esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Performance Evaluation Based on Actual Sensing Results  
Figure 13 shows the cumulative distribution function (CDF) of RMSE in the sensing results of Figure 11. From this figure, \"Cancel w/ Adaptation\" achieves the minimum RMSE among all the results. As \(\rho_{\gamma}\) and\(\rho_{\varepsilon}\) are changed from 0.999999 to 0.99999, the RMSE is degraded. The temperature sensor has the high correlation among sensors. Therefore, each sensing result takes the similar value. As a result, in the detection by FC, the duplication of sensing results frequency occurs. In the proposed technique, the data set is updated from the past data set. If the updating of the data set continues for long time slots, the mismatch of the detected CSI, CFO, frequency spectrum between detecting data set and past one becomes larger. Therefore, the high time correlation of CSI and CFO are required for achieving smaller RMSE.  
Figure 12: Results of temperature sensor in Part 2.  
Figure 13: CDF of RMSE in temperature sensor of Part 1.  
Figure 14 shows the CDF of RMSE in the sensing results of Figure 12. We can see that all the performances of this figure are better than those of Figure 13. This is because the duplication of sensing results occurs in the fewer times. In addition, the difference of \"Cancel w/ Adaptationf between \(\rho_{\epsilon}=\rho_{\gamma}=0.99999\) and \(\rho_{\epsilon}=\rho_{\gamma}=0.9999\) becomes smaller. This reason is as follow. Although the time correlation of CSI and CFO becomes low, the better data tracking is achieved owing to the highly time continuity of sensing results. From the evaluations with using actual sensing results, our proposed technique achieves the better data separation with the lower correlation of sensing results among sensors and the more highly time correlation of sensing results.
## 3 Experimental Setup
### Li-Fi Transmission Link Testbed Based on Sparse Coded Mask Model
* Feasibility in Indoor Infrastructure: Many Li-Fi use cases (e.g., smart offices, classrooms, hospitals) are characterized by short to medium range communication, typically within 2-5 m. Therefore, the proposed 1 m setup serves as a relevant first step and is representative of core mechanisms applicable to these environments. * Optical Lens System to focus and collimate the LED beam, increasing directionality and intensity. - Multiple Input Multiple Output (MIMO) architectures to aggregate spatial diversity and mitigate path loss. - Higher sensitivity photodiodes (e.g., SiPM or APD arrays) to enhance detection under lower illumination at longer ranges. - Adaptive masking ratio tuning based on channel state information (CSI) to preserve signal recoverability as the channel degrades. The proposed MAE model consists of a patch encoder and decoder designed to process multivariate time-series DMT waveforms. Each DMT frame is split into fixed-length patches (e.g., 16-sample segments), and a random subset (e.g., 75-85%) is masked before being input to the encoder. The encoder embeds the unmasked patches using a 1-D positional embedding and passes them to a multi-layer transformer. The decoder reconstructs the full DMT frame by predicting the missing (masked) patches using both the encoded representations and learnable mask tokens. Reconstruction loss is computed only over the masked patches to guide self-supervised learning. To ensure reproducibility and transparency, we have now included a table summarizing the key hyperparameters, as shown in Table 4.  
We tuned these hyperparameters through an empirical grid search using a validation set split from the waveform dataset. We observed that smaller patch sizes resulted in improved fine-grained reconstruction but increased model size, while 16-sample patches provided the best trade-off between performance and complexity. Similarly, a masking ratio above 85% degraded EVM sharply, while the 75-85% range maintained reliable reconstruction.
## 2 Background
### Wireless Sensor Network (WSN)  
Wireless Sensor Networks (WSNs) are an emerging technology which consists of the improvement of three technologies [20]: microprocessors, wireless communications and micro-electromechanical systems (MEMs). A WSN is typically formed by thousands of small nodes, with low computational power, few available resources (e.g., memory), a short-range communication and generally non-rechargeable battery. The set of nodes allows the network to be able to perform complex tasks, which may be difficult to be performed by a single node [21]. WSNs have usually a dynamic topology and each node has a type of sensing equipment to analyze a given phenomenon (e.g., temperature, luminosity). Through this device, it can capture the phenomenon occurred, and transmit it to a sink node, responsible for disseminating the data to the observer (or the Internet). Due to the large number of participating nodes in the network, it can be considered as an advantage the short distance between them, using a multi-hop communication (as opposed to using long-range communication) to save energy in data transmission [22].
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 5 Modeling and Results Discussion  
### Modeling  
In this section, we present our enhanced model that could generate both M2M and H2H traffic while adding priorities and queuing for both traffic.  
