esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Materials and Instrument  
PV were cultivated in semi-natural conditions under a forest at Tra Linh Farm, at the height of 1.600 m on Ngoc Linh Mount, Quang Nam Province, Vietnam. The seeds were set in February, and the plants grew naturally in the original forest conditions without any fertilization. The underground parts of fresh 2-7 year old cultivated PV were collected in December, during the winter when PV hibernates. Each age group comprised five different samples. A voucher specimen was deposited in the herbarium of the College of Pharmacy, Seoul National University (SNUP-2014-A-03, Seoul, Korea). The underground parts were cleaned and dried at 50 \({}^{\circ}\)C in a drying oven until dry. The dried material was then ground and sieved to give a fine powder with particle size less than 355 \(\upmu\)m.  
Figure 6: Change of saponin content of the PV rhizome from 2 to 7 years. Results are expressed as mean \(\pm\) SD (\(n=5\)), \({}^{\star}p<0.05\), \({}^{\#}p<0.01\) compared with that at the age of two years.  
Before conducting this study, reference standards, including G-Rb1, G-Rd, G-Rg1, G-Re, N-R1, M-R1, M-R2, V-R2, V-R11, and p-RT4 were prepared in the laboratory following our previous reports [1; 2; 3]. HPLC-grade solvents were purchased from J.T. Baker (Deventer, Netherlands).  
HPLC analyses were performed on a Perkin Elmer series 200 HPLC system (Perkin Elmer, Inc., Waltham, MA, USA) equipped with vacuum degasser, quaternary gradient pump, auto-sampler, UV-vis detector coupled with Sedex 80 LT ELSD (Sedere, Alfortville Cedex, France), connected to a TotalChrom Workstation software. A Gemini C\({}_{18}\) column (150 mm \(\times\) 4.6 mm. i.d., 5 \(\upmu\)m) and a Gemini C\({}_{18}\) guard column (Phenomenex, Torrance, CA, USA) were used.
## 2 Background and Proposed System Model
### Cfcpsc
#### 2.4.2 CFCPSC with DF  
The CFCPSC with DF, denoted as CFCPSC-DF, deals with each antenna as a single receiver. In [11], it is shown that the alteration of the original CFCPSC, in this case, is similar to having \(L\) single-antenna receivers rather than a single \(L\)- antenna SU. Every one of the SUs divide the whole bandwidth into \(S\) sub-bands, makes an occupation decision in the sub-band level, combines these decisions, finds a local occupation decision regarding the presence or absence of a PU signal in the sensed band, and forwards this occupation decision to the fusion center in order to make a global collaborative decision. The steps 1, 2, 3, 4, and 5 of this altered CFCPSC-DF are the same as the first ones of the modified CFCPSC-SF in Section 2.4.1. The remaining steps are as follows:  
1. Find the CFCPSC \(T_{\text{CFCFPSC}_{s,s}}=r_{u,s}\) statistic at each SU for the \(s\)-th sub-band.
2. Compare \(T_{\text{CFCFPSC}_{u,s}}\) with a local decision threshold \(\gamma_{u}\) in order to decide on the occupation state of the \(s\)-th sub-band: when \(T_{\text{CFCFPSC}_{u,s}}\) is greater than \(\geq\gamma_{u}\), decides \(\mathcal{H}_{1}\), otherwise decide \(\mathcal{H}_{0}\).
3. At each SU, make a occupation decision as \(\mathcal{H}_{0}\) if all local sub-band decisions are also \(\mathcal{H}_{0}\) or as \(\mathcal{H}_{1}\) if at least one local sub-band decision is \(\mathcal{H}_{1}\) and then send this decision to the FC.
4. At the fusion center, combine the \(U\) decisions and make a global collaborative decision as \(\mathcal{H}_{0}\) when all received decisions are \(\mathcal{H}_{0}\) or as \(\mathcal{H}_{1}\) otherwise.
