esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a CDM was designed to improve OFDM-based image reconstruction in wireless communication systems. The proposed scheme addressed the limitations of traditional OFDM-based image reconstruction by leveraging the robustness of diffusion models and incorporating conditional factors that adapt to varying levels of OFDM-based image distortion. A variance learning mechanism can further enhance the reconstruction performance across various modulation schemes, with the simulation results demonstrating significant improvements, particularly in low-SNR scenarios.  
The efficiency of the proposed CDM is expected to be particularly useful under the low-SNR conditions of LEO satellite communications, suggesting that it can be widely applied to autonomous systems for estimating communication parameters and reconstructing distorted images. However, challenges remain in addressing real-world LEO complexities such as the variable Doppler shift, atmospheric interference, and dynamic link conditions. Future research will focus on mitigating these issues and further optimizing the CDM for different communication scenarios.  
Conceptualization, S.K., J.K. ([PERSON]), and Y.S.; methodology, S.K., and J.K. ([PERSON]); simulation, S.K. and J.K. ([PERSON]); formal analysis, S.K., S.L., and J.S.; resources, S.K., B.H., and Y.S.; writing--original draft preparation, S.K., J.K. ([PERSON]), J.K. ([PERSON]); writing--review and editing, J.S., K.K., and Y.S.; visualization, S.K., J.K. ([PERSON]) and S.L.; supervision, Y.S. and J.K. ([PERSON]); project administration, S.K. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in [CIFAR-10] at [[https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)]([https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)) (accessed on 12 December 2024).  
This work was partly supported by an Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Ministry of Science and ICT (MSIT) (No. 2021-0-00892-005, \"Research on Advanced Physical-Layer Technologies of Low-Earth Orbit (LEO) Satellite Communication Systems for Ubiquitous Intelligence in Space\") and supported by the MSIT, Korea, under the TIRC (Information Technology Research Center) support program (IITP-2025-RS-2023-00258639) supervised by the IITP.  
Author [PERSON] was employed by the company SMARTEVER, Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial of financial relationships that could be construed as a potential conflict of interest.
## 3 Controller Design
### Controller for the Case without In-Track Control
Then the switching control law combining the neural network control law and the adaptive sliding mode control law is designed as:  
\[\mathbf{U}_{2}=-\hat{m}\overline{\mathbf{A}}_{2}+(1-\hat{h}_{a,b}(\mathbf{e}_{\rho}))\mathbf{ u}_{n2}+\check{h}_{a,b}(\mathbf{e}_{\rho})\mathbf{u}_{s2}+\mathbf{u}_{2s}, \tag{35}\]  
with  
\[ \begin{split}&\mathbf{u}_{n2}=-\hat{\mathbf{W}}_{2}\mathbf{{}^{T}}\mathbf{h}( \mathbf{X})-\text{diag}(\hat{\phi}_{21},\hat{\phi}_{22})\text{sgn}(\mathbf{s}_{2}),\\ &\mathbf{u}_{s2}=-\hat{\delta}_{2}.\end{split}  \tag{36}\]  
The adaptive laws of \(\hat{m},\hat{\mathbf{W}}_{2},\hat{\mathbf{\phi}}_{2}\) and \(\hat{\delta}_{2}\) in Equation (35) are designed as:  
\[\left\{ \begin{array}{l}\dot{\hat{m}}=\gamma_{2}s_{2}^{\text{T}}\overline{A }_{2},\\ \dot{\mathbf{W}}_{2}=(1-\check{h}_{a,b}(\mathbf{e}_{\rho}))\mathbf{\zeta}_{1}\mathbf{h}(\mathbf{X}) \mathbf{s}_{2}\mathbf{{}^{T}},\\ \dot{\mathbf{\phi}}_{2}=(1-\check{h}_{a,b}(\mathbf{e}_{\rho}))\mathbf{\xi}_{2}|\mathbf{s}_{2}|,\\ \dot{\delta}_{2}=\check{h}_{a,b}(\mathbf{e}_{\rho})\mathbf{\zeta}_{3}\mathbf{s}_{2},\end{array} \right. \tag{37}\]  
where \(\gamma_{2}>0\), \(\mathbf{\zeta}_{1}\), \(\mathbf{\zeta}_{2}\) and \(\mathbf{\zeta}_{3}\) are positive definite diagonal matrices. Consider the Lyapunov function \(V_{2}\) designed as:  
\[V_{2}=(1/2)(\mathbf{s}_{2}^{\text{T}}\mathbf{ns}_{2}+\gamma_{2}^{-1}\tilde{m}^{2}+ \text{tr}(\tilde{W}_{2}\mathbf{{}^{T}}\mathbf{\xi}_{1}\tilde{W}_{2})+\mathbf{\phi}_{2}^{ \text{T}}\overline{\xi}_{2}^{-1}\widetilde{\mathbf{\phi}}_{2}+\widetilde{\delta}_ {2}^{\text{T}}\overline{\xi}_{3}^{-1}\widetilde{\delta}_{2}) \tag{38}\]  
where \(\tilde{W}_{2}=\tilde{W}_{2}-{W}_{2}{}^{*}\), \(\widetilde{\mathbf{\phi}}_{2}=\hat{\mathbf{\phi}}_{2}-\mathbf{\phi}_{2}\) and \(\widetilde{\delta}_{2}=\hat{\delta}_{2}-\delta_{2}\) are the estimation errors of \(W_{2}^{*}\), \(\mathbf{\phi}_{2}\) and \(\delta_{2}\), respectively. Similar to the method used in the stability proof of radial underactuated, the same asymptotic stability of the closed-loop system can be proved for the case without in-track control and will not be repeated.
