esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A substantial number of off-grid olive oil mills in Morocco are powered by diesel-fired generators, which hugely contribute to air pollution and greenhouse gas emissions. In this research work, a biomass gasification combined heat and power (CHP) plant fueled with local by-products was explored as a renewable alternative to electrify off-grid olive oil mills in this country. The case study considered a gasification CHP plant with a rated power of 80 kW\({}_{e}\), in order to enable adaptation of the producer gas flow rate to abrupt changes in the power generation unit under dynamic operation. A downdraft gasifier and a producer gas conditioning unit were modeled under steady state operation using Cycle-Tempo, while the power generation unit was modeled in the Thermoflex simulation environment under partial and full load operation. Olive cake pellets and olive pruning chips were evaluated as biomass feedstock, with moisture contents ranging from 5% to 20% (wet basis). The results from the simulation of the gasification CHP plant showed net electrical efficiencies and CHP efficiencies around 18% and 35%, respectively. Finally, a profitability assessment of the gasification CHP plant was developed for 2 months of continuous operation, together with a sensitivity analysis. The results for the baseline scenario reveal a payback period of 7-8 years and a 68.5% accumulated profit based on the capital investment, which suggest that biomass gasification CHP plants can represent an economically feasible and sustainable solution for the electrification of off-grid areas in Morocco.  
combined heat and power (CHP); downdraft gasifier; load profile adjustment; off-grid electrification; producer gas; sensitivity analysis Article
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Machine Learning Approaches  
#### 4.2.1 Logistic Regression  
Logistic regression is a machine learning technique to estimate the parameters of a logistic model, where the logarithm of the equation with a value labeled as \"1\" is a linear combination of one or more independent variables. The probability of a value being labeled \"1\" can vary between 0 and 1, and the function that converts the equation to a probability is the logistic function.  
Logistic regression is useful in various fields, such as data analysis, medical, economics, and social sciences. The advantages of logistic regression are simplicity, ease of use and calculation, applicability to any type of inputs, and identification of the probability of output values. However, logistic regression is unsuitable for complex problems. In addition, it is sensitive to noise and can easily suffer from overfitting.  
Consider a model with two variables \(x_{1}\), \(x_{2}\) and one binary response Y, which have probability \(p=P(\text{Y}=1)\). Consider a linear relationship between the variables and the log-odds of \(p\) that Y = 1. This linear relationship can be expressed as follows:  
\[l\ =\ \log_{b}\frac{p}{1-p}\ =\ \beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}, \tag{1}\]  
Figure 9: Data collected similar to a traffic environment: (**a**) underground parking, (**b**) corresponding GPS signal, (**c**) outdoor environment, (**d**) corresponding GPS signal.  
where \(\ell\) is the log-odds, \(b\) is the base of the logarithm, and \(\beta_{i}\) represents the model's parameters.  
Here, we can calculate that  
\[p\ =\ \frac{1}{1+b^{-(\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2})}},\]  
is the probability of Y = 1.  
The formula shows that once \(\beta_{i}\) are fixed, we can effortlessly compute the probability that gives Y = 1 for a given observation. The main purpose of a logistic model is to calculate observation (\(x_{1}\), \(x_{2}\)) and estimate the probability that \(P(\text{Y}=1)\). Logistic regression, which is a general statistical model, was originally developed and introduced by [PERSON] [22].
