esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Office Buildings  
The building studied in this research consists of several administrative offices occupied by employees of the Ministry of Public Works of the city of Antsiranana for more than 20 years. The same office was built in the three other cities of the country with different climatic characteristics like that showed in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cities** & **Latitude** & **Longitude** & **Altitude (m)** & \multicolumn{2}{c}{**Temp. (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**RH (\%)**} & \multicolumn{2}{c}{**Wind Speed (m/s)**} \\ \cline{4-9}  & & & **Max.** & **Min.** & **Max** & **Min.** & **Max.** & **Min.** \\ \hline Antsiranana & 12.25 & 49.27E & 40 & 33.5 & 20.0 & 85.0 & 60.0 & 9.0 & 4.5 \\ Mahajunga & 15.53S & 46.29E & 20 & 37.5 & 15.0 & 90.0 & 40.0 & 8.5 & 0.1 \\ Toamasina & 18.19S & 49.34E & 06 & 35.5 & 17.0 & 80.0 & 50.0 & 8.5 & 0.0 \\ Taolagnaro & 25.1S & 46.69E & 539 & 30.0 & 17.0 & 90.0 & 17.0 & 5.6 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Climatic characteristics of four cities.  
Figure 2: East facade of the studied office building.  
The entire building was built on an area of 288 m\({}^{2}\) and height of 3.5 m. This building comprises 12 offices with 20 workers and one meeting room with a capacity of 50 people and two tool storage stores. The office occupancy schedule is from Monday to Friday, 8:00-12:00 and 2:00 pm-5:00 pm. To assess indoor air quality in more natural conditions, certain parameters were previously defined for this purpose (stop Heating, ventilation, and air conditioning (HVAC) system, Lighting: LED; activity of occupant...). The activity of the occupants can be considered as sedentary, that is, the workers were not subjected to any physical constraint. An estimation of the resistance of workers' clothes was around 1 Clo in the rainy season and 0.5 Clo in the dry season. In addition, the offices were analyzed under mechanical and natural ventilation conditions. Finally, the different materials making up this building and their characteristics are presented in Table 2. Air conditioning system: Fan Coil Unit (4-pipe) with seasonal cooling system (COP (Coefficient of Performance) = 1.8).  
The multi-zone model was created by the simulation tool. It was possible to have the data for each zone. However, the data that interests us are those of the entire building studied. The schedule of occupants was detailed in Section 2.3: The office occupancy schedule is from Monday to Friday, 8:00-12:00 and 2:00 pm-5:00 pm.  
Other internal heat gains:  
1. Computer: Power density 1.1 W/m\({}^{2}\), radiant fraction 0.2; schedule: Work day from Monday to Friday, 8:00-12:00 and 2:00 pm-5:00 pm.
2. Office equipment: Power density 0.8 W/m\({}^{2}\), radiant fraction 0.2.
## 2 Materials and Methods
### Agarose Gel Electrophoresis  
For the evaluation of the influence of CaCl\({}_{2}\) on the electrophoretic mobility of the pDNA molecules, 5 \(\mu\)L of plasmid was mixed with 45 \(\mu\)L of HEPES medium supplemented with different concentrations (0, 0.25, 0.5, or 1 mM) of CaCl\({}_{2}\). Then, the samples were loaded into 0.5% agarose gel. For the gel preparation, agarose (Thermo Fisher Scientific, Washington, DC, USA) was melted in 1\(\times\) TAE buffer (Thermo Fisher Scientific, Washington, DC, USA) and mixed with ethidium bromide (Sigma-Aldrich, St. Louis, MO, USA) at a final concentration of 1 \(\mu\)g/mL. Each sample was mixed with 2-3 \(\mu\)L of 2\(\times\) DNA loading dye (Thermo Fisher Scientific, Washington, DC, USA). Finally, a 1 kb DNA ladder (Thermo Fisher Scientific, Washington, DC, USA) was added to the gel, and the reaction was performed at 80 V for 45 min. For the visualization of the results, a UV transilluminator (Cleaver Scientific, Rugby, UK) was used.
