esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Data  
#### 2.2.1 Satellite Data  
In this study, all the datasets used are available from the GEE repository. GEE provides atmospherically corrected, tier 1 surface reflectance imagery along with many other datasets. Landsat 5 and 8 were used for the years 2010 and 2020 for LC change mapping. Table 1 shows the band combination of Landsat 5 and 8 data. Both satellites have sun-synchronous orbit and have a temporal resolution of 16 days. The data are available as approximately 185 km \(\times\) 180 km scenes defined in a worldwide reference system (WRS) of the path and row coordinates [51; 52]. Landsat 5 data were atmospherically corrected using Landsat ecosystem disturbance adaptive processing system (LEDAPS) [53]. Landsat 8 data were atmospherically corrected using land surface reflectance code (LaSRC) [54] that includes a cloud, shadow, water and snow mask produced using the C function of Mask (CFMASK) [55], as well as a per-pixel saturation mask.  
Since Nepal's Kaski district is a cloudy area, most of the Kaski district areas lie in the cloud covered portion of Landsat images. Hence, obtaining a full scene without a cloud of the same day or month is challenging. Hence, we performed cloud masking in the scene and applied median filtering to the scene to get smooth and continuous coverage of the scene without clouds resulting in the annual median composite.  
In addition to surface reflectance, the slope was also used to aid the classification. The slope was derived from the shuttle radar topography mission (SRTM), an international research effort that obtained a digital elevation model (DEM) on a near-global scale [56]. This SRTM V3 product (SRTM Plus) is provided by National Aeronautics Space Administration Jet Propulsion Laboratory at a resolution of 1 arc-second (approximately 30 m) [57]. This dataset has undergone a gap-filling process using open-source data (ASTER GDEM2, GMTED2010 and NED) instead of other versions that contain voids or were gap-filled with commercial sources. Dem helps to eliminate the confusion created by the shadow of terrain as it has similar spectral characteristics to water in the optical image. Figure 3 shows the map representing elevation and slope from SRTM dem. The elevation of the Kaski district ranges from 450 m to 8091 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No.** & ** Landsat 5 Bands** & ** Landsat 8 Bands** & **Wavelength Range** & **Band Name** \\ \hline
1 & & B1 & 0.435\(\pm\)0.451 & Coastal Aerosol \\
2 & B1 & B2 & 0.452\(\pm\)0.512 & Blue \\
3 & B2 & B3 & 0.533\(\pm\)0.590 & Green \\
4 & B3 & B4 & 0.636\(\pm\)0.673 & Red \\
5 & B4 & B5 & 0.851\(\pm\)0.879 & NIR \\
6 & B5 & B6 & 1.566\(\pm\)1.651 & SWIR1 \\
7 & B7 & B7 & 2.107\(\pm\)2.294 & SWIR2 \\
8 & Pixel\_QA & Pixel\_QA 1 & & \\ \hline \hline \end{tabular}  
* Pixel quality assurance band.  
\end{table}
Table 1: The bands, ranges and names of Landsat 5 and Landsat 8 data.  
Figure 3: Maps obtained from shuttle radar topography mission (SRTM) dem: (**a**) elevation; (**b**) slope.
## 4 Obstacle Avoidance Planning
### Artificial Potential Field Function
#### 4.1.1 Attraction Potential Field Function  
The distance between the target point and the end-effector is proportional to the attraction potential field. The farther the distance is, the stronger the potential field is generated, and vice versa. Thus, the attraction potential field function can be expressed as:  
\[\left\{\begin{array}{l}U_{att}(X)=\frac{1}{2}\eta d^{2}(X,X_{g})\\ d^{2}(X,X_{g})=\|X-X_{g}\|^{2}=\left[(x-x_{g})^{2}+(y-y_{g})^{2}\right]\end{array} \right., \tag{24}\]  
In the formula, \(\eta\) refers to attraction potential field gain factor, \(X=(p_{x},p_{y},p_{z})\) refers to the position of the end-effector, \(X_{g}\) refers to the position of the target point, \(d(X,X_{g})\) refers to the Euclidean distance between the end-effector and the target point.  
