esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis
### Signal Interruption Comparison Experiment  
In order to verify the reliability of the \"dynamic memory + Kalman filter\" scheme in the signal interruption scenario, a comparative experiment was carried out in the Deji Square area (actual area: 85,000 m\({}^{2}\)). In the experiment, two additional control groups were set up to calculate and compare their area errors, and the number of memory allocation failures was counted through the FreeRTOS memory management hook function. The memory overflow rate is the number of allocation failures divided by the total number of allocations.  
* Control group 1: GPS + fixed memory: u-blox NEO-M8N module, memory pre-allocated 100 nodes
* Control group 2: BeiDou + fixed memory: use ATGM332D chip (Zhongke Microelectronics, Hangzhou, China) but disable dynamic memory management, memory pre-allocated 100 nodes
* This design: dynamic memory + Kalman filter mechanism  
During the experiment, the antenna is occluded for ten seconds (analog signal interruption) after three minutes of measurement, and the measurement is repeated five times. The test results are shown in Table 7.  
Results analysis:  
* The advantages of a physical layer: The error of the BeiDou group is 12.9% lower than that of the GPS group, which verifies the reliability of the BeiDou satellite in a weak signal environment.
* Software-layer enhancement: The dynamic memory scheme further compresses the error by 45.7%. There is no trajectory fracture in the experimental group, while the fixed memory group has an average data loss of 1.4 due to queue overflow caused by satellite positioning errors or signal interruption recovery.
# Collective Memory: Transposing [PERSON]'s Experiment to Robot Swarms
## 2 Experimental Setup
### The Learning and Testing Phases  
The [PERSON] experiment relies on two main stimuli, the unconditional stimulus (food) that triggers an unconditional response (salivation by anticipation), and a conditional stimulus (bell sound) which does not a priori elicit a specific reaction from the subject. In a first training phase, the subject is presented the unconditional stimulus together with the conditional stimulus. Later, in a testing phase, the subject is only presented the conditional stimulus. When the subject has learned to associate the two stimuli together, it reacts to the conditional stimulus and produces what is called a conditioned response. As a control, in the absence of training, the conditional stimulus does not elicit the conditioned response.  
The [PERSON] experiment is rather straightforward to adapt to a group of robots, starting by defining the specific stimuli that the robot can use. The unconditional stimulus is a target (implemented here as an acoustic message) that robots are tasked and rewarded to detect. When robots make a successful detection, they provide a positive response and advertise the finding with their status LEDs, displaying the equivalent of the dog's salivation. The conditional stimulus (CS) is also an acoustic message that can be perceived by all the robots at once when triggered. This stimulus has no specific reward associated.  
Figure 4 shows the unfolding of a collective memory experiment with a group of robots. The experiment starts with the initial state of the robots which are randomly scattered in the pool. In the training phase, the robots are presented either the unconditional stimulus alone or the unconditional and the conditional stimuli together. During that time, the robots move with a random walk and may stop when they are in range of the central beacon, thus aggregating together. In the testing phase, the robots are presented only the conditional stimulus and their response is observed. The robots are expected to collectively respond to this stimulus if they have learned to associate it with the unconditional stimulus. They advertise their response using their status LEDs. If the conditional stimulus was not presented jointly with the unconditional stimulus during the training phase, the robots are expected to provide a negative response with their status LEDs.  
Figure 3: The experimental setup in our simulations is a circular pool (12.5 m diameter), with a beacon in its center that periodically emits acoustic messages and acts as an aggregation device to maintain the robots together (1.75 m range). Conditional and unconditional stimuli can be presented to the robots at any time, by triggering long-range acoustic signals.
