esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Dynamic Reconfiguration of Security in FamiWare
In the case of the security reconfiguration, the plans need to be generated at runtime, because the information to go from the existing configuration to a new one is dynamically generated by the policy engine + policy interpreter modules. This information is in the SDS file that is used to transform the VSpec tree of the running product to the VSpec tree of the adapted produced, as shown in Figure 9.  
In that SDS file, we see how the output of the policy interpreter indicates that the encryption concern using the encr_algorithm_2 need to be deployed and that the authorization concern must be undeployed from the system. Using this SDS file, the running product (C2) and the variability model (C1), FamiWare automatically obtains the configuration that represents the adapted product (C3). In this configuration, the DataSecurity and cypher features will be selected and the authorization feature unselected. Then, using the difference operator, a reconfiguration plan with two tasks (one to add the encryption components and another to remove the authorization ones in all sensors of the WSN) is automatically generated:  
<Sequence>  <AtomicProcess ID=\"AddService\">  <Input ID=\"AllSensors\"/>  <Input ID=\"Encryption\"/>  <AtomicProcess ID=\"RemoveService\">  <Input ID=\"AllSensors\"/>  <Input ID=\"Authorization\"/>  </Sequence>  
As we explained in the previous section, this plan is interpreted by the FamiWare reconfiguration service deployed in each WSN node. The specific reconfiguration code for reconfiguring the security policies must be provided, so we use the mapping defined in the previous section between the security reconfiguration tasks and the specific implementation actions (label E1 in Figure 9), which needs to be executed for the system reconfiguration. In the mapping of the example, we can observe that for adding an encryption algorithm to a sensor, as FamiWare follows a publish/subscribe architecture, we need to execute the following actions: (1) from now on, this sensor will publish its data under the encrypted topic; (2) this sensor is now subscribed to the encrypted topic (_i.e._, it will receive encrypted messages); (3) the components that implement the selected encryption algorithm have to be added to this sensor. In a similar way, to remove the authorization, this sensor has to unsubscribe from this topic.
## 4 System Model and Description of the Proposed Protocols
### Proposed Protocol 1 (EP-VIR-Three)
#### 4.1.1 Network Configuration  
In the proposed protocol, the network model is housed with multiple sinks (nine sinks) and relay nodes varying from (150-450) [27]. The responsibility of these nodes is to collect and forward the DPs towards the sink by collecting their own DPs and from their current forwarder nodes. The multiple sinks are placed on the water surface and these sinks are assumed to be stationary. These sinks are housed with both radio and acoustic modem. The radio modem is used for the communication among sinks and base stations, while the acoustic modem is used for underwater communication and data collection among sinks and underwater sensor nodes (using multi-hop). Moreover, we assumed that:  
1. Firstly, sinks have high energy. Moreover, they are connected with each other to balance the load of DPs on respective sinks.
2. Secondly, if the data is successfully delivered from any of the relay nodes to any of the sink, then it means that data is successfully delivered to the control station.  
Figure 8: System model of LMPC.  
3. In the end, we have not considered the movement of nodes in a vertical direction, which is almost negligible, whereas, we have only considered the mobility of the nodes in the horizontal direction (due to water currents).
