esa-sceva/satcom-chunk-collection huggingface dataset:
# THz Rectennas and Their Design Rules
## 6 Thermal Effect
### Seebeck Effect
Primarily, Seebeck effect diodes are paired with detection applications as most of the reviewed literature reveals [101; 102; 103]. This is, for example, reinforced by [PERSON] et al. [103] who have experimented with two thermocouple antenna designs under two sources with different intensities. In their experiment, the response of their distributed bolometric effect rectenna system to a \(10.6\,\mathrm{\SIUnitSymbolMicro m}\) laser, with irradiance intensity of \(1\times 10^{8}\,\mathrm{W}\,\mathrm{m}^{-2}\), is \(15\,\mathrm{\SIUnitSymbolMicro V}\). Therefore, the focus is on the thermal antennas noise performance where the dominant parameter to quantify the performance is the noise equivalent power (NEP). It can be identified as \"the value of the power \(P\) that gives a unity signal-to-noise ratio for a bandwidth \(\Delta\
u=1\,\mathrm{Hz}\)\" as defined by [PERSON] et al. [45]. In their model, they managed to reach NEP values around \(100\,\mathrm{p}\mathrm{W}\mathrm{Hz}^{-1/2}\) for a barrier thickness around \(10\,\mathrm{\SIUnitSymbolMicro A}\) and frequency \(10\,\mathrm{GHz}\) with a fast roll off for THz frequencies and NEP values more than \(5\,\mathrm{n}\mathrm{W}\mathrm{Hz}^{-1/2}\). [PERSON] et al. [101] for their uncooled BiSb/Sb thermocouple, experimentally managed to reach an NEP value below \(200\,\mathrm{p}\mathrm{W}\mathrm{Hz}^{-1/2}\) for a close-to-terahertz frequency of \(812\,\mathrm{GHz}\). While it is quite normal for tunneling diode models to vary many orders of magnitude compared to experimental results, the Seebeck effect diode models usually express accuracy the same order of magnitude as their experimentally-tested prototypes, reaching 75% accuracy as the case of [PERSON] et al. [103]. The reported results by [PERSON] et al. [101] show also good agreement between measured and modeled results.
## 2 Proposed Feed-Forward NN Structure-Based Estimation Methods
### Proposed NN-Based Estimation Method 1: FFNN Structure
#### 2.2.2 Number of Hidden Neurons  
For a NN model with a single hidden layer, the number of neurons in the hidden layer must be chosen carefully since use of a larger number of hidden neurons can generate a model that more accurately reflects the training data. However, use of a large number of hidden neurons makes the model increasingly complex and may lead to overfitting. On the flip side, use of only a few hidden neurons may cause underfitting. Several researchers have proposed approaches to determine the ideal number of hidden neurons. A review that discusses how to fix the number of hidden neurons in NNs was presented by [PERSON] and [PERSON] [54], but no single method is effective in every circumstance. In this paper, the well-established [PERSON]'s Bayesian criterion, or the Bayesian information criterion (BIC), is used to determine the number of hidden neurons. This criterion is defined as  
\[BIC=n*ln\left(\frac{1}{n}\sum_{i=1}^{n}E^{2}\right)+p*ln\left(n\right) \tag{9}\]  
where \(n\) and \(p\) denote the sample size and the number of parameters, respectively. In BIC, the term \(ln\left(n\right)\) generally penalizes free parameters strongly. In addition, the accuracy of the NN model will increase as the sample size increases.
# Multimodal Attention-Based Instruction-Following Part-Level Affordance Grounding
## 2 Related Work
### Object Affordance Detection  
The concept of affordance encompasses both object affordance and environment affordance [57]. We mainly focus on object affordance in this paper. Vision-based object affordance understanding aims to learn object affordance from vision, which falls within the domain of affordance learning. Visual object affordance is normally associated with three main tasks: object affordance categorization, object affordance detection, and affordance reasoning.  
Similar to vision tasks such as object detection and semantic segmentation, affordance detection aims to detect the object and assign pixel-level labels for the object according to the affordance categories [58; 59; 60; 61; 62; 63]. In early work, manually designed geometric features were extracted from RGB-D images to detect the affordance of objects [58]. As deep learning has progressed, an increasing number of deep-learning-based methods have been proposed to avoid hand-designed features. [PERSON] et al. [59] trained an encoder-decoder convolutional network to obtain pixel label predictions. Subsequently, [PERSON]. [60] employed a sequence of deconvolutional layers with a resizing strategy and introduced a multitask loss function to effectively handle the multiclass problem in the affordance mask. AffordanceNet [62] uses two branches to localize the object and assign affordance labels separately. Without object candidates, [PERSON] et al. [63] designed a relationship-aware network to produce pixel-wise affordance segmentation in an end-to-end way. Considering the object shape, ADOSMNet [16] uses a novel shape mask-guided encoder that aggregates global features and locally relevant object features. In addition, several researchers have introduced weakly supervised and few-shot affordance detection methods [64].  
