esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Prediction of the Fatigue Crack Growth Rate
Therefore, the subsequent development of a prediction model is possible:  
\[Log_{10}\bigg{(}\frac{da}{dN}\bigg{)}=\beta_{1}Log_{10}\bigg{(}\frac{dE_{AE}}{ dN}\bigg{)}+\beta_{2}, \tag{13}\]  
where \(\frac{da}{dN}\) is the crack growth rate, \(\frac{dE_{AE}}{dN}\) is the AE energy release rate, and \(\beta_{1}\) and \(\beta_{2}\) are the model parameters. The proposed model is significant, in that it estimates the crack propagation rate by analysing the AE energy generated during fatigue loading. This allows the crack propagation length to be estimated within the monitoring period, thus facilitating timely warnings and real-time assessments of the severity of piston rod fracture failures. Figure 18: The quantitative relationship between the AE parameters and crack propagation process. AE counts (**a**); AE energy (**b**); linear correlation between the fatigue crack growth rate and AE counts and AE energy (**c**).
## 6 Experimental Evaluation
### Simulation Settings  
In the contract incentive algorithm, we use the CVX tool to solve the optimization problem. In the resource allocation algorithm, we deploy 20 to 50 VUs under the coverage of one UAV. The coverage radius of the UAV is set to 3 km, the coordinates of the VUs are randomly generated with the UAV located directly above the road, and the height of the UAV is set to 100 m. The values of \(a,b,\eta_{\text{LoS}}\), and \(\eta_{\text{NLoS}}\) are set to 12.08, 0.11, 1.6, and 23, as employed in [36].  
Taking the landmark recognition task as an example, we conduct the VUs' FL experiments on the MNIST dataset, which is a grayscale handwritten image dataset that includes 60,000 training pictures and 10,000 test pictures. The CNN model is used as the backbone model of FL, which is composed of two convolutional layers, two activation functions, two pooling layers, and two fully connected layers. Among them, the two convolutional layers have 10 and 20 (5 \(\times\) 5) convolution kernels. The two fully connected layers have 50 and 10 neurons, respectively. We exported the CNN parameter file (including all weights and biases) with a size of approximately 0.05 Mbit.  
Due to the small size of the transmission parameters, only one RB is needed to complete the transmission. An RB is typically set to 180 kHz in the 4G protocol and also in the 5G protocol. The other simulation parameters are set as in Table 2. Among them, the maximum transmission power refers to the definition of 3 GPP [38; 39].
## 4 Experimental Results
### Results of Subset3  
Table 5 shows the RMSE and MAPE results obtained for Subset3. It can be seen from the table that these results are lower for Model1 and Model2 than for Model3. When Model1 and Model2 are compared, it can be seen that Model1 has more successful results in terms of RMSE and MAPE values.  
In Figure 13, a graph is given showing the prediction of 30 days-ahead by applying Model1, Model2, and Model3, respectively, using Subset3. If these results are compared between models, it is seen that the closest approach to the real load value for Subset3 is achieved by Model1, and the results in Table 4 confirm that.  
Figure 14 shows a visualization of the LIME results of the predictions made with Model1, Model2, and Model3, respectively, using Subset3. The XAI results for Subset3 were obtained using a random first sample in the test set for all models. As can be seen from the LIME results, the prediction of Model1 is 0.89, and many time-lagged values of the 'SYSLoad' feature and a one-day lagged value of the 'DryBulb (t-1)' feature in the right figure contribute positively to the prediction score. The predictive value of Model2 is 0.88, and as in Model1, the 'DryBulb' and 'SYSLoad' features have a positive impact on this prediction result. While the values of the 'DryBulb (t-1)', 'SYSLoad (t-20)', 'SYSLoad (t-13)', 'DryBulb (t-7)' features positively improve the prediction of Model2, the 'SYSLoad (t-17)' and 'SYSLoad (t-18)' features have a negative effect. The prediction value of Model3 is 0.90, and the features that contribute positively to this model are the values of the 'DryBulb' and 'SYSLoad' features, as in Model1 and Model2. As shown in the table, the values of 'SYSLoad (t-13)', 'SYSLoad (t-5)', 'SYSLoad (t-6)', 'SYSLoad (t-7)', 'SYSLoad (t-13)', and 'SYSLoad (t-20)' contributed the most to the prediction of Model3. When the XAI results from Subset3 are evaluated on a feature basis, it can be inferred that the most effective features are 'SYSLoad' and 'DryBulb'.  
