esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Artificial Neural Network Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & **Architecture** & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**\(\mathbf{R^{2}}\)**} & \multirow{2}{*}{**\(\mathbf{R}\)**} & \multirow{2}{*}{**MAPE (\%)**} & \multirow{2}{*}{**Regression Line Equation**} \\  & **ANN** & & & & & & \\ \hline \multirow{8}{*}{Hyperbolic tangent} & 7–2–1 & 0.3215 & 0.9052 & 0.9514 & 60.1 & \(v_{d,sim}\) = 0.8637\(v_{d,exp}\) + 0.3047 \\  & 7–4–1 & 0.2611 & 0.9174 & 0.9578 & 37.0 & \(v_{d,sim}\) = 0.9377\(v_{d,exp}\) + 0.0727 \\  & 7–6–1 & 0.2331 & 0.9384 & 0.9687 & 44.1 & \(v_{d,sim}\) = 0.9643\(v_{d,exp}\) + 0.0955 \\  & **7–8–1** & **0.1912** & **0.9647** & **0.9822** & **31.9** & \(v_{d,sim}\)**= **0.9865\(v_{d,exp}\)**+ 0.1000** \\  & 7–10–1 & 0.1718 & 0.9672 & 0.9835 & 31.5 & \(v_{d,sim}\) = 0.9641\(v_{d,exp}\) + 0.0906 \\ \hline \multirow{8}{*}{Logistic sigmoid} & 7–2–1 & 0.3096 & 0.8896 & 0.9432 & 49.6 & \(v_{d,sim}\) = 0.8304\(v_{d,exp}\) + 0.1417 \\  & 7–4–1 & 0.3377 & 0.8802 & 0.9382 & 52.6 & \(v_{d,sim}\) = 0.8331\(v_{d,exp}\) + 0.0665 \\ \cline{1-1}  & 7–6–1 & 0.3020 & 0.9111 & 0.9545 & 45.8 & \(v_{d,sim}\) = 0.8568\(v_{d,exp}\) + 0.0327 \\ \cline{1-1}  & **7–8–1** & **0.2139** & **0.9521** & **0.9757** & **25.4** & \(v_{d,sim}\)**= **0.8704\(v_{d,exp}\)**+ 0.1169** \\ \cline{1-1}  & 7–10–1 & 0.2467 & 0.9430 & 0.9711 & 37.4 & \(v_{d,sim}\) = 0.8903\(v_{d,exp}\) + 0.0127 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANN network results using the hyperbolic tangent and logistic sigmoid transfer function in the hidden layer. Figure 6: Optimal ANN architecture developed for the estimation of drying velocity. Thus, the mathematical function that describes the behavior of the solar drying process under study was given by Equation (3). Table 6 summarizes the weights and biases of the neuronal model for the hyperbolic tangent transfer function. The neural model used these parameters to reproduce the values of \(v_{d}\) during the optimization process.
## 3 Results
### Effect of Metformin on Acute Pulmonary Thrombosis and Bleeding Time in Mice  
The antithrombotic therapeutic effect of metformin in mice was also evaluated with respect to preventing mortality by the effect of acute pulmonary thromboembolism (Figure 8A). Metformin at 250 mg/kg, but not 150 mg/kg, significantly reduced the ADP (0.7 mg/g)-induced mortality rate from 100% (12 dead, \(n\) = 12; PBS) to 83.3% (10 dead, \(n\) = 12; metformin 150 mg/kg) and 50% (6 dead, \(n\) = 12; metformin 250 mg/kg). All the animals were alive in the sham group (injected PBS without ADP; \(n\) = 12). However, heparin (1.5 U/g) had no effects (11 dead, \(n\) = 12). We also evaluated the bleeding time through transection of the mouse tails 30 min after metformin was intraperitoneally administered; the bleeding times were 204 \(\pm\) 50 s (PBS-treated group; \(n\) = 8), 228 \(\pm\) 27 s (150 mg/kg metformin-treated group; \(n\) = 8), 295 \(\pm\) 81 s (250 mg/kg metformin-treated group; \(n\) = 8), and 526 \(\pm\) 33 s (1.5 U/g heparin-treated group; \(n\) = 8; Figure 8B). To detect if there are any relbeeding occurrences, each mouse was separately watched for 15 min, even after the bleeding had stopped. The results suggest that metformin reduced acute pulmonary thrombosis without significantly prolonging bleeding time. These findings provide a possibility that metformin may be employed in the protection of cardiovascular events and reduction in mortality in patients with diabetes [23].  
