esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Environmental Impact of Dust Storms and Wind Erosion
#### 3.7.3 Implications for Sustainable Development in Saudi Arabia
The preservation of natural resources and environmental sustainability are key components of Vision 2030. The study's findings support the objectives of Vision 2030 to strengthen the Kingdom's resistance to climate change through the promotion of sustainable land management techniques that lessen desertification and soil erosion. In particular, controlling dust storms and their effects on soil and air quality is crucial to fostering a healthier environment--an objective that is highly valued in Vision 2030. Furthermore, the variation in dust deposition and its consequences at various locations emphasizes the significance of tailored approaches that take topographical and meteorological variables into account. In dry regions like KSA, these tactics are crucial for handling difficulties connected to soil and dust. By tackling these issues, KSA can achieve the sustainability goals contained in Vision 2030 and fulfill its commitment to the global SDGs with great success. This study's conclusions not only offer insightful information about how dust storms affect the ecosystem, but they also highlight the necessity of focused interventions that support more general development and sustainability objectives. Implementing such interventions will be key to mitigating the adverse effects of wind erosion and dust deposition, thereby supporting Saudi Arabia's long-term vision for a sustainable, resilient future.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 4 Implementation and Evaluation Setup
### Software Implementation  
The Zolertia Re-Mote supports the Contiki OS which includes a full IoT protocol stack for wireless mesh backbones using open IPv6 supported standards such as RPL and 6 lowpan. To implement the MAC layer, a custom implementation was required. To this end, the multi-technology MAC protocol was implemented using the Time Annotated Instruction Set Computer (TAISC) framework [33], which supports the design of TDMA MAC protocols that use multiple radios.  
Since easy installation is one of the main design goals, we also support easy configuration of the system. High level settings of the MAC protocol (e.g., ranging scheme configuration, slot size) and the anchor selection (e.g., number of anchors to range with, anchor selection approach) and low-level settings on the PHY (e.g., bitrate, preamble bits) are run-time (re)configurable from a central server using the Constrained Open Application Protocol (CoAP) [34] open standards for resource reconfigurations. To report the ranging results to the Localization Engine (LE), the MQTT protocol is used.
## 4 Feature Extraction  
### Signal Preprocessing  
The pre-processing of the signal is crucial because, if the redundant values are not eliminated, the results of feature extraction will be affected. The signals of take-off and landing were removed, and the acceleration signals below 5 Hz were also removed because  
Figure 4: Fault states: (**a**) the damaged propeller; (**b**) loosening of the motor screw; (**c**) loosening of the arm screw.  
we only need vibration signals rather than the those relating to the maneuver. The high-pass filter was used to filter the low signals, and only the frame and mechanism vibration signal were retained. The vibration signal was taken completely using the built-in high-pass filter function of MATLAB(r). Furthermore, after feature extraction, datapoints that lay outside of two standard deviations were removed. This constituted the extreme value removal method applied to eliminate extreme values, which enabled more stable classification by the model.
## 2 Materials and Methods  
### A. System Model  
Figure 1 shows a schematic diagram of a multi-hop FSOC system. The modulated beam emitted by the source node is transmitted to the destination node through \(N-1\) relay nodes. The relay nodes only amplify and forward the received signal without regeneration and shaping. We adopt two AF relay strategies: CSI-assisted relay and FG relay. It is assumed that all relay nodes can receive and transmit signals simultaneously, and there is  
no delay in transmission. In IM/DD system, the received electrical signal at the destination node is [29; 30]:  
\[y_{N}=\prod_{i=1}^{N}G_{i-1}h_{i}x+\sum_{i=1}^{N-1}n_{i}\left(\prod_{j=1}^{N}G_{ j}h_{j+1}\right)+n_{N} \tag{1}\]  
where \(G_{i}(G_{0}=1)\) represents the gain at the \(i\)-th hop, \(h_{i}\) represents the composite channel fading of the \(i\)-th hop, \(x\) represents the transmission signal of each hop, \(n_{i}\sim N(0,\sigma_{n}^{2})\) with power \(N_{0,i}\) represents the additive white gaussian noise (AWGN) at the \(i\)-th hop. Therefore, the end-to-end SNR of the multi-hop AF relay system, \(\gamma_{\text{end}}\), is [23]:  
\[\gamma_{\text{end}}=\frac{\prod_{i=1}^{N-1}G_{i}^{2}\prod_{i=1}^{N}h_{i}^{2}}{ \sum_{i}^{N-1}N_{0,i}\prod_{i=1}^{N-1}G_{i}^{2}\prod_{j=i+1}^{N}h_{j}^{2}+N_{0,N}} \tag{2}\]
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 3 Performance of Single Link  
### Experiment Designs  
In the single link experiment under the two forests shown in Figure 1, we denote the two nodes by \(A\) and \(B\), and the two directional links between them are denoted by \(A\)-\(B\) (\(A\) sends data to \(B\)) and \(B\)-\(A\) (\(B\) sends data to \(A\)), respectively. In experiments, \(A\) first broadcasts 600 probing messages to \(B\) with a period of 10 ms. After the completion of \(A\)'s probe sending, \(B\) starts broadcasting 600 probes back to \(A\), still with the time interval of 10 ms. When a probe is received by \(A\) or \(B\), the receiver will measure the RSSI and the LQI of this probe by invoking the functions supported by the CC2420 radio chip. To record the link information, each node is connected to a laptop via the USB port. The PRR is calculated at the laptop by the ratio of the successfully received probes and the transmitted probes. Most wireless sensor networks in practice pursue long-term monitoring, so the sensor nodes often use as low transmit powers in communication as possible.  
