esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Case Study  
The Cluj-Napoca Metropolitan Area is located in the north-west of Romania, in the historical region of Transylvania, approximately in the center of Cluj County, at the intersection of 46\({}^{\circ}\)46\({}^{\prime}\)00.0\({}^{\prime\prime}\) north latitude parallel with the 23\({}^{\circ}\)36\({}^{\prime}\)00.0\({}^{\prime\prime}\) east longitude meridian (Figure 1).  
From the hydro-morphological perspective, the area is located predominantly in the Somesul Mic river basin, at the intersection of the Somes Plateau, the Transylvania lowlands, and the eastern part of the Apuseni mountains (Masivul Gilau and Muntele Mare).  
One of the largest in Romania, Cluj-Napoca's Metropolitan Area covers a territory of approximately 1600 km\({}^{2}\) and includes 19 administrative units with about 100 settlements, totaling around 415,000 inhabitants (according to the General Population Census in 2011 and The Romanian National Statistics Institute); with an increase of approximately 7% compared to the total population recorded in the 2002 census.  
The area is very important in terms of socio-economic and has experienced an impressive urbanization in the recent years, especially in peripheral areas around the municipality of Cluj-Napoca. The largest increase in both the population and the built-up areas, more than 90% of the total, occurred in the first ring of the metropolitan area, in the immediate neighborhoods of the municipality. Considering this, the analysis was focused on this area, administratively formed from the city of Cluj-Napoca and the following communes: Apahida, Baciu, Chinteni, Feleacu, and Floresti.
## 4 Results and Discussion
### Received Power Estimation
The results demonstrate that path loss and received power are influenced by factors such as operating frequency, the distance between transmitter and receiver antennas, antenna location, antenna height, and their respective  
Figure 16: Received power of an Urban Macro environment at 60 GHz. Figure 14: Received power of an Urban Macro environment at 28 GHz. Figure 15: Received power of an Urban Macro environment at 38 GHz. positions. Among the models considered, the 5 GCM model is found to minimize path loss and maximize receiver power specifically in the urban micro environment. On the other hand, the 3 GPP model is suitable for both urban and rural macro environments, surpassing other models by producing the lowest path loss in those respective environments. These models are recognized as optimal choices for enhancing system performance in terms of path loss. Service providers can leverage these models to improve the quality of service in both indoor and outdoor 5G mmWave wireless networks.
## 3 Results and Discussion
### Fractography Investigation  
The fatigue fracture morphologies of the G20 Mn5 QT cast steel and the weld, after they were corroded using 5% alcohol nitrate solution, were observed using an S-3400N scanning electron microscope at the School of Material Science and Engineering, Tianjin University. Compared to that of G20 Mn5 QT, the crack surface of the butt weld is relatively flatter and smoother, and the necking zone of the butt weld appears much later (see Figure 16a,b). In stage I, the micromorphologies of both G20 Mn5 QT and the butt weld exhibit flat facets and fine fatigue striations with little secondary cracks, as shown in Figure 16c,d. As the fatigue crack propagates into stage II, the spacing of fatigue striations becomes wider and extremely evident. At the same time, secondary cracks appear extensively, as shown in Figure 16e,f. When the crack grows into stage III, dimples, tearing ridges, and secondary cracks begin to appear extensively. At the end of the cracks, ductile overload fracture morphologies characterized by microvoids and microcracks are predominant in the crack surfaces, as shown in Figure 16g,h.  
It is worth noting that shear bands are observed on the fracture surfaces of G20 Mn5 QT and the butt weld. Furthermore, the shear bands are not continuously distributed along the surface but occur probabilistically around defects caused by casting or welding, as shown in Figure 17a,b. Therefore, the shear cracks appearing in the fracture surface of a CT specimen that should primarily exhibit tensile cracks may be caused by the multi-state of stress and strain occurring due to the casting or welding defects. This phenomenon verifies the existence of cluster 3 signals generated by shear crack fractures and the discontinuous occurrences pertaining to the discontinuous distributions of defects. Furthermore, as the average energy released by a shear crack fracture of G20 Mn5 QT is less than that of the weld, the material weakening caused by the defects of G20 Mn5 QT is larger than that of the weld.  