# Tuning of Multivariable Model Predictive Control for Industrial Tasks
## 2 MPC Problem Formulation
The typical rudimentary MPC optimisation problem is:  
\[\min_{\triangle u(k)}\left\{J(k)=\sum_{p=1}^{N}\|y^{\text{SP}}(k+p|k)-\hat{y} (k+p|k)\|_{\mathbf{\pi}_{p}}^{2}+\sum_{p=0}^{N_{\text{u}}-1}\|\triangle u(k+p| k)\|_{\mathbf{\Lambda}_{p}}^{2}\right\}\] (4) subject to: \[u^{\min}\leq u(k+p|k)\leq u^{\max},\ p=0,\ldots,N_{\text{u}}-1\] \[\triangle u^{\min}\leq\triangle u(k+p|k)\leq\triangle u^{\max},\ p=0,\ldots,N_{\text{u}}-1\] \[y^{\min}\leq\hat{y}(k+p|k)\leq y^{\max},\ p=1,\ldots,N\]  
where the vectors \(u^{\min}\) and \(u^{\max}\) define the minimal and maximal values of the manipulated variables, respectively, the vectors \(\triangle u^{\min}\) and \(\triangle u^{\max}\) define the minimal and maximal changes of the manipulated variables, respectively, and the vectors \(y^{\min}\) and \(y^{\max}\) define the minimal and maximal predicted values of the controlled variables, respectively. In spite of the fact that as many as \(n_{\text{u}}N_{\text{u}}\) decision variables (1) are computed at every sampling instant \(k\), only the first \(n_{\text{u}}\) of them are actually applied to the process; in the consecutive sampling instants, the whole calculation procedure is repeated. In this work, the Dynamic Matrix Control (DMC) algorithm [38] is considered. A characteristic feature of the DMC algorithm is the fact that for prediction, i.e., to calculate the scalars \(\hat{y}_{n}(k+p|k)\) or the vectors \(\hat{y}(k+p|k)\), a discrete-time step-response model of the controlled process is used. The model may be obtained in a very simple way from the real process; no complicated identification algorithms need be used, which is a huge advantage. Since the step-response model is linear in terms of the manipulated variables, minimisation of the MPC optimisation task (4) is, in fact, a quadratic optimisation problem. Details related to the implementation of the DMC algorithm for MIMO processes may be found in [6].
# Calculations of Cross-Sections for Positron Scattering on Benzene
## 1 Introduction
The motivation for this paper is to provide cross-section data for positron-benzene scattering, particularly for cross-sections of direct ionization, which are interesting for applications, e.g., in the further development of PET technology [1; 13]. New-generation PET scanners utilize Ps as a biomarker [15; 16]. For a deeper understanding of the formation and lifetime of Ps in biomaterials, it is important to have knowledge about the interaction of positrons with organic molecules. In this aspect, the interactions between positrons and benzene are interesting. The available data for positron-benzene scattering include the measured total cross-sections (TCSs) by [PERSON] [17], [PERSON] [18], [PERSON] et al. [19; 20], and [PERSON] et al. [21]. The elastic cross-section for low collision energies has been computed by [PERSON] et al. [22], [PERSON] and [PERSON] [23], [PERSON] and [PERSON] [24], [PERSON] et al. [25], and [PERSON] et al. [26]. [PERSON] et al. [26] also present cross-sections for direct ionization, excitation, and Ps formation. [PERSON] [27] has computed the cross-section for electronic excitation. The datasets for electronic excitation cross-sections are very different from each other. In this paper, we present new calculations of the cross-sections for direct ionization using binary-encounter Bethe models [28; 29] and for electronic excitation using the binary-encounter Born approximation [30]. Furthermore, we present an estimation of the cross-section for Ps formation by subtracting the elastic cross-section and the cross-sections for direction ionization and electronic excitation from the experimental total cross-section. This paper is organized in the following way. Theoretical models and computational procedures of the calculations are summarized in Section 2. The results are presented and discussed in Section 3. The paper ends with our conclusions in Section 4.
