esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 7 Conclusions  
This paper introduced several novel techniques for WSNs including path planning, an efficient clustering algorithm, an efficient real-time routing algorithm, and novel path planning. The routing algorithm considered real-time messages, sensors energy, and urgent messages while the path planning considered the environmental effect on the monitored areas. The method for clustering took into account the sensor nodes' energy, the quality of the wireless links, and the distance parameter, which represented the average distance between clusters. The path planning utilized the areas' priority, environmental parameters, and urgent messages. The suggested routing method is environment and energy aware, taking into account load balancing, environmental data, network quality, latency, and hop count. These algorithms serve as the foundation for WSN operations. Based on the comprehensive experiments employed in this research, we draw the conclusion that the suggested framework is effective and has surpassed current methods. The limitation of the proposed algorithms is the time complexity; they take into account more realistic parameters than the comparison algorithms and this will take more computation time. In the future of this research, it may be extended to take into account multiple sink nodes. The research may also be expanded to include other uses, such as scheduling and mobility for drones.  
Figure 13: Influence of average traffic rate on average end-to-end delay for the second scenario.  
**Author Contributions:** Conceptualization, F.H.E.-F. and R.A.R.; methodology, F.H.E.-F. and R.A.R.; software, F.H.E.-F. and R.A.R.; validation, F.H.E.-F., A.B.A. and R.A.R.; formal analysis, F.H.E.-F., A.B.A. and R.A.R.; investigation, F.H.E.-F. and R.A.R.; resources, F.H.E.-F., A.B.A. and R.A.R.; data curation, F.H.E.-F., A.B.A. and R.A.R.; writing--original draft preparation, F.H.E.-F. and R.A.R.; writing--review and editing, F.H.E.-F., A.B.A. and R.A.R.; visualization, F.H.E.-F.; supervision, R.A.R.; project administration, R.A.R.; funding acquisition, A.B.A. and R.A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Ground Tilt Data  
The ground tilt is measured on Mt. Vesuvius by a network of sensors, able to detect slight tilt variations, in both direction and amplitude, of the volcano edifice caused by the endogenous (e.g., hydrothermal activity, possible dyke intrusions) and exogenous dynamics (e.g., earth and ocean tides).  
INGV-OV manages the current tilt network composed of seven stations: three equipped with surface short base-length platform tiltmeters deployed in shallow wells and four borehole digital tiltmeters. The signals are acquired with a resolution less than \(5\times 10^{-9}\) radians and a sampling rate of one sample per minute; the whole dataset is telemetered in real time to the surveillance center in Naples. Details can be found in [1; 19].  
The elevation and installation depths of the borehole tiltmeters on which our study has focused are as follows: IMB (974 m a.s.l., \(-22\) m), CMT (842 m a.s.l., \(-20\) m), TRC (372 m a.s.l., \(-28\) m) and CMG (117 m a.s.l., \(-25\) m) (Figure 1).
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 4 Results  
### RadCaTS Results  
There were 12 successful collects at RadCaTS in the 19 months following the commissioning phase of Landsat 9. The comparison between the OLI and RadCaTS for both the TOA spectral radiance and TOA reflectance data products is shown in Figure 14 as the ratio of the OLI values to those determined using the RadCaTS. There was a higher amount of snow and precipitation than normal at Railroad Valley from January 2023 to April 2023, which is why there is a lower number of successful collects than would be anticipated. Efforescent salts typically become present while the playa is drying, which can lead to surface uniformity issues when using point measurements, such as those with a finite number of GVRs.  
The RadCaTS results for each of the two data products agree to within the uncertainties of the methodology, which is summarized here as \(\pm\)4% (k = 1). One noticeable result in Figure 14 is that of the slight offset between the results of the two L1 TP imagery products. This bias is highest in the Coastal Aerosol band (band 1) and the SWIR 2 band (band 7), which are those most affected by atmospheric effects and the low SNR of the ground data. This pattern has also been observed in previous Landsat 8 work, but it should be noted that the offset is within the combined uncertainties of the methodologies.
## 3 Materials and Methods
### Overview of GAN Architecture
#### 3.1.2 Generator Loss  
The central objective of the GAN's generator is to guarantee the creation of data samples that the discriminator can wrongly group as genuine or not falsified. It is computed using the formula below.  
\[LG=-(1/2)Ez\sim pz(z)[\log(D(G(z)))] \tag{4}\]As shown in the above Equation (4), the generator receives a random noise vector \(z\) sampled from a noise distribution \(p_{z}(z)\), then this noise is transformed by the generator \(G\) into a synthetic data sample, and the discriminator \(D\) then evaluates this sample to estimate the probability that it is real. The term \(D(G(z))\) represents the discriminator's confidence that the generated data is authentic. By taking the logarithm of this value and computing its expectation over all sampled noise inputs, the generator aims to maximize the likelihood that the discriminator is \"fooled\" into classifying fake data as real. The negative sign and the scaling factor \(-\frac{1}{2}\) indicate that the generator is trained to minimize this loss, thereby improving its ability to generate realistic samples. This formulation ensures that the generator continually learns to produce data that better resembles the true data distribution as training progresses.
