esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State-of-the-Art and Related Work
### Investigating Geometric Part Deviations in FDM Using Empirical Predictive Models  
Machine learning (ML) offers promising techniques and algorithms to predict geometric part deviations and has already proven its applicability for conventional manufacturing processes, such as milling [24] and turning [25]. In context of AM, the methods of ML imply major advantages as it offers the possibility of discovering implicit knowledge and the identification of relationships between process parameters in large data sets as for example their influence on geometric deviations of additively manufactured parts [26]. Using ML techniques, predictive models can be built and thus be used for prediction and performance optimisation [23]. The complexity of the AM-process makes it highly suitable for the common ML techniques as supervised learning methods, like Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Gaussian Processes (GP), because of the accessibility of labelled data sets [23; 26]. [PERSON] et al. developed a predictive model for geometric deviations resulting through shrinkage in the FDM process. The predictive model is able to learn from the data obtained from a certain number of tested part shapes. This allows to take effective countermeasures for compensating part deformations of new and not yet tested parts in advance [27]. [PERSON] et al. compared the results from experiments determining geometric part deviations of additively manufactured ABS-parts with fuzzy decision-making logic [28]. In order to predict and subsequently compensate geometric part deviations resulting from the FDM-process, [PERSON] et al. trained a GP on previously gained training data. It was concluded that it is purposeful to determine resulting part deviations before fabrication, so the design can be adapted a priori [29]. [PERSON] et al. analysed different process parameters on their influence on geometric deviations of additively manufactured parts using ANN and Taguchi method [17]. [PERSON] et al. developed a predictive model for investigating the actual dimensions of FDM parts by training ANNs with experimental data, gained through tests and measurements. The authors claim that the achieved accuracy of the prediction is proven to be more accurate than the manufacturing accuracy of the FDM process itself [30].
## 1 Introduction  
The use of Internet of Things (IoT) devices and low-power wide area network (LPWAN) communication technology is increasing [1]. LPWAN communication technology has advantages such as high energy efficiency and low cost. However, recent studies have applied only one network, and no study has compared various communication methods in the same environment. Limited studies have applied Sigfox to a hospital environment. This paper introduces the structure of a proposed Internet of Medical Things (IoMT) system for a hospital environment, describes the equipment and data types, and presents the results obtained following the implementation of a location and temperature measurement system with IoT technology according to the network method: Sigfox, Hospital and Non-Hospital Networks.  
The significance of this study is in the implementation of three different IoMT system in a hospital environment and the comparison between the accuracy of sending and receiving signals of various targets including infusion pumps, pharmaceutical refrigerators, and people, e.g., patients and nurses). The communication method and architecture for the application of the IoMT system in a hospital environment is described in the following sections. In this study, the communication system was applied to increase the work efficiency of medical staff by allowing the location tracking of patients and medical equipment in a hospital environment.
