esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation-Based Performance Evaluation
### Preliminaries  
FTC is applicable to a network when all nodes are randomly and uniformly deployed, because the training data derived from Equation (1) assumes that all nodes are randomly and uniformly deployed. However, FTC can be applied to other deployment strategies as long as the node degree distribution is able to be obtained. In our simulations, nodes are uniformly deployed at random in a 100 \(\times\) 100 m\({}^{2}\) field. All nodes are stationary after the deployment. The maximum communication ranges, denoted as \(r_{max}\), for each node are the same, and \(r_{max}=30\) m. Each node is capable of adjusting its communication range within [0 \(r_{max}\)]m. In this paper, we call one \"round\" simulation when all nodes finish the process of executing a topology control algorithm one time. Recall that \(n\) is the total number of nodes deployed in the field, and \(k\) is the desired node degree (namely, \(ND_{ref}=k\)). In Section 4.4, in order to simulate different node densities, the number of nodes deployed in the area varies from 50 to 90 nodes. We randomly generate 50 networks for each specific number of nodes. For each network, we run different algorithms separately. The results are the average of 50 deployments.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned aerial vehicle \\ LEO & Low earth orbit \\ IoRT & Internet of Remote Things \\ NTN & Non-terrestrial network \\ HAP & High-altitude communication platform \\ CN & Core network \\ IAB & Integrated access and backhaul \\
5G-NR & Fifth-generation new radio \\ QoS & Quality of service \\ IoT & Internet of Things \\ RST & Remaining service time \\ AWGN & Additive white Gaussian noise \\ EIRP & Effective isotropic radiated power \\ \end{tabular}
# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 6 Conclusions and Future Work  
In this paper, we investigate the issue of location privacy preservation for non-uniform distributed road network. According to the user's distribution, the side-weight inference attack happens as the adversary would infer that the target user belongs to a high-density road section with high probability. In other words, the adversary would track a target user with a high probability, and location privacy is poorly protected. We propose a privacy-preserving method based on road truncation. The road section with high user density is designed to be truncated to reduce the divergence between different road sections. We also consider the cost function and formulate the object as a mixed integer programming problem. The simulation results show that our scheme not only achieves \(k\)-anonymity and \(l\)-diversity, but also resists the attack of side-weight inference. Meanwhile, the proposed scheme runs fast at a low cost. As a result, our scheme is proved to protect users' location privacy effectively and efficiently for non-uniform distributed road network.  
In the future, we are planning to study the problem of trajectory privacy in continuous LBS. Moreover, machine learning is widely used in many aspects and the Generative Adversarial Networks (GAN) is a promising tool to protect privacy.  
Conceptualization, Q.L. and H.W.; methodology, Q.L. and H.W.; software, Q.L.; validation, Q.L., H.W., X.W., N.Z. and L.D.; formal analysis, Q.L.; investigation, Q.L.; resources, Q.L.; data curation, Q.L.; writing--original draft preparation, Q.L.; writing--review and editing, H.W., X.W., N.Z. and L.D.; visualization, Q.L.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2018 YFE0207600) and the Fundamental Research Funds for the Central Universities, China (2020 JBZD005).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: The average time computation of generating an anonymous set with the increase of \(l_{0}\). (\(k_{0}=10\), \(\lambda_{0}=2\)).
# Evaluating Optical Clock Performance for GNSS Positioning
## 6 Discussion
SPP application users, in contrast, can expect less than a centimetre level error from the optical clocks, which is approximately a 10 times better result than the Galileo FOC satellite clocks. Furthermore, an individual constellation's clock prediction errors in the corresponding navigation message intervals are discussed based on the estimated stability of the satellite clocks. For example, a similar setup of Galileo FOC and BeiDou BDS3 satellite clocks will contribute an error of a few millimetres to centimetres to the clock prediction within their navigation data upload intervals. Whereas the stability of GLONASS and GPS satellite clocks will have clock prediction errors of less than a decimetre over 1800 s and 7200 s, respectively. The three optical clocks, for comparison, will result in millimetre to micrometre levels of errors in the window of upload intervals of all constellations ranging from 600 s to 7200 s, despite the fact that the stability of the Iodine MTS and CROC clock have degradation after 10,000 s.  
