esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 3 Results and Discussion
### Partial Least-Squares Regression Applied to Wavelength and Water Status  
Partial least squares (PLS) regression is an alternative statistical regression technique that employs data compression to reduce the number of independent variables. PLS regression, followed by a calibration regression stage consisting of a least-squares fit of the parameters to the obtained regression factors [2; 28], was employed to calibrate the relationships between the spectral variables derived from the hyperspectral data (RAW, DIFF, and LOG) and the tomato leaf water status (WC and MC).  
To evaluate the effectiveness of the PLS regression model, cross-validation was employed in this experiment. Moreover, the root-mean-square error (RMSE) was calculated by using the following formula:  
\[RMSE=\sqrt{\frac{1}{n}\sum\
olimits_{i=1}^{n}{(y_{i}-\hat{y}_{i})}^{2}}. \tag{9}\]  
The largest coefficient of determination of PLS regression for the wavelength and water status was obtained by the analysis of DIFF and MC, as shown in Figure 7f, where the coefficient of determination for calibration is \(R^{2}=0.5822\) and that for the validation is \(r^{2}=0.5144\). In addition, the two wavelength bands are the only two factors adopted in this model. The use of fewer factors will ensure model stability in practical applications. This is because the use of fewer factors will decrease the noise that always accompanies wavelength acquisition. The PLS regression results show a higher coefficient of determination for MC than that for WC.  
Some conclusions can be drawn in terms of the effect of hyperspectral data preprocessing before PLS regression: (1) Inverse-logarithm relative reflectance transformation helped improve the model performance, enabling a higher coefficient of determination and lower RMSE compared with RAW in WC modeling. Furthermore, the opposite effect was observed in MC modeling. (2) The first derivative of relative reflectance transformation degraded the model performance, leading to a lower coefficient of determination and higher RMSE compared with RAW in WC modeling. Meanwhile, the opposite effect was observed in MC modeling. The aforementioned aspects are considered the differences between WC and MC in terms of physics and the applicability of the data processing methods.
## 3 Results
### sEMG vs. iEMG  
The performance resulting from the use of sEMG and iEMG data was compared using four combinations of datasets, including healthy and amputee data classified with SSAE and LDA. The same cross-validation scheme was used as in Section 3.2. Results for this analysis are shown in Figure 6 as the average of the 10 healthy and six amputee subjects.  
No significant difference was observed between iEMG and sEMG when using SSAE (_p_ > 0.05), whereas sEMG outperformed iEMG with LDA (_p_ < 0.05).  
Figure 5: Mean (and SD) classification error for SSAE and linear discriminant analysis (LDA) classifiers for the four sets of EMG data. The diamond symbol indicates the best classifier with statistical significance (_p_ < 0.001).  
Figure 6: Mean (and SD) classification error obtained with two different classifiers for both kinds data for healthy and amputee subjects. The diamond symbol indicates the best EMG data type, with statistical significance.
## 1 Introduction  
Supplying adequate agrifood products and byproducts, the demand for which has increased as the global population rises, requires diverse strategies, including (i) incrementing the cultivation area and (ii) improving the production per unit area. However, although the first approach (e.g., land use change) increased production, the conversion of natural land-forms into farming land led to an environmental challenge through land degradation [1; 2; 3]. This problem has become increasingly important in the Mediterranean basin, which demonstrates obvious movements of degradation, especially in areas where climate changes and meteorological conditions contribute extremely to it [4]. As an alternative strategy, the application of agrochemicals (e.g., artificial fertilizers, herbicides, etc.) and intensive farming management practices to increase crop production has brought the major disadvantage of the increasing contamination of agricultural products and the environment [5; 6]. Moreover, the strong demand for agrochemicals from domestic and global markets has driven up their prices and caused economic challenges in the agricultural sector [7]. One of the most significant current discussions is a reconsideration of technologies to boost plant production, focusing on alternative strategies, mainly the application of beneficial biological approaches and bio-based products. The application of beneficial microbial consortia, mainly plant growth-promoting rhizobacteria (PGPR), has become one of the most widely used biological alternatives in sustainable agriculture. Such beneficial rhizobacteria can be considered plant biostimulants, which, according to Regulation (EU) No. 2019/1009 of the European Parliament [8], can be used as fertilizing products to promote the plant nutritional value, increase the nutrient locked-up availability in the rhizosphere/soil, and improve plant tolerance against abiotic and biotic stresses [9]. Moreover, the presented sustainable alternatives of agrochemicals receive considerable critical attention in fulfilling part of the United Nations Sustainable Development Goal 15, including how microbial-based biofertilization can promote the sustainable use of agroecosystems and preserve farmlands from degradation. Recently, investigators have attempted to evaluate the potential of identified PGPR and their mechanisms of action as agents of biofertilization, phytostimulation, bioremediation, and bioprotection (Figure 1). Table 1 presents an overview of some identified PGPR and their functional attributes, as further discussed in Sections 2 and 3.
