esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a System for Verification of Automatic Guided Vehicle Routes Using Computer Emulation
## 2 Materials and Methods  
### Designing a System for Verification of AGV Routes Using Computer Emulation  
The proposed system for the verification of AGV routes using computer emulation comprises several partial components (modules) that communicate, process, and evaluate each other's information while providing feedback. Subsequently, the solutions transform these sub-proposals into a comprehensive, integrated system. This system integrates computer simulation and the emulation of manufacturing, assembly, and logistics systems with modules for the communication of real logistics elements and virtual emulation models of manufacturing and assembly systems. Figure 1 depicts a simplified representation of the proposed integrated system structure.  
The integrated system consists of three basic levels. The first level is made up of elements that represent real logistical handling equipment such as forklifts and automatically guided logistic tractors (Section 2.1.1). The second level consists of systems for 3D modelling, optimisation, and simulation of the manufacturing system, and for the visualisation of the system through immersive technologies depicting the virtual world [26] (Sections 2.1.3 and 2.1.4). Between these two levels, there is a communication interface, the task of which is to transform the simulation model into an emulation model (Section 2.1.2).  
The real system simulation model has been designed as a parametric model. The model's parameters are quantities obtained from the real system through radio-frequency identification (RFID) tags, on which individual commands to be executed by the device during operation are programmed. Data from the real system is processed and stored in a Structured Query Language (SQL) database and subsequently transformed into a simulation/emulation model through software bridging.  
These elements combine to form a functional unit that encapsulates the essential ideals of the digital manufacturing concept.
## 3 Results and Discussion
### Optical Properties
We conclude that the synthesis of SeNPs in this way is beneficial in DNA healing by reducing or eliminating oxidative stress in the immune system, thus preventing ROS from stimulating lipid peroxidation responsible for subsequent DNA damage. Hence the stability of energy metabolism [38] and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **No Damage** & **Low Damage** & **Medium Damage** & **High Damage** \\
**Groups** & **\% (Mean \(\pm\) SD)** & **\% (Mean \(\pm\) SD)** & **\% (Mean \(\pm\) SD)** & **\% (Mean \(\pm\) SD)** \\ \hline \hline Neg. control & A42.478 \(\pm\) 0.975 & A43.41 \(\pm\) 0.553 & D7.066 \(\pm\) 0.942 & D7.045 \(\pm\) 0.599 \\ \hline STZ & D33.189 \(\pm\) 1.565 & D33.698 \(\pm\) 0.878 & A15.025 \(\pm\) 0.554 & A18.088 \(\pm\) 1.072 \\ \hline STZ + Met & C37.909 \(\pm\) 0.856 & C38.37 \(\pm\) 0.878 & B10.852 \(\pm\) 0.557 & B12.869 \(\pm\) 0.329 \\ \hline STZ + Se & BC38.681 \(\pm\) 1.33 & C38.487 \(\pm\) 0.939 & BC10.484 \(\pm\) 0.613 & B12.347 \(\pm\) 1.693 \\ \hline STZ + Se NPs & B39.705 \(\pm\) 0.406 & B40.087 \(\pm\) 0.724 & C9.487 \(\pm\) 0.605 & C10.721 \(\pm\) 0.712 \\ \hline LSD & 1.659583 & 1.215511 & 1.010036 & 1.505262 \\ \hline \hline \(p\)-value & 0.00075 & 0.00008 & 0.00006 & 0.00047 \\ \hline \hline \(\bullet\) & STZ = streptozotocin diabetic mice & & & \\ \(\bullet\) & Met = metformin & & & \\ \(\bullet\) & Different capital and small latter represent significant \(p\leq 0.05\). & & \\ \(\bullet\) & LSD: least significant difference. & & & \\ \(\bullet\) & \(p\) value: probability value. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The result of the comet assay in sperm after treatment with SeNPs. Figure 10: Positive control group (DM): high numbers of cells with high comet and medium comet by using comet score program. this result agrees with the previous study by [PERSON] et al. and [PERSON] et al. [39; 40]. Treatment with SeNPs alone was more effective than either MET or Se use.
# New Model for Defining and Implementing Performance Tests
## 3 Problem Formulation and Proposed Solution  
It is natural to strive for maximum resilience of systems and software against errors and failures resulting from performance problems and overloads. As [PERSON] noted: \"Program testing can be used to show the presence of bugs, but never to show their absence!\" [28]. It follows that the process of identifying bugs is inherent in the process of developing, implementing, and operating the system. It should be emphasized, however, that the application should not be tested in isolation from the hardware parts on which it resides. To begin with, let us look a little more closely at the types of performance tests encountered in the literature. We can distinguish three main groups of tests [29]:  
* Performance testing: The process of measuring and evaluating aspects of software system performance. Metrics tested include response time, throughput, and resource utilization.
