esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking
### Watermark Embedding
#### 2.2.1 Original Video-Based Watermarking Algorithms  
For an original video-based watermarking algorithm, the original host video is treated as an aggregate of a series of still images that are temporally continuous. Watermark information is embedded into the original video, and then the watermarked video is recompressed [16]. The advantages of these kinds of algorithms include (i) that the implementation of the algorithm is relatively simple, and many watermarking schemes applied to still images are also suitable for the algorithm and (ii) the algorithm does not rely on specific video compression standards and has strong universality. Its disadvantages include (i) watermark extraction requires complete decoding, which leads to high complexity; (ii) watermark information can be easily removed by a compression standard with a high compression ratio; and (iii) the compressed host video needs to be decoded first, and then encoded after watermark embedding.
# A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection
## 2 Related Works
### Non-Deep-Learning Object Detection  
As for the domain of non-deep-learning sliding-window-styled object detection, there are several effective schemes with various complexity. The renowned Viola-Jones detector [15] achieved good human face detection performance, employing a set of simple features describing local image intensity patterns and an AdaBoost classifier. [PERSON] and [PERSON] [1] designed the effective HOG feature to be used with a linear-SVM classifier, achieving big improvement in pedestrian detection. To adapt to deformable objects, [PERSON] et al. [2] proposed a root window filter in connection with several part filters whose positions are adjustable, in combination with a Latent-SVM classifier; this proposal gained considerable enhancement in multi-class object detection. [PERSON] et al. [3] further generalized this part-based model to grammar models which allow more flexible subdivision of parts and embodied ability to represent partial occlusions. Based on the idea of organizing multiple window filters with graph structure as in the case of grammar models, [PERSON] et al. [16] proposed the And-Or Graph model with more complex graph structure to represent variations of vehicle appearances, and even the appearances of multiple vehicles altogether, being able to handle more various occlusions.  
Some other non-deep-learning object detection methods employ 3D geometry models to assist detection. [PERSON] et al. [17] adopted 3D voxel models to represent several frequently appearing vehicle appearances, being able to perform accurate 2D detections as well as 3D detections. [PERSON] et al. [6] established fine-grained 3D wireframe models for 3D vehicle detection, and thus enabled inference of occlusions to the grain level of wireframe vertex.
## 3 System Design and Development
### Communication Server  
Table 3 shows the component servers and their descriptions of the Communication Server. The Communication Server consists of an Application Server and Relay Server. The  
Figure 4: Devices’ IDs and IP addresses management.  
Figure 5: Functions of FOS modules.  
Figure 3: Virtual Object Manipulation: (**a**) Right-hand Model; (**b**) Grab and move gesture to move a virtual object; (**c**) Put down gesture. (Source: Microsoft Hand tracking MRTK2 [22]).  
Application Server manages and provides information necessary for system operation. This server stores information on BIM virtual objects, QR codes for spatial alignment, IDs of FOS and OMS devices, and provides information requested by clients through the web. The Relay server enables direct communication between FOS and OMS based on Web Real-Time Communication (WebRTC). WebRTC is a technology for communication between devices to which dynamic IP addresses are assigned. In general, mobile devices use dynamic IP addresses, and for remote collaboration, a connection must be maintained even when the IP addresses of FOS and OMS devices changes frequently. To this end, this study built a WebRTC-based Relay Server for devices that can track IP using ICE and STUN provided by Google (stunl.google.com:19302 (accessed on 17 May 2022)) [23].
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 2 Materials and Methods
### Earth Observation Data  
_Cystoseira_ forests in the area typically occur in shallow reefs with a maximum depth of 10 m [34]. Their distance from the shore varies with geomorphology. The 10 m pixel resolution of Sentinel-2 was insufficient for mapping _Cystoseira_ forests due to mixed pixels representing multiple habitats. After evaluating commercial options, we selected PlanetLabs imagery--available under the Education and Research Program--because other providers (e.g., Airbus France, MAXAR, Westminster, CO, USA) lacked consistent coverage for the study area. On-demand tasking was possible but cost-prohibitive and subject to uncertain sea surface conditions (e.g., wave and sunlight interference).  