## 4 Description of the Proposed Topology Construction
### Design Issues of the Proposed Logical Topology  
Design issues that need to be discussed in relation to the proposed logical topology include the number of chains in the system, the number of nodes in a chain, and the time when the leaders should be changed or the chains should be reconstructed/updated. Other issues regarding network management include the arrival of a new node, or dead/aberrant nodes. These issues are discussed below.
## 4 Discussion
### Case-Level Nodal Status Prediction: A Classic Needle-in-Haystack Challenge
On the one hand, we can show that image-based data augmentation techniques do not work for MIL approaches based on pre-trained models. On the other hand, we can show that for small data, de novo model training does not work. Thus, the data augmentation needs to take place after feature vector creation. There, however, the number of published data augmentation techniques is limited. Therefore, we adapted the query bag approach described by [PERSON] et al. that works on tabular data [39]. Furthermore, we add random noise to every row in the table containing the instances. By combining these data augmentation steps, we can prevent over-fitting and can show that a MIL model can be trained to generalize. Besides modifying the tabular data, in the MIL setting, there are approaches based on resampling the tabular data. This resampling is often called bag mixing [33; 50]. Unlike the random sampling from bags employed in this study, bag mixing approaches proved ineffective for our data set. Under bag remixing, an early over-fitting repeatedly occurred for the data set used here. This can possibly be explained by the small overall data set size. Besides resampling, there are several publications on generating synthetic tabular data for the MIL model. This synthetic data can be generated by just interpolating between two rows. Albeit, in our case, there is no clear separation in the latent space, and the informative instances are not known a priori. Therefore, such approaches do not seem promising. And indeed, in isolated experiments, we could not demonstrate any additional benefit from the incorporation of synthetic data. Nevertheless, these were preliminary experiments, and a comprehensive assessment of all methods for generating such data exceeds the intended scope of this work [51; 52].
# The Influence of Capacitance and Inductance Changes on Frequency Response of Transformer Windings
## 2 Parameters Influencing the Frequency Response of Transformer Windings
The low-frequency range is often omitted in test result analysis, because it shows natural differences between the three phases, due to various magnetic flux distributions in the middle and side columns of the core, which results in a single or double minimum of the main parallel resonance. This frequency range can also be affected by the previous DC measurements, resulting in core magnetization or by core construction [17; 20]. The low frequency range finishes at the inflection point of the increasing capacitive slope of the FR curve until the next series resonance, from which FR analysis usually starts for the medium frequency range. The position of the first resonance depends on the winding self-inductance and total series and shunt capacitances. The medium frequency (MF) range is sensitive to changes caused by local deformations or displacement, therefore it is usually used for the analysis of measured data. The high frequency HF range is influenced by test setup itself, connection quality and wave phenomena in windings, so it is usually omitted in the analysis. The changes of inductive or capacitive parameters of the transformer have strong influence on LF range, especially on the position of the first resonance. Additionally, they also influence more complex phenomena resulting in visible changes in the medium frequency range. By observing changes in both mentioned frequency ranges it is possible to determine whether visible changes are the results of deformation or if they come from some construction changes, e.g., the wrong tap position of OLTC during transformer measurement. FRA data are usually interpreted after performing measurements on-site, therefore it is difficult to check if there were no accidental mistakes during tests (like the mentioned wrong tap position). The analysis presented in this paper allows to understand the influence of different parameters on FR curve.
