esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model  
A multi-user, single-input, single-output (MISO) IRS-aided ISAC system is presented in Figure 1, with \(K\) single-antenna users and a base station (BS) equipped with \(M\) antennas. Specifically, the BS deployed the uniform linear array (ULA) antennas, and the IRS employed the uniform planar antenna (UPA). Our work considers a case wherein direct links of BS users are obstructed by dense obstacles. Therefore, the IRS with \(N\times N\) reflecting elements is employed to aid the user's wireless data transmission and to provide target sensing service in NLoS areas. We can denote the set of users, BS antennas, and IRS elements as \(\mathcal{K}=\{1,2,\ldots,K\}\), \(\mathcal{M}=\{1,2,\ldots,M\}\), and \(\mathcal{N}=\{1,2,\ldots,N\}\), respectively. The transmitted information-bearing symbol vector is denoted as \(\mathbf{s}(t)=\left[s_{1}(t),\cdots,s_{K}(t)\right]^{T}\in\mathbb{C}^{K\times 1}\). The signal transmitted by BS is given by  
\[\mathbf{x}(t)=\mathbf{Ws}(t), \tag{1}\]  
where \(\mathbf{W}=\left[\mathbf{w}_{1},\mathbf{w}_{2},\cdots,\mathbf{w}_{K}\right] \in\mathbb{C}^{M\times K}\) represents the transmit beamforming matrix, with \(\mathbf{w}_{m}\in\mathbb{C}^{M\times 1}\) denoting the transmit beamforming vector for user \(k\).  
The covariance matrix of the transmit signal is computed by  
\[\mathbf{R}_{\mathbf{X}}=\mathbb{E}\left[\mathbf{x}\mathbf{x}^{H}\right]= \mathbf{W}\mathbf{W}^{H}. \tag{2}\]  
Therefore, the transmit power budget can be obtained by  
\[\text{Tr}[\mathbf{R}_{\mathbf{X}}]\leq P^{\text{max}}, \tag{3}\]  
where \(P^{\text{max}}\) is the transmit power budget.  
Figure 1: System model.
# Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
## 6 Abnormal Pattern Detection: Case Study  
Due to this bridge undergoing pavement replacement work in March 2020, with half of the bridge deck still open to traffic during the replacement process, this paper will use the data collected during this period as abnormal patterns of the bridge structure to validate the proposed abnormality detection method and its effectiveness. When using the 3\(\sigma\) criterion as the upper- and lower-limit values for the normal state of the structure, the results of the recognition of the abnormal event of bridge deck pavement replacement using the previous Lasso regression model are as shown in Figure 10.  
As can be seen from Figure 10, both the daily- and annual-scale Lasso regression models can identify the abnormal patterns of the structure. However, the Lasso regression model under the annual scale has a smaller dispersion of regression residuals, indicating that the Lasso regression model on the annual scale is more sensitive to overall changes in the bridge structure and can more effectively identify abnormal patterns.  
Figure 8: Predictive residual on test set.  
Figure 9: Comparison before and after establishing Lasso regression model: (**a**) total residual, (**b**) original structural frequency.  
In conclusion, this paper suggests that, in practical engineering applications, a Lasso regression model on an annual scale should be established to identify abnormal patterns of the structure by utilizing the SHM data.
# Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances
## 3 Control Strategy  
Proper adaptive scheme should be selected to cope with general uncertainties and disturbances, while compensators is employed to improve the transient response. In this section, \(L_{1}\) adaptive control method augmented with a modern AW compensator is presented for systems with uncertainties and input saturation. Firstly, the L1 AC is introduced to guarantee fast adaption and robustness. Then, Riccati-based AW compensator is chosen to augment the L1 AC architecture to cope with input saturation.
## 4 Inverse Problems  
Modeling physical phenomena can be thought of as a mapping operation, where a set of inputs \((B)\) is transformed into a set of outputs \((E)\) by applying the model operator \((M)\) such that \(M(B)=E\). In the realm of physics, there must be a cause-effect relation between the inputs and outputs, and forward modeling consists of designing and applying a mapping function capable of producing outputs that closely follow experimental measurements \((e)\), meaning that the difference \(E-e\) should be close to zero (Figure 5a) [95].  
