esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Efficiency K-band MMIC Linear Amplifier Using Diode Compensation
## 4 Measurement Results  
The fabricated three-stage K-band linear amplifier MMIC is shown in Figure 12. The MMIC size is as small as \(2.00\times 1.40\) mm\({}^{2}\) with a GaAs substrate thickness of 50 \(\upmu\)m. Linear S-parameter and large signal measurements were performed at a drain voltage of 5 V and gate voltage of \(-0.8\)V on a wafer. Figure 13 shows the measured S-parameter of the MMIC from 19.5 GHz to 22.5 GHz. The design has achieved higher than a 26 dB small signal gain and better than a 10 dB input return loss. Figure 14 illustrates the measured output power and PAE for the amplifier at fixed input drive levels of +3.5 dBm under a continuous wave (CW). At the nominal supply of VD = 5 V and VG = \(-0.8\) V, the amplifier demonstrates higher than 30 dBm P1 dB with 33-35% PAE over a 20-22 GHz frequency. Very flat power and gain characteristics were achieved for this design. Further PAE improvement should be possible with a more precise power match by the result of a load-pull test.  
Two-tone measurement of the amplifier has been performed with 10 MHz tone spacing under the drain voltage of 5 V and gate voltage of \(-0.8\) V. The intermodulation distortion was measured on a wafer with 10 MHz two-tone spacing. The two carrier sources were connected to the chip through a power combiner with an isolator in order to reduce the intermodulation contributions of the setup. As shown in Figure 15, IMD3 better than 20 dBc and PAE higher than 33% were measured at an output power of 27 dBm per carrier over a 20-22 GHz band. Compared to the design without an inner paralleled diode configuration, there is more than a 5 dB improvement of IMD3. Table 4 summarizes the performance comparison of this work with other published linear amplifiers working in close frequency ranges. The characteristics of the amplifier, including power, gain, efficiency, and IMD3, are better than the previously reported ones.  
Figure 11: (a) Simulated AM-to-AM versus input power at 21 GHz; (b) simulated AM-to-PM versus input power at 21 GHz.  
Figure 12: Photograph of the fabricated K-band linear amplifier.  
Figure 14: Measured output power and PAE at 1 dB compression.  
Figure 13: Measured small-signal performance of the amplifier under the bias of VD = 5 V and VG = \(-0.8\) V at 25 Celsius.  
Figure 15: Measured IMD3 and PAE versus output power under a two-tone test with different frequencies.
## 3 Results and Discussion
### Measurements in an Experimental Open Channel  
The proposed method shows a new setup to use a widely studied piezoresistive force micro-sensor based on a strain gauge as it derives the shear stress from bending (e.g., [PERSON] et al. [30], [PERSON] et al. [31], [PERSON] et al. [11], and [PERSON] et al. [32]) instead of pure axial deformation. Preliminary results in controlled laboratory experiments were able to measure small forces due to local flow changes, as we observed a significant linear relationship between the change in strain gauge signal and the relative fluid-sensor velocity for flow depths greater than 5 cm, while flow depth less than 2 cm did not show changes in the strain gauges [33]. The experimental setup considered a granular bed and sensors located in the vicinity of the bed to relate flow depth to dynamic pressure inside the boundary layer and the pressure variation of the surface flow. The channel used was a rectangular prismatic section 0.48 m wide and 0.45 m high with an acrylic bed and walls. The laboratory flume is of variable slope, 12 m long, and was left with zero slope for experimental activities. The experimental test protocol proposed by [PERSON] [33] was used, which adapted the ISO 3455:2007 standard for windlass calibration to the characteristics of operation and functioning of the sensor. [PERSON] [33] assumed valid the principle of action and reaction of the force sensor on liquid water at rest, where it is expected that the constant movement of the sensor along the channel is equivalent to the movement of the fluid with the sensor at rest.  
