esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 3 Methodology
### Modeling the Cascading Failures of the Freeway Network
#### 3.2.1 Preliminaries
A \"normal\" edge refers to one that functions properly, with the traffic volume \(D_{a}\big{(}\hat{t}\big{)}\) below its maximum capacity \(C_{a}\). A \"failed\" edge occurs when the load on the edge exceeds its capacity \(C_{a}\). This section assumes that edges that fail in subsequent time steps are unrecoverable. Therefore, the state of a road \(a\) at time step \(t\) can be expressed as follows:  
\[s_{a}(t)= \begin{cases}1,&if\ D_{a}\big{(}\hat{t}\big{)}\leq C_{a}\\ 0,&if\ D_{a}\big{(}\hat{t}\big{)}>C_{a}\end{cases}  \tag{3}\]  
where \(\hat{t}\) refers to a previous time step before the current time step \(t\). It is used to assess whether the traffic volume \(D_{a}\big{(}\hat{t}\big{)}\) on road I has exceeded its capacity \(C_{a}\) in the last step, which then determines the current state \(s_{a}(t)\) of the road. In the simulation of cascading failures, when a road segment fails, its impedance is adjusted to infinity. The segment is not deleted, but travelers will avoid this segment when choosing their travel paths.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 1 Introduction  
Having timely access to accurate and reliable onsite information is vital for the efficient management of construction operations. On the contrary, late detection of onsite operations can be problematic, leaving insufficient time for members of the project team to react which can result in negative impacts on project cost and schedule [1]. Real-time information can provide feedback to support tracking project operations [1; 2; 3; 4; 5], safety management [5], productivity analysis, and progress reporting [1; 6]. This is especially critical for tracking activities with highly dynamic nature such as lifting activities [7].  
Using the conventional manual progress reporting methods is time-consuming, labor-intensive and may not be accurate, even for small projects. Besides, the integration of manual data collection and available electronic interfaces can be a challenging task [1]. To overcome the drawbacks stated above, one can make use of automated site data acquisition for schedule updating and progress reporting. Such automation can provide project management teams with needed timely information on the status of onsite construction operations with sufficient accuracy [8]. The use of various remote sensing (RS) technologies for onsite automated data-capturing can result in more robust progress reporting. The captured data can be compared to related as-planned data to perform progress reporting [6; 9].  
This article encompasses four main areas: an overview of research on automated site data acquisition, standalone RS technologies, integrated use of RS technologies and a general discussion. It is based on a carefully selected set of articles from 20 diverse scientific journals and a couple of conference proceedings in the domain of construction engineering and management. In this search, keywords such as'remote sensing', 'automated data acquisition', and 'progress tracking' were used. A total of 56 selected studies are used in this review, among which over 50% were published within last five years. Table 1 presents a list of selected journals and conference proceedings.
# CRC-Based Reliable WiFi Backscatter Communication for Supply Chain Management
## 3 CRCScatter Decoder Design
### XOR and Differential Decoder
Due to the limited length of the tag data and the simplicity of the parity check step, the time of parity check will not affect the decoding efficiency of the system. In practical applications, the system can select the suitable redundant bit length and type based on the requirements to balance the tradeoff between the bit error rate (BER) and the verification complexity.
## 4 Colorless ONU
The side-mode suppression ratio is larger than 40 dB and the combined tuning range of 43 nm exceeds the C-band. The dropping of the laser output power at wavelength above 1560 nm is evidently affected by the band-edge of the TFE, as indicated by the dashed curve in Figure 12b. The transmitter loss away from the band-edge region is around 7 dB from the GC to the fiber, including the 3-dB intrinsic loss of the Y-branch. Both lasers show almost identical thermal tuning behavior and the efficiencies are plotted in Figure 12c. For each laser to reach 23 nm wavelength tuning, only \(\sim\)26 mW heater power is needed. The thick polymer bottom cladding and the deep-etched air trenches surrounding the buried heaters function as ideal thermal buffers [24,26,27]. The tuning efficiency reaches 0.88 nm/mW. The Tx laser can be direct modulated on the GC at 10 Gb/s data rate [24,28,29]. The eye diagram after transmission along an 11.3-km-long standard single mode fiber (SSMF) is shown in Figure 12d. The signal to noise ratio is 5.85 dB and the bit error rate (BER) is lower than 1 \(\times\)10\({}^{-13}\). No band-pass filter is used in the experiment. On the Rx side, a broadband tunable laser source is injected into the fiber and the signal PD responsivity is shown in Figure 13. Above 1580 nm, the PD responsivity remains at 0.14 A/W, corresponding to a receiver loss of \(\sim\)7 dB from the fiber to the PD. The mirror loss amounts to \(\sim\)1 dB. The fiber/waveguide coupling and the waveguide propagation loss add up to \(\sim\)2 dB. The rest is attributed to the TFE loss, \(\sim\)2 dB concluded from the measurement using fibers, and the associated scattering loss at the TFE/waveguide interface in the slot. The scattering loss is caused by the non-optimal wave-front matching from the free-space region (TFE and excess slot width) to the receiving waveguide, _i.e._, the  
Figure 12: (**a**) Tx characteristics of the ONU. Tuning spectra of the lower arm laser; (**b**) tuning spectra of the upper arm laser; (**c**) total tuning range and efficiency of both lasers; (**d**) eye diagram at 10 Gb/s after transmission through an 11.3-km-long standard single mode fiber. mismatch between the broadened beam (location and size) and the near-field mode profile of the waveguide. This loss can be reduced by introducing proper tapers and waveguide offsets at the slot region. The optical signal to noise ratio (OSNR) is measured to be 34 dB at 1580 nm and 1540 nm.
