esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation Results Discussion: Analysis and Evaluation
### Evaluating RSU Availability and Overnight Connectivity
#### 7.2.3 Analysis of Communication Types over Overnight Percentage  
Figures 19 and 20 offer a visual analysis of how overnight connectivity percentages affect communication patterns for vehicles in rural areas, where internet access is often conditional on being near home networks or designated parking lot hotspots. This research measures connectivity on a scale from complete absence (0) to full coverage (100), revealing a direct relationship between the degree of connectivity and the quantity of reputable communications (RC). This trend suggests that, as vehicles gain better internet access overnight, they are more capable of updating their reputation metrics, showcasing the Pre-Signature scheme's potential in enhancing vehicular communication.  
Notably, the graph sheds light on ORC, signifying that, even without RSU range, vehicles can still engage in trustworthy exchanges by leveraging pre-signed data, ensuring secure and dependable communication in areas with limited connectivity.  
Observe that all graphs in Figures 19 and 20 are approximately linear. Having a large proportion of vehicles update their reputation overnight is one of the most effective ways to boost the quality of our approach.
###### Abstract  
The determination of flavor compounds using headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) can be severely interfered with by complex food matrices in food systems, especially solid samples. In this study, dispersive liquid-liquid microextraction (DLLME) was applied prior to HS-SPME to efficiently reduce the matrix effect in solid seafood samples. The method had high sensitivity (the quantification limits of maltol and ethyl maltol were 15 and 5 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, respectively), an excellent linear relationship (R2 \(\geq\) 0.996), and the sample recovery rate was 89.0-118.6%. The relative standard deviation (RSD %) values for maltol and ethyl maltol were lower than 10%. Maltol (from 0.7 to 2.2 \(\mathrm{\SIUnitSymbolMicro g}\)/g) and ethyl maltol (from 0.9 to 34.7 \(\mathrm{\SIUnitSymbolMicro g}\)/g) in seafood were detected in the selected samples by the developed method. Finally, DLLME coupled with HS-SPME effectively removed the influence of sample matrix and improved the sensitivity of the method. The developed method was applicable in the analysis of flavor enhancers in complex matrix foods.  
dispersive liquid-liquid microextraction; HS-SPME; flavor enhancer; seafood; GC-MS +
Footnote †: journal:  
Traditionally, solid phase extraction (SPE) or liquid-liquid extraction (LLE) have been applied to remove most sample matrix [21; 22], however, these traditional methods are time-consuming and consume large volumes of solvent [23; 24]. As compared with traditional methods, the dispersive liquid-liquid microextraction (DLLME) technique can reduce solvent consumption and can concentrate analytes rapidly, which significantly improves the extraction efficiency [25; 26; 27]. In addition, DLLME had been reported to significantly reduce matrix interference in analyses of contaminants in wine [27; 28], polycyclic aromatic hydrocarbons (PAHs) in roasted cocoa beans [29], and acrylamide in coffee samples [30]. To the best of our knowledge, there are no reports on HS-SPME combined with DLLME applied to detect flavor enhancers in seafood.  
In this study, solid seafood was pretreated using DLLME/HS-SPME, and the flavor substances (maltol and ethyl maltol) in seafood were detected and analyzed by gas chromatography-mass spectrometry. The experimental parameters of DLLME and HS-SPME were optimized, and the accuracy and performance of DLLME/HS-SPME/GC-MS were further evaluated.
## 4 Results and Evaluation
### Assessing Energy Footprint and Battery Efficiency
It is interesting to mention that for the moderate transmit power of the LoRa radio settings, the main source of energy consumption during the activity cycles was not the LoRa radio but rather the ML2 instrument and the elongated measurement period it required for reliable output. It must be noted, though, that the LoRa transceiver has the potential for more aggressive settings in order to tolerate higher noise environments and to cover larger distances. Nevertheless, this comes at the cost of increased time on air (ToA) for the packets and a reduction in the maximum achievable rate. In this regard, further experiments involving the specific HPD13 LoRa wireless module that is found on the LoRa 32u4 board and that uses the Semtech SX1276 chip design indicated an energy consumption profile where the LoRa becomes the dominant energy drain source per activity cycle. The latter performance analysis was facilitated by the meticulous study of the SX1276 datasheet [45] (especially of the equations explained on page 31 of the datasheet) and the LoRa Air-Time Calculator tool [46] based on the datasheet. Figure 15a depicts the behavior of the system for spreading factor SF12 (instead of SF7) and coding rate 4/8 (instead of 4/5), resulting in an elongated duration of 3050 ms (instead of 100 ms), approximately, with the spreading factor change responsible roughly for the x20 increase and the coding rate adding another 40% to this amount. Figure 15b depicts the estimate by the LoRa Air-Time Calculator of the transmitter behavior for chip simulation settings close to the ones of the real system.
