esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 6 Edge Resource Optimization
### Problem Formulation
#### 6.2.3 Objective Functions  
The goal is to find the optimal mapping of virtual resources to edge servers for the next day (\(X_{ij}\), \(Y_{j}\quad\forall i=1,\ldots,n,\quad\forall j=1,\ldots,m\)) that meets particular objective functions. We explore three different objective functions, as follows:  
1. To minimize the total number of cores used in the edge infrastructure, which can be formulated as follows: \[\text{Minimize}\quad Total\_cores\] (4) where \[Total\_cores=\sum_{j=1}^{m}S_{j}^{c}\cdot Y_{j}\] (5) In the particular scenario where all servers are homogeneous and have an equal number of cores, i.e., \(S_{j}^{c}=S^{c},\forall j=1,\ldots,m\), this objective function is equivalent to minimizing the number of servers in use, which can be formulated as follows: \[\text{Minimize}\quad Total\_servers\] (6) where \[Total\_servers=\frac{Total\_cores}{S^{c}}=\sum_{j=1}^{m}Y_{j}\quad\text{if} \quad S_{j}^{c}=S^{c},\forall j=1,\ldots,m\] (7)
2. To balance the average load of all the cores used in the edge infrastructure. To formulate this objective function, we first define the average (daily) load per core of an edge server, \(S_{j}^{load}\), as follows: \[S_{j}^{load}=\frac{\sum_{i=1}^{n}\sum_{t=0}^{23}X_{ij}\cdot V_{i}^{c}\cdot V_{ i,t}^{u}}{24\cdot S_{j}^{c}},\quad\forall j=1,\ldots,m\] (8) Then, the load balancing objective function can be expressed as follows: \[\text{Minimize}\quad Max\_load\] (9) where \[Max\_load=max\{S_{j}^{load},\quad\forall j=1,\ldots,m\}\] (10) It is worth noting that the maximum function (\(max\)) is not linear. Therefore, in order to incorporate it into the ILP formulation, it is necessary to transform it into one or more linear expressions. This can be achieved by re-formulating the \(Max\_load\) function as follows: \[Max\_load>=S_{j}^{load}\quad\forall j=1,\ldots,m\] (11)
3. To minimize the number of virtual resources affected by latency violations, regarding the preferred location of each virtual resource, \(Pref_{ij}\), and the actual location selected for this virtual resource, \(X_{ij}\), which can be formulated as follows: \[\text{Minimize}\quad Total\_latency\_violations\] (12) where \[Total\_latency\_violations=\frac{\sum_{i=1}^{n}\sum_{j=1}^{m}|Pref_{ij}-X_{ij}|}{2}\] (13)As in the previous case, the absolute value function is also non-linear. Therefore, it is necessary to transform it into a linear expression using the following auxiliary variable, \(D_{ij}\):  
\[\begin{split} D_{ij}>=Pref_{ij}-X_{ij}&\forall i=1, \ldots,n\quad\forall j=1,\ldots,m\\ D_{ij}>=-(Pref_{ij}-X_{ij})&\forall i=1,\ldots,n \quad\forall j=1,\ldots,m\\ D_{ij}>=0&\forall i=1,\ldots,n\quad\forall j=1, \ldots,m\end{split} \tag{14}\]  
Then, the \(Total\_latency\_violations\) function can be re-formulated as follows:  
\[Total\_latency\_violations=\frac{\sum_{i=1}^{n}\sum_{j=1}^{m}D_{ij}}{2} \tag{15}\]
# Research on the Parametric Design and Application of Ceramic Modeling Based on Python
## 2 Theoretical Basis and Technical Framework of Parametric Design
### Parametric Study of Ceramic Shape  
Ceramic modeling parametric design is a highly systematic and meticulous design process that achieves flexible and precise implementation through three closely interconnected core layers: the parameterized layer, the code layer, and the application layer [12] (see Figure 1).  
