esa-sceva/satcom-chunk-collection huggingface dataset:
# Occurrence of Fungi on Duck Egg Shells and Drug Resistance Analysis of _Aspergillus_ spp. Isolates
## 1 Introduction  
Fungi are a typical part of the microbiome of poultry houses. They are present in the litter, feed and the organic dust emitted by farms. Some of the fungal genera, such as _Aspergillus_ or _Candidia_, may cause poultry and human infections [1, 2]. Aspergillus is one of the most common respiratory infections in poultry. Out of all animals, birds are the most susceptible group, due to their anatomic particularities such as the absence of an epiglottis and limited number of ciliated epithelial cells in the respiratory system, which allows the conidia of these fungi to directly reach the lower parts of the respiratory tract. The lack of diaphragm prevents the active expulsion of particles out of the lungs, and the lack of superficial macrophages and the presence of heterophils instead of neutrophils makes the poultry immune response less effective against aspergillillosis [3, 4].  
The main route of infection in birds is inhalation or ingestion of conidia from the environment. Sick birds show respiratory symptoms such as dyspnea and an acceleratedrespiratory rate, but may also present suppressed growth and nervous symptoms [5; 6; 7]. The most susceptible are newly hatched and young birds in which infection is generally acute and the mortality rate is high (up to 90% of the flock) [6]. Early infection is possible, because _Aspergillus_ species may penetrate eggshells and infect embryos. The eggshell porosity provides a means of entrance for microorganisms into the egg from the nesting material and environment. Fungi can proliferate due to the high air humidity and diffusion of vapor from the egg content while stored and incubated, and cracking up contaminated eggs in incubators generates spores that contaminate hatchery equipment, eggs and embryos [7; 8; 9]. The infected embryos' mortality is mostly observed between the 15 th and 18 th day of incubation, which causes decreased hatchability even up to 30%. Potential embryo infection occurs when the dust contains more than eight hundred colonies per gram [9; 10]. The eggshell infested by fungi can be a hazard for embryos and young birds, causing respiratory problems. Because aspergillosis can cause direct loss through higher mortality, impaired growth-feed conversion and immunosuppression, it is the major threat among mycotic diseases in birds. The lack of information about the occurrence of fungal genera on the eggshell of duck eggs and sparse data about the fungal contamination of eggshell of other poultry species resulted in a need to supplement the data.  
To assess eggshell contamination and the potential risk to developing embryos and workers having contact with the eggs, we conducted studies to identify the species of fungi present on the eggshell surface acquired from duck reproductive flocks, with particular emphasis on _Aspergillus_ contamination, and conducted a drug resistance analysis of isolated _Aspergillus_ spp. isolates.
## 5 Designing an NMG Testbed and Numerical Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Standard** & **Protection** & **Control** & **Monitoring** & **Operation** & **Billing** \\
**Message** & **Message** & **Message** & **Message** & **Message** \\ \hline ETSI TR 102 935 [68] & Up to 10 ms & 100 ms & 1 s & N/A & 1 h to 1 day \\ IEC 61850 [69] & 4 ms & 100 ms & 1 s & 1 s & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Communication response time requirements in intelligent electronic devices (IED) [66; 67]. Figure 9: The changes in the mean RTTs at different congestion levels indicate that different protocols result in varying QoD performances. The text box provides the relative congestion level and the mean of the RTTs. TCP and TCP-based background traffic causes increased RTTs. Figure 10: Greater variability in RTTs occurs when high-variance background traffic, such as TCP or a combination involving TCP, is injected into the communication network. The relative congestion levels and standard deviation of RTTs are given in a text box. Figure 11: The y-axis represents the mean RTTs in seconds. The x-axis indicates the relative congestion level as a percentage. Text boxes provide the relative congestion levels and standard deviation of RTTs for each experiment. The TCP’s congestion control limits the relative congestion level. Analysis shows that TCP and ICMP + TCP background traffic cause increased RTTs. Figure 12: The y-axis represents the standard deviation of RTTs in seconds. The x-axis indicates the relative congestion level as a percentage. High-variance bit rate scenarios, e.g., TCP or ICMP + TCP, lead to a higher standard deviation of RTTs. Figure 14: A scatter plot depicts the positive correlation between the mean and standard deviation of RTTs across varying congestion levels. The mean and standard deviation of RTTs surpass the 100 ms threshold when variance in background traffic increases. Figure 13: A positive linear correlation between the mean and standard deviation of RTTs is illustrated. The text box presents the mean and standard deviation of RTTs and the relative congestion level for each data point. As the mean of RTTs increases, the variability in RTTs also increases, particularly when the congestion level is between low and med-high relative congestion levels.
