esa-sceva/satcom-chunk-collection huggingface dataset:
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 2 System Model
### Spectrum Sensing  
Since SUs are using CR technology, the separate spectrum-sensors are deployed on top and inside the smart building, which are responsible for detecting the vacant spots in the spectrum for SUs. The transmitter detection (the signal received by the spectrum-sensors)-based hypothesis model can be given by Equation (2) as follows [28]:  
\[r_{k}(t)=\begin{cases}\sigma_{k}^{2}(t)&ifH_{0}\\ \tilde{s}(t)h(t)+\sigma_{k}^{2}(t)&ifH_{1}\end{cases} \tag{2}\]  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbols** & **Meaning** \\ \hline \(\mathcal{F}\) & Objective function \\ \(N\) & Available channels \\ \(C\) & Traffic classes \\ \(Q\) & Spectrum-sensors \\ \(I\) & IoT devices \\ \(M\) & Mobile users \\ \(c\) & Subscript of class \\ \(u\) & Subscript of the SU belongs to the \(c\)th class \\ \(j\) & Subscript of the swarm particle \\ \(k\) & Subscript of channels \\ \(NM\) & Network monitor \\ \(HSV\) & History status vector of a channel \\ \(SU\) & CR-based mobile user, IoT device or NM \\ \(T\) & Total history slots \\ \(\bar{P}\) & Transmission power of SU \\ \(Z\) & Partitions of HSV \\ \(\omega\) & Weights for partitions of HSV \\ \(E\) & Energy of the PU signal used for spectrum sensing \\ \(V\) & Velocity of the swarm particle \\ \(S\) & Position of the swarm particles \\ \(Pb\) & Global best of the swarm particles \\ \(a_{min}\) & Lower limit of data rate requirement \\ \(\beta_{min}\) & Upper limit of BER tolerance \\ \(\gamma min\) & Lower limit of the channel stability requirement \\ \(a_{k}\) & Data rate of the \(k\)th channel \\ \(b_{k}\) & BER of the \(k\)th channel \\ \(\
abla_{c}\) & Represents SUs of the \(c\)th class \\ \(\theta_{k}\) & Stability index of the \(k\)th channel \\ \(x_{k}^{x,u}\) & Stability index of the \(k\)th channel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols and notations.  
where \(r_{k}(t)\) is the signal received by a spectrum-sensor on the \(k\)th channel, \(\tilde{s}\)(t) is the transmitted signal of the PU, \(h\)(t) is the channel response and \(\sigma_{k}^{2}(t)\) represents the noise content. For the sake of simplicity, we employ a double threshold-based energy detector, which calculates the energy of a PU signal on the \(k\)th channel as follows:  
\[E_{k}=\sum_{y=1}^{Y}|r_{k}|^{2} \tag{3}\]  
if the value of \(E_{k}\) is greater than threshold-1, it is assumed that the PU is present over the \(k\)th channel, and the SU cannot use the \(k\)th channel for its transmission. However, if the value of \(E_{k}\) is less than threshold-2, it declares that the channel is available to SUs.
# Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section
## 3 Materials and Methods
### Data and Measures  
The data were collected in 2 stages: the first stage was the 2 nd day after CS, and the second stage was the 6 th week after CS. During the first stage, from 469 women, we collected medical and demographic data from the socio-demographic questionnaire and past traumatic life events from the Life Events Checklist-5 (LEC-5) of DSM-V and Criterion A from the adapted first Criterion of PTSD. At the second stage, the PTSD symptoms from the Post-Traumatic Stress Checklist (PCL-5) of DSM-V are collected (The dataset that was used can be found in: [[https://users.uowm.gr/chorovas/appsci/nn_ptsd.html](https://users.uowm.gr/chorovas/appsci/nn_ptsd.html)]([https://users.uowm.gr/chorovas/appsci/nn_ptsd.html](https://users.uowm.gr/chorovas/appsci/nn_ptsd.html)) (accessed on 20 June 2022)). The life events checklist (LEC) is the only measure that individuals can determine different levels of exposure to a traumatic event in their lives [25]. For a PTSD diagnosis, 8 criteria must be met. For the first criterion (Criterion A), the individual must have been exposed to death, threatened death, serious injury, or sexual violence in one of the following ways: (a) direct exposure, (b) witness to the event, (c) information of the event, and (d) exposure in the working space [26]. For this study, Criterion A was adjusted accordingly. The post-traumatic stress checklist (PCL-5) is a self-report scale, which was developed to measure and evaluate PTSD and PTSD Profile symptoms [1,27]. In the present study, the postpartum women replied via telephone to 20 questions during the 6 th postpartum week, corresponding to 20 symptoms of the criteria B (re-experiencing), C (avoidance), D (negative thoughts and feelings), and E (arousal and reactivity). All replies are scored on 5-point scales (range zero to four). A score of one or more in the categories of criteria B and C and two or more in categories D and E are considered PTSD symptoms. Depending on the symptoms, the postpartum women were diagnosed with (a) provisional diagnosis of PTSD and (b) PTSD profile [27,28]. The demographics, prenatal health, and mental health variables that were collected are presented in Tables 1-3 (statistical tests with IBM SPSS Statistics v.20).
