esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### CES-EDUPAC Material Selection  
The utilization of the software CES-EDUPAC allowed us to reduce the number of candidate materials as expressed in Table 3, where the third stage of the selection displays the performance index chart that can be found in Figure 9. Furthermore, the list of candidates that has been proven to be useful in building applications is shown in Table 4,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Beam** & **Load (Q+) KN** & **Load (Q\(-\)) KN** \\ \hline C1–C2 & 3.13 & 6.98 \\ C2–C3 & 1.31/1.76 & 1.47/1.92 \\ C3–C4 & 5.95 & 3.85 \\ C5–C6 & 3.46 & 12.17 \\ C6–C7 & 2.90 & 4.10 \\ C7–C8 & 8.86 & 3.83 \\ C9–C10 & 3.31 & 6.58 \\ C10–C11 & 1.89/0.99 & 0.7/2.57 \\ C11–C12 & 6.17 & 4.01 \\ C1–C5 & 5.17 & 4.02 \\ C5–C9 & 2.44 & 2.35 \\ C4–C8 & 5.45 & 3.97 \\ C8–C12 & 2.59 & 2.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation loads.  
Figure 8: Beam load result.  
where the materials are sorted in alphabetical order and do not reflect any preference. In this sense, it is feasible to consider fibers that are reinforcement of concrete as expressed in the methodology, since, over the years, different kinds of fibers have been used by researchers as construction materials due to the price, local availability and improving properties [14].
## 3 Results and Discussion
Figure 8: Time course of the reference signal \(n_{\mathrm{r}}\) for the stirrer system of the laboratory batch bioreactor with an AMFC system (i.e., output of AMFC controller). Figure 7: Time course of the dissolved CO\({}_{2}\) concentration \(\gamma\) of the laboratory batch bioreactor with an AMFC system; additionally, time courses of the CO\({}_{2}\) concentration of the reference model and non-controlled laboratory bioreactor are presented for comparison. The actual stirrer's speed is shown in Figure 9.  
From Figures 5-7, we can see that the adaptive controller assures that the product quantity of the fermentation process follows the prescribed trajectory of the reference model in spite of the unknown parameters of the controlled plant. From Figures 8 and 9, it is clear that the output signal of the adaptive controller stays in the feasible range. The controller's output does not exceed the stirrer's drive maximum or minimum limits (operating range 0-300 min\({}^{-1}\)). For comparison, we also carried out the control of the fermentation process of the laboratory batch bioreactor with a conventional linear Proportional-Integral (PI) controller. Since we did not know the mathematical model of the laboratory bioreactor, we used the Ziegler-Nichols step response method for tuning the parameters of the PI-controller. The proportional gain \(k_{\text{p}}=12\) and the time constant \(T_{\text{i}}=1.17\) of the PI-controller were determined. Figure 10 shows the time course of the dissolved CO\({}_{2}\) concentration of the laboratory bioreactor if the conventional PI-controller was used instead of the adaptive controller. Significantly worse tracking of the reference course was the result of a lack of knowledge of the mathematical model of the fermentation process, and, consequently, the approximate synthesis of the regulator parameters. The procedure used to tune the parameters is more intended to eliminate disturbances than track the reference value. Figure 10: Time course of the dissolved CO\({}_{2}\) concentration \(\gamma\) of the laboratory batch bioreactor with a PI control system; additionally, the time course of the CO\({}_{2}\) concentrations of a non-controlled laboratory bioreactor and the time course of the reference CO\({}_{2}\) concentration are presented for comparison. Figure 9: Time course of the measured stirrer speed \(n\) of the laboratory batch bioreactor with an AMFC system.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 3 Open Collaborative Platform
### Open Collaborative Platform for Multiple Drones
#### 3.2.2 Communication between Krypto and Open Platform  
Information is exchanged between the Krypto module and the Open Collaborative Platform using the common web protocol using GET and POST. While a drone carries the Krypto module and flies around the disaster area, a trajectory (i.e., set of location points) information is recorded and stored. When a Wi-Fi signal is detected, the sensed RSSIs are also logged. The following is the basic information sent from Krypto to the platform: _Krypto ID, trajectory, current power level, and timestamp_. If a Wi-Fi signal is detected, additional information will also be sent: _RSSI_ logs, _IP_, and _MAC_ address of the mobile phone, and _estimated location_ of the mobile phone.  
