esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technological Platform for the Wireless Sensor Node
### ARM System and Wireless Radio Networking  
This section provides an overview of the ARM microcontroller systems, focusing on the LPC2148F model of the LPC2000 family. Table 1 provides a summary of the LPC2148F hardware and a picture and the block diagram of the wireless sensor node is shown in Figure 1.  
The LPC2148F model has an ARM7 TDMI-S [24] processor of the ARM architecture, where the S means that it has a synthesized VHDL core. For a more in depth description of the LPC2148F system, the following references should be consulted [25-27]. The agricultural platform is equipped with 802.15.4 compliant radios, namely the XBEE Pro Zigbee radios from Maxstream. These radios were chosen due to their combination of lightweight, long transmission range, serial interface compatibility with the ARM processor, and packet interface. As shown in Figure 1, the small size of the agricultural platform designed at the SITELDI Solutions Laboratory possesses the advantages of being inexpensive, energy efficient and highly resistant in outdoor environments.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **LPC2148F** \\ \hline Processor & ARM7 TDMI-S 60 MHz \\ RAM memory & 32 Kbyte (expandable module external) \\ ROM memory & 512 Kbyte EEPROM (electrically-erasable programmable ROM) \\ Serial ports & UART: serial (38,400 bauds) + UART1: modem \\ Clocks & RTC 32.768 KHz \\ Timers & T0, t1: 15 MHz (CCLK = 60 MHz/PDIV = 4) \\ others & Gpio, spi, pwm, i2c, and can \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of LPC2148F specifications.  
Figure 1: Picture and the block diagram of the wireless sensor node.
## 5 Service Discovery Mechanism  
In previous sections, we justified the use of a cross-layer approach to define an efficient service discovery protocol for MANETs. Based on this, in our previous works, we presented a new service discovery protocol based on OLSRv1. In 2014, a new version of OLSR was presented; then, we started to work on a new version of our service discovery protocol based on this second version of OLSR.  
The behavior of the service discovery mechanism integrated into OLSRv2 is simple. Nodes that have one or more services to announce include service advertisements, as a TLV structure, in HELLO messages, with the name, a brief description of the service announced and a time to live parameter. HELLO messages are exchanged between neighbor nodes periodically, and all nodes at a one-hopdistance receive them. Each neighboring node stores in a local remote service table the source node that offers the service, the service type, the time this service will be available in the network (time to live) and a brief service description.  
At this point, it is worth mentioning that our protocol can use any kind of service description for service advertisements. In our implementation, we use a simple service description based on the format defined for the SLP protocol, but other more flexible service descriptions could be used to deploy our protocol in the Internet of Things (IoT) scenarios nowadays, so more advanced matchmaking techniques could be used to provide a better service discovery [51].  
From all of the nodes that are at a one-hop distance, only MPR nodes retransmit service advertisements in the network. MPR nodes use TC messages to retransmit service announcements. For that purpose, TC messages also include a TLV structure with the same attributes as HELLO messages. This way, all of the nodes in the network are aware of the services announced within it.  
When a node needs a service, it searches for servers on its remote service table. The route to reach a server is available through the routing table.  
On the other hand, when a node leaves the network, it sends a notification message with the time to live parameter equal to zero. This means that the service or services that this node offers are no longer available.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 3 System Model and the Novel Schedulers  
### System Model  
We consider an uplink SC-FDMA system with one eNB, n active H2H devices and m active M2M modules. Every transmission time interval (TTI) of 1 ms, according to a defined algorithm, the eNB selects users among active users to be scheduled and allocates N resource blocks to them. The transmission power, as defined by the standard [32], is given in Equation (1).  
\[P_{TX}=\min(P_{max},P_{0}+10*\log_{10}(M)+\alpha*PL+\Delta_{TF}+f), \tag{1}\]  
where:  
\(P_{max}\) is the maximum transmission power;  
\(P_{0}\) is the open loop path-loss-power value;  
\(M\) is the number of RBs allocated to the user;  
\(\alpha\) is the open loop path-loss factor;  
\(PL\) is the downlink path-loss measured in the user;  
\(\Delta_{TF}\) is a parameter related to the used modulation scheme;  
\(f\) is a user parameter related to closed loop correlation.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 3 System Model and Design Goal
### Design Goal  
Our goal is to design a congestion-aware incentive scheme for single-copy routing in DTNs, which can stimulate selfish nodes to effectively forward messages to others when the buffer is limited. More specifically, the scheme is to achieve the following goals:  
1. Incentive compatible. Our incentive scheme can effectively stimulate selfish nodes to forward messages to others and truthful participation is adopted by all the forwarding nodes despite their selfish nature.
