esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Periodic Feature-Based Time Series 2D Transformation  
Unlike other types of sequential data, such as language and video, time series data is recorded continuously, with scalar values typically stored at each time point. However, a time series at a single time point often lacks sufficient semantic information for analysis. In the context of hoist fault diagnostics, particularly in bearing diagnostics, the signals exhibit complex time series patterns, including rising, falling, and fluctuating behaviors. Bearings may fail due to factors such as wear, inadequate lubrication, unbalanced loads, or other mechanical issues during operation. These failures can result in damage to the inner ring, outer ring, or ball, with each fault type generating a distinct vibration signal signature. As damage progresses or multiple faults overlap, the vibration signal exhibits increasingly complex and irregular patterns.  
[PERSON] et al. [27] enhanced the deep convolutional neural network by converting input time series data into a 32 x 32 matrix and incorporating adaptive learning rates and momentum components into the weight update process. They proposed an adaptive deep convolution neural network, which can automatically extract features from vibration signals. [PERSON] et al. [25] adopted the overlap sampling method to rearrange the original data, enabling the automatic extraction of relationships between different interval signals in periodic mechanical signals. [PERSON] et al. [28] converted multi-source vibration signals into a two-dimensional matrix and applied a CNN for feature extraction and classification. However, such transformations may result in the loss of information, particularly long-term dependencies in time series data, which can be lost during the rearrangement. Additionally,2D transformations increase computational complexity and overhead, potentially affecting efficiency, especially when working with large datasets. [PERSON] et al. (2019) proposed a fault identification method based on multi-sensor data fusion and a bottleneck layer optimized convolutional neural network, which integrated information by converting multi-sensor vibration signals into images, allowing the model to extract richer features. [PERSON] et al. (2019) introduced a signal-to-image method using integer binary encoding, which converts bearing fault signals into binary images for a two-stage binary coding fully connected classifier based on a residual network. By converting time series data into image format, these approaches leverage the power of deep learning in image recognition, enhancing fault diagnosis and prediction efficiency. They also enable the use of image enhancement techniques, such as interpolation and smoothing, to improve data features, thereby enhancing model performance and generalization. However, this conversion can lead to a loss of detailed information, which may impact the overall performance of the model.
# Ship Network Traffic Engineering Based on Reinforcement Learning
###### Abstract  
This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects--traffic balance, instruction priority, and complex network structure--to evaluate reinforcement learning performance in these scenarios. Performance: We developed a reinforcement learning framework for ship network traffic engineering that treats the routing policy as the state and the network state as the environment. The agent generates routing changes and uses actions to optimize traffic services. The experimental results show that reinforcement learning optimizes network traffic balance, reasonably arranges instruction priorities, and copes with complex network structures, greatly improving the network's quality of service (QoS). Through an in-depth analysis of the experimental data, we noticed that network consumption was reduced by 9.1% under reinforcement learning. Reinforcement learning effectively implemented priority routing of high-priority instructions while reducing the occupancy rate of the edge with the highest occupancy rate in the network by 18.53%.  
ship network; traffic engineering; reinforcement learning; quality of service (QoS)
## 4 Simulation Results and Discussions
### Results and Discussions  
In this section, we compare the performance of the proposed ASA system with that of the other proposed systems under the same measures in terms of system utilization and other QoS metrics, such as average waiting time and TBP. The average holding time for all users is the same. Therefore, the increase in request arrival rates (i.e., offered traffic loads) corresponds to the increase in traffic intensity. Consequently, most of the performance measures are plotted against the total offered load measured in Erlang (Erl).  
In the following subsections, we discuss several experiments to investigate the full impact of our proposed system. The first experiment evaluated the performance of our proposed ASA system and compares it with the SSA system. The second experiment studied the impact of adding a maximum tolerable delay parameter on the ASA system. The third experiment evaluated the effect of changing the priority ratios of the slices in both ASA and DA-ASA systems. The fourth experiment explicated and compares the performance of ASA and DA-ASA at different queuing time limits. The last experiment evaluates the performance of ASA and DA-ASA at different queue size.  
