esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 2 Related Works  
### Rotation Feature Extraction  
[PERSON] et al. [27] addressed the issue of traditional convolutional neural network (CNN)-based detectors mainly extracting input feature maps within square windows, which can include a large amount of irrelevant information from surrounding areas when processing elongated objects. They proposed Strip R-CNN, which effectively combines the advantages of square convolutions and strip convolutions while encountering less feature redundancy. To tackle the feature misalignment challenge in the refinement phase, R\({}^{3}\)Det [28] employs feature interpolation to re-encode the positional information of the bounding box under refinement into corresponding feature points. Subsequently, it reconstructs the entire feature map at the pixel level, thereby achieving feature reconstruction and alignment. To guide the neural network model to pay more attention to effective features and suppress background noise, [PERSON] et al. [29] designed Non-local Awareness Pyramid Attention (NP-Attention). By learning both the spatial multi-scale nonlocal dependency and channel dependency, It mitigates the discrepancy between the network's attention focus and the real-world objects in images.  
[PERSON] et al. [30] proposed RoI Transformer, which transforms horizontal regions of interest (HRoIs) into rotated regions of interest (RRoIs). Subsequently, a Rotation-Position-Sensitive RoI Align (RPS-RoI-Align) module is incorporated to extract rotation-invariant regional features. Aiming at the problem of large target scale variations in remote sensing images leading to insufficient and unbalanced sampling in feature maps of different levels, [PERSON] et al. [31] proposed an adaptive scale sampling (ADS) strategy. This strategy establishes a mapping relationship between the size of target scales and the levels of feature maps, allocating more samples from high-level feature maps to large-scale targets and the opposite for small-scale targets. This strategy achieves sufficient sampling and a more balanced distribution of samples across different scales.
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 2 Multiwavelength Laser Attenuation Characteristics Test in Water-Filled Karst Caves
### Laser Attenuation Characteristics Test System
#### 2.1.3 Measurement Module of Water Turbidity and Laser Attenuation  
Turbidity, as a parameter to evaluate water quality, is used to quantitatively measure the degree of water opacity caused by particles. There are three main kinds of turbidity measurement methods: transmission, scattering, and scattering-transmission. Scattering-transmission measurement features the highest accuracy [23], and is also the most commonly method used at present. The water turbidity measurement module used a SGZ-200 BS portable turbidimeter (made by Yuefeng Device Co., Ltd. Shanghai, China). Formazine was selected to prepare turbidity liquid samples with upper and lower limits. According to the international standard ISO7027, the unit of measurement of turbidity is NTU (Nephelometric Turbidity Unit).  
The VLP-2000 laser power meter was selected in this test to measure the initial laser emission power and the power attenuated through the water simulator. The meter uses a pyroelectric probe and converts the laser light energy into thermal energy and then an electrical signal through the thermopile structure so that the laser power can be accurately measured. This device does not require wavelength calibration and features high resolution, fast measuring speed, convenience, and reliability. Its measuring range is 0-200 mw, and the accuracy can reach 0.1 mw. The laser power measurement module calculates the attenuation coefficient of the laser after passing through a certain distance of water by measuring the laser power before and after the attenuation of different wavelengths.
## 4 Conclusions  
The synthesis and a critical review of the literature presented above indicate that the hypothesis of the study is proven. Over and above the offsite effect of sedimentation and non-point source pollution, accelerated soil erosion is also an important source of major GHGs (CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O) emitted during all three stages of the erosion process.  
The discussion presented has also led to accomplishing the major objectives of the study:  
1. Accelerated soil erosion (i.e., water, wind, and other processes) has a strong impact on the GCB. Soil carbon transported by erosional processes is partitioned as follows: (i) redistribution over the landscape, (ii) deposition in channels and other depressed sites, and (iii) transportation into the oceans. Because the eroded site is depleted of its SOC stock, its NPP is adversely affected, and the gaseous flux from soil to the atmosphere is decreased. Depending on the soil temperature and moisture regimes, gaseous fluxes (e.g., CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O) from redistribution and depositional sites may increase.
