esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Simulation Results and Analysis
#### 4.2.3 Bytes Overhead  
In Figure 14, we assess the routing overhead of the three protocols relative to vehicle density. The routing overhead is quantified in bytes and is computed as the cumulative sum of control packet bytes generated between relay nodes on a road segment. It is expressed as a ratio by comparing the data packet delivery ratio to the size of the control packet bytes. A lower byte count signifies reduced overhead.  
\[\text{Bytes Overhead}=\frac{\text{Hello Packet}+\text{Unicast Routing Control Pakcet}+\text{Data stored in buffers}}{\text{Data packet delivery rate}} \tag{10}\]  
Figure 13: Comparison between proposed GSR, GyTAR, and MJTAR for end-to-end delay; (**a**) number of vehicles (5 packets/s); (**b**) speed of vehicles (300 nodes and 5 packets/s); (**c**) packet size (300 nodes and 5 packets/s).  
As depicted in Figure 14, all three protocols exhibit a proportional increase in the percentage of control packets generated with the number of nodes. However, MJTAR demonstrates lower routing overhead than the other protocols. GSR tends to incur higher routing overhead due to its increased generation of Hello packets to gather neighbor position information. Conversely, MJTAR minimizes routing overhead by exclusively acquiring position information within its cell area through the E-IFITS mechanism. Moreover, GSR's tendency to select roads with lower traffic density, in contrast to the MJTAR protocol, leads to a higher frequency of Hello packets due to frequent recovery mode activations. Consequently, the frequency of Hello packets generated by GSR is approximately three times higher than that of MJTAR.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 4 Experiments and Results
### Pre-Training  
Convolutional neural network models are complex with a large number of parameters and require a large amount of labeled data for training. The size of our constructed dataset was much smaller than the dataset with millions of images. Therefore, we borrowed the idea of migration learning to train a neural network using other large image datasets and trained a new model based on the parameters of the trained model. The YOLOv4 model was pre-trained using the ImageNet dataset [27], and since the backbone network of the improved model was the same as the YOLOv4 model, the parameters of the backbone part of the pre-trained model were used as initialization parameters instead of random initialization for training, which effectively improved the model convergence speed.
## 5 Results and Discussion
### Results of Sensitivity Analysis
When the smoothing level increased from 0 to 5, the RMSE of change date, the RMSE of the number of changes, and omission errors decreased rapidly, indicating greater accuracy of the detected change date and number of changes; meanwhile, the MSE of the number of changes increased from negative to positive, or from underestimation to overestimation, and the number of false changes increased. When the smoothing level increased from 6 to 20, the RMSE of change date rebounded to the values without smoothing, the RMSE of the number of changes increased to greater values, and the MSE of change date became more negative, while the other indices had little variation. Briefly, the smoothing process at low or intermediate level (_i.e._, level \(\leq 5\)) improved the accuracy of the detected changes with sacrifice of false changes, but too much smoothing reduced the accuracy of the detected change date and did not improve the accuracy of other indices. Figure 5: The influence of noise and change intensity on the RMSE of change time (**a**); RMSE of number of changes (**b**); and omission errors (**c**) of simulated time series, and the influence of noise on stable time series represented by the ratio of false change calculated for Classes 1–6 (**d**). In each plot, the X-axis represents the standard deviation (\(\sigma\)) of white noise added to the time series.