## 4 Results and Discussion
### Durability against Sulphate Attack  
External sulphate resources can severely damage concrete due to sulphate attack, especially in the solution mode where sulphate ions can freely infiltrate the concrete specimens. Sodium sulphate attack on calcium hydroxide and calcium hydro aluminate generates compounds including calcium sulphate (plaster) and ettrinigite, accompanied by an increase in volume. Figure 16 shows the compressive strength of specimens after being placed in a 20% sodium sulphate solution. Higher values for specimens with 5 and 10% cement replacement with waste might be due to a filler effect and the latent pozzolanic reactivity of waste, which was explained in Section 4.1. Durability loss against sulphate attack increased in specimens with more than 10% cement replacement. The compressive strength reduction percentage of specimens at 120 days after being placed in 20% sodium sulphate solution is also shown in Figure 17. Similar to the trend at 28 days, it is evident that, by increasing the cement replacement, the compressive strength reduction increased. A greater decline in compressive strength of specimens containing waste powders than the control specimens (pure cement) can be attributed to the slow pozzolanic reactivity of the added SCMs.  
Figure 14: Electrical resistivity of all mortar mixes at 7, 28, and 42 days.  
Figure 15: Relative electrical resistivity at the ages of 7, 28, and 42 days.
# A Polarization-Dependent Frequency-Selective Metamaterial Absorber with Multiple Absorption Peaks
## 6 Conclusions  
A simple design of a polarization-dependent frequency-selective metamaterial absorber based on a circular patch with a C-shaped slot is presented. In contrast to previous studies on compression of multi resonators, in the proposed design, a multi-band absorption is obtained using a simple resonant structure. Furthermore, changing the polarization angle of the incident wave, a triple-band, a dual-band, and a single-band absorptions can be achieved, which is required in the polarization-dependent detection. In order to demonstrate the absorption mechanism, the electric field distribution, the surface current distribution, and the power-loss distributions were obtained. The proposed design was verified both by simulations and experiments, which were performed using the free space measurement method. A good agreement between simulated and measured results was achieved, which proves a high absorption performance of the proposed MA.  
This work was supported by the National Natural Science Foundation of China (No. 51607050) and the National Defense Pre-Research Foundation of China (Grant No. 6140239010106).  
[PERSON] and [PERSON] conceived and wrote the paper; [PERSON] and [PERSON] performed the experiments; [PERSON] guided the experimental design and analyzed the data.  
The authors declare no conflict of interest.
## 2 System Model and Problem Formulation
### Multi-Agent Q-Learning  
Inspired by [30; 31], we aim at the case where UAVs are influenced by co-channel interference and design a cooperative anti-jamming channel selection algorithm based on multi-agent Q-learning. In the proposed multi-agent Q-learning, each user maintains and updates a Q table \(\tilde{Q}^{n}\), which is based on joint action \(\mathbf{a}\left(t\right)\). Similar to single Q-learning, the Q function updates using the following rule:  
\[\tilde{Q}_{t+1}^{n}\left(s,\mathbf{a}\left(t\right)\right)=\left(1-\widetilde {\lambda}_{n}\right)\tilde{Q}_{t}^{n}\left(s,\mathbf{a}\left(t\right)\right)+ \widetilde{\lambda}_{n}\left[\widetilde{r}_{n}\left(t\right)+\widetilde{ \gamma}_{n}\widetilde{V}_{n}\left(s^{\prime}\right)\right], \tag{13}\]  
where \(\widetilde{\lambda}_{n}\) is user \(n\)'s learning rate under joint action and \(\widetilde{\gamma}_{n}\) is the discount factor correspondingly. \(\widetilde{r}_{n}\left(t\right)\) denotes the user \(n\)'s immediate reward when taking joint action \(\mathbf{a}\) under state \(s\). Moreover, \(\widetilde{r}_{n}\left(t\right)\) represents the normalized utility under joint action, which can also be shown as:  