The CC2420 transceiver equipped in the TelosB node supports eight transmit power levels that can be programmed at runtime; they are \(0\), \(-1\), \(-3\), \(-5\), \(-7\), \(-10\), \(-15\) and \(-25\) in units of dBm, and they are represented in programs with parameters (tx) 31, 27, 23, 19, 15, 11, 7 and 3, respectively. The two last transmit power levels are too weak to form a valid link; we therefore choose the first six levels in experiments. In the experiments, nodes \(A\) and \(B\) are placed \(4\times d\) meters apart, where \(d\in\{1,2,\cdots,12\}\). The longest distance of the sender and receiver is 48 m in the Bajia experiment, beyond which the links almost always fail in the direction from \(A\) to \(B\), or the direction from \(B\) to \(A\), or both. Note here that in the Campus experiment, the longest distance between the two nodes is set to be 36 m, because it is the longest line-of-sight distance we can obtain there; the laser ranging device in use works only under the line-of-sight scenario. We repeatedly conduct the single-link experiment under different link distances and different transmit power levels.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 2 Groundwork
### Disaster Management  
DM involves different actions which depend on previous preparation and response capabilities when a disaster strikes. Such actions often occur in harsh conditions (in emergency scenarios) or in preparation stages where conditions are more favorable. As such, DM is usually divided into four (Figure 1) different stages, occurring after and before a disaster strikes [3].  
* **Mitigation**: Actions before disaster strikes. It consists of all types of actions taken to identify vulnerabilities and reduce or eliminate the risks of future events, such as the permanent removal of affected populations from dangerous areas and reinforcing weakened structures prone to collapse.
* **Preparedness**: Actions before disaster strikes, strongly tied to the mitigation process. However, preparedness works with the assumption that hazards cannot be avoided entirely. The goal is to devise workarounds and preparations when the disaster occurs, such as the creation of evacuation plans, training & warning systems [17].
* **Response**: Immediately before, during, and after a disaster occurs. It consists but it is not limited to evacuation, rescue, and needs assessment to save lives and minimize the damages on properties.
* **Recovery**: Actions which take place after a disaster. They aim to reestablish life as it was before the disaster. It involves the reconstruction and monitoring of affected communities/areas.  
When it comes to _in situ_ tasks involving DM, there are different options, including rescue team incursions by land, air, or water. Still, affected regions can be dangerous for the rescue team--e.g., 9/11 terrorist attack or the Fukushima Daiichi nuclear accident, where first-responders heroically gave their lives to save others. These situations are examples where disaster robots can play a crucial role, keeping the rescue team safe or even freeing them to execute other tasks.
## 2 Optimized EEMD Based 2D-MUSIC Method
### Impact Localization Process  
The implementing process of the impact location method based on the proposed method is shown in Figure 2. The fast HHT with an optimized EEMD algorithm is introduced to extract IMFs from impact signals. The spatial spectrum \(\mathbf{P}_{\text{MUSIC}}\) of each IMF is evaluated versus all \((r,\theta)\) in the \(r-\theta\) plane to find the steering vector \(\mathbf{A}(r,\theta)\) which matches the actual impact signal vectors. When the right steering vector is found, the denominator of Equation (2) approaches zero due to the orthogonal properties, resulting in a peak in the spatial spectrum which corresponds to the estimated impact position.  
