esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 3 Results
### Our Best Tracker Proposal versus Two Recent Trackers  
Two different recent trackers were selected for contrast: Struck [45] and SiamMask [10]. Struck remains relevant because it is a purely online tracking proposal that requires no kind of training and is still able to participate in the latest issues of the VOT challenge [64]. On the other hand, SiamMask is considered among the best tracker proposals that participated in the latest VOT challenge (top 20) in terms of accuracy, also several techniques that take inspiration from SiamMask are positioned in the toptier. SiamMask relies on pre trainined models as it is based on convolutional neural networks. This section compares the performance of this two state of the art trackers against our best proposal (GPU BEE) in terms of speed and accuracy using 14 videos of the ALOV dataset, one per category.  
The analysis of Struck and SiamMask revealed that both trackers preprocess the image frames before actually performing tracking, in the case of SiamMask this is usefull to reduce latency caused by the CPU to GPU communication. Taking that into account, a simple reduction of the frame size is performed in our proposal, by scaling the picture accordingly, whenever the width or the height of the frame is greater than 300 pixels. The effect on the accuracy caused by the reduction of frames can be considered negligible compared to the full resolution frame. On the other hand, a relevant reduction of the average time per frame is obtained.  
Figure 17a shows that the accuracy of GPU BEE is inferior to that of SiamMask and Struck. This is not surprising since GPU BEE uses NCC as a tracker, and different sources have found it to be surpassed in accuracy by Struck. However it is interesting to note that Struck showed a better accuracy than SiamMask in this particular setting, which is not the general case. The conclusion that is drawn is that Struck shows greater robustness in certain cases: low resolution videos and videos with background objects that resemble the target. In addition, it is clear that the parallelization of the HSA does not improve the accuracy of its fitness function (Figure 13), but a significant acceleration is observed compared to the conventional NCC tracker. As shown in (Figure 17b), the average time per frame of our best proposal (GPU BEE) is almost four orders of magnitude smaller than that obtained for CPU NO BEE. Moreover, the survival curve of average time per frame of GPU BEE is in the same window (from 0.2 to 0.9 spf) of Struck and SiamMask, a remarkable improvement against CPU NO BEE.  
Figure 17: **(a)** Comparison of F-score survival curves between GPU BEE (our best tracker proposal), Struck and SiamMask. **(b)** Comparison of average time per frame survival curves between GPU BEE, CPU NO BEE (conventional NCC), Struck and SiamMask. In the average time per frame axis, the scale from 0.02 to 0.1 is linear but logarithmic above 0.1, in order to contrast substantial difference of average time per frame between CPU NO BEE and the rest of the trackers.
## 3 Results and Discussions  
### Thermal and Morphological Properties  
The thermal stabilities of TPA-1A were investigated by TGA and DSC measurements; these data are shown in Figure 1 and summarized in Table 1. TPA-1A exhibited a high glass transition temperature of 176 \({}^{\circ}\)C, while demonstrating a high decomposition temperature of 474 \({}^{\circ}\)C for 5% weight reduction. The observed values are more suitable for thermally stable device applications and which enhance the morphological properties during device fabrication. Consequently, our materials revealed a higher melting point of 351 \({}^{\circ}\)C. TPA-1A showed outstanding thermal properties than well-known reference hole-transporting materials NPB (98 \({}^{\circ}\)C) and TPD (65 \({}^{\circ}\)C) due to its highly conjugated structure and rigid diphenyl acridine moiety attached to the triphenylamine central core [38; 39].  
The morphological studies of films are supported by scanning electron microscope (SEM) imagery and depicted in Figure 2. We could observe a well-defined multilayer structure with visible interfaces. Each layer shows proper contact with their adjacent layer, which helps to enhance the device efficiencies through proper charge transportation.  
Figure 1: Thermal gravimetric analysis (TGA) and differential scanning calorimetry (DSC) distribution of TPA-1A.