In these studies, the affordance detection and segmentation problem is considered a classification or pixel-level prediction problem. Due to the predefined and fixed nature of the affordance category, it is challenging for robots to directly understand language instructions. Consequently, existing approaches often fail to consider the crucial relation between visual features and task-specific semantic contexts. Our approach innovatively integrates natural language instruction with affordance detection and bridges the gap between language instructions and visual affordance, facilitating a more convenient interaction between humans and robots.
## 3 Experimental Procedure
### Equipment for Verifying Radio Wave Absorption Effect  
#### 3.3.1 Outline of the Radio Wave Absorption Verification System  
The outline of the verification system is shown in Figure 5.  
The radio wave absorption verification system is roughly divided into a transmission system and a reception system:  
The transmission system consists of Sweep Generator (HP 8350B & 83595A) and Multiplier (HP 83554A). By multiplying the 13.25 GHz to the 14.75 GHz RF signal oscillated from the Sweep Generator by Multiplier, an RF signal from 26.5 GHz to 29.5 GHz is created and transmitted from the horn antenna for transmission. The receiving system consists of a Scalar network analyzer (WILTRON 562) and an RF Detector (WILTRON 560-7K50). The RF signal received from the receiving horn antenna is detected by the RF Detector and transmitted by the Scalar network analyzer. The loss is measured. Figure 6 shows the Sweep Generator (HP 8350B & 83595A) and the Scalar network analyzer (WILTRON 562).  
Figure 4: Each sample used for verification: (**a**) CNT spray paint. Coating thickness: 16 \(\upmu\)m [left], 35 \(\upmu\)m [middle], 76 \(\upmu\)m [right]; (**b**) Carbon black paint: Coating thickness: 16 \(\upmu\)m [left], 35 \(\upmu\)m [middle], 76 \(\upmu\)m [right]; (**c**) Graphite paint: Coating thickness: 15 \(\upmu\)m [left], 36 \(\upmu\)m [middle], 74 \(\upmu\)m [right].  
Figure 5: Outline of the radio wave absorption verification system.  
Figure 6: Sweep Generator (HP 8350B & 83595A) and Scalar network analyzer (WILTRON 562).
## 3 Results and Discussion  
### Phase Composition  
Figure 1 shows the XRD patterns of the substrate and coatings prepared at various temperatures. Phases were identified from the XRD pattern using the International Center for Diffraction Data (ICDD) database. It should be noted that since the coating thickness of the samples is too thin, the grazing-incident X-ray and asymmetric-Bragg diffraction was employed to test the phases. As can be seen, the coating in the S30 sample consists mainly of FeS and FeS\({}_{2}\). When the temperature rises to 50 \({}^{\circ}\)C, the FeS intensity reaches a maximum and a new Fe\({}_{1-\text{x}}\)S phase is produced. It is because that in the range of 50 to 70 \({}^{\circ}\)C, the evaporation of water in the chemical solution is accelerated, and the increase in the concentration of the solution causes the intensity of Fe\({}_{\text{x}}\)S\({}_{\text{y}}\) (FeS, FeS\({}_{2}\), and Fe\({}_{1-\text{x}}\)S) to rapidly increase. As the temperature increases to 90 \({}^{\circ}\)C, the density of Fe\({}_{\text{x}}\)S\({}_{\text{y}}\) decreases significantly. This is because that the moisture in the chemical solution evaporates sharply at high temperature and a large amount of solute precipitates to lower the concentration of the solution, resulting in a decrease in the reaction product of Fe\({}_{\text{x}}\)S\({}_{\text{y}}\). Moreover, Fe\({}_{2}\)O\({}_{3}\) is identified in the S90 sample, indicating that the substrate has been oxidized at this temperature. From the XRD results, it can be concluded that the optimum chemical solution temperature is between 50 and 70 \({}^{\circ}\)C, since the coatings mainly consist of Fe\({}_{\text{x}}\)S\({}_{\text{y}}\) (FeS FeS\({}_{2}\), and Fe\({}_{1-\text{x}}\)S).  
