esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations
### Impact of Some Parameters on the Invulnerability Performance
#### 4.2.2 Impact of Single-Hop Cluster Head Node Capacity  
In the experiment, we found that the damage of the network is related to the single-hop cluster head node to a certain extent. We divide the cluster head nodes into single-hop cluster head nodes and general cluster head nodes, and their capacity size is determined by Equation (9). Single-hop cluster head nodes, as nodes directly adjacent to the sink nodes, become the mandatory path to the sink nodes. Let \(\sigma=2\), \(\lambda=0.55\), and then we explored the impact of single-hop cluster head node capacity on the invulnerability of the whole network.  
As shown in the Figure 5, the evaluation index is set to \(M\). It is clear to see that increasing the capacity of single-hop cluster head nodes does not increase the number of available nodes in the whole network in the small case. In contrast, when the capacity of single-hop cluster head nodes is gradually increased, the propagation of cascading faults can be effectively suppressed. However, when the overall capacity of the network is large enough, expanding the capacity of single-hop nodes does not significantly improve the resilience of the network to damage; it only causes a waste of resources.  
Figure 4: Effect of capacity regulation parameters: (**a**) the effect of exponential adjustment parameter on invulnerability performance and (**b**) the effect of overload tolerance on invulnerability performance.  
As shown in Figure 6, when the evaluation index is the original evaluation index \(G\), the value of \(M\) is much smaller than the value of \(G\) when \(\beta\in[0.3,0.4]\). This is because, although the number of surviving nodes is large, the critical paths that can be transmitted to the sink node are destroyed, resulting in a substantial increase in the shortest paths of the surviving nodes. This can directly reflect the reasonableness of the improved evaluation index.  
In reality, the increase of \(\beta\) means an increase in cost. Therefore, this experiment provides an important theoretical guidance for constructing an economical and more destructive network.
## 6 Discussion  
### Efficacy of Bilateral Ear Acoustic Authentication  
The EER for left ear acoustic authentication was 1.31%, and the AUC was 0.9979. Contrastingly, the EER and AUC of bilateral ear acoustic authentication (Joint method) was 0.39% and 0.9995, respectively. By combining the signals from both ears, the EER was reduced by 0.92% and AUC was increased by 0.0016. The F-test and \(t\)-test were performed to determine the existence of a difference between the accuracy of bilateral (Joint method) and single (Left method) authentication. The result of F-test showed that the population variances were not equal; therefore, [PERSON]'s \(t\)-test was performed. The \(p\)-value was less than 0.01, indicating a significant difference between accuracies. This implies that the use of signals from both ears for ear acoustic authentication is effective in improving accuracy.  
Figure 8: Feature extraction method for bilateral ear data. Plus is a method of adding data to the left ear and the right ear. Minus is a method of subtracting data from the left and right ear. Joint is a method for concatenating data from the left and right ears.  
Figure 9: Authentication accuracy for bilateral ear acoustic authentication (Plus, Minus, and Joint) and single-ear acoustic authentication (Left and Right). (**a**) EER. (**b**) AUC.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 1 Introduction
### Banking Malware Communication (C&C) Architecture
It is particularly effective against decentralized P2P C&C architectures, like the Zeus banking malware, as it can model peer relationships and detect botnet traffic. Supervised algorithms such as random forest and unsupervised methods like clustering can help to optimize feature selection, improving detection accuracy while reducing computational overhead. By leveraging behavioral analysis, ML provides a robust and dynamic solution for identifying and mitigating sophisticated malware C&C activities.
## 3 Results and Discussion
### Performance Experiment Results  
#### 3.3.1 \(T_{l}\) Variation  
As \(T_{basic}\) was set to \(-\)5.0 \({}^{\circ}\)C, \(T_{radiation}\) was set to \(-\)40.0, \(-\)20.0, and 0 \({}^{\circ}\)C, the result showed that the decrease rate of \(T_{l}\) was 14.0 \({}^{\circ}\)C-h\({}^{-1}\) at the first 0.5 h, which was same as the control group. This means that the cooling rate of \(T_{l}\) was primarily affected by the basic refrigeration unit. After the combined action conducted over 3.0 h, \(V_{cool}\) became higher than that of the control group, whereas \(T_{l}\) was \(-\)3.8, \(-\)2.9, and \(-\)0.4 \({}^{\circ}\)C, respective to \(T_{radiation}\) set at \(-\)40, \(-\)20, and 0 \({}^{\circ}\)C. Therefore, the cold radiation source further reduced \(T_{l}\) when the influence from basic refrigeration unit tended to be stable (Figure 8a).  
