esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Jamming Attack Models
### Jamming Attack Models from Academia  
#### 2.2.1 Models of [PERSON] _et al._[1]  
[PERSON] _et al._[1] have suggested four types of models, described below:  
* _Constant Jammer_ is not aware of the existing protocols of the network (bit-rate, packet-size etc.) and, therefore keeps transmitting bits constantly over a period of time without following any protocol. They are not energy efficient.
* _Deceptive Jammer_ is aware of the target network's protocol and jams the network by transmitting legitimate packets constantly over a period at a high rate to keep the carrier captured. It is highly effective but is as energy inefficient as the constant jammer.
* _Random Jammer_ functions either like a constant jammer or a deceptive jammer but does so randomly. It is less effective than the jammer whom it imitates (constant or deceptive) but is more energy efficient than it.
* _Reactive Jammer_ also knows the communication protocols of the target network. It keeps listening to the network passively, and attacks the network at its chosen time in a manner as if it is part of the network, following its protocols. It is most effective but not very energy-efficient as it spends considerable amount of energy in constantly listening to the network.
## 4 Experimental Research  
### Data Source  
The experimental data were derived from dataset III of Brain Computer Interface (BCI) Competition II provided by BCI Lab, Graz University of Technology in Graz, Austria ([[http://www.bbci.de/competition/ii/](http://www.bbci.de/competition/ii/)]([http://www.bbci.de/competition/ii/](http://www.bbci.de/competition/ii/))). The dataset was obtained by collecting the EEG signals of a healthy adult female while she was imagining left hand or right hand movement. The dataset was composed of 280 trials, of which 140 were used for training and 140 were used for testing. The 140 trials used for training and testing included 70 trials imagining left hand movement and 70 trials imagining right hand movement. Each trial lasted for 9 s, and the timing diagram of the experiment is shown schematically in Figure 2.  
As shown in Figure 2, for the first two seconds the subject remained quiet and relaxed; when the time reached 2 s, a short beep indicated the start of the trial and the '+' cursor appeared on the monitor simultaneously. When the time was 3-9 s, the visual cue (left-right arrow) was displayed as the direction of motor imagery. At the same time, the subject imagined the hand movement according to the direction indicated by the arrow. The data were sampled at 128 Hz. The three channels, C3, Cz, and C4, were applied to acquire EEG, using Ag/AgCl as an electrode, and the placement of the electrode is shown in Figure 3.
## 3 Results and Discussion  
### Measured Wideband Data Analysis  
Figure 13 shows the experimental locations of the direction-finding system and the distribution of nearby long-wave stations. The two receivers are arranged in a north-south orientation, with geographic coordinates of 36.37\({}^{\circ}\) N, 120.37\({}^{\circ}\) E (main) and 36.33\({}^{\circ}\) N, 120.37\({}^{\circ}\) E (reference). The distance between the receivers is approximately 5.18 km, and their positions nearly overlap on the map. In the figure, the BPC (68.5 kHz) (34.47\({}^{\circ}\) N, 115.84\({}^{\circ}\) E) transmitter corresponds to the Shangqiu low-frequency time-code broadcasting station [31], NDI (54 kHz) (26.32\({}^{\circ}\) N, 127.85\({}^{\circ}\) E) is a US Navy submarine communications transmitter station in Okinawa, Japan, and JJI (22.2 kHz) (32.04\({}^{\circ}\) N, 130.81\({}^{\circ}\) E), also known as the Ebino VLF transmitter (Transmission Station, Ebino Soshinsho), is a transmitter operated by the Japan Maritime Self-Defense Force located in Ebino City, Miyazaki Prefecture, Japan [32]. JJY60 (60 kHz) (33.47\({}^{\circ}\) N, 130.18\({}^{\circ}\) E) and JJY40 (40 kHz) (37.37\({}^{\circ}\) N, 140.85\({}^{\circ}\) E) are call signs for low-frequency time signal stations in Japan [33]. Among these, BPC, JJY60, and JJY40 use ASK modulation, while JJI and NDI use MSK modulation [34; 35].  