2. Congestion aware. Our scheme can prevent congested nodes from discarding messages deliberately, and improve the routing performance when nodes are congested.
# On Anisothermal Electromagnetic Elastic Deformations in Flight in Fair Weather and Lightning Storms
## 2 Fundamental Equations of Dissipative Electromagnetic Thermoelasticity  
The fundamental equations of dissipative electromagnetic thermoelasticity are the four equations of electricity, magnetism, momentum and energy/entropy (Section 2.2), with the substitution of constitutive and dissipation relations arising, respectively, from the first and second principles of thermodynamics (Section 2.1). The three vector equations and one scalar equation specify the electric and magnetic fields, the displacement vectors and the temperature, and simplify for isotropic media in a steady state (Section 2.3).
# Anti-Slip Gait Planning for a Humanoid Robot in Fast Walking
## 3 Swing Leg Motion Planning
### Inverted Pendulum Yaw Moment  
When the height of the CoM is constant, given the initial position and velocity and taking x direction as an example, the motion of the 3D inverted pendulum can be expressed as follows:  
\[x_{pend}(t)=x_{pend}(0)\cosh(t/T_{c})+T_{c}\dot{x}_{pend}(0)\sinh(t/T_{c}) \tag{13}\]  
\[\dot{x}_{pend}(t)=x_{pend}(0)/T_{c}\sinh(t/T_{c})+\dot{x}(0)\cosh(t/T_{c}) \tag{14}\]  
therefore,  
\[\ddot{x}_{pend}(t)=x_{pend}(0)/T_{c}^{2}\cosh(t/T_{c})+\dot{x}_{pend}(0)/T_{c} \sinh(t/T_{c}) \tag{15}\]  
where, \(T_{c}\equiv\sqrt{z_{pend}/g}\), \(x_{pend}(0)\) and \(\dot{x}_{pend}(0)\) are the initial position and velocity of the inverted pendulum in x direction respectively, and \(t\) is the time.  
Before we plan the gait, the ZMP and walking period \(T\) are usually specified. As shown in Figure 3, the step lengths in the x direction and the y direction are \(L_{x}\) and \(L_{y}\) respectively. Based on these parameters, the motion Equations of the inverted pendulum in the x direction are:  
\[x_{pend}(t)=L_{x}[\sinh(t/T_{c})(1+\cosh(T/T_{c}))/\sinh(T/T_{c})-\cosh(t/T_{c})] \tag{16}\]  
\[\ddot{x}_{pend}(t)=\frac{L_{x}}{T_{c}^{2}}[\sinh(t/T_{c})(1+\cosh(T/T_{c}))/ \sinh(T/T_{c})-\cosh(t/T_{c})] \tag{17}\]  
Similarly, the motion Equations of the inverted pendulum in the y direction can be obtained:  
\[y_{pend}(t)=\frac{L_{y}}{2}[\sinh(t/T_{c})(1-\cosh(T/T_{c}))/\sinh(T/T_{c})+ \cosh(t/T_{c})] \tag{18}\]  
\[\ddot{y}_{pend}(t)=\frac{L_{y}}{2T_{c}^{2}}[\sinh(t/T_{c})(1-\cosh(T/T_{c}))/ \sinh(T/T_{c})+\cosh(t/T_{c})] \tag{19}\]  
We can obtain the moment that is required to perform the motion of the inverted pendulum by substituting Equations (16)-(19) into Equation (5). The conclusion is \(M_{z}^{pend}=0\), which indicates that the the inverted pendulum does not produce a yaw moment on the ground, and only the swing leg is required to satisfy the frictional constraint.
# Mechanical Integrity Analysis of a Printed Circuit Heat exchanger with Channel Misalignment
## 2 Numerical Simulation  
### Approach and PCHE Design  
The nuclear industry uses PCHEs as reactor components. PCHEs are used in advanced nuclear reactors because they offer a high surface-area-to-volume ratio, high thermal effectiveness, and low overall pressure drop, as mentioned in [24].  
A PCHE is a steam generator and an important component of a sodium-cooled fast reactor. In a steam generator system, a countercurrent flow is formed by the sodium and steam flow. Steam is produced at inlet and outlet temperatures of around 230 and 503 \({}^{\circ}\)C, respectively, with inlet and outlet pressures of 18 and 16.7 MPa, respectively. Sodium flows at inlet and outlet temperatures of around 528 and 322 \({}^{\circ}\)C, respectively, with a pressure of 0.5 MPa [25].  