\(T_{basic}\) was set to 0 \({}^{\circ}\)C and at the first 0.5 h, \(V_{cool-leaf}\) was 12.0, 12.0, and 10.0 \({}^{\circ}\)C-h\({}^{-1}\), respective to \(T_{radiation}\) set at \(-\)40.0 \({}^{\circ}\)C, \(-\)20.0 \({}^{\circ}\)C, and 0 \({}^{\circ}\)C. \(V_{cool-leaf}\) of the control group was 7.0 \({}^{\circ}\)C-h\({}^{-1}\). After the combined action conducted over 3.0 h, \(T_{l}\) was \(-\)0.9, 2.7, 2.4, and 4.3 \({}^{\circ}\)C. This showed that there was an enhanced effect of the cold radiation source unit on \(T_{l}\) (Figure 8b).  
\(T_{basic}\) was set to 5 \({}^{\circ}\)C and at the first 0.5 h, \(V_{cool-leaf}\) was 9.8, 7.8, and 4.0 \({}^{\circ}\)C-h\({}^{-1}\), respective to \(T_{radiation}\) at \(-\)40.0, \(-\)20.0, and 0 \({}^{\circ}\)C. \(V_{cool-leaf}\) of the control group was 2.6 \({}^{\circ}\)C-h\({}^{-1}\). After the combined action conducted over nearly 3.0 h, the \(T_{l}\) was 2.3, 3.8, 7.0, and 7.8 \({}^{\circ}\)C, respectively. This showed a further enhanced effect of the cold radiation source unit on \(T_{l}\) strengthened (Figure 8c).  
Figure 6: Temperature contour.  
Figure 7: Leaf–air temperature difference.
## 4 Experimental Results and Comparative Analyses
### Performance Evaluations
#### 4.2.1 Comparisons of LLM-Based Pre-Processing Techniques
In contrast, short-summarization techniques stand out in key point extraction, where the goal is to quickly distill the most crucial information and high-speed process tasks, enabling rapid and efficient handling of large volumes of text. These findings can guide future research and application development in 6G patent data analysis, enabling more efficient and accurate utilization of the vast amount of patent information in the 6G domain.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Manual Fruit Count
#### 2.3.3 Examples
[33] reported sampling of a 17 ha apple orchard using an SUR approach in 5 h. A marketable yield estimate of \(356.6\pm 89.2\) t compared well to the 374.9 t packed for export.
## 5 Discussion  
In this paper, we unveil a methodological framework designed to assist designers in regard to the sizing of power electronics applications. Several noteworthy aspects of this methodology merit attention, as follows:  
* A specialized 'white-box' solver has been meticulously crafted to simulate systems governed by ordinary differential equations (ODEs), particularly those that are hybrid in nature. The expressions governing these systems are subject to change in response to unpredictable events;
* This solver possesses the remarkable ability to automatically detect when a power electronics application has attained a steady state;
* It is also equipped to extract salient features from both time-domain and frequency-domain simulations, which can subsequently be utilized as outputs for optimization;
* Furthermore, the solver is amenable to differentiation; the derivatives concerning the optimization inputs are computed through automatic differentiation for time-domain simulations, while formulas are employed for feature extraction.  
The methodology has been implemented within the open-source Python library, NoLOAD [33], enabling its application to various types of power electronics systems. Notably, it has been employed for the sizing of an aircraft power channel application, demonstrating superior performance with SQP and exact gradients compared to previous methodologies [11]. Although the application discussed herein pertains to a diode-based circuit, the methodology can be extended to encompass circuits featuring active switches. Moreover, the methodology is applicable to other domains, such as mechanical engineering. In particular, economic considerations could be incorporated into the optimization problem.  
In summary, this methodology enables designers to efficiently size their dynamic models, especially those composed of hybrid ODEs subject to unpredictable events, by employing gradient-based algorithms with automatic and accurate gradient computation. No a priori knowledge on the application's operating modes is required. As a result, the resolution's capabilities and optimization scope are significantly enhanced compared to prior work [11]. Furthermore, it represents a generic and easily implementable approach for the designer, addressing a genuine limitation in terms of the current state of the art [49; 50].
