esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Results of EIS Tests
These results confirmed the previous findings regarding the optimal inhibitor concentration (750 ppm) and the extract mixing ratio (40:60 BPE:RSE). It should be noted that the inhibition efficiency in potentiodynamic polarization tests was determined using the following formula:  
Figure 4:1  
where \(i_{corr0}\) and \(i_{corr1}\) are the corrosion current densities, which are related to the blank case (0 ppm) and the solution with inhibitor (\(\mu\cdot\text{cm}^{-2}\)). Figure 4:2 Phase angle plots of EIS tests for steel specimens in HCl solution in the absence and presence of green corrosion inhibitors: banana peel extract (**a**), rice straw extract (**b**), mixture of extracts at different mixing ratios at 750 ppm (**c**), and single and mixture of extracts at 750 ppm (**d**). Validation of EIS data using Kramers-Kronig Transformation (KKT) can ensure the accuracy of testing and help in the correct selection of corrosion inhibitors. Validation of EIS data in KKT is a topic for our future work. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inhibition Efficiency (\%)** & **Deviation** & **Deviation** & \\
**Comparison** & **Causal** & **(Comparison** & **Causal** & \\
**of Experi-** & **Factor** & **of Experi-** & **Factor** & \(i_{corr}\) & **Concentration** \\
**of Experi-** & **mental and Theoretical** & **According** & **mental and Theoretical** & **according to 2** & **(\(\mu\)-cm\({}^{-2}\))** & **(ppm)** \\
**of Experi-** & **Values) (\%)** & & **Values) (\%)** & \\ \hline — & 4.67 & 3.14 & 9.00 & 2.18 & 1065 & 0 & blank \\
30.70 & 1.67 & 3.05 & 2.50 & 1.95 & 738 & 250 & \\
61.03 & 4.00 & 2.88 & 1.00 & 2.02 & 415 & 500 & banana peel \\
78.40 & 1.00 & 2.97 & 4.00 & 2.08 & 230 & 750 & \\
80.19 & 3.33 & 3.10 & 2.00 & 2.04 & 211 & 1000 & \\
51.27 & 1.33 & 2.96 & 7.00 & 2.14 & 519 & 250 & \\
88.92 & 4.33 & 2.87 & 2.00 & 2.04 & 118 & 500 & \\
94.55 & 3.00 & 3.09 & 2.50 & 1.95 & 58 & 750 & \\
95.02 & 7.00 & 3.21 & 5.50 & 1.89 & 53 & 1000 & \\
95.59 & 5.33 & 3.16 & 8.50 & 2.17 & 47 & 750 & 40/60 & banana \\
95.87 & 1.67 & 3.05 & 6.50 & 2.13 & 44 & 750 & 50/50 & peel/rice \\
92.86 & 3.67 & 3.11 & 8.50 & 2.17 & 76 & 750 & 60/40 & straw \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Inhibition efficiency results obtained from potentiodynamic polarization tests and comparison of experimental and theoretical values in determining causal factors (according to standard values 2 and 3). Figure 9: The equivalent circuit used for fitting data obtained in EIS.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 3 Methodology
### Preprocessing
#### 3.3.3 Data Labeling  
Security experts in SOC meticulously investigated the enterprise dataset alerts with deterministic evidence gathered to manually identify vital incidents. They examined the content of the communications captured in archived packet capture. They determined the criticality level of each alert after analyzing files using a sandbox, verifying the contents of the accessed URL, and comparing them with URL blacklists, among other methods. Owing to the alert fatigue that commonly occurs during security alert investigations, there is a substantial chance that security experts will miss or ignore highly critical alerts. Therefore, we hired AI experts to use visualization tools to review labeled alerts. Alerts with key incidents were labeled as highly critical and identified as positive alerts based on the gravity of the possible impact.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 5 Results and Comparison  
This section presents the detailed results obtained from our implementation of the BEC model for ECC. We discussed the hardware and software utilized in our implementation in Section 5.1. The performance analysis of our cryptographic accelerator design considers various metrics, which are described in Section 5.2. Additionally, in Section 5.3, we provided a comparative analysis between our proposed design and existing implementations to highlight the achieved performance improvements. This comparison includes different factors, including the utilization of hardware resources, operational clock frequency, the ratio of the throughput to the area, and other pertinent parameters.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 6 Discussion
### Tests for Algorithm Convergence  
As the algorithm continues to iterate, auction information spreads globally, and satellites continuously update their local cognition, gradually achieving global consistency, and then c-CBBA tends to converge. This experiment is used to analyze the changes in various indicators during the convergence process of c-CBBA, where c-CBBA only uses the TPS mechanism.  
