esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Strength Theory of Unsaturated Soil
### Nonlinear Shear Strength  
The shear strength of soil in unsaturated states is conventionally depicted in a linear fashion; nevertheless, a substantial corpus of empirical evidence attests to the pronounced nonlinearity exhibited by shear strength of soil in unsaturated conditions, particularly  
Figure 1: The spatial variations in matric suction within unsaturated soils: (**a**) uniform distribution; (**b**) linear increase with burial depth.  
when subjected to the effects of matric suction [44; 45]. This article posits several nonlinear equations to delineate the inter-relation between the shear strength of unsaturated soil and matric suction. [PERSON] et al. [46] encapsulated a selection of emblematic nonlinear equations, as shown in Table 1. In this context, \(S\) signifies saturation, while \(S_{r}\) signifies residual saturation. \(\theta_{w}\), \(\theta_{s}\), and \(\theta_{r}\) correspond to volume moisture content, saturated volume moisture content, and residual volume moisture content, respectively. Furthermore, \(c_{ult}\) designates the ultimate undrained soil shear strength in a desiccated state, and \(\psi_{r}\) designates residual matric suction. And AEV refers to the Air Entry Value.  
Table 2 enumerates the parameters characterizing the Soil-Water Characteristic Curve (SWCC) for four distinct soil classifications in accordance with the study by [PERSON] and [PERSON] [47]. Specifically, NO.1 corresponds to sandy soil, NO.2 corresponds to silt, NO.3 corresponds to clay, and NO.4 corresponds to ultra-fine-grained soil. In Table 2, \(a_{f}\), \(n_{f}\), and \(\kappa\) stand for the suction value, slope, and fitting parameter, respectively, which correspond to the inflection point in Figure 2b. It can be observed that various soil types exhibit substantial disparities in their shear strength profiles.  
\begin{table}
\begin{tabular}{l l} \hline
**Author and Year** & **Apparent Cohesion Induced by Matric Suction** \\ \hline [PERSON] et al. (1978) [34] & \(c_{app}=(u_{a}-u_{w})\tan\varphi^{b}\) \\  & \(c_{app}=(u_{a}-u_{w})\Theta_{d}{}^{\kappa}\tan\varphi^{f}\) \\  & where \(\kappa\) is a fitting parameter, \(\Theta_{d}=\frac{\theta_{w}}{\theta_{s}}\) \\ [PERSON] et al. (1996) [36] & \(\theta_{w}=\left[1-\frac{\ln\left(1+\frac{u_{w}-u_{w}}{\ln\left(1+\frac{u_{ w}}{\ln\left(1+\frac{u_{w}}{\ln\left(1+\frac{u_{w}}{\ln\left  
Furthermore, for the aforementioned representative soil specimens, Figure 2a illustrates the nonlinear correlation existing between matric suction and shear strength. Meanwhile, Figure 2b delineates the SWCC of a prototypical soil sample. It is clear from the AEV that shear strength demonstrates a notably linear association with saturated soil conditions. Upon surpassing the critical AEV, the shear strength envelope of the soil shows a nonuniform expansion pattern within the nonlinear domain. As the matric suction approaches the residual zone, sand demonstrates a diminishing tendency with the augmentation of matric suction, whereas plastic soil and silt exhibit a proclivity toward horizontal dispersion.
## 4 Discussion
### Advantages, Limitations, and Future Work  
The RF model coupled with RS data for estimating FCO\({}_{2}\) has been proved in this study. Compared to traditional methods, the proposed RF model can quantify the nonlinear relationship between the target variable and the independent variables. It also overcomes the influences of multicollinearity that might be encountered in high-dimensional datasets [14; 42]. Therefore, it provides a huge opportunity to utilize massive amounts of current or future RS data to monitor the carbon flux of ecosystems, especially for the harsh and remote tropical-subtropical forest ecosystems.  
