esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### PV and \(\upmu\)-CHP: Electricity Production  
Figure 4b indicates the weekly PV output in mean value with an average of 37 kWh, which is the same for \(\upmu\)-CHP power generation, shown in Figure 4c. The winter atmospheric conditions in the Netherlands have a consequent impact on the PV output, and \(\upmu\)-CHP power generation shows a good way of balancing the seasonal renewable energy output. This energy comes at an additional cost of gas consumption, which can be provided from sustainable sources such as biogas.  
Gas consumption was 1700 m\({}^{3}\), which is higher than the Dutch average (1400 m\({}^{3}\)) [38], but considering the surfaces varying between 150 and 199 m\({}^{2}\) in the PMC, this values gets close to the Dutch average: 10.6 m\({}^{3}\)/m\({}^{2}\) household surface. As the PV and \(\upmu\)-CHP provided a part of the consumption, the households could decrease their bill considerably, especially when they were potentially purchasing one electricity rate category lower. \(\upmu\)-CHP gas consumption costs 390\(\upmu\)/year on average, similar to other heating means, and helps to maintain the seasonal electricity balance. Moreover, in time of use or dynamic tariff scenarios, their role will be much more important, as the peak hour purchase will vary.
## 3 Lightweight Cryptography for Smart Objects
### Public-Key (Asymmetric) LWC Algorithms  
Public-key (asymmetric) cryptography requires the use of a public key and a private key. Public keys can be associated with the identity of a node by including them into a public certificate, signed by a Certification Authority (CA) that can be requested to verify the certificate. Public-key cryptography requires the significant effort of deploying a Public Key Infrastructure (PKI). Moreover, asymmetric cryptography requires higher processing and long keys (at least 1024 bits for RSA [37]) to be used. Alternative public-key cryptographic schemes, such as Elliptic Curve Cryptography (ECC) [38], might require shorter keys to be used in order to achieve the same security than RSA keys. However, because of these reasons, symmetric cryptography is preferred in terms of processing speed, computational effort, and size of transmitted messages. Public-key ciphers are usually used to setup symmetric keys to be used in subsequent communications.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CN0 & carrier-to-noise density ratio \\ ADS-B & automatic dependent surveillance-broadcast \\ AE & autoencoder \\ AGC & automatic gain control \\ AI & artificial intelligence \\ AOA & angle of arrival \\ API & application programming interface \\ ATC & automatic toll collection \\ CNN & convolutional neural network \\ COTS & commercial-off-the-shelf \\ CPU & central processing unit \\ DL & deep learning \\ DME & distance measurement equipment \\ DOP & dilution of precision \\ DSP & digital signal processor \\ DT & decision tree \\ DVB & digital video broadcasting \\ EM & electromagnetic \\ ES & electronic support \\ FDoOA & frequency difference of arrival \\ FFT & fast Fourier transform \\ FPGA & field-programmable gate array \\ GLRT & generalized likelihood ratio test \\ GNSS & global navigation satellite system \\ GPIO & general purpose input/output \\ GPSD & GPS service daemon \\ GPU & graphics processing unit \\ GUI & graphical user interface \\ HIL & human-in-the-loop \\ IQ & in-phase and quadrature-phase \\ ISR & interference-to-signal ratio \\ ISS & international space station \\ LEO & low earth orbit \\ LNA & low-noise amplifier \\ LOS & line-of-sight \\ LTE & Long-Term Evolution \\ MA & moving average \\ MCD & Monte Carlo dropout \\ ML & machine learning \\ NLOS & non-line-of-sight \\ NMEA & National Marine Electronics Association \\ OFDM & orthogonal frequency division multiplexing \\ OOB & out-of-bag \\ pHat & Raspberry Pi hat \\ PNG & portable network graphic \\ POI & probability of intercept \\ PPD & privacy protection device \\ PPS & pulse per second \\ PQF & polyphase quadrature filter \\ PRN & pseudo-random noise \\ PSD & power spectral density \\ PVT & position, velocity, and time \\ RAM & random access memory \\ ResNet & residual neural network \\ RF & random forest \\ \end{tabular}