In addition, among the features used for all subsets, the lagged values of the features generally close to 30 days contribute positively to the prediction of the models. This shows that all the feature selection processes performed in the feature engineering and time-lagged up to 30 days are correct and useful processes in terms of this study.  
Figure 13: Forecasting results of the models based on Subset3: (**a**) Model1 results; (**b**) Model2 results; (**c**) Model3 results.  
\begin{table}
\begin{tabular}{c c c} \hline
**Subset3** & **RMSE** & **MAPE** \\ \hline Model1 & 0.046 & 0.044 \\ \hline Model2 & 0.049 & 0.045 \\ \hline Model3 & 0.073 & 0.076 \\ \hline \end{tabular}
\end{table}
Table 5: Results of Subset3.
# Intelligent TCP Congestion Control Policy Optimization
## 2 Related Work  
### Fundamentals of Congestion Control  
Network congestion is a phenomenon that often occurs in the operation of computer networks, and from its manifestation, network congestion is the phenomenon that the cache in the router drops packets because of overflow. When the packet arrives at the router, the packet is forwarded according to the configured forwarding rules and output to the corresponding link. Due to limited network link resources (including cache size, fixed bandwidth, processing power, etc.), queues form in the link and network congestion occurs when packets arrive too quickly. Increasing the buffer area can absorb excess packets and prevent packet loss, but if you blindly increase the size of the buffer without improving the link bandwidth and processor capacity, this will cause the waiting time in the queue to greatly increase, and the upper protocol can only retransmit them, so simply expanding the cache space can not solve the network congestion problem, but will cause a waste of network resources. In addition, network nodes process thousands of data streams per second, sharing bandwidth between data streams, and the maximum rate of data transmission is limited by the bottleneck link. Congestion occurs when a network node needs to process more data than it can handle. Therefore, the job of congestion control is to prevent data senders from sending large amounts of data into the network, causing the transmission link to be overloaded. The principle of congestion control is shown in Figure 1.  
Figure 1: The role of congestion control.
# Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks
## 3 Description of the Dataset
### Data Extraction  
We used the annotations for aneurysm extraction. We chose a size of 64 \(\times\) 64 pixels concerning the maximum dimensions of the annotations. A total of 1230 aneurysm images were annotated and cropped. For each aneurysm annotation, we cut off 10 vessel slices of non-aneurysm vessels around an aneurysm. We were careful not to overlap the sections with the annotation. We obtained an image database with 1230 aneurysm and 12,300 non-aneurysm image slices.  
All cropped images pass by zero-mean normalization. Thus, the original 10-bit grayscale pixel was converted from an original range of 0-1023 to a unit value and shifted by the mean value to a final range from \(-\)0.5 to +0.5. Figure 6 illustrates the procedure we used to cut the data for aneurysms and non-aneurysms from the original image. The red rectangle represents the original annotation. For the purpose of training the model, it is inconvenient for us that the annotation region has different dimensionality for different images, patients, and aneurysm sizes. For training, we used a square region centered by the original annotation. It is shown by the green square. Around the annotation, we clipped non-aneurysm vasculars. If a random cut interfered with the annotated region, we ignored it and replaced it with another cut. Because there are disproportionately more healthy vessels, we limited the number of non-aneurysm clippings to 10.  
We divided the dataset into training, validation, and test sets. Table 1 specifies the numbers of data used in training and testing.  
Figure 5: Example of an annotation visualization. (**a**) The aneurysm represents only a tiny part of the image. (**b**) The detail of the annotated area.  
