esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
In Figure 10, the confusion matrixes for GoogLeNet and ResNet50 tests are represented. As it was expected, transfer-learning approaches led to best results over classical machine-learning classification models. Choosing a pre-trained deep-learning network and adapting it to the microstructure image dataset was easy and consumed adequate GPU time. The experiments yielded excellent results in test confusion matrix 9 and, in addition, it can be stated that ResNet50 is the deep-learning network that better fits this three-class steel microstructure classification problem. If the confusion matrixes are analyzed, it can be stated that annealing is the class that presents a higher number of false positives. This appears coherent with the complexity established by the authors as a starting point based on the significant heterogeneity degree associated with the corresponding microstructures. Although annealing micrographs are the easiest to be classified, according to the essential number of key points, their heterogeneity suggest a higher probability that the system throws more false positives. We highlight the absence of false positives and negatives related to the other two categories, i.e., quenching and quenching-tempering. The authors expected some degree of confusion between these two categories since there is some visual affinity of the involved micrographs. Thus, it can be stated that the results obtained are excellent and, particularly, better with ResNet50 transfer learning, which can be considered as a good choice in future research in the field of steel microconstituent recognition.
## 5 Performance Analyses
To obtain the numerical results with the closed-form expressions and Monte Carlo simulation, we assume a total transmission power (dBm) given by \(P_{\text{T}}=\sum_{l=0}^{2N-1}P_{\text{x}}[i,i]\), and \(L_{\text{CP}}\in\{14,18,30\}\) for representing the CP length values, and \(\Delta\in\{0,3,7\}\) for the STO. Moreover, the gap factor \(\text{Y}=0\) dB for the data rate analysis and the square 16-quadrature amplitude modulation (QAM) constellation for the BEP analysis. As for the \(\mathcal{B}\)-geometry of the DOST, we consider \(\mathcal{B}=64\). The main parameters are listed in Table 3. Finally, all of the following numerical results were obtained from a Python 3.9 script written by the authors.
# Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement
## 4 Experimental Results
### One-Dimensional NMR T\({}_{2}\) Distribution Measurement Results  
The fluid content and composition within the rock core can be effectively determined by analyzing the area and morphology of the one-dimensional T\({}_{2}\) distribution. The total area under the T\({}_{2}\) distribution curve decreases as the fluid content in the rock core diminishes, reflecting the progressive dissipation of fluids. Moreover, distinct peaks within the T\({}_{2}\) distribution correspond to specific fluid components, such as bounding water and crude oil. Therefore, changes in the morphology of the T\({}_{2}\) distribution provide valuable insights into the fluid state of different components, enabling a detailed analysis of their dissipation behaviors and interactions within the pore structure. The T\({}_{2}\) distribution of the two core samples at various dissipation times are obtained through NMR measurements, as shown in Figure 4. The amplitudes of the T\({}_{2}\) distribution for both cores exhibit a gradual decline; however, the patterns of changes in the T\({}_{2}\) distribution differ significantly between the two cores. Due to the lower viscosity of oil in sample A, the two fluid components in sample A are more distinctly separated in terms of transverse relaxation time at the initial state, whereas those in sample B are harder to distinguish. Over time, however, the two fluid components gradually differentiate as the rapid evaporation of bound water causes the signal with shorter relaxation times to decrease more quickly, reducing the overlap between the two components.  
Core A initially exhibits a bimodal feature, with the amplitude of the right peak significantly greater than that of the left peak. As dissipation progresses, the amplitudes of both peaks begin to decrease, exhibiting no significant difference in the rate of decline. Furthermore, as the amplitudes of the two peaks decrease, their T\({}_{2}\) distributions gradually decrease: the left peak shifts from 0.8 ms to approximately 0.15 ms, while the right peak shifts from 10 ms to 5 ms.  
Core B initially does not display a clear bimodal feature, with the T\({}_{2}\) distribution ranging from 0.1 ms to 100 ms. During the oil-water dissipation process, the reduction in peak amplitude is distributed between 0.4 ms and 40 ms.
