esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 3 Optimization of Real-Time Performance for RISC-V Processors
### Optimization of the Bran_Pre Module (Branch Prediction Module)
#### 3.2.2 Optimization of the Branch Predictor
Evaluate the \"PC_inst\" signal; if it is a branch instruction, extract the target address and prepare to output the branch address (prediction address). 3. Based on the \"PC_number\" and the instruction type obtained through \"PC_inst\", index the \"Index\" value in the BHR. 4. Use the \"Index\" value to access the corresponding 2-bit saturating counter in the PHT (Pattern History Table). 5. Output the branch result (prediction result) based on the state of the indexed 2-bit saturating counter. The simplified branch history predictor module operates in the first stage of the pipeline (fetch stage). It primarily uses combinational logic design, which does not consume additional clock cycles. The input and output signals of this module are shown in Table 3.  
The workflow for SLBHP (simplified local branch history predictor) to update the BHR status and interact with external signals is as follows:  
1. Based on the input \"PC_inst\" signal and \"PC_number\", along with the state of the PHT (Pattern History Table), the SLBHP first predicts the branch and outputs the prediction result to the \"PC_generate\" module, which is used to generate subsequent PC values (SLBHP Prediction Workflow). 2. The SLBHP updates the BHR records within it based on the actual executed instructions and the actual calculated branch flag signals provided by the subsequent \"EX\" (Execution) module. 3. The actual calculated branch flag signal and the branch outcome from the corresponding 2-bit saturating counter are used to determine if the prediction was correct. If the prediction was accurate, no pipeline flush occurs, and the saturating counter state shifts towards a stronger branch direction. If the prediction was incorrect, the saturating counter state shifts towards a weaker branch direction, and a pipeline flush signal is sent to the \"Pi_Ctrl\" (pipeline control) module to flush the pipeline and refetch instructions from the target address for execution. The principle diagram of the branch prediction module is shown in Figure 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Instruction Name** & \
\begin{tabular}{c} **Input/** \\ **Output** \\ \end{tabular} &
\begin{tabular}{c} **Description** \\ \end{tabular} \\ \hline PC\_inst & Input & The instruction from the PC\_IF (fetch) module. \\ PC\_number & Input & The PC value of the instruction from PC\_IF module. \\ Ex\_jump\_valid & Input & The true branch result from the EX \\  & & (execution) module. \\ EX\_inst & Input & The instruction from the EX (execution) module. \\ To\_CTRL\_not\_flash & Output & The pipeline flush signal output to the \\  & & Pi\_Ctrl module. \\ To\_PC\_If & Output & The predicted PC value output to the PC\_IF module. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Signals related to the simplified branch predictor.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Evacuation Coordination in Smart Building
#### 3.2.1 Distributed Architecture
This allows, on the one hand, to monitor the real-time situation of the building and, on the other hand, to trigger an evacuation process and to execute control actions in such a process. SB agent's _evacuation route recommender_ module computes optimised evacuation routes for each locally present user agent by distributed computation and communication with the rest of the SB agents in a multi-hop fashion. In this process, the algorithm uses: (i) data regarding the building topology, (ii) general knowledge about emergency and evacuation scenarios (e.g., facts that people with strong affiliate ties should always be evacuated together, the appropriateness of certain routes for people with limited mobility, and the influence of certain events like fire and smoke on the security level), and (iii) the current physical space situation awareness of the SB agent itself, as well as regarding the evacuees that are currently in its space and evacuation network's safety values. During evacuation, the global safety situation of the building is dynamically updated in real time and each SB agent recalculates the evacuation routes if necessary.
