esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyclic Detectors in the Fraction-of-Time Probability Framework
## 2 Materials and Methods
### Threshold Derived Analytically  
If the distribution of the decision statistic under \(\mathrm{H}_{0}\), \(\mathcal{T}_{0}\), is known, then the threshold \(\lambda_{\mathrm{an}}\) can be derived analytically from an assigned value of the probability of false alarm \(P_{\mathrm{fa}0}\) (referred to as _nominal or design_\(P_{\mathrm{fa}}\)). For example, from (72), we have  
\[\lambda_{\mathrm{an}}=F_{\lambda_{2\mathrm{FS}}^{2}}^{-1}(1-P_{\mathrm{fa}0})\,. \tag{78}\]Then, starting from this analytically derived threshold \(\lambda_{\text{an}}\), from data under H0, the effective probability of false alarm \(P_{\text{fa}}\) (referred to as _Monte Carlo_\(P_{\text{fa}}\)) is computed using Monte Carlo simulations as  
\[P_{\text{fa}}=\frac{\#(\mathcal{T}_{0}(i)>\lambda_{\text{an}})}{N_{\text{MC}}} \tag{79}\]  
If the statistical description of \(\mathcal{T}_{0}\) is correct, then one should expect that the Monte Carlo \(P_{\text{fa}}\) is approximately equal to the nominal \(P_{\text{fa0}}\).  
From data under H1, the probability of detection \(P_{\text{d}}\) (_Monte Carlo_\(P_{\text{d}}\)) is computed using Monte Carlo simulations as  
\[P_{\text{d}}=\frac{\#(\mathcal{T}_{1}(i)>\lambda_{\text{an}})}{N_{\text{MC}}} \tag{80}\]  
The computation of \(\lambda_{\text{an}}\)_does not require_ the availability of \(\mathcal{T}_{0}(i)\); that is, of side-data under H0.  
Detectors whose decision statistic is a quadratic form with known distribution (see Section 2.6) have a threshold that can be determined analytically.
## 3 Pre-Processing of Multi-Sensor Data from the Unmanned Ship
### System Modeling
#### 3.1.2 Measurement Equation of ZigBee  
According to the principle of ZigBee positioning networking in this paper, and taking the sampling time \(T_{k}\) of basic particle filter (BPF) as the time reference, the measurement equation of \(RSSI\) signal collected by the ZigBee positioning system was defined [15]. The specific steps were as follows:  
Step 1 Establish the signal strength power model \(\widetilde{P}_{i}^{\text{R}}\) of the \(i\)th reference node according to the path loss principle.  
\[\widetilde{P}_{i}^{\text{R}}=P_{0}-10\theta\text{lg}(\sqrt{\left(x^{z}-x_{i}^ {R}\right)^{2}+\left(y^{z}-y_{i}^{R}\right)^{2}}/d_{0}) \tag{5}\]  
where \(\forall\ i\in[1\), \(n]\), \((x_{i}^{R},y_{i}^{R})\) is the coordinate of the \(i\)th reference node; \(P_{0}\) is the received power of the reference node at \(d_{0}\) from the unmanned ship; \(\theta\) is the path loss coefficient, and \((x^{z},y^{z})\) is the blind node coordinate.  
Step 2 Establish the covariance matrix \(\mathbf{R}_{RSSI}\) of the observation equation.  
\[\mathbf{R}_{RSSI}=\text{diag}(\sigma_{dB_{1}^{R}}^{2},\sigma_{dB_{2}^{R}}^{2}, \cdot\cdot\cdot,\sigma_{dB_{n}^{R}}^{2}) \tag{6}\]  
where \(\forall\ i\in[1\), \(n]\), \(\sigma_{dB_{i}^{R}}^{2}\) is the initial variance of the signal strength of the \(i\)th ZigBee reference node.  
Step 3 Define the observation equation for \(RSSI\) measurements collected by ZigBee.  
\[\mathbf{z}_{RSSI}(k)=\left[\widetilde{P}^{\text{R}}(k),\ \mathbf{R}_{RSSI}(k)\right]^{T}+v(k) \tag{7}\]  
where \(\widetilde{P}^{\text{R}}=\left\{\widetilde{P}_{1}^{R},\widetilde{P}_{2}^{R}, \cdot\cdot\cdot,\widetilde{P}_{n}^{R}\right\}\) is the signal strength power model; \(v\) is the covariance matrix of the observed noise.
