esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation and Analysis
### Results and Analysis
#### 6.3.2 Analysis of Marginal Abatement Costs  
The marginal abatement cost refers to the economic cost required to reduce (or increase) one unit of carbon emission under a reasonable input-output allocation, reflecting the marginal utility of the resource allocation to the actual output. To further investigate the impact of carbon-peaking targets on RIESs, the marginal abatement costs of HP-RIES and CP-RIES in the FEL and FTL operating modes are calculated, and the results are illustrated in Figure 9. The findings indicate that in all three development scenarios, the FEL mode exhibits significantly lower marginal abatement costs compared to the FTL mode. Furthermore, in the same mode, HP-RIES has slightly lower marginal abatement costs  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Rapid Scenario (Tons)**} & \multicolumn{2}{c}{**Steady Scenario (Tons)**} & \multicolumn{2}{c}{**Green Scenario (Tons)**} \\ \cline{3-8}  & & **2020** & **2040** & **2020** & **2040** & **2020** & **2040** \\ \hline \multirow{3}{*}{Constraint} & HP-RIES-FEL & \(3721\) & \(5201\) & \(3721\) & \(4712\) & \(3721\) & \(4378\) \\  & \(-29.5\%\) & \(-11.8\%\) & \(-29.5\%\) & \(-16.88\%\) & \(-29.5\%\) & \(-20.39\%\) \\ \cline{2-8}  & HP-RIES-FTL & \(5278\) & \(5897\) & \(5278\) & \(5669\) & \(5278\) & \(5499\) \\ \hline \multirow{3}{*}{No constraint} & HP-RIES-FEL & \(3422\) & \(4887\) & \(3322\) & \(4152\) & \(3222\) & \(3920\) \\  & \(-30.26\%\) & \(-14.02\%\) & \(-31.6\%\) & \(-25.64\%\) & \(-32.83\%\) & \(-28.73\%\) \\ \cline{2-8}  & HP-RIES-FTL & \(4907\) & \(5684\) & \(4857\) & \(5584\) & \(4797\) & \(5500\) \\ \cline{2-8}  & CP-RIES-FEL & \(2308\) & \(3819\) & \(2296\) & \(3409\) & \(2284\) & \(3305\) \\ \cline{2-8}  & \(-30.63\%\) & \(-16.67\%\) & \(-30.51\%\) & \(-20.44\%\) & \(-30.56\%\) & \(-16.14\%\) \\ \cline{2-8}  & CP-RIES-FTL & \(3327\) & \(4583\) & \(3304\) & \(4285\) & \(3289\) & \(3941\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: The carbon emissions of HP-RIES and CP-RIES.  
compared to CP-RIES. The optimization results further confirm the superior environmental benefits of the FEL mode over the FTL mode in terms of operating mode selection. It is also observed that although HP-RIES has a lower renewable energy penetration compared to CP-RIES, it demonstrates stronger economic competitiveness. This suggests that although renewable energy has a better low-carbon performance, its economic costs are higher compared to those of traditional energy sources.
# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
## 4 Discussion  
Autoencoder or iForest models separately have worse performance because training data are anomaly-contaminated. For the autoencoder-based anomaly detection model, since the training data are contaminated with anomalies, it also performs well in reconstructing anomalous data. Additionally, it is difficult to distinguish abnormal data from normal data because the reconstruction error between the two is not large. For the iForest-based anomaly detection model, the data were recursively divided, and the length of the path from data to the root node is chosen as the anomaly score. Since metallurgical energy data have a high proportion of abnormal data, the length of the path from abnormal data to the root node is similar to the normal ones, which will lead to a poor anomaly detection performance. The SVM-based anomaly detection algorithm is a supervised algorithm that needs to be carried out with a large amount of labeled data. It can behave well in detecting abnormal data that have been trained, while failing to do so with abnormal data that the model has not encountered.  
The proposed algorithm first used the iForest algorithm to calculate the anomaly score of the data, and then selected the data with a lower anomaly score to train the autoencoder model, which avoided the worse detection effect caused by the abnormal data in the training set. The reconstruction error of normal data and abnormal data became larger, so it was easy to distinguish normal data from abnormal data.
