esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel Agent-Based Framework for Analyzing Urban Agricultural Supply Chains
###### Abstract  
This work presents a parallel agent-based framework designed to analyze the dynamics of vegetable trade within a metropolitan area. The system integrates agent-based and discrete event techniques to capture the complex interactions among farmers, vendors, and consumers in urban agricultural supply chains. Decision-making processes are modeled in detail: farmers select crops based on market trends and environmental risks, while vendors and consumers adapt their purchasing behavior according to seasonality, prices, and availability. To efficiently handle the computational demands of large-scale scenarios, we adopt an optimistic approximate parallel execution strategy. Furthermore, we introduce a credit-based load balancing mechanism that mitigates the effects of heterogeneous communication patterns and improves scalability. This framework enables detailed analysis of food distribution systems in urban contexts, offering insights relevant to smart cities and digital agriculture initiatives.  
parallel computing; digital infrastructure; food distribution networks Article
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 3 Experimental Setup and Results
### Model Assessment and Implementation
#### 3.2.1 Reconstruction Validation  
Using the encoder-decoder architecture, we assessed the reconstruction task by comparing the estimated input pattern \(\hat{\vec{x}}\) and real value \(\vec{x}\) using the root mean squared error (**RMSE**) and mean absolute error (**MAE**) as performance metrics. For evaluating the _denoising_ effect on the reconstruction, we used the estimated input pattern \(\hat{\vec{x}}\) and measure the auto-correlation (**Auto-C**) and the mean of the differences (**Diff-M**) between consecutive values. A high Auto-C and low Diff-M, is associated with a smoother time series. For measuring the structure left within the _residual noise_ (difference between real and estimated time series), we used an information theory score called spectral entropy (**Spectral-H**) [53]. A high value of this entropy means a less structured residual in terms of signal frequencies.  
Smoothing a time series is a classical signal processing task. Unfortunately, this is not straightforward for transits because transits are (statistically speaking) isolated behaviors  
Figure 4: Examples of the different representations that can be obtained from a periodic light curve by knowing the period \(P\). The example images on every column are the raw detrended light curve, the folded (phase-space), and global-folded (setting the bins to \(T=1000\)), respectively. The third column is the representation used in this work. The time unit is BKJD.  
or anomalies as shown in Figure 4. Therefore, a standard denoising model will remove transits by considering them anomaly deviations of the \"normal\" stellar flux. Based on this, we selected the following signal processing baselines to compare our models: the Butterworth passband filter [54], a moving average filter with different window sizes, and the RAE\({}_{t}\)[10]. In addition, we performed a Mandel-Agol (M-A) fit over the global-folded light curves, modeling points on the phase-space of this representation. The M-A fit was performed to obtain a reasonable time period for the exoplanet population analysis, using the same model calculation given by Kepler. This was implemented using the _ktransit_ library [55] with an LS optimization based on a quadratic limb darkening model and the star coefficients of the Kepler metadata, given by [56]. At this point, the LS optimization hyper-parameters were set to the library defaults.  
To make a fair comparison, the initial estimate for the period and mid-transit time was the same as that used in the folding of the light curves.
## 2 Modeling and Analytical Benchmarking of Equivalent Virtual Inertia for DFIGs
### The Model Overview of DFIG with Limited Control Information  
To solve the above problems, this paper proposes the \"limited control information\" framework, which categorizes wind turbine control knowledge through a hierarchical dual-layer structure in Figure 1, explicitly distinguishing between parametric identifiability (Gray Frame) and structural identifiability (Colored Frame). In this framework, a \"module\" refers to a functionally independent subsystem within the wind energy conversion system, each characterized by different levels of structural transparency and parameter accessibility. The system is decomposed into the following modules:  
1. **Known Structural Layer**: including modules whose functional structures are known from theory but whose parameters must be identified from data. This layer comprises the induction machine, rotor dynamics, and maximum power point tracking (MPPT) modules. These components exhibit a high degree of consistency with standard DFIG models and can be modeled using established physical equations with unknown coefficients.
