esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The IEEE 802.15.4 standard does not describe by itself the required mechanisms to deal with cluster-tree topologies. It has been complemented by the ZigBee specification [6] that defines both network and application layers. In the network layer, ZigBee defines a set of rules for network formation, addressing and routing, providing mechanisms to build cluster-tree networks. These mechanisms employ a hierarchical addressing scheme and use tree-based routing algorithms. Cluster-tree topologies have the benefits of being beacon-oriented, which allows synchronisation, collision-free time slots, and duty-cycle operation. However, they still suffer from some relevant limitations in what concerns real-time aspects (latency and communication delays) and energy-efficiency [12, 13], beacon frame collisions [7, 14, 15, 16, 17] and congestion control [18]. Additionally, as all cluster-tree paths go through the PAN coordinator, communications are prone to higher delays whenever the coordinator is not the final destination of the message streams [19].
## 3 Coherent Feature Extraction and Evaluation
### The Model of Coherent Feature Extraction  
According to the simulation analysis above, define the maximum peak at the zero point as the main peak. Except for the maximum peak, the maximum of the remaining peaks is defined as the secondary peak. The discrimination model of coherent characteristics can be named as the ratio of the secondary peak value to the main peak value (SMR). The model parameter is given as below.  
\[SMR=\frac{\max[F(n)]}{F(1)}\ \,\ \ \ n>1 \tag{14}\]  
where \(F(1)\) is the main peak value at the zero point.  
In terms of the conditions of Experiments 1-3 to calculate the SMR of different signal types, take the average of 100 times of calculation results. SMRs are computed without noise in Table 1.  
As shown in Table 1, it can be seen that the SMRs of the coherent pulse train are close to 1, while the SMRs of the noncoherent pulse train are much smaller than 1.  
In order to verify the rationality of the discrimination threshold of SMR in different SNRs, according to the conditions of Experiments 1 to 3, SMRs of different signal types are computed based on the Monte-Carlo for 100 times in different SNRs. The results are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Signal Type**} & \multicolumn{3}{c}{**Coherent Pulse Train**} & \multicolumn{3}{c}{**Noncoherent Pulse Train**} \\ \cline{2-7}  & **CW** & **LFM** & **NLFM** & **CW** & **LFM** & **NLFM** \\ \hline SMR & 0.904 & 0.889 & 0.928 & 0.463 & 0.408 & 0.392 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the secondary peak value to the main peak value (SMR) with different signal types.  
Figure 4: Autocorrelation result of coherent and noncoherent pulse trains; signal type is nonlinear frequency modulation (NLFM).  
As can be seen in Figure 5, when SNR = \(-\)5 dB, SMRs of different signal types are overlapped as SMR cannot distinguish the difference very well. When SNR \(\geq\) 0 dB, the difference in SMRs between the coherent and noncoherent pulse trains is obvious. This is due to the instantaneous autocorrelation processing, which can weaken the noise and strengthen the signal. To a certain extent, SNR of the signal has been improved. From the simulation analysis above, we can conclude that SMR can be used as a feature parameter for REID, and it is insensitive to SNR and signal type changes.  
Furthermore, we can see when SNR \(\geq-\)5 dB, SMR of the coherent pulse train is always above 0.6, while SMR of the noncoherent pulse train is always below 0.6. Additionally, each signal type has the same rule. Therefore, we can set the discrimination threshold at 0.6. When SNR \(>\) 0.6, the pulse train is coherent, otherwise the pulse train is noncoherent.
## 2 Robust Principal Component Analysis (RPCA)  
Robust principal component analysis (RPCA), as the name suggests, is a modification of the well-known data analysis method, principal component analysis (PCA). However, RPCA is shown to perform well for noise-corrupted data by reducing the deteriorating effect of outliers compared with PCA. The central idea of RPCA is to decompose a data matrix V into another two matrices, L and S, as follows:  
\[\text{V}=\text{L}+\text{S}, \tag{1}\]  
where L is a low-rank matrix and S is a sparse matrix with most of its entries being zero. One of the most widely used methods to achieve the above decomposition is the Principal Component Pursuit (PCP) method [28], which solves the constrained optimization problem below.  
\[\text{min}(\|\text{L}\|_{*}+\lambda\|\text{S}\|_{1})\text{ subject to }\text{L}+\text{S}=\text{V} \tag{2}\]  
where \(\|\text{L}\|_{*}\) is the sum of singular values of matrix V, \(\|\text{S}\|_{1}\) is the sum of all entries of matrix S, and \(\lambda\) is a weighting factor set to \(\frac{1}{\sqrt{n}}\), n being the dimension of matrix V.  
