esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Control Strategy Design
### ECMS Problem Formulation
#### 3.3.1 Equivalent Consumption Minimization Strategy
\tag{22}\]  
In order to maintain a relatively stable SOC, an SOC-based feedback control is designed to adjust the value of EF based on the SOC tracking error:  
\[s(t)=\alpha\Big{(}1+\beta\Big{(}SOC(t)-SOC_{ref}\Big{)}\Big{)} \tag{23}\]  
where \(\alpha\) is the nominal value of EF, \(\beta\) is the proportional gain of the SOC tracking error, and \(SOC_{ref}\) is the reference SOC to sustain. With given values of \(\alpha\) and \(\beta\), the optimal control can be solved:  
\[p_{g}^{*}(t)=\arg\min_{p_{g}(t)\in[p_{\text{min. }P_{\text{max}}}]}\big{(} \dot{m}_{eqv}\big{(}p_{g}(t),p_{d}(t),s(t),t\big{)}\big{)} \tag{24}\]  
Furthermore, based on the solved optimal output power of the engine-generator set, the engine combustion mode can be determined by checking whether \(p_{g}^{*}(t)\) is located within the HCCI operation range or the SI operation range. Note that the battery parameters, such as SOC, SOP, and resistance, cannot be measured accurately. It will have an impact on the optimization results, as can be seen from Equation (19). Take the resistance, \(R\), as an example: if it is overestimated, then the battery loss will also be overestimated, leading to less usage of the battery and more deviation from the optimal operating point of the engine; on the contrary, if \(R\) is underestimated, then the engine will have more opportunity to operate at the most efficient point, which is similar to the thermostat strategy. In the simulation, these parameters are obtained from pre-stored lookup tables and assumed to be accurate. In real applications, these parameters will be estimated by an advanced algorithm, such as the extended Kalman filter (EKF), to guarantee sufficient accuracy. Since this is not the focus of this study, the details will be omitted here.
## 4 Experiments
### Application in Low Textured Scenes
#### 4.4.1 Experimental Setup  
Since it is unreliable to use feature-based methods to obtain the pose information of objects in low-texture situations, perturbing the actual pose to simulate inaccurate pose conditions does not conform to reality. Therefore, in this experiment, we use scenes without pose data. In such cases, the reconstructed scenes can obtain estimated poses and render new viewpoint images. Then, by comparing the estimated poses with the real poses and  
Figure 8: Performance difference between smooth warm-up learning rate scheduling strategy and non-smooth scheduling strategy in the scene of Lego.  
Figure 9: Partial data of a scene with low texture, reflective ceramic.  
rendering new viewpoint images and depth images for display, we collectively verify the ability of our method to reconstruct scenes with challenging textures, demonstrating its great potential in research and application fields. The software and hardware environments used in the experiments are consistent with those in previous experiments.
## 3 Framework of Forecasting AOG Models in Aviation
### AOG Events and Financial Implications  
The AOG events are an incessant financial challenge for airlines, affecting their direct expenses and related demand. According to the Aviation Outlook Newsletter [19], AOG-related costs can range from EUR 10,000 to EUR 150,000/h, mainly depending on the airline's operational scale, aircraft type, and the nature of the event. Moreover, such expenses are usually accompanied by the urgency of the need for spare parts, additional maintenance work, and compensating affected passengers (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Financial Factor**} & \multicolumn{1}{c}{**Estimated Cost Range (EUR)**} & \multicolumn{1}{c}{**Factor Metrics**} \\ \hline Hourly AOG Cost (per hour) & EUR 10,000–EUR 150,000 & Costs increase based on aircraft type (e.g., narrow-body vs. wide-body) and route profitability. \\ \hline Expedited Parts Costs & EUR 5000–EUR 60,000 & Costs depend on the part’s criticality, availability, and location; for example, a replacement engine can cost EUR 2–10 million. \\ \hline Maintenance Labor Costs & EUR 1000–EUR 25,000 & Hourly labor for technical staff (e.g., EUR 100–EUR 200/h) for emergency repairs. \\ \hline Passenger Compensation & EUR 10,000–EUR 50,000 & Includes vouchers, refunds, and legal compensation under regulations like EU261. \\ \hline Storage Fees for Aircraft & EUR 500–EUR 15,000 & Storage costs in major hubs are significantly higher, depending on airport congestion and local regulations. \\ \hline Hotel Accommodation for Passengers & EUR 5000–EUR 50,000 & Costs increase with the number of passengers affected and the location of the incident. \\ \hline Revenue Loss (Unutilized Aircraft) & Up to EUR 200,000+ & Based on average revenue per flight: a wide-body aircraft like a Boeing 777 may lose EUR 150,000+ in daily revenue. \\ \hline Replacement Aircraft Costs & EUR 50,000–EUR 300,000 & Leasing a replacement aircraft, including crew and fuel. Larger aircraft and international routes are more costly. \\ \hline Logistical Support & EUR 2000–EUR 20,000 & Includes towing, fueling, and ground support equipment. Varies by location and aircraft size. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Financial impacts and cost breakdown of AOG events in aviation industry [19].
