esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Metabarcoding Sequencing  
A modified three-step PCR methodology [27; 28] was carried out to amplify the regions of interest prior to NGS. The universal primers ITS1-ITS4 and LROR-LR3 for the characterization of fungi and yeast, and the 341 FR-806R primers for bacteria, were used for the first PCR step. The PCR product was submitted to a second amplification using primers with a frameshift section and sequencing adaptors encompassed in the ITS1 f1-f6 and ITS4 f1-f6, LROR f1-f6 and LR3 f1-f6, as well as the 341R f1-f6 and 806R f1-f6 primers (Table 1). For the final amplification, a forward primer was used with a reverse primer containing a barcode sequence to amplify the PCR product from the previous step (Table 1). The barcode sequence was different for each sample. Each PCR reaction was carried out in a total volume of 25 \(\upmu\)L using 10 ng of DNA, 12.5 \(\upmu\)L of green GoTaq (Promega, Madison, WI, USA), 10 \(\upmu\)M of each primer and DNA-free water. The conditions for each PCR amplification were an initial step of 95 \({}^{\circ}\)C for 5 min, followed by 10 cycles at 95 \({}^{\circ}\)C for 1 min, 52 \({}^{\circ}\)C for 2 min and 72 \({}^{\circ}\)C for 2 min, with the final step at 72 \({}^{\circ}\)C for 2 min. The generated fragments were electrophoresed at 120 volts for 20 min using 10 \(\upmu\)L of the PCR product with 1 \(\upmu\)L of loading dye (Promega, Madison, WI, USA) in a 1.5% agarose gel run in 1\(\times\) TAE (Tris base, boric acid and EDTA 0.5 M, pH 8.0). The PCR products were quantified using the NanoDrop 2000 spectrophotometer.  
All the final PCR products containing the different barcodes were pooled into a single tube. A total of 50 \(\upmu\)L from the pooled products was run in a 2% electrophoresis gel with TAE 1\(\times\) (Tris base, boric acid and EDTA 0.5 M, pH 8.0) at 110 volts for 45 min (50 \(\upmu\)L of DNA and 10 \(\upmu\)L of loading dye (Promega, Madison, WI, USA)). The PCR products were gel-purified using the Quick Gel Extraction and PCR Purification Combo kit and submitted to a service lab for sequencing on an Illumina-MiSeq platform.
## 1 Introduction
In the case of the cold plasma, there will not be a local thermodynamic equilibrium between the highly energetic electrons generating plasma and other particles (T\({}_{\text{electrons}}\)\(>>\) T\({}_{\text{plasma}}\)) because the density of the electrons in the plasma is small compared to that of other constituents. Thus, the term \"cold\" refers to the plasma with the temperature between 40 \({}^{\circ}\)C and 70 \({}^{\circ}\)C [33]. This plays a key role if plasma is used to modify heat-sensitive materials because plasma modification of a solid surface is one of its common applications. It can be used for food decontamination [34] and in medicine [35], nanotechnology [36], and surface engineering [37]. The high-energy constituents of the plasma interact with a surface and can change a wide range of its properties such as surface free energy, wettability, roughness, surface charge, and biocompatibility. The ability of plasma modification to induce any changes in the bulk of a solid is still an open question. The aim of this study was to obtain a new silica-multiwalled carbon nanotubes/poly (dimethylsiloxane) (silica-MWCNTs/PDMS) composite coating. The hydrophobic properties of the product were to be ensured by the modification of carbon nanotubes with poly(dimetlysiloxane) (PDMS) as well as hydrophobization of the surfaces with hexamethyldisilazane. The influence of cold plasma modification of the support on the surface properties of the coating was investigated. The physicochemical properties of the obtained coatings were comprehensively studied using scanning electron microscopy, X-ray diffraction, optical profilometry, thermal analysis, contact angle measurements, and surface free energy evaluation.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion  
The desired radiation characteristic of an electromagnetic problem is achieved by utilizing an LAA. Different optimization algorithms have been applied for the synthesis of these types of antenna arrays. In this section MAOA algorithm is proposed for optimizing the position and amplitude of the LAA elements. Here, the optimization goal is to synthesize an LAA having reduced PSLL, minimum CSLL, suppressed side lobes, and null positioning under the condition of fixed and variable beamwidth requirement by controlling the interelement spacing and current amplitude of the antenna elements separately. Eleven designs are showcased to compute the performance nature of the proposed algorithm, alongside a comparison with other prevalent intelligent optimization algorithms. The programming was carried out using MATLAB software (2024a) on a computer with an Intel Core i5 processor having 16 GB of RAM. The process was executed 15 times to obtain the desired optimized values. MAOA is a population-based iterative optimization algorithm; hence, the number of search agents and the iteration count are set to be 40 and 1000, respectively. Designs 1 to 5 illustrate that the position of the array elements is optimized, whereas designs 6 to 10 illustrate the amplitude-optimized LAA using the proposed algorithm. Design 11 showcases the amplitude-optimized CAA using the proposed algorithm.
