esa-sceva/satcom-chunk-collection huggingface dataset:
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 3 Production of Bioplastics from Volatile Fatty Acids
### Physico-Chemical Properties of Bioplastics and Their Applications
#### 3.3.1 Polyhydroxyalkanoates (PHAs)
Comparing other properties, PHB shows a better barrier to moisture and oxygen gas than PP and PET, respectively [199]. In general, the properties of PHAs, such as processability, mechanical properties, and UV resistance, are like those of conventional fossil-based polymers such as PP and polystyrene (PS). Depending on the composition of their monomers, the properties and functionalities of PHA vary [200]. Some of the various applications of the different types of PHA are shown in Table 4 of this document. In terms of biodegradability, P3(HB) is a biopolymer that degrades in a reasonable time when in contact with microorganisms in biologically active environments such as soil, freshwater, etc. Therefore, it is a sustainable alternative to synthetic polymers with which it is compatible [201]. Although PHAs are biodegradable, as indicated in Table 5, it has recently been shown that the biodegradability of P3 HB and P3 HB/valerate copolymers is highly dependent on molecular weight, processing conditions, and crystallinity [202]. This biodegradability is an advantage over plastics of fossil origin, such as PE, PP, and nylon, which are xenobiotic. The release of these plastics into the environment causes pollution, and their mechanical degradation into microplastics is also an additional problem [203; 204]. As mentioned above, PHA can be used in packaging. Most traditional plastics used in packaging are non-biodegradable, such as PE, PP, PS, PVC, or PET. These non-biodegradable plastics are the main source of plastic waste, causing a serious environmental pollution problem. This is a serious global environmental problem known as 'white pollution' [205; 206]. However, despite recent advances in bio-based and biodegradable polymers, more research is needed before these biopolymers can fully replace petroleum-based plastics [207]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Polymer**} & **Melting** & **Tg** & **ME** & **EF** & **E** & **B** \\  & **Point** (\({}^{\circ}\)**C) & **(\({}^{\circ}\)**C)** & **(GPa)** & **(MPa)** & **(\%)** & **Biodegradation** \\ \hline P(3 HB) & 180 & 4 & 3.5 & 40 & 5 & High rate \\ P(3 HB-co-20 mol\% 3 HV) & 145 & \(-1\) & 0.8 & 20 & 50 & High rate \\ P(3 HB-co-6 mol\% 3 HV) & 133 & \(-8\) & 0.2 & 17 & 680 & High rate \\ P(3 HV) & 103 & \(-15.8\) & - & - & - & High rate \\ P(75.3\% 3 HB-co-13.1\% 3 HV) & 102.3 & \(-1.8\) & - & - & - & High rate \\ P(73.8\% 3 HB-co-7.6\% 4 HB-co-18.6\% 3 HHx) & - & \(-11.7\) & 0.003 & - & 143 & High rate \\ Polypropylene & 176 & \(-10\) & 1.7 & 38 & 400 & Slow rate \\ LDPE & 130 & \(-30\) & 0.2 & 10 & 620 & Slow rate \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of the physical properties of different polymers. LDPE: low-density polyethylene; Tg: glass transition temperature; ME: modulus of elasticity; EF: elongation at fracture; E: elongation at break.
# Effects of Shading on the Growth and Carbon Storage of _Enhalus acoroides_
## 3 Results
### Density  
When the shading period was 3 months, the shoot density of _E. acoroides_ decreased but was not significantly different from that of the control (\(p\) > 0.05) (Figure 2). After 6 months of shading treatment, the shoot density in the moderate shading and high shading treatments was significantly lower than that in the full-light treatment (\(p\) < 0.05), decreasing by 55.36% and 59.82%, respectively, compared with that of the control. After the restoration of light, the shoot density increased in all shading treatments but did not return to the same level as the control.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & **Treatments** & **Leaf Length/cm** & **Leaf Width/cm** & **Leaf Number** \\ \hline S3 & CK & 34.98 \(\pm\) 0.96 a & 1.65 \(\pm\) 0.02 a & 4.34 \(\pm\) 0.09 a \\  & MS & 29.78 \(\pm\) 1.17 b & 1.47 \(\pm\) 0.02 b & 3.60 \(\pm\) 0.11 b \\  & HS & 25.25 \(\pm\) 1.16 c & 1.38 \(\pm\) 0.02 c & 3.35 \(\pm\) 0.11 b \\ S6 & CK & 54.98 \(\pm\) 1.55 a & 1.84 \(\pm\) 0.02 a & 6.63 \(\pm\) 0.08 a \\  & MS & 22.79 \(\pm\) 1.80 b & 1.49 \(\pm\) 0.03 b & 5.00 \(\pm\) 0.12 b \\  & HS & 21.47 \(\pm\) 1.40 b & 1.40 \(\pm\) 0.02 c & 4.84 \(\pm\) 0.12 b \\ R3 & CK & 24.31 \(\pm\) 1.26 a & 1.75 \(\pm\) 0.02 a & 5.41 \(\pm\) 0.14 a \\  & MS & 19.12 \(\pm\) 0.87 b & 1.50 \(\pm\) 0.03 b & 4.23 \(\pm\) 0.12 b \\  & HS & 16.30 \(\pm\) 0.91 b & 1.35 \(\pm\) 0.03 c & 3.58 \(\pm\) 0.12 c \\ \hline \hline \end{tabular} Note: S3, shading for 3 months; S6, shading for 6 months; R3, recovery for 3 months. CK-Control; MS-Moderate shading; HS-High shading. Different letters indicate significant differences at the 0.05 level. The same below.  