This experiment takes two scenarios, local-1080-90 and global-1080-90, as examples to test the convergence from three perspectives: (a) number of repeated tasks in all task bundles, (b) the average length of task bundles, and (c) the total profit. The results are shown in Figure 12.  
From Figure 12, it can be seen that the trend of the above indicators with iterations is roughly the same in global and local distribution scenarios. The global repeated tasks in all task bundles are the most intuitive indicator for testing convergence. The statistical method of this indicator is: if the task bundles of satellite \(s_{1}\), \(s_{2}\), and \(s_{3}\) all contain task \(t_{j}\), the number of global repeated tasks will be increased by 2. Figure 12a shows that this indicator continues to decrease during the iteration process, dropping to 0 at convergence. When using TPS, the convergence speed is significantly accelerated; the smaller the setting of \(\alpha\), the faster the convergence speed.  
Indicator (b) also shows a downward trend. This is because satellites have less understanding of global auction information in the early stages of algorithm operation, and greedily add tasks to their own task bundles, resulting in longer average task bundle. With the iteration, the satellites' understanding of the global bidding information deepens, and there are more and more tasks that cannot be added to the bundle set due to their own bids being lower than other satellites, coupled with the constraints of the algorithms, which gradually reduces the number of tasks in the task bundles. This indicator also reflects the planning effect to some extent. The longer the average length at convergence, the better  
Figure 11: Parameter \(\alpha\) testing of TPS. The vertical axis represents the percentage of the communication volume and profit of c-CBBA (using TPS policy with different parameter \(\alpha\)) to the basic CBBA. (**a**) Example of local distribution; (**b**) example of global distribution.  
the planning effect. When \(\alpha\) is small, the indicator is poor, and as \(\alpha\) increases, the indicator gradually approaches the basic CBBA.  
Indicator (c) considers the repeated execution of tasks as a non-profit observation, and only calculates the profit once for repeated tasks. In the early stage of algorithm, due to a large number of tasks being repeatedly added to the task bundles, the global total profit is lower with limited resources, so this indicator shows the opposite trend compared to the first two indicators.
# Primitive Shape Fitting in Point Clouds Using the Bees Algorithm
## 4 Experimental Method
### Data Sets Used  
Each data set comprised of 591 3D models of \(10^{3}\) data points each. Each model represented one primitive shape (sphere, box, or cylinder) of different proportions and orientation. Namely, each data set was composed of:  
* 181 individual models of spheres. where the radius was varied from 1 to 10 units in steps of 0.05;
* 220 individual models of boxes, where the width, height and depth were varied from 1 to 10 units in steps of 1, and took all possible combinations of these levels (full factorial design). The orientation was randomly determined;
* 190 individual models of cylinders, where the radius was varied from 0.5 to 5 units in increments of 0.25, the height from 1 to 10 units in steps of 1, and radius and height took all possible combinations of these levels (full factorial design). The orientation was randomly determined;  
The centre of the shapes was set at the origin. Each PC was created first forming and rotating the primitive shape, and then uniformly sampling \(10^{3}\) data points from its surface. The three data sets differed for the amount of noise (error, see Figure 1) in the sampling of the points. Namely:  
* Clean set: there is no error, the data points lie exactly on the surface of the primitive shape;* Error set: the position of each point was randomly perturbed with uniform probability within a 0.1 unit radius;
* Double error: the position of each point was randomly perturbed with uniform probability within a 0.2 unit radius;  
In the tests, it is assumed that the type of sought shape is known, and the goal is to find the shape size and orientation. That is, each algorithm is run three times on each data set, each time to fit one specific kind of shape. Each time an algorithm is run on one data set, it is shown only the subset of shapes that needs to be fitted: for example, if the algorithm is required to find the size and orientation of cylinders in the clean data set, only the subset of 190 cylinder models will be used.
