esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Amendments
#### 3.1.2 SEM and XRD Analysis of Steel Slag  
The SEM analysis illustrates (Figure 2A) the presence of numerous agglomerates on the surface of the steel slag, interspersed with a modicum of non-agglomerated entities, exhibiting an irregular disposition. A plethora of investigations affirms the stellar mesoporous adsorbent qualities of steel slag, characterized by robust affinities and efficacious removal rates for metallic ions [23]. The compositional scrutiny of steel slag, validated via XRD (Figure 2B), delineates predominant constituents in the form of oxygenated compounds of Fe, Ca, and Si, with preeminent phases encompassing FeO, CaCO\({}_{3}\), Ca\({}_{3}\)SiO\({}_{5}\), Ca\({}_{2}\)SiO\({}_{4}\), Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\), and SiO\({}_{2}\)[24]. The elevation in soil pH ostensibly ensues from the neutralizing influence of alkali compounds intrinsic to steel slag. Oxidative interactions of slag-borne  
Figure 1: SEM images of vermicompost (**A**); FTIR spectra of vermicompost (a) and vermicompost after Cu\({}^{2+}\) adsorption (b) (**B**).  
substances with water yield -OH moieties, which subsequently engage in reactions with heavy metal cations within the soil milieu, precipitating hydroxides. In acidic soils replete with Cu\({}^{2+}\), a profusion of copper sulfide emerges via chemical precipitation reactions, culminating in the formation of Cu(OH)\({}_{2}\)[25; 26]. Furthermore, Ca\({}^{2+}\) within the steel slag engages in ion exchange with heavy metal ions in the soil, effectually securing heavy metals in situ [24]. It concurrently forms conglomerates with mineral colloids and organic constituents, thereby fostering a structural amelioration in acidic and heavy metal-afflicted soils [27].
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 2 Status and Challenges of VLC
### Modulation Format in VLC
In [40], a 3-Gbit/s OFDM VLC system based on bit loading and power loading is demonstrated, indicating that OFDM has great potential of combining with adaptive bit- and power-allocation algorithms. DMT is similar to OFDM, except that it uses Hermitian symmetry before IFFT, so that the signal after IFFT is real-valued. The step of up-conversion is not needed. In [41], using the maximum ratio combination a 2.3-Gbit/s underwater DMT VLC system is realized. Additionally, adaptive bit- and power-allocation algorithms can also be applied for DMT. It is reported that using a bit-loading and power-loading scheme, the data rate of the underwater VLC system based on Si-substrate LED has achieved 3.37 Gbit/s. Although OFDM and DMT offer desirable resistance to ISI and flexible bit and power allocation, they are faced with a high peak-to-average power ratio (PAPR). DFTS-OFDM is proposed to mitigate the problem. The difference is that DFTS-OFDM employs an extra FFT operation between the serial-to-parallel conversion and IFFT. In [42], the authors have proven that by employing DFTS-OFDM the PAPR can be significantly reduced. The complementary cumulative distribution function (CCDF) of the transmitted signal with different modulation formats is illustrated in Figure 2. The results indicate that the PAPR of OOK-NRZ is the lowest, followed by PAM 4. while the PAPR of CAP and PAPR of DFTS-OFDM are higher but exhibit similar performance. Obviously, OFDM has the highest PAPR. As a result, signals using different modulation may experience different nonlinear channel responses, which further aggravates the complexity.
## 2 Research Methodology
### ML Algorithm
#### 2.4.2 Support Vector Machines (SVM)  
SVM was initially developed for binary classification problems and, due to various complexities, its extension to multiclass problems is not straightforward. The SVM algorithm creates a line or hyperplane that separates the data points to different classes. This line or hyperplane is called the decision boundary and the elements of the input data that defines the boundary are called support vectors. For SVM, the best hyperplane is the one that maximises the margins from both the classes. In general, the larger these margins are, the lower the generalisation error of the classifier. SVM algorithm uses a set of mathematical functions called kernels to take the data as input and transform it into the required form. The kernel functions return the inner product between two points in a suitable feature dimension. In general, it is used to transform a nonlinear decision surface to a linear equation, in a higher number of dimensions.  
