esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 3 Results
### Model Validation  
To verify the applicability of the model, the cotton yield estimation model based on SIF was validated in the flowering and boll period. The model was validated using data from 2023. Among them, the RF model had the best verification effect (R\({}^{2}\) = 0.6407, RMSE = 243.0 kg\(\cdot\)ha\({}^{-1}\), RPD = 0.7547, Figure 15), followed by the Bagging model (R\({}^{2}\) = 0.4234, RMSE = 290.8 kg\(\cdot\)ha\({}^{-1}\), RPD = 0.4600, Figure 15). The model validation effect was the worst in the BPNN model. The cotton yield estimation model based on SIF was validated. The RF model had the best verification effect (R\({}^{2}\) = 0.7381, RMSE = 162.0 kg\(\cdot\)ha\({}^{-1}\), RPD = 1.833, Figure 16), followed by the Bagging model (R\({}^{2}\) = 0.6563, RMSE = 180.3 kg\(\cdot\)ha\({}^{-1}\), RPD = 1.391, Figure 16).  
Figure 14: Yield estimation model of cotton based on canopy SIFy from 14:00 to 15:00 using BPNN for different growth periods. (**a**): budding period; (**b**): flowering period; (**c**): flowering and boll period; (**d**): boll period. Black samples represent the results of the training set, and blue samples represent the results of the verification set.  
Figure 12: Yield estimation model of cotton based on canopy SIFy from 14:00 to 15:00 using RF for different growth periods. (**a**): budding period; (**b**): flowering period; (**c**): flowering and boll period; (**d**): boll period. Black samples represent the results of the training set, and blue samples represent the results of the verification set.  
Figure 13: Yield estimation model of cotton based on canopy SIFy from 14:00 to 15:00 using Bagging for different growth periods. (**a**): budding period; (**b**): flowering period; (**c**): flowering and boll period; (**d**): boll period. Black samples represent the results of the training set, and blue samples represent the results of the verification set.
# Towards Robust Neural Rankers with Large Language Model: A Contrastive Training Approach
## 4 Experiment
### Baselines  
To demonstrate the superiority of our proposed approach in terms of retrieval performance, we selected mainstream IR models as baselines, such as traditional BM25 and RM3 models, as well as neural network-based DRMM and KNRM. BM25 [58] was a probabilistic IR model that extends the Binary Independence Model (BIM) by incorporating term frequency and document length normalization. It has been proven to be effective in various information retrieval tasks and serves as a strong baseline for many modern approaches. RM3 (Relevance Model 3) [59] was a query expansion technique that employs pseudo-relevance feedback to improve retrieval performance. By estimating the relevance model from top-ranked documents and using it to expand the original query, RM3 was able to capture more relevant terms and provide better retrieval results. DRMM (Deep Relevance Matching Model) [60] was a neural network-based model that captures local interactions between query and document terms using histograms. It employed feed-forward neural networks to learn term-matching patterns and aggregated them to produce a final relevance score. KNRM (Kernelized Neural Ranking Model) [61] was another neural network-based model that integrates term-matching signals into a learning-to-rank framework using kernels. It mapped the semantic matching signals into a continuous space, allowing the model to learn fine-grained relevance patterns.  
Additionally, the baselines included the original PLM-based Dense Retriever and Cross Encoder (Reranker) architectures. There are also some excellent studies in the community that focus on retrieval robustness. In this paper, we explored two other advanced robust retrieval methods, the typo-aware model [10] and Dual Contrastive Learning [11], to showcase and compare the robustness of different robust-enhanced retrieval methods. In the typo-aware method, the authors addressed the issue of decreased retrieval performance due to typos in queries, by proposing a simple spelling typo-aware training framework for both Dense Retriever and Reranker. In the Dual Contrastive Learning method, the authors analyzed the distribution of embeddings in the space and proposed an effective training paradigm that utilized contrastive learning to learn optimized representations for queries and documents, ensuring smoothness and uniformity in the embedding space. Note that Dual Contrastive Learning was only proposed for the DR architecture. To ensure fairness in the experiments, whenever a method involves using PLM as the encoder, we set the encoder to bert-base-uncased from the Hugging Face community.