# Nematic Dispersive Shock Waves from Nonlocal to Local
## 7 RNLS and NLS DSW Regimes  
The KdV approximation (44) gives that the DSW changes from KdV to NLS type at \(\
u=q^{2}/(4u_{+}^{2})\) due to the change in sign of the coefficient of the third derivative, as discussed above, and as shown in Figure 3. It should be noted that numerical solutions do not show a distinct change in DSW type, but a transition from a KdV-type DSW to an NLS-type DSW, as seen in Figure 2d,e and as shown by the two regimes RNLS and NLS type in Figure 3. The TDSW regime is characterized by a negative polarity solitary wave connecting the resonant wavetrain to the intermediate level, as in Figure 2d. As the nonlocality \(\
u\) decreases, the beam power in increases, and the height of this solitary wave decreases, resulting in the RNLS regime which consists of a Whitham shock connecting a resonant wavetrain to the intermediate level \(u_{i}\). Ahead of the resonant wavetrain is a partial NLS-type DSW  
Figure 8: Comparison between the Whitham shock velocity \(U_{s}\) given by the modulation equation jump conditions (92)–(93) with (94) and numerical solutions of the nematic Equations (11) and (12) in the TDSW regime. Solution of jump conditions: red (full) line; numerical solution: red boxes. Here, \(u_{-}=1.0\) and \(q=2\).  
which connects to the level ahead \(u_{+}\); see Figure 2e. As \(\
u\) decreases further, the resonant wavetrain shrinks, and the partial DSW becomes a full NLS DSW with solitary waves at its trailing edge and linear waves at its leading edge, resulting in an NLS DSW for sufficiently small \(\
u\); see Figure 3b. In this limit, the NLS DSW alone links the intermediate level to the level ahead. As \(\
u\) decreases in the RNLS regime, the waves at the trailing edge of the partial DSW evolve from weakly nonlinear Stokes waves to fully nonlinear periodic waves, and then solitary waves in the NLS DSW regime.  
The intermediate RNLS state, illustrated in Figure 2e, consists of a resonant wavetrain with the height of the negative polarity solitary wave at the Whitham shock negligible. To compare the KdV-NLS DSW boundary \(\
u=q^{2}/(4u_{+}^{2})\) with numerical solutions, the choice of the height of this solitary wave being \(5\times 10^{-3}\) above \(u_{i}\) was chosen for the onset of the RNLS regime in numerical solutions. It can be seen that there is good agreement for this regime boundary for \(u_{+}\) close to \(u_{-}\), but there is increasing disagreement as \(u_{+}\) decreases. This is expected as the reductive nematic eKdV Equation (42) was derived under the assumption that \(|u_{-}-u_{+}|\) is small.
## 3 Building Load Type
### Classify Based on Time Span  
During the process of load forecasting, the types of load forecasting can be subdivided into four main categories based on the length of the prediction period: very short-term load forecasting, short-term load forecasting, medium-term load forecasting, and long-term load forecasting [39].  
