esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 6 Case Study: Topic-Grounded Consultant Agents vs. LLM
### Quantitative Performance Evaluation
#### 6.7.5 Scalability Analysis: Addressing State-Space Explosion
* _Coordination Overhead Inversion:_ Rather than increasing coordination overhead with agent population, TB-CSPN demonstrates improving efficiency. Measured throughput increases 11.2% from 10 to 100 agents, indicating that coordination becomes more efficient at scale through shared semantic representations and hierarchical control. * _Large-Scale Deployment Viability:_ Our experiments with 100 agents achieving a 92% success rate and only 21.5 MB total memory usage confirm practical viability for enterprise-scale deployments. * _Architectural Foundations of Sub-Linear Scaling:_ TB-CSPN achieves sub-linear scaling through three core innovations:
* _Shared Semantic Representations:_ Topic-based tokens create shared semantic spaces where a single topic distribution coordinates multiple agents simultaneously. As agent populations grow, the semantic spaces converge rather than explode--100 agents share the same core topics used by 10. * _Token-Based State Compression:_ Agents are stateless between cycles; coordination state is fully encoded in tokens. Memory usage scales with active tokens (constant per task), not agent population. * _Hierarchical Coordination Efficiency:_ The three-tier structure enables supervisors to coordinate larger populations through identical rule-based mechanisms. The Petri Net substrate provides formal guarantees without increasing memory overhead. * _Error Tolerance and Reliability:_ Success rates above 90% across all scales demonstrate strong error tolerance. Mislabeling of topics below threshold values results in inaction rather than incorrect action, yielding graceful degradation and full traceability via token logs. These findings confirm that TB-CSPN's separation of semantic and coordination layers enables efficient, reliable scaling, without the combinatorial explosion that hampers traditional Petri Net systems. Topic-based coordination may thus be foundational for the next generation of scalable, interpretable multi-agent AI.
## 3 Data Analysis
### Storage Time  
The storage time of each charge/discharge cycle is understood as the number of days the battery has been unused between the previous charge and the new use. Long storage periods can be detrimental to batteries as they produce greater capacity losses in relaxation, in addition to irreversible capacity losses [19]. Unfortunately, it was not possible to study these phenomena directly with the available data.  
Regarding the relationship of this parameter with internal resistance, it was observed during measurements that prolonged periods of storage have served to consolidate increases in internal resistance. There is no evidence to indicate that storage time directly influences resistance, but it seems that longer storage time is linked to an increase of internal resistance by stabilizing all cells to an increased value at which they were tending.  
Figures 15 and 16 compare the average storage time values of each battery with the final number of cycles of use and with the increase in internal resistance. It is observed how  
Figure 14: Voltage dispersion (w.c.) vs. temperature for battery 5.  
there is a clear relationship between these parameters that supports the theory, that longer storage times lead to a reduction in cyclic battery life and more pronounced increases in internal resistance.  
Because of this direct relationship, storage time will be one of the parameters considered in the model.
## 3 Materials and Methods
### Ultrasonic Solvent Extraction (USE)  
Ultrasound-assisted solvent microextraction (USE) was performed in an ultrasound cleaning bath (Clean 01, MRC Scientific Instruments, London, UK) by the indirect sonication mode at a frequency of 37 kHz at 25 \(\pm\) 3 \({}^{\circ}\)C. The advantage of using USE is the isolation of volatile and semi-volatile as well as water-soluble organic compounds without the application of heat. Different solvents were used for USE: a mixture of pentane/diethyl ether, 1:2 (\(v/v\)), dichloromethane, pentane, and diethyl ether. The mixture and dichloromethane were separately used for the extractions. A previously developed USE method was modified with the solvent sequences that were applied for the honey extraction. Sequence I consisted of the extraction with pentane followed by the extraction with diethyl ether (pentane \(\rightarrow\) diethyl ether). Sequence II consisted of pentane extraction followed by the extraction with pentane:diethyl ether 1:2 (\(v/v\)) and afterwards with dichloromethane (pentane \(\rightarrow\) pentane:diethyl ether 1:2 (\(v/v\)) \(\rightarrow\) dichloromethane). For each extraction, 40 grams of the honey was dissolved in distilled water (22 mL) in a 100-mL flask. Magnesium sulfate (1.5 g) was added and vortexed (10 min). The solvent volume was 20 mL and the sonication was applied for 30 min. After the sonication, the organic layer was separated by centrifugation and filtered over anhydrous MgSO\({}_{4}\). The aqueous layer was returned to the flask and another batch of the same extraction solvent was added and extracted for 30 min. The organic layer was separated in the same way as the previous layer and filtered over anhydrous MgSO\({}_{4}\), and the aqueous layer was sonicated a third time for 30 min with another batch of the extraction solvent. Combined organic extracts were concentrated to 0.2 mL by distillation with a Vigreaux column, and 1 \(\upmu\)L was used for GC-FID/GC-MS analyses. The experiments were performed in triplicate.