Figure 16: Scanning electron micrographs of crack surfaces: (**a,c,e,g**) G20 Mn5 QT cast steel; (**b,d,f,h**) butt weld; (**a,b**) crack surface; (**c,d**) fatigue striations at stage I; (**e,f**) fatigue striations at stage II; (**g,h**) dimples at stage III.
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 4 Proposed Algorithm: HOUM
Black dots: majority data; blue stars: minority data; red dots: data selected from the majority class for removal; red stars: oversampled minority data. 3. The data are divided into 80% for training and 20% for testing. (Note: oversampling and undersampling operations are applied only to the training data.) 4. The balance of the training dataset is determined (a balanced condition indicates that the class distribution is within 50 \(\pm\) 5%). If this condition is met, the process proceeds to step 8. 5. Undersampling is applied to the majority of the data far from the decision boundary as follows. * The first quartile (25 th percentile) of decision values is considered to belong to the majority class. * The aforementioned first 25 th percentile is removed from the dataset. 6. The balance of the training dataset is determined. If the dataset is balanced, the process proceeds to step 8. 7. Oversampling is applied using SLS. Then, the process returns to step 4. 8. If the data are balanced, they can be classified using classification algorithms. 9. The classifiers are evaluated with the test data, and the results are interpreted using accuracy and G-mean metrics. The complexity analysis of the HOUM algorithm consists of three main steps: SVM-based undersampling, SLS-based oversampling, and iterative balancing. In the first step, SVM-based undersampling, the training of the SVM in HOUM-R with a radial kernel, typically has a complexity between \(O(n^{2})\) and \(O(n^{3})\) as it involves calculating the kernel matrix, where n represents the total number of samples in the dataset [42]. In the second step, the SLS method determines the safe levels of the minority-class samples using the nearest neighbors and has a complexity of \(O(n\cdot k\cdot d)\), where \(n\) is the number of data points, \(k\) is the number of neighbors, and \(d\) is the number of features [2]. In the iterative balancing step, these two processes are repeated until the dataset is balanced, leading to an overall complexity of \(O(l\cdot(n^{2}+n\cdot k\cdot d))\), where \(l\) represents the number of iterations. Figure 4: Flowchart of the proposed HOUM algorithm.
## 2 Methodology and System Architecture  
This section discusses the proposed smart cyber-physical system consisting of the layers mentioned in Section 1; the physical resources layer of the smart factory platform is shown in Figure 3, which is composed of a robotic arm (KUKA KR 6 sixx r900), PLC Siemens S7-1200, universal web camera, variety of sensors, a drilling unit and a PC with a range of communication technologies to be discussed later.  
The considered smart manufacturing is a machining (drilling) process where the workpiece is transported to a certain slot where the PLC and the web camera detect its arrival. The machine vision system calculates the position and orientation of the workpiece in order for KUKA robot to know the coordinates of the workpiece's gripping point. The robot that is responsible for pick-and-place tasks receives the corresponding position and picks the item and places it in the designated drilling unit slot. A proximity sensor is used to check its arrival at the drilling table to start the drilling process. A limit switch is triggered after the drilling process has been completed so the robot can move the workpiece to the designated storage unit. This process repeats itself each time a piece is present at the starting slot. The PLC is programmed using the ladder logic via a _Totally Integrated_Automation (TIA)_ portal, and the KUKA is programmed using KUKA Robot Language (KRL) via its own Human-Machine Interface (HMI).  
The sensors used are connected to the PLC, and the LabVIEW Application Program Interface (API) transfers data between the KUKA robot and the PLC to the cloud. The following subsections describe the system architecture in more detail.