Here, a new design for the printed circuit steam generator is proposed. Figure 1 shows a schematic drawing of the geometry, which consists of water and sodium channels. The sodium channel is surrounded by the water channel. The water channel is rectangular with 0.1 mm fillets. The sodium channel is semi-rectangular with an ellipsoidal diameter ratio of 0.5 mm to 3 mm.
# Research on Multi-Robot Formation Control Based on MATD3 Algorithm
## 3 Multi-Robot Formation Control Based on Multi-Agent Reinforcement Learning  
### Multi-Agent Reinforcement Learning  
In single-agent reinforcement learning, the environment is stable for the agent. However, in the multi-agent system, each agent will interact with the environment, which will cause the environment to become unstable for each agent. To solve this problem, the centralized training and decentralized execution framework (CTDE) [28] is often used in research. At the beginning, all agents participate in the training simultaneously to jointly improve the parameters of the policy network and the value network. After the training, the agents make decisions and take actions according to their respective policy networks.  
Figure 3: Toggle formation navigation.  
The Markov decision process (MDP) is defined as the process of the interaction between an agent and its environment [30]. The Markov decision process for a multi-robot formation can be represented explicitly by a multivariate group \(\{S,A,R,O,P\}\), where \(S\) denotes the state space of the environment and \(s^{t}\in S\) denotes the state at moment \(t\). \(A=\{A_{1},\cdots,A_{N}\}\) denotes the joint action space of all robots, and the joint action of all robots is denoted as \(a=\{a_{1},\cdots,a_{N}\}\), where \(a_{i}\in A_{i}\) denotes the action of robot \(\mathbf{r}_{i}\). \(R=\{R_{1},\cdots,R_{N}\}\) denotes the set of reward functions of all robots, and \(r_{i}=R_{i}\Big{(}s^{t},a_{i}\Big{)}\) denotes the reward value of robot \(\mathbf{r}_{i}\) when it performs action \(a_{i}\) in state \(s^{t}\). The observation space of all robots can be denoted as the set \(O=\{O_{1},\cdots,O_{N}\}\), and the observations of all robots are denoted by \(o=\{o_{1},\cdots,o_{N}\}\). The state transfer probability function is denoted as \(P\Big{(}s^{t+1}\mid s^{t},a_{i}\Big{)}\), that is the probability distribution specifying when the environmental state \(s^{t}\) changes to the state \(s^{t+1}\) at the next moment when the robot \(\mathbf{r}_{i}\) performs the action \(a_{i}\) in state \(s^{t}\).
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Data Preprocessing
#### 3.2.3 Contrast Limited Adaptive Histogram Equalization (CLAHE)  
To address contrast-related issues, such as noise and grayscale inhomogeneity commonly observed in CT-scans, the Contrast Limited Adaptive Histogram Equalization (CLAHE) [49] method is implemented following the data-scaling step. CLAHE is specifically utilized to alleviate these problems and enhance the visualization of CT-scans. By applying the CLAHE method, image noise is effectively reduced, while simultaneously generating pixels with improved grayscale contrast [50]. Consequently, CT-scans become more visually discernible, aiding in the accurate analysis and interpretation of the images. A comparative illustration of CT-scan samples before and after the application of the CLAHE method is presented in Table 4[14], highlighting the significant enhancement achieved through this preprocessing technique.
## 3 Results
### Spatial Resolution with the GEMPix
#### 3.3.1 Edge Spread Function  
For the edge response, the 2.5 cm thick lead block was placed in the same position as for the measurement with the LaGEMPix (see Figure 5). A ROI (green region) was set perpendicularly to the edge. Figure 10 shows the edge response profile calculated from the average of 200 equalized images as explained earlier. The spatial resolution was estimated by fitting the edge profile by the logistic function of Equation (1). The FWHM obtained with the GEMPix is 5.20 \(\pm\) 0.10 mm.  
Figure 10: Edge response profile with the GEMPix for 40 kV X-rays obtained using a 2.5 cm-thick lead block. The red line represents a logistic function fit to the data. The signal fluctuations at around 1.4 cm are caused by the physical gap between the Timepix chips, which may lead to distortions of the electric field close to the edge. Other features, such as those around 1.7 and 1.9 cm, are due to imperfections in the detector that the equalization procedure cannot compensate for. These artefacts are most likely the result of radiation effects, as the detector was already used in several experiments with ionizing radiation.  
Figure 9: (a) Line pair mask type 53 made of 0.05 mm thick lead used as an imaging target. (b) Heat map of the MTF mask after irradiation with 30 kV X-rays.