# Improving Structural Power Content Analysis Robustness for Satellite Navigation Applications +
## 3 Jammer Models
To demonstrate this, the false alarm rate that resulted from the addition of different jammer signals was calculated. Similar jammer parameters as those used to produce the results in Figure 1a were used, except that the AM and FM message frequencies were both set to 100 kHz and the chirp repeat rate to 10 kHz. For each jammer type, the experiment was performed at different jamming-to-signal ratios (JSR), and at each JSR point the experiment was repeated 10 times and the average false alarm rate was calculated. For computing the JSRs, the noise density was measured in the recorded clean signal, and a typical carrier-to-noise-density ratio of 45 dB-Hz for GPS L1 C/A was assumed [9]. The results shown in Figure 1b confirm the above analysis and indicate that jammer signals of the types CW, AM, FM, and chirp can cause false positive detections with the SPCA algorithm. Furthermore, the presence of AGWN does not seem to affect the false alarm rate significantly, as expected from the lack of spectral structure for this kind of jamming signal. Finally, we note that a noise jammer with cyclic properties similar to PRN codes, however, would add significant excess power to the spectral lines corresponding to the GNSS signals and thus would be more likely to cause false positive detections with the SPCA algorithm than a completely random noise jammer. Figure 1: The impact of different jamming signal types on the SPCA algorithm: (**a**) the power spectral density (PSD) of the DAM output, and (**b**) the achieved false alarm rate.
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.11. Denial of Service (DoS) Attack  
In the proposed scheme, even if the adversary may render BASN unavailable by replaying old messages \(B_{3},B_{4},B_{5},\) and \(T_{2}\) repeatedly, BASN firstly verifies whether the time gap meets \(|T_{c}-T_{2}|>\Delta T\) or not. If it does, BASN directly terminates this session. Furthermore, even if the adversary updates timestamp \(T_{2}\) to obtain \(|T_{c}-T_{2}|<\Delta T\), BASN also ignores this session due to the following verification failure of value \(B_{5}\), where \(B_{5}\) can only be derived by the original timestamp. Thus, the denial-of-service (DoS) attack cannot succeed. Similarly, the terminal of \(GWN\) does not suffer from this DoS attack.
# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 2 Materials and Methods
### Methods
#### 2.2.3 Evaluation Metrics
The Mean Absolute Percentage Error (MAPE) measures each prediction's average absolute percent error, providing a scale-independent assessment of error magnitude. It expresses accuracy as a percentage, which can be easier to interpret than other metrics. The formula of MAPE is given by Equation (3). \[\text{MAPE}(y,\hat{y})=\frac{1}{n}{\sum_{i=1}^{n}}\left|\frac{y_{i}-\hat{y}_{i }}{y_{i}}\right|\times 100\% \tag{3}\]  
The Root Mean Squared Error (RMSE) assesses the standard deviation of errors within predictions. It is computed by taking the square root of the mean of squared differences between actual and predicted values, and it provides error magnitude in the same units as the variable being predicted, which can be directly interpretable. This metric is sensitive to large errors and can be more informative than MAE when large residuals are undesirable or costly. The formula of RMSE is given by Equation (4). \[\text{RMSE}(y,\hat{y})=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{ 2}} \tag{4}\]  
The Coefficient of Determination (R\({}^{2}\) Score), while not an error metric per se, is a statistical indicator that measures the ratio of the variance in the target variable that is attributable to the predictor variables in a regression model. It indicates the goodness of fit and, therefore, measures how well unseen samples are likely to be predicted by the model. An R\({}^{2}\) value of 1 indicates that the regression predictions perfectly fit the data, whereas an R\({}^{2}\) value of 0 signifies that the model does not explain any variation in the response data around its mean. The formula of R\({}^{2}\) is given by Equation (5). \[\text{R}^{2}(y,\hat{y})=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{ i=1}^{n}(y_{i}-\overline{y})^{2}} \tag{5}\]where \(\overline{y}\) is the mean of the observed values \(y\). It is calculated as \(\overline{y}=\frac{1}{n}\sum_{t=1}^{n}\ y_{i}\). To calculate the percentage reduction in each evaluation metric (MAE, MAPE, and RMSE) from a baseline model (proprietary data) to a final model (open data), we computed the following difference ratio:  
\[\textit{Percentage Reduction}=\left(\frac{\textit{Metric value}_{ baseline}-\textit{Metric value}_{final}}{\textit{Metric value}_{baseline}}\right)\times 100\% \tag{6}\]  
The result shows how much improvement in prediction accuracy (in terms of MAE, MAPE, and RMSE) our final model has achieved compared to the baseline model. A higher percentage indicates a more significant improvement. To calculate the percentage gain (or improvement) in the R\({}^{2}\) score from the baseline model to the final model, we follow a similar approach as with the other evaluation metrics, with some adjustments for the nature of the R\({}^{2}\). The percentage gain can be calculated as follows:  
\[\textit{Percentage Gain}=\left(\frac{R^{2}_{final}-R^{2}_{baseline}}{R^{2}_{ baseline}}\right)\times 100\% \tag{7}\]
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 3 Proposed Methods
### Abnormal Early Warning
#### 3.3.2 Transfer Learning  
For any system, the abnormal state has a small probability compared with normal functionality, and the application scenarios of private 5G networks require high-reliability services, which determines that it is a highly reliable system itself, and its abnormal state is highly improbable. Therefore, the number of anomalous samples is too small in proportion to the samples of normal cases, leading to difficult training of the network and difficulty in fitting the anomalous part of the samples, which is usually referred to as few-shot learning.  