## 4 Event Extraction from Text  
Along with entities, event recognition is one of the major tasks within information extraction [12]. In ARCOMEM, we refer to an event as a situation within the domain (states, actions, processes, properties) expressed by one or more relations. This perspective on events as relational constructs between participants is in line with foundational ontological definitions [13] and the conceptual organization of existing _de facto_ event models (e.g., linked open descriptions of events (LODE) [14], event-model-F [15], and the event ontology [16]. Events can express these relational constructs at any level of semantic granularity: coarse-grained events, such as the first landing on the moon or a natural disaster, regularly occurring events, such as elections or TV serials, or fine-grained events, such as the resignation of a politician.  
There are various strategies to recognize events that can be broadly categorized into top-down and bottom-up approaches, both of which were implemented in the ARCOMEM event extraction module. The top-down method involves predefined sets of relations, possibly structured into an ontology, which makes such tasks domain-dependent, but feasible. This approach gained great popularity within the ACE context [17] and has been adopted in ARCOMEM for semantically well-defined events, such as strikes and music performances. The top-down approach involves a form of template filling, by selecting a number of known events in advance, and then identifying relevant verbs and their subjects and objects to match the slots. For example, a \"performance\" event might consist of a band name, a verb denoting some kind of \"performing\" and, optionally, a date and location. This kind of approach tends to produce high precision, but relatively low recall. Domain-independent techniques involve a more bottom-up identification of entities and relations, which can incrementally be generalized into events [18]. Exploiting natural language technology enables the discovery of n-tuples of relevant items (entities) belonging to an n-ary relation in natural language documents in an unsupervised way [19]. In ARCOMEM, we follow a bottom-up approach, which consists of identifying verbal relations in the text and classifying them into semantic categories, from which new events can be suggested. This kind of approach produces higher recall, but lower precision.  
The key NLP components for event extraction are named entity recognition, term extraction and relation extraction. Once extracted, acquired event knowledge is formalized and stored according to (_de facto_) standard representation models. The GATE application we have developed for event recognition is similar in structure to the one for entity recognition and is designed to be run after the entity recognition application has first been run on the corpus.
# The BCL Method for DSSC: Basis and Applications +
## 4 A Big Challenge: Understanding the Absorption Band of Ruthenium Dyes
### Results
#### 4.1.2 Absorption Spectra of Complexes
The HOMO energy varies between \(-6\) and \(-6.5\) eV, showing two nearly degenerated ligands, one centered on the metal (orbitals d) and the other a mixture of d orbitals with \(\pi\) orbital from the chromophoric ligand. The composition is similar for all the complexes with LH ligand independent of the substituent of the bipyridine ligand  
The first six unoccupied orbitals are dominated for the LUMO's of the bipyridine ligands and the chromophoric ligand. Electronic density analysis shows that the LUMO is localized in the P fragment of the chromophoric ligand. Otherwise, the LUMO + 1 (0.11 eV higher in energy) is localized on the bipyridine ligand. In complexes 1 and 3, the LUMO is centered in the bipyridine ligand that presents a lower energy than that of the chromophoric ligand LH. When the dimethylamino substituent is introduced in complexes 2 and 4, the HOMO energy raises above \(-5.8\) eV as a consequence of the competition between the electron-attracting effect of the substituted bipyridine and the effect of the electron-donor substituent on the chromophoric ligand. The electron density distribution of the HOMO is also altered, being principally located on the F fragment of the chromophoric ligand. The HOMO-LUMO gap is reduced about 40% in these complexes, in comparison to the corresponding complexes with no amino substitution.
