esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The use of machine learning (ML) has been applied to different industries such as sustainability of materials [36], techno-economics [37], molecular crystals engineering [38], energy [39], diagnostics in medicine [40] and, more recently, food/beverages [17; 18; 22; 29; 41] and agriculture [42; 43; 44]. This has been an effective tool to aid in the prediction and rapid assessment of products; however, a common issue found when using ML is the overfitting of the models because the generalization of the data is not achieved. This is usually found in noisy datasets and when the number of selected neurons is too high, which provides high accuracy, but poor performance [45; 46]. Among the supervised ML algorithms, artificial neural networks tend to be the most robust due to their nonlinearity and good capacity of finding patterns among the inputs and targets. Furthermore, this is most convenient when a single multitarget model is required, which is a feature that is not possible in other ML algorithms [47; 48]. Some studies that have presented multitarget ANN models include the identification of proteomics from beer foamability analysis [49], prediction of beer aromas and physicochemical data using foam-related parameters obtained from a robotic pourer [50], prediction of beer acceptability of sensory attributes using e-nose data [22], and prediction of sensory profiles of wine using near-infrared spectroscopy [51]. This study aimed to predict coffee aromas and roasting intensity using a newly developed low-cost and portable (wireless) e-nose coupled with machine learning (ML) modeling. Model 1 was developed using the e-nose data as inputs to classify samples into low-, medium-, and high-intensity according to that reported in their label. On the other hand, Model 2 was developed using the e-nose outputs as inputs to predict the relative abundance based on the peak area of 45 aromas measured using GC/MS used as targets. By adding low-cost sensor technology with coffee pouring devices or normal coffee machines, the aroma profile from specific coffee and provenance can be maintained as a quality assurance method. It would also offer an automated system to detect aroma profile variations due to unforeseen factors, such as water quality, temperature or other problems in the brewing process that may affect consumer perception.
## 4 Results and Discussion
### Interpretation of the Fracture Network Model
Despite the high fracture density, the hydraulic properties, i.e., transmissivity and porosity values in the claystone, do not increase with depth; transmissivity is lower than \(10^{-8}\) m\({}^{2}\)/s, and porosity is in the \(10^{-2}\)-\(10^{-3}\) range. The aperture coefficients are 3-4 orders of magnitude smaller than typical for the sandstone body. The various water-rock interaction processes such as dissolution and cementation along the fracture planes could significantly alter the hydrodynamic properties. Small values of the aperture coefficient usually coincide with more closed, highly cemented and self-sealed fractures. In the lowermost segment of the well, FZI values vary significantly, likely due to tiny changes in porosity or permeability. As a result, distinct and wide HFUs failed to form in the deepest part of the well. In two zones (520-630 and 700-775 m), the FZI values are lower than those typical in the claystone. In these zones, fracture densities (P10 and fractal dimension) also increase compared to their surroundings. This increased fracture number and better hydraulic properties could indicate a coarsening of the average grain size of the formation. Sedimentological descriptions show that fine sandstone interbeds are common in the BCF. In these sections, the number of sandstone layers could be increased compared to the average claystone. Although fracture density data and hydrodynamic properties change at the boundary between the two lithologies, DFN models along the entire borehole based on ten independent runs show no lithology-related pattern concerning fracture network communication. Despite the distinctly different hydrogeological behaviour of the sandstone and the claystone, the fracture network is connected between the sandstone and the upper segment of claystone in all runs. Nevertheless, the results of some simulations indicate that two fracture subgroups may form along the well, which are not connected. The boundary of these subgroups is located at 670 m in the studied well. The explanation for this horizon is not clear based on the available information. At about the same depth (690 m), within the otherwise homogeneous claystone, there is a four-metre-wide reductive layer whose rheological properties could be different. In the reductive zones, the proportions of phyllosilicates are different and the predominant clay mineral in this zone is chlorite instead of illite [16]. However, the influence of the reductive zone on the geometry of the fracture network is unclear. In this zone, the fracture system should be near its percolation threshold; a slight change in modelling parameters can significantly affect the connectivity of the fracture system [43]. Therefore, the connectivity of the fracture network at this depth cannot be predicted with high certainty.
