esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 3 The Proposed Method for Fault Diagnosis
### BPA Determination
#### 3.3.3 Support Degree Calculation of Propositions with Multiple Elements  
In this part, the support degree of the test model to compound proposition \(\{F_{n_{1}},F_{n_{2}},...,F_{n_{i}}\}\) denoted as \(Sup(\{F_{n_{1}},F_{n_{2}},...,F_{n_{i}}\})(n_{1}=1,2,...,M,n_{2}=1,2,...,M,..., n_{i}=1,2,...,M,n_{1}<n_{2}<...<n_{i})\) is obtained.  
Taking into account the nature of the model curve, firstly let us calculate the integral interval:  
\[InMin=\min(\min_{n_{j}}(\bar{X}_{n_{j}}+3\sigma_{n_{j}},\bar{X}_{n_{j}}-3 \sigma_{n_{j}}),\bar{X}_{T}+3\sigma_{T},\bar{X}_{T}-3\sigma_{T})\quad(n_{j}=n _{1},n_{2},...,n_{i}), \tag{27}\]  
\[InMax=\max(\max_{n_{j}}(\bar{X}_{n_{j}}+3\sigma_{n_{j}},\bar{X}_{n_{j}}-3 \sigma_{n_{j}}),\bar{X}_{T}+3\sigma_{T},\bar{X}_{T}-3\sigma_{T})\quad(n_{j}=n _{1},n_{2},...,n_{i}). \tag{28}\]Then, the intersection area between the test model curve \(\mu_{T}(x)\) and the fault model curves \(\mu_{F_{n_{j}}}(x)\) is obtained as:  
\[Area(\{F_{n_{1}},F_{n_{2}},...,F_{n_{i}}\})=\int_{IntMin}^{InMax}\min(\min_{n_{ j}}(\mu_{F_{n_{j}}}(x)),\mu_{T}(x))dx\quad(n_{j}=n_{1},n_{2},...,n_{i}). \tag{29}\]  
For example, \(Area(\{F_{1},F_{2},F_{3}\})\) is as shown in Figure 2c if \(M=3\).  
Finally, the support degree \(Sup(\{F_{n_{1}},F_{n_{2}},...,F_{n_{i}}\})\) of the test model to compound proposition \(\{F_{n_{1}},F_{n_{2}},...,F_{n_{i}}\}\) is obtained:  
\[Sup(\{F_{n_{1}},F_{n_{2}},...,F_{n_{i}}\})=\frac{Area(\{F_{n_{1}},F_{n_{2}},...,F_{n_{i}}\})}{Area_{T}}, \tag{30}\]  
where the \(Area_{T}\) is the area under the test model curve \(\mu_{T}\left(x\right)\), \(Area_{T}=\sqrt{2\pi}\sigma_{T}\).
# An Analysis of the Altitude Impact on Roots Compressor Operation for a Fuel Cell System
## 1 Introduction
### Contribution and Objectives  
The main objective of this paper is to investigate and analyze the impact of altitude on a Roots compressor operation within a heavy-duty PEM fuel cell system. For this purpose, a balance-of-plant evaluation will be made, coupling a previously existing physical and chemical model of a PEM fuel cell [45] and detailed operating maps of the target roots compressor [44]. By achieving this goal, the following specific objectives will be accomplished:  
* Understand the matching between a roots compressor characteristic map and a fuel cell sized for a heavy-duty truck application;
* Evaluate the effect of the compressor operating conditions (pressure ratio and efficiency) on the compressor power consumption and balance-of-plant efficiency;
* Quantify the implications of altitude on the maximum achievable power and energy balance;
* Discuss the relationship between the compressor map and the altitude impact.
# Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
## 2 Wind Turbine and Control Algorithm  
### Target Wind Turbine  
The wind turbine targeted in this study is a 100 kW turbine designed and manufactured for commercial use, which is currently undergoing installation and system optimization for field validation. Additionally, this wind turbine is designed with three blades to enable integrated pitch control and has a mechanism similar to that of typical large wind turbines. Its specifications include a hub height of 30 m, a rotor diameter of 24 m, a rated rotor speed of 50 rpm, and a maximum power coefficient of approximately 0.45 at a TSR of 7.3. Figure 1 shows the shape of the targeted wind turbine, and Table 1 presents its brief specifications. Wind turbine modeling was performed using commercial software based on the specifications provided by the manufacturer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Unit** & **Value** \\ \hline Wind Turbine Type & - & HAWT, VSVP, Upwind \\ Rated Power & kW & 100 \\ Rotor Diameter & m & 24 \\ Hub Height & m & 30 \\ Rated Wind Speed & m/s & 10 \\ Rated Rotor Speed & rpm & 50 \\ Fine Pitch Angle & deg & \(-\)0.8 \\ Optimal Tip Speed Ratio & - & 7.3 \\ Max-Cp & - & 0.45 \\ Gear Ratio & - & 1:1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of target wind turbine (100 kW model).
