esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Hardware Configuration  
The constitution of the hardware in our system is straightforward. Intuitively shown in Figure 2a, a six degree of freedom (6 DoF) IMU [17] and a Teensy 3.2 board [18] were connected with each other. We built these on a breadboard as a testing device with an Arduino Mega board in our previous work for the wire-tension measurement [12]. The 6 DoF means there is a tri-axial accelerometer and a tri-axial gyroscope, which can return the acceleration and the angular speed, respectively, on the X, Y, and Z axis of a coordinate system. In other words, 6 DoF can be described as the freedom of movement of a rigid body in three-dimensional space, which refers to the following: Forward/back (on X axis), left/right (on Y axis), up/down (on Z axis), roll (around X axis), pitch (around Y axis), and yaw (around Z axis). In our particular case, we do not need the magnetometer, which can be potentially combined with the  
Figure 1: The 3D motion capture of hammer throw. (**a**) The set-up of the data collection; (**b**) a sample of the 3D data.  
accelerometer and the gyroscope to construct a 9 DoF IMU. In our application, the IMU is designed as a combo board, which has the accelerometer, ADXL345, and the gyroscope, TG3200. The Teensy 3.2 board is a breadboard-friendly microcontroller, which can be programmed in Arduino IDE (Integrated Development Environment). Compared to several Arduino boards, the current one is smaller than the Arduino UNO and Mega boards, and it has its own USB (Universal Serial Bus) port while Arduino Mini does not have one, which makes it relatively easier to be programmed.  
In addition, as shown in Figure 2b, we attached three motion capture markers (two are 9 mm in diameter and one is 5 mm in diameter) on the sensor device for constructing a capture model for 3D motion capture using a 10-camera VICON MX40 motion capture system (VICON Motion Systems, Oxford Metrics Ltd., Oxford, England). The motion capture rate was set at 200 frames/s. Calibration residuals were determined in accordance with VICON's guidelines and yielded positional data accurate within 1 mm. The VICON data was used to help us develop a tracking algorithm of the IMUs in the vertical direction.
## 2 Related Work
### Delay Mechanisms in Information Propagation  
The incorporation of delay mechanisms in diffusion models represents a significant advancement over classical models. Following the pioneering delay-aware approach of [PERSON] et al. (2010) [6], researchers have developed increasingly sophisticated temporal models. These mechanisms reflect the realistic time lag between receiving and sharing information. [PERSON] et al. (2024) [14] developed the SEIHR model which added \"exposed\" and \"hibernated\" states to capture different attitudes toward information sharing.  
[PERSON] and [PERSON] (2022) [15] proposed a novel action-reaction-diffusion model with temporal constraints. Their model evaluated the influence between users by analyzing meta-paths in user-content relationships. [PERSON] et al. (2021) [8] specifically addressed delays in warning information diffusion by incorporating warning timeliness parameters in their population dynamics model.  
[PERSON] and [PERSON] (2024) [2] extended the reaction-diffusion modeling approach by introducing media correction, self-correction, and time delays into rumor propagation systems. Their work demonstrated that increasing the time delay decelerates rumor propagation, while higher degrees of cross-diffusion and correction mechanisms can accelerate information spread. Our work builds upon these delay-focused approaches by systematically comparing three distinct delay mechanisms (constant, uniform, and exponential) across different network structures. This comparative analysis has not been previously undertaken in the literature.
## 1 Introduction
The agent can learn from the feedback of the environment and adjust its strategy when the channel quality or transmission traffic changes. The learning process of the agent does not depend on channel model assumptions or accurate global CSI. * A deployment method based on a transfer learning mechanism was proposed to facilitate implementation in a large-scale LEO satellite constellation. By fixing the first several convolutional layers and fine-tuning the last layers, the converged DRL network in the simulation environment can be efficiently transferred to the actual space environment, thus reducing the computation expense and promoting system efficiency considerably.
