esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SEM, BET, MIP, and TGA Tests Results
The volume of microporous and mesoporous attapulgite and dual-additives-amended clay increased significantly. Therefore, the dual-additives decreased the microporous size diameter of clay powder and increased its specific surface area. The TGA test results of clay amended by different additives are shown in Figure 13. The water in the clay was classified into tightly bound water (TBW) connected with clay minerals by hydrogen bond, loosely bound water (LBW) connected with clay minerals by molecular force [52; 53], and free water; the limit of water decomposition temperature was 120-230 \({}^{\circ}\)C, 75-120 \({}^{\circ}\)C, and 25-75 \({}^{\circ}\)C respectively [54]. The DTG curve was obtained from the differentiation of the TG curve. Each minimum point at the valley on the DTG curve represents the water decomposition point. Figure 13a shows that the temperature limits of TBW, LBW, and free water of unamended clay were 29.4 \({}^{\circ}\)C, 101.55 \({}^{\circ}\)C, and 184.41 \({}^{\circ}\)C respectively. The LBW temperature limit of attapulgite-amended clay was 106.68 \({}^{\circ}\)C, an increased of 5.05% compared with unamended clay, and its TBW temperature limit was 210.46 \({}^{\circ}\)C, an increase of 14%. The LBW temperature limit of diatomic-amended clay was \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multirow{2}{*}{**Dosage**} & \multirow{2}{*}{**Ratio** **(**Attapulgite to** **Diatomic)**} & \multirow{2}{*}{**Specific Surface** **Area (m\({}^{2}\)/g)**} & \multirow{2}{*}{**Less than** **Less than** **1.863 nm**} & \multirow{2}{*}{**Average** **Microporous Size**} \\  & & & & **1.863 nm** & & **19.577 nm** \\ \hline Unamended & - & - & 43.5843 & 0.01119 & 0.05508 & 0.7847 \\ Attapulgite-amended & 5\% & - & 49.7271 & 0.01284 & 0.07191 & 0.7829 \\ Diatomic-amended & 5\% & - & 45.4255 & 0.01201 & 0.05729 & 0.7803 \\ Dual-additives-amended & 5\% & 4 & 49.3243 & 0.01284 & 0.06779 & 0.7811 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The BET test results. Figure 11: MIP results of amended compacted clay specimens: (**a**) cumulative intruded pore volume vs. pore size diameter; and (**b**) incremental intruded pore volume. 105.10 \({}^{\circ}\)C, an increase of 3.5%, and that of dual-additives-amended clay increased by 8.9%. The TBW and free water have no relationship with the moisture retention performance of clay. The LBW is double-diffuse layers (DDLs) of clay minerals [55; 56], which is the critical factor in the moisture retention performance of clay. Figures 4,5 and 13, present that the trend of the temperature limit of clay amended by different additives is consistent with the results of moisture retention percent and liquid limit. It illustrates that the moisture retention percent of amended clay increases with the increasing the temperature limit of LBW. That is attributed to the fact that the thermal energy required to lose the same amount of LBW is greater with the increasing temperature limit; therefore, its moisture
## 2 Materials and Methods
### Data Collection and Pre-Processing  
Meteorological parameters (temperature, wind speed, pressure, and air humidity) were taken from [29] (Table S2 in Supplementary Materials).  
Descriptive statistics of the VOC concentrations during study periods are presented in Table 3. Data of VOCs concentrations lower than LODs (23.8% from the total number of measurements) and outliers (4.9% of the total number of samples) were omitted.  
The mean concentrations of benzene, toluene, methylene chloride, and \(n\)-heptane in all sampling days and sampling sites ranged from 23 to 64 \(\mu\)g m\({}^{-3}\). The maximum benzene concentration exceeded Kazakhstan 24-h limit (100 \(\mu\)g m\({}^{-3}\)[30]) by 3.4 times during the sampling period. The Kazakhstan benzene limit is 2.5 and 25.6 times higher than those in Belarus (40 \(\mu\)g m\({}^{-3}\)) and Israel (3.9 \(\mu\)g m\({}^{-3}\)), respectively [31]. While the mean concentrations of ethylbenzene, \(m\)-, \(p\)-xylenes, \(o\)-xylene, 1,2,4-trimethylbenzene, phenol, benzaldehyde, 3-picoline, naphthalene, 1,3,5-trimethylbenzene, and \(n\)-decane were 1.2-8.5 \(\mu\)g m\({}^{-3}\) and for propylbenzene, and fluorene--0.53-0.96 \(\mu\)g m\({}^{-3}\).  