From the Open Platform, searching path information and update information are sent to a Krypto drone (or drone's pilot) if necessary. The information exchange between drones and the platform is encrypted with standard public/private encryption with public/private from the Platform's registration service on the payload as defined in the Message Encryption standard for Web Push. When a new drone joins the SAR operation, an update on the searching path also is sent to the Krypto module (or the drone operator) from the open platform using RESTful protocol's POST method through the common wireless connection. Different searching path planning approaches are discussed in the Section 3.3.
## 3 Experimental Methods and Results  
### Experimental Setup  
#### 3.1.1 Structural Components of the System Unit  
The residual-input tube-cutting system for plasma bags is a simplified version of the plasma-bag cutting machine that focuses on the cutting function of the input tube. The plasma-bag cutting machine consists of input, cutting, and output functions, while the plasma-bag tray is reciprocated on the bottom disk. This system only retains the function of cutting the input tube to facilitate research. The system is composed of a plasma-bag rotating device, a bottom support plate, an image-acquisition device, a screw-slide table, a cutting component, and a device control system, as shown in Figure 4.  
The plasma bag was placed on a tray with a clamping mechanism at the top to secure its bottom edge, while baffles on both sides prevented any relative movement between the bag and tray during rotation. The plasma bag rotation device was driven by a reducer stepper motor with a maximum speed of 174 r/min. A side optical sensor was mounted to detect whether the input tube was in contact with the bottom surface. The image acquisition device, comprised of a photoelectric sensor, a light source, and a camera, was positioned above the rotation path of the bag. The camera lens was positioned 300 mm above the bottom support plate and had a resolution of 640 \(\times\) 480. It captured 120 frames per second. The cutting device was composed of a lead screw slide, an electromagnetic actuator, and a cutting blade. The lead screw slide table had a 50 mm stroke and was powered by a separate stepper motor. It was mounted parallel to the edge of the lower support plate. The electromagnetic actuator was fixed to the lead screw slide, and the cutting blade was attached to the push rod's end. The cutting device moved along the \(Y\)-axis of the global coordinate system, as shown in Figure 4. Using the distance read by the vision system, the cutting device moved to the corresponding position on the \(Y\) axis and waited for the end of the remaining input tube. Once the end arrived, the electromagnetic switch was turned on, causing the cutting blade to move downwards along the \(Z\) axis of the global coordinate system and cut the input tube. The maximum distance that the electromagnetic switch could travel was 1 mm from the support surface, which satisfied the design requirements for cutting the end of the input tube. The electromagnetic switch had a travel distance of less than 10 mm, allowing for fast response times.
## 2 Materials and Methods
### Methods
#### 2.3.2 MCMC Algorithm  
The MCMC method was chosen for the MODIS-PSN model parameter optimization process because it is based on the assumed prior probability, the probability of observing different data under the given assumptions, and the observed data itself. Unlike other methods, the Bayesian estimation method does not consider the parameter to be estimated as a fixed unknown, but rather as a random variable that conforms to some prior probability distribution. Based on the Bayesian theory, the ideal Bayesian inference process is approximated by constructing a Markov chain with an equilibrium distribution and iterating continuously, leading to parameter optimization and convergence of samples obtained through the recommended distribution. In practical applications, parameter estimation methods provide more accurate and efficient tools, especially in small sample situations. The efficiency advantage of the Bayesian estimation method in estimating the shape parameters of this distribution makes it a more reliable choice and helps improve the science and accuracy of decision-making.
