esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
## 4 Experimental Results and Discussion
### Ablation Experiment  
In Section 3.2.3 we mentioned that when a 224 \(\times\) 224 image is fed into the essential features extraction network, it is divided into 16 \(\times\) 16 blocks at the feature-optimization layer, but we also tried dividing it into 32 \(\times\) 32 blocks and 8 \(\times\) 8 blocks and comparing the results obtained in these cases. Figure 20 shows an example of the reconstruction results from the model for the feature-optimized image in these three blocking scenarios.  
From this figure, we can see that when the feature-optimization image is divided into 8 \(\times\) 8 blocks, its reconstructed image is more likely to have defective residues. When the feature-optimization image is divided into 16 \(\times\) 16 blocks, its reconstruction effect is better compared to the other two cases. When the feature-optimization image is divided into 32 \(\times\) 32 blocks, in addition to defective residues appearing in some of the images, non-defective parts of the image may also be disturbed by other factors interference, which leads to the problem of inconsistent texture of non-defective regions before and after reconstruction. The comparison of the detection precision for the three cases is shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Original Image** & **Normalization Data** & **Partial Area Image** & **Area-Feature** & **Reconstructed** & **Area-Feature** \\  & & & **Image** & **Image** & **Image** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sample identification examples.  
From Table 6, it can be seen that the method dividing the image into 16 \(\times\) 16 blocks has the best detection effect, that dividing it into 32 \(\times\) 32 blocks has the second best detection effect, and that dividing it into 8 \(\times\) 8 blocks has the weakest detection effect among the three.
## 4 Quantitative Methods for the Detection of AD
### Support Vector Machines
They constructed a feature vector containing a mean standardized uptake ratio (SUVR: ratio of observed tracer uptake to that of a standardized reference region), cortical thickness from MR, and the product of amyloid and tau burden for each ROI. It was determined that this combination of features outperformed each individual feature set and was able to differentiate between patients with MCI, AD, and non-AD dementis (progressive cortical atrophy and lopogenic variant primary progression aphasia) with an AUC approaching 0.9 [129]. [PERSON] et al., published research on an SVM-based classifier that used amyloid PET and structural MR data to predict conversion from MCI to AD with 87.2% accuracy, 81.7% sensitivity, 90.1% specificity, and an AUC = 0.94 [130]. [PERSON] et al., were able to use amyloid PET data in combination with CSF protein markers, neuropsychological scores, and an SVM classifier to achieve AD vs. HC classification with AUC = 0.93 and sMCI vs. pMCI with AUC = 0.83 [131]. [PERSON] et al., investigated a series of quantitative methods for predicting conversion from MCI to AD from FDG-PET, CSF amyloid and tau markers, structural MRI, mini-mental state examination (MMSE) scores, a measure of cognitive resilience, genetic data, and demographic data. It was determined that linear kernel SVM outperformed multi-kernel learning SVM and GLM with elastic regularization in this task with sensitivity = 0.93, specificity = 0.77, accuracy = 0.81, and AUC = 0.93 [132]. [PERSON] et al., used longitudinal FDG-PET images to predict MCI to AD conversion and achieved an accuracy of 89.9% with an AUC of 0.892 [133]. Their work made use of a novel brain atlas (Brainnetome Atlas), a novel normalization procedure, and an engineered distance metric for their network [134,135]. Furthermore, their work is unique for making use of ADNI subjects with four separate FDG-PET studies.
## 3 Base Station Deployment and Self-Optimization Positioning Method
### Self-Optimization Positioning Method for Base Stations
#### 3.3.2 B-Type Transition Area  
Taking Figure 3 as an example, when the coordinates from the previous moment are within the B-type transition area, the two nodes at the ends of the current boundary, namely nodes labeled 6 and 7, are fixedly selected. Concurrently, the four nearby base station nodes, namely nodes labeled 2, 3, 10, and 11, are iterated through to serve as the third ranging node. This process yields four positioning coordinates with associated weights. During the iteration, if a ranging measurement with a weight less than 0.5 is encountered, the positioning coordinate obtained from that measurement is immediately discarded. Ultimately, the weighted average Equation (6) is applied to derive the final positioning coordinates.