# Cardiac Radiofrequency Ablation Simulation Using a 3D-Printed Bi-Atrial Thermochromic Model
## 3 Results
### X-ray Imaging, Mapping, and Ablation  
Figure 9 show examples of X-ray fluoroscopy images taken during the simulated procedure. The bi-atrial model is clearly visible and there is good epicardial contrast between the Layfomm-40 and the surrounding saline solution. The cone beam CT image (512 \(\times\) 512 \(\times\) 400 matrix size, 0.46 mm\({}^{3}\) voxel size) was used to verify the dimensions of the heart model. The overall model size was 72 \(\times\) 66 \(\times\) 160 mm. The LA dimensions were 38-44 mm, which fall within the typical range [8]. The RA dimensions were 29-35 mm, which also fall within the typical range [15]. Figure 10 shows the exported chamber geometries and ablation points from CARTO3 and the corresponding lesions formed on the heart model. During the first five ablations, the ablative power was increased gradually from inferior to superior and there was a clear corresponding effect on the lesion size. For the final five ablations, the ablative power was kept constant, and the mean lesion diameter was measured to be 3.4 \(\pm\) 0.6 mm (\(\pm\)1 SD, n = 5). The results showed that the RFA only  
Figure 8: **(A) Fusion360 model of simulator base (1) with inserted bi-atrial model (2). (B) Simulator base inside the Really Useful Box (3) with connecting silicone tubing (4) leading to standard hose connectors (5) with silicone plugs (6) to act as hemostatic valves. (C) Silicone tubing extension (7) to IVC entry point showing insertion of a sheath (8) and ablation catheter (9). (D) lid assembly (10) with PVC tubes (11) to accommodate the CARTO3 patches (12). (E) PVC tube end cap (13) with CARTO3 patch connector (14). (F) Complete simulator assembly with wiring to CARTO3 system and RF generator.**affected the internal coatings of the heart model and had no effect transmurally or on the outer coatings.  
Figure 10: CARTO3 mapping and ablation. **(A)** Exported geometry and ablation points (numbered 1 to 10) from the CARTO3 system showing the RA and the venae cavae. **(B)** Cut-away of the heart model immediately after the ablation experiment showing the lesions and their correspondence to the ablation points. **(C)** Partial CARTO3 map of the LA. **(D)** Cut-away of the dry heart model. **(E)** Ablation points clearly visible on the internal surface. **(F)** External surface is not affected by the RFA. **(G)** Transmural cut that shows no effect inside the model wall.  
Figure 9: X-ray fluoroscopy imaging of the simulator. **(a)** With no catheters inserted, **(b)** ablation catheter in the RA, **(c)** ablation catheter across the septum, and **(d)** ablation catheter with transseptal sheath in the LA.
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 3 Experimental Results
### Calibration Results  
The calibration experiment was carried out three times, and the experimental results are shown in Figure 10. In order to eliminate the influence of errors caused by a single measurement, the average of three measurements was taken as the final calibration result, which is shown in Table 2. As can be seen from Figure 10, the calibration results have a good repeatability in both the X and Y directions. Through three repetitive experiments, we can conclude that the difference between them was within 3 \(\upmu\)m in the X direction and within 1 \(\upmu\)m in the Y direction. The experimental results shown in Table 2 are approaching the theoretical design values in two directions, which directly proves the correctness of the calibration method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Elliptical Paraboloid** & **Relative Pitch Angle/Arcsec** & **Relative Yaw Angle/Arcsec** \\ \hline
1 & 0 & 0 \\
2 & \(-\)149.8 & 103.9 \\
3 & \(-\)81.7 & 124.3 \\
4 & 176.7 & \(-\)71.5 \\
5 & 186.5 & 155.8 \\
6 & 82.7 & \(-\)34.0 \\
7 & 107.1 & \(-\)92.2 \\
8 & \(-\)44.1 & 166.7 \\
9 & \(-\)175.4 & 185.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calibration results of the relative pitch angle and yaw angle.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline
**X/mm** & 0 & 49.9681 & 100.0044 & 149.9427 & 199.9558 & 250.0217 & 300.0113 & 350.0063 & 400.0362 \\
**Y/mm** & 0 & 0.0306 & 0.0398 & 0.0359 & 0.0194 & \(-\)0.0158 & \(-\)0.0377 & 0.0488 & \(-\)0.0344 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibration results of the elliptical paraboloid array.