Figure 14 displays the time-frequency spectrum of the main receiver's magnetic and electric antennas for a one-minute. The figure displays amplified time-frequency spectra for the JJI (22.2 kHz), NDI (54 kHz), and JJY60 (60 kHz) frequency bands, focusing on a ten-second segment of data. It is evident that JJI and NDI use MSK modulation, with energy uniformly distributed over time and concentrated within a 200 Hz bandwidth. In contrast, JJY60 uses ASK modulation, with its energy distribution appearing discrete over time.  
Figure 15 displays the power spectral density measured by the receiver in Qingdao, Shandong, China. The spectral range from approximately 3 kHz to 300 kHz is primarily dominated by various long-wave radio transmitters (approximately 11 kHz-257 kHz), ocean navigation (LORAN) transmitters (approximately 70 kHz-130 kHz), radio-controlled clocks (approximately 60 kHz-77.5 kHz), and lightning discharges (around 5 kHz-50 kHz) [19].  
Figure 13: Position distribution of transmitter and receiver and electromagnetic propagation path.
## 5 Results and Analysis
### Performance Comparison
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Recall@5** & **Recall@10** & **MRR** \\ \hline FPMC & 0.2126 & 0.2970 & 0.1701 \\ PRME & 0.2236 & 0.3105 & 0.1712 \\ LSTM & 0.2724 & 0.3288 & 0.1851 \\ PLSPL & 0.3678 & 0.4523 & 0.2806 \\ STAN & 0.4570 & 0.5728 & 0.3236 \\ GETNext & 0.4417 & 0.5997 & 0.3194 \\ STHGCN & 0.5308 & 0.6169 & 0.3823 \\ \hline HGL (Ours) & **0.5468** & **0.6267** & **0.3915** \\ Improvement & 3.01\% & 1.59 \% & 2.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of a comparison between the Recall@k and MRR values of different methods on the NYC dataset. Numbers marked in bold and underlined text represent the best and second-best results, respectively. dataset exhibits more balanced improvements across metrics. This difference may be attributed to the distinct urban structures and mobility patterns in these two cities. New York's grid-like street layout and concentrated points of interest may benefit more from our hierarchical approach's ability to capture neighborhood-level patterns, while Tokyo's more complex and distributed urban structure might require the multi-scale modeling that our approach provides.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 1 Introduction
### Captive Portals  
Apart from EAP, there exist other alternatives for access control to networks. Captive portal systems filter the traffic at the network or link layer to control the access to network resources [8]. Captive portals are websites displayed in end devices after network attachment to an Access Point (AP) before granting access to network resources or the Internet. These systems are typically found in hotels, airports or in shopping centers.  
There are several ways to implement a captive portal. Most of the solutions nowadays are proprietary implementations delivered in business routers, but other open-source or custom router firmware offer this solution. For this work, we are interested in open-source alternatives for integrating WebAuthn authentication within the captive-portal system. The most well-known solutions are pfSense and OpenWRT. They both implement a captive-portal system, filtering access to network resources and displaying a captive-portal web page to the user. In our work, we will modify this captive-portal web page and integrate WebAuthn authentication in this system.  
pfSense is one of the most popular open-source firmware available in the market, mainly developed by Netgate. Similar to other proprietary solutions, pfSense offers authentication based on local-configured accounts or an external RADIUS server [9]. Their firmware is compatible with several routers, including Netgate, but also others like some TP-Link and Protetti models.  
On the other hand, OpenWRT is a Linux-based operating system that targets embedded systems [10]. This firmware can be installed in some routers from Lynksis, Asus, TP-Link, and Netgear, among others. OpenWRT has many available add-on packages that add features to the core light firmware. One of the available packages, built as a separate open-source project, is OpenNDS. OpenNDS, based on NoDogSplash, is captive-portal software developed in C compatible with this firmware. Among their options, it is worth mentioning that it adds the possibility to externalise the web server used in the captive portal. This configuration is known as the Forwarding Authentication Service (FAS) [11].
