esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 2 System Model
### PD-NOMA Transmission  
The basic principle of PD-NOMA is to simultaneously serve multiple users over the same spectrum resources (i.e., time, frequency, and code) but with different power levels. For the considered system model and without loss of generality, assume that all of the users \(U_{1}\),..., \(U_{M}\) are ordered based on their channel gains:  
\[h_{1}\leq\cdots\leq h_{k}\leq\cdots\leq h_{M} \tag{8}\]  
Based on PD-NOMA concept, signals for all users are superposed in the power domain. Thus, a DC bias added to ensure that the transmitted signal at the LED consists of \(M\) non-negative signals \(s_{1}\),...,\(s_{M}\) and is expressed as in [11] by:  
\[x=\sum_{l=1}^{M}a_{l}\sqrt{P_{elc}}s_{l}+I_{DC} \tag{9}\]  
where \(a_{l}\) represents the power allocation coefficient for the \(l\)-th user, \(P_{elc}\) represents the total electrical power, \(s_{l}\) is the message intended for the \(l\)-th user, and \(I_{DC}\) is the DC bias. Each message is assumed to have unity variance and zero mean. The use of PD-NOMA implies that \(a_{1}\geq\cdots\geq a_{M}\) and power allocation coefficients should satisfy [11]  
\[\sum_{l=1}^{M}a_{l}^{2}=1. \tag{10}\]  
The LED optical power is directly proportional to the LED driving current, the optical power can be expressed as:  
\[P_{opt}=E[x]=I_{DC} \tag{11}\]  
where \(E[.]\) denotes the statistical expectation. The received signal at the \(k\)-th user is given by:  
\[y_{k}=\sqrt{P_{elc}}h_{k}\sum_{l=1}^{M}a_{l}s_{l}+n_{k} \tag{12}\]  
Figure 1: Visible light communication (VLC) system model.  
where \(n_{k}\) denotes the real-valued additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma_{n}^{2}\).
## 4 SOC Estimation Based on EKF and UKF
### Extended Kalman Filtering  
As there have been plenty of published literatures on the EKF approach, the algorithm principle was shortly introduced, to avoid repetitions. The nonlinear state-space equation and measurement discretized equation can be represented as follows:  
\[x_{k+1}=f(x_{k},u_{k})+\Gamma_{k}\omega_{k} \tag{15}\]  
\[y_{k+1}=g(x_{k},u_{k})+v_{k} \tag{16}\]  
where \(f(x_{k},u_{k})\) is the nonlinear state function and \(g(x_{k},u_{k})\) is the nonlinear measurement function. Then, \(\left.\hat{A}_{k}=\frac{\partial f(x_{k},u_{k})}{\partial x_{k}}\right|_{x_{k}  
The key point for implementing the EKF to our proposed model is to deal with the \(C_{k}\). The detailed derivation process is below:  
Assume that the vector of the parameters is \(\theta=[R_{0}^{+},\ R_{0}^{-},\ M,\ \lambda]\), where \(R_{0}^{+}\) is the ohmic resistance when the cell is charging and \(R_{0}^{-}\) is the ohmic resistance when the cell is discharging. Then, to calculate \(C_{k}^{\theta}\):  
\[\frac{dg(x_{k},\ u_{k},\ \theta)}{d\theta}=\frac{\partial g(x_{k},\ u_{k}, \theta)}{\partial\theta}+\frac{\partial g(x_{k},\ u_{k},\ \theta)}{\partial x_{k}}\frac{dx_{k}}{d\theta} \tag{17}\]  
\[\frac{dx_{k}}{d\theta}=\frac{\partial f(x_{k-1},\ u_{k-1},\ \theta)}{\partial\theta}+ \frac{\partial f(x_{k-1},\ u_{k-1},\ \theta)}{\partial x_{k-1}}\frac{dx_{k-1}}{d\theta} \tag{18}\]  
The equation can be calculated recursively, and the initial value of \(\frac{dx_{k}}{d\theta}\) (\(k=0\)) can be set to zero. In this particular case:  
\[\frac{\partial g(x_{k},\ u_{k},\theta)}{\partial\theta}=\left[I^{+}\ I^{-}\ 0\ 0\right] \tag{19}\]  
\[\frac{\partial g(x_{k},\ u_{k},\theta)}{\partial x_{k}}=\left[1\ \frac{ \partial OCV(S_{k})}{\partial S_{k}}\right] \tag{20}\]  
\[=\left[\begin{array}{ccc}0&0&0&0\\ 0&0&\left(1-\exp\Bigl{(}\left|\frac{\eta i_{k-1}\gamma_{k}}{C_{N}}\right| \Bigr{)}\Bigr{)}\text{sgn}(i_{k-1})&\left(h_{k-1}-M(S,\ \hat{S})\right)&\left|\frac{\eta i_{k-1}\gamma_{k}}{C_{N}}\right|\exp \Bigl{(}\left|\frac{\eta i_{k-1}\gamma_{k}}{C_{N}}\right|\Bigr{)}\right]\\ 0&0&0&0\end{array}\right. \tag{21}\]  
\[\frac{\partial f(x_{k-1},\ u_{k-1},\ \theta)}{\partial x_{k-1}}=\left[ \begin{array}{ccc}\exp\Bigl{(}-\frac{T_{k}}{R_{1}C_{1}}\Bigr{)}&0&0\\ 0&\exp\Bigl{(}-\left|\frac{\eta i_{k}\gamma T_{k}}{C_{N}}\right|\Bigr{)}&0\\ 0&0&1\end{array}\right] \tag{22}\]  
With the above equations, EKF can be executed recursively by repeating the steps in Table 2.