Changes in the coefficient of determination between flow velocity and the signal from the strain gauge were independent of temperature changes, but a high scattering of the data between tests is attributable to experimental errors such as orthogonality and/or twisting of the sensor in the direction of motion. In other experimental runs considering a moving fluid, the proposed sensor detected changes in flow depth, flow magnitude, and flow surface oscillations. Sensors located at the bed and near the bed (5.0 mm) showed a sudden decrease in the signal and then increased exponentially until the signal became constant. In contrast, for the sensor located 35 mm from the bed, the signal did not show a sudden decrease but an exponential and oscillatory increase consistent with the flow pressure gradient, until it reached a plateau. The magnitude of the instrumental signal change correlated positively with flow magnitude only for the sensor located 35 mm from the bed in all tests. Differences in response related to the location are due to the presence and influence of the boundary layer at the bed that affects the response of the signal. The sensor located outside the boundary layer (free zone) showed a coherent development with the changes of the flow variables. For sensors located inside the boundary layer, the hydraulic load was the most relevant variable measured by the instrumental signal. Figure 9 shows some pictures of the experimental test in an open channel with water in motion.
## 2 Theories
### Sc-LeGo-Loam  
SC-LeGO-LOAM [4], an extension of the LeGO-LOAM [21] method, incorporates a loop detection technique based on the scan context for enhanced loop identification. The process begins with the 3D LiDAR data points obtained from the scan, which are initially categorized into ground and segmented points using a ground segmentation method. Subsequently, an image segmentation method refines the segmented points into distinct clusters, each assigned a specific label for identification purposes.  
By evaluating the roughness values c of both ground and segmented points and comparing them with a predefined threshold c\({}_{th}\), the classification of edge and planar features is then carried out. The roughness c is defined as follows:  
\[\text{c}=\frac{1}{|S|\cdot\|\mathbf{r}_{i}\|}\|\sum_{\text{j}\in\text{S}_{j} \
eq i}(\mathbf{r}_{j}-\mathbf{r}_{i})\| \tag{4}\]  
where \(r_{i}\) and \(r_{j}\) are the Euclidean distances from points \(p_{i}\) and \(p_{j}\) in set S to LiDAR, respectively.  
The estimation of the robot's motion follows, involving a comparison between the current feature set and that of the previous time step, with label matching enhancing the precision of matching results. The subsequent application of a two-step Levenberg-Marquardt (L-M) Optimization computes the pose transformation matrix. Loop detection is facilitated through the integration of a scan context method, transforming the 3D point cloud into polar coordinates and extracting global descriptors.  
Utilizing a K-dimensional (KD) tree and conducting the nearest neighbor search, the frame with the highest similarity score is identified as the loopback frame, facilitating loop detection. The similarity score between queried and the candidate point clouds can be calculated by  
\[d(I^{q},I^{c})=\frac{1}{N_{\text{s}}}\sum_{j=1}^{N_{\text{s}}}(1-\frac{c_{j}^{ q}\cdot c_{j}^{c}}{\|c_{j}^{q}\|\|c_{j}^{c}\|}) \tag{5}\]  
where \(N_{\text{s}}\) represents axial sector number in scan context \(I\) and \(c_{j}^{q}\), \(c_{j}^{c}\) represent column vectors at the same index in queried and the candidate frame, respectively.  
The final stages encompass map optimization via the iterative closest point (ICP, [22]) method and the integration of positional data, culminating in the determination of an accurate final position and estimation.
###### Abstract  
This report evaluates the pulsed breakdown performance of \(C_{4}F_{7}N\) under a 6.8 kV/ns voltage excitation. The pulsed dielectric strength of \(C_{4}F_{7}N\) is compared to \(SF_{6}\) in the same experimental setup, and it is found that \(C_{4}F_{7}N\) concentrations of 50% or greater are required to achieve a dielectric strength greater than or equal to \(SF_{6}\). Pure \(C_{4}F_{7}N\) demonstrated higher electric field hold-off for longer time periods and less statistical variance under pulsed conditions when compared to \(SF_{6}\). Mixtures of 50% \(C_{4}F_{7}N\) with \(N_{2}\) or \(CO_{2}\) as buffer gases showed no appreciable difference in pulsed dielectric strength.  
\(C_{4}F_{7}N\); \(SF_{6}\); Novee\({}^{TM}\) 4710; pulsed breakdown; dielectric strength; electrical breakdown; capacitive voltage divider; Rogovski coil; insulating gas; sulfur hexafluoride +
Footnote †: journal: Applied sciences  
or coated in a thin layer of grease whenever possible. Second, \(C_{4}F_{7}N\) and its mixtures remain in the testing apparatus for no more than 90 min at a time and at room temperature, after which they are evacuated. This setup is designed to minimize the amount of material degradation induced by \(C_{4}F_{7}N\).  
