esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The weather is one of the main concerns in the Canary Islands, especially in those islands with very abrupt orography. On the one hand, the agricultural sector in the Canary Islands can be described as segmented, very sensitive to rainfall and without permanent watercourses. On the other hand, the main economic activity in the Canary Islands is tourism, which attracts large numbers of floating population. In addition, the wide variety of meteorological factors occurring in the Canary Islands make rainfall prediction an important issue and, together with its temporal estimation, constitutes an essential factor for decision making and to minimize potential risks linked to sudden increases and the considerable disperse distribution of the affected areas.  
The Canary Islands are part of the Macaronesia and are located northwest of the Atlantic Ocean reaching from 27\({}^{\circ}\)37\({}^{\prime}\) to 29\({}^{\circ}\)25\({}^{\prime}\) N and from 13\({}^{\circ}\)20\({}^{\prime}\) to 18\({}^{\circ}\)10\({}^{\prime}\) W. The Canary Islands archipelago is made up of eight main islands lined from East to West and ten smaller isles (Figure 1).  
The island of Tenerife, with a population close to 900,000 inhabitants and the annual visit of more than six million tourists, is the most populated island and the largest in the archipelago. In fact, it is one of the areas with the highest population density in Europe (440 inhabitants/km\({}^{2}\)).  
The island, located approximately 300 km west of the northern coast of Africa, is characterized by an uneven and steep orography with sharp slopes from the inner part to the coast. The central area is crowned by a mountain range with Teide volcano on top (3718 m), which divides the island into two highly differentiated climatic areas: the southern and the northern area. The southern area is drier because the mountains block the passage of trade winds and the northern area is relatively wetter precisely because of the effect of those trade winds. Overall precipitation is low [1], with an annual average of 233 mm on the whole island. Distribution of rainfall is heterogeneous, with a large percentage of rainfall occurring in short periods of time and sparse in space. These factors mean that sudden increases in rainfall constitute a serious issue that has led to the loss of human lives as well as economic loss in sectors such as tourism, agriculture, and communications infrastructure. Therefore, weather forecasting and climate analysis are lines of research that are prioritized.  
In this paper, we develop and compare several rainfall prediction models using machine learning techniques (ML) for the island of Tenerife resulting in a classification of rainy or dry months. In order to do this, we used a meteorological dataset [2] for a period of time of 41 years of registered data, from 1976 to 2016.
## 5 Conclusions and Future Work
### Future Work  
In the future, we aim to investigate various extensions. Primarily, we intend to evaluate the performance of our methodology using embeddings besides doc2 vec. While we anticipate that the algorithm will function effectively with BERT [18] or Longformer [61] embeddings, a comprehensive analysis is warranted. Additionally, we plan to delve deeper into the reasons for the lack of improvement in certain models. Our conjecture is that models with distinctly separated data in high-dimensional space may not necessitate this enhancement. However, this can only be confirmed using labeled data, making the analysis of non-labeled data challenging.  
Conceptualization, B.R., P.H. and P.-V.V.; methodology, B.R., P.H. and P.-P.V.; software, B.R. and P.-P.V.; validation, B.R.; formal analysis, B.R. and P.-P.V.; investigation, B.R., P.H. and P.H.; resources, B.R. and P.-P.V.; data curation, B.R. and P.-P.V.; writing--original draft preparation, B.R. and P.-P.V.; writing--review and editing, B.R., P.H. and P.-P.V.; visualization, P.-P.V.; supervision, P.H. and P.-P.V. ; project administration, P.-P.V.; funding acquisition, P.-P.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by PID2021-122136 OB-C21 from the Ministerio de Ciencia e Innovacion, Spain, by 839 FEDER (EU) funds.  
The data used in our validation experiments are public, and we provided the references where they can be obtained.  
