esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Purpose: This study aimed to measure the amount of change in the mandibular angle, interregional width, and ramus angulation due to intersegmental interference and changes in condey position after mandibular bilateral sagittal split ramus osteotomy (BSSRO) in patients with mandibular pragnathism and to evaluate the correlation between them. Materials and Methods: A total of 32 patients who underwent mandibular setback using the BSSRO of the mandible to manage skeletal propathathism during the years 2018 to 2020 at the Department of Oral and Maxillofacial Surgery, Pusan National University were followed both clinically and with cone beam computed tomography (CBCT) for at least one year. Those who were also treated with genioplasty or other orthopathic surgery were excluded from the study. The mandibular angle (gonal angle: Ar-Go-Me), interregional width (Go-Go), and total angle (sum of left and right proximal segmental angle) were recorded. Changes in the ramus were compared and analyzed before surgery (T1), immediately after surgery (T2), and one year following surgery (T3). Results: The mandibular angle increased by an average of 0.14 degrees immediately after surgery (T2-T1) and increased by 0.97 degrees at 12 months postoperatively (T3-T2). No correlation was observed with the amount of change in each group relative to the amount of mandibular setback. The mandibular width decreased by 0.01 mm on average immediately after surgery (T2-T1), and by 4.2 mm on average at 12 months after surgery (T3-T2). The angle of the mesial fragment of the mandible increased by 1.04 degrees immediately after surgery (T2-T1), compared to the preoperative state. It decreased by 0.86 degrees at 12 months postoperatively (T3-T2). Conclusion: The increase in the mandibular angle from immediately after surgery to 12 months after BSSRO reflects the counterclockwise rotational tendency to prevent opening restriction when intentionally selecting the condylar position. The decrease in the interregional width immediately after surgery is thought to be due to the effect of internal trimming to minimize the bone interference between the outer surface of the distal bone fragment and the inner surface of the mesial fragment, and the decrease 1 year after surgery can be considered to be due to bone remodeling. Interosseous interference during mandibular setback osteotomy does not necessarily cause an increase in the width of the mandibular angle after surgery.  
orthognathic surgery; relapse; bone remodeling; stability; intergional distance; interference +
Footnote †: journal: Medical Imaging
## 1 Introduction
We propose that the fermentation of prodegraded PS (Figure 2) could be one application of a fraction of global plastic solid waste (PSW) using oxidative degradation as promoted by specific microbial strains [26]. The prodegraded PS had an acid number of 22 (see Section 3.1), indicating the presence of carboxylic groups in addition to ketone, acid, or aldehyde C=O groups [5; 27]. From previous studies, the hydrophilicity of samples can be increased using thermal treatment to increase the acid number, followed by ultrasound sonication to reduce the average molecular mass to stimulate bacterial metabolism [5]. For alkane metabolism (aerobic), the most common pathway is the oxidation of the terminal methyl group into a carboxylic acid through an alcohol intermediate, which is succeeded by complete mineralization via the \(\beta\)-oxidation pathway [5; 28; 29]. The degradation of styrene has been reported previously in _Pseudomonas putida_ CA-3, where epoxidation of styrene occurs, followed by isomerization of the epoxide to form phenylacetaldehyde, which is then oxidized to phenylacetic acid and converted to phenylacetyle coenzyme A (CoA) before further oxidization to acetyl-CoA [30]. The novelty of this study is that prodegraded PS is the carbon source used, and NMR has been tasked to analyze the thermal degradation process in detail, and, therefore, show the viability of PS as a carbon source with fermentation analysis and polymer sequence characterization. The bacterial strain, _Cupriavidus reactor_ H16 (formerly _Ralstonia eutropha_), was selected for this study as it is a reliable producer of PHAs, grows well at relatively low temperatures, has limited immunity to some heavy metals, and has a stable and well-documented genetic profile [5; 31; 32; 33]. After the PHAs are generated, they are stored as granules, sometimes referred to as \"carbonosomes\" [31; 32; 33; 34]. These microbes are known to effectively synthesize PHAs (up to 85% PHA per dry cell weight) under famine and environmentally harsh conditions using a wide range of carbon sources, including: Fatty acids, crude glycerol, hemicellulose, liquefied wood, and methane gas [35; 36; 37; 38; 34]. The removal of PHAs from biomass uses lyophilization and hot solvent extraction, followed by precipitation in hexane [5; 33; 35]. In summary, this study details the molecular level structural analysis of PHAs synthesized using _C. necator_ as fed by prodegraded PS (PS0) waste and pretreated samples with thermal oxidation  
Figure 2: Proposed recycling stages, adapted from [22]. processes (PS1 to 4), as additional carbon sources in nitrogen rich tryptone soya broth (TSB) growth media and nitrogen limited, basal salts media (BSM) during optimal fermentation periods.
