esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design Overview
### Functional Components  
#### 3.2.1 Expert Profile Generation  
The expert profile component supports the creation and maintenance of expert profiles by merging user-provided information with automatically gathered data. Experts begin by entering their details through a user interface, supplying elements such as expertise, experience and education. This core data forms the initial profile and serves as a baseline for further enrichment.  
The component employs two submodules for data acquisition and processing. The web scraper retrieves supplemental data from specified external platforms, such as LinkedIn, ResearchGate, ORCID, etc. By focusing on well-defined interfaces, it can be updated to incorporate additional sources as they become relevant. Once the web scraper acquires the raw data, the data extractor processes it, using configurable extraction tem  
Figure 1: SPARK-IT platform architecture.  
plates designed to handle different source formats. Because these templates are pluggable, the data extractor can be adapted without altering the overall system whenever new formats or platforms arise.  
The expert profile component integrates these steps into a single workflow. First, it verifies user input and identifies missing or outdated information. Next, it triggers the web scraper to gather relevant details that could fill the gaps or refresh the profile. Then, the data extractor refines and structures the retrieved data to align with the expert's existing information. After processing, the system presents the updated profile for expert verification or approval. If needed, the expert can correct or refine data within the interface, ensuring that the final profile remains accurate and aligned with the professional record.  
This approach reduces the time experts spend manually updating their profiles and helps maintain current, consistent records. By supporting multiple data sources and flexible extraction methods, the component remains resilient to changes in the data landscape. Through its integrated workflow, profile creation, and maintenance process, the component helps experts present up-to-date information in a single, organized location.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN
### Management of Enabling Technologies
#### 5.6.2 Enabling Hardware  
Enabling hardware forms part of the infrastructure of WSNs based on SDN and an important component for managing and enabling the SDN integration into WSNs is the SDN-enabled sensor node. The design and management of the nodes needs to meet the requirements of SDN and a major part of the design is that the node should be reconfigurable even after deployment via wireless communications. Examples of reconfigurable nodes presented for WSNs include a sensor node based on modularity [138] using the Field Programmable Gate Array (FPGA) as the node control unit [139] due to the re-configurable property of FPGA hardware unlike micro-controllers. This enables the remote reconfiguration of sensor hardware over wireless networks which is essential for SDN-based management of WSNs. To reduce the energy consumption of FPGAs, state of the art designs have been developed to operate in ultra low power modes [62]. Another implementation of sensor re-configurability is a node design with a soft core processor and the capacity to be reconfigured remotely via OTAP techniques post deployment based on the FPGA as the control unit [140]. The systems proposed in [139; 140] use a micro-controller to re-configure the FPGA as a minimisation technique for power consumption.  
Further efforts in proposing implementations of reconfigurable nodes (SDN-enabled nodes) in SDN-based WSNs have been made in recent years. TinySDN [69; 118] uses an SDN-enabled node structured into three parts namely the TinySdnP which performs a flow table update upon receiving a flow setup resonse, TinyOS application which creates data packets and places them on the network and ActiveMessageC which is a component of TinyOS managing and providing a programming interface with radio hardware on the sensor. [PERSON] et al. [136] propose a SDWSN based on role generation and delivery to and from a reconfigurable node. The node makes use of a modular combination of FPGA and micro-controller for an energy efficient node that can be reprogrammed via OTAP to perform various roles. The micro-controller is used to alter the network behaviour while the FPGA handles heavier data processing tasks. Other enabling hardware such as computer servers for role or task generation and communication modules for the multi-hop wireless link have been presented for implementation in SDN-based management for WSNs [65; 136].
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 2 Technology Reviews
### Differential Privacy  
Differential privacy (DP) ensures a similar distribution of responses to queries, regardless of whether an individual's record exists in a database [7]. Because an attacker cannot distinguish responses, DP makes it impossible to infer more information about a particular individual. In this way, it protects personal data while ensuring the usefulness of data utilization. DP is defined below [34].  
