esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 1 Introduction
First, we developed a comprehensive database by collecting road anomaly images and video feeds, leveraging both open-source data and a self-collected database, along with real-time access to road video streams. This effort resulted in a dataset of road anomalies, consisting of 15,223 images, with 80% allocated for training and 20% for testing and validation. Second, we compared the AI models for adaptability for this monitoring purpose and selected InternVL as the primary model, with ResNet serving as the comparison model. Third, we designed a Chain of Thought framework to evaluate the pre-trained visual language model, focusing on the identification of anomalies and assessing the safety level. This model was optimized through the use of customized prompts and retrieval-augmented generation, which targeted specific monitoring indicators. We emphasized the selection and assignment of reference objects, facilitating the continuous optimization of the monitoring workflow. This step was essential for conducting analyses of video streams, thereby ensuring accurate detection results. Lastly, we evaluated the models to assess their performances.
## 2 Basic Principles
### Physics-Informed Loss Function  
The physics-informed loss function is a valuable tool for using physical insights to inform the training of deep learning models, which plays the most important role in PINNs [10]. Generally, the performance of ANNs highly depends on the construction of their loss function. In PINNs, such physical constraints, including both governing equations and boundary condition equations, are imposed upon the loss function in the form of a partial differential that is directly described by automatic differentiation. The incorporation of physical constraints into the loss function helps to reduce the complexity of the model, leading to improved generalization and better performance. Additionally, the physical constraints can be used to ensure that the model conforms to certain physical laws, ensuring that the model is providing reliable and correct predictions. Owing to the popular graph-based utilization of neural network libraries (e.g., TensorFlow and PyTorch), the automatic differentiation technique is easy to carry out, thus enabling the embedding of physics constraints in the loss function [23].  
The physics-informed loss function, \(\mathcal{L}\), including data loss and physics loss, can be written as follows:  
\[\mathcal{L}=\mathcal{L}_{d}+\mathcal{L}_{p} \tag{6}\]  
where  
\[\mathcal{L}_{d}=\parallel u_{i}^{*}-\hat{u}_{i}\parallel_{2}^{2} \tag{7}\]  
\[\mathcal{L}_{p}=\parallel\sigma_{ijj}+f_{i}\parallel_{2}^{2}+\parallel u_{i} -\overline{u}_{i}\parallel_{2}^{2}+\parallel\sigma_{ij}n_{j}-\overline{f}_{ i}\parallel_{2}^{2} \tag{8}\]  
The symbol \(\hat{u}_{i}\) refers to the predicted displacement, which is a function of the input variables and represents the solution to the physical problem being studied. The symbol \(u_{i}^{*}\) represents the actual values of some data. The notations in \(\mathcal{L}_{p}\) have the same meanings as described in Section 2.1. In the context of this paper's two-dimensional problem, the indices \(i\) and \(j\) correspond to either 1 and 2 or \(x\) and \(y\), respectively, and each index represents a component of a tensor.  
Forward problems can be solved using \(\mathcal{L}_{p}\) without any labeled data within physics domains. As for inverse problems, such as those related to identifying the Young's modulus of an unknown material, \(\mathcal{L}_{d}\) is required in addition to \(\mathcal{L}_{p}\).
# The Effects of Adhesive and Bonding Length on the Strain Transfer of Optical Fiber Sensors
## 1 Introduction
[PERSON] _et al._[15] developed a simple model to calculate the percentage of strain applied to the host structure actually transferred to the embedded fiber optic sensor. [PERSON] _et al._[16] investigated the influence of coating and adhesive on the strain transmission of embedded fiber optic sensors. [PERSON] _et al._[17] investigated the influence of four geometric parameters of the adhesive, side width, top thickness, bonded length and bottom thickness, on the strain of the optical fiber. Most of the existing works that have studied the strain transfer between the optical fiber and host material have focused on the embedded optical fiber. In this investigation, the fiber optic sensor is surface bonded on the host structure. Previous work [18] developed a theoretical model to predict the strain transfer between the host structure and surface bonded optical fiber. Present work employed the theoretical model to study the effects of the adhesive and bonding length on the optical fiber strain sensor. Experimental tests were conducted to verify the theoretical model. A series of experimental tests is performed to evaluate the influences of the adhesive and the bonding length on the strain transferred from the host structure to the optical fiber.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol
### Route Measurement and Scoring
#### 3.4.1 Congestion Degree  
The \"white list\" mechanism of the AODV routing protocol often leads to a situation where, once the link has been determined, it cannot easily be changed. This situation will cause the energy of the nodes on the link to be exhausted rapidly. In addition, due to the weakness of the node performance, the receiving and sending queues of the route will be full, resulting in a high packet loss rate and larger delay. Therefore, the study used the congestion degree as one of the routing metrics. The calculation of the congestion degree (CD) is shown in Equation (8):  
\[CD=\frac{L_{free}}{L_{all}}\text{, }CD\in[0,1] \tag{8}\]  
where \(L_{free}\) is the size of the idle sending queue at a certain moment, and \(L_{all}\) is the size of the total sending queue. Therefore, the calculated score must be between 0 and 1, where the higher the score is, the lower the congestion degree of the node is and vice versa.
