esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Challenges of MAC Protocol for IBSN
### Requirements of MAC Protocol Design for IBSN
#### 3.1.1 Energy-Efficiency  
To deliver the levels of comfort and unobtrusiveness required for widespread adoption, IBSN sensor nodes must be small and have energy sources that last for months and years, depending on the application. The size requirement obviously limits the size of the batteries that will power the nodes (energy scavenging is another option, but the amount of power available from such techniques is relatively small), so IBSN nodes must be extremely efficient in their energy usage. The IEEE 802.15.6 standard recommends a star topology, in which the coordinator node is rechargeable and is placed outside the body. Most network management activities, such as time slot allocation, routing information, and prioritization are performed by the coordinator node. By doing so, energy wastage, due to performing such management overheads by the client nodes, is minimized. However, in some of the closed-loop applications of IBSN, the implanted client nodes are also responsible for such management activities in order to provide high reliability [8]. Furthermore, energy efficiency is also increased by mechanisms that allow sensor nodes to enter a low-power sleep mode for a long time (several beacon periods) before transmitting/receiving. Low power WuR is required to achieve such long sleep duration without affecting the reliability.
## 4 Results
### Effect of Deep Rolling Offset
While a small offset like 0.1 mm enhances the uniformity of stress and  
Figure 16: (**a**) Total deformation map during the deep rolling process with 0.1 mm offset. (**b**) Time-based deformation variation during the deep rolling process. deformation, it also increases the number of tool passes required to cover the entire surface. This can prolong the process, but the trade-off is improved material properties due to the consistent deformation pattern. The 0.1 mm deep rolling offset applied in both the experimental and FEA setups ensures uniform deformation across the material surface, with the highest deformation reaching 2.744 mm directly under the tool. The time-based deformation variation shows consistent peaks, reflecting the tool's even pressure application throughout the process. This small offset helps achieve uniform stress distribution and enhances the material's mechanical properties, particularly its fatigue resistance. Figure 16b presents the deformation behavior during the deep rolling process with a 0.1 mm offset. The green line reflects maximum deformation (~2.744 mm) at points directly under the tool, while the blue line shows moderate deformation (~2.1 mm) at adjacent areas. The red line represents negligible deformation at regions unaffected by the tool's direct influence. This variation demonstrates the localized nature of the deep rolling process, where deformation is concentrated under the tool and decreases with distance, ensuring uniform stress distribution and improved material performance.
## 2 Materials and Methods
### Feature Selection Method  
The process of feature selection entails picking the most relevant subset from the pool of original features [39], reducing dimensionality, eliminating redundant features, and improving the performance and generalization ability of a model [40]. In this study, light gradient-boosting machine (LGBM) was employed to optimize the model. LGBM was used to identify the best features and rank them based on their importance values, ultimately selecting the features with importance values greater than the threshold, the 'average feature importance value' [41].
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 3 Problem Description and Formulation  
### System Model  
The multi-satellite cooperative computing system consists of multiple heterogeneous satellites, each tasked with various remote sensing operations. The primary objective is to achieve efficient task execution through task decomposition and resource mapping techniques. Each satellite \(S=\{s_{1},s_{2},\ldots,s_{n}\}\) has unique computational, storage, and communication capabilities, enabling resource sharing to enhance overall system performance. The system architecture comprises satellite nodes, communication links, and a task scheduling center, where components interact via inter-satellite communication for data transmission and processing.  
The core components of the system include satellite nodes, the task scheduling center, and the communication network. Each satellite node \(s_{i}\) possesses resources, characterized as computational resources \(r^{b}(s_{i})\), communication resources \(c(s_{i})\), and payload resources \(r^{p}(s_{i})\). A task \(m_{i}\) is decomposed into \(k\) subtasks \(\{T_{i1},T_{i2},\ldots,T_{ik}\}\), with each subtask \(T_{ij}\) requiring specific resources, expressed as  
\[R(T_{ij})=\left[r^{p}(T_{ij}),\,r^{b}(T_{ij}),\,c(T_{ij})\right], \tag{1}\]  
The task allocation matrix \(\mathbf{X}\) represents the assignment of subtasks to satellites:  
\[\mathbf{X}_{ij}=\begin{cases}1,&\text{if subtask $T_{ij}$ is assigned to satellite $s_{i}$},\\ 0,&\text{otherwise}\end{cases} \tag{2}\]  
Data flows begin with the observation phase, generating observation data \(D_{o}\), which are transmitted via communication links to other nodes or the task scheduling center, followed by compression and processing phases. The amount of observation data is represented as  
\[D_{o}=\omega_{s}^{p}\cdot r_{s}^{p}(t). \tag{3}\]  
The compressed data can be represented as  
\[D_{c}=\eta\cdot D_{o}, \tag{4}\]  
where \(\eta\) is the compression efficiency. The data flow model can be represented through task dependency matrices \(\mathbf{Z}\) and time constraints.
