esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The scheme is conservative in space and time everywhere in the computational domain, including its parts adjacent to the boundaries of the body. The Thomas waveform parameter method [19], originally developed to obtain pressure signatures during tests in a wind tunnel, is now widely used for obtaining far-field pressure signatures based on the near-field pressure signatures generated by CFD solvers. Currently, there are several codes for the propagation of these signals, including the Thomas code, PC Boom, and boom codes, as well as a novel implementation of the Thomas algorithm, the SUBoom code, the description of which is presented in [20]. The approach for calculating PLdB [21] includes the PyLdB code, which is based on the Mark VII algorithm [22]. The wide possibilities of flow control using TSSs have been shown previously [14; 15; 16; 17]. However, the question remained as to what noise could be introduced to the ground during flow/flight control with the use of TSSs. The present paper gives an answer to this question; however, so far, in a limited range of the characteristics of flow and AD models. This work is devoted to the study of the problem of noise generation during flights of supersonic civil aircraft (Sonic Boom problem) (M\({}_{\infty}\) = 1.5-2). The fields of the flow parameters and the dynamics of the drag forces of a pointed cylinder aerodynamic model by means of a TSS with a different number of layers and different temperatures in the layers are studied. The influence of the TSS used for the flow control on the near-field pressure signatures (NFPS) and the ground pressure signatures (GPS) is considered using the approaches from [18; 19], accordingly, and the impact of the perceived loudness in decibels (PLdB) is estimated on a base of the code from [21]. It has been shown that when controlling the flow with the help of TSS of the considered parameters, no additional noise impact on the ground is introduced, which is a step forward in expanding the ability to control high-speed flows using TSS.
## 4 Simulation Studies
### Bode Plot Analysis of the Proposed and Traditional Data Transfer Channel  
The Bode plots of the proposed data transfer channel (\(G_{dd1}\) shown in Equation (16)) from \(V_{d}\) to \(V_{od1}\) and traditional data transfer channel (\(G_{dd2}\) shown in Equation (19)) from \(V_{d}\) to \(V_{od2}\) are shown in Figure 10, which shows that the data transfer capacity of the proposed channel is larger than the  
Figure 8: Bode plots of \(G_{pp1}\) and \(G_{pp2}\).  
Figure 9: Bode plot of the interference from power transfer to data transfer.  
traditional channel at the data carrier frequency, so the proposed method for transferring data when power transfer is blocked is more suitable for data transfer.
## 4 Simulation Results and Analysis  
In order to evaluate and compare the presented estimation algorithms, computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink, as shown in Figure 4. CarSim is commercial vehicle dynamic software. The vehicle model in CarSim contains a steering system, tires, and driver model, and is extensively validated and correlated to real-world results, as measured and observed by many automotive OEMs around the world. Therefore, in this study, the embedded vehicle model equipped with VSC in CarSim serves as a real vehicle, providing control input, reference vehicle states and measured signals, while the estimation algorithms are built in Matlab/Simulink. The vehicle parameters used in the simulation are listed in Table 1. 5% differences of these parameters are added to the UKF 3-DOFs vehicle model in the simulation to imitate modeling uncertainties.
# AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
## 4 Case Studies
### Step 2: Agent Configuration
#### 4.2.5 Knowledge Graph Agent  
The knowledge graph agent is a central component of the ODET pipeline that not only identifies relationships between entities but also refines them through retrieval-augmented generation (RAG). RAG makes it possible to extract relevant information directly from the original data, which improves the accuracy of the reports because the generated summaries are supported by real data. Furthermore, the knowledge graph agent makes it possible to capture the dynamics of events by representing not only static but also changing relationships. This feature helps to track the progression of a disaster event in real time and to analyze the impact of decisions or external factors on the event. Combining the extracted knowledge graphs with RAG allows ODET to create more accurate and up-to-date reports that are invaluable to decision-makers in crisis situations. The input for this agent comes from the results of the classification agent, and the output is processed in the last step of the pipeline. This agent uses the same text inference endpoint as the classification endpoint and differs only in the grammar, prompt, and RAG settings. An example of the agent configuration is shown in Figure A6.  
