esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2014**, _14_, 17621-17654; doi:10.3390/s140917621  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
MirOS: A Hybrid Real-Time Energy-Efficient Operating System for the Resource-Constrained Wireless Sensor Nodes  
[PERSON] 1,2, [PERSON] 2,*, [PERSON] 2, [PERSON] 2 and [PERSON] 2  
Internet and Information Technology Laboratory, Wuhan University, Road LuoJia, 430072 Wuhan, China  
LIMOS Laboratory UMR 6158 CNRS, Blaise Pascal University, Les Cezeaux, BP 10125, 63173, Clermont-Ferrand, France; E-Mails: [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (C.V.); [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (K.E.G.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-4-7340-5026.  
Received: 7 June 2014; in revised form: 28 August 2014 / Accepted: 12 September 2014 /  
_Published: 22 September 2014_  
**Abstract:** Operating system (OS) technology is significant for the proliferation of the wireless sensor network (WSN). With an outstanding OS; the constrained WSN resources (processor; memory and energy) can be utilized efficiently. Moreover; the user application development can be served soundly. In this article; a new hybrid; real-time; memory-efficient; energy-efficient; user-friendly and fault-tolerant WSN OS MIROS is designed and implemented. MIROS implements the hybrid scheduler and the dynamic memory allocator. Real-time scheduling can thus be achieved with low memory consumption. In addition; it implements a mid-layer software EMIDE (Efficient Mid-layer Software for User-Friendly Application Development Environment) to decouple the WSN application from the low-level system. The application programming process can consequently be simplified and the application reprogramming performance improved. Moreover; it combines both the software and the multi-core hardware techniques to conserve the energy resources; improve the node reliability; as well as achieve a new debugging method. To evaluate the performance of MIROS; it is compared with the other WSN OSes (TinyOS; Contiki; SOS; openWSN and mantisOS) from different OS concerns. The final evaluation results prove that MIROS is suitable to be used even on the tight resource-constrained WSN nodes. It can support the real-time WSN applications. Furthermore; it is energy efficient; user friendly and fault tolerant.  
**Keywords:** operating system; wireless sensor network; real-time; hybrid scheduling; dynamic memory allocation; middleware; multi-core; energy conservation; reliability
## 4 Conclusions  
In this article authors evaluated the radioactivity and heavy metals amount in twenty different samples of Italian wines, belonging to controlled origin denomination (DOC) geographic areas of the Calabria region, south of Italy.  
The activity concentration of the main natural radionuclide present, \({}^{40}\)K, was measured using HpGe gamma spectrometry, with the aim to estimate the health risk, for the age category higher than 18 years, by the effective dose due to their ingestion. The coefficient of the effective dose for ingestion was reported by the Italian Legislative Decree 230/95 and successful modifications. The calculated annual effective dose was found to be under allowable levels (1 mSv/year), thus excluding the risk of ionizing radiation effects on human beings.  
The anthropogenic radioactivity was evaluated through the \({}^{137}\)Cs specific activity measurement. It was lower than the minimum detectable activity value, thus excluding its presence as a pollutant.  
With regards to the metal's concentration, it was lower than the contamination threshold value for Cu, Zn, Pb, B, As and Cd, thus excluding their presence as contaminants.  
[PERSON] performed gamma-spectrometry analysis, collected data, designed the study and drafted the manuscript; [PERSON] and M.D. contributed to perform gamma-spectrometry analysis; [PERSON] performed ICP-MS analysis; [PERSON] contributed to perform ICP-MS analysis; [PERSON] and D.P. supervised the study.  
This research received no external funding.  