## 2 Engineering Application and Experimental Design
### Engineering Application
T is the thickness of the pavement, which is related to H. The sample names of all the specimens for each set of parameters are shown in Table 1. The naming convention of the specimens was in the form of \"s (spacing of teeth) w (width of teeth) h (height of teeth)\". As shown in Figure 5a, the compression-shear test was carried out using a universal testing machine. The loading rate was 2 mm/min. The angle of compression-shear was \(\alpha=45^{\circ}\). Two linear variable differential transformer sensors were installed at the ECC materials and steel plate to measure the shear displacement. The shear load was measured by an external load sensor and continuously collected using a DH3820 acquisition system. The loading details are shown in Figure 5b. Based on the digital image correlation (DIC) method, the deformation fields of the composite structure specimens during the loading process were continuously measured using a VIC-3D non-contact full-field strain measurement and analysis system, as shown in Figure 5a. The raw materials used to prepare the ECC were as follows: 42.5 R ordinary Portland cement; Fly ash; Sika-III superplasticizer; 70-140 mesh quartz sand, used as fine aggregate; and polymer fiber: K-II PVA fiber. The properties of the fibers are shown in Table 2. Table 3 shows the proportion of each material. Common domestic water was used. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**No.** & **S** & **W** & **H** & **No.** & **S** & **W** & **H** & **No.** & **S** & **W** & **H** \\ \hline s1w1h1 & 30 & 10 & 5 & s1w2h1 & 30 & 15 & 5 & s1w3h1 & 30 & 20 & 5 \\ s1w1h2 & 30 & 10 & 10 & s1w2h2 & 30 & 15 & 10 & s1w3h2 & 30 & 20 & 10 \\ s1w1h3 & 30 & 10 & 20 & s1w2h3 & 30 & 15 & 20 & s1w3h3 & 30 & 20 & 20 \\ s2w1h1 & 40 & 10 & 5 & s2w2h1 & 40 & 15 & 5 & s2w3h1 & 40 & 20 & 5 \\ s2w1h2 & 40 & 10 & 10 & s2w2h2 & 40 & 15 & 10 & s2w3h2 & 40 & 20 & 10 \\ s2w1h3 & 40 & 10 & 20 & s2w2h3 & 40 & 15 & 20 & s2w3h3 & 40 & 20 & 20 \\ s3w1h1 & 50 & 10 & 5 & s3w2h1 & 50 & 15 & 5 & s3w3h1 & 50 & 20 & 5 \\ s3w1h2 & 50 & 10 & 10 & s3w2h2 & 50 & 15 & 10 & s3w3h2 & 50 & 20 & 10 \\ s3w1h3 & 50 & 10 & 20 & s3w2h3 & 50 & 15 & 20 & s3w3h3 & 50 & 20 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test parameters and sample number (unit: mm). Figure 4: Model specimens: (**a**) front elevation and (**b**) side elevation. The preparation process of the ECC materials was described elsewhere [19]. Dumbbell thin plate specimens were used in the uniaxial tension tests. Tensile stress-strain curves and strain field nephograms based on the DIC are shown in Figure 6. With an increase in the strain level, many cracks in the calculation region saturated. Most importantly, even at high stress levels of 4%, cracks were not confined to local areas.
## 2 General Methods for the Synthesis of 1,2,4,5-tetrazines
### Synthesis of 1,2,4,5-Tetrazines Using Thiocarbohydrazide  
A method to synthesize substituted 1,2,4,5-tetrazines without an oxidation step involves the reaction of hydrazinecarbontiohydrazide (**26**) with triethyl orthoesters (Scheme 10), yielding 3-(dodecylthio)-1,2,4,5-tetrazine derivatives (**27**) [80, 81]. The key thiocarbohydrazide **26** is first converted into a salt by reaction with 1-iodododecane and subsequently reacted with triethyl orthoesters in the presence of triethylamine. Introducing a long-chain dodecyl substituent into the sulfur atom reduces the volatility of the resulting 1,2,4,5-tetrazine, facilitating the isolation process. This is particularly important for alkyl 1,2,4,5-tetrazines, which are highly volatile and difficult to purify. Alkyl derivatives of 3-(dodecylthio)-1,2,4,5-tetrazine (**27**) are valuable intermediates for synthesizing other reactive derivatives, such as 3-hydrazinyl-1,2,4,5-tetrazine and 3-bromo-1,2,4,5-tetrazine.  
Another transformation described in the literature is the reaction of hydrazinecarbontiohydrazide (**26**) with methyl iodide, giving the condensation product methyl hydrazinecarbontihoate (Scheme 11). This intermediate, treated with trimethyl orthoacetate and subsequently oxidized with sodium nitrite in trifluoroacetic acid, was converted into 3-methyl-6-(methylthio)-1,2,4,5-tetrazine, with a yield of 23% [82].  
Figure 10: Synthesis of 1,2,4,5-tetrazine derivatives from thiocarbohydrazide.  
The symmetrical 3,6-bis(methylthio)-1,2,4,5-tetrazine (**28**) was synthesized through a multi-step sequence of transformations involving hydrazinecarbontiohydrazide (**26**), bis(carboxymethyl) trithiocarbonate, and methyl iodide (Scheme 12). In the first step, thiocarbohydrazide reacted with bis(carboxymethyl) trithiocarbonate in the presence of sodium hydroxide, forming cyclic 1,2,4,5-tetrazine-3,6-dithione. This intermediate subsequently methylated with methyl iodide. The resulting 3,6-bis(methylthio)-1,4-dihydro-1,2,4,5-tetrazine then oxidized with iron(III) chloride in ethanol, yielding the final product **28**, with a yield of 84% [83].  