Conceptually, solving an inverse problem consists of using the measured effects to estimate the causes. That is, solving a problem of the type \(B=M^{-1}(e)\), which could be straightforward if \(M\) was a bijective function, with \(M\) and \(M^{-1}\) being continuous and differentiable, and \(e\) was a continuous and smooth distribution (Figure 5b). The main difficulties with inverse problems are the possible nonlinearity of the inverse mapping function, the multiplicity of solutions, and the sparsity and noise of the measured effect data [15; 95].  
The development of advanced measuring techniques along with advances in computer science brought attention to the practical applications of inverse problems. A growing body of research has been built to address the afore-mentioned difficulties and to apply the inverse modeling methodologies to problems from many different engineering applications. To attend to the necessity of opening wide discussion of concepts, methodologies, and methods related to inverse formulations, specialized journals started circulating by the late 80s, e.g., the Inverse Problems and Inverse Problems in Engineering Journals (today  
Figure 5: Data processing pipeline for patient-specific (**a**) forward problems and (**b**) direct inverse problems. Symbols, \(B\): forward problem inputs, \(E\): forward problem outputs, \(e\): experimental data.  
Inverse Problems in Science and Engineering) among many others [95]. In this section, several solution methodologies for inverse problems in mechanics are reviewed, highlighting their respective advantages and drawbacks when incorporated into cardiovascular biomechanics analyses.
## 4 Vehicle Storage Analysis
### Performance and Reliability at Extreme Temperatures  
Performance degradation at extreme temperatures can severely compromise real-time sensor fusion and object recognition tasks, both of which demand rapid read/write operations from the storage system. At higher temperatures, random read speeds can drop by as much as 70%, delaying retrieval of AI models or sensor logs and hindering split-second decisions in autonomous systems. Meanwhile, below-freezing conditions (e.g., -20 \({}^{\circ}\)C) may slow down program/erase operations, causing delayed writes from LiDAR or camera sensors. We plan to further investigate cold-weather performance in future work, as recommended by multiple reviewers. Although firmware details are largely proprietary, we hypothesize that drives with advanced ECC, thermal throttling, and robust heat dissipation--often found in industrial-grade SSDs--sustain better performance under thermal stress. Other factors, such as a higher 3D NAND layer count or proactive wear-leveling, can also reduce performance drops. To address these challenges in real-world automotive deployments, we recommend employing active cooling or heat sinks, optimizing firmware for thermal management, scheduling intensive writes under cooler conditions, and using industrial-grade SSDs designed for broad temperature ranges.  
Figure 4: Flash Read/Write Operation Throughput.  
Figure 5: Flash Read/Write Operation IOPS.  
Figure 6: Flash Read/Write Operation Latency.
# Challenges of PBFT-Inspired Consensus for Blockchain and Enhancements over Neo dBFT
###### Abstract  
Consensus mechanisms are a core feature for handling negotiation and agreements. Blockchain technology has seen the introduction of different sorts of consensus mechanism, ranging from tasks of heavy computation to the subtle mathematical proofs of Byzantine agreements. This paper presents the pioneer Delegated Byzantine Fault Tolerance (dBFT) protocol of Neo Blockchain, which was inspired by the Practical Byzantine Fault Tolerance (PBFT). Besides introducing its history, this study describes proofs and didactic examples, as well as novel design and extensions for Neo dBFT with multiple block proposals. Finally, we discuss challenges when dealing with strong Byzantine adversaries, and propose solutions inspired on PBFT for current weak-synchrony problems and increasing system robustness against attacks. **Key Contribution**: Presents an overview of the history of PBFT-inspired consensus for blockchain, highlighting its current importance on the literature, challenges and assumptions. Contributes to the field of Distributed Consensus, proposing novel extensions for the Neo dBFT (dBFT 2.0+, dBFT 3.0 and dBFT 3.0+), with new insights on innovative consensus mechanisms.  
P +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 129_, doi:10.3390/612080129
# Insider Threat Detection Using Machine Learning Approach
## 2 Literature Review
### Deep Learning
#### 2.2.1 Recurrent Neural Networks  
The use of traditional techniques, such as cumulative sum and exponentially weighted moving averages, for monitoring data logs has proven to be challenging. In conventional log data monitoring systems, long data sequences are common; this results in longer processing times; therefore, there is a need to achieve pre-determined monitoring results and detection, which the aforementioned approaches are unable to achieve. These approaches need to feature a pre-determined time window in the detection of significant changes in an underlying data distribution. One significant benefit of RNNs is that stacking deep layers in RNNs allows the network to learn over different time scales [46].  
