esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 System Performance Analysis
### Model Drive Response Time  
For the response time test, each model's actions were divided into three groups, driving axes 1, 2, and 3, respectively. The model's three-axis markings and driving directions are shown in Figure 15.  
Figure 14: Comparison diagram of model loading time with ABB RobotStudio.  
Figure 15: Model driving direction diagram (KUKA, top left; ABB, bottom left; system, right).  
Each group of actions was tested 25 times. In the same environment, the control command issuance for the models was tested. The response time test results for our system's model driving are shown in Figures 16 and 17.  
According to the test results, the average time for driving KUKA KR150 axes 1, 2, and 3 with our system is 43.72 ms, 46.48 ms, and 54.08 ms, compared with KUKA Sim Pro's 76.92 ms, 79.64 ms, and 90.08 ms, resulting in loading efficiency improvements of approximately 43.16%, 41.64%, and 39.96%, respectively. The overall average response time improvement is about 41.50%.  
For the ABB IRB2400, the average time for driving axes 1, 2, and 3 with our system is 62.76 ms, 64.12 ms, and 57.12 ms, compared with RobotStudio's 85.32 ms, 89.48 ms, and 83.44 ms, resulting in loading efficiency improvements of approximately 26.44%, 28.34%, and 31.54%, respectively. The overall average response time improvement is about 28.75%.  
The improvement in response times can be attributed to the limitations of traditional simulation software's teach pendants, where commands must be transmitted to the model through protocols like TCP/IP, EtherNet/IP, CAN bus, and RAPID. In contrast, our system's commands are directly transmitted via WebSocket, resulting in enhanced model driving response times.  
Figure 16: Comparison chart of response time with KUKA Sim Pro driver.  
Figure 17: Comparison chart of response time with ABB RobotStudio driver.
## 5 Intrusion Detection Scheme Based on Relaxed Flow Conservation Constraints
### Two-Hop Energy-Efficient and Secure Reporting Scheme
#### 5.2.3 Security Mechanisms for the Reporting Scheme  
The observations that are exchanged among nodes are subject to many attacks, which aim at spoofing the source of observations and/or altering their contents. Thus, we enhance the two-hop energy-efficient reporting scheme by adding some security mechanisms.  
In order to prevent the relay node \(r\) from altering the observations sent by \(w\), each node \(i\) creates its two-hop broadcast key \(THK_{i}\), which is a shared key between the node and its two-hop neighbors. This key is unknown for the one-hop neighbors. The creation of this key, as described in [43], is as follows: Each node \(i\) is preloaded with a transitory initial key \(K_{IN}\) and a random number \(RN_{i}\). It first computes its master key \(MK_{i}=G(K_{IN},ID_{i})\), where \(G\) is pseudo random function and \(ID_{i}\) is node \(i\)'s identifier. Subsequently, it computes its two-hop broadcast key \(THK_{i}=G(MK_{i},RN_{i}||RN_{i})\). Node \(i\) sends \(RN_{i}\) encrypted with \(K_{IN}\) to its two-hop neighbors. The latter use the received \(RN_{i}\) to derive \(MK_{i}\) and \(THK_{i}\). We assume that an adversary can compromise a node only after \(T_{min}\) time units from network deployment. Before the expiration of \(T_{min}\), each node \(i\) erases \(K_{IN}\) and \(RN_{i}\) from its memory in order to prevent an adversary from compromising the keys of other nodes. Node \(i\) uses \(THK_{i}\) to encrypt its observations.  
An attacker might forge its identity to masquerade as another node and send fake observations. To allow for two-hop authentication of the observation, each node \(i\) generates a two-hop one-way hash chain (TOHC) \((H_{i}^{0},H_{i}^{1},H_{i}^{2},\cdots,H_{i}^{K})\). Each node within \(i\)'s two-hop neighborhood is preloaded with the last value (\(H_{i}^{K}\)). Upon generation of the \(d\)'th observation, node \(i\) appends (\(H_{i}^{K-d}\)) to \(Rep_{i}\). Each monitoring node that receives the \(d\)'th observation can authenticate the source \(i\) by applying the one-way hash function on the received value \(H_{i}^{K-d}\) and verifying whether the result is equal to the pre-loaded TOHC value \(H_{i}^{K-(d-1)}\); \(F(H_{i}^{K-d})=H_{i}^{K-(d-1)}\).  
