esa-sceva/satcom-chunk-collection huggingface dataset:
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
## 2 Materials and Methods
### Pharmacokinetics of Veratraldehyde in Sprague-Dawley Rats  
Rats were starved 12 h before a single dosing test, hairs on the abdomen were removed (8 \(\times\) 6 cm), and then 300 or 600 mg/kg of repellent was applied to the exposed skin using a patch (5 \(\times\) 4 cm) for 24 h. The three Sprague-Dawley rats were grouped as a control (G1, vehicle) and two treatments (G2, 300 mg/kg and G3, 600 mg/kg). Using a disposable syringe (1 mL, 26G needle), each blood sample (300 \(\mu\)L equivalent to \(\geq\)100 \(\mu\)L plasma) from the jugular vein was collected at 0, 0.25, 0.5 1, 2, 4, 6, 8, 10, 12, 24, 48 and 72 h in a BD Vacutainer (K2 EDTA) tube with anticoagulant. To determine the veratraldehyde in blood samples, a calibration curve was made using 3 to 1000 mg/mL solution (R\({}^{2}\) = 0.9986). An internal standard solution (10 \(\mu\)L) was added to the calibration and plasma samples, and they were precipitated with 300 \(\mu\)L of 0.2% formic acid in acetonitrile. The prepared plasma solutions were centrifuged at 4 \({}^{\circ}\)C and 15,000 rpm for 5 min after shaking at 1000 rpm for 10 min and were stored in a freezer (\(-\)20 \({}^{\circ}\)C) for pK (pharmacokinetics) analysis. Quantitative analysis was carried out using LC-MS/MS (1290 series Infinity II, 6495 Triple Quadrupole, Agilent, USA) based on a previous report [29], and the analysis conditions used to determine the veratraldehyde in blood samples were as follows: a Unison UK-C18 column (3.0 \(\mu\)m, 2.0 \(\times\) 50 mm) was kept at 30 \({}^{\circ}\)C, and the mobile phase was composed of 0.2% formic acid in water (A) and 0.2% formic acid in acetonitrile (B) and was flowed at a rate of 0.5 mL/min with 80% A + 20% B at 0 min and 20% A + 80% B at 2.5 min. Each supernatant (2 \(\mu\)L) was injected and detected by coupled MS/MS. The detector conditions were as follows: gas temperature (200 \({}^{\circ}\)C), gas flow rate (14 L/min), nebulizer (200 psi), sheath gas temperature (250 \({}^{\circ}\)C), sheath flow rate (11 L/min), capillary (positive 3000 V, negative 3000 V), nozzle voltage (positive 1500 V, negative 1500 V), polarity (positive precursor 167.07 m/z), product (139.0 m/z // 123.7m/z), retention time (1.61 min) and CE (10 eV // 15 eV).
# Towards a Reliable Evaluation of Local Interpretation Methods
## 3 Human-Friendly Evaluation
### Framework of Evaluation  
Our evaluation framework consists of model decision process (a), generating interpretation process (b) and user evaluation process (c) (Figure 2). In the decision-making stage, we adopt a pre-trained model (Vgg-16). It is worth noting that we pay more attention to the model prediction results. At the interpretation stage, we investigate different interpretation methods. Specifically, we constructed a complete set of evaluation data sets at this stage. In the process of user evaluation, we focus on three dimensions of explanations: accuracy, persuasibility and class-discriminativeness. Study [26] has shown that visual interpretation methods are making mistakes. For example, methods assign high attributions to unimportant features (False Positive/FP) and low attributions to important features (False Negative/FN). Accuracy refers to the explanation should assign high attributions to important features (True Positive/TP) and low attributions to unimportant features (True Negative/TN). Persuasibility means that the user can accurately understand the explanation and the explanation result is meaningful. Class-discriminativeness represents the explanation only be given on the relevant objects and not cause user ambiguity. These three dimensions might reflect essentially what a good explanation is.  
**Definition 1:** We define the accuracy of explanation as accurately highlighting image features that play a key role in model decision making.  