2. The magnitude of soil C transported by water erosion may be 10%-15% of the total anthropogenic emissions of -11.5 Pg in 2019. In addition, there is a large transport by wind erosion in arid and semi-arid climates. Such a large flux, with severe negative impacts on NPP and other biotic and abiotic processes, must be accounted for in the GCB. Considering the multi-gas flux (CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O), and the fact that some gases have a large global warming potential (CH\({}_{4}\) and N\({}_{2}\)O), the magnitude of erosion-induced flux of GHGs must be accounted for and included along with those of other anthropogenic activities affecting global warming.
3. The SOC erosion by water and wind accelerates anthropogenic climate change and also decreases soils' C sink capacity to mitigate global warming. Thus, accelerated soil erosion must be effectively managed to minimize the risks. eroded soils, which are already severely depleted of their SOC stock, must be restored to improve soil health and enhance essential ecosystem services. Adoption of conservation-effective measures for restoration of eroded soils may represent a net significant carbon sink.
4. Additional research is needed on developing methodologies to account for the SOC erosion into the GCB.
5. Because SOC erosion is a source of multiple GHGs, erosion-induced emissions as CO\({}_{2}\) eq must be considered among the sources of anthropogenic emissions.
6. Adoption of site/region-specific conservation-effective technologies (conservation agriculture, mulch farming, cover cropping, afforestation, REDD, and shelterbells) must be promoted to conserve soil and water and reduce the emission of erosion-induced GHGs.  
Support received from [PERSON] and [PERSON], the staff of the Carbon Management and Sequestation Center of the Ohio State University, is appreciated and acknowledged.  
The authors declare no conflict of interest.
## 1 Introduction
### Online Classification  
In online classification, the decision about which packet (or flow) belongs to which particular class is based on the traffic speed. This is the same as any hardware classifier (Packet Shaper, SANGFOR [11]) which is installed on the network path in order to classify the traffic at the network speed. Therefore, the online classifier is normally installed in line with the switch/router, to identify the total traffic that passes through this device. An online classifier is very important to manage threats in the traffic, such as denial of service, flooding attacks and other similar threats. Online classification provides early flow prediction while the flow is in progress [12]. Furthermore, most of the published articles have only focused on the classifier's accuracy, with the classifier trained based on the full flow. However, this approach cannot be implemented successfully for online classification [13]. One of the problems in online classification is the high traffic speed. The challenge will be to do all the following steps in the case of high network traffic speed, i.e., (i) capturing the traffic, (ii) dividing it into flows, and (iii) calculating the statistical features or checking the payload.  
Offline classification is not helpful for online management and control, mainly due to the performance [14]. Online network traffic classification is very important for several reasons:  
* Online classification is the basis for managing the real time network traffic. Therefore, in order to manage and control the Internet traffic, there is a real need for online classification;
* Online classification helps to prevent network threats and abnormal behaviors, such as denial of service, flooding attacks and other such threats;
* Developing of effective software-based online classification algorithms helps to reduce the use of hardware classifiers (such as Packet-shaper) which have very high costs.  
One of the goals of this paper is to differentiate between \"real Internet traffic\" and \"online Internet traffic\". There is a big difference between these two terms: real traffic can be defined as any real Internet traffic which is captured on any network level, and at the current time this is not live traffic, while online traffic means the traffic which is currently running in the network (live traffic). Figure 1 illustrates the difference between real traffic and live traffic. Real traffic is a more extended definition than live traffic. In other words, the live online traffic is a real traffic, but the real traffic is not always an online traffic. In the same manner, there are big differences between online classification and real traffic classification. Real traffic classification is the identification of the real network traffic, which can be called offline classification. This paper defines online classification as a system which can receive and classify the Internet traffic at that traffic's running time.