When a node is physically captured, the adversary can retrieve all of the embedded security credentials and turn the node into a liar that fabricates fake reports, without being detected by the above security mechanisms. In the following subsections, we show how this issue is tackled under two models of selective routing attacks.
## 4 Experiments and Results  
### Materials and Method  
To verify the performance of the proposed method, six test images were employed from the infrared image databases of IRData ([[http://www.dgp.toronto.edu/~nmorris/data/IRData/](http://www.dgp.toronto.edu/~nmorris/data/IRData/)]([http://www.dgp.toronto.edu/~nmorris/data/IRData/](http://www.dgp.toronto.edu/~nmorris/data/IRData/))) and LTIR ([[http://www.cvl.isy.liu.se/research/datasets/ltir/version1.0](http://www.cvl.isy.liu.se/research/datasets/ltir/version1.0)]([http://www.cvl.isy.liu.se/research/datasets/ltir/version1.0](http://www.cvl.isy.liu.se/research/datasets/ltir/version1.0))); these images are shown in Figure 2. Our experiments were performed on a PC with the Intel CPU 2.8 GHz and 8 GB RAM using MATLAB R2014a. Six different SRR methods were adopted for comparison, including the Bicubic method, the MFT method [23], the TV-based method [13], the TGV-based method [21], the TV4-based method [31], and the FTV4-based method [33]. Among the six methods, the Bicubic method was implemented by the function \"imresize(0)\" in the images toolbox of MATLAB; the MFT method used the scripts provided in [23]; other methods were taken by self-produced scripts according to [13; 21; 31; 33].  
For the objective evaluation, we calculated the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [49]. These can be defined as follows:  
\[\mathrm{PSNR}(\mathbf{X},\mathbf{Y})=10\log_{10}\frac{255\times 255}{\frac{1}{N^{2}} \sum_{i=1}^{N}\sum_{j=1}^{N}(\mathbf{X}_{ij}-\mathbf{Y}_{ij})^{2}}, \tag{39}\]  
\[\mathrm{SSIM}(\mathbf{X},\mathbf{Y})=\frac{(2u_{\mathbf{X}}u_{\mathbf{Y}}+(255k_{1})^{2})(2 \sigma_{\mathbf{X}\mathbf{Y}}+(255k_{2})^{2})}{(u_{\mathbf{X}}^{2}+u_{\mathbf{Y}}^{2}+(255k_{1 })^{2})(\sigma_{\mathbf{X}}^{2}+\sigma_{\mathbf{Y}}^{2}+(255k_{2})^{2})}, \tag{40}\]  
where \(\mathbf{X}\) and \(\mathbf{Y}\) represent the ground truth and reconstructed image, respectively. \(u_{\mathbf{X}}\) and \(u_{\mathbf{Y}}\) are the mean values of \(\mathbf{X}\) and \(\mathbf{Y}\), respectively. Further, \(\sigma_{\mathbf{X}}^{2}\) and \(\sigma_{\mathbf{Y}}^{2}\) are the variances in the \(\mathbf{X}\) and \(\mathbf{Y}\), respectively. \(\sigma_{\mathbf{X}\mathbf{Y}}\) is the covariance of \(\mathbf{X}\) and \(\mathbf{Y}\). The parameters \(k_{1}\) and \(k_{2}\) are set in a manner that ensures that the denominator of SSIM is a non-zero number. In this study, we set \(k_{1}=0.01\) and \(k_{2}=0.03\).  
Throughout all the experiments, we set the penalty parameters \(\rho=1\times 10^{-5},\eta=1\) and \(p=0.5\) of the \(L_{p}\) quasinorm. The blur matrix \(\mathbf{H}\) in Equation (7) is set as a corresponding matrix to the blur kernel which is generated by the MATLAB built-in function \"fspecial('gaussian', '9, 1)\"; \(\mathbf{S}\) is set as a K-fold downsampling operator which is generated by the MATLAB built-in function \"downsample(X,K)\".
