esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The SDOF System with a Grounded TLIMD
### Equations of Motion (EOMs)  
In terms of the two coordinates and motion parameters defined in the primary analogue TLIMD-SDOF system, as shown in Figure 1, the kinetic energy \(T\), the potential energy \(V\) and the dissipation function \(D\) of this coupled system can be obtained:  
\[\begin{array}{l}T=\frac{1}{2}m_{1}\left(\dot{x}_{1}+\dot{x}_{g}\right)^{2}+ \frac{1}{2}m_{2}[(-\dot{\theta}l_{2}\cos\theta+\dot{x}_{g})^{2}+(-\dot{\theta}l _{2}\sin\theta)^{2}]\\ +\frac{1}{2}\int_{-l_{1}}^{l_{0}}\rho[(-\delta x\cos\theta+\dot{x}_{g})^{2}+( -\delta x\sin\theta)^{2}]dx\\ V=\frac{1}{2}k_{1}x_{1}^{2}+\frac{1}{2}k_{2}(x_{1}-l_{1}\sin\theta)^{2}\\ D=\frac{1}{2}c_{1}\dot{x}_{1}^{2}+\frac{1}{2}c_{2}(\dot{x}_{1}-\dot{\theta}l_{ 1}\cos\theta)^{2}\end{array} \tag{1}\]  
Thus, the original nonlinear EOMs for the primary analogue TLIMD-SDOF system can be developed using the Lagrangian equation,  
\[\begin{array}{l}m_{1}\dot{x}_{1}+k_{1}x_{1}+k_{2}(x_{1}-l_{1}\sin\theta)+c_ {1}\dot{x}_{1}+c_{2}(\dot{x}_{1}-\dot{\theta}l_{1}\cos\theta)=-m_{1}\dot{x}_{g }\\ [m_{2}l_{2}^{2}+\frac{1}{3}m_{3}(l_{3}^{2}-l_{1}l_{3}+l_{1}^{2})]\ddot{\theta} -k_{2}(x_{1}-l_{1}\sin\theta)(l_{1}\cos\theta)-c_{2}(\dot{x}_{1}-\dot{\theta}l _{1}\cos\theta)(l_{1}\cos\theta)\\ =\ddot{x}_{g}\cos\theta[m_{2}l_{2}+\frac{1}{2}m_{3}(l_{3}-l_{1})]\end{array} \tag{2}\]  
For the development of closed-form formulae for the TLIMD optimal parameters, the small-angle approximation that is commonly utilized in the lever-type damper design [31; 41] is imposed. The large-angle situation, which may introduce some nonlinear coupling terms, is investigated in our previous work, cf. [22]. The linearized EOM for this coupled system yields the following expressions:  
\[\begin{array}{l}m_{1}\ddot{x}_{1}+k_{1}x_{1}+k_{2}(x_{1}-x_{2})+c_{2}(\dot{ x}_{1}-\dot{x}_{2})=-m_{1}\ddot{x}_{g}\\ [m_{2}\alpha_{1}{}^{2}+\frac{1}{3}m_{3}(\alpha_{2}{}^{2}-\alpha_{2}+1)]\ddot{ x}_{2}+k_{2}(x_{2}-x_{1})+c_{2}(\dot{x}_{2}-\dot{x}_{1})=\alpha_{1}m_{2} \ddot{x}_{g}+\frac{1}{2}m_{3}(\alpha_{2}-1)\ddot{x}_{g}\end{array} \tag{3}\]  
where, \(\alpha_{1}=l_{2}/l_{1}\) and \(\alpha_{2}=l_{3}/l_{1}\) are the length ratios, which are crucial to the TLIMD performance, as demonstrated in the following sections. In practical applications, where the maximal inertial force is always desired, these two length ratios are commonly set to the same value (i.e., \(\alpha_{1}=\alpha_{2}=\alpha\)).
