esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Experiment Flow  
The experiments were exercised with different ROM sizes from 8 \(\times\) 32, 16 \(\times\) 32, 32 \(\times\) 32, 64 \(\times\) 32, 128 \(\times\) 32, to 256 \(\times\) 32. For each ROM size, 100 trials are exercised, and each trial runs 10\({}^{6}\) times of fault injection and error detection evaluation. The flow chart of the provided experiments is shown in Figure 7 and described step by step as follows:  
Step 1. Specify ROM size and target simulation times (sim_times). The parameter \"sim_times\" is defined as 10\({}^{6}\) times here.  
Step 2. Repeat step 3 to step 9 100 times, and go to step 10.  
Step 3. Generate a random content of ROM.  
Step 4. Based on generated random content in the step 3, generate the encoding results as golden patterns of 1-bit parity, 2D parity, 7-bits ECC and 2D-VLEDC.  
Step 5. Repeat step 6 to step 8 10\({}^{6}\) times, and go to step 9.  
Step 6. Inject four random errors into the ROM randomly.  
Step 7. Perform the decoding flow of 1-bit parity, 2D parity, 7-bits ECC and 2D-VLEDC with the injected-errors ROM.  
Step 8. Check error detection of each decoding result. Accumulate the number of un-detected errors into the un-detected error counter and then go to step 4.  
Step 9. Log undetected error rate (Rate\({}_{\text{ud}}\)) and size overhead of each memory protection technology in the log-files. Go to step 2.  
\[\text{Rate}_{\text{ud}}=\text{(un-detected error times/sim\_times) }\times 100\% \tag{1}\]  
\[\text{size\_overhead}=\text{(redundant bits)/(total bits of memory)} \tag{2}\]  
Step 10. Analyze 95% confidence interval from the log-files.  
Step 11. Finish experiment. Therefore, there are 100 Rate\({}_{\text{ud}}\) data for each ROM size with 1-bit parity, 2D parity, 7-bits ECC and 2D-VLEDC.  
\begin{table}
\begin{tabular}{c c} \hline
**Count of Zero** & **ECC Encode** \\ \hline
0\(\sim\)15 & 1-bit parity \\
16\(\sim\)31 & 7-bit ECC \\ \hline \end{tabular}
\end{table}
Table 2: Filter rule of the proposed 2D-VLEDC.
## Abstract  
Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain-computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.  
major depressive disorder; electroencephalography; brain-computer interface; emotion induction; International Affective Picture System; support vector machine; machine learning
## 2 Materials and Methods
### Methods
#### 2.2.2 Data Preprocessing  
We applied data preprocessing, such as PCA and PLS, to improve prediction performance and speed [6; 8].  
* PCA [26] is an unsupervised feature extraction method that projects high-dimensional data with \(p\) variables into low-dimensional data with \(k\) variables using only explanatory variable information. The principal component \(PC_{i}\) (i = 1, 2,\(\cdots\), \(k\)), which is a new variable  
Figure 3: Example of multiple imputations.  
generated using PCA, can be generated as many times as the value of \(p\), which is the maximum number of original variables. In general, the number of principal components that explain more than 70-80% of the variance of the original variable is selected and used.  
The variable \(PC_{i}\) created by PCA has the following three characteristics. (1) the principal component is generated as a linear combination of the original variables; (2) an axis is created and projected in the direction of the greatest dispersion; (3) it is a useful method to solve the problem of multicollinearity because each \(PC_{i}\) is independent of each other.  
* PLS  
PLS [27] is a method that reduces the dimension of variables by creating new variables through linear combinations between variables when there is a problem of multicollinearity between variables in multivariate data with multiple variables. In particular, this is a useful method when the number of variables (\(p\)) is greater than the number (\(n\)) of data.  
Unlike PCA, which uses only explanatory variable information, it is a supervised feature extraction method that also uses response variable information. The latent variable (\(T\)), which is a variable extracted through linear combination with PLS, can be created as many times as the value of \(p\), which is the maximum number of original variables. The optimal number of latent variables is selected based on the mean square error (MSE) [21].
