esa-sceva/satcom-chunk-collection huggingface dataset:
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 1 Introduction
On the other hand, code jammers need to know the boundary of access slots and the access code used to send preambles or the messages since they generate and send fake signals with the same spreading code being used by the legitimate SNs. The access slot can be known if the code jammers successfully look at the synchronization channel from HS and the access code is normally open in common air protocol specifications. Code jamming hence gives different effects compared with power jamming. At the stage of preamble transmission, code jamming reinforces the power level of SNs that use the same access code, but reduces, similarly to power jamming, the signal quality of SNs choosing different codes. As a result of code jamming, some SNs can receive AI more easily and have more chances to transmit the message, which is again jammed by the code jammers using the same code. Code jamming looks like RF spoofing where the jammers transmit a fake signal that masquerades as an actual signal [18]. The code jamming hence increases the failure probability in sending the information messages and intends to mislead the adequate allocation of radio resources. In this paper, we also present the probability of successful preamble transmission with power ramping according to the jamming types and the resulting throughput and delay of information transmission of SNs, respectively. With numerical investigation, the effect of the two jamming attacks on RA performance of SNs is compared in terms of throughput, as well as delay. The results show that, compared to RA without jammers, power and code jamming degrade the throughput by up to 30.3% and 40.5%, respectively, while the delay performance by up to 40.1% and 65.6%, respectively. The remainder of the paper is organized as follows. The system model, RA procedure, a power capture model according to jamming types and power ramping for preamble transmission are presented in Section 2. In Section 3, we present the success probability of preamble transmission, the throughput and the access delay. We compare RA performances of SNs attacked by power and code jamming with numerical investigation in Section 4. Finally, conclusions are presented in Section 5. The main notations used in this paper are listed in Table 1 in Appendix C.
## 6 Therapies Targeting Gut Microbiota's Composition
#### Therapies Targeting Microbiota Composition in Atopic Dermatitis
By inhibiting the growth of harmful bacteria and promoting the growth of beneficial ones, SCFAs contribute to a healthier skin microbiome, which is important for reducing inflammation in conditions such as atopic dermatitis. (v) Reduction of oxidative stress: SCFAs have antioxidant properties, which means they can help neutralize harmful reactive oxygen species (ROSs) and reduce oxidative stress in the skin. Oxidative stress is known to exacerbate inflammation in various skin disorders, including atopic dermatitis. (vi) Improvement of mucosal immunity: SCFAs play a role in maintaining the health of mucosal surfaces, including the gut and respiratory tract. A strong mucosal immune system can help regulate systemic inflammation, which may indirectly benefit skin conditions such as atopic dermatitis [160]. Phage therapy is also being studied as microbiome-based therapeutics to treat AD microbiota dysbiosis. The use of staphylococcal phage SaGU1 was tested in combination with commensal bacteria _S. epidermidis_ with good results using a mouse model [161]. The use of a recombinant phage endolysin, Staphefekt SA.100, is also being tested; however, the results are not yet conclusive [162; 163]. In Table 3, therapies targeting the microbiota' composition in AD are exemplified. To summarize, the results indicate that microbiome-based therapeutics offer several promising treatment avenues for AD. However, further investigation is needed to determine the specific strains of commensal bacteria that can yield long-lasting effects [171; 172]. \begin{table}
\begin{tabular}{c c c} \hline
**T**** therapy** & **Examples** & **References** \\ \hline \hline Probiotics & _Lactobacillus, Bifidobacterium_ & [152; 158; 159] \\ \hline \multirow{3}{*}{Prebiotics} & Inulin, resistant starch, polydextrose, pectic oligosaccharides & \\  & derived from pectin (fructooligosaccharides (FOS) and & [164; 165; 166] \\  & galactooligosaccharides (GOS)) & \\ \hline Synbiotics & _Bifidobacterium_ and galactooligosaccharide & [167; 168; 169; 170] \\ \hline Improving nutritional status & Fibers, fruit, and Mediterranean diet consumption & [152] \\ \hline Short-chain fatty acids (SCFAs) & Acetate (C2), propionate (C3), and butyrate (C4) & [160] \\ \hline Phage therapy & Staphylococcal phage, SaGU1; phage endolysin, Staphefekt SA.100 & [161; 162; 163; 171] \\ \hline \end{tabular}
\end{table} 
Table 3: Therapies targeting microbiota’ composition in AD.
# Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties
###### Abstract  
Model reference adaptive control (MRAC) schemes are known as an effective method to deal with system uncertainties. High adaptive gains are usually needed in order to achieve fast adaptation. However, this leads to high-frequency oscillation in the control signal and may even make the system unstable. A robust adaptive control architecture was designed in this paper for nonlinear aircraft dynamics facing the challenges of input uncertainty, matched uncertainty, and unmatched uncertainty. By introducing a robust compensator to the MRAC framework, the high-frequency components in the control response were eliminated. The proposed control method was applied to the longitudinal-direction motion control of a nonlinear aircraft system. Flight simulation results demonstrated that the proposed robust adaptive method was able to achieve fast adaptation without high-frequency oscillations, and guaranteed transient performance.  
model reference adaptive control; fast adaptation; robust compensator; uncertain nonlinear aircraft +
Footnote †: journal: Applied Sciences  
_Article_  
Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties  
[PERSON], [PERSON], and [PERSON]
## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### Trajectory and Energy Management (TE)
#### 4.2.4 TE with SN-CP Association  
In many of the proposed works, researchers consider a single UAV for data gathering, which might be very challenging in large-scale WSNs, especially considering the limited energy storage of the UAV. In such large-scale networks, it is expedient to have nodes clustered to facilitate easier network coordination, thus emphasizing the need for sensor nodes to be associated with the most appropriate CPs. As opposed to these works using a single UAV, the authors in [91] consider a multi-UAV-assisted WSN with a data center and multiple sensor nodes as shown in Figure 11b. The UAV flies at a fixed velocity and altitude. Particularly, the authors propose a cooperative framework for energy-constrained multi-UAV data collection for time-sensitive WSNs. Because of the UAV's limited energy supply, its flight trajectory is designed to reduce the max AoI of the ground sensor nodes. Each UAV is expected to collect data from the sensor nodes inside of its coverage area. As a result, the UAV's flight path is reduced to a series of ordered CPs, where it hovers to collect the data. The authors decomposed the formulated problems into three different parts. For the first, a graph-theory-based approximation was used to determine a set of CPs and to establish the SN-CP association. Afterwards, the kernel K-means clustering method was used to establish CP-UAV association. Finally, the planning of AoI-optimal trajectory for each UAV was performed subject to the UAV's energy constraint. The simulation results show that the proposed scheme outperforms the traditional ones with respect to AoI performance. Future work can explore other more efficient clustering algorithms for CP-UAV association.
# Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model
###### Abstract
However, to the best of the authors' knowledge, a very small number of studies have investigated the issues of imbalanced travel mode choice data and proposed a solution for it [33]. So far, many scholars have provided useful strategies to manage the issue of class imbalance. These strategies have been helpful and competent in explaining the issue partially through enhancing classifiers' performance. The majority of models developed for binary category imbalance issues are improper for the multiclass imbalanced datasets like work mode choice. In addition, rare studies have provided a solution for the imbalanced mode choice datasets. The shortcomings mentioned above prompted the authors to cope with the multiclass imbalance mode choice data issue and contribute to the body of research on this topic. Thus, this study developed the adjustable kernel-based SVM classification algorithm (SVM\({}_{\text{AK}}\)) that is suitable for handling multiclass imbalanced data. Initially, the estimated hyperplane is obtained employing the regular SVM model. Subsequently, the parameter function and the weighting factor concerning each support vector in every iteration is determined. The likelihood-ratio chi-square test is utilized to estimate these parameters. Following this, the kernel transformation or the new kernel functions are determined. The unequal boundaries of class are enlarged, and data skewness is adjusted, thanks to this function of kernel conversion. Consequently, the estimated hyperplane is remedied through the developed model, and it also solves the problem of performance degradation. The rest of this paper is structured as follows. In Section 2, the source of data, dataset characteristics, and methodology used for improving the performance of the SVM model for classification of imbalanced data are presented, and evaluation metrics are provided. Section 3 presents the results obtained with the model as well as a series of comparisons against other ML models and SVM-based models for classifying imbalanced datasets. Section 4 describes the sensitivity analysis method and its outcomes. Finally, a conclusion of the paper is presented in Section 5.
