esa-sceva/satcom-chunk-collection huggingface dataset:
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 6 Risk Analysis
### STRIDE Application to System Components
#### 6.3.4 Vehicle Status Analysis  
The Vehicle Status Analysis (VSA) module monitors real-time data (e.g., seat occupancy and belt usage), playing a vital safety role. Threats could compromise its functionality and reliability. **Spoofing** attacks could involve sending false status data, disrupting system responses. **Tampering** with the module or ports could alter critical information, leading to inappropriate warnings. **Repudiation** risks arise when unauthorized modifications cannot be traced. **Information Disclosure**, while less critical, could compromise operational insights if communication is intercepted. **DoS** attacks threaten availability, potentially causing system malfunctions due to missing data. **Elevation of Privilege** risks involve exploiting VSA vulnerabilities to gain wider system access.  
Mitigations involve strong authentication protocols for input data to counter **Spoofing**. Encryption and intrusion detection mechanisms protect against **Tampering**, potentially enhanced by tamper-evident hardware, integrity checks, and AI-based monitoring for unusual data patterns. Digital signatures ensure accountability for **Repudiation**[37], with blockchain offering a potential future avenue for immutable logging of status changes. Encrypting communications addresses **Information Disclosure**. Rate-limiting and redundant configurations ensure operational continuity during **DoS** attacks. Role-based access controls (AAA) restrict privileges to mitigate **Elevation of Privilege** risks [32].  
Residual risks include potential latency/overhead from authentication (**Spoofing**). Sophisticated physical attacks may bypass **Tampering** defenses. **Repudiation** mitigation depends on secure key infrastructure or the complexities of blockchain deployment. Long-term cryptographic resilience (**Information Disclosure**) requires planning (e.g., quantum-safe crypto). Advanced **DoS** attacks might evade detection. **Elevation of Privilege** mitigation relies on a comprehensive design-phase assessment and ongoing monitoring. Trade-offs between security, cost, and complexity need further research, especially considering interactions within broader vehicle ecosystems.
## 5 Experiments and Evaluation
### Results and Discussion
The dynamic threshold indicates if the predicted probability of an instance is classified as an anomaly. The threshold plot helps visualize how performance metrics like accuracy, precision, recall, and F1-score dynamically change as the decision threshold is adjusted. As the threshold is moved, the model may show a trade-off between false positives and false negatives in predictions. Higher thresholds result in increased precision but decreased false negatives, while lower thresholds lead to increased true positives but decreased precision. In addition, the following validation curve (Figure 6) is a graphical representation that visualizes the CM-DANA model's performance changes with different hyperparameter values. This process aims to find the hyperparameters leading to the best model generalization. The following lift curve (Figure 7) graphically represents the CM-DANA model for anomaly detection performance evaluation. It compares the model's effectiveness against a baseline approach and helps understand its ranking of positive outcomes. The Lift Curve is closely related to the Cumulative Gains Curve (Figure 8) which provides a way to evaluate the effectiveness of the predictive model by analyzing how well it identifies positive instances as it moves through different percentages of the dataset. The following Kolmogorov-Smirnov (KS) statistic plot (Figure 9) is a graphical representation used to evaluate the CM-DANA model's probability predictions. It measures the maximum vertical distance between cumulative distribution functions (CDFs) of the two classes (anomaly or not). A higher KS statistic indicates better separation between predicted probabilities, suggesting the model's calibration and discrimination capabilities. Figure 5: Threshold Plot of CM-DANA. Figure 6: Validation Curve Plot of CM-DANA. Figure 7: Lift Curve Plot of CM-DANA. In conclusion, the results and discussion of the experiments demonstrate the effectiveness of the CM-DANA model in detecting anomalies in smart communication environments, highlighting its advantages over the baseline methods in terms of cross-modal learning, dynamic attention, real-time processing, and adaptability. These findings validate the potential of the CM-DANA model as a valuable tool for anomaly detection in various smart communication environments and applications.
