esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### GC-IMS Combined with PCA
The relative anethol content in GC-MS was greater than 80%, whereas that in GC-IMS was approximately 50%. This could be attributed to the relatively low GC-IMS threshold [28]. GC-IMS detected components that were not identified by GC-MS, causing an increase in the total amounts, and the relative amounts of the main compounds in GC-IMS were lower than those in GC-MS. The main component of SAEO is anethol, which was consistent with the essential oils extracted using the four different methods. However, the amounts of \(\alpha\)-terpineol, terpinene-4-ol, linalool, gamma-terpinene, limonene, 1,8-cineoldelta-3-carene, \(\alpha\)-terpinene, \(\alpha\)-phellandrene, mycrene, \(\beta\)-pinene, \(\alpha\)-thujene, and \(\alpha\)-pinene were lower in ESE. The amounts of 2-methylbutanal, acetone, hexanal, pentanal, hexanol, 1-pentanol, acetoin, 2-methylbutanol, 2-pentanone, 1-butanol, 3-methylbutanal, 2-butanone, isobutanol, and butanal in SAEOs extracted by SE were higher than those in SAEOs extracted by other methods. The ethanol, acetal, and ethyl acetate amounts of SAEOs extracted by SCD and ESE were higher, whereas the isopropyl acetate and methyl acetate amounts of SAEOs extracted by HD were higher than those of SAEOs extracted by the other methods. These results are consistent with those shown in the corridor diagram of the signal peak area in Figure 5. In general, the characteristic fingerprints of the volatile compounds of SAEOs extracted by the four different methods could be established by GC-IMS, which provides theoretical guidance for SAEO investigation. Furthermore, GC-IMS data were analysed using the PCA analysis method. PC1 and PC2, which represented 52% and 30% of the total variance, respectively, represented the entire sample. The SAEOs extracted using the four extraction methods were independently distributed and dispersed in the principal component space (Figure 6). These results suggested that SAEOs extracted by the different methods can be effectively distinguished through the combination of non-targeting feature markers and PCA.
## 3 Spectral Scattering Imaging  
Scattering occurs, as photons traveling within biological tissues are encountering cellular structures, which serve as scattering particles, or the interfaces of two tissue layers with different refractive indexes, thus resulting in the change in the direction of light propagation. Light scattering depends on the density, cell structures, extra- and intra-cellular matrices of tissues, and it can thus provide insights into the structural and textural properties of biological tissues [24; 38; 39]. Quantifying light scattering, however, poses a challenge, because the scattering event is inevitably interwoven with light absorption, another fundamental phenomenon taking place during the interaction of light with tissues, which relates to tissue chemical composition.  
[PERSON] and colleagues [38] reported on the use of a light transmission technique coupled with integrating sphere measurements to quantify light scattering for assessment of pork quality. However, the transmission technique is not suitable for detecting fresh horticultural produce. Since the mid-1990s, there has been growing interest in non-destructive assessment of fruit texture, i.e., firmness, which led to the development of a number of light scattering techniques. In earlier studies, a single laser was explored as a light source for acquiring scattering images or profiles at a specific wavelength [40; 41; 42]. Single laser-based techniques, although having shown correlation between scattering features and fruit firmness, have limitations as the selected wavelength may be not optimal or insufficient for predicting firmness and other quality attributes like SSC. Our laboratory first proposed the concept of using spectral scattering imaging, including multispectral and hyperspectral, to measure scattering characteristics to enhance fruit quality assessment [24; 43]. Multispectral scattering imaging allows acquiring scattering images at several discrete key wavelengths, while hyperspectral imaging enables acquisition of scattering images over a series of contiguous wavelengths, thus enhancing the detection capability. In implementing spectral scattering imaging, a broadband light source is used to illuminate the sample and backscattered images are acquired by a camera coupled with a wavelength selection device. In recent years, researchers have explored laser-based imaging systems for fruit quality assessment [44; 45; 46; 47], which use a laser module as a light source that consists of several lasers operating at different wavelengths to acquire multispectral scattering images.  
