esa-sceva/satcom-chunk-collection huggingface dataset:
# Strength Analysis of Real-Life Passwords Using Markov Models
## 5 Results
### Character Frequency Analysis  
The character frequency distribution is the basis for constructing a Markov model for the purposes of either cracking passwords or computing its Markov score i.e., its strength. We performed character frequency analysis on our datasets of disclosed real-life passwords (see Appendix A).  
Since the datasets are acquired from different sources, we expect them to differ in terms of character frequency distribution, which is important for our research, since we are trying to create different independent models. We present a couple of findings that point to this assumption. First, in almost all of the datasets, the most frequent characters are _numbers_, _lower-case letters_ and _upper-case letters_, but the _10k_most_common_ and _uniqpass_preview_ lack _upper-case letters_. Likewise, special characters from the basic ASCII table are not present in all the datasets. Second, the RockYou dataset contains characters from the extended ASCII table, which other datasets do not, so we expect this dataset to produce significantly different password scores from the testing dataset.  
In order to confirm our assumption about the statistically significant difference between the datasets in terms of character frequency distribution, we performed a statistical analysis of the character frequency for all datasets. We used the nonparametric [PERSON]'s ANOVA statistical test for multiple repeated measures since the Shapiro-Wilk test for normality showed that all of the differences between pairs of datasets do not conform to a normal distribution (\(p<0.05\) in all cases). The [PERSON]'s ANOVA test did find that there are statistically significant differences (\(\chi 2(11)=259\), \(p<0.001\)). This confirms our assumption and gives us the ability to continue with our experiment and analysis since this meets the condition for diversity; this condition is met when at least one dataset is different from the others.
## 4 The Proposed 2 FAKA-C/S Protocol
### Registration Phase  
To obtain authentication from \(S_{j}\), the user \(U_{i}\) needs to carry out the following registration steps (R. 1-3) and then complete the operation of registration in the terminal of \(S_{j}\).  
**R. 1**.: _The user \(U_{i}\) chooses an identity \(1D_{i}\) and a pre-secret value \(R_{i}\); by the secure channel, \(U_{i}\) transmits \(\{1D_{i},R_{i}\}\) to the aggregated server \(S_{j}\)._  
**R. 2**.: _Upon receiving \(\{1D_{i},R_{i}\}\), \(S_{j}\) selects a random number \(r_{i}\in Z_{p}^{*}\) and computes \(PID_{i}=H(ID_{i}\parallel x)\) and \(V_{i}=H(PID_{i}\parallel x\parallel r_{i})\). \(S_{j}\) then stores \(\{PID_{i},r_{i},Sum=0,R_{i}\}\) in its database, where Sum indicates the number of allowed login failures before the smart card is revoked. If the number of login failures exceeds the allowed limit, the smart card is marked as revoked in the server's database, and the user is notified to request a new smart card [28; 29]. Finally, \(S_{j}\) writes \(\{H(x),\ P,\ V_{i},PID_{i}\}\) to a new smart card \(SC_{i}\) and securely transmits \(SC_{i}\) to \(U_{i}\)._  
**R. 3**.: _Upon the user \(U_{i}\) obtaining the smart card \(SC_{i}\) from \(S_{j}\), \(SC_{i}\) selects \(a_{i}\,\epsilon Z_{p}^{*}\) and randomly generates a number \(2^{4}\leq n_{0}\leq 2^{8}\). Then, \(SC_{i}\) calculates parameters: \(RPW_{i}=H(ID_{i}\parallel PW_{i})\) mod \(n_{0},B_{i}=H(RPW_{i}\parallel a_{i})\bigoplus V_{i}\), \(A_{i}=H(ID_{i}\parallel PW_{i}\parallel a_{i})\) mod \(n_{0}\). Finally, \(SC_{i}\) contains the parameters: \(\{a_{i},A_{i},B_{i},X,P,n_{0}\}\)._  
The operations are also summarized in Figure 2, to provide researchers with a quick understanding of the registration phase.
