esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A Two-Round Optimization Design Method of Aerostatic Spindles Considering the FSI Effect  
According to the existing design philosophy of aerostatic bearings [19; 20; 21], the design of air bearing is usually first carried out based on its performance requirements and design restrictions, and then the crucial solid parts are designed empirically based on the dimensions of air film. The elastic deformation of crucial parts caused by high air pressure and its impact on the overall performance of the aerostatic spindle are ignored. However, when the FSI effect is considered, the design of the aerostatic spindle consists of the design of the geometrical parameters of air bearing and the design of key structural dimensions. Both aspects impact the overall performance of the spindle system significantly. To facilitate the optimal design of aerostatic spindles considering FSI, a two-round optimization design method is proposed in this research, as shown in Figure 1. Its basic idea is to optimize the geometrical parameters of aerostatic bearing and the key structural dimensions of solid parts sequentially so that the static and dynamic performance of the spindle system can be optimized comprehensively.  
In the first-round design stage, the static performances of the aerostatic bearing are improved by optimizing its geometrical parameters. The FEM model of aerostatic bearing is built in this stage. Then, the influence of the crucial design parameters (such as the orifice diameter, the air film thickness, and the orifice number) on the static performance of the aerostatic bearing can be acquired. Finally, the optimized design parameters for air bearing design can be acquired by selecting a good combination of LCC, stiffness, and volume flow rate (VFR).  
In the second-round design stage, the static and dynamic performances of the aerodynamic spindle are further investigated with consideration of the FSI effect. A two-way FSI simulation model is built to calculate the structural deformation of the spindle and its impact on the static performance of the aerostatic bearing. The crucial structural dimensions that greatly influence the structural rigidity and weight of solid components are analyzed. Furthermore, to clarify the effect of structure dimension on the natural frequency of the spindle, modal analysis with varying structure dimensions is carried out, and finally, the optimal structural dimensions are acquired.  
In the following sections, the optimal design of an aerostatic spindle is implemented as a case study to elaborate the detailed procedures of the proposed two-round optimization design method.
## 3 Materials and Methods
### Optimal Neighborhood Scale Determination  
The selection of an optimal neighborhood scale is critical for accurate feature extraction and subsequent leakage detection. Traditional scale selection often relies on heuristic knowledge or empirical rules. However, such methods may lack generalizability across diverse datasets. To address this, various data-driven approaches have been proposed, such as analyzing local surface variations [33], iterating geometric features for scale determination [34], using eigenentropy for scale selection [35], and aligning scales with object-specific recall thresholds [32]. These methods ensure scale selection is aligned with the dataset characteristics and analytical objectives.  
In this study, the focus is on optimizing the F1-score, which is a harmonic mean of precision and recall, to balance extraction completeness and accuracy of leakage detection. The determination of optimal neighborhood scale follows a systematic evaluation approach. For each candidate neighborhood scale (5, 10, 20, 40, 60, 80, and 100), the method generates distinct training and validation datasets using the corresponding scale parameters. Each classifier undergoes independent training on its scale-specific training dataset, with subsequent performance evaluation conducted on the corresponding validation dataset. The scale yielding the highest F1-score is selected as the optimal value. This systematic approach guarantees that the selected scale maximizes segmentation accuracy while minimizing the influence of noise and outliers. By systematically testing a broad range of scales, this method provides a robust framework for determining the optimal scale, enhancing both the precision and reliability of tunnel leakage detection.
## 3 Results
### Ara-C Treatment  
Expression of survivin was investigated in a panel of AML-derived cell lines (Figure 1a). We did not detect any substantial difference among the relative survivin expression levels in individual cell lines with respect to their characteristic gene mutations (AML1/ETO in Kasumi-1, Flt3-ITD in MV4-11, DNMT3A in OCI-AML2 and OCI-AML3, NPM1 in OCI-AML3, or p53 in Kasumi-1, KG-1 and HL-60). In accordance with our previous results [49], ATRA induced attenuation or no change in survivin expression in the cell lines (Figure 1b and Figure S2). On the other hand, Ara-C caused survivin overexpression in a broad interval of concentrations (50 nM-5 \(\upmu\)M, Figure 1b). It also dramatically slowed the proliferation (Figure 1c), but a large drop in viability was observed only in HL-60 and OCI-AML2 at higher concentrations (Figure 1d).  
Expression of survivin is cell-cycle-regulated and reaches its maximum in mitosis [16]. Ara-C is known to arrest cells in S phase [50; 51]. Enhanced survivin expression, thus, cannot be attributed to altered fraction of mitotic cells. Indeed, we detected increased survivin level in the nuclei of interphase cells and, simultaneously, we did not find any mitotic cells in Ara-C-treated samples (Figure 2).  
In subsequent experiments, we used 5 uM Ara-C for all cell lines except for HL-60, which was considerably more sensitive; thus, it was treated with 0.5 uM Ara-C.  
