esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Datasets and Methods  
### Dataset  
The dataset used in this project consisted of nine different public datasets formed by tagged tweets, each dataset related to the detection of different typologies of toxicity in social networks (we chose nine files from the ones available at the following links to make up the whole starting dataset: [[https://github.com/Akshitalha/NLP_CSS_2017](https://github.com/Akshitalha/NLP_CSS_2017)]([https://github.com/Akshitalha/NLP_CSS_2017](https://github.com/[PERSON]/NLP_CSS_2017)), https://ha socfire.github.io/hasoc/2019/dataset.html, [[https://github.com/zeeraktalat/hatespeech](https://github.com/zeeraktalat/hatespeech)]([https://github.com/zeeraktalat/hatespeech](https://github.com/zeeraktalat/hatespeech)), [[https://github.com/melsherief/hate_speech_icwsm18](https://github.com/melsherief/hate_speech_icwsm18)]([https://github.com/melsherief/hate_speech_icwsm18](https://github.com/melsherief/hate_speech_icwsm18)), [[https://ckan.hatespeechdata.co](https://ckan.hatespeechdata.co)]([https://ckan.hatespeechdata.co](https://ckan.hatespeechdata.co)) m/dataset/founta-et-al-hate-and-abusive-speech-on-twitter/resource/9 ccd6c1e-a7d2-429-8-be72-fe78f529364e, [[https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offe](https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offe)]([https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offe](https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offe)) nsive-language-dataset, accessed on 20 June 2022); the main ones are:  
* Hate Speech.
* Offensive Content.
* Sexiam.
* Racism.  
Therefore, when we merged the datasets to create a unique one, we preprocessed the entire set of tweets, taking care to adhere to the same labelling logic: \"0\", neutral and \"1\", toxic. Examples of neutral and toxic labelled tweets are, respectively:  
* \"True happiness is when we make our women smile. Celebrating the spirit of #WomensDay with the two most important women in my life\", \"0\".
* \"bro take it down u are just a fake as friend and an ungrateful piece of shit u should be thank faggot\", \"1\".  
To have a better comprehension of the data, Figure 1 shows an overall datasets class proportion.  
After the separate analysis, the nine sets were merged to obtain a single dataset consisting of 111,131 unique English-labelled tweets. We have 63,823 tweets belonging to class \"0\", neutral, and 47,308 tweets belonging to class \"1\", offensive, representing 57.43% and 42.57% of the total, respectively. The latter is the focus of the classification.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 2 System Model
### The Definition of UAV Dynamic Communication Topology  
The location state vector and the state vector of the communication coverage radius determined the communication connectivity of the whole UAV swarm. The communication connectivity of the UAV swarm was similar to that in graph theory. Taking a directed graph as an example, when \(j\)-th UAV could sense \(i\)-th UAV, it was believed that there was a directed path from the \(i\)-th UAV to the \(j\)-th UAV. When the communication topology of the UAV swarm was non-connected topology, strongly connected topology, and completely-connected topology, respectively, they corresponded to the concepts of the non-connected graph, strongly connected graph, and completely-connected graph in graph theory, respectively. For more basic concepts of graph theory, please refer to [34].
# Computational Modeling of U-Shaped Seismic Dampers for Structural Damage Mitigation
## 3 Mesh Convergence Analysis for Different Loading Protocols
### Impact of Mesh Size on Local Strain and Energy  
The convergence results of the AEPS initially show a large distance between the curves due to the cumulative nature of this parameter. However, all three curves exhibit similar convergence trends, with the AEPS increasing until reaching a stagnation at the 4.8 mm mesh size. For the energy absorbed by the strain, a logarithmic behavior is observed, starting with significantly high values for larger element sizes and converging at less than 5 J. Additionally, a separation in the curves is observed because the number of cycles directly affects the amount of energy during the simulated loading protocols. These results show that the number of cycles is not an influential factor in the stress levels produced in the damper. However, the number of applied cycles influences the deformability and energy dissipation, considering the same level of maximum displacement.  