According to [28], the two matrices L and S in Equation (2) can be determined accurately without any prior knowledge of them. For more details about the PCP method, one can refer to the literatures [28; 29].
# Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments
## 2 Related Work
### Smart Dust  
Smart dust originated with [PERSON] of the University of California, Berkeley, who developed a 1-2 mm mini-sensor. Though these sensors are very tiny (like dust), they possess significant computing ability, an electric power supply, bi-directional wireless communication, and a solar battery [18; 19].  
A smart dust network is shaped by multiple smart dusts, enabling not only mutual communication but also information collection. Management of areas such as military zones or inaccessible forested areas is a driving force in smart dust development. Although equipment such as unmanned surveillance drones and artificial satellites are currently used for surveillance, they risk exposure and their capacity for real-time information collection is limited [20; 21]. Smart dust overcomes the limitation of their capacity by deploying sensor nodes to a variety of environments and collecting the real-time information as shown in Figure 1. dust-sized surveillance equipment distributed to necessary zones makes real-time information collection possible, which enables natural communication without environmental limitations. This equipment can also be tracked by recognizing its precise location and timing information.  
Like smart dust, the Internet was initially designed to serve military purposes; as the Internet's sphere of application has been extended to daily life, the applications of smart dust are also evolving. Intel and UC Berkeley built a wireless network using smart dust that remotely monitors the condition of sea swallows. The technology is also being used in to prevent cold-weather damage of crops, make ecological observations in inaccessible areas, measure biochemical pollution, prevent forest fires, observe the weather and earthquakes, detect the movement of troops and equipment, and manage distribution. For example, smart dust that prevents crop damage from cold weather may collect soil information in real time by planting smart dust in the soil. In the Telecommunication Engineering laboratory of Twente University in the Netherlands, smart dust distributed in mountains form a wireless network that is used to conduct smart dust research with real-time information delivery to a monitoring center [22].  
Potential applications of smart dust are inexhaustible; although the technology is currently highly utilized, its use is expected to spread as it is combined with a variety of additional technologies [23].
## 4 Experiments and Analysis of Results
### Experiments Based on the DeepPCB Dataset  
To rigorously assess PCES-YOLO's performance on PCB defect detection, comparative experiments were conducted using the DeepPCB dataset [39]. This specialized dataset comprises 1500 image pairs, each containing a defect-free reference image and its corresponding annotated test image. The images were captured using a linear scanning CCD device with a native resolution of approximately 48 pixels/mm and were subsequently processed into aligned 640 \(\times\) 640 pixel sub-images. The dataset provides comprehensive annotations for six prevalent PCB defect categories: open, short, mousebite, spur, copper, and pin-hole. The experimental results are detailed in Table 8. The visualization experiment results are shown in Figure 10.  
Table 8 and Figure 10 demonstrate PCES-YOLO's superior performance on the DeepPCB dataset. It consistently outperforms YOLOv9t, YOLOv10n, and YOLOv11n in mAP50, mAP50-95, precision, and recall. The model achieves higher accuracy than YOLOv11n across all defect categories, confirming its exceptional capability in small target detection with low false and missed detection rates. These results demonstrate the excellent performance of the model on different PCB defect datasets and validate its strong generalization performance.
## 2 Materials and Methods  
According to Regulation (EU) 2016/429 [16], once the Veterinary Services detect the presence of the SHB in the sentinel colonies, they capture or collect adults and/or larvae  
Figure 2: A pair of sentinel honey bee colonies sealed following SHB detection.  
* Store the packed sentinel in the official laboratory at \(-12\)\({}^{\circ}\)C or less (core temperature) for at least 24 h [18] before proceeding with its visual inspection;
* Remove the four heavy plastic bags one by one and carefully inspect them for the presence of beetles;
* Cut and remove the polyethylene film and look for the presence of the parasite or its parts with the help of a magnifying glass. Carefully inspect the adhesive tape (Figure 6) and the cracks of the hive since SHBs could have been trapped by the adhesive tape's glue;
* Once the packaging is removed, open the lid of the hive and inspect it for the parasites with a magnifying glass and under an appropriate source of lighting;
* Remove the honeybees on the lid by dropping them inside the hive;
* Shake all the bees inside the hive from the top of the frames and from the hive edges;
* Extract and clean the frames from the honeybees one by one, dropping them inside the hive and placing the frames on a sheet of white paper for inspection;
* Carefully inspect all cells of the frames looking for _A. tumida_ larval or egg forms.;
* Remove honeybees from inside the cells to look for the presence of the parasite under them (Figure 7) and perform the same operation on all frames.
