esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis  
### Baseline Scheme Without Energy Signal Transmission  
Since the proposed scheme in (15)-(18) considers energy signal transmission, unlike the conventional scheme in [15], its effectiveness can be evaluated by comparing its performance with that of the following BL scheme that does not involve energy signal transmission:  
\[P_{d,\mathrm{I}}^{\mathrm{BL}}=\min\Bigg{(}\Bigg{(}\frac{\rho_{c }^{\mathrm{ND}}\sigma_{c,\mathrm{cov}}^{2}-(1-\rho_{c}^{\mathrm{ND}})^{2}P_{b} |h_{\mathrm{bc}}|^{2}}{(1-\rho_{c}^{\mathrm{ND}})|h_{dc}|^{2}}\Bigg{)}^{+},P_{ d}^{\mathrm{max}}\Bigg{)}, \tag{19}\] \[P_{d,\mathrm{E}}^{\mathrm{BL}}=0,\] (20) \[\rho_{c}^{\mathrm{BL}}=\frac{\rho_{c}^{\mathrm{ND}}P_{b}|h_{bc}|^ {2}}{P_{b}|h_{bc}|^{2}+P_{d,\mathrm{I}}^{\mathrm{BL}}|h_{dc}|^{2}},\] (21) \[\rho_{d}^{\mathrm{BL}}=\Bigg{[}\frac{Q_{d}}{\zeta T(P_{b}|h_{bd}|^ {2}+P_{d,\mathrm{I}}^{\mathrm{BL}}|h_{dd}|^{2})}\Bigg{]}_{0}^{1}. \tag{22}\]  
The BL scheme in (19)-(22) can be derived through the process of obtaining the proposed scheme in (15)-(18), with the assumption that \(P_{d,\mathrm{E}}=0\). Let \(R_{d}^{\mathrm{O}}\) and \(R_{d}^{\mathrm{BL}}\) denote the D2D rates achieved with the optimal and BL schemes, respectively, which are obtained by substituting (15)-(18) and (19)-(22) into (6), respectively.
## 4 Impacts of HTST and UHT on Food Quality
### Bioactive Compounds
#### 4.3.3 Other Bioactive Substances  
[PERSON] et al. [25] showed a small decrease of 4.9% in the selenium content of clear Se-enriched kiwifruit juice preserved in parameters of 110 \({}^{\circ}\)C/8.6 s, while there was a significant decrease in this element on level of 25% in cloudy juice. The content of chlorophyll, the compound responsible for the attractive and green color of kiwifruit and the juices derived from them, also dropped significantly, by 80.9% and 47.8%, respectively. The researchers found that such a change is related to the thermal denaturation of a protein in chlorophylls. Bioactive compounds that also determine the color of products, and have antioxidant properties, are carotenoids. [PERSON] et al. [39], [PERSON] et al. [18] and [PERSON] et al. [44] showed that heating under conditions of 110 \({}^{\circ}\)C/8.6 s did not change the content of carotenoids. It can lead to isomerization of various compounds and the formation of new compounds that were absent before the preservation process, such as the _cis_-13-lycopene in tomato juice [18]. In lettuce juice preserved by UHT (115 \({}^{\circ}\)C/5 s), there were significant decreases in the total chlorophyll content by 14.1% and total \(\beta\)-carotene content by 35.5% [12]. The same researchers observed also a significant decrease in vitamins E, K1, B6, B9 and B12, and negligible decreases in B1 and B3, and a negligible increase in B2. [PERSON] et al. [40] indicate degradation of betacyanins and betaxanthins by 63.1% and 45%, respectively, as a result of heating at 80 \({}^{\circ}\)C for 30 s. These thermal conditions result in a 14.2% increase in gingerol content due to disruption of plant cells and higher extraction of the inner compounds [38]. Thermal processing causes isomerization of carotenoids and can result in a reduction in their content, for example, of 20.5% in sea buckthorn juice preserved with HTST [56]. On the other hand, heating can cause their extraction through cell wall degradation or the formation of new carotenoid compounds, as well as concentrating the sample and thus the bioactive components [88]. [PERSON] et al. [40] indicated degradation of betacyanins and betaxanthins by 14.8% and 14.4%, respectively, following heating at 80 \({}^{\circ}\)C for 30 s (Table 3).
