esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
HAR has gained popularity in different domains including sports, healthcare, robotics, human-computer interaction, security, surveillance, and entertainment. This review paper provides a comprehensive overview of the latest advancements in HAR with a focus on multimodality, DRL, and LLMs. It explores the diverse range of human activities and sensors used in Human Activity Recognition. Then, it surveys recent research on vision and non-vision based HAR. Different multimodal techniques have also been explored in this survey. Ablation studies performed in some of the papers reviewed showed that multimodal approach enhances the precision of HAR systems. Multimodal HAR systems can distinguish between similar activities with subtle differences and adapt to environmental conditions by combining multiple sensor data and even contextual information. They also need multimodal datasets, and these datasets with physiological data are also reviewed in this survey. The integration of DRL in HAR tasks represents a shift from traditional supervised learning methods. It combines the strength of deep learning and reinforcement learning and enables the HAR system to learn optimal policies through trial and error. Thistechnique has been applied to identify key features in complex datasets, determine optimal weights for the fusion of multimodal systems, and also improve the system's adaptability to new activities without extensively labelled data. LLMS have been introduced to the task of HAR for their ability to understand and predict activities based on textual and sensor data and also personalise their output based on individual preferences. They can be used in the annotation of large datasets and refining models based on contextual information and user preferences.  
Despite the advances in the field of HAR, there still remain some challenges such as data privacy, data collection and annotation, accuracy and reliability, ethical considerations, and standardisation. In order to overcome some of these challenges, future research should focus on explainable AI, collecting and annotating massive and diverse datasets, multimodality, standardisation, and personalised HAR systems. This review highlights the potential of multimodal techniques, DRL, and LLMs in advancing HAR. Future research should continue to innovate and refine these techniques, paving the way for more user-centred, accurate and reliable HAR systems that will thrive in real-world scenarios.  
Conceptualisation, U.O., R.O. and A.S.A.; methodology, U.O. and R.O.; resources, R.O.; writing--original draft preparation, U.O.; writing--review and editing, R.O., A.S.A. and U.O.; supervision, R.O. and A.S.A.; project administration, R.O.; funding acquisition, R.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Correlation between _Ea_ and _ln_ As  
The linear correlation between pre-exponential factor and the activation energy is clear (R = 0.986 and residual sum of squares = 0.39). This correlation has been already noticed in a previous work dealing with polysaccharide-reinforced bitumens [57] and even in simple liquids. This is an important aspect also for applicative purposes: in fact, although there is no theory explaining such correlation [59] and if this correlation holds, then the temperature dependence of the viscosity of our materials can be expressed through one parameter only--the only one known (Ea or As)--and since the other can be derived from it, it is a fact which can be surely helpful for practical uses. Our data are compared, in the inset of Figure 3, with the data of other literature works dealing with polysaccharide-additivated bitumens at working temperature (50 \({}^{\circ}\)C) [57].  
The present work interestingly shows a nice consistency between the two sets of data, extending the number and types of systems for which this linear correlation holds, and suggesting a quite universal behavior. The data of the present paper thus reinforce our general view of fluids, where the key ingredients dictating rheological behavior lie at the molecular basis in the same way for different systems despite obvious specific differences at the macro-scale. For this reason, the data shown in this manuscript can be used for deeper theoretical investigation opening the way to the construction of a proper model describing why different fluids share similar rheological behavior.
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
## 2 Analysis of Interference Mechanisms
### Analysis of RA Receiver Interference Mechanisms
A reduction in the IF signal power of the receiver will impact the counting process of the counter in the RA receiver, consequently influencing the height output of the RA.