\end{table}
Table 2: Effects of different light treatments on morphological characteristics of _E. acoroides_.
## 2 Overall Technical Parameter Requirements and Architecture Analysis
### Hardware Model  
The hardware model mainly includes the following devices:  
1. Front-end sensors: these consist of 3D LiDAR and cameras for real-time detection of obstacles in the environment and capturing image information to obtain the three-dimensional position information of objects and improve system detection accuracy.
2. Processors: responsible for receiving data collected by sensors and conducting algorithmic processing for multi-modal data fusion, obstacle detection, and identification.
3. Communication module: this ensures real-time communication between LiDAR, cameras, and processors.
4. Human-machine interaction interface: the interface displays detection results, allowing operators to view obstacle information and alarm prompts.
## 3 Methods
### Development of the Fixed-Jilin-1 Method  
Although the Jilin-1 GP01/02 PMS and Landsat 5/7 TM share identical band configurations, discrepancies were observed in their spectral response functions, particularly within the red and NIR bands, Figure 3 illustrates the comparative spectral response curves of these two sensors. Furthermore, in contrast to the Landsat TM, which is limited to imaging at the subsatellite point, the Jilin-1 GP01/02 PMS is capable of a more expansive side-swing angle, extending up to \(\pm 45^{\circ}\). Consequently, the fixed parameters originally derived for the Landsat TM may not be directly applicable to the Jilin-1 GP01/02 PMS. It is thus imperative to develop a distinct set of fixed parameters that are tailored for the Jilin-1 GP01/02 PMS. In this study, we produced normalized BRDF parameters for the Jilin-1 GP01/02 PMS, employing both the AFX and fixed parameter methodologies. The procedure is outlined in the following steps: (1) AFXs of each band of the Jilin-1 GP01/02 PMS were calculated based on the Landsat TM's fixed parameters; (2) the normalized BRDF parameters (\(F_{iso}\), \(F_{vol}\), and \(F_{geo}\)) were determined according to the AFX range of the calculated AFX values of each band in Table 4; and (3) the NBAR of the Jilin-1 GP01/02 PMS was generated by bringing normalized BRDF parameters into the RTISR BRDF model. Table 6 presents the fixed AFX values and the corresponding normalized BRDF parameters for the green, red, and NIR bands of the Jilin-1 GP01/02 PMS, as calculated by employing the proposed Fixed-Jilin-1 method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Jilin-1 GP01/02** & & & & \\
**PMS Band** & **AFX** & \(F_{iso}\) & \(F_{vol}\) & \(F_{geo}\) \\ \hline
4 Green & 0.8963 & 0.5 & 0.2700 & 0.0806 \\
5 Red & 0.8792 & 0.5 & 0.2029 & 0.0845 \\
6 NIR & 0.9469 & 0.5 & 0.3135 & 0.0679 \\ \hline \hline \end{tabular}
\end{table}
Table 6: AFXs and normalized BRDF parameters for green, red, and NIR bands of Jilin-1 GP01/02 PMS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Landsat Band** & \(f_{iso}\) & \(f_{vol}\) & \(f_{geo}\) \\ \hline
2 Green & 0.1306 & 0.0580 & 0.0178 \\
3 Red & 0.1690 & 0.0574 & 0.0227 \\
4 NIR & 0.3093 & 0.1535 & 0.0330 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The fixed BRDF parameters for Landsat TM green, red, and NIR bands.