The authors declare no conflict 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.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DR & Dimensionality Reduction \\ DT & Decision Trees \\ KNN & K-Nearest Neighbors \\ MLP & Multilayer Perceptron \\ PaCMAP & Pairwise Controlled Manifold Approximation Projection \\ SVM & Support Vector Machines \\ trimap & Large-scale Dimensionality Reduction Using Triplets \\ t-SNE & t-Distributed Stochastic Neighbor Embedding \\ UMAP & Uniform Manifold Approximation and Projection \\ XGBoost & Extreme Gradient Boosting \\ \end{tabular}
###### Abstract  
This paper presents an analytical method based on the shear flow distribution law to study the shear lag effect of thin-walled single- and double-cell box girders. The first step in this method is to determine the box girder's shear flow distribution. Subsequently, a series of novel improved longitudinal displacement functions mathematically expressed as cubic parabolas are established. The parabolic origin of these functions is located at the zero point of the shear flow corresponding to each plate; the unknown parameters used to describe the function form can be determined according to the shear flow distribution, the continuity conditions, and the axial force balance condition. Then, the variational energy method is adopted to derive the governing differential equations. The shear lag effect in thin-walled single- and double-cell box girders under several boundary conditions and load cases is studied and analytical expressions for the shear lag coefficient are derived. Finally, results obtained using the proposed method are validated via comparison with numerical results. The results show that the proposed method can provide reasonable predictions for the shear lag effect of single- and double-cell box girders, and that this method is more straightforward and practical. In addition, the shear lag coefficients at different webs are not entirely equal, which is related to the distance from the web to the zero point of the shear flow.  
thin-walled box girders; shear lag effect; shear flow distribution; theoretical analysis; variational energy method; numerical simulation +
Footnote †: journal: Applied sciences
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
###### Abstract  
The rise of social media has led to an increasing online cyber-war via hate and violent comments or speeches, and even slick videos that lead to the promotion of extremism and radicalization. An analysis to sense cyber-extreme content from microblogging sites, specifically Twitter, is a challenging, and an evolving research area since it poses several challenges owing short, noisy, context-dependent, and dynamic nature content. The related tweets were crawled using query words and then carefully labelled into two classes: Extreme (having two sub-classes: pro-Afghanistan government and pro-Taliban) and Neutral. An Exploratory Data Analysis (EDA) using Principal Component Analysis (PCA), was performed for tweets data (having Term Frequency--Inverse Document Frequency (TF-IDF) features) to reduce a high-dimensional data space into a low-dimensional (usually 2-D or 3-D) space. PCA-based visualization has shown better cluster separation between two classes (extreme and neutral), whereas cluster separation, within sub-classes of extreme class, was not clear. The paper also discusses the pros and cons of applying PCA as an EDA in the context of textual data that is usually represented by a high-dimensional feature set. Furthermore, the classification algorithms like naive Bayes', K Nearest Neighbors (KNN), random forest, Support Vector Machine (SVM) and ensemble classification methods (with bagging and boosting), etc., were applied with PCA-based reduced features and with a complete set of features (TF-IDF features extracted from \(n\)-gram terms in the tweets). The analysis has shown that an SVM demonstrated an average accuracy of 84% compared with other classification models. It is pertinent to mention that this is the novel reported research work in the context of Afghanistan war zone for Twitter content analysis using machine learning methods.  
