative interference of regular placed jammers is_  
\[E[I_{j}]=\frac{1}{\mu}\sum_{m=1}^{n}E[I_{j}(m)]. \tag{8}\]  
We present the proof in Appendix A.  
We then derive the probability \(P_{e}\) that one transmission has been eavesdropped, which is given by Theorem 2.  
**Theorem 2**: _In FJ-Reg scheme, the probability \(P_{e}\) that one transmission has been eavesdropped is_  
\[P_{e}=\int\limits_{r>0}e^{-\mu T_{p}r^{\alpha}(\sigma^{2}+E[I_{j})-\pi r^{2} \lambda(\rho(T_{p},\alpha)+1)}2\pi\lambda rdr, \tag{9}\]  
_where \(\rho(T_{p},\alpha)=T_{p}^{-2/\alpha}\int_{T_{p}^{-2/\alpha}}^{\infty}\frac{1}{ 1+\mu^{\alpha/2}}d\mu\) and \(E[I_{j}]\) is given by Equation (8)._  
**Proof.** First, the SINR at a random distance \(r\) from its nearest transmitter can be expressed as \(\text{SINR}\;=\;\frac{hr^{-\alpha}}{\sigma^{2}+I_{t}+I_{j}}\). Then, from the definition of \(P_{e}\), we have  
\[P_{e} =\int\limits_{r>0}\;P\left[\frac{hr^{-\alpha}}{\sigma^{2}+I_{t}+I_ {j}}>T_{p}|r\right]e^{-\lambda\pi r^{2}}2\pi\lambda rdr\] \[=\int\limits_{r>0}P[h>T_{p}r^{\alpha}(\sigma^{2}+I_{t}+I_{j})|r]e^ {-\lambda\pi R^{2}}2\pi\lambda rdr.\]  
According to the channel model (given in Section 3.2), we have  
\[P[h >T_{p}r^{\alpha}(\sigma^{2}+I_{t}+I_{j})|r]\] \[=E_{I_{t}}[\exp(-\mu T_{p}r^{\alpha})(\sigma^{2}+I_{t}+I_{j})|r]\] \[=e^{-\mu T_{p}r^{\alpha}(\sigma^{2}+E[I_{j}])}\cdot E_{I_{t}}[\exp (-\mu T_{p}r^{\alpha}I_{t})]\] \[=e^{-\mu T_{p}r^{\alpha}(\sigma^{2}+E[I_{j}])}\cdot L(\mu T_{p}r^{ \alpha}),\]  
where \(L(\mu T_{p}r^{\alpha})=\exp(-\pi r^{2}\lambda\rho(T_{p},\alpha))\), \(\rho(T_{p},\alpha)=T_{p}^{-2/\alpha}\int_{T_{p}}^{\infty}\frac{1}{1+(\mu)^{ \alpha/2}}d\mu\) and \(E[I_{j}]\) is given by Equation (8).  
It is shown in Theorem 2 that the probability \(P_{e}\) heavily depends on the path loss factor \(\alpha\), the Rayleigh fading factor \(\mu\), the noise \(\sigma\) and the placement parameter \(d\). Section 5 will give the numerical results that will further confirm this observation.
## 3 Preliminaries
### HCR Protocol
#### 3.2.1 Gradient Field Establishment  
In gradient field establishment, all nodes obtain their gradient \(k\) by flooding an advertisement (ADV) message all over the network. At first, the sink sets its gradient \(k=0\) and broadcasts the ADV message, which contains \(k_{ADV}=0\) within range \(R_{t}\). The node that receives this ADV message will set its gradient as \(k=k_{ADV}+1=1\) and then broadcast the ADV message with \(k_{ADV}=1\) in range \(R_{t}\). The flooding process continues until all nodes obtain their gradients. If a node receives more than one ADV messages, it only recognizes the first ADV message and ignores all of the following ADV messages.  
It is worth noting that the gradient \(k\) depicts the minimum hop that counts from the node to the sink. Besides, the gradient field establishment updates only once at the beginning of the network operation. Thus, it does not increase the complexity of the protocol.  
