esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing
## 3 Design of Golay Complementary Coded Radar
### The Self-Positioning Module  
As the relative positions between radars are needed for human respiration detection, the acquisition of all radars' positions in the architecture of the proposed multiple observation point detection system cannot be neglected. Normally, the relative positions between radars are measured manually in an actual environment, which is not suitable for field applications because of manual measurement errors and the time required. To overcome this problem, a self-positioning module is deployed in each radar to sense its relative position and upload it to the control host automatically.  
As shown in Figure 2e, the self-positioning module is composed of a DWM1000 ultra-wideband module, an STM32 Microcontroller Unit (MCU) and a self-positioning controller in FPGA. The DWM1000, as the key component of the self-positioning module, is designed based on Decawave's DW1000 chip. It integrates antenna, all RF circuitry, power management and clock circuitry into one module, supports four radio frequency bands from 3.5 GHz to 6.5 GHz, provides the function of timestamp and precise control of transmission times and can be used in the two-way ranging or Time Difference of Arrival (TDOA) positioning with an error within 10 cm [29; 30; 31; 32]. Here, the two-way ranging is realized by the double-sided two-way ranging algorithm executed in the STM32 MCU to achieve the self-positioning of the radars.  
Although the workflows of the network clock synchronization module and self-positioning module are similar, it should be noted that the CC1310 and the DWM1000 are not interchangeable; the clock frequency of the CC1310, which serves the timestamp register,is too low to provide accurate self-positioning, while the configuration and algorithm of the DWM1000 are cumbersome and need to be controlled by an MCU, the random response time of which might cause a network clock synchronization error.
# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 4 Discussion of the Implementation
### Distributed and Decentralized Training  
In a multi-party setting, MPC-based aggregation can collect encrypted gradient updates from each participant. At a high level, each participant locally encrypts its gradient, sending it to an aggregator that operates under an MPC protocol. The aggregator computes the combined gradients without decrypting individual contributions. In principle, specialized libraries (e.g., MP-SPDZ) or secure enclaves can be employed to implement this aggregator securely. For large LLM architectures, top-\(k\) sparsification or 8-bit quantization methods can be layered atop encryption to reduce the communication overhead. We specifically chose top-k sparsification because it typically offers a strong balance between gradient fidelity and the bandwidth savings. Alternatively, 8-bit quantization can reduce the communication load further while preserving the model's performance, though it may be more sensitive to certain architectures.  
When deploying MPC-based aggregation in practice, system designers should carefully evaluate the communication overhead and latency. In many real-world scenarios, multiple sites contribute gradients at varying rates, making their synchronization non-trivial [49]. To alleviate congestion, techniques such as dynamic batching of updates and adaptive scheduling can be used. For instance, participants might only pass updates when their gradient norm exceeds a specific threshold, thus minimizing unnecessary cryptographic transmissions. Additionally, several groups have begun to research trust frameworks that combine multi-party computation and threshold cryptography [50]. In such a model, the decryption keys are shared among multiple secure computation environments, with a majority required for decryption. No single environment can decrypt the data independently; only when a threshold number of participants agrees can the global model state be partially decrypted for verification. This approach offers an extra layer of assurance if the aggregator resides in an untrusted or semi-trusted domain.  
Integration with container orchestration platforms (e.g., Kubernetes) and infrastructure-as-code (IaC) tools can also streamline the large-scale deployment of SecureLLM. Each participant runs a secure container instance housing the cryptographic modules and the local data, while the aggregator node(s) operate(s) the MPC protocols. Configuration files can define secure networking policies--such as strict mutual TLS--to prevent eavesdropping on gradient transmissions.
# Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process
## 3 Vulnerability Assessment of Cites
### Performance of a U-city under Hazards
#### 3.3.2 The Impact of Disasters on ICTs  
For the analysis purposes, the first step is to establish the ranking for man-made and natural hazards using AHP. Then the impacts of these hazards were estimated on ICTs. As demonstrated in Figure 6 the same process for natural and man-made hazard was followed to rank likelihood, area of influence, and extent of influence (susceptibility) of different ICTs. All disasters were first ranked for their likelihood to occur based on expert opinion as input of the AHP process. In a second step AHP process was carried out to assess the susceptibility of various ICTs against different hazards, which was a tedious and efforts taking process. The vulnerability analysis was carried out without AHP and without considering the area of influence. Those results were rejected by the expert panel and the process was repeated using AHP and considering the area of influence of different disasters. As a result, the outcomes obtained were quite reliable. Figure 7 shows which technologies are affected by some natural disasters. Some hazards being more destructive and catastrophic than others while some ICTs are more susceptible to others some hazards. It is also quite noticeable that natural hazards heavily affect technologies that require physical infrastructure. The exception being GNSS which is satellite technology and resides out of reach of most natural disasters. Floods are the most destructive natural hazard under the climatic and infrastructural conditions of Melbourne. All ICTs are somehow in the same range of vulnerability. However, Cable and ADSL are among the top three due to heavy reliance on these ICTs, and Radar being the one that needs attention due to weather conditions can influence its functioning.  
