esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of System Embedded Optical Interconnect in Sub-Top-of-Rack Data Center Systems
## 3 LightningValley--First-Generation Optically Enabled Data Center System
### Optically Enabled Data Storage Switch Controller Daughters with Polymer Waveguide Interconnect  
The controller daughtercards were modified such that 12 high-speed bidirectional electronic links from the SAS switch (expander) on the controller daughtercard to the midplane were converted to optical signals through the use of a Finisar BOA midboard optical transceiver module. The optical link on the controller daughtercards between the midboard optical transceiver modules and the optical midplane connector was conveyed across discrete, interchangeable dual-layer polymer optical ribbons. Figure 5 shows the optically modified LightningValley controller card with one BOA midboard optical transceiver mounted and a freestanding polymer waveguide flexible ribbon assembled providing the optical link between the midboard transceiver and the optical midplane connector. Each ribbon comprised two separate layers of polymer waveguides fabricated on an S-curved Kapton polyimide strip. Each layer contained 12 multimode polymer waveguides with a square cross-sectional profile of size 50 \(\upmu\)m \(\times\) 50 \(\upmu\)m and separated by a center-to-center pitch of 250 \(\upmu\)m, thus matching the horizontal interchannel separation used in MT ferrules. At each end of the ribbon cable, both layers of 12 waveguides were terminated into a bespoke ferrule, the interface of which was fully compliant with a single 2 \(\times\) 12 MT interface.  
Figure 4: Partially optically enabled data storage system (LightningValley): (**a**) standard 4U24 OneStor storage array system by Seagate Technology, (**b**) photo of LightningValley system, (**c**) schematic view of optically enabled storage system [12].  
The Swiss company Vario-optics manufactured the two types of dual-layer polymer optical waveguide flexible ribbon. The proprietary polymer acrylate material sets used to fabricate the waveguides were compatible with PCB manufacturing processes such as lamination, through-hole plating and reflow soldering and exhibited acceptable loss changes for long-term (2000 h) tests at elevated temperature (85 \({}^{\circ}\)C) and relative humidity (85%) as part of standard reliability tests, e.g., Telcordia GR-1221 [15].
## 3 Proposed Framework
### Localization with Combined Dictionary
#### 3.4.1 Steps to Create a Combined Dictionary  
Step 1: Concatenate All Track Dictionaries. We initialize \(\mathcal{I}_{\text{combined}}\) as the union of all \(\mathcal{I}_{k}\):  
\[\mathcal{I}_{\text{combined}}\ =\ \bigcup_{k=1}^{K}\mathcal{I}_{k} \tag{1}\]  
where \(K\) represents the six static tracks considered.  
Step 2: Remove Duplicate or Overlapping Entries. At certain time instants \(t\), interactions may occur with multiple tracks. These are simultaneous interactions. If the same ego-vehicle cluster \(C_{\text{ego}}\) is repeated with minor variations, we merge or remove duplicates to ensure the dictionary remains concise.  
Step 3: Store Track-Specific Indices. To preserve which track each interaction came from, we store an additional track ID label in each dictionary entry:  
\[\big{\{}t,\,C_{\text{ego}},\,C_{\text{track}},\,L,\,x,\,y,\,\text{track\_ID} \big{\}}.\]  
This helps in quickly referencing the relevant transition matrices or covariance structures for that track.  
Step 4: Recompute Interaction Statistics. For each unique pair \((C_{\text{ego}},C_{\text{track}})\), we can compute:  
\[(\mu_{x},\mu_{y},\Sigma_{x},\Sigma_{y})\]  
to represent the mean and covariance of the ego positions \((x,y)\) encountered in those interactions. These statistics become vital when updating or predicting the ego state from the combined dictionary.