2. **Unknown Structural Layer**: including the inertia control module, whose internal structure and control logic are not disclosed due to manufacturer confidentiality. As such, this module is treated as a black box, requiring both structural and parametric identification through input-output data.
## 3 Methodology
### Determination of the Sub-Basin Boundary  
Alos Pulsar Digital Elevation Model (DEM) data, which are freely available and have a spatial resolution of 12.5 m, were used to determine the sub-basin boundary [63]. First, the WGS84 UTM Zone36N projection was projected to DEM data by ArcGIS Pro 3.2.0 software. Then, fill/flow, direction/flow, accumulation/basin, and watershed were applied to DEM data using spatial analysis tools, respectively. The sub-basin including the Bogacay River and its tributaries were determined by subsetting. Lastly, the river and its tributaries were converted into vector format and prepared for use in the HEC-RAS 6.5 software.
# Design of Time-Synchronized Switch Based on Zynq
## 2 WR Technology
### Digital Dual Mixer Time Difference (DDMTD)  
The DDMTD features good linearity and a wide dynamic range, and it has no dead zone. Compared to traditional DMTD structures, it is simpler and more cost-effective. The DDMTD can convert phase deviation from high frequency to low frequency, making it very suitable for measuring two closely spaced signals. This can greatly eliminate several common types of phase noise.  
Figure 3 illustrates the internal structure of the DDMTD. It samples two square wave clock signals, \(clk_{A}\) and \(clk_{B}\), using two D flip-flops in order to measure the phase difference between them. The offset frequency is generated from one of the measured signals through a phase-locked loop (PLL), as shown in Equation (3) [23].  
\[f_{DDMTD}=\frac{2^{N}}{2^{N}+1}f_{in} \tag{3}\]  
Figure 2: PTP message exchange process.  
The principle of the DDMTD can be described by making a visual comparison with a conventional vernier caliper. The vernier caliper has two scales: the millimeter scale and the vernier scale. The vernier scale is used to enhance the measurement precision of the millimeter scale by providing a decimal fraction. A typical vernier caliper's vernier scale is divided into ten intervals, with each interval measuring 4.9 mm in length. When any interval on the vernier scale aligns with any interval on the millimeter scale, the measurement result will include a decimal fraction. The millimeter scale corresponds to the input clock, while the vernier scale corresponds to the offset clock. Whenever the intervals on the two scales align, the output signal will change. Figure 4 shows an example of the two measured clocks and the output signal they generate.  
In this design, N is set to 14. The sampling operation performed by the flip-flops is similar to analog mixing and low-pass filtering. Therefore, the frequencies of the output clock signals clk\({}_{\text{Aout}}\) and clk\({}_{\text{Bout}}\) are proportional to the frequencies of the input clock signals. The phase between the input signals is equal to the phase between the output signals; thus, the difference between the edges of the input and output clock signals is proportional, as described in Equation (4).  
\[X_{in}=\frac{f_{out}}{f_{in}}.X_{out}=\frac{f_{in}-f_{DDMTD}}{f_{in}}.X_{out}= \frac{1}{2^{N}+1}.X_{out} \tag{4}\]  
When the input clock is 62.5 MHz, the output DDMTD clock frequency is 62.496185 MHz after passing through a PLL. The down-converted clock frequency produced by the DDMTD is 3.814 kHz. Since each rising edge of these clock signals is timestamped, the phase information is provided to the PLL at the DDMTD frequency. This is indeed the sample rate of the SoftPLL, and thus its Nyquist frequency is 1.9 kHz. According to the current parameters, the time resolution can be calculated as 0.977 picoseconds using Equation (5).  
\[\Delta=\frac{T}{2^{N}+1} \tag{5}\]  
Figure 4: The signal generated by the DDMTD.  
Figure 3: The internal structure of the DDMTD.
## 2 Integrating 5G and Satellite Networks for Enhanced IoT Applications in Smart Cities: A Review
By including rain and atmospheric attenuation factors alongside traditional propagation models,this approach allows for better accuracy in predicting signal strength, which is crucial for IoT applications in 5G-satellite integrated networks. This model supports the design of resilient communication systems, enabling IoT deployments that can withstand varied environmental conditions and maintain connectivity, thereby enhancing the network's robustness in both urban and remote areas.