## 2 Materials and Methods  
### Design Conditions  
As shown in Figure 1, the seedling growth and development are uniform as produced in plug trays. It is a key step for greenhouse production to timely and efficiently transplant seedlings from a growth tray into some larger pots. Some design conditions for seedling transplanting are established according to cultural practices for vegetable production in China.  
(1) The widely used soft plastic trays were made of injection molded polystyrene. The overall dimensions are 280 mm width \(\times\) 540 mm length for each type of tray. Two widely used trays have inverted truncated pyramid-type cells with the interval arrangement of 16 \(\times\) 8 (128-cell tray) and 12 \(\times\) 6 (72-cell tray), respectively. The cell dimensions are 48 mm height \(\times\) 32 mm top \(\times\) 14 mm bottom and 45 mm height \(\times\) 40 mm top \(\times\) 20 mm bottom for the 128-cell tray and the 72-cell tray, respectively.  
(2) The transplanting machine should adapt to different kinds of tray cells and irregular-shaped bedding plants by adjusting structural parameters. The limited growing space of the tray cell may make the seedling roots adhere around the perimeter of the tray cell. Therefore, it needs moderate mechanical action to grasp seedlings and extract them from tray cells.  
(3) To ensure the survival rate, the flexible design is important to minimize bruising damages when picking up the seedling in transplanting. The transplanting machine needs to be equipped with a dexterous gripper that can grasp and plant a variety of seedlings. Its design also influences the allowable approaches and departures of the transplanting system from the work object.
# A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning
## 4 Experiments  
### Datasets and Experiment Setup  
We conducted extensive experiments and compared our model with 14 competitive denoising methods to demonstrate the significant denoising performance of TripleDIP, which are BM3D [18], DnCNN [22], FFDNet [25], DIP [34], N2V [32], VDNet [44], WINNet [45], BCDNet [46], DAGL [47], DeamNet [48], DRUNet [49], IRCNN [50], Restormer [30], and SwinIR [28] (noted that we obtain the data of WINNet and BCDNet directly from the original paper due to the lack of source code). The benchmark datasets used in experiments are Set12 [22] and BSD68 [51]. As the basic image dataset, Set12 is widely used in various studies. It consists of 12 grayscale images, including seven images in size 256 \(\times\) 256 (Cameraman, House, Pepper, Fishstar, Monarch, Airplane, Parrot) and five images of the size 512 \(\times\) 512 (Lena, Barbara, Boat, Man, Couple). The BSD68 is also a common dataset in visual tasks composed of 68 images randomly selected from the BSD500 dataset [51]. The images in BSD68 contain rich texture details and make them ideal for testing the model's robustness. Furthermore, to quantify the performance of different methods, we adopt PSNR and SSIM (structural similarity index measure) as the evaluation metrics. The PSNR evaluates the intensity similarity between undistorted and generated images, and a higher PSNR value demonstrates a more satisfactory denoising ability. The SSIM is an image quality evaluation metric more closely related to how humans perceive image quality. Higher SSIM implies more satisfactory synthesis performance visually. All experiments were performed on the same hardware platform (i.e., 11 th Gen Intel(R) Core(TM) i7-11700 @ 2.50 GHz 2.50 HGz RAM 32.0 GB) and software environment (i.e., Windows 10 operating system). To maximize the performance of the internal and external mixed priors.
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 3 Materials and Methods
### Generating the Time Series
#### 3.4.1 Detection Series  
To generate the series corresponding to the number of detections, for each '.csv' file resulting from the video processing, detections were grouped by time interval and the maximum value was obtained within these groups. This value was then used in the time series to refer to that interval. Choosing the maximum over other statistics, such as mean or median, helps correct potential false negatives that may occur. In certain detection intervals, some people may not be detected due to either the precision of the YOLO model or the SSIM threshold; however, detections may occur throughout the interval.  
We used 50% of the available data to extract statistics, aiming to extend the series. We group this partition by day of the week, hour, and minute (w, h, m), obtaining both the median (Figure 3) and the interquartile range (IQR) (Figure 4) for each of the groups.  
