esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbial Growth Dynamics in Minced Meat Enriched with Plant Powders
###### Abstract  
Plant powders with antimicrobial properties can be used in food manufacturing and must comply with the demands of consumers regarding microbiological safety, nutritional value, and sensory properties of foods. The present study aimed to assess the microbial growth inhibitory ability of different plant powders, including by-products of horticultural primary processing (e.g., pomace) in raw and cooked minced pork. The total counts of aerobic mesophilic bacteria, pseudomonads, yeasts, and moulds were studied to assess the microbial growth dynamics in meat samples. Additionally, for the plant powders, which were able to suppress the microbial growth in a total counts dynamics study, the growth potential of _Listeria monocytogenes_ in ready-to-eat (RTE) minced meat samples was estimated by challenge testing. The results showed that the most effective combinations of plant powders in raw minced pork, in relation to the total counts of microorganisms, were 3% apple+1% onion+2% blackcurrent berries (Apple+On+BCDer); 3% apple+1% garlic+2% tomato (Apple++Ga+Tom); and 3% apple+2% tomato+1% rhubarp petioles (Apple+Tom+Rubub). However, challenge tests revealed that some plant powders were unable to inhibit the growth of _L. monocytogenes_. The lowest _L. monocytogenes_ growth potential (\(\delta\) = 2.74 log cfu/g) was determined for cooked minced pork samples enriched with 2% rhubarb petioles, followed by Apple+On+BCDer (\(\delta\) = 3.63 log cfu/g) and Apple+Tom+Rubub (\(\delta\) = 3.74 log cfu/g). In minced pork samples without plant additives, the _L. monocytogenes_ growth potential was 7.30 log cfu/g. In conclusion, blends of plant powders may have good potential for developing meat products with acceptable microbiological quality.  
plant powders; minced meat products; microbial growth inhibition; food safety Article
# Comparative Studies on Nanocellulose as a Bio-Based Consolidating Agent for Ancient Wood
###### Abstract  
In this work, nanocellulose aqueous dispersions were studied as a bio-inspired consolidating agent for the recovery and conservation of ancient wood and compared with two of the most used traditional consolidants: the synthetic resins Paraloid B-72 and Regalrez 1126. The morphology of crystalline nanocellulose (CNC), determined using Scanning Electron Microscopy (SEM), presents with a rod-like shape, with a size ranging between 15 and 30 nm in width. Chemical characterization performed using the Fourier-Transform Infrared Spectroscopy (FT-IR) technique provides information on surface modifications, in this case, demonstrating the presence of only the characteristic peaks of nanocellulose. Moreover, conductance, pH, and dry matter measurements were carried out, showing also in this case values perfectly conforming to what is found in the literature. The treated wood samples were observed under an optical microscope in reflected light and under a scanning electron microscope to determine, respectively, the damage caused by xylophages and the morphology of the treated surfaces. The images acquired show the greater similarity of the surfaces treated with nanocellulose to untreated wood, compared with other consolidating agents. Finally, a colorimetric analysis of these samples was also carried out before and after a first consolidation treatment, and after a second treatment carried out on the same samples three years later. The samples treated with CNC appeared very homogeneous and uniform, without alterations in their final color appearance, compared to other traditional synthetic products.  
nanocellulose; cultural heritage; ancient wood; consolidation +
Footnote †: journal: selected
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 4 Applications of AI in Early Fault Diagnosis of Rotating Machinery
### Svm  
The SVM-based fault diagnosis is very similar to KNN-based methods. [PERSON] et al. [155] used statistical parameters and characteristic amplitude ratios of frequency bands as fault features, then the PCA was used to reduce the fault feature dimensions. The selected features were put into SVM for gear fault type identification. [PERSON] et al. [156] used the impact time frequency dictionary as fault features, and then SVM was trained for fault recognition. [PERSON] et al. [157] used band-pass filters and Hilbert transform to extract fault features and one-class SVM was used to discriminate between normal and faulty conditions. [PERSON] et al. [158] used statistical feature as input, and a two-layer structure consisted of support vector regression machine was used to recognize bearing fault patterns and track the fault sizes. [PERSON] et al. [159] proposed a method for wind turbine high-speed shaft bearings which used spectral kurtosis as fault features, and support vector regression for fault classification.  
