esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Acetabular Inclination Angles
#### 3.5.1 Inclination Angles  
First, the representative values of the acetabular inclination angles were extracted (Table 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Acetabular Inclination Angles**} \\ \hline \hline  & \multicolumn{2}{c}{**3D-Printed Implant**} & \multicolumn{2}{c}{**Bone Graft**} \\ \hline \hline  & Reconstructed & Healthy & Reconstructed & Healthy \\ \hline Max & 61.4 & 63.5 & 48.8 & 61.1 \\ Min & 42.9 & 51.9 & 15.3 & 50.2 \\ Median & 50.1 & 56.0 & 32.5 & 55.3 \\ Standard Deviation & 6.3 & 4.0 & 9.5 & 3.4 \\ \hline \hline \end{tabular} Note: there are two conventional safe zones [67,68], the former ranges from 30 to 50 degrees and the latter from 30 to 45 degrees.  
\end{table}
Table 6: Characteristic values of the acetabular inclination angles in degrees.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Greater Trochanter Offset Magnitudes**} \\ \hline \hline  & Healthy & Reconstructed & Healthy & Reconstructed \\ \hline Max & 51.0 & 59.2 & 58.5 & 57.7 \\ Min & 40.4 & 42.1 & 42.2 & 37.0 \\ Median & 44.2 & 46.7 & 48.6 & 48.7 \\ Standard Deviation & 44.5 & 49.1 & 49.4 & 48.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The characteristic values of the greater trochanter offset magnitudes. All values are in millimeters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Greater Trochanter Offset Deviation**} \\ \hline \hline  & \multicolumn{2}{c}{**3D-Printed Implant**} & \multicolumn{2}{c}{**Bone Graft**} \\ \hline \hline Max & 15.7 & 10.4 \\ Min & \(-\)1.0 & \(-\)11.1 \\ Median & 2.6 & \(-\)0.4 \\ Standard Deviation & 5.5 & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristic values of the greater trochanter offset deviations. All values are in millimeters.  
The acetabular inclination angle was assessed using a Chi-squared test. This was performed twice, each time considering the different one of the two safe zones in (Figure 7), both with a null hypothesis being that there is no difference between both methods at an alpha level of 0.05. No significant difference was found between both groups in all cases.
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 2 Background and Related Work  
### WSN and Its Architecture  
WSNs [18; 19] are a type of distributed network system that consists of a large number of small, low-cost and low-power devices called sensor nodes, which are equipped with sensors, processing units, and wireless communication capabilities. These nodes are typically deployed in a remote or harsh environment to monitor physical or environmental conditions, such as temperature, humidity, light, sound, vibration, and motion. However, their reliability and robustness in adverse conditions remain a major challenge, which has led to extensive research on WSN reliability modeling and analysis.  
WSN architecture can be divided into three layers [20; 21]: the physical layer, the network layer, and the application layer. The physical layer [20; 21] is responsible for data acquisition and transmission and includes the sensor nodes, the data transmission medium, and the physical interface. The network layer [20; 21] provides the routing and data aggregation functionalities and consists of the communication protocols and the routing algorithms. The application layer [20; 21] defines the tasks and objectives of the WSN and includes the data processing and analysis algorithms, as well as the user interfaces.
## 2 Materials and Methods
### Sorption Studies of EF on Alternator, Ignition coil, and Starter Motor  
The EF sorption on the alternator, ignition coil, and starter motor was evaluated in a 0.275 m\({}^{3}\) fumigation chamber. For the alternator and starter motor, 35 g/m\({}^{3}\) EF was applied and fumigated for 4 h at 23 \({}^{\circ}\)C. The concentration of EF inside the fumigation chamber was determined from gas samples collected at 0.1, 1.0, 2.0, and 4.0 h after EF application using Shimadzu-GC 17A (Shimadzu, Kyoto, Japan) equipped with a flame ionization detector (FID) after separation on a DB5-MS Column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness; J&W Scientific, Folsom, CA, USA). The oven temperature was maintained at 100 \({}^{\circ}\)C. The injector and detector temperatures were maintained at 250 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. Helium was used as a carrier gas at a flow rate of 1.5 mL/min. The concentration of EF was calculated as the peak area against a series of external EF standards. Sorption was expressed as concentration loss ratio (C/C\({}_{0}\)), where C = EF concentration determined at one of the time intervals and C\({}_{0}\) = EF concentration at 0.1 h.
