esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 4 Experimental Results
### Results
Likewise, for the AID dataset, the accuracies are notably high at 70% and 73%, and the NWPU dataset attains accuracies of 66% and 72%, further highlighting the robust performance of these models. To delve deeper into the nuances of zero-shot classification at the class level, we provide a comprehensive view through confusion matrices for the Merced, AID, and NWPU datasets. These matrices depict the performance of both xlm-roberta-large and ViT-H-14, offering valuable insights. Remarkably, eleven classes in the Merced dataset, eleven in the AID dataset, and fifteen in the NWPU dataset achieved classification accuracy surpassing 90%. Unveiling the challenges encountered, the Merced dataset exhibited particular difficulty in distinguishing sparse residential scenes, often misclassified as intersections, while dense residential scenes were sometimes confused with intersections or medium residential areas. Additionally, building classes were occasionally misinterpreted as parking lots. In the AID dataset, complexities emerged with dense and sparse residential categories for xlm-roberta-large, while ViT-H-14 faced challenges with dense and medium residential classes. Within the NWPU dataset, xlm-roberta-large grappled with the identification of airplanes, ground track fields, and terrace scenes, while ViT-H-14 encountered hurdles with meadows and medium and sparse residential classes. These findings underscore the remarkable capabilities of vision-language models (VLMs) in accurately classifying RS scenes despite their training in entirely distinct domains. Nevertheless, further enhancements in analyzing and interpreting these results can offer deeper insights into their implications and potential applications.
## 3 Results and Discussion
### LC-ESI-MS/MS Analysis  
Tentative identification of the compounds presented was performed in the multiple reaction monitoring (MRM) mode, by comparing their retention time and precursor ions with authentic standard solutions. Quantification of the detected compounds was achieved by comparing the peak areas of the detected compounds with those of standard solutions. Correlation coefficients of all the generated calibration curves were higher than R\({}^{2}\) = 0.99 (Table S2-Supplementary Material).  
Caffeic acid derivates, phenolic compounds and hydrogenzoic acids were identified and quantified (Figure 1). Esters of caffeic acid, namely rosmarinic acid and chlorogenic acid, were presented in all the tested extracts. In particular, rosmarinic acid was the predominant compound identified at the methanolic extract (3375.67 \(\pm\) 38.02 ug/g), followed by chlorogenic acid (3225.10 \(\pm\) 16.44 ug/g). In addition, hesperidin (711.57 \(\pm\) 44.79 ug/g), luteolin-7-glucose (597.46 \(\pm\) 12.85 ug/g), luteolin (327.88 \(\pm\) 0.55 ug/g), apigenin glucoside (260.64 \(\pm\) 3.61 ug/g), caffeic acid (175.14 \(\pm\) 9.14 ug/g) and protocatechic acid (110.16 \(\pm\) 2.92 ug/g), were identified in abundance, respect to the other compounds, at the extract of methanol. In general, the use of methanol as an extraction solvent, appeared to be the most efficient, in terms of quantification, for the majority of the compounds presented.  
in combination to the presence of the other identified compounds. Given the rich chemical profile of _Ziziphora taurica_ subsp. _cleonioides_ in polyphenols and taking into consideration that these molecules are suggested to improve human wellbeing, it can be concluded that the plant under study can be classified as a promising candidate to use in therapeutics.
## 5 Results and Discussion  
This part presents the results and the subsequent discussion, which were obtained from this research output. The analysis started from the well log scale, then continued to seismic scale. The analysis started at the synthetic well dataset to test the efficacy of the proposed workflow, followed by the testing on well data of well A-1 and A-2 in field \"X\". Then, the analysis was finished by testing the model on seismic data from field \"X\".  
The results begin from the unsupervised learning analysis at the well log scale. The analysis was started from performing fluid replacement modelling first to create a simulation of an oil and gas reservoir. Then, several AVO attributes were extracted to be used as the inputs for the unsupervised learning model. In this part, AVO attributes can also be analyzed by determining the capability to distinguish hydrocarbons from brine qualitatively.  