# Temperature Changes in Composite Materials during Photopolymerization
## 4 Discussion
revealed no impact on flexural strength when a LED lamp was used instead of a halogen lamp to polymerize the Filtek Silorane (3M ESPE) and Herculite XRV (Kerr) composite materials. However, a rise in flexural strength was demonstrated as being due to increased polymerization duration when using a halogen lamp for polymerization of Filtek Silorane. In the study, lower values of flexural strength were obtained for methacrylate resin-based materials (Gradia Direct Anterior and Gradia Direct Posterior, GC Japan) [25]. According to our findings, polymerization time affected flexural strength, not the mode of light curing. Another study revealed higher flexural strength values for hybrid composite materials compared to nanofill resins. The authors used two composite materials with different filler types, namely the Filtek P60 3M ESPE (hybrid) and Filtek Supreme 3M ESPE (nanofil) [26]. Another study using the Z250 (3M ESPE) composite and the Scotchbond Multi-purpose Plus (3M ESPE) bonding system revealed that the photoactivation method applied had no impact on the performance of the composite material (including flexural strength), regardless of the material storage medium (water vs. ethanol) [27]. The Filtek Z250 (3M ESPE) and Heliomolar (Ivoclar Vivadent) composite materials were tested by [PERSON] et al. at polymerization energy doses of 6-24 J/cm\({}^{2}\). A rise in flexural strength was observed for increasing energy doses for the Filtek Z250 material, whereas no changes in mechanical properties were observed for the Heliomolar composite [28]. The authors' results confirmed the hypothesis that the polymerization energy dose influences the flexural strength value. All in vitro studies have their limitations. The baseline test stand temperature in our study was lower than the temperature within the cavity. However, even starting at lower temperatures, some values exceeded the limit value of 42 \({}^{\circ}\)C. The effects of thermal absorption within the dentin tissue, potentially leading to lower values during polymerization, were not taken into account in the tests. In addition, in the case of deep cavities, it is not always possible to reach the composite layer with the polymerization tip, and the energy dose, leading to the rise in the temperature, is reduced with increasing distance. The effect of the bonding system on the temperature rise was not taken into account in our study, so as to maintain constant conditions for all materials for which different bonding systems are recommended by their manufacturers. Subsequent studies should be based on the model of human teeth, in which natural heat dissipation in hard tissues will be obtained. In addition, it would be advisable to examine how liners and their thickness affect the transmission of thermal energy to the tooth pulp. Such studies would allow obtaining a safe clinical procedure for the treatment of deep carious lesions.
# Application of Sorbent-Based Extraction Techniques in Food Analysis
## 4 SPME in Food Analysis--Main Fields of Applications
### Advances in Geometries of SPME  
The traditional SPME consists of coated fused silica fiber. This device, developed in the early 1990s, has certain limitations described in the previous section. To overcome these limitations, improvements and new geometries of SPME were proposed in recent years, as presented in Figure 2. The main direction of new developments was increasing the surface area of the coating to increase the extraction rate. An example of a device with an increased surface area is SPME arrows, which are based on a stainless-steel rod fiber with an arrow tip [133]. Compared to fibers, arrows improve the robustness, help to avoid background contamination and have higher sensitivity due to the possibility of longer and thicker sorbent phases [134]. This leads to the limitation of this device, which is the longer extraction time needed to reach equilibrium. Another drawback of arrows is the need to modify the GC inlet with specially designed liners with higher diameters compared to SPME fibers. SPME arrows have been successfully applied for the analysis of compounds in food matrices, including fermented fish sauce [135], Korean distilled spirit [136] and Korean edible plants [137]. Thin-film solid-phase microextraction (TF-SPME) has been validated as a novel sampling device and it demonstrated potential as an extraction method used in food analysis. TF-SPME is characterized by a larger extraction phase volume and surface area-to-volume ratio compared to traditional SPME fibers [138]. Therefore, TF-SPME enables enhanced capacity and a higher extraction rate than the traditional form. This format of SPME was applied in combination with LC, GC, and direct MS. Coated blade spray (CBS), which is a sorbent spread on the metal plates coupled directly to the MS device, was successfully used for the analysis of 1-5 veterinary drugs in meat [139]. A sorbent spread on aluminum foil was used for the determination of five individual polychlorinated n-alkanes from cod liver oil samples [140]. Recently, sorbent spread on carbon mesh was also used as an extraction tool for odorants from beer [2]. TF-SPME demonstrated a high potential for food analysis since it enables analysis of a wide range of compounds, and more varieties of sorbent phases have been utilized in this form of SPME [2]. The only drawback of this device compared with SPME fibers is the lack of full automation. Although the desorption of TF-SPME might be fully automated, for both LC and GC, their geometry still poses a barrier for online extraction.
