esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
## 2 Materials and Methods
### Experimental Design  
This chapter describes the design of the testing activities, focusing on the sensor's integration within the facade prototype and the test sequence. The facade prototype is composed of 6 prefabricated modules with dimensions of 1400 \(\times\) 3450 mm. Indeed, the realized 1:1 scale facade mock-up total dimension is 6.90 \(\times\) 4.50 mt, and the technology used is aluminum frames and double skin glaze. The facade modules were assembled off-site while the sensors were integrated on-site once the facade was installed in the laboratory. The sensor integration was deeply investigated, considering the sensor characteristics and facade components or orientations. In particular, the position of the sensor within the facade was defined according to the following:  
* The test standards (EN 13830 [16]), which defined the facade positions and parameters to assess the facade behavior. Indeed, the positions correspond to the most stressful facade position in the central part of aluminum profiles and glazed panels.
* Facade axis. Both FBG and accelerometers were positioned with different orientations with the aim of testing the facade behavior in all directions. As a reference for sensor monitoring, the different axes were considered, such as \(X\)--left, right; \(Y\)--up, down; and \(Z\)--inside, outside.
* Avoiding impact position test--the standard EN 14019 and EN 12600 [39] determine several facade positions where to conduct the impact test; the sensors were not positioned there to avoid damage.  
Table 2 shows the list of sensors integrated within the facade prototype for the testing activities.  
The FBG sensors are period refractive index modulation in fiber and are completely solid state. The sensitivity is, therefore, not limited by the FBG sensor but by the measurement system connected to it. At the maximum sample rate of 19.2 kHz, the switchgator interrogator can measure strain changes of 1 microstrain or, when measuring temperature, a temperature change of 0.1 \({}^{\circ}\)C. By resampling to lower speeds, this sensitivity is improved by the square root of the resampling factor.
## 5 Evaluation of the Security Aspects of the Designed Protocol
### Perfect Forward Secrecy  
The security feature of Perfect Forward Secrecy (PFS) is indeed crucial in authentication and key-agreement protocols, and it should be an integral component of any protocol aiming to safeguard the generating session keys and confidential parameters during the protocol process. PFS offers the significant advantage of generating distinct session keys for each session without any correlation between the keys of different sessions.  
As elucidated in the description of the presented protocol, random number generation has been employed to compute the S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), SA, A, and N\({}_{\text{i}}\) parameters, which play a pivotal role in session-key generation. The utilization of random numbers in this process ensures that the session keys calculated in each session are not only unique but also entirely unrelated to those generated in previous sessions. Consequently, even if an attacker manages to acquire keys from prior sessions under specific conditions, they would be unable to calculate the current session's key or establish any connection between session keys.  
The use of random numbers helps the protocol session-key generation process so that the keys calculated in the session are unique and completely different from previous sessions. Therefore, if the attacker is able to obtain the keys that are generated during prior sessions, it is beyond his/her capacity to perform the calculation of the previous keys and formulate the connection among the session keys.
## 4 Results and Discussion  
### Spectrum Curves Seasonal Variation of Forest Canopy in the Footprint  
#### 4.1.1 Reflectance Curves Characteristics in Different Seasons  
Vegetation reflectance varies in different wavebands and seasons. The reflectance spectrum in the growing season (Figures 3a,b,d,e) illustrates a peak in the green region and absorption features in blue and red regions. The red edge is positioned in the range of 700-720 nm wavelengths, which represents a large jump from the bottom of chlorophyll absorption (660-670 nm) to the near infrared plateau in the growing seasonal spectrum. As a result of the vegetation's high reflectance in the near infrared region and three moisture absorption valleys at 1450 nm, 1950 nm, and 2550 nm, there are two reflection peaks in the regions of 1600-1650 nm and 2150-2200 nm.  
In contrast, the reflectance spectrum in the dormant season (Figure 3c,f) shows dissimilar reflectance amplitude and spectral features to the spectrum in growing season. The absorption features in the blue and red regions and reflection peaks in the green region almost disappear. The red edge features recede, the NIR shoulder decreases, and the reflection peaks flatten in SWIR region.  