Authors thank the Italian Sommelier Association, in the person of the President, [PERSON], for having kindly provided the samples analyzed in the present article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**ICP-MS**} \\ \hline  & \multicolumn{6}{c}{**Metals Concentration (\(\mu\)g/L)**} \\ \hline
**Sample ID** & **Cu** & **Zn** & **Pb** & **B** & **As** & **Cd** \\ \hline S1 & 59 & 507 & 4 & 8443 & 2 & 0.17 \\ S2 & 53 & 672 & 59 & 8949 & 6 & 0.25 \\ S3 & 60 & 492 & 27 & 7685 & 9 & 0.23 \\ S4 & 43 & 967 & 42 & 7635 & 6 & 0.32 \\ S5 & 52 & 751 & 24 & 8600 & 17 & 0.34 \\ S6 & 85 & 826 & 29 & 8977 & 7 & 0.25 \\ S7 & 62 & 546 & 5 & 7751 & 5 & 0.22 \\ S8 & 81 & 473 & 44 & 9429 & 17 & 0.19 \\ S9 & 456 & 634 & 19 & 9489 & 35 & 3.7 \\ S10 & 137 & 547 & 18 & 7383 & 7 & 0.25 \\ S11 & 77 & 551 & 3 & 2696 & 2 & 0.34 \\ S12 & 14 & 289 & 5 & 4914 & 4 & 0.32 \\ S13 & 43 & 409 & 9 & 4773 & 13 & 0.30 \\ S14 & 37 & 478 & 14 & 7501 & 7 & 0.22 \\ S15 & 80 & 679 & 11 & 4326 & 9 & 0.32 \\ S16 & 19 & 371 & 7 & 3825 & 4 & 0.31 \\ S17 & 22 & 837 & 11 & 4532 & 6 & 0.37 \\ S18 & 187 & 560 & 5 & 4114 & 5 & 0.21 \\ S19 & 20 & 352 & 4 & 1995 & 3 & 1.06 \\ S20 & 54 & 590 & 3 & 4758 & 5 & 0.27 \\ _Contamination threshold_ & _1000_ & _5000_ & _200_ & _10,200_ & _200_ & _10_ \\ \hline \hline \end{tabular}
\end{table}
Table 5: Element contents, determined by ICP-MS in the twenty investigated wine samples.
## 3 Signal Model
### NR Signal Components
5G NR employs an ultra-lean transmission paradigm: Only SSB maintains periodic transmission, while other physical channels are transmitted on demand. SSB, CSI-RS, and DMRS embedded in PDSCH/PDCCH are predictable for the receiver and can be used as a local replica signal in NR navigation receiver. All these predictable navigation signals are referred to as reference signals for brevity in the following paper. Based on the reference signal, time delays between the transmitter and receiver can be estimated through code tracking loops to provide PNT service.
# All-Optical Tuning of Fano Resonance for Quasi-BIC and Terahertz Sensing Applications
## 3 Results and Discussion
Moreover, compared with filled aluminum, the transmission intensity of Fano linear region of photosensitive silicon structure is increased by 2.63 times, which means that the dynamic range of the sensor is also improved. What's more important is that the lineshape can be freely adjusted and controlled by using this structure, meanwhile the sensitivity can be optimized. Therefore, this method opens the window for ultra-high-sensitivity sensing applications.
# Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
## 2 Establishment of Dataset
### Input Parameters for Models
#### 2.1.4 Uniaxial Compressive Strength  
The strength of rocks is affected by their mineralogy, grain size, and porosity. Rocks with larger grain sizes and considerable porosity have lower UCS than that of other dense rocks [2]. Furthermore, an increase in quartz content will increase the strength of rocks as well as the TC ([PERSON] and [PERSON] [59] and Figure 6). Hence, the greater the UCS is, the larger the rocks' TC is (Figure 7). [PERSON], [PERSON], and [PERSON] [43] found that concrete mixtures with higher quartz content had higher TC and compressive strength.  
Different groups of researchers ([PERSON] et al. [62]; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [30]; and [PERSON] and [PERSON] [12]) proposed models for TC prediction using only the P-wave velocity and porosity. Although their models had an acceptable degree of accuracy, the discussions on the role of the rock properties in TC prediction indicated that these properties were not enough. Other rock properties can also be interesting and give accurate predictions of TC (rock type, TC, UCS, density, saturating fluid characteristic, quality, and geometry of the contact between the grains).  
Figure 5: Measured values of and the relationship between porosity and density.  