A considerable difference in results was obtained between the 14 mm mesh size and 5.6 mm mesh size in terms of the equivalent plastic strain and the associated strain energy, indicating that to achieve good numerical results in U-shaped metallic dampers, the number of elements and size should not be selected randomly. In [PERSON] et al. [26], four elements in the damper thickness adequately captures the stress distribution, confirming our results; however, the low number of elements induces errors close to 10% in the equivalent plastic strain and lower accuracy for strain energy absorption compared to results with six elements per thickness, according to the convergence results reported in Figure 4f,g. In [PERSON] et al. [38], the deviations between experimental and finite element modeling results still exist, possibly due to the low number of elements in the damper thickness (only two) and the consequent lack of accuracy in the local strain distribution compared to our numerical results. Similar analysis for the 14% higher damper stiffness and 9.4% of lower dissipated energy determined with one element in thickness were obtained in [PERSON] et al. [15].  
This analysis demonstrated that mesh size significantly impacts stress and strain distribution in U-shaped dampers, influencing both the computation of strain energy and performance characteristics. Considering this convergence analysis and mesh refinement, a suitable grid for determining the behavior performance of the investigated U-shaped metallic damper via finite element-based computational analysis was established with a 4.8 mm element size, comprising six elements in thickness.
## 3 Results and Discussion
### Lipoxygenase Inhibitory Activity  
Lipoxygenenses catalyse the stereo selective addition of oxygen to polyunsaturated fatty acids such as linoleic and arachidonic acids, forming leukotrienes and lipoxins. The role of 15-LOX in the regulation of AD has been recognized [59]. In fact, the level of 15-LOX increases during the early phase of AD, thereby indicating its implication in the pathogenesis of AD [60]. It therefore makes sense that 15-LOX can be considered another target used to stop the progression of AD. This study identified three crude extracts (MLPC, MEGD and MSTB) and two compounds, (**5**) and (**8**), with inhibitory effect against 15-LOX. The results of the IC\({}_{50}\) values determined with the software GraphPad Prism 6.0 are presented in Table 1. It was found that two active compounds, globimetulin B (**5**) and bangwausrene B (**8**), were highly potent against 15-LOX with IC\({}_{50}\) values of 7.14 \(\upmu\)g/mL (16.22 \(\upmu\)M) and 23.31 \(\upmu\)g/mL (43.16 \(\upmu\)M), respectively. These compounds were even more effective than quercetin, which had an IC\({}_{50}\) value of 23.97 \(\upmu\)g/mL (79.37 \(\upmu\)M). As 15-LOX has been recognized to be involved in the progression of various inflammation-related diseases such as psoriasis, atherosclerosis and rheumatoid arthritis [61, 62] and the discovery of very potent human 15-LOX inhibitors is still underway [63], our study therefore identified  
Figure 3: Nitric oxide inhibitory activity of crude extracts and isolated compounds in LPS-stimulated RAW 264.7 macrophage cells (**A**) and their respective percentage of cell viability (**B**). The percentage of NO inhibition was determined based on the efficacy of test samples to inhibit NO production by RAW 264.7 cells compared with the negative control (RAW 264.7 cells treated with LPS and DMSO at 0.5% without test samples), which was considered to be 0% inhibition. Quercetin was used as a positive control. Data represent the mean \(\pm\) standard deviation of three independent experiments, MLPC: methanol leaf extract of _P. capitata_, MEGD: methylene chloride/methanol (1:1) entire plant extract of _G. dinklagei_, MSTB: methylene chloride/methanol (1:1) seed extract of _T. bangwensis_. Compounds (**1–9**) were isolated from the above-mentioned plant extracts. Querc. (Quercetin). Data are presented as means of triplicate measurements \(\pm\) standard deviation;(*) means a statistical difference (\(p\) < 0.05) between the tested samples versus the negative control (Crtl).  
two terpenoids, globimetulin B (**5**) and bangwausrene B (**8**), which can be considered potential anti-15-LOX agents for the management of these diseases.