## 5 Results and Discussion
### Contextualizing Vulnerability and Guiding Actionable Insights
Explainable AI (XAI) models, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations  
Figure 5: Model confusion matrices: original vs. attacks. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attack Type** & **Accuracy** & **PAVI** & **Confusion Matrix** \\ \hline Original data & 0.973684 & N/A & TN = 70, FP = 2, FN = 1, TP = 41 \\ \hline Adversarial FGSM attack & 0.614035 & 0.385965 & TN = 28, FP = 44, FN = 0, TP = 42 \\ \hline Data-poisoning attack & 0.894737 & 0.105263 & TN = 63, FP = 9, FN = 3, TP = 39 \\ \hline Adversarial evasion attack & 0.622807 & 0.377193 & TN = 29, FP = 43, FN = 0, TP = 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy, PAVI and confusion matrices. (LIME), and Saliency Maps, could also play an integral role in the detection of adversarial attacks. Moreover, research could address a key gap by performing a sensitivity analysis to quantify the relationship between attack magnitudes, such as varying \(\epsilon\) or \(\sigma^{2}\), and their impact on model performance and the PAVI. Understanding this relationship would help researchers and practitioners better assess the robustness of AI models under different levels of adversarial stress, paving the way for more resilient AI systems. Recent advancements in this domain have started focusing on cybersecurity attacks and vulnerabilities, particularly concerning LLMs [33]. [PERSON] and [PERSON] [27] comprehensively analyzed the vulnerabilities of LLMs and highlighted the need for advanced safeguards to mitigate adversarial risks. [PERSON] et al. [28] explored transferable adversarial attacks and demonstrated how misaligned models remain vulnerable to universal perturbations that compromise their decision-making processes. [PERSON] et al. [34] emphasized the potential of adversarial training to bolster the resilience of LLMs. They provided a framework that could inspire similar approaches in the context of DL models for healthcare. These studies suggest that extending defense mechanisms to healthcare applications can bridge the gap between theoretical advancements in LLM safety and their practical deployment in high-stakes scenarios.
## 5 Evaluation Measures
### Usability
#### 5.2.3 Task Performance and Cognitive Workload
The NASA-TLX operationalizes workload across six dimensions by asking participants to evaluate their experience in terms of mental, physical, and temporal demands, performance, effort, and frustration. In our sample, the NASA-TLX was the most used method for evaluating cognitive workload. For instance, [PERSON] et al. [30], [PERSON] et al. [33], and [PERSON] et al. [64] used this questionnaire to measure task workload. In some cases, other subjective measures were employed in the user studies, such as asking questions about usage effort [27] or evaluating mental demand by asking participants \"_How mentally demanding was it to understand how this AI makes decisions?_\" [42]. In contrast, we found only one study in our sample that evaluated cognitive workload using objective measures. This is likely because these measurements are more challenging to implement in user studies than self-report-based measures. To measure the mental workload, [PERSON] et al. [48] logged the time spent on the explanation interface. In summary, our review shows a strong consensus on the theoretical construct of performance, often defined in terms of effectiveness, efficiency, and mental workload. However, the operationalization of this construct varies significantly depending on the specific design goals and the nature of the task. Evaluating this construct effectively requires a well-defined understanding of the context and a precise specification of the task, ensuring that the measurement of task performance is both relevant and rigorous.
# Characterization and Prediction of Air Transport Delays in China
## 5 Effect of Weather on Delay Dynamics  
Results in the previous section indicate that the average delay has not strongly been correlated with the traffic level; additionally, it presents a complex evolution, with a weak overall decrease, but with stronger peaks during the summer season. In order to understand if these peaks can be explained through the presence of exogenous factors, we here focus on identifying potential relationships between weather conditions and the appearance of abnormal delays.  
Two complementary approaches are considered. Firstly, in Section 5.1, a standard statistical analysis is presented; afterwards, in Section 5.2, a machine learning model is constructed, aimed at forecasting the average level of delay observed for each day.  
In order to simplify the test, and reduce the level of noise in the data, all variables (thus including the average delay and all weather metrics) have been binarized. Mathematically, this corresponds to the transformation:  
\[v^{*}=\begin{cases}0,&\text{if }v<M(v)\\ 1,&\text{otherwise}\end{cases} \tag{1}\]  
where \(v\) is the variable to be transformed, and \(M(\cdot)\) is the median operator. To illustrate, let us suppose that the delay at day \(i\) is \(d_{i}\); this value is transformed to 1 if \(d_{i}\) is among the half largest observed delays, and 0 otherwise. A similar transformation is applied to all other weather metrics.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 4 Solder Joint Failure Modeling and Lifespan Prediction
### Solder Joint Lifespan Prediction
By monitoring the degradation process of solder joints in accelerated thermal cycling tests, the solder joint is considered to have exhausted its lifespan upon reaching point B. Based on the remaining lifespan data of solder joints at different positions on the chip, effective prediction of the overall lifespan of electronic packaging is achievable.
