esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Welding Current  
The results of the evaluation show that, as the welding current decreases, the layer thickness increases while the wall thickness decreases (Figure 3).  
Under the condition that the framework parameters (Table 1) are constant and the welding current decreases, this also applies to the reduction in the energy input per unit length \(E\) Equation (1).  
\[E=\frac{U_{S}\cdot I_{S}\cdot\eta}{v_{S}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Parameter Settings** \\ \hline \multirow{3}{*}{Control factors} & Welding current: & 120 A (105 A; 135 A) \\ \cline{2-3}  & Welding speed: & 48 cm/min (36 cm/min; 60 cm/min) \\ \cline{2-3}  & Powder mass flow: & 23 g/min (18.3 g/min; 27.9 g/min) \\ \hline \multirow{3}{*}{Framework parameter} & Plasma gas amount: & 1.51/min \\ \cline{2-3}  & Shielding gas amount: & 121/min \\ \cline{2-3}  & Conveying gas: & 31/min \\ \cline{2-3}  & Anode type: & Standard \\ \cline{2-3}  & Gas: & 100\% Ar \\ \cline{2-3}  & Working distance: & 10 mm \\ \cline{2-3}  & Electrode set back: & 1.5 mmm \\ \cline{2-3}  & Electrode angel: & 30\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setup for tests.  
This reduction leads directly to a smaller melt pool size. Figure 4 shows an example of the length of the melt pool as an indicator of the melt pool volume at a welding speed of \(v_{\text{s}}=36\) cm/min and a powder mass flow of \(m_{\text{p}}=18.3\) g/min and varied welding current. It was seen that the reduction in the welding current from \(I_{\text{s}}=135\) A to \(I_{\text{s}}=105\) A causes a reduction in the weld pool length of 27%. This reduced weld pool length can be used as a direct indication of a lower melt pool temperature due to the constancy of all other parameters. Fused metals with a lower temperature are characterized by a higher viscosity, which is why a perpendicular build-up direction is preferred and the wall thickness is reduced. The results obtained are consistent with what is stated in various sources of the literature [17,18].
## 2 Performance Testing of SCA and Rock Material
### Performance Testing of SCA  
The properties of static crushing agent (SCA) are mainly influenced by factors such as material ratio, mixing water temperature, ambient temperature, and water-cement ratio. Due to constraints in underground coal mine conditions, field tests often focus on the water-cement ratio of the ready-made SCA. In this section, to guide the on-site construction of the roadway expansion project, the expansion characteristics of SCA with different water-cement ratios were obtained by experiment tests based on the standard of \"Soundless Crushing Agent: JC506-2008\" [23]; the test schematic of SCA expansion force is shown in Figure 2.  
The expansion stress of SCA is measured by the resistance strain gauge method, and a constant temperature water bath is used to obtain the expansion stress-time curve at the underground temperature. The expansion stress is calculated using Equation (1) [23]:  
\[P=Es\cdot(K^{2}-1)\left[\frac{\epsilon_{\theta}}{2-\upsilon}\right] \tag{1}\]  
where \(P\) represents the expansion stress (MPa); \(Es\) is the elastic modulus of Q235 steel pipe, which is 206 GPa; \(K\) is the ratio of the steel pipe's outer diameter to inner diameter (mm); \(\epsilon_{\theta}\) is the circumferential strain of the steel pipe; and \(\upsilon\) is the Poisson's ratio, taken as 0.3.  
Based on experiment tests, the time-history curves of expansion stress of SCA with different water-cement ratios are shown in Figure 3. From Figure 3, the expansion stress of SCA increases at a faster rate within 0-30 h. After 30 h, the expansion stress continues to rise with the growth of reaction time, but the rate of increase gradually decreases and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Rock Type**} & **Density** & **Compressive** & **Tensile** & **Elastic** & **Poisson’s** & **Internal** & **Cohesion** \\  & **(kg/m3)** & **Strength** & **Strength** & **Modulus** & **Ratio** & **Angle (°)** & **(MPa)** \\ \hline Sandstone & 2720 & 67.4 & 5.82 & 16.874 & 0.25 & 36.2 & 14.374 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The obtained rock mechanical parameters are listed in Table 1.  
