esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hybrid Protocols
### Binary Tree Slotted Aloha  
The reader in the Binary Tree Slotted Aloha (BTSA) uses a dynamic frame length adjustment and BTSA algorithm [65]. Each tag from the interrogation area randomly chooses a slot and transmit its ID. If the reader successfully identifies a tag it will not be activated in the subsequent slots. When a collision occurs, the collided tags are resolved by binary tree splitting, while the rest of the tags will wait until that process is successfully completed.  
The collided tags are continually split into two sets until each set has only one tag. This operation is performed by the BT. The initial frame length is \(L=2^{Q}\) and the highest efficiency is achieved when the initial frame size is close to the number of tags. Since BTSA has no estimation of the tag set  
Figure 21: An example of Tree Slotted Aloha (TSA).  
size, the reader cannot set the initial frame size according to the number of tags. Some protocols are presented in order to achieve higher efficiency in a wide range of number of tags. An example of BTSA is shown in Figure 22.
## 3 Result and Discussion
### Computational Analysis  
In addition to thermodynamically favorable energy levels, molecular orbital positions are critical for facile, productive electron transfers in DSC devices. Ideally, the highest occupied molecular orbital (HOMO) of the dye should be located away from the TiO\({}_{2}\) surface to avoid back electron transfers after electron injection, and the lowest unoccupied molecular orbital (LUMO) of the dye should be localized closer to the TiO\({}_{2}\) surface for efficient electron injection to TiO\({}_{2}\) after photoinduced intramolecular charge transfer (ICT). Density functional theory (DFT) calculations at the B3 LYP/6-311G(\(d\),\(p\)) level were carried out in Gaussian09 to visualize the frontier orbital distribution of the target structures [34]. The HOMO was found to be distributed across both donors on each dye with some presence on the quinoxaline benzene ring. This HOMO location is well positioned to be far from the TiO\({}_{2}\) surface, as desired. The LUMO was localized on the quinoxaline bridge and carboxylic acid anchors at the TiO\({}_{2}\) surface (Figure 4). The LUMO is ideally positioned for electron transfer to TiO\({}_{2}\). Significant HOMO-LUMO overlap was observed at the quinoxaline (primarily at the benzene ring), which shows the role of this group as a \(\pi\)-bridge. Thus, efficient ICT from donor to acceptor in the DD-\(\pi\)-AA molecular framework is possible, which is critical to absorbing lower energy light with reasonable molar absorptivities.
## 1 Introduction
Recent research emphasized the integration of UAV-assisted wireless communication systems with RISs, where UAVs equipped with RISs intelligently relay and manipulate signals to enhance link quality [24; 29]. The authors carried out a comprehensive examination of RIS-assisted UAV systems with consideration given to multiple aspects like optimization, communication methods, deep reinforcement learning, secrecy performance, efficiency improvement, and the Internet of Things [30]. For RIS-assisted relay wireless communication, the above studies did not analyze the performance impact of malicious jamming using Unmanned Aerial Vehicles (UAVs) on the RIS-assisted dual-hop system. Motivated by the above discussion, this study investigates a dual-hop wireless communication system with a RIS-Equipped UAV, where the receiver is jammed by a malicious jammer with a RIS-Equipped UAV. These systems have significant applications in various fields, such as the military and defense for security, and in areas affected by natural disasters to maintain connections. To the authors' greatest knowledge, no work has investigated RIS-Equipped UAV dual-hop wireless communication systems when there is a RIS-Equipped UAV jamming. The main contributions of this work are presented as follows:* We derived and presented a closed-form expression for the probability density function (PDF) of the legitimate channel and jamming channel in the RIS-Equipped UAV dual-hop wireless communication systems. Additionally, we derived a novel closed-form expression for the PDF of the received signal-to jamming ratio (SJR). * Using the Gauss-Laguerre Approximation, we derived expressions for the ABER under BPSK and QAM modulation. * We analyzed the effects of jamming probability, the location of the legitimate RIS, and different channel conditions on the performance of the ABER through analytical and simulation results. The subsequent sections in this paper are organized as follows: Section 2 presents an overview of wireless communication systems, including the jammer and relevant channel models. Section 3 focuses on deriving the closed-form expressions for the ABER under both the BPSK and QAM schemes. Section 4 presents the numerical results and corresponding discussions, and Section 5 concludes this paper.
# Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities
## 4 Experiment
### Comparison of Multi-Level SFL and FL Time Cost  
This section compares the time overhead of multi-level SFL and multi-level FL when training large (ResNet18) and small (LeNet) machine learning tasks. Both use a four-level multi-level architecture, and the number of clients is 20. In order to simulate the limited computing resources of the client and the efficient computing resources of the master server, the local training of the client uses the CPU for computing, while the split model trained by the master server uses the GPU for training. We test the impact of multiple levels of SFL on resource-limited clients by the time overhead on model training.  
Figure 6: Time required for LeNet model to reach convergence in number of clients.  
As shown in Figure 8, centralized learning has the lowest time cost for both small and large machine learning tasks, at 25.32 min and 85.57 min, respectively. In the training of small model LeNet, the time cost of multi-level FL is slightly lower than that of multi-level SFL, and the difference in time cost between the two is not obvious. This is because when training the small network LeNet, the computing cost of the two-layer network in the local training of the client is relatively small, and the master server only needs to train the three-layer network, so the impact of split learning on the global training of the AIoT system cannot be reflected. Furthermore, because the master server and client also communicate via the MQTT protocol, multi-level SFL has a partial longer topic release time than multi-level FL. Therefore, in the small network training, multi-level SFL cannot present its advantage. However, on a large network, such as ResNet18, which has 18 layers, 15 of which are computed by the master server, the time cost of multi-level SFL is significantly lower than that of multi-level FL. And because the split learning process of multi-level SFL is run in parallel in the main server, rather than a linear run similar to split learning, the time cost of multi-level learning in large machine learning tasks is lower. The experimental results show that multi-layer SFL is more suitable for clients in AIoT with limited resources than multi-layer FL.  
Figure 8: Time cost of multi-level SFL and multi-level FL train different models.  
Figure 7: Testing convergence of LeNet on non-IID dataset.
## 3 Methods
### Crop Identification Based on Time-Series NDVI Clustering  
According to ground investigations, wheat is the dominant crop in the study areas, accounting for 90% of the summer harvest crops. The human-managed wheat has different seasonality characteristics from single season natural vegetation and man-made features. Therefore, wheat areas can be identified by analyzing various phenological characteristics. Each wheat survey point was plotted as a time-series NDVI profile (Figure 2); the shaded areas indicate the differences between the maximum and minimum NDVIs for wheat. A large amount of research has proven that time-series images can provide improved classification accuracy compared with single images. Herein, a simple unsupervised Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA) and maximum likelihood (MLC) classification techniques, which have been used in several studies, were adopted for the time-series PROBA-V 300-m and the synthetic 100-m image clustering [16; 44]. Firstly, the NDVI time series were created separately for the 300-m and 100-m dataset by stacking all the NDVI images covering the whole growing season of the individual crop. Afterwards, pixels with specific spatial intervals from the images were selected as training samples (a gridded set of samples) and the ISODATA algorithm was applied to those training samples to generate several classes. Each class (a group of many separated pixels) again serves as the training samples for the MLC method which was employed to the whole image to cluster the time-series NDVI. We classified the NDVI time series into 10 classes because only no more than ten land cover types existed and therefore 10 classes are basically enough for the identification of wheat fields. The time-series mean NDVI values of each class were then extracted and displayed as the NDVI temporal profile (10 profiles in total). Finally, the wheat fields were identified based on the classification results and NDVI temporal profiles in combination with the prior maximum and minimum NDVI curves (Figure 3). Specifically, if the profile of a corresponding class falls in the shaded areas of Figure 3, the class is considered to be wheat. Otherwise, it belongs to another land cover type.
## 3 Materials and Methods
### Key System Purposes  
#### 3.3.1 Object Distinction and Scene Understanding  
The object detection module provides labels for each detected object, which are crucial for scene understanding, as shown in Figure 5. This information enables the AR system to do the following:  
* Provide users with object details or identify potential risks.