Many researchers have applied SVM with adaptive decomposition techniques for bearing fault diagnosis in recent years. [PERSON] et al. [160] used EEMD to decompose the signal into IMFs, and multi-scale fuzzy entropy value of selected IMF was used to form the fault features. The SVM was used to diagnose the fault types and fault severities for motor bearing. [PERSON] et al. [161] used EMD to decompose the signal into IMFs, then the characteristic energy ratios of IMFs and other statistical parameters were used as input of SVM for gear tooth surface damage diagnosis. [PERSON] et al. [162] used WPD to denoise the signal, the fault features extracted by EEMD were put into SVM to detect small defects on roller bearings under different operating conditions. [PERSON] et al. [163] used CWT to decompose the signal, then put wavelet coefficients into a tree kernel-based SVM to identify bearing faults. [PERSON] et al. [164] used CWT and Hilbert transforms to extract fault information, then rough set theory (RST) and a genetic algorithm (GA) were used to refine the fault features. Finally, SVM was used  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Methodologies** \\ \hline [PERSON] et al. [150] & Symbolic Aggregate approximation + KNN \\ [PERSON] et al. [151] & Stransform + morphological pattern spectrum + KNN \\ [PERSON] et al. [152] & EEMD + hybrid binary bat + KNN \\ [PERSON] et al. [153] & Hidden Markov model + KNN \\ [PERSON] [154] & Filter bank + KNN \\ \hline \hline \end{tabular}
\end{table}
Table 10: Applications of k nearest neighbor (KNN) method in early fault diagnosis of rotating machinery.  
to detect six types of induction motor faults. [PERSON] et al. [165] applied a singular value decomposition (SVD) algorithm for feature extraction, and a multi-layer support vector machine was used for fault classification of induction motors. The above mentioned methods are summarized in Table 11.
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 8 Quantum Circuit Toolchain
### Circuit Specification and eDSL  
FQT is developed in the functional programming language Haskell [47]. In FQT, a circuit (Circ) is defined as a list of gates that comprise the quantum circuit. A core set of gates is supported as primitives. Custom gates can be defined by composition of primitives. Circuits are defined by functions which return a Circ type; list concatenation (the \(++\) operator in the code snippets below) is used to compose more complex circuits.  
At the level of our Haskell eDSL, qubits are given unique identifiers which are used as parameters for our circuit generation functions. Qu represents a single qubit identifier and QReg is an arbitrarily sized list of Qus. High-level operators allow for complex control patterns to be specified for a gate. control(s) takes one control or a list of controls to apply to a gate which follows. negControl(s) are the equivalents for negative controls. For a mix of controls and negative controls, the \(.==\) operator is provided to make specification easier. For example ([a,b,c].= \"011\") (x d) applies the \(X\) gate to qubit \(d\) only when \(a\) is 0 and \(b\) and \(c\) are 1. Additional convenience operators include an inverse operator.! which gives the inverse of a quantum circuit by iterating through all gates, finding their inverse, and returning them in reverse. This is particularly useful when using the uncomputation pattern.  
As a brief example of circuit specification in the eDSL, Figure 20 defines a specialised 4-qubit comparator circuit used to check if the input value is less than 9. Line 1 describes the type signature of the lt9q4 circuit generator, ascertaining it takes a quantum register QReg and a single qubit Qu, and returns a Circ. As QReg is an arbitrarily sized quantum register, the Line 2 pattern matches on its contents and tells the compiler to expect exactly four elements. Lines 3, 4, and 5 define and concatenate the three sub-circuits which form the comparator. Each of x, cnot, and \(.==\) are (informally) defined as 'circuit generators'. In general, a circuit generator takes any number of arguments and returns a Circ. High-level operators (including \(.==\) are defined as functions which take at least one Circ argument.  
