esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis  
### A. Outage Probability  
In the FSOC system, OP is a key indicator to judge the reliability of the communication link. It is defined as the probability that the system SNR falls below the threshold SNR (\(P_{\text{out}}=\Pr(\gamma_{\text{ub}}\leq\gamma_{\text{th}})\)). Equation (24) is the closed expression for the OP of single-hop link. Equation (37) is the closed expression for the OP of CSI-assisted relay and FG relay.
# On the Link between Diesel Spray Asymmetry and Off-Axis Needle Displacement
## 3 Results  
### Hole-to-Hole Difference on Spray Penetration  
As a first step in analyzing the results, it has been checked whether the off-axis of the needle is tied to significant differences on the spray; accordingly, the penetration trends over time for each nozzle hole have been observed. The penetration distance is defined as the distance along the spray axis to the boundary of the spray, according to [51]. The curves of Figure 6 originate at time zero ASOI (After Start Of Injection) and reach the penetration of 0.01 m at different times. Incidentally, up to 0.01 m, the spray is in the Eulerian-Eulerian domain; when droplets exceed this limit, they are taken over by the Eulerian-Lagrangian simulation. The spray emerging from the hole \(N2\) is the slowest, both in the Eulerian tract for the half part of the Lagrangian tract. Referring to the off-axis configuration of Figure 3, the comparisons were made in couples, first \(N4\) and \(N1\), then \(N2\) and \(N3\) and finally the pair \(N1\)-\(N3\); the related trends, in terms of percentage differences, are shown in Figure 12 (right).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rail Pressure** **(MPa)** & **Ambient Pressure** **(MPa)** & **Ambient Temperature (K)** & **Nozzle Number** & **Fluid** \\ \hline
150 & 6 & 900 & 210677 & N-C12-H26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spray-A conditions.  
Since the opening of the nozzle up to 0.1 ms ASOI, spray N4 prevails over spray N1; this difference decreases rapidly, vanishes in 0.11 ms ASOI and reverses back, up to more than half of the injection process. In the closing phase, the spray N4 returns to prevail on the spray N1 but with a more limited deviation to what occurs at the opening (3% versus 18%).  
During the opening, the pair N3-N2 behaves likewise to the pair N4-N1, with the prevalence of N3 on N2, but, thereafter, the difference, while decreasing, does not fade and 0 is not reversed.  
The trend of the pair N1-N3 shows the spray N1 below N3, but not in the period between 0.11 and 0.2 ms ASOI.  
A graphical evolution of the spray shape, accompanied by the cut view of the nozzle flow, is reported in Figure 13; even if the thorough analysis of the flow structures within the nozzle is beyond the aim of the current contribution, it is desirable to focus on how the penetration dispersion among the holes is reflected by the shape of the spray plumes; the asymmetry of velocity and cavitation patterns is clearly evident.
## 4 Results and Discussion
### Theoretical and Comparative Evaluation of the Model
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criterion** & **Traditional Model** & **Multi-Agent Model (Proposed)** & **Reference** \\ \hline Allocation of Spaces & Manual or semi-automatic with high occupancy times. & Dynamic optimization with heuristics, expected 25\% reduction in occupancy times. & [29] \\ \hline Route Optimization & Static, based on operational experience. & Dynamic calculation with A*, Dijkstra, and reinforcement learning, expected reduction of 15–30\% in transit times. & [30] \\ \hline Traffic Management & Reactive, without real-time adjustments. & Continuous adaptation based on prediction and IoT sensors. & [31] \\ \hline Synchronized Boarding & Based on fixed schedules, without dynamic optimization. & Dynamic adjustment with adaptive scheduling, 20\% reduction in waiting times. & [32] \\ \hline System Monitoring & Based on manual reports and subsequent adjustments. & Predictive analytics and real-time optimization using AI. & [33] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison multi-agent model vs. traditional model. nals operate with legacy systems that might require upgrades or interoperability modules to enable seamless communication between smart agents and port traffic control systems. Another key challenge is the technological infrastructure needed for real-time data collection and processing. Optimal system operation requires the implementation of IoT sensors, advanced communication networks, and servers capable of handling distributed information processing. Although many terminals have already adopted digital technologies, heterogeneity in automation levels between different ports can represent a barrier to model standardization. From an operational point of view, the interaction between the autonomous system and port operators is essential to ensure the adaptability and efficiency of the model. Staff training and redefining roles within the terminal can facilitate the transition to AI-based management. In addition, the system's acceptance will depend on its ability to demonstrate tangible improvements in reducing operating times, optimizing internal traffic, and efficiency in shipment planning. In this sense, validation through pilot projects in controlled environments would allow the performance of the model to be evaluated before its large-scale deployment.