# The Value of Simple Heuristics for Virtualized Network Function Placement
## 3 Placement Heuristics
### Fast and Simple Heuristic Algorithm (FACE)  
The final heuristic uses the same method as the previous heuristic in choosing a shortest path. Additionally, the FACE algorithm keeps a decreasing list of shortest paths based on their maximum-minimum nodal resource. In each stage of the placement process, if the placement was not possible for the chosen shortest path, it tries the next shortest path in the list. The following strategy will be deployed to place the NFs along the chosen shortest path.  
The NFs are sorted based on the number of possible candidate nodes in an increasing order. A candidate node parameter \(candid_{f_{i}}\) is defined for each NF of the SG and is equal to the number of nodes along the shortest path that can be used for the placement of that specific NF. In choosing the nodes along the shortest path we consider two parameters: a node has to provide sufficient nodal resources, and the NFs that previously were placed. The order of the NFs in the SG is fixed and we cannot re-organize them. Furthermore, we do not want to have a placement that passes a physical link more than once. E.g. if the third NF of the SG is being placed in the second node of the shortest path, subsequent NFs in the SG cannot be placed in the first node. The candidate nodes are being chosen based on the placement of previous NFs to avoid loops and backtracking in the placement. If there are no candidate nodes for any of the NFs at any stage of placement, the chosen path is infeasible and the placement process will choose the next shortest path with maximum-minimum nodal resource and repeat the process of NF placement.  
To place the chosen NF in one of the nodes along the shortest path we sort its candidate nodes based on their index difference and choose the node with the lowest index difference. The index of the nodes along the shortest path is equal to their order in the shortest path e.g., the source node's index is one. The index of a NF is equal to its order in the SG, e.g., the index of the first NF of the SG is one and the index of second NF is two. We compare the index of the chosen NF with the index of the candidate nodes and choose the one with the minimum index difference with the chosen NF. In the end, the available resources of the nodes, BW of the links, and the list of candidate nodes for the remaining NFs will be updated.  
We applied FACE heuristic to the same example as previous sections. Figure 5 shows the result of placement by FACE heuristic. As all of NFs had three options for placement, the algorithm started from the first NF and placed it in the node with minimum index difference, which is the source node. The second NF is placed in the second node of the chosen shortest path as it has minimum index difference and the third NF is placed in the third node of the chosen shortest path.  
Figure 4: All shortest path placement example.  
Figure 5: FACE heuristic placement.
## 4 Experiment
### Experimental Results  
#### 4.4.1 Comparison Experiment with Traditional Methods  
We compared the AE-GRU (D) model with three existing forecasting models, including a statistical forecasting method: a climatologically-aware forecasting technique (A), a dynamical and numerical cyclone prediction method, the [PERSON] barotropic technique (B), and a numerical method, the NWP model (C). The average APE of the 12, 24, 48, and 72 h forecast in the Western North Pacific of all methods are shown in Table 4. Because Methods A and B only provided prediction errors for certain years, the cumulative average error was used. In addition, Method A provided no record of the 12 h prediction results. As we can see, compared with the traditional methods, the statistical forecast method (A) had a better prediction performance than the Sanders barotropic technique (B), but it was not as good as the NWP model (C). In addition, the Sanders barotropic technique (B) and the NWP model (C) outperformed our model (D) in the 12 h forecast. However, our model (D) showed better results in the 24 h, 48 h, and 72 h forecasts. Besides, the performances of Methods A, B, and C declined dramatically as the prediction time range increased, whereas the AE-GRU method produced relatively stable performance in long-term prediction.  