## 4 Results and Analysis
### Perceptual Behavior of Visual Elements
#### 4.2.2 Eye Movement Indicators of Different Visual Elements  
A multivariate model with the proportion of visual elements as a covariate was used to analyze the influence of visual element types on visual behavior and to determine the differences. Again, we selected the eye-tracking experimental data for A-01, B-02, and C-02 as the data sources and used SPSS 21.0 (IBM Corp.) to conduct a one-way ANOVA for six eye movement indicators of seven visual elements in the landscape architecture image: landmark buildings, low-rise buildings, high-rise buildings, super high-rise buildings, water systems, mountains, and green plants. We then conducted a least significant difference  
Figure 5: Viewers’ visual behavior data for different landscape architecture elements.  
multiple comparison test and obtained statistical data results (Figure 6, Supplementary Materials Table S2).  
From the data in Figure 6, after examining the eye movement indicators of visual elements in various landscape architecture images viewed from observation decks, it becomes evident that a significant difference exists for TFD and AFD at the 0.01 significance level (\(p<0.01\)); the FC, FFD, and VC indicators display a significant difference at the 0.05 significance level (\(p<0.05\)). However, no significant difference can be observed in TFF (\(p>0.05\)).  
Among the eye movement indicators for different visual elements, those of super high-rise buildings and landmark buildings showed the greatest significance. In terms of the AFD and FC eye movement indicators, the average values of the visual elements of  
Figure 6: Viewers’ visual behavior data for different landscape architecture elements (** represents \(p<0.01\), which is significantly correlated at the 0.01 level).  
super high-rise buildings and landmark buildings were relatively high. This implies that participants faced the most difficulty in extracting data from super high-rise and landmark buildings, as this demanded a larger cognitive load. Generally, the trends for the FFD, TFF, AFD, and VC eye movement indicators were largely in line with that of TFD. They also corresponded to the distribution outcomes of the eye movement heat map.  
In summary, the eye-tracking indicators revealed differences in the TFD, FC, FFD, AFD, and VC data between the visual behavior indicators of the viewers when looking at urban landscape architecture (\(p<0.05\)). Different groups of people have consistent perceptions of the same landscape, but their perceptions of different visual elements vary. Therefore, we can verify that H1 is valid.
## 5 Simulation Results
### Cases Evaluated  
These are the cases studied in the simulation tool:  
* Direct position, orientation, and angular velocity at single-rate \(T\) (SR): In this experiment, output measurements \((\omega_{1},\omega_{2},\omega_{3},\omega_{4},X,Y,\psi)_{k}^{T}\) are directly sampled from the simulator block at different periods \(T\) = 0.1 s and \(T\) = 0.3 s. No noise is considered. In addition, for the case SR 0.1 s, the modified version of the Pure Pursuit path-tracking algorithm presented in this work is compared with the original method under the same conditions.
* Dual-rate Extended Kalman Filter (DREKF): This is the filter introduced in [23], which is used in the present work to be compared with the proposed NUDREKF. Gaussian noises are added to the robot outputs: distances \((d_{1},d_{2},d_{3},d_{4})_{k}^{NT}\), which are sensed at sampling period \(NT\), being \(N\) = 10; velocities \((\omega_{1},\omega_{2},\omega_{3},\omega_{4})_{k}^{T}\); and vehicle orientation \((\psi)_{k}^{T}\), which are sampled at period \(T\) = 0.1 s. Regarding possible losses of the distance values, three options for the loss probability \(P\) are simulated: \(P\) = 0.1, \(P\) = 0.3, and \(P\) = 0.5. Due to the nature of the DREKF, if one or more distances are lost at the current sensing time, the full set of distances will be considered lost.
* Nonuniform dual-rate extended Kalman filter (NUDREKF): This is the case presented in Section 3.2. The simulations are carried out under the same considerations as in the DREKF scenario; now, however, as the NUDREKF is able to face nonuniform sampling patterns, individual losses for each distance can be treated.  
