esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
## 3 Results and Discussion
### Results of GNSS/GPS Network Validation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Station** & \(\mathbf{X}\) & \(\mathbf{Y}\) & \(\mathbf{Z}\) & \(\mathbf{s_{X}}\) & \(\mathbf{s_{Y}}\) & \(\mathbf{s_{Z}}\) & \(\mathbf{s_{3D}}\) \\  & \([\mathbf{m}]\) & \([\mathbf{m}]\) & \([\mathbf{m}]\) & \([\mathbf{mm}]\) & \([\mathbf{mm}]\) & \([\mathbf{mm}]\) & \([\mathbf{mm}]\) \\ \hline CB1 & 4,288,747.3357 & 1,230,643.5523 & 4,542,659.7027 & 0.99 & 1.15 & 0.75 & 1.69 \\ CB2 & 4,288,755.2340 & 1,230,468.2036 & 4,542,699.6583 & 0.99 & 1.15 & 0.75 & 1.69 \\ MDVG & 4,278,144.0819 & 1,221,927.0432 & 4,555,592.3033 & 1.58 & 1.40 & 1.69 & 2.70 \\ BRSK & 4,307,965.4474 & 1,200,393.7500 & 4,532,779.1697 & 2.44 & 2.98 & 1.38 & 4.09 \\ \hline \end{tabular}
\end{table} 
Table 11: Earth-Centered–Earth-Fixed (ECEF) GNSS/GPS network station positions in ITRF2020 (epoch 2023.45) with corresponding positional accuracies expressed as standard deviations, based on the application of IGS type-mean antenna PCC models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Station** & \(\mathbf{X}\) & \(\mathbf{Y}\) & \(\mathbf{Z}\) & \(\mathbf{s_{X}}\) & \(\mathbf{s_{Y}}\) & \(\mathbf{s_{Z}}\) & \(\mathbf{s_{3D}}\) \\  & \([\mathbf{m}]\) & \([\mathbf{m}]\) & \([\mathbf{m}]\) & \([\mathbf{mm}]\) & \([\mathbf{mm}]\) & \([\mathbf{mm}]\) & \([\mathbf{mm}]\) \\ \hline CB1 & 4,288,747.3376 & 1,230,643.5514 & 4,542,659.7015 & 0.75 & 0.88 & 0.57 & 1.30 \\ CB2 & 4,288,755.2354 & 1,230,468.2027 & 4,542,699.6572 & 0.75 & 0.88 & 0.57 & 1.30 \\ MDVG & 4,278,144.0785 & 1,221,927.0438 & 4,555,592.3017 & 1.21 & 1.07 & 1.29 & 2.07 \\ BRSK & 4,307,965.4476 & 1,200,393.7511 & 4,532,779.1737 & 1.87 & 2.29 & 1.06 & 3.14 \\ \hline \end{tabular}
\end{table} 
Table 12: Earth-Centered–Earth-Fixed (ECEF) GNSS/GPS network station positions in ITRF2020 (epoch 2023.45) with corresponding positional accuracies expressed as standard deviations, based on the application of LMMT individual antenna PCC models. \[ \begin{split} s_{\text{M}}^{\text{ICS}}=1.57\text{ mm},\\ s_{\text{M}}^{\text{LMMT}}=1.21\text{ mm}.\end{split}  \tag{9}\]  
By analyzing the local network accuracy, i.e., the standard deviations of station positions (Tables 11 and 12), a higher achieved network accuracy based on the usage of LMMT individual antenna PCC models is evident. Furthermore, the higher global accuracy of the network, as quantified by the standard deviation of the point position following [PERSON]'s methodology, further confirms that individual GNSS receiver antenna calibration at LMMT leads to improved accuracy in network realization. To summarize, in the GNSS/GPS network validation test conducted, the application of LMMT individual receiver antenna PCC models positively transferred into the spatial domain and yielded approximately 23% higher network realization accuracy compared to the use of type-mean antenna PCC models provided by IGS.