## 3 Results
### Test Scenario Description  
To execute the offline tests, conducted to produce the results presented in this paper, a Dell XPS 13 7390 with a Intel(R) Core(TM) i7-10510U CPU @ 1.80 GHz-2.30 GHz and 16 GB of RAM was used. In particular, the tests were performed working on data relative to three different geographical areas located in Mallorca Island (Spain). All the areas considered in this work are coastal regions with an average distance from the coast of about 30m and they can be classified as very shallow waters, as the depth within them ranges between 3 m to 15 m. However, note that the GPP algorithm has been designed on the assumption of working in a 2D scenario, as the slope of areas where PO uses to grow is usually low. Thus, the bathymetry of the inspected area does not impact the behaviour of the algorithm nor the execution of the mission. In any case, the AUV is equipped with an altitude sensor and it is controlled to maintain a fixed distance from the sea bottom during all the missions. This allows the AUV to maintain good acquisition conditions about the images used to disclaim the presence of PO.  
In each area, three different scenarios were considered by varying the origin point of the path. The extension and shape of PO meadow in the environment was modelled through a GP trained on data describing, in probabilistic terms, the presence of PO in a specific location of the target area. Such data were produced by segmentation of images through a pre-trained CNN. The training of the GP was carried out using GpFlow [43], library of Tensorflow [44], in Python environment. Figure 4 shows a satellite view of the three areas of inspection. In each of them, the variance map obtained by the GP prediction is represented as a colormap, where high variance regions are depicted in yellow, while the areas with low variance are displayed in blue. The a priori known obstacles are depicted in brown and the different starting points are shown on each map, respectively, in red, blue, and orange, according to their order.  
Figure 4: Aerial view of the three geographic areas of interest. (**a**) Starting points Area \(1\); (**b**) Starting points Area \(2\); (**c**) Starting points Area \(3\).
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 3 Agriculture 4.0 Security Threats in Multi-Layered Paradigm
### Security Risks at the Physical Layer  
This is primarily concerned with physical equipment such as sensors and actuators. Physical devices can fail due to unintentional or intentional human behavior, viruses, spyware, or cyber-attacks. Smart farming applications use a wide range of sensors and technologies, and this diversity opens up several security risks, including the following:  
Random device incidents: This is the unintended alteration of physical equipment that causes it to deviate from its normal performance. The physical equipment may be damaged by farm machinery, causing short- or long-term physical damage, as a result, generates false data and affects data accessibility.  
Mobile node hijacking: It is the theft of farm equipment such as tractors, drones, and sowing robots. Drones can indeed be pesticides, fertilizers sprinkling could be performed by Drone, and robots could do weeding detecting disease. The intruder may access and use the farm equipment remotely without having access permission.  
Fake node: An intruder introduces imitation or harmful nodes into the automated agriculture field to disturb the smooth functioning of the system [55]. This attack could be triggered by capturing a node and making a replica of it. This type of attack typically aims to either alter or modify data-shuttered devices and applications.  
Abnormal Measurement: It senses unusual observations due to data falsification, energy exhaustion, radio radiation, detection of various connections, extreme storms, malfunctions, or wrong inputs. Incorrect data can jeopardize making decisions that result in the analysis of data will be inaccurate, which minimizes system correctness.  
Sleeplessness attack: The sleeplessness attack sends a series of seemingly legitimate requests in order to keep the devices awake for prolonged life. As a result, the device's battery runs out, and the device stops working [56; 57]. When the devices are turned off, the observed information is not properly sent, jeopardizing decision-making and system efficiency.