Many researchers have employed LSTM as an unsupervised anomaly detection method, such as [15; 21; 27]. This approach is meant to overcome the challenges of RNNs,such as the reduction in time gradients in long data sequencing. The inefficiencies associated with the use of RNNs were also highlighted by [47]; namely, this technique offers poor stability, especially when applied to a long sequence of imbalanced data. Large training datasets and an imbalanced distribution in such training sets in most practical cases of anomaly detection make RNN a less favourable approach.  
[PERSON] et al. [19] suggested using LSTM-RNNs and Kernel PCA to analyze insider threats. CMU CERT Insider Threat dataset v6.2 was used to build and test the model. Performance comparison evaluation of the proposed technique was conducted against traditional ML algorithms (e.g., SVM and isolation Forest); it was not compared against deep learning models. This approach achieved a TPR of 92.46%, an FPR of 6.8%, a precision of 95.12%, and an accuracy of 93.85%. This accuracy was achieved through thoroughly pre-processing the initial log data. First, events were standardised and aggregated into a format around the behaviours and attributes of individuals; next, features were extracted for the training and testing phases, respectively.  
[PERSON] et al. [23] proposed an insider threat detection method based on an attention-LSTM that models normal user behaviour and indicates anomalies as malicious behaviour. They used a multi-head attention-LSTM (that has shown high priority in neural language processing) to separate the attacked data from normal data in parallel, given different types of features. The Dempster-Shafer theory was then employed to determine whether a given set of input data qualifies as an attack.  
A different approach proposed by [PERSON] et al. [38], known as DANTE, uses system logs to generate a sequence of events for each user over a given time frame to establish ground truth. Their model uses LSTM to process the sequences and classify normal and outlier behaviours. They further classify threats into one of five categories provided in the CERT insider threat dataset. Although the model achieves high accuracy, it suffers from a high FP rate.
# Distribution Grid Stability--Influence of Inertia Moment of Synchronous Machines
## 4 Practical Verifying  
The synchronous machine MEZ-A 225 MO 4 with the parameters \(P_{s}\) = 50 kVA, \(n\) = 1500 min\({}^{-1}\), \(k\) = 1140 N\(\cdot\)m, and \(B\) = 14 N\(\cdot\)m\(\cdot\)s was chosen as an example. MEZ-A 225 MO 4 is a synchronous hydroalternator with four poles, without a dumper, with a brushless exciter powered by an external electronic source with a constant current of 1.2 A. This corresponds to excitation at a nominal voltage of \(3\times 400\) V in the idle state.  
This excitation current of the brushless exciter was kept constant throughout the experiment.  
The shaft of the machine was connected by a rigid coupling with a steel flywheel with a diameter of 1300 mm mounted in its own bearings.  
The moment of inertia \(I\) = 160 kg\(\cdot\)m\({}^{2}\) was determined by calculation from the geometric dimensions and the catalog data. The damping constant \(B\) = 14 N\(\cdot\)m\(\cdot\)s was calculated from the attenuation of the transient during phasing into the distribution network.  
The unit was started at synchronous velocity by using a friction coupling from a diesel engine.  
During the experiment, the synchronous machine ran as a motor on the distribution network without any additional load. It was loaded by mechanical losses in the bearings and the mechanical power consumption of its own ventilation and exciter. The total load was estimated at 3 kW, which is less than 8% of the nominal power of the synchronous machine. This state was close to the idling state, which is theoretically discussed above.  
Under the permissible load angle oscillation amplitude \(\beta_{A}\) = 0.1 rad (equal to the overflow of active power \(P_{a}\) = 20 kW), the intersection point P presented the maximum value \(I\) = 160 kg\(\cdot\)m\({}^{2}\).  
Figure 4 gives evidence of conformity to the calculated value of an average amplitude (\(I\) = 160 kg\(\cdot\)m\({}^{2}\)) with a recording of grid frequency fluctuation and the corresponding response of the load angle. This recording was measured from the machine during the experiment.