## 2 Literature Review
### Evolution of the Digital Twin Concept  
The Digital Twin (DT) concept dates back in 2002 when the idea of a virtual space containing the information of and linked to the real space emerged in the field of study of complex systems, in particular regarding the Product Lifecycle Management (PLM) [19]. When the concept emerged, it was not referred to as DT, but it was presented as the \"Conceptual Ideal for PLM\" [19], evolved then to Mirrored Spaces Model in 2005 [47] and to Information Mirroring Model in 2006 [48] and 2011, when also the term Digital Twin was first used to describe the model [49]. In recent years, the concept of a DT has been studied also in the aerospace sector: the DT represents an ultra-realistic digital replica of real flying vehicles, considering one or more interconnected systems allowing for probabilistic simulations that take into account physical characteristics and models, sensor data, and history of previous flights and vehicles [50; 51; 52]. Recent definitions of DTs can be found in various sectors, with a wide use and diffusion of the concept of a virtual replica of physical entities whose purpose is to manage, optimize, and control the physical asset itself. In the infrastructure sector, DT was defined as a realistic virtual representation of the corresponding infrastructure, adding the built or natural context in which the object is contained and to which it is connected [53]. In the manufacturing sector, the idea of the connection between physical components and virtual models is widened, adding the necessary mono- or bi-directional flow of data between the physical asset and its virtual counterpart in order to real-time monitoring the actual object, supporting simulations, analytics, and control capabilities of the dynamic virtual model [54]. The construction industry can be still considered in its beginning regarding the definition of a DT for buildings. Despite the various attempts to define a DT in construction industry [21; 24; 53; 55; 56], a comprehensive definition was proposed by [PERSON] and [PERSON] [57]: \"an approach for connecting a physical system to its virtual representation via bidirectional communication (with or without human in the loop) using temporally updated Big Data [...] to allow for exploitation of Artificial Intelligence and Big Data Analytics by harnessing this data to unlock value through optimization and prediction of future state\". This definition includes all the fundamental parts of a DT, which are described in detail in the following paragraph.
# IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance
## 3 Results  
### Dose Linearity and Reproducibility  
Results for the detector's performance in terms of dose linearity are shown in Figure 3. After an appropriate dose calibration, dose readings resulted to be linear with respect to delivered MUs in the full range considered. The linear fit of the MUs-dose plot shows a squared correlation coefficient of 1. Moreover, the 100 MU dose reproducibility test resulted in a recorded dose average value of 99.8 cGy with a standard deviation of 0.2 cGy.  
Figure 3: Dose linearity test performed delivering 10–5000 MU with the 60 mm reference collimator. Dashed line is the linear fit (\(y=0.9998x+0.8147\), R\({}^{2}=1\)) of the collected points.
## 1 Introduction  
In general, in the event of a chatter, severe vibration is found for tools and workpieces, causing diverse damages such as quality deterioration, decrease in tool lifespan, and machine damage with a chatter mark on the surface. Therefore, there have been many attempts to avoid such self-excited vibration [1]. Previous studies have performed chatter detection based on diverse signals such as signal measurement using a tool dynamometer [2, 3], and analysis of sound-pressure signals with a microphone [4]. In terms of a chatter detection method, analysis of signal frequency has been primarily used [5, 6]. Many other methods have also been suggested, including amplitude detection using the standard deviation of sound-pressure signals [4]. However, there are limitations in applying a tool dynamometer with relatively high signal reliability. In terms of detection through analysis of sound-pressures signals, various issues such as operator/motor noise can occur. To check a chatter frequency range through frequency analysis, a modal analysis must be performed in advance, using related techniques such as an impact hammer. For this, information on changes in machining environments (e.g., materials, tools, cutting conditions, acceleration/deceleration, supplementary axis) is necessary [1, 7]. Therefore, these methods have some difficulties in actual application. Recently, a cutting force-based optimization software has been used for optimization [8, 9]. In fact, machining efficiency and accuracy have been considerably improved [10; 11; 12; 13; 14]. However, this method cannot perfectly predict cutting dynamics such as tool wear, material non-uniformity, chatter vibration, and spindle deformation during machining. In particular, effects are minor in mass-production or automotive parts in which products with the same shape are being processed consecutively.