## 2 Materials and Methods
### Target Gene Prediction, Functional Annotation and Pathway Analysis  
Based on the sequences of known and novel miRNAs, the candidate target genes were predicted using the psRNA Target Server ([[http://plantgrn.noble.org/psRNATarget/](http://plantgrn.noble.org/psRNATarget/)]([http://plantgrn.noble.org/psRNATarget/](http://plantgrn.noble.org/psRNATarget/)), accessed on 20 September 2020) [38]. All predicted miRNA targets and differentially expressed miRNA targets were annotated based on the following databases: Nr (NCBI non-redundant protein sequences), Nt (NCBI non-redundant nucleotide sequences), Swiss-Prot (Swiss-Prot protein database), Pfam (Protein family), COG (Cluster of OrthologousGroups database), GO (Gene Ontology database, [[http://www.geneontology.org/](http://www.geneontology.org/)]([http://www.geneontology.org/](http://www.geneontology.org/))) and KEGG (Kyoto Encyclopedia of Genes and Genomes pathway database) pathway analysis ([[https://www.kegg.jp/kegg/pathway.html](https://www.kegg.jp/kegg/pathway.html)]([https://www.kegg.jp/kegg/pathway.html](https://www.kegg.jp/kegg/pathway.html)), accessed on 22 September 2020) [39]. For GO and KEGG analysis, the calculated \(p\)-value was processed through FDR (false discovery rate) correction, taking FDR \(\leq\) 0.05 as a threshold.
# Tradeoffs in Key Rotation Strategies for Industrial Internet of Things Devices and Firmware
## 2 Representative Architectures for Secure Boot
### Minimal Immutable Bootloader (A)  
To baseline the conversation, consider the idea of a small primary bootloader that cannot be modified by any firmware operation [19]. Some chips come from the vendor with an immutable bootloader and that looks to a specific memory slot for a CRC checksum or a raw public key. The raw public key is loaded at the time of manufacturing and its corresponding private key must be protected for the lifetime of the manufactured devices. Some product makers break the secure boot process by only using the CRC checksum to boot to a bootloader that contains a raw public key used to verify the firmware. This preserves the chain of trust but does not protect against private key compromise. Considering the attributes in Table 1, the most obvious challenge with an immutable bootloader is that if it contains the public key and is write protected, then there is not a default mechanism for key rotation (therefore, in tradeoff analysis in Figure 5, it scores in the lowest tier in the \"Rotate\" impact category.) The tradeoff is that we can optimize boot time (\"Boot Time\" in Table 1) and reduce time to market (\"Dev Time\" in Table 1). The pros and cons of this approach are summarized in Table 2 below.
## 2 Selection of Parameters Affecting the Sealing Performance of Caprocks  
### Sealing Mechanism of Caprocks  
Sealing failure of caprocks in underground gas storage of depleted gas reservoirs can threaten the storage safety. Both microscopic and macroscopic characteristics can affect the sealing performance of caprocks. When the gas is injected into the target reservoir, it will gradually move upward to the bottom of caprocks. Gas will enter the pores of caprocks when the displacement pressure is larger than the capillary pressure. The gas breakthrough process of caprocks can be divided into three stages, as shown in Figure 2. In the first stage, gas enters the pores of caprocks, but there is no connected pore channel of gas. In the second stage, with the continuous increase in gas pressure in the reservoirs, a connected pore channel forms locally in the caprocks. In the third stage, when the gas-driving pressure is continuously higher than the gas breakthrough pressure in the caprocks, the pore water in the caprocks is replaced by gas, forming the dominant gas migration pathways. The liquid present in the pores will be displaced, and the gas will continuously migrate through the thick caprock, resulting in sealing failure of caprocks [35]. Therefore, gas breakthrough pressure is a key index reflecting the sealing performance of caprock. Additionally, the geometry of the pores will affect the displacement process [36; 37].  
Caprock thickness, mechanical properties, fault distribution, etc., are the macro factors that may affect the sealing ability of caprocks. If the thickness of caprock is too thin, the injected gas may easily penetrate the caprock and sealing failure occurs. Faults may cut through caprocks and cause sealing failure problems. But if the faults only partially cut into the interior of caprocks, the sealing of caprocks still works. The lithology will also affect the sealing of caprocks because different minerals can result in different capillary pressure. The failure mode of caprock is related to in situ stress state. With rapid gas extraction, the formation pressure of the reservoir drops rapidly, and the caprock will also be damaged [38; 39].  
Figure 2: Schematic of gas breakthrough process in the caprock with continuous injection of natural gas (the black arrows in the left figure represent the migration paths of gas).