## 2 Results  
Components Identification from MCG\({}_{5\text{-}10\text{-}15\text{-}years}\) and GCG\({}_{5\text{-}10\text{-}15\text{-}years}\)  
Using the optimal HS-SPME-GC-MS conditions described in Section 4.3, the representative Based Peak Intensity (BPI) chromatogram of ginseng samples is presented in Figure 1. MCG and GCG samples with different growth years have similar chemical profiles, but the difference in content of some compounds can be visually noted. Based on the NIST database and the Wiley library, a total of 46 components were preliminarily identified, including 29 sesquiterpenes, 7 carbonyl compounds, 1 pyrazine, and 9 others. The relative contents of 46 components in different samples were calculated using 2-heptanone as the internal standard (I.S.), and the detailed information is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Compounds** & \multicolumn{2}{c}{**MCG**} & \multicolumn{2}{c}{**CGCG**} \\ \cline{3-8}  & **5 Years** & **10 Years** & **15 Years** & **5 Years** & **10 Years** & **15 Years** \\ \hline  
E)-3-Nonreal & 59.39 \(\pm\) 1.00 & 8.38 \(\pm\) 1.72 & 17.44 \(\pm\) 5  
scattered around the edge area, and only the compounds represented by these few ions contributed to the separation observed in the OPLS-DA score plots. In the present study, five variables (marked in red) with Variable Importance for the Projection (VIP) \(>\) 1 and \(p\) < 0.05 were selected as the potential chemo-markers, which directly led to the differentiation between \(\text{MCG}_{\text{5-15-years}}\) and \(\text{GCG}_{\text{5-15-years}}\). Table 2 summarizes the detailed information of the five potential cultivation-dependent markers, including hexanal, \(\beta\)-gurjunene, 2-oxo-pentanoic acid, heptanal, and octanal. The contents of hexanal, 2-oxo-pentanoic acid, heptanal, and octanal in \(\text{MCG}_{\text{5-15-years}}\) were significantly higher, while \(\beta\)-gurjunene in \(\text{GCG}_{\text{5-15-years}}\) was significantly higher (Figure 2D). Therefore, our data suggests that the above 5 volatile components might be used as the unique chemo-markers for discrimination between \(\text{MCG}_{\text{5-15-years}}\) and \(\text{GCG}_{\text{5-15-years}}\).
## 4 Materials and Methodology
### Materials
#### 4.2.2 Municipal Wastewater Data for Kuwait  
Municipal and industrial wastewater is managed and treated by the Ministry of Public Works (MPW). The total wastewater collected in 2019 was 378 \(\times\) 10\({}^{6}\) m\({}^{3}\)--generated from residential, governmental, and commercial sectors [46]. There existed (in 2019) six wastewater treatment plants (WWTPs), where five plants operated using a three-phase system; primary, secondary, and tertiary (sand filtration and disinfection). The Sulaybia plant, which treats 53% of all wastewater reserves, includes an additional reverse osmosis (RO) and UV disinfectant treatment phase. The majority of effluent is distributed locally for reuse in irrigation, natural reserves, artificial ponds, injection wells, and other commercial uses. The wastewater treatment system generates approximately 3.2 \(\times\) 10\({}^{6}\) m\({}^{3}\) of wet sludge annually [46], equivalent to 158,000 tonnes of dry sludge (assuming wet sludge  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Wastewater Influent** & **COD Influent** & **COD Influent** & **COD Influent** & **COD Loading** \\  & **2019 (10\({}^{6}\) m\({}^{3}\)/year) a** & **mg/L** & **mg/L** & **10\({}^{3}\) Tonnes** \\ \hline
**UAE** & 771 & 452 b  & 22 b  & 0.44 b  & 348 \\
**Bahrain** & 154 & - & - & - & - \\
**KSA** & 3083 & 533 c  & 24 c  & 0.60 c  & 1642 \\
**Oman** & 95 & 583 d  & 24 d  & 0.51 d  & 56 \\
**Qatar** & 278 & 441 e  & - & 0.41 f  & 123 f  \\
**Kuwait** & 334 & 442 g  & 29 g  & 0.46 g  & 147 \\ \hline \hline \end{tabular}  
\end{table}
Table 2: GCC wastewater characteristics.  
has a 6%-solids content and a density that is similar to water at ambient temperature). Wet sludge is dewatered, solar dried, and stored on sight or landfilled. Previously, dry sludge was employed for agricultural purposes, but the Kuwait Environmental Protection Authority (KEPA) has recently banned this practice. Dry sludge samples were collected from the Sulaybia WWTP between October and December of 2020, and analyzed for physico-chemical characteristics (Table 3).