# Dynamic Modeling and Characteristic Analysis of Articulated Steering Vehicles
## 3 Results and Discussion  
After determining the process in Section 2, we established a simulation platform with the dynamic model of ASVs using Matlab/Simulink software. The system contained a power train system, a steering system, a braking system, and the lateral and longitudinal dynamic responses of the frames. The inputs were the signals of the acceleration pedal, the braking pedal, and the steering handle, just as in a real LHD vehicle. The simulation interval was set as 0.01 s, i.e., 100 Hz, and the parameters are shown in Table 1.  
In order to validate the embedded dynamic model, we performed a field test on a load-haul-dump vehicle. A diagram of the field test system is shown in Figure 6. The states of the frames, steering system, and power train were recorded. The data involved during the field test, including velocity, angular velocity, articulated angle data, torque, and pressure, were recorded, and the sample rate was set to 100 Hz. An LMS SCADAS (1, SCM05) was used to record the data during the field test. Two inertial measurement units (IMUs) (2, 3, SC-AHRS-200A) were installed in the front and rear frames. Two kinds of encoders were used to measure the articulated angle of the frame (4, MIRAN WOA-C) and the speed of transmission (5, SCHMEASAL IFL 5-18M-10P). Pressure sensors (6, AE-H) were installed to measure the pressure of the steering system and the braking system. The acceleration pedal and the state of the motors were recorded in the controller of the LHD vehicle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(m_{1}\) & 12,850 kg \\ \(m_{2}\) & 15,650 kg \\ \(l_{21}\) & 307,420 kg\(\cdot\)m\({}^{2}\) \\ \(l_{22}\) & 380,320 kg\(\cdot\)m\({}^{2}\) \\ \(B\) & 0.993 m \\ \(l_{71}\) & 0.19 m \\ \(l_{72}\) & 1.94 m \\ \(L_{71}\) & 0.16 m \\ \(L_{r2}\) & 1.59 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main involved parameters.  
Figure 6: Field test system of target ASV.  
The obtained input signal of the test vehicle was input into the simulation model, and the responses were compared with the test results to validate the model. For the ASV, off-road conditions were used for the running ground in order to examine a compacted earth road, with a standard road roughness of level E. If the road conditions are not modeled accurately, then differences between the field test and simulation are inevitable.
## 5 Experimental Test  
To verify the effectiveness of the proposed controller in real systems, the Furuta pendulum is implemented in real-time. A comparison with the results in [24] is provided to evaluate performance and efficiency. In that sense, the QNET 2.0 Rotary Pendulum board from Quanser (119 Spy Ct, Markham, ON L3R 5H6, Canada), based on the Furuta pendulum mechanism (see Figure 11), was used to verify the proposed control scheme via hardware-in-the-loop experiments.  
The two cases in the simulations are considered using the same development for the linearized system (21), the control law (23), and the design of the HGO (22), in such a way that the implementation is based on the same model (Furuta pendulum). Additionally, for the design of the fault observer, a simple modification is provided by the following analysis: it is well known that the observer performs better as more information becomes available. That is to say, for reconstructing the states as well as the actuator fault, we set the output as \(\bar{g}(t)=Cx\), assuming that all state information is available (whole information regulation problem). On the other hand, the output defined in (21) holds. Therefore, since the design of the HGO is independent of the UI observer, an observation gain is designed based on the following output (it gives account of 2 position sensors):  
Figure 10: Comparison between the real actuator fault and the estimated fault.  
Figure 9: Comparison between the unknown reference signal \(\Psi_{2}\), the estimated signal by the HGO and the state \(x_{1}\).  
\[\bar{y}=\begin{bmatrix}1&0&0&0\\ 0&1&0&0\end{bmatrix}x(t),\]  
such that \(\tilde{C}_{a}=[C_{a}\ \ 0_{0\times(n+p_{n}m)}]\). Thus, by applying the LMI (16) for the fault actuator observer of the fifth order with a decay rate of 4, one can obtain  
\[L=\begin{bmatrix}48&55\\ -12&23\\ 1274&1883\\ 365&847\\ 1151&1721\\ 10395&15563\\ 54576&81712\\ 155867&233343\\ 187914&281266\end{bmatrix}.\]  
The results in [24] show that, although the simulation and real-time results are sufficiently close to validate the effectiveness of the regulator, there is still room for improvement. Considering the error between the behavior of the physical plant and the reference signal as an actuator fault, it is treated as an estimable signal and is discarded from the regulator.
