esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 2 Single-Robot Semantic Mapping  
Single-robot semantic mapping is the process in which a single robot explores the environment and builds its semantic map. As shown in Figure 1, this process is mainly composed of two major tasks: data acquisition and processing, and data representation. Regarding the first task, the robot collects two types of data: geometric data and semantic data. Geometric data is usually acquired by the robot's sensors and is processed to generate a geometric map of the environment. Semantic data can be acquired from different sources and is used to add semantic meanings to the mapped features. The three main sources of semantic data are the extraction of interesting features from sensor data, the incorporation of data through HRI, and the extraction of data from a knowledge database, which is a model of the concepts involved in the explored environment and the relationships between them. As for the representation task, it consists of organizing those geometric and semantic data in a structured representation, namely, the semantic map, so that the robot can exploit them to understand its environment. In what follows, we study in depth the solutions proposed in the literature to solve these two tasks. It is important to mention that this work does not cover semantic SLAM systems [18; 19; 20], which benefit from object recognition to tightly integrate metric and semantic information in map localization and estimation [21]. However, it focuses on approaches that extract metric and semantic information in two separate processes and then merge the results into a semantic map.
# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 3 Methodology
### Dataset  
In this research, the Cyber-Troll publicly available dataset ([[https://www.kaggle.com/datasets/dataturks/dataset-for-detection-of-cybertrolls](https://www.kaggle.com/datasets/dataturks/dataset-for-detection-of-cybertrolls)]([https://www.kaggle.com/datasets/dataturks/dataset-for-detection-of-cybertrolls](https://www.kaggle.com/datasets/dataturks/dataset-for-detection-of-cybertrolls)) (accessed on 9 February 2022)) was used. Data-Turk created this dataset for aggression detection purposes. The dataset contains English language tweets, classified into two classes; cyber-aggressive (CA) and non-cyber-aggressive (NCA) by the Data-Turk society. Cyber-aggressive tweets contain messages intended to insult or hurt someone online. On the other, non-cyber-aggressive are tweets that contain no negative meaning, with no harm directed toward others. The dataset contains 20,001 tweets, of which 12,179 tweets are NCA and 7822 tweets are CA, as shown in Table 1. As depicted in Figure 1, CA tweets represent about 39% of the data while the NCA represents 61%.
## 5 Conclusions  
In this work, we presented a 3D surface-to-surface contact algorithm using a surface reconstruction technique. Validation examples of 3D contact problems were calculated to examine its accuracy and capability. The calculated results showed that the present contact algorithm can deal with both frictionless and frictional contact problems. It can provide a fine treatment for contact problems with small deformation and achieve high accuracy. It is also capable of handling impact contact problems with significant deformation. The algorithm was successfully applied to the simulation of material failure during projectile impact on a ceramic plate. The deformation modes of the projectile and the ceramic plate were correctly reproduced, and the predicted residual velocity was in good agreement with the experimental one.  
The present contact algorithm preserves the advantages of the SPH method over Lagrangian mesh-based methods in simulating large deformation and material failure. It enables the SPH method to simulate a wide range of contact problems with an accurate way. As an extension of the previously proposed 2D algorithm [10], it allows us to model engineering problems in 3D space and consequently provides results agreeing better with the reality. Thus, it is promising in many engineering applications, such as contact stresses between mechanical components, impact resistance of protective structures and fluid-structure interaction.  
Conceptualization, Y.X.; methodology, Y.X.; software, Y.X.; validation, Y.X. and K.L.; formal analysis, Y.X.; investigation, Y.X. and K.L.; resources, Y.X.; data curation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, K.L.; visualization, Y.X. and K.L.; supervision, Y.X.; project administration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No.: 11862005) and Natural Science Foundation of Jiangxi Province of China (Grant No.: 20181 BAB211012).  
Not applicable.  
Figure 19: Comparison between the present SPH simulation result and the simulation results of [PERSON] et al. with two different approaches: (**a**) FEM with erosion technique [1]; (**b**) Fragmentation approach (7 radial sectors for projectile) [1]; (**c**) The present SPH method.  