To clarify the descriptions, we have the following definitions:  
**Definition 1**.: _(Gradient) Given the transmission range \(R_{t}\), the gradient of node \(i\), which is denoted as \(k(i)\), is the minimum hop count by forwarding a packet from node \(i\) to the sink with range \(R_{t}\)._  
**Definition 2**.: _(Ring) The set of nodes that have the same gradient \(j\) is defined as Ring \(j\), which is denoted by,_  
\[\mathcal{R}(j)=\{i:k(i)=j,i\in\mathcal{S}\} \tag{3}\]  
_where \(\mathcal{S}\) is the set of all sensor nodes._  
**Definition 3**.: _(Distance) Given node \(i\) with gradient \(k\), the distance from the node \(i\) to the sink is denoted as \(d(i,0)\). The shortest distance from the node \(i\) to the edge of \(\mathcal{R}(k-1)\) can be formulated as \(d_{toEdge}\)_  
\[d_{toEdge}=d(i,0)-(k-1)\times R_{t} \tag{4}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(R_{c}\) & Clustering range \\ \(R_{t}\) & Inter-cluster transmitting range \\ \(N\) & Number of nodes \\ \(k\) & Gradient, which equals the minimum hop count to the sink \\ \(\mathcal{R}(j)\) & Ring, the set of nodes with the same gradient \(j\) \\ \(d_{toEdge}\) & The estimated distance to the edge of the gradient field \\ \(d(i,j)\) & Distance between node \(i\) and \(j\) \\ \(d(i,0)\) & Distance between node \(i\) and the sink \\ \(T_{b}\) & Backoff timer \\ \(E_{max}\) & Initial energy of nodes \\ \(E_{r}\) & Residual energy of nodes \\ \(E_{ave}\) & Average residual energy of nodes \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of variables.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Vision-Based Detection  
Object detection is a crucial task in the domain of computer vision that involves the detection of instances of certain types of visual objects (e.g., animals, people, or plants) in an image. By creating computational models and techniques to detect objects, computer vision applications can provide valuable information: what object is where? Object detection is widely recognized as a fundamental challenge, and it provides the basis for numerous other associated tasks. For instance, methods for object detection can be extended to instance segmentation, image captioning, and target tracking tasks. From an application standpoint, research on object detection can be broadly categorized into two primary topics: \"general object detection\", which aim to detect various types of objects within a unified framework, mimicking the visual perception and recognition capabilities of human beings, and \"detection applications\", which focuses on designing specialized approaches tailored to specific application scenarios, such as pedestrian detection, face detection, and other relevant domains. Over the past few years, rapidly advancing deep learning techniques have injected a new dynamic into object detection, bringing significant breakthroughs. Object detection has become increasingly popular across a diverse spectrum of real-world applications, including path planning, robot vision, video surveillance, and many others. The following section will introduce more typical target detection methods, including traditional object detection methods and deep learning-based approaches, as well as the commonly used UAVs object detection datasets at present. Table 6 shows the results of some state-of-the-art methods on different datasets.
# Exploiting Frequency Characteristics for Boosting the Invisibility of Adversarial Attacks
## 3 Methodology
### Frequency Domain Loss
Therefore, we use the ratio of the strengths of the perturbation in different regions as the loss function, so we set frequency domain loss as:  
\[\frac{\partial\mathcal{L}_{fre}(\mathbf{x}_{\text{FFT}}^{\prime},\mathbf{x}_ {\text{FFT}};\Omega)}{\partial\mathbf{x}_{\text{FFT}}^{\prime}}=\frac{ \partial\bigg{(}-\frac{d_{low}}{\left(d_{high}+\theta\right)}-\frac{d_{other}} {\left(d_{high}+\theta\right)}\bigg{)}}{\partial\mathbf{x}_{\text{FFT}}} \tag{14}\]  
In the optimization process, the strength of the perturbation may be zero, so we add a tiny factor \(\theta=1e-3\) to the denominator to avoid a zero denominator. We use \(d_{low}\) and \(d_{other}\) as the numerator and \(d_{high}\) as the denominator to form the frequency domain loss, respectively. Moreover, we set the frequency domain loss to a negative value to avoid increasing the gradient. The use of the frequency domain loss can adjust the frequency domain distribution of the perturbation by minimizing the fractional equation and increasing \(d_{high}\) in exchange for decreasing \(d_{other}\) and \(d_{low}\). Since the DNN has a high sensitivity to the perturbation within \(\omega_{high}\), by reducing the unnecessary frequency domain components in the non-sensitive region, we can achieve an adversarial attack with a lower perturbation strength. In addition, placing the perturbation more in the high-frequency region can improve the overall invisibility of the adversarial examples.