## 2 Mobility Models in FANETs
### Comparison of Existing Mobility Models for FANETs  
A UAV's mobility is governed by several parameters, including path, flying altitude, UAV speed, direction, and atmospheric condition. These parameters are not considered in simple mobility models despite the level of random motion employed for each mobility model being one of the first aspects to consider. The curve of a UAV is then compared to an actual curve that is smooth and is represented by many basic mobility models as a general rapid change of direction. Then, there is the avoidance of connection, which is described as the distance between the UAVs. Finally, the deployment area and safety standards, such as safety distance, must be considered to avoid collision between UAVs. Table 2 summarizes these characteristics and all previously described FANETs mobility models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Mobility** \\ **Model** \\ \end{tabular} & **Reference** & **Categories** & **Randomness** & \begin{tabular}{c} **Smooth** \\ **Curves** \\ \end{tabular} & **Connectivity** & \
\begin{tabular}{c} **Collision** \\ **Avoidance** \\ \end{tabular} &
\begin{tabular}{c} **Deployment** \\ **Area** \\ \end{tabular} \\ \hline RW & Ref. [36] & Random & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 2D \\ \hline RWP & Ref. [39] & Random & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 2D \\ \hline RD & Ref. [41] & Random & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 2D \\ \hline MG & Ref. [43] & Random & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 2D \\ \hline CLMN & Ref. [47] & Group & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & 3D \\ \hline ECR & Ref. [50] & Group & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 3D \\ \hline NC & Ref. [53] & Group & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 3D \\ \hline PRS & Ref. [55] & Group & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & 3D \\ \hline BSA & Ref. [56] & Time-Dependent & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 3D \\ \hline GM & Ref. [57] & Time-Dependent & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 3D \\ \hline ST & Ref. [60] & Time-Dependent & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 3D \\ \hline EGM & Ref. [63] & Time-Dependent & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 3D \\ \hline FP & Ref. [66] & Path-Planned & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & 3D \\ \hline SRCM & Ref. [69] & Path-Planned & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & 2D \\ \hline PPRZM & Ref. [72] & Path-Planned & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 2D \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of FANETs mobility models.  
Figure 18: The trajectory of FANETs using PPRZM models.
## 2 Materials and Methods
### Identification of Phenolic Compounds with HPLC-QTOF-Ms  
An UHPLC system with an HPG-3400 pump (Dionex UltiMate 3000 RSLC, Thermo Fisher Scientific, Germany) coupled to a QTOF mass spectrometer (Maxis Impact, Bruker Daltonics, Bremen, Germany) was used for the analysis. Negative electrospray ionization mode was applied. Separation was carried out using an Acclaim RSLC C18 column (2.1 \(\times\) 100 mm, 2.2 \(\upmu\)m) purchased from Thermo Fisher Scientific (Driesch, Germany) with a pre-column of ACQUITY UPLC BEH C18 (1.7 \(\upmu\)m, VanGuard Pre-Column, Waters (Waters Corporation(r), Wexford, Ireland). Column temperature was set at 30 \({}^{\circ}\)C. The solvents used consisted of (A) 90% H\({}_{2}\)O, 10% methanol, and 5 mM CH\({}_{3}\)COONH\({}_{4}\) (Mobile phase A) and 100% methanol and 5 mM CH\({}_{3}\)COONH\({}_{4}\) (Mobile phase B). The adopted elution gradient started with 1% of organic phase B with flow rate 0.2 mL min\({}^{-1}\) during 1 min, gradually increasing to 39% for the next 2 min and then increasing to 99.9% and flow rate 0.4 mL min\({}^{-1}\) for the following 11 min. These almost pure organic conditions were kept constant for 2 min (flow rate 0.48 mL min\({}^{-1}\)), and then initial conditions (1% B-99% A) were restored within 0.1 min (flow rate decreased to 0.2 mL min\({}^{-1}\)) to re-equilibrate the column for the next injection.  
