esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 5 Evaluation
### Xorshift PRNG  
The Xorshift PRNG is a type of Linear Feedback Shift Register (LFSR) PRNG that uses a set of bitwise operations to create a series of random numbers. This PRNG was first introduced by [PERSON] in 2003 and is known for its simplicity, speed, and efficiency, which was improved in 2022 [45]. The Xorshift PRNG has the advantage of statistical quality and can generate a sequence of high-quality random numbers, which reduces skewing and gives more uniformity in distribution. The Xorshift32 PRNG takes an initial seed value and uses a series of bitwise XOR operations (left shift 13 bits, right shift 17 bits, and left shift 15 bits) to generate the next value in the random number sequence, which makes it much faster than other more complex PRNGs [45]. This PRNG has a period of \(2^{32}-1\), which means it can create \(2^{32}-1\) unique numbers before repeating itself. The workflow of Xorshift32 is shown in Algorithm 1.  
```
/*Initialization:state=seed*/ /*Output:A32-bitpseudorandomnumber*/ FUNCTIONXorshift32() state~-(state<<13);//Leftshiftby13 bitsandXOR state~=(state>>17);//Rightshiftby17 bitsandXOR state~=(state<<5);//Leftshiftby5 bitsand~XOR seed=state;//Updateseedwithnewstate RETURNstate;//Returnthenewpseudorandomnumber ENDFUNCTION
```  
**Algorithm 1**Xorshift32 Algorithm
## 3 Methods
### Temporal Embedding with LSTM  
When used in the NPL domain, the transformer model produces good results, and its potential application to time series prediction is still being investigated. Time series forecasting requires data embedding before the encoder of the initial transformer model can identify the historical data directly. Data embedding turns the historical data into vectors. This study opts to extract the temporal features from the historical data through the LSTM for better time series modeling work, since the temporal features of the traffic passenger flow data are easily overlooked by conventional conversion of historical information. Because LSTM is frequently used in time series prediction models and can ignore insignificant features in historical time series while keeping critical information, it was chosen for this project.  
The forgetting gate, memory gate and output gate are the three components of the LSTM, which comprises three pieces in total. The formula expression and the possible functions are as follows:  
Step1: Select the data you want to erase using the forgetting process. The values on each dimension are translated using the sigmoid function in this phase by reading the last state output \(\mathrm{h_{t-1}}\) and the current input \(\mathrm{x_{t}}\). The information on the dimension represented by the values close to 1 will be maintained, while the values around 0 will be forgotten.  
\[\mathrm{f_{t}}=\sigma(W_{\mathrm{f}}[\mathrm{h_{t-1}},\mathrm{x_{t}}]+\mathrm{ b_{f}} \tag{1}\]  
Step2: The input mechanism chooses which portion of the information can be input after the recurrent neural network discards the data that does not need to be recalled. This information is needed to supplement the important data. In this stage, a single new candidate value that will be added to the state sequence is created using the tanh layer.  
\[\mathrm{i_{t}}=\sigma(W_{\mathrm{i}}[\mathrm{h_{t-1}},\mathrm{x_{t}}]+\mathrm{ b_{i}} \tag{2}\]  
\[\mathrm{\widetilde{C}_{t}}=\mathrm{\tan h}(W_{\mathrm{c}}[\mathrm{h_{t-1}}, \mathrm{x_{t}}]+\mathrm{b_{C}} \tag{3}\]  
Step3: The output for the current instant through the output gate must be made after the calculation to acquire the new state. The output mechanism chooses what value should be output and then presents a filtered version, after adding the new candidate values produced by the memory mechanism to the additional state information.  