Figure 1: X-ray diffraction (XRD) patterns of the AISI 5140 steel before and after chemical bath deposition coating treatment at different temperatures.
# ndInIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks
## 5 Performance Evaluation
### PIT Size  
One of the main topics of discussion about NDN performance is the number of entries kept at the PIT, which scales directly with network traffic leading to congestion situations. By introducing aggregation to our pub-sub solution, when services of the same DCU are concerned, we aimed to reduce the number of entries necessary to be kept at PIT tables. Proving the efficiency of this PIT management, the results in Figure 13(a) exhibit a strong reduction in the number of PIT entries across all use cases.  
To understand how efficient Name aggregation can be in the pub-sub mechanism considered, we can look further into the results of ndnIoT-FC in use case 3, with emphasis on the number of PIT entries that were newly created or updated throughout the simulation, as depicted in Figure 13(b).  
Considering that no changes were made to the native forwarding mechanism in the context of this work, it is expected that no improvements are noticeable. In ndnIoT-FC, however, by using part of the  
Figure 13: Use case 3—Network overhead metrics.  
Figure 14: PIT usage optimization.  
interest packet Names, which may include information about the kind of service under subscription, as opposed to the full name in order to construct a PIT entry, we were able to reduce the number of PIT entries required by one third using name aggregation, as services from the same location can be grouped into one single entry. This is especially meaningful when considering that an increased number of services made available will result in a higher number of PIT entries, which can be better controlled by the solution proposed. Additionally, the number of PIT entries reduces the energy consumption on each node by reducing the lookup stage searching for the pretending entry [27].
# Zero Drift Infrared Radiation Thermometer Using Chopper Stabilised Pre-Amplifier
## 2 Materials and Methods  
Two IRTs were configured in order to directly compare and contrast a zero-drift IRT against a precision IRT. The InAsSb photodiode used within each IRT was an uncooled Hamamatsu P13243-011 MA (Hamamatsu Photonics, Hertfordshire, UK), with a quoted specific detectivity (\(D^{\star}\)) and peak responsivity values of \(1\times 10^{9}\) cm-Hz\({}^{\frac{1}{2}}\)/W and 4.5 mA/W, respectively. No additional filter was placed in front of the detector, ensuring a broadband response defined by the detector spectral sensitivity. Each IRT comprised an identical TIA circuit, except for the type of op-amp used. A Linear Technologies LTC2050 chopper stabilised op-amp (Linear Technologies, Milpitas, CA, USA) and a Linear Technologies LT1012 precision op-amp (Linear Technologies, Milpitas, CA, USA) were used for the zero-drift and precision IRTs, respectively. The TIA feedback network resistance and capacitance were 10 M\(\Omega\) and 10 pF, leading to an RC time constant of 100 \(\upmu\)s. This provided a faster response time than could be achieved by thermocouples or thermal detector based IRTs, whilst still providing sufficient low-pass filtering. The circuits were not shielded, therefore, enabling some degree of electromagnetic coupling (EMC) related pickup to affect both IRTs to similar extents.
# Deep Belief Networks (DBN) with IoT-Based Alzheimer's Disease Detection and Classification
## 4 Result and Discussion
### Rmse  
Figure 8 shows the results of an RMSE comparison of the DBN-MOA method to those of other existing methods. The lower RMSE value achieved by the ML method is graphically displayed in the figure. On data set 100, for instance, the RMSE is 20.685%, while the SVM, RF, KNN, LR, and DT models achieve marginally improved RMSE of 45.839%, 36.811%, 32.851%, 28.652, and 24.685%, respectively. In contrast, the DBN-MOA model has demonstrated its best performance across various data sizes while maintaining a small RMSE. Similarly, under 600 data points, the RMSE values for SVM, RF, KNN, LR, and DT models are 52.965%, 44.832%, 36.789%, 31.789, and 27.981%, respectively, while DBN-RMSE MOA's value is 23.785%.
## 6 Experimental Verification  
In order to verify the calculation results of core loss, a load core loss experiment was carried out on the prototype. The motor prototype, controller, and experimental bench are shown in Figure 20.  
The experimental verification method was as follows:  
1. Increase the motor speed to slightly more than 6000 rpm through the inverter;
2. The inverter stops working, and the motor slows to a standstill; record the relationship between time and speed when the motor speed is 6000 rpm and zero;
3. The power loss at different moments can be obtained through the deceleration curve of the motor.  
It can be seen from Figure 21 that the calculation result was basically consistent with the core loss calculation result, and the maximum error of each point did not exceed 9.6%, which can meet the needs of the project. The motor core loss measured by this experimental method included the  
Figure 19: Cloud diagram of load core loss at different speeds.  
