esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Cost BLE Transceiver with RX Matching Network Reusing PA Load Inductor for WSNs Applications
## 3 Transceiver Circuit Implementation
### Synthesizer  
As shown in Figure 7, the LO is generated by a 4.8 GHz fractional-N synthesizer [34], consisting of a phase-frequency detector (PFD), a charge pump (CP) current source, a loop filter, a third  
Figure 10: The simulated magnitude-frequency response of the 4 th order complex filter.  
Figure 9: The simplified schematic of a 2 nd order complex filter.  
order multi-stage noise shaping (MASH) delta-sigma modulator (DSM), a dual-modulus prescaler, programmable dividers, and a voltage-controlled oscillator (VCO). Considering the phase noise and respond time, the PLL loop bandwidth of 120 kHz was adopted in this work. However, this loop bandwidth is significantly smaller than the BLE specified 1 Mb/s rate with GFSK modulation. To extend the modulation bandwidth, a two-point modulation scheme is applied to reuse the synthesizer. This simultaneously guarantees reduced noise performance in both TX and RX modes.  
Figure 11 shows the simplified VCO schematic. The NMOS only cross-coupled topology is employed due to its better phase noise performance [35] in low power design. The charge pump output is applied to MOS-varcators C\({}_{22}\) and C\({}_{23}\) for fine frequency and phase-locking after the 3-order loop filter. To improve the linearity of the VCO gain (Kvco), both C\({}_{22}\) and C\({}_{23}\) were designed to have two combined varactors biased to ground. An 8 bit DAC is added as HF (the second point) to extend the frequency modulation bandwidth beyond the PLL bandwidth. The designed frequency resolution is about 2 kHz. A symmetric and differential customized inductor is adopted to increase the Q-value and reduce current consumption. Current biasing is achieved by a variable poly resistor R\({}_{21}\), which suffers lower noise as comparing to a tail current source. The output impedance of NM\({}_{21}\) and NM\({}_{22}\) are increased through NM\({}_{23}\) and NM\({}_{24}\) by using negative feedback, which makes the node (V\({}_{\text{F}}\)) voltage be less sensitive to external interference and provide steadier current for resonance device. Therefore, the phase noise of the VCO is much less sensitive to bias noise in low frequencies [23].  
To cover frequency range from 4.8 GHz to 4.967 GHz (two times of ISM band frequency), a 7-bit capacitor array was used as shown in Figure 11. In order to speed up the lock time, initial coarse frequency algorithm is implemented in digital domain during startup period. The control word of the capacitor array is scanned and the corresponding frequency is calculated and stored. When channel switching, the required control word will be automatically picked up to achieve a fast lock. After the PLL has settled to the channel frequency, the HF data is then applied to other MOS varactors consisting of C\({}_{24}\) and C\({}_{25}\) in TX mode.
## 4 Mechanical Properties of Geopolymer Concrete
### Efficiency of Alkaline Solution and Ambient Condition on GPC  
In this part, based on the investigations carried out in the previous sections regarding the efficiency of inorganic polymers such as fly ash with considerable amounts of silicon and aluminum on the improvement of concrete properties, especially compressive strength, it is urgent to take a step forward. Other key factors including activator solution ratio and the ambient conditions of concrete are also essential to be considered. Therefore, other experimental attempts carried out by other researchers have been reviewed here to open the new lines of inquiry for this topic. There have been numerous studies to assess the improvement of GPC mechanical properties affected by various ratios of alkaline solution used in this concrete type. [PERSON] et al. (2018) also explained that alkaline-activated solutions, for instance NaOH, have a main role in the improvement of compressive strength for GPC concrete. This research was centralized on the fly ash with diverse NaOH concentrations as 8M, 12M, 16M and different curing conditions such as 24, 48, 72 h. The laboratory results of this investigation clearly showed that there was an increase in compressive strength with an increase in NaOH concentration. The compressive strength test was performed with the use of three cubes for each set at the ages of 7 and 28 days. Therefore, 7 days compressive strength can be increased with an enhancement in the concentration of NaOH from 8M to 16M for the same curing time. Furthermore, the increase of compressive strength can be observed in the curing time from 24 h to 48 h, however, no significant alteration in compressive strength was observed over the curing time from 48 to 72 h.  