## 3 Results  
The standard measure of quality for the generated neural model is the RMS error, which is defined as follows:  
\[RMS=\sqrt{\frac{\sum_{i=1}^{n}\left(Tm_{i}-Tp_{i}\right)^{2}}{n}} \tag{5}\]  
where:  
* \(n\)--number of cases;
* \(Tm_{i}\)--measured values of temperature;
* \(Tp_{i}\)--values of temperature predicted with the use of the neural model. The RMS error values for the MLP 4-5-1 neural model were:
* 0.4324 for the training file;
* 0.4462 for the validation file;
* 0.4554 for the test file. The RMS error values for the MLP 4-7-1 neural model were:
* 0.2604 for the training file;
* 0.2780 for the validation file;
* 0.2809 for the test file. The error values for the MLP 4-10-1 neural model were:
* 0.1437 for the training file;
* 0.1626 for the validation file;
* 0.1578 for the test file. The created MLP with 4-5-1, 4-7-1 and 4-10-1, respectively, were characterized by generally low error values with a falling tendency together with the increase in the number of neurons in the hidden layer. Complete and full statistics of regression for all created models are presented in Table 3. Having noticed the fact that the network with 10 neurons within the hidden layer showed a tendency to over-learn, a decision was made to use a model with an MLP 4-7-1 network in the process of modeling temperature layout in the rock regenerator. Notably, this model showed good generalization abilities/capabilities. Figure 3: The structure of the model developed—MLP 4-7-1. Figure 4 presents the information on the linear regression between the temperature values obtained using the MLP 4-7-1 model and the values of that parameter for the testing file. Information on the absolute error of temperature determination was also included in the form of a histogram (Figure 5). The regression coefficient for the model was 0.998. Thus, one may assert that the results of the temperature prediction as a function of position, and charging time obtained using the model that was generated, closely reflected the results of the natural experiment included in the testing file. The high quality of the generated MLP 4-7-1 network as a prediction instrument was identified by the regression statistics presented in Table 3. The statistics included the following indicators:  
* Data Mean--the mean value of the output variable calculated based on the required values of this variable, collected in the training, validation or testing files, as appropriate. The regression statistics were determined independently for the training, validation and testing files. * Data S.D.--the standard deviation calculated for the required (as above) values of the output variable. * Error Mean--mean error (the modulus of difference between the required value and the obtained output value) for the output variable. * Error S.D.--standard deviation of errors for an output variable. * Abs Mean--mean absolute error (difference between the required value and the obtained output value) for the output variable. * S.D. Ratio--the ratio of standard deviations for the errors and for the data. This was the main quality coefficient of the regression model generated by the network.
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
## 5 Conclusions  
Since there was no efficient method for comparing the performance of damper-equipped buildings, this paper employed a novel method and performed original work on the reliability analysis of response-controlled buildings using fragility curves. The building models were 10-story steel buildings consisting of two different sections (trimmed and conventional), and hysteresis and oil dampers were installed in the trimmed section of the building. The building models were created and analyzed with the use of a single type of damper and with a combination of different types of dampers. Moreover, the combination of hysteresis and oil dampers proposed in this paper significantly improved the seismic performce of the buildings. The following points are the main conclusions of this study:  
* The IDA is an effective method that can accurately predict the fragility assessment and reliability of response-controlled buildings.
* Sa(T\({}_{1}\)) is an efficient IM, since it has a good correlation with the seismic damage measures of first-mode-dominated structures. On the other hand, the story drift ratio \(\theta\)max appeared to be an effective DM index. The lognormal cumulative distribution function provides a good understanding of damage probability with respect to the intensity of an earthquake at the certain damage level.
* Hysteresis and oil dampers effectively improved the seismic performance of the TS building.
* On the basis of the fragility curves, hysteresis dampers are more effective for larger ground motion accelerations than oil dampers.
* Oil dampers dissipated energy before hysteresis dampers for small deformations.
* Combined hysteresis and oil dampers in the TS buildings exhibited better performance than the individual hysteresis and oil damper buildings.
* The TS building equipped with a Hysteresis-Oil-Hysteresis damper configuration has almost the same performance as the CS building.  
