esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Temporal Patterns of WUEs and SIF Emission  
The mean of summer (June to August, three months) daytime WUE, WUE\({}_{i}\), IWUE, and uWUE were 0.14 g C/kg H\({}_{2}\)O, 13.45 umol C/mmol H\({}_{2}\)O, 0.14 g C hPa/kg H\({}_{2}\)O and 0.13 g C hPa/kg H\({}_{2}\)O, respectively (Figure 1), which were much higher than their values in the spring/winter seasons. It suggested that plants became more efficient in using water resources when they were water stressed. Among the four formulations, IWUE and uWUE demonstrated a stronger seasonal pattern, as both of them showed a rapid increase in late April and May, maintained a high value for about two months in summer, and started to decline in September (Figure 1c,d). Compared to IWUE, uWUE showed a relatively smaller range and variability in summer (Figure 1d), most likely due to the use of the square root of VPD. In contrast, WUE and WUE\({}_{i}\) showed less substantial changes in the spring and autumn seasons and had a longer plateau of high values (Figure 1a,b). Note that WUE\({}_{i}\) contained more missing values, reflecting additional difficulties in estimating WUE\({}_{i}\) (Figure 1b).  
The three SIF time series also demonstrated a clear seasonal pattern. Among the three SIF bands, SIF\({}_{687}\) showed less variation in both spring and autumn seasons (Figure 2a) and remained roughly stable, with values around 0.14 mW/m\({}^{2}\)/sr/nm from the late spring to early autumn. In contrast, the dynamics of SIF\({}_{720}\) and SIF\({}_{761}\) in 2014 showed a stronger seasonal pattern, as they increased very fast in spring, maintained stability in the mature period, and dropped rapidly in autumn (Figure 2b,c). Among these three SIF bands, SIF\({}_{761}\) had the most pronounced emission, as its hourly emission peak reached about 1.6 mW/m\({}^{2}\)/sr/nm, which was about 5 times higher than the emission peak of SIF\({}_{687}\) (Figure 2a,c). The number of SIF\({}_{687}\) observations is less than that of SIF\({}_{761}\), which indicates that strong a (re)absorption effect and weak emitted energy in red SIF may cause additional difficulties in retrieving SIF\({}_{687}\).  
Figure 1: The time series of hourly (**a**) water use efficiency (WUE, g C/kg H\({}_{2}\)O); (**b**) intrinsic water use efficiency (WUE\({}_{i}\), umol C /mmol H\({}_{2}\)O); (**c**) inherent water use efficiency (IWUE, g C hPa /kg H\({}_{2}\)O) and (**d**) underlying water use efficiency (uWUE, g C hPa /kg H\({}_{2}\)O) at the Harvard Forest site in 2014. The red lines represent 4-day daytime averages. The values in the top \(x\)-axes represent months.  
Figure 2: _Cont._
# Stride Length Impacts on Sagittal Knee Biomechanics in Flat Ground Baseball Pitching
## 3 Results  
The drive knee dynamic data presented in Table 1 and Figure 1A depict that the knee flexion was significantly greater during the generation phase (\(p\leq\) 0.001, ES = 1.13) followed by a greater drive knee extension during brace-transfer (\(p\leq\) 0.001, ES = 1.11) with longer strides. Increased stride lengths exhibited a greater drive knee extensor velocity at stride foot contact (\(p\leq\) 0.05, ES = 0.82), a lower mean flexor velocity during the generation phase (\(p\leq\) 0.05, ES = 0.36), and greater drive knee extensor moments during brace-transfer (\(p\leq\) 0.001, ES = 2.83). In contrast, a greater drive knee flexor velocity was observed with shorter strides during the acceleration phase (\(p\leq\) 0.05, ES = 2.37). Drive knee extensor moments were observed for increased strides whereas shorter strides demonstrated drive knee flexor moments at MER (\(p\leq\) 0.001, ES = 0.35). Throughout the acceleration phase, knee moments between the stride length conditions demonstrated differences in power generation and absorption. Pitching with longer strides evoked an eccentric knee extensor action that absorbed power by slowing the rate of drive knee flexion whereas under-stride-generated power was evidenced by concentric knee flexor moments that increased the rate of drive knee flexion (\(p\leq\) 0.001, ES = 2.56).  