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.7 Three-Dimensional Networking  
The evolution to 6G involves integrating ground and airborne networks, forming a three-dimensional (3D) landscape that extends vertically. Low-orbit satellites and UAVs contribute to 3D base stations, providing coverage even in challenging terrains like oceans and mountains [224; 225].
# Role of Kir4.1 Channel in Auditory Function: Impact on Endocochlear Potential and Hearing Loss
## 5 Conclusions  
The Kir4.1 channel has an objective role in maintaining cochlear fluid homeostasis and endocochlear potential. To date, the relationship between deafness and mutations of the gene coding for kir4.1 has been demonstrated in two syndromes, Pendred and EAST/SeSAME, which are closely tied to the homeostasis of cochlear fluids. In several knockout mouse studies on the development of the auditory system, it has been consistently shown that the absence or deficiency of Kir4.1 channel expression affects the generation of endocochlear potential. Recent investigations suggested that an impairment of the Kir4.1 channel function might be linked to progressive hearing loss in the elderly. This is a consequence of the deterioration of the physiological condition of the vascular stria and/or spiral ligament (both involved in the K+ cycle). The extensive clinical and experimental evidence, which encompasses the complicated network of ion channels and pumps, and the gap and tight junctions between cells in different tissue layers, does not allow for a clear overview that provides effective suggestions for directing therapies aimed at resolving the aforementioned auditory pathologies. Our view is that the most likely drugs are those that can regulate blood pH or prevent cell damage, for instance, avoiding the production of reactive oxygen substances and the accumulation of free radicals. It has been shown in studies on ototoxicity that aminoglycosades can inhibit the function of the Kir4.1 channel. The imbalance in potassium homeostasis is inevitable when Kir4.1 is silenced, as it acts as a channel that compensates for the deficiency of other potassium channels. In this context, it is feasible to develop new therapies that increase gene expression to prevent damage from Kir4.1 deficiency. Preventing changes in the homeostasis of cochlear fluids would be crucial for the maintenance of endocochlear potential.  
**Author Contributions:** Conceptualization, L.A. and G.A.; methodology, S.F. ([PERSON]) and F.H.; data curation, S.F. ([PERSON]) and F.H.; writing--original draft preparation, L.A., G.A., S.F. ([PERSON]) and F.H.; writing--review and editing, L.A., G.A., S.F. ([PERSON]), F.H., S.F. ([PERSON]), G.M. and D.B.; visualization, L.A., G.A., S.F. ([PERSON]), F.H., G.M., D.B., S.F. ([PERSON]) and E.Z.; supervision, L.A. and G.A.; funding acquisition, L.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by MIUR, grant number PRIN-2022 YRBE8B.  
**Acknowledgments:** Figures were created under license using BioRender.com, [[https://biorender.com/](https://biorender.com/)]([https://biorender.com/](https://biorender.com/)).  
The authors declare no conflicts of interest.
# Indoor MIMO-VLC Using Angle Diversity Transmitters
## 3 MIMO-VLC Using ADTs
### Structure of ADT  
We assume that each ADT consists of totally \(N\) LEDs, including one center LED and \(N-1\) inclined side LEDs. More specifically, the center LED is placed horizontally, while the \(N-1\) side LEDs are inclined with the same inclination angle. It is further assumed that all the LEDs in the ADT have the same optical and electrical performance and the same radius \(r\). The top view and the side view of the designed ADT are illustrated in Figure 2a,b, respectively. It can be seen from Figure 2a that the side LEDs are placed at the circumference of a circle with radius \(R\), while the center LED is located at the center of the circle. Moreover, the gap between the center LED and the side LED is \(l\), and the azimuth angle and the inclination angle of the \(n\)-th side LED are \(\omega_{n}\) and \(\theta\), respectively. The azimuth angle \(\omega_{n}\) can be given as follows:  
\[\omega_{n}=\frac{2\pi(n-1)}{N-1},n=1,2,\ldots,N-1. \tag{6}\]  
For example, when there are four LEDs (\(N=4\)) in the ADT, the corresponding azimuth angles of the three side LEDs are \(0^{\circ},120^{\circ},240^{\circ}\), respectively.