## 5 Conclusions  
In this manuscript, a strategy for a novel Transmit and Reflect Array (TRA) metasurface employing a multi-layered approach is proposed. Comprising three metallic layers interspersed with a Roger RT 6006 substrate, this metasurface serves as a versatile tool for transmitting and reflecting electromagnetic waves. Demonstrating its adaptability to distinct frequencies, specifically 6.3 GHz and 9.3 GHz, the metasurface dynamically responds to the polarization of incident waves. In the x-polarized (TM) mode, it acts as a transmit array at the lower frequency and transforms into a reflect array at the higher frequency. Alternatively, in the y-polarized (TE) mode, its function inversely transitions from a reflect array at 6.3 GHz to a transmit array at 9.3 GHz. Noteworthy attributes include its commendable gain performance and impressively low side lobe levels, collectively underlining its efficacy across wide electromagnetic applications.  
Conceptualization, A.A. and D.-Y.C.; methodology, A.A. and J.A.; software, A.A.; validation, A.A., J.A. and D.-Y.C.; formal analysis, A.A.; investigation, A.A. and D.-Y.C.; resources, D.-Y.C.; data curation, A.A., J.A. and D.-Y.C.; writing--original draft preparation, A.A.; writing--review and editing, J.A. and D.-Y.C.; visualization, D.-Y.C.; supervision, D.-Y.C.; funding acquisition, D.-Y.C. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2022R11 IA3064544).  
Not applicable.  
The authors declare no conflict of interest.
# Demand-Response Control in Smart Grids
## Nomenclature  
\begin{tabular}{l l} DR & Demand Response \\ CAi & Consumer Agent \(i\) \\ \(N\) & Number of consumer agents \\ \(\pi_{i}^{\min}\) & Minimum power consumption required by the consumer agent \(i\) for a period of time T \\ \(\pi_{i}^{\max}\) & Maximum power consumption required by the consumer agent \(i\) for a period of time T \\ t & Time slot (representing hours) \\ \(\pi_{i,t}\) & Required power consumption by the consumer agent \(i\) at time slot \(t\) \\ \(\pi_{i,t}^{\min}\) & Minimum required power by the consumer agent \(i\) at time slot \(t\) \\ \(\pi_{i,t}^{\max}\) & Maximum required power by the consumer agent \(i\) at time slot \(t\) \\ \(s_{i}^{a}\) & Up ramp limit related to the required power for the consumer agent \\ \(s_{i}^{d}\) & Down ramp limit related to the required power for the consumer agent \\ \(\pi_{i,t}^{\min}\) & Received power by the consumer agent \(i\) at time slot \(t\) \\ \(\pi_{t}^{\min}\) & Minimum power requirements limit of all consumer agents at time slot \(t\) \\ \(\pi_{t}^{\max}\) & Maximum power requirements limit of all consumer agents at time slot \(t\) \\ \(\pi_{t}\) & Total required power inside the microgrid \\ \(U(\pi_{i},\omega)\) & Utility function corresponding to the benefit gained by consumer agent \(i\) when it \\ \(W(\pi_{i},\omega)\) & Wellare of consumer agent \(i\) \\ \end{tabular}
## 2 Research Data and Research Methods
### Research Data
#### 2.2.2 GPP Data  
Gross Primary Productivity (GPP) refers to the total amount of primary productivity generated by plants through photosynthesis in an ecosystem. It is a key indicator for measuring energy flow and biomass accumulation [16]. The GPP data used in this study were sourced from the Zenodo platform ([[https://zenodo.org/records/6518002](https://zenodo.org/records/6518002)]([https://zenodo.org/records/6518002](https://zenodo.org/records/6518002)), accessed on 3 December 2024). This dataset was constructed using the GLOBMAP Leaf Area Index (LAI), CRUJRA meteorological data, and ESA-CCI land cover data, in conjunction with an updated Two-Leaf Light Utilization Efficiency Model (TL-LUE). The model was calibrated and validated against Eddy covariance measurements from 68 FLUXNET sites (480 site years) for parameter optimization, and an independent validation using 25 FLUXNET sites (170 site years) yielded an overall coefficient of determination (R\({}^{2}\)) of 0.76 and root mean square error (RMSE) of 2.1 g C m\({}^{-2}\) day\({}^{-1}\)[17]. The resulting global GPP dataset has a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 8 days, covering the period from 1992 to 2020. For this research, GPP data from 2001 to 2020 were selected and resampled to a 1 km \(\times\) 1 km spatial resolution using the nearest neighbor method to match the scale requirements of the study area.