Data curation, E.A.M.A.; Investigation, A.K.K. and T.S.; Methodology, A.K.K., E.A.M.A. and T.S.; Writing--original draft, A.K.K.; Writing--review & editing, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interests.
## 5 Simulation  
Simulations were utilized to examine the performance of the proposed solution. The proposed SDP initial solution and the optimal GN solution are denoted by \"SDP\" and \"GN\" in the following figures. The TDOA and FDOA measurement noises are assumed to be independent Gaussian random variables, so their covariance matrices are set to \(\mathbf{Q}=\sigma^{2}\big{(}\mathbf{I}_{M-1}+\mathbf{I}_{M-1}\mathbf{I}_{M-1} ^{T}\big{)}/2\) and \(\mathbf{Q}=0.1\mathbf{Q}\), where \(\sigma^{2}\) is the measurement noise level in m\({}^{2}\). The observation interval \(\tau\) is fixed at 1 s. The number of ensemble runs is \(T=200\). The proposed SDP algorithm is implemented using the CVX toolbox with the solver SeDuMi, where the precision is set to the best. The performance is evaluated by the root mean-squared error (RMSE), defined as  
\[\begin{cases}RMSE(\mathbf{\hat{u}})&=\sqrt{\frac{1}{T}\sum_{t=1}^{T}\|\mathbf{ w}^{\alpha}-\mathbf{\hat{u}}\|^{2}},\\ RMSE(\mathbf{\hat{u}})&=\sqrt{\frac{1}{T}\sum_{t=1}^{T}\|\mathbf{\hat{u}}^{ \alpha}-\mathbf{\hat{u}}\|^{2}},\end{cases} \tag{53}\]  
where \(\mathbf{\hat{u}}\) and \(\mathbf{\hat{u}}\) are the estimates at the \(t\)-th test. The values of \(\eta_{1}\) and \(\eta_{2}\) are selected from \(\{10^{-2},10^{-3},\cdots,10^{-10}\}\) and \(\{1,10^{-1},\ldots,10^{-8}\}\) through the strategy elaborated at the end of Section 3.2.  
The proposed method is applicable for both 2D and 3D cases. If the sensors and source are unmanned ground vehicles (UGVs) that move on a plane, the localization scenario is 2D, while for unmanned aerial vehicles (UAVs) or underwater moving sensors and source deployed with different depths, the localization scenario is 3D. Thus, separate simulations were conducted to evaluate the algorithm's performance in both 2D and 3D scenarios. Of the large number of papers investigated, only a few studied TMA using TDOA or FDOA [45; 46]. However, they are neither applicable to hybrid measurements nor the same scenario or problem. Thus, the proposed SDP and GN methods are compared with the CRB, the optimal theoretical lower bound for evaluating the performance, to validate the effectiveness and feasibility.
## 1 Introduction  
The construction industry confronts multifaceted challenges arising from its diverse activities and projects, compounded by the involvement of various stakeholders and specialties such as owners, contractors, architects, and engineers [1, 2]. Adhering to building regulations and standards, which vary across locations, further complicates planning, design, and execution [3]. To navigate these complexities, the integration of modern technology and innovation becomes imperative [4]. Technologies like 3D design, construction robots, virtual reality (VR), and augmented reality (AR) streamline construction processes, enhance visualization, and improve project accuracy [5]. Additionally, analytical tools and artificial intelligence aid in decision making and resource allocation, while big data management enhances operational efficiency [5]. However, despite technological advancements, bridging the gap between virtual and real-world construction remains a challenge. The study seeks to address this gap by leveraging intelligent tools, notably aerial imagery captured by drones, to monitor and track construction progress [6]. By employing Pix4D software version 4.9, the study processes aerial data to generate maps and 3D models, facilitating the comparison between actual and virtual designs [6]. This approach enables precise project oversight, ensuring alignment with predetermined objectives and plans, thereby enhancing project management and quality assurance. In conclusion, the integration of aerial imagery and advanced software in construction monitoring represents a pivotal advancement in addressing the industry's challenges. By combining real-time data from drones with virtual design schemes, construction stakeholders can optimize project efficiency, mitigate risks, and ensure compliance with standards and specifications, ushering in a new era of streamlined and effective construction management.
# Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks
## 3 A Periodic Watermark-Based Detection of Replay Attacks
### Detection of Replay Attacks
If the delay of the replayed data \(\Delta k=k-k_{1}=(n-n_{1})T+i-m_{1}\) is not an integer multiple of the watermark signal period, i.e., \(i\
eq m_{1}\), as indicated by (27), the detection signal \(G(k)=\alpha(k)-i=m_{1}-i\
eq 0\) at this point, indicating the presence of a replay attack. Employing the method proposed in this paper, a replay attack can be detected whenever the condition \(|\Sigma_{r}^{-1}|\left\|\mathbb{Y}^{f}(j)-\mathbb{Y}^{f}(i)\right\|_{2}^{2}\) is satisfied in (25). Therefore, in an ideal scenario, the detection rate of replay attacks using this scheme reaches \(1\). However, due to the presence of random noise disturbances and computational errors, especially assuming \(\Sigma_{o}\) is small, there is a possibility of false detection occurring. To compromise system stability, an attacker must record and store system signals for an extended period prior to launching a replay attack. Conversely, control watermarks can be integrated into the control variables at the onset of system operations. It is established that \(n_{0}\) is sufficient to ensure that \(A_{di}^{(n_{0}-1)T}\approx 0\). Consequently, this paper assumes that no replay attack occurs within the initial \(n_{0}\) periods. If the delay of the replayed data \(\Delta k=k-k_{1}=(n-n_{1})T+i-m_{1}\) is an integer multiple of the watermark signal period, i.e., \(i=m_{1}\), as indicated by (27), the detection signal \(G(k)=\alpha(k)-i=m_{1}-i=0\) at this point, which can result in a false positive. However, this method has certain limitations on the condition that the length of the chosen watermark period is sufficiently large that the occurrence of such cases can be minimized. Future work will focus on addressing this issue in more depth.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
###### Abstract  
Spoofing of global navigation satellite system (GNSS) signals threatens positioning systems. A counter-method is to detect the presence of spoofed signals, followed by a warning to the user. In this paper, a multi-antenna snapshot receiver is presented to detect the presence of a spoofing attack. The spatial similarities of the array steering vectors are analyzed, and different metrics are used to establish possible detector functions. These include subset methods, Eigen-decomposition, and clustering algorithms. The results generated within controlled spoofing conditions show that a spoofed constellation of GNSS satellites can be successfully detected. The derived system-level detectors increase performance in comparison to pair-wise methods. A controlled test setup achieved perfect detection; however, in real-world cases, the performance would not be as ideal. Some detection metrics and features for blind spoofing detecting, with an array of antennas, are identified, which opens the field for future advanced multi-detector developments.  
GNSS; beamforming; server-based processing; antenna array +
Footnote †: This paper is an extended version of “Blind Spoofing Detection using a Multi-Antenna Snapshot Receiver” by [PERSON], [PERSON], [PERSON], and [PERSON], published in the Proceedings of the 2019 9 th International Conference on Localization and GNSS (ICL-GNSS), Nuremberg, Germany, 4-6 June 2019.
## 5 Results and Analysis
### The Effect of UAV Speed
According to the results, ML-OLSR can achieve the maximum throughput compared to other protocols because it considers both the load balance and the length of the UAV queue when deciding which MPR to use. OLSR has a low throughput due to a high number of dropped packets, which contributes to the protocol's poor performance. Compared to the D-OLSR protocol, the P-OLSR protocol can achieve greater throughput at all UAV speeds. Figure 7: Latency vs. speed of UAVs.
