esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Collaborative Caching for Information-Centric IoT in Fog Computing
## 3 Smart Collaborative Caching for IoT
### Model Establishment  
In the information-centric IoT, the situation is not as simple as before. Here are two practical examples: when a node is supposed to cache the original key, the capacity is not big enough; when a node leaves the system without sending the notification, there will be no original key which was cached in this node. Therefore, \"map a key onto a node\" is not appropriate anymore in such scenarios. According to the target of ICN, the original key should be stored in the entire network, not just in a single node. Our basic idea is to separate the traditional binding between the key identifier and the original key. The former will be handled by the leader of IoT cluster (introduced in Figure 1), the latter will be stored by the leader itself or members insider cluster. It is also possible to cache the latter inother clusters. Then all clusters should establish a \"Pointer Table\" to maintain the mapping between the key identifier and the original key. However, the additional complexity must be well controlled. These collaborative operations are crucial for SCC, which enable it to be a strong tool in fog computing.  
A schematic circle of SCC is constructed in Figure 1a. For simplification, the term \"content\" (\"cluster\") will refer to both the original content (IoT cluster) and content (cluster) identifier under the hash function. The distinction is easy to find based on context. Each cluster should maintain a constant identifier which is hashed from the information of cluster leader, cluster member, or other related components. It should not be changed until the last member has left the network. The cluster leader can represent the whole cluster and mark itself with the cluster identifier. Assuming \(m\) is equal to 4, there will be 16 empty slots (characterized by the circles with white) for both clusters and contents. The position of slots is not proportionally spaced to better illustrate the main part. Four existing clusters represented by their cluster leaders (marked by the circles with yellow) are 2, 4, 9 and 10, respectively. The cluster members (characterized by the thin circles with white), i.e., 9-1, 9-2 and 9-3, should have a direct connection with cluster leader 9. The circle linked them is not another Chord circle. The identifiers of cluster members are just the extensions of cluster leaders, which means it is only visible inside cluster by the local members. The six content (marked by the squares with white) are 0, 2, 3, 4, 6 and 8, respectively. Three finger tables for cluster 2, 4 and 9 are given to locate the cluster that is in charge of the content. The content 6 and 8 (marked by the squares with green) are cached in 9-1, 9-2, 9-3 and 10 due to the limited capacity of cluster leader 9. It is suggested that the backup content of a cluster leader should be stored in its successors. They could be easily found even if this cluster is disappeared. A pointer table of cluster leader 9 is also given to identify the mapping between the content and its location.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 5 Algorithm Design and Implementation
### Genetic Operations
#### 5.3.3 Crossover and Mutation  
The self-learning mechanism dynamically adjusts the crossover and mutation probabilities based on the evolutionary generation, fitness, and optimal solution [36], as shown in Equations (44) and (45). Here, \(f^{\prime}\) is the fitness value of the individual with the larger fitness between the two crossed individuals, \(f_{\text{max}}\) is the maximum fitness value in each generation, \(f_{avg}\) is the average fitness value of the population in each generation, \(t\) is the current evolutionary generation, \(S\) is the number of individuals whose optimal solution has not changed during the evolution process, \(M\) is the maximum number of iterations, and \(U\) is the population size. \(P_{c1}>P_{c2}\), \(P_{m1}>P_{m2}\), \(P_{c1},P_{c2}\in[0.4,0.99]\), \(P_{m1},P_{m2}\in[0.01,0.1]\).  
\[P_{c}=\left\{\begin{array}{l}P_{c1}-\frac{(P_{c1}-P_{c2})(f^{\prime}-f_{avg} )}{\left(f_{\text{max}}-f_{avg})}[1+\exp(-\frac{f_{avg}}{f_{avg}})],\,f^{\prime }\geq f_{avg}\\ P_{c1}^{[1+\exp(-\frac{f_{c1}}{f_{c1}M})]},f^{\prime}<f_{avg}\end{array}\right. \tag{44}\]  
\[P_{m}=\left\{\begin{array}{l}P_{m1}-\frac{(P_{m1}-P_{m2})(f_{max}-f)}{\left( f_{\text{max}}-f_{avg}\right)[1+\exp(\frac{f_{avg}}{f_{avg}})]},f\geq f_{avg}\\ P_{m1}^{[1+\exp(\frac{f_{avg}}{f_{avg}})]},f<f_{avg}\end{array}\right. \tag{45}\]  
The crossover operation uses a two-point multi-point crossover. For identifying duplicate customer points in the offspring chromosomes after crossover, the missing customer points are sequentially used to replace the duplicates, ensuring that each customer point appears only once in the chromosome.  