## 2 The Proposed Methodology
### PID (Proportional-Integral-Derivative) Controllers  
The responses of the first floor of structures such as displacement, velocity, acceleration, and total acceleration are considered as the feedback (x) and the error signal (e(t)) is found according to the reference signal (\(x_{r}\)), which is taken as zero. After the determination of the error signal (Equation (7)) for each step of an earthquake, the control signal using Equation (8) is calculated. The final equation of the control signal of the nth floor (u\({}_{n}\)) is given in Equation (9). The design variables are the PID controller parameters such as K\({}_{p}\) (proportional gain), T\({}_{d}\) (derivative gain), and T\({}_{i}\) (integral gain):  
\[e(t)\ =x_{r}\text{-}x \tag{7}\]  
\[u(t)=K_{p}\left[e(t)+\frac{1}{T_{i}}\int_{0}^{t}(e(t)dt)+T_{d}\frac{de(t)}{dt}\,\right] \tag{8}\]  
\[u_{n}=K_{p}\left[-x+\frac{1}{T_{i}}\int_{0}^{t}(-xdt)-T_{d}\frac{dx}{dt}\right] \tag{9}\]
# Comparison of Achieved Rolling Resistance Values of Two Selected Tires on a Solid Surface
## 3 Results  
As mentioned above, the methodology of measuring the rolling resistance of the tire was simple and based on pulling the main frame with the tire using a braking and winding device (Figure 2a, pos. 5). Thus, the values of the internal component of rolling resistance were obtained.  
During the measurement, the tensile resistance of the trailing wheel was recorded using a force sensor HBM S9M/10 kN with a sampling frequency of 5 Hz. The given value of the sampling frequency was chosen based on the work of the authors [PERSON] and [PERSON] [15], who used the given value of the sampling frequency in the measurements of the traction indicators of tractors. The measured data were transferred to a personal computer via the HBM Quantum X MX 840A measuring recorder, and transferred to MS Excel files via the HBM Catman Easy measuring software where they were also subsequently processed. The speed of movement of the trailing wheel was 0.1 m\(\cdot\)s\({}^{-1}\). The sets of measured tensile resistances at individual values of vertical load and corresponding values of inflation pressures of the monitored tires were statistically processed in the program Statistica 12 CZ. An example of the course of the measured tensile strength for a Mitas TS05 10.0/75-15.3 10 PR tire at an inflation pressure of 100 kPa and a vertical load of 350 kg is shown in Figure 4. An example of statistical processing of tensile force data is shown in Figure 5 for a Mitas TS05 10.0/75-15.3 10 PR tire with an inflation pressure of 100 kPa and a vertical load of 350 kg.  
Figure 4: The course of the recorded tensile resistance of the Mitas TS05 10.0/75-15.3 10 PR tire at an inflation pressure of 100 kPa and a vertical load of 350 kg.  
Figure 5: Example of statistical processing of tensile force sets-Mitas TS05 10.0/75-15.3 10 PR tire, inflation pressure 100 kPa, vertical load 350 kg.  
The hypothesis of distribution normality of measured data at the significance level of 0.05 is not rejected by the Shapiro-Wilk test as the calculated \(p=0.24574\) is a value greater than the considered significance level of the test (0.05). Similar results were obtained when processing other measured values of tensile force.  
The measuring process of the rolling resistance of selected tires on a concrete surface is documented in Figure 6. From the obtained results of measuring the rolling resistance of monitored tires, the dependence of rolling resistance of Mitas TS05 10.0/75-15.3 10 PR on inflation pressure for a concrete surface was shown (Figure 7). Figure 8 shows the same dependence for the Mitas TS04 6.00-16 6 PR tire and the concrete pad. Figure 9 shows the dependence of the rolling resistance on the tire load at individual inflation pressure levels for the Mitas TS04 6.00-16 6 PR tire and the concrete pad.
