esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Antiviral Activity of the Styrylquinoline Integrase Inhibitor Class
### Modulation of the spacer  
The antiviral evaluation of the linker modified compounds is depicted in Table 12. The reduction of the central double bond which did not affect the integrase inhibition potency dramatically reduced the antiviral activity [26]. Likewise, replacement of the double bond with a simple amide (**92**), or urea (**94,95**) drastically reduced activity. In this context, the amide **93** is a noteworthy exception [18]. Modulation of the hydroxyl substituent pattern on the ancillary ring did not modify the picture. Only, **96**, **101** and to a lesser extent **98** possessed an antiviral activity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compd.**} & **Antiviral Activity on CEM cells** & **Cytotoxicity** & \multirow{2}{*}{**Reference**} \\  & **CEM cells IC\({}_{\text{50}}\) (\(\upmu\)M)** & & **IC\({}_{\text{50}}\) (\(\upmu\)M)** & \\ \hline
**77e** & 95.0 & 100.0 & 26 \\
**77f** & 50.0 & 100.0 & 26 \\
**77g** & NR & 43.0 & 26 \\
**77h** & NR & 47.0 & 26 \\
**77i** & 110.0 & \textgreater{}100.0 & 25 \\
**77j** & 57.0 & 63.0 & 30 \\
**35** & 1.3 & \textgreater{}100.0 & 14 \\
**77i** & \textgreater{}100.0 & \textgreater{}100.0 & 26 \\
**77m** & 80.0 & \textgreater{}100.0 & 14 \\
**77n** & 30.0 & 110.0 & 19 \\
**77o** & 30.0 & 50.0 & 23 \\
**77p** & 10.0 & 50.0 & 23 \\
**77q** & 3.0 & 95.0 & 26 \\
**77r** & 54.0 & 95.0 & 26 \\
**77s** & 62.0 & \textgreater{}100.0 & 14 \\
**77i** & 1.2 & 10.0 & 14 \\
**77u** & 1.5 & 250.0 & 26 \\
**77w** & 9.0 & 100.0 & 26 \\
**77x** & 36.0 & 22.0 & 37 \\
**77y** & 50.0 & \textgreater{}100.0 & 26 \\
**77 aa** & 6.0a & \textgreater{}100.0 & 19 \\
**77 ab** & 14.5a & \textgreater{}100.0 & 19 \\
**77 ac** & 26.0a & 230.0 & 19 \\ \hline \end{tabular}  
* NR: not reached, a Hela P4 cells.  
\end{table}
Table 12: Antiviral activity and cytotoxicity of spacer modified inhibitors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compd**} & **Antiviral Activity on CEM cells** & **Cytotoxicity** & \multirow{2}{*}{**Reference**} \\  & **IC\({}_{\text{50}}\) (\(\upmu\)M)** & & **IC\({}_{\text{50}}\) (\(\upmu\)M)** & \\ \hline
**35** & 1.3 & \textgreater{}100 & 14 \\
**78** & NR & 61 & 26 \\
**92** & 30.0 & \textgreater{}100 & 18 \\
**93** & 2.0 & \textgreater{}100 & 18 \\
**94** & \textgreater{}100.0 & 70 & 18 \\
**95** & 55.0 & \textgreater{}100 & 18 \\ \hline
**96** & 2.0 & \textgreater{}100 & 18 \\
**97** & 40.0 & \textgreater{}100 & 18 \\
**98** & 10.0 & \textgreater{}100 & 18 \\
**99** & \textgreater{}100.0 & \textgreater{}100 & 18 \\
**100** & 25.0 & \textgreater{}100 & 18 \\
**101** & 4.0 & \textgreater{}100 & 18 \\
**102** & \textgreater{}100.0 & \textgreater{}100 & 18 \\ \hline \end{tabular}  
* NR: not reached, a Hela P4 cells.  
\end{table}
Table 11: Influence of the ancillary ring substitution on antiviral activity.
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## 4 Deficiencies of the Conventional MPPT Method from Other Perspectives  
Apart from the narrow tracking bandwidth and slow dynamic response under conditions with low wind speed, the conventional MPPT method shows its deficiencies because of the following two assumptions.