The QTOF-MS system was equipped with an electrospray ionization interface (ESI), operating in negative mode with the following settings: capillary voltage of 3500 V, end plate off-set of 500 V, nebulizer pressure of 2 bar (N\({}_{2}\)), drying gas of 8 L min\({}^{-1}\) (N\({}_{2}\)), and drying temperature of 200 \({}^{\circ}\)C. A QTOF external calibration was daily performed with sodium formate (cluster solution), and a segment (0.1-0.25 min) in every chromatogram was used for internal calibration, using calibrant injection at the beginning of each run. The sodium formate calibration mixture consisted of 10 mM sodium formate in a mixture of H\({}_{2}\)O/isopropanol (1:1). Full scan mass spectra were recorded over the range of 50-1000 \(m/z\), with a scan rate of 2 Hz. MS/MS experiments were conducted using AutoMS data-dependent acquisition mode based on the fragmentation of the five most abundant precursor ions per scan. The instrument provided a typical resolving power (FWHM) between 36,000 and 40,000 at \(m/z\) 226.1593, 430.9137, and 702.8636.
# Predicting Human Location Using Correlated Movements
## 2 Related Work
### Human Mobility Analysis  
A number of studies were aimed at revealing human movement characteristics [25; 26; 27]. For example, [PERSON] et al. [27] classified the properties of human mobility into three groups: spatial, temporal, and connectivity. With regard to the spatial characteristic, they focused on geographic movement, i.e., how far a person moves and where a person goes. Flight was defined as a Euclidean distance between two consecutive spots visited by the same individual. Temporal features were also considered, e.g., pause-time indicates the time period a person stays at a specific location. Meanwhile, the connectivity property reflects the contact or encounter between two people, e.g., inter-contact time was defined as the elapsed time between two adjacent contacts for a pair of people.  
In addition, a few studies considered the effect of social relationships on the human movement [8; 28]. [PERSON] et al. [8] observed that people are likely to visit a distant place where a friend stays nearby, based on the datasets of online location-based social networks and cell phone location traces. Meanwhile, short-distance travel is less affected by social ties.  
Those studies mainly focused on capturing human mobility characteristics, whereas we plan to design a movement prediction model to estimate a person's current or future location. Note that embedding the properties of human mobility will be helpful for accurately predicting human movement. Therefore, in this work, a mobility prediction model is proposed, considering human movement characteristics such as encounter frequency and social correlation.
## 4 Performance Evaluation
At  
Figure 11: Comparative analysis of the AOTL-CDA3S system with recent approaches. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Sensitivity** & **F-Score** \\ \hline AOTL-CDA3S & 98.00 & 96.11 & 95.87 & 98.66 \\ \hline Gabor & 71.95 & 61.88 & 61.84 & 61.78 \\ \hline BoW-SRP & 79.95 & 68.25 & 67.75 & 68.32 \\ \hline BoW-LBP & 84.26 & 74.36 & 74.37 & 74.74 \\ \hline GLCM-SVM & 80.20 & 75.57 & 73.63 & 87.99 \\ \hline GoogleNet & 84.51 & 83.16 & 84.94 & 80.92 \\ \hline VGGNet & 84.49 & 85.72 & 82.74 & 84.76 \\ \hline MDTL-ICDDC & 96.94 & 93.24 & 93.11 & 93.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of the AOTL-CDA3S system with recent approaches. the same time, the SqueezeNet model derives the significant features from the preprocessed input images for the classification process. In addition, the DL model suffers from several issues: namely, gradient vanishing, overfitting, hyperparameter tuning, and extensive computation. The DL model encompasses several hyperparameters and the selection of optimal configuration for these parameters in such a high dimensional space is challenging. These hyper-parameters act as knobs, which can be tweaked during the training of the model. Because of continual deepening of the model, the number of parameters of DL models also increases quickly which results in model overfitting. At the same time, different hyperparameters (learning rate, epoch count, and batch size) have a significant impact on the efficiency of the CNN model, particularly the learning rate. It is also necessary to modify the learning rate parameter for obtaining better performance. Therefore, in this study, we employed the AO technique for the hyperparameter tuning of the SqueezeNet model, which in turn enhances the classification performance. In addition, the unique characteristics of the AO algorithm, such as faster optimization speed, global exploration ability, high search efficiency, and fast convergence speed helps to attain improved crowd classification results over other models.