The internal representation (IR) of a circuit defined in this way is simply a list of gates defined over some set of primitive gate types. Figure 21 shows the IR of the example 4-bit comparator, specialised for less-than-9.  
Figure 20: A specialised four-qubit less than 9 operator expressed in the FQT eDSL. On the right is the circuit diagram of the comparator for reference.  
[X test [],X test [\"r3\"],X r2 [],X r1 [],X r0 [], X test [\"r3\",\"r2\",\"r1\",\"r0\"],X r2 [],X r0 [],X r1 []]
## 2 Materials and Methods  
### Study Area  
This study was conducted in the Great Lakes region of Sub-Saharan Africa (GLR-SSA), occupying the surface area of approximately 6.3 million km\({}^{2}\). This region extends over the African Great Lakes countries including the Democratic Republic of Congo (DRC), Burundi, Rwanda, Tanzania, Kenya and Uganda in east-central Africa and Zambia, Malawi, Mozambique and Zimbabwe in southern Africa. The European Space Agency ClimateChange Initiative, Land Cover, ESA-CCI-LC [22], indicated that the major land cover types are: forest (~65.32%), cropland (~23.14%) and grassland (~7.42), while the other physical land cover (LC) types including built-up, wetlands, inland water and barren areas cover the remaining surface extent (~4.12%).  
Koppen-Geiger climate types indicated that this region comprises ~70% of the African equatorial/tropical (T = 25 \({}^{\circ}\)C, P = 1170 mm yr\({}^{-1}\)), ~20% of sub-tropical (T = 24\({}^{\circ}\)C, P = 535 mm yr\({}^{-1}\)) and ~40% of temperate zones (T = 18 \({}^{\circ}\)C, P = 286 mm yr\({}^{-1}\)mm). The Climate Research Unit Time Series (CRU TS) land surface temperature datasets (2000-2020) indicate that this region consists of ~25 \({}^{\circ}\)C, ~1200 mm and ~650 mm of the overall annual average temperature, precipitation and evapotranspiration (ET), respectively [23; 24]. This region also experiences four climatic seasons [25]: the two rainy seasons, namely, long rainy (late February to late May) and short rainy (end September to early December); and two dry seasons, namely, long dry (June-September) and short dry (mid-December to mid-February), respectively [26]. These two rainy seasons correspond to vegetation growing seasons due to the high precipitation occurrence [26; 27]. The topographical elevation varies between 200 m in the lowest lands and plateau and 5818 m above sea level in the upper mountains (i.e., Mount Kilimanjaro) (Figure 1).  
According to a soil atlas of Africa, the geospatial lithology and physical soil types of GLR-SSA are dominated by tropical and sub-tropical soil types features such as Acrisols (42%), Arenosols (19%), Chernozems (11%) and Greysol (4%); water bodies (8%); and other soil type features that make up the remaining 16% [28]. These diverse ecological patterns exert important influences on soil development and vegetation processes that may significantly contribute to carbon fluxes [26; 29].
# Unexpected Ferromagnetism--A Review
## 2 Examples of \(d^{0}\) Ferromagnetism
### Carbon Nanostructures
#### 2.3.1 Fullerenes  
Fullerenes were discovered in 1985 by [PERSON] et al. [55]. The most stable fullerene is C\({}_{60}\), and even though in its un-polymerized form it is considered to be diamagnetic, there have been reports of fullerene-based structures that exhibited ferromagnetism [56]. The first report of ferromagnetic behavior in the polymer-C\({}_{60}\) was observed in 1994 by [PERSON], when C\({}_{60}\) was ultrasonically dispersed in a dimethylformamide solution of polyvinylidene fluoride [57]. Since then, there have been numerous reports on fullerene-based structures with ferromagnetic behavior.  