Figure 6: A vulnerability assessment of U-city under natural and man-made hazards for ICTs and areas using AHP.  
Figure 8 presents the impacts of man-made hazards on ICTs. A very clear and obvious threat is vandalism following by Malware. These two hazards are more influential due to their higher probabilities all over the city and susceptibility of the ICTs to these two types of disasters. Vandalism is a physical-based hazard and Malware is the digital-based or connected to internet hazard which has appeared influential over recent times and can be special to U-cities only due to heavy reliance on ICTs.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Wideband Channel Modeling for Over-the-Sea Wave Propagation  
The channel model was developed for use in the Aegean Sea, Greece, for a mobile vessel-to-vessel communication system. The frequency used in this channel model [8] is 1.9 GHz. The focus of the present study lies in the delay profile that is nothing more than a normalized plot of received power against delay, if the pulse is transmitted and faces erosion. The large-scale characterization of the measured environments was carried out using the long-distance path loss model [6]. The path loss equation for the channel model [8] is:  
\[PL(d)=PL(d_{0})+10n\log(\frac{d}{d_{0}})(dB) \tag{8}\]  
where \(n\) is the attenuation factor, \(PL(d_{o})\) is the measurement of path loss at a particular location at a distance of \(d_{o}\) from the transmitter (TX), where \(d\) is used to denote the distance of interest. For small-scale characterization, a time dispersion analysis along with a time and space domain analysis were performed. Figure 10 shows a mobile vessel-to-vessel communication scenario for the channel model.  
The outstanding part of the work in [8] is that it presents authentic real-time data, based on real life implementations. The main problem of this model was the consideration of multipath effects, which arise only because of coastline hills. Multipath effects caused by fluctuating sea waves were ignored. It has a high delay for the nLoS propagation links and the system is much too dependent on the global positioning system (GPS).
## 5 Numerical Simulation and Discussion
### Simulation Results and Discussion
#### 5.2.3 Input Voltage Variation  
For evaluating the robustness of the suggested control approach in comparison with the three remaining control algorithms in the case of input voltage variation, a step-changing input voltage is adopted as follows:  
\[v_{in}=\left\{\begin{array}{ccc}16,&\text{if}&0\leq t<0.15\\ 14,&\text{if}&0.15\leq t<0.35\\ 18,&\text{if}&t\geq 0.35\end{array}\right.\quad(\text{V}) \tag{55}\]  
The out voltages of the FIBC in the circumstance of the input variation are demonstrated in Figure 6. As depicted in this figure, equivalent to the two above case studies, the C4 controller with the PI control algorithm employed for both inner and outer control loops still attains the longest settling time and the biggest steady-state error. In contrast, the three remaining control approaches are able to achieve better voltage regulation performance. However, it is clearly seen that the proposed control framework obtains the best performance with the smallest overshoot and fast convergence speed in comparison with C3 and C4 control approaches. The results consistently indicate the advantage of the suggested control method and its appropriateness to the voltage regulation problem of the FIBC for fuel cell applications.  
Through the three above case studies, it is worth noting that the proposed control approach can be considered a valuable solution in exactly regulating the output voltage of FIBCs for fuel cell applications, which is more robust against input variation and load change conditions and shorter settling time in comparison with popular existing control methodologies.  
Figure 5: The output voltage of the FIBC under the four controllers in the case of step load change.
# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 3 Green Underwater Sensor Network Framework using Adapted Whale and Wolf Optimization  
In this section, the proposed framework for the green underwater network using whale and wolf optimizations (W-GUN) is presented. In UWSNs, researchers are currently aiming to increase network lifetime and improve the data delivery rate, considering constrained underwater network scenarios. For this, the data dissemination path should consider natural underwater characteristics along with the shortest path possible. It will lower the energy consumption and also packet delay in the underwater scenario. However, the best deployment of underwater relay nodes optimizes each network resource and routing performance. Therefore, optimizing the number of underwater relay nodes and the deployment strategy of relay nodes considering underwater scenarios has been an essential downside to green communication in underwater networks.  