The preparation of 3,6-bis(methylthio)-1,2,4,5-tetrazine from bis(carboxymethyl)trithiocarbonate.
# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
## 3 Results and Discussion
### Measure Ten Dial Gauges  
After completing the above experiment, it is time to complete the last step of the whole algorithm: reading. As shown in Table 5, the average relative error in the algorithm from [9] is 7.044%, the algorithm from [10] is 1.200% and our algorithm is 0.994%. Therefore, compared with the previous two methods, the relative error of our algorithm is significantly reduced. In Table 5, the algorithm [9] has a larger relative error for images 7-10. This is because the algorithm [9] is based on Hough line detection. There are many straight lines detected during processing that lead to large false detections. Moreover, the division value of images 7-10 is large. A small angle deviation during positioning will cause large reading error. Although the absolute error of center extraction of our algorithm is about 2.5%, it does not affect the overall reading error. It indicates that the accuracy of the center detection has little effect on the reading results, and the main influencing factor is the accuracy of the pointer positioning, especially for instruments with high division value.  
According to the experimental results, there are still errors in the recognition of the proposed dial gauge readings. The main reason is that the pointer positioning accuracy is not high enough. When the angle method is used to obtain readings, slight angle deviations will cause large reading errors. In addition, light and shadow may affect the image quality when collecting images, which leads to incomplete pointer extraction and deviation of positioning.
## 2 UAV Platform Evolution
### Infrared Sensors  
Infrared (IR) imaging measures land-surface temperature (LST) and sea-surface temperature (SST) by detecting the infrared radiation emitted from surfaces. The integration of lightweight thermal cameras on UAVs has proven transformative across industries by enabling accessible aerial thermal data collection. UAVs with thermal sensors can readily identify heat signatures and efficiently gather accurate surface temperature measurements over large areas. This provides new capabilities for applications, such as assessing commercial building insulation, monitoring wildfires, and inspecting solar installations. The combination of infrared thermography and UAV mobility offers an accessible and cost-effective approach to thermal mapping that unlocks new possibilities across sectors. Common IR sensors include microbolometers detecting longwave IR (7.5-14 \(\upmu\)m) and InGaAs detectors for shortwave IR (0.5-2.55 \(\upmu\)m) [117]. The resolution varies from a centimeter scale for object identification to meters for thermal mapping, with FLIR sensors being considered the most commonly used thermal sensors for UAVs, as illustrated in Figure 8[118]. High-resolution IR thermography from UAVs can pinpoint building envelope deficiencies prone to energy loss. IR cameras identify areas of heat leakage, moisture accumulation, and insufficient insulation when temperature gradients exist between interior and exterior surfaces [119]. Frequent UAV IR surveys can help track building deterioration and prioritize retrofits. In addition, thermal mapping enhances the urban heat island research, linking surface temperatures to land-cover types [79]. Combined with elevation data, UAV IR enables the modeling of microclimate variations across neighborhoods to guide the heat mitigation research [120]. IR sensors can help quantify the effects of urban vegetation on outdoor thermal comfort and verify thermal anomalies in buildings envelopes through time-lapse thermography techniques [121].
## Materials  
### Study Area  
The analysis framework developed within this study is tested on the city of Milan (Northern Italy). Milan is located in one of the most densely populated European regions, the Padana Plain (see Figure 1), affected by poor wind circulation and high urbanization which favour the stagnation of pollutants as well as the persistence of a historically well-known UHI [28]. This makes Milan a suitable test area to investigate the influence of land cover on local air temperature that is the primary goal of the proposed analysis.  
The study region is characterized by a flat terrain with an average altitude of 120 m above sea level. Minimum and maximum altitudes are 85 m and 175 m above sea level, respectively. Due to this small range, we did not account for any relevant variation of temperature produced by orographic phenomena. The region climate characteristics can be summarized according to the Koppen-Geiger Climate Classification [29] in warm temperate, fully humid with hot summer (Cfa zone). Such environmental peculiarities are also common to other cities in Italy and Southern Europe [30], thus the same work might be replicated or adapted in other sites.  