## 5 Conclusions  
In the present study, the effect of simvastatin systemic administration after RME was evaluated using uCT images for microtomographic parameter extraction and histomorphometric analysis in cortical bone. The samples treated with this drug showed, in the descriptive analysis, greater regularity in the bone surface radiographic density when compared to the CT group. This description was corroborated by the microtomographic parameter extraction, which showed a greater intermingled bone volume (BV/TV), cortical bone area fraction (Ct.Ar/Tt.Ar) and cortical thickness (Ct. Th) in the SVT group compared to the CT group. Additionally, in the linear measurement, it was verified that the CT group had greater measurements at points P1 to P4 compared to the SVT group. Furthermore, the histomorphometric analysis showed intense cellular activity of clastic cells in the CT group and a more discreet activity in the SVT group. Therefore, the analyses carried out evidenced that the samples treated with simvastatin presented an osteoinductive potential compared to those samples from the CT group. However, we emphasize the importance of further immunohistochemical studies that can elucidate the cell signaling process that occurs in the expanded suture treated with this drug. Likewise, studies with uCT in vivo allowed for a longitudinal evaluation of the drug effect in different time frames in the same sample.  
Conceptualization, J.R.B.N. and J.d.A.C.-M.; methodology, J.R.B.N., I.L., S.C.S., A.T.N.N.A., C.M.S.d.F.F.d.S., C.F.M., M.D.C.-M. and J.d.A.C.-M.; software, J.R.B.N., I.L., C.M.S.d.F.F.d.S. and R.T.L.; formal analysis, J.R.B.N., S.C.S. and C.M.S.d.F.F.d.S.; investigation, J.R.B.N., S.C.S., K.J., I.D. and J.d.A.C.-M.; resources, R.T.L., K.J., I.D., C.F.M. and M.D.C.-M.; data curation, J.R.B.N. and I.L.; writing--original draft preparation, J.R.B.N.; writing--review and editing, C.F.M., M.D.C.-M. and M.D.C.-M.; visualization, J.R.B.N., S.C.S., K.J., I.D. and J.d.A.C.-M.; supervision, M.D.C.-M. and J.d.A.C.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The research protocol for this work was approved by the Federal Fluminense University Ethics Committee on Animal Use (CEUA/UFF) under the number CEUA 5072191118.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions.  
The authors declare no conflicts of interest.
###### Abstract  
The evaluation of the structural health of a bridge and the monitoring of its bearing capacity are performed by measuring different parameters. The most important ones are the displacements due to fixed or mobile loads, whose monitoring can be performed using several methods, both conventional and innovative. Terrestrial Laser Scanner (TLS) is effectively used to obtain the displacements of the decks for static loads, while for dynamic measurements, several punctual sensors are in general used. The proposed system uses a TLS, set as a line scanner and positioned under the bridge deck. The TLS acquires a vertical section of the intrados, or a line along a section to be monitored. The instantaneous deviations between the lines detected in dynamic conditions and the reference one acquired with the unloaded bridge, allow to extract the displacements and, consequently, the elastic curve. The synchronization of TLS acquisitions and load location, obtained from a Global Navigation Satellite System GNSS receiver or from a video, is an important feature of the method. Three tests were carried out on as many bridges. The first was performed during the maneuvers of a heavy truck traveling on a bridge characterized by a simply supported metal structure deck. The second concerned a prestressed concrete bridge with cantilever beams. The third concerned the pylon of a cantilever spar cable-stayed bridge during a load test. The results show high precision and confirm the usefulness of this method both for performing dynamic tests and for monitoring bridges.  
laser scanner; line scanner; structure monitoring; deformation; dynamic measurements 1
## 7 Power Consumption Models
### Router Node Power Consumption Model  
In this work, each router node consists of an Atmega 328p microcontroller and the XBee S2C wireless protocol. Therefore, the power consumption of the router and main router nodes depends only on these two components. The power consumption of the router and the main router nodes without and with the sleep/wake scheme can be expressed as Equations (12) and (13), respectively:  
\[I_{RN}=I_{Atmega}+I_{XBee\ S2C} \tag{12}\]  
\[I_{RN\_avg}\ =I_{Atmega\_avg}\ +I_{XBee\ S2C\_avg} \tag{13}\]  
The total current consumption of the router nodes without and with the sleep/wake scheme can be expressed as Equations (14) and (15), respectively:  
\[I_{RN\_total} =\ \sum_{1}^{m}I_{RN_{m}}\, \tag{14}\] \[I_{RN\_average\_total} =\sum_{1}^{m}I_{RN_{m}\_average}\, \tag{15}\]  
where \(I_{RN_{m}}\) is the current consumption of each router node, as presented in Equation (12); \(I_{RN_{m}\_average}\) is the average current consumption of each router node, as modeled in Equation (13); and \(m\) is the number of router nodes, where \(m=4\) in the current work. Equations (10) and (11) can be utilized to calculate the average power consumption and life span of the router and main router nodes based on the sleep/wake scheme, respectively.