## 5 Comparative Analysis of Incremental Launching Monitoring Results
### Construction Results  
In this project, the structural stress state of the bridge was comprehensively monitored throughout the entire incremental launching construction process. Through in-depth comparative analysis with the finite element theoretical calculations, the internal force characteristics of the structure during the process were studied, which provided valuable guidance for the construction process. The monitoring results revealed that the alignment of the steel girder is smooth and the installation of each component is precise, and all monitoring data related to the structural stress state met the design requirements. This indicates that the incremental launching construction technology of the small-curvature curve steel box girder bridge has been successfully applied and verified in this project, offering some practical basis for the application of this technology in similar bridge projects and ensuring the smooth acceptance of the bridge, thus guaranteeing the subsequent normal operation of this bridge. Figure 15a shows the scene of the curved steel box girder bridge across the highway after being incrementally launched into position, and Figure 15b shows the scene after the construction is completed and the bridge is open to traffic.
## 5 Results
### Considered FttC Realistic Scenario and Improvements of FttC Access Network
The second update strategy is similar to the first one. Even in this case, all the distributors at distance lower than \(d_{OE}\) are connected by fiber to the old Cabinet but now the new Cabinet is positioned at distance a \(d_{k}>d_{OE}\), i.e., it is close to the first distributor at distance greater than \(d_{OE}\). To account for practical installation of the new Cabinet, in our calculation we set the new Cabinet distance at \(d_{k}-d_{0}\) m and \(d_{0}=20\) m. In both cases, if \(d_{n_{D}}\leq d_{OE}\) all the distributors are connected by optical fiber to the Cabinet and no new Cabinet is installed. As shown in the following, the second strategy allows obtaining better sum bit rate coverage performance at the expense of increased costs with respect to the first strategy. Since the considered two strategies are applied for each cable from the single Cabinet, depending on the distance of distributors along the cable, the number of added new Cabinets for one existing Cabinet is lower or equal to the number of cables from the old Cabinet. Assuming 5G nodes can be connected to the new Cabinets the considered strategies allow to increase the density of the wireless nodes in the service area. Considering the Italian FttC network, the density of 5G nodes including the added Cabinets is shown in Table 2 for each group of cities as a function of \(d_{OE}\). As expected, the number of added Cabinets and then the corresponding node density decreases with \(d_{OE}\). Even the number of distributors connected to the added Cabinets decreases with \(d_{OE}\). This fact is shown in Figure 4 where we plot the percentage of these distributors with respect to the total number of distributors in the city areas. From Figure 4 the number of distributors rapidly decreases. In particular, due the short secondary network characteristics of the considered FttC network, the percentage of distributors still connected by copper rapidly decreases with \(d_{OE}\) for all the three groups of cities. \begin{table}
\begin{tabular}{l c c c}  & Group1 & Group2 & Group E \\ \hline \(d_{OE}\)**=200m** & 120.9 & 41.3 & 16.6 \\ \(d_{OE}\)**=400m** & 113.4 & 38.9 & 15.8 \\ \(d_{OE}\)**=600m** & 109.7 & 37.8 & 15.3 \\ \(d_{OE}\)**=800m** & 108.2 & 37.3 & 15.0 \\ \(d_{OE}\)**=1000m** & 107.4 & 37.1 & 14.9 \\ \end{tabular}
\end{table} 
Table 2: Updated average density of radio access nodes including the added new Cabinets for each group of cities and for variable \(d_{OE}\).
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 7 Application Control Policy  
The ACS manages the DSP applications deployment through a local policy (executed by the OMs) and a global policy (executed by the AM).  