# Healing Effects of Photobiomodulation on Diabetic Wounds
## 2 Diabetic Wound Healing  
Chronic wounds rarely occur in healthy individuals and frequently occur as a comorbidity with other diseases and conditions, such as DM, obesity, and spinal cord injury. A chronic wound is one which has failed to proceed through an orderly and timely reparative process. These wounds often become stuck in the inflammatory phase of healing and typically do not heal within three months. Diabetic ulcers of the lower extremities occur as a common complication of DM and involves a multifactorial pathogenesis including peripheral neuropathy and peripheral vascular disease, repetitive external trauma to the feet, and infection [7]. Most infected DFUs require some surgical intervention, ranging from minor to major interventions, including debridement and amputation, respectively. Infected DFUs are a major cause of lengthy hospital admission and contribute to more than a million amputations per year [8], with a 50% 5-year mortality rate amongst diabetic amputees [9].  
Wound healing in diabetes is impaired by extrinsic and intrinsic factors. Extrinsic factors include repeated trauma or mechanical stress to the foot. Intrinsic factors play a major role in the development of DFUs. Hyperglycemia leads to the formation of advanced glycation end-products (AGEs) which prompt the production of inflammatory cytokines. There is also a decrease in collagen production and other essential extracellular matrix (ECM) proteins, and an increase in their destruction by matrix metalloproteinases (MMPs). There are also alterations in cellular morphology, abnormal differentiation of keratinocytes and fibroblasts, decreased cellular proliferation, altered immune function, and altered bioavailability of cytokines and growth factors [10]. There are also conditions of hypoxia, which impact negatively on wound healing.  
Treatment of DFUs is centered on eliminating infection, the use of dressings to maintain a moist wound bed and to absorb exudate, offloading high pressure from the wound bed, and debridement to accelerate endogenous healing and facilitate the effectiveness of topically applied substances [11]. With the advancement of technology, new treatments for diabetic ulcers have been developed and include the incorporation of nanoparticles and growth factors into wound dressings, tissue-engineered human skin equivalents, stem cell therapy, vacuum-assisted closure (VAC) devices, hyperbaric oxygen therapy (HBOT), and gene therapy. Advances in the area of photonics and biophotonics has led to the efficacious use of light in the treatment of diabetic wounds.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 2 Material and Methods
### The UWB Life-Detection Radar Hardware System Design
#### 2.1.2 The Radar System  
A novel Golay complementary coded UWB radar prototype system was designed to verify the proposed detection strategy. The prototype system contains several radars and a control host. The generation, transmission, reception of electromagnetic signal are integrated in the Golay-coded radars, while the human-machine interface, collaborative management, and data processing are handled by the control host. Each radar in the system has a full-featured transceiver, which can work alone at stationary operating mode. For the scanning operating mode, several additional modules would be activated for collaborative work, and the most important parts are network clock module and self-positioning module. By setting the time division multiplexing (TDM) architecture, the control host coordinates several radar nodes during the multiple observation points detection process to prevent mutual interference. All communication and control between radars are carried out wirelessly, and the number of observation points can be changed flexibly for different observation environments.
## 4 Experiments  
### Dataset, Testing Environment and Testing Plan  
To validate the effectiveness of the proposed algorithm, it was applied to the physics experiment system of the Hubei University of Technology (wlysy-main (wjygrit.cn)). The experimental dataset was sourced from 203,495 experiment reports uploaded to this system during the 2022-2023 academic year, from which 100,299 images were randomly selected. The images were captured indoors using mobile phones, tablets, etc., either from an overhead or horizontal perspective. The lighting conditions were stable, with moderate intensity. Due to side lighting, more than 95% of the images exhibit varying levels of shadow. The shooting environments were complex, featuring multi-colored backgrounds, and in addition to the primary subjects, the images contained extraneous elements such as furniture, appliances, decorations, and stationery. Approximately 2% of the images contain Gaussian noise, which results in blurred object contours and textures. The images are in JPEG format, with file sizes ranging from 100 KB to 400 KB.  