## 3 Measurement Results and Discussion
### Experiments under Winter Conditions
#### 3.2.2 Measured Images with Visible, Near-Infrared, and Far-Infrared Cameras  
Figure 11 shows the images of the first measurement at the Imperial Palace in the winter season. Compared to the summer image in Section 3.1, the far-infrared wavelength image looked very different. In the summer image, the ground was white, and the sky was black, whereas, in this winter image, the ground and sky had a similar appearance in the far-infrared wavelengths. The heat load on the human body is discussed in the next section, as well as the summer data.
## 2 Materials and Methods  
**Chip Fabrication**. All chips were printed on our Stratasys Objet500 Connex 1 3D printer (Stratasys, Rehovot, Israel). The chips were initially designed in the 3D Computer-aided Design software SOLIDWORKS 2022 (SolidWorksCorp., Waltham, MA, USA) and then converted for compatibility with the Objet500 Connex 1. All sets of chips were printed in batches.  
**Materials**. Three different photopolymer materials were used in this study. The core material used for all chip architectures was Agilus30 Clear FLX985 (Stratasys, Eden Prairie, MN, USA), a soft resin. That material was chosen based on its chemical resistance and deformability. SUP706B (Stratasys, Eden Prairie, MN, USA) was used throughout as the sacrificial material to create negative features, i.e., microchannels. In some chips Agilus30 Black FLX985 (Stratasys, Eden Prairie, MN, USA), a black resin, was used because it provides a good optical contrast, for visual demonstration purposes. It was used to visualize microchannels instead of sacrificial material by filling negative spaces normally filled with sacrificial material (e.g., Figure 1D). All chips were printed monolithically in a single process with SUP706B or Agilus30 Black FLX985 and embedded in Agilus30 Clear FLX985 to form the microchannels in each specific architecture.  
**Visual Assessment of Clearance Magnitude**. The microchannels of all printed chips were cleared from the SUP706B sacrificial material as previously described [59]. In some chips, the success and magnitude of the clearance procedure were visually assessed by injecting blue food dye (McCormick Assorted Food Colors, Hunt Valley, MD, USA) into the cleared microchannels, at a concentration of 5 \(\mu\)L in 1 mL water.  
**Analysis of Serpentine Clearance Percentage**. Single serpentine-shaped microchannels were printed in flat chips. The dimensions of a chip were 41 mm \(\times\) 53 mm (width \(\times\) length). Four chips were printed, where each chip contained sets of four cross-sectional sizes (XL = 700 \(\mu\)m; L = 550 \(\mu\)m; S = 400 \(\mu\)m; XS = 250 \(\mu\)m), referred here as a super-array of arrays. The degree of success of the clearance procedure was assessed by measuring the linear length of the cleared fraction. This fraction was expressed as a percentage of the entire length of the channel.  
**Electrical Tests and Measurements**. Electrical resistance measurements to test wired chips were conducted using a Fluke 87V Industrial Multimeter (Everett, WA, USA) and Agilent 34410A Multimeter (Santa Clara, CA, USA). Actuation voltage was provided by Spellman SA60 (Hauppauge, NY, USA).
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 4 CMOS Transimpedance Amplifier (TIA)
### Resistor-Based TIA  
The most intuitive way to implement the TIA is employing a single resistor as shown in Figure 21. In this configuration, the calculations of the gain, \(R_{T}\), and the bandwidth, \(f_{-3 dB}\), are straightforward and can be expressed as follows:  
\[R_{T}=R_{TIA} \tag{4}\]  
\[f_{-3 dB}=\frac{1}{2\pi R_{TIA}C_{PD}} \tag{5}\]  
As evident from the above equations, there is a severe tradeoff between the gain and the bandwidth. On the other hand, the total integrated noise at the output is given by:  
\[\overline{V_{n,out}^{2}}=\frac{kT}{C_{PD}} \tag{6}\]  
where \(k\) is the Boltzmann constant and \(T\) is the absolute temperature. Further, the signal-to-noise ratio (SNR) can be defined to assess the performance of the TIA as follows:  
\[SNR=\frac{C_{PD}}{kT}I_{in}^{2}R_{TIA}^{2} \tag{7}\]  
Figure 20: Categorization of PD by coupling schemes.  