\[\widetilde{r}_{n}\left(t\right)=\left(1-f\left(a_{n}\left(t\right),a_{j}\left( t\right)\right)\right)\frac{1}{I_{n}\left(c_{n},t\right)}-w_{s}\delta_{s} \left(a_{n}\left(t\right),a_{n}\left(t-1\right)\right)-w_{c}\delta_{c}\left( I_{n}\left(c_{n},t\right)\right). \tag{14}\]\(\widetilde{V}_{n}\left(s^{\prime}\right)\) is the user \(n\)'s value function in multi-agent Q learning, which is:  
\[\widetilde{V}_{n}\left(s^{\prime}\right)=\widetilde{Q}_{t}^{n}\left(s^{\prime}, \mathbf{a}^{*}\right), \tag{15}\]  
where \(\mathbf{a}^{*}\) represents the best joint action when all users' total benefit reaches the maximum. \(\mathbf{a}^{*}\) can be expressed using the following equation:  
\[\mathbf{a}^{*}=\arg\max\sum_{n=1}^{N}\widetilde{Q}_{t}^{n}\left(s^{\prime}, \mathbf{a}\right). \tag{16}\]  
Without loss of generality, either in single Q-learning or in multi-agent Q-learning, the \(\varepsilon\)-greedy policy is introduced for the purpose of avoiding the local optimum. Moreover, it is obvious that indicator function \(\delta_{c}=0\) in single Q-learning and \(\delta_{c}=1\) in multi-agent Q-learning. As in single Q-learning, users take actions dependently, while in multi-agent Q-learning, users cooperate with each other to avoid co-channel interference. Note that in [34], the author investigated the convergence of multi-agent Q-learning and proved that the multi-agent Q-learning can converge to an optimal joint action, so we will not discuss the convergence analysis in our paper. Moreover, as the cooperation cost is introduced, the utilities of users may decrease in multi-agent Q-learning due to joint Q-table sharing.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Modes Control
#### 3.2.6 Temperature Monitoring and Control  
DC fast charging produces heat because of the substantial charging currents it employs. Temperature monitoring and control techniques have been implemented to safeguard the battery against overheating. Temperature sensors are positioned inside the battery pack to monitor its temperature throughout the charging process [134]. The charging system utilizes these data to modify charging parameters like current or voltage, ensuring the battery remains within safe temperature thresholds. This practice is crucial for preserving optimal battery performance and extending its longevity [135]. The good news is that advancements in lithium-ion batteries continue to be made, opening doors for next-generation options like solid-state batteries. Material research is key to overcoming challenges and making electric vehicles more sustainable. By focusing on improving the heat resistance of materials, using venting mechanisms, and employing eco-friendly bio-based flame retardants, researchers are working to prevent thermal runaway, a critical safety concern.
## 3 Result and Discussion
### Dynamic Non-linearity Parameter  
Simulation is carried out considering the change of the non-linearity parameter with temperature independently. The HIFU irradiation time is set to 5 s to ensure the validity of the dynamic non-linearity parameter used in the range of 30 \({}^{\circ}\)C to 75 \({}^{\circ}\)C, and the non-linearity parameter and acoustic field are updated here every 0.5 s. In Figure 5b, the axial profile of peak acoustic pressure at 5 s is almost identical for dynamic and constant \(\left(B/A\right)_{\mathit{liver}}\). This phenomenon can be attributed to the local increase  
Figure 4: **(a)** The evolution of \(\alpha_{\mathit{liver}}\) at t = 1 s and 3 s. The effects of the dynamic acoustic absorption coefficient on: **(b)**\(p\)**(c)**\(Q_{\mathit{ext}}\)**(d)**\(T\) at the ultrasonic focus **(e)** thermal damage zone **(f)**\(t_{\text{43}}\) at t = 3 s.  
of the non-linearity parameter near the ultrasonic focus with the increase of HIFU irradiation time, as shown in Figure 5a. According to Formula (5), the value of \(Q_{ext}\) for dynamic \((B/A)_{liver}\) is almost the same as that for constant \((B/A)_{liver}\). Consequently, the change of the focal temperature with time and the thermal damage zone are almost identical for dynamic and constant \((B/A)_{liver}\), as shown in Figure 5c,d. It can be concluded that the dynamic acoustic non-linear parameter has little effect on the HIFU hyperthermia.
