esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
###### Abstract
In addition to NDVI, other vegetation-based indexes (VIs), such as the leaf area index (LAI) [25], enhanced vegetation index (EVI) [26], perpendicular vegetation index (PVI) [9], green area index (GAI) [27], vegetation condition index (VCI) [28; 29], fraction of photosynthetically active radiation (FPAR) [30], and wide dynamic range vegetation index (WDRVI) [31; 32], have also been used as independent variables to build a regression model considering yield [33]. In meteorological data-based yield estimation models, input variables can be original meteorological factors (such as precipitation, temperature, solar radiation, etc.) or agrome-terological indexes calculated by original meteorological factors (such as various drought indexes) [34; 35; 36]. For example, [PERSON], et al. [34] used average air temperature, rainfall, solar radiation, etc., as input variables to build spatial regression models for predicting corn yield in Brazil from 2012 to 2014. [PERSON], et al. [35] compared the correlations of fifty-eight agro-climatic indexes and corn yield anomalies in the United States and noted that temperature and the standardized precipitation evapotranspiration index (SPEI) obtained in July are the two best yield predictors. A large number of studies have reported the impact of adverse weather conditions on crop yield. For example, [PERSON] [37] established a quadratic equation to explain maize yield losses on the Songliao Plain of China. [PERSON], et al. [38] analyzed the regression relationship between the detrended maize yield and the standardized precipitation evapotranspiration index (SPEI) in the North China Plain (NCP) and found that the three-month SPEIs in August, which reflect water conditions during June and July, had the best relationship with the detrended maize yield. [PERSON], et al. [39] built a multivariate regression model between the detrended winter wheat yield and the SPEI in Jiangsu Province, China. [PERSON], et al. [40] established seven aggregate drought indexes to quantify the relationships between them and the anomalies of the climatic yields (or standardized climatic yields) of wheat by using two statistical regression models applied in the NCP. [PERSON], et al. [41] set up a logistic function that related a drought hazard index to the yield loss rate for maize in China. A previous study noted that agrometeorological indexes can help improve yield prediction accuracy in the presence of adverse weather conditions [42]. In this paper, remote sensing data and meteorological data are used together to develop a yield estimation model utilizing the random forest (RF) method with two different variable selection methods, and experiments are conducted in Liaoning and Jilin provinces in Northeast China. Our main objectives include (1) to verify the accuracy of yield estimations models built in this study; (2) to compare the differences of the two variable selection methods; (3) to compare the importance of different variables on yield estimation; and (4) to compare the differences in modeling between the two provinces.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Fluorescence Diffuse Optical Spectroscopy (TD-FS) and Tomography (TD-FT)  
#### 5.4.1 Fundamental Equations for TD-FS and TD-FT  
Fluorescence diffuse optical spectroscopy (FS) and tomography (FT) have been investigated as one of the modalities for molecular imaging [195; 196], and TD-FT is an extension of TD-DOT to the coupled fluorescence excitation and emission phenomena of light propagation. Fundamental equations of light propagation for TD-FS and TD-FT are usually described by the coupled TD-DEs for excitation and emission light,  
\[\bigg{[}\frac{1}{c}\frac{\partial}{\partial t}-\
abla\cdot D_{x}(\textbf{r}) \
abla+\mu_{ax}(\textbf{r})+\varepsilon N(\textbf{r})\bigg{]}\phi_{x}(\textbf {r},t)=q_{x}(\textbf{r},t), \tag{55}\]  
\[\bigg{[}\frac{1}{c}\frac{\partial}{\partial t}-\
abla\cdot D_{m}(\textbf{r}) \
abla+\mu_{am}(\textbf{r})\bigg{]}\phi_{m}(\textbf{r},t)=\frac{\gamma(\textbf {r})\varepsilon N(\textbf{r})}{\tau(\textbf{r})}\int_{0}^{t}\phi_{x}(r,tt) \exp(\frac{t-tt}{\tau(r)})dt, \tag{56}\]  
where subscripts \(x\) and \(m\) refer to excitation and emission light, respectively, \(N(\textbf{r})\), \(\gamma(\textbf{r})\), and \(\tau(\textbf{r})\) are the spatial distributions of the fluorescence properties, i.e., concentration, quantum efficiency, and lifetime of the fluorophore, respectively, and \(\varepsilon\) is the extinction coefficient of the fluorophore at the excitation wavelength. The goal of FT is to reconstruct the distributions of fluorescence properties, and \(N(\textbf{r})\) and \(\tau(\textbf{r})\) are the main targets for molecular imaging. In general, the image reconstruction algorithm for TD-DOT is extended for TD-FT, which uses the emitted fluorescence light intensities measured at the object surface as the input data in addition to the measured reemitted excitation light intensities. The optical and fluorescence properties are assumed first, and Equations (55) and (56) are solved as the forward problem to obtain the calculated excitation and fluorescence light intensities, which are compared with the measured ones. If they do not agree, the optical and fluorescence properties are upgraded by an optimization procedure introduced in Section 5.3.1, and Equations (55) and (56) are  
Figure 18: TD-DOT images of the human forearm. (**Left**) MRI of the forearm showing the ulna and radius. (**Center Two**) Reconstructed \(\mu_{a}\) and \(\mu_{s}{{}^{\prime}}\) images for 759 nm at rest state. \(\mu_{s}{{}^{\prime}}\) image reflects the positions of the two bones. (**Right**) Change in \(C_{\textit{oxy-HB}}\) from rest to task states. Red areas indicate the positions of thick blood vessels while deep-blue areas indicate muscles. Modified from Reference [47].  