###### Abstract  
The inevitable vibration caused by the normal operation of the spacecraft in orbit will interfere with sensitive instruments, such as space telescopes, reconnaissance cameras, and spatial interferometers. Severe vibrations can impact the accuracy and reliability of these sensitive instruments, potentially leading to mission failure. To address this issue, active-passive integrated six-dimensional orthogonal vibration isolation (APIVI) platform has been proposed for vibration isolation in spaceborne sensitive instruments. The APIVI platform is composed of three orthogonal isolation modules, each made up of an active piezoelectric actuator and passive rubber isolator. Taking into account the parameter uncertainties of the actual system, the H\({}_{\infty}\) controller was designed, and the \(\mu\)-synthesis method was proposed to establish a parameter uncertainty model for the APIVI platform. Finally, experimental studies were conducted on the APIVI platform. The results demonstrated the excellent vibration isolation performance of the APIVI platform, with the vibration isolation frequency band above 18 Hz. With the addition of active control, it was able to fully attenuate the first-order resonance peak of the system, with a maximum attenuation of 18 dB.  
2025  
2025
# A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding
## 2 Theoretical Design of CLSDAT
### Obtaining Pixel Points on the Circular Contour by Grid Method  
After obtaining the circular contour, the minimum bounding rectangle is chosen. Within this rectangle, a set of horizontal and vertical lines are evenly drawn, as shown in Figure 1a,b, respectively. Each line intersects with the circular contour at two points. The coordinates of the intersection points of the horizontal line set with the circular arc form set \(S_{1}\), while the coordinates of the intersection points of the vertical line set with the circular arc form set \(S_{2}\). Finally, the data from these two sets are combined and recorded in the data set \(W\).  
In theory, the smaller the spacing between the lines, the larger the number of valid data points within the data set. According to the CLSDAT algorithm proposed in this paper, the ability to overcome interference and reduce errors increases with a larger number of valid data points. However, this also significantly increases the computational complexity. To minimize the computational load while ensuring positioning accuracy, the CLSDAT algorithm adopts a stepwise incremental approach to increase the number of dividing lines. Based on the final positioning accuracy, an appropriate number of valid data points is determined.
## 3 Methodology
### System Model
#### 3.1.3 Node Discovery  
This stage denotes the discovery of the nodes. Nearby nodes will be discovered using the nearby node selection process. So, the NNS procedure is applied here for discovery and identification. Each node is made aware of the presence and location of adjacent nodes, as well as sink nodes deployed in the water. This step ensures that each node is conscious of its neighbors and potential communication paths.
###### Abstract  
This paper compares the vibroacoustic behavior of a tapered annular circular plate having different parabolic varying thickness with different combinations of rectangular and concentric stiffener patches keeping the mass of the plate and the patch constant for a clamped-free boundary condition. Both numerical and analytical methods are used to solve the plate. The finite element method (FEM) is used to determine the vibration characteristic and both Rayleigh integral and FEM is used to determine the acoustic behavior of the plate. It is observed that a Case II plate with parabolic decreasing-increasing thickness variation for a plate with different stiffener patches shows reduction in frequency parameter in comparison to other cases. For acoustic response, the variation of peak sound power level for different combinations of stiffener patches is investigated with different taper ratios. It is investigated that Case II plate with parabolic decreasing-increasing thickness variation for an unloaded tapered plate as well as case II plate with 2 rectangular and 4 concentric stiffeners patches shows the maximum sound power level among all variations. However, it is shown that the Case III plate with parabolically increasing-decreasing thickness variation with different combinations of rectangular and concentric stiffeners patches is least prone to acoustic radiation. Furthermore, it is shown that at low forcing frequency, average radiation efficiency with different combinations of stiffeners patches remains the same, but at higher forcing frequency a higher taper ratio causes higher radiation efficiency, and the radiation peak shifts towards the lower frequency and alters its stiffness as the taper ratio increases. Finally, the design options for peak sound power actuation and reduction for different combinations of stiffener patches with different taper ratios are suggested.  