## 4 Implementation and Operations
### Message Delivery between Components  
As like other Observer design pattern [18] based MOMs, there are single Subject and multiple Observers in TinyONet. _InfoBus_ is the subclass of the Subject class and it is responsible for broadcasting incoming messages to the registered Observers. _SensorPool_, _SensorMapper_ and _SyncSlice_s are the subclasses of the Observer class and they listen to _InfoBus_. If a new data packet arrives at _SensorMapper_ from a sensing field, the packet is packaged into a _Msg_ and the _Msg_ is published on _InfoBus_. Then, this _Msg_ is delivered to _SensorPool_ and the group of _SyncSlice_s. If _SensorPool_ is the _Msg_'s target, it will parse the _Msg_ and perform an ordered processing with _Command_ and _Info_ of the _Msg_. In the case of _SyncSlice_s, they promptly process the _Msg_s and perform the action on receiving _Msg_s. Conversely, if there are some data to be delivered to physical sensors, _SyncSlice_s or _SensorPool_ will publish the data on _InfoBus_. Then, this data is handed over _SensorMapper_ and then it is transmitted to physical sensors through _TxRx_. This message-oriented communication is depicted in Figure 5.  
Figure 4: Cache structure of _VirtualSensor_.
## 3 Performance Indices
### Index \(I_{3}\)  
This index is connected to minimum-time control that requires the velocity constraint \(\dot{y}=v_{\rm max}\) to be introduced, otherwise the input command becomes infinite. The performance index is of the form  
\[J_{3}=\int_{t_{0}}^{t_{f}}dt\,. \tag{35}\]  
There are no constraints imposed on the translation in \(x\) axis, since the movement in \(x\) axis is proportional to time (\(\varphi_{x}(t)=gt\)), thus minimum time implies minimal value of \(\varphi_{x}\). Taking this constraint into consideration, the performance index takes the form  
\[J_{3}=\int_{t_{0}}^{t_{f}}\left(1+\lambda(v_{\rm max}-\dot{y})\right)dt\,. \tag{36}\]  
Euler equations derivatives for \(y\):  
\[\frac{\partial f}{\partial y} = 0\,, \tag{37}\] \[\frac{\partial f}{\partial\dot{y}} = -\lambda\,,\] (38) \[\frac{d}{dt}\left(\frac{\partial f}{\partial\dot{y}}\right) = -\lambda\,, \tag{39}\]  
and for \(\lambda\):  
\[\frac{\partial f}{\partial\lambda} = v_{\rm max}-\dot{y}\,, \tag{40}\] \[\frac{\partial f}{\partial\lambda} = 0\,,\] (41) \[\frac{d}{dt}\left(\frac{\partial f}{\partial\dot{\lambda}}\right) = 0 \tag{42}\]  
yield in the following set of E-L equations:  
\[\lambda = 0\,, \tag{43}\] \[\dot{y} = v_{\rm max}\,, \tag{44}\]  
with the solutions:  
\[\lambda = C_{1}\,, \tag{45}\] \[y = v_{\rm max}t+C_{2}\,. \tag{46}\]  
Border conditions become:  
\[y(0) = 0\,, \tag{47}\] \[y(t_{f}) = \varphi_{y}(t_{f})\,, \tag{48}\]  
and, as a result,  
\[at_{f}^{2}-(2 ap+v_{\rm max})t_{f}+ap^{2}+q=0\,. \tag{49}\]From the latter equation, one can obtain the interception time. In the \(x\) axis, it is assumed that the equation of motion becomes  
\[x(t)=C_{3}\exp^{-\frac{t}{t}}+C_{4}\exp^{\frac{t}{t}} \tag{50}\]  
with border conditions:  
\[x(0) = 0\,, \tag{51}\] \[x(t_{f}) = gt_{f}\,, \tag{52}\]  
where \(t_{f}\) is already calculated for \(y\).