## 3 Continuous-Time Average State-Space Model
### Output (DC-AC) Stage  
The DC-AC stage architecture and the equivalent diagram are presented in Figure 5(a) and Figure 5(b), respectively. Based on Figure 5(b), the KVL is applied and can be expressed as Equation (42), followed by Equations (43) and (44). Further, the voltage across the capacitor and current through the load and capacitor are presented in Equation (45), Equation (46) and Equation (47), respectively. Equation (48) denotes the inverter output. Thereafter, a small signal change was applied to Equation (44), Equations (49), and (50) are obtained. Then, applying the Laplace transform to Equation (50) yields Equation (51), and the ratio is presented in Equation (52). In order to sm  
\[\text{sI}_{\text{ac}}(\triangle\text{i}_{\text{ac}})=\triangle\text{U}_{0}( \text{s})-\triangle\text{U}_{\text{C}}(\text{s}) \tag{51}\] \[\frac{\triangle\text{U}_{0}(\text{s})}{\triangle\text{U}_{\text{C}}( \text{s})}=1+\frac{\text{s}^{2}\text{Lac}\text{C}_{\text{ac}}\triangle\text{i}_ {\text{ac}}(\text{s})}{\triangle\text{i}_{\text{c}}(\text{s})}\] (52) \[\triangle\text{i}_{\text{ac}}(\text{s})=\triangle\text{i}_{\text{c}}( \text{s})+\triangle\text{i}_{\text{Load}}(\text{s})=\triangle\text{i}_{\text{C} }(\text{s})+\frac{\triangle\text{U}_{\text{C}}(\text{s})}{\text{Z}}\] (53) \[\frac{\triangle\text{i}_{\text{ac}}(\text{s})}{\triangle\text{i}_ {\text{C}}(\text{s})}=1+\frac{1}{\text{s}\text{C}_{\text{ac}}\text{Z}}\] (54) \[\frac{\triangle\text{U}_{0}(\text{s})}{\triangle\text{U}_{\text{C }}(\text{s})}=1+\text{s}^{2}\text{Lac}\text{C}_{\text{ac}}+\frac{\text{s} \text{I}_{\text{ac}}}{\text{Z}}\] (55) \[\text{Thus, }\frac{\triangle\text{i}_{\text{C}}(\text{s})}{ \triangle\text{U}_{\text{C}}(\text{s})}=\frac{\text{s}\text{C}_{\text{ac}}}{ 1+\text{s}^{2}\text{Lac}\text{C}_{\text{ac}}+\frac{\text{s}\text{I}_{\text{ac }}}{\text{Z}}}\] (56) \[\text{G}_{\text{op}}=\frac{0.00312\text{s}}{3.9\times 10^{-9} \text{s}^{2}+8.113\times 10^{-4}\text{s}+1}\] (57) \[\text{G}_{\text{op}(\text{Con})}=\frac{0.1647\text{s}}{3.047 \times 10^{-14}\text{s}^{3}+6.338\times 10^{-9}\text{s}^{2}+4.195\times 10^{-4} \text{s}} \tag{58}\]  
The state-space matrix of the above-mentioned equation is given as  
\[\text{A}=1\times 10^{10}\begin{bmatrix}-0.000&-1.3768&0\\ 0&0&0\\ 0&0&0\end{bmatrix}\]  
\[\text{B}=\begin{bmatrix}1&0&0\end{bmatrix}\]  
\[\text{C}=1\times 10^{12}\begin{bmatrix}0\\ 0\\ 5.4053\end{bmatrix}\]
## 2 Related Works  
### Target Searching and Tracking  
Search theory originated from [PERSON]'s research during World War II [13]. As a branch of search theory, target searching and tracking aims to find more unknown targets and track the perceived targets using limited resources through cooperation among multiple agents [14]. A widely used approach in the search problem is to divide mission area into cells and link each cell with a target existence probability to construct a probability map of entire area [15]. Force vectors method is also commonly used to solve the problem. In this method, control of multiple agents is achieved by adding up the force vectors that are attractive to nearby targets and repulsive to nearby agents [16; 17]. In [18], the authors constructed the issue as a Multi-Objective Optimization (MOO) problem and propose a MOO algorithm to solve it. In [19], the authors proposed constraint programming (CP) to address target search problem for multiple UAVs.  