thick annular circular plate; Rayleigh integral; finite element modeling; rectangular and concentric stiffener patches; taper ratio; thickness variation Article
## 3 Results and Discussion
### Quasi-Trapped Modes of Metasurfaces Composed of MoS\({}_{2}\) Disks
The unit cell is the parallelepiped extended in the direction that is perpendicular to the lattice plane, which contains only one disk form the metasurface. The side faces of the unit cell comprise periodic conditions, which allows one to simulate an infinite array of the disks. Top and bottom bases comprise a perfectly matched layer (PML) that solves the problem of reflecting waves back into the interior of a unit cell. Additionally, the top base includes an emitting port from which a plane incident wave is emitted. At the same time, top and bottom bases also comprise detection ports that detect waves reflected from the structure and transmitted through it, respectively. Note that the nonmonotonic behavior of the polarizability curve in the studied spectral region in Figure 2a gives two more zeros of the function \(U=\left|S_{z}^{\left(\mathrm{R}\right)}-\mathrm{Re}\left(\frac{1}{\alpha z^{ \mathrm{cr}}}\right)\right|\) at the wavelengths \(1619\) nm and \(1665\) nm, which, together with \(\lambda_{QTM}\), determines the excitation of three QTM resonances at once in the considered spectral range. Indeed, the numerical simulation allows one to observe these two additional resonances at the wavelengths \(\lambda_{QTM2}=1610\) nm and \(\lambda_{QTM3}=1675\) nm, respectively. A feature of the resonances shown in Figure 3b, in contrast to Ref. [8], is the suppression of light reflection by the metasurface at QTM wavelengths. At the same time, the \(p_{x}\) component for the individual disk from the metasurface remains almost at the same level in the whole spectral range in Figure 3b,c, while a resonant decrease in the \(m_{y}\) component is observed through its bianisotropic coupling with \(p_{z}\) determined by the term \(\alpha_{zy}^{em}H_{y}\) in Equation (10); see Figure 3c. As a result, for the considered wavelengths \(\lambda_{QTM}\), the energy of the wave incidenting on the metasurface is reallocated between the excitation of the QTM and the resonant enhancement of the wave transmitted through the metasurface, which can be used for frequency selection. The important feature of QTM formation using strongly anisotropic NPs is the asymmetric field distribution inside the metasurface. This can be used to create near-field waveguide systems [29; 30] on the metasurface. Note that effects considered in this work can be experimentally detected in finite-size metasurfaces fabricated using the electron-beam lithography method. At the same time, it has already been shown that the fabrication errors of both individual building blocks and periodicity of the structure affect the characteristics of the effect but do not destroy it [31].
# Meta-Learner for Amharic Sentiment Classification
## 2 Literature Review
### Feature Selection  
Feature selection plays a prominent role in the efficient and successful application of machine learning algorithms. Features are used to discriminate observations (samples) to classify into categories. To apply machine learning algorithms, the first task is feature preprocessing and feature selection. All the input features to the machine learning should be transformed into numerical forms. For example, text features need to be converted into numerical feature sets. The most common feature sets include Bag-Of-Words (BOW), Term Frequency-Inverse Document Frequency (TF-IDF), \(n\)-grams (character/word) language modeling, topic modeling features, and word embedding. Studies revealed that bi-gram word feature sets perform better than uni-gram word feature sets for text classification. In similar research, BOW word feature set shows better performance for distinguishing text categories compared to TF-IDF and word embedding [23].  
In contrast, character \(n\)-grams are working effectively in different tasks of NLP, such as language identification, authorship attribution, text categorization, plagiariism detection, sentiment analysis, and so on. Character \(n\)-grams can also be used in a task, regardless of the language. Character \(n\)-grams can be used with new languages, and they can also detect rare words that are out of vocabulary (OOV) or misspelled [24; 25; 26]. According to the findings, \(n\)-grams with \(n=3\) are the most common. However, for a more salient \(n\)-grams feature, a higher value of \(n\succcurlyeq 4\) is suggested [27]. The choice of the best text feature representation for an application like sentiment classification has no strict scientific recommendation and no alignment in the existing literature, as it depends on application data and the type of language being processed.
# Low-Cost Active Anomaly Detection with Switching Latency
## 4 Numerical Results  
In this section, we present numerical examples to illustrate the performance of the proposed DMSC policy as compared to the Random selection Sequential Probability Ratio Testing policy (R-SPRT policy) and CL-\(\tau cN\) policy [22] in the finite regime.  
