esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Design of OCPSI Mode
### Single-Camera Constant Speed OCPSI Mode
\begin{cases}\Delta\theta_{frame}\times rem\Big{(}\frac{n-1}{2(N-1)} \Big{)},\quad\quad\quad rem\Big{(}\frac{n}{2(N-1)}\Big{)}\leq N\\ \Delta\theta_{frame}\times\Big{[}2(N-1)-rem\Big{(}\frac{n-1}{2(N-1)}\Big{)} \Big{]},\quad rem\Big{(}\frac{n}{2(N-1)}\Big{)}>N\end{cases}  \tag{18}\]  
where \(rem(\;)\) is the remainder. We guarantee the coverage in the vertical orbit direction, and then, we analyze the coverage along the orbit direction. The overlap between the inside and outside of the pendulum motion imaging ensures complete ground coverage. Since there is no inclination in the orbit direction, the magnification is the same in the orbit direction. In Figure 11, the overlapping portion of the inner image \(\Delta d_{in}\) is significantly smaller than the overlapping portion of the outer image \(\Delta d_{out}\), so we design based on the overlapping of the inner image. We define the pendulum period scanning overlap as  
\[\beta_{\eta_{p-cycle}}=\frac{\Delta d_{in}}{d_{in}} \tag{19}\]  
where \(d_{in}\) is the axial width of the inner image. Therefore, the pendulum scanning period is  
\[T_{pend}=\frac{(1-\eta_{p-cycle})d_{in}}{V_{s}} \tag{20}\]  
From this, the angular velocity of the pendulum scanning can be obtained as  
\[\omega_{c}=\frac{2(N-1)\times\Delta\theta_{frame}}{T_{pend}}=\frac{2(N-1) \times\Delta\theta_{frame}V_{s}}{\Big{(}1-\eta_{p-cycle}\Big{)}d_{in}} \tag{21}\]  
Then, according to the above-mentioned cone scanning and pendulum scanning imaging, the OCPSI mode is a coupling of the two. The schematic diagram of the OCPSI ground coverage is shown in Figure 12. In order to simplify the derivation of the cone and pendulum scanning image, we use a set of spiral equations to describe the trajectory of the optical axis of the camera, the origin is the position of the sub-satellite point at the start time. \[ \begin{cases}\begin{aligned} x=r_{i}\times\cos(\theta_{i}), \\ y=r_{i}\times\sin(\theta_{i})+V_{s}t,\;i=1,2,\dots,n.\end{aligned}\end{cases}  \tag{22}\]where \(r_{i}\) can be given by Equation (6), and \(\theta_{i}\) is given by  
\[\theta_{i}=\omega_{s}t \tag{23}\]  
According to Equation (22), it can be seen that the cone and pendulum scanning imaging is performed by three-dimensional motion coupling. In the formula, \(V_{s}\) represents orbital motion, \(\theta_{i}\) represents cone scanning motion, and \(r_{i}\) represents pendulum scanning motion. Since the instantaneous linear velocity generated by the cone scanning on the ground at the initial moment is much greater than the velocity of the sub-satellite point caused by the orbital motion, the pendulum scanning period is changed to  
\[T_{pend}=\frac{(1-\eta_{p-cycle})d_{in}}{\omega_{s}r_{1}} \tag{24}\]  
The pendulum scanning angular velocity is  
\[\omega_{c}=\frac{2(N-1)\times\Delta\theta_{frame}}{T_{pend}}=\frac{2(N-1) \times\Delta\theta_{frame}\omega_{s}r_{1}}{\Big{(}1-\eta_{p-cycle}\Big{)}d_{ in}} \tag{25}\]
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing
## 4 QEPAS Sensor Based on Custom QTF
### QEPAS Sensor Employing Overtone Flexural Mode of Custom QTF  
If the fundamental resonance frequency of custom QTF is small (a few kHz), the resonance frequency of the first overtone flexural mode should not be too high. Therefore, the first overtone flexural mode of QTF can be used in a QEPAS sensor [71; 72; 73; 74]. The schematic of the fundamental flexural mode and the first overtone flexural mode of QTF is shown in Figure 7. In [71], a custom QTF with fundamental mode resonance \(\sim\)2.87 kHz was adopted and the first overtone at \(\sim\)17.7 kHz was utilized. The measured Q factor for the first overtone resonance was 31,373.81, which is \(\sim\)2.6 times higher than when it operated in fundamental flexural resonance mode (12,098.97). A high Q factor is beneficial to improve the QEPAS signal level. To evaluate the sensor performance, a near infrared (IR) diode laser emitting at 1.37 \(\upmu\)m and water vapor (H\({}_{2}\)O) as the target gas were selected. The comparison of the first overtone mode and the fundamental mode was carried out at the same conditions, in terms of a 1.7% water concentration, a pressure of 75 Torr, an optimized laser wavelength modulation depth, and laser beam focusing point. The normalized peak value of measured second harmonics for the first overtone mode is \(\sim\)5.3 times higher than that achieved using the fundamental mode.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**QTF** & \(f_{0}\) **(kHz)** & **Length (mm)** & **Width (mm)** & **Thickness (mm)** & **Gap (mm)** \\ \hline Standard & 32.768 & 3.6 & 0.6 & 0.36 & 0.3 \\ Custom & 4.246 & 20 & 1.4 & 0.8 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for standard and custom QTFs.