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 5 Discussion and Future Work
To detect these types of gunshot sounds: we need to collect samples of gunshot sounds with silencers, add them to the gunshot class dataset, and then retrain the model. In the future, we plan to make a new gunshot sound dataset recorded inside a shooting range with the same microphone used in the device. After training the model with this dataset, the system will be tested with actual shooting with different types of firearms--with and without silencers--inside a shooting range. We plan to test the system in the shooting range with different distances and measure its performance considering environmental noise in the future. If there are multiple gunshot detector devices, such as in different classrooms of a school, and a gunshot is detected by more than one device, then the central server can prioritize the location of the device that detected the largest sound volume. The device can measure the maximum volume of the sound and send it to the server when a gunshot is detected. We plan to implement this feature in the future. The proposed device needs a Wi-Fi connection to send data to the central server when a gunshot is detected. As Wi-Fi is generally available indoors, the device will work indoors only and will not work outdoors. However, the device can be used outdoors by interfacing with a cellular modem, which will give Internet access to the device outdoors. In the outdoors, the power supply might be a challenge. We plan to explore the possibility of outdoor usage of the device in the future.
## 3 Timing Predictability in CPSs
### Predictability in Time-Critical Embedded Systems  
This subsection discusses timing predictability in embedded systems with regards to on-board embedded systems in each hauler in the example provided in Section 2. In time-critical embedded systems, it is required that all actions by these systems are performed in a timely manner such that all specified timing requirements are met. Hence, at the design time, the developers of these systems need to verify that the systems are timing predictable. Timing predictability is a well-defined concept in the real-time systems domain [6; 7; 8; 9; 10]. For a given system model and a set of assumptions, the system is considered to be timing predictable if it is possible to show, prove or demonstrate that all specified timing requirements will be satisfied when the system is executed.  
The timing requirements can be specified on various elements of the system model, e.g., on the individual tasks, set of tasks and task chains in a node, network messages and chains of tasks and messages in a distributed embedded system. Note that a node can be a single-core processor or a multi-core processor. Traditionally, the timing requirements referred to the deadlines corresponding to the response times of individual tasks, messages and task chains [18]. The response time of a task, message or a task chain is counted from the arrival of the input value at the input interface until the delivery of the corresponding computed output value. The response time of a task, message or a task chain shall not exceed the corresponding deadline. In the past few years, the research community extended the use of timing requirements beyond the traditional deadline requirement by considering several other timing requirements by means of timing constraints. The reaction and age constraints are two such constraints, among others. These requirements have been included in several domain-specific modelling languages [19], e.g., Timing Augmented Description Language (TADL2) [20], EAST-ADL [21] language, the Rubus Component Model (RCM) [22] and AMALTHEA [23]. These requirements have also been incorporated with the domain-specific standards such as the automotive standard AUTOSAR [24].  
The predictability of a time-critical embedded system is supported by verifying its timing behaviour at the design time and providing a support for predictable execution environment at the runtime. The timing behaviour of the system can be verified by using the schedulability analyses, whereas the predictable runtime environment can be provided by means of a real-time operating system (RTOS). There is a wealth of schedulability analysis techniques developed by the research community [18; 25; 26]. Furthermore, there are many RTOSs that support predictable runtime environment for embedded systems, e.g., VxWorks, Rubus and FreeRTOS, to mention a few. In summary, the development of timing predictable embedded systems has already achieved significant level of maturity.
# Demystifying Quantum Gate Fidelity for Electronics Engineers
## Appendix A  
In quantum mechanics, a quantum state is pure when it provides a fully informative description of a quantum system. This implies that the quantum system is closed and isolated from its environment, so that Schrodinger's Equation is an adequate tool for describing the dynamics of the system [63]. Nevertheless, when addressing open quantum systems, an additional mathematical tool is required: the density matrix operator \(\rho\), or density matrix for short. For a quantum pure state \(\ket{\psi}\), \(\rho\) is defined as the outer product of the state \(\ket{\psi}\bra{\psi}\). On the other hand, a mixed quantum state is defined as a state whose density matrix \(\rho_{MIX}\) is a linear combination (mixture) of the density matrix of pure quantum states \(\ket{\psi_{X}}\), each contributing with a probability \(p_{X}\)[43]:  
\[\rho_{MIX}=\sum_{X}p_{X}\ket{\psi_{X}}\bra{\psi_{X}} \tag{10}\]  
Equation (10) shows that the density state of a mixed state is not simply the outer product of the quantum state. Since the probabilities are normalized, \(\sum_{X}p_{X}=1\), Equation (10) suggests that you can think of a quantum system in a pure state as an ensemble of quantum subsystems, all in the same pure quantum state.  