# A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
###### Abstract  
Due to the fact that the quality of the received signal is adversely affected by the beam squint phenomenon, which is especially pertinent to the millimeter wave (mmwave) bands, many studies have been utilised by other researchers to provide light on some of the challenges that come with analysing this type of occurrence. Squint causes several issues, the most important of which are its detrimental effects on gain, line of sight, angle of arrival, progressive phase, usable bandwidth, and fading effect. As a result of these obstacles, the advantages of adopting a high-frequency band such as mmwave in modern wireless communication systems are severely limited. Squint-phenomena-related difficulties, such as decreased channel capacity, increased bit error rate (BER), and lowered quality of services, may have a substantial detrimental impact on channel performance. The squint phenomenon and associated issues become more pressing with the expansion of the frequency spectrum and the deviation of the arrival angle from boresight. The purpose of this article is to provide a comprehensive overview of the relevant literature and to compare and contrast various methods in order to identify the most fruitful lines of inquiry for future research.  
beam squinting; massive MIMO; millimeter waves +
Footnote †: journal: Electronics Electronics
## 4 Real-Time Test from Slipping to Being Locked Phase
As shown in Figure 9, in order to find the sub-boundary, tests for the task scheduling period \(T\) ranging from 0.001 to 0.32 s are performed every 0.001 s. The test runs for 10 s in each case. The vehicle jerk is calculated, and the maximum value is recorded. The evolution of the maximum vehicle jerk and frictional loss with respect to the task scheduling period is plotted in Figure 8. It can be seen that the maximum vehicle jerk and frictional loss increase rapidly when the task scheduling period \(T\) is longer than 0.3 s, which is the critical period that separates the stable and unstable regions. When the task scheduling period \(T\) is less than 0.3 s, the rate of increase is rather low. In particular, the maximum vehicle jerk is less than 10 m/s\({}^{3}\) when the task scheduling period is in the range of \(T\) < 0.01 s. Therefore, the sub-boundary here is 0.01 s.  
The influence of the task scheduling period is summarized in Table 3. The critical period obtained in the first step is significant for the whole process because it is an obvious turning point in terms of the clutch engagement performance. As seen from the real-time hardware-in-the-loop test results, the critical period for the clutch engagement process is 0.30 s. Furthermore, as a practical reference, the scheduling period should be selected within the sub-boundary, which is 0.01 s, so as to realize a smooth engagement process.
## 3 Results and Discussion  
### Analysis of Thermodynamic Flow Characteristics  
Figure 4 illustrates the velocity vector diagrams at four distinct time points under a pressure of 0.6 MPa. It can be observed that the heated fluid near the wall ascends due to natural convection caused by density reduction, while the colder fluid at the center descends under the driving force, forming a distinct vortex region. The flow pattern of the gas-phase vortex is notably different, primarily characterized by an upward airflow from the center, which is likely attributed to the greater driving force induced by liquid-phase evaporation compared to that generated by thermal effects. Furthermore, as time progresses, the velocity of the fluid near the wall gradually increases, promoting a more uniform motion throughout the fluid domain.  
In terms of spatial distribution, the region near the wall exhibits an upward flow, whereas the central region demonstrates a descending trend. The highest fluid velocity is observed near the gas-liquid interface. Over time, both the fluid velocity and its maximum value progressively increase, leading to a more homogenized flow pattern.  
Figure 5 illustrates the temperature distribution at different time points under a pressure of 0.6 MPa. In the vapor phase, a notable and continuous temperature rise is observed. Numerical results indicate that the vapor temperature increases from 220.03 K at 0 s to 223.45 K at 300 s, yielding a total increment of approximately \(\Delta\)T = 3.42 K. This temperature rise originates near the heated wall and gradually propagates toward the vapor core, forming clear thermal stratification.  
Figure 4: The velocity vector diagrams at 0.6 MPa.  
Figure 3: Comparison of simulation results with experimental data: pressure (**a**) and temperature (**b**).  
In contrast, the temperature in the liquid phase remains nearly constant, with a total increase of less than 0.03 K throughout the same duration. This negligible change is due to the higher thermal conductivity and specific heat of the liquid phase, as well as the latent heat absorption during phase change, which inhibits sensible heating.  
In summary, due to the absence of an absolute insulation structure, heat leakage is inevitable. The fluid near the wall absorbs heat, leading to density variations and the formation of natural convection, which drives the entire fluid domain into relative motion. As illustrated in Figure 4, a distinct vortex is observed in the liquid phase, where the flow phenomenon is more pronounced due to the thermophysical property differences between the liquid and gas phases. However, as shown in Figure 5, the temperature rise in the liquid phase is not significant, primarily because the absorbed heat is initially converted into latent heat, and the higher specific heat capacity of the liquid phase further mitigates temperature changes. In contrast, the gas phase exhibits a more noticeable temperature gradient, reaching up to 3.5 K, owing to its lower specific heat capacity and thermal conductivity.