* Grammar and JSON schema--The agent analyzes the semantic relationships between entities based on the context of tweets. ODET allows the definition of a JSON grammar that specifies the structure of the output data as triples (subject, relation, and object). These triples are supported by a prompt similar to that used by the classification agent. An example is shown in Figure A10.
* Retrieval-augmented generation (RAG)--The knowledge graph agent combines the generated graphs with RAG using the generated headlines to access the original data set and collect further information. This ensures higher accuracy and increases the validity of the reports.
* Grouping of entities--Since it can be assumed that messages with similar content and a similar creation time refer to a single event, the agent groups such messages. This reduces the number of requests for the model and optimizes processing speed. Grouping similar data points increased the efficiency of knowledge graph generation.
* Visualization of results--The knowledge graph agent creates the results as a structured network of entities in the form of triples, which supports the creation of reports and simplifies data analysis. The generated knowledge graphs can be displayed graphically in the report if desired, making it easier to identify correlations and validate relationships.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 3 Double Panel Air Cavity, Sandwich and Composite Structures
### Sandwich Core Support Structures
The result of their findings showed that at the broad band frequency range, the pyramidal core produced the best sound insulation performance than the other two core types. [PERSON] et al. [65] conducted some experiments to validate their theoretical model on the STL of sandwich panels with pyramidal truss cores. The effects of truss stiffness, incident angles, elevation angles, face-sheet thickness and material properties on the STL of the pyramidal truss core were carefully studied. For example, it was shown that at an incident angle of \(0^{\circ}\), the STL of the pyramidal truss core produced higher results than when incident angles of \(30^{\circ}\), \(45^{\circ}\) and \(60^{\circ}\) were used. In recent time, [PERSON] et al. [31] also developed theoretical model to predict the STL of sandwich structures whose pyramidal truss core are made of two layers and with fiberglass subjected to external mean flow. The influence of various Mach numbers of the external mean flow along the downstream and upstream directions on the STL was compared. In their results, the STL increased with increased Mach number along the downstream direction while it decreased along the upstream directions. In addition, the authors observed minimal shifts in the peaks and dips of the STL curves toward higher frequency during downstream event and toward lower frequency during upstream event. In addition, other parameters such as Young's modulus, fibrous material filling and distribution beams were investigate to evaluate their effects on the STL of the sandwich pyramidal truss cores.
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 4 Verifiable Threshold Multi-Party Fully Homomorphic Encryption
### Construction of the VTMFHE Scheme
This process effectively isolates incorrect inputs and prevents invalid data from affecting the correctness of the system. If all proofs pass, the evaluation proceeds with \(\hat{ct}\leftarrow\mathrm{F.Eval}\left(F,\left\{ct_{i}\right\}_{i\in[k]} \right)\), and the resulting ciphertext \(\hat{ct}\) is returned. * V.TDec \(\left(\hat{ct},P_{online}\right)\rightarrow\mu\) : Before decrypting \(\hat{ct}\), the oracle query is executed again to obtain the set of online parties \(P_{online}\). If \(|P_{online}|<t\), \(\bot\) is returned; otherwise, the threshold decryption protocol is executed and the decryption result \(\mu\) is output. We will provide a detailed analysis of the security and performance of this scheme in Section 5. Before that, we will explain the specific process of the distributed threshold decryption result in detail.
