esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconstruction of Ultra-High Vacuum Mass Spectra Using Genetic Algorithms
## 2 Materials and Methods
### Synthetic Data Generation and Normalization  
The samples used for the comparative metrics have been obtained by synthetic generation, as described in a previous work [2]. Ideally, one should be able to use true residual gas samples obtained from RGA in UHV systems, but the availability of these is very limited, so this is an effective and reliable method for the controlled generation of a large number of samples. The generation has been performed following the mathematical model for MS simulation and using standard fragmentation patterns described in the NIST 2017 standard library [8]. The equations that model the data generation are:  
\[I_{1} = c_{1,1}\alpha_{1}P_{1}+c_{1,2}\alpha_{2}P_{2}+\ldots+c_{1,N} \alpha_{N}\;P_{N}\] \[I_{2} = c_{2,1}\alpha_{1}P_{1}+c_{2,2}\alpha_{2}P_{2}+\ldots+c_{2,N} \alpha_{N}\;P_{N}\] \[\vdots\] \[I_{M} = c_{M,1}\alpha_{1}P_{1}+c_{M,2}\alpha_{2}P_{2}+\ldots+c_{M,N} \alpha_{N}P_{N}\]  
These equations then can be written in a matrix form:  
\[\mathbf{I}=\mathbf{C}\cdot\boldsymbol{\alpha}\cdot\mathbf{P} \tag{1}\]  
where each element \(I_{m}\) of vector \(\mathbf{I}\) corresponds to the total ion current measured for a particular \(m/z\) ratio (up to \(M\)). The resulting ion current vector corresponds to a bar graph spectrum where only ion current values at integer \(m/z\) ratios are represented. The remaining matrices participating in Equation (1) are:\[\mathbf{C}=\begin{pmatrix}c_{1,1}&c_{1,2}&\cdots&c_{1,N}\\ c_{2,1}&c_{2,2}&\cdots&c_{2,N}\\ \vdots&&\ddots&\\ c_{M,1}&c_{M,2}&\cdots&c_{M,N}\end{pmatrix},\quad\boldsymbol{\alpha}=\begin{pmatrix} \alpha_{1}&0&\cdots&0\\ 0&\alpha_{2}&\cdots&0\\ \vdots&&\ddots&\\ 0&0&\cdots&\alpha_{N}\end{pmatrix},\quad\mathbf{P}=\begin{pmatrix}P_{1}\\ P_{2}\\ \vdots\\ P_{M}\end{pmatrix}\]  
In Equation (1), \(\mathbf{C}\) is deterministic (the fragmentation pattern matrix). All \(\alpha_{n}\), \(n=1\),..., \(N\) can be set to 1 for the purpose of this study (meaning that the RGA exhibits the same sensitivity to all gases). Hence, the only variable that affects the shape of the spectrum is the vector of partial pressures \(\mathbf{P}\). By generating a large number (\(K\)) of random \(\mathbf{P}_{k}\) vectors (\(k=[1,\ldots,K]\)), an arbitrary large number \(K\) of synthetic spectra examples can be generated.  
Finally, the used model requires the values of the generated ion currents to be normalized according to the following logarithmic transformation:  
\[I_{norm}=\frac{log_{10}(I)+16}{10} \tag{2}\]  
This normalization covers 10 orders of magnitude of ion currents from \(10^{-16}\) A to \(10^{-6}\) A, which results in a sufficiently large range for the most common UHV systems.The purpose of this normalization is to bring out components near the limits of detection of the spectrum that could initially be mistaken as noise.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
###### Abstract
In the next sections we review a powerful sensing technique, which allows to reach sensitivities orders of magnitude higher than that of classical sensing methods, even when compared to standard commercial electronic noses: fluctuation-enhanced sensing (FES). At first, we review the basics of FES and the idea from which it takes its origins, then we review the evolution and the state-of-the-art of FES, together with the most common implementation issue including the dedicated instrumentation required for reaching the highest sensitivity. Prospects and future developments will also be discussed.
## 3 Materials and Methods
### Preprocessing of Dataset  
After acquiring data, the dataset underwent preprocessing, which included handling missing values, normalization, and outlier treatment. Missing values were addressed using mean imputation, while outlier analysis revealed no significant deviation. The data were normalized using min-max scaling to transform values between 0 and 1, ensuring uniform input for the models. Finally, the output variable was converted from multiclass to binary, where 1 indicates the existence of heart attack, whereas 0 denotes no heart attack.