For the few-shot learning of anomalous samples, this study used transfer learning to solve this problem. The idea of transfer learning comes from human analogy learning, which aims to apply features learned from a high-quality dataset to other datasets under similar domains. In the environment of this study, although there is a gap between abnormal sample features and normal sample features, both occur in the same network environment and their feature extraction processes are similar; therefore, transfer learning can be used to apply the feature extraction framework learned from normal samples to abnormal samples to improve the accuracy of abnormal sample prediction.  
In the previous section, a three-layer dense network was proposed as the temporal feature extraction of wireless base station data and is the shared network part of normal and anomalous samples, which was trained using transfer learning which is not consistent with the training method of anomaly detection. First, the proposed model is trained with normal samples to obtain a prediction network with good performance. Then, the LSTM of LstmAE-TL is frozen and trained again with a small number of normal samples mixed with anomalous samples to achieve accurate prediction of the anomalous part.  
Figure 5: Implementation process of the LstmAE-TL model.
# Functional Magnetic Resonance Imaging in Research on Dog Cognition: A Systematic Review
## 4 Discussion
### Research Articles Without Stimuli (Resting State Network)  
The latest research, published in 2024, investigated resting-state functional connectivity to predict trainability in working dogs. By analyzing brain scans and behavioral measures at three stages--before training, immediately post training, and three months after training--the study aimed to identify connectivity changes associated with training outcomes. Findings revealed a stable core brain network, with stronger interactions in dogs that successfully became detector dogs. Notably, connectivity strength at the initial time point predicted future training success. Moreover, the research suggested parallels between canine and human neural mechanisms involved in odor learning and retrieval [15].  
Several works have also been published mapping whole-brain functional networks and connectivity patterns. Using resting-state fMRI under sevoflurane anesthesia, seven distinct resting state networks were identified, including the default mode network and primary and higher-order visual, auditory, somatosensory, and sensorimotor networks. The results demonstrated that resting-state fMRI is feasible in anesthetized dogs [14]. In conscious dogs, the key networks identified included those involving the prefrontal cortex, anterior cingulate, posterior cingulate, and hippocampal regions, as well as sensorimotor, auditory, frontal, cerebellar, and striatal networks. Researchers noted an anteroposterior connectivity pattern in the posterior cingulate area, suggesting similarities to primate brain organization [17] and highlighting the cingulate gyrus as a central feature [16].  
Another aspect of this group of research was the functional connectivity in anxious dogs, revealing increased global and local efficiency within the amygdala, which correlated with anxiety symptoms. Increased connectivity was observed within the anxiety circuit, including regions such as the amygdala-hippocampus, amygdala-mesencephalon, amygdala-thalamus, frontal lobe-hippocampus, frontal lobe-thalamus, and hippocampus-thalamus, suggesting that anxious dogs may share a similar topological organization of functional brain networks. This finding also drew parallels with anxiety-related brain network dysfunction observed in humans [18].