* Look for _A. tumida_ on the hive walls and on spacers;
* Accumulate the honeybees in the lower part of the hive and drop about 300 honey bees in a tray covered with a sheet of white paper;
* Look for the presence of beetles among the honeybees, with the help of a magnifying glass (Figure 8) and under adequate lighting, and separate them with a tweezer;
* Repeat the same operation to examine all the honeybees in the hive;
* At the end of the inspection, put all the material in a clean container for later storage or disposal by incineration;
* If SHB is detected, remove it with a tweezer and place it in a Petri dish for further identification under the stereomicroscope in order to differentiate it from morphologically similar coleopterans.  
Between December 2014 and January 2015, a preliminary investigation has been carried on other Nitidulidae beetle species occurring on rotten fruit in the Reggio Calabria province infested with small hive beetle [19]. A total of 78 adult beetles belonging to eight different species of the Nitidulidae family were detected on rotten citrus fruit. The beetles collected were characterized by the clubbed antennae typical of Nitidulidae family, but in general by thin elongated bodies that macroscopically differed from the much broader _A. tumida_[1]. Similar results have been obtained also in further field surveys carried out in the following years until 2020 ([PERSON], personal communication).  
Figure 6: Seek the beetle on the adhesive tape used to seal the package.
###### Abstract  
We conducted 24-h real-time monitoring of temperature, salinity, dissolved oxygen, and nutrients in the near-shore (M4-1), front (M4-8), and offshore (M4-13) regions of the 31\({}^{\circ}\) N section of the Changjiang (Yangtze) River estuary plume in summer. Carbon dioxide partial pressure changes caused by biological processes (pCO\({}_{2}\)bio) and net ecosystem production (NEP) were calculated using a mass balance model and used to determine the relative contribution of biological processes (including the release of CO\({}_{2}\) from organic matter degradation by microbes and CO\({}_{2}\) uptake by phytoplankton) to the CO\({}_{2}\) flux in the Changjiang River estuary plume. Results show that seawater in the near-shore region is a source of atmospheric CO\({}_{2}\), and the front and offshore regions generally serve as atmospheric CO\({}_{2}\) sinks. In the mixed layer of the three regions, pCO\({}_{2}\)bio has an overall positive feedback effect on the air-sea CO\({}_{2}\) exchange flux. The contribution of biological processes to the air-sea CO\({}_{2}\) exchange flux (Cont) in the three regions changes to varying extents. From west to east, the daily means (\(\pm\)standard deviation) of the Cont are 32% (\(\pm\)40%), 34% (\(\pm\)216%), and 9% (\(\pm\)13%), respectively. In the front region, the Cont reaches values as high as 360%. Under the mixed layer, the daily means of potential Conts in the near-shore, front, and offshore regions are 34% (\(\pm\)43%), 8% (\(\pm\)13%), and 19% (\(\pm\)24%), respectively. The daily 24-hour means of NEP show that the near-shore region is a heterotrophic system, the front and offshore regions are autotrophic systems in the mixed layer, and all three regions are heterotrophic under the mixed layer.  
biological processes; air-sea CO\({}_{2}\) exchange flux; net ecosystem production; potential CO\({}_{2}\) emissions; trophic status; Changjiang River estuary plume +
Footnote †: journal: Water
## 4 Case Study--Safety Assessment of the Rockfill Specim in Triaxial Test
### Preliminary Investigation  
The twelve triaxial tests used in this study to calibrate material parameters for the safety assessment of the reference specimen form part of a broader laboratory campaign on embankment materials undertaken for the ongoing dam project in Asia. All tests are carried out in the ISMGEO laboratory in Bergamo, Italy. The examined material is the rockfill that constitutes the dam slopes and protects the central clay core. The tests are run with four different values of confining cell pressure \(\sigma^{\prime}_{3}\): 250 kPa, 500 kPa, 1000 kPa and 2000 kPa (three samples for each \(\sigma^{\prime}_{3}\)).  
The deviatoric behavior observed in triaxial compression is presented in Figure 6. All specimens exhibit non-linear strain hardening from very small strains up to the peak strength. After the peak, the specimens tested at lower confining pressures show a slight tendency to soften.  
