esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Realistic Modeling of Resource Constraints
### Privacy Loss Modeling  
Client participation in FL can result in varying degrees of privacy exposure depending on the nature of their local data. We quantify the privacy cost of client \(i\) as follows:  
\[\Delta P_{i}=\alpha_{i}\cdot f(D_{i}) \tag{11}\]  
where \(\alpha_{i}\in[0,1]\) is the intrinsic privacy sensitivity of device \(i\), and \(f(D_{i})\) captures the sensitivity of the data being used for local training. The privacy sensitivity coefficients \(\alpha_{i}\) for different device types are summarized in Table 4.  
This cost term, \(\lambda_{P}\cdot\Delta P_{i}\), is integrated into the reward function, discouraging frequent selection of highly privacy-sensitive clients unless they provide critical contributions.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Device Type** & \(\alpha_{i}\) \\ \hline IoT Devices & 0.8–1.0 \\ Edge Devices & 0.4–0.6 \\ Smartphones & 0.2–0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Privacy sensitivity by device type.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Device Type** & **Examples** & **CPU** & \
\begin{tabular}{l} **Battery** \\ **(mAh)** \\ \end{tabular} & **Uplink** &
\begin{tabular}{l} **Privacy** \\ **Risk** \\ \end{tabular} & **Notes** \\ \hline IoT Devices [34] & ESP32, Arduino & Low & 200–500 & Slow & High & Battery constrained and privacy-sensitive \\ \hline Edge Devices [35] & Raspberry Pi, Jetson Nano & Medium & 2000–4000 & Moderate & Medium & Moderate performance, battery-aware \\ \hline Smartphones [36; 37] & Pixel, Galaxy, etc. & High & 3000–5000 & Fast & Low & High performance, latency-sensitive \\ \hline \hline \end{tabular}
\end{table}
Table 2: Device classes and characteristics.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Device Type** & \(B_{\text{self}}^{i}\) **(mAh)** & \(B_{\text{self}}^{i}\) **(mAh)** & **Avg. Drop (\%)** \\ \hline IoT Devices & 5–15 & \textless{}1 & 3–4\% \\ Edge Devices & 50–100 & 5–10 & 5–7\% \\ Smartphones & 150–250 & 20–30 & 8–10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Battery usage estimates per 100 rounds.
## 4 Results
### Experimental Results
#### 4.3.1 Simulation Log
Therefore, it can be concluded that although the filtering methods used in the experiment have different accuracy rates for event logs with different frequencies and quantities of random positioning activities, the chaotic activity filtering method proposed in this paper can effectively improve the accuracy of chaotic activity filtering compared with other chaotic activity filtering methods. The following conclusions can be drawn from the running time required by different filtering methods shown in Tables 3-5 to complete the filtering of chaotic activities. First, with the increase in k value, the running time required to filter all chaotic activities in the log increases. Meanwhile, frequent random positioning activities have a greater impact on the running time than infrequent random positioning activities. Therefore, both the number and frequency of random positioning activities have a certain influence on the effect of the chaotic activity filtering algorithm, and the number and frequency of chaotic activities are proportional to the running time of the filtering method. Secondly, the direct filtration method proposed in this paper has a shorter running time and higher efficiency than the entropy-based filtration method. Moreover, compared with the entropy-based indirect chaotic activity filtering method, the proposed indirect chaotic activity filtering method has a higher operating efficiency. Therefore, by using the entropy based chaotic activity filtering method and the direct chaotic activity filtering method proposed in this chapter, it can be concluded that although the filtering method used in the experiment presents a different time performance for event logs with different frequencies and quantities of random activities, that is, the running time difference between the proposed filtering method and the traditional filtering method is different, compared with other chaotic activity filtering methods, the proposed method can improve the running efficiency of the chaotic activity filtering algorithm. In Table 3 to Table 5, the filtration methods proposed in this paper are Direct(drr) and Indirect(drr). The methods used for comparison experiments with the proposed method are the direct entropy-based activity filtering approach (Direct), direct entropy-based activity filtering approach with Laplace smoothing (Direct(\(\alpha=1/\left|\,\mathrm{A}\,\right|\))), indirect entropy-basedactivity filtering approach (Indirect), and indirect entropy-based activity filtering approach with Laplace smoothing (Indirect(\(\alpha=1/\left|\text{A}\right|\))).