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 3 System Model  
A practical wireless relay-enabled biomedical implant system is proposed, where \(N\geq 1\) relays are employed to serve a series of \(K\geq 1\) implanted sensors. The relay and implanted sensor sets are denoted as \(\mathcal{N}\) and \(\mathcal{K}\), respectively, with \(|\mathcal{N}|=N\) and \(|\mathcal{K}|=K\). Each relay performs an on-body physiological signal collection and has a computation task that needs to be completed locally. Since the intra-body environment is complex and different from person to person, a reasonable assumption that all devices have the same \(Z\) values is made, and we only consider the XY plane. More accurate work regarding WBAN data transmission can be investigated by extending the proposed system model to three-dimensional (3D) scenarios.
## 3 System Model and Problem Statement  
### System Model  
Network Model  
A WSN consists of a large number of sensors, and wireless links representing direct communication between the sensors within the radio range. A WSN is modeled as an undirected graph G(\(V\),\(E\),\(W\)), where \(V=\{v_{i},v_{2},\cdots,v_{n}\}\) is the set of all the nodes. Each \(v_{n}\) has its maximum communication radio range with radius \(R\). \(E\) is the set of all bidirectional wireless links _(i, j)_ (\(i,j\in V\)). A link _(i,j)_, denoted by \(e(i,j)\in E\), exists between \(v_{i}\) and \(v_{j}\) if \(d(v_{i},v_{j})\leq\) R. It indicates that node \(v_{i}\) and node \(v_{j}\) can directly communicate with each other. \(d(v_{i},v_{j})\) is the distance between node \(v_{i}\) and node \(v_{j}\). W is the weight set of all directed links _(i,j)_. The weight \(e_{ij}\) of link _(i,j)_ is the energy consumption of communication between \(i\) and \(j\). Let \(S_{e}=\{v_{I},v_{2},\ldots,v_{I}\}\) be the set of all nodes in the event area. Note that \(S_{e}\) is the subset of \(V\) (\(S_{e}\subseteq V\)). Let \(N_{i}\) denote the set of neighbors of node \(i\) and \(N_{i}=\{j\mid d_{j}\leq R,\,j\in V\}\).  
In this paper, the following assumptions are adopted:  
* N sensor nodes are uniformly distributed within a square field. Each sensor nodes has a unique ID. Sensor nodes in the event area are grouped into clusters.
* All sensor nodes keep static or less movement after being deployed.
* The energy of the sensor nodes cannot be recharged.
* Sensor nodes are location-unaware, _i.e._, a sensor node need not rely on the expensive devices, such as Global Positioning System (GPS), to receive the position information for finding the shortest path routing to the sink.
* Communication is symmetric. Nodes can estimate distance based on the signal strength of each other, and at the same time the radio power can be controlled.
* We assume ideal MAC layer conditions, that is, perfect transmission of data on a node-to-node link.
###### Abstract  
Within this work, an advanced control algorithm was proposed to eliminate the non-linear vibrations of the rotor electro-magnetic suspension system. The suggested control algorithm is known as the Adaptive Linear Integral Positive Position Feedback controller (ALIPPF-controller). The ALIPPF-controller is a combination of first-order and second-order filters that are coupled linearly to the targeted non-linear system in order to absorb the excessive vibratory energy. According to the introduced control strategy, the dynamical model of the controlled rotor system was established as six non-linear differential equations that are coupled linearly. The obtained dynamical model was investigated analytically applying the asymptotic analysis, where the slow-flow equations were extracted. Based on the derived slow-flow equations, the bifurcation behaviors of the controlled system were explored by plotting the different bifurcation diagrams. In addition, the performance of the ALIPPF-controller in eliminating the rotor lateral vibrations was compared with the conventional Positive Position Feedback (PPF) controller. The acquired results illustrated that the ALIPPF-controller is the best control technique that can eliminate the considered system's lateral vibrations regardless of the angular speed and eccentricity of the rotating shaft. Finally, to demonstrate the accuracy of the obtained analytical results, numerical validation was performed for all obtained bifurcation diagrams that were in excellent agreement with the analytical solutions.  