## 3 Calibration
### Thermal Radiometric Calibration  
G-LiHT's thermal imaging camera has factory radiometric calibration for surface temperatures between \(-\)20 \({}^{\circ}\)C and 120 \({}^{\circ}\)C, with a Noise Equivalent Temperature Difference (NETD) \(>\)50 mK. This wide dynamic range allows for airborne operations of the thermal image over a diverse range of surface temperatures. Thermal calibration stability as a function of a microbolometer operating temperature range of 25 \({}^{\circ}\)C to 50 \({}^{\circ}\)C was verified against a GSFC Calibration Facilities blackbody for stable operation over the target temperature range of \(-\)5 \({}^{\circ}\)C to 85 \({}^{\circ}\)C (Figure 8).
# Centralized Energy Prediction in Wireless Sensor Networks
## 2 Related Work
#### 3.2.3 Communication Domain  
Two approaches are used to calculate the energy consumption in the communication module. One approach is based on the number of bits transmitted and received [32; 34]. The other approach is based on operation states, measuring the time spent on each state to estimate the total energy consumption [31; 35], as explained for the processing and sensing modules. We chose to use the operation states approach to give homogeneity to the model.  
Figure 1 depicts a simplified block diagram for a WSN communication module, based on four IEEE 802.15.4-compliant radios modules (CC2420 [36], CC1120 [37], MRF24J40 [38], and CC2520 [39]). Then, based on the simplified block diagram, four states were defined: transmitting, receiving, idle, and sleeping. When the module is in the transmitting state, the transmitter circuitry and the control logic block are active. In the receiving state, the receiver circuitry and control logic block are active. When the receiver and transmitter circuitry are turned off and the control logic block is turned on, the module is in the idle  
state. Last, when the receiver and transmitter circuitry are turned off and the control logic block is in the sleeping mode, the module is in the sleeping state.  
Equation (9) express the energy consumption for a certain period of time. The terms \(P_{Tx}\), \(P_{Rx}\), \(P_{com-id}\), and \(P_{com-sl}\) are the _transmitting_, _receiving_, _idle_, and _sleeping_ states average power consumption. The terms \(T_{Tx}\), \(T_{Rx}\), \(T_{com-id}\), and \(T_{com-sl}\) are the time spent on _transmitting_, _receiving_, _idle_, and _sleeping_ states. The terms \(N_{q}\) and \(P_{\{ct,q\}}\) are the number of \(q\) transitions and its average power consumption, respectively.  
\[E_{Com}=P_{Tx}T_{Tx}+P_{Rx}T_{Rx}+P_{com-id}T_{com-id}+P_{com-sl}T_{com-sl}+ \sum_{q=1}^{Q}N_{q}P_{\{ct,q\}} \tag{9}\]
# Predicting Electromagnetic Performance Under Wrinkling in Thin-Film Phased Arrays
## 4 Discussion
### Dual-Domain Displacement Patch Analysis
It not only shifts the resonant frequency but also significantly worsens matching and reduces far-field gain, leading to lower antenna efficiency and making it a primary contributor to electrical performance degradation. The \(d_{zm}\) error has the mildest effect; as seen in the gain patterns and S11 curves, its main impact appears to be on the system's radiation uniformity, with little effect on resonance or matching. In summary, the \(c_{fz1}\) error has the most direct impact on resonant frequency and should be prioritized to avoid system-level instability caused by frequency drift; the \(d_{pch}\) error significantly affects matching and gain, requiring strict control of manufacturing precision; the \(d_{zm}\) error poses relatively mild harm but still warrants attention for its effect on far-field radiation. During antenna design and fabrication, the impacts of these errors should be comprehensively considered to ensure system stability and reliable performance.
## 3 Experiments and Analysis
### Experimental Settings
#### 3.1.2 Evaluation Metrics  
This evaluation includes comparisons with recent bi-temporal CD methods and ablation studies. We evaluate the CD quality using four metrics: Precision (Pre.), Recall (Rec.), F1-measure (F1), and Intersection over Union (IoU), computed from True Positives (TPs), False Positives (FPs), and False Negatives (FNs). Higher scores across all metrics indicate superior performance.  
\[\text{Pre.}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{26}\]  
\[\text{Rec.}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{27}\]  
\[\text{F1-measure (F1)}=2\times\frac{\text{Pre.}\times\text{Rec.}}{\text{Pre.}+ \text{Rec.}} \tag{28}\]  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{29}\]
## 4 Experimental Results
### Similarity Analysis Between Original and Tampered Images Using SIFT  
The comparison of the average matching distance between Korean and English text images using Scale-Invariant Feature Transform (SIFT) revealed significant differences based on the structural characteristics of the two languages. As shown in Figure 11, statistical analysis confirmed that the matching distance for Korean text is significantly higher than that for English text (\(p<0.05\)), indicating notable differences in visual complexity. This can be attributed to the fact that Korean characters have a more complex structure than  
Figure 10: Graph of the changes in each region of the GLCM metric when Korean and English texts are forged using the copy–move method: (**a**) contrast, (**b**) correlation, (**c**) IDM, and (**d**) ASM.  
