dataesr/scientific-paragraphs-categorization huggingface dataset:
Surface flooding and snow cover Snow cover on top of sea ice can facilitate nutrient delivery to the sea-ice matrix from surface waters by depressing the ice-snow interface below the sea surface and causing surface water flooding of the snow and upper ice layers  (Tison et al. , 2017). Subsequent formation of snow ice, under the weight of overlying snow as the flooded snow/slush layer freezes, can incorporate seawater nutrients into the ice matrix  (Maksym & Jeffries, 2000; Sturm & Massom, 2009 , 2017). In our dataset, approximately half of the ice cores are accompanied by measurements of snow thickness (n=128), of which most had snow cover of less than 0. 5 m and only five had a ratio of snow thickness to ice thickness of >1:3 indicating flooding. Amongst the small number of cores that have freeboard measurements (n=11), a negative freeboard required for surface flooding was only observed in one first-year ice core from the Dronning Maud Land coast.
Although there was little evidence for surface flooding at the time of sampling across our dataset, C-shaped salinity and nutrient profiles may be indicative of surface flooding having occurred over the ice-covered period, particularly for profiles that were or had been close to the ice edge or cracks and leads in the ice cover. Seawater inflow to the upper ice layers would have become possible through spring as increasing air temperatures (Figure  2b ) warmed the upper ice, producing a more permeable matrix than during winter when these layers were cold and impermeable. These effects of surface flooding would be superimposed onto the seasonal evolution of salinity and nutrient profiles driven by variability in brine drainage and convection with depth and time  (Cozzi, 2014)  and biological processes  (Section 4.
J o u r n a l P r e -p r o o f Substantial accumulations of DIN and phosphate superimposed onto overall trends of biological drawdown during spring and summer are observed in several fast-ice cores. Salinity-normalised concentrations in excess of surface water values (Figure  9 ) and bulk values plotting above the TDLs (Figure  10 ) indicate biological enrichment of these nutrients by organic matter remineralisation and nutrient recycling within the ice matrix. Our dataset shows significantly higher salinity-normalised ammonium concentrations in sea ice than surface water values for most of the seasonal cycle in all ice layers and bulk values plot consistently above the TDL. The largest ammonium accumulations occur in bottom ice in October and November, and in interior ice in November, particularly in the Ross Sea and adjacent to the Adélie Land coast.
g. , biofilms, with distinct biogeochemical dynamics  (Roukaerts et al. , 2021; Section 4. 5). Uptake by the ice algal community of ammonium as an alternative nitrogen source to nitrate becomes more important as the growing season proceeds  (Kristiansen et al. , 1998)  and has been shown to make a larger contribution to primary production in the interior ice than in bottom ice close to the ice-water interface  (Roukaerts et al. , 2016). Such uptake of ammonium as well as nitrate is likely to influence concentrations of both inorganic nitrogen forms in our dataset  (Priscu & Sullivan, 1998; Thomas & Dieckmann, 2002). Nitrification plays a key role in nitrogen recycling and provides a critical regenerative pathway back to nitrate, as evidenced in winter by increases in salinity-normalised nitrite concentration in all ice layers between May and August and in spring/summer by substantial accumulations of nitrate in some bottom ice in October and November and elevated nitrite values in all ice layers in January.
4). Nevertheless, we propose that light limitation linked to ice and potentially snow thickness is the primary control on spring/summer nitrification in our dataset, because this is observed most readily in January when all ice sampled was thicker than 1. 4 m (Figure  3 ). Phosphate also shows significant accumulation above surface water values in bottom ice through November and December, as well as in interior ice in January and in the same cores from the Ross Sea and adjacent to the Adélie Land coast from November that showed large peaks in ammonium concentration. This biological enrichment of phosphate concurrent with that of inorganic nitrogen provides further evidence for intense organic matter remineralisation. Such accumulations of phosphate, ammonium and nitrate well above J o u r n a l P r e -p r o o f Journal Pre-proof surface water values strongly suggest efficient nutrient pumping into the ice matrix, high levels of accumulation into biomass and subsequent high rates of heterotrophic activity by bacterial communities and grazers coupled with substantial nitrogen and phosphorus recycling (e.
