esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a joint PAM constellation and IRS reflectance optimization problem was formulated to improve the BER performance by maximizing the minimum distance between received constellation points under the dimming constraint. The problem was divided into a convex receive constellation optimization problem and a feasible transmit constellation and IRS reflectance problem; the latter can be further decomposed and relaxed into two constrained least square problems that can be solved alternatively. The simulation results showed the performance improvement benefits from the additional IRS-reflected NLoS gain and the dimming constraint relaxation when the dimming coefficient is greater than 0.5.  
**Author Contributions:** Conceptualization, L.J.; data curation, Q.W.; formal analysis, L.J.; funding acquisition, L.J.; methodology, L.J.; project administration, Y.Z.; resources, Y.Z.; software, L.J. and Q.W.; supervision, L.J.; validation, Y.Z.; visualization, Q.W.; writing--original draft, L.J.; writing--review & editing, L.J. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded in part by the National Natural Science Foundation of China under grant 62301257, and in part by the Natural Science Foundation of Jiangsu Province under grant BK20200488.  
**Institutional Review Board Statement:** Not applicable  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} VLC & Visible light communication; \\ IRS & Intelligent reflecting surface; \\ LoS & Line of sight; \\ NLoS & Non-light of sight; \\ SISO & Single-input single-output; \\ BER & Bit error rate; \\
6G & Sixth-generation; \\ LED & Light-emitting diode; \\ RF & Radio frequency; \\ MIMO & Multiple-input multiple-output; \\ DPC & Dirty-paper coding; \\ IM/DD & Intensity modulation and direct detection; \\ PAM & Pulse amplitude modulation; \\ CCU & Central control unit; \\ PD & Photo-detector; \\ DC & Direct channel; \\ FoV & Field of view; \\ LD & Laser diode; \\ OOK & On-Off keying; \\ PPM & Pulse position modulation; \\ SER & Symbol error rate; \\ ML & Maximum likelihood; \\ SNR & Signal-to-noise ratio. \\ \end{tabular}
## 4 Design of Switching Strategy  
As was confirmed in the previous section, the proposed LC/S compensated IPT system can achieve CCO and CVO characteristics at two distinct ZPA operating frequencies through appropriate parameter design, without the need for redundant AC switches (ACS). Figure 9 shows the switching strategy for the system charging mode. Figure 10 depicts the mode-switching part in the green box in Figure 9. More specifically, when the IPT system starts to work, the charging voltage \(U_{\text{bat}}\) and current \(I_{\text{bat}}\) of the battery are synchronously measured by the voltage and current sensors, and the values are sent to the controller. \(U_{\text{set}}\) is the charging voltage specified in CV mode, namely, the critical voltage at the transition point (\(U_{\text{set}}\) = 45 V) between CC mode and CV mode. When \(U_{\text{bat}}<U_{\text{set}}\), the controller charges in CC mode at the CC frequency \(f_{\text{cc}}\). As the voltage continues to rise until \(U_{\text{bat}}\geq U_{\text{set}}\), the controller charges in CV mode at the CV frequency \(f_{\text{cv}}\). As the charging current decreases, the IPT system stops working when the charging current \(I_{\text{bat}}<I_{\text{min}}\) is detected. \(I_{\text{min}}\) is the specified minimum charging current (\(I_{\text{min}}\) = 0.25 A).
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## Abbreviations  
The following abbreviations are used in this manuscript.  
\begin{tabular}{l l} AWGN & Additive White Gaussian Noise \\ AWEMS & Adaptive Weighted Extended Min-sum \\ AWMS & Adaptive Weighted Min-sum \\ BCH & Bose-Chaudhuri-Hocquenghem \\ BER & Bit Error Rate \\ BICD & Bit-interleaved Coded Demodulation \\ BICM & Bit-interleaved Coded Modulation \\ BP & Belief Propagation \\ CG & Coding Gain \\ CNN & Convolutional Neural Network \\ CRC & Cyclic Redundancy Check \\ DSP & Digital Signal Processing \\ EDFA & Erbium Doped Fiber Amplifier \\ EMS & Extended Min-sum \\ EXIT & Extrinsic Information Transfer \\ FEC & Forward Error Correction \\ FER & Frame Error Rate \\ GF & Galois Field \\ LDPC & Low-density Parity-check \\ LLR & Log-likelihood Ratio \\ MS & Min-sum \\ NCG & Net Coding Gain \\ NF & Noise Figure \\ NN & Neural Network \\ NR & New Radio \\ NNEMS & Neural Normalized Extended Min-sum \\ NNMS & Neural Normalized Min-sum \\ OSC & Optimized Successive Cancellation \\ PB & Protograph-based \\ PCM & Parity-check Matrix \\ PDF & Probability Density Function \\ PEG & Progressive-edge Growth \\ QAM & Quadrature Amplitude Modulation \\ QC & Quasi-cyclic \\ ReLU & Rectified Linear Unit \\ RRC & Root Raised Cosine \\ RM & Real Multiplication \\ RNN & Recurrent Neural Network \\ SC & Spatially coupled \\ SS-PAN & Simple Scaling and Parameter-adapter Networks \\ SCL & Successive Cancellation List \\ SWD & Sliding Window Decoder \\ WBP & Weighted Belief Propagation \\ WDM & Wavelength Division Multiplexing \\ WEMS & Weighted Extended Min-sum \\ WMS & Weighted Min-sum \\ \end{tabular}
## 2 Antenna Design
### Antenna Array Design and Measurement  
We construct a 4-element antenna array to improve the gain and thereby increase the conversion efficiency using the proposed antenna. The configuration of the antenna array is shown in Figure 5. It consists of two series subarrays. In order to ensure the equal amplitude and phase of the two elements of the subarray, the meandered feed lines and terminal lines are optimized. A Wilkinson power divider is used to feed the array [20].  