## 5 Fault Location in Direct Current (DC) Grid Having Cable and Overhead Line Segments
### Three-Line Segment Model
#### 5.1.2 Case 2  
Let us consider fault \(F_{2}\). It occurs in the middle segment, \(L_{2}\) as shown in Figure 4. If this fault occurs at \(t~{}=~{}0\), then arrival times of traveling waves at terminals \(T_{1}\) and \(T_{2}\) are:  
\[t_{1-F2}=\frac{x_{F2}}{v_{2}}+\frac{L_{1}}{v_{1}} \tag{13}\]  
\[t_{2-F2}=\frac{L_{2}-x_{F2}}{v_{2}}+\frac{L_{3}}{v_{3}} \tag{14}\]  
The difference between arrival times is given by:  
\[\Delta t_{12-F2}=\frac{2\cdot x_{F2}}{v_{2}}+\frac{L_{1}}{v_{1}}-\frac{L_{2}}{ v_{2}}-\frac{L_{3}}{v_{3}} \tag{15}\]  
The distance of the fault location from the start of \(L_{2}\) segment is given by:  
\[x_{F2}=\left(\Delta t_{12-F2}-\frac{L_{1}}{v_{1}}+\frac{L_{2}}{v_{2}}+\frac{L _{3}}{v_{3}}\right)\times\frac{v_{2}}{2} \tag{16}\]
# Application of Three-Dimensional Porous Aerogel as Adsorbent for Removal of Textile Dyes from Water
## 2 Materials and Methods
### Determination of Residual Dissolved Total Organic Carbon  
A Shimadzu TOC total organic carbon analyzer (Shimadzu Corporation, Kyoto, Japan) was used to determine the residual dissolved total organic carbon in aqueous solutions after the adsorption test under static conditions. It is a fast and reliable method for monitoring the concentration of organic matter dissolved in water using carbon as an indicator.  
The Shimadzu analyzer system consists of the following blocks: TOC-VCSN--analyzer; SSM-5000A--solid-sample combustion unit; and ASI-V--automatic liquid sample collection system.  
The process of testing a liquid sample for total organic carbon includes several stages, detailed as follows:  
* Thirty minutes before starting the test, the analyzer should be turned on, and the carrier gas should be released;
* Special 40 mL test tubes are prepared. A detergent is added to an empty test tube (for example, 2.5 g of iodine and 12.5 g of potassium iodide are added to a liter of 1% (volume) sulfuric acid), and the walls of the bottle are properly covered by shaking, followed by a 15 min wait. The solution is poured out, and the test tubes are rinsed thoroughly with tap water and then with distilled water; finally, they are dried;
* The liquid samples need to be filtered. Suspended solids are removed by using a glass-fiber filter (1.2 \(\upmu\)m). The samples are poured into special test tubes with a capacity of 40 mL so that \(\sim\)1 cm of the test tube remains empty;
* The sample tubes are placed in the drum of the automatic liquid sampling system and analyzed. Samples containing carbon compounds are heated to 680 \({}^{\circ}\)C in an oxygen-rich environment in carbon combustion tubes filled with a platinum catalyst. The carbon dioxide produced during oxidation is detected by using an infrared gas analyzer. The signal from the detector produces a peak reading, where its area is proportional to the concentration of total organic carbon in the sample. For the preparation of standard TC solutions, the following steps are necessary:
* A total of 2.125 g of pure potassium hydrogen phthalate, previously dried for 1 h at 105-120 \({}^{\circ}\)C and cooled in a drying cabinet, is weighed;
* The weighed amount of potassium hydrogen phthalate is added to a measuring flask with a capacity of 1 L and dissolved in distilled water;
* The standard stock solution is diluted with distilled water to prepare a standard stock solution of the selected concentration.  
Based on the methodology of the research described above, experimental studies were carried out in which an attempt was made to assess whether the dyes Congo red, Naphthol green B, Rhodamine B and Methylene blue that are used in the textile industry could be removed from an aqueous solution by using an adsorbent made from paper and cardboard waste.
# Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
## 2 Ad Hoc Network Structure and Hybrid Checkpointing Model
### Problem of Hybrid Checkpointing Model to Be Solved  
The hybrid checkpoint model takes both of the communication features of hosts in same cluster and different clusters into consideration, and thus reduces the number of wireless communication messages greatly and saves the kinds of resources of MH\({}_{\rm s}\). What is more, this model has less dependence on cluster heads (it just records the dependence among checkpoints of processes in different clusters, and it will be discussed in Part 5). Therefore, this model has better flexibility.  