Figure 6: Thermal conductivity of basic rock-forming minerals and their compositional relationship with rocks for volcanic and sedimentary rocks.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 4 Discussion and Analysis
### G-Code Program Protection  
After a watermarked model was translated into a G-code program, our decoder did not explicitly embed any signatures into the result. It seems that this content is not protected. However, the G-code program contains instructions, which generate the watermark. To remove the watermark from printed parts, the attackers have to figure out these instructions first and remove or modify them later. It is impossible to identify these instructions by browsing the G-code program. Using a simulator to emulate the printing process might offer some clues. Nonetheless, beside the simulator invoked by our decoder, most G-code interpreters can display only the toolpaths and resultant model by drawing thick lines or solid tubes [29, 30]. If the watermark is irregular or small, it is very unlikely to comprehend the watermark during the simulation process, not to mention locating the G-codes.  
Even when these G-codes were discovered, removing or editing them may produce profound damages in the G-code program. As a result, the G-code program becomes useless and no physical part can be manufactured.
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 2 Materials and Methods
### Elman Neural Network  
The Elman Neural Network is a dynamic recurrent neural network with local feedback connections, known for its self-organizing, adaptive capabilities, and strong learning, fault tolerance, and interference resistance. It typically consists of four layers: the input layer, hidden layer, context layer, and output layer. The context layer neurons correspond one-to-one with the hidden layer neurons. The output of the hidden layer is fed back to the input of the hidden layer through the context layer's delay and storage, achieving short-term memory and seeking local information correlations within debris flow samples [16]. In this case study, the Elman recurrent neural network model is primarily constructed using the Net function from MATLAB's (R2023a) Neural Network Toolbox. The Net function in MATLAB (R2023a) represents a specific neural network object, encapsulating the network's structure, weights, biases, and training parameters, facilitating rapid deployment of the Elman code for analysis and computation. The structure of the Elman Neural Network is depicted in (Figure 2).  
The mathematical expression for its network structure diagram is given as follows:  
\[y(k)=g(w_{3}\cdot\chi(k)) \tag{1}\]  
\[\chi(k)=f(w_{1}\cdot\chi_{c}(k)+w_{2}(u(k{-}1))) \tag{2}\]  
\[\chi_{c}(k)=\alpha\chi(k{-}1)+\chi(k{-}1) \tag{3}\]  
where \(y\) is the \(m\)-dimensional output node vector; \(\chi\) is the \(n\)-dimensional intermediate layer node unit vector. \(u\) is the \(r\)-dimensional input vector; \(\chi_{c}\) is the \(n\)-dimensional feedback state vector; \(w_{3}\) is the connection weight from the intermediate layer to the output layer; \(w_{2}\) is the connection weight from the input layer to the intermediate layer; \(w_{1}\) is the connection weight from the context layer to the intermediate layer; \(0\leq\alpha{<}1\) is the self-feedback gain factor; \(g(\cdot)\) is the transfer function of the output neuron, representing the linear combination of the intermediate layer outputs; and \(f(\cdot)\) is the transfer function of the intermediate layer neuron, commonly using the Sigmoid function.  
Figure 2: Schematic Diagram of the Operational Principle of the Elman Neural Network.
# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
## 5 Conclusions  
Regarding an expanded vision of 6G as a wired/wireless integrated joint communication and sensing platform, we propose and evaluate advanced machine-learning-based image processing techniques for traffic monitoring in smart cities using a low-cost phase-sensitive OTDR. The investigated algorithms are assessed using an experimental low-cost DFOS system installed over a fiber-optic cable buried within a university campus. Data are simultaneously collected by the \(\phi\)-OTDR setup and a dedicated camera whose videos are processed by the YOLOv8 algorithm, defined as ground truth. The collected data are labeled with vehicle density and average speed and fed to MobileNetV2 and ViT models for training. The inference drawn from unseen data provides a robust estimation of traffic behavior. The ViT model exhibits increased performance compared with the MobileNetV2 model when applied to both vehicle counting and average speed estimation. As a case study, the ViT model is applied to monitor the traffic for eight entire days, revealing interesting patterns and grasping the actual traffic profile on the campus. While the ViT necessitates a significantly larger number of parameters, its complexity is similar to that of a CNN when considering multiply-accumulate operations and random access memory usage. Finally, the dataset used to generate this work is made public for benchmarking.  