It has been observed that the magnetic behavior exhibited in polymerized fullerenes is strongly related to certain preparation conditions, such as temperature, pressure and time [58]. For example, the appearance of ferromagnetic behavior was noticed in conditions where the fullerene structure is almost destroyed. This effect was believed to be correlated with intra-molecular defects [59]. The effect of pressure and temperature was also confirmed in a study on rhombohedral C\({}_{60}\) polymer by generating different degrees of ferromagnetic behavior [60]. Another mechanism of induced ferromagnetism was observed in C\({}_{60}\) crystal when irradiated by light in oxygen [61]. An interesting example of exhibited ferromagnetism is a system composed of Pd/C\({}_{60}\) bilayers. Intriguingly, neither C\({}_{60}\) nor Pd films are ferromagnetic; however, experimental and theoretical studies showed ferromagnetic behavior in stacked bilayers of Pd/C\({}_{60}\)[62].
## 2 Theory and Methods
### Variable Weight Theory  
The weight of various factors will change dynamically in the actual project [20]. The weights can be dynamically adjusted based on the measurement data using the state variable weight vector Si(X). The calculation of the state variable weight vector is:  
\[S_{i}(X)=\left\{\begin{array}{ll}e^{c_{1}(d_{1}-X)}+m-1&X\in[0,d_{1})\\ m&X\in[d_{11},d_{2})\\ e^{c_{2}(X-d_{2})}+m-1&X\in[d_{2},d_{3})\\ e^{c_{3}(X-d_{3})}+e^{c_{2}(d_{3}-d_{2})}+m-2&X\in[d_{3},d_{4}]\\ \end{array}\right. \tag{7}\]  
where, \(m\), \(c_{1}\), \(c_{2}\), and \(c_{3}\) are the parameters for weight adjustment, and \(d_{11}\), \(d_{22}\), \(d_{33}\), and \(d_{4}\) are the threshold for classifying the grades of each index li. [0, \(d_{i1}\)) is the penalty interval. The punishment intensity is inversely proportional to the index size. [\(d_{i1}\), \(d_{i2}\)) is the non-penalty and non-incentive intervals. The weight will not be punished or stimulated in this interval. [\(d_{i2}\), \(d_{i3}\)) is a weak excitation interval. The amplitude of the incentive increases as the value of the indicator increases in the interval. [\(d_{i3}\), \(d_{i4}\)] is a strong excitation interval and the excitation degree is the highest. The interval distribution is shown in Figure 5.  
The variable weight is determined as:  
\[w_{i}(X)=\frac{w^{\prime}_{i}S_{i}(X)}{\sum_{m=1}^{n}w^{\prime}_{m}S_{m}(X_{m})} \quad i=1,2,\cdots,n \tag{8}\]  
where \(w_{i}\) and \(w^{\prime}_{i}\) are the variable weight and constant weight of the evaluation index, respectively.
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
###### Abstract  
The consolidation of encryption and big data in network communications have made deep packet inspection no longer feasible in large networks. Early attack detection requires feature vectors which are easy to extract, process, and analyze, allowing their generation also from encrypted traffic. So far, experts have selected features based on their intuition, previous research, or acritically assuming standards, but there is no general agreement about the features to use for attack detection in a broad scope. We compared five lightweight feature sets that have been proposed in the scientific literature for the last few years, and evaluated them with supervised machine learning. For our experiments, we use the UNSW-NB15 dataset, recently published as a new benchmark for network security. Results showed three remarkable findings: (1) Analysis based on source behavior instead of classic flow profiles is more effective for attack detection; (2) meta-studies on past research can be used to establish satisfactory benchmarks; and (3) features based on packet length are clearly determinant for capturing malicious activity. Our research showed that vectors currently used for attack detection are oversized, their accuracy and speed can be improved, and are to be adapted for dealing with encrypted traffic.  