###### Abstract
SDG contains starch, cellulose, protein, and lipid, and has been utilized as animal feed [19]. However, SDG is difficult to store since it rots rapidly because of its high moisture content and fermentable sugar content. Using distillers grain to prepare biochar by pyrolysis has been proposed as an alternative method since these fermentation residues have the potential to be converted into carbonaceous material. Although biochar prepared from brewers' spent grain [17], also a fermentation residue, has been reported as an adsorbent for ammonium nitrogen capture, there is a lack of studies on the physicochemical properties and ammonium nitrogen adsorption performance of biochar produced from SDG. It is well known that the feedstock source has a critical impact on the properties of the prepared biochar, including the specific surface area and content of surface functional groups [12, 20]. In addition, chemical activation is considered an efficient method to improve the biochar adsorption capacity [21, 22], and reports regarding the use of activators in biochar preparation from SDG are scarce. Therefore, the current study focuses on the physicochemical characterization of chemically activated biochar produced from SDG and the evaluation of its adsorption capacity for ammonium nitrogen. In this work, biochars derived from SDG through NaOH- and KOH-activated pyrolysis processes were studied for the adsorption of ammonium nitrogen from a simulated aqueous solution. Detailed characterizations, including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetry (TG), and specific surface area (SAA) analyses, were carried out to offer a broad evaluation of the prepared biochars. The effects of activators, pyrolysis temperature, and adsorption pH on the biochar adsorption capacity were analyzed. Moreover, the adsorption process was further analyzed based on the adsorption kinetics and isotherm, with the aim to discover the possible adsorption mechanism of ammonium nitrogen on the biochar.
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 3 Frequency Domain Simulation
### SPICE Equivalent Circuit Model Of Bonding Wire
\begin{array}{c}S_{11}(\omega)=\frac{[Z_{11}(\omega)-Z_{0}]\cdot[Z_{22}( \omega)+Z_{0}]-Z_{12}(\omega)\cdot Z_{21}(\omega)}{[Z_{11}(\omega)+Z_{0}]\cdot [Z_{22}(\omega)+Z_{0}]-Z_{12}(\omega)\cdot Z_{21}(\omega)}\\ =\frac{2\cdot\big{[}R+Z_{0}\big{(}1-LC_{2}\omega^{2}+j(RC_{2}+L)\omega\big{]} \big{]}}{-RC_{1}C_{2}Z_{0}^{2}\omega^{2}-L(C_{1}+C_{2})Z_{0}\omega^{2}+2Z_{0} +R+j(-LC_{1}C_{2}Z_{0}^{2}\omega^{2}+RC_{1}Z_{0}+RC_{2}Z_{0}+C_{1}Z_{0}^{2}+C_ {2}Z_{0}^{2}+L)\omega}-1}-1\\ \end{array}  \tag{6}\]  
\[S_{21}(\omega)=\frac{2Z_{21}(\omega)\cdot Z_{0}}{[Z_{11}(\omega)+Z_{0}]\cdot [Z_{22}(\omega)+Z_{0}]-Z_{12}(\omega)\cdot Z_{21}(\omega)} \tag{7}\]  
In order to compare the data accurately and eliminate the inconsistency of the software, the simulation tools of ADS software were adopted. The diameter, flat length ratio, span, height and different loops of bonding wire will certainly affect the resistance R, inductance L and two different capacitors C1 and C2 in SPICE equivalent circuit model. As previously mentioned, the S-loop wire is the best loop modes. Meanwhile, based on the design rules, SP12 bonding wire is appropriate for the model and the parameters of this model were selected (L = 4500 \(\upmu\)m, H1 = 250 \(\upmu\)m, H2 = 254 \(\upmu\)m, d = 25 \(\upmu\)m). Compared with the result file solved by the HFSS electromagnetic field simulation software and S parameter curve obtained by the ADS frequency domain simulation solver, the results of the S11 and S21 are shown in Figure 14. As is shown below, the two curves are basically well fitted, which can verify the correctness of the SPICE equivalent circuit model. Figure 13: The equivalent impedance model of \(Z11\).