### Linking Voice Physiology to ADHF  
Voice is produced when air flows from the lungs past the vocal folds, which are held in an approximately closed position. The pressure difference above and below the vocal folds forces bursts of air through them, creating a periodic signal. Biomechanical properties of the vocal folds affect their readiness to vibrate in response to air pressure from the lungs [9]. The phonation threshold pressure (PTP) is defined as \"the minimum lung pressure required to initiate phonation\" [10]. The relevant biomechanical vocal fold properties include tissue elasticity, thickness, and viscosity [11], which is closely linked to hydration level in the vocal folds [12].  
PTP and hydration level have both been linked to phonatory effort. In [13], healthy adults were given the diuretic Lasix (furosemide), which decreases systemic hydration. For participants who received the diuretic, PTP increased by 23% within 5 to 12 h of treatment. In contrast, control speakers not given the diuretic showed no increase in PTP during the same period. That study is particularly relevant since Lasix is also widely used to treat decompensated HF [6]. Additionally, several acoustic measures of vocal perturbation (jitter, shimmer, and signal-to-noise ratio) have been found to improve in response to hydration treatments for speakers with laryngeal nodules or polyps [14]. These speakers also reported reduced phonatory effort after hydration treatments. Other studies have found that dehydration can reduce vocal quality, and conversely, that increasing hydration can improve jitter, shimmer, and maximum phonation times [15].  
The findings linking hydration to PTP and vocal effort indicate a direct relationship among systemic fluid levels, vocal fold physiology, and vocal function. These results suggest that HF-related congestion and edema, if present in the vocal folds, could also affect the resulting voice signal in detectable ways. The amount of laryngeal edema required to measurably change the voice is expected to be small, especially in contrast to the large amount of systemic edema needed to produce an easily detectable increase in body weight. Therefore, voice monitoring may allow patients and clinicians to detect and track HF-related congestion at an earlier stage than weight monitoring does. This sequence of events is summarized in Figure 1.
## 2 Related Work
For each node in the cluster, once it detects an event, it is transmitted to the sink; otherwise the node transmits a dummy packet after a specific time instead. Packets are transmitted to the cluster head that has the higher remaining energy. The cluster head forwards the packet whether it is real or fake to the sink. This technique does not guarantee the delay of the event and delivery ratio, which means it is not suitable for real-time or time sensitive applications. One more technique, which is presented in [15], is Fortified Anonymous Communication Protocol (FACP), which transmits a real or a dummy packet every interval and could consume a huge amount of energy and increase the overhead of the network. In addition, only one packet message is considered, which makes their technique unable to deal with multimedia data that consists of multiple packets. Generally, in the literature, most of the papers focus the effort on how to mislead the adversary on both the location and the time of the real event, but rarely consider confusing the adversary about the existence of the real event. The proposed techniques avoid most of the drawbacks of previous solutions. Some solutions in the literature do not successfully control the delivery ratio and delay of the real events. CAD increases the delivery ratio and decreases the delay without sacrificing the anonymity of the origin. This allows the real event to be delivered within a specific time. Another disadvantage in some of the previous solutions that they assume a central location for the sink in the network. This is not practical for many applications where the sink could be located anywhere. Therefore, we developed our solutions while considering flexible and adaptive sink location in the network. FACP used fake messages in each interval, which causes high power consumption. In our techniques, we avoided this by introducing probability-based techniques, which reduces the number of overhead messages compared to FACP. A major contribution in this paper is introducing three different analysis models developed to prove the performance of the proposed techniques. Many of the current related work techniques do not have an approval of their performance and how they react to a global adversary attack. Those analysis models show that the proposed techniques protect the location and time of the real event by misleading the adversary about the existence of the real event. The models are output visualization, neural network, and anonymity equation.