# Intrusion Detection for Industrial Control Systems Based on Improved Contrastive Learning SimCLR
## 3 Contrastive Learning-Based Intrusion Detection Model for Industrial Control Systems
### Data Pre-Processing  
#### 3.1.1 Normalization  
The data used in this experiment contains some null values and some infinite data, for which the processing method is to delete the rows containing these data. After the outliers are processed, normalization is also required. The data normalization process limits the data to a range, which can eliminate the adverse effects caused by odd sample data, speed up the training of the model, and improve the accuracy of the model in some models. The calculation of data normalization is shown in Equation (9).  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{9}\]  
where \(x^{\prime}\) is the normalized data, \(x_{\text{max}}\) and \(x_{\text{min}}\) are the maximum and minimum values of the same feature attribute in all data, respectively.
## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Closing the Knowledge Gap and Technical Expertise
To overcome this challenge, SMEs can implement the following actionable strategies:  
* Develop Internal Capabilities through Training. Invest in continuous role-specific training for employees in areas such as data analysis, AI fundamentals, and algorithmic thinking. Training should be modular and accessible, allowing gradual learning and practical application in daily operations. * Leverage Low-Code and Pre-Trained AI Tools. Adopt user-friendly, cloud-based platforms offering pre-configured AI models. These reduce the technical entry barrier and enable SMEs to gain value from AI without requiring advanced programming or data science skills. * Build Strategic External Partnerships. Collaborate with universities, research centers, AI consulting firms, and technology providers. These partnerships provide access to expert guidance, reduce learning curves, and help SMEs co-develop solutions tailored to their business context. * Engage in Open Innovation Networks. Participate in innovation ecosystems and industry alliances to exchange knowledge, gain exposure to best practices, and remain informed about AI trends and tools suitable for SME environments. In summary, closing the knowledge and skill gap is essential for SMEs to effectively integrate AI into their business strategy. By combining internal training, accessible tools, and external collaboration, SMEs can build the capabilities needed to adopt AI in a sustainable and scalable way, strengthening both technological readiness and organizational maturity.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 3 System Model
### 5G Threats  
The 5G telecom network is divided into four major network elements: RAN, core network, transportation network, and interconnection network. Again, each of the network elements consists of three planes for carrying various types of network traffics: control plane, user plane and management plane. These planes are vulnerable to new threats if they are exposed to the attackers. Furthermore, there are threats in 5G cellular networks, which are classified by authentication and privacy approaches, meaning pattern behavior of the attacks in 4G and 5G networks [56]. There are other classifications according to various metrics including passive or active, internal, external, etc. Four clusters of attacks have been described in [12; 56], (1) attacks against privacy, (2) attacks against integrity, (3) attacks against availability, and (4) attacks against authentication. All of the mentioned threats are given in Figure 8. Some of the threats in 5G are discussed below.
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 4 Taxonomy for Services Composition
### Services Composition Using BPEL
#### 4.1.5 Domain-Specific Approaches  
A grid service is a type of Web service that allows for the sharing of resources, such as computational power or data storage, across a network of computers. Grid services are relevant to Web service composition because they provide a way to aggregate the capabilities of multiple Web services into a single, cohesive system. By utilizing grid services, developers can create complex, distributed systems that are able to leverage the resources of multiple machines to perform tasks that would be infeasible for a single machine to accomplish alone. Additionally, grid services can be used to manage and orchestrate the interactions between multiple Web services in a composite application, making it easier to create and maintain large-scale, distributed systems.  