In order to solve the above mentioned problem, this paper presents a novel approach for the optimization of underwater relay nodes prioritization using an adapted whale and wolf optimization algorithm. The adapted optimization algorithm has been developed to realize the W-GUN framework by incorporating moving underwater whale characteristics in underwater data dissemination. Here, the optimal best relay nodes are obtained simultaneously from the algorithms, including whale optimization and wolf optimization, and the final relay node decision is taken to select the best among them during each iteration. At the completion of every iteration, the best underwater relay node solution will be given to both algorithms to generate a better solution for the underwater relay node than the previous one, and the same cycle is repeated. Here, the deployment of underwater sensor nodes comes in the form of normal random distribution at the initial underwater network establishment stage.  
By utilizing the proposed adapted optimization algorithms in W-GUN, the minimum number of underwater relay nodes required is specifically determined once the network parameters are provided, such as the variety of source nodes, the communication range, and amount of routing paths. Once the underwater relay nodes are optimally determined, the average path length and, therefore, the range of control packets were significantly reduced, which may doubtless minimize energy consumption and packet delay and increase network lifetime, and packet delivery ratio. Moreover, the duty cycle of underwater relay nodes is adjusted adaptively using the modified echo state network (MESN).
## 2 Materials and Methods
### Processing Software  
#### 2.2.1 Onboard Pre-Processing  
The control and processing system was managed by a mini-PC interfaced with the other equipment in different ways. Two USB/RS485 converters were connected with the weather station and the AHR, respectively. An Ethernet port was used to connect the GNSS receiver to obtain the data and to a 4G router for Internet connectivity.  
Figure 1: GNSS meteorology equipment onboard ships. (**a**) System architecture, (**b**) Photos of GNSS antenna (**top**), GNSS receiver and data collector (**bottom left**), weather station (**bottom right**).  
The software for the onboard instruments and data management was a dedicated C++ code designed using a multi-task and multi-thread approach. The main process of this software managed the following independent threads:  
* GNSS time and position data acquisition;
* Weather station data acquisition;
* AHRS data ingestion;
* Data publication (on a local HTML server) and storage.  
A child process was run for retrieving the AHRS data. The GNSS receiver automatically stored internally the data received by different satellite constellations, and created aggregated RINEX files. The aggregation period could be selected; for reasons of synchronisation with other cascade processing (e.g., the assimilation into numerical meteorological models), we opted for a one-hour period. Finally, a third synchronised process sent the RINEX and surface weather data by FTP to the central ground server for successive processing.
## 5 Case Study
### Sensitivity Analysis  
Some indexes were chosen for the purpose of conducting a single-factor sensitivity analysis for the index weighting. The indexes are unit operation and maintenance cost (0.0491), generation system efficiency (0.0492), annual amount of coal conservation (0.0450), and contributions to clean generation increment (0.0474) whose weighting values are bigger than 0.044. With weight changes of \(\pm\)10%, \(\pm\)20% and \(\pm\)40%, the sensitivity analysis results of project B are shown in Figure 8.  
Figure 8: Sensitivity analysis results of index weighting on project B.  
Figure 7: Evaluation result comparison of different methods.  
From the results in Figure 8, we can find that the weighting of generation system efficiency has the most positive effect on the evaluation results, which are more sensitive than the weighting changes of annual amount of coal conservation and contributions to clean generation increment. On the other hand, the weight changing of unit operation and maintenance cost also has a negative impact on the evaluation results. This suggests that a greater focus needs to be placed on enhancing the generation system's efficiency and controlling the unit operation and maintenance cost in order to optimize the performance of distributed PV-generation project. On this developing stage, the government should play a crucial role in encouraging customers to use more distributed power and provide effective support for technology innovation and cost control.
# Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography
## 4 Discussion
First, participants only walking at a single speed were studied; previous work showed that peak value of KAM is positively related to walking speed [16; 17]. Therefore, the results may not be applicable to the healthy or knee OA population, especially to people with knee arthroplasty. Second, the musculoskeletal model used in OpenSim was scaled using weight of the subject and 16 markers. The simulation of the joint moments and knee JRFs could be improved by using more markers. Third, the musculoskeletal model does not include ligament and soft tissues, which also are associated with TF contact forces directly. Finally, the difference in lower-limb kinematics, dynamics, and EMG caused by FPA gait modification were compared in this paper but long-term effects were not assessed. Further work is needed to address these limitations, the TF contact force may alter over time [14]. The results presented in the paper were based on an exploratory study with a relatively small sample size; however, FPA modification results might be used by physiotherapists to help OA patients to relieve pain and could provide loading information for engineers when they design knee replacement implants for patients who may have toe-in or toe-out gaits. It seems more reasonable to assume that toe-in and toe-out gaits could relieve patient pain by redistributing TF contact forces of medial and lateral condylar due to the change of lateral contact center and the shift of bilateral contact points. When exploring new conservative treatment methods of OA in the future, it might be more reasonable to study the joint contact forces, contact locations, and contact areas in addition to TF joint KAM and KFM.