The experiments consider a wide range of offered traffic, from a low value of 50 Erl to a larger value of 350 Erl to capture the behavior of the proposed systems in all traffic load conditions (i.e., at low, moderate, and high traffic loads). In the first experiment, we investigate the general behavior of our proposed ASA system against the SSA system.  
In addition, we consider a wide range of offered traffic values, ranging from 20 Erl to 250 Erl, to evaluate the systems' behavior under various traffic volumes (i.e., low, moderate, and high traffic loads). The comparison is under the same conditions regarding system utilization and other QoS metrics, such as TBP, and average waiting time. We then plot the performance measures against the total offered load measured in Erl.
## 3 Results
### Journals and Authors
#### 3.3.2 Authors
Researchers' interests have gradually expanded from early studies in agricultural remote sensing to encompass a wide range of fields, including environmental monitoring, forest management, and disaster prevention. Over time, both UAV technology itself (drones) and its applications in remote sensing have shown a steady upward trend in research attention. Moreover, domain-specific applications of UAVs--such as volcanic monitoring, hydrological station observation, and precision forestry--have emerged as key research focuses. Alongside these developments, technological advancements related to UAVs--particularly in image processing and data analysis--have also become prominent research hotspots. For instance, the application of convolutional neural networks (CNNs) and biosensors has played a crucial role in improving image interpretation and data analytics. Additionally, research on image co-registration and image correlation is vital for enhancing the accuracy and reliability of UAV-RS data. Through the researchers' quantitative analysis of keywords, this study reveals that beneath the surface-level prosperity of rapid growth in UAV-RS applications lies a weak foundation in basic research. The research agenda is largely driven by immediate application needs, while critical foundational technologies--such as sensors and data standards that are essential for the field's long-term development--receive insufficient attention.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 7 Introducing the System of Systems Lifecycle Management Model
### An Implemented System Integration for Arrowhead Using the Newly Developed SoSLM Methodology
#### 7.2.2 Transition  
The transition subcycle is basically about how the developed application can be brought to life. This partly aligns with the deployment stage of our PLM model.  
* Deployment: now, the application is fit for deployment. Here, the target systems are the described IIoT framework, the Arrowhead, and the exemplary chosen WSO2 (Web Services Oxygenated 2) communication infrastructure, which can establish a connection between SOA-based environments [106].
* Initiation: mostly, this involves the operating environment's parameters and the necessary inputs of the developed application. In the current case, the developed workflow engine needs, on the one hand, a production document as input and, on the other hand, the address and other attributes and necessary artifacts of the chosen delivery platform.
## 2 Materials
### Eddy Covariance System  
In this study, two meteorological variables (_i.e._, vapor pressure deficit (VPD) and air temperature) measured by the eddy covariance system at every half hour interval were used to estimate the ET and gross primary productivity. The eddy covariance system is an essential part of the mass conservation equation, which provides the framework to estimate the net ecosystem exchange (NEE, in mgC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)) between the photosynthetic carbon assimilation and releasing respiration. Negative NEE is expressed as another parameter, net ecosystem productivity (NEP, in mgC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)). The GPP is calculated using the following equation:  
\[GPP=NEP+R_{e}=-NEE+R_{e} \tag{1}\]  
where \(R_{e}\) is the total ecosystem respiration. The ground-measured GPP was validated with the MODIS-based GPP in this study.  
