esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Control Design
### Design of an EKF  
Due to the presence of sensory measurement noises, the vehicle and manipulator positions measured by sensors are not inaccurate. Therefore, it is necessary to utilize a nonlinear filter to estimate the system's states. As the extended Kalman filter (EKF) is simple and easy to complete and has low computational complexity, the EKF is used in this study. It is necessary to obtain a linear model during the KF design process. The dynamic equations of the UVMS can be linearized by ignoring the higher order terms in the expended Taylor series. The state vector of the system, e.g., position/attitude and velocity vectors, is defined as \(\mathbf{X}=[\mathbf{\xi}^{T},\mathbf{\xi}^{T}]^{T}\). The measurement model can be expressed as \(\mathbf{Z}=\mathbf{h}(\mathbf{X})=\mathbf{\xi}\).  
Based on (2) and (3), the time derivative of the system state vector \(\mathbf{X}\) can be obtained as  
\[\dot{\mathbf{X}}=f(\mathbf{X},t)=\begin{bmatrix}\mathbf{J}_{\xi}^{-1}(\mathbf{\eta}_{2})\mathbf{ \dot{\xi}}\\ \mathbf{M}^{-1}(\mathbf{\tau}-\mathbf{\tau}_{d}-\mathbf{b})\end{bmatrix} \tag{9}\]  
where \(f(\mathbf{X},t)\) is considered as the estimated model of the system.  
With the additive Gaussian white noise, the predicted system state vector \(\mathbf{\hat{X}}_{k+1}^{-}\) at \(t=t_{k+1}\) is given in (10), and the predicted measurement state vector \(\mathbf{Z}_{k+1}^{-}\) at \(t=t_{k+1}\) is shown in (11). Then the covariance matrix of the predicted state vector can be obtained as shown in (12).  
\[\mathbf{\hat{X}}_{k+1}^{-} =\mathbf{\hat{X}}_{k}+f(\mathbf{\hat{X}}_{k},t)+\mathbf{Q}_{k} \tag{10}\] \[\mathbf{Z}_{k+1}^{-} =\mathbf{h}(\mathbf{\hat{X}}_{k+1}^{-})+\mathbf{\Gamma}_{k}\] (11) \[\mathbf{P}_{k+1}^{-} =\mathbf{\phi}_{k}\mathbf{P}_{k}\mathbf{\phi}_{k}^{T}+\mathbf{Q}_{k} \tag{12}\]  
where \(\mathbf{\hat{X}}_{k}\) is the predicted state vector at \(t_{k}\). \(\mathbf{Q}_{k}\) is the vector of system noises. \(\mathbf{\Gamma}_{k}\) is the vector of measurement noises. \(\mathbf{P}_{k}\) is the covariance matrix of the estimated system states.  
Therefore, the estimated system states can be obtained through the following correction step:  
\[\mathbf{K}_{k+1} =\mathbf{P}_{k+1}^{-}\mathbf{H}_{k+1}^{T}(\mathbf{H}_{k+1}\mathbf{P}_{k+1}^{-}\bm {H}_{k+1}^{T}+\mathbf{\Gamma}_{k+1})^{-1} \tag{13}\] \[\mathbf{\hat{X}}_{k+1} =\mathbf{\hat{X}}_{k+1}^{-}+\mathbf{K}_{k+1}(\mathbf{Z}_{k+1}-\mathbf{Z}_{k+1}^{-})\] (14) \[\mathbf{P}_{k+1} =(\mathbf{I}-\mathbf{K}_{k+1}\mathbf{H}_{k+1})\mathbf{P}_{k+1}^{-} \tag{15}\]  
where \(\mathbf{\phi}_{k}=\frac{\partial f(\mathbf{X},t)}{\partial\mathbf{X}^{T}}\Bigg{|}_{\mathbf{X} =\mathbf{\hat{X}}_{k}}\) and \(\mathbf{H}_{k+1}=\frac{\partial h(\mathbf{X})}{\partial\mathbf{X}^{T}}\Bigg{|}_{\mathbf{X}=\bm {\hat{X}}_{k+1}^{-}}\).
# Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things
## 5 Simulation Results
### Computation Complexity  
In this subsection, the computation complexity (i.e., TNRO) of the ML decoder used in the proposed EFGSM receiver side is calculated based on (38) and weighted toward the computation complexity of ML decoder used in the SM, GSM, FGSM receiver sides, respectively. As explained in Section 4, the proposed scheme receives two independent signals; the first one is related to the primary signal constellation, and the other is related to the second signal constellation. Each one is like the conventional FGSM in the TNRO and the complexity of the two signals (y\({}_{1}\), y\({}_{2}\)) can be calculated as in (41). As in the ABER evaluation, in regards to achievable data rate, we calculate the system complexity based on, 8 and 9 bpcu. Assume the same TX/RX antennas are employed with different modulation orders in the different SMSs. Figures 6 and 7 show the computational complexity reduction achieved by using the proposed scheme compared to the traditional SM, GSM, and FGSM. As shown, the computation complexity of the ML decoder in the receiver side is reduced significantly, as transmission through two different constellations provides an orthogonal signal which reduces the complexity of the ML decoder.  
Figure 5: Power saving ratio of the proposed EFGSM at multiple Nt compared to conventional SM, GSM, and FGSM referenced to M (1024) QAM.  
Figure 6: The computation complexity of the proposed EFGSM compared to the SM, GSM and FGSM computational complexity for 8 bpcu transmissions.
## 2 Measurement Setup
### Wearable Sensor System  
The developed sensor system for hand motion acquisition is presented in the form of a block diagram in Figure 3. Hand motion acquisition with the use of inertial sensors is related to the implementation of such a system in a wearable form. The solution presented in the article consists of a central module (MAIN), to which sixteen LSM9 DS1 sensors [38] (S1 to S16) are connected via the SPI (Serial Peripheral Interface) bus (SPI BUS). Each of the sensors has a built-in accelerometer, gyroscope, and magnetometer. The sensor system is powered by a 3.7 V lithium polymer battery (1 s). The battery is connected to the CHARGE CONTROL system, which, while charging the battery, is powered from an external 5 V source connected to the CHARGE INPUT. The CHARGE CONTROL output is connected to the DC-DC CONVERTER input. The converter output voltage is 3.3 V and is used to power both the ESP32 module and the LSM9 DS1 sensors. ESP32 [39] is a single 2.4 GHz Wi-Fi-and-Bluetooth combo chip, which in the presented wearable solution downloads data from the LSM9 DS1 sensors, processes the received data and transmits them to the computer. The main board of the central module (MAIN) is shown in Figure 4a,b.  
Figure 2: Station for measuring the load on the sensor network.  
Figure 4: View of WSS: (**a**) from the Wi-Fi module side, (**b**) from the connectors side.  
Figure 3: Block diagram of the wearable sensor system (WSS).  
The location of the individual sensors of the hand motion acquisition system is presented in Figure 5a,b. Sensors S1-S15 (external) are connected to the MAIN module via multi-core cables, whereas the S16 (internal) sensor is integrated with the main board of the MAIN module. The S15 sensor is placed on the forearm, whereas each of the other external sensors (S1 to S14) is placed on the corresponding phalanx of individual fingers (Figure 5). There are two sensors for the thumb, whereas for the other fingers, three sensors are implemented in the system. The applied location of the sensors allows both to monitor the movement of each of the fingers as well as the position of the hand in relation to the forearm.
## 2 Proposed Enhanced PNCC Algorithm
### Proposed Enhanced PNCC Algorithm  
Preprocessing is the first stage in the block diagram. In this stage, a high pass pre-emphasis Finite Impulse Response (FIR) filter \(H(z)=~{}1-0.97z^{-1}\) is applied at the input speech waveform in all methods except RASTA-PLP. Then, the pre-emphasized waveform is divided into short overlapped frames with 25.6 ms frame duration and with 10 ms overlap between frames. Each frame is multiplied by the Hamming window. In the next stage, the fast Fourier transform (FFT) \(Y(k)\) is applied with 256-bit resolution. The power spectral density (PSD) is obtained by computing the magnitude-square of the output frequencies as \(\left|Y(k)\right|^{2}\).  
