esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Energy Performance of LoRaWAN
###### Abstract  
LoRaWAN is a flagship Low-Power Wide Area Network (LPWAN) technology that has highly attracted much attention from the community in recent years. Many LoRaWAN end-devices, such as sensors or actuators, are expected not to be powered by the electricity grid; therefore, it is crucial to investigate the energy consumption of LoRaWAN. However, published works have only focused on this topic to a limited extent. In this paper, we present analytical models that allow the characterization of LoRaWAN end-device current consumption, lifetime and energy cost of data delivery. The models, which have been derived based on measurements on a currently prevalent LoRaWAN hardware platform, allow us to quantify the impact of relevant physical and Medium Access Control (MAC) layer LoRaWAN parameters and mechanisms, as well as Bit Error Rate (BER) and collisions, on energy performance. Among others, evaluation results show that an appropriately configured LoRaWAN end-device platform powered by a battery of 2400 mAh can achieve a 1-year lifetime while sending one message every 5 min, and an asymptotic theoretical lifetime of 6 years for infrequent communication.  
LoRaWAN; LoRa; energy; energy modeling; performance evaluation; Internet of Things; IoT; smart cities; LPWAN +
Footnote †: journal: Sensors
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods
### Materials
#### 2.1.3 FY-4B/AGRI Operational Precipitation Product  
The operational precipitation product is the pure satellite estimated precipitation results, generated using FY-4B/AGRI infrared channel data, without ground rain gauge correction [29]. The FY-4B/AGRI Level1 data at each time step are inverted to obtain the corresponding precipitation product; thus, the temporal resolution of the precipitation product is also 15 min and the spatial resolution is 4 km.
# Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm
## 4 Performance Evaluation of the Embedded GA-Based Method
The plots are shown in Figures 9 and 10 and show that the best solutions returned by the GA allows a correct I-V curve reproduction. Figure 8: Photovoltaic system with the MPPT and online parameter identification technique. (**a**) Functional scheme of the proposed digital controller; (**b**) flowchart of the perturb and observe algorithm integrated with the parameter identification procedure. As for the numerical results returned by the GA, there is no difference in comparison with the execution on the desktop PC because the C compiler for the STM32 microcontroller supports the double data type. The execution time on the microcontroller, instead, is very different from that on the desktop PC. Aiming to reduce it as much as possible, the Selector function of the GA available in [21] has been suitably modified. In particular, to pick up the individuals that survive and reproduce, the original function used a linear search over a vector holding the values of the cumulative fitness function. The worst-case computational complexity of the linear search is \(O(n)\). Instead, in the proposed approach, a binary search algorithm is used, which has a worst-case computational complexity of \(O(log(n))\). Table 8 reports the average execution time of the GA in different conditions and shows that an appreciable speed gain has been obtained. Overall, the obtained execution time of the GA using \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **Parameter** & \(\mathbf{I_{ph}}\) **(A)** & \(\mathbf{I_{s}}\) **(A)** & \(\mathbf{\eta}\) & \(\mathbf{R_{s}}\) **(\(\mathbf{\Omega}\))** & \(\mathbf{R_{h}}\) **(\(\mathbf{\Omega}\))** & **Fitness** \\ \hline Test \#2 & Guess solution (A) & 3.07 & \(3.56\times 10^{-9}\) & 1.09 & 1.9 & 102 & 2.23 \\ Test \#2 & Best GA solution (B) & 2.78 & \(3.56\times 10^{-7}\) & 1.36 & 1.37 & 4405 & 85.33 \\ \hline Test \#2 & Variation (B/A) & 0.90 & 100 & 1.25 & 0.72 & 43.2 & 38.26 \\ \hline Test \#3 & Guess solution (C) & 1.62 & \(5.96\times 10^{-10}\) & 1.13 & 4.1 & 206 & 5.24 \\ Test \#3 & Best GA solution (D) & 1.46 & \(5.30\times 10^{-9}\) & 1.13 & 0.876 & 740.2 & 25.96 \\ \hline Test \#3 & Variation (D/C) & 0.90 & 8.89 & 1.004 & 0.214 & 3.59 & 4.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SDM parameters comparison for Tests #2 and #3. Figure 9: Comparison of the I-V and P-V photovoltaic curves for Test #2. (a) Photovoltaic I-V curves; (b) photovoltaic P-V curves. the binary search on the STM32 microcontroller is under 10 min, thus more than adequate for on-line monitoring/diagnosis applications.
