esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Privacy-Utility Trade-Off in Differentially Private Hierarchical Text Classification
## 7 Evaluation
### Empirical Privacy and Utility
In contrast to the CNN, the MI attack against the BoW and Transformer models is only reaching the baseline at \(\epsilon=1\) and \(\epsilon=1.5\), respectively. The results for the Reuters dataset are provided in Figures 4b and 5b. Compared to BestBuy, the decrease in model utility on Reuters is smaller for all three HTC models, which can be explained with a significantly higher amount of training examples and a smaller amount of hierarchical classes. The BoW classifier's utility is most robust to the addition of noise to the training process, yet it is closely followed by the Transformer model. However, the CNN model exhibits the most severe decrease in model utility. Figure 5b indicates that the MI adversary's advantage drops to the baseline level again for very weak DP guarantees of \(\epsilon>10^{2}\) for all models. This behavior can be explained with the high amount of training examples and the smaller amount of hierarchical classes. Therefore, the gap between the empirically measured membership advantage and the upper bound on membership advantage diverges widely. Figure 4: Target model test accuracy per dataset over \(\epsilon\). (**a**) Target model test accuracy for BestBuy utility over \(\epsilon\); (**b**) Target model test accuracy for Reuters utility over \(\epsilon\); (**c**) Target model test accuracy for DBPedia over \(\epsilon\). For DBPedia in Figure 4c, the BoW model is again the most robust, and the Transformer model is least robust to the added noise during the training process, which is similar to the observations made on the BestBuy dataset. This is in line with our formulated expectations. The only exceptions are the measured utility metrics for \(\epsilon\approx 10^{-1}\), for which the BoW model performs worse than the CNN and Transformer model. MI metrics for the DBPedia HTC models are provided in Figure 5c. We see that the MI metrics for the BoW and Transformer models drop to the baseline level for very weak DP guarantees, similar to the Reuters models. Therefore, our MI adversary does by far not reach the theoretical upper \(Adv\) bound. Notably, the MI metrics for the CNN model do not drop to the baseline level for the considered range for \(z\) and resulting \(\epsilon\). Hence, the gap between the measured \(Adv\) and theoretical upper bound on \(Adv\) reaches its lowest value for this model. Overall, the privacy and utility results support our expectation that the utility of a high bias model such as BoW is less affected by the introduced noise than models with high variance such as Transformer. On the other hand, Transformer models are less prone to the MI attack due to better generalization, which has generally been demonstrated aside from MI in related work [18; 40].
## 2 Related Work
### Lightweight Object-Detection Methods for Embedded Devices in UAVs  
Numerous studies have utilized the Jetson platform for deploying lightweight target-detection models. In 2023, [PERSON] et al. launched the LODNU network, specifically designed for UAVs, demonstrating superior performance in real-time tests on the NVIDIA Jetson [24]. In 2024, [PERSON] et al. introduced UAV-Net 18 using the NVIDIA Jetson Nano. This model enhanced detection precision and reduced complexity through advanced pruning techniques [25]. Concurrently, the YOLOv8n model improved small-target detection on UAVs, with [PERSON] et al. optimizing it for the Jetson TX2, thereby increasing precision and speed in complex UAV-captured scenes [26]. The following year, [PERSON] et al. adapted the Jetson Xavier for their LUD-YOLO model, achieving efficient and suitable target detection for real-time UAV operations [27]. Despite these advancements on high-performance Jetson-based platforms, challenges remain in developing UAV-specific algorithms for more cost-effective, lower-power embedded platforms. Studies like [15; 16] have proposed models with about 1 MB of parameters, showing significant improvements. While many models are still tested on relatively resource-rich Jetson-based platforms, general-purpose object-detection methods like YOLO-Fastest [28] and FastestDet [17] have been adapted for constrained embedded platforms. YOLO-Fastest, a highly streamlined version within the YOLO series, drastically reduces the number of convolutional layers and channels. This significant simplification transforms it into an extremely lightweight model, capable of achieving minimal latency and ultra-fast inference speeds, often exceeding 50 FPS [28]. FastestDet enhances real-time, high-precision inference through features like lightweight detection heads and dynamic positive-negative sample distribution [17]. Although these models perform well as general-purpose object-detection solutions on constrained platforms like Rockchip, specific enhancements for UAV applications remain limited.