The data of PM\({}_{2.5}\) and inorganic pollutants with concentrations lower than LODs (6.2% of the measurements) were omitted. The mean concentrations of NO\({}_{2}\), PM\({}_{2.5}\), SO\({}_{2}\), and CO during the studied period were 81, 44, 8.4, and 1.2 \(\mu\)g m\({}^{-3}\), respectively (Table 4). Maximum concentrations of NO\({}_{2}\) (436 \(\mu\)g m\({}^{-3}\)) and PM\({}_{2.5}\) (260 \(\mu\)g m\({}^{-3}\)) exceeded WHO limits by 17 times (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Analytes**} & \multirow{2}{*}{**N *} & \multicolumn{4}{c}{**Concentration, \(\mu\)g m\({}^{-3}\)**} \\ \cline{3-6}  & & & **Mean** & **SD** & **Minimum** & **Maximum** \\ \hline
1 & Benzene & 179 & 64 & 67 & 2.3 & 341 \\
2 & Toluene & 180 & 39 & 43 & 1.4 & 223 \\
3 & Ethylbenzene & 180 & 1.8 & 1.4 & 0.13 & 9.5 \\
4 & \(m\)-Xylene & 177 & 3.8 & 3.9 & 0.2 & 41 \\
5 & \(p\)-Xylene & 180 & 2.7 & 2.5 & 0.15 & 20 \\
6 & \(o\)-Xylene & 1,2,4- & 180 & 2.3 & 3.3 & 0.12 & 36 \\  & Trimethylbenzene & & & & & \\
8 & Trimethylbenzene & & & & & \\
9 & Propylbenzene & 117 & 0.53 & 2.5 & 0.10 & 27 \\
10 & Phenol & 180 & 3.1 & 3.9 & 0.19 & 31 \\
11 & Chlorobenzene & 156 & 0.21 & 0.68 & 0.040 & 8.2 \\
12 & Benzaldehyde & 180 & 3.4 & 2.7 & 0.11 & 13 \\
13 & 3-Picoline & 131 & 2.5 & 3.7 & 0.10 & 23 \\
14 & Naphthalene & 180 & 2.5 & 3.3 & 0.39 & 26 \\
15 & Fluorene & 101 & 0.96 & 1.39 & 0.14 & 11 \\
16 & 1,2- & 84 & 4.0 & 4.8 & 1.7 & 42 \\  & Dichloroethane & & & & & \\
17 & Methylene & 137 & 23 & 29 & 0.66 & 168 \\  & chloride & & & & & \\
18 & \(n\)-Decane & 142 & 8.5 & 5.7 & 1.5 & 28 \\
19 & \(n\)-Heptane & 99 & 64 & 48 & 8.7 & 235 \\ \hline \hline \end{tabular}  
* Note: N—number of measurements.  
\end{table}
Table 3: Descriptive statistics of the VOC concentrations during the study period.
## 3 Approach and Architecture  
The goal for the development of the ARCOMEM crawler architecture is to implement a socially-aware and semantic-driven preservation model. This requires thorough analysis of the crawled Web page and its components. These components of a Web page are called Web objects and can be the title, a paragraph, an image or a video. Since a thorough analysis of all Web objects is time-consuming, the traditional way of Web crawling and archiving is no longer working. Therefore, the ARCOMEM crawl principle is to start with a semantically-enhanced crawl specification that extends traditional URL-based seed lists with semantic information about entities, topics or events. The combination of the original crawl specification with the extracted information from the reference crawl is called the intelligent crawl specification. This specification, together with relatively simple semantic and social signals, is used to guide a broad crawl that is followed by a thorough analysis of the crawled content. Based on this analysis a semi-automatic selection of the content for the final archive is carried out.  