\[\mathrm{C_{t}}=\mathrm{f_{t}}*\mathrm{C_{t-1}}+\mathrm{i_{t}}*\mathrm{ \widetilde{C}_{t}} \tag{4}\]  
\[\mathrm{y_{t}}=\sigma(W_{\mathrm{y}}[\mathrm{h_{t-1}},\mathrm{x_{t}}]+\mathrm{ b_{y}} \tag{5}\]  
\[\mathrm{h_{t}}=\mathrm{y_{t}}*\mathrm{\tan h}(\mathrm{C_{t}}) \tag{6}\]  
Figure 2: LSTM-based transformer model architecture.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 4 Proposed Method PDTR  
This work is categorized among the location-based deterministic and probabilistic protocols. It proposes a probabilistic and deterministic tree-based routing approach called PDTR, which aims to attend an efficient and balanced energy consumption. It is built from the leaves to the root, the sensing nodes (leaves) transmit the sensed data to the sink (root) node, through a deterministic tree structure. Each node selects a parent with a higher probability in the initial probabilistic routing table, to relay the sensed data toward the sink. After time, when any node \(n_{i}\) loses \(\ell_{e}\) of its energy, the sub-tree routed at the node \(n_{i}\) cuts one of its sub-sub-trees, and the root node of the cut sub-subtree selects probabilistically a new parent, according to the updated probabilistic routing table. Then, the sub-subtree routed at a cut node will be rooted probabilistically at the new parent node. This process is repeated for the node \(n_{i}\), each time it loses \(\ell_{e}\) of its energy, starting from the first energy loss, until the value of its remaining energy is less than \(\ell_{e}\). The used probabilistic protocol in the removed child is classified among the adaptive probabilistic schemes. Each node \(n_{i}\) is aware of its location \((x_{i},y_{i})\), and has a hop-count \(h(n_{i})\) that indicates the minimum number of hops required to reach the sink node, as defined in Table 1. All sensors in the network are fully charged with the same initial energy \(E^{*}\), except for the sink node, which has a different initial energy, \(\mathrm{E_{n_{0}}}^{*}\), as given in Table 2. Every source node collects data and has to choose the appropriate forwarder to send the collected data to, according to the probabilistic routing table.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 3 Results
### Frequency Domain Response  
In Figure 2, the blue trace corresponds to the ideal microgeometry, the red trace represents the pitch error case, and the black trace shows the response with both measured microgeometry and pitch error. The same data set, analyzed in the frequency domain, reveals additional insights into how each error family redistributes spectral energy. The detrended FFT in Figure 3 confirms that pitch deviation introduces a distinct shaft order component at 16.66 Hz, a feature that is absent in the ideal configuration. When measured microgeometry is included, this tone persists, and mesh frequency side-bands become visible, indicating stiffness modulation by pitch error [5]. The primary mesh peak remains at 383.3 Hz for all cases, but its amplitude drops slightly once pitch error or microgeometry redistributes the mesh load.
## 4 Results and Discussion
The design must consider the practical  
Figure 14: Longitudinal position, lateral position and heading angle, slip angle, yaw rate and heading angle. Figure 15: Tracking performance of MPC and SMC. demand of the elderly or disabled users. As the conclusion of the questionnaires, functions of the rollator, such as height adjustable, safety, GPS location, and wireless communication, etc., has been reorganized. A Kano model analysis is conducted and the TRIZ theory is utilized to redesign a novel rollator for meeting the requirements of the elderly. Compared to the designed rollator in the state of art [3; 37; 38; 39], the novel rollator is more suitable for elderly users, featured with a simple geometric shape, the adjustable seat, and suitable materials, etc. Furthermore, safety [40; 41] and reliability are also important factors for the rollator. Uncertain human-robot interactions [42; 43; 44; 45] cannot be avoided during the usage of the rollator. However, there are few studies achieving lateral stability control of the systems of the rollator using MPC. Hence, we validate the novel designed rollator with the lateral stability tracking control, which is a main challenge of the commercial rollator. Hence, we try to design the lateral stability of an active control system for the elderly walker. In this paper, we have presented an MPC based path-tracking controller to improve the safety of elderly rollator. The simulation results indicate that the proposed algorithm keeps the lateral position error in a reasonable range. In the co-simulation of ADAMS-MATLAB, the trajectory of the rollator is smooth with constrained position error within 0.1 m, the turning angle and speed can achieve stable tracking control within 5 s and the heading angle is accurate and the speed is stable. In addition, a compared experiment with MPC and SMC show that MPC controller has faster response, higher tracking accuracy and smoother trajectory on the novel designed rollator. Thus, the novel designed rollator can be available in practical life. The research on the rollator focuses on the novel design and balance control of the novel rollator, while the main issue in this paper is to design the stability system of the novel designed elderly rollator for practical engineering. The characteristics of the novel rollator are with high security, low-speed stability and reliability. Adopting the MPC algorithm in the actual engineering application of rollator system is to meets the requirements of the low-speed stability of the elderly rollator. At the same time, MPC has the advantages of higher tracking accuracy, lower position error, and robustness, which meet the requirements of high security.
# Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
###### Abstract  
Connected devices in IoT systems usually have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyberattacks. Implementing security measures like cryptographic authentication, access control, and firewalls for IoT devices is insufficient to fully address the inherent vulnerabilities and potential cyberattacks within the IoT environment. To improve the defensive capabilities of IoT systems, some research has focused on using deep learning techniques to provide new solutions for intrusion detection systems. However, some existing deep learning-based intrusion detection methods suffer from inadequate feature extraction and insufficient model generalization capability. To address the shortcomings of existing detection methods, we propose an intrusion detection model based on temporal convolutional residual modules. An attention mechanism is introduced to assess feature scores and enhance the model's ability to concentrate on critical features, thereby boosting its detection performance. We conducted extensive experiments on the ToN_IoT dataset and the UNSW-NB15 dataset, and the proposed model achieves accuracies of 99.55% and 89.23% on the ToN_IoT and UNSW-NB15 datasets, respectively, with improvements of 0.14% and 15.3% compared with the current state-of-the-art models. These results demonstrate the superior detection performance of the proposed model.  
IoT; cyber attacks; intrusion detection; deep learning; attention mechanism +
Footnote †: journal: Future internet
# Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN
## 4 Performance Evaluation
### Simulation Results  
#### 4.2.1 Network Lifetime  
In this work, the network lifetime is defined as the total time that the nodes are alive, which can be considered the number of rounds. When the residual energy of a node decreases to zero, the node is considered a dead node. In our simulation, we compared three protocols to evaluate the network lifetime while varying the number of nodes in 1000 rounds, as shown in Figure 4. In Figure 4a, we consider a network of 450 nodes in 1000 rounds with a transmission range at 200 m; the results show that nodes began to run out of energy at round 300 for LEACH, DBR, EEBLC, and EECMR. The number of dead nodes of the proposed protocol was the lowest compared to the other protocols.  
In Figure 4b, we varied the number of nodes in 1000 rounds with the transmission range at 200 m. The LEACH protocol had the highest number of dead nodes compared to the DBR, EEBLC, and EECMR. Since EECRM allows the nodes to form a cluster according to their depth level, nodes only communicate with the nodes at the same depth level in the same cluster. However, nodes in the LEACH protocol became cluster heads in turns according to the generated random number. The nodes may have consumed high energy as a result of increasing the number of transmissions during cluster formation. In EEBLC, the clustering allows unequal clusters in the networks in which nodes can elect to become cluster head considering the number of neighbors and its residual energy. As a result, the number of dead nodes in EEBLC is lower than that of DBR and LEACH, as in Figure 4. In the case of DBR, instead of forming into clusters, the nodes sent data to the sink via a multi-hop routing path according to their depth level. The node would send packets to the forwarder node at the upper depth level without considering the number of neighbors sending packets to the same destination. The forwarder node may have had a high load, which causes high energy consumption while sending packets.  
Figure 3: Network deployment after clustering.  
In Figure 4c,d, we evaluate the network performance when the transmission range was 150 m. It is clear that the network performance depended on the transmission range of the node. The number of dead nodes of the three protocols was higher when the transmission range decreased. However, the network performance of clustering protocols of LEACH, EEBLC, and EECMR was similar, which was better than DBR. Therefore, the clustering protocol showed better performance than the multi-path routing protocol.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 3 Star Identification Algorithm  
The star identification algorithm is primarily divided into two parts: initial matching and similarity calculation. First, the initial matching process narrows down the range for navigation star identification. Subsequently, among the selected candidate stars, the degree of matching between the reference star and each candidate star is calculated, ultimately selecting the star with the highest matching degree as the result for the reference star.