**Definition 1**.: _(\(\epsilon\), \(\delta\)): differential privacy_  
_If databases \(D\) and \(D^{\prime}\) with a record difference for any algorithm \(M\) are established as follows, then (\(\epsilon\), \(\delta\))--differential privacy--is satisfied._  
\[\epsilon>0,\;\delta\geq 0,\;Pr\big{[}M(D)=t\big{]}\;\leq\;e^{\epsilon}\;Pr \big{[}M(D^{\prime})=t\big{]}+\delta. \tag{1}\]  
_If \(\delta\) is 0, then \(\delta\)-difference privacy is satisfied. The smaller the \(\delta\) value, the smaller the distribution difference between the two databases, thus ensuring greater privacy. In general, the value of \(\delta\) is less than \(1/\left|D\right|\)._  
In the literature, differential privacy has been applied to federated learning scenarios. The authors in [35; 36] investigate a federated learning setup where a coordinator is assumed to be trusted and is responsible for adding noise to aggregate local parameters in order to ensure the privacy of all of the data from users. In another setting, the assumption of a trusted coordinator is eliminated, and data owners are responsible for perturbing private information locally before uploading it to the untrusted coordinator [37; 38; 39]. With an untrusted coordinator, the research works in [40; 41] examine an additive noise mechanism where local parameters are perturbed with less noise but then encrypted to ensure privacy before forwarding to the coordinator.
### Literature Review
#### Integration of Electronic Health Records with Injury and Trauma scores and Location Awareness  
Injuries and disease can be classified according to the International Classification of Diseases (ICD) classification codes, and it can be used with clinical classification codes like the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), and Logical Observation Identifiers Names and Codes (LOINC) [28; 29]. Injury can be described in two ways using ICD-10: (1) the external cause of injury, and (2) the nature of the injury. As an example of ICD coding, if death and the causes of death for a particular patient have to be coded according to ICD-10, then the external cause of injury and the nature of Injury have to be determined. The External Cause codes describe the mechanism or the cause of the injury (e.g., motor vehicle crash), and the manner or intent of the injury (e.g., unintentional). The Nature of Injury codes describe the body region or the site of the injury (e.g., hip) and the diagnosis (e.g., fracture) [30; 31]. The ICD injury matrices are frameworks that are designed to organize ICD-coded injury data into meaningful groupings that are agreed upon by the global medical community. The matrices were developed specifically to facilitate national and international comparability in the presentation of injury statistics [32].  
Once the early warning and trauma scores have been calculated, the EHR with the public healthcare service provider can be updated using ICD and SNOMED/LOINC classification codes using Health Level 7 (HL7) standards. FHIR is an HL7 standard that provides interoperability specifications for web services and EHR databases [33].  
A very important application for wearable IoT healthcare monitoring devices is the ability to locate the individual when the trauma-related events take place. With the availability of low-cost wearable GPS and Global System for Mobile Communication/General Packet Radio Service (GSM/GPRS) receivers, which can be embedded into the wearable kits, such a provision can made available. The GPS receiver can provide the location-specific information, and the composite sensor can provide the physiological information and trauma scores. This composite payload can be transmitted to the healthcare service provider and can enable them to get ready for an emergency. It can also help the ambulance critical care team can get ready for the emergency procedures while they are on their way to the incident location. Several GPS/GNSS systems already exist in mobile service vans tracking systems; e.g., an efficient vehicle tracking system has been implemented for tracking the movement of vehicles using a smartphone application with a microcontroller interfaced with GPS/GNSS technology to track the location in real time. The vehicle tracking system uses the GPS module to obtain geographic coordinates at regular time intervals. The system also uses Google Maps Application Programming Interface (API) to display the vehicle on the map in the smartphone application, and it can estimate the distance and time for the vehicle to arrive at a given destination [34].
###### Abstract  
Electrohystergography (EHG) is a promising technique for pregnancy monitoring and preterm risk evaluation. It allows for uterine contraction monitoring as early as the 20 th gestational week, and it is a non-invasive technique based on recording the electric signal of the uterine muscle activity from electrodes located in the abdominal surface. In this work, EHG-based contraction detection methodologies are applied using signal envelope features. Automatic contraction detection is an important step for the development of unsupervised pregnancy monitoring systems based on EHG. The exploratory methodologies include wavelet energy, Teager energy, root mean square (RMS), squared RMS, and Hilbert envelope. In this work, two main features were evaluated: contraction detection and its related delineation accuracy. The squared RMS produced the best contraction (97.15 \(\pm\) 4.66%) and delineation (89.43 \(\pm\) 8.10%) accuracy and the lowest false positive rate (0.63%). Despite the wavelet energy method having a contraction accuracy (92.28%) below the first-rated method, its standard deviation was the second best (6.66%). The average false positive rate ranged between 0.63% and 4.74%--a remarkably low value.  