## 2 Material and Methods
### The Optimization Model
#### 2.1.2 The Genetic Operators  
There are three basic genetic operators that are used to generate a new population in each generation: selection, crossing, and mutation [27]. The selection corresponds to the process of sampling solutions of the current population. It is represented by a skewed selection process that is used to determine which solutions should be included in the new population. The method used in this implementation was selection by tournament [34]. The solution selection criterion, to integrate the next generation, is based on the fitness value of each solution, which is converted into a probability of being selected. The most appropriate solutions are more likely to be chosen compared to the least appropriate solutions [35].  
The crossover operator is used to reproduce the chromosomes of the offspring by crossing between four parent chromosomes as in the below [32; 36].  
Parent 1 = 1 2 3 4 5 6 7 8 9 10 11 121 Parents: Parent 2 = 1 3 2 8 1 4 9 6 5 7 12 10 111 After three random cut-off points: Parent 1: 1 12 3 4 5 6 1 7 8 9 10 11 121 Parents: Parent 2: 1 3 2 1 8 1 4 9 1 6 5 7 12 10 11 The genes are copied between the two cut-off points of the first parent. The remaining genes are copied from the second parent from the second cut-off point, excluding genes already transferred to the descendant and preserving order [36].  
Offspring 1: 1 1 2 1 8 1 4 9 1 7 8 9 12 10 11 1 Offspring 1: 1 3 2 1 3 4 5 6 6 5 7 10 11 121 First, a random position (cut zone) is selected in both parents, which divides the information from the parent to be included in the descendant. This cutting position must ensure that there is at least one position occupied in the partial information of both parents, otherwise the offspring may lose zones in the process and generate invalid solutions. The offspring is conceived by including partial information from both parents, assigning the spatial location and sequential position information to the new positions [36]. At this stage, the same zone can be assigned to more than one location. However, since there can be no duplicate installations, a replacement must be made in advance. In other words, in these cases, the position of the first occurrence in the duplicate zones in the offspring, which corresponds to the partial information of the second parent, must be determined. This position is used to find a different zone in the first parent. If this new zone already exists in the offspring, the process is repeated until the chosen zone is not removed from the descendant. Otherwise, the new zone will be included in the descendant. If there is a duplicate zone that cannot be avoided (no substitute zone has been found), then the descendant is deleted and the relative is included in the offspring [32; 36].  
The mutation operator is used to introduce randomness into a solution, preventing it from getting stuck in a minimal place. The purpose of this mutation operator is to exchange only the status and equipment information between two positions chosen on a chromosome [37].
## 4 Implementation System and Results
### Scanning Sample with Four Transducers at the Same Time  
One of the highlights of our design is its ability to operate with four transducers simultaneously. With this capability, the SAM system reduces the uptime by only a quarter compared with that of using the UT320 equipment. Moreover, the maximum trigger frequency of the UT320 was 20 KHz, compared with our P/R device's 50 KHz. That means the SAM system that worked with our equipment had the highest possible speed, which was 10 times faster than the SAM system using the UT320. Moreover, the highest amplitude of the echo signal from our P/R is 3.5 V, and the UT320 is 1 V. The echo  
Figure 19: **(a)** Coin scanning image with an 85 MHz transducer; **(b)** Real image of the Spatarm-6 XC6 SLX9-144; **(c)** Scanning image of the inside of the Spatarm-6 with a 25 MHz transducer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Transducer** & **CNR (with Damping Resistor)** \\ \hline
1 & 25 MHz & 8.2 \\
2 & 85 MHz & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Compared contrast-to-noise ratios (CNRs) of images with a damping resistor.  
signal with high amplitude create high resolution for B-Mode images. Below is Table 9, comparing the main parameters.  
In this test, we used another SAM system. The new system could work with four transducers simultaneously and used two digitizers, which had two analog channels (PCI-5152, National Instruments Corporation. Austin, TX, USA). The motor control modules were similar to the old system. The new system model is described in Figure 20.  
As is shown in the model of the system in Figure 20a, the sample was divided into four regions with similar areas. We performed experiments many times with different transducers, and the results are described in Figure 20c.  