# Metachrylate-Based Polymeric Sorbents for Recovery of Metals from Aqueous Solutions
## 5 Adsorption of Metal Ions on Polymeric Sorbents--General Remarks  
Metal sorption from aqueous solutions proceeds through the following mechanisms: coordination, precipitation, ion exchange, Van der Waals, and electrostatic interactions, depending on solution pH, type of the active sites on the sorbent surface, point of zero charge of polymeric sorbent, etc. (Figure 1) [5; 69].  
Generally, adsorption experiments include determining maximum adsorption capacity under static and dynamic conditions. In order to optimize adsorption conditions, experiments should be performed under different conditions, such as adsorption time, pH, adsorbent mass, initial metal ions concentrations, and temperature. Adsorption capacity (\(q_{t}\)) is calculated according to the Equation (1):  
\[q_{t}=\frac{(C_{i}-C_{t})*V}{m} \tag{1}\]  
Figure 1: Schematic representation of possible metal adsorption mechanisms by polymer sorbents.  
where \(C_{i}\) is the initial concentration and \(C_{t}\) is the concentration at time \(t\) for metal ions in aqueous solution, \(V\) is the volume of the aqueous solution, and \(m\) is the mass of the adsorbent.  
The research on metal ions removal also requires the determination of adsorption equilibrium and kinetics, i.e., analysis of experimental data with adsorption isotherms and kinetic models. Calculation of thermodynamic parameters such as the standard free energy, enthalpy, and entropy change enables the prediction of the nature of the adsorption process, endothermal or exothermal. Last but not least, experiments in several adsorption/desorption cycles provide insight into the possibility of regeneration and repeated use of the adsorbent.  
The most important criteria for the evaluation of sorbent applicability and efficiency are the nature and location of functional groups in a sorbent, sorbent capacity, selectivity, and the rate of complexation of metal ions [42]. In order to facilitate sorption kinetics, it is preferable for functional groups to be located at the surface or nearby. For example, amino-functionalized macroporous polymers have accessible functional groups on the particle surface, which promote the metal sorption process. Therefore, several parameters must be considered when sorbent usability and capabilities are assessed, such as adsorption isotherms and kinetics, selectivity, adsorption thermodynamics, and capability of the metals' desorption and recovery.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design
### Tree Construction Algorithm
#### 3.5.1 Registration of Data Sources  
When a node is designated as the source node for a multicast group, it generates a MID and registers the association between the MID and its NA with both the GNRS and its corresponding ENRS (if exists). Each tree node TN\({}_{i}\) maintains the accumulated delay, \(D_{accum}\), for packets transmitted from the multicast source node to itself within the tree. The root node (RN), which also functions as a multicast tree node (TN), maintains an accumulated delay of 0.
## 7 Research Gaps and Future Opportunities
### Protection against Communication Attacks
#### 7.1.2 Lack of Awareness  
BC-SCM systems deliver information security by storing the information exchanged among different interacting stakeholders in an encrypted chain of blocks (i.e., distributed ledger). The chain is tamper-proof and able to detect any kind of modification of data. Also, such solutions are good at detecting what information has been compromised. However, since they are not aware of the information semantics (i.e., business process implementation that generated the information), they failed to determine potential impact of the compromised information. For instance, in case of a security incident, current solutions could not exactly identify what is the incident. In this case, they have very limited resolution information, resulting in failure to mitigate the impact of the incident on one hand; on the other hand, they are not able to initiate a relevant recovery strategy. Since blockchain-based solutions demand high credibility of the involved stakeholders, it becomes challenging for stakeholders to establish trust in the business if no victim is identified when something goes wrong. More recently, there have been some efforts [92; 93] to develop semantic aware blockchain and smart contracts that partially consider the meta-data (i.e., process level information) to analyze the system security.
## 5 Experiments and Results  
### Experimental Scenario  
In this experiment, the flight mission was located on Xuefu Road, Xiangtan City, Hunan Province, China, as shown in Figure 8. The Xuefu Road is an asphalt pavement with eight two-way lanes and a one-way road width of 16 m. The UAV aerial photography covered a distance of 1.5 km. The road was built and opened to traffic in 2010. After more than 13 years, the road surface has suffered significant damage, including transverse cracks, longitudinal cracks, alligator cracks, and no-cracks. The experiment was conducted at 10:00 a.m. on a sunny day with relatively sparse traffic. Based on the previously obtained UAV flight parameters, the flight height (\(\mathbf{H}\)) was set to 22.5 m and the flight velocity (\(\mathbf{v}\)) was set to 5.0 m/s.
