esa-sceva/satcom-chunk-collection huggingface dataset:
# Contrastive Enhanced Learning for Multi-Label Text Classification
## 4 Experiments
### Results  
The comparative experimental results on the AAPD and RCV1-v2 datasets are presented in Tables 2 and 3, respectively. From these tables, some conclusions can be drawn. Firstly, XML-CNN shows a significant performance gap compared to other models, primarily because it does not consider label information and fails to adequately extract document features. Among the models that do incorporate label information, DXML, Attention-XML, and LSAN demonstrate good performance, highlighting the importance of leveraging label information to enhance classification effectiveness. Secondly, HTTN, which is specifically designed for long-tail MLTC tasks, exhibits superior performance on the AAPD dataset, underscoring its effectiveness in handling imbalanced label distributions. Thirdly, X-transformer also utilizes label information to construct the label space for classification, but its drawback lies in constructing the label space using only the textual semantic information of the labels without considering their relationship with the document. Notably, models such as Attention-XML and LightXML achieve more outstanding performance on the RCV1-v2 dataset. This is due to the more sophisticated data preprocessing of the RCV1-v2 dataset, which better facilitates model learning. Additionally, these models are particularly effective at leveraging the larger label set present in the RCV1-v2 dataset, enabling them to capture more fine-grained label relationships and improve classification performance compared to datasets with fewer labels. This advantage highlights their ability to handle complex multi-label scenarios more efficiently. Overall, the MPCM achieves competitive results on both datasets, owing to its comprehensive consideration of various label information to construct the label space in addition to the ability to leverage the label space to enhance classification performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P@1** & **P@3** & **P@5** & **nDCG@3** & **nDCG@5** \\ \hline XML-CNN & 74.37 & 53.85 & 37.78 & 71.12 & 75.93 \\ SGM & 75.67 & 56.75 & 35.65 & 72.36 & 75.35 \\ DXML & 80.54 & 56.30 & 39.16 & 77.23 & 80.99 \\ Attention-XML & 83.70 & 60.70 & 41.96 & 80.10 & 84.52 \\ EAXM & 83.26 & 59.77 & 40.66 & 79.10 & 82.79 \\ HTTN & 83.84 & 59.92 & 40.79 & 79.27 & 82.67 \\ X-Transformer & 84.45 & 61.53 & 42.16 & 75.89 & 85.07 \\ LSAN & 85.28 & 61.12 & 41.84 & 80.84 & 84.78 \\ LightXML & 85.07 & 60.43 & 41.40 & 76.47 & 84.06 \\ \hline
**Ours** & **86.58** & **62.37** & **42.22** & **81.93** & **85.32** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results on AAPD.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P@1** & **P@3** & **P@5** & **nDCG@3** & **nDCG@5** \\ \hline EAXM & 93.67 & 75.80 & 52.73 & 86.85 & 87.71 \\ XML-CNN & 95.75 & 78.63 & 54.94 & 89.89 & 90.77 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results on RCV1-v2.
## 4 Discussion
To clarify the cause of the systematic discrepancy in spring peak (i.e., EOR) between sap flow and leaf phenology, further data accumulation and combined measurements of leaf phenological metrics and physiological properties, such as \(g_{\mathrm{s}}\), chlorophyll fluorescence, and pigment compositions, are necessary. We are aware of the limitations of our study that should be addressed in future studies. First, the sap flow phenology of individual trees was represented by a single sensor installed at a 0-2 cm depth in the sapwood. Although \(\mathrm{SFD}_{\mathrm{day}}\) generally decreases with increasing sapwood depth, previous studies have shown that ignoring radial variations in \(\mathrm{SFD}_{\mathrm{day}}\) causes considerable errors in estimates of tree transpiration, e.g., [38]. Furthermore, the radial profile of \(\mathrm{SFD}_{\mathrm{day}}\) can vary during the season and is correlated with changes in environmental conditions, such as air VPD [61], soil-water content [37; 62; 63], and irradiance [64]. If such seasonality was present in the radial profile of the \(\mathrm{SFD}_{\mathrm{day}}\) of our investigated species, the SFD obtained from single-sensor measurements would not reproduce sap flow phenology correctly. Therefore, future studies should investigate spatiotemporal variations in SFD within stems to reproduce sap flow phenology at the tree level. Second, reductions in \(\mathrm{SFD}_{\mathrm{day}}\) due to drought stress are rare at this site because of frequent rain events. However, since inter-annual variations in precipitation have tended to increase in Japan since the 1970s (Japan Meteorological Agency), drought conditions may be created in future rainless years. Previous studies have demonstrated that camera-derived leaf phenological metrics cannot detect drought-dependent decreases in physiological properties when canopy color does not change with drought, e.g., [9]. Therefore, two years of observation may be too short to characterize flux-leaf phenology relationships. To enhance the generic applicability of our study results, further data accumulation through long-term monitoring is necessary. Third, image acquisitions using the UAV were often conducted in variable illumination conditions because of rapidly changing regional weather (i.e., frequent fog and cloud cover, Table A1), which causes large spatiotemporal variations in brightness on the canopy surface, creating a bias in phenological metric estimation at the individual tree level. Although the seasonal amplitude of the phenological metrics is markedly greater than that of noise induced by variable illumination conditions in deciduous trees [27; 65], homogeneous illumination conditions are still considered essential for precise estimation of phenological metrics. In unstable weather conditions, such as those in the study site, flight time should be shortened by increasing flight height and adopting higher resolution cameras.