* Load testing (load test): The process of evaluating the behavior of a system under a typical load to detect problems that only occur when the system is operating at an increased rate, such as memory leaks or system instability.
* Overload testing (stress testing): The process of subjecting a system to extreme conditions to test its resilience and detect problems associated with overloading, such as saturation of CPU or memory resources or database errors.  
In addition, we can find additional types of tests in the literature:  
* Spike Testing assesses the performance of a system when there is a sudden and significant increase in the number of simulated end users. Spike testing helps determine whether a system can handle a sudden, drastic increase in workload over a short period of time. As with stress testing, the IT team typically conducts spike tests be fore a major event in which the system is likely to be exposed to higher-than-normal workloads [1].
* Endurance Testing simulates a steady increase in the number of end users over time to test the long-term stability of the system. During the test, the test engineer monitors Key Performance Indicators (KPIs), such as memory usage, and checks for failures, such as memory shortages. Stress tests also analyze throughput and response times after long-term use to show whether these indicators are consistent with their state at the start of the test [30].
* Scalability Testing--a non-functional testing method that measures the performance of a system or network when the number of user requests is increased or decreased. The purpose of scalability testing is to ensure that the system can handle anticipated increases in user traffic, data volume, transaction count frequency, etc. [31].
* Volume Testing is a type of software testing in which software is subjected to a huge amount of data. It is also referred to as flood testing. Volume testing is carried out to analyze system performance by increasing the amount of data in the database. Using volumetric testing, it is possible to study the impact on response time and system behavior when the system is exposed to a large amount of data [1].
## 2 Related Work
### Head for Object Detection  
Existing object detectors mainly consist of an anchor-based detector and an anchor-free detector. The anchor-based detectors can be subdivided into two-stage detectors and one-stage detectors. The classical two-stage detector is Faster-RCNN [21], which has superior detection performance; however, it cannot realize real-time detection. The one-stage detector RetinaNet [22] surpasses the second-stage detectors in detection accuracy by weighting the sample loss. YOLOv3 [4] inherits the idea of YOLOv1 [23] and YOLOv2 [24] and adds the FPN structure to achieve multi-scale prediction. While retaining the advantages of real-time monitoring of one-stage detectors, the detection accuracy is greatly improved. RoI-Trans [25], DAL [26], and others add attributes such as rotation angle to the anchor to increase the proportion of effective pixels in the ground truth, thereby improving the detection performance of the network for small targets; however, due to its large network volume and complex structure it has not been widely used in practical projects. Anchor-free detectors such as CenterNet [27], FCOS [28], RepPoints [29], and Orientated RepPoints [30] have advantages in small target detection due to their pixel-level prediction of feature maps; however, their huge amount of computation makes the reasoning speed of the network very slow.  
YOLOv5 simplifies the backbone of YOLOv4 [6], adds the PANet structure to achieve the fusion of deep and shallow features, uses k-means clustering to obtain the appropriate anchor frame size, and improves the problem of missed detection of small targets by eliminating grid sensitivity. In addition, similar to EfficientNet, YOLOv5 sets two parameters, the depth multiple and width multiple, to control the depth and width of the network, that is, the number of convolution layers and channels, respectively. YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, representing the four versions of model volume and accuracy in increasing order, have been released to adapt to different task requirements. YOLOv5 has been applied to various visual tasks after several iterations as an efficient one-stage object detection model. Unless otherwise specified, the YOLOv5 model mentioned in this paper refers specifically to the 5.0 version.
# Metabolomic Insights into the Potential of Chestnut Biochar as a Functional Feed Ingredient
## 4 Discussion
It is likely that components of the chestnut biochar extract, particularly hydroxybenzoic acid, interfere with these bacterial processes. Hydroxybenzoic acid and its derivatives have demonstrated a quorum-sensing inhibitory activity. For example, [PERSON] et al. (2020) [45] synthesized derivatives such as 3,4,5-trihydroxybenzoic acid methyl ester, which inhibited biofilm formation and communication between cells in Chromobacterium violaceum. Similarly, [PERSON] et al. (2022) [46] showed that 4-hydroxycinnamic acid, a hydroxybenzoic acid derivative, inhibited quorum sensing in Agrobacteriumium tumefaciens, reducing biofilm formation and pathogenicity. In addition to its effects on pathogenic bacteria, the biochar extract was tested on two probiotic strains, _L. plantarumum_ and _L. reuteri_. Preliminary results suggest that at low concentrations, biochar extract may have a prebiotic effect, particularly on _L. plantarumum_. The interactions between biochar and probiotics appear to depend on the unique metabolic pathways and genetic characteristics of each strain, as observed by [PERSON] et al. (2019) [47], who confirmed that the effects of biochar on bacterial growth are strain-dependent. Further research into the use of biochar as a feed additive could improve gut health in livestock by supporting beneficial probiotic populations, increasing nutrient absorption, and reducing disease incidence. The differing responses of _L. plantarumum_ and _L. reuteri_ to biochar highlight the importance of understanding strain-specific interactions in optimizing the role of biochar as a functional ingredient for promoting gut health in animals. The translation of in vitro data to in vivo doses is challenging, largely due to the inherent complexity of biological systems. A review of the literature revealed that biochar can be included in the feed in different concentrations, ranging from 0.3% to 6%. However, the reported outcomes are inconsistent, potentially due to the influence of dose-dependent effects [48]. A high inclusion percentage can have a negative effect on digestibility due to the adsorbent nature of the materials. It is therefore hypothesized that an inclusion percentage of less than 2% (percentage of additive included) is reasonable.
