esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 4 Improved Saturation Attack Method
### Basic Attack Operation  
Before presenting the detailed process of the attack, we define two basic operations (i.e., primitives) to infer the state of flow entries in Algorithm 1.  
The first operation, named _search_timeout_, is like the incremental search in the probing phase. After generating a flow rule, the attacker sends probing packets with increased packet intervals and returns only when a probing packet triggers a table-miss. The attacker can use this operation to obtain the exact timeout value of the previously inserted flow rule. However, this process is time-consuming. Moreover, this operation ends up with a new rule installation, which means that the previous flow rule state is lost.  
The other operation, _compare_timeout_, compares the current timeout with a given value. Given a target value \(x\), we wait for \(x\) seconds after the rule is installed, and send a probing packet to measure whether it hits. If it hits, it indicates that the installed entry is still active, so the idle timeout is larger than \(x\). Otherwise, the timeout is less than \(x\). Compared with _search_timeout_, this operation is more straightforward, but only obtains a rough result. Note that the flow rule expires only when this operation ends with a table-miss event.  
Figure 5: The overview of attack phases.  
```
0: R: an installed flow entry
0: the exact timeout value of the rule;
1:functionsearch_timeout
2:\(t=1\);
3: rttt = send_probe()
4:while\(rtt<HitThreshold\)do
5:\(t=t+1\)
6: Sleep(\(t\));
7: rtt = send_probe()
8:endwhile
9:return\(t-1\);
10:endfunction
```  
**Require:** x : packet interval value; R: an installed flow entry  
**Ensure:** whether the timeout is larger than x;
1:function\(compare\_timeout(\)x)
2: sleep(\(t\));
3: rtt = send_probe()
4:if\(rtt<HitThreshold\)then
5: return True
6:else
7: return False
8:endif
9:endfunction ```
**Algorithm 1** Primitives to infer the flow rule state
## 2 Calculation Methods
### Selection of the Best Fitting Copula Function  
The empirical distribution function can be used to estimate the marginal distribution of the seismic requirements of a component since it is more flexible and has fewer limitations. Because the normal function and \(t\) copula function can describe the symmetry between component seismic requirements, an empirical distribution-based analysis method was used in this study to select the most suitable copula from the commonly used copula functions (normal function and t copula function). The normal copula function and \(t\) copula function with correlation coefficient \(\rho\) are expressed as:  
\[C(X_{1},X_{2}\cdots,X_{n};\rho)=\Phi_{\rho}\Big{(}\Phi^{-1}(u_{1}),\Phi^{-1}(u _{2})\cdots,\Phi^{-1}(u_{n})\Big{)} \tag{11}\]  
\[C(X_{1},X_{2}\cdots,X_{n};\rho,\
u)=\Phi_{\rho,\
u}\Big{(}{T_{\
u}}^{-1}(u_{1} ),{T_{\
u}}^{-1}(u_{2})\cdots,{T_{\
u}}^{-1}(u_{n})\Big{)} \tag{12}\]  
where, \(\Phi_{\rho}\) is an n-dimensional standard normal distribution with \(\rho\), \(\Phi^{-1}\) is the inverse normal distribution, \(\Phi_{\rho,\
u}\) is the \(n\)-dimensional \(t\) distribution with \(\rho\) and \(\
u\), and \(T^{-1}\) is the inverse \(t\) distribution.  
Furthermore, the marginal distribution function based on the seismic demand of the component, and the empirical joint distribution function expressed by the empirical copula function are defined as:  
\[\hat{C}(u_{1},u_{2},\ldots,u_{n})=\frac{1}{n}\sum_{i=1}^{n}I_{[F_{n}(x_{1}) \leq u_{1}]}\bullet I_{[F_{n}(x_{2i})\leq u_{2}]}\cdots\bullet I_{[F_{n}(x_{mi} )\leq u_{m}]} \tag{13}\]  
where \(F_{n}(x_{ji})\) (\(i=1,...,n;j=1,...,m\)) denotes the marginal probability distribution function of components. \(I(\bullet)\) denotes the indicator function, which is equal to 1 if the value of component marginal distribution is smaller than \(u_{i}\), otherwise, it is equal to 0.  
For the above two copula functions as alternative functions, the copula function with the smallest distance is the best fitting copula function describing the correlation between component seismic requirements. The squared Euclidean distance between the alternative function and the empirical copula function is defined as:  
\[d^{2}=\sum_{i=1}^{n}\big{|}\hat{C}(u_{1i},u_{2i},\cdots,u_{mi})-C(u_{1i},u_{2i },\cdots,u_{mi})\big{|}^{2} \tag{14}\]  
where \(C(\bullet)\) is the alternative copula function, \(\hat{C}\) is the empirical copula function.  