Figure 1: Mechanics test of rock samples.  
Figure 2: Schematic diagram of the expansion force test equipment.  
eventually levels off. Comparing the expansion stresses of SCA with water-cement ratios of 0.26, 0.3, 0.34, and 0.38, when the water-cement ratio is 0.3, the SCA slurry has the fastest rate of increase in expansion stress and reaches the highest value (75.9 MPa) after trending toward stability. This means that SCA at a water-cement ratio of 0.3 has the highest expansion stress, resulting in a more pronounced fracturing effect.
# AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments
## 3 Overall System Architecture  
The software system we designed is an animal behavior analysis platform that can be deployed in medical laboratories. It automatically acquires the behavioral information of animals in open-field videos and generates corresponding data reports to be stored in a database. The platform allows researchers to flexibly select existing behavioral recognition algorithms or extend new ones into the platform for use according to different experimental needs. In addition, the platform utilizes an integrated natural language query interface service to provide high usability, as researchers are no longer limited by preset functions when conducting data retrieval or analysis. We selected relational databases for the platform because the relational database can effectively manage and maintain relationships between data, ensure data integrity and consistency, and support complex query operations in SQL language. We used a model-template-view (MTV) pattern similar to the model-view-controller (MVC) pattern, which can effectively separate data, business logic, and user interface. The pattern can improve the maintainability and scalability of the system and make the platform easier to develop and maintain. The model layer handles the application's data logic and database interaction. The template layer is responsible for building the structure and style of the page, typically using hypertext markup language (HTML) and template languages. As a traditional controller, the view layer receives and processes user requests and passes the model data to the template for display. In addition, we selected JavaScript object notation (JSON) as the structural form for data exchange to achieve lightweight data transmission and parsing, which can improve system performance and efficiency. We developed the algorithm library using Python. We used Flask, Pytorch frameworks, and open-source libraries (such as OpenCV-python, Numpy, Scikit-video, and others) in the development project. We deployed the platform, pose estimation service, behavior recognition service, and natural language query interface service on four identical devices. Each device adopted the Ubuntu 18.04 system with a CPU model of 2 vCPU Intel (R) Xeon (R) Platinum 8352V, Intel Corporation and NVIDIA Corporation, Santa Clara, California, USA and had 90 GB of memory. The device that was deployed for the pose estimation, behavior recognition, or natural language query interface service had anRTX4090 (24 GB) GPU, Santa Clara, California, USA. Figure 2 shows the overall architecture of the platform, where the gray part indicates the main service modules, and Table 2 describes the relevant information of each module.
## 1 Introduction  
The importance of unmanned aerial vehicles (UAVs) for personal and professional use has increased rapidly over the last 2 years. UAV systems are used for a variety of applications, including aerial photography, surveying, and reconnaissance. There are many types and classes of UAVs; however, the most usable ones are the vertical takeoff and landing (VTOL) arcrafts, which combine efficient plane flight and multicopter hover ability. Control system design for this drone type is very challenging due to the complex and nonlinear airframe dynamics. Existing control methods, such as PID or LQ controllers, have significant limitations including poor performance in the presence of disturbances and changing environmental conditions. The importance of this research is determined by the potential widening of the range of applications based on a high-performing and stable control system. During this research, the kinematics of the specific VTOL drone \"FIXAR-007\" are used for the design and simulation of the linear and adaptive control systems. This adaptive system design is the main focus of this paper, which presents a comparison of these systems. The motivation for the research was the drone's simplistic design, which can be utilized with a universal control system to be used by a VTOL drone during all flight phases. The drone mentioned above is designed and manufactured by the FIXAR-Aero company [1], which owns the patent for autopilot, ground control stations and the aerodynamic scheme. Therefore, the paper considers the author's personal ideas and assumptions about the control system design, without having access to the intellectual property of the company.