## 3 Tests, Results, and Discussion
### Data Collection  
The test vehicle was equipped with a NovAtel GNSS receiver, a low-cost INS616 IMU, and an odometer in Beijing on 23 December 2021. The sampling rates of GNSS, IMU, and odometer measurements were set to 1 Hz, 125 Hz, and 100 Hz, respectively. The designed test route, available satellite number, and PDOP, along with the trajectory, are shown in Figures 2 and 3. The trajectory is mainly on urban environments with many buildings on both sides of the road. The average number of satellites of GPS, BDS-3, and BDS-3/GPS are 6.1, 8.5, and 14.5. The corresponding PDOP are 2.3, 2.1, and 1.4, respectively. As is shown, the continuity of this test is poor, especially for GPS-only and BDS-only. The combination of GPS and BDS-3 can improve it, but there are still many periods with GNSS outages. For example, the 1500 s to 2500 s and the 6500 s to 7500 s are the most typical scenes. Based on the precise satellite orbit/clock recovered by the corrections obtained by BDS PPP-B2b service, six data processing strategies, namely undifferenced PPP, BSSD PPP, BSSD PPP/INS TCI, BSSD PPP/INS TCI, BSSD PPP/INS/ODO TCI, and undifferenced PPP/INS/ODO TCI will be implied. The results will be compared to the solutions of RTK/INS TCI calculated by the Inertial Explorer (IE) software.  
Figure 1: Algorithm of BSSD PPP/INS/ODO TCI structure.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Extraction
#### 2.2.1 Liquid-Liquid Extraction (Partitioning)
Unfortunately, there was no sufficient method performance data regarding centrifugal partition chromatography-based aflatoxin analysis for naturally contaminated samples. While liquid-liquid extractions possess the advantage of a simple operation and ease of apparatus handling, the methodology is often a cumbersome process requiring a large amount of organic solvent, especially when processing multiple sample extractions. To optimize extraction conditions, one has to exhaustively test various combinations of different solvents [83; 84]. Liquid-liquid extractions are also prone to error resulting in analyte dilution from handling large solvent volumes and reduced recovery from the formation of sample emulsions. Due to the use of more effective clean-up (solid phase extraction (SPE) or immunoaffinity columns) and detection (LC-FLD and LC-MS) technologies with better sensitivity and specificity, liquid-liquid extraction has been significantly simplified and mainly used as an extraction technique. In general, a one-step extraction is performed for aflat, followed by SPE, immunoaffinity clean-up or direct injection of the extraction [85; 86; 87; 88; 89; 90].
## 3 Analysis of the Fault State and Establishment of the Fault Data Sample
### Analysis of Fault Signal  
#### 3.1.1 Fuel Injection Failure of the Injector  
Fuel injection failure of the injector is mainly achieved by increasing the fuel injection pulse width in the engine fuel injection pulse. When the 3 W engine is working normally, the ECU controls the fuel injection pulse width by querying the engine pulse spectrum data according to the current throttle opening and speed information. When failure occurs, compared with the normal state, the fuel injection pulse width increases, and the fuel injection amount increases. In addition, under the same throttle opening, the engine speed is different when the normal state and the fuel injection pulse width are abnormal. So, the engine speed will increase when the fuel injection amount increases.  