## 4 Simulations and Results
### Simulation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & **Vehicle** & **Test** & **_p-Value_** & **Is It Significant** \\
**Metric** & **Density** & **Statistic** & **2 Sides** & _p-Value \(<\) 0.05?_ \\ \hline Packet losses & all together & 10.55 & 2 & 0.005 & Yes \\ Average delay & all together & 18.95 & 2 & 0.0001 & Yes \\ Average No. of hops & all together & 42.35 & 2 & 0.0001 & Yes \\  & 67 & 9.8 & 2 & 0.007 & Yes \\ Average No. & 100 & 2.6 & 2 & 0.273 & No \\ of neighbors & 133 & 9.614 & 2 & 0.008 & Yes \\  & 166 & 9.614 & 2 & 0.008 & Yes \\ \hline \hline \multicolumn{5}{c}{**(a)**} \\ \hline
**Performance** & **Vehicle** & **Test** & **df** & _p-Value_** & **Is It Significant** \\
**Metric** & **Density** & **Statistic** & **df** & _p-Value \(<\) 0.05?_ \\ \hline Packet losses & all together & 1.55 & 2 & 0.461 & No \\ Average delay & all together & 10.4 & 2 & 0.006 & Yes \\ Average No. of hops & all together & 22.05 & 2 & 0.0001 & Yes \\  & 67 & 5.6 & 2 & 0.061 & No \\ Average No. & 100 & 1.4 & 2 & 0.497 & No \\ of neighbors & 133 & 4.66 & 2 & 0.097 & No \\  & 166 & 5.59 & 2 & 0.061 & No \\  & all together & 4.899 & 2 & 0.086 & No \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _p_-Values from the Friedman test to determine the effect of the building attenuation model. (**a**) Barcelona scenario: one AP; uniformly-distributed traffic. (**b**) Tarragona scenario: six APs; exponentially-distributed traffic. vehicle density do not detect any difference in any of the cases. One reason that explains this result is that the effect size of the interaction is lower in the Barcelona scenario. We used a pairwise comparison to determine the models among which there exists a difference in terms of the performance results. Table 4 shows the \(p\)-values for this comparison by using the Wilcoxon statistical test [22]. We employed the Bonferroni correction in these pairwise comparison. That is, the new significance threshold is 0.017, obtained by dividing 0.05 by the number of options compared (_i.e._, 0.05/3). From Table 4a for the Barcelona scenario, we can see that the results of the performance metrics are significantly different (see Rows 1,4 and 7 and 16) if we model the effects of building presence as
# Communication Technologies in Emergency Situations
## 3 Desk Research
As already stated, while in most of the ECS surveys, this topic is poorly (or not at all) addressed, in the present work it is considered of major importance and thus included thoroughly in Sections 3-5. Ref. [26] presents a review on security challenges of wireless communications in disaster emergency response and crisis management situations. Indeed, the distributed nature, the heterogeneity of the networks, and the requirement of availability of real-time communications make a challenge in the security of the proposed communications architecture. The paper reviews several proposals for emergency communications to conclude that, so far, there is no system that provides, in any network, all security services. Security is also a particularly crucial point often disregarded in the research works of the ECS domain and, thus, will be addressed in our review. Among the latest surveys related to ECS but not so related to the present review as the works described so far, the following references have also been identified:  
Ref. [27], a survey on 6G technologies, scenarios, challenges, and related issues. It claims that 6G will have a profound impact on the intelligence process of communication development, which consists of intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and thus ECS domain is identified among the key scenarios for 6G. Ref. [28], a state-of-the-art and trend of emergency rescue communication technologies for coal mines. It includes the review of four types of emergency communication systems, namely through-the-air, through-the-wire, through-the-earth, and mixed medium types, but of course, it is too specific to a single application scenario. Finally [29], a survey of Indian disaster communication systems and spectrum allocation, including an interesting review on the spectrum allocation for emergency communications in other world regions such as US or Europe, among others.
