esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Electrochemical Tests
#### 2.3.3 Electrochemical Impedance Spectroscopy (EIS)  
EIS is a non-destructive alternative current technique with which the electrochemical impedance of all studied samples was evaluated in a wide frequency range (100 mHz to 100 kHz).  
Electrochemical processes that happen slowly, like long-term stability or diffusion, can often be seen at low frequencies (100 mHz to 1 Hz). This helps to identify mass-transport limitations, electrode porosity or faradaic reactions. Medium frequencies (1 Hz to 10 kHz) are crucial for analyzing double-layer capacitance and charge-transfer resistance. This frequency range offers important information about ion diffusion and interfacial interactions at the electrode-electrolyte boundary. High frequencies (10 kHz to 100 kHz) provide information on the bulk electrolyte properties, including ionic conductivity, capacitive behavior and dielectric response.  
Frequencies below 100 mHz were excluded because they prolong the measurement times and are often affected by noise interference. Frequencies above 100 kHz were notconsidered either, as they provide minimal additional information for most electrochemical systems. This frequency is also beyond the operational limits of the available equipment.  
This test followed the ISO 16773-1-4:2016 standard [41]. The obtained impedance data were represented using Nyquist and Bode diagrams. Additionally, equivalent circuits (ECs) were utilized to model and interpret the electrochemical behavior of the material, providing insights into its impedance characteristics.
# Pollen Food Allergy Syndrome in Southern European Adults: Patterns and Insights
## 1 Introduction  
Food allergy is a significant global public health concern, affecting approximately 8% of children and 10% of adults worldwide [1]. This condition not only impacts the individuals diagnosed but also affects their families, influencing daily life and overall well-being [1,2]. In Europe, Immunoglobulin E (IgE)-mediated food allergy remains a significant clinical issue, with a pooled point prevalence of 2.7% (95% CI: 1.7-3.7). Recent findings suggest that food allergies might be more common in adults than previously recognized, with numerous cases of adult-onset allergies being reported [3].  
IgE-mediated food allergy is defined as the presence of symptoms combined with a positive IgE response to at least one food [2]. The ability of linear or conformational epitopes of food allergens to elicit, and bind to elicited IgE immunoglobulins, is the cornerstone of IgE-mediated food allergies. Linear epitopes, typically associated with the life-threatening reactions of food allergies, are continuous stretches of amino acids. Incontrast, conformational epitopes, which are implicated in the development of Pollen Food Allergy Syndrome (PFAS), are formed by protein folding that brings distally located amino acids or peptides into spatial proximity.  
The term Oral Allergy Syndrome (OAS) was initially used in the 1980s to describe the oral mucosal symptoms caused by the cross-reactivity of airborne allergens and food allergens [4; 5]. PFAS is currently used to better describe the (mainly located to) orophnx symptoms due to a cross-reaction of allergens present in both pollen and plant foods [6]. The primary sensitization to these allergens is occurring in the respiratory system and leads to the development of allergic rhinitis [6; 7]. The elicitation of symptoms after the ingestion of homologous food proteins (called also panallergens) usually involves the oral cavity. Nevertheless, there are cases of PFAS that are also involving the skin, gastrointestinal tract, and the respiratory system [8]. Systemic reactions depend on the allergenicity of the involved food but are also influenced by certain risk factors and cofactors, such as the liquid form of the food, its quantity consumed, and fasting [9].  
A wide variation in the incidence of PFAS is noticed around the globe, attributed to factors such as differences in climate and vegetation, dietary habits, the prevalence of atopy in each country, and the syndrome's registration by healthcare professionals [10]. The aim of the present review is to present current insights on PFAS in Southern Europe. This review assesses epidemiological data on PFAS in adults and the key panallergens in the Mediterranean and Southern European region, providing a comprehensive analysis of this emerging concern.