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## 4 Performance Evaluation
### LFM Radar Simulation
These results show the effectiveness of the proposed method for maneuvering targets (not only the targets with constant acceleration). In addition, the simulation results are consistent with the theoretical analysis described in the previous subsection. Figure 4: Simulation results for \(c_{\rm RD}=-0.5\) (**a**) and \(0.5\) (**b**). Figure 3: Simulation scenario. (**a**) true orbit of the target and radar position; (**b**) true acceleration of the target; and (**c**) true and measurement range for \(c_{\rm RD}=\pm 0.5\).
# Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
## Appendix A  
In the following, we make clear how the two sufficient conditions (38) and (39) for the LF-CLT are satisfied for \(\Lambda=\sum_{i=1}^{N}v_{i}\) under hypothesis \(\mathcal{H}_{j}\), for \(j=0,1\). Notice that in the LF-CLT, essentially, the objective of the two conditions is to ensure that no single random variable is dominate in its contribution to the summation operator.  
In the first step we shall prove that the first condition (38) is satisfied. As can be seen from (21) and (34), the variance of \(v_{i}\), for \(i=1,2,\cdots,N\), under the hypotheses \(\mathcal{H}_{1}\), can be represented as  
Figure 10: The global probability of error detection as a function of the number of SVUs.  
\[\begin{array}{ll}D[v_{i}\left|\mathcal{H}_{1}\right.]&=P_{Di}\left(1-P_{Di}\right) \\ &=\left[\frac{1}{2}+\left(P_{di}-\frac{1}{2}\right)\sqrt{\frac{\gamma_{i}}{1+ \gamma_{i}}}\right]\left[\frac{1}{2}-\left(P_{di}-\frac{1}{2}\right)\sqrt{ \frac{\gamma_{i}}{1+\gamma_{i}}}\right]\\ &=\frac{1}{4}-\left(P_{di}-\frac{1}{2}\right)^{2}\left(\frac{\gamma_{i}}{1+ \gamma_{i}}\right).\end{array} \tag{10}\]  
It is sufficient to show that, under the hypothesis \(\mathcal{H}_{1}\), the variance of \(v_{i}\) is lower bounded by a positive value, as long as the \(P_{di}\) are bounded away from 0 and 1. Certainly, the \(P_{di}\) can't be equal to 0 and 1 in the practical vehicular networks.  
In the next step, we show that the second condition (39) is satisfied. Let us make use of the fact that \(v_{i}\) is a binary random variable which follows a Bernoulli distribution characterized by the associated \(P_{Fi}\) and \(P_{Di}\). It is plain enough that under the hypotheses \(\mathcal{H}_{1}\),  
\[\begin{array}{ll}\mathrm{E}\left[|v_{i}-\mathrm{E}[v_{i}]|^{3}\left| \mathcal{H}_{1}\right.\right]&=P_{Di}\left(1-P_{Di}\right)\left(P_{Di}^{2}+(1 -P_{Di})^{2}\right)\\ &<P_{Di}\left(1-P_{Di}\right).\end{array} \tag{11}\]  
A similar analysis can be conducted, \(P_{Di}(1-P_{Di})\) is upper bounded by a positive value.  
In the above analysis, we prove that the two sufficient conditions (38) and (39) in the LF-CLT are satisfied under the assumption of hypothesis \(\mathcal{H}_{1}\) explicitly, but the same proved method can be carried out to demonstrate the effectiveness under the hypothesis \(\mathcal{H}_{0}\).
