esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 3 Optimization of Real-Time Performance for RISC-V Processors
### Optimization of the DIV Module (Division Module) Based on Data Dependency
#### 3.3.2 Division Instruction Data Dependency Judgment and Optimization  
To improve the execution efficiency of division operations in the processor, we adopt two methods to enhance the performance of the divider.  
First, increase the driving clock of the divider from 50 M to 300 M to speed up the divider.  
Second, add a data dependency judgment module. If the subsequent instructions do not use the result of the division, the pipeline will not be paused, thereby improving the execution speed.  
The data dependency judgment module is designed to evaluate the data relationship between division instructions and subsequent instructions. Therefore, the data dependency judgment logic is placed in the Id (decode) stage of the pipeline, which is the stage before the Ex (execute) stage. The optimized data dependency module, located in the Id (decode) stage, outputs corresponding signals to the pipeline control module (Pi_Ctrl) based on the results of the data dependency judgment. The signals in the Id (decode) module that involve data dependencies are shown in Table 4.  
The flowchart for the data dependency judgment of division operations is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Name** & **Input/** & **Description** \\  & **Output** & \\ \hline Div\_busy\_i & Input & The signal indicating that a division operation is in progress. \\ Div\_reg\_waddrr & Input & The address to which the division result is written. \\ PC\_inst & Input & The instructions following the division instruction. \\ Div\_hold\_flag & Output & The pipeline stall signal corresponding to the division calculation. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Introduction to signals of the division dependency module.  
Figure 10: Division data dependency module working principle diagram.  
During the division calculation, the \"Div_busy_i\" signal remains high (1), and the \"Div_reg_waddr\" signal provides the write address for the division result. When the subsequent instruction (PC_inst) is decoded, it decodes the data address of that instruction. If the data address of the subsequent instruction (PC_inst) matches the write address of the division result, the pipeline is paused to wait for the completion of the division operation. Otherwise, the pipeline is not paused.
# Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy
###### Abstract
Some applications of bioacoustic PLF techniques in chickens are as follows: the identification of acoustic parameters for estimating the welfare of broiler chickens [7], the automatic detection of flapping, purring, coughing, and crowing with 60 acoustic classification features [8], stress detection in broiler chickens using acoustic features for voice quality assessment and vocal emotion recognition [9], the detection of symptoms of respiratory diseases in poultry through the cepstral coefficient of mel frequency [5], and the acoustic features of the vocalization signal in poultry to identify and monitor bronchitis and Newcastle disease [10]. This work aims to use PLF technology to characterize the vocalization of hens on a commercial farm before and after the administration of the ILT vaccine, and during their reactions to the ILT vaccine, as well as to identify and analyze the most important features for automatic detection. A second objective is to quantify the effectiveness of a treatment that reduces the vaccination reaction in the animal. The research objectives are designed to answer the following research question: can the vocalizations of hens be utilized to detect and quantify their vaccine response? This is based on the hypothesis that vocalisations in poultry encode information about their welfare status, and the side effects of vaccination generate discomfort that should be detectable through animal calls. This objective is achieved by capturing the vocalizations emitted by two groups of hens from a commercial farm: a control group, composed of vaccinated hens, and a group of vaccinated hens to which the anti-inflammatory product was administered. The data was processed to extract the 13 Mel-frequency cepstral coefficients (MFCC1-13), the spectral centroid (centroid), and the number of vocalizations (vocalNum) features. A visual inspection was performed to find stress-related patterns in the periods before, during, and after the vaccination reaction. Furthermore, to automatically detect reactions and identify the most important acoustic features, different machine learning algorithms were used. Detecting reactions in real time through acoustics and understanding variations in calls can improve animal management and animal welfare. Additionally, the performance of an administered product can be determined by monitoring the percentage of animal reaction classifications. This paper is structured as follows. The above introduction is used as framework of this project and is detailed in Section 1. The technical specification of this project is detailed in Section 2.1 and the methods used, audio pre-processing, feature extraction, data campaign, and machine learning models are detailed in Section 2.2. The experiments and results are detailed in Section 3. A discussion of the key aspects is detailed in Section 4 and the conclusion of the experiments and a discussion of future work can be found in Section 5.