which indicates that increasing \(R_{TIA}\) enhances the SNR indefinitely. However, a large \(R_{TIA}\) introduces an inter-symbolic interference (ISI) and thus reduces the bandwidth, which limits the maximum value of \(R_{TIA}\) for a given \(C_{PD}\). In general, the minimum bandwidth sufficiently suppressing the ISI is chosen to be 0.5-0.7 times the target data rate. Thus, the only way to enhance the SNR is increasing the input optical power, thereby providing a large input current. Furthermore, a large \(C_{PD}\) renders this type of TIAs unsuitable for high-speed optical receivers. Nevertheless, this type of TIAs exhibits a distinct advantage in that it consumes virtually zero power. Recently, by effectively cancelling the ISI, a resistor-based TIA is successfully demonstrated at moderately high speed with low power consumption [70].
## 5 Numerical and Simulation Results  
### System Setup  
In this subsection, we present the Monte Carlo simulation results obtained from the proposed system under various conditions. The simulation parameters of the proposed model are shown in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline
**Description of Parameter** & **Parameter** & **Value** \\ \hline Path loss exponent for \(\theta_{r},\theta_{t}=\pi/2\) & \(\epsilon_{\pi,\gamma}^{t},\epsilon_{\pi/2}^{t}\) & 2.2 \\ \hline Path loss exponent for \(\theta_{r},\theta_{t}=0\) & \(\epsilon_{\varphi_{r}}^{t},\epsilon_{0}^{t}\) & 3.5 \\ \hline Path loss exponent for \(\theta_{G}=0\) & \(\epsilon_{0}^{G}\) & 2.8 \\ \hline Path loss exponent for \(\theta_{G}=\pi/2\) & \(\epsilon_{\pi/2}^{G}\) & 2.2 \\ \hline Rician factor for \(\theta_{r},\theta_{t}=\pi/2\) & \(\kappa_{\pi/2}^{r},\kappa_{\pi/2}^{t}\) & 15 dB \\ \hline Rician factor for \(\theta_{r},\theta_{t}=0\) & \(\kappa_{\varphi}^{r},\kappa_{0}^{t}\) & 5 dB \\ \hline Rician factor for \(\theta_{G}=0\) & \(\kappa_{0}^{G}\) & 15 dB \\ \hline Rician factor for \(\theta_{G}=\pi/2\) & \(\kappa_{\pi/2}^{G}\) & 10 dB \\ \hline Path loss at 1 meter & \(\chi_{1}\) & \(-\)30 dB \\ \hline Maximal height of UAV & \(h_{max}\) & 100 m \\ \hline Minimal height of UAV & \(h_{min}\) & 10 m \\ \hline Distance between U1/U2 and \(O\) point & \(L\) & 10 m \\ \hline BS position & \((r_{bs},h_{bs})\) & \((50,10)\) \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
# Conductivity Enhancement of Graphene and Graphene Derivatives by Silver Nanoparticles
###### Abstract  
In this article, a facile way for the doping of graphene and graphene derivatives with silver nanoparticles at different Ag ratios is described. Ag nanoparticles were formed directly on the surface of two different graphene substrates dispersed in dimethylformamide by the reduction of Ag cations with NaBH\({}_{4}\). A few layered graphene nanosheets (FLG) produced from graphite and reduced graphene oxide functionalized with amino arylsulfonates (_f_-rGO) were used as substrates. The final graphene/Ag nanoparticle hybrid in the form of solid, dense spots showed enhanced electrical conductivity, which can be attributed to the formation of conductive interconnections between the 2D nanosheets. Importantly, electrical conductivities of 20 and 167 10\({}^{3}\) S m\({}^{-1}\) were measured for the hybrids of _f_-rGO and FLG, respectively, with the higher Ag percentage without an annealing process. A representative hybrid _f_-rGO with Ag nanoparticles was used for the development of a highly conductive water-based gravure ink with excellent printing properties.  
conductive ink; graphene hybrid; gravure printing; silver nanoparticles Article
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 3 Code-Frequency Two-Stage Compression Acquisition
### Algorithm Process  
The detailed and straightforward processes of proposed GNSS acquisition algorithm are shown in Algorithm 1 and Figure 4.  
