esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Environmental pollution from trace elements (TEs) is an increasing concern due to the potential impacts of these toxic substances on ecosystems and human health [1, 2]. Mining activities are a major source of TE emission from the soils which contaminate water resources and affect the quality of agricultural crops [3, 4, 5]. Both essential elements (iron and copper) and non-essential elements (lead and nickel) can accumulate in food chains, exposing local populations to significant health risks. Various scientific studies worldwide have addressed the impact of TE on the environment, highlighting the risks associated with the accumulation of heavy metals in agricultural soils and their transfer to crops and animals [6, 7].  
In Cameroon, similar studies have recently evaluated the effects of TE in mining areas: [PERSON] et al. [8] examined trace elements pollution and their potential health risks in the cobalt-nickel-bearing areas of Lomie, and [PERSON] et al. [9] studied contamination levels in the abandoned gold mining area of Bindiba. Likewise, [PERSON] et al. [8] assessed the risks associated with TE in the gold-mining region of the country's east [3]. However, despite the economic importance of the rutile deposits in Akonolinga [10], little research has focused on the current contamination levels and the environmental and health risks related to TE in this region, which is potentially vulnerable to significant pollution effects. It is important to note that rutile (TiO\({}_{2}\)) found in the aquatic environment can interact with organisms and induce toxic effects [11]. [PERSON] et al. [12] showed that TiO\({}_{2}\) affected the immune system and digestive gland function of mussels. Hence, the importance and necessity of assessing the level of environmental pollution in Akonolinga, where fishing and farming are the main activities of the inhabitants  
This study investigates the ecological and health issues related to trace elements (TEs) in Akonolinga, Cameroon, for the first time before the planned rutile mining activities in the coming years. Although rutile exploitation has not yet begun in the study area, ongoing exploration activities are contributing to environmental pollution. Thus, because exploration implies the excavation of soil and the release of dust into the aquatic and terrestrial environments, it becomes imperatively essential to assess the current level of contamination in order to provide baseline data for comparison with assessments conducted during and after exploitation. The aim of this study is to determine the current level of TE contamination in soils and assess the potential risks to human health and the environment. In addition, this research will help public health policymakers and international researchers to better understand the effects of TE and also increase awareness among local stakeholders about the importance of sustainable management of mineral resources.
## 4 Field Test and Validation  
### Test Scheme  
The field observation aimed to measure the displacement of the foundation pit soil and pipeline. Since the direct arrangement of measuring points on the aviation oil pipeline can disturb the pipeline greatly, 23 points (GXC-1 to GXC-23) for measuring pipeline settlement were set on the soil above the aviation oil pipeline, and five points (TST-1 to TST-5) for measuring horizontal displacement were set within the nearest range of the station pipeline and foundation pit, with the specific location of each point shown in Figure 14.  
In this paper, measuring point refers to displacement measuring point mainly used for the measurement of surface vertical displacement and stratum horizontal displacement. Surface vertical displacement is mainly measured with a level gauge. Before the construction of support structure, three measurements were carried out by vertical displacement measuring points already arranged in the surrounding environment, and the mean value was taken as the initial value within the tolerance range. In the event of measurement of stratum horizontal displacement, an inclinometer was used to measure the deformation of the inclinometer pipe buried in the soil layer from bottom to top in order to make clear the horizontal displacement of soil layer around the envelope structure at different depths during the excavation of the foundation pit, as shown in Figure 15. During measurement, the inclinometer probe went down slowly to the bottom of hole along the guide groove, so as to measure the horizontal displacement every 500 mm from bottom to top. To figure out the deep lateral deformation, the fixed starting point, supposedly located at the top of the inclinometer pipe, was determined. Pipe orifice displacement was measured by a total station; for calculation, the actual displacement of orifice was corrected.