The time frame for very short-term load forecasting is limited to within one day [40], with a time resolution accurate to fine-grained units such as seconds, minutes, and hours. When the time span of the forecast extends from one day to one week, it is referred to as short-term forecasting, with its time resolution primarily measured in days and weeks. On the other hand, medium- to long-term load forecasting focuses on load fluctuations from quarters to years, providing crucial information for strategic planning. As for predictions spanning several years or more, they are categorized as long-term forecasting, which holds profound significance for strategic planning and decision-making [41].
## 4 The Proposed CIM-Oriented Ontology Database Framework for DMS
### Development Process of the CIM-Oriented DMS Using an Ontology Database  
Integrating an ODB into a DMS involves five distinct stages. First, determining the appropriate ontology types for the DMS is essential. This requires analyzing use cases to consider the types of data that will be represented and managed within the system. Second, based on the results of the requirements analysis, designing an ontology becomes imperative. This ontology can be represented as an information model comprising classes, attributes, and associations, enabling the expression of relationships between entities and objects. Third, it is possible to construct an ODB in the form of an RDF triple graph based on the designed information model. Fourth, integrating the ODB into the DMS necessitates a database inference engine. This engine functions as the input-output interface of the internal components of the DMS, receiving a profile input in the form of a schema and producing the corresponding payload as output. The components of DMS can exchange data stored in the ODB through an inference engine. Finally, the system should be adaptable to future changes or expansions. The information model can be modified, added to, or deleted according to evolving business needs, and the ODB is designed with a flexible structure to handle this. It must be possible to maintain additions, changes, and deletions of new or existing classes, attributes, and associations.  
Figure 10 illustrates the CIM-oriented DMS development process based on the integration of MDD and CBD methodologies. In the MDD approach, an information model is first designed as a Platform-Independent Model (PIM) using standard modeling tools such as EA. This model is then transformed into a Platform-Specific Model (PSM) by deriving interface specifications, including RDF Schema (RDFS), XML Schema (XSD), and Interface Definition Language (IDL) based on the CIM structure [30].  
From the PSM outputs, interface-specific software components such as adaptors are automatically generated using a code generator, allowing a seamless transition from abstract models to implementation. Meanwhile, the CBD approach supports the modularization of the system into independently manageable components. Each component is connected to the middleware through its own adaptor, and the middleware facilitates communication with the ODB via a data agent.  
The data agent, equipped with RDF inference capabilities, enables semantic reasoning and accommodates changes in information models. All internal components conform to CIM-based interface profiles, allowing for consistent and standardized data exchange across the DMS. This integrated MDD-CBD methodology supports automatic interface generationand flexible service composition, thereby enhancing the scalability, maintainability, and interoperability of the system under evolving policy and operational requirements.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### Cyber Security of IECPS
#### 5.4.3 Attack Detection and Identification  
Deliberate attack detection and identification are key elements in ensuring the safe operation of IECPS. Effective deliberate attack detection methods help intrusion detection systems (IDS) identify potential deliberate attacks in a timely manner, thereby strengthening the defense capabilities of IECPS against deliberate attacks. In Section 4.2.1, methods for deliberate attack detection are analyzed based on underlying principles. In practice, the aforementioned deliberate attack detection methods are integrated into the IDS. By strategically deploying IDS within IECPS, the detection algorithm's capability can be maximized. A centralized IDS uses a single sensor to monitor all traffic entering and exiting measurement devices, as well as access system logs. This helps identify internal attacks in the IECPS network and detects systemic intrusions by scanning access records and analyzing the anti-tampering signals provided by the measurement devices. Embedding IDS into the deployment of measurement instruments can significantly improve the detection accuracy and the reliability of early warnings, though it comes at a higher cost. Additionally, there are IDS deployed in cloud service platforms and distributed IDS, among others. In the future, attention should be given not only to upgrading deliberate attack detection algorithms but also to IDS deployment strategies, in order to enhance the system's defense capabilities.
## 2 Modeling Methodology
### Soil Liquefaction Model  
If shear deformation is applied to the saturated soil element under undrained conditions, excess pore pressure is generated owing to slippage between the soil particles. This decreases the effective stress and simultaneously expands the volume of the soil. In each cyclic shear deformation step, the compatibility conditions for this volume change can be expressed as shown in Equation (1) ([PERSON] et al., 1975 [24]). In other words, the change in the void ratio under undrained conditions (water compression amount) is equal to the value obtained by subtracting the volume expansion of the soil element caused by pore pressure from the volume change of the soil element under drained conditions.  