# Research on Renewable Energy Planning Considering the Flexible Region of the Microgrid
## 3 Model and Solution of Distributed Energy Allocation in the Microgrid under Dynamic Boundary
### Related Component Model of the Microgrid
#### 3.2.2 Constraints  
Therefore, referring to various studies in the literature [24; 25; 26; 27], the constraints considering the demand side response are as follows:  
1. System operation power balance constraint \[P_{PV}(t)+P_{ES}(t)+P_{G}(t)-P_{load}(t)=0\] (28)
2. Renewable energy penetration constraint To ensure the power supply reliability of the microgrid, there is a minimum power of the permeability of the renewable energy. \[R_{new}\geq R_{new,min}\] (29) where \(R_{new}\) is the penetration of the renewable energy, and \(R_{new,min}\) is the minimum penetration of the renewable energy.
3. Battery charge and discharge constraints \[S_{SOCmin}\leq S_{SOC}(t)\leq S_{SOCmax}\] (30)\[\left\{\begin{array}{l}P_{ESint}(t)\leq P_{dl\_max}\\ P_{ESout}(t)\leq P_{d\_max}\end{array}\right. \tag{31}\]
4. Backward power constraint \[P_{gridout}\leq P_{gridout,\max}\] (32) where \(P_{gridout}\) is the output power from the microgrid to the distribution network, and \(P_{gridout,\max}\) is the upper limitation of the output power from the microgrid to the distribution network.
5. Load transfer constraint \[m_{load}\leq M_{load}\] (33) where \(m_{load}\) is the value of the load transfer, and \(M_{load}\) is the upper limitation of the load transfer.  
2.3 Algorithm Flow of Distributed Energy Allocation Model under the Dynamic Boundary of the Microgrid  
When the distribution network breaks down, the disconnectors and breakers in the distribution network with a dynamic boundary can also be used to optimize the dynamic boundary of the microgrid.  
The formation method of the dynamic boundary can adopt the following algorithm.  
1. Obtain the distribution network structure and the system data, including the conventional generator output power data, wind turbine data, wind speed data, energy storage data and reliability requirement.
2. Suppose that the proportion of controllable load in the load \(P_{L}\) connected to node \(N\) is \(a\), and the uncontrollable load is \(1-a\). If \(a>0\), an additional node is set up to connect with \(L\) through the branch with a switch, so the value of the load at the node is \(aP_{L}\), and the load connected to \(L\) becomes simply uncontrollable.
3. The DG are taken as the root node to form the tree model. The power required by the node \(v\) is \(P_{v}\).
4. The nodes at both ends of the line with no switch are merged. The load of the combined node is the sum of the loads of the two nodes. Merge the zero-demand node with its upstream node.
5. Then, calculate the power flow of the formed microgrids according to (4).
6. Check whether the microgrids meet the requirements listed in Equations (28) to (33). If not, go to (4); if yes, go to (7).
7. Calculate the cost of the microgrid according to formula (8) and mark it as \(C_{MG,i}\).
8. If \(C_{MG,i}>C_{MG}\), \(C_{MG}=C_{MG}\), otherwise \(C_{MG}=C_{MG,i}\); then, \(i=i+1\).
9. If \(i<n\), go to (3); otherwise, end.
## 4 Design Overview
### Solution Architecture  
The model design is illustrated in Figure 3. The core component of our system is the Controller, which comprises two main modules: the hybrid predictor and the global scheduler. The hybrid predictor forecasts future request arrivals based on historical data stored in the function's log. It combines ARIMA and Random Forest (RF) prediction methods to produce a final, accurate forecast of request arrivals at specific times. The other key component is the global scheduler, responsible for routing requests to the most appropriate invoker among those available in the system. Additionally, the global scheduler manages scaling in and out of invokers based on the current resource status of each invoker. This resource states that information is maintained in the Invokers Info Table. At each invoker level, a local scheduler is deployed. This scheduler uses the hybrid predictor's forecast as input and is fully responsible for managing the container pool within that invoker. The local scheduler combines the hybrid prediction with the current state of each container to decide whether there is a warm container ready to handle a request, if the request should be queued until a container becomes available, or if a new container must be spawned (resulting in a cold start). This decision-making process is based on abuffer-aware greedy approach, which is explained in more detail in Section 6. Information about each request is stored in the function's log, including execution time, resource usage (CPU and memory), and batch size. This data serves as a training set for the profiling model to estimate the expected execution time of specific functions. While the Hybrid Predictor serves as the brain of our system, its value emerges only when coupled with the Global Skewness-Aware Scheduler and Local Buffer-Aware Scheduler; without this integration, predictions alone cannot mitigate cold starts or balance infrastructure load. The Monitor component continuously tracks cold starts, latency, and resource utilization. It is also responsible for retraining the hybrid prediction model whenever performance degrades, specifically, when the RMSE increases by 10% in our experiments.