## 2 Materials and Methods  
### Coronary Artery Disease Dataset  
#### 2.1.1 Data Acquisition  
Between 16 February 2018 and 28 February 2022, the Department of Nuclear Medicine at the University Hospital of Patras conducted a study involving 2036 consecutive patients who underwent gated-SPECT MPI using 99 mTc-tetrofosmin. They employed two-hybrid SPECT/CT gamma-camera systems (Varicam, Hawkeye, Infinia, Hawkeye-4, GE Healthcare) for MPI. Attenuation correction (AC) based on computed tomography was applied to stress and rest images for all patients. Among these patients, 506 individuals underwent ICA within sixty days of MPI for further examination. After excluding twenty patients due to inconclusive MPI results or missing ICA reports, the final study population consisted of 594 patients, with 43.82% showing CAD-positive results.  
The ethical committee of the University General Hospital of Patras approved the data collection process (Ethical and Research Committee of the University Hospital of Patras, protocol number 108/10-3-2022). As this study was retrospective in nature, informed consent from the participants was not required. All data-related procedures were conducted anonymously and in accordance with the Declaration of Helsinki. The diagnostic results of MPI SPECT were provided by three experienced nuclear medicine physicians, who independently inspected the polar maps and resolved any discrepancies through consensus.  
The raw image data were tomographically reconstructed on a dedicated workstation (Xeleris 3, GE Healthcare, Chicago, IL, USA) using the Ordered Subset Expectation-Maximization (OSEM) algorithm with two iterations and ten subsets. Subsequently, a low-pass filter (Butterworth) was applied with specific parameters for stress and rest images. The dedicated software (Xeleris 3.0513) automatically generated polar maps, which are 2D circular representations summarizing the results of the 3D tomographic slices. These polar maps were saved in Digital Imaging and Communications in Medicine (DICOM) format for further processing.
## 6 Inflammation and Oxidative Stress in PVAT. The Role of Ghrelin in Vasoreactivity  
PVAT influences vasodilation and vasoconstriction and regulates vascular tone and diameter. PVAT-derived ROS can directly inhibit smooth muscle cell contractions by activating the receptor of NO, soluble guanylyl cyclase. Hydrogen peroxide release is another mechanism of PVAT-induced anticontractile vascular effects [51]. Healthy perivascular adipocytes inhibit vasoconstriction by activating the potassium channels in smooth muscle cells through the adipocyte-derived relaxing factor (ADRF). However, the vasodilatory effect of PVAT is mostly present in an association of intact endothelium with intact PVAT [14]. In non-obese type 2 diabetes animal models, no relaxing properties of PVAT from rat aortas were found [52].  
Ghrelin, a modulator of gastric motility, has kinetic properties which also impact the vascular tone. PVAT modifies the local production of angiotensin, which regulates blood pressure [53]. Ghrelin inhibits angiotensin II-induced proliferation and contraction of human aortic smooth muscle cells [54]. Ghrelin inhibits the vasoconstrticr angiotensin-II by increasing the intracellular concentration of cAMP and increasing the bioavailability of NO, which has vasodilatory properties. The decrease in blood pressure may be also mediated by the brain, where ghrelin suppresses sympathetic activity [38]. The vasodilatory effects of ghrelin [55] are confirmed by the finding that low ghrelin levels are associated with increased blood pressure [56]. Ghrelin was found to suppress oxidative stress in hypertensive rats [57]. High levels of ghrelin were associated with hypertension only in obese women [38]. Ghrelin inhibits a major source of intravascular ROS formation, the isoform cyclooxygenase-2 (COX-2) found in hypertensive patients [30].  
Ghrelin secretion declines with age [58] and aging is a major risk factor for atherosclerosis. The decreased levels of ghrelin are aggravated by MS, as obese and diabetic individuals are known to have low levels of ghrelin [59, 60]. The increase in adipose tissue mass has major implications on the characteristics of PVAT, with detrimental effects on vascular health. Due to its cardiovascular protective effects, ghrelin could compensate for these vascular dysfunctions. However, various ghrelin polymorphisms, such as Leu72 Met, indicate an increased risk of coronary artery disease [33]. Moreover, despite its orexigenic effect, circulating ghrelin levels decline in obesity, diabetes, and MS, but this decrease applies to des-acyl ghrelin concentrations, the most abundant form of ghrelin, whereas acylated ghrelin remains unchanged or increases [41].
