esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Automated Predictions of Veracity: BERT Models  
In the first experiment, BERT models were fine-tuned and evaluated on the same type of data: either typed or transcribed. Table 2 presents the precision, recall, F1, and accuracy of the three BERT models for typed utterances. As described in Section 2.4.1, the results were achieved in a 10-fold cross-validation with validation subsets for model selection. Table 3 presents similar data but for spoken transcribed utterances. The bottom of each table includes the results of [PERSON]'s Q.  
As indicated by the results presented in Tables 2 and 3, there were significant differences between the tested classification models. Because the \(p\)-value is less than 0.05, we can reject the null hypothesis and conclude that there is a difference between the classification accuracies.  
Of the three models, the best performing one was HerBERT, with accuracy reaching 0.68 in the case of typed utterances. PolBERT was the second best, with an accuracy of 0.62 for typed and 0.60 for transcribed utterances. The multilingual BERT by Google failed to recognize deception, as the results are no better than the random baseline. It is possible that the multilingual pre-training does not include enough of the Polish language. As indicated in the Section 2.4.1, PolBERT and HerBERT were pre-trained using much larger, cleaner, and more carefully selected collections of texts. It is also possible that the multilingual tokenizer and embedding vectors are not as good for processing the Polish language as the monolingual equivalents in the case of PolBERT and HerBERT.  
It is interesting to note that the models performed better on typed utterances. Transcriptions of spoken utterances were more difficult to learn from. This finding is interesting because spoken language leaves less time for consideration to the speaker, so deception features associated with constrained resources [4] should positively influence the performance of the classifiers. However, this did not happen or the models were not able to utilize the information.  
Figure 1 illustrates 10-fold accuracy of the HerBERT model for the typed utterances, computed over 10 epochs and measured on validation sets. The results, recorded at an  
interval of 500 steps, indicate that the best performance was achieved between 400 and 700 steps. The worst models exceeded the accuracy of 0.6, whereas the best ones reached 0.8.  
We also tried to merge all texts, spoken and written, and train models on a combined dataset. On one hand, this setup could improve model training as the data size doubles. On the other, transcribed and spoken texts may be too different to extract features related to veracity. In fact, the second circumstance turned out to be true, as the models trained on the combined dataset were marginally better than the random baseline.
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 2 Materials and Methods
### Description of the Base Station Coordinator Node
#### 2.2.1 Real-Time Clock (RTC) Module of the Base Station  
In the development of systems for precision agriculture, it is fundamental to consider the temporal reference in which the information of the sensors is obtained, to establish the fertigation strategies in an appropriate way. Without such information, precision irrigation would be impossible. For this purpose, the coordinator module incorporates an RTC module DS3231, which is connected to the Arduino DUE platform that manages the set through communication signals using the I2C bus. To safeguard the operation of the RTC, it has a backup battery apart from the general power supply of the control module.  
The information received from the RTC module is used to create a timestamp system. This timestamp is incorporated in two ways: in the properties of the image file, and in the name assigned to the file. Some problems have to be solved regarding the number of characters in the name of the file, since it is limited to 8 characters due to the file structure of the hardware of the RTC module.  
Figure 6: Sample view and diagram of the coordinator node. (**a**) Final prototype assembly of the node. (**b**) Schematic diagram of the node. (**c**) Diagram of the elements in a stacked mount; the elements are placed in a stacked mount to save space and increase the capabilities of the prototype.
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 1 Introduction  
With fifth-generation (5G) wireless communication technology, new and higher frequency bands, beamforming, and massive multiple-input multiple-output (MIMO) techniques are introduced to further enhance the efficiency of the radio access network (RAN), allowing ultra-reliable communications, broadband communication, and massive deployments of wireless IoT devices. As electromagnetic field (EMF) exposure quantities are time-averaged quantities [1, 2], knowledge of the duty cycle--a measure of the fraction of time during which a mobile phone is transmitting--for realistic use cases plays a key role in the accurate evaluation of EMF exposure assessments.  