# Profile Control Using Fly Ash Three-Phase Foam Assisted by Microspheres with an Adhesive Coating
## 4 Conclusions  
We present a novel fly ash three-phase foam system assisted by high-temperature resistant microspheres coated with an adhesive layer. This novel foam system allows mitigating gas channeling that occurs during steam injection into heterogeneous heavy-oil reservoirs. Therefore, it is a promising candidate for enhanced oil recovery via profile control. Our study allows drawing the following conclusions.  
1. The foaming volume increases as the consolidation agent concentration decreases, suggesting a stronger foamability at a lower consolidation agent concentration. However, the half-life period of foam is remarkably declined with a descending consolidation agent concentration, showing weaker stability. Fluid drainage does not appear when the consolidation agent concentration reaches 20%. Additionally, increasing consolidation agent concentration yields a longer settling time of solid particles.
2. The presence of MCL in TPF enables an improvement in plugging performance under high temperature. The plugging rate is close to 90% when the temperature is as high as 300 \({}^{\circ}\)C, demonstrating a well-accepted plugging effect. The lower is the permeability, the higher is the breakthrough pressure.
3. As the permeability ratio increases, a more effective redistribution of the fractional flow is anticipated. Nevertheless, if the permeability ratio is overlarge, the effectiveness of profile control is indistinctive and less fraction of fluid diverts to the low-permeability channels.
4. A larger pore volume injection of TPF results in a higher EOR in parallel cores, which substantiates the effectiveness of the three-phase foam system in sealing high-permeability channels.  
Conceptualization, J.H.; methodology, Y.Y., T.C. and T.L.; validation, Y.Y. and Z.Y.; formal analysis, Y.Y. and T.C.; investigation, Y.Y. and T.C.; resources, J.H.; data curation, T.C.; writing--original draft preparation, Y.Y.; writing--review and editing, Z.Y.; supervision, J.H.; project administration, J.H.; funding acquisition, Y.Y. and J.H. All authors have read and agreed to the published version of the manuscript.  
Financial supports from the National Natural Science Foundation of China (Grant No. 51804316, Grant No. 41702249, and Grant No. U1762211) and the Science Foundation of China University of Petroleum, Beijing (Grant No. 2462020 XKBH013) are greatly acknowledged.  
Data is contained within this article.  
The authors declare no conflict of interest.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 2 Polypyrrole-Based Electrodes
### 1D Nanostructures  
One-dimensional nanomaterials are popular sensing elements in analytical systems as they enhance the efficiency and performance of the associated biosensors via the rapid transfer of electrical signals and their large surface-to-volume ratio. These nanostructures significantly decrease the detection  
Figure 1: **(a)** Schematic of CPPy nanoparticle-based biosensor electrodes for the detection of peptide hormones [79]. Copyright 2012, American Chemical Society. **(b)** Platinum-coated CPPy nanoparticle-based non-enzyme biosensors for dopamine detection [80]. Copyright 2015, Wiley. **(c)** A nanodisk-functionalized bioelectronic nose based on nickel-coated CPPy nanoparticles [81]. Copyright 2019, American Chemical Society.  
limit of biosensors by providing numerous immobilization sites, which, in turn, increases the number of biomolecular interactions [82]. Moreover, their high porosity facilitates fast penetration of the biomolecules, thereby reducing the detection time [83].  
