esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Time-Dependent Results
When the disturbance reached the trailing edge, the instability of the trailing edge increased, and the strength of the pressure waves radiating upstream also intensified, initiating the upstream movement of the shock wave. During the downstream movement of the shock wave (as shown in Figure 13(b)), as the distance from the shock wave position to the trailing edge continuously increased, the disturbance of the boundary layer instability at the trailing edge diminished. Consequently, the strength of the pressure waves radiating upstream weakened, leading to the shock wave starting to move downstream again. This resulted in the periodic oscillatory motion of the shock wave on the upper wing surface. The power spectral density (PSD) of the lift coefficient obtained with the parameter autoregressive method is shown in Figure 15. The experimentally observed main frequency of the transonic shock buffet was 69 Hz. The PSD results indicated a computed main frequency of 67 Hz, which closely matched the experimental findings, with a relative error of less than 3%. In previous numerical studies [11; 15], DDES [15] yielded a main frequency of 80.5 Hz, approximately 17% higher than the experimental result. In comparison, the ZDES employed in this study exhibited a better agreement with the experiment, as shown in Table 3. Due to the influence of Kelvin-Helmholtz instability, a peak region was observed in the high-frequency range at 2000 Hz and 5000 Hz, generated by secondary oscillations with a central frequency approximately 40 times the experimental main frequency. This corresponded to the frequency of the Karman vortex shedding in the downstream wake region observed in the density gradient contour maps. Figure 14: Instantaneous wave pattern of shock oscillation.
## 5 Results and Discussion
### Spatial Interaction Between HDT Behaviors and Clustered Freight Zones
#### 5.4.2 General Cargo Truck (GT)  
Figure 10 shows a systematic analysis of the trip patterns of GTs. As a critical interface between trunk haulage and regional distribution networks, GTs exhibit an industry-oriented spatial agglomeration, reflective of their role in integrating urban industrial sectors. A Sankey diagram analysis indicates that Cluster 1 (53.61%) and Cluster 2 (34.23%) account for 87.84% of GT trips, yet unlike tractor trucks that are port-centric, GT clustering is  
Figure 9: Systematic analysis of trip patterns of tractor trucks. The upper two subplots depict heatmaps of trip ends and trips, respectively. The Sankey diagram in the lower-left corner illustrates the proportions of TT trips across clustered functional zones, while the ID entropy heatmap in the lower-right corner visualizes entropy values in grid cells with high trip volumes.  
concentrated around automotive plants (e.g., SAIC Jiading, GM Jinqiao, and Tesla Lingang), express logistics hubs (Qingpu Express Zone), industrial logistics parks (Malu Logistics Agglomeration), and air cargo nodes (Pudong Air Cargo Center). This pattern underscores their integration into the manufacturing-logistics-commerce value chain, with a more balanced trip distribution, evidenced by a 9.03% share in Cluster 4 (significantly higher than that of tractor trucks), highlighting their dual role in bulk transportation and regional distribution.  
Concentration metrics reveal an \(HHI_{E}\) of 0.413 (the second highest value), indicating a strong spatial focus on industrial/logistics nodes driven by the high-frequency demand for a medium-load capacity. Conversely, an \(HHI_{R}\) of 0.061 (substantially lower than that of tractor trucks) suggests dispersed service networks organized around regional closed-loop chains, enabling flexible multi-client operations. An ID entropy analysis further differentiates the organizational models: the low entropy in automotive zones reflects captive or long-term contract fleets, whereas the high entropy in logistics/express sectors signals market-driven, dynamic capacity allocation.  
The greater penetration in non-port nodes of GT stems from their adaptive load capacity and versatility in navigating diverse cargo scenarios, filling service gaps inaccessible to tractor trucks due to size constraints. While tractor trucks optimize long-haul bulk transportation, GTs specialize in intra-regional distribution and door-to-door services, jointly constructing a synergistic \"trunk-feeder\" network. This labor division exemplifies how vehicle specialization aligns with urban industrial geography to enhance supply chain efficiency and throughput.  