Figure 1: (**a**) Relative survivin expression at messenger RNA (mRNA, gray) and protein (black) level in a panel of acute myeloid leukemia (AML) cell lines. Values represent an average of at least three independent measurements \(\pm\) SD (**b**) Effect of 24 h all-_trans_-retinoic acid treatment (ATRA) or cytarabine (Ara-C) treatment on survivin expression. \(\upbeta\)-Actin levels served as the loading control. (**c**) Cell proliferation after 48 h of Ara-C treatment. (**d**) Cell viability assessed by trypan blue uptake after 24 h (left) or 48 h (right) Ara-C treatment. Original blots with molecular weight (MW) markers are provided in the Supplementary Materials, Figures S1 and S2  
Figure 2: Immunofluorescence detection of survivin expression (Alexa Fluor555, red) after 24 h in control (upper row) and 5 uM Ara-C-treated (lower row) AML cell lines. Nuclei are counterstained with Hoechst33342. Arrows indicate mitotic cells. Bars represent 10 um.
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 3 Results and Discussions
### Detection of Acetamiprid in Fortified Tomatoe Sample  
Tomatoes are one of the foodstuffs that could be affected by the acetamiprid and where the EFSA legislation was fixed the MRL to 0.01 ppm [4]. To perform the detection of acetamiprid on tomatoes, the sample were prepared following the method reported by [PERSON] et al. [46]. Briefly, 5 g of tomatoes samples were extracted with 10 mL of methanol for 30 min, and then centrifuged for 20 min at 10,000 rpm (4 \({}^{\circ}\)C) to remove the solids. The supernatant was filtered through a 0.45-micron membrane. Then, aliquots were doped initially with acetamiprid (\(c_{1}\) = 65 fM) that will be confirmed later via the standard addition method. This procedure allows the calibration of analytical devices taking into consideration the matrix effects. The capacitance curves in tomatoes samples solution showed a proportional increase of the capacitance signal with the addition of increasing target concentrations (Figure 6a). The obtained calibration plot (Figure 6b) allowed to determinate \(c_{i}\) by extrapolation to be found equal to 62.1 \(\pm\) 0.51 fM. In addition, the obtained recovery values for the measurements performed in tomatoes and in PBS are gathered in Table 2. High recovery values are comprised from 95 to 104%, depending on concentration. Thus, the ability of the method to measure small amount of pesticides in food demonstrates the potential of this biosensor of the acetamiprid detection in food.  
Figure 5: Histograms giving the relative variation of redox capacitance variation after incubation with different interferents.  
Figure 6: (a) Capacitance curves before (a: 0 fM) and (b to e) after incubation with various acetamiprid concentrations in fortified tomatoes samples (b: \(c_{i}\), \(c_{i}\) + 55 fM, d: \(c_{i}\) + 155 fM and e: \(c_{i}\) + 255 fM); (b) Calibration curve of the biosensor displaying the relative variation of redox capacitance versus the target concentration.
## 2 Ground Fault Protection of Ungrounded System
### GPT (Ground Potential Transformer)  
GPT is a protective device that detects ground faults through the magnitude of sequence voltage induced in response to a ground fault by forming a loop through which the ground fault current can flow. The internal configuration of GPT is depicted in Figure 3. The primary side of GPT is Y-connected and its neutral point is directly grounded. The secondary side of GPT is also Y-connected to ground the neutral point, and the tertiary side is composed of an open \(\Delta\) connection using a CLR. When in occurrence of a ground fault in the system, a closed circuit is formed through the fault point and the neutral point of the primary side of the GPT, causing the fault current to flow. Accordingly, the current of the tertiary side of the GPT is induced and the zero-phase voltage is induced across the current limiting resistor. Based on the magnitude of the corresponding zero-phase voltage, GPT is used as an operating element of the linked OVGR or SGR.  
Figure 1: Example of square wave injection type insulation monitoring device (IMD) connected to ungrounded system.  
Figure 2: Equivalent circuit of isolation impedance and IMD.
## 5 DDoS Attacks Emulation on SDN-Based IoT Architecture
### SDN-Based IoT Testbed  
The physical testbed consists of two IoT-DAG, two IoT Attackers, one IoT-DS, three OpenFlow switches and one SDN Controller as shown in Figure 5. In order to have a realistic setup for IoT applications, IoT-DAG-1, IoT-DAG-2, IoT Attackers, IoT-DS and SDN Controller use Raspberry Pi 3; this is because IoT devices are expected to be low cost, low battery consumption and low CPU capacity.  
Figure 4: Comparison of available bandwidth.  
Figure 3: Network architecture in the Mininet.  