c 2019 9 3723; doi:10.3390/app9183723
## 3 Discussion
### Wind Speed and Aerosol Optical Depth  
[PERSON] and [PERSON] [68] introduced an ANN retrieval algorithm to estimate the wind speed of the ocean surface and the optical depth of aerosols using CALIPSO lidar measurements. A neural network algorithm was trained with CALIPSO ocean surface and atmospheric backscatter data, combined with collocated AMSR-E (Advanced Microwave Scanning Radiometer-EOS) ocean surface wind speed data. The study demonstrated the effectiveness of the ANN algorithm by comparing the retrieved wind speeds with AMSR-E and AMSR-2 (Advanced Microwave Scanning Radiometer 2), a passive microwave radiometer onboard the GCM-W1 (Global Change Observation Mission 1 st--Water) satellite, which is operated by JAXA, the Japan Aerospace Exploration Agency. The neural network algorithm was tested by applying it to CALIPSO measurements for the entirety of 2008, to retrieve ocean surface wind speed. The results showed that the retrieval was unbiased, with an average wind speed difference between CALIPSO and AMSR-E of around 0.001 m/s. The standard deviation of the wind speed difference between CALIPSO and AMSR-E, which was considered an instantaneous error, was around 1.05 m/s. The retrieved CALIPSO wind speeds agreed with AMSR-E for both low and high wind speeds. The neural network also automatically adjusted aerosol lidar ratios, minimizing wind speed retrieval errors using ocean surface and atmospheric signals. This eliminated the need to assume predetermined lidar ratios, making the algorithm more versatile with respect to the traditional inversion methods. The authors used the MATLAB embedded functional fitting neural network function, fitnet. The input variables for the neural network included various CALIPSO measurements related to the ocean surface and atmospheric backscatter, as well as latitude. The output variable was the wind speed of the ocean surface. The study highlighted the importance of turbulence in the exchange of momentum, energy, and greenhouse gases between the atmosphere and the ocean. The retrieval of high spatial resolution ocean surface wind speed from CALIPSO lidar measurements contributed to understanding the vertical turbulence exchange and air-sea interaction. Additionally, the algorithm allows for the derivation of aerosol optical depths (AOD) using CALIPSO's ocean surface backscatter signal and theoretical ocean surface reflectance calculated from wind speed and wind-surface slope variance relation. The study emphasized the applicability of the algorithm to most clear skies with optical depths up to 1.5, without assuming aerosol lidar ratios. The findings contributed to advances in aerosol distribution research, climate dynamics, and the accuracy of wind speed and aerosol optical depth retrievals.
## 5 Discussions of Simulation Analyses
### Temperature and Flow Distributions of the Cooling Regions  
Figure 6 shows the temperature distributions in the inlet and outlet sections of the four radiators. In the air inlet section, the averaged air temperature is almost uniform at about 323 K. In the air outlet section, the temperature in the regions covered by cooling fans is obviously lower than that out of the fan action regions (red area). The average temperature of the torque converter radiator is lower than that of the engine radiator, because in the lower part the thermal load of 101 kW is about twice as large as that of the torque converter radiator, although there are four fans directly working for the engine radiator. The hydraulic oil radiator achieves a good cooling mark as it is relatively independent and not greatly influenced by the other fan groups.  
Figure 7 shows the flow field of the whole air region. The velocity of most cooling air is about 5 m/s, except that on the edge of the fans, which could be up to 40 m/s. There are two positions needing additional attentions, circled by red lines in the figure. One is the top air inlet. Since the top air inlet is opened near to where the front outlet grids are, the air backflow problem is still occurring in this region and it will increase the intake air temperature. The same phenomenon also appears in the bottom air inlet, and this is even more serious because of the ground effect. Therefore, one suggestion in future modifications is that the location of the top air inlet can be slightly farther away from the front outlet grids to reduce air mixing and backflow in this area. Another suggestion is that the section  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Experimental Value**} & **Simulation Value** & **Relative Error** \\ \hline Temperature of torque & inlet & 321 & 330 & 2.8\% \\ converter radiator (K) & outlet & 356 & 360 & 1.1\% \\ \hline Temperature of engine & inlet & 323 & 318 & \(-1.5\%\) \\ radiator (K) & outlet & 348 & 349 & 0.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment and simulation comparison on radiator temperature.  
Figure 5: Cooling air temperature curve of different radiators by experiments.  
of the bottom air inlet can be made smaller so that the cooling air can be sucked into the region from the upper air inlet as much as possible for better heat dissipation.