The NIR reflectance varies with different seasons. Some reflectance values were much higher than 0.4 during the growing season, but some were lower than 0.2 in the dormant season.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 1 Introduction
The authors of [12] considered UAV mounted base stations to serve as data relay between IoT devices. In [13; 14; 15], UAV-based small cells are used to fill the coverage holes in a cellular network in a post-disaster situation. In disaster scenarios, where the permanent connectivity is not required or is not possible due to the limited number of UAVs and their restricted coverage, UAVs are employed for delay tolerant data collection. In [16], UAV based mobile base station is considered that serves user along its path in post-disaster communication. In [17], UAVs are deployed to collect data from IoT devices on ground and the optimal trajectory of UAVs is studied to minimize energy consumption to enhance their flight time. The conventional Internet protocol (IP)-based network architecture for ground IoT network has several loopholes in terms of caching, mobility, security, and flexibility [18]. Moreover, the current IP infrastructure is not equipped to deal with disaster situations adequately, especially when the network is fragmented due to damage to the communication infrastructure. Therefore, an efficient and reliable system is needed for ground IoT network in such situations to detect and monitor the fire as well as send notifications to the users. Recently, named data networking (NDN) architecture has received much attention which evolves from the content-centric networking [19]. NDN is a data-oriented future Internet architecture (which retrieves data based on named content) offering several advantages over transmission control protocol (TCP)/IP networking including content delivery efficiency and data security [20; 21]. Forwarding strategy is the key feature of NDN to realize dynamic, adaptive and intelligent forwarding. Moreover, NDN architecture in ground IoT network can reduce the overall delay between IoT nodes,which increases the robustness of the system. Therefore, it is important to develop ground IoT network based on NDN architecture that can efficiently handle remote monitoring.
## 2 System Model
### System Model with Hardware Impairments  
Based on the model (11), we then obtain the system model expression with HWI. In this paper, two kinds of HWI are considered, i.e., RIS HWI and transceiver HWI. The RIS HWI mainly includes intrinsic hardware imperfections, imperfect CSI, and quantization errors of the RIS hardware [16; 17; 40; 41]. Note that the phase estimation error can be modeled as a zero-mean von Mises variable [41]. However, since the RIS HWI in this paper contains three types of error, we model it as \(\gamma_{i_{in},i_{n}}\sim\mathcal{U}[-a_{\text{HWI}},a_{\text{HWI}}]\)[17], where \(a_{\text{HWI}}\in[0,\pi/2]\). It is noteworthy that \(\gamma_{i_{in},i_{n}}\) is independent and identically distributed at time \(t\). Accordingly, \(\Theta_{i_{in},i_{n}}^{\text{HWI}}(t)\triangleq\Theta_{i_{in},i_{n}}(t)e^{- \gamma_{i_{in},i_{n}}}\). The transceiver HWI includes HWI in the transmitter and receiver. More precisely, the transceiver HWI refers to the distortion noises generated by the transmitter due to inaccurate modeling, which can be modeled as \(\eta_{t}\sim\mathcal{CN}(0,\text{Y}_{t})\), where \(\text{Y}_{t}\) is equal to a proportionality coefficient \(\kappa_{t}\) times transmit power [16; 17]. Similarly, the received distortion noises generated by the receiver at time \(t\) can also be modeled as \(\eta_{r}\sim\mathcal{CN}(0,V_{r})\), where \(V_{r}\) is equal to the proportionality coefficient \(\kappa_{r}\) times received power.  
Therefore, the total received signal \(y^{\text{HWI}}(t)\) can be obtained as  
\[y^{\text{HWI}}(t)=S_{0}^{\text{HWI}}(t)\Theta_{0}(t)+\sum_{i=1}^{K}\sum_{m=1,t =1}^{M,N}S_{i_{in},i_{n}}^{\text{HWI}}(t)\Theta_{i_{in},i_{n}}^{\text{HWI}}(t) +\eta_{r}+w, \tag{12}\]  
where  
\[S_{0}^{\text{HWI}}(t)=A_{0}(t)\bigg{\{}\sqrt{P_{t}}x(t-\tau_{0}(t))+\eta_{t} \bigg{\}}, \tag{13}\]  
and  
\[S_{i_{in},i_{n}}^{\text{HWI}}(t)=A_{i_{in},i_{n}}(t)\bigg{\{}\sqrt{P_{t}}x(t- \tau_{i_{in},i_{n}}(t)-\frac{\phi_{i_{in},i_{n}}(t)+\gamma_{i_{in},i_{n}}}{2 \pi f_{c}})+\eta_{t}\bigg{\}}. \tag{14}\]  
It is noteworthy that since the proposed model (12) is only related to the positions of the vehicle at time \(t\), which are already known at the BS, we regard the PI as the CSI. Therefore, the distances \(d_{0}(t)\), \(d_{\text{BS}}^{i_{in},i_{n}}\) and \(d_{i_{in},i_{n}}^{\text{V}}(t)\) are assumed to be already known in the rest of the paper. In addition, we discuss the case when the PI has random errors in Section 6.4.
# Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts
## 4 Procedure Execution
### Prototype  
We designed and built a working prototype of an augmented reality headset that provides astronauts hands-free instructions for procedure execution. The prototype used three single-board computers, one Intel Edison and two Raspberry Pi devices (see Figure 6 for a networking diagram). The Edison ran a web server that stored the procedure and kept track of the user's progress. One of the Raspberry Pi computers served as the server for the front-end of the web application whose user interface was displayed on an iPad mini, which was mounted face-down from a helmet worn by the user. The AR interface was achieved by reflecting the user interface on the iPad mini running the web application onto a sheet of teleprompter glass that hung in front of the user (see Figure 7a).  
A second Raspberry Pi computer, which was worn by the user, served as a Bluetooth beacon reader to scan for Bluetooth Low Energy (BLE)-based beacons affixed to tools needed to execute the procedure. This Raspberry Pi examined the strength of a Bluetooth signal being broadcast from a tool to roughly determine its distance (in increments of tens of feet) from the user in a room. The Raspberry Pi passed this distance information to the Intel Edison over Wi-Fi, and, combined with an experimenter who controlled a user interface directional arrow, the AR interface then displayed a visual indicator of distance and direction to help the user determine how to locate and retrieve the tool or object needed to perform the current procedure step (see Figure 7b). Hands-free operation of this prototype was achieved by incorporating speech recognition, made possible with a speech recognition engine on a Windows 8-based mobile phone with a headset worn by the user that captured verbal commands (e.g., \"Ready for next step\"), translated them to text, and sent them to the Intel Edison. If this converted phrase corresponded to a valid voice command, the system updated the user interface accordingly for the user.  
Figure 6: Networking diagram showing the connections over Wi-Fi of the two Raspberry Pis, Intel Edison, Windows Phone, and Web App.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 6 Conclusions  
IS is a new technology that is emerging nowadays. However, in recent years, various works have pointed to it as an opportunity to improve the wireless communication system for the next generations. This is due to the improvements obtained mainly for THz communication. This paper reviewed the impact of the IS on THz communication modeling. Furthermore, it summarized the architecture of the IS, THz propagation models, and IS-assisted THz propagation models. Finally, it discussed the challenges that need further exploration and examination for future work. Although there is little variety of work on this topic, a great effort by researchers has been made due to the great potential, more importantly in THz communication. It highlights the opportunities that must still be addressed before next-generation network technology employs IS and achieves higher KPIs than the 5G.  
**Author Contributions:** Conceptualization, J.D.S.E.S., J.A.P.R., V.F.A. and L.L.M.; methodology, J.D.S.E.S., J.A.P.R., V.F.A. and L.L.M.; formal analysis, J.D.S.E.S., J.A.P.R. and V.F.A.; investigation, J.D.S.E.S., J.A.P.R. and V.F.A.; writing--original draft preparation, J.D.S.E.S., J.A.P.R. and V.F.A.; writing--review and editing, J.D.S.E.S., J.A.P.R., V.F.A., L.L.M., S.B.M., R.A.A.d.S. and Y.L.; supervision, R.A.A.d.S.; project administration, R.A.A.d.S.; funding acquisition, R.A.A.d.S. and L.L.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by CNPq (Grant References 311470/2021-1 and 403827/2021-3), by Sao Paulo Research Foundation (FAPESP) (Grant No. 2021/06946-0), and by RNP, with resources from MCTIC, Grant No. 01245.020548/2021-07, under the Brazil 6G project of the Radiocommunication Reference Center (Centro de Referencia em Radiocomunicaoes--CRR) of the National Institute of Telecommunications (Instituto Nacional de Telecomunicacoes--Intel), Brazil, and by Huawei, under the project Advanced Academic Education in Telecommunications Networks and Systems, contract No PPA6001 BRA23032110257684, and by FCT/MCTES through national funds and, when applicable, co-funded by EU funds under the project UIDB/50008/2020-UIDP/50008/2020.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methodology and Model Establishment
### Rock-Breaking Criterion and Constitutive Relation of Rock  
The strength criterion for rock commonly adopts the Drucker-Prager (D-P) criterion. Building upon the Mohr-Coulomb criterion, the D-P criterion incorporates the influence of intermediate principal stresses and accounts for the effect of static hydrostatic pressures. This criterion is suitable for explaining the yield or failure behavior of rock materials under static hydrostatic pressures. Therefore, the model opts for the D-P criterion as the yield criterion for rock. It can be specifically formulated as follows:  
\[f=\alpha I_{1}+\sqrt{I_{2}}-K=0 \tag{3}\]  
\[I_{1}=\sigma_{1}+\sigma_{2}+\sigma_{3} \tag{4}\]  
\[J_{2}=\frac{(\sigma_{1}-\sigma_{2})^{2}+(\sigma_{2}-\sigma_{3})^{2}+(\sigma_{ 3}-\sigma_{1})^{2}}{6} \tag{5}\]  
\[\alpha=\frac{2\sin\phi}{\sqrt{3}(3-\sin\phi)} \tag{6}\]  
\[K=\frac{6c\cos\phi}{\sqrt{3}(3-\sin\phi)} \tag{7}\]  
where \(I_{1}\) is the first invariant of stress, \(J_{2}\) is the second invariant of stress deviation, \(\alpha\) and \(K\) are experimental constants related only to rock internal friction angle \(\varphi\) and cohesion \(c\), and \(\sigma_{1}\), \(\sigma_{2}\), \(\sigma_{3}\) are the first, second and third principal stresses, respectively.  