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 6 Prototype Implementation and Evaluations
### Major User Interfaces of CoVSCode Prototype System
#### 6.1.2 Real-Time Collaborative Programming  
Following the initialization process in the previous step, the programmer starts to work in the real-time collaboration session. Figure 8 presents the UI snapshots of two _CoVSCode_ clients used by two programmers, _[PERSON]_ (Site A, the upper figure) and _[PERSON]_ (Site B, the lower figure), in a real-time collaboration session. At this particular moment, Site A (_[PERSON]_) is editing Line 52 of the source code file, while Site B (_[PERSON]_) is editing Line 37 of the same file. Both of them can see each other's work in real-time.  
As presented, the client user interface looks the same as the original single-user _VS Code_ IDE, and all existing functionalities have been fully preserved. The document explorer on the left looks similar to that in the original _VS Code_, but real-time collaboration functionalities have been integrated: whenever the programmer creates, deletes or renames any source code item in the explorer, the operation is immediately transmitted to remote sites; and conversely, remote programmers' changes in the source code tree can also be replayed in the local explorer in real-time.  
Similarly, the IDE's built-in source code editor has also been equipped with additional real-time collaboration features. During the programming process, each local operation is instantly detected and propagated to remote sites, while remote operations are also replayed and viewed in a real-time fashion. The source code consistency is always maintained by the system, provided that all operations have arrived at remote sites.
# Monte Carlo Study of Imaging Plate Response to Laser-Driven Aluminum Ion Beams
## 2 Experimental Setup  
The experiments were performed on the Trident laser facility at Los Alamos National Laboratory (LANL) [33; 34]. Figure 1 shows the schematic layout of the experimental setup. 80 J, 650 fs, 1054 nm laser pulses were focused using an f/3 off-axis parabola, and irradiated 110 nm thick aluminum foils with a peak laser intensity of about \(2\times 10^{20}\) W/cm\({}^{2}\)[33]. The laser-driven Al ion beams diverged with a 20\({}^{\circ}\) cone half-angle [35; 36]. The ions fly into a high resolution and high dispersion Thomson Parabola Spectrometer (TPS) [33; 37], which measures an energy spectrum of the ions separately depending on their charge to mass (\(Z/A\)) ratio. The TPS symmetry axis is aligned with the ion propagation direction, and the ion flux into the TPS is limited by a pinhole aperture along that axis. Over a portion of the ion flight within the TPS, strong electric (\(E\)) and magnetic (\(B\)) fields parallel to each other and normal to the symmetry axis deflects ions depending on their \(Z/A\) and kinetic energy (\(E_{ion}\)). After a drift distance within the TPS, they arrive at the detector plane laid normal to the axis. The \(Z/A\) and \(E_{ion}\) are given by their location on this plane [33; 37]. Specifically, the TPS disperses a given \(Z/A\) on the detector plane along a narrow (as defined by the pinhole) parabolic curve in the \(\hat{\kappa}\) (horizontal, B-field deflection) and \(\hat{y}\) (vertical, E-field deflection) directions originating at the intersection with the symmetry axis according to \(E_{ion}\). The origin (= zero point in Figure 2) corresponds to an infinite \(E_{ion}\), and X-rays or neutral particles are recorded as well. The TPS uses 10 cm long magnets producing 0.82 T and 50 cm long electrodes that can be charged up to 15 kV potential., enabling high energy resolutions of \(\Delta\)E/E \(<\) 5% at 100 MeV/nucleon [33; 37]. The IP used in the TPS was covered with an 18 \(\upmu\)m thick Al filter in order to eliminate background noise originating from scattered laser lights, low energy protons (<1 MeV), low energy electrons (<50 keV), and soft X-rays [33]. Incident Al ions with kinetic energy greater than 50 MeV penetrated through the filter and reached the IP surface. For counting the absolute number of aluminum ions, strip-shaped CR-39 track detectors with a width of a few mm were placed on the IP surface [33].  