Figure 18: Stator core loss harmonics under SVPWM.  
mechanical loss of the motor (bearing loss and wind abrasion loss), so there is a certain error between the calculation results and the experimental results.
## 2 System Overview
### Power Transfer Principle  
For the LCC-S compensation circuit, \(C_{\text{T}},C_{\text{P}}\), and \(C_{\text{S}}\) satisfy the following equations [19]:  
\[\left\{\begin{matrix}C_{\text{T}}=\left(\omega^{2}L_{\text{T}}\right)^{-1}&C_{ \text{P}}=\left[\omega^{2}(L_{\text{P}}-L_{\text{T}})\right]^{-1}&C_{\text{S}} =\left(\omega^{2}L_{\text{S}}\right)^{-1}\\ C_{\text{DP}}=\left(\omega_{\text{D}}{}^{2}L_{\text{P}}\right)^{-1}&C_{\text{S }}=\left(\omega_{\text{D}}{}^{2}L_{\text{S}}\right)^{-1}\\ C_{\text{P1}}=\left(\omega^{2}L_{\text{PP}}\right)^{-1}&C_{\text{S1}}=\left( \omega^{2}L_{\text{SS}}\right)^{-1}\\ C_{\text{P2}}=\left(\omega_{\text{D}}{}^{2}L_{\text{PP}}\right)^{-1}&C_{\text{ S2}}=\left(\omega_{\text{D}}{}^{2}L_{\text{SS}}\right)^{-1}\end{matrix}\right. \tag{1}\]  
where \(\omega\) and \(\omega_{\text{D}}\) are the operating angular frequencies of power transfer and data transfer, respectively, and satisfy \(\omega=2\pi f,\omega_{\text{D}}=2\pi f_{\text{D}};f\) and \(f_{\text{D}}\) are the operating frequencies of power transfer and data transfer, respectively. The phase shift control strategy is adopted for power control, and the fundamental output voltage in phasor form can be expressed as [8]:  
\[\dot{U}_{\text{in}}=\frac{2\sqrt{2}U_{\text{dc}}}{\pi}\sin\frac{\delta}{2} \angle 0^{\circ} \tag{2}\]  
where \(\dot{U}_{\text{in}}\) is the output voltage vector of the inverter, \(U_{\text{dc}}\) is the input DC voltage, and \(\delta\) is the conduction angle.  
By using Kirchhoff Voltage Law (KVL) in the power transfer channel, the following equations can be derived:  
\[\left\{\begin{matrix}\dot{U}_{\text{in}}=\left(j\omega L_{\text{T}}+\frac{1}{ j\omega C_{\text{T}}}\right)\dot{I}_{\text{T}}-\frac{1}{j\omega C_{\text{T}}} \dot{I}_{\text{P}}\\ 0=\left(j\omega L_{\text{P}}+\frac{1}{j\omega C_{\text{P}}}+\frac{1}{j\omega C_ {\text{T}}}+\frac{1}{j\omega C_{\text{P1}}}+j\omega L_{\text{PP}}\right)\dot{I }_{\text{P}}-\frac{1}{j\omega C_{\text{T}}}\dot{I}_{\text{T}}-j\omega M\dot{I }_{\text{S}}\\ 0=-j\omega M\dot{I}_{\text{P}}+\left(j\omega L_{\text{S}}+\frac{1}{j\omega C_{ \text{S}}}+\frac{1}{j\omega C_{\text{S1}}}+j\omega L_{\text{SS}}+R_{\text{eq} }\right)\dot{I}_{\text{S}}\\ \dot{U}_{\text{R}}=R_{\text{eq}}\dot{I}_{\text{S}}\\ U_{\text{L}}=\frac{\pi\sqrt{2}}{4}U_{\text{R}}\end{matrix}\right. \tag{3}\]  
where \(\dot{U}_{\text{R}}\) and \(U_{\text{R}}\) are the input voltage vector and the RMS value of the rectifier, respectively. \(R_{\text{eq}}\) is the equivalent input resistance of the rectifier. Because the value of \(C_{\text{P2}}\) and \(C_{\text{S2}}\) is  
Figure 1: The proposed power and data parallel transmission circuit.  
very small, it approximates an open circuit for low frequency, so it can be ignored for power transfer.  