Figure 7: Influence of metformin in vasodilitor-stimulated phosphoprotein (VASP) phosphorylation in human platelets. Washed platelets (3.6 \(\times\) 10\({}^{8}\) cells/mL) were directly stimulated by prostaglandin E1 (PGE1, 20 nM), nitroglycerin (NTG, 10 \(\upmu\)M), or metformin (1.8 mM) in the presence of H89 (100 \(\upmu\)M) or KT5823 (10 \(\upmu\)M) for immunoblotting of VASP phosphorylation. Results are expressed as mean \(\pm\) standard error of the mean (\(n\) = 4). *** \(p\) < 0.001 vs. PBS-treated group; *** \(p\) < 0.001 vs. PGE1- or NTG-treated group.
This eliminates the default crucial need to know the extension number of the called person, going through complex and confusing trees to reach the right representative, or making a customer repetitively listen to \"your call is important to us\" while the call is forwarded to a representative who does not exist in the office. This is believed to enhance the Average Waiting Time (AWT), the time spent by an inbound call to be answered. LTC can also be expanded to greater extents, as will be discussed later in Section 6. The rest of the paper is organized as follows. Section 2 outlines the contributions of the paper. Section 3 reviews related research in the literature and highlights their contributions and shortcomings. Section 4 delineates the building blocks of the proposed system model. Section 5 provides a feasibility study and a use case implementation of the proposed LTC system. Section 6 discusses the future work considering other application scenarios of the proposed system that could take it to greater extents. Lastly, Section 7 concludes the paper and provides final remarks.
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 4 Model Introduction and Application
### MCNN Transformer Model  
The original transformer model first converts a single moment sample in the input sequence into a high-dimensional feature vector through the embedding layer, and then applies positional encoding to process the information of each position in the sequence. Subsequently, the multi-head attention mechanism is used to map the input features into three feature vectors: query (Q), key (K) and value (V). Through computing the correlation between the query (Q) and the key (K), the values (V) are subsequently weighted and summed to generate the features of the next time step.  
However, the prediction of underwater pump shaft seal water pressure is a regression prediction problem. Since the embedding layer of the transformer model is not suitable for feature extraction of multi-time series data, the original model needs to be optimized. In this study, an MCNN-transformer model is proposed, Figure 13 illustrates the details of model. The original data sequence is input into MCNN and multi-scale features are extracted. Subsequently, these feature sequences are input into the embedding layer of the Transformer model, and the self-attention mechanism of the Transformer model is used to model the global relationship of the sequences. The advantage of this dual model combination is that MCNN can effectively extract multi-time scale local features from the  
Figure 13: MCNN-transformer structure.  
original data, while Transformer can understand and model sequence data on a global scale, thus improving the performance of the model in processing complex sequence data.
# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 4 Simulations and Experiments  
In this section, the proposed controller is verified under simulations and experiments. The raspberry Pi mouse V3 robots (see Figure 4) from RT-Corporation are selected as autonomous vehicles, utilized and controlled by the Robot Operation System (ROS Noetic) in the Ubuntu 20.0.04 LTS environment. The Raspberry Pi mouse V3 robot is a small mobile platform that uses a Raspberry Pi 3B+ as the primary computation board together with a 1000 mAh LiPo battery, four distance sensors, and two four-phase step motors to drive the left and right wheels that operate independently. Since Raspberry Pi mouse robots are differential-drive robots, they can be modeled as unicycles:  
\[\dot{x}_{i} =v_{i}\cos\phi_{i} \tag{22a}\] \[\dot{y}_{i} =v_{i}\sin\phi_{i}\] (22b) \[\dot{\phi}_{i} =\omega_{i}, \tag{22c}\]  
where \(\mathbf{p}_{i}=(x_{i},y_{i})\) is the position of robot \(i\), \(\phi_{i}\) its heading, and \(v_{i}\), \(\omega_{i}\) are the translational and angular velocities. Since the CBF algorithm provides desired motions in terms of \(\dot{\mathbf{p}}_{i}\), we map these onto \(v_{i}\), \(\omega_{i}\) through [58]To verify the feasibility of the proposed CBF-based controller, we conduct simulations in the Gazebo simulator, which is based on the Open Dynamics Engine and Object-Oriented Graphics Rendering Engine. In Gazebo, the robot's 3D Unified Robotics Description Format (URDF) model, which is a replica of the real robot shown in Figure 4, is utilized for the simulation. The Raspberry Pi mouse's URDF model can be found on GitHub (version 2.0.0, latest release: 18 November 2024) [59]. Simulations are performed on a desktop featuring an Intel i9-11900F CPU, 64 GB RAM, and an NVIDIA RTX 3090 GPU.
# Towards a Reliable Evaluation of Local Interpretation Methods
## 2 Related Work
### Attribution Method  
Attribution method is the classical feature-level interpretation method to explore the connection between input and output via identifying features importance with respect to model prediction. The final result of this interpretation method is to generate a FSM highlighting the importance of each input feature to the prediction. The gradient-based contribution method mainly uses gradient information to obtain the importance of input features. Three different strategies have been proposed to capture input features relative importance. The y are backpropagation-based method [27; 28], activation-based method [16; 29] and perturbation-based method [17; 18; 30]. [PERSON] et al. [27] assumes the most important feature is the input pixel that makes the output the most sensitive, and quantifies the importance of each input via the pixel gradient itself. Guided prop [28] modifies the backpropagation condition and only allows the positive gradient to propagate back to each neuron. CAM [29] and Grad-cam [16] leverage high-level information of deep learning model and weight sum of the last convolutional feature maps. Perturbation-based attribution method is another technique for explaining predictions of deep learning model. These methods perturbate the input with some certain reference value and find the smallest mask through an optimized way to maximize the output result. [PERSON] et al. [17] optimized the method to make the generated mask smooth and meaningful. [PERSON] et al. [18] ran domly performs binarization sampling on the input image, and finally obtains the saliency map by weighting the predicted value. [PERSON] et al. [30] improved the optimization process with integrated gradient.
# Reliability Assessment of Cyber-Physical Distribution Systems Considering Cyber Disturbances
## 1 Introduction
A breakthrough from the limitations of the traditional distribution network reliability assessment indices is needed from the perspective of cyber-physical integration. The contributions of this paper are listed as follows:  
1. Considering the complexity of distribution network CPS in terms of structure and characteristics, a combination of fault tree and Petri net is used to model the reliability of the system, which realizes the unification of the physical system and cyber system in modeling and gives full play to their own and the combined advantages of fault tree and Petri net. 2. To take into account the influence of cyber disturbances on system reliability, the system states are divided, and the probability of the system being in each state is calculated based on the probability of successful cyberattacks, thus achieving an accurate calculation of each load point and system reliability indices. 3. In response to the lack of reliability indices for the comprehensive quantitative assessment of cyber-physical system reliability in the existing studies, the generalized distribution network CPS reliability assessment indices are defined, which can accurately assess the system reliability under the influence of cyber disturbances. This paper consists of five sections, and the parts of this paper are organized as follows. A general description of the research object and the methods used in this paper is provided in Section 2, with a brief description and explanation of each method. In Section 3, reliability modeling of the distribution network CPSs is proposed, with simple examples of how the fault tree model and the Petri net model are modeled as well as the advantages of the modeling method. The generalized distribution network CPS reliability assessment indices considering cyber disturbances and a detailed description of the new indices are defined in Section 4, with the process of the reliability assessment of the distribution network CPS described in detail. In Section 5, a modified IEEE RBTS BUS2 system is taken as an example to verify the validity of the proposed modeling method and indices. Section 6 concludes the paper.