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 5 Motivating Examples
### Analyzing the LS-Based and LQI-Based Estimation Procedures
For instance, assume node 1 is receiving a packet from node 3. Because node 3 is hidden from node 4, node 4 is unaware of node 3's transmission and transmits at the same time, causing strong interference and possibly packet loss at node 1. If the packet is indeed lost, which is likely because of the similar distances between nodes 1 and 3 and between nodes 1 and 4, then such an event is _not_ captured in the LQI average because the MAC drops the packet and no information is sent to the network layer. Later, when node 4 is no longer transmitting, node 3 retransmits the packet, which then arrives at node 1 without interference and with a high LQI. As a result, the LQI-based estimates are based on only high LQI packets that are transmitted during times of no hidden node. It should be mentioned that, if node 4 were farther away from node 1, then the interference power would be lower; the probability of successful reception would increase; and a packet with lower LQI would be received and considered by the LQI-based procedure. However, this example shows that there are reasonable scenarios in which the LQI-based procedure would not be able to detect hidden node problems.
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 6 Pricing and Cost Considerations
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Category** & **Subcategory** & **Azure DevOps** & ** GitHub** & **Remarks** \\ \hline CI/CD Pipeline Security & Pipeline Isolation & Self-hosted agents in private networks, artifact signing & Ephemeral workflow environments, self-hosted runners & Azure DevOps offers enhanced isolation for enterprise needs. \\ \hline  & Workflow Approval & Release approvals, immutable builds & Workflow approval processes for third-party actions & Azure DevOps enforces stricter workflow controls. \\ \hline  & Threat Detection & Microsoft Defender for DevOps, anomaly detection & GitHub Advanced Security (code scanning, Secret Scanning) & Both offer strong threat detection, but Azure DevOps integrates better with enterprise tools. \\ \hline  & Malicious Code Prevention & Immutable build verification, gated check-ins & Automated security checks in pull requests & Azure DevOps is more decisive for preventing malicious code injections. \\ \hline Supply Chain Security & Dependency Monitoring & Integration with Microsoft Security Graph, SROM compliance & Dependabot for dependency updates, npm security advisories & GitHub excels in automated dependency patching. \\ \hline  & Vulnerability Detection & Real-time threat detection, compliance-driven security assessments & Code scanning, Secret Acsanning, GitHub Advisory Database & Both platforms provide robust vulnerability detection. \\ \hline  & Third-Party Integrations & Zero Trust model, end-to-end security validation & GitHub Actions for supply chain verification & Azure DevOps is better for compliance-driven enterprises. \\ \hline  & Open-Source Security & Limited focus on open-source workflows & Strong focus on open-source security intelligence & GitHub is better suited for open-source projects. \\ \hline Compliance and Governance & Regulatory Compliance & Tailored for regulated industries (HIPAIA [12], GDPR [11]) & Limited compliance frameworks & Azure DevOps is preferred for compliance-heavy industries. \\ \hline  & Audit Logging & Comprehensive audit logs, integration with Azure Policy & Basic audit logging & Azure DevOps provides better audit capabilities. \\ \hline  & Disaster Recovery & Advanced disaster recovery & Limited disaster recovery & Azure DevOps is stronger for mission-critical applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._additional execution time. Azure Boards, which serves as a project management tool, is included in all paid plans. Azure Test Plans, which support automated testing, are billed separately based on the number of users. Azure Artifacts, which provides package management, charges organizations based on storage and usage [32; 33].