The reference trajectory to be tracked is the Lissajous curve \((1,2)\) shown in Figure 12, where the subset of via-points can also be seen.  
Figure 11: Real robot.  
Figure 12: Lissajous curve reference trajectory.
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## 2 MIMO System Design with CoI Cancellation  
### System Overview  
The structure of the proposed receiver is illustrated in Figure 1. The proposed method works with a number of \(M\) CE-DFE. For each CE-DFE, there are two stages: one channel estimator with interference cancellation (IC), Doppler estimation and compensation, and one decision feedback equalizer with IC. The proposed receiver works in training mode if the decoded symbols are known; otherwise, it works in the decision-directed mode. Before channel estimation and channel equalization, the Doppler estimation and Doppler compensation are performed using decoded symbols. In the channel estimator, we utilize the decoded symbols to construct projection matrix \(\mathbf{P}_{m}\), whose purpose is to project the interference to a specific subspace and to free the interference in the subspace. As a result, the channel estimation performance will be improved. If the projection matrix equals the unit matrix, the proposed channel estimation model reduces to the traditional channel estimation model, which cannot mitigate the interference. In Figure 1, filters \(\mathbf{g}_{ff}\) and \(\mathbf{g}_{fb}\) are the feedforward and feedback filters, respectively. We added filter \(\mathbf{g}_{ic}\) based on traditional CE-DFE in [30]. The purpose of this filter \(\mathbf{g}_{ic}\) is to mitigate the interference. Differing from DA-DFE, which uses various criteria to achieve filter taps, the filters \(\mathbf{g}_{ff}\), \(\mathbf{g}_{fb}\), and \(\mathbf{g}_{ic}\) are measured by channel estimates. Compared with traditional CE-DFE, the proposed receiver not only removes the CoI in channel equalization, but also removes it in channel estimation; therefore, the performance of the proposed method will be significantly improved. In the following, we will demonstrate the proposed receiver in detail.
###### Abstract  
Intrusion and malware detection tasks on a host level are a critical part of the overall information security infrastructure of a modern enterprise. While classical host-based intrusion detection systems (HIDS) and antivirus (AV) approaches are based on change monitoring of critical files and malware signatures, respectively, some recent research, utilizing relatively vanilla deep learning (DL) methods, has demonstrated promising anomaly-based detection results that already have practical applicability due low false positive rate (FPR). More complex DL methods typically provide better results in natural language processing and image recognition tasks. In this paper, we analyze applicability of more complex dual-flow DL methods, such as long short-term memory fully convolutional network (LSTM-FCN), gated recurrent unit (GRU)-FCN, and several others, for the task specified on the attack-caused Windows OS system calls traces dataset (AWSCTD) and compare it with vanilla single-flow convolutional neural network (CNN) models. The results obtained do not demonstrate any advantages of dual-flow models while processing univariate times series data and introducing unnecessary level of complexity, increasing training, and anomaly detection time, which is crucial in the intrusion containment process. On the other hand, the newly tested AWSCTD-CNN-static (S) single-flow model demonstrated three times better training and testing times, preserving the high detection accuracy.  