# Retinal Nerve Fiber Layer in Patients with Alcohol Use Disorder
## 3 Results  
The final sample included 21 patients over 18 years old (42 eyes in total) and 21 healthy controls (42 eyes in total) paired by age. In both groups, participants were Caucasian males with a mean age of \(51.7\pm 4.6\) for the AUD group and \(52.3\pm 5.3\) for the control group. The best-corrected visual cavities, anterior and posterior segment examinations, intraocular pressure, and pupillary light reflexes were normal for both eyes of all participants. None of the eyes were excluded because of unreliable SD-OCT scans. The mean initial age for alcohol consumption was \(14.57\pm 2.60\), and the mean age at which AUD patients met the criteria for harmful drinking was \(24.14\pm 6.75\). The mean quantity of alcohol consumption using the SDU was \(24.43\pm 12.51\). Finally, the meantime of abstinence, considering that a 3-month period of abstinence was required for inclusion in this study, was \(8.67\pm 7.25\) months. The mean score on the TEDCA was \(11.37\pm 2.45\) points. The average macular thickness for the right eye was significantly thinner in AUD patients (\(282.30\pm 11.58\) um) than in healthy controls (\(289.99\pm 10.79\) um) (\(p=0.032\)). Similar results were obtained for the left eye (Table 1). Macular thickness was lower in main quadrants (superior, inferior, temporal, and nasal) in AUD patients compared to controls, with the exception of central (see Table 2). For a better understanding of the data obtained, we included a box diagram with the values obtained, differentiating each eye and quadrants (Figure 5), which provides complete information on the degree of dispersion of the data and the degree of asymmetry of the distribution in the quadrants set forth above. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Quadrants** & \begin{tabular}{c} **Control Subjects** \\ _n = 21_ \\ \end{tabular} & \begin{tabular}{c} **SD Control** \\ _(\(\pm\))_ \\ \end{tabular} & \begin{tabular}{c} **AUD Patients** \\ _n = 21_ \\ \end{tabular} & \
\begin{tabular}{c} **SD Alcohol** \\ _(\(\pm\))_ \\ \end{tabular} &
\begin{tabular}{c} **Difference** \\ _CI_ \\ \end{tabular} & \(p\) **Value** \\ \hline Right (μm) & 289.99 & 10.79 & 282.30 & 11.58 & 0.71 to 14.67 & 0.032 * \\ \hline Left (μm) & 289.93 & 10.22 & 281.68 & 10.22 & 1.76 to 14.74 & 0.014 * \\ \hline \hline \end{tabular}  
* Mean (SD) value of the macular thickness (μm) in each of the 90\({}^{\circ}\) quadrants around the macula of control subjects and alcoholic patients. Confidence intervals for the difference in means (Difference CI). Significance of difference \(t\)-test: \(p\)-value.
* Statistically significant data (\(p<0.05\)).  
\end{table} 
Table 1: Average macular thickness (μm). At the papilla level, no significant differences were found in the average of each eye (Table 3) nor in any quadrant specifically (Table 4). We also included a box diagram with the values obtained, differentiating each eye and quadrants, for better comparison (Figure 6). As the figure shows, there was a smaller variance of the data than in the case of macular thickness.