## 3 Simulation of Underwater Electric Field Communication
### Analysis of Underwater Electric Field Communication Simulation Results  
Based on the calculation results of the underwater electric field communication simulation parameters, a communication experiment with two nodes at a fixed distance of 15 m was conducted to verify the effective communication range of the two nodes. Finally, a comparative analysis of the communication delay and packet delivery rate of the five nodes under different protocols was performed.
## 5 Discussion
### Refined Impact Assessment of AI on Supply Chain Demand Forecasting  
Artificial intelligence in demand forecasting has ushered in a new era of precision and speed, particularly in the healthcare and retail sectors. Advancements in machine learning (ML) and deep learning (DL) have brought significant improvements in predictive analytics, leading to refined operational strategies. However, these developments are not without challenges: data quality issues, algorithmic biases, and integration with existing systems demand a balanced view on the impact of AI on core systems.  
The demand forecasting process can be significantly enhanced by various AI techniques and tools. Long short-term memory (LSTM) networks are well-suited for handling complex time-series data, while support vector machines (SVMs) excel in both classification and regression tasks. Additionally, deep learning tools like convolutional neural networks (CNNs) and generative adversarial networks (GANs) fine-tune estimation processes, improving the depth and accuracy of training datasets and enhancing the analysis of sequential data. Furthermore, blockchain technology offers robust and transparent record-keeping, which enhances reliability in demand forecasts across complex supply chains. AutoML democratizes the use of AI and scales solution availability by simplifying model selection and deployment for advanced forecasting, making these tools accessible to non-experts.  
This detailed perspective not only highlights the role of AI in improving supply chain efficiency but also connects specific AI technologies to their practical applications. It offers valuable insights for stakeholders who seek to make informed decisions regarding the strategic implementation of AI in supply chain management.
## 2 Methods  
### CRC  
The CRC is a widely employed technique for detecting errors in digital data [23]. Particularly valuable in data transmission and storage contexts, CRC helps ensure the integrity of data by detecting accidental changes to raw data arising from noise or other disturbances.  
CRC operates through polynomial division, a mathematical concept borrowed from algebra, but applied here in a binary framework. Each block of data to be transmitted is taken as a large number, often visualized as a sequence of bits. This number is then divided by a predetermined, non-zero binary polynomial, known as the CRC polynomial. The key mathematical operation involved is the modulo-2 division, which is analogous to the long division one learns in arithmetic, but adjusted for binary calculations. Here, subtraction and addition are equivalent, simplifying the arithmetic operations to XOR operations.  
The essence of CRC's error-checking capability lies in its generation and use of a 'checksum'--a small block of data derived from the modulo-2 division. Before transmission, the checksum is appended to the data. Upon reception, the combined block of data and checksum is once again divided by the same CRC polynomial. If the remainder of this division is zero, the data are considered unchanged; otherwise, an error is flagged.  
Mathematically, if \(D\) is the original data represented as a binary number, and \(P\) is the CRC polynomial, then the CRC process can be described by the following steps:  
1. Multiply \(D\) by \(2^{n}\), where \(n\) is the degree of \(P\). This step effectively appends \(n\) zero bits to the end of \(D\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Research Work**} & \multicolumn{2}{c}{**Long Code**} & \multirow{2}{*}{**High Reliability**} & \multicolumn{2}{c}{**Balancing Complexity**} \\  & **Length Adaptability** & & **and Performance** \\ \hline [PERSON] et al. [18] & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [19] & \(\times\) & \(\times\) & \(\surd\) \\ [PERSON] et al. [20] & \(\surd\) & \(\times\) & \(\surd\) \\ [PERSON] et al. [21] & \(\surd\) & \(\times\) & \(\surd\) \\ [PERSON] et al. [22] & \(\times\) & \(\surd\) & \(\surd\) \\ Our Scheme & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scheme comparison.  