# Performance Analysis of Direct Torque Controllers in Five-Phase Electrical Drives
## 4 Results and Discussion
### Load Torque Rejection  
Then load torque rejection tests were performed. The results obtained are summarized in Figure 11, where the reference speed is 500 rpm and the coupled DC machine imposes a heavy load torque within the system limits at t = 0.5 s. A drop in the speed is observed when the load is suddenly applied; although, the controller successfully manages this disturbance, upper-left plot. The estimated electrical torque is also regulated to be the referred one in steady and transient states, as shown in the upper right figure, while stator phase currents increase to manage the increment in the load (see bottom-left timing diagram). The estimated stator flux value is regulated in steady and transient states to coincide with the references, as can be appreciated in the bottom right figure. Then, these results, which summarize the ones obtained under different operating conditions, prove a controlled electrical torque in the multiphase drive. Note that flat lines are also observed in the \(x\)-\(y\) plane polar diagrams of the stator current, similar to the ones shown in Figures 9 and 10.  
Figure 10: Experimental steady-state operation test where the reference speed is set at 500 rpm and a load torque of 2.75 N\(\cdot\)m is applied. Upper row: speed and torque responses. Second row: stator flux waveforms. Third row: current trajectories of the stator in the \(\alpha\)–\(\beta\) and \(x\)–\(y\) planes. Last row: stator phase currents.
# A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
## 2 Hardware Architecture
### Communication  
All the raw data provided by the sensors, and the processed data generated by the computers, need to be shared. Data from one sensor might be needed by more than one computer, and the processed data generated by an algorithm from one computer may be needed by another computer. In order to share all this information, a star topology is adopted in which all the devices are connected to a gigabit switch. The main advantage of this topology is that it is easily extensible if more sensors or computers are added to the network.  
Apart from the communications between the systems inside the vehicle network, an Internet connection is also required in order to obtain GNSS differential corrections, download digital maps or connect to V2X networks. For that purpose, a 4G router is connected to the switch, providing Internet access to every computer in the network.
## 4 Experimental Results and Analysis
### Analysis of Algorithm Performance
#### 4.3.2 Influence of Coding Format on PBIL  
The encoding format is the main difference between the real-coding PBIL algorithm and the traditional PBIL with binary coding. Different encoding formats usually determine the length of chromosomes and the size of the probability matrix, thus having a significant effect on the efficiency of the evolution process.  
In this paper, we implement two encoding formats of binary coding and real coding for the PBIL algorithm and solve the multi-satellite scheduling problem under the same conditions, respectively. Ten simulation experiments are carried out, and the statistical results are shown in Table 4.  
As can be seen from the table above, the chromosome length of real coding is 50, which is equal to the quantities of targets. In contrast, the length of binary coding is 729, about 14 times that of real coding. Obviously, the chromosome length has a significant effect on the efficiency of constraint checking and the evolution process; therefore, shorter chromosome lengths can improve search speed. This is consistent with the results in the table that the running time of real-coding PBIL is significantly shorter than that of binary-coding PBIL. The simulation results show that the running time of binary-coding PBIL is about 1.5 times that of real-coding PBIL, which indicates that the encoding format plays an important role in the performance of the PBIL algorithm. More notably, real-coding PBIL can obtain higher optimal fitness values; that is, it can obtain higher quality solutions than binary-coding PBIL for multi-satellite scheduling.
# Encapsidation of Staufen-2 Enhances Infectivity of HIV-1
## 2 Materials and Methods
### GST Pull-Down Assay  
HEK293T cells were co-transfected with either 1.2 \(\upmu\)g of pkGST vector alone (control) or pkGST-HIV-1 Gag and 1.2 \(\upmu\)g of Staufen-2 FLAG, or Staufen-1 FLAG construct (positive control). After 48 h of transfection, the cells were lysed using mild lysis buffer (50 mM Tris (pH 8), 1 mM PMSF, 10% glycerol, 0.8% NP-40, 150 mM NaCl, and 1X complete protease inhibitor). The clarified cell lysate was incubated with Glutathione Sepharose 4B beads for 4 h at 4 \({}^{\circ}\)C, with end-over-end rotation. After binding, the GST beads were washed three times with mild lysis buffer, and proteins bound to GST beads were eluted by boiling beads in SDS gel loading buffer at 95 \({}^{\circ}\)C for 5 min. The interactions were analyzed by immunoblotting with respective antibodies. As described in the immunoprecipitation section, RNase A treatment was performed to study if the protein-protein interaction is RNA-bridged.