The OpenFlow switch is Zodiac-FX, a low-cost simple device, which has 4 physical ports: port 4 for a connection between the controller and the switch, ports 1 to 3 for peer connections between switches. In order to manage multiple switches via a single controller, a traditional router is used to link the controller to all the three switches. IoT-DAG tries to send data to the IoT server, while IoT attackers will try to impact the transmission from IoT-DAG to the server by depleting bandwidth resources. IoT attackers have been deployed under two switches as shown in Figure 5, so that both IoT-DAG-1 and IoT-DAG-2 will be impacted. iPerf is adopted to generate traffic in the network as explained in the next subsection, where two scenarios are considered:  
* _In scenario-1_, IoT-DAG-1 generates periodical and random TCP or UDP traffic one after another to verify the nature of each traffic type in the network.
* _In scenario-2_, IoT-DAG-1 generates periodical UDP and periodical TCP traffic, and IoT-DAG-2 generates random UDP and random TCP traffic.  
The IoT-DS is responsible for collecting TCP and UDP traffic and generating test results for each scenario. In order to distinguish periodical and random TCP/UDP traffic on the server side, different port numbers have been assigned to these four types of traffic.  
iPerf is also used on the IoT attacker to emulate the DDoS attack in the testbed. It is worth noting that we are using UDP flooding attack against the control plane. The attack frequency can be modified through the interval of transmission. The attacker sends a flow to a new destination each time to trigger a request to the controller. Thus, as long as it produces a huge amount of requests within a short period, the controller has to consume enormous resources to process these fake packets so that DDoS attacks can take effect on the controller. Meanwhile, the switch has to process these requests as well, so that it may not be able to process normal packets. Therefore, the impact is on both the control and data planes. Each experiment runs 10 times for 20 min.  
We use the topology in Figure 1 to verify the behaviour of each scenario under normal circumstance, under DDoS attacks and running SECOD to resist DDoS attacks.
## 3 Analysis of Alignment Tolerance on the Double-Diffracted Grating Interferometer
### Analysis of the Stand-off Error  
As Figure 3 shows, the stand-off error changes the distances between the grating and the reading-head while keeping the incident beams vertical. The diffracted beams of the first diffraction influenced by stand-off (shown in red or blue) and the original position of the diffracted beams (shown in transparent white) are parallel but with a deviation. The diffracted beams entered the retro-reflectors respectively, creating spot offsets in the surfaces of the RRs. Considering the center symmetry feature of corner-cube retro-reflectors [15], the output beams, as the oblique incident beams of the second diffraction, follow the offset and propagate in the opposite directions. Finally, the second diffraction beams vertically transmit away from the grating with symmetrical deviation. Qualitatively, the bigger stand-off error is, the larger the deviation that occurs. After the second diffraction, in a beam-combined prism implemented by a NPBS in the 45\({}^{\circ}\) position, the beams with symmetrical deviation can still entirely overlap (but with a deviation). Only when the actual interfering zone exceeds the boundary of the PD, can the stand-off error have no effect on the interference signal.  
Figure 3: Geometrical analysis of the stand-off error and its influence on the measurement beam caused by spots deviation.  
Figure 2: Distinguishing the misalignment angles (roll, yaw, and pitch), stand-off and offset.
## 3 Data Used in This Study  
### Data Collection  
Data collected from three buildings in different regions is employed to evaluate the models, i.e., Hawthorn Campus--ATC Building (denoted as _DatasetS1_), Hawthorn Campus--AMDC Building (denoted as _DatasetS2_), and Wantimra Campus--KIOSC Building (denoted as _DatasetS3_) are incredibly valuable as it provides real-time insights into the building's performance, energy consumption, and operational efficiencies, allowing for a swift response to potential issues. This real-time data not only enhance decision-making capabilities for building management, maintenance, and optimization but also provides a basis for developing more accurate forecasting models. Furthermore, it can guide strategic energy management, potentially leading to significant cost savings, improved sustainability  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Forecasting Model** & **Year** & **Country** & **Forecast Horizon** & **Ref.** & **Accuracy** \\ \hline \hline \multirow{6}{*}{\(11\)} & Hybrid Neural Fuzzy Interface System (HyFS) & \multirow{2}{*}{2019} & \multirow{2}{*}{Portugal} & day & \multirow{2}{*}{[28]} & \multirow{2}{*}{8.71\%} & \multirow{2}{*}{**MAE**} \\ \cline{2-2} \cline{6-6}  & [PERSON] and Mendel’s Fuzzy Rule Learning Method (WM) & & & & & \\ \cline{2-2} \cline{6-6}  & A genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL) & & & & \\ \hline \hline \(12\) & XGBoost & \multirow{2}{*}{2022} & \multirow{2}{*}{Spain} & day & \multirow{2}{*}{[29]} & \multirow{2}{*}{8.83} \\ \cline{2-2} \cline{6-6}  & XGBoost & & & & & \\ \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Common categories of Forecasting model in the literature.  
ity, and increased occupant comfort over time. The two datasets _DatasetS1_ and _DatasetS2_ contain the energy consumption from 2017 to 2019, and the dataset _DatasetS3_ contains the energy consumption from 2018 to 2019. The prediction value is the difference between the previous and the intermediate next time in using energy, or the cumulation of energy. The historical value is 96 data points every 15 min to predict the next value. Figure 2 Indicates the location of the buildings from the Hawthorn Campus and the Wantrina Campus in the context of Melbourne and Figures 3 and 4 indicate the Electricity accumulation in every 15 min and one hour for the Hawthorn campus (ATC building).