In [63], the authors provide an introduction to the concept of grid services and how they can be used to create distributed systems that can leverage the resources of multiple machines. The authors also discuss how grid services can be used to manage and orchestrate the interactions between different Web services in a composite application.  
For instance, the authors in [64] introduced BPEL4 WS (Business Process Execution Language for Web Services) for defining process flows between grid services based on the Open Grid Services Infrastructure (OSSI) standard.  
Geographic Information System (GIS) for Web Service Composition  
A geographic information system, or GIS, is a domain-specific framework for storing, managing, analyzing, and displaying spatial data. GIS systems can be used to create and display maps, perform spatial analysis, and manage and query large spatial databases. The importance of GIS in Web service configuration is that the user can integrate the GIS functionality into Web-based applications using Web services. Web service composition allows developers to combine the functionality of multiple Web services to create morecomplex applications [65]. By integrating GIS capabilities into Web-based applications via Web services, developers can add spatial analysis and mapping capabilities to their applications. This allows GIS capabilities to be integrated into a wide range of applications, such as urban planning, emergency management, transportation, and natural resource management. GIS Web services can also be used to make GIS data and functionality available to other systems through basic or semantic-based discovery capabilities [66]. This allows other systems to expose and consume GIS data and functionality for Mashups such as MashMaker (cf. Section MashMaker).  
Most of the described work considers Web services from a system perspective. However, in recent years, end-users have become the focus of various technologies, including Web services composition, as explained in the next section.
## 2 Proposed Method
### Detailed Procedure of the Proposed Method  
Step 1. The vibration signals are collected under various operating conditions for each domain using an accelerometer. The target domain has fewer labeled data than the source domain. There are labeled training and test data in the target domain. The labeled training data is used to fine-tune the pre-trained model to obtain the target diagnosis model, and the test data is not used for training but to evaluate the model performance.  
Step 2. The acquired acceleration signals are converted into spectrogram images in the time-frequency domain through signal processing methods. Spectrogram images are used as input for a deep residual network. A pre-processed input image has the form of a [224, 224, 3] three-channel RGB.  
Step 3. A deep residual network (ResNet50) is trained using source domain data as an initial pre-trained model. The last fully connected layer is initialized to be suitable for the task class, and training starts with random initial weights. The initial pre-trained model has feature extraction layers and parameters suitable for fault diagnosis in the source domain.  
Step 4. The initial pre-trained model is transferred to target domain and fine-tuned using a small number of labeled target domain data. The initial deep transfer residual network is obtained. Detailed procedure of this step is presented in Figure 3.  
Step 5. The MMD between the source domain instance and target domain are calculated. In addition, the KLD of the source domain instances are derived from the initial deep transfer residual network. Multi-objective source instance weights are derived from the calculated MMD and KLD.  
Step 6. A final pre-trained model is trained using source domain data and multi-objective instance weights. By assigning instance weights, each instance in the source domain has different influence on the subsequent model training process. The learning rate was fixed at 0.001, and the training process was controlled so that no overfit occurs. After training a specific epoch, the structure of the trained model and all parameters are saved.  
Step 7. The optimized pre-trained model is fine-tuned using target domain training data, which generates a final instance-based deep transfer learning network. Detailed procedure of this step is presented in Figure 3. To fine-tune the pre-trained model, we freeze beginning layers before residual blocks and train the parameters of the remainder layers including the fully-connected layer. During the fine-tuning procedure, we use Adam optimizer, and the batch size is set to be 32. The initial value of the learning rate is 0.001, and it is gradually decreased by multiplying by 0.5 to find the optimal parameters when the loss value over 50 epochs does not improve.  
Step 8. The instance-based deep transfer learning network is applied to predict the label of the target test data, which is used to validate the performance of the designed model on the target fault diagnosis task.