## 3 Results and Discussion  
### Structures of the Isolated Compounds  
The isolated compounds were identified as obliquumol (12-O-acetylplateroxylinol or ptaeroxylinolacetate) [30; 31], mixture of lupeol and a minor triterpen, possibly \(\beta\)-amyrin [32; 33], and \(O\)-methylallopteroxylin (Appendix A, Figure A1) [34]. The structures were assigned on the basis of mass spectrometry and NMR spectroscopy data, which were in agreement with those already reported for the compounds [31; 35]. The molecular formula of 12-O-acetylplateroxylinol was confirmed to be C\({}_{17}\)H\({}_{16}\)O\({}_{6}\) from the HRESIMS data, which showed a protonated molecular ion peak at \(m\)/\(z\) 317.1027 [M + H]\({}^{+}\) (Cald For C\({}_{17}\)H\({}_{17}\)O\({}_{6}\), 317.1025). The MS of the mixture of lupeol and \(\beta\)-amyrin showed the molecular ion peaks at \(m\)/\(z\) 427.4 [M + H]\({}^{+}\) and 425.3 [M - H]\({}^{-}\), in agreement with the proposed structures. Several studies on O-methylallopteroxylin report \({}^{1}\)H NMR data only [34; 35]. Therefore, the \({}^{1}\)H and \({}^{13}\)C NMR data of O-methylallopteroxylin is provided in Table A1 (Appendix B). The assignment was based on 1D and 2D NMR data. The \({}^{1}\)H NMR spectrum showed two one-proton singlets at \(\delta_{\rm H}\) 5.99 (H-3) and 6.26 (H-6), and two three-proton singlets at \(\delta_{\rm H}\) 2.28 (CH\({}_{3}\)-2) and 3.91 (OCH\({}_{3}\)-5). The signals of the dimethylpyran unit appeared as two doublets at \(\delta_{\rm H}\) 6.69 (H-1\({}^{\prime}\)) and 5.56 (H-2\({}^{\prime}\)), and a singlet integrating for six protons at \(\delta_{\rm H}\) 1.47 ((CH\({}_{3}\))\({}_{2}\)-3\({}^{\prime}\)). The \({}^{13}\)C and dept-135 NMR spectra showed signals of the 5-methoxy and 2-methyl substituents at \(\delta_{\rm C}\) 56.3 and 19.7, respectively. Those of the chromone core appeared at \(\delta_{\rm C}\) 162.6 (C-2), 111.8 (CH-3), 177.6 (C = O, C-4), 108.4 (C-4a), 160.6 (C-5), 96.3 (CH-6), 157.6 (C-7), 102.3 (C-8), 154.2 (C-8a), and the signals of the dimethylpyran scaffold resonated at \(\delta_{\rm C}\) 115.2 (CH-1\({}^{\prime}\)), 127.3 (CH-2\({}^{\prime}\)), 77.9 (C-3\({}^{\prime}\)) and 28.2 ((CH\({}_{3}\))\({}_{2}\)-3\({}^{\prime}\)). The molecular formula of the compound was confirmed to be C\({}_{16}\)H\({}_{16}\)O\({}_{4}\) from the HRESIMS data, which showed a protonated molecular ion peak at \(m\)/\(z\) 273.1140 (Cald for C\({}_{16}\)H\({}_{17}\)O\({}_{4}\), 273.1127).
# Shear Performance for Prestressed Concrete Hollow Core Slabs
###### Abstract  
This study evaluated the shear performance of prestressed concrete hollow core slabs (HCS), which are convenient to use as floor structures of flexible spaces. A total of 18 specimens, with cross-sectional height and presence of topping concrete as variables, were fabricated by extrusion. A four-point loading test was conducted using simply supported beams. The results showed that shear performance satisfied the requirements of ACI 318-19 regardless of cross-sectional height or presence of topping concrete. Through comparison with past studies, the web-shear strength of HCS was found to be influenced by compressive stress due to prestress at the centroid, compressive strength of concrete, and shear span-to-depth ratio.  
hollow core slabs; shear strength; precast concrete; prestressed concrete +
Footnote †: journal: Applied sciences
## 3 Name metadata and the Semantic Web  
Name metadata records are well positioned to become an integral and valuable part of the emerging Semantic Web. Systems such as the Simple Knowledge Organization System (SKOS) are designed to represent standardized or controlled datasets, such as names in a name database. Each name in SKOS would have a uniform resource identifier (URI) that could be used to represent the name in many different, linked systems. SKOS offers a great potential to aid in effective name disambiguation and collocation of variant names.
# Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
## 2 Insights on Data Collection and Analytics Opportunities in the COVID-19 Era  
In recent years, data have been regarded as an oil of the economy, as they can assist nations and countries in countless ways, including human behavior analysis, recommendations, policy planning, and improving the living standards of people, to name a few. The latest technologies are very good at finding insights from large-scale data [7]. We highlight the data collected and processed in the COVID-19 era and corresponding analytics opportunities offered by them in Figure 3.  
Figure 3: Conceptual overview of the data sources, digital solutions, and analytics (DDA) in the COVID-19 era.  
Apart from these techniques, Big Data analytics on existing data, such as commodities data, have profound benefits in separating the vulnerable population [8]. In addition, the collection of facilities' visit log data can be employed in individual profiling for mobility analysis. Despite the comprehensive analytics given in Figure 3, we provide the following analytics functions that can be exploited to curb the disease spread.  
* Finding the most exposed people through location analysis (e.g., church/moviegoers).
* Extraction of people where physical presence for a job is mandatory.
* Identification of people working in high-risk zones.
* Alerting people when they have a high chance of getting COVID-19.
* Cluster identification by fusing multiple source data.
* Analyzing contact among people and predicting the risk score based on multicriteria.
* Identifying close contacts in a privacy-preserving manner.
* Analyzing the dynamics of the disease with respect to age groups.
* Identifying lethal combinations of the diseases to lower mortality chances.
* Providing the probability of infection score based on multiple parameters.
* Finding the connection of the diseases and demographics to better guide people to improve their health.
* Analyzing the dynamics of the disease with ambient conditions.
* Analyzing the correlation between symptoms and ICU transfer for resource planning.
* Predicting discharge dates based on demographics and comorbidity data.  
The analytics documented above open up the era of research with new data that have not been analyzed before. With the help of the latest technologies, all of the above tasks can be achieved with sufficient accuracy to improve healthcare. By utilizing the above functions, the burden of healthcare can be significantly reduced. Furthermore, automated diagnosis and prescriptions can be provided with ease.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 1 Introduction
### Statement of Purpose  
There have been a few articles related to LoRa in the literature. In [13; 14], different long-range technologies, including LoRa, are compared. [PERSON] et al. [15] studied the coverage of LoRa and proposed a channel attenuation model. In [16], the authors analyzed the LoRa capacity and proposed LoRaBlink to support multi-hop communications.  
In complementing the work of these articles, the goal of this paper is three-fold: (i) given the semi-proprietary nature of LoRa (parts of the protocol are well documented; other parts are not), to provide an overview and functional description of LoRa and to present as much information as could be (experimentally and otherwise) gathered; (ii) to independently provide a quantification and evaluation of the performance of LoRA and of LoRaWAN, especially the spreading factor; and (iii) based on the analysis and performance evaluation, to propose possible solutions for performance enhancement.  
The remainder of this paper is organized as follows: Section 2 provides a functional overview of LoRa, followed by Section 3, which describes and analyzes the LoRa physical layer in detail and provides experimental performance studies hereof. Following, the LoRaWAN MAC protocol is described in Section 4, with Section 5 presenting the evaluation hereof for LoRaWAN. Section 6 concludes this paper.