## 4 System Simulation and Performance Analysis
### Performance Degradation Analysis
#### 4.2.2 Effect of Solenoid Valve Performance Parameters  
The solenoid valve is an important part of the electrohydraulic actuator. The dynamic characteristics of the high-speed solenoid valve have a significant effect on the action characteristics of actuator. There are many influencing factors in its dynamic change [11]. This paper does not discuss the factors that  
Figure 10: (**a**) First-stage exhaust buffer tank pressure; (**b**) second-stage exhaust buffer tank pressure.  
Figure 9: Valve plate, actuator displacement, and cylinder pressure under different spring stiffness values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spring Stiffness** & **First Stage Buffer Tank Pressure** & **Second Stage Buffer Tank Pressure** \\ \hline
38 kN/m & 264 kPa & 580 kPa \\
30 kN/m & 253 kPa & 555 kPa \\
25 kN/m & 241 kPa & 542 kPa \\
15 kN/m & 230 kPa & 508 kPa \\ \hline \hline \end{tabular}
\end{table}
Table 3: The pressure varies with the stiffness of the spring.  
affect the dynamic response of the high-speed solenoid valve but only the influence of the dynamic response of the high-speed solenoid valve on system capacity regulation performance.  
The influence of the dynamic response of the solenoid valve on the dynamic process of the suction valve plate, and the dynamic pressure in the cylinder is illustrated in Figure 11. The dynamic characteristics of the solenoid valve affect both the actuator's extension and retraction movements.  
The high-pressure stage actuator was normal, and the solenoid valve response of the low-pressure stage actuator with different degree changes of response is shown in Figure 12. The load of the first stage and the second stage were both set as 80%. Under normal regulation conditions, the pressure of the first and second exhaust buffer tanks were 270 kPa and 533 kPa, respectively. The pressure variation of the buffer tank under different solenoid valve responses is shown in Table 4.  
When the solenoid valve responded 8 ms in advance of the normal response, the pressure of the first-stage exhaust buffer tank was reduced by 25 kPa, and the pressure of the second-stage  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Change in Response** & **First-Stage Buffer Tank Pressure** & **Second-Stage Buffer Tank Pressure** \\ \hline
0 ms & 270 kPa & 533 kPa \\
3 ms & 262 kPa & 545 kPa \\
5 ms & 253 kPa & 555 kPa \\
8 ms & 245 kPa & 563 kPa \\ \hline \hline \end{tabular}
\end{table}
Table 4: Buffer tank pressure variation under different responses of the solenoid valve.  
Figure 11: Solenoid valve response, valve plate displacement, and dynamic pressure.  
Figure 12: (**a**) First-stage exhaust buffer tank; (**b**) second-stage exhaust buffer tank.  
exhaust buffer tank was increased by 30 kPa. Therefore, if the solenoid valve changed in response, the accuracy of the capacity regulation decreased under the original design parameters. If the response changes greatly, control instability will be caused, which will affect the safe and stable operation of the compressor.
# GLClick: Interactive Segmentation Combining Global and Local Features
###### Abstract  
Convolutional neural networks (CNNs) are the backbone of most modern interactive segmentation algorithms. However, the limited receptive field of CNNs restricts their ability to capture long-range semantic relationships. Recently, transformers have gained significant attention for their capacity to capture long-range dependencies. Nevertheless, CNNs still outperform Transformer in extracting local information. An effective interactive segmentation algorithm should accurately capture fine-grained local details alongside global semantic relationships. Therefore, we propose GLClick, a global-local click-based interactive image segmentation model that integrates local and global information through a novel fusion mechanism. We design an efficient global-local feature fusion module (GLFM) that integrates fine-grained features from various layers of ResNet50 with those from the Transformer feature pyramid. This approach maintains ResNet50's ability to extract local features while effectively leveraging the Transformer to capture global context. Additionally, we enhance the multi-layer perceptron (MLP) to improve performance. Extensive experiments on diverse benchmark datasets demonstrate significant improvements in interactive image segmentation, confirming the effectiveness of our approach. Moreover, we conduct experiments on medical image datasets, further illustrating the model's versatility and effectiveness across different domains.  