Figure 10: Systematic analysis of trip patterns of general cargo trucks. The upper two subplots depict heatmaps of trip ends and trips, respectively. The Sankey diagram in the lower-left corner illustrates the proportions of GT trips across clustered functional zones, while the ID entropy heatmap in the lower-right corner visualizes entropy values in grid cells with high trip volumes.
## 3 Experimental Results and Discussion
### Performance of Hardware System  
The objective of evaluating the hardware system performance is to evaluate the reliability of the proposed system for real-time monitoring. This includes the performance evaluation of the Raspberry Pi Zero W, RS485 Modbus to WiFi converter, soil sensor, air sensor, thermal sensor, Haiwell IoT Cloud HMI hardware, and Modbus protocol.  
In the experiments, the system was run continuously (twenty-four hours) from 7 June 2023 to 11 June 2023 in the outdoor environment (home terrace), as shown in Figure 8. During experiments, the Raspberry Pi Zero W stores the reading data from the DHT22 and AMG8833 sensors into the Modbus registers for supplying the Modbus data to the Haiwell IoT Cloud HMI and SDCard memory for data logging at one-minute intervals. The Haiwell IoT Cloud HMI requests the Modbus TCP data to the Raspberry Pi Zero W and RS485 Modbus to WiFi converter every five seconds. However, as described earlier, the data are saved to the HMI memory every minute.  
Figure 12: Historical trend display of air temperature parameters.  
Figure 13: Historical data display of Plant-1 soil sensor.  
Based on the data logger from the Raspberry Pi Zero W and historical data from the Haiwell IoT Cloud HMI, we evaluate the system performance as follows. We define the data recording success rate (DRSR) as the percentage of the number of stored data from the expected number of data at a particular time interval. The DRSRs in the Raspberry Pi Zero W and Haiwell IoT Cloud HMI are given in Table 11. As described earlier, the data of DHT22 and AMG8833 sensors are both stored in the Raspberry Pi Zero W and the Haiwell IoT Cloud HMI. Therefore, their DRSRs are calculated in Raspberry Pi Zero W and the Haiwell IoT Cloud HMI, as shown in the table. However, since the JXCT sensors (and RS485 Modbus to WiFi converter) do not store the data locally, the DRSRs for Plant-1 and Plant-2 are only calculated in the Haiwell IoT Cloud HMI.  
The DRSR can be used to evaluate the following performances: (a) hardware faults, (b) processing delay time, and (c) Modbus communication error. Observing Table 11, all values of the DRSR in the Haiwell IoT Cloud HMI are 100%. The HMI hardware always runs smoothly during five days of evaluation. The HMI's DRSR of 100% means that the HMI can process the task without delay and indicates that the Modbus communication runs appropriately. The average DRSR of the Raspberry Pi Zero W does not achieve 100%. It reaches 86.5% and 99.8% in Plant-1 and Plant-2, respectively. The DRSR of Raspberry Pi Zero W on Plant-1 each day, which is lower than 100%, denotes no hardware fault but represents the processing delay time. Further examination shows that the processing delay time is caused by the execution time uncertainty of the DHT22 sensor, where sometimes it is longer than one minute. It complies with the results in Plant-2, where the DSRR achieves almost 100% without a DHT22 sensor.
