esa-sceva/satcom-chunk-collection huggingface dataset:
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 5 Proof of Identity
### Proposed SRUP Implementation
#### 5.2.1 Human-In-The-Loop system  
The first of these is a system with a human operator in the loop. This approach might be taken when a trusted human operator is present with the device at the time of the join operation (as might be the case for a sensor or other device being manually deployed).  
In this class of system, the join operation is expected to be conducted with relation to a specific (pre-known) device being deployed or installed. The device initiates the process by sending a SRUP_MESSAGE_TYPE_HM_JOIN_REQ message.  
In response to this, the server would send a SRUP_MESSAGE_TYPE_HM_JOIN_RESP message, which would contain an encrypted value (a suitable randomly-selected 128-bit nonce value, encrypted using the device's public key). On receiving this message, the device would present the decrypted version of that data for observation by a human observer. In this way, the trusted human observer, in communication with the operator of the server (outside of the protocol) or who is operating theserver themselves, would be able to compare the identity presented to the server and the identity presented by the device being observed and deployed. This enables the operator of the server to be assured that the physical device that was observed was the device that has the identity associated with the join request. Depending on the device (and the nature of the system, as well as technical proficiency of the operator), this presentation could be done in a number of different ways. In a simple case, the device could display the decrypted value on a suitable display on the device.  
A sequence diagram showing the information flow during a human-moderated join operation is shown in Figure 1.  
Other techniques for presenting a UUID in a human-readable form are explored in Section 8.
# Free-Hand Input and Interaction in Virtual Reality Using a Custom Force-Based Digital Thimble
## 6 User Study 2: Teleportation and Sorting
### Design  
This study employed a within-subject design and two sessions: teleportation and sorting. The independent variable was the input device, which had three levels: the finger mouse, Digital Thimble, and controller. In the teleportation session, participants teleported to eight predetermined targets, while the sorting session involved sorting ten sequences. Both the session and device orders were counterbalanced to minimize order effects. The dependent variables were **task completion time** and **accuracy** performance metrics. The task completion time represents the average time taken by participants to correctly complete a teleportation or sorting task, measured in milliseconds. Accuracy, measured as a percentage, indicates the average proportion of tasks accurately performed by the users. For teleportation, an error is recorded when participants select a location outside the intended teleport target. In the sorting task, errors occur when the completed sequence deviates from the correct order.
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 4 Conclusions  
1. A robust multi-objective optimization framework was developed by integrating the RSM predictive model, NSGA-III, CRITIC weighting method, and VIKOR ranking technique. The framework effectively addresses trade-offs among conflicting performance objectives, including R\({}_{\text{f}}\)/R\({}_{\text{c}}\), shrinkage, and porosity, demonstrating its ability to enhance the rationality and feasibility of concrete mix design under complex engineering conditions, demonstrating its practical value for designing durable pavement concrete in harsh environments, and enhancing the rationality and feasibility of concrete mix design under complex engineering conditions.
2. The RSM-based predictive models exhibited excellent fitting performance, with \(R^{2}\) values of 0.8574, 0.8346, and 0.9824 for R\({}_{\text{f}}\)/R\({}_{\text{c}}\), shrinkage, and porosity, respectively. Statistical analysis confirmed their significance (\(p<0.01\)) and reliability. Among the influencing factors, AEA was identified as the most critical determinant for R\({}_{\text{f}}\)/R\({}_{\text{c}}\) and porosity, while W/C primarily influenced shrinkage. The accuracy and reliability of these models offer important theoretical value for optimizing concrete mix designs.
3. The Pareto-optimal front generated by NSGA-III revealed trade-offs among performance metrics. By applying the CRITIC and VIKOR methods, the optimal mix proportions were identified as follows: W/C = 0.35, sand ratio = 36%, and AEA = 0.200%. Experimental validation confirmed that under the proportions, the concrete achieved an R\({}_{\text{f}}\)/R\({}_{\text{c}}\) of 0.141, shrinkage of 0.0446%, and porosity of 2.82%.
4. Microstructural analysis using LF-NMR and SEM further validated the optimization framework. LF-NMR results showed a shift in pore distribution, characterized by a reduction in larger pores and an increase in smaller pores, indicating a denser microstructure. SEM analysis confirmed that the number of pores was significantly reduced, thus improving the densification and durability of the concrete. The enhanced microstructure further supports the feasibility of this optimization framework for practical applications, particularly in regions prone to extreme environmental stresses.