ALIPPF-controller; rotor magnetic bearings system; non-linear control; periodic; quasiperiodic; chaotic motions; Poincare map; frequency spectrum +
Footnote †: journal: Applied Physics Letters
# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Analysis of the PKU Dataset  
During our study, we initiated our experiments on the PKU dataset, which is a well-known publicly available vehicle dataset. Table 1 briefly describes the various vehicle categories in the PKU dataset. Generally, the PKU dataset is a collection of diverse vehicle images that are captured under diverse conditions [31]. As shown in Table 1, this dataset contains a total of 3977 diverse vehicle images. The developers of the PKU dataset divided the vehicles into five distinct categories, which they refer to as G1, G2, G3, G4, and G5. Out of 3977 vehicle images, the PKU dataset also contains a total of 4263 visible license plates, whose pixel resolution varies from 20 to 62 pixels. In Figure 3, we demonstrate a few detection results for both vehicles and license plates for each category of the PKU dataset. We show different vehicles from each category to demonstrate a fair understanding. **Vehicle+LP detection: G1-category:** The first row in Figure 3 demonstrates a few images from this category. It is evident for this category that for different-shaped vehicles, the detection module performs well by drawing a red rectangle around the object of interest, which is a vehicle in this case. The detected vehicle image is then analyzed by the LP localization module. In all four of the sample images in Figure 3 from the G1 category, the visible LP is accurately localized by our developed method. **Vehicle+LP detection: G2-category:** The second row in Figure 3 demonstrates a few images from the G2 category. As indicated in Table 1, this category mostly contains vehicle images that are captured during different times of the day. In all four images shown for this category, both the vehicle and the LP localization module are in the correct position, thereby indicating the correct position of both of these objects. The first image shown for this category is of the truck, and the rest are the cars. However, the detectors applied to capture these objects are intelligent enough to discriminate between these shapes. **Vehicle+LP detection: G3-category:** The third row in Figure 3 demonstrates a few images from the G3 category. Most of the images in this category are nighttime captures of small cars and trucks. It can be observed in the third row of Figure 3 that both objects are accurately located. To fairly discriminate the vehicle and the LP detection for nighttime captured images, we draw the white color bounding box around both the detected vehicle and the LP area.
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
## 4 Bacteria Used for Hydrogen Production During Dark Fermentation
### Clostridium  
_Clostidium_ spp. has been widely used in dark fermentation studies for H\({}_{2}\) production, especially _C. butyticum_, _C. beijerinkii_, _C. acetobutylticum_, and _C. thermocellum_. These strict anaerobic bacteria are found in soil, water, and animal intestines and are used in pure and mixed culture systems for dark fermentation H\({}_{2}\) production [64]. _Clostidium_ spp. bacteria break down carbohydrates to produce organic acids, alcohols, and gases. H\({}_{2}\) production by these bacteria involves the conversion of pyruvate to acetyl-CoA by the enzyme PFOR, releasing CO\({}_{2}\) and transferring electrons to ferredoxin, which, combined with protons, forms molecular H\({}_{2}\). Hydrogenase is the key enzyme for H\({}_{2}\) production, catalyzing the reversible oxidation of molecular H\({}_{2}\). pH range of 5-7 and a temperature of 30-37 \({}^{\circ}\)C are optimal for H\({}_{2}\) production by these bacteria [65].