## 2 Materials and Methods
### Projection Micro-Stereolithograph (P\(\mu\)SL)
#### 2.1.2 Projection Micro-Stereolithography (P\(\mu\)SL) Experimental Set-Up  
The AM system used in this work is a custom-built P\(\mu\)SL system capable of manufacturing micro-scale features. It consists of a linear stage (Thorlabs), on which the sample holder is attached. A projection lens (Thorlabs) is used to achieve a lateral resolution of 12 \(\upmu\)m. A digital micromirror device (DMD) (Texas Instruments, Dallas, TX, USA) is used for generating projection patterns according to cross-sectional digital images of a 3D model. UV LED (365 nm, Hamamatsu, Hamamatsu City, Japan) is used as a UV illumination source. The UV light reflected from the DMD is projected on the surface of the resin inside the vat. Once a layer is formed, the sample holder is lowered by the layer thickness, and the next image is projected to cure a new layer on top of the previous one. This process repeats until all layers are completed. The actual system used in this study is shown in Figure 2. The setup is kept in a printing chamber where environmental factors such as external light and oxygen concentration are controlled.  
Figure 1: (**a**) Schematic diagram of P\(\mu\)SL and resulting surface roughness on a printed part. (**b**) Microscope images of 3D printed struts in different printing angles (90\({}^{\circ}\) and 60\({}^{\circ}\)). The polymer used is HDDA and curing time per 80 \(\upmu\)m thick layer is 3 s. These side profiles clearly display the surface roughness caused by the layer-wise process.
# Tree Seedings Detection and Counting Using a Deep Learning Algorithm
## 4 Conclusions  
This study presents a method for detecting and counting three types of tree seedlings based on the YOLOv5 object detection network. The data were augmented via 13 data augmentation methods to improve the accuracy of the detection model and prevent overfitting. The detection model was trained with 7000 images and validated with 2000 images. The optimal model was obtained at 400 epochs and the average detection time of dragon spruce, black chokeberries, and Scots pine tree seedlings was 0.695, 0.713, and 0.728 s, respectively. The detection of dragon spruce tree seedlings exhibited the highest accuracy value, whereas the detection of black chokeberries tree seedlings exhibited the lowest accuracy value. The accuracy of the detection model reached 95.10% on average (98.58% for dragon spruce tree seedlings, 91.62% for black chokeberries tree seedlings, and 95.11% for Scots pine tree seedlings). Moreover, this study has demonstrated the great significance of incorporating deep learning techniques into agriculture and forestry to aid in the statistical tasks of the number of trees, yield estimation of seedlings, and plant breeding in nurseries. Experimental results demonstrated that the YOLOv5 model could achieve accurate results and satisfactory performance for detecting and counting tree seedlings. In addition, the Yolov5 model can detect small, medium, and big objects due to the capability of its model head to achieve multi-scale prediction by generating three different sizes of feature maps. These advantages made YOLOv5 more suitable and eligible for the detection of tree seedlings.  
Methodology, D.M. and D.L.; resources, D.M. and X.Y.; software, D.M. and X.Y.; writing, D.M.; format calibration, D.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that are presented in this study are available on request from the corresponding author. The data are not publicly available due to them being necessary for future works and graduation thesis.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Accuracy Evaluation Method
#### 2.3.1 Fitting Residual  
The discrete points of clock offset are fitted to obtain the fitted clock offset, and the interval between discrete points is 1 s. The differences between the fitted clock offset and the actual clock offset is calculated to obtain the fitting residual of clock offset. The root mean square (RMS) is usually used to represent the fitting residual of clock offset, which can be obtained as follows:  
\[\sigma=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(\Delta T_{i}^{\mathrm{C}}- \Delta T_{i}^{\mathrm{O}}\right)^{2}}{n-1}} \tag{7}\]  
where \(n\) represents the number of observation data, \(\Delta T_{i}^{\mathrm{C}}\) represents the calculated value of the \(i\)th fitted clock offset, and \(\Delta T_{i}^{\mathrm{O}}\) denotes the observed value of the \(i\)th actual clock offset. Generally speaking, the smaller RMS of the fitting residual indicates the better stability of clock offset, and the higher accuracy of time synchronization. it is noted that the actual clock offset is the post precision satellite clock offset [24].
## 3 Results
### Cure Modeling Using the Hill Equation  
To numerically understand how trapped solvent affected the cure kinetics of the adhesive, the Hill equation was used to model the curing process for each of the samples. First, to better visualize the curing process, the curves shown in Figure 6a were normalized and plotted in Figure 7a. This graph represents the degree of conversion \(\alpha\) of the adhesive, which ranged from zero (at the beginning of curing) to one (when the adhesive was fully cured). As previously seen in Figure 6b, it is also clear in Figure 7a how increasing IPA content delayed the converging process. Figure 7b compares the experimental and theoretical curves of the baseline adhesive sample containing no IPA. The Hill model gives a good estimate of the curing kinetics of the adhesive.  
