esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Equilibrium Analysis under Arbitrary Transmission Limit
### Equilibrium Analysis for S-ALOHA System
#### 5.2.1 Equilibrium Analysis in the Saturation State  
If the S-ALOHA system is in a saturation state when it converges to the equilibrium state, there must be \(s=1\) according to Section 3.2. A successful device should first pass the ACB check and then succeed in the data transmission procedure without collision. Therefore, we can rewrite the system balance equation as follows:  
\[\begin{array}{ll}f&=pe^{-\frac{Xp}{L/\delta}}\\ &=pe^{-1}\\ X&=\frac{L/\delta}{p}\\ &=\lambda\frac{1-(1-f)^{Q}}{f}\end{array} \tag{36}\]Then we can derive that:  
\[\begin{array}{l}p=e\bigg{[}1-\left(1-\frac{L/\delta}{e\lambda}\right)^{1/Q} \bigg{]}\\ f=1-\left(1-\frac{L/\delta}{e\lambda}\right)^{1/Q}\\ P_{S}=\frac{L/\delta}{e\lambda}\end{array} \tag{37}\]  
Due to ACB scheme, there must have \(0\leq p\leq 1\), so we can derive the following inequation:  
\[0\leq L\leq e\lambda\delta\bigg{[}1-\left(1-e^{-1}\right)^{Q}\bigg{]} \tag{38}\]  
In this paper, we define the saturation threshold in the equilibrium state as:  
\[L_{th}^{S-ALOHA}=e\lambda\delta\bigg{[}1-\left(1-e^{-1}\right)^{Q}\bigg{]} \tag{39}\]  
When \(L=\ L_{th}^{S-ALOHA}\), the system load \(\rho_{th}\) at the saturation threshold is obtained as:  
\[\rho_{th}=\frac{\lambda}{L_{th}^{S-ALOHA}/\delta}=\frac{e^{-1}}{\left[1-\left( 1-e^{-1}\right)^{Q}\right]} \tag{40}\]  
**Remark 4**.: _Based on the above analysis, there must be \(\rho\geq\rho_{th}\Big{(}L\leq\ L_{th}^{S-ALOHA}\Big{)}\) if the system converges to a saturated equilibrium state. Until this step we just have demonstrated that \(\rho\geq\rho_{th}\Big{(}L\leq\ L_{th}^{S-ALOHA}\Big{)}\) is the necessary condition for a saturated equilibrium state. In the following subsections, we still need to prove that \(\rho\geq\rho_{th}\Big{(}L\leq\ L_{th}^{S-ALOHA}\Big{)}\) is also the sufficient condition to ensure the system is saturated and to prove that there is only one equilibrium point on s\(\in\) (0,1] if \(\rho<\rho_{th}\)._
# TC-Verifier: Trans-Compiler-Based Code Translator Verifier with Model-Checking
###### Abstract  
Code-to-code translation, a critical domain in software engineering, increasingly utilizes trans-compilers to translate between high-level languages. Traditionally, the fidelity of such translations has been evaluated using the BLEU score, which predominantly measures token similarity between the generated output and the ground truth. However, this metric falls short of assessing the methodologies underlying the translation processes and only evaluates the translations that are tested. To bridge this gap, this paper introduces an innovative architecture, \"TC-Verifier\", to formally employ the Uppaal Model-checker to verify trans-compiler-based code translators. We applied the proposed architecture to a trans-compiler translating between Swift and Java, providing insights into the verified and unverified aspects of the translation process. Our findings illuminate the strengths and limitations of using Model-checking for formal verification in code translation. Notably, the examined trans-compiler reached a verification success rate of 50.74% for the grammar rules and productions modeled. This study underscores the gaps in trans-compiler-based translations and suggests that these gaps could potentially be addressed by integrating Large Language Models (LLMs) in future work.  
code translation; formal verification; model checking; Uppaal; trans-compiler
## 4 IEC61850 Multicast Communication for Wide-Area Applications
### R-SV/R-GOOSE vs. Routable-Tunneled SV/GOOSE  
As stated above, R-SV/R-GOOSE messages support IP and, therefore, become capable of WAN communication via the use of routers, which are the networking devices for forwarding IP packets between LANs. Moreover, R-SV/R-GOOSE can be applied in Point-to-Multipoint wide-area applications via IP-multicast technologies. As shown in Figure 2, sender IED (in Substation 1) sends R-SV/R-GOOSE messages to the group of receivers in Substation 2 and Substation 3 using IP-multicast technology.  