# Efficient Top-\(k\) Spatial Dataset Search Processing
## 4 Grid Distribution-Based Spatial Dataset Similarity Measurement
: _Given two spatial datasets \(S_{i}\) and \(S_{j}\), and the corresponding grid distributions are \(D_{i}\) of \(D_{j}\), the similarity between \(S_{i}\) and \(S_{j}\), denoted as \(sim(S_{i},S_{j})\), is measured as Equation (5),_  
\[Sim(S_{i},S_{j})=\sigma DSim+(1-\sigma)QSim, \tag{5}\]  
_where_  
\[DSim=\sum(T_{i,x},T_{j,y})\in T\ \min\{T_{i,x},r,T_{j,y},r\}, \tag{6}\]  
\[QSim=1-\frac{|\sum_{(T_{i,x},T_{j,y})\in T}T_{i,x}\cdot N-\sum_{(T_{i,x},T_{j,y})\in T}T_{j,x}\cdot N|}{\max\{\sum_{(T_{i,x},T_{j,y})\in T}T_{i,x}\cdot N,\sum_{(T_{i,x},T_{j,y})\in T}T_{j,x}\cdot N\}}, \tag{7}\]  
\[T=\{(T_{i,x},T_{j,y})|T_{i,x}\in D_{i},T_{j,y}\in D_{j}\wedge T_{i,x}\cdot g=T _{j,y}\cdot g\}, \tag{8}\]  
\(\sigma\) _is a similarity regulator, and \(\min\{*\}\) and \(\max\{*\}\) are to obtain the minimum and maximum values of a set, respectively._  
Definition 4 indicates that the densities and quantities of overlapping non-empty grids are incorporated into the measurement of spatial dataset similarity. For two spatial datasets, their density distribution similarity and quantity distribution similarity in the MCG are considered. Furthermore, the similarity regulator \(\sigma\) can adjust the ratio of density to quantity similarity. A higher \(\sigma\) value places greater emphasis on density similarity, whereas a lower \(\sigma\) emphasizes quantity similarity. The introduction of the similarity regulator enhances flexibility, allowing for adaptation to a broad spectrum of real-world requirements. Figure 2: An example of grid distribution representation. We also take the same example to illustrate Definition 4 as shown in Figure 2. For the datasets \(S_{i}\) and \(S_{j}\), there are three overlapping non-empty grids, \(g_{2,2}\), \(g_{3,2}\) and \(g_{3,4}\). Different similarity regulators yield varying levels of similarity. Assuming that the similarity regulators are set at 0.5 and 0.8, the similarities between datasets \(S_{i}\) and \(S_{j}\) are calculated as follows: when the regulator is 0.5, the similarity \(sim(S_{i},S_{j})=0.5\times 0.4+0.5\times 0.8=0.6\); and when the regulator is 0.8, the similarity \(sim(S_{i},S_{j})=0.8\times 0.4+0.2\times 0.8=0.48\), according to Equation (5). In summary, the GDSS can demonstrate the distribution feature of locations in spatial datasets by dividing the space into grids. Compared to MBR-based similarity, it provides a more detailed representation of spatial datasets, and compared to EMD-based similarity, it avoids complex similarity calculations. In practice, if smaller grids are generated by dividing the spatial space, the closer the similarity between points located in the same grid is, and the more fine-grained spatial dataset similarity measurement is achieved. However, the computational cost of the similarity calculation will be increased. We will perform experiments to evaluate the impact of space-dividing resolution in Section 7.
# Stabilized Sewage Sludge as Fertilizer: Risks Related to the Presence of Microplastics
## 2 Materials and Methods
### Identification of Microplastic Polymers via Raman Spectroscopy  
The identification of microplastic (MP) particles was performed using Raman spectroscopy based on the comparison of the characteristic Raman peaks of each particle with the reference spectra. This method allows for the differentiation of polymers based on their distinct vibrational modes, which are highly specific to their molecular structure.  