Figure 3: Weekly distribution of median detection series. \(X\)-axis in intervals of 15 min.  
Figure 2: SSIM comparison results. (**a**) Displays detections provided by Yolo. (**b**) Shows various cutouts from the background frame corresponding to examples of a false positive, a false negative, and a true positive, respectively. (**c**) Presents cutouts from the original frame representing examples of a false positive, a false negative, and a true positive, respectively.  
For modeling the artificial series, a \(Gumbel\) distribution [22] (Equation (1)) was employed. This distribution allows for modeling the distribution of maximum or minimum values.  
\[Gumbel(x|\mu,\beta)=e^{-e^{-\frac{x-\mu}{\beta}}} \tag{1}\]  
We generate the values corresponding to 8 weeks with a frequency of 15 min, where \(\mu\) is the median of the corresponding (w, h, m), and \(\beta\) is the quartile deviation (\(IQR/2\)) of the corresponding (w, h, m) (see Figure 5).  
To evaluate the consistency of the expanded series with actual observational data, the Kolmogorov-Smirnov test [23] was employed. The Kolmogorov-Smirnov test is a nonparametric method designed to assess the equality of continuous, one-dimensional probability distributions, enabling verification of whether a sample derives from a specific reference distribution or if two samples share a common distribution. With a \(p\)-value of 0.1782 obtained, and at a 95% confidence level, the results suggest that the null hypothesis--that the data originate from the same distribution--cannot be rejected.
# A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization
## 3 Proposed Localization Algorithm
### TDOA Localization Algorithm
#### 3.1.2 Location Model Based on Swarm Intelligence Algorithm  
Taking a two-dimensional scenario as an example, we suppose there are \(M\) anchor nodes distributed on the field. The coordinates of the anchor node \(i\) and the unknown node are \((X_{i},Y_{i})\), \((x,y)\), the distance between the anchor node \(i\) and the unknown node is \(d_{i}\), and the calculation is:  
\[d_{i}=\sqrt{\left(X_{i}-x\right)^{2}+\left(Y_{i}-y\right)^{2}}+\varepsilon_{i}. \tag{1}\]  
We considered anchor node 1 as the main reference node, \(d_{i,1}\) is the measured value of the distance difference between the unknown node and the anchor node \(i\) and the anchor node 1 and the unknown node. The equation is:  
\[d_{i,1}=d_{i}-d_{1}+\varepsilon_{i,1} \tag{2}\]  
\[d_{i,1}=\sqrt{\left(X_{i}-x\right)+\left(Y_{i}-y\right)}-\sqrt{\left(X_{1}-x \right)+\left(Y_{1}-y\right)}+\varepsilon_{i,1} \tag{3}\]  
where \(d_{i,1}\) is calculated as the time difference between the two anchor nodes receiving a broadcast from the unknown node and \(\varepsilon\) is the measurement noise. Equation (3) identifies the unknown node on a particular hyperbola, which requires at least three anchor nodes in the two-dimensional plane to unite the set of equations, and at least four anchor nodes in the three-dimensional plane, converting Equation (3) to matrix form as:  
\[\Delta D=D-D_{1}+N \tag{4}\]  
Assuming that:  
\[\begin{array}{c}\Delta D_{(m-1)*1}=[d_{2,1},d_{3,1},\cdots,d_{m,1}]^{T}\\ D_{(m-1)*1}=[d_{2},d_{3},\cdots,d_{m}]^{T}\\ D_{1}=[d_{1},d_{1},\cdots,d_{1}]^{T}\\ N_{(m-1)*1}=[\varepsilon_{2,1},\varepsilon_{3,1},\ldots,\varepsilon_{m,1}]^{T} \end{array}\]  
where \(\varepsilon_{i,1}\) are independent Gaussian white noise random variables with zero mean and known variance of \(\delta^{2}\), the maximum likelihood method is used to determine the location of the unknown node \((x,y)\). The likelihood function can be followed as:  
\[\prod_{i=1}^{M-1}\left\{\frac{1}{\sqrt{2\pi\delta}}\exp[-\frac{\left(d_{i,1} -d_{i}+d_{1}\right)^{2}}{2\delta^{2}}]\right\}=[\frac{1}{\sqrt{2\pi\delta}}]^ {M-1}\exp[-\frac{\left(\Delta D-D+D_{1}\right)^{T}\left(\Delta D-D+D_{1} \right)}{2\delta^{2}}] \tag{5}\]  
for getting the coordinate of maximizing the likelihood function of Equation (5), it can be transformed into solving the objective function of Equation (6).  
