esa-sceva/satcom-chunk-collection huggingface dataset:
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 3 Geo-DMP Mobile System
### The Development of the Map Adaptor Module
To address this issue, with the associated information of streets and subdistricts [104], we approximately determine the borders of subdistrict regions through joining the block polygons (Figure 11). Subsequently, to form a hierarchy, we should construct another level between the block layer and the subdistrict layer. Likewise, we produce such level via joining the block regions. For the sake of better practical significance, the joining operations abide by the following principles: (1) The size of these regions should be in the middle of the blocks and the subdistricts; (2) they should keep the subordination relationship with the upper subdistrict boundaries; (3) contiguous block regions that have empirical social ties should be joined together as a priority. The result of the new layer is shown in Figure 12. The scale of this layer can be analogous to the communities. As it can be seen from the statistics in Table 1, the three layers are well differentiated in terms of size and form a reasonable hierarchy. Roughly speaking, each subdistrict contains about 3-5 communities and one community contains about 3-4 blocks, which conforms to the actual situation to a large extent. Moreover, as the aim of the map segmentation is to serve content retrieval, we hope that it would support the multi-hop relay progress as well. Since the community layer is at the intermediate level, it should be capable of reflecting some invariability, i.e., accommodating movements and data exchange happened in a small range. Given that the typical communication range of nodes in VANET can be up to 300-500 m [105], the diameter of derived community regions is approximately 2-3 km, so 4-5 hops of forwarding are possible inside one community region at least, which we think is appropriate. Up to this point, the generation of hierarchical map segmentation is finished. Figure 11: Segmentation result of subdistrict regions.
## 3 Experiments and Data Processing
### Battery Testing Platform
The collected data included battery voltage, current, temperature, and capacity with a sampling interval of 2 s. Five data subsets selected from the 20 datasets were used as test sets, and the rest were used as training sets. The test data sets were absolutely different from the training datasets. The test datasets were DST at 0 \({}^{\circ}\)C, FUDS at 10 \({}^{\circ}\)C, BJDST at 20 \({}^{\circ}\)C, DST at 30 \({}^{\circ}\)C, and US06 at 45 \({}^{\circ}\)C.
## 3 Methods
### Data Acquisition
After representing all communication tools and protocols, data acquisition and transmission occur as follows: the robotic arm is connected to the PC(gateway) with RTDE and MODBUS protocols over Wi-Fi. On the PC, there are Node-Red nodes that read and transmit all necessary data to the server using MQTT broker. On the same PC, the Node-Red Dashboard Nodes offers the data in a graphical user interface accessible through a web browser in a real-time manner. Node-red dashboard allows the data to be presented in various forms such as charts, text fields and gauges, commands are also triggered from the GUI using sliders, switches, text fields and buttons. Additional details about communication protocols and KPI computations of the cobot on the dashboard are given in the aforementioned papers [18; 31].
## 5 Application of eNO\({}_{3}\)R to Brackish Groundwater: A Case Study
### Effect of Chloride Anions  
The primary ions present are chlorides, which are found in substantial concentrations in this type of water. Generally, the pathway for ammonium production by eNO3R is limited in this case, as it necessitates a cathode with high faradaic efficiency (FE) and an anode with the highest oxygen evolution reaction (OER) compared to chlorine ion oxidation reactions (CIOR). Additionally, research on this topic is currently limited. Thermodynamically, CIOR has higher potentials compared to OER, making it prone to activation under high cell voltages required for industrial current densities. In acidic conditions, CIOR manifests as a chlorine evolution reaction (CIER), while in alkaline conditions, it forms hypochlorite, both of which are corrosive and detrimental to catalyst longevity [203]. Materials such as Mn-based oxides and non-noble metal catalysts like transition metal hexacyanometallates [204], cobalt phosphates [205], selenides [206], and borates [207] have shown promise in stabilizing OER in NaCl-containing electrolytes. However, they struggle to deliver industrially relevant current densities at overpotentials below 490 mV. Recently, electro-catalysts resistant to chloride ion oxidation for industrially relevant current densities and low overpotentials have been extensively studied, even though their primary application is aimed at hydrogen production from seawater. Researchers such as [PERSON] et al. [208] have  
Figure 15: Pourbaix diagram of nitrogenous forms [202].  