## 1 Introduction
As a result, statistical techniques such as regression [25], Support Vector Machines (SVMs) [26], Bayesian networks [27], and ANNs [28] have been implemented which utilise data from a multitude of engine parameters to perform diagnostics on the main engine holistically or its individual components. These techniques rely on the collection of training and validation data sets from large sensor networks in both healthy and faulty conditions, which are hard to obtain. This is due to the lack of such sensor networks and high quality data for the majority of the world fleet [29], as well as data sharing issues [30]. As a result, first-principles modelling is employed to reduce the required number of measured parameters and identify the cylinders condition. Specifically, thermodynamics modelling has been used in conjunction with some measured parameters in order to identify engine faults such as excessive peak combustion pressure [31], fuel injectors condition [32; 33] and cylinder compression as well as blowby [34]. These techniques also demonstrated their usefulness by using engine models to derive baseline conditions, which facilitate comparisons with the current engine operation, making it easier to reveal faults and degradation effects. From the above literature review, the following research gaps were identified: Despite the usefulness of the ICT measurement, there is no study for large two-stroke diesel engines that utilises said measurement to identify specific types of engine faults. Subsequently, the aim of this study is to provide a framework for assessing the performance large two-stroke diesel engines, by mapping the relationship of specific malfunctioning conditions with the engine ICT, through the comparison with engine healthy (baseline) conditions. This is accomplished by utilising a novel combination of less data-intensive first-principles models, which implies coupling a thermodynamics and crankshaft dynamics model to predict the engine's ICT at various malfunctioning conditions. The following four most frequently encountered malfunctioning conditions are considered:1. Change in the Start of Injection (SOI); occurs due to fuel injector wear and engine detuning [32]. 2. Change in the Rate of Heat Release (RHR); occurs due to poor fuel quality, and fuel injector or fuel pump wear [33; 35]. 3. Change in the scavenge air pressure; occurs due to turbocharger compressor or scavenge air cooler fouling [34]. 4. Cylinder blowby; occurs due to wear in the cylinder liner or piston rings [34; 36]. An initial study on a modelling approach for predicting a marine engine's shaft dynamics was presented by the authors [37]. The present study is an extension of that, and includes improved thermodynamics model formulations, and additional analysis on the modulus and phase angle of the most affected frequency components of the engine ICT signal.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 2 K-L-Based Local Path Planning Algorithm
### Adaptive Clustering Method Based on Kohonen Network  
#### 2.2.1 Implementation of Adaptive Clustering Method Based on Kohonen Network  
In accordance with the number of sensors shown in Figure 1, five neurons are set up in the network input, receiving the measured distances of each sensor as data \(X\). The input layer has an extra neuron, which is defined as constant \(C\) and represents the largest reward of the AGV in the current action. Each neural unit of the output layer consists of a triplet \((S,\ Q,\ a)\), where \(S\) is the corresponding state, \(Q\) is the score of each action in the current state, and these two values are used to select the action \((a)\).  
The computation process is as follows:  
Figure 1: Sensor placement and movement direction. (**a**) shows the position where the sensor is placed, and (**b**) shows the forward direction of AGVs.  
1. The input sample represents the distance between the AGV and the obstacle. The input initialization vector is \(X=[x_{1},x_{2},\ldots x_{n}]\), and the weights of the connections between all input and output nodes are \(\omega\), \(\omega_{i}=[\omega_{i1},\omega_{i2},\ldots\omega_{in}]\). The number of neurons in the input layer is \(n\), and the output layer is \(m\).
2. Construct the Kohonen network.
3. Initialize the learning rate (\(a_{0}\)), the neighborhood (\(N\)), and the maximum number of iterations (\(T\)).
4. Feed input feature vector \(x_{i}\).
5. Calculate the similarity between \(x_{i}\) and \(\omega_{ij}\) by Euclidean distance. Eventually, the output node (\(j^{*}\)) with the minimum distance is selected. \[d_{j}=\sqrt{\sum_{i=1}^{n}\left(x_{i}-\omega_{ij}\right)^{2}}\,\ \ \ \ \ j=1,2,\ldots,m\] (1)  
The neuron with the shortest \(d_{j}\) is the winning neuron. In the formula, the weights of the connections between all input and output nodes are \(\omega_{ij}\); the number of neurons in the input layer is \(n\), and the output layer is \(m\). The input initialization vector is \(x_{i}\).