## 3 Proposed GaN HEMT MMIC LNA Design
### Reverse Isolation and Stability Performance of GaN HEMT Devices
Similar to the series resistor-based technique (Figure 2c), it readily decreases \(S12\) and improves reverse isolation by offering a higher output impedance at the operating frequency. However, the proposed technique does not cause significant noise performance degradation owing to its inherently low resistance. Moreover, the added series inductor partially cancels the parasitic capacitance at the output node. Consequently, this mitigates the MAG degradation caused by the series impedance in the signal path. Figure 4 shows the \(S12\) and \(\mu\) simulation results of the single-stage CS amplifier (4F25) (Figure 3) by adopting different values of the series inductor (\(L_{SE}\)) at the drain of the GaN device at \(28\) GHz frequency. As expected, the simulated \(S12\) and \(\mu\) values are improved when larger inductors are employed. Figure 2: Stability enhancement configurations: (**a**) a cascode stage, (**b**) a single stage with source degeneration, and (**c**) a single stage with an output series resistor. In addition, the stability performance of the single-stage CS amplifier was evaluated in simulations with matching networks (MNs) to reflect all the scattering parameters (\(S11\), \(S12\), \(S21\), \(S22\)) of the network. Figure 5 depicts the single-stage CS amplifier configurations with matching networks and series inductor(s) at the input and/or output nodes. In these simulations, source degeneration (L = 130 \(\upmu\)m and W=15 \(\upmu\)m) is also utilized in combination with the series inductor-based technique to further enhance the overall stability performance. The simulated small-signal gains (\(S21\)s) and \(\mu\) values are shown in Figure 6. As can be seen from Figure 6, larger values of \(L_{SE}\) result in a more enhanced stability (\(\mu\)) performance in the overall frequency band. Moreover, when series inductors are utilized at both the input and output ports (Figure 6b), stability performance is further enhanced. Although the small-signal gains (\(S21\)s) and NFs of the single-stage amplifiers are degraded owing to the effect of series impedance (\(L_{SE}\)) in the signal path, the amount of degradation is mitigated through partial cancellation of the parasitic capacitance at the source or drain node with series inductors. Figure 4: Simulation results of the common-source single stage adopting series inductor: (**a**) a \(S12\) and (**b**) a stability \(\mu\) criterion. Figure 5: Single-stage common-source GaN HEMT-based amplifier configuration (4F25) (**a**) with a series inductor at the drain node and (**b**) with series inductors at both gate and drain nodes. Figure 3: Common source single stage (4F25) adopting series inductor-based stability enhancement technique. Figure 6: Simulated performance of the single-stage common-source GaN HEMT-based amplifier configuration with different values of series inductors at the drain node (output) (Figure 5a) and at both gate and drain nodes (input and output) (Figure 5b); (**a**) stability \(\mu\) criteria, (**b**) small-signal gains (S21s), and (**c**) noise figures.
# New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System
## 2 Related Works
### Audience Segmentation  
Audience segmentation is the process of partitioning the target audiences into smaller segments based on certain criteria or traits, such as demographics, behaviors, hobbies, or requirements [24]. It is widely used in the marketing and advertising domains to discover more specialized and focused audience groups for businesses. In existing papers, audience segmentation is usually performed with two different segmentation approaches, which are the clustering-based [4; 5; 6; 7; 9; 11] and score-based [8; 10] approaches to segment the audience's behaviors.  
The clustering-based segmentation utilizes the unsupervised machine learning technique, which is the clustering algorithm. The clustering algorithm aims to generate audiences that are related to each other in the same group and distinct from the audiences in other groups. The audience is grouped based on distance or similarity functions, allowing for the formation of clusters from the audiences that are closely related to each other in the same group [25]. It is able to identify the natural groupings and patterns in the data without establishing any rules or criteria beforehand and discovering the patterns that might not be apparent from prior knowledge. Therefore, with the help of the clustering algorithms, the underlying patterns and trends for marketing and advertising motives with the huge amount of data can be used to classify the audience into meaningful categories [26].  
Score-based segmentation is a rigid approach that relies on pre-established rules or criteria to assign the scores. To take an example from the existing paper [10], the pre-established rules to assign each criterion of RFM model are from score 1 to score 4. The upper limit of each criterion's score is determined by three quartiles, which are 25%, 50%, and 75%. The larger the score assigned to the specific criterion, the better the performance of the specific criterion. By assigning four different scores (score 1 to score 4) to three criteria (recency, frequency, and monetary), there might be a total of 72 different groups interpreted. It is hard to gain insight into the huge number of groups for interpreting the behavioral patterns. Therefore, clustering-based segmentation was chosen as the audience segmentation approach in this study to investigate and interpret the behavior patterns of the audience for the businesses.  
Broadly, there are three types of clustering algorithms that are being applied in the existing papers [4; 5; 6; 7; 9; 11], which are centroid-based, hierarchical-based, as well as model-based clustering [27; 28]. Table 2 illustrates the categories of clustering algorithms that are stated in Table 1. The key characteristics of each category are different. Therefore, each category will be included in the experiments of this study.