The volumetric contraction occurs up to approximately the peak strength; after that, the material behavior becomes dilative, as shown in Figure 7. Samples tested at the lower confining pressures display more tendency for dilatancy. Although testing continues to \(\epsilon_{s}\approx 25\%\), not all specimens reach a constant-volume state.  
Figure 7 also plots the stress ratio \(\sigma^{\prime}{}_{1}/\sigma^{\prime}{}_{3}\) against the deviatoric strain, giving a clearer visual basis for comparing peak and constant-volume (CV) states. The difference between peak and CV stress ratios is clear at \(\sigma^{\prime}{}_{3}=250\) kPa and diminishes as the confining pressure rises, becoming almost imperceptible at \(\sigma^{\prime}{}_{3}=2000\) kPa.  
The four test groups reveal how the material responds at different confinement levels. Key stress-strain characteristics--strength, stiffness and dilatancy--vary with \(\sigma^{\prime}{}_{3}\) as follows:  
* Strength rises non-linearly with confining pressure; the \(\sigma^{\prime}{}_{1}/\sigma^{\prime}{}_{3}\) ratio at failure decreases as \(\sigma^{\prime}{}_{3}\) increases (Figure 7).
* Stiffness increases with the higher confinement level (Figure 6).
* Dilatancy becomes less pronounced at higher stresses and is almost negligible for \(\sigma^{\prime}{}_{3}=2000\) kPa (Figure 7).  
Figure 6: Stress–strain behavior in a shearing phase. (**a**) Deviatoric stress versus axial strain; (**b**) deviatoric stress versus deviatoric strain.  
Figure 7: Stress–strain behavior in a shearing phase. (**a**) Change in volumetric strain versus deviatoric strain; (**b**) stress ratio \(\sigma^{\prime}{}_{1}/\sigma^{\prime}{}_{3}\) versus deviatoric strain in the shearing phase.
# Compact Broadband Antenna with Vicsek Fractal Slots for WLAN and WiMAX Applications
## 2 Antenna Geometry Design
### Parametric Analysis of the Suggested Antenna
#### 2.3.3 The Vicsek Slot Length Variation Effects  
The parametric study in this section looks into the effect of the length of the Vicsek fractal slot on the impedance bandwidth and on the resonant frequencies. Figure 7 depicts the variation of \(S_{11}\) when the length \(a\) of the Vicsek fractal slot is changed and all other dimensions remain constant. It can be claimed from the figure that the parameter \(a\) has no effect on the suggested antenna's impedance bandwidth but it does change the position of the resonant frequencies. The range of this parameter (\(a\)) is between 0.7 and 1.6 mm. Additionally, the value \(a\) = 1.3 mm is found to provide satisfactory impedance matching in the 3.6 and 5.8 GHz frequencies corresponding to the WLAN and WiMAX bands.
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 3 Results
### Tracing the Geometric Elements of the Analyzed River Sector  
After processing and validating the DEM, the geometric elements required for modeling in the HEC-RAS 6.5 software were delineated on the river sector considered as a case study (Figure 4), specifically:  
- the river centerline (thalweg) represents the line along the deepest part of the riverbed and is used to define the geometric configuration of the riverbed within the hydraulic model and to correctly position the cross-sections;  
- the left bank and the right bank define the edges of the river and separate the water flow in the minor riverbed under normal conditions from adjacent areas, such as the floodplain;  
- floodplain; this allows the identification of regions vulnerable to flooding under various flow scenarios. In this case, analyzing a leveled watercourse, the floodplain is considered as the area between the two levees (Figure 4).  
In the river segment considered as a case study, 32 cross-sections were outlined perpendicular to the river's course. These sections describe the shape and size of the river at various points along its course and are used to define the river's geometry, including its width, depth, and bank configuration.  
The cross-sections play a particularly important role in the simulation and analysis of water flow, providing information about the river's geometry and allowing for the calculation of hydraulic parameters such as the area through which flow occurs, wetted perimeter, hydraulic radius, and channel bed slope. These parameters are used in flow equations to determine the water velocity and discharge.  
The analysis of the cross-section characteristics for the river segment considered as a case study highlights the following aspects: there are significant variations between the defined sections in terms of the height of the left and right banks, indicating a variable relief along the river; the lengths of the left and right banks vary considerably, showing differences in the geometry of each section; the Cutline to XS ratio is consistently one, indicating that the sections are consistently aligned with the river's course without significant deviations.