## 2 Data and Methods
### Generalized Split-Window Model  
The LSA SAF applies the GSW model with a formulation similar to that proposed by [PERSON] and [PERSON] [13, 14] and adapted by [PERSON] _et al._[15] and [PERSON] _et al._[12] to the SEVIRI split-window channels. LST is obtained through a semi-empirical regression of SEVIRI 10.8- and 12.0-\(\upmu\)m TOA brightness temperatures, where the correction of atmospheric influences is based on the different absorption of two adjacent infra-red bands [12]. The LST is estimated through a linear regression of the split-window TOA brightness temperatures. The regression coefficients depend explicitly on the land surface emissivity and implicitly on the TCWV obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecasts and the satellite view zenith angles (VZA) [15]. The surface emissivity is provided for the split-window channels using a method based on the fraction of vegetation cover (FVC), also estimated by the LSA SAF from seviri visible and near-infrared channels [12, 24]. Thus, the emissivity computation is driven by the vegetation state and takes into account daily FVC estimates from SEVIRI measurements and a global land cover classification [12, 24].  
Reported uncertainties for the LSA SAF LST dataset are in the range of 1-2 K [12], except for very moist atmospheres. A detailed description of the LSA SAF model can be found in the corresponding Algorithm Technical Base Document [15]; see also [12]. We used LST data from the LSA SAF archive for model inter-comparisons, which we label \"GSW\" LST in the following.
## 4 Experiments
### Comparative Analysis  
#### 4.2.1 Datasets  
The simulation dataset comprises three primary categories: personnel, projects, and contracts. Table 5 provides an overview of the experimental data, presenting relevant details for each category.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Row** & **Column** & **Sensitive** & **Non-Sensitive** & **Sensitive Type** \\  & **Number** & **Number** & **Columns** & **Columns** & **Sensitivity Type** \\ \hline Personnel Information & 22,618 & 111 & 19 & 92 & Name, gender, phone number, email address, company name \\ Project Information & 23,208 & 301 & 31 & 270 & Information about departments and personnel involved in the project \\ Contract Information & 6351 & 37 & 15 & 22 & Contract payment information \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of simulation data.  
In the simulation dataset, the sensitive information in each column was identified through expert auditing, and the distribution of sensitive information in this dataset is illustrated in Figure 9. Among the various data columns, the contract data contained the highest proportion of sensitive information, accounting for over 40% of the dataset. On the other hand, the project data exhibited a relatively smaller percentage of sensitive data, mainly due to a significant number of missing values present in the data.
## 3 Experimental Methods and Results
### Comparison of Different Judgment Methods  
Figure 6 shows the average power spectrum information entropy under each segment number calculated by this method. It can be seen that the average power spectrum information entropy of the HIFU echo signals under each segment number are greater than that of the noise signals, and there is a certain degree of differentiation. Only an appropriate threshold under each segment number needs to determine, and then it can be judged whether or not the sampled signal is the HIFU echo signal. After a comprehensive analysis of the average entropy value and three evaluation indicators under each segment number, this method was compared with classic power spectrum method when the segment number is 17.  
When the segment number is 17, the average entropy of the HIFU echo signals is 2.32, and the average entropy of the noise signals is 1.51. In addition, when the threshold is determined to be 1.98, the optimal recognition rate of distinguishing the two types of sampled signals can reach 98.60%. Similarly, if classic power spectrum method is used for distinguishing, the optimal recognition rate reaches 95.20% after determining the threshold. When the segment number is 17, it can be obtained that the recognition rate of this method to distinguish the two types of sampled signals is higher than that of the classic power spectrum method.  