## 3 Solution Procedure of Mathematical Physics Equations
### Analysis of Sphere (Interface) Movement Shape Parameters  
The parameters of the sphere movement form include acceleration, displacement (collision velocity), movement time, and the normal stress of the interface particle. To obtain the relationship between the parameters, the following solution paths are set:  
\[f=\iint\limits_{x^{2}+y^{2}\leq 2 Ru_{0}-u_{0}^{2}}\lambda_{3}\frac{\partial u}{ \partial z}dxdy=\iint\limits_{r^{2}\leq 2 Ru_{0}-u_{0}^{2}=r_{0}^{2}}\lambda_{3}\frac{ \partial u}{\partial z}rdrdr\varphi=\pi\lambda_{3}\frac{\partial u_{0}}{ \partial z}\cdot\int_{0}^{2 Ru_{0}-u_{0}^{2}}\sqrt{1-\frac{r^{2}}{r_{0}^{2}}}dr ^{2}=\frac{2\pi\lambda_{3}}{3}\frac{\partial u_{0}}{\partial z}(2 Ru_{0}-u_{0}^{2 })\approx\frac{4\pi\lambda_{3}}{3}\frac{\partial u_{0}}{\partial z}Ru_{0} \tag{89}\]  
The equation of motion of the sphere can be described as:  
\[m\frac{\partial^{2}u_{0}}{\partial t^{2}}=mg-\frac{4\pi R\lambda_{3}}{3}u_{0} \frac{\partial u_{0}}{\partial z} \tag{90}\]Together with Equation (83), the equation set can be formed as:  
\[\left\{ \begin{array}{l}\frac{\partial^{2}u_{0}}{\partial t^{2}}=g-\frac{4\pi R \lambda_{3}}{3m}u_{0}\frac{\partial u_{0}}{\partial z}\\ \frac{\partial^{2}u_{0}}{\partial t^{2}}u_{0}=c^{2}\bigg{[}\frac{\eta}{2}\left( \frac{\partial u_{0}}{\partial z}\right)^{2}-\frac{2u_{0}}{R}\bigg{]}\end{array} \right. \tag{91}\]  
The following equation can be obtained:  
\[\eta\left(\frac{\partial u_{0}}{\partial z}\right)^{2}+\frac{8\pi R\lambda_{3 }}{3 mc^{2}}u_{0}^{2}\frac{\partial u_{0}}{\partial z}-\frac{4u_{0}}{R}-\frac{2 gu _{0}}{c^{2}}=0 \tag{92}\]  
The relationship between the interface collision center point strain (\(\partial u/\partial z\)) and the corresponding collision depth \(u_{0}\) can be described as:  
\[ \begin{array}{l}\frac{\partial u_{0}}{\partial z}=\frac{\sqrt{\frac{(8\pi k \lambda_{3}}{3 mc^{2}}u_{0}^{2})^{2}+\frac{16u_{0}\eta}{R}+\frac{8 cu_{0}\eta}{ c^{2}}-\frac{8\pi k\lambda_{3}}{3 mc^{2}}u_{0}^{2}}}{2\eta^{2}c^{2}(\eta c^{2} )}+\frac{8\pi k\lambda_{3}}{3 mc^{2}}u_{0}^{2}\\ =\sqrt{\frac{4\eta c^{2}u_{0}+2 gRu_{0}\eta}{Rc^{2}\eta^{2}}}\sqrt{\frac{16 \pi^{2}R^{2}\lambda_{3}^{2}u_{0}^{2}}{9m^{2}c^{2}(\eta c^{2}+gR\eta)}+1}- \frac{4\pi R\lambda_{3}}{3 mc^{2}\eta}u_{0}^{2}\\ =\sqrt{\frac{4c^{2}u_{0}+2 gRu_{0}}{Rc^{2}\eta}}\sqrt{\frac{8\pi^{2}R^{3} \lambda_{3}^{2}u_{0}^{3}}{9m^{2}c^{2}(\eta c^{2}+gR\eta)}+1}-\frac{4\pi R \lambda_{3}}{3 mc^{2}\eta}u_{0}^{2}\end{array}
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
## 2 The Campaign Sites and Instrumentations
### Instruments
#### 2.2.2 CETESB Air Quality Stations  
The air quality index and particulate matter concentration in the Sao Paulo state is provided by CETESB, which started air pollution and air quality monitoring in 1975 [7]. The CETESB has a total of 61 automatic stations to monitor concentrations of inhalable particles (\(PM_{2.5}\) and \(PM_{10}\)) and gas concentration such as sulfur dioxide (\(SO_{2}\)), nitrogen monoxide and dioxide (\(NO\) and \(NO_{2}\)), nitrogen oxides (\(NO_{X}\)), carbon monoxide (\(CO\)), and ozone (\(O_{3}\)). The CETEB air quality network provides near real-time data based on hourly averages that are available in a web platform and since 2013 has changed its regulations in order to include standards for fine particle and gas concentrations more restrictive and based on guidelines established by WHO [7; 46]. According to the CETESB annual report [46], the main sources of pollution in Paraiba valley is light-duty vehicles (LDV, e.g., passenger cars) running on gasbool or hydrated ethanol, heavy-duty vehicles (HDV, e.g., trucks and buses) running on diesel, and motorcycles running on gasbool commuting by the Presidente Dutra Highway; industries of several types, such as chemical, pharmaceutical, petrochemical, and steel and fertilizer manufacturing; and also long-range pollutant transportation from sugarcane burning and forest fires. In order to provide an air quality overview of the Paraiba valley region, monthly mean particulate matter concentrations were retrieved from four CETESB stations installed in three different cities, i.e., in Sao Jose dos Campos (with two stations--S. J. dos Campos and S. J. dos Campos-Satellite), Taubate and Guaratingueta, as can be seen in Figure 1.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) Data Mining