The eddy covariance system was applied to the flux observation. This method has been widely used for flux tower measurements worldwide, since the energy and water exchange between the surface and atmosphere can be directly determined:  
\[NEE=Storage\;\;flux+eddy\;\;flux+adveve_{v}flux+adve_{h}flux \tag{2}\]  
The first component on the right side of the equation is the amount of flux stored below the measurement height, second is the flux generated by vertical turbulent motions, third is the amount of flux advected by the mean vertical flow in the presence of the vertical gradient of concentrations, and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **KoFlux Gwangneung Supersite (GDK)** \\ \hline Latitude & 37.75\({}^{\circ}\)N \\ Longitude & 127.15\({}^{\circ}\)E \\ Elevation & 340 m \\ Slope & 10–20\({}^{\circ}\) \\ Land cover & Mixed forests \\ Soil texture & Sandy loam \\ Predominant species & Quercus sp., Carpinus sp. (80–200 years old) \\ Depth of soil moisture & 10, 30, 60 cm \\ Canopy height & 18 m \\ Maximum Leaf Area Index (LAI) & 6 \\ Mean annual temperature & 11.5 \({}^{\circ}\)C \\ Mean annual precipitation & 1,365 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Site descriptions.  
last component is the fluxes generated by the horizontal flow divergence of the mean flows and the concentrations below the measurement height. Among these components, the conventional eddy covariance system depends on the measurement of the second term, eddy flux. This term is considered to be very useful and efficient tool under steady state conditions over flat terrain with an extended upwind fetch of the underlying vegetation [33]. Due to the inherent complexity, heterogeneity, and variability of the eddy flux in nature, the eddy covariance method has been used over various landscapes and extended temporal scales, resulting in many problems and subsequent remedies [33]. We selected flux tower data measured by the eddy covariance system in order to validate the MODIS ET product.
## 2 Design of Low-Pass Filter
### Reconfigurable Structure of the Filter
Furthermore, maximally flat passband response does not impact the signal-to-noise ratio (SNR) at large signal conditions, since minimal back off is not needed for margin against peaking in passband. It can be noted that connecting a 2 nd and 4 th order filters of the proposed reconfigurable LPF in cascade produces a 6 th order LPF transfer function (1), which is not equal to a 6 th order Butterworth transfer function (2):  
\[H(s) = 1/\Big{(}\Big{(}s^{2}+\,s/0.541\,+\,1\Big{)}\,\times\,\Big{(}s^{2} +\,s/1.31\,+\,1\Big{)}\Big{)}\,\times\,1/\Big{(}s^{2}+\,s/0.707\,+\,1\Big{)}, \tag{1}\] \[H(s) = 1/\big{(}\big{(}s^{2}+\,s/0.518\,+\,1\big{)}\,\times\,\big{(}s^{2} +\,s/1.932\,+\,1\big{)}\,\times\,\big{(}s^{2}+\,s/0.707\,+\,1\big{)}\big{)}. \tag{2}\]  
Cascading two filters with a Butterworth response retains the flatness in the passband, though if both 2 nd and a 4 th order filters have equal bandwidths, the overall path bandwidth will be reduced by \(\sim\)14%, and have a slower roll-off around the transition band. Figure 4: Schematic of 4 th order active-RC ladder topology low-pass filter. Figure 3: Schematic of 2 nd order active-RC multiple feedback topology low-pass filter. Designing each stage with a higher maximum bandwidth limit can counter the aforementioned bandwidth reduction. Looking at (3), increase of the bandwidth limit for both 2 nd and 4 th order filters allows to relax the requirements by about 20% for OA GBWP for the same bandwidth, compared to implementing a 6 th order Butterworth transfer function. The small change of the roll-off near the transition band can be easily corrected in the digital domain by a channel equalizer, which can be found in most wireless communication systems [26]. \[\text{GBWP}_{\text{OA}}\approx\ 2\ \times\ \omega_{\text{max}}\times\ Q/ \Delta Q, \tag{3}\]  
where \(\omega_{\text{max}}\) is the maximum passband frequency; \(Q\) is the maximum quality factor and \(\Delta Q\) is the tolerable quality factor variation for the filter [22]. The requirement for OA GBWP can still be further reduced, if the bandwidth limit for the 2 nd order filter is increased more than for the 4 th order filter. In this scenario, the 2 nd order filter would add additional signal suppression in stopband, while the 4 th order filter would retain the near maximally flat response in the passband.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.13 The Indicator for Environmental Monitoring  
This indicator promotes land-use efficiency, diversity of uses, and increase density through the building height. It has been promoted by SBTool\({}^{\text{PT}}\) Urban, BREEAM C, and LEED ND, while addressed by ISO 37120, and SDGs. Therefore, this indicator is recognized as a prioritized environmental concern. The calculation methodology provided by the SBTool\({}^{\text{PT}}\) Urban Guide is through a verification checklist. To comply with the list, an Environmental Monitoring Plan must be developed. In this case, based on the findings of the study, this indicator is not possible to be calculated though using the CIM model, as the criteria cannot be added and assessed by the model.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 8 Undistorted Airy Pulses
### Physical Attenuation Compensating Airy Pulse  
In the previous section, we have seen that a complex Airy pulse can compensate pulse attenuation. However, these pulses cannot be normalized. One option to turn these pulses to a finite energy ones,  
Figure 28: Same as the right figure in Figure 24, but with \(\eta/\theta=0.2\).  
