esa-sceva/satcom-chunk-collection huggingface dataset:
# Life-Cycle Oriented Risk Assessment Using a Monte Carlo Simulation
## 4 Results
### Risk Model and Assessment Tool  
Using a combination of a generic cost model and a Monte Carlo simulation, \(c_{\Delta}\) and \(f_{\Delta}\) are estimated within a risk model for the different use cases. The generic cost model considers a product in line with the structure of Figure 4, where a single part is changed. The aim of the generic cost model is to predict the expected savings from changing this part. The difference in costs consists of three elements: the costs for testing, incident handling,  
Figure 4: Generic breakdown structure of the type of system investigated.  
and savings due to modified procurement costs. The change in costs is dependent on the number of parts sold per time unit \(N\):  
\[c_{i}=\underbrace{\Delta t_{t}\cdot N\cdot f\cdot\delta c_{t}}_{\text{testing}}+ \underbrace{\Delta t_{r}\cdot N\cdot\delta c_{i}}_{\text{recalls}}-\underbrace{ \Delta t_{s}\cdot N\cdot\delta c_{s}}_{\text{savings}} \tag{4}\]  
The total savings depend on the savings per product sold \(\delta c_{s}\) and the time span \(\Delta t_{s}\) during which the saving is active. The testing costs depend on the costs per test \(\delta c_{t}\), the duration \(\Delta t_{t}\) during which the tests are performed and the fraction \(f\) of products to be tested. Recall costs arise if the product fails during the required lifespan and a recall is required. \(\Delta t_{i}\) is the timespan between the first modified product sold and the first incident detected and \(\delta r\) are the costs per recalled product.  
The timespans \(\Delta t_{t}\), \(\Delta t_{r}\) and \(\Delta t_{s}\) depend on the selected switching strategies. Table 1 lists the distinction of cases for the different switching strategies. Relevant for the time spans is the new lifetime \(t_{i}\) after the switch is made. This new lifetime is compared to the required lifetime \(t_{0}\). It is further assumed that the test can speed up the load cycles by a factor \(g\).  
The true lifetime \(t_{i}\) after the switch is subject to uncertainties. Hence, the result of (4) is also subject to uncertainties. To evaluate (4) for the different switching strategies (3) a Monte Carlo simulation is used. A thousand randomized values for \(t_{i}\) are drawn according to the expected lifetime effect, and certainty for the new part. To facilitate the application of the model and integrate it into the decision-making process and risk assessment, a simple tool is implemented, Figure 5. The tool requires the input of product specific parameters such as expected lifetime, cost structure and others. Based on these inputs, the cockpit enables the risk assessment to consider different degrees of certainty regarding the lifetime of the product after switching parts. The results are shown as a histogram of the expected saving. Additionally, the expected cost-development over time is characterized.
## 5 Discussion
### Theoretical and Analytical Implications  
From a theoretical standpoint, this study validates the applicability of TPB as an explanatory model of digital behavior in the context of GAI, as previously shown in [17]. The use of Random Forest and XGBoost enables model fitting with over 90% accuracy, while also providing strong predictive power.  
The three dimensions of the TPB provide empirical support, albeit with uneven importance. Attitude (operationalized via INNOV), perceived behavioral control (via KNOW), and belonging to younger generations are stronger predictors than subjective norms (SU, SR, and REG), suggesting that GAI usage is influenced more by internal factors than by social pressures.  
Analytically, the use of non-parametric methods such as decision trees and their extensions allows alternative analytical perspectives to traditional linear models, especially useful in technological adoption, which is mediated by nonlinear relationships and complex interactions. Implementing the SHAP measure in the best predictive method (RF) enables a transparent decomposition of each variable's impact on model predictions, providing a methodological contribution that can be replicated in future research on technology acceptance. The robust model fit (R\({}^{2}\) > 90% in RF and XGBoost) strengthened the validity of the findings.  
One of the most interesting theoretical findings was the central role of KNOW as the main predictor of GAI use. This suggests that perceived self-efficacy and technological familiarity carry more weight than do concerns about social risks or formal education levels. Similarly, the high relevance of INNOV, despite not being a primary split in the DTR, reinforces the idea that individual dispositions toward innovation act transversally in the adoption of new technologies.  