* Convey spatial information for recognizing specific locations (e.g., living rooms, classrooms, offices).
* Differentiate between non-rigid objects (people, animals, vehicles) and rigid objects (typically considered background).  
This distinction is vital, as the AR system positions digital elements between the background and foreground.  
Figure 4: A simple illustration of our pipeline with an example frame, where the features are two modules for object detection and segmentation. The object detector generates bounding boxes from the image. Subsequently, segmentation is applied to each box, generating an individual mask.
## 4 Training Result and Comparison
### Comparative Study
Segmentation integrity is investigated in this section. SoUNet-fusion has good segmentation integrity in the images of single cracks, multiple cracks, and net-shaped cracks. There were relatively few misjudged areas. When interference occurs, the crack area can still be completely segmented. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Our Test Datasets** & \multicolumn{2}{c}{**FISSURES Datasets**} \\ \hline
**Metrics** & **MIoU** & **MPA** & **MioU** & **MPA** \\ \hline SegNet & 63.77 & 69.92 & 56.34 & 60.65 \\ HED & 64.56 & 70.70 & 58.30 & 65.86 \\ VGG16-U-Net & 66.99 & 74.57 & 59.12 & 67.66 \\ SoUNet-Basic & 67.46 & 75.59 & 59.15 & 68.45 \\ SoUNet-BN & 68.46 & 74.65 & 60.07 & 65.56 \\ SoUNet-GF & 68.41 & 77.28 & 61.04 & 67.81 \\ SoUNet-Fusion & **69.32** & **80.33** & **61.05** & **68.60** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation and comparison results of different methods on two datasets. Figure 14: Comparison of prediction results on the FISSURES dataset. Figure 13: Comparison of prediction results on our dataset.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 4 Methodology
### Sea Level Data Analysis  
After computing the SLA for each cycle and each retracking, the time series can be analyzed to retrieve useful oceanography information. Because of the monsoon, the annual and semi-annual signals are both significant near the HK coast. Therefore, at a first order, SLA variations can be modeled as follows:  
\[\begin{array}{l} SLA(t)=a_{1}\cos(2\pi t/T_{year})+a_{2}\sin(2\pi t/T_{year})\\ +a_{3}\cos(4\pi t/T_{year})+a_{4}\sin(4\pi t/T_{year})+a_{5}t+a_{6}+\varepsilon (t)\end{array} \tag{5}\]  
where \(T_{year}\) = 365.2425 days, \(\varepsilon(t)\) is the residual \(SLA\), \(a_{1}\) to \(a_{6}\) are the regression coefficients to be estimated: \(a_{6}\) is a bias, \(a_{5}\) is the sea level trend, the annual/semi-annual amplitude and phase can be deduced from \(a_{1}\) to \(a_{4}\):  
\[A_{annual}=\sqrt{a_{1}^{2}+a_{2}^{2}};A_{semi-annual}=\sqrt{a_{3}^{2}+a_{4}^{ 2}} \tag{6}\]  
Figure 6: 20-Hz sea level anomaly (SLA) before (**left**) and after (**right**) editing. Jason-2 cycle 5 is considered here.  
\[\Phi_{annual}=\arctan(a_{2}/a_{1});\Phi_{semi-annual}=\arctan(a_{4}/a_{3}) \tag{7}\]  
Coefficient uncertainty is defined as the square root of the diagonal elements in the covariance matrix of the coefficient vector.  
In practice, we estimated the seasonal terms and trend separately: the seasonal coefficients were removed first from the detrended time series; then the short-term trend was estimated from the residuals (i.e., initial time series corrected for the seasonal terms) after reintroducing the initial trend. In the latter step, a nine-point moving window (corresponding to an about 3-month cutoff frequency) was applied to the residuals to reduce the intrinsic 59-day erroneous signal presented in Jason altimetry missions [32; 33].  
Note that the computed trends based on only 6.5 years of data essentially represent the inter-annual variability. These short-term trends are expected to be significantly different for long-term trends estimated over the whole altimetry era. In the following the term 'trend' means'short-term trend'. In this work, it is used as a diagnostic to analyze the quality of near-coastal altimetry data and not the long term sea level trend related to climate change.