Assume that our LTE-A network, which utilizes the FIFO (First In-First Out) queue in MATLAB, consists of a total number of resources c = 100. M2M requests are served by an LTE-M network with 6 resources (\(\text{rb}_{\text{(m)}}=6\)), while H2H requests will be handled by the remaining 94 resources (\(\text{rb}_{\text{(h)}}=94\)).  
To investigate the mutual consequences of H2H and M2M in normal and/or emergency scenarios, we use the theory of queuing to produce exact theoretical results for some performance measures that facilitate a comparison of empirical outcomes with corresponding theoretical outcomes.  
To fit our CHANAL design, we made numerous modifications to the model described in Section 4.1, including:  
* Poisson processes with the two parameters \(\lambda_{\text{(h)}}\) and \(\lambda_{\text{(m)}}\) correspondingly determine arrivals in the architecture, which consists of two servers with two traffic sources (H2H and M2M). With respect to the rate parameter \(\mu\), service times follow an exponential distribution, with the mean service time being 1.
* Assuming a fixed arrival rate \(\lambda_{\text{(h)}}\) for H2H traffic and a service rate \(\mu_{\text{(h)}}=1\).
* Additionally, we assume that the five variable arrival rates for M2M heterogeneous traffic are \(\lambda\text{(m)}\in\{5,10,15,20,25\}\) and that the service rate is \(\mu\text{(m)}=1\).
* M2M and H2H traffic are prioritized equally.
* We employ a FIFO queue type and take into account the following queue sizes: \(\text{n}=\text{o}=0\) for H2H traffic and M2M traffic, respectively.
* The duration of the modulation is 1000 s.  
Table 3 provides an overview of all the notations utilized in the simulations.
## 6 Results and Discussion
### Model Implementation and Training Protocols
#### 6.2.4. Levenberg-Marquardt (LM) Neural Network  
Using the Levenberg-Marquardt algorithm (trainlm) on a 70/15/15 data split, a 15-neuron network was trained for up to 1000 epochs. This method blends Gauss-Newton and gradient-descent updates via an adaptive damping factor (\(\mu_{0}\) = 1 \(\times\) 10\({}^{-3}\), decrease factor = 0.1, increase factor = 10) and applies a weight-decay term of 0.1. The model converged after 25 epochs, achieving R\({}^{2}\) = 0.951 and RMSE = 1.737 MPa, as displayed in Figure 16.  
Figure 16. Comparison of actual vs. predicted UCS: Levenberg–Marquardt neural network (15 neurons; \(\mu_{0}\) = 1.0 \(\times\) 10\({}^{-3}\); 25 epochs; RMSE = 1.737 MPa; R\({}^{2}\) = 0.951).  
Figure 15. Comparison of actual vs. predicted UCS: scaled conjugate gradient neural network (15 neurons; \(\lambda_{0}\) = 1.0 \(\times\) 10\({}^{-3}\); \(\sigma\) = 1.0 \(\times\) 10\({}^{-6}\); RMSE = 1.490 MPa; R\({}^{2}\) = 0.964).
## 3 DTN Solutions for IoT
### Motivation for Enabling Delay Tolerant IoT  
There is a very strong dependency between DTN characteristics and IoT constraints. Figure 2 and the previous work of [PERSON] _et al._[17] show the importance of considering delay tolerant schemes for IoT applications. The diagram combines DTN main performance metrics and IoT characteristics. This dependency makes it easier to understand how to tailor DTN solutions by enhancing some performance metrics and considering IoT issues at the same time. As declared in [17], most IoT applications constitute a huge number of interconnected objects (e.g., smart grid, smart industry, etc.). Hence, the great amount of exchanged data is a concern since the application needs to process every required information from the environment. Obviously, the huge quantity of data introduces a bigger challenge for DTN architecture to enhance delivery ratio and data efficiency. Since DTN schemes aim to increase delivery ratio, it would be even harder to achieve when the application has many exchanged information. Furthermore, the increasing number of connected objects make the scalability another challenge to consider, especially in sparse environment where data may take a long way to be delivered in an intermittently connected network (e.g., smart city). Delivery latency and network coverage are then two main DTN characteristics concerned by the scalability in the IoT. Furthermore, resource management is another constraint in the IoT and DTN architectures. The limited battery supply and memory buffer in small objects should be considered to design efficient and optimized DTN solutions. Mobility and security are yet additional IoT issues directly connected to DTN characteristics. We conclude that designing delay tolerant schemes, should give more attention to specific IoT constraints considering the same performance metrics. In what follows, we discuss the existing DTN solutions in the literature that have been especially designed for IoT applications.  
