esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 3 Performance Degradation Trend Analysis and Fault Early Warning of Marine Diesel Main Engines
### Performance Degradation Analysis and Fault Early Warning
#### 3.4.2 Performance Evaluation and Degradation Trend Analysis
\begin{table}
\begin{tabular}{c c} \hline
**Performance Parameter** & **Mean Value** \\ \hline Cylinder Jacket Cooling Water Inlet Temperature (°C) & 83.773101089477535 \\ Cylinder 1 Jacket Cooling Water Outlet Temperature (°C) & 89.696554016113282 \\ Cylinder 2 Jacket Cooling Water Outlet Temperature (°C) & 89.8497976744048 \\ Cylinder 3 Jacket Cooling Water Outlet Temperature (°C) & 89.1630899637888 \\ Cylinder 4 Jacket Cooling Water Outlet Temperature (°C) & 89.092101951599119 \\ Cylinder 5 Jacket Cooling Water Outlet Temperature (°C) & 89.83531893127446 \\ Cylinder 6 Jacket Cooling Water Outlet Temperature (°C) & 89.51670715930181 \\ Cylinder Jacket Cooling Water Inlet Pressure (MPa) & 0.47260199856758117 \\ Cylinder 1 Exhaust Temperature (°C) & 358.52707760620116 \\ Cylinder 2 Exhaust Temperature (°C) & 352.2829172736327 \\ Cylinder 3 Exhaust Temperature (°C) & 351.78961108398437 \\ Cylinder 4 Exhaust Temperature (°C) & 343.93701009698242 \\ Cylinder 5 Exhaust Temperature (°C) & 356.396980276367 \\ Cylinder 6 Exhaust Temperature (°C) & 352.38188513183593 \\ Lubricating Oil Inlet Temperature (°C) & 45.5445690536499 \\ Scavenging Air Temperature (°C) & 51.22465405175165 \\ Scavenging Air Pressure (MPa) & 0.08449000284492974 \\ Speed (RRTM) & 58.23421699053956 \\ Power (kW) & 11,815.623912109375 \\ Fuel Flow Rate (kg/h) & 2035.4115 \\ Specific Fuel Consumption (g/kW-h) & 172.31603485696255 \\ Fuel Type & 3 \\ \hline \end{tabular}
\end{table} 
Table 8: Mean Values of Performance Parameters in the Initial Performance Sample Space for the Common Operating Range Condition. Figure 5: Degradation trend curve of performance. To validate this hypothesis, sequential elimination of individual performance parameters was conducted with subsequent Mahalanobis distance recalculations to reconstruct degradation trend curves. The results demonstrate that removal of the \"No. 2 cylinder jacket cooling water outlet temperature\" parameter yielded a gradual degradation profile devoid of abrupt surges or significant fluctuations (Figure 6). In contrast, elimination of other parameters maintained degradation trends consistent with Figure 5 (Figures 7 and 8). Subsequent analysis revealed substantial amplitude variations in the No. 2 cylinder jacket cooling water temperature profile between late February and March 2021 (Figure 9), suggesting probable mechanical fault occurrence within this period. The localized temperature anomalies indicate fault origination proximate to the No. 2 cylinder
## 3 Dual Sparsity Pruning Federated Learning Framework
### Sparse Federated Training for Object Detection
#### 3.2.1 Local Object Detection Loss  
Nowadays, many electronic devices equipped with cameras have the ability to take pictures and upload them to cloud data centers. For example, smartphones, watches, tablets, and other devices all need to complete real-time and fast object detection tasks.  
Therefore, by combining the advantages of federated learning and model sparsification, we propose an efficient sparse federated learning framework, which is very suitable for deploying sparse object detection models on edge electronic devices.  
The detection model used at the edge is YOLO (You Only Look Once) [45], which is recognized as a high-speed algorithm for implementing real-time object detection tasks. Its design principle involves dividing the input image into an \(S\times S\) grid of cells, generating \(B\) bounding boxes within each cell, and subsequently calculating the confidence score \(C\) of objects existing within each bounding box. Specifically, each bounding box is represented by a quintuple (\(x\), \(y\), \(w\), \(h\), \(C\)), where (\(x\), \(y\)) denote the horizontal and vertical coordinates of the center point, \(w\) and \(h\) represent the width and height of the bounding box, and \(C\) indicates the probability of an object within the bounding box. Following this, the bounding box with the highest Intersection over Union (IoU) among the \(B\)-generated bounding boxes is selected. As the YOLO algorithm has continuously evolved, we adopted the YOLOv8 [46] version as the foundation for object detection tasks. In YOLOv8, the local loss function at device \(i\) includes coordinate loss (\(L_{coo}\)) and classification loss (\(L_{cls}\)) formulated as follows:  
\[L(\mathbf{\theta})=\lambda_{cls}L_{cls}(\mathbf{\theta})+\lambda_{coo}L_{coo}(\mathbf{ \theta}), \tag{4}\]  
where \(\lambda_{cls}\) and \(\lambda_{coo}\) are hyperparameters. YOLOv8 uses CIoU (Complete IoU) and DFL (Distribution Focal Loss) loss functions for bounding box regression, which have improved object detection performance, especially for smaller objects. For classification, YOLOv8 employs binary cross-entropy loss.
# Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs +
## 5 Accelerated Matrix Multiplication for GPU-Only ConvMM
### cuBLAS Accelerated Matrix Multiplication Convolution (ConvCAMM)  
CUDA Basic Linear Algebra Subroutines (cuBLAS) library provides the high-performance functions for the numerical operations and shows the order of magnitude performance improvements over the other libraries [23; 24]. This library also contains various General Matrix-Matrix Multiply (GEMM) routines for the different types of operands (like complex, single data type, double data type etc.) that can be useful to accelerate the ConvMM layer [25; 26]. However, some constraints need to be considered before using the subroutines of cuBLAS.  
The first aspect to consider is the compute capability of the GPU platform because the cuBLAS library cannot be used on a GPU having compute capability less than 3.5. Next constraint is the memory storage layout of the cuBLAS library, which is in column-major order. Since C++ language follows the row-major order, applications written in C/C++ language cannot use the native semantics for the 2-dimensional arrays and data needs to be transformed from row to column-major order for the cuBLAS subroutines. This transformation between the row and the column-major order for every subroutine can break the performance of a program instead of accelerating it.  
In this work, single precision GEMM (SGEMM) routine of cuBLAS is used to accelerate the matrix multiplication of the input and the filter matrices (for GPU devices having compute capability greater than 3.5). This cuBLAS accelerated Matrix Multiplication (CAMM) is employed in the proposed GPU-only Convolution scheme.  
The problem of storage layout is resolved for the matrix multiplication subroutine. Since the cuBLAS always assume that the matrices are stored in the column-major order, so the matrices are transformed implicitly before passing to the multiplication subroutine and after getting back the resultant matrix. This does not cause a buffer overrun, but it effectively transposes the matrices without actually moving data around in the memory. Thus, the problem of memory layout is resolved by passing the matrices in reverse order (C\({}^{\prime}\) = B\({}^{\prime}\) \(\times\) A\({}^{\prime}\) instead of C = A \(\times\) B), and the resultant matrix  
Figure 9: Flow of the accelerated matrix multiplication-based convolution with unified memory.  
would be implicitly transposed before passing to main application that is the required result (C = (C')') in the row-major order for the ConvMM layer.
## 4 Evaluation
### Discussion  
There are/have been several other initiatives and projects working in the area for creation of a kind of Internet of things architecture such as e.g., Fi-Ware, Hydra, Runes, IoT-A, iCore and Sofia. Each of these projects has had different application cases into which they have focused, the resulting architectures have been interoperable only within the referred project and their approaches have varied from relying on open source solutions, some open API based implementations and own interpretation on applied standards. Here, one of the projects has been selected for comparison, and provided architectural principles are compared in the applicable level with main blocks of generic elements (GE) of FI-Ware architecture [51] in the Table 2. It is seen that the provided architectural principles are quite well in line with Fi-Ware architecture, however, an essential difference seems to be that we rely more on open standards and have an open multiple stakeholder system as the goal, and FI-Ware is more relying on open API based implementations of specific industrial companies. However, it is estimated that there are several lessons to be learned from Fi-Ware architecture and related evaluations, applicability of some parts and GEs related to M2M information management, M2M security and interaction with constrained embedded M2M devices are open areas for future research.  
The evaluation of the architecture principles have been carried out in such a manner that main enablers have been developed and evaluated in different experimental systems in parallel as described earlier. This means that the key enablers have not yet been integrated and executed within a single end  
Figure 27: Example of device metadata for electric bike.  
to end experimental scenario, which means that performance analysis of the complete system has not yet been feasible to be done. However, the aim in the next step is to create a combined experimental system, where the key enablers are executed in an integrated manner. It is planned and expected that in that phase also quantitative results related to performance can be evaluated.