In the test, the trigger signals for the P/R and Digitizers 1 and 2 were completely the same. Besides that, we also used four completely identical transducers. The gain of four amplifier modules on the P/R device was adjusted differently. The trigger signals entering channels 1 and 2 of the P/R device and the digitizers were similar. This sample scan result is described in Figure 20c.  
Based on the resulting image in Figure 20c, we can see the signal uniformity of four different regions of this sample. Each transducer scanned a region, so the time to scan this sample was also four times faster than using a transducer.
Remote Sens._**2014**, \(6\), 5306-5324; doi:10.3390/rs6065306  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
**Applicability of Multi-Frequency Passive Microwave**  
**Observations and Data Assimilation Methods for Improving**  
**Numerical Weather Forecasting in Niger, Africa**  
**[PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
\({}^{1}\) Department of Civil Engineering, The University of Tokyo, Tokyo 113-8656, Japan;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of  
Sciences, Lanzhou 730000, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-358-418-874.  
_Received: 7 February 2014; in revised form: 23 May 2014 / Accepted: 26 May 2014 / Published: 6 June 2014_  
The development of satellite-based forecasting systems is one of the few affordable solutions for developing regions (e.g., West Africa) that cannot afford ground-based observation networks. Although low-frequency passive microwave data have been used extensively for land surface monitoring, the use of high-frequency passive microwave data that contain cloud information is very limited over land because of strong heterogeneous land surface emissions. The Coupled Atmosphere and Land Data Assimilation System (CALDAS) was developed by merging soil moisture information estimated from low-frequency data with corresponding high-frequency data to estimate cloud information and, thus, improve weather forecasting over Niger, West Africa. The results showed that the assimilated soil moisture and cloud distributions were reasonably comparable to satellite retrievals of soil moisture and cloud observations. However, assimilating soil moisture alone within a mesoscale model produced only marginal improvements in the forecast, whereas the assimilation of both soil moisture and cloud distributions improved the simulation of temperature and humidity profiles. Rainfall forecasts from CALDAS also correlated well with satellite retrievals. This indicates the potential use of CALDAS as a reliable forecasting tool for developing regions. Further developments of CALDAS and the inclusion of data from several other sensors will be researched in future studies.  
**Keywords:** passive microwave remote sensing; data assimilation; numerical forecast; soil moisture; clouds; precipitation
# Fault-Tolerant Topology of Agricultural Wireless Sensor Networks Based on a Double Price Function
## 3 The Related Theory  
### Agricultural WSN Model  
Suppose that \(N\) nodes that sense the environment are evenly distributed in the farm, and can be represented by an undirected graph \(G\) (\(V\),\(E\),\(P\)). \(V=\{1,2,\ldots,N\}\) represents the set of nodes in the network, and \(E=\{e_{ij},i\in V,j\in V,i\
eq j\}\) represents the link set between nodes. When any two nodes \(i\) and \(j\) can communicate directly, \(e_{ij}=1\), otherwise, \(e_{ij}=0\). \(P=\{(p_{1},p_{2},\ldots,p_{N}),p_{i}\in[p_{\min}p_{\max}]\}\) represents the transmit power set of all sensing nodes. The ID of all nodes in the network is unique. Each node can obtain the neighbor node ID, residual energy, communication distance, and the minimum communication power between nodes within the communication range. In order to facilitate the description and calculation, the following commonly used definitions are given:  
**Definition 1**.: _Network connectivity. For a connected network topology graph \(G\) (\(V\),\(E\)), if there is a transmission path between two nodes \(i\) and \(j\) in the topology graph \(G\), nodes \(i\) and \(j\) are connected; if any two nodes \(i\) and \(j\) in \(G\) meet the connectivity condition, the network \(G\) is connected._  
**Definition 2**.: _Global cut vertex. For a connected network topology graph \(G\) (\(V\),\(E\)), \(\exists i\in V\), \(E_{i}\) represents undirected edges connected to node \(i\). If \(G^{\prime}=G\)(\(V\)-\(i\), \(E\)-\(E_{i}\)) are not connected, node \(i\) is a cut vertex._  
**Definition 3**.: _k-hop cut vertex. For a network topology graph \(G\) (\(V\),\(E\)), if node \(i\) is the cut vertex of its k-hop local topology \(S_{G}\), node \(i\) can be called the k-hop cut vertex of graph \(G\)._
## 4 Results and Discussion
### Development of the AGB Estimation Model  
We developed four regression models for estimating the AGB, and these included linear regression model, power function model, exponential model, and support vector machine model (SVM-AGB). We randomly selected 46 biomass field samples and the corresponding synthetic NDVI pixels as the training set to construct the four models. We then used the other 22 samples to assess the accuracy of the four models. The accuracy assessment results indicated that for both MODIS NDVI and synthetic NDVI, the SVM-AGB model had higher accuracy than the other three models (Tables 5 and 6). We further compared the synthetic NDVI-derived SVM-AGB model and MODIS NDVI-derived SVM-AGB model and found that the accuracies of the synthetic NDVI-derived SVM-AGB model data were higher than the accuracies of MODIS NDVI-derived SVM-AGB model data, as indicated by the \(R^{2}\), RMSE, and RMSEr for both the training set and testing set (Tables 5 and 6). Therefore, we finally chose to integrate the SVM-AGB model and the synthetic NDVI to predict AGB for grasslands in Xilinhot during 2005-2013.  