The attraction force of the target point on the manipulator is generated by the attraction potential field. It is a negative gradient of the attraction potential field function and draws the pickup manipulator towards the target point. The function expression is:  
\[F_{att}(X)=-\
abla U_{att}(X)=-\eta d(X,X_{g})\frac{\partial d(X,X_{g})}{ \partial X}, \tag{25}\]
## 3 Preliminaries
### Tensor Robust Principal Component Analysis with Weighted Tensor Nuclear Norm Minimization  
The RPCA is designed to accurately separate a low-rank matrix \(L\) and a sparse matrix \(C\) from a corrupted matrix \(G\in\mathbb{R}^{n_{1}\times n_{2}}\). A direct solution to this problem is as follows:  
\[\min_{L,C}rank(L)+\lambda\ \left\|C\right\|_{0}\ \text{, s.t. }G=L+C, \tag{10}\]  
Here, the \(\ell_{0}\)-norm is denoted by \(\left\|\cdot\right\|_{0}\), and the weight-adjusting parameter \(\lambda\) is positive. The rank of the matrix is presented by \(rank(\cdot)\). Equation (10) represents the matrix rank minimization, which is a non-convex and NP-hard problem. A more feasible approach to the low-rank matrix reconstruction problem is to minimize a weighted linear combination of the matrix nuclear norm and the \(\ell_{1}\)-norms [28]. The approach is as follows:  
\[\min_{L,C}\ \left\|L\right\|_{*}+\lambda\ \left\|C\right\|_{1}\ \text{, s.t. }G=L+C \tag{11}\]  
In Equation (11), \(||L||_{*}=\sum_{i=1}^{min(n_{1},n_{2})}\sigma_{i}\) is the matrix nuclear norm, and \(\sigma_{i}\) is the \(i_{th}\) singular value of \(L\). The \(\ell_{1}\)-norm is denoted as \(\left\|\cdot\right\|_{1}\). Introducing the weighted nuclear norm allows different singular values to shrink differently, thereby preserving major data components [43]. This is expressed as follows:  
\[\min_{L,C}\ \left\|L\right\|_{*,\omega}+\lambda\ \left\|C\right\|_{1}\ \text{, s.t. }G=L+C \tag{12}\]  
In Equation (12), \(||L||_{*,\omega}=\sum_{i=1}^{min(n_{1},n_{2})}\sigma_{i}\omega_{i}\) is the weighted nuclear norm (WNN), and \(\omega\geq 0\) is the weight parameter. Additionally, the RPCA is expanded to handle multi-dimensional data while maintaining the tensor structure via the TRPCA. This technique has been effective in several fields including image processing, background removal, and image reconstruction. It has proven particularly useful in addressing noise damage issues in IoTs. As shown in Figure 4, the TRPCA is used to restore low-rank tensors damaged by sparse noise. The different colors present different columns or attributes in the data tensor.  
The weighted tensor nuclear norm (WTNN) minimization for the TPRCA [44] is given as follows:  
\[\min_{\mathcal{L},\mathcal{L}}\left\|\mathcal{L}\right\|_{*,\omega}+\lambda \left\|\mathcal{C}\right\|_{1},\quad\text{s.t. }\mathcal{G}=\mathcal{L}+\mathcal{C} \tag{13}\]  
Figure 4: Decomposition of low-rank and sparse components from corrupted TRPCA observation.  
where \(||\mathcal{L}||_{*,\omega}=\sum_{j=1}^{n_{3}}\sum_{i=1}^{min(n_{1},n_{2})}\sigma_{ ij}\omega_{ij}\), the weight parameters, are arranged such that a more significant singular value is paired with a smaller weight and vice versa. This configuration ensures equal treatment of all singular values within the objective function. Following this, the weight parameters were initialized and fixed in non-decreasing order as follows: \(0\leq\omega_{1}\leq\omega_{2}\leq...\leq\omega_{min(n_{1},n_{2})}\).
# Boundary-Aware Hashing for Hamming Space Retrieval
## 2 Related Work
### Linear Scan  
The deep supervised hashing method can effectively reduce the semantic gap between feature representation and hash encoding by using supervised information to achieve satisfactory retrieval performance. DSH [15] used contrastive loss to measure the similarity between the pairwise data and controlled the quantization error effectively using a regularization constraint. Deep hashing network (DHN) [23] and deep pairwise-supervised hashing (DPSH) [24] leveraged generalized sigmoid function as a probability function to design pairwise loss, learned pairwise similarity by the equivalent form of the inner product of Hamming distance, and effectively reduced the relaxation quantization error by controlling the quantization error function. In addition, Hashnet [25] introduced a weighted similarity training pairwise based on DHN [23] to solve the data imbalance problem and achieved a lower quantization error by using the continuation method. In order to better learn the Hamming ranking of hash codes, central similarity quantization (CSQ) [26] proposed a concept of hash center to learn the center similarity and aggregated the hash codes to the corresponding hash centers with deep neural networks. However, the above methods are all based on Hamming ranking design to maximize retrieval performance, which cannot pull similar data into the small Hamming ball well, resulting in a low level of hash code aggregation.