###### Abstract  
The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.  
federated Kalman filtering; high-speed railway; track geometry state detector; inertial/GNSS/odometer; integrated navigation Article  
The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection. _Appl. Sci._**2021**, _11_, 5244. [[https://doi.org/10.3390/appl1115244](https://doi.org/10.3390/appl1115244)]([https://doi.org/10.3390/appl1115244](https://doi.org/10.3390/appl1115244))
# GPR55 and GPR119 Receptors Contribute to the Processing of Neuropathic Pain in Rats
## 4 Materials and Methods
### Western Blot Analysis  
Western blot analysis was used to detect changes in the expression of GPR55 or GPR119 receptors in the ipsilateral lumbar dorsal spinal cord and sciatic nerve of rats subjected to L5-L6 spinal nerve injury. For this purpose, sham rats and L5-L6 spinal nerve-ligated rats (7 and 14 days) were submitted to euthanasia by decapitation. After, the lumbar region of the spinal cord and the left sciatic nerve were excised, and the lumbar spinal cord was dissected to isolate the ipsilateral dorsal spinal cord section. Immediately, tissues were blended in ice-cold lysis buffer (containing: 150 mM NaCl, 50 mM Tris-HCl, 5 mM EDTA, 1 mM phenylethylsulfonyl fluoride, 10 \(\upmu\)g/mL aprotinin, 10 \(\upmu\)g/mL leupeptin, 10 \(\upmu\)g/mL pepstatin A and 0.1% TritonX-100) at pH 7.4. Then, the homogenates were centrifuged at 14,000 rpm for 10 min to remove the cellular rubbish. The resultant supernatant was used to measure protein concentration by the Quick Start(tm) Bradford Protein Assay (Bio-Rad, Hercules, CA, USA). Fifty micrograms of protein from each sample were loaded into wells and they were resolved by 10% SDS-polyacrylamideide gel electrophoresis. Next, proteins were transferred to polyvinylidene fluoride membranes and the membranes were blocked with 5% non-fat milk in ice-cold phosphate buffered saline with Tween 20 (containing:137 mM NaCl, 2.7 mM KCl, 10 mM Na\({}_{2}\)HPO\({}_{4}\), 2 mM KH\({}_{2}\)PO\({}_{4}\) and 0.1% Tween 20) at pH 7.4 for 1 h. After that, membranes were incubated with the primary GPR55 antibody (rabbit anti-GPR55, 1:2000, ab203663, Abcam, Waltham, MA, USA) or with the primary GPR119 antibody (rabbit anti-GPR119, 1:5000, bs-12016R, Bioss) at 4 \({}^{\circ}\)C overnight. Membranes were washed three times with gentle agitation for 5 min each in ice-cold phosphate buffered saline with Tween 20. Later, horseradish peroxidase-conjugated secondary antibody (donkey anti-rabbit, 711-035-152, 1:10,000, Jackson ImmunoResearch, West Grove, PA, USA) was added to the membranes at room temperature for 1 h to recognize the primary antibody. Protein signal detection was achieved with the ECL chemiluminescence system (ECL plus, Amersham, UK). The membranes were stripped and incubated with the primary \(\beta\)-actin antibody (mouse anti-actin, GT5512, 1:10,000, Genetex, Irvine, CA, USA) and its respective horseradish peroxidase-conjugated secondary antibody (anti-mouse, 115-035-003, 1:10,000, Jackson ImmunoResearch Laboratories Inc., West Grove, PA, USA), which was employed as loading control to normalize GPR55 and GPR119 protein expressions. The densitometry analysis of the bands was carried out using ImageJLab 5.0 software (Bio-Rad, Hercules, CA, USA).
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 5 Results and Discussion
### Performance Analysis  
Initially, the images from the dataset were acquired and considered as the video frames acquired from the vehicles between the CAVs. The images were processed under superpixel generation to minimize the complexity during the classification. Subsequently, the features from the images were extracted and a feature vector was generated for each vehicle of the CAVs. Finally, the classification of the drivable and non-drivable areas was accomplished by using the TLFC. For example, a preprocessed sample and detected image using the KITTI dataset are presented in Figures 5 and 6. Figures 5 and 6 also show a shadow region image and extreme sunlight image, respectively.  
Figure 5: Road detection in shadow region scenario: (**a**) sample image, (**b**) pre-processed image, (**c**) detected image.  
