esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Testing of Key-Point Detection Model  
The verification set was selected as the model test object, the scatter plot of the key cross-section perpendicular to the \(Z\)-axis direction was selected, and the thickness of the data section was 10 mm. Then, the trained object detection model was used to identify the key points, and Figure 10 is one of the test results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Node Number** & **X-Coordinate** & **Y-Coordinate** & **Z-Coordinate** &
\begin{tabular}{c} **Velocity** \\ **Magnitude** \\ \end{tabular} & **X-Velocity** & **Y-Velocity** & **Z-Velocity** \\ \hline
1 & 0.726608 & 0.541836 & 0.028558 & 2.734538 & 2.509488 & 1.084060 & \(-0.055024\) \\
2 & 0.727234 & 0.542215 & 0.027279 & 2.845202 & 2.604854 & 1.14  
Figure 9: _Cont._  
It could be seen that the errors between the results from the key-point model and manual labeling were less than 2.5% (in Table 4). So, the model could be applied in practice.  
Figure 10: Test results of YOLOv8l model.  
Figure 9: The loss curves and mAP curves of the model training process. The \(x\)-axis is the number of iteration steps. (**a**) The loss curves and mAP curves of the YOLOv8n model training process, (**b**) the loss curves and mAP curves of the YOLOv8n model training process, (**c**) the loss curves and mAP curves of the YOLOv8m model training process, (**d**) the loss curves and mAP curves of the YOLOv8l model training process, and (**e**) the loss curves and mAP curves of the YOLOv8x model training process.
## 4 Methods: Experiments for Validation
### Experiment: Moving Source on a Circular Path  
Two PEPT experiments were performed of a tracer particle following a circular path. The first experiment was a button tracer with 60 \(\upmu\)Ci \({}^{18}\)F activity undergoing circular motion. The amplitude and frequency were 71.19 mm and 1.06 Hz, respectively. The circular motion  
Figure 3: Source positions used within the FOV for the validation tests. A button tracer was used with radiolabelled activity 60 \(\upmu\)Ci \({}^{22}\)Na. O lies at the centre of the FOV, while 1 and 3 lie on the \(X\) axis, 2 and 4 lie on the \(Y\) axis, and 5 and 6 lie on the \(Z\) axis of the scanner FOV.  
took place in the \(XY\) plane, with the centre of the circular motion at \(X=-1.30\) mm and \(Y=-0.54\) mm, and the listmode data were acquired with a 5.0 s duration. The source was formed as before with \({}^{18}\)F as the radionuclide. The tracer was attached using adhesive to a wooden rotating disc with a radius of approximately 70 mm. The second experiment was performed with an NRW-100 tracer of radius 400 \(\upmu\)m with 591 \(\upmu\)Ci activity of \({}^{68}\)Ga. The NRW-100 tracer underwent circular motion with an amplitude and frequency of 27.64 mm and 19.74 Hz, respectively. The circular motion took place in the \(XZ\) plane, with the centre of the circular rotation at \(X=1.14\) mm and \(Z=1.73\) mm, with a 0.5 s duration of listmode acquisition. The tracer was attached using adhesive tape to a wooden disc with an approximate radius of 30 mm. It should be noted that the parameters describing the circular motion were calculated from the sinusoidal fits described by the circular motion [46], to produce a series of locations derived from processing experimentally obtained LORs with PEPT-ML to achieve a location rate of approximately 1.0 kHz. These fit parameters for the circular motion were then used to define the ground truth in the corresponding simulations.  
During processing, additional oscillations were observed in the experimentally derived location data that likely arose from additional vibrations in the equipment. These were added to the model, and there is a short description of the implementation in Appendix B.
## 3 Results and Discussion
### Granular Activated Carbon
#### 3.2.2 Rapid Small-Scale Columns  
The RSSC test is a confirmed method used to predict the breakthrough on full-scale GAC columns [49]. The adsorption profile curve was performed for CarboTech DGF 8x30 GL and NRS Carbon 0.5-2.5 (Figure 6). The implementation of the kinetic models was necessary to compare both GACs properly.  
The curve fitting for the first- and second-order kinetics is presented in Figure S2 for both GAC types. The coefficient of determination (R\({}^{2}\)) was also calculated to select the best-fitting model. In the case of the first-order kinetic model, the R\({}^{2}\) was 0.992 and 0.999 for CarboTech DGF 8x30 GL and NRS Carbon 0.5-2.5, respectively. Contrary, the R\({}^{2}\) of the second-order kinetics were 0.884 and 0.897 for CarboTech DGF 8x30 GL and NRS Carbon 0.5-2.5, respectively (Table 2). The significantly higher R\({}^{2}\) of the first-order kinetics models evidenced their high accuracy for describing the adsorption process. Thus, the first-order kinetics model was selected to compare the two GAC types, and the second-order kinetics hypothesis was discarded. The constant values of the first-order kinetics model are shown in Table 2. They can be used as adsorption parameters to describe the adsorption process when different materials are compared. The calculated q shows the maximum amount of model compound adsorbed per mass of GAC; CarboTech DGF 8x30 GL is the one with the higher capacity.  