## 2 Related Works
### Design Framework of Private Set Intersection Protocol
#### 2.1.3 Design Framework Based on Oblivious Transfer  
Oblivious Transfer (OT) [23] is a cryptographic protocol that allows a sender to transmit information to a receiver without revealing any private information. In the OT protocol, the sender has two options, but the receiver can only obtain information about one of them without access to the other, and the sender does not know which option the receiver has chosen. OT is widely used in many secure areas due to its cryptographic robustness and privacy features. Its applications include secure protocol negotiation, secure online auction systems, and secure voting systems. In 2013, [PERSON] et al. [24] proposed a new data structure--the Garbled Bloom Filter (GBF)--and based on the GBF and OT extension, they introduced a PSI protocol. This protocol utilized efficient symmetric encryption operations and could handle billions of elements. However, this protocol faced two issues: one is that the malicious sender might send incorrect shared information, and the other is that the input datasets are not independent. To address these problems, in 2016, [PERSON] and [PERSON] [25] proposed a new randomized garbled Bloom filter using the \"cut-and-choose\" technique. They successfully implemented a two-party malicious model PSI protocol. Subsequently, [PERSON] et al. [26], based on this scheme, further proposed and implemented a multi-party PSI protocol, ensuring malicious security in the presence of two non-colluding servers, with computational and communication costs depending on the number of participants. [PERSON] et al. [27], based on the OOS17-OT [28] protocol, built a maliciously secure PSI protocol. [PERSON] et al. [29], based on the semi-honest secure [PERSON] et al. [30] protocol and the maliciously secure [PERSON] et al. [31] protocol, respectively, proposed maliciously secure and semi-honest secure PSI protocols. Overall, PSI protocols based on oblivious transfer typically feature lower computational and communication overhead.
## 2 Performance Degradation Analysis of Space TWTA  
The space TWTA is the most widely used vacuum electronic device consisting of a traveling wave tube (TWT) and an Electronic Power Conditioner (EPC). The TWT includes a slow-wave structure, an electron gun, RF input and output couples, a magnetic focusing system, and a collector [19], and its structure is shown in Figure 1. Engineering practices indicate that the main reasons for its failure are insufficient cathode emission ability and the deterioration of the vacuum degree. Through in-depth research on the failure mechanisms of the TWTA, helix current and anode voltage are selected as the in-orbit performance characteristic parameters, and the data are used to study the performance degradation of the TWTA.
## 3 Antenna Pointing Direction and Its Blockage by UAVs
### Roll Angle to Avoid Blocking  
Because the angles of the antennas are driven by two-axis gimbal changes, corresponding to the attitudes and positions of the UAV, it is important to know the conditions for avoiding blocking in advance. To return to Equation (7), when \(\psi_{\mathrm{P}}=90\) degrees, \(\theta_{\mathrm{GC}}\) is as low as possible. Thus, if \(\theta_{\mathrm{GC}}<0\) degrees when \(\psi_{\mathrm{P}}=90\) degrees, blocking will inevitably occur. Conditions to avoid blocking can be clarified with Equation (1) by assigning \(\theta_{\mathrm{GC}}=0\) degrees and \(\psi_{\mathrm{P}}=90\) degrees, as shown Equation (15).  
\[\phi<\tan^{-1}\frac{1}{\alpha+\beta} \tag{15}\]  
Using Equation (4), the blocking area can be clarified in relation to \(\beta\) and \(\phi\). As examples of realistic values assumed in HAPS, the results in the case of \(\alpha=0.075\) and \(\alpha=0.15\) are shown in Figure 10a,b, respectively. Blocking can be prevented by flying HAPS under flight conditions that avoid the yellow blocking area in Figure 10. The results also confirm that the smaller that \(\beta\) and the velocity are, the bigger that \(\alpha\) is and, in turn, the lower the risk of blocking.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Data Augmentation Methods
#### 2.3.3 Background Noise (BN)  
While keeping the pitch and speed of the audio constant, the audio samples were mixed with background noise at different signal-to-noise ratios (SNR). Each instance was generated by blending the original audio with four environmental audio samples, which included indoor-related background noises such as air conditioning, rainfall, television, and fan sounds. The mixing process followed the formula below:  
\[y_{out}(i)=y+y_{noise}(i) \tag{6}\]  
where \(y_{out}(i)\) represents the output of the mixed audio. \(i\) denotes a different SNR level, ranging from \(-10\) dB to \(20\) dB. The range used in this study includes \(-10\), \(-5\), \(0\), \(5\), \(10\), \(15\), and \(20\). A larger \(i\) indicates a greater SNR. Similar mixing methods have been adopted in the previous literature [22,34].