# A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis
###### Abstract
A reference architecture made up of 4 sub-architectures: CAVs, Devices & Peripherals, the Edge and the Cloud formed of a hybrid Functional and Communication viewpoint. 2. A methodology to use the reference architecture to synthesis the attack surface in the form of attack trees. \begin{table}
\begin{tabular}{c c l l} \hline \hline
**Level** & **Name** & **Description** & **Example** \\ \hline
0 & None & The human driver is in full control. & Anti-lock Braking System \\ \hline \multirow{3}{*}{1} & \multirow{2}{*}{Driver Assistance} & The human driver is assisted by a driver assistance system of steering or acceleration/deceleration using information about the driving & \multirow{2}{*}{Cruise Control} \\  & & environment. The human performs all other tasks. & \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Partial} & The human driver is assisted by one or more driver assistance systems of both steering and acceleration/deceleration using information about the driving environment. The human performs all other tasks. & \multirow{3}{*}{Lane Centring} \\  & & the autonomous vehicle controls all aspects of driving, with & \\ \cline{1-1}  & & the expectation that the human driver will respond appropriately to & \\ \cline{1-1}  & & a request to intervene. & \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{High} & The autonomous vehicle controls all aspects of driving, even if a human driver does not respond appropriately to a request to intervene. & \multirow{3}{*}{Driverless Valet Parking [20]} \\ \cline{1-1}  & & the autonomous vehicle is in full control and no human input related to & \\ \cline{1-1}  & & driving is expected. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Levels of Vehicular Autonomy [19] from no autonomy (where the driver is in full control of the vehicle) at level 0 to level 5 where the vehicle is in full control. Two case studies to demonstrate the applications of attack surface and attack tree analysis in deepening the security knowledge of the system. The remainder of this paper is organised as follows. In Section 2 we present relevant related work, including examining existing vehicular reference architectures. Section 3 describes our proposed reference architecture, its components and relevant attack surfaces and Section 4 describes the methodology for using the attack surface; including using attack trees as a method to perform the analysis. In Section 5 two case studies of example applications are presented as instantiations of our reference architecture. The implications of the reference architecture is discussed in Section 6; and future work is presented in Section 7, before the paper concludes with Section 8.
## 3 Underwater Acoustics Channel Statistics
### RMS Delay Spread  
In wireless communication, the RMS delay spread is referred to as the multipath spread and provides a good measure of time dispersion of the channel [26]. This parameter can reflect the complexity of the channel. In this paper, instant and average RMS delay spread of the channels were measured. The instant RMS delay spread is defined as [27]  
\[\tau_{RMS}(n)=\frac{1}{f_{s}}\sqrt{\frac{\sum\limits_{t=1}^{M_{tap}}\left(m_{ t}-\overline{m}\right)^{2}\left|h(n,m_{t})\right|^{2}}{\sum\limits_{t=1}^{M_{tap}} \left|h(n,m_{t})\right|^{2}}}, \tag{4}\]  
where \(M_{tap}\) denotes the number of taps, and \(f_{s}\) is the sample rate. \(m_{t}\) is the tap index used for calculating the RMS delay spread. The average delay spread \(\overline{m}\) is determined as  
\[\overline{m}=\frac{\sum\limits_{t=1}^{M_{tap}}m_{t}|h(n,m_{t})|^{2}}{\sum \limits_{t=1}^{M_{tap}}\left|h(n,m_{t})\right|^{2}}, \tag{5}\]  
The amplitude of one tap should satisfy Equation (2). The delay interval between two adjacent taps must be greater than or equal to the time resolution. The average RMS delay spread is defined as  
\[\overline{\tau_{RMS}}=\frac{1}{N}\sum\limits_{n=1}^{N}\tau_{RMS}(n). \tag{6}\]
# Evaluation of the Radiological and Chemical Risk for Public Health from Flour Sample Investigation
## 3 Results and Discussion
### Heavy Metal Analysis  
Table 4 reports Cd and Pb mean concentrations (mg kg\({}^{-1}\)) for each type of investigated flour, as obtained through ICP-MS analysis.  
As can be seen from the table, the experimental values are lower than the contamination thresholds established by the commission regulations [52]. Therefore, Cd and Pb cannot be considered as pollutants, and are generally expected not to cause severe effects on human health.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Heavy Metal Concentration (mg kg\({}^{-1}\))**} \\ \hline
**Sample ID** & **Cd** & **Pb** \\ \hline S1 & 0.010 & 0.17 \\ S2 & 0.010 & 0.08 \\ S3 & 0.020 & 0.08 \\ S4 & 0.020 & 0.11 \\ S5 & 0.003 & 0.08 \\ S6 & 0.070 & 0.10 \\ S7 & 0.010 & 0.15 \\ S8 & 0.030 & 0.17 \\ S9 & 0.004 & 0.18 \\ S10 & 0.010 & 0.07 \\ S11 & 0.010 & 0.08 \\ Contamination threshold & 0.100–0.200 & 0.20 \\ EU Reg. 1881/06 & 0.100–0.200 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cd and Pb mean concentrations in investigated flour samples.  
