esa-sceva/satcom-chunk-collection huggingface dataset:
# Pilot-Scale Fenton-like System for Wastewater Treatment Using Iron Mud Carbon Catalyst
## 3 Results and Discussion
### Catalytic Performance Test Using Sequential Batch Experiments
#### 3.2.2 Optimization of the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) System for COD Removal
The Removal Performance of COD in the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) System at the Pilot Scale  
Figure 6 shows the COD removal performance of the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system on secondary effluent at the pilot scale. As shown in Figure 6a, when pH increases from 6.33 to 8.15, the COD removal rate presents an overall decreasing trend. When pH = 6.5 \(\pm\) 0.3, the COD removal rate is 60.7%, and the effluent COD is lower than 35 mg\(\cdot\)L\({}^{-1}\), reaching the discharge standard of main water pollutants for municipal wastewater treatment plant & key industries of Taihu area (DB32/1072-2018) (hereinafter referred to as Standard). With the increase in pH to about 8.1, the COD removal rate drops to 4.2%, and the effluent COD reaches 66 mg\(\cdot\)L\({}^{-1}\), which does not meet the effluent standard. Under high pH conditions, the reason for the decrease in COD removal rate might be that the accelerated decomposition of H\({}_{2}\)O\({}_{2}\) limits the regeneration of hexavalent iron (Fe(VI)) [29]. Additionally, the precipitation of Fe(OH)\({}_{3}\) may lead to the passivation of the catalyst surface [30] (Equation (5)). \[\text{Fe}^{3+}+3\text{OH}^{-}\rightarrow\text{Fe(OH)}_{3} \tag{5}\]Figure 6b shows the dynamic trends of influent COD, effluent COD, and COD removal rate at the pilot scale within 65 days. At the initial stage of the pilot test, the influent COD experienced a brief fluctuation due to the variation in the actual secondary effluent. With the extension of the running time, the influent COD gradually tends to be stable (70.3 mg\(\cdot\)L\({}^{-1}\)). However, the stable effluent COD concentration (\(<\)40 mg\(\cdot\)L\({}^{-1}\)) demonstrated that the increased organic load did not affect the process efficiency, indicating that the system has sufficient active sites (Figures 2 and 3) and oxidizing species to cope with load fluctuations. In addition, in the primary stage of oxidation, the removal rate of COD is the highest (64.59%), which is due to the presence of a large number of active sites when the catalyst is just added. When the catalyst was operated for 500-1000 h, the activity was still better, and COD removal rate was reduced by 9.59%. The average effluent COD value is 33 mg\(\cdot\)L\({}^{-1}\). After reaction for 1000 h, the COD removal rate decreased and the effluent COD increased slightly, because the catalyst sites gradually reached saturation and the degradation efficiency of organic matter slowed down. In particular, the COD degradation rate was 38.4% after 65 days of operation. However, effluent COD is still less than 40 mg\(\cdot\)L\({}^{-1}\). Therefore, the effluent COD treated by the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system can meet the Standard and has great engineering application potential.
## 3 Proposed Method  
### Overview  
The overview of the entire network structure is shown in Figure 2. GLSNet revolves around three major modules: the global shallow branch, the local deep branch, and the global-local fusion module. In global shallow branching, shallow neural networks are used to collect global contextual information. The local deep branch uses a deep neural network to extract fine local features of the cropped array blocks in parallel. The global-local fusion module combines these branches with GL-Deep Fusion, which comprises a dual-cross encoder and a single decoder. Embracing the transformer's potential, GL-Deep Fusion skillfully combines high-quality features that hold both global semantics and local details. Using the cooperative relationship between these three fundamental components, GLSNet can effectively accomplish image segmentation assignments on extremely ultra-high resolution images.  
The two main modules proposed are GL-Deep Fusion and the global shallow branch, along with the local deep branch. In Section 3.2, the intricacies of GL-Deep Fusion are explored. Subsequently, Section 3.3 is dedicated to an exploration of the nuances of the global shallow branch and the local deep branch.