To solve a multiclass classification problem, several binary SVM classifiers are combined. The most common approaches for combining binary classifiers are one vs. one (OvO), one vs. rest (OvA), directed acyclic graph (DAG) and error-corrected output coding [32]. For this study the one vs. one method was selected as this method requires lesser training data vectors for each classifier and the memory required to create the kernel matrix is much smaller. For '\(M\)' classes, the OvO algorithm constructs one binary classifier for every pair of distinct classes and a total of \(M\times(M-1)/2\) binary classifiers are constructed. The training samples are made to be inputs to each classifier and the output from each classifier is in the form of a class label. The max wins algorithm is used to combine these classifiers and the class label that occurs the most is assigned to that point in the data vector. A comprehensive explanation of multiclass classification using SVM is provided in [33]
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 3 Results and Discussion
### FT-IR Analyses  
Fourier Transform Infrared (FT-IR) spectroscopy was applied to further verify the polymer type of each unknown sample of plastic analyzed by HSI. Unknown marine plastic sample spectra were compared against reference polymer libraries to ascertain the reliability of polymer assignment by HSI.  
The results confirmed the polymer identification obtained from hyperspectral imaging analysis. In Figure 11, the results obtained for the PET fragment in the Foce Verde sample set are presented as an example.
# A Simple AMC Antenna for Liquid Monitoring in an Infusion Bag
###### Abstract  
Running-out detection of the liquids in an infusion bag is important for medical treatment. This paper proposed a simple low-cost sensing scheme with an artificial magnetic conductor (AMC) antenna for liquid-running-out detection in infusion bags. The proposed antenna consists of a dipole antenna supported by an AMC layer. It operates in the 2.4 GHz ISM band in the without-liquid state, in the 2.0 GHz ISM band in the with-liquid state, and can be used for liquid sensing. The AMC layer isolates interference from the surrounding environment such as the standing pole. It also enhances antenna performance and improves monitoring sensitivity. This gives a peak gain of 6.45 dBi and a radiation efficiency of 98% in the without-liquid state. Meanwhile, the with-liquid state can achieve a peak gain of 4.5 dBi and a radiation efficiency of 93%. The proposed antenna is fabricated and measured, verifying its sensing performance of the liquid in the infusion bag. This antenna's design is flexible, compact, precise, and suitable for biomedical wireless sensing.  
artificial magnetic conductor (AMC); liquid detection antenna; wireless local area network (ISM) Article
## 5 Application Research for Timber Damage Monitoring
In order to intuitively display the classification outcomes, we used the confusion matrix to represent the discrimination outcomes, as displayed in Figure 8.  
To demonstrate the superiority of the approach in this paper, it was compared with ALIF, EMD and EEMD decomposition algorithms and the same dataset was utilized for the test. To avoid the randomness of the test results, the above method was used for 10 repetitive experiments. The average accuracy of different methods for timber damage classification is demonstrated in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Network Layers (Type)** & **Out SHAPE** \\ \hline Input Layer & (None, 7, 7, 1) \\ Conv2d & (None, 7, 7, 32) \\ Batch\_Normalization & (None, 7, 7, 32) \\ Max\_Pooling2d & (None, 3, 3, 32) \\ Conv2d\_1 & (None, 3, 3, 64) \\ Batch\_Normalization\_1 & (None, 3, 64) \\ Flatten & (None, 64) \\ Dense & (None, 1024) \\ Dropout & (None, 1024) \\ Dense\_1 & (None, 6) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CNN model structure. Figure 7: (**a**) The accuracy of training set and validation set; (**b**) The loss function of training set and validation set. It can be seen from Table 3 that the classification accuracy of the approach proposed in this paper achieves the highest results. The classification results of timber damage conditions are the best when compared to the results obtained by other algorithms. The experimental results validate the efficacy and superiority of the proposed approach for detecting timber damage based on VMD, MSE and CNN.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Methods
#### 2.3.5 Analysis of Climatic and Anthropogenic Factors  
A regression model (Equation (3)) quantified the contributions of precipitation (P, mm), ENSO (El Nino 3.4 index, dimensionless), and extraction (EXT, mm/year) to GWS changes:  