In the following subsections, we give a detailed description of several scattering configurations developed for scattering measurement, analysis of spectral scattering images, and applications of spectral scattering imaging for quality evaluation of apple and other horticultural products.  
Figure 3: Three sensing modes commonly used for hyperspectral image acquisition: (**a**) reflectance, (**b**) transmittance and (**c**) interactance.
## 1 Introduction  
In recent years, many applications have been developed for retrieving information over the _World Wide Web_, and analyzing the structure of the underlying _Web Graph_, which contains currently more than 1 trillion different URLs ([[http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html](http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html)]([http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html](http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html))). This large-scale graph has too many links to be stored in the main memory which forces several random seeks to a disk. Since disk access is (by five orders of magnitude) slower than main memory access,this leads to unacceptable retrieval times. To mitigate this problem, several compression techniques have been proposed for large graphs, aimed at reducing the number of bits per link required in graph representation [1; 2; 3; 4; 5; 6; 7].  
In this paper, we focus on the efficient storage of and rapid access to compressed graphs. In contrast to other techniques that make use of lexicographic ordering of URLs, and thus are specifically tailored for the Web Graph, however, the scheme presented here does not need to refer to the URLs and therefore may be applied to graphs of more general nature. The specific aim of our method is to produce a compressed graph supporting queries of the kind:  
* _For two input pages X and Y, does X have a hyperlink to page Y?_
* _For input page X, list the neighbours of X_  
In summary, our method produces a compressed Web Graph featuring high compression ratio, short retrieval time of the adjacency list of a node, and fast testing of whether or not two pages share a hyperlink. The paper is organized as follows. Section 2 stipulates some notation and reviews previous work. Our method is presented in Section 3. Section 4 outlines the structure of a new universal code inspired by our method, to be further analyzed in a forthcoming paper. Finally, Section 5 documents the performance achieved.
## 3 Results and Discussion
### Antimicrobial Activities of Greek Fir Essential Oils  
Seed and cone scale EOs of _Abies cephalonica_ were evaluated for their antibacterial effectiveness against two reference strains: _S. aureus_ ATCC 29213 and _E. coli_ ATCC 8739. In the present study, it was shown that both reference strains were susceptible to the tested EOs. The results exhibited that the cone EO had greater antibacterial activity than that isolated from seeds; however, both were characterized by bactericidal effectiveness. Moreover, in comparison to the thymol and gentamicin sulfate (positive controls), the cone EO exhibited higher and lower antibacterial activities, respectively. The detailed results of the experiments are shown in Table 3.  
According to the available literature, there are few data on the antibacterial effectiveness of _Abies cephalonica_ EO against microorganisms; however, in experiments that were based on EO from the needles of twigs, [PERSON] et al. [7] showed that the MICs of Greek fir EO against the _S. aureus_ ATCC 6538 and _E. coli_ ATCC 8739 strains were 0.62 and 10.0 mg/mL, respectively. Comparable results were observed in a study by [PERSON] et al. [1], in which the antimicrobial activity of Greek fir needle EO was confirmed against _S. aureus_ ATCC 6538 and _E. coli_ ATCC 35210 (MIC = 2.5 \(\pm\) 0.010 mg/mL). It is worth emphasizing that the MIC  
Figure 2: The viability of skin fibroblasts, keratinocytes, and melanoma cells (A375, C32) treated with fir seed and fir cone EOs for 24 h in MTT assay. The data are presented as the mean \(\pm\) standard deviation of three independent experiments performed in triplicate (* \(p<\) 0.05 compared to the 0.1% DMSO-treated group).  
results obtained in the current study were lower in comparison to the abovementioned data but quite similar to the results obtained in our former studies [18]. We proved the antimicrobial properties (the MIC ranged from 25 to 33 \(\mu\)L/mL) of _Abies concolor_ EOs isolated from seeds and cones against the _E. coli_ ATCC 25922 and _S. aureus_ ATCC 43300 strains.
