esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Load-Displacement Plots  
Displacements obtained from DIC analysis are compared with the experimentally recorded dial gauge values at varying load values. Displacement values of DSLR images are obtained separately from UAV images and plotted. Small variation in the investigations is observed in dial gauge to DSLR displacements, with an accuracy of 95%, whereas the variations are higher in investigations observed form dial gauge to UAV displacements, with an accuracy of 88%. A sudden spike is observed in UAVIC results at some intervals due to occurrence of vibrations and magnetic attractions in the drone. Figure 16a displays a load-displacement graph of beam-A with failure load as 88 kN and maximum displacement as 4.82 mm.  
Figure 16: Load displacement plots of three RCC beams.  
Figure 15: Strain in the (**a**) longitudinal direction and (**b**) lateral direction.  
Similarly, beam-2 graph as shown in Figure 16b indicated failure load as 109 kN and maximum displacement as 5.35 mm, and beam-3 as shown in Figure 16c signifies failure load as 116 kN and maximum displacement as 6.45 mm, respectively. A good result has been observed between the two studies, with a deviation of 12% of experimental values.
## 3 Results and Discussion  
### Preparation Conditions of CC  
For preparation of CC, the influences of reaction conditions on the degree of substitution are shown in Figure 1a. The experimental data presented a slow upward trend of the DS with the increase of temperature in the range of 135 \({}^{\circ}\)C and 150 \({}^{\circ}\)C for 3 h, in a weight ratio of \(W_{\text{urea}}\)/\(W_{\text{cell}}\) = 0.334. We noted from Equations (1) and (2) that the reaction between urea and cellulose first goes through a deammoniating process of urea into isocyanic acid at high temperature, and then the HNCO reacts with cellulose to form CC. A high temperature above the melting point (132.7 \({}^{\circ}\)C) of urea was an important factor for CC synthesis for two reasons: One is that above the melting point of urea the solid-solid system became a liquid-solid reaction, which can enhance the contact between the reactants. The other reason is that above the melting point of urea, the reactive HNCO can be obtained in large quantities by the decomposition of urea [29]. However, urea is unstable at high temperature and the thermal decomposition products involve many by-products that are unfavorable for the synthesis of CC. For example, when heated to 150-240 \({}^{\circ}\)C at a heating rate of 10 K/min, an \(\sim\)73% weight-loss ratio of urea occurred along with the formation of the by-products, such as biuret, triuret, cyanuric acid, ammonia, carbon dioxide, and so on [30]. In addition, when the temperature was higher than 145 \({}^{\circ}\)C, the color of CC was yellowing due to the Maillard reaction. Therefore, too high temperature was not needed for the production of CC. When the temperature was 145 \({}^{\circ}\)C, the DS of CC reached 0.136.  
Under conditions of weight ratio \(W_{\text{urea}}\)/\(W_{\text{cell}}\) at 0.334 and the reaction temperature at 140 \({}^{\circ}\)C, the DS increased with time up to 0.143 at 4 h, and after that decreased rapidly. As the pyrolysis of cellulose at 140 \({}^{\circ}\)C is negligible [31], and the thermochemical reactions of urea and its pyrolysis products are affected by high temperature and by oxygen [29; 30], the phenomenon of decreased DS at reaction time 5 h in Figure 1b could be explained by the thermal degradation of carbamate of CC. Therefore, the reaction time should be controlled within 4 h. Later in this article, we will analyze the thermogravimetric performance of CC. In Figure 1c, the influence of material weight ratio on substitution degree of CC was also shown. When the urea/cellulose weight ratio increased from 0.259 to 0.334, DS rapidly increased from 0.055 to 0.123.
# Buckle Pose Estimation Using a Generative Adversarial Network
## 1 Introduction  
Intelligent manufacturing has transformed the manufacturing industry. Although it improves production efficiency and product quality, it also faces new challenges in the automated production of mechanical parts. In industrial production, computer vision technology aims to process collected images into relevant information needed for industrial production, such as the pose of materials [1] and the presence of defects [2], through algorithm processing. The information is converted into instruction codes and transmitted to industrial robots that perform operations such as grasping and disassembly. Newly manufactured lenses must first be removed from their plastic plates, and their encapsulating buckles must be disassembled before being placed on an aluminum plate for coating. Manual buckle disassembly is laborious and characterized by temporal, efficiency, and quality constraints. Therefore, to automate the coating process, it is necessary to design an accurate real-time buckle pose estimation algorithm that assists these robots in automatically disassembling the buckle.  
