esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Model
### A Mixed Evaluation Model
#### 3.3.2 Vikor  
The first nonlinear evaluation method, the VIKOR, was proposed by [PERSON] et al. [41; 42]. As indicated in (7), the method first normalizes the \(Q\) that is supplied to the model, where \(z\) and \(m\) stand for the size of the action space and the number of objects, respectively. The greatest and minimum values in the action-value function are calculated by (8)-(11), commonly known as the positive ideal solution (PIS) and the negative ideal solution (NIS). Equations (12) and (13) determine the collective utility value \(C\) and the individual regret value \(I\). Lastly, the trade-off index value \(Y\) is calculated using (14), and all actions are ranked from smallest to largest according to \(Y\). \(v\) defines the weight of the group utility value \(C\) and the individual regret value \(I\), which is often set to 0.5.  
\[r_{i,j}=\frac{Q(s,a_{i},o_{j})-\min(Q(s,\ast,o_{j}))}{\max(Q(s,\ast,o_{j}))-\min (Q(s,\ast,o_{j}))},i=1,2,3,\cdots,z,j=1,2,3,\cdots,m \tag{7}\]  
\[PIS=u_{1}^{+},u_{2}^{+},u_{3}^{+},\cdots,u_{m}^{+} \tag{8}\]  
\[NIS=u_{1}^{-},u_{2}^{-},u_{3}^{-},\cdots,u_{m}^{-} \tag{9}\]  
\[u_{j}^{+}=\max(r_{s,j}),j=1,2,3,\cdots,m. \tag{10}\]  
\[u_{j}^{-}=\min(r_{\ast,j}),j=1,2,3,\cdots,m. \tag{11}\]  
Figure 3: Action selection process.  
\[C_{i}=\sum_{j=1}^{z}w_{j}\cdot\frac{u_{j}^{+}-r_{i,j}}{u_{j}^{+}-u_{j}^{-}},i=1,2, 3,\cdots,z \tag{12}\]  
\[I_{i}=\max_{1\leq j\leq m}w_{j}\cdot\frac{z_{i}^{+}-r_{i,j}}{z_{j}^{+}-z_{j}^{-} },i=1,2,3,\cdots,z \tag{13}\]  
\[Y_{i}=v\cdot\frac{C_{i}-\min(C)}{\max(C)-\min(C)}+(1-v)\cdot\frac{I_{i}-\min(I )}{\max(I)-\min(I)},i=1,2,3,\cdots,z \tag{14}\]
# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 3 Problem Definition and Modeling
### Modeling of the Tour Itinerary Recommendation Problem Based on Comprehensive Satisfaction  
We employ a mathematical model to represent the actual tour itinerary recommendation problem, as indicated in (8)-(13). Equation (8) represents the objective function, where our aim is to find a tour itinerary with routes and time arrangements that maximize the comprehensive satisfaction score. Simultaneously, it adheres to the constraints represented by (9)-(13), where \(x_{ij}=1\) when the itinerary includes the visit from \(a_{i}\) to \(a_{j}\) in order; otherwise, \(x_{ij}=0\).  
\[Max\ CSS(I) \tag{8}\]  
\[\sum_{j\in I}x_{ij}=1 \tag{9}\]  
\[\sum_{i\in I}x_{ij}=1 \tag{10}\]  
\[x_{ij}\times x_{ji}=0 \tag{11}\]  
\[DepartureT+ItinDur\leq LatestEndT \tag{12}\]  
\[\sum_{a_{i}\in I}Cost_{a_{i}}\leq CostConstraint \tag{13}\]  
Equations (9)-(11) signify that each node is visited exactly once, and the itinerary comprises no fewer than two nodes. Equation (12) ensures that the ending time of the itinerary does not exceed the expected ending time, while Equation (13) guarantees that the total cost of visiting scenic spots during the itinerary does not surpass the specified cost constraint. Additionally, the itinerary must start at the starting point and end at the destination, while adhering to the user's personalized requirements.  
Our tour itinerary recommendation problem is modeled on a variant of the combinatorial optimization problem, which has been proven to be NP-hard [26; 27]. Moreover, this problem involves penalty functions related to time, further complicating its complexity. Due to its NP-hard nature, finding an optimal solution is infeasible. To address these challenges, we propose the MCTSLKH_TR algorithm, which is based on the LKH algorithm and a variant of the MCTS algorithm. These algorithms heuristically search for optimal solutions within a more promising solution space, ensuring real-time performance and effectiveness. In Section 3, we will present the implementation of this algorithm.