# Power Control Techniques for Interference Management-
## 2 Systematic Review Method
### Planning the Review
#### 2.1.2 Development of the Review Procedure  
The review procedure is carried out in the same way that research design is carried out in social science studies [25]. The authors in [24] emphasized the need for a review procedure for rigorous systematic reviews. The authors in [27] endorse the need for a review procedure. [PERSON] et al. [28] suggested the elements of the review procedure, including \"the purpose of the study, research questions, inclusion criteria, search strategies, quality assessment criteria and screening procedures, strategies for data extraction, synthesis, and reporting\". The following paragraphs discuss how each procedure is applied in the context of this research.  
1. The purpose of the review This article investigates models that have been proposed in the evolving 5G networks to address resource management. The objective is to search for models in the literature developed to address resource management in 5G networks. The successful completion of this research will inform future research.
2. Research questions Based on the research objectives outlined in the Introduction section, this study examined the following research questions (RQs):  
* RQ1: What are the current publication trends in power control techniques for addressing interference in 5G wireless networks?
* RQ2: Which are interference management's most influential authors, countries, and journal articles?  
Figure 1: Systematic literature review process. Adapted from [25].  
* RQ3: What is the current state of collaboration involving power control to address interference in 5G wireless networks?
* RQ4: Which areas involving power control in 5G wireless networks need additional study? (This will point out opportunities and directions for future research.)  
This study was carried out to answer these research questions by implementing the steps shown in Figure 1.
## 4 Dataset Generation and Simulation Environment
### Simulation Design and Ground-Truth Generation  
The evaluation's foundation is a synthetic dataset generated by the MHMM class configured in \"Expert Mode,\" acting as a ground-truth generator. This dataset comprises longitudinal data for 100 simulated participants, each performing 200 distinct procedural tasks, totaling 20,000 individual data points. Each data point represents multimodal observations HRV, TCT, and NASA-TLX intrinsically linked to a known underlying proficiency state (Novice, Intermediate, or Expert).  
To ensure ecological validity, the MHMM's generative parameters were meticulously initialized using empirical benchmarks from the surgical training literature ([PERSON] et al., 2025 [24]). The initial state distribution for participants was set to favor Novice states (\(\tau_{Novice}=0.80,\tau_{Intermediate}=0.15,\tau_{Expert}=0.05\)). The base transition probability matrix (A_base) (Equation (2)) was designed to reflect typical learning curves, with high self-transition probabilities and smaller probabilities for progression (e.g., Novice to Intermediate) or regression (e.g., Intermediate to Novice). For emission parameters, the means and standard deviations for each modality (TCT as Lognormal, HRV and NASA-TLX as Normal distributions) were directly matched to empirical findings from [PERSON] et al. (2025) [24], creating distinct multimodal signatures for each proficiency state.  