## 3 Results
### Annual Mean Patterns of Satellite-Based GPP  
The annual mean GPP from flux measurements (GPP\({}_{\rm EC}\)), BESS (GPP\({}_{\rm BESS}\)), the MODIS (GPP\({}_{\rm MOD}\)), and GOSIF (GPP\({}_{\rm SIF}\)) at the six flux sites from 2014 to 2015 are shown in Figure 6. Firstly, at the S_Ar site, although both the GPP\({}_{\rm BESS}\) and GPP\({}_{\rm SIF}\) were overestimated, the GPP\({}_{\rm BESS}\) had the best performance. Furthermore, the three GPP products were underestimated in varying degrees at other EC sites. For instance, at the S_Sdq and S_HhI sites, the three GPPs were severely underestimated by more than half. Similarly, the GPP\({}_{\rm BESS}\) and GPP\({}_{\rm MOD}\) were only approximately half of the GPP\({}_{\rm EC}\) at the S_Dm site. Finally, the GPP\({}_{\rm SIF}\) had an obvious advantage at the S_Dm site among the three GPP products.  
Figure 5: Comparisons of the seasonal patterns of GPP observations (GPP\({}_{\rm EC}\)) and the remotely-derived products (GPP\({}_{\rm BESS}\), GPP\({}_{\rm MOD}\), and GPP\({}_{\rm SIF}\)) at the six flux sites during the periods of spring, summer, autumn, and winter, respectively. The box range and whisker range were 25%–75% and 10%–90%, respectively.
## 6 Results and Discussions
### Polishing Application  
A polishing task was performed to demonstrate the effectiveness of the force control. In Figures 11 and 12, the force measurements for polishing with a reference force of 5 N and 10 N, are presented. For these experiments, we used an inner velocity loop with an outer PIV force loop. The figures display the measured cartesian forces, the reference force, and the force in Z--direction without force control.  
In both cases, the force \(\mathbf{F_{z}}\) remains stable in both experiments. Forces \(\mathbf{F_{x}}\) and \(\mathbf{F_{y}}\) are shown. The force \(\mathbf{F_{y}}\) was produced by the friction along the trajectory. The force \(\mathbf{F_{x}}\) was produced by the radial force due to the polishing tool. It can be seen as the force \(\mathbf{F_{x}}\) and \(\mathbf{F_{y}}\) increase if the force \(\mathbf{F_{z}}\) increases.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Std. Deviation**} & \multicolumn{2}{c}{**N\({}^{v}\) Picks**} & \multicolumn{2}{c}{**N\({}^{v}\) Picks**} & \multicolumn{2}{c}{**Max Pick**} & \multicolumn{2}{c}{**Min Pick**} & \multicolumn{2}{c}{**AdjustingGain**} & \multicolumn{2}{c}{**Total**} \\  & \multicolumn{2}{c}{**(46\%)**} & \multicolumn{2}{c}{**(19\%)**} & \multicolumn{2}{c}{**>6 N (11\%)**} & \multicolumn{2}{c}{**<4 N (11\%)**} & \multicolumn{2}{c}{**(4\%)**} & \multicolumn{2}{c}{**(4\%)**} & \multicolumn{2}{c}{**(6\%)**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-13}  & **Pt** & **\%** & **Pt** & **\%** & **Pt** & **\%** & **Pt** & **\%** & **Pt** & **\%** & **Pt** & **\%** & **Pt** & **\%** & **Pt** & **\%** & \\ \hline P & 4 & 1.82 & 5 & 0.96 & 4 & 0.43 & 4 & 0.43 & 4 & 0.16 & 4 & 0.16 & 5 & 0.29 & 4.25 \\ PV & 3 & 1.37 & 4 & 0.77 & 3 & 0.32 & 5 & 0.54 & 4 & 0.16 & 4 & 0.16 & 5 & 0.29 & 3.60 \\ PD & 3 & 1.37 & 4 & 0.77 & 3 & 0.32 & 4 & 0.43 & 4 & 0.16 & 3 & 0.12 & 5 & 0.29 & 3.46 \\ PI & 4 & 1.82 & 5 & 0.96 & 5 & 0.54 & 3 & 0.32 & 5 & 0.20 & 5 & 0.20 & 5 & 0.29 & 4.33 \\ PIV & 4 & 1.82 & 5 & 0.96 & 4 & 0.43 & 5 & 0.54 & 4 & 0.16 & 5 & 0.20 & 5 & 0.29 & 4.40 \\ P + FF & 5 & 2.28 & 5 & 0.96 & 4 & 0.43 & 2 & 0.21 & 5 & 0.20 & 3 & 0.12 & 3 & 0.18 & 4.37 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prioritization matrix of outer loops.  