## 3 Research Design of the Fieldwork and Results: Comparing Three Paradigms
### Research Design of the Fieldwork
Furthermore, the _AI4 SI International Summer School_, directed by the author, serves as an experimental platform where researchers from the Global North and South engage in critical debates on the intersection of AI, Web3, and decentralized technologies, offering a comparative global perspective on how these technologies affect different governance systems. The inclusion of digital justice and equity frameworks further elevates the research's societal relevance and potential impact, ensuring that decentralized technologies are evaluated not only for their technological innovation but also for their potential to foster inclusivity in global governance. By positioning the research within these interconnected global and academic ecosystems, the design not only allows for an in-depth exploration of emerging governance paradigms but also ensures that the findings are embedded within real-world practices and interdisciplinary dialogue. This comprehensive approach increases the replicability of this study and strengthens its potential impact, contributing to the ongoing academic and policy discussions around decentralized governance, sovereignty, and Web3 technologies. With the addition of Table 1, which outlines the milestones and key timelines, the research design now provides clear, actionable steps for future scholars and practitioners to replicate or extend these findings in other contexts, further enhancing this study's methodological rigor and applicability. This action research approach, rooted in immersive fieldwork and collaborative engagements, provides a robust methodological framework for understanding the new emerging forms of nation-statehood driven by the libertarian Web3 ideology [22; 60]. The rise in a decentralized platformization trend, as observed through case studies such as (i) _Cabin_, (ii) _Afropolitan_, (iii) _Edge City_, (iv) _Embassy Network_, (v) _Regen Network_, (vi) _Burning Man Foundation_, (vii) _HanHemen_ e-diaspora platform, (viii) _e-Estonia_ residence program, (ix) RefiDAO, and (x) _Designing Opportunities_ platform connecting the Global South and North, exemplifies the spread of this ideology and its challenge to traditional models of sovereignty. The convergence of AI and data governance within this context further highlights the emergence of new paradigms that question the very foundations of the nation-state power, role, and function. These findings will inform the comparative analysis of the three emerging paradigms, which is the focus of this discussion. As a result of the fieldwork research, three paradigms can be distinguished. Each paradigm has been examined across five layers (Table 2): (i) ideological, (ii) governance, (iii) development and economic, (iv) technological, and (v) territorial and legal.
## 3 Results
### Fiumicino  
Fiumicino is the area located west of the city of Rome, in the proximity of the Tiber River delta plain. There, it is also hosted the main airport of the city, i.e., the international airport \"Leonardo da Vinci\", which has more than 40 million passengers per year. Due to the soft alluvial sediments surrounding the Tiber River, many subsidence phenomena affect the area, making it potentially dangerous for civil infrastructures [25; 26; 27].  
To investigate the deformation phenomena occurring in this area, we used a dataset of 38 C-band ENVISAT SAR images collected from 2003 to 2010 along the descending orbit. Multi-look factors of two and 10 along the range and azimuth directions were applied, thus resulting in a pixel posting of about 40 m \(\times\) 40 m. The SRTM DEM was used for removing the topography. Goldstein filtering [24] and unwrapping with minimum cost flow algorithm [25] was also applied to each interferogram. We discarded the interferogram with unwrapping errors and large atmospheric artifacts, which can affect the results producing spikes and over and underestimation displacements in the InSAR time series. Then, by applying the IPTA-MB approach, the mean deformation velocity and time series were retrieved (Figure 4). InSAR analysis reveals some of the signals of subsidence surrounding the Tiber Valley and in the proximity of the municipalities of Ostia and Fiumicino, which are close to the coastline (points P1 and P3).  
The most significant subsidence was observed along the southeastern part of runway three of the Leonardo da Vinci airport (point P2), where a subsidence rate of more than 5 mm/year was recorded. The stratigraphy of subsoil showing different subsoil features explains the deformation gradient along the two sections of the runway [26; 28].