**Definition 2:** We define the persuasibility of explanation as the satisfaction degree to human users, aiming to measure the subjective satisfaction of explanation.  
Figure 2: Overview of our evaluation framework, which contains three parts model decision process (**a**), generating interpretation process (**b**) and user-oriented evaluation process (**c**) (Note that the blue box represents the existing research results, the orange box is our innovative work).  
**Definition 3:** We define the class-discriminativeness as whether the explanation method has the ability to distinguish different object targets.
## 3 Actual Platform Benefits
### Reduce Deaths from Sepsis  
The organization improved sepsis mortality rates and improving care outcomes by using the advanced analytic component of the platform. Sepsis impacts almost 1.7 million adults in the U.S. and is responsible for nearly 270,000 annual deaths. One-third of all hospital deaths are patients with sepsis [27]. The machine learning prediction model used in the platform was developed by one of the authors of this paper, as described before. It is still too early to mention the results of the utilization of this feature. However, the goal of the organization is to reduce its sepsis mortality rate, the costs of the creation of its sepsis care transformation team, and the implementation of an evidence-based sepsis care practice.
## 3 System Model  
In this section, we firstly provide an energy evaluation model. Then, we construct a communication range judgement model. Finally, we design a reception state adjustment mechanism. The related symbols in this paper are listed in Table 1.  
Before the modeling process, the following assumptions should be followed:  
1. \(n\) sensor nodes are randomly distributed in a \(M\times M\) two-dimensional plane, and the positions of all nodes are settled.
2. Nodes in the network are homogeneous, where the maximum battery capacity is \(E_{m}\), and all nodes have the same transmitting power.
3. Positions of all nodes (denoted by \((x,y,)\)) can be known by position discovery techniques [22; 23]. If node \(j\) is within the communication range of node \(i\) (i.e., neighbor node), a wireless link exists between these two nodes. The extreme case (i.e., the routing loop problem) would be handled in the process of routing protocol design.
4. Each node is equipped with a solar device that can harvest additional energy from the surrounding environment. The energy harvesting rate \(EH(t)\) follows a Gaussian random distribution \(\mathcal{N}(b,c^{2})\), where the detailed expression is written as \(EH(t)=ae^{-\frac{(t-b)^{2}}{2a^{2}}}\) (\(t\) denotes time, and \(a\), \(b\), \(c\) represent the coefficients affected by light intensity).
5. The data buffer of each node is limited. Each node can sense its residual energy and data buffer occupancy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline \(E_{r}\) & Residual energy \\ \(E_{m}\) & Battery maximum capacity \\ \(EH\) & Energy harvesting rate \\ \(EH_{max}\) & Maximum energy harvesting rate \\ \(E_{0}\) & Initial residual energy \\ \(E_{tx}\) & Energy consumption for sending a packet \\ \(E_{rx}\) & Energy consumption for receiving a packet \\ \(E_{s}\) & Dissipation energy consumption of hardware \\ \(DB\) & Data buffer occupancy \\ \(B\) & The number of packets in buffer \\ \(Flag_{g}\) & Buffer packet reception state mark \\ \(p_{t}\) & Transmission power \\ \(P_{r}\) & Receive power \\ \(d_{0}\) & Maximum communication distance \\ \(d_{ij}\) & Distance between node \(i\) and \(j\) \\ \(NE(i)\) & Neighbor nodes set of node \(i\) \\ \(FN(i)\) & Forward transmission nodes set of node \(i\) \\ \(CN(i)\) & Candidate nodes set of node \(i\) \\ \(L\) & Packet length \\ \(R\) & Data transmission rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of core notations related to the system model.
# Compact N-Band Tree-Shaped Multiplexer-Based Antenna Structures for 5G/IoT Mobile Devices
## 6 Conclusions  
In this paper, a new multi-band antenna design concept is proposed, explained and proved through some practical prototypes. The main basis is the use of a feeding structure which ensure the impedance matching at a given operating frequency, isolating the structure at the remaining frequencies. When compared with an eventual alternative of a radiator with dual resonance, the option here presented, besides maximizing the efficiency, it avoids the usage of any bulky filters at the input.  