The values for the rate constant \(k\) and the shape parameter \(\theta\) obtained for each sample type after fitting the experimental data to the Hill equation using a GRG algorithm are summarized in Table 2. As expected, the rate constant decreased with increasing IPA content, although the decreasing rate decayed with increasing solvent content. On the other hand, the shape parameter for all the samples ranged between 1.35 to 1.48 and no particular trend was observed. The sum of errors (between the experimental and theoretical values) squared was small, meaning that the modeling resembled the experimental results.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 5 Conclusions and Perspectives  
Although solar water treatments have produced significant interest in research, they have not yet reached commercialization; there are only a few examples of medium to large-scale solar wastewater processing plants in industry. However, recent literature results demonstrate that solar wastewater treatment has the potential to be successfully employed both as a cheaper and more environmentally-friendly alternative to conventional processes, or integrated in existing plants, thereby increasing efficiency and reducing operating costs. However, further research towards industrialization is needed. Two directions emerge as particularly promising: advances in materials science towards immobilized photocatalysts, which can effectively use sunlight, and switching to the investigation of real, rather than model, wastewaters.  
Regarding the former, photocatalysts working with UV-visible light would make it possible to better harness sunlight and ease the design requirements for solar collectors and photoreceptors. For example, by eliminating the need for UV radiation, it would be possible to use conventional mirrors, which are significantly less expensive and more durable than UV-reflecting mirrors.  
Regarding wastewaters, investigations which initially began with water models have turned to real wastewater sources in recent years. These are much more complicated samples, generally containing a variety of species which can interfere with the treatment. This complexity needs to be tackled by directed research efforts, particularly by the chemistry and chemical engineering community, in order to progress towards industrial application.  
Conceptualization, M.A.F., M.O., A.M. and M.B.; writing--original draft preparation, M.A.F.; writing--review and editing M.O., A.M.; supervision A.Q. and A.M.  
This research was funded by the University of Trento, project ERICSOL, in a collaboration between the industrial engineering and physics departments.  
The authors declare no conflict of interest.
# The Link between Purine Metabolism and Production of Antibiotics in _Streptomyces_
## 5 Guanosine Metabolism Impinges on Antibiotic Production
### Production of Actinofudin (ACT) and Undecylprodigiosin (RED)  
Production of antibiotics such as ACT and RED (Figure 2) in _S. coelicolor_ generally takes place in stationary phase when (p)ppGpp accumulates (Figure 3) and genes encoding the biosynthetic enzymes are activated by cluster-situated regulators ActII-ORF4 and RedD, respectively [62; 63]. Inactivation of _relA_ in _S. coelicolor_ A2(3) abolishes (p)ppGpp synthesis and production of both ACT and RED under nitrogen-limiting conditions, and this correlates with the reduced accumulation of both _actil_-ORF4 and _redD_ transcripts (Table 1) [64]. Under conditions of nutrient sufficiency, induced production of (p)ppGpp results in activation of _actil_-ORF4, but not _redD_, indicating that accumulation of (p)ppGpp _per se_ does not induce RED production [21; 65]. While inactivation of either _relA_ or _rshA_ in _S. coelicolor_ M600 leads to a complete lack of ACT production in batch culture, RED production is higher than in wild-type cells [20]. Based on the analysis of (p)ppGpp levels and antibiotic production in a strain carrying a mutation in the principal sigma factor (a mutation that was speculated to reduce levels of substrates for (p)ppGpp synthesis), it was inferred that (p)ppGpp-mediated induction of antibiotic production is extremely sensitive to differences in cellular (p)ppGpp pools [66]. In _S. coelicolor_ and _S. griseus_, the cyclic AMP (cAMP)-binding protein EshA is essential for ensuring adequate (p)ppGpp synthesis by mechanisms that remain unclear and this, in turn, is required for (p)ppGpp-mediated control of GTP levels. EshA is therefore important for morphological differentiation and ACT/streptomycin production [67]. This analysis also concluded that finely tuned (p)ppGpp accumulation is essential for ensuring antibiotic production.  