In the experimental program by [PERSON] et al. (2018), it was proven that GPC typically has high strength, low creep and sufficient resistance to sulphate attacks, as well as acceptable acid resistance. The focus of this investigation was on the evaluation of GPC mechanical properties, such as GPC compressive strength at different ages and concrete workability utilizing slump test. The results of this experimental program supported the hypothesis that the GPC compressive strength was enhanced while increasing the alkaline solution to fly ash ratio up to 0.5 so that the compressive strength of the mix design
# A Real Time Algorithm for Versatile Mode Parking System and Its Implementation on FPGA Board
## 2 Autonomous and Versatile Mode Parking System (AVPS)
### Determining the Space Constraints  
In order to avoid collision during the parking process, the possibility of collision was studied by calculating appropriate distances for each parking mode configuration. Moreover, the constraint conditions were analyzed according to the environment of the vehicle  
Figure 3: Robot description in the Simplorer environment.  
and the parking characteristics. Figure 4 illustrates the constraint conditions according to the selected mode by the proposed AVPS. The values of each distance can be easily calculated, where:  
* \(\mathrm{D_{1}}\) represents the required free distance for each parking mode.
* \(\mathrm{D_{2}}\) represents the distance between the first and second corner of the same vehicle.
* \(\mathrm{D_{3}}\), \(\mathrm{D_{4}}\) and \(\mathrm{D_{5}}\) represent the minimum distance required for safe parking.
* \(\alpha\) represents deviation angle of the parked vehicles.  
Figure 5 illustrates the robot parameters, which affect the minimum distance required for safe parking in parallel and perpendicular parking modes.  
Distances \(\mathrm{D_{3}}\) and \(\mathrm{D_{4}}\) in these two modes can be expressed as follows.  
For parallel mode:  
Figure 4: Presentation of the four combined parking modes.  
Figure 5: Parameters affecting the minimum distance required for safe parking.  
\[\mathrm{D}_{3}=\sqrt{2\mathrm{RE}+\left(\mathrm{K}-\mathrm{B}\right)^{2}}+\ \mathrm{B}+0.1*\mathrm{K} \tag{1}\]  
For perpendicular mode:  
\[\mathrm{D}_{4}=\sqrt{\mathrm{B}^{2}+\left(\mathrm{R}+\left(\mathrm{E}/2\right) \right)^{2}}-\sqrt{\left(\mathrm{R}-\left(\mathrm{E}/2\right)\right)^{2}-\left( \mathrm{R}-\left(\mathrm{E}/2\right)-\mathrm{F}\right)^{2}}+0.1*\mathrm{E} \tag{2}\]  
We proposed to add a safeguard equal to 10% of the vehicle length (parallel mode) and 10% of wheel base (perpendicular mode), to the parking space to minimize the possibility of a collision due to non-ideal conditions. Distances \(\mathrm{D}_{2}\) and \(\mathrm{D}_{5}\) in the head-out and head-in angled modes can be expressed as follows.  
For head-out angled mode:  
\[\mathrm{D}_{2}=\mathrm{K}*\ \cos\ \alpha \tag{3}\]  
\[\mathrm{D}_{5}=\left(\left(\mathrm{D}_{4}+\mathrm{E}\right)/\sin\ \alpha\right)- \mathrm{D}_{2}-\left(\mathrm{E}*\ \sin\ \alpha\ \right) \tag{4}\]  
For head-in angled mode:  
\[\mathrm{D}_{2}=\mathrm{E}*\cos\ \alpha \tag{5}\]  
\[\mathrm{D}_{5}=\left(\left(\mathrm{D}_{4}+\mathrm{E}\right)/\cos\ \alpha\right)- \mathrm{D}_{2}-\left(\mathrm{K}*\cos\ \alpha\ \right) \tag{6}\]
## 4 Experimental Results
### Model Training and Experimental Setup
Figure 13 presents confusion matrices that compare the classification performance of models trained with and without dummy traces. Figure 13a shows the results for models trained without dummy traces, where a high misclassification rate indicates limited classification capability. In contrast, Figure 13b shows the results for models trained with dummy traces, highlighting improved accuracy through a more balanced distribution of true positives and true negatives. In the experimental results, the superior performance of MLP over RNN-based models when dummy data were added can be attributed to the fact that dummy power traces could be distinguished based on pattern features that appear at specific time points, rather than relying on temporal dependencies [17; 25]. Although RNN-based models are designed to capture long-term dependencies, the data in this study emphasizes pattern changes at individual time points, which is why MLP showed better classification performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dummy** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\