###### Abstract
Therefore, the above analysis shows that there is a strong need to enhance these routing protocols in terms of high Packet Delivery Ratio (PDR), low packet loss ratio with minimum Energy Consumption (EC) and minimum E2E delay. Therefore, in the current work, two proactive routing protocols are implemented. In both protocols, the sender based approach is implemented to minimize the probability of void hole occurrence for reliable data delivery. Therefore, multi-hop communication is adapted for reliable data delivery and the shortest routing path selection for minimum EC to extend the network lifespan. The contributions of this work are:  
1. The Energy-efficient Path-based Void hole and Interference-free Routing (EP-VIR-Three) protocol is proposed, which selects the next forwarder node by checking 3-hop neighbors information (the 3-hop forwarder nodes information is based on the depth difference of current and the previous node) to avoid the void hole problem with enhanced PDR. 2. The Bellman-Ford Shortest Path-based Routing (BF-SPR-Three) protocol is proposed, which selects the next forwarder node using 'Bellman-Ford (BF)' algorithm. This algorithm provides the shortest and fastest path for routing. In addition, the binary tree approach with horizontal layering concept is implemented for reliable data delivery. Furthermore, it solves the problem of routing loop problem. 3. The problem in this work is tested over multiple simulations and verified by calculating feasible regions for the proposed routing protocols using linear programming. Furthermore, the scalabilityanalysis of the proposed routing protocols is also performed by varying the number of nodes in the UWSN. 4. Comparative analysis is also performed with state-of-the-art proactive routing protocols. Moreover, the proposed protocol shows efficacy in terms of high PDR and minimum EC with affordable E2E delay. Further, some state-of-the-art routing protocols are reviewed and compared on the basis of some specific parameters, i.e., reliability, packet size and number of hops while communicating in an underwater environment. Moreover, these routing protocols are categorized into three different categories, i.e., energy efficient routing, topology-based routing and void hole avoidance based routing. The achievements, challenges and the limitations of these routing protocols are also discussed below.
## 5 Conclusions  
Long-term fertilizer treatments increased soil N and P significantly in _T. grandis_ orchards, which increased leaf N and P concentrations, but there was no obvious positive impact on _T. grandis_ growth but only a negative impact on seed yield under heavy fertilizer application. In our study, N enrichment was indicated by stable and decoupling relationships between the soil and leaf [N]. Leaf [P] was highly correlated with soil [N], and N enrichment amplified the physiological P limitation (deficiency). Excessive N addition  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Y** & **X** & **Slope** & **Intercept** & \(\mathbf{R^{2}}\) \\ \hline Leaf N & Soil TP & 0.24 & 1.26 & 0.63 * \\ Leaf N & Soil HN & 0.21 & 0.87 & 0.48 * \\ Litter N & Leaf N & 0.9 & 0.25 & 0.72 ** \\ Litter P & Soil TP & 0.69 & \(-\)0.02 & 0.59 * \\ Litter C & Leaf C & 1.63 & \(-\)1.12 & 0.51 * \\ Leaf C & SOC & 0.18 & 1.42 & 0.88 ** \\ Leaf C:P & Leaf P & \(-\)1.03 & 1.70 & 0.99 ** \\ Leaf C:P & Leaf P & \(-\)1.02 & 1.36 & 0.98 ** \\ PRE & Leaf P & \(-\)1.02 & 1.36 & 0.98 ** \\ \hline \hline \end{tabular}
* and ** are 2-tail sig. \(p\) \(<\) 0.1 and 0.05.  
\end{table}
Table 4: Relationships of nutrients among soil, leaves, and litter through SMA analyses.  
Figure 5: Nutrients resorption efficiency of N (NRE), P (PRE) and relative nutrient resorption (RR = NRE \(-\) PRE) of T. grandis under different fertilizer treatment scenarios. The lowercase letters indicate a significant difference among treatments, \(p\)\(<\) 0.05.  
interfered _T. grandis_'s sensibility to P availability in this N-enrichment area, which is indicated by increasing PRE with both modes of fertilization intensity. Therefore, leaf [P] or the N:P ratio may not reflect the actual demands of the plant under the P addition conditions in our study. Furthermore, large amount of fertilizer application might waste resources and alter soil physicochemical properties (e.g., soil acidification). Decreasing fertilizer application should be taken into consideration to accomplish the sustainable development of the orchard ecosystems.  
Idea development, G.G.W. and T.W.; field experiment design, S.J., W.C. and Y.J.; data collection, W.C. and Y.J.; data interpretation, Y.H. and S.J.; data analysis, Y.H.; writing--original draft preparation, Y.H.; writing--revision and editing, G.G.W. and T.W. All authors have read and agreed to the published version of the manuscript.  
The National Key Research and Development Program of China (2019 YFE0118900), the National Natural Science Foundation of China (31971641), Jiyang College of Zhejiang A&F University under Grant ( 0525170003 ), and the Young People Fund of Natural Science Foundation of Zhejiang province, China (LQ19C160011).  
