esa-sceva/satcom-chunk-collection huggingface dataset:
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work  
IoT systems frequently generate vast quantities of data, posing substantial management and analysis challenges. Researchers have introduced several frameworks and architectures to address these challenges in IoT Big Data management and knowledge extraction. One such proposal is the Cognitive-Oriented IoT Big Data Framework (COIB Framework), as outlined in [PERSON]'s works [47; 48]. This framework encompasses an implementation architecture, layers for IoT Big Data, and a structure for organizing data. An alternative method involves employing a Big-Data-enhanced system, adhering to a data lake architecture [49]. Key features of this system include a multi-threaded parallel approach for data ingestion, strategies for storing both raw and processed IoT data, a distributed cache layer, and a unified SQL-based interface for exploring IoT data. Furthermore, blockchain technologies have been investigated for their potential to maintain continuous integrity in IoT Big Data management [50]. This involves five integrity protocols implemented across three stages of IoT operations.
###### Abstract  
The study aimed to investigate probiotic characteristics, and low moisture silage fermentation capability of selected lactic acid bacteria (LAB) isolated from Alfalfa (_Medicago sativa_ L). Morphological and physiological properties, carbohydrates fermentation, enzymes, and organic acids production, anti-fungal activity, antibiotic sensitivity patterns, and probiotic characteristics (acidic and bile salt tolerances, hydrophobicity and aggregations natures) of LAB were examined. 16 SrRNA sequencing was carried out to identify isolated strains. The identified strains _Lactobacillus plantarum_ (KCC-37) and _Lactobacillus plantarum_ (KCC-38) showed intense antifungal activity, survival tolerant in acidic and bile salt environments, cell surface and auto aggregations ability, enzymes and organic acids productions. At ensiled condition, KCC-37 and KCC-38 enhanced acidification of Italian ryegrass silages by producing a higher amount of lactic acid, a key acid for indicating silage quality with less extent to acetic acid and succinic acid at low moisture level than non-inoculated silages. Notably, the addition of mixed strains of KCC-37 and KCC-38 more potentially enhanced acidification of silage and organic acid productions than the single-culture inoculation. The overall data suggested that these strains could be used as an additive for improving the quality of the fermentation process in low moisture silage with significant probiotic characteristics.  
Lactobacillus plantarum; antifungal; probiotic; Italian ryegrass; low moisture silage +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data
#### 2.2.4 Meteorological Data  
The Final Operational Global Analysis data (FNL) with 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) spatial resolution and 6-h interval from the National Centers for Environmental Prediction (NCEP) [90; 91] were used as meteorological initial and boundary conditions of the regional CO flux inversion system.  
The NCEP ADP Global Upper Air and Surface Weather Observations [92; 93], which are composed of a global set of surface and upper-air reports operationally collected by NCEP, were used as meteorological observations in this research. The meteorological observations from a total of 71 World Meteorological Organization (WMO) meteorological stations were used in this research. Their locations are shown in Figure S4.
# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 2 Principles and Methodology  
With the rapid development of computer technology, deep convolutional neural networks have been promoted and are now the mainstream research method in the field of target detection, thanks to their better performance and the complete automation of feature engineering, even replacing the traditional target detection algorithm based on region filtering + feature extraction + feature classification, and eliminating the need to manually design feature extractors, which has been widely used in areas such as handwritten text transcription, image search, autonomous driving, pose estimation and instance segmentation.  
Supervised learning-based target detection methods at the current stage can be divided into two categories: The first category is based on the anchor mechanism, and the second is based on the anchor-free or key-point mechanism. While the target detection algorithm based on the anchor mechanism can be roughly divided into two types: one-stage and two-stage. Two-stage target detection algorithms based on candidate regions, which first generate candidate frames through regional proposal networks (RPN), and then use convolutional neural networks for classification and non-maximum suppression (NMS) to remove the duplicated detections for the same instance by computing Intersection over Union (IoU). The process is more accurate but slower and difficult to meet real-time requirements due to more candidate frames, such as the R-CNN series [40; 41]; And one-stage target detection algorithms based on regression, represented by SSD (Single Shot MultiBox Detector) and YOLO (You only look once) [42; 43], one-stage detectors slide a complex arrangement of possible bounding boxes, called anchors, over the image and classify them directly without specifying the box content. Then the algorithm based on anchor-free or key-point mechanism detects directly by learning the key features of the input image instead of generating a series of anchor box, omitting the process of RPN(Region Proposal Network) and NMS(Non-Maximum Suppression), which makes the prediction process more direct and faster, and the representative algorithms include CornerNet, FCOS, ExtremeNet, CenterNet, etc.  