## 2 Related Work
### Feature Extraction and Processing
#### 2.2.2 Deep Feature-Based  
A trained CNN contains a set of convolutional layers with fixed weights that can capture the hierarchical information of an image. A convolutional _filter_ in the convolutional layer generates different activations of the input image as pre-layer outputs and encodes the spatial information of discriminative regions into a _feature map_[27; 28]. A feature map may alternatively be thought of as detection scores acquired after applying a filter over particular spatial places in a 2D image; the activation value at the \(i\)-th location quantifies the significance of the pixel there [29]. In CNNs, lower convolutional layer feature maps contain local information such as edge, line, and corner, while filters in deeper layers take the outputs of lower layers as input and generate feature maps that contain more semantic information [30; 31]. Therefore, in many tasks, a trained CNN can be treated as a more robust feature descriptor and replace local image feature descriptors. For example, [PERSON] et al. [32] used CNNs' deep features for change detection in satellite images. [PERSON] et al. [33] leveraged deep features to help retrieve similar fabric images. [PERSON] et al. [34] used deep features for RGB-D image tracking.  
The potential of a deep feature-based approach in repeated pattern detection was presented by [PERSON] et al. [12]. The authors demonstrated that repeated patterns' key points in two-dimensional images have spatial consistency with certain activations within feature maps; hence, extracting activations can assist in repeated pattern detection. Nevertheless, their method took almost all the feature maps of a trained CNN model into consideration for repeated pattern detection, which is computationally expensive and results in poor efficiency. In another related study, [PERSON] et al. [2] reduced computation by selecting filters (feature maps) that are active more than others. A similar idea of data reduction was used in landscape image analysis: [PERSON] and [PERSON] [29] used principal component analysis (PCA) to compress the CNN feature maps to a compact representation that best encodes landscape image information for landscape similarity analysis.  
Overall, these cases support the view that deep-feature-based methods are robust but not concerned with the accuracy of the repeat pattern size. On the contrary, the approaches in use for autocorrelation models are accurate, efficient, yet noise-sensitive.
## 7 Complexity
### Theoretical Analysis
: According to Lemma 4, the subset that contains the node with the highest weight \(i\in G\) and its neighbors at a distance \(\leq k\) (\(i\bigcup N_{\leq k}(i)\)) stabilizes after at most \(2\times k+2\) successive rounds. The same process is repeated in the graph \(G^{{}^{\prime}}=\{G\ /\ i\bigcup N_{\leq k}(i)\}\). As \(G\) contains at most \(\big{[}\frac{n}{k+1}\big{]}+1\) clusters (Lemma 5) and each cluster takes \(2\times k+2\) rounds to reach a stable state. Thus, the proposed protocol requires at most \(\big{(}\big{[}\frac{n}{k+1}\big{]}+1\big{)}\ \times\ (2\times k+2)\ =\ ( \big{[}\frac{n}{k+1}\big{]}+1)\ \times\ 2(k+1)\ =\ 2(n+k+1)\) rounds to form all the clusters in the network. **Lemma 7**. : _DC2 HC algorithm has a linear time complexity of \(O(n)\) rounds._  
Proof. : Based on Lemma 6, the upper bound of the time needed for the execution of DC2 HC is \(2\times(n+k+1)\) rounds. Thus, it is obvious that the time complexity (convergence time) of the algorithm is \(O(|E|+k)\). As \(|E|\geq k\), the proposed algorithm converges after at most \(O(n)\) rounds. **Lemma 8**: _DC2 HC algorithm has a linear space complexity._  
Proof. : Each node has to maintain the 2-hop neighbors information in its \(CRL\) data structure. Therefore, the space complexity of each node will not increase as long as the local density remains constant. The space complexity of DH2 HC is \(O(d_{2})\), where \(d_{2}\) represents the 2-hop local density. As \(d_{2}\leq n\), the complexity of the proposed algorithm is linear, which is needed in a resource constrained environment. The number of nodes inside clusters depends upon the network density and the _k-hop_ constraint. Indeed, in the case of complete graph, all nodes can be grouped within a singleton cluster. Hence, the maximum number of nodes in a cluster is n, where n is the number of nodes in the network. However, this situation may not often occur in real world networks. Therefore, by considering a related graph, the maximum number of clusters that can be generated by the proposal is \(n/(k+1)\) (Lemma 5), thus, the minimum limit of member inside each cluster is equal to \(\frac{n}{n/(k+1)}=\frac{n(k+1)}{n}=k+1\).