Keywords:ResNet50; Transformer; global-local feature fusion module; interactive segmentation; MLP +
Footnote †: journal: Applied sciences
## 4 Results: Model Application and Experiments  
To verify its applicability and provide a better understanding on how the model operates, this research proposed its application to a real case study. Specifically, it refers to one of the most relevant airports in Southern Italy, namely Catania airport.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline Strategy 1 (H1) & For each critical work-zone w, application of a single M&R activity during the planning period T \\ \hline Strategy 2 (H2) & For the critical work-zones w requiring rehabilitation activities, merging of them at the same year t, identified as the minimum one among all those found \\ \hline Strategy 3 (H3) & For all critical work-zone w, merging of all the M&R activities in the year t in which there are the greatest number of interventions. \\ \hline Strategy 4 (H4) & For each critical work-zone w, implementation of a single M&R activity or, if it is considered more convenient, of two less invasive activities \\ \hline Strategy 5 (H5) & For each critical work-zone w, implementation of only superficial M&R activities to mitigate any risk of interruption of the airport operations \\ \hline \end{tabular}
\end{table}
Table 1: Strategies used in the heuristic algorithms implemented for this study.  
Catania airport has only one runway, 2666.01 m long and 45 m wide, with 2 \(\times\) 7.50 m wide shoulders. The Aerodrome Reference Code for Catania Airport is 4D [9]; therefore, the flight infrastructures are subject to high loads due to the types of aircraft that can transit. Focusing on the pavement of the runway, it has a flexible pavement type, with a Pavement Classification Number (PCN) 97/F/A/W/T, between the progressive 200.00 and 2545.93 m. Having a single runway and constantly growing traffic at Catania airport, it is essential to optimize a pavement maintenance program, and also take into account the duration of the activities, to manage any operational restrictions.  
The following sub-section details the parameters and the assumptions setting for the clustering and heuristic methods.
# Secure Internal Data Markets
## 3 Security Issues of Internal Data Markets
### Knowledge on Data  
Similar to the data volume, global data markets strive to be data agnostic, i.e., they either simply broker the data without local storage (see data locality), or provide a data storage engine that is as flexible as possible in order to be able to provide a wide variety of data sets and sources. This also reduces the capabilities for sanity checks and other security features on the data itself. This is especially reinforced in cases where the market only handles the data in an encrypted form. Internal markets, on the other hand, often do not need to be built as all-purposes markets, but only in order to deal with rather specific data sets. Furthermore, encryption of the data might not be an issue, depending on the design of the system and the entities involved. Thus, the platform could apply sanity checks and detection mechanisms against manipulation attacks in order to, for example, thwart adversarial machine learning [28].  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Global Data Markets** & **Internal Data Markets** \\ \hline Dedicated security personal & Typically available & Typically not available \\ \hline System and Infrastructure & Modelled after DM requirements & Already existing infrastructure \\ \hline Management capabilities & Can be patched/fixed/changed & External software \\ \hline Volume & High & Diverse \\ \hline Data intelligence & Little to none & Can be high \\ \hline Users & Little control & High level of control possible \\ \hline Market model & Generic & Can be specifically tailored \\ \hline Interfaces & Control possible & Typically no control \\ \hline Data locality & Typically low & Typically high \\ \hline GDPR responsibility & None & Can be fully responsible \\ \hline Institutional attention & DM is main focus & DM is a side issue \\ \hline \hline \end{tabular}
\end{table}
Table 1: Security-related key differences between internal and global data markets.