Figure 6: Example of data clipping.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
###### Abstract  
Resource bound security solutions have facilitated the mitigation of spatio-temporal attacks by altering protocol semantics to provide minimal security while maintaining an acceptable level of performance. The Dynamic Window Secured Implicit Geographic Forwarding (DWSIGF) routing protocol for Wireless Sensor Network (WSN) has been proposed to achieve a minimal selection of malicious nodes by introducing a dynamic collection window period to the protocol's semantics. However, its selection scheme suffers substantial packet losses due to the utilization of a single distance based parameter for node selection. In this paper, we propose a Fuzzy-based Geographic Forwarding protocol (FuGeF) to minimize packet loss, while maintaining performance. The FuGeF utilizes a new form of dynamism and introduces three selection parameters: remaining energy, connectivity cost, and progressive distance, as well as a Fuzzy Logic System (FLS) for node selection. These introduced mechanisms ensure the appropriate selection of a non-malicious node. Extensive simulation experiments have been conducted to evaluate the performance of the proposed FuGeF protocol as compared to DWSIGF variants. The simulation results show that the proposed FuGeF outperforms the two DWSIGF variants (DWSIGF-P and DWSIGF-R) in terms of packet delivery.  
wireless sensor network; node selection; malicious nodes; routing protocol; security; fuzzy logic; geographic forwarding +
Footnote †: journal: Sensors
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 3 A Practical Non-Stationary of 3-D Massive MIMO Channel Model
### Energy Transferring  
Next, let's consider the energy transferred between a transmitter and a receiver. A transmitter emits energy over time and the energy emitted per unit time is the power watts. If the transmitter is emitting radiation equally in all directions with power \(P_{Ti}\), then the flux \(F_{i}\) at a distance R from the transmitter is simply:  
\[F_{i}=\frac{P_{Ti}}{4\pi R^{2}} \tag{33}\]
# Sensitivity Estimation for Differentially Private Query Processing
## 4 Sensitivity Estimation for Join Queries
### Discussion  
The sampling-based and sketch-based sensitivity estimation methods proposed in this study significantly enhance the efficiency and accuracy of differential privacy protection for complex join queries. This breakthrough enables critical real-world applications, such as real-time anomaly detection in social networks, to achieve near non-private query utility while maintaining rigorous individual privacy protection with sub-second response times. By addressing the fundamental limitations of existing approaches, our work overcomes the key barriers to deploying large-scale privacy-preserving systems in practice. However, there are still some limitations that need to be considered. First, the sampling-based approach requires the careful tuning of sampling rates for optimal performance across different data distributions, which may increase implementation complexity. Second, the performance of our sketch-based approach may be impacted by data dimensionality and the number of join attributes, where high-dimensional join operations can amplify approximation errors due to the curse of dimensionality in sketch compression, and more join attributes require larger sketch sizes to maintain target accuracy levels. This paper establishes an effective framework for privacy-preserving query processing with immediate practical applications, and the limitations discussed provide promising directions for our future research.
## 1 Introduction
proposed a method to optimize the element array so that the sound pressure distribution in the main lobe is similar to that of a densely arranged element array, while reducing the grating lobe level [18]. As the majority of the aforementioned studies optimized element arrays for handheld probes, where a large FOV was not a necessity, the optimized element arrays were small, approximately 10 mm square. In contrast, for skin-attachable probes, the use of large element arrays is necessary to achieve a wide FOV, particularly in the lateral direction. As increasing the element spacing results in a deterioration of image quality [17], it is impractical to directly apply the optimization methods from previous studies to skin-attachable probes. While uniform focusing across the entire 3D space was required in the previous studies, focusing only on a single cross-section is sufficient for 2D imaging with skin-attachable probes. The required number of elements for 2D imaging may be less than that for 3D imaging, although it must accommodate attachment errors. In this study, we propose an element array-optimization method to enable clear imaging of target cross-sections with skin-attachable probes, even in the presence of positional and angular errors. Previous studies have only optimized element coordinates; however, US focus performance also depends on element shapes [1; 19]. Therefore, in this study, both the center coordinates and the side lengths of the elements were optimized. These parameters were iteratively updated based on the main lobe level, main lobe width, and side lobe level when focusing US on the imaging points. To enable the simultaneous optimization of high-dimensional parameters, we made the calculation of acoustic pressure field differentiable, allowing for using a gradient-based optimization method. The optimized element array successfully visualized simulated wires, cysts, and blood vessels with clarity, even under a 3 mm positional error and 20\({}^{\circ}\) angular errors.