## 2 RPEM Economic Evaluation Method  
### The Basic Principles of RPEM  
RPEM, also known as moment estimation method, is often used to purposefully select points composed of special values under the condition of unclear distribution of random variables. The mean and variance are used to find the first four moments of the functions point by point and to calculate the reliability of the evaluated project. This method has a simple principle, fewer calculations, and does not need to consider the probability distribution of basic input random variables in advance. The processing of related variables is simple, while the requirements of engineering accuracy can be met [31]. The calculation process of RPEM is shown in Figure 1.  
Figure 1: Flow chart of RPEM.  
When using RPEM for mine technical and economic evaluation, the target state function is first established: \(Z=Z\) (\(x_{1}\), \(x_{2}\), \(\ldots\), \(x_{i}\), \(\ldots\), \(x_{n}\)), among which, \(x_{i}\) represents a random variable subject to normal distribution, that is, mining and dressing cost, ore geological grade, concentrate sales price, dressing recovery rate, and other indicators; next, the value points are selected, and within the interval (\(x_{i}\) min, \(x_{i}\) max), two value points are symmetrically seleceted (such as the positive and negative standard deviation of the mean of the state variable). Using \(\mu\), \(\sigma\) representing the mean and standard deviation of variables, there are:  
\[x_{i1}=\mu_{xi}+\sigma_{xi} \tag{1}\]  
\[x_{i2}=\mu_{xi}-\sigma_{xi} \tag{2}\]  
At this point, there are \(2^{n}\) value points and \(2^{n}\) combinations of corresponding value points for n random variables. When the probabilities of these \(2^{n}\) combinations occurring are equal and independent of each other, the first moment of the mean function is:  
\[\mu_{z}=\frac{1}{2^{n}}\sum_{j=1}^{2^{n}}Z_{j} \tag{3}\]  
When a group of variables is related and the probability of occurrence is not equal, the probability of occurrence \(P_{j}\) for each group of variables is related to its correlation coefficient:  
\[P_{j}=\frac{1}{2^{n}}(1+e_{1}e_{2}\varphi_{12}+e_{2}e_{3}\varphi_{23}+\cdots+e _{(n-1)}e_{n}\varphi_{(n-1)^{n}}) \tag{4}\]  
where \(j=1\), \(2\), \(3\), \(\ldots\), \(n\). When \(x=x_{i1}\), \(e_{i}=1\). When \(x=x_{i2}\), \(e_{i}=-1\); \(\varphi_{ij}\) is the correlation coefficient of a random variable. The first moment of the state function is:  
\[\mu_{z}=\frac{1}{2^{n}}\sum_{j=1}^{2^{n}}P_{j}Z_{j} \tag{5}\]  
The second moment is:  
\[\sigma_{z}^{2}=\sum_{j=1}^{2^{n}}P_{j}Z_{j}^{2}-\mu_{z}^{2} \tag{6}\]  
Reliability indicators \(\beta\) can be established as shown in Equation (7):  
\[\beta=\frac{\mu_{z}-1}{\sigma_{z}} \tag{7}\]  
Then, the calculation of reliability \(R\) can be performed using:  
\[R=\phi(\beta) \tag{8}\]
## 2 Preliminaries and Problem Formulation
### Dual-Mode Security Control Scheme
\begin{split} u(t)&=\alpha(t)K_{r(t)}^{a}(x(t-\tau( t))-e_{a}(t))+(1-\alpha(t))\\ &\times\xi_{r(t)}K_{r(t)}^{b}(x(t-\tau(t))-e_{a}(t)),\;t\in\left[t _{n}^{\ell},t_{n}^{\ell+1}\right)\end{split}  \tag{11}\]  
where \(\tau_{\min}\) and \(\tau_{\max}\) denote the minimum and maximum time delays, respectively. Figure 2: Evolution of update data in the presence of DoS attacks. In view of the designed resilient event-triggered rule (8) and dual-mode security control protocol (11), the resulting closed-loop systems can be described as  
\[ \begin{cases}\dot{\pi}(t)=A_{r(t)}x(t)+\left[\bar{B}^{u}_{r(t)}+(\alpha(t)-\alpha )\bar{B}^{u}_{r(t)}\right]\bar{K}_{r(t)}\\ \quad\times\left(x(t-\tau(t))-e_{a}(t)\right)+B^{\omega}_{r(t)}\omega(t)\\ z(t)=\mathbb{C}_{r(t)}x(t)+\left[\bar{D}^{u}_{r(t)}+(\alpha(t)-\alpha)\bar{D}^ {u}_{r(t)}\right]\bar{K}_{r(t)}\\ \quad\times\left(x(t-\tau(t))-e_{a}(t)\right)\end{cases}  \tag{12}\]  
where \(\bar{\kappa}=1-\alpha\), \(\bar{K}_{r(t)}=\text{col}\{K^{a}_{r(t)},K^{b}_{r(t)}\}\), and  