## 3 Node Key Construction Technology
### Cantilever Truss Node
#### 3.3.2 Node 2 Structural Design  
The lower chord rod XXG, oblique belly rod FG1A-B, and the vertical rod FG2 form Node 2, which is shown in Figure 7. The lower chord rod XXG1 is made of a 1700 mm \(\times\) 1500 mm \(\times\) 80 mm \(\times\) 80 mm box-shaped steel pipe, and the oblique rods FG1A to B are 700 mm \(\times\) 700 mm \(\times\) 40 mm \(\times\) 40 mm box-shaped steel plates. Moreover, the vertical rod FG2 is a 700 mm \(\times\) 700 mm \(\times\) 60 mm \(\times\) 60 mm box-shaped steel plate. In the design of Node 2, the lower chord rod XXG1 is connected using the function of horizontal load transfer and support, and it is responsible for connecting the inclined belly rods FG1A to B and the vertical rod FG2. The description is shown in Table 2. The local tearing problem caused by excessive steel plate thickness should be considered. The oblique bars FG1A to B are used to transfer the internal force downward in order to increase the stability and stiffness of the node. The vertical bar FG2 is mainly responsible for providing vertical support to enhance the overall stiffness of Node 2. The design of the node fully takes into account the impact of seismic action. The oblique rod forms a single K-node structure, and it also forms a stable triangular structure with the vertical rod. The stress distribution is more uniform, ensuring the coordination between the components and effective force transmission. This operation can effectively absorb and disperse seismic energy when a rare earthquake occurs. It can also reduce the degree of damage to the structure and can improve seismic performance of the overall structure.  
\begin{table}
\begin{tabular}{c c} \hline
**Component** & **Depict** \\ \hline  & 1700 mm \(\times\) 1500 mm \(\times\) 80 mm \(\times\) 80 mm box-shaped steel pipe (lower chord rod) \\  & 700 mm \(\times\) 700 mm \(\times\) 40 mm \(\times\) 40 mm box-shaped steel plate (oblique rod) \\  & 700 mm \(\times\) 700 mm \(\times\) 60 mm \(\times\) 60 mm box-shaped steel plate (vertical rod) \\ \hline \end{tabular}
\end{table}
Table 2: Component information of Node 2.  
Figure 6: Diagram of Node 1.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 2 Positioning Foundation of the Roadheader and Modeling
### SLAM Mathematical Model of Roadheader  
In the moving process of the roadheader, the airborne camera observes the environmental signposts and estimates its own motion. As shown in Figure 3, the SLAM process of a roadheader equipped with a camera can be visualized, and the pose state and signposts are represented by nodes, and processes of camera movement and observation are represented by edges. Further, at time \(i\), let the pose variable of the airborne camera be \(x_{i}\), the position variable of waypoint is \(y_{i}\), the motion input is \(u_{i}\), and the observation data of airborne camera are \(z_{i,j}\); then, the mathematical model of visual SLAM for the roadheader is shown as Equation (7).  
\[\begin{array}{ccc}x_{i}=f_{\rm p}(x_{i-1},u_{i},w_{i})&i\in(1,\ldots,m)\\ z_{i,j}=g_{\rm o}\big{(}y_{j},x_{i},v_{i,j})&j\in(1,\ldots,n)\end{array} \tag{7}\]  
where \(f_{\rm p}\) and go are the process function and observation function, respectively; \(w_{i}\) and \(v_{i,j}\) are the motion noise and observation noise at time \(i\), respectively.