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
###### Abstract  
Mobile edge computing (MEC) has been recently proposed to bring computing capabilities closer to mobile endpoints, with the aim of providing low latency and real-time access to network information via applications and services. Several attempts have been made to integrate MEC in intelligent transportation systems (ITS), including new architectures, communication frameworks, deployment strategies and applications. In this paper, we explore existing architecture proposals for integrating MEC in vehicular environments, which would allow the evolution of the next generation ITS in smart cities. Moreover, we classify the desired applications into four major categories. We rely on a MEC architecture with three layers to propose a data dissemination protocol, which can be utilized by traffic safety and travel convenience applications in vehicular networks. Furthermore, we provide a simulation-based prototype to evaluate the performance of our protocol. Simulation results show that our proposed protocol can significantly improve the performance of data dissemination in terms of data delivery, communication overhead and delay. In addition, we highlight challenges and open issues to integrate MEC in vehicular networking environments for further research.  
mobile edge computing (MEC); intelligent transportation systems (ITS); smart cities; vehicular ad hoc networks (VANETs); vehicular edge computing (VEC) +
Footnote †: journal: Sensors
## 5 Conclusions  
The present paper introduced a method for synthesizing iron silicate slags suitable for the evaluation of their inherent reactivity in SCM applications. The relevance of this method was verified, concluding the following:  
* The reactivity of an industrial slag granulated on an industrial scale does not differ from the reactivity of the same slag granulated using the method developed in the present study;
* Therefore, results on reactivity achieved using this method are directly applicable to industrial slags. Furthermore, the robustness of this methodology was evaluated by producing a triplicate sample from chemical reagents, concluding the following:
* The targeted chemical composition can be synthesized with high repeatability, and the constituents are successfully incorporated in an entirely amorphous phase;
* The standard deviation of the developed heat in the R3 isothermal calorimeter experiments was 3.6% of the average value of the triplicate, suggesting that this method is robust.  
Therefore, the developed method can be used to study the influence of chemical composition on the inherent reactivity of iron silicate slags while generating results that translate directly to industrial slags. Furthermore, the results suggest that iron silicate glasses suitable for SCM applications can be generated outside the context of the copper-making process. The implications suggest that synthesizing iron silicate slags can be used to identify the optimal composition for reactivity as well as study mechanisms that are lost in the noise of uncontrolled parameters when comparing copper slags from different smelters.  
Conceptualization, A.A., L.B., A.L., A.R. and F.E.; methodology, A.A. and L.B.; formal analysis, A.A. and L.B.; investigation, A.A. and L.B.; writing--original draft preparation, A.A.; writing--review and editing, A.A., L.B., A.L., A.R. and F.E. All authors have read and agreed to the published version of the manuscript.  
The financial support by Boliden AB was limited to funding, i.e., the participating researchers determined the design of this study, the collection, analysis, and interpretation of data, as well as the writing of the manuscript.  
Not applicable.  
All data is available in this paper.  
The work was conducted within the Centre of Advanced Mining and Metallurgy (CAMM) at Lulea University of Technology. The assistance of [PERSON] and [PERSON] is greatly acknowledged.
## Abbreviations  
AHC  
Agglomerative hierarchical clustering  
ANN  
Artificial Neural Network  
BGWO  
Binary grey wolf optimization  
BPNN  
Backpropagation Neural Network  
AUC
Area Under the ROC Curve  
CLSTM  
Convolutional Long Short-Term Memory  
CSA  
Current Signature Analysis  
CWRU MFPT  
Case of Western Reserve University Machinery Failure Prevention  
Technology Dataset  
DWT  
Discrete wavelet transform  
EDMFE  
Ensemble Deep Models Features Extraction  
EFS  
Ensemble feature selection  
EHD  
Empirical mode decomposition  
EPO  
Emperor penguin optimizer  
EPRI  
Electric Power Research Institute  
FCM  
Fuzzy C-Means  
FFT
Fast Fourier transform  
FSDD  
Feature selection based on distance discriminant  
HANFIS  
Hierarchical Adaptive Neuro-Fuzzy Inference System  
HGBCSO  
Hybrid genetic binary chicken swarm optimization  
HVF  
Harmonic voltage factor  
IEC  
International Electrotechnical Commission
# Laplacian Controllability and Observability of Multi-Agent Systems: Recent Advances in Tree Graphs
## 2 Problem Setting and Preliminary Results
### Recent Engineering Applications  
The above framework is adopted in several modern technological applications such as, for example, those reported below.  