## 1 Introduction
Scintillation causes errors in the received signal, leading to degradation of communication performance, making it insufficient to establish seamless communication systems. Therefore, we analyzed the relationship between pointing error and scintillation from the aspect of beam divergence angle. Furthermore, we verified that a slight increment in pointing error causes a rapid increase in the scintillation index, and a large beam divergence angle can reduce the effect of scintillation. Hence, the proposed adaptive beam adapts its divergence angle according to the pointing error between the communicating optical transceivers to reduce the effect of scintillation due to pointing error. Figure 1: UAV-based vertical and horizontal FSO links.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 2 Materials and Methods
### Method for Anomaly and Trend Detection Model Selection
#### 2.2.1 Overall Method and Selected Models  
To select suitable AD and TD models for the presented system a three step approach was followed to ensure that the most suitable models are chosen. Firstly, potential models were identified in the literature. Secondly, these models were applied on synthetic data meeting defined characteristics of the considered application and evaluated in respect of different quality criteria to reduce the solution space. Thirdly, the best performing models were evaluated using real world data taken from accelerated wear tests of industrial robots. The overall selection process is summarised in Figure 3. In the following, these steps are explained in detail.  
Figure 3: Overview of the model evaluation method.  
Figure 2: Overview of the condition monitoring system.  
As described in Section 1.1, a large number of AD and TD models exist. Hence, a holistic comparison of existing approaches is not feasible. Therefore, models from the classes as described in [18] were chosen for the AD model comparison. In detail, the models listed in Table 1 were used. The models are explained in detail above in Section 1.1.2. For TD model comparison, the MK test, the CS test as well as the LR and MA based approaches described in Section 1.1.3 were chosen. The implementation of the models is described in an open source repository [38].
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 2 Materials and Methods
### Analysis of the Working Mechanism of the Filter's Operation  
The designed filter is based on four cylindrical resonators operating in TM\({}_{010}\) mode to reduce the sensitivity in the thickness direction, thereby reducing the difficulty of the processing, as shown in Figure 1a. The working mechanism of the filter can be summarized in a few steps: First, the feeding rectangular waveguide is located at the non-centrally symmetric part of resonator 1 (or 4). The arrangement ensures that the effective magnetic field of the TM\({}_{010}\) is parallel to that of the feeding waveguide. Therefore, the TE\({}_{10}\) mode transmitted in the fed rectangular waveguide can excite the TM\({}_{010}\) mode in resonator 1; Then, resonators 1 and 2 (or resonators 3 and 4) are coupled through an inductive iris; Finally, resonators 2 and 3 are coupled via a slot. Since the whole structure is symmetric, the mode distribution on the right side of the structure is consistent with that on the right side, as shown in Figure 3.