Conceptualization, R.A.C., L.R., E.C. and D.A.A.M.; Methodology, R.A.C., L.R., E.C. and D.A.A.M.; Software, R.A.C.; Validation, E.C.; Formal analysis, R.A.C.; Investigation, R.A.C., E.C. and D.A.A.M.; Resources, E.C. and D.A.A.M.; Data curation, R.A.C.; Writing--original draft, R.A.C.; Writing--review & editing, R.A.C., L.R., E.C. and D.A.A.M.; Visualization, L.R.; Supervision, E.C. and D.A.A.M.; Project administration, D.A.A.M.; Funding acquisition, E.C. and D.A.A.M. All authors have read and agreed to the published version of the manuscript.  
Parts of this work appear in [32] (CNN processing) and [34] (data collection with YOLOv8 labeling). This research was partially supported by grants from three Brazilian agencies: the Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP): 2022/12917-5, 2022/07488-8, 2022/11596-0 (EMU), 2021/06569-1, 2021/11380-5 (CPTEn), and 2021/00199-8 (SMARTNESS); the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq): 402081/2023-4, 405940/2022-0, 317133/2023-3, and 314539/2023-9; and the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES): 88887.954253/2024-00.  
Data underlying the results presented in this paper are available in Ref. [38]. The dataset is composed of images labeled by the YOLOv8 algorithm and applied to camera recordings.  
The authors would like to thank [PERSON] and [PERSON] for their invaluable contributions in bridging the optical and wireless domains.  
The authors declare no conflicts of interest.
## 2 Experimental Section  
Design scheme of the regenerative amplifier is shown in Figure 1. It consists of a fiber seed, an optical isolator and a regenerative amplifier. After passing through the beam collimator and optical isolation, the seed pulses are derived into two parts. One part enter into the photodetector (PD) as the clock signal, the other part inject into the regenerative amplifier.  
The seed we chose is a commercial mode-locked all fiber oscillator that produces 150 mW at repetition rate 46.8 MHz. The pulse duration of the seed pulses is 20 ps with a central wavelength of 1064.06 nm and spectral width 0.2 nm. The optical splitter is composed of a half wave plate and a polarized beam splitter (PBS). By adjusting the half wave plate, we can control the seed power injected into the regenerative amplifier and PD. After adjustment, 100 mW power is injected into the regenerative amplifier, and the rest of the 50 mW entered into the PD. Optical isolator consists of a polarizer, a half wave plate and a Faraday (FR) that just allows the light transmission in the direction of injection. Regenerative amplifier is composed of a polarizer, a quarter wave plate, an electro-optic Q-switch, a gain medium, a pumping source and many total reflection mirrors. The total length of the cavity is about 2200 mm.  
Inside the regenerative amplifier, the Nd:YVO\({}_{4}\) crystal is end pumped by 888 nm continuous wave (CW) laser. The 0.5-at. % Nd:YVO\({}_{4}\) crystal has dimensions of 3 \(\times\) 3 \(\times\) 20 mm\({}^{3}\) with 2\({}^{\circ}\) wedge angle in one end. To improve the cooling efficiency, Nd:YVO\({}_{4}\) crystal is welded on heat sink that cooled in both \(\mathbf{x}\)- and \(\mathbf{y}\)- directions. Used as a pulse picker, Pockels cell (PC) consists of two BBO crystals with single crystal size of 3 \(\times\) 3 \(\times\) 20 mm\({}^{3}\). PC is operated with quarter-wave voltage. The rise and fall times of the PC driver are about 6 ns. The chirp of Nd:YVO\({}_{4}\) and BBO crystals is about 1-2 ps. M1 and M5 are two plano-concave high reflectance (HR) mirrors with R = -2000 mm. M2 and M4 are two plane-convex HR mirrors with R = 1500 mm to compensate the thermal focal length of the medium. M3 is a 45\({}^{\circ}\) plane mirror coated with antireflection at 888 nm and highly reflective at 1064 nm. The pump source is an 888 nm semiconductor fiber coupling module with maximum output power 100 W. Output fiber core diameter is 400 \(\upmu\)m, and the pump beam is coupled into the crystal by a 1:4 coupling lens telescope.