## 4 Energy Consumption
### Average Energy Consumption during Normal Cycles
#### 4.6.1 C1 Sensor Nodes  
The average energy consumed by RN1 in a _normal_ cycle can be expressed as being composed of different terms, each of them depends on the possible outcomes of the RN1 contention process: (i) it completes a successful PF transmission, \(E_{tx,s}^{nr,1}\) ; (ii) a PF transmitted by RN1 collides, \(E_{tx,f}^{nr,1}\) ; (iii) one or more C1 SNs, different from RN1, win the contention and transmit a PF, \(E_{oh}^{nr,1}\).  
\[\begin{split} E_{tx,s}^{nr,1}&=\sum_{i=1}^{Q_{1}} \sum_{k=0}^{M_{1}}\pi_{1}(i,k)P_{s,k,1}T_{1}^{nr,1}P_{sl}\,,\\ T_{1}^{nr,1}&=T-T_{sc,1}-\left(BT_{s,k,1}+t_{RTS}+ \alpha_{1}\,t_{DATA}+t_{CTS}+t_{ACK}+4D_{p}\right),\\ \alpha_{1}&=min(i,F_{1})\,.\end{split} \tag{24}\]  
\[\begin{split} E_{tx,f}^{nr,1}&=\sum_{i=1}^{Q_{1}} \sum_{k=1}^{M_{1}}\pi_{1}(i,k)P_{f,k,1}T_{2}^{nr,1}P_{sl}\,,\\ T_{2}^{nr,1}&=T-T_{sc,1}-\left(BT_{f,k,1}+t_{ RTS}+D_{p}\right).\ \ \.\end{split} \tag{25}\]  
\[\begin{split} E_{oh}^{nr,1}&=\sum_{i=1}^{Q_{1}} \sum_{k=1}^{M_{1}}\pi_{1}(i,k)KP_{s,k,1}\Big{(}T_{3}^{nr,1}-BT_{s,k,1}\Big{)}P_ {sl}\\ &+\sum_{i=1}^{Q_{1}}\sum_{k=2}^{M_{1}}\pi_{1}(i,k)\widehat{P}_{f,k,1}\Big{(}T_{3}^{nr,1}-BT_{f,k,1}\Big{)}P_{sl}\\ &+\sum_{k=0}^{M_{1}}\pi_{1}(0,k)(T-T_{sc,1})P_{sl}\,,\\ T_{3}^{nr,1}&=T-T_{sc,1}-\left(t_{RTS}+D_{p}\right). \end{split} \tag{26}\]  
The average energy consumed by RN1 in a _normal_ cycles can be expressed as,  
\[E_{nr,1}=E_{tx,s}^{nr,1}+E_{tx,f}^{nr,1}+E_{oh}^{nr,1}. \tag{27}\]
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 4 Embedded Programming
### Robustness Enhancement of Kalman Prediction
#### 4.3.2 Emergency Mechanism Design  
When the device detects that the C/N0 in the chip output NMEA-0183 protocol is less than 20 and exceeds three seconds, the emergency mechanism is triggered, and the system automatically switches to the Kalman filter prediction mode. The flow chart of this process is shown in Figure 6.
# Capabilities and Limitations of Using Desktop 3-D Printers in the Laser Sintering Process
## 3 Results and Discussion
### Differential Scanning Calorimetry  
Thermal analysis (DSC) showed a semicrystalline structure of the tested materials. The obtained DSC curves are similar to single peaks during heating and cooling, as shown in Figure 5. The extracted start melting temperature (T\({}_{\text{m-start}}\)), start crystallization temperature (T\({}_{\text{c-start}}\)), and processing window width (\(\Delta\)T) are listed in Table 4.  
The widest processing window is provided by PA2200, while PA12 Smooth offers the narrowest one. The lowest melting and crystallization enthalpies are given by PA2200, and significantly differ from PA12 powders of desktop LS manufacturers. The found differences indicate that PA12 Smooth and PA12 Black contain a higher value of crystal domains in the tested materials. Additionally, a high melting enthalpy is favourable for precise contour melting. The threshold for thermal bleeding of nonmelted powder particles rises, which allows for a higher surface quality to be achieved [28].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Material** & **T\({}_{\text{m-onset}}\)**[\({}^{\circ}\)C]** & **T\({}_{\text{c-onset}}\)**[\({}^{\circ}\)C]** & **AT**[\({}^{\circ}\)C] & **\(\Delta\)H\({}_{\text{m}}\)**[J/g]** & **\(\Delta\)H\({}_{\text{c}}\)**[J/g]** \\ \hline PA2200 & 180.6 & 151.0 & 29.6 & 108.6 & 64.0 \\ PA12 Smooth & 176.5 & 157.4 & 19.1 & 157.3 & 84.6 \\ PA12 Black & 179.9 & 158.4 & 21.5 & 175.7 & 92.7 \\ \hline \end{tabular}
\end{table}
Table 4: Thermal Properties of Powders PA2200, PA12 Smooth, and PA12 Black.  