# Improving the Performance of Turbo-Coded Systems under Suzuki Fading Channels
###### Abstract  
In this paper, the performance of coded systems is considered in the presence of Suzuki fading channels, which is a combination of both short-fading and long-fading channels. The problem in manipulating a Suzuki fading model is the complicated integration involved in the evaluation of the Suzuki probability density function (PDF). In this paper, we calculated noise PDF after the zero-forcing equalizer (ZFE) at the receiver end with several approaches. In addition, we used the derived PDF to calculate the log-likelihood ratios (LLRs) for turbo-coded systems, and results were compared to Gaussian distribution-based LLRs. The results showed a 2 dB improvement in performance compared to traditional LLRs at \(10^{-6}\) of the bit error rate (BER) with no added complexity. Simulations were obtained utilizing the Matlab program, and results showed good improvement in the performance of the turbo-coded system with the proposed LLRs compared to Gaussian-based LLRs.  
log-normal distribution; Rayleigh distribution; Suzuki distribution +
Footnote †: journal: Low Power Electronics and Applications
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 2 Background
### Full-Duplex and Self-Interference  
Full-duplex enables simultaneous uplink and downlink communication in the same frequency at the same time [38] which is useful in this context: [PERSON] receives the transmitted signal and at the same time emits the jamming signal. The main drawback in FD is the self-interference that is generated due to the signal created on the transmitter antenna being fed back into the receiver antenna of the same device. On top of that, the SI signal has higher gain than the intended one. Thus, [PERSON] will not only receive the information signal but also receive the jamming signal (with higher gain) that is generated resulting in a lower SINR of the channel [15].  
Furthermore, to successfully retrieve the signal of interest it is required to reduce the SI signal to decode it correctly, so the goal of SI cancellation is to predict and model the distortions and compensate for them at the receiver antennas.
# Production Plant and Warehouse Automation with IoT and Industry 5.0
## 7 Challenges on the Platform Shift
### Protocols  
Device communication protocols are commonly called the major driver behind IoT applications, and they serve as the primary route for the flowing of data between sensors and the outside world. Meanwhile many MAC algorithms with frequency division multiple access have been proposed for several domains free of collision and traffic [14]. The transport layer has protocols similar to TCP, which is used for providing reliable communication and controlling the congestion between communicating devices; however, the communication protocols do not usually provide a high level of reliability.
# Reputation-Driven Dynamic Node Consensus and Reliability Sharding Model in IoT Blockchain
## 4 Experiment and Result Analysis
### Reputation Replacement Test  
Reputation is the most basic idea in this paper and is the guarantee of the reliability of master node election, the mathematical basis of node elimination, and the theoretical core of reliable sharding. Ten rounds of consensus reputation reward and punishment tests were carried out on 10 nodes. Node 1,2,3,4,5,6,8 is the normal working node, node 10 is the continuous malicious node, node 9 is the discontinuous malicious node, node 7 is the malicious node when it becomes the main node. The change of node reputation is shown in Figure 6. It is observed that the reputation of the No. 10 node decreased after it acted maliciously in the first round of consensus, and it was judged as an abnormal node by the LOF algorithm and removed. Node 9 did not act maliciously continuously, and it worked normally before it acted maliciously. It can be seen that the reputation increased first and then decreased. Although the reputation remained around 25 after the second round of consensus, there was a big gap between it and the normal working node, and it was judged as an abnormal node by the LOF algorithm. The first four rounds of consensus of Node 7 all worked normally, and the reputation gradually increased. When the fifth round of consensus became the main node, Node 7 began to behave maliciously, reputation decreased considerably, and it was judged as an abnormal node by the LOF algorithm. All the rest of the nodes were slowly building reputation according to the rules.
## 3 SRB Activities in Acidic Environments
### Sulphate Reduction at Low pH
#### 3.2.1 Proton Concentration  
Sulphate reduction (reactions 2-5) is a proton consuming process; thus, the Gibbs free energy is more negative with decreasing pH [65]. This results in more energy release from the reactions at low pH. For example, assuming concentrations of 1 mol/L for all reactants, except H\({}^{+}\), the \(\Delta\)G of hydrogenotrophic sulphate reduction (H\({}_{2}\) as electron donor) is \(-\)198 kJ/mol at pH 3 compared with \(-\)175 kJ/mol at pH 5 [25]. The extra energy gained can be consumed by pumping protons out of the cytoplasm to maintain a suitable cytoplasmic pH for bacteria to survive and grow. The sulphate reduction rate increases as a consequence. The maximum biogenic sulphide production of _D. vulgaris_ at pH 5 was 60% higher than at pH 7.4 [17]. Similar findings were reported for sulphate reduction rate of SRB in different zones of venting sediments (Milos Island, Greece) at pH 5 and 6 [66]. The sulphate reduction rate at pH 4 was close to zero [66] which indicates the limit of bacterial growth. In this case, SRB may have lost energy to pump protons out of the cells and lag phase in growth occurred when they change their system to respond to low pH conditions.