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 4 Optimization Problem
### Economic and Cost Analysis  
The COE for a specific system is an economic evaluation of the system's costs and of the associated cost in its lifespan. The COE is a function of the NPC, it actually helps to select the lowest energy prices from different feasible hybrid configurations, which means the least overall investment cost in a renewable power system plant, after fulfilling the energy dependability limitations. While the NPC represents the current value of the capital investment and operating costs over the lifespan. The NPC and the COE in ($/kWh) can be computed as follows [3; 30]:  
\[\text{COE}=\frac{\text{NPC}}{\sum_{\text{I}}^{\text{S760}}\text{P}_{\text{L}}} \text{C}_{\text{RF}} \tag{13}\]  
\[\text{NPC}\text{=C}_{\text{Ann}}^{\text{T}}/\text{C}_{\text{RF}} \tag{14}\]  
where, \(\text{C}_{\text{Ann}}^{\text{T}}\) is the total annual cost of the proposed hybrid system, and \(\text{C}_{\text{RF}}\) is the capital recovery factor, which is a ratio for the current cash value calculation and it can be estimated over a lifespan of years (S = 25 years) and an interest rate (\(\text{I}_{\text{r}}\) = 6%). \(\text{C}_{\text{RF}}\) and \(\text{C}_{\text{Ann}}^{\text{T}}\) are modeled as:  
\[\text{RF}\;(\text{I}_{\text{r}}\;,\;\text{S})\text{=}\frac{\text{I}_{\text{r}} \times(\text{I}_{\text{r}}+1)^{\text{S}}}{\left(\text{I}_{\text{r}}+1\right) ^{\text{S}}-1} \tag{15}\]  
\[\text{C}_{\text{Ann}}^{\text{T}}\text{=}\sum\text{C}_{\text{Ann}}^{\text{u}} =\text{C}_{\text{Ann}}^{\text{PV}}+\text{C}_{\text{Ann}}^{\text{WT}}+\text{C }_{\text{Ann}}^{\text{BG}}+\text{C}_{\text{Ann}}^{\text{Bat}}+\text{C}_{ \text{A}}^{\text{inv}} \tag{16}\]  
\[\text{C}_{\text{A}}^{\text{u}}=\text{C}_{\text{Ann},\text{Cap}}^{\text{u}}+ \text{C}_{\text{OM}}^{\text{u}}+\text{C}_{\text{Ann},\text{Rep}}^{\text{u}}+ \text{C}_{\text{Ann},\text{fuel}} \tag{17}\]  
where, \(\text{C}_{\text{Ann}}^{\text{u}}\) is the annual cost of each unit, \(\text{C}_{\text{Ann},\text{Cap}}^{\text{u}}\) is the total annualized cost of each unit, \(\text{C}_{\text{OM}}^{\text{u}}\) is the operation and maintenance cost of each unit, \(\text{C}_{\text{Ann},\text{Rep}}^{\text{u}}\) is the replacement cost for each unit, and \(\text{C}_{\text{Ann},\text{fuel}}\) is the annual fuel cost of the biomass unite which is computed by applying the following formula [25; 27]:  
\[\text{C}_{\text{Ann},\text{fuel}}\text{=C}_{\text{Bio}}\times\text{Bi}_{\text {T}} \tag{18}\]  
\[\text{Bi}_{\text{T}}=\sum_{\text{I}}^{\text{S760}}\text{FS}_{\text{Frat}}( \text{t}) \tag{19}\]  
where, \(\text{C}_{\text{Bio}}\) is biomass fuel cost, and \(\text{Bi}_{\text{T}}\) is the total feedstock consumption of the generator (kg/year).  
Figure 6: The biomass consumption of the studied area.
# Innovative Vaporization of Wood Panel Waste into Activated Biochar for Efficient Phenol Adsorption
###### Abstract  
Construction and demolition byproducts include substantial amounts of wood panel waste (WPW) that pose environmental challenges. They also create opportunities for sustainable resource recovery. This study investigates the potential of WPP-derived biochar as an efficient adsorbent for phenol removal from aqueous solutions. Biochar was produced via pyrolysis at 450 \({}^{\circ}\)C and subsequent activation at 750, 850, and 950 \({}^{\circ}\)C. The biochar's physicochemical properties, including surface area, pore volume, and elemental composition, were characterized using advanced methods, including BET analysis, elemental analysis, and adsorption isotherm analysis. Activated biochar demonstrated up to nine times higher adsorption capacity than raw biochar, with a maximum of 171.9 mg/g at 950 \({}^{\circ}\)C under optimal conditions: pH of 6 at 25 \({}^{\circ}\)C, initial phenol concentration of 200 mg/L, and biochar dosage of 1 g/L of solution for 48 h. Kinetic and isotherm studies revealed that phenol adsorption followed a pseudo-second-order model and fit the Langmuir isotherm, indicating chemisorption and monolayer adsorption mechanisms. Leaching tests confirmed the biochar's environmental safety, with heavy metal concentrations well below regulatory limits. Based on these findings, WPW biochar offers a promising, eco-friendly solution for wastewater treatment in line with circular economy and green chemistry principles.  
biochar; wood panel waste; phenol adsorption; pyrolysis; activated carbon; circular economy; water treatment; heavy metals; sustainable materials Article
## 2 Model Constructions and Hypothesis
### Critical Analysis of Theoretical Constructs
#### 2.1.3 Social Influence  
Social influence (SI) is a key factor in the adoption of new technologies within the UTAUT framework. It refers to the user's perception of social network influence while using the system [31]. The lack of studies on the social influence of human perceptions of robots at work has been highlighted by [44].  