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 2 Related Work
### Sentiment-Lexicon-Based Sentiment Analysis  
The sentiment dictionary-based analysis method relies on the construction of sentiment dictionaries as its core technology. This method involves extracting emotion words from input texts or sentences using techniques like word cutting and word splitting. Theseextracted words are then matched and compared with existing words in the emotion dictionary to determine the emotional tendency of the input text or sentence. Early sentiment dictionaries typically included only three categories of sentiment words: positive, negative, and neutral. One of the earliest sentiment dictionaries is the SentiWordNet sentiment dictionary, constructed by [PERSON] et al. [1]. This sentiment dictionary assigns sentiment polarity scores to each word to reflect the user's sentiment attitude. [PERSON] et al. [2] proposed that dictionaries and semantic features can effectively represent information in text data, and they used this as the basis for building an emotion detection system. [PERSON] et al. [3] constructed a three-layer sentiment dictionary that combines sentiment words, sentiment objects, and sentiment polarity. By utilizing this combination, the researchers were able to reduce the ambiguity of multiple meanings and achieve a more detailed sentiment analysis, resulting in positive outcomes. [PERSON] et al. [4] expanded on [PERSON]'s work by generating domain-specific sentiment dictionaries and applying them to sentiment feature extraction, thereby enhancing the accuracy of sentiment analysis. [PERSON] et al. [5] utilized lexical dictionaries with semantic directions, such as polarity and intensity, through the use of the semantic orientation calculator (SO-CAL). This approach considered aspects like reinforcement and negation to identify the overall sentiment tendencies of the text. With the rapid emergence of new words, [PERSON] et al. [6] employed co-occurrence information of sentiment words and sentence sentiment labels to construct a sentiment lexicon. They expressed sentiment categories based on average sentiment intensity and successfully performed sentiment classification tasks.
# AUV Adaptive Sampling Methods: A Review
## 4 Mission Objectives of Sampling
### Source Localisation
#### 4.1.1 Bacterium-Inspired Behaviours  
As the plume is formed, the turbulent nature of the fluid flow breaks the plume into smaller patches that have a relatively higher concentration than the surrounding fluid [78]. Therefore, while traversing the plume or to trail the tracer upstream to the source, bacterium use signal gradients to locate the food source. The simple chemotactic process consists of two behaviours, Run and Tumble. Run allows swimming straight in a particular direction, while Tumble allows reorientation to a new direction.  
[PERSON] et al. [79] developed a gradient-following detection method for seeking and tracking a pheromone source. Their approach was based on a biased random walk, inspired by chemotaxis  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chemotaxis** & **Detection Strategy** & **Tracking Strategy** \\ \hline E. coli (bacterium) & Random walk & Increasing signal: Rotate a small random angle Else: New random direction is selected \\ \hline \multirow{4}{*}{Silkworm moth} & & 1: Surge (A rapid upwind for 0.4 s) \\  & & 2: Cast (Side-to-side with increasing amplitude for 4 s) \\  & Passive monitoring & 3: Turn in circle for 10 s \\  & & 4: Turn with random direction for 20 s \\  & & Procedure repeats when a new signal is detected. \\ \hline \multirow{4}{*}{Dung beetle} & & Zigzag diagonally upwind \\  & & When leaving the edge of the plume: change the \\ \cline{1-1}  & & When losing the plume: assume the source is passed \\ \hline Gradient following & Gradient ascending & Manoeuvre the agent toward the stronger sensing signal \\ \hline \hline \end{tabular}
\end{table}
Table 9: Detection and tracking strategies of four chemotaxis algorithms, developed from information in [54].  
of bacterium. In the absence of a concentration gradient, the vehicle executes 10 units of distance (Run) along a grid before altering its direction randomly (Tumble). When the vehicle detects a positive gradient, its Tumble frequency is reduced so that the length of the Run is increased, resulting in what is called a biased random walk. The bias value used in their method was 10% of the mean free path (MFP) that is usually observed in bacterium in nature [80]. A methodical description of the original biased random walk model and its differential-integral equations are provided by [PERSON] [81]. The simulated results compared two strategies, a pure random walk and a biased random walk; the latter demonstrated a superior performance in finding the source.
## 2 Materials and Methods
### Thermal Conductivity  
Thermal conductivity measurements were performed following the ASTM D5334-14 [30] standard using a portable thermal conductivity meter (Thermtest TLS-100). SSP-reinforced composites containing 0% to 3.5% SSP by weight were cast into cube-shaped molds with a volume of 1000 cm\({}^{3}\). To standardize testing, a central hole measuring 2.5 mm in diameter and 5 cm in depth was drilled into each cured specimen. The Thermtest sensor probe was inserted into this cavity to obtain direct real-time readings of the thermal conductivity coefficient. This non-destructive method ensured accurate and efficient evaluation of the composites' heat transfer properties. Measurements were performed in triplicate for each formulation, and mean values were reported.