\[\Delta\text{GWS}=\beta_{1}\times\text{P}+\beta_{2}\times\text{ ENSO}+\beta_{3}\times\text{EXT}+\epsilon \tag{5}\]  
where:  
P: is precipitation (mm);  
ENSO: is the El Nino 3.4 index (dimensionless);  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Aquifer**} & **Area** & \multirow{2}{*}{**N\({}^{\circ}\) of Wells**} & **Annual** & \multirow{2}{*}{**Annual Decline**} & **Recharge** \\  & **km\({}^{2}\)** & & & **(hm\({}^{3}\)/year)** & **(m/year)** & **Deficit (\%)** \\ \hline Caplina (Tacna) & 922 & 200+ & 17.50 & 1.00 & 30.00 \\ Tumbes & 250 & 150 & 11.00 & 0.75 & 12.50 \\ Chillon-Rimac (Lima) & 866 & 1000+ & 252.00 & 2.00 & 12.50 \\ Lurin (Lima) & 230 & 200 & 25.00 & 1.50 & 25.00 \\ Chilica (Cafete-Lima) & 99 & 50 & 5.00 & 1.00 & 17.50 \\ Locumba (Tacna) & 505 & 100 & 8.00 & 0.70 & 12.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Groundwater extraction, average groundwater level decrease (annual decline), and percentage by which extraction exceeds estimated natural recharge (recharge deficit) by aquifer (ANA, 2024).  
EXT: is human extraction (mm/year);  
\(\epsilon\): is the residual error (mm/year);  
\(\beta_{1}\) (mm/year), \(\beta_{2}\) (relates ENSO-Dimensionless), and \(\beta_{3}\) (mm/year) are the coefficients that indicate the magnitude of each explanatory variable.  
This approach, adapted from [33], enables the identification of the relative contributions of climatic and anthropogenic factors to the hydrological dynamics of aquifers such as the La Yarada aquifer in Tacna (Peru), providing a quantitative basis for sustainable resource management.
## 3 Results
### Emissions Results
Also, it has the most advanced SOC at \(-2.45\) compared with other cases since expediting the SOC in the RCCI engine mitigates soot emissions by enhancing premixed combustion. In contrast, when SOC is retarded, soot emissions will increase owing to fuel-rich localized zones. Moreover, the peak heat-release value is greater than in the other cases, resulting in the highest PFP and peak temperature. The CA 50 is 3.33 CA aTDC. This case produced a power of 6.35 kW and an IE of 49% and utilized a higher quantity of diesel fuel, 2.44 mg, injected at a pressure of 365.4 bars at 2620 rpm. Case D produces the highest power at 7.03 kW, indicating that the most suitable power should be less than 0.89 from the maximum allowable design load of the original diesel engine, primarily due to the high NOx emissions at higher loads. For this case, the SOC, CA 50, and CD are (\(-1.78\), 5.72), CA aTDC, and 11.38 CA, respectively. The IE was 49% and used less diesel fuel, at 2.06 mg injected at a pressure of 496 bars and at 3010 rpm, as shown in Figures 9-11. Figure 8: Scatter plot for objectives and constraints. Figure 9: In-cylinder pressure, HRR, and temperature for selected optimum cases. The tradeoff results between NOx with UHC and CO emissions can be interpreted physically. As seen in Figure 12, for case A, the temperature is high near the wall in the piston bowl region due to rich fuel in this region, which is why the NOx in this case is higher, and due to the the lower HC and CO emissions due to the enhanced combustion characteristics. In contrast, in case B, the low combustion temperature can increase the UHC and CO due to incomplete combustion, mostly in the center of the combustion chamber, while NOx emissions can be emitted in the lower range primarily in the squish region, as shown in Figure 13. Table 6 presents a comparison of emissions and performance outcomes based on data collected from the literature. This study achieves advanced IE and lower UHC and CO emissions, in addition to comparable results for soot and NOx, which are better than those of some studies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **IE** & **ISFC** & **NOx** & **UHC** & **CO** & **Soot** \\  & **\%** & **g/kWh** & **g/kWh** & **g/kWh** & **g/kWh** & **g/kWh** \\ \hline
[26] & 48.7 & - & 0.57 & 0.8 & 0.2 & 0.085 \\
[27] & - & - & 0.07 & 6.2 & 22.1 & - \\
[31] & - & 205.6 & 1.66 & - & - & 0.01 \\
[57] & 48.8 & 170.8 & 0.11 & 2.6 & 4 & 0.0009 \\ Present Study & 50 & 148 & 0.4 & 0.202 & 0.103 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison of the results for the present study with the literature’s results. Figure 11: Comparison figure for ISFC and rpm, and HC, CO, and NOx emissions for selected optimum cases. Figure 10: Parallel plot power, CH\({}_{4}\) mass, diesel mass, duration (D), MES, Lambda, IE, SOC, CA50, and CD for all selected optimum cases.