**Type** & **Traces** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline RNN & With & 97.50 & 99.01 & 95.97 & 97.46 & 99.68 \\ Bi-RNN & With & 97.38 & 98.77 & 95.96 & 97.34 & 99.65 \\ MLP & With & 97.81 & 99.71 & 95.90 & 97.77 & 99.76 \\ RNN & Without & 52.46 & 75.26 & 7.51 & 13.66 & 44.05 \\ Bi-RNN & Without & 48.15 & 32.11 & 3.20 & 5.83 & 44.05 \\ MLP & Without & 48.89 & 39.54 & 3.94 & 7.18 & 46.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Deep learning model performance comparison for detecting Hiding operations. Figure 12: Comparison of model performance for detecting hiding operations in Hash algorithms. Figure 14 illustrates the training and testing times under both conditions. Figure 14a shows the training times of models trained on either the original power traces or the power traces augmented with dummy traces, and Figure 14b shows the corresponding testing times. The training was conducted using 710,428 data samples, with 10 epochs performed for both the RNN and Bi-RNN models and 50 epochs for the MLP model. Testing was uniformly performed on 177,607 test data samples. In terms of training time, the RNN and MLP models took longer to train when using dummy traces, whereas the Bi-RNN model required more time when trained without dummy traces. For testing time, all three models exhibited faster performance when trained on power traces augmented with dummy traces. Notably, although the MLP model showed an increase in training time of approximately 70 s, its testing time was significantly reduced, decreasing from 3.18 s to 0.13 s.  
Figure 14: Training and testing times of models with and without dummy traces: (**a**) Training times; (**b**) Testing times. Figure 13: Confusion matrices of classification results for models trained with and without dummy traces: (**a**) without dummy traces; (**b**) with dummy traces.
###### Abstract
Recently, the INS measurements are also being integrated with acoustic/vision-based systems to produce a solution that, beyond reducing the accuracy drifts of the INS, will have high positioning accuracy in short-ranges. All these technologies are addressed in Section 2 of this work, which is organized as shown in Figure 1, including the main sensors used and the different approaches taken. After solving the problem of self-localization and navigation, other challenges must be addressed to implement a collaborative team of AUVs. Since there is a need for sharing information between the vehicles, communication is an important concern. The amount and size of the messages will depend on the collaborative scheme used, the number of vehicles and the communication system capabilities. Acoustic-based performs better than light-based communication in terms of range, but not in data rates. It also suffers from many other shortcomings such as small bandwidth, high latency and unreliability [13]. Despite its notable merits in the terrestrial wireless network field, radio-based communication has had very few practical underwater applications to date [14]. The collaborative navigation scheme is also a mandatory issue to be considered. The underwater environment is complex by itself to navigate at, and now multiple vehicles have to navigate among each other. A proper formation has to ensure safe navigation for every single vehicle. These topics are analyzed in Section 3, which also includes a review on the main collaborative AUV mission: surveillance and intervention. Since there is no need to interact with the environment, survey missions are simpler to implement and have been performed successfully for different applications, such as mapping or object searching and tracking. Intervention missions are usually harder due to the complexity of the manipulators or actuators needed. In either case, since an experimental set up is difficult to be reached, many of the efforts made are being tested in simulation environments.
## 4 IoT Protocols
### Service Discovery Protocols  
* **Multicast DNS (m-DNS):** It resolves host names to IP addresses without using a unicast DNS server [56]. It operates on multicast UDP packets, through which a node acquires terms of all nodes in the local network. This protocol can be implemented irrespective of infrastructure failures.
* **DNS-Service Discovery (DNS-SD):** It uses standard DNS messages to discover services of an IoT network [57]. Host names of the service provider are resolved, and IP addresses are paired with host names using mDNS.
* **Simple Service Discovery Protocol (SSDP):** This protocol is the basis for UPnP used in small-scale networks to advertise and discover network services [58]. It does not use any server-based configuration mechanism such as DHCP or DNS, based on an IP suite.  