## 1 Introduction
### Construction Safety  
The Architecture, Engineering, Construction and Operation (AECO) industry is considered one of the most hazardous in the world [1]. Its work dynamics, interactions between diverse professionals, and construction site characteristics that include outdoor work, unpredictable environments, dangerous machinery and tools and unique and varied projects, make safety management complex [2]. Thus, multiple factors affect construction safety, ranging from organisational concerns to job site characteristics, materials and equipment, in addition to factors associated with workers and work teams [3]. Despite the sector's efforts to improve safety, its high accident rates have not decreased in recent years, and challenges for improving safety persist, though the goal is still 'zero accidents'.  
Today, the sector is evolving within the framework of Construction 4.0. This new way of working proposes the digitalisation of the work environment and incorporates emerging technologies that use robotisation and automation. Alongside this, the integration of management and collaboration methodologies, such as Lean Construction and Building Information Modelling, establish new frameworks that aim to reduce losses and increase continuous improvement and efficiency [4]. The aim is to integrate and connect physical work environments with virtual environments by using digital twins (DT) that make it possible to control and anticipate the behaviour of physical environments to improve the planning and execution of construction and operation processes and infrastructure (under development or during their useful life) [5].  
Faced with the challenge of reducing the accident rate in the working environment as proposed by Construction 4.0, extended reality demonstrates multiple advantages in various aspects of safety management [6; 7]. Various tools can be developed using virtual reality, which creates total immersion; augmented reality, the superimposition of virtual elements on reality; and mixed reality (MR), a combination of virtual reality and augmented reality that creates environments in which real and virtual elements interact [7; 8; 9]. The immersive possibilities of first-person BIM models, such as recreation of real environments, collection of site data and worker behaviour, overlap with reality and safe, virtual interactions for users with these elements offer potential improvements in occupational health and safety in construction [10; 11].  
Extended reality is relevant today as a new platform for human-machine interactions (HMIs) in the context of the new work dynamics proposed in Construction 4.0 [12; 13]. Its great advantage, like other digitalisation tools, is prediction, the ability to anticipate real events and scenarios in controlled virtual environments. This allows us to model reality, anticipate problems and prevent them [14; 15].
## Materials and Methods
### Milk and Feed Compositional Analysis  
Total nitrogen (TN), crude protein (CrP), non-protein nitrogen (NPN), non-casein nitrogen (NCN), and true protein (TP) were determined as outlined by the International organisation for standardization ISO (10; 11) using the Kjeldahl method and a nitrogen-to-milk protein conversion factor of 6.38. These N values were then used to calculate true protein (TP), casein protein (C\({}_{\text{p}}\)), and whey protein (W\({}_{\text{p}}\)) contents as outlined by [PERSON] et al. (12) where TP = TN-NPN \(\times\) 6.38, C\({}_{\text{p}}\) = (TN-NCN) \(\times\) 6.38 and W\({}_{\text{p}}\) = (NCN-NPN) \(\times\) 6.38. Milk samples were analysed for fat, lactose, and total solids contents by infrared absorption spectroscopy using a FT6000 Milkoscan (Foss Ireland Ltd, Dublin, Ireland). Milk yield from each of the diets are shown in Table 4. Feed samples were collected throughout lactation from paddocks at time of grazing. Samples were dried at 60 \({}^{\circ}\)C for 48 h, milled, and stored prior to analysis. Grazed grass and offered silage samples were analysed using near infrared reflectance spectroscopy using a FOSS 6500 (FOSS Ireland Ltd, Dublin, Ireland). Feed supplement samples during the trial were bulked to one sample per diet and analysed for the major FAME by Dairygold Analytical Services Laboratory (Lombardstown, Mallow, Co. Cork, Ireland) (Table 5).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & **CONC** & **PALM** & **SOYA** & **BEET** & **SE** & **P-Value** \\ \hline
**Daily Milk Yield (kg/cow/day)** & 19.2 1 ** & 19.8 21.4 3  & 21.4 3  & 0.27 & 0.001 \\
**Milk Fat (g/kg)** & 43.3 1 ** & 48.8 2  & 45.4 3  & 44.8 1  & 1.01 & 0.01 \\
**Milk Protein (g/kg)** & 35.7 & 35.3 & 35.2 & 35.0 & 0.43 & NS \\
**Milk Solids Yield (kg/cow/day)** & 1.52 1  & 1.64 1  & 1.72 2  & 1.60 1  & 0.029 & 0.001 \\ \hline \end{tabular} a,b and c denote significant difference between feeding systems.  