Similar to observations of pack ice  (Fripiat et al. , 2017) , our dataset shows that phosphate accumulation in fast ice is greater than that of nitrate and/or ammonium, according to the Redfield ratio of 16N:1P (Figure  11a ), which leads to a reduction in the molar N:P ratio through spring and early summer (Figure  S2 ). This strongly suggests that in addition to organic matter remineralisation producing both phosphate and inorganic nitrogen, there must also be preferential assimilation and remineralisation of phosphorus relative to nitrogen. This would be consistent with greater resource allocation by ice algae to phosphorus-rich ribosomes (growth machinery) during rapid growth  (Arrigo, 2005; Klausmeier et al. , 2004) , coupled with convective loss of excess DIN, and/or organic phosphorus being more labile and thus having a shorter turnover time than organic nitrogen  (Clark et al.
, 2016b). In contrast, complete exhaustion of inorganic nitrogen, phosphate and silicic acid has been documented and shown to have significant effects on ice algal biochemistry  (Gleitz et al. , 1996; Gleitz & Thomas, 1993; Lizotte & Sullivan, 1992; McMinn et al. , 1999). To assess the potential for nutrient limitation across our Antarctic fast ice dataset, we estimate the concentration of DIN, phosphate and silicic acid available to sympagic organisms in sea-ice brine, following  Fripiat et al. (2017). Measurements of sea-ice temperature were not available for most ice cores in our dataset, therefore brine nutrient concentrations could not be estimated using brine volume fractions  (Cox & Weeks, 1983). Instead, we approximate brine volume fraction as the maximum expected ice porosity typical of warm and saline Antarctic sea ice, 20% (e.
, 2001). The much higher value for K Si in fast ice  (Lim et al. , 2019)  is supported by unpublished experimental results from C. W. Sullivan  (referenced in Arrigo et al. , 1993)  and previous modelling work  (Arrigo & Sullivan, 1994; Saenz & Arrigo, 2012; Saenz & Arrigo, 2014). Figure  12b  shows that using the representative value for pelagic diatoms (3. 9 µmol L -1 ;  Sarthou et al. , 2005)  would indicate that silicon limitation is not prevalent in our dataset, and only occurs sporadically during the summer, similar to nitrogen-and phosphorus-limitation. However, a higher value of K Si , as indicated by  Lim et al. (2019) , would mean that diatoms could experience silicon limitation in all ice layers in summer, with the most intense limitation occurring in upper and interior ice in January (and December) and in bottom ice in December.
Conclusions and implications We present a major international compilation of nutrient concentration data from Antarctic fast ice, and describe the key biogeochemical processes cycling nitrogen, phosphorus and silicon within the ice matrix and the exchange of nutrients between fast ice, surface waters and snow cover. Nutrients are supplied to fast ice primarily from underlying surface waters by direct exchange across the ice-water interface. This exchange is driven largely by convective processes associated with desalination, and fluxes are enhanced by higher surface water nutrient concentrations, platelet ice accumulation, and under-ice tidal currents. Comparison of our nutrient dataset with the AFIAC compilation of fast-ice chlorophyll a data J o u r n a l P r e -p r o o f  (Meiners et al.
Accumulation of silicic acid occurs later and to a lesser degree due to slower rates of diatom frustule dissolution. We have shown that sporadic limitation of primary production by nitrogen or phosphate can occur in Antarctic fast ice, although limiting conditions for these nutrients are neither widespread nor sustained across our dataset. A lower affinity of sea-ice diatoms than pelagic diatoms for silicic acid, as well as strong drawdown and slower recycling of silicic acid, could mean that silicon limitation is more widespread in fast ice, but further work is required to better-quantify the affinities for all nutrients amongst sea-ice algal communities, which could differ significantly from pelagic phytoplankton. Overall, we found that concentrations of nitrate, ammonium and silicic acid were all higher in fast ice than in pack ice around Antarctica, and we attribute this difference to greater supply from underlying seawater and more intense recycling of organic matter.