Figure 13: Protocol categorization of IWF layers.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 3 Linear Programming Problems
### Linear Duality
#### 3.2.1 The Dual Simplex  
When the simplex method solves a primal maximum problem, we begin with a primal feasible solution but a dual infeasible solution. Through the sequence of simplex iterations, the primal feasibility is maintained all the time. An optimal primal solution is only obtained when it is also dual feasible (yields useful insights in sensitivity analysis [24]). In many situations it could be more efficient to solve an LP by beginning with a tableau (of a maximization problem) in which each variable in row 0 has a non-negative coefficient and at least one constraint has a negative right-hand side (RHS), i.e., primal infeasible.The dual simplex maintains the non-negative reduced costs (dual feasibility), and when it obtains a tableau with non-negative RHS (primal feasibility), an optimal solution is achieved.  
The main steps of the dual simplex for a maximization problem are as follows:  
1. Check the sign of the RHS of each constraint. If all are negative then an optimal solution is at hand. Otherwise, at least one constraint has a negative RHS and we go to step 2.
2. The most negative basic variable leaves the basis. Select the variable to enter from the row in which the leaving variable is basic (pivot row) using an absolute ratio test. The variable with the maximum absolute value of the corresponding coefficient in row 0 divided by its coefficient in the pivot row.
3. An infeasibility is indicated if there exists a constraint for which the corresponding RHS is negative and the coefficient of each of its variables is negative in the current tableau. If the problem is feasible we go back to step 1.
## 4 Wiener Filtering
\[\mu_{{}_{3}}\left(t_{u},f_{v}\right) = \frac{1}{L^{2}}\sum_{i=u-M_{1}}^{u+M_{2}}\sum_{j=v-M_{1}}^{v+M_{2}} s\left(t_{i},f_{j}\right) \tag{7}\] \[\sigma_{s}^{2}\left(t_{u},f_{v}\right) = \frac{1}{L^{2}}\sum_{i=u-M_{1}}^{u+M_{2}}\sum_{j=v-M_{1}}^{v+M_{2 }}\left(s\left(t_{i},f_{j}\right)-\mu_{s}\left(t_{u},f_{v}\right)\right)^{2} \tag{8}\]  
with  
\[\hat{y}\big{(}t_{u},f_{v}\big{)}=\alpha s\big{(}t_{u},f_{v}\big{)}+\beta \tag{2}\]0  
\[\hat{y}\big{(}t_{u},f_{v}\big{)}=\alpha s\big{(}t_{u},f_{v}\big{)}+\beta \tag{2}\]1  
The use of different window sizes (\(L\) value, \(M_{1}+M_{2}+1=L\)) affects the resulting estimated interference \(\hat{y}(t_{u},f_{v})\). If \(L\) is too small, the noise filter algorithm is not effective. On the other hand, if \(L\) is too large subtle details of the interference will be lost in the filtering process. Figure 6 shows the simulated error in the estimation of the noise power as a function of the RFI power present in the received signal; assuming that the thermal noise power (\({\sigma_{n}}^{2}\)) is perfectly known. It is observed that the value of \(L\) depends on the actual RFI level: if the RFI level is 17 dB lower than the thermal noise, the best choice is a 5 \(\times\) 5 window, otherwise, a 6 \(\times\) 6 window will outperform. In our case of study, the 6 \(\times\)6 window is chosen as the maximum value of the algorithm error is 0.74 K.  
\[\hat{y}\big{(}t_{u},f_{v}\big{)}=\alpha s\big{(}t_{u},f_{v}\big{)}+\beta \tag{2}\]2 Error in the estimation of the antenna temperature as a function of the Interference-to-Noise Ratio (INR).
