esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
With the dual arms and dual grippers configuration, the proposed bimanual robotic teleoperation system shows strong potential in dealing with challenging manipulation tasks, ranging from one hand grasping with different weights, shapes, sizes, and hardnesses to bimanual collaborative tasks such as target approaching, grasping, lifting, and holding. Compared with traditional teleoperation system with one arm and one gripper, the bimanual configuration with dual arms and dual grippers enables the robotic system to perform more complex manipulation tasks, such as picking and placing heavy objects, grasping, passing, holding, and assembling. Based on the bio-inspired design philosophy, the bimanual configuration with dual arms and dual grippers is very similar to human beings. Undoubtedly, the two hands configuration is much better than a one-hand system in collaborative tasks. It is more common in our daily life and industrial applications.  
Based on the haptic device and the joint space mapping strategy, the human operator has the ability to manually control the robotic arm according to his intuitive observations. However, the current setup is only limited to the motion control of the robotic arm. The force information is not included. Besides, the current system is established on the open loop control. The time delay and dynamic uncertainties have not been taken into consideration. To further improve the performance of the teleoperation system, a feedback control is indispensable. Multimodal perception, including visual information and tactile sensing, would be considered to incorporate with the teleoperation system to enhance the bilateral interaction between the human operator, the robot, and the environment. More scenarios and multimodal control strategies could be established, ranging from supervised autonomy to full teleoperation.  
The Leap Motion Controller provides a markerless, natural user interface for hand motion tracking in the teleoperation of the robotic gripper. Compared with various wearable devices, it gives the user a more natural and comfortable environment to interact with the slave devices. Due to the similar configurations between the human hand and the anthropomorphic hybrid robotic gripper, the gesture-based control strategy enables the human operator to control the robotic gripper more intuitively and much more easily. However, it is observed that the accuracy of the hand tracking falls significantly when the hand moves away from the Leap Motion Controller, or moves far left or  
Figure 10: Execution of the teleoperation-based bimanual tasks for the manipulation of an aluminum frame.  
far right, especially in the dynamic setup. The self-occlusion is another problem. When the finger is covered by the palm, or when the palm is sideways, some of the fingers would be overlapped by others. In such scenarios, self-occlusion occurs. To overcome these shortcomings, some other sensors and corresponding sensor fusion techniques should be considered to improve the tracking performance of the Leap Motion Controller.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 5 Conclusions  
This paper introduced a high-order tensor model tailored for ETC gantry data that adeptly captures temporal and spatial structural information. Through the utilization of an Improved Dynamic Tensor Decomposition (IDTD) approach, we successfully addressed the challenges posed by the considerable sparsity of ETC gantry data. By employing a Laplacian matrix within the IDTD framework, we effectively mitigated computational burdens and preserved essential traffic characteristics in the data completion process. Our comparative analysis, illustrated through a case study, confirms the superior accuracy and reduced computational complexity of the IDTD-based algorithm over traditional methods. The IDTD method, therefore, holds substantial promise for comprehensive ETC gantry data completion and has the potential for broader application across diverse datasets and scenarios. Future research will explore extending the reach of IDTD in handling large-scale ETC gantry datasets and its adaptability to other data-intensive domains.  
Methodology, Y.R. and Y.Z.; validation, Y.R. and Y.Z.; data curation, W.L. and C.W.; writing--original draft preparation, Y.R. and Y.Z.; writing--review and editing, Y.R., Y.Z., W.L. and C.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Key R&D Program of Shandong Province, China. No. 2020 CXGC010118.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 4 Evaluation and Limitations
### Counting Results  
The following section evaluates three different traffic scenes. Each of them has a specific characteristic in order to get a broader sense of the proposed classification and counting system. The evaluation of the scenes is done with different request intervals, due to the fact that the performance of the system changes rapidly when operating under different settings. For each request interval there is a screenshot showing the prediction model as well as a table with the corresponding counting results. The prediction model shows the estimated driving paths during operation which are marked as coloured lines. Each of the resulting tables for every analysed request interval shows the actual vehicles on the scene, the counted objects by the program as well as the sum of them. Furthermore, each table is split up into the detected vehicle types. These tables are used to calculate the actual error rate of lost vehicles missed by the program or of additional vehicles detected by the system.