Pulsed breakdown measurements are performed on a pressurized experimental apparatus that allows for the measurement of breakdown behavior with nanosecond temporal resolution. The \(C_{4}F_{7}N\) mixing percentages tested are 50%, 50/50, and 20%, 20/80, with a buffer gas as the remaining percentage. The buffer gases used are \(CO_{2}\) and \(N_{2}\). Custom voltage and current diagnostic probes were manufactured and calibrated to handle pulsed excitations with a frequency bandwidth greater than 350 MHz. An electrically triggered Marx generator is used to produce a consistent voltage pulse for statistical comparisons. The experimental apparatus is a 3.8 L chamber with pressures ranging from 101.3 kPa to 456 kPa being tested. All pressures measurements in this report are recorded as absolute pressure.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 5 Real-World Application
### Real-Time Performance Aspects  
Real-time responsiveness is critical in warehouse environments. Although LLMs are not traditionally designed for low-latency applications, several factors make them suitable here:  
* Prompt processing times for smaller models are typically less than 500 ms, which is acceptable for scheduling, contract evaluation, and strategic proposal processing.
* Batch processing of similar requests can improve throughput.
* Session caching in stateful prompting modes enables incremental decision updates, reducing the size of prompts and speeding up response times.  
In high-decision-frequency environments, hybrid architectures can be used where LLMs are supplemented by faster rule-based systems for routine or low-risk tasks.
# Awareness to Action: Student Knowledge of and Responses to an Early Alert System
## 3 Results
### How Do Students Respond to Receiving an ASR?
#### 3.2.2 Motivations and Concerns  
Students agreed that they understood why they had received an ASR (\(M\) = 4.9, \(SD\) = 1.2). Moreover, when receiving an ASR, whether positive or negative, they felt relatively supported by the instructor (\(M\) = 4.3, \(SD\) = 1.4), and they felt motivated to improve their performance (\(M\) = 4.5, \(SD\) = 1.2) and stay in the course (\(M\) = 4.5, \(SD\) = 1.3). Likewise, participants reported that they felt like they could improve their performance (\(M\) = 4.6, \(SD\) = 1.2) and stay in the course (\(M\) = 4.8, \(SD\) = 1.1). Aligned with these results, participants disagreed that they felt pressured to drop or withdraw from the course after receiving an ASR (\(M\) = 2.6, \(SD\) = 1.7). Overall, students reported that receiving an ASR was a somewhat positive experience (\(M\) = 4.0, \(SD\) = 1.5).  
Although the above results suggest that participants felt motivated and able to persist and improve in the course after receiving an ASR, they also reported holding many concerns after receiving an ASR (Table 10). Students were especially concerned about maintaining their GPA, (68.8%; n = 75), failing the class (59.6%; n = 65), and their permanent academic records (44.0%; n = 48). In addition, a fair number of students were concerned about the instructor's perception of them as a student (42.2%; n = 46), and their parents learning about the ASR (39.4%; n = 43).
# Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors
## 2 Proposed SRAM Structure and Operation Principles
### Read and Write Operation  
As illustrated in Figure 3a, the read operation in this SRAM cell begins by pre-charging both the bit line (BL) and its complementary bit line bar (BLB) to the supply voltage (VDD), which is typically set at 1 V. This pre-charging phase ensures that both lines are initially at an equal voltage level before the read access. Once the word line left (WLL) and word line right (WLR) are activated, the stored data in the storage nodes (denoted as Q and QB) dictate the behavior of the bit lines. If the value stored in node Q is a logic '1', the voltage on the signal line corresponding to BL remains unchanged at VDD, indicating that no discharge occurs. Conversely, if the value stored in node Q is a logic '0', the signal line associated with BL discharges toward 0 V, resulting in a detectable voltage difference between BL and BLB. This differential voltage can then be amplified and interpreted by the sense amplifier, allowing the stored values in the nodes Q and QB to be accurately read and transmitted to the output. This mechanism leverages the inherent differential sensing capabilities of the SRAM architecture to enhance both the speed and reliability of the read operation.  