\[(x,y)=\arg\{\min[\left(\Delta D-D+D_{1}\right)\left(\Delta D-D+D_{1}\right)]\} \tag{6}\]  
\[\text{fitness}=[\left(\Delta D-D+D_{1}\right)^{T}\left(\Delta D-D+D_{1}\right)] \tag{7}\]  
Using general methods to solve nonlinear Equation (6) will lead to high computation costs, which are very inconvenient in practical problems. After transforming Equation (6) into objective Equation (7), this paper proposes a HBES algorithm, which obtains the optimal solution for the target function by finding the individual with the highest level of adaptability.
## 4 Discussion
### Quantification of the Generated (Reactive) CAP Species
#### 4.1.2 Generation of ROS/RNS
In order to comment on the effect of the plasma characteristics on the generation of RNS, more research will be required to (i) examine the formation of nitrite/nitrate as function of the treatment time and (ii) to assess if other (short-living) RNS were generated as well. The pH value of the CAP treated water (Figure 4), which is linked to the generation of ROS and RNS, is also an important factor to be considered. For three examined CAP treatment conditions, i.e., DBD-0.0% (_v/v_) oxygen-21.88 V, DBD-1.0% (_v/v_) oxygen-21.88 V, and DBD-0.0% (_v/v_) oxygen-13.88 V, the pH values ranged between 2.6 and 3.9. As mentioned in the previous paragraph, this low pH value most likely contributed to the generation of certain lethal CAP species such as peroxnitrite. However, literature indicated that the pH drop itself cannot have been sufficient to (significantly) contribute to the inactivation of the biofilm-associated cells. _Listeria monocytogenes_ (planktonic) cells can grow until a pH level of 4.2, while up to 72% of the cells proved to survive pH 3 for 20 min [48; 49; 50]. For _Salmonella_ spp., on the other hand, (planktonic) cells can grow until a pH level of 3.6, while these cells proved to survive pH values up to 2.5 for several hours [49; 51; 52]. Nevertheless, the low pH value can result in an additional stress factor for the cells, eventually leading to cell death due to synergistic effects with ROS, RNS, and/or UV photons [53]. However, it should be taken into account that the actual drop in pH inside a biofilm might not be as pronounced as for the small volume of water tested, since the biofilm environment is buffered, and other reactions might occur due to the presence of the cells and the biofilm matrix. Regarding the influence of the plasma characteristics on the pH of the CAP treated water, it was concluded that the oxygen level of the gas flow did not result in significant differences. For the electrode configuration, it was observed that generally lower pH values were reached when the DBD configuration was used. This can be explained based on (i) the generally higher dissipated plasma power resulting in a more effective generation of reactive species (Table 1) and (ii) the mode of exposure. Using the DBD electrode, the water (and the biofilm sample) was directly exposed to the generated CAP species, meaning that a high number of reactive species was able to interact with or diffuse into the water [19; 43]. The significantly lower pH values at the highest power discharge (using the DBD electrode) can again be explained based on the higher dissipated plasma power.
## 2 Assessing Sensory Processing and Motor Skill Performance of Reactive Agility
### Within and Between-Group Differences in Sensory and Motor Skills  
By comparing simple reaction time, multi-choice reaction time and movement time it is possible to estimate the contribution of sensory processing and change of direction speed to agility performance [20,21]. For instance, simple reaction time, multi-choice reaction time and agility time (representing both choice reaction time and movement time) in two athletes with different demands on their agility skills was compared [22]. Subject A, with both longer simple and choice reaction times, was able to achieve shorter agility time than subject B. Subject A was able to initiate his/her foot responses more rapidly due to the fact that he/she was an elite karate-kata competitor who does not usually respond to any stimuli but must be able to move a short distance very quickly. On the other hand, subject B, in spite of shorter simple and choice reaction times, was not able to transfer this capability into better agility time. This may be ascribed to the genetic disposition (fast reaction time in subject B) and adaptations to sport-specific training (fast movement time in subject A).  