## 6 Conclusions
We thank the method used by Erzincan Mengucek Gazi Training and Research Hospital for their support in obtaining the material used in this study. Special thanks to the editors of the journal and to the anonymous reviewers, for their constructive criticism and improvement suggestions. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & \\ \hline
**M** & **Feature (Units)** & **Type** & \(V_{\textbf{n},1}\) & \(V_{\textbf{n},2}\) & \(A_{\textbf{n}}\) & **Surv.** & **Non-Surv.** & **Surv.** & **Non-Surv.** & **Surv.** & **Non-Surv.** & \\ \hline
1 & ALT (U/L) & 1 & 34.84 & 35.36 & 0.518 & 0.483 & 0.876 & 0.975 & 0.143 & 0.646 & 0.246 & 0.15892 \\
2 & AST (U/L) & 1 & 32.949 & 33.472 & 0.536 & 0.492 & 0.979 & 0.996 & 0.16 & 0.699 & 0.275 & 0.18123 \\
3 & Albumin (g/L) & 1 & 36.81 & 49.08 & 0.808 & 0.826 & 0.627 & 0.957 & 0.262 & 0.887 & 0.37 & 0.38219 \\
4 & ALP (U/L) & 1 & 85.852 & 85.305 & 0.725 & 0.701 & 0.97 & 0.996 & 0.242 & 0.823 & 0.388 & 0.31932 \\
5 & Amylase (U/L) & 1 & 72.92 & 76.79 & 0.922 & 0.917 & 0.974 & 0.997 & 0.535 & 0.955 & 0.691 & 0.6599 \\
6 & CK-MB (U/L) & 1 & 184.8 & 18.86 & 0.832 & 0.816 & 0.996 & 0.999 & 0.347 & 0.898 & 0.515 & 0.46247 \\
7 & D-Bll. (mg/dL) & 1 & 0.04995 & 0.836 & 0.845 & 0.974 & 0.971 & 0.322 & 0.904 & 0.45 & 0.4068 \\
8 & Glucose (mg/dL) & 1 & 135.631 & 136.854 & 0.557 & 0.514 & 0.991 & 0.998 & 0.167 & 0.679 & 0.286 & 0.19419 \\
9 & Creatinine (mg/dL) & 1 & 0.05469 & 1.1665 & 0.617 & 0.595 & 0.845 & 0.975 & 0.171 & 0.739 & 0.284 & 0.20988 \\
10 & CK (U/L) & 1 & 92.88 & 116.1 & 0.663 & 0.636 & 0.931 & 0.989 & 0.201 & 0.774 & 0.331 & 0.25619 \\
11 & LDH (U/L) & 2 & 253.26 & 597.64 & 0.876 & 0.877 & 0.867 & 0.985 & 0.411 & 0.928 & 0.557 & 0.5169 \\
12 & eGFR & 1 & 82.5742 & 146.250 & 0.77 & 0.772 & 0.755 & 0.97 & 0.246 & 0.859 & 0.371 & 0.31869 \\
13 & UA (mg/dL) & 1 & 0 & 39.01 & 0.818 & 0.824 & 0.755 & 0.972 & 0.298 & 0.892 & 0.427 & 0.38088 \\
14 & BASO (10\({}^{3}\)/uL) & 2 & 0.0098 & 0.01026 & 0.322 & 0.277 & 0.772 & 0.926 & 0.096 & 0.426 & 0.17 & 0.07242 \\
15 & EOS (10\({}^{3}\)/uL) & 2 & 0.01323 & 0.1907 & 0.574 & 0.596 & 0.352 & 0.903 & 0.079 & 0.718 & 0.129 & 0.09262 \\
16 & HCT (\%) & 2 & 30.1257 & 44.0946 & 0.293 & 0.246 & 0.768 & 0.915 & 0.091 & 0.388 & 0.163 & 0.06324 \\
17 & HGB (\%) & 2 & 9.5125 & 15.3997 & 0.252 & 0.193 & 0.85 & 0.929 & 0.094 & 0.319 & 0.169 & 0.05391 \\
18 & LVM (10\({}^{3}\)/uL) & 2 & 0.5936 & 1.2672 & 0.481 & 0.466 & 0.635 & 0.928 & 0.105 & 0.62 & 0.18 & 0.1116 \\
19 & MCH (pg) & 2 & 29.6058 & 35.6706 & 0.722 & 0.762 & 0.313 & 0.918 & 0.115 & 0.833 & 0.168 & 0.13994 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of classification according to the two-threshold approach of surviving and non-surviving COVID-19 patients, for the balanced dataset.