As the proposed TLFC-RD method uses deep learning classifiers for road detection, the performances of the proposed TLFC-RD method were compared with some deep learning classifiers: Lenet-5 and ResNet. On the other hand, the TLFC-RD method was also compared with three different conventional classifiers: the Artificial Neural Network (ANN), SVM and Random Forest Classifier (RFC). Therefore, the TLFC-RD method was evaluated with five different classifiers. Lenet-5, ResNet and the deep learning classifiers used in the TLFC-RD method use the same activation function, namely ReLU, whereas the conventional classifiers are operated with a default setup. Here, the performance metrics considered for the evaluation were accuracy, sensitivity or recall, specificity, precision, F-score and max F measure. The performance evaluation of the TLFC-RD method is described as follows.  
The performance evaluation of the TLFC-RD method with ANN, SVM and RFC for KITTI and CamVid datasets is shown in Tables 1-3. Tables 1 and 2 show the performances of the KITTI dataset for extreme sunlight and shadow region images, respectively. Table 3 provides the performance evaluation of the TLFC-RD method with the CamVid dataset. From the evaluation, it could be concluded that the TLFC-RD method achieved better performance than the three existing classifiers (ANN, SVM and RFC). The fusion of feature maps from the LeNet 5, LSTM and SVM was used to improve the classification between the road and background for the autonomous vehicles. Additionally, the beneficial features from the images were used to improve the classification accuracy of the images. After performing the classification, we considered the detected information to be transmitted between the CAVs for ease of driving.  
Figure 6: Road detection in extreme sunlight region scenario: (**a**) sample image, (**b**) pre-processed image, (**c**) detected image.
## 3 Inductance Identification and Inductance Analytical Expressions
### The Proposed NAM
#### 3.2.2 Inductances' Identification of Branches Considering Spatial Position  
When there are multiple branches shorted at the same time, the stator circuit schematic is as shown in Figure 6. The inductance matrix of coil \(L\) (10) must be obtained, in which \(L_{ij}\) means the mutual inductance between the coils numbered \(i\) and \(j\). When \(i=j\), \(L_{ij}\) represents the self-inductance of the coil numbered \(i\). Since all coils are divided into two parts, the inductance matrix of coils is a 72 nd-order matrix.  
\[L=\left[\begin{array}{cccccc}L_{411h}&M_{411h11f}&\cdots&M_{411h21f}&M_{411h2 2h}&M_{411h22f}&\cdots&M_{411h2c3f}&M_{411h34f}&M_{411h34f}\\ M_{411f41h}&L_{411f}&\cdots&M_{411f21f}&M_{411f22h}&M_{411f22f}&\cdots&M_{411f 33f}&M_{411f34h}&M_{411f34h}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ M_{622h11h}&M_{622h11f}&M_{622h1f}&L_{422h}&M_{622h2f}&\cdots&M_{622h3f3}&M_{ 622h34f}&M_{622h34f}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ M_{534h11h}&M_{534h11f}&\cdots&M_{534h21f}&M_{534h22h}&M_{534h22f}&\cdots&M_{ 534h23f}&L_{534h}&M_{534h34f}\\ M_{534f411h}&M_{534f411f}&\cdots&M_{534f21f}&M_{534f21f}&M_{534f22f}&\cdots&M_{ 534f33f}&M_{534f34f}&L_{534f}\\ \end{array}\right] \tag{10}\]  
\[L_{ABCf}=C_{N_{c},72}\cdot L\cdot C_{N_{c},72}^{T} \tag{11}\]  
Figure 6: The stator circuit schematic with ITSF in multiple branches.  
To calculate the inductances of the branch, the position matrix \(C_{Ne,72}\) of the branch coils is established, in which \(N_{e}\) is equal to the sum of the branch number and the faulty coil number. The element of \(c_{i,j}\) in the position matrix indicates that whether the coil \(j\) belongs to the branch \(i\). The value is set to 1 when the coil \(j\) belongs to the fault branch \(i\), otherwise it is 0. Based on the position matrix and inductance matrix, the inductance matrix \(L_{ABCf}\) is calculated by Equation (11) and it is a \(N_{e}\)th-order matrix. The inductances of all branches, including the fault branch, can be acquired from the inductance matrix \(L_{ABCf}\).