## 3 Results
### Study Characteristics  
The papers included in this review span sixteen years, with the earliest paper published in 2004 and the most recent in 2020. Most studies used rodent models, except for [PERSON] et al. [39], and [PERSON] et al. [47], who both used zebrafish models, while [PERSON] et al. [40], [PERSON] et al. [66], and [PERSON] et al. [21] examined human brain tissue. Twenty-three studies used mouse models [31; 32; 33; 34; 36; 37; 41; 42; 45; 46; 48; 49; 53; 54; 55; 58; 60; 61; 63; 64], seven used rat models [50; 51; 52; 59; 62; 65; 66], three used both rat and mouse models [29; 35; 38], and one used a guinea pig model [56]. In addition, fourteen of the studies applied 2 PI to visualize ex vivo cells in brain slices [42; 43; 44; 45; 46; 47; 48; 50; 51; 52; 55; 56; 62; 66], while seventeen studies employed 2 PI in vivo [31; 32; 33; 36; 37; 39; 43; 47; 49; 57; 58; 59; 60; 61; 63; 64; 65]. Three studies utilized a combination of in vivo and ex vivo approaches [44; 46; 53]. In vivo 2 PI was performed by a small craniotomy in the animal, allowing direct access to the brain for visualization and uncaging. The animals were lightly or fully sedated during experiments. For experimental seizure-induction, different pharmacological agents were used across the studies, including kainic acid (KA), 4-aminopyridine, pilocarpine, and bicuculline methiodide. Note, however, that in the studies conducted by [PERSON] et al. [60] and [PERSON] et al. [36], the effects of interictal epileptiform activity were investigated by infusion of bicuculline methchloride, inducing focal interictal spiking. Finally, nearly all studies employed an additional electrophysiological recording technique (local field potential recording, patch-clamp recording, or electroencephalography (EEG)) to confirm the induction of epileptiform activity and to correlate imaging data (structural) with electrophysiological data (functional) for temporal precision.
# Sialic Acids: An Important Family of Carbohydrates Overlooked in Environmental Biofilms
###### Abstract  
Sialic acids in the structural matrix of biofilms developing in engineered water systems constitute a potential target in the battle against biofouling. This report focuses specifically on the presence of sialic acids as part of the extracellular polymeric substances (EPS) of biofilms forming in cooling towers and the potential effect of nutrient starvation on sialic acid presence and abundance. Two cooling water compositions were compared in parallel pilot-scale cooling towers, one poor in nutrients and one enriched in nutrients. Fresh deposits from the two cooling towers were collected after a five-week operation period. EPS extractions and analyses by Fourier transform infrared spectroscopy (FTIR) and high-resolution mass spectrometry (MS), along with 16S rRNA gene amplicon sequencing were performed. The results of MS analyses showed the presence of pseudaminic/legionaminic acids (Pse/Leg) and 2-keto-3-deoxy-p-glycero-p-galacto-nononic acid (KDN) in both biofilm EPS samples. FTIR measurements showed the characteristic vibration of sialic acid-like compounds \(\
u\)(C=O)OH in the nutrient poor sample exclusively. Our findings, combined with other recent studies, suggest that bacterial sialic acids are common compounds in environmental biofilms. Additionally, the conservation of sialic acid production pathways under nutrient starvation highlights their importance as constituents of the EPS. Further in-depth studies are necessary to understand the role of sialic acids in the structural cohesion and protection of environmental biofilm layer.  
biofouling; industry; EPS; sialic acids; mass spectrometry; environmental biofilm; cleaning
# Drive-by Bridge Damage Detection Using Continuous Wavelet Transform
## 4 Parametric Study
### Multiple Damage  
Damage in bridges is not necessarily confined to one portion of the bridge, but, especially if created by environmental conditions, can occur simultaneously at different sections. To investigate the efficacy of the proposed method to detect damage at multiple locations, we simulated two scenarios where damage is introduced at two separate sections. In the first scenario, damage is simulated between 7 m and 8 m and also between 12 m and 13 m measured from the left-hand support. In the second scenario, the damage is between 7 m and 8 m and between 16 m and 17 m. In both scenarios, we focused only on 20% damage because detecting and locating this damage level ensures that higher damage levels can also be detected and located. The numerical analysis was then repeated for both scenarios with road roughness, and the residual accelerations on the vehicle were computed. The vehicle speed was 2 m/s. Figure 17 depicts the variation of the wavelet coefficients with location at a scale of 600.  
Figure 16: Wavelet coefficients at a scale of 600 for different boundary conditions.  
Comparing Figures 12 and 17, we can observe that multiple damage leads to two separate peaks (two each with positive and negative sign) with virtually the same amplitude in the wavelet coefficients when two separate sections of the bridge are damaged. On the other hand, only a one peak in the wavelet coefficients (one with negative and one with positive sign), that has a much higher amplitude, occurs when a single section of the bridge is damaged. Hence, presence of multiple damage can be detected using the proposed method in the form of multiple peaks in the wavelet coefficients.  