The translation of these steps into the ARCOMEM system architecture foresees four processing levels: the crawler level, the online processing level, the offline processing level and cross crawl analysis, which revolve around the ARCOMEM storage, as depicted in Figure 1. The ARCOMEM storage, consisting of an object store and a knowledge base, is the focal point for all components involved in crawling and content analysis. It stores all information from the crawl specification over the crawled content to the extracted knowledge. Therefore, a scalable and efficient implementation together with a sophisticated data model is necessary (see Section 4). The different processing levels are described as follows:
## 2 Problem Formulation
### Dynamic Cooperative Path Planning Constraints of UAVs
#### 2.1.2 Environmental Limitations  
In the dynamic planning environment, there are five different types of threats, i.e., terrain threats, radar, anti-aircraft artillery, missiles, and sudden threats. Of these, the sudden threats refer to threats that suddenly appear (i.e., radar, anti-aircraft artillery, and missiles). Sudden threats can be static or moving. The modeling process of the first four threats has been described in detail in our previous work [38]. This paper mainly describes sudden threats and environmental limitations.  
Considering the time uncertainty and state uncertainty of sudden threats, we describe the sudden threats in Definition 2.  
**Definition 2 (sudden threats).**_For planning space \(\mathcal{X}\), all UAVs in \(\text{UAV}_{N}\) fly according to the currently planted cooperative path, and the flight time interval is \(\left[t_{s},t_{g}\right]\). At \(t\in\left[t_{s},t_{g}\right]\), m new threats \(\mathcal{X}_{new}^{threat}:\left\{\mathcal{X}_{1}^{threat},\ldots,\mathcal{X}_{m}^ {threat}\right\}\) are sudden threats. The position change rule \(\mathcal{X}_{new}^{threat}\) is expressed as:_  
\[\Delta\mathcal{X}_{new}^{threat}=\underset{i=1}{\cup}^{m}\Delta\mathcal{X}_{m}^ {threat},\text{ where }\Delta\mathcal{X}_{m}^{threat}=\left\{\begin{array}{l} \varnothing\\ f(x,t_{i})\\ \text{unpredictable}\end{array}\right. \tag{5}\]  
_where \(\Delta\mathcal{X}_{m}^{threat}=\varnothing\) means that the sudden threat is static, \(\Delta\mathcal{X}_{m}^{threat}=f(x,t_{i})\) means that the dynamic sudden threat is moving, and \(\Delta\mathcal{X}_{m}^{threat}=\text{unpredictable}\) means that the threat movement is unpredictable._  
The definition of environmental limitations is given in Definition 3.  
**Definition 3 (environmental limitations).**_For any \(\text{UAV}_{i}\) in-planning space \(\mathcal{X}\), the limit of the planning environment is:_  
\[\left\{\begin{array}{l}z_{i}-z_{(x_{i},y_{i})_{\text{DMC}}}\geq h_{\text{ min}}\\ (p_{i},p_{i+1})\cap\mathcal{X}^{threat}=\varnothing\end{array}\right. \tag{6}\]  
_where \(z_{(x_{i},y_{i})_{\text{DMC}}}\) represents the terrain threat altitude when the UAV is located at \(p_{i}\) and \(\mathcal{X}^{threat}=\left\{\mathcal{X}_{known}^{threat}\cup\mathcal{X}_{new}^ {threat}\right\}\) represents the known radar, anti-aircraft artillery, and missile threats, as well as sudden threats._