## 4 Results
### Feature Analysis
following_users_graph_in_degree_). It is fair to say that the concept of followers plays an important role in the selection of contributing features. Apart from that, keywords are also a superior contributing feature, especially in the form of features resulting from centrality measures in/out-degree, in-strength and clustering coefficient (in Figure 3 those are the features 5, 9, 11 i 12). A detail to note is that in-degree centrality of keywords has a greater impact than out-degree. In terms of layers/graph types, the replay network has spawned a larger number of features with valuable impact than the mention layer/graph. Last but not least, network metadata also make a satisfactory contribution to the retweet prediction, for example number of followers (follower count), number of changed statuses of the user (statuses count), presence of media in the tweet (entities media), or presence of URL in the tweet (entities URLs) are important features that are positioned at the top of the list. Compared with other similar studies, our results are in line with the findings of [PERSON] et al. (2019). They also examined the content and contextual features in the task of retweet prediction, but to a much lesser extent than our study. In general, their findings suggest that retweetability has a very close relationship with the social network context of the authors and the informational content and value contained in tweets, similar to our results. In particular, they showed that among content features, URLs and hashtags have strong potential in the prediction of retweeting. Furthermore, among contextual features which are related to the network, the number of followers and followees as well as the age of the account affect retweetability. In our results, the number of followers also has a high correlation with retweeting. However, according to our SHAP analysis, the presence of media has a much stronger impact than the presence of URLs. Next, the in-degree measures of the follower layer and the tweet layer are ranked as high, as well as the impact of the in-strength and out-strength centrality measures of the retweet layer. This suggests that multilayer network measures have the potential for retweet prediction which has not been shown before.
# Plasma-Activated Water Affects the Antioxidant Contents in Water Spinach
## 1 Introduction
Heavy metal contamination of the environment is an important concern because plants can absorb chemical toxicants and further facilitate the entry of toxic metals into the food chain [30]. Plant cells growing in soil containing heavy metals are damaged by the imbalance between ROS and the protective systems of cells [31]. Heavy metals, such as lead (Pb) and cadmium (Cd), are of particular importance because of their dive consequences on the environment and human health [32; 33]. Plants have automatically developed a range of protective systems, including protective systems to overcome invasion, such as increasing antioxidative enzymes viz. catalase, peroxidase, superoxide dismutase, and ascorbate peroxidase, involved in the metabolism of antioxidants [30; 31; 34]. Several studies have shown that plants' secondary metabolites increase under heavy metal stress [34; 35; 36] and that heavy metals exhibit the ability to induce oxidative stress by generating ROS [37]. [PERSON] et al. [38] found that plasma could significantly increase the total phenolic (TP) content and antioxidant activity of mandarin and freshly cut pitaya fruits [39]. By contrast, [PERSON] et al. [40] indicated that the effects of plasma on the antioxidant content and activity of kiwifruit were not significant. Meanwhile, studies investigating the effect of PAW on the antioxidant content and activity of leafy vegetables under heavy metal stress are lacking. Therefore, this study aimed to elucidate whether PAW treatment induced changes in the antioxidant potential of water spinach by investigating the TP and total flavonoid (TF) contents of water spinach planted in soil contaminated with Pb or Cd.
## 3 Experiment  
In general, there are two stages during the whole cycle life of underwater vehicles, i.e., storage and operation. It is well known that the batteries possess self-discharge characteristics and are sensitive to ambient temperature. Therefore, the storage and discharge experiments have been conducted. The lithium argon-sulfuryl chloride battery (ER48690), which is a power source for a certain type of underwater vehicle, is taken as the test object and the basic parameters are listed in Table 2. The test system, used in this paper, is shown in Figure 1, including a discharge panel, XW5005C8, manufactured by NEWARE, a thermal chamber from Giant Force to control the storage and operation temperature, a computer to program and store the experimental data. The acquisition accuracy of current and voltage was 1 mA and 1 mV, respectively.  
The purpose of the storage experiments is to explore the influence of temperature and storage time on battery capacity. The specific experimental process is shown in Figure 2. First, a total of 72 cells were selected to form an experimental sample. Then, the samples were divided into four groups and stored at four temperatures, i.e., 0 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, and 54 \({}^{\circ}\)C. Finally, with an interval of 15 days, three cells were taken out at each temperature and discharged at a nominal current. Three independent cells were used to ensure the reliability of experimental data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Nominal Capacity** & **Nominal Voltage** & **Nominal Current** & **Lower Cut-Off Voltage** \\ \hline ER48690 & 22 Ah & 3.6 V & 2.2 A & 3 V \\ \hline \hline \end{tabular}
\end{table}
Table 2: The basic parameters of the tested battery system, i.e., ER48690.  
Figure 1: The test platform with a (**left**) NEWARE discharge penal and (**right**) Giant Force thermal chamber.  
Figure 2: The flow chart of the storage experiments.  