## 3 Results
### Mode 1: Charging
#### 3.1.2 Cold Energy Stored, Energy Storage Rate and Time Required to Charge the PCM  
Figure 9 shows the PCM instantaneous cold energy and the PCM energy storage rate against time during the charging test with a compressor power 30%. It was observed that from the second to the fifth minute the energy storage rate is quite higher than in the rest of the process. This is explained by the fact that most of the PCM is changing phase during this period, allowing for a higher temperature difference between the refrigerant and the PCM.  
Figure 7: Temperature profiles in Mode 1 for compressor power at (a) 30%, (b) 45%, (c) 60% and (d) position of the sensors. Dotted lines denote PCM temperature at top level, dashed lines at middle level, and solid lines at the bottom.  
The final cold energy stored in the PCM and charging time are reported in Figure 10 for the different compressor powers investigated to evaluate the influence of compressor power on the total cold energy that can be stored. It was observed that the lower the compressor power the higher the time of charging. Moreover, the highest energy storage capacity was achieved at a compressor power of 30%, for which 294 kJ of cold energy were stored in 17 min. The second higher value of cold energy stored in the PCM was achieved at a compressor power of 60%, for which 273 kJ of cold energy were stored in 6.7 min. This represents a decrease of about 7% in the energy stored, but the charging duration was considerably reduced from 17 min to less than 7 min, which represents a reduction of about 60%. Therefore, if the charging duration is a critical parameter of the real application, the charging at 60% compressor power could be a good option.  
Figure 8: PCM temperature profile for compressor power at 15% in Mode 1. Dotted lines denote PCM temperature at top level, dashed lines at middle level, and solid lines at the bottom.  
Figure 9: Cold energy and energy storage rate against time, for PCM charging at 30% compressor power.
# Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method +
## 4 Simulation for Correntropy Induced Metrics and Cyclic Correntropy Spectrum  
### The Effect of Correntropy Induced Metrics  
A simulation was made to illustrate the capabilities of CIM. Five simulation signals \(s_{1}\), \(s_{2}\), \(s_{3}\), \(s_{4}\), and \(s_{5}\) are generated. \(s_{1}\) is a sine signal, \(s_{2}\) is a square wave signal, \(s_{3}\) is an amplitude modulated signal, \(s_{4}\) is random noise, where, \(s_{1}\), \(s_{2}\), and \(s_{3}\) are expressed as (11)-(13), \(s_{4}\) consists of random numbers from \(-1\) to \(1\). \(s_{5}\) is the mix of \(s_{1}\), \(s_{2}\), \(s_{3}\), and \(s_{4}\), expressed in (14). The sampling frequency is \(16\) kHz, and the number of sampling points is \(2048\).  
\[s_{1}=\sin(200\times\pi\times t) \tag{11}\]  
\[s_{2}=\text{sign}(700\times\sin(200\times\pi\times t)) \tag{12}\]  
\[s_{3}=\sin(200\times\pi\times t)\times\sin(2000\times\pi\times t) \tag{13}\]  
\[s_{5}=s_{1}+s_{2}+s_{3}+s_{4} \tag{14}\]  
The waveform and frequency spectrum of the five signals are shown in Figure 3.  
Calculate the CIM between the first four signals and \(s_{5}\), respectively. From (7), it can be conducted that when \(X=Y\), CIM = 0; CIM > 0, for other cases. Additionally, the greater the difference between the variables \(X\) and \(Y\), the greater the CIM value. Set \(X\) in (6) to \(s_{5}\), set \(Y\) in (7) to \(s_{1}\), \(s_{2}\), \(s_{3}\), \(s_{4}\), respectively. Then four CIM values are displayed in Figure 4, it can be seen that the CIM between \(s_{2}\) and \(s_{5}\) is the smallest which can be explained by the frequency spectrum (Figure 3b).  