Figure 4: Cumulative distribution and distribution density of the tested polyamides powders.  
The thermal properties show that PA2200 has the highest tolerance of temperatures divergence, while also having the lowest enthalpies. This can result in less precise contour melting when compared to the other two polyamides.
###### Abstract  
This study explores the quality of data produced by Global Precipitation Measurement (GPM) and the potential of GPM for real-time short-term nowcasting using MATLAB and the Short-Term Ensemble Prediction System (STEPS). Precipitation data obtained by rain gauges during the period 2015 to 2017 were used in this comparative analysis. The results show that the quality of GPM precipitation has different degrees efficacies at the national scale, which were revealed at the performance analysis stage of the study. After data quality checking, five representative precipitation events were selected for nowcasting evaluation. The GPM estimated precipitation compared to a 30 min forecast using STEPS precipitation nowcast results, showing that the GPM precipitation data performed well in nowcasting between 0 to 120 min. However, the accuracy and quality of nowcasting precipitation significantly reduced with increased lead time. A major finding from the study is that the quality of precipitation data can be improved through blending processes such as kriging with external drift and the double-kernel smoothing method, which enhances the quality of nowcast over longer lead times.  
rainfall; satellite precipitation; GPM; hydroclimatic changes; nowcasting; STEPS; Mexico 2021  
20 May 2021  
DMPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Results
### Fate Choice  
After the recovery period, the NSCs were fed with neuronal specification medium (NSM) [6] in order to obtain neurons. Exposure to a space environment did not alter the NSCs potential to choose the neuronal fate. Interestingly, neuroblasts proliferated for more than 1 week after being in NSM before moving toward maturation. The timeline for neuronal specification is shown in Scheme 2.  
Neuronal Specification. Upon their return to Earth, the cells were allowed to recover in NSC medium and standard conditions for 9 days. NSCs were introduced to neuronal specification medium (NSM) on Day 10. After 10 or 14 days of having been in NSM, we examined the electrophysiological properties of these cells.  
Electrophysiological recordings of SPC-NSC-derived neurons were obtained 10 days or 14 days after being in NSM using standard procedures, as detailed in our previous publication [8] Figure 4.  
A total of seven plates containing SPC-NSCs were used, and 16 cells were recorded in voltage and current clamp modes using a K-gluconate-based internal solution. First, passive membrane properties were determined in the voltage clamp mode. The cell membrane capacitance was relatively small (\(27.3\pm 1.9\) pF, mean \(\pm\) SEM), while the input resistance was high (\(553.4\pm 84.7\) M\(\Omega\)). The decay time constant was very fast (\(0.4\pm 0.05\) ms). These properties are similar to those obtained from implanted NSCs [8]. After measuring passive membrane properties, the recording was switched to the current clamp mode. The mean resting membrane potential was relatively depolarized (\(-38.1\pm 1.8\) mV), and upon injection of depolarizing current steps, most cells displayed a small-amplitude action potential, suggesting that these SPC-NSCs were in an immature state. In the gap free mode, we determined that spontaneous synaptic activity was not yet apparent.  
Figure 3: SPC-NSCs and ground control NSCs were recovered from the passive hardware (**A**) and treated in the same manner. (**B**) Representative example of post-flight NSCs that continued to actively proliferate, as shown by the arrows. (**C**) After 39.3 days in space, NSCs display enhanced energy consumption. OCR—oxygen consumption rate; ECAR—extracellular acidification rate; SPC-NSCs in the space environment. Each experiment consisted of n = 12. The values are expressed as mean + SD. * The Student’s t test revealed that these differences were significant (p < 0.05).
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 4 Data Acquisition and Handling
### Solar Radiation
#### 4.3.2 RGB Conversion to Radiance  
Another photometric alternative is to quantify the visible spectrum into red, green, and blue segments and calculate the radiance in the visible range. The daylight simulation software called \"radiance\" uses a complex algorithm for analysis known as backward ray tracing. In this process, the rays are traced from viewpoint back to the source of light after considering the reflection and refraction of the light rays from different surfaces. The program uses the following formula for calculation of radiance:  
\[R=[0.263\times Rr+0.655\times Rg+0.082\times Rb\;]\frac{Watts}{meter^{2}}. Steradian \tag{1}\]  
where \(R\) denotes radiance, and \(r\), \(g\), \(b\) correspond to red, green, and blue channels of light [28].  