Our results are in line with previous ones, which reported that CarboTech DGF 8x30 GL was able to adsorb approximately 80 mg of model compound per gram of GAC, while NRS Carbon 0.2-2.5 adsorbs around 65 mg g\({}^{-1}\). Better fresh GAC performance was expected, as regenerated GAC usually has a lower efficiency [50]. However, even with a 15% lower q, regenerated GAC can be considered a suitable alternative for its lower environmental impact.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**CarboTech DGF 8x30 GL**} & \multicolumn{2}{c}{**NRS Carbon 0.5–2.5**} \\ \hline \multirow{3}{*}{First-order kinetic} & q & 86.38 & 73.21 \\  & K & 5.601 x 10\({}^{-4}\) & 6.069 x 10\({}^{-4}\) \\ \cline{1-1}  & R\({}^{2}\) & 0.992 & 0.999 \\ \hline \multirow{3}{*}{Second-order kinetic} & A & 0.477 & 0.479 \\ \cline{1-1}  & B & 0.016 & 0.018 \\ \cline{1-1}  & R\({}^{2}\) & 0.884 & 0.897 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constant values of the first- and second-order kinetic models.  
Figure 6: Modelled adsorption of indigo carmine in the RSSC under the first-order kinetics hypothesis.
# A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
###### Abstract  
In the practical scenario of construction sites with extremely complicated working environment and numerous personnel, it is challenging to detect safety helmet wearing (SHW) in real time on the premise of ensuring high precision performance. In this paper, a novel SHW detection model on the basis of improved YOLOv3 (named CSYOLOv3) is presented to heighten the capability of target detection on the construction site. Firstly, the backbone network of darknet53 is improved by applying the cross stage partial network (CSPNet), which reduces the calculation cost and improves the training speed. Secondly, the spatial pyramid pooling (SPP) structure is employed in the YOLOv3 model, and the multi-scale prediction network is improved by combining the top-down and bottom-up feature fusion strategies to realize the feature enhancement. Finally, the safety helmet wearing detection dataset containing 10,000 images is established using the construction site cameras, and the manual annotation is required for the model training. Experimental data and contrastive curves demonstrate that, compared with YOLOv3, the novel method can largely ameliorate mAP by 28% and speed is improved by 6 fps.  
YOLOv3; cross stage partial network (CSPNet); safety helmet wearing (SHW) detection; spatial pyramid pooling (SPP); feature fusion Article
## 5 Modulation of a Femtosecond Ti:sapphire Oscillator
Here we follow the process up to three Stokes and anti-Stokes sidebands, _i.e._, the final spectrum includes contributions from \(F(\
u-3\
u_{M})\), \(F(\
u-2\
u_{M})\),..., \(F(\
u+3\
u_{M})\). In this calculation, we assume 60% conversion efficiency to each sideband. typical femtosecond oscillator (1 nanojoule energy per pulse, 20 femtosecond pulse width, 100 MHz repetition rate). For many applications such as quantum control, nanojoule per pulse level energies will not be sufficient. For such applications, an amplified Ti:sapphire system (larger than 1 microjoule energy per pulse, about 10 kHz repetition rate) can be used as a mixing laser. The peak power of such lasers is quite high, and these pulses would interfere with the molecular state preparation by the pump and the Stokes laser beams. To overcome this problem, one idea would be to chirp the pulse so that its peak power is greatly reduced. In the spirit of the technique of _chirped pulse amplification_, peak power can be dramatically reduced by stretching the pulses to picosecond times scales. Also, for modulating amplified Ti:sapphire systems, pulsed molecular modulators such as those utilizing photonic crystal fibers may be used [57]. We conclude this section by noting that there are other schemes that can be used to broaden the spectrum of a Ti:sapphire laser. Early experiments used nonlinear self-phase modulation inside a photonic crystal fiber to broaden the Ti:sapphire spectrum [58,59]. Recently, multi-octave broadband spectra and single-cycle pulse synthesis have been demonstrated using a number of approaches including coherently combining the supercontinuum output from multiple highly nonlinear fibers and using multiple optical parametric amplifiers seeded by a common laser source [5, 6, 7, 8]. These are exciting developments that may eventually allow for the synthesis of arbitrary optical waveforms. However, we believe that the simplicity and the coherent nature of the molecular modulation process make it the most promising route for constructing an arbitrary optical waveform generator. As mentioned before, the molecular modulation technique was the first to synthesize single-cycle pulses and this technique still holds the world record for the shortest pulses that have ever been synthesized in the optical region of the spectrum [18, 19, 20].