However, in a hybrid checkpointing strategy, the checkpoints of a process cannot be handled according to the fully synchronous or asynchronous checkpointing algorithm because of the existing of \(SC_{\rm s}\) and \(DC_{\rm s}\). The main problems for checkpoints processing are as follows:  
1. What are the correctness criteria? In the hybrid checkpoint model, because there are mutual dependence relations among checkpoints in the same cluster and different clusters, so it is necessary to research a brand new global consistency criterion for checkpoints to assure the global consistency of checkpoints.
2. When is the checkpoint triggered? In order to reduce the checkpoint operations and storage overhead, different triggering occasions should be taken for different kinds of checkpoints according to the ad hoc network features and hybrid checkpointing model.
3. When is the checkpoint eliminated? The existing of \(DC_{\rm s}\) makes the elimination of \(SC_{\rm s}\) be complicated, and so creates a lot of checkpoints. In order to save space, it needs to clear away the useless \(SC_{\rm s}\) in time and coordinate the useless \(DC_{\rm s}\) actively.
4. What is the strategy for processing handoff? The checkpointing strategy for the hybrid checkpoint model is decided by the same cluster and different clusters relations of MH\({}_{\rm s}\). However, the same cluster and different clusters relations of MH\({}_{\rm s}\) will change when MH\({}_{\rm s}\) take handoff. Hence, the checkpointing strategy should dispose the change of MH\({}_{\rm s}\) correctly and automatically maintain the new cluster relation to guarantee the correct checkpoint operations of processes in handoff MH\({}_{\rm s}\).
## 6 Results and Discussion
### Feature Analysis  
Figure 3 depicts the Prewitt and Gaussian (PG) filter feature vectors for four distinct forms of distortion from CSIQ datasets: JPEG compression, white noise, Gaussian blur, and JPEG 2000 compression. Each subfigure depicts the PG feature vector distribution for a distinct type of distortion.  
As seen in the image, each form of distortion has a distinct feature vector pattern. In the first subfigure, for example, the feature vector of JPEG compression shows a different pattern when compared with the other forms of distortion. This shows that the suggested technique is capable of collecting and learning the unique characteristics of each form of distortion.  
Furthermore, the results show that by extracting and selecting relevant features with the Information Gain Score algorithm and then training the SVM regression model, the proposed framework can accurately predict the quality of distorted images. On widely used benchmark datasets such as TID2013, CSIQ, and LIVE, approach outperforms state-of-the-art methods in terms of accuracy and robustness.
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
## 3 Impact on _i_GNSS-R Altimetric Performance
### Signal to Thermal Noise Ratio  
The SNR degradation is one of the major issues in \(i\)GNSS-R due to the cross correlation of the thermal noise. From [13; 16], the final SNR of the power waveform at the output of the cross-correlator is expressed as  
\[SNR(\tau)=\frac{\langle|z_{S}(\tau)|^{2}\rangle}{\langle|z_{S_{N}}(\tau)|^{2} \rangle+\langle|z_{S\hat{N}}(\tau)|^{2}\rangle+\langle|z_{N}(\tau)|^{2}\rangle} \tag{3}\]  
where \(\langle|z_{\hat{S}_{N}}(\tau)|^{2}\rangle\) is the average power of cross-correlated up-looking signal and down-looking noise, \(\langle|z_{S\hat{N}}(\tau)|^{2}\rangle\) is the average power of cross-correlated down-looking signal and up-looking noise, and \(\langle|z_{N}(\tau)|^{2}\rangle\) is the average power of cross-correlated up- and down-looking noises.  
Using Equations (2) and (3), the SNR of the interferometric power waveform is simulated at the delay corresponding to the specular point (SP), i.e., \(\tau=0\), as a function of the receiver baseband bandwidth. As shown in Figure 6, the extra transmitted power of the IM component increases the SNR by \(\sim\)0.7 dB with the bandwidth larger than 10 MHz, which should be taken into account during the design and optimization of \(i\)GNSS-R instruments, for example in the link budget calculation and the antenna design. It is worth mentioning that the SNR decreases with the receiver bandwidth, as the thermal noise power is proportional to the latter. The same effect of the receiver bandwidth on the SNR was also reported in [28].  