## 6 Discussion  
The preliminary test presented in the last section shows the empirical relation between the size of the sampling set based on the reference focus map, the overall length of the survey (in km), and the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Points**} & \multicolumn{3}{c}{**Route Length (km)**} & \multicolumn{3}{c}{**Covered Buildings**} & \multicolumn{3}{c}{**Covered Policies**} \\  & \multicolumn{3}{c}{**(30-m Buffer)**} & \multicolumn{3}{c}{**(30-m Buffer)**} \\ \cline{2-10}  & **Focus** & **Rand** & **\% Diff** & **Focus** & **Rand** & **\% Diff** & **Focus** & **Rand** & **\% Diff** \\ \hline
**50** & 38.5 & 42.1 & \(-\)9\% & 3629 & 2128 & 70\% & 6098 & 3460 & 76\% \\
**100** & 53.7 & 63.2 & \(-\)16\% & 5279 & 4685 & 13\% & 8955 & 8161 & 10\% \\
**200** & 70.8 & 78.3 & \(-\)10\% & 6414 & 58 expected visual coverage in terms of buildings and, more importantly in this application, earthquake insurance policies. By developing such empirical models, a complete optimization and tuning of the field surveys is possible, accounting both for the users' priorities encoded into the focus map and the constraints related to available resources and environmental conditions.  
Looking at the values in Table 7 and the plots in Figure 15, it can be noted that the \"focused\" sampling is consistently more efficient than the \"random\" sampling; in the latter case, the route length, for instance, is on average longer.  
Under the same conditions, the focused survey was able to systematically capture more buildings with respect to a random survey. Even stronger is the comparison between the policy coverage.  
The focused survey therefore always outperformed the random survey in the considered tests. We have to stress that these are preliminary evaluations, based on a small number of realizations (four realizations related to different sampling sets). More thorough testing should be undertaken (and is envisaged), but the current outcomes already provide an indication of the potential of the proposed approach. These results are not surprising, considering that the computed focus map increases the inclusion probability of the locations with higher density of buildings and policies.  
The relationship between the route length and the number of the covered policies is expected to be linear in the focused case using a PPS sampling scheme (since is linearly depending on the density of buildings and policies, which we suppose as roughly constant). This is easily verified and is visible in Figure 15 (lower).
## 4 Results and Discussions
Figure 6:7 Effect of computational complexity on V2V link quality. Furthermore, the proposed method can improve the computational issues of connected cars in complex and dense environments, especially in underdeveloped nations where incidents occur in large numbers. Moreover, the beam management strategy, which takes into account the power density, significantly reduces power losses. The potential of using an omnidirectional antenna is also explored to assess the challenges faced by switched beam antennas. The proposed algorithm is competitive in various environments and conditions, as demonstrated by different parametric and evaluation metrics. Additionally, the anti-failure system that directly relates to the asphalt value is a crucial aspect of safety.
## 4 Results and Discussion
### Fitting Parameter Comparison
During nighttime, the absence of solar forcing and the reduced working activities contribute to mitigation of the heating intensity and to modification of the SUHI footprint and orientation of BG, as also visible in BG panels of Figure 5.  
The same behavior is found for CM, where the daytime direction of the SUHI footprint along the NE-SW quarters follows the layout of residential and commercial zones [40]. During the nighttime, there is a significant cooling effect of the urban core and outskirts, causing a reduction of Ar61%, a0, and a \(\varphi\) modification, as also noticeable in CM panels of Figure 5.  
Figure 10 shows the central location (x\({}_{0}\), y\({}_{0}\)) of the Gaussian surface expressed as distance from to the center of the map (0, 0); the map center roughly corresponds to the city center. Except for BG, whose urban extension is considerably wider than the other cities, the mean shift of the center of the Gaussian footprints (with respect to the map center) is small both seasonally and daily. For BG, (x\({}_{0}\), y\({}_{0}\)) moves always towards southeast, with a displacement of the order of 5-10 km in daytime. This central location is positioned in the core of the high-density residential and commercial zone, also including government institutes and public utility zones [28; 39]. At nighttime, the BG footprint shift is pronounced in the south direction, indicating the permanence of an urban heating towards the coastal zone. This fact could suggest a nocturnal sea influence, as discussed in [41]. Figure 9: Intercity comparison of orientation angle \(\varphi\) (degree) during summer (**upper panel**) and winter (**bottom panel**); \(\varphi\) is averaged over the 14 years, considering the diurnal variation at 10:00, 14:00, 22:00, and 02:00. The black bar is the standard deviation across the years.