Figure 1: Location of the study area. Coordinate Reference System (CRS): WGS84/UTM zone 32 N. Map data: © OpenStreetMap contributors.
## 3 Data Sources Description
### TBM Data and Geological Survey Data  
During tunneling, field data of the TBM were collected every second. The TBM operational data had 199 kinds of monitoring data. They were collected from subsystems of the TBM, including cutterhead system, driving system, supporting system, belt conveyor system, electrical system, and hydraulic system.  
Figure 4: (**a**) Structure of the TBM cutterhead; (**b**) Schematic diagram of the cutterhead.  
As for the geological parameters, a preliminary check of geological conditions was carried out through boreholes before excavating. During tunneling, the geological changes were carefully monitored by geological engineers via observation of the conditions of fragments cut by the TBM. Once the degree of rock fragmentation was considered to be changed, the rock samples were tested to determine different parameters such as rock-saturation uniaxial compressive strength and fissure coefficient, and then rock mass basic quality was calculated according to [40]. Although the results of geological surveys did not reveal the whole geological conditions of the tunnel, application of image processing technology made it possible to monitor geological changes through rock fragments in real time [41].  
Besides daily inspections, disc cutter conditions were inspected routinely after three strokes of TBM excavating. During inspections, disc cutters over their wear limit and abnormal disc wear cutters were replaced. In this study case, 1692 disc cutters were replaced. The total numbers of disc cutter replacements in each position are shown in Figure 5. As can be seen from Figure 5, the central disc cutters were less often replaced because they had a small radius compared to their diameter. The low numbers of central and face disc cutter replacements were mainly related to their position radii. For gage disc cutters there was no obvious pattern.
# Corrosion Susceptibility and Microhardness of Al-Ni Alloys with Different Grain Structures
## 3 Results and Discussion
### Thermal Gradients as a Function of Time  
The thermal gradients for each adjacent thermocouple were calculated as a quotient between the difference in temperatures and the difference in distance between the two. In Figure 2, the graph of gradients can be observed as a function of time for Al-3.wt %Ni alloy.  
It is observed in the graph that the gradients at the beginning take higher values, both at the base and at the upper zone of the samples. Later, as the cooling progresses, it can be observed that in the zone where the CET occurs, the gradient takes a minimum value and gives way to the subsequent growth of the equiaxed grains, hence the formation of the CET zone. From this graph, what is observed and given as a constant behavior in the different tests is that where we see a minimum of the thermal gradient, G\({}_{\rm c}\), the CET occurs. It can also be seen in Figure 2 that CET does not occur in a line but in a transition zone of the order of 1 cm or greater, as was reported before [23; 24; 25; 26; 27]. The values of the critical temperature gradients, G\({}_{\rm c}\), are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Alloy**} & \multirow{2}{*}{\
\begin{tabular}{c} **V\({}_{\rm e}\) Li-** \\ **(c/min)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **V\({}_{\rm S}\)** \\ **(mm/s)** \\ \end{tabular} } & **Gc** & **Solidification Time** & **CET\({}_{\rm Min}\)** & **CET\({}_{\rm Max}\)** & **Columnar Grain** & **D\({}_{\rm Ge}\)** \\  & & & **(c\({}^{\circ}\)C/cm)** & **(s)** & **(mm)** & **(mm)** & **Width (mm)** & **(mm)** \\ \hline Al-1 wt \%Ni & 16.20 & 1.25 & 1.9 & 743 & 65 & 75 & 2 & 1.45 \\ \hline Al-3 wt \%Ni & 10.26 & 0.79 & 1.3 & 814 & 50 & 60 & 3 & 1.60 \\ \hline Al-5.7 wt. \%Ni & 5.04 & 0.67 & 2.9 & 905 & 45 & 55 & 5.5 & 6.86 \\ \hline Al-8 wt \%Ni & 6.52 & 1.18 & 2.5 & 864 & 43 & 52 & 4.6 & 1.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of thermal parameters obtained for different Al-Ni alloys.  
Figure 1: (a) Cooling curves of (a) Al-1 wt. %Ni, (b) Al-3 wt. %Ni, (c) Al-5.7 wt. %Ni and (d) Al-8 wt. %Ni alloys.  