## 3 Results and Discussions
### Soil Reinforcement Tests
#### 3.2.2 UCS Comparisons among Different Treatment Methods  
Figure 6 compares the UCS of the soils reinforced by the improved EICP containing the optimal concentration of organic material with that of the control group. The mean UCS and the strength increasing rates are summarized in Table 4. The reinforcement effect was generally weak when any one of the three organic materials was added alone. The average strength increased by 169.46 kPa  
Figure 5: Strength for soil modified by the improved EICP containing organic material.  
(52.18%) compared with that of the untreated soil, most likely due to the high viscosity of brown sugar, whose reinforcement effect was the best herein. The second was skim milk powder, which had a mean strength of 117.42 kPa (36.16%) higher than the original soil. When only glutinous rice powder was mixed, the strength did not increase substantially (6.25%). The mean UCS of specimens treated with EICP could be enhanced by 259.41%, which was higher than the untreated soil. However, when 8 g/L glutinous rice powder, 8 g/L skim milk powder or 16 g/L brown sugar was added into the treatment solution separately, the soil mean strength increased by 380.72 kPa, 297.29 kPa, and 288.18 kPa, which is 32.62%, 25.47%, and 24.69% higher than that of soil treated by EICP and 376.64%, 350.95%, and 348.15% higher than that of the original soil, respectively. \(R_{i}\), \(R_{\text{EICP}}\), and \(R_{\text{EICP}+i}\) were defined respectively as the strength increasing rate of soil treated with only organic material EICP, as well as the improved EICP containing organic material compared to untreated soil. Through the calculation of data in Table 4, it is not difficult to find the existence of formula (1), indicating that the strength increasing rate of EICP combined with organic materials is not the simple arithmetic superposition of the two alone, and EICP with added organic materials is really a highly efficient reinforcement method.  
\[R_{\text{EICP}+i}>R_{\text{EICP}}+R_{i} \tag{1}\]
## 4 Results  
### Time-Dependent Behavior of Operational Noise  
The background noise from the operating system was processed for the entire measurement period and displayed in Figure 8. This figure shows the aggregated minima and maxima of the amplitude, energy, and spectral centroid. The data from sensor 3 at the transition piece are shown in the left column, while the data from sensor 7 at the foundation are shown in the right column. By studying the statistical spread and directly comparing the sensors, the position-specific variation of the background noise becomes apparent. Sensor 3 shows a higher intensity of background noise than sensor 7. Here, it is worth noting that the spread is significantly lower for sensor 3. This observation suggests that the proximity to the nacelle and the mounting on the steel structure of the tower play a role. Obviously, the vibrations of the generator are transmitted with greater intensity up to thispoint in the tower. In contrast, the lower intensity at sensor 7 could be related to a higher attenuation due to the concrete structure.  
On closer analysis, a correlation with maintenance activities was identified. Vertical drops can be seen in the data at various points, particularly in the amplitude plot between the lowest minima at around 60 dB (electrical noise level) and the bands above. These regular drops could be clearly assigned to the periods stated in the maintenance logs. During maintenance work, the entire system is switched off and on, entered by personnel,  
Figure 8: Representation of the minima and maxima of signal features (peak amplitude in decibel, energy in engineering unit, and spectral centroid in kilohertz) throughout the entire measurement period: (**a**) for Sensor 3 at the transition piece and (**b**) Sensor 7 at the foundation. Data gaps, such as those between October 2021 and December 2021, resulted from storage limitations due to the large volume of recorded data and delays in maintenance work required for hardware replacement.  
and thus exposed to different source characteristics. These changes are visible in the form of the detected drops in the data.  
For this reason, the maintenance periods were excluded from the data sets for subsequent correlation analyses in Section 4.2. This also applies to a special operating condition at the beginning of May, which occurred for three days and could not be observed with comparable intensity in the following period. Due to the lack of information related to the circumstances, this period was also excluded from the data for the correlation analyses.  
Based on the filtered data of the 15-minute maxima, quantiles were determined for the sensors and the outer limits for each feature were defined as evaluation thresholds. The respective values included in the PoD model in Section 4.4 are summarized in Table 1. These thresholds exclude a significant proportion of the remaining operational noise, as the maxima and minima already represent aggregated periods.