## 5 Experiments and Results
### Recovery Results  
The first two previously mentioned image databases were concatenated, obtaining a single broad image base; the second image database is Shullani (Sh). The natural scenes are: forests, skies, coasts, mountains, fields, and rivers. The results were taken based on two performance evaluation methods: (i) in resubstitution (see Figure 7) and (ii) in cross-validation at 70% and 30% (see Figure 7b). Regarding the number of centers in the k-means algorithm, a value of \(K=9\) was used, since it provided the best results.  
As can be seen in Figure 7a,b, the search or query image is recovered along with the 5 more similar images.The proposed methodology gives 100% result if the the recovery of the most similar image is taken into account in the resubstitution method. In contrast, for the cross-validation method, the five recovered images belong to the same type of natural scene.  
Finally, the quantification of the performance of the proposed system is given in Table 2 via confusion matrix showing 100% recovery of each of the natural scene.  
Figure 7: Image retrieval by scene and validation type.
## 3 Design of the Stress Wave-Based Communication System
### Hardware Design  
#### 3.2.1 Piezoceramic Transducer Design  
The new transducer was designed to generate high-amplitude stress waves upon touching. The motivation for developing such a piezoceramic transducer is for future robotics applications and the actuator design can be incorporated on the hand of a robot to enable stress wave communication upon \"touch.\" The developed transducer consists of eight piezoceramic arc segments assembled in parallel to increase vibration amplitude. PZT (lead zirconate titanate), a type of piezoceramic material with strong piezoelectric effect, is used in this research. When excited by an alternating voltage, the eight piezoelectric segments will experience radial deformation. This design of the PZT transducer enables the transmission of the stress wave from the transducer to a pipeline through contact or upon touching. There is no requirement to permanently install the PZT transducer. Table 2 lists the key parameters of the transducer. Figure 3 shows the detailed design of the transducer, which includes the components, dimensions, and a photo. In Figure 3, the red segments represent positive electrodes, and the blue segments represent negative electrodes.  
Figure 2: Architecture of the proposed stress wave communication system.  
Figure 3: The piezoceramic transducer: (**a**) assembled piezoceramic transducer and components; (**b**) dimensions of the piezoceramic transducer (units in mm); (**c**) photo of the piezoceramic transducer.
## 3 Results and Discussion
### Engine Exhaust Emissions
#### 3.3.1 NOx Emissions
Although the ISNOx emissions were approximately similar in Stage 1, when comparing the 1500 and 2000 rpm cases, the ISNOx emissions were observed to be approximately 10% to 20% higher at 1500 rpm compared to 2000 rpm in Stages 2, 3, and 4 (Segment 3 and onwards). This could be due to the higher combustion temperatures produced at 1500 rpm, which is also evident from the higher exhaust temperatures at 1500 rpm, consequently producing higher NOx [6; 8; 35; 38]. It has been observed that the raw NOx emissions (g/s) decreased with a decrease in load, at both engine speeds, as reported in the literature [21; 35]. However, the ISNOx emissions (g/kWh) have been observed to increase with decreasing loads, in Stages 2, 3, and 4 (Segments 3 and onwards), with the only exception to the trend at 25% load at 2000 rpm, where the ISNOx emissions were the lowest amongst all loads, which could have been due to the unusual combustion taking place at low load at 2000 rpm, such as longer combustion duration, with retarded injection, producing a comparatively lower in-cylinder temperature, leading to lower ISNOx. It has been observed that the ISNOx emissions were higher at 50% load, at both engine speeds, with the magnitude of increase being more prominent at 1500 rpm compared to 2000 rpm. The reason for this could have been related to combustion deterioration at lower loads (50% and 25% loads), due to the ignition retard in Stage 2 of engine warm up. As the combustion would deteriorate, the ECU might decrease the combustion duration to cause more fuel to burn in the premixed combustion stage, to have rapid heat release rates, leading to higher in-cylinder temperature and higher NOx.