## 3 Results
### Bone Formation and Scaffold Degradation  
Histology reports indicated that the local tissue reaction to both the porous and non-porous materials was similar. All specimen showed implanted material surrounded and divided by new bone formation (indicated by presence of cartilage, woven bone, lamellar bone, and bone marrow). An increase in neovascularization as compared to non-treated bone was also observed, a result consistent with healing. Furthermore, the amount of woven bone decreased between 4 and 8 weeks to 13 weeks in both porous and non-porous samples, indicating mature bone development and remodeling. The pathologist concluded that the porous material had faster resorption and increased new bone formation within the implant sites, as compared to the non-porous material. At 8 weeks, approximately 25-50% of the HA remained, with no evidence of tissue toxicity in either group. New bone formation and residual scaffold volume were measured by microCT at 8 and 13 weeks after implantation. Bone and scaffold characterization were performed by microCT for the entire volume of the defect (Figure 3). New bone and residual scaffold material were independently identified and measured within the region of interest by utilizing the difference in opacity between the newly-formed bone and the scaffold materials within the easily identifiable defect geometry. All data are summarized in Table 1 and reported as a comparison of porous and non-porous HA at 8 and 13 weeks, as well as a temporal comparison by material at 8 vs. 13 weeks. There were statistically significant increases in bone volume, bone fill percentage (bone volume/total volume), connectivity density, trabecular thickness, and mean bone density with porous granules as compared to the non-porous at the 8- and 13-week time points, as well as bone surface area at 13 weeks. The average rate of bone regeneration and volume of new bone for each animal is graphically illustrated in Figure 4. There were statistically significant differences between the materials in new bone volume at both time points (\(p<0.01\)). The increase of new bone for each material between the 8- and 13-week time points was only significant for porous HA (\(p<0.005\)) and corresponded to resorption of the implant material. Bone volume after implantation was statistically equivalent between 8 and 13 weeks for non-porous HA. Resorption of scaffold materials between the two time points was significant for both materials, with 35% less volume in porous scaffolds (\(p<0.02\)) and 21% less volume in non-porous scaffolds (\(p<0.05\)).
## 3 Results and Discussion
### Thermal Stability Analysis
#### 3.3.2 Pyrolysis Kinetics Analysis
The apparent activation energy of F7N7 is 2.7 kJ-mol\({}^{-1}\) more than EP, while the remaining three-phase composite is reduced. The activation energy of the three-phase composites in the second stage is all higher than EP. This is mainly due to that EP is burned out, while CF and CNTs can protect EP from combustion to an extent. In summary, a certain amount of CNTs and CF can synergistically inhibit the generation of free radicals activated by EP, thereby reducing its pyrolysis.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 3 Results  
The FD process of HFDF-EffNetV2 is shown in Figure 4. First, the vibration signals collected by the sensors during the machinery operation are transmitted to the digital device. Subsequently, data preprocessing using the CWT is applied to obtain time-frequency FM, which are then divided into training and test sets. The model-training process outputs the predicted probabilities in stages, computes the loss using the dynamic adaptive weighted cross-entropy (DAWCE) function during forward propagation, and updates the weights via backpropagation. Finally, the test set is fed into the trained model for diagnosis, and the diagnostic results are visualized and analyzed from multiple perspectives.
# Practical Adaptive Sliding-Mode Control Approach for Precise Tracking of Robot Manipulators
## Appendix A How to Choose the TDC Gain  
If the TDC gain \(\bar{\mathbf{M}}\) can be chosen as the diagonal elements of \(\mathbf{M}(\mathbf{q}_{t})\), we can achieve the best tracking performance when operating in the system without noise. However, for real systems, given that the information of \(\mathbf{M}(\mathbf{q}_{t})\) is not known exactly, the TDC gain \(\bar{\mathbf{M}}\) should be tuned through the trial-and-error method. Fortunately, the stability criteria (Equation (11)) introduced in this paper implies that the smaller the TDC gain \(\bar{\mathbf{M}}\), the easier it is to stabilize the system. Therefore, we recommend a tuning method that increases the TDC gain \(\bar{\mathbf{M}}\) at very small values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Strategies** & **Joint 1 (Deg)** & **Joint 2 (Deg)** \\ \hline TDC approach [32] & 6.70 \(\times\) 10\({}^{-2}\) & 40.66 \(\times\) 10\({}^{-2}\) \\ ASMC approach [17] & 2.92 \(\times\) 10\({}^{-2}\) & 10.46 \(\times\) 10\({}^{-2}\) \\ Proposed SS-ASMC approach (Equation (12)) & 0.78 \(\times\) 10\({}^{-2}\) & 4.59 \(\times\) 10\({}^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The root-mean-square (RMS) tracking errors for sinusoidal command joint angle trajectory.