**R-SPRT Policy:** The random SPRT (R-SPRT) policy is the policy with the lowest switching cost, where a series of SPRTs are performed in random order and the switchings occur only when the current probed process is identified. Once all the abnormal processes or all the normal processes are declared, the detection procedure of R-SPRT will be terminated.  
**CL-\(\mathbf{\pi cN}\) policy**: CL-\(\tau cN\) policy was proposed to solve the anomaly detection problem with a similar objective function that aims at minimizing the expected cumulative observation cost without considering the switching cost. It was shown to be asymptotic optimal on its objective function in the finite regime.  
In this paper, we focus on the scenario where the number of abnormal processes and the total number of all processes are both known. In addition, we consider the case where the distribution under each hypothesis is completely known: \(f\sim Poi(\lambda_{f})\) and \(g\sim Poi(\lambda_{g})\), which means that the observations from the normal processes follow the Poisson distribution with parameter \(\lambda_{f}\) and the observations from the abnormal processes follow the Poisson distribution with parameter \(\lambda_{g}\). The KL divergences can be expressed as:  
\[D(g||f)=\lambda_{f}-\lambda_{g}+\lambda_{g}\log(\frac{\lambda_{g}}{\lambda_{ f}}),\]  
\[D(f||g)=\lambda_{g}-\lambda_{f}+\lambda_{f}\log(\frac{\lambda_{f}}{\lambda_{ g}}).\]  
Let \(C_{LB}^{*}\) be the asymptotic lower bound on the expected cumulative cost as \(\alpha\to 0\), which is given by  
\[c\cdot\min\left[\frac{L(L+1)B}{2D(g||f)}+s\frac{L(L-1)}{2},\frac{L(M-L)B}{D(f ||g)}+sL(M-L-1)\right]\]  
in Appendix A.1 Lemma A.1. Define  
\[L_{\Gamma}\triangleq\frac{E(C|\Gamma)-C_{LB}^{*}}{C_{LB}^{*}}\]  
as the relative cost in terms of the expected cumulative cost under policy \(\Gamma\), as compared to the asymptotic lower bound. Following Theorem 1 and Theorem 2, we expect \(L_{\text{DMSC}}\) to approach 0 in Scenario 1 and to be bounded by a constant in Scenario 2 as \(\alpha\to 0\).
# FenceTalk: Exploring False Negatives in Moving Object Detection
## 6 Conclusions  
A reliable image recognition model is crucial for security surveillance. Using image data containing moving objects from the specific area as training data can significantly enhance the model's accuracy in recognizing the area. To simplify the process of selecting target data from a large amount of image data, FenceTalk categorizes data based on brightness and utilizes SSIM and optimal thresholds to compare differences between current images and background images. It automatically selected suspicious images with moving objects that the Yolo model failed to recognize. This approach enables the model to learn the features of moving objects in the area using more training data. In the experimental results of FenceTalk, the recall values (Yolo + SSIM) surpassed 99%, demonstrating the universality of SSIM optimal thresholds. This also confirmed that FenceTalk effectively captures moving objects with motion characteristics.  
FenceTalk can be deployed on the embedded device Jetson Nano, ensuring smooth system operation while reducing hardware costs. In the future, we will continue researching methods to minimize misjudgments of moving objects due to shadow and lighting changes. FenceTalk has a detection box to visualize recognized individuals; in the future, we will add the visualization feature (YOLOv7-gradCAM) [27] to FenceTalk. Additionally, GUI packages related to FenceTalk will be developed, making the system more user friendly and reducing deployment complexity.  
Until August 2023, we have technically transferred FenceTalk to the Department of Education of Keelung City Government, Acton Technology Inc., Quanta Computer Inc., China Medical University, Asia University, and National Cheng Kung University.  
Conceptualization, Y.-W.L. and Y.-B.L.; methodology, Y.-W.L. and Y.-B.L.; software, J.-C.H.; validation, Y.-H.L.; data curation, J.-C.H.; writing--original draft preparation, Y.-B.L.; writing--review and editing, Y.-W.L.; supervision, Y.-W.L.; project administration, Y.-B.L.; funding acquisition, Y.-B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Council (NSTC) 112-2221-E-033-023, 110-2622-8-A49-022, NSTC112-2221-E-A49-049, NCKU Min Wu School of Computing, Research Center for Information Technology Innovation.  
The authors declare no conflict of interest.  
Figure 12: FenceTalk’s processing speed and GPU usage on Jetson Nano.
###### Abstract  
In this study, a hyperspectral imaging system of 866.4-1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.  
hyperspectral imaging; shrimp freshness; standard normal variate; competitive adaptive reweighted sampling; extreme learning machine; object-wise +
Footnote †: journal: Applied Soft Soft