## 4 Discussion
### Impact of LLIE Value on MODIS GPP  
The value of \(\varepsilon_{max}\) is biomopecific, representing the maximum light use efficiency (LUE) of the corresponding vegetation in the process of photosynthesis. For a given biome type, the value of \(\varepsilon_{max}\) is constant and assigned by the BPLUT. Although the BPLUT has corrected and updated \(\varepsilon_{max}\) values, the colossal diversity of terrestrial environments and climatic conditions does not allow a value of \(\varepsilon_{max}\) at one location to be used reliably in another location, even for the same biome type [47]. The optimized \(\varepsilon_{max}\) that we deduced for the eighteen sites in this study greatly differed from the MOD17A2H \(\varepsilon_{max}\) that was specified by the BPLUT (Figure 7, Table 5). Because the \(\varepsilon_{max}\) value used in the MOD17A2H GPP algorithm sets the foundation for the actual conversion of light energy, the misestimation of its value will inherently reduce the accuracy of GPP estimations.
## 1 Introduction  
Since its discovery in 2014, laser-induced graphene (LIG) has shown great prospects in the field of wearable devices and systems [1]. This graphene material has a flexible three-dimensional (3D), porous structure and possesses excellent electrical and mechanical properties. The maskless direct laser writing process makes the material suitable for large-scale, low-cost device fabrication [2, 3]. Moreover, because LIG has few constraints regarding its substrate material and growing environment, it is a promising candidate to meet the massive demand for high-performance wearable devices for applications such as health care and HCI [4, 5, 6].  
In our previous work, we reviewed trends in wearable devices over the recent years [7, 8, 9]. The first trend encompassed the multifunctionality of the devices. The reason for their development was to meet the requirements of long-term daily usage when applied to health care and HCI. Such requirements can generally not be met using a single signal or a single function, so the development of devices has gradually shifted from a device level to a system level [10, 11, 12]. Wearable devices can now not only perform individual functions such as sensing or actuating, but they are also becoming integrated systems that can realize multi-mode sensing [13, 14], and are even endowed with a power supply [15] and signal acquisition-processing-transmission-feedback [16]. The second main trend treats intelligent systems. Wearable devices are becoming smarter systems that can not only realize basic signal processing functions but can also use intelligent algorithms for disease diagnosis or intelligent HCI [17; 18]. These two development trends are not independent of each other. At present, quite a number of research reports on wearable systems for health care and HCI comprehensively consider endowing devices with multifunctional and intelligent characteristics to achieve systems more in line with the requirements of the applications [19; 20; 21].  
In this review, given the requirements for applications of wearable systems for health care and HCI, we introduce the recent advances in LIG-based multifunctional and intelligent wearable systems for health care and HCI applications, each with eight typical examples. Their design ideas, preparation processes, performance, and application demonstrations are summarized and analyzed. Moreover, the possible development directions of multifunctional and intelligent LIG-based wearable systems will be discussed.