The research of this paper is based on Yolov4 [44], through combining the MobileNet backbone feature extraction network with the Yolov4 network to simplify the Yolov4, thereby reducing the number of parameters and achieving the purpose of improving the detection speed.
# Assessment of Spatial Variability in Ground Models Using Mini-Cone Penetration Testing
## 2 Theoretical Background
### Coefficient of Variation (\(CV\))  
The CV is defined as the standard deviation (\(\sigma\)) divided by the mean (\(\mu\)), representing the numerical uncertainty of an object relatively. It normalizes the variability in data with respect to the mean value, allowing for the comparison of variability among different measurements. The \(CV\) for cone penetration resistance within a soil layer can be defined as follows:  
\[CV=\left(\frac{\overline{q_{c}}}{\mu_{qc}}\right) \tag{1}\]where  
\(q_{c}=\) standard deviation of cone penetration resistance.  
\(\mu_{qc}=\) mean of cone penetration resistance.
###### Abstract
The proposed method computes approximated reachable sets and the attainable safe landing zone with information about propellant consumption and time and is applied to a generic Moon landing mission. [PERSON] et al. [22] proposed a method based on the analytical homotopic approach to analyze the flight capability of a Mars atmospheric entry vehicle. Based on the solutions to optimization problems, the reachable longitudinal area can be determined iteratively by setting a proper continuation parameter. The reachable zone calculated by the profile design method is easy and practical. Firstly, the entry process constraints are translated into a generalized flight profile, and a flight corridor is formed. Then, entry-designed profiles are planned along the corridor boundaries to form the maximum and minimum range profiles as well as all the feasible profiles. Finally, the corresponding trajectory endpoints of each profile are connected to obtain the reachable zones. [PERSON] et al. [25] proposed a new landing footprint generation algorithm that considers multiple uncertainty effects based on an improved 3D acceleration profile planning method. In short, there are several gaps in the current works on the reachable zone of boosting gliding vehicles, such as low computational efficiency and poor adaptability. What is more, the current literature is not conducive to the study of online prediction for the reachable zone because of low computational efficiency. Aimed at solving the above problems, this paper proposes a fast prediction method for the target reachable zone of a boosting gliding vehicle based on a database. The database of the target reachable zone can be built in advance using the standard trajectory that was previously designed. In the process of the gliding phase, when the vehicle encounters an emergency, the commander can quickly predict the reachable zone at the current moment based on the database method, which provides theoretical support for the online trajectory replanning of the gliding vehicle. The structure of this paper is as follows: Section 2 establishes the motion formulation and the constraints for the boosting gliding vehicle; the preliminary D-V profile method is introduced in Section 3; in Section 4, the effective reachable zone prediction method is proposed; the simulated examples demonstrating the validity and accuracy of the reachable zone prediction method are detailed in Section 5. We conclude this paper in Section 6.
## 4 Mechanisms of Our Proposed Protocol
### XSalsa20 Algorithm
Because the permutation process is a fundamental part of XSalsa20's architecture, it is a dependable option for systems that need robust cryptographic protection: this is the reason why we suggested it as a crucial method in our proposed system. The significance of the permutation procedure in XSalsa20 is defined as follows:  
* All of the output bytes of XSalsa20 depend on all of the input bytes of the key, nonce, and block counter. An attacker will find it difficult to identify any patterns or connections between the cipher's input and output due to its properties of confusion and diffusion. * By implementing the core function over several rounds (20 rounds in XSalsa20), the algorithm distributes any possible vulnerabilities throughout the rounds, thus resulting in a high level of security. * By using multiple rounds of permutation, attackers are prevented from taking advantage of biases and linearity, which might be used to infer details about the plaintext or key. * XSalsa20 uses both the key and the nonce in every permutation cycle to make sure that variations in either parameter produce radically different results. This feature is essential to security, because it keeps adversaries from analyzing patterns in plaintext or ciphertext to determine the key or nonce. * XSalsa20's permutation procedure ensures that the cipher functions securely under a range of circumstances and inputs, thus ensuring that encrypted data--in the case of the suggested system, the payee and payer information--are kept private and impenetrable.