## 4 CSAMT Experiment in the Jinding Lead-Zinc Deposit
### CSAMT Acquisition Design  
#### 4.1.1 Line Layout  
For CSAMT field data acquisition, a total of seven survey lines were laid out. The specific locations of the survey lines are shown in Figure 1. From bottom to top, they are Line 700, Line 1100, Line 1900, Line 2100, Line 2400, Line 3600, and Line 4500. The total length of the survey lines is 13.68 km. Among them, Lines 1900, 2100, and 2400 cross the main ore body and are the key research objects.
## 5 Results and Discussion
### Feature Selection Results
#### 5.1.2 Hierarchical Correlation Clustering Results  
Figure 5 presents the correlation matrix of the weather parameters, clearly showing clusters of similar variables, for instance, groups related to temperature, wind, and radiation. The rationale behind this step is to pinpoint and eliminate features that largely overlap in the information they provide. Only those features contributing unique information are retained by applying [PERSON]'s linkage distance to group parameters with strong correlation  
Figure 4: The normalized variance of weather parameters.  
similarities. This filtering process not only streamlines the feature set but also helps minimize redundancy and reduce multicollinearity in the following stages of the analysis.  
A linkage distance matrix is initially computed using [PERSON]'s method, translating correlation values into a distance measure. A clustering algorithm then partitions this matrix based on a specified distance threshold, grouping highly similar (i.e., correlated) parameters together. Each distance threshold effectively defines how strictly the parameters must differ to remain in the reduced set; if the correlation between two parameters surpasses \(\pm\)0.7, they are considered highly correlated and thus potential candidates for removal. Figure 6 depicts how the maximum absolute correlation changes with different linkage distance thresholds. By finding the intersection point where the correlation threshold of 0.7 meets the Ward linkage distance, we identify an optimal cutoff that excludes parameters displaying excessive redundancy while preserving features that capture unique information.  
Figure 7 shows a dendrogram containing clusters of weather parameters, along with their linkage distances. A distance of 1.2 was selected based on the results of the grid search. This threshold ensures that parameters with correlations exceeding \(\pm\)0.7 are grouped, enabling the removal of redundant features. Within each cluster, only the parameter exhibiting the highest absolute correlation to the other members is retained, while the remaining parameters are discarded. This step ensures that each cluster is represented by a single, most relevant feature, preserving diversity in the dataset and minimizing duplication.  
Figure 5: The correlation between weather parameters.  
Figure 8 presents the final set of weather parameters after the correlation-based selection process. Combining a linkage distance threshold and a strict correlation cutoff ensures that only a few redundant features are retained. Each cluster is represented by a single candidate feature, chosen for its higher absolute correlation with other parameters in that cluster. As a result, the dataset is streamlined to include the most informative variables, minimizing overlap while preserving a comprehensive picture of the underlying conditions.  
Figure 6: The relationship between the linkage distance and the maximum correlation value.  
Figure 7: The dendrogram of hierarchically clustered parameters showing the linkage distance.
## 3 QPSO-RBF-SVM Model
### Model Construction Steps  
The flow chart of the QPSRBF-SVM model is depicted in Figure 2.  
(1) Preprocess the collected bridge sample data by removing any ambiguous, erroneous, or incomplete entries to ensure data quality and consistency. Subsequently, apply feature scaling and feature expansion to address scale differences between features and then split the data into training and testing sets.  
(2) Evaluate each particle's position by training a Support Vector Regression (SVR) model and calculating the Mean Squared Error (MSE) for all particles as their fitness values. The particle with the lowest MSE is selected as the current global best parameter configuration.  