Furthermore, simple and two-choice reaction time was found to be significantly shorter in karate-kumite than the karate-kata competitors. This most likely contributed to shorter agility time (travelling distance of 0.8 m), as there were no significant differences in movement velocity between these groups. Simple and two-choice reaction time was also significantly shorter in both hockeyball and soccer goalies than players. In contrast, movement velocity was significantly higher in both hockeyball and soccer players than in goalies. Faster movement time most likely contributed to significantly shorter agility time in both hockeyball players (travelling distance of 1.6 m) and soccer players (travelling distance of 3.2 m) than in goalies. These findings were corroborated by a higher Agility Index in karate-kumite than karate-kata competitors and in hockeyball and soccer goalies than in players. Its higher values signify the predominant contribution of a sensory component to agility performance. Longer travelling distances make higher demands on change-of-direction speed and anaerobic/aerobic capabilities of players. While the motor component of agility performance seems to be predominant in players in terms of faster movement execution, in goalies it is the sensory component which allow them faster decision making.  
These findings indicate that both speed of decision making and change of direction speed contribute to agility performance in athletes, although to a different extent [20,21].
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 4 Results
### Quantitative Evaluation
#### 4.2.3 Runtime Analysis  
We show the runtime comparison of RD-SLAM with other systems in Table 7. Because of the efficiency of the rasterizing 3D Gaussians and G-ICP front-end, RD-SLAM runs 55 times faster than Point-SLAM and 60 times faster than SplaTAM, achieving the best tracking accuracy and rendering quality at the fastest run speed.
## 3 Results and Discussion
### Textural and Chemical Characterization of the Adsorbents
Four distinct types of O-functionalities including -C=O (531.1 eV); R-O-C=O, O=C-NH\({}_{2}\), O=C-O-C=O, C-OH, R-O-R (532.3 eV), R-O-C=O, O=C-O-C=O (533.3 eV), and -COOH (534.2 eV), as well as the contribution of chemisorbed H\({}_{2}\)O (536.1 eV), can be identified [30]. This indicated that the oxygen atoms on aACF were primarily present as -OH and C = O groups. It is noted that on the surface on nACF, the existence of a great amount of chemisorbed water was detected, which is likely to be related to the introduction of nitrogen functionalities. Figure 2: FESEM images of the samples: (**a**) as-received ACF; (**b**) KOH-activated ACF (aACF); (**c**) TPEA-modified ACF (nACF). The composition and the atomic percent of N-functionalities on the surface of the adsorbents are shown in Figure 4b. Six nitrogen-based compounds were identified [31], including the aromatic  
Figure 4: Results of the fits of the high resolutions X-ray photoelectron spectroscopy (XPS): (**a**) O\({}_{18}\) region; (**b**) N\({}_{18}\) region. The values were given in the atomic ratio (%) of total intensity. Figure 3: The X-ray photoelectron spectroscopy (XPS) of the samples: (**a**) survey scan spectra; (**b**) atomic ratios; (**c**) N\({}_{18}\) peaks; (**d**) O\({}_{18}\) peaks. N-times (395.7 eV), the pyridine-type N (398.4 eV), the pyrrolic N or pyridonic N (400.1 eV), the quarternary N (401.2 eV), the pyridine N oxides (402.4 eV), and the chemisorbed NO\({}_{2}\) (405 eV). Pyridonic N and pyrrolic N could not be differentiated from each other based on the current measurement technology [32]. In view of the fact that oxygen existed on the surface of the adsorbents and the pyrrolic N was much more unstable than pyridonic N at an elevated temperature [33], the functional groups occurring at 400.1 eV were probably pyridonic N. Arising from the precursor of ACF, the major N-functionalities were featured with the quarternary N and the pyridine type N. Even though the evolution of nitrogen took place significantly during the KOH activation process, pyridonic N was much more heat-resistant compared with other species. Since the nitrogen on the aACF originated totally from the ACF, it is believed that the pyridonic N was a more stable form. The distribution of N-functionalities on nACF displayed an increase in pyridonic N, which could be due to the functional groups in TEPA. The TGA profiles are illustrated in Figure 5a, and the temperatures for the maximum rate of weight loss for ACF, aACF, and nACF were 600, 588, and 655 \({}^{\circ}\)C, respectively (in Figure 5b). Comparing the profiles of aACF with ACF, the oxidation resistance of the samples was not significantly changed after KOH activation. Possibly due to the total or partial blinding of micropores by N-functionalities, the oxidation resistance of ACF modified with TEPA was improved significantly.