However, considering the clock technology readiness level (TRL), which describes the maturity of technologies and Size, Weight, and Power (SWAP) parameters, these three clocks may require further enhancement. The Sr Lattice optical clock, for instance, needs to be miniaturised to meet the satellite payload requirement, which is one of the critical features in choosing an appropriate clock, besides stability, for a reliable, long-term satellite operation [15; 56]. The current size and weight of the Sr Lattice optical clock are approximately 968 L and 250 kg, respectively [13]. It was also mentioned that these parameters could feasibly be reduced to 500 L and 100 kg. The Galileo PHM clock's parameters, in contrast, are 40 L and 25 kg [15]. The Iodine MTS and CROC optical clocks, on the other hand, have a much more compact setup occupying 40 L and 15 L, respectively,which allows the clocks to meet the SWAP requirements. Even the Iodine MTS clock was flown successfully on a sounding rocket to test its maturity and robustness [41; 42]. But the stability of both clocks degrades after 10,000 s due to the cavity reference on the Iodine MTS, and the out-of-loop measurements of the cell temperature and laser power of the CROC.
# Robotic Systems and Navigation Techniques in Orthopedics: A Historical Review
## 2 Robotic Systems (RSs)
### Autonomous RS  
#### 2.1.1. _R_obodoc \(\rightarrow\) Tsolution One  
The ROBODOC surgical system (Curexo Technology Corporation, Fremont, CA, USA) is a fully autonomous robotic system initially designed for total hip arthroplasty (THA) in the 1980s [9; 27], and introduced to be used on patients in 1992 [26; 28]. It is the first robot that was clinically used in orthopedic surgery [2]. ROBODOC had its first system installed in Germany after being approved for sale by the European Union in 1994 [2; 4]. ROBOTDOC was approved by the FDA (ROBODOC FDA 2008 file (accessed on 28 June 2023): [[https://www.accessdata.fda.gov/scripts/cdrh/cdocs/cfpmn/pmn.cfm?ID=K072629](https://www.accessdata.fda.gov/scripts/cdrh/cdocs/cfpmn/pmn.cfm?ID=K072629)]([https://www.accessdata.fda.gov/scripts/cdrh/cdocs/cfpmn/pmn.cfm?ID=K072629](https://www.accessdata.fda.gov/scripts/cdrh/cdocs/cfpmn/pmn.cfm?ID=K072629))) in 2008 for use in THA [19].  
The whole system includes ORTHODOC (a 3D preoperative computer modeling and planning workstation) and ROBODOC surgical assistant (a 5-axis SACARA-type surgical robot) [25]. The system conducted its work, e.g., bone milling and preparing for stem implantation, based on preoperative computed tomography (CT) imaging. The workstation generates a 3D virtual model and produces a customized surgical plan [19]. The system employed fiducials implanted about 5 mm deep in the bone for bone motion detection and tracking; thus, the tracking accuracy is high and unaffected by debris and fluids [9]. Since ROBODOC is a fully autonomous system, once it starts to work, the surgeon only has control over the emergency stop [19].  
The company Curexo Technology Corporation changed its name to Think Surgical, Inc. in 2014 [2; 29]. Based on ROBODOC technology, its next-generation system named TSolution One (THINK Surgical, Inc., formerly Curexo Technology Corporation, Fremont, CA, USA) (THINK Surgical website (accessed on 28 June 2023): [[https://thinksurgical.com/](https://thinksurgical.com/)]([https://thinksurgical.com/](https://thinksurgical.com/))) was developed. Its applications have been expanded to total knee arthroplasty (TKA) [4; 19]. The TSolution One was approved by the FDA (TSolution One FDA 2019 file (accessed on 28 June 2023): [[https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K191369](https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K191369)]([https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K191369](https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K191369))) in 2019 for use in TKA. Currently, the TSolution One system has upgraded to a computer-aided system from the earlier CT-based system [2; 4].
# Dynamically Triggered Damage Around Rock Tunnels: An Experimental and Theoretical Investigation
## 2 Experimental Setup
### Test System  
The SHPB test system cleverly avoids the difficulty of directly measuring the strain stress of an object under high-speed loading [23]. It is being increasingly used to test the dynamic mechanical properties of materials [24; 25]. The modified SHPB system, as illustrated in Figure 3, was used for the dynamic impact, primarily comprised of the driving device, an incident bar, a transmission bar, an energy trap at the rear end, and the data acquisition system. High-speed photography and digital image correlation (DIC) technology were also employed to observe the deformation evolution of the specimens during the dynamic impact process. DIC is a non-contact surface deformation measurement technique widely used in dynamic and static mechanical performance testing of materials [12; 13; 26]. In this study, the high-speed photography resolution was set as 256 \(\times\) 256 pixels at a frame rate of 79,161 fps. Two coaxial lights were used as the light source to ensure that no shadows or reflective areas were produced on the specimen. After the experiment, the pictures were processed using VIC-2D v7 software with the default parameters, which achieved good results.  