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 1 Introduction
The convergence of MEMS sensing with wireless transmission and big data analytics has emerged as a novel research frontier in geotechnical disaster monitoring, though applications specifically targeting unstable rock mass collapse remain limited. Unstable rock mass collapse prediction constitutes a multivariate challenge influenced by numerous factors [15, 16, 17], compounded by harsh environmental conditions including construction disturbances, blasting activities, poor network connectivity, and power supply limitations [18]. In order to achieve the early warning of unstable rock mass collapse and solve the problem of the real-time evaluation of unstable rock mass stability, this study investigates MEMS sensor operational principles, establishes vibration mechanics-based quantitative relationships between NF/RMS-VAR and rock stability, and proposes a dual dynamic index algorithm. Furthermore, we discuss real-time stability evaluation models and critical implementation considerations for MEMS-based monitoring methods. The developed MEMS sensing methodology provides enhanced temporal warning capabilities and novel non-engineering control strategies for preventing sudden geotechnical disasters.
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 5 Methodology
### Machine Learning Models Evaluation
#### 5.6.1 Performance and Evaluation  
The classifier was evaluated using standard metrics derived from the confusion matrix (Table 7): accuracy, precision, recall, and F1-Score. The 80/20 training-validation split was used to assess model generalizability while preventing overfitting.  
The matrix will monitor the classifier and the errors it makes in the following ways:  
* **True Positive (TP):** Correctly identified phishing email;
* **True Negative (TN):** Correctly identified ham email;
* **False Positive (FP):** Ham email incorrectly classified as phishing;
* **False Negative (FN):** Phishing email incorrectly classified as ham.
## 2 Methodology and Data
### Discriminant Analysis  
Discriminant analysis is a statistical technique used to identify differences between two or more groups based on a set of predictor variables. The technique aims to find a linear combination of the predictor variables that maximally discriminate between the \(k\) groups of the outcome variable. This linear combination is known as the discriminant function \(f(x)\). Its formula is as follows [52; 53]:  
\[f(x)=\alpha_{0}+\alpha_{1}x_{1}+\alpha_{2}x_{2}+\cdots+\alpha_{p}x_{p}\]  
where \(p\) is the number of predictor variables, \(x=\big{(}x_{1},\,x_{2},\,\dots,\,x_{p}\big{)}\) is an observation of the \(p\) predictor variables, and \(\alpha_{0},\alpha_{1},\alpha_{2},\,\,\dots,\alpha_{p}\) are parameters to be determined; their estimations will be \(a_{0},\alpha_{1},a_{2},\dots,a_{p}\).  
The goal is to find the estimates \((a_{0},\alpha_{1},a_{2},\dots,a_{p})\) of the coefficients \(\alpha_{0},\alpha_{1},\alpha_{2},\,\,\dots,\alpha_{p}\), such that the discriminant function \(f(x)\) separates the groups of the target variable as well as possible. One common approach is maximising the discriminant function of between-group variance relative to the within-group variance. Specifically, we seek to maximise the ratio of the between-group variance to the sum of the within-group variances, known as Fisher's discriminant ratio.  
For linear discriminant analysis, the solution can be expressed in terms of the inverse of the pooled covariance matrix, which is a weighted average of the group covariance matrices. We assume that the covariance matrices are equal across groups for the linear discriminant analysis but not necessarily for the quadratic discriminant analysis. For the quadratic discriminant analysis, we need to estimate separate covariance matrices for each group, and the solution is given using a quadratic function of the predictor variables [52; 54; 53].  
When the target variable has \(K\) possible values, this involves fitting \(K\) into separate discriminant functions, one for each class, where each function is trained to distinguish that class from all other classes of the outcome. Specifically, for class \(k\), we define a binary response variable of \(y_{k}\), where \(y_{k}=1\) if the observation belongs to class \(k\), and \(y_{k}=0\) otherwise. We then fit a discriminant function of \(f_{k}(x)\) for each \(k\), using the same predictor variables as before. Finally, to classify a new case, we compute the values of all \(K\) discriminant functions and assign the case to the class with the highest value of \(f_{k}(x)\).  