# On Neighborhood Structures and Repair Techniques for Blocking Job Shop Scheduling Problems +
## 4 Representations of a Schedule
### Distance of Schedules  
The distance of feasible solutions is an important measure in analyzing search spaces and neighborhood structures of discrete optimization problems, cf. [43; 44]. When a heuristic search method is applied, the distance of two consecutively visited solutions refers to the size of the search step. In such a procedure, the step size may act as a control parameter or observed key measure to guide the search. Intensification and diversification are strategically implemented by conducting smaller or bigger steps to avoid an early entrapment in locally optimal solutions.  
In scheduling research, the distance \(\delta(s,s^{\prime})\) of two feasible schedules \(s\) and \(s^{\prime}\) is commonly defined by the minimum number of basic operators required to construct one schedule from the other, cf. [44; 45]. Here, the adjacent pairwise interchange (API) of two neighboring operations in the machine-based representation of a schedule is used as the basic operator. Formally, it can be described by the introduction of an indicator variable for all pairs of operations \(O_{i,j}\) and \(O_{i^{\prime},j^{\prime}}\) with \(i<i^{\prime}\) requiring the same machine as follows:  
\[h_{i,j,i^{\prime},j^{\prime}}=\begin{cases}1,&\text{if an ordering }O_{i,j}\to O_{i^{\prime},j^{\prime}}\text{ or }O_{i^{\prime},j^{\prime}}\to O_{i,j}\text{ in }s\text{ is reversed in }s^{\prime},\\ 0,&\text{else}.\end{cases} \tag{4}\]  
Consequently, the distance of two schedules is determined by  
\[\delta(s,s^{\prime})\ =\ \sum_{\begin{subarray}{c}O_{i,j}O_{i,j^{\prime}}\in \mathcal{O}\text{ with }\\ Ma(O_{i,j})=Ma(O_{i^{\prime},j^{\prime}}),\ i<i^{\prime}\end{subarray}}h_{i,j,i^{\prime},j^{\prime}}. \tag{5}\]  
Note that, when describing the BJSP with a mixed-integer program and implementing the pairwise ordering of operations with binary variables, the given distance measure is highly related to the well-known Hamming distance of binary strings, see [15] for further explanations.
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## Nomenclature  
\(A\) reference surface  
\(C_{R}\) solar radiation pressure coefficient  
\(D_{n}\) nodal day  
\(J_{l,m}\) coefficients of gravitational harmonics  
\(M\) mean anomaly  
\(P_{sr}\) solar radiation pressure  
\(R_{M}\) Martian equatorial radius  
\(T\) thrust  
\(T_{max}\) maximum available thrust  
\(T_{n}\) nodal orbital period  
\(U\) Martian gravitational potential  
\(V\) Lyapunov function  
\(\mathbf{a}\) non-Keplerian acceleration vector  
\(a_{d}\) desired semimajor axis  
\(\mathbf{ap}\) perturbation acceleration  
\(\mathbf{a}_{T}\) thrust acceleration  
\(e\) osculating eccentricity  
\(e_{d}\) desired eccentricity  
\(f\) true anomaly  
\(i\) osculating inclination  
\(i_{d}\) desired inclination  
\(l\), \(m\), \(n\), \(s\), \(q\) modified equinoctial elements (MEE)  