## 3 ML Application in O-RAN
### Resource Management
The chosen resource allocation policy's effectiveness is evaluated at the end of each period by the Near-RT RIC, which computes a reward indicative of the transmission rate. This reward is then sent to the Non-RT RIC. Through this iterative learning process, the ML agent gradually acquires knowledge and discerns the optimal resource allocation policy capable of adapting to dynamically changing environments.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 6 Performance Evaluation
### Performance Evaluation of the Proposed Defense System against FakeBob Attacks
Furthermore, the average running time for FakeBob has been significantly increased by applying the noise-adding defense method. For example, when \(\sigma=0.002\), the noise-adding method slows down the processing speed of the FakeBob attacks more than 5 times. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plugin** & \(\sigma\) & **Avg Iter** & **Avg Time (s)** & **Avg ASR (\%)** \\ \hline Original & 0 & 168.88 & 6080.47 & 95.00 \\ \hline Denoising & 0.001 & 97.40 & 3702.36 & 55.68 \\ \hline Denoising & 0.002 & 100.58 & 3825.02 & 38.63 \\ \hline Denoising & 0.005 & 344.53 & 13,130.24 & 17.73 \\ \hline Noise-Adding & 0.001 & 556.33 & 20,041.00 & 8.98 \\ \hline Noise-Adding & 0.002 & 918.23 & 33,017.30 & 0.50 \\ \hline Noise-Adding & 0.005 & 921.48 & 33,103.39 & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of proposed defense system against FakeBob attacks in I-Vector SV systems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plugin** & \(\sigma\) & **Avg Iter** & **Avg Time (s)** & **Avg ASR (\%)** \\ \hline Original & 0 & 23.00 & 158.68 & 100.00 \\ \hline Denoising & 0.001 & 18.90 & 192.02 & 77.20 \\ \hline Denoising & 0.002 & 22.85 & 235.96 & 56.05 \\ \hline Denoising & 0.005 & 22.30 & 235.78 & 51.00 \\ \hline Noise-Adding & 0.001 & 92.60 & 614.92 & 24.35 \\ \hline Noise-Adding & 0.002 & 604.95 & 3992.88 & 5.20 \\ \hline Noise-Adding & 0.005 & 694.95 & 4350.35 & 4.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of proposed defense system against FakeBob attacks in GMM SV systems. In summary, the experimental results indicate that the noise-adding defense with a reasonable value of the standard deviation of noise (e.g., \(\sigma=0.002\)) can effectively and efficiently counteract FakeBob attacks. Moreover, such a defense method is simple to implement, is very light-weight, and only slightly degrades the performance of normal operations of an SV system.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 2 The Plane Rotation Model of UHS
### Parametric Analysis of RCS
#### 3.2.3 Influence of Curvature Radius of Contact Surface on RCS  
The influence of the curvature radius of the contact surface on the RCS was studied with \(R_{0}=1.5\) m and \(R_{0}=\infty\) (the contact surface is plane), under the premise of ensuring the same radius for the contact surface (\(R_{2}\)). The critical torques are shown in Figure 11 with different \(R_{2}\) or \(R_{0}\).  
Figure 10: Related curve of the RCS-central angle.  
Figure 9: Torque–displacement curves with different central angles.  
Figure 11 shows that the difference between the critical torque was less than 5% with \(R_{0}\) taking a different value. Thus, it can be concluded that \(R_{2}\) and \(F\) must be unchanged. At this point, it can be considered that the rotational performance of UHS is independent of the radius of curvature.