To verify what is shown in the thermal gradient graph, the zone where the CET takes place in the macrograph of Figure 2 was compared (corresponding to Al-3 wt. %Ni) and related to the gradient graph. It is observed that when the CET occurs, the gradient takes a minimum corresponding to the thermocouple located at this position. For example, in the case of Figure 2, the minimums are displayed between thermocouples #3 and #4, and in the macrograph, the CET occurs right at this height of the sample.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Problem Solution Techniques
#### 5.2.1 Mathematical Programming-Based Approaches  
These methods frame the scheduling problem as an optimization challenge.  
For task scheduling in fog computing, two mathematical programming models are used: Integer Linear Programming (ILP) and Mixed Integer Linear Programming (MILP). However, these approaches demand significant computational resources. They may be affected by the size and intricacy of the problem, thus being better suited for small and medium-sized fog computing systems than larger-scale ones.  
Integer Linear Programming is a well-known mathematical technique involving developing a mathematical model representing the task scheduling problem as a set of linear equations and constraints. In fog computing task scheduling, ILP can allocate tasks to devices to minimize the overall completion time or maximize the utilization of available resources while also considering constraints related to device capabilities and network conditions [21].  
Mixed Linear Integer Programming is frequently utilized to address optimization problems involving discrete and continuous variables and non-linear functions in the constraints or objective function. In fog computing task scheduling, MILP can be used to assign tasks to devices to minimize the overall completion time or maximize the utilization of available resources. MILP can also incorporate constraints related to device capabilities and network conditions, ensuring that the assigned tasks can be executed efficiently.
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 4 Results
### Influence of Receiver Coding Mode  
In order to eliminate the influence of other interferences except for Gauss white noise in the system, the zero forcing algorithm and the least mean square error algorithm are introduced into the module to recover the signal at the receiver. The system error rate variation regularity with the signal to noise ratio is shown in Figure 13.  
Figure 12: Output spectrum of the two symbols. (**a**) Fixed form symbol; (**b**) Pseudo random code.  
As can be seen from the curves, the system error rate is reduced by the zero forcing algorithm or the least mean square error algorithm, and the latter algorithm is even better. This is because the zero forcing algorithm will eliminate other interference factors except the additive Gauss white noise in the system, and the least mean square error algorithm takes the noise into consideration. Although the minimum mean square error algorithm can greatly reduce the system error rate, the algorithm complexity is higher, resulting in long operation time in the system. The BER results under the influence of the SPDT in Figure 8 are much lower compared with the results in [34] especially when the SNR is ranged from 0 dB to 5 dB. As for the high SNR, the comparison between the two methods remains robust. Take the BER results at 0 dB for an example, compared with the single RF MIMO with Electronically Steerable Parasitic Array Radiators under ideal or non-ideal situation, the system structure proposed in our paper is lower than 0.1, while the single RF MIMO system with Electronically Steerable Parasitic Array Radiators is higher than 0.1, which verifies the effectiveness and superior performance of the proposed system.
# Hom Antenna on Chip Operating at 180 GHz Using the SiGe CMOS Process
## 3 Simulation and Measurement
### Gain  
The gain of an antenna refers to the increase in the power of electromagnetic waves transmitted and received by the antenna in a specific direction relative to an ideal feed antenna. The gain of an antenna is usually expressed in dBi. If the gain increases, the antenna transmits and receives signals in that direction more effectively. Antenna gain can be calculated in Formula (2). Among them, \(\mathrm{G_{dB}}\) is the gain of the antenna, \(\mathrm{\eta}\) is efficiency, and \(\mathrm{A}\) is the aperture area. Figure 9 shows the gain of this design. The gain is \(-4.21\) dBi at 180 GHz.  
\[\mathrm{G_{dBi}}=10\mathrm{log}10\left(\frac{4\pi\mathrm{\eta}\mathrm{A}}{ \lambda^{2}}\right) \tag{2}\]  
Figure 8: At 180 GHz, the 2D radiation pattern of the antenna. (**a**) XY Plane; (**b**) XZ Plane; (**c**) YZ Plane.  
Figure 9: Antenna gain simulation.