###### Abstract  
The quality of machined marine propellers plays a critical role in underwater propulsion performance. Precision casting is the predominant manufacturing technique; however, deformation of wax models and rough blanks during manufacturing frequently cause deviations in the dimensions of final products and, thus, affect propellers' performance and service life. Current inspection methods primarily involve using coordinate measuring machines and sampling. This approach is time-consuming, has high labor costs, and cannot monitor manufacturing quality in real-time. This study developed a real-time online automated measurement system containing a high-resolution CITIZEN displacement sensor, a four-degree-of-freedom measurement platform, and programmable logic controller-based motion control technology to enable rapid, automated measurement of blade deformation across the wax model, rough blank, and final product processing stages. The measurement data are transmitted in real time to a cloud database. Tests conducted on a standardized platform and real propeller blades confirmed that the system consistently achieved measurement accuracy to the second decimal place under the continual measurement mode. The system also demonstrated excellent repeatability and stability. Furthermore, the continuous measurement mode outperformed the single-point measurement mode. Overall, the developed system effectively reduces labor requirements, shortens measurement times, and enables real-time monitoring of process variation. These capabilities underscore its strong potential for application in the smart manufacturing and quality control of marine propellers.  
automated inspection system; cloud database; data acquisition; propeller measurement; real-time online measurement Article
###### Abstract  
This paper presents an integrated tunable hybrid multi-laser module designed to simultaneously generate multiple radiofrequency (RF) local oscillator (LO) signals through optical heterodyning. The device consists of five hybrid InP/Si\({}_{3}\)N\({}_{4}\) integrated lasers, each incorporating an intracavity wavelength-selective optical filter formed by two micro-ring resonators. Through beating the wavelengths generated from three of these lasers, we demonstrate the simultaneous generation of two LO signals within bands crucial for satellite communications (SatCom): one in the Ka-band and the other in the V-band. The device provides an extensive wavelength tuning range across the entire C-band and exhibits exceptionally narrow optical linewidths, below 40 kHz in free-running mode. This results in ultra-wideband tunable RF signals with narrow electrical linewidths below 100 kHz. The system is compact and highly scalable, with the potential to generate up to 10 simultaneous LO signals, being a promising solution for advanced RF signal generation in high throughput satellite payloads.  
hybrid integrated laser; integrated microwave photonics; satellite communications 2025, 12, 77 [[https://doi.org/10.3390/photonics12010077](https://doi.org/10.3390/photonics12010077)]([https://doi.org/10.3390/photonics12010077](https://doi.org/10.3390/photonics12010077))
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 1 Introduction
### Radiometric and Spectral Resolution  
Remote-sensors record the electromagnetic properties of land surface and the energy reflected (optical sensors), emitted (thermal infrared or passive microwave sensors) or scattered (active radar sensors), and hence provide a variety of information on land properties [5]. Currently, satellite images are simultaneously recorded in several or more electromagnetic radiation ranges, i.e., spectral bands: multispectral (MS) or hyperspectral (HS). Radiometric resolution, i.e., the precision with which the sensor can distinguish the variability of the electromagnetic radiation reaching it, is recorded in bits (eight and above). In other words, the number of digital levels used to express the data collected by the sensor. An 8-bit recording value of the variability of the reflected one-spectral-band varied between 0 and 255, thus representing greyscale. Therefore, the recorded information (of pixels in the acquired image) contains a set of different values (integers) with respect to the number of spectral bands. The values differ depending on the observed object (feature and place), condition and wavelength. Sometimes, features are indistinguishable in a certain spectral band, while they are quite different in another [3]. Spectral resolution is the number of spectral bands in which the sensor can collect reflected radiance; hyperspectral sensors collect hundreds of bands, moderate ones--in tens of bands, and low--three bands. These two definitions concerning radiometric and spectral resolution define two important parameters for the proposed algorithm, which is described further as values of pixels and the number of spectral bands. Other characteristics of a satellite image concerning spatial and temporal resolution are not currently taken into account in this paper due to the omission of these dimensions.  