The algorithm was developed and executed using OpenCV (version 4.6.0) on a Windows operating system with hardware specifications including an 11 th Gen Intel(R) Core (TM) i5-1135G7 @ 2.40 GHz CPU. The performance of the Magefreehome algorithm was compared with the proposed algorithm in terms of effectiveness, performance, and the impact of different strategies.  
Initially, these images were processed using the Magefreehome algorithm, resulting in 15,064 images with suboptimal results. The deficiencies were categorized as follows:  
(1) Incomplete or excessive segmentation due to fixed thresholds (10,436 images, 69.28%); (2) Segmentation errors caused by improper selection of the image center point (3905 images, 25.92%); (3) Incomplete segmentation due to weak anti-interference capabilities (723 images, 4.80%).
## 3 Results and Discussion
### Transcription of the Artificial Operon P16S-g10-merT-merP-merB1-merB2-ppk-rpsT  
BL21-0, BL21-1, BL21-2 and BL21-7 were cultured in LB liquid medium at 37 \({}^{\circ}\)C for 16 h. The mRNAs of these bacteria were extracted and qPCR was performed. The cDNA of BL21-0 was used as a control. The results showed that the transcripts of _ppk-rpsT_ in BL21-1, BL21-2 and BL21-7 were 1373 \(\pm\) 62 copies/ng, 8245 \(\pm\) 119 copies/ng and 8326 \(\pm\) 141 copies/ng, respectively (Figure 2). The experiment was performed four times. The transcripts of _ppk-rpsT_ in BL21-7 were approximately six-fold those in BL21-1 (Figure 2). No transcript was detected in BL21-0. These results demonstrated that the vector p5 ( pET28a harboring _P16S-g10-merT-merP-merB1-merB2-ppk-rpsT_) could provide sufficient transcripts. Sufficient transcripts may produce high-abundance protein [6]. The Hg tolerance of BL21-2 was approximately fivefold greater than that of BL21-1 (Figure 3). In BL21-1, the artificial operon _g10-ppk-rpsT_ was regulated by the _T7_ promoter, while the transcription of the same artificial operon was promoted by _P16S_ in BL21-2. This showed that the transcription initiated by _P16S_ was stronger than that of the _T7_ promoter. The Hg tolerance of BL21-7 (Figure 3) was approximately sevenfold greater than that of the strain transformed with _merR-o/p-merT-merP-merB1-ppk_[20]. This might be due to the strong promoter _P16S_, the translation enhancer _g10_ and the terminator _rpsT_ in the artificial operon.  
Figure 1: Schematic diagram of the designed artificial operon.
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 3 Economy-Financial Analysis
### National Economy Analysis of the Plant
#### 3.2.1 Meeting the Demand for Electricity Load Growth  
The floating PV plant at Da Mi hydropower reservoir, Binh Thuan province will partially meet the demand for the increased electricity load of the Vietnamese power system. On the other hand, the plant also contributes to increasing the proportion of renewable energy in the system, contributing to stabilizing the electricity supply for the economy.