## 3 Challenges in Terahertz-Based FSO Communication
### Line-of-Sight (LOS) Alignment
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Challenge** & **Cause** & **Solution** & **Key Limitation** \\ \hline ([PERSON] et al., 2018) & Misalignment & Turbulence, vibrations & Adaptive optics & High complexity \\ \hline ([PERSON] et al., 2018) & Precision requirement & Short wavelengths & Beam-steering mechanisms & Costly hardware \\ \hline ([PERSON] et al., 2018) & Environmental instability & Platform vibrations & Vibration compensation systems & Increased energy usage \\ \hline ([PERSON] et al., 2018) & Beam divergence & Large aperture size constraints & Phased array antennas & Complex manufacturing \\ \hline ([PERSON] et al., 2018) & Dynamic conditions & Moving platforms & Adaptive real-time controls & Hardware scalability \\ \hline ([PERSON] et al., 2018) & Atmospheric effects & Refraction and scattering & Dynamic path optimization & Computational overhead \\ \hline ([PERSON] et al., 2018) & Alignment drift & Structural vibrations & Feedback-based alignment & Delay in adjustments \\ \hline ([PERSON] et al., 2018) & High sensitivity & Shorter wavelengths & Precision beamforming & High computational requirements \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Challenges in LOS alignment for THz-FSO communication. Figure 5: Atmospheric absorption.
# Person Identification and Gender Classification Based on Vision Transformers for Periocular Images
## 3 Materials and Methods
### Proposed Approach
#### 3.4.1 ROI Extraction  
The iris center has been used as a region of interest (ROI) by [PERSON] et al. [7]. This method fails when gaze angle and pose variations are involved. The individual's eyes are occasionally partially or wholly closed. Due to this, we need to identify other techniques to help recognize our image. The metadata and the UBIPr dataset enable us to retrieve the periocular region of interest, of which the cathus points are the most helpful. We utilized the approach illustrated in [PERSON] et al. [20,32] to extract the ROI from images. The sequence of steps to extract the ROI is mentioned as follows.  
* Let \((a_{1},b_{1})\) be the coordinates of the medial cathus points (inner corner) and \((a_{2},b_{2})\) be the coordinates of the lateral cathus points (outer corner). The Euclidean distance (\(D\)) between the medial and cathus points is determined as mentioned in (2). \[\mathrm{D}=\sqrt{\left(a1-a2\right)^{2}+\left(b1-b2\right)^{2}}\] (2)
* Compute a 2-D coordinate \(E_{p}\) = (\(E_{px}\), \(E_{py}\)) using (3). This is a point where the line connecting the medial and lateral cathus points meet. \[E_{px} =\left(a1\ +a2\right)/2,\ E_{py} =\left(b1\ +b2\right)/2\] (3)
* Calculate the rectangle ROI's upper-left coordinate (\(a_{3}\),\(b_{3}\)) and lower-right coordinate (\(a_{4}\),\(b_{4}\)) as mentioned in (4) and (5), respectively. \[(a_{3},b_{3}) =\left(E_{px}\ -1.2\ \times\ \mathrm{D},\ E_{py}-0.8\ \times\ \mathrm{D}\right)\] (4) \[(a_{4},b_{4}) =\left(E_{px}\ +1.2\ \times\ \mathrm{D},\ E_{py}+0.8\ \times\ \mathrm{D}\right)\] (5)
* To get the rectangular ROI, use the calculated points (\(a_{3}\),\(b_{3}\)) and (\(a_{4}\),\(b_{4}\)).