```
0: Incoming signal, \(r(n)\); Spreading code vector, \(\mathbf{c}_{0}\); Measurement matrix, \(\mathbf{\Phi}\); Decision threshold, \(V_{t}\);
0: Code phase, \(\tau\); Doppler frequency offset, \(f_{\text{d}}\);
1: Mapping incoming signal \(r(n)\) to different carrier frequencies \(f_{o}^{i}\);
2: Overlapping the mapped signals to the same code phase to obtain \(r_{l}^{\prime\prime}(n)\);
3: Padding zeros to the end of the mapped signals \(r_{l}^{\prime\prime}(n)\) to form a signal vector \(\mathbf{r}^{\prime\prime}\);
4: Shifting spreading code vector \(\mathbf{c}_{0}\) to obtain the code matrix \(\mathbf{C}\);
5: Performing FFT operation to find out the measurement data \(\mathbf{Y}\);
6: Using the greedy algorithm to obtain the correlation result \(\mathbf{X}\);
7: Comparing the first \(K\) elements from the correlation result \(\mathbf{X}\) with the threshold \(V_{t}\) to obtain the code phase \(\tau\) and Doppler shift \(f_{\text{d}}\);
8:return\(\tau,f_{\text{d}}\);
```  
**Algorithm 1** The proposed GNSS acquisition algorithm.
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 3 System Model
### Energy Consumption Model  
The energy model used in the proposed scheme is adopted from Ref. [31]. The radio energy dissipation model of a transceiver consists of three primary components: a transmitter, amplifier, and receiver, which are illustrated in Figure 3.  
The energy model formulates the energy expended by the \(i\)th sensor node amid the transmission and reception of a \(p\)-bit packet to \(j\)th sensor node as shown by Equations (2) and (3):  
\[E_{TX}\big{(}p,d_{ij}\big{)}=\big{(}E_{elec}+\varepsilon_{amp}\big{)}\cdot p \tag{2}\]  
\[E_{RX}(p)=E_{elec}\cdot p \tag{3}\]  
where \(d_{ij}\) represents the distance between the \(i\)th and the \(j\)th node, and \(E_{TX}\) and \(E_{RX}\) are the transmission and reception energy dissipation, respectively. \(E_{elec}\) is a distance-independent entity, and it accounts for the energy consumption by the transceiver circuitry. \(\varepsilon_{amp}\) denotes the energy consumption of the transmitter's amplifier, which is formulated as:  
\[\varepsilon_{amp}=\left\{\begin{array}{ccc}\varepsilon_{fs}\cdot{d_{ij}}^{2 }&for&d_{ij}\leq d_{th}\\ \varepsilon_{mp}\cdot{d_{ij}}^{4}&for&d_{ij}>d_{th}\end{array}\right. \tag{4}\]  
and  
\[d_{th}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{5}\]  
where \(d_{th}\) is the threshold distance, which is defined in Equation (5), \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are the propagation models for the amplification energy dissipation in free space and multi-path, respectively. In the free space model, a direct line-of-sight (LOS) path is considered between the transmitter and receiver nodes, whereas a multipath represents the non-line-of-sight (NLOS) signal propagation from various routes at different time intervals after reflection from the ground.  
The amount of energy consumed by a CM in a cluster: \(E_{CM}\), and the amount of energy required for a CH: \(E_{CH}\), are given by Equations (6) and (7), respectively, as:  
\[E_{CM}=E_{init}-E_{TX}\big{(}p,d_{ij}\big{)} \tag{6}\]  
\[E_{CH}=E_{init}-E_{std} \tag{7}\]  
\[E_{std}=E_{TX}\big{(}p,d_{ij}\big{)}+E_{DA}+E_{RX}(p) \tag{8}\]  
Figure 3: Radio energy dissipation model.  
where \(E_{init}\) represents the initial energy of the sensor node. \(E_{std}\) is the standard energy consumption of a node taking part in the CH selection phase, and \(E_{DA}\) denotes the energy consumption of the node for the data aggregation process.