###### Abstract  
In the last decade, many SAR missions have been launched to reinforce the all-weather observation capacity of the Earth. The precise modeling of radar signals becomes crucial in order to translate them into essential biophysical parameters for the management of natural resources (water, biomass and energy). The objective of this study was to demonstrate the capabilities of two statistical algorithms (i.e., multiple linear regression (MLR) and random forest (RF)) to accurately simulate the backscattering coefficients observed over bare agricultural soil surfaces. This study was based on satellite and ground data collected on bare soil surfaces over an agricultural region located in southwestern France near Toulouse. Multi-configuration backscattering coefficients were acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, in co-(abbreviated \(\sigma^{0}_{\text{HH}}\) and \(\sigma^{0}_{\text{VV}}\)) and cross-polarization states (abbreviated \(\sigma^{0}_{\text{HV}}\) and \(\sigma^{0}_{\text{VH}}\)) and at incidence angles ranging from 24\({}^{\circ}\) to 53\({}^{\circ}\). Models were independently calibrated and validated using a ground dataset covering a wide range of soil conditions, including the topsoil moisture (range: 2.4-35.3%), root-mean-square height (range: 0.5-7.9 cm) and clay fraction (range: 9-58%). Higher-magnitude correlations (r) and lower errors (RMSE) were obtained when using RF (r values ranging from 0.69 to 0.86 and RMSE from 1.95 to 1.00 dB, depending on the considered signal configuration) compared to MLR (r values ranging from 0.58 to 0.77 and RMSE from 2.22 to 1.24 dB). Both surpass the performance presented in previous studies based on either empirical, semi-empirical or physical models. In the linear approach, the information is mainly provided by the surface moisture and the angle of incidence (especially in the case of co-polarized signals, regardless of the frequency), while the influence of roughness or texture becomes significant for cross-polarized signals in the C-band. On the contrary, all the surface descriptors contribute in the approach based on RF. In future work, the use of the RF algorithm developed in this paper should improve the estimation of soil parameters.  
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# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 3 Case Study
### Experimental Design
#### 3.4.1 Effect of Sequence Length  
The predicted sequence length was fixed to the 1200 mm ring width. Three groups of nineteen experiments were designed in terms of data interval length (interval_len), historical sequence length, and labelled sequence length, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups Parameters** & **No** & \begin{tabular}{c} **interval\_len** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **seq\_len** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **label\_len** \\ **(mm)** \\ \end{tabular} \\ \hline \multirow{7}{*}{A. Data interval length} & 1 & 1200 & 2400 & 1200 \\  & 2 & 240 & 2400 & 1200 \\  & 3 & 120 & 2400 & 1200 \\ \cline{1-1}  & 4 & 80 & 2400 & 1200 \\ \cline{1-1}  & 5 & 60 & 2400 & 1200 \\ \cline{1-1}  & 6 & 48 & 2400 & 1200 \\ \cline{1-1}  & 7 & 24 & 2400 & 1200 \\ \hline \multirow{7}{*}{B. History sequence length} & 1 & 48 & 1200 & 1200 \\  & 2 & 48 & 2400 & 1200 \\ \cline{1-1}  & 3 & 48 & 3600 & 1200 \\ \cline{1-1}  & 4 & 48 & 4800 & 1200 \\ \cline{1-1}  & 5 & 48 & 6000 & 1200 \\ \cline{1-1}  & 6 & 48 & 7200 & 1200 \\ \hline \multirow{7}{*}{C. Label sequence length} & 1 & 48 & 7200 & 1200 \\  & 2 & 48 & 7200 & 2400 \\ \cline{1-1}  & 3 & 48 & 7200 & 3600 \\ \cline{1-1}  & 4 & 48 & 7200 & 4800 \\ \cline{1-1}  & 5 & 48 & 7200 & 6000 \\ \cline{1-1}  & 6 & 48 & 7200 & 7200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sequence length parameter setting comparison experiment.  
Figure 7: Heatmap of Pearson correlation coefficient.  
In this experiment, Group A trials fixed the length of the history sequence and the length of the label sequence used in the model and then varied the sampling interval to examine the effect of this parameter on model accuracy. Group B experiments varied the length of the history sequence used in the model and observed a trend in the change in model accuracy. Group C sought to investigate the effect of the length of the label sequence on the model. Initially, the results of Groups A and B were used to determine the appropriate length of the data interval and the length of the history sequence, and then the effect of the choice of label length on model performance was further analyzed.