One of the key advantages of discriminant analysis with multiple target variables is that it allows for identifying the most important predictors of group membership. Additionally, this method can identify which variables are the most influential in discriminating between groups, providing insight into the underlying mechanisms driving group differences [55].
# The Feasibility of Shadowed Image Restoration Using the Synthetic Aperture Focusing Technique
## 5 Conclusions  
Medical diagnosis is only as good as the diagnostic tools used. In sonographic diagnosis, the US can only detect lesions from the anterior; it is rarely used to detect human soft tissue lesions in circumferential 360 degrees. The use of US could fail to provide reliable diagnoses of tumors located either between the heterotopic ossification and joint, or eyeball tumors, due to the skull barrier behind the eyeball, causing acoustic shadowing. Making use of a forward model study, the phenomena of acoustic shadowing were demon  
Figure 7: Schematic of (**a**) B-mode and (**b**) pitch/catch mode scans and the theoretical echo-paths for the active transceiver(s). SP stands for scan position, and subscripts OBS, FR, EMT, and REC are the obstacle, obstacle-free, emitter, and receiver, respectively.  
strated, and the digitized echo signals were obtained. Acquired data were processed by a restoration technique, i.e., SAFT. Restoration output commends a target that is imaged under acoustic shadowing could be conditionally revealed and encourages the feasibility of revealing the existence of a potential lesion behind the larger and harder tissues or lesions. However, there are some limitations. Unless a target to be imaged can be partly echoed in the multi-channel scans, a shadowed image will not be unveiled. In other words, only a shadowed lesion that can be echoed using ultrasonography could be diagnosed, which is theoretically reasonable. Once the targets of potential lesions are positioned in a totally anechoic area, i.e., umbra in eclipse, the scanning task will fail.  
Conceptualization, C.-H.C.; Data curation, J.-W.C.; Funding acquisition, K.-Y.H.; Methodology, C.-H.C. and Y.-F.C.; Project administration, K.-Y.H.; Software, Y.-F.C. and J.-W.L.; Writing--original draft, C.-H.C.; Writing--review & editing, K.-Y.H. All authors have read and agreed to the published version of the manuscript.  
The research leading to this paper was financially supported by the Ministry of Science and Technology Taiwan under grant no. MOST 108-2314-B-006-048-MY2, MOST 109-2116-M-415-001-, and MOST 110-2314-B-006-020.  
We wish to express our appreciation to anonymous reviewers for providing very constructive suggestions and comments in revising this paper. Our appreciation also goes to [PERSON] and [PERSON] for their valuable effort and time in editing this paper.  
The authors declare no conflict of interest.
# Temperature-Dependent Dynamic on Resistance in Gamma-Irradiated AlGaN/GaN Power HEMTs
###### Abstract  
Dynamic R\({}_{\text{ON}}\) is a key parameter in terms of device reliability and the efficiency of power-switching converters. In this study, commercial off-the-shelf GaN-on-Si power high-electron-mobility transistors (HEMTs) were irradiated using different regimes of accumulative gamma rays with a \({}^{60}\)Co source of photon energy (1.33 MeV), while a base temperature of 53 \({}^{\circ}\)C and 133 \({}^{\circ}\)C during the irradiation test was applied. This test campaign had the objective of investigating how the combination of gamma irradiation and temperature affects dynamic on-resistance (R\({}_{\text{ON}}\)) behaviour. The results indicated that gate voltage bias stress affected the degradation of dynamic on-resistance when irradiation was applied, and that temperature was an accelerating factor in dynamic on-resistance degradation. Finally, we obtained a partial reduction in dynamic R\({}_{\text{ON}}\) when a total ionising dose of around 140 krad(SiO\({}_{2}\)) was applied and the base temperature during the irradiation test was not high.  
high-electron-mobility transistor (HEMT); gallium nitride (GaN); radiation hardness; assurance testing; radiation effects; total ionising dose (TID) +
Footnote †: journal: Applied Sciences
# A Chinese Few-Shot Text Classification Method Utilizing Improved Prompt Learning and Unlabeled Data
## 2 Related Work
### Few-Shot Text Classification  
Few-shot text classification aims to build a classifier using a limited number of annotated resources. Traditional text classification methods, such as the Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Transformers, are not well suited for this scenario as they require a large number of labeled samples for model convergence [15; 16; 17; 18; 19]. For the past few years, the meta-learning approach has emerged as a popular framework for few-shot text classification, which aims to help us solve tasks by learning a set of universal \"meta-tasks\" with few samples. There are two main ways to leverage meta-learning for target tasks: (1) the optimization-based method and (2) the metric-based method. The former treats the meta-task as a universal parameter optimization process and its modification in a large number of similar tasks. The latter focuses on measuring the similarity between the query and support samples by generating sample representations with better clustering properties, which are then used to model the classification probability of the query samples [20].  
