esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 5 Evaluation  
### Experiment Setup  
To evaluate the detection performance of the system, some real experiments are conducted in three typical rooms from several aspects. The three rooms are a meeting room, a typical living room, and a large office, and the sizes of the three room are 5 m \(\times\) 4 m, 5 m \(\times\) 4 m and 10 m \(\times\) 6 m, respectively. The layout of the three rooms and transceiver deployment are shown in Figure 5. There are desks with glass dam-boards and chairs in the office, while a meeting table and chairs in the meeting room, which causes different multipath effects. Especially, in order to present a reasonable evaluation in a smart home scenario, a typical living room was used as a scenario. In the living room a television, there is a television on the wall, a sofa, a piano, a refrigerator, some other furniture, and some doors to other rooms, which will cause much more complex multipath effects. A TP-Link 802.11n wireless router with a single antenna is used as the transmitter and a Lenovo laptop equipped with a three-antenna Intel WiFi Link 5300 (iwl 5300) NIC running Ubuntu 11.04 OS as the receiver. The firmware of the NIC is modified in order to extract CSIs from data packets utilizing the CSI tools. In addition, we upgrade the antennas of the NIC using three 6 dbi gain antennas as shown in Figure 6 in order to increase the signal-noise-ratio.  
According to CSI tools, the sensing data is the CSIs of the respond packets when the transmitter is continuously sending ICMP packets to the receiver. We recruited four volunteers in our experiments with the basic information shown in Table 1. During data collection period, only a single person  
Figure 5: Experimental scenario.  
moves back and forth in different moving patterns respectively in the room without a fixed path. The transmission rate in our experiments is configured to 200 Hz. A few cycles of data collection process are conducted for one person, while each cycle contains only one moving pattern and lasts for 100 s. Data collection lasts for one week, and about 20 min moving data is collected for one person moving in one pattern.  
False negative (FN), false positive (FP), and the probability of detection (PD) are used as the evaluation metrics in this paper. False negative is the ratio that RDFID fails to detect intrusion, while false positive is the ratio it reports intrusion when nobody is in the room. The probability of detection is the ratio that it successfully detects the existence of the intruder. The three metrics can be illustrated by Figure 7, where P1-P4 are the elements of the confusion matrix in the form of percentage. As shown in Figure 7, P4 represents FN and P1 represents FP. PD is described in Equation (7).  
\[PD=\text{P3}/(\text{P3}+\text{P4}), \tag{7}\]
# Quantum Synchronization and Entanglement of Dissipative Qubits Coupled to a Resonator
## 5 Strong Damping Semiclassical Regime  
We find that in the opposite limit of strong damping, the semiclassical approximation is quite successful. To illustrate this, we consider a cavity coupled to two qubits with all dissipation terms in Equations (4) and (5), both for cavities and qubits with equal dissipation \(\gamma=\gamma_{s}\). In this case, the dissipation is chosen to be of the same order of magnitude as the detuning between qubits and cavities. This is, thus, a regime where we expect the semiclassical theory to give a very good description of the steady state. This is confirmed by numerical simulations presented in Figure 5, which shows that both the mean spin orientation and the mean cavity susceptibility variables, \(\alpha_{x}=\mathrm{Re}\langle\hat{a}\rangle\) and \(\alpha_{y}=\mathrm{Im}\langle\hat{a}\rangle\), are reproduced almost perfectly by the minimization of the semiclassical functional \(S\). We see that in addition to the main resonance at \(\omega_{r}=0\), the cavity susceptibility shows features at resonance with the qubits \(\omega=\Omega_{1,2}\), in agreement with the exact RWA solution. For these strong values of damping, the rate equation series are no longer converging for the range of detuning explored here.  
At weaker frictions, the semiclassical results become less accurate but can still reproduce the qualitative trends of the qubit response to cavity excitation. To illustrate this, we show the semiclassical results for the case from Figure 6 for one qubit. We see  
Figure 4: Comparison between RWA and rate equation series for weak damping \(\gamma=\gamma_{s}=0.005\lambda\). As expected in this regime, the rate equation series (R), from Equation (19), converges. Excitation was reduced to avoid overheating at resonance with \(F=0.1\lambda\) and \(0.2\lambda\). As in the previous figures \(\Omega-\omega_{0}=2\lambda\).  
Figure 5: Spin projections of the two spins as functions of the detuning \((\omega-\omega_{0})/\lambda\) for excitation strength \(F=\lambda\) and \(F=5\lambda\). Dashed lines show the spin projection predicted by the semiclassical functional Equation (38), and dotted lines show the RWA steady state. The qubit-cavity detunings are as follows: \(\Delta_{1}=\Omega_{1}-\omega_{0}=2\lambda\), \(\Delta_{2}=\Omega_{2}-\omega_{0}=-\lambda\). The dissipation is fixed to \(\gamma=\gamma_{s}=2\lambda\), and the relatively large values of the dissipation rates ensure good agreement with the semiclassical predictions.  
that--although this method fails to capture the multiphoton resonance--the broad shape of the response is reproduced correctly.
