esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Theory-Based Characterization and Classification of Household Photovoltaics
## 3 Methodology  
### Modeling  
In this study, PV arrays will be modeled using digraphs in the following manner:  
* Panels are represented by arcs, which point in the direction that the photo-current is created. This characterization shows the fact that panels have a determined polarity. The head represents the positive pole and the tail the negative one.
* Vertices represent nodes in the network structure. If different panel poles represented by heads or tails of arcs are incident to or from a given vertex, there exists a cable connection between them.  
An example is found in Figure 3. The number of panels \(p\) of a given structure determines the size of the associated digraph, and imposes a restriction in its order, which can go from 2 to \(p+1\). The first case being the parallel association of all \(p\) panels, while the second corresponds to all panels in a series connection.  
An array-digraph is defined as a digraph that represents a PV panel structure, this is, a digraph \(D\) such that:  
1. There exists only one vertex \(s\in V(D)\), called the source, such that \(d^{-}(s)=0\), i.e., there is only one vertex \(s\), such that there is no arc adjacent to \(s\).
2. There exists only one vertex \(t\in V(D)\), called the target, such that \(d^{+}(v)=0\), i.e., there is only one vertex \(t\), such that there is no arc adjacent from \(t\).
3. For every arc \(a\in A(V)\) there exists at least one path \(C\) from \(s\) to \(t\), such that \(a\in A(C)\), i.e., every arc in the digraph belongs to a path from \(s\) to \(t\).  
It can be observed that every array-digraph \(D\) is weakly connected, as it is directly deduced from condition three.
## 3 Results  
### Effects of DRE on Cell Viability and Lipid Accumulation in PA-Induced AML12 Cells  
To evaluate the cytotoxicity of and lipid accumulation inhibition by DRE, AML12 cells were treated with PA in the presence or absence of DRE. Cell viability was not significantly affected by either DRE concentrations, indicating the safety of DRE under the tested experimental conditions (Figure 1A). PA significantly increased the intracellular TG levels compared to those in the control group (Figure 1B). However, co-treatment with DRE at 25 and 50 \(\upmu\)g/mL significantly reduced TG accumulation in a dose-dependent manner. Oil red O staining revealed markedly increased lipid accumulation in the PA-treated cells, which was significantly attenuated by DRE (Figure 1C). This attenuation was more pronounced at high DRE concentrations (50 \(\upmu\)g/mL).  
Figure 1: Effects of the _Distylium racemosum_ ethyl acetate fraction (DRE) on cell viability, triglyceride (TG) levels, and lipid accumulation in palmitic acid (PA)-induced AML12 cells. AML12 hepatocytes were treated with PA (0.5 mM) to induce lipid accumulation and co-treated with DRE at 25 and 50 \(\upmu\)g/mL for 24 h. (**A**) Cell viability was assessed using the 3-(4,5-dimethyl-2-thiazol)-2,5-diphenyltetrazololium bromide (MTT) assay. No significant cytotoxicity was observed at the tested DRE concentrations. (**B**) Intracellular TG levels were significantly increased by PA (* \(p\) < 0.05 vs. control). Co-treatment with DRE significantly reduced TG accumulation in a dose-dependent manner (# \(p\) < 0.05 vs. PA-treated group). (**C**) Oil red O staining revealed markedly increased lipid accumulation in the PA-treated group, but this effect was significantly attenuated by DRE treatment at both concentrations. Data are represented as the mean \(\pm\) standard deviation (SD). * \(p\) < 0.05 vs. control group; # \(p\) < 0.05 vs. PA-treated group.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 4 Implementation
### Use Cases  
We implemented two redundant node algorithms in WSN-Maintain with the aim to extend the network lifetime while still preserving coverage and connectivity. The two algorithms, _i.e._, greedy-maintain, a centralised greedy algorithm, and local-maintain, a localised algorithm, identify coverage redundant nodes. A sensor node is coverage redundant if, when turned off, the quality of coverage does not fall below the coverage threshold and the network is not disconnected. Coverage redundant nodes can be put to sleep to save energy and will be turned on to cover some part of an uncovered area if active nodes that sense this area fail or run out of energy. The on/off status of nodes to turn on redundant nodes and the status of the remaining energy to inform other nodes when active nodes run out of energy are piggybacked on control messages. Both algorithms, with a slight modification, can be used to configure the initial network, as well as to turn on redundant nodes. To differentiate the two functionalities, we call the algorithms to configure the initial network the greedy initial network configuration and the local initial network configuration.