Also noteworthy is the lack of influence of traditionally relevant variables, such as gender and income, which did not appear as either primary or surrogate conditions in the DTR models. This calls for a re-evaluation of the centrality of these variables in classical digital divide models, at least in the specific context of GAI.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 2 CNNs and Normalization Layers
### Pooling Layer and ReLU Activation  
Pooling layers efficiently reduce the model complexity and redundant information passed between the layers in CNNs. Each pooling layer consists of kernels that slide through the input array similar to the convolution layer kernels. Pooling layers rely on sub-sampling each local area under the kernels' receptive field. The most common pooling layers are the max-pooling and the average-pooling layer. Max-pooling has been shown to consistently outperform average pooling [34]. A max-pooling layer compresses the signals between layers by concatenating the maximum values of each local area for every channel separately. Equation (2) demonstrates the max-pooling operation:  
\[\hat{y}_{j}^{l,k}=max(\mathbf{y}_{j}^{l,k}) \tag{2}\]  
where \(\mathbf{y}_{j}^{l,k}\) denotes the values under the max-pooling kernel receptive field from the \(k\)-th local area of the \(j\)-th channel between convolutional layers \(l\) and \(l+1\), and \(\hat{y}_{j}^{l,k}\) is the corresponding maximum value.  
Typically between consequent cross-correlation computations, the output values are processed with a non-linear activation function, such as the rectified linear unit (ReLU) shown in Equation (3). The processed output values \(x_{j}^{l+1,i}\) can then be passed forward to the next layer \(l+1\).  
\[x_{j}^{l+1,i}=ReLU(y_{j}^{l,i})=max(0,y_{j}^{l,i}) \tag{3}\]
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### Static PPP Results
#### 4.4.3 Tropospheric Estimates  
The differences between the estimated ZTDs with PPP after convergence and the IGS products were calculated to obtain the time series and the RMSE of ZTDs. Figure 15 shows the ZTD error series of station SOD3, WTZR, and SPTU. The RMSE of all the stations is shown in Figure 16. The mean RMSE of ZTDs for each network and their comparison between different processing modes are shown in Table 11.  
Figure 15 shows that the ZTD estimates are dispersed as the latitude decreases. From Figure 16 and Table 11, we can see that the ZTD estimates of GLONASS are less accurate than that of GPS in all three networks, which are 10.10%, 6.82%, and 14.08% lower in the high, middle, and low latitude networks, respectively. The addition of GLONASS will improve the accuracy of tropospheric estimates, and the accuracy improvements are 5.19%, 6.98%, and 7.79% in the high, middle, and low latitude networks, respectively. The accuracy of estimated ZTDs decreases as the latitude decreases for GPS, GLONASS, and GPS+GLONASS.
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 2 Materials and Methods
### Simulation and Phantom Experiment Setups  
Experiments were devised to verify the effectiveness of our algorithm on sidelobe suppression by simulated and phantom experimental data. Several parameters related to the sidelobe level, resolution, and contrast were measured during the experiment.
## 4 Implementation and Evaluation
### Implementation
#### 4.2.2 State Search and Insertion Time  
In Section 3.3.2 we discussed two methods for comparing states: (1) compare state with match fields, as illustrated by the global state [gs, 0, 8] in Figure 13a; and (2) compare state with actions, as shown in Figure 13b, by comparing the values of global state [gs, 0, 8] with actions that represent if logic.  
In this experiment, we compared the performance of two methods with varying numbers of states. At the start of the experiment, set the global state [gs, 0, 8] to 0. When a new state value is entered by using the first method, an entry is added to the table shown in Figure 13a. Adding a new comparison state to the second method will add a set of actions before the action in Figure 13b \"IF [gs, 0, 8] == 0\". In the experiment, a 10 Gbit, 64-byte IPv4 packet with the destination 10.0.0.1 was sent to the pipeline.  