To address this need, this paper proposes a model that identifies the pose of a buckle on a plastic plate by obtaining its center position and deflection angle relative to the horizontal plane. To achieve this task, three key requirements must be met:  
1. The model must be generalizable enough to adaptively handle significant buckle shape differences. Figure 1 presents three common buckle types (buckles are inside the red box).
2. The model must distinguish the buckle from its background. The differences between these two are small. Figure 1 illustrate this scenario.  
3. The accuracy of buckle pose estimation must be high enough to reduce the loss caused by the clumsy disassembly of the machine.  
To meet these requirements, this paper proposes a generative adversarial network (GAN)-based [3] buckle pose estimation algorithm. A generator applies an edge extraction network to classify each pixel in the image, and the outer contours and center edge lines of buckles are regressed to estimate their center position and deflection angle. A discriminator-assisted generator is applied during training for edge refinement. First, its encoder is used for feature extraction and decoder feature reconstruction. A dilated convolution [4] is then performed so that the network can obtain information about the larger receptive field while paying attention to the overall target characteristics. Thus, the network can regress the overall outline information of the target to determine its pose to meet Requirements (1) and (2). Discriminators are introduced during training to assist the generator. Spatial attention is added to the discriminator, causing it to pay more attention to the difference between the generated image and its edge in the ground truth image. An auxiliary generator increases the attention paid to the detailed edge information and improves the accuracy to meet Requirement (3).
# Transient Attitude Motion of TNS-0#2 Nanosatellite during Atmosphere Re-Entry
## 3 Attitude Motion Reconstruction Technique  
In this section, a technique for the sensors' measurements processing of TNS-0 #2 is presented. A mathematical model of the angular motion of the nanosatellite with relevant assumptions is provided.
## 3 Methodology
### Fault and No-Fault Data Generation  
For training the data-mining models, a data window of 32 samples was taken 0.01 s after the inception of the faults.  
Fault case simulation includes:  
Figure 2: Flowchart of the proposed methodology of HVDC fault classification and location.  
Four types of faults such as positive pole-to-ground (P-G), negative pole-to-ground (N-G), pole-to-pole (P-P), and pole-to-pole-to-ground (P-P-G) were simulated in all 4 lines (Line-1 through Line-4).
2. Thirteen (13) fault resistances, namely \(R_{fault}=0.01\)\(\Omega\), 0.1 \(\Omega\), 1 \(\Omega\), 10 \(\Omega\), 20 \(\Omega\), 30 \(\Omega\), 40 \(\Omega\), 50 \(\Omega\), 60 \(\Omega\), 70 \(\Omega\), 80 \(\Omega\), 90 \(\Omega\), and 100 \(\Omega\) were considered during the simulation of fault cases.
3. Nine (9) fault locations in each line were considered during the fault simulation, namely 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% of the line. Therefore, a total of 4 \(\times\) 4 \(\times\) 13 \(\times\) 9 = 1872 fault cases were simulated. Each fault case in a line when considered from the relays present at both ends of the line was equivalent to 2 cases. The total number of fault cases presented to the classifier was 1872 \(\times\) 2 = 3744. No-fault transient cases presented to the classifier were as follows:
4. External DC fault cases: These were the voltage and current waveforms at the relaying points of a line during a DC fault in another line. For example, during a fault in Line-1, voltage and current waveforms obtained at the relaying points R-3, R-4, R-5, R-6, R-7, and R-8 were taken as external DC fault cases. The total number of external DC fault cases present was 11,232.
5. External AC fault cases: external AC faults were simulated at each of 4 AC sides as follows * Five types of faults A-G, A-B, A-B-G, A-B-C, and A-B-C-G were simulated in all 4 AC sides. * Thirteen (13) fault resistances, \(R_{fault}=0.01\)\(\Omega\), 0.1 \(\Omega\), 1 \(\Omega\), 10 \(\Omega\), 20 \(\Omega\), 30 \(\Omega\), 40 \(\Omega\), 50 \(\Omega\), 60 \(\Omega\), 70 \(\Omega\), 80 \(\Omega\), 90 \(\Omega\), and 100 \(\Omega\), were considered during the simulation of fault cases. * The number of external AC fault cases present was 2080. Therefore, the total no-fault transient cases present for classification was 13,312.