6. Adjust the connection weights of the winning neuron and its neighborhood, \(N\left(t\right)\). \[\left\{\begin{matrix}\omega_{ij}^{new}=\omega_{ij}^{old}+a_{t}\left(x_{i}- \omega_{ij}\right),j\in N\left(t\right),\ \ 1<i<n\\ \omega_{ij}^{new}=\omega_{ij}^{old},j\
otin N\left(t\right)\end{matrix}\right.\] (2) where \(a_{t}\) represents the learning rate at moment \(t\), which increases as time (t) decreases; \(N\left(t\right)\) is the neighborhood of output node \(j\) at time \(t\), and its size decreases with increased time until the neighborhood contains only one node, and it does not change thereafter. This formula is updated as follows: \[a_{t}=a_{0}e^{-\frac{t}{T}}\] (3)
7. Check if the network converges. Implement (1) if it converges; otherwise, exit.  
According to the above design, the number of states generated by the Q-learning algorithm in the learning process is \(m*n\).
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
## 4 Aerosol Scattering Losses
(**c**) Aerosol scattering losses versus link distances under clear atmospheric conditions at 1550 nm for various cities not exceeded 99.9% of the time. (**d**) Aerosol scattering losses versus link distances under clear atmospheric conditions at 1550 nm for various cities not exceeded 99.99% of the time. Similarly, scattering losses of \(\sim\)0.15, 0.50, 1.54, and 4.29 dB would be encountered by the same FSOC links over a distance of 1 km in the city of Mafikeng, as shown in Figure 4a-d, respectively.
## 2 Routing Protocols in Large-Scale WSNs
### Energy Balance Algorithms
###### Meleach-L
The operation of MuMHR can be divided into two stages: set-up and data transmission. During the set-up stage, cluster heads are elected and clusters are created. The sink selects 5% of all the nodes as cluster heads stochastically and broadcasts this information by a discovery message. Every node which has received the discovery message changes its state from \"waiting\" to \"discovered\", and checks whether it has been elected as cluster head or not. If it is, it broadcasts an advertisement message for forming a new cluster. Otherwise, it forwards this message to its neighbors. It is worth noting that every node regards the node from which it received the discovery message as the upstream node along the path nearest to the sink. This path will be used only when the cluster failure occurs. When a node has joined a cluster, it will ignore any other advertisement messages. Moreover, if the back-off waiting timer is activated, the node will choose a cluster head with which the node is separated with the minimum hop. Then based on the residual energy, the node calculates a value indicating its desire to be a cluster-head in the next cluster set-up stage, and inserts the value in the registration packet that the node sends back to the chosen cluster-head. Afterwards the cluster head chooses the highest value and appends its corresponding sender into the cluster-head backup list and registers the node as a member of the cluster, and repeats the selection process among the residual nodes until the cluster round time is ending. When the cluster round time is over, the current CH node flooding an announcement message for renouncing the CH role. Then the node which is the first in the backup nodes list substitutes the CH role after that it receives an announcement message, and has no use for further communication. The CH role will also be handed over to the backup node when a failure occurs in the current cluster-head node. During the data transmission stage, the non-cluster head nodes transmit sensed messages to their CHs by TDMA schedule. The CHs aggregate the received data, and then deliver the aggregation packet to the sink. The communication of each cluster using different code division multiple access (CDMA) codes to avoid interference with traffic generated by other clusters. The back-off waiting time gives more time to receive a smaller number-of-hops value, and the minimum hop-count method for choosing the CH nodes shortens the path and also makes the nodes within any clusters be balanced. The application of backup CHs improves the robustness and reliability of network. The back-off waiting timer can reduce broadcast message further, whereas it leads to much more delay. Another weakness is that numerous overhead will be generated due to the construction of cluster which is executed every round.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### Advanced Encryption Standard (AES)  
The AES is a symmetric block cipher developed as a replacement of the Data Encryption Standard (DES) algorithm which has been broken in the seventies. AES is a standardized and widely used cipher which allows the use of three different key lengths: 128 bits, 192 bits, and 256 bits. It was first named \"Rijndael\", name which derives from the two belgian cryptographers who developed it. In 2002, it was re-named the Advanced Encryption Standard and it was published by the NIST (National Institute of Standards and Technology). It was, also, approved by the National Security Agency (NSA) institute in order to handle top secret information and it has become a standard cryptographic mechanism since then. Nowadays, AES is very widespread. Plenty of libraries have been developed in several programming languages such as C, C++, Java, and Python. Even WhatsApp and Facebook, today, use AES in order to make the exchanged messages confidential. Furthermore, it is so standardized that there are several hardware implementations for AES operations in Intel and AMD processors.
# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 3 The Proposed Method
### Residual Transformer YOLO  
The structure of the RT-YOLO algorithm is shown in Figure 3 and consists of three components. The first part is the backbone feature extraction network. After the initial feature extraction from the input image, it generates four feature maps at layers 4, 6, 9, and 12. Following this, these feature maps undergo channel adjustment through convolutional groups (Conv) and are subsequently connected to the Neck backbone for down-sampling, thereby achieving feature fusion.  
Neck enhances feature extraction by merging shallow and deep features, with the aim of combining information from different scales. The feature maps from Backbone are stacked with Neck's main path for up-sampling to achieve feature fusion, and the already obtained feature maps continue to down-sample after BOTrans and ELAN to achieve feature fusion once more. Finally, the obtained feature maps are sent to the YOLO Head for decoupling.  
Figure 3: The complete structure of RT-YOLO.  
The third part is the YOLO Head, which has four detection heads, including two small-scale, occluded object prediction heads named BTDetect. These heads receive two feature maps containing the richest detailed information. Afterward, a 1 \(\times\) 1 Conv layer is applied to decouple the output results for object location and category information. In addition, introducing NAM attention before the residual branches can reduce the interference of irrelevant features and enhance the algorithm convergence efficiency without increasing computational and spatial complexity.
## 3 Results
### Destruction Removal Efficiency (DRE)
#### 3.1.2 Results of Measurement  
In order to confirm the optimal conditions for each gas of the POU scrubber, the DRE for each volume flow rate and the consumption rates of plasma energy were measured. When the treatment volume flow rate of the POU scrubber increased to 300 SLM, it was confirmed that the DRE decreased to some extent. In particular, in the case of NF3, it was necessary to increase the energy consumption for optimal DRE conditions, and 300 SLM was set as the applicable optimal processing capacity in display sectors. The measurement results are shown in Table 7. As an additional experiment, the DRE, according to the treatment capacity of the POU scrubber, was estimated based on CH4, which was destroyed the least by the POU scrubber, and the measurement results are shown in Tables 7 and 8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Volume Flow Rate**} & \multirow{2}{*}{**Gas**} & \multirow{2}{*}{**Power Value (kW)**} & \multicolumn{2}{c}{**FT-IR (ppm)**} & \multicolumn{2}{c}{**QMS (SLM)**} & \multirow{2}{*}{**DRE (\%)**} \\ \cline{3-4} \cline{6-8}  & & & **Inlet** & **Outlet** & **Inlet** & **Outlet** & \\ \hline \multirow{4}{*}{100 SLM} & SF6 & 7.095 & 5998.84 & 0.37 & 100 & 143 & 99.99 \\  & 10.73 & 5998.84 & NA & 100 & 143 & 99.99 \\ \cline{2-8}  & NF3 & 6.058 & 4109.57 & NA & 100 & 143 & 99.99 \\  & 10.73 & 4109.57 & NA & 100 & 143 & 99.99 \\ \hline \multirow{4}{*}{300 SLM} & SF6 & 6.032 & 5414.71 & 163.84 & 322 & 365 & 96.57 \\  & 10.73 & 5414.71 & NA & 322 & 365 & 99.99 \\ \cline{1-1} \cline{2-8}  & NF3 & 6.058 & 3786.96 & 1640.30 & 322 & 365 & 49.32 \\ \cline{1-1} \cline{2-8}  & NF3 & 10.73 & 3786.96 & 143.33 & 322 & 365 & 95.57 \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of DRE values according to the volume flow rate variation in the POU scrubber based on the applicability to the display sector.  
Figure 7: Diagram for measuring destruction removal efficiency (DRE).