esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 5 Conclusions and Future Work
### Experimental Conclusions and Summary  
We conducted research and innovation on the concept of \"real-time performance\" for processor hardware. We propose a \"Hardware Real-Time Performance Parameter (H\({}_{\text{rtp}}\))\". This parameter not only reflects whether the hardware can meet the real-time requirements of the system but also evaluates the quality of real-time performance when these requirements are met. We validated the reasonableness of this parameter using data from the processor before and after real-time optimization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **CoreMark/MHz** \\ \hline Hummingbird E203 [22] & 2.14 \\ Cortex M0 [24] & 2.33 \\ Cortex M0+ [24] & 2.46 \\ Cortex M3 [24] & 3.53 \\ Rev-riscv & 2.57 \\ Rev-riscv-II & 2.70 \\ \hline \hline \end{tabular}
\end{table}
Table 15: The CoreMark test results for similar-level processors.  
We optimized the branch prediction module of the processor and innovatively proposed a \"Simplified Local Branch History Predictor (SLBHP)\". The comparative experiment shows that, with an additional consumption of approximately 2700 logic units, the predictor's accuracy for conditional branch prediction is 12% higher than that of a static branch predictor. The division calculation of the processor has been optimized. The experiments show that, with an additional consumption of approximately 1200 logic units, the computation speed of the optimized division module can be improved by more than 75%. Through these two optimizations, the processor's execution time and execution time variability can be effectively reduced, thereby enhancing the \"hardware real-time performance\" of the processor.  
The final experiments showed that when running the CoreMark benchmark program, the processor's average execution time decreased by 3%, and the execution time variability (variance) decreased by 21%, indicating an improvement in the \"quality of hardware real-time performance\". The \"Hardware Real-Time Performance Parameter (Http)\" changed from 4.4001 to 4.3651, consistent with the trend of improved hardware real-time performance. When executing the self-test program instructions for division, the \"Hardware Real-Time Parameter (Http)\" decreased from \(-8.2173\) to \(-8.9997\).  
The results of running both test programs are consistent with the trend of improved hardware real-time performance. The parameter has a certain reference significance for evaluating \"hardware real-time performance\".  
Additionally, we compared the performance of the Rev-riscv-II with that of currently mainstream embedded processors, demonstrating that the Rev-riscv-II processor has significant usability.
# An Effective Hybrid Routing Algorithm in WSN:
## 4 ACOHCM Routing Algorithm  
The goal of the ACOHCM routing algorithm is to maximize the lifetime of the WSNs. Moreover, an effective routing algorithm must converge quickly and have high rate of successful searching for the best solution for the optimal path, as well as low overhead in route discovery, route update and maintenance. Therefore, the ACOHCM algorithm uses the hop count-classification network topology, which is a kind of planar network topology. It is based on the node location information, so it is easy to maintain. The routing protocol on this topology has the advantages of simple structure and low maintenance overhead. In order to search for an optimal path with minimum total energy consumption and balanced energy consumption of each node, a dynamic energy threshold strategy and a mutation strategy have been proposed. Especially, we revise the heuristic factor and replace a new pheromone update strategy in the probabilistic node state transition formula for path selection. The new algorithm takes into comprehensive account energy efficiency, load balancing, convergence speed, and routing reliability as well as low maintenance. Energy efficiency here refers to the ratio of total packet delivered at the sink node to the total energy consumed by the network's sensor nodes on the data transmission path during the data transmitting process (Kbit/J). So it is expected to minimize the total energy consumption of the sensor nodes on the routing path. In addition, It is expected to reduce the redundant transmissions involved in route discovery and data delivery to reduce the energy consumption of a node.
## 4 A Privacy-Aware and Secure Decentralized Framework
### Decentralized Data Mining  
In this section, we show how data mining can be distributed over a set of nodes. While the majority of existing DDM solutions presented in Section 3.2 rely on computing the machine learning models (ML models) locally at WSN nodes and then combining their knowledge in a global ML model, our proposed approach starts with computing an initial ML model at a (randomly) chosen WSN node and then iteratively \"augment\" it by updating it with the data at the next WSN node. By repeating this procedure following a (predefined) path through the nodes of the WSN, the last visited node will contain an approximation of the global ML model, as would be computed on all the data from all the WSN nodes on the traversed path.  