electrohystergography; wavelets; uterine contraction detection; pregnancy monitoring
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 3 Fusion of Lane Location Information and SLAM Positioning Algorithm
### Location Prediction Based on SLAM Information  
As shown in Figure 4. Firstly, \(d_{lane}\) is still obtained through the local positioning method of the lane line. The map candidate points are translated to the current vehicle position to generate a virtual trajectory, and a batch of new map candidate points are obtained. Taking the motion information from SLAM as the predicted excitation \(X_{k+1}=X_{k}+(\delta x,\delta y)\), \((\delta x,\delta y)\) is the representation of the SLAM position change in the world coordinate system. After obtaining the changes combined with the SLAM positioning information, the position is updated on the basis of the K map candidate point positions obtained after the last calculation. For each candidate point, with the updated position \(X_{k+1}=X_{k}+(\delta x,\delta y)\) as the center, select \(K\) points according to the nearest neighbor principle, and calculate the probability of the updated position of the vehicle at each candidate point.  
\[\begin{array}{c}P_{X_{k}n}^{k+1}=P_{X_{k}n}^{k+1}+P_{X_{k}n}^{k}*\lambda*\exp \Bigl{(}-\frac{1}{2}{X_{k+1}}^{T}*Q^{*}X_{k+1}\Bigr{)}\\ X_{k+1}=\{x-x_{k+1},y-y_{k+1}\}\\ \sum\limits_{n=1}^{K}P_{X_{k}n}^{k+1}=1\end{array} \tag{11}\]  
\(P_{X_{k}n}^{k+1}\) represents the probability that the vehicle is at position \(n\) at time k + 1. For each candidate point at time k, \(K\) new candidate points are obtained and normalized. Through the prediction of K candidate points, \(K^{2}\) probability updates can be generated, and \(K_{n}\) new candidate points \(K<K_{n}<K^{2}\) can be obtained. Through K calculations, the probability of the vehicle at each new candidate point is accumulated and normalized. Each candidate point has direction and position information in the representation of the directed graph, and can be established by constraining \(d_{lane}\) by the position of the lane line, the angle \(\theta_{lane}\) between the observed lane projection, the vehicle coordinate system and the map point contact, and update the probability of these \(K^{2}\) results as observations.
# On the Delayed Scaled Consensus Problems
## 6 Conclusions  
Scaled consensus problems have found diverse applications in cooperative tasks in practical multi-agent systems. This paper discusses the stability of scaled consensus problems with and without time delays. We distinguish between signal transmission delays and signal processing delays by deriving sufficient and necessary scaled consensus conditions and calculating the final scaled consensus values in each case. These two types of delays are shown to possess distinct features on the scaled consensus processes, as well as the scaled consensus tracking on associated manifolds.  
The scales encoded in \(\alpha\) in this paper are assumed to be constant. It would be interesting to explore time-varying scales \(\alpha=\alpha(t)\) (leading to a non-autonomous linear system) in the delayed system framework. It is noteworthy that our results assume a fixed communication topology and homogeneous agents. Therefore, delayed scaled consensus processes with switching/uncertain topologies and heterogeneously motivated agents (e.g., [22]) can be meaningful subjects of future work.  
The author would like to thank the anonymous referees for their valuable suggestions that improved the presentation of the paper. The work is funded in part by the National Natural Science Foundation of China (11505127), the Shanghai Pujiang Program (15 PJ1408300) and the Program for Young Excellent Talents in Tongji University (2014 KJ036).  
The author declares no conflict of interest.  
Figure 2: State evolution of agents under (**a**) signal transmission delays for system (1) with (6) and (**b**) signal processing delays for system (1) with (16).
