esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions
**Glossary**  
**Notations**  
A area  
A\({}_{\text{L}}\) area of the laminar flame front  
A\({}_{\text{T}}\) area of the turbulent flame front  
C\({}_{\text{f}}\) friction coefficient  
C\({}_{\text{P}}\) pressure loss coefficient  
Dx  
discretization length  
D equivalent diameter  
D\({}_{3}\) fractal dimension--tuning constant of combustion model  
E total internal energy per unit mass  
f\({}_{\text{d}}\) decay function of tumble  
H total enthalpy per unit mass/heat transfer coefficient  
K turbulent kinetic energy  
K mean flow kinetic energy  
L\({}_{\text{max}}\) maximum flame wrinkling scale  
L\({}_{\text{min}}\) minimum flame wrinkling scale  
M mass  
\({}_{\text{n}}\) air mass flow rate  
m\({}_{\text{b}}\) burned mass  
m\({}_{\text{EGR}}\) EGR mass flow rate  
N number of boundaries  
P pressure  
P dissipation rate of mean flow kinetic energy  
Q\({}_{\text{w}}\) heat exchanged through wall  
R gas constant  
S\({}_{\text{L}}\) laminar flame speed  
T time  
t\({}_{\text{T}}\) characteristic timescale of tumble  
T temperature  
T\({}_{\text{m}}\) tumble momentum  
U velocity at boundary  
V volume  
v\({}_{\text{pm}}\) mean piston speed  
x\({}_{\text{crev}}\) fraction of combustion chamber volume  
**Greeks**  
A temperature exponent in laminar flame speed formulation  
B pressure exponent in laminar flame speed formulation  
\(\delta_{\text{L}}\) laminar flame thickness  
E dissipation rate of turbulent kinetic energy \begin{tabular}{l l} \(\
u_{t}\) & turbulent viscosity \\ \(\rho_{u}\) & density of unburned gas \\ \(\Phi\) & equivalence ratio \\
**Subscripts** & \\ B & burned zone \\ Crev & crevice \\ Cyl & cylinder \\ Inc & incoming \\ L & laminar \\ Max & maximum \\ Min & minimum \\ Out & outcoming \\ Quenc & quenching \\ Ref & reference \\ T & turbulent \\ Tum & tumble \\ U & unburned zone \\ W & wall \\
**Acronyms** & \\
0D/1D/3D & zero-/one-/three-dimensional \\ A/F & air-to-fuel \\ AFTDC & After Top Dead Center \\ ANFIS & Adaptive Neuro-Fuzzy Systems \\ ANN & Artificial Neural Network \\ CAD & crank angle degree \\ CFD & computational fluid-dynamic \\ CI & compression ignition \\ CR & compression ratio \\ ECU & Engine control unit \\ EGR & exhaust gas recirculation \\ EIVC & Early Intake Valve Closure \\ EVC & Exhaust Valve Closure \\ EVO & Exhaust Valve Opening \\ FE & finite element \\ HCCI & homogeneous charge compression ignition \\ HP & high pressure \\ ICE & internal combustion engine \\ IMEP & indicated mean effective pressure \\ ISFC & Indicated Specific Fuel Consumption \\ IVC & Intake Valve Closure \\ IVO & Intake Valve Opening \\ KLSA & knock limited spark advance \\ LFS & laminar flame speed \\ LP & low pressure \\ LTC & Low-Temperature Combustion \\ MBT & maximum brake torque \\ MFB 10\% & 10\% of mass fraction burned \\ MFB 50\% & 50\% of mass fraction burned \\ MFB 10–50\% & combustion core duration \\ MON & motor octane number \\ NG & natural gas \\ PID & proportional integral derivative \\ PFI & port fuel injection \\ PMEP & pumping mean effective pressure \\ RANS & Reynolds Averaged Navier Stokes \\ ROHJR & rate of heat release \\ \end{tabular}