Data Availability Statement: The data used to support the findings of this study are available from the corresponding author upon reasonable request.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 4 Results and Discussion
### POI Tracking
As the helicopter turns to face the front of the POI, a larger inner bounding box becomes possible, leading to a lower IoU score, but the tracking remains relevant. Interestingly, this is one of the few failure cases of the SAM 2 tracker. It starts out perfect, but about halfway, the tracker only starts to focus on the left half of the building, leading to low IoU scores. However, the remaining segmentation mask is still decent, but not complete, as shown in Figure 11(b). The MedianFlow tracker is not always accurate, as illustrated by the second appearance of the chapel on the _Maur van Geraardsbergen_, where the tracking IoU is 0% throughout the entire live broadcast time window. This is because the POI was detected 52 s in advance, and the helicopter circled around it during this time, causing the tracker to lose the POI entirely. A similar behavior is observed with the _Vinkemolen_, as shown in Figure 12(a), where the helicopter circled around the POI for 12 s before the direc  
Figure 11: Visualizations of the tracking results of the _Sint-Martinuskerk_ at the end of the POI time window using (**a**) the MedianFlow tracker (green box = ground truth, blue box = prediction) and (**b**) the SAM 2 tracker (blue mask = prediction). Both trackers perform very well on this POI. Figure 12: Visualization of the tracking results of the _Kartuizerpriorij_ at the middle of the broadcast time window using (**a**) the MedianFlow tracker (green box = ground truth, blue box = prediction) and (**b**) the SAM 2 tracker (blue mask = prediction). The IoU of the MedianFlow tracker seems low, but the resulting tracking is good and stable. SAM 2 loses track of the right side of the building, which also leads to lower IoU scores. view. This highlights a limitation of the current tracking solution. The MedianFlow tracker performs well with linear or limited rotational movement but struggles when the helicopter flies around the POI, often losing track once the original surface disappears. In contrast, this is where the SAM 2 tracker really excels. Figure 12(b) also shows the _Vinkemolen_ at the end of the POI timeframe. Despite the large orientation and zoom level difference, SAM 2 is still able to track the mill perfectly. Although SAM 2 produces more accurate and robust tracking results than MedianFlow, the output of this traditional tracker is sufficient and stable for most POIs. When used live, the director could give instructions to the helicopter camera crew on which types of movement to avoid. Moreover, the semi-automatic approach allows for manual input to reset the tracking to the correct POI location, which can be performed shortly before the director switches to the helicopter view. However, if enough computational power is available, the SAM 2 tracker can be used for its superior tracking performance.
## 2 State of the Art Review of Video-Based Smoke Detection Algorithm
Some of the techniques seen above [14; 15; 16; 17; 21; 29; 30; 31] perform basically a background subtraction, so they just want to recognize the foreground. Other papers, like [19], implement more sophisticated video processing techniques, like Kalman based motion estimation, but with fixed parameters not aware of the specific camera configuration. Techniques such as in [18; 28], based on CNNs, are difficult to implement in real scenarios due to the lack of a large dataset for the training videos. This work also outperforms a preliminary algorithm, studied and presented by the authors in [22; 23], which was using SAD (sum of absolute differences) block matching as motion estimation, plus other image processing tasks, such as morphological filter, bounding boxes extraction, features extraction, correct bounding boxes selection, and space and time analysis. The new approach, discussed in Sections 3 and 4, refines the motion estimation method by replacing it with a Kalman estimator, properly tuned for smoke detection. After color analysis, image segmentation, blob labeling, features analysis, and M out of N design, an alarm signal is generated. When comparing the new algorithm with that in [22; 23], it can be observed that there is an increase of all the evaluation metrics, such as recall, accuracy, F1 score, precision, and MCC (Matthews correlation coefficient) and a reduction of the algorithm implementation complexity that allows implementing the new measuring system in real-time and with low power.
## 4 Simulations
### Results  
In order to confirm the convergence and scalability of the scheme, this section first conducts simulations in a scenario with 3, 4, or 5 SNs randomly distributed. The simulation results are shown in Figure 4. It can be seen from the resulting diagram that the more SNs in the scene, the more training rounds are required for algorithm convergence. At the same time, the average reward obtained by the UAV agent becomes higher, which shows that the UAV can intelligently plan its trajectory, so that when facing a growth in the number of SNs during the data collection, rewards can be obtained by the UAV with fewer action steps.  