Further, the peaks, and more specifically the location where the wavelet coefficient crosses the horizontal axis between the negative peak and the positive peak clearly indicates the location of the damage.  
As such, we can confidently state that, for low vehicle speeds and relatively low road roughness levels, the proposed method can detect and locate multiple damage occurring concurrently on the bridge.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
### QKD Protocols  
A number of QKD protocols exist, each with their own strengths and weaknesses. BB84 is a notable protocol with many other variations similar to it, including B92, SARG04, SSP99, and some error correction and throughput increasing modifications. E91 is another notable QKD protocol based on quantum entanglement. In this section, we provide a review of the protocols.
# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 2 Related Work
### Mask-Detection Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Type \({}^{\dagger}\)**} & \multirow{2}{*}{**Mask Type**} & \multirow{2}{*}{**\#Images**} & \multirow{2}{*}{**\#Faces**} & \multicolumn{4}{c}{**Available Labels (Reported Only)**} \\ \cline{5-8}  & & & & & & **\#Mask** & **\#No Mask** & **\#Inc. Worn** & **Other \({}^{\ddagger}\)** \\ \hline MFDD [5] & IM & Real-world & 4343 & _n/a_ & 24,771 & 0 & 0 & _n/a_ \\ RMFRD [5] & IM & Real-world & 92,671 & 92,671 & 2203 & 90,000 & 0 & _n/a_ \\ SMFRD [5] & IM & Simulated & 500,000 & 500,000 & 500,000 & 0 & 0 & _n/a_ \\ MAA [18] & IM & Real-world & 30,811 & 36,797 & 35,806 & 991 & 0 & G, E, P \\ FMD [26] & IM & Real-world & 853 & 4072 & 3232 & 717 & 123 & _n/a_ \\ MMD [27; 28] & IM & Real-world & 6024 & 9067 & 6758 & 2085 & 224 & _n/a_ \\ FMD (ours) & AN & Real-world & 41,934 & 63,072 & 29,532 & 32,012 & 1528 & G, E, P \\ \hline \hline \end{tabular} \({}^{\dagger}\) IM—Image dataset, AN—Annotation dataset; \({}^{\ddagger}\) G—Gender, E—Ethnicity, P—Pose; _n/a_—Information not available.  
\end{table} 
Table 1: High-level comparison of the main characteristics of existing datasets with masked faces and the annotation dataset FMLD presented in this work. Please note that the number of reported images and annotations does not always correspond to the actual numbers publicly available online.
###### Abstract  
In the in situ leaching process of ionic rare earth ore, high-pressure moving water jet technology has been applied to improve floor leakage problems. However, it has been found that the morphology of the erosion surface formed by a moving water jet cannot be accurately predicted. The range of the erosion surface has an important influence on the stability of liquid collection engineering and the liquid collection rate of rare earth resources, which is an important basis for the design of liquid collection engineering. To study this, soil-breaking tests using high-pressure moving water jets for a Pingnan mine were conducted. The influences of jet pressure and nozzle moving speed on soil-breaking morphology were analysed. The mechanism of erosion surface formation was revealed by erosion theory, and a method of predicting the erosion surface geometry based on jet pressure and nozzle speed is proposed. The results show that, in the jet pressure range of 5-10 MPa, with increases in jet pressure and decreases in nozzle speed, the geometric shape of the erosion surface tends to expand, and these parameters have no effect on the morphological characteristics of the erosion surface. In the soil-breaking process, the erosion speed decreases with increases in jet distance. As the nozzle speed becomes greater, the degree of the attenuation of erosion speed at a given jet pressure also becomes greater. Soil-breaking depth has a quadratic relationship with jet pressure and a 0.5 power function relationship with nozzle speed. The geometry of the erosion surface has self-similarity and can be divided into straight and curved sections. Accordingly, an empirical formula of the erosion surface geometric shape is proposed. The error of predicting the soil-breaking depth is less than 11%, which can provide a reference for engineering applications.  
moving water jet; soil-breaking characteristics; erosion theory; soil-breaking depth; prediction equation +
Footnote †: journal: Applied Sciences