## 3 Results and Discussion
### X-ray Photoelectron Spectroscopy (XPS)  
XPS was subsequently carried out for the binding energies (BEs) of C 1s, N 1s, O 2p, and Pd 3d\({}_{5/2}\) to determine the chemical environment of the surface palladium species in the PdAS(x)-MA polymers. For comparison, the XPS characterization of PdAS was also carried out. As shown in Figure 6, the wide range spectra of Pd 3d\({}_{5/2}\) at 338.40 eV, C 1s at 285 eV, N 1s at 400.0 eV, and O 2p at 533.8 eV were obtained. In the case of PdAS, XPS analysis showed BEs for Pd 3d\({}_{5/2}\) and 3d\({}_{3/2}\) at 338.4 and 343.7 eV, respectively, while for N 1s the BE was detected at 400.0 eV. The Pd and N XPS contributions are therefore in line with the reported results for Pd-SALEN complexes [31; 58]. As shown in the inset of Figure 6, the Pd contribution in the PdAS(x)-MA polymers can be more clearly observed upon magnification of the Pd core level region, where the BE of Pd 3d\({}_{5/2}\) can be clearly seen at 338.40 eV with typical spin-orbital splitting (\(\sim\)5.30 eV) for Pd 3d\({}_{3/2}\). The observed BEs of Pd 3d\({}_{3/2}\) and Pd 3d\({}_{5/2}\) therefore confirm the presence of palladium metal in the +2 oxidation state in all prepared catalysts [58]. This result confirms the successful immobilization of the PdAS MCM onto the resin structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & **Pd wt.\%** & \multirow{2}{*}{**Pd exp/Pd nom**} & **Imobilized** & \multicolumn{2}{c}{**Binding Energy**} \\  & & & **PdAS** & \multicolumn{2}{c}{**eV**} \\  & & & **mmol**\({}_{\text{Sat}}\)\({}^{-1}\) & **Pd** 3d\({}_{5/2}\) & **N 1s** \\ \hline PdAS & - & - & - & 338.4 & 400.0 \\ PdAS(1)-MA & 0.36 & 1.3 & 0.034 & 338.0 & 400.4 \\ PdAS(2)-MA & 0.51 & 0.8 & 0.050 & 338.5 & 400.0 \\ PdAS(5)-MA & 0.77 & 0.6 & 0.074 & 338.5 & 400.0 \\ PdAS(10)-MA & 1.38 & 0.5 & 0.132 & 338.3 & 400.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pd wt.% by inductively coupled plasma optical emission spectrometry (ICP-OES), Pd experimental/Pd nominal ratio, immobilized PdAS and X-ray photoelectron spectroscopy (XPS) core level of Pd 3d\({}_{5/2}\) and N1s for PdAS(x)-MA polymers.  
Figure 6: Survey XPS spectrum of the PdAS complex and PdAs(x)-MA catalysts (Pd 3d region; inset). Lines, PdAS complex (black), PdAS(1)-MA (red), PdAS(2)-MA (blue), PdAS(5)-MA (magenta) and PdAS(10)-MA (green).
## 5 Performance Evaluation  
In this section, the performance of our proposed method is evaluated and analyzed by comparing it with the Greedy algorithm [45], Always-Migrate [46] and Random Process [46].  
The time complexity of our method is \(O(n^{5})\), where \(n\) is the size of the dataset. Hence, the computation time with our model is high. To evaluate the performance of our proposed algorithm, we compare it with three other algorithms, i.e., the Greedy method, Always-Migrate method, and Random method. The time complexities of the Greedy method, Always-Migrate method, and Random method are \(O(n^{2})\), \(O(n^{2})\), and \(O(n^{4})\), respectively, where \(n\) is the size of the dataset. The time complexity of our proposed method is \(O(n^{5})\). Consequently, the time complexity of the proposed method is higher than that of the other methods. Consequently, the proposed method increases the time significantly. Therefore, we can see from the simulation results that the proposed method outperforms the Greedy, Always-Migrate, and Random methods in terms of the success rate. To decrease the time complexity, an algorithm based on the above method and ANN technology is proposed, which reduces the service time significantly [47]. In the ANN algorithm, we train ANN from the dataset obtained from the previous proposed method. The time complexity of the new proposed algorithm based on ANNs is \(O(nlog(n))\)[48]. Once the ANN is trained, the time complexity of our method becomes constant, i.e., \(O(1)\); thus, the scheme based on ANNs reduces the service time to the minimum, and the time complexity becomes lower than that of Greedy, Always-Migrate, and Random methods.