\[\bar{B}^{u}_{r(t)}=\left[ \begin{array}{cc}\alpha&\bar{\alpha} \bar{\alpha}_{r(t)}\\ \end{array} \right]\otimes B^{u}_{r(t)},\bar{B}^{u}_{r(t)}=\left[ \begin{array} []{cc}1&-\xi_{r(t)}\\ \end{array} \right]\otimes B^{u}_{r(t)},\] \[\bar{D}^{u}_{r(t)}=\left[ \begin{array}{cc}\alpha&\bar{\alpha} \bar{\alpha}_{r(t)}\\ \end{array} \right]\otimes D^{u}_{r(t)},\bar{D}^{u}_{r(t)}=\left[ \begin{array} []{cc}1&-\xi_{r(t)}\\ \end{array} \right]\otimes D^{u}_{r(t)}.\]  
In general, malicious attackers can launch three different types of DoS attacks on the CPS communication network, including periodic DoS attacks, stochastic DoS attacks, and time-constrained DoS attacks as shown in Figure 3. From the perspective of attack concealment, we consider time-constrained DoS attacks, whose properties can be characterized by frequency and duration. **Assumption 1** (DoS Frequency [12]). : _There exist scalars \(\eta\in\mathbb{R}_{\geq 0}\) and \(\tau_{D}\in\mathbb{R}_{\geq\underline{\Delta}}\) such that_  
\[n(t_{s},t_{f})\leq\eta+\frac{t_{f}-t_{s}}{\tau_{D}}\]  
_for any \(0\leq t_{s}<t_{f}\)._  
**Assumption 2** (DoS Duration [12]). : _There exist scalars \(\xi\in\mathbb{R}_{\geq 0}\) and \(T\in\mathbb{R}_{\geq 1}\) such that_  
\[|\mathcal{H}(t_{s},t_{f})|\leq\xi+\frac{t_{f}-t_{s}}{T}\]  
_for any \(0\leq t_{s}<t_{f}\)._
## 2 Related Work  
### Traversability Detection  
Determining drivability in off-road environments is crucial for enhancing the safety and efficiency of autonomous driving systems. Consequently, considerable research [7; 8; 9; 10; 11; 12; 13] has been conducted to detect traversable regions. [PERSON] et al. [14] introduced the RUGD dataset along with a traversability estimation approach for off-road environments. Although their work provides valuable insights into traversable regions based on unstructured terrain, it is limited to categorizing drivability into six discrete levels, which restricts the granularity of the traversability information. [PERSON] et al. [15] proposed OFFSEG, a semantic segmentation framework designed for off-road driving. The study focused on handling irregular terrain and complex class boundaries by employing an advanced deep learning architecture. However, performance degradation was observed when the model encountered environments that differed from those in the training dataset, highlighting the need for improvements in its generalization capabilities. [PERSON] and [PERSON] [16] proposed BumpyPatch for analyzing ground textures using 3D LiDAR data to detect less bumpy paths. This approach involves complex preprocessing, which may hinder its application in dynamic environments where real-time decision-making is critical. In this study, we address the limitations of previous studies by introducing a probabilistic representation of drivability (%), rather than categorizing traversability into fixed levels. This allows a more efficient deployment in off-road environments for unmanned ground vehicles (UGVs). Additionally, in scenarios where detailed traversability information is necessary, the system can be simplified by omitting scenes with low drivability probabilities, thereby optimizing the system efficiency.
# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
## 3 Fuzzy Clustering and Neuro-Fuzzy Modeling  
### Database  
The database for this study was built from 17 local sites of South Korea, as shown in Figure 4, along the coastal line, and six sites were used for verification of the model. The closed circle indicates the location where training data for training the fuzzy membership function were extracted, and the open square indicates the location where the verification data for verifying the completed fuzzy soil classification system were obtained. Table 1 summarizes the site location, number of PCPTs, and soil type classified by USCS. The measured values from piezocone penetration tests--\(q_{c},\ f_{s},\ u_{bt}\)--were averaged within the interval of 5 to 10 cm and picked up at the same depth where SPT and undisturbed samples were taken. The database contained 5173 data points in total. Table 2 shows the classification results of the database into six categories following USCS. A huge number of clayey soil (CH, CL) samples were, included while the number of silty soil and sandy soil samples was relatively small due to the difficulty in soil sampling with a thin wall tube sampler.  