###### Abstract
The contributions of this paper are summarized as follows:  
* Exploring two emerging technologies: IoT and UAV, which will be the pioneers of smart farming in the coming years. * Outlining some major applications of IoT and UAV in smart farming as well as the agricultural industry. * Exploring the communication technologies, network functionalities and connectivity requirements needed to ensure seamless connectivity for Smart farming
* Identifying the connectivity limitations and challenges of smart agriculture in remote areas with two case studies. In case study-1, we propose and evaluate meshed Long Range Wide Area Network (LoRaWAN) gateways to address connectivity limitations of Smart Farming--while, in case study-2, we explore satellite communication systems to provide connectivity to smart farms in remote areas in Australia. * Identifying future research challenges on this topic and outlining directions to address those challenges. Table 1 presents the acronyms used in this paper. The remainder of the paper is structured as follows. Section 2 explains how IoT and UAVs are used in smart farming, while Section 3 explores various applications of IoT and UAV in smart farming. Section 4 explicitly discusses the smart communication technologies along with network functional  
ities and connectivity requirements. Section 5 presents connectivity limitations of smart farming in remote areas with two case studies on smart farming. Section 6 discusses some open research issues on this topic and finally, Section 7 concludes the paper.
# Insights into PCDD/Fs and PAHs in Biomass Boilers Envisaging Risks of Ash Use as Fertilizers
## 3 Experimental Part
### Conformity of PCDD/Fs and PAHs in Ashes with the Proposed Limits for Fertilizers
The contamination of ashes is affected by mixes: bottom ashes and fly ashes may contain other flows produced in different zones, such as superheaters, economizers, cyclones, baghouses and ESP, which was also highlighted elsewhere [143]. This is another important subject to be addressed in fertilizer regulations, for example, the definition of the constitution of ash streams that may be used as fertilizers. The provenience of ashes must also be better defined; the simple designation as \"biomass ashes\" as a corollary for safe use appears to be inadequate. Ashes from residual biomass originated in field plague control, wood and food industries, agriculture, farming, or other types of biomass/bio-residues, such as sludges, compost and organic fractions of MSW, may exhibit much higher contamination than natural biomass ashes. Besides POPs and their precursors, possible contamination of fuels with toxic and bioaccumulative metals (Cd, Pb, Hg, As, etc.) results in higher contents of these metals in ashes. Furthermore, metals like Cu may have a memory effect along the furnace systems, catalyzing PCDD/Fs formation for prolonged periods.
## 2 Materials and Methods
### Chemicals and Reagents  
Then, reference standard compounds (Figure S1) were selected as marker components for the quality control of DSHT using HPLC. The compounds were purchased from commercial companies: gallic acid (CAS No., 149-91-7, Cat no. G7384, purity 100.0%), naringin (CAS no. 10236-47-2, Cat no. 71162, purity 95.0%), benzoic acid (CAS no. 65-85-0, Cat no. 242381, purity 99.9%), and baicalein (CAS no. 491-67-8, Cat no. 2465119, purity 98.0%) from Merck KGaA (Darmstadt, Germany); albiflorin (CAS no. 39011-90-0, Cat no. 016-22201, purity 99.8%), baicalin (CAS no. 21967-41-9, Cat no. 024-15691, purity 98.0%), and wogonin (CAS no. 632-85-9, Cat no. 236-02321, purity 98.9%) from Fujifilm Wako Pure Chemical Co. (Osaka, Japan); and paeoniflorin (CAS no. 23180-57-6, Cat no. DR10579, purity 99.4%), poncirin (CAS no. 14941-08-3, Cat no. DR11185, purity 98.9%), and wogonoside (CAS no. 51059-44-0, Cat no. DR10630, purity 98.9%) from Shanghai Sunny Biotech Co., Ltd. (Shanghai, China).  
HPLC-grade solvents, methanol, acetonitrile, and water, and the reagent, formic acid, were purchased from J.T.Baker (Phillipsburg, NJ, USA) and Fujifilm Wako Pure Chemical Co. (Osaka, Japan).