# Thermographic Stall Detection by Model-Inspired Evaluation of the Dynamic Temperature Behaviour
## 2 Principle of Measurement
### Transient Thermodynamic Response  
In the case of transient input signals, the output signal is also in a transient form and the temporal gradient of the surface temperature field is used to distinguish between different boundary-layer flow regions.  
The temperature field of the entire measured object can be described analytically by coupling energy balances and kinetics of heat conduction. For measuring objects with constant thermal conductivity \(\lambda\), the temperature field \(T\) depends on the location, the time \(t\) as well as the material properties summarised in the thermal conductivity coefficient \(a\) and can be calculated by solving the heat equation [25]  
\[\frac{\partial T}{\partial t}=a\
abla^{2}T. \tag{3}\]  
Thus, the general solution of this differential equation depends on the initial temperature distribution as well as the shape and material parameters denoted as cross-sensitivities \(T_{0}\) and \(M\), respectively. In addition, the following boundary condition applies to the surface  
\[-\lambda\cdot\left(\frac{\partial T}{\partial n}\right)_{\mathrm{s}}=\alpha \cdot\left(T_{\mathrm{s}}-T_{\mathrm{fl}}\right)+\dot{q}_{\mathrm{r}}, \tag{4}\]  
where \(\partial/\partial n\) means a normal differentiation on the surface. As a result, the surface temperature response to a transient step-like input signal converges over the time to a steady state temperature. The transient signal processing approach is thus to evaluate the temporal gradient of the surface temperature response. Since transient heat inputs cause enhanced temperature differences between the surface and the fluid temperature, the flow-dependent convective heat flow is the dominant mechanism for the local response behaviour. As a result, the evaluation of temporal temperature gradients leads to a high thermal contrast between the different flow regions. Since the temporal gradient of the surface temperature decreases over time, the measurement will take place at the beginning of the transient input signal.
# A Review on SIW and Its Applications to Microwave Components
## 7 Substrate Selection for SIW Implementation  
In the design of substrate-integrated waveguide devices/components, substrate selection is considered vital for the structural and electrical performance in comparison to other types of transmission line structures including microstrips, coplanar waveguides, etc. Using an SIW filter as a case study, [PERSON] and [PERSON] [33] showed the relationship between the device substrate features and the size, the insertion loss, the thermal stability, the mean power management capability, and the peak power management capability.  
* The physical size of an SIW device is directly linked to the permittivity or dielectric constant of the material used for the device implementation. The higher the material dielectric constant, the smaller the physical size of the SIW device.
* The amount of insertion loss observed in an SIW device relates to three factors: substrate thickness, dielectric loss, and copper foil.
* Thermal expansion and thermal coefficient of permittivity of a substrate material determines the temperature stability of an SIW device.
* SIW components are well known for their good power handling capabilities. The average power handling capability is based on the substrate thermal expansion, the thermal coefficient of permittivity, the thermal conductivity, and the glass transition temperature, while the peak power handling capability is only linked to the substrate dielectric strength.  
It is important to understand that the utilisation of a particular type of substrate in the SIW components design will also depend on the method employed in the component development. Hence, several parts of the SIW component design and implementation are interrelated.
###### Abstract  
Soil water use efficiency (SWUE) was proposed as an effective proxy of ecosystem water use efficiency (WUE), which reflects the coupling of the carbon-water cycle and function of terrestrial ecosystems. The changes of ecosystem SWUE at the regional scale and their relationships with the environmental and biotic factors are yet to be adequately understood. Here, we aim to estimate SWUE over northeast China using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity data and European Space Agency climate change initiative (ESA CCI) soil moisture product during 2007-2015. The spatio-temporal variations in SWUE and their linkages to multiple factors, especially the phenological metrics, were investigated using trend and correlation analysis. The results showed that the spatial heterogeneity of ecosystem SWUE in northeast China was obvious. SWUE distribution varied among vegetation types, soil types, and elevation. Forests might produce higher photosynthetic productivity by utilizing unit soil moisture. The seasonal variations of SWUE were consistent with the vegetation growth cycle. Changes in normalized difference vegetation index (NDVI), land surface temperature, and precipitation exerted positive effects on SWUE variations. The earlier start (SOS) and later end (EOS) of the growing season would contribute to the increase in SWUE. Our results help complement the knowledge of SWUE variations and their driving forces.  