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 2 Related Work
A single prediction branch is used, thus successfully lightweighting the model while maintaining the pose estimation accuracy above a level. Lite-HRNet [17] is based on HRNet [3] and uses the shuffle block from ShuffleNetV2 [18]. This block divides the input channels into two, sequentially performs bottleneck operations on one partition, connects it with the other partition, and performs channel shuffle. The authors noted the high time complexity of 1 \(\times\) 1 convolutions and replaced them with element-wise weighting operations to achieve their reduction. Additionally, when creating new sub-branches in each branch of the original HRNet, the feature maps of all previous branches were combined, and convolutions were applied. Subsequently, they were redistributed to existing and generated branches, facilitating information exchange across different scales. This approach improves model efficiency; however, relying on multiple branches and redistributing feature maps can increase the overhead.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 6 Guidance Navigation and Control (GNC)
### Navigation  
The navigation parameter settings in this study were determined using the Mission Planner software. There are two essential parameters that need tuning. Turn G max, for example, indicates the maximum lateral acceleration (in multiples of 9.81 m/s\({}^{2}\)) that a USV can withstand while staying stable. The USV may tip over in turns if it is set too high, and the USV will not be able to turn sharply enough if it is set too low. The Lateral Acceleration Period (Lat Acc Control Period) is the parameter for aggressiveness control. Raising the LatAcc Control Period if the USV weaves along the straights, lowering it if the USV does not turn sharply enough.  
For this research, the turn G maximum lateral acceleration is given 5 m/s\({}^{2}\), and the lateral acceleration central period is given 20 s. These parameters depend on the shape of USV and are obtained experimentally.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
## 5 Conclusions  
A metamaterial inspired dual band antenna comprising of an AMC Backing, aimed for ISM and RFID applications was sketched, simulated, and fabricated. With the dimensions of 70 \(\times\) 31 \(\times\) 1.6 mm\({}^{3}\), the antenna absorbed relatively little power. The metamaterials used on either side of the antenna created a pathway for it to resonate at two frequencies making it a dual band antenna. According to the simulation results, the metamaterial inspired antenna, performed well at both lower (0.905 \(\approx\) 0.923 GHz) and higher (2.382 \(\approx\) 2.516 GHz) frequencies. The designed antenna provided high gain when placed beneath the AMC Backing structure resulting in a change from 1.53 dBi to 2.87 dBi, and 4.49 dBi to 6.8 dBi, at 0.915 GHz and 2.45 GHz frequencies, respectively. The AMC Backing structure caused the radiation properties of the simulated system to shift from omnidirectional to directional. The AMC loaded antenna structure offered improvement in radiation efficiencies compared to the antenna without the AMC backing. The surface current distributions were observed and discussed for the antenna structures with and without AMC Backing. The parametric properties were obtained with negative values of permittivity, permeability and zero degrees of the reflected phase, making the unit cell perfect. In view of the above, the prototype antenna is an attractive candidate for microwave and UHF bands at Bluetooth, 802.11 wireless network protocol, WIFI, 4G, and LTE applications.  