## 2 Advances within 3 GPP Standardization on D2D
### IoT and Wearables
From the research point of view, the effect on the cell energy efficiency and the battery life-time for all involved devices has not been thoroughly studied. It is clear that with UE relaying, the devices willing to operate as relays consume more power than the remote UEs. However, the device power consumption model used in [33] was rather simplified and no clear view on the spatial distribution of the power consumption was achieved. The 3 GPP has plans to continue the normative work on bringing the relaying support for cellular IoT and wearables into standards. Currently, the corresponding work item has been proposed, but it is yet unclear whether the work will take place in release 15 or 16 [44]. D2D communication support in different 3 GPP releases is depicted in Figure 3.
## 1 Introduction  
Cryogenian-Ediacaran granitic rocks are widely distributed in the Northern corner of the Arabian-Nubian Shield (ANS) [1; 2; 3]. ANS represents one of the largest continental crusts (after collision of East and West Gondwana), formed by accretionary terrane [4; 5; 6; 7]. The ANS crops out in the Eastern Desert, South Sinai as well as in the southern corner of the Western Desert, and comprises Neoproterozoic Island arc assemblages overthusted by ophiolitic rocks and injected by variable granitic rocks.  
Among the Neoproterozoic Egyptian rocks, the highly evolved (fractionated) granitic rocks are accompanied by rare metals mineralization [2; 8; 9; 10]. They possess high contents of rare metals such as Nb, Ta, Y, Sn, Zr, REEs and highly radioactive mineralization [11; 12; 13]. These granitic bodies are abundant in the Northern and Central Eastern Desert (CED) of Egypt with varying shapes and reliefs. They are characterized by enrichment in REEs, Nb, Ta, U, Th, Zr and the high content in total alkalis may be related to magmatic and/or hydrothermal alterations [9; 14].  
The highest contents of rare metal-bearing granitic rocks cropping out in the Egyptian Eastern Desert are Nuweibi, Homrit Waggat, Abu Dabbab, Abu Diab, Abu Rusheid and Gattar granitic intrusions [9; 15]. Moreover, plutonic rocks including granites due to their abundance (60% of the total Egyptian crystalline rocks) are widely utilized as an ornamental stone due to high durability as well as good appearance [2].  
The aeromagnetic survey is a helpful technique for geological mapping as it provides information at a fraction of the cost of ground mapping. One of the major purposes of the aeromagnetic survey is to distinguish the relationship between magnetic anomalies and tectonic pattern that is typically present in survey areas. This is because magnetic anomalies preserve the tectonic history of the rocks by their magnitude and pattern. As a result, any extensive magnetic measurements will contain anomalies with a non-random pattern.  
A magnetic survey is widely used to examine the subsurface anomalies caused by the magnetic characteristics of the underlying rocks. Magnetic content (susceptibility) of rocks varies depending on the type of rock and the environment in which it is found. The texture of the anomaly field is the trends, sizes, and configurations of the numerous anomaly centers; the presence of shape gradients is the most important aspect in the interpretation of aeromagnetic maps [16]. The susceptibility, geometry, depth of burial, remnant magnetization, striking direction, and local magnetic inclination and declination all influence the form and magnitude of a magnetic anomaly. Changes in these factors, within and between rock types, give the aeromagnetic map particular distinguishing characteristics that reflect the underlying geology.  
The current work aims to investigate and classify the various Homrit Waggat granitic rocks through petrographical investigation, evaluate the subsurface structure affecting the studied area and determine their depth using aeromagnetic method.