## 3 HCL Polystyrene Based Nanocomposites
### Nanocomposites Containing Metal Nanoparticles
The obtained systems were tested as catalysts for the oxidation of D-glucose, revealing that best catalytic efficiency, in terms of conversion and selectivity, are obtained at intermediate complex/resin compatibility, when nanoparticles size ranges between about 4 and 2 nm. A further contribution on the understanding of the catalytic potentiality of Pd nanoparticles supported on HCL polystyrenes was given by [PERSON] et al. [86], who evaluated the relevance of the solvent effect for these systems for the selective hydrogenation of 2-methyl-3-butyn-2-ol to 2-methyl-3-butene-2-ol. Recently, Pd nanoparticles that were supported on HCL polystyrenes were also tested as catalysts for stearic acid hydrodeoxygenation, revealing that the use of HCL polystyrene containing about 1 wt % Pd allows for converting stearic acid with up to 97% heptadecane yield [94]. Also, Ru nanoparticles have been embedded in HCL polystyrenes by treatment of the resin with a Ru(OH)Cl\({}_{3}\) solution, followed by treatment with NaOH and H\({}_{2}\)O\({}_{2}\)[95]. Very small nanoparticles were obtained (mean diameter 0.5-0.8 nm), coexisting with less numerous larger nanoparticles (mean diameter up to 3.0 nm), and some aggregates. The latters are attributed to the hydrophobic nature of the polymer matrix, that prevents the homogeneous distribution of the metal during the impregnation of the resin. The obtained nanoparticles have a mixed valence structure, with both oxidic and metallic components. The catalytic activity and selectivity of the realized systems was tested towards the oxidation of d-glucose to d-gluconic acid, and the system containing 0.74 wt % of Ru was found to be the most performant with respect to systems containing 0.05 and 2.71 wt % of Ru. Other HCL resin systems containing Ru nanoparticles were tested as catalysts. [PERSON] et al. [96] realized a nanocomposite system containing about 5 wt % Ru and found it comparable with commercial Ru/C systems for the selective hydrogenation of levulinic acid to \(\gamma\)-valerolactone, with yields \(>\)99%. A similar system was tested for the catalytic hydrogenation of d-glucose to d-sorbitol, revealing two routes for the reaction, one based on the interaction of d-glucose with the spilled-over hydrogen supplied by the catalyst, the other based on the interaction of the substrate with hydrogen coming  
Figure 7: Schematic representation of Pd complexes embedded in HCL polystyrene depending on the complex/resin compatibility: I for low complex/resin compatibility; and, II for high compatibility. Reproduced from ref. [93] with permission of the Royal Society of Chemistry (London, UK). from the reaction medium [97]. Moreover, with an interesting strategy, Ru-containing catalysts were also realized on HCL polystyrene resins, either unfunctionalized, or functionalized with amino and sulphate groups. The catalyst based on unfunctionalized resin and containing 1.0 wt % Ru was found the most active for cellulose hydrogenolysis to sorbitol and mannitol [98].
## 4 Simulation Experiment and Results
### Case 1: Simple Scenario  
In the simple scenario, the target point coordinates of the PMCU are \(\left[x_{f},y_{f}\right]_{case1}=[27.2,\,39.2]\), the velocity of target point \(v_{fcase1}=0\), and the orientation angle \(\theta_{fcase1}=-\frac{\pi}{2}\). Among them, since the traditional RPM algorithm uses random initial value guesses, it often converges to different solutions and, in some cases, even fails to converge within the maximum number of iterations. Therefore, the results of the traditional RPM algorithm are compared with the PH-RPM using the optimal results of all experiments.
## 2 Study Area and Data Used
### Landslide Explanatory Variables
#### 2.3.3. Forestry Factors  
Vegetation prevents erosion on a slope by buffering the impact of rain falling on the slope, and vegetation roots increase the shear strength of the slope by increasing the shear strength of the soil. The forestry factors include timber type, timber diameter, timber age, and timber density. Here, timber type and timber age mean the species and average age of planted trees, respectively. In addition, timber diameter represents the size of the diameter at chest height. Timber density refers to the degree of closure of the crown canopy. These values were obtained from a 1:5000 scale forest map produced by the Korea Forest Research Institute.