As per Equation (3), during the process of cutting and drilling into rock, the stress within the rock progressively increases, inducing plastic deformation. When the rock's plastic strain reaches a certain critical value, rock failure initiates. With the continuous increase in plastic strain, the rock's strength gradually diminishes. Until the plastic strain of the rock reaches the equivalent plastic strain corresponding to complete failure, the surface layer of the rock detaches from the rock mass, resulting in the formation of debris. Therefore, the equivalent plastic strain is used as the criterion of rock failure, i.e.,  
\[\varepsilon^{p}\leq\ \widetilde{\varepsilon}_{f}^{p^{l}} \tag{8}\]  
where \(\varepsilon^{p}\) is the equivalent plastic strain, and \(\widetilde{\varepsilon}_{f}^{p^{l}}\) is the equivalent plastic strain of rock failure.  
The concept of \"damage factor\" from damage mechanics is introduced into the context of rock failure and detachment. Currently, the elastic modulus is primarily employed to define the damage factor D, as follows:  
\[D=1-\frac{E^{\prime}}{E}=\left\{\begin{array}{ll}0&\varepsilon\leq\overline{ \varepsilon}_{f}^{st}\\ 1-\frac{\sigma}{\overline{\sigma}}&\varepsilon>\overline{\varepsilon}_{f}^{ st}\end{array}\right. \tag{9}\]  
where \(E\) is the elastic modulus of rock without failure, \(E^{\prime}\) is the equivalent elastic modulus of rock failure, \(\varepsilon\) is the strain, \(\sigma\) is the stress, \(\overline{\sigma}\) is the equivalent stress, \(\sigma_{y0}\) is the yield stress when the initial damage occurs to the rock, and the damage variable D = 0 at this time; when the plastic strain reaches \(\overline{\varepsilon}_{f}^{st}\), D = 1, the rock fails.
# Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves
## 2 Methods
### Dataset Construction for the Verification of Our Proposed Method
#### 2.2.2 Data Pre-Processing  
Since the measured pulse wave contained noise, pre-processing was necessary. In particular, if the pressure applied to the finger wearing the PPG changed, a trend appeared in the pulse wave, as shown in Figure 3. Therefore, it was necessary to remove the trend. First, trend elimination was performed on the measured pulse wave. For trend elimination, the trend elimination method proposed by [PERSON] et al. was used [11]. Next, a band-pass filter (0.5 to 10 Hz) was applied to the trend-removed pulse wave to remove mainly high-frequency noise. By performing this process, the trend and noise of the pulse wave could be removed, as shown in Figure 4. Next, each subject's experimental data with a data length of 480 s was divided into 60 segments with a data length of 8 s. In each segment, unitary pulse waveforms were extracted, and the features obtained from the unitary waveforms in the 8-s interval were averaged to be further used for multiple regression. Each unitary pulse waveform was obtained by segmenting the pulse wave based on the position of the detected pulse wave trough. Blood pressure measured with a continuous sphygmomanometer was similarly divided into 60 segments, with a data book of 8 s from one experimental data with a data length of 480 s. In each segment, the mean value of systolic blood pressure was used for multiple regression analysis. Figure 5 shows a box plot with the 60 systolic blood pressures (sbp) of each subject, which are averaged in each window of 8 s.