## 4 Results and Discussion
### Optimized S-Parameters, VSWR, and Propagation Constant Plots of the Proposed Transition Model  
The optimized S-parameters, in terms of the return loss and insertion loss, are depicted in Figure 10a, and the corresponding VSWR plot with frequency is displayed in Figure 11b. Figure 10a shows the minimum insertion loss of \(-0.36\) dB and lowest return loss of \(-16.72\) dB at a frequency of 29.75 GHz. A 66.15% fractional bandwidth was achieved for the concerned Ka-band structure. The \(-10\) dB return loss with VSWR between the two ports of transition was 1.10, as shown in Figure 10b, demonstrating the good transmission of power flow between the two ports of the waveguide and microstrip line. The lowest insertion loss of \(-0.36\) dB and return loss of \(-21.135\) dB were obtained at the frequency 40 GHz. The transition exhibited return loss below \(-10\) dB and an insertion loss below \(-0.50\) dB for a 66.15% fractional bandwidth from 23.52 to 45.0 GHz. The attenuation constant, \(\alpha\), and phase constant, \(\beta\), plots of real and imaginary parts of the propagation constant are displayed in Figure 10c. The phase constant was higher and attenuation went down to almost zero with a cut-off frequency of 21.25 GHz for the dominant TE\({}_{10}\) mode. This shows that the proposed model covers some portions of the K-band (23.52-26 GHz out of the 18-26 GHz band), Q-band (33-45 GHz out of the 33-50 GHz band), and U-band (40-45 GHz out of the 40-60 GHz band), along with the entire Ka-band (26-40 GHz) application coverage. Ideally, the cut-off frequency is 21.09 GHz. Thus, only a 0.753% relative error in cut-off frequency was recorded. The zero attenuation constant value after the cutoff  
Figure 9: (**a**) Ka-band RWG-to-MSL transition model, (**b**) prototype of the nature-inspired fish-tail microstrip line.  
frequency guarantees that the designed transition model has extremely low losses and provides the highest transmission of EM power between the MS Line and RWG.  
Figure 10: _Cont._  
Electric Field (E), Magnetic Field (H), and Surface Current Density (I) Distribution Inside the Designed Transition  
The simulated electric field (E), magnetic field (H), and current density distribution (J) inside the WR-28-to-MSL transition at a frequency of 30 GHz for the design optimized parameters and dimensions are displayed in Figure 11a-c, respectively. It is clear from the field distribution that the field alters its direction after traveling \(\lambda_{\mathrm{g}}/2\) and repeats after traveling a distance of \(\lambda_{\mathrm{g}}\). This ensures that the length of the standard RWG (WR-28) provides a better transmission and low insertion when it is of the order of nearly an integer multiple of the half-guided wavelength (\(\lambda_{\mathrm{g}}/2\)). The confined electric field within the transition also ensures that perfect matching between the quasi-TEM mode of the 50 \(\Omega\) microstrip line and the nearly 500 \(\Omega\) dominant mode (TE\({}_{10}\)) of WR-28 is achieved.
# Prediction Models for Diabetes in Children and Adolescents: A Review
## 5 Machine Learning and Deep Learning Models to Predict Diabetes in Children and Adolescents  
In 2021, 355,900 new cases of T1D were diagnosed in children and adolescents globally, with projections indicating an increase of over 100,000 cases by 2050 [1]. The delayed diagnosis of T1D is a significant problem, contributing to up to 70% of deaths in those under 25, with diabetic keto acidosis (DKA) being a life-threatening complication in undiagnosed cases. Diagnosis is challenging because symptoms like fatigue, weight loss, and increased thrust are non-specific. Although blood glucose tests can confirm the condition, the lack of immediate lab results complicates timely diagnosis. Genetic risk scores are underutilized in healthcare due to integration challenges. On the other hand, T2D is a chronic disease that affects millions of people worldwide, and its early diagnosis is a challenge. As already mentioned, genetic, environmental, and lifestyle factors are key to the occurrence of T2D. There are more biological indicators that indicate T2D, but the priority is an elevated level of glucose in the blood as well as insulin resistance [32]. Traditional methods of T2D prediction often rely on subjective judgments, leading to delays and suboptimal outcomes. Recent advancements, such as machine learning (ML) algorithms, have shown promise in identifying T1D and T2D not only in adults but also in children and adolescents. It is believed that this tool could have significant potential to improve early detection of diabetes. As a result, it is expected that 72% of children with diabetes will be flagged 90 days prior to diagnosis. To ensure the benefits for all populations, dissemination over the world is necessary.  