## 3 Event-Triggered Extended-State Observer  
An ETESO was developed to estimate uncertainties including model uncertainties and time-varying disturbances, and an event-triggered condition was employed to avoid unnecessary communications. Inspired by [33], an ETESO was designed as follows:  
\[\dot{\eta}_{i}(t) = -\frac{3}{\epsilon_{i}}(\dot{\eta}_{i}-\xi_{i})+R_{i}\phi_{i}, \tag{15}\] \[\dot{\psi}_{i}(t) = -\frac{3}{\epsilon_{i}^{2}}R_{i}^{T}(\dot{\eta}_{i}-\xi_{i})+ \dot{\sigma}_{i}+M_{i}^{-1}\tau_{i},\] (16) \[\dot{\sigma}_{i}(t) = -\frac{1}{\epsilon_{i}^{3}}R_{i}^{T}(\dot{\eta}_{i}-\xi_{i}), \tag{17}\]where \(\hat{\eta}_{i}=[\hat{x}_{i},\hat{y}_{i},\hat{\psi}_{i}]^{T}\); \(\hat{\upsilon}_{i}=[\hat{a}_{i},\hat{\upsilon}_{i},\hat{r}_{i}]^{T}\); and \(\hat{\sigma}_{i}=[\hat{\sigma}_{i,1},\hat{\sigma}_{i,2},\hat{\sigma}_{i,3}]^{T}\); \(\hat{x}_{i},\hat{y}_{i},\hat{y}_{i},\hat{a}_{i},\hat{\upsilon}_{i},\hat{r}_{i},\hat{\sigma}_{i,1},\hat{\sigma}_{i,2}\) and \(\hat{\sigma}_{i,3}\) are the estimates of \(x_{i},y_{i},\hat{y}_{i},u_{i},v_{i},r_{i},\sigma_{i,1},\sigma_{i,2}\) and \(\sigma_{i,3}\), respectively; \(\epsilon_{i}\) is a positive constant; and \(\bar{\xi}_{i}\) is the transmitted signal given by  
\[\bar{\xi}_{i}(t)=\left\{ \begin{array}{ll}\eta_{i}(t_{k}),&\text{if }d(t)=0\\ \eta_{i}(t),&\text{otherwise};\end{array} \right. \tag{18}\]  
\(t_{k}\) is the previous event-triggering time instant; \(d(t)\) is an event-triggered mechanism designed as  
\[d(t)= \left\{ \begin{array}{ll}0,&\text{if }\|q(t)\|<c_{q}\\ 1,&\text{otherwise}.\end{array} \right. \tag{19}\]  
\(c_{q}>0\) is the design threshold; and  
\[q(t)=[q_{1},q_{2},q_{3}]^{T}=[\eta_{i}(t_{k})-\eta_{i}(t)]/\epsilon_{i}^{2}. \tag{20}\]  
**Remark 2**. : _The continuous-time control scheme is emulated by a computer with a sampler and a zero-order holder, and the control command is updated periodically. At each sampling instant, \(q(t)\) is monitored to determine whether or not to transmit the information (\(\eta_{i}(t)\)) to the observer. If \(\|q(t)\|\geq c_{q}\), the event-triggering time instant (\(t_{k}\)) is updated to the current time (\(t\)), and \(\eta_{i}(t)\) is transmitted to the observer. Otherwise, only \(\eta_{i}(t_{k})\) is available to the observer.
# Parametric Analysis of Tensegrity Plate-Like Structures: Part 2--Quantitative Analysis
## 4 Examples
### Sixty-Four-Module Tensegrity Plate-Like Structures  
The last taken into account structure consists of sixty four modified Quartex modules (Figure 13a). Three models (models: P16-1-P16-3) with different way of support were considered in Part 1 [45]. All analyzed models (Figure 13b) are structures with tensegrity features of 1 class and the quantitative analysis is carried for these models. The minimum prestress level depends on the way of support. This level there is much higher than that obtained for the previous models and for three analysed models, i.e., P64-1, P64-2, and P64-3 equals, respectively, \(S_{\text{min}}~{}=~{}16\) kN, \(S_{\text{min}}~{}=~{}45\) kN and \(S_{\text{min}}~{}=~{}38\) kN. The maximum prestress level is like in previous plates--\(S_{\text{max}}~{}=~{}60\) kN.  
Figure 12: Impact of the initial prestress level \(S\) on the: (**a**) global stiffness parameter \(GSP\), (**b**) effort of structure \(W_{\text{max}}\).  
Figure 13: Sixty-four-module tensegrity plate-like structure: (**a**) geometry, (**b**) models of structures with tensegrity features of class 1.  