## 3 Learning-Based Action Representation Approach
### Deep Learning-Based Approaches
#### 3.2.1 Generative/Unsupervised Models  
Generative/unsupervised deep learning models do not require the target class labels during the learning process. These models are specifically useful when labelled data are relatively scarce or unavailable. Deep learning models have been investigated since the 1960s [133] but researchers have paid little attention towards these models. This was mainly due to the success of shallow models such as SVMs [134], and unavailability of huge amount of data, required for training the deep models.  
A remarkable surge in the history of deep models was triggered out by the work of [135] where the highly efficient DBN and training algorithm was introduced followed by the feature reduction technique [136]. The DBN was trained layer by layer using RBMs [137], the parameters learned during this unsupervised pre-training phase were fine-tuned in a supervised manner using backpropagation. Since the introduction of this efficient model there has been a lot of interest in applying deep learning models to different applications such as speech recognition, image classification, object recognition, and human action recognition.  
A method using unsupervised feature learning from video data was proposed in [138] for action recognition. The authors used independent subspace analysis algorithm for learning spatio-temporal features and combined with deep learning techniques such as convolutional and staking for action representation and recognition. Deep Belief Networks (DBNs) trained with RBMs were used for human action recognition in [139]. The proposed approach outperforms the handcrafted learning-basedapproaches on two standard datasets. Learning continuously from the streaming video without any labels is an important but challenging task. This issue was addressed in [140] by using unsupervised deep learning model. Most of action datasets have been recoded under controlled environment; action recognition from unconstrained videos is a challenging task. A method for human action recognition from unconstrained video sequences was proposed in [141] using DBNs.  
Unsupervised learning played a pivotal role in reviving the interests of the researchers in deep learning. However, it has been overshadowed by the purely supervised learning since the major breakthrough in deep learning used CNNs for object recognition [142]. However, an important study by the pioneers of latest deep learning models suggest that unsupervised learning is going to be far more important than its supervised counterpart in the long run [13] since we discover the world by observing it rather being told the name of every object. The human and animal learning is mostly unsupervised.
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 2 Native Bacterial Approach for Colorectal Cancer (CRC) Treatment
### Colonization Resistance for Pathogenic Bacteria  
The occurrence of CRC is closely related to environmental factors, diet, and gut microbiome composition [27]. The development of CRC was usually accompanied by an increase in the abundance of intestinal pathogenic bacteria in a multi-cohort analysis of 526 CRC patients' samples; these bacteria included _Fusobacterium nucleatum_, _Peptostreptococcus anaerobius_, _Prevotella intermedia_, _Alistipes fingogldi_, _Thermanerovibrio acidaminovorans_, and enterotoxigenic _Bacteroides fragilis_[28], which contribute to tumor proliferation by promoting inflammation, causing DNA damage, etc. Another pathological epidemiology database of 1069 rectal and colon cancer cases revealed that the amount of _F. nucleatum_ DNA in colorectal cancer tissue is associated with shorter survival [29]; thus it can serve as a prog nostic biomarker. Furthermore, most probiotics with a protective effect against CRC have been exhausted, including _Lachnospiraceae_ species, _Bifidobacterium animals_, and _Streptococcus thermophilus_, and are found to be depleted in CRC patients [30]. Thus, probiotic intervention can resolve the dysbiosis of the gut ecosystem. Through colonization resistance, probiotic strains were able to reduce the colonization of pathogens such as _Clostridum difficile_, which has been recognized as a cancer-stimulating bacterium, _in ApcMin/+ mice_[31]. In addition, metabolites produced by probiotics such as lactic acid and acetic acid make an acidic lumen environment to inhibit the growth of pathogenic bacteria [8]. Metabolite bacterioxins can also directly kill pathogens to maintain intestinal microbial health [32]. Thus, these properties of probiotics help restore the intestinal dysbiosis associated with CRC.