2. Divide the modified \(D\) by \(P\) and find the remainder \(R\), where \(R\) is a binary number less than \(P\) in degree.
3. Append \(R\) to the original data \(D\), forming the transmitted message \(T\). On the receiver's end, \(T\) is divided by \(P\). A non-zero remainder indicates that an error has occurred during transmission, prompting corrective measures or retransmission requests.  
This method's reliability and simplicity have made CRC a fundamental tool in digital communications, widely applied in various networking protocols and storage systems to ensure data integrity.
## Materials and Methods
### Search Strategy  
A systematic search of the literature was conducted to identify any studies related to the use of phytochemicals for the treatment of IBD. Articles including clinical data of the phytochemicals' application on humans affected by IBD were screened. Description of the potential molecular antioxidant or anti-inflammatory pathways targeted by the phytochemicals was evaluated. The influence of phytochemicals on the symptoms, clinical scores, prevention of relapse, maintenance of remission, quality of life and avoidance of complications was also examined.  
We searched two major databases (PubMed/Medline and Scopus) until January 2024. Searches were conducted using the keywords \"inflammatory bowel disease\", \"photochemical\", \"antioxidant\" and \"anti-inflammatory\". Specifically, various combinations of the following search terms and phytochemical compounds or category headings were used in the database \"Advanced Search\" feature of PubMed/Medline and in Scopus: \"inflammatory bowel disease\", \"phytochemicals\", \"antioxidant\" or \"anti-inflammatory\" along with \"curcumin\", \"alkaloids\", \"organosulfur\", \"phenolics\", \"terpenes\", \"carotenoids\" or \"couramins\". Filters were applied so that only randomized controlled trials (RCTs), systematic reviews and clinical trials written in English were included. After deduplication, titles and abstracts were screened for relevance to our topic during the initial search. Following retrieval of screened articles, full-text evaluation was done to reject or accept each study for inclusion. References from included studies were also screened to ensure a thorough literature review.  
Figure 1: PRISMA flow diagram for the records identified after searching the databases.
## 2 Materials and Methods
### Satellite Retrievals and Gauge Data
Considering data availability and quality, this study uses the best hourly gauge data covering the warm period from 1 April-30 September 2014. Among all of the stations, there are 52 in the Yellow River basin, 66 in the Yangtze River basin and 69 in the Brahmaputra basin. The gauge densities in the Yellow River, Yangtze River and Brahmaputra basins are 2.01 and 1.34, 1.78 per ten thousand square kilometers, respectively. Moreover, a field snowfall experiment from April 2014-February 2015 at a site (99\({}^{\circ}\)52\({}^{\circ}\)54\" E, 38\({}^{\circ}\)1606\" N, 2980 m) of the northeastern TP (Figure 1) was performed by [28], and the data are used for IMERG snowfall error analysis. Details of the field site information can be found from [28].
## 1 Introduction
(3) To what extent does the proposed enhanced TransD model generalize on both a self-constructed California Traffic Accident Knowledge Graph (TAKG) and the benchmark datasets FB15K-237 and WN18 RR? To answer these questions, the main contributions of this paper are as follows: (1) A California Traffic Accident Knowledge Graph (TAKG) comprising more than 110 entity and relation types is constructed following a top-down ontology design principle, thereby filling the gap in fine-grained traffic accident graph resources. (2) An enhanced TransD embedding model is proposed, which introduces entity attribute projection vectors into the dynamic mapping mechanism and employs a dual-limit loss to efficiently model complex multi-mapping relations. (3) Extensive benchmark comparisons and ablation studies on FB15K-237, WN18 RR, and the self-constructed TAKG systematically demonstrate robust improvements across multiple key metrics. Given TransD's advantageous balance between expressive capacity and computational efficiency in handling complex multi-mapping relations, it was selected as the baseline for enhancement. The TAKG was built from the publicly available California traffic accident dataset on Kaggle, which was manually cleaned and field-standardized. The remainder of this paper is organized as follows: In Section 2, we review related works; Section 3 presents the methodology for constructing the traffic accident knowledge graph; Section 4 details the theoretical foundations and algorithmic implementation of the enhanced TransD model; Section 5 describes the experimental setup, presents results, and provides analysis; Section 6 discusses the implications of our findings; finally, Section 7 concludes the paper and outlines future research directions.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Prediction Intervals Formulation and Evaluation Metrics
#### 2.5.6 Prediction Interval Covered-Normalised Average Width (PICAW)  
The indices described above are only about the PIs covered by the actual values. PICAW estimate involves the PIs not covered by the actual values, since this affects the PI widths negatively; it is therefore a new evaluation index for the width given in Equation (26) [21].  