## 3 Materials and Methods  
### Study Area and Field Data Collection  
Limpopo province is located on the northern part of South Africa. This province hosts Makhuduthamaga (Figure 1), which is the focus of this study. The area has rural villages that focus on smallholder maize farming [39]. Hence, due to the dominance of smallholder farms in the area, it was selected as a case study. Weather stations from the Agricultural Research Council located in Nchabeleng, Ga-Rantho, and Leeuwkraal have recorded an average annual rainfall of 536 mm and average annual temperatures of 7 \({}^{\circ}\)C in winter and 35 \({}^{\circ}\)C in summer. Makhuduthamaga has an undulating topography with rock habitats in the form of rock outcrops, rocky ridges, rocky flats, and rocky refugia [40].  
Field surveys for the collection of training and validation data for different landcover types within the smallholder boundaries occurred from 18 to 21 February 2019. A hand-held Garmin Global Positioning System (GPS) device which has a positional accuracy of 1.5 meters (on average mode) used to capture the coordinates of different land cover classes. The dominant land cover classes in the study area were captured; these include maize, bare land, and vegetation. The bare land and vegetation classes were combined to generate training samples (\(n=9895\) pixels) for the non-planted areas. The maize class consisted of \(n=9802\) pixels training samples. The samples were randomly selected into 80% training and 20% validation for each class. Constraining the land cover classes to two classes reduced the potential of classification errors from using the classes individually due to the variations in the natural occurrence of certain features. Limiting the area of investigation to the smallholder boundary excluded the farming activities in residents' backyards, thus only land that was demarcated as smallholder farmland was considered. A total of 18 smallholder farms were randomly selected in the field for validation purposes. Their areas were measured using a GPS. Most of these farms do not have proper access roads, which made it difficult to survey more farms.
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 2 Operation Mode and Coordination Control of Microgrid in Forest Area
### Operation Mode of the Forest Microgrid
#### 2.2.2 Emergency Mode  
When the wind is relatively weak or the output of the biomass power generation unit is limited, the DC voltage of the microgrid will drop below 0.98 pu. Under these circumstances, the energy storage system discharges to supplement the power gap. When the output of the wind power generation system is so high that the DC voltage rises above 1.02 pu, the biomass power generation unit withdraws automatically. In this case, if the system power is still excessive, the battery can be charged only when its state of charge is below the upper limit.
###### Abstract  
_Enterococcus faecalis_ and _Enterococcus faetium_ are frequently associated with persistent endodontic infections due to their ability to form biofilms and resist conventional treatments. The aim of this study was to evaluate the antibacterial and antibiofilm effects of _Lavandula dentata_ L. essential oil at 100% against the standard and clinical strains of _Enterococcus_ spp. The minimum bactericidal concentration (MBC) of _L. dentata_ essential oil for each bacterial strain was determined. The antibiofilm effect was evaluated by an MTT assay. Data were statistically analyzed by the ANOVA and Tukey test, with a significance level of 5%. The MBC value was 32% (292.8 mg/mL) against all the tested strains. _L. dentata_ significantly reduced _E. faecalis_ and _E. facecium_ biofilms at 16-64% (146.4-585.6 mg/mL) concentrations, with effects comparable to 2% chlorexidine (CHX) (\(p\geq 0.05\)). Biofilm reduction was strain-dependent at 30 min but showed broader efficacy after 24 h, particularly against _E. facecium_ clinical strains (\(p\leq 0.001\)). _L. dentata_ demonstrated effective antimicrobial activity against planktonic and biofilm forms of _E. faecalis_ and _E. facecium_ as it reduced biofilm formation at a concentration of 16% and 64% (146.4 and 585.6 mg/mL), with results comparable to 2% CHX after 30 min and 24 h. _L. dentata_ EO may serve as a potential alternative or adjunctive antimicrobial agent in endodontic treatment. However, the study's limitations included the in vitro design and lack of cytotoxicity assessment.  