The performance of using the action comparison state improves when the number of comparison states is less than 12, as shown in Figure 14. However, as the number of comparison states grows, so does the number of executed actions, resulting in a decrease in performance. The state's performance as a match field is consistent, which is related to the experiment's hash-based table lookup algorithm (DPDK/librate_table/EM). Aside from performance, the two methods take up different amounts of space. When the state is  
Figure 12: Time required to search or update the global state 1,000,000 times with an offset of 8 bits and different lengths. (**a**) Search state. (**b**) Insert state.  
a match field, every time a comparison value is added, an entry (526 bytes) is added. When using actions, each additional set of comparison forwarding actions takes up only 32 bytes of space.  
If the number of comparison states is small, or several commonly used states can be predicted, then, using the action to compare the state, bring the majority of the hit values to the front to improve performance and save space. On the other hand, using the state as a match field can ensure consistent performance.
# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 4 Proposed Method  
This paper introduces a lightweight method for UWB NLoS multi-class identification based on 1D-CLANet. The 1D-CLANet architecture is designed to extract the spatiotemporal features of CIR signals for channel model classification. As illustrated in Figure 4, the proposed network is composed of two main components: a feature extraction module and a feature fusion and classification module. At the heart of the network is the feature extraction module, which integrates CNN and LSTM. This module includes a CNN-based section for spatial feature extraction and an LSTM-based section for temporal feature extraction. The SE attention mechanism module further adjusts the weights of each channel. The feature fusion module then combines the spatiotemporal features with the key features highlighted by the SE attention mechanism and feeds them into a classifier. The following sections provide a detailed overview of each module.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
###### Abstract  
The development of software-defined radio (SDR) systems using field-programmable gate arrays (FPGAs) compels designers to reuse pre-existing Intellectual Property (IP) cores in order to meet time-to-market and design efficiency requirements. However, the low-level development difficulties associated with FPGAs hinder productivity, even when the designer is experienced with hardware design. These low-level difficulties include non-standard interfacing methods, component communication and synchronization challenges, complicated timing constraints and processing blocks that need to be customized through time-consuming design tweaks. In this paper, we present a methodology for automated and behavioral integration of dedicated IP cores for rapid prototyping of SDR applications. To maintain high performance of the SDR designs, our methodology integrates IP cores using characteristics of the dataflow model of computation (MoC), namely the static dataflow with access patterns (SDF-AP). We show how the dataflow is mapped onto the low-level model of hardware by efficiently applying low-level based optimizations and using a formal analysis technique that guarantees the correctness of the generated solutions. Furthermore, we demonstrate the capability of our automated hardware design approach by developing eight SDR applications in VHDL. The results show that well-optimized designs are generated and that this can improve productivity while also conserving the hardware resources used.  
Keywords: reconfigurable computing; field programmable technologies; field programmable gate arrays; FPGAs; design automation; software-defined radio; domain-specific language; high-level synthesis +
Footnote †: This paper is an extended version of our paper published in the International Conference on Field Programmable Technologies (FPT 2017).
## 3 Methodology  
### GNSS Satellite Reflected Signal  
SNR is a measurement parameter representing the strength quality of a signal received from the receiver antenna, including the direct and the reflected signals [39; 58; 59]. At any time, the SNR is a function of direct power, reflected power, and the phase difference between them. Changes in SM will change the phase of the reflected signal and the soil reflectivity [38; 39]. Thus, SNR oscillations can provide information about changes in near-surface SM [41]. The geometric principle is shown in Figure 3. Evidently, there is a definite relationship between SNR and the ground environment. Therefore, SNR could be used to establish a multipath effect assessment model, which was conducive to estimating surface environmental parameters.  
Figure 1: Environment (**left panel**) and effective observation area (**right panel**) of P043. The yellow mark indicates the site location of the receiver. The colored ellipse on the ground is the reflection area of each satellite during the research periods when setting an elevation interval between 5\({}^{\circ}\)and 20\({}^{\circ}\). These images can be accessed at [[http://earth.google.com/](http://earth.google.com/)]([http://earth.google.com/](http://earth.google.com/)), accessed on 5 May 2023 and [[http://www.unavco.org/](http://www.unavco.org/)]([http://www.unavco.org/](http://www.unavco.org/)), accessed on 5 May 2023.  