developed a robust electrocataly based on nickel-iron hydroxide with low nucleophilicity. The EA-Ni\({}_{0.82}\)Fe\({}_{0.18}\)-OH electrocatalyst provides industrially relevant current densities of 400 and 1200 mA cm\({}^{-2}\) in simulated alkaline seawater, with respective overpotentials of 312 mV and 411 mV. Moreover, it exhibits impressive durability at current densities of 500 mA cm\({}^{-2}\) over 50 h of simulated alkaline seawater electrolysis [208]. We need to advance research in developing anodes that will play a crucial role in the large-scale application of ammonium production from brackish waters. On the other hand, the pathway for nitrogen selectivity remains reasonable and motivates its large-scale application, especially with the presence of a high chloride concentration in this type of water, which plays a significant role in converting generated ammonium to gaseous nitrogen, as discussed in the previous section (Cl-electrochemical advanced oxidation).
## 2 Materials and Methods
### Contact Efficiency and DLVO Theory  
Particle collisions were quantitatively predicted using classical filtration theory. Filtration theory states that diffusion, sedimentation, and interception are the three mechanisms governing particle collision from solutions to sand surfaces. The key formulae are [24]  
\[\eta_{\rm D}=2.4A_{\rm S}{}^{3}N_{\rm R}{}^{0.081}N_{\rm vdw}{}^{1.2}Pe^{0.715} \tag{6}\]  
\[\eta_{\rm I}=0.6A_{\rm S}{}_{\rm N}{}^{1/8}N_{\rm R}{}^{-2.675} \tag{7}\]  
\[\eta_{\rm G}=0.22N_{\rm R}{}^{0.24}N_{\rm vdw}{}^{0.053}N_{\rm G}{}^{2.11} \tag{8}\]  
where \(\eta_{\rm D}\), \(\eta_{\rm I}\), and \(\eta_{\rm G}\) represent collection efficiencies under diffusion, interception, and sedimentation, respectively; \(A_{\rm S}\) is the porosity-dependent parameter of [PERSON]'s model; \(N_{\rm R}\) is the aspect ratio; \(N_{\rm vdw}\) is the van der Waals number; \(Pe\) is the Peclet number; \(N_{\rm A}\) is the attractive number; and \(N_{\rm G}\) is the gravity number. The formula for calculating the collision efficiency (\(\eta_{0}\)) is as follows:  
\[\eta_{0}=\eta_{\rm D}+\eta_{\rm I}+\eta_{\rm G} \tag{9}\]  
Classical Derjaguin-Landau-Verwey-Overbeek (DLVO) theory incorporates van der Waals force (\(\Phi_{\rm VDW}\)) and the electric double-layer repulsion force (\(\Phi_{\rm EDL}\)) to model particle interactions. This theory can be used to predict particle retention stemming from physicochemical factors when suspended particles and sand particles interact [25]. Formulae for \(\Phi_{\rm VDW}\) and \(\Phi_{\rm EDL}\) are listed in Text SI [26].
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
## 5 Optimal Design of Freezing Scheme and Analysis of Numerical Simulation Results
### Path Analysis  
#### 5.4.1 Path Selection  
This paper explores the optimal design of pipe curtain freezing schemes under seepage conditions by reducing the freezing pipes in the strong and weak freezing zones of the original scheme. Four paths are established in the model of the three schemes, positioned upstream and downstream of the strong and weak freezing zones, to compare and analyze the differences between the two optimized schemes and the original scheme. These paths are situated in the cross-section where (Z = 10) m. Paths DJ-1 and DJ-2 are situated within the strong freezing zone. The first observation point is set at the center of the pipe curtain interval (point 0), followed by one point every 1 m interval, extending to both the inner and outer sides. The outer side is designated as positive, while the inner side is negative. Each path is equipped with a total of 7 observation points. Paths DJ-3 and DJ-4 are located in the weak freezing zone, with the first observation point set 1 m from the edge of the steel pipe on each side. Observation points are then set every 1 m interval, with each path having a total of 6 points. The path diagram is depicted in Figure 11.
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 2 Materials and Methods
### Rice Seedling Segmentation Procedure  
After determining the threshold \(T\), the classification process can be carried out to classify the pixel into either background or rice seedling in the current image. In this subsection, the detailed steps of the proposed segmentation algorithm for rice seedlings are described:  
* Step 1: Convert an input image into the YCrCb color space with Equation (1) and compute Cb component using Equation (2).
* Step 2: Calculate the color-index, defined as 2 Cg-Cr-Cb, to obtain the gray image.
* Step 3: Compute the total mean \(\mu\) of the gray image obtained from Step 2.
* Step 4: Set the initial threshold \(t\) to the total mean \(\mu\).  