Figure 2: Dependency between DTN and IoT [17].
## 2 Study Area and Data
### Data  
#### 2.2.1 Satellite Remote Sensing Data  
We used the Collection 6 MOD13Q1 Normalized Difference Vegetation Index (NDVI) data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautics and Space Administration's (NASA's) EOS/Terra satellite to assess the vegetation dynamics in the study region [40]. MOD13Q1 is provided at a temporal resolution of 16 days and a spatial resolution of 250 m. The QA band in MOD13Q1 indicates the quality of the retrieved NDVI (i.e., whether and how the retrieval is contaminated by clouds, cloud shadows, and aerosols). We used the Collection 6 MOD13Q1 data for the period from 2000 to 2019.  
The land cover data were obtained from Finer Resolution Observation and Monitoring--Global Land Cover (FROM-GLC10) dataset, which provides land cover type data of the whole globe at a 10 m resolution [41]. We used the FROM-GLC10 data of 2017 and resampled them to a resolution of 250 m. According to FROM-GLC10, the main land cover types of this semi-arid region are grasslands (44.93%), barren land (29.92%), cropland (13.46%), forests (8.26%), and impervious surfaces (2.49%) (Figure 1).  
We also used the MODIS Collection 6 Land Surface Temperature (LST) product MOD11A1 [42] to assess the relationship between vegetation greenness and temperature. The daily temperature was used in our investigation. When calculating atmospheric vapor pressure deficit, we needed land altitudes [2]. The Shuttle Radar Topography Mission (SRTM) digital elevation data [43] were used in our calculations.
## 4 Optimisation Results
### CHP Control Algorithm Optimisation  
To get a more precise CHP control result, load prediction plays a crucial role. Based on simple load prediction rule proposed by Equations (6) and (7), Figure 8 shows the differences between the actual demands and the predicted demands. Figure 8 reveals that the electrical demand prediction error can be very high compared to the heat demand prediction error. However, when using the predicted demands just to control binary CHP, as discussed in Section 2.2 and shown in [33], this approach gives a low error rate, which is 2.9% in this particular case study. Figure 9 shows the times that the CHP control algorithm generates errors in typical day.  
By applying the CHP control algorithm, the domestic building energy operational cost is reduced to 563 pence, which offers 38 pence saving a day. However, by applying this algorithm, the state of CHP changes very frequently, especially at night peak demand time. This is shown in Figure 11. In Figure 11, the dotted line shows the state of CHP in a day with the CHP control algorithm. To reduce dynamic ramp up times and cycling stress on the CHP, it is necessary to reduce the CHP switching frequency.  
Figure 8: Heat and electrical demand prediction error by using proposed prediction algorithm.  
Figure 9: The CHP control algorithm errors in sampling date.
# The LHAASO PeVatron Bright Sky: What We Learned
## 1 Introduction
### Status of the Field after the LHAASO Results
Despite the fact that the portion of the galaxy observable from the LHAASO site is smaller than that from the HAWC site (and it does not include the galactic center), LHAASO tripled the number of known sources emitting above 100 TeV, demonstrating that this feature is quite common for galactic sources. The number of sources for which a hadronic scenario is largely excluded similarly increased. Moreover, the detection of photons near 1 PeV challenges the astrophysical explanation of what we are observing (assuming both hadronic or leptonic origins). Let us also note that LHAASO obtained these results with only 10 months of observations, suggesting these 12 sources are only the tip of the iceberg of what will be detected in future observations by both LHAASO and other instruments based on the same technology (i.e., SWGO [73] at the South and TAIGA [74]). In Section 2, we briefly summarize what we know about the source types that may contribute to galactic CR acceleration: SNRs (Section 2.1), MSCs (Section 2.2), and PWNe (Section 5). We also consider TeV halos (Section 2.4) because, even if they are expressions of already accelerated CR diffusion, more than one LHAASO source could be associated with a halo. For completeness, we is evident include microquasars in this section (Section 2.5) because, although these kinds of source are not associated with any LHAASO PaVatron, they are considered to be among the possible hadronic PeVatrons. In Section 3, we focus on each of the 12 LHAASO UHE detections and on the information collected about them up to now. Finally, we conclude with a look at the expectations regarding future instruments (Section 4) and with final remarks (Section 5). Figure 3: Figure from [75]. Here, the all-particle CR spectrum is explained with the contributions from different kinds of galactic source. In particular, the dominant contribution from the SNRs in MSCs is evident.