1D PPy-based nanomaterials are also synthesized using various manufacturing methods. [PERSON] et al. synthesized human taste receptor-functionalized CPPy nanotubes to detect bitter compounds that produced phenylthiocarbamide and propylthiouracil [84]. CPPy nanotubes were fabricated as taste receptor templates using reverse microemulsion polymerization. Recently, [PERSON] et al. reported synthesizing CPPy nanotubes via a facile self-degradation method utilizing fibrillary templates composed of methyl orange and iron chloride (Figure 2a) [85]. Here, the carboxyl pyrrole monomer was incorporated into the fibrillar template via electrostatic interactions between the pyrrole monomer and the sulfite group of methyl orange. Simultaneously, there was a reaction between carboxyl pyrrole and Fe\({}^{3+}\) cations on the surface of the fibrillar template. The synthesized CPPy nanotubes were then applied as sensing transducers to detect 17\(\beta\)-estradiol, a renowned inhibitor that affects hormone immunity and other physiological processes.  
[PERSON] et al. fabricated platinum-coated CPPy nanofibers using an oxidative template and sonochemical methods (Figure 2b) [86]. Here, CPPy nanofibers were formed when the electrode underwent charge transfer processes via an oxidative template composed of etrimonium bromide and ammonium persulfate. The oxidative templates exhibited lamellar micelle structures and were composed of folded, twin-tailed complexes of the cetylammonium cations and peroxydisulfate anions. The unique micelle structure generated by the cetylammonium cation-peroxydisulfate anion interactions resulted in the polymerized nanofibers' fibrous shape. Next, composite nanomaterials were formed using a simple chemical reduction of the platinum precursors. The platinum-coated CPPy nanofibers were useful for nonenzymatic biosensors to detect oxalic acid, a notable indicator of renal tubular acidosis, hyperparathyroidismism, medullary sponge kidney, hyperoxaluria, and [PERSON]'s diseases.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### Multiclass Model  
This queue model's uninterrupted serving (transmission, reception, or processing) of messages belonging to different classes. Such classes are used to categorize messages of IoT interactions that have different characteristics. For example, sensor data, video streaming services and transnational messaging require different transmission and processing resources. Each class corresponds to the common M/M/1 queue (see Figure 0(a)), featuring Poisson arrivals and exponential service times; thus, multiple M/M/1 queues are used to form the multiclass queue (see Figure 2(a)).  
A multiclass queue (\(q_{\text{mcl}}\)) is defined by the tuple:  
\[q_{\text{mcl}}=(\lambda,\mu,C) \tag{8}\]  
where \(\lambda=\{\lambda_{c_{n}}:c_{n}\in C\}\) is the input rate of messages to the queue of each class \(c_{n}\in C\) and \(\mu=\{\mu_{c_{n}}:c_{n}\in C\}\) is the service rate for the processing of messages of \(c_{n}\in C\). On the basis of standard solutions [14] for the multiclass queue, and given a message that belongs to a class \(c_{k}\), its time that remains in the system is given by  
\[\Delta^{\text{mcl}}\ =\ \frac{1}{\mu_{c_{k}}\ -\ \mu_{c_{k}}\sum_{c_{n}\in C} \lambda_{c_{n}}/\mu_{c_{n}}}. \tag{9}\]
# A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images
## 3 Results and Discussion  
### Datasets Description and Training Details  
To verify the effectiveness of our proposed method, we conducted various experiments using two publicly available datasets: the Inria Aerial Image Labeling dataset (Inria aerial dataset) [68] and the Satellite dataset II (East Asia) [69]. Considering building shapes are usually regular and mostly rectangular, we use thousands of images in 2 databases as the training set to ensure the generalization performance of our method.  
The Inria Aerial Image Labeling Dataset, released by [PERSON] et al., covers an area of 810 square kilometers, with a total of 360 images, each with 5000 \(\times\) 5000 pixels. The dataset includes 10 densely populated cities and remote villages (Austin, Bellingham, Bloomington, Chicago, Innsbruck, Kitsap, San Francisco, Western and Eastern Tyrol and Vienna). The spatial resolution of the images reaches 0.3 m and the shape and structure of the buildings are clearly visible. The images cover dissimilar urban settlements, ranging from densely populated areas (e.g., San Francisco's financial district) to alpine towns (e.g., Lienz in Austrian Tyrol). Instead of splitting adjacent portions of the same images into the training and test subsets, different cities are included in each of the subsets. The datasets are constructed by combining public domain imagery and public domain official building footprints. The Satellite dataset II (East Asia) consists 17,388 images which are 512 \(\times\) 512, of six neighboring satellite images covering 860 km\({}^{2}\) on East Asia with 0.45 m ground resolution. In this experiment, we use 3135 images in the training set and 903 images in the test set. Unlike the Satellite dataset II, the testing set in the Inria aerial dataset does not have the labels that be used for segmentation comparative experiment, so we chose four group images and their labels from training set as the testing set of Inria aerial dataset in our experiments.  