In comparison to unicast communication, multicast technology provides better bandwidth utilization since the sender IED (e.g., PMU) sends only one message for several receiver IEDs (e.g., PDCs) in Substation 2 and Substation 3. The sender IED publishes IP-multicast traffic (R-SV/R-GOOSE messages) to the First Hop Router (FHR), which is the name for the multicast router that is connected to the sender. Then, FHR uses routing protocols and sends messages toward the Last Hop Router (LHR) that is the multicast router which is connected to the receiver IEDs. FHR and LHR are called edge routers, which communicate via WAN.  
Figure 1: Frame structures of SV/GOOSE and R-SV/R-GOOSE.  
There are two types of routing messages between the sender and receivers: R-SV/R-GOOSE (Figure 2) and Routable-Tunneled SV/GOOSE (Figure 3). If both sender and receivers support IP-multicast, R-SV/R-GOOSE messages are communicated (Figure 2) between them. However, Routable-Tunneled SV/GOOSE communication (Figure 3) is used if the sender and receivers (or one of them) have no support for IP-multicast or even for the IP protocol. For example, this is often the case when inter-substation communication is needed for traditional IEDs supporting only Layer 2 SV/GOOSE. In such a scenario (Figure 3), the Sender sends OSI model Layer 2 SV/GOOSE messages to a FHR that must be capable of encapsulating these data into IP-multicast packets to generate Routable-Tunneled SV/GOOSE messages. The FHR sends these messages to the LHRs which receive them, decapsulate the SV/GOOSE data, and forward them to the receivers. Alternatively, IEC 61850-90-5 Bridge [31], which has sending/receiving functionality, can be used for encapsulation/decapsulation of the SV/GOOSE data into IP-Multicast messages. In Figure 3/Substation 3, it is assumed that decapsulation is carried out at an IEC 61850-90-5 Receiver Bridge.  
Rutable-Tunneled SV/GOOSE messages contain the IP-multicast address as well as the Ethernet-multicast address in order to forward Routable-Tunneled SV/GOOSE messages between the routers/bridges, as well as to send encapsulated GOOSE/SV messages to the end-devices. These multicast addresses can be seen in Figure 4, which shows the Wireshark capture of R-GOOSE and Routable-Tunneled GOOSE messages generated by the software tool [32].  
Figure 2: Multicast communication of R-SV/R-GOOSE from Substation 1 to Substation 2 and 3.  
Figure 4: Routable GOOSE (R-GOOSE) vs. Routable-Tunneled GOOSE.  
Figure 3: Multicast communication of Routable-Tunneled SV/GOOSE from Substation 1 to Substation 2 and 3.
## 3 Dual-Fusion Active Contour Model
### Original Image Fitting Energy  
The local inhomogeneous intensity and noise problems can be solved by fusing the local intensity fitting and CV model. However, the missing contour information of the saliency image still needs to be solved. Therefore, the original underwater images are used to make up the missing contour information.  
In this paper, we use the local image fitting model (LIF) [27] to extract the contour of original underwater images. The energy functional \(E_{\text{org}}\) can be expressed as:  
\[E_{\text{org}}(\phi)=\frac{1}{2}\int_{\Omega}\left|I(x)-I^{\text{LF1}}(x) \right|^{2}dx,x\in\Omega, \tag{15}\]  
where \(I^{\text{LF1}}(x)\) is a local fitted image, as shown in Equation (6). Although the models, such as LBF [29; 30], LGIF [31], and RMPCM [3] can extract the target contours of underwater images very well, as shown in Figure 1, the LIF model has higher efficiency. This higher efficiency is because the energy functional of the LIF model does not include a kernel function. Moreover, the LIF model can fit the original image well while reducing the noise significantly by minimizing the difference between the fitted image and the original image.  
As shown in Figure 1, the LBF, LGIF, and LIF models could extract the target contour better, but LBF was more sensitive to the initial contour curve (as shown in the green dashed area). The energy functional of LGIF and RMPCM both involved the kernel function. The kernel function performs more than one convolution operation for each iteration step, so the evolution speed is slow. The running time of the above models is shown in Table 1.  