## 3 Results and Discussion  
### Case Study 1: Paintings  
#### 3.1.1 Microscopic Observation  
The original pictorial surfaces, the area of pictorial integrations (localized through an initial observation under UV LEDs), the applied protective layer, the surface morphology, the overlap between continuous layers, and the characteristic craquelure were preliminarily examined and documented using a portable digital microscope. The following were observed in Painting 1: (a) the original brown pictorial layer was compromised of severe thinning; (b) the original canvas and a trace of reddish ground layer in correspondence with a color lacuna; (c) a pink-reddish pictorial layer along the craquelure fractures with minimal retouching; and (d) a UVF image confirmed contours of thin retouchings and the presence of recent protective and minimal residues of an older varnish (Figure 3A-D). In the case of Painting 2 (Figure 3E-H), there was also an area where the original pictorial layers were covered by a non-original protective layer and more recent pictorial retouching (both observable under UV LED sources). The darker portions (brown) were attributable to residues of the original protective layers (solubilized and not completely removed before the application of the current thick layer of varnish, as shown in Figure 3E,F). Figure 3G shows retouching on the original surface. It was highlighted under UV LEDs (the dark area in Figure 3H); a blue-white fluorescence of the thick protective layer was also documented.  
Figure 3: Digital optical microscopy images on selected areas of Painting 1 (**A–D**) and Painting 2 (**E–H**). (**A,B**): areas of the original painting, where the reddish-brown ground is also evident in some portions affected by thinning, and the original canvas is visible in correspondence of a lacuna; (**C**): the large fractures of the craquelure are retouched with thin pictorial integrations evident even under UVF (**D**); (**E,F**): original painting with traces of both original and non-original superficial protective varnish. In particular, the darker portions (brown) in (**F**) are attributable to residues of the original protective layer (solubilized and not completely removed before the application of the current thick layer of protective varnish); (**G,H**): area showing both original and non-original pictorial brushstrokes. The repainting visible on the right side of both images is accentuated above all in H through the use of UV (dark area).  
In both paintings, preliminary observations using the optical microscope revealed several previous restorations. These substantially altered the correct reading of the original pictorial surfaces, as confirmed by the following multispectral investigations.
# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
###### Abstract  
In estimating the global carbon cycle, the net ecosystem exchange (NEE) is crucial. The understanding of the mechanism of interaction between NEE and various environmental factors of ecosystems has been very limited, and the interactions between the factors are intricate and complex, which leads to difficulties in accurately estimating NEE. In this study, we propose the A-DMLP (attention-deep multilayer perceptron)-deep learning model for NEE simulation as well as an interpretability study using the SHapley Additive exPlanations (SHAP) model. The attention mechanism was introduced into the deep multilayer perceptual machine, and the important information in the original input data was extracted using the attention mechanism. Good results were obtained on nine eddy covariance sites in China. The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. The SHAP method was used to investigate the relationship between the input features of the A-DMLP model and the simulated NEE, and to enhance the interpretability of the model. The results show that the normalized difference vegetation index, the enhanced vegetation index, and the leaf area index play a dominant role at most sites. This study provides new ideas and methods for analyzing the intricate relationship between NEE and environmental factors by introducing the SHAP interpretable model. These advancements are crucial in achieving carbon reduction targets.  
n
## 2 Data and Methods
### Data Source and Preprocessing  
#### 2.2.1 Remote Sensing Data Acquisition and Preprocessing  
The remote sensing data come from the MODIS product of NASA ([[http://search.earthdata.nasa.gov/](http://search.earthdata.nasa.gov/)]([http://search.earthdata.nasa.gov/](http://search.earthdata.nasa.gov/))) (accessed on 31 March 2022). The MODIS satellite was launched in 1999, and most of the MODIS data products started in 2000. The orbit number of the remote sensing satellite is h27v05, and the data format is hdf format. The surface reflectance data were obtained from the MOD09A1 product with a temporal resolution of 8 d and a spatial resolution of 500 m; NDVI and EVI data were obtained from the MOD13Q1 vegetation index product with a temporal resolution of 16 d and a spatial resolution of 250 m; LAI (leaf area index) data were obtained from the MOD15A2H product with a temporal resolution of 8 d and a spatial resolution of 500 m; and surface cover type data were obtained from the MCD12Q1 product with a temporal resolution of 1 a and a spatial resolution of 500 m.  
By using the MRT (MODIS Reprojection Tool) software batch process, the image projection was converted to the WGS84 projection, the resampling method used was the nearest neighbor natural method, the resampling resolution was 250 m, and the data format was converted to geotiff format. The nearest neighbor natural method was used for any resampling required to go from coarse to high resolution to limit any loss of information [32].  