In this study, we focused on assessing the uplink (UL)--from a smartphone towards a radio access network--duty cycle of a 5G-enabled smartphone. Before a smartphone is introduced to the market, the smartphone must comply with exposure limits in terms of the local specific absorption rate (SAR) averaged over 10 g [1] or 1 g of body tissue [2] following standardized measurement methods [3]. However, in realistic use cases, a smartphone will almost never be continuously transmitting, i.e., at a duty cycle of 100%, depending on the application or service used. The assessment of realistic EMF exposure to the uplink of a smartphone thus requires knowledge of the duty cycle. Time-averaged output powers of user devices have been investigated for GSM [4], 3G [5, 6], 4G [7], and 5G [8] devices. Furthermore, [PERSON] et al. [9] investigated the duality between UL local and DL whole-body exposure, and [PERSON] et al. [10] determined the duty cycle for Wireless Local-Area Networks (WLANs) for several data and voice applications and in a variety of environments. Such knowledge of realistic duty cycles will aid the determination of upper bounds on the time-averaged output powers of mobile equipment for different applications. The novelties of this study are as follows:  
1. A method for assessing uplink duty cycles using the drive test tool QualiPoc (Rohde & Schwarz, Munich, Germany);
2. An assessment of the in situ UL duty cycle of a smartphone for 4G and 5G New Radio (5G NR) for typical voice and data applications in the proximity of a 4G/5G base-station site in Belgium;An investigation of the influence of the Physical Uplink Shared Channel (PUSCH) transmit power, the reference signal received power (RSRP), and the Modulation Coding Scheme (MCS) on the uplink duty cycle.  
Section 2, the Materials and Methods section, presents the method for assessing duty cycles in realistic usage patterns for voice, video, and data applications based on the parameters obtained from the drive test tool. Section 3 discusses the observed duty cycles as well as uplink parameters that provide more profound insights in the assessment of duty cycles. Finally, Section 4 puts this study into a broader context and points out the limitations of this study. Section 5 concludes this in situ study and proposes the next steps beyond the scope of the present study.
# Robotics Applications in the Hospital Domain:
## 4 Robotic Systems in Hospitals Settings  
### Surgical Robots  
Robotic surgery is a significant application of robotics in the field of medicine. Robotic systems are used to assist in surgical procedures, including minimally invasive surgery [24]. These systems have been developed to overcome the limitations of traditional surgical methods. Robotic-assisted surgery (RAS) technologies, such as the da Vinci Surgical System, have rapidly evolved in recent years. They aim to improve surgical precision, dexterity, and access to minimally invasive procedures [25].  
Although autonomous surgery presents ethical, legal, and liability issues, it offers potential benefits, such as increased precision and a reduced risk of human error [26]. Examples of the current state of autonomous robotic surgery and its future potential include the following:  
* STAR (Smart Tissue Autonomous Robot), a vision-guided robotic system featuring a powered laparoscopic suturing tool, is currently in the experimental phase. It can perform continuous sutures from image-based commands. The interface supports two modes; manual mode allows the user to specify each stitch's position, and automatic mode enables automatic calculation of each stitch with equal spacing based on the incision's contour [27].
* The Da Vinci robot with partial automation, although still controlled by humans, is currently in an initial implementation phase. This system allows for the automation of surgical subtasks, leading to the partial automation of certain elements of surgical procedures. This partial automation of certain elements of the surgical procedure enhances safety and precision. The automation process employs AI applications to improve surgical accuracy and targeting [28].
* Robots for ophthalmic surgery such as Preceyes Surgical System, an intraocular robotic surgery system specifically for delicate retinal procedures, are currently in a clinical trials phase. Partial automation, such as pre-programmed movements by the surgeon, allows for more controlled and safer surgical procedures with less risk of human error [29].