## 3 Results and Discussion
### Physico-Chemical Characterisation of Textile Effluent Samples
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Compound**} & **Raw Effluent (RE)** & **Treated Effluent 1 (TEI)** & **Removal Percentage** \\  & **(ug L\({}^{-1}\))** & **(ug L\({}^{-1}\))** & **(\%)** \\ \hline Phenol & 0.03 & 0.03 & 23.08 \\ \hline Hexamine & 0.04 & 0.03 & 24.49 \\ \hline Ethlyl-tetramethyl-heptane & 0.02 & 0.01 & 29.29 \\ \hline
2.6-Tetr-butyl-quinone & 1.62 & 1.20 & 25.64 \\ \hline Tetramethyl-dodecane & 0.02 & 0.01 & 29.27 \\ \hline Ionol (2.6-di-tert-butyl-4 methyl-phenol) & 0.02 & 0.01 & 28.21 \\ \hline Diethylphthalate & 0.09 & 0.07 & 21.67 \\ \hline Nonylphenol & 0.36 & 0.27 & 26.95 \\ \hline
3.5-Di-tert-butyl-4-hydroxi-benzoic acid & 0.03 & 0.02 & 30.65 \\ \hline
2.6-Di-tert-butyl-4-ethyl-phenol & 1.69 & 1.23 & 26.89 \\ \hline
2.6-Di-tert-butyl-4-methoxy-phenyl-phenol & 0.02 & 0.01 & 25.64 \\ \hline Dimethyl-carboxymethyl-phtalate & 0.26 & 0.20 & 24.35 \\ \hline Octadecane & 0.11 & 0.08 & 29.40 \\ \hline Butlyl-2-ethylhexyl-phtalate & 0.16 & 0.11 & 28.29 \\ \hline Benzoic acid phenyl ester & 0.31 & 0.25 & 21.62 \\ \hline
2.2-dimethyl-2-phenyl-acetophenone & 0.02 & 0.01 & 27.04 \\ \hline Butlyl-octyl-phtalate & 0.21 & 0.15 & 30.79 \\ \hline
4.4\({}^{\prime}\)-Methylenebis-phenol & 0.02 & 0.02 & 26.83 \\ \hline Benzil-quinoline & 0.02 & 0.01 & 25.64 \\ \hline Docosane & 0.02 & 0.01 & 26.70 \\ \hline Tributyl-O-acetylcitrate & 0.08 & 0.06 & 24.43 \\ \hline Tricosane & 0.03 & 0.02 & 27.98 \\ \hline
7-(Diethylamino)-4-methyl-2H-1-benzopyranan-2-one & 0.13 & 0.09 & 31.92 \\ \hline Benzyl-butyl-phtalate & 0.03 & 0.02 & 23.08 \\ \hline Triphenyl-phosphonic acid & 0.25 & 0.19 & 24.31 \\ \hline Disoscotylphtalate & 0.21 & 0.15 & 29.58 \\ \hline Hexacosane & 0.26 & 0.19 & 25.85 \\ \hline Triacontane & 0.66 & 0.47 & 29.41 \\ \hline Dotriacontane & 0.39 & 0.28 & 28.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chemical composition found using GC-MS in the textile effluent RE and TE1 samples. microorganisms. However, algae were the most susceptible organisms, recording a 96-h biomass EC\({}_{50}\) of 0.70 \(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\) for _Scenedesmus subspicatus_ and 0.97 \(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\) for _Anabaena flos-aquue_. The aquatic plant _L. gibba_ was shown to be more resistant, recording an EC\({}_{50}\) of 62.5 \(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\)[37]. The values recorded for trticlosan in the RE and TE1 samples (Table 2) were lower than the EC\({}_{50}\) values reported by [PERSON] et al. [37]. However, most of the compounds present in the effluent samples studied are not included in the legislation, in particular the Directive 2008/105/EC of 16 December 2008 [16], nor the Law by Decree n\({}^{\circ}\) 236/98 of 1 August [30]. Nevertheless, the possible toxic effects and the potential risk of the effluent due to synergistic interactions between the different compounds cannot be ignored, even at low concentrations.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 2 System Model