This strategy was implemented in a dual-band and a tri-band prototypes, both suitable to integrate in IoT sensors. Individually these antennas present good impedance bandwidths for all frequencies of interest, since the smallest measured value is 400 MHz.  
Regarding the antennas' gain, and considering that these structures are based in monopoles, these values are also satisfactory, especially when allied to the efficiency values witnessed. More importantly, the proposed concept of producing a _n-band_ antenna has proven not only to work, but it also assures higher efficiency values than what is commonly seen in the state-of-the-art for multi-band antennas.  
All the authors have contributed to this paper. [PERSON] has designed the proposed antennas, performed the simulations and the measurements, and wrote the paper. [PERSON] and [PERSON] have supervised the entire research, the approach used, the results analysis, and discussion. Additionally, both have strongly contributed to the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by FCT/MCTES through national funds and when applicable cofunded EU funds under the project UIDB/50008/2020-UIDP/50008/2020 and the European Regional Development Fund through the Competitiveness and Internationalization Operational Program, Regional Operational Program of Lisbon, Regional Operational Program of the Algarve, in component FEDER, and the Foundation for Science and Technology, Project RETIOT, POCI-01-0145-FEDER-016432. Authors declare no conflict of interest.
# Special Issue: New Trends in Recycled Aggregate Concrete
## 4 Long-Term Behavior of Recycled Aggregate Concrete  
[PERSON] [8] analyzed the effect of different types of recycled concrete aggregates (RCAs) on the equivalent concrete strength and drying shrinkage properties. A total of six mixes were proportioned using the modified equivalent mortar volume (EMV) method with three RCAs. The test results show that the concrete with RCAs produced from concrete sleepers exhibited compressive strength, Young's modulus, and flexural strength values equivalent, within 2% variation, to those values of the companion natural aggregate concrete. In other mixes, compressive strength was found to decrease to 11-20%. For 100% replacement, the Young's modulus increased up to 10% and the drying shrinkage increased up to 8%, while for 50% replacement, the Young's modulus decreased up to 8% and the drying shrinkage dropped up to 4%.  
[PERSON] et al. [9] performed an analysis and modelling of the shrinkage and creep of reactive powder concrete (RPC) with various steel fiber contents. It was revealed that the compressive strength and modulus of elasticity increased with increasing steel fiber content, contrary to shrinkage and creep. A good linear relationship was found between the axial stress ratios and creep strain. All four existing models were unable to accurately predict the shrinkage and creep of RPC. However, a good agreement between the experimental results and the proposed shrinkage and creep numerical models was observed.
## 3 Methodology
### Motivations and Deployment Considerations  
The deployment of a third-party certification authority (CA) for monitoring urban traffic and implementing a separate authentication communication scheme in vehicular AdHoc networks (VANETs) is motivated by several key factors. This section provides an overview of the motivations and considerations behind using the third-party CA, the impact of capacity and speed on network signaling overhead, and the importance of cluster management.
## Materials and Methods  
### Experimental Site  
The experiment was conducted in Burkina Faso, a West African country with an area of 274,200 km\({}^{2}\), at latitude 12\({}^{\circ}\)30\({}^{\prime}\)00.00\({}^{\prime\prime}\) N and longitude 1\({}^{\circ}\)48\({}^{\prime}\)00.00\({}^{\prime\prime}\) W. Its capital, Ouagadagou, is located in the center of the country. Burkina Faso shares its borders with six neighboring countries: Mali at the northwest, Ivory Coast at the southwest, Ghana at the south, Togo and Benin at the southeast, and Niger at the northeast. The geographical position of Burkina Faso on the world map can be found in the reference [23].  
As shown on the global rain-rate map [24], Burkina Faso is situated in the rain-rate zones, called the E, K, and N zones, in the ITU recommendations, that is to say, into the zones of 22 mm/h, 42 mm/h, and 95 mm/h. This particularity will be discussed in a next paper concerning CML data processing and results.