The parameterized layer serves as the foundation of the entire study, constructed based on established script logic. Its primary task is to extract editable logical abstraction parameters and allow designers to make flexible modifications and adjustments to these  
Figure 1: Roadmap of ceramic modeling parametric technology.  
parameters. This process ensures that the parameter layer can precisely control the design of ceramic modeling according to the predetermined plan, thereby enhancing the flexibility and accuracy of the design. The code layer utilizes the Python programming language to construct the ceramic 3D digital modeling. At this level, the system delves into the deep relationships between parameters and parameters, as well as between parameters and logic, establishing the ceramic 3D digital modeling based on Python language. The work of the code layer includes data sources and preprocessing, parameter extraction, surface generation, and other stages. Leveraging the powerful capabilities of the Python programming language, the code layer can efficiently process large amounts of data and accurately generate ceramic modeling surfaces that meet design requirements. The application layer is a user-facing plugin that provides various ceramic modeling options and five core functions based on parametric design. Additionally, the application layer has also established an efficient indexing program allowing users to select different ceramic shapes for editing or generation while enjoying the convenience brought by a simplified user interface. It can quickly respond to user operations and query requests. The model visualization function enables users to intuitively view and adjust the generated ceramic shape models, while the continuously expanding model library provides users with more styling options and inspiration.  
The parametric design of ceramic shapes has successfully achieved a flexible and efficient process through the deep integration of parametric layers, code layers, and application layers. This layered design not only enhances the flexibility and scalability of the system but also significantly improves the user experience. Through this architecture, designers can more efficiently create high-quality ceramic shapes that meet personal or market demands.
# Statistical Process Control with Intelligence Based on the Deep Learning Model
## 4 Experiment and Discussion
### The Influence of the Number of Network Layers  
In the field of deep learning, this view is recognized by scholars. With the deepening of the network, the performance of it will be improved. This is because multilayer structure can improve the capacity and flexibility of the model [45]. However, some scholars a point out that the increase of network layers will increase the risk of over-fitting [51]. Therefore, this experiment compares the influence of the number of different network layers on training loss and testing accuracy, as shown in Figure 8. In this experiment, the network structure of Figure 5 is used, that is, the output of the first forward layer of the network is the input of the second forward layer, and the output of the first backward layer is the input of the second backward layer. According to this stacking rule, a multilayer network is formed. The data and training parameters used are the same as the above experiments. The training loss and testing accuracy of different networks are collected.  
Figure 8: The testing accuracy and training loss under different number of layers.  
Figure 7: The training time and loss under different batch sizes.  
It can be seen from Figure 8, with the increase of the number of layers of the Bi-LSTM network, the training loss of the training set data continues to decrease, which shows that the network fits these training data better and better. On the contrary, the recognition rate of the trained multilayer Bi-LSTM network to the data of the test set increases first and then decreases. This is a typical over fitting phenomenon, that is, with the increase of the number of layers, the network continues to deepen, the fitting degree of the training set data is too high, and the generalization ability of the test set data is reduced. According to the above experimental results, it is finally determined that the number of layers of multilayer Bi-LSTM is 3.
## 3 Results
Most of the DCs fit equation E1 with a high confidence (R\({}^{2}\) > 0.9 for 41 curves out of 56) while time constants range from 7 to 280 s. Four curves resulted in a very poor fit and are indicated by italic numbers in Table 1. As expected, the time constants for periods 1-4 and 2-3 yielded similar results. According to these results, if a diver stays more than 3 min at a depth where the temperature exhibits a stepwise change (thermocline), a noticeable change in temperature of around 50% will be observed from the log of a DC. The resulting protocol for divers is described below:  
Step 1. Mark the geolocation of the dive site. Step 2. Make sure that the DC time and date are set correctly. \begin{table}
\begin{tabular}{c c c c} \hline