To support the polymer classification process, a list of characteristic Raman peaks for the most commonly encountered microplastic types was compiled from the literature and spectral databases. These peaks served as the basis for identifying the types of polymers in the collected samples. Table 1 summarizes the leading Raman bands, their vibrational assignments, and the corresponding references for each polymer type analyzed in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polymer** & **Main Raman Peaks (cm\({}^{-1}\))** & **Vibrational Assignment** & **References** \\ \hline Polystyrene (PS) & \(\sim\)620, \(\sim\)1000, \(\sim\)1030, \(\sim\)1600, \(\sim\)2900 & Aromatic ring deformation, ring breathing, C=C stretching, C–H stretching & [PERSON] et al. (2018) [16] \\ \hline Polyethylene (PE) & \(\sim\)1060, \(\sim\)1130, \(\sim\)1295, \(\sim\)1440, 2880–2950 & C–C stretching, CH\({}_{2}\) twisting, C–H stretching & [PERSON] et al. (2018) [17] \\ \hline Polypropylene (PP) & \(\sim\)840, \(\sim\)973, \(\sim\)1165, \(\sim\)1330, \(\sim\)1450, 2880–2950 & CH\({}_{3}\) rocking, C–C stretching, CH bending, and stretching & [PERSON] et al. (2015); [PERSON] et al. (2018) [16,18] \\ \hline Polyvinyl chloride & \(\sim\)638, \(\sim\)695, \(\sim\)830, \(\sim\)1430, \(\sim\)2900 & C–Cl stretching, C–H deformation, skeletal vibrations & [PERSON] et al. [17] \\ \hline Polyethylene & \(\sim\)860, \(\sim\)1000, \(\sim\)1240, \(\sim\)1615, \(\sim\)C–C, C–O stretching, aromatic C=C, ester C=O stretching & [PERSON] et al. (2018) [16] \\ \hline Polyamide & \(\sim\)935, \(\sim\)1200, \(\sim\)1440, \(\sim\)1640, \(\sim\)2900 & C–N stretching, CH\({}_{2}\) deformation, amide C=O stretching & [PERSON] et al. (2015); [PERSON] et al. (2018) [16,18] \\ \hline Polycarbonate (PC) & \(\sim\)640, \(\sim\)1130, \(\sim\)1600, \(\sim\)1770 & C–O stretching, aromatic C=C, carbonate C=O stretching & [PERSON] et al. [17] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic Raman peaks of selected microplastic polymers.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 4 Node Clustering
### Clustering Parameter Enhancement  
The coordinates of the cluster centers (\(x^{\prime}_{C_{i}}\), \(y^{\prime}_{C_{i}}\)) are iteratively updated using the K-means optimization method based on the average coordinates of the nodes within each cluster \(C_{i}\). To assign nodes to their nearest clusters, the algorithm computes the Euclidean distance \(d(N,C_{i})\) between each node \(N\) and the updated cluster centers (\(x^{\prime}_{C_{i}}\), \(y^{\prime}_{C_{i}}\)) as shown in Equation (1), starting from the previous centers (\(x_{C_{i}}\), \(y_{C_{i}}\)). This process establishes proximity-based clusters with each node's coordinates denoted by \(x_{N}\) and \(y_{N}\), contributing to the calculation of the updated cluster centers.  
Figure 1: Pseudocode for K-means cluster center initialization and updating.  
\[d(N,C_{i})=\sqrt{\left(x_{N}-x_{C_{i}}\right)^{2}+\left(y_{N}-y_{C_{i}}\right)^{2}} \tag{1}\]  
Equations (2) and (3) ensure that the cluster centers \((x^{\prime}c_{i},y^{\prime}c_{i})\) consistently improve to accurately reflect the spatial distribution of nodes within each cluster \(C_{i}\) after the initial assignment, as represented by \(\left|N_{C_{i}}\right|\). Additionally, our method employs the elbow method and Silhouette Score in Equation (4) to determine the optimal number of clusters \(k\). The elbow method identifies the ideal number of node clusters by detecting the point where the reduction rate in the within-cluster sum of squares (WCSS) significantly changes. Similarly, the Silhouette Score measures the compactness and separation of clusters, with higher scores indicating more distinct clusters. The algorithm illustrated in Figure 2, outlines the procedures involved in the optimization process, including node assignment and optimal \(k\) selection.  