###### Abstract  
Radial basis function (RBF) is gaining popularity in function interpolation as well as in solving partial differential equations thanks to its accuracy and simplicity. Besides, RBF methods have almost a spectral accuracy. Furthermore, the implementation of RBF-based methods is easy and does not depend on the location of the points and dimensionality of the problems. However, the stability and accuracy of RBF methods depend significantly on the shape parameter, which is primarily impacted by the basis function and the node distribution. At a small value of shape parameter, the RBF becomes more accurate, but unstable. Several approaches were followed in the open literature to overcome the instability issue. One of the approaches is optimizing the solver in order to improve the stability of ill-conditioned matrices. Another approach is based on searching for the optimal value of the shape parameter. Alternatively, modified bases are used to overcome instability. In the open literature, radial basis function using QR factorization (RBF-QR), stabilized expansion of Gaussian radial basis function (RBF-GA), rational radial basis function (RBF-RA), and Hermite-based RBFs are among the approaches used to change the basis. In this paper, the Taylor series is used to expand the RBF with respect to the shape parameter. Our analyses showed that the Taylor series alone is not sufficient to resolve the stability issue, especially away from the reference point of the expansion. Consequently, a new approach is proposed based on the partition of unity (PU) of RBF with respect to the shape parameter. The proposed approach is benchmarked. The method ensures that RBF has a weak dependency on the shape parameter, thereby providing a consistent accuracy for interpolation and derivative approximation. Several benchmarks are performed to assess the accuracy of the proposed approach. The novelty of the present approach is in providing a means to achieve a reasonable accuracy for RBF interpolation without the need to pinpoint a specific value for the shape parameter, which is the case for the original RBF interpolation.  
radial basis function (RBF); taylor series; partition of unity; PUM; interpolation +
Footnote †: journal: Applied Mechanics and Physics Letters  
1
## 4 Existing Jamming Attack Detection Methods and Their Analysis
### Comparison of Existing Methods  
A comparative study of the existing jamming detection methods is as given in Table 1.
# Ultrawideband Cross-Polarization Converter Using Anisotropic Reflective Metasurface
## 2 Design of a Unit Cell
\right]\left[ \begin{array}{c}E_{x}\\ E_{y}\\ H_{x}\\ H_{y}\end{array} \right] \tag{2}\]  
Considering the \(E_{y}\) polarized incidence wave i.e.,  
\[\left[ \begin{array}{c}\mathbf{E}\\ \mathbf{H}\end{array} \right]=\left[ \begin{array}{c}0\\ E_{y}\\ H_{x}\\ 0\end{array} \right] \tag{3}\]  
Figure 2: Design process along with respective reflection characteristics of the proposed anisotropic reflective metasurface with (**a**) a 45\({}^{\circ}\) rotated square patch, (**b**) a rectangular split width of 0.4 mm is employed at 45\({}^{\circ}\) rotation with respect to \(xy-\)axes, (**c**) a triangular part is cut off from the lower portion, and (**d**) a rhombus\(-\)like shape is removed from 45\({}^{\circ}\) rotated square patch. then, \(J_{x}\) and \(M_{y}\) will radiate the \(E_{x}\) polarized wave because  
\[J_{x}=i\omega\big{(}P_{exxy}E_{y}+P_{emxx}H_{x}\big{)}\
eq 0 \tag{4}\]  
\[M_{y}=i\omega\ \big{(}P_{meyy}E_{y}+P_{mmyx}H_{x}\big{)}\
eq 0 \tag{5}\]  
As we have a nonzero cross\(-\)polarized field, and orthogonal polarization is also nonzero, as represented by the following equations,  
\[p_{x}=P_{exxy}E_{y}+P_{emxx}H_{x}\
eq 0 \tag{6}\]  
\[m_{y}=P_{meyy}E_{y}+P_{mmyx}H_{x}\
eq 0 \tag{7}\]  
Furthermore, the effective impedance, permittivity, and permeability are related to the reflection and transmission coefficients. The below equations describe the relationship [17]. \[Z=\pm\sqrt{\frac{\left(1+S_{11}\right)^{2}-S_{21}{}^{2}}{\left(1-S_{11}\right) ^{2}-S_{21}{}^{2}}} \tag{8}\]  