solved again. This process is repeated until convergence is reached. Many studies have been conducted on TD-FS and TD-FT, and some studies are reviewed in the following.
## 5 Conclusions  
By exploring remote sensing data from 14 deciduous broadleaf forest sites and 12 evergreen needleleaf forest sites, this research analyzed the difference in seasonal variations in the S1 backscatter coefficient between deciduous forest and coniferous forest and assessed their accuracy to estimate forest phenology. The co-polarization of the two forests is both positively correlated with the forest vertical structure and follows similar variations, while the cross-polarization differs. During the leaf-on period, the foliage of deciduous forests can absorb and front scatter the cross-polarization signals, which causes a decrease in backscattering. Coniferous forests do not show significant foliage dynamics, but the annual amplitude of the cross-polarization is possibly negatively correlated with the length of needle leaves, which may cause instability in seasonal variation of VV-VH signals. We also discovered that both VH/VV and VV-VH can be exploited to monitor deciduous forest phenology, and VV-VH is more stable in annual amplitudes and performs better at coniferous forest sites. Although the time series can satisfactorily characterize seasonal variations, the satellite data performed poorly in extracting EOS compared with Gcc measurements. Further research can focus on exploring the non-linear relationships of phenological retrieval between different data sources. Overall, S1 backscatter measurements can better characterize the SOS of DB but are not satisfactory for EOS of DB and EN phenology.  
Conceptualization, Y.L., S.T. and C.L.; data curation, Y.L.; methodology, Y.L., C.L. and J.D.; validation, Y.L., S.T., C.L., J.D., H.M. and J.P-G.; writing-original draft preparation, Y.L.; writing-review and editing, S.T., C.L., J.D., H.M. and J.P.-G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Key Research and Development Program of China (2018 YFC1506502).  
Not applicable.  
Restrictions apply to the availability of these data. PhenoCam data was obtained from [NEON] and are available [[[https://www.neonscience.org/field-sites/explore-field-sites](https://www.neonscience.org/field-sites/explore-field-sites)]([https://www.neonscience.org/field-sites/explore-field-sites](https://www.neonscience.org/field-sites/explore-field-sites))] (accessed on 25 June 2020) with the permission of [NEON]. Sentinel-1 and -2 data was obtained from [European Space Agency] and are available [[[https://sentinel.esa.int/web/sentinel/sentinel-data-access/sentinel-products](https://sentinel.esa.int/web/sentinel/sentinel-data-access/sentinel-products)]([https://sentinel.esa.int/web/sentinel/sentinel-data-access/sentinel-products](https://sentinel.esa.int/web/sentinel/sentinel-data-access/sentinel-products))] (accessed on 25 June 2020) with the permission of [European Space Agency].  
The authors declare no conflict of interest.
# Review of Indoor Positioning: Radio Wave Technology
## 3 Radio Signals-Based Positioning
### ZigBee Technology  
The ZigBee technology is a short-range wireless communication technology, based on the IEEE 802.15.4 standard as its medium access control (MAC) layer and physical layer (PHY) standard. It operates at the 2.4 GHz frequency with a lower bit rate. ZigBee can be applied with a star, tree networks, and mesh networks by relating to a microcontroller [118, 119, 120]. The ZigBee design classifies three types of devices such as the ZigBee coordinator, ZigBee Router, and ZigBee End Device that combines ZigBee radios. A ZigBee End Device is cheaper to produce than a ZigBee coordinator or ZigBee Router [54].  
