esa-sceva/satcom-chunk-collection huggingface dataset:
# Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
## 3 Methodology
### Survey of the Air Quality Models  
A survey of various air quality-modeling techniques was conducted to identify the most suitable model for predicting air quality impacts in a flat-terrain setting typical of the study area. Table 2 provides a comparative assessment of popular models.  
Figure 3: Methodology adopted in air quality assessment and decision-making.  
It has been observed that models at Sl. Nos. 1 to 4 (Table 2) are either too complex or are applicable on a regional scale or complex terrain. So, the Industrial Source Complex Short Term (ISCST3) model was selected for air pollution modeling, specifically for assessing total SPM and PM\({}_{10}\) emissions from iron ore-mining operations, as also supported by various other authors, e.g., [26]. ISCST3 requires input parameters such as emission rates, meteorological data, and geographical information to simulate the dispersion of pollutants in the air [27]. The model is well-regarded for its applicability to short-term area source modeling in flat terrains, making it suitable for the study area [28; 29].  
The Gaussian plume approach in the ISCST3 allows for simultaneous consideration of various source types, capturing the dispersion of pollutants from mining activities comprehensively [27; 30]. The ISCST3 is suitable for short-term area source modeling, which aligns with the need of this study, to assess impacts during both construction and operation phases, matching the emissions characteristics of mining activities. Studies have shown that ISCST3 is a refined dispersion modeling technique that provides accurate predictions of pollutants like nitrogen oxides (NO\({}_{X}\)) and total suspended particles [31]. The model uses rural dispersion and regulatory defaults, in conformity with the Central Pollution Control Board (CPCB, New Delhi, India) guidelines, thus simplifying the implementation and ensuring timely results [32]. One of the advantages of the ISCST3 model is that it assumes a flat terrain and, hence, is suitable for the topography of the study area, ensuring appropriate predictions in the area.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 3 The Novel Anti-Jamming Technique  
In this section, first, the prior distribution of MNCM is assumed to be a Gaussian distribution, and the problems of the VBAKF algorithm are analyzed according to this assumption. Second, on the basis of the above analysis, we propose estimating the MNCM and completing the anti-jamming task of INS/GNSS integration by using the BBVI algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distribution** & **Gaussian** & **Inv-Gamma** & **Inv-Wishart** \\ \hline AKLD & 1.557984 & 2.913936 & 2.639924 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The AKLD values.  
Figure 5: The Gaussian, Inv-Gamma, and Inv-Wishart distributions.
# Multi-Beam Focusing and Deflecting Characteristics of Liquid Crystal Optical Phased Array
## 5 Conclusions  
In this paper, we developed a programmable 1D transmissive liquid crystal optical phased array device. The device is based on a fully embedded system, leveraging a touch screen for human-machine interaction instead of conventional computer-based desk software. These improvements can remarkably enhance portability and reduce the costs of LCOPA devices. We also have realized the composite function of multi-beam fan-out and focusing, which may have potential applications in some special scenarios.  
Figure 9: Experimental results of multi-beam fan-out with focusing: (**a**) two light beams, with each beam deflected by \(-1.8\) mrad and \(1.8\) mrad, respectively; (**b**) two light beams, with each beam deflected by \(-1.8\) mrad and \(-2.7\) mrad, respectively; (**c**) two light beams, with each beam deflected by \(-1.8\) mrad and \(-3.6\) mrad, respectively; (**d**) three light beams, with deflection angles of \(-1.8\) mrad, \(0\), and \(1.8\) mrad, respectively; (**e**) three beams of light, with deflection angles of \(0.9\) mrad, \(0\) mrad, and \(1.8\) mrad, respectively; and (**f**) there are five light beams, with deflection angles of \(-1.8\) mrad, \(-0.9\) mrad, \(0\), \(0.9\) mrad, and \(1.8\) mrad, respectively.  