Figure 3: (**a**) Read operation and (**b**) write operation.  
Figure 2: Overall SRAM-CIM architecture.  
For the write operation, as depicted in Figure 3b, we assume an initial condition where the logic value '1' is already stored in node Q, and its complement, logic '0', is stored in node Q. In order to overwrite these values, specific voltages are applied to the bit lines. In this scenario, a voltage of 0 V is applied to the bit line (BL), and a voltage of 1 V is applied to the complementary bit line bar (BLB). These applied voltages force the signal lines to undergo charging and discharging processes, respectively. The BL, set to 0 V, induces a discharge at node Q, flipping the stored value from a logic '1' to a logic '0'. Simultaneously, BLB, which is set to 1 V, induces a charging process at node QB, flipping its stored value from logic '0' to logic '1'. This coordinated charging and discharging of the bit lines effectively overwrites the previous data in the storage nodes Q and QB. The write operation thus ensures that the new values are successfully written into the SRAM cell, replacing the old data. This operation is crucial for maintaining data integrity and is carefully controlled to prevent any contention between the BL and BLB during the switching process, ensuring reliable data storage within the memory array.
## 3 Simulation Results
### Single Objective Optimization  
#### 3.1.1 Without Considering the Limitations of Power Exchange with Upstream Grid  
For solving the single-objective problem in the unlimited power exchange mode with the network, both cost and pollution (gas emission) targets have been studied separately. The results are compared with the PSO and DE methods and the convergence diagrams are presented in Figures 2 and 3. Both forms show better convergence of the IDE algorithm compared to the other methods. Table 5 to Table 6 illustrate how to distribute power to reduce costs and gas emission without considering the limitations of upstream network power exchange with two objectives. The last column of the tables illustrates the unlimited power exchange with the upstream utility grid. Tables 7 and 8 show the results of 10 consecutive runs of each method. The proposed algorithm is better than all other methods in all the best, worst, and average of the indices, and therefore, the superiority in both targets is quite clear. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hour** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline WT (kW)/installed (kW) & 0.1190 & 0.1190 & 0.1190 & 0.1190 & 0.1190 & 0.0610 \\ PV (kW)/installed (kW) & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Hour & 7 & 8 & 9 & 10 & 11 & 12 \\ WT (kW)/installed (kW) & 0.1190 & 0.0870 & 0.1190 & 0.2060 & 0.5850 & 0.6940 \\ PV (kW)/installed (kW) & 0.00 & 0.0080 & 0.1500 & 0.3010 & 0.4180 & 0.4780 \\ Hour & 13 & 14 & 15 & 16 & 17 & 18 \\ WT (kW)/installed (kW) & 0.2610 & 0.1580 & 0.1190 & 0.0870 & 0.1190 & 0.1190 \\ PV (kW)/installed (kW) & 0.9560 & 0.8420 & 0.3150 & 0.1690 & 0.0220 & 0.00 \\ Hour & 19 & 20 & 21 & 22 & 23 & 24 \\ WT (kW)/installed (kW) & 0.0867 & 0.1190 & 0.0867 & 0.0867 & 0.0610 & 0.0410 \\ PV (kW)/installed (kW) & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Production rate of wind turbine and photovoltaic per one unit of production. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hour** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Load (Kw) & 52 & 50 & 50 & 51 & 57 & 62 \\ Hour & 7 & 8 & 9 & 10 & 11 & 12 \\ Load (Kw) & 70 & 74 & 76 & 79 & 78 & 75 \\ Hour & 13 & 14 & 15 & 16 & 17 & 18 \\ Load (Kw) & 72 & 71 & 76 & 80 & 85 & 87 \\ Hour & 19 & 20 & 21 & 22 & 23 & 24 \\ Load (Kw) & 90 & 87 & 77 & 71 & 65 & 58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Rate of daily loading. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hour** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Price (\$) & 0.23 & 0.19 & 0.14 & 0.12 & 0.12 & 0.20 \\ Hour & 7 & 8 & 9 & 10 & 11 & 12 \\ Price (\$) & 0.23 & 0.38 & 1.5 & 4 & 4 & 4 \\ Hour & 13 & 14 & 15 & 16 & 17 & 18 \\ Price (\$) & 1.5 & 4 & 2 & 1.95 & 6 & 0.41 \\ Hour & 19 & 20 & 21 & 22 & 23 & 24 \\ Price (\$) & 0.35 & 0.43 & 1.17 & 0.54 & 0.30 & 0.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Market price for upstream power exchange.