However, because the performances of Methods A and B were unknown for certain years, it would be unfair to compare them with the AE-GRU model when applying data from 1945 to 2017. Therefore, we compared the results per year of our proposed model with those of the NWP model (C), which performed the best among Methods A, B, and C. The NWP model results were from [43]. The comparison results per year are shown in Figure 6. In addition, the number of tropical cyclones and the number of records per year for this comparison are indicated in Figure 7. From Figure 6, it is clear that our proposed model achieved better results than the NWP model for almost every year. For the year 1998, the error of the proposed model peaked, but was still lower than that of the NWP model. According to Figure 7, there were fewer tropical cyclones and records in 1998 than during the other years. The reason for the larger error in 1998 was due to insufficient data, which proved that the AE-GRU model was data-driven. However, the AE-GRU model showed a higher robustness and stability than the NWP method.
## 3 General Framework and Methodology
### Keyword Expansion Technique  
#### 3.2.1 Tf-IWF  
TF-IWF (Term Frequency-Inverse Word Frequency) is an algorithm for assessing the importance of words in a specific corpus. It improves on TF-IDF (Term Frequency-Inverse Document Frequency) by focusing more on the rarity of words in a particular corpus. In TF-IDF, TF indicates the frequency of occurrence of a word in a text, while IWF reflects the  
Figure 1: Research framework of the thesis.  
rarity of the text containing the word. Specifically, it better distinguishes between different text categories if a word occurs in only a few texts.  
TF (Term Frequency):  
\[\text{TF(t,d)}=\frac{\text{f(t,d)}}{\sum_{t^{\prime}\in\text{d}}\text{f(t^{ \prime},d)}} \tag{1}\]  
where \(\text{TF(t,d)}\) denotes the word frequency of vocabulary word t in document d, f(t,d) denotes the number of occurrences of vocabulary word t in document d, and \(\sum_{t^{\prime}\in\text{d}}\text{f(t^{\prime},d)}\) denotes the sum of occurrences of all vocabulary words in document d.  
IWF (Inverse Word Frequency):  
\[\text{IWF(t)}=\text{log}\frac{\text{N}}{\text{df(t)}} \tag{2}\]  
where IWF(t) denotes the inverse document frequency of vocabulary word t, N denotes the total number of documents in the document collection, and df(t) denotes the number of documents that contain vocabulary word t.  
TF-IWF is obtained by multiplying TF and IWF, calculated as follows:  
\[\text{TF-IWF(t,d)}=\text{TF(t,d)}\times\text{IWF(t)} \tag{3}\]  
Important keywords are identified by calculating the TF-IWF values of each word in the document. Then, semantic extensions can be performed based on these keywords, such as using synonym dictionaries or word vector models to find synonyms, near-synonyms, etc., related to the keywords in order to enrich the keyword list and improve the accuracy of information retrieval and text analysis.
# Internet of Things (IoT) and the Energy Sector
## 4 IoT in the Energy Sector
### Smart Use of Energy in Industry  
IoT can be employed to design a fully connected and flexible system in the industry to reduce energy consumption while optimizing production. In traditional factories, a lot of energy is spent to produce the end product and control the quality of the end product. Moreover, monitoring every single process requires human resources to be involved. However, using an agile and flexible system in smart factories helps to recognize failures at the same time rather than recognizing them by monitoring the products at the end of production line. Therefore, a suitable action can be taken promptly to avert wasteful production and associated waste energy.  
In terms of monitoring processes during manufacturing, IoT, and its enabling technology play a crucial role. Gateway devices, IoT hub networks, web servers, and cloud platforms, which are accessible with smart mobile devices (e.g., smart phones or personal computers) can be examples of monitoring equipment. Wireless communications such as Wi-Fi, Bluetooth, ZigBee, Z-wave, or wired communications, such as Local Area Network (LAN) can be used to connect all pieces of equipment [126]. Moreover, to use IoT more efficiently, by installing sensors on each component of an industrial site, the components that consume more energy than their nominal energy level can be detected. Thus, every single component can be easily managed, the faults of components can be fixed, and the energy consumption of each component can be optimized. This eminently results in reducing the energy losses in smart factories.  