## Materials and Methods
### Stacked Unit and Free Movement Space (FM)  
The stacked unit was located above the rear axle, using a column stack fixturing to prevent vertical motion. This fixturing method is generally used by the ASTM D7386-12 [11] and ISTA 3A [12] protocols to hold the stacked unit together during the test time and to avoid free movement (FM) of the unit in lateral and longitudinal orientations. To determine the effect of the role of free movement (FM) space around the parcel shipment (in the lateral and longitudinal directions), the measurements of FM were divided into three groups in the field measurements, as described here:  
1. No FM (0% free movement space);
2. FM of \(\pm\)5% in the width/length of the box (total of 10% FM);
3. FM of \(\pm\)10% in the width/length of the box (total of 20% FM).  
The reasons for choosing these amounts of free movement space as parameters during the measurements were as follows: (a) The operators usually try to load these parcels as close to each other as possible in practice. (b) They are not able to perfectly fill the loading area, and some free spaces always remain. (c) If there is too much space available, the goods are stacked in groups but not fastened together, and smaller spaces remain between them. (d) If the spaces between the parcels are too great, then the parcels will not vibrate but will bump into each other, causing shock impulses, not vibration events. (e) During the simulation technique in packaging testing laboratories, the equipment generally used has a maximum table size to perform these tests, which limits the possibilities in laboratory practice.  
In the 10% and 20% free movement experiments (Figure 3b,c), the space allowed the boxes planar movement, but did not let them fall down or change their placement angle from the horizontal position. The differences in box coverage could be up to 36 mm (20%) when one of the boxes moved in one direction while the box above or below moved to the totally opposite side.
## 6 Implementation Results
When the SIP-DRDoS attack was applied, the SIP server's CPU usage increased to 72%, 67%, and 74% for registered, nonregistered, and random users, respectively. In Figure 8, the CPU load of the SIP server under attack is given for the first 6 min after the initiation of the attack while our defense mechanism was running. Our implementation results show that the proposed mechanism effectively performed a deep packet analysis for SIP traffic, detected the SIP-DRDoS attack, and mitigated it by dropping attack packets while letting legitimate packets pass through. While the SIP-DRDoS attack sharply increased the CPU usage of the SIP server, our defense mechanism was triggered within 3 min and mitigated the attack by dramatically reducing the CPU load in the following 3 min. As given in Figure 9, with our defense mechanism, the CPU usage of the SIP server under attack decreased from 72% down to 19%, from 67% down to 17%, and from 74% down to 20% for registered, nonregistered, and random users, respectively. Considering all types of users, the CPU usage fell from 71% down to 18% on average, as shown in Figure 10. Moreover, as given in Table 2, the false positive rate of our SIP-DRDoS attack detection mechanism was low, with only a small number of legitimate packets being mistakenly dropped. Hence, the proposed SIP-based DRDoS attack detection and mitigation mechanism exhibited good performance in terms of both detecting and mitigating the attack in real time while also maintaining a low false positive rate. These results suggested that the proposed mechanism
## 2 Mathematical Modeling and Methods
### Pump-Motor Hydraulic System Model
#### 2.1.2 Control Unit of the Swash Plate Inclination  
The control unit of the inclination angle of the swash plate is composed of two parts: a valve-piston cylinder unit and a piston-swash plate inclination unit.  
The dynamic balance equation of the valve-piston cylinder unit is shown as follows:  
\[\left\{\begin{array}{l}Q_{L}=K_{q}x_{c}-K_{c}P_{L}\\ Q_{L}=A_{p}\frac{dy}{dt}+C_{tp}P_{L}+\frac{V_{t}}{d\beta_{p}}\frac{dP_{L}}{dt} \\ A_{P}P_{L}=M_{t}\frac{d^{2}y}{dx}+B_{p}\frac{dy}{dx}+Ky+F_{L}\end{array}\right. \tag{2}\]  
where \(Q_{L}\) represents the load flow; \(K_{q}\) represents flow amplification factor; \(K_{C}\) represents the flow pressure coefficient; \(x_{c}\) represents the spool displacement; \(P_{L}\) represents the load pressure difference; \(A_{p}\) represents the effective area of hydraulic cylinder piston; \(Vt\) represents the volume of the hydraulic cylinder; \(y\) represents the piston displacement; \(C_{tp}\) represents the total leakage coefficient of the hydraulic cylinder; \(M_{t}\) represents the total mass of the piston and the load; \(B_{p}\) represents the viscous damping coefficient of the piston and the load; \(K\) represents the spring stiffness of the piston and the load; \(F_{L}\) represents the load force on the piston.  