In this way, instead of resorting to ingenious ways of combining multiple local ML models into one global ML model, we can use techniques from data stream mining (DSM) to iteratively update our initial local ML model at each subsequent WSN node. Moreover, our approach also minimizes the amount of data that has to be sent through the WSN--the descriptions of the local ML models are only shared/sent between the nodes on a  
Figure 1: A layered system architecture.  
predefined path, as opposed to multiple paths that are needed for sending the local ML models to a sink node (for the computation of the global ML model) in the traditional scenario of the \"combined\" local-to-global ML model computation.  
A comprehensive review of the DSM methods is provided by Albert Bifet et al. in their book [65]. We decided to use decision trees as our preferred ML algorithm due to their space/time efficiency and explainability. The Hoeffding tree [66] is a version of the decision tree that can be learned from streams of data. Thus, our DDM method of choice is the Hoeffding tree, which we first learn from the batch data provided by some (randomly) chosen sensor in our WSN and then send through the WSN, following a predefined path of nodes, to be updated by streams of data at each subsequent WSN node. The described procedure is depicted in Figure 2.  
The initial Hoeffding tree (\(HT_{1}\)) is first learned in batch from the data provided by the first sensor--\(Sensor_{1}\) (\(Data_{1}\)). This tree is then propagated through the WSN, and in each subsequent WSN node (\(Sensor_{k}\)), an updated Hoeffding tree is generated (\(HT_{k}\)) from the previous tree (\(HT_{k-1}\)) and the data from the current sensor--\(Sensor_{k}\) (\(Data_{k}\)). The final decision tree is then represented by the last Hoeffding tree in this chain--\(HT_{n}\) (the output of \(Sensor_{n}\)). Notice that only partial Hoeffding trees (\(HT_{k}\)'s) are propagated through the WSN (not the actual sensor data), which is most suitable for privacy preserving and also diminishes the load on the WSN.
## 3 Experiments and Results
### Synthetic Data
#### 3.1.3 Computational Time  
In this section, we perform 50,000 independent trails to analyze the computational time of our proposed method, the GP4 Pf and the [PERSON]'s method executed on a 3.3 GHz 4-core laptop. At each trail, we randomly select three 2D-3D point correspondences from synthetic data without any noise added to the 2D image points for our proposed method and [PERSON]'s method, and four 2D-3D point correspondences for the GP4 Pf. We report the average computational time in milliseconds (ms) in Table 1.  
Figure 7: Experiment results of noise sensitivity.  
From Table 1, we observe that our proposed method is faster than the GP4 Pf and is slightly slower than [PERSON]'s method, but [PERSON]'s method has a multi-solution phenomenon that is up to four solutions and needs a fully calibrated camera.
## 4 Results  
### Coal Permeability and Cleat Compressibility  
The experimental resulting permeability values for a different test series (\(\Delta\)P varies from 0.01 to 0.1 MPa for each \(p\)-value) are shown in Figure 3. Additionally, the average permeability values are considered. As the confining hydrostatic pressure increased by 8 times, the coal permeability decreased by 6.7 times (from 60 mD at P = 1 MPa to 9 mD at P = 8 MPa) (Figure 5). A literary analytical review indicated that these values are typical for intensely fractured coals [24]. The permeability measurement error varied from 4.8 to 7.9%.  
The coal cleat compressibility C\({}_{\text{f}}\), obtained by a method described in [26], was 0.0845+/\(-4\times 10^{-3}\) MPa\({}^{-1}\). When the cleat compressibility was not assumed to be a constant and was assumed to depend on the stress conditions, the obtained initial compressibility ratio C\({}_{0}\) and a rate of compressibility ratio variation \(\alpha\) were 0.0715+/\(-7.8\times 10^{-3}\) MPa\({}^{-1}\) and 1.83 * 10\({}^{-4}\), respectively [26; 27]. The obtained cleat compressibility ratios C\({}_{\text{f}}\) and C\({}_{0}\) and rate \(\alpha\) were utilized for further coal permeability estimation.  