The algorithm begins by randomly initializing cluster centers and assigning nodes to the nearest cluster based on Euclidean distance. For each \(k\) value in the specified range, the algorithm computes the WCSS using Equation (4), with \(x\) and \(y\) coordinate points and \((\mu_{i})\) centroids used to evaluate the clustering quality and iteratively update the cluster centers. Consequently, the algorithm shown in Figure 3, determines the optimal number of clusters for the final K-means clustering phase.  
\[x^{\prime}c_{i} =\frac{1}{\left|N_{C_{i}}\right|}\sum_{N\in N_{C_{i}}}x_{N} \tag{2}\] \[y^{\prime}c_{i} =\frac{1}{\left|N_{C_{i}}\right|}\sum_{N\in N_{C_{i}}}y_{N} \tag{3}\]  
Figure 2: Pseudocode for node granting and optimal K selection: Coordinate clustering.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 5 Discussions
### The Contribution of Each Component in the HC-MMK Approach
#### 5.2.4 Quality Control  
Quality control is a critical step for producing a widely-used land cover product [11; 55; 56]. The procedures need to be applied to the whole classification process, including satellite image collection and preprocessing, field data collection, automatic classification and accuracy assessment. Only if each sub-step obtained a high-precision result could a high accuracy land cover product be achieved. This paper proposed a practical, interactive quality control method based on prior knowledge, which obviously eliminated many classification errors and spatial discontinuity phenomena (Figures 9 and 10) and improved the quality of the CLC-SW2010 product. In addition, many strict criteria were also developed in the other stages; for instance, the accuracy of image-to-image co-registration was less than 2 pixels in mountainous areas for data preprocessing, and the total cloud cover was less than 10% for each selected satellite image.
## 2 Materials and Methods  
Figure 1 shows the three traditional helmet designs considered in the present study. The full-face helmet covers the entire head, and comprises a rear section which covers the back of the skull and the top of the neck, a protective section which covers the top of the head and the cheekbones, and a further protective section which covers the jaw bone and chin (Figure 1a). The open-face helmet covers the same area as the full-face helmet, but leaves part of the face (usually the lower chin) exposed (Figure 1b). Finally, the half helmet has essentially the same design as the open-face helmet, but leaves the lower rear region of the head and the cheeks exposed (Figure 1c).  
The crashworthiness of motorcycle helmets is generally evaluated by means of free-fall drop tests, in which the helmet is positioned on a test headform and then dropped vertically, onto a fixed steel anvil. Figure 2 presents a schematic illustration of the test rig used in the present study to investigate the impact performance of the full-face helmet. When performing the test, the helmet was placed over an aluminum headform attached to a ball arm and was then dropped onto a flat tetragonal anvil containing a circular impact area with a diameter of 127 mm. The moving assembly (minus the helmet) had a total mass of 5 kg and exhibited no resonant frequencies below 2000 Hz. The free-fall height of the ball arm was set to 1830 mm; resulting in an impact velocity of 598.9 cm/s. The helmet was positioned on the headform in such a way that the impact occurred against the top of the helmet; thereby avoiding the air vent regions. The headform acceleration during impact was measured using a MAU-1055 accelerometer (Superpro Technology Corp., Taipei, Taiwan).  
The present study evaluated the performance of the four helmets (i.e., full-face, open-face, half-face, and open-face with ventilation) with regards to three different standards, namely ECE R22.05, Snell M2010, and DOT FMVSS 218. As shown in Table 1, all three standards prescribe a peak acceleration limit for the headform center of gravity (COG), while the DOT standard imposes an additional limit on the acceleration duration [8].  
Figure 1: Motorcycle helmet designs: (**a**) full-face; (**b**) open-face; and (**c**) half.  
Figure 2: Experimental setup for free-fall impact tests.
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 2 Speech-Coding Background
However, as these standards evolved, codec complexity increased substantially as well. It is clear from the above discussions, however, that most current speech-coding research investigates performance compared to the AMR-WB codec. This is because of the prevalence of the AMR codecs in applications including VoIP, digital cellular, and first responder communications. In this paper, we describe and analyze the performance of a codec structure with much lower complexity and delay than AMR and EVS for coding narrowband speech. An evolution to wideband speech-coding is also suggested. Overall Block Diagram of the Multimode Tree Coder with Pitch Prediction and Pre- and Post-Perceptual Weighting  
Figure 1 is the block diagram of the Multimode Tree Coder with backward pitch predictor and perceptual pre- and post-weighting filters, designated as MMT-WP. This diagram shows the basic structure of the codec and the locations of the several components. We see from the figure that the codec involves both pole \(A(z)\) and zero \(B(z)\) short-term predictors and a long-term predictor \(PI(z)\). This combination is called a code generator in tree-coding. Additionally evident from the figure is a now classical analysis-by-synthesis loop involving a distortion measure and a tree search for the best excitation. The figure also shows the Mode Decision outside of the encoding loop and the new location of the perceptual weighting outside of the encoding and decoding sequence. These components and the others are developed in detail in subsequent sections.