Several studies have demonstrated the effectiveness of these meta-learning ap-proaches in few-shot text classification [21]. Metric-based meta-learning, especially, can outperform traditional text classification models trained from scratch. For example, [PERSON] et al. [22] proposed Siamese Neural Networks, a neural network model containing two identical neural networks, to solve single-sample learning problems by discriminating whether the query and support samples belong to the same class. Inspired by the twin networks, [PERSON] et al. [23] proposed matching networks, which use global information within each episode to represent the query samples and then use an attention mechanism to weigh the aggregated label information of the support samples to model the classification probability of the query samples. [PERSON] et al. [24] proposed a simple and effective few-shot learning algorithm called the Prototype Network to form a circular representation of this category and then measure the class works, averaging the sample representations of the same category in the support samples' classification probability of the query samples by their similarity to each prototype representation. Furthermore, [PERSON] et al. [25] proposed a Relation Network to model the similarity metric function using deep neural networks to replace the traditional fixed distance calculation methods, such as the L1 distance, L2 distance, and cosine similarity.  
Although metric-based meta-learning creates insufficiently labeled sample scenarios for each meta-task through clustering characteristics and text similarity, they still need to filter and retain a large amount of representative annotation data for training and debugging.
## 3 Materials and Methods
### New Evaluation Metric  
We present Enhanced Tree Matching (ETM), a new evaluation metric that compares queries based on their abstract syntax tree (AST) rather than the set-based matching approach of ESM. ETM applies a set of verifiable equivalence rules to transform queries into normalized forms before comparison, reducing false negatives present in ESM (Section 3.2). Figure 6 shows a high-level overview of ETM's process for comparing SQL queries. Queries are parsed into Abstract Syntax Trees (ASTs) then normalized using predefined equivalence rules such that the normalization recognizes that structural differences like aliases do not affect the query meaning. After normalization, the originally different ASTs become equivalent, enabling semantic rather than textual query comparison. ETM addresses all the issues in Sections 3.1 and 3.2, as well as other critical issues. Tables 1-3 provide a full list of equivalent queries and verifiable assumptions incorporated into ETM. The preprocessing rules (Table 1) handle basic syntactic variations like case sensitivity, table prefixes, column ordering, and aliasing, while the advanced equivalence rules (Tables 2 and 3) capture more complex semantic relationships that require database schema constraints for verification. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Equivalent Queries** & **Verifiable Assumptions** \\ \hline \multirow{2}{*}{1} & SELECT \_ FROM t1 WHERE c1 = (SELECT MIN/MAX(c1) FROM t1); & **c1** is UNIQUE \\  & SELECT \_ FROM t1 ORDER BY c1 ASC/DESC LIMIT 1; & \\ \hline \multirow{2}{*}{2} & SELECT DISTINCT c1 FROM t1; & **c1** is UNIQUE \\  & SELECT c1 FROM t1; & \\ \hline \multirow{2}{*}{3} & SELECT c1 FROM t1 WHERE d1 INTERSECT/UNION SELECT c1 FROM t1 WHERE d2; & **c1** is UNIQUE \\  & SELECT c1 FROM t1 WHERE d1 AND/OR d2; & \\ \hline \multirow{2}{*}{4} & SELECT \_ FROM t1 WHERE GROUP BY c1,c2,...; & **c1** is UNIQUE \\  & SELECT \_ FROM t1 WHERE GROUP BY c1; & \\ \hline \multirow{2}{*}{5} & SELECT c1 FROM t1 EXCEPT (q1); & **c1** is UNIQUE \\  & SELECT c1 FROM t1 WHERE c1 NOT IN (q1); & and NON\_NULL \\ \hline \multirow{2}{*}{6} & SELECT CONT(*) FROM t1; & **c1** is NON\_NULL \\  & SELECT CONT(c1) FROM t1; & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Equivalent queries with verifiable assumptions implemented in ETM. **t***: table, **c***: column, **d***: condition, **q***: full query. **Case 1**: a primary key-foreign key relation, where t1.c1 is the primary key and t2.c2 is the foreign key. **Case 2**: t1.c1 must be non-composite and X can be any column(s) in t2. / denotes options, but consistency is required in selecting between options across corresponding elements of the queries. Figure 6: Overview of the Enhanced Tree Matching (ETM) process. Queries are parsed into their ASTs, which are then normalized using a set of predefined equivalence rules (Table 2) before comparison. After the rules are applied, queries are reduced to their most basic form, allowing us to compare them for equality.