## 3 Results and Discussion
### Total Phenolic Content and Antioxidant Capacity of the Samples  
Oxidative stress plays a role in the development of several diseases, such as diabetes mellitus, cardiovascular diseases, inflammatory conditions, neurodegenerative disorders, and tumors. An antioxidant-rich diet theoretically helps to prevent these diseases; however, further research is required to better understand the complex role of reactive oxygen species in disease pathogenesis and the possible harmful consequences of excessive antioxidant consumption [44]. In addition to potential health benefits, antioxidant-rich food ingredients may contribute to product stability as they can effectively prevent the oxidation of high-fat foods like meat, fish, oil, and vegetables, which leads to aesthetic deterioration and the development of rancid flavors [45]. Table 4 shows the total phenolic content (TPC) and antioxidant capacity of our samples determined by different in vitro methods (FRAP, CUPRAC, TEAC, DPPH). The TPC of the samples ranged between 2.28 and 164.82 mg GAE/g sample, with an average of 32.05 mg GAE/g and a median of 14.93 mg GAE/g. Acerola powder contained exceptionally high amounts of phenolic compounds, followed by aronia, rosehip, moringa, and turmeric powders. Only these samples contained polyphenols in concentrations above the mean. In contrast to other fruit powders, lucuma powder was characterized by a very low total phenolic content. Chlorella, spirulina, duckweed, beetroot, wheat grass, and barley grass powders were also relatively poor in phenolic compounds (TPC < 10 mg GAE/100 g). Among the antioxidant capacity tests, the CUPRAC assay resulted in the highest values, followed by the DPPH, FRAP, and TEAC assays. These variances are attributed to the fact that these tests are based on different mechanisms and are sensitive to different types of antioxidants. To obtain a more comprehensive parameter applicable to evaluate the relative antioxidant potential of different samples, the relative antioxidant capacity index (RACI) has been proposed, which standardizes the results of different antioxidant capacity assays, thus providing a more accurate ranking of samples [32]. This approach has been applied to a wide range of food categories, such as fruit and vegetable juices [46], teas and herbal infusions [47], coffees [48], black soybeans [49], and bee pollens [50]. Several studies show that the so-called \"superfoods\" typically have a high antioxidant potential [51]; however, limited information is available on the relative antioxidant capacity of superfood powders originating from different food groups.
## 4 Results and Discussion
### FDS vs. FLUENT
The results for the mass flow rate through the forced extraction grilles obtained with both codes (not shown in the paper) compare very well. Figure 18: Contours of CO\({}_{2}\) mass fraction (\(Y_{\text{CO}_{2}}\times 10^{4}\)) at \(t=240\) s and heights \(z=2.95\) m (left column) and \(z=2.05\) m (right column), obtained as in Figure 13. (**a**) FDS; (**b**) FLUENT. Figure 20 shows the comparison presented in Figure 19c, but with more detail and for a shorter period of time. Besides the net mass flow rates, Figure 20 also compares the outward and inward mass flow rates through the west vent. Note the fairly good agreement between the results for the outward mass flow rate through the west vent obtained with FDS and FLUENT. This implies that the larger net mass flow rate through the west vent obtained with FDS is mainly due to a smaller inward mass flow rate. This, in turn, determines the larger inward mass flow rate through the east vent predicted by FDS. Figure 19: **(a)** Mass flow rates of CO\({}_{2}\), **(b)** heat flow rates, and **(c)** mass flow rates (including the net mass flow rate leaving the compartment) through the ventilation systems obtained with FDS and FLUENT using the simplified fire model with \(T_{\text{in}}=1500\) K (Case G-4). Despite the discrepancies mentioned above, the overall agreement between FDS and FLUENT is reasonably good. The higher discrepancies between the results of the two codes reported in the literature for some fire simulations, which do not seem to be justified when similar mathematical fire models are solved, might in part be attributed to underresolved numerical solutions in space and/or time obtained with FLUENT, which requires substantially more computational resources than FDS for a given grid resolution. The main disadvantage of FLUENT over FDS, at least for the scenarios simulated and code versions used in this work, is the substantially higher CPU times required (as much as twice or even more) when standard settings are used, a drawback that is not accompanied by appreciable differences in the numerical predictions. However, differences in CPU time consumption should be taken with care since changes in certain settings may produce substantial variations. For example, using the PISO algorithm instead of SIMPLEC for solving the coupling between mass and momentum conservation equations through pressure increases CPU time consumption in FLUENT by a factor of the order of 20%. Choosing different convergence criteria and numerical settings may also result in appreciable variations in CPU time consumption. On the other hand, FLUENT offers much more flexibility in grid generation for arbitrary geometries and postprocessing of results, and makes simulation set up easier than FDS, which requires a more careful specification of boundary conditions and grid partitions. It should also be pointed out that more recent releases of ANSYS FLUENT [43] are likely to improve the performance of the code, specially in terms of the amount of CPU time consumed.