Figure 1 and Table 1 intuitively show that the LIF model has advantages regarding both the speed and contour extraction results. So, we use the LIF model to extract the original image contour to correct the contour information of the salient target.
###### Abstract  
Due to the complexity of the oil and gas station system, the operational data, with various temporal dependencies and inter-metric dependencies, has the characteristics of diverse patterns, variable working conditions and imbalance, which brings great challenges to multivariate time series anomaly detection. Moreover, the time-series reconstruction information of data from digital twin space can be used to identify and interpret anomalies. Therefore, this paper proposes a digital twin-driven MTAD-GAN (Multivariate Time Series Data Anomaly Detection with GAN) oil and gas station anomaly detection method. Firstly, the operational framework consisting of digital twin model, virtual-real synchronization algorithm, anomaly detection strategy and realistic station is constructed, and an efficient virtual-real mapping is achieved by embedding a stochastic Petri net (SPN) to describe the station-operating logic of behavior. Secondly, based on the potential correlation and complementarity among time series variables, we present a MTAD-GAN anomaly detection method to reconstruct the error of multivariate time series by combining mechanism of knowledge graph attention and temporal Hawkes attention to judge the abnormal samples by a given threshold. The experimental results show that the digital twin-driven anomaly detection method can achieve accurate identification of anomalous data with complex patterns, and the performance of MTAD-GAN anomaly detection is improved by about 2.6% compared with other methods based on machine learning and deep learning, which proves the effectiveness of the method.  
digital twin; GAN; anomaly detection; temporal Hawkes attention; knowledge graph attention +
Footnote †: journal: _aciences_  
_Article_
## 2 Framework of UAV Inspection System  
To enhance the practical application of UAV inspection systems in road crack detection, this study presents a comprehensive DL-based method and technical solution framework. As illustrated in Figure 1, the technical framework consists of four main components: (1) Data Acquisition: a flight suitability parameter model is established to ensure high-quality pavement imagery acquisition by the UAV. Prior image data are utilized to create crack datasets for model training, while the subsequent phase is directly employed for pavement crack detection. (2) Model Training and Evaluation: UAV imagery is pre-processed through frame extraction, image dividing, and data enhancement, and then labeled according to five major categories of cracks (longitudinal, transverse, diagonal, mesh, and no cracks) to create the datasets. Based on this, four mainstream DL target detection algorithms (Faster-RCNN, YOLOv5, YOLOv7, and YOLOv8) are individually conducted for the road crack detection model training. Finally, the models are compared and validated using precision (_P_), recall (_R_), F1-score, and mean accuracy precision (_mAP_) as evaluation metrics, and the best model is selected. (3) Model Application and Road Crack Detection: The preferred model is employed to identify road crack targets using UAV imagery. To reduce the computing resources, the full-scale images are divided into smaller images before detection. (4) Road Distress Evaluations: Quantitative assessments (for instance, cracks count, cracks length, and crack area etc.) are conducted to evaluate pavement distress, which provide factual evidence and a solid data foundation for evaluating road damage and planning road repair work for transportation departments.  
Figure 1: A framework of UAV inspection system based on deep learning for pavement distress.
# Identification of Tire Model Parameters with Artificial Neural Networks
## 2 Artificial Neural Networks (ANNs)
In its reduced form, the Magic Formula has six parameters that must be identified to know the whole curve. The output of the ANN will, therefore, have six outputs, one for each parameter. It is important to realize that this model (1) is very nonlinear since it has some nested trigonometric functions, which makes its complexity very high and, as will be seen, will affect the selection of the chosen ANN (see Section 3). \[F_{i}=f_{y}+FzD\cdot\sin(C\cdot\arctan(B\cdot(x_{i}+f_{x})-E\cdot(B\cdot(x_{i }+f_{x})-\arctan(B\cdot(x_{i}+f_{x}))))) \tag{1}\]  
The model (1) has the following nomenclature: xi is the longitudinal (or lateral) slip in % and Fi is the longitudinal (or lateral) force, in N. The pairs of values (xi, Fi) are known from tests performed on tire test benches. The remaining variables are the parameters that need to be identified. The parameter _fx_ reflects the \"horizontal shift\" of the curve and _fy_ the \"vertical shift\" that show the deviations in x and y from the curve that does not exactly pass through the coordinate origin. The parameter D is the peak factor, which multiplies to the _Fz_ (vertical force on the tire). Both parameters together _FzD_ stand for the maximum amplitude height of the curve. Parameter C represents the shape factor of the curve and B is the stiffness factor. Finally, the parameter E is the \"curvature factor\". Usually, ANNs are used for classification issues or discrete outputs. In this case, all the parameters to be identified are continuous variables, so it is necessary that the ANN support real-valued continuous output variables. The input and output data of the network are known, so a supervised training will be used. The ANN will be based on a Multi-Layer Perceptron (MLP), with nodes totally connected to each other between successive layers (Feed-Forward) [24], activated through the sigmoid function (with continuous derivative) that allows training through the Backpropagation Algorithm [21]. The selection of the chosen ANN topology is detailed in the following section as part of the working methodology.