\end{table}
Table 4: The effect of dietary treatment (parlour concentrate, palm kernel expeller, soya hulls, and molassed beet pulp) on milk production and milk composition, during the experimental period (23 July to 31 August).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **CONC** & **PALM** & **SOYA** & **BEET** \\ \hline
**Dry Matter** & 869 \(\pm\) 1.6 & 911 \(\pm\) 22.7 & 869 \(\pm\) 6.7 & 876 \(\pm\) 2.4 \\
**Organic Matter** & - & 657 \(\pm\) 70.2 & 800 \(\pm\) 58.1 & 871 \(\pm\) 59.7 \\
**Digestibility** & 163.7 \(\pm\) 0.8 & 149.2 \(\pm\) 9.2 & 93.8 \(\pm\) 0.8 & 84.5 \(\pm\) 0.8 \\
**Crude Fibre** & 51.0 \(\pm\) 0.9 & 199.0 \(\pm\) 32.3 & 360.0 \(\pm\) 7.5 & 120.5 \(\pm\) 5.8 \\
**ADF1** & - & 539 \(\pm\) 34.1 & 446 \(\pm\) 21.2 & 202 \(\pm\) 17.3 \\
**NDF2** & 248 \(\pm\) 40.8 & 810 \(\pm\) 41.2 & 622 \(\pm\) 33.1 & 401 \(\pm\) 24.8 \\
**Oil** & 26.8 \(\pm\) 0.8 & 71.
# Study on the Dynamic Generation of Subcooled Water Using a Compact Heat exchanger
## 2 Experimental Setup and Experimental Conditions
### Experimental Setup  
The setup for the dynamic generation of subcooled water mainly consists of a low-temperature NFL loop (left) and a loop for the dynamic generation of subcooled water (right), which are connected by a compact heat exchanger 1, as shown in Figure 2. The components in front of the heat exchanger inlet in both loops are identical and arranged symmetrically. The low-temperature NFL loop provides the cooling capacity as a closed loop and is driven and controlled by a variable frequency pump 2. Then, through a pre-filter (40 \(\upmu\)m), the NFL is filtered before entering the heat exchanger to prevent blockage due to impurities. Next, it passes through a turbine flow meter 6, a pressure gauge 8, and a K-type thermocouple 5 into the compact heat exchanger, where the pressure gauge mainly monitors the loop pressure status and is not used as a parameter for collection. After heat exchange with the tap water, the NFL flows back into the low-temperature thermostatic device 3 with a capacity of 33 L, determined through temperature measurement by the K-type thermocouple at the outlet. A semi-open loop, driven by two variable frequency pumps, is used for the subcooled water's dynamic generation. From the high-temperaturethermostatic device 2 to the compact heat exchanger inlet, the tap water passes through the same components as the low-temperature non-freezing liquid. After heat exchange with the non-freezing liquid, the temperature of the generated subcooled water is measured at the outlet and then flows into the ice storage tank 4 for release through subcooling to form ice or ice slurry. Finally, a variable frequency pump is used to pump away excess water from the storage tank, and the water is then injected into a high-temperature thermostatic device with a capacity of 25 L to reduce the thermostatic load. However, some subcooled water is left in the ice storage tank out of necessity. The subcooled water generation setup also includes a ball valve 4 and the insulation material of the pipeline.  