Usually, quantum systems constituted by elementary subsystems exhibit non-separability (entanglement) [64]; the single elementary subsystems are therefore dubbed as entangled. The non-separability implies that the state of a quantum system with entanglement cannot be factorized in a tensorial product of pure quantum states. It is worth pointing out that if a composite quantum system with entanglement may be in a pure state, its single entangled subsystems are surely mixed [43]. The entanglement of each quantum subsystem with the rest of the quantum system, i.e., its environment, causes a loss of phase coherence, which, in turn, makes the state mixed. Therefore, a quantum gate exposed to the environment induces phase decoherence in the output state, which therefore results in a mixed state.  
For a given mixed state, the pure entanglement quantum state, within which the mixed state is entangled, can be obtained through a procedure known as purification [38]. Since purification is essentially a reverse procedure, several entanglement pure states are possible for a given entangled mixed state. This remark explains the use of the maximum probability mentioned in the definition of Uhlmann's fidelity, introduced in Section 7.
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 2 Related Work
### Cross-Layer Design Approaches  
The limited resources of WSNs--such as memory, computational power, and energy--motivated the research community to modify the traditional layered protocol stack. The work described in [29] presented a cross-layer management plane for low-power sensor networks that allows sensor nodes to use cross-layer information for efficient use of resources. The authors of [30] realize an energy efficient routing by selecting routing paths based on the residual energy of the constituent nodes, following a learning automata method that selects the route based on energy and their distance from the sink. There have been attempts based on cross-layer design, i.e., to realize cross-layer interactions between the routing layer and the MAC layer to enhance various performance metrics. One such work [7] utilizes the integrated MAC and routing layer for wireless sensor networks, i.e., AIMRP, which organizes the network into various tiers around the BS. The communication pattern used in AIMRP is multi-hop. The nodes report events to the BS from one ring to another in the direction of the latter. The MAC control packets are responsible for selecting the next hop node for relaying the data. Some of the interesting features of AIMRP include integration of MAC and a routing layer that reduces the overhead in terms of delays and energy consumptions. Yet this results in the restricted modularity of the protocol stack.  
In Table 1, the base station position is fixed outside the sensing region. However, in our proposed scheme the base station can be deployed anywhere in the network, either inside or outside the sensing region depending on the feasibility of the location.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 4 Prototype Assembly and Testing  
### Assembling the System  
The entire system was assembled according to the circuit diagram present in Figure 15. Figure 15 shows the circuit completely assembled, powered through the battery represented by the number 5. Also visible, is the wireless charging system, at number 6; the temperature and humidity sensor, at the number 1; the ESP32 microcontroller is identified by number 2; the EMG sensor by the number 3; and the two buck converters are represented by the number 4. The number 7 symbolizes the stimulation electrode (this LED lights up when a stimulus is sent by the microcontroller) and the number 8 represents the state of the power supply circuit--if the battery is being charged this LED stays on, otherwise it is always off.  
Figure 14: (**A**) Application login window and (**B**) application parameterization window.  
Figure 15: Final assembly of the circuit.
# Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks
## 2 Observed Data Analysis and Processing  
In this section, the functional relationship between traffic flow and travel time is resolved based on the analysis of congested networks' traffic characteristics. Multi-source observed variables are transformed to a uniform format, and further trajectory of observed route data is reconstructed using cluster analysis technology.