## 4 Residue Number System and Secret Sharing
It includes a secure encryption function with a private key, which is designated as ENC. In addition, the algorithm implements a computationally secure \((k,\,n)\) SSS. It is also worth noting that the space of both the secret and the message in this scheme is the same as for the encryption function \(ENC\). ```
1. Choosing a random encryption key \(K\); secret \(s\) is encrypted by ENC, \(e=\mathrm{ENC}_{\mathrm{K}}(\mathrm{s})\). 2. \(e\) is divided into \(n\) fragments--\(e_{1},\,\,e_{2},\ldots,\,\,e_{n}\) by the scheme. 3. \(K\) is represented as a tuple of \(n\) numbers \(\mathrm{K}_{1},\,K_{2},\ldots,\,\,K_{n}\) by Asmuth-Bloom SSS. 4. Shares \(m_{i}=(e_{i},K_{i})\), \(i=\overline{1,n}\) are distributed between participants
```  
**Algorithm 1**Secret Sharing of Krawczyk scheme. In Algorithm 2, every share \(m_{i}\) has a bit length \(|e_{i}|+|K_{i}|\), where \(|x|\) is a bit number of \(x\). Evidence of this, as well as confirmation of the secrecy of the scheme, is given in [38]. Despite the obvious advantages of these schemes (low redundancy, scalability, flexibility), they have several disadvantages, such as the inability to add new participants without recovering the secret and re-sharing it which is important for smart city infrastructure. The advantages and limitations of the DT-RRNS scheme are discussed in the next sections.
# TBRNet: Two-Stream BiLSTM Residual Network for Video Action Recognition
###### Abstract  
Modeling spatiotemporal representations is one of the most essential yet challenging issues in video action recognition. Existing methods lack the capacity to accurately model either the correlations between spatial and temporal features or the global temporal dependencies. Inspired by the two-stream network for video action recognition, we propose an encoder-decoder framework named Two-Stream Bidirectional Long Short-Term Memory (LSTM) Residual Network (TBRNet) which takes advantage of the interaction between spatiotemporal representations and global temporal dependencies. In the encoding phase, the two-stream architecture, based on the proposed Residual Convolutional 3D (Res-C3D) network, extracts features with residual connections inserted between the two pathways, and then the features are fused to become the short-term spatiotemporal features of the encoder. In the decoding phase, those short-term spatiotemporal features are first fed into a temporal attention-based bidirectional LSTM (BiLSTM) network to obtain long-term bidirectional attention-pooling dependencies. Subsequently, those temporal dependencies are integrated with short-term spatiotemporal features to obtain global spatiotemporal relationships. On two benchmark datasets, UCF101 and HMDB51, we verified the effectiveness of our proposed TBRNet by a series of experiments, and it achieved competitive or even better results compared with existing state-of-the-art approaches.  
action recognition; bidirectional long short-term memory; residual connection; temporal attention mechanism; two-stream networks +
Footnote †: journal:
## 6 Conclusions  
We developed a state-of-the-art speech watermarking technique called the LP-DSS scheme that satisfies the four requirements of inaudibility, robustness, blind-detectability, and confidentiality. For blind-detectability and confidentiality, we proposed two forms of data embedding for blind detection with frame synchronization, and we investigated ways of mitigating the trade-off between inaudibility and watermark detection rate by varying the embedding-strength level. The evaluation results showed that, with the embedding-strength level of \(-\)15 dB, the proposed method satisfies the inaudibility and watermark detection rate requirements until 8 bps. We also found that the proposed method is inaudible and robust in detecting security codes, and that it has sufficient capability for blind detection. Regarding confidentiality, we demonstrated that if the security code is embedded on the front-side or back-side form, and vice versa, it cannot be detected.  