# Period Cycle Optimization of Integrated Energy Systems with Long-Term Scheduling Consideration
## 3 Results and Discussion
### Economic Feasibility  
Figure 5 illustrates the projected capital recovery period for the system within the energy structure of this case study. The year 0 represents the total investment during the construction phase. Through the iterative optimization method applied to the one-year operational scenario, it can be inferred that the capital will be fully recovered within ten years, achieving initial profitability for the case. This further validates the economic feasibility of this case.  
Table 6 presents the economic parameters of the energy structure for this case over one project cycle (20 years). The pre-tax and post-tax _IRR_ are 23.14% and 21.96%, respectively. These values significantly exceed the global inflation rate of 8.8% in 2022, and the International Monetary Fund (IMF) forecasted a global inflation rate of 6.5% for 2023, indicating that the energy structure of this case holds substantial investment value. The pre-tax and post-tax _NPV_ are 66.38 and 59.27 million, respectively. The results demonstrate that the energy structure of this case, under current energy economic and environmental competitiveness, can maximize economic benefits while meeting energy conservation and emission reduction constraints.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item (Unit)** & **Value** \\ \hline Total investment cost (million) & 509.40 \\ Pre-tax _IRR_ (\%) & 23.14 \\ Post-tax _IRR_ (\%) & 21.96 \\ Pre-tax _NPV_ (million) & 66.38 \\ Post-tax _NPV_ (million) & 59.27 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Economic data output of the system.  
Figure 5: Cost recovery projection chart.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimization Period** & \
\begin{tabular}{c} **Carbon Emission Reduction** \\ **of the System/t** \\ \end{tabular} &
\begin{tabular}{c} **Other Emission Gas** \\ **Reduction of the System/t** \\ \end{tabular} & **Proportion/\%** \\ \hline
1–5 & 91.90 & 30.19 & 11.07\% \\
6–10 & 162.28 & 53.30 & 19.55\% \\
11–15 & 185.15 & 60.82 & 22.30\% \\
16–20 & 193.11 & 63.43 & 23.26\% \\
21–25 & 197.74 & 64.91 & 23.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Energy saving and emission reduction in different cycle stages.  
The case study results reveal that utilizing renewable energy in the energy structure is highly worthwhile, primarily due to significantly reduced energy costs and superior energy conservation and emission reduction effects compared to traditional energy systems. However, the additional investments required for renewable energy technology, as well as ongoing technological advancements in this field, will lead to increased investment and maintenance costs for renewable energy systems.
# A Multi-Level Optimization Method for Elastic Constants Identification of Composite Laminates
###### Abstract  
A new and effective elastic constants identification technique is presented to extract the elastic constants of a composite laminate subjected to uniaxial tensile testing. The proposed technique consists of a new multi-level optimization method that can solve different types of minimization problems, including the extraction of material constants of composite laminates from given strains. In the identification process, the optimization problem is solved by using a stochastic multi-start dynamic search minimization algorithm at the first level in order to obtain the statistics of the quasi-optimal design variables for a set of randomly generated starting points. The statistics of the quasi-optimal elastic constants obtained at this level are used to determine the reduced feasible region in order to formulate the second-level optimization problem. The second-level optimization problem is then solved using the particle swarm algorithm in order to obtain the statistics of the new quasi-optimal elastic constants. The iteration process between the first and second levels of optimization continues until the standard deviations of the quasi-optimal design variables at any level of optimization are less than the prescribed values. The proposed multi-level optimization method, as well as several existing global optimization algorithms, is used to solve a number of well-known mathematical minimization problems to verify the accuracy of the method. For the adopted numerical examples, it has been shown that the proposed method is more efficient and effective than the adopted global minimization algorithms to produce the exact solutions. The proposed method is then applied to identify four elastic constants of a [0\({}^{\circ}\)/\(\pm\)45\({}^{\circ}\)]s composite laminate using three strains in 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\) directions, respectively, of the composite laminate subjected to uniaxial testing. For comparison purposes, several existing global minimization techniques are also used to solve the elastic constants identification problem. Again, it has been shown that the proposed method is capable of producing more accurate results than the adopted available methods. Finally, experimental data are used to demonstrate the applications of the proposed method.  