## 5 Simulation and Analysis
### The Analysis of the Compensated Denoising Module
The compensated denoising process weakens the noise thus the recovered signal is more accurate than the unprocessed recovered signal, so the BER of the signal is also improved in different degrees. It can be seen from Figure 7 that when the number of sampling channels is 30 and 50, the BER of \(\hat{z}(t)\) is decreased by 6.16% and 3.17%, compared to \(z(t)\), respectively.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 5 Application Examples
### Max-Min Fair Allocation  
As mentioned earlier, the proposed model can be applied to various scenarios and objectives. In this section, we consider another optimization problem, a max-min fair allocation, whose objective function is defined as follows:  
\[\max\;\min_{f\in F}r_{f}. \tag{48}\]  
The above objective is inherently nonlinear, and thus it is difficult to directly employ it in the proposed model. To address this issue, we introduce an additional auxiliary variable, denoted as \(w\), add a constraint, and change the objective function as follows:  
\[\max\;w \tag{49}\] \[w\leq r_{f},\hskip 28.452756 pt\forall f. \tag{50}\]  
In this evaluation, the nodes are set to selectively use MIMO or OFDMA, and we use the same configuration of the previous evaluation except that \(N=12\) and \(F=3\), as shown in Table 6. Figure 4 compares two resource allocation results, and Tables 7 and 8 show the corresponding detailed resource allocation results. First, we can clearly observe that flow 2 (i.e., blue arrows) is served only in the max-min fair allocation (Figure 4b). This allows for better fair allocation, but it also has performance degradation in terms of the sum-rate: with the sum-rate maximization the sum-rate is \(4/3\) (\(=1+0+1/3\)), while with the max-min fair allocation, it is reduced to \(1\) (\(=1/3+1/3+1/3\)). On the other hand, for the first case, relatively more resources are allocated to flow 1, resulting in a higher sum-rate, but no resources could be given to flow 2. Overall, the max-min fair allocation exploits OFDMA more than MIMO, compared to the sum-rate maximization case. OFDMA is used for all transmissions associated with flow 2. Node 3 fully uses its available bandwidth for OFDMA on links \(3\to 4\) and \(3\to 9\) at \(t=2\), and for receiving data from two different sources, node 7 and node 11, at \(t=3\). We also can see that most potential interference issues are resolved by allocating different subchannels, similar to the previous evaluation.
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
###### Abstract  
In beyond 5G (B5G) non-terrestrial network (NTN) systems, satellite technologies play an important role. Especially for data collection systems (DCS), low-earth orbit satellites have many advantages. Such as global coverage, low latency, and high efficiency. As a miniaturization technology, CubeSat has attracted extensive attention from a large number of scholars. Satellite constellations can coordinate for distributed tasks. This paper proposes a B5G NTN-based data collection system. A CubeSat constellation achieves global coverage as the basic space platform for DCS. The 5G terrestrial network is used as the data bearer network of the gateway station. A traffic load balance strategy is proposed to optimize the system's efficiency. As a unified hardware platform, software-defined radio (SDR) is compatible with various sensor data models. Finally, the design was verified by a series of experiments.  
b 5G; non-terrestrial-network; data collection system; satellite constellation +
Footnote †: journal: _aerospace_  
00232023333
## 6 Conclusions and Future Research  
The goal of this research was to look at ways to create synthetic time series that are realistic and follow similar distributions of real time-series data. The model and the components were built after performing an extensive literature review. Six stacked GRU layers were used to develop the MTS-TGAN, and the hyper-parameters were selected. The proposed model combines the adaptability of the unsupervised GAN technique with the control over conditional temporal dynamics provided by supervised autoregressive models. MTS-TGAN generates realistic time-series data by utilizing the contributions of the supervised loss and jointly trained embedding network, showing consistent and considerable gains over state-of-the-art benchmarks. This paper concluded that when the MTS-TGAN model was implemented, it produced realistic and varied multivariate time-series data, which were then processed as if they were actual data by external models. The model was evaluated both in terms of qualitative measures, namely t-SNE, PCA, discriminative and predictive scores and quantitative evaluation measures were implemented using an RNN model, which calculated MAE and MSLE scores for three training phases: Train Real Test Real, Train Synthetic Test Real and Train Real and Test Synthetic. The model was able to reduce the overall error up to 13% and 10% in predictive and discriminative scores, respectively.  
Despite the encouraging findings of this research, future work may choose to include more features, such as adding more multivariate features or taking a dataset with more multi-modal elements in it so as to assess how well the proposed model handles these  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **MAE** & **MSLE** \\ \hline TRTR & 0.004862 & 0.000309 \\ \hline TRTS & 0.005861 & 0.000365 \\ \hline TSTR & 0.006604 & 0.000620 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Regression scores.  
Figure 16: TRTS and TSTR graphs for UniMIB dataset.  
conditions, or instead of more feature, we can also add different types of data, such as mixed data (which include both tabular data as well as time-series data). It might also be fascinating to combine features with other forms of data, including geospatial data, to create mixed data, as they depend on a number of objects and events. Future research on assessment measures is often required for data production utilizing GANs so that a framework may be agreed upon for a straightforward comparison of findings across investigations.  