feature selection; network attack detection; supervised learning Article
# Adsorption of Ammonium, Nitrate, and Phosphate on Hydrochars and Biochars
## 3 Results and Discussion
### Adsorption of Ammonium
The Langmuir model demonstrates superior descriptive capabilities for the adsorption process of NH\({}_{4}\)+, NO\({}_{3}\)-, and PO\({}_{4}\)3- on the BCs, HCs, and AC, as evidenced by the higher R\({}^{2}\) values. Through the utilisation of both BCs and HCs, it was found in the study in [11] that the Langmuir isotherm model exhibited a higher degree of accuracy in describing NH\({}_{4}\)+ adsorption in comparison to the Freundlich isotherm model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**System**} & \multicolumn{8}{c}{**Material**} \\ \cline{2-9}  & & **BC300** & **BC500** & **BC700** & **HC30** & **HC180** & **AC** \\ \hline \multicolumn{9}{c}{Ammonium} \\ Freundlich & K\({}_{\text{F}}\) & 1.177 & 1.016 & 0.755 & 0.192 & 0.186 & 1.117 \\  & n & 2.737 & 2.700 & 2.502 & 2.187 & 2.134 & 2.778 \\  & R\({}^{2}\) & 0.886 & 0.846 & 0.896 & 0.866 & 0.944 & 0.886 \\ Langmuir & q\({}_{0}\) & 9.833 & 8.183 & 8.137 & 2.970 & 3.434 & 9.050 \\  & K\({}_{\text{L}}\) & 0.028 & 0.032 & 0.020 & 0.016 & 0.012 & 0.029 \\  & R\({}^{2}\) & 0.968 & 0.946 & 0.980 & 0.975 & 0.993 & 0.967 \\ \hline \multicolumn{9}{c}{Ammuite} \\ [PERSON] & K\({}_{\text{F}}\) & 0.036 & 0.073 & 0.091 & 0.033 & 0.093 & 0.093 \\  & n & 1.565 & 1.583 & 1.632 & 1.971 & 1.523 & 1.539 \\  & R\({}^{2}\) & 0.688 & 0.674 & 0.651 & 0.826 & 0.748 & 0.708 \\ [PERSON] & q\({}_{0}\) & 1.637 & 3.051 & 3.261 & 0.698 & 1.399 & 4.529 \\  & K\({}_{\text{L}}\) & 0.008 & 0.009 & 0.010 & 0.013 & 0.007 & 0.008 \\  & R\({}^{2}\) & 0.879 & 0.880 & 0.871 & 0.953 & 0.902 & 0.893 \\ \hline \multicolumn{9}{c}{Ammuite} \\ [PERSON] & K\({}_{\text{F}}\) & 0.119 & 0.272 & 0.301 & 0.033 & 0.098 & 0.477 \\  & n & 1.992 & 2.147 & 2.193 & 1.971 & 2.107 & 2.092 \\  & R\({}^{2}\) & 0.829 & 0.809 & 0.860 & 0.826 & 0.805 & 0.832 \\ [PERSON] & q\({}_{0}\) & 2.413 & 4.184 & 4.699 & 0.698 & 1.615 & 8.292 \\  & K\({}_{\text{L}}\) & 0.013 & 0.017 & 0.014 & 0.013 & 0.016 & 0.014 \\  & R\({}^{2}\) & 0.955 & 0.948 & 0.956 & 0.953 & 0.947 & 0.943 \\ \hline \end{tabular}
\end{table} 
Table 2: Langmuir and Freundlich isotherm parameters for the adsorption of ammonium, nitrate, and phosphate on BCs, HCs, and AC.
# Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks
## 2 The Proposed Algorithm
### The Training of AE and FCN
Let region \(m\) be the ground truth of the pixel \(y_{k}^{j}\). The estimated probabilities are trained by the proposed FCN network. The estimated probability is said to be accurate when  
\[P_{k}^{j}(m)=\max_{n=1,...,N}P_{k}^{j}(n). \tag{10}\]  
Based on the facts stated above, the corresponding loss function for the training of FCN is  
\[L=\sum_{k=1}^{K}\sum_{j=1}^{M}\log\Big{(}\frac{1}{P_{k}^{j}(m)}\Big{)}, \tag{11}\]  
where \(M\) is the dimension of \(Y_{k}\), and \(K\) is the number of training images. Clearly, the loss \(L\) will increase when \(P_{k}^{j}(m)\) does not meet the condition in (10). In fact, the loss function will penalize at each pixel \(y_{k}^{j}\) the deviation of \(P_{k}^{j}(m)\) below 1.0. Therefore, the training of FCN minimizing the loss function \(L\) is able to maximize \(P_{k}^{j}(m)\) for each \(k\) and \(j\). After the FCN is trained, given a test image \(Y\), the network is then able to produce \(P^{j}(n)\), the estimated probability of the \(j\)-th pixel \(y^{j}\) belonging to region \(n\). The test image \(Y\) is subsequently segmented to regions \(Y(1),...,Y(N)\), where  
\[Y(i)=\{y^{j}:P^{j}(i)=\max_{n=1,...,N}P^{j}(n)\},i=1,...,N. \tag{12}\]  
The operations shown in (12) can be viewed as the function \(G(Y)\) defined in (5). In addition to training, the validation is required for the avoidance of overfitting. In the proposed algorithm, the validation operates in conjunction with the training. However, both processes are based on different data sets. After the completion of each epoch during the training process, the values of the loss function for the training set and validation set are measured, respectively. We stop the training process only after the convergence of the loss function values for both the training set and validation set are observed. The samples in the training set and validation set are based on the data augmentation process presented in (8). However, the data set for testing in our experiments consists of real images acquired from a camera without data augmentation. Furthermore, the samples in the testing set are different from those in the training and validation sets. The effectiveness of the proposed algorithm can then be evaluated from the real images for defect detection.
## 4 Numerical Experiments and Results
### Example and Parameter Setting
#### 4.1.2 Parameter Setting  
The tolerance coefficients are set to 0.025, 0.05, 0.075 and 0.1. With the increase in the tolerance coefficient, the solution quality gradually decreases, the running time increases greatly and the solution timeliness worsens. When the tolerance coefficient is 0.025, the optimal value of the objective function value is 868.606, and the average running time is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category of** & **Weight Ratio** & **Meaning of Scenario** \\
**Scenarios** & **between Costs** & **whening Costs** \\ \hline S1 & \(w_{1}:w_{2}=1:0\) & Only the carbon tax policy is adopted. \\ S2 & \(w_{1}:w_{2}=0.75:0.25\) & The carbon tax policy is dominant, and the carbon trading policy is supplemented. \\ S3 & \(w_{1}:w_{2}=0.5:0.5\) & The carbon tax policy and the carbon trading policy are equal. \\ S4 & \(w_{1}:w_{2}=0.25:0.75\) & The carbon trading policy is dominant, and the carbon tax policy is supplemented. \\ S5 & \(w_{1}:w_{2}=0:1\) & Only the carbon trading policy is adopted. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cost weight ratios and implications of different mixed carbon policy scenarios.  
1019.405 s. The reduction rates of the tolerance coefficients are set to 0.05, 0.35, 0.65 and 0.95. When the decrease rate of the tolerance coefficient is 0.95, the solution quality is the best, and the optimal value, worst value and average value of the objective function value are all optimal. The gap in the solution timeliness is not large, and the maximum differences of the optimal value, the worst value and the average value of the running time are 86.6703 s, 100.2706 s and 30.0703 s, respectively. The maximum number of iterations in the initial solution generation and pre-optimization stage are set to integer values of 1, \(n/5\), \(2n/5\), \(3n/5\), \(4n/5\) and \(n\), where \(n\) is the number of customers. With the increase in the maximum number of iterations, the optimal value, worst value, average value, standard variance, range and range percentage of the objective function value gradually decrease, the quality and stability of the initial scheme improve and the diversity decreases.  
Therefore, considering the solution quality, solution timeliness and diversity of the initial scheme, the tolerance coefficient, the decline rate of the tolerance coefficient and the maximum number of iterations are, finally, set as 0.025, 0.95 and the integer of \(n/5\), respectively.  
Effectiveness and Optimization Ability Analyses of the Two-Stage VNS-TA Algorithm for Solving the Basic MCVRP