## 4 Results and Discussion
### Thermal Evolution History
#### 4.6.3 Thermal History Simulation  
Currently, the Easy%Ro method proposed by [PERSON] & Burnham (1990) [37] is the most accurate approach for thermal history simulation [31; 46; 55]. By using a single-well simulation method, the theoretically calculated Ro values are matched with the measured data, adjusting geological model parameters until the theoretical and measured Ro trends achieve the best fit. This method can therefore be used to infer paleo geothermal gradient values [49; 60].  
In this thermal history simulation, stratigraphic thickness, geological age, and erosion thickness during key periods were used as important constraints. Combined with geothermal field parameters and measured vitrinite reflectance values, Petro-Mod simulation software was applied using the Easy%Ro method to simulate the thermal history of Well Hong 7 in the central sag and Well Hong 6 in the southern sag. The simulated Ro values closely match the measured Ro values, indicating reliable results.  
The thermal history simulation indicates that the source rocks of the Tamulangou Formation in the Hongqi Depression have undergone two main thermal evolution stages: a rapid heating phase during the Late Jurassic-Early Cretaceous and a slow cooling phase from the Late Cretaceous to the present (Figure 8). The simulation results reveal certain differences in thermal evolution history across different locations. Well H6, located in the southern part of the Hongqi Depression, shows that the Tamulangou Formation source rocks entered the hydrocarbon generation threshold at 138 Ma, with Ro reaching 0.5%.  
Figure 7: Erosion thickness restoration diagram for wells H6 and H7.  
Around 119 Ma, they entered the hydrocarbon generation peak, with Ro reaching 1.0% and the maximum paleotemperature exceeding 140 \({}^{\circ}\)C. At present, they are in a highly mature hydrocarbon generation stage (Figure 9). Well H7, situated in the central part of the Hongqi Depression, indicates that the Tamulangou Formation source rocks reached the hydrocarbon generation threshold at 128 Ma, with Ro reaching 0.5%. Around 74 Ma, they entered the moderately mature stage, which has continued to the present, with Ro reaching 0.7% (Figure 10).  
Figure 8: Relationship diagram of maturity, temperature, and time in the Hongqi Depression.
# Predicting the Evolution of Syntenies--An Algorithmic Review
## 5 Accounting for Gene Trees
### Evolution through Segmental Duplications and Losses
#### 5.3.1 Synteny Tree Reconstruction  
The first problem can be handled as a classical phylogenetic reconstruction problem using an alignment of the sequences composing \(\mathcal{X}\), each synteny considered to be a single sequence obtained from the concatenation of its gene sequences. However, with this method, the specificity of each gene family is ignored, in addition to being unsuitable in the case of gene rearrangements obscuring the initial alignment. Rather, if a gene tree \(T_{i}\) is available for each gene family \(\Gamma_{i}\), then a synteny tree may be obtained from those individual trees. In fact, if the set of trees are \"consistent\", i.e., do not present contradictory phylogenetic information, then a synteny tree may be represented by a \"supertree\". The consistency problem of rooted trees has been widely studied. The BUILD algorithm [96] can be used to test, in polynomial time, whether a collection of rooted trees is consistent, and if so, construct a compatible, not necessarily fully resolved, supertree, i.e., a tree displaying them all. This algorithm has been generalized to output all compatible minimally resolved supertrees [97; 98; 99], which may be exponential in the number of genes.  
If the gene trees are not consistent, a synteny tree may be obtained from a greedy consensus tree method (strict, majority rule or singular majority rule consensus) [100] reconstructing a \"consensus tree\", i.e., a tree minimizing a given distance with the set of input trees. Alternatively, we may want to minimally correct the input gene trees in a way they become consistent. In the case of gene families likely containing multiple copies in the same synteny, a way of doing can be to keep a single copy in each synteny. This is actually required for the super-reconciliation model considered in [94; 95], as tandem duplications are not allowed in this model. Formally, given a set of gene trees represented as MUL-trees, i.e., trees with potentially repeated leaf-labels, the problem is to prune them in an appropriate way, keeping a single copy of each label.  