## 2 Materials and Methods
### Performance Metrics  
Coefficient of determination (\(R^{2}\)) is a widely used regression performance metric to measure how well the predictions of a model fit the actual data [28]. The metric's domain is \(R^{2}\leq 1\), where \(R^{2}=1\) means perfect predictions, \(R^{2}=0\) is the baseline model that predicts \(\hat{y}\), and \(R^{2}<0\) means poor predictions; it is calculated as shown in Equation (4). It is composed of both the sum of squared estimates of error (SSE) and the sum of squares total (SST), Equations (5) and (6), respectively, and was calculated for each target feature (\(F_{x}\), \(F_{y}\), \(F_{z}\)) using the testing dataset. When the error between predictions and the reference value is minimized, \(SSE\approx 0\), so \(R^{2}=1\), which is concordant with the aforementioned fact, as the minimal error between samples would mean the predictive model is perfect, and hence, \(R^{2}=1\).  
\[R^{2}=1-\frac{SSE}{SST} \tag{4}\]  
SSE, known as the deviation between predicted and reference values, was calculated as shown in Equation (5). \(\hat{y}_{i}\) refers to the ith prediction and \(y_{i}\) to the reference value, and their squared difference is summed across \(n\) testing samples.  
\[SSE=\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{5}\]SST, known as the total variation in the data, was calculated as shown in Equation (6). \(y_{i}\) refers to the ith sample and \(\bar{y}\) to the target feature's mean, and their squared difference is summed across \(n\) testing samples.  
\[SST=\sum_{i=1}^{n}(y_{i}-\bar{y})^{2} \tag{6}\]  
The Pearson correlation coefficient (\(r\)) was also used as a performance metric to measure the linear relationship between predictions and reference values [28]. The metric's domain is \(-1\leq r\leq 1\): \(r=1\) means a strong positive linear relationship, \(r=-1\) means a strong negative linear relationship, and \(r=0\) means a poor linear relationship. It is calculated as shown in Equation (7). \(x_{i}\) is the ith sample of \(X\) series (in this case \(\hat{y}\) for predictions) and \(y_{i}\) is the ith sample of \(Y\) series (in this case \(y\) for reference).  
\[r_{xy}=\frac{\sum_{i=1}^{n}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i=1}^{n }(x_{i}-\bar{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}}} \tag{7}\]
## 1 Introduction
### Internet of Things (IoT)  
The International Telecommunication Union (ITU) defines IoT as _\"A global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies\"_[13]. The infrastructure that allows devices and/or assets to connect to one another is known as the _IoT_. The term IoT has been deployed in many different fields, and it takes the name of the corresponding field; in Industrial internet of things (IIoT) [14], Internet of Service (IoS) [15], the merging of Robotics and IoT technologies gives birth to a new concept known as IoT-aided Robotics or Internet of Robotic Things (IoRT) [16].  
Figure 1: Pillars of Industry 4.0.  
In manufacturing systems, every component, including sensors, actuators, and electronic software, is embedded with physical devices, and it is all connected to various Internet networks. The Internet of Things makes it possible for manufacturing devices to share and exchange data both among themselves and with other manufacturing devices, as well as with the service providers and customers of manufacturing devices [17; 18].  
Recently, there have been numerous studies on IIoT and its uses in Industry and other applications. A number of studies offered insights and contributions that are focused on issues associated with protocols and standardizations [19], architecture and connectivity [20], and semantic and ontological issues for Industry 4.0 [21; 22], while other studies focused on merging an IoT platform with CPSs towards smart digital manufacturing for additive manufacturing [23], in smart factory and smart manufacturing [24; 25; 26].