## 2 Materials and Methods  
### Experimental Process  
The PABR is a high-rate anaerobic system designed by [PERSON] and [PERSON] [7]. It consists of two concentric cylinders, as shown in Figure 1, with the inner one operating as heat exchanger. The space between the two cylinders is divided into four compartments, each of which is divided in two sections, one downflow and one upflow, thus resembling a simple anaerobic baffled reactor (ABR), only arranged in a circular structure.  
The innovative approach of this bioreactor is its ability to periodically change the inflow and outflow compartments. The PABR introduces a novel operational parameter called switching period (T), which along with the hydraulic retention time (HRT) rearranges the flow patterns of the reactor. T is the period for one complete switching of compartment roles. Through this parameter, the time each compartment will be used as the inlet and the outlet is determined. The switching between the compartments is achieved with valves fitted on the external piping, which determine whether the compartment will be used as the inlet or outlet or as an intermediate compartment. This flexibility gives the biomass the opportunity to withstand fluctuations of the feed concentration, leading to easier culture adaptation under extreme or varying conditions.  
When the HRT/T ratio is equal to 1, every compartment receives the same amount of untreated substrate. On the other hand, if we do not switch periodically the role of each compartment, the flow pattern of the reactor resembles that of a four-compartment anaerobic baffled reactor. A pilot-scale 77L active volume PABR was utilized and both operation modes (switching and ABR mode) were examined and evaluated for similar organic loading rates and HRTs.  
Figure 1: Experimental setup of the PABR used in the experiments. A,B,\(\Gamma\),\(\Delta\) indicate the compartments of the reactor. Valves 1,4,7,10 are the inlet valves of compartments A,B,\(\Gamma\),\(\Delta\) while valves 2,5,8,11, are their effluent valves. Valves 3,6,9,12 control the communication between the compartments.  
A number of studies have examined the performance of the PABR under various OLRs, HRTs, and TS and by utilizing different types of feedstock other than typical municipal wastewaters [8; 9; 10; 11; 12].
## 2 Methodology
### Image Partitioning  
Since the original images were of high resolution, they were partitioned into smaller fragments that we refer to as segments throughout this paper. The idea was to study the contents of each segment and to determine whether it depicted a pavement defect or not. The total of all of these segments formed the basis for training the artificial neural network.  
Segments were extracted automatically from the annotated images described in the previous section. The resulting dataset may also be augmented as needed, i.e., the number of images depicting the defects was artificially increased by applying various transformations to existing images such as translation and rotation; in theory, this should improve the efficiency of the ConvNet training.  
The partitioning algorithm extracted the initial segments based on a simple grid also capturing some redundant segments on the edges to ensure maximum coverage of the orthoframe area of interest. Only those segments that fell into the unmasked area were kept, though there was also the option to ignore segments that were partially masked. The segments were exported into a large number of PNG image files into two folders: defect_0 containing segments that depicted no defects and defect_1 containing segments with pavement defects. The procedure of division into these two classes was carried out based on the defect masks manually obtained via digitization during the preprocessing step, as discussed above.
## 2 Material and Methods
### Airborne Laser Scanning Processing Chain
First, to calculate the refractive index, the sine of the angle of incidence was divided by the sine of the angle of refraction [36]. Refractive index varies depending on properties of transmission media (e.g., temperature, pH) and wavelength of incoming radiation. For variable temperature and salinity conditions, and for a green laser (532 nm wavelength), the refractive index is \(n=1.33538\)[36]. Second, the coordinates of the laser origin and the incidence angle were obtained from the trajectory information recorded by IMU and POS. Third, the Air-Water Interface (AWI) was modelled in Quantum Geographic Information System (QGIS) by creating a raster in which all pixels values corresponded to the height of the water surface according to the daily measurements of the master data gauge at Stock, Chiemsee [37]. The water level used (517.93 m.a.s.l.) for the water surface modelling was the measurement taken when the scanning flight took place. The laser penetration depth in the water body was about 10 m, which was approximately 8 m after correcting for light refraction.
# Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships
###### Abstract  
The Artificial Potential Field (APF) method is a classical path planning method for unmanned ships, relying on Global Positioning System (GPS) positioning information for path planning. Unfortunately, once the path planning algorithm uses inaccurate or even fake data, it will lead to ship collision, grounding, or deviation from the course, causing severe economic losses and causing significant security risks to other sailing ships. This paper aims to study the impacts of GPS spoofing on the path planning of unmanned ships. We propose a GPS attack and study GPS spoofing of path planning based on the APF method for an unmanned ship by a low-cost software-defined radio, which causes the unmanned ship to deviate from the course. Our simulation tests show that this method has significant impacts on the path planning results of the APF method.  
unmanned surface ships; artificial potential field; GPS; attack; spoofing; jamming; path planning 2022
## 2 G-LiHT Design and Instrumentation
### Thermal Imaging  
G-LiHT's thermal remote sensing capability originates from the Gobi-384 thermal imaging camera (Xenics, Leuven, Belgium). The instrument measures long wave infrared (LWIR) radiation over a broad spectral range (8 to 14 \(\upmu\)m) using an uncooled amorphous silica (ASi) microbolometer detector with 384 cross track pixels and a 30\({}^{\circ}\) FOV. This compact camera (72 \(\times\) 60 \(\times\) 50 mm) uses a Gigabit Ethernet TCP/IP (Transmission Control Protocol and the Internet Protocol) interface to deliver 16-bit radiometrically calibrated thermal imaging data at a 25 \(\mathrm{Hz}\) frame rate to the computer running Xenics's Xeneth infrared camera software. Each image frame is coded with a computer timestamp synchronized to a GPS Timing Receiver (see Section 2.6).  
Figure 6: **(a)** True color quick look data product (Keyhole Markup Language (KML) format) viewed in Google Earth, illustrating image georegistration in a turbulent atmosphere near Plymouth, NC, USA (28 July 2011; 35.8437\({}^{\circ}\)N 76.6994\({}^{\circ}\)W); **(b)** Coincident downwelling solar irradiance and upwelling radiance spectra over a forested area in the swath; **(c)** reflectance spectra for bare soil and forest targets in (a).
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 3 Experimental Validation
### Field Deployment  
The proposed sensing system was deployed on a steel box girder bridge in Seoul, South Korea. The bridge consists of three girders, and the system was installed on the outer side of the bridge. The webcam, mounted on a bracket to allow for tilt adjustments, was securely fixed to the concrete surface using an epoxy. A ChArUco marker, serving as a fiducial reference, was printed on an acrylic pad and attached to a 3D-printed jig with magnets, ensuring stable placement on the bridge bearing. To facilitate visibility at night, an array of LEDs was affixed to the rear side of the marker (see Figure 9e). These LEDs were connected to the MCU's 3.3 V output and were activated only when the MCU was operational. The distance between the marker and the camera was maintained at approximately 417 mm, consistent with the setup used during laboratory testing. The camera was configured to capture images six times daily at four-hour intervals. These images were then transmitted to a server via an LTE connection, where they were systematically archived according to a predefined database schema. A solar panel was installed on the upper structure of the bridge to provide a continuous power supply, ensuring the system's long-term operability. The three axes of displacement were designated on the marker, as illustrated in Figure 9d,e. The \(x\)-axis represents the longitudinal direction, the \(y\)-axis denotes the vertical direction, and the \(z\)-axis corresponds to the transverse direction. Figure 9c compares the static behavior of the bearing by overlaying images from September and January. This comparison reveals that the superstructure of the bearing is moving in the negative \(x\)-axis direction.  
Figure 9: Field deployment setup: (**a**) installed setup; (**b**) installed camera and marker; (**c**) change between the September and the January image (**d**) day image; (**e**) night image.