## 2 Materials and Methods
### Web-Based Platform
#### 2.2.4 API Endpoints  
As discussed in previous sections, the web platform incorporates an API that serves as a communication interface between the WSN and the backend. This communication is facilitated through HTTP requests initiated by the master node and handled by the web server. It is important to note that conventional IoT techniques, such as implementing MQTT servers, were not employed in this system. The decision to minimize implementation costs led to the adoption of shared hosting, which supports MySQL database, PHP: hypertext preprocessor, and the ability to install dependencies using a composer.  
The primary endpoint utilized by the master node is \"/ API/getConfig.\" It utilizes a GET request with a JSON payload in the request body. If the provided credentials match those stored in the database, the configuration is returned in JSON format. This endpoint serves the purpose of retrieving the necessary configuration parameters required for the WSN's operation.  
The second crucial endpoint is \"/API/addMeasurements.\" As the name implies, this endpoint is responsible for capturing and adding the measurements obtained from the WSN. The data are received in a POST request in JSON format, following the structure depicted in Figure 6. The payload includes the node ID and passkey, with the timestamp being optional but preferred. The _data_ field consists of a list of JSON objects that carry information from the WSN, including addresses, sensor types, and the corresponding parameter values encapsulated under the _data_ key.  
By utilizing these endpoints, the web platform establishes a seamless connection between the WSN and the backend, ensuring the secure transfer of configuration data and measurements. This architecture guarantees the integrity and reliability of the collected information, enabling the platform to effectively process and analyze the data captured by the WSN.
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Precision (\%)**} & \multicolumn{1}{c}{**SS**} & **SB** & **TR** & **Mean** \\ \hline \multicolumn{1}{c}{**Model**} & **SS** & **SB** & **TR** & **Mean** \\ \hline \multicolumn{1}{c}{Multi-task XGBost model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with ADYSN.} & 99.95 & 100 & 99.93 & 99.96 \\ \hline \multicolumn{1}{c}{Independent XGBost model for classification of PA types (SS, SB, TR) with ADYSN.} & 99.90 & 99.86 & 99.52 & 99.76 \\ \hline \multicolumn{1}{c}{**Recall (\%)**} & \multicolumn{1}{c}{} & & & \\ \hline \multicolumn{1}{c}{**Model**} & **SS** & **SB** & **TR** & **Mean** \\ \hline \multicolumn{1}{c}{Multi-task XGBost model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with ADYSN.} & 99.99 & 99.79 & 99.66 & 99.82 \\ \hline \multicolumn{1}{c}{Independent XGBost model for classification of PA types (SS, SB, TR) with ADYSN.} & 99.95 & 99.66 & 99.18 & 99.60 \\ \hline \multicolumn{1}{c}{**Fl Score (\%)**} & \multicolumn{1}{c}{} & & & \\ \hline \multicolumn{1}{c}{**Model**} & **SS** & **SB** & **TR** & **Mean** \\ \hline \multicolumn{1}{c}{Multi-task XGBost model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with ADYSN.} & 99.97 & 99.90 & 99.79 & 99.89 \\ \hline \multicolumn{1}{c}{Independent XGBost model for classification of PA types (SS, SB, TR) with ADYSN.} & 99.93 & 99.76 & 99.35 & 99.68 \\ \hline \multicolumn{1}{c}{**Table A.** Precision, Recall, and F1 score for APS Classification (CGBoost models).} & & & & \\ \hline \multicolumn{1}{c}{**Model**} & **NS** & **EAS** & **MS** & **Mean** \\ \hline \multicolumn{1}{c}{Multi-task XGBost model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with ADYSN.} & 94.02 & 99.67 & 99.56 & 99.75 \\ \hline \multicolumn{1}{c}{Independent XGBost model for classification of APS types (NS, EAS, MS) with ADYSN.} & 89.78 & 99.48 & 98.32 & 95.86 \\ \hline \multicolumn{1}{c}{**Recall (\%)**} & **NS** & **EAS** & **MS** & **Mean** \\ \hline \multicolumn{1}{c}{**Model**} & **NS** & **EAS** & **MS** & **Mean** \\ \hline \multicolumn{1}{c}{Multi-task XGBost model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with ADYSN.} & 99.32 & 98.57 & 98.88 & 98.92 \\ \hline \multicolumn{1}{c}{**Independent XGBost model for classification of APS types (NS, EAS, MS) with ADYSN.} & 98.71 & 97.49 & 97.32 & 97.84 \\ \hline \multicolumn{1}{c}{**Fl score (\%)**} & **RS** & **EAS** & **MS** & **Mean** \\ \hline \multicolumn{1}{c}{**Model**} & **NS** & **EAS** & **MS** & **Mean** \\ \hline \multicolumn{1}{c}{Multi-task XGBost model for classification of APS types (NS, EAS, MS) and PA types (SS, SB, TR) with ADYSN.} & 96.60 & 99.12 & 99.22 & 98.31 \\ \hline \multicolumn{1}{c}{Independent XGBost model for classification of APS types (NS, EAS, MS) with ADYSN.} & 94.03 & 98.47 & 97.81 & 96.77 \\ \hline \end{tabular}
\end{table} 
Table A3: Precision, Recall, and F1 score for PA Classification (XGBost models).