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 3 Methods  
### Overall Framework  
The overall framework of FEHA-OD shown in Figure 2 mainly consists of the feature extraction network, prediction module, and loss function. The FEFN and HAM are parts of the feature extraction network for feature enhancement. The key modules are designed as follows:  
1. Feature extraction network: Firstly, an input image is sent to ResNet101 Conv1-5, which is the backbone. Then, FEFN and HAM modules perform the feature enhancement. Finally, a feature map that is 4 times smaller (scale s = 4) than the input image is output.
2. Feature enhancement fusion network (FEFN): Firstly, a 1 \(\times\) 1 convolutional is used to adjust the number of the high-level layer channels and bilinear interpolation is used to upsample the high-level layer. The upsampled result is combined with the low-level layer through element-wise addition. Then, the combined feature map is sent into three sub-networks containing different receptive fields to enhance original features. Finally, the results of these sub-networks are fused to obtain multi-scale representation.
3. Hybrid attention mechanism (HAM): A supervised pixel attention mechanism and a channel attention mechanism are combined, which makes the network focus on both the pixel-level information regarding an object and valid channel information, effectively reducing the disturbance of background noise and detecting small objects.
4. Prediction module: The image is predicted by four branches, namely heatmap, offset, box parameters, and orientation, and these four branches work together to achieve oriented object detection.
5. Loss function: A multi-task loss function is used to train and combine the above modules better.  
The input RGB image is \(I\in R^{3\times W\times H}\). After the feature extraction network, the feature map \(A\) is output as follows.  
\[A=f_{FEN}(I),A\in R^{C\times W/s\times H/s} \tag{1}\]  
where \(f_{FEN}\) denotes the processing of the feature extraction network. \(C\) denotes the channel numbers, which is set to 256. \(S\) denotes the downsampling multiplier, which is set to 4. \(W\) and \(H\) are the width and height of the input image.  
Then, \(A\) is transformed into four branches in the prediction module: heatmap \(HM\in R^{K\times W/s\times H/s}\), offset \(O\in R^{2\times W/s\times H/s}\), box parameters \(B\in R^{10\times W/s\times H/s}\), and orientation  
Figure 2: Overall framework of the proposed FEHA-OD.  
\(\theta\in\mathbb{R}^{1\times W/s\times H/s}\), where \(K\) represents the number of object categories. Each branch gets a corresponding loss. Finally, by combining all the losses, the output image with detection results can be obtained.
# Sustainable Development of Concrete through Aggregates and Innovative Materials: A Review
## 4 Organic Aggregates
### Coconut Fiber  
Coconut fiber, extracted from the outer shell of coconuts, has great properties to be used in concrete. For instance, coconut fiber can achieve 4 to 6 times more strain in comparison with other natural fibers [103]. Moreover, coconut fiber has the highest toughness among all-natural fibers. These mechanical characteristics reduce plastic cracking, which leads to having coconut fiber concrete with enhanced flexural behavior and high impact resistance compared with traditional concrete. Nevertheless, coconut fiber concrete can reduce its compressive strength and workability [104].  
[PERSON] et al. [104] developed experimental tests in coconut fiber concrete and observed that 4% replacement of coconut fiber in mortar can decrease its compressive strength about 50%. In addition, water absorption and flexural strength of coconut fiber mortar can be increased up to 33% and 50%, respectively. Based on the stress-strain curves of coconut fiber concrete, [PERSON] et al. [104] reported that its elastic modulus decreased while its maximum strain achieved \(6\times 10^{-3}\), which is two times more than conventional concrete [105]. Thus, coconut fiber can be used as a polymer fiber in concrete for boards, slabs and pavements [106].  
Ropes composed by coconut fibers have been employed as vertical reinforcement of concrete walls to increase the structures stability against earthquakes [107]. To evaluate the tensile strength of coconut ropes, [PERSON] and [PERSON] [108] developed experimental tests of coconut ropes. They proposed that the length of coconut ropes incorporated in concrete should be above 200 mm. Additionally, they observed that coconut fiber concrete and boiled treatment of ropes increase their bond strength and pullout energy [108]. Regarding the performance of coconut fiber and rope reinforced concrete columns tested on shaking tables, [PERSON] [109] verified that nonvisible degradation occurred prior to the cracking these columns. Furthermore, one rope configuration could maintain the column attached to its support after being tested. On the other hand, [PERSON] and [PERSON] [110] used coconut fibers (see Figure 11) to improve concrete properties under load impact. They determined that coconut fiber reduced spalling and cracking of coconut fiber concrete. Finally, to determine the complete behavior of coconut fiber and ropes in concrete, it is recommended to carry out experimental tests about the ductility and microstructure of columns and beams containing coconut fibers.  