Keywords:optimization; composite plate; elastic constants identification; strain analysis +
Footnote †: journal: Applied Soft. 2019, 9, 4267; doi:10.3390/app9204267
## 2 Preliminaries
### Attack Algorithms
#### 2.2.3 Carlini and Wagner Attack  
The Carlini and Wagner (C&W) attack [7] solves the optimization problem in Equation (4) to obtain the change applied to the input image. In the equation, \(D(x,x+\delta)\) is the distance between the original input \(x\) and the adversarial image \(x+\delta\). \(C(x+\delta)\) is the class determined by the target network and \(t\) is the target class. In the problem, each pixel of an image has a value in the range [0, 1]. Thus, the pixel values of the adversarial image should also fall into this range. Therefore, we have the condition \(x+\delta\in[0,1]^{n}\).  
\[\begin{split}\text{minimize}& D(x,x+\delta)\\ \text{such that}& C(x+\delta)=t\\ & x+\delta\in[0,1]^{n}\end{split} \tag{4}\]  
The optimization cannot be directly solved because of the constraint \(C(x+\delta)=t\). Thus, the authors substituted \(C\) using an approximation function \(f\), which is designed so that \(C(x+\delta)=t\) if and only if \(f(x+\delta)\leq 0\). One example of \(f\) is Equation (5), where \(Z\) is the output of the network for input \(x^{\prime}\), before applying the final softmax function.  
\[f(x^{\prime})=(\max_{i\
eq t}(Z(x^{\prime})_{i})-Z(x^{\prime})t)^{+} \tag{5}\]  
In the optimization problem, the distance metric \(D\) could be any metric including \(L_{0}\), \(L_{2}\) and \(L_{\infty}\) distance. The final row of Figure 1 shows the images created using C&W with \(L_{2}\) distance as the distance metric. Compared to images created by FGSM and JSMA, the images look more similar to the original inputs, with some light strokes added near the digit. The C&W attack is shown to be effective in creating adversarial images that looks very similar to original inputs [21; 22] and is successful at attacking networks armed with defensive methods such as defensive distillation [23].
## 3 Thermal Coupling of Power Components
### The Experimental Platform  
The thermal coupling platform is illustrated in Figure 9. This experimental platform includes: one 30 V DC power supply for the MOSFET gate driving, a controlled DC current supply for the testing circuit (5 A), two power components (MOSFET: IRF540 NPBF, Infineon, Munich, Bavaria, Germany, and diode: MBR20100 CT, ON Semiconductor, Phoenix, Arizona, USA) packaged with TO-220, a digital multimeter which is used for observing the voltage-drop of the diode, a current sensor which is used to measure the current levels, a data Taker Data Logger (DT80 with eight bi-directional channels, Thermo Fisher Scientific, Waltham, MA, USA) which is employed for collecting the measurement data, K-type thermocouples attached to the case of the devices for \(T_{c}\) testing [24; 25], and real-time data monitoring by a computer and a FR4 PCB board.  
In power converter applications when the MOSFET and the diode are working, power losses (the conduction loss and switching loss, etc.) will be generated and all the power losses will become factors of component heating. Therefore, a given power loss [11] or controlled currents [26] can heat up the devices. In this study, we focus on the on-state mode and after the devices have undergone conduction loss, thermal dissipation will ensue.  
The two power devices are measured in the thermal coupling experimental platform with the distances of 12 mm, 18 mm, and 22 mm and under three different working currents, respectively. In one minute, 30 \(T_{c}\) values (a maximum of 600 data can be collected) for the MOSFET and the diode are collected by a Data-Taker and for every testing current it takes 20 min at the separation distances, as shown in Figure 10. When the devices are powered on, \(T_{c}\) increases at a fast rate and then reaches its  
Figure 9: Thermal coupling testing platform.  
steady state. Since the diode has a higher power loss than the MOSFET, it has a faster rate of increase and a higher steady temperature. When the devices are powered off, \(T_{c}\) decreases at a considerable rate and then reaches \(T_{a}\) and the diode also decreases at a faster rate than the MOSFET. All measured \(T_{c}\) data in steady state, \(T_{c\text{-}M}\) for the MOSFET and \(T_{c\text{-}D}\) for the diode, are shown in Table 1.