## 1 Introduction
Several studies have investigated the relationship between spatial resolution or temporal resolution on accuracy of different input parameters [23; 24] used to estimate irrigation performance by remote sensing--for example, normalized difference vegetation index (NDVI) [25; 26], energy balance components [27], ETa [28], or net (NPP) or gross Primary Productivity (GPP) [29]. Further, much effort has been placed on continuously improving the resolution of these products [30] or aggregating images with varying resolutions to maximise information (i.e., aggregating high-spatial and low-temporal-resolution images with low-spatial and high-temporal-resolution images) [31]. A recent study compared the accuracy of wheat yield with varying spatial and temporal resolutions andfound that, for yield (or NDVI trends), a higher spatial resolution was preferred to a higher temporal resolution [20]. Another study suggested at a regional scale Landsat ETM+ 30 m and MODIS 250 m data can both estimate agricultural area and NDVI to a reasonable level of accuracy [32]. However, despite some investigations into these parameters and spatial resolutions, there is gap between research projects and the impact of image resolution on the quality and accuracy of irrigation performance indicators at medium to high resolution scales for a given irrigation scheme or farm scale. The recent online United Nations Food and Agricultural Organization (FAO) portal to monitor water productivity through open access of remotely sensed-derived data (the Water Productivity Open Access Portal--WaPOR) avails evapotranspiration and interception (ETIa) and yield related datasets in Africa and the Middle East at dekadal time steps at three different spatial resolutions (250 m, 100 m, and 30 m), depending on geographic location. The availability of these datasets provides an opportunity to monitor farming practices and irrigation management from space and is thus ideal for regions with limited in-situ or local datasets. In this study, we utilize WaPOR datasets to derive three irrigation performance indicators--namely, adequacy, equity, and productivity--in five irrigation schemes--Wonji, Metehara, Koga, Zankalon, and Office du Niger (ODN)--in Africa. We evaluate the impact on each of the indicators at inter-plot and scheme levels and suggest the best resolution for each based on farm plot size.
## 2 Results and Discussion
### Determination of Phenolic Compounds
Figure 2: Mean oil yields obtained in the different oil mills, according to Table 1. The white bars indicate the tons of milled olives; the yellow and green bars indicate the oil yields obtained with and without the use of the sono-heat-exchanger (SHE), respectively. The hydrolysis was applied to obtain a more accurate determination of the total tyrosol and hydroxytyrosol (free and linked forms) derived from their secoiridoidic precursors [23]. This approach, already applied in previous works [20; 21; 22], helped to better estimate the antioxidant potential of the analyzed oils. Comparing the amount of total phenols obtained before and after hydrolysis, the main differences were highlighted only for few samples: OMU, P4U, P7U, and C3U (Figure 3A). Data obtained before and after the hydrolysis showed a wide variability in terms of total phenols, ranging from values lower than 100 mg/kg (C4N before hydrolysis) to values higher than 500 mg/kg (OMU after hydrolysis). The results in Figure 3B pointed out that no significant differences were found between the EVOOs obtained from a traditional technology (N) and those obtained by the help of ultrasounds (U). Figure 4 shows the percentages of hydroxytyrosol in the sum of tyrosol and hydroxytyrosol. It emerges that the percentages of hydroxytyrosol ranged from 30% to 45% for most of samples, with exceptions only pointed out for oils from Cellina di Nardo cv (C4 NI, C5 UI, C3 UI, C6 UI), which showed lower percentages (10%-25%). Unlike expected, the results obtained after hydrolysis suggested that the main secoiridoidic components of the virgin olive oils were not oleuropein derivatives, but ligstroside analogues, which contain tyrosol in their structures. Recent findings, obtained by applying the same hydrolytic method on virgin oils from various provenance and varieties [22; 23],  
Figure 3: Total phenols obtained before and after the acidic hydrolysis for each EVOO (**A**) and averaged over all oils obtained in each oil mill (**B**). highlighted that the most of oils showed hydroxytyrosol below 50%, confirming the prevalence of tyrosol precursors. In another study, it was proposed to sum the tyrosol derivatives and the hydroxytyrosol ones in order to evaluate the total useful phenolic content to apply the EFSA health claims [26]. The application of the acidic hydrolysis on a larger number of oils will help to acquire a more realistic picture of the secoiridoid pool in virgin olive oils.
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 1 Introduction
The training data created using different shapes and geometries was trained in the software, and a prediction model between the input parameters and the raster angle was created. A total of 50 model designs were made, and as a result, a dataset of 1050 lines was created. According to the performance criteria applied to the prediction models, the accuracy of RFR was determined to be higher than that of the other ML algorithms. The novelty of this study is that the implementation of ML for FDM raster angle prediction has never been implemented. The findings of the study will contribute to the growth of the AM industry by reducing the product lead time and cost without compromising accuracy. The proposed model can predict the optimum raster angle for any geometry, which will further increase the applicability of digitally fabricated parts.