Figure 3: SHPB test system.  
Figure 2: Sample model (**a**); and photograph (**b**).  
In each experimental process, the striker was carefully repositioned to a fixed location, ensuring consistent application of pressure as the driving force. This meticulous action aimed to minimize the external disturbances and maintain uniform incident stress for every specimen. The focus of this experiment was on analyzing the fracture process surrounding the arched hole under constant incident stress, with a peak value of approximately \(\sigma_{d}=100\) MPa.  
As the striker hits the end of the incident bar, the stress wave will propagate ahead as strain in the bar and be recorded by the strain gauges on the bars. The stress at both ends of the sample near the incident and transmission bars \(\sigma_{in}\) and \(\sigma_{tr}\) can be written as follows [23]:  
\[\left\{\begin{array}{l}\sigma_{in}=E\cdot\left(\epsilon_{i}^{-}+\epsilon_{r }^{+}\right)\\ \sigma_{tr}=E\cdot\epsilon_{r}^{-}\end{array}\right. \tag{1}\]  
where \(E\) is the elastic modulus; and \(\epsilon_{i}\), \(\epsilon_{t}\), \(\epsilon_{r}\) are the incident, transmission, and reflected strains, respectively. It is worth noting that the incident and transmission strains represent compressive strain, marked as negative (\(-\)), and the reflected strain denotes the tensile strain, marked as positive (\(+\)). Afterward, the stresses and strains in the specimen can be determined by the three-wave method [13; 27]. Table 2 lists the main equipment used in this test.
# Effect of Methylammonium Iodide (MACI) on MAPbI\({}_{3}\)-Based Perovskite UV-C Photodetectors
## 2 Materials and Methods  
### Experimental Materials  
During this study, we purchased all experimental materials from commercial companies and used them without any additional purification process. Firstly, a quartz glass substrate (2 cm \(\times\) 2 cm, Substrate thickness \(<\) 0.1 mm) coated with Indium Tin Oxide (ITO, Film thickness \(<\) 150 nm, Sheet resistance \(<\) 8 ohm/sq) was provided by the company TMA (Seoul, Republic of Korea). Tin(IV) oxide (SnO\({}_{2}\), Alfa Aesar, Haverhill, MA, USA) was used as colloidal solution of 15 wt.% in water. We used methylammonium iodide (CH\({}_{3}\)NH\({}_{3}\)I, MAI), methylammonium hydrochloride (CH\({}_{6}\)ClN, MACI), and formamidinium bromide (CH\({}_{5}\)BrN\({}_{2}\), FABr) that were produced from Greatcell Solar Materials.  
Ethyl alcohol (\(\geq\)99.5%) was purchased from Duksan Pure Chemical Company. Pb(II) iodide (PbI2, 99.999%), 1-butyl alcohol (C4H9 OH, \(\geq\)99%,), sodium dodecylbenzenesulfonate (SDBS, CH3(CH2)11C6H4 SO3 Na), acetonitrile (ACN, C2H3N, 99.93%), dimethyl sulfoxide (DMSO, (CH3)2 SO; \(\geq\)99.9%), N,N-dimethylformamide (DMF, HCON(CH3)2; 99.8%), spiro-MeOTAD (C81H88A4O8; \(\geq\)99%), 2-propanol (IPA; 75 wt.%), lithium bistrifluoromethane-sulfonimidate (CF3 SO2 NLiSO2 CF3; \(\geq\)99.0%), toluene (99.9%), and 4-tert-Butylpyridine (C9H13N, 98%) were used and supplied from Sigma-Aldrich Chemical sciences company (St. Louis, MO, USA).
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 5 Performance Results and Evaluation
### Trade-Offs and Inefficiencies  
While the proposed fire monitoring system demonstrates significant advantages in scalability, energy efficiency, and real-time monitoring, certain trade-offs and inefficiencies must be acknowledged, particularly in relation to hardware limitations and computational demands.