Figure 27: Same as Figure 2, but for the intensity (\(|A(t,z)|^{2}\)) of the pulse presented by Equation (92).  
and therefore normalizable, is to multiply them by an exponential function, i.e., to choose the following initial pulse profile:  
\[A(z,t)=A_{0}\text{Ai}\left(\frac{t-i\eta}{\theta}\right)\exp(at) \tag{95}\]  
Then, following Equations (8) and (93), the pulse after a distance \(z\) has the following form:  
\[A(z,t)=A_{0}\text{Ai}\left[\frac{t-i\eta+i\beta_{2}az}{\theta}-\left(\frac{ \beta_{2}z}{2\theta^{2}}\right)^{2}\right]\exp\left[i\frac{\beta_{2}}{2}a^{2} z+at+i\frac{\beta_{2}z(t-i\eta+i\beta_{2}az)}{2\theta^{3}}-\frac{i}{12}\left( \frac{\beta_{2}z}{\theta^{2}}\right)^{3}\right] \tag{96}\]  
The intensity of this pulse  
\[\left|A(z,t)\right|^{2}=\left|A_{0}\text{Ai}\left[\frac{t-i\eta+i\beta_{2}az}{ \theta}-\left(\frac{\beta_{2}z}{2\theta^{2}}\right)^{2}\right]\right|^{2}\exp \left[2 at-\frac{\beta_{2}z(\beta_{2}az-\eta)}{\theta^{3}}\right] \tag{97}\]  
is presented in Figures 29 and 30. One can easily see that unlike Figure 25, the pulse's amplitude increases in short distances, but unlike Figure 28, it has finite energy, and therefore, unlike Equation (94), can be implemented in real physical scenarios.  
Clearly, however, the pulse's amplitude will eventually decay. It will reach its maximum intensity at \(z=\eta/\beta_{2}a\), beyond which it will decay to zero. Therefore, this pulse may be implemented to compensate for the medium's losses when the width of the medium is a given parameter.  
Figure 30: Same as Figure 2, but for the intensity \(\left(\left|A(t,z)\right|^{2}\right)\) of the pulse presented by Equation (97) for \(a=0.2/\theta\) and \(\eta/\theta=1\). The horizontal line corresponds for the maximum intensity distance \(z=\eta/\beta_{2}a\).  
Figure 29: Same as Figure 28, but for the pulse presented by Equation (97), with \(\eta/\theta=0.2\) and \(a=0.1/\theta\).