The land surface albedo calculation was carried out by the method of Liang [33] and the Modis algorithm formula is as follows:  
\[\text{Ashort}\,=0.160a_{1}+0.291a_{2}+0.243a_{3}+0.116a_{4}+0.112a_{5}+0.081a _{7}-0.0015 \tag{1}\]  
Figure 1: Location of the Yiluo River Basin.  
Monthly NDVI and LAI data were obtained by the maximum value synthesis method; monthly EVI and surface albedo data were obtained by the mean value method.
# Buffer Allocation via Bottleneck-Based Variable Neighborhood Search
## 3 Solution Methodology
### Notations  
This section presents the notations used in the proposed algorithm:  
* \(i\): A node that models a server or buffer.
* \(n_{i}\): Number of jobs at node \(i\).
* \(\pi(n_{i}=k)\): Steady-state probability of \(k\) jobs being at node \(i\).  
* \(\lambda_{i}\): External Poisson arrival rate at node \(i\); i.e., the average number of jobs that enter a production line from node \(i\) per unit time.
* \(\mu_{i}\): Exponential mean service rate at node \(i\); i.e., the average number of jobs that leave node \(i\) per unit time.
* \(j\): Number of buffer updates.
* \(T_{i}\): Throughput at which jobs leave node \(i\) per unit time.
* \(T_{hig}\): Temporary highest throughput rate.
* \(T_{hig}^{j}\): Obtained highest throughput rate at \(j\)th buffer update.
* \(T_{sol}\): Throughput in the solution.
* \(N_{hig}^{j}\): Buffer size corresponding to the obtained highest throughput rate at \(j\)th buffer update.
* \(N_{sol}\): Buffer size in the solution.
* \(B_{hig}^{j}\): Buffer configuration vector corresponding to the obtained highest throughput rate at the \(j\)th buffer update.
* \(B_{sol}\): Buffer configuration vector in the solution.
* \(C_{ori}\): Search times of the initial neighborhood.
* \(C_{add}\): Search times of an additional neighborhood.
* \(V_{bot}\): Number of nodes that may be the bottleneck.
* \(V_{mer}\): Number of merging nodes.
* \(V_{spl}\): Number of splitting nodes.
* \(\beta_{i}\): Active probability index.
# Examination of Viscosity Effect on Cavitating Flow inside Poppet Valves Based on a Numerical Study
## 4 Discussion
### Viscosity Effect on Vortex Cavitation and Potential Core
#### 4.1.3 Discussion on Viscosity Effect on Vortex Cavitation and Potential Core
poppet valves with a chamfered valve seat as exhibited in Figures 7a and 9f, the thin layered vortex even triggers a thin cavity, probably due to the imposed chamfered groove, which confines the viscous diffusion of the jet potential core and contributes to an enhanced shearing intensity within the wall shearing layer. However, due to the diverging potential core and limited thickness of wall shearing layer, the thin layered vortex fails to achieve a size comparable to the counterpart in the oil case. In contrast, in the oil-flow case, the wall shearing layer has a thickness comparative to the free shearing layer. And the mild laminar-turbulent transition process conduces to the gradual growth of the vortex ring within the free shearing layer into larger size comparable to the potential core thickness before saturation, which facilitates the vortex development across the potential core at the wall shearing side. Therefore, the two vortexes at either side of potential core exert an interactive effect to each other, and constitute the paired configuration across the potential core, which is responsible for the waving pattern of the potential core in the oil-flow case. Such an interaction could be explained according to Kutta-Joukowski theorem [21]. The counter-rotating vortexes at either side of the potential core give rise to the opposite forces exerted on the two vortexes and further to the produced waving pattern. The persistent presence of paired configuration of vortex cavitation in oil-flow case suggests that such discrepancy is a common flow phenomenon as a result of the viscosity effect, irrespective of the valve seat structure. Figure 9 also includes a sensitivity study concerning viscosity effect on flow dynamics such as jet potential core pattern and vortex structure organization. As shown in Figure 9g, the waving pattern is created at kinematic viscosity of \(2.2\times 10^{-5}\), and obvious paired structure appears at kinematic viscosity of \(3.2\times 10^{-5}\).