machine learning; deep learning; system calls; host-based intrusion detection; malware +
Footnote †: journal: Applied Sciences
# On the Use of FOPID Controllers for Maintenance Phase of General Anesthesia
## 2 Materials and Methods
### Patient Model  
In the design of the controller, a key role is played by the model used to represent the patient during propofol administration. The well-known Schnider PK/PD model [24; 25] was employed in this study. Pharmacokinetics describes the relationship between the administered drug and its plasmatic concentration, while pharmacodynamics describes the relationship between the plasmatic concentration and the clinical effect on the human body. A mammillary three-compartmental model is used for the pharmacokinetics of propofol. In this model structure, there are two peripheral compartments and a central compartment; see Figure 1. Each compartment describes cumulative volumes that have the same drug concentration. These compartments interact with each other by transferring the drug according to the following mass flow equations, which describe this exchange:  
\[\begin{array}{l}\dot{q}_{1}(t)=-(k_{10}+k_{12}+k_{13})q_{1}(t)+k_{21}q_{2}(t) +k_{31}q_{3}(t)+u(t)\\ \dot{q}_{2}(t)=k_{12}q_{1}(t)-k_{21}q_{2}(t)\\ \dot{q}_{3}(t)=k_{13}q_{1}(t)-k_{31}q_{3}(t)\end{array} \tag{1}\]  
where \(u(t)\) (mg/min) is the drug infusion rate, \(q_{i}(t)\) (mg) is the quantity of drug in each compartment, and \(k_{ij}\) (for \(i\
eq j\)) (min\({}^{-1}\)) represents the drug transfer frequency from the \(i\)th to the \(j\)th compartments. The output of this model is the plasmatic concentration of propofol, that is:  
\[C_{p}(t)=\frac{q_{1}(t)}{V_{1}},\quad\left[\frac{\text{mg}}{\text{L}}\right] \tag{2}\]  
where \(V_{1}\) represents the volume of the primary compartment. The output \(C_{p}(t)\) of the PK model is the input of the PD model, which describes the relation between the plasmatic concentration and the BIS value. A linear dynamic part relates the plasmatic concentration of propofol in the central compartment to the drug concentration in the effect-site compartment. From the metabolism in the cerebral cortex, it is possible to write:  
\[\dot{C}_{e}(t)=k_{e0}(C_{p}(t)-C_{e}(t)), \tag{3}\]  
where \(k_{e0}\) quantifies the lag time, and its estimated value is:  
\[k_{e0}=0.0495.\quad\left[\text{min}^{-1}\right] \tag{4}\]  
The last part of the PD model is an algebraic nonlinearity that describes the relationship between the effect-site concentration and the BIS value (the clinical effect of the drug). It is represented by a sigmoid curve referred to as the Hill function:  
\[BIS(t)=E_{0}-E_{max}\left(\frac{C_{e}(t)^{\gamma}}{C_{e50}^{\gamma}+C_{e}(t)^ {\gamma}}\right), \tag{5}\]  
where \(E_{0}\) is the baseline effect that represents the initial drug-free state of the patient, \(E_{0}-E_{max}\) is the maximum achievable clinical effect, \(C_{e}\) is the value of the drug concentration inside the effect site, \(\gamma\) represents the slope of the sigmoidal curve, and \(C_{e50}\) is the drug concentration that is necessary to reach 50% of the maximal effect.
# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 2 Materials and Methods
### Statistical Analysis
Generalised Additive Models (GAM) were used for estimating the relationships between GPP and the RSVIs to test for nonlinear responses. We then determined if the seasonalities of the RSVIs (average intra-annual variability) were correlated with the seasonality of GPP using linear models. Seasonality was estimated as the PV of the 12 monthly values within a year, averaged over all complete years in which GPP and RSVI data were available. We next determined if the interannual variability of GPP was correlated with the interannual variability of the RSVIs, also using linear models. The interannual variabilities of GPP and the RSVIswere calculated as the interannual monthly PV of each month averaged over the 12 months of a year. For both seasonality and interannual variability, we only used sites in which monthly time series of GPP and the RSVIs were available for five or more years [31]. Finally, we determined if the trends in annual GPP could be inferred from the trends in the annual average and the annual peak-season values of the RSVIs by calculating the annual trends of GPP and the annual average and peak-season RSVIs of the sites for which the time series were at least seven years [32]. Trends were calculated using the robust Theil-Sen's slope estimator [33]. All statistical analyses were performed with R statistical software [34]. The amount of variance explained by the predictors in a model was assessed using the \"_Img_\" metric [35] in the \"_relainpo_\" package [36].
# CRC-Based Reliable WiFi Backscatter Communication for Supply Chain Management
## 2 System Model
### Codeword Translation  
HitchHike [35] proposes a novel technology called codeword translation for tag modulation. This method allows for the piggybacking of tag data information while ensuring that the backscatter signal remains an 802.11b WiFi signal. The backscatter signal after codeword translation can be received and demodulated by the receiver. Specifically, the 802.11b 1 Mbps signal utilizes two codewords to encode packets, with a 180\({}^{\circ}\) phase difference between them. During tag modulation, the tag uses BPSK modulation to modify the codeword by a phase offset while simultaneously modulating and backscattering the exciting signal. Table 1 shows the relationship between phase offsets and tag bits in tag modulation.  