ZigBee devices can control their own data and prevent some data damage by using carrier-sense multiple access/collision avoidance (CSMA/CA). ZigBee devices are defined by aspects, for instance, of energy detection and link characteristics that permit RSS measurements to be simply resolved. ZigBee technology has a wider range than BLE technology, as such it is able to communicate further by using a mesh network of relay nodes to arrive at a destination [107]. The ZigBee-based localization system used to link quality indication (LQI) instead of the RSSI [121]. The ZigBee standard-based wireless technology has many advantages such as its low cost, low power (17.68 mW average power consumption) [107], safety, reliability, robustness, and low data rates. In addition to its light weight, it has low-bandwidth and a faster computation processing. [122, 123, 124].  
The ZigBee technology was commonly used to measure indoor positioning and tracking previously because of its advantages [35, 36, 119, 121]. Conversely, ZigBee-based positioning impacts accuracy because of the interference and strength of the signals [38]. ZigBee positioning also has a definite constraint on positioning in real time when using RSSI, due to the short-range and great latency shortcoming of 802.15.4 wireless technology [117]. Furthermore, it requires extra hardware and is not a trend among current IoT users. The low power features of ZigBee technology have not happened because of its limitations in data transmissions. This network usually allows a device to succeed in its data transmission over almost 100 m despite its low powered characteristics. In the network, each node can connect directly with other nodes or through neighbouring nodes in the network [125, 126].
## 2 Main Architecture of the Classification System  
The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for evaluating the language abilities of non-native speakers of Italian.  
Interestingly, in 2001 the European Union introduced the Common European Framework of Reference for languages (commonly abbreviated as CEFR) [14] which recommends the use of a six-level scale in order to assess the language abilities of non-native speakers. Since then, the vast majority of institutions dealing with language teaching and assessment in Europe have adopted this scale. Hence, there is a large amount of texts used as assignments in language evaluation tasks which can be exploited in order to train a supervised classification model. The six CEFR levels, increasingly ordered by complexity, are: A1, A2, B1, B2, C1, and C2. However, in the language certification context, the texts used for the first two levels A1 and A2 are very short and elementary, thus practically useless for our purposes. For these reasons, our dataset was formed by collecting a corpus of texts used in the reading sections of language certification exams for the CEFR levels B1, B2, C1, and C2, which were manually labeled by language testing experts.  
Using machine learning terms, we have a dataset of texts labeled with four different classes, which can be used to train a predictive model that, in turn, allows us to predict the complexity class of a previously unseen text.  
As is common in the text classification field [37, 38], we first convert any text into a numeric vector. This numeric representation allows us to directly use the most common models and algorithms available in the machine learning literature [39, 40].  
The numeric vector corresponding to a given text \(t\) is formed by the purposely defined quantitative linguistic features, which are computed from linguistic data structures obtained by running a natural language processing (NLP) pipeline on \(t\). An NLP pipeline [41, 42] includes a variety of processing steps (such as e.g., tokenization, part-of-speech tagging, and parsing) aiming at highlighting the linguistic structure of a text.  
Hence, the working scheme of our classification system can be divided in two phases:  
1. the _training phase_, depicted in Figure 1, to be performed once (or sporadically, as new reliably labeled texts become available) and whose final goal is to train a predictive model by feeding a machine learning algorithm with a training set of numeric vectors obtained by extracting the linguistic features from the original dataset of texts correctly labeled by linguistic experts;
2. the _prediction phase_, depicted in Figure 2, where an unlabeled text undergoes the process of features extraction and is then predicted to one of the four complexity levels B1, B2, C1, or C2.  
Further details about the dataset, the NLP pipeline, the features extraction process, and the classification models adopted are provided in the following sections.  
Figure 1: Training phase of the classification system.
# Design of an Underwater Acoustic Waveform and Integrated System for Communication and Detection
###### Abstract  
Combining underwater communication and detection can reduce system size and power consumption as well as improve secrecy. This paper presents a waveform that integrates continuous phase modulation (CPM) for data communication with linear frequency modulation (LFM) for detection. It is shown that the velocity ambiguity and range performance with this waveform are similar to those with only LFM. An integrated underwater acoustic system is also proposed to achieve simultaneous communication and detection. A symbol suffix (SS) is introduced into the signal frame at the transmitter and a two-step algorithm is employed at the receiver for frequency and phase offset estimation. Results are presented, showing that this decreases the sensitivity to CPM and reduces the range ambiguity side lobes. Further, the proposed system can effectively recover the data from the received signal. The bit error rate (BER) is shown to be better than that of traditional systems without a symbol suffix. With changes in the signal-to-noise ratio (SNR), the bit error rate (BER) in underwater acoustic environments is comparable to that in an additive white Gaussian noise (AWGN) channel, indicating a significant improvement in communication performance within the underwater acoustic channel.  