A key innovation of this simulation is its ability to model how contextual factors dynamically influence proficiency transitions. The compute_dynamic_transition_matrix_expert function modulates the base transition probabilities at each procedural step based on simulated workplace challenges. These include task complexity (impeding skill acquisition), skill decay and forgetting (increasing regression probabilities based on time gaps between procedures), cumulative fatigue and stress (increasing regression probabilities based on physiological strain and perceived workload), and collaboration intensity (adjusting learning and regression based on interaction with AI). The data generation process is an iterative, step-by-step procedure in Python 3.11.13, where at each step, the next hidden state is probabilistically sampled based on dynamic transitions, and corresponding multimodal observations are generated from their defined emission distributions, with all contextual metadata recorded. This ensures a rich, longitudinal dataset with known ground-truth trajectories for rigorous model evaluation.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 1 Introduction
In this study, the primary objective is to comprehensively investigate the behavior and response of IRP under lateral loads. To achieve this, a multifaceted approach combining experimental data analysis, numerical simulation, and analytical calculation is employed. On the one hand, these experimental data provide essential insights into the physical response of the IRP when subjected to lateral loads, allowing for an in-depth understanding of its structural behavior. On the other hand, numerical simulations have been developed to model and predict the intricate response of the IRP under different lateral load scenarios, offering a virtual platform for further analysis and optimization. To complement these experimental and numerical approaches, a standard analytical calculation is employed, enabling the formulation of mathematical expressions that capture the essential mechanical properties and responses of the four-point bending mechanism. By synergistically integrating these three methodologies, a comprehensive understanding of the complex behavior of IRP under lateral loads is achieved. This study focuses on numerical investigation of the impact of various surface preparation methods (unclean, cleaned, and ablated) on the adhesive strength of repair systems using a debonding finite element analysis (FEA) mechanism. This work includes a comprehensive parametric analysis of discontinuous host pipes with varying crack widths of 12.7 mm (0.5 inch) and 152.4 mm (6 inch), as well as continuous host pipes. It also highlights the critical role of adhesive strength in the IRP repair method, which is essential for optimizing the design and application of trenchless repair systems. This optimization leads to more efficient, cost-effective, and durable infrastructure maintenance solutions. The findings from this research provide valuable insights for advancing trenchless repair techniques, with the potential to improve system performance and extend service life. The sections of the paper are organized as follows: Section 2 details the FEA methodology, covering the modeling techniques and validation of results, with particular emphasis on debonding phenomena. Section 3 explores the influence of surface preparation on debonding behavior. Finally, Section 4 concludes the paper by summarising the key findings and discussing their implications for advancing IRP technology.
## 3 Results and Discussion
### Calculating Process of the Parameter Self-Calibration  
To illustrate the data processing results of each step, one of the test results in the first test stage was taken as an instance in which the target application rate of seeding and fertilizing were 375 kg/ha and 525 kg/ha, respectively. First, the cumulative increment signal \(D(t)\) of seeds and compound fertilizers were measured by the application rate measuring system, as shown in Figure 7. The large fluctuation in the signal was due to the lifting and lowering of the tractor when the tractor turned around.  
Figure 6: Original calibration relationship model between the mass flow rate and rotation speed.  
Figure 7: Weighing sensor signals for seeding and fertilizing.  
Secondly, the zero-crossing linear fitting between \(D(t)\) and \(S(t)\) of the fertilizing and the seeding area was carried out, and the dynamic cumulative application rate information \(Q(t)\) was calculated, as shown in Figure 8.  
Finally, the dynamic cumulative application rate Q(t) and the dynamic cumulative rotation cycles N(t) of the fluted rollers were linearly fitted, and the relationship between the mass flow rate and rotation speed was obtained, as shown in Figure 9, from which the updated calibration model parameters (\(k\), \(b\)) can be obtained.
## 2 Materials and Methods
### Materials  
Sodium alginate was purchased from MP Biomedicals, LLC. (Tokyo, Japan). Calcium chloride, D-(+)glucose, NaCl, KCl, NaHCO\({}_{3}\), NaH\({}_{2}\)PO\({}_{4}\), Na\({}_{2}\)HPO\({}_{4}\), ethanol, isoflurane, streptozotocinocrit (STZ), penicillin-streptomycin-L-glutamine (PSG) solution, pEBMulti-Hyg plasmid, StemSure Monothioglycerol Solution (MTG), and 1, 1\({}^{\prime}\)-dioctadecyl-3, 3, 3\({}^{\prime}\), 3\({}^{\prime}\)-tetramethylindocarbocyanine perchlorate (DiCi18(3); DiJ) were purchased from FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan). Fetal bovine serum (FBS) and Opti-MEM were purchased from Thermo Fisher Scientific (Waltham, MA, USA). Lipofectamine 3000 and a PureLink HiPure Plasmid Miniprep Kit were purchased from Invitrogen (Carlsbad, CA, USA). Hygromycin B gold, Luria-Bertani (LB) broth base, and LB agar were purchased from InvivoGen Co. (San Diego, CA, USA). KOD-Plus-Neo was purchased from DYOBO Co., Ltd. (Osaka, Japan). Competent NEB 10-beta _Escherichia coli_ (high-efficiency), SOC medium, _EcoRV_-HF, and _KpnI_-HF were purchased from New England Biolabs, Inc. (Ipswich, MA, USA). Nucleopin gels and PCR cleanup reagents were purchased from Takara Bio Co. (Shiga, Japan). O.C.T compound was purchased from Sakura Fintek Japan Co., Ltd. (Tokyo, Japan). Food Plus 75 was purchased from Yamaichi Chemical Industries Co., Ltd. (Tokyo, Japan). A NanoGlo Luciferase Assay System was purchased from Promega (Madison, WI, USA). Heparin sodium salt, 0.5% trypan blue solution, and 2.5 g/L-trypsin /1 mmol/L-EDTA solution were purchased from NACalai Tesque, Inc. (Kyoto, Japan). Dulbecco's modified eagle's medium (DMEM) was purchased from Nissui Pharmaceutical Co., Ltd. (Tokyo, Japan).