antibacterial; antibiofilm; _Lavandula dentata_ L.; _Enterococcus faecalis_; _Enterococcus faetium_
## 2 Experimental Programs
### Loading and Measuring-Point Arrangement  
Strain gauges were arranged along the height direction of both webs at \(1/4\), mid-span and \(3l/4\) positions, within the span of the test T-beam. In addition, strain measuring points were also arranged on the surface of the bottom plate at the above positions of the beam to measure the strain distribution on a concrete surface. Displacement measuring points were arranged at \(l/4\), mid-span and \(3l/4\) positions within the span of the T-beam to record the deflections under various loads. Figure 2 shows the measuring-point arrangement. To accurately represent the stress variation of the steel bar in the test beam, strain gauges were arranged at the \(l/4\), \(l/2\) and \(3l/4\) positions of the bottom tensile steel bar. Meanwhile, a strain gauge was arranged on the stirrup at the supports of the test beam. The position and number of measuring points of strain gauges for reinforcement are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Test Beam** & **Location of Crack** & **Depth of Crack/mm** & **Diameter of Steel Bar at Crack/mm** \\ \hline WS & - & - & - \\ ZSS1 & mid-span & 20 & 12 \\ ZSS2 & mid-span & 60 & 12 \\ ZSS3 & mid-span & 60 & 10 \\ DSS1 & \(1/4\) position & 20 & 12 \\ DSS2 & \(1/4\) position & 60 & 12 \\ DSS3 & \(1/4\) position & 60 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test-condition settings of T-beam for pre-cracking test.  
Figure 1: Section-size layout and reinforcement diagram of test beam (unit: mm).  
The test beam was simply supported and loaded by three-point bending. A pre-loading test was first conducted before formal loading. The control force in the pre-loading process was 20 kN. The load was unloaded to 1 kN after 5 min, which can test the accuracy of data acquisition at each measurement point and eliminate the gaps between the structures. Hydraulic graded loading was used for the formal loading process. The load of each stage was set at 5 kN before beam cracking, and the load of each stage was 10 kN after cracking. The force index was used to control the loading process when the load was in the range of 0-200 kN, and the loading rate was 0.4 kN/s. The displacement index was used when the load exceeded 200 kN, and the loading rate was 1 mm/min. The load was held for three minutes before data recording at each loading level. Two termination criteria were set for the loading test: the cumulative deflection increment of the beam was larger than 6 mm within 30 min, or the single measured mid-span deflection increment in the last 5 min was larger than 0.5 mm. Figure 4 shows the loading test device.  
Figure 4: Loading test device.  
Figure 3: Layout diagram of reinforcement strain gauge in the test beam.  
Figure 2: Arrangement of measuring points (unit: mm).
# A Survey of Big Data Archives in Time-Domain Astronomy
## 2 Big Data in Astronomical Domain  
The term \"big data\" refers to the huge amount of data that can be found in various forms and formats. Traditional relational data are not the only source of unstructured data, which are continuously expanding [13]. It is typical for big data to be available from various sources. Data derived from machines, for example, grows exponentially and holds a wealth of information that will be uncovered in the future. However, even though the data gathered by humans are more textual, valuable insights can still be retrieved from them [14].  
For computer scientists interested in astronomy, most surveys make their entire data collection and any derived parameters readily available online, in the form of enormous databases. In today's world, data are becoming increasingly large, unstructured, and fast-moving, making traditional data management methods ineffective. Over a billion stars have been studied, millions of objects have their spectra taken, and hundreds of new planetsare still being identified. The Sloan Digital Sky Survey (SDSS) [15] is one of the largest astronomical surveys. More than 200 million galaxies and more stars have been discovered in the almost a million field images capture by the SDSS telescope per night. Future surveys are expected to yield significantly more information. The Legacy Survey of Space and Time (LSST), now known as the Vera C. Rubin Observatory, is another prospective future survey that will offer wide-field images of the sky and expose galaxies that are currently too faint to be noticed.
## 4 Case Study  
To illustrate the effectiveness and efficiency of our proposed method, we present identification results using patents related to unmanned aerial vehicles (UAVs). UAVs are widely used in military, transportation, agroforestry, and contemporary disaster-relief work, to perform tasks such as investigation, transport, and filming, due to their advantages of light weight, flexible maneuverability, and user-friendly control. In recent years, more and more companies have invested in the development of UAVs. A total of 19235 patents were gathered from Patsnap ([[https://analytics.zhihuiya.com](https://analytics.zhihuiya.com)]([https://analytics.zhihuiya.com](https://analytics.zhihuiya.com)), accessed on 12 November 2019) using the search query \"DESC: (\"unmanned aerial vehicle\" OR \"UAV\") AND PATENT_TYPE: (granted) AND APD: [20000101 TO 20191112]\" (date: 12 November 2019).  