Figure 5: Modelled L1 reflected power waveform for the spaceborne scenario in Table 2 with and without considering the IM component. Case 1: GPS L1 without the IM component, Case 2: GPS L1 with the IM component.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Analysis of Results
#### 3.3.2 Analysis of the Results of the Composite Test Group
Future research could explore integrating multispectral data to improve plant segmentation accuracy in dense canopy conditions or incorporating LiDAR data to reduce errors in height estimation caused by shading and occlusion. Further optimization of preprocessing algorithms, such as advanced image alignment and filtering techniques, may also enhance point cloud quality under challenging environmental conditions. In addition, we compared the proposed column space approximation segmentation algorithm with commonly used segmentation techniques, such as the region-growing algorithm and Euclidean clustering, to further validate its effectiveness. The region-growing algorithm, which segments point clouds based on curvature and normal vector consistency, performs well for sparse datasets but tends to merge multiple plants into a single cluster in densely planted or shaded environments. Similarly, Euclidean clustering is effective in simple geometric arrangements but struggles to separate overlapping plants in field populations. By contrast, the proposed column space approximation algorithm leverages normal vector orientation and AABB bounding box constraints to achieve more accurate segmentation, even in challenging field conditions. For example, in shaded areas with overlapping canopies, the region-growing algorithm often fails to isolate individual maize plants, where it produces merged clusters. In comparison, the proposed algorithm successfully segmented distinct point clouds for each plant. Quantitatively, the proposed method achieved higher segmentation accuracy under dense canopy conditions, with the accuracy exceeding 90%, compared to approximately 80% for the region-growing algorithm and 75% for Euclidean clustering. Furthermore, the computational efficiency of the proposed algorithm was significantly higher, reducing processing time while maintaining segmentation accuracy. Despite these advantages, the proposed algorithm may encounter challenges in cases of extreme plant morphology irregularity, or substantial environmental noise. Future research could integrate multispectral or LiDAR data to enhance segmentation robustness, particularly in complex environments. Moreover, refining preprocessing algorithms, such as advanced filtering techniques, could further improve point cloud quality and reduce errors caused by shading or occlusion. In summary, the maize single-plant height extracted based on point cloud data has strong feasibility and accuracy at the field scale. However, the study highlights the need for further improvements to address the challenges posed by varying environmental conditions (e.g., lighting and wind) and maize growth stages (e.g., dense canopies and shading). Incorporating additional data sources like LiDAR or multispectral imagery and optimizing the segmentation process could further improve the method's robustness and applicability in diverse field scenarios.
# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 9 Discussion
### 2D VS 3D: Pros and Cons  
The use of 3D facial data overcomes some problems encountered on solutions based on 2D models, but, at the same time, it has some drawbacks that make three-dimensional techniques convenient only for a set of specific applications. Table 7 lists the main advantages and disadvantages of 2D and 3D methods, next highlighting the common challenges.  
The main common challenges that both 2D and 3D methods still have to overcome are listed below:  
* Current publicly available databases include posed facial expressions; lack of spontaneous datasets
* Many features or facial points are marked by hand
* Algorithms usually depend on an excessive number of feature points
* Few works are fully automatic; many of the systems still require manual intervention
* Face images collected in commonly used facial expression databases ignore the effect of time and age
* Due to the large-scale dissimilarity in the nature of the facial data, systems can accurately recognize or classify expressions only for the human faces for which it is trained
* The detection of subtle AUs or the combinations of several co-occurring AUS is still challenging
* More expressions are needed to be handled by future facial expression recognition methodologies as well as arbitrary and spontaneous ones, and micro expressions
* All the expressions have to be recognized with equal accuracy
## 2 Thrust, Torque, and Power Estimation Model for Modified EPB TBM in Mixed-Face Ground  
### Power Model  
A TBM's operating system is split into two components: the mainframe system and the backup system. The mainframe system, which has the most significant influence on the tunneling performance of the equipment, is primarily responsible for moving the cutterhead forward and attaining cutterhead rotation. The back support system is mainly in charge of conveying residue, illumination, water supply, firefighting, etc. The TBM consumes energy in many aspects, such as excavating the ground and overcoming friction. Each portion of the energy will transform into another. In addition, there are various factors affecting the energy consumption in excavation, such as the geological parameters (uniaxial compressive strength, tensile strength, cohesion, etc.), operational parameters (penetration rate, rotational speed, excavation speed, etc.), tool parameters (tool type, number, mounting position, etc.), cutterhead structure parameters (diameter, opening ratio, etc.), etc. Therefore, establishing a power model based on energy consumption is impractical. The TBM's energy is mainly consumed by the thrust provided by the main advance system and the torque supplied by the cutterhead rotation system, so the tunneling power prediction model can be developed based on the energy consumption theory. For TBM excavation in a hard rock formation, [PERSON] et al. (2012) proposed a model for calculating tunneling power, which can be expressed as Equation (1), and mechanical analysis models for tunneling thrust and cutterhead driving torque were developed. However, these models contain many coefficients that need to be identified in the context of specific projects. The following two parts, Sections 2.2 and 2.3, develop theoretical models of the tunneling thrust and cutter torque of modified EPB TBM in the mixed-face stratum, which are then substituted into Equation (1) to estimate the TBM's tunneling power.  
