esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Results at the Material Level  
The experimental results that were obtained with the procedure described in Section 2.1.1 are summarised in Table 4. The measured emissivity of the aluminium foil and of the pure aluminium paint are respectively \(\sim\)0.04 (sample c) (The emissivity \(\varepsilon\sim\) 0.04 of a commercial shine alluminium foil (sample c) was also measured by using a portable emissiometer according to ASTM C1371-15 [19]) and \(\sim\)0.41 (sample a100). It is worth to be mentioned that these results are in line with the values reported in ([5,7,18]).  
In Figure 10, a linear dependency of the emissivity as a function of the Al paint concentration is demonstrated. The results reveal that a change of 10% in the balance between the Al paint and the matt paint can determine a variation of approximately 0.047 of the final emissivity of the sample.  
One of the main drivers for a large diffusion of these materials is to minimise the metallized effect of the finishing since these paints can be used as interior radiation control coating in buildings. Considering this aspect, an interesting result in terms of emissivity value (\(\varepsilon\sim\) 0.6) was reached by the sample a50 (50% of aluminium paint concentration). As demonstrated in the following section this material can have a non-negligible influence on the indoor mean radiant temperature with a valuable aesthetic value (Figure 6).  
Figure 9: Axis orientation for the planar radiative temperature measurements \(T_{pr}\).
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 4 Performance Evaluation
### Performance Measures  
The simplest and basic performance measures are based on the calculation of prediction rates given a set of known class labels and classification labels, resulting in the counting of four prediction consequences, including the number of true-positive (TP), true-negative (TN), false-negative (FN), and false-positive (FP) predictions. With these counts, simple accuracy measures, including the confusion matrix, the Overall Accuracy (\(OA\)), and the Average Accuracy (\(AA\)), can be defined. These simple accuracy measures may be misleading in practice, particularly when the learning data is imbalanced. In this work, a comprehensive set of performance metrics, including scalar and graphic metrics, are adopted.  
Precision and recall are improved performance measures in the field of information retrieval and statistical classification, also widely used in the deep-learning-based objection literature. The precision is the ratio of the number of positive samples to the total number retrieved (defined as \(TP/(TN+FP)\)). It reflects the ability of a model to predict only the relevant instances. The recall rate refers to the ratio of the number of positive samples retrieved and the number of all truly positive samples in the dataset (defined as \(TP/(TP+FN)\). The recall indicates the ability of a model to find all relevant instances. The two measures are coupled; in general, when both measures approach 1, they reflect a more accurate model. However, practical models often achieve higher precision and low recall or vice versa. Based on precision and recall, the \(F_{1}\) score, which is the harmonic mean of precision and recall, quantifies the balanced performance of a classification model.  
The precision and recall can be evaluated using a default threshold value (i.e., 0.5) in the classification layer. By varying the underlying classification threshold, a precision-recall curve (PRC) can be plotted. Technically, a maximal F-1 measurement (and the optimal threshold) can be recognized from this PRC curve. Another graphical evaluation approach, called the Receiver Operating Characteristic (ROC) curve, is often used in the literature on machine learning. A ROC is created by plotting the true-positive rate (which is the same as the recall measure) against the false-positive rate, during which the classification threshold varies as well. Given a PRC or ROC associated with an acceptable classification model, one usually observes that as the recall (or the true-positive rate) increases, the precision decreases, and the false-positive rate increases. Last, it is noted that with the ROC curve, the area under the ROC curve (\(AUC\)) can be used as a lumped measure that indicates the overall capacity of the model. In this paper, the baseline confusion matrices, four performance statistics \(AUC\), \(F_{1}\) score, \(AA\), and \(OA\), and two graphical curves (PRC and ROC curves) are used as performance measures.