\(m\) instantaneous mass of the vehicle  
\(m_{0}\) initial spacecraft mass  
\(\dot{m}\) mass time rate  
\(p\) osculating semilatus rectum  
\(p_{d}\) desired semilatus rectum  
\(\mathbf{r}\) position vector  
\(r\) radius  
\(r_{A}\) apoapse radius  
\(r_{p}\) periapse radius  
\(t\) actual time  
\(t_{f}\) time of flight  
\(\Omega\) osculating RAAN  
\(\Omega_{d}\) desired RAAN  
\(\zeta\) heading angle  
\(\mu\) Martian gravitational parameter
# Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations
## 3 Algorithms for Self-Localization and Navigation-Aid Applications
### Aid to Underwater Navigation
The derivation can be followed in ([22] Section II-B); we summarize it here for completeness. First, we reformulate (6) as an equivalent problem:  
\[\min_{u\in\mathbb{R}^{2}} \sum_{i=1}^{n}\alpha-2x_{i}^{\top}u+\|x_{i}\|^{2}-\hat{d}_{i}^{2}\] (7) s.t. \[\|u\|^{2}=\alpha.\]  
By introducing \(y=(u,\alpha)\) in Problem (7), we get:  
\[\min_{y\in\mathbb{R}^{3}} \|By-c\|^{2}\] (8) s.t. \[y^{\top}Dy+2f^{\top}y=0\]  
where \(B\), \(c\), \(D\) and \(f\) are defined as follows:  
\[B= \begin{pmatrix}-2x_{1}^{\top}&1\\ \vdots&\vdots\\ -2x_{n}^{\top}&1\end{pmatrix} ,\ \ \ c= \begin{pmatrix}\hat{d}_{1}^{2}-\|x_{1}\|^{2}\\ \vdots\\ \hat{d}_{n}^{2}-\|x_{n}\|^{2}\end{pmatrix} ,\ \ \ D= \begin{pmatrix}\mathbf{I}_{n}&\mathbf{0}_{n \times 1}\\ \mathbf{0}_{1\times n}&0\end{pmatrix} ,\ \ f= \begin{pmatrix}\mathbf{0}_{n\times 1}\\ -0.5\end{pmatrix} . \tag{9}\]  
The optimization Problem (8) is an instance of a quadratic problem with a single quadratic equality constraint. These kinds of problems have strong duality between the primal and dual formulations provided certain constraint qualifications are satisfied ([23] Th.3.2), which Problem (8) fulfills. Problem (8) can then be solved with the dual formulation:  
\[ \begin{split}\underset{\lambda\in\mathbb{R}}{\text{solve}}& \quad\hat{y}(\lambda)^{T}Dy(\lambda)+2f^{T}\hat{y}(\lambda)=0\\ \text{s.t.}&\quad\hat{y}(\lambda)=(B^{T}B+\lambda D )^{-1}(B^{T}c-\lambda f)\\ &\quad B^{T}B+\lambda D\succeq 0.\end{split}  \tag{10}\]  
The objective function of (10) is monotone in \(\lambda\), so a bisection algorithm can be applied and converges very quickly to the unique root of the system [22; 23]. The specific steps are described in Algorithm 2. ```
1:Initialize \(\underline{\lambda}=-1/\max\text{eig}(\text{D},\text{B}^{\text{T}}\text{B})\), \(\overline{\lambda}=100\). 2:while\(\hat{y}(\lambda)^{T}Dy(\lambda)+2f^{T}\hat{y}(\lambda)>=0\)do\(\overline{\lambda}\gets 2\overline{\lambda}\)
3:endwhile
4:Set \(\lambda\leftarrow\frac{1}{2}(\overline{\lambda}+\underline{\lambda})\). 5:while\(\overline{\lambda}-\underline{\lambda}\geq\epsilon\)do
6:if\(\hat{y}(\lambda)^{T}Dy(\lambda)+2f^{T}\hat{y}(\lambda)>=0\)then\(\underline{\lambda}\leftarrow\lambda\)
7:else\(\overline{\lambda}\leftarrow\lambda\). 8:endif
9:endwhile
10:Set \([u^{T},\alpha]\leftarrow\hat{y}^{T}(\lambda)\). Return\(u\). ```  
**Algorithm 2** Bisection algorithm for UUV localization