The gammatone filter banks provide more robust features rather than Mel-filter banks and bark filters for the MFCC and RASTA-PLP methods, respectively. Therefore, a set of 25 gammatone filters [18; 19] were generated from the frequency response of the gammatone kernel basis functions from Equation (1) with center frequencies ranging from 100 Hz to 4 kHz, as shown in Figure 2.  
\[g(n)=an^{\theta-1}e^{-2\pi b\xi n}cos(2\pi bf_{c}n+\varphi) \tag{1}\]  
where \(a\) is the amplitude, \(n\) is the discrete-time index, \(\theta\) is the filter's order, \(b\) is the filter's bandwidth, \(f_{c}\) is the center frequency, and \(\varphi\) is the phase of the carrier.  
Figure 1: Difference between the block diagrams of enhanced PNCC, PNCC, GFCC, RASTA–PLP, and MFCC feature extraction techniques.  
In order to decrease the computational time, the gammatone filter response is set to be zero if the filter bank is less than 0.5 percent of its maximum value. The magnitude of each gammatone filter is then multiplied by the PSD of each frame, and the summation is calculated according to the following equation:  
\[P[m,l]=\sum_{k=1}^{M/2}\bigl{|}Y[m,k]\bigr{|}^{2}G_{l}(k) \tag{2}\]  
where \(m\) is the frame index, \(l\) is the gammatone filter index, \(M\) is spectrum resolution, and \(G_{l}(k)\) is the function of each filter bank in the frequency domain.
## 5 Performance Evaluation
### Throughput Analysis over a Number of D2D Users  
Figure 6 shows the average D2D throughput, average cellular user throughput and the average system throughput over the number of D2D users. We can observe that D2D throughput increases with the increase of D2D users, but on the other hand, cellular user throughput decreases. System throughput is the summation of D2D and the cellular user throughput, which also increases with the increment of the D2D users. For example, our proposed method provides a cellular user throughput equal to 2.912 for 10 D2D users. The proposed method yields 0.2880 as D2D user throughput, which gives a system throughput equal to 3.2. When we increase the number of D2D users to 20, the figure shows a cellular user throughput, D2D throughput, and overall system throughput of 2.8259, 0.5477 and 3.3736, respectively.  
All methods show these same trends over the number of D2D users. From this experiment, we can also investigate the issue about the appropriate number of D2D users which provides the better trade-off between D2D and cellular user throughput in a single cell scenario. Here, we can observe that moderate number of D2D users, for example, 50 D2D users provide suitable amount of D2D and cellular user throughput. Our proposed method outperforms the other methods regarding D2D throughput, cellular user throughput and overall system throughput at every number of D2D users.  
Figure 4: D2D throughput versus transmit power.  
Figure 5: Joint D2D throughput and cellular user throughput optimization.
# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## Appendix A Appendix
### Faithfulness Assessment
The agentic pipeline distinguishes itself by orchestrating the outputs of Embeddings RAG, GraphRAG, and Web Search, leveraging each pipeline's retrieval and reasoning capabilities through multi-step, tool-augmented decision-making. As a result, it achieved the highest overall faithfulness, with 99.7% of statements supported by evidence and 96.7% of answers fully grounded. However, it is crucial to recognize that these faithfulness results are fundamentally dependent on the accuracy and coverage of the underlying pipelines. The agentic pipeline does not generate new evidence directly; rather, it maximizes factual support by strategically combining, cross-verifying, and synthesizing the results retrieved by Embeddings RAG, GraphRAG, and web search. Latency results, as presented in Table 4, provide us with an overview of how fast each pipeline generated each answer, allowing comparative analysis. Both Embeddings RAG and GraphRAG were the most efficient, with mean latencies of 13.41 and 15.06 s, respectively. These pipelines, operating on a single internal data source with relatively simple retrieval operations, provided the fastest user experience. In contrast, Web Search was slower, with a mean latency of 18.79 s.  
This increased response time is likely attributable both to the overhead of live internet querying and the need to process much larger contexts retrieved from entire web pages or to receive an error or timeout while fetching the website. This same dependency is reflected in the latency statistics. The agentic pipeline's response time inherently incorporates the latencies of all three underlying pipelines, since it must invoke and await the completion of each one in sequence or combination before reasoning over their combined results. Consequently, the agentic pipeline's average latency, 62.04 s per question, is a product of both the complexity of its multi-step reasoning and the cumulative time required to run the Embeddings RAG, GraphRAG, and web search pipelines. In some cases, responses took well over two minutes, reflecting the overhead of coordinating and synthesizing multiple retrieval processes. Thus, both the superior faithfulness and the increased latency of the agentic pipeline should be understood as outcomes of its reliance on the performance and interplay of the distinguished pipelines it orchestrates. In summary, when comparing pipelines that use the same underlying data, GraphRAG demonstrates superior faithfulness over Embeddings RAG, with only a minor increase in latency. Web search and agentic pipelines offer even higher factual support by leveraging larger and more diverse information sources, but these benefits come at a significant cost in response time. The optimal pipeline choice will thus depend on the application's requirements for speed versus factual thoroughness.