## 4 Vehicular Ad Hoc Networks
### Routing
#### 4.2.3 Mobility Modeling and Predicting  
To create a path between vehicles, we must acquire information about the position of nodes and their movement pattern. However, it is difficult to predict their movement because vehicles have different movement patterns. Thus, designing a mobility model based on a predefined road model including network traffic, speed of nodes, and their behavior is very essential for creating an efficient routing protocol in the network. In this case, a traffic simulator can be beneficial when there are no real vehicular traces. However, this traffic simulator can affect the network performance. When researchers use the real vehicular traces in their simulation, the packet delivery rate is significantly lowered in comparison with the results of unrealistic traces. Therefore, when the mobility modes are closer to the real model, evaluation results of the routing protocol are more reliable [31; 33].
# Enhanced Path Planning by Repositioning the Starting Point +
## 3 Proposed Method
### Distance Analysis
#### 3.1.2 Starting Point Factor  
To provide additional analytical perspective, we can view SPD as a proportion of the total mission distance. This leads us to define the Starting Point Factor (SPF) in Equation (4) as follows:  
\[\textit{Starting Point Factor (SPF)}=\frac{\textit{Starting Point Distance (SPD)}}{\textit{Total Distance}} \tag{4}\]  
Since SPD represents a subset of the total distance, SPF values fall within the range \([0,1]\). The lower bound \(SPF=0\) occurs in the theoretical scenario where starting, ending, and depot points coincide. Lower SPF values indicate that starting point distances constitute a small proportion of total distance, offering limited optimization potential. Conversely, higher SPF values suggest that starting point distances represent a significant portion of total distance, providing substantial opportunities for improvement through SPD minimization, as demonstrated in Figure 5.
# Marination as a Hurdle to Microbial Pathogens and Spoilers in Poultry Meat Products: A Brief Review
## 1 Introduction
### Camplyobacter and Salmonella, Foodborne Pathogens  
Since 2005, camplyobacteriosis is the most routinely reported zoonosis in humans across the European Union (EU), with 120,946 confirmed cases, representing 40.35 cases per 100,000 people in 2020 [8]. According to the last EU zoonoses report, the prevalence of _Campylobacter_-positive non-RTE turkey and broiler samples was 30.5% and 21.5%, respectively, the highest prevalence rates for all the fresh meats in the same class.  
Although many _Campylobacter_ species are the causative agents of gastrointestinal diseases in humans, _Campylobacter jejuni_ and _Campylobacter coli_ are implicated in most infections originating from contaminated foods, such as poultry meat [9; 10; 11]. The vast majority of campylobacteriosis cases are self-limiting, with few instances requiring treatment with antibiotics [12]. Farm-based studies have shown that the major routes of _Campylobacter_ transmission to poultry are the environment and the horizontal dissemination between flock mates [10].  
During the slaughter process, cross-contamination can effortlessly happen among the various processing steps of the carcass: plucking, evisceration, carcass washing, and refrigeration. These steps are crucial control points since they provide a path for bacteria to relocate from the intestines to the carcass or the processing environment [13]. In the pores of the removed feathers, _Campylobacter_ will find a microaerophilic environment that will shield this microorganism from atmospheric oxygen and low refrigeration temperatures [14]. Factors such as the protection offered by the food matrix, the low infectious dose, the capability to form monoculture and mixed-culture biofilms, and the viable but non-culturable (VBNC) state justify the perception of poultry meat as a primary reservoir of _Campylobacter_.  
In the EU, salmonellosis is the primary cause of foodborne outbreaks, remaining the second most commonly reported zoonoses, with 52.702 confirmed cases in 2020, representing 13.7 occurrences per 100,000 individuals [8]. According to the 2020 EU zoonoses report, the prevalence of _Salmonella_-positive non-RTE broiler samples was around 8%, much less prevalent than _Campylobacter_ but still significant. Salmonella is a self-limiting gastroenteritis syndrome, typically caused by non-typhoidal _Salmonella enterica_ serotypes. _Salmonella Typhimurium and _S. enteritidis_ are considered the main cause of foodborne salmonellosis.