Although the RF model driven by RS data performed well in estimating the FCO\({}_{2}\), there are still some limitations. Firstly, this study only focused on the dry season, the performance of the proposed model in the wet season is still needed to be tested because of the short length of FCO\({}_{2}\) observation and the different driving mechanisms of FCO\({}_{2}\) during different phenological periods. Besides, the contribution of the FCO\({}_{2}\) in the dryseason to the annual total \(\text{FCO}_{2}\) needs to be further explored. Secondly, the moderate spatial resolution of the MODIS and soil moisture products may introduce uncertainties in model prediction. Nevertheless, using these RS datasets to estimate the soil \(\text{CO}_{2}\) efflux at the global and regional scales is increasingly attracting researcher's interests [5; 37; 39], which not only contributes to the prediction accuracy of \(\text{FCO}_{2}\) but also offers the possibility of mapping \(\text{FCO}_{2}\) at high spatial and temporal resolution [8; 37]. Finally, other factors such as soil properties, land-cover change that may affect the \(\text{FCO}_{2}\) emission should be included in future studies.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 5 Conclusions  
This paper constructs an improved YOLOv4 model based on lightweight CNN to detect the transmission line insulator defects in UAV aerial images. Case studies were carried out and different influence factors on the detection results were analyzed. The detection performance was also compared to other object detection models. The conclusions can be drawn as follows:  
1. The data augmentation method based on Graphcut segmentation is effective to expand the defective insulator images, which can avoid the problem of sample imbalance, improve the training effect and test accuracy, and therefore improve the generalization ability and robustness of the detection model.
2. The lightweight YOLOv4 model improved by MobileNet, DSC-SPP, and DSC-PANet has good performance for detection of transmission line insulator defects, with a high detection accuracy and a fast detection speed. The _mAP_ and _FPS_ respectively reach 93.81% and 53 pictures/s, and the overall performance is better than that of Faster RCNN, SSD, YOLOv3, and YOLOv4 models.
3. Laplace sharpening is able to compensate the detection accuracy of the lightweight YOLOv4 model, which increases the _mAP_ to 97.26%, and its property is better than other sharpening methods including sobel, prewitt, and log. The proposed method is helpful for accurate detection of defective insulators on transmission lines, and applicable to meet the requirements of real-time detection in practical engineering.  
Conceptualization, Z.Q. and X.Z.; methodology, Z.Q. and X.Z.; software, W.Q.; validation, Z.Q. and X.Z.; formal analysis, D.S.; investigation, W.Q. and D.S.; resources, C.L.; data curation, Z.Q. and C.L.; writing--original draft preparation, Z.Q. and X.Z.; writing--review and editing, Z.Q., X.Z. and W.Q.; supervision, C.L.; project administration, Z.Q. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Jiangxi \"Double Thousand Plan\" Innovative Leading Talents Long-term Project, grant number jssq2019101071, and Jiangxi Natural Science Foundation for Young Scholars, grant number 20192 BAB216028.  
Figure 12: Detection results of different models under simulated environment.
## 4 Result and Discussion  
In this section, the simulation results demonstrate the performance of the proposed strategy. First, to verify the effectiveness of the proposed control strategy, two driving cycles, which both include the flight phase and the ground phase are used as test driving cycles. Second, the proposed strategy is compared and analyzed with PMP, DP, and RB methods. Finally, the effectiveness of the proposed strategy is verified. Moreover, all the results were calculated on a laptop computer with a 1.60 GHz CPU and 16.00 GB of memory.