English ones. Korean text consists of syllabic blocks formed by combining consonants and vowels, and this block-based structure results in a densely arranged text. Due to the high complexity and density of these syllabic blocks, even slight pixel changes can significantly affect the relative positions and arrangements of keypoints, thereby increasing the matching distance. As a result, the complex and dense structure of Korean text leads to higher SIFT matching distance values compared to English text, reflecting lower similarity between the original and tampered images. On the other hand, the simpler structure and smaller number of keypoints in English text result in relatively lower matching distance values.
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 4 Experimental Results and Analyses
### Sound Pressure Level Conversion  
The components filtered based on information gain are subject to the conversion of sound pressure level to enhance the discrimination between components for easier feature extraction. In Figure 7, a comparison is made between the sound pressure signals and the signals following sound pressure level conversion.  
As is clear from Figure 7a, for a normally operating pipeline, the inflection points of signals are not easily distinguishable regardless of whether there is a lateral connection or not. As shown in Figure 7b, the inflection points of signals that underwent sound pressure level conversion are distinguishable. The conversion of sound pressure level can better reflect the local features of signals, which can enhance the differentiation between pipe conditions and improve the sensitivity of acoustic signals.
## 3 Calculation Model
### Validation  
In Figure 5, the authors validate the dynamic characteristics of the Calculation Model, focusing on the first natural bending frequency and the corresponding modal damping. This validation was conducted through a study employing the hybrid modelling technique outlined in Section 2.2 and published in [12]. The model was constructed using linear elements (C3D8) in the Abaqus software environment [40]. The dynamic characteristics of the Calculation Model are derived from impulse-generated decay curves.  
The pulse is characterised by its transit time \(\Delta t=\min(^{1}/_{12~{}f_{R}}\); 0.005 s. The amplitudes of the applied impulses are insignificant, as the models lack nonlinearities. The Abaqus modal dynamics solver is used to compute the system response in the time domain with a resolution of \(\Delta t\). The mesh size is determined as a function of the slab height, ensuring a minimum of three elements over the height. Mesh widths in adjacent components are kept similar.  
For reference, the analytical frame frequency \(f_{R}\)[22] and the limit \(a_{0,G}\) according to Equation (14) are plotted in Figure 5. The figure shows that the natural frequencies \(f_{1}\) of the shorter spans show a slight stiffening compared to the analytical natural frequency \(f_{R}\). This deviation decreases as the span increases. The observed effect results from the stiffening of the backfill, which is more pronounced for shorter bridges due to the assumed geometries. Regarding the first eigenfrequency, there is also a good agreement between the different modelling methods in regions where the limit \(a_{0,G}\) is exceeded. Considering the primary structural stiffness dependence of the eigenfrequencies, this correlation is expected [12]. As anticipated, the damping ratios show increasing discrepancies between the modelling methods when the application limit is exceeded (here, \(E_{b}\lesssim 450\) MPa in Figure 5a).  
Further results for a span of 17 m are provided in Figure 6.  
Here, the abutment width is varied while keeping the span constant. As the dimensionless frequency increases (\(a_{0}>1\)), it can be seen that the predicted damping ratios increasingly deviate from the hybrid modelling approach. These differences can be related  
Figure 5: A comparison of the dynamic characteristics of the frame using the hybrid modelling technique and the Calculation Model when varying the span: (**a**) damping ratio; (**b**) natural frequency.  
Figure 6: Comparison of the dynamic characteristics on the 17m frame using the hybrid modelling technique and the Calculation Model when varying the abutment width: (**a**) damping ratio; (**b**) natural frequency.  
to the unaccounted SSSI effect in the Calculation Model, highlighting the importance of imposing limitations to reduce such discrepancies.  
Considering the imposed limitation, there is a satisfactory agreement between the Calculation Model and the results obtained through the hybrid modelling approach.