**Computer** & **Dive** & **1/k** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{Sensus Ultra No.2} & 1 & 192.0657 & 0.9998 \\  & 2 & 210.8371 & 0.9978 \\  & 3 & 202.1318 & 0.9998 \\  & 4 & 203.6378 & 1.0000 \\ \hline \multirow{4}{*}{Sunuto D4} & 1 & 71.8781 & 0.9710 \\  & 2 & 93.6115 & 0.8035 \\  & 3 & 31.0285 & 0.8399 \\  & 4 & 91.5816 & 0.9661 \\ \hline \multirow{4}{*}{Sunuto D6} & 1 & 203.3433 & 0.8472 \\  & 2 & 7.3099 & 0.9009 \\  & 3 & 9.8825 & 0.7992 \\ \hline \multirow{4}{*}{Sunuto} & 1 & 39.4032 & 0.9654 \\  & 2 & 25.6803 & 0.3220 \\ \cline{1-1}  & 3 & 22.7505 & 0.0492 \\ \cline{1-1}  & 4 & 50.1622 & 0.9692 \\ \hline \multirow{4}{*}{Uwatec Galileo Sol} & 1 & 99.3289 & 0.9597 \\ \cline{1-1}  & 2 & 2.6356 & _0.0963_ \\ \cline{1-1}  & 3 & 94.1056 & 0.8101 \\ \cline{1-1}  & 4 & 102.1418 & 0.9663 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 7: Sample curve fit for the dive computer Mares Smart. The resulting time constant is 66.06 s with an R\({}^{2}\) of 0.9877. Step 3. Mark the DC model and serial number. Step 4. During the pre-dive briefing, if dive team members are not familiar with the CS project, inform them that you will stop for at least 3 min to mark the thermocline. Step 5. Before the dive, avoid exposing your DC to extreme temperature differentials; if possible, let it be immersed in water at a depth shallower than the one that triggers depth profile logging and decompression computing (30 cm is an adequate depth to immerse for most of the available DCs in the market). Step 6. During the dive, when you will stop for at least 3 min, make sure that this stop does not impose any danger to you and other team members. Such dangers might include but not limited to disadvantageous decompression obligations, drifting with the current or boat accidents. Step 7. Do not limit your contribution with a single dive at the same location. If possible, provide multiple dive data samples for the same location.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 6 Challenges and Limitations
### Technical Challenges
#### 6.1.1 Data Privacy and Security
Organizations must carefully assess the security measures of cloud providers, consider the implications of storing data in different geographic locations, and possibly opt for private or hybrid cloud solutions to maintain greater control over their data [164]. LLMs are vulnerable to data leakage risks, especially when handling sensitive information such as voyage routes, crew data, or vessel technical details. For instance, there is a risk of reproducing fragments of confidential data in model responses. To mitigate these risks, the following measures should be implemented:  
* Data encryption--All data should be encrypted both in transit and at rest. * Local processing--Deploying models on onboard local servers instead of cloud systems minimizes the risk of data breaches. * Differential privacy--Incorporate techniques that prevent individual data points from being revealed in model outputs. In summary, addressing data privacy and security is paramount when integrating LLMs into maritime operations. Organizations must implement comprehensive cybersecurity strategies, ensure compliance with relevant data protection laws, and adopt best practices in data management to protect sensitive information and maintain trust among stakeholders.
## 3 Proposed Optimal Orchestration Process
### Automation of Algorithm Selection
#### 3.5.1 Automated Algorithm Selection Using LazyPredict  
LazyPredict is particularly advantageous due to its ability to provide a comprehensive overview of various machine learning algorithms and their performance metrics without extensive prior configuration. This capability allows for rapid experimentation and iteration, which is crucial in dynamic environments where requirements may change frequently.  
Additionally, LazyPredict streamlines the initial selection process, significantly reducing the time needed to identify suitable algorithms for specific tasks.  
Figure 7 illustrates the implementation of the LazyPredict automation workflow process. The implementation demonstrates the framework's capability to analyse multiple algorithms simultaneously, generate performance metrics for comparison, visualise results for algorithm selection, and adapt to changing environmental conditions.  
Figure 7: LazyPredict process flow.
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 4 Through Metal Communication System
### Prototype  
The built prototypes for the outside and inside subsystems are shown in Figure 17. A communication protocol for this specific application was designed to enable automatic power transfer to the inside subsystem and bidirectionally data communications using one pair of coils. Four different operation modes were defined: Wireless Power Transfer (WPT), Tx, Rx and Sleep. The operation of the system is as follows:  
Figure 16: Measured harvested AC and DC power and total efficiency versus equivalent input impedance \(Z_{in}\).  
Figure 15: Complete schematic of harvesting subsystem.  
1. Outside subsystem enters WPT mode, and transmits a high-power signal to charge the inside subsystem through the 1 mm-thick aluminum plate.
2. Inside subsystem harvests energy. If enough power is available, it enters Rx mode and waits for an incoming data stream from the outside subsystem, otherwise toggles between Sleep and Rx modes.
3. Outside subsystem finishes its WPT mode and power transfer stops. Then, Tx mode begins and the outside subsystem transmits a data frame at a data rate of 100 bps.
4. Inside subsystem is in Rx Mode and demodulates the incoming data. If data is received correctly, the inside subsystem enters Tx mode.