## 4 Results
### Run-off Risk Assessment (FFPI)
#### 4.3.2 Application of the MLP Model for FFPI Computation
\) & \(\textbf{MLP}_{\textbf{1989}}\) & \(\textbf{MLP}_{\textbf{2019}}\) \\ \hline \multirow{4}{*}{Slope} & 0-3° & 0.00 & 0.10 & 0.33 & 0.20 & 0.373 & 0.404 \\  & 3–7° & 0.20 & 0.18 & 0.00 & 0.10 & & \\  & 7–15° & 0.43 & 0.27 & 0.25 & 0.17 & & \\  & 15–25° & 1.98 & 0.90 & 1.93 & 0.67 & & \\  & 25–45° & 1.20 & 0.58 & 2.72 & 0.90 & & \\ \hline \multirow{4}{*}{TPI} & –25.1 to –4.74 & 1.47 & 0.90 & 1.35 & 0.90 & 0.097 & 0.103 \\  & -4.73 to –1.31 & 0.93 & 0.33 & 1.21 & 0.75 & & \\  & -1.3 to 1.52 & 1.01 & 0.41 & 1.03 & 0.56 & & \\  & 1.53–5.15 & 0.98 & 0.38 & 0.70 & 0.21 & & \\  & 5.16–26.32 & 0.72 & 0.10 & 0.60 & 0.10 & & \\ \hline \multirow{4}{*}{TWI} & 3.09–6.04 & 1.27 & 0.90 & 1.14 & 0.66 & 0.104 & 0.194 \\  & 6.05–7.74 & 1.13 & 0.81 & 1.09 & 0.64 & & \\  & 7.75–10.1 & 0.14 & 0.10 & 0.37 & 0.28 & & \\  & 10.11–14.75 & 0.90 & 0.64 & 1.62 & 0.90 & & \\  & 14.76–24.64 & 0.67 & 0.48 & 0.00 & 0.10 & & \\ \hline \multirow{4}{*}{Land use/land cover} & Built-up areas & 2.13 & 0.59 & 2.21 & 0.56 & 0.287 & 0.236 \\  & Agriculture zone & 0.28 & 0.15 & 1.01 & 0.29 & & \\  & Shrub & 2.21 & 0.60 & 3.16 & 0.78 & & \\  & Fruit trees & 3.46 & 0.90 & 2.58 & 0.65 & & \\  & Forests & 0.08 & 0.10 & 0.16 & 0.10 & & \\  & Pastures & 0.64 & 0.23 & 0.37 & 0.15 & & \\  & Water bodies & 2.45 & 0.66 & 3.70 & 0.90 & & \\ \hline \multirow{4}{*}{Lithology} & Sandstone flysch & 0.91 & 0.65 & 0.58 & 0.17 & 0.146 & 0.128 \\  & Gravels, sands & 0.00 & 0.10 & 1.02 & 0.30 & & \\  & Clay with blocks & 0.90 & 0.64 & 0.79 & 0.23 & & \\  & Sandstone, shales & 1.12 & 0.77 & 1.54 & 0.46 & & \\  & Sandstone, marls & 0.47 & 0.38 & 2.79 & 0.84 & & \\  & Sandstone, tuffs & 0.70 & 0.52 & 2.79 & 0.84 & & \\  & Sandstone, conglomerates & 1.09 & 0.75 & 3.00 & 0.90 & & \\  & Sandstone-shale & 1.34 & 0.90 & 0.35 & 0.10 & & \\ \hline \multirow{4}{*}{Lithology} & 0.9–1.4 & 1.52 & 0.90 & 1.79 & 0.90 & 0.059 & 0.045 \\  & 0–0.9 & 0.86 & 0.10 & 1.11 & 0.40 & & \\  & \(-1.6\) to 0 & 0.99 & 0.26 & 0.71 & 0.10 & & \\ \hline \multirow{4}{*}{Aspect} & Flat surfaces & 0.00 & 0.10 & 0.00 & 0.10 & 0.051 & 0.032 \\  & North & 0.98 & 0.63 & 1.09 & 0.86 & & \\  & North-East & 1.32 & 0.81 & 1.12 & 0.88 & & \\  & East & 1.25 & 0.77 & 0.92 & 0.74 & & \\  & South-East & 0.72 & 0.49 & 0.72 & 0.60 & & \\  & South & 0.79 & 0.53 & 1.15 & 0.90 & & \\  & South-West & 0.67 & 0.46 & 1.00 & 0.80 & & \\  & West & 0.56 & 0.40 & 0.96 & 0.77 & & \\  & North-East & 1.48 & 0.90 & 1.06 & 0.84 & & \\ \hline \multirow{4}{*}{Convergence index} & –99.3 to –3 & 0.76 & 0.10 & 0.40 & 0.14 & 0.05 & 0.084 \\  & –3 to –2 & 0.79 & 0.16 & 0.34 & 0.10 & & \\  & \(-2\) to –1 & 1.13 & 0.90 & 1.04 & 0.54 & & \\  & \(-1\)–0 & 1.10 & 0.85 & 1.61 & 0.90 & & \\  & 0–100 & 1.09 & 0.