Figure 8 illustrates the statistics of the granted patents retrieved. In general, the yearly filing number of UAV patents increased moderately from 2000 to 2010, which indicates that UAV technology was developing slowly during that time. After 2010, the patent number witnessed a significant growth and reached at its peak in 2016, as shown in Figure 8a. This shows that UAV technology has been constantly adapting to market demand and constantly being upgraded. The patent numbers from 2017 to 2019 were slightly decreased because of incomplete data, due to the invention review process. Figure 8b shows the annual filing number of UAV patents for each geographical authority. The United States Patent and Trademark Office (USPTO) and China National Intellectual Property Administration (CNIPA) have the most patents applied. In particular, the growth of annual filing of UAV patents in CNIPA has kept pace with the progress of global technology. Although the initial development was slow, it has moved rapidly since 2013, and surpassed the USPTO in 2016, becoming the competitive center of UAV technology. Boeing, Dajiang Innovation (DJI), and Raytheon were the top three companies in terms of the total number and share of filed UAV patents, as shown in Figure 8c. DJI has gradually become a leading enterprise in UAV technology, since 2014 (Figure 8d).  
Figure 8: _Cont._  
Figure 8: (**a**) Yearly filing number of UAV patents; (**b**) Annual filing number of UAV patents for different geographical authorities; (**c**) Total number and share of patents granted to and owned by the top ten companies; (**d**) Yearly filing number of patents applied for by DJI, BOEING, and RAYTHEON.  
Figure 9 shows the evolution of DJI's UAV series products. In terms of market performance, DJI's global sales climbed about 160-fold from 2011 to 2016 [54]. In 2017, DJI was listed as one of 50 smartest companies by the MIT Technology Review. In addition, it was selected as the \"2019 Forbes China Most Innovative company\". Considering its excellent technological competence and market performance, DJI has achieved radical inventions in UAVs [55]. Therefore, DJI's patents were further selected as experimental data to demonstrate the proposed method more clearly.
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 4 Experiments
### Node Disambiguation
#### 4.2.6 Execution Time Analysis  
The runtime of FONDUE-NDA is linear \(\mathcal{O}(n)\) on each node, and is trivially parallelizable. In Figure 4, we show the execution speed of FONDUE-NDA and baselines in node identification and splitting. FONDUE-NDA is faster than CC, and by nearly one order of magnitude than NC in most datasets. Note that FONDUE-NDA approximates Equation (5) by aggregating two different approximation heuristics (i.e., randomized sampling and eigenvector thresholding as listed in Section 3.3.2). Although the best results (in terms of NDCG and ARI) were obtained by the latter approximation. Thus, the runtime results reflect only the execution time of latter heuristic. This is listed in details in Table 7. All the experiments have been conducted on a Intel \(i7-7700\)K CPU 4.20 GHz, running the Ubuntu 18.04 distribution of Linux, with 32 GB of RAM.