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 3 Experimental Setup
### Datasets
#### 3.1.1 VoiceBank+DEMAND  
VoiceBank+DEMAND dataset [33] is an open-sourced dataset widely used in the SE research. It consists of a training set of 11,572 utterances from 28 speakers and a test set of 824 utterances from another 2 unseen speakers. The speakers of clean speech are selected from the VoiceBank corpus [39], with around 400 utterances per speaker. The 28 speakers in the training set include 14 males and 14 females, while the 2 unseen speakers in the test set include 1 male and 1 female. The clean audios in the training set are mixed with 10 types of noise, including 2 artificially generated noises (speech-shaped noise and babble) and 8 real noise recordings from the DEMAND database [40] (domestic noise in a kitchen, office noise in a meeting room, cafeteria noise, restaurant noise, subway station noise, car noise, metronoise, and street noise at busy traffic intersection). The mixed SNRs in the training set is {0 dB, 5 dB, 10 dB, 15 dB}. In the test set, the clean audios are mixed with five other types of noise from the DEMAND database that do not appear in the training set, namely, domestic noise in a living room, office noise in an office space, bus noise, open area cafeteria noise, and public square noise. The mixed SNRs in the test set is {2.5 dB, 7.5 dB, 12.5 dB, 17.5 dB}.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 3 Architecture and Key Challenges
### Architecture
However, it is challenging to enforce laws for the autonomous UAVs where there  
Figure 2: Overview of UAV-enabled IoT platform for disaster management. is no pilot. Researchers are investigating law enforcement for autonomous UAVs and Transport Canada has also defined Canadian aviation regulations. It is important to design and deploy IoT platform while considering the requirements of wildfire management department and satisfying the rules of Transport Canada (or similar authorities in other countries). Despite this available literature, wireless connectivity, data analytics, and law enforcement still need to be comprehensively included in the UAVs enabled IoT platform for wildfire management to enable effective deployment and easy adaptation by the wildfire management department. The architecture of UAV-enabled IoT platform can help in multiple ways for wildfire management: (i) fire crew and people in the affected areas will have coverage through UAVs enabling their long-range communication; (ii) autonomous UAVs can spot forest fires at early stages before they get out of control and also identify people in need in the affected areas; (iii) the ground IoT network can be helpful to collect useful information in wildfire areaa; and (iv) many data collected from the ground and UAV system can be relayed to the cloud infrastructure where the data can be analyzed to improve the performance of the system and predict future conditions, in addition to providing recommendation to the wildfire management authorities.
# Generic Digital Twin Architecture for Industrial Energy Systems
## 4 Proof-of-Concept: Digital Twin Instantiation
### Knowledge Representation Inside the Shared Knowledge Base
#### 4.1.2 Simulation Service--Domain Ontology  
A View on the Virtual Entity of the DT consists of a Simulation Service in combination with context information, stored in the Shared Knowledge base. The information about the simulation models used by the Simulation Services is captured by the Simulation Service--Domain Ontology. This ontology can be seen as a reference implementation for other domain ontologies. The main concepts of this ontology and their relations are shown in Figure 5.  
The domain service ontology inherits all classes and properties from the Base Service Ontology and adds domain-specific knowledge. For the implementation of the Simulation Service--Domain Ontology, ML-Schema is used to describe data-driven and physical simulation models within a Simulation Service. ML-Schema is developed to represent and interchange information on machine learning algorithms,  
Figure 4: Part of the Base Service Ontology concepts and their relations.  
Figure 5: Main Simulation Service—Domain Ontology concepts and their relations.  
datasets, and experiments [30]. The concepts in the ontology which are reused from ML-Schema are depicted with grey background in Figure 5. More Information about ML-Schema can be found in [31]. The additional concepts of the domain ontology are depicted with a white background.  
The SimulationService is a special type of service for describing simulation models inside the DT. It inherits all properties from the BaseService concept in the top-level ontology. The Simulation Service executes a certain ML-Schema:Software which consists of an ML-Schema:Implementation and ML-Schema:Model. The model is trained on a certain Dataset, which has properties to describe how the data can be used (dataset location, dataset format, feature names, etc.).  