A greater stride knee extension was observed during brace-transfer (\(p\leq\) 0.001, ES = 4.17) and the acceleration phase (\(p\leq\) 0.05, ES =1.80) when pitching with longer strides. However, the angular velocities were no different at any point in the pitching cycle between the stride conditions (Table 2). Overall, shorter strides resulted in greater stride knee extensor moments at a maximal external rotation and acceleration phase with a moderate to large effect size, respectively (\(p\leq\) 0.05), with a greater mean joint power generated during the acceleration phase (\(p\leq\) 0.001, ES = 3.26) (Table 2 and Figure 1B).  
Figure 1: (**A**) Drive knee flexion/extension dynamics. Flexion (\(-\))/extension (+) drive knee dynamics. OS indicated greater knee flexion during the generation phase, with greater knee extension and moments during brace-transfer as well as increased knee extension moments at maximal external rotation. US presented greater knee flexion angular velocities during the generation and acceleration phases, knee flexion moments at maximal external rotation, and concentric knee flexion power during the acceleration phase. OS indicated small eccentric knee extension power during the acceleration phase, slowing the rate of knee flexion. (**B**) Stride knee flexion/extension dynamics. Knee kinematic conventions describing closed kinetic chain (CKC) movement following SFC (73% and 80% time for under- and over-stride pitching). Stride knee flexion (\(-\))/extension (+) dynamics; OS indicated greater knee extension from SFC to BR. No significant differences were reported in angular velocities. US indicated greater knee extensor moments at MER and ACC with greater power generation during ACC.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms  
This section will focus on references that use ML algorithms to support Smart Transportation. The algorithms and their use will be thoroughly analyzed in the following subsections. Table 1 summarizes the combined findings from the reviewed literature, presenting the algorithm name, the number of times each algorithm is used in the literature, the algorithm type and the respective learning type.  
In the following subsections, the aforementioned ML algorithms are grouped by their algorithm type and they are briefly analyzed.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**No** & **Algorithm** & **References** & **Algorithm Type** & **Learning Type** \\ \hline
1 & AdaBoost & [17] & Ensemble & Supervised \\
2 & Bayesian Network Seasonal & [18] & Bayesian & Supervised \\  & Autoregressive Integrated & & & \\  & Moving **A**verage (BN-SARIMA) & & & \\
3 & Convolutional **N**eural **N**etwork & [19; 20; 21] & Deep Learning & Reinforcement \\  & (CNN) and **D**eep CNN (DCNN) & & & \\
4 & Coupled **H**idden **M**arkov & [22] & Markov Model & Reinforcement \\  & Model (CHMM) & & & \\
5 & Decision Tree & [23] & Decision Trees & Supervised \\
6 & Deep **B**elief **N**etworks (DBN) & [12; 24] & Deep Learning & Reinforcement \\
7 & Deep **R**eurrent **A**ttention & [25] & Recursive Neural & Reinforcement \\  & Model (DRAM) & & Networks-Deep & \\  & & & Learning & \\
8 & Fuzzy **C**-Means (FCM) & [26] & Clustering & Unsupervised \\
9 & Feed **F**orward **N**eural **N**etworks & [18; 23; 27; 28; 29; 30] & Artificial Neural & Supervised \\  & (FF-NN) & & Networks & \\
10 & Fully **C**onnected **N**etworks & [20] & Deep Learning & Reinforcement \\  & (FCN) & & & \\
11 & **S**tacked **A**uto **E**ncoder (SAE) & [31] & Deep Learning & Reinforcement, \\  & with Greedy Layer-wise training & & Unsupervised \\
12 & Inception Neural Networks & [32] & Deep Learning & Reinforcement \\
13 & K-Means & [12; 33; 34] & Clustering & Unsupervised \\
14 & k-Nearest **N**eighbor (\(k\)-NN) & [24; 29; 35] & Instance Based & Supervised \\
15 & Logistic Regression & [23] & Regression & Supervised \\
16 & Markov **D**ecision **P**rocess (MDP) & [36] & Discrete Time & Reinforcement \\  & & Stochastic Control & & \\
17 & Markov **R**andom **F**ield (MRF) & [14] & Markov Model & Unsupervised \\
18 & Nonlinear **A**utoRegressive & [18] & Recursive Neural & Reinforcement \\  & e**X**ogenous model (NARX) & & Networks-Deep & \\  & & Learning & \\
19 & Q-Learning & [36] & Stochastic & Reinforcement \\  & & Control-Markov & & \\  & & Model & & \\
20 & Random **F**orest (RF) & [23; 27; 34; 35; 37] & Ensemble & Supervised \\
21 & Regression Tree & [27; 29] & Decision Trees & Supervised \\
22 & Support-**V**ector **M**achine (SVM) & [14; 23; 24; 35; 37; 38] & non-probabilistic & Supervised \\  & & Linear Classification & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Machine Learning algorithms in Internet of Things (IoT) smart transportation applications.