In short, according to the component seismic requirements obtained using IDA, the correlation parameters of two commonly used copula functions were estimated, and the squared Euclidean distance was calculated with Equation (14). Then the minimum distance criterion was used to select the best fitting copula function. Finally, the system fragility was obtained by substituting the fragility of each component and the best fitting copula function into Equation (10).
# Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction
## 5 Real-World Tests  
### Mobile-ROS Interaction Test  
The mobile-ROS interaction test was conducted on mobile devices and a portable laptop running ROS. RPLIDAR A1M8 LiDAR [41] and Sprint RoboSense 16-wire LiDAR [42] were used to collect data and enabled the system to perform real-time mapping and data processing. The testing environment included an underground parking lot at Tongji University, Jiading Campus, Shanghai, China, as shown in Figure 5(a). During the test, the system demonstrated its capability to accurately capture and visualize the surrounding environment. The corresponding mapping result is illustrated in Figure 5(b). Notably, gaps in the scanned image correspond to the positions of parked cars, demonstrating the system's precision at detecting and mapping obstacles.  
Specifically, the RPLIDAR A1M8 LiDAR was employed for detailed 2D environmental mapping, as shown in Figure 6(a). As depicted in Figure 6(b), the real-time scanning process involved the LiDAR performing real-time scanning and mapping. The data were then transmitted from the laptop to the mobile device for visualization. The scanned results were displayed on the mobile device in real time, allowing users to control and interact with the system directly through the mobile interface. Then, users could view the real-time environmental map on the mobile device and make necessary adjustments and controls. This enables users to monitor and manage environmental data more conveniently.  
To further illustrate the mobile-ROS capabilities of the proposed AMSEWL-R system, a real-world test was conducted to switch between the 2D and 3D views using ROS on the portable laptop. The switching process involved three steps, as depicted in Figure 8. The seamless transitions between 2D and 3D views demonstrated the system's capability to adapt to different visualization needs and provided users with flexible and intuitive control over the mapping and interaction processes. This feature is critical for enhancing the safety and efficiency of remote autonomous operations.  
The excellent mobile-ROS interaction ability significantly enhances the usability and functionality of the proposed AMSEWL-R system, making it a powerful tool for real-time environmental monitoring and interaction.  
Figure 6: (**a**) The location of the mapping experiment: an underground parking lot at Tongji University, Jiading Campus, Shanghai, China, where we conducted our mapping tests. (**b**) Visualization on a mobile device using Iviz that show the scanning results from the parking lot. Notably, the gaps in the scanned image correspond to the positions of cars in the parking lot. This illustrates the effectiveness and accuracy of the SLAM data captured and visualized using Iviz.
## 4 Results
In contrast, the soil at EM 6 is classified as a Sand with typical surface composition of its dominant soil series (Delhi) of roughly 4% clay and 95% sand. EM 3 is classified as a Silty Clay Loam, and the surface composition of its dominant series (Egbert) lies in between, with 38% clay and 18% sand fractions. While preliminary, these results suggest that some soil compositional properties may be sufficiently spectrally distinct to be mapped using their position in the feature space-at least in this study area. Figure 8 illustrates the complexity that can be measured by hyperspectral imagery on even a relatively small spatial scale. The spatial subset shown in this figure is only 9 \(\times\) 15 km. Even this small spatial area exhibits considerable diversity in vegetation and substrate spectra. Substantially more prominent clustering is notable within the AVIRIS feature space than within the Landsat feature space, as the subtle spectral differences between fields are better resolved by the hyperspectral sensor than the multispectral sensor. The full complexity of these spectra could not be captured in any three band false color composite image, or resolved with the six-band Landsat or even 11-band Sentinel-2 sensors. Individual pigment and mineral absorptions are distinguishable that would be lost completely in multispectral data. Notably, despite the severely limited spatial domain of this subset, spectra are identifiable (e.g., 2 and 5) that are again remarkably similar to global EMs. Figure 7: AVIRIS and Landsat substrate EM variability. The PC 1 vs. 4 projection is useful in both transforms as a means of discriminating between the spectral shape of substrate EMs. The remarkable topological similarity between the AVIRIS and Landsat feature spaces (**upper left**) extends into the fourth dimension. The bright (high PC1) edge of the point cloud splays out in both AVIRIS and Landsat to reveal a continuum of rock and soil substrate EM spectra. Multipixel mean spectra for each of these EMs (**lower left**) are displayed as observed by both Landsat and AVIRIS. The locations of the spectra are indicated on the AVIRIS flight lines (**right**). USDA-Ncess soil survey data were used to find the soil type at the location of each EM. The order and suborder of each soil are shown on the EM spectra plots. The spectral variability captured by the EM continuum corresponds to consistent soil property variations. From EM1 to EM6, the soil types are characterized by a tradeoff between decreasing clay content and increasing sand content.