## 3 Online Root Fault Identification Method
### Online Root Fault Diagnosis
#### 3.2.2 Root Fault Label  
To identify the root fault of an alarm list, the weighted distances between the alarm vector and the feature vectors of every fault should be calculated. Thus, we can obtain \(P\)-weighted distances \(D_{W}(V^{\prime},C_{k})\), where \(k=1,2,\ldots,P\). The smaller the weighted distance is, the higher the similarity is. Thus, the minimum weighted distance is selected and expressed as:  
\[D_{W}(V^{\prime},C_{\mu})=\min\bigl{\{}D_{W}(V^{\prime},C_{1}),D_{W}(V^{ \prime},C_{2}),\ldots,D_{W}(V^{\prime},C_{P})\bigr{\}}, \tag{26}\]  
where \(D_{W}(V^{\prime},C_{\mu})\) is the weighted distance between an unknown alarm vector and the feature vector of fault \(\mu\). The detection threshold is expressed as \(T_{\mu}\). If \(D_{W}(V^{\prime},C_{\mu})\leq T_{\mu}\), and the root fault of the alarm list is labeled as a fault \(\mu\). If \(D_{W}(V^{\prime},C_{\mu})>T_{\mu}\), the root fault of the alarm list does not belong to the known \(P\) faults.  
The detection threshold \(T_{\mu}\) is determined using the available fault cases of fault \(\mu\) in the fault-template database; \(T_{\mu}\) is the maximum weighted distance between the fault cases of the fault \(\mu\) and the feature vector of the fault \(\mu\).
###### Abstract
[PERSON] [18] proposed using aluminum film resistors as temperature sensors to compensate for the output of heat sensitivity and bias displacement pressure sensors. In order to optimize the process parameters of this kind of temperature resistance, Al films with different thicknesses and heat treatments were studied experimentally. The results showed that the Al films with a thickness of 1.2 \(\upmu\)m have good long-term stability after annealing at 330 \({}^{\circ}\)C for 72 h. The specific resistance is 2.85 \(\upmu\Omega\cdot\)cm, and the resistance temperature coefficient is 43.3 \(\times\) 10\({}^{-4}\) K\({}^{-1}\). [PERSON] [19] studied the resistance temperature coefficients of Au and Al films with thicknesses ranging from 20 nm to 200 nm at 298 K and atmospheric pressure. The measured TCR value decreased with the decrease in film thickness, and the value decreased by about 13% compared with the corresponding volume value. Using cylindrical PET monofilaments as the main substrate, [PERSON] [21] used RF magnetron co-sputtering to deposit unpatterned Ni-Cr films. By changing the sputtering power of the target material, Ni-Cr thermal-resistant films with different Cr contents were obtained. The higher the Cr concentration, the longer the diameter and height of the grain, and the more obvious the effect. In addition, the increase in Cr concentration worsened the crystallinity of the NiCr films, becoming closer to the amorphous phase. The TCR of different NiCr concentration ratios showed different characteristics, and the maximum TCR value was 30 \(\times\) 10\({}^{-4}\) K\({}^{-1}\). Tungsten films have potential applications in temperature sensors due to their low cost, good conductivity, and stable physical/chemical properties [26]. However, their TCR values are relatively lower than that of typical industrial Pt resistance thermometers (-3.85 \(\times\) 10\({}^{-3}\) K\({}^{-1}\)[31; 32]). Thin-film Pt has the advantages of a high temperature coefficient of resistance, small volume, small heat capacity, and low thermal noise, which make it the focus of scientific research and industrialization. The TCR is a key technical index to characterize resistance thermometers' performance. The key factors affecting TCR value include film thickness, substrate roughness, and thermal annealing treatment. Interface scattering, annealing-induced grain growth, and defect reduction have a significant impact on TCR values [33; 34; 35; 36]. Therefore, the purpose of this work is to alloy Ti into W to fabricate solid-solution-type WTi alloy films. The alloying helps decreases the melting point of the alloys and thus the sputtered alloy films have a larger grain size. In addition, annealing was applied to improve the TCR values and stability of the WTi resistance thermometers.
_Int. J. Environ. Res. Public Health_**2015**, _12_, 9920-9937; doi:10.3390/ijerph120809920  
International Journal of  
**Environmental Research and**  
**Public Health ISSN 1660-4601**  
www.mdpi.com/journal/ijerph  
_Article_  
**Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1 and [PERSON] 1**  
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital  
Earth, Chinese Academy of Sciences, Beijing 100101, China;  
E-Mails: [EMAIL_ADDRESS] (Z.N.); [EMAIL_ADDRESS] (C.W.)  
Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese  
Academy of Sciences, Beijing 100094, China; E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed. E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-10-6480-6258; Fax: +86-10-6480-6258.  
Academic Editor: [PERSON]  
_Received: 7 July 2015 / Accepted: 14 August 2015 / Published: 20 August 2015_  
**Abstract:** The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (_r_) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.  