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.4 Network Virtualization
Recently, researchers have suggested adding applications with policies to integrate blockchain technology for network virtualization to protect owners of wireless infrastructure from double-spending attacks that allocate the same radio frequency slice to multiple virtual wireless networks [523].
## 4 Discussion
### Three-Dimensional Geological Modeling of Wuhan' MDA  
It is of great significance to clarify the urban groundwater environment and the structural characteristics of aquifers for the protection of urban groundwater resources and the development and utilization of underground space. Considering the high price of hydrogeological boreholes, it is obviously impractical to carry out hydrogeological tests on a large scale. In fact, most cities globally are currently facing the issue of scarce hydrogeological data. However, during long-term continuous urban construction, large quantities of other types of geological data have been accumulated, including engineering geology surveys, basic geological surveys, and Quaternary geological surveys. According to theories related to geological genesis and evolution, geological bodies and their internal attribute characteristics, which are influenced by similar internal and external factors, inevitably exhibit close spatial structural correlations. Therefore, in this study, we analyzed the correlation between the stratigraphic structure and its internal hydrogeological structure in the study area, and deduced the aquifer types of each stratigraphic layer. Based on this, we constructed a 3D hydrogeological structural model of the study area, as shown in Figure 14, for which the comprehensive accuracy reached 79.5%.  
Figure 14: 3D aquifer system of Wuhan’s MDA (vertical magnification 25\(\times\)).
## 4 Case Study
### Solution of Non-Splittable Model Under Adequate and Insufficient Capacity Scenarios  
This section explores two scenarios--adequate capacity and insufficient capacity--using the non-splittable model to find a solution.  
In the adequate capacity scenario, the high-speed express transportation demand does not exceed the transport capacity of the high-speed rail line based on the cargo flow information from Table 9. In the insufficient capacity scenario, the express transportation demand exceeds the rail line's capacity. Here, the cargo flow volume is multiplied by four, while the volume of each single express parcel remains unchanged, reflecting the constraints such as loading and unloading capacity. This scenario is also based on the cargo flow information from Table 9.  
Gurobi is a new generation of large-scale mathematical programming optimizers developed by Gurobi. The non-splittable model (M3) was solved using Gurobi in both scenarios. The parameter MIPGap represents the gap between the optimal solution and the current solution, and is set to 0.0001. When the gap between the target value of the current solution found by the solver and the optimal solution is less than 0.0001, the solver will stop solving and return the optimal solution. The solution time was 6.36 s for the adequate capacity scenario and 40.79 s for the insufficient capacity scenario. The optimization results, including the transportation modes of different train services, the transportation paths of different express cargo flows, and optimization indicators such as cargo flow distribution, train capacity utilization, and passenger capacity loss, were analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Symbol** & **Parameter Name** & **Parameter Unit** & **Parameter Value** \\ \hline \(e^{train}\) & Penalty factor for the use of special high-speed trains for the transport of unit express cargoes & CNY/kg & 15 \\ \(e^{delay}\) & Penalty factor for delays per unit of time & /h & 0.5 \\ \(e^{vir}\) & Penalty factor for a single express shipment in case of retention of the shipment due to lack of capacity & - & 5 \\ \(e^{store}\) & Warehousing costs per unit of express container in a day’s time & CNY/collector/day & 1 \\ \(e^{pha}\) & Revenue per unit of passenger transport & CNY/person & 403.5 \\ \(C^{box}\) & Express consolidation capacity & kg & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Parameter settings for numerical experiments.