## 3 Results
### Ablation Experiments
The DynamicHead module remarkably improved the model's recall, which increased from 78.0% to 82.1%. However, the detection speed significantly decreased to 6.5 ms, with slight increases in parameter values and memory overhead. This suggests that DynamicHead is more suitable for scenarios where high recall is required but may not be appropriate for tasks with strict real-time requirements. The WIoU loss function and DynamicATSS sample allocation strategy had relatively limited impacts on model performance. While WIoU slightly improved [EMAIL_ADDRESS] to 86.7% and [EMAIL_ADDRESS]-0.95 to 64.4% while maintaining a high detection speed of 1.2 ms, DynamicATSS significantly increased the detection time to 3.8 ms with no significant performance improvement. Ultimately, when all these modules were combined, the model achieved optimal comprehensive performance. Indeed, the precision, recall, F1 score, and [EMAIL_ADDRESS] reached 92.6%, 85.4%, 88.9%, and 90.2%, respectively, and [EMAIL_ADDRESS]-0.95 increased to 72.5%. Meanwhile, the detection speed remained at 1.6 ms, the number of parameters was reduced to 9.41 M, GFLOPs decreased to 19.3 M, and memory overhead was reduced to 16.4 MB. This indicates that the synergistic action of these modules significantly enhanced detection accuracy and optimized computational efficiency and memory usage, offering higher practicality and deployability for real-world applications. In summary, the RepLKNet and CBAM modules improved the model's precision and detection accuracy. At the same time, the DynamicHead and DynamicATSS strategies enhanced the model's comprehensive performance by optimizing recall and sample allocation. The WIoU loss function also improved the model's localization accuracy and generalization ability without increasing computational complexity. Integrating these modules enabled the YOLOv11-RCDWD model to perform exceptionally well in maize leaf disease detection while successfully balancing detection speed and model complexity.
# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 3 Feature Transformation
### Feature Extractor  
We first train a standard classification network \(f\) using the base dataset \(D_{B}\). The base dataset is divided into a training set \(D_{B_{train}}=\{(x_{i},y_{i})\}_{i=1}^{b_{0}}\) and a test set \(D_{B_{test}}=\{(x_{i},y_{i})\}_{i=1}^{b_{1}}\), where \(b_{0}\) and \(b_{1}\) denote the number of samples in the training and test sets, respectively. The classification network is trained by minimizing the empirical risk:  
\[\arg\min_{f}\sum_{(x_{i},y_{i})\in D_{B_{train}}}l(f(x_{i}),y_{j}) \tag{2}\]  
where \(l(\cdot,\cdot)\) is the loss function, typically the cross-entropy loss used in classification tasks.  
The classification network consists of two parts: a feature extractor and a fully connected (FC) layer. It can be represented as  
\[f(x)=W^{T}\phi(x)+\text{bias} \tag{3}\]  
where \(\phi(\cdot):\mathbb{R}^{D}\rightarrow\mathbb{R}^{d}\) denotes the feature extractor, which maps the input from the original feature space \(\mathbb{R}^{D}\) to a lower-dimensional feature space \(\mathbb{R}^{d}\), extracting compact and discriminative features. \(W^{T}\in\mathbb{R}^{d\times|\Upsilon_{B}|}\) and _bias_ represent the weights and bias of the FC layer, respectively. Once the network is fully trained and converged, the FC layer is removed, and only the feature extractor \(\phi(\cdot)\) is retained. In all subsequent stages of the proposed method, feature representations are obtained using the fixed feature extractor \(\phi(\cdot)\).
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 2 Theoretical Background
### Concurrency  
The capability of a system to handle multiple tasks at the same time is known as concurrency. Concurrency in Java [5; 10] may be achieved through the usage of threads. The program can perform multiple tasks simultaneously using threads, which are self-contained subunits that can run in parallel. Concurrency is an extensively explored principle for optimizing resource utilization and increasing system efficiency. By using multiple threads to parallelize the execution, response times can be reduced, and a better distribution of workload can be achieved.  
Race conditions and deadlocks can occur as a result of complex and error-prone concurrency management in multi-threading environments [11].  
A race condition happens when the state of a resource depends on the sequence or timing of uncontrollable events, producing inconsistent or unexpected results. The execution of each thread may take a different amount of time than expected, they can finish in a different order than expected, generating unanticipated behavior.  
A deadlock occurs when two or more threads are waiting for resources that are blocked by each other, leading to a state of complete standstill, which has the potential to severely compromise application stability and performance. There are different strategies available to prevent, detect, or resolve these problems, such as using advanced resource access scheduling algorithms or sophisticated deadlock avoidance mechanisms [12].  
To streamline concurrency management as well as improve performance and scalability, alternative concurrency models have emerged, such as event-driven and reactive programming.