## 2 Preliminaries  
### Logistic Map  
The logistic map results from the logistic equation, sometimes called the Verhulst model [17], which is often used to predict the growth of the population of living beings or the occurrence of natural phenomena. The logistic equation is described by the following differential Equation (1).  
\[\frac{dN}{dt}=\frac{rN(K-N)}{K} \tag{1}\]  
where \(r\) is the Malthusian parameter [18] that determines the growth of a living organism at a given time, and \(K\) is called a carry. If we let \(x=N/K\), then the described Equation (1) after dividing both sides with \(K\) becomes the following Equation (2).  
\[\frac{dx}{dt}=rx(1-x) \tag{2}\]  
which can be written as Equation (3).  
\[x_{n+1}=rx_{n}(1-x_{n}) \tag{3}\]  
In the logistic map, \"\(r\)\" is a positive value known as the \"biotic potential\". When the value of \"\(r\)\" is greater than 3.5 and an initial parameter, \"\(x_{0}\in[0,1]\),\" is used, the map produces a chaotic system that can be used for encryption. This has been demonstrated in reference [19], where the logistic map is used to encode the selected content of an image. The combination of the logistic map and sine map results in a complex, chaotic map that offers good security performance. The use of these maps in image encryption is rapidly increasing, as demonstrated in references [19; 20; 21; 22]. This trend is likely to continue as researchers continue to explore the potential of chaotic maps for image encryption.
## 4 Discussion
### Future Outlook and Research Directions
#### 4.6.1 In the Context of Techno-Economic Analysis Research  
There is a consistent recommendation across studies for more focused research on process optimisation and model improvements, particularly informed by experimental and kinetics data. The integration of renewable energy sources (e.g., solar PV, wind farms) is a recurring theme in the viability of e-fuel processes [66; 72]. Future research should further explore the efficient coupling of e-fuel production with fluctuating renewable energy sources. The broader implications of these studies suggest a shift towards more sustainable fuel options. However, this shift requires not only technological advancements but also supportive policy frameworks and market mechanisms.
## 2 Materials and Methods
### Algorithmic Approach (Figure 1)
#### 2.2.3 Feature Selection  
A systematic review [20] found that most researchers abandon the feature selection stage of physiological processing, though it can greatly decrease the computational complexity. Principal component analysis (PCA) is a dimensionality reduction technique used to simplify complex datasets by transforming the original variables into a new set of uncorrelated variables called principal components, which capture variance in the data [31]. PCA can be used to select a smaller set of the combination of features that give the most information from the larger set. By focusing on these principal components, PCA reduces the number of features while preserving the essential information. PCA was used in the present research to decrease the number of features and the computational power required. However, PCA has its weaknesses: it assumes linear relationships among variables, which may not capture the true structure of the data, and it can be sensitive to scaling, requiring standardization of data [29]. Additionally, PCA may discard important information if too many dimensions are reduced, and the new principal components are often less interpretable compared to the original features. To decrease the number of features and select the most important ones, principal component analysis (PCA) was performed using all 40 trials for each of the 32 participants using the DEAP dataset. The explained variance ratio for each was used to select the features using a cutoff of 95% of data. PCA was conducted on each signal independently as well as on the combined signals.  
PCA was chosen over kernel PCA and LDA (Linear Discriminant Analysis) based on results from [PERSON] et al. [10]. [PERSON] et al. used wavelets to extract features and then selected features using kernel PCA, PCA, and LDA [10]. Kernel principal component analysis (Kernel PCA) is an extension of PCA that allows for nonlinear dimensionality reduction. While standard PCA is limited to linear transformations, Kernel PCA uses kernel methods to project data onto a higher-dimensional space where linear separations can capture complex, nonlinear relationships in the original data [10]. Linear Discriminant Analysis (LDA) is a supervised dimensionality reduction and classification technique used to separate two or more classes by finding the linear combination of features that maximizes the separation between them [10]. [PERSON] et al. compared Kernel PCA, LDA, and PCA; PCA was most accurate, with up to 100% accuracy [10].