It is known that the IP response does not depend on the charge state of the incident ions [6; 23; 38; 39]. This is because the incident ions quickly arrive at an equilibrium charge state as soon as they enter the target surface. This characteristic is assumed by [PERSON] et al. [23], and is confirmed by [PERSON] et al. [6] and [PERSON] et al. [27] in experiments using multiply charged carbon ions and titanium ions, respectively. Therefore, we do not need to specify the charge state of Al ions incident on the BAS-TR IP in our SRIM simulations.
## 2 Materials and Methods  
### Preparation of Al/n-Decane Nanofluid Fuel  
Al nanoparticles (Dk Nanotechnology Co., Ltd., Beijing, China.) were synthesized by the plasma vapor deposition method. The nanoparticle surface was covered with a thin layer of Al\({}_{2}\)O\({}_{3}\) (3-5 nm) by passivated treatment to prevent Al nanoparticle from being oxidized. The median particle size of Al nanoparticles was \(\sim\)50 nm determined by a laser particle size analyzer. The n-decane, purchased from Macklin Biochemical Co., Ltd., Shanghai, China, was selected as the base fluid. Its density, surface tension, viscosity, and relative permittivity are 0.735 g/cm\({}^{3}\) (20 \({}^{\circ}\)C), 23.8 \(\times\) 10\({}^{-3}\) N/m (25 \({}^{\circ}\)C), 0.92 \(\times\) 10\({}^{-3}\) Pa-s (20 \({}^{\circ}\)C) and 2.0, respectively. Nonionic sorbitan monooleate (SP-80, MacklinBiochemical Co., Ltd., Shanghai, China) was selected as a surfactant to improve the dispersion and stability performances of Al nanoparticle in n-decane. The density and viscosity of SP-80 are 0.994 g/cm\({}^{3}\) (20 \({}^{\circ}\)C) and 1.0-2.0 Pa-s (20 \({}^{\circ}\)C), respectively.  
The two-step method was used to prepare the Al/n-decane nanofluid fuels: (1) SP-80 was firstly added into a certain amount of base fluid n-decane according to the volume ratio of 1:99 (SP-80:n-decane), and the mixture of n-decane and SP-80 was stirred to make them compatible. Then a certain amount of Al nanoparticles was weighed and added into the mixture to produce the Al/n-decane nanofluid fuel suspensions with the Al nanoparticle concentration ranging from 1.0-10.0 mg/mL with an interval of 1.0 mg/mL. (2) These suspensions were exposed to an ultrasound field for 30 mins to reduce the coagulation of Al nanoparticles in n-decane. The stability observation experiments revealed that the nanofluid fuels as prepared could keep stable for 2 h, which was long enough to conduct the secondary breakup of each Al/n-decane nanofluid fuel suspension in the electric field (30 mins). The properties of Al/n-decane nanofluid fuels as prepared have been measured accurately and can be found in our previous work [20].
# AI-For-Mobility--A New Research Platform for AI-Based Control Methods
## 3 Sensor Architecture
### Horizontal Dynamics  
The horizontal dynamics describe all quantities of the vehicle's planar motion and is dominant compared to the vehicle's vertical dynamics. In addition to accelerations and velocities, this also includes the vehicle's position (both translational and rotational). Acquisition of these quantities is essential for many vehicle control algorithms (see also control pyramid in Figure 2, layers \"Vehicle Application\" and \"Motion Execution\"). Utilizing these quantities, advanced nonlinear state estimation algorithms such as [21,22] can be implemented on the AFM. The sensors of the AFM measure:  
**Accelerations, velocities and position of the vehicle's center of gravity**  
* A high-precision 6D Inertial Measurement Unit (IMU) installed close to the center of gravity captures all components of a vehicle motion. Two roof antennas additionally allow for fusing measurements by the global navigation satellite system (GNSS).