## 3 Results
aeruginosa_ biofilms were statistically significant for both contact times and antiseptics applied (Kruskal-Wallis test with post-hoc [PERSON]'s analysis, \(p<0.0001\)), while no significant differences in antibiofilm activity were observed within each of two aforementioned clusters of oral pathogens (Kruskal-Wallis test with post-hoc [PERSON]'s analysis, \(p>0.0001\)). Regarding the comparison between the antibiofilm activities of specific antiseptics, there were no statistically significant differences between P versus PO and PO versus B against the tested pathogens (Kruskal-Wallis test with post-hoc [PERSON]'s analysis, \(p>0.0001\)). In the case of B versus P, differences were also insignificant with the exception of _C. albicans_ biofilms, where B displayed a significantly higher activity in both contact times (Kruskal-Wallis test with post-hoc [PERSON]'s analysis, \(p<0.0001\)). Next, as a proof-of-concept, we evaluated the potential applicability and adhesiveness of BC carriers to various specific surfaces of the oral cavity. To do so in in vitro conditions, we used oral cavity typodont models and raw bovine loin tissue. Results are presented in Figures 9 and 10, respectively. BC antiseptic dressings were easily modeled and stuck firmly to various surfaces of the oral cavity model and bovine tissue. Moreover, as can be seen in Figures 9 and 10, BC dressings that were saturated with colorless antiseptic (e.g., P, PO-BC) displayed a high transparency. Results presented in Figures 9 and 10 may raise questions concerning the mechanical strength and elasticity of BC dressings regarding their potential applicability within the oral cavity. The mechanical strength and elasticity modulus of BC dressings used in experiments presented in this manuscript was \(2.00\pm 0.86\) and \(26.18\pm 9.25\) MPa, respectively. Figure 8: Percentage reduction of biofilm-forming cells from HA discs after 30 min of contact time. B, PO, and P stand for Braunol, ProntOral, and Prontosan, respectively. Aa, Ef, Sa, Pa, and Ca stand for _A. actinomycetemcomitans_, _E. faecalis_, _P. aeruginosa_, and _C. albicans_, respectively. Figure 10: Adherence of BC dressings to bovine tissue. Uneven surfaces of bovine tissue are marked with green, yellow, and red arrows. (**A**)—Bovine tissue with uneven surfaces. (**B**,**C**)—BC dressings chemisorbed with polihexanidine adhered to challenging surfaces of bovine tissue. (**D**)—BC dressings chemisorbed with PVP-iodine adhered to challenging surfaces of bovine tissue. Figure 9: Proof-of-concept of applicability of BC antiseptic dressings for periodontal applications. (**A**,**B**)—Covering of oral cavity tissues in a typodont with B-BC dressings; 1: folded B-BC dressing fits and adheres to vestibular oral mucosa; 2: hooked B-BC dressing covered firmly over the teeth and gingiva. (**C**,**D**)—Multiple applications in the oral cavity with BC dressings of various sizes; 3,4,5,8,9,13—smaller BC dressings adhered to uneven surfaces; 6,7,11,12—larger BC dressings were retained by teeth and covered more area. Note the transparency of the 3–13 BC dressings.
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 3 Atomization Processes  
Table 2 briefly describes the different types of atomization processes and its characteristics.  