## 3 Determination of Key Parameters  
### The Control Mechanism of Roof Cutting Height on Roof Breaking  
The cutting height of the roof is determined by the expansion coefficient of the broken rock block and the mining height of the coal seam. Therefore, the cutting height \(H\) of the caving gangue in the goat to fill the goat and finally form a stable state of compaction is:  
\[H=\frac{M}{K_{\text{P}}-1} \tag{1}\]  
In Equation (1): \(K_{\text{P}}\) is the expansion coefficient of rock, taking 1.3; \(M\) is the mining height of the coal seam (m), taking 2.2 m. According to Equation (1), the roof cutting height required for the roof broken gangue to fill the goat should not be less than 7.3 m.  
When the thickness of the main roof strata is small, the dense drilling can completely penetrate the main roof to break and sink. At this time, the roof cutting height can be determined as the sum of the thickness of the immediate roof and the main roof. However, when the thickness of the basic roof strata is large, it is difficult to achieve dense drilling through the basic roof strata completely. At this time, only part of the basic roof strata can be considered to be cut off, so that the part of the roof pre-splitting cutting seam that is not penetrated can be smoothly broken in the process of roof rotation and sinking in the gob. The mechanical model of the main roof strata when partial roof cutting is established is shown in Figure 3.  
\[\sigma_{w}=\frac{M_{\text{(d)}}}{W_{t}} \tag{2}\]  
\[M_{\text{(d)}}=\frac{1}{2}{ql_{2}}^{2} \tag{3}\]  
\[W_{t}=\frac{1}{12}b(h-h_{1})^{2} \tag{4}\]  
In the formula: \(\sigma_{w}\) is the tensile stress on the non-penetrated surface of the basic roof rock; \(M_{\text{(d)}}\) is the bending moment on the cutting top surface when the cantilever beam rotates and sinks; \(l_{1}\) is the width of the roadway; \(l_{2}\) is the lateral cantilever length of the main roof; \(h\) is the thickness of the main roof; \(h_{1}\) is the height of dense drilling.  
By substituting (3) and (4) into (2), the relationship between \(\sigma_{w}\) and \(h_{1}\) is obtained as shown in Figure 4:  
Figure 2: Support section of roadway and borehole histogram.  
From Figure 4, it can be seen that the greater the height of the roof cutting seam of the intensive drilling hole \(h_{1}\), the greater the tensile stress \(\sigma_{w}\) acting on the non-penetrating surface of the roof cutting. When \(\sigma\)w is greater than the tensile strength of the rock mass, the roof is broken. For instance, when \(h_{1}=7.8\) m, \(\sigma_{w}=1.86\) MPa, which is greater than the tensile strength of the rock mass, so that when the roof cutting height \(>7.8\) m, the roof can be broken and the broken gangue can be filled with the goat.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 1 Introduction
[PERSON] and [PERSON] proposed a cloudizing storage method for unstructured LiDAR point cloud data with the distributed file system GridFS based on MongoDB, and the results showed that the proposed method performed better than the local file system [16]. Current studies mostly concentrate on the management of typical GIS data, such as point, line and surface, and less on remote sensing data, while this paper focuses on constructing a distributed storage strategy for massive remote sensing data to improve the management and service level. The primary focus of this study is to promote the storage efficiency and access efficiency of remote sensing data by establishing a distributed sharding cluster based on the MongoDB database. In our approach, we propose a distributed storage and access method for remote sensing data based on MongoDB. Firstly, we established a distributed cluster for remote sensing data using the sharding technology of MongoDB. Secondly, we constructed an archiving model to realize the unified management of remote sensing data referring to prevalent international standards and metadata structures. Furthermore, for remote sensing data, metadata and image data are stored separately, with the former stored as documents, and the latter stored with the GridFS mechanism, which is related by the data filename. Finally, with Landsat metadata and various satellite data used as experimental data sources, we conducted various experiments to verify the availability and effectiveness of the proposed method. The results show that the method proposed for remote sensing data has higher performance in data storage and access, and can effectively manage massive data. It can also provide guidance for data management strategies and meet the demand for massive data management.