## 6 Model Validation using Monte Carlo Simulation  
The CGRR results obtained by the proposed analytical technique in Sections 2 and 4 are compared with the results obtained using a sequential Monte Carlo simulation (MCS) method developed in Section 5 in order to validate the proposed method. The analysis is performed on the IEEE-RTS. The priority loading order, failure rate, operating capacity limits, and the operating cost parameters of the IEEE-RTS are given in Table 4[13].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Load (MW)**} & \multirow{2}{*}{**No. of Units**} & \multirow{2}{*}{**CGRR \(\times\) 10\({}^{6}\)**} & \multicolumn{3}{c}{**Error (\%) with Included Order of Contingencies**} \\ \cline{4-7}  & & & **1 st** & **2 nd** & **3 rd** & **4 th** \\ \hline
900 & 7 & 2686.33119 & 0.3476 & \(3.8\times 10^{-4}\) & \(3.7\times 10^{-7}\) & 0 \\
1300 & 9 & 2685.91680 & 0.5419 & \(1.3\times 10^{-3}\) & \(1.5\times 10^{-6}\) & 0 \\
1497 & 10 & 2688.12089 & 0.7280 & \(1.9\times 10^{-3}\) & \(3.0\times 10^{-6}\) & 0 \\
1694 & 11 & 2691.40242 & 0.9524 & \(3.1\times 10^{-3}\) & \(5.2\times 10^{-6}\) & 0 \\
2004 & 13 & 2701.18317 & 1.5167 & \(6.2\times 10^{-3}\) & \(1.3\times 10^{-5}\) & 0 \\
2159 & 14 & 2707.68269 & 1.8555 & \(8.3\times 10^{-3}\) & \(1.9\times 10^{-5}\) & 0 \\
2459 & 17 & 2725.82489 & 2.7522 & \(1.5\times 10^{-2}\) & \(4.5\times 10^{-5}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Order of contingency states and accuracy in CGRR evaluation.  
An operating condition was considered with a load of 1995 MW. Using the conventional N-1 unit commitment criterion, a total of 13 generating units were committed from the priority loading order. An economic load dispatch by gradient method was used to dispatch the committed units. Table 5 shows the unit loadings for the operating condition.  
The CGRR results obtained from the two methods are shown in Table 6 in eight decimal places for comparison. It can be seen that the two results are very close. The proposed analytical method is simple to apply and provides very close results to that obtained from a sequential MCS method that simulates the actual operation of a system condition in time chronology.
# Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
## 5 Results and Discussion
### Extristic Evaluation
Existing cross-lingual language model pretraining approaches force the model to maintain its multi-linguality [13; 40; 44; 45]. However, in a realistic and practical scenario, the goal is often maximizing the performance on a single language at interest. Our results demonstrates that under this scenario, XPT and completely adapting the model to a single language in general are superior to polyglot models.
# Macro-Modelling Approach for the In-Plane Cyclic Response of Cold-Formed Steel Partition Walls
## 3 Development of the Proposed Analytical Model
### Structural Characteristics of CFS Partition Walls
#### 3.2.2 Gypsum Boards  
GBs behave with rigid-body motion and develop no resistance before vertical displacements, \(\delta_{v}\) due to their racking reach to the joint gap size. After \(\delta_{v}\) overcomes the gap size, the behavior of GBs is represented using the contact property with the RC slab. The contact phenomenon is defined by the initial gap, \(g_{gb}\) chosen to absorb the construction tolerance of GBs. To derive the contact properties, the critical surface is required to be defined as shown in Figure 4. Since the micro-crack at the GB is initiated from a location where their edge distance is the smallest, the failure surface can be defined as the area including two screw connections installed at the corner of a GB. This kind of corner crushing damage has been observed by many experimental studies which were performed to categorize the damage states of CFS partition walls with typical construction practices. The effective failure area, \(A_{e}\) is calculated by multiplying the corner length, \(l_{e}\) with the thickness, \(t\) of a GB. Assuming the corner crushing is concentrated on the effective failure area, the crushing strength of GBs,\(f_{gb}\) is calculated as:  
\[f_{gb}=NA_{e}\sigma_{b} \tag{12}\]  
where \(N\) is the number of GBs, and \(\sigma_{b}\) is the compressive strength of the gypsum board material. Using the material test results of GBs [28], \(\sigma_{b}\) is assumed to be 1.48 MPa. Finally, the maximum load-bearing capacity can be obtained from the summation of \(f_{gb}\) at expected locations of the corner crushing. The stiffness, \(K_{gb}\) and ductility, \(\mu_{gb}\) of the GBs are, respectively, defined as 1.4 kN/mm and 5.7, values based on the modelling properties prescribed in ASCE/SEI 41-17 [24]. Therefore, the lateral force, \(F_{GB}\) of the GBs due to the contact between the corner of a GB and RC slab is derived from:  
\[F_{GB}=K_{gb}(\delta_{h}-\delta_{h,gap,GB})\ \ \left(F_{GB}\leq\sum_{i}f_{gb}\right) \tag{13}\]The monotonic behavior of GBs can be idealized to the bi-linear behavior, as presented in Figure 4. Before the closing of the gap between the corner of a GB and a slab where \(\delta_{h}<\delta_{h,gap,GB}\), the resisting force of the GB is not developed. Once \(\delta_{h}\) reaches \(\delta_{h,gap,GB}\), the bearing behavior of the GB is initiated and the lateral force is increased with the stiffness of \(K_{gb}\). After reaching the crushing strength, the lateral force is slightly increased with the post-yield stiffness ratio of 0.01. At larger displacements corresponding to the ductility, \(\mu_{gb}\), strength degradation occurs due to corner crushing of GBs.