# Lower Limb Muscle Activation in Young Adults Walking in Water and on Land
## 3 Results  
### Inter-Trial Reliability  
The inter-trial reliability of the measures recorded here was moderate to excellent, with the exception of stance phase Co-A (water 3.5 mph with jets), swing phase GM RMS (water 3.5 mph; water 3.5 mph with jets), and swing phase Co-A (water 3.5 mph; water 3.5 mph with jets; \(p<0.001\); see Tables 2 and 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Environment** & **Measure** & **2.5 mph** & **3.5 mph** & **3.5 mph + Jets** \\ \hline \multirow{4}{*}{Land} & Stance time (s) & 0.71 (0.61–0.81) & 0.94 (0.91–0.96) & \\  & TA RMS (\(\mu\)V) & 0.57 (0.45–0.70) & 0.69 (0.58–0.80) & \\  & GM RMS (\(\mu\)V) & 0.84 (0.77–0.90) & 0.90 (0.85–0.94) & \\  & Co-A (\%) & 0.65 (0.54–0.77) & 0.80 (0.72–0.88) & \\ \hline \multirow{4}{*}{Water} & Stance time (s) & 0.87 (0.81–0.92) & 0.81 (0.74–0.88) & 0.77 (0.68–0.85) \\  & TA RMS (\(\mu\)V) & 0.73 (0.63–0.82) & 0.78 (0.70–0.86) & 0.69 (0.59–0.80) \\ \cline{1-1}  & GM RMS (\(\mu\)V) & 0.63 (0.51–0.75) & 0.56 (0.44–0.69) & 0.83 (0.75–0.89) \\ \cline{1-1}  & Co-A (\%) & 0.61 (0.49–0.73) & 0.66 (0.54–0.77) & 0.46 (0.34–0.61) \\ \hline \hline \end{tabular}  
TA = tibialis anterior; GM = medial gastrocnemius; RMS = root-mean-square amplitude; Co-A = co-activation index.  
\end{table}
Table 2: Inter-trial reliability of measures during the stance phase of walking.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Environment** & **Measure** & **2.5 mph** & **3.5 mph** & **3.5 mph + Jets** \\ \hline \multirow{4}{*}{Land} & Swing time (s) & 0.87 (0.81–0.92) & 0.89 (0.84–0.93) & \\  & TA RMS (\(\mu\)V) & 0.82 (0.74–0.89) & 0.87 (0.80–0.92) & \\  & GM RMS (\(\mu\)V) & 0.61 (0.49–0.73) & 0.87 (0.81–0.92) & \\  & Co-A (\%) & 0.66 (0.55–0.77) & 0.81 (0.73–0.88) & \\ \hline \multirow{4}{*}{Water} & Swing time (s) & 0.88 (0.83–0.93) & 0.80 (0.71–0.87) & 0.81 (0.73–0.88) \\  & TA RMS (\(\mu\)V) & 0.89 (0.83–0.93) & 0.88 (0.82–0.92) & 0.92 (0.88–0.95) \\ \cline{1-1}  & GM RMS (\(\mu\)V) & 0.84 (0.77–0.90) & 0.35 (0.24–0.50) & 0.46 (0.34–0.61) \\ \cline{1-1}  & Co-A (\%) & 0.83 (0.76–0.90) & 0.46 (0.34–0.61) & 0.45 (0.33–0.60) \\ \hline \hline \end{tabular}  
TA = tibialis anterior; GM = medial gastrocnemius; RMS = root-mean-square amplitude; Co-A = co-activation index.  
\end{table}
Table 3: Inter-trial reliability of measures during the swing phase of walking.