Figure 5: \(D_{\text{{mg}}}\) for all investigated samples, compared to the threshold level imposed by legislation.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 5 Preliminary Results and Validation of QKD Emulation with PUF  
In this section, we provide preliminary error rate results of our emulation system that validate the functionality and correctness of the system. We considered three tests:  
1. Transmitting 75 trits for a constant basis and input trit combination.
2. Transmitting 75 trits by cycling through different basis and input trit combinations.
3. Transmitting a sample key of length 75 trits.  
We used 75 trits to allow the output to fit the terminal which facilitates manual analysis and verification.  
The first test aimed to verify that we consistently obtained matching input and output for matching basis combinations and approximately 50% matching input and output for mismatching basis combinations. We held the combination constant and transmitted 75 trits to eliminate errors that might have been introduced by polarization rotation hardware and alignment.  
The second test aimed to observe potentially added error introduced by changing polarization states. This test changed the basis combination after each transmitted trit, allowing for observation of potential error.  
The third test simulated real-world quantum key distribution, where the basis and inputs are randomly generated. This test allowed us to observe the behavior of the system, when all the components were changing state at random, potentially revealing the effect a previous state may have on the next.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## Materials  
### Study Site  
The study site examined is the western part of Catalonia region of north east Spain (Figure 1a). Figure 1b shows the location of the S1 footprints used in this study. The area covered by the used footprints corresponds to 55% of the total area of Catalonia and compromises 64% of the total agricultural areas in the region. Figure 1c is a digital elevation model (DEM) derived from the shuttle radar topography mission (SRTM) data and Figure 1d shows the agricultural areas derived from the SIGPAC (geographical information system for agricultural parcels) data of Catalonia. The hatched zone in Figure 1d represents the area finally used in the classification.  
Catalonia is a region of 32,000 km\({}^{2}\) located in the north east corner of the Iberian Peninsula along the northeastern Spanish Mediterranean coast. The agricultural areas of Catalonia are concentrated in the northeastern part and the interior plane in the western part as shown in the agricultural map of Figure 1d. The climate in the region is typically Mediterranean; mild in winter and warm in summer with very dry season. Figure 2 presents the precipitation and temperature data recorded at a local meteorological station located in Tornabous village of the interior plane (Figure 1b) ([[https://ruralcat.gencat.cat/web/guest/agrometeo.estacions](https://ruralcat.gencat.cat/web/guest/agrometeo.estacions)]([https://ruralcat.gencat.cat/web/guest/agrometeo.estacions](https://ruralcat.gencat.cat/web/guest/agrometeo.estacions))). The average annual precipitation in this station is around 400 mm.  
During the dry summer season between May and September, irrigation mainly occurs in the study site. Different irrigation techniques exist. In the old irrigation systems, water is distributed using open channels and irrigation is performed by inundation every two weeks. In the new irrigation systems, water is distributed by pressure where sprinklers or drip irrigation are used depending on the culture type. Irrigation here can be applied daily.  
Figure 1: (**a**) Location of the study site (in black), Catalonia Spain, (**b**) Sentinel-1 footprints over Catalonia used in the study, (**c**) digital elevation model (DEM) from shuttle radar topography mission (SRTM) data, (**d**) agricultural areas of Catalonia derived from geographical information system for agricultural parcels (SIGPAC) data. The hatched area represents the zone finally used for classification.  
Figure 2: Precipitation and temperature records for a local meteorological station in Tornabous of the interior plain of Catalonia, Spain.
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 3 Data and Experimental Design
### Experimental Configuration  
The CNN-BiConvLSTM model and basic DNNs were all optimized with [PERSON] [50] by setting \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\), and the initial learning rate was set to 0.0005 with an exponential decay according to the formula:  
\[lr=0.0005\times 0.98^{\frac{i}{208}}, \tag{2}\]  
where \(i\) denotes the number of iterations. The loss function is a combination of \(L1\) losses according to the formula:  
\[\mathcal{L}=L1_{\text{all}}+w_{1}\times L1_{\text{all\_thrd}}+w_{2}\times L1_{ \text{missing}}, \tag{3}\]  
where \(L1_{\text{all}}\) denotes \(L1\) losses for all output radar images and \(L1_{\text{missing}}\) denotes only missing positions of output radar images; \(L1_{\text{all\_thrd}}\) denotes \(L1\) losses for values of reflectivity factors larger than 30 in all input radar images; \(w_{1}=3\) and \(w_{2}=8\) are chosen as the weights of \(L1\) losses. \(L1_{\text{missing}}\) and \(L1_{\text{all\_thrd}}\) are given more weight in order to emphasize the importance of missing frames and strong radar echoes. All the inputs were scaled by 80, and the outputs were limited to the range of 0 to 80. To simulate missing frames in real radar sequences, the input radar images were randomly masked before they were fed to the model during the training, following the rules below:  
(1) Generate a random mask for a radar sequence of length 10. The number of masked radar images ranges from 0 to 10 with equal probability. All the masked images are set to zero.  