**Keywords:** HJ CCD; GF-1 WFV; STDFA; ESTARFM +
Footnote †: journal: Journal of Physics A
# A Review of Light Shelf Designs for Daylit Environments
## 2 Overview
### Performance Analysis of Static Horizontal Light Shelves
(2014) compared the performance of two light shelves, one mirrored and one using diffused reflection as this was achieved by a combination of various shapes of crystal faces located on the flat upper surface of the light shelf. A South oriented room in Seoul, Korea (Latitude: 37.56\({}^{\circ}\)) with dimensions 4.9 \(\times\) 6.6 \(\times\) 2.5 m was used which had 32% Window-to-wall Ratio. The results indicate that there is a substantial increase in uniformity. This increase was 29.9-34.3% for the external light shelf and 10.4-13.7% for the internal, compared to the existing flat mirror-type light shelf. In addition, lighting energy savings can increase by 7.98-13.3% (external light shelf) and by 1.8-4.4% (internal light shelf) when compared to the flat mirror light shelf. Among technical papers so far examined, the work of [PERSON] and [PERSON] [48] is very interesting since it examines the influence of a light shelf -among other systems- on the aesthetic perception of a small office. Four different daylight systems have been installed in an office room in Trondheim, Norway (Latitude: 63.43\({}^{\circ}\)) and fifty participants evaluated stereoscopic images rating nine architectural quality attributes under clear and overcast sky conditions. The results under clear sky show an increase on luminance contrast when a light shelf is used giving rise to glare problems and making the room less pleasant than other daylighting systems (such as reflective blinds). After analyzing the results, it seems that the reflective blinds rated highest in nearly all attributes.
# Estimation of Direct Normal Irradiance at Antarctica for Concentrated Solar Technology
## 2 Materials and Methods
### Description of the Solar Metric Station and Observations  
As part of the XXVIII scientific expedition of the PNRA in the austral summer season (October 2012/February 2013), ENEA oversaw the installation at MZS of a solar-metric acquisition station with the aim of carrying out high frequency and strong validated observations to create a data repository for solar irradiance modeling in the Antarctic region. The solar-metric station, produced by EKO Instruments, is equipped with three sensors--a single pyrheliometer and two pyranometers--for the independent measurement of the fundamental quantities for the purpose of characterizing the solar radiation on the ground: Global irradiance on the horizontal plane (GHI); Diffuse irradiance on the horizontal plane (Diff.HI) and Direct irradiance on the normal plane to the Sun's beams (DNI).  
The instrumentation also includes a Sun tracker, a device with moving parts that instantly detects the position of the Sun and allows the instrumentation to be aimed in the direction of the solar disc. The measurement error with the Kipp and Zonen CMP11 instrument is \(\pm\)3% (Figure 2), with a width of spectrum ranges is from 335 to 2200 nm, while the sensitivity of the instrument ranges from 4 to 6 \(\mu\)V\(\cdot\)Wm\({}^{-2}\). The station is equipped with an automatic data acquisition system that stores the observations, mean value and the average square deviations of each quantity. All the data are recorded on an internal memory module capable of holding the data acquired for a period of several months.
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 5 Performance Comparison and Discussion
### Ablation Experiments for the Multi-Head Structure
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variant** & \multicolumn{2}{c}{**Two Heads**} & \multicolumn{2}{c}{**Three Heads**} & \multicolumn{2}{c}{**Five Heads**} \\ \cline{2-7}
**Model** & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** \\ \hline RMSE (FD004) & 20.578 & 0.935 & 19.693 & 0.479 & 19.749 & 0.690 \\ Score (FD004) & 3168.767 & 1101.832 & 2391.744 & 351.663 & 2433.758 & 398.082 \\ \hline
**Variant** & \multicolumn{2}{c}{**Six Heads**} & \multicolumn{2}{c}{**Seven Heads**} & \multicolumn{2}{c}{**Nine Heads**} \\ \cline{2-7}
**Model** & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** \\ \hline RMSE (FD004) & **19.401** & **0.349** & 19.786 & 0.601 & 19.664 & 0.530 \\ Score (FD004) & **2184.660** & **158.393** & 2654.577 & 400.604 & 2233.338 & 392.941 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Effect of the number of heads in DMSAM_Data on the performance of RUL prediction. In summary, MSA-FCN designed by the multi-head structure can effectively and adequately focus on various aspects of the raw monitoring data including different degradation trends, degradation stages, and degradation rates. Therefore, the multi-head structure enables the neural network to achieve more accurate and robust RUL prediction results.