### Baseband Channel Model  
The discrete-time baseband channel is modeled as a finite impulse response filter between each pair of transmit and receive antennas. Let \(x_{n}[t]\) be the symbol of transmit antenna \(n\) at time \(t\) and let \(\mathbf{x}[t]=(x_{1}[t]\ \ldots\ x_{N}[t])^{T}\). Similarly, let \(y_{k}[t]\) be the received symbol of UE \(k\) at time \(t\) and let \(\mathbf{y}[t]=(y_{1}[t]\ \ldots\ y_{K}[t])^{T}\). The channel model is  
\[\mathbf{y}[t]=\sum_{\tau=0}^{L-1}H[\tau]\mathbf{x}[t-\tau]+\mathbf{z}[t] \tag{1}\]  
where the noise \(\mathbf{z}[t]=(z_{1}[t]\ \ldots\ z_{K}[t])^{T}\) has circularly-symmetric, complex, Gaussian entries that are independent and have variance \(\sigma^{2}\), i.e., we have \(\mathbf{z}\sim\mathcal{CN}(\mathbf{0},\sigma^{2}\mathbf{I})\). The \(\mathbf{H}[\tau]\), \(\tau=0,\ldots,L-1\), are \(K\times N\) matrices representing the channel impulse response, i.e., we have  
\[\mathbf{H}[\tau]=\begin{pmatrix}h_{11}[\tau]&h_{12}[\tau]&\ldots&h_{1N}[\tau]\\ h_{21}[\tau]&h_{22}[\tau]&\ldots&h_{2N}[\tau]\\ \vdots&\vdots&\ddots&\vdots\\ h_{K1}[\tau]&h_{K2}[\tau]&\ldots&h_{KN}[\tau]\end{pmatrix} \tag{2}\]  
where \(h_{kn}[.]\) is the channel impulse response from the \(n\)-th antenna at the base station to the \(k\)-th UE. For instance, a Rayleigh fading multi-path channel with a uniform power delay profile (PDP) has \(h_{kn}[\tau]\sim\mathcal{CN}(0,1/L)\) and these taps are independent and identically distributed (iid) for all \(k,n,\tau\).  
The vector \(\mathbf{x}[t]\) is constrained to have entries taken from a discrete and finite alphabet  
\[\mathcal{X}=\{0\}\cup\left\{\sqrt{\frac{P}{N}}\,\mathrm{e}^{\mathrm{j}2\pi q/2 ^{b}};q=0,1,2,\ldots,2^{b}-1\right\}. \tag{3}\]  
The transmit energy clearly satisfies \(\|\mathbf{x}[t]\|^{2}\leq P\) and we define \(\mathrm{SNR}=P/\sigma^{2}\). The inequality is due to the 0 symbol that permits antenna selection. Antenna selection was also used in [45] to enforce sparsity. Our intent is rather to allow antennas not to be used if they do not improve performance.
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 4 Simulation Experiments and Result Analysis  
### Description to Hyperparameters  
To achieve a better forecasting performance, it is essential to systematically optimize the relevant hyperparameters of the models. These hyperparameters include the number of hidden layer nodes, learning rate, and the size of the training batch. To ensure the control of the variables in later comparison experiments among different sub-datasets and deep learning models, we used empirical values of 1 for the number of hidden layers, 1000 for the max iteration epochs, 128 for the batch size, and 0.01 for the initial learning rate of BP neural networks. To achieve a higher accuracy, the number of hidden nodes can be tuned sequentially in the range given by Equation (11), while keeping the other parameters unchanged. Figure 8 presents the grid search result of the number of hidden nodes. According to Figure 8, the number of nodes in the hidden layer of BP neural networks was taken as 13.