## 4 Discussion  
By establishing a mathematical model of the temperature control value system and analyzing and comparing the simulation and experimental results, it can be concluded that changing the gas flow rate at the cold path in this temperature control value system can effectively change the temperature at the outlet of the hot path. The movement of the butterfly valve at the inlet of the cold path changes the cross-sectional area of the cold path inlet pipe to control the flow of the cold gas, thereby affecting the heat exchange between the heat exchanger and the hot gas, and it is possible to control the temperature at the outlet of the hot path.  
In addition, the temperature at the outlet of the hot path is affected not only by the rate of the gas at the cold path but also by other factors at the outlet of the hot path, including the rate at the inlet of the hot path, the heat exchange efficiency, and the NTU coefficient. It was confirmed by experiments that the heat exchanger efficiency and NTU coefficient varied with the operating conditions, so the heat exchanger was fitted based on the collected data.  
According to the result of the temperature control valve system characteristic test experiment, the factors affecting of heat exchanger efficiency under different working conditions were obtained. In order to achieve the temperature adaptive control system, based on 25-point orthogonal experimental data, a second-order polynomial interpolation model of the heat exchanger was identified by experiment. The experimental results show that the fitting accuracy of the heat exchanger model reached 0.985. Based on this model, we took the unit step hot-side outlet stable temperature as the heat exchanger model input and the hot-side outlet temperature as the output to obtain the transfer function of the heat exchanger under different flow conditions.  
Figure 14: Unit step response of heat exchanger: (**a**) linear simulation results, (**b**) heat exchanger frequency response diagram.
# A Concise Review of Silver Diamine Fluoride on Oral Biofilm
## 3 Results
### Application of Silver Diamine Fluoride on Fungus  
Two studies explored the anti-candidalal effect of SDF [16; 33] (Table 3). One study of _Candida albicans_ on a dentin block taken from human teeth reported that the anti-candidal effect was a dose-related property. No significant differences were found between 3.8% and 38% SDF solutions for the colony-forming unit counts, the colorimetric quantification of _Candida_, the real-time polymerase chain reaction-based quantification, and the scanning electron microscopy. The 38% SDF caused severe damage to candida cell walls, whereas the 3.8% SDF caused only partial damage. In another study, _Candida albicans_, _Candida glabrata_, _Candida parasilosis_, _Candida tropicalis_, _Candida krusei_, and _Candida dubliniensis_ were isolated from the carious lesions of preschool children. Zones of growth inhibition, a multiplex polymerase chain reaction, and minimal inhibitory concentrations (MIC50 and MIC90) were adopted for assessing the anti-candidal effect of SDF and the controls. _C. tropicalis_ was more resistant to SDF based on the diameter of the inhibitory zone of the culture growth. Meanwhile, _C. krusei_ and _C. glabrata_ were more sensitive to SDF.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 5 Limitations  
Our systematic review and meta-analysis present several limitations that should be considered when interpreting the results. The included studies exhibit considerable heterogeneity in methodology, such as the choice of the study population and the recording time, which contributes to the observed variability in the accuracy of SRs for measuring sleep. For this reason, the results of this study should not be interpreted as conclusive. Additionally, many studies had relatively small sample sizes [53] or a limited number of recording nights [43; 44; 50], potentially restricting the generalizability of our findings. Future research with larger and more diverse patient populations is needed to validate SRs' accuracy and reliability in health monitoring. Publication bias may have influenced the studies included in our analysis, with null or unfavourable results possibly remaining unpublished [83], leading to an overestimation of SRs' efficacy in health monitoring. Our review primarily focused on sleep and cardiovascular outcomes, but SRs can potentially monitor a broader range of health parameters. Further research is needed to evaluate their accuracy and reliability in monitoring other health parameters, such as mental health outcomes. Moreover, we did not assess the economic implications and financial sustainability of SRs in healthcare systems. Future studies should investigate cost-effectiveness and scalability to better understand their potential impact on healthcare systems and accessibility for specific patient populations. The review's limitation to English language studies may have excluded relevant research published in other languages. Furthermore, the literature shows that different ages are correlated with different sleep patterns, quality, and quantity [84]. These differences should be accounted for in the design of future studies analysing SR performance. Furthermore, general agreement on the standard acceptable cut-off error between the SR measurement and the gold-standard device measurement regarding the time of different sleep phases was not found in the current literature. Taking these limitations into account, our findings offer a preliminary understanding of the potential role of SRs in health monitoring. More comprehensive research is needed to overcome these limitations and provide a clearer picture of SRs' true capabilities in healthcare.
# DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks
## 1 Introduction
However, the access of the mobile node will influence the transmission of the fixed nodes. Thus, we need consider the transmission of both the mobile and fixed nodes in the MAC protocol designing. There are some MAC protocols proposed for the UASN with mobile nodes [17; 18]. In the work of [PERSON] and [PERSON] [17], according to the real-time location information of the mobile node, the sink node schedules the transmission time slot of the sensor node dynamically, in order to avoid transmission collisions. A code division multiple access (CDMA)-based MAC protocol is proposed by [PERSON] and [PERSON] [18], in order to avoid the transmission collision, where different orthogonal spread codes are allocated to different mobile nodes. However, the CDMA technique introduces the near-far problem. In this paper, we propose a MAC protocol for a two-tier DCO-UASN. In the proposed MAC protocol, the network is partitioned into two kinds of sub-networks according to the traffic load, and the suitable MAC protocols are designed for the different kinds of sub-networks. For the sub-network with a light traffic load, a ConTention-based MAC (CT-MAC) protocol is carried out. In the proposed CT-MAC protocol, when one of the sensor nodes having data packet(s) to be transmitted reserves the channel, the receiving node will give a response to the received channel reservation control packet, as well as inviting other sensor nodes having data packet(s) to be transmitted in the same sub-network, to join in the transmission process. For the sub-network with a heavy traffic load, a ReSerVation-based MAC (RSV-MAC) protocol is performed. In the RSV-MAC protocol, the receiver initiates the transmission process. To avoid the transmission collision and achieve a high throughput, the receiver pre-schedules the transmission of both the control packets and data packets. Furthermore,in order to support the access of mobile nodes, the extra transmission time is reserved in both the CT-MAC and RSV-MAC protocols. The remainder of the paper is structured as follows. In Section 2, we review the related work about MAC protocols in UASNs. The system model is described in Section 3. In Section 4, we present a hybrid MAC protocol in the UASN for data collection, in detail. Section 5 gives the performance analysis of the proposed MAC protocol in the renewal theory. Numerical results and discussions are given in Section 6. Finally, we conclude the paper in Section 7.
# Defense and Security Mechanisms in the Internet of Things: A Review
## 4 Security Mechanisms in IoT Environments
### Key Agreement Protocols for IoT Environments  
[PERSON] et al. in [133] introduced a key agreement protocol to enhance communication security in Decentralized Edge Computing Networks. This protocol uses the Computational Diffie-Hellman. The authors used the ROM model and ProVerif tool to analyse this protocol's security. The protocol resists stolen verifier, impersonation, replay, DoS and eavesdropping attacks. They determined that their solution suits low-power and resource-constrained IoT environments.  
[PERSON] et al. in [134] presented a novel cloud-based quantum secure framework with a key agreement protocol. This solution was dedicated to medical IoT environments. The authors built their framework on the Kyber post-quantum cryptosystem [135; 136]. The authors verified their protocol using the AVISPA tool and informal analysis. These analyses confirmed that this protocol resists MITM, replay, impersonation, and node capture attacks. Also, it meets the following security features: anonymity, untraceability, data confidentiality, authentication, and session key agreement.  
[PERSON] et al. in [137] proposed key agreement protocol for IoT environments based on Hyper Elliptic Curve Cryptography [138; 139] and Chinese Remainder Theorem [140] built on four co-prime integers. This protocol allows multiple shared keys between devices, facilitating a subset construction with more excellent device support and full connectivity till depth four. The authentication mechanism treats device identities as secret information during the exchange, eliminating the selective device attack. The proposed solution is secure against various attacks, mainly reply attacks, impersonation attacks, user anonymity, and non-repudiation. The authors verified these features using the ROR model and informal analysis.