**Author Contributions:** Conceptualization, X.X. and L.X.; methodology, X.L.; simulation, L.X.; validation, X.X. and L.X.; writing--original draft preparation, X.X.; writing--review and editing, L.X.; supervision, L.X.; project administration, J.D.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Research Initiation Fund OF Jiangsu University of Science and Technology, grant number 1052932208.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Findings of the Study
### Extrinsic Evaluation
On the other hand, the AdaBoost (Ada) is a meta-learning classifier that makes predictions based on constructing and adjusting the weights from multiple weak classifiers [69]. During preprocessing, the attributes in the training set were transformed into unigram, bigram, and trigram using n-gram mechanism and limited the minimum number of attribute frequency to 3 as well as vocabulary size to 3000, 1500 for unigram features of _Sentiment140_ and _myPersonality_, respectively. In the case of bigram and trigram, we fixed the vocabulary size to 1500 and 1000 for respective _Sentiment140_ and _myPersonality_ corpuses. The values were fixed based on trial and error to eliminate issues caused by sparsity. The probability of the attribute \(a\) in the given instances was approximated based on (8), and the conditional probabilities of the n-gram language models were computed using (9), where the size of bigram and trigram attributes represented as \(n=2\), and \(n=3\) respectively. \[p(a_{1},\dots,a_{z})\approx\prod_{i=1}^{z}p(a_{i}|a_{i-(n-1)},\ \dots,\ a_{i-1}) \tag{8}\]  
\[p(a_{i}|a_{i-(n-1)},\ \dots,\ a_{i-1})=\frac{Count(a_{i}\Big{|}a_{i-(n-1)},\ \dots,\ a_{i-1},\ a_{i})}{ Count(a_{i}\Big{|}a_{i-(n-1)},\ \dots,\ a_{i-1})} \tag{9}\]  
Similar to the previous topic modeling process, this supervised learning also treated the problem as multiclass and one vs. all classification. In this investigation, the training set was comprised of \(n\) pairs of numbers, i.e., (\(x_{1}\), \(y_{1}\)), (\(x_{2}\),\(y_{2}\)), \(\dots\), (\(x_{n}\),\(y_{n}\)) for multiclass and one vs. all distributions, where each \(x_{n}\) was a measurement referring to a single data point, whereas \(y_{n}\) was the label for that point. Then, the Synthetic Minority Oversampling (SMOTE) class balancing technique with a synthetical seed number of 5 was applied on both distributions of _myPersonality_ and _Sentiment140_ to improve the machine learner's performance due to the high distribution variation nature of the classes. In order to measure the consistency of the topics predicted by SLDA, the training documents were partitioned according to 10-fold cross-validation. In this experiment, we used a personal computer with the specification of 8 GB RAM and Intel(R) Core (TM) i7-3610 QM CPU@ 2.30 GHz processor.
## 3 Technical Background
### Experimental Platform and Design  
The Xilinx Zynq-7010 is an SoC-FPGA that includes a dual-core ARM Cortex-A9 processor, a 4-way L1 set-associative cache for instructions, and one for data, each 32 KB. The Xilinx Zynq-7010 also has an 8-way set-associative L2 cache (512 KB in size) and a cache line length of 32 bytes. The L2 cache is the last level cache (LLC) for the Xilinx Zynq-7010 and is shared between the two ARM Cortex-A9 cores and the master interface in the hardware accelerator connected to the accelerator coherency port (ACP) presented in the following sub-section.  
Figure 4 shows the experimental design implemented in the Xilinx Zynq-7010 SoC [17] for this work, and Figure 5 shows the memory hierarchy of this SoC. The hardware IPs of the programmable logic part of the SoC-FPGA are partitioned in two: secure IPs (in green in Figure 4) and non-secure IPs (in red in Figure 4), using the advanced extensible interface (AXI) functionality. Both IPs have direct access to the memory using the ACP. In the processing system, each core of the ARM processor is dedicated to a world, the secure ARM core (in green in Figure 4) runs critical applications, the non-secure ARM core (in red in Figure 4) runs normal applications. The external memory is also partitioned into a secure region (in green in Figure 4) and a non-secure region (in red in Figure 4) using the TrustZone configuration registers (called TZMA). The secure region of the external memory stores critical applications and the non-secure region contains the rest of the applications. More details about this implementation can be found in [18].  
Figure 4: Experimental design (secure memory region/core/IP are in green, non-secure in red) in-plemented in a Xilinx Zynq SoC-FPGA.
## 4 Results and Discussion  
### Cutting Force  
Maximum cutting force results are shown in Figure 5. Force ranged from 1600 to 1903 N when the conventional system was used, while a considerable reduction in of up to 16.41% was recorded when Cut-list was employed, at a lower feed rate of 0.1 mm/rev. This could be attributed to the high adhesion action of the impinged fluid at a lower feed rate, which gave the fluid's droplets enough time to adhere to the workpiece and cutting tool surfaces. Additionally, the relatively higher impinging jet velocity (up to 10.83 m/s, see Table 4) was achievable by the Cut-list and helped the cutting fluid to penetrate effectively into the machining zone and form the boundary of the oil film between workpiece and cutter, resulting in minimising contact pressure. A similar phenomenon was reported by [PERSON] et al. [60], where the high momentum jet produced a hydraulic wedge between the tool and the workpiece interface, offering adequate lubrication with a substantial reduction in friction. In addition, the results also showed analogous trends in increasing cutting force with increased cutting speed regardless of the cooling system used. This is due to the increased cutting energy associated with increased cutting speed [61].  