###### Abstract  
In order to solve the problem of ineffective pre-splitting blasting and reduce the damage caused to preserved rock bodies in the Niukutou open-pit quarry in the alpine region of Qinghai Province, China, this study investigated the influence of different delay times on the damage caused to the rock mass, combining numerical calculations with field tests. Using the finite element software ANSYS/LS-DYNA 19.0 and based on the constitutive model of saturated skarn, numerical models of pre-splitting blasting with four different delay times (0 ms, 12 ms, 18 ms, and 24 ms) were established. These models systematically analyze the damage evolution of rock and the effective stress distribution in specific elements under different delay times. The results indicate that a maximum damage depth of 32.56 cm occurs when the inter-hole delay is 0 ms, while a minimum damage depth of 30.61 cm is observed when the inter-hole delay is 18 ms. Further analysis reveals that the peak effective stress is highest when the inter-hole delay is 0 ms, and that the peak effective stress at 18 ms is higher than that at 12 ms and 24 ms. Considering the need to control the damage caused to the surrounding rock and the blasting effectiveness, the best pre-splitting blasting effect is achieved with an inter-hole delay of 18 ms. Field tests show that the damage depth of the retained rock mass is 1.62 m when the inter-hole delay is 0 ms; however, it decreases to 0.90 m when the delay is 18 ms, representing a 44% reduction in rock mass damage depth. This study provides theoretical support and practical guidance for the optimization of pre-splitting blasting in saturated skarn in alpine regions.  
high-altitude open-pit mining site; water-saturated skarn; pre-splitting blasting; delay time; rock mass damage +
Footnote †: journal: Applied sciences
# Multivariate Time Series Data Prediction Based on ATT-LSTM Network
## 4 Experiment
### Experimental Results and Analysis  
In this section, we conduct a practical evaluation of our proposed model. First, compare the ATT-LSTM model with the other six models (VAR, LSTM, GRU LSTM+Zoneout, LSTM-RNN, Attention-RNN) on two data sets. Then use the ATT-LSTM model to predict and visualize the prediction results to further prove the model's effectiveness.
## 5 Discussion and Best Practice
### Sensors
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Description (Novelies)** & **Sensor Type** & **Sensor** & **Content** & **Reference** \\ \hline \multirow{3}{*}{2008} & Calibration and application of & \multirow{3}{*}{Multispectral 2D} & Canon EOS 350D & \multirow{3}{*}{C, P} & [190] \\  & spectroradiometrically characterized RGB & & imager & Sony DSC-F828 & \\ \hline \multirow{3}{*}{2009} & Multi-camera multispectral 2D imager on & Multi-camera spectral & \multirow{3}{*}{MiniMCA} & \multirow{3}{*}{C, P} & [130] \\  & UAV for vegetation monitoring & & 2D imager & & \\ \hline \multirow{3}{*}{2012} & Small hyperspectral pushbroom UAVs & \multirow{3}{*}{Pushbroom} & \multirow{3}{*}{Micro-Hyrespec VNIR} & \multirow{3}{*}{C, P} & [125] \\  & system for vegetation monitoring & & & & \\ \hline \multirow{3}{*}{2012} & Characterization and calibration of & Multi-camera spectral & \multirow{3}{*}{MiniMCA} & \multirow{3}{*}{C} & [50] \\  & spectral 2D imager & & & & \\ \hline \multirow{3}{*}{2013} & Processing chain for sequential band & \multirow{3}{*}{Sequential band} & \multirow{3}{*}{Rikola FPI} & \multirow{3}{*}{P} & [44] \\  & spectral 2D imager for spectral and 3D & & & & \\ \hline \multirow{3}{*}{2014} & Point spectrometer on UAV & \multirow{3}{*}{Point spectrometer} & \multirow{3}{*}{STS-VIS} & \multirow{3}{*}{C, I} & [22] \\  & spectralometer for irradiance & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key publications on novel sensors, concepts, or methods for calibration (C), integration (I), or data pre-processing (P) for UAV spectral sensors and data. RGB: red–green–blue.