## 3 Results
### Classification Using Machine Learning
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1 Score**} \\ \cline{2-13}  & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** \\ \hline Logistic regression & 0.8095 & 0.6054 & 0.7664 & 0.8044 & 0.5588 & 0.774 & 0.8095 & 0.6054 & 0.7664 & 0.8001 & 0.5744 & 0.7574 \\ \hline Decision tree & 0.9138 & 0.907 & 0.9388 & 0.9155 & 0.9067 & 0.9393 & 0.9138 & 0.907 & 0.9388 & 0.9144 & 0.9069 & 0.939 \\ \hline Random forest & 0.9524 & 0.9252 & 0.9546 & 0.952 & 0.9245 & 0.9541 & 0.9524 & 0.9252 & 0.9546 & 0.952 & 0.9248 & 0.9541 \\ \hline Gradient boosting & 0.9637 & 0.9252 & 0.9456 & 0.9642 & 0.9241 & 0.9454 & 0.9637 & 0.9252 & 0.9456 & 0.9635 & 0.9244 & 0.9454 \\ \hline K-nearest neighbors & 0.9138 & 0.8617 & 0.8503 & 0.9156 & 0.8588 & 0.8479 & 0.9138 & 0.8617 & 0.8503 & 0.9127 & 0.86 & 0.8474 \\ \hline SVC & 0.8254 & 0.8095 & 0.8095 & 0.736 & 0.7219 & 0.7238 & 0.8254 & 0.8095 & 0.8095 & 0.7736 & 0.7573 & 0.7577 \\ \hline Gaussian NB & 0.8866 & 0.8639 & 0.8707 & 0.8914 & 0.861 & 0.8669 & 0.8866 & 0.8639 & 0.8707 & 0.8883 & 0.8574 & 0.8679 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input set 1, input set 2, and input set 3 performance comparison. Figure 6: Visual representation of model performances. The logistic regression classifier initially achieved decent performance, but it demonstrated the most substantial decrease in performance when lighter datasets were provided, making it unsuitable for such classifications. The Gaussian NB model was also robust.
## 3 Results and Discussion
### Communication and Cooperation  
Interactive maps and control panels are used between participants to quickly communicate about the current situation during the monitoring and to be informed about the expected development of the situation. Interactive dashboards, like the one shown in Figure 7, are a particularly useful tool because they display key indicators and provide summary critical information.  
Figure 6: Modeling and prediction of spreading the infection in the Epidemic Location Intelligence System. The figure shows current reports with epidemic curves and instantaneous weekly reproductive numbers (Rt).  
This work, like other aforementioned research, recognized the advantages of GIS to prevent and control COVID-19, but also some of the difficulties and challenges. Some local specifications of the country with complex and heterogeneous health information system created difficulties in development and maintenance of such platforms. At the beginning, the only continuous data source was from available public news portals, which were not official. These data were shown on the spatio-temporal map for the first six months of epidemic. With the inclusion of more and more official data, other problems arose, such as different data formats and their structures. The ANUBIH created the Center for Disease Control and GeoHealth Studies (CDC/GHS) to be responsible for the back office of the ELIS and to coordinate with health institutions. The center includes multidisciplinary teams with members who are experts in the field of health (epidemiologists, microbiologists, veterinarians), computer science, and geoinformatics.  
The purpose of the geoportal is to serve as a support for the exchange of epidemiological and other data between public health institutions and the public during the pandemic crisis.  
It refreshes its dashboard and thematic maps every day. Data services from international sources like WHO comes every day, but from local public health institutions usually only on working days. There are 20 to 30 visits by named users daily, and they spend approximately 30 min per visit. Named users are from public health institutions and the administrative authorities of two entities, the district and the ten cantons. There are an average of 30 visits per day by public users who spend about 10 min per visit. Data download is mostly done by scholars and data analysts who are interested in time series.