# 5G Mobile Phone Network Introduction in Colombia
## 4 MIMO Systems
### Comparison of MIMO in 5G vs. 4G  
\(2\times 2\) MIMO systems are successfully used for 4G mobile networks, and a large number of antenna elements are added for 5G communications. Several types of antennas have recently been proposed for 5G MIMO smartphones. Cell phones that operate with several 5G MIMO antennas at frequencies Sub-6 GHz must be accommodated in a limited size environment and coexist with the frequency antennas used by previous generations. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Band Frequency** & **Advantage** & **Disadvantage** \\ \hline \multirow{3}{*}{\(<1\,\mathrm{GHz}\)} & Greater coverage than higher & Lower channel capacity than other \\  & frequencies (1 GHz-6 GHz), (mm-W & frequencies (1 GHz-6 GHz), (mm-W \\  & bands up to 66 GHz) & bands up to 66 GHz) \\ \hline \multirow{3}{*}{\(1\,\mathrm{GHz}\)–\(6\,\mathrm{GHz}\)} & Balanced combination of coverage & There are other mobile bands in the 1 to 6 6 GHz range, currently used for 3G and 4G services, which could be gradually \\  & drives the first wave of 5G & 4G services, which could be gradually \\  & deployments & reallocated for their use in 5G. \\ \hline \multirow{3}{*}{\(>6\,\mathrm{GHz}\)} & Lower coverage than other & \\  & frequencies (1 GHz-6 GHz) and & Higher data rate than other \\ \cline{1-1}  & (mm-W bands up to 66 GHz) & frequencies (1 GHz-6 GHz), (mm-W \\ \cline{1-1}  & Supports ultra-fast mobile broadband & bands up to 66 GHz) \\ \cline{1-1}  & speeds envisioned for 5G technology & bands up to 66 GHz) \\ \cline{1-1}  & Will include unlicensed mobile bands & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Advantage and disadvantage in 5G band frequency [26].  
scheme of 5G MIMO antennas for smartphones is to have four or eight antenna elements in separate regions of the antenna itself where isolation is ensured by the distance between the elements obtaining low mutual coupling on unrelated channels. This is a difficult task for space limited smart phones. For this reason, the locations for the 3500 MHz 5G band antenna on the mobiles is along the two long edges of the chassis ground, as the 4 Gs usually occupy the space along the short edges. For example, some 4G services like Long Term Evolution (LTE) and GSM 850/900 have the primary antenna size of \(45\times 15\) mm while the 5G auxiliary antenna size is 19 \(\times\) 15 mm [27; 30].
## 4 Experimental Validation
When using four permanent magnets, the output power density is increased by 0.529 mW/dm\({}^{2}\) compared to a uniform cross-section piezoelectric cantilever beam at 250 rpm, and the output power increased by 11.51%, indicating the success of optimizing the piezoelectric cantilever beam through non-uniform cross-section. The maximum power output test results of the electret-piezoelectric coupling energy-harvesting structure, based on alternating positive and negative polarities and optimized non-uniform cross-section piezoelectric cantilever beams at different rotational speeds, are shown in Figure 15.  
In Figure 15, A represents the coupling structure using identical positive-polarity electrets, while B represents the coupling structure using alternating positive- and negative-polarity electrets. Due to the coupling of the magnetically actuated piezoelectric component with the electret electrostatic component, the energy-harvesting structure operates over a wide range rather than being concentrated at a specific working point. As the rotational speed increases, the output of the device gradually rises, reaching a maximum power output of 6.74 mW. The magnetically actuated piezoelectric component's output increases with the rotational speed because the intermittent magnetic driving becomes more frequent, in line with the previous analysis. The device in this paper is designed for low-frequency  
Figure 15: Testing of rotational energy harvesting for coupling devices under different electret configurations, with optimized piezoelectric component. The identical component only uses negatively charged \(\delta\) electrets. excitation; if the rotational speed is too high, causing the frequency of the permanent magnet exciting the piezoelectric cantilever beam to even reach its resonance frequency, the model proposed in this paper may not be applicable. Then, compared to the identical arrangement of positively charged electrets, the alternating arrangement of positive and negative electrets increases the rate of induced charge variation and the relative charge difference, resulting in an average power output increase of 0.71 mW for components of the same volume, confirming the validity of the theoretical and simulation models in Section 3.1. Additionally, the output power density of the piezoelectric component is greater, compensating for the insufficient output power when using electrostatic components. Based on the aforementioned test results, a comparison of the device designed in this study with other rotational energy-harvesting devices is presented in Table 1.  
In summary, the coupling structure proposed in this study has the advantages of non-contact and facilitating integrated measurement and supply. The experimental results demonstrate the correctness of the theoretical analysis and modeling, achieving the optimized design of the coupled structure and confirming the advantages.