The construct of social influence (SI) is based on [31]. The first two items, SI1 and SI2, originated from the \"Subjective Norm\" variable from [38; 45; 46; 47; 48; 49], and these items have been used in the Theory of Reasoned Action (TRA). The items SI3 and SI4 originated from the \"Social Factors\" variable [50], and these items have been used in Innovation Diffusion Theory (IDT).  
Subjective norm has been used in robot acceptance models as an independent variable linked to the intention to use [26; 28]. These studies, which used the UTAUT framework in robot acceptance, showed a significant relationship between social influence and intention to use [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 32].  
Therefore, the following hypothesis was proposed:  
**H3**.: _There is a significant relationship between social influence (SI) and behavior intention (BI) to use the robot as a co-worker._
## 1 Introduction
For missions with such infrared (IR) payloads and stringent thermal requirements, active thermal control systems are used. Satellite active thermal control systems (ATCS) rely on input power for operation and have been shown more effective in maintaining the required temperature within required limits [10] but this increases the total power budget of the satellite and eventually the cost. In general, small satellites cannot generate more power due to limited solar array size. Some of the active thermal systems are thermo electric coolers, electric heaters, and pumped fluid loop systems. Besides cost and input power, these methods require additional volume which is limited in small satellites. Unlike active systems, a passive thermal control system (PTCS) does not require any additional input power from the satellite and it also can be implemented at lower cost. Some of the passive thermal control systems are, multi-layer insulation (MLI), thermal straps, radiators, thermal louvers, heat pipes, phase change materials, and thermal switches [11]. No small satellite missions with thermally sensitive IR instruments (operating at cryogenic range of temperatures) have flown and it is only due to lack of thermal control systems. NASA's JPL has developed an active cryocubesat (ACCS) [12] for such a thermally sensitive IR instrument, which is a breakthrough technology for many future cryogenic small satellite missions. To save cost, power, and volume inside the satellite, a few PTCS are studied for a nano/micro satellite carrying a an IR instrument.
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 1 Introduction
### Proposed Approach  
Despite the valuable contributions, there is still a palpable gap in the literature on topics relevant to studying the aggregated traffic classes of brownfields to enable the next step of integration with new technologies. While network performance metrics are considered for developing methodologies for new traffic types integration, there are still no key parameters for characterization or measuring the performance of the consolidated networks. The existing related work, study industrial network communication for means of network traffic modeling. However, there are still unaddressed issues in studying the dynamic behavior of networks that call for new and continuous probes and assessments. There is no proper model that considers network traffic as a whole; the focus so far has been on the characterization and modeling of each traffic flows in the network. Indeed this approach poses difficulties and complexities to network resource management considering the IIoT application demands in terms of scalability and flexibility. While the existing work-cycles in the traffic patterns, due to the operational work-flows, are acknowledged, it is not incorporated into solution development for modeling or characterization of network traffic.  
This paper addresses the support and securing of the performance of brownfield and avoiding performance bottlenecks in new integration, considering the challenge of future industrial networks for network resource management. The main goal is to model the network traffic dynamics to identify various communication states and profile their characteristics. The identified states and the profiled characteristics then can create the bases of network monitoring, and provide the required insight in decision making for provisioning or scaling up by safeguarding the requirements of the ongoing traffic in the network. The contributions of this work are as follows:  
* Two network parameter indicators (PIs), transmission volatility and transmitter volatility, are introduced to capture the temporal and spatial dynamics in bandwidth utilization to formulate the communication intensity and identify the work-cycles.
* Work-cycles are modeled, and their communication states are profiled based on their statistical summary and dynamic characteristics, including the introduced parameters.  
A two-step approach is proposed to model the aggregated traffic classes collected from brownfield, with respect to transmission intensity and throughput, utilizing the introduced parameters. The proposed approach is validated through comparative analysis of its performance in terms of accuracy of prediction and consistency of generated labels, with a set of unsupervised learning algorithms.