Figure 2: The impact localization process.
## 3 System Design and Platform Subsystems
### Electrical Power Subsystem (EPS)  
The EPS provides electrical power to the spacecraft's different subsystems such that the satellite can function during the entire length of the mission. The power system will consist of three main components: the solar panels, the battery, and the power distribution system. Several options were considered for the solar panel configuration. The two most significant approaches suggested are the fixed solar panel configuration and the side deployable solar panel configuration. While the side deployable solar panel configuration appears to be appealing given the significantly higher power generation compared to the fixed solar panel configuration, power budget estimates (typical 6.24 Whr and maximum 7.33 Whr) suggest that the fixed solar panel configuration will provide the required power necessary. A total of 22 solar cells are used to generate the satellite's power with the solar cell distribution around the structure being as shown in Figure 10. The power system is also equipped with a Li-Ion battery pack with a density of 38.4 Whr. The EPS can perform the Li-Ion battery charging without interrupting the payload operation.  
Figure 9: MeznSat ADCS modes of operation.
## 3 Application of CNCs  
### CNC-Based Nanocomposites  
In the case of nanostructured organic composites, the surface area between the filler material and the organic matrix must be large. The dispersibility of the filler is the most important factor affecting the available surface area. If the dispersion is poor, the filler will form aggregates owing to van der Waals bonding; this will reduce the surface area in contact with the organic material and result in poor-quality nanocomposites. In addition, strong bonding at the interface between the filler and the organic material is essential. Otherwise, any external force acting on the nanocomposite will not be transferred to the filler, resulting in poor mechanical performance. CNCs can be used as reinforcing materials in multiphase composite materials based on polymers, as they lead to significant improvements in the mechanical strength, even at very low volume fractions. In addition, CNCs have been employed in biomimetic foams, reinforced paper, and flexible panels for flat panel electronics and displays [32]. Table 2 lists the various technologies that currently use nanocellulose materials.
## 2 Methodology
### Three-Dimensional Numerical Simulation
#### 2.2.2 Governing Equations and Boundary Conditions  
Numerical analysis of the flow field was performed with the incompressible Reynolds-averaged Navier-Stokes equations, including the solution of continuity Equation (2) and momentum Equation (3).  
\[\
abla\cdot\mathbf{u}=0 \tag{2}\]  
\[\rho\left(\frac{\partial u}{\partial t}+(\mathbf{u}\cdot\
abla)\mathbf{u}\right)=- \
abla P+\
abla\cdot(\mu\
abla\mathbf{u})+\rho\mathbf{g} \tag{3}\]  
where \(\mathbf{u}\) is the velocity vector, \(P\) represents the pressure, \(\rho\) is the density of the fluid, \(\mu\) represents the dynamic viscosity, and \(\mathbf{g}\) represents the gravity vector.  
In addition to the aerodynamic loads, the lifetime of turbine blades was significantly influenced by centrifugal forces and thermal loads. Equation (4) illustrates the relationship between the thermal load and the temperature distribution.  
\[\sigma=\Delta T\alpha E \tag{4}\]  
where \(\sigma\) represents the thermal stress, \(\Delta T\) is the temperature gradient, \(\alpha\) represents the coefficient of thermal expansion, and \(E\) represents the material's elastic modulus.  
The temperature field of the high-pressure turbine blade is governed by the heat conduction Equation (5).  
\[\
abla(k_{m}\
abla T_{m})+Q=\rho_{m}C_{p}\frac{\partial T_{m}}{\partial t} \tag{5}\]  
where \(k_{m}\) represents thermal conductivity, \(T_{m}\) stands for blade temperature, \(Q\) is the heat source, \(\rho_{m}\) is material density, and \(C_{p}\) represents the material's specific heat capacity.  
The centrifugal tensile stress of a rapidly rotating body significantly affects the strength distribution, which is determined by Equation (6).  
\[\sigma_{c}=\frac{m\omega^{2}R}{S} \tag{6}\]  
where \(m\) is the mass of the blade, \(\omega\) represents the rotational speed of the blade, \(R\) is the distance from the blade's center of mass to the rotation center, and \(S\) represents the cross-sectional area of the blade.  