# Investigating Models for the Transcription of Mathematical Formulas in Images
###### Abstract  
The automated transcription of mathematical formulas represents a complex challenge that is of great importance for digital processing and comprehensibility of mathematical content. Consequently, our goal was to analyze state-of-the-art approaches for the transcription of printed mathematical formulas on images into spoken English text. We focused on two approaches: (1) The combination of mathematical expression recognition (MER) models and natural language processing (NLP) models to convert formula images first into LaTeX code and then into text, and (2) the direct conversion of formula images into text using vision-language (VL) models. Since no dataset with printed mathematical formulas and corresponding English transcriptions existed, we created a new dataset, _Formula2 Text_, for fine-tuning and evaluating our systems. Our best system for (1) combines the MER model _LaTeX-OCR_ and the NLP model _BART-Base_, achieving a translation error rate of 36.14% compared with our reference transcriptions. In the task of converting LaTeX code to text, _BART-Base_, _T5-Base_, and _FLAN-T5-Base_ even outperformed _ChatGPT_, _GPT-3.5 Turbo_, and _GPT-4_. For (2), the best VL model, _TrOCR_, achieves a translation error rate of 42.09%. This demonstrates that VL models, predominantly employed for classical image captioning tasks, possess significant potential for the transcription of mathematical formulas in images.  
mathematical formula transcription; formula-to-text; LaTeX-to-text; image captioning; computer vision; natural language processing; vision-language models +
Footnote †: journal: Applied Sciences, 99084 Erfurt, Germany
# From Lean Production to Lean 4.0: A Systematic Literature Review with a Historical Perspective
## 3 Results
### Answering RQ3. Historical Evolution of the Term \"Lean\"
#### 3.2.3 Lean Logistics (1994), Lean Supply (1996) and Lean Supply Chain (1999)  
The first paper on lean logistics (LL) was authored by [PERSON] et al. in 1994, _From Lean Production to Lean Logistics: The Case of Microsoft Ireland_[56], which illustrated the expansion from production to supply chain management.  
Figure 6: Lean production vs. lean manufacturing evolution.  
In 1996, the first paper to use using \"lean supply\" was _Squaring Lean Supply with Supply Chain Management_[57], in which the authors extended the concept to supply chain management.  
In 1999, the first paper to use \"lean supply chain\" (LSC) was _Vertical Integration in a Lean Supply Chain: Brazilian Automobile Component Parts_[58], which related the term \"lean\" to a broader perspective on supply chain management.  
The three surnames can be considered quite equivalent, in that they focus on: the efficiency of material flows inside and outside the factory; the integration and development of suppliers; and the integration of different actors and information across the supply chain [59].  
[PERSON] et al. (2020) [60] presented a conceptual evolution map of the concepts behind lean supply chain management over the last 22 years.  
Relationships between lean supply chain and I4.0 have appeared in recent years in publications on different topics, such as the impact of these on performance improvement [61; 62; 63; 64] and their further relationships with information and digital technologies [63].  
To conclude, these specifiers appeared as lean expanded to supply chain management: \"Lean logistics [...] is based around extended TPS right along supply chain from customers right back to raw material extraction\" [64] (p. 171).  
The three surnames in this field extend lean perspective to supply chain management, with interest having increased moderately since 2007 (Figure 7).