(2) Randomly replace two positions of the mask by ones to ensure at least two real radar images in a sequence.  
During inference, the input radar images were masked according to the real missing patterns. The scheme chart for the mask procedure and DNN model is shown in Figure 4. This procedure enabled the model to deal with any possible missing patterns. If no masks were given to the input radar images, the model would abstract features from every input radar image in the training procedure. However, in the inference model, the model will extract features from zero inputs as real radar images and subsequently obtain poor results. In the study, the missing pattern of a radar sequence of length 10 was denoted as 10 binary numbers, where element '0' indicated missing frames and element '1' indicated normal frames. The batch size was set to 8, and the training was stopped after ~160,000 iterations (i.e., 80 epochs). The mask ratio was gradually increased up to 100% at 10,000 iterations. The models were evaluated for every epoch, and the optimal model was chosen according to the highest validation loss for each model.
## 2 Material Properties  
### Base Materials  
GGBFS and zeolite powder were selected as geopolymer base materials due to their environmental friendliness, optimized properties, and wide range of applications [19; 20]. Compared with the elevated energy consumption of metakaolin, the restricted availability of silica fume, and the concomitant environmental hazards linked with fly ash, GGBFS possesses the benefit of water-hardening and performance-enhancing properties as a byproduct of iron and steel. The zeolite powder stands out for its porosity, adsorption, and energy-saving features that do not require calcination. Therefore, industrial solid waste (S95 GGBFS powder and 200 mesh zeolite powder) was used as the base material for the preparation of the grouting material, and the materials are shown in Figure 1.  
S95 GGBFS powder, with a specific surface area of 412 m\({}^{2}\)/kg, a density of 2.9 g/cm\({}^{3}\), a specific gravity of 2.45, and a particle size range of 5-20 \(\upmu\)m, was purchased from Hengyuan New Material Company, located in Zhengzhou City, Henan Province, China. The 200-mesh natural zeolite powder was purchased from Henan Gongyi Hengxin Co., Ltd., China (located in Gongyi City, Henan Province, China), with a density range of 1.75 g/cm\({}^{3}\), a  
Figure 1: Basic materials: (**a**) GGBFS powder; (**b**) zeolite powder.  
specific surface area of 560 m\({}^{2}\)/kg, a specific gravity of 2.1, and a particle size range of 74-100 \(\upmu\)m. Figure 2 shows the XRD patterns of the primary base materials, while Table 1 provides a comprehensive overview of their chemical compositions. As shown in Figure 2 and Table 1, the primary chemical composition of GGBFS powder consists of SO\({}_{2}\) and CaO, accounting for 72.35%, followed by Al\({}_{2}\)O\({}_{3}\), at 15.04%. The predominant minerals include calcite (CaCO\({}_{3}\)), anhydrite (CaSO\({}_{4}\)), and calcium alumina feldspar (Ca\({}_{2}\)Al\({}_{2}\)SiO\({}_{7}\)). The primary chemical composition of zeolite powder consists of SO\({}_{2}\), accounting for 73.35% of the total, followed by Al\({}_{2}\)O\({}_{3}\), at 12.45%. The predominant mineral compositions are gismondine (CaAl\({}_{2}\)Si\({}_{2}\)O\({}_{8}\)-4H\({}_{2}\)O) and warakite (CaAl\({}_{2}\)Si\({}_{6}\)O\({}_{16}\)-6H\({}_{2}\)O).
# Design and Implementation of 24-GHz and 48-GHz VCOs Using Noise Filtering Technique in 90-nm CMOS
###### Abstract  
This work proposes two voltage-controlled oscillators using noise-filtering technique. The first one is a 24-GHz voltage-controlled oscillator, and the second one is based on a push-push architecture with a \(\lambda/4\) transmission line to further increase the frequency up to 48 GHz. The designs are implemented and verified in a standard 90-nm CMOS process. Typically, the current mirror transistor in the tail current has a nonlinear effect. When the transistor operates in the nonlinear region, noise will be introduced. Therefore, a set of LC filters with a resonant frequency at \(2f_{0}\) are added to the design of this section to filter the noise at \(2f_{0}\) through the capacitor to the ground. The measurement results show that the design of a single oscillator has an oscillation frequency of 24.37 GHz, a tuning range of 6.5%, and a phase noise of \(-97.19\) dBc/Hz @1 MHz. The measurement results of the push-push architecture show that the double oscillation frequency is 49.8 GHz, the tuning range is 7.2%, and the phase noise is \(-80.52\) dBc/Hz @1 MHz. The chip areas of 24-GHz LC VCO and 48-GHz push-push LC VCO are 0.68 mm \(\times\) 0.69 mm and 0.7 mm \(\times\) 0.7 mm, respectively.  