#### 2.2.3 SAR Amplitude Change Detection
The assignment of the three RGB colors to the bands can be freely chosen. For this study, the assigned color scheme was as follows: R = master; G = slave; B = slave. When the slave is the same in both G and B channels, the RGB is actually a red-cyan (RC) composite. This choice allowed us to identify the changes due to the decrease in the backscatter signal in red (Figure 5a), while the changes due to the increase in the backscatter signal in cyan (Figure 5c). * \"Ratio\": through a subtraction operation between bands of the slave image and the master image (values in both expressed in dB; see Section 2.2.2), the ratio map highlights any areas where changes in the backscatter radar occurred. The output of this operation is in grayscale, which, however, can be replaced by a freely chosen combination of colors for better viewing. The decrease or increase of the backscatter signal is displayed with dark colors (Figure 5b) and light colors (Figure 5d), respectively. While the RGB/RC composites provide information on \"where\" the change occurred, the ratio maps provide information on the magnitude of such changes. These two outputs were the basis to identify the change patterns that have occurred in the Rimac River Basin and could inform about either the extent of floods or presence of urban changes (e.g., new residential areas) that were relevant due to their potential impact on local susceptibility to flooding.
## 4 Conceptual Framework for Augmented Reality Application
### Display Device  
In this step, tests will be carried out with the visualization system chosen for the application. Visualization systems can be classified according to the devices used. These systems employ a combination of optical, electronic, and mechanical components to generate images somewhere along the trajectory of human vision, i.e., between the observer's eyes and the real environment where the virtual object will be inserted. Depending on the optical system used, the image of the virtual object can be generated on a plane or a more complex surface [9].  
Ref. [88] classified these types of systems as follows: (i) non-immersive video-monitor-based, (ii) immersive video-monitor-based, (iii) direct optical vision optical see-through, (iv) video see-through, (v) entirely graphic video display environments, and finally (vi) entirely graphic display environments with interference from real objects (Figure 13). In the non-immersive video-monitor-based system, as the name implies, computer-generated elements are digitally superimposed through monitors, whereas in the immersive video-monitor-based system, immersive head-mounted devices are used. The direct optical vision system refers to a head-mounted device equipped with a display, where computer-generated elements can be overlaid and directly viewed in the real environment. On the other hand, in the video vision system, the real environment is viewed through a video feed, and virtual objects are superimposed.  
Fully graphical display environments present the real-world environment through video and incorporate physical objects that interact with the synthetic environment, allowing users to reach or pick up objects using their hands. Alongside conventional devices, there are ongoing developments in other systems designed for specific user interactions [88]. The device type selection, alongside equipment costs, relies heavily on the intended application. For instance, smart glasses are particularly well-suited for equipment maintenance tasks, where workers must keep their hands free. However, their broader adoption is hindered primarily by the lack of knowledge in effectively integrating smart glasses into production workflows [89]. Conversely, smartphones are more commonly used in AR applications due to their affordability, user-friendliness, and widespread accessibility. The versatility and convenience offered by smartphones make them a preferred choice for AR experiences. Continued research and improvements are essential to unlock smart glasses' full potential and expand their adoption in various industries and applications.
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 4 Experimental Results
### Simulated Data
#### 4.1.2 Gaussian Mixture Model  
Inspecting results using [PERSON]'s analysis, the performance of experiments with larger anomaly ratios bear low F\({}_{1}\) values due to high counts of FN. The main reason for the aforementioned is the presence of an outlier curve in the scores' distribution. The latter occurs due to the high proportion of outliers formed by a specific mechanism, in this case an abnormal DBN. GMM score-analysis is thus employed in the same experiments, affecting solely the threshold computation in the score-analysis phase.  