# Nanomaterials' Influence on the Performance of Thermal Insulating Mortars--A Statistical Analysis
## 3 Results and Discussion
### Application of the Multivariate Statistical Methods  
The application of these statistical methods allowed us to analyse the current state-of-the-art thermal mortars' formulations. However, each method can be applied by itself, providing additional information related to the data being analysed. This way, each method can be further applied in other research studies using the following considerations:  
* Due to the ease of application, the uni and bivariate analysis allow us to obtain a general overview of the sample's characteristics, the scatter of the data, and the existence of outliers. Therefore, this analysis is so simple to apply that it is considered a good starting point before applying any other statistical technique;
* The PCA and factor analysis were revealed to be the techniques that allowed to obtain more insights that can further help to develop these products. They allowed us to find the interdependence between several characteristics and to verify the influence that the use of nanomaterials has on the performance of these formulations. These techniques can be an interesting tool to develop new formulations in the future;
* The cluster analysis, although allowing us to separate formulations into different origins, only served as a verification for the results obtained in the PCA and factor analysis, reinforcing the influence that the use of nanomaterials has on the performance. However, when considering a large amount of data, it can help the researchers to discover products that are intimately related;
* The regression models allow us to find predictive equations, when possible, for a desired dependent variable. In this case, they allowed us to predict the thermal conductivity at 10 \({}^{\circ}\)C and in the dry state, when knowing the thermal conductivity at 10 \({}^{\circ}\)C at 28 days, several days earlier.  
These methods allow us to analyse substantial amounts of data and to transform it into structured knowledge, with their relevance and usefulness increasing when used together.
# Object-Level Semantic Map Construction for Dynamic Scenes
###### Abstract
Then, the motion information of each object in the database will be identified through object-oriented 3D tracking against the camera pose, and its motion status will be updated in the current frame. By creating associations between the results of fusion segmentation combining the semantic and geometric cues and the object database, the dynamic part in the current frame will be found and eliminated, so as to obtain the camera pose accurately. Based on the camera pose accuracy and instance segmentation results, we produced semantically-labelled surfel (surface element) reconstructions of object instances without strong a priori knowledge of the object types present in a scene and integrated object attribute information with the system to improve the system efficiency and accuracy. Our underlying assumption is that objects of interest can be detected and segmented in real time using efficient segmentation algorithms. We used a Mask RCNN [6] to provide 2D instance mask predictions, utilized geometric information in the depth images to enhance the object segmentation, and then fused these masks online into the surfel reconstruction along with a 3D \"voxel mask\" to fuse the instance foreground. This approach enables the problems in dense and accurate camera pose tracking, semantic instance segmentation, object dynamic detection, and map construction to be solved in a dynamic environment. The main contributions of this work can be summarized as follows. 1. An object motion detection method involving object-oriented 3D tracking against the camera pose based on prior knowledge of a previously constructed object database is provided. 2. This approach enables dynamic objects to be found in the current frame by creating associations between the results of the instance segmentation algorithm combining the semantic and geometric cues and the object database updated by object motion detection. 3. The camera motion estimation method reduces the overall drift by leveraging the 3D static model for frame-to-model alignment, as well as dynamic object culling in the current frame. 4. To achieve an accurate 3D object-level semantic map representation of a scene, conduct object motion detection, and perform accurate instance segmentation and camera pose estimation, ElasticFusion is utilized, creating a complete and novel dynamic RGB-D SLAM framework. The remainder of this paper is organized as follows: Section 2 presents the related work conducted in the past few years on dynamic object-level semantic RGB-D SLAM. Section 3 describes our proposed approach. The experimental results obtained by testing the method on the public TUM datasets are given in Section 4. Section 5 provides a discussion and, finally, Section 6 summarizes the conclusions.