An adiabatic wall surface is the boundary interface between the fluid domain and the turbine blades. The fluid domain was configured as a rotating domain, with a rotational speed of zero assigned to the non-rotating boundaries. The high-speed fluid adopted the Total Energy heat transfer mode, and the medium was considered an ideal gas. In the solver, absolute total temperature and absolute total pressure were specified at the gas inlet while static pressure was provided as the initial condition at the gas outlet. The temperature data obtained from the flow field analysis were imported into the steady-state thermal analysis module to obtain the thermal stress results. In addition to the effects of thermal stress and aerodynamic forces, centrifugal forces were also obtained by inputting the rotational speed, completing a multi-field coupled analysis.
## 3 Results and Discussion
### Preparation and Characterization of Silver Nanoparticles
Figure 5b shows the UV-Vis absorption spectra of nanoparticles stabilized with 16-O-16. These AgNPs were synthesized similar to the previous ones, but 16-O-16 was taken as capping agent (experiment 4-6). We observe that the peak of absorbance gradually increases with the decrease of gemini surfactant concentration as well, as in the case of experiments 1-3. The peaks of absorbance for experiments 4, 5, and 6 are 0.185, 0.313, and 0.397, respectively. The stabilizing effect of 16-O-16 on AgNPs is stronger for molar ratios of nAg/n16-O-16 = 5 and 10, whereas it has weaker effect for reaction with nAg/n16-O-16 molar ratio of 2.5. Same trend was observed for of 16-6-16. UV-Vis absorption spectra of AgNPs obtained in experiments 7 and 8 are shown in Figure 6. Strong absorption peaks at approximately 410 and 419 nm come from the surface plasmon absorption of nanosized silver particles. These spectra stand out; the good symmetric absorption peaks with a nearly unaltered width, suggesting that the size of the nanoparticles is very homogenous [89; 92]. All UV-Visible absorption spectra exhibit the absorbance peaks in the range of 410-435 nm (Table 3), which is typical feature for colloidal silver nanoparticles [93], confirming our statement that gemini surfactants in the Ag nanodispersion ensures a stabilizing effect on the nanoparticles. These results are in good correlation with what was described previously [87; 94; 95]. Consequently, the colloidal stability is also confirmed by the polydispersity index (PDI), which is given in DLS analysis and should be less or about 0.3. If the PDI is equal or higher than 1, the observable precipitation may occur [96]. The PDI values were between 0.2 and 0.4 in experiments 1-3 and 7-8, illustrating good colloidal features, and PDI values around 0.5 were found to be for the experiments 4-6. Higher PDI values in experiments 4-6 supports relatively broad and slightly distorted width of the UV-Vis spectra (Figure 5b) as well as the visual minor nanodispersions, which were of white color. \begin{table}
\begin{tabular}{c c c} \hline
**Experiment** & **Wavelength [nm]** & **Absorbance** \\ \hline
1 & 420 & 0.286 \\
2 & 435 & 0.332 \\
3 & 425 & 0.359 \\
4 & 420 & 0.185 \\
5 & 420 & 0.313 \\
6 & 415 & 0.397 \\
7 & 410 & 0.690 \\
8 & 419 & 0.596 \\ \hline \end{tabular}
\end{table} 
Table 3: The wavelength and absorbance data obtained from UV-Vis spectra. Figure 6: UV-Vis spectra of silver nanoparticles stabilized with 16-6-16 obtained in procedure with excess of reductant.
## 5 Experiment Evaluations
### Experiment Setup  
To evaluate the proposed approach, we used the NPS dataset from the Health Insurance Review and Assessment (HIRA) service in Korea [55]. The National Patients Sample (NPS) dataset consists of electronic health records of 3% of the Korean people sampled in 2011. We randomly selected 5 M records with seven attributes (_Age_, _Sex_, _Length_ of stay in the hospital, _Location_, _Surgery_ status, _Disease_, and _Height_ of patience) from the NPS dataset. We consider the first five attributes (_Age_, _Sex_, _Length_, _Location_, _Surgery_) to belong to _QA_, and the disease attribute belonging to \(S\). In the experiments, we focused on the following range query:  
SELECT_Age_, _Sex_, _Length_, _Location_, _Surgery_, _Disease_ FROM R WHERE \(min_{height}\leq Height\) AND \(Height\leq max_{height}\).  