Figure 9: Curves of before and after weighted background variance and within-class variance of the image given in Figure 8A.  
* Step 5: Classify the gray value \(i\) according to the threshold \(t\). If \(i<t\), assign the pixel to background class, or assign it to rice seedling class.
* Step 6: Calculate the gray level probability \(P_{1}(t)\), the average value \(\mu_{1}\) and the variance \(\sigma_{1}^{2}(t)\) in the background class, and perform the same operation for \(P_{2}(t)\), \(\mu_{2}\) and \(\sigma_{2}^{2}(t)\) in the rice seedling class.
* Step 7: Compute the within-class variance \(\sigma_{w}^{2}(t)\) according to Equation (14) and \(t=t+1\).
* Step 8: Loop the process from Step 5 to Step 7 and find the minimum within-class variance using Equation (15). Next, set the threshold \(T\) to \(t\) at the minimum within-class variance.
* Step 9: Classify the pixel as rice seedling if \(i>T\), or as belonging to the background otherwise.
# Update class center using momentum-based smoothing
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.3.2 Comparative Experiment and Analysis  
The evaluation of deep learning models extends beyond conventional accuracy metrics to encompass critical computational characteristics, including parameter efficiency and computational complexity, which jointly determine practical deployability. In order to better compare the performance of this paper's method and the latest face expression recognition algorithms, this paper reproduces some important expression recognition algorithms and extracts their key indexes for comparison experiments. Relevant experiments are conducted on Fer2013, CK+ and RAF-DB datasets, and the results are shown in Tables 4 and 5. Analyzing Table 5, it can be seen that the proposed method in this paper has the highest accuracy on Fer2013, CK+ and RAF-DB datasets, which are 69.17%, 97.37, and 85. 97%, respectively. Compared with the Improved MobileNet V2 network in study [32] (the Improved MobileNet V2 network in study [32] has further improved the accuracy over the original MobileNet V2 network on all three datasets), the accuracy of this paper's method on the Fer2013, CK+, and RAF-DB datasets in absolute terms improved by 0.55%, 1.41%, and 2.54%, respectively. The number of parameters is reduced by 6.57% and the computational complexity of the model is reduced by 4.66% compared to the MobileNet V2 network. Compared with recent lightweight expression recognition algorithms IEDBN [19], Improved Mobilenet V2 [32], Model by [PERSON] et al. [20], Self-Cure Net [21], Improved MobileViT [33], MFN [34], MFN+ [34], Ada-CM [23], Parallel CNN [35], etc. This paper's method has a smaller number of parameters, moderate model inference time, and the highest accuracy rate. The above experiments verify the effectiveness of this paper's method for facial expression recognition on faces.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters/M** & **Flops/M** \\ \hline VGG16 & 138.4 & 15,517 \\ VGG19 & 143.7 & 19,682 \\ ResNet50 & 25.6 & 4100 \\ ResNet101 & 44.7 & 7900 \\ ResNet152 & 60.4 & 11,000 \\ MobileNet & 4.3 & 600 \\ MobileNetV2 & 3.5 & 312.86 \\ MobileNet V3S & 1.52 & 58.79 \\ MobileNet V3L & 4.21 & 227.95 \\ DenseNet121 & 8.1 & 5690 \\ Improved Mobilenet V2 [32] & 3.26 & \(\backslash\) \\ MFN [34] & 31.00 & 16,950 \\ MFN+ [34] & 8.26 & 8110 \\ Ada-CM [23] & 11.18 & 1819 \\ Parallel CNN [35] & 5.8 & \(\backslash\) \\
**Ours** & **3.27** & **298.27** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of parameters of various models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Fer2013 Acc/\%** & **CK+ Acc/\%** & **RAF-DB Acc/\%** \\ \hline IE-DBN [19] & \(\backslash\) & 96.02 & \(\backslash\) \\ Improved & 68.62 & 95.96 & 83.43 \\ Mobilenet V2 [32] & 66.1 & \(\backslash\) & \(\backslash\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of various models.
## 2 Submillimeter-Level Microwave Ranging
### Ranging Error Sources Analysis  
Carrier phase measurement errors are mainly caused by the following reasons: local oscillator phase noise, system noise, and dynamic stress error.  