## 3 Results
### Training MOGP Kernels for VWC Time Series Modelling
As observable in Figure 5, the convolutional model reaches the best results in the predictions of VWC values for both 2020 and 2021 winter wheat test sites. The complete phenological profile of the crop (green dots) is fully recovered, and all latent VWC values are predicted accurately by the CONV model in the absence of optical observations. Consequently, the CONV model is selected for the posterior task of reconstructing VWC at a pixel level. Although CSM and SM-LMC models indeed delivered good results for  
Figure 5: Performance of the SOGP and MOGP models predictions for VWC data reconstruction based on the S1 & S2 synergy. The green dots represent the latent S2 GP VWC data used to compute the error metrics whereas the red dots correspond to the S2 GP VWC cloud-free observations utilized to train the regression models. The red-shaded area represents the artificially created S2 data gap. SOGP and MOGP predictions performance for the selected winter wheat ROI-1 of the year 2020 (**a**) and 2021 (**b**). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**S2 GP VWC and S1 RVI Orbit 68**} \\ \hline
**MOGP Kernel** & **MAE [g m\({}^{-2}\)]** & **MAPE [\%]** & **RMSE [g m\({}^{-2}\)]** & **NRMSE [\%]** & **Time [s]** \\ \hline MOSM & 1606.97 & 91.26 & 1746.35 & 77.76 & 11.59 \\ CSM & 1420.06 & 79.84 & 1549.94 & 69.02 & 19.85 \\ SM-LMC & 1229.57 & 64.98 & 1362.06 & 60.65 & 13.9 \\ CONV & 238.07 & 41 & 328.01 & 14.61 & 22.31 \\ SM & 1408.52 & 75.06 & 1550.67 & 69.05 & 7.23 \\ \hline \multicolumn{6}{c}{**S2 GP VWC and S1 RVI orbit 141**} \\ \hline MOSM & 1606.95 & 91.26 & 1746.33 & 77.76 & 9.96 \\ CSM & 864.12 & 54.02 & 928.28 & 41.33 & 18.25 \\ SM-LMC & 1262.46 & 69.72 & 1378.87 & 61.4 & 12.24 \\ CONV & 274.33 & 43.77 & 352.11 & 15.68 & 21.78 \\ SM & 1408.52 & 75.06 & 1550.67 & 69.05 & 6.98 \\ \hline \multicolumn{6}{c}{**S2 GP VWC, S1 RVI orbit 68 and S1 RVI orbit 141**} \\ \hline MOSM & 1640.51 & 94.6 & 1778.6 & 79.2 & 21 \\ CSM & 1446.8 & 82.65 & 1576.08 & 70.18 & 36.08 \\ SM-LMC & 1395.58 & 74.98 & 1535.22 & 68.36 & 24.21 \\ CONV & 190.44 & 25.69 & 227.12 & 10.11 & 45.02 \\ SM & 1408.52 & 75.06 & 1550.67 & 69.05 & 10.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error metrics and training time of the MOGP and SOGP evaluated kernels for the 2021 winter wheat averaged ROI-2. the 2020 winter wheat ROI, they have almost zero capacity when applied to the 2021 site. Furthermore, SOGP showed an extremely low capacity to reconstruct VWC in the artificially created gapped windows.
## 5 Correlation Analysis of \(C_{y}\) and \(T\) on the \(S_{d}\) Ranking  
It is concluded that the discreteness of \(S_{d}\) calculated by different ground motions is very large based on the bilinear model and modified Clough model. Therefore, this section mainly analyzes the correlation of the \(S_{d}\) rankings calculated at different \(C_{y}\) and different periods. The \(C_{y}\) is taken as the nonlinear response parameter to characterize the degree of nonlinear response. The \(T\) is closely related to the dynamic response of the structure.  
[PERSON] et al. [25] first proposed the concept of damage potential ranking. The damage potential ranking of ground motions refers to ground motions that are ranked according to the intensity measures (_IMs_) that can characterize the structural damage subjected to  
Figure 4: Analysis of the effect of different yield strength coefficients on the discreteness of the \(S_{d}\) spectrum. (**a**) Bilinear model, (**b**) odidied Clough model.  
Figure 3: The \(S_{d}\) mean spectrum under different \(C_{y}\) conditions. (**a**) Bilinear model, (**b**) modified Clough model.  
ground motions. When the damage potential \(IM\) of ground motions is determinate (\(S_{d}\) is selected in this paper), and the damage potential values (\(S_{d1}\), \(S_{d2}\), \(S_{d3}\),..., \(S_{dn}\)) of different ground motion records (\(a_{1}\), \(a_{2}\), \(a_{3}\),..., \(a_{n}\)) are calculated, the damage potential ranking of ground motions can be obtained by ranking these damage potential values. The damage potential value will change when the structural parameters are changed. It is important to analyze how the damage potential ranking change with structural parameters. To ensure the correctness and simplify the calculation of the conclusion, four different values of \(C_{y}\) = 0.2, 0.4, 0.6, and 0.8 are selected for analysis in this section.