# A Circularly Polarized Non-Resonant Slotted Waveguide Antenna Array for Wide-Angle Scanning
## 3 Design of Antenna Array  
### Linear Array  
In this section, the compact antenna element proposed in Section 2 is arranged into a linear array, with each antenna element featuring a center-set waveguide and a compact circular polarizer. They are fed by a straight-through waveguide tube, and the structure of this linear array is shown in Figure 11. The linear array is a non-resonant array that is excited by a traveling wave. In non-resonant arrays, the slots are excited with different phases, resulting in the main lobe beam deviating from the normal direction. The phase difference between the slots varies with the frequency, causing the main lobe direction to change accordingly. Thus, non-resonant arrays possess the capability of frequency scanning. In drawing on fundamental phased array antenna theory and waveguide theory, the relationship between the slots distance \(d\) and the beam angle \(\theta\) satisfies the following:  
\[\theta=arcsin(\frac{\lambda}{\lambda_{g}}-\frac{\lambda}{2d}) \tag{4}\]  
The distance between the slots also impacts the resonant frequency of the linear array. When the slot distance is \(0.5\lambda_{g}\), the non-resonant array exhibits a very high VSWR at the resonant frequency [35; 36]. This phenomenon can be mitigated by increasing the slot distance. However, excessively long slot distances will result in a high grating lobe  
Figure 10: The relationship between the simulated radiation efficiency and the cylinder height under the resonant condition.  
level. The suppression condition for grating lobes can also be described as in Equation (1). In conclusion, the maximum value of the slot distance is constrained by the performance of the grating lobe level, while the minimum value is determined by the VSWR performance. Additionally, the main lobe direction must be considered in the determination of the slot distance.  
The control of the first side-lobe level was also achieved in this work. Side-lobe control in slotted waveguide arrays is accomplished by regulating the radiation power weighting of the elements. The weighting factor is determined using the Taylor Pattern Synthesis Method. In building upon the findings from Section 2, the differential power weighting of the elements is achieved through adjustments in the height of the cylinder and the length of the slot.
## Materials and Methods
### Impedance Measurement  
ECIS system was utilized to study the concentration-based cytotoxicity of MgO/Mg(OH)\({}_{2}\) NPs in hFOB 1.19 cells [7] as an alternative to the trial-and-error of animal testing. Here, array type 8W1E PET (Applied Biophysics, NY, USA) consisting of eight wells, each well with a single circular 250 \(\upmu\)m diameter active gold electrode, was used for ECIS. Arrays were attached to the ECIS system (Model ECIS Z0, Applied Biophysics, NY, USA), and alternative current (I) of 4000 Hz was applied to the eight-well sensing electrodes. A voltage (V) across the electrodes created an impedance (Z), which was measured by [PERSON]'s law (Z = V/I). With the morphological changes in hFOB 1.19 cells in response to different concentrations of MgO/Mg(OH)\({}_{2}\) NPs [7] in the arrays, impedance was measured.  
After the third passage of the hFOB 1.19 cells in a complete growth media, 400 uL of the cells (5000 cells/well) were seeded in two sets of eight-well arrays for control and experimental groups. Arrays were incubated under standard cell culture conditions (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)) with humidified air for 24 h [7]. After 24 h, different concentrations (0, 1, 5, 10, and 50 mM) of MgO/Mg(OH)\({}_{2}\) NPs [7] were exposed to the respective wells of the arrays, followed by incubation for another 24 h. Impedance was measured after 24 h of MgO/Mg(OH)\({}_{2}\) NP exposure.
## 2 Materials and Methods
### Soil Moisture Forecasting (SMF) Model Construction
#### 2.2.2 The Proposed SMF Model  
The forecasting of soil moisture is a type of time-series data forecast [30]. We experimented with using a moving average, which is a common way to statistically forecast time-series data. Table 1 shows the forecasting errors of the moving average with a window size between 2 and 17. The smallest error (7.4) was with a moving average window size of 16. The worst average error was for size 17 (175.7). However, the results were no better than using the continuously segmented records as the regression function.  
The proposed SMF formatted every sample record as a vector \(v=\{H_{s}\), \(T\), \(H_{a}\), \(D\), \(P\)}. For each element in the vector, \(H_{s}\) is the sampled moisture of the soil, \(T\) is the air temperature, \(H_{a}\) is the air humidity, \(D\) is the depth layer of soil of the sensor, and \(P\) is the irrigation interval.  