## 4 Classification of MAC Protocols Based on Channel
### Cluster-Based Single-Channel MAC Protocols
#### 4.8.1 Pdmac  
Priority-based enhanced TDMA MAC was designed to prevent accidents via in-time delivery of time-critical safety messages and non-safety messages using the priority assignment technique. The protocol provides better clock synchronization, reduced message loss, latency, and improved throughput in the network. This protocol was based on an intra-cluster V2V model on two-directional highways and inter-cluster clock synchronization to reduce overhead and to improve channel utilization. It describes a three-tier priority assignment method for better delivery of warning messages estimating the type of message, severity level, and direction components. A single bit field called Validate_timer in the message header is used to check whether the timer is synchronized. The node's timer is considered synchronized when Validate_timer= 1 and, hence, validated. Conversely, the clock needs synchronization when Validate_timer= 0 and Validate_timer remains invalid for all other nodes on the network. Validate_timer is set the default value of 0 to make synchronization of clock mandatory for each and every node upon entering the highway [95]. The clustering mechanism of PD MAC in a bidirectional scenario is depicted in Figure 15.  
The protocol's performance in urban or city scenarios (dense and highly mobile networks) shows less efficiency.
## 3 Results and Discussion  
### Characterization of the Cement Pastes  
#### 3.1.1 Preliminary Experiments: Selection of the Formulations  
As introduced earlier, phosphate additives can be incorporated into MgO/SiO\({}_{2}\) formulations to improve the fluidity of the cement paste without increasing the amount of water used. Sodium orthophosphate (OP) has been recently demonstrated to be the most efficient fluidificant for M-S-H cements [52]. Here, the potential effects of combining Hydroxypropyl methylcellulose/Carboxymethyl methylcellulose (HPMC/CMC) with OP on the consistency and fluidity of the cements were initially evaluated by manually monitoring the hardening of the pastes. As described in Section 2.3.1, the formulations were periodically checked with a spatula to control their setting times and to understand how the combined use of these additives influenced the hardening of the pastes. The results of the tests, reported in Figure 1, reveal that the presence of cellulose derivatives does not influence the hydration reaction, and thus the hardening of the cements, while OP increases the setting time and the fluidity of the pastes up to a threshold value. In fact, systems with a P/Mg ratio of 0.02 showed the longest setting times and highest fluidity, regardless of the HPMC/CMC concentration. The presence of a threshold value for phosphate as a water-reducing and retarding admixture was already observed in analogous systems, where 1 wt% of (NaPO\({}_{3}\))\({}_{6}\) was reported as the correct dosage of fluidificant (corresponding to a P/Mg ratio of about 0.01) to achieve the highest fluidity for Mg(OH)\({}_{2}\)/silica fum pastes [58]. As a comparison, other M-S-H extrudable inks were previously prepared using 1.5-2.5% of sodium hexametaphosphate [25] or without phosphates [1]. Here, a P/Mg ratio of 0.02 was selected to extend the setting times of the investigated pastes as much as possible and facilitate the 3D printing of such formulations. Considering that HPMC and CMC did not affect the appearance and hardening of our samples, only two cellulose concentrations were selected to evaluate their effect on the physico-chemical properties of the 3D printed materials: 5 wt% and 10 wt% of HPMC/CMC. According to the literature, these water retention additives could influence the strength of 3D-printed calcium silicate cements [47], and here their effect on magnesium silicate cements was preliminary investigated.
# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q2. What Is the Importance of Resource Allocation in 5G?
Moreover, the authors investigated the algorithm's spectral efficiency associated with the modification of users' requirements for two slices, and it achieved better spectral efficiency performance. The researchers [156] presented a utility function based on the signal-to-interference-and-noise ratio (SINR). The small cells quantity in a cluster demonstrated the weighted mean. From all clusters, a small cell was chosen as having an extreme value for the second utility function based on the lowest path loss values across the small cells and the microcell base station. The small cell having a high priority performed similarly to a spectrum manager of a set. The remaining small cells presented a price value created based on the required data rate and the user type for a subcarrier to the high priority small cell spectrum manager. The small cell that had a high priority assigned resources to SCs which were being used for the projected algorithm relying on the third utility function along with the price value. In the presented work, they calculated the spectral efficiency, SINR, and power consumption of the system. The power consumption of the presented system was reduced by up to 30%, and spectral efficiency and SINR improved almost 40% and 45% compared with corresponding existing methods. The authors of [157] proposed an effective resource allocation method for 5G C-RAN named Bee-Ant-CRAN. The problem discussed was to develop joint mapping logically among User Equipment (UE) and RRHS along with BBUs. This was tested under network load circumstances, aiming to minimize the overall costs of the network along with managing the QoE and QoS of the user. The load was articulated as a mixed integer nonlinear problem with several restrictions. Afterward, the expressed optimization problem was classified as a two-step problem of resource allocation: RRH-BBU mapping and UE-RRH association. In [158], the authors proposed a game theory-based ideal method for resource allocation, which focused on enhancing the coverage probability and sum rate for uplink communications in critical scenarios. The presented classified game theory architecture improved the performance of a multitier heterogeneous network having uplink communications in alliance with femto access points and pico base stations in the domain of a macro base station. The experimental simulations were based on a real-time data set that was being observed for a predefined period. Then the data set was deployed to generate real-world critical scenarios. The result was achieved by using a Nash equilibrium strategy for a noncooperative game. The authors performed simulations that had various failure rates, and the outcomes showed that the presented method enhanced the sum rate coverage probability, obtaining a remarkable margin with or without considering the critical scenario.