The coal permeability was determined according to theoretical models. The curves obtained by the [PERSON] et al. (1992) and [PERSON] (2006) models are shown in Figure 6. The standard deviations between the calculated and experimental data for both cases were calculated and compared. The standard deviation for the [PERSON] et al. (1992) model was lower than that of the [PERSON] (2006) model, and thus the [PERSON] et al. (1992) model more accurately described the coal permeability variation under constant confining pressure conditions [33; 34]. Furthermore, the most suitable [PERSON] et al. (1992) model was utilized [33]. The unloaded coking coal calculated by the [PERSON] et al. (1992) model was 72.62 mD.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 3 Experimentation Environments
### Free Range Routing Protocol Suite  
The Free Range Routing (FRR) protocol suite is a fork of the Quagga project that provides IP routing services. Thus, its role in a networking stack is to exchange routing information with other routers, make routing and policy decisions, and inform other layers of these decisions. In the most common scenario, FRR installs routing rules into the OS kernel, allowing the kernel networking stack to make the corresponding forwarding rules. FRR supports the full range of L3 configuration: dynamic routing, static routes, addresses, router advertisements, among other capabilities.  
In terms of the routing protocols considered in this paper, FRR supports BGP (including specific configuration for the data center), OSPF, Openfabric, and ISIS.
## 2 Methods
### Iterative Peak-Separation Method  
As previously mentioned, a valid measurement depends on the accurate extraction of the peak locations. The base of the peak location is to find the maximum in the existing method, so it is called the maximum-point-based SIM (MP-SIM). When the MDR has only one peak shape due to the film being thinner, as shown by the blue curve in Figure 3a, the MP-SIM only detects one peak and fails to calculate the film thickness. Therefore, the detection threshold is limited. We propose the iterative curve fitting technique to break the limit.  
To shorten the time required to fit the full-field MDR curve, the Nelder-Mead simplex algorithm is utilized. According to Equation (6), the model function \(M^{*}(x,y,z)\) is defined as:  
\[M^{*}(x,y,z)=R_{c1}(x,y)exp\left[-\left(\frac{z-p_{1}(x,y)}{kF_{1}}\right)^{2} \right]+R_{c2}(x,y)exp\left[-\left(\frac{z-p_{2}(x,y)}{kF_{2}}\right)^{2}\right], \tag{8}\]  
where the linear variables are the component coefficients--i.e., \(R_{c1}(x,y)\) and \(R_{c2}(x,y)\)--and the nonlinear variables are the full width at half maximum (i.e., \(F_{1}\) and \(F_{2}\)) and the peak positions (i.e., \(p_{1}(x,y)\) and \(p_{2}(x,y)\)). Finding the location of overlapping peaks--i.e., \(\mathbf{p}_{opt}=\left(p_{1 opt},\ p_{2 opt}\right)\)--can be transformed into an optimization problem, such as:  
\[\mathbf{p}_{opt}=arg\underset{\mathbf{p}}{\min}RMS[M^{*}(x,y,z)-M(x,y,z)] \tag{9}\]  
Suppose that the thickness measurement error is the criterion for the threshold. The comparison proves that the smallest detectable thickness of the IPS-SIM is lower than the MP-SIM, as illustrated in Figure 3. When the film thickness is 822 nm, the measurement error of the MP-SIM equals 135.5 nm. On the contrary, the IPS-SIM accurately identifies the two peaks, as shown in Figure 3b. Furthermore, the presented algorithm still maintains a low measurement error when the two components are completely merged into one, as explained in Figure 3c. The thickness measurement error as a function of film thickness in  
Figure 3: Comparison of the detection threshold of the iterative peak separation structured illumination microscopy (IPS-SIM) and the maximum-point-based SIM (MP-SIM): (**a**) the simulated MDR in different thickness D when \(FWHM_{1}=696\) nm and \(FWHM_{2}=800\) nm; (**b**) the fitting result by the IPS-SIM when the thickness is 822 nm; (**c**) The fitting curve by the IPS-SIM when the thickness is 500 nm; (**d**) the thickness measurement error as a function of film thickness in the IPS-SIM and MP-SIM.  
the two methods is illustrated in Figure 3d. The results show that the IPS-SIM has a lower detection threshold than the MP-SIM.