Figure 11: Polishing with force control with reference force 5 N.  
As can be observed, the force response is better with the polishing tool than with the probe. This proves that the inner velocity loop is affected by the stiffness of the tool. Further, in Figure 11, it is possible to observe the polishing task without force control (green line). This demonstrates that without external force control is not possible to maintain the reference force. Thus, we obtain a poor surface polish. Table 8 shows the result of the polishing task.  
Table 8 includes values of mean, standard deviation, and error of the force control. Also, since the polishing tool is more flexible, it allows to obtain smaller measurement errors than the reference. This effect is reflected in the fewer number of observed picks.
## 3 System Model
### Geometric Angle Formation  
The message-forwarding mechanism uses relay nodes to send messages from a source node \(s\) to a destination node \(d\). Figure 1. illustrates an idea of probing and selecting a relay at one- and two-hop transmission distances from a message-carrying node. Several referential lines \(\overrightarrow{sd}\), \(\overrightarrow{sd}^{\prime}\), and \(\overrightarrow{sd}^{\prime\prime}\) show directional information and angle calculation. To select a one-hop relay, only the front direction with respect to \(d\) is scanned, as shown in Figure 1a. Thus, only \(n_{1}\) and \(n_{2}\) will be considered by relay selection and nodes \(n_{7}\) and \(n_{8}\) will be ignored. The scanning process helps in filtering relay nodes in a broadcast coverage and avoids redundant message overhead to other nodes in the backward direction. To select one- and two-hop relays, we measure the geometric angle at each of the one- and two-hop distances, as described in the following two steps.  
First, the one-hop geometric angle, also known as the first offset angle, indicates that the angle of the first relay relative to \(\overrightarrow{sd}\) is calculated. As Figure 1b depicts, two signs \(+\) and \(-\) are used to indicate the clockwise and counterclockwise direction with reference to \(\overrightarrow{sd}\), respectively. Then, \(\theta_{1}\) and \(\theta_{2}\) with respect to \(\overrightarrow{sd}\) are two geometric angles formed at a one-hop distance. To determine the sign of any offset angle \(\theta_{i}\), the outer product of the vectors is used. For example, given \(\overrightarrow{sd}=(x_{1},y_{1})\) and \(\overrightarrow{si_{i}}=(x_{2},y_{2})\), \(\overrightarrow{sd}\times\overrightarrow{si_{i}}=x_{1}y_{2}-x_{2}y_{1}\) will determine the sign of \(\theta_{i}\).  