# Numerical Investigation on Handling Stability of a Heavy Tractor Semi-Trailer under Crosswind
## 4 Multi-Body Dynamic Simulation
In the simulation, the driving speed was set to 30 m/s, and a crosswind with different strengths was added in the vertical direction of the vehicle driving direction. To some degree, the lateral displacement, lateral acceleration and yaw rate could evaluate the handling stability of the tractor semi-trailer under crosswind. Figure 15 shows the variation of lateral displacement, lateral acceleration and yaw rate with time under different crosswinds. In order to obtain the dynamic response of the vehicle more clearly and accurately, the simulation time is set to 10 s. In general, when the crosswind speed increases from 0 to 16.7 m/s, the lateral displacement, lateral acceleration and yaw rate also increase gradually. Due to the rise of the lateral force coefficient and the yawing moment coefficient, the growth rate of lateral displacement increases, and the variation gradient of lateral displacement also agrees well with the trends of the lateral coefficient and the yawing moment coefficient (see Figure 15a). Because the dynamic model is an open-loop, the lateral displacement of the tractor semi-trailer will increase continually. As to the lateral acceleration, it will increase to the maximum instantly once the vehicle encounters a crosswind, and reduces gradually then. Because the effective area of crosswind decreases with the tractor semi-trailer deviated from its original path, the lateral force and yawing moment also decrease so that the lateral acceleration decreases (see Figure 15b). The yaw rate shows a similar trend with the lateral acceleration. Previous studies [40] have indicated that the yaw rate is the most important factor on the handling stability, and the greater the yaw rate is, the worse the crosswind stability is. Figure 15: The evaluation indexes of crosswind stability at different crosswinds: (**a**) lateral displacement; (**b**) lateral acceleration; (**c**) yaw rate.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 6 Conclusions  
In this paper, we considered a pulse-Doppler radar and multiuser wireless communications systems that share the same spectrum. We proposed a joint transmit waveform with the same time-varying amplitude and quadratic phase functions, and we developed a design method to select waveform parameters by optimizing joint system performance metrics. Using multiobjective optimization, the tradeoff in reducing both overall interference and state parameter estimation error was demonstrated. Note that the designed parameters only include the frequency-modulation rate and duration of the waveform but not the waveform's phase function. It may be possible to improve the aforementioned performance tradeoff when the time-varying phase function is also used in the design, especially at varying output SNR values. As we demonstrated in References [17; 21] using a deviation measure of the Barankin bound from the Cramer-Rao lower bound on the estimated target state covariance, waveforms with a logarithmic phase function result in lower MSE than waveforms with a quadratic phase function.  
Conceptualization, A.P.-S.; methodology, J.S.K.; software, J.S.K.; validation, J.S.K.; formal analysis, J.S.K. and A.P.-S.; writing--original draft preparation, J.S.K.; writing--review and editing, A.P.-S.; funding acquisition, A.P.-S.  
This research was funded by DARPA grant HR0011-13-1-0004 and by AFOSR grant FA9550-17-1-0100. The views expressed are those of the authors and do not reflect the official policy or position of the United States Department of Defense.  
The authors would like to thank [PERSON] from MITRE Corporation for his insightful discussions that influenced the progress of this work.  
The authors declare no conflict of interest.
# Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats
## 6 Results
Figure 11: RSSI reception levels (dBm) in the port area with LoRa’s SF12. Figure 10: RSSI (Received Signal Strength Indicator) reception levels (dBm) in the port area with LoRa’s SF7. the link robustness with this LoRa configuration is achieved at the expense of severely decreasing the transmission data-rate and, consequently, increasing the ToA of each transmitted message (see Table 1). This fact is not negligible in the case of having an important number of nodes simultaneously transmitting to the same base station due to potential interfering signals [29]). In addition, due to duty-cycle restrictions associated with the unlicensed transmission bands, employing low data-rates (high SF) implies the use of short payloads; hence, the information included in each transmitted frame is limited (Table 1). Therefore, when possible, it is preferred to employ low values for the SF (high data-rates) in order to increase the achieved throughput. Another test was also conducted in order to evaluate the transmission range in open sea. In this case, we employed SF12 in order to evaluate the maximum distance reached by using the deployed architecture. The RSSI levels attained in this experiment are presented in Figure 13. Observe that, due to the limitations of the sailboats under study, they had to sail close to the shore for safety reasons. The maximum coverage range attained in this scenario was 4 km. Although other works [10] have shown greater transmission distances in land, we justify the attained outcomes by the adverse propagation conditions around the base station, due to close big obstacles, as well as the proximity of the on-board transmission equipment to the water, as explained in the previous section. However, we consider that a range of 4 km from the port is long enough for monitoring maneuvers inside the port or activities in areas close to the shore, especially for boats with a stringent payload and energetic restrictions such as the sailboat under study (Optimist Class) or radio-control devices. Finally, regarding the energy efficiency of this proposal, we have measured the power consumption of the end-device when transmitting packets every 5 s and employing LoRa's SF12. In these measurements, all the sensors were connected to the board and active. With this configuration, we attained a power consumption of 140 mA, with a voltage of 5 V. This consumption level permitted an autonomy of around 6.5 h with the employed power bank of 900 mAh, and would provide a battery lifetime of one day with an inexpensive power bank of 22,000 mAh. This consumption level is far from other alternative communication technologies that may fit in the scenario under consideration such as the cellular ones (2G/3G) as shown in [30; 31]. Figure 12: RSSI reception level (dBm) attained during the tracking of an Optimist training session.
# Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form
## 3 Grey Decision-Making for Product Form Driven by Fuzzy Relationship between Kansei Images
### Identification of Kansei Image Weight Based on Variable Weight Theory  
#### 3.1.1 Determination of Kansei Image Weights  
A questionnaire is constructed based on selected representative samples and Kansei words, and the semantic difference (SD) method is used to investigate users to obtain the Kansei image cognitive evaluation matrix, namely:  
\[R=\begin{bmatrix}r_{11}&r_{12}&\cdots&r_{1n}\\ r_{21}&r_{22}&\cdots&r_{2n}\\ \vdots&\vdots&r_{ij}&\vdots\\ r_{m1}&r_{m2}&\cdots&r_{mn}\\ \end{bmatrix} \tag{1}\]  
where \(r_{ij}\) is the user's evaluation value for the \(j\)-th Kansei image of the \(i\)-th sample.  
Equation (2) is used to standardise the Kansei image cognitive evaluation result \(R\)[20] to ensure the commensurability and comparability of the data and obtain the standardization matrix \(X=\begin{bmatrix}x_{ij}\end{bmatrix}_{m\times n}\).  
\[x_{ij}=\frac{r_{ij}-min(r_{j})}{max(r_{j})-min(r_{j})} \tag{2}\]  
To reduce the influence of subjective user factors in the process of weighting, this study uses the entropy weight method [21; 22] to obtain the weight of each Kansei image. The specific steps are as follows:  
Step 1: Using Equation (3) to calculate the probability of the target Kansei image.  
\[P_{ij}=\frac{x_{ij}}{\sum\limits_{i=1}^{m}x_{ij}} \tag{3}\]  
Step 2: Substituting \(P_{ij}\) into Equation (4) to calculate the Kansei image entropy \(Q_{j}\).  
\[Q_{j}=-k\sum\limits_{i=1}^{m}P_{ij}lnP_{ij} \tag{4}\]  
Step 3: According to Equation (5), the Kansei image weight \(W_{j}\) is determined.  
\[W_{j}=\frac{1-Q_{j}}{\sum\limits_{j=1}^{n}\bigl{(}1-Q_{j}\bigr{)}} \tag{5}\]
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 5 Experiment and Results
### Evaluation Criteria  
In this paper, the following three items are used as the evaluation criteria for the accuracy of the forecasting model: root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE), which are calculated as Equations (27)-(29).  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(\hat{y}_{i}-y_{i}\right)^{2}}{N}} \tag{27}\]  
\[MAPE=\frac{1}{N}\sum\limits_{i=1}^{N}\left\|\frac{\hat{y}_{i}-y_{i}}{y_{i}} \right\|\times 100\% \tag{28}\]  
\[MAE=\frac{1}{N}\sum\limits_{i=1}^{N}\left|\hat{y}_{i}-y_{i}\right| \tag{29}\]  
where \(\hat{y}_{i}\) is the forecasting load of the model; \(y_{i}\) is the true value of the load; \(N\) is the number of predicted time nodes. In this paper the load value is predicted for the next 24 time nodes, so \(N=24\).
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Interpretation of Model Outputs
#### 3.2.3 Time-Dependent Variations and Interactions  
The Random Forest model, in particular, was able to capture the complex interactions and time-dependent variations of the factors affecting water quality. For instance, the model detected the increase in pollution levels during peak residential sewage discharge times and heavy rainfall events, which lead to increased stormwater runoff.  
The ability of the RF model to identify these patterns suggests that any measures to improve water quality must take into account the timing and intensity of residential sewage input and stormwater flow. Strategies like optimizing the timing of sewage release to off-peak times and implementing green infrastructure to manage stormwater may help reduce the pollution load in the lake.  
Interpreting the outputs of our machine learning models provides valuable insights into the primary contributors to water quality degradation and the complex dynamics of urban water pollution. These findings are critical to inform effective strategies for urban lake water management and restoration.  
Figure 3: Feature importance.
## 5 Assessing Dataset Availability and Practicality
### Fabric Defect Detection  
Fabric defect detection has emerged as a critical focus within textile manufacturing, responding to the increasing necessity for automated quality assurance in a sector that has predominantly depended on conventional manual visual inspection. A variety of datasets have been established to expand the research and practical applications in this area, each responding to specific challenges.  