Due to GPU memory limitations, we resize the images from the datasets. We cropped the image and its corresponding label to 512 \(\times\) 512 size. In order to improve the image utilization of the datasets, the images and corresponding labels in the original dataset were randomly cropped. In order to facilitate the observation of the experimental results, the test images were cropped to a size of 512 \(\times\) 512 pixels with an overlap of 10 pixels between the images. These operations can ensure that the network has enough data and prevent the model from overfitting and improve its robustness.
## 6 Conclusions  
In this paper, segmentation approaches consisting of FCMPSO-mED and KMPSO-mED have been developed in order to specify the spatial intra-plaque distribution of VH-IVUS components. Furthermore, three commonly used statistical methods, including LBP, GLCM, and MRL, are presented to extract the texture features. Based on the experiments, the best performance of classification was achieved by applying the combined plaque and texture features. The experimental results proved the superiority of the proposed method, with a high accuracy rate of 98.61% for the diagnosis of TCFA plaque.  
Author Contributions: Author Contributions: Conceptualization and Methodology: A.S., Z.R., M.S.M.R., M.R.A.K.; Data and Sources Searching: Z.R, A.S. A.T; Writing-Original Draft Preparation: [PERSON], A.S., M.S.M.R., M.R.A.K.; Writing-Review & Editing: M.P., O.K., K.K., E.H.-V., H.F.; Visualization: [PERSON], A.S., M.S.M.R; Supervision: [PERSON], M.S.M.R., M.R.A.K.  
**Acknowledgments:** This research was funded by Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-02G31, and the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS Vot-4F551) for the completion of the research. The work and the contribution were also supported by the project Smart Solutions in Ubiquitous Computing Environments, Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2018). Furthermore, the research is also partially supported by the Spanish Ministry of Science, Innovation and Universities with FEDER funds in the project TIN2016-75850-R.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
ANN Artificial Neural Network  
CDC Confluent DC  
CLBT Close Lumen Tracing  
CNC Confluent NC  
DC Dense Calcium  
DCCL Dense Calcium in Contact with the Lumen  
DCL DC Layering  
DWPF Discrete Wavelet Packet Frame  
ECC Extracting Confluent Component  
ECG Electrocardiogram  
FCMFuzzy C-means  
FCMPSO Fuzzy C-means with Particle Swarm Optimisation  
FF Fibro-Fatty Tissue  
FI Fibrotic Tissue  
FO First Order  
GLCM Grey-Level Co-occurrence Matrix  
HOG Histogram of Oriented Gradients  
IVUS Intravascular Ultrasound  
KMPSO K-means and PSO  
k-NN K-Nearest Neighbour  
LBP Local Binary Patterns  
mED minimum Euclidean Distance  
MRL Modified Run Length  
NC Necrotic Core  
NCCL Necrotic Core in Contact with the Lumen  
NCL Necrotic Core Layering  
NGL Neighbouring Grey- Level  
OCT Optical Coherence Tomography  
OLBT Open Lumen Tracing  
PBA Plaque Burden Assessment  
PSO Particle Swarm Optimisation  
ROI Region of Interest  
ST Scattering Transforms  
SOM Self-Organising Maps  
SVM Support Vector Machine  
SW Silhouette Weight  
TCFA Thin cap fibroatheroma  
VH-IVUS Virtual Histology--Intravascular Ultrasound  
VIAS Volcano Corporation, San Diego, CA, USA  
WF Wavelet Features
## 3 Results and Discussion
### Bubble Coverage and Actual/Effective Current Density on the Anode--Experiment 1
In Table 2, the bubble size distribution on three tested anodes at 0.9 A\(\cdot\)cm\({}^{-2}\) are summarized and compared. The 100 mm \(\times\) 50 mm flat anode case showed a higher percentage of small bubbles (for every step that bubble size \(<\) 300 mm\({}^{2}\)), in contrast to the 50 mm \(\times\) 22 mm flat anode case. For the 50 mm \(\times\) 22 mm flat anode case, a large amount of bubbles was concentrated in the size of 300-500 mm\({}^{2}\) (Table 2 and Figure 5a), while this phenomenon was not observed on the 100 mm \(\times\) 50 mm flat anode (Table 2 and Figure 8). Instead, the large bubbles (bubble size \(>\) 300 mm\({}^{2}\)) on the 100 mm \(\times\) 50 mm flat anode were relatively uniformly distributed, suggesting that the bubble evolution is less affected by the size of the anode. A larger anode could provide a higher level of freedom for the bubbles during its evolution process. A larger anode is supposed to be less sensitive to the evolution process of single bubbles, although these bubbles affect the local actual current density regularly. This is because the anode has enough space to accommodate many bubbles at the same time. All these bubbles evolve independently at different stages of evolution. As a result, the group effect of the bubbles becomes evident, and the evolution character of single bubbles becomes less obvious. This trend is very clear, and it leads to different evolution behavior on anodes; for example, the average bubble size within 60 s on the 50 mm \(\times\) 22 mm flat  
Figure 8: The size distribution of the bubbles on the 100 mm \(\times\) 50 mm flat anode within 60 s.  