## 4 Simulation Modeling
### Externally Modulated Laser (EML) Static Simulation Model  
Firstly, we created the static simulation model for the employed optical modulator. Its static response was matched with the respective experimental characterization results (S21 response, power-voltage curves) of a fabricated EML. The fabricated EML was monolithically integrated on an InP platform [55], designed to operate in the 1550 nm window. It consisted of the laser section co-integrated on the same chip with the electro-absorption modulator (EAM). Figure 2a shows the power versus input voltage curves for the simulation model and the experimentally-measured EML device. As it can be seen, the measured and the simulated responses were identical, having a linear regime between \(-1\) V and \(-0.2\) V, corresponding to an extinction ratio (ER) of 12 dB. Moreover, Figure 2b shows the perfectly matched measured and simulated electro-optic S21 curves of the EML, exhibiting in both cases a flat-top response of 11 GHz.
# SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
## 6 Evaluation
### Effects of Interventions  
To study the progression of disease in the population, the government and health authorities are often interested in the impact of different interventions. Fortunately, SIM-D is capable of modelling the non-pharmaceutical interventions as well. To see the impact of interventions on the disease progression, an experiment was designed. In the experiment, the SIR model was simulated, and two types of interventions were applied to it: isolation and vaccination. In the isolation, the activities of individuals are limited to homes and only 10% of the time are they allowed to perform interactions outside the house. In the vaccination, 50% of the population is randomly vaccinated. A vaccinated person is nine times less susceptible to disease than a healthier person. Figure 9 shows that when 50% of the population is vaccinated, the progression of disease slows down significantly. The peak is also delayed by 10 days. Delay in peak is important as it gives the health authorities more time to respond to the outbreak. Figure 9 also shows the impact of isolation or lockdown on the disease progression. When a lockdown is applied around the 23 rd iteration, the number of infections start falling. The disease picks up again slightly around the 50\({}^{th}\) iteration, when the lockdown was eased (40% of outside the house interactions were allowed).  
Figure 8: SIM-D showing the effect of transmissibility on the number of total infections. At very small values of transmissibility (0.0001), the attack rate is 10%. At transmissibility of 0.0008, the attack rate is 33%. At transmissibility of 0.002, the attack rate is 40%.  
Figure 9: Effect of interventions on the spread of disease. In no intervention case, the attack rate is 35%. In the isolation case, the attack rate is 18%. In the vaccination case, the attack rate is 13%.
# A Low-Overhead Message Authentication and Secure Message Dissemination Scheme for VANETs
###### Abstract
Following are the major contributions of our work:  
* We propose a low overhead message authentication and secure message dissemination scheme for VANETs. Vehicles themselves do not authenticate messages. \(RSU\)s are responsible for collecting, aggregating, authenticating and disseminating messages to vehicles. * To reduce the message authentication overhead, \(RSU\)s can select some vehicles in its region as group leaders (\(GL\)s) to collect/aggregate messages from vehicles in their subregions and send them to the \(RSU\) for further aggregation and dissemination. * Our scheme ensures authenticity and integrity of messages using digital signature based on public key cryptography. The rest of the paper is organized as follows. We discuss some related works in Section 2. In Section 3, we describe our proposed approach. In Section 4, we present the security and privacy analysis of our approach. Finally, Section 5 concludes the paper. Next, we discuss some related works.
# Key Factors for a Successful Telemedicine Solution for Cardiovascular Diseases: A Systematic Review
## 2 Materials and Methods
### Eligibility Criteria
#### 2.1.1 Population  
All studies between 2020 and 2024 analyzing the effects of a telemedicine intervention on patients aged 18 years or older were considered eligible for inclusion if they had one or more of the following cardiovascular diseases: hypertension, heart failure, or atrial fibrillation.
## 2 Robot Control System
### Large-Scale Surface Orthogonal Motion Planning
#### 2.3.1 Surface Reconstruction  
To plan a path on a surface, the geometrical shape of the surface must be known. Especially for inspection tasks, it cannot be assumed that the actual state of the workpiece still corresponds to its computer-aided design (CAD) data. In our implementation, the surface is reconstructed from a point cloud using the Point Cloud Library [39] (PCL). The point cloud is provided by the RGB-D camera at the end effector of the manipulator. In the first step, the point cloud is down-sampled, and then the surface is reconstructed by applying B-spline fitting [40]. Figure 7 shows the reconstructed surface of an exemplary rotor blade segment.  
Figure 6: A\({}^{*}\) based global path planning in a 60 \(\times\) 60 m sized grid-based costmap. (**a**) Default behavior; (**b**) right driving behavior.