Results are available in Table 2, being noticeable the considerable increase in recall for experiments with \(P_{O}\) of 20% when compared to results from Table 1. Such is confirmed in existing literature [38]. In general, the count of FP is higher when employing GMM. Such is caused by the GMM's assumption of the existence of an abnormal model even in its absence. Due to similarities between DBNs, especially when considering model \(B\), scores from both networks tend to mix together around the threshold being thus difficult to discern them. The GMM approach has typically higher recall but lower precision with thresholds smaller in module. The latter is more noticeable in higher outlier ratios, since in the presence of fewer anomalies [PERSON]'s method displays higher F\({}_{1}\) scores.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Model B**} & \multicolumn{4}{c}{**Model C**} \\ \hline \(P_{O}\) & \(N\) & **PPV** & **TPR** & **ACC** & **F\({}_{1}\)** & \(N\) & **PPV** & **TPR** & **ACC** & **F\({}_{1}\)** \\ \hline \multirow{3}{*}{5} & 100 & 0.88 & 0.70 & 0.98 & 0.78 & 100 & 0.89 & 0.73 & 0.98 & 0.80 \\  & 1000 & 0.93 & 0.96 & 0.99 & 0.94 & 1000 & 0.91 & 0.98 & 0.99 & 0.94 \\  & 10,000 & 0.95 & 0.98 & 0.99 & 0.96 & 10,000 & 0.94 & 1.00 & 0.99 & 0.97 \\ \hline \multirow{3}{*}{10} & 100 & 0.96 & 0.38 & 0.94 & 0.54 & 100 & 0.89 & 0.73 & 0.97 & 0.80 \\  & 1000 & 0.99 & 0.87 & 0.99 & 0.93 & 1000 & 0.97 & 0.87 & 0.98 & 0.92 \\  & 10,000 & 0.99 & 0.91 & 0.99 & 0.95 & 10,000 & 0.99 & 0.87 & 0.98 & 0.93 \\ \hline \multirow{3}{*}{20} & 100 & 1.00 & 0.19 & 0.83 & 0.32 & 100 & 0.90 & 0.22 & 0.84 & 0.35 \\  & 1000 & 1.00 & 0.20 & 0.84 & 0.33 & 1000 & 1.00 & 0.37 & 0.87 & 0.54 \\ \cline{1-1}  & 10,000 & 1.00 & 0.16 & 0.83 & 0.28 & 10,000 & 1.00 & 0.29 & 0.86 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subject outlier detection of METEOR on simulated data using [PERSON]’s strategy.
## 3 Processing Method of Vibration Piezoelectric Signal
### Environmental Influence  
In the experimental setup, it is essential for the piezoelectric detection unit to operate under stable airflow conditions to ensure the accurate detection of particle collision signals. To achieve this, fan inverters were employed to regulate airflow speed, thereby enhancing the overall stability and reliability of the system.  
Fan Inverter Adjustment: By fine tuning the fan inverter, researchers were able to precisely control the airflow velocity. In this study, the airflow speed was set to 3 m/s--a value commonly adopted to maintain consistent experimental conditions. This setting was optimized to ensure the repeatability and stability of signal acquisition throughout the drying process.  
Airflow Direction Control: The direction of airflow was regulated by adjusting the angle of a deflection plate. This allowed real-time optimization of the airflow trajectory, guiding particles to naturally impact the piezoelectric detection unit while minimizing signal interference caused by misaligned airflow.  
Maintaining stable and uniform airflow is critical to ensuring consistent particle trajectories. The use of a fan inverter enabled controlled particle motion within the airstream, ensuring that particles followed a predictable path and made reliable contact with the piezoelectric sensor.  
However, in practical applications, airflow speed may need to be adapted to account for differences in seed type, particle size, or specific experimental requirements. Fan inverters offer the flexibility to dynamically adjust airflow parameters, enabling rapid adaptation to various experimental conditions and enhancing the robustness of the system.
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 6 Evaluation
### Network Performance
design derives from the combined adoption of the single-flit packet format (packets require less time to be processed), and the traffic decoupling between ringlets (local traffic) and routers (global traffic).