# A Full-Duplex LED-to-LED Visible Light Communication System
## 4 Full-Duplex LED-to-LED VLC
Thus, we can conclude that the interference mostly occurs due to the noise path shown in Figure 4a. As a result, it seems that slight performance degradation in the full-duplex system is caused by the reflection of the opposite LEDs. Since the transmission distance in our system was short, the optical signal power was much stronger than the ambient background light. Thus, the effect of ambient light from other lights can be ignored in our experimental results. Therefore, we think that the main reason for the signal degradation is the reflection of opposite LEDs. As shown in our results, the LED-to-LED VLC system has a few limitations, such as its low data rate and short transmission distance. However, we think that a higher data rate and longer transmission distance can be achieved with advanced detection techniques (e.g., artificial neural-network-based detection and optimally weighted non-coherent detection) [19; 20]. In LED-to-PD VLC such as in [21], the data rate is much higher than for LED-to-LED VLC. The PD used in [21] (a New Focus 1601 FS-AC instrument) had a rise time value of 400 ps and a peak responsivity value of 0.5 A/W. In contrast, our LED-to-LED VLC system achieved a maximum data rate  
Figure 5: Analytical and measured SNR values for the half-duplex and full-duplex LED-to-LED VLC systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Dimension** \\ \hline I(0) & \(2.09\times 10^{-3}\) & W/sr \\ m & 4.8188 & - \\ \(R_{p}\) & \(5.6\times 10^{-4}\) & A/W \\ \(r\) & 0.005 & m \\ \(R_{f}\) & 0.0186 & - \\ \(P_{n}\) & \(2.4\times 10^{-21}\) & W \\ \(d_{tr}\) & 0.013 & m \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters used in the SNR analysis of half- and full-duplex LED-to-LED VLC systems. of about 50 kbps, because the receiver LED had a rise time value of 44 us and a peak responsivity value of 0.56 mA/W. It is obvious that PDs shows much better performance than LEDs as optical receivers. However, we believe that LED-to-LED VLC can be commercialized because it has several advantages, such as its low complexity and low cost.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 3 PPP Models
### PPP-RTK  
GNSS precise centimeter-level positioning can be achieved by RTK or PPP. RTK has the advantage of short convergence time, e.g., a few seconds. Nevertheless, RTK depends on the communication between the rover receiver and one or more reference stations, introducing baseline and bandwidth limitations. On the other hand, PPP enables the absolute positioning of a single receiver, but the convergence time of the ambiguity-fixed PPP, or PPP-AR, exceeds 20 min [77; 78].  
The term \"PPP-RTK\" was introduced in [85] to reduce the gap between PPP and RTK and enable centimeter-level accuracy in a few seconds with a single rover receiver. Although \"PPP-AR\" and \"PPP-RTK\" are used interchangeably in many research papers to denote the PPP with ambiguity resolution, in this article, we adopt the convention that PPP-RTK is not the same as PPP-AR [69; 85].  
As described in Section 3.3, PPP-AR is the ambiguity-fixed PPP employing the standard IF observations. In real-time PPP-AR, the receiver obtains precise corrections for the satellites' orbits, clocks, and biases. The IF combinations mostly eliminate the ionospheric delay; nonetheless, they contain more noise than their corresponding uncombined observations, as illustrated in Section 2.1. Therefore, PPP-AR requires a long convergence time.  
In contrast, in PPP-RTK, the atmospheric errors are estimated on the network side and sent to the users along with the other precise corrections to enable fast ambiguity resolution. Furthermore, the PPP-RTK model can rely on a global network of reference stations on the server side or a regional network similar to RTK for a more accurate estimation. While RTK adopts the Observable Space Representation (OSR), PPP-RTK, similar to real-time PPP-AR, employs the State-Space Representation (SSR), reducing the communication bandwidth and allowing more users to be served.  
In summary, PPP-RTK is a state-of-the-art precise positioning model that combines the advantages of both PPP and RTK. More details on PPP-RTK can be found in [69; 76; 85].
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### Library Integration and Compatibility  
Our implementation carefully manages the integration between PyQC (for CRYSTALS-Kyber) and TenSEAL (for CKKS FHE):  
* Key Format Compatibility: Kyber shared secrets are processed through the HKDF to generate standard AES-256 keys, ensuring compatibility with TenSEAL's encryption wrapper functions.
* Serialization: FHE contexts and keys use TenSEAL's built-in serialization methods, which produce binary data that is then encrypted using the AES-GCM channel.
* Error Boundary Management: All cryptographic operations are wrapped in try-catch blocks to handle library-specific exceptions and provide meaningful error messages.