Figure 2: SM reference and precipitation data. Shown are daily averages for the study area, which were obtained from the International Soil Moisture Network ([[https://ismn.geo.tuwien.ac.at/en/](https://ismn.geo.tuwien.ac.at/en/)]([https://ismn.geo.tuwien.ac.at/en/](https://ismn.geo.tuwien.ac.at/en/)), accessed on 5 May 2023).  
According to existing research, under the assumed condition that reflection occurs only once, there is a sine (or cosine) functional relationship between SNR observations and the multipath interference phase [38; 39]:  
\[\mathrm{SNR}^{2}=S_{\mathrm{d}}^{2}+S_{\mathrm{r}}^{2}+2S_{\mathrm{d}}S_{ \mathrm{r}}\mathrm{cos}\psi \tag{1}\]  
where \(S_{\mathrm{d}}\) is the direct signal, representing the low-frequency trend term of the SNR; \(S_{\mathrm{r}}\) is the reflected signal, representing the high-frequency modulation term of the SNR [56]; \(\psi\) is the phase difference between the direct and the reflected signals.  
Since the SM retrieval of GNSS-IR is only related to the reflected signal, it is necessary to remove the direct signal of SNR in the low satellite elevation range. After that, there is still a sine (or cosine) function relationship of fixed frequency between the reflected signal and \(\sin\theta\), and the reflected signal \(\mathrm{SNR}_{\mathrm{r}}\) can be expressed as [38].  
\[\mathrm{SNR}_{\mathrm{r}}=A\mathrm{cos}\bigg{(}\frac{4\pi h}{\lambda}\mathrm{ sin}\,\theta+\varphi\bigg{)} \tag{2}\]  
where \(\lambda\) represents the carrier frequency; \(A\) and \(\varphi\) are the characteristic parameters of the reflected signal to be solved, i.e., amplitude and phase, which can be used to retrieve the variation of the surface environment around the site [38; 39].
## 2 Experimental Method  
### Sample Fabrication  
The sample investigated in this paper was manufactured by using the AM equipment (SLM 125, SLM Solutions NA, Inc., Lubeck, Germany). Figure 1 demonstrates the layout of the AM equipment.  
A Yb:YAG continuous wave (CW) fiber laser (IPG YLR-400, IPG Photonics Corporation, Oxford, MA, USA) with a wavelength of 1060 nm, nominal output power of 400 W, and spot size of 100 \(\upmu\)m was used in the sample fabrication process. Usually, the minimum size that can be manufactured by the SLM machine is no less than the laser spot size, which is smaller than the minimum size that can be detected by the LUT system in this work. Near spherical titanium aluminide alloy Ti-6 Al-4V powder (AP&C, GE Additive, Munich, Germany) with a diameter range of 15-45 \(\upmu\)m was used. The building chamber was set with a protective argon atmosphere (oxygen content of less than 0.2%) to prevent oxidation of the material during the AM process. The building platform was preheated to 473.15 K, to reduce the thermal gradient and residual-stress formation in the sample during manufacturing process.  
The specific AM process parameters are shown in Table 1. The sample with four internal holes with diameters of 0.4, 0.8, 1.0, and 2.0 mm was manufactured. The dimensions of the Ti-6 Al-4V sample were set to 50 \(\times\) 10 \(\times\) 5 mm\({}^{3}\). The center-to-center distance among four holes was designed to be 12 mm. All four holes were located at the center along the thickness direction. During the AM process, these holes were formed as near hole-type defects, thus representing a common defect formed in the AM process. Note that the AM process used a laser beam to manufacture the part in a layer-by-layer style. During AM process, issues including balling and porosity can be formed due to the high-power density [2] and occur in the inner surface of four holes. Next, the sample was cut by using wire-cutting equipment (FH-020C, Suzhou Xingjie CNC Technology Co., Ltd., Suzhou, China). In order to obtain lower surface roughness for better signal reception, the sample was then sand-blasted (BNP-55, Clemco Industries Co., Ltd., Washington, MO, USA) on the top and bottom surfaces of the sample, with the blasting distance of 150 mm and flow rate of 3 m\({}^{3}\)/min.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter (unit)** & **Value** \\ \hline Laser power (W) & 275 \\ Scanning speed (m/s) & 0.76 \\ Hatch spacing (mm) & 0.12 \\ Powder layer thickness (\(\upmu\)m) & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: AM parameters of the Ti-6 Al-4V sample.  