\[(\Delta\upnu\cdot\mathrm{n_{e}})/K_{\mathrm{w}}=\Delta\epsilon_{\mathrm{yd}} \ \text{-}\ (\Delta\upnu/\mathrm{E_{r}}) \tag{1}\]  
Figure 2: Determination of zone of liquefaction ([PERSON], 1971 [18]).  
where \(\Delta\)u is the pore pressure increment, \(\Delta\varepsilon_{\rm vd}\) is the volume reduction, \(\rm E_{r}\) is the tangential coefficient of the unloading curve, \(\rm n_{e}\) is the porosity, and \(\rm K_{w}\) is the bulk modulus of water. The volume strain increment that induces a change in the pore pressure in Equation (1) can be expressed using Equation (2).  
\[\Delta\varepsilon_{\rm vd}=C_{1}(\gamma\ \text{-}\ C_{2}\ \varepsilon_{\rm vd})+(C_{ 3}\ \varepsilon_{\rm vd}{}^{2})/(\gamma+C_{4}\ \varepsilon_{\rm vd}) \tag{2}\]  
where \(\varepsilon_{\rm vd}\) is the cumulative volume strain up to N cycles, and \(\gamma\) is the shear strain action in cycle N + 1.  
In Equation (2), \(C_{1}\), \(C_{2}\), \(C_{3}\), and \(C_{4}\) are the volume change constants, which vary depending on soil conditions. The volume change constants, which are important variables for liquefaction analysis, can be calculated through indoor experiments. For practical purposes, [PERSON] (1991) [25] proposed a simplified equation (Equation (3)) that can replace Equation (2). Equation (3) represents an empirical method. [PERSON] (1991) [25] also proposed a calculation equation (Equation (4)) for the volume change constants \(C_{1}\) and \(C_{2}\) using the relative density of the soil.  
\[\Delta\varepsilon_{\rm vd}/\gamma=C_{1}\ \text{exp}\ (\text{-}C_{2}\ ( \varepsilon_{\rm vd}/\gamma)) \tag{3}\]  
\[C_{1}=7600(D_{\rm r})^{-2.5},\ C_{2}=0.4/C_{1} \tag{4}\]  
The Finn model constructed above can identify the direct, real-time pore pressure development caused by soil shear deformation. It was coupled with the Mohr-Coulomb elastoplastic model in FLAC 3D for the liquefaction modeling ([PERSON] & [PERSON], 2004 [19], [PERSON] et al., 2010 [26], [PERSON] & [PERSON], 2014 [27]). By incorporating both constitutive model, basic failure criteria and a related physical mechanism was modeled by Mohr-Coulomb criteria, and additional complex behaviors due to liquefaction, such as dynamic pore pressure generation, reduction of effective stress, etc. were captured by the Finn liquefaction model.
# Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data
## 3 Materials and Methods  
### Study Area and Data Used  
The study covers the territory of seven Polish counties (an administrative unit corresponding to the European Territorial Units for Statistics NUTS 4), which are located in different physical-geographical mesoregions and reflect the diversity of both the natural and anthropogenic environment, making them representative (Table 1). Due to various threats, including the violation of state borders' integrity, they are of strategic importance for security. The area of interest (see Figure 1) covers 3.1% of Poland. Piaseczno and Otwocki counties are located in the central part of Poland, adjacent to the capital city of Warsaw, Ostrowski, in Great Poland. The next two study areas are situated along the eastern border of Poland, namely, Sokolski (in the north) along the border with Belarus, and Sanocki (in the south) on the Polish-Ukrainian and Polish-Slovak border. The sixth county Slupski is located in the Baltic Sea coastal zone. Last but not least, Miedzyrzecki county is located in western Poland. Five of the regions of interest have previously been the subject of OSM data quality assessments ([PERSON], 2018; [PERSON], 2018). Two topographic datasets were investigated, namely, the National Database of Topographic Objects (BDOT10k), managed by the Head Office of Geodesy and Cartography, and OpenStreetMap, created on a voluntary basis. Six thematic layers were analyzed in detail: three polygon layers, such as buildings, forests and water bodies, and three linear ones--streams and canals, paved roads and railway lines. Topographic data were chosen because they reflect the complex relationships between components of the geographical environment, related to morphology, geology, hydrology, vegetation, and microclimate.
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 4 Analysis of Numerical and Experimental Results
### Necessary Hydrodynamic Results  
In this section, hydrodynamic coefficients and retardation functions obtained by the hydroelastic code are presented and analyzed. The added masses of \(A_{33}\) and \(A_{55}\), damping coefficients of \(B_{33}\) and \(B_{55}\), and the retardation functions of \(K_{33}\) and \(K_{55}\) of the target ship with forward speeds 5 kn and  
Figure 7: Acquisition of instantaneous wetted surface during computation.  