## 6 Conclusions
### Perspectives for Future Research  
Future directions for remote sensing in peatland research and management involve refining approaches and methodologies, improving calibration techniques, and integrating multi-sensor data. The review highlights the need for validation using higher-resolution data, such as satellite and airborne hyperspectral imagery, and in situ measurements to improve the accuracy of remote sensing-based estimations. Incorporating environmental covariates and machine learning algorithms and considering the complexities of peatland ecosystems are crucial for advancing our understanding and modelling of these unique en vironments. Additionally, further research is needed to address the limitations identified in the review, such as limited spatial and temporal coverage, cost constraints, data processing complexity, sensor selection, interpretation, and validation challenges. Finally, the review underscores the need to explore underutilised applications like peatland degradation and fire dynamics monitoring further to strengthen our understanding of northern peatland dynamics during environmental changes. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16030591/s1](https://www.mdpi.com/article/10.3390/rs16030591/s1)]([https://www.mdpi.com/article/10.3390/rs16030591/s1](https://www.mdpi.com/article/10.3390/rs16030591/s1)), Supplementary Table S1: List of the applications and grouping themes in the systematic literature search for review (for Figure 4); Supplementary Table S2: The total number and percentage of studies that used different remote sensing platforms (such as satellites, airborne, in situ, or mixed sensors) in northern peatlands over the years 2017-2022; Supplementary Table S3: The total number and percentage of studies that applied remote sensing-based applications in northern peatlands over the years 2017-2022; Supplementary Table S4: The total number and percentage of studies that used satellite included in the SLR over the years 2017-2022; Supplementary Table S5: The total number and percentage of studies that used airborne included in the SLR over the years 2017-2022; Supplementary Table S6: The total number and percentage of studies that used UAV included in the SLR over the years 2017-2022; Supplementary Table S7: The total number and percentage of studies that used in situ included in the SLR over the years 2017-2022; Supplementary Table S8: The total number and percentage of studies that used mixed or multiple platforms included in the SLR over the years 2017-2022. Conceptualization, data curation, methodology, formal analysis, writing--original draft preparation, A.Y.A.A. ; Conceptualization, writing--review and editing, funding acquisition, supervision, R.J. All authors have read and agreed to the published version of the manuscript. The authors declare no conflicts of interest.
## 3 Experimental Results and Discussion
### Sensor Data Analysis
#### 3.5.2 Leaf Temperature  
Figure 17 shows the recorded air and leaf temperature data graphs over five days (from 7 June 2023 to 11 June 2023), where the yellow line represents the air temperature, and the blue and red lines represent the leaf temperatures of Plant-1 and Plant-2, respectively. Since the air temperature affects the leaf temperature [5,8,12], the air temperature profile is shown for easy analysis. The figure shows that the patterns of leaf temperatures follow the air temperature. This also conforms with the results in [12] that the leaf temperature is highly correlated with the air temperature.  
Figure 17 shows that the Plant-2 leaf temperature tends to be lower than the air temperature over a whole day. However, the Plant-1 leaf temperature tends to be lower than the air temperature in the daytime (07:00 to 14:00 h) only. These behaviors are clearly shown in Figure 18, which depicts daily air and leaf temperature data graphs. Figures 17 and 18 show that when the air temperature decreases (afternoon until early  
Figure 16: Recorded air temperature and humidity data graphs.  
Figure 17: Recorded air and leaf temperature data graphs.  
morning), the Plant-1 leaf temperature is higher than the air temperature, contrary to Plant-2. One possible reason, as addressed by [58], is caused by the leaf size, where due to its larger leaf size (see Figures 7 and 8), Plant-1 leaf tends to be warmer than Plant-2.  
Another interesting finding from Figures 17 and 18 is that the Plant-1 leaf temperature exhibits significantly daily fluctuations at about 08:00 h. From observation of the data, it is caused by the abrupt air temperature changes during the sunshine. The thermal sensor (AMG8833) cannot track these rapid changes, thus producing an error reading. This phenomenon also occurs occasionally in Plant-2.
## 4 Results and Discussion
### MAE and RME Comparison of Different Predictive Models  
The RME comparison of the predictive models is shown in Figure 14. We calculated the relative mean errors with full features set, basic features, and important sparse matrix features. Figure 14A shows the RME of all 14 sparse matrix features, while Figures 12C and 14B show the basics and important features, respectively. With the full  
Figure 13: MAE Comparisons of (**A**) full, (**B**) basic, and (**C**) important sparse matrix features.  
and basic features set, DT, RF, and GB show almost similar performance, while RR under-performs in both cases.
# Modeling the Submergence Depth of Oil Well States and Its Applications
## 3 Results
### Submergence Depth Models' Numerical Solutions
#### 3.1.2 Least Squares Curve-Fitting Method  
For the submergence depth \(h\) of the full-pumping state model (Formula (5)) in the rising phase, it is assumed that the number of sampling nodes is \(N(\geq 2)\). The submergence depths corresponding to time points \(0=t_{0}<t_{1}<t_{2}<\cdots t_{N}\) are \(0=t_{0}<t_{1}<t_{2}<\cdots t_{N}\) respectively. For \(1\leq i\leq N\), it is assumed that \(H_{i}=h_{i}-h_{0}\), and \(H_{E}=h_{E}-v/\Big{(}C_{f}\cdot S\Big{)}-h_{0}\); then, the two-norm error function is:  
\[g\Big{(}C_{f},H_{E}\Big{)}=\sum_{i=1}^{N}\Big{[}H_{E}\Big{(}1-e^{-C_{f}t_{i}} \Big{)}-H_{i}\Big{]}^{2} \tag{25}\]  
The parameters \(C_{f}\) and \(H_{E}\) are calculated, as shown below, to minimize the value of the two-norm error function.  