Figure 4: Thermocouple arrangement for workpiece temperature measurement (dimensions in mm).  
Figure 5: Cutting force results for the two systems in various cutting conditions.
## 3 Results and Discussion
### Dynamic Characteristic Analysis Results and Discussion  
#### 3.2.1 Modal Analysis Results and Discussion under Milling Conditions  
The modal analysis of the direct-drive turntable was carried out according to the finite element modal analysis method in Section 2.4.1. The first six modes of the direct-drive turntable under milling conditions are shown in Figure 14.  
The first six vibration modes and natural frequencies of the direct-drive turntable under milling conditions are summarized in Table 8.  
The first six modes of the direct-drive turntable under turning conditions are shown in Figure 15.  
The first six vibration modes and natural frequencies of the direct-drive turntable under turning conditions are summarized in Table 9.  
In the first six modes of the direct-drive turntable under the two working conditions, the first four modes are mainly located on the C-axis and the workpiece, and the fourth to sixth modes occur on the overall structure of the direct-drive turntable. Aside from the workpiece, processing should pay more attention to the direct-drive turntable C-axis part.
## 2 Results and Discussion
### TPS2 Catalyze \(\alpha\)/\(\beta\)-Fruesene and \(\beta\)-Ocimene Formation In Vitro  
The full-length _TPS2_ and _TPS5_ were cloned from _C. sinensis_, and phylogenetic analysis (Figure 5c) showed that _TPS2_ and _TPS5_ belong to TPS-a and TPS-c family, respectively. _TPS2_ clustered together with CaFAS, which have been reported to be involved in the production of \(\alpha\)-farnesene and \(\beta\)-ocimene in vitro but could only form \(\alpha\)-farnesene in _C. sinensis_[20]. There is a farnesene synthase from _Vitis vinifera_, which also belongs to the TPS-a family [30].  
Figure 5: Expression of three _TPSs_ in different tissues and their response to MeJA. (**a**) q-PCR results of the expression level of _TPS1-TPS5_ responds to MeJA, red line to highlight the changes of _TPS2_, _TPS3_ and _TPS5_. (**b**) q-PCR showing the different expression levels of _TPS2_, 3 and 5 in five tissues, (**c**) the phylogenetic tree of _TPS2_ and _TPS5_, which was obtained by using MEGA-7 software with the neighbor-joining method. At, _Arabidopsis thaliana_; Vv, _Vitis vinifera_; CsAFS, \(\alpha\)-farnesene synthase in _Camellia sinensis_; _CsOCS_, \(\beta\)-ocimene synthase in _Camellia sinensis_; CsNES, nerolidol synthase in _Camellia sinensis_; CsNES/LS, nerolidol/linaloid synthase in _Camellia sinensis_.  
To verify our gene-to-terpene network and to confirm whether _TPS2_ is involved in the formation of farnesene, _TPS2_ was cloned and expressed in _E. coli_ Rosetta (DE3) cells. In vitro chemical products of the resulting recombinant proteins were analyzed by comparison to authentic standards. The main product produced by _TPS2_ using FPP as a substrate was \(\beta\)-farnesene, followed by \(\alpha\)-farnesene (Figure 6a). In contrast, \(\beta\)-ocimene is the resulting product when using GPP is used as the substrate. Together, our results indicate that _TPS2_ is a multifunctional enzyme that could catalyze the formation of \(\alpha\)- and \(\beta\)-farnesene, as well as \(\beta\)-ocimene in vitro.
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 4 Sample Preparation and Analytical Methods for Cyanotoxins and Prymnesins Detection
### Sample Preparation
#### 4.1.1 MCs and NODs  
There are numerous literature examples of procedures for the extraction of MCs from a variety of matrices, such as tissue, listed in Table 3, including some that have systematically evaluated various extraction conditions. Because of their similar structure and chemical properties to MCs, there is little to differentiate extraction conditions for NODs, and as seen in Table 3, the conditions are comparable. The most common methods for the extraction of MCs have used methanol:water mixtures of varying ratios, in some cases with added acid, typically acetic acid. Because MCs are a diverse class of species with varied amino acid substitutions, one complicating factor is the variability in hydrophobicity/hydrophilicity of MCs that could be present in a sample. This has been found to be an issue in extractions with pure methanol, for which lower recovery of more hydrophilic congeners, such as MC-RR, has been reported. Instead, most procedures have used 75% to 90% methanol [50], the remainder being water, which has been seen to be generally appropriate for a broad suite from hydrophobic to hydrophilic congeners (See Table 3). An additional complication, however, has been observed in extractions using methanol when used in conjunction with some ELISA based toxin measurements. In at least some studies, interferences resulting in false positive detections were observed and confirmed by comparison with LC-MS/MS, where no MCs were observed [49]. While it is possible that the targeted methods were missing unknown MCs, this was further evaluated by comparison with a direct monoclonal ELISA with known higher specificity to MCs, and the absence of MCs via that method was confirmed.  