PlanetLabs provides daily or more frequent imagery, enabled by a constellation of \(\sim\)200 CubeSats. We used Planet Explorer to select imagery that matched temporally with the field expedition to collect spatial data from the _Cystoseira_ peak growth period in summer [36]. The image of 6 September 2024 (image ID: 20240906_083046_72_24 bf_3B_AnalyticMS_8b_clip.tif) from the SuperDove sensor was ordered as Top of Atmospheric Radiance (TOAR) after visually confirming minimal capillary wave action/whitecap effects along the shore and the absence of sunlight interference (Figure 3). The imagery is from EPSG 32635 WGS 84/UTM zone 35N, which is used in all cartographic representations.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 5 Target Identification Method Based on Multi-View Multi-Task Sparse Learning  
Several previous research works have been studied for multi-view multi-task sparse learning. [PERSON] et al. [49] applied the multi-view sparse learning method to the field of target identification. [PERSON] et al. [50] proposed a generative tracker based on multi-view learning, with impressive tracking performance. The tracker proposed by [PERSON] et al. [51] cannot be directly used to perform visual ship tracking tasks because the features to be tracked are color and intensity, which may be very similar between different ships.  
To address the above issues, we propose a multi-view multi-task sparse learning for target identification. Suppose there are \(n\) candidates, each has \(v\) different perspectives (e.g., SIFT, HOG, LBP, hu feature). Defined \(X^{v}\in R^{d\times n}\) as the feature matrix, it is a normalized \(n\)-column \(d\)-dimensional particle image feature vector. The \(dv\) is the \(v\)-dimensional viewing angle and the \(n\) is the number of candidates. Especially, defined \(D^{v}\in R^{dv\times n}\) as a target dictionary, where each column is a target of the \(v\)-th perspective, and \(n\) is the number of target templates. In this paper, it means four high-dimensional vectors of the SIFT, HOG, LBP, hu feature extraction methods. In this manner, it can effectively integrate multiple feature extraction methods. The target dictionary constructs a complete dictionary \(A^{v}=[D^{v},I_{dv}]\) by combining the background template \(I_{dv}\). Each representation matrix \(C^{v}\) is composed of two cooperative parts \(P^{v}\) and \(Q^{v}\). Component \(P\) captures shared features  
Figure 7: LBP histogram of ship.  
where \(Q_{i}^{\varphi}\) is the \(i\)-th row of \(Q^{\varphi}\), it is defined as outliers, and the observation probability is set to zero, and the resampling process of outliers is ignored. Define the number of detected outlier tasks as \(N_{0}\), and update the threshold \(\sigma\) as follows:  
\[\left\{\begin{array}{l}\sigma nev=\sigma old\kappa,n0>N0\\ \sigma nev=\sigma old/\kappa,n0=0\\ \sigma nev=\sigma old,0<n0\leq N0,\end{array}\right. \tag{7}\]  
where \(\kappa\) is a scale factor, \(N0\) is a threshold for the number of predefined outliers.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
###### Abstract  
The fast motion of low Earth orbit (LEO) satellites provides rapid geometric changes in a short time, which can accelerate the initialization of precise point positioning (PPP). The rapid convergence of ambiguity parameters is conducive to the rapid success of ambiguity fixing. This paper presents the performance of single- and four-system combined PPP Ambiguity Resolution (AR), enhanced with an ambiguity-float solution LEO. Two LEO constellations were designed: L was a typical polar orbit constellation, with a higher number of visible satellites at high latitudes than at low and middle latitudes; and M was designed to compensate for the lack of visible satellites at low and middle latitudes. The ground observation data of the LEO satellites at the MGEX stations were simulated. Because the global navigation satellite systems (GNSSs) were fully operational, the GNSS data were real observation data from the MGEX stations. Based on the daily observation datasets collected at 258 stations in the global MGEX observation network over three days (from 1 January to 3 January 2022), in addition to the LEO simulation data, we evaluated the positioning performance of LEO ambiguity-float solution-enhanced PPP ambiguity resolution and compared it with LEO-enhanced PPP. The L+M mixed constellation was able to reduce the time to first fix (TTFF) of the four-system combined PPP-AR to 5 min, and four LEO satellites were sufficient to achieve this. L+M mixed constellation was able to reduce the convergence time of the four-system combined PPP to 2 min. Unlike PPP-AR, PPP required more LEO satellites for augmentation to saturate.  