## 4 Results and Discussion
### CTAM Performances under Different Compaction Methods
#### 4.2.4 Dry Shrinkage Coefficient  
The variations of the dry shrinkage coefficients of Aggregate A and B are shown in Figure 13.  
Figure 13a,b shows that the dry shrinkage coefficients of Aggregate A and Aggregate B both presented parabola variations with increasing moisture loss rates. The maximum dry shrinkage coefficients under the different compaction methods happened between 6-8 h. Compared with Aggregate A, Aggregate B had higher moisture absorption, which led to higher aggregate shrinkage. Thus, Aggregate A had a higher dry shrinkage resistance than Aggregate B.  
Figure 12: Unconfined compressive strength of (**a**) Aggregate A and (**b**) Aggregate B.  
The specimen under the vibration compaction method had the minimum moisture loss rate and shrinkage strain, resulting in the minimum dry shrinkage coefficient. The shrinkage coefficient difference under three compaction methods gradually enlarged with the increasing moisture loss rate, and was then reduced to a small extent.
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Complex Indoor and Outdoor Scene Classification Algorithm Based on Spatio-Temporal Features
#### 2.6.2 BiLSTM Network  
Although multi-scale neural networks effectively extract spatial features, their ability to capture temporal features is less satisfactory. To address this limitation, we incorporate a BiLSTM (bidirectional long short-term memory) network into the model for temporal feature extraction. LSTMs, or long short-term memory networks, consist of forget gates, input gates, and output gates, which work together as a memory unit. These gates dynamically regulate the storage and output of memory information at each time step.  
In recent years, various LSTM variants have been proposed to enhance its capabilities [33]. For example, [PERSON] et al. introduced a spatio-temporal attention mechanism to improve video classification models. This approach utilizes an attention mechanism to weight important regions and time periods within video frames, enhancing the model's ability to manage complex spatio-temporal dependencies in sequences. Additionally, they proposed a multi-scale LSTM model, which addresses sequential data across different time scales. This model enables LSTMs to capture features over varying time spans by integrating a multi-scale mechanism, making it suitable for processing temporal data with multiple cycles or hierarchical dependencies [34].  
Moreover, [PERSON] et al. combined BiLSTM with graph neural networks (GNNs) for text classification tasks [35]. In this setup, the BiLSTM handles serialized features of the text, while the GNN further processes the graph structure, allowing for the better modeling of dependencies within the text.  
In this paper, we utilize a BiLSTM network, and its structure is illustrated in Figure 13.  
This architecture significantly enhances the recognition of long-distance dependent patterns by simultaneously capturing the forward and backward temporal dependencies of data sequences. In this structure, the data first enter the bidirectional network, where the information flow to the hidden layer is precisely controlled through the coordinated tuning of two sets of parameters. Finally, the softmax layer is employed to effectively classify the extracted features. The theoretical expression for this process is as follows:  
\[\overrightarrow{h_{t}}=f\Big{(}\vec{\omega}\cdot x_{t}+\vec{v}\cdot h_{t-1}+ \overrightarrow{b}\Big{)} \tag{11}\]  
data to influence earlier judgments. This limitation prevents the integration of front and back sequences for comprehensive learning. In contrast, the model proposed in this paper possesses the capability to utilize contextual information, allowing it to make informed judgments by linking both past and future sequences.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 4 Chipless RFID Array Design
### Linear Array Tags  
The linear array configuration is a fundamental arrangement of unit cells in a straight-line formation characterised by the equal spacing between the unit cells. In this study, we use CST Microwave Studio to design unit cells and three specific linear configurations, namely, \(1\times 2\), \(1\times 3\) and \(1\times 4\) arrays as shown in Figure 8.  