* With the aim of improving the accuracy of the satellite measurements (GNSS), a mobile differential GPS (dGPS) base station provides correction data to the IMU/GNSS platform installed in the vehicle.  
**Longitudinal and lateral velocities along with complementary vehicle side-slip angle**  
An optical speed sensor scans the road surface to measure the vehicle's over ground velocity. Based on these measurements, the vehicle's side-slip angle is determined. Note that both longitudinal and lateral velocity can be measured precisely.
## 4 Result and Discussion
### Results for Delay with Random UAV Movement  
Figure 9 shows the value of data transmission delay for bandwidth allocation using the BRFFNN and LSGB ML models.  
Figure 8: PDR using BRFFNN and LSGB for bandwidth allocation with random UAV movement.  
Figure 9: Delay using BRFFNN and LSGB for bandwidth allocation with random UAV movement.  
The results show that the average delay for the BRFFNN model, represented by the red line, is 0.0456 ms, while the LSGB model, represented by the blue line, experiences a slightly higher delay of 0.0542 ms. These results indicate that the BRFFNN model achieves lower delays than the LSGB model. This performance, measured within a randomly set area, highlights the effectiveness of BRFFNN-driven bandwidth allocation in enhancing QoS metrics in 5G UAV communication scenarios.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 5 Results and Discussions
### Comprehensive Comparison across All Road Segments  
#### 5.1.1 Short-Term Travel Time Prediction  
As depicted in Table 4, SLIT significantly outperforms all other methods in short-term prediction across all metrics. This superior performance is attributed to the integration of attention mechanisms across periodic segments and within the final prediction phase through the proposed decoder, SLI-D. This design is intended to allow SLIT to dynamically weigh the importance of short-term versus long-term data, adapting responsively to changes in traffic patterns. The improvements demonstrate the effectiveness of the design of SLIT in handling the intricacies of short-term traffic data. Historical Average (HA), a well-known traditional method, demonstrates the highest error, highlighting the complexity of traffic prediction that requires more complex models. LSTM, DNN, and DE-SLSTM show significant improvements over HA, which emphasizes the importance of deep learning in traffic forecasting. DHM performs notably in MAE, and PASS2S excels in RMSE among competitive methods. However, the design of SLIT aims to integrate attention across multiple temporal segments, which is expected to facilitate a more comprehensive understanding of traffic dynamics.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 2 Cross-Domain Fusion Constellation Design Method
### Constellation Design Scheme  
Constellation design refers to the simultaneous design of multiple satellite tasks to jointly complete a specific task, generally including satellite type, number, working orbit, etc. This is for the traditional single-domain constellation design.The CDFC is a multi-domain universal constellation, and its completed tasks are no longer limited to one category. Communication, navigation and remote sensing are the typical domains in this paper. First, it is necessary to analyze the system requirements and constraints of the constellation. The requirements of each domain should be merged as much as possible, and those that cannot be merged should be designed separately. Then, it is necessary to give the constraint conditions of the corresponding payload. Though the satellite of CDFC can define satellite functions through software in orbit, it is constrained by the payload carried by the satellite itself. The third step is to set the basic parameter constraints of the constellation, such as orbit height, orbit inclination, etc. If the accurate value cannot be determined, the approximate limit range can be given. Based on the above inputs, we carry out a multidisciplinary constellation design to obtain the basic constellation of each functional domain, and then conduct a comprehensive design. Roughly removing the satellite is the first step during a synthesis. Then, in the reverse design phase, optimize the deployment position of the payload first, and then make the satellite fusion. After reverse optimization based on constraints, the final target constellation is finished.