## 2 Materials and Methods
### Model Construction and Structural Parameter Setting
#### 2.3.2 Physics Modeling and Boundary Conditions  
In this study, the concave herbaceous field is selected as a LID facility. The concave herbaceous field is used to collect the runoff generated by the surrounding hardened surface and achieve the purposes of runoff, flood peak reduction, and flood peak delay through the rainwater retention and infiltration of the soil and gravel layers in its structural layer. The working principle is as follows: During the rainfall process, the ground rainwater is collected into the green belt of the planting soil and is permeated and stored through the planting soil. The excess rainwater flows into the underground space through the vertical well and is discharged through the transverse seepage pipe. The results show that the rainwater control efficiency of the concave green space is significantly related to the depth of the concave, the thickness of the planting soil, the thickness of the gravel layer, the slope of the planting soil, the porosity of the planting soil, the porosity of the drainage pipe, and the recurrence period of the designed rainstorm (Figure 2a).  
The medium-sized concave herbaceous field is taken as the model calculation area under actual working conditions. The model is 300 m long and 60 m wide (Figure 2b). The physical modules involved in the calculation include gravity module, porous media module, viscous flow module, and turbulent flow module. The boundary condition of the model inlet adopts the velocity boundary, and the design water level of the initial condition is equal to the ground height of the inlet. The front and back sides of the seepage structure layer and the top of the model are set as impervious solid wall boundaries. The side wall itself is impervious to water, and the outer side is set as a continuous boundary, which can only allow water flow out of the drainage seepage pipe. The bottom of the model is set as a continuous boundary, which allows the structural layer to seepage rainwater out. The total duration of the simulated precipitation scenario is 2 h, and the initial time step is set to 1 min. The minimum time step is the default value. In addition, the flow monitoring surface is set at the mouth of the green drainage pipe; this allows one to judge the stormwater control effect of LID facilities more intuitively through the total runoff overflow characteristics of the overall structure.  
Moreover, in order to explore the runoff control benefits and optimization schemes of the concave herbaceous field with different structure layer sizes, the initial dimensions of each structure layer were set according to relevant norms and literature, as shown in Table 1.  
Figure 2: Schematic diagram of LID numerical analysis module. (**a**) Concave herbaceous field concept structure; (**b**) Three-dimensional numerical structure diagram.
## 4 Discussion  
The study aimed to quantify the mangrove dynamics arising from the massive LUSI mudflow diversion operation that followed the extreme mud volcano eruption, in Sidoarjo, Indonesia. Our investigation started in January 2009 and continued until November 2019. This investigation of mangrove dynamics combined usage of UAV-based and satellite analyses with GEE cloud computing. This approach included the successful retrieval of mangrove biophysical properties in terms of canopy height and the individual position of mangrove trees (Figure 8) as well as a time series of mangrove belt development (Figure 10). This approach resulted to a new set of mangrove extent maps for our study location that is not covered by other existing products such as the WMA [1] and GMFD v1 [8] that were released in 2000. The GCMFC-21 [9], GMW v2.0 [10] and at the national-level provided by the Indonesian National Institute of Aeronautics and Space (LAPAN) [99], whose latest product at the time of writing dates from 2012, 2016, and 2019, respectively. The mangrove dataset provided by the GMW v2.0 from the period of 1996, 2007, 2008, 2009, 2010, 2015, and 2016, while LAPAN products were for the period of 2014, 2016, and 2019. Therefore, from those datasets the mangrove dynamics in Porong Estuary cannot be deduced. The spatial and temporal resolution of those dataset is not sufficient as Porong has been experiencing extreme mud influx from LUSI and its transformation has been so rapid. This study resulted in a higher temporal resolution and detailed three monthly mangrove classification and their biophysical properties, especially in the highly dynamic delta lobes that expand seaward.