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 2 Related Work
### DCNN in the Remote Sensing Domain  
In recent years, an increasing number of deep learning methods have been applied to remote sensing image segmentation. Although the evolution of deep convolutional networks in remote sensing has been rapid, most of these models are variants of traditional segmentation networks. [PERSON], [PERSON] et al. found that small objects tend to be overlooked when applying Deeplabv3+ to drone datasets. As a result, they proposed EMNet, which is based on edge feature fusion and multi-level upsampling [25]. [PERSON], [PERSON] et al. achieved promising results when applied to high-resolution remote sensing images using a joint model constructed from improved UNet and SegNet [26]. [PERSON], [PERSON] et al. employed a structure similar to FCN with skip connections, merging the image representation information and global information of the network, and achieving more accurate segmentation precision [27]. Multi-level cascaded networks have also been widely adopted. [PERSON], [PERSON] et al. introduced a method similar to Adaboost, cascading multiple lightweight UNets. The results demonstrated higher accuracy than those obtained with a single UNet [28].  
In addition to improvements in some standard networks, the attention mechanism of DCNN has also caught the attention of researchers. Such methods utilize transformations of different scales to extract multi-scale features of segmentation targets. [PERSON], [PERSON] et al. enhanced the UNet structure by adding a SE (squeeze-and-excitation) module [29], allowing the network to focus more on the most crucial feature maps in the upsampling section,thereby improving landslide detection results [30]. [PERSON], [PERSON] et al. also used the channel attention mechanism to achieve impressive results in building extraction [31]. [PERSON], [PERSON] et al. incorporated both channel and spatial attention mechanisms into the network to address the issues of inadequate boundary and detail extraction in building detection from drone remote sensing images [32]. In summary, DCNN and its variants have been widely accepted by scholars in the remote sensing domain [33].  
To recap and summarize, the current methods deployed to enhance segmentation accuracy in the remote sensing domain are as follows:  
1. Employing skip connections to link the encoder and decoder modules of the network, effectively merging global and local features.
2. Adopting the spatial pyramid approach, capturing semantic information of different scales through receptive fields of varying sizes, as seen in modules such as ASPP (atrous spatial pyramid pooling) and SPP (spatial pyramid pooling).
3. Integrating attention mechanisms, allowing the network to fuse information across multiple scales.
4. Enhancing the model using multi-level cascading methods. However, this is achieved under the cascade of various networks and does not necessarily indicate an enhancement in the segmentation accuracy of a single network.
## 4 Results and Discussion  
### Model Validation from Experimental Data  
#### 4.1.1 Temperature  
The comparison of simulated and measured temperature of the measurement points in the kitchen (A1-A3, B1-B5) and exhaust time system (C1-C3, F1-F3 and G) is shown in Figure 7. It can be seen in the figure that the simulated temperatures of points in the kitchen were slightly lower than the measured values, with a deviation of 10%. The simulated temperatures of points in the exhaust system were closer to the measured values, with deviation generally within 5%.
## 4 Results
### Spatial Distribution and Evolution of Sedimentary Facies  
#### 4.2.1 Single-Well Sedimentary Facies Evolution  
The stratigraphic architecture and vertical facies evolution of the Triassic succession in the Wushi Depression are exemplified by the ST1 borehole, revealing distinct depositional patterns across the Kelamayi and Huangshanije formations. Within the Kelamayi Formation, deposition is dominated by braided delta plain and delta front systems. The delta front facies, concentrated in the middle interval, comprises gray fine- to medium-grained sandstones exhibiting gamma-ray (GR) log responses of bell-shaped and box-shaped patterns. The lower interval of the formation is characterized by braided delta plain deposits featuring thick-bedded reddish-brown to brown mudstones, which display notably higher GR values compared to upper plain deposits, reflecting increased clay content and reduced hydrodynamic energy in proximal settings (Figure 8).  