The unsupervised learning analysis was started by performing features selection to identify which features are best to be used as inputs, and then the process proceeded to the anomaly detection. In this research, self-organizing maps combined with mean-median and heatmap correlation was used to perform features selection to select the AVO attributes to reduce the redundancy.  
Then, the performance of the hydrocarbon delineation was analyzed using self-organizing maps. The delineation result was validated with water saturation well log. The model was evaluated based on its accuracy in delineating hydrocarbons.  
After the desired result was obtained at the well log scale, the analysis was shifted to the seismic scale, where the SOM algorithm was applied to delineate the potential hydrocarbons in seismic data using AVO attributes that were selected in the previous workflow. Before the SOM is applied, well log correlation and horizon picking need to be completed for the result validation and input preparation. The result will be shown in seismic section and horizon map slice to show the prediction result distribution and validated with the water saturation well log data that have been tied with seismic data.
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 4 Hybrid ARIMA-Elman Model  
Firstly, lifting wavelet is used to preprocess the gyro data, to separate the high frequency white noise and low frequency drift component. Then, the low-frequency drift error belongs to the non-stationary random sequence, which can be further decomposed into linear components and nonlinear components. After that, the linear part is modeled using the ARIMA model, and the nonlinear part is modeled by the Elman neural network. Finally, the two partial prediction values are combined to compensate for drift error of the fiber-optic gyro.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 5 Results and Analysis
### Performance of CTINet based on the \(\alpha\).
#### 5.2.2 Goodput  
Figure 6 illustrates the comparison of algorithms in terms of goodput when receiving CTI under different SNR conditions based on WLAN in the simulation setup. The goodput is averaged over all WLAN STAs. The results show that measurement-based algorithms generally outperform packet error-based algorithms, which are implicit methods, because they perform rate adaptation based on explicit channel state measurements. Moreover, packet error-based algorithms send ACK messages for every packet, resulting in additional control message overhead that reduces goodput compared to measurement-based algorithms.  
M-CARA achieves the highest performance across all WLAN SNRs by preemptively identifying potential CTI situations and selecting an appropriate MCS to minimize packet loss. This proactive approach enhances throughput. In addition, M-CARA reduces control overhead by exchanging MCS feedback only during CTI-induced situations, leading to further throughput improvements.  
Packet error-based rate adaptation inherently has delayed reactions in adjusting rates, resulting in similar performance among packet error-based algorithms. Moreover, CARA's rate adaptation in response to CTI tends to select lower rates compared to traditional algorithms when CTI is present, which leads to better performance in terms of reliability. However, this also results in reduced throughput. In algorithms like PE, where the rate adaptation response is slower, the effort to improve throughput by minimizing packet loss is relatively less effective compared to MB algorithms, leading to less improvement in goodput.  
Figure 5: Comparison of packet error rate under different WLAN SNRs with CTI.
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
## 3 Advanced RF PA Architecture Comparison
 \\ \cline{2-5}
1s the basis for all advanced architectures. & 5 dB–8 dB-8D; Different architecture variations available. & 27\% possible; Linearization and bandwidth difficult. & Predistortion possible; & No additional impedance filtering can be increased without hardware changes. & \begin{tabular}{c} No additional impedance \\ matching networks. \\ Bandwidth comparable to that of _III-V_ semiconductors. \\ \end{tabular}  \\ \hline \multicolumn{5}{c}{**Circuit blocks utilized by architecture**} \\ \hline Active device, & \multirow{3}{*}{\ \begin{tabular}{c} Train and Auxiliary PAs, \\ impedance matching \\ impedance structure, \\ impedance structure, \\ impedance structure. \\ \end{tabular}  } & \multirow{3}{*}{ \begin{tabular}{c} Two nonlinear or linear PAs \\ in parallel, impedance \\ network, supply modulator. \\ matching network, supply modulator. \\ \end{tabular}  } & Multiple identical/different \\ \cline{2-5}
1s the basis for all advanced architectures. & 5 dB–8 dB-8D; Different architecture and bandwidthefficiency and bandwidth. Most of the reported advanced RF PA architectures are suitable to undergo linearization using the currently promising adaptive digital and other predistortion techniques.
# Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions
As WSN applications require compact systems, new directional antennas have been developed [24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Many studies on the use of directional antennas in WSNs are theoretical. The few measurement results have demonstrated that directional antennas can effectively reduce the transmit power for the same range and minimize interference in LOS propagation. Although higher-gain antennas may provide more received power, it is necessary to evaluate how directional antennas behave in environments with attenuation. In these situations, signal transmission is strongly affected by the multipath phenomenon. Therefore, although a directional antenna increases the received signal in a given direction, the number of received reflections is reduced due to its narrower beam. In this work, the performance of directional antennas was compared to omnidirectional antennas in a ZigBee network under NLOS propagation conditions. For the study, measurements were performed in environments affected by Wi-Fi and other ZigBee networks. The performance evaluation considered the RSSI and the Packet Delivery Rate (PDR) parameters. A deconvolution method was applied to estimate the signal distribution around the receiving antenna. These results aim to evaluate the effect of the environment on the received power for different radiation patterns.
## 3 Results and Discussion
#### 3.1.2 Effect of Refrigeration Times  
For the classification of volatile compounds in raw meat with different refrigeration times (Figure 1c), the values of R\({}^{2}\)X, R\({}^{2}\)Y, and Q\({}^{2}\) were 0.969, 0.924, and 0.852, respectively. Correspondingly, for non-volatile compounds in raw meat with different refrigeration times (Figure 1d), the values of R\({}^{2}\)X, R\({}^{2}\)Y, and Q\({}^{2}\) were 0.917, 0.851, and 0.605, respectively. Four distinct clusters were also identified along the two first predictive components, t[1] and t[2], of the score plot for volatile compounds modeled according to refrigeration times (Figure 1c). 24, 72, and 120 h subjects were well discriminated along the t[1]. Variability between 1 and 120 h subjects was collected by t[2]. In the score plot of the non-volatile compounds calibration model (Figure 1d), excellent inter-class comparability between the 1 and 120 h samples could be observed for the first predictive component, where negative scores correspond to 120 h samples and positive scores to 1 h samples. Score plot for the non-volatile compounds also showed a grouping of 24 h and 72 h samples that distributed prevalently as positive scores on the t[2].  
The transformation of muscle into meat after slaughter is accompanied by many biochemical and structural activities. These processes have been shown to be influenced by post-slaughter handling [29; 30] and to affect the volatile and non-volatile compounds, tenderness, color, and other qualities of the raw meat [31]. The conduction of volatile and non-volatile compounds at different times after slaughter were affected by protein oxidation and lipid oxidation [32]. The formation of post-mortem peptides, free fatty acids, and oxidative free radicals led to the generation of flavor precursors, which in turn produced different volatile and non-volatile compounds [1; 33].