# Investigation of In Situ and Ex Situ Passivation of Pyrophoric Uranium-Niobium Alloy Powder
## 2 Materials and Methods
### Passivation  
For ex situ passivation, no modifications were made to the spheroidization process. The unpassivated spherical powder was transferred to a Nalgene low-density polyethylene screw-top bottle in an argon atmosphere. The bottle was then removed from the glovebox, sealed around the cap with electrical tape, and placed in a stainless steel secondary container. The powder was left undisturbed in air for seven months, allowing oxygen to permeate the bottle and oxidize the powder at ambient temperature. After this period, the bottle was returned to the argon glovebox without further exposure to oxygen.  
In situ passivation was performed during spheroidization by introducing a pre-mixed 2% O\({}_{2}\)/98% Ar gas into the plasma chamber. The passivation gas flow rate was set at a value between 0 and 10 SLPM, while the inert Ar/He gas input was held constant. The powder fell through the 0.67 m length of the chamber at a speed of approximately 4.6 m/s [28], passivating in less than 0.15 s. Due to the limited availability of materials, oxygen content (via SLPM) was the only parameter that varied during in situ passivation, as it was expected to have the greatest impact on the passivation. Unless noted otherwise, the in situ-passivated powders characterized were produced with a 5 SLPM passivation gas flow.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Essential Oils/Bacterial Strains**} & \multicolumn{2}{c}{_Ocimum_} & \multicolumn{2}{c}{_Thymus_} & \multicolumn{2}{c}{_Melaleuca_} & \multicolumn{2}{c}{_Thymus vulgaris_} & \multicolumn{2}{c}{_Matricaria_} \\  & \multicolumn{2}{c}{_basilicum_} & & \multicolumn{2}{c}{_capitatus_} & \multicolumn{2}{c}{_alternifolia_} & & \multicolumn{2}{c}{_Thymus vulgaris_} & \multicolumn{2}{c}{_chammilla_} \\ \cline{2-10}  & **MIC** & **MBC** & **MIC** & **MBC** & **MIC** & **MBC** & **MIC** & **MBC** & **MIC** & **MBC** \\ \hline MRSA & 0.25–4 & 0.25–4 & 0.06–0.5 & 0.06–0.5 & 0.12–1 & 0.06–2 & 0.06–1 & 0.06–1 & 2.>4 & 2.>4 \\ VRE & \(\geq\)4 & \(>\)4 & 0.25–1 & 0.5–1 & 1–4 & 0.75–4 & 0.5–2 & 0.32–4 & \(>\)4 & \(>\)4 \\ SA 29213 & 0.25 & 0.25 & 0.25 & 0.25 & 0.5 & 0.5 & 0.25–0.5 & 0.25 & \(>\)4 & \(>\)4 \\ SA 43300 & 0.5 & 0.5 & 0.12 & 0.12 & 0.25–0.5 & 0.25 & 0.25 & 0.25 & \(>\)4 & \(>\)4 \\ SE 12228 & 0.25 & 0.25 & 0.12–0.25 & 0.12 & 0.25–0.5 & 0.25–0.5 & 0.12–0.25 & \(>\)4 & \(>\)4 \\ EF 51299 & 4 & 4 & 0.25 & 0.25 & 1 & 1 & 0.25–0.5 & 0.25–0.5 & \(>\)4 & \(>\)4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Range of Minimal Inhibitory Concentration (MIC) and the Minimal Bactericidal Concentration (MBC) values % (_v/v_) of selected essential oils against the tested bacteria. MRSA: methicillin-resistant _Staphylococcus aureus_; VRE: vancomycin-resistant enterococci. Figure 1: Comparison of MIC values of selected essential oils against wild and ATCC staphylococci (**A**), wild and ATCC enterococci (**B**).