## 3 New Generation Sensor Web Enablement
### SOAP Binding Introduced  
In many IT infrastructures, SOAP [69] enables communication with web services. In its basic form, SOAP simply adds a well-defined envelope around the XML encoded operation request, without any other implications. This resembles the binding style of the well-known OGC standards (e.g., WebMapping Service, or Web Feature Service) which is based on communication of plain XML operation requests and responses but without any surrounding envelope. If additional communication functionality, such as reliability or security mechanisms, need to come into play, SOAP can be regarded as an enabling and proven technology.  
In SWE 1.0, the SOS and SPS standards were silent about a possible SOAP binding. Information items that are needed to fully enable the SOAP binding of SWE services were missing, such as so called action URIs--both for use with SOAP itself, but also with WS-Addressing [70]--as well as a SOAP fault mapping. SWES defines all these information items in the SOAP binding for its operations. Furthermore, it defines how asynchronous communication as well as publish/subscribe functionality shall be achieved in SOAP bindings of SWE standards (such as SOS 2.0 and SPS 2.0). The corresponding technologies are well established IT standards: WS-Addressing and WS-Notification [71], respectively. To not exclude support for IT deployments that use older versions of SOAP, SWES does not prescribe usage of either SOAP 1.1 or SOAP 1.2.
## 5 Experimental Validation and Benchmarks
### Applied Solutions in Real-World Networks
#### 5.2.4 Key Takeaways and Impact  
The real-world applications discussed above highlight the versatility of AI in communication networks. The following key takeaways can be drawn from these deployments:  
* Privacy-First AI: Federated learning and differential privacy ensure that AI models can be deployed in privacy-sensitive environments like 5G networks without compromising performance.
* Scalable Solutions: AI models can be scaled to operate across large networks, handling real-time data from thousands of devices while maintaining high accuracy.
* Efficiency Gains: AI-driven systems not only improve performance but also contribute to energy savings, reduced congestion, and cost efficiency, making them suitable for large-scale deployments in cities and network infrastructure.  
These solutions provide concrete examples of how AI can be practically applied in communication networks and urban management, proving that AI technologies can deliver both innovation and tangible benefits in real-world scenarios.
## 1 Introduction
Through comprehensive analysis of previous research and existing model limitations, we propose an optimized detection framework, YOLO-AFR, based on YOLOv12, which addresses the current challenges in real-time dangerous driving behavior detection. The framework of YOLO-AFR is illustrated in Figure 1, and the main contributions are summarized as follows:  
* We propose A2C2f-FRFN, an enhanced attention mechanism for the R-ELAN module in YOLOv12. This novel module integrates a Feature-Refinement Feedforward Network [19] (FRFN) to dynamically enhance spatial feature representations while suppressing redundancy, thereby improving the discriminative capacity for risky driving behaviors. This addresses the limitations of the original Area Attention by also considering channel-wise redundancy. * We develop C3k2_SC-Conv structure within the backbone and neck of YOLOv12, introducing self-calibrated convolution [20] (SC-Conv). This integration broadensthe receptive field and improves its ability to capture crucial contextual information for dangerous driving behavior detection without increasing computational costs. SC-Conv adaptively adjusts feature representations based on spatial and channel information, improving robustness. * We develop Detect_SEAM by incorporating the Separated and Enhanced Attention Mechanism [21] (SEAM) into the Detect module of YOLOv12. This enhancement specifically addresses the challenges of dynamic occlusion and complex background interference common in driving scenarios. SEAM leverages depthwise separable convolution and cross-channel fusion to boost responses in unobstructed regions and compensate for occlusion-induced feature loss, improving the detection of occluded dangerous behaviors. The rest of this paper is structured as follows: Section 2 provides an overview of dangerous driving behavior detection technologies and the YOLOv12 model. Section 3 describes the architecture and technical details of the proposed model. Section 4 presents the experimental datasets, setup, evaluation metrics, and results. Section 5 discusses the experimental results, and Section 6 concludes the study and outlines future research directions.