The experiments were conducted using a turbine flowmeter and K-type thermocouple to collect the flow magnitude and the inlet and outlet temperature of the heat exchanger on the two loop sides, respectively. The data recorder, which could communicate with the computer through the RS485 module, then collected the parameters, which were output as  
Figure 2: Subcooled water generation setup. (**a**) Schematic of subcooled water generation setup. (**b**) Photo of subcooled water generation setup. 1�  
an Excel file to facilitate the analysis of the experimental results. The turbine flow meters had a flow range of 0.600-4.100 L/min with an uncertainty of \(\pm\)1.0%, and the K-type thermocouples had a temperature range of \(-\)50-50 \({}^{\circ}\)C with an uncertainty of \(\pm\)1 K.
## 1 Introduction
\begin{tabular}{c c c c c} \hline \hline
**System** & \begin{tabular}{c} **Frequency Band** \\ **[MHz]** \\ \end{tabular} & **Max Eirp 2 [mW]** & **Duty Cycle 3** & **References** \\ \hline Wi-Fi 2G & 2400–2483.5 & 100 (Class 1) & 0.02–0.95 & \begin{tabular}{c} ETSI EN 300 328 \\ V2.2.2 (2019-07) [23] \\ \end{tabular} \\ \hline LTE; LTE-M; NB-IoT & 1880–1920; 1920–1980; 2110–2170; 2300–2400; 2496–2690 & 1250 (Class 1); 200 (Class 3) & 0.02–0.95 & \begin{tabular}{c} ETSI TS 136 101 \\ V18.6.0 (2024-08) [24] \\ \end{tabular} \\ \hline UMTS; HSPA+ & 1885–2025; 2110–2200 & 2000 & 0.02–0.95 & \begin{tabular}{c} ETSI TS 125 101 \\ V15.3.0 (2019-05) [25] \\ \end{tabular} \\ \hline Bluetooth (LE); ZigBee & 2400–2483.5 & \begin{tabular}{c} 100 (Class 1); \\ 2.5 (Class 2); \\ 1 (Class 3) \\ \end{tabular} & \begin{tabular}{c} ETSI EN 300 328 \\ V2.2.2 (2019-07) [23] \\ \end{tabular} \\ \hline SHF RFID & 2446–2454 & \begin{tabular}{c} 500 (4000 in building) \\ \end{tabular} & \begin{tabular}{c} 0.01–0.7 \\ (0.01–0.15 in building) \\ \end{tabular} & \
\begin{tabular}{c} ETSI EN 300 440 \\ V2.1.1 (2017-03) [26] \\ \end{tabular} \\ \hline WBAN & 2360–2400; 2400–2483.5 & 1 &
\begin{tabular}{c} ETSI TR 103 711 \\ V1.1.1 (2020-10) [27] \\ \end{tabular} \\ \hline \hline \end{tabular}   
* 1 2.45 GHz ISM band—the 2.45 GHz (i.e., 2.400–2.483 GHz) ISM (Industrial, Scientific, Medical) frequency band;
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 6 Experimental Results
### Segmented Global Path Planning Results
#### 6.3.2 Success Rate
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Radius (m)** & **NA** & **MB** & **T** & **NAS (\%)** & **MBS (\%)** & **CR (\%)** \\ \hline \multirow{3}{*}{Home} & 0.18 & 12 & 9 & 12 & 100 & 75 & 25 \\  & 0.23 & 11 & 7 & 12 & 91.67 & 58.33 & 33.33 \\  & 0.28 & 2 & 1 & 12 & 16.67 & 8.33 & 8.33 \\ \hline \multirow{3}{*}{Office} & 0.18 & 18 & 13 & 18 & 100 & 72.22 & 27.78 \\  & 0.23 & 18 & 9 & 18 & 100 & 50 & 50 \\ \hline Average & - & 61 & 39 & 72 & 84.72 & 54.16 & 30.56 \\ \hline \hline \end{tabular}  
NA: the number of successful navigation paths when using the proposed algorithm based on the ANPs. MB: the number of successful navigation paths when using the ROS move_base stack. T: the total number of navigation paths. NAS: navigation success rate when using the proposed algorithm based on the ANPs (NA/T). MBS: navigation success rate when using ROS move_base stack (MB/T). CR: the improvement when compared to ROS move_base (NAS-MBS). Average values are also included.  