Figure 4: CIM between the first four simulation signals and \(s_{5}\).  
Figure 3: Simulation signals. (**a**) waveform of signals. (**b**) frequency spectrum.  
The frequency spectrum of \(s_{2}\) contains the most frequency components of \(s_{5}\), so for a method based on frequency component analysis, it is appropriate to choose \(s_{2}\) with the lowest CIM to represent \(s_{5}\). Moreover, the largest value of CIM is produced by \(s_{4}\) and \(s_{5}\), where \(s_{4}\) is the random noise, which also shows that CIM-based selection can effectively reduce noise interference.
## 2 Related Work  
Anchor-based detectors: Faster R-CNN [4] first demonstrated the method of employing anchor boxes to detect objects, inspired by the traditional sliding-window and proposal-based detectors. After that, the anchor boxes with fixed scales and aspect ratios were widely used in modern object detectors. The anchor boxes are regarded as reference boxes or proposal boxes in single-stage detectors [5; 6; 8; 23; 24; 25]. SSD [7] utilizes the feature maps from multi-layers to detect objects with different scales. YOLOv2 [5] applies the anchors for classification and box regression to achieve better performance than YOLOv1 [9]. RetinaNet [8] proposes focal loss to mitigate the classification imbalance problem. Compared with single-stage detectors, two-stage or multi-stage detectors [4; 26] usually implement the Region Proposal Network (RPN) to generate regions of interests (RoIs). Then, RoI Pooling and RoI Align layers are used to extract aligned features of the RoI. In addition, several detectors [24; 27; 28] adopt a cascade layer to refine detection bounding boxes step by step.  
Refined anchor-based detectors: In recent years, a large number of works have aimed to improve the pre-defined anchor strategy. For the anchor assignment strategy, ATSS [17] acquires the IoU scores of the anchor boxes near the ground-truth box and calculates the mean and variance to determine the IoU threshold of each bounding box adaptively. PAA [29] separates the anchors into positive and negative samples for the ground-truth boxes according to the learning status of the model. For the anchor generation strategy, MetaAnchor [30] proposes an anchor function to generate anchor boxes from the arbitrary customized prior boxes dynamically. Guided Anchor [22] refines basic boxes with the shape prediction branch Based on the pre-defined anchor boxes.
## 6 Discussion  
Table 3 provides a comparison of F1-scores and AUC metrics obtained from our experiments and those derived from the LogEvent2 Vec paper. Our best results are significantly better than those obtained by the authors of the LogEvent2 Vec approach. The experimental environments were configured in the same way, except for the length of the sequences and model validation method. The authors of LogEvent2 Vec divided the input data into sequences of length 5000, resulting in 943 sequences split randomly into training and validation subsets in a ratio of 90 to 10. In this paper, we used much shorter sequences: with the length ranging from 3 to 100 log templates, resulting in a total number of sequences ranging from 1,582,654 to 47,479. To validate a model, we used the repeated stratified K-fold cross-validation method, which is much more suitable for the imbalance classification task than is the classic train/test split validation method used by the authors of the LogEvent2 Vec approach. Repeated stratified K-fold cross-validation sustains the class distribution in each fold, which results in an estimate of model performance that is neither too \"optimistic\" nor too \"pessimistic\". For the most adequate comparison, the feature vector length of 100 was preserved.  
Splitting the dataset into shorter sequences implies a greater number of sequences, which in turn improves the model training data. A narrower semantic context allows binary classifiers to better identify the features responsible for anomalies and decreases the number of false negatives. Anomalies, by definition, are rare; thus, in long sequences, the features responsible for the anomaly are averaged over the others, making the task of binary classification more difficult and prone to errors.  
Furthermore, long log template sequences reduce the usefulness of the model in production environments. Usually, any sequence classified by the model as anomalous is forwarded to a system administrator, whose task is to identify whether it is a false-positive. With a sequence length of 5000 log templates, finding a potentially anomalous log or log series within 5000 entries may be very tedious and time consuming. The model presented in this paper can identify much more precisely which logs should be manually analyzed, making it more suitable for use in production environments.