\[PICAW=\frac{1}{R}\big{(}\frac{1}{m_{p+}}\sum_{i=1}^{m_{p+}}PIW_{i}+\lambda\frac {1}{m_{p-}}\sum_{i=1}^{m_{p-}}PIW_{i}\big{)}, \tag{26}\]  
where \(m_{p+},m_{p-}\), respectively, represent the number of the actual values that the PIs does or does not cover. A control parameter that widens the difference between the PIs and the actual values is \(\lambda>1\) else if \(\lambda=1\), PICAW becomes PINAW. PICAW gives more accurate PI construction evaluation when actual values are farther off from the PIs.
# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 4 Results
### Implementation Details  
To comprehensively train the proposed model, enabling it to enhance low-light regions and suppress overexposed areas, we select multi-exposure sequence images from the SICE dataset [36] as our training set, totaling 2002 images. This dataset encompasses scenes under various exposure conditions, providing a rich array of samples for the model to learn image features under different lighting scenarios. All the training images are resized to \(512\times 512\) pixels to ensure input consistency and enhance training efficiency. We implement our proposed method using the PyTorch 2.1.0 framework and conduct training and testing on a desktop computer equipped with an NVIDIA RTX 3090 GPU, whose manufacturer is NVIDIA company and was made in Santa Clara, CA, USA. For the training parameter settings, the batch size is set to 8. Network weight initialization employs a standard Gaussian distribution with zero mean and a standard deviation of 0.02, while the bias term is initialized as a constant. The optimization algorithm used is the Adam optimizer with a fixed learning rate of 0.0001. The weight parameters in the loss functions, \(w_{spa}\), \(w_{exp}\), \(w_{col}\), and \(w_{tr(g)}\), are set to values consistent with those provided in the Zero-DCE source code to balance the contribution of different loss components.  
We conducted comprehensive evaluations across the following six datasets: LOLv1 [28], LOLv2-Real, LOLv2-Synthetic [37], LSRW-huawei [38], DICM [39], and MEF [40], obtaining extensive experimental data. We adopted a comprehensive evaluation strategy, combining qualitative and quantitative analyses to verify the effectiveness of the proposed method. Qualitative assessment involved analyzing the visual perception of enhanced images in terms of detail preservation, color restoration, and contrast balance. Quantitative evaluation employed two categories of objective evaluation metrics: reference-based metrics, including PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index Measure) [41], and LPIPS (Learned Perceptual Image Patch Similarity) [42], and no-reference metrics, such as NIQE (Natural Image Quality Evaluator) [43]. Note that higher values are preferred for PSNR and SSIM, while lower values indicate better performance for LPIPS and NIQE. The comparison methods included RetinexNet [28], Zero-DCE [33], R2 RNet [38], RUAS [44],Zero-DCE++ [34], CLIP-LIT [31], GCP [45], and CoLIE [32]. Comprehensive experimental results demonstrated that our proposed method achieves satisfactory performance in both subjective visual quality and objective evaluation metrics. Particularly, we also conducted a comparison of the execution efficiency of various methods. The experimental data indicates that the proposed model is the most efficient in terms of execution speed, achieving the intended design objectives.