## 5 Results and Discussion
### System Design Requirements  
It is recommended that the system design requirements are in the system so that the patient and physician have more options.  
Location features for health recommendation and notification services  
Several features, such as notification and geographical location features, can be added to the system to improve patient healthcare services, for instance, notifications [58] and geographical location features. Expert 4 says, \"If a patient has asthma and the weather is dusty, the patient is supposed to know this information. So, the system could send an alert to take a sprayer or change his place, or even if a patient is in a high area, which may cause shortness of breath.\"  
Accessibility features and the right to be forgotten  
Because some patients may not be able to utilize the app properly, a guide should be considered. Patients also suggest improving accessibility, such as for those with visual impairments [11]. Another aspect is when a patient wants to remove their record, as the patient has the right to be forgotten in terms of their data, in compliance with the GDPR [7].  
Two sections for inpatient and outpatient details  
According to the conducted interviews, almost all healthcare centers have separate sections for inpatient and outpatient treatment; these sections have different procedures. However, this needs to be conducted correctly for all stakeholders. As Expert 7 explains, \"When you open the patient's record, the system should automatically open it to a window like a bifurcation... for example, if the patient is in an inpatient clinic, a bed icon appears, and if in an outpatient clinic, it shows another icon, and the visit itself, if I open it, shows me the name of the treating physician with each visit, the medical licenses, even the external medical prescriptions\".
# A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium
## 1 Introduction
aeruginosa_, _Moraxella catarrhalis_) [19] or to differentiate control samples from those containing _E. coli_ or _P. aeruginosa_[20]. Similarly, a hybrid E-nose with organic-inorganic nanocomposite sensors plus MOS sensors was proposed to differentiate _Enterobacter cloace_, _S. aureus_, _E. coli_, _P. aeruginosa_, and _S. enterica_, grown in a synthetic medium [21]. Recently, an E-nose with carbon-dots was developed allowing, in combination with PCA, to differentiate _E. coli_, _P. aeruginosa_, _S. aureus_, and _B. subtilis_, also grown in synthetic medium [22]. An E-nose-MOX allowed differentiating _E. coli_, _Klebsiella pneumoniae_, and _Salmonella enterica_ in pasteurized milk samples, with a predictive classification rate of 95% [23]. Optical E-noses were also applied to detect and differentiate (using PCA and/or HCA) different bacteria grown in synthetic media or in drinking water samples (_E. aerogenes_, _E. coli_, _E. faecalis_, _Klebsiella pneumoniae_, _Listeria monocytogenes_, _Protein mirabilis_, _P. aeruginosa_, _Streptococcus agalacliae_, and/or _S. aureus_) [24; 25]. However, fewer studies reported the use of E-noses for bacteria quantification. [PERSON] et al. [23; 26] verified the feasibility of using an E-nose-MOX device to semi-quantitatively assess the contamination levels of milk samples [23] or drinking water [26] with _E. coli_. Also, [PERSON] et al. [27] showed that an E-nose, with a single nanowire gas sensor, could accurately assess the total viable count (TVC) during trout fish spoilage,allowing estimating the decimal logarithmic of TVC with an error lower than 5%, using an SVM model. Similarly, an E-nose-MOX device allowed for the predicting of the TVC of aerobic bacteria in sardines, using partial least squares models (correlation coefficient of 0.91), which accuracy was attributed to the capacity of the device to assess the emission of VOCs generated from the sardines' bacterial degradation [28]. In this sense, the present study aims to evaluate not only the classification, but also the quantification performances of a lab-made E-nose, with commercial MOS gas sensors, for bacteria evaluation, grown in solid media. Four target microorganisms were selected taking into account their use as quality/contamination indicators for food/water samples, namely, two Gram-positive (_E. faecalis_ and _S. aureus_) and two Gram-negative (_E. coli_ and _P. aeruginosa_) bacteria. Previously, the research team has demonstrated that a potentiometric electronic tongue, comprising lipid polymeric sensor membranes could be used for these purposes, although it required the resuspension of each bacterium dried mass in water, which does not allow a direct analysis [29]. Alternatively, the E-nose-MOS device, coupled with LDA or multiple linear regression models (MLRM), could be used as a green, direct (i.e., not requiring any sample's pre-treatment) and non-invasive recognition/counting sensing platform for sniffing bacteria growth in solid media.