###### Abstract  
The aim of the study was to investigate the effect of the addition of graphene oxide (GO) on the rheological and mechanical properties of extruded polyamide 12 (PA12) filaments with high aluminum oxide (Al2O3) content used for 3D printing using the fused filament fabrication (FFF) method. Firstly, Al2O3-based mixtures with 0.10, 0.25 and 0.50 vol.% GO content were prepared. These mixtures were dried and subsequently combined with paraffin wax (PW), stearic acid (SA) and polyamide 12 (PA12) in an organic solvent. After drying in a vacuum oven and sifting, powder compositions of 74 wt.% (Al2O3 + GO)/26 wt.% (PA12 + PW + SA) with different GO content were obtained. All compositions were successfully extruded into filaments for 3D printing. Rheological, microstructural and mechanical studies of the compositions and filaments were carried out. X-ray diffraction phase analysis and Raman spectroscopy were also performed. It was shown that 0.10 and 0.25% vol. GO proved to be a universal additive that resulted in an increase in the rheological and mechanical properties of the highly filled polymer and also improved its 3D printability, which ultimately helped obtain a ceramic product with complex shape using the FFF method.  
rheological and mechanical properties; highly filled filaments; graphene oxide; Al2O3; 3D printing of ceramic +
Footnote †: journal: applied sciences
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
## 3 Experimental Investigation
### Propagating Regularity of the Coda Wave in Concrete
#### 3.3.2 Influence of the Relative Position of Transmitters and Receivers  
Waveforms of the receivers set on the opposite side and top of the transmitter are shown in Figure 7. It was found that the coda wave of an acoustic signal launched from the top surface and received on the side is more developed than that launched and received on the opposite surface. The main reasons for this are listed as follows. Firstly, the received signal launched on the top surface and received on the side is an example of non-collinear propagation. In the process of propagation, the direct wave cannot be perceived. Secondly, the waveform launched on the top surface and received on the side contains a certain component of transverse wave due to the different wave field. There is a large angle between the polarization direction and propagation direction of the transverse wave, but the polarization direction of the longitudinal wave is the same as the propagation direction. Therefore, the scattering probability of the transverse wave is larger, the scattering energy is strong, and the coda wave is more developed.  
Figure 6: Frequency spectrum of the received signal at different frequencies: (**a**) 300 kHz, (**b**) 150 kHz.
# DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data
## 2 Materials and Methods
### Differential Analysis  
The multi-omics datasets encompassing mRNA, miRNA, and DNA methylation for breast cancer patients are sourced from The Cancer Genome Atlas (TCGA) [31]. In the field of the breast cancer research, four main subtypes stand out due to their extensive validation and high rate of replication: Basal-like (Basal), Her2-enriched (Her2), Luminal A (LumA), and Luminal B (LumB). The key characteristics of these breast cancer subtypes depend on the expression levels of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the cell proliferation marker Ki67 [32; 33; 34]. The distribution of sample counts across these breast cancer subtypes is detailed in Table 1.  
For these three omics data, we conduct the fundamental preprocessing steps. This includes the removal of missing values and the consolidation of duplicate features. Following these preprocessing steps, we perform a differential analysis on each of the three omics datasets separately. Specifically, we collect initial samples from different omics platforms and categorize them into healthy and disease groups. Specifically, the mRNA dataset includes 194 samples from healthy individuals and 986 samples from breast cancer patients; the miRNA dataset contains 104 samples from healthy individuals and 1103 samples from breast cancer patients. The DNA methylation dataset consists of 97 samples from healthy individuals and 785 samples from breast cancer patients. Subsequently, we calculate the average expression levels of each feature within these omics datasets and perform \(t\)-tests on the data from the healthy and disease groups to identify features with significantly different expression levels. Through a rigorous differential expression analysis, we identify 3692 DE-mRNA features, 502 DE-miRNA features, and 4679 DE-DNA methylation features with differential expression. The criteria for selecting these significantly different features include the fold change in feature expression and the \(p\)-value of the \(t\)-test. The results of differential analysis are shown in Table 1 and Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Omics Types**} & \multicolumn{2}{c}{**Original Data**} & \multicolumn{2}{c}{**Differential Analysis Data**} \\ \cline{2-5}  & **Sample** & **No. of Features** & **Sample** & **No. of Features** \\ \hline mRNA & 1180 & 19,961 & 520 & 3692 \\ miRNA & 1207 & 1881 & 520 & 503 \\ DNA methylation & 882 & 12,264 & 520 & 4679 \\ \hline \end{tabular} In the entire sample set (520 samples), the sample distribution of breast cancer subtypes is Basal (81), Her2 (31), LumA (283), and LumB (125).  