The mutation operation employs dynamic range mutation, where the mutation range \(l_{en}\) is adjusted dynamically based on the current iteration number and the maximum iteration number, as described in Equation (46). In this equation, \(I\) represents the current iteration number, and \(I_{\text{max}}\) represents the maximum iteration number.  
\[l_{en}=\left[\frac{i-i}{2}\cdot\left(1-\frac{I}{I_{\text{max}}}\right)\right] \tag{46}\]  
As shown in Figure 6, where the black points represent the selected mutation points, a portion of the genes between the mutation points are swapped.
# Potential Imaging Capability of Optical Coherence Tomography as Dental Optical Probe: A Mini-Review
## 5 Conclusions  
Although OCT can visualize periodontal tissue and peri-implant conditions with high resolution, there is no clear evidence that supports the imaging capability of OCT as a dental optical probe to visualize the subgingival area. This might be due to the limited availability, usability, and portability of OCT, coupled with lack of clinical trials in dentistry. Considering the advantages of OCT as discussed in this review, further development of handheld OCT systems, especially for dental use, is encouraged to improve its feasibility, reduce its expenses, and, more importantly, attract researchers to optimize its application in clinical practice. Hence, OCT is expected to improve diagnostic imaging performance in dentistry through high-resolution and non-invasive imaging without the use of ionizing radiation.  
Conceptualization, N.Y. and K.S.; data curation, R.H.P. and N.Y.; formal analysis, R.H.P. and E.R.A.; methodology, R.H.P., N.Y., and E.R.A.; funding acquisition, N.Y.; supervision, E.R.A. and K.S.; writing--original draft, R.H.P.; writing--review and editing, N.Y. and K.S. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by Grant-in-Aid for Scientific Research (C: 19K10198) from Japan Society for the Promotion of Science.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
# SINR-Based MCS Level Adaptation in CSMA/CA Wireless Networks to Embrace IoT Devices
## 4 Demonstrations Using Simulations
### Multiple Users in Multiple Cells
Figure 7: (**a**) changes of SINR measured at AP0 for WD10; (**b**) changes of the moving averaged SINR used at AP0 for WD10. The simulation results of the achieved throughputs by WD10 in Figure 8b show that WD10 achieves less than the corresponding throughput of each MCS level. This is because AP0 (along with WD10) shares network resources with other nodes in this case according to the CSMA/CA mechanism. From the simulation results in Figures 7 and 8, we can see that the proposed algorithm works as expected in this multi-cell, multi-user environment.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 1: Collect System Information
#### 3.1.3 Step 1.3: Define Critical Loads and MDI Scores  
The next step is to define the importance of various loads in relation to national security. We suggest using MDI [28] which is a unitless measure that can be tied to the importance of a specific load to national security in spite of the imperfections in the measure [29]. As other better measures become available, they can be substituted for MDI. These scores become the MI used in [PERSON] et al.'s method [22] which is implemented later in this methodology.  
It is important to note that all loads within this case study are considered critical. Further, the responsibility of assigning an MDI value lies with the base energy manager and installation leadership. When a base energy manager or installation leadership deem that a load has no or very low MDI, it can likely be ignored as being unimportant to national security. We do not recommend a specific cutoff value for low MDI scores; however, in some situations where there are many loads and challenges exist identifying true critical loads, a pre-established cutoff value may be justified. For instance, the loads associated with a recreational facility are likely not critical to national security and thus will have a very low MDI score while the loads associated with a radar system may be very critical to national security and then are expected to have a very high MDI score. The loads that are deemed critical can then be carried forward through the rest of the methodology.