## 2 Related Work
### RT-DETR Technology and Analysis  
A Real-Time Detection Transformer (RT-DETR) is the first real-time, end-to-end object detection framework that eliminates Non-Maximum Suppression (NMS), enhancing both speed and accuracy. Its architecture comprises three core components: a ResNet backbone for multi-scale feature extraction, an efficient hybrid encoder, and a flexible transformer decoder. The hybrid encoder integrates attention mechanisms and convolutional operations, utilizing an Attention-based Intra-scale Feature Interaction (AIFI) module for single-scale processing and a Cross-scale Convolutional Feature Fusion (CCFF) module for efficient multi-scale feature integration. The transformer decoder supports adjustable layers to balance speed and accuracy without retraining, making it highly adaptable to diverse application scenarios. A standout feature is its uncertainty-minimal query selection strategy, which models and minimizes uncertainty in classification and localization tasks. By optimizing a loss function to align positional and categorical probabilities, RT-DETR ensureshigh-quality query generation, improving detection robustness and precision. The design advantages of RT-DETR lay in its comprehensive architecture, removing the requirement for post-processing, and its combination of a hybrid encoder with a flexible decoder, which substantially improves computational efficiency and offers high adaptability for a wide range of real-time visual tasks.  
RT-DETR shows superior precision and reasoning speed, particularly excelling in lightweight models. Experiments show that RT-DETR demonstrates robust performance on large datasets, maintaining high accuracy and efficiency. When working with smaller datasets, it still remains reliable and efficient, showcasing its adaptability to various real-world scenarios. [PERSON] et al. [27] developed Drone-DETR, integrating ESDNet and EDF-FAM, and achieving an 8.1% mAP50 improvement over RT-DETR-R18 on Vis-Drone2019 with 28.7 M parameters. [PERSON] et al. [28] proposed RS-DETR, a model combining cascaded attention, enhanced feature fusion, and Focaler-GIoU loss to improve the remote sensing object detection performance. [PERSON] et al. [29] proposed the FHB-DETR model, improving railway-turnout defect detection accuracy by 3.5% in mAP50, reducing the parameter count by 25%, and lowering computational complexity by 6% while maintaining high real-time performance. [PERSON] et al. [30] proposed EMSC-DETR, an improved chicken detection model, achieving 98.6% AP50 and surpassing RT-DETR by 8.2%, 9.2%, and 28.3% in three independent testing sets. [PERSON] et al. [31] introduced an enhanced RT-DETR-Tomato model, integrating a Swin Transformer backbone and a BiFormer module, significantly enhancing the detection accuracy and reducing training time. [PERSON] et al. [32] proposed FedsNet, an RT-DETR-based network for pedestrian detection, achieving a 1.7% accuracy improvement, a 15.1% reduction in parameters, and a 14.5% decrease in model size, with detection speeds of 109.5 FPS and 100.3 FPS on self-built and public datasets, respectively.
# Study on Siphon Drainage Capacity of Slopes with Long-Horizontal Pipe Sections
## 4 Discussion  
### Comparison of Siphon Flow Rates  
The siphon drainage rate is directly related to the efficiency of rescue and relief in engineering practice and provides a reference basis for long-term effective groundwater level reduction and maintenance on slopes. Therefore, the siphon drainage rate under different lift, pipe length, and pipe diameter conditions is also vital to improve its engineering applicability.  