During the discharge experiments, temperature and discharge rate were taken into account to explore the battery capacity characteristics. The detailed experimental steps are shown in Figure 3. Herein, 36 cells were randomly selected from the sample with good consistency. At four different temperatures, i.e., \(-10\)\({}^{\circ}\)C, \(0\)\({}^{\circ}\)C, \(25\)\({}^{\circ}\)C, and \(40\)\({}^{\circ}\)C, \(36\) cells were divided into four groups. Then, each cell was discharged at three different rates, i.e., 0.05 C, 0.1 C, and 0.2 C, resulting in 12 different working conditions, where the discharge experiments were conducted and experimental data were recorded.
## 3 Results
\begin{table}
\begin{tabular}{l c c c} \hline  & \multicolumn{3}{c}{**Hzt FV Profile**} \\ \hline  & **F\({}_{0}\) (N-kg\({}^{-1}\))** & **P\({}_{\rm max}\) (W-kg\({}^{-1}\))** & **V\({}_{0}\) (m-s\({}^{-1}\))** & **T10m (s)** \\ \hline Professional & 7.31 \(\pm\) 0.43 & 16.42 \(\pm\) 1.02 & 9.04 \(\pm\) 0.33 & 1.73 \(\pm\) 0.04 \\ Amateurs & 6.92 \(\pm\) 0.6 & 15.64 \(\pm\) 1.4 & 9.11 \(\pm\) 0.3 & 1.77 \(\pm\) 0.07 \\ U18 DH & 7.1 \(\pm\) 0.73 & 15.86 \(\pm\) 1.72 & 9.01 \(\pm\) 0.44 & 1.76 \(\pm\) 0.09 \\ U18 LN & 6.74 \(\pm\) 0.49 & 15.09 \(\pm\) 1.37 & 9.02 \(\pm\) 0.42 & 1.79 \(\pm\) 0.07 \\ \hline  & \multicolumn{3}{c}{**Performance Tests**} \\ \hline  & **M505 test (s)** & **505 test (s)** & **M505\_CODD** & **505\_CODD (s)** \\ \hline Professional & 2.45 \(\pm\) 0.09 & 2.21 \(\pm\) 0.07 & 0.72 \(\pm\) 0.07 & 0.49 \(\pm\) 0.07 \\ Amateurs & 2.48 \(\pm\) 0.07 & 2.27 \(\pm\) 0.09 & 0.70 \(\pm\) 0.05 & 0.5 \(\pm\) 0.11 \\ U18 DH & 2.50 \(\pm\) 0.11 & 2.29 \(\pm\) 0.08 & 0.74 \(\pm\) 0.09 & 0.53 \(\pm\) 0.09 \\ U18 LN & 2.57 \(\pm\) 0.08 & 2.39 \(\pm\) 0.07 & 0.77 \(\pm\) 0.07 & 0.60 \(\pm\) 0.08 \\ \hline \end{tabular} Data are mean \(\pm\) SD; F\({}_{0}\) = maximum horizontal force production; F\({}_{\rm max}\) = maximum power output; V\({}_{0}\) = maximum velocity application; N = newtows; W = watts; m = meters; s = seconds; kg = kilograms; T10m = time in 10 m; M505 test = modified 505 test; M505_CODD = change of direction deficit in M505 test; 505_CODD = change of direction deficit in 505 test.  
\end{table} 
Table 1: Mean and standard deviation (SD) of the mechanical capabilities of Hzt FV profile (F\({}_{0}\), V\({}_{0}\) and P\({}_{\rm max}\)) and performance tests. Figure 1: Correlations between 505 test, M505 test and the mechanical variables of Hzt FV profile (F\({}_{0}\), V\({}_{0}\) and P\({}_{\rm max}\)). \({}^{*}\) Correlation is significant (\(p<0.05\)). \({}^{**}\) Correlation is significant (\(p<0.01\)). Confidence interval at 90%; F\({}_{0}\) = maximum horizontal force production; P\({}_{\rm max}\) = maximum power output; V\({}_{0}\) = maximum velocity application; N = newtows; W = watts; m = meters; s = seconds; kg = kilograms. Table 2 displays the explanatory parameters from the multiple linear regression analysis employed to determine the relevance of the variables of Hzt FV profile on the performance prediction in the different tests analyzed.