# Meta-Learner for Amharic Sentiment Classification
## 4 Results and Discussions
### Discussions  
In this subsection, we discuss the above-mentioned results from four aspects, which are presented as follows. (i) Effects of SMOTE: As we can see in Table 4, the application of SMOTE on imbalanced data sets is improving performance of all the classifiers (i.e., both stack classifier and base learners) on sentiment classification of Amharic user generated texts. With character \(n\)-grams feature, the results of stack classifier with the five evaluation metrics (accuracy of 86%, recall of 86%, precision of 87%, F1 of 86%, and AUC of 94%) has revealed that the use of SMOTE is significantly improving sentiment classification performance over the other experimental settings. Figure 4 is visually revealing that application of SMOTE has shown a rise in performance when it is used in both character \(n\)-grams (those bars in green) and word gram feature sets (those bars in orange). (ii) Effects of Features: As shown in Table 4, the use of the character \(n\)-grams features increases the performance of sentiment classification of all the classifiers while comparing them to the word \(n\)-grams features regardless of application of SMOTE. The result of all the classifiers trained with TF-IDF character \(n\)-grams (as shown Exp I) has better performance over classifiers trained with TF-IDF word uni-grams (as shown Exp III). For example, the stack classifier in experiment I has achieved better results in accuracy of 79%, recall of 74%, precision of 77%, F1 of 75%, and AUC of 84% as compared to the results in experiment III (accuracy of 73%, recall of 64%, precision of 71%, F1 of 64%, and AUC of 76%). To clearly analyze the result in Table 4, Figure 5 is plotted to show that similar performance gains have achieved by all the individual classifiers with character \(n\)-grams features (those bars
## 3 Advancements and Alternatives to Modal-Based Damage Identification
### Non-Modal Damage Sensitive Features  
Due to many modal-based DSFs' sensitivity to environmental and operational conditions, in addition to their various extraction challenges, there has been an increase in damage detection and identification assessments that utilise non-modal-based DSFs. The majority of these non-modal features can be subdivided into two classes; vibration-based and time-series-based, some of which are highlighted herein.
## 3 Joint Angle and Delay Estimation (JADE)
### GWO vs. Combining \(|\)Embedding Is (Is-Gwo\(|\)Gwoeis)
**Step (5.a) [GWO or IS-GWO]:**  
\[(\mathbf{\chi}_{*}^{(r)})_{j}{=}(\mathbf{\Sigma}_{t-1}^{*})_{j}-g_{j,t}^{*}\text{dist}(*,( r),j). \tag{36}\]  
**Step (5.b) [GWOEIS]:**  
\[(\mathbf{\chi}_{*}^{(r)})_{j}{=}(\mathbf{\Sigma}_{t-1}^{*})_{j}-g_{j,t}^{*,(r)}\text{dist }(*,(r),j). \tag{37}\]  
Hence, before coming back to Step (2) if \(t<T_{H}\), each individual's location is updated in either case based on these intermediate variables \(\mathbf{\chi}_{*}^{(r)}\) as follows:  
\[(\mathbf{\Sigma}_{t}^{(r)})_{j}=\frac{(\mathbf{\chi}_{\alpha}^{(r)})_{j}+(\mathbf{\chi}_{ \beta}^{(r)})_{j}+(\mathbf{\chi}_{\delta}^{(r)})_{j}}{3}. \tag{38}\]  
Figure 1 depicts the way to update a search agent's position according to \(\alpha\)-type, \(\beta\)-type, and \(\delta\)-type wolves based on (37) and (38) for the proposed GWOEIS algorithm. Finally, the joint AoA-TD estimate \(\widehat{\mathbf{\Sigma}}_{\text{Tech}}=\big{[}\widehat{\mathbf{\theta}}_{\text{Tech}}, \widehat{\mathbf{\tau}}_{\text{Tech}}\big{]}\) is selected as the last position of the lead wolf \(\alpha\):  
\[\widehat{\mathbf{\Sigma}}_{\text{Tech}}=[(\mathbf{\Sigma}_{T_{H}}^{\alpha})_{1},\dots,(\mathbf{\Sigma}_{T_{H}}^{\alpha})_{Q},(\mathbf{\Sigma}_{T_{H}}^{\alpha})_{Q+1}, \dots,(\mathbf{\Sigma}_{T_{H}}^{\alpha})_{2Q}], \tag{39}\]  
where the choice of Tech \(\in\) [\"GW0\", \"IS-GWO\", \"GWOEIS\"] determines the cases considered in Steps (1), (3), (4), and (5). We summarize the GWOEIS algorithm in Figure 2.  
Figure 1: Position updating in GWOEIS.