## 4 Case Studies
### Reliability Uncertainty Analysis on an AEPS  
#### 4.2.1 Case Description  
The AC subsystem of the AEPS shown in Figure 1 was taken as an example to illustrate the proposed method. The AC subsystem supply power to the AC electrical equipment mainly through two sink nodes: LG BUS and RG BUS. The sink node LG BUS has three minimal path sets: {LG BUS,LGB,LG}, {APUG,APUGB,APUG BUS,BTB1,LG BUS}, and {RG,RGB,RG BUS,BTB2, APUG BUS,BTB1,LG BUS}; the sink node RG BUS also has three minimal path sets: {RG, RGB,RG BUS}, {APUG,APUGB,APUG BUS,BTB2,RG BUS}, and {LG,LGB,LG BUS, BTB1,APUG BUS,BTB2,RG BUS}. Eleven components are involved in these six minimal path sets, which are LG, LGB, LG BUS, BTB1, APUG BUS, APU GB, APUG, BTB2, RG BUS, RGB, and RG. For the convenience of description, the components are numbered from 1 to 11. The failure rate of each component obeys a triangular distribution and the parameters are shown in Table 3. In aircraft engineering, by taking the general changes of flight conditions into consideration, different experts may have different opinions on the exact value that the component's failure rate should be, but they always have a consensus on the order of magnitude of the failure rate. In addition, triangle distribution is always widely used in depicting a random variable that one only knows its maximum, minimum, and the most likely value. Therefore, we used the component's failure rate as a variable that obeys the triangle distribution, where the mode parameter \(\lambda_{mode}\) is the value that is repeated most often in [14; 17], and the upper limit parameter \(\lambda_{upper}\) and lower limit parameter \(\lambda_{lower}\) is the maximum and minimum value in the magnitude order of \(\lambda_{mode}\). Since RG BUS and LG BUS are completely symmetrical in structure, the power supply reliability of them is equal, and the contribution of component failure uncertainty to the power supply reliability uncertainty of the sink node LG BUS is discussed here.
## 3 Results
### ROS Generation in HepG2 with and without Exposure GNPs  
ROS generation was also examined in HepG2 cells, when exposed to GNPs at different concentrations (25, 50, and 100 \(\mu\)g/mL) for a 24 h incubation period (Figure 7). Compared to control, ROS levels increased in cancer cells, once exposed to GNPs-1, GNPs-2, and GNPs-3, as shown in Figure 7. At 25 \(\mu\)g/mL, ROS generation was increased to 123%, 156%, and 194% in HepG2 cells, respectively; at 50 \(\mu\)g/mL, ROS generation upsurged to 116%, 138%, and 164% in HepG2 cells, respectively. At a higher concentration (100 \(\mu\)g/mL), ROS generation increased to 110%, 128%, and 153% in HepG2 cells, respectively, as compared with controls (Figure 7).  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Concentrations**} & **GNPs-1** & **GNPs-2** & **GNPs-3** \\  & **(\% Cell Viability)** & **(\% Cell Viability)** & **(\% Cell Viability)** \\ \hline Control & 100.00 \(\pm\) 2.23 & 100.00 \(\pm\) 2.93 & 100.00 \(\pm\) 6.13 \\ \hline
1 \(\mu\)g/mL & 101.91 \(\pm\) 1.82 & 102.00 \(\pm\) 2.89 & 100.86 \(\pm\) 6.44 \\ \hline
2 \(\mu\)g/mL & 96.39 \(\pm\) 5.02 & 98.70 \(\pm\) 2.49 & 99.02 \(\pm\) 5.49 \\ \hline
5 \(\mu\)g/mL & 86.86 \(\pm\) 6.96 & 90.56 \(\pm\) 3.64 & 94.77 \(\pm\) 1.60 \\ \hline
10 \(\mu\)g/mL & 69.08 \(\pm\) 1.00 & 83.50 \(\pm\) 3.92 & 87.35 \(\pm\) 2.88 \\ \hline
25 \(\mu\)g/mL & 61.87 \(\pm\) 2.62 & 77.35 \(\pm\) 1.86 & 79.58 \(\pm\) 4.65 \\ \hline
50 \(\mu\)g/mL & 48.23 \(\pm\) 2.06 & 53.79 \(\pm\) 2.23 & 67.89 \(\pm\) 2.21 \\ \hline
100 \(\mu\)g/mL & 17.05 \(\pm\) 0.37 & 46.49 \(\pm\) 1.52 & 55.94 \(\pm\) 2.17 \\ \hline \end{tabular}
\end{table}