**Author Contributions:** Methodology, investigation, writing--original draft, writing--review and editing, T.I.; investigation, writing--review and editing, S.K.; conceptualization, supervision, project administration, investigation, writing--review and editing, M.U. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by I-O DATA foundation and KDDI Foundation.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 4 Discussion
### Enhancement of Model Realism and Robustness  
In our research, while the proposed model incorporates a driver-state factor to account for cognitive delays and stress responses, it does not fully capture the inherent variability in human driving behavior. Individual differences in reaction times and unexpected road conditions remain oversimplified, such as delayed responses due to distractions or fatigue and sudden obstacles. Future iterations would integrate stochastic modeling and machine learning techniques to dynamically adjust reaction time parameters based on real-time behavioral data, thereby enhancing adaptability to diverse driver profiles. In addition to driver variability, our current work only considered driving environments under clear weather conditions. Environmental factors can significantly alter braking dynamics and vehicle interactions. These factors include adverse weather (rain, snow, and city roads) and varying road surface conditions. These changes would compromise the model's accuracy. To address this issue, our future work plans to incorporate real-time environmental data by using V2X communication. For instance, V2X-enabled systems could share critical information about sudden braking, lane changes, and intersection movements, which is particularly crucial in scenarios like heavy rain, snow, or icy roads, where friction coefficients and visibility are drastically reduced. This integration would allow dynamic updates to IDM parameters, such as adjusting safety distances based on real-time friction coefficients from road sensors or incorporating weather forecasts (e.g., snowfall intensity or ice formation alerts) transmitted via V2X communication. By refining braking variables and safety distance calculations under these adverse conditions, the model's adaptability and predictive capabilities in extreme weather scenarios would be improved. Furthermore, while ITTC effectively quantifies collision risk, its reliance on time-to-collision thresholds may overlook contextual factors like road geometry or driver intent. Complementing ITTC with multi-dimensional metrics could improve reliability, such as the deceleration rate to avoid a crash and surrogate safety measures derived from vehicle kinematics. Future work will focus on developing hybrid approaches that combine ITTC with data-driven classifiers, such as random forests. The improved method would enable a more comprehensive analysis of collision risks.
## 4 Results of Parameter Identification and Optimization
### Functioning Principle of the Measuring Control System  
Of the larger number of types of transmitters-sensors, a particularly important place is held by pneumatic transmitters, the installation of which, especially in conjunction with electronics, can form very powerful and increasingly widely used automated measurement and control systems. The principle and essence of the functioning of pneumatic transmitters-sensors is shown in the functional diagram below (Figure 5).  
Pressurized air from the network is introduced into the preparation phase (separation of condensate (3), filtering from mechanical impurities (4), and pressure reduction in the pressure regulator (5) via the supply connection (1) on the network and the tap (2)). The fine stabilization of the pressure is carried out in the pressure stabilizer (6). The air prepared in this way is introduced into the measuring and control device (7) with a variable measuring scale (8) and a semaphore indicator (9), and then passed through the drain line (10) to the measuring head (11), which is placed next to the measuring object at the precisely calculated distance (z). At the exit from the measuring head, a corresponding air inlet is formed in the (z) with the parameters according to the equation continuity and the energy equation of fluid (air) movement.  
Figure 5: Functional measurement and control scheme with a pneumatic sensor: (1) Air supply from the system. (2) Faucet. (3) Moisture separator (condensate). (4) Filter. (5) Pressure regulator. (6) Pressure stabilizer. (7) Measuring device. (8) Measuring scale with float. (9) Indicator of measuring quantity. (10) Air line. (11) Measuring head. (12) Measuring object (\(x^{T}\), measurement size with tolerance \(T\); \(\pm\Delta x\), change of measurement size \(x^{T}\); \(z\), clearance between measuring head and measuring object).  
Each change in the measured value leads to a change in the size (z), which causes a change in the parameters of the formed output jet of air (pressure, speed v). The end result is, therefore, a change in the pressure of the jet, which is registered by moving the float on the measuring scale in the appropriate range.