Conceptualization: M.N., A.S.C.S. and B.T.P.M.; methodology: S.D., A.S.C.S., S.S.A.; software: M.N., A.S.C.S. and M.A.; validation: N.H., B.T.P.M., S.S.A., and M.A.; writing--original draft preparation: M.N.; writing--review, and editing: S.D., M.A., and S.S.A.; supervision: B  
Data Availability Statement: The data presented in this research are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
Measurements for individuals MOS001-MOS054 were made using a Bragg-Brentano geometry diffractometer, Siemens D-500, working with the Ni-filtered Cu-K\(\alpha\) radiation (\(\lambda\) = 1.5406 A) at a working power of 1.2 kW (45 kV and 30 mA), from (4 to 70) C28, at 1 C28/min (step size = 0.05 C20; time = 3 s). Measurements for individuals MOS061-MOS083 were made using a Bragg-Brentano geometry diffractometer PANalytical X'Pert PRO MPD Alpha-1 (radius = 240 mm) equipped with an X'Celerator detector (active length = 2.122 C), using the Ni-filtered Cu-K\(\alpha\) radiation (\(\lambda\) = 1.5406 A) at a working power of 45 kV and 40 mA, from (5 to 80) C28, with a 0.026 C step size and an acquisition time of 50 s, spinning the sample at 1 Hz. The crystalline phases in each analysed specimen were evaluated using the PANalytical X Pert HighScore Plus software package, which includes the Powder Diffraction FileTM (PDF(r)) of the International Centre for Diffraction Data (ICDD). Twenty-three individuals (MOS009, 021, 022, 023, 027, 029, 030, 032, 039, 047, 048, 061, 063, 064, 065, 066, 067, 069, 070, 072, 075, 077, and 079) were also analysed in terms of thin section petrography (PE) at the University of Sheffield. Following [PERSON]'s system, petrographic groups were defined and described [33; 34] (Supplementary Material 1: detailed petrographic descriptions). Finally, a subsample of eleven individuals was studied by scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) (MOS-005, 007, 012, 023, 032, 048, 054, 061, 062, 068 and 069) according to the classification by WD-XRF and PXRD analysis of meaningful compositional groups, by petrographic criteria, and according to specimen availability [35]. SEM-EDX observations were performed on a fresh cross-section fracture passing through the oro-aboral axis of the body wall to observe the microstructure, estimate the degree of sintering and the vitrification state of the matrix, and enable microanalysis of features of interest. Bulk specimens were fixed on metal specimen stubs using silicone adhesive, and the non-conductive ceramic specimens were made conductive. Colloidal silver paint was applied on the ceramic bulk specimen's excess silicone adhesive and lateral sides. Subsequently, the specimen surface was coated with a thin carbon film (-10 nm) by vacuum evaporation. The observations were made using JEOL JSM-6510 and Quanta 200 microscopes coupled with an energy-dispersive X-ray spectroscopy system (EDX). The observations were performed using an acceleration voltage of 20 kV and a working distance of 15 mm.
## 4 Experimental Analysis and Results
### General Tagging of Objects  
We will now discuss the tagging of the objects in the case-study. The advent of Web 2.0 with its desktop-like experience and focus on social applications helped tagging be established as a concept worthy of being considered as an open decentralized way of structuring and sharing information and meta-data in the knowledge society [50]. [PERSON] [51] even suggests that the \"folksonomies\" of tags created from social and collaborative projects on the web could be effective tools for Information Retrieval on the Web and \"could be studied in that same way that search engines have been studied in the past\". In collaborative projects, such as OSM, contributors can add, edit, delete tags easily [52]. For objects in OSM these tags can be updated to reflect changes over time and space. For example: changes in landcover type, building usage, highway designation, _etc_. In Figure 4 the distribution of the number of tags at the final current version of each object in our case study is shown. The mean number of tags assigned to features is \(3.45\). In the case of many edits to features in OSM some editor software automatically embeds their own \"tag\" mark to indicate which editor software performed the edit. In some cases we found this automatic tag as the only tag existing. Without tags objects cannot be properly rendered by map-tile generation software or queried from the OSM spatial database. In Figure 5 a scatter-plot shows the relationship between the number of unique contributors to each object against the number of tags associated with that object at its final or current version. The correlation (\(0.17\)) is very weak and statements about statistical significance could not be made based on this. In a similar  
Figure 3: Using the Levenshtein distance and JaroWinkler distance metrics to visualize changes to “name” attribute tags of \(412\) objects in our OSM case-study area.  
fashion Figure 6 plots the number of versions of each object against the number of tags associated with that object at its final or current version.  