# Detection of Induced GNSS Spoofing Using S-Curve-Bias
## 2 Signal Model
Thus the corresponding cross-correlation of the induced spoofing with the local replica can be expressed as:  
\[R\left(\tau^{\prime}-\left(\Delta_{c}-\Delta_{r}t\right)\right)= \begin{cases}1 -\left|\tau^{\prime}-\left(\Delta_{c}-\Delta_{r}t\right)\right|,&\left|\tau^ {\prime}\right|\leq 1\\ 0,&\text{otherwise},\end{cases}  \tag{6}\]  
where \(\Delta_{r}\) is the code rate difference between the induced spoofing and the authentic signal. Therefore, the correlator output for prompt channel with neglected carrier phase error is shown below:  
\[P\left(\tau^{\prime}\right)=\sqrt{P^{a}}R\left(\tau^{\prime}\right)\text{sinc }\left(\Delta f_{d}^{a}\right)+\sqrt{P^{s}}R\left(\tau^{\prime}-\left(\Delta_ {c}-\Delta_{r}t\right)\right)\text{sinc}\left(\Delta f_{d}^{s}\right)+\bar{n}, \tag{7}\]  
where \(\text{sinc}(x)=\frac{\sin(\pi x)}{\pi x}\), \(\Delta f_{d}^{a}=f_{d}^{a}-f_{d}^{a}\) and \(\Delta f_{d}^{s}=f_{d}^{a}-f_{d}^{s}\) are the Doppler shift residuals of the authentic signal and the spoofing, respectively. The \(\bar{n}\) comes from AWGN and cross-correlation results between local replica and other satellite signals, which can be expressed as the following:  
\[\bar{n}=\int_{0}^{1}x_{n}\ell_{i}\left(t,\tau_{i}\right)dt+\sum_{j\
eq i}\int _{0}^{1}x_{a,j}\ell_{i}\left(t,\tau_{i}\right)dt+\sum_{j\
eq i}\int_{0}^{1}x_ {s,j}\ell_{i}\left(t,\tau_{i}\right)dt, \tag{8}\]  
where \(x_{a,j}\) and \(x_{s,j}\) are separately the \(j\)-th satellite signal including in the authentic signal and spoofing. It is assumed that the receiver has stably tracked the authentic signal before the induced spoofing attack. At the same time, for simplicity, it is assumed that the Doppler frequency of the spoofing signal is the same as that of authentic signal, which is called a \"frequency lock\" in reference [39]. Then the correlation value of the prompt channel is modeled as:  
\[P\left(\tau^{\prime}\right)=\sqrt{P^{a}}R\left(\tau^{\prime}\right)+\sqrt{P^{ s}}R\left(\tau^{\prime}-\left(\Delta_{c}-\Delta_{r}t\right)\right)+\bar{n}. \tag{9}\]
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 2 Second-Order Systems
### Overdamped Behavior (\(\zeta>1\))  
In the overdamped case, the two poles of a transfer function of Equation (1) have real numbers but with different values. Its response \(C_{O}(s)\) in the Laplace domain is modeled under the following formulation:  
\[C_{O}(s)=\frac{\omega_{n}^{2}}{s\Big{(}s+\zeta\omega_{n}+\omega_{n}\sqrt{ \zeta^{2}-1}\Big{)}\Big{(}s+\zeta\omega_{n}-\omega_{n}\sqrt{\zeta^{2}-1}\Big{)}}. \tag{6}\]  
After applying the inverse Laplace transform, it is obtained the temporal response of the overdamped behavior \(c_{O}(t)\) defined as follows:  
\[c_{O}(t)=1+e^{-(\zeta-\sqrt{\zeta^{2}-1})\omega_{n}t}. \tag{7}\]  
Under the Overdamped behavior, the system slowly reacts until reaching the value of one. The deceleration of the response depends on the value of \(\zeta\). The greater the value of \(\zeta\), the slower the response will be. The output of this behavior is visualized in Figure 2 for the case of \(\zeta=1.67\).  
Figure 2: Temporal responses of second-order system considering its different behaviors: Underdamped (\(0<\zeta<1\)), critically damped (\(\zeta=1\)), and overdamped (\(\zeta>1\)).