## Materials and Methods
### DPPH Radical Scavenging Assay  
The DPPH assay was based on hydrogen atom transfer (HAT) reactions [30; 31]. 2,2-Diphenyl-1-picrylhydrazyl or DPPH will generate a stable free radical with an unpaired electron that is delocalized over the entire molecule. Then, the changing of the color from yellow to purple was detected by the UV spectrophotometer. Briefly, various concentrations of the rice sprout extracts dissolved in methanol (between 5 to 1000 \(\upmu\)g/mL) and the DPPH reagents were added and mixed in the ratio of 1:1 in the 96 well plates for 30 min in the dark at room temperature. The loss of DPPH radical's absorbance at 517 nm was measured by using a microplate spectrophotometer (EnSight \({}^{\text{TM}}\)). Gallic acid, a standard antioxidant was used as a positive control (y = 21.216x + 2.5088, R\({}^{2}\) = 0.9822). The linear curve was obtained from the plot between gallic acid concentration and DPPH radical scavenging power. The experiments were repeated in five replicates. The DPPH radical scavenging capacity is represented as the percentage of DPPH radical inhibition at 50% (IC\({}_{50}\)). The percentage of inhibition or %DPPH scavenging effect was calculated following Equation (2).  
\[\%\text{DPPH scavenging effect}=[\text{Abs of control}-\text{Abs of sample}]/[\text{Abs of control}]\times 100 \tag{2}\]  
Abs of control = absorbance of control or a reaction mixture in an absence of antioxidant of sample Abs of sample = absorbance of the reaction mixture in the presence of sample
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
###### Abstract  
In emerging energy-harvesting wireless sensor networks (EH-WSN), the sensor nodes can harvest environmental energy to drive their operation, releasing the user's burden in terms of frequent battery replacement, and even enabling perpetual sensing systems. In EH-WSN applications, usually, the node in energy-harvesting or recharging state has to stop working until it completes the energy replenishment. However, such temporary departures of recharging nodes severely impact the packet routing, and one immediate result is the routing loop problem. Controlling loops in connectivity-intermittent EH-WSN in an efficient way is a big challenge in practice, and so far, users still lack of effective and practicable routing protocols with loop handling. Based on the Collection Tree Protocol (CTP) widely used in traditional wireless sensor networks, this paper proposes a loop-aware routing protocol for real-world EH-WSNs, called La-CTP, which involves a new parent updating metric and a proactive, adaptive beaconing scheme to effectively suppress the occurrence of loops and unlock unavoidable loops, respectively. We constructed a 100-node testbed to evaluate La-CTP, and the experimental results showed its efficacy and efficiency.  
energy-harvesting wireless sensor networks; loop-aware routing; parent updating; adaptive beaconing +
Footnote †: journal: Sensors
## 4 Results and Discussion
### Analysis of Metrics for the Implementation of V2G with Power Smoothing Methods
#### 4.4.2 Crest Factor  
The crest factor (see Figure 15), which evaluates the relationship between the maximum values and the square root of the mean square, did not show notable improvements with smoothing methods. All methods maintained a relatively constant prominence of peaks in energy generation.
# Modeling of Traffic Flows Sustainability on Highway Network Stretches
## 1 Introduction  
Traffic flow and traffic jams are major societal, economic, and environmental issues caused by road transportation in developed nations' urban areas. Therefore, traffic monitoring on congested highways requires a clear understanding of what causes road congestion, which determines traffic jams' time and place, how congestion spreads along the road network, etc. [1]. Over the past fifty years, numerous traffic flow theories and models have been developed to address these research questions. Using traffic flow modeling is a cost-effective way of studying transportation, which allows for reliable predictions of delays and traffic congestion. The variety of traffic controlling options leads to using numerous criteria, making it possible to model and predict the sustainability of traffic flows and traffic jam conditions [2; 3].  
Transport flow density is the main factor in traffic modeling [4; 5; 6]. For example, in research [4], the transport flow density is considered an equilibrium, constant value. In this case, equilibrium flux densities were obtained analytically by the authors of this study. Theinvestigation [5] provides obtaining method for transport flow density corresponding to driving near intersections. The research [6] shows the application of variable flow density in models. The authors obtained solutions for different density values. This approach improves prediction accuracy.  
An executed review of current research on solving traffic management problems has provided the following framework for the main approaches and methods applied in this field (Figure 1).  
Our study corresponds to the first group--mathematical models classified into three types (Figure 1). Therefore, a detailed literature review of the current studies for each mathematical model type is provided in the following sections.