# RJ-RRT: Improved RRT for Path Planning in Narrow Passages
## 4 Simulation and Result
### Bug Trap  
The expanded size also affects the number of iterations and memory usage of the RRT and RRT variants. In RJ-RRT, the magnitude of each reduction in the sampling space is also determined by the size of the random tree expansion. Therefore, to analyze the influence of different expand sizes on the performance of different algorithms, we use 0.1/0.3 as the expand size for discussion in this experiment. Tables 3 and 4 are the experimental data results when the expand size is 0.1 and 0.3, respectively, and Figure 10 shows the planning performance when the expand size is 0.3. It can be seen from Tables 3 and 4 that RJ-RRT and RRV perform better than RRT in various performance aspects under different expand size settings. When the expand size is 0.1, RJ-RRT is higher than RRV in generating the total number of nodes. This is because the environment in this experimental, the long and narrow trap passage and short expand size lead to multiple \(GapSpaces\) in the RJ-RRT planning process, resulting in the need for multiple fallback and forward operations. These processes increase the number of samples and planning time, so the final performance is not as good as RRV. When the expand size is set to 0.3, since the expand size increase reduces the number of Gap Spaces, RJ-RRT can still plan a path using a smaller number of nodes and a faster time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Average Time (s)** & **Min Time (s)** & **Max Time (s)** & **Average Number of Collisions** & **Average Number of Nodes** \\  & & & & **Detections** & **of Nodes** \\ \hline RRT & 4.937 & 1.575 & 10.489 & 7648 & 1347 \\ RRV & 2.338 & 1.031 & 5.944 & 3327 & 958 \\ RJ-RRT & 1.276 & 0.475 & 1.749 & 678 & 227 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of three algorithms in narrow passage.  
Figure 9: The performance of the three algorithms in narrow passage. (**a**) RRT; (**b**) RRV; (**c**) RJ-RRT.
# Rethink Motion Information for Occluded Person Re-Identification
## 2 Related Work
### Image Person Re-Identification  
Image person re-identification, including holistic and occluded parts, aims to deal with retrieving a person of interest in other camera views. With the generation of large-scale datasets and the development of deep learning methods, recent works that utilize transformers to obtain refined human features, have achieved the best performance on holistic person ReID tasks. [PERSON] et al. [7] proposed a Part-Aware Transformer to deal with occlusion situations, which utilizes the transformer encoder-decoder architecture with learnable part prototypes for occluded person Re-ID and achieves a competitive performance. [PERSON] et al. [8] proposed a feature diffusion model, including a non-pedestrian occlusion augmentation strategy, an occlusion erasing module, and a feature diffusion module, to help the model distinguish diverse occluded situations and precisely perceive target pedestrians. [PERSON] et al. [6] designed a dynamic prototype mask for occluded person Re-ID, which does  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Reference** & **Classification** & **Evaluation Metrics** & **Main Findings** \\ \hline PAT [7] & CVPR2021 & Image & Rank-1, mAP & Learning human part prototypes \\ PMFB [19] & TNNLS 2021 & Image & Rank-1, mAP & Utilize the pose estimation to \\  & & & & localize the human body \\ Co-attention [20] & ICIP2021 & Image & Rank-1, Rank-3 & Use semantic segmentation to \\  & & & & accomplish Partial-REID \\ FED [8] & CVPR2022 & Image & Rank-1, mAP & Non-pedestrian occlusion \\  & & & & augmentation strategy \\ \hline Flow [21] & IVC 2021 & Video & Rank-1, mAP & Use optical flow to obtain \\  & & & & spatio-temporal information \\  & & & & Use temporal relations to obtain \\  & & & & human features \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 1: A brief summary of classic ReID methods.  
Figure 1: MAF model is capable of handling ReID tasks based on both image and video inputs, and is able to achieve competitive results on the ReID task for both images and videos.  
not rely on extra pre-trained networks, but uses a hierarchical mask generator to enrich the holistic prototype, and simultaneously retains the information from the whole image and achieves automatic alignment. Although those methods achieve satisfactory performance in the holistic or occluded ReID benchmarks, some occlusion-based models will still be affected by unpredictable occlusion and are largely influenced by pedestrians' appearance.