Here, the values of \(min_{height}\) and \(max_{height}\) were varied during the experiments. In addition to reporting the experimental results for the method proposed in this paper (which is based on local anonymization in a distributed environment), we also report the results for the _k_-anonymity algorithm that is based on global anonymization in a distributed environment.  
One way to evaluate the performance of the proposed approach is to implement the proposed scheme on commercial or open source distributed DBMSs and conduct comprehensive experiments in real application environments. This, however, is out of scope at this stage of the research. Thus, in this paper, we simulated a distributed query processing environment as following: we used a cluster with one master node and five slave nodes for the experiments. Each node has a 3.30 GHz of CPU. 1 Gbps LAN is used for node communication. The data used in the experiment is horizontally partitioned into five fragments which are distributedly stored in the five slave nodes. That is, each slave node has a relation with the same set of attribute (i.e., _Age_, _Sex_, _Length_, _Location_, _Surgery_, _Disease_, and _Height_) and records are randomly and evenly distributed among five slave nodes. Each slave node has its local (standalone) DBMS, MySQL [56], managing local data. The communication between a master and a slave node is implemented using standard TCP/IP. Upon receiving a user query, the master node sends it to slave nodes which run in parallel. Then, each slave node runs the user query against the local data, and returns query results to the master node. We ran each query five times and the averaged values are presented in the paper.
# Phase-Sensitive 2D Motion Estimators Using Frequency Spectra of Ultrasonic Echoes
## 3 Experimental Results  
To evaluate the spatial resolution of the imaging method described in Section 2.4, an echo from a fine stainless wire of 15 \(\upmu\)m in diameter was measured in water. Figure 3 shows a B-mode image of the wire obtained with rectangular apodization. The lateral and axial widths at half maximum of the point spread function were 0.81 mm and 0.27 mm, respectively. Figure 4a shows the B-mode image of the sponge phantom. The nine red points show the points of interest, at which the lateral and axial motion velocities are estimated. Examples of the estimated lateral and axial velocities are shown in Figure 5. As shown in Figure 5, at the beginning of the movement, there are undesirable vibrations of the metal arm, which supports the ultrasonic probe, due to the rapid acceleration of the automatic stage. Therefore, errors in the estimated lateral and axial velocities were evaluated during the period from 154 to 235 ms (25 frame sets) in Figure 5, when the  
Figure 3: B-mode image of a stainless wire at 15 \(\upmu\)m in diameter. Figure 2: Illustration of frame averaging procedure in velocity estimation. undesired vibrations were attenuated. In the present paper, the bias error \(e_{\text{bias}}\) and standard deviation \(\sigma\) of the estimated lateral and axial velocities were evaluated as follows:  
\[e_{\text{bias}}=\text{E}_{p,n}\left[v_{p,n}-v_{\text{true}}\right], \tag{22}\]  
\[\sigma=\sqrt{\text{E}_{p,n}\left[\left(v_{p,n}-\bar{v}\right)^{2}\right]}, \tag{23}\]  
where \(v_{p,n}\) is the lateral or axial velocity at \(p\) and \(n\), which denote the points of interest (\(p=0,1,2,...,8\)) and the frame set number (\(n=0,1,2,...,24\)), respectively, \(\text{E}_{p,n}[\cdot]\) denotes averaging with respect to \(p\) and \(n\), \(\bar{v}\) is the lateral or axial velocity averaged with respect to the points of interest and frame sets, and \(v_{\text{true}}\) is the actual lateral or axial velocity, which was set to the automatic stage. Tables 1 and 2 show the bias errors \(e_{\text{bias}}\) and standard deviations \(\sigma\) of the lateral and axial velocities estimated by the conventional 2D motion estimator without and with frame averaging, respectively. Figure 4: B-mode image of phantom. Red points are points in shallow (a) and deep (b) regions of interest to be tracked. Figure 5: Example of estimated lateral and axial motion velocities. Lateral and axial velocities set at the automatic stage were 2.0 mm/s and -1.0 mm/s, respectively. The bias errors \(e_{\rm bias}\) in the estimated lateral velocities were large when the lateral window size was less than \(\pm 1.5\) mm (\(M=15\)). On the other hand, the bias errors \(e_{\rm bias}\) in the estimated axial velocities were very low (equal to or less than 3%) at all the examined window sizes. Both for the lateral and axial velocities, the standard deviations \(\sigma\) were significantly reduced by frame averaging.