It is given that \(x=e^{-C_{f}}\). The least squares curve-fitting characteristic function corresponding to the two-norm error Formula (25) is [12]:  
\[f(x)=\sum_{1\leq i<j\leq N}\Big{[}\Big{(}x^{t_{i}-t_{1}}{\sum_{k=0}^{t_{j}-2}a _{k}x^{k}}\Big{)}\Big{(}H_{i}{\sum_{k=0}^{t_{j}-1}x^{k}}-H_{j}{\sum_{k=0}^{t_{ i}-1}x^{k}}\Big{)}\Big{]}, \tag{26}\]  
where \(a_{k}=\left\{\begin{array}{ll}(k+1)t_{i},&0\leq k\leq t_{j}-t_{i}-1\\ (t_{j}-k-1)(t_{j}-t_{i}),&t_{j}-t_{i}\leq k\leq t_{j}-2\end{array}\right..\)  
According to a reference [17], the characteristic equation \(f(x)=0\) has a unique solution in \(x\in(0,1)\), which can be solved by using the dichotomy method, and we can obtain the following:  
\[H_{E}=\frac{\sum_{i=1}^{N}[H_{i}\big{(}1-x^{t_{i}}\big{)}]}{\sum_{i=1}^{N}(1- x^{t_{i}})^{2}}=\frac{\sum_{i=1}^{N}[t_{i}H_{i}x^{t_{i}}]}{\sum_{i=1}^{N}[t_{i}x^{t_ {i}}(1-x^{t_{i}})]}. \tag{27}\]  
Therefore, the least squares curve-fitting solution for the full-pumping state model given in Formula (5) is:  
\[\left\{\begin{array}{l}C_{f}=-ln(x)\\ h_{E}=H_{E}+h_{0}+\frac{v}{C_{f}\cdot S}\end{array}\right.. \tag{28}\]  
Referring to the analysis for the full-pumping state model, for the nonfull-pumping state model (Formula (8)), it is given that \(x=e^{-C_{n}}\), and for \(1\leq i\leq N\), \(H_{i}=h_{i}-h_{0}\)and \(H_{E}=h_{E}\big{[}1-v/\big{(}C_{n}\cdot S\cdot h_{p}\big{)}\big{]}-h_{0}\). Similarly, using Formula (26), the least squares curve-fitting solution of its corresponding characteristic equation is:  
\[\left\{\begin{array}{l}C_{f}=-\Big{(}ln(x)+\frac{p}{S\cdot h_{p}}\Big{)}\\ h_{E}=(H_{E}+h_{0})\Big{(}1+\frac{p}{C_{f}\cdot S\cdot h_{p}}\Big{)}\end{array} \right.. \tag{29}\]  
Solutions (28) and (29) are also suitable for cases in which the submergence depth \(h\) is in the falling phase.  
However, in the above two solutions, when the submergence depth is in the holding phase, the value of the comprehensive seepage parameter \(C_{f}\) of the oil well cannot be obtained using Formula (16) or (26). Instead, it can be obtained by changing the pumping speed such that the submergence depth is in the rising or falling phase.
## 4 Discussion
### Potential Application for Postural Control Assessment  
By manipulating instability levels and sensory integration strategies, the efficiency of an individual's postural control system can be observed [38]. This approach can identify the threshold at which the system can no longer compensate for increased instability, offering quantitative insights into the individual's adaptability and reserves.  
The potential to assess different sensory systems separately enhances the utility of this approach. It expands the understanding of postural control and can mimic real-life scenarios--like navigating in low-light or nighttime environments, where falls are more likely to occur.  