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Ambiguity Rate**} & \multicolumn{2}{c}{**10\%**} & \multicolumn{2}{c}{**1\%**} & \multicolumn{2}{c}{**0.1\%**} \\ \hline  & **Method** & **FONDUE-NDA** & **MCL** & **FONDUE-NDA** & **MCL** & **FONDUE-NDA** & **MCL** \\ \hline \multirow{9}{*}{**FONDUE-NDA**} & **fb-sc** & 0.582 & 0.781 & 0.781 & 0.829 & 0.947 & 0.936 \\  & **fb-pp** & 0.405 & 0.583 & 0.504 & 0.583 & 0.610 & 0.600 \\  & **email** & 0.122 & 0.248 & 0.214 & 0.276 & – & – \\  & **student** & 0.215 & 0.124 & 0.131 & 0.183 & – & – \\  & **lesmis** & 0.597 & 0.304 & – & – & – & – \\  & **polbooks** & 0.391 & 0.338 & – & – & – & – \\  & **ppi** & 0.033 & 0.120 & 0.055 & 0.115 & 0.338 & 0.321 \\  & **n**etscience** & 0.610 & 0.777 & 0.642 & 0.809 & – & – \\  & **GrQc** & 0.498 & 0.665 & 0.661 & 0.682 & 0.762 & 0.784 \\  & **CondMat** & 0.471 & 0.709 & 0.582 & 0.713 & 0.574 & 0.718 \\  & **HepTh** & 0.447 & 0.556 & 0.521 & 0.552 & 0.465 & 0.542 \\  & **cm05** & 0.471 & 0.722 & 0.674 & 0.732 & 0.488 & 0.741 \\  & **cm03** & 0.479 & 0.724 & 0.575 & 0.730 & 0.513 & 0.690 \\ \hline \multirow{9}{*}{**FONDUE-NDA**} & **fb-sc** & 0.609 & 0.952 & 0.887 & 0.985 & 0.862 & 0.998 \\  & **fb-pp** & 0.409 & 0.586 & 0.509 & 0.564 & 0.546 & 0.637 \\  & **email** & 0.136 & 0.315 & 0.278 & 0.413 & – & – \\  & **student** & 0.197 & 0.139 & 0.079 & 0.078 & – & – \\  & **lesmis** & 0.405 & 0.289 & – & – & – & – \\  & **polbooks** & 0.449 & 0.577 & 0.638 & 0.655 & – & – \\  & **ppi** & 0.036 & 0.139 & 0.067 & 0.151 & 0.331 & 0.399 \\  & **n**etscience** & 0.725 & 0.819 & 0.722 & 0.897 & – & – \\  & **GQc** & 0.534 & 0.678 & 0.665 & 0.683 & 0.895 & 0.872 \\  & **CondMat** & 0.470 & 0.708 & 0.548 & 0.712 & 0.475 & 0.686 \\  & **HepTh** & 0.469 & 0.571 & 0.529 & 0.561 & 0.404 & 0.455 \\  & **cm05** & 0.466 & 0.726 & 0.549 & 0.777 & 0.450 & 0.723 \\  & **cm03** & 0.482 & 0.722 & 0.570 & 0.727 & 0.567 & 0.697 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Adjusted Rand index score for FONDUE-NDA and MCL.
# Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention
## 4 Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Twitter-G**} & \multicolumn{3}{c}{**Twitter-P**} & \multicolumn{3}{c}{**Reddit-Pol**} \\ \hline
**Method** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline NBOW & 0.7303 & 0.7444 & 0.7281 & 0.8039 & 0.8027 & 0.8033 & 0.6983 & 0.6933 & 0.6950 \\ TextCNN & 0.7488 & 0.7635 & 0.749 & 0.7993 & 0.7965 & 0.7978 & 0.7180 & 0.7183 & 0.7181 \\ Bi-LSTM & 0.7752 & 0.7885 & 0.7784 & 0.7994 & 0.8029 & 0.8009 & 0.7062 & 0.7100 & 0.7073 \\ SIARN & 0.7464 & 0.7601 & 0.7480 & 0.7897 & 0.7924 & 0.7909 & 0.7018 & 0.7020 & 0.7019 \\ SMSD & 0.7508 & 0.7662 & 0.7447 & 0.7966 & 0.7979 & 0.7972 & 0.7326 & 0.7331 & 0.7328 \\ BERT & 0.8178 & 0.8337 & 0.8217 & 0.8399 & 0.8465 & 0.8424 & 0.7339 & 0.7266 & 0.7292 \\ mirchlog & 0.8211 & 0.8344 & 0.8251 & 0.8395 & 0.8441 & 0.8413 & 0.7525 & 0.7402 & 0.7427 \\ SDCK-maj & 0.8249 & 0.8376 & 0.8292 & 0.8516 & 0.8605 & 0.8546 & 0.7532 & 0.7519 & 0.7525 \\ SDCK-min & 0.8552 & 0.8598 & 0.8573 & 0.8361 & 0.8426 & 0.8386 & 0.7555 & 0.7554 & 0.7555 \\ SDCK-con & 0.8349 & 0.8471 & 0.8392 & 0.8393 & 0.8413 & 0.8402 & 0.7610 & 0.7609 & 0.7610 \\ SDCK-att & 0.8341 & 0.8467 & 0.8385 & 0.8404 & 0.8417 & 0.8410 & 0.7570 & 0.7571 & 0.7571 \\
**Model (ours)** & **0.8761** & **0.8744** & **0.8751** & **0.8592** & **0.8638** & **0.8612** & **0.7641** & **0.7716** & **0.7658** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results for sarcasm detection. The best result is in boldface. SDCK-maj model on the F1 metric. Similarly, Model-min achieves 1.12%, 1.70%, and 1.20% improvement compared to the SDCK-min model on the F1 metric.