# HBIM: Background, Current Trends, and Future Prospects
## 5 Applications and Data Management
### Common Data Requirements
#### 5.1.2 Non-Geometric Data
NDT methods, such as ultrasonic pulse velocity, infrared thermography, ground penetrating radar, and spectral analysis of surface waves, have proven effective in estimating the material properties of traditional building materials like stone and rammed earth [83; 84; 85; 86]. The structural condition of a building, among other factors, is inherently dynamic, introducing a fourth dimension into HBIM: time. While 4D standard BIM models are commonly used to capture changes over time in construction or demolition processes, early HBIM models were static and developed to represent the current or \"final\" state of the building [1]. Since then, several studies have explored the development of 4D HBIM models, mostly to capture large-scale variations over time in CH assets, such as construction phases [87; 88; 89; 90], past interventions [91; 92], and global deterioration and damage [93; 94; 95]. However, when designed for management purposes, HBIM models should also accommodate future modifications resulting from interventions, whether or not they involve geometric changes [6; 96]. Additionally, if the model is used to store and manage the building's structural condition, it must incorporate both quantitative and qualitative data about material properties, detected defects, and damage states, with the ability to update this information over time [91; 95; 97].
## 5 Results and Discussions
### Comparison Results and Discussion of Various Methods
#### 5.4.1 Necessity and Practicality of Data Leveling
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Element** & **Region** & **Min** & **25\%** & **50\%** & **75\%** & **Max** & **StdDev** & **Mean** & **Kurt** & **Skew** \\ \hline \multirow{2}{*}{Ag} & North & \(-6.17\) & \(-3.81\) & \(-3.51\) & \(-3.16\) & \(3.64\) & \(0.68\) & \(-3.45\) & \(12.86\) & \(1.79\) \\  & South & \(-5.93\) & \(-3.46\) & \(-3.22\) & \(-2.98\) & \(1.40\) & \(0.55\) & \(-3.21\) & \(6.36\) & \(0.54\) \\  & North & \(-2.94\) & \(1.09\) & \(1.45\) & \(1.90\) & \(7.88\) & \(0.97\) & \(1.53\) & \(4.50\) & \(0.65\) \\ \multirow{2}{*}{Cu} & South & \(-1.90\) & \(1.44\) & \(1.63\) & \(1.81\) & \(8.80\) & \(0.53\) & \(1.63\) & \(17.11\) & \(1.34\) \\  & North & \(-0.78\) & \(1.44\) & \(1.78\) & \(2.06\) & \(4.89\) & \(0.54\) & \(1.74\) & \(2.12\) & \(-0.07\) \\ \multirow{2}{*}{Pb} & South & \(-2.88\) & \(1.66\) & \(1.83\) & \(2.23\) & \(5.68\) & \(0.68\) & \(1.89\) & \(4.35\) & \(-0.46\) \\  & North & \(-0.52\) & \(2.66\) & \(2.89\) & \(3.15\) & \(6.91\) & \(0.55\) & \(2.95\) & \(5.77\) & \(1.18\) \\ \multirow{2}{*}{Zn} & South & \(0.54\) & \(2.85\) & \(2.96\) & \(3.30\) & \(5.79\) & \(0.43\) & \(3.07\) & \(4.37\) & \(0.78\) \\  & North & \(-2.55\) & \(-1.01\) & \(-0.68\) & \(-0.34\) & \(4.22\) & \(0.67\) & \(-0.60\) & \(9.15\) & \(2.13\) \\ \multirow{2}{*}{W} & South & \(-3.57\) & \(-0.88\) & \(-0.73\) & \(-0.39\) & \(5.78\) & \(0.63\) & \(-0.57\) & \(6.21\) & \(1.44\) \\  & North & \(-3.20\) & \(-0.23\) & \(0.12\) & \(0.60\) & \(6.59\) & \(1.02\) & \(0.34\) & \(6.66\) &Figure 15: _Cont._  
Figure 15: _Cont._
## 3 Materials and Methods
### HS-SPME Procedure  
A time-effective and solventless (HS-SPME) method was applied for VOMs extraction. With slight modifications, the HS-SPME procedure was performed based on the conditions described by [PERSON] et al. [22]. For headspace sampling, 1 g of spice powder, 0.3 g of NaCl (to promote the \"salting-out\" effect by decreasing the solubility of volatile metabolites in the water-based phase) and 6 mL of ultra-pure Milli-Q water were placed into a 20 mL amber headspace glass vial containing a magnetic stirring microbar. Before sealing the vial with a PTFE-faced silicone septum, 5 \(\upmu\)L of 3-octanol (102 \(\upmu\)g/mL) used as an internal standard was added. Then, the vial was placed in a thermostatic bath at 45 \(\pm\) 1 \({}^{\circ}\)C under constant magnetic stirring (450 rpm). HS-SPME extractions were carried out by exposing the SPME fiber (DVB/CAR/PDMS) to the sample's headspace for 50 min. Finally, the fiber was withdrawn into the holder needle, removed from the vial, and the VOMs extracted by SPME were thermally desorbed by the direct insertion of the fiber into a GC injector at 250 \({}^{\circ}\)C for 6 min, in splitless mode. Experiments were carried out in triplicate (\(n=3\)) for all samples. The SPME fiber was thermally conditioned according to the manufacturer's instructions before use, and a daily conditioning for 10 min was carried out before the first extraction to ensure the absence of carryover.