Initially, our study used the above equation in the IDE code for the calculation of radiance from the three primary colors of light. The TCS34725 was used for detection of red, blue, and green bands. Further data included color temperature and illuminance. After overlaying the graphs, it was outlined that the r, g, b readings follow the illuminance pattern (Figure 8). This means the sensor readings and illuminance have a direct relationship.  
The RGB sensor readings of 0-65535 were distributed into 16-bit segments for each color. After conversion of the r, g, b scale to 8-bits (0-255), the results were similar to the irradiance reading from illuminance through the estimated value of \"0.0079 W/m\({}^{2}\) per lux\" (Figure 9). The reason for slight differences in peak values is considered to be due to the different techniques used for estimation. Also, the scales are for two different units of solar radiation, i.e., radiance in comparison to irradiance.  
The photometric sensors used in the prototype are IR blocking. The related sensors must be added in the prototype for the addition of IR and UV components of the solar spectrum.
# Effect of Buildings on the Radiation Characteristics of MF Broadcast Antennas
## 5 Conclusions  
The influence of the presence of buildings on the radiation pattern of a monopole and a two-element linear antenna array, operating at the MF band, was examined in this paper, in an attempt to assess the distance from a broadcast antenna at which we may construct buildings without obstructing its operation. The buildings were modeled as wire-grid bodies; this assumption constitutes the worst case in terms of their impact on the radiation characteristics of the antenna, although it may not ensure accurate modeling. Our results indicate that the height of the buildings is the most important parameter that may alter the free-space radiation characteristics of antennas. The length and the width of buildings, as well as their distance from the antenna, may only have an impact on the radiation pattern if the height exceeds a critical value; the latter was found to be roughly equal to 0.2\(\lambda\) when the radiation of the monopole was obstructed by two buildings, as well as when one or two buildings were situated in the vicinity of the two-element array, whereas it was slightly greater (about 0.3\(\lambda\)) when the monopole radiated with one building nearby. Thus, the worst-case scenario suggests that buildings may be constructed as close as 0.5\(\lambda\) from a radiating broadcast MF antenna without obstructing its operation, provided that they have less than eight stories (each about 5 m high). Our future work may include the examination of other shapes (such as cylinders) and materials for building modeling, in conjunction with different methods for the calculation of EM fields around radiating antennas.  
Conceptualization, G.S.L. and I.O.V.; methodology, G.S.L. and I.O.V.; software, G.S.L., N.S.P. and I.O.V.; validation, G.S.L., M.P.I., N.S.P., A.T.B., T.N.K., C.D.N. and I.O.V.; formal analysis, G.S.L., M.P.I., N.S.P. and I.O.V.; investigation, G.S.L., M.P.I., N.S.P., T.N.K., A.T.B., C.D.N. and I.O.V.; resources, G.S.L., N.S.P., C.D.N. and I.O.V.; data curation, G.S.L., N.S.P., T.N.K., A.T.B., C.D.N. and I.O.V.; writing--original draft preparation, G.S.L., M.P.I. and I.O.V.; writing--review and editing, G.S.L., M.P.I. and I.O.V.; visualization, G.S.L., M.P.I., N.S.P., A.T.B., T.N.K., C.D.N. and I.O.V.; supervision, I.O.V.; project administration, I.O.V.; funding acquisition, G.S.L., N.S.P., C.D.N. and I.O.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in this study are available on request from the corresponding author.  
This research is financed by the project \"Strengthening and optimizing the operation of MODY services and academic and research units of the Hellenic Mediterranean University\", funded by the Public Investment Program of the Greek Ministry of Education and Religious Affairs.  
The authors declare no conflict of interest.
## 4 RF Energy-Harvesting Techniques
### Narrowband vs. Broadband RF Energy Harvesting
Narrowband systems offer high efficiency and simpler design but are limited to specific RF sources, making them suitable for applications with known, stable frequency environments. Conversely, broadband systems provide greater adaptability in capturing energy from diverse RF sources but require more complex designs to ensure consistent performance across a wide frequency range [64; 65]. In scenarios such as applications of wireless sensor networks, mobile health monitoring, and smart city deployments, the ability of broadband systems to harvest energy opportunistically from multiple sources may outweigh their slightly lower peak efficiencies, particularly when energy availability is unpredictable. The decision between these approaches should be guided by the specific application requirements and the characteristics of the RF environment in which the system will operate.