In addition, to mitigate the mutual coupling effect among the resonators in the array design, a copper strip with a width of 1 mm is intentionally removed between each resonator element. This deliberate removal of the copper strip concentrates the surface current distribution around the resonators and the ground plane, reducing mutual coupling and improving the array's performance based on reflector array theory. According to Figure 9a, the surface current distribution is spread over all resonators, making the strong mutual coupling effect. However, in Figure 9b, the surface current distribution is controlled by removing these copper strips and mitigating the mutual coupling effect.
## 2 Numerical Method and Calculation Procedure  
### Governing Equations  
In the MPS-MAFL method, the continuity, Navier-Stokes, and energy equations for incompressible viscous flows are as follows [42]:  
\[\
abla\cdot\overset{\rightharpoonup}{u}=0 \tag{1}\]  
\[\rho\left(\frac{D\overset{\rightharpoonup}{u}}{Dt}+\left(\overset{ \rightharpoonup}{u}-\overset{\rightharpoonup}{u}\right)\cdot\
abla\overset{ \rightharpoonup}{u}\right)=-\
abla p+\mu\
abla^{2}\overset{\rightharpoonup}{u}+ \rho\overset{\rightharpoonup}{g}+\overset{\rightharpoonup}{F}_{S} \tag{2}\]  
\[\frac{DT}{Dt}+\left(\overset{\rightharpoonup}{u}-\overset{\rightharpoonup}{u} ^{c}\right)\cdot\
abla T=\alpha\
abla^{2}T \tag{3}\]  
where \(\rho\) is the liquid density, \(\mu\) is the dynamic viscosity, \(\overset{\rightharpoonup}{u}\) is the fluid flow velocity, \(\overset{\rightharpoonup}{u}^{c}\) is the velocity of computing Lagrangian points after reconfiguration, \(p\) is the pressure, \(\overset{\rightharpoonup}{F}_{s}\) is the surface tension force, \(T\) is the temperature, and \(\alpha\) is the thermal diffusivity.  
In the MPS-MAFL method, calculation consists of three parts: Lagrangian, reconfiguration, and Eulerian parts. In the Lagrangian part, Laplacian and gradient models as well as the Pressure Poisson Equation (PPE) are adopted to semi-implicitly solve governing equations except for the convection term in the momentum equation as stated in the MPS method [48]. Then, Lagrangian computing points are adaptively reconfigured according to the flow directional local grid. Finally, the convection term in the momentum equation is solved by the first-order upwind scheme. In this paper, we introduce some up-to-date particle interaction models into the Lagrangian part, which are indicated in followed parts. Discussion about reconfiguration and Eulerian parts is outside the scope of this paper; readers can find more details about these two parts in other previous studies [41; 43].
## 3 Results and Discussion
### Performance of PVDF Composite Ultrafiltration Membrane
Water molecules adsorbed on the surface will then form a hydration layer. This hydration layer helps to isolate the contact between the membrane surface and pollutants, such that the pollutants can be removed more easily and the FRR can be improved. Moreover, composite ultrafiltration membrane with CGO has more hydrophilic functional groups on the surface, which makes it easier to form a large hydration layer, so the FRR is higher. Comparing the FRR with the addition of GO or CGO, it was found that this trend was similar to water flux and porosity, which reached the maximum at 0.75 and 1.0 wt%, respectively. Figure 5d shows a schematic diagram of the composite ultrafiltration membrane. As seen in the figure, the principle of preparing composite materials and the function of each component are as follows: Chrome leather scraps are waste, but they can be degraded to collagen by alkaline and enzyme degradation (this collagen may become a small molecular amino acid or peptide chain with multiple amino acids). The chemical structure of collagen contains functional groups similar to amino acids. The oxygen-containing functional groups on the graphene oxide structure include epoxy group, hydroxyl and carboxyl. CGO can be obtained by a ring-opening addition reaction between the amino group on collagen and the epoxy group on graphene oxide structure (or a reaction between the amino group on collagen and the carboxyl group on graphene oxide structure). CGO has a higher carboxyl content than GO, so it has better hydrophilicity. Because of the hydrophobicity of PVDF, its water flux is not high as that of a separation membrane. Improving the hydrophilicity of PVDF can improve its water flux. At the same time, it can also improve the elution of the membrane after separating protein pollutants, so as to improve the recycling efficiency. CGO was used as filler to form a composite material with PVDF in solvent NMP. PVDF is the matrix of the ultrafiltration membrane, PVP is the porogen, NMP is the organic solvent and CGO is the hydrophilic additive. The film-casting solution obtained by mixing these materials was heated to volatilize the organic solvent so as to form a membrane. After that, the CGO/PVDF composite ultrafiltration membrane was formed by putting the obtained membrane in water to remove NMP and porogen PVP. Compared with GO, CGO has higher carboxyl content and better hydrophilicity, so the water flux of the composite material formed with PVDF was also improved.