Lift is one of the fundamental influencing factors of a siphon system, which directly affects the drainage performance of the siphon, as shown in Figure 8a,b. In the legend, the number before the short line represents the siphon diameter, and the number after the short line represents the total length of the siphon. The intersection of the curves with the x-axis is the siphon limit lift for the corresponding conditions. In general, the influence of pipe diameter on the trend of siphon velocity change with a lift is small, and the curves of lift velocity for different pipe diameters show similar trends. From the graphs, the following observations can be made: (1) as the lift increases, the absolute value of the slope of the curve increases, which confirms the principle that the formation of dry plug flow causes high pressure drop under low capillary number conditions; (2) for short siphons, the limit value of the lift increases with the increase of the pipe diameter, but in siphons with long horizontal sections, this law no longer appears. These phenomena are caused by the differences in the manifestations of the slug flow in siphons of different diameters. First, the fluid has enough space and time to form a fully developed slug flow in a siphon with a long horizontal section. Second, the effects of gravity and surface tension in different pipe diameters have different percentages of the effect, as shown by the fact that the smaller the pipe diameter, the more pronounced the effect of surface tension; the more effective the pipe diameter, the more pronounced the effect of gravity. Returning to the problem discussed earlier, in D-3-4 tubes, fluid surface tension plays a dominant role, and bubbles form a symmetrical structure in the tube and form a gas-liquid-solid three-phase moving contact line. The adverse effects of moving contact lines on pressure transfer have been discussed in the previous section, and this is a problem we should try to avoid in our engineering design. Due to the unique dimensions of the D-5 tube, between the capillary tube and standard diameter tube, it possesses transitional characteristics: the tube can form both wet plug flow and ensure that bubbles form a regular shape inside the tube. In the D-6-5-8 tube, gravity plays a dominant role, which leads to bubbles floating up against the wall, while the liquid is mainly distributed in the lower part of the tube. The bubble floating phenomenon will cause an uneven force of fluid in the tube, leading to the phenomenon of \"liquid discharge and gas accumulation in the tube\". When the gas in the pipe accumulates to a certain level, the siphon will fail.
# Method and System for Heart Rate Estimation Using Linear Prediction Filtering +
## 2 Materials and Methods
### Experimental Setup
The selected solution for the HMI was DFRobot LCD Keypad Shield ([[https://wiki.dfrobot.com/LCD_KeyPad_Shield_For_Arduino_SKU_DFR0009](https://wiki.dfrobot.com/LCD_KeyPad_Shield_For_Arduino_SKU_DFR0009)]([https://wiki.dfrobot.com/LCD_KeyPad_Shield_For_Arduino_SKU_DFR0009](https://wiki.dfrobot.com/LCD_KeyPad_Shield_For_Arduino_SKU_DFR0009)), accessed on 18 March 2025) displayed in Figure 8. It is a module that allows the development of a navigable interface. It consists of a 16 \(\times\) 2 white character LCD with blue backlight and five keys that allow selection, up, right, down, and left. Initially, the system only showed the heart rate value calculated using the LPC method, but a menu could be developed in future versions. The system is powered by a shield for charging two 18650 lithium-ion batteries, as illustrated in Figure 9. It features output voltages of 5 volts (2 amps) and 3.3 volts (1 amp), as well as battery overcharge and over-discharge protection. Additionally, it includes 0.5 amp USB (Micro, C) connectors and LED indicators for charging and full charge status. MATLAB v24.1 (Matrix Laboratory) is a programming and numerical computing platform for analyzing data, developing algorithms, and creating models that combine an environment tailored for interactive analysis and design processes with a programming language expressed in matrix and array mathematics ([[https://www.mathworks.com/](https://www.mathworks.com/)]([https://www.mathworks.com/](https://www.mathworks.com/)),  
Figure 8: DFRobot LCD keypad shield. Figure 9: Shield for 18650 lithium-ion batteries. accessed on 18 March 2025). It validates the LPC frequency estimation algorithm before deploying the code in the ESP32-Dev-Kit. The firmware implementation is performed through Arduino v2.0 IDE, which uses a C++ programming language with some modifications. This choice was based on ease of use, availability of sample codes, and libraries for accessing peripherals. An AFG1022 Arbitrary Function Generator (Tektronix\({}^{\text{TM}}\), Beaverton, OR, USA), illustrated in Figure 10, which can emulate an ECG signal, was used in the system validation. The system was evaluated by generating ECG signals at heart rates of 30, 60, 120, and 180 beats per minute. The heart rate was estimated using 100 cardiac cycles per frequency, and performance was assessed by calculating the accuracy.