## 3 Results and Discussion
### XRD Analysis of Optimized CaCO\({}_{3}\) Nanoparticles  
Based on the aforementioned parameters, the optimal conditions for the formation of spherical nanoparticles of CaCO\({}_{3}\) were 50 mL of DDW with 1 h feeding rate of K\({}_{2}\)CO\({}_{3}\) in ethanolic solution, using the freeze-drying technique. Figure 6 shows the XRD analysis of the composition and phase of the optimized CaCO\({}_{3}\) nanoparticles derived from cockle shell. The presence of CaCO\({}_{3}\) can be observed at 20 of 23.0, 29.5, 36.0, 39.3, 43.2, 48.5, 57.5, 60.9, 63.1, 64.4, 65.6, and 83.7\({}^{\circ}\). These 20 positions correspond to (0 1 2), (1 0 4), (1 1 0), (1 1 3), (2 0 2), (1 1 6), (1 2 2), (2 1 4), (1 2 5), (0 3 0), (0 0 12), and (1 3 4) crystal planes, respectively. Other parameters such as a = b = 4.9900 A, c = 17.0020 A, \(\alpha\) = \(\beta\) = 90.0000\({}^{\circ}\), and \(\gamma\) = 120.0000\({}^{\circ}\) were also obtained. The assignments of 20 position and other parameters for CaCO\({}_{3}\) from XRD were in agreement with the reference code 98-000-5340 of a hexagonal crystal system and space group R-3c. The crystallite size of CaCO\({}_{3}\) nanoparticles was 61.4 nm, which was calculated from the Debye-Scherrer equation, L = K/\(\beta\) cos 20), where K is a constant; \(\lambda\) is to the X-ray wavelength (\(\lambda\) = 1.54060 A), \(\beta\) is the full-width at half maximum, and \(\theta\) is the angle. The formation of the calcite  
Figure 5: FESEM images of CaCO\({}_{3}\) prepared in (**a**,**d**) ethanol; (**b**,**e**) methanol; and (**c**,**f**) propanol after freeze drying (**left**) and oven drying (**right**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feeding Rate of K\({}_{2}\)CO\({}_{3}\) (h)** & **Surface Area (m\({}^{2}\)/g)** & **Average Pore Diameter (Å)** \\ \hline
1.0 & 26 & 580 \\
1.5 & 8.4 & 606 \\
2.0 & 5.9 & 758 \\
2.5 & 4.1 & 325 \\ \hline \hline \end{tabular}
\end{table}
Table 3: BET surface area and average pore diameter analyses of CaCO\({}_{3}\) at different feeding rates of K\({}_{2}\)CO\({}_{3}\).  
phase of CaCO\({}_{3}\) in the current investigation matched well with the results of [PERSON] et al. [46], with the characteristic peak positioned at 20 = 29.5\({}^{\circ}\) and the crystal plane of (1 0 4).
# MOCA: A Network Intrusion Monitoring and Classification System
## 3 Connection Features  
Researchers classify features into two particular classes: _basic features_ and _connection features_. Basic features represent a single connection, while connection features are extracted from multiple connections. The preeminent objective of the two features is to identify two general types of attacks in network intrusion detection: attacks that require single connections, such as Web SQL Injection, and attacks that contain multiple connections, such as DDoS and Botnet attacks.  
Distributed Denial of Service (DDoS) is a malicious attack in which numerous systems use malicious traffic to confuse a target server. In the CICDDoS2019[9] dataset, there are 13 DDoS attacks, such as DNS, MSSQL, SSDP, and UDPLag attacks. The following connection features are used to find the behavior of a client with a 100-connection window:  
* When there are multiple connections whose source IP address is the same as the current connection, the _source count_ is extracted.
* When there are multiple connections whose destination IP address is the same as the current connection, the _dst count_ is extracted.
* When there are multiple connections whose source port is the same as the current connection, the _sport count_ is extracted.
* When there are multiple connections whose destination port is the same as that of the current connection, the _dport count_ is extracted.
* When there are multiple connections whose destination and source IP addresses are the same as those of the current connection, the _dst source count_ is extracted.
* When there are multiple connections whose destination IP address and source port are the same as those of the current connection, the _dst sport count_ is extracted.
* When there are multiple connections whose source IP address and destination port are the same as those of the current connection, the _source dport count_ is extracted.  
The datasets used in this paper do not contain connection features. As a result, based on the original files, the seven connection features were derived for each sample as mentioned above.