## 3 Related Work
### ASD Prediction Using ML Approach
#### 3.1.2 ASD Prediction Using ML with FS Methods  
[PERSON] et al. [25] compared several FS algorithms to classify ASD. They performed two experiments. First, with all features, they calculated the accuracy of the random tree (RT) classification algorithm and the result was 95.1%. Second, to improve the efficacy of the RT classifier, they used chi-square, correlation feature selection (CFS), bagged tree feature selector (BT), recursive feature elimination (RFE), subset evaluation, and information gain (IG). The optimal selection of each feature selection algorithm was assisted by a 10-fold cross-validation RT classification algorithm. The results show that the BT model with the RT classifier had a high accuracy of 95.7% compared with 95.2% for REF.  
[PERSON] et al. [26] analyzed four ASD datasets for toddler, child, adolescent, and adult. They applied different feature selection algorithms on ASD datasets such as relief feature, IG, and CS, and relief feature outperformed the others. They also used some classification techniques and the sequential minimal optimization (SMO) algorithm worked best for the detection of ASD cases for all of the ASD datasets. A 10-fold cross-validation method also was used to assess the datasets.  
This study by [PERSON] et al. [27] used IG filter with three ML classifiers. They used gene expressing profiles to compare the performance of ML classifiers such as decision tree (DT), KNN, and NB after applied IG filter. The results showed that the Naive Bayes had an accuracy of 86.67%, while the accuracy was 83% for KNN and 53% for DT.  
[PERSON] et al. [28] proposed an SVM-based classification system that used brain images to classify 6-month-old infants at high risk for ASD. Two feature selection algorithms were performed. First, a t-test and followed by the LASSO logistic regression. LASSO logistic regression is a widely used feature selection algorithm that can pick a parsimonious collection of features from a wide range of potential candidates to improve the classification accuracy. It only maintains the most discriminatory features, thus discarding the obsolete ones. The outcome achieved an accuracy of 76%.  
The purpose of a study by [PERSON] et al. [29] was to solve high-dimensional and heterogeneous dataset problems like the Autism Brain Imaging Data Exchange (ABIDE) dataset. Previous works on the ABIDE dataset have reported accuracies less than 60%. In their study, they investigated the predictive power of MRI in ASD utilizing three classifiers: RF, SVM, and gradient boosting machine (GBM). They used RFE for the feature selection technique and the results showed that the classification accuracy could reach 60%.
## 5 Limitations and Future Directions  
This paper specifically addresses the significant impact of moisture on the carbonation reaction, without establishing correlations with other influencing factors. Future investigations should aim to comprehensively explore the carbonation mechanism of steel slag by concurrently studying moisture, temperature, CO2 pressure, and other relevant variables. The objective of this paper is to emphasize the importance of moisture in the carbonation mechanism through controlled manipulation of its content; however, implementing such control measures in practical engineering scenarios may prove challenging based on the methods employed herein. Therefore, it is imperative for researchers to develop a suitable approach for regulating water content.
###### Abstract  
In 5G networks, heterogeneous cloud radio access network (H-CRAN) is considered a promising future architecture to minimize energy consumption and efficiently allocate resources. However, with the increase in the number of users, studies are performed to overcome the energy consumption problems. In this study, we propose a power control algorithm with mobility prediction to provide a high-energy efficiency for 5G H-CRAN. In particular, the proposed algorithm predicts UE mobility in vehicular mobility scenarios and performs remote radio head (RRH) switching operations based on % prediction results. We formulate an optimization problem to maximize the energy efficiency while satisfying the outage probability requirement. We then propose an RRH switching operation based on Markov mobility prediction and optimize the transmission power based on a gradient method. Simulation results demonstrate the improved energy efficiency compared with those of existing RRH switching-operation algorithms.  