## 3 Operation and Models of the VANET Testbed
### Protocol Layers and Operations
This is a revision of the fixed WiMAX IEEE 802.16e-2005 standard [48] and it provides functionalities such as Base Station handoffs, MIMO (Multiple Input Multiple output) transmit/receive diversity, and scalable FFT (Fast Fourier Transform) sizes. Table 1 lists some of the more relevant features. Although WiMAX can support high data rates providing up to 128 Mbps downlink and 56 Mbps uplink using its MIMO antenna techniques, we shall use SISO antenna technique, which supports up to 1 Mbps uplink and downlink. We use the WiMAX MAC architecture to define service flows that can be mapped into gradual IP sessions to enable end-to-end IP based QoS. Of the two-duplexing modes allowed by mobile WiMAX, we shall use the TDD (Time Division Duplexing) mode (the other is FDD (Frequency Division Duplexing) mode) as done in most deployments in order to accommodate the asymmetric VANET traffic. That is, our uplink traffic \begin{table}
\begin{tabular}{c c c} \hline \multirow{3}{*}{Sub-layers 2} & Standard & IEEE 802.16e–2005 \\ \cline{2-3}  & Multiplexing & OFDMA \\ \cline{2-3}  & FFT size & Scalable (512, 1024, 2048, _etc._) \\ \cline{2-3}  & Duplexing mode & TDD \\ \hline \multirow{3}{*}{Sub-layers 1} & Modulation scheme & QPSK, 16-QAM, and 64-QAM \\ \cline{2-3}  & Subcarrier spacing & 10.94 kHz \\ \cline{1-1} \cline{2-3}  & Signal bandwidths & 5, 7, 8.75, and 10 MHz \\ \cline{1-1} \cline{2-3}  & Spectrum & 2.3, 2.5, and 3.5 GHz \\ \hline \end{tabular}
\end{table} 
Table 1: IEEE 802.16e characteristics. requires more bandwidth than the downlink, due to the uploading of safety (not downloading entertainment video that the other researchers are focused on) driving video traffic to the RSU. The WiMAX technology uses OFDMA frames for multiple users to receive data from the BS at the same time thereby increasing bandwidth utilization. The frames are structured into UL (Uplink) and DL (Downlink) subframe as shown in Figure 3[49]. All the MSes receive the frame control section of a DL subframe. The DL mapping field in the frame control section specifies the location and duration of DL data burst in the frame from a particular MS. Conversely; the UL mapping field specifies the location and number of time slots allotted in the frame for a particular MS to transmit its data on UL. All MSes listen to the control section in the DL frame, and receive or transmit data during the slots assigned to them by the BS[50]. As seen from Figure 3, this structure enables dynamic allocation of DL and UL resources to efficiently support asymmetric DL/UL traffic. A subchannel is allocated to each user there by reducing the channel interference in the frequency domain. OFDMA (Orthogonal Frequency Division Multiple Access) is the scheme used allowing multiple accesses to every user on our network.
## 4 Discussion  
### Possible Reasons for the Inconsistent Performance of the Four Products  
#### 4.1.1 Inconsistency of Model Inputs  
One main reason for the inconsistent performance of the four products could be the difference in the input datasets and their usage (Table 6). Firstly, the meteorological driving data for each ET product are different. For example, the meteorological driving data of MOD16, PML-V2 and BESS come from GMAO-MERRA, GLDAS and NCEP/NCAR, respectively. These three datasets have different temporal and spatial resolutions (Table 6). Previous studies suggested that GMAO-MERRA may overestimate the net radiation and  
Figure 8: Comparison of multi-year average ET from equatorial climate zone (**a**), warm climate zone (**b**), and snow and polar climate zone (**c**) among the four products and ET values from literature in other regions. shortwave radiation in the tropical climate region, while GLDAS may overestimate the net radiation and underestimate the incoming shortwave radiation [61]. The meteorological elements used in these three products are also different. MOD16 requires air temperature, atmosphere pressure, relatively humidity and downward shortwave radiation. BESS requires air temperature and wind speed. In addition to the same meteorological elements as required by MOD16, PML-V2 also requires precipitation, downward longwave radiation and wind speed. Secondly, the remote sensing input datasets of these ET products are inconsistent. Although all ET products use the same land cover product (MOD12Q1), only MOD16 integrates it into the ET algorithm. The biome property thresholds of leaf stomata, VPD and other related factors are set on the basis of land cover type in MOD16, while PML-V2 and BESS just used the land cover product to construct a mask of the terrestrial area. The sources and usage of LAI in these products are also different. LAI in both MOD16 and BESS comes from Terra, while PML-V2 comes from a combination of Terra and Aqua. Consequently, the spatial and temporal resolution of the three input LAI datasets are also different. For example, the spatiotemporal resolution of MCD15A3 (500 m/4 day) in PML-V2 LAI is better than that of MOD15A2 and MCD15A2 (1 km/8 day) in MOD16 and BESS. In addition, LAI is used to upscale the leaf conductance (including stoical, cultural and leaf boundary layer conductance) to canopy conductance in MOD16, while it is used to divide the total available energy into canopy absorption and soil absorption in PML-V2. Unlike MOD16 and PML-V2, LAI is used to estimate the downward longwave radiation and net radiation absorbed by the shaded and sunlit leaves in BESS. Additionally, the input albedo and emissivity datasets of each ET product are inconsistent. In terms of albedo, MCD43A3 is used in PML-V2, with a spatial resolution of 500 m and a temporal resolution of 8 days, while MOD43C1 (0.05\({}^{\circ}\)/16 days) and MCD43B3 (1 km/16 days), with lower spatial and temporal resolution, are used in MOD16 and BESS, respectively. The higher spatiotemporal resolution of albedo in PML-V2 is most likely to obtain a more accurate radiation energy in