One alternative to attempting to extract individual MC congeners has been developed using Lemieux oxidation, which has been demonstrated to react with a variety of MCs and convert them to a common product from all MCs with \"adda\", producing MMPB, which can be extracted instead. Quantification of MMPB can then serve as a proxy measurement for the total of MC congeners present in the sample. This does have limitations in that MMPB oxidation is known to generate positive results when parent MCs may be partially metabolized or otherwise transformed, as in drinking water treatment where oxidized MCs give a false positive signal, but recent publications indicate correlations seen between solution phase MC detections and detections in fish tissue [51].
## 2 Proposed Antenna Design
### Trade-Off between Total Efficiency and Axial Ratio Depending on Chord Length \(L_{c}\)  
To achieve effective proximity coupling and stable axial ratio with a small number of array elements and minimum gap condition imposed by the fabrication limitation, a simulation-based parametric study on the effect of \(L_{c}\) is conducted. The evaluation is based on the average total efficiency and average axial ratio of the target frequencies of 23.4, 23.7, and 24 GHz. As \(L_{c}\) increases, the coupling between the feed and elements increases, leading to an enhancement in both the total efficiency (\(e_{tot}\)) and operational bandwidth, as depicted in Figure 4. However, the increase in \(L_{c}\) results in a poor axial ratio since the rotation velocity of the surface current changes regardless of the given path length. Therefore, \(L_{c}\) is set to 2.1 mm, which provides \(e_{tot}\) of over 80% and an axial ratio below 3 dB, as shown in Figure 4a. The initial value of \(R\) for circular patches was set using the standard circular patch equation [31]. This results in an increase in the element impedance and a higher resonant frequency of the antenna. The optimal values are derived from the simulation, which is described in Figure 2.
# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 3 Experiments and Discussion
### Analysis of SIR Spreading Model
When \(t<5\), the slope of the curves in DC, BC, PageRank, and TPR algorithms are equivalent, whereas the slope of the curve in the HITS algorithm is slightly smaller than those of the other algorithms. In the Euroroad network (Figure 7b), after the propagation of 20 steps, the numbers of infected and recovered nodes and the infection rate of the seed nodes in the network are TPR \(>\) DC \(>\) PageRank \(>\) HITS \(>\) BC. In the Power Grid network (Figure 7d), the top 10 nodes identified by the DC have strong abilities to spread. The number of infected nodes and the infection rate of the top 10 nodes are DC \(>\) TPR \(>\) PageRank \(>\) BC \(>\) HITS and DC \(>\) TPR \(>\) PageRank \(>\) HITS \(>\) BC, respectively. In the above experimental results, when \(0<t<15\), the numbers of infected and recovery nodes of each algorithm increase. When \(15<t<20\), each algorithm is in a stable state, the \(F(t)\) function of the TPR algorithm has higher initial curve slope, and the growth rate is obvious in the Email, Euroroad, and Protein networks. TPR is second only to DC in Power Grid, indicating that the top 10 nodes identified by the TPR have advantages in initial propagation. Figure 7: Growth of infected and recovered nodes for different networks.
# Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset
## 3 Results
### Results of Illumination Level on Detection Scores
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Angle and Lux** & **Class** & **Reading 1** & **Reading 2** & **Reading 3** & **Average** & **Measured Lux** \\
**Level of Lamp** & & **(\%)** & **(\%)** & **(\%)** & **Reading (\%)** & **Value (lm/m2)** \\ \hline \multirow{6}{*}{180\({}^{\circ}\) Level 0} & Red cylinder & 85 & 77 & 75 & 79.00 & 25 \\  & Red cube & 83 & 80 & 69 & 77.33 & 23.5 \\  & Yellow cylinder & 85 & 77 & 78 & 80.00 & 25.7 \\  & Yellow cube & 91 & 77 & 85 & 84.33 & 25.7 \\  & Blue cylinder & 77 & 78 & 75 & 76.67 & 24.1 \\  & Blue cube & 77 & 73 & 75 & 75.00 & 24.1 \\ \hline \multirow{6}{*}{180\({}^{\circ}\) Level 1} & Red cylinder & 80 & 85 & 75 & 80.00 & 187.9 \\  & Red cube & 80 & 62 & 65 & 69.00 & 187.8 \\  & Yellow cylinder & 85 & 80 & 85 & 83.33 & 188.6 \\  & Yellow cube & 92 & 57 & 86 & 78.33 & 188.9 \\  & Blue cylinder & 75 & 73 & 83 & 77.00 & 188.1 \\  & Blue cube & 83 & 70 & 83 & 80.00 & 187.5 \\ \hline \multirow{6}{*}{135\({}^{\circ}\) Level 0} & Red cylinder & 78 & 83 & 86 & 82.33 & 50 \\  & Red cube & 80 & 83 & 83 & 82.00 & 48 \\  & Yellow cylinder & 78 & 80 & 83 & 80.33 & 50 \\  & Yellow cube & 77 & 78 & 85 & 80.00 & 49 \\  & Blue cylinder & 77 & 82 & 77 & 78.67 & 50 \\  & Blue cube & 85 & 71 & 83 & 79.67 & 49 \\ \hline \multirow{6}{*}{135\({}^{\circ}\) Level 1} & Red cylinder & 75 & 87 & 83 & 81.67 & 175 \\  & Red cube & 86 & 83 & 86 & 85.00 & 172 \\  & Yellow cylinder & 76 & 75 & 83 & 78.00 & 177 \\  & Yellow cube & 80 & 89 & 83 & 84.00 & 174 \\  & Blue cylinder & 73 & 80 & 78 & 77.00 & 175 \\  & Blue cube & 75 & 83 & 86 & 81.33 & 176 \\ \hline \multirow{6}{*}{90\({}^{\circ}\) Level 0} & Red cylinder & 89 & 51 & 89 & 76.33 & 28 \\  & Red cube & 91 & 80 & 88 & 86.33 & 25 \\  & Yellow cylinder & 91 & 39 & 83 & 71.00 & 28 \\  & Yellow cube & 94 & 48 & 85 & 75.67 & 26 \\  & Blue cylinder & 80 & 57 & 78 & 71.67 & 28 \\  & Blue cube & 89 & 70 & 69 & 76.00 & 26 \\ \hline \multirow{6}{*}{90\({}^{\circ}\) Level 1} & Red cylinder & 90 & 65 & 85 & 80 & 206 \\  & Red cube & 93 & 76 & 83 & 84 & 189 \\  & Yellow cylinder & 91 & 49 & 82 & 73.67 & 209 \\  & Yellow cube & 92 & 62 & 89 & 81 & 192 \\  & Blue cylinder & 80 & 53 & 86 & 73 & 207 \\  & Blue cube & 88 & 80 & 65 & 77.67 & 201 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Detection scores and lux level for the different angles and intensities of the lamps. is more even and that there is less surface reflection with a 135-degree lamp of level 0 illumination because of its low variance.
## 4 Discussion and Conclusions
### Future Research  
Although the smart pipe demonstrator unit described in this paper and the associated research has provided valuable information on producing smart pipes, there are certain areas that need further research if this concept is to become reality. These include:  
* Powering the sensors--methods of harvesting energy from the surrounding environment are essential to power the sensors for the lifetime of the pipe. This is particularly important if Smart Pigs for data collection are to be avoided within the water distribution network.
* Communications--further work on sensor-to-sensor and sensor-to-Smart Server communication is required.  
Research into these components is currently under way at the University of Birmingham and results will be presented in future papers.
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)
### Summary of Network Development Status
#### 3.2.4 5G Networks
The 5G + satellite integrated communication network will integrate the space-air-ground-sea 5G networks, provide a ubiquitous communication network with high-speed data, low delay, and low power consumption, and realize man-machine and things interconnection. To summarize, the emergency wireless private network is evolving toward the PDT narrowband digital cluster and B-Trunc broadband cluster private networks. Currently, it is mainly based on the 370M narrowband private network, and it is developing towards multi-system convergence communication technologies such as fixed-mobile integration, broadband-narrowband integration, and public-private complementarity. In remote or extreme areas of sea, air, and ground, especially during the rescue and relief of major natural disasters, the mobile satellite communication system will be a more reliable choice. Mobile satellite communication systems can provide users with regional and even global communication services by using mobile communication satellites in high and low orbits. In view of the characteristics of high speed, high reliability, low delay, and low power consumption of 5G communication networks, and the rapid development of ad hoc network technology, the integration of ad hoc network technology, 5G communication technology, satellite communication technology, and private network technology will also be the focus of future development.