## 1 Introduction  
### Multinuclear MRI  
Magnetic resonance imaging (MRI) systems with a strong main magnetic field, \(\mathrm{B}_{0}\), such as 7T MRI scanners, can produce highly detailed anatomical images [1, 2, 3, 4]. The increase in \(\mathrm{B}_{0}\) not only affects the number of spins available for excitation, but also the Larmor's frequency at which the spins enter into resonance [5, 6, 7]. This increase in the Larmor frequency and the number of spins for excitation also allows additional nuclei besides protons (\({}^{1}\)H) to be used for acquiring MR images; this phenomenon is referred to as multi-nuclei MR imaging [8, 9, 10, 11, 12, 13] or X-nuclei MRI. As X-nuclei MRI can be used for diagnosing early stage cancers, tumors, lesions, and pathogens, it has become a widely studied technique in recent years. Sodium \({}^{23}\)Na, phosphorus \({}^{31}\)P, and fluorine \({}^{19}\)F are among the most used nuclei for X-nuclei imaging.  
Various applications have been proposed for X-nuclei MRI and MRI spectroscopy (MRIS). Several studies have shown that \({}^{23}\)Na imaging can be used to examine multiple sclerosis, cartilage degradation, strokes, and tumors [14, 15, 16, 17, 18]. It is most commonly applied to the brain and knee regions; in addition, some studies have also been done in kidneys due to the high local \({}^{23}\)Na concentrations [19]. Reports have shown that \({}^{31}\)P MR spectroscopy can be used to study metabolism in the human body [20], including the biochemistry of cells such as adenosine triphosphate (ATP), adenosine diphosphate (ADP), and phosphocreatine. \({}^{31}\)P MRIS has been applied to the heart [21, 22], brain [23, 24, 25], breast [26], neck [27], and even to small animals [28]. While the concentration of \({}^{19}\)F in the body is limited, there are some applications for these nuclei when used in combination with MRI. For instance, \({}^{19}\)F has been used to study tumors in mice, monitor inflammation after cardiac and cerebral ischemia, image the gastrointestinal system, and track stem cells [29, 30, 31].
# Ranking of Electricity Accumulation Possibilities: Multicriteria Analysis
## 2 Comparative Description of Selected Energy Storage Technologies
### Lead-Acid Battery  
The most common batteries for energy storage are lead-acid batteries used as backup energy sources. The basis of their operation relies on electrochemical reactions involving the exchange of charges between the positive electrode, which consists of lead dioxide, and the negative electrode, which consists of porous lead. These electrodes are submerged in an electrolyte composed of sulfuric acid in water, which actively participates in the charge and discharge reactions.  
In recent years, new types of lead-acid batteries have been invented. One such battery is the valve-regulated lead-acid battery which is sealed and does not need to be topped up with water, and thus the maintenance cost of this type of battery is cheaper. Gel-type lead-acid batteries, which are filled with gel instead of liquid, reduce the possibility of leakage [9]. Taking into account the challenge of the modern world, even these batteries continue to be improved and currently efforts are being made to improve the performance of lead-acid batteries and extend their life cycle. One of the examples most often cited in recent articles is to improve the discharge capacity of lead-acid batteries, for example, through the use of graphene oxide [10].  
Figure 1: Electricity storage technologies selected for the comparative analysis.
## 5 Simulation Study
### Performance Comparison  
We compare the performance of the three protocols under the default parameters, with the results presented in Table 4.  