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
###### Abstract  
Internet of Things (IoT) technology is rapidly emerging in medical applications as it offers the possibility of lower-cost personalized healthcare monitoring. At the present time, the 2.45 GHz band is in widespread use for these applications but in this paper, the authors investigate the potential of the 915 MHz ISM band in implementing future, wearable IoT devices. The target sensor is a wrist-worn wireless heart rate and arterial oxygen saturation (SpO2) monitor with the goal of providing efficient wireless functionality and long battery lifetime using a commercial Sub-GHz low-power radio transceiver. A detailed analysis of current consumption for various wireless protocols is also presented and analyzed. A novel 915 MHz antenna design of compact size is reported that has good resilience to detuning by the human body. The antenna also incorporates a matching network to meet the challenging bandwidth requirements and is fabricated using standard, low-cost FR-4 material. Full-Wave EM simulations are presented for the antenna placed in both free-space and on-body cases. A prototype antenna is demonstrated and has dimensions of 44 mm \(\times\) 28 mm \(\times\) 1.6 mm. The measured results at 915 MHz show a 10 dB return loss bandwidth of 55 MHz, a peak realized gain of \(-\)2.37 dBi in free-space and \(-\)6.1 dBi on-body. The paper concludes by highlighting the potential benefits of 915 MHz operation for future IoT devices.  
IoT; ISM band; small antenna; wearable; medical; SpO2; wireless sensor; wrist-worn antenna 2018  
18222017
## 4 Node Insertion Algorithm for Determining Viewpoints
### Node Propagation Stoppage Criteria
#### 4.2.1 Indirect Method (Using Gauss Maps)  
The idea behind this method comes from the concept of Gauss maps in differential geometry (Refer, e.g., [14]). The Gauss map is a map that maps any surface to a unit sphere, which transforms the unit normal at every point on the surface to the corresponding point on the unit sphere. If we draw the Gauss map for developable surfaces, i.e., plane, cylinder, and cone, we observe that the normals lie at the same point, along a great circle, or along a small circle, respectively (Figure 15a). On the other hand, for a highly non-developable surface such as a hemisphere, the Gauss map occupies a large area on the unit sphere. In fact, the Gauss map of a hemisphere is also a hemisphere (Figure 15b).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline WIDTH & 1280 & Pixels \\ HEIGHT & 738 & Pixels \\ \(\theta_{VFOV}\) & 18 & Degrees \\ \hline \hline \end{tabular}
\end{table}
Table 2: Camera parameters.  
From the above observations, we define the concept of a near-developable patch on the surface as being an area where the normals diverge from each other by a maximum of some angle (say 60 degrees). In this region, we can still fit a planar grid onto the surface with no significant deviation. Thus, the stoppage criteria for the direct approach is arrived at. If the normal vector of the current node being inserted is more than a certain angle \(\theta_{N}\) with respect to that of the starting node, we will stop the node propagation at this node.  
The process is detailed in Algorithm 4.  
```
Input: Unit normal vector at current point \(\mathbf{n}\), Unit normal vector at the start point \(\mathbf{n}_{0}\) Output: Boolean, true if node propagation should be continued \(\theta\gets cos^{-1}(\mathbf{n}\cdot\mathbf{n}_{0})\) if\(\theta>\theta_{N}\)then  return false else  return true end
```  
**Algorithm 4**Indirect method--Stoppage criteria  
This method has a drawback that developable surfaces such as a cylinder will still stop the node propagation at that value of normal angle divergence \(\theta_{N}\), even though the node propagation can still continue without any distortion (as the surface is developable). We will then need to take viewpoints from other starting points as well after saving the current set of viewpoints. This can be fixed to some extent by considering angular deviations in two directions, but as that also has some drawbacks, we do not explore it here. Instead, we use an alternative technique named the 'Direct method', explained in the next section.