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 2 Comparative Study of BPv7 and IPv6 Functionalities
### Data Plane  
Compared to the BP, IPv6 lacks a fundamental component: a way to manage packets in a store-and-forward manner. The need for a store-and-forward mechanism comes from the fact that, in DTNs, an end-to-end uninterrupted path may never be possible to achieve. The intermittent nature of the links, due to the changing network topology, the long propagation delays, and the high error rates of the links, make end-to-end connectivity quite challenging. Therefore, if a non-DTN node receives a packet, but the link on which it should be forwarded is down, this packet will normally be dropped by the stack. If an upper layer provides retransmissions, then this would trigger a full retransmission after a certain time, which will have a similarly low chance of reaching its destination. To address this problem, the idea is to equip intermediate nodes with storing capabilities to be used to store the packet and wait for the link to the next hop to become available. A mechanism for hop-by-hop reliability was included in BPv6 as the so-called \"custody mechanism\"; this implemented additional signaling between BP nodes that transferred custody of a bundle along the path. In BPv7, it was deemed as a non-core functionality and was removed in favor of end-to-end retransmission by the application, enabled by new status reportingadded to BPv7. This provided a more flexible and less strict solution, while the stack now relied on the Licklider Transmission Protocol [17], a widely used lower layer for the BP to provide hop-by-hop reliability. The IP suite is not equipped with such mechanisms, so an analogue solution must be designed.  
In some implementations focused on point-to-point links, for example in European Space Agency (ESA) and National Aeronautics and Space Administration (NASA) missions, the store-and-forward mechanism is usually located at the data-link layer (L2) of the protocol stack [18], as it can be implemented simply by defining policies on the interfaces and buffering the data there. However, the main drawback of this solution is that routing, performed at the network layer (L3), is computed at packet arrival and applied immediately. This means that a packet stored at an interface (L2) awaiting forwarding is forever bound to the decision taken at reception, even if alternative paths to the destination become available in the meantime. In the worst-case scenario, this may lead to data loss if the expected contact does not happen due to anomalies. For this reason, implementing the storage function at L3 directly inside IPv6 would provide a much greater degree of control and enable reactive routing, achieving efficient network usage.
## 2 Results
### CuAAC Reactions in Batch  
The catalytic properties of supported complexes were initially tested in batch CuAAC runs, carried out with the bead-type materials **P7**-CuX1-2 (Table 1).  
In the case of **P7**-CuCl in THF, the material was active in its pristine form and led to quantitative consumption of the benzylazide substrate in 20 h at room temperature at the substrate to catalyst ratio of \(S/C\) = 10 (Table 1, entry 1). Direct reuse of the resin recovered each time by filtration allowed four recycle runs, carried out with a progressive increase of \(S/C\) up to 50, to be successfully performed (Table 1, entries 2-5).  
By contrast, the Cu(II)-supported complexes **P7**-Cu(OTf)\({}_{2}\) and **P7**-CuSO\({}_{4}\) had to be subjected to reduction before being used in CuAAC runs. Initial tests, aimed to optimized this stage, showed that phenylhydrazine in CH\({}_{2}\)Cl\({}_{2}\) and hydroquinone in THF or MeOH were poorly effective in the activation of **P7**-Cu(OTf)\({}_{2}\) (Table 1, entries 6-8). On the contrary, the exposure of the same supported complex to sodium \(\iota\)-ascorbate in MeOH/H\({}_{2}\)O (4:1) afforded a brownish-yellow material with high activity in a series of consecutive CuAAC runs between benzylazide and propargyl alcohol (Table 1, entries 9-12).  
Figure 2: Appearance of the **M7**-Cu(OTf)\({}_{2}\) monolith (**a**) and relative intensity of the L\({}_{\alpha\cdot\beta}\) Cu lines, collected by SEM-EDS analysis along the scanning path A-B shown in the inset (**b**).  
As long as the same benchmark CuAAC reaction is concerned, **P7**-CuSO\({}_{4}\) reduced with sodium l-ascorbate in aqueous MeOH also proved to be a remarkably effective catalyst (Table 1, entry 13). However, when the recovered supported complex was employed for promoting the CuAAC between benzylazide and 1-hexyne, an appreciably slower reaction was observed (Table 1, entry 14).  
Repetition of the last two runs above, by replacing **P7** with the soluble TBTA ligand **1** (Table 1, entries 15 and 16), showed that the catalytic activity of the homogeneous system was higher than reduced **P7**-CuSO\({}_{4}\) under otherwise identical conditions Even in these experiments, however, 1-hexyne was found to react at a substantially slower rate than propargyl alcohol.