# Architecting and Deploying IoT Smart Applications: A Performance-Oriented Approach
## 2 Background and Related Work
### IoT Architectures and Platforms
These platforms may use multiple protocols and standards or even proprietary solutions. This plethora of platforms leads to interoperability problems since developers must adapt their applications to each platform, using specific API, information models, and protocols [25]. Thus, standard interfaces are needed to fulfill this interoperability issue among IoT applications and heterogeneous IoT platforms. Also, security requirements must be addressed, such as authentication, authorization and access control, and secure communication [26; 27]. FIWARE [28] is an open-source IoT platform that has been attracting widespread attention in the last years, comprised of a series of generic enablers (GEs) that provide different services. A combination of GEs builds different applications that exchange data through a REST API based on OMA NGSI [29]. Entities and their attributes represent the key aspect of the FIWARE NGSI context management information model. Orion is the main FIWARE GE, a publish/subscribe context broker and an efficient data distributor, providing an interface where producers publish entities that are further notified to consumers. Orion only stores the last entity version published so that it must work together with other GEs to keep historical data. The IoT Agent GE translates sensor data in different formats to NGSI and publishes it in Orion. There are different implementations of the IoT Agent, where each one maps different IoT communication protocols and data models to NGSI. The standard FIWARE IoT Agent receives MQTT data from the sensors formatted in the Ultralight 2.0 protocol [30]--a lightweight text-based protocol aimed to constrained devices and communications where the bandwidth and device memory may be limited resources. Currently, the official FIWARE IoT Agent implementation that supports LoRaWAN communication is unstable. Therefore, we developed our own version of IoT Agent capable of mapping LoRaWAN messages in the NGSI format.
# Multi-Term Attention Networks for Skeleton-Based Action Recognition
## 5 Experiments and Analysis
### Experimental Results
#### 5.4.3 Northwestern-UCLA Dataset Results  
According to Table 9, we can see that our proposed MTANs achieve the best performance of 93.73% on the Northwestern-UCLA dataset. The previous advanced model Ensemble TS-LSTM [8] extracts short-term, medium-term, and long-term temporal dynamics respectively, similar to our temporal feature extraction structure. However, our method has a 4.51% improvement compared to [8]. This shows that the addition of CNNs is effective for spatial feature extraction of skeleton sequences.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **CS Acc. (\%)** & **CV Acc. (\%)** \\ \hline STA-LATM (AAAI2017) [17] & 73.4 & 81.2 \\ C-CNN + MTLN (CVPR 2017) [6] & 79.57 & 84.83 \\ Ensemble TS-LSTM (ICCV 2017) [8] & 74.6 & 81.25 \\ Two-stream RNN (CVPR 2017) [29] & 71.30 & 79.50 \\ CNN + Motion + Trans (ICMEW2017) [22] & 83.2 & 89.3 \\ STGC (AAAI 2018) [30] & 74.85 & 86.28 \\ ST-GCN (AAAI 2018) [31] & 81.50 & 88.30 \\ Beyond Joints (TIP 2018) [32] & 79.5 & 87.6 \\ MAN (IJCAI 2018) [9] & 83.01 & 90.66 \\ DPRL (CVPR 2018) [18] & 83.50 & 89.8 \\ SR-TSL (ECCV 2018) [33] & 84.8 & 92.4 \\ HCN (IJCAI 2018) [34] & 86.5 & 91.1 \\ SkeleMotion(AVSS 2019) [35] & 69.6 & 80.1 \\ TSRJI (SIBGRAP 2019) [36] & 73.3 & 80.3 \\ VA-NN (TPAMI 2019) [37] & 79.4 & 87.6 \\
2s-SDGCN (ICCV Workshop 2019) [38] & 83.04 & 90.33 \\ Motif-STGCN (AAAI 2019) [39] & 84.2 & 90.2 \\ \hline MTANs & 84.74 & 93.23 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison on the NTU RGB+D dataset with CS evaluation and CV evaluation in accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Acc. (\%)** \\ \hline Skeleton joint features (CVPR 2013) [41] & 87.90 \\ Histograms of 3D joints (IEEE 2012) [26] & 90.92 \\ Elastic functional coding (CVPR 2015) [42] & 94.87 \\ ST-LSTM + Trust Gate (ECCV 2016) [15] & 95.00 \\ Geometric features (WACV 2017) [43] & 95.96 \\ Ensemble TS-LSTM (ICCV 2017) [8] & 96.97 \\ GCA-LSTM (CVPR 2017) [40] & 99.00 \\ DPRL (CVPR 2018) [18] & 98.50 \\ MAN (IJCAI 2018) [9] & 99.00 \\ \hline MTANs & 99.26 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison on the UT-Kinect Action dataset in accuracy.
# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 3 Materials and Methods
### Article Selection and Filtering  
A total of 3021 articles were retrieved according to the search procedure. After the removal of 1012 duplicate articles, 2009 articles were left for the eligibility screening. Following the screening of articles for eligibility using the inclusion and exclusion criteria and evaluation of abstracts, 1965 articles were excluded (articles not written in English: 3, other exclusion criteria: 1962), leaving behind 44 articles for further assessment. The abstract evaluation specifically targeted the identification of articles that utilised computational analysis of EEG data collected from rodent models of epilepsy. After reading the full text, 23 studies were discovered to have used nonmachine learning and deep learning techniques. These studies were excluded, and 21 articles based on ML/DL techniques met the eligibility criteria. One article from the reference list was also found to be relevant. The articles were independently filtered by two researchers. A total of 22 papers were considered eligible and included in this review study.  
* Inclusion criteria: 1. Conference papers published by ACM, IEEE, or Springer; 2. Published between 1 January 1994 and 1 January 2024; 3. Focused on automatic seizure detection and prediction with machine learning and deep learning techniques; 4. The study is conducted using EEG on rodent models of epilepsy; 5. Full text available; 6. The study reported in the article is empirical.
* Exclusion criteria: 1. The study uses EEG from humans or other animals, not rodents; 2. Full text is inaccessible; 3. Literature review studies, books, short papers/abstracts; 4. The study is focused on other neurological disorders (Alzheimer's, dementia, sleep disorder, etc.); 5. The study uses other clinical methods of determining epilepsy (e.g., genomics); 6. Studies not written in English; 7. Studies focusing on clinical studies only; 8. Studies performed using other nonmachine or deep learning techniques; 9. Journals not listed in Journal Citation Report (JCR).
* D1: The aim of the article.
- D2: The rodent models of epilepsy analysed.
- D3: The epileptiform discharge recognition task: detection or prediction of seizures.
- D4: The feature engineering techniques adopted.
- D5: Employed ML or DL technique, either classical or a combination of techniques and evaluation result.
- D6: The implementation of the ML or DL models.
# Identical Parallel Machine Scheduling Considering Workload Smoothness Index
## 4 Theoretical Analysis  
### NP-Hardness  
For problem \(P_{m}||SI\), we prove its NP-hardness via reduction from the PARTITION problem.  
**Theorem 2**.: _Problem \(P_{m}||SI\) is NP-hard._  
Proof.: We prove the theorem by reducing PARTITION to the considered problem. We first phrase the classical PARTITION problem below.  
**PARTITION**: Given \(n\) positive real numbers \(S=\{a_{1},a_{2},\ldots a_{n}\}\) with \(k=\frac{1}{2}\sum_{j=1}^{n}a_{j}\), do there exist two disjoint subsets \(S_{1}\), \(S_{2}\) such that \(S_{1}\bigcup S_{2}=S\) and \(\sum_{j\in S_{1}}a_{j}=\sum_{j\in S_{2}}a_{j}=k\)?  
For problem \(P_{m}||SI\), we construct a job input instance with \(n+m-2\) jobs that are to be processed on \(m\) (\(2\leq m\leq n\)) machines. Let \(p_{j}=a_{j}\) for \(1\leq j\leq n\), and \(p_{j}=k\) for \(n+1\leq j\leq n+m-2\). Hence, \(\sum_{1\leq j\leq n}p_{j}=2\cdot k\) and \(\sum_{1\leq j\leq n+m-2}p_{j}=m\cdot k\). We claim that if there exists a YES answer to the PARTITION problem, implying the existence of two disjoint sets \(S_{1},S_{2}\) with \(\sum_{i\in S_{1}}a_{j}=\sum_{j\in S_{2}}a_{j}=k\), then there is an optimal solution to problem \(P_{m}||SI\), in which the first \(n\) jobs are partitioned into two job sets with the same total processing time equal to \(k\) and assigned to two machines with the same workload of \(k\). For the remaining \(m-2\) jobs, each of them is assigned to one of the remaining machines. As shown in Figure 3, all the machines are of the same workload of \(k\) and \(SI=0\) in the optimal solution.  
On the other hand, if there exists an optimal solution to problem \(P_{m}||SI\), then we conclude that the first \(n\) jobs must be partitioned into two job sets with the same total processing time. With \(p_{j}=a_{j}\) for \(1\leq j\leq n\), it indicates the existence of two disjoint subsets \(S_{1},S_{2}\) with \(\sum_{j\in S_{1}}a_{j}=\sum_{j\in S_{2}}a_{j}=k\). The proof is completed.