## 2 Methodology
### Shape searching Algorithm Based on Contour Point Distribution Information  
#### 2.3.1 Algorithm Description  
The outline of a shape is composed of points, and the positional relationship of the points constitutes different shapes. The positional relationship of the shape contour points can be used to describe a shape [27]. The specific steps of the proposed algorithm are as follows.  
First, the centroid position of the shape is calculated and taken as the pole to establish a polar coordinate system.  
Second, the azimuth direction is divided into 12 intervals, and the number of contour points falling into each direction bin is counted, as shown in Figure 6. In this figure, point _O is_ the centroid position, and point P is a point on the shape contour falling within the first direction bin.  
Third, we count the number of contour points in each direction bin with the assumption that the number of contour points in each direction bin is stored in a variable named _Direct_, where _Direct_ = (\(d_{1}\), \(d_{2}\), \(d_{3}\),..., \(d_{12}\)). The contour points of a regular shape are usually evenly distributed over all directions. Therefore, we propose the following judgement conditions as Formula (1) to determine whether or not a shape is regular:  
\[\left\{\begin{array}{l}N_{\rm d}>N\\ d_{\rm min}<S_{\rm d}=\rm Deviation(\emph{Direct})<d_{\rm max}\end{array}\right. \tag{1}\]  
where \(N_{\rm d}\) is the number of direction bins covered by all shape contour points. The operator \(\rm Deviation(\cdot)\) is used to calculate the normalized standard deviation of the number of contour points in each direction bin. Take Figure 3 as an example. The number of direction bins covered by the contour points of the shape is 8, \(N\) is a threshold value that defines the minimum number of direction bins covered by the contour points, and \(S_{\rm d}\) is the normalized standard deviation of the number of contour points in each direction bin. The normalized standard deviation is calculated with the standard deviation of the number of contour points in each direction bin divided by the largest standard deviation of all direction bins to adapt to the contour shapes of different sizes. \(d_{\rm min}\) and \(d_{\rm max}\), respectively, denote the low threshold and high threshold. The thresholds define the allowable range of the normalized standard deviation of the number of contour points in each direction bin; the more uniform the  
Figure 6: Shape and its contour point distribution information.  
distribution direction is, the lower the standard deviation will be, resulting in a high upper-limit threshold. Note that the standard deviation of the circle contour is considerably low, that is, it is close to 0. As the circular registration region cannot determine the rotation angle of the image, the circle shape is not suitable as an image registration region. Therefore, we must define the minimum value of the standard deviation. In the actual test, the values of the above parameters are \(N=8\), \(d_{\text{min}}=0.01\), \(d_{\text{max}}=0.07\). The maximum value of \(n\) can be 11.
# Nanoelectrode Ensembles Consisting of Carbon Nanotubes
###### Abstract  
Incorporating the nanoscale properties of carbon nanotubes (CNTs) and their assemblies into macroscopic materials is at the forefront of scientific innovation. The electrical conductivity, chemical inertness, and large aspect ratios of these cylindrical structures make them ideal electrode materials for electrochemical studies. The ability to assemble CNTs into nano-, micro-, and macroscale materials broadens their field of applications. Here, we report the fabrication of random arrays of CNT cross-sections and their performance as nanoelectrode ensembles (NEEs). Single ribbons of drawable CNTs were employed to create the CNT-NEEs that allows easier fabrication of nanoscale electrodes for general electrochemical applications. Surface analysis of the prepared NEEs using scanning electron microscopy showed a random distribution of CNTs within the encapsulating polymer. Electrochemical testing via cyclic voltammetry and scanning electrochemical cell microscopy revealed voltammetric differences from the typical macroelectrode response with the steady-state nature of NEEs. Finally, when the NEE was employed for Pb\({}^{2+}\) detection using square-wave anodic stripping voltammetry, a limit of detection of 0.57 ppb with a linear range of 10-35 ppb was achieved.  
carbon nanotubes; nanoelectrodes; SECCM; nanoelectrode ensembles and arrays +
Footnote †: journal: Applied Sciences
## 2 Related Works and State of the Art Review
### Acquisition Processes  
The acquisition process is divided into physical, logical, and file system acquisition. These processes are summarized below.  