## 4 Performance Evaluation
### Enhancement Layer Service  
To evaluate the mean amount of resources required to deliver the additional layers of video via unicast service, we first estimate the resource amount needed to deliver each of the \(L\) layers to the specified share of users. We model the delivering process of a layer as \(M/M/C\) queuing system [31], where \(\lambda\) is the intensity of requests for the layer delivery, \(\mu^{-1}\) is the mean duration of the requested video fragment that is supposed to follow exponential distribution, and \(C\) is the number of active unicast sessions that should be maintained to fulfill the specified probability of successful delivery. The drop probability, \(p_{C}=E_{C}(\rho)\), for the queue can be found by utilizing the celebrated Erlang-B expression  
\[E_{C}(\rho)=\frac{\rho^{C}/C!}{\sum_{m=0}^{C}\left(\rho^{m}/m!\right)},\ 0\leq\rho<\infty.\]To avoid computational complexity we use the recursive algorithm for calculating the \(M/M/C\) steady-state distribution [32], i.e.,  
\[l_{0}(\rho)=1,I_{0}(\rho)=1+\frac{\rho}{v}I_{v-1}(\rho),v=1,2\ldots,\]  
where \(I_{0}(\rho)=[E_{v}(\rho)]^{-1}\).  
Finally, we evaluate the mean demand for delivery of \(l\)-s enhancement layer as  
\[E_{l}[U]=C_{l}\big{[}p_{B,i}b_{U_{l},B}+(1-p_{B,i})b_{U_{l},L}\big{]},\]  
where \(b_{U_{l},B}\) and \(b_{U_{l},L}\) are the demands for a single UE to download the \(l\)-s layer in LoS blocked and non-blocked conditions.
## 5 Proposed Multi-EC Enabled Air-Ground VN Scenarios
### Cost Efficient Federated Learning Platform for Vehicular Applications
#### 5.1.3 Proposed Solutions  
We have considered two different approaches for implementing an efficient FL platform over VN assisted by multiple EC layers.  
Hierarchical FL Platform over Multi-EC VN Architectures  
In this approach, we develop a hierarchical learning-based FL platform over the Joint T-NTN (Figure 7). The HAP with larger coverage and resources acts as FL-server, while VUs (FL-devices) with local datasets perform model training. For the conventional FL process, VUs transmit the local model parameters to the HAP. This can result in higher transmission times, high energy costs, and often VUs dropouts from the training process mainly because of unreliable long-distance communication channels between VUs and HAP. In the case of the proposed hierarchical learning approach, instead of transmitting their model parameters to the HAP server over long distance/unreliable communication links, VUs resort to sending them to the nearby EC layers that includes LAP and RSU based EC servers. After receiving data from VUs, nearby EC servers perform the averaging operation to create a sub-global model parameter and transmit them to the HAP server. In this way, the amount of connections over long-distance links is limited and, as a result, saves the training process time and energy for each FL communication round. Furthermore, BC-EC nodes are used as a backup for avoiding service failures when HAP fails to connect with other platforms, mainly because of security issues or environmental conditions. In this case, nearby ECs transmit their model parameters to the BS instead of HAP.  
Computation Offloading Based FL Platform over Multi-EC VN Architectures  
In the case of a conventional FL process, many VUs drop out from the training process mainly because of limited computation/communication resources onboard. If a VU has limited computation resources, performing a local training process can result in a larger computation time and may not be able to send their updates to the FL server in time. For avoiding such issues during FL, a computation offloading-based FL platform can be used, exploiting the computation offloading services from the nearby EC servers for allowing VUs with scarce resources to participate in the FL process (Figure 8). In this approach, VUs mobility, limited coverage range of nearby EC servers, the security threats of EC platforms are required to be taken into account.  