Acknowledgements: SFH was supported by the  United Kingdom Natural Environment Research Council  through grant  NE/K010034/1. KMM was supported by the  Australian Government  through  Australian Antarctic Science  Project #4546, through the  Antarctic Science Collaboration Initiative program  (project  ID ASCI000002 ), and through the  Australian Research Council's Australian Centre for Excellence   in Antarctic Science  (Project Number  SR200100008 ). These funders had no influence or involvement in the study design, execution or the preparation of the article. This study was carried out under the auspices of the international expert group on  Biogeochemical Exchange Processes at Sea-Ice Interfaces (BEPSII) , which was originally a Working Group of the Scientific  Committee on Oceanic Research  (SCOR; WG-140) and has since been endorsed by the  Scientific Committee on Antarctic Research (SCAR) , the  Surface Ocean Lower Atmosphere Study (SOLAS )  and  Climate and Cryosphere (CliC ).
Data availability statement: All data used in this study are publicly available or were obtained from published work. Compiled nutrient concentration data from Antarctic fast-ice cores and platelet ice are available through the Australian Antarctic Data Centre (URL: https://data. aad. gov. au/metadata/BEPSII_2023_Fast_Ice_Nutrients). Seawater nutrient concentration data from Rothera Station (2013Station ( -2016) )  are available from the British Oceanographic Data Centre at https://www. bodc. ac. uk/data/published_data_library/catalogue/10. 5285/98cc0722-e337-029c-e053-6c86abc02029/ (Henley and Venables, 2019). All other data are published, with references given in the text, tables and/or figure captions. Henley, S. F. , and Venables, H. J. (  2019 ). Water Column Nutrient, Particulate Organic Matter and Isotopic Data for Ryder Bay, West Antarctic Peninsula 2013-2016.
The map scores are processed using a combination of a fast Gaussian smoother and a rank-order filter to improve accuracy. Only the blocks in motion are coded and counted for transmission. The accuracy of our approach has been evaluated on a large dataset using key performance metrics. It has been found that our algorithm outperforms other state-of-theart techniques in terms of true positive rate (TPR), with 80. 84% on sensitivity metric, while exhibiting a well-balanced accuracy for all categories. A careful examination of the computational complexity confirms the low overhead. The energy and bitrate savings could achieve nearly 90% and 98%, respectively.
I. INTRODUCTION V IDEO Content Analysis (VCA) techniques involve au- tomatically analyzing video to detect and determine spatial and temporal events. VCA is used in a wide range of domains, including video browsing and retrieval  [1] , image and video coding  [2]  [3], video surveillance, etc. The analyze-then-compress (ATC) paradigm employs VCA to first analyze the content before compressing it. As a result, feature extraction is carried out before the compression and the transmission of the visual data captured from a visual sensor node (VS). This paradigm has been put forth as a substitute for the conventional compress-then-analyze (CTA) paradigm, which compresses the entire captured video before transmitting it to be processed further when it is received. The CTA paradigm typically employs highly complex video coding standards  [4] , such as MJPEG  [5] , H264  [6] , and HEVC  [7].
We contribute by proposing an energy-efficient method and validating its detection efficiency on a large dataset containing nearly all surveillance conditions classified into 11 categories  [24]. Additionally, we validate the energy efficiency through detailed modeling of computational complexity and energy cost to prove the neglected extra cost compared to the saved energy. By avoiding unnecessarily processed and compressed blocks, the proposed pre-encoding scheme significantly reduces computational complexity. A Block-based movIng Region Detection (BIRD) technique is proposed. BIRD detects the difference created between frames using a kind of Sum of absolute Frames Difference (SFD)  [11]  to address video coding in resourceconstrained systems. The SFD operation is followed by heavy yet efficient morphological filtering to enhance the accuracy of the moving-region detection.
ROI detection has also been a solution to control the memory usage  [45] , the bitrate  [46] , and the quality of the video encoder. This is accomplished by managing the bit allocation mechanisms, as shown in  [47]. The authors in  [32]  have provided a more accurate and energy-efficient strategy, in which a good trade-off between energy efficiency, image quality, content awareness, bitrate, and effective machinebased monitoring tasks at the destination have been reached. The strategy seeks to create a new pre-processing method, named Successive Sum of Absolute Differences (S-SAD), to identify the ROI and divide it into many classes based on their importance. Table  1  summarizes the related work on ROIbased video coding for WVS. While the works mentioned above provide effective energy-saving solutions for WVS systems, the majority of them did not fully consider the efficiency of the used moving object detection method because they only considered a few evaluation metrics and small datasets.