# The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
## 4 Processed Dataset
### Low-Level Jet Information as Part of the NEWA Ferry Lidar Dataset  
The processing of the data from the NEWA Ferry Lidar Experiment was complemented with the derivation of essential low-level jet (LLJ) information. Following the definition of [PERSON] et al. (2018), an LLJ is defined at the lowest (local) maximum of the wind speed profile that is at least 2 m/s and 25% faster than the next minimum above. Note that this definition includes the upper and lower edge of the profile defined by the measurement range of the lidar instrument. The LLJ information in the dataset includes the horizontal wind speed and height of the identified profile maximum, the position of the measurement (longitude/latitude coordinates), and the timestamp.  
Figure 7 (left plot) shows the wind profiles with LLJs found for the 4-day period already presented in Figure 6. The wind speed maxima appear for measurement heights between 100 m and 175 m, and all on 27 February 2017. The right plots of Figure 7 show all LLJ events found for the month of February (in total 20) with their geo-coordinates in comparison with those found for the month of May  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{8}{c}{**Availability per Measurement Height [\%]**} \\ \cline{2-13}  & **65 m** & **75 m** & **90 m** & **100 m** & **110 m** & **130 m** & **150 m** & **175 m** & **200 m** & **225 m** & **250 m** & **275 m** \\ \hline February 2017 & 98 &  
(in total 392). This initial comparison indicates that considerably more LLJ events were observed in the spring month than in a comparable period in winter, including a relevant number of wind-speed maxima at the lower limit of the profile range (cf. the color differentiation in Figure 7). This simple observation can be explained by the positive air-sea temperature difference that is typically largest in spring when the water body with its significantly higher heat capacity is still cold while the land at the coastlines typically already heats up. Offshore-oriented winds and the associated step change in the surface roughness can then lead to the development of LLJs. This process is well known (e.g., Reference [19]) and due to the cold waters in late spring very common for the Baltic Sea (e.g., Reference [20]).
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Reinforcement Learning Integration (Same as Previous Work)
#### 3.5.2 Q-Value Table  
We employ a Q-value table \(Q(s,a)\), where each entry represents the expected cumulative reward for taking action \(a\) in state \(s\). The RL update loop leverages either a Temporal Difference (TD) or Monte Carlo (MC) method to iteratively refine these values. As the set of parameters included in the fuzzy state representation may vary depending on the active profile, the state space adapts accordingly. However, the core mechanics of the Q-value update remain unchanged.  
The reward function evaluates the impact of the BC/DNB decision on network performance, incentivizing actions that optimize Coverage while minimizing redundant transmissions:  
\[R(s,a)=\begin{cases}+1&\text{if }a=\text{BC with no duplication},\\ -1&\text{if }a=\text{BC causes duplication},\\ +0.5&\text{if }a=\text{DNB avoids unnecessary BC},\\ -0.5&\text{if }a=\text{DNB leaves nodes uncovered}.\end{cases} \tag{4}\]This structure encourages high Coverage and penalizes redundant broadcasts or Coverage gaps. To balance exploration and exploitation, an \(\epsilon\)-greedy policy is applied:  
\[\pi(a\mid s)=\begin{cases}1-\epsilon+\dfrac{\epsilon}{|A(s)|}&\text{if }a=\arg \max_{a^{\prime}}Q(s,a^{\prime}),\\ \dfrac{\epsilon}{|A(s)|}&\text{otherwise.}\end{cases} \tag{5}\]  
The dynamic selection of parameters in each profile directly influences the observed states \(s_{t}\), allowing for the RL agent to adapt its decisions to the current network environment without altering the reward logic.
