esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design
### Sensing, Control, Communication, and Localization  
#### 2.4.1 Sensing  
In different components, different types of sensors are equipped to measure the relative position and orientation. The camera (image sensor: OV5640) and infrared sensor (940 nm) are used to guide the docking process in different stages. As shown in Figure 4, color markers (red) and Apriltags are used as the passive markers to be recognized by the camera. The color marker can indicate the alignment and distance. The Apriltag can provide the coordinates of the marker in the frame of the camera. An infrared emitter is installed on the passive part of the docking mechanism and three infrared receivers are distributed along the transverse side of the active part of the docking mechanism. According to the infrared signal strength, the robot can measure the distance and alignment in a short range. In addition, an inertial measurement unit (ATK-IMU901, Alientek, China) is integrated inside to provide feedback regarding the attitude in extended functions.
## 4 Conclusions  
SiC NWs were successfully obtained via CS in a Si-C-N system using W as a catalyst. The W-catalyzed CSed SiC NWs products were found to be fluffy from surface to the core, and they were about several hundred micrometers long, with diameters less than 1 \(\mu\)m. W could improve the yield of whiskers, and it could decrease the harmful residual Si and C elements. In order to understand the initial growing progress, we investigated the absorption behavior of C, N, and Si atoms on the crystal planes of W (100), W (110), and W (111) surfaces by using first-principles calculations. The calculated \(E_{\text{surf}}\) of the studied W surfaces and the absorption energy of C, N, and Si atoms on  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Surface**} & \multirow{2}{*}{\(E_{\text{surf}}\)/(J\(\cdot\)m\({}^{-2}\))} & \multirow{2}{*}{**Adsorption Site**} & \multicolumn{3}{c}{\(E_{\text{ad}}\)/eV} \\ \cline{3-6}  & & & **C** & **N** & **Si** \\ \hline \multirow{2}{*}{W (100)} & \multirow{2}{*}{3.96} & top1 & \(-\)6.66 & \(-\)8.63 & \(-\)4.20 \\  & & top2 & \(-\)11.12 & \(-\)10.82 & \(-\)7.38 \\  & & bridge & \(-\)8.87 & \(-\)9.98 & \(-\)8.15 \\ \hline \multirow{2}{*}{W (110)} & \multirow{2}{*}{3.28} & top1 & \(-\)6.06 & \(-\)7.55 & \(-\)4.12 \\  & & top2 & \(-\)9.31 & \(-\)10.03 & \(-\)5.76 \\  & & bridge & \(-\)8.30 & \(-\)8.25 & \(-\)4.97 \\ \hline \multirow{2}{*}{W (111)} & \multirow{2}{*}{3.76} & top & \(-\)5.21 & \(-\)7.12 & \(-\)4.47 \\  & & hcp & \(-\)6.71 & \(-\)7.38 & \(-\)4.73 \\ \cline{1-1}  & & fcc & \(-\)7.79 & \(-\)7.36 & \(-\)6.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The calculated surface energy (\(E_{\text{surf}}\)) of the studied W surfaces, and the absorption energy of C, N, and Si atoms on different sites. Different adsorption sites are depicted in Figure 5.  
different sites indicated that the C atom has a priority to sink to the nanometer catalysts grain of W, and the pre-sunk C atom could react with a Si atom to form NWs.  
Data curation, H.-Y.G.; Project administration, M.X.; Writing--original draft, M.I.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities (FRF-TP-16-001A3).  
The authors declare no conflict of interest.
# Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
## 2 Materials and Methods
### MT Prediction Subnet  
We review the haze removal method based on dark channel prior [15], which is widely used in harsh visual scenarios such as fog, dust, and underwater [16; 17; 18]. The image formation model can be expressed as [19]:  
\[I^{c}(x)=J^{c}(x)T(x)+A^{c}\big{(}1-T(x)\big{)},\quad c\in\{r,g,b\}. \tag{1}\]  
This equation is defined on three RGB color channels. \(I\) represents the observed image, \(A\) is the airlight color vector, \(J\) is the surface brightness vector at the intersection of the scene and the real world light corresponding to the pixel \(x=(x,y)\), and \(T(x)\) is the transmission along with the light. [PERSON] et al. [20] proposed a new Dark Channel Prior (DCP) algorithm that can effectively estimate ambient light and is suitable for enhancing foggy, hazy, sandstorm, and underwater images. Inspired by DCP, as transferred \(T(x)\) has wide applicability, we use the medium transmission (MT) map (\(\overline{T}\)) as our attention map. It is worth mentioning that effectiveness will be demonstrated in ablation experiments. From [20], the actual input underwater image does not have a corresponding ground truth medium transmission map. It is difficult to train a deep neural network to estimate the medium transmission map. So the medium transmission map can be estimated as:  
\[\widetilde{T}(x)=1-\min_{y\in\Omega(x)}\bigg{\{}\min_{c\in\{r,g,b\}}\frac{I^{ c}(y)}{A^{c}}\bigg{\}}, \tag{2}\]  
where \(\bar{T}\) is the estimated medium transmission map, \(\Omega(x)\) is a local patch centered at \(x\), and \(c\) is the RGB channel. We use the MT map as a feature selector to weigh the importance of different spatial locations of features, as shown in Figure 3. We assign more weight to high-quality pixels (pixels larger MT values), which can be expressed as:  
\[F=O\oplus O\otimes\overline{T}. \tag{3}\]  
\(F,O\) represent the characteristics of the output and input, respectively. In detail, the MT map prediction subnetwork consists of four blocks to extract features. Each block has a convolution operation, a group normalization [21], and a proportional exponent linear unit (SELU) [22]. Then, it uses lateral connections to influence the detailed information decoded in the underwater feature map. Finally, another convolution operation is used, plus a sigmoid function, to return to \(\overline{T}\) by adding a supervision (input MT map in the training data).
###### Abstract  
Conceptually, this paper aims to help reduce the communication blind spots originating from the design of millimeter-wave (mmW) beamforming by deploying radio units of an open radio access network (O-RAN) with free-space optics (FSOs) as the backhaul and the fiber-optic link as the fronthaul. At frequencies exceeding 24 GHz, the transmission reach of 5G/6G beamforming is limited to a few hundred meters, and the periphery area of the sector operational range of beamforming introduces a communication blind spot. Using FSOs as the backhaul and a fiber-optic link as the fronthaul, O-RAN empowers the radio unit to extend over greater distances to supplement the communication range that mmW beamforming cannot adequately cover. Notably, O-RAN is a prime example of next-generation wireless networks renowned for their adaptability and open architecture to enhance the cost-effectiveness of this integration. A 200 meter-long FSO link for backhaul and a fiber-optic link of up to 10 km for fronthaul were erected, thereby enabling the reach of communication services from urban centers to suburban and remote rural areas. Furthermore, in the context of beamforming, reinforcement learning (RL) was employed to optimize the error vector magnitude (EVM) by dynamically adjusting the beamforming phase based on the communication user's location. In summary, the integration of RL-basedmmW beamforming with the proposed O-RAN communication setup is operational. It lends scalability and cost-effectiveness to current and future communication infrastructures in urban, peri-urban, and rural areas.  
fiber/FSOs communication; open radio access network; mmW beamforming
# Improved Smart Pillow for Remote Health Care System
## 3 Results
### Thermal Simulation of the Smart Pillow  
Energy2D is employed to simulate the heat transfer of head resting in the pillow, a simulation tool developed by [PERSON] [27]. The tool applies the Finite-Difference Time-Domain (FDTD) process and iterative methods to solve the heat equations. Before simulation, the thermal properties of the involved material are shown in Table 2. When person rests on the pillow, the material between the head to the bed is hair, pillowcase, and the pillow, and of course they are all exposed in air. Thermal conductivity \(K\) is the main concern. There is no argument about thermal properties of air. For hair, it is traditionally accepted that the thermal conductivity \(K\) is 0.37 W/mK [28; 29].  