The Virtual Environment provides a description of the environment in which a model is executed. A Container is one possibility of such a concrete realization of a Virtual Environment. The presented implementation uses a Docker container for providing such an environment. Each ML-Schema:Model is related with a OWL-Time:TimelInstant to capture the date and time of its training or parameter identification. This information is used to handle various versions of a model.
# Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions
## 1 Introduction  
Usually, people rely on a Trusted Third Party (TTP), such as an online bank operator, to transfer assets over the internet. A TTP is responsible for guaranteeing a secure exchange and intervenes in case of failure or security breach. However, the centralized design of TTPs makes them vulnerable to the single point of failure problem. Nowadays, blockchain [1] has been widely adopted as an immutable (if some constraints are respected, such as, the majority of the nodes are honest) Distributed Ledger (a distributed database that shares transaction records between all the participants) technology (DLT) allowing transaction information to be securely replicated over a set of nodes, thus eliminating the need for TTP and avoiding the single point of failure problem. However, transaction data on the blockchain is publicly accessible, and at the same time, depending on the requirements of their application, users may need, in some cases, on-demand (as opposed to full) data disclosure, leading to privacy concerns that should be handled in the design of the blockchain for this particular solution. On-demand data disclosure requires fine-grained access control to check who is the recipient of the data before applying required security (encryption strategies) and privacy (data protection) measures.  
We define privacy-sensitive data as any data that is subject to disclosure constraints. Such data is highly dependent on the application domain (medical care, production chain, etc.). Diverse solutions are available to overcome the privacy challenge (such as k-anonymity, l-diversity, t-closeness, etc.). In this context, privacy solutions are combined with security measures (such as mix network [2], Zero-Knowledge Proofs (ZKPs) [3], ring signature [4], etc.) that provide required services to protect data from unwanted disclosure, control data access, and prevent identity theft. Although severalstudies [5; 6; 7; 8; 9; 10] focus on security or privacy, a comprehensive and structured analysis of blockchain requirements, the latest advances and their relevance in this context is still missing as discussed below.
# Electromagnetic Particle Algorithm for Beam-Wave Interaction in Traveling Wave Tube of Symmetry
## 1 Introduction
It is also based on the characteristics of the FDTD algorithm, which it is mostly used as the time domain algorithm of electromagnetic field in electromagnetic PIC particle simulation. The electron beam emitted from the cathode of TWT is accelerated by the beam-wave interaction in the high-frequency system and emits high power electromagnetic waves. The study of beam-wave interaction in TWT is basically to solve Maxwell equation with a source (current source formed by electron beam) and the dynamic equation of electron motion.
## 3 Information-Theoretic Results
### Realism Constraints
#### 3.1.2 Perfect and Imperfect Realism Constraints  
The concept of perfect realism [25; 29] is analogous to the lossless scenario in rate-distortion problems, requiring that the output distribution closely matches the source distribution in terms of a perceptual measure. This aligns closely with lossy coding under distribution constraints [31], where the output sequence must adhere to a specified i.i.d. distribution while also satisfying distortion constraints. The near-perfect realism constraint requires that the reconstruction distribution be asymptotically arbitrarily close to the source distribution. [PERSON] [25] demonstrated that achievability with perfect realism is equivalent to achievability with near-perfect realism when the distortion-perception pair is uniformly integrable. Specifically, deterministic coding is sufficient when \(D>0\) and \(P>0\)[26].