# Associating Images with Sentences Using Recurrent Canonical Correlation Analysis
## 4 Experimental Results
### Image and Sentence Matching
To evaluate the cross-dataset generalization of the proposed model, we use the train splits of Flickr30K and Microsoft COCO datasets to train two models: RCCA\({}^{*}\)-f30k and RCCA\({}^{*}\)-coco, respectively, and then perform test on the test split of Flickr8K dataset. We can see that either RCCA\({}^{*}\)-f30k or RCCA\({}^{*}\)-coco achieves worse performance than the original RCCA\({}^{*}\). It mainly results from the fact that the image and sentence contents are quite different between the two datasets. RCCA\({}^{*}\)-coco performs much better than RCCA\({}^{*}\)-f30k, because the train split of Microsoft COCO has a larger size than that of Flickr30K. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Image Annotation**} & \multicolumn{4}{c}{**Image Retrieval**} & \multirow{2}{*}{**Sum**} \\ \cline{2-2} \cline{4-9}  & **R@1** & **R@5** & **R@10** & **Med \(r\)** & **R@1** & **R@5** & **R@10** & **Med \(r\)** \\ \hline DeViSE [1] & 4.5 & 18.1 & 29.2 & 26 & 6.7 & 21.9 & 32.7 & 25 & 113.1 \\ SDT-RNN [5] & 9.6 & 29.8 & 41.1 & 16 & 8.9 & 29.8 & 41.1 & 16 & 160.3 \\ RVP (T+I) [6] & 12.1 & 27.8 & 47.8 & 11 & 12.7 & 33.1 & 44.9 & 12.5 & 178.4 \\ Deep Fragment [2] & 14.2 & 37.7 & 51.3 & 10 & 10.2 & 30.8 & 44.2 & 14 & 188.4 \\ DCCA [3] & 16.7 & 39.3 & 52.9 & 8 & 12.6 & 31.0 & 43.0 & 15 & 195.5 \\ NIC [47] & 17.0 & - & 56.0 & 7 & 17.0 & - & 57.0 & 7 & - \\ DVSA (BRNN) [10] & 22.2 & 48.2 & 61.4 & 4.8 & 15.2 & 37.7 & 50.5 & 9.2 & 235.2 \\ MNLM [7] & 23.0 & 50.7 & 62.9 & 5 & 16.8 & 42.0 & 56.5 & 8 & 251.9 \\ LRCN [48] & - & - & - & - & 17.5 & 40.3 & 50.8 & 9 & - \\ m-RNN [45] & 35.4 & 63.8 & 73.7 & 3 & 22.8 & 50.7 & 63.1 & 5 & 309.5 \\ FV (HGLMM) [4] & 34.4 & 61.0 & 72.3 & 3 & 24.4 & 52.1 & 65.6 & 5 & 309.8 \\ FV \({}^{*}\)[4] & 35.0 & 62.0 & 73.8 & 3 & 25.0 & 52.7 & 66.0 & 5 & 314.5 \\ m-CNN-st [8] & 27.0 & 56.4 & 70.1 & 4 & 19.7 & 48.4 & 62.3 & 6 & 283.9 \\ m-CNN \({}^{*}\)[8] & 33.6 & 64.1 & 74.9 & 3 & 26.2 & 56.3 & 69.6 & **4** & 324.7 \\ RTP \({}^{*}\)[9] & 37.4 & 63.1 & 74.3 & - & 26.0 & 56.0 & 69.3 & - & 326.1 \\ \hline \hline
**Ours:** & 27.5 & 53.3 & 66.9 & 5 & 20.9 & 46.7 & 58.8 & 7 & 274.1 \\ RCCA-na & 34.4 & 61.6 & 71.2 & 3 & 23.9 & 51.1 & 61.8 & 5 & 304.0 \\ RCCA-fc & 32.2 & 60.4 & 72.3 & 3 & 23.7 & 53.1 & 65.0 & 5 & 306.7 \\ RCCA-lc & 32.2 & 61.2 & 72.4 & 3 & 23.9 & 53.4 & 65.8 & 5 & 308.9 \\ RCCA-ac & 36.0 & 65.8 & 75.6 & 3 & 25.8 & 53.9 & 65.7 & 5 & 322.8 \\ RCCA \({}^{*}\) & **39.3** & **68.7** & **78.2** & **2** & **28.7** & **57.2** & **69.8** & **4** & **341.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of image annotation and retrieval on the Flickr30K dataset (\({}^{*}\) indicates ensemble or multi-model methods, and \({}^{\dagger}\) indicates using manual annotations).