Figure 1: Photograph of the additive manufacturing (AM) equipment (SLM 125). Zoom out viewport: The manufacturing process of the investigated sample.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 5 Experiment and Analysis
### Pseudo Stars  
In the simulation of the actual identification process, false star points may occur due to interference from dust and high-energy particles. Therefore, one to five false star points are randomly placed in the simulated star maps to validate the robustness of various star identification algorithms against false star interference. As shown in Figure 9, compared to other identification algorithms, the algorithm based on dynamic angle matching demonstrated the highest recognition rate, while the dynamic distance ratio algorithm's recognition rate also showed no significant decrease. Even in the presence of five false stars, the algorithm was able to maintain an average recognition rate of 94.91%. Thus, the proposed algorithm demonstrates good robustness against pseudo star interference.  
It should be clarified that although in Sections 5.2 and 5.3 the identification rate of the dynamic angle matching algorithm is comparable to that of the dynamic distance ratio method, there is a significant difference in identification rates between the two in the focal length error and image distortion experiments. This will be discussed in detail in the next section.  
Figure 8: The relationship between star spot position noise and star identification rate.  
Figure 9: The relationship between number of pseudo stars and star identification rate.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 7 Results of All-Bands BadNets Attack and Defense
### Results of Defense Mechanisms
#### 7.2.2 Activation Clustering (AC), DeepInspect (DI), Pixel-Backdoor (PB), and TABOR  
We applied AC [14], DI [18], PB [19], and TABOR [13] to detect backdoors in deep learning models and the experiment configurations are the same as those for Neural Cleanse. Again, the trigger was embedded into all 13 bands of the data samples. Table 4 shows the detection results. The DI algorithm was successful in detecting the backdoor for \(2\times 2\) and \(5\times 5\) trigger sizes but ineffective in the detection for all other scenarios. We also seen a similar pattern in other defense approaches. Any 6 out of the 36 scenarios were successful.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trigger Size** & **DI [18]** & **PB [19]** & **TABOR [13]** & **AC [14]** \\ \hline \(2\times 2\) & ✓ & ✓ & \(\times\) & \(\times\) \\ \(3\times 3\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \(4\times 4\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \(5\times 5\) & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \(6\times 6\) & \(\times\) & \(\times\) & ✓ (multiple) & \(\times\) \\ \(7\times 7\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \(8\times 8\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \(9\times 9\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \(10\times 10\) & \(\times\) & \(\times\) & ✓ (multiple) & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of DeepInspect (DI), Pixel Backdoor (PB), Tabor, and Activation Clustering (AC) on the deep convolutional neural network (DCNN) architecture, where ‘multiple’ indicates that more than one label has been identified as the target label.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trigger Size** & **ResNet20\({}_{\text{B}}\)** & **ResNet56\({}_{\text{B}}\)** & **EfficientNet\({}_{\text{B}}\)** & **DCNN\({}_{\text{B}}\)** \\ \hline \(2\times 2\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \(3\times 3\) & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \(4\times 4\) & \(\times\) & \(\times\) & ✓ & \(\times\) \\ \(5\times 5\) & ✓ & \(\times
## 4 Vehicle State Estimation, Localization, and Sensors
### Sensor Fusion  
In order to fuse output data from both the wheel encoder units and the visual-inertial odometry algorithm, Robot_localization package [46] in the Robot Operating System (ROS) is employed. The package contains two types of estimators, namely an extended Kalman filter (EKF) and an unscented Kalman filter (UKF). The extended Kalman filter in the \"robot_localization\" package is defined as a node named \"ekf_localization_node\". The node implements an extended Kalman filter that employs an internal omnidirectional model for motion. The model is used to project states forward (in time) and rectify the projected estimate using data from the visual-inertial odometry and wheel odometry altogether. The EKF imposes less computational costs on processors as compared with the UKF. The sensor fusion node estimates six degrees of freedom position and velocity of the vehicle.