Figure 4 represents the different types of commercially used atomizers, the device used for the atomization process to develop atomized sprays for various applications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & **Energy Source** & **Mechanism** & **Class of Spray** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Hydraulic} & \multirow{4}{*}{Fluid pressure} & Pressure exerted on the fluid & & & \\  & & drives it through the nozzle to & & & Not relevant for \\  & & generate fluid sheets with & & & \\  & & high velocity and leading its & & & \\  & & disruption to fragments and & & rough & drying & \\  & & then to droplets by friction & & & \\  & & between liquid and air & & & \\ \hline \multirow{4}{*}{Pneumatic} & \multirow{4}{*}{Air pressure} & Fluid at low velocity passing & & & \\  & & through the nozzle is & & & \\  & & surrounded by high velocity & & & \\  & & flow of air, boosting friction & & average roughness & & \\  & & between two medium causing & & & \\  & & disruption of fluid sheet & & & \\ \hline \multirow{4}{*}{Rotary} & \multirow{4}{*}{Centrifugal force} & \multirow{4}{*}{Atomizer has a spinning disc  at the center to which the fluid  is introduced through the  nozzle; centrifugal force takes & & Uniform droplets, & Expensive, not \\  & & the fluid to the margin of the & & & \\  & & disc and flip off the boundary & & & \\  & & setting up ligaments that then & & & \\  & & breaks into droplets & & & \\ \hline \multirow{4}{*}{Electrostatic} & \multirow{4}{*}{Electric charge} & \multirow{4}{*}{An electric field applied  between atomizer and  workpiece to make it  conductive; fluid passed  through the electric field and  the repulsive force disrupt the & & Fine \& uniform & \\  & & fluid into droplets and is & & & \\  & & gathered at the workpiece & & & \\ \hline \multirow{4}{*}{Ultrasonic} & \multirow{4}{*}{Electromechanical device} & Fluid is passed through a  vibrating electromechanical  device causing the disruption & & & \\  & & of the fluid into droplets & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & Fluid is passed through a  vibrating electromechanical  device causing the disruption & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & of the fluid into droplets & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different atomization processes and its characteristics; Based on [1; 47; 48; 49; 50; 51; 2].
# Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
## 2 Materials and Methods
### Technical Analysis
An Optical Parametric Oscillator (OPO) as the oscillator and a Hot-electron Bolometer (HEB) as the detector were selected as the best solution for the setup. An OPO generates short pulses with an amplitude up to several watts with tunability over 2 THz. Above about 1 THz, superconductor-based Hot-electron Bolometers offer the best parameters in terms of the sensitivity and speed [35; 36; 37]. Despite such undisputed advantages, the THz technology is rather bulky and is in constant development.
## 4 Discussion of the Fault-Diagnosis Results
### Performance under Variable Load  
When the aircraft is in flight, the EMA's workload changes according to the work tasks. Accordingly, the current and angle of the motor and the position response of the EMA will change, so will the signal measured by the sensor. Figure 13 shows the normalized status signals of the EMA under different loads.  
As can be seen from Figure 13, there are certain differences in signal waveform and amplitude under different loads, and the greater the load changes are, the more obvious the differences will be, which will make the classifier unable to correctly classify the extracted features, thus reducing the accuracy of the intelligent diagnosis system. Therefore, it is of great practical significance to use the data under a single load to train the model and then use the trained model to fault diagnose the signal when the load changes.  
In this section, the MSFFCNN is trained with data under loads of 0.3 N\(\cdot\)m, 0.5 N\(\cdot\)m, and 0.7 N\(\cdot\)m, respectively, and then, the signals under the other two loads are used as the test set. Data acquired under 0.3 N\(\cdot\)m, 0.5 N\(\cdot\)m, and 0.7 N\(\cdot\)m loads are defined as datasets A, B, and C respectively. After combination, there are six test conditions, namely A\(\rightarrow\)B, A\(\rightarrow\)C, B\(\rightarrow\)A, B\(\rightarrow\)C, C\(\rightarrow\)A, and C\(\rightarrow\)B. Moreover, in order to verify the reliability of the results, MSCNN, WDCNN, and 1 DCNN are compared as the benchmark models. The variable load test results are shown in Figure 14.  
Figure 13: Normalized signals of the EMA under different loads.  
As shown in Figure 14, it was found that the diagnostic performance of 1 DCNN and WDCNN are comparable under six variable load conditions, with an average diagnostic accuracy of about 85%. In contrast, the average diagnostic accuracy of the MSCNN model is nearly 5% higher than the previous two models, indicating that multi-scale feature learning has strong adaptability to variable load. The average diagnostic accuracy of the MSFFCNN proposed in this paper is about 95% under six variable load conditions, which is 10% higher than that of 1 DCNN and WDCNN. To summarize, it can be shown that MSFFCNN has strong self-adaptive ability of variable load and can adapt to variable working environments.