All the procedures described below (for an end user) are assumed to operate on the same radiometric resolution (a condition which is fulfilled e.g., for Landsat ETM+), regardless of the number of spectral bands. For example, the EnMAP project acquires 14-bit resolution images. However,the resulting images are corrected to obtain radiometrically-calibrated radiance data. EnMAP final products characterize the strictly defined quality of images, which are different for different processing levels (level 0-8 bits, time-tagged instrument raw data with auxiliary internal information, level 1B, radiometrically corrected, spectrally and geometrically-characterized radiance, etc.) [6].
## 2 Experiment Setup  
The schematic diagram of the microwave-generated plasma system used in the experiment is shown in Figure 1.  
The magnetron was used to generate continuous-wave signals at a fixed output frequency of 2.45 GHz. The output power of the magnetron varied from 0-800 W. The output wave was launched into the WR340 waveguide using TE10 mode to transfer. An isolation circulator with a water load in an isolated way was connected to the magnetron. A waveguide coaxial connector was used for waves to enter the coaxial plasma reaction chamber from the WR340 waveguide. An automated, three stobs tuner was used to match the impedance, so that the maximum amount of power could be coupled with the plasma.  
This reaction cavity used a coaxial waveguide for microwave transmission. The external diameter of the coaxial waveguide was 150 mm, and its inner conductor was 8 mm. A hollow glass rod was used to surround the coaxial inner diameter. The inner diameter of the glass tube was 29 mm, and the external diameter was 33 mm. Using two O-ring seals to form a vacuum-sealed cavity in the middle of the glass rod's outer wall, both ends of the glass rod were connected to an O-ring seal to form a vacuum-sealed cavity between the glass rod's outer wall and the reaction cavity inner surface.  
Figure 1: Schematic diagram of the coaxial microwave-driven plasma with Langmuir probe and emission spectroscopy.  
There were several KF flanges on the outer wall of the cavity, and the Langmuir probe was mounted on a KF flange, which is shown in detail in Figure 2.  
In the experiment, firstly, a vacuum pump was used to pump the pressure below 10 Pa. Then, the argon flow rate was adjusted to stabilize the working pressure to 80 Pa. After entering the microwave with 800 W power, the plasma was discharged, and the light could be observed from the observation window, as shown in Figure 3.
## 3 EUV Spectra Analysis in the Plasma Recovery
### Impurity Behavior Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Shot Num.**} & **I\({}_{\rm p}\) (kA)** & **n\({}_{\rm e}\) (10\({}^{19}\) m\({}^{-3}\))** & **V\({}_{\rm loop}\) (V)** & **Accumulation** \\  & **(t1/t2)** & **(t1/t2)** & **(t2)** & **(t2)** \\ \hline
84602 & 52/300 & 0.2/2.2 & 0.778 & 0 \\
84603 & 54/300 & 0.2/2.3 & 0.694 & \(\sim\)18 \\
84605 & 53/300 & 0.2/2.7 & 0.690 & \(\sim\)100 \\
84606 & 55/300 & 0.2/3.1 & 0.691 & \(\sim\)226 \\
84607 & 56/300 & 0.3/2.6 & 0.671 & \(\sim\)286 \\
84609 & 55/300 & 0.2/2.5 & 0.684 & \(\sim\)379 \\
84611 & 56/300 & 0.2/2.3 & 0.628 & \(\sim\)428 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Plasma parameters of I\({}_{\rm p}\) ramp-up phase (t1 = 0.08 s) and I\({}_{\rm p}\) platform phase (t2 = 5.0 s) during lithium granule injection. Figure 6: EUV spectra comparison during I\({}_{\rm p}\) platform phase (t = 5.0 s) in recovery (red lines) and normal (blue lines) discharges. (**a**) 8–65 Å, (**b**) 65–190 Å, (**c**) 190–320 Å, and (**d**) 32–495 Å. Spike noise are indicated with red arrows.
## 5 Object Detection Application Domains
### Object Detection in Sports Videos  
Deep CNNs have shown great performance in detecting objects in still images. Due to the ImageNet [70] task introduced, detecting objects from the video (VID) shifted the task of object detection into the video domain. Object detection in sports videos contains three parts: ball detection, player detection, and action recognition. The major challenge of object detection in a sports video is that it is usually desirable to track the identities of various objects between frames.  
[PERSON] et al. [224] proposed improved YOLOv3 + K-means clustering methods to achieve better performance, especially for detecting small objects, such as a sports ball or a tennis racket. [PERSON] et al. [225] implemented a convolutional neural network-based classifier to perform ball detection in sports videos, and the system is robust to illumination changes and flickering issues. [PERSON] et al. [226] implemented a temporal CNN for object detection on video tubelets. [PERSON] et al. [227] implemented the YOLOv3 object detector to detect active players in real time, tested on a custom handball video dataset.  