###### Abstract  
The accurate prediction of roof stress in mined-out areas is crucial for ensuring mine safety. However, existing study methods often overlook the increasingly available image data and fail to balance the model predictive capability with interpretability. To address these issues, this study innovatively integrates 3D laser scanning image features into the prediction of roof stress in mined-out areas. Image features are extracted using pre-trained deep-learning models and combined with traditional geological parameters to construct multiple machine-learning models for prediction. The experimental results demonstrate that models incorporating image features significantly outperform traditional models that rely solely on geological parameters in terms of prediction accuracy, interpretability, and complexity, revealing the critical role of image features in stress prediction. Furthermore, the use of SHapley Additive exPlanations (SHAP) to interpret the random forest model uncovers new domain knowledge, such as the relationship between spatial patterns and stress concentration. This study theoretically validates the effectiveness of image data and effectively balances the predictive capability and interpretability of the model, facilitating knowledge discovery in the field. On a practical level, it also provides guidance for mine safety management.  
mined-out area; roof stress prediction; 3D laser scanning images; interpretable machine learning +
Footnote †: journal: applied sciences  
Article
## 3 Evolution of Wireless Communication Technology (1G-6G)
### Fifth Generation (5G) Technology  
To obtain a consistent QoS, low end-to-end latency, reduced cost, and massive device connectivity, the Fifth Generation (5G) communication technology was established to support applications such as AR, home and industrial automation, and machine vision. Fifth Generation technology was first offered in South Korea in 2019. It provides a data rate of 20 Gbps in the downlink and 10 Gbps in the uplink, and is aimed to support three generic services; Enhanced Mobile Broadband (eMBB), Massive Machine-type Communications (mMTC), and Ultra-Reliable Low-Latency Communications (URLLCs) [30]. eMBB aims to deliver peak download speeds of over 10 Gbps to support applications such as Ultra-High Definition (UHD) videos and AR. mMTC defines the requirement to support one million low-powered economical devices per Km\({}^{2}\) with a battery life of up to 10 years. It can support applications such as smart homes and industrial automation that involve several sensors, controllers, and actuators. URLLC sets the requirement of high reliability (99.99%), extremely low latencies (<1 ms), and support for low data rates (bps/Kbps) to support applications such as social messaging services, traffic lights, self-driving cars, and smart healthcare. In 2020, Huawei, as part of the 5.5G vision, proposed three additional sets of services; Uplink-centric Broadband Communication (UCBC), Real-Time Broadband Communication (RTBC), and Harmonized Communication and Sensing (HCS) [31]. UCBC aims to increase the uplink bandwidth by 10-fold to support applications involving machine vision. For Augmented Reality (AR), Virtual Reality (VR), and Extended Reality (XR) applications, RTBC would provision large bandwidth and low latency services with a certain level of reliability. HCS focuses to offer communication and sensing functionalities for connected cars and drone scenarios. However, with the emergence of an interactive and connected IoT, communicating IoV, and holographic applications, the 5G networks could not manage the stringent high computing and communications requirements of those applications. Consequently, the 6G network generation was instigated.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 5 Application to Wildlife Tracking
### Tag Electronics  
Tags were developed that include a standard GPS RF front-end (Maxim MAX2769B Universal GPS Receiver) connected to a high-performance low-power 32-bit STM32F103 CB [26] microprocessor. The microprocessor is programmed to wake at intervals, read data from the GPS RF front-end and store this data in a file on an micro Secure Digital (microSD) Memory Card. The un-packaged electronics from one of these FastFix tags are shown in Figure 7. A packaged solar-powered version of the tag, designed to attach to seabird feathers was developed [9] for deployment on seabirds. This tracking tag uses a small Taoglas AP.12F.07.0045 active patch antenna [27].  
Each fix stores 4 ms of 1-bit baseband data sampled at 16.368 MHz, which requires 64 kb of data. A 2 GB SD-card can store approximately 244,000 fixes. A solar-powered tag could operate continuously storing data for a position estimate every five minutes for more than two years, limited only by storage limits.