## 2 Method
### Autonomous Vehicle Pipeline and Vehicle Setup
#### 2.1.4 Control  
The vehicle control computes the front wheel steering angle and throttle/brake command to track the reference trajectory. Similar to the work presented in [18; 42], a MPC strategy is applied to compute the acceleration/deceleration and front wheel steering angle commands for the racing vehicle. Two different MPC controllers are used to achieve the lateral and longitudinal vehicle dynamics, respectively.  
The MPC dealing with the lateral vehicle dynamics applies an internal vehicle model defined by the state space representation in Equation (14) to predict the future behavior of the controlled system on the prediction horizon \(T_{p}\). The MPC takes as input the crosstalk error \(e_{1}\) and the relative yaw angle \(e_{2}\) defined in (4) and (5).  
Then, it solves an open-loop optimal control problem to determine the optimal commands, that is defined as \([u(k),\ \ldots,\ u(k+T_{c}-1)]\), where \(T_{c}\) is the control horizon. Therefore, only the first input of the optimal control sequence is applied to the system, and the prediction horizon is shifted one step forward to repeat the prediction and optimization procedure on the new available vehicle states. The optimization problem to be solved at each time step is  
\[\min_{u}J=\sum_{j=1}^{N_{y}}\sum_{i=1}^{T_{p}}\left\|y_{j}(k+i|k)\ -\ y_{j,ref}(k+i|k)\right\|_{Q_{y}}+\sum_{j=1}^{N_{u}}\sum_{i=0}^{T_{c}-1} \left\|u_{j}(k+i|k)\ -\ u_{j}(k+i-1|k)\right\|_{R_{u}} \tag{15}\]  
subject to  
\[x(k+j+1|k)\ =Ax(k+j|k)\ +\ B_{u}u(k+j|k)\ +\ B_{d}v(k+j|k) \tag{16}\]  
\[x(k|k)\ =x(k) \tag{17}\]  
\[y(k+j|k)\ =Cx(k+j|k) \tag{18}\]  
\[\left|u(k+j|k)\right|\ \leq u_{limit} \tag{19}\]  
where \(u\) is the control command, \(y_{j}(k+i|k)\) is the predicted value of the \(j\)-th output plant at the \(i\)-th prediction horizon step, and \(y_{j,ref}(k+i\left|k\right)\) is the reference value for the \(j\)-th output plant at the \(i\)-th prediction horizon step. The weighted norms of vector \(y=\ [y_{1},\ y_{2},\ y_{3}]\) and \(u=\ [u_{1},u_{2}]\) are equal to  
\[\left\|y_{j}(k+i|k)\ -\ y_{j,ref}(k+i|k)\right\|_{Q_{y}}=y(k+i|k)^{\mathsf{T}}Q_{ y}y(k+i|k) \tag{20}\]  
\[\left\|u_{j}(k+i|k)\ -\ u_{j}(k+i-1|k)\right\|_{R_{u}}=u_{j}(k+i|k)^{\mathsf{T}}R _{u}u_{j}(k+i-1|k) \tag{21}\]  
\(Q_{y}=\text{diag}([Q_{11},Q_{22},Q_{33}])\) and \(R_{u}=\text{diag}([R_{11},R_{22}])\) are the design matrices chosen according to the desired performance trade off.  
The optimization problem is formulated to minimize the sum of the weighted norms of the control input variables and the error between the predicted output vector \(y\) and the reference output vector \(y_{ref}\) which are computed as  
\[y=\ [U_{x}\ e_{1}\ e_{2}] \tag{22}\]  
\[y_{ref}=\ \left[U_{ref}\ 0\ 0\right] \tag{23}\]  
while the operational constraints to which the optimization problem is subject are  
\[\left|\dot{U}_{x}\right|\ \leq a_{max} \tag{24}\]  
\[\left|\delta\right|\ \leq\delta_{\max} \tag{25}\]
# Learning Weighted Forest and Similar Structure for Image Super Resolution
## 5 Experimental Results
### Comparisons  
We compare our algorithm with most algorithms included in [17], SR forests method (RFL) [21], and SRCNN [22]. The methods in [17] include Bi-cubic, NE+LLE [13], Zeyde's [16], GR and ANR. Bi-cubic interpolation is the benchmark comparison method in the experiment. The implementation codes of other algorithms are offered by their authors. These approaches share the same training dataset. They are compared quantitatively according to PSNR. The result shows that PSNR generally associates well with visual quality.