Table 1 shows the Accuracy, Sensitivity and Specificity of this method and classic power spectrum method compared with empirical judgments when the segment number is 17. It can be concluded from Table 1 that this method has higher values on the Accuracy, Sensitivity and Specificity, which verifies that this method is more consistent with empirical judgments.  
Figure 6: The average power spectrum information entropy under each segment number.
# Pulsed Thermography Dataset for Training Deep Learning Models
## 4 Experiments with Deep Models for Defect Segmentation
### Efficient Data Segmentation Methods
#### 4.5.2 Experiment with Fewer Thermal Frames  
The thermal profiles of six different areas, one sound and five defective, are compared in Figure 9. The results demonstrate that the majority of the thermal value difference occurs in the early stage. Hence, using only the initial portion of the thermal sequence as input data may prove to be an effective approach for deep learning models to detect defects.  
To assess the impact of the number of frames on the model performance, the Average Intersection over Union (IoU) metric is used on the validation dataset. The results of the experiment, as shown in Table 3, highlight that the best performance is obtained with the use of the initial 200 frames of the thermal measurement. This suggests that the majority of the relevant information lies within the early stages, and that including additional trailing frames may not contribute significantly to the defect evaluation. The training hyperparameters are kept consistent with those used in Section 4.4. Additionally, using 140 or 200 frames resulted in faster processing times, with the models running two to three times faster compared to the original dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average IoU** & **Model Size** \\ \hline U-Net on original data & 0.748 & 18.4 MB \\ SegNet on original data & 0.729 & 17.2 MB \\ U-Net on PPT & 0.732 & 14.5 MB \\ SegNet on PPT & 0.717 & 13.3 MB \\ U-Net on PCT & 0.638 & 14.5 MB \\ SegNet on PCT & 0.625 & 13.3 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results on validation dataset of PPT and PCT images.  
Figure 9: Thermal profiles comparison from sound area and defective areas with different defect depths.
# Estimation of Aircraft-Dependent Bumpiness Severity in Turbulent Flight
## 3 Experiments and Analysis
### Analysis of Vertical Acceleration Response
#### 3.2.2 Aircraft Maneuvers Effects  
Figure 12 shows in-depth analysis results in WF8. The longitudinal and vertical turbulence components of 120 s are shown in Figure 12a. Between 79 s and 82 s, the aircraft experienced sudden vertical turbulence. At the same time, the deflection of elevators and spoilers recorded by flight data are shown in Figure 12b,c, respectively. It can be found that both the elevator \(\delta_{e}\) and high-speed flight spoiler \(\delta_{\textit{SHS}}\) experienced big deflections. The deflection of elevators had a greater impact on aerodynamics force than that of spoilers.  
Figure 12d shows the computation results of aerodynamic force with or without control deflections by using UVLM. By differentiating the aerodynamic force \(F\) from \(F^{0}\), the longitudinal aerodynamic force induced by control deflections or turbulence is able to be distinguished effectively. The effects of turbulence on vertical acceleration were separated from aircraft maneuvers effects. Therefore, the change of incremental vertical acceleration only induced by turbulence was obtained. Besides, it can be found that the sudden deflections of control surfaces may cause an increase in aerodynamic force and deteriorate the bumpiness severity.  
Figure 11e shows the EDR indicator by wind-based estimation algorithm given in [39]. The EDR value is commonly output once a minute. In this paper, in order to analyze the dynamic process of EDR and aircraft bumpiness estimation, the 90 th percentage EDR value is given in a sub-window of 10 s with 50% overlap.  
Figure 11f shows the acceleration variation with or without aircraft maneuvers. The turbulence intensity was first obtained by Equation (3) according to the EDR value indicated once a minute. Then the longitudinal and lateral turbulence components were generated by Equation (6) in a sampling frequency of 8 Hz. With the turbulence components as the excitation, the time series of vertical acceleration is computed by UVLM. After obtaining  
Figure 11: Mean Square Error (MSE) and Peak value of Acceleration.  
the acceleration response, the data length of one-minute vertical acceleration data is 480 points. In the process of bumpiness severity estimation, the band-pass frequency \(f_{l}\) and \(f_{h}\) are typically chosen as 0.1 and 1.0 Hz respectively for civil aviation aircraft. The one-minute acceleration data are further divided by six sections, while the length of Tukey-Hanning window is set as 160 and the overlapping is set by 50%. Figure 12f shows the total variance estimation, \(\sigma_{az}^{2}\), which is blended by aircraft maneuvers. In the computation process of incremental acceleration, the effects of control surface deflection are eliminated by \(\sigma_{az}^{2}-\sigma_{az,0}^{2}\). It can be concluded that during the severity estimation of aircraft bumpiness, the acceleration response model based on UVLM is capable of effectively avoiding the adverse effects of aircraft maneuvers.