Not applicable.  
The data supporting reported results can be supplied by the authors upon request.  
The authors declare no conflict of interest.
## 7 Analysis of Results
### Longitudinal Profiles
#### 7.1.1 Temperatures  
Figure 3 shows the longitudinal temperature profiles after 10 min from the start of the fire and at a height of 2 m along the escape path. The burning vehicle is assumed to be, in this case, in the middle of the tunnel length. With reference to a pre-movement time of both 90 s and 150 s (only for the bus), Figure 3 shows the positions occupied by the last user, which is escaping from the tunnel in direction of portal A, after a time t = 10 min from the start of the fire. One may note that the above-mentioned user is at a distance of 47 m from portal A (i.e., 378 m from fire) in the event of 8, 50, and 100 MW fire (red user), and 89 m (i.e., 336 m from the fire) in the event of 30 MW fire (green user). Figure 3 shows that temperatures higher than 60 \({}^{\circ}\)C are observed only for a distance from the fire of: 5 m, 30 m, 50 m, and 170 m in the case of 8 MW, 30 MW, 50 MW, and 100 MW fires, respectively. This means that also the last user escaping is safe since he/she is exposed during the evacuation to temperatures lower than the tenability limit of 60 \({}^{\circ}\)C [37].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Criterion** & **Reference** \\ \hline Temperature & \(\leq\)60 \({}^{\circ}\)C & [37] \\ Radiant heat flux & \(\leq\)2–2.5 kW/m\({}^{2}\) & [37] \\ Carbon monoxide & \(\leq\)1200 ppm & [38] \\ Carbon dioxide & \(\leq\)40,000 ppm & [38] \\ Visibility distance & \(\geq\)10 m & [37] \\ \hline FED\({}_{\text{toxic gases}}\) & \(<\)1 & [35] \\ FED\({}_{\text{heat}}\) & \(<\)1 & [35] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Acceptable safety criteria for tunnel users in the event of a fire.  
Figure 3: Longitudinal temperature profiles (t = 10 min) at a height of 2 m along the escape path when the fire source is in the middle of the tunnel length.
## 5 Results and Discussion  
In this study, we use the machining of a marine diesel engine piston as an example to demonstrate our proposed method for identifying key machining features and constructing a machining quality prediction model. Through the information entropy method, we identified the 43 rd quality feature as the key quality feature. Subsequently, we employed the XGBoost algorithm to establish the corresponding machining quality prediction model. The optimal hyperparameters for this model were determined using Bayesian optimization and validated on a test set, resulting in relatively accurate prediction outcomes.  
The analysis indicates that the 43 rd quality feature has the highest entropy value, suggesting significant quality fluctuations during the machining process, which establishes it as the key quality feature. These large fluctuations in the 43 rd quality feature are associated with various factors, including machining processes, equipment stability, and process parameter settings. By further optimizing these factors, we expect to enhance the consistency of machining quality.  
Through training the XGBoost model, we observed a decreasing trend in the root mean square error (RMSE) on both the training and test sets. During the first 30 iterations, the RMSE decreased rapidly for both sets, with the RMSE on the training set consistently lower than that on the test set. After 50 iterations, the model's RMSE reached 0.0307, meeting the required accuracy threshold. The observed decreasing trend in RMSE across different iterations indicates that XGBoost is highly adaptable in addressing machining quality prediction problems. Notably, the model optimized through Bayesian optimization outperformed the model with default parameter settings on the test set, underscoring the significance of hyperparameter optimization in enhancing model accuracy.  
An analysis of the feature importance indicators in the XGBoost model revealed that the feature ranking met our expectations, further validating the model's reliability. Key process parameters, including spindle speed, feed rate, and cutting depth, significantly impacted machining quality. Adjustments to these parameters during optimization directly influenced the model's prediction accuracy, reinforcing their crucial role in machining quality.  
Future research should aim to expand the scale of the training dataset to enhance the model's generalization capabilities. Additionally, factors such as ambient temperature and tool wear, which may also affect machining quality, should be considered to improve the model's applicability. Furthermore, integrating other machine learning algorithms, such as deep learning models, for multi-model comparisons could lead to more effective machining quality prediction methods.  
Figure 11: Prediction comparison of Models 1 and 2 on the test set.