# Performance Analysis of Mesh Based Enterprise Network Using RIP, EIGRP and OSPF Routing Protocols +
## 5 Time Testing  
Time testing is performed after line termination when transmitting packets. The following table summarizes the findings of the time tests performed in Table 2 for full mesh routing using RIP, EIGRP and OSPF. First, conduct continuous ping tests, then pause; a delay time will display. In addition, Table 2 summarizes the experimental findings for routing RIP, EIGRP and OSPF on the mesh's entire topology.
## 5 Conclusions  
This study examined the structural behavior of a partially earth-anchored cable-stayed bridge with a long main span length when using a new key segment closing process utilizing the thermal prestressing technique based on simple thermal expansion. The validity of the new key segment closing method was demonstrated through a detailed construction sequence analysis. The main contributions and conclusions of this study are summarized as follows:  
(1) A new method utilizing the thermal prestressing technique offers an effective way to connect key segments while avoiding large movements resulting from the removal of longitudinal restraints arising from asymmetric of axial forces in girders near the pylons. The new method becomes more effective with increasing span length since bridges with longer spans show substantial movement with the removal of longitudinal restraints.  
(2) The applied closing method develops new member forces through a heating process in the cantilever system before closing the key segment and cooling the system continuously after closing. The resulting forces developed by the new key segment closing method enhance the structural efficiency of a partially earth-anchored cable-stayed bridge by reducing both the axial forces in the girders. The application of the new closing method may change the bending moments in the girders. However, these changes are not significant since the absolute values of the bending moments are relatively small in relation to their capacities. With regard to cables, only earth-anchored cables show increased tensile forces, whereas other cables anchored to a side span show decreased tensile forces.  
Figure 19: Cable forces with various numbers of earth-anchored cables. (**a**) Cable forces after completed construction; (**b**) Cable force ratio.  
(3) Parametric studies have shown that the elongation length is an important factor that can enhance the effects of the new method. The ratio of the peak compressive axial force is 0.97 for an elongation length of 100 mm elongation length, 0.96 for 150 mm, and 0.95 for a 200 mm.  
There is latitude for additional research on the new applied key segment closing method, particularly concerning the operation of the heating device, the feasible heating times during connection, the enhancement of the economy of its operation, and other facets. In addition, further research is needed on the stress distribution created by the heat flow and in the cantilever state before closing a key segment as well as on torsion and distortion of the box section.  
This study has been supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport of the Korean government (14 CTAP-C078811-01).  
[PERSON] proposed the topic of this study and designed the process. [PERSON] performed the analysis and wrote the paper. [PERSON] and [PERSON] modified the final paper.  
The authors declare no conflict of interest.
## 4 Experiments and Results
### Results and Discussion
#### 4.2.2 Distribution, Frequent Words and Training Examples of Tweets
Tokenization method has been incorporated as a parser to remove specific unwanted words from the collected tweets and finally extracting features. We used count vectorizer in Scikit-learn to accomplish this task [39; 40; 41; 42; 43; 44; 45]. The outcome of count vectorization provides a typical sparse matrix in terms of counts. The reason that we chose count vectorization is that data sparsity is very common in tweets as we intend to detect hate speech; moreover, the irregular form of hate speech in a short text of tweets makes it sparser by nature. To the best of our knowledge, none of the authors have reported the visualization of tweets, especially hateful words, which helps us to understand the specific words from the tweets that play a key role in generating hateful sentiments among youth. In the word cloud, a larger font size of hateful words presents its importance in a more prominent way than the other words of the tweets that are in the overall cluster. As we have an ample number of Twitter data, we obtained a better word cloud visualization. We were able to represent semantically more meaningful visual outcomes.
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation
### Service-Forwarding Architecture
#### 3.1.2 Data Plane  
The data plane includes a forwarding module and service module. The forwarding module is responsible for forwarding packets. In the existing NS4's data plane, we added service ports. These ports are identified with a specific port number and bound to two queues, which are used to cache the packets entering or leaving the service module. The pre-processing module and post-processing module are used to process the data packets before or after the P4 pipeline. Their main function is to determine whether the data packets are entering or leaving the service module.  
The service module of the data plane is the module that formulates and executes the algorithms. The service module mainly includes algorithm module and basic function module. The algorithm module includes the GP selection module, RP selection module, multicast state reduction module and packet scheduling module. Users can modify the methods in these modules to flexibly deploy experiments. The basic function module  
Figure 1: Service-forwarding architecture.  
includes a protocol handler module, network status measurements module, protocol conversion module and the discrete event encapsulation module.  