In a smart factory, data processing is the key element in the whole system, through which data in the cloud platform (acts as a brain) will be analyzed to help managers making more efficient decisions in time [127]. In terms of monitoring and maintaining assets of manufacturing, the big problem in factories is the depreciation of machines and mechanical devices. With an appropriate IoT platform and tools, the proper device size can be selected to reduce wear and tear and the associated maintenance costs. IoT-based conditional monitoring ensures the mechanical device never reaches its threshold limit. This simply means the device lasts longer and suffers fewer failures. Moreover, the failures that cause energy loss can be anticipated to be tackled.  
IoT-based agile systems can provide a smart system for collaboration between customers, manufactures, and companies. Therefore, a specific product will be manufactured directly according to customers' order. Therefore, energy consumed during the process of storing spare parts as well as the energy wasted in warehouses to keep the spare parts will be dwindled significantly. Only a certain number of products in various kinds will be manufactured and stored, which enhances the management of energy consumption and production efficiency [126].
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AoA & angle of arrivals \\ BS & Base Station \\ IoE & Internet of Everything \\ m-MIMO & Massive MIMO \\ CSI & Channel State Information \\ CGD & Conjugate gradient-descent \\ SCMI & Sum conditional mutual information \\ SMSE & Sum mean square error \\ FDD & Frequency Division Duplex \\ DL & Downlink \\ UL & Uplink \\ TDD & Time division duplex \\ Massive MIMO & m-MIMO \\
5G & Fifth generation \\
6G & Sixth generation \\ iid & Independently and identically distributed \\ SINR & Signal-to-interference-plus-noise ratio \\ OR & One ring \\ BF & Eigenbeamforming \\ RZF & Regularized zero forcing \\ \end{tabular}
## 4 Description of the Approach  
This section introduces the details of the developed approach. Section 4.1 summarizes the measure selection process, which is based on a hybrid MCDM approach with AHP and entropy for criteria weighting and MAUT for the prioritization of the paths. Then, Section 4.2 outlines the process for measure classification into preventive and reactive by utilizing the categorization of energy flexibility measures in the VDI 5207 standard [37]. Finally, Section 4.3 integrates the selected measures in the production schedule with the help of a mixed-integer linear problem (MILP) and a branch-and-bound optimization, minimizing the cost of additionally purchased energy. The integration of measures is understood as the creation of new, risk-treated scheduling that, through preventive measures, contains less risk potential. In addition, reactive measures are integrated so that backup schedules are created that are used when faults occur.
## 2 Methodology
### Validation of the Analytical Model  
The proposed analytical model was validated against numerical simulation evaluating the response of buried operating pipeline under longitudinal PGD, using the finite element software ABAQUS/Standard [33]. First, the system performance was analyzed numerically within the beam on Winkler foundation theory. Then, the numerical results were comparedto those obtained using the state-of-the-art analytical procedure [8; 11; 13] and the proposed analytical model, demonstrating the capacity of the latter to accurately evaluate the pipeline response.  
The calculation example considers a X42 steel grade pipeline with diameter of 0.508 m and thickness of 7.1 mm, buried in dense sand with a cover depth \(H_{c}\) = 1.5 m, measured from the soil surface to the pipe crown. The analyzed soil-pipeline parameters are summarized in Table 3.  
The pipeline performance was evaluated considering the presence and absence of service loads, including internal pressure \(P_{i}\), and temperature variations \(\Delta T\), demonstrating the accuracy of the proposed analytical method to assess the system response under operating conditions.  
It is assumed that the unpressurized pipeline (\(P_{i}\) = 0 MPa) has no temperature variation with respect to its installation temperature (\(\Delta T\) = 0 \({}^{\circ}\)C). Conversely, the pressurized pipeline is assumed to operate at an average temperature of \(\Delta T\) = 50 \({}^{\circ}\)C, compared to the pipe installation temperature, and internal pressure \(P_{i}\) = 4.4 MPa, which is 75% of the maximum allowed pressure, \(P_{max}\), according to [34]:  
\[P_{max}=0.72\cdot\left(2\sigma_{y}\frac{t}{D}\right) \tag{4}\]
## 3 Results  
Users defined the initial values of the input parameters based on the baseline scenario during the validation workshop. The baseline scenario corresponds to the current situation of Day 0 in the Area of Operation. When the model is run with the baseline scenario (without any shock effect), we get the normal capacity of the baseline requirements, resilience main domains, and the normal risk values. When the model is run with shock injections (extraordinary scenario), we see the effects of the shocks on the output values. Figure 7 shows a sample graph depicting both baseline and extraordinary scenario outputs for continuity of government baseline requirements.  