## 4 Discussion  
The liver plays various important roles in the human body, such as metabolism, detoxification, storage, and secretion. Acute or chronic liver failure can occur for various reasons. Among them, acute liver failure occurs suddenly from various causes and can exhibit clinical symptoms of severe liver damage [22]. The main causes of liver damage leading to liver failure show wide geographical variation and depend on liver viral infections and drug use patterns [23; 24]. Studies have shown that viral causes dominate in developing countries, including India, with Hepatitis A, B, and E virus infections as common causes [22; 25]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Groups**} & \multicolumn{6}{c}{**Change Form Baseline**} \\ \cline{3-10}  & & & **Mean** & **SD** & **Median** & **Min** & **Max** & **Within** & **Within** & **Between** \\  & & & & & & & & **Group*** & **Group*** \\ \hline \multirow{2}{*}{**ALT**} & CA-HE50 & \(-19.9\) & \(9.9\) & \(-21\) & \(-42\) & \(1\) & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\  & Placebo & \(-1.8\) & \(11.3\) & \(1\) & \(-39\) & \(8\) & \(0\).3323 & \(0\).28991 & \(<\)0.001 \\ \hline \multirow{2}{*}{**AST**} & CA-HE50 & \(-11.4\) & \(9.8\) & \(-11\) & \(-32\) & \(9\) & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\  & Placebo & \(0.4\) & \(5.1\) & \(0\) & \(-17\) & \(12\) & \(0\).6388 & \(0\).3940 & \(<\)0.0001 \\ \hline \multirow{2}{*}{**GGT**} & CA-HE50 & \(-6.8\) & \(6.3\) & \(-5\) & \(-25\) & \(1\) & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\  & Placebo & \(-1.5\) & \(4.2\) & \(-2\) & \(-12\) & \(9\) & \(0\).0271 & \(0\).0202 & \\ \hline \multirow{2}{*}{**TG**} & CA-HE50 & \(-18.3\) & \(31.5\) & \(-6\) & \(-108\) & \(52\) & \(0\).0007 & \(0\).0004 & \(0\).0008 \\  & Placebo & \(5.7\) & \(29.7\) & \(2\) & \(-56\) & \(90\) & \(0\).2362 & \(0\).3221 & \\ \hline \multirow{2}{*}{**TC**} & CA-HE50 & \(-8.9\) & \(19.2\) & \(-6\) & \(-62\) & \(29\) & \(0\).0055 & \(0\).0028 & \(<\)0.0001 \\  & Placebo & \(16.3\) & \(25.5\) & \(9\) & \(-40\) & \(97\) & \(0\).0003 & \(0\).0001 & \\ \hline \multirow{2}{*}{**LDL-C**} & CA-HE50 & \(3.6\) & \(25.1\) & \(-3\) & \(-44\) & \(75\) & \(0\).3738 & \(0\).5692 & \(0\).0100 \\  & Placebo & \(19.7\) & \(29.1\) & \(13\) & \(-40\) & \(105\) & \(0\).0001 & \(0\).0001 & \\ \hline \multirow{2}{*}{**HDL-C**} & CA-HE50 & \(-1.7\) & \(3.8\) & \(-11\) & \(-11\) & \(3\) & \(0\).0075 & \(0\).0290 & \(<\)0.0001 \\  & Placebo & \(3.\) & \(6.1\) & \(2\) & \(-9\) & \(20\) & \(0\).0308 & \(0\).0032 & \\ \hline \hline \end{tabular}  
* Within-group comparison was performed using paired \(t\)-test + 1 Within-group comparison was performed using a non-parametric method (Wilcoxon signed-rank test).
* Between-group comparison was performed using a two-sample \(t\)-test that compared IP and placebo. ALT, alanine aminotransferase; AST, aspartate aminotransferase; GCT, gamma-glutamyl transferase; TG, triglyceride; TC, total cholesterol; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol.  
\end{table} 
Table 6: Summary of the parameter changes from baseline.