Table 1: Comparative anticancer activity of gold NPs against liver cells by MTT assay.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Concentrations**} & **GNPs-1** & **GNPs-2** & **GNPs-3** \\  & **(\% Cell Viability)** & **(\% Cell Viability)** & **(\% Cell Viability)** \\ \hline Control & 100.00 \(\pm\) 3.24 & 100.00 \(\pm\) 5.97 & 100.00 \(\pm\) 5.31 \\ \hline
1 \(\mu\)g/mL & 100.48 \(\pm\) 3.95 & 102.16 \(\pm\) 5.97 & 100.97 \(\pm\) 5.12 \\ \hline
2 \(\mu\)g/mL & 98.84 \(\pm\) 1.93 & 99.35 \(\pm\) 1.46 & 100.19 \(\pm\) 4.07 \\ \hline
5 \(\mu\)g/mL & 89.95 \(\pm\) 0.69 & 91.57 \(\pm\) 4.77 & 98.95 \(\pm\) 4.80 \\ \hline
10 \(\mu\)g/mL & 77.34 \(\pm\) 3.20 & 85.02 \(\pm\) 1.80 & 95.31 \(\pm\) 3.45 \\ \hline
25 \(\mu\)g/mL & 63.03 \(\pm\) 4.46 & 76.49 \(\pm\) 2.32 & 83.26 \(\pm\) 2.19 \\ \hline
50 \(\mu\)g/mL & 51.46 \(\pm\) 2.35 & 57.54 \(\pm\) 5.74 & 66.60 \(\pm\) 3.14 \\ \hline
100 \(\mu\)g/mL & 15.10 \(\pm\) 0.52 & 47.66 \(\pm\) 3.46 & 49.54 \(\pm\) 3.21 \\ \hline \end{tabular}
\end{table}
Table 2: Comparative anticancer activity of gold NPs against liver cells by NRU assay.
# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 3 Materials and Methods
### Network Model
The Fog control nodes will estimate their own cost and find the cost of all possibly linked neighbouring candidate Fog access nodes with a minimum number of hops between the Fog control nodes and the corresponding candidate Fog access nodes.
# Bioremediation Methods for the Recovery of Lead-Contaminated Soils: A Review
## 1 Introduction  
Lead is one of the major contaminants present in soil on a global scale [1; 2], and numerous studies have pointed out its harmful effects on the environment and human health. Its occurrence in the environment is due to mineral rock weathering and anthropogenic sources, with the latter including the extraction and melting of metals, the combustion of coal, industrial activities, atmospheric deposition, agriculture, and waste disposal [3]. The production of arsenate-based insecticides, batteries, pigments, and fertilizers [4; 5] contributes significantly to the lead pollution of soils. On the other hand, atmospheric deposition is closely related to past use of tetraethyl lead (TEL) as an additive in gasoline [5] and to fuel combustion to produce heat and electricity [6].  
Lead is present in soil in various forms, characterized by different mobility, toxicity, and bioavailability. Metal bioavailability accounts for the amount of element available for absorption by living organisms such as humans, animals, and plants [7] and is strictly connected to the metal chemical form. In turn, its chemical form in soil is affected by numerous processes, e.g., precipitation and dissolution, ion exchange, absorption and desorption, complexation, immobilization and biological mobilization, and absorption by plants. Lead is frequently detected in soil as ionic lead (e.g., Pb(II) in the form of PbSO\({}_{4}\)), oxides and hydroxides, and lead-metal oxyanion complexes [8]. Phosphates, carbonates (at pH above 6), hydroxides/oxides, sulfides, and pyromorphites are some of the most stable and insoluble forms in which lead occurs in soils in a wide range of pH values [9; 10].  
In view of the widespread lead pollution and the threat posed to the environment and human health, there is growing attention on the recovery or reduction of its concentration in soils. This reviewaims to focus on the different bioremediation strategies applied so far and will consider various studies concerning both the use of plants for phytoremediation and the exploitation of fungi and bacteria for the removal or stabilization of lead in the soil. Indeed, several processes have been extensively studied in recent years, with the aim of replacing traditional chemical and physical methods and implementing the use of environmentally friendly techniques to restore soils.