However, it's hard for the traditional planning and optimization methods to pursue optimal solution when problem scale is large. To reduce computational complexity, swarm intelligence algorithm is proposed to address the target searching and tracking issue. In [20], the authors proposed a method called motion-encoded particle swarm optimization (MPSO) to locate mobile targets using UAVs. [PERSON] et al. proposed an intelligent collaborative mission planning method for multiple UAVs to search and locate time-sensitive moving objects in an unpredictable dynamic setting, utilizing a combination of artificial potential field and ant colony optimization (HAPF-ACO) technique [21]. In [22], the authors presented a dynamic discrete pigeon-inspired optimization (PIO) algorithm to solve search-attack mission planning problem for UAVs. [PERSON] et al. presented a multi-objective optimization approach based on genetic algorithm (GA) to allocate tasks for UAVs [23].  
In light of the recent advancements in RL, certain studies have also endeavored to employ RL techniques to address the issue of target searching and tracking. [PERSON] et al. presented a centralized RL method for UAV target searching and tracking, assuming that global state and actions of other UAVs are accessible to each individual UAV [24]. [PERSON] et al. formulate the multi-object tracking issue as a motion planning problem and use deep reinforcement learning (DRL) to train a shared policy across all agents [25]. In [26], the authors presented the experience sharing Reciprocal Reward Multi-Agent Actor-Critic (MAAC-R) algorithm to learn cooperative tracking policy for UAV swarm. [PERSON] et al. considered cooperative search for stationary targets by UAVs and proposed DNQMIX algorithm to solve the problem [27]. The comparison between the existing works are listed in Table 1.
# Ni-Al Alloys as Alternative EUV Mask Absorber
## 5 EUV Imaging Simulations
Thinner absorbers are preferable as they reduce the contribution of absorber shadowing to pattern shift through focus, as it causes an imbalance in the diffraction orders intensity [5]. Furthermore, with \(n\) is close to unity due to Al, there is less phase imbalance between illumination poles under off-axis illumination [6]. Imaging simulations predict all modeled Ni\({}_{x}\)Al\({}_{y}\) absorbers to surpass TaBN and Ni in mitigating pattern shift through focus. The contrast is expressed in normalized intensity log-slope (NILS), with better contrast with higher NILS. In Figure 14e NILS of the Ni absorber at 32 nm pitch is chosen as reference for each illumination. For the Quasar illumination, TaBN absorber exhibits the best NILS, outperforming all Ni\({}_{x}\)Al\({}_{y}\) absorbers. However, with the DipoleY illumination, which is superior for small patterns, 39 nm Ni\({}_{3}\)Al and 38 nm NiAl absorbers exceed both TaBN and Ni absorbers in NILS. Lastly, the overlapping process windows under the DipoleY illumination at 0.33 NA are compared between 60 nm TaBN, 32 nm Ni, 39 nm Ni\({}_{3}\)Al--as the Ni\({}_{x}\)Al\({}_{y}\) stack with best NILS, and 31 nm Ni\({}_{2}\)Al\({}_{3}\)--as the Ni\({}_{x}\)Al\({}_{y}\) stack with best M3D reduction. These process windows are determined as the range of exposure dose and focus in which a 16 nm trench line/space pattern varies \(\pm\)10% from target CD. Mask bias has been applied for each pitch, as to print the trenches on target CD at the specified exposure. The overlap of process windows (oPW) for pitch 32 nm, 44 nm and 60 nm, are shown in Figure 15.  
The oPW of Ni, Ni\({}_{3}\)Al, and Ni\({}_{2}\)Al\({}_{3}\) are larger comparing to TaBN. The smallest best focus shift is recognized as the most symmetric oPW, which is the case for the Ni\({}_{2}\)Al\({}_{3}\) absorber. The overlapping depth-of-focus (oDoF) has increased more than 10% for the Al containing absorbers, compared to the TaBN and the Ni absorber. Ni\({}_{3}\)Al has a similar oDoF compared to Ni\({}_{2}\)Al, but is more preferred for its higher NILS. Figure 15: **(a)** Overlapping process windows, and **(b)** overlapping depth-of-focus under the DipoleY illumination at 0.33 NA of 16 nm trenches at a pitch of 32 nm (red), 44 nm (blue), and 60 nm (green), for 60 nm TaBN, 32 nm Ni, 39 nm Ni\({}_{3}\)Al, and 31 nm Ni\({}_{2}\)Al\({}_{3}\) mask absorbers. In conclusion, based on rigorous EUV imaging simulations, there is a trade-off between phase distortion reduction, and NILS. High Al-content alloys are expected to exhibit significant contrast loss, despite superior phase distortion reduction, especially at reduced absorber thickness. A 39 nm Ni\({}_{3}\)Al turns out to be the best candidate out of the simulated absorber models, with superior M3D mitigation combined with high NILS under DipoleY illumination, when compared to 60 nm TaBN and 32 nm Ni absorbers.