We compared our SVM-AGB model (\(R^{2}\) = 0.77, RMSE = 17.22 g/m\({}^{2}\), RMSEr = 24.8%) with other AGB estimation models in the same region, e.g., the exponential model based on 250-m MODIS NDVI (\(R^{2}\) = 0.447) by [PERSON] _et al._[57]; the ANN model based on elevation, Landsat NDVI, and Landsat reflectance data (\(R^{2}\) = 0.82, RMSE = 60.01 g/m\({}^{2}\), RMSEr = 40.61%) by [PERSON] _et al._[35]; and the power function model based on 250-m MODIS NDVI (\(R^{2}\) = 0.568, RMSE = 673.88 kg/ha) by [PERSON] _et al._[47]. The studies by [PERSON] _et al._[57] and [PERSON] _et al._[47] mainly used 250-m MODIS NDVI data, and the accuracies of their models were lower than that of our estimation model (\(R^{2}\) = 0.447 by [PERSON] _et al._; \(R^{2}\) = 0.568 by [PERSON] _et al._; \(R^{2}\) = 0.77 by our synthetic NDVI-derived SVM-AGB model). Meanwhile, in the study of [PERSON] _et al._[35], although their ANN model was built on Landsat NDVI and reflectance images and possessed higher accuracy, the limitations of sparse temporal data associated with Landsat images restricted their model's application to the requirements of frequent time series and dynamic monitoring. In general, our synthetic NDVI-derived SVM-AGB estimation model had both higher spatial resolutions (30 m) and temporal resolutions (8 d) than the other models in Tables 5 and 6 and showed improvements compared to other AGB estimation models generated from a single type of remotely sensed data [33; 47; 56].
## 1 Introduction
In addition, often, these standards are not integrated into the models. As a result, databases with exchange standards often only serve as archives and cannot be meaningfully connected in practice [9]. Especially during the planning phase and the exchange of data between BIM and different tools used for energy simulations, these rigid and inert constructs are an obstacle to early energy design optimization. The challenges addressed in the following work can be summarized as follows: (1) semantic problems due to different designations, (2) mapping information issues due to complex requirements, and (3) the need for flexible and adaptable structures. The identified challenges in alphanumeric interoperability motivate the present work: How can project- and tool-specific parameters make the integration of BIM and BEM more efficient? To what extent can flexible, toolchain-based approaches close existing gaps?Interoperability was recognized as a central problem in the BIM2 IndiLight project [10]. The aim was to develop a BIM2 BEM framework that connects Autodesk Revit with DALEC--a web-based tool for thermal, daylight, and artificial lighting simulation [11]. Standardized properties and the use of IFC [12] play a key role in optimizing building performance in the early planning phase, achieving interoperability at the semantic level. The experience that pure standardization without a functional link to BIM tools is insufficient motivated the follow-up project BIM2 BEM-Flow. This developed a web-based platform (YAPS), which is used to manage project and tool-specific parameters and integrate them into BIM applications using defined workflows (PWM and RWM) (Figure 1). The basis of the approach is the mapping of alphanumeric information between different tools. The integration of exchange parameters, with a stored mapping of different BEM/simulation tools, in the BIM application ensures that the IFC export works correctly, depending on the use case of the current work processes. This article provides the outcomes from the finished BIM2 IndiLight project and the current BIM2 BEM-Flow project and presents an overview of interoperability approaches in BIM2 BEM research. It is divided into three sections: (I) the results of the BIM2 IndiLight project, (II) the creation of the web-based parameter management platform, and (III) a discussion of the findings.