Figure 4: Distribution of the number of tags from all objects in the case study.  
Figure 5: Scatter plot of the number of tags against the number of contributors for all objects in the case study area.
## 3 Using Classifiers to Predict Relevant Change  
Using the same per-tile feature signatures that the GeoCDX system uses to organize image tiles into clusters [19], we propose methods for predicting areas of relevant change based on prior, manual classification of a subset of a pair. A high-level flow chart of the proposed change prediction methodology can be seen in Figure 4.  
A user begins by inspecting tiles in the GeoCDX user interface and performing change analysis to determine if a relevant change has occurred within the tile. These change/no-change annotations are recorded on a per-tile basis in the system database. If change occurs, but is not relevant, it is to be marked as no-change by the analyst. This information can then be used in conjunction with the per-tile features used for change clustering that were described in Section 2 and explained in detail in Section III.A. of [19]. These features are 16-bin histograms that encode information about the 14-pixel level features used by the GeoCDX system. As was the case in [19], we concatenate these histograms together to construct a single feature vector that represents the signature for each tile. We use these signatures along with accumulated change/no change annotation data for a pair to produce a classifier (_i.e._, the relevant change model) that can be used to predict relevant change for the remaining tiles within a pair.  
Figure 4: Overall workflow for using binary classification to predict relevant change.
## 4 Model Building
### Main Model for Trajectory Prediction
This helps the model handle sequence data more effectively without relying on a recurrent structure. The multi-head attention layer takes a sequence with positional information as an input. The multi-head self-attention mechanism [19] calculates attention scores between each position and the other positions. Based on the attention scores, it weights the feature vectors at each position and obtains the context for each position. The attention calculation formula is as follows:  
\[Attention(Q,K,V)=softmax\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V \tag{8}\]  
\(K\) denotes the key vector used to compute the attention weight, \(Q\) denotes the query vector, \(V\) denotes the value vector, and \(d_{k}\) denotes the dimension of the key vector. In the model, the dimensions of \(K\), \(Q\), and \(V\) are equal. The multi-head self-attention mechanism enables the model to capture dependencies between different positions in the input sequence. This helps the model understand long-range dependencies and effectively acquire contextual information. Additionally, in practical applications, self-attention allows for parallel computation, greatly speeding up both the training and inference processes compared to sequential models like RNN. The dropout layer normalizes and randomly drops out based on the output of the previous layer to prevent issues of vanishing or exploding gradients during multi-layer computations. Through the above optimizations, the main model has not only achieved significant improvements in terms of parameter quantity and learning ability, but also its endpoint prediction module takes into full consideration of the road environment information collected from the vehicle's driving perspective. Comprehensive comparison and ablation experiments are set up in the next section to demonstrate the effectiveness of these improvements.
## 2 Methods
### Process Description
#### 2.1.4 Compressor Leak Consideration  
The potential problem of a movable LNG liquefaction plant is a refrigerant leak from the compressor. Compressor leaks without refrigerant make-up causes severe operational problems such as poor efficiency and variations in operating conditions [31]. All compressors in the liquefaction processes compared in this study are assumed to be reciprocating compressors because of their small capacity.  
The main cause of leaks in reciprocating compressors is valve leakages. Therefore, according to [32], newly installed packing has been reported to leak an average of 2 m\({}^{3}\)/h and worn packing may leak up to an average of 26 m\({}^{3}\)/h. Further, the leakage rates vary with production size; thus, the average leakage rate for a small-size production compressor is 0.31-0.34 m\({}^{3}\)/h, and it is the large compressors may leak 0.68-4.25 m\({}^{3}\)/h for large compressors [33]. [PERSON] [34] reported that the leakage through the clearance of the compressor depends on the actual radial clearance and thus, the total leak mass varies in the range of 0.017-0.443 kg/h with 2.75-12 m of radial clearance. Based on previous studies and our project experience, the leakage rate of reciprocating compressors is fixed to 0.05 wt. % of the total amount of refrigerant in this study. The compressor leak can be also solved using compressors with high leakage resistance such as a Labyrinth sealed compressor; however, this solution is not considered in this paper [22].  