To verify the performance of the DRA array, a prototype is fabricated and measured, as shown in Figure 6. The simulated and measured S11 of the antenna array is shown in Figure 7. The simulated impedance bandwidth with S11 \(<\) 10 dB in the lower band is 2.42-2.51 GHz and the measured result is 2.4-2.52 GHz. For the upper band, the simulated bandwidth is 5.74-5.84 GHz, and the measured impedance bandwidth is 5.29-5.91 GHz. It can be observed that a good impedance matching is obtained. The difference between simulated and measured results at upper pass band might be due to the larger machining dimensions and the uneven distribution of dielectric substrate materials.  
Figure 4: Simulated radiation patterns of the proposed DRA: (**a**) 2.45 GHz; (**b**) 5.8 GHz.  
Figure 3: Electric field distributions on the x-z plane: (**a**) 2.45 GHz; (**b**) 5.8 GHz.  
Figure 6: Test environment of the prototype DRA array.  
Figure 7: Simulated and measured S11 of the proposed DRA array.  
Figure 8 presents the radiation patterns of the proposed DRA array at lower and upper frequencies. It can be seen that the measured x-z plane radiation patterns at 2.45 GHz and 5.8 GHz are in good agreement with the simulated ones. However, the measured y-z plane radiation patterns (especially the value of E_theta) are somehow different from the simulation. The dielectric block needs to be fixed by the reinforcing tape during the radiation pattern measurement (not considered in the simulation), which leads to a slight difference in the radiation pattern.  
Figure 9 depicts the simulated and measured gain of the DRA array. The measured and simulated results show good agreement. The simulated and measured gains of the antenna array are 6.2 dBi and 5.5 dBi at 2.45 GHz, respectively. The simulated and measured gains at 5.8 GHz are 14.2 dBi and 12.9 dBi, respectively. Differences are probably owing to two main factors: (1) The insertion loss of the fabricated feeding network is larger than the simulation; (2) The insertion loss of the SMA connector utilized to test the prototype is not included in the simulations.  
Figure 8: Simulated and measured radiation patterns of the proposed DRA array: (a) 2.45 GHz; (b) 5.8 GHz.
## 1 Introduction
[32] extracted the same phenological metrics to study climate and phenology interactions in catchment areas in the Northern Hemisphere, showing that an increase in temperature strongly influenced the advance of SOS and delay of EOS in the study area. [PERSON] et al. [33] compared field data with NDVI extracted SOS and EOS, concluding that NDVI enabled more accurate estimates for the two metrics. This study also reported that an increase in mean spring temperature leads to the advancement of budburst. This technique has also been applied to conifer forest [34] and alpine forest [35] to study the land surface phenology of the crop. However, the study of land surface phenology of plantation crops such as rubber (_Hevea brasiliensis_) is still relatively unexplored. Rubber is a plantation crop that produces latex. The key phenological stages that occur in rubber involve annual defoliation and refoliation processes during what is called the wintering period. Wintering occurs in the dry season, which in Sumatra is between July and September. Leaf fall in the refoliation period naturally causes distinct changes in the tree canopy. This biophysical property is an excellent indicator for detecting, measuring, and tracking growth using remote sensing imagery. Previous studies have exploited this unique characteristic to differentiate rubber from other vegetation, such as forest and oil palm, for mapping purposes [36; 37; 38]. Several vegetation indices such as NDVI, Land Surface Water Index (LSWI), Normalized Burn Ratio (NBR), Atmospherically Resistant Vegetation Index (ARVI), and Normalised Difference Moisture Index (NDMI) have been effectively deployed for detection of wintering in rubber [39; 40]. Rubber phenology is highly sensitive to climate change, particularly to rainfall and temperature [9; 41; 42]. Theseresponses have a direct impact on yield [43]. The SOS and EOS are affected by temperature and rainfall, respectively, as changes in these climatic factors may delay or advance the season [9]. Our knowledge on how climatic factors influence land surface phenology metrics such as SOS and EOS is currently limited. To our knowledge, no previous study has investigated and analysed phenological trends using multi-temporal remote sensing data for rubber plantations. This is particularly important as rubber plantations have a relatively long economic lifespan of up to 35 years. In this study, we aimed to investigate the influence of climate on rubber phenological changes over the past ten years using remote sensing datasets. The specific objectives of the paper are three-fold: (1) to demonstrate the applicability of remote sensing data to extract the rubber phenology of SOS and EOS; (2) to examine the trend and changes of two major climatic data; and (3) to assess SOS/EOS response to rainfall and temperature. This information is essential for differentiating natural leaf drop during wintering with abnormal leaf drop. Consequently, this could assist in developing an early warning system for disease and triggering interventions.