## 2 Material and Methods
### Preprocessing
#### 2.3.2 Discrete Wavelets Based Decomposition  
We used Discrete Wavelet Transform (DWT) for decomposition to enhance the contrast [23]. The level-2 decomposition of Haar wavelet used low (L) and high (H) pass filter banks, that generate approximation (LL) and details (LH, HL and HH) sub-band images as shown in Figure 4. We selected LL and diagonal-details (HH) of level-2 followed by inverse-DWT to level 1 before being merged to DWT image (256 \(\times\) 256). This transformation follows downsampling to 224 \(\times\) 224, to ensure consistency, consequently rejecting LH and HL matrices with contrast improvement.
# An NLP-Based Perfume Note Estimation Based on Descriptive Sentences
## 5 Experiments
### Results and Discussion
#### 5.2.2 Experiment 2: Qualitative Analysis  
Different from Experiment 1, discussed above, Experiment 2 is designed to qualitatively evaluate the proposed system (see Table 3 for details). To this end, the estimated perfume notes using the MiniLM (six Layers) with and without the proposed fine-tuning are shown in Table 5 together with the query descriptions \(d_{test}\) and the relevant ground truth perfume notes \(b_{test}\). Here, the MiniLM (six Layers) is chosen due to its outstanding estimation performance (see Table 4 and Figure 4). Although we report the estimated results of four test samples only due to page limits, a similar trend in results can be observed from other test samples. Similarly, only the first \(k=5\) estimated notes are analyzed.  
From the first two samples shown in Table 5, it is observed that the MiniLM (six Layers) at \(k\leq 5\) and without the fine-tuning has failed to correctly estimate the perfume notes. For the remaining two test samples, the model could estimates one and two perfume notes, respectively. As shown in the last column of Table 5, however, applying the proposed fine-tuning strategy to the MiniLM (six Layers) model has significantly improved the estimation accuracy. The fine-tuned MiniLM (six Layers) correctly estimates one to four perfume notes. Particularly, for the first and third test samples, the fine-tuned model has successfully estimated three and four notes from the first place (at \(k=1\)). However, for the fourth test sample (the bottom-most row of Table 5), the fine-tuned MiniLM (six Layers) estimates one less perfume note correctly than that of the original MiniLM (six Layers).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Query Description, \(d_{test}\)** & **Ground Truth Perfume Notes, \(b_{test}\)** & **Estimated Perfume Notes without the Proposed Fine-Tuning** & **Estimated Perfume Notes with the Proposed Fine-Tuning** \\ \hline A woody, smoky fragrance. The bottle is rounded and designed to fit the hand... It contains a tea note. & Lapsang souchong tea, Bergamot, Amber, Jasmine, Cedar, Sandalwood, Musk, Leather, Vanilla & 1 & Silk tree flower & Musk \\ \hline Bright and lush, mysterious and provocative,... Finally, she added patchuli. & Basil, Lily of the valley, Galbanum, Patchouli, Jasmine, Rosewood, Green botanics, Basil, Lavender, S