Figure 8: Sequence stratigraphic histogram and core description diagram of ST1 well.  
The Huangshanjie Formation records two vertically stacked coarsening-to-fining cycles, documenting the transition from braided delta front to lacustrine depositional systems. The lower cycle initiates with thick-bedded medium-grained sandstones of the delta front subfacies, displaying GR log motifs of bell and box shapes. This transitions upward into lacustrine deposits dominated by gray mudstones with intercalated coal seams, marked by elevated GR values and leftward deflection on acoustic logs. The upper cycle exhibits reduced thickness and finer grain sizes, with lacustrine facies containing massive and wavy bedding structures, along with abundant carbonaceous fragments exemplified by dark gray mudstone at 6674.8 m depth (Figure 6d).  
Vertical succession analysis demonstrates a basinward deepening trend with progressive hydrodynamic energy attenuation in the ST1 borehole. This evolutionary trajectory reflects a depositional regime shift from terrestrial delta plain-floodplain environments to delta front-lacustrine systems, controlled by base-level fluctuations and sediment supply variations during the Triassic. The facies stacking pattern correlates with episodic delta progradation interrupted by lacustrine transgressive events, as evidenced by cyclic GR log signatures and lithofacies alternations between fluvial-dominated coarse clastics and suspension-settled lacustrine fines.
## 3 System Model  
### Network Model  
In this paper, we investigate a zero-trust QKD relay network where no relay is fully trusted. The adoption of a zero-trust architecture, traditionally used in cybersecurity, assumes that threats could exist on both internal and external networks and that no actors or systems should be automatically trusted [31]. This philosophy is particularly powerful in QKD, where trust assumptions can be critical vulnerabilities [32].  
The proposed system comprises a central key management server (KMS) that functions as the control plane (CP), responsible for overseeing network operations and managing key distribution to the data plane (DP). Each quantum node is equipped with quantum devices, a classical information processing unit for encryption and decryption, and a quantum key pool (QKP) to manage local keys with neighboring nodes. This architecture reflects the principles of software-defined networking (SDN), which separates network control from data forwarding, facilitating flexible network management--a concept increasingly applied in QKD networks to enhance scalability and adaptability [33, 34].  
In the proposed model, the controller dynamically selects route combinations based on the involved relays, while the QKD nodes temporarily transmit key pairs. Implementing an adaptive MTD approach, the model aims to confuse potential attackers by altering multidimensional parameters. This strategy, discussed in more detail in Section 4.1, leverages the unpredictable nature of quantum processes to enhance security dynamically.  
To outline security challenges, this paper introduces general relay types and the adversary model within the network in subsequent sections.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 4 Proposed Neighborhood-Enabled TDMA Approach  
The neighborhood information of devices \(C_{i}\) can play a vital role in developing an ideal TDMA-enabled communication approach for the networks in general and for the Internet of Things (IoT) in particular. If number of slots are equal to that of member devices \(C_{i}\), which reside in closed proximity, then it is highly likely that every member device \(C_{i}\) can transmit its collected data in its dedicated time slot. Furthermore, this refined mechanism has the capacity to resolve numerous other challenging issues, e.g., an average packet delivery ratio (APDR), starvation, collision, a minimum ratio of the latency and time interval (waiting) for a particular slot, and improvement of the lifespan of the respected IoT network. The proposed TDMA scheme is based on neighborhood information of the server \(S_{j}\), where the number of time frames is divided into slots where the ratio of proportionality is direct to the respected devices \(C_{i}\) that reside in the vicinity of the respective server.