Figure 2: Working principle diagram of variable pump control fixed motor.
## 3 XAI Approaches for Chest X-ray Image Analysis and Report Generation
### Local Interpretable Model-Agnostic Explanations (LIME)  
LIME is an interpretability method that is model agnostic; this means that LIME explains why an AI model makes a particular prediction without making any assumptions about the architecture of the model. For the case of ConvNets, these models might base their decisions on a large number of features. Model explanations need to be understandable to humans, so explanations must be based on a subset of features that humans can comprehend and relate to the predictions made by a model. Therefore, LIME approximates a ConvNet with a simpler interpretable model that behaves like the ConvNet for a specific prediction (this is called _local interpretability_).  
LIME works by first partitioning an image into blocks of homogeneous regions consisting of pixels with similar attributes (such as color and brightness). These blocks are called _superpixels_. Then, a set \(S\) of new images is created by graying out a random selection of superpixels. A ConvNet is then used on each image of \(S\) to make a prediction, and a weight is assigned to each superpixel denoting its importance for making the prediction. In an image, the superpixels with the largest weight are highlighted, yielding a heat map showing the most important parts of the image over which the ConvNet based its prediction.  
Figure 4: Attention-based explanation. The refined features are highlighted through attention feature maps and represented via a heat map image.  
A number of works have been proposed for COVID-19 detection since the pandemic began in 2020. To reduce dependency on limited COVID-19 test kits, an alternative is the use of screening systems for chest X-rays. Two studies on the effectiveness of COVID-19 detection using different ConvNet-based models were presented in [57; 63]; LIME was used to identify the features that the ConvNets used to distinguish patients with COVID-19 from patients without COVID-19.  
A ConvNet-based system to identify lesions in X-rays is presented in [62]. In this work, a combination of predictions from different classifiers was used to detect abnormalities using frontal and lateral X-rays. Radiologists highlighted the regions of X-rays they would focus on to make a diagnosis, and this human-made highlighting was compared to that produced by Grad-CAM and LIME.  
A spiking neural network technique is presented in [58] to detect COVID-19 positive cases using a spike neural network (SNN) with supervised synaptic learning. Three additional works on the use of ConvNets to detect COVID-19 using X-ray images are presented in [59; 60; 61]. All these works use LIME to identify the regions of an X-ray showing a COVID-19 infection.  
Explanations obtained through LIME have demonstrated their importance in COVID-19 related research. In analyzing chest X-rays, localization and segmentation are considered crucial parts of the deep learning process as prediction of various chest pathologies can be effectively explained by LIME as reported by recent research discussed in this section.
## 3 Beamforming Processing  
In the previous section, the architecture of the SDR system was presented, as well as the description of each analog and digital component used. In this section, taking advantage of the absolute control over each of the four Tx/Rx channels and the processing capacity that this system has, an example demonstrating some of its potentialities is presented. In this work, some examples will be presented using two possibilities, beam shaping and beam steering, to highlight the functionality of the proposed system. Nevertheless, a multitude of techniques may still be explored in the future. Figure 10 shows a block diagram of a beamforming system, applied to a linear array with N number of isotropic elements, in reception. In this structure, each antenna element has an adjustment, by multiplying each received signal by a weight factor, \(W_{n}=a_{n}e^{ja}\), where \(a_{n}\) is the amplitude excitation, and \(\alpha\) is the relative phase excitation to the previous element. By varying the weight factor of each channel, and properly estimating its values, it is possible to control the radiation pattern of the array.  
Figure 8: Measurements of downconverter module: (**a**) RF output spectrum (M1@4 GHz, M3@12 GHz; (b) downconverter conversion gain at different RF frequencies.  
Figure 9: Planar antenna array \(4\times 4\).  