IV. RESULTS AND DISCUSSION To validate the proposed method, we present the Change Detection 2014 Dataset (CDnet)  [50]  results. CDnet 2014 is a very challenging dataset composed of 51 video sequences from 11 categories ( more than 150000 frames + their ground truths). Since each category is associated with a specific change detection problem, e. g. , dynamic background, shadows, CDnet enables an objective identification and ranking of methods that are most suitable for a specific problem as well as competent overall. The experimental values for each used parameter are summarized in Table  2. We consider first a qualitative assessment based on visual observation of the obtained binary mask for the moving regions compared with ground truth masks.
B. PERFORMANCES OF BIRD OVER THE CDNET 2014 Table  3  shows the performance of BIRD indicating the algorithm's visual accuracy in detecting all the ROI candidates for compression and transmission. The presented sample frames from all categories of the benchmark dataset in Table  3  show that the algorithm successfully detects the blocks in which a high movement occurs. Objects are entirely detected in most videos, which could be a good enabler for a variety of applications, especially as a pre-encoder for ROI-based video coding  [23]. It should be noted that, for some video scenarios (like the Office video sample), the algorithm is unable to detect the target object for some time due to the object's stability. Even though the object information has already been delivered to the destination, the reported numerical results are reduced.
C. COMPARISON WITH OTHER TECHNIQUES Table  5  shows the overall results of our method on CDnet 2014 dataset compared with the state-of-the-art techniques namely, KNN in  [51] , GMM in  [52] , KDE in  [53] , Mahalanobis Distance and Euclidean Distance techniques presented in  [54]  and another GMM-based technique in  [55]. The proposed method exhibits good results in the recall and FNR metrics with the best results against other techniques and shows competitive results for the specificity metric. The weaknesses of the algorithm in the precision and Fmeasure values (0. 1893 and 0. 2678) can be explained by the adopted block-based techniques which allow the detection of additional pixels with the moving object, (i. e. : high FPR). According to Table  4 , the results of BIRD are considered very high in the context of the studies that aim to integrate object detection as a pre-processing step for WVS in very low-complexity platforms.
E. THE IMPACT OF THRESHOLDING ON DETECTION We select three sequences from the used dataset to empirically validate the BIRD accuracy and low-overhead assumptions. Highway with a size of (320 × 240) contains high activity with a number of moving vehicles. The pedestrians sequence of size (360 × 240) is of low activity with relatively high stability in the background. The Snowfall sequence of size (720 × 480) is a long sequence that contains moving objects with very high activity in the background ( Snow and winter). Figure  (5)  plots the TPR against the FPR when varying the threshold value (0. 10). The obtained ROC curves show that low thresholds imply a high true positive rate. However, this adversely affects the specificity of the detection, since a high number of blocks is wrongly labeled as activity blocks,     which means that more data is to be considered for delivery.
F. METHOD COMPLEXITY To evaluate the consumed energy on embedded sensor conditions, we have considered what follows, a sensor node equipped with an ARM Cortex M3 micro-controller  [57]. Table  8  shows the processor characteristics. Using MATLAB 2020a and C++ running on a PC intel Core i7-2670QM 2. 2Ghz, with 8GB RAM on Windows 7 OS, 2. 6 ms to process one frame of 320×240 is recorded allowing processing of 384 frames per second (fps). The total energy budget of the proposed BIRD algorithm is directly proportional to its computational complexity and could be expressed as follows:  (12)  The total computational budget of the method is presented in Table  9. The number of operations for FGS is reported in  [48] , while the ROF budget is R = K(K -1)/2, where K is set to 4 for the proposed method and represents the size of the sliding vector.