## 4 Technological Challenges and Integration Issues
### Cybersecurity and Reliability of the Digital Twin
Likewise, operational protocols must be in place for graceful degradation or manual override in cases where the digital twin's recommendations cannot be trusted. Early-stage deployments should maintain human-in-the-loop control structures to prevent overdependence on automated recommendations until reliability is fully established [4]. Coordinated incident response is another critical element. The distributed and cross-border nature of the twin means that cyber events will rarely be isolated. Procedures must be developed for detecting, communicating, and responding to incidents across jurisdictions [29]. This may include pan-European cyber drills, shared threat intelligence channels, and integrated response protocols supported by institutions such as ENISA or a potential EU-level cybersecurity coordination center for digital twin infrastructure [42]. Cost and capacity imbalances across stakeholders present an additional hurdle. Larger TSOs may have in-house cybersecurity teams, but many smaller DSOs do not. To ensure system-wide resilience, shared cybersecurity services or federated monitoring centers could be established, allowing smaller actors to meet baseline requirements without duplicating high-cost infrastructure [25]. Such arrangements could be coordinated by a central European entity or through regulated consortia [4]. Cybersecurity and privacy are closely interconnected and cannot be treated in isolation. Although most of the digital twin's data is technical and anonymized, certain use cases,such as demand-side management or the use of disaggregated smart meter data, raise concerns under the General Data Protection Regulation (GDPR). These concerns must be addressed through privacy-preserving data governance strategies that include data aggregation, pseudonymization, and strict access controls. Technical safeguards must be complemented by clear legal frameworks that define how data may be used, shared, and protected within the architecture of the digital twin. In conclusion, cybersecurity and reliability are not peripheral concerns but fundamental prerequisites for the successful implementation of a European grid digital twin. As the system grows in scale and becomes more integrated into grid operations, it is essential to build a secure and resilient architecture from the beginning. This requires embedding security by design, aligning with evolving European regulations, ensuring consistent protection across all stakeholders, and preparing for both technical and organizational risks. The digital twin initiative may also act as a catalyst for strengthening the cybersecurity posture of the entire energy sector, transforming it from a fragmented system with uneven protections into a coordinated and secure digital infrastructure that supports Europe's decarbonized energy future.
## 3 FANET Routing Algorithms
### Reinforcement-Learning-Based Routing Algorithms
While addressing the challenges of topology changes and frequent link switching, it is important to reduce the demands on node storage and computational capabilities (considering that UAV and satellite nodes have significantly lower computational and storage capacities compared to ground-based nodes). Therefore, novel routing algorithmsshould integrate the characteristics of the integrated network scenario while ensuring the adaptability of the routing process. In the study of SAGIN routing, the complexity of the protocol framework also serves as a significant hindrance to the development of routing algorithms [107], as SAGIN employs separate protocol frameworks for satellite-ground, aerial-ground, and aerial-satellite connections. Among them, there are numerous base stations, drones, or satellites with specialized purposes that adopt independent protocol frameworks for security reasons. This is also one of the most challenging issues in current research on SAGIN routing algorithms. To address this challenge, we can draw on the layered control approach proposed by [PERSON] et al. [108] and the idea of designing a software-defined network based on a layered hybrid architecture proposed by [PERSON] et al. [109]. By introducing the concept of separating control and transmission in a software-defined network into the heterogeneous SAGIN context [110], we can attempt to add a controller to each layer of the existing network structure, ensuring unified protocols and programming interfaces between layers.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 2 Related Work
### Mobility Support in ICN
#### 2.2.1 Anchor-Based Proposals
However, the use of a fixed HA brings problems, such as handover path extension and single points of failure. Finally, the common problem of anchor-based mobility proposals is that interests are always transmitted through the anchor node, and the length of the forwarding path may be much longer than the shortest path calculated in the routing plane.
## 4 Applications
### Concrete Fatigue in Axial Compression
#### 4.1.2 Finite Element Simulation
This application example demonstrates that the fatigue damage evolution equation constructed based on the literature [31] could reflect the performance degradation of materials under similar conditions, and the proposed equivalent fatigue damage constitutive model could be applied to simulate fatigue loading under axial compression reasonably well. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Material**} & **Dilatancy** & \multirow{2}{*}{**Eccentricity**} & \multicolumn{2}{c}{**Ratio of**} & \multirow{2}{*}{\
\begin{tabular}{c} **Shape Factor** \\ **of Yield** \\ **Surface** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Viscosity** \\ **Parameter** \\ \end{tabular} } \\  & **Angle (\({}^{\circ}\))** & & & & \\  & & & & & \\ \hline Concrete & 55 & 0.1 & 1.16 & 0.667 & 0.0005 \\ \hline \end{tabular}
\end{table} 
Table 7: Plasticity parameters of CDPM adopted in the analysis of fatigue tests under axial compression from the literature [42]. The definition of plasticity parameters and their default values in Abaqus are shown in Section 2.1.2. Figure 22: Simulated monotonic stress–strain curves of concrete cylinders in fatigue tests under axial compression from the literature [42]: (a) Specimen F1; (b) Specimen F3.