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 6 Conclusions  
Aimed at the flying formation and AAR of UAVs, this paper proposes a UAV pose measurement method based on rigid skeleton structure, which uses the spatial linear equation of rigid skeleton combined with the geometric model to solve the aircraft pose. This paper first proposes a key point extraction method that integrates 2D image data with 3D depth information to solve the problem of reduced feature point mismatch. This method improves the accuracy and stability of the pose measurement method. Compared with other methods, the proposed method in this paper can solve the problem of UAV attitude calculation without the need for auxiliary equipment, such as a theodolite, or remote transmission of data signals. Thus, the proposed method effectively improves the reliability and robustness of the pose measurement method and provides a safety guarantee for the aerial flight of UAVs. The proposed method can measure the position and attitude of UAVs without the need for knowledge of the aircraft model and size. The RMS error of the attitude angle is within 2.1\({}^{\circ}\) and the RMS error of distance measurement is within 0.61 m. The calculation frequency is 12 fps, which meets the collision avoidance measurement conditions of short-range UAVs in flight. The effectiveness and feasibility of the proposed method are verified in a simulation environment. The proposed method does not have a complex framework, which is easy to be implemented and used in actual project engineering.  
Conceptualization, J.Z. and Z.L.; methodology, J.Z.; software, J.Z.; validation, J.Z.; data analysis, J.Z.; writing-original draft preparation, J.Z.; writing-review, Z.L and G.Z.; supervision, Z.L; project administration, Z.L. and G.Z.; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Aviation Science Foundation Project (No:201946051001).  
Not applicable.  
Data Availability StatementData available on request due to restrictions eg privacy or ethical.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned Aerial Vehicle \\ AAR & Automated Aerial Refueling \\ \end{tabular}  
Figure 16: Attitude angle measured by comparison algorithm.
# Machine Learning for Quantitative Finance Applications: A Survey
## 8 Sentiment Analysis  
As already mentioned, social media usage has increased over the last 10 years, becoming the first place to obtain news. In particular, it has been noted that social media can affect not only public opinion but also political and economic events. Many researchers have investigated how social media could affect financial market in order to gain information for forecasting financial time-series.  
Satisfactory results have been achieved by analyzing textual analysis of breaking financial news. The proposed method [44] collected more than 9000 financial news articles and 10,000,000 S&P 500 stock quotes for a period of 5 weeks. The core idea is to estimate a discrete share price by analyzing news 20 min after its release. Several textual representations have been used to analyze news accurately: bag of words, noun phrases and named entities. Furthermore, the authors developed a SVM-based system to perform discrete numerical prediction. The experiments demonstrated not only that Bag of Words is the representation that works better than others but also SVM is extremely accurate according to MSE values.  
Another significant work in which a machine learning-based system is applied to analyze Twitter data in the financial domain is [45]. The authors suggest that the stock market trend is high correlated with public opinion. In particular, they focused on analyzing users' tweets using two different textual representations, called Word2 vec [46] and N-gram [47]. The first representation was used to map each single word in a vector, the second one is used to match the corpus of a text. The experimental results confirmed the strong correlation existing between stock market trend and public opinion expressed in tweets. In Table 6, we provide a summary of surveyed studies focused on sentiment analysis.
## 2 LMMSE Estimator within GNSS Augmentation Systems
### Estimation Algorithm Considerations  
In order to use Equation (4), the following vector and two matrices have to be known:  
* \(\boldsymbol{\mu_{\xi}}\) approaches \(\boldsymbol{\mu_{\eta}}\) after a certain initialization time where a considerable amount of measurements is performed to assume that the average operation drives the contribution of \(\boldsymbol{\epsilon}\) to zero.
* \(\boldsymbol{R_{w}}\) depends on the quality of the GNSS receiver and is intrinsic to it. In fact, \(\sigma_{w}^{2}\) can be estimated offline and is considered to be a known vector.