The OM local policy implements the Analyze and Plan phases of the decentralized MAPE loop, which controls the execution of a single DSP operator. Running on decentralized components, this policy has only a local view of the system, which consists of the execution status of each operator replica (in terms of response time and resource utilization). We consider two different local policies. The first is a threshold-based policy whereby scaling decisions are based on the replicas CPU utilization compared to predefined threshold values (Section 7.1). The second resorts on a more sophisticated RL solution, where the scaling policy is learned over time by interacting with the system (Section 7.2). Whenever a scale-out decision is taken, the OM places the new operator replica within the same region where the other operator replicas are running. In the case of a scale-in decision, the OM terminates one (randomly selected) operator replica among the available ones.  
To accept or reject the reconfiguration actions proposed by the OM, the AM resorts on a global policy, which implements the Analyze and Plan steps of the centralized MAPE loop. Its main goal is to coordinate the reconfiguration actions to satisfy the DSP application requirements, while minimizing the number of used computing resources. We consider a simple global policy that is only aimedto solve reconfiguration conflicts, i.e., it rejects adaptation actions when they try to allocate the same computing resource to multiple operator replicas (Section 7.3). More sophisticated approaches (e.g., based on a token bucket to limit the number of performed reconfigurations) can be proposed as well [8].  
In the next section, we present the idea behind the different policies. Nevertheless, their details can be found in [8].
## 4 Proposed Routing Algorithm for Wireless Sensor Networks
### Routing Strategy for Hierarchical Architecture  
When a node wants to send a packet to the sink, it sends a packet \"d\" (discovery) to its cluster head (Figure 3).  
Figure 3: Routing Strategy for hierarchical architecture.  
The cluster head source seeks the route in its routing table. If it has the route, the cluster head source sends a packet \"r\" (route) to the sink. Otherwise, the cluster head source sends a package \"s\" (search) to search for a route to the sink. After this, the sink receives an \"s\" package and it replies to the cluster head source by sending a \"t\" (target) packet that leads to the cluster head source as the target that generated the \"s\" package. This package contains the path to the sink. However, if the route to the sink becomes invalid, the cluster head source generates an \"f\" (fail) package indicating that it was not possible to reach the sink, and rebuilds a package \"s\" to find a new route to the sink.
# Radio-Frequency Undulators, Cyclotron Auto Resonance Maser and Free Electron Lasers
## 1 Introduction
The request, regarding the electron beam parameters, becomes challenging (in terms of peak current) for the operation at short FEL wavelength and relax with increasing \(\lambda_{w}\). A large amount of power density necessary to obtain reasonable strength undulator values is not a secondary issue and will be carefully discussed in the concluding section. The scientific literature on the subject of RF undulators has significantly grown in the past and a partial list can be found in [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. We would like to underscore that the design numbers we have foreseen can be made more accessible at short \(\lambda_{w}\) if we consider FEL operation in the VUV-X region, which requires low-energy electron beams. We have not specified yet the type of source providing the RFU. The region above 1-9 mm can be achieved with gyrotrons, gyro-klystrons or any other device (see below). Even though all these sources are candidates for RFU, a few elements of discussion are necessary to decide in favor of one or the other. Gyrotrons work as oscillators and the only way to drive several undulator sections is to be phase locked. The drawback of this configuration is that stable phase locking oscillator operates at 10% only of the locked power. Such an efficiency drop makes this solution scarcely appealing and therefore will not be discussed here. The gyroklystron is a promising source [30]. It operates at 3 mm wavelength, however, for shorter wavelength the size of the rf cavity has to be reduced too, which will reduce the output power due to the breakdown problems. The peak power of those amplifiers is around 100 kW and the duty factor is about 10%. It is possible to use the gyro-TWT [31]. Its peak power is 80-100 kW at 60-80 GHz. The Cyclotron Auto Resonance Maser (CARM) [32; 33] promises good performances in the region 1-5 mm, therefore we choose it as RFU candidates in the forthcoming discussion. In Section 2, we summarize the physics of CARM and fix the design conditions to get sufficient power for RFU operation. In Section 3 we discuss an actual RFU configuration and discuss the associated critical issues.