## 4 Discussion
### Discussion Benchmarking Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Overall (\%)**} & \multicolumn{2}{c}{**N (\%)**} & \multicolumn{2}{c}{**V (\%)**} & \multicolumn{2}{c}{**S (\%)**} \\
**Methods** & **Acc** & **SEN** & **SPEC** & **PPV** & **SEN** & **SPEC** & **PPV** & **SEN** & **SPEC** & **PPV** \\ \hline [PERSON] et al. [22]* & 99.89 & 99.87 & 98.56 & 99.84 & 99.98 & 99.97 & 99.94 & 96.69 & 99.98 & 97.06 \\ [PERSON] et al. [23]* & 99.53 & 99.68 & 96.05 & 99.55 & 99.94 & 99.97 & 99.50 & 88.94 & 99.72 & 92.57 \\ [PERSON] et al. [1] & 96.68 & 98.72 & 62.46 & 96.22 & 68.08 & 98.43 & 77.91 & 23.27 & 99.97 & 94.80 \\ [PERSON] et al. [70] & 75.20 & 80.20 & - & 78.20 & 50.20 & - & 48.50 & 03.20 & - & 10.30 \\ [PERSON] et al. [71] & 98.70 & 99.90 & - & 99.10 & 97.10 & - & 99.10 & 94.70 & - & 96.80 \\ [PERSON] et al. [72] & 94.69 & 97.79 & - & 95.69 & 72.26 & - & 94.09 & 27.12 & - & 32.44 \\ [PERSON] et al. [73] & 97.66 & 97.35 & 71.09 & 96.47 & 73.26 & 96.42 & 71.67 & 70.28 & 99.44 & 82.90 \\ This method & 96.05 & 98.00 & 60.34 & 95.99 & 69.54 & 97.68 & 70.79 & 02.81 & 99.93 & 32.08 \\ \hline \hline \end{tabular}  
* Trained with balanced data.  
\end{table} 
Table 6: Benchmarking of the proposed algorithm with state-of-the-art work for the inter-patient scheme. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Overall (\%)**} & \multicolumn{2}{c}{**N (\%)**} & \multicolumn{2}{c}{**V (\%)**} & \multicolumn{2}{c}{**S (\%)**} \\
**Methods** & **Acc** & **SEN** & **SPEC** & **PPV** & **SEN** & **SPEC** & **PPV** & **SEN** & **SPEC** & **PPV** \\ \hline [PERSON] et al. [22]* & 99.97 & 100.00 & 99.97 & 99.70 & 100.00 & 100.00 & 99.97 & 97.65 & 100.00 & 100.00 \\ [PERSON] et al. [23]* & 99.92 & 100.00 & 99.86 & 98.87 & 99.50 & 99.97 & 99.98 & 96.48 & 100.00 & 100.00 \\ [PERSON] et al. [1] & 97.37 & 91.64 & 85.17 & 96.01 & 94.07 & 95.08 & 98.74 & 89.04 & 94.76 & 98.77 \\ [PERSON] et al. [70] & 96.50 & 98.70 & 96.30 & - & 82.60 & 97.80 & - & 72.40 & 94.50 & - \\ This method & 98.28 & 99.50 & 79.71 & 97.83 & 87.66 & 99.47 & 92.50 & 55.05 & 99.91 & 94.49 \\ \hline \hline \end{tabular}  
* Trained with balanced data.  
\end{table} 
Table 7: Benchmarking of the proposed algorithm with state-of-the-art work for the intra-patient scheme. In general, despite the deteriorated performance of the proposed method regarding the S category recognition. RCs technology applications in heartbeat classification and ECG signal analysis are paramount and promising avenues. They need more investigations, especially in their improvement which must take into consideration the training speed and hardware amenability. RCs have the characteristic to be physiologically plausible which makes them a powerful hot research topic.
## 3 System Description
### Geomagnetic Recording System
The control electronics is based on PIC 18F4550 and 16F877 microcontrollers managing the acquisition of our two PPM magnetometers. The first PIC is also used to control the triaxial fluxgate acquisition, with an Analog-to-Digital (A/D) converter to digitize its output. Two low-consumption (embedded) PCs are used to log the data, and a satellite transmission system is used to transfer the raw geomagnetic data to the corresponding INTERMAGNET GIN.