1. Carrier phase measurement error due to system noise The carrier phase measurement error due to system noise can be given as (1\(\sigma\)):\[\sigma_{nois}=\frac{\lambda}{2\pi}\sqrt{\frac{B_{n}}{c/n_{0}}\left(1+\frac{1}{2 Tc/n _{0}}\right)}\quad(\upmu\upmu)\] (8) where \(B_{n}=2\) Hz, the noise bandwidth of the phase-locked loop (PLL); \(\lambda\) is the length of the carrier wave (K band \(12.3\times 10^{3}\)\(\upmu\)m, Ka band \(9.2\times 10^{3}\)\(\upmu\)m); \(T=0.02\) s, the PLL pre-detection integration time; and \(c/n_{0}=10^{65/10}\) is the carrier-to-noise ratio (\(C/N_{0}=65\) dB-Hz). With calculation, the measurement errors due to system noise are \(\sigma_{nois-K}=15.4\)\(\upmu\)m(\(1\sigma\)), \(\sigma_{nois-Ka}=11.5\)\(\upmu\)m(\(1\sigma\)).
2. Carrier phase measurement error caused by phase noise of local oscillator The carrier phase measurement error (for the third-order tracking loop) introduced by the local oscillator's Allan variance can be given by the following empirical formula: \[\sigma_{Allan3}=160\cdot\frac{\lambda}{360}\cdot\frac{\sigma_{A}\left(\tau \right)f_{c}}{B_{n}}\quad(\upmu\upmu)\] (9) where \(B_{n}=2\) Hz, the bandwidth of the PLL; \(\tau=1/B_{n}=1\) s, the short-term stability length of the Allan variance; \(\sigma_{A}\left(\tau\right)=3\times 10^{-13}\), the Allan variance; \(f_{c}\) is the carrier frequency; K = 24.5 GHz; and Ka = 32.7 GHz. With calculation, the phase measurement errors due to local oscillator Allan variance are \(\sigma_{Allan3-K}=20.1\)\(\upmu\)m(\(1\sigma\)) for the K band and \(\sigma_{Allan3-Ka}=15\)\(\upmu\)m(\(1\sigma\)) for the Ka band.
3. Phase measurement error due to dynamic stress Dynamic stress error is closely related to the tracking loop order. For three-order PLL, the dynamic stress is given as: \[\sigma_{PLL3}=0.4828\frac{d^{3}R/dt^{3}}{B_{n}^{3}}\quad(\upmu\upmu)\] (10) where \(d^{3}R/dt^{3}\) is the line of sight (LOS) acceleration rate (\(\upmu\)m/s\({}^{3}\)). For the PLL bandwidth \(Bn=2\) Hz, the LOS acceleration rate is 10\(\upmu\)m/s\({}^{3}\), and the dynamic error \(\sigma_{PLL3}=0.60\)\(\upmu\)m(\(1\sigma\)).
4. Summary of phase measurement error The total measurement error while using three-order PLL can be modeled as: K band: \(\sigma_{\sum-K}=\sqrt{\sigma_{nois-K}^{2}+\sigma_{Allan3-K}^{2}+\sigma_{PLL3} ^{2}}=\sqrt{15.4^{2}+20.1^{2}+0.6^{2}}=25.3\)\((\upmu\upmu)\) Ka band: \(\sigma_{\sum-Ka}=\sqrt{\sigma_{nois-Ka}^{2}+\sigma_{Allan3-Ka}^{2}+\sigma_{PLL3} ^{2}}=\sqrt{11.5^{2}+15^{2}+0.6^{2}}=19\)\((\upmu\upmu)\)
###### Abstract  
3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), as the rate-limiting enzyme in the mevalonate pathway, is essential for the biosynthesis of shikonin in _Lithospermrum erythrorhizon_. However, in the absence of sufficient data, the principles of a genome-wide in-depth evolutionary exploration of _HMGR_ family members in plants, as well as key members related to shikonin biosynthesis, remain unidentified. In this study, 124 _HMGR_s were identified and characterized from 36 representative plants, including _L. erythrorhizon_. Vascular plants were found to have more HMGR family genes than nonvascular plants. The phylogenetic tree revealed that during lineage and species diversification, the _HMGR_s evolved independently and intronless _LerHMGR_s emerged from multi-intron _HMGR_ in land plants. Among them, _Pinus tabiformis_ and _L. erythrorhizon_ had the most _HMGR_ gene duplications, with 11 _LerHMGR_s most likely expanded through WGD/segmental and tandem duplications. In seedling roots and M9 cultured cells/hairy roots, where shikonin biosynthesis occurs, _LerHMGR1_ and _LerHMGR2_ were expressed significantly more than other genes. The enzymatic activities of LerHMGR1 and LerHMGR2 further supported their roles in catalyzing the conversion of HMG-CoA to mevalonate. Our findings provide insight into the molecular evolutionary properties and function of the _HMGR_ family in plants and a basis for the genetic improvement of efficiently produced secondary metabolites in _L. erythrorhizon_.  