As shown in Figure 5, in the case of four SNs, the algorithm proposed in this chapter can achieve 90% of the performance of the benchmark DQN algorithm under the condition of known and precise location information, even in the presence of three and five SNs. In the scenario of networked devices, it reaches 98% of the performance of the benchmark DQN algorithm. The above shows that an increase in the number of SNs does not necessarily affect the performance of the algorithm.  
Then, we conduct a simulation experiment on the scenarios with uncertain SN locations. Table 1 sums up the major parameters of the DNN. We only know that each SN is located in an area composed of \(2\times 2\) cells, but we cannot obtain its detailed location information. There are three SNs distributed within square areas of ([550, 650], [150, 250]), [[250, 350], [450, 550]], [[850, 950], and [850, 950]], respectively. Specifically, each SN may be located in any of the four cells. To simplify the problem and make the algorithm more universal, we assume that the SNs are always located in the center of the cells. Therefore, we can collect data from the SNs without their precise location information being known.  
Figure 4: Convergence performance of DQN algorithm under different numbers of SNs.  
Figure 5: Comparison of the proposed algorithm and the baseline algorithm.  
Figure 8 compares the performance of the proposal and benchmark algorithms with the precise position information known. We define the minimum task completion time with the precise position information perfectly known as \(T_{min}\) and take the quotient of \(T_{min}\) divided by the mission completion time as an indicator, \(C\). The indicator \(C\) can be defined as  
\[C=\frac{T_{min}}{T}. \tag{16}\]  
Obviously, if the performance of the proposed algorithm is satisfactory, \(C\) will be very close to 1. We use the data of the last 3200 episodes of the training process to generate this figure and test it 50 times on average in each possible scenario. The proposed algorithm can achieve 96.1% of the efficiency of the benchmark algorithm on average in all possible scenarios and 89.6% even in the worst-performing scenario.
# Effect of PAS-LuxR Family Regulators on the Secondary Metabolism of _Streptomyces_
## 2 PAS-LuxR Family Regulators
### PAS-LuxR Family Regulators Are Present in the Type I Polyketide Synthase (PKS) Pathway  
As shown in Figure 2, PAS-LuxR regulatory genes are predominantly found in the PKS pathway. For example, the most well-reported biosynthetic gene cluster of pimaricin (also known as natamycin) has five PKS genes (_pimS0_, _pimS1_, _pimS2_, _pimS3_, and _pimS4_), three oxidoreductase genes (_pimD_, _pimF_, and _pimG_), three glycosylation genes (_pimC_, _pimJ_, and _pimK_), three transporter genes (_pimA_, _pimB_, and _pimH_), one pleiotropic effect regulatory gene (_pimT_) [9], one thioester hydrolase gene (_pimI_), and one sterol oxidase gene (_pimE_) [11; 12]. In addition to the PKS and postmodifying genes, two regulatory genes, namely, _pimM_ and _pimR_, encode a PAS-LuxR family regulator and a SARP-LAL family regulator. Reedsmycin is a nonglycosylated polyene macrolide, and its gene cluster includes four PKS genes (_rdmG_, _rdmH_, _rdmI_, and _rdmI_), a resistance protein gene (_rdmK_), a Paal family thioesterase gene (_rdmM_), an acyl carrier protein (ACP) gene (_rdmN_), an acyl coenzyme A synthase gene (_rdmO_), two genes with the methyltransferase gene (_rdmB_), the ferritin gene (_rdmL_), and five regulatory protein genes (_rdmA_, _rdmC_, _rdmD_, _rdmE_, and _rdmF_) that encode a PAS-LuxR family protein [13]. The PAS-LuxR family regulators are also present in the biosynthetic gene clusters of all polyene macrolides, including nystatin, filipin, tetramycin, aureofucin, candicidin/FR-008, and amphoteric. CFA-L-Ile is an amino acid analogue, and the CFAL-Ile gene cluster consists of two PKS genes (_cfa6_ and _cfa7_), one acyl carrier protein (ACP) gene (_cfa1_), two acyl coenzyme A ligase genes (_cfa5_ and _cfa1_), one oxidoreductase gene (_scab79681_), a cytochrome P450 monooxygenase gene (_sabc79691_), two reductase genes (_cfa8_ and _sabc79721_), a hydroxybutyryl coenzyme A dehydrogenase gene (_sabc79711_), and two regulatory genes (_orf1_ and _cfaR_, which encode a PAS-LuxR family protein) [14]. Wuyiercin is an aminoglycosa antibiotic [15; 16] that was mainly studied in biocontrol. The gene cluster has only been partially sequenced and it contains PAS-LuxR gene _wysR_. PAS-LuxR family regulators are mainly present in the PKS pathway.  