5G; heterogeneous cloud radio access network; vehicular mobility; remote radio head switching operation +
Footnote †: journal: Sensors
# Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
## 4 Conclusions  
A UAV-aided mmWave downlink cooperative CP-less GFDM system is proposed in this paper. UAVs are combined with a terrestrial cellular network, and emphasis is placed on physical layer security for massive machine-type communication (mMTC) users in such a network. In this study, the implementation of an efficient low-complexity T-transformation spreading code as well as MU-MIMO Gyre precoding resulted in the reduction in both MUI and the enhancement of the achievable ergodic rate. Utilizing null subcarriers at both ends of a GFDM block for sub-time symbols and better than raised cosine (BTRC) filter resulted in significant reduction in out-of-band (OOB) spectrum power by eliminating the effects of the fading channel at mmWave. The 3D mobility model resulted in reducing the effects of the Brownian motion of UAVs. In this proposed system, the performance in terms of BER is not quite satisfactory. However, the BER performance is reasonably acceptable with the combined implementation of (3,2) SPC and RA channel-coding schemes and outperforms other proposals found in the literature. In the future, introducing massive MIMO to the proposed system can improved it and make it more robust.  
Conceptualization, J.J.S., S.E.U. and R.R.; Data curation, J.J.S., S.E.U., R.R. and M.R.I.; Formal analysis, J.J.S., S.E.U., R.R., M.R.I. and M.M.R.; Funding acquisition, R.R., M.R.I. and M.A.P.M.; Investigation, J.J.S., S.E.U., R.R. and M.M.R.; Methodology, J.J.S. and S.E.U.; Project administration, S.E.U., R.R., M.R.I. and A.Z.K.; Resources, J.J.S. and S.E.U.; Software, J.J.S. and S.E.U.; Supervision, S.E.U., R.R., M.R.I. and A.Z.K.; Validation, R.R., M.R.I. and M.M.R.; Visualization, J.J.S., M.M.R. and M.A.P.M.; Writing--original draft, J.J.S. and S.E.U.; Writing--review & editing, R.R., M.R.I., M.M.R., A.Z.K. and M.A.P.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Testing Protocol
#### 2.5.2 Instruments for On-Snow Assessments  
We selected the CARV system [44] (Carv Digital Ski Coach, London, UK) because it offers real-time biomechanical feedback relevant to balance, edging, and pressure--key aspects of skiing performance. The smart insoles (dimensions: 90 \(\times\) 45 \(\times\) 18 mm; weight: 27 g) contain an accelerometer, a gyroscope, a magnetometer, a barometer, and 36 pressure sensors, collectively sampling at 25 Hz. The insole was placed inside the ski boot, between the shell and the liner, while a small data logger was attached to the boot's exterior. Data were transmitted via Bluetooth Low Energy to the accompanying smartphone app. In total, 35 metrics were processed, aligning with our goal to assess technique improvements following balance training. The system's battery (Li-Po) lasts up to 8 h, allowing for comprehensive on-snow evaluations.
## 5 Test Results and Evaluation
### Evaluation of the Amplitude of Shape Imperfections in the Axial Direction
Thus, to confirm the shape imperfections of the cylinder, we made plots using Equations (16) and (17). \[x=r\cos\theta+Q(w_{\theta}-w_{0})\cos\theta \tag{16}\]  
\[y=r\sin\theta+Q(w_{\theta}-w_{0})\sin\theta \tag{17}\]  
Figure 13 shows the plot for cylinder No. 191. The maximum (\(-\)minimum) amplitude of local directional-shape imperfections can be identified in this plot. Note that the shape imperfections in the figure have been enlarged by \(Q\) = 30 times. Figure 14 shows the relationship between the KDF and offset angle for the various specimens. The offset angles in this case are 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), and the shape imperfections are the maximum and minimum values. As shown, the KDF values have some scatter. The offset direction and distance both affect the KDF, i.e., the KDF does not have a simple correlation with offset direction, shape imperfections, or offset distance. This conclusion is consistent with the ANOVA. Figure 13: Plot of shape imperfections of cylinder No. 191 (0\({}^{\circ}\) offset).