## 4 Conclusions  
It was demonstrated in this work that guided electromagnetic waves in K-band are a promising tool for reliable damage detection in airspace structures under real-world conditions. It was shown through an experimental campaign that damage in the order of only 2 mm can be identified during controlled flight operation in the laboratory with severe disturbances, showing robustness against operative flight conditions. Although a simplified device under test was studied here, i.e., an aluminum plate containing holes, the approach can easily be applied to more complex aerospace components.  
Future research can use flexible dielectric substrates that conform to the irregular shape of the shell, e.g., via flexible dielectric sheets [25]. It is expected that temperature  
Figure 11: RMS-based damage indicators of the reference and the seven damaged states given by removed screws for the hovering drone.  
Figure 10: (**top**) Time domain data of the first two reference measurements and the first measurement of every waveguide leak state. (**bottom**) Frequency domain data for every measurement shown above.  
variations will also influence the detectability of damage, because the sensor extends and shrinks. Temperature influence studies under controlled laboratory conditions have been planned to quantify such effects; see related studies in the context of ultrasonic SHM systems [26]. Suitable compensation strategies in post-processing must be developed. Another aspect is related to the performance assessment of the SHM system, e.g., in terms of probability of detection (POD). In this context, the authors have already proposed some preliminary developments that might by transferred and extended in the context of guided electromagnetic waves [27].  
Conceptualization, M.M., J.S., D.d.R.V., M.R., S.B. and J.M.; methodology, M.M., J.S., C.G., M.R., S.B., V.M., J.M. and V.K.; software, [PERSON], [PERSON] and S.B.; validation, [PERSON], [PERSON] and S.B.; formal analysis, [PERSON], [PERSON], S.B. and V.M.; investigation, [PERSON], J.S., D.d.R.V., M.R., S.B. and J.M.; resources, C.G., D.d.R.V. and J.M.; data curation, [PERSON], [PERSON] and S.B.; writing--original draft preparation, [PERSON], [PERSON], D.d.R.V., M.R., V.M. and J.M.; writing--review and editing, [PERSON], [PERSON], M.R., V.M. and J.M.; visualization, [PERSON], D.d.R.V. and M.R.; supervision, D.d.R.V., J.M. and V.K.; project administration, D.d.R.V., J.M. and V.K.; funding acquisition, D.d.R.V. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Federal Ministry for Economic Affairs and Energy, grant number 20Q1911C.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company IMST GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Mod2 VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2
## 6 Related Classical and Quantum Methods for Solving Linear Systems
### Quantum Linear Solvers
We are not looking for a unique solution to a fixed system of equations, but _any_ solution to any subset of this system of equations. The Mod2 VQLS works especially well for this, because the optimized variational circuit produces a superposition over multiple solutions to \(Ax=b\). On the flip side, while HHL and VQLS _promise_ an exponential speed-up over classical solvers, because they leverage a dense amplitude encoding that requires \(\log_{2}n\) qubits to represent the linear system \(A^{\prime}x^{\prime}=b^{\prime}\) with an \(n\times n\) coefficient matrix \(A^{\prime}\), Mod2 VQLS can only promise a polynomial speed-up over the fastest-known classical solvers (which are already polynomial), because Mod2 VQLS requires circuits with \(m+n\) qubits for solving the system \(Ax=b\) with an \(m\times n\) coefficient matrix \(A\). Thus, it is important to note that Mod2 VQLS promises at most a polynomial, not an exponential speed-up. Also, the ability to work with non-square matrices comes at a computational cost: as seen in Figure 3, we produce a mapping from \(\mathbb{F}_{2}^{n}\) to \(\mathbb{F}_{2}^{m}\) using \(n+m\) qubits, but seen as a whole, the circuit is really a reversible unitary transformation on the space \(\mathbb{C}^{2(n+m)}\). So, the ability to map between spaces of different dimensions in Mod2 VQLS comes at the cost using disjoint sets of qubits for the input and output spaces.