## 1 Introduction
Different features extracted from sMRI data have been widely evaluated for MCI conversion prediction: gray matter (GM) signal intensities [14], histogram-based features generated from individual connectivity networks [15], region of interest (ROI) [16], and sMRI measurement of the hippocampus [17]. The main objective of feature selection is to find an informative subset of features with the aim of improving prediction accuracy and avoiding the course of dimensionality. Many studies were conducted using different feature selection methods for MCI conversion prediction. Filter, wrapper, and embedded methods are the main three types of feature selection methods [18]. Filter-based methods analyze the feature worth based on typical characteristics of data without using a learning algorithm. Wrapper methods use the output of a classifier to evaluate the importance of features. Applying the wrapper methods leads to better accuracy yet a higher computational cost and time requirement [18]. Embedded methods have the advantages of both methods [19]. Embedded methods such as sparse learning-based methods use learning algorithms in the selection process [18]. The least absolute shrinkage and selection operator (LASSO) was used in [20]. A multi-modal feature selection algorithm using feature correlation and feature structure fusion was applied in [21]. Three feature selection methods, a random subset feature selection algorithm, minimal redundancy maximal relevance, and a sparse linear regression feature selection algorithm were applied in [22]. The last stage of an MCI conversion prediction framework is classification and evaluation. Many algorithms are used for MCI conversion prediction, such as support vector machine (SVM), k-nearest neighbor [23], decision trees [24], and random forest [25]. Among all classification methods, the SVM classifier [26] is widely used for AD detection due to its high accuracy compared to other classifiers. In this study, we proposed a new MCI conversion prediction framework based on the 3D-Zernike Moment (3D-ZM) model and an improved dynamic particle swarm optimization (IDPSO) algorithm. The 3D-ZM model generates statistical features (e.g., shape, texture, and symmetry information) from 3D-sMRI scans. These statistical features can be considered a lower-dimensional feature space which are representing sMRI images. IDPSO is a meta-heuristic algorithm that can be used as a robust feature selection technique to find the optimal feature subset. We assessed the proposed MCI conversion prediction framework on a large set of sMRI data at the baseline from the ADNI dataset by means of SVM as the classifier. We will show that the proposed MCI conversion prediction framework has a strong capability to identify MCI patients who are at risk of conversion to the AD stage using only baseline MRI data.
## 3 Data and Method
For spatial analysis, the Portland metropolitan area was divided into two subareas: a CBD area that covered the Portland Downtown area and a non-CBD area. The CBD area is the regional center of employment, shopping, and recreation destinations and witnessed the most improvement in transit, biking, and walking infrastructure between 1994 and 2011. A CBD dummy variable was coded for HBW trips: if either the origin Traffic Analysis Zone (TAZ) or the destination TAZ of a trip was located inside the CBD area, the trip was coded as a CBD trip (CBD = 1), otherwise it was coded as a non-CBD trip (CBD = 0). There were 84.5% CBD trips and 15.1% non-CBD trips in the final dataset (Table 1). We conducted both aggregate and disaggregate analyses on the 1994 and 2011 surveys. For the aggregate analysis, descriptive analysis and \(t\)-tests were conducted to test the constant commuting time hypothesis. For the disaggregate analysis, regression analysis was performed to examine how factors influenced the commuting time at the trip level. To avoid multicollinearity between independent variables, the Variance Inflation Factor (VIF) was calculated for the independent variables. Variables with a VIF larger than 5 were excluded in the regression analysis. Commuting time was the dependent variable in the regression model; it is a continuous variable and meets a normal distribution. Multiple linear regression models were estimated to examine how factors influenced the commuting time at the linked trip level. Multiple linear regression is a multivariate statistical technique to model the relationship between one dependent variable and two or more independent variables. The estimates of multiple linear regression models illustrate how the independent variables quantitatively influence the dependent variable. A violation of the ordinary least squares (OLS) assumptions was diagnosed, and the results indicate that the models are robust for valid inferences.
# Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent
## 3 Framework for Generic Blue Agent Training
### Framework's Component Details
#### 3.1.3 Data Collection--Observations and Action Tuples  
The proposed framework employs supervised learning to train a generic blue agent for ACOs. Since supervised learning requires training data, the framework includes a data collection mechanism. Initially, a separate blue agent is trained against each red agent using RL, allowing the blue agent to learn the best possible actions based on the current network state/observations against its respective red agent. Similarly, the generic blue agent needs to learn optimal actions based on various network states and also identify the type of red agent attacking the network.  
To train the generic blue agent, the framework collects a sufficient number of observation-action tuples by running scenarios where each trained blue agent defends against its corresponding red agent. This process should be repeated for all trained blue agents as described in Section 3.1.2. The goal is to gather enough observation-action tuples from each trained blue agent to compile a comprehensive dataset. This dataset can then be used to train a generic blue agent capable of identifying the type of red agent attacking the network and learning to take appropriate actions. The collected observation-action tuples should be stored for further processing. Algorithm 1 outlines the data collection process.  
```
1 Input:\(blue\_agents\), red\(agents\);
2 Output:\(list\_of\_obs\_actions\_files\);
3\(list\_length=len(blue\_agents)\);
4\(i=0\);
5 for\(i<list\_length\)do
6\(new\_tuples\_file=create\_file()\);
7\(i++\);
8\(episode=0\);
9\(env=create\_aco\_env(blue\_agents[i]\), red\(agents[i])\);
10\(model=load\_trained\_model(blue\_agents[i]\), red\(agents[i])\);
11\(terminated=False\);
12\(obs=model\_reset(env)\);
13 forepisode\(<MAX\_EPISODES\)do
14 whilenot terminateddo
15\(action=model.predict(obs)\);
16\(prev\_obs=obs\);
17\(obs\), reward, terminated, \(info=env.step(action)\);
18\(new\_tuples\_file(prev\_obs,action)\);
19
20
21 end for
22
23 end for
24
25\(list\_of\_obs\_actions\_files.add(new\_tuples\_file)\);
26\(new\_tuples\_file.close()\);
27
28 end for
29 return\(list\_of\_obs\_actions\_files\);
```  
**Algorithm 1**Collection of Observation-Action Tuples
# Bottlenose Dolphins' Clicks Comply with Three Laws of Efficient Communication
## 2 Materials and Methods
### Statistical Analysis  
We grouped the collected dataset of clicks using the affinity propagation (AP) method [42]. Unlike the traditional k-means cluster analysis, AP does not require the number of clusters to be predetermined. Instead, by viewing each data point as a node in a network, the AP algorithm takes measures of similarity between pairs of data points as input. It recursively sends real-valued _messages_ along the network's edges until an optimal set of clusters and final exemplars are identified. The algorithm allows for the adjustment of the sensitivity or granularity of the clustering process by setting the input preference parameter or selecting a specific quantile of the input similarities. Using the _apcluster_ package in R [43], we first computed a similarity matrix for the amplitude data in the final dataset. Negative squared errors (Euclidean distance) were used as the similarity measure. On the conservative side, we set the initial input preference parameter value at 0.8, which calls for a higher degree of similarity and potentially a higher number of clusters. We consider the cluster size a good approximation for the occurrence frequency of the corresponding click types.  
To investigate whether the length of a sequence influences its frequency of use along the lines of [PERSON]'s law of brevity, we fitted the observed sequence-length distribution against theoretical gamma, Weibull, and log-normal distributions using the _fitdistrplus_ package in R. The fits were evaluated via the Kolmogorov-Smirnov (K-S) criterion, whereby a larger p-level value indicates a better fit, and by a visual inspection of the respective cumulative distribution function plots.  