Figure 2 shows the change of the speed signal and the air-fuel ratio signal under normal and fuel injection fault conditions. Figure 2a reflects the change of the rotational speed with the throttle opening, the red line represents the change of the rotational speed when the fuel injection fault of the injector occurs, and the black line represents the change of the rotating speed under the normal state. With the figure, it can be gotten that the throttle opening of both is the same at around 2500 r/min. As the throttle opening increases, the difference in the rotational speeds of the two states become larger and larger. Figure 2b reflects the change of the air-fuel ratio of the first cylinder within 0.1 s under the condition of 3500 r/min, wherein the red line represents the air-fuel ratio when the fuel injection is abnormal, and the black line represents the air-fuel ratio under normal conditions. Compared with the two air-fuel ratios, the air-fuel ratio when the fuel injection is abnormal is much smaller than the  
Figure 1: Fault diagnosis test platform for the engine control system.  
normal value. This is because the fuel injection amount is too large, causing the incomplete combustion of the fuel which leads to a decrease in the engine air-fuel ratio.  
The vibration signal can not only reflect the time domain variation of the engine speed, but the peak value of it can also reflect the amount of function of the engine. At the same rotating speed, the increase of the injection pulse width leads to an increase in the amount of fuel injection, and the engine increases the work per unit time, thus releases more energy. Taking the engine operating at 4000 r/min as an example.  
Figure 3 shows the time-domain waveforms of the cylinder vibration under different conditions in 0.1 s. Figure 3 represents the time domain waveform diagram of the cylinder vibration when the fuel injection is abnormal, and Figure 3 represents the cylinder vibration signal in the normal state. As the figure shows, the waveform change law between the two conditions is basically the same, but the peak of the cylinder vibration in the normal state is lower than the peak when the fuel injection is abnormal. This phenomenon further illustrates the energy relationship mentioned above.
## 3 Results and Discussion
### Plant Growth
#### 3.2.2 Analysis of MDA and Enzymatic Activity of Mung Beans after Heat Treatment Residue Cultivation  
Plant organs tend to undergo membrane lipid peroxidation in response to adverse conditions. MDA is one of the most important products of membrane lipid peroxidation in plants, and its production also aggravates membrane damage. SOD is the only enzyme in nature that uses oxygen radicals as substrates and can quench the toxicity of superoxide anions and alleviate the toxic effects caused by superoxide anions on organisms. Therefore, this antioxidant enzyme plays a vital role in the oxidative and antioxidant balance of the body. Peroxidase can be used as a physiological indicator of tissue aging because of the high antioxidant dismutase (POD) activity in aging tissues in general. MDA, SOD, and POD were selected as indicators to determine the response of the plants to pollutant stress. The MDA, SOD, and POD activity in plants exposed to three toxic residues is illustrated in Figure 4.  
When the three residues examined herein were at higher concentrations, the MDA and peroxidase content in the mung beans increased gradually, whereas SOD content decreased gradually. These results indicate that the residues caused a certain degree of plant membrane damage, resulting in an imbalance between oxidation and antioxidant in the plants. Higher residue concentrations resulted in aging, lodging, and other adverse effects after 14 days. This might be due to the inhibition or degradation of plant enzymes caused by the heavy metals in the residues.  
Figure 4: Effect of heat treatment residues on the enzymatic activity of mung beans. (**a**) the content of malondialdehydedehyde; (**b**) the content of superoxide dismutase; (**c**) the content of peroxide dismutase.  
The effect of high-temperature oxidation residues on the MDA and enzyme activity of the mung bean plants was the most significant. The MDA content of the mung beans after planting was 1.5-2 times higher than that of the control when the high-temperature oxidation residue was mixed in a proportion of 70%, meaning that the plant membrane damage was more severe. The high-temperature oxidation residue had an inhibition rate of approximately 45% for plant SOD, which severely disrupted the antioxidant balance of the mung bean plants. High-temperature oxidation residue contains dioctyl adipate and 2,4-di-tert-butylphenol, in addition to straight-chain alkanes. Dioctyl adipate is classified as a slightly toxic substance and a category three carcinogen by the World Health Organization (WHO). The compound 2,4-di-tert-butylphenol is toxic to organisms and may have long-term adverse effects on aquatic environments [39]. Therefore, the high-temperature oxidation residue was found to have the most serious impact on plant malondialdehydedehyde and enzyme activity.