## 3 Proposed Framework
### Offline Training Phase  
Our proposed framework is depicted in Figure 1. The dataset used in our work was collected at the University of Carlos III, Madrid, using a vehicle called \"iCAB\" equipped with a Velodyne LiDAR Puck (VLP-16) sensor [46]. The Velodyne VLP-16 LiDAR was selected due to its widespread use in AV research and compatibility with real-time processing constraints. Despite its efficiency and 360\({}^{\circ}\) horizontal field of view, the sensor has limitations related to its 16-beam resolution, which may lead to lower vertical fidelity compared to more advanced LiDARs. In adverse weather conditions, such as rain or fog, signal degradation and increased noise may occur, which can affect point cloud density and downstream tracking accuracy [47]. In our work, these effects are partially mitigated through temporal filtering and clustering.  
The environment has been captured using 393 frames of point cloud data. Two vehicles are included in this dataset: iCAB1 overtakes another vehicle iCAB2 at a specific point in time. In addition, the dataset contains static objects, such as buildings, trees, and poles, that are crucial for vehicle navigation and localization. Furthermore, in the offline training phase, static and dynamic tracks from the LiDAR-based dataset were classified based on previously established mathematical formulations detailed in our previous work [29]. The process involves analyzing individual tracks, extracting relevant kinematic and positional features, and applying clustering methods to segregate dynamic objects from static environmental elements.  
The dataset utilized for this work includes different track sequences from the same LiDAR-based source, distinctly separate from those presented in our prior work. This ensures the robustness and generalizability of our classification approach under varying scenarios and track interactions.  
It is important to remove ground points and other irrelevant data from the raw point cloud data, as part of the preprocessing step to improve the accuracy of the subsequent  
Figure 1: Our framework providing a visual representation of the steps involved in the classification of tracks, allowing the generation of interaction dictionaries and the localization through the MJPF.  
steps. Filtering out noise and identifying significant features in the data are also part of this process [48].
# Temporal Encoding to Reject Background Signals
## 1 Introduction  
Conventional optical wireless communications (OWC) involves the modulation of the optical emission from a light source, such as a light-emitting diode (LED) or laser, and detection of the output light with a photoreceiver [1]. When transmitting over long distances, or through high loss media, received power will become greatly reduced, and eventually be lost in noise from background light or within the receiver electronics themselves. Single photon detection and counting methods are used to achieve high receiver sensitivity with intensity modulated optical signals [2; 3; 4; 5; 6]. With the use of forward error correction (FEC) codes and high order pulse position modulation (PPM) [7], photon counting systems can operate with extremely low numbers of photons per bit [8]. In combination with arrayed receivers, the high sensitivity of single photon counting techniques has potential for deep-space communication links, operating at megabit rates [9; 10].  
The link performance of a single photon counting link can suffer significantly under the presence of noise counts [11], which can occur due to background light in the channel, or dark counts occurring within the detector. These additional counts cause erroneous detection of bits, necessitating the use of powerful FEC codes [2; 12]. Photon counting links using coincident photon pairs can overcome noise limitations [13; 14; 15; 16], and can be used for quantum key distribution [17; 18; 19]. However, suchsystems typically require high efficiency photon pair sources, putting large form factor requirements on the internal and transceiver optics. In fact, many single photon communication links make use of complex, large, and/or costly equipment, such as cryogenic receivers [3; 10], CW lasers with external modulators [2; 6] and arbitrary waveform generators [4]. This makes such systems difficult to deploy in application areas where size, weight and power budgets may be limited.  
Here, we demonstrate a novel optical transmission scheme, suitable for OWC with single photon detection, requiring a single, low photon flux channel. Compared with existing methods, this scheme is implemented with simple and widely available semiconductor components and electronics. A gallium nitride (GaN) micro-LED transmitter, silicon single-photon avalanche diode (SPAD) receiver and field-programmable gate array (FPGA) electronics provide a compact system and with low power consumption. The transmission method operates under the presence of both constant and modulated background noise, which is enabled by the encoding of data in the timing statistics of the received photons. The following sections discuss the details of the transmission scheme, its current implementation, data transmission results and a demonstration of the system's suitability for inter-satellite communications, such as shown in Figure 1a.