# Biodegradable Contrast Agents for Photoacoustic Imaging
## 4 Gold Nanoparticles as Biodegradable PA Agents
### Biodegradable Gold Nanovecisles as Theranostic Agents  
In addition to PAI, AuNPs can simultaneously be used as photothermal therapy agents. [PERSON] et al. produced biodegradable gold nanovecisles (BGVs) consisting of poly(ethylene glycol)-\(b\)-poly(\(\varepsilon\)-caprolactone) (PEG-\(b\)-PCL)-tethered AuNPs [34]. Each BGV had a single layer of AuNPs with a hollow interior cavity (Figure 4A). The average PA signal intensity of the BGVs  
Figure 3: Polymer-stabilized gold nanoparticle (AuNP) clusters. (**A**) The formation process for AuNP clusters. Aggregation of the AuNPs and the polymer increased the volume fraction of the particles (\(\lx@overaccentset{{\circledR}}{\phi}\)). (**B**) Electron microscope image of 4 nm sized primary AuNPs. (**C**) Low- and high-magnification images of biodegradation of the AuNP clusters in live cells at 24 and 168 h. The red squares in the low-magnification images correspond to the areas shown in the high-magnification images. (**D**) An ultrasound image of the hypoechoic inclusion with hyperechoic background (left). Photoacoustic images of the phantom with inclusions containing AuNPs (middle) or gold nanorods (right). Reproduced with permission from [36;56]. Copyright, American Chemical Society (2010) and Optical Society of America (2012).  
was approximately ten times higher than the background and two times higher than that of GVs, which composed of nonbiodegradable PEG-\(b\)-polystyrene polymer-tethered AuNPs (Figure 4B). The intense PA signal was monitored in the tumoral area after intratumoral injection of the BGVs in a mouse model with an MDA-MB-435 tumor (Figure 4C). Owing to strong near-infrared absorption properties, the BGVs showed a higher photothermal conversion efficiency (37%) than GVs (18%), gold nanorods (22%) and gold nanoshells (13%).  
The BGVs gradually began to break down when the system temperature exceeded 70 \({}^{\circ}\)C. Because PCL has a melting point of approximately 60 \({}^{\circ}\)C, most of the BGVs were degraded after 10 min. Under irradiation with an 808 nm laser, tumors injected with the BGVs showed retarded growth and tumor regression (Figure 4D). Therefore, these BGVs are promising multifunctional theranostic platforms for PAI and photothermal therapeutic applications. More recent works on BGVs used smaller primary AuNPs to increase the biocompatibility and enhance the renal excretion of the degraded AuNPs [35].
# Second-Order Statistical Properties of Vehicle-to-Vehicle Rician Fading Channel
## Appendix A  
In this appendix, we employed the same derivation procedures for (16)-(21) to obtain \(b_{1}^{SBT}\), \(b_{1}^{SBR}\), \(b_{1}^{DB}\), \(b_{2}^{SBT}\), \(b_{2}^{SBR}\) and \(b_{1}^{DB}\) using the equality \(\int\limits_{-\pi}^{\pi}e^{\text{acos}(c)+bsin(c)}dc=2\pi I_{0}(\sqrt{a^{2}+b^ {2}})\) and \(\int\limits_{-\pi}^{\pi}e^{\text{cos}(\theta)}\text{cos}(m\theta)d\theta=2\pi I _{m}(x)\) ([21], Equations (3.3339) and (8.4111)], where \(I_{0}\) denotes zeroth-order Bessel function of the first kind and \(I_{m}\) is the \(m\)th-order Bessel function of the first kind, the closed-form expressions can be obtained. Also, let \(\beta_{T}=\alpha_{T}-\mu_{T}\) and the appropriate value of \(m\) be substituted into the equation. **1. Derivation of (16) and (17)**  
Note that (16) and (17) are similar for the derivation for parameters \(b_{1}^{SBT}\) and \(b_{1}^{SBR}\), and hence only \(b_{1}^{SBT}\) was derived here. The expression for \(b_{1}^{SBT}\) was derived with \(m=1\), and the first (\(Y_{1}\)) and second (\(Y_{2}\)) terms of the equation can be readily obtained as  