# Design and Analysis of Yoshimura Tubular Origami Mechanisms
## 3 The Topological Matrix Representation Method for Yoshimura Origami Configurations
### Examples of the Topological Matrix Representations
For a standard-form origami structure, when the distance between the upper and lower faces on one side decreases, a bending motion is induced in the structure, corresponding to a bending angle \(\theta_{b}\). In contrast, for the derived skewed form, while a bending angle \(\theta_{b}\) also exists, there is an additional difference: after derivation, misalignment occurs between originally aligned circumferential upper and lower faces on one side. This misalignment corresponds to a twist angle, \(\theta_{t}\), which is significant for further analysis of the derived skewed forms of the origami structure. Section 3.2 of this paper provides a detailed analysis of two different skewed crease shape elements, which correspondingly derived into left-biased and right-biased origami structures. On a macro level, these two structures exhibit a twisting motion, which is distinct from the majority of standard origami structures. As shown in Figure 20, the left-biased origami structure in its initial compressed state consists of several origami segments. For a single segment, if the distance between the upper and lower faces on one side increases or decreases, it results in a bending motion. Alternatively, if the distance between the upper and lower faces on all three sides simultaneously increases or decreases, an extension motion occurs. The motion of a single origami segment, when arranged axially, is transmitted throughout the origami structure, leading to an overall extension motion. However, as analyzed in this paper, unlike standard origami structures, a single segment may experience a bending motion, but with an additional circumferential twist angle \(\theta_{t}\). Although this twisting might not be apparent in a single segment, if every segment bends on the same side, the twist angles accumulate through the arrangement. At the macro level, this accumulation results  
Figure 19: Partial morphology. Figure 18: Standard form. in a twisting motion of the entire origami structure. For the left-biased homomorphic tube-derived origami structure, this twisting motion is viewed from above and is in the clockwise direction. Similarly, as shown in Figure 21, the right-biased origami structure can also exhibit both extension and twisting motions. For the right-biased homomorphic tube-derived origami structure, the twisting motion, when viewed from the top, occurs in the counterclockwise direction.
###### Abstract  
An intelligent analytical technique which is able to accurately identify maceral components is highly desired in the fields of mining and geology. However, currently available methods based on fixed-size window neglect the shape information, and thus do not work in identifying maceral composition from one entire photomicrograph. To address these concerns, we propose a novel Maceral Identification strategy based on image Segmentation and Classification (MISC). Considering the complex and heterogeneous nature of coal, a two-level coarse-to-fine clustering method based on K-means is employed to divide microscopic images into a sequence of regions with similar attributes (i.e., binder, vitrinite, liptinite and inertinite). Furthermore, comprehensive features along with random forest are utilized to automatically classify binder and seven types of maceral components, including vitrinite, fusinite, semifusinite, cutinite, sporinite, inertodetrinite and micrinite. Evaluations on 39 microscopic images show that the proposed method achieves the state-of-the-art accuracy of 90.44% and serves as the baseline for future research on maceral analysis. In addition, to support the decisions of petrologists during maceral analysis, we developed a standalone software, which is freely available at [[https://github.com/[PERSON]/MISC-Master](https://github.com/[PERSON]/MISC-Master)]([https://github.com/[PERSON]/MISC-Master](https://github.com/[PERSON]/MISC-Master)).  
maceral components; image segmentation; coal petrography; random forest; two-level clustering +
Footnote †: journal: Applied
# Experimental Study on the Shear Strength of Silt Treated by Xanthan Gum during the Wetting Process
## 3 Results and Discussion
### Direct Shear Test
#### 3.4.5 Discussion on Strength Mechanisms  
According to the mercury intrusion test result, the pore size distribution of the silt specimen is bimodal. Therefore, in the shear process, the internal stress of the silt specimen is not uniform. By comparing Figures 15(a) and 16(a), it is obvious that the pore structure in the silt greatly affects the shear stress of the soil and thus further affects the strength and stability of the soil in the range of low water content. According to SEM pictures and mercury intrusion test results, with the increase in XG content, the pores of silt are gradually filled with XG cement. Compared with Figures 15 and 16, it can be concluded that the stress-hardening phenomenon of the silt treated by XG is more obvious than silt when the water content is 2%, 4%, and 8% because the pores of the silt treated by XG are supported by XG, which expands after absorbing moisture. Therefore, the water content of stress softening into stress hardening increased from 8% to 16%. The results indicate that XG can improve the strength and stability of soil after improving the micropore structure of the soil.  
Figure 17: Wetting strength of the silt specimens with different water contents under a vertical stress of 100 kPa: (**a**) \(m_{bp}/m_{s}=0\%\); (**b**) \(m_{bp}/m_{s}=2\%\).  