## 3 The Human Role in Teleoperation
### Frame of Reference  
Many teleoperation visuomotor tasks such as telemanipulation or telesurgery can be highly demanding for the operator because the frames of reference for vision and action are misaligned [93; 94]. The operator's control actions are harder when there is no alignment between his/her point of view, the input device, and the coordinate frame of the robot. Any mental transformation, such as rotation or translation, imposed by the input/output interface to control the robotic system can increase mental workload and consequently decrease task performance. Thus, the interface should be intuitive enough, so when the operator acts on the input device, the robotic agent should move in the expected direction coherently with the input gesture. Moreover, the operator should observe or sense the robotic agent moving in the expected direction. Researchers have shown that the angular misalignment between the visualized axis of rotation and the controller's hand axis rotation can cause control response delays and decrease accuracy in telemanipulation tasks [95; 96]. Human path following performances degrade for non-orthogonal angles relating control and visual frames [97]. The eye-hand coordination tasks should be as natural as if the operator were physically in the remote place. In teleoperation systems, a one-to-one correspondence between control and display devices is desirable, so for a given control movement, the consequent change in the image display should appear to be in the same relative place [10]. Humans can easily adapt to a lateral displacement between the expected point of view of a movement and the observed movement through a 3D perspective display, if less than 15\({}^{\circ}\)[44]. For higher angles \(>\)15\({}^{\circ}\), the adaption decreases.  
One way to achieve the alignment between the operator's point of view, the input/output device coordinates, and the robot's coordinate frame is to design the teleoperator agent anthropomorphically,or ensure that the extent of the control action is easily perceived and mapped through the feedback of the agent (e.g., ensure that a steering wheel rotation is proportional to the robot's linear displacement in the diagonal). The egocentric visual feedback suggested in the present work aims to fulfill one of these natural and one-to-one correspondences (visual, haptic, proprioceptive).
# Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge
## 2 Related Work
### Related Definitions  
Information security is becoming more and more important to people's life [11]. How to discover abnormal traffic or hosts from a high-speed network is one of the important topics in the field of security research. Super points detection is one of the important methods for locating anomaly hosts [12]. All of the super points detection algorithms are based on network traffic and belong to passive network measurement. The original data used in the algorithm is the IP address collected from the network. For network managers, the measuring place is usually located at the boundary of the managed network, as shown in Figure 1. Observation node is a server beside a router, from which the packets between two networks could be collected and inspected. The host in \(\mathbb{A}\) communicates with those hosts in \(\mathbb{B}\) through the boundary router. IP address pairs such as \(<a,b>\) can be extracted from each packet passing through the border router, where \(a\in\mathbb{A}\), \(b\in\mathbb{B}\). For the host \(a\) in \(\mathbb{A}\), its cardinality is defined as follows:  
**Definition 1** (Opposite host set/cardinality).: _In time window \(\mathcal{T}\), for a host \(\mathrm{a}\in\mathbb{A}\), the set of all hosts in \(\mathbb{B}\) that communicating with it is called the opposite host set of \(\mathrm{a}\), and is denoted as \(\mathbb{S}^{\mathbb{B}}_{\mathrm{a},\mathcal{T}}\). The size of \(\mathbb{S}^{\mathbb{B}}_{\mathrm{a},\mathcal{T}}\) is called the cardinality of \(\mathrm{a}\), which is denoted as \(|\mathbb{S}^{\mathbb{B}}_{\mathrm{a},\mathcal{T}}|\)._  
The cardinality is one of the important network attribute [13], and it is the criteria to judge if the host is a super point.  
**Definition 2** (Super point).: _In the time window \(\mathcal{T}\), the host whose cardinality exceeds the specified threshold \(\theta\) is called a super point._  
In this paper, without losing generality, it is assumed that the super points detection is only for \(\mathbb{A}\). Threshold \(\theta\) is set by the users according to different situations, such as detecting DDoS attacks, locating servers and so on.  
Cardinality estimation is the basis of super points detection. The next section will introduce the commonly used algorithm for cardinality estimating in super points detection.