waveform design; underwater acoustics; integrated communication; detection; signal ambiguity; bit error rate +
Footnote †: journal: Marine Science and Engineering  
Article
# Optimization of Multi-Day Flexible EMU Routing Plan for High-Speed Rail Networks
## 5 Conclusions  
In this study, we propose a flexible multi-day EMU routing optimization model that accounts for dynamic coupling/decoupling operations and flexible inspection location assignment. The presented model integrates connection time and empty-run mileage into a unified objective, enabling the dynamic assignment of EMUs across planning days while respecting maintenance constraints. To efficiently solve this complex optimization problem, a heuristic decomposition algorithm is developed, which involves the following: (i) the generation of daily train connection task chains; (ii) the construction of the EMU assignment network; (iii) multi-day routing plan optimization with inspection and connection continuity constraints.  
We validated the proposed method using a real-world case study on the Beijing-Baotou high-speed railway corridor. The results demonstrate that compared to the fixed routing strategy, the flexible scheme reduces EMU fleet requirements (from 43 to 42 units), decreases total deadheading mileage, and achieves more balanced inspection scheduling. Moreover, it maintains high operational feasibility, generating solutions within 30 min on standard computational hardware.  
In summary, the presented model enhances EMU operational efficiency by comprehensively leveraging the flexibility of modern high-speed EMUs and station infrastructure. It offers a scalable and practical framework for EMU routing plans in dense and dynamic railway networks, particularly under scenarios with variable demand and constrained maintenance resources.  
Future research could further extend the model to incorporate energy efficiency and passenger flow dynamics. Additionally, to improve practical applicability, future extensions may include explicit coupling/decoupling capacity constraints and multi-level maintenance requirements. These enhancements will enable the model to more accurately reflect  
Figure 11: Iteration curves under different weights.  
complex operational realities and provide even more robust decision support for large-scale railway networks.  
Conceptualization, X.S., Y.Y. and B.G.; methodology, X.S. and Z.C.; software, X.S. and Z.C.; validation, X.S.; data curation, Y.Y. and B.G.; writing--original draft preparation, X.S.; writing--review and editing, X.S., Y.Y., B.G. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by China State Railway Group Co., Ltd. (No. P2024X002) and the Research Project of China Railway Beijing Group Co., Ltd. (No. 2024 BY01).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors thank everyone who contributed to this article.  
The authors declare no conflicts of interest.
## 4 Conclusions  
The effect of steam explosion pre-treatment of WS on AD was evaluated in parallel with the provision of nitrogen to balance the high C/N ratio of WS. The steam explosion pre-treatment offered a 12-21% enhancement in methane production in all examined scenarios, while the adjustment of the C/N ratio was clearer when combined with a steam explosion pre-treatment step. Furthermore, all four different examined feedstocks (WS, SE, DDGS and RM) performed well as sole substrates in a batch AD system with a preferable I/S of 1.75 over that of 3.5 (_w/w_). It was also found that the addition of nitrogen, either inorganic (NH\({}_{4}\)Cl) or organic, as a co-substrate was more efficient towards gas production for steam-exploded straw than towards untreated straw. This study has shown, for the first time, that DDGS or RM can be used as an AD supplement to stimulate gas production and  
Figure 4: Methane production after 30 days of co-digestion of untreated and steam-exploded wheat straw after subtracting the expected methane production of the RM and DDGS, respectively.  
improve wheat straw biodegradability. Additionally, it was shown that the addition of 10% of either DDGS or RM on an AD system operating with WS can enhance gas yields at levels similar to those achieved by steam-exploded straw. Further techno-economic evaluation and life cycle assessment (LCA) could determine the financial sustainability of commercial biomethane production based on these co-digestion strategies.  
A.C., K.-A.G.K. and F.K. conceived the original idea and designed the experiments for this study. The experiments were conducted by F.K. and J.D. while F.K. and J.D. also collected the experimental data and ran the data analysis. K.-A.G.K., A.C. and D.C. verified the analytical methods and provided supervision during the experiments and data analysis. The original manuscript was written by F.K. and was reviewed/edited by [PERSON], K.-A.G.K. and D.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors of this manuscript would like to thank the Engineering and Physical Sciences Research Council (EPSRC EP/M50660%/1) and the company Future Biogas Ltd. for providing the financial support for this study. Special thanks to FoodWasteNet BBSRC NIBB for funding a Vacation Scholarship (VS18_07) for J.D.  
The authors would like to declare no conflicts of interest.