The proposed method's overall efficiency was evaluated using a standard dataset as a benchmark. The performance assessment shows a satisfactory balance between the proposed method's detection accuracy, energy efficiency, and memory. In these respects, our approach effectively relieves the burden of processing and compressing video sequences for resourceconstrained surveillance devices. The proposed method has two main drawbacks: (1) It has only been tested on fixed cameras, and (2) It has, in some cases, poor results using some performance metrics (like F-measure) because of its commitment to meet constrained WVS. Future studies include improving the performance of the algorithm and its implementation in an embedded WVS system while taking into account channel and network characteristics.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/ACCESS. 2023. 3248067 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4. 0 License. For more information, see https://creativecommons. org/licenses/by-nc-nd/4 • few evaluation metrics Rehman et al. (2016) [29] • divide the frame into 4 blocks • moderate accurate detection • limited datasets • select ROI from sub-blocks • simple and efficient algorithm • high bitrate • background modeling • dedicated to WMSN context • high complexity for WMSN node • compression using DWT Aliouat et al. (2022) [30] • edge detection using Canny filter • automatic thresholding • high complexity • 8 × 8 SAD of the edge map • accurate detection • limited dataset • automatic multi-threshold selection • content-aware coding • high bitrate (50% reduction) • multi-Otsu thresholding • allocate more resources to the ROI • no energy consumption model • compression priority to the ROI • dedicated to WMSN context • few evaluation metrics Aliouat et al.
(2022) [16] • edge detection using Sobel filter • good accuracy on the used dataset • high complexity for WMSN con- • 4 × 4 SAD of the edge map • efficient in different weather cond. text • 2-D Rank order map filtering • high bitrate and processing reduc- • limited dataset • fixed threshold tion • no energy consumption model • background update each GOP • dedicated to WMSN context • few evaluation metrics Ko. et al. (2018) [23] • edge detection using Sobel filter • accurate detection • limited dataset (2 sequences) • 8 × 8 SAD • optimal circuit design • no comparison to the state of the art • bitrate control using PID-controller • high processing and bitrate reduc- • few evaluation metrics • optimal enhancement algorithm tion • prototyping on 130nm sensor node. • dedicated to WMSN context • FPGA implementation Ko.
et al. (2015) [31] • edge detection using Sobel filter • optimal circuit design • limited dataset (4 sequences) • perform Frame difference • high processing and bitrate reduc- • no comparison to the state-of-the- • 8 × 8 SAD tion art • rate control (channel cond. -BER-) • content and energy-aware • few evaluation metrics • thresholding using PID controller • dedicated to WMSN context • detection accuracy not reported Aliouat et al. (2023) [32] • novel (S-SAD) introduced • accurate detection • no detection accuracy comparison • multi-classes coding 2 based on • energy model provided • medium dataset ROI. • high bitrate and processing saving • fixed threshold • assessed for Human and Machine • content-awareness based monitoring • resources/quality tradeoff achieved • dedicated to WMSN context Sengar et al.
(2020) [33] • MOD detection suing Optical flow • deal with moving cameras • limited dataset (4 sequences) • Ostu for thresholding • good efficiency comparison with • no energy consumption model • particle swarm optimization (PSO) state-of-the-art • not dedicated to WMSN context for redundancy exploring • good rate-distortion performance • few evaluation metrics BIRD (Proposed) • 8 × 8 SFD • low complexity • tested only for fixed camera • 1-D ROF on the activity map • high detection accuracy • fixed threshold • FGS filter on the activity map • energy modeling (ARM Cortex • a pre-encoder for video coding. M3) • large dataset (51 sequences) • dedicated to WMSN context VOLUME 4, 2016 3 • low complexity • fast image compression algorithm • dedicated to WMSN context • less accurate • few datasets.
The electrical energy required during the test is monitored, as well as indoor temperatures and a range of external weather parameters, such as: air temperature; wind speed and direction; and incident solar radiation, ideally measured on the vertical plane of the building facade expected to receive the highest proportion of solar gains. Additional measurements (e. g. , heat flux density through building elements, relative humidity, local wind conditions, etc. ) may also be collected to gain further information on the thermophysical behaviour of the building fabric. Owing to the quasi-stationary nature of the test, linear regression models are generally adopted to analyse the data collected and evaluate the aggregate heat transfer coefficient of the building. These models are based on a steady-state energy balance of the building: 𝑃 h = 𝐻 Δ𝑇 -𝐴 sw 𝑞 sw (1) where 𝑃 h is the power supplied to heat up the indoor space Among the most common data analysis methods for the estimation of the whole-building aggregate heat transfer coefficient are the Siviour and multiple linear regression analysis methods  [12].