## 3 Simulation Results  
A simulation in accordance with Figure 1 is performed through OptiSystem. The linewidth and frequency of the laser are 50 MHz and 193.1 THz, respectively, the output power is 10 dBm. An MZM is adopted in the settings whose upper and lower ports have opposite electrical phases, and the MZM has an extinction ratio of 40 dB. In addition, the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations** & **Meaning** \\ \hline LD & Laser diode \\ MZM & Mach–Zehnder modulator \\ EDFA & Erbium-doped fiber amplifier \\ PD & Photodetector \\ EF & Electrical filter \\ PA & Power amplifier \\ LNA & Low-noise amplifier \\ MUC & Microwave upconverter \\ EC & Electrical combiner \\ DAC & Digital to analog converter \\ ADC & Analog to digital converter \\ FS & Frequency synthesizer \\ \hline \hline \end{tabular}
\end{table}
Table 1: The meaning of the abbreviations in Figure 1.  
Figure 2: Schematic diagram of the optical spectrum under CS-DSB modulation, (**a**) for upconversion, (**c**) for downconversion, and the electrical spectrum after beating, (**b**) for upconversion, (**d**) for downconversion.  
switching bias voltage and the bias voltage of the upper arm are set to 6 V in order to carry out CS-DSB modulation. The PD has a responsivity of 0.8 A/W.  
The results of upconversion and downconversion are shown in Figures 3 and 4. We assume that the IF signal has a frequency of 1.8 GHz and adjust the frequency of RF to 0.8-18 GHz in the simulation system, which corresponds to LO over 21-38.2 GHz. As can be seen from Figure 3, the modulation optical spectrum of HF signal and LO signal indicates that the CS-DSB modulation is finished by controlling parameters of the dual-port MZM. Between them, the HF signal originates from upconverting the IF/RF signal via a 22 GHz signal. Furthermore, the +1-order sidebands of HF and LO signals exhibit good power consistency and give greater optical power than \(-\)20 dBm for both the upconversion and downconversion (Figure 3). The beating frequency from 0-20 GHz is shown in Figure 4a,b. The electrical spectrum illustrates that RF and IF signals produced by upconversion and downconversion, respectively, can obtain an in-band spurious suppression of at least 25 dBc. Near the target signals, no obvious spurs can be observed because the mixing spurs are outside the system frequency range of 0.8-18 GHz. For the flatness of output power, Figure 4c,d show power fluctuation of 2.2 dB for upconversion and 1.88 dB for downconversion within the LO frequency of 21-38.2 GHz. Therefore, from a simulation point of view, the proposed transceiver system demonstrates improved conversion capability by applying the microwave photonic converter.  
Figure 3: Simulated optical spectrum of upconversion (**a–c**) and downconversion (**d–f**) when RF signal has the frequencies of 0.8 GHz, 9 GHz, and 18 GHz.
# Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks
## 5 Weighted Grid Clustering Algorithm
### Multi-Hop Routing  
The cluster head is now responsible for route formation and routing the data towards the sink node. The routing of data is divided into two main types:  
1. Inter cluster data routing
2. Intra cluster data routing  
Figure 4: Node’s status (\(\mathrm{W_{u}}\)) at different simulations for being a cluster head (CH).  
For route selection and data transfer, the node, which is considered as a coordinator, forwards the route selection ACK message to the NextHop. After receiving the message, NextHop replies to the coordinator and thus the hop tree updating process starts. These steps go on repeating until the sink node is found or a node is reached which is already located on the route. Once a route is established, multi-hop data routing also starts. The main goal of this phase is to update the HopToTree value of all the nodes so that each node obtains the new next-hop ID for further routing. Finally, the new relay node initiates the hop tree updates after implementing the Hop Tree Algorithm.  
When a member node of a CH has some data to send, CH will wait for the data from any other member node. CH will collect the data from its entire descendants and forwards the aggregated data to the NextHop node. Using this routing technique, energy consumption is highly reduced. The whole process is explained in Algorithm 3.  
```
1. The leader node (Cluster Head) v, broadcasts the route selection Acknowledgment Message (ACK) to all the neighboring nodes
2. NextHop \(\leftarrow\) NID
3. End
4. Repeat
5. If u = NextHop then, // u is the member node which receives the ACK message from its cluster head v
6. HopToTree(u) \(\leftarrow\) 0 // Node u is part of newly built route
7. Role(u) \(\leftarrow\) Relay
8. Node u is broadcasting ACK message to its neighboring nodes
9. Run line 1 to line 4
10. HopToTree = 1
11. A node that receives the HCM message sent by node u runs algorithm 1 from line 3 to 13.
12. End
13. Until // Finds the sink node
```  
**Algorithm 3** Multi-hop routing and Hop Tree update