## 5 Numerical Investigation
### Failure Mode  
Table 6 enables a comparison of failure mode between numerical predictions and test results. Similar failure modes occurred for all three numerical models: buckling of the lower rebar and lattice steel wire near the end of the models. However, the test specimens of 135-ED1-3.2A and B and 150-ED3-3.8B led to quite different failure modes from the numerical models, where these specimens failed due to the buckling of the upper truss near the center of the specimen. This may be attributed to the step-by-step loading method from the center to the end, described previously, in the tests, which indicates that the deck plate with truss girder can fail near the end rather than the center in practice. On the other hand, the specimen 150-ED3-3.8A failed by buckling of lattice near the end, similar to the corresponding numerical model. Also, the welding failures for the specimens of 200-ED5-4.5A and B  
Figure 20: Simulation results for Specim 135-ED1-3.2. (**a**) von Mises stress distribution for deck plate. (**b**) Axial stress distribution for truss girder.  
can be regarded as reasonably comparable to the numerical results because the failure locations are similar and these failures can occur due to the buckling and the stress concentration observed in the numerical results.  
The buckling for each numerical model is also identified in the numerically-calculated von Mises stresses as a function of distributed load, shown in Figure 21. It is evident that the von Mises stresses abruptly decreases due to the buckling when particular loads are reached, noting that the buckling loads are identified quite after the specified construction loads. An observation is also made that the stresses are less localized on the center of upper rebar than other components. Figure 22 shows contours of von Mises stresses and effective plastic strains for 135-EDI-3.2 and is provided to clearly give the specific failure locations, which is situated near but somewhat away from the end.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen ID** & **Test Specimens** & **Numerical Models** \\ \hline
135-EDI-3.2A & Buckling of upper truss near the center & Buckling of lower truss and lattice \\
135-EDI-3.2B & Buckling of upper truss near the center & near the end \\ \hline
150-ED3-3.8A & Buckling of lattice steel wire near the end & Buckling of lower truss and lattice \\
150-ED3-3.8B & Buckling of upper truss near the center & near the end \\ \hline
200-ED5-4.5A & Welding failure at joints between the & \\  & bottom rebar and lattice steel wire & Buckling of lower truss and lattice \\
200-ED5-4.5A & Welding failure at joints between the & near the end \\  & bottom rebar and lattice steel wire & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Failure modes between numerical models and test specimens.  
Figure 21: von Mises stresses as a function of distributed load for numerical models. (**a**) Specimen 135-EDI-3.2. (**b**) Specimen 150-ED3-3.5. (**c**) Specimen 200-ED5-4.5.
## 3 Collision-Free Hybrid MAC Protocol Based on Pipeline Parallel Transmission
### Optimization of the Handshaking and Data Transmission Schedules
#### 3.4.3 Duration of the Pipeline Stage  
Obviously, the duration of data transmission varies for each transmission cycle because the source and destination nodes will change with time. When each node can obtain the data packet schedule of all nodes in the network, it can synchronize with other nodes by predicting the beginning of the next pipeline stage. It means that the duration of a pipeline stage can be variable even when the handshaking is implemented with TDMA. Therefore the best choice for the duration of a pipeline stage is:  
\[T^{stage}=\text{max}\Big{(}T^{TDMA},T^{data}\Big{)} \tag{10}\]  
In such cases, all data packets can be transmitted no matter the data transmission duration is longer than the TDMA period or not. On the other hand, when the data packet schedule cannot be known by all nodes in the network, like in a multi-hop situation, the pipeline stage needs to be consistent with the TDMA period for synchronizing, i.e.;  
\[T^{stage}=T^{TDMA} \tag{11}\]  
It means that some data packets need to be postponed when the duration of data transmission is larger than the TDMA period, which will lead to performance degradation.