## 5 Conclusions  
In this paper, a novel satellite-based WSN is first proposed, which integrates the WSN with the cognitive satellite terrestrial network. Then, the energy-efficient optimal power allocation schemes in cognitive satellite terrestrial networks are proposed for non-real-time and real-time applications, respectively. For both scenarios, AIP constraint is adopted to guarantee the interference power at the primary terrestrial user under a tolerable limit, while ATP and PTP constraints are employed for the transmit power constraint of the satellite user, respectively. In this context, the energy-efficient optimal power allocation problem can be formulated as a nonlinear fractional programming problem, which is solved by combining the Dinkelbach's method and the Lagrange duality method. Extensive numerical results evaluate the impact of interference power limit, transmit power limits and the interference link quality on the EE of the satellite user. It can be observed that in the same scenario, the optimal EE of the satellite user under ATP constraint is larger than that under PTP constraint. In addition, strong interference link fading is favorable to the performance of the satellite user.  
The work of [PERSON], [PERSON], [PERSON] and [PERSON] was supported by National Natural Science Foundation of China (No. 61571464, 61601511, 91338201, 91438109 and 61401507). The work of [PERSON] was supported by the UK EPSRC under grant number EP/N007840/1. The author is deeply grateful for all help from the papers quoted. Finally, the authors would like to give gratitude to the reviewers for their constructive comments.  
[PERSON] and [PERSON] proposed the architecture of integrated wireless sensor and cognitive satellite terrestrial network. [PERSON] and [PERSON] derived the optimal power control schemes for the satellite user. The simulations were implemented by [PERSON] and [PERSON]. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] read and approved the final manuscript.  
The authors declare no conflict of interest.
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 5 Experimental Setup and Results
### Data Communication  
To demonstrate the convergence of communication and sensing by using the same 16-QAM OFDM waveforms, we also measured data communication performance in terms of BER. A subframe wise processing is used, where 12 OFDM symbols form a subframe, and each OFDM symbol has 300 active subcarriers. In one subframe, four OFDM symbols are multiplexed with pilot subcarriers (\(4\times 50\) pilot subcarrier in a subframe), and a code  
Figure 10: Range-speed plots for the speed measurements. (**a**) Range–speed plot of a single target moving with 2.34 m/s, whereas the moving assembler acts as a static unit. (**b**) Range–speed plot of the target in (**a**) with speed increased to 5 m/s. (**c**) Range–speed plot of a static target, placed at 0.6 m, and a moving target with speed of 2.34 m/s.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{4}{c}{**Actual Values**} & \multicolumn{4}{c}{**Measured Values**} \\ \cline{2-9}  & \(\mathbf{d_{1}}\) & \(\mathbf{v_{1}}\) & \(\mathbf{d_{2}}\) & \(\mathbf{v_{2}}\) & \(\mathbf{d_{1}}\) & \(\mathbf{\vartheta_{1}}\) & \(\mathbf{d_{2}}\) & \(\mathbf{\vartheta_{2}}\) \\  & **m** & **m/s** & **m** & **m/s** & **m** & **m/s** & **m** & **m/s** \\ \hline Single static target & 0.605 & 0 & N.Arate of 0.76 is used. In the experiment, we placed the receiver and transmitter in the LOS link distance of 0.6 m, with a bit rate of 8.08 Gbps. Due to non-ideal environment for the measurement such as limited dimensions of the lab and surrounding objects, multiple reflections of the transmitted signal arrive at the receiving antenna. Changes in the spectrum of the received signal in Figure 11 (red color) confirm the presence of the multiple reflections. Therefore, channel equalization is necessary to recover the transmitted data, which increases BER compared to AWGN channel [38]. We performed frequency domain equalization (zero-forcing) by using the transmitted pilot subcarriers for the channel estimation. As a result, a BER of 0.01 is recorded by comparing the transmitted and received data bits, when the average received SNR is around 15 dB, which is below the soft-decision forward error correction (SD-FEC) threshold of \(1.5\times 10^{-2}\)[39]. As a comparison, a BER of \(3.6\times 10^{-4}\) is obtained in the simulation due to the absence of the background reflections. A comparison of the received spectrum and the corresponding constellations is provided in Figure 11, for illustration purposes.