18 kn in head regular waves are presented in Figures 8-10. As can be seen from Figure 8, both \(A_{33}\) and \(A_{55}\) vary significantly with respect to the natural circular frequency \(\omega\) of the incident wave when \(\omega\) is less than 0.5 rad/s. For \(\omega\) greater than 1.5 rad/s, these two added mass coefficients change very slightly. Different from \(A_{33}\), ship velocity plays a more vital role in \(A_{55}\) when \(\omega\) is less than 0.3 rad/s. As velocity increases from 5 kn to 18 kn, positions of occurrence of the peak values in \(B_{33}\) and \(B_{55}\) both move leftward a little bit, with the peak value of \(B_{33}\) changing less significantly than that of \(B_{55}\). Similar to \(A_{33}\) and \(A_{55}\), the variations of \(B_{33}\) and \(B_{55}\) with respect to \(\omega\) are much more evident when \(\omega\) falls in the interval of 0.1 rad/s to 1.5 rad/s. The general shapes of retardation functions \(K_{33}\) and \(K_{55}\) are similar, and small oscillations can be found in these two curves. Both \(K_{33}\) and \(K_{55}\) decay quickly with respect to time, as can be observed in Figure 10.
# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
## 4 Experimental
### Comparison of Conventional Lightweight YOLOs  
To highlight the excellent performance of the Light-YOLO, we start with a traditional lightweight YOLO family comparison. Specific data are shown in Table 6.  
In the comparative analysis of the traditional YOLO family, the Light-YOLO model demonstrated its exceptional performance under lightweight design. This model achieved an accuracy of 89.3% and a recall rate of 80.7%. Although its accuracy is slightly lower than YOLOv3-tiny and YOLOv7-tiny, its recall rate surpasses all other comparison models, including YOLOv7-tiny. This result indicates a clear advantage of Light-YOLO in accurately identifying the correct targets. In more detailed performance metrics, Light-YOLO reached 86.7% in [EMAIL_ADDRESS] and achieved a high score of 51.8% under the more stringent [EMAIL_ADDRESS]-0.95 evaluation standard, leading other models and showcasing its high precision and reliability in various object detection scenarios.  
In terms of computational efficiency, the number of parameters of Light-YOLO is only 4.4 million, which is far less than that of YOLOv5s (34.7 million) and YOLOv10n (8.06 million), and its GFLOPs is only 6.1, compared with YOLOv5s (23.8) and YOLOv10n (24.8), separately. This low computational complexity makes Light-YOLO especially suitable for running on devices with limited computational resources, such as mobile devices and embedded systems.  
Although the frame rate (FPS) of 121.95 of Light-YOLO is not as good as that of 833.33 of some other models, such as YOLOv5n, the FPS provided by Light-YOLO is sufficient to meet the needs of most real-time application scenarios, especially in video surveillance and mobile devices, while maintaining high accuracy and low computational requirements. In summary, Light-YOLO is an ideal choice for efficient, high-precision target detection in resource-constrained environments due to its excellent balance between performance and efficiency. These features make it an outstanding performer in lightweight target detection models and provide a necessary reference value for research and applications in related fields.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **Precision** & **Recall** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]–0.95 (\%)** & **Params** & **GFLOPs** & **FPS** \\ \hline YOLOv3-tiny [26] & **91.4** & 77.1 & 83.5 & 43.3 & 17.4 & 12.9 & 434.78 \\ YOLOv7-tiny [27] & 91.1 & **81.3** & 85.5 & 47.8 & 12.3 & 13.2 & 123.46 \\ YOLOv5n [28] & 91.0 & 77.6 & 85.6 & 49.6 & 9.5 & 7.1 & **833.33** \\ YOLOv5s & 90.0 & 77.0 & 84.1 & 49.2 & 34.7 & 23.8 & 263.15 \\ YOLOv8n & 87.6 & 78.1 & 83.7 & 47.6 & **3.0** & 8.2 & 322.58 \\ YOLOv10n [29] & 90.2 & 78 & 84.5 & 48.7 & 8.06 & 24.8 & 230.49 \\ Light-YOLO & 89.3 & 80.7 & **86.7** & **51.8** & 4.4 & **6.1** & 121.95 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative experiments of the traditional YOLO family.