Figure 10: Bamboo reinforcement bars used in concrete slabs. Reprinted with permission from [102]. Copyright © 2018, Elsevier B.V.
# Ultra-Broadband Bending Beam and Bottle Beam Based on Acoustic Metamaterials
## 3 Results  
### Bending Beams  
As an example, we present the generation of a bending beam with the radius \(R=0.35\) m. With the aid of the expressions of the phase \(\phi(y)\) and the depth \(h(y)\) mentioned above, the desired phase profile and corresponding depth profile for the bending beam can be obtained, as shown in Figure 3a,b, respectively. The depth profile in Figure 3b has been discretized based on the period \(p\) of the metastructure so that each dot in (b) shows the depth of the corresponding tube within the structure shown in Figure 3c. The structure will be used to generate the bending beam. We have simulated the reflected fields \(|p|^{2}\) by the metastructure shown in Figure 3c for different frequencies; see top panels in Figure 4. The bending beams are successfully generated within a broadband frequency range (2 kHz to 17 kHz) by the same structure. The results are then compared with the fields generated by an ideal source of the same size; see bottom panels. From the results, the fields generated by the metastructure agree well with the fields generated by the ideal source, whose phase profile is shown in Figure 3a.  
Figure 4: Simulated bending fields. (**Top** panels) bending beams generated by the metastructure at the frequencies: 2000, 4500 and 17,000 Hz; (**Bottom** panels) bending beams generated by an ideal source for comparison. White arrows indicate the incident plane waves.  
Figure 3: Desired parameters for the generation of bending beams. (**a**) The phase profile at the interface of the structure for the bending beam with \(R=0.35\) m, and (**b**) the corresponding depth profile for the bending beam (see Equation (2)). (**c**) The metastructure is designed based on the depth profile in (**b**).
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 6 In-Context Learning
### Chain-of-Thought Learning
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **PT, FT Batch-Size** & **Context Size** & **PT, FT Epochs** & **Activation, Optimizer** & **Finetuning Methods** \\ \hline Transformer-base [24] & - & - & 100,000 & & \\ Transformer-big [24] & - & - & 300,000 & -, Adam & Feature-based \\ BERT-base [28] & 256,32 & 128,512 & 40,4 & & \\ BERT-large [26] & 256,32 & 128,512 & 40,4 & & \\ GPT-1 [27] & 64 & 512 & 100 & & \\ GPT-2 [28] & 512 & 1024 & - & & \\ GPT-3 [29] & 3.2M & 2048 & - & - & few-shot, one-shot, zero-shot \\ TS [25] & 128,128 & 512 & 219,218 steps & RELU, AdaFactor & FT \\ REALM [30] & 512, 1 & - & 200 k steps, 2 & - & - \\ Jurassic-1 [31] & 3.2 M tokens & 2048 & - & - & few-shot, zero-shot \\ mTS [32] & - & - & - & GeGLU, & FT, zero-shot \\ Fangui-Alpha [33] & - & 1024 & 130 K 260 K & GeLU & few-shot, one-shot, zero-shot \\ CPM-2 [34] & - & - & - & - & FT, PT \\ Yuan 1.0 [35] & - & - & - & - & few-shot, zero-shot \\ HyperCova [36] & 1024,- & - & - & -, AdamW & few-shot, zero-shot, PT \\ GLAM [37] & - & 1024 & - & -, Adafactor & zero-, one-shot \\ ERNINE 3.0 [38] & 6144 & 512 & - & GeLU, Adam & FT, zero- and few-shot \\ Gopher [39] & - & 2048 & - & Adam & FT, few-shot, zero-shot \\ Chlhuillia [40] & - & - & - & AdamW & FT, zero-shot \\ AlphaCode [41] & 2048 & - & 205 K & - & finetuning \\ CodeGEN [42] & 2 M & 2048 & - & - & - \\ CodeGECAT [43] & 3072 & - & - & FastGELU, Adam & finetuning \\ FLAN [44] & - 8192 & 1024 & 30 K & -, Adafactor & Instruction Tuning, zero-shot \\ InstrCRT [45] & 3.2 M & 2048 & - & - & RLHF \\ Lambda [46] & - & - & - & gated-GELU, FT & FT \\ TO [47] & - & - & - & RELU, AdaFactor & FT, zero-shot \\ GPT NeoX 20B [48] & 3.15 M tokens & 2048 & 150 K steps & -, AdamW with ZERO & few-shot \\ OPT [49] & 2 M tokens & 2048 & - & ReLU, AdamW & few-shot, zero-shot \\ MINERVA [50] & - & 1024 & 399 K & SwiGLU, Adafactor & few-shot, chain-of-thought context \\ AlexandTo 2.0 [51] & 2 million tokens & - & Adam & Finetuning, few-shot, one-shot, zero-shot \\ GLM-130 B [52] & 4224 & 2048 & - & GeGLU, & zero-shot, few (5) shots \\ XGLM [53] & 512, 1024, 2048 (1, 2, 4 M & 2048 & 1 (255 k steps) & SwiGLU, Adafactor & few-shot, chain-of-thought, finetuning \\ PalLM [54] & tokens). - & 2048 & 4 epochs & GeLU & zero-shot \\ Galactic [55] & 2 M & 2048 & 4 epochs & GeLU & zero-shot \\ Pall [56] & - & - & - & - & - \\ LLMA [57] & 4 M tokens & - & - & SwiGLU, AdamW & zero-shot, few-shot, Instruction Tuning \\ UL2 [58] & 128 & 512 & 500 K steps & SwiGLU, Adafactor & finetuning, instruction tuning \\ Pythia [59] & 1024 & 2048 & 1.5 Epochs & Adam & zero-shot, few-shot \\ WeLM [60] & 2048 & 2048 & - & - & zero-shot, few-shot \\ BLOMOM [22] & 20,482,048 & 2048 & - & GELU, - & (fine)-tuning \\ GLLM [61] & 1024 & & 200 K Steps & & FT \\ GPT-J & & 2048 & 383,500 steps & - & FT \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Finetuning details of LLMs.
# Image Completion with Hybrid Interpolation in Tensor Representation
## 1 Introduction
Motivated by several works [48; 49; 50; 51] on the use of various interpolation methods for solving image completion problems, other recent works [52; 53; 54; 55; 56] on image processing aspects, and the concept of tensor product spline surfaces [57], we show the relationship of the Tucker decomposition with factorizable radial basis function (RBF) interpolation and use it to compute the factor matrices. RBF interpolation is a mesh-free method, which is very profitable for recovering irregularly distributed missing pixels, but it may incorrectly approximate linear structure. Hence, we combine it with low-degree polynomial interpolation, and both interpolation methods can be expressed by the Tucker decomposition model. Adopting the idea of PDE-based inpainting, we propose to compute the interpolants using only restricted surrounding subareas, instead of all accessible pixels. Hence, the whole image is divided into overlapping blocks, and the Tucker decomposition model is applied to each block separately. As many interpolation methods do not approximate the boundary entries well, the overlapping is necessary to avoid discontinuity effects. The proposed methodology is applied to various image completion problems, where the incomplete images are obtained from true images by removing many random pixels or many small holes. One of the experiments is performed for resolution up-scaling, where a low-resolution image is up-scaled to high resolution using the proposed algorithm. All the experiments demonstrate that the proposed method considerably outperforms the baseline and state-of-the-art low-rank image-completion methods in terms of the performance, and it is much faster than the methods based on tensor decompositions. The remainder of this paper is organized as follows. Section 2 reviews some preliminary knowledge about fundamental tensor operations, the Tucker decomposition model, low-rank tensor completion, and RBF interpolation. The proposed algorithm is described in Section 3. The numerical experiments performed on various image completion problems are given and discussed in Section 4. The last section contains the conclusions.