###### Abstract  
Underwater sensor networks (UWSN) include a large number of devices and sensors which are positioned in a specific area to carry out monitoring in cooperation with each other as well as data collection. In this paper it has been studied and simulated the performance of an extremely important parameter for communication in UWSN such as the acoustic channel capacity as function of water temperature and salinity arise. The performance's knowledge on acoustic channel may be improved with a deep study of its dependence by season, weather conditions or environmental parameters variation. If an accurate estimation of the acoustic communication capacity utilization for a given area is required, we must consider also the bottom materials of this area. The simulation results presented in this study through an improved algorithm, will help to understand better the underwater acoustic channel performance as a function of all these factors. This is of particular importance for acoustic modems designing, in order to implement suitable functionalities able to adapt the data transmission capacity of the acoustic link to the structure of the oceanic bottom and its component material.  
shallow; bottom material; capacity; lifetime; propagation; acoustic channels; package size 1
# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
###### Abstract
During the COVID-19 pandemic, the growing body of evidence for disease-specific patterns in lung US, mostly B-lines and pleural line irregularities, has led to the advocacy for an amplified role of LUS [22,23,24,25]. It was argued that LUS could be performed routinely in COVID-19 suspects and become part of the diagnostic toolkit for COVID-19 differential diagnosis [23,24,26,27]. Notably, in COVID-19 patients, LUS has a higher diagnostic sensitivity than CXR [28]. Moreover, radiologists reported inter and intra-observer agreement between US and CT findings [29,30]. Some studies found the diagnostic accuracy of LUS for COVID-19 to be _on par_ with CT [31,32], and even more sensitive in detecting pulmonary imaging biomarkers [10]. Hence, in triage or resource limited settings, LUS can be a valuable technique [33,34] and serve as a globally available first-line examination method to guide downstream testing [35]. Despite this encouraging evidence, US is not (yet) widely adopted in clinical practice for cardiac or lung imaging and its wider deployment is hampered for a multitude of reasons such as operator-dependent acquisition [36]. Notably, a recent \"perspective\" paper in _The Lancet_ has pointed out the adoption of deep learning (DL) technologies for the guidance and interpretation of US images as a major challenge for the digitation of medicine [26] Citing: \"Theoretically, if these (POCUS) devices were widely used, rapid, point-of-care imaging could become routine and reduce the need for formal studies in a dedicated radiology suite. But that shift of adoption is predicated on the ability of clinicians to become facile in obtaining high-quality ultrasound scans. To date, that has not occurred for various reasons including cost of the devices, issues for reimbursement, and difficulty with performing image capture, which has historically been a specialised task for sonographers\". Moreover, the relevant LUS pattern can be hard to discern, requiring time and trained personal [37], This calls into play medical image analysis systems based on machine learning (ML) which aim to be utilized as clinical support tools for physicians that aid data acquisition, patient diagnostics or monitoring. Here, we release a novel LUS dataset with diverse pulmonary manifestations and perform a first study on the automatic detection of lung pathologies for differential diagnosis.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
###### Abstract  
Automatic static analysis tools (ASATs), also known as static analyzers, have demonstrated their significance and practicability in detecting software defects. ASATs assist developers to identify potential vulnerabilities, errors, and security hazards in source code without executing the software. As software systems grow in scale and complexity, ASATs are replacing manual security audits and becoming crucial for detecting issues in code. However, ASATs often generate numerous warnings with high false positive rates, while developers typically only take measures on a small portion of actionable alarms. To cope with this problem, we propose an innovative method that combines the pre-trained CodeBERT model and neural networks to reduce false positives detected by ASATs. Our approach was evaluated on the Defects4J dataset, which comprises 835 real-world software defects extracted from 17 open-source Java projects. The experimental results explicitly manifest the effectiveness in processing static analysis alarms. By employing a bidirectional recurrent neural network for context embeddings, our approach achieved an accuracy of 95.77% and an AUC score of 98.3%. This research enables developers to minimize false positive alarms and ensure a reasonable number of actionable warnings while guaranteeing software quality and security.  
static analysis; false alarms; deep learning +
Footnote †: journal: Applied Sciences
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 4 The Optimization Algorithm: Ad-BBR  
In this paper, we propose the Ad-BBR algorithm to address these issues by introducing a novel minimum RTT determination mechanism, determining the link state based on the minimum RTT, formulating finer-grained rules for adjusting transmission rates according to different link states, and modifying the trigger logic of the ProbeRTT phase. This approach aims to mitigate over-injection effects, improve the fairness and competition between long and short RTT streams, and enhance the stability of data transmission. Consequently, the fairness between long and short RTT streams is improved, and data transmission stability is enhanced.  
Figure 3: A comparison of the theoretical optimal operating points and the actual operating points for flows with varying RTTs.