The material of the pillow is latex, although it is cellularized rubber, so the \(K\) for natural rubber is 0.15, but for latex the engineer toolbox [30] recommends 0.045, it is reasonable as the air inside the rubber works as insulation, so at least it is better than air. On the contrary, the \(K\) of cotton is 0.06, but the pillowcase is cotton fabric, since it is not pure cotton, its \(K\) is marginally greater with a recommended value of 0.077. Those parameters are set in Energy2D to run the simulation; with the result is shown in Figure 11.  
The simulation settings are exactly equivalent to the actual scenario. The size of the testbed is 100 cm \(\times\) 50 cm, the pillow dimensions are 55 cm (width) by 10 cm (height), the head is modeled as an eclipse with 20 cm height and 15 cm width. Three temperature sensors are distributed on the surface of pillow, with the middle sensor close to the edge of the head to simulate head resting right on the sensor. The head temperature is set to 37 \({}^{\circ}\)C as a stable heat source, as this is the simulation of CBT, and the environmental temperature is set to 25 \({}^{\circ}\)C.  
From the simulation process, it took about 2 min for the middle sensor to reach 36.8 \({}^{\circ}\)C, and 5 min to reach 36.9 \({}^{\circ}\)C, for this is the maximum level reached. Ideally, the result proves that despite the hair and pillowcase between the skin and the sensor, if the head is right on the sensor and is present for a long enough time, the value of sensor is very close to the CBT.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Name** & \(K\) (W/m\({}^{\circ}\)C) & \(\rho\) (kg/m\({}^{3}\)) & \(C\) (J/kg\({}^{\circ}\)C) \\ \hline Hair & 0.37 & 1300 & 10,000 \\ Air & 0.026 & 1.17 & 1007 \\ Cellularized Rubber & 0.045 & 100 & - \\ Cotton Pillowcase & 0.077 & 245 & 1300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of materials related in the pillow experiment.  
In the actual experiment, for head resting right on the sensor for more than 10 min, the value is very close to a traditional thermometer measured orally, with their difference being in the margin of 0.1 \({}^{\circ}\)C--thus proving that the simulation result and experimental result is generally consistent.
# Instantaneous Networking Service Availability for Disaster Recovery
## 4 Networking Service Availability: An Integrated Measure of User Service Restoration
### Networking Service Availability Improvement by Utilizing LI-Clouds  
We at first suppose that an LI-Cloud is immediately available for local users when the conventional networks breaks down. In later sections, we will discuss in detail about the network restoration process by taking into account the launching time of LI-Clouds. By utilizing the LI-Clouds that are immediately available for local users in an emergency, the conditions that [PERSON] can communicate with Y are (1) [PERSON] and Y reside in the same recovered LI-Cloud area even when there is no global network connection, or (2) the area where X resides is locally recovered with both local and global network connection, and the area where Y exists is provided with both local and Internet connections. Suppose that an LI-Cloud is immediately available for local users in an emergency. By utilizing an LI-Cloud, we have  
\[P_{LACS}(X\sim Y)(t)=P(\text{X in Ax, Y in Ay, t})\cdot P_{local}(Ax,t)\cdot 1\cdot P_{local}(Ay,t)\cdot 1. \tag{2}\]  
when \(Ax=Ay\).  
Consequently, the improvement ratio on I-NSA by utilizing an immediately available LI-Cloud can be expressed as  
\[\begin{split} ImproveRatio_{One}&=\frac{P_{LACS}(X \sim Y)(t)}{P_{Convention}(X\sim Y)(t)}-1\\ &=\frac{1}{P_{global}(Ax,t))\cdot P_{global}(Ay,t)}-1.\end{split} \tag{3}\]  
when X and Y are in the same area--that is, for the two users in the same area, the improvement of I-NSA is in reverse proportion to the product of the probabilities of the global connectivity in the areas where X and Y reside. We consider that the improvement of I-NSA by utilizing the LI-Cloud is mainly due to that LI-Clouds provide the alternative networking service of global networks if the LI-Cloud can be quickly deployed before the global network restoration.
## 3 Results
### Sampling of Continuous Apertures  
It is well known that continuous apertures and arrays can share similar characteristics and an antenna array can be given by sampling an aperture. To have an aperture or an array with a desired LFoV, we must extend the design in the appropriate enlargement.  