MUL-trees remain relatively little studied compared with single-labeled phylogenetic trees, mainly due to the fact that many problems that are tractable for phylogenetic trees become NP-hard when extended to MUL-trees. For example, most generalizations of the tree distances [101], as well as generalizations of consensus tree methods are exponential in the case of MUL-trees [102; 103; 104]. In a recent publication [105], we consider two problems related to pruning MUL-trees. First, given a set of MUL-trees, the Set Pruning for Consistency, or MULSETPC, problem asks for a leaf-pruning of each tree leading to a set of consistent trees. Second, proceeding each gene tree at a time, the MUL-tree Pruning for Reconciliation (MULPR) problem asks for a pruning minimizing a reconciliation cost with a given species tree. Both problems are shown NP-hard. Nevertheless, an accurate greedy heuristic for MULPR has been developed.
## 2 Materials and Methods  
### Materials  
PLGA (Mw: 10,000, monomer composition of DL-lactic acid/glycolic acid = 75/25) was purchased from Taki Chemical Co., Ltd. (Kakogawa, Japan). PLGA-PEG-PLGA1004 (Mw: 4000/1000/4000, monomer composition of DL-lactic acid/glycolic acid/ethylene oxide = 62/22/16) was kindly donated by Taki Chemical Co., Ltd. CsA (C\({}_{62}\)H\({}_{111}\)N\({}_{11}\)O\({}_{12}\), purity \(\geq\) 97.0%), and \(N\)-methyl-2-pyrrolidone (NMP, C\({}_{5}\)H\({}_{9}\)NO, purity \(>\) 98%) was purchased from Fujifilm Wako Pure Chemical Corp. (Osaka, Japan). Coumarin-6 (C\({}_{20}\)H\({}_{18}\)N\({}_{2}\)O\({}_{2}\)S, purity \(>\) 98%) was purchased from Sigma-Aldrich (St. Louis, MO, USA). DMEM (1.0 g/L glucose) with L-gln, and sodium pyruvate was purchased from Nakalai Tesque, Inc. (Kyoto, Japan). Additionally, 0.5 M EDTA (pH 8.0) and 10 \(\times\) TBS Buffer (pH 7.4) were purchased from Nippon Gene Co., Ltd. (Tokyo, Japan). 3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2\(H\)-tetrazolium bromide (MTT, C\({}_{18}\)H\({}_{16}\)BrN\({}_{5}\)S) was purchased from Dojindo Laboratories (Mashiki, Japan). DAPI Fluoromount-G\({}^{\textregistered}\) (DAPI) was purchased from Southern Biotechnology Associates, Inc. (Birmingham, AL, USA). 1,1-Dioctadecyl-3,3,3,3-tetramethylIndotricarbocyanine iodide (DiR, C\({}_{63}\)H\({}_{101}\)N\({}_{2}\)) was purchased from AAT Biogust, Inc. (Pleasanton, CA, USA). Imiquimod (IMQ) cream 5% (\(w/w\)) was purchased from Glennmark Pharmaceuticals, Co., Ltd. (Mumbai, India). Protopic\({}^{\otimes}\) ointment 0.1% (TAC cream, JP tacrolimus hydrate 1.02 mg/g) was purchased from Maruho Co., Ltd. (Osaka, Japan). New Hematoxylin Type M and New Eosin Type M were purchased from Muto Pure Chemicals Co., Ltd. (Tokyo, Japan). Other chemicals were of the highest reagent grade commercially available.
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
## 5 Virtual and Real Mapping  
Virtual-real mapping includes motion behavior mapping and mechanical property mapping. The motion behavior mapping needs to construct the motion model of the radio telescope and fuse it with the angle data of the acceleration sensor. With the help of the communication module, the real-time interaction of the motion is realized. The mechanical property mapping is to ensure that the DTs can quickly, accurately, and intuitively reflect the mechanical state of the current actual working conditions on the twin model.