## 4 Application of HS to VM Consolidation
### Harmony Search for VM Consolidation
#### 4.2.1 Solution Representation  
For VM placement, HM contains the PM-VM mapping information, which is the VM assignment to PM information. One HM is in a permutation form, where each array represents the VM index, and each index value represents the PM index to which the VM is assigned. This solution representation is schematically shown in Figure 5.  
Figure 5: Solution representation for physical machine (PM)–virtual machine (VM) mapping.
## 2 System Model
### Computation Model
#### 2.4.3 Cloud Execution  
Since the edge node operates within the constraints of limited resources within the E-RAN [5; 7]. In some cases, the computational requirements of UE \(k\) goes beyond the computation resources (\(f_{l,k}^{edge}\)) of E-AP \(l\). Therefore, to tackle the computation demand, the agent \(l\) selects the cloud tier (\(s_{k}^{cloud}=1\)), and the UE \(k\) offloads its task through C-RAN mode. Generally, the delay associated with offloading to the cloud tier encompasses both an uploading delay and a processing delay. This can be formulated as follows:  
\[D_{k}^{cloud}=\frac{M_{k}}{\sum_{j\in\bar{l}}R_{k,j}}+\frac{M_{k}}{\sum_{j\in \bar{l}}R_{j,CP}}+\frac{C_{k}}{f_{CP,k}^{cloud}}, \tag{11}\]  
where \(R_{k,j}\) signifies the achievable transmission rate from the UE \(k\) to RRH \(j\) and \(R_{j,CP}\) represents the transmission rate from RRH \(j\) to the cloud processor \(CP\). \(f_{CP,k}^{cloud}\) illustrates that the CP allocates \(f\) amount of computing resources to the UE \(k\). It is important to note that in our work, the volume of data uploaded is notably larger than that of processed data. Since the RRH is near the UE, the downloading delay from edge nodes to the UE is negligible. However, given that the cloud server is positioned at a considerable distance and is connected through fiber with the core networks [1], the downloading delay from the CP to RRH is significantly non-negligible in this E-RAN system. The total offloading delay for the cloud tier can be expressed as:  
\[D_{k}^{cloud}=\frac{M_{k}}{\sum_{j\in I}R_{k,j}}+\frac{M_{k}}{\sum_{j\in I}R_{j, CP}}+\frac{C_{k}}{f_{CP,k}^{cloud}}+\frac{M_{k}^{processed}}{\sum_{j\in I}R_{CP,j}}, \tag{12}\]  
where \(M_{k}^{processed}\) is the output data after processing at the cloud servers. The corresponding energy consumption due to transmission is expressed as:  
\[E_{k}^{Cloud}=p_{k,j}\bigg{(}\frac{M_{k}}{\sum_{j\in I}R_{k,j}}+\frac{M_{k}}{ \sum_{j\in I}R_{j,CP}}\bigg{)} \tag{13}\]  
It is worth mentioning that each piece of UE \(k\) can choose a single mode to accomplish their computation demand. In this condition, the computation will be performed at the local computation tier (SUE), cloud, or edge computing tier. The overall energy consumption conditions can be represented as follows:  
\[E_{k}^{service}=s_{k}^{local}E_{k}^{local}+s_{k}^{edge}E_{k}^{edge}+s_{k}^{cloud }E_{k}^{cloud}, \tag{14}\]  
where \(s_{k}^{local}+s_{k}^{edge}+s_{k}^{cloud}=1\) and \(s_{k}^{local},s_{k}^{edge},s_{k}^{cloud}\in\{0,1\}\); if \(s_{k}^{local}=1\) the task will be executed by the SUE by itself. If \(s_{k}^{edge}=1\), the computation process will be accomplished at the edge tier. On the other hand, if \(s_{k}^{cloud}=1\), the computation tasks will be executed at the cloud computing zone. Furthermore, condition \(s_{k}^{local}+s_{k}^{edge}+s_{k}^{cloud}=1\) strictly denotes that the user \(k\) can opt for only one mode based on the computation demand.