The protocol handler module is responsible for the construction and maintenance of the multicast path. The status management module mainly manages and maintains the multicast forwarding table (MFT) according to the multicast protocol message. This module also manages the load information of the node and its neighboring domains.  
The protocol conversion module is responsible for the protocol conversion between the emulation network and real network. The advantage of this module is that it can avoid modifying the protocol of emulation network or real network and, thus, significantly reduce the experiment workload. The discrete event encapsulation module encapsulates the operations of the service module into discrete events. One of the core concepts of ns-3 is to model the packet forwarding processing in the network system as discrete events occurring in a certain sequence. Encapsulating the actions of the service module as discrete events is in line with the design concept of ns-3.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### UWB IPS Topologies
#### 3.2.2 Time Difference of Arrival (TDoA)  
This algorithm determines the position by measuring the difference in the arrival time of the signal sent from the tag to the different anchors. The difference in distance from each anchor is calculated by the difference in arrival time at each anchor. This results in multiple hyperbolas passing through the tag's location. The intersection of these hyperbolas is the  
Figure 6: SDS-TWR tag–anchor signal propagation times.  
tag's location. One of the advantages of this method is that there is no need to send a response message from the anchors to the tag, as the arrival time of the signal at each anchor is calculated separately, and then the difference between the arrival times is subtracted to determine the location of the moving tag. This saves processing time and increases the speed of the system. This method requires synchronization between anchors, which adds complexity to the implementation of this algorithm. However, this algorithm has great flexibility in the number of tags that can be tracked at the same time. Synchronization can be implemented using a direct wired connection between the anchors, which is more accurate. Synchronization can also be achieved wirelessly, and in this case, synchronization requires periodic calibration to obtain the required accuracy [15; 16; 17; 18]. Figure 7 explains how the algorithm works. The tag begins sending a request to the neighboring anchors, depending on their location on the tag. After the signal reaches all three anchors, it takes different times to arrive at t1, t2, and t3. By calculating the difference between the three times, the location of the tag can be determined.  
This method can be used differently, where the poll UWB signals are sent by the synchronized anchors, the different times of arrival for each anchor are calculated separately in the mobile tag, and the location is determined based on the difference between the different times of arrival for the anchors. This method is called reverse time difference of arrival (RTDoA).
## 3 Obstacle Avoidance Scenario
### Obstacle Avoidance Techniques
#### 3.4.3 Collision Cone Method  
The collision cone concept was first proposed by [PERSON] and [PERSON] [45] for a 2D movement scenario. The authors assumed any obstacle as a circular area, and the distance from the UAV's position to the obstacle area is calculated within a collision cone using the UAV's velocity vector. This concept works for any irregular-shaped unknown obstacle and prevents collision between two irregular-shaped objects or vehicles. Following the same strategy, [PERSON] and [PERSON] [47] calculated a radar detection cone to find the possible collision-free path. To solve the collision between two aircraft in 3D space, [PERSON] et al. [300] used the collision cone method with mixed geometry. [PERSON] et al. [301] used a 2D passive vision sensor and collision cone approach in order to examine the obstacles from a critical distance. In Figure 11, the vehicle's position is \(X_{v}\), the obstacle position, \(X_{obs}\), is inside the safe boundary, and \(X_{ap}\) is the vehicle's aiming point.  
Later on, [PERSON] and [PERSON] [302] extended the collision cone approach to detect the moving obstacle in 3D space. [PERSON] et al. [303] used this method to avoid shape-shifting targets like shape-shifting snake robots, a swarm of vehicles, and oil split. They first developed the collision cone between a point object and a deformable object and subsequently extended that to the case of an engagement between a circular object and a deforming object. A real-time collision avoidance algorithm, called Tangent Plan Coordinate, addressing multiple obstacles, was proposed by [PERSON] and [PERSON] [51]. They used a stereo-vision sensor with a limited field of view to assume the unknown obstacle. The collision cone was calculated by straight lines using an affine transformation that are tangent to the ellipsoid and that passes through the position of the quadrotor.  
Figure 11: Collision cone and aiming point [301].  
Figure 10: Artificial potential field [298].