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 3 Enhancements and Improvements to the Original Protocol
### Using SRUP to Initiate Application-Specific Actions  
In addition to SRUP data messages (Section 3.5), SRUP may be used to initiate application-specific actions. A trivial example of this might be to use an action message to switch on or off a connected peripheral.  
This can easily be accomplished by adding a new message type (SRUP_MESSAGE_TYPE_ACTION), which consists of the \"base\" message, plus an ACTION_ID field, which would take an application-specific value to describe the action to be taken. Each action would be a simple atomic operation such as turning a device on or off. As such, two different action IDs would be required to cover both halves of a binary operation such as turning on and off.  
The ACTION_ID is sent as a byte-stream so it could consist of a string or use a numeric value. The recipient may optionally send a SRUP_MESSAGE_TYPE_RESPONSE message to signal the outcome of the action. Messages with an action ID value unknown to the recipient should be ignored; although the recipient may optionally send a response message with a status value reflecting an unknown action.  
For more complex actions where additional parametric data are required, a SRUP data message (Section 3.5) can be used to send the appropriate data, before initiating the action.  
Using this approach simplifies the action message and helps to reduce the volume of data being sent. This way, additional data are only sent where they are required, as opposed to a model where (for example) additional data are specified as a parameter to the action message.  
Whilst this message-type might be viewed as blurring the lines between system \"management\" messages and \"operational\" messages, it is easy to imagine scenarios where the functionality for the remote C2 server to securely pass such an operational message to the device might be highly desirable: for example, activating an emergency beacon on the device. Rather than extending the core protocol on the occasion that a custom message action is required or trying to specify a very large number of such message types in the protocol specification, this approach means that anyone implementing a system using SRUP will be able to easily use the protocol for these types of message. This is especially important if the protocol is being used from a language other than C++. Although the examples implemented thus far have been using C++ directly, it is ultimately desirable to provide wrappers to the library so that it could be used natively from (for example) Python.
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 3 Results
### The Comparison of the Random Variables of Scenario 3. Sea Ferry and Scenario 4 Cruise Ship  
Similar to Scenario 2, in Scenarios 3 and 4, similar relationships were observed between the arithmetic mean of the distribution of the random variable of the ship's hull distance from the assumed reference point in the independent groups, obtained in all the variants of Scenario 3 for the sea ferry and all five variants of Scenario 4 for the cruise ship.  
In Scenario 3, the difference between the largest value of the distribution of the random variable X3_ECDIS obtained in the variant 3_ECDIS (passages using the ECDIS system) and the smallest value of the mean distribution of the random variable X3_AV obtained in the variant 3_AV was more than nine-fold (Table 11).  
In Scenario 4, the difference between the largest value of the mean distribution of the random variable X4_ECDIS obtained in variant 4_ECDIS and the smallest value of the mean distribution of the random variable X4_AV obtained in the variant 4_AV was over eleven-fold (Table 11).  
It can be noted from the results that the value of the mean distribution of the random variable of the cruise ship distance from the east shore of Gryfia Island obtained for the passage variant 4_Pprecise (using the Pilot_precise system) is lower than for the sea ferry passage variant with the same system, while the values obtained for the other systems are in the inverse relationship. This is due to different moments of measuring the significant value for each variant. The shortest distance from the east quay of Gryfia Island during cruise ship turning was the lowest value of all recorded populations in the test sample. With a clearly specified reference in the form of the shoreline, the difficulty of the navigational task came down to get the ship's contour skillfully as close as possible during the turning maneuver.  