Second, to select a two-hop relay, the sum of the first offset angle and the two-hop offset angle is calculated, which is denoted as \(\theta_{m}\). For instance, by referring to Figure 1b, there are \(\theta_{m}=|(+\theta_{1})+(+\theta_{3})|\) for \(s\), \(n_{1}\), and \(n_{3}\), and \(\theta_{m}=|(+\theta_{1})+(-\theta_{4})|\) for \(s\), \(n_{1}\), and \(n_{4}\). Similarly, other examples are \(\theta_{m}=|(-\theta_{2})+(+\theta_{5})|\) for \(s\), \(n_{2}\), and \(n_{5}\), and \(\theta_{m}=|(-\theta_{2})+(-\theta_{6})|\) for \(s\), \(n_{2}\), and \(n_{6}\). Note that \(\theta_{3}\) and \(\theta_{4}\) are formed with respect to the \(\overrightarrow{sd}^{\prime}\), while \(\theta_{5}\) and \(\theta_{6}\) are formed with respect to the \(\overrightarrow{sd}^{\prime\prime}\).  
With the above two cases, it is seen that the relay selection involves the use of threshold values, which will be further discussed in the next section. According to the relay scanning and angle formulation in one- and two-hop neighborhood areas, we develop a routing policy for message forwarding in OppNets.
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture
### System Model
#### 3.1.2 Authentication Model  
Based on the above networking model, an authentication model is proposed. All the legal drones are registered in the database with a set of data consisting of a static key \(key_{i}\), a communication address \(IP\), a fingerprint PUF \(P_{fp}\), an ID, and extraction information of identity PUF \(P_{ID}\). The registration scheme is shown in Table 2. Before the drones execute tasks, the GS and the drones authenticate each other, and authentication only occurs once at takeoff. The authentication scheme is shown in Table 3.  
The fingerprint PUF consists of a 64-byte contiguous sequence in RAM, while the ID PUF is a 32-byte sequence of discontinuous RAM segments. The PUF based on RAM belongs to weak PUFs. Therefore, this study uses random position, random direction, and random length as challenges for the PUF. The read position and read direction are provided by the GS (vector \(a\) in Table 3), while the read length for each position is provided by the drone (vector \(b\) in Table 3). The drone strings the read sequence into a 32-byte fingerprint \(\hat{fp}\) and sends it to the GS, which computes the challenge given by the drone based on the database and uses the hash value of that challenge as the secret key \(h(b)\) for the next stage. Considering that the fingerprint PUF is limited, a static secret key \(key_{i}\) is reserved in the drone since static secret keys are easily readable. Therefore, during the first stage of authentication, GS tags the drone information associated with the key to prevent counterfeit drones. In Table 3, it should be noted that in the second step, GS generates four 1-byte signed numbers with a maximum representation range of \(-128\) to \(127\), with the sign \(\text{sgn}(a)\) representing the direction of the read and \(|a_{i}|\) representing the read position (offset from the start address). However, the fingerprint PUF has 64 bytes (512 bits), so it is necessary to divide the PUF into cells of 4 bits each. The advantage of this is not only that GS only needs to perform a hexadecimal convolution of the fingerprint when matching the fingerprint, which saves 3/4 of the time compared to a unit convolution, but also that the drone can send it directly in hexadecimal, eliminating the process of converting binary strings to hexadecimal arrays. To make the GS more efficiently matched, this paper requires that the random length be no shorter than 8 cells (32 bits). In the fifth step, the method of adding perturbations \(\delta\) to GS is discussed in detail in Section 3.4.2. In the sixth step, the reason why \([ID^{\prime}s,C^{\prime}s]=ECC(\hat{ID},\hat{C}_{\delta})\) is discussed in detail in Section 3.4.1. It is important to note in particular that the identity PUF is used for the second stage of authentication, where the power-up current is the challenge and the \(\hat{ID}\) is the response (the ID used for enrollment in Table 2 is one of its responses).
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 3 System Model
### Block Diagram of a Fog-Based WBAN  
The functional block diagram of the proposed fog-assisted architecture for the real-time health monitoring system is shown in Figure 2. The three layers in the monitoring framework are as follows:  
1. Sensor layer
2. Body controller layer
3. Central coordinator layer  
The sensor layer collects the body vitals and processes the signals that must be transmitted to the next layer. The body controller layer stores and transmits the data about the fog layer or central coordinator layer. Here, simple mathematical modeling has been used to make a decision regarding the priority of the data. From this layer, the prioritized data are transmitted to the physician through the cloud server. The role of a fog node in the proposed model are:  
1. Collecting the human vital signs from sensor nodes
2. Computing and analyzing the sensed data using simple modeling techniques
3. Sending the consolidated report to the cloud server
4. Assigning the priority of the sensed data
5. Coordinating operations of the body sensor nodesThe patient vitals is transmitted to the base station through the body controllers, using a trusted tree formed with n number of body controllers within each block. The fog nodes determine the priority of the data with the help of the prioritization scheme and send the data towards the destination through a cloud server. The back-end part of the system is the cloud server, whose function includes storing, processing, and transmission of data along with back-end services for real-time data interpretation and visualization. The tree formation between the body controllers and the next hop node selection and dynamic time slot assignment is explained in the following sections.