The far field of the array factor can be written according to the Equation (1), which relates the contribution of each element for the total radiation of a linear array.  
\[AF=\sum_{n=1}^{N}\ a_{n}e^{i[(n-1)kd\alpha\cos\theta+a]} \tag{1}\]  
In Equation (1), the summation of independent phasors (weights), the progressive phase is represented by \(\psi=\text{kd}\ \text{cos}\theta+\alpha\), where k = (2\(\pi\)\(\lambda\)) and d the distance between elements of the array.
## 1 Introduction
Since the network infrastructures in disasters are corrupted, the estimation of DAWNs includes four aspects, e.g., energy consumed (EC), number of connected nodes (CN), hop counts to ECVs (HC), and distance to the nearest ECVs (DIS). We consider these four aspects as decision criteria that cover all of the decision-making aspects in disasters for next-hop selection. We use the analytic hierarchy process-Technique for Order Preference by Similarity to an Ideal Solution (AHP-TOPSIS) method to calculate the weights and the judgment matrix for the selected criteria. * We propose a multi-criteria decision-making method for emergency communication protocol (MCDM-ECP) algorithm, which can update routing tables and choose the optimal routing path according to the real-time routing information in DAWNs. The algorithm can not only reduce energy consumption for mobile nodes, but also extend the network lifetime for DAWNs, which is of great importance for disaster relief. Two classical algorithms: Ad hoc On-demand Distance Vector (AODV), and Low Energy Adaptive Clustering Hierarchy (LEACH), are compared with MCDM-ECP, and the performance evaluation results show that the proposed algorithm performs better than other classical protocols both in energy consumption and packet received rate (PRR) for long-term emergency communications. The rest of this paper is organized as follows. In Section 2, related works about decision making and routing protocols on DAWNs are presented. In Section 3, we design a network structure and topology in an emergency communication scenario and analyze link characterization; then, we propose the MCDM-ECP algorithm for our designed DAWNs in Section 4, followed by simulations and results analysis in Section 5. Finally, Section 6 concludes this paper.
# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 2 Background
### Consistency-Based Methods  
In consistency-based methods, a model is encouraged to give consistent results on perturbed versions of the same inputs, such as input images with Gaussian noise added [21]. In other words, if an input \(x_{i}\) is classified as class \(c\), then the perturbed input \(x_{i}^{\prime}\) must also be classified as class \(c\). As previous research has shown, an ensemble of neural networks can considerably improve the ability of the network to generalize [24; 25; 26]. One of the most dominant consistency-based SSL models is the Mean Teacher (MT) [27], which is based on the Temporal Ensembling [24] method. This method involves self-ensembling in neural networks, wherein the results of the same network over different epochs are combined during training to infer new training targets from the unlabeled data. Each training epoch has the same network but different regularization and input augmentation conditions to enforce consistency. Due to the use of dropout regularization, the prediction generated from each epoch is from an individual sub-network, making the final result an ensemble prediction of many different sub-networks. An exponential moving average (EMA) prediction is maintained for each training instance and updated after each training step with the new predictions. As a result, the EMA prediction is the ensemble result of the model generated after the latest training step and all the models generated in the previous steps. The predictions obtained through the temporal self-ensembling method are closer to the real labels of the unlabeled part of the dataset, and as such, these inferred labels can be used as training targets for unlabeled data. The Mean Teacher (MT) [27] builds on the temporal self-ensembling method. The model takes on the role of a student and a teacher; as a student, the model normally learns, while as a teacher, the model creates targets for learning which the student then uses to train. The teacher is an ensemble of the student model, similar to the previous method. This model uses the average weights of the models as opposed to the average of the predictions. Thus, the teacher maintains the EMA weights of the student model. At training step t, the EMA(\(\theta_{t}^{\prime}\)) of successive \(\theta\) weights is defined as:  
\[\theta_{t}^{\prime}=\alpha\theta_{t-1}^{\prime}+(1-\alpha)\theta_{t} \tag{1}\]  
where \(\alpha\) is a smoothing coefficient hyperparameter. Mean Teacher maintains a total cost function which is a weighted sum of the consistency cost and classification cost. The consistency cost is calculated by comparing the outputs of the teacher and student, while the classification cost involves comparing the student's prediction and the target label. The Mean Teacher method allows the model to work with large datasets and improves classification accuracy.