## 5 Numerical and Simulation Results
### The Performance of the Proposed Technique  
The simulation is run for five consecutive time intervals of interest (\(5\times 50\) s) with the same IoV map routes to see if the trajectory planner can generate a kinodynamic-aware path for the UAV satisfying motion constraints. The starting point is \(\xi[0]=\begin{bmatrix}0&0&0\end{bmatrix}^{\top}\measuredangle\psi=0\). To obviate obstacles, the Benders cut method was used [38].  
In the first scenario, the minimum UAV altitude limit is lower than the height of the building (\(Z_{min}=30\), \(Z_{max}=150\)) to evaluate the crash avoidance performance of the trajectory optimization technique. The output results of the trajectory optimization problem are shown in Figure 5c,d through 3D and 2D plots. The proposed optimization technique generates kinematics-aware paths where the motion constraints, i.e., the speed and rate of heading change (nonholonomic) constraints, have been satisfied.  
In the second scenario, to narrow the navigation space, the UAV's maximum altitude is also limited to be lower than the average height of a tall building (\(Z_{max}=100\)) so that the solution space does not include multiple different homotopies. The results are shown in Figure 6, indicating that the navigation objective, i.e., the kinodynamic-aware crash avoidance trajectory design, is achieved. However, it may end up with a less optimal solution due to the imposed motion constraints. The performance of the proposed smoothing technique is shown in Figure 7.  
According to the MATLAB \(tic-toc\) function, the running time of the method is \(9.3\) s on average plus \(3\) s for smoothing for each time interval of \(50\) s. The results indicate that the optimizer has sufficient time for real-time trajectory optimization. For example, while the UAV follows the trajectory of the current time interval, it has \(50\) s to solve the trajectory optimization problem for the next \(50\) s time interval.  
Figure 5: Simulation results of the proposed trajectory optimization method in the first scenario: (**a**) 3D occupancy map of the simulated dense urban area; (**b**) 2D view of the map, including BSs (shown by black triangles) and routes of four ground intelligent vehicles (with colored squares as the waypoints corresponding to discretized sample times) (**c**) 3D view of the generated trajectory for the proposed method (shown by the green line with red dots indicating the waypoints); (**d**) 2D view of the trajectories.
## 3 State-of-the-Art Results on the Detection of Identity Deception
### Detection Methods  
Although heuristic methods exist for detecting malicious accounts, they are fairly rudimentary and can be easily bypassed by an experienced adversary (e.g., requiring an account to have at least one post). As such, all identity deception detection methods use some machine learning algorithm. These may use either social or user behavior features, and the effectiveness of these may vary depending on the choice of algorithm (e.g., regression, random forest, or neural networks). For example, clustering algorithms have been utilized in many studies that have used different features and approaches for pre-processing the data [2; 6; 21].  
Supervised vs. unsupervised machine learning: Machine learning models are also used for anomaly and intrusion detection in order to solve some of the shortcomings of supervised machine learning models [28]. Supervised classifier models require large datasets that contain examples of each class that they are classifying (e.g., malicious or legitimate accounts). However, because the most advanced adversaries typically use novel techniques (i.e., behaviors), gathering this representative dataset becomes very difficult. The challenge with these types of models is that we expect to find deviations from normal behavior without having the necessary data to actually \"know\" what these irregularities are [29]. Furthermore, because of the high consequences of allowing attackers to remain undetected in OSNs, gathering data representative of the attackers becomes more challenging. As such, anomaly detection models (e.g., unsupervised models) are instead used. These are machine learning models that have been trained on normal data (i.e., legitimate user behavior). Any deviations from that behavior are classified as anomalies and probable malicious behaviors that need to be further examined by an administrator. As such, the bigger challenge associated with crafting these models is to have a homogeneous legitimate user behavior on the OSNs and identify features that can discern this behavior with high accuracy.  
Computational overhead: The choice of machine learning algorithm will also influence the computational power needed for effective detection. Data, as well as the frequency of invoking and updating a machine learning model all result in computational needs for identity deception detection. Much of the research on new machine learning models has focused on leveraging domain knowledge in order to achieve better performance and low computational costs. However, a more recent school of thought in machine learning posits that building models that are finely tuned to the domain often ignores the fact that computation over time becomes less expensive, and as such, it should be leveraged more [30].