## 3 Results
### Wall Displacement Wd  
The analysis of wall displacement indicated that for the iliac arteries, an increase of EV from 70 mL to 115 mL and FP from 60 min\({}^{-1}\) to 120 min\({}^{-1}\) caused an increase in Wd from 5.79 \(\pm\) 0.043 mm to 10.30 \(\pm\) 0.089 mm (Figure 7a-d). Similar changes in Wd were observed for the silicon tubes (from 1.45 \(\pm\) 0.032 mm to 3.60 \(\pm\) 0.024 mm) (Figure 7a-d). However, the wall of the silicon tubes showed a twice as low change in displacement as iliac arteries. Furthermore, with an increase of FP and EV, no significant increase of differences in Wd values between iliac arteries and silicon tubes was observed (\(p>0.05\)).  
In addition, there was a positive correlation between trends in hemodynamic parameters and Wd; with the largest increase in Wd in the group of EV for the iliac arteries (rho = 0.976; \(p<0.05\); Spearman's rank correlation coefficients). A similar trend was observed for the group of FPs where the largest increase was observed for the iliac arteries (rho = 0.968) (Figure 8a-d).  
Figure 7: Wall displacement Wd (mm) for all analyzed vessels for ejection volume equal to: **(a)** 70 mL; **(b)** 85 mL; **(c)** 100 mL; **(d)** 115 mL. Values are presented as mean \(\pm\) SD, \(n=5\). For all analyzes, \(p<0.0001\).
## 3 Results and Discussion
### Mechanical Properties
#### 3.1.3 Ultimate Tensile Strength  
The PLA\({}_{\text{x}}\)/poly(S-_co_-MMA)\({}_{\text{y}}\) blends also showed compatibility in tensile strength (Figure 1). In general, it is observed that blend PLA\({}_{90}\)/poly(S-_co_-MMA)\({}_{10}\) presented the highest value (56 MPa), which even surpassed the neat PLA value (52 MPa). The PLA\({}_{75}\)/poly(S-_co_-MMA)\({}_{25}\) blend had a lower value than poly(S-_co_-MMA) copolymer with 45 MPa and 46 MPa, respectively.  
The PLA, in some cases, can be used to increase the ultimate tensile strength for specific blends, for instance, PLA/PS blends. This improvement was attributed to low interfacial tension and high-stress transfer parameters [42]. Similarly, PLA\({}_{70}\)/HDPE\({}_{30}\) blends showed an increasing effect for the ultimate tensile strength of about 49 MPa [38]. In the same manner, the PLA\({}_{85}\)/LLDPE\({}_{15}\) blends displayed a maximum value of 43 MPa [39]. Table 3 displays the mechanical properties of PLA, PLA\({}_{\text{x}}\)/poly(S-_co_-MMA)\({}_{\text{y}}\) blends, and poly(S-_co_-MMA).
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 2 Materials and Methods
### Factor Graph Model of INS/GPS/VO Integrated Navigation System
#### 2.1.1 INS Factor Graph Model
The factor \(f^{Equiv}\) encapsulates all IMU readings spanning the temporal interval between \(t_{i}\) and \(t_{j}\). However, integrating it into the factor graph entails introducing solely the variable \(x_{i}\). This stands in juxtaposition to the conventional IMU factor scenario, where variables are added at the IMU sampling rate. Figure 2 shows the comparison between using the conventional IMU factor and using the equivalent IMU factor. The shown factor graph contains prior, IMU, equivalent IMU, and bias factors. From Equations (3), (5), and (6), the equivalent IMU factor node \(f_{k+1}^{Equiv}(x_{k+1},x_{k},\alpha_{k})\) is connected to the state variable node \(x_{k}\), bias variable node \(\alpha_{k}\), and state variable node \(x_{k+1}\); The bias factor node \(f_{k+1}^{Bins}(\alpha_{k},\alpha_{k+1})\) connects the bias variable node \(\alpha_{k}\) and the bias variable node \(\alpha_{k+1}\). Figure 3a,b shows the factor graph models at two distinct time intervals. New nodes are continually integrated into the model as time progresses; therefore, the INS factor graph model is actually a dynamic graph model.
## 3 Experimental Verification
### Data Acquisition and Preprocessing
#### 3.1.2 Experimental Data Preprocessing  
The monitoring system utilizes an integrated receiver capable of observing dual-frequency signals from three satellite navigation systems, with an epoch interval of 1 s. Once installed, the equipment continuously performs the real-time monitoring of the scaffolding structure.  