## 3 RSSI Signal Based MLT Location Estimation
### Transformation to RSSI Values to Distances  
A computer used PLE to convert the RSSI values from all FNs into distance values [25]. This equation states that the distance calculated in a 3 \(\times\) 3 m examination area can be used to calculate the RSSI. Equations (1) and (2), which calculate the average RSSI in \(dBm\) based on the relationship between the \(RSSI_{i}\) and the associated distance, show the PLE. At a reference distance from the \(RSSI_{i}\) transmitter, the average RSSI (\(dBm\)) is calculated [25]. The path-loss index, denoted by the symbol \(k\), displays the rate of signal loss as a function of distance. After gathering RSSI readings from a test field where the separation between the transmitter and receiver was known, the \(RSSI_{i}\) were calculated.  
\[RSSI_{i}(dBm)=RSSI_{i_{0}}(dBm)-\left[10\times k\times\log_{10}\frac{d}{d_{0}}\right] \tag{1}\]  
\[d=10^{\frac{RSSI_{i_{0}}(dBm)-RSSI_{i}(dBm)}{10\times k}},\ for\ d_{0}=1 \tag{2}\]
# A DFB-SOA Based Optical Vector Network Analyzer for Characterization of Bandpass Optical Devices
## 3 Experimental Demonstration
Additionally, the measurement range is primarily determined by the bandwidth of the employed optoelectronic devices and the EVNA, limiting our proposed OVNA to the capability of measuring transmission spectra encompassing up to 40 GHz. Utilizing broad bandwidth optoelectronic devices and EVNA within our system may potentially expand the measurement range of the proposed OVNA. The phase response of the ODUT, which is measured by our proposed OVNA, is illustrated in the inset with a red line. It is essential to highlight that the observed ripple characteristics in the measured magnitude and phase response in our experiment are mainly resulted from the spontaneous emitting noise of the DFB-SOA. However, the application of a meticulously packaged and precisely controlled DFB-SOA within our OVNA could markedly mitigate the noise impacts effectively. Although the absence of a commercially available OVNA, such as the LUNA, in our laboratory precludes the comparison of phase responses with our proposed OVNA. However, from the phase response we measured, it is clear that it conforms to the phase response law of a bandpass filter, thus indicating the correctness of our measurement. Furthermore, as optoelectronic technology advances, our proposed OVNA exhibits considerable potential for integration into a chip utilizing the InP/InGaAsP platform. Consequently, this is expected to result in a reduction in costs, concomitant with enhanced performance outcomes in the future [22]. It is worth noting that, our proposed  
Figure 7: Single-sideband modulated optical signal with suppressed carrier (blue line) and the recovered optical signal with reinstated carrier following the DFB-SOA processing (red line). OVNA is focus on achieving spectrum characterization for ODUT with bandpass response. However, if another direct branch is added between the TLS and the PD, and the DFB-SOA is exchanged position with the ODUT, those slight modifications would make it possible for measuring bandstop optical filters [16].
## 2 Materials and Methods
### Data Sources  
1. Remote sensing data. The remote sensing data are the MOD13Q1 and MOD17A2H data products from the National Aeronautics and Space Administration (NASA) of the United States. MODIS data products can be divided into four topics according to data characteristics--ocean, land, atmosphere, and ice and snow. The data used in this study are MODIS land data products. The MOD13Q1 data product is called MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid, with a time resolution of 16 day and spatial resolution of 250 m. The MOD13Q1 data has 12 bands, including two primary vegetation layers, NDVI and EVI, which can be used for vegetation condition and land cover change monitoring. The MOD17A2H data product is called MODIS/TERRA Gross Primary Productivity 8-Day L4 Global 500 m SIN Grid, with a time resolution of 8 day and spatial resolution of 500 m. The MOD17A2H has three bands, including information on Gross Primary Productivity (GPP) and net photosynthesis (PSN), which can be used to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The original dataset is in HDF format and has been processed via atmospheric correction and geometric correction. The MODIS remote sensing image was set to a unified UTM projection by MODIS Reprojection Tools (MRT) software.