low Earth orbit satellite; global navigation satellite system; precise point positioning; ambiguity resolution; convergence speed
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Underserved and Remote Areas with Limited Connectivity
#### 5.10.2 Existing Solutions  
To address the aforementioned challenges, in addition to edge computing and fog computing, discussed in the third section of this paper, several other methods can be employed. Firstly, developing and deploying offline AI models can be highly effective. These models, typically installed on local devices or servers, can function independently without needing an internet connection [164]. For example, portable ultrasound devices used in remote areas, such as those from GE Healthcare, utilize offline AI for image analysis. These devices can deliver diagnostic services without a network connection by storing results locally and uploading them once connectivity is restored. This capability allows doctors to make immediate diagnoses in areas with unstable connectivity. Secondly, adopting data compression and efficient data transmission technologies can significantly reduce the amount of data transmitted [165]. For instance, remote health monitoring systems can use data compression techniques like Gzip or Brodi to shrink data sizes, making transmission feasible even in low-bandwidth environments. Wearable devices such as Fitbit employ these techniques to effectively upload health data to servers despite limited network conditions. Thirdly, designing and deploying lightweight artificial intelligence models that require fewer computing resources can be advantageous [166]. Models optimized for low-power devices, like MobileNet and TinyYOLO, can perform real-time processing directly on smartphones and embedded devices, reducing reliance on cloud computing. An example of this is Huawei's Atlas 200 AI accelerator card, which supports lightweight AI models for real-time image recognition and analysis in resource-constrained environments. Finally, implementing low-bandwidth optimization methods can help minimize network bandwidth requirements during data transmission. AI algorithms and optimization technologies tailored for such environments can improve efficiency [167]. For example, in drone data transmission, adaptive compression algorithms adjust data compression rates and transmission frequencies based on real-time network conditions. This method proves particularly useful in disaster relief scenarios, where critical environmental data and video information must be transmitted effectively even with limited bandwidth.
## 3 Preliminaries
### Fundamental Concepts of the Echo State Network
#### 3.1.2 Echo State Network  
In the ESN model, the reservoir is seen as a dynamic system in which the hidden state is moved from each time step of the input according to the dynamics expressed by the reservoir function, also called the state transition function. The ESN is effective when the reservoir is contractive, satisfying the echo state property (ESP). ESP causes the reservoir to be dependent only on the driven input signal, forgetting the initial condition (the first hidden state, usually represented as \(\mathbf{h}(0)\)). An ESN model is depicted in Figure 1. The reservoir equation is formalized as  
\[\mathbf{h}(t)=tanh(\mathbf{W}_{x}\mathbf{x}(t)+\mathbf{W}_{h}\mathbf{h}(t-1)+ \mathbf{b}_{h}) \tag{1}\]  
where \(t\) is the current time step, \(\mathbf{x}\in\mathbb{R}^{N_{x}}\) is the input time series, \(\mathbf{h}\in\mathbb{R}^{N_{h}}\) is the vector of the hidden state, \(\mathbf{W}_{x}\in\mathbb{R}^{N_{h}\times N_{x}}\) is the matrix of input weights, \(\mathbf{W}_{h}\in\mathbb{R}^{N_{h}\times N_{h}}\) is the matrix of recurrent weights, \(\mathbf{b}_{h}\in\mathbb{R}^{N_{h}}\) is the bias vector of the reservoir, and _tanh_ is the non-linearity function. The ESN readout equation is formalized as  
\[\mathbf{o}(t)=\mathbf{W}_{o}\mathbf{h}(t)+\mathbf{b}_{o} \tag{2}\]  
where \(\mathbf{o}\in\mathbb{R}^{N_{o}}\) is the predicted output vector, \(\mathbf{W}_{o}\in\mathbb{R}^{N_{h}\times N_{h}}\) is the matrix of readout weights, and \(\mathbf{b}_{o}\in\mathbb{R}^{N_{h}}\) is the bias vector of the readout. The trained parameters are only \(\mathbf{W}_{o}\) and \(\mathbf{b}_{o}\).  
Figure 1: Echo state network architecture.  
An efficient and effective training function used to fit the readout is the ridge regression, a one-shot training function formalized in the following way:  
\[\mathbf{W}_{o}=\mathbf{Y}^{T}\mathbf{H}(\mathbf{H}^{T}\mathbf{H}+\lambda\mathbf{ I})^{-1}\quad\mathbf{b}_{o}=\mathbf{Y}^{T}\mathbf{s}(\mathbf{s}^{T}\mathbf{s}+ \lambda\mathbf{I})^{-1} \tag{3}\]  
where \(\mathbf{Y}\in\mathbb{R}^{N_{n}\times N_{n}}\) is the matrix of the output of the training set, \(\mathbf{H}\in\mathbb{R}^{N_{n}\times N_{h}}\) is the matrix of the hidden states, \(\mathbf{s}\in\mathbb{R}^{N_{n}}\) is a vector of ones, and \(\lambda\) is the scalar Thikonov regularization hyperparameter (\(N_{n}\) represents the number of examples in the training set).
# Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning
## 3 Traffic Light Cycle Recommendation Methods
### Q-Learning  
Q-Learning [36] is a model free off policy reinforcement learning algorithm. The working principle is to update the expected value of the action in each state, and get a value for each possible state. Q-learning converges through repeated actions to achieve maximum expected return. This is indicated by updating the Q-value as below. \[Q(S_{t},A_{t})=Q(S_{t},A_{t})\ +\ \ \alpha[R_{t+1}\ +\ \gamma max_{t}Q(S_{t+1},a)\ -\ Q(S_{t},A_{t})] \tag{8}\]  
where \(Q\) is the expected value of the action \(A_{t}\) in the state \(S_{t}\), \(S_{t}\) is the state of the vector \(t\), \(A_{t}\) is the action of the vector \(t\), \(R\) is the reward, \(\alpha\) is the learning rate or step size and \(\gamma\) is the discount factor. Q-learning is able to learn the value of the action of all states. The cost of this algorithm is high, but it does get a good answer. The algorithm of the Q-learning approach is described in Algorithm 1. Algorithm parameters: Step size \(\alpha\in(0,\ 1]\), small \(\varepsilon>0\)  
Initialize \(Q(s,\ a)\), for all \(s\ \in\ S^{+}\), \(a\in A(s)\), arbitrarily except that (_terminal_, \(\cdot\)) = 0  
Loop for each episode:  
Initialize \(S\)  
Loop for each step of episode:  
Choose \(A\) from \(A\) using policy derived from \(Q\) (e.g., \(\varepsilon\)-greedy)  
Take action \(A\), observe \(R\), \(S^{\prime}\)  
\(Q(S,A)\gets Q(S,A)\ +\ \alpha[R\ +\ \gamma max_{t}Q(S^{\prime},a)-Q(S,A)]\)  
\(S\gets S^{\prime}\)  
until \(S\) is terminal  
Figure 8: Road traffic data from 5:00 p.m. to 6:00 p.m. on Minsheng W. Rd. and Yanping N. Rd. The _\(\varepsilon\)-greedy_ algorithm [37] is one of the Multi-armed Bandit Algorithms. It can be divided into two parts: These are \"explore\" and \"exploit.\" \"Explore\" refers to the use of a random strategy to select the action to be performed. \"Exploit\" means using the original policy to select the action to be performed. In reinforcement learning, \"explore\" is often applied in the early stages of model training to help update more Q-values. \"Exploit\" is often used in the later stages of model training, where the information learned is enough, so you can follow the past experience. The \(\varepsilon\)-greedy action \(A\) that is used in Q-Learning is as follows.
## 4 Channel Modeling  
This section delivers a comprehensive channel model for an RIS-assisted M-MIMO system, which functions at mmWave frequencies. This narrowband channel model considers a random number of scatterers (clusters), shadowing, sharing between clusters, probability of LoS, etc. Moreover, this considers array responses and realistic channel gains of RIS components. This channel model describes the modeling of amplitude and phase, considering the RIS phase shifts. This is widely used in 3 GPP [20, 21, 57].
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
#### 4.1.3. OMNeT++  
Overview  
OMNeT++ is a component-based modular simulation framework written in C++ that is primarily used to develop network simulators [88]. Its adaptability and extensibility make it a popular choice for modeling various network types, including IoT systems.  
Features
* Modular Architecture: Facilitates easy extension and customization through reusable modules.
* Comprehensive Frameworks: Supports various frameworks like INET and Castalia for simulating different network protocols and environments.
* Graphical Simulation Environment: Provides a robust GUI for designing, executing, and visualizing simulations.  
Support for Complex Network Scenarios: Capable of simulating large and complex networks with diverse protocols and configurations.
* Integration with Other Tools: Allows integration with external tools and libraries for enhanced simulation capabilities.
* Active Community Support: Backed by a strong community providing support, tutorials, and extensions.