\[\varepsilon_{effective}=\frac{n}{Z} \tag{9}\]  
\[\mu_{effective}=n\times Z \tag{10}\]  
Following the steps represented by simulation results, we can control the reflection and transmission coefficients and can meet impedance\(-\)matching conditions. As a result, there will be minimum co polarization reflection and maximum polarization conversion. \(Z\) represents impedance, and \(n\), \(k\), and \(h\) represent the unit cell's refractive index, wave number, and thickness, respectively. Secondly, the resonance frequency \(f\propto\frac{1}{\sqrt{LC}}\) is inversely proportional to inductance and capacitance effects. As depicted by the above four steps, the inductive and capacitive effect varies when we break the unit cell symmetry, which can be analyzed from the simulated results of the co\(-\)polarized and cross\(-\)polarized coefficients of the reflected wave. When increasing or decreasing the gap between the metallic patches, the capacitance will decrease and increase. We can achieve a strong electric/magnetic resonance by adequately adjusting the L and C through variations of top metallic patches, as shown in Figure 2d.
# Effects of Manure Waste Biochars in Mining Soils
## 2 Materials and Methods
### Incubation Experiment  
In order to study the effect of biochar addition on soil properties, each soil was amended with 10% in weight of biochar (BPL450, BPL600, BRM450, or BRM600), leading to 40 soil treatments. This dose of biochar was already used in previous works [35; 36]. In addition, 10 soils without biochar addition were used as control. Then, 50 g of each treatment was introduced into 1-L airtight jars. For jars with control soils, 50 g of soils were added, and, for jars with amended soils, 45 g of soil + 5 g of biochar were combined. All experiments were performed in triplicate. Later, all treatments were incubated for 180 days at constant temperature (21 \(\pm\) 2 \({}^{\circ}\)C) and humidity (60% field capacity) in a Radiber AGP-360-HR incubator. The soil microbial respiration was measured as follows: CO\({}_{2}\) was collected in a 0.3 N NaOH solution, which was titrated using 0.3 N HCl after the BaCl\({}_{2}\)-assisted precipitation of carbonates [37]. After the incubation experiment, pH, EC, mobile metal content, soil microbial biomass carbon, and enzymatic activities of soils were determined. Cu, Cd, Cr, Fe, Ni, Pb, and Ni mobile fractions were extracted with CaCl\({}_{2}\) 0.01 M, filtered and flushed with distilled water, and measured in an AAnalyst 400 PerkinElmer (AAS).  
Soil metabolic quotient (qCO\({}_{2}\)) and enzymatic dehydrogenase, phosphomonoesterase, and \(\beta\)-glucosidase activities were determined as follows: qCO\({}_{2}\) was calculated as the ratio between microbial respiration and microbial biomass C. Microbial biomass carbon was measured by the difference between the carbon content of the fumigated and unfumtigated samples (following a chloroform fumigation-extraction method) with a commonly used factor (Kc = 0.45) for soils amended with biochar [38; 39]. Then, qCO\({}_{2}\) was expressed as micrograms of CO\({}_{2}\)-C released per milligrams of biomass carbon per hour. In the case of dehydrogenase activity, 2-(_p_-iodophenyl)-3-(_p_-nitrophenyl)-5-phenyltetrazolzolium chloride (INT) was used as substrate, following the method of [PERSON] et al. [40], and the activity of the enzyme was expressed in umol INTF-g\({}^{-1}\)-h\({}^{-1}\)Phosphomonoesterase and \(\beta\)-glucosidase activities were determined after incubating soils with a substrate containing a \(p\)-nitrophenyl moiety; then, in the enzymatic hydrolysis, the amount of released \(p\)-nitrophenol was measured (mmol \(p\)-nitropenol\(\cdot\)g\({}^{-1}\).h\({}^{-1}\)) [38]. Finally, to integrate information from these enzymatic activities, the geometric mean (GMea) was calculated as follows:  
\[GMea=(Delhydrogenase\times\beta-Glucose\times Phosphomonoesterase)^{1/3}. \tag{2}\]