## 4 Performance Evaluation and Result Analysis
### Graphical Analysis
A larger value of coding gain implies that the decoding scheme requires a lower SNR to achieve the target error rate as compared to the benchmarked scheme. 3. The channel conditions and noise interference level are closely reflected by the BLER. A lower BLER indicates a cleaner radio channel or better SNR for a particular modulation scheme, which reduces the likelihood of the transport block being received in error. Random linear codes enable efficient error correction and offer flexibility for maintaining the low-latency requirements of URLLC applications in 6G [51]. The plot in Figure 5 highlights the error rate performance of AED, OSD, and GRAND decoders for random linear code with dimensions (128, 103) and (128, 64). Table 5 lists the parameters used for simulations. We evaluate the performance of the decoders for random linear code (128, 103) and (128,64), where N = 128 is the block length and K = 103, 64 are the information bits. For each decoding scheme, simulations are carried out in MATLAB 2024 for BPSK modulation and the AWGN channel. AWGN follows the normal distribution with zero mean and variance \(\sigma^{2}\). Noise variance is expressed as  
\[\sigma^{2}=\frac{1}{2R(E_{b}/N_{o})}=\frac{1}{SNR} \tag{10}\]  
where \(R=K/N\) is the code rate. For simulation, the number of iterations per \(E_{b}/N_{o}\) value is \(10^{5}\). As shown in Figure 5, a BLER of the order of \(10^{-5}\) is achieved at SNR \(\approx 10\) dB for the GRAND decoder for RLC with the code rate R \(\approx 0.8\). For the OSD decoder, the performance for both the code rates is similar, and a BLER of the order of \(10^{-4}\) is attained at \(\approx 11\) dB. The AED decoder for code rate R = 0.5 achieves the BLER of \(10^{-4}\) at an SNR \(\approx 11.8\) dB. The plot verifies better error correction by the GRAND decoder for a code with a high code rate, whereas the performance of the AED decoder degrades as the code rate increases. 4. Figure 6 is the three-dimensional scatter plot characterizing the relationship between energy efficiency, latency, and throughput of the given decoders. On the X-axis, latency in nanoseconds (ns), on the Y-axis, throughput in giga bits per second (Gbps), and on the Z-axis, energy efficiency in pico joules per bit (pJ/bit) are marked, respectively. The color bar maps color to numeric values and indicates the variations in EE values. The performance metrics for AED, SCAL, GRANDAB, and Fast Log-SCS decoders are taken from Table 3. The value for energy efficiency is calculated using Equation (8) and given as an estimated value. For the given throughput value of the decoder, consumed power \(P_{c}\) is taken to be \(\approx\)0.1 Watt and the unit for energy efficiency is taken as pJ/bit. From the plot, it is evident that the AED decoder shows better energy efficiency than other decoders, while Fast Log-SCS is the least energy efficient.
## 2 Numerical Methods and Model Setup
### Spray Characterization  
#### 2.6.1 Spray Pattern  
It is well-known that an accurate representation of the spray is critical to a CFD simulation of the reductant supply process in the SCR system. Therefore, the spray process of a commercial three-hole, pressure-driven, non-air-assisted injector was modeled. The three 0.19 mm diameter nozzles of the injector were uniformly distributed on a 1.9 mm diameter circle, which means the spray consists of three plumes. A single spray plume was set like a solid cone with a cone angle of 11\({}^{\circ}\), and the inclination angle of a single spray plume was set to 9\({}^{\circ}\). The main characteristics of spray are shown in Figure 4. The spray parameters, such as the number of spray plumes, inclination angle, cone angle, mass flow rate, and initial velocity, were obtained from the experiment [31] and calibrated with our previous numerical study [32], which is shown in Table 2. It has been shown that water and UWS behave similarly in terms of bulk spray characteristics [33]. Therefore, water was adopted as the liquid phase in the spray model to simplify the model and facilitate comparison with experimental results.