In the case of the sea ferry berthing variant, the value of the lowest distance of the hull from the assumed (not visible) reference point was measured at the moment of the'stop' command given by the navigator. The discretely monitored measurement of the distance using a marker was burdened with an error of marker setting and the absence of a clearly defined reference point. This situation did not refer to the AV system, and in the case of large inaccuracy in the estimation of distances using other systems and visual observation (place of the ship not visible from the bridge), it had a lower impact on the overall value.  
The differences observed (at least one pair) in the values of the mean distribution of the random variable distance of the sea ferry from the assumed reference point in the independent groups, obtained for five variants of Scenario 3 and the mean distribution of the random variable of the cruise ship distance from the east shore of Gryfia Island in independent groups, obtained for five variants of Scenario 4, made the basis for further analysis, conducted similarly to the previous passages.
# Method and Test Course for the Evaluation of Industrial Exoskeletons
## 4 Results
### Applicability and Effectiveness of Exoskeleton Types
#### 4.2.1 Mode of Actuation  
* In comparison to passive exoskeletons, active systems are more suitable for use in particular tasks with dynamic movement sequences and high variance due to the versatile adaptation of the support performance and its basic possibility, as the application of exoskeletons mainly in IT01, IT02, IT07, and IT08 shows.
* Passive systems are mainly suitable for static holding and stabilization tasks with only minor variations (e.g., IT04 and IT05). Due to the passive drive (e.g., spring), the energy for force support must first be actively supplied to the system by the user. Accordingly, passive systems have proven to be especially suitable for activities without required load changes.
* Both types often offer a possibility to deactivate the force support, whereby active systems can automatically switch off the support for selected movements (e.g., OR03, OR16). On the other hand, passive systems usually have to be manually unlocked, though not all exoskeletons possess this option (e.g., OR04, OR16).
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
## 1 Introduction  
A covert channel (CC) is an unforeseen communication channel in a system design. While the first covert channels for local computers were described in the 1970s (cf. [1]), the research of recent decades has discovered a plethora of new and sophisticated covert channels that aid the secret exchange of information between hosts, databases, network hosts and IoT devices. Due to their stealthy and policy-breaking nature, covert channels enable several actions related to cybercrime, such as the secret extraction of confidential information, barely detectable botnet command and control channels, and unobservable communication for cybercriminals. While legitimate use cases are imaginable as well, e.g., journalists using covert channels for secure exchange of dissident-related information, criminal use seems foremost, so that presentation of new covert channels also serves presentation of countermeasures.  
We present the first CC that exploits cryptographic hash chains, which have become popular because some form (blockchain) is used in cryptocurrencies, although they had been used much earlier, e.g., by [PERSON] [2]. Hash chains are widely used, which renders them an attractive carrier for covert information that can be applied in various cybersecurity contexts, such as authentication systems, cryptocurrencies and blockchain-driven systems of all kinds. Thus, our proposed covert channels can be applied in several domains and contexts, which is an advantage in comparison to covert channels designed for legitimatecommunication systems (such as covert channels for specific smart building communication protocols).  
Our covert channels can be considered an example of plausible deniability: [PERSON] communicates with [PERSON] over the covert channel. Both can state that every possible hash value is equally likely to occur. By modifying (alternating) bits of hash values, [PERSON] and [PERSON] can thus plausibly deny the existence of the covert channel. Moreover, we show that our channel allows [PERSON] to restore the original message before forwarding it to the overt receiver, making our covert channel reversible. [PERSON] and [PERSON], the covert sender and receiver, can be located directly with sender and receiver of the overt channel, but they can also be located on the communication path, realizing a man-in-the-middle (MitM) scenario.  
Moreover, we present the CC in a parameterized manner so that our proposal really comprises a large number of possible CC instances that differ in steganographic bandwidth, robustness and stealthiness.  
We sketch possible countermeasures against this CC, which are non-trivial because hash values are random-looking bit strings. To this end, we also perform experiments with MD5 and SHA-3 as cryptographic hash functions in [PERSON]'s application of hash chains as one-time passwords, and give experimental results on the performance and detectability of CC use.