Transcription of _actII_-ORF4 is under control of numerous regulators, whereas _redD_ is activated by RedZ; _redZ_ expression, however, is controlled by some of the same factors that regulate _actII_-ORF4 expression (reviewed in [40]). The transcriptional regulator XdhR, which was previously shown to control expression of the _xdh_ gene cluster by mediating a (p)ppGpp-dependent increase in _xdh_ expression during nutrient limitation and stationary phase [51; 56], also binds the promoter regions of _actII_-ORF4 and _actII_-ORF1. The gene _actII_-ORF1 encodes another cluster-situated regulator of ACT biosynthesis. The binding of XdhR leads to the repression of genes encoding ACT biosynthetic enzymes and hence production of ACT in _S. coelicolor_ M145 [58]. Since (p)ppGpp is a ligand for XdhR, entry into stationary phase or stringent response would be expected to lead to dissociation of XdhR from the _actII_-ORF4 and _actII_-ORF1 promoters, thus facilitating ACT biosynthesis. Accordingly, XdhR directly links regulation of purine metabolism with (p)ppGpp-mediated control of antibiotic production.
## 3 Results
### Response of CUE to LSP Variation  
After analyzing the correlation between CUE and LSP in the growing seasons, it was found that CUE was negatively correlated with SOS in about 70% of the study area (Figures 10a and 11). This indicated that earlier SOS would encourage higher CUE. In 72% of areas covered by GRA, CUE was negatively correlated with SOS. In 67% of the SNP, the later EOS would result in higher CUE. In 80% of areas covered by DBF, late EOS dates might have the positive effect on the increase of CUE (Figure 10b). CUE was positively correlated with LOS in more than 70% of areas covered by DBF, GRA and WET. The average CUE of MF with the longest growing season was highest (0.529). GRA with the second longest growing season (0.482). Although the LOS of GRA was the shortest, its average CUE (0.482) was greater than that of DBF (0.477). This would be because GRA in cold and dry regions consumed less energy to maintain growth. The area proportions of correlation coefficients after significant test for all the pixels were obtained (Figure 11).  
Figure 10: Spatial distribution of correlation coefficients (R) between CUE, SOS, EOS and LOS in the SNP during 2001–2015. (**a**) CUE and SOS; (**b**) CUE and EOS; (**c**) CUE and LOS.  
Figure 9: Spatial distribution of land surface phenology metrics in the SNP from 2001 to 2015: (**a**) average SOS; (**b**) average EOS; (**c**) average LOS.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 3 Adaptive Sub-Network Organization
### Estimating the Sub-Network Consistency  
It is evident that the selection of the threshold value \(k\) controls the overall performance of the network. Setting a high value might create unnecessary re-evaluations of the sub-network, thus leading to overspending the energy resources of the devices. In order to avoid such cases, the network administrator is provided with two mechanisms in order to suitably set the parameter value of \(k\):  
**FixK**--in controlled deployments where the density of the network is uniform, the value of the parameter \(k\) will remain fixed for the full lifetime of the network.
**AverageK**--in deployments where the network density varies between the different locations, each device is allowed to have a different threshold \(k\). During the evolution of the network, each device can independently modify the value of \(k\) by observing the consistency of its sub-network during the past intervals. An adaptive algorithm is introduced (see Algorithm 1) that smoothly modifies the threshold gradually during each period.  