# Two Scenario-Based Heuristics for Stochastic Shift Design Problem with Task-Based Demand
## 3 Solution Approach
### Task Assignment Algorithm  
Before describing the TAA, we state the following definition:  
For a given designed shift plan \(H^{d}\), denote the \(i\)-th shift in \(H^{d}\) by \(h^{d}_{i}\), and the corresponding shift has the following information \(\left\{s_{h^{d}_{i}},e_{h^{d}_{i}},w_{h^{d}_{i}}\right\}\), where \(s_{h^{d}_{i}}\) and \(e_{h^{d}_{i}}\) are the start time and end time of shift \(h^{d}_{i}\), respectively, and \(w_{h^{d}_{i}}\) is the staffing for shift \(h^{d}_{i}\). The inherent cost of shift plan \(H^{d}\) is \(c_{H^{d}}=\alpha\sum_{h^{d}_{i}\in H^{d}}w_{h^{d}_{i}}\left(e_{h^{d}_{i}}-s_{h ^{d}_{i}}\right)\).  
Also for any shift \(h^{d}_{i}\), there are \(w_{h^{d}_{i}}\) subshifts, and each subshift has the following information \(\left\{s_{h^{d}_{i}},e_{h^{d}_{i}},1\right\}\). The subshifts of each shift \(h^{d}_{i}\) are collected to generate a new set of subshifts \(H^{s}\), and the \(i\)-th shift in \(H^{s}\) is denoted by \(h^{s}_{i}\).  
And for a given set of tasks \(K^{e}\), \(k^{e}_{j}\) is the \(j\)-th task in \(K^{e}\) with the following information \(\left\{s_{k^{e}_{j}},e_{k^{e}_{j}},w_{k^{e}_{j}}\right\}\), where \(s_{k^{e}_{j}}\) and \(e_{k^{e}_{j}}\) are the start time and end time of task \(k^{e}_{j}\), respectively, and \(w_{k^{e}_{j}}\) is the workforce required to perform task \(k^{e}_{j}\).  
To some extent, each shift can also be viewed as a task, and we will use this feature in the first phase of the TSH.  
The specific process of TAA is as follows:  
1. For a given designed shift plan \(H^{d}\), generate the corresponding set of subshifts \(H^{s}\) and make the tasks in \(K^{e}\) sorted by the start time of the tasks. Initialize \(j=0\), create an empty set \(K^{u}\), and go to step 2.
2. If \(j<|K^{e}|\), then let \(j=j+1\), collect the subshifts that can dispatch workforce to the \(j\)-th task from the set \(H^{s}\) to generate a new set \(H^{k^{e}_{j}}\), where \(H^{k^{e}_{j}}=\left\{h^{s}_{i}\middle|s_{h^{e}_{i}}\leq s_{k^{e}_{j}},e_{h^{e}_ {i}}\geq e_{k^{e}_{j}},h^{s}_{i}\in H^{s}\right\}\), and sort the shifts in \(H^{k^{e}_{j}}\) from front to back according to the start time of shifts. Use  
\(\delta_{\mu^{d}}\) is the over- and understaffing cost of the shift plan \(H^{d}\) when this plan is used in the scenario \(K^{e}\), and \(K^{u}\) is the corresponding set of those tasks without workforce to perform.
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 2 Materials and Methods
### Data Preprocessing Module  
The experiments utilize vibration sensors to collect fault signals from rotating components of complex equipment; therefore, the experimental data in this study consist of vibration time-series data. First, the data undergo preprocessing, utilizing the Z-score method to standardize the raw data. This step is essential to prevent certain outlier samples from exerting excessive influence on the model's training. The standardization is calculated as follows:  
\[z=\frac{x-\mu}{\sigma} \tag{1}\]  
Samples are divided using a sliding window approach, as shown in Figure 2. In order to reflect the strong recognition ability of the model, a window as small as possible is selected and input into the model. It is proved that the proposed model can identify the fault within a small range of data and windows. Generally, the sample window of the existing research is usually set to 1024 or 2048. In this experiment, the sliding window is set to 105, and the step size is set to be consistent with the window, that is, no repeated sampling. The test set accounts for approximately 20% of the total samples.  
Figure 1: MultiDilatedFormer model framework.  
Figure 2: Schematic diagram of data sliding window sampling.