soil moisture; soil water use efficiency; land surface phenology; MODIS; northeast China 2019  
[PERSON], [PERSON], and Huan Zhai  
School of Geographical Sciences, Northeast Normal University, Renmin Street No. 5268, Changchun 130024, China; [EMAIL_ADDRESS] (H.Q.); [EMAIL_ADDRESS] (H.Z.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-431-8509-9550
# DIFFBAS: An Advanced Binaural Audio Synthesis Model Focusing on Binaural Differences Recovery
## 2 Methodology
### IPD Calculation of Binaural Audio  
Given binaural audio \(\mathbf{y}(t)=[\mathbf{y}_{L}(t),\mathbf{y}_{R}(t)]\), the corresponding spectrograms \([\mathbf{Y}_{L},\mathbf{Y}_{R}]\in C^{F_{s}\times T_{s}}\) can be computed as  
\[\begin{array}{l}\mathbf{Y}_{L}(\omega,t)=\mathbf{A}_{L}(\omega,t)\cdot e^{i \mathbf{\phi}_{L}(\omega,t)}\\ \mathbf{Y}_{R}(\omega,t)=\mathbf{A}_{R}(\omega,t)\cdot e^{i\mathbf{\phi}_{R}( \omega,t)}\end{array} \tag{8}\]  
where \(\mathbf{A}_{L}(\omega,t)\) and \(\mathbf{A}_{R}(\omega,t)\) denote the amplitudes of the left and right ears signals at time \(t\) and frequency \(\omega\), \(\mathbf{\phi}_{L}(\omega,t)\) and \(\mathbf{\phi}_{R}(\omega,t)\) represent their initial phases. The IPD \(\Delta\mathbf{\phi}\) between two ears can be computed as  
\[\Delta\mathbf{\phi}(\omega,t)=\mathbf{\phi}_{L}(\omega,t)-\mathbf{\phi}_{R}(\omega,t)\in [-2\pi,2\pi] \tag{9}\]  
We need to limit the phase difference within one cycle. Leveraging [PERSON]'s theorem and Equation (8), the spectral disparity between the left and right ears can be computed as  
\[\frac{\mathbf{Y}_{L}(\omega,t)}{\mathbf{Y}_{R}(\omega,t)}=\frac{\mathbf{A}_{L }(\omega,t)}{\mathbf{A}_{R}(\omega,t)}\cdot(\cos\Delta\mathbf{\phi}(\omega,t)+j \sin\Delta\mathbf{\phi}(\omega,t)) \tag{10}\]  
Furthermore, \(\Delta\mathbf{\phi}\) can be obtained by calculating the phase angle of this complex matrix  
\[\Delta\mathbf{\phi}(\omega,t)=\angle\bigg{(}\frac{\mathbf{Y}_{L}(\omega,t)}{ \mathbf{Y}_{R}(\omega,t)}\bigg{)}\in[-\pi,\pi] \tag{11}\]  
where \(\angle\) is the depression angle for calculating the complex matrix, and \(\Delta\mathbf{\phi}\) represents the initial phase difference between the left and right ears that we ultimately calculate.  
Therefore, for an arbitrary binaural audio \(\mathbf{y}(t)=[\mathbf{y}_{L}(t),\mathbf{y}_{R}(t)]\), the IPD can be represented by a complex matrix  
\[IPD(\mathbf{y}(t))=\angle\bigg{(}\frac{\mathbf{Y}_{L}}{\mathbf{Y}_{R}}\bigg{)}\in C ^{F_{s}\times T_{s}} \tag{12}\]  
where \(F_{s}\) and \(T_{s}\) are the frequency and time resolutions of STFT. Using this formula, the phase difference of binaural signal \(\mathbf{y}_{L}(t)\) at a specific time and frequency after STFT transformation can be calculated. Combined with Equation (7), it can be shown that the phase difference contains the orientation information of the sound source at a certain moment.