As we can seen, the ARP protocol achieves the highest delivery ratio with lower transmission overhead (indicated by the average copies for each message) and the lowest average delivery delay, compared with other two protocols. The LBEL scheme performs worst in terms of delivery ratio. This is reasonable because the sensor nodes here only transmit messages to the node belonging to the same group (same label) as the destinations. It is ineffective for message delivery in the network. Moreover, we observe that for the flooding protocol, the delivery ratio is higher and its average delay is much longer than that of the LABEL scheme. This stems from the queue management of flooding where only messages newly generated by the source sensor are allowed into the queue after the queue is full. Accordingly, messages in the data queue can be divided into two types: message copies created by the flooding stage and single data messages coming afterwards. The former can be delivered to the sink node by different sensor nodes, while the latter can be delivered to sink nodes by the source node only. As a result, the overall delivery ratio of flooding protocol outperforms the LABEL scheme. Since the single data messages can only be transmitted to the sink nodes by the source sensor node, its average delay is much longer.  
\begin{table}
\begin{tabular}{|l|c|l|l|} \cline{2-4} \multicolumn{1}{c|}{} & **ARP** & **LABEL** & **Flooding** \\ \hline
**Delivery ratio (\%)** & 73.5 & 4\(\hat{9}\)0 & 66.8 \\ \hline
**Average copies for each message** & 2.7 & 2.6 & 4.3 \\ \hline
**Average delay(s)** & 120.5 & 134.2 & 671.2 \\ \hline \end{tabular}
\end{table}
Table 4: Simulation results with default parameters.  
Figure 5: Node distributions at 12 AM.  
We also examine how the thresholds of Th and \(\lambda\) affect the performance of ARP. e.g., the data delivery ratios of ARP with different values of Th are shown in Figure 6. As we can see, the delivery ratio of ARP decreases with the increase of Th. This is natural as upgrading threshold Th for too long makes sensor nodes update their transmission ranking at quite a slow speed. Thus sensor nodes' transmission rankings will be inaccurate, resulting in missed transmissions in the network. Figure 7 depicts how different values of \(\lambda\) affect the performance of ARP. We acquire that with the increase of \(\lambda\), the data delivery ratio increases. This is because with too small value a of \(\lambda\), the time interval that a sensor node uses to reduce its transmission ranking is improperly short. Therefore, transmission rankings for sensor nodes would be unreasonable and they may not accurately reflect the sensor nodes' communication strength with sink nodes. As data transmission is based on the transmission rankings of sensor nodes, thus the delivery ratio is relatively small.
## 4 RMNCE-Based Adaptive UKF
### INS/GNSS System Description  
The state vector in the proposed tightly-coupled system is given by:  
\[\mathbf{X}=\left[\phi_{E},\phi_{N},\phi_{U},\delta V_{E},\delta V_{N},\delta V _{U},\delta L,\delta\lambda,\delta h,\varepsilon_{x},\varepsilon_{y}, \varepsilon_{z},\
abla_{x},\
abla_{y},\
abla_{z},\delta t_{u},\delta t_{u} \right]^{T} \tag{22}\]  
where \(\left[\phi_{E},\phi_{N},\phi_{U}\right]^{T}\) is the misalignment vector between the true and estimated local navigation frame in East-North-Up (ENU) coordinate, \(\left[\delta V_{E},\delta V_{N},\delta V_{U}\right]^{T}\) is the velocity error, \(\left[\delta L,\delta\lambda,\delta h\right]^{T}\) is the position error, \(\left[\varepsilon_{x},\varepsilon_{y},\varepsilon_{z}\right]^{T}\) is the gyroscope bias, \(\left[\
abla_{x},\
abla_{y},\
abla_{z}\right]^{T}\) is the accelerometer bias, \(\delta t_{u}\) and \(\delta i_{u}\) are respectively the GNSS receiver clock offset and clock drift. The nonlinear differential equations of INS states are well known and can be found in [38; 39; 40]. The biases of IMU sensors are considered constant, and \(\delta t_{u}\) is modeled as a first-order Gauss-Markov process.  