2. Ground measured data. The field survey data were collected by the research team supported by the Institute of Geography, Mongolian Academy of Sciences and Mongolia National University. The sampling time was August 2013 and August 2014, which was a period of vigorous vegetation growth. The survey included grassland production, grassland type, coordinates, terrain, and landscape description. The sample plot size was 10 m \(\times\) 10 m, and three 0.5 m \(\times\) 0.5 m sample squares were randomly selected. The grass in the sample square was collected by ground-level moving, then weighed and recorded, taking the average of the three sample data as the actual grassland production data.
3. Other data. Socioeconomic data (population, livestock data, etc.), mean annual temperature, and mean annual precipitation data were obtained from the official website of the Mongolian National Bureau of Statistics [20] ([[http://www.en.nso.mn/](http://www.en.nso.mn/)]([http://www.en.nso.mn/](http://www.en.nso.mn/))). The meteorological data were the annual temperature and precipitation data of Mongolia from 2006 to 2015, including  
Figure 1: Location of study area.  
22 meteorological stations. The meteorological station data were interpolated by ArcGIS 10.2 software. Mongolian administrative boundary data, including grassland type and DEM data (1 km resolution) were provided by the Thematic Database for Human-Earth System, Chinese Academy of Sciences ([[http://www.data.ac.cn](http://www.data.ac.cn)]([http://www.data.ac.cn](http://www.data.ac.cn))).
## 4 On-Site Measurement  
In the SDS A3 workshop, the oil contamination concentration on the workbench in seven positions was measured on-site. Figure 2 shows the internal layout of the SDS A3 workshop. The solid numbered circle with a red ellipse shows the position of the measured workbench. The red dashed box shows three different types of production units. The red dotted arrow indicates the delivery route of the workpiece from the production area to the assembly area.  
The SDS A3 workshop has different production units, such as the solid axle, internal and external transfer wheels, and three-pin shaft joint. Assume that the production area has 24 production units, and each production unit is equipped with two workbenches. A total of 24 workbenches are in the assembly area, and different types of workpieces are delivered to the corresponding assembly area. The solid axles, internal and external transfer wheels, and three-pin shaft joints are delivered to assembly areas 1, 2, and 3, respectively. Seven workbenches were selected from each production area or assembly area and numbered from 1 to 7. The oil contamination concentrations on surfaces were measured. The steps of the measurement method can be described as follows: arrange the measuring points on the workbench, clean the workbench at the initial moment, cover more than 50% of the surface with several clean filter papers as much as possible, use an electronic balance to weigh all of the filter papers, and record the initial weight as \(\mathrm{x_{1}}\) (mg). All clean filter papers were placed on the surface of the measuring point, as shown in Figure 3. After four hours, the contaminated filter papers were retrieved and weighed again with an analytical  
Figure 2: Internal layout of the SDS A3 workshop.  
balance, and the weight was recorded as x\({}_{2}\) (mg). The mass of oil stains deposited on the surface of the measuring point is given by x\({}_{1}\)-x\({}_{2}\) (mg). The surface area of a single piece of filter paper is approximately 0.01 m\({}^{2}\), and the surface oil concentration of the measuring point represents the weight of the oil deposited on the surface of all filter papers at a single measuring point divided by the total area of the filter paper.  
Table 1 shows the masses (in g) of oil contamination on the seven surfaces and the initial mass of oil contamination (in mg/m\({}^{2}\)) carried by each workpiece from different production units. The mass of oil contamination on the surface is the total mass of oil contamination deposited in four hours.