### Basic Assumption
#### 2.2.4 Auxiliary Equation  
(1) Porosity evolution equation  
The change in pore volume is mainly caused by the erosion and migration of fine particles and the deformation of solid skeleton. For the erosion and migration of fine particles, the rate of change in pore volume is equal to the volume of eroded fine particles per unit time, then:  
\[\frac{\partial(dV_{V})}{\partial t}=\frac{\dot{m}dV}{\rho_{p}} \tag{21}\]Which can be simplified as:  
\[\frac{\partial\phi}{\partial t}=\frac{\dot{m}}{\rho_{p}} \tag{22}\]  
For pore volume change caused by deformation of solid skeleton, the porosity equation proposed in reference [40] is as follows:  
\[\phi=\frac{\phi_{0}+\varepsilon_{V}}{1+\varepsilon_{V}} \tag{23}\]  
where, \(\phi_{0}\) is the initial porosity.  
Equation (23) can be written as:  
\[\frac{\partial\phi}{\partial t}=\frac{1-\phi_{0}}{\left(1+\varepsilon_{V} \right)^{2}}\frac{\partial\varepsilon_{V}}{\partial t} \tag{24}\]  
Then, based on the erosion and migration of fine particles and the deformation of solid skeleton, the porosity evolution equation can be expressed as:  
\[\frac{\partial\phi}{\partial t}=\frac{\dot{m}}{\rho_{p}}+\frac{1-\phi_{0}}{ \left(1+\varepsilon_{V}\right)^{2}}\frac{\partial\varepsilon_{V}}{\partial t} \tag{25}\]  
(2) Relationship between permeability and porosity  
The Kozeny-Carman equation was used to describe the relationship between permeability and porosity [41; 42; 43]:  
\[k=k_{0}(\frac{\phi}{\phi_{0}})^{3}(\frac{1-\phi_{0}}{1-\phi})^{2} \tag{26}\]  
where: \(k_{0}\) is the initial permeability (m\({}^{2}\)).  
Equations (3)-(5), (11), (19), (20), (25) and (26), constitute the two-phase fluid-solid coupling mathematical model considering the effects of fine particles migration.
## 3 Materials and Methods
### Gas Chromatography-Mass Spectrometry Analysis (GC-MS)  
VOCs isolated from _P. ramossissima_ were analyzed using a gas chromatograph (type 8890 Agilent Technologies, Palo Alto, Santa Clara, CA, USA) and a tandem mass spectrometer detector (type 597/E MSD, Agilent Technologies). The HP-5 MS capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness, Agilent Technologies, Palo Alto, Santa Clara, CA, USA) was used to separate VOCs. The conditions for GC-MS analysis and the procedure for compound identification were described in detail previously by [PERSON] et al. [37]. Results for all samples were measured in three independent analyses and expressed as the average area percentage (%) of each compound.  
Ultra High-Performance Liquid Chromatography--High-Resolution Mass Spectrometry (UHPLC-ESI-HRMS) of Methanol Extract  
The UHPLC-ESI-HRMS analyses were performed on the UHPLC ExionLC AD system (AB Sciex, Concord, ON, Canada) equipped with the ExionLC modules: Controller, AD Pump, AD Degasser, solvent delivery system, AD Autosampler, AD Column oven tandem quadrupole time-of-flight (Q-TOF) mass spectrometer TripleTOF 6600+ (AB Sciex, Concord, Canada) containing Duospray ion source. The chromatographic separations of the compounds were carried through the analytical column Acquity UPLC BEH Phenyl-Hexyl (Waters, Milford, MA, USA; dimensions 2.1 mm \(\times\) 100 mm and particle size 1.7 \(\upmu\)m). Both mobile phases, water (A) and acetonitrile (B), contained 0.1% formic acid and were pumped with a continuous flow rate of 0.4 mL/min. The oven temperature was constantly set at 30 \({}^{\circ}\)C. The elution started isocratic with 2% of B (0.6 min) followed by the gradient program: 0.6-18.5 min (B linear gradient to 100%), 18.5-25 min (100% B). An amount of 4 \(\upmu\)L of the sample was injected [38].  
Electrospray ionisation was set in positive mode (ESI+) with the collision-induced dissociation (CID) in information-dependent acquisition (IDA) mode for MS/MS mass spectra recording. A detailed description of the parameters could be seen in [PERSON] et al. [38].  