In Table 4, the most important characteristics of the proposed solutions for the cost-efficient FL platform implementation over Multi-EC enabled VN architecture are summarized.  
Figure 7: The hierarchical FL platform over a Multi-EC VN architecture.
###### Abstract  
The importance of accurate livestock identification for the success of modern livestock industries cannot be overstated as it is essential for a variety of purposes, including the traceability of animals for food safety, disease control, the prevention of false livestock insurance claims, and breeding programs. Biometric identification technologies, such as thumbprint recognition, facial feature recognition, and retina pattern recognition, have been traditionally used for human identification but are now being explored for animal identification as well. Muzzle patterns, which are unique to each animal, have shown promising results as a primary biometric feature for identification in recent studies. Muzzle pattern image scanning is a widely used method in biometric identification, but there is a need to improve the efficiency of real-time image capture and identification. This study presents a novel identification approach using a state-of-the-art object detector, Yolo (v7), to automate the identification process. The proposed system consists of three stages: detection of the animal's face and muzzle, extraction of muzzle pattern features using the SIFT algorithm and identification of the animal using the FLANN algorithm if the extracted features match those previously registered in the system. The Yolo (v7) object detector has mean average precision of 99.5% and 99.7% for face and muzzle point detection, respectively. The proposed system demonstrates the capability to accurately recognize animals using the FLANN algorithm and has the potential to be used for a range of applications, including animal security and health concerns, as well as livestock insurance. In conclusion, this study presents a promising approach for the real-time identification of livestock animals using muzzle patterns via a combination of automated detection and feature extraction algorithms.  
livestock identification; livestock muzzle pattern identification; horse identification; automated horse identification; yolo; equine biometrics; livestock biometrics; computer vision 2023
# Security Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes
## 3 Methodology and Solutions
### Blockchain-Based Key Exchange Solution  
A blockchain, originally known as block chain, is a growing list of records called blocks. Blockchain consists of a sequence of blocks, and the latter block is linked to the previous one by including the cryptographic hash of the previous block. In addition, the block also contains Index, Time-stamp, Previous Hash, Hash, and Data [27]. All nodes sync using a gossip protocol, which is illustrated in Figure 5. When you change any of these data, you will change the entire block, and the subsequent blocks will notice that something has changed.  
The four main types of blockchain networks are public, private (or managed), consortium, and hybrid blockchains. The types are selected based on the access, confidentiality, efficiency, and trust requirements. For ADS-B application, it is desirable to have private and permissioned blockchains to satisfy the following requirements:  
* Access--trusted members need permission to join;
* Confidentiality--read/write access limitation among members;
* Efficiency--higher efficiency in transactions; and
* Trust--the degree of trust.  
Hyperledger Fabric (Fabric) is considered the best option, as the framework offers excellent security and performance as well as adaptable tools for access management, privacy, and integrating business logic. A wide variety of use cases specifically make use of Fabric's characteristics [28; 29].  
Our goal is to explore how blockchains can protect the sharing of sensitive data. To be more specific, we want to highlight a high-level blockchain-based concept that offers a decentralized nature for flight information, particularly for enabling blockchain technology for key exchange and the positioning of information storage. Figure 6 shows the blockchain network topology for key and data exchange. Node 1 is considered as the administrator and thus can add each node to the network and assign private and public keys to each node for data exchanges. Other nodes use the public key of node 1 to encrypt data based on the RSA scheme and publish it via the MQTT protocol to the admin, Node 1, which receives the information and decrypts the data via an RSA private key, and then the data is stored in the blockchain nodes. In addition, an FFX encryption/decryption key is encrypted by admin node according to each node's corresponding public key, and it is published to the node. Thus, every node receives the key via a secured channel.  
Figure 5: Example of Blockchain Blocks.