1. _Physical Acquisition:_ Physical acquisition on mobile devices consists of copying information from the device by direct access to the internal storage memory. The process creates a copy of the entire file system bit by bit. Such an approach is similar to that adopted in computer forensic investigations. A physical acquisition can acquire all data present on a storage device, including deleted data that has not yet been overwritten, in addition to copying unallocated space [9]. It is considered to be the most effective in forensic terms and is performed using specific tools. According to [PERSON] [10], the least amount of information that an OS can read in a filesystem is a block that, at the physical disk level, is equivalent to a cluster. Copies and readings are made block by block at the filesystem level. The File Systems widely used by Android devices were Flash-Friendly File System (F2 FS) [11], and currently, Fourth Extended Filesystem (EXT4) [12]. Both file systems adopt 4 KB-sized logical blocks. Still, according to [PERSON] [10], there are software such as _dd_ and _dc3 dd_ that can read a physical sector, which has 512 bytes by default and is the smallest unit of information that can be read on an HD or pen drive by its controller.
2. _File System Acquisition:_ The file system acquisition is technically seen as a type of logical acquisition [9]. However, it is more abundant in data, as the entire file system of the device is copied. It contains files and directories that the device uses to populate applications, system settings, and user settings, along with user storage areas. It also includes files not directly accessible to the user through the device interface, which requires specialized tools to access such artifacts. However, unlike physical acquisition, this type of acquisition does not copy the unallocated space of physical memory.
3. _Logical Acquisition:_ Logical acquisition is a copy of logical storage objects, such as file systems, directories, and files. Data is copied from the allocated space on disk, still accessible to the user in the file system. Such data includes the phone book, calls, messages, some application data, and other data that can be expected from a software backup with iTunes or Android itself, that is, what we can see if we manually examine the device [13]. It is observed as the fastest and least invasive, but it is the most limited of acquisitions.  
Figure 2 compares, in a summarized way, the different data that can be acquired by the types of acquisitions presented.
###### Abstract  
In this study, we investigate the impact of soil moisture at varying depths on the stability of Chinese ecosystems, with ecosystem stability assessed using the Enhanced Vegetation Index (EVI) and Gross Primary Productivity (GPP). A multi-perspective analysis is conducted across different climatic zones and land cover types. Sen's Slope Estimation and the Mann-Kendall trend test, combined with linear regression and correlation analyses, are employed to analyze the long-term trends of EVI and GPP in different climatic zones and land cover types and to assess the effects of soil moisture changes on ecosystem stability. The research reveals the following findings: (1) On a national scale, both EVI and GPP exhibit positive growth trends, with more significant increases in humid areas and relatively slower growth in arid areas. In addition, EVI and GPP of different land cover types exhibit positive inter-annual variation trends, reflecting a gradual enhancement in ecosystem productivity. (2) Cluster analysis shows that EVI has strong spatial correlation, with a distribution pattern of low-low (L-L) clusters in the north and high-high (H-H) clusters in the south. L-H clusters are concentrated in the Huaihai, Southwest Rivers, and Pearl River basins, while H-L clusters are scattered along the eastern coast. The spatial correlation of GPP is mainly concentrated in the south and the northeast, with a distribution pattern of L-L in the northeast, L-H in the Yangtze River basin, and H-H in the south. H-L clusters are dispersed in the downstream area of the Yangtze River. Both EVI and GPP show a tendency for high-value aggregation in space, with high-value areas of EVI located in the south and low-value areas in the central and western regions. High-value areas of GPP are in the south, while low-value areas are in the northeast, particularly in the Yangtze River Delta. (3) The correlation between EVI, GPP, and soil moisture varies significantly across different climatic regions. Arid and semi-humid regions show significant correlations between specific soil moisture depths and EVI and GPP, while such correlations are not significant in humid regions. The EVI and GPP values of croplands and grasslands are significantly and negatively correlated with soil moisture at depths of 150-200 cm (SM4). Conversely, wetland GPP values increase significantly with increasing soil moisture. Other vegetation types do not show significant correlations with soil moisture. The results of this study provide an important basis for understanding the impact of climate change on ecosystem stability and offer scientific guidance for ecological protection and water resource management.  
**Keywords: ecosystem stability; soil moisture; EVI; GPP; trend analysis; correlation analysis**