## 2 Problem Statement and Model Formulation
### Time-Space State Network Modeling Perspective  
Although the integer linear programming model for multi-task ISRPTWC is well established and can be mathematically explicit, the multi-task ISRPTWC with similar properties to the multi-vehicle VRPPDTW [42] is also an NP-hard problem in nature and is difficult to solve efficiently using existing solvers. Therefore, based on the time-extended network [43], we propose a novel mathematical idea, which is to formulate a time-discrete multi-commodity flow model based on the time-space state network for the multi-task ISRPTWC. In this section, we construct the time-space state network for multi-task ISRPTWC. As shown in Figure 3, we first use the virtual topology method to obtain the set of time slices. After that, the time-space state network is constructed based on the topological distribution relationship between satellites in different time slices. In the network, there are four states of a task transmission process, namely upload, wait, transmit, and downlink, and the transition relationship between the states is shown in Table 2. For example, the transfer state matrices for task 1 and task 2 are \([S_{1},S_{2},S_{3},S_{3},S_{2},S_{4}]\), \([S_{1},S_{2},S_{2},S_{3},S_{2},S_{4}]\), respectively. After constructing the time-space state network, we redefine a node index in the network as \((i,s,w)\), in which \(i\) represents the satellite index, \(s\) represents the current time index, and \(w\) represents the current state of a task. The link can be represented as \((i,j,s,e,w,w^{\prime})\),\(t(d,i,j,s,e,w,w^{\prime})\), and \(c(d,i,j,s,e,w,w^{\prime})\)and can be expressed as the transmission time (the transmission time has been simplified above as the number of transmission hops) and the transmission cost of the task \(d\) on link \((i,j,s,e,w,w^{\prime})\), respectively. In addition, the capacity of nodes and links has been embedded in the time-space state network. The specific parameters are defined in Table 3. Therefore, we can obtain the transmission route of task 1 in Figure 3 as \((0,06,Waiting\ for\ unload)\rightarrow(7,07,S_{1})\rightarrow(7,08,S_{2}) \rightarrow(6,09,S_{3})\rightarrow(9,10,S_{3})\rightarrow(9,11,S_{2}) \rightarrow(GS,12,S_{4})\), and the transmission cost is also obtained. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\frac{w}{w}\) & **Upload/\(S_{1}\)** & **Waiting/\(S_{2}\)** & **Transit/\(S_{3}\)** & **Download/\(S_{4}\)** \\ \hline Upload/\(S_{1}\) & No change & True & True & True \\ Waiting/\(S_{2}\) & False & No change & True & True \\ Transit/\(S_{3}\) & False & True & True & True \\ Download/\(S_{4}\) & False & False & False & False & — \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Task state transition representation. Figure 3: Time-space state network.
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 1 Introduction
### Problem Statement and Research Objectives  
The volume of source code developed by software companies or programmers' communities is continuously increasing as a result of technology trends. For example, the software included into a connected and autonomous car is estimated to reach more than 100 million lines of code in different programming languages and the next generation networks and cloud infrastructures are transforming into software defined networks. Securing the software is critical to build trust of people in technological evolution. There is a strong need to support software developers with tools able to identify the security flaws within their code as early as possible, independent of the programming languages used by their organizations or communities. Complementing SAST technologies with machine learning-based solutions is a cost-effective way to reduce the software related weaknesses.  
The main objective of our research is to provide developers a practical solution for security weaknesses detection within source code, language agnostic, and leveraging the behavioral patterns of programmers of large organizations and communities in writing code. This solution is not meant to replace existing technologies such as SAST or FOSS (Free and Open-Source Software) scanners, but to complement them in order to reduce costs and to increase programming languages coverage.