As the target LNG liquefaction plant is movable, an additional leakage makeup system for each process is required to make up the refrigerants. The schematics for each refrigerant make up system are shown Figure A1(3). The SMR system is supposed to have separate tanks for all component of the refrigerants and a mixing tank for mixing the refrigerants [35]. In the case of dual nitrogen expansion, liquid nitrogen is supplied for the leakage make up [36]; for propane pre-cooled self-refrigeration, only propane makeup facilities are required.  
Figure 4: Process flow diagram of the propane pre-cooled self-refrigeration system.
# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 2 Related Work
### Detection Network  
According to the rapid development of deep learning, convolutional neural networks (CNNs) have demonstrated outstanding performance in the domain of anomalous sound detection. CNNs exhibit superior feature extraction capabilities, data understanding, and computational efficiency compared to other neural networks. They offer clear advantages, particularly in real-time tasks such as sound classification and abnormal detection. [PERSON] et al. [31] introduced a CNN architecture tailored for respiratory anomalous sound detection. This architecture leverages the potent feature extraction capabilities of the CNN to discern fine texture details within respiratory acoustic features. This work provides valuable insights for the clinical application of the respiratory anomalous sound detection model. [PERSON] et al. [32] harnessed the MobileNetV3 network to extract refined feature representations from machine sound signals.  
They adopt global depthwise convolution (GDConv) instead of global pooling, which significantly improves the performance of the anomalous sound detection system. In recent years, attention-based techniques have gained prominence in the field of anomalous sound detection. The attention mechanism empowers models to concentrate effectively on vital segments of input data, thereby augmenting sound detection accuracy. [PERSON] et al. [33] proposed a method for anomalous sound detection using an attention-based autoencoder. By assigning different weights to each audio frame within the input sound, the sound signal extraction ability and understanding ability of the network are enhanced. [PERSON] et al. [34] proposed an attention network called SPIDERnet for a one-shot anomaly detection task. The SPIDERnet attention network focuses on critical aspects of the input sound data, constructing a robust similarity function for time-frequency structure changes by absorbing time-frequency stretching.  
Drawing inspiration from the works mentioned above, this paper augments the feature extraction capabilities of the MobileNetV3 network through the utilization of the ECA attention module and the SoftPool method, referred to as the ES attention module. This method captures local channel information via the ES attention module, determining the significance of each feature channel and suppressing irrelevant feature information for the current task.
## 2 RUL Prediction Methods
### Exponential Smoothing  
Due to the significant noise in the monitoring features of the C-MPSS dataset, the simple exponential smoothing (SES) algorithm is introduced to smooth the raw monitoring features of the engine. This is carried out to enable the prediction algorithm model to better capture the changing trend of the feature signal. The current actual value of the engine sensor monitoring parameter is weighted averaged with the previous parameter value to obtain the smoothing result [16]. The formula for the calculation is:  
\[\left\{\begin{array}{l}y_{t}=x_{t},t=1\\ y_{t}=\alpha x_{t}+(1-\alpha)y_{t-1},t\geq 2,\end{array}\right. \tag{4}\]  
In the equation, \(x_{t}\) represents the true value at time \(t\) and \(y_{t-1}\) represents the observed value at time \(t-1\). \(\alpha\in(0,1)\) is the decay factor. The smoothing parameter \(\alpha\) controls the rate of the weight reduction of the exponential smoothing. When the value of \(\alpha\) is closer to 1, the recent characteristic parameters have a greater influence on the prediction results; when \(\alpha\) is closer to 0, the data parameters are smoother.