Keywords:CMOS 90-nm technology; voltage-controlled oscillators; push-push VCO; transmission line; slow-wave factor (SWF); noise filtering; reduce phase noise +
Footnote †: journal: _micromachines_
# Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems
## 5 Performance Evaluation
In order to ensure the long-term stability of the data queue and avoid the high queuing delay of the data packet, which makes the total end-to-end delay exceed the deadline, the system sets high priority and low priority queue length should not exceed the threshold \(\varpi\xi\), thatis, \(\lim\limits_{T\rightarrow\infty}\frac{1}{T}\sum\limits_{t=0}^{T-1}E\big{\{}Q_{ n}^{H}(t)\big{\}}\leq\varpi\) and \(\lim\limits_{T\rightarrow\infty}\frac{1}{T}\sum\limits_{t=0}^{T-1}E\big{\{}Q_ {n}^{L}(t)\big{\}}\leq\xi\). When the average packet arrival rate is \(\overline{\hbar}=\sum\limits_{u=0}^{U}u\theta_{u}=1\), the system sets \(\varpi=40\), \(\xi=90\), that is, the queue length of high priority should not exceed 40 MB, and the queue length of low priority should not exceed 90 MB. As shown in Figures 7 and 8, the increase in packet arrival rate increases the system's queue length. Furthermore, the threshold \(\varpi\xi\) will increase with an increase in packet arrival rate. Figures 6-8  
Figure 4: Curve of high-priority queue length changing with weight. Figure 5: Curve of low-priority queue length changing with weight. Figure 6: Comparison of average delay(s) under different packet arrival rates. show that the system's average delay and queue length can be manipulated under different packet arrival rates and finally converge. The results show that the DDPS strategy proposed in this paper can dynamically adjust the packet scheduling strategy according to the change in packet arrival rate. This method ensures the system's optimal performance and stability. The above experiments analyze the performance of the DDPS and compare it to the EAS and QS to prove the advantages of the DDPS. We set the experiment weight to \(V=2\). The simulation results are shown in Figures 9-11. Figure 9 shows the average delay of the system, and Figures 10 and 11 compare the high- and low-priority queue lengths. The simulation results show that when the system works for a long time, the average DDPS delay is low. When it tends to be stable, the sum of high- and low-priority queue lengths is also low. DDPS improves system efficiency by dynamically adjusting the queue and the number of packets sent according to the speed status of passing vehicles and the length of high- and low-priority queues. On the other hand, the EAS's insensitivity to vehicles' high-speed mobility and packet queue length affects its performance. The QS only optimizes the system regarding queue length without considering the effect of vehicle speed on transmission delays. The DDPS proposed in this paper has obvious advantages over these two strategies. Figure 8: Comparison diagram of low-priority queue lengths under different packet arrival rates. Figure 7: Comparison of high-priority queue lengths under different packet arrival rates.
# Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study
## 1 Introduction
Based on the simulation, a ground source heat pump (GSHP) with a heat recovery ventilator (ERV) yielded the most energy savings, but this option was also the most expensive one. The most cost-effective option was an air source heat pump (ASHP) without the heat recovery system. [PERSON] [17] studied the combined PV and the solar thermal system and concluded that the combined system would be taking a leading role in meeting the electricity and thermal energy needs for residential buildings toward zero energy. Achieving the zero-energy goal in small low-rise commercial buildings is not overly complex; however, it is much more challenging for large high-rise office buildings [18]. There are few studies found in the literature to report how an existing large high-rise office building can potentially become zero-energy. [PERSON] [19; 20] explored the potential of an existing commercial building with near 20,000 m\({}^{2}\) total floor area in a mild climate in the US moving toward zero energy. Simulation results showed that the commercial building could achieve the zero-energy goal through a deep energy retrofit. However, the economic feasibility was the biggest concern. This paper presents a case study on how a large high-rise office building located in the hot summer and warm winter zone in China can potentially become zero energy without any significant alterations to the building. The intention is to mobilize owners of similar facilities.