## 4 Discussion
### Outlook  
In future work, additional data sources such as the Clinical Trials Information System (CTIS) or the European Database on Medical Devices (EUDAMED) could serve as useful resources for clinical data retrieval [26; 58]. Incorporating adverse effect announcements could facilitate post-market surveillance and monitoring use cases by verifying the benefits of medical devices in the face of newly published information [7].  
Subsequently integrating retrieved heterogeneous data into interactive knowledge graphs yields the potential for cluster analysis, visualizations, and improved search performances [59]. For instance, by linking publications to clinical trials and other publications via citations, additional relevant data could be retrieved based on already identified relevant documents without the need for further searches [60].  
However, despite the potential benefits of integrating such automated search methods into systematic review processes [61], the applicability in the scope of the MDR clinical evaluation remains uncertain [29]. The usability of TAR methods must be evaluated for the search for clinical data in the scope of the MDR clinical evaluation.  
A promising opportunity could be an ensemble of both developed search methods complementing each other to address respective retrieval challenges and solve transparency concerns while increasing retrieval performances.  
Overall while first results indicate high precision and recall, further evaluations on a larger scale should be conducted, ideally with medical device manufacturers who already certified devices under the MDR and could therefore provide data serving as a future gold standard.
## 4 Results and Analysis
### Performance Comparison  
Generally, the performance of a clock is reported in terms of its stability. Allan deviation is the most common form to express such stability. The lower the value of the Allan deviation, the higher the stability. Stable 32 is a well-known software that helps to generate such stability results [32]. This software is used to analyze the results for this paper too.  
The stability graphs are plotted for the cases before the application of the correction factor and after the predicted correction factors are applied. In Figure 8, the dashed curves show the stability of the receiver before the application of the correction factor and the solid curves show the stability of the receiver after the application of the correction factor. The red and the blue colors are for the data from NTU and Stirling Road, respectively. It can be noted that the stability of the Ublox for the two locations look very similar for both the cases of before and after the application of the correction factor.  
There is a clear shift in the stability curves after the predicted correction factors were applied. The stability is improved for all the averaging times. This shows that the linear-regression-based model is able to improve the performance of a low-cost oscillator. For an averaging time of 1 day (x-axis reading of 86,400 s), the stability is improved by \(10^{3}\) times. This huge improvement in the stability validates that a simple data-driven algorithm-based steering is very much effective in correcting the performance of a low-cost oscillator.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Encryption Quality
#### 3.5.2 Mean Absolute Error  
The MAE is another statistical metric used to evaluate the quality of an image encryption algorithm. It measures the average absolute difference between the pixel values of a plaintext image and its cipher image [56]. A lower MAE value indicates a greater similarity between the plaintext image and its cipher, whereas a higher MAE value indicates a lower similarity between the plaintext image and its cipher; therefore, a higher MAE value means higher-quality encryption. The MAE can be calculated as follows:  
\[MAE=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}|P(i,j)-C(i,j)|, \tag{25}\]  
From Equation (25), the minimum and maximum possible MAE values for an image, regardless of its size, can be calculated. For the minimum value of MAE, if we assume the two images to be identical, then \(P(i,j)-C(i,j)\) will be 0. Thus, the MAE minimum value is 0. For the maximum value, we assume that the two images are completely different from each other (one with 255-pixel values, and the other with 0-pixel values), the maximum possible MAE can be calculated as follows:  