\end{table} 
Table 5: Results of navigation success rate. of collision. In the indoor scene, the connection channels of different topological areas are usually the right angle. For example, 20 of the 30 paths in our experiment's home and office scenes were the right angle. By observing the move_base navigation trajectory (expressed by red line in Figure 10a,c), it can be seen that the crossing door trajectory is circular-like. This makes the distance between the two sides of the robot and the obstacle uneven and increases the probability of collision. The proposed method forces the robot to pass through the door along the door area's centerline, keeping the maximum distance from the obstacle on both sides, so as to improve the success rate and security. * As the robot's radius increases, both of the success rates of our method and move_base are degraded. The increase in the robot's radius narrows the feasible area when passing the door, so it is more likely that the valid path cannot be sensed or the collision occurs. For example, the door's width in the home scene was 0.74 m, and as the diameter of the robot increased, the feasible area for the robot to pass the door gradually decreased, respectively 0.34 m, 0.24 m, and 0.14 m.
# Automated Code-Smell Detection in Microservices Through Static Analysis: A Case Study
## 1 Introduction  
Microservices are becoming the preeminent architecture in modern enterprise applications [1]. There are several advantages to utilizing this architecture, which have led to its rise in popularity [2]. The distributed nature of a microservice-based application allows for greater autonomy of developer units. While this provides greater flexibility for faster delivery, improved scalability, and benefits in existing problem domains [3], it also presents the opportunity for code smells to be more readily created within the application. This is especially true since distinct teams manage different distributed modules of the overall system.  
Code smells [4] are anomalies within codebases. They do not necessarily impact the performance or correct functionality of an application. They are patterns of poor programming practice and deteriorate program quality. They can affect a wide range of quality attributes in a program including reusability, testability, and maintainability. If code smells go unchecked in a microservice-based application, the benefits of using a distributed development process can be mitigated. It is therefore crucial that the code smells in an application are appropriately detected and managed [4, 5].  
Microservices present a unique situation when it comes to code smells due to the distributed nature of the application. Microservice-specific code smells often focus on inter-module issues rather than an intra-module issue. Traditional code-smell detecting tools cannot detect code smells between discrete modules, so these issues go unchecked during the development process. This paper shows that, when we augment static code analysis to recognize enterprise development constructs, we can effectively detect code smells in distributed microservice applications.  
We share a case study targeting eleven recently identified code smells for this architecture to demonstrate our approach. Furthermore, we develop a prototype code smell detector for microservices and share it with the community as open source. Our prototype is based on code analysis and recognizes Java code along with Enterprise Java platform constructs and standards [6; 7; 8; 9; 10]. Next, it identifies the eleven microservice code smells targeted in this paper.  
The rest of the paper is as follows. Section 2 assesses related work for code smells detection and the shortcomings for distributed systems. Section 3 introduces the code smells used in this paper. Section 4 describes the static code analysis of enterprise systems. Section 5 proposes our solution for automatic code-smell detection for microservices. Section 6 verifies our approach on two existing microservice benchmark applications. Lastly, Sections 7 and 8 conclude the work and highlight future perspectives.
## 4 Experiments and Results
### Results on Real-World Datasets
At significance level \(\alpha=0.05\), the corresponding \(q_{\alpha}=2.41\), thus we have CD = 2.41 (\(n\) = 8, \(T\) = 18). Figure 3 show the average ranks of the algorithms based on aRRMSE metrics. As can be seen in Figure 3, although MTR-MLS is not significantly different from MMR and MSLR for all datasets, there is a significant improvement in performance between the above algorithms and other MTR algorithms. Because these algorithms consider the correlation between different target outputs and impose sparsity constraints in the modelling process, which makes they are significantly superior to the problem transformation algorithms (SST, RLC and ERC) on different datasets. Furthermore, these algorithms are based on the multi-layer sparse latent variable regression framework, which can make full use of the correlation information between different outputs to obtain better prediction results than mSVR, which only considers multiple output errors, and OKL, which only considers the positive correlation between targets. The MTR-MLS algorithm maintains better performance than MMR and MSLR on the datasets with a high number of output targets (OES10, OES97, OSALES, SCM1d, SCM20d, WQ), indicating that the proposed algorithm can learn the complex structured noises by sparse learning of the complex structured noise among the targets of the datasets.