Such a method could be applied to evaluate the effectiveness of balance maintenance, serving as a measure of rehabilitation or training efficacy. The assessment of balance maintenance under demanding conditions could indirectly gauge the efficiency of the neural controller and identify potential deficiencies contributing to balance disorders.  
Moreover, heightened sensitivity to body sway at higher instability levels has practical applications for designing tailored interventions in balance training and rehabilitation. For instance, identifying when postural control breaks down can inform personalized rehabilitation programs targeting the specific sensory or motor deficits of an individual. This can help optimize balance training protocols, enhance fall prevention strategies, and contribute to the development of adaptive equipment or environments that gradually increase instability to safely challenge and improve postural control in clinical or athletic populations.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 2 Local Filter  
Considering the multi-sensor fusion positioning discrete system with uncertain time-varying noise variance, this is defined as  
\[x_{k+1}= F_{k}x_{k}+B_{k}w_{k} \tag{1}\] \[z_{k}^{h}= C_{k}^{h}x_{k}+v_{k}^{h},\quad h=1,2,\ldots,H \tag{2}\]  
where \(k\in\mathbb{R}\) represents discrete time, \(x_{k}\in\mathbb{R}^{n}\) represents the state of fusion positioning system. \(F_{k}\), \(B_{k}\) and \(C_{k}^{h}\) represent the time-varying state transition matrix, input noise matrix and observation matrix with known appropriate dimensions, respectively. \(H\) is the number of sensors. \(z_{k}^{h}\) represents the measurement of h-th sensor at time \(k\). \(w_{k}\) and \(v_{k}^{h}\) are model and observation noise of the h-th sensor, respectively. \(w_{k}\) and \(v_{k}^{h}\) have unknown noise variance \(\bar{Q}_{k}=E(w_{k}w_{k}^{T})\) and \(\bar{R}_{k}^{h}=E(p_{k}^{h}(v_{k}^{h})^{T})\), respectively, and the conservative upper bound of \(\bar{Q}_{k}\) and \(\bar{R}_{k}^{h}\) are \(Q_{k}\) and \(R_{k}^{h}\), respectively.  
\[\bar{Q}_{k}\leq Q_{k},\qquad\bar{R}_{k}^{h}\leq R_{k}^{h},\qquad h=1,2,\ldots,H \qquad\forall k\in\mathbb{R}\,. \tag{3}\]  
Based on the minimax estimation [30; 31] and linear unbiased minimum variance rule, the local filters under time-varying noise variance one-step predictors are given by [31]  
\[\hat{x}_{k}^{h}=\psi_{k}^{h}x_{k-1}^{h}+K_{k}^{h}x_{k}^{h},\quad h=1,2,\ldots,H \tag{4}\]  
where the filtering gains \(K_{k}^{h}\) are  
\[K_{k}^{h}=P_{k|k-1}^{h}(C_{k}^{h})^{T}[C_{k}^{h}P_{k|k-1}^{h}(C_{k}^{h})^{T}+R _{k}^{h}]^{-1} \tag{5}\]  
the estimation error covariance matrices are  
\[P_{k+1|k}^{h}= C_{k}^{h}P_{k|k-1}^{h}(C_{k}^{h})^{T}+B_{k}Q_{k}B_{k}^{T} \tag{6}\] \[P_{k|k}^{h}= [I-K_{k}^{h}C_{k}^{h}]P_{k|k-1}^{h} \tag{7}\]  
where \(\psi_{k}^{h}=[I-K_{k}^{h}C_{k}^{h}]F_{k}\), \(I\) represents the identity matrix. The local estimation variance matrix satisfies the Lyapunov equation to ensure the robustness [32]  
\[P_{k+1|k+1}^{h}= \psi_{k+1}^{h}P_{k|k}^{h}(\psi_{k+1}^{h})^{T}+[I-K_{k+1}^{h}C_{k+ 1}^{h}]B_{k}Q_{k}B_{k}^{T}[I-K_{k+1}^{h}C_{k+1}^{h}]^{T}\] \[+K_{k+1}^{h}R_{k+1}^{h}(K_{k+1}^{h})^{T}\,. \tag{8}\]  
The key issue of multi-sensor fusion positioning algorithm with an unknown motion model, unknown covariance and uncertain time-varying noise variance is how to realize the track fusion efficiency of local filters and design an accurate smoother.