Figure 1: Structures of secondary metabolites regulated by the PAS-LuxR family regulators.
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 4 ESPEA for Solving the PVSP-MSS
### Insertion Decoding
#### 4.4.2 Decoding the OSC  
\(Y_{m}\) and \(T\) are used to determine the corresponding yard \(M_{k}\) and pickup time \(t_{i,j,k}\). For vehicle operation \(O_{i,j}\) and yard \(M_{k}\), if \(O_{i,j}\) is the first operation of vehicle \(i\) and the first operation of yard \(M_{k}\), it can be inserted directly into the 0 moment for processing; if \(O_{i,j}\) is the first operation of \(M_{k}\) but not the first operation of vehicle \(i\), \(O_{i,j}\) can be inserted directly at the completion time of the previous operation \(O_{i,j-1}\). Otherwise, find all idle time periods \([TS_{x},TE_{x}]\) for yard \(M_{k}\), where \(TS_{x}\) is the start time of the \(x-th\) idle time period and \(TE_{x}\) is the end time of the \(x-th\) idle time period. During yard \(M_{k}\)'s work process, if \(O_{i,j}\) satisfies the condition \(max\{C_{i,j-1},TS_{x}\}+t_{i,j,k}\leq TE_{x}\), it can be inserted into the idle time \([TS_{x},TE_{x}]\) with the start time of \(\{C_{i,j-1},LM_{k}\}\), where \(LM_{k}\) is the earliest available time for the current yard \(M_{k}\).  
Figure 5 shows an insertable example in \(M_{2}\). \(O_{4,1}\) is the first operation of \(I_{4}\), and the pickup time \(t_{4,1,2}\) is 3, which is less than the idle time period \([TS_{1},TE_{1}]\) of \(M_{2}\). Therefore, \(O_{4,1}\) can be inserted ahead of \(O_{1,2}\) without affecting the normal operation of \(I_{1}\).  
Figure 6 shows a non-insertable example in \(M_{2}\). \(O_{3,2}\) is the second operation of \(I_{3}\), and has \(S_{3,2}\geq C_{3,1}\geq TE_{1}\). Therefore, \(O_{3,2}\) cannot be inserted into the idle time of \(M_{2}\) and it is assigned to the next operation after \(O_{1,2}\).  
Figure 5: An example of an insertable operation.
# Real Geometrical Imperfection of Bow-String Arches--Measurement and Global Analysis
## 4 Discussion and Conclusions  
The comparison of several analyses executed in two different software indicates the relative small influence of geometric imperfection on the stress state of arches of considered, from a 112-metres-span bow-string arch bridge. Nevertheless, this study shows that taking into account geometric imperfections can produce more relevant results. The comparison of the stresses, if either real or theoretical imperfections were taken into account, shows the influence of the considered shape of geometrical imperfection. Basically, in the same cross-section of the arch, a nonlinear analysis can produce stress values that are higher or lower than those obtained by the linear analysis, depending on the initial shape of imperfection.  
The other task is the type and precision of the FEM model, especially the application of the type and size of finite elements, the modelling of structural details, and the approximation of connection, bearings, etc. As mentioned above, the presented two different approaches produced differences in the stresses in some cross-sections up to 22%, while the effects of taking into account geometric imperfections in geometrically nonlinear analysis were much smaller. Of course, this conclusion cannot be automatically generalised.  
The present study pointed out the possibility of applying laser scanning during the inspection or diagnostic of existing bridges. The obtained data can be subsequently used for relevant analyses when assessing arches or the entire bridge superstructure. Whether the second order nonlinear analyses are to be applied or the simplified method is used, (e.g., by the means of a so-called equivalent column method), the information about the actual imperfection are certainly valuable data for a possible refinement of the results, [19,20]. In the case of bridges, the consideration of real imperfections can lead into a better determination of its load-carrying capacity.  
Scanning using the 3D laser scanner seems to be a relatively convenient and unpretentious method for obtaining basic data about the real geometric imperfections of steel bridge arches. In order to collect much more data, enhanced measurement was started and today is almost finished. Two tenths of the arch bridges have been already scanned and now the attention is paid to more sophisticated data processing and comparison methodology.  