## 3 Measurement Errors of PLLs
### MEMS Accelerometer-Aided PLLs
Figure 7: Phase errors of MEMS accelerometer-aided PLL with a carrier-to-noise ratio (CN0) of 40 dB-Hz. We summarize the measurement error of PLLs as follows. Under strong earthquake dynamics, the bandwidth of the stand-alone PLLs cannot be less than 17 Hz, and the phase measurement error increase with GNSS signal fades. MEMS accelerometer-aided PLLs with narrow bandwidth can track GNSS signal with little phase error under strong earthquake dynamics. Above error models are helpful to the analysis of phase error and the selection of optimal bandwidth.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 3 System Model
### Energy Model  
The energy model of EH-WSNs in this paper consists of three components, _i.e._, harvested energy, packet energy, and node's residual energy, which will be presented in detail as follows.  
(a) **Harvested energy** (\(E_{h}\)) is the amount of energy harvested from the environment at each node during a unit time to maintain the operation of the sensor node (e.g., sensing, transmitting, and receiving packets). Even when sensor nodes are located where it is difficult to recharge or replace a battery, they should harvest energy from the environment or other energy sources, such as vibration, thermoelectricity, or acoustic noise, to prolong their lifetime.  
In this paper, sensor nodes that harvest energy through solar cells were chosen. The amount of harvested energy at each node depends on light intensity. Because of the amount of harvested energy at each node is different, the effect of random light intensity in the environment can be expressed as  
\[E_{h}=\beta_{i}\times E_{max} \tag{2}\]  
where \(E_{max}\) is the harvested energy corresponding to the highest light intensity, \(\beta_{i}\) represents a multiplication coefficient which is uniformly distributed in \([0,1]\).  
(b) **Packet energy** (\(E_{e}\)) is the amount of energy consumed by a node when sensing, receiving, or transmitting a packet with a packet length of \(L\) (bits), and the amount of energy consumed is denoted as \(E_{se}\), \(E_{rx}\), and \(E_{tx}\), respectively. The wireless propagation channel is affected by the large-scale path loss model [25]. Thus, \(E_{se}\), \(E_{rx}\), and \(E_{tx}\) are expressed as  
\[E_{se}=\alpha_{1}\times L \tag{3}\]  
\[E_{rx}=\alpha_{2}\times L \tag{4}\]  
\[E_{tx}=(\alpha_{3}+\alpha_{4}d^{\beta})\times L \tag{5}\]  
where \(d\) (\(d\leq R\)) is the distance between transmitter and receiver, \(\beta\) is the path loss exponent, \(\alpha_{1}=50\) nJ/bit, \(\alpha_{2}=135\) nJ/bit, \(\alpha_{3}=45\) nJ/bit, \(\alpha_{4}=10\) pJ/bit/m\({}^{2}\) and \(0.001\) pJ/bit/m\({}^{4}\) when \(n=2\) and \(4\), respectively [26].  
(c) **Residual energy at node \(\vec{v}_{i}\)** (\(RE_{i}\)) is a sink node supplied by unconstrained energy with the residual energy of other nodes considered. The residual energy of node \(v_{i}\), (\(RE_{i}\)), after transmitting through path \(p_{k}\), can be expressed as:  
\[RE_{i}(p_{k})=B\ -\rho\times E_{e}+\beta_{i}E_{max} \tag{6}\]  
Figure 2: The sample network illustrates the benefit of a stability-aware routing scheme for minimizing energy consumption and reliable data transmission in energy harvesting wireless sensor networks (EH-WSNs).  
where \(\rho\) represents the number of packets transmitted from node \(v_{i}\).  
A specific sensor node can communicate with others if the residual energy of node \(v_{i}\) is greater or equal to a packet energy, _i.e._, \(RE_{i}\geq E_{e}\).