The procedure incorporates the following steps:  
1. The scaling factor is derived from the range of view.
2. The FFoV indices \(\mathit{SLL_{\mathrm{F}}}\), \(\mathit{HPBW_{\mathrm{F}}}\), and \(\mathit{D_{\mathrm{F}}}\) are derived from the LFoV ones.
3. The aperture distribution for the FFoV is found.
4. A sampling procedure is followed to transform the continuous distribution to an array.
5. The scaling transformation is enforced on the array to produce the final result.  
Figure 3: The flowchart of the design procedure.
# Study on Microwave Deicing of Carbon-Fiber-Modified Concrete under Multi-Factor Coupling Effect
###### Abstract  
To explore the law of microwave deicing of carbon-fiber-modified concrete under the action of multiple factors and improve its application in pavement, in this study, we designed a test of the heat absorption and deicing effect of concrete under the action of multiple factors. We found that the law of heat absorption and deicing of CFRP is influenced by the coupling effect of fiber length and dosage, height (straight-line distance between the microwave receiving surface and bell component), initial temperature and ice cover. The temperature rises fastest when the fiber dosage is 0.2% and fiber length is 6 mm without ice. Further analysis of other factors shows that the deicing effect is optimal when the height is 40 mm, and the presence of ice on fiber-reinforced concrete weakens the microwave deicing efficiency, although the reduction is small. The test results of these two factors are in agreement with the simulation results and conform to expectations. The initial temperature has a considerable influence on the deicing efficiency. In practical applications, the deicing time should be adjusted according to the initial temperature in order to prevent the phenomenon of secondary cing when the heating time is too long. Based on heat generation and heat dissipation, the four stages of microwave deicing were analyzed, and the relationship with the temperature increase rate was deduced. It was proven that carbon fiber affected the deicing efficiency by changing the microwave absorption and reflection effect of concrete.  
carbon-fiber-modified concrete; microwave deicing; electromagnetic wave simulation calculation +
Footnote †: journal: Applied Sciences
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 3 Materials and Methods
### Crowd Representation and Behaviour Descriptors  
This sub-section explains the methods used to represent the crowd in detail. Firstly, we will explore the methods employed for local feature extraction and their significance in representing individuals within a scene, specifically within the context of crowd anomaly detection. Prominent local feature extraction techniques such as FAST, SIFT, and AKAZE are revisited in detail to emphasise that these algorithms can be utilised to extract a comprehensive set of local features, enabling us to analyse each frame's content effectively. These detected interest points serve as valuable observations to represent objects or people in the scene. Subsequently, we delve into the implementation of a sparse feature tracking framework. This framework allows us to track the identified local features over consecutive frames. As the tracking accuracy improves, the emphasis shifts from detecting new features to accurately tracing existing trajectories. Additionally, we explore the spatial representation aspect of crowds by employing the Delaunay triangulation method. This approach aids in creating a structured representation of the scene, facilitating the analysis of individual and interactive behaviours within the crowd. Furthermore, we investigate the utilisation of individual and interactive behaviour descriptors, which play a pivotal role in characterising and understanding the observed crowd dynamics. These descriptors provide valuable insights into anomalies or abnormal behaviours within the crowd. By combining the tracked features and descriptors, we aim to classify the anomalies in the video effectively.
# Possibility of Bridge Inspection through Drive-By Vehicles
## 5 Effect of Road Roughness on Drive-By Bridge Inspection
### Effect of Roughness on Initial Conditions of VBI interaction Simulations  
Initial vehicle status can affect the detection of bridge properties, which has been verified by [PERSON] [1]. To find how the road roughness affects vehicle status, a series of model simulations are conducted.  
A 30 by 10 m ground was generated in ABAQUS. It has the same property as the short bridge. The vehicle will drive through the ground in 10, 20, 25, and 30 m/s with  
Figure 19: Roughness profile PSDs versus spatial frequencies.  
Figure 18: Class B road roughness generated.  