Conceptualization, J.O.; data curation, M.F. and J.C.; formal analysis, J.O., and J.K.; investigation, M.F. and J.C.; methodology, J.O. and J.G.; supervision, J.O.; validation, J.O., J.G. and M.F.; visualization, J.O. and M.F.; writing--original draft preparation, J.O.; writing--review and editing, M.F. and J.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Research Project No. 1/0336/18 of the Slovak Grant Agency.  
The authors declare no conflict of interest.  
Figure 11: Percentage differences in the stresses produced by the common “beam” model in the SCIA Engineer compared to the more complex “shell” model in ADINA.
###### Abstract  
Predicting hard disk failure effectively and efficiently can prevent the high costs of data loss for data storage systems. Disk failure prediction based on machine learning and artificial intelligence has gained notable attention, because of its good capabilities. Improving the accuracy and performance of disk failure prediction, however, is still a challenging problem. When disk failure is about to occur, the time is limited for the prediction process, including building models and predicting. Faster training would promote the efficiency of model updates, and late predictions not only have no value but also waste resources. To improve both the prediction quality and modeling timeliness, a two-layer classification-based feature selection scheme is proposed in this paper. An attribute filter calculating the importance of attributes was designed, to remove attributes insensitive to failure identification, where importance is gained based on the idea of classification tree models. Furthermore, by determining the correlation between features based on the correlation coefficient, an attribute classification method is proposed. In experiments, the models of machine learning and artificial intelligence were applied, and they included naive Bayesian, random forest, support vector machine, gradient boosted decision tree, convolutional neural networks, and long short-term memory. The results showed that the proposed technique could improve the prediction accuracy of ML/AI-based hard disk failure prediction models. Specifically, utilizing random forest and long short-term memory with the proposed technique showed the best accuracy. Meanwhile, the proposed scheme could reduce training and prediction latency by 75% and 83%, respectively, in the best case compared with the baseline methods.  
ML/AI; disk failure prediction; timeliness; feature selection
## 2 Materials and Methods
### Lipophilic Fraction Characterization
#### 2.4.2 Analysis by GC-MS  
Before GC-MS analysis, nearly 20 mg of extract was converted into trimethylsilyl (TMS) derivatives according to previously optimized methodology [39]. GC-MS analysis was performed using a Trace Gas Chromatograph 2000 Series equipped with a Thermo Scientific DSQ II mass spectrometer (Shimadzu, Kyoto, Japan) using helium as carrier gas (35 cm/s) equipped with a DB-I &W capillary column (30 m \(\times\) 0.32 mm i.d., 0.25 m film thickness, Clara, CA, USA). The chromatographic conditions were as follows: initial temperature 80 \({}^{\circ}\)C for 5 min, temperature rate of 4 \({}^{\circ}\)C/min up to 260 \({}^{\circ}\)C, and 2 \({}^{\circ}\)C/min until the final temperature 285 \({}^{\circ}\)C, then maintained at 285 \({}^{\circ}\)C for 13 min, injector temperature of 250 \({}^{\circ}\)C; transfer-line temperature of 290 \({}^{\circ}\)C, split ratio: 1:50. The MS was operated in the electron impact mode with an electron impact energy of 70 eV and data collected at a rate of 1 scan/s over a range of _m/z_ 33-700. The ion source was maintained at 250 \({}^{\circ}\)C. Compounds were identified as TMS derivatives by comparing their mass spectra with the GC-MS spectral library (Wiley-NIST Mass Spectral Library 1999), with literature MS fragmentation and also by injection of standards. Lipophilic compounds were quantified by their peak areas, being GC-MS calibrated with pure reference compounds, representative of each family, namely hexadecanoic acid for fatty acids, nonadecanol for alcohols, stigmasterol for phytosterols, and unsolic acid for triterpene compounds, relative to tetracosane (the internal standard). The respective response factors were calculated as the average of six GC -MS runs. Three aliquots of the lipophilic extract were injected in duplicate, and the results represent the average of the concordant values obtained for the six runs (_n_ = 6, 3 extracts, obtained from 3 independent extractions, each extract was injected in duplicate).  
Figure 1: Scheme of the workflow followed in this work.