The measurement equation of pseudo-range is shown in Equation (15). The pseudo-range rate observation model is formulated as:  
\[\dot{\rho}_{GNSS}=e_{x}(v_{s,x}-v_{x})+e_{y}\big{(}v_{s,y}-v_{y}\big{)}+e_{z} (v_{s,z}-v_{z})+\delta i_{u}\;+\;\Delta\dot{\rho} \tag{23}\]  
where \(\big{[}v_{s,x},v_{s,y},v_{s,z}\big{]}^{T}\) is the satellite velocity, \(\big{[}v_{x},v_{y},v_{z}\big{]}^{T}\) is the receiver's velocity, \(\big{[}e_{x},e_{y},e_{z}\big{]}^{T}\) is the unit vector between the receiver and the satellite, \(\delta i_{u}\) denotes the receiver clock drift and \(\Delta\dot{\rho}\) is the measurement noise of pseudo-range rate.  
Besides, altitude information is employed to augment the observation system. The ellipsoid equation is employed to serve as an auxiliary measurement equation [27]:  
\[\frac{x^{2}}{(R_{e}+h)^{2}}\;+\;\frac{y^{2}}{(R_{e}+h)^{2}}\;+\;\frac{z^{2}}{ (R_{p}+h)^{2}}=1 \tag{24}\]  
Figure 1: The flow chart of the proposed satellite selection.  
where \(h\) is the altitude information, \(R_{e}\) and \(R_{p}\) are the lengths of the Earth's semi-major and semi-minor axes respectively.
## 3 Delay Inversion and Jitter Optimizations  
This section analyses the delay inversion effect, a side-effect of applying uniformly distributed jitter on route request (RREQ) flooding. In order to counter the delay inversion effect, an approach called \"window jitter\" is introduced, in Section 3.2, applicable for networks using a simple hop count metric. Of course, hop count metrics have their own issues, notably the worst-path-first syndrome, and therefore, Section 3.3 presents a generalization of \"window jitter\" to non-trivial link metrics.
# Dielectric Spectroscopy Studies and Modelling of Piezoelectric Properties of Multiferroic Ceramics
###### Abstract  
Compounds and solid solutions of bismuth ferrite (BiFeO\({}_{3}\))--barium titanate (BaTiO\({}_{3}\)) system are of great scientific and engineering interest as multiferroic and potential high-temperature lead-free piezoelectric materials. In the present paper, the results of research on the synthesis and characterisation of 0.67 Bi\({}_{102}\)FeO\({}_{3}\)-0.33 BaTiO\({}_{3}\) (67 BFBT) ceramics in terms of crystal structure and dielectric and piezoelectric properties are reported. It was found that the produced 67 BFBT ceramics were characterised by a tetragonal crystal structure described by the \(P4 mm\) space group, an average crystallite size \(<\)\(D\)\(\approx\) 80 nm, and an average strain \(<\)\(c\)\(>\) = 0.01%. Broad-band dielectric spectroscopy (BBDS) was employed to characterise the dielectric response of polycrystalline ceramics. The frequency range from \(\
u\) = 10\({}^{-1}\) Hz to \(\
u\) = 10\({}^{5}\) Hz to \(\
u\) = 10\({}^{7}\) Hz and the temperature range from \(T\) = -20\({}^{\circ}\)C to \(T\) = 50\({}^{\circ}\)C. The electric equivalent circuit [R\({}_{\circ}\)CE\({}_{1}\)([L\({}_{1}\)R\({}_{1}\)C\({}_{1}\)]C\({}_{0}\)]) was used, and good fitting quality was reached. The relevant calculations were performed, and it was found that the piezoelectric charge coefficient exhibited a value of \(d_{31}\) = 35 pC/N and the planar coupling factor was \(k_{\rm p}\) = 31% at room temperature. Analysis of impedance spectra performed in terms of circumferential magnetic field made it possible to establish an influence of magnetic field on piezoelectric parameters of 67 BFBT multiferroic ceramics. Additionally, the \"magnetic\" tunability of the modulus of the complex dielectric permittivity makes 67 BFBT a sensing material with vast potential.  
Electro ceramics; structure; impedance spectroscopy; piezoelectric properties; modelling; \"magnetic\" tunability +
Footnote †: journal: Applied Physics Letters