## 3 Results
### Covariate Sets  
#### 3.2.1 All-Relevant Variable Set  
In the BQ region, the Boruta technique retained a series of vegetation, LST (land surface temperature), and precipitation covariates, composing the all-relevant (AR) set of clayalr (Figure S1). The s2_dn_e_1 (the diurnal LST in summer of the sampling year) was the most relevant covariate to explain the clayalr variation. The AR set of saltalr was composed of a series of vegetation, terrain, LST, and precipitation covariates, in which the NDVI_9 was the most relevant covariate. In terms of the ILR-transformed fractions, the AR set for clayilr prediction composed of vegetation, terrain, LST, and precipitation covariates (Figure S1). Slope (Slp) was the most relevant covariate. Whereas for saltllr, the s2_dn_e_1 was the most relevant covariate. None of the topographic covariates was relevant to ILR-transformed fractions. Moreover, the air temperature was not relevant to neither of the transformed PSFs in this region.  
In the WQ region, vegetation and LST covariates were retained in AR sets to infer the variance of ALR- and ILR- transformed PSFs (Figure S2). The GPP_1 and s4_n_i_9 showed the most robust relevance to clayalr and saltalr, respectively. The s4_df_e_1 and GPP_1 were the most relevant covariates of clayllr and saltllr, respectively (Figure S2).  
In this study, the common covariate categories selected in both study areas were vegetation and LST. The AR sets contained terrain and precipitation covariates in the BQ  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**PSF**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**SD**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Range**} & \multirow{2}{*}{**Skew**} & \multirow{2}{*}{**Kurtosis**} \\  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{3}{*}{BQ} & \multirow{3}{*}{49} & Clay & 8.8 & 2.9 & 4.7 & 17.8 & 13.1 & 0.9 & 1.3 \\  & & Silt & 42.0 & 13.1 & 14.6 & 60.4 & 45.8 & \(-\)0.6 & \(-\)0.9 \\  & & Sand & 49.2 & 14.9 & 28.9 & 79.5 & 50.6 & 0.5 & \(-\)1.0 \\ \hline \multirow{3}{*}{WQ} & \multirow{3}{*}{59} & Clay & 14.0 & 5.7 & 3.8 & 28.8 & 25.1 & 0.5 & \(-\)0.3 \\  & & Silt & 9.5 & 3.4 & 3.5 & 18.9 & 15.4 & 0.5 & \(-\)0.1 \\ \cline{1-1}  & & Sand & 76.5 & 8.7 & 53.7 & 90.8 & 37.2 & \(-\)0.5 & \(-\)0.3 \\ \hline \hline \end{tabular}  
* SD stands for standard deviation, Min stands for the minimum, and Max stands for the maximum.  
\end{table}
Table 2: Descriptive statistics of soil particle size fractions (PSF) of observations.  
Figure 3: The soil particle size fractions of topsoil observations presented in the United States Department of Agriculture (USDA) texture triangle.  
region but not in the WQ region. The difference of all-relevant covariate sets probably indicated the difference in the formation of soil PSFs in two study areas.
# Gypsum-Dependent Effect of NaCl on Strength Enhancement of CaO-Activated Slag Binders
## 3 Results and Discussion  
### Compressive Strength  
The compressive strength test results of all hardened paste samples are provided in Figure 2. Each strength result shows an averaged value obtained from six identical samples.  
A previous study [12], reported that the use of gypsum significantly increased the strength of CaO-activated GGBFS. Similarly, in this study, the 5 wt % of gypsum incorporation (Ca-N0-G5 in Table 2) significantly increased the strength, which is consistent with the previous study.  
When NaCl was used without gypsum (Ca-N1-G0), when compared to the mixture without NaCl or gypsum (Ca-N0-G0), no strength improvement was achieved at 7 days, and the strength was even lower at 28 days. However, the sample with both NaCl and gypsum (Ca-N1-G5) demonstrated noticeably higher strength results than Ca-N0-G5 at 7 and 28 days. This shows that the use of NaCl was only beneficial in improving strength when paired with gypsum in this study.  
It is worth noting that the beneficial effect of NaCl combined with gypsum on strength seems to have mostly occurred before 7 days. This is evidenced by the fact that most of the strength gains of Ca-N0-G5 and Ca-N1-G5 were obtained before 7 days.