* \(\boldsymbol{R_{\xi}}\) is the most critical estimate, and is defined by two variables, the pseudorange DC standard deviation \(\sigma\) and the correlation distance \(D\) as seen from Equations (3) and (5). These two variables have to be estimated by surveying campaigns beforehand (two methods are described in [10]) and depend on the environmental conditions which can vary in time. Therefore, it is important to assess the situation when the modeling parameters diverge from true values due to several reasons, such as geometric changes and severe ionospheric storms or sudden atmospheric changes [10]. The objective of this paper is thus to analyze the sensitivity of the LMMSE algorithm to variations of \(\sigma\) and \(D\).
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of Noise Power
#### 4.3.3 Performance of JMART  
Finally, Figure 13 shows the variation of the Fisher information and the corresponding error bounds by the JMART under different deviations of the metrics of RSS, AOA and TOF. From this figure, we can find that both the Fisher information and error bounds are significantly determined by AOA deviation, which are in accordance with the results under the metrics of single and double measurement given above.
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 2 Related Work  
### Video Anomaly Detection  
Video anomaly detection (VAD) has evolved significantly with advancements in deep learning, focusing on modeling normal patterns to identify deviations. Early approaches, such as autoencoders (AEs) [13], reconstructed normal frames under the assumption that anomalies would result in higher reconstruction errors. However, these methods often struggled with complex scenes due to their sensitivity to appearance noise and limited semantic understanding.  
Prediction-based approaches emerged to address temporal dynamics, utilizing autoregressive models [14] and generative adversarial networks (GANs) [15] to predict future frames. For instance, [PERSON] et al. [16] proposed a method using FlowNet and GANs to predict future frames, leveraging temporal coherence for anomaly detection. These methods improved performance but faced challenges in handling long-term dependencies and computational costs for real-time applications.  
Recent works introduced structured representations to enhance interpretability and generalization. [PERSON] et al. [17] decomposed skeleton trajectories into global motion and local posture, modeling their interactions via a message-passing encoder-decoder RNN. This approach achieved competitive performance by leveraging low-dimensional semantic features. Similarly, memory-augmented methods, such as VideoPatchCore [18], optimized normality memorization through coreset subsampling and multi-stream memory banks, enabling efficient anomaly detection without training.  
Transformer-based models have also gained traction for their sequence modeling capabilities. [PERSON] et al. [19] proposed a self-context-aware Transformer for few-shot VAD, predicting frame features using initial non-anomalous frames. This approach reduced the training data requirement and captured video-specific non-anomalous nature effectively. Language-guided paradigms, such as LaGoVAD [20], integrated textual definitions to dynamically adapt anomaly criteria, addressing concept drift in open-world scenarios.
# Effect of Soil Reinforcement on Tunnel Deformation as a Result of Stress Relief
## Material and Methods
### Finite Element Analysis
#### 2.2.4 Numerical Modeling Scheme  
It should be noted that there is no ground reinforcement in the centrifuge model test. However, the numerical modeling schemes include the cases with and without ground reinforcement. The centrifuge tests provide a benchmark for verifying the correctness and reasonability of the numerical results with no reinforcement. Furthermore, testing of the effect of the reinforcement on tunnel response due to stress relief can be conducted. The ground is assumed to be reinforced by grouting. All parameters of the ground mechanical behavior change after reinforcement may affect tunnel response. A parametric study was conducted to investigate the effect of soil friction angle, cohesion, etc., on the tunnel response. It was found that the strength indexes had only a little influence on tunnel deformation. However, the Young's modulus may play an important role in predicting the tunnel response. Thus, this study focused on the study of the soil's Young's modulus. Figure 5 shows the schematic diagram of the basement bottom reinforcement. The soil mass between the basement bottom and the tunnel was divided into parts 1, 2, and 3, with each part being 10 mm in the model (0.6 m in the prototype). Note that reinforcement S1 was equal to area 1, S2 was equal to area 1 plus area 2, and S3 was the sum of areas 1, 2, and 3. In the first scheme, the effect of the reinforced area's Young's modulus on the behavior of the tunnel was investigated, assuming that the reinforcement area underneath the basement was S1. The reinforced soil's Young's modulus varied between 1, 5, and 10 times the value of the soil's initial Young's modulus. As for the 5-times and 10-times values,  
Figure 4: A sketch of the modeling procedure.  
they may not be close to reality but are certainly helpful for practice engineers to judge the effect of soil Young's modulus on the tunnel response. In the second scheme, the Young's modulus of the reinforced soil was constant (\(E_{\mathrm{r}}=5E_{\mathrm{s}}\)), and the effect of the reinforcement depth on the tunnel's behavior was investigated. The reinforcement depth varied from area S1 = 0.6 m and S2 = 1.2 m to S3 = 1.8 m in the prototype.