FFT Results of the vehicle acceleration responses when driving from a 20 m offset distance with a speed of 10 m/s is shown in Figure 24, which shows that there are two acceleration vibration frequencies. One frequency is at 3 Hz, while the other one is at 28 Hz. Their corresponding amplitudes at different driving distances for the two-frequency component are shown in Tables 9 and 10.  
Figure 23: Right front axle acceleration history when approaching the bridge at 30 m/s.  
Figure 22: Right front axle acceleration history when approaching the bridge at 25 m/s.  
From the two amplitude results, the vibration of the vehicle keeps decreasing with longer driving distance. This decrease is caused by the damping of the vehicle. Furthermore, with the driving speed increasing, the ending amplitude becomes larger. This is because the damping of the vehicle does not have enough time to release the energy. However, to simulate a VBI model accurately, it is important to set adequate driving distance from the bridge's entrance. Otherwise, additional frequencies will be included in the model and affect the simulation accuracy. From the above simulations, 10 m offset distance is needed for speed less than 20 m/s, 15 m offset distance is needed for speed between 20-25 m/s, while more than 20 m offset distance is needed for speed at 30 m/s and above.
## 2 Geopolymer Concrete (GPC)
### Mix Design  
The development of appropriate and rational mix design is required to achieve the desired strength and workable GPC. Mix design of GPC is a complex process because of the influence of numerous variables like alkaline content [67], curing time and temperature [68], water to solid ratio [69], pH and molarity of activators [70], aluminosilicate composition and type [71], aluminates to silicate ratio [72], and silicate to hydroxide ratio [73] involved in the geopolymerization process [69].  
Different researchers used different types of mix design for GPC based on the parameters considered in their studies. They adopted mix design calculations that were based on the hit and trial method [74], strength considerations [66], activator to binder ratio [66], or binder to sand ratio. The different factors involved in different approaches of mix design calculations are discussed in the following sections.
## 3 Proposed Methods
### Secure Optimal Path-Routing Protocol  
With the process of localizing the self-organization as well as with the function of key generation, the network has been classified into clusters. Every cluster has a cluster head. The key-sharing for the intra-cluster between the cluster heads of the nearest clusters and the key-sharing for the inter-cluster between the subsequent cluster head of the nearest clusters are generated. In the proposed SOPR, the transmitter node creates the operation of route discovery by transmitting the request for the route (REQR) to the receiver node. When the receiver node and their central cluster head possess the probable route, they will accept the REQR, create the reply for the route (REPR), and send it back to the transmitter node. Every REPR comprises the acknowledgment code for the message, which is known to be a MAC that has been evaluated through the key-sharing of the inter-cluster. Every REPR packet has two stages of verification, which are simulated as follows: initially, the cluster head for the next cluster is created. The REPR packet is validated by sending a pending authentication message from the cluster head to the next hop en route to the receiver node. The MAC, which has been verified through the intra-cluster key exchange, makes up the authentication message. The authentication message accepted by the next hop sends the authentication output to the transmitter. When this process is accomplished, the node forwards the REPR packet of those data. Otherwise, when the authentication is not accomplished or the node cannot obtain the output for authentication within a particular  
Figure 5: Decryption process of the OTP algorithm.  
time, the REPR packet is not transmitted. Second, the cluster head of the nearest cluster receives the REPR packet, which must validate the authentication of the REPR packet by the MAC, because the key-sharing of the inter-cluster is performed only between the cluster heads. When authentication is accomplished, the cluster head of the nearest cluster transmits the REPR packet to the earlier node. Otherwise, it must be withdrawn. Among the two cluster heads of the nearest cluster nodes, the transmission of the REPR packet is accepted instantly when the REQR packet is received, although the packet does not possess the path without a black-hole attack. Here, to detect this attack, the random mechanism for the authentication of data is used. With this mechanism, a source node is chosen at random. The message to the receiving node is controlled by the source node and transmits the authentication. The authentication comprises the MAC that uses the key-sharing between the transmitter and the receiver node. When the transmitter receives authentication along with the validation, it is then considered to be successful against a black-hole attack. The path is considered to possess the node of the black hole within it. REPR of the prohibited node is not admitted with the source node. An appropriate routing table is built during the discovery of a secure route. The table contains the route to the sink node along with information about the next hop.