\end{table}
Table 1: The original data and differential analysis data for mRNA, miRNA, and DNA methylation.
## 3 RPL Overview
### RPL Objective Function  
The Objective Function is used to define one or more metrics to help RPL nodes translate these metrics into ranks. It is responsible for selecting and optimizing the routes in a DODAG. Rank computation is carried out using the Objective Function depending on defined routing metrics such as link quality, delay, and connectivity. The Objective Function is used to define the rank of a node, which is expressed as the node distance from a DODAG root node. RPL main specification has no default objective function. Therefore, Objective Function 0 (OF0) is considered the default function, which is common to all implementations. The efficient objective function's design is still a critical research issue. In this paper, we are focusing on the evaluation of two objective function's implementations: One using Hop Count (HC) and the other using Expected Transmission Count (ETX) as a link metric in the rank calculation.  
Hop Count (HC): It is the metric used to identify the number of hops from source to destination. In Contiki operating system, Objective Function zero (OF0) is the default OF and selects the path to the root with the minimum number of hops. Contiki uses a 16-bit rank value in units of 256 that allows a maximum of 255 hops. Each node calculates its rank with respect to its parent rank using the summation of the parent rank and the (default-min-hop-rank-increase) value that is defined as 256 in the RFC (6550) [28]. The rank calculation based on hop count Objective Function can be calculated as follows:  
\[\mathrm{R}(\mathrm{n})=\mathrm{R}(\mathrm{P})+(\mathrm{default\_min\_hop\_rank \_increase}). \tag{1}\]  
while \(\mathrm{R}(\mathrm{n})\) represents the rank of the node \(\mathrm{n}\) and \(\mathrm{R}(\mathrm{P})\) is the rank of the node parent, node (\(\mathrm{n}\)) selects the parent node that minimizes the value of \(\mathrm{R}(\mathrm{n})\)  
Figure 3: RPL DIO Message Mechanism.  
Expected Transmission Count (ETX): ETX is defined as the expected number of transmissions which are required to send a packet over the communication link. The path ETX is the sum of the ETX of all the links along the path. When ETX is applied, the nodes must select the parent that has the lowest ETX value. Each node uses ETX to calculate the path to the root node and select its parent, which has the minimum overall ETX to the root node. ETX over a link can be calculated as follows:  
\[\text{ETX}=1/(\text{DF}\cdot\text{DR}). \tag{2}\]  
DF represents the probability of receiving a packet from the neighbor node and DR is the probability of receiving an acknowledgment successfully. Node (n) can calculate its rank based on ETX using this formula:  
\[\text{R}(\text{n})=\text{ETX}+\text{R}(\text{P}) \tag{3}\]  
where \(\text{R}(\text{P})\) represents the rank of the parent node, so the total node rank can be expressed as the rank of its parent with the addition to ETX of the overall path.
# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
## 3 Contact Fatigue State Identification
### Rolling Contact Fatigue Test  
The test was carried out on the RCF-A type testing machine, as shown in Figure 7. The specimen and the accompanying specimen are processed according to the YB/T 5345.2014 test standard, the material is 40 Cr, and the quenching and tempering process is used. During the test, set the rotational speed of the main shaft (specimen) to 1000 r/min, the rotational speed of the accompanying shaft (accompanying specimen) to 1100 r/min, the slip rate to be 10%, and the radial load to be 2071 N. The 1A307E accelerometer is installed on the headstock box by magnetic attraction, and the EM9118B-6/ICP data acquisition card is used to collect vibration signals. The image acquisition system selected LINEA series line scan cameras from Canada DALSA company and MLM-3 XMP lens from Japan COMPUTAR company to acquire the contact surface image of the specimen.  