[PERSON] et al. [228] proposed Mask-RCNN + Spatiotemporal interest points in detecting active players on handball custom video datasets. The same authors [229] implemented Mask-RCNN for detecting active players and tested on recorded handball practice videos. [PERSON] et al. [230] have given an overview of various deep CNNs used for object detection in sports videos. [PERSON] [231] implemented a real-time multi-person detection using YOLOv2 CNN and achieved better results on the NCAA Basketball Dataset.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 7 Discussion  
This section discusses our results and presents the lessons learned. We address the implications of the observations for IoT security architectures and implementations. We also compare our work to existing energy efficiency simulation approaches and to existing benchmarking results in order to study the advantages and disadvantages of our approaches.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **[PERSON]/[PERSON]** & **[PERSON]/[PERSON]** & **NH/[PERSON]** & **NH/[PERSON]** & **SC/[PERSON]** & **SC/[PERSON]** \\ \hline Transmitted bits & 91,016 & 90,368 & 14,592 & 16,384 & 1248 & — \\ Transmitted symbols & 885 & 878 & 150 & 167 & 21 & — \\ Transmission time & 3.5 ms & 3.5 ms & 0.60 ms & 0.66 ms & 0.084 ms & — \\ Transmission average power & 2640 mW & 2640 mW & 2640 mW & 2640 mW & 5280 mW & — \\ \hline Transmission Energy & 9.34 mJ & 9.27 mJ & 1.58 mJ & 1.76 mJ & 0.44 mJ & — \\ \hline Received bits & 90,368 & 91,016 & 16,384 & 14,592 & — & 1248 \\ Received symbols & 878 & 885 & 167 & 150 & — & 21 \\ Reception time & 3.5 ms & 3.5 ms & 0.66 ms & 0.60 ms & — & 0.084 ms \\ Reception average power & 1980 mW & 1980 mW & 1980 mW & 1980 mW & — & 1980 mW \\ \hline Reception Energy & 6.95 mJ & 7.01 mJ & 1.32 mJ & 1.18 mJ & — & 0.17 mJ \\ \hline Total Energy & 16.29 mJ & 16.28 mJ & 2.90 mJ & 2.95 mJ & 0.44 mJ & 0.17 mJ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Estimated costs of radio transmission in key exchange.
## 4 Traffic Filtering-Based Techniques
### Traffic Control (TC)  
#### 4.2.1 TC Concepts  
Like QR, TC [12] proposed a simple method to remove duplicated and circulated frames from a ring. The TC approach is applied for terminal rings. The TC selects one of nodes within the ring as the traffic control node. The traffic control node will make decisions of discarding duplicated frames.
## 3 In Situ Measurement and Analysis on Deformation Pattern  
### In Situ Measurement Overview  
#### 3.1.1 Valley Narrowing Process  
Seven survey lines have been set up to monitor the valley deformation at the dam site. The survey line measures the distance between two points on both sides of the valley, i.e., the valley width, by the laser range finder. The information of the survey lines is shown in Table 1.  
Figure 5 shows the location and deformation process of seven survey lines for the valley width with the reservoir level-rising process. The valley width continues decreasing since the beginning of the impoundment. A special trend is observed: All seven survey lines share similar tendencies and deformation amplitudes, regardless of their installation position and elevation. The deformation process has slowed since the first flood season but has not ceased until now. The latest monitoring data shows that the shrinkage deformation is up to 78 mm from 20 December 2012 to 26 July 2019 on average.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Elevation/m** & **Length/m** & **Distance to the Dam Axis/m** & **Position** \\ \hline VD01 & 749.00 & 791.98 & 350.00 & Upstream \\ VD02 & 611.00 & 593.19 & 260.00 & Upstream \\ VD03 & 722.00 & 786.71 & 50.00 & Upstream \\ VD04 & 611.00 & 568.27 & 50.00 & Upstream \\ VD05 & 707.00 & 632.19 & 300.00 & Downstream \\ VD06 & 610.00 & 524.55 & 350.00 & Downstream \\ VD07 & 561.00 & 415.79 & 350.00 & Downstream \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on the survey line to monitor valley deformation.  
Figure 4: Schematic diagram of groundwater circulation along the A–A1 section in Figure 1.