###### Abstract
Normally, there are two par structures in this foramen: the pars vascularis (or pars venous), located in the posterolateral section of the jugular foramen and involving the vagus nerves, spinal accessory nerves, the sigmoid sinus, and the ascending pharyngeal artery. The ascending pharyngeal artery becomes the posterior meningeal artery in the dura region, and the pars nervosa resides in the anteromedial region of the jugular foramen and includes the glossopharyngeal nerve, the inferior petrosal sinus, and the ascending pharyngeal artery [9]. Some exceptional cases demonstrate integration of the vagus nerves and hypoglossal nerves immediately after departing the hypoglossal canal and jugular foramen, respectively [10]. The jugular foramen is diverse and critical in otorhinolaryngology, radiology, and neurosurgical procedures involving the base of the skull and the middle ear. This foramen is also challenging to enter surgically [11]. Multiple techniques for approaching the jugular foramen are considered case by case because of their anatomical variations. The anteroposterior and mediolateral widths are factors that determine the neurovascular content, which passes through this foramen. Variations in neurovascular structures and their complexity make each surgical procedure unique, and knowledge of the anatomy is essential [12]. Intracranial or extracranial lesions in this section may occur, such as a metastatic lesion, a schwannoma or intracranial meningioma [13]. The jugular ganglion of the vagus nerve can also develop slow-growing glomus jugular tumors that generate multiple cranial nerve palsies, such as jugular foramen syndrome or paresis of the glossopharyngeal nerve, vagus nerve, and spinal accessory nerve [14; 15; 16]. The dome of the jugular foramen presents a superior bulb of major paired venous return from the brain, called the internal jugular vein [13]. The dome characteristic is related to clinical implications because kinking of the vein can occur, leading to thrombosis. Obstruction from thrombosis or cancer metastasis can also occur and depress the cranial nerve and adjacent vessels. This study considered the morphometric and anatomical disparities in the foramen magnum, occipital condyles, hypoglossal canal, and jugular foramen to improve knowledge of the base of the skull anatomy and optimize surgical procedures for anatomical variations.
# Smart Roads for Autonomous Accident Detection and Warnings
## 2 Related Works
### Hybrid Approaches
They also introduce a combination of blockchain and 5G-based MEC vehicular networks as a possible solution for the security, privacy protection, and content caching issues in the existing 5G V2X environment. The study [46] looks at how well vehicle visible light networks (VVLNs) perform in terms of increasing message delivery rates via full-duplex transmission (FD). According to their tests in which they compared their results with half-duplex (HD), it was discovered that using FD in urban environments increased message delivery ratio by 10%. By putting sensors on the road, the VANET is integrated with the low-cost wireless sensor network (WSN) to buffer and convey information concerning unsafe situations [47]. The designed system's prototype has been implemented and tested in the field. The findings show a variety of design tradeoffs and show that right parameters can provide sufficient safety and energy efficiency. In contrast to our work, this study started with the goal of improving driving safety by providing information about road conditions. They gathered and processed sensor data in order to extract information useful for safe driving and transmit it to vehicles that require it. A novel biologically inspired networking architecture [48] was presented in order to take advantage of the first available DSRC-equipped vehicles as temporary RSUs. Cars acting as temporary RSUs can halt for short periods of time and operate as a communication bridge for other vehicles in the network. Designing local rules and the algorithms that apply them is the basis for the suggested approach. According to results, the proposed method significantly improves message reachability and connection. In order to reduce RSUs, they deployed vehicles as RSUs to improve message reachability and network connectivity. A reinforcement learning (RL) algorithm [49] is utilized for partially observable intelligent transportation systems (ITS) based on dedicated short-range communications (DSRC). This system's performance is evaluated for various car flows, detection rates, and road network topologies. This method can effectively reduce the average waiting time of vehicles at a junction even with a poor detection rate. However, their work focuses on reducing traffic congestion by controlling traffic lights, instead of reducing or responding to traffic accidents efficiently. A deep reinforcement learning-based resource allocation system [50] was developed that includes remote radio head grouping and vehicle clustering. It balances system energy efficiency with service quality and dependability. In terms of performance, noise ratio, feasible data rate, and system energy efficiency, the new method is compared to three current algorithms using simulations. In comparison to our work, this study aims to assign various communication resources in order to improve system capacity while consuming the least amount of energy possible from periodic message traffic overheads.