\[ \begin{array}{c}Y_{1}=\frac{f_{max}}{2\pi I_{0}(k_{T})}\int\limits_{-\pi}^{ \pi}\left[e^{k_{T}\text{cos}\beta_{T}}\times\text{cos}\left(\beta_{T}+\mu_{T} -\gamma_{T}\right)\right]d=f_{Tmax}\text{cos}(\mu_{T}-\gamma_{T})\frac{I_{1} (k_{T})}{I_{0}(k_{T})},\\ Y_{2}=\frac{f_{Runx}}{2\pi I_{0}(k_{T})}\int\limits_{-\pi}^{\pi}\left[e^{k_{T} \text{cos}\beta_{T}}\times[\Delta_{T}\sin(\gamma_{R})\text{sin}(\alpha_{T})- \text{cos}\,\gamma_{R}]d\beta_{T}=f_{Rmax}[\Delta_{T}\text{sin}\left(\gamma_{ R}\right)\text{sin}\,\mu_{T}\frac{I_{1}(k_{T})}{I_{0}(k_{T})}-\text{cos}\, \gamma_{R}],\\ b_{1}^{SBT}=(2\pi)b_{0}^{SBT}[f_{Tmax}\text{cos}(\mu_{T}-\gamma_{T})\frac{I_{ 1}(k_{T})}{I_{0}(k_{T})}+f_{Rmax}[\Delta_{T}\text{sin}\left(\gamma_{R}\right) \text{sin}\,\mu_{T}\frac{I_{1}(k_{T})}{I_{0}(k_{T})}-\text{cos}\,\gamma_{R}] \\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad=(2 \pi)b_{0}^{SBT}[Y_{1}+Y_{2}].\end{array}  \tag{18}\]  
**2. Derivation of (19) and (20)**  
Herein, adopting the same procedure, only \(b_{2}^{SBT}\) was derived with \(m=2\), and hence the six terms of the equations (\(Y_{3}\), \(Y_{4}\), \(Y_{5}\), \(Y_{6}\), \(Y_{7}\), and \(Y_{8}\)) can obtained accordingly. \[
## 3 Network and Energy Models
### Network Model  
The proposed method addresses a heterogeneous sensor network, with the following characteristics:  
1. Deployed sensor nodes possess limited heterogeneous energy levels within a specified range.
2. All deployed sensors remain static and do not change their locations.
3. The sensors have the ability to adjust their transmission power levels.
4. Sensors regularly monitor the environment, with data rates varying based on events or sensor node types.
5. The base station remains static and does not change its location.
6. The network may contain any number of nodes, accommodating various shapes and scales, with fully heterogeneous sensor nodes.  
To elaborate on the concept of multi-level heterogeneity, consider a network with \(n\) levels of energy heterogeneity and m levels of data rate variability, where '\(n\)' and '\(m\)' are positive integers greater than zero.  
where \(n,m>0\) & \(n,m\in Z^{+}\)
## 3 Results
### Effects of ABF(r) on Lipid Accumulation in 3T3-L1 Adipocytes  
To explore the effects of ABF(r) on lipid accumulation in 3T3-L1 adipocytes, 3T3-L1 cells were treated with ABF(r), and lipid accumulation in the cells was measured by Oil Red O staining. As shown in Figure 5, treatment with ABF(r) (0.025 and 0.05 mg/mL) significantly decreased oil droplet formation compared with untreated control cells. In particular, the levels of lipid accumulation were significantly reduced by ABF(r) 0.05 mg/mL relative to that by ABF(r) 0.025 mg/mL (i.e., it acted in a dose-dependent manner).  
Effects of ABF(r) on the mRNA and Protein Expression of lipoprotein Lipase (LPL) in 3T3-L1 Adipocytes  
We assessed the effects of ABF(r) on the mRNA and protein expression of LPL in 3T3-L1 adipocytes. As shown in Figure 6A, the LPL mRNA ratio was significantly reduced in a dose-dependent manner in the 0.025 and 0.05 mg/mL (73% and 42%, respectively) ABF(r) groups compared to the ABF(r) non-treatment group (100%). Additionally, LPL protein expression was significantly decreased in the ABF(r) at 0.025 and 0.05 mg/mL (0.645 \(\pm\) 0.039 and 0.559 \(\pm\) 0.080, respectively) compared to that in the ABF(r) non-treatment group (0.855 \(\pm\) 0.062) (Figure 6B).  
Figure 4: Effects of _Aronia_ bioactive fraction (ABF®) on 3T3-L1 adipocyte cell viability. Cell viability was measured in 3T3-L1 cells treated with various concentrations of ABF® (0.025, 0.05, 0.1, 0.2, and 0.5 mg/mL) using EZ-CYTOX kit. Data represent the mean \(\pm\) SEM. * \(p\) < 0.05 vs. MDI control group (ABF® 0 mg/mL).  