## 3 Reference Architecture
### Deployment View  
In Figure 7, the deployment view is presented, in which the software system would be realized on hardware and infrastructure. A detailed overview and their interaction with the physical deployment of various components of the system in an actual operational environment is presented. The domain model view supports an understanding of the  
Figure 6: Domain Model Diagram. 0...1 infers that the relationship is optional (and associates with at most one entity), 1...* denotes a mandatory relationship (can associate with one or more entities), 0...* denotes that the relationship is optional, and thus can associate with zero or more entities.  
system's hardware, software, network, and physical architecture. In the case of virtualised art galleries, the Local PC or HPC plays a significant role in hosting the functionalities of the gallery's content. Local data storage is provided for high-resolution replicas and interactive media files suitable for onsite operations. Connected to a data management system, bilateral communication between the servers and users of the gallery is accomplished via local or mobile connectivity. Camera Units/sensors are used to monitor and digitize the gallery environment, while actuators provide physical controls within the virtual physical gallery. The proof of concept further enhances the end user's experience by enabling features such as immersive 3D visualization, narrative digital storytelling, and gamification. Remote monitoring services provide the gallery's stakeholders with real-time insights into visitor data and systems performance. Additionally, cloud services can support the digitized gallery infrastructure by providing short-term and long-term data storage and streaming features. Pixel streaming cloud service ensures high-quality digitized results across a variety of devices (PC, Tablet, XR, etc.).
# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 2 Materials and Methods
### Neural Network-Assisted Visual-Inertial Person Localization Method under a Moving Base
#### 2.3.1 Data Pre-Processing and Initialization  
We pre-processed the collected IMU data to reject incorrect data values and fill in gaps. After LSTM network processing, the IMU data are pre-integrated to obtain the state quantities of the current moment: position, velocity, and rotation.  
The initialization part includes purely visual SFM (structure from motion) and visual-inertial fusion alignment, which aims to provide good initial values for visual-inertial tight-coupling optimization within the sliding window below. The purely visual SFM is tracked by point feature extraction between consecutive frames, solving the relative transformation between frames by a pair of polar geometric models and selecting the two keyframes within the sliding window with the same observation but farthest away for triangulation to obtain the inverse depth of 3D points.  
Since the IMU comes with zero bias and zero bias is included as a state variable to be estimated in the sliding window optimization mechanism below, the visual inertial joint initialization can be used to estimate the initial value of the gyroscope zero bias using the rotation constraint of the visual SFM. Accelerometer zero bias has little effect due to comparison with gravity action that is neglected here. The gravity direction, velocity, and scale information of the camera can be estimated using the translational constraint.
## 3 Results and Discussion
### Energy Utilization in The Convective Solar Dryer
#### 3.5.3 Electrical and Solar Energy Intervention  
The contribution of both solar and electrical energy during all drying experiments at different aero-thermal parameters is shown in Figure 11.  
The trend of both solar energy and total energy utilization are similar, which means, an increase in air-drying temperature (60-80 \({}^{\circ}\)C) results in a decrease in solar energy, this is because at higher values of air-drying temperatures the drying time decreases. Based on these data, it can be concluded that solar energy constitutes the predominant used energy in all drying experiments compared to electrical energy (Figure 11), which allows more savings on energy costs and respects the environment.  
Figure 10: Evolution of specific energy utilization at 60, 70, and 80 \({}^{\circ}\)C.
## 1 Introduction
The 3D model obtained by photogrammetric surveying is optimized and prepared for its implementation in a game-engine for creating the VR presentation, which is explained in detail. The method is designed according to the current state of the art in the field of VR and photogrammetry application for cultural heritage, adopting the most widely used software tools. The workflows connecting photogrammetry and VR represent an important topic in the field of cultural heritage [19; 20; 26; 27; 28]. [PERSON] et al. [20] presented a digital reconstruction of a historical building as an interactive and immersive experience in virtual reality. However, the authors focused on employing digital imaging tools to recover deteriorated mural paintings. Several works used multi-source 3D data, i.e. the combination of photogrammetric and terrestrial laser scanning data for cultural heritage 3D modeling [26; 27; 28], while [PERSON] et al. [19] used photogrammetry, but did not deal with the on-site surveying plan. They pointed out the relationship between the texture resolution and ground sample distance (GSD) as a subject of future work. The aim of this paper is to create the workflow for the representation of real-world heritage and interaction with it in the virtual environment. The workflow focuses on achieving an accurate 3D reconstruction based on photographs; 3D model preparation for its further usage in VR in terms of mesh optimization and texture mapping; and, finally, the process of creating a VR experience using game-engine. The proposed workflow was applied and validated on the case study of the iconostasis of the Serbian Orthodox Cathedral church of Saint Nicholas in Sremski Karlovci. The VR for the iconostasis is significant as it provides a user with a possibility to interact with the iconostasis and to visualize and disclose its individual icons through different perspectives and multiple levels of detail, which is otherwise not possible inside a church interior.