## 4 Results and Discussion  
### Comparison of SOE Estimation with Different Operating Conditions  
EVs operate under complex conditions in practice. Accurate SOE estimation is challenged by the impact of diverse operating conditions on battery performance [28]. We aimed to verify the model's performance under various operating conditions and evaluate the potential of mechanical force in SOE estimation. Predictions are conducted based on FUDS and US06 at 25 \({}^{\circ}\)C with 500 N. Additionally, the performance of the fusion model is compared with or without the use of force as an input feature.  
Table 2 shows the SOE estimation results under different operating conditions. The addition of the force signal reduced the RMSE from 2.35% to 0.64% and the MAE from 2.06% to 0.53% for the FUDS. The MAXE was also significantly reduced from 5.34% to 1.64%. Similarly, for the US06, the RMSE and MAE were reduced to 0.56% and 0.39%, respectively, while the MAXE decreased from 5.62% to 2.22%.  
Figure 8 demonstrates that the BiLSTM-PSO-KF model, incorporating the force signal, dramatically improves the SOE estimation accuracy of the LFP battery under both operating conditions, particularly in the SOE range of 0.4 to 0.9. A long voltage plateau exists in this region where voltage and temperature variations are subtle, making it prone to large errors when using only traditional features. The estimation results under FUDS and US06 strongly demonstrate the significant advantage of incorporating the force signal in improving the accuracy of SOE estimation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Data** & **Test Data** & **RMSE\%** & **MAXE\%** & **MAE\%** \\ \hline DST, BJDST & FUDS & 2.35 & 5.34 & 2.06 \\ (without force) & US06 & 2.02 & 5.62 & 1.50 \\ \hline DST, BJDST & FUDS & 0.64 & 1.64 & 0.53 \\ (with force) & US06 & 0.56 & 2.22 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SOE estimation results under different operating conditions.  
Figure 7: The entire framework of the BiLSTM–PSO–KF model.
## 1 Introduction
For evaluating the service life in the target structure, it is very important to quantify not only the design parameters such as cover depth, diffusion coefficient, and mix conditions but also external environmental conditions such as external sulfate concentration and temperature. The consideration of exterior temperature is significantly important since constant design parameters are changed with temperature and it gives an impact on service life. For this reason, the temperature effect on the diffusion coefficient, which is a key parameter for service life design, should be evaluated. For chloride ion and CO\({}_{2}\) diffusion, research on temperature effects has been performed, but its effect on sulfate ions has barely been studied since sulfate ions are mainly considered in footing and foundation systems on the ground (soil). However, high sulfate ions in ground water or cooling water in drainage RC systems may have more severe durability problems due to high temperature. In the study, the service life of an RC drainage structure exposed to high sulfate ions was evaluated through a linear degradation depth model, considering the temperature-dependent diffusion coefficient. For this purpose, the diffusion coefficient of sulfate ions was experimentally evaluated through the natural diffusion cell test from 20 \({}^{\circ}\)C to 50 \({}^{\circ}\)C (10 \({}^{\circ}\)C interval), and the probabilistic parameters (mean and COV) were evaluated for each temperature. The activation energy of sulfate ions was derived based on the Arrhenius Law and utilized for the temperature-dependent diffusion coefficient. The changes in service life were evaluatedthrough deterministic and probabilistic methods, and the effect of design parameters on service life was also investigated with the temperature effect.
## 5 Simulations
### Performance of the ECHR Protocol
Because the ECHR protocol is able to manage the network with features of energy-balancing and coverage-preserving, the locations of dead nodes are more evenly distributed over the network, which can effectively prevent coverage holes from occurring. Furthermore, Figure 11(d) depicts the distributions of nodes before the network fails to maintain 100% sensing coverage. In this case, the dead nodes are equal to 53. The experiment results demonstrate that using the overlapped sensing ranges the proposed ECHR protocol utilizes the energy of the nodes more efficiently. Thus, the duration for maintaining full sensing coverage can be significantly prolonged.