# Ecological Dynamics and Evolution of Cooperation in Vehicular Ad Hoc Networks
## 3 The Proposed Model Based on Evolutionary Public Goods Game
### Using the Game to Inform the Model  
Now we employ replicator equations to analyze the frequency-dependent evolutionary dynamics of cooperators, defectors, and empty spaces. Let us assume \(x(t)\), \(y(t)\), and \(z(t)\) represent the relative frequency of cooperator, defector, and empty space, at time \(t\), respectively. Additionally, \(\dot{x}(t)\), \(\dot{y}(t)\), and \(\dot{z}(t)\) denote the changing rate of the phenotypes' frequency. In this system we assume that new vehicles can only emerged into empty space. Hence, birth rates are proportional to empty spaces \(z\) and their payoffs. Dynamics of the individuals in this system are calculated by the following equations  
\[\dot{x}(t) =x(t)(z(\pi_{\text{C}}+b)-d), \tag{8}\] \[\dot{y}(t) =y(t)(z(\pi_{\text{D}}+b)-d),\] \[\dot{z}(t) =-\dot{x}(t)-\dot{y}(t),\]  
where \(d\) and \(b\) indicate death and birth rates of vehicle (i.e., departure rates), \(\dot{x}(t)\), \(\dot{y}(t)\), and \(\dot{z}(t)\) show rate of change of cooperator, defector, and empty space frequencies in the population, respectively. Equation (8) represents an extension on the replicator dynamics equations [23]. Replicator dynamics determine whether individuals in the process of evolution converges to which proportions within the population [24]. Equation (8) is converted to normal replicator [25] if \(\frac{dz}{dt}=0\) and \(d=z\frac{x\Pi_{\text{C}}+y\Pi_{D}}{x+y}\). The equilibrium of the phenotype frequencies is calculated by setting our equation system (i.e., Equation (8)) equal to zero. As the frequencies of three phenotypes cooperator, defector, and empty space should clearly sum to one, the set of possible frequency states will be a simplex that defined by:  
\[S_{3}=\{\forall\;(x,y,\;z)\in\mathbb{R}^{3}|x+y+z=1\} \tag{9}\]  
The replicator dynamics equations have several equilibrium (fixed) points that describe situations in which the population is no longer evolving. These equilibrium points have two types, monomorphic and polymorphic. Monomorphic point indicates a pure population state composed of one player type, where polymorphic point represents a mixed population state including more than one player type.
# Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan
## 3 Automatic System Design
### Scenario Parameterization Design
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario Element Class** & **Function Name** & **Parameterization** & **Direct Assignment** \\ \hline \multirow{8}{*}{Remote Vehicle (RV) Information} & The Speed of RV & \(\alpha_{speed}\) & \(\surd\) \\  & The Acceleration of RV & \(\alpha_{acceleration}\) & \(\surd\) \\  & The Deceleration of RV & \(\alpha_{acceleration}\) & \(\surd\) \\  & The Distance between HV and RV & \(\alpha_{distance\_hr}\) & \(\surd\) \\  & The Offset Rate of RV & \(\alpha_{offset}\) & \(\surd\) \\  & Vehicle Type of RV (Car, SUV, Bus & & \\  & and Truck) & \(\beta_{\text{Vehicletype}}[1/2/3/4]\) & \(\times\) \\  & The Direction of RV (left, straight and right) & \(\beta_{\text{road}}[1/2/3]\) & \(\times\) \\ \hline \multirow{8}{*}{Host Vehicle (HV) Information} & The Distance between HV and The Center & \multirow{2}{*}{\(\alpha_{distance\_cp}\)} & \multirow{2}{*}{\(\surd\)} \\  & Point of the Current Lane Stop Line & & \\  & Longitudinal Distance between HV and the & & \\  & Start of In-Vehicle Signage & & \\  & Longitudinal Distance Between HV and the & & \\  & Start of The Congestion Location & & \\  & The Speed of HV & \(\alpha_{speed}\) & \(\surd\) \\  & The Acceleration of HV & \(\alpha_{acceleration}\) & \(\surd\) \\  & The Deceleration of HV & \(\alpha_{acceleration}\) & \(\surd\) \\  & The Direction of HV (left, straight & & \\  & and right) & \(\beta_{\text{road}}[1/2/3]\) & \(\times\) \\ \hline \multirow{8}{*}{Pedestrian (PED) Information} & The Speed of PED & \(\alpha_{speed}\) & \(\surd\) \\  & The Acceleration of PED & \(\alpha_{acceleration}\) & \(\surd\) \\  & Pedestrian Delay Duration & \(\alpha_{delay}\) & \(\surd\) \\  & Pedestrians Type (adults and children) & \(\beta_{\text{pedtype}}[1/2]\) & \(\times\) \\ \hline \multirow{2}{*}{Environmental Information} & Lighting Conditions & \multirow{2}{*}{\(\alpha_{light}\)} & \multirow{2}{*}{\(\surd\)} \\  & Weather (sunny, rainy and snowy) & \(\beta_{weather}[1/2/3]\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on partially parameterizable representations of scenarios. With the above approach, logical scenarios are represented in a parameterized form. When generating a specific scenario, only the specific parameters of the scenario need to be determined, and the complete specific scenario can be generated.