Keywords:duplication and loss; evolutionary; enzymatic activities; HMGR; _Lithospermrum erythrorhizon_
# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 2 Materials and Methods  
### H\(\delta\)N Dataset  
Both imaging and clinical data of the \"Head-Neck-PET-CT\" TCIA open-source dataset [35] have been involved in our retrospective analysis. The dataset collects 298 patients, belonging to four different institutions (HGJ, CHUS, HMR and CHUM), with Head and Neck Squamous Cell Carcinoma (HNSCC), locoregional recurrence and distant metastasis. Per each patient, fluorodeoxyglucose (FDG) PET and CT diagnostic images have been scannedbefore treatment. Further information about specific cohorts, image acquisition, Regions of Interest (ROI) contours and clinical data have been presented in a reference study [36]. In our work, 201 patients with oropharyngeal primary tumors have been selected and patients with a primary tumor site in the larynx (n = 45), nasopharyngealrx (n = 28) and hypopharynx (n = 13) or unknown (n = 9) have been excluded. Moreover, patients with Overall Stage equal to I have been excluded from this analysis (n = 2). To assess the validity and robustness of the methods, the dataset has been split into a training set and testing set. CHUM (n = 57), CHUS (n = 71) and HGJ (n = 54) patients belong to the training set, and HMR (n = 19) ones to the testing set. In the following, we will denote the training set with \(DS_{1}\) and the testing set with \(DS_{2}\). As regards the chosen outcomes for the classification, for pN-Stage, we evaluated the presence/absence of the lymph nodes (N0/N+); for pT-Stage, we classified the stage as belonging to T1/T2 vs. T3 vs. T4; and, finally, we classified the Overall Stage as II, III or IV. In Table 1, we report the class distribution for the chosen outcomes, as well as information about gender and age. Notably, the strong imbalance of data is particularly evident.
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 5 Numerical Results
### Network Layer Routing Performance  
The literature review has highlighted how energy efficiency is crucial in UWSNs. In this direction, we finally present some results related to routing, demonstrating how the presence of collaborative nodes brings benefits in terms of nodes energy saving. Specifically, we refer to a simulation scenario including several intermediate nodes, ranging from 1 to 8, acting as collaborative relays for the transmission of packets from source to destination. Given CSI as available, path selection is performed through exhaustive search of the hop-by-hop link with the highest channel quality. At each hop, a cooperative DF-based SW-ARQ is performed, where retransmission is firstly asked to those neighboring nodes with good channel quality and, if needed, to the transmitting node. All the mechanisms at physical and link layer are implemented as described in the previous subsections. Performance are measured first in terms of energy efficiency, defined as the ratio between the number of packets to be transmitted and the number of packets (including retransmissions) actually generated within the network. We first refer to a normalized transmit power level for nodes, namely \(P_{t}=P_{\text{ref}}=1\) W. Moreover, we consider another case with nodes using a transmit power equal to \(P_{t}=0.75P_{\text{ref}}=0.75\) W. The results are shown in Figure 9.  
An interesting aspect to discuss is related to the fact that cooperative routing with nodes exploiting a lower transmit power provides the highest energy efficiency, especially when the number of available relay nodes grows. The reason of this result is the following. The use of a high transmit power allows in principle the packet to reach the destination with few hops, but with the potential risk of having a higher number of retransmission requests. On the other hand, a lower transmit power for nodes forces routing to be realized based on a larger number of hops, that however follow shorter and more reliable links. Hence, packet retransmission may be reduced, with a corresponding energy saving for nodes. Furthermore, Figure 9 reports the performance of a non-cooperative routing strategy where, at each hop, error control is managed between transmitting and receiving node without any support by relays. So, packet retransmission in ARQ may be more prone to errors due to the larger distance between the involved nodes. As a result, more transmissions are required by the nodes, impacting on energy consumption.  
The presented results suggest how an efficient routing passes through the optimization of many different aspects, including power control, multi-hop scheduling and error control. Overall, the use of a cooperative paradigm in the context of UWSNs guarantees improved performance with respect to conventional approaches.  
Figure 9: Energy efficiency performance for underwater cooperative and non-cooperative routing.