The performance degradation of the specimen is a gradual process, and the period of rolling contact fatigue test is relatively long. In order to avoid too much repetitive  
Figure 6: The structure of the self-developed RCF-A test machine. (**a**) 3D model, (**b**) Prototype.  
Figure 7: Schematic diagram of the shaft box of the RCF-A test machine. 1. Accompanying axle box; 2. Spindle box.  
information in the data, the vibration signal is collected by interval sampling during the test. The signal acquisition parameter settings are shown in Table 7, and the control interface of the testing machine is shown in Figure 8.
## 1 Introduction  
Wireless sensor networks (WSNs), the Internet of Things (IoT), and unmanned aerial vehicle (UAV) technologies have many benefits and thus have been utilized in a huge variety of applications, including healthcare, disaster management, agriculture, and transportation. In WSNs, sensors are deployed for data collection and aggregation. They are also useful within the more advanced IoT networks (which help to connect different smart entities) for sensing, actuation, and computations through the Internet [1; 2]. Data generated by these sensors and smart devices can be used in the aforementioned application domains [3], including smart grids [1]. Sensor nodes in WSNs and IoT are mainly characterized by limited transmission range and energy constraints; therefore, they cannot transmit sensed data over large distances efficiently. This motivates the deployment of UAVs for relaying sensed data in WSNs and IoT (i.e., UAV-assisted WSN/IoT) to their required destination. In other words, UAVs fly to collect data sensed by WSNs/IoT devices and transmit that data to the data center or base station (BS). However, one of the main challenges of UAVs' deployment in WSN/IoT networks is to effectively send the required data in real-time. Due to the numerous limitations of WSNs and IoT themselves, there have been many studies investigating the many aspects that could be enhanced to ensure the data's freshness using the Age of the Information (AoI) metric. The AoI is the amount of time that elapses after the generation of the most recent update before it is received at the destination. This study investigates a few key components of AoI optimization, including the planning and designing of the UAV's trajectory.
# Non-Plaque Induced Diffuse Gingival Overgrowth: An Overview
## 4 Gingival Enlargement Induced by Hormonal Changes
### Acromegaly  
Acromegaly is a rare disease with slow but progressive development, due to an excess of the growth hormone (GH) caused in more than 95% of cases by a pituitary adenoma secreting the hormone [34]. Hypersecretion of GH leads to the overproduction of insulin-like growth factor (IGF1) which results in a multisystem disease characterized by somatic overgrowth, multiple comorbidities, physical abnormalities and increased mortality [6; 7; 27; 35; 36].  
The estimated prevalence of acromegaly is 50-70 cases per 1,000,000 people [6; 27]. This disease generally arises between the 3 rd and 5 th decade of life with equal involvement of men and women [6]. The incidence of gingival volume increase in these patients is around 70% [7].  
Acromegaly clinically appears with an enlargement of the major organs and with other alterations that can affect the oral cavity, such as thickening of the lips, prognathism, diastemas and hypercementosis, macroglossia and periodontal disease, with tooth mobility and loss [6; 7; 27; 29]. Diffuse gingival volume increases were described too.  
Macroglossia is a classic clinical sign of acromegaly, with enlarged furrows and dental impressions on the lateral edges of the tongue. It can cause difficulties in speaking [7].  
Another typical feature of these patients is the median maxillary and mandibular diastema and is a very suggestive element for the diagnosis [7]. Changes in the size of the jaws and macroglossia are responsible for the formation of spaces between the incisors of acromegalic patients [7].  
The increase in the length of the jaw is one of the most frequent findings in these patients. It is the consequence of an enlargement of the condylar cartilage of the temporomandibular joints [7].  
Gum lesions in these patients are slow-growing and asymptomatic, presenting with a hard consistency and a light pink color and with a fibrous collagen component [6,7].  
These are non-reversible alterations even after the correction of excess growth hormone secretion [7].  
In acromegaly, gingival overgrowth is caused by alterations that act on three different levels: the increase in connective and epithelial volume, the secondary advent of inflammatory complications and the thickening of the alveolar edges due to the excessive influence of growth hormone [6].