Machine learning (ML) and artificial intelligence (AI) are recognized in pathology and laboratory medicine as useful techniques in data preprocessing and as basic supervised concepts for disease prediction [80]. It is also the case for diabetes [81]. The advantage of the application of ML and AI is the possibility of processing a large amount of data and detecting diabetes in individuals [82]. Early identification of diabetes remains critical for enabling preventive actions and improving outcomes [83].
# Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
## 5 Evaluation and Discussion
### ANN Adversarial Example Generation
#### 5.3.1 Binary Classification ANN Model  
Using the targeted FGSM to generate adversarial examples showed decreased accuracy when the epsilon value reached 0.8. As explained in Section 3.3, a decreased recall metric shows an increase in false negatives, enabling an attacker to bypass the IDS. Figure 5(a) shows that as the epsilon value increases to 1, the accuracy falls from 99.8% to 92.7%, and the recall score falls from 99.7% to 56.3%. In addition, the confusion matrix seen in Figure 3(a), shows a significant increase in false-negative classifications than the original confusion matrix in Figure 2(a). The learning outcome is that while the accuracy remains high, the recall score falls significantly. For example, if an organisation accepts a model  
Figure 5: Comparing recall scores in targeted/non-targeted Support Vector Machines label flip.  
with over 90% accuracy, then they are at a greater risk of the success of an undetected attack using the FGSM. Using the non-targeted FGSM to generate adversarial examples showed a more significant negative impact than the targeted FGSM. In Figure 5(b), the accuracy score fell from 99.6% to 76.8%, and the recall score fell from 99.6% to 77.1%. It was noted in Section 4.2 that the CleverHans package could perform a targeted and non-targeted attack. The non-targeted attack focuses on trying to make the labels incorrect [48]. The attack on the labels forces the model to incorrectly classify any label, which could be why the accuracy is falling more significantly compared to the targeted attack.  
However, taking the confusion matrices in Figure 5(a) into consideration, it is observed that the non-targeted FGSM created adversarial examples results in a higher number of false negatives. The recall score falls significantly in the targeted FGSM and seen in Figure 6(a) because the model's ability to classify true positives correctly remains high.
## 3 Results and Discussion
### MEMS Tuning Simulation  
To illustrate the potential tuning capability at the MEMS scale, COMSOL FEM was used to simulate the results. A silicon cantilever with dimensions of 9.5 \(\times\) 11 \(\times\) 0.025 mm (w \(\times\) 1 \(\times\) t) was developed based on previous research [5; 6]. A silicon proof mass (650 \(\upmu\)m thick) with an array of 38 \(\times\) 38 cavities that were 235 \(\times\) 235 \(\upmu\)m squares was simulated. A solid rectangle of W with a density of 4.33 g cm\({}^{-3}\) was used to fill the cavities. The result of filling consecutive rows in both directions (free end to tethered end and tethered to free) is illustrated in Figure 8. In the figure the x-axis represents consecutive filled rows, so 2  
Figure 6: Experimental results illustrating 1 st mode resonant frequency as a function of filling consecutive columns, starting with the most lateral (C1), using W powder for both a 5 \(\times\) 7 array and a 7 \(\times\) 9 array.  
Figure 7: Experimental results illustrating 1 st mode resonant frequency as a function of filling consecutive rows in both directions (black—free end to tethered end, and red—tethered end to free end).  
on the x-axis represents the red line filling rows 1 and 2, whereas the black line represents filling rows 37 and 38. The results demonstrate the potential to have a tuning range from 178 Hz to 59 Hz using standard MEMS energy-harvesting devices. The tuning range could be further increased by increasing the density of the filler or decreasing the density of the proof mass. The results illustrate the potential to use this method in a scaled-down device. However, developing methods of dispensing the particles into sub-mm cavities would need to be investigated further.