Figure 3: Schematic representation of 3T3-L1 adipocytes differentiation. MDI: 0.5 mM 3-isobutyl-l-methylxanthine, 1 mM dexamethasone, and 10 mg/mL insulin. ABF®: _Aronia_ bioactivity fraction.  
Figure 5: Effects of _Aronia_ bioactive fraction (ABF\({}^{\otimes}\)) on lipid accumulation in 3T3-L1 adipocytes. (**A**) Lipid accumulation in the cells viewed under an inverted microscope (100\(\times\) magnification) and (**B**) determined by Oil red O staining. (**C**) Lipid accumulation measured after Oil Red O elution. (**a**) 3T3-L1 cells cultured in the non-differentiation medium, (**b**) adipogenic differentiation medium only (MDI control), (**c**) adipogenic differentiation medium + ABF\({}^{\otimes}\) 0.025 mg/mL, and (**d**) and adipogenic differentiation medium + ABF\({}^{\otimes}\) 0.05 mg/mL. Data represent the mean \(\pm\) SEM. * \(p\) < 0.05 compared with the MDI control group.  
Figure 6: Effects of lipoprotein lipase (LPL) on the _Aronia_ bioactive fraction (ABF\({}^{\otimes}\)) in 3T3-L1 adipocytes were treated with ABF\({}^{\otimes}\) (0.025 and 0.05 mg/mL). (**A**) RT-qPCR measured mRNA expression of LPL. ABF\({}^{\otimes}\) (0.05 mg/mL) significantly reduced LPL mRNA in 3T3-L1 adipocytes. (**B**) Protein expression of LPL was measured by Western blot. ABF\({}^{\otimes}\) (0.05 mg/mL) significantly reduced LPL protein in 3T3-L1 adipocytes. Data represent the mean \(\pm\) SEM. * \(p\) < 0.05 compared with the MDI control group.
## 5 Simulation and Results
### Positioning Accuracy Assessment
The errors significantly decreased, although the corner area still showed the least accuracy, due to multipath reflections and noises. Furthermore, positioning quality is also better expressed in the remaining areas in which the SVM and RF algorithms provided the best results. Although each method has its own advantages and disadvantages, they all showed a significant improvement in positioning accuracy after applying both area division and noise reduction.
## 5 Conclusions  
This study investigated elevational gradient of climate driving effect on interannual change trend in cropland ecosystem NPP in the alpine region of the southwest China. The conclusions are as follows:  
1. The cropland ecosystem NPP was the highest at mid-elevations between 1600-2600 m ASL with an average value of 837 \(\pm\) 25 gC m\({}^{-2}\) year\({}^{-1}\) (range: 801-866 gC m\({}^{-2}\) year\({}^{-1}\)). This finding indicated that cropland reached its peak capacity in assimilating NPP under the optimal environmental conditions in the study area (annual average temperature: 10.11-15.35 \({}^{\circ}\)C, annual total precipitation: 983-1098 mm).
2. Cropland ecosystem NPP increased significantly under climate warming and drying and increasing solar radiation in the study area. The increasing NPP was mainly driven by increasing solar radiation. Climate warming had an elevation-dependent driving effect on increasing NPP. The driving effect of climate warming on NPP change converted from significantly negative at low elevations (below 1700 m ASL) to significantly positive at high elevations (above 2600 m ASL).
3. Croplands were more vulnerable to high temperature and precipitation anomalies at low elevations than other regions. The vulnerable feature was caused by the conflicts between climate drying, frequent seasonal drought, and the strong water requirement of cropland ecosystem at low elevations.
4. When we investigated climate driving effects on NPP change across the entire study area, climate warming showed different effects than the conclusion (2). Cropland at low elevations below 1700 m ASL hosts 63% of the total cropland. The cropland distribution and the negative driving effect of climate warming on cropland ecosystem NPP at low elevations brought about nonsignificant negative impact of climate warming on NPP change across the entire study area.  
The results not only remind us to take the elevational gradient of climate driving effects into account when we manage agriculture production and food security to deal with ongoing climate change in the study area but also identify the most susceptible areas to climate change, located at low elevations.  
Conceptualization, J.T. and J.Z.; methodology, J.T.; validation, J.T. and Y.X.; writing, J.T. and W.W.; supervision, Y.Z. and X.Z.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research & Development Program of Tibet Autonomous Region (grant number XZ202101 ZY0008N), the National Social Science Fund of China (grant number 21 ZDA116), the National Natural Science Foundation of China (grant number 41501054).  
The data presented in this study are available upon request from the first author.  
The authors declare no conflict of interest.