# Electrically Tunable Hydrogen-Bonded Liquid Crystal Phase Control Device
###### Abstract  
Terahertz waves have attracted much attention mainly because of their potential in imaging, security checking, nondestructive testing, and information and communication technologies. In the past few years, there has been an extensive effort to investigate terahertz wave control devices. Liquid crystal (LC) devices are strong candidates for high-performance terahertz wave control devices because of their controllability at low drive voltages and their low power consumption. In this study, we fabricated an electrically tunable phase control device by using a hydrogen-bonded LC material. We investigated the performance of the LC phase shifter by using a far infrared continuous wave laser. We also estimated the birefringence and absorption properties of the hydrogen-bonded LC at 2.5 THz by using Jones matrix calculations. The measurements and calculation results indicated that the hydrogen-bonded LC showed no dichroism at 2.5 THz. Based on the absorption properties, we believe that it could be a strong candidate for use in future terahertz devices.  
Keywords:terahertz wave; liquid crystal; phase control device +
Footnote †: journal: Applied Physics Letters
# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 4 Discussion
We computed the F1 score across the entire sample, encompassing both normal and seizure recordings, and achieved an average F1 score of 0.76, with a sensitivity of 75% and a specificity of 78.8%. To enhance these outcomes, comparable to those reported in the literature, and to increase their clinical relevance in a hospital setting, we categorised these recordings based on potential epilepsy etiologies. This approach yielded F1 scores exceeding 0.80 in all examined subgroups. Notably, this was accompanied by a significant boost in sensitivity, reaching up to 100% in some cases, without compromising specificity, which remained balanced across all subgroups with values around 80%. This stratification of seizures according to underlying brain pathology, which influences the onset of epilepsy, necessitates a tailored analysis due to the unique characteristics of each type of brain damage. For instance, seizures resulting from cardiorespiratory arrest exhibit different patterns compared to those stemming from a brain haemorrhage, with variations observed across different pathologies. Such differentiation underscores the need for medical professionals to identify the cause of suspected seizures when patients are admitted. While this requirement might seem challenging, it is generally manageable as most patients at risk of seizures have undergone brain imaging, such as MRI or CT scans, before ICU admission. Our proposed model paves the way for an ensemble-based approach, enabling more personalised diagnoses that consider clinical observations and complex EEG characteristics. This includes clinical factors, quantitative analysis of mathematical transforms, and machine learning techniques. However, it is important to acknowledge a primary limitation of our study: the uneven number of samples between normal and seizure recordings and their distribution across subgroups. This disparity introduces variability, particularly when considering larger sample sizes. We analysed multiple parameters to mitigate this, allowing us to extrapolate our findings due to the balanced distribution observed in the confusion matrices.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 6 Therapeutic Targeting of CSCs
### Targeting Signaling Pathways  
Among signaling pathways that are important for HNSCC stemness, already explored in terms of therapy (Hippo (YAP/TAZ), Wnt, and Notch) [151; 152; 153; 154; 155], the SHh signaling pathway has recently been recognized as significant for CSC maintenance and could serve as a possible target for their removal. For example, Gli3 knockdown diminishes stemness, cell proliferation, and invasion in oral squamous cell carcinoma [156]. Furthermore, an SHh pathway inhibitor Vismodegib, a drug currently used for the treatment of basal-cell carcinoma that is also undergoing clinical trials for therapy of different types of cancer, effectively reduced the expression of CSC-related genes in the HNSCC cell line [157].