### Machine Learning in VANET Routing Prediction  
In the realm of vehicular ad hoc networks (VANETs), the integration of machine learning techniques for routing prediction has emerged as a pivotal area of research. One noteworthy approach involves utilizing supervised learning models such as support vector machines (SVM) and random forests. SVMs excel in classifying and predicting data by finding the optimal hyperplane that separates different classes, making them well-suited for discerning intricate patterns in vehicular movement. On the other hand, random forests leverage the collective decision-making power of multiple decision trees, enhancing the robustness of predictions. Their ability to handle large datasets and adapt to dynamic network conditions positions them as formidable tools in predicting optimal routes within VANETs. These models offer advantages over traditional routing algorithms by discerning patterns in real-time traffic data, considering factors like congestion, road conditions, and vehicular movement, resulting in more adaptive and efficient routing decisions.  
Furthermore, the advantages of machine learning-based routing prediction extend beyond individual model choices. Figure 4 shows Machine Learning models used in VANET routing prediction. Ensemble methods, which combine the strengths of multiple models, have demonstrated exceptional performance in enhancing prediction accuracy and generalization. Hybrid models that integrate machine learning with traditional routing algorithms capitalize on the strengths of both approaches, offering a balanced solution that benefits from the adaptability of machine learning and the stability of conventional routing methods. The dynamic nature of vehicular environments necessitates models that can adapt to changing conditions, and machine learning techniques in VANET routing prediction pave the way for intelligent, context-aware decision-making, ultimately contributing to safer and more efficient transportation systems.  
Figure 3: Illusion of pollution, impersonation, and DoS cyber attack scenarios in VANET [26].
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 4 BackCom: Signal Processing and Detection
### Signal Detection in BackCom  
Signal detection has a vital role in BackCom receivers. The received signals by BackCom readers are degraded mainly due to noise, interference, attenuation, and very low frequency, as compared to carrier signals. Considering the degraded signals, some threshold levels need to be fixed for understanding the desired variations of signals. These thresholds are decided by averaging two voltage levels and can be done by a comparator [12]. However, if the resultant of the input signals is higher than the threshold level, \(V_{1}\) (first voltage) can be decided. Moreover, if the result of averaging is less than the threshold level, \(V_{2}\) (second voltage) is considered [12]. Authors further proposed a resistive/capacitive (RC) circuit to average the variation in TV signals, as shown in Figure 15. The circuit diagram has two stages: (i) an envelope detection and averaging stage and (ii) a compute threshold stage. The envelope detection and averaging stage generates an average envelope of the signal, whereas the compute threshold stage compares the averaged signal with the certain threshold level. BackCom receivers' detection sensitivity is also one of the main challenges in passive device communications. [PERSON] et al. increased the detection sensitivity of a receiver by increasing the transmit signal time duration [32]. Let us now discuss methods of detection of signals. One way includes the phase information, while the other does not need any. These are discussed below:
# Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms
## 3 Tensor-Based RLS Algorithms  
The faster convergence rate is one of the main advantages of the RLS methods, with respect to the performances obtained by the LMS-based family of algorithms. However, this convergence aspect comes together with a very high computational complexity. It was previously demonstrated that tensor-based algorithms could produce better results than the classical RLS approach, by splitting the long filter associated with the unknown system identification problem into multiple smaller filters, i.e., into multiple smaller system identification processes. The main advantages of this category of adaptive algorithms are two-fold: a significant decrease in number of mathematical operations and improved convergence rates.