# Fatigue Behaviour of Concrete Using Siderurgical Aggregates
## 1 Introduction
There are also a large number of methods to characterise concrete fatigue. The reference method for fatigue characterisation is S-N curves, but this method requires a large number of tests (minimum 10) of long duration. The Staircase method is another widely used method, although it also requires a large number of tests and only enables the fatigue limit to be determined. Another method used to determine the fatigue limit is the Locati method, which aims to define the fatigue limit by means of a single fatigue test, thus reducing both the time and cost of fatigue characterisation tests. Besides using different test methods, there are other ways to reduce the characterisation time, such as, for example, characterisation by resonant fatigue. Resonant fatigue is a type of fatigue test in which the fatigue test is performed at the resonant frequency of the machine + specimen assembly. [PERSON] et al. [25] recommend frequency range for concrete fatigue testing between 1 and 15 Hz. Low frequency (<1 Hz) reduces the fatigue limit because it increases the test time, creep damage. Comparing moderate frequency (10 Hz) and tests at very high frequency (90 Hz) it has been shown that temperature increases above 65 \({}^{\circ}\)C can significantly magnify creep damage. Resonant fatigue is a relatively widespread type of fatigue test for metals, whereas only a few studies have been carried out for concrete. In the case of conventional concretes, only one work was found in which fatigue at very high frequency was characterised. [PERSON] et al. [17] tested and compared a high-performance self-compacting concrete at moderate and very high frequency by means of S-N curves. In the case of recycled concrete, three works were found [18; 26; 27], [PERSON] et al. [18] analysed the effect of different percentages of recycled aggregate and cement. [PERSON] et al. [26] compared the Locati method and the Staircase method in resonant fatigue. [PERSON] et al. [27] compared the fatigue limits obtained by the Locati method of 3 recycled concretes tested at moderate friction and resonant frequency. The most common scenarios in which today it can be detected fatigue failures are slender structures. It is increasingly common to reduce thicknesses, due to the increase in the resistance of materials, as is the case of skyscrapers, railway sleepers and bridges. In these cases, it is considered convenient to take into account possible fatigue damage. In this research, resonance Locati fatigue tests have been performed on two different concrete mixes; a reference limestone concrete and a concrete mix with fine and coarse siderurgical aggregates. During these tests strain, resonance frequency and temperature were recorded. Based on these parameters, the fatigue limit of both types of concrete could be determined and, subsequently, the effect of the presence of EAFS on the fatigue behaviour of the concretes was analysed.
## 3 Results and Discussion
### Effect of Experimental Parameters
#### 3.1.5 Selected Standard Experimental Conditions  
The selected standard experimental conditions were as follows: MPB initial concentration: 50 mg/L; adsorbent dose: 20 mg/L of AC and 60 mg/L of AOS; stirring speed: 240 rpm; initial methylparaben solution pH: 7.
# CO\({}_{2}\) Adsorption Reactions of Synthetic Calcium Aluminum Ferrite (CAF)
## 4 Conclusions  
In order to investigate the carbonation reaction, SCAF was synthesized in this study with a ternary system of CaO-Al2O3-Fe2O3, and the mechanism of the carbonation reaction of SCAF was analyzed.  
1. As a result of Rietveld-XRD analysis by firing CAF compounds at sintering temperatures of 1000 \({}^{\circ}\)C and 1100 \({}^{\circ}\)C, CF- and CA-based products (about 87.3%) accounted for the major proportion at 1100 \({}^{\circ}\)C, and at 1000 \({}^{\circ}\)C, CF- and CA-based products accounted for 64.6% in total.
2. In the carbonation of SCAF with the wet method, the peak intensity of the products by carbonation was larger than that in the dry carbonation, and regardless of the synthesis temperature of the CAF compound, from 3 h of the reaction time, as hydration products from reactions of the CAF compound with CO2, formation of CAC(Ca4 Al2O6 CO3-11H2O) and CACH(Ca4 Al2O6 CO3-11H2O), which are the calcium carbo aluminate compounds, and CFC(Ca4 Al2 Fe2O12 CO3(OH)2-22H2O), the calcium carbo alumino-ferrite compound, was confirmed.
3. In the case of carbonation with the wet method, CAH and CAFH, which are hydrates, are produced in up to 1 h of reaction time, but from 3 h of reaction time, calcium carbo aluminate and calcium carbo alumino-ferrite compounds, which are carbo compounds, are formed by carbonation.
4. Irrespective of the SCAF synthesis temperature, SCAF reacts with CO2 to produce calcite and calcium carbo compounds, and the carbonation reaction becomes more active over time.  
Therefore, it is expected that the SCAF compound synthesized in this study can be utilized as an excellent material for CO2 capture capable of absorption and fixation of CO2, because with the material, carbo compounds are easily formed by the carbonation reaction and carbonate is produced by reaction with CO2.  
Figure 7: Scanning electron micrographs (\(\times\)5000) of carbonation sintered at 1000 \({}^{\circ}\)C and 1100 \({}^{\circ}\)C. (a) OH; (b) 1H; (c) 3H; (d) 6H.  
**Author Contributions:** Investigation, W.-G.L.; Writing--review & editing, M.-S.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21 CTAP-C163942-01).  
**Conflicts of Interest:** The authors declare no conflict of interest regarding the publication of this paper.