* Multi-robot systems and robotic networks [32]. The multi-agent setting (3) allows robot units of a team to coordinate and perform global actions without relying on a central supervisory device. Common global tasks are robot rendezvous (agents meeting at a common location), deployment (agents spreading out to cover an area), and formation control (agents keeping a predefined formation).
* Security and intrusion detection in cyber-physical systems, such as fault-tolerant distributed algorithm [34] and cyber-physical security [35]. In [34], controllability of (3) is the assumption to achieve almost sure resilient consensus. In [35], it is shown that observability, together with the absence of systems zeros, is required to achieve security.
* Wireless Sensor Networks. The multi-agent setting is employed to address fundamental issues in wireless sensor networks, such as synchronization [36], or averaging algorithms [37].
* Automotive Networking and UAV formation control. The above multi-agent setting is widely employed to achieve formation control through distributed algorithms in vehicle networks and UAVs [38; 39; 40].
* Electrical Power Systems. With the evolution of the electric grid toward a smart grid, the multi-agent setting is widely adopted to tackle new emerging challenges such as load balancing, fault detection and isolation, demand response, and others [41; 42]. Cyber-physical attacks in power networks using the above models are studied in [43].
## Author Contributions  
[PERSON] initiated the idea of this research. [PERSON] formulated the optimization problem, proposed the procedure of the algorithm, and wrote the initial manuscript, which is critically reviewed by [PERSON] and [PERSON]. [PERSON] also helped with the PSO design. [PERSON] helped with the Matlab coding and did the simulations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{2}{c}{**Distribution**} & **Uniform** & **Exponential** & **Pareto** & **Rayleigh** & **Weibull** \\ \hline \multirow{4}{*}{Mean} & Random & 60.8031 & 70.3932 & 46.3667 & 49.4641 & 54.7102 \\  & Optimal & 41.2141 & 43.0155 & 40.6554 & 40.0270 & 40.2594 \\  & Proposed10 & 41.2141 & 43.0481 & 40.6614 & 40.0270 & 40.2604 \\  & Proposed50 & 41.2141 & 43.0222 & 40.6568 & 40.0270 & 40.2595 \\ \hline \multirow{4}{*}{Standard} & Random & 31.9083 & 35.4822 & 7.3149 & 22.8160 & 27.3215 \\  & Proposed10 & \(7.1776\times 10^{-15}\) & 0.0449 & 0.0061 & \(1.9842\times 10^{-5}\) & 0.0021 \\ \cline{1-1}  & Proposed50 & \(7.1776\times 10^{-15}\) & 0.0125 & 0.0022 & \(1.8236\times 10^{-6}\) & \(3.6255\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 4: Performance comparison (Case B). Note that “Proposed10” and “Proposed50” stands for “Proposed algorithm with 10 iterations” and “Proposed algorithm with 50 iterations”, respectively.
## 5 Unbalanced PSI-CA Protocol Based on Cuckoo Filter
### Summary of This Chapter  
This chapter presents the development and analysis of an unbalanced PSI-CA protocol that utilizes Cuckoo filter to efficiently handle datasets with significant size disparities. The protocol is designed to overcome the limitations observed in traditional PSI-CA protocols such as Cristofaro's, which struggles with efficiency under unbalanced conditions.  
Experimental analyses demonstrate the protocol's robustness, showing minimal runtime increases even as dataset sizes grow significantly, which marks a substantial improvement over traditional methods. The protocol proves particularly effective in real-world scenarios where dataset imbalances are common, providing a reliable solution that ensures privacy and efficiency.  
In essence, this chapter confirms the efficacy of integrating Cuckoo filter into PSI-CA protocols, offering enhanced performance and security, making it a valuable addition to the field of data privacy and secure computation.  
However, in this protocol, the receiver still has to bear the burden of complex cryptographic computations and storing the Cuckoo filter. The next chapter will focus on optimizing this aspect.