The models were loaded by uniformly distributed load \(q~{}=~{}-1.5\)\(\mathrm{kN/m^{2}}\) applied on the top surface of the structure. The influence of the initial prestress level \(S\) on the non-zero displacements of 117 th node (\(q_{350},~{}q_{351}\)), the global stiffness parameter (\(GSP\)), and the effort (\(W_{max}\)) are illustrated in Figure 14. Additionally, in Table 6 values of analyzed static parameters for the minimum and maximum prestress level are shown. Due to the quite high value of the minimum prestress level, the effect of non-linearity is not very significant. For example, in the case of the model P64-3, the relative error between the second (II) and third (III) order theory is equal to 19.8% for \(q_{351}\) and 28.3% for \(q_{350}\) for \(S_{\mathrm{min}}\) and, respectively, 8.92% and 18.7% for \(S_{\mathrm{max}}\) (Figure 14a,b). Additionally, it can be observed that for the models P64-2 and P64-3, the direction of supports is not as significant as in the case of analogically supported plates built with four modules (models P4-2 and P4-3).  
Figure 14: Impact of the initial prestress level \(S\) on the: (**a**) displacement \(q_{351}\), (**b**) displacement \(q_{350}\), (**c**) global stiffness parameter \(GSP\), (**d**) effort of structure \(W_{\mathrm{max}}\).  
The insignificant effect of non-linearity is also noticeable in the case of the global stiffness parameter (Figure 14c) and the effort (Figure 14d). The dependences on the initial prestress level \(S\) are almost linear, especially for the models P64-2-P64-3. Like in the previous plates, the effort of cables is higher than that of struts. For example, for the model P64-1, the effort of cables is 53.2% higher than of struts for \(S_{\text{min}}\) and 59.7%. for \(S_{\text{max}}\).
## 1 Introduction  
Biofilms are usually defined as the coexistence of microbes and extracellular polymeric substances (EPS) [1, 2, 3]. Microbial populations secrete vast quantities of EPS and are embedded in the viscous EPS matrix, which has a highly self-organizing heterogeneous structure and can protect the biofilm from virulence and stripping [4, 5, 6, 7, 8]. Their potent sorptive capacity enables biofilms to grow even in environments extremely lacking in nutrition [3]. Secretion of EPS is therefore conducive to the adsorption and degradation of pollutants in subsurface porous media such as soil and rock. Biofilms are involved in environmental restoration and protection applications such as remediation of toxic metals, radionuclides, oil spill, and microplastics [9, 10, 11, 12, 13, 14, 15]. The growth of biofilms can be artificially stimulated to promote the degradation of pollutants or to prevent further infiltration of subsurface contaminant plume. However, biofilms growing in the subsurface can clog the porous media and even reduce its permeability, which may have negative effects near groundwater recharge wells, oil recovery water injection wells, or deep geothermal systems [16, 17, 18].  
Therefore, it is essential to evaluate the degree of clogging and analyze the spatial and temporal distribution of biofilms in porous media. Measurement and simulation of the biofilm formation process in porous media are essential for understanding the associated mechanisms that can provide a theoretical foundation for monitoring, qualitative and quantitative prediction, and data interpretation. Understanding the mechanisms that can promote or reduce biofilm growth in porous media is therefore important in the modeling and design of in situ biostimulating and/or alleviation of bioclogging schemes. Related experimental mechanism studies tend to use columns packed with typical porous media (e.g., soil sample or aquifer materials), which make the system opaque and thus not available for direct observation [1]. The invasive examination methods often damage the sample at the cost of terminating of the experiment. Hence, non-invasive and in situ methods are imperative to implement direct imaging or to provide spatiotemporal information of biofilm-mediated processes and the related impacts within the porous media [19; 20]. The modeling methods are restricted by the limited knowledge about such processes in three-dimensional (3D) pore space, and the long computational time for model resolution, whereas the existing experimental methods are restricted by the progress of instrumental performance.  
This review summarizes methods dealing with the relationships of the growth of biofilms, fluid hydrodynamics, and mass transport in porous media. To facilitate future work and promote advances in field site monitoring, we focus our attention on providing an overview of the non-invasive and in situ measurement methods as well as the mathematical simulation methods. We also discuss the application scope and provide our perspective on opportunities for in situ monitoring at field sites.