5. Inside subsystem transmits data frame and goes back to Rx mode.
6. Outside subsystem is in Rx mode. The data is received and demodulated.
7. System is ready to start the cycle again.  
Using this configuration, the outside subsystem transmits a power signal with a \(P_{tx}=35\) W during 7 s to fully charge the inside subsystem. Then, power transfer stops and data are transmitted. At the inside subsystem, the receiver demodulates the incoming data and operates during 1 s with a power consumption of 19 mW. Then, it transmits data to the outside subsystem during 1 s with a power consumption of 39 mW. The inside subsystem consumes 240 \(\upmu\)W in Sleep mode, 19 mW in Rx Mode, and 39 mW in Tx Mode. Using only the harvested power from outside, the inside subsystem can transmit and receive data for more than 2 s.
## 4 Experiments
### Effectiveness of Enriching Both Intra-Variation and Invariant Features
#### 4.4.3 Ablation Study  
We further evaluated the contribution of the enriched invariant features and the enriched images on improving SSPP face recognition performance. The comparison results on FERET-b and LFW-a are shown in Table 3. It can be found that the recognition accuracy improved after we fused the match scores of the original images (denoted by \(O\)) with the match scores of enriched invariant features extracted from albedo (denoted by \(A\)) or the match scores of features extracted from the enriched images (denoted by \(E\)), even though the original images already set up relatively high baselines. Figure 6 shows the false rejection rates at different thresholds when fusing different match scores on the FERET-b and LFW-a databases. As can be seen, our proposed matchers \(A\) and \(E\) significantly reduced the FRR of the original matcher \(O\). It is worth mentioning that both \(A\) and \(E\) helped in reducing FRR. However, according to the experimental results, \(A\) (using albedo-based features) was more effective than \(E\) (using augmented images).
###### Abstract  
An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging. Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals.  
indoor positioning system; acoustic ranging; Wi-Fi fingerprinting; data and model dual-driven; adaptive unscented Kalman filter 2023  
2  
[PERSON], [PERSON], [PERSON], R. Hybrid Indoor Positioning System Based on Acoustic Ranging and Wi-Fi Fingerprinting under NLOS Environments. _Remote Sens._**2023**, _15_, 3520. [[https://doi.org/10.3390/rs15143520](https://doi.org/10.3390/rs15143520)]([https://doi.org/10.3390/rs15143520](https://doi.org/10.3390/rs15143520))
# Sphere Fitting with Applications to Machine Tracking
### Related Work  
#### 1.1.1 Thinnest Sphere Annulus  
For fitting a sphere to a set of points, the acceptable compromise is the approximation of the thinnest sphere annulus (called the \(\ell_{\infty}\)-norm of the distance vectors), that minimizes maximum of distances to the input set, \(\max_{p\in P}\mid\|p-x\|-r\mid\) over every \(r\in\mathbb{R}\) and \(x\in\mathbb{R}^{d}\).  
The main drawback of this optimization function is sensitivity to a single point (outlier) in the input data set that may completely change the optimal solution even compared to the sum of squared distances that was discussed in the previous section.
## 3 Permeability Calculation Method  
### Klinkenberg Effect  
When gas flows in a low permeability porous medium, in contrast to liquid laminar flow, there is a non-zero velocity at the contact with the solid phase, known as gas slippage or the Klinkenberg effect. Gas slippage only occurs when the mean-free-path of the gas molecules and the diameter of the flow channel are comparable, and the lower the rock permeability, the more pronounced the Klinkenberg effect [12; 24]. Apparently [12], the mean-free-path of the gas molecules is equivalent to the pore size (10\({}^{-7}\)\(\sim\)10\({}^{-9}\) m) of the salt rock when the gas pressure is 0.06-6.00 MPa, and the Klinkenberg effect is significant at this condition, so calibration is needed. [PERSON] et al. [28] found that the slippage effect has a great influence on gas seepage when the permeability of a reservoir sandstone is less than 1 mD (millidarcy).  
[PERSON] [29] observed through experiments that in single-phase gas flow through a porous medium, the permeability is:  
\[K_{g}=K_{\infty}(1+\frac{b}{\overline{p}}) \tag{6}\]  
where \(K_{g}\) is the gas permeability of the rock in millidarcy (mD,1 mD = 10\({}^{-3}\)\(\mu\)m\({}^{2}\)); \(K_{\infty}\) is the absolute permeability (or Klinkenberg permeability) in mD; \(b\) is the slip factor (the strength of the slippage effect in the porous medium in MPa); and \(\overline{p}\) is the average gas pressure in MPa.  