During the satellite-based monitoring, interference from surrounding high-rise buildings and other complex environmental factors can degrade the quality of satellite signals during certain periods. This degradation results in the inability to obtain precise fixed solutions, leading to significant deviations in the monitoring data. To illustrate the effectiveness of gross error elimination, 12 h of monitoring data from a selected day were analyzed. As shown in Figure 5a, the raw monitoring data exhibit significant outliers at the fourth and eighth hours, along with additional deviations of approximately 1 m at certain other time points. To address these anomalies, gross error elimination was applied to the original monitoring data. As shown in Figure 5b, after outlier removal, the fluctuation range of the monitoring data is stabilized within \(\pm\)0.05 m. This improvement indicates a significant enhancement in data stability and reliability, allowing for a more accurate representation  
Figure 4: Monitoring station distribution diagram: (**a**) overall receiver deployment; (**b**) monitoring station deployment; and (**c**) reference station deployment.  
Figure 3: Project schematic diagram: (**a**) Tianjin CITIC city plaza; and (**b**) 3D model illustration of the monitoring site.  
of the actual monitoring conditions. The refined dataset provides a robust foundation for subsequent data analysis and research.
## 4 Expedition Cruising in Svalbard and the Application of ICT
### ICT Tools Developed by AECO to Perform Collective Action  
AECO makes use of two key ICT solutions in support of tourism management, a cruise database and a vessel tracking system, both of which are discussed in greater detail in the following sections. The cruise database features three tools, one for the booking of landing sites, another one to lodge a cruise itinerary, and a third one to upload and manage post visit reports. The vessel tracking system allows for real-time localization and tracking of the expedition cruise vessels of all the network members with a maximum delay of 15 min.  
Publicly available ICT, such as an automatic identification system (AIS) for vessel tracking or very high frequencies (VHF) for maritime communication with vessels, supports AECO's in-house ICT management tools. AIS and VHF play crucial roles in terms of ensuring the safety of operations at sea. ICT technologies are constantly evolving to better fit challenging Arctic environment but prior studiesas well as research participants note the limitations of ICT use in the Arctic [10; 68] due to bandwidth issues and the availability of certain technologies. For instance, research participant 1 stresses that _\"some operators have good iridium satellite connections and some do not. And these limitations are real constraints in regard to safety but also communication in regard to other things. So, this area is very important.\"_ Improving communication technologies, such as satellite phones, will enhance communication between vessels. While we acknowledge the importance of ICT in support of safety, which has been the focus of previous research [77], our paper concentrates on AECO's in-house ICT management tools and their use in collective action to address risks associated with environmental degradation and crowding.
## 4 DA Experiments
### Experiment Characteristics
#### 4.1.1 Monitoring Configurations  
Depending on the availability of velocity data at various locations in the domain of interest, estimates of how the velocity improved using DA varied. By identifying two velocity monitoring configurations, we argue that increasing the density (i.e., number) and the distribution (i.e., spatial coverage) of observations in the channel will lead to improvement of velocity and residual currents results in our domain. To assess DA performance, two monitoring Set-Ups were used in addition to the Base-Test:  
**Set-Up I:** Using one fixed ADV deployed 0.5 m below the surface at the pontoon (Figure 1). This set-up investigated the impact of DA on velocity estimates, assimilating 21 h of velocity data at ten-second intervals.  
**Set-Up II:** A combination of Base-Test and Set-Up I, using both drifters and ADV velocity data simultaneously. Table 3 summarises the characteristics of the DA experiments and monitoring network used for evaluating the DA performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Asimilation Type** & \
\begin{tabular}{c} **Measurement Type and Number** \\ \end{tabular} & **Measurement Location** &
\begin{tabular}{c} **Asimilation Duration and Frequency** \\ \end{tabular} \\ \hline
**Free-Run** & x & x & x & x \\ \hline
**Base-Test** & Pseudo Lagrangian & 8 drifters & From point A to B in Figure 1 & 3 h*/1 min \\ \hline
**Ensemble test** & Pseudo Lagrangian & 8 drifters & From point A to B in Figure 1 & 3 h/1 min \\ \hline
**Frequency test** & Pseudo Lagrangian & 8 drifters & From point A to B in Figure 1 & 3 h/1 min \\ \hline
**Density test** & Pseudo Lagrangian & 8 drifters & From point A to B in Figure 1 & 3 h/1 min \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics and Parameters of the Experiments.