\[\begin{split} MAE&=\frac{1}{H\times W}\sum_{i=1}^{H }\sum_{j=1}^{W}[255-0]\\ &=\frac{1}{H\times W}\times H\times W\times[255]\\ &=\frac{H\times W\times[255]}{H\times W}\\ MAE&=255\\ \end{split} \tag{26}\]
## 4 Results
### Quantitative Evaluation
#### 4.2.2 Rendering Performance  
Tables 4 and 5 present a comparison of the mapping quality on the TUM-RGBD and Replica datasets, respectively. As shown in Table 4, RK-SLAM achieved comparable results. Compared to the top-performing SplaTAM algorithm, RK-SLAM demonstrated a competitive performance and even surpassed it in the fr1/desk sequence. As shown in Table 5, RK-SLAM achieved state-of-the-art results on the Replica dataset. Although it lags slightly in the SSIM metric, it shows significant advantages in the PSNR and LPIPS metrics. In the PSNR metric, RK-SLAM achieved the best results in six out of the eight sequences, outperforming other methods by a considerable margin. Similarly, for the LPIPS metric, RK-SLAM secured the best results in four out of the eight sequences. In the comparisons above, RK-SLAM demonstrates a strong performance on both the Replica dataset, with complete depth information, and the TUM-RGBD dataset, which \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Metrics** & **fr1/desk** & **fr2/xyz** & **fr3/Office** & **Avg.** \\ \hline \multirow{4}{*}{Point-SLAM} & PSNR \(\uparrow\) & 13.79 & 17.62 & 18.29 & 16.57 \\  & SSIM \(\uparrow\) & 0.625 & 0.710 & 0.749 & 0.695 \\  & LPIPS \(\downarrow\) & 0.545 & 0.584 & 0.452 & 0.527 \\ \hline \multirow{4}{*}{SplaTAM} & PSNR \(\uparrow\) & 21.49 & 25.06 & 21.17 & 22.57 \\  & SSIM \(\uparrow\) & 0.839 & 0.950 & 0.861 & 0.883 \\  & LPIPS \(\downarrow\) & 0.255 & 0.099 & 0.221 & 0.192 \\ \hline \multirow{4}{*}{RK-SLAM (Ours)} & PSNR \(\uparrow\) & 22.31 & 22.47 & 20.67 & 21.82 \\  & SSIM \(\uparrow\) & 0.741 & 0.729 & 0.710 & 0.727 \\ \cline{1-1}  & LPIPS \(\downarrow\) & 0.254 & 0.220 & 0.251 & 0.242 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Map quality on TUM-RGBD dataset. \(\uparrow\) means that the bigger the metric, the better, and \(\downarrow\) means that the smaller the metric, the better. The table marks the first, second, and third best results.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Asynchronous Duty-Cycled MAC Protocols
#### 5.1.6 DCM-MAC  
DCM-MAC [33] was the first MAC protocol to introduce a multi-channel property to the asynchronous duty-cycling mechanism. DCM-MAC handles issues like decreasing synchronization overhead, allocating multi-channels dynamically, and managing multi-channel hidden node problems. It uses a receiver-initiated mechanism, and does cross channel handshaking to reserve a channel for transmitting data. This protocol provides three types of channels: (i) one control-channel (CC), (ii) multiple data-channels (DC) and (iii) one broadcast-channel (BC).  
Figure 10 shows both the unicasting and broadcasting scheme used in DCM-MAC. In unicasting, if a sender node has any packet to send then it will wake up and silently wait for the receiver's wakeup beacon. To the receiver's beacon packet, the receiver will add the specific DC's ID number where it intends to receive the data packet, and instantly switches to that channel. After receiving the beacon packet, the sender will switch to that specific DC and send RTS. After getting a CTS packet from the receiver, the sender will start sending the data packet and wait for the ACK. In order to avoid collision, it follows a random back-off procedure, the same as RI-MAC. DCM-MAC also provides a broadcasting mechanism whereby it assigns a BC for that procedure. If a sender has a broadcast-packet, then it will switch to BC and continuously send broadcast-packets N number of times. In order to receive the packet, the neighbor nodes will wake up once every N-1 time interval. The balance between energy consumption and latency is handled by the value of N. The simulations provided by [33] showed that this protocol improved energy consumption compared to RI-MAC in the case of unicast transmission. However, the energy efficiency of the broadcasting scheme using multi-channel with duty-cycling was not addressed.