(3) Update each particle's position by computing the update amount based on the difference between the particle's current position and the global best position, incorporating a random factor to determine the update direction. This update amount is combined with the global best and individual best positions to adjust and determine the new particle positions. To ensure that all particle parameter values remain within the predefined range, normalization is applied.  
(4) After completing all iterations of the QPSO algorithm, identify the optimal parameters by minimizing the MSE on the training data. Use these optimal parameters to retrain the model on the training set, further enhancing the model's performance and ensuring that the obtained parameter configuration performs optimally on the training data.  
(5) Utilize the optimized SVR model to make predictions on the test set. Calculate the mean relative error and the coefficient of determination (R\({}^{2}\)) of the predictions and evaluate the model's performance on the test set. This process helps validate the effectiveness and generalization capability of the QPSO-RBF-SVM model and provides the final prediction results.
# Fast Fourier Asymmetric Context Aggregation Network:
## 3 Proposed Method
### The Encoding Stage  
In the encoding stage, we first concatenate the design sketch \(S\), texture patch \(T\), and clothing mask \(M_{c}\) along the channel dimension to obtain the fused feature \(f_{0}\):  
\[f_{0}=\textbf{Concat}(S,M_{c},T), \tag{1}\]  
where **Concat** denotes the concatenation operation along the channel dimension. \(S\) and \(T\) provide structural and texture guidance, respectively, while \(M_{c}\) prevents content overflow. During training, \(S\), \(T\), and \(M_{c}\) are extracted from real clothing images. Subsequently, FCAN employs \(N\) layers of sequential lightweight encoding blocks, \(\textbf{Encoder}_{i}\), based on vanilla convolution, where \(i\in\{1,2,\dots,N\}\), to encode \(f_{0}\) into multi-scale features \(\{f_{1},f_{2},\dots,f_{N}\}\). Additionally, in the first encoding block, we introduce sinusoidal position encoding to embed positional information of the garment regions \(P\), enhancing the efficiency of feature alignment. A detailed implementation can be found in [61].  
The structure of encoding block is shown in Figure 3. The embedding process of \(P\) is illustrated in Figure 4, and the multi-scale encoding process can be expressed as follows:  
\[\begin{split}& f_{1}=\textbf{Encoder}_{1}(f_{0},P)\\ & f_{2}=\textbf{Encoder}_{2}(f_{1})\\ &\dots\\ & f_{N}=\textbf{Encoder}_{N}(f_{N-1}).\end{split} \tag{2}\]
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 3 System Design and Components
### System Architecture  
The QESIF is a holistic and extensible architecture that is also scalable, and it is able to integrate within existing smart city infrastructure through matching QKD at the network layer with classical application layer-operating IoT protocols. It consists of four central layers: (1) IoT device layer, (2) edge-QKD gateway layer, (3) quantum network layer, and (4) smart city control center.  
(1) IoT Device Layer: This layer holds heterogeneous IoT devices distributed throughout various city domains, such as traffic, meters associated with electricity, cameras, and health monitors. These devices are limited with respect to cryptographics and are reliant on higher layers to establish secure keys.  
(2) Edge-QKD Gateway Layer: Acting as intermediaries, these edge gateways are equipped with QKD-compatible hardware (e.g., single-photon detectors and QRNGs) and classical quantum communication interfaces. They handle session establishment, QKD-based key negotiation, and device-level data encryption using quantum-derived keys. They also house lightweight modules of the Q-IDS for initial anomaly detection. QKD integration in the QESIF is limited to the edge gateway layer to maintain cost-efficiency and deployment feasibility. These gateways are equipped with QKD-compatible hardware--such as QRNGs and photon detectors--now increasingly available via compact photonic modules. This design reflects practical deployments observed in initiatives such as SECOQC, the Beijing-Shanghai QKD backbone, and systems from ID Quantique and Quantumium. IoT devices themselves do not perform QKD but receive securely distributed session keys from the gateways via classical channels.  
(3) Quantum Network Layer: This layer facilitates secure key exchange between trusted QKD nodes using protocols like BB84 or E91. The QKD links may be fiber-based or free-space, and they enable the secure generation and distribution of encryption keys across distributed smart city regions. In terms of protocol compatibility, QKD protocols such as BB84 and E91 operate below the transport layer and are responsible solely for key exchange. In the QESIF, these keys are passed to the middleware layer, which injects them into the session layer (e.g., TLS for MQTT or DTLS for CoAP) to establish secure sessions. Quantum repeaters and entanglement sources are deployed to extend secure key distribution over longer distances. These components help to overcome distance-related losses by enabling entanglement swapping and trusted relays at backbone nodes, supporting scalable QKD in urban-scale deployments.  