Figure 9: The variation of actual current density on the 100 mm \(\times\) 50 mm flat anode within 60 s.  
anode was 324 mm\({}^{2}\); in contrast, the average bubble size decreased to 160 mm\({}^{2}\) on the 100 mm \(\times\) 50 mm flat anode. When a slot was introduced on the 50 mm \(\times\) 22 mm anode, the average bubble size was significantly reduced to 40 mm\({}^{2}\) from 324 mm\({}^{2}\). It was observed that the bubbles could not cross over the anode slot (Figures 2 and 3). During the experiments, once a bubble touched the edge of the anode or anode slot, the bubble was released immediately. Therefore, due to the restriction from the anode slot, the bubble size was limited to below 200 mm\({}^{2}\), as most of the bubbles were less than 100 mm\({}^{2}\). Owing to the limitation from the anode size, experiments carried out on the 50 mm \(\times\) 22 mm flat and slotted anodes give more information on the evolution characters of a single bubble, while the investigation on the 100 mm \(\times\) 50 mm anode provides details that are closer to the realistic anode working condition.
# Quantum LDPC Codes Based on Cocyclic Block Matrices
## 2 Preliminaries
### Error of Quantum Error-Correction Code and Bound  
All errors that occur in different quantum codes can be labeled with string vector over field \(F_{2}\) when the states are transmitted through quantum channels. Single qubit error correction for quantum codes includes three classes, i.e, flip error, phase error or phase-flip error, which can be described with three corresponding Pauli operators: \(\sigma_{x}\), \(\sigma_{z}\) and \(\sigma_{y}\), respectively. Assuming a single error qubit described as flip error \(X(a)\) and phase error \(Z(a)\) for \(a\in F_{2}\), a state \(|x\rangle\in C\) acted by error is denoted as  
\[X(a)|x\rangle=|x+a\rangle\quad\text{and}\quad Z(a)|x\rangle=\omega^{a\times x} |x\rangle. \tag{8}\]  
Correspondingly, the errors on \(N\) qubits can be denoted as vector \(e=\sigma_{x}^{X}\sigma_{z}^{Z}\), for \(X=(x_{1},x_{2},\ldots,x_{N})\) and \(Z=(z_{1},z_{2},\ldots,z_{N})\in F_{2}^{N}\). Hence, it acts on an \(N\)-qubit basis state \(|Q\rangle=(q_{1},q_{2},\ldots,q_{N})\) over \(F_{2}^{N}\)  
\[e|Q\rangle=(-1)^{Z\cdot Q}|X+Q\rangle=(-1)^{\sum_{l=1}^{N}z_{l}\cdot q_{i}}| \sum_{i=1}^{N}x_{i}+q_{i}\rangle. \tag{9}\]  
Furthermore, the quantum Hamming bound of a general quantum code \(\mathcal{C}[[N,k,d]]\) is required to satisfy the following inequality condition [28]  
\[\sum_{l=0}^{t}3^{l}\binom{N}{l}\leq 2^{N-k}, \tag{10}\]  
which may correct up to \(t=[(d-1)/2]\) quantum error bits.