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 4 Network Indicator System Construction
### Packet Loss Rate  
The packet loss rate is a critical metric for assessing the reliability of network transmission, and serves as an essential indicator of network performance. High packet loss rates indicate network abnormalities. By tracking the number of packets dropped by each task at every vehicle node, the packet loss rate of each task can be calculated, leading to the overall packet loss rate for the network. Specifically, assuming that the number of lost packets and the total packets of network tasks are \(\{loss_{1},\cdots,loss_{n}\}\), \(\{\varphi_{1},\cdots,\varphi_{n}\}\), respectively, where \(n\) is the total number of tasks at the current moment, the overall packet loss rate of the network is:  
\[loss\_rate=\frac{\frac{loss_{1}}{\varphi_{1}}+\cdots+\frac{loss_{n}}{\varphi_ {n}}}{n} \tag{6}\]
# Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach
## 2 Methods and Design  
### PV System Design of Photovoltaic System 5 kW \"Chikalov\", Simtili, Blagoevgrad, Bulgaria  
In the studied photovoltaic installation mounted on the roof of a residential building, the three-phase inverter Sunny Tripower 5000 TL (STP9000 TL-DEN130516w, SMA Solar Technology AG, Nisetetal, Germany) with a power of 5 to 9 kW was used. It features Opti-flex technology, a maximum efficiency of 98%, temperature management with OptiCool,  
Figure 2: The location of the PV rooftop system in a residential house in Simitil, southwestern Bulgaria.  
and shade management with OptiTrac Global Peak technology to generate the highest yields. An external Bluetooth antenna is used to communicate with the user. Also, the PV system has an SMA Webconnect connector that allows direct connection and communication to Sunny Portal, Bluetooth, and multi-function relay. Sunny Tripower has integrated network management functions and is suitable for operation with 30 mA [25; 26].  
BYD photovoltaic panels, BYD P6-30 Series-3 BB (HK:1211), were used to construct the PV system, which is based on the NES technology, and increase the average efficiency of the cells to 17.4%. In addition to the following specifications, they contain polycrystalline silicon solar cells measuring 156 m \(\times\) 156 m/6 inch, with a front glass of 3.2 mm tempered glass, an anodized aluminum alloy frame, an IP65 junction box, an IP67 plug connector, 6 bypass diodes (IEC)/3 bypass diodes (UL), and a type of connector that is MC4-compatible, 0-1394462-4/6-1394461-2 [27].  
In our study, we collected data from a platform called Sunny Portal, which monitors a PV system in real time (Sunny Portal: SMA Solar Technology AG, Niestetal, Germany) [28]. It provides information about system status data and current energy flow, monitors communication to gateways and inverter performance, and gives location and time information.  
From Figure 3, we can see that the PV system generated more power during May, June, July, and August. These results are expected during this time of year since solar energy is the most powerful in this area at this time. The investigated PV system can generate energy that is useful for a household regardless of the weather conditions.  
Figure 3: _Cont_.