Mass spectrometer data were processed using ACD/Spectrus Processor 2021.1.0. (ACD/Labs, Toronto, Canada) and the identification of the compounds was performed based on their mass spectra and the elemental compositions which were combined with the results of the search in the ChEBI, Lipid maps and MassBank database.
## 4 Experimental Setup and Results Analysis
### VLPTs Recognition  
According to the method described above for recognizing straight lines on the spectrogram, Figure 13 shows the results of vertical line recognition in different spectrograms.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Spectral Clustering** & **K-Means++** \\ \hline Accuracy (\%) & 0.96 \(\pm\) 0.01 & 0.98 \(\pm\) 0.01 \\ Missed Detection Rate (\%) & 0.04 \(\pm\) 0.01 & 0.02 \(\pm\) 0.01 \\ Error Rate (\%) & 0 & 0 \\ Time (s/per image) & 0.042 & 0.013 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical analysis of spectral clustering and improved K-means ++ clustering Vertical lines recognition results.  
Figure 12: Comparative analysis of different line recognition experiment results. (**a**) Original image; (**b**) Hough line recognition; (**c**) hierarchical clustering model; (**d**) DBScan model; (**e**) spectral clustering model; (**f**) K-means++ model after dimensionality reduction.  
By comparing Figure 13, we can observe that the greater the number of recognized lines, the greater the disturbance. To optimize the recognition process, we introduced a variable _linesnum_, which represents the number of column vectors representing row clusters detected in an 8 s spectrogram. Analyzing _linesnum_, we found that spectrograms with _linesnum_ > 60 usually indicate significant perturbations. We recorded the spectrograms with _linesnum_ > 60. Using this method, we can not only recognize prominent VLPTs that indicate significant perturbations but also determine their spatial and temporal perturbations. The following two sets of spectrograms are examples of VLPTs found in a massive dataset, as shown as Figures 14 and 15, all of which satisfy _linesnum_ > 60. This method effectively filters out high-energy VLPTs from vast amounts of data and obtains their spatial and temporal information.  
Figure 14: A set of pulse sequence disturbances occurred in the latitude range (\(-8.65\), \(-0.85\)) and longitude range (\(-79.27\), \(-80.8\)) between 7:14:58 and 7:17:01 on 30 December 2019 from CSES with orbit number 105801.  
Figure 15: _Cont_.
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 4 Proposed Approach
### Trajectory Denoising and Generations  
The effect of the denoiser to generate trajectories mainly depends on the ability of the network model to pay attention to the condition state \(S\) and the noise level \(\theta_{k}^{i}\). These two factors motivated our choice to use Transformer as the last major decoder construction. First in the encoder stage, the model's scene encoder architecture encodes scene elements such as road maps, historical trajectories, and traffic light states into a set of latent embeddings. The denoiser takes as input a real trajectory corresponding to each vehicle, which is disturbed by random noise. During denoising, the noisy input undergoes repeated blocks of self-attention between agents and cross-attention to the set of context tags for each agent, and finally the result is projected to the same feature dimension as the inputs [28].  
As shown in Figure 4, self-attention helps the diffusion model learn the noise distribution across vehicles, while cross-attention is used for the model to learn a more accurate conditional distribution of states.  
Specifically, transformer can achieve similar functionality to graph convolution due to its self-attention mechanism, which is insensitive to the order of information. Therefore, we use it to constrain the current state information of the vehicle. Before processing the social features between vehicles [29], the past trajectory features and state information features are obtained through the past trajectory encoder \((X,Y)\) and the state information \(S\). The vehicle feature tensor \(H\) is obtained by concatenating the two feature tensors with the high-speed noise parameters. The vehicle feature tensor \(H\) is encoded with self-attention, and the tensor \(H\) is used as the Q-value, K-value and V-value of the attention mechanism. The attention feature \(P\) computed by the attention mechanism is as follows.  
\[[P_{i}]_{i=1}^{N}=\frac{Softmax([Q_{i}^{T}K]_{i,j=1:N})}{\sqrt{d}_{K}}[V_{i}]_{ i=1}^{N} \tag{14}\]  
Here the subscripts \(i\) and \(j\) denote the sequence of vehicles in the scene. The purpose of \(j\) is to help vehicle \(i\) determine the encoded features of the remaining vehicles in the scene.  
Figure 4: The denoising generation of the trajectory consists of two stages. After embedding the sampled noise into the real trajectory, the result is projected to the same feature dimension as the input after passing through the self-attention and cross-attention module.