## 3 Proposed Approach
### Sentiment Analysis Feature Extraction
Compute the Root Mean Square Energy \(RMS=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x(i))^{2}}\) End \(w_{t}=w_{t}+1\); \(prev\_lag=0.5\); \(post\_lag=1.5\); Output: Statistical features computed over the time-frequency spectrogram features into a 1D vector. end for
```  
**Algorithm 1**SER: Features Extraction  
TF-IDF consists of two components: (i) Term frequency (TF) measures how frequently a term appears in a document. TF is calculated by dividing the number of times a term occurs in a document by the total number of terms in the document. (ii) Inverse document frequency (IDF) measures the rarity of a term across all documents in the corpus. It is calculated by dividing the total number of documents by the number of documents containing the term, and then taking the logarithm of that quotient. The TF-IDF score for a term in a document is obtained by multiplying its TF and IDF values. Higher TF-IDF scores indicate terms that are more important or relevant to the document. Algorithm 2 presents the step by step for the SA features extraction. ```
Input: Text data from audio signal (speech2 text) Result: Feature vector for sentiment analysis classification
1. Pre-processing: 1. **Tokenization**: -Tokenize the text data into individual words or tokens. 2. **Text Cleaning:** -Remove punctuation, special characters, and irrelevant symbols. -Convert all text to lowercase for consistency. 2. Bag of Words Representation: -Represent the text data as a bag of words by counting the frequency of each word in the corpus. 3. Feature Extraction: 1. **Term Frequency (TF)**: -Compute the TF score for each term in each document. -TF(_t, d_) = (_Number of times term t appears in document d_) / (_Total number of terms in doc d_) 2. Inverse Document Frequency (IDF): -Compute the IDF score for each term across the entire corpus. -IDF(t) = log_e(_Total of documents / Number of documents containing term t_) 3. TF-IDF Score_: -Calculate the TF-IDF score for each term in each document. -TF-IDF(_t, d_) = TF(_t, d_) \(\times\) IDF(_t_)
4. Feature Vector Construction: -Concatenate the TF-IDF scores for all terms in each document into a vector. -Each document is represented as a vector of TF-IDF scores, where each dimension corresponds to a unique term in the vocabulary. Output: Feature vector representing the features for Sentiment classification. ```  
**Algorithm 2. SA: Features Extraction**
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 2 Background
### Illuminant Estimation
In these methods, all pixels equally attend in illuminant estimation (e.g., GW), or some pixels are radically selected for processing and the others are ignored (e.g., WP, Bright Pixel [41], PCA-based method [21], Gray Pixels [42], Gray Index [43]). Naturally, the explanation can be extended as a weight-based technique, i.e., all pixels are used, but the weights for different pixels are not the same. And, the weights can be implicitly or explicitly presented. GGW, GE, and SoG are included in this explanation since they can be explained to have implicit weights with the structure of the \(p\)-th Minkowski norm. Local surface reflectance (LSR) [44] which uses WP to weight GW can also be seen to have implicit weights [40]. According to this explanation, illumination estimation becomes the regression function of the pixels distribution features and the corresponding weights. Similarly, in learning-based methods, some direct regression-based methods have been proposed [20; 21; 23; 40; 45] in recent years, which simply map image features to illuminant. One of the remarkable methods is based on the Corrected Moment (CM) [20]. The CM-based method provides an efficient methodology to yield the illuminant estimation using a polynomial regression from multiple low-level-based methods. Other techniques, e.g., regression tree [30], fuzzy logic [19], deep learning [46; 47], also can be regarded as regression-based methods with complex structures. Although these algorithms can achieve outstanding performances, their disadvantage is that they make illuminant estimation work in a black box without further considering the specific features of different images. These methods can be improved if the regression or weighting model is adjusted with image intrinsic features. So it is very important to assess the impacts of selected image features on illuminant estimation and determine which features are more correlated to illuminant estimation. The optimal combination of selected features will make illumination estimation regression more efficient.