# Strategies for Reducing Automobile Fuel Consumption
## 5 Transportation Policies, Alternatives, and Externalities
### Alternatives to Motorized Road Transportation  
In the passenger sector, _shared mobility_ (e.g., ride-hailing) can encourage efficiency in each vehicle's utilization. This leads to a more efficient use of resources and reduces fuel consumption per passenger-kilometer. Another important alternative to road transportation is shared _bicycle and electric scooter systems_. They represent sustainable modalities in the transportation landscape, characterized by zero fuel consumption (disregarding production). These platforms provide environmentally conscious alternatives for short commutes, thereby diminishing dependence on conventional fuel-driven vehicles. Within supply chain operations, the adept forecasting of supply and demand, together with the fine-tuning of distribution pathways, is instrumental in reducing fuel consumption.  
Furthermore, technologies related to _machine learning_ (ML) also have the potential to reduce fuel consumption associated with road transportation. One example is ML-powered additive manufacturing (AM) (i.e., 3D printing), which has the potential to produce lighter automobile components and enable vehicle production closer to the consumer. Additionally, ML can enhance virtual communication (i.e., telepresence). As evidenced by public agencies and the scientific community (and especially by the general worldwide population during the COVID-19 pandemic), this shift to telepresence reduced fuel consumption drastically during this period, due to reduced general commuting. By utilizing ML to improve virtual communication, we can further reduce the need for physical travel, consequently reducing worldwide fuel consumption. Machine learning (ML) and artificial intelligence (AI) are crucial in revolutionizing terrestrial transportation, enhancing efficiency, safety, and user experience. They enable real-time traffic optimization, reduce congestion, and curtail emissions through data analysis. AI-driven systems enhance vehicular safety via advanced driver-assistance features and predictive maintenance, mitigating risks preemptively. Moreover, the synergy of ML and AI is accelerating autonomous vehicle technology, enabling real-time sensory data processing and decision-making. These technologies personalize user experiences, adapt public transport services, and introduce new urban mobility solutions, marking a transformative era in transportation with comprehensive applicationsin operational efficiency, safety enhancements, and innovation. With the advent of _big data_, some general approaches to increase the energy efficiency in urban travel (and to improve human well-being) include fewer trips through ICT-improved communications, multimodal trips enabled by navigation systems, trajectory-based trip planning using mobile technologies, intelligent trip planning with real-time updates on mobile devices, and city profile-based trip planning utilizing comprehensive data from various sectors.
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 3 Problem Description and Assumptions
We consider a simple star topology network that has one VLAP and several VLNs. Currently, it is not realistic to achieve full duplex communication in VLC-based access networks. This is because of challenges such as interference, glare and user equipment power limitation. However, VLC can be complimented by other spectrums in order to achieve full duplex communication. Two ways have been used to complement VLC: (i) using infra-red for uplink channel and VLC for downlink channel and (ii) using radio spectrum (WiFi) for uplink channel and VLC for downlink channel. In this study, we consider infra-red as the means of providing uplink channel and VLC for downlink channel as applied in LiFi. This is due to the fact that the uplink channel consumes less bandwidth than downlink channel. Therefore, the benefit of huge bandwidth available in VLC can be more fruitful if exploited for the downlink channel. Moreover, existing research publications suggest that currently infra-red can support 4 Mbps data rate. Moreover, while assigning bandwidth to each VLN, we consider a beacon-enabled channel access methods stated in [12]. A sample star topology network is presented in Figure 1. The problem is assigning a shared VLAP to multiple VLANs, where minimum waiting time, low packet collision, high system utilization and fairness are key performance metrics. To the best of the authors' knowledge, this is the first study to address this problem considering these performance metrics, the non-contention approach and the physical layer properties of the OWNs.