# MBHAN: Motif-Based Heterogeneous Graph Attention Network
## 5 Experiments
### Baseline Algorithms  
MBHAN was compared with the following baseline algorithms, which together cover homogeneous graph representation learning methods, heterogeneous graph representation learning methods, and graph neural networks.  
DeepWalk [43]: is a graph representation method based on a random walk strategy that is usually applied to homogeneous graphs. In the experiments, it treated the datasets as homogeneous graphs because it could not handle the heterogeneity of the nodes.  
metapath2 vec [44]: is a heterogeneous graph representation learning method based on a meta-path random walk that leverages Skip-Gram to learn the embedding of the nodes. In the experiments, all possible meta-paths consisting of three node types were processed. The reported results give the average performance.  
GCN [9]: is a semi-supervised graph convolutional neural network designed for homogeneous graphs. As not all nodes in the datasets have features, the experiments used a meta-path based heterogeneous graph transformation operation. For the Author node classification and clustering task in the DBLP_four_area dataset, the meta-paths \(A-P-A\), \(A-P-C-P-C-A\), and \(A-P-T-P-A\) were employed for the transformation. For the Paper node classification and clustering task, the meta-paths \(P-A-P\), \(P-C-P\), and \(P-T-P\) were employed, with the given results being the average performance. For the ACM dataset, the meta-paths \(P-A-P\) and \(P-S-P\) were employed, with the average performance again being reported.  
HAN [19]: is a hierarchical graph attention neural network that can be used for heterogeneous graphs. It takes into account both node-level attention and meta-path-level attention. We adopted the meta-path selection scheme given in the literature. The meta-paths \(A-P-A\), \(A-P-C-P-C-A\), and \(A-P-T-P-A\) were used for the Author node classification and clustering task in the DBLP_four_area dataset, while the meta-paths \(P-A-P\), \(P-C-P\), and \(P-T-P\) were used for the Paper node classification and clustering task. For the ACM dataset, the meta-paths \(P-A-P\) and \(P-S-P\) were used.  
GTN [21]: is a heterogeneous graph neural network representation learning method that requires no prior knowledge. It generates new graph structures from multiple candidate adjacency matrices from the original graph to achieve more efficient graph convolution operations.  
MBHANnon_type: is a simplified version of MBHAN that does not distinguish between the node types when calculating the motif subgraph attention. To be precise, after calculating the importance coefficients for the different motif subgraphs in Equation (7), a SoftMax function is applied to normalize all the node types.
## 5 Accuracy and Uncertainty of COSMIC-2 Refractivity, Temperature, and Water Vapor Retrievals  
In this study, we use RS41 measurements from June to October 2019 as references to quantify the accuracy and uncertainty of refractivity, temperature, and moisture profiles derived from COSMIC-2.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 2 Related Work
### Data Fusion Mechanism  
[PERSON] et al. [25] demonstrated, using data fusion technology, that the larger the network or the higher the number of source nodes, the more significant the effect of saving energy is. There are various classifications of data fusion from different perspectives. Based on the spanning tree, [PERSON] et al. [26] proposed the DCTC (dynamic convey tree-based collaboration) algorithm. The convergence nodes perform data fusion on the data of their subgenerating tree nodes. [PERSON] et al. [27] proposed the MFST (minimum fusion Steiner tree) algorithm for energy-efficient data collection in data fusion mode in WSNs. Based on the spatiotemporal correlation, the TiNA model (temporal coherency aware in network aggregation) was proposed [28]. Its basic idea is that the node sends the data only when the difference between the currently collected data and the last collected data is greater than the tolerance limit specified by a certain user. The spatial fusion model [29] measures the correlation by the distance between nodes. The spatiotemporal fusion model [30] is the development trend of data fusion research to eliminate spatiotemporal correlation. The LEACH protocol (low-energy adaptive clustering hierarchy) [31] is a typical WSN routing protocol which forms a hierarchical structure-based routing mechanism.  