## 3 System Configuration
### Testing of Laser Performance  
The near-infrared DFB laser (1520 nm, Nanoplus) is driven by a self-developed driver. The output wavelength of the laser is tuned by controlling the drive current and temperature, and the laser's temperature was set to 20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C by the self-developed temperature controller. As the driving current increases (20 to 150 mA), the emitting peak wavelength gradually increases, and the current tuning factor is approximately 0.011 nm/mA. At a stable driving current, the emitting peak wavelength increases with temperature, and the temperature tuning factor is approximately 0.113 nm/\({}^{\circ}\)C. The tuning characteristics of the laser are shown in Figure 4a.  
The driving current is set to 80 mA, and the temperature is set to 25 \({}^{\circ}\)C with a monitoring experiment for 4 h (8:30-12:30). The laser's optical emission spectrometry is shown in Figure 4b. The emitting peak wavelength varies by approximately 0.001 nm (between 1520.011 and 1520.012 nm).  
Figure 3: (**a**) Structure of the C\({}_{2}\)H\({}_{2}\) detection system. (**b**) C\({}_{2}\)H\({}_{2}\) concentration detection experimental platform.  
The monitoring experiment results show that the laser's emission peak wavelength slightly changes, and the working state is stable during a long-term continuous operation.
## 2 Materials and Methods
### Moderate Resolution Imaging Spectroradiometer Data and Vegetation Indices  
The time series site-specific VIs for the targeted wetland EC tower were extracted from the MODIS datasets. We mainly used seven spectral bands to study vegetation and land surface: blue (459-479 nm), green (545-565 nm), red (620-670 nm), near infrared (NIR, 841-875 nm, 1230-1250 nm), and shortwave infrared (SWIR, 1628-1652 nm, 2105-2155 nm). In this study, based on the location (latitude and longitude) of the targeted EC tower, we downloaded the eight-day MOD09A1 data over 2009-2010 from Earth Observation and Modeling Facility (EOMF), the University of Oklahoma website ([[http://www.eomf.ou.edu](http://www.eomf.ou.edu)]([http://www.eomf.ou.edu](http://www.eomf.ou.edu))). Then, we calculated EVI [37], LSWI [33], and NDVI [32] based on the reflectance values of the eight-day MODIS dataset from the blue, red, NIR, and SWIR spectral bands [33]. Owing to some defects of the NDVI, the EVI was proposed by using a feedback-based approach that incorporates both background adjustment and atmospheric resistance concepts into the NDVI, resulting in a feedback-based, soil and atmosphere resistant vegetation index [37]. Furthermore,VPM model used EVI and LSWI as important input data to simulate the dynamics of GPP at the targeted coastal wetland.  
\[EVI=2.5\times\frac{\rho_{\text{nir}}-\rho_{\text{red}}}{\rho_{\text{nir}}+(6 \times\rho_{\text{red}}-7.5\times\rho_{\text{blue}})+1} \tag{4}\]  
\[LSWI=\frac{\rho_{\text{nir}}-\rho_{\text{swir}}}{\rho_{\text{nir}}+\rho_{ \text{swir}}} \tag{5}\]  
\[NDVI=\frac{\rho_{\text{nir}}-\rho_{\text{red}}}{\rho_{\text{nir}}+\rho_{\text{ red}}} \tag{6}\]  
where \(\rho_{\text{blue}}\), \(\rho_{\text{red}}\), \(\rho_{\text{nir}}\), and \(\rho_{\text{swir}}\) represent the surface reflectance values of blue, red, NIR, and SWIR bands, respectively [33; 39; 49].  
For time series of VIs calculated from surface reflectance, a simple approach was employed to fill those cloudy pixels [36; 50]. The gap-filling procedure that was employed for cloudy pixels of VIs was reported in the earlier studies [22; 36; 39]. A three-point time series filter X(t - 1), X(t), and X(t + 1)) was employed to correct a cloudy pixel using values of non-cloudy pixels in the window. If X(t + 1) and X(t - 1) pixels were cloud-free, the average of X(t +1) and X(t - 1) was calculated and was used as X(t). If only one pixel (either X(t + 1) or X(t - 1)) was cloud-free, then we used that pixel to substitute X(t). If the three-point time series filter was not available, then we expanded it to a five-point time series filter (X(t - 2), X(t - 1), X(t), X(t + 1), and X(t + 2)) [36]. The gap filling procedure was same as the above three-point time series filter.