## 1 Introduction
In the first stage, 9623 augmented ROIs from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. The second stage deployed a DCNN for feature extraction followed by feature selection and random forest classifier. Pathway evolution was carried out iteratively using the genetic algorithm (GA), with tournament selection based on counter-preserving crossover and mutation. Using leave-one-case-out cross-validation, the second stage was trained on 9120 DBT ROIs from 228 mass lesions. The 2-class classification resulted in an 0.90 area under the ROC curve (AUC). In [PERSON], [PERSON] et al. [11], demonstrated fully automated deep-learning approaches for detecting breast lesions on a publicly available dataset. They looked at how well two data augmentation strategies (channel replication and channel concatenation) worked with five cutting-edge deep learning detection models. The implemented DL models were YOLO-Small (S), YOLOMedium (M), YOLO-Large (L), YOLO-XLarge (XL), and Faster R-CNN. This paper proposes a computer-aided multi-class diagnosis system for classifying DBT scans as benign, malignant, or normal. DBT augmentation techniques and colour mapping are integrated with a modified deep learning architecture (Mod_AlexNet) in the multi-class diagnosis model. An optimization layer with multiple high performing optimizers is added to the proposed modified deep learning architecture (Mod_AlexNet) so that it can be analysed and optimised using optimization algorithms. This research highlights some of the key deep learning models as well as a newly proposed modified deep learning architecture (Mod_AlexNet). To efficiently classify DBT slices, two experimental scenarios are applied. The first scenario proposed a computer-aided diagnosis (CAD) model that integrated DBT augmentation, image enhancement techniques, and colour feature mapping with six deep learning models for feature extraction, including ResNet-18, AlexNet, GoogleNet, MobileNetV2, VGG-16, and DenseNet-201. The features were then classified using a support vector machine (SVM) classifier. The second scenario used several optimization techniques to compare the performance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, and different evaluation performance metrics were computed. For different batch sizes, the optimization techniques included adaptive moment estimation (Adam), root mean squared propagation (RMSProp), and stochastic gradient descent with momentum (SGDM). This paper is organised into five sections. Section 2 contains the proposed model's architecture, deep learning models that have been implemented, and a detailed description of each phase. Section 2 also includes a description of the dataset, the experimental setup, and the performance measures. Section 3 shows and compares the results of the two experimental scenarios. Section 4 presents a discussion of the proposed model and its results. Finally, in Section 5, conclusions are drawn.
## 6 60 GHz WiFi: IEEE 802.11 ay
### Modulation and Coding Schemes (MCS)  
Following its predecessor, IEEE 802.11 ay includes three different physical layer modes, as follows:* Control mode;
* Single-carrier mode;
* OFDM mode.  
The control mode is based on DBPSK (differential binary phase shift keying), with a coding rate of 1/2. Therefore, the data rate is around 27.5 Mbps, which is low compared to other modes [59]. However, because the control mode is used for control signalling such as beacon transmission, beamform training, sweep messages, and other management messages, it is important to use a lower-order modulation scheme to ensure easier detection of control and management messages, even under low-SNR conditions.  
The single-carrier (SC) mode offers the advantage of simpler transceiver design and low energy consumption. However, the data rates in this mode can reach as high as 34 Gbps due to higher-order modulation schemes and low coding rates. Similarly, the OFDM mode also reaches close to 40 Gbps of the maximum bandwidth [60]. However, the OFDM transceivers are complex and power-hungry compared to SC transceivers. A complete list of MCS of these modes is given in Table 2.  
It is worth noting that the data rates discussed here are for a single spatial stream. Depending on the number of additional spatial streams used, the data rate will multiply.