* Wireless and Routing Algorithm Support: OMNeT++ supports multiple networking standards, including Wi-Fi, LTE, and 5G, via extensions such as INET. It enables detailed routing algorithm simulations, fault tolerance analysis, and network behavior modeling under various noise conditions.  
Limitations
* Complexity: The extensive features and configurations can be overwhelming for new users.
* Performance Concerns: Simulating networks exceeding 10,000 nodes may lead to performance bottlenecks due to high memory consumption, increased event scheduling overhead, and computational complexity of detailed protocol modeling.
* Steep Learning Curve: Requires a good understanding of C++ and network simulation concepts.  
Notable Studies Using OMNeT++
* Ref. [47]: Integrated OMNeT++ with Matlab for detailed network and system co-simulation.
* Ref. [5]: Simulated and analyzed specific protocols for RFID-based IoT networks.
* Ref. [88]: Developed FLoRaSaT using OMNeT++ to simulate satellite-based IoT networks with realistic orbital mechanics.  
Performance Metrics
* Scalability and Flexibility: OMNeT++ is highly flexible, capable of modeling complex networks, and is often used for multi-layered simulations.
* Latency and Packet Loss: OMNeT++ simulations often focus on measuring latency and packet loss, particularly in complex, large-scale network environments.
* Energy Consumption: While OMNeT++ is not specialized for energy monitoring, it can be used to evaluate energy-related metrics in extended simulations.
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 5 Surface Morphology of Phobos
### Blocks and Boulders  
Another morphological feature on the surface of Phobos includes blocks and boulders (Figure 19). Similar to the Moon, it is believed that these features originated from impact cratering on the Martian moon. They were first identified in images captured by the Viking Orbiter. The smallest blocks observed at that time had a diameter of 15-30 m, while the largest, observed inside the Stickney crater, had a diameter of 100 m [12].  
In the MGS 55103 image, covering an area of 16 km\({}^{2}\) (Figure 19), 900 rock fragments with diameters ranging from 2 to 16 m were counted [12].  
[PERSON] and [PERSON] [53] also counted less-visible, meter-sized protrusions interpreted as blocks covered with regolith or partially destroyed by micrometeoroid impacts. These meter-sized blocks are randomly distributed around small craters and on their edges, sometimes forming small chains and clusters [53].  
Additionally, a shortage of boulders, responsible for creating grooves, has been observed. [PERSON] and [PERSON] [50] suggest the possibility of rock blocks escaping from Phobos' surface due to varying gravity and escape velocities. They state that some blocks, after forming grooves on a surface with higher gravity, may leave Phobos' surface upon reaching an area with lower escape velocity.  
The missing boulders, after creating grooves, could have been removed or fragmented in subsequent years and centuries due to secondary impacts from boulders originating from the Stickney crater [51]. It is highly likely that fragments of boulders are present on Phobos but buried in regolith.  
Figure 19: Boulders (white arrow) with diameters ranging from a few meters on the surface of Phobos. Image excerpt from MGS 55103 [12].
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 4 Cross-Regional Identity Authentication Model Based on the Hash Chain
### Consensus Algorithm  
The traditional Bitcoin system requires about 10 min to generate blocks, which obviously cannot meet the low latency requirements of cross-regional authentication in SAGIN. Therefore, we simplified the consensus algorithm of the traditional blockchain and implemented Kafka distributed message processing instead of PoW, PoS, and other consensus mechanisms to achieve the verification and backup of records on the block. This article compares and analyzes the advantages and disadvantages of the two consensus mechanisms of Kafka and PBFT, and performs simulations in subsequent experiments. Kafka consensus selects several fixed nodes to implement the Kafka cluster to maintain partition logs, and the remaining nodes are used as transaction production and consumers to manage messages in the queue. The advantage of the Kafka consensus is that it has the characteristics of high throughput and low latency. The disadvantage is that it can only tolerate 1/2 of the maximum node failure, and cannot tolerate the existence of malicious nodes. The PBFT algorithm contains a total of three stages, the pre-preparation, preparation, and committing, which realizes the Byzantine problem in the case of a limited number of nodes. The advantage of PBFT consensus is that it can tolerate 1/3 of the failed or malicious nodes, which significantly improves the network's ability to defend against attacks. The disadvantage is that as the number of nodes increases, the broadcast cost also increases, the time complexity of PBFT is as high as \(O(n^{2})\), and the delay also increases exponentially.