## 3 Current Measurement Techniques  
In the following, we review and discuss the main measurement techniques for soil moisture and snow properties that are currently applicable to the alpine environment (see also Tables 1 and 2 that provide a summary of the main applicable techniques for soil moisture and snow properties, respectively).  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Soil Moisture (SM)** & **Manual (M)** & **Output** & **Time** & **Spatial Range** & **Accuracy** & **Remarks** & **Represented** \\
**Measurement Methods** & **Semi-Auto** & **Parameters** & **Interval** & **Resolution** & & & **Depth** \\  & & **(S-A) Auto (A)** & & & & & & \\ \hline _In-situ_ & estimation & A & top layer SM & as required & typ: 10 \(\times\) 10 m & limited & Includes & depends \\ stations & from fluxes & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline inference & A & SM & & 10 cm or along & moderate & Includes & depends \\ from thermal & & & & & a line 1–5 km & model & & \\ properties & & & & & & & & \\ \hline neutron & A & vertically & & m possible (10’s & high & depends \\ probes & & & integrated SM & & of km usual) & & \\ \hline TDR & A & SM in a small & & & high & 1 cm–1 m \\  & & & & volume & & & & (typ. 10 cm) \\ \hline Satellite-based & ERS- & A & top layer SM & 0.5 day & 50 \(\times\) 50 km & & large-scale & 5 cm \\  & scatterometer & & & & & & & missions \\ \hline SMOS & A & top layer SM & 1–3 days & 35–50 km & 4\% & & 5 cm \\ \hline GRACE & A & mass changes & depends on & 1,000 \(\times\) & \(\pm\)cm water & large \\  & & in large areas & spatiotemporal & 1,000 km & column & \\  & & & & 1 averaging & & & \\ \hline \end{tabular}
\end{table}
Table 1: Typical soil moisture measurement methods applicable for the alpine environment.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline
## 2 Methods and Materials
### Materials
#### 2.2.1 Beamforming Parameters  
This part describes the details and parameters used for the three beamformers compared: DAS, FDMAS, and \(p\)-DAS. For all of these beamformers, the raw RF data are previously oversampled at \(2f_{s}=41.6\) MHz (corresponding to \(8f_{0}\)). For this paper, the over sampling frequency was empirically determined to ignore the potential aliasing of artificial harmonics when using \(p\)-DAS. A dynamic aperture with a constant F-number of 1.75 is used, as by [5]. For DAS, uniform receive apodization is used. For FDMAS, the band-pass filter used is the same as that described by [16], as a Kaiser finite impulse response (FIR) filter with a centred frequency at \(2f_{0}\) and a pass-band defined on the range \(1.5f_{0}-2.5f_{0}\). For \(p\)-DAS, two \(p\)-values are investigated: \(p=2\) and \(p=3\). The band-pass filter required to remove artificial harmonics on the image is performed along the depth dimension. The RF image is first low-pass filtered using a Butterworth filter (order 11; cut-off frequency \(1.7f_{0}\)), and then it is high-pass filtered using another Butterworth filter (order 11; cut-off frequency \(0.4f_{0}\)).
## 2 Preliminaries
### System Model  
Three co-ordinates are used for attitude-tracking control: Earth-fixed co-ordinates \(\mathcal{F}_{e}\), body-fixed co-ordinates \(\mathcal{F}_{b}\), and target co-ordinates \(\mathcal{F}_{d}\). These are illustrated in Figure 1.  
There are a few methods to describe the orientation of a rigid body, and Euler angles have been widely used to express the attitude, which is convenient and intuitive. However, Euler angles have a singularity angle at \(\pm\pi/2\). Rodrigues-parameter representation is a three-dimensional alternative form of Euler representation. Compared with Euler angles, Rodrigues parameters have double the range of angles without singularity, as it has a singularity at \(\pm\pi\). In this paper, we chose the Rodrigues parameters to express the attitude.  