## 1 Introduction
Since the deployment of picocells decreases the average inter-BS distance, HCNs bring about much more severe ICI than homogeneous networks. To model the CS game with severe ICI, a practical approach is to introduce ICI into the well-known game models for NS. However, this is tough work, because ICI could completely change the features of the payoff functions, hence changing the PSNE in the game. Fortunately, the future network has the key feature of hyper-dense deployment of sensor and mobile devices, guaranteeing the rationality to evaluate the ICI on a given device by taking an average of all of the devices that interfere with it. Meanwhile, no matter whether a device transmits directly or indirectly to a BS, it requires certain resources (_i.e._, time slot and frequency) and will interfere with near-by devices using the same resource. For the densely-deployed sensors in the near future, coordinators (such as mobiles) will be also densely deployed and be closely surrounded by their sensors. Therefore, the wireless transmission between each coordinator and its BS will also interfere with other transmitters using the same resource. In this article, we consider a simple, but suitable model, the congestion game [19, 20, 21], in which the cost function is defined as the congestion that each device undergoes. Then, we add ICI into the payoff function as a weighted summation of congestion and ICI, changing it into a more complicated game. To further evaluate the features of this game, we also consider channel capacity as the payoff function under the conditions of both fractional frequency reuse (FFR) and orthogonal frequency division (OFD). Three theorems are proven for PSNE in the above games. In the end, extensive simulations show the impact of ICI on the PSNE of these CS games. Figure 1: Scenario with multiple picocells and densely-deployed devices in a single macrocell. The contribution of this study is two-fold. On the one hand, we theoretically find the circular boundary between the devices selecting the macrocell and those selecting picocells, _i.e._, the PSNE of the CS game among densely-deployed sensor and mobile devices. Three theorems are proven, and their correctness is verified by simulations with the ICI formulated by integrals. On the other hand, we systematically analyze the severe impact of ICI on the PSNE by extensive simulations. The collapse of the PSNE (_i.e._, a large number of picocell devices changes their CS preferences to the macrocell in the PSNE) and the degradation of the system performance are clearly revealed, which is quite helpful for the future CS scheme design in the HCN and IoT integrated environment. The remainder of this article is organized as follows. In Section 2, a summary of CS studies is provided. In Section 3, we describe the system model. In Section 4, we theoretically analyze the PSNE of three game models. In Section 5, extensive simulations are performed to demonstrate the correctness of the theorems and the impact of ICI on the CS games and the collapse points. Finally, in Section 6, we draw the conclusions.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 5 Details of Proposed Protocol  
In this section, we describe the secure and lightweight cloud assisted video reporting protocol over 5G-enabled vehicular networks in details. In Table 1, we summarize the notations used for the proposed protocol and the overall operations of the protocol are depicted in Figure 2.  
\begin{table}
\begin{tabular}{c l} \hline _Term_ & **Notation** \\ \hline \(5G_{ID}\) & Unique 5G identity for each OBU’s vehicle \\ _TA_ & Trusted Authority \\ _DMV\({}_{k}\)_ & Department of Motor vehicle’s server \\ _RSC\({}_{j}\)_ & Roadside cloud’s server \\ _DV\({}_{j}\)_ & Identity of designated vehicle’s OBU \\ _R-ered_ & \(v_{j}\)’s credential issued by _DMV\({}_{k}\)_ \\ _KN_ & Key word within a vehicle’s credential \\ _List\({}_{RN}\)_ & List of generated _KNs_ periodically \\ _Tok\({}_{DV}\)_ & A duty token for _DV\({}_{j}\)_ generated by _DMV\({}_{j}\)_ \\ _alias_, & \(v_{j}\)’s pseudo identity \\ _is_ & time stamp \\ _\(\delta_{i}\)_ & certificateless signature of entity \(i\) \\ \(G\) & Elliptic curve group with the same order \(q\) \\ _P_\(\in G_{1}\) & A generator of \(G_{1}\) \\ _sk\({}_{i}\)pk\({}_{id}\)_ & private, public key pair of an entity X \\ _\(t_{i}\)_ & Master secret for each attribute \(i\) \\ _ABE.amk_ & Attribute master key \\ _T\({}_{i}\)_ & Public key for each attribute \(i\in U\) \\ _alias\({}_{v_{i}}\)_ & \(v_{i}\)’s pseudonym \\ \(U\) & Universe of attribute \\ _\(\Gamma\)_ & Access tree \\ _\(\omega\)_ & Attribute set \\ _AS\({}_{j}\)_ & Access Structure corresponding to entity \(j\) \\ \(D\) & Set of secret share \(D_{last_{j}}\) in \(\Gamma\) \\ _Enq\({}_{k}\)_(.) & Symmetric encryption under key \(k\) \\ \hline \end{tabular}
\end{table}
Table 1: Terminology.