## 4 Experiment Setup and Results  
The experimental system includes a KUKA KR120 six-axis industrial robot arm and the proposed end-effector as shown in Figure 8. The polishing wheel speed is 500 rpm with a diameter of 50 mm. A kind of magnetorheological fluid with Nano-diamond is used in this experiment. The magnetorheological flowrate is 800 mL/min, and the ribbon height is adjusted to 2 mm.  
Figure 6: Constant force control system with ADRC topology.  
Figure 7: Unit step response of ADRC and PID.  
The experiment is arranged as follows: First, verify the performance of the designed controller with several tests. Second, compare the robotic magnetorheological finishing stability before and after the constant force control. Third, conduct a mirror deterministic polishing experiment to verify the effectiveness of the proposed system. In all experiments, the six-axis robot carries the end-effector in vertical contact with a workpiece of quartz mirror (200 mm \(\times\) 200 mm \(\times\) 10 mm). The normal force \(F_{n}\) during the polishing process is monitored and recorded by the application based on the software LABVIEW.
## 2 Materials and Methods
### Antiplasmodial Activity  
Antiplasmodial potential of the studied extracts of _O. genistifolia_ against chloroquine-sensitive _P. falciparum_ (strain 3D7) was evaluated as described by [PERSON] et al. [21] using parasite lactate dehydrogenase (pLDH) activity as a biomarker. Malaria parasites (_P. falciparum_ strain 3D7) were maintained in RPMI 1640 medium containing 2 mM L-glutamine and 25 mM hepes (Lonza, Basel, Switzerland). The medium was further supplemented with 5% Albumax II, 20 mM glucose, 0.65 mM hypoxantine, 60 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\) gentamycinin and 2% hematocrit human red blood cells. The parasites were cultured at 37 \({}^{\circ}\)C under an atmosphere of 5% CO\({}_{2}\), 5% O\({}_{2}\) and 90% N\({}_{2}\) in a sealed T75 culture flask.  
To determine the antiplasmodial activity of the extracts, serial dilutions of the extracts were added to in vitro cultures of _P. falciparum_ (strain 3D7) in 96-well plates and incubated for 48 h. Thereafter, an aliquot of 20 \(\mathrm{\SIUnitSymbolMicro mL}\) of culture was removed from each well combined with 125 \(\mathrm{\SIUnitSymbolMicro mL}\) of a mixture of Malsat and NBT/PES solutions in a fresh 96-well plate. These solutions measure the activity of the parasite lactate dehydrogenase (pLDH) enzyme in the cultures. A purple product was formed when pLDH was present, and this product was quantified in a Spectramax M3 microplate reader (Abs\({}_{620}\)). The Abs\({}_{620}\) reading in each well is thus an indication of the pLDH activity and hence the number of parasites present. For the IC\({}_{50}\) determination, the extracts were tested in a range testing from 100 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\) and extended using a 3-fold serial dilution. For each extract, percentage parasite viability--the pLDH activity in extract-treated wells relative to untreated controls--was calculated. The extracts were tested in duplicate and standard deviation (SD) was derived. For each compound and each extract, percentage viability was plotted against Log (extract concentration) and the IC\({}_{50}\) (50% inhibitory concentration) obtained from the resulting dose-response curve by non-linear regression. For comparative purposes, chloroquine (a standard antimalarial drug) was used as a control.
## 1 Introduction
### Thermoelectric Cooling System  
Thermoelectric coolers (TEC) are solid-state heat pumps based on the Peltier effect. It enables converting electrical energy into a temperature gradient. In commercial types, TECs are composed of many of n-type and p-type semiconductor junctions. When a direct current power source is connected, the electrical current flows from the n-type element to the p-type element, and thermoelectric cooling effect occurs. When the electrons pass from a lower energy level element (p-type) to a high energy level element (n-type) the temperature of the cold junction decreases. At the same time, the electrons carry the absorbed heat to the hot junction. This heat is transferred to the heat sink, while the electrons return to a lower energy level in the p-type semiconductor (the Peltier effect) [26].  
There is also a possibility to use thermocouples reverse. That means, it possible to generate electrical energy by the temperature difference. If the thermal gradient exists between the cold and hot junction, a voltage (Seebeck voltage) directly proportional to the temperature difference is generated.  
TEC systems have no mechanical moving parts and fluids, which transfer heat from the cold side to the hot side of the modules [27]. TECs have many advantages such as high reliability, low weight, and flexibility in integration. TEC systems have been applied to thermoelectric refrigerators [27; 28], car seats [29; 30], electronic cooling devices [31; 32]. It has been also used in military, aerospace, instrument, and industrial products [33; 34; 35; 36]. One of the areas of new research of thermoelectric technology is applying thermoelectric modules (TEM) to domestic space cooling. TEC systems can be also powered by a photovoltaic (PV) without the inverter. Some of the recent researches focused on thermoelectric coolers and generators (TEC and TEG) are defined by the combination of solar energy and PCMs. TEG is an alternative choice for converting of solar thermal energy into electricity. The technology described in this article can use energy from PV for TEC in combination with the PCM-based TES.