Figure 4: Seventeen local sites for this study.  
Figure 3: Fuzzy membership functions developed by [PERSON] and [PERSON] [7].
## 3 Background  
### Edge Computing  
The world is becoming more digitalized, producing enormous amounts of data from across various industries that need to be processed quickly for real-time applications. Although small- and medium-sized enterprises and research organizations can now perform computations without owning infrastructure thanks to cloud technologies, end users still need to send and receive data to and from data centers [11].  
On the other hand, edge computing (Figure 1) is perfect for real-time applications since it computes at the location of the data. Computational power is brought closer to the locations where data are generated and consumed by positioning edge servers closer to end users. This lowers latency and allows for faster data processing. By allocating computing resources among cloud data centers, edge cloud architecture uses gadgets such as smartphones and Internet of Things sensors as endpoints and data sources to improve data processing, storage, and transmission efficiency.  
The primary benefit of edge computing is its capacity to process data locally, at, or close to the location where it is generated. Applications where quick decision making is critical, such as industrial automation, smart cities, autonomous cars, and healthcare, require local processing capabilities. Edge computing increases the speed and efficiency of data processing, resulting in faster insights and actions by reducing the need to transfer massive volumes of data to remote cloud data centers [12].  
Infrastructure for edge computing usually comprises edge servers and other devices that are positioned carefully to perform particular tasks. These edge servers, which are often located at the edge of the network and closer to end users, offer processing capabilities to support cloud data centers. Better resource usage, increased scalability, and enhanced resilience to network outages and congestion are all made possible by this distributed architecture. Furthermore, edge devices--such as smartphones and Internet of Things  
Figure 1: Edge computing architecture.  
sensors--serve as data sources and processing nodes, adding to the ecosystem's adaptability and durability [2].  
Despite its numerous advantages, edge computing also presents challenges, particularly in terms of resource management, security, and interoperability [13]. The distributed nature of edge infrastructures requires sophisticated algorithms for efficient resource allocation, task scheduling, and load balancing. Additionally, ensuring the security and privacy of data processed at the edge is critical, given the potential exposure to various threats. Interoperability between diverse devices and systems is another key consideration, necessitating standardization and robust communication protocols.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 2 Measurement Geometry under LEO Augmentation  
The GNSSs have experienced rapid development in the past decades. As of 1 February 2022, the GPS has 30 operational satellites, among which 16 are operational satellites transmitting signals on the L1 (1575.42 MHz), L2 (1227.6 MHz) and L5 (1176.45 MHz) for positioning and navigation purposes [16]. At the same time, the Galileo system of the European Union (EU) is also increasing its size and on its way to a full constellation, having 3 In-Orbit-Validation (IOV) satellites left operational in the system, and 19 Full Operational Capability (FOC) satellites usable by 1 February 2022. All the currently operational Galileo satellites are transmitting five-frequency signals on E1, E5a, E5, E5b and E6, which have overlapped frequencies on E1 and E5a with the GPS on L1 and L5. In 2020, the Chinese BDS completed its full constellation. Until 1 February 2022, it has 42 operational satellites, including 12 satellites of BDS-2 and the remaining 30 of BDS-3. In addition to 24 MEO satellites, different from the GPS and the Galileo, the BDS also has 8 Geostationary (GEO) satellites and 10 Inclined Geosynchronous orbit (IGSO) satellites. The BDS also occupies a relatively complicated frequency structure, having the BDS-2 satellites transmitting signals on B1I (1561.098 MHz), B2I (1207.14 MHz) and B3I (1268.52 MHz) and the BDS-3 satellites sending open signals in addition to B1I, B3I and also on B1C (1575.42 MHz), B2a (1176.45 MHz) and B2b (1207.14 MHz) [17]. The frequency structure of the three constellations is illustrated in Figure 1. It can be observed that the L1 of 1575.42 MHzand the L5 of 1176.45 MHz are the only two frequencies that are currently shared by all three systems.  
For a dual-frequency user, Figure 1 also shows that the tracking combination of L1 and L5 would lead to a maximal number of usable satellites from these three systems, i.e., 68 in total. In addition to the satellite numbers, the signals on L5 were also shown to have smaller code noise [18] and to be more robust against interferences [19]. Due to all these advantages, these two frequencies are planned to be used for the US/EU dual-frequency multi-constellation (DFMC) positioning service in aviation, both for the case using the GNSS broadcast ephemeris with the advanced Receiver Autonomous Integrity Monitoring algorithm (ARAIM) [20; 21], and the case using DFMC Satellite-Based Augmentation System (SBAS) corrections [22; 23].