To analyse the possible relation between the length of a click sequence and the duration of its components along the lines predicted by the Menzerath-Altmann law, we fitted linear mixed-effect models using the _lme4_ package [44] in R v.4.0.2 [45]. Click duration was included as a response variable, and sequence length was the only fixed factor. In addition, sequence code and recording session were included as random effects to account for repeated measurements. The original distribution of click durations showed a slight positive skew. Consequently, the dependent variable was square-root-transformed for the model; the model residuals were visually inspected for homoscedasticity and normal distribution via _qqplot_ and their distribution against the fitted values. We used the likelihood-ratio test to evaluate the significance of the entire model, which included the fixed and random factors, against the null model, which only had the random factors. The 95% confidence intervals (CIs) and \(p\)-values for the obtained estimates were computed using the Wald approximation.
## 3 Construction of Local and Global Approximations
### Local Approximation of High Order  
The accuracy of the local approximation depends on the size of space. The Legendre polynomials have excellent properties including orthogonality. The orthogonality can adjust the properties of coefficient matrix. The basis function \(L_{n}(x)\) of n degree will be defined on the interval \([-1,1]\) as  
\[L_{n}(x)=\frac{1}{2^{n}n!}\frac{\mathrm{d}^{n}}{\mathrm{d}x^{n}}(x^{2}-1)^{n} \tag{5}\]  
When \(n=0\), it is the above case (\(L_{ij}=1\)). The nodes are uniformly preset in the domain, and star points are located at different positions. The original Legendre polynomials should be moved or scaled to satisfy the boundary of mathematical patch, which can maintain the good properties of Legendre polynomials. Thus, the final high order NMM approximation is  
\[\phi^{*}=\sum_{ijk}w_{i}a_{ij}^{k}L_{ij}^{k} \tag{6}\]  
where \(k\) is the order of Legendre polynomials and \(a_{ij}^{k}\) is the degrees of freedom of the \(k\)th order.  
The higher order of the polynomials is used, the closer to real solution the approximation will be, due to polynomial base is dense in space \(C[a,b]\) (continue function space on interval \([a,b]\)).  
If discontinuity occurred as mentioned above, the new physical patches generated by old patches and discontinuities will inherit the old function of original patches. In many fields, however, the increased order may result in \"linear dependence\" (LD) issue, in which the global matrix is rank deficient even after sufficient constraints are coerced. Encouragingly, in this paper, no LD issue is incurred while obtaining high accuracy, which will be verified in follow section.
# Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
## 3 Methodology
### Air Quality Assessment
#### 3.3.5 Modeling Procedure  
The Key model settings for evaluating AQ at Ground-Level Concentrations (GLCs) for the study area are as follows:  
* Plume rise estimation: Briggs formulae, limited to the mixing layer.
* Buoyancy induced dispersion: Captures plume dispersion during ascent.
* Processing routine: Calms by default.
* Wind profile exponents: Default 'Irwin'.
* Terrain: Flat terrain computation
* Transformation and removal: No physiochemical transformations or pollutant removal assumed.
* Washout by rain: Not considered.  
These model settings ensured the model accurately reflected site-specific conditions. The emission data for SPM stemming from various phases of the mining operation, including excavation, and loading activities, were also documented (Table 4).  
Figure 5: (**a**) Mixing height of particulate matter during the day in the pre-monsoon season. (**b**) Standard deviation of the wind-direction fluctuations along with frequency of occurrence by stability classes A to F, as in Table 3. (**c**) Coefficients of the lateral and vertical dispersion in different stability classes.  
This table outlines both the operational parameters and the resulting emissions calculations for the proposed iron ore mine (IOM). Parameters such as 'ore production', 'total working days', 'operational hours', and 'activity rate' are inputs used to estimate emissions. 'Uncontrolled emission rate' and 'emission factor' are used to derive'source emission rate in area' and 'controlled emissions', which are the calculated emissions after applying a control efficiency of 90% thereby incorporating potential environmental impacts under typical operational conditions and implemented control measures.