## 6 Conclusions
\begin{array}{l}f_{1}^{\prime}=\frac{h_{s}}{D_{0}^{2}}\bigg{[}\omega ^{2}+\frac{1}{h_{s}}\bigg{(}\frac{v_{0}^{2}}{v_{0}}-g\bigg{)}-\frac{4D_{0}^{2} }{v_{0}^{2}}+3\dot{D}_{0}\bigg{(}\frac{1}{v_{0}}-\frac{\dot{D}_{0}}{D_{0}^{2} }\bigg{)}+\frac{2\dot{D}_{0}}{D_{0}}+\frac{\dot{c}_{D_{0}}^{2}}{\dot{c}_{D_{0 }}^{2}}-\frac{\bar{c}_{D_{0}}}{C_{D_{0}}}\bigg{]},\\ f_{2}^{\prime}=\frac{h_{s}}{D_{0}^{2}}\bigg{(}2\xi\omega-\frac{3D_{0}}{v_{0}}+ \frac{2\dot{D}_{0}}{D_{0}}\bigg{)},\\ f_{3}^{\prime}=\frac{h_{s}}{D_{0}^{2}}\bigg{[}-\frac{2D_{0}v_{0}}{h_{s} \cdot r_{0}}+\frac{8D_{0}^{3}}{v_{0}^{2}}+\frac{3\dot{D}_{0}D_{0}}{v_{0}^{2}}- \frac{\dot{c}_{D_{0}}D_{0}^{2}}{C_{D_{0}}v_{0}^{2}}\bigg{]}.\end{array} \right.\] (A22)  
Because it is difficult to obtain the value of \(\delta\dot{D}\), it can be substituted by \(\delta\dot{h}=\dot{h}-\dot{h}_{0}\), and then the small increment \(\delta(L\cos v/D)\) is written as:  
\[\delta\bigg{(}\frac{L\cos v}{D}\bigg{)}=f_{1}\delta D+f_{2}\delta\dot{h}+f_{3 }\delta v\] (A23)  
Taking the variation of the small increments \(\delta v\), \(\delta D\), \(\delta\dot{D}\), and \(\delta\dot{h}\) into Equation (16), only the first-order terms are retained:  
\[\delta\dot{D}=-\frac{D_{0}}{h_{s}}\delta\dot{h}+\frac{2D_{0}^{2}}{v_{0}^{2}} \delta v-\bigg{(}\frac{4D_{0}}{v_{0}}+\frac{\dot{h}_{0}}{h_{s}}-\frac{\dot{c}_ {D_{0}}}{C_{D_{0}}}\bigg{)}\delta D\] (A24)  
Substituting Equation (A24) into Equation (A15) and collecting with some algebraic skills, there is:  
\[\left\{ \begin{array}{l}f_{1}=f_{1}^{\prime}-f_{2}^{\prime}\bigg{(}\frac{4D_ {0}}{v_{0}}+\frac{\dot{h}_{0}}{h_{s}}-\frac{\dot{c}_{D_{0}}}{c_{D_{0}}}\bigg{)},\\ f_{2}=-f_{2}^{\prime}\frac{D_{0}}{h_{s}},\\ f_{3}=f_{3}^{\prime}+2f_{2}^{\prime}\frac{D_{0}^{2}}{v_{0}^{2}}.\end{array} \right.\] (A25)  
Thus, the required lift-to-drag ratio is given by:  
\[\bigg{(}\frac{L\cos v}{D}\bigg{)}_{c}=\bigg{(}\frac{L\cos v}{D}\bigg{)}_{0}+f_ {1}\delta D+f_{2}\delta\dot{h}\] (A26)  
\[\left\{ \begin{array}{l}\delta D=D-D_{0},\\ \delta\dot{h}=v\theta+h_{s}\bigg{(}\frac{2D_{0}}{v}+\frac{\dot{D}_{0}}{D_{0}}- \frac{\dot{c}_{D_{0}}}{C_{D_{0}}}\bigg{)}.\end{array} \right.\] (A27)