```
1:interval\({}_{counter}\)++
2:consistent\(\_{mess_{total}}\)++=consistent\(\_{mess_{received}}\)
3:k\({}_{current}\leftarrow\)ceil\((consistent\_{mess_{total}}\)/interval\({}_{counter})\)
4:k\({}_{new}\leftarrow\)k\({}_{pre}\)*w +k\({}_{current}\)*(1-w)
5:consistent\(\_{mess_{received}}\gets 0\)
```  
**Algorithm 1****AverageK** method for calculating a new value for \(k\)  
The advantage of the **AverageK** approach is in _dynamic_ deployments where it is common for devices to be relocated, or deployments with uneven distribution of devices. In order to better understand the benefits of having a fixed threshold or an adaptive threshold, a set of experiments are presented in Section 5 that provide feedback on the benefits of each method.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 2 PLC Transmission via Overhead MV Power Lines
### Multi-Conductor Transmission Lines  
An MTL consisting of \(n+1\) lines parallel to the \(z\)-axis supports \(n\) forward and \(n\) backward traveling waves propagating with distinct propagation constants. These waves are jointly described by a coupled set of \(2n\), first-order, partial differential equations relating the line voltages \(V_{l}(z,t)\) and the line currents \(I_{l}(z,t)\), \(i=1,2,...,n\). Each pair of forward and backward traveling waves constitutes a mode [7,8]. An MTL consisting of four conductors and a ground return supports four modes, whereas a three-conductor MTL with ground return supports three modes. The modal wire to ground voltages \(\mathbf{V}_{m}(z)=[V_{1}^{m}(z)\ V_{2}^{m}(z)\...\ V_{n}^{m}(z)]^{T}\) and modal wire currents \(\mathbf{I}_{m}(z)=[I_{1}^{m}(z)\ I_{2}^{m}(z)\...\ I_{n}^{m}(z)]^{T}\) are related to the line voltages \(\mathbf{V}(z)=[V_{1}(z)\ V_{2}(z)\...\ V_{n}(z)]^{T}\) and currents \(\mathbf{I}(z)=[I_{1}(z)\ I_{2}(z)\...\ I_{n}(z)]^{T}\) via the similarity transformation  
\[\mathbf{V}(z) =\mathbf{T}_{V}\mathbf{V}_{m}(z), \tag{4a}\] \[\mathbf{I}(z) =\mathbf{T}_{I}\mathbf{I}_{m}(z), \tag{4b}\]  
where \(\mathbf{T}_{V}\) and \(\mathbf{T}_{I}\) are \(n\) x \(n\) matrices that depend on frequency, the physical properties of the power lines and the lossy earth, and the geometry of the MTL configuration. Equations (4a) and (4b) relate the line voltages and currents with the corresponding modal quantities. Under specific assumptions [8], the three modes propagating along three-wire overhead MV power lines may be decoupled, giving rise to three independent transmission channels which can simultaneously transfer PLC signals. The common (or ground) mode is characterized by a comparatively higher attenuation and propagates via all the phases (wires) returning via the ground. The differential (or aerial) modes, propagating and returning via the wires, exhibit a lower attenuation compared to the common mode [7]. In PLC transmission, depending on the way the signal is coupled onto the power lines, either wire-to-wire (WTW) or wire-to-ground (WTG) injection is possible. Under WTW injection, differential modes are mostly excited. Under WTG injection with coupling to the middle phase, both the common mode and the differential modes are excited. Under certain conditions, which are practically satisfied for frequencies up to 100 MHz, the PLC modes supported by MV power lines are orthogonal [10].
## 3 Proposed Approach: Quantum Key Distribution (QKD)
### Detailed Protocol Description and Evaluation Framework  
The proposed protocol consists of three primary phases. The first phase focuses on Quantum Key Distribution (QKD) using the BB84 protocol. In this phase, polarized photons are transmitted from the UAV to the ground station, followed by key sifting and error correction using LDPC codes. The second phase involves data encryption and compression. The raw agrotechnical data collected by UAVs are compressed using the LZW algorithm and then encrypted using AES-128 with the quantum key generated in the previous phase. The third phase encompasses secure data transmission, where the encrypted and compressed data are transmitted from the UAV to the ground station, followed by decryption and decompression at the receiving end.  
Evaluation Framework  
The proposed protocol was evaluated based on the following metrics:  
Key Generation Rate: Measured in bits per second (bps), this metric evaluates the efficiency of QKD in generating secure keys under varying environmental conditions.  
Quantum Bit Error Rate (QBER): Assesses the integrity of the quantum channel, with higher QBER values indicating potential eavesdropping or environmental interference.  
Encryption/Decryption Speed: Evaluates the time taken for AES encryption and decryption processes, crucial for real-time UAV operations.  
Computational Overhead: Measured in CPU cycles per byte to assess the protocol's efficiency, especially given the limited processing power of UAVs.  
Security Analysis: Includes resilience against common cyber-attacks such as man-in-the-middle (MITM) attacks, data interception, and data forgery.  
Simulations were conducted under various environmental scenarios, including clear weather, cloudy conditions, and heavy fog, to assess the protocol's robustness. Additionally, stress tests involving simulated cyber-attacks were performed to validate security claims.