The ZJU-Leaper [43] initiative represents a comprehensive benchmark dataset comprising 98,77 images classified into 19 distinct fabric categories. This dataset introduces five distinct task configurations and an innovative evaluation protocol designed to reconcile the disparity between conventional inspection techniques and automated visual systems, highlighting the imperative for extensive, annotated datasets to propel advancements in defect detection technologies. As such, the paper addresses the need for efficient quality control in textile manufacturing using computer vision technology. It presents the ZJU-Leaper dataset and multiple-stage models for defect detection, emphasizing fast deployment and easy upgradability. The paper also introduces a new evaluation protocol to ensure accurate comparisons between different inspection methods. Similarly, ISL-Knit [44] specifically addresses knitted fabrics, bridging a significant gap by providing a dataset that includes 3018 defective and 357 flawless images. Through practical applications on devices such as Raspberry Pi, it demonstrates the practicality of deep learning in industrial quality control. Another dataset, Textil-5k [46], encompasses a compilation of 4100 images, where labelled and unlabeled data promote advancements in both supervised and semi-supervised learning, moving the industry closer to scalable, automated solutions.  
Publicly annotated databases also aspire to standardize evaluation practices. For instance, [PERSON] et al. [47] presents a plain fabric dataset to address the inconsistencies associated with the utilization of private collections in verifying defect detection methodologies. By categorizing detection strategies and underscoring diverse imagery, it establishes a benchmark for future research evaluations. In a parallel context, a newly developed patterned texture [48] dataset tackles challenges related to anomaly detection, with methodologies such as reverse distillation demonstrating superior performance compared to traditional approaches in assessing a variety of defect types.
## 3 Background
### Per-Stream Filtering and Policing (PSFP)  
The IEEE 802.1 Qci--PSFP introduces new functionalities, such as per-stream metering and monitoring, error detection, and error mitigation. To improve reliability, PSF allows for blocking a stream or a port so as to enforce error containment and to prevent the error propagation. By applying ingress policing and filtering, PSFP improves security by blocking the source of unforeseen or noncompliant traffic. PSFP defines the _stream gates_, which are placed on top of the transmission queues (blue blocks in Figure 1). Stream gates are traversed by frames, and each frame is mapped to a single stream gate. The PSFP mapping may use one or multiple fields of the Ethernet frame VLAN tag to assign a stream gate to the frame [27]. The stream gates maintain the status of their gates (open or closed) and an Internal Priority Value (IPV). If a gate is closed, the frames traversing it are dropped, otherwise the frames are forwarded to the priority transmission queue assigned by the stream gate IPV. Both the stream gate status and IPV are modified following a _stream gate control list_, i.e., a list of operations that cyclically repeats. The repetition cycle is called a stream gate cycle. Each operation in the stream gate control list is activated at a precise  
Figure 1: Ethernet port forwarding architecture according to the IEEE 802.1Q-2018.  
instant within a stream gate cycle. Using the PSFP, it is possible to change the mapping between the frames and transmission queue (i.e., the priority) over time.
## 1 Introduction
At low inlet gas velocity (i.e., at low input power), extinction is likely to occur and, thus, low-power systems require insulating materials, and conversely, at high inlet gas velocity (i.e., at low residence time), blowout is likely to occur and, thus, high-power systems require conductive materials [10; 35]. Once a clear distinction of roles is identified, differentiating the thermal conductivity between inner and outer walls could lead to systems capable of working at both low inlet gas velocity, thanks to the resistance to extinction imparted by the material with low thermal conductivity, and high inlet gas velocity, thanks to the resistance to blowout imparted by the material with high thermal conductivity. The present paper fits in this framework focusing on the effect of differentiating the thermal conductivity between inner and outer walls on the stability, assessed in terms of critical inlet gas velocities (i.e., the velocity below which extinction occurs and the velocity beyond which blowout occurs), of a non-symmetric U-shaped catalytic heat-recirculating micro-combustor. In this configuration, the inlet channel is protected by the outlet channel on the one side and directly exposed to external heat losses on the other side. The aim was to understand the role played by the inner and outer walls in determining extinction and blowout, and whether differentiating the thermal conductivity could be an effective strategy to improve stability, thus enlarging the operating window of the catalytic micro-combustor compared to the use of the same material for both walls. To this end, a two-dimensional CFD model was developed and, for different combinations of materials (i.e., different combinations of values for the inner and outer thermal conductivities), simulations of lean pre-mixed propane/air combustion were performed by varying the inlet gas velocity.