# Visual Tracking with FPN Based on Transformer and Response Map Enhancement
## 3 Proposed Method
### Transformer Feature Pyramid
The above process can be expressed by the following formula. \[ \begin{split}\text{F3}^{\prime}=\text{TFE}(\text{Feat4},\text{ Feat3},\ \text{Feat3});\text{F4}^{\prime}=\text{TFE}(\text{Feat4},\text{Feat4},\ \text{Feat4});\text{F5}^{\prime}=\text{TFE}(\text{Feat4},\text{Feat5},\ \text{Feat5})\\ \text{F}^{\prime}=\text{F3}^{\prime}+\text{F4}^{\prime}+\text{F5} ^{\prime}\\ \text{F}_{\text{-final}}=\text{TFE}(\text{F}^{\prime},\text{F} ^{\prime},\text{F}^{\prime})\end{split}  \tag{2}\]  
Figure 2: Schematic diagram of the proposed TFE block. The module consists of three parts: Multi-Head Attention, Layer Norm and MLP. Compared with the traditional FPN, the Transform can help overcome the limitations of the receptive field. Our proposed method can calculate the correlation between all positions of the input feature sequence, and enhance the target features by using the cross attention between different feature layers to establish the global interaction. In this fusion mode, by transmitting the semantic information to the multi-layer feature map, this paper not only fuses the deep and shallow features, but also adaptively realizes the interactive fusion of key feature information. Therefore, features obtained by TR-Siam are more significant, which can effectively enhance the robustness of the algorithm to deal with the interference and occlusion of similar targets.
## 5 Parameter Analysis
### The Influence of Shear Span vs. Depth Ratio of SC Composite Beam  
Based on the developed finite element models, the influence of the shear span versus depth ratio (\(\lambda\)) on the minimum shear connection ratio of the SC composite beam under cyclic loading was investigated. The shear span versus depth ratio of four SC composite beams varied from 3.33 to 6.67. For each SC composite beam, the minimum shear connection ratio could be determined by the same assumption, i.e., the plastic strain occurs in tensile steel plate when the SC composite beam reached yield capacity. After simulation, the minimum shear connection ratios of the SC composite beams with 3.33, 4.44, 5.56, and 6.67 shear span versus depth ratio are 1.70, 1.60, 1.55, and 1.55, respectively.
# Optimization Design of RV Reducer Crankshaft Bearing
## 2 Performance of crankshaft Bearings of Rotate Vector (RV) Reducers
### Structure of RV Reducer  
The RV reducer is a two-stage planetary transmission mechanism. The first stage is involute gear transmission. The second stage is cycloid gear transmission. Figure 1 is the structure of the RV reducer. In a cycloidal-pinwheel transmission, the crankshaft and cycloidal gear are connected by the crankshaft bearing. The off-center section of the crankshafts causes the cycloidal gear to rotate in eccentricity. The cycloidal gear meshes with the pin gear. Therefore, the cycloid gear also rotates around its own axis. The crankshaft transmits the spinning motion of the cycloidal gear to the output mechanism of the reducer.  
Figure 1: Structure of rotate vector (RV) reducer.  
Crankshaft bearing is mainly composed of rollers, inner raceways, and outer raceways. Because of the restriction of available space, a cylindrical roller bearing without inner and outer rings is used for the crankshaft bearing. As shown in Figure 2, the outer raceway is integrated into the cycloidal gear.  
The size of the outer raceway is limited by the root circle and the center hole of the cycloidal gear. The inner raceway is integrated into the off-center section of the crankshaft. The radius of the crankshaft bearing distribution circle determines the installation position of the crankshaft bearing in the cycloidal gear. The width of the off-center section is the same as the width of the cycloidal gear, which limits the length of the roller.  
Although the components of the crankshaft bearings appear simple, the macro geometry has significant effects on mechanical behavior of bearing. The macro-geometric parameters mainly include the roller length \(L_{ave}\), roller diameter \(D_{ave}\), bearing pitch diameter \(D_{m}\), and radial clearance \(P_{d}\). As shown in Figure 2, the boundary dimensions include the outer diameter (\(D_{U}\)), minimum diameter of crankshaft (\(D_{L}\)), bearing width (B), diameter of the center hole (\(D_{h}\)), and the root circle of the cycloidal gear (\(D_{c}\)).