As presented above, the energy harvesting technology and energy saving mechanisms (including low-duty cycle and data fusion) are the two main means to extend the service life of wireless sensor networks. However, in previous studies, they often focused on only one technology and did not comprehensively consider the problem of extended service life span of wireless sensor networks. Based on this case, we comprehensively used technologies such as energy harvesting, neighbor discovery and data fusion to analyze the energy supply and energy consumption of nodes, and hoped to propose a new approach to achieve a reliable and efficient way to secure energy in WSNs.
## 1 Introduction
Based on the aforementioned problems, a full loading-based MVDR beamforming method is proposed in this paper, which combines the advantages of multiple loading parameters and iteration. In this method, a full and an appropriate loading matrix was constructed to correct the steering vector of the target source backward and further suppress the interferences, respectively. In addition, spatial response power was used to derive a more accurate direction of arrival (DOA) of the target source. The remaining parts of this paper are organized as follows: Section 2 describes the conventional diagonal loading methods; Section 3 gives the proposed full loading method; Section 4 shows the simulations and the results analysis; and Section 5 draws the conclusions.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 5 BPMN24 IoT-A Holistic Framework for IoT-Driven BPs
### Executing IoT-Driven Business Processes  
When executing IoT-driven BPs, a distinction is made between the deployment and execution phases. To illustrate this, the communication between the components of the architecture is shown in Figure 12 as well. Note that the deployment phase is represented by Step (1) and the execution phase by Steps (2)-(4).  
**Deployment Phase**. The process engineer models an IoT-driven BP and configures its IoT parameters (e.g., endpoint and expressions). The configured BP model then contains both information specific to IoT and not specific to IoT that is required to execute the IoT-driven BP. The BP model is encoded as an XML file, which is deployed to the process engine. Note that this XML file is machine-readable and, thus, is executable (cf. Step (1)).  
**Execution Phase**. When creating a new process instance, the IoT process engine reads the XML file and then starts the execution of this process instance. As soon as the engine encounters an IoT artifact or event during the process execution, the _IoT Element Parser_ parses the IoT-specific information. In turn, the _IoT Execution Orchestrator_ ensures that the required action is taken. If sensor values are required, e.g., to check expressions in the IoT start event, the _IoT Subscription Manager_ enables sensor subscriptions at the _IoT Middleware_ in Step (2.1). The middleware responds regularly with the most recent sensor data as long as the subscription is necessary. These data are then sent to the _IoT-driven Business Expression Evaluator_, which uses it to evaluate expressions, e.g., for the IoT start event, IoT boundary event, or the IoT catch artifact. Another option to query the data is an active (pull) middleware request to instantly fetch sensor values (see Step (2.2)). Additionally, it is possible to send commands to actuators. The communication between the actuators and the IoT-driven BPMS is depicted in Step (2.3). In Step (3), all progress updates as well as failures during the execution of an IoT-driven process are sent to the _IoT Process Monitoring_ via execution updates. The latter then displays these execution updates.
# Improved Low-Depth SHA3 Quantum Circuit for Fault-Tolerant Quantum Computers
## 3 SHA3 Quantum Circuit
### Quantum Cost Analysis for SHA3
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cipher** & **\#Toffoli** & **\#CNOT** & **\#X** & **\#Full Depth** \\ \hline \multirow{6}{*}{LSH [34]} & 256/224 & 63,488 & 145,152 & 1536 & 210,051 \\  & 256/256 & 63,488 & 145,152 & 3492 & 210,049 \\ \cline{1-1}  & 512/224 & 139,104 & 312,832 & 7663 & 421,851 \\ \cline{1-1}  & 512/256 & 139,104 & 312,832 & 7696 & 421,851 \\ \cline{1-1}  & 512/384 & 139,104 & 312,832 & 7668 & 421,850 \\ \cline{1-1}  & 512/512 & 139,104 & 312,832 & 7680 & 421,852 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quantum resources required for LSH hash function proposed in [34]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cipher** & **\#Toffoli** & **\#CNOT** & **\#X** & **\#Full Depth** \\ \hline \multirow{6}{*}{LSH [14]} & 256/224 & 62,464 & 170,752 & 59,392 & 6879 \\  & 256/256 & 62,464 & 170,752 & 59,392 & 6879 \\ \cline{1-1}  & 512/224 & 138,000 & 375,760 & 134,688 & 14,517 \\ \cline{1-1}  & 512/256 & 138,000 & 375,760 & 134,688 & 14,517 \\ \cline{1-1}  & 512/384 & 138,000 & 375,760 & 134,688 & 14,517 \\ \cline{1-1}  & 512/512 & 138,000 & 375,760 & 134,688 & 14,517 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quantum resources required for LSH hash function proposed in [14]. (Results for depth reduction compared to Table 7 through quantum resource trade-off).