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 1 Introduction  
Rolling elements present in, e.g., the automotive or aeronautics industry are required to satisfy high quality standards. A single failure can lead to enormous financial and fatal damages. To assure these quality standards, manufacturers employ surface inspection systems. These scan the roller bearings for defects. This quality assurance step sits between the manufacturing of the element and the bearing's final assembly.  
Surface inspection is either done through human experts or with automated machine vision systems. The former exhibit human errors, especially after extended hours of working on the tedious inspection task. Most automated approaches implement a set of manually parameterized operations. This requires extensive domain knowledge. The resulting classification system is highly specialized for the task [1]. Even minor changes in the manufacturing process can make the system unusable. With changes in the production process, more expert knowledge is required to adapt the classification system to the new conditions. This leads to production downtime and lower profits.  
Classical machine learning systems solve this problem only to a certain extent. Such systems combine feature extraction algorithms built on expert knowledge with learned classificationalgorithms [2]. The latter can automatically find optimal classifiers for a given set of training data. No domain specific expert knowledge is needed for the classification algorithm. While this automates parts of the whole classification system, expert knowledge is still required for finding descriptive features. The feature extraction is not automatically learned from training data. Therefore, it can't be automatically adapted when the need arises.  
Modern neural network approaches can fully automate the expert's task of injecting domain specific knowledge into the system [3]. Neural networks are self-learning systems and can automatically identify useful features in an example set of data. As will be discussed in Section 2 of this work, literature already proposes the use of convolutional neural networks (CNN) to reduce the need for expert knowledge. However, most of the proposed systems require extensive amounts of labeled data with a balance of defect and non-defect parts to train their CNNs [4]. Since the production of rolling elements is already a highly optimized process, defects are a rarity. This makes collecting the necessary amount of defect samples a time-consuming process. To avoid this, we train our network on simulated defects.  
The rest of this paper will be structured as follows: Section 2 analyzes current proposals in the literature for the given problem. Section 3 explains the necessary background on CNNs required for the rest of this paper. In Section 4 we elaborate on the process of collecting the training data and simulating the defects. Section 5 describes the classification system, including the architecture of our neural network and its training. In the last section, we evaluate the proposed system on previously unseen training data.
###### Abstract  
Research and development have been performed to investigate the effect of total pressure and microwave power on the electrical conductivity of nitrogen (N) atoms' grain boundaries incorporated ultrananocrystalline diamond (N-UNCD) films grown by microwave plasma chemical vapor deposition (MPCVD). Insertion of N atoms into the UNCD film's grain boundaries induces N atoms chemical reaction with C-atoms dangling bonds, resulting in release of electrons, which induce electrical conductivity. Four-point probe electrical measurements show that the highest electrically conductive N-UNCD films, produced until now, exhibit electrical resistivity of \(\sim\)1 Ohm.cm, which is orders of magnitude lower than the \(\geq\)106 Ohm.cm for undoped ultrananocrystalline diamond (UNCD) films. X-ray diffraction analysis and Raman spectroscopy revealed that the growth of the N-UNCD films by MPCVD do not produce graphite phase but only crystalline nanodiamond grains. X-ray photoelectron spectroscopy (XPS) analysis confirmed the presence of nitrogen (N) in the N-UNCD films and the high conductivity (no electrical charging is observed during XPS analysis) shown in electrical measurements.  
MPCVD; carbon materials; thin films; nitrogen atoms; nitrogen-ultrananocrystalline diamond (N-UNCD) Article