In tag modulation, one tag data bit corresponds to one data bit. This correspondence indicates that the encoding scheme is efficient and redundancy-free. HitchHike proposes an efficient XOR decoder for extracting tag data from backscatter data and original data. The decoding formula of the XOR decoder can be written as:  
\[\text{tag data}=\text{backscatter data}\oplus\text{original data}. \tag{1}\]  
In CRCScatter, there is only one access point that receives signals. Thus, while the backscatter data for the XOR decoder can be obtained directly, the original data require calculation by the CRCScatter decoder from the backscatter packet. Therefore, the decoding function for CRCScatter can be expressed as follows:  
\[(\text{original packet},\text{tag data})=\text{CRCScatter}(\text{backscatter packet}). \tag{2}\]
# Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging +
###### Abstract  
Employing a cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework was developed recently, enabling CP-OFDM sensing to surpass the conventional limits imposed by underlying communications. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CFs) in CP-OFDM waveforms. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to remove false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution. In particular, our design can reduce the false alarm rate caused by false targets by over 50% compared with the prior art.  
integrated sensing and communications (ISAC); joint communications and sensing (JCAS); OFDM; cyclic prefix (CP); false targets +
Footnote †: Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
### Aim and Novelty  
The aim of this study is to analyze the contribution of the statistical metrics commonly used in the field of meteorology and climate science to understand their potential to define the conditions leading to moisture on masonry surfaces, the frequency of their occurrence in a historical building, and their validity in determining the severity of the dampness risk. The aim is not to establish universal risk thresholds for moisture-related deterioration as the impacts vary based on material composition, construction techniques, and historical significance, moving away from a one-size-fits-all approach [33].  
By analyzing parameters including temperature, relative humidity, dew point, and mixing ratio, we propose some indicators and assess their effectiveness in identifying moisture frequency, persistence, and severity across 13 case studies in Northern Italy, covering the Alpine region, Po valley, and Lake Como. Comparative analysis was used to validate the limitations and strengths of each indicator, as well as explore their correlations and potential synergies.  
The novelty of this research lies in bridging the gap between theoretical microclimatic indicators and practical conservation needs, avoiding prescriptive thresholds due to material and contextual variability [34].
## 3 Materials  
In this study, a WMSN monitoring system designed by our laboratory was used to perform the task of capturing images of wildlife. The system was deployed in the Saihan Ula National Nature Reserve in the Inner Mongolia province, which is located in the southern mountainous area of the Greater Khingan Range. It is a forest ecological nature reserve, which has a medium-temperate, semi-humid, and warm climate. This reserve is home to 37 primary species of wildlife, including three kinds of secondary, nationally protected mammals, such as _Cervus eleplus_, _Naemorhedus goral_, and _Lynx lynx_.  
The WMSN wildlife monitoring system is used to capture wildlife images using industrial-grade cameras with terminal node equipment embedded. The system, which is mostly composed of coordination nodes, terminal nodes, gateway nodes, and servers, achieves real-time, remote monitoring images. The detailed distributions are depicted in Figure 1.  
The WMSN node establishes a sensor network in a self-organizing manner by using the ZigBee network protocol. Detailed parameters are given in Table 1. When the wild animals enter the monitoring range, the camera is triggered by the infrared sensor of the terminal node to capture the images. The captured images are then sent to the coordination nodes in a multi-hop manner. After the coordinating nodes successfully receive the image data information, the information is transmitted to the server center through the gateway node in the form of a 4G signal. If there is no target shown in the monitoring region, the nodes stop working to reduce the energy consumption.  
Figure 1: Wildlife monitoring system.  
More than 20 sensor nodes were deployed in Saihan Ula National Nature Reserve, and the distance between every two sensor nodes was 150 m. In this study, more than 2000 images of 12 species of wildlife were collected in the nature reserve using the monitoring system, including _Cervus elaphus_ and _Lynx lynx_, which are nationally protected animals. The monitoring images are shown below in Figure 2.