###### Abstract  
Our research aim was to apply UV/Vis spectrophotometric techniques for the rapid monitoring of the quality of water sourced from on-farm root vegetable washing processes. To achieve this goal, the quality assessment of the washing water and wastewater at different stages of the technological processes was performed using physicochemical, biological, and UV/Vis absorbance measurements as well as statistical methods, such as principal component analysis (PCA) and partial least squares (PLS) regression. Limit values of UV/Vis absorbance at specific wavelengths were predicted in order to adapt them for routine testing and water quality monitoring at the farm packhouses. Results of the lab analyses showed, that the main problems of the water quality were caused by suspended solids (470-3400 mg L\({}^{-1}\)), organic substances (BOD\({}_{5}\) 215-2718 mg L\({}^{-1}\); COD 540-3229 mg L\({}^{-1}\)), nitrogen (3-52 mg L\({}^{-1}\)), phosphorus (1-6 mg L\({}^{-1}\)), and pathogenic microorganisms (TVC \(>\) 300 cfu mL\({}^{-1}\), _E. coli_ 5.5 \(\times\) 10\({}^{3}\)-1.0 \(\times\) 10\({}^{4}\) cfu mL\({}^{-1}\), intestinal enterococci 2.8 \(\times\) 10\({}^{2}\)-1.5 \(\times\) 10\({}^{4}\) cfu mL\({}^{-1}\), coliform bacteria 1.6 \(\times\) 10\({}^{3}\)-2.0 \(\times\) 10\({}^{4}\) cfu mL\({}^{-1}\)). Suspended solids exceeded the limit values by 10-50 times, organic matter by 10-25 times, dissolved organic carbon by 3-5 times, nitrogen by 3-7 times, total phosphorus by 3-12 times, and microorganisms by 3-10 times. UV/Vis limit values calculated were as follows: A210 nm--3.997-4.009 cm\({}^{-1}\), A240 nm--5.193-5.235 cm\({}^{-1}\), A254 nm--4.042-4.047 cm\({}^{-1}\), A320 nm--7.387-7.406 cm\({}^{-1}\), and A660 nm--3.937-3.946 cm\({}^{-1}\). UV/Vis measurements at A320 nm are proposed for the routine water quality monitoring.  
Keywords:on-farm packhouses; spectrophotometry; principal component analysis (PCA); partial least squares regression (PLS) +
Footnote †: journal: Applied Soft
## 4 Discussion
### Abiotic Factors
EC indicates the number of charged ions circulating in the water column, so the more ions (nutrients) circulating, the higher the value of conductivity [38,39]. In an aquaponic system, the fish feed is the main nutrient source [40]. Fish metabolize the feed, and their feces along with the uneaten food, become the nutrient source for plants through oxidation of the produced ammonia to nitrate by the nitrifying bacteria. It has been suggested that 80% of the required nutrients for plants can be provided by the fish feed [41]. The rest 20% concern nutrients that are absent or in low levels in fish feed, such as Ca, K and Fe, and should be supplemented either directly to the water or as foliar fertilization. Nitrogen can be absorbed by plants in two forms, nitrate or ammonium, depending on the concentration and the plant physiology [42]. In the present study, the low ammonium mean values and the gradual rise of nitrate levels (Figure 2) proved the efficiency of the filter in oxidizing the produced ammonia. During the 30-days study period, the mean nitrate concentration (mg/L) was 122.56 \(\pm\) 7.367, 120.82 \(\pm\) 5.911, 120.32 \(\pm\) 8.584 for the control, Fe, and Fe + K treatment, respectively. The daily supply of 13.47-14.03 g of fish feed provided efficient nitrogen for plant nutrition. According to [PERSON] et al. [43], leaf number and yield of the rocket are low with NH\({}_{4}\) nutrition, whereas they reach the highest values with the 50:50 NH\({}_{4}\):NO\({}_{3}\) ratio. In their study, water and N-use efficiencies increased in the rocket with the increase in NO\({}_{3}\)-N percentage in the nutrient solution. In our work, the mean Fe concentration (mg/L) for the 30-days study period was 3.61 \(\pm\) 0.338 for Fe treatment and 3.55 \(\pm\) 0.354 for Fe + K treatment, while the K concentration (mg/L) for the Fe + K treatment was 112.00 \(\pm\) 9.953. These values are close to the optimum target values of 5 mg/L for Fe and 120 mg/L for K.