## 3 Methodology
### Loss Function  
In remote sensing image semantic segmentation, precise object recognition requires high accuracy in both region classification and boundary localization. This is crucial for high-resolution images, where complex object shapes can lead to blurred boundaries and concentrated errors. This study presents a composite loss function, the Multi-scale Boundary Focal Loss Function, to optimize both region recognition and boundary delineation. This loss function consists of two components: the first component is the Multi-class Region Focal Loss, which is used to optimize pixel-wise classification within regions; the second component is the multi-scale boundary loss, which strengthens the model's sensitivity to boundary pixels. The final combined loss function is expressed as follows:  
\[L_{total}=(1-\beta)\cdot L_{region}+\beta\cdot L_{boundary} \tag{19}\]  
where \(L_{region}\) denotes the multi-class region focal loss for the region, \(L_{boundary}\) represents the multi-scale boundary loss, and \(\beta\) is the balancing coefficient controlling the relative importance of the two components. Among them, the two loss terms are defined as:  
\[L_{region}=\alpha_{\text{region}}\cdot(1-p_{true})^{\gamma}\cdot\log(p_{true}) \tag{20}\]  
\[L_{boundary}=\frac{1}{N_{scales}}\sum_{s=1}^{N_{scales}}\text{BCEWithLogitsLoss} (p_{foreground}^{s},\text{boundary\_mask}^{s}) \tag{21}\]  
Figure 5: The main structure of the graph fusion module.  
The number of classes is set to six, addressing the requirements of multi-class segmentation tasks in remote sensing images. For the multi-class region focal loss, the focusing parameter \(\gamma\) is set to 2.0 to emphasize hard examples, and the class balancing factor \(\alpha_{\text{region}}\) is set to 1.0 to ensure equal treatment across all categories. The multi-scale boundary loss introduces a separate coefficient \(\alpha_{\text{boundary}}\), which is set to 0.3 to increase the model's sensitivity to boundary pixels. To enhance boundary awareness across spatial resolutions, this loss is computed over three different scales: 0.5\(\times\), 1.0\(\times\), and 2.0\(\times\) of the original size. Finally, the coefficient \(\beta\) is empirically set to 0.3, balancing the influence of regional classification and boundary localization in the final loss.
## 3 QualiChain Architecture, Pilot Concept, Research Questions, and Execution Methodology
### QualiChain Architecture
#### 3.1.2 Selection of Blockchain  
Towards maximizing the impact of the QualiChain platform over time, the blockchain service was decided to be developed in a future-proof manner. In practice, regarding the blockchain aspects of the platform, this translated to a requirement to abstract over the specific blockchain implementation, and to support the storage and verification of qualifications on multiple blockchains. This approach allows for easy adaptation of the platform to new developments in the future. The project reference implementation was built on Ethereum, as a widely used blockchain with support for smart contracts, but the processes were designed in such a way as to offer extensibility in the future. Moreover, the selection of Ethereum means that there is no encryption employed for data transactions. However, public/private key cryptography is used in the production of digital signatures for data transaction authentication (e.g., certificate verification), which was the main goal of QualiChain [36].  
Blockchain-agnosticism and multiple blockchain support also offer other practical benefits. Given that different blockchain technologies have different trust features, we cannot assume that all stakeholders will trust the same blockchains equally. An educational institution may, as a matter of policy, select the blockchain platform that it prefers to use  
Figure 1: QualiChain technical architecture.  
to issue qualifications. A graduate of that institution may have a different opinion. If we require that, e.g., students simply have to trust an institution's choice, we replicate the current scenario of requiring centralized trust. If, however, the system is designed from the outset to allow the use of multiple blockchains for a single certificate, all stakeholders can have their requirements met with no conflict.