Within the baseline scenario, the simulation starts at time \(t_{0}\) (Run 1), and resilience capacity of continuity of government follows a steady pattern around 0.8 for 150 days. When the extraordinary scenario where all shock types are turned on takes place (Run 2), the resilience capacity of continuity of government follows a pattern converging to the NATO resilience concept. In the latter scenario, the effects of shocks start to be seen around \(t_{10}\), and those effects increasingly continue until around \(t_{50}\) (shocks absorb area in the graph). After the absorption phase, continuity of government baseline behaves for recovering the effects of shocks (shocks recover area in the graph) until around \(t_{90}\). The resilience capacity starts to go up to the normal pattern as depicted in the shock adaptation area in the graph (\(t_{90}\)-\(t_{150}\)).  
Figure 6: Sample graphs from output dashboard.  
Figure 7: Sample output graph of continuity of government baseline requirements for baseline and extraordinary scenarios.  
We have determined the risk assessment functions by an inverse function of the resilience capacity. The higher the resilience capacity occurs, the lower the level of risk for four different risk assessment functions (_Command and Control, Protection, Movement/Manoeuvre, and Sustain_). Figure 8 provides risk assessment output graphs.  
The above graphs in Figure 8 show that all risk functions have acceptable levels of risk when the model runs the baseline scenario (Run 1). When the extraordinary scenario is inserted into the simulation (Run 2), the risk levels behave in the opposite direction of the resilience capacities.  
Figure 9 gives the resilience capacities of resilience main domains in baseline and extraordinary scenarios. In the baseline scenario conditions, three main domains follow an almost steady pattern between \(t_{0}\) and \(t_{150}\) (Run 1). When the simulation runs in extraordinary conditions, having the shocks turned on, main resilience domains behave in parallel to the baseline requirements (Run 2).
###### Abstract  
Hearing loss significantly impacts daily communication, making accurate speech audiometry (SA) assessment essential for diagnosis and treatment. However, SA testing is time-consuming and resource-intensive, limiting its accessibility in clinical practice. This study aimed to develop a multi-class classification model that predicts SA results using pure-tone audiometry (PTA) data, enabling a more efficient and automated assessment. To achieve this, we implemented and compared MLP, RNN, gradient boosting, and XGBoost models, evaluating their performance using accuracy, F1 score, log loss, and confusion matrix analysis. Experimental results showed that gradient boosting achieved the highest accuracy, 86.22%, while XGBoost demonstrated a more balanced classification performance. The MLP achieved 85.77% and the RNN achieved 85.41%, exhibiting relatively low accuracy, with the RNN showing limitations due to the low temporal dependency of PTA data. Additionally, all models faced challenges predicting class 2 (borderline hearing levels) due to overlapping data distributions. These findings suggest that machine learning models, particularly gradient boosting and XGBoost, outperform deep learning models in SA prediction. Future research should focus on feature engineering, hyperparameter optimization, and ensemble approaches to enhance performance and validate real-world applicability. The proposed model could contribute to automating SA prediction and improving hearing assessment efficiency and patient care.  
speech audiometry; pure-tone audiometry; multilayer perceptron; recurrent neural network; gradient boosting; XGBoost; regression analysis in audiology; deep learning; machine learning; hearing assessment
# Non-Interactive Decision Trees and Applications with Multi-Bit TFHE
## 3 Preliminaries
### Combinational Digital Circuits
#### 3.2.2 Multiplexer  
Multiplexing means transmitting many information units over a smaller number of channels or lines. The function of a multiplexer is to choose an output from a selection of inputs such as a rotary switch. A digital multiplexer (MUX) is a combinational circuit that selects one of \(2^{n}\) data input lines based on an n-bit address, directing it to one output line. A set of selection lines controls the selection of a particular input line.  
A typical multiplexer has \(2^{n}\) information inputs \((I_{(2^{n}-1)},\ldots,I_{0})\), \(n\) select inputs \((S_{n-1},\ldots,S_{0})\) and one information output \(Y\). The simplest multiplexer when \(n=1\) is a \(2^{1}\)-to-\(1\) multiplexer, shown in Figure 3. The single selection variable S has two values, 0 and 1. When \(S=0\), the multiplexer selects and outputs the first input, \(A\). Similarly, when \(S=1\), the multiplexer selects the second input \(B\). Optimizing the truth table with Karnaugh maps gives the equation \(Out=\bar{S}A+SB\).