## 1 Introduction
Phenomena such as slip, inertia, gravity, collisions, traces left by the moving robot, etc., are simulated, so that robot traversal is realistic enough to test mapping and navigation algorithms. We do not model soil compacting or settlement, as it is not crucial for vision-based situational awareness. In a similar way, terramechanics simulators do not provide configurable, photorealistic rendering, hardware-in-the-loop or standard software integration capabilities, as they are not relevant to their focus area. The simulator can generate any quantity of ground truth data for training and testing the onboard machine learning and deep learning image processing (segmentation, object  
Figure 1: View from the LunarSim environment. detection, depth maps) paired with realistic images. It can also support the development of vision-based navigation--visual odometry (VO) and simultaneous localisation and mapping (SLAM)--paving the way to increased AI-assisted rover autonomy. The simulator offers a range of desirable properties that are typically only partially present in other solutions. The principal contributions of this paper are outlined as follows:  
* Introduction of a simulator and toolkit achieving a high degree of rendered scene realism, which is demonstrated by improved performance of the segmentation neural network model, using generated synthetic data for transfer learning when compared to an alternative solution. * Interface-level compatibility with ROS 2, an industry-standard robotics software platform enabling rapid development and testing of applications, which is shown by running and evaluating the visual odometry engine, using simulated data. * Compatibility with a wide range of hardware and simulation scenarios, which is demonstrated using deep learning image analysis, sensor data processing, and navigation functions using real devices, ranging from embedded devices with integrated AI accelerators to complex system-on-chip devices, including custom, programmable hardware.
## 3 Experimental Results and Discussion
### Experimental Result
#### 3.2.1 The Relation between the Current and Induced Voltage  
According to Equation (3), the exciting current is a key factor of the sensor output. The z-axis direction height was set to 1 mm, and the iron debris moved along the x-axis at 2.12 m/s.  
The peak-to-peak values of inductive voltage profiles with different exciting currents are shown in Figure 9. Each experimental condition in the figure is counted using 12 sets of data, with the top line and bottom line representing the maximum and minimum values, and the middle line representing the median. The top and bottom of the box are the upper quartile and the lower quartile respectively.  
Figure 8: Output wave (0.90 mm length and 0.90 mm diameter iron debris, 2.12 m/s, 0.4 A exciting current).  
Figure 7: Experimental rig.  
The result indicates that there is a linear relationship between the peak-to-peak values and exciting current, which correlates well with Equation (3). However, by calculation, when the excitation current gradually becomes larger, the standard deviation of the experimental data increased from 6.2 to 22.1. Figure 10 shows the induced voltage noise variation under different drive current conditions. When the drive current varies from 0.1 to 0.5 A, the peak-to-peak value of the noise does not change significantly. The drive current of the excitation coil thus has little effect on the background noise of the output voltage.
## 3 Results and Discussion
### Cohesion Analysis  
As shown in Table 2, the percentages of retained materials were all larger than 60%, which is recommended from the Ontario Ministry of Transportation [1; 8]. It indicates that three kinds of patching materials can meet the requirement of cohesion to alleviate the problems of reveling and stripping. Of them, the OG mixtures had the highest losing mass of 34.8 g, which increased by 1.83 and 0.37 times separately, when compared with the DG mixtures and PADG mixtures. This is because the continued gradation of the DG mixtures and the PADG mixtures is conducive to the denser compaction. The PADG mixtures have the highest residual ratio due to the cohesion enhancement of adhesive. The functional groups, with low surface energy in adhesive, can participate in the formation of the adhesive layer and reduce the surface energy between binder and aggregate, which will improve the infiltration ability of the binder [29].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mixtures Type** & **Losing Mass (g)** & **Residual Ratio (\%)** \\ \hline DG mixtures & 12.3 & 98.46 \\ OG mixtures & 34.8 & 95.65 \\ PADG mixtures & 25.4 & 96.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of cohesion tests.  
Figure 8: Paving friction of cold mixtures at different temperatures (at 5 \({}^{\circ}\)C and 20 \({}^{\circ}\)C).  
Figure 9: Paving frictions of cold mixtures at different storage times (at 5 \({}^{\circ}\)C).