[PERSON] gave this expression for the slip factor \(b\):  
\[b=\frac{4C_{1}\overline{\lambda}p}{r} \tag{7}\]  
where \(C_{1}\) is a constant (close to one); \(r\) is the average radius of the pore in \(\mu\)m; \(\overline{\lambda}\) is the mean-free path of gas molecules under average gas pressure, which is given by [30]:  
\[\overline{\lambda}=\frac{C_{2}\mu(\frac{RT}{M})^{\frac{1}{2}}}{p} \tag{8}\]  
where \(C_{2}\) is a constant (close to two); \(\mu\) is the gas viscosity in mPa\(\cdot\)s (10\({}^{-3}\) Pa\(\cdot\)s); \(R\) is the universal gas constant (0.86367 L\(\cdot\)MPa\(/\)(g\(\cdot\)mol)); \(T\) is the absolute temperature, K; \(M\) is the relative molecular weight of the gas.  
Substituting Equation (8) into Equation (7):  
\[b=\frac{a\mu(\frac{T}{M})^{\frac{1}{2}}}{r} \tag{9}\]  
where \(a=4C_{1}C_{2}R^{\frac{1}{2}}\).  
Clearly, the smaller the pore throat, the higher the value of \(b\)[31] and the lower the specimen permeability. The relationship between permeability, porosity and pore throat radius for a circular equivalent pore throat is given by [32; 33]:  
\[K=\frac{\phi r^{2}}{8} \tag{10}\]  
where \(K\) is the liquid permeability in mD, comparable to the Klinkenberg permeability (\(K_{\infty}\)); and \(\phi\) is rock porosity.
## 3 Kinematic Analysis
### Singularity Analysis  
When a mechanism is in a singular configuration, the degree of freedom of the mechanism will change, which will seriously affect the performance of the mechanism. A singularity analysis of the mechanism under consideration is very important for research on parallel mechanisms.  
According to the Jacobian matrix, the singular configuration of the proposed mechanism is analyzed. The relationship between the input velocity \(\dot{\mathbf{q}}\) of the driving pair and the output velocity \(\mathbf{v}\) of the moving platform is obtained after the derivation of the two sides of Equations (22)-(24):  
\[\mathbf{J}_{q}\,\dot{\mathbf{q}}=\mathbf{J}_{p}\mathbf{v} \tag{25}\]  
where \(\mathbf{J}_{q}\) is the input-velocity Jacobian matrix, and \(\mathbf{J}_{p}\) is the output-velocity Jacobian matrix. Their expressions are as follows:\[\mathbf{J}_{q}=\begin{bmatrix}2l_{1}&0&0&0\\ 0&2l_{2}&0&0\\ 0&0&2l_{3}&0\\ 0&0&0&2l_{4}\end{bmatrix};\mathbf{J}_{p}=\begin{bmatrix}f_{11}&f_{12}&f_{13}\\ f_{21}&f_{22}&f_{23}\\ f_{31}&f_{32}&f_{33}\\ f_{41}&f_{42}&f_{43}\end{bmatrix} \tag{26}\]  
Since \(\mathbf{J}_{p}\) is a 4 \(\times\) 3 matrix, it is impossible to determine whether \(\mathbf{J}_{p}\) is singular by judging whether the determinant of \(\mathbf{J}_{p}\) is 0, but it can be determined by judging whether the determinant of \(\mathbf{J}_{p}\mathbf{J}_{p}\) is 0.  
When \(\mathbf{J}_{q}\) is singular and \(\mathbf{J}_{p}\) is non-singular, that is, the determinant of \(\mathbf{J}_{q}\) is 0 and the determinant of \(\mathbf{J}_{p}\mathbf{J}_{p}\) is not 0, an inverse kinematic singularity of the mechanism occurs.  
When \(\mathbf{J}_{p}\) is singular and \(\mathbf{J}_{q}\) is non-singular, that is, the determinant of \(\mathbf{J}_{q}\) is not 0 and the determinant of \(\mathbf{J}_{p}\mathbf{J}_{p}\) is 0, a forward kinematic singularity of the mechanism occurs.  
When both \(\mathbf{J}_{p}\) and \(\mathbf{J}_{q}\) are singular, a combined singularity occurs.  
If the determinant of \(\mathbf{J}_{q}\) is not 0, then Equation (25) can be written as  
\[\dot{\mathbf{q}}=\mathbf{J}\mathbf{v}=\mathbf{J}_{q}^{-1}\mathbf{J}_{p}\mathbf{v} \tag{27}\]  
where \(\mathbf{J}\) is the mechanism Jacobian matrix.