Conceptualization, P.Y. and S.S.; Methodology, P.Y. and N.F.; Software, N.F.; Validation, P.Y. and N.F.; Formal analysis, P.Y. and M.G.; Investigation, P.Y.; Resources, P.Y. and S.S.; Data curation, N.F.; Writing--original draft, N.F.; Writing--review & editing, P.Y.; Funding acquisition, P.Y. and M.G. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Department of Science & Technology (IHUB-NTHAC/2021/01/25).  
The authors declare no conflict of interest.
# Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
## 2 Methods
### Geo/Igso/GPS Raw PPP Model
Therefore, a priori ionosphere delay (e.g., by using the Kolubchar model [36] and global ionosphere mapping [37]) is used to separate them as much as possible, and the corresponding function can be expressed as  
\[I_{r,j}^{s}=I_{r,priori}^{s}=40.28\cdot VTEC_{priori}/(f_{j}^{2}\cdot\cos\theta ),\sigma_{I_{r,priori}}^{2} \tag{8}\]  
where \(\sigma_{I_{r,priori}}^{2}\) is the priori variance of the ionosphere model. Since the ionosphere delay is a frequency dependent error (Equation (5)), \(I_{r,2}^{s}\) can be expressed as \(I_{r,2}^{s}=I_{r,1}^{s}f_{1}^{2}/f_{2}^{2}\). Therefore, only one ionospheric parameter is estimated for each satellite. Besides, since the receiver hardware delay \(d_{r}\) is correlated to ionospheric delay, it is hard to directly estimate the absolute values for \(d_{r}\). Thus, only the differential form (i.e., \(DCB_{r}=d_{r,1}-d_{r,2}\), \(DCB\) stands for differential code bias) between \(d_{r,1}\) and \(d_{r,2}\) is estimated. Finally, the state vector can be written as  
\[\mathbf{x}=\left[\mathbf{p}_{r,G/B},\mathbf{v}_{r,G/B},\mathbf{t}_{r},\dot{\mathbf{t}}_{r},z_{wet },b_{B-G},DCB_{r,G},DCB_{r,B},\mathbf{F}_{r,1}^{s},\mathbf{N}_{r,1}^{s},\mathbf{N}_{r,2}^{ s}\right]^{T}\!\!. \tag{9}\]  
Then, the EKF [31] is applied to estimate the parameters. The corresponding state prediction and measurement update functions can be respectively written as  
\[L_{r,j}^{s}=\rho_{r}^{s}+c(t_{r}-t^{s})+c(b_{r,j}-b_{j}^{s})+T_{r}^{s}-I_{r,j}^ {s}+\lambda_{j}N_{r,j}^{s}+\varepsilon_{L,j} \tag{2}\]0  
\[L_{r,j}^{s}=\rho_{r}^{s}+c(t_{r}-t^{s})+c(b_{r,j}-b_{j}^{s})+T_{r}^{s}-I_{r,j}^ {s}+\lambda_{j}N_{r,j}^{s}+\varepsilon_{L,j} \tag{2}\]1
# CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway
## 2 System Model  
### Platooning Model  
We consider a highway platoon of vehicles moving in unison. Vehicles in a platoon are either fully autonomous or semiautonomous, exchanging V2V control messages, including motion state data such as acceleration, velocity, steering, and so on [6]. Many distance-measuring sensors, such as radar, camera, and laser light interferometry (LIDAR), are available in automobiles. A vehicle's following and preceding distances may be calculated using distance sensors. Platoons use CACC [15; 16; 17; 18] to keep their separation constant based on distance measurements and communications sent.  
V2V transmissions are safeguarded using cryptographic primitives to secure the platoon operation. C-V2X communication relies on a public/private key pair (\(pk_{X}\), \(sk_{X}\)) and digital certificate (\(cert_{X}\)) provided by a PKI following the standard (3 GPP TS 33.185 [1]). Using it, the platoon vehicles may build confidence and validate the source of information. Digital identity and platoon secret key management are outside this project's scope. It is also explored how non-platooning cars behave in various autonomous platooning circumstances. For this reason, cars that are not platooning are divided into non-priority and priority categories.