## 4 ANN Modeling  
An artificial neural network (ANN) model was applied to identify the optimal design parameters of the inclined porous plate as a wave absorber. The ANN model has the advantage of learning complex nonlinear relationships, handling complex data, and parallel-processing capability. This study utilizes the multilayer perceptron (MLP) regression algorithm of the ANN model, a supervised learning algorithm that trains on a labeled dataset. This algorithm possesses strong nonlinear mapping capabilities, enabling it to make accurate predictions. The neural network architecture is flexible, allowing for adjustments in the number of hidden layers and neurons in each layer to tailor the model training to the research and training goals.  
The ANN model includes an input layer with two input features such as non-dimensional plate length (\(l/h\)) and inclined angle (\(\theta\)), several hidden layers, and an output layer with a single output feature of averaged reflection coefficient (\(R_{f}=\frac{1}{M}\sum\limits_{i=1}^{M}|R_{i}|\), \(M=\) total number of incident wave periods). Another important parameter affecting the reflection coefficient is the porosity of an inclined porous wave absorber. However, it is excluded from the input features because previous articles have already clarified that the optimal porosity is around 0.1 [5,15,28]. Figure 2 illustrates the architecture of the ANN model and the mapping of input and output with its weights (\(w\)) and activation function (\(\Gamma\)) of artificial neurons. Each layer is composed of nodes or artificial neurons that are interconnected, each with its activation function. Each node in the network has its weight and threshold. When the node's output surpasses the specified threshold, it is activated and transmits data to the next layer of the network. If not, the data are not passed on to the next layer. The activation function of a neural network is crucial for activating the neurons of the hidden layer, enabling them to generate preferable outputs and inhibiting nonlinearity in the model. The performance of the neural network model is affected by several hyperparameters, including the number of hidden layers, the number of neurons in each layer, batch size, learning rate, regularization parameters, solver, activation function, and the number of epochs. Optimizing these hyperparameters can significantly enhance the training rate of the ANN model and improve its prediction accuracy. Figure 3 illustrates all the processes involved in ANN modeling and predictions. Specifics of the tasks are outlined in the following sections.  
Figure 2: Illustration of (**a**) ANN model architecture (**b**) mapping of input and output of artificial neurons.
## 5 Network Layer
### Spectrum Awareness and Routing
However, this scheme does not necessarily yield the optimal overall performance in terms of the amount of channel switching when taking into account the whole map. For example, it is possible that a combination of second-best sub-route decisions may together lead to a better overall frequency hopping reduction.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 3 Decision Function
### Description and Efficiency
#### 3.2.2 Choosing the Threshold  
As we stated in Section 2.4, the efficiency of an attack may depend on many factors such as the attacker's connection speed, the current network topology and probably other factors. To detect an attacker disposing of an optimal relay setup, we should set the decision function to the highest sensitivity that can be supported. This is achieved by letting the threshold be as low as possible while keeping some breathing room to avoid the maximum of false positives as well. From Figure 9, we see that the minimum threshold value getting 0% of false positive on our tests lies around 192 ms. Nevertheless, we observed on Section 2.3.2 that small variations on the samples might emerge over long periods of times (Figures 5 and 6). Hence, users can choose to slightly loosen the sensibility of the decision function with a higher threshold.  
Allowing the samples to live about 5 to 10 ms higher than normally expected trades off the insurance of very few false positives against the possibility of an attack, assuming that such an efficient relay is achievable between those nodes.
# Performance Analysis of Continuous-Variable Quantum Key Distribution with Multi-Core Fiber
## 2 The MCF-Based CVQKD
To restrict the impact of the inter-core crosstalk, we propose the channel wavelength distribution scheme in Figure 3, where all the wavelengths of quantum channels are different. Under this condition, the wavelength of the inter-core crosstalk in each core will be different from the wavelength of quantum channel and this crosstalk can be removed out at the receiver. Therefore, a good channel wavelength management can put down the impact of the inter-core crosstalk. Figure 3: Different distribution of quantum channel wavelength in each core.