Figure 1: Mechanical configuration and co-ordinates of the negative-buoyancy quad-tilt-rotor autonomous underwater vehicle (NQTAUV).  
The NQTAUV shares the same kinetics equations of the previous version of NTAUV. The kinematics and kinetics equations of NQTAUV are in References [1; 9; 32]  
\[\dot{\xi} = H(\xi)\omega \tag{1}\] \[J\dot{\omega} = -S(\omega)J\omega-I_{A}\dot{\omega}-C(\omega)\omega-D(\omega)+\tau \tag{2}\]  
where \(\xi=\bar{\mathbf{e}}tan(\Phi/2),\bar{\mathbf{e}}\in\mathbb{R}^{3},||\bar{ \mathbf{e}}||=1\) are the Rodrigues parameters, \(\omega\) is the body-angle velocity vector, \(J\) is the rigid-body inertia tensor with respect to the origin of body-fixed co-ordinates, \(I_{A}\) is the added inertia matrix, \(C(\omega)=S(I_{A}\omega)\) is the hydrodynamic Coriolis and centripetal matrix, \(D(\omega)\) is the hydrodynamic damping matrix, and \(\tau\) is the control torque. \(S(\lambda)\) is defined as  
\[S(\lambda)=\begin{bmatrix}0&-\lambda_{3}&\lambda_{2}\\ \lambda_{3}&0&-\lambda_{1}\\ -\lambda_{2}&\lambda_{1}&0\end{bmatrix} \tag{3}\]  
in which \(\lambda=[\lambda_{1},\lambda_{2},\lambda_{3}]^{T}\), which satisfies \(\lambda^{T}S(\lambda)\equiv 0\). \(H(\xi)\) is defined as  
\[H(\xi)=\frac{1}{2}[I_{3}+S(\xi)+\xi\xi^{T}] \tag{4}\]  
which satisfies  
\[\xi^{T}H(\xi) =\frac{1}{2}\xi^{T}[I_{3}+S(\xi)+\xi\rho^{T}] \tag{5}\] \[=\frac{1}{2}(\xi^{T}+\xi^{T}\xi\xi^{T})\] \[=\frac{1}{2}(1+\xi^{T}\xi)\xi^{T}\]
# Incorporating Setup Effects into the Reliability Analysis of Driven Piles
## 6 Conclusions  
This paper presented an increase factor for the ultimate resistance for driven piles to modify the reliability index calculation formula. Meanwhile, the study conducted the uncertainty analysis of load and resistance to determine the ranges of relevant parameters. Finally, the impact of four critical parameters on the reliability index were investigated and compared with the existing results.  
Through parameter analysis, it is concluded that FOS has a significant influence on the reliability index of driven piles considering setup effects. The reliability index is essentially unaffected by \(\rho=Q_{\text{D}}/Q_{\text{L}}\), so it can be used as a constant when calculating the reliability index. \(M_{\text{setup}}\) was a critical parameter in this study and has a significant impact on the reliability index of driven pile considering setup effects. Therefore, the value of \(M_{\text{setup}}\) is particularly important in the reliability analysis of driven piles considering setup effects and is generally selected according to the type of soil. Meanwhile \(\rho_{\text{R0,Rsetup}}\) has a significant influence on the reliability index of driven piles, and when the \(\rho_{\text{R0,Rsetup}}\) value is smaller, the corresponding reliability index is higher. Through validation example analysis, the proposed formula in this paper is feasible. Additionally, it is concluded that more uncertainties will be considered when using the formula proposed in this paper to calculate the reliability index of driven pile considering setup effects.  
To summarize, if the setup effect is not entirely considered, the reliability index obtained is very conservative. Therefore, the reasonable evaluation of setup effects is crucial for the reliability analysis of driven piles.  
Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]) and [PERSON]; validation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), J.C. and K.Z.; writing--original draft preparation, [PERSON] ([PERSON]) and J.C.; writing--review and editing, [PERSON] ([PERSON]), [PERSON] and K.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52078396, 51708428).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 5: Reliability indices with time intervals for driven piles in clay and sand.