## 6 Method Description
The overlapping of the signal of interest and the interference noise can be seen in Figure 14. The signal of interest (between two specific nodes) is represented in green, starting after the observation time and hopping from channel to channel within the operation time. All the contributions from different coexisting networks to the interference noise are represented in red, and the overlaps between signal and noise are represented in black. Figure 13: Representation of observation (blue) and operation (purple) times and example of channel occupancy with two static networks (WLAN and LR-WPAN) and a dynamic network (Bluetooth) that hops over three frequency channels. The coexisting networks will be analysed during the observation time to determine the operation of the network of interest during the operation time. In the last step, determination of the topology, the topology of the WSN can be determined taking into consideration the distances between nodes and the resulting overall PER. Figure 15 shows two types of topologies where each node communicates directly to the gateway (single-hop topology) or through other nodes (multi-hop topology). When a node communicates to the gateway through other nodes, the PERs of all the transmissions are multiplied to obtain the overall PER. In terms of energy consumption, it might be preferable to hop among different nodes, so that the communication distances are reduced and the transmission power of the nodes can be lowered. In return, this may involve increasing the overall PER. On the contrary, to minimize the overall PER, it is most likely that each node has to communicate directly to the gateway, but in exchange for increasing the transmission distances and the consequent power consumption. Assigning different weights to the overall PER and the maximum transmission distance may result in different topologies, and will help to choose the topology with the best equilibrium between overall PER and energy consumption. Figure 14: Signal (green) and noise (red) overlapping. There are different sources that contribute to noise: static networks and dynamic networks that hop over different frequencies. The noise is analysed during the observation time (blue) to determine the hopping sequence of the signal of interest. The signal of interest is only transmitted during the operation time (purple). The overlapping of signal and noise is represented in black. This overlap only implies time and frequency coexistence; for an error to occur, the SNR must be higher than the receiving sensitivity of the nodes too. Figure 15: Different network topologies: **(a)** Single-hop: each node (black dots) communicates directly to the gateway (red dot); **(b)** Multi-hop: the nodes communicate to the gateway hopping through other nodes.
## 3 Main Results
Replacing \(-V^{-1}\) with its over-approximation and pre- and post-multiplying (16) with \(\text{diag}\{\mathbf{I},\mathbf{I},V^{\kappa},\mathbf{I}\}\) and its transpose, the convex condition (9) is established. With the aid of the inner-approximation strategy (12), Theorem 1 establishes a convex approximation for the non-convex constraint (9). Note that given known matrices \(G^{\kappa},G_{r}^{\kappa}\) satisfying \(\mathbb{L}+\text{He}\{\mathbb{X}^{\kappa}N\mathbb{Y}^{\kappa}\}<\mathbf{0}\), the condition (9) is formulated in the form of linear matrix inequality (LMI), and it suffices to check the feasibility of the original non-convex problem by alternatively solving the convex surrogate optimization problem \[ \begin{cases}\min\gamma\\ \text{s.t. }P=P^{T}>\mathbf{0},G_{r}\succeq 0,(9).\end{cases}  \tag{18}\]  
In addition, the superscript \(\kappa\) can be viewed as an iteration indicator, and once given the feasible initial condition \(G^{0},G_{r}^{0}\), one can obtain a sequence of reduced-order models as the iteration proceeds. Next, we summarize the above procedures into the following SCO algorithm (Algorithm 1). ```
1:\(\epsilon\): given tolerance level; \(IMax\): the maximum number of iterations. 2: Given the initial condition \(G_{r}^{0}=\mathbf{0}\), determine the initial condition \(G^{0}\) by solving \[ \begin{bmatrix}P-G^{0}-G^{0}&G^{0}(\bar{A}+\bar{F}G_{r}^{0}\bar{M})\\ \star&-P\end{bmatrix} <\mathbf{0}\] (19) and fix \(\kappa\gets 1\)
3:while\(\kappa<IMax\)do
4: Solve (18)
5:if\(\frac{\gamma^{\kappa}-\gamma^{\kappa+1}}{\gamma^{\kappa}}\leq\epsilon\)then
6:\(\gamma\leftarrow\gamma^{\kappa+1},G_{r}\gets G_{r}^{\kappa+1}\)
7:break;
8:endif
9:\(\kappa\leftarrow\kappa+1\)
10:endwhile
11:Result\(G_{r}\): the reduced-order model; \(\gamma\): the \(H_{\infty}\) performance index. ```  
**Algorithm 1** Successive convex optimization algorithm for the computation of reduced-order models  
**Remark 3**. : _Algorithm 1 provides a straightforward solution to the non-convex BP problem without any parametrization techniques. In addition, it is worth noting that Algorithm 1 is performed iteratively, which presents a potential computational burden relative to the conventional method. As mentioned above, an essential prerequisite for implementing Algorithm 1 is that the initial condition \(G^{0},G_{r}^{0}\) should satisfy \(\mathbb{L}^{0}+\operatorname{He}\{\mathbb{X}^{0}N\mathbb{Y}^{0}\}<\mathbf{0}\). Nevertheless, finding a feasible initial solution can be as equally challenging as solving the original non-convex problem. Fortunately, the original high-order system is stable, and the reduced-order model with \(A_{r}=\mathbf{0}\) enables the resulted error system \(A_{\epsilon}= \begin{bmatrix}A&\mathbf{0}\\ \mathbf{0}&A_{r}\end{bmatrix} \) to be stable. In this setting, compared with [24], the proposed algorithm is easier to implement by avoiding the arduous initialization step._