(4) Smart City Control Center:The control center manages the centralized orchestration of the network, including quantum key management, session auditing, anomaly aggregation from edge layers, and policy enforcement. It serves as a global observer for the Q-IDS, correlating quantum noise patterns, network flow metadata, and device behavior logs to detect coordinated or advanced persistent threats, as illustrated in Figure 1.
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 4 Discussion  
This study evaluates the effectiveness of network feature extraction in remote-sensing images when faced with complex and variable semantic information. The dual-ASPP structure proves to be more efficient in extracting multi-scale semantic information related to roads and completing the fusion process. The findings suggest that DANet is better equipped to extract various types of road semantic information from remote-sensing images, leading to improved segmentation results.  
In Section 3, the segmentation results of DANet are displayed for various road situations. Unlike classical networks, DANet performs well in complex road situations with fewer instances of missing and misclassification. It also effectively differentiates road and background information. However, the network structure still struggles with jagged segmentation edges. According to the research, DANet outperforms classical networks in terms of segmentation results in all situations. The output of DANet has smooth target edges, and the occurrence of missed and wrong judgments is rare. Even when dealing with complex and irregular road information, the segmentation results are still effective.  
In the current research, the attention mechanism is also advancing rapidly. In future research, we plan to incorporate the attention mechanism into the network structure to enhance DANet's ability to acquire road information from remote-sensing images. Additionally, we believe that the semantic segmentation of multi-spectral remote-sensing images in current remote-sensing image processing should receive equal attention. Therefore, in future research, we intend to focus more on this aspect.
## 1 Introduction  
The mechanical properties derived from quasi-static monotonic loadings are far from being sufficient with regard to the real loadings that concrete structures undergo during their lifespan. Existing structures [1] are very often subjected to dynamic loading, either moderate such as wind loads or high such as explosive charge or vehicle impact. These types of dynamic loadings are becoming more and more frequent due to climate change (floods, coastal flooding, earthquakes, storms, and hurricanes). Their consideration in the design of concrete constructions is, therefore, becoming a major issue. Current regulation codes do not constitute tools to address this issue. They are not sufficiently based on physical foundations. Thus, the literature indicates that concrete responds very differently in case of dynamic loading. Indeed, all experimental research points in the same direction: concrete displays a high strain-rate dependency, especially for tensile loading. Concerning the strain-rate dependency of concrete, previous studies [2; 3] support that it has a physical origin: the presence of free water inside the hydrates of the cement paste which could generate the well-known Stefan effect. The present work concerns the development of a numerical model capable of considering dynamic loadings and based on this physical reality vocated above. This numerical model is the extension of a numerical cracking model developed and validated for quasi-static loadings. It is a 3D probabilistic semi-explicit cracking model [4; 5; 6]. Its design and numerical development in statics have been carried out with a focus on simplicity and physical relevance. In dynamics, the development follows the same approach. The goal is to create a design and analysis tool for concrete structures that can be used by an engineering company (or its academic counterpart). All of the mechanical parameters of the model, in both statics and dynamics, are clearly defined and easily measurable. The use of a Monte Carlo procedure, linked to the probabilistic aspect of the model, could permit performing a statistical analysis of the structural behavior and, in this way, determine the realistic structural safety coefficients.
## 3 Results and Discussion  
The fs laser beam travels through the water layer that is transparent to its wavelength and lies above the surface of the target material. The beam is focused onto the surface of the material, the material is ablated, and a plasma plume is formed, which consists of carbon species from the target material (ions, atoms, or clusters). The plasma plume appears as a small volume of intense white light on the target surface, and this light is concentrated at the laser beam's focal point. Shock waves are also generated. Due to the high temperature of the plume at the plume-liquid interface, the liquid can be vaporized or even decomposed (atomized or ionized) and can be converted into water vapor, molecular/atomic oxygen or hydrogen, or radicals (water plasma). A cavitation bubble is generated, which consists of the vaporized liquid, and the bubble surrounds the plasma plume from the ablated material. The oxygen and hydrogen species can react with the carbon plasma species at the region of the interface, be mixed with the carbon plume plasma, and interact with its species in the volume of the ablation plume; alternatively, the species can interact with ablated material that is in the form of hot gas in the volume of the cavitation bubble. Inthe present case, because of the fs pulse, there is no plasma heating effect as in the case of using ns or ps pulses. The NPs start to form by nucleation in the plasma plume from the material under the conditions of supersaturation, which is followed by their growth and coalescence in the plasma plume and cavitation bubble. It is generally believed that the polyynes are formed in the plasma plume [4].