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 2 Methodology  
We selected 18 mid-range and low-end Android smartphones from various device manufacturers, running different versions of the Android OS. In our test sample, one device was running Android 7.0, three devices were running Android 6.x, five devices were running Android 5.x, eight devices were running Android 4.x, and one device was running Android 2.3. These devices and their essential characteristics are listed in Table 1.  
To perform power consumption measurements, a test smartphone was connected to the digital programmable power supply Rigol DP832A with the high-resolution option [20], and the digital programmable power supply was linked to a notebook PC via USB interface for loggingsubsequent data analysis. The schematic diagram of the experiment and the test setup are illustrated in Figure 1.  
To automate the process of power consumption measurements in different modes of operation, we developed a custom Android application that periodically performed microphone audio recording and digital signal processing using several signal processing algorithms with different levels of complexity. The audio signal from the microphone was captured using the Android _AudioRecord_ class [21] at a sampling rate of 44.1 kHz, and all digital signal processing algorithms were implemented in optimized fixed-point C++ code and integrated into the test application via the Android Native Development Kit [22].  
To minimize the influence of other hardware and software components on the measurement results, we turned off 2G/3G/LTE mobile data transfer, disabled Wi-Fi and Bluetooth radios, and uninstalled or disabled all non-system applications (social networks, messengers, etc.) that could consume battery power in background mode.  
Firstly, we ran audio recording tests without any signal processing at all. In this mode of operation, the audio samples that were received in the audio recording callbacks were simply ignored and the processing thread was inactive. Secondly, we run three real-time digital signal processing algorithms: \"simple\" (algorithm #1), \"moderately complex\" (algorithm #2), and \"fairly complex\" (algorithm #3), listed in Table 2. Each algorithm was active for 15 min with a 2 min \"cool down\" interval between runs, and then the measurement cycle was repeated multiple times. Each experiment lasted for at least 24 h. Instant (second-by-second) power consumption measurements were recorded using a digital programmable power supply and transferred to the notebook PC via USB interface. A typical sequence of recorded power consumption measurements is illustrated in Figure 2. As one can see, the instant power consumption never drops below a certain minimum level (in our example, 170 mW), and has occasional spikes as high as 650 mW (algorithm #3). These power variations are caused by the complex and dynamic nature of Android OS behavior. Moreover, all modern Android smartphones use dynamic central processing unit (CPU) frequency scaling (CPU throttling). Due to computational
## 2 Related Work
### Registration Methods
#### 2.1.3 Learning-Based Methods  
Deep learning-based methods have addressed many of the limitations of traditional point cloud registration techniques, with some approaches building on and improving the Iterative Closest Point (ICP) framework. These methods can be broadly divided into several key steps: First, feature extraction involves selecting key points and using specific algorithms to identify more effective point pairs between the source and target point clouds. Second, mismatches in the obtained point pairs are further refined. Finally, Singular Value Decomposition (SVD) is employed to solve for the registration results. One of the earliest works in this domain is PointNetLK [36] proposed by [PERSON] et al. This method maps two point clouds into a high-dimensional space using PointNet [37] and treats them as images. It then iteratively optimizes the distance between the features of the two point clouds using an optical flow approach, similar to that used in image matching, to achieve registration. [PERSON] et al. introduced DCP [38], which employs Transformer [39] to calculate soft correspondences between two point clouds when identifying point pairs. The transformation is subsequently computed using SVD. [PERSON] et al. proposed IDAM [40], integrating an iterative distance-aware similarity convolution module into the matching process. This method overcomes the limitation of using inner products to obtain point-to-point similarity. PRNet [41] introduces a keypoint detector and employs keypoint-to-keypoint correspondences in a self-supervised manner to address local point cloud registration problems. DeepGMR [42] extracts pose-invariant correspondences between the original point cloud and Gaussian mixture model parameters, and then recovers the transformation from the matched Gaussian mixture models. RPMNet [43] proposes a network that predicts parameters for the Sinkhorn algorithm, an optimal annealing algorithm, and uses it to obtain soft correspondences from local features, thereby enhancing robustness. However, these models are primarily based on global matching. Although they can handle part-to-part point cloud registration to some extent, there is still room for improvement in terms of accuracy and robustness. The aforementioned deep learning registration algorithms based on global distribution have achieved certain results on point cloud models generated from graphics. However, their performance on acquired datasets with large differences between point clouds and complex geometric structures may be limited, making effective registration challenging. Due to the complexity of the computational methods and the large volume of data transmitted, there is still room for improvement in their applicability to real-world scenarios. Limitations remain in how to perform registration quickly and efficiently.