Let a newly captured soil moisture data point in the depth layer \(d\) be \(h_{s}\), the air temperature be \(t\), and the air humidity be \(h_{a}\). The SMF model uses the vector \(v^{\prime}=\{h_{s}\), \(t\), \(h_{a}\), \(d\}\) to represent these data. To forecast the soil moisture after a time interval \(P^{\prime}\) where \(P^{\prime}\) is the possible irrigation interval adapted in this system, the SMF model computed the distance between every stored record \(v_{i}\) and the vector \(v^{\prime}\) if they satisfied the following constraints: (1) \(P=P^{\prime}\), (2) \(h_{a}=H_{a}\pm 1\), (3) \(t=T\), (4) \(h_{s}=H_{s}\pm 5\). The SMF model selected all newest records that had the shortest distance to \(v^{\prime}\) and put these into set \(Z=\{v^{*}1\), \(v^{*}2\), \(v^{*}3\),..., \(v^{*}m\}\), where \(m\) was the number of records that were found.  
Next, each element \(v^{*}_{i}\) in \(Z\) searched out its previous adjacent records. The number of records used to compute the regression function \(f_{i}(x)\) was the window size. Finally, the forecast soil moisture value after interval \(P^{\prime}\), denoted as \({h_{i}}^{+}\), could be computed from \(f_{i}(x)\). The \(m\) elements in \(Z\) contributed \(m\) forecasting soil moisture values \({h_{i}}^{+}|_{i=1,2,\ldots,m}\). The average value of the \(m\) forecasting values was used as the final forecasting value of the soil moisture after interval \(P^{\prime}\). The experimental deductions related to this model are given in Section 3.2.
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 6 Conclusions  
Aiming at the problem of mining and forecasting vessel trajectory, this study presents a method of constructing a vessel trajectory prediction model based on the Attention Spatiotemporal graph Convolution network (ASTGCN). The core of this method is to capture the spatio-temporal characteristics of vessel trajectories by introducing spatio-temporal attention mechanism and spatio-temporal graph convolution. In addition, to adapt the model to the graph structure corresponding to the vessel trajectory and improve the prediction accuracy, a method to determine the prior matrix is also designed.  
The experimental results proved the effectiveness of ASTGCN, which demonstrated significant advantages in several performance indicators. This is mainly due to the attention mechanism introduced in time and space dimensions, and the powerful processing ability of nonlinear trajectory data based on the graph structure of non-Euclidean space. This design allows the model to better capture major feature associations and spatiotemporal dependencies in vessel trajectory data. The excellent performance of ASTGCN indicates that the trajectory prediction model proposed in this study, based on the convolution network of attentional spatiotemporal graphs, has a good performance in capturing the spatiotemporal characteristics of vessel trajectory, which not only provides an accurate prediction for the future trajectory of vessels but also has a certain interpretability for the spatiotemporal attention mechanism of the network structure and the convolution of spatiotemporal graphs. It provides a more transparent and reliable forecasting basis for decision-makers.  
Since the computational complexity of the ASTGCN model is mainly affected by the number of nodes, the number of neighbors, the filter parameters, and the convolution order (in the graph convolution layer), as well as the number of time steps and the vector dimensions (in the attention mechanism), the scalability of the ASTGCN model can be improved by considering the strategies of data parallelism, model parallelism, lightweight design, and algorithmic optimization in the subsequent study. This will allow for effective resource utilization and performance improvement when dealing with large spatio-temporal data.  
Conceptualization, X.C. and R.Z.; methodology, R.Z. and L.Y.; validation, W.Y.; investigation, B.Z.; resources, J.L.; writing--original draft preparation, R.Z. and L.Y.; writing--review and editing, X.C.; visualization, J.L.; supervision, X.C.; project administration, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 42371438 and No. 42301526).  
Not applicable.  
The data supporting the findings of this study can be found at [[https://www.hifleet.com/data/index.html](https://www.hifleet.com/data/index.html)]([https://www.hifleet.com/data/index.html](https://www.hifleet.com/data/index.html)) (accessed on 23 October 2023).  
The authors declare no conflicts of interest.