By submitting (1) and (2) into (3), the output voltage \(U_{\mathrm{L}}\) can be obtained:  
\[U_{\mathrm{L}}=\frac{MU_{\mathrm{dc}}}{L_{\mathrm{T}}}\sin\frac{\delta}{2} \tag{4}\]
# Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure
## 3 D Model Generation: Methodology Assessment in a Controlled Environment
### Data Acquisition  
The performed test addressed the assessment of the potentialities of a manual data acquisition for the updating of an object or a small area. The survey campaign was realized by employing the OnePlus 5 smartphone device in the video mode, in order to ensure a stabilized acquisition and to avoid the blur effect. At the same time, some images were captured with a NIKON D800E camera (Table 4), a high quality DSLR. Indeed, thanks to its performances from a technological point of view, it was employed for the generation of a reference dataset for the assessment of the geometric quality of the 3D point clouds produced with the smartphone.  
The acquisition of the frames must be carefully designed and must take into account the distance between the different acquisition points, defined according to the scale of representation, the scale of the frame and the resolution of the image. In general, the acquisition geometry must be constructed in such a way that between one image and the next there is sufficient overlap both in the longitudinal and in the transversal directions, which for an approach with techniques deriving from Structure from Motion algorithms reaches values of 60-75% [41].  
The smartphone device was held by the operator, who was moving around the object, trying to have a complete description: the auto-focus functionality of the camera sensors was disabled. In a few minutes it was possible to collect a dataset composed of more than 5000 frames, but only some of them were employed for the 3D reconstruction (Figure 4).  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **NIKON D800E** \\ \hline
**Sensor** & \(35.9\times 24.0\) mm CMOS (complementary metal-oxide \\ semiconductor) sensor (Nikon FX format) \\
**Focal length [mm]** & 24–50–60 \\
**Image resolution [pixel]** & 7360 \(\times\) 4912 \\
**Shutter Type** & Mechanical \\
**Fastest–Slowest speed [s]** & 1/-30.0 \\
**Price** & 750–1200 \(\mathbf{\epsilon}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: NIKON D800E specifications, according to the settings used during the data acquisitions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **OnePlus 5** & \\ \hline
**Focal length** & f [mm] & 4.574 \\
**Principal point** & x\({}_{0}\) [mm] & 0.034 \\  & y\({}_{0}\) [mm] & \(-\)0.017 \\  & k\({}_{1}\) & 0.1764 \\
**Radial distortion** & k\({}_{2}\) & \(-\)0.1549 \\  & k\({}_{3}\) & \(-\)1.4567 \\
**Tangential distortion** & P1 & 0.0017 \\
**Skew** & P2 & \(-\)0.0131 \\  & & \(-\)30.4633 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated calibration parameters.
## 3 Numerical Results and Discussions
### Detection of Diverse PQD Types  
Within a power grid, various PQD events can occur. Herein, we focus on six major PQD events--sag, swell, interruption, harmonics, notch, and oscillatory transient--and two combined PQD events--well with harmonics and sag with harmonics, which can often be generated within the power grid and cause serious problems in the connected electric devices or facilities.  
Table 7 summarizes the numerical results of the proposed detection scheme for the diverse randomly phased PQD events (\(\varphi_{m}\sim U[0,\ 2\pi)\); that is, random PQDs) that are generated over the IMPN channels with SNR = 40 dB, \(\mu\) = 400, and \(\psi\) = 0.02%. In the simulation, the critical PQD events specified by IEEE1159 (Table 1) (e.g., a swell with 110% \(pu\), a sag with 90% \(pu\), and harmonics (\"Har\") with 10% \(pu\)) are assumed to occur. In this simulation, we assume that such PQD event signals have a limited duration of two cycles (which are specified as \"instantaneous\"- or \"momentary\"-type signals by IEEE1159).  
Table 7 shows that a swell with 1.1 \(pu\) (or a sag with 0.9 \(pu\)) exhibits much more degraded DR than that with 1.2 \(pu\) (or a sag with 0.8 \(pu\); Table 5). This indicates that the amplitude of the PQD signals substantially affects the detection performance. In addition, a comparison of MV and RMS indicates that MV is only slightly better in terms of DR than RMS.  
The table also shows that the proposed scheme exhibits almost 100% DR for harmonics, interruption, notch, oscillatory transient, and swell (or sag) with harmonics [29], which indicates that these PQD events are relatively easy to detect as compared to pure sag or swell.