\[P_{T}=Fv+Tn \tag{1}\]  
where \(P_{T}\) is the total tunneling power, \(F\) is the TBM's tunneling force, \(T\) is the cutterhead driving torque, \(v\) is the advance speed, and \(n\) is the rotation speed of the cutterhead.
## 2 Experiments
### Experimental Scheme  
In each test, two cylinders were mounted symmetrically on the rotor test stand, and the model samples rotated clockwise. The computer models are shown in Figure 4. In the present study, the radius of the rotor test stand was designed as 0.3 m, which was determined according to the outlet size of the wind tunnel and previous calibrations of the icing wind tunnel [26]. The rotor test stand is driven by a three-phase asynchronous motor, and the rotational speed was accurately modulated by a variable-frequency drive (VFD).  
Figure 4: Three-dimensional model of the rotor test stand.  
Figure 3: King wind tunnel experimental system. (**a**) Experimental system; (**b**) picture of the test section.  
The experimental conditions in the icing wind tunnel system were measured according to previous work [26]. In the present study, the wind speed for the icing tests was 4 m/s. The liquid water content (LWC) was 0.58 g/m\({}^{3}\). The average medium volume diameter (MVD) was 50 \(\upmu\)m. The experimental temperature in the test section was \(-\)12 \({}^{\circ}\)C. For exploring the effect of the rotational speed on the characteristics of icing on the rotating cylinder, the tip speed ratio (TSR) \(\lambda\), which is the ratio of the linear velocity of the rotor to the wind speed, was used in this paper to characterize the rotational state of the rotor. The TSR is also an important parameter for characterizing the working state of a wind turbine, as expressed by Equation (1).  
\[\lambda=\frac{R\omega}{U}=\frac{\pi Dn}{60U} \tag{1}\]  
where \(\lambda\) is the tip speed ratio, \(R\) is the radius of the rotor (m), \(\omega\) is the angular velocity of the rotor (rad/s), \(U\) is the wind speed (m/s), \(D\) is the diameter of the rotor (m) and \(n\) is the rotational speed of the rotor (rpm).  
In this study, six values of TSR were selected: 0.2, 0.6, 1, 1.5, 2.5 and 3.5. The experimental scheme and key experimental conditions are listed in Table 1.
## 3 Future CONERSTONE Platforms
### Electronic-Photonic Integration
#### 3.4.1 Codesign of CMOS Driver and Si Modulator for Si Photonics Transmitters
As highlighted in Figure 18, the PD chip and TIA chip were carefully placed within the cavity of a fully custom designed PCB. During the packaging process, the output pads of the PD and input pads of the TIA were precisely aligned using an industry-standard flip-chip bonding machine. Through the use of a programmable ball-bonding machine, the length and height of the bonding loops were established as 350 \(\upmu\)m and 80 \(\upmu\)m, respectively (Figure 18b). Furthermore, in order to minimize the current spike within the power supply rails, double-wire bonding was applied to the DC power pads within the TIA. Besides the on-off keying (OOK) performance, measurement results also prove that the integrated receiver can detect 64 Gb/s pulse amplitude modulation-4 (PAM-4) signals when the input OMA is \(-\)8.2 dBm (Figure 18d).