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 6 Discussion  
Video data frames generated from data sources verify the cause of an accident when it occurs. To handle forensic evidence, the integrity of the data stemming from the evidence should be preserved. AI solutions like machine learning are used routinely across many industries. To ensure the governance and trustworthiness of AI models, it is necessary to introduce AI TRiSM, which stands for AI Trust, Risk, and Security Management. For this reason, ensuring the rapid and media-independent integrity of training data is becoming increasingly important.  
The results of this experiment confirm that performing the XOR operation requires minimal time for encoding sensitive data during the image detection process. Furthermore, through the parallel processing of multiple threads, feature extraction, metadata construction, and data transmission could be performed without delay for 30 images per second. In order to mitigate security vulnerabilities, a filter employing XOR encoding will be designed, incorporating Hardware Security Modules (HSMs) for robust cryptographic key management. HSMs, as physical devices, provide high assurance of security for cryptographic operations, including the generation, storage, and management of cryptographic keys. Both the logger and verifier interact with their respective HSMs to generate a random number, which serves as an input for the filter.  
The YOLOv8 used in the experiment is an object detection model designed for general-purpose object recognition. This experiment has a limitation, in that it did not perform performance evaluation using a separate dataset due to the purpose of detecting sensitive objects without performing separate fine-tuning for Yolov8. During the experiment, as shown in Figure 17, there are limitations such as false positives and false negatives, which can lead to the exposure of sensitive objects. To address this issue, it is necessary to reconstruct the learning model by retraining and fine-tuning it on the dataset acquired from the given environment, thereby improving the accuracy of detected objects. The experiment  
Figure 16: Elapsed time of encoding images.  
observes instances of incorrect object detection, indicating the need for the development of specialized algorithms and further fine-tuning to enhance accuracy.
## 2 Deformation Rate Threshold-Based Solution for Slope Safety Factor
### Program Realization Process  
The FISH language of 3 DEC software is used to write the program to implement the method of this paper. In order to reach the critical state of the slope as soon as possible and reduce the number of calculations, the bifurcation strategy is used for iterative calculations, and the specific implementation process is as follows (Figure 1):  
1. First, set the parameters, including the deformation rate threshold \(v_{\text{th}}\), the lower limit \(K_{1}\), the upper limit \(K_{2}\) and the tolerance \(\delta\) of the folding coefficient \(K\), as well as the maximum number of iterative steps \(n\);
2. Then, make \(K=K_{1}\), obtain the new strength parameters, \(c/K\), \(\tan\varphi/K\), using these parameter for calculation. If the deformation rate of the key point is \(v>v_{\text{th}}\), the slope is unstable, and its safety factor is less than \(K_{1}\), exit the calculation; if \(v\leq v_{\text{th}}\), the slope is stable, perform the third step of the calculation;
3. Then make \(K=K_{2}\), obtain the new strength parameters, calculate again. If the key point deformation rate is \(v\leq v_{\text{th}}\), then the slope safety factor is greater than \(K_{2}\), exit the calculation; if \(v>v_{\text{th}}\), then the slope is unstable, perform the fourth step calculation;
4. Make \(K=(K_{1}+K_{2})/2\), calculate again. If the slope is stable, then take \(K_{1}=K\); if the slope is unstable, then take \(K_{2}=K\), so that the range of \(K\) is reduced by half. Iterate this process until the slope reaches critical instability. When \(K_{1}\) and \(K_{2}\) are very close, with a difference less than the tolerance \(\delta\), take their average as the result of the calculation for the safety factor.  
Figure 1: FISH calculation flow.