Figure 18: Comparison of shear strength parameters before and after treatment: (**a**) \(w\)-\(c\) relation curve; (**b**) \(w\)-\(\phi\) relation curve.  
The mercury intrusion test results indicate that the pore size distribution of the silt specimen is bimodal. The dispersed pore distribution of the specimens results in an unstable structure. The silt specimen is easily disturbed by external loads; thus, the strength of the silt specimen is small. After adding XG, the silt pore size decreased, the pore size distribution changed into a unimodal pore size distribution, and the pore structure stability improved. The water retention capacity and shear strength of the silt, therefore, significantly increased. The SEM test results showed that the cohesion and internal friction angle of the silt specimens under different water content was significantly higher because XG blocks the pores of soil and produces a hydrogel that can bond silt particles together [39; 55; 56; 57].
## 6 Results
### Production Plan Based on Optimal Assignment  
The production plan based on the proposed mathematical model established that the filtration stages and bottling lines will operate jointly, assigning filtration line F2 to work exclusively with bottling line 1 (L1), and filtration line F1 to work in conjunction with bottling line 2 (L2) (Figure 5). This decision is likely due to the fact that switching between filtration lines requires an additional 90 min, which would significantly impact the total production time.  
Under this assignment, the model allocated batches 1, 2, 3, 5, 6, and 9 to the F2-L1 set, while batches 4, 7, 8, and 10 were assigned to the F1-L2 set, as shown in Figure 3. For the F2-L1 set, the total estimated time was 535 min, of which 457 min were allocated to the bottling process and 78 min to adjustments and transitions. In the case of F1-L2, the total time was 510 min, with 441 min for bottling and 69 min for transitions and adjustments.  
Compared to the initial assignment, where batches were distributed without considering the specific properties of the wine or its fouling potential, this new assignment achieves an 8% reduction in total production time for Line 1 (L1-O) and a 7% reduction for Line 2 (L2-O). This demonstrates that the optimal allocation takes into account not only the hydraulic properties of the wine and their impact on processing time but also the filtration capacity of each line.  
Figure 6 shows the result of the optimal allocation of production batches to Lines 1 and 2. This graph complements the analysis by providing an example of how the production lines are affected in the time of each stage when a change in planning is made. In gray, the transition between one wine batch and another is presented, which is determined by the system's state, whether in cleaning processes or line adjustments. Light blue represents the bottling of wine, and the values contained in the graph indicate the duration time of each stage in minutes.  
The model optimized the process by assigning batches with higher fouling properties to the filtration line with the larger area (F2, with 18 m\({}^{2}\)), thus reducing the impact of particle accumulation on processing time. Additionally, the joint assignment of filtration and bottling lines, combined with optimized transition times between stages, allowed for  
Figure 5: Production plan with allocation to line 1 and 2, comparing the value considered in the plan and the observed.  
a reduction in total production time without compromising operational efficiency. These results highlight that the planning based on the mathematical model not only improves the overall efficiency of the system but also addresses technical limitations without incurring additional costs.
## 3 Data
### Global Land Data Assimilation System (GLDAS)  
The Global Land Data Assimilation System (GLDAS) is a near-real terrestrial modeling system, incorporating ground-based and spaceborne observations to provide information on surface states and fluxes on a global scale [54]. It was developed by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center and the National Oceanic and Atmospheric Administration National Centers for Environmental Prediction (NCEP). In this study, the GLDAS Noah Land Surface Model L4 3 hourly 0.25 \(\times\) 0.25 degree V2.1 (GLDAS_NOAH025_3H_2.1) product was used, which is providing data from January 2000 to present with a spatial resolution of 0.25\({}^{\circ}\)[55]. The model uses National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields [56], the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields [57; 58], and the Air Force Weather Agency's Agricultural Meteorological modeling system (AGRMET) radiation fields as forcing input variables [59].