## 3 Materials and Research Methods
### Creating a \(QoE_{i}\) Probability Model  
The main goal of this research stage is to find the density function of the probability distribution _f(x)_, which allows the calculation of the probability that \(QoE_{i}\) will take a value from a certain interval [a, b]. Using the SPC tool for Excel and the distribution fitting procedure, data fitting, i.e., \(QoE_{i}\) values with several common distributions, was tested. As a result of this procedure, the Anderson-Darling (AD) statistic test, as well as corresponding \(p\)-values, were given for data fitting with a certain type of distribution, and the distributions were ranked according to the Akaike information criterion (\(AIC\)). AIC, as one of the most common tools in statistical modeling, is a means of selecting the best model in a set of models. Therefore, this criterion is an indicator of the degree of the statistical model fitting with real data (goodness of fit--GOF). Its mathematical formulation is an extension of the maximum likelihood principle [34]:  
\[AIC=-2\ln(\hat{L})+2K \tag{3}\]  
where \(\hat{L}\) is the maximum value of the likelihood function, and \(K\) is the number of model parameters [35]. \(AIC\) is an indicator of the relative amount of information lost by creating a given model over the actual data, with \(AIC\) making a compromise between GOF and model simplicity. This means that the less information is lost, the lower its value and the higher the quality of the model.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 3 Overview of Single-Satellite Navigation and Orbit Determination Methods
### Satellite-to-Ground Laser Measurement Technology  
Satellite Laser Ranging (SLR) is currently the most accurate technology for precise satellite orbit determination and inspection. Its single ranging accuracy reaches centimeter level or even sub-centimeter level. The United States and the Soviet Union successively installed five sets of laser reflector arrays on the lunar surface, and collected a large amount of observation data as well as noting significant scientific achievements.  
SLR is used to accurately measure the round-trip time interval of the laser pulse from the ground observation point to the satellite equipped with a corner reflector, expressed in terms \(\Delta t\), to calculate the distance from the ground observation point to the satellite \(R\). \(R\) and \(\Delta t\) have the following relationship [43]:  
\[R=\frac{1}{2}c\Delta t=\frac{1}{2}cNT_{0} \tag{14}\]  
where \(c\) is the speed of light.  
The schematic diagram is shown in Figure 5. \(\rho_{TP}\) is the satellite observation value vector and \(\rho_{TD}\) is the ground target calibration observation value vector. \(\overset{\rightarrow}{OD}\) is the distance  
Figure 4: Schematic diagram of velocity measurement navigation.  
value for ground target calibration and should be accurately measured by a ranging instrument during the construction of the ground target. \(\overset{\rightharpoonup}{OF}\) is the distance vector of the ground center point, which should also be accurately measured during the construction of the ground center point. \(\overset{\rightharpoonup}{PS}\) is the satellite center of the mass correction value vector, determined according to the shape of the satellite. \(dx\) is the system delay correction value, which is obtained by each measurement of the ground target. Laser ranging observation is to measure the distance from the ground reference point to the satellite. The ground reference point can be the intersection of the two axes of the telescope as the reference point, or the ground center point as the reference point.  
The observation vector from the telescope reference point to the satellite's center of mass can be expressed as:  
\[R_{OS}=\rho_{TP}+\overset{\rightharpoonup}{PS}-|\rho_{TD}|+\left|\overset{ \rightharpoonup}{OD}\right| \tag{15}\]  
The observation vector from the ground center point to the satellite's center of mass can be expressed as:  
\[R_{FS}=\rho_{TP}+\overset{\rightharpoonup}{PS}-\overset{\rightharpoonup}{ OF}-|\rho_{TD}|+\left|\overset{\rightharpoonup}{OD}\right| \tag{16}\]
# Fault Tolerant DHT-Based Routing in MANET
## 4 Fault Tolerant DHT Network (FTDN)
### Logical Network Construction/LIDs Assignment and Key-Invariants
In other words, the distance function returns the sub-tree to which the destination address belongs and the node then looks up the next-hop table to find the next-hop towards the destination. In brevity, we omit diagrammatic and algorithmic details for the logical network construction/routing. For complete detail please see [14]. Our main objective is fault tolerance. The greedy forwarding and the prefix sub-graph constraint ensure the successful delivery of packets in the logical network.
## 2 Methods
### Global Stitching and Error Adjustment
For multiple connected components, independent adjustments are performed. Absolute corrections align each component to a unified reference frame using manually identified stable ground control points (GCPs, e.g., non-deforming pavement areas or building corners), with the deformation rates and elevation residuals set to zero. This method, by using graph theory connectivity constraints and a weighted least squares adjustment, effectively solves the rank deficiency problem caused by isolated sub-blocks in traditional block stitching and achieves the high-precision seamless integration of large-scale PS networks.
# Study of Fast and Reliable Time Transfer Methods Using Low Earth Orbit Enhancement
## 2 Experimental Principles
### LEO Constellation Design and Observation Simulation
Finally, all types of errors are added to the geometric distance between the satellite and ground station to obtain the final observed data at the ground station.