# Assessment of ZTD Derived from COSMIC Occultation Data with ECWMF, Radiosondes, and GNSS
## 2 Materials and Methods
### ZTDs from RO, ECMWF, and Radiosondes  
The RO method obtains the refractive index of the earth's atmosphere with a horizontal resolution of about 300 km and a vertical resolution of -1 km by measuring the phase delay caused by the GNSS radio signal recorded in low earth orbit [22]. The COSMIC is one of the most important scientific occultation projects in recent years, providing more than 1500 atmospheric and ionospheric RO observations every day in its early days [23; 24], making significant contributions to operational numerical weather forecasting and ionospheric research [25]. The COSMIC Data Analysis and Archiving Center (CDAAC) is a comprehensive data center responsible for data processing for COSMIC and several other occultation missions. The site provides users RO data at different processing levels. In this work, the atmospheric parameter profiles retrieved from atmospheric occultation data and their matched files co-located with the ECMWF high-resolution reanalysis (ECH) model and radiosonde observations were obtained from CDAAC/UCAR for 1 January to 31 December in the years of 2008 and 2012 [26]. In CDAAC, these atmosphere profiles are marked as \"atmPrf\", \"echPrf\", and \"sonPrf\", respectively. The atmPrf files from CDAAC/COSMIC did not provide humidity information. The echPrf files from CDAAC/COSMIC provided water vapor pressure. The sonPrf file from CDAAC/COSMIC contains temperature, pressure, and moisture profiles generated from the NCAR mass store, dataset 353.4. Radiosondesdo not continuously observe high-altitude atmospheric properties. The location of an occultation event may not coincide with the location of a high-altitude observation station. Therefore, temporal and spatial matching is required. In the process of data matching, the temporal resolution is 1 h, and the spatial resolution is 300 km.  
Specifically, in the calculations of occzTD, sonZTD, and echZTD, the effective range of height and sampling interval of the obtained data are not consistent. Since the influence of ZTD is closely related to height, (1) we select the common height part of the profile and use the cubic spline interpolation method to interpolate the refractive indexes to the uniform height region and resolution (\(\rm{\hbar=1\ m}\)); and (2) we integrate the refractive indexes to obtain ZTDs via Equation (1):  
\[\rm{ZTD}=10^{-6}\times\sum\rm{Ref(h)\delta h}, \tag{1}\]  
to reduce the error caused by the height inconsistency.
# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
## 5 Conclusions
### Discussion and Implications
In addition, this study identified mainstream technological trends in photonics machine learning and strategies and positioning in the patent layout to provide reference for enterprises to devise patent layout strategies, identify investment opportunities, and develop the industry strategically. Few studies have investigated the patent layout and patentee positioning in the synergy between machine learning and photonics. Machine learning in photonics will become indispensable, and analyzing the distribution of its technology is crucial and was therefore the motivation for this study. This study bridged this research gap and used a new perspective to explore the technological foci of each technology developer. This study proposes a technology road map of machine learning in photonics. The conclusions of this study can be used by businesses to allocate their research and development resources and used by the government to promote new technologies. More attention is required on companies and academia involved in photonics, applications of machine learning, and the usage of photonics to improve the performance of AI. This study determined that current research on machine learning in photonics was related to image data processing, electric digital data processing, wireless communication networks, and healthcare informatics and diagnosis. Therefore, the government can provide funding to train researchers in these fields to improve the future development of the photonics industry.
###### Abstract  
Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l'Observation de la Terre 5 (SPOT5) High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW), an area of over 800,000 km\({}^{2}\). Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008-2011) for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC), a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (\(\pm\)0.51% standard error), while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (\(r^{2}\) = 0.90) with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and is already widely used for natural resource management in the state.  
SPOT5; Landsat; lidar; foliage projective cover; tree cover +
Footnote †: journal: Remote Sensing