## 3 Background  
MQTT is a client-server publish/subscribe messaging transport protocol [47]. The message exchange involves two types of agents: MQTT clients and MQTT brokers. MQTT clients can be either publishers (information producers) or subscribers (information consumers), and they can perform both roles simultaneously. The architecture of MQTT is depicted in Figure 1.  
The MQTT protocol requires that information provided by a publisher be associated with a topic that categorizes the information. Subscribers express interest in receiving certain information by specifying its topic.  
Unlike traditional client-server architectures, MQTT uses a broker to mediate communication between publishers and subscribers rather than direct communication. This process is illustrated in Figure 1. Subscribers interested in a given topic (e.g., \(T\)) send a subscribe message \(sub(T)\) to the broker, specifying the topic. When a publisher sends a message to the broker, it includes the information \(I\) and the topic \(T\) to which the information should be published. The broker then forwards the message to all subscribers interested in that topic.  
MQTT messages have minimal overhead, with a protocol header of only 2 bytes, and the payload size can be up to 268,435,456 bytes. These characteristics make MQTT suitable for low-bandwidth networks and resource-constrained clients, such as IoT devices. To conclude this section, we provide some details on the structure of an MQTT packet. This packet, depicted in Figure 2, consists of a fixed header, variable header, and payload (which contains the actual message content being transmitted).  
The main fields of the header are as follows:  
* Packet type: Identifies the type of packet, e.g., whether it is a publish packet.
* Remaining length: Length of the variable header and payload.
* Topic name: UTF-8 string that indicates the topic to which the message is published.  
Figure 1: MQTT architecture.  
Figure 2: General MQTT packet structure.  
* Properties: User-defined properties that add metadata to MQTT messages in order to transmit additional user-defined information.
# UAV Formation Trajectory Planning Algorithms: A Review
## 5 Local Trajectory Planning Algorithm
### Model Predictive Control
The problem of multi-UAV trajectory planning in the target tracking scenario is solved. [PERSON] et al. [162] proposed a new trajectory planning algorithm for model predictive control (NMPC). They introduced a virtual target to move along the patrol trajectory at a predetermined speed and designed a decentralized estimator for each UAV to estimate the state of the virtual target. Then, they used a new model prediction algorithm to calculate a feasible trajectory for the formation of UAVs, which addresses the problem of formation reconstruction and trajectory planning in multi-UAV aerial patrol missions. [PERSON] and [PERSON] [163] proposed a model for predicting flushing force under drones (PMDFF). It regards each UAV as a virtual structure to form a cylindrical UAV model and then uses the cluster and Optimal Interactive Collision Avoidance (ORCA) algorithm to solve the collision-free trajectory. They addressed an issue where downwash effects have an impact on neighboring drones in UAV formation trajectory planning. Figure 28: Schematic diagram of Model Predictive Control. Table 10 summarizes the contents of our survey on model predictive control algorithms.
# Equivalent Electrical Circuit Modeling of CNT-Based Transparent Electrodes
## 2 Modeling of a Carbon Nanotube
### Atomistic Modeling  
Atomistic modeling employs atomic interactions and force fields to obtain the position of atoms in the nanostructure, which is needed to solve the Schrodinger equation. In this way, fundamental quantities that provide information about all the properties of the nanotube are obtained. Atomistic modeling includes ab initio methods (also referred to as first principles methods) [18], based on Monte Carlo (MC) and Quantum Monte Carlo (QMC) approaches, more accurate but also requiring a huge computational effort, Density Functional Theory (DFT) techniques, molecular dynamics (MD)-based approaches [19], including Tight Binding (TBMD) approximation, and several others. Due to the great computational effort required for the solution of at the nanoscale level, atomistic models are usually employed for the investigation of a limited number of atoms, representing volumes or surfaces at the nanometric scale.