esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Information Leakage Analysis of Merkle Tree-Based Authentication Schemes
### Previous Schemes and Their Vulnerabilities
#### 3.2.2 Authentication without a Merkle Tree  
[PERSON] and [PERSON] [26] pointed out the possibility of a replay attack on Merkle tree-based data authentication and proposed a probabilistic protocol inserting randomness based on hardcore function to achieve resilience against the replay attack (the protocol is summarized in Algorithm 1).  
Figure 3: Merkle tree-based authentication for data \(M\) with eight blocks.  
Unfortunately, in their scheme, the size of data to be verified is known to the public (because the hash function \(G\) in Algorithm 1 specifies the size of the output dependent on the data). Due to this assumption, this approach does not prevent the leakage of size information.  
The protocol requires the verifier to perform the same computation as the prover unless the same random seed \(s\) is used repeatedly. If the same seed value is used repeatedly, the proof becomes always the same, thus an adversary can bypass authentication for the entire data with a single eavesdropping on the proof. Consequently, as long as a newly chosen seed is used for every authentication attempt, the efficiency on the verifier side would be reduced because it uses all of the data in the verification process and pre-computation cannot occur.  
In addition, according to their analysis based on the Goldreich-Levin theorem, the probability that an adversary deceives the verifier is at most \(1/|M|\), which is not negligible on the data size. Because the bitstring \(r\) (**Step** 2 in Algorithm 1) can become known to the adversary, the adversary can extract at most \(\log(|M|)\) bits from the transferred proof. To overcome this problem and prevent further information leakage, they recommended encrypting all traffic using a session key generated by additional protocols for secure connection establishment (e.g., SSL/TLS, IPSed), which requires non-negligible overhead in practice. This is dealt with in Sections 3.2.3 and 3.2.4.  
There are also other approaches that do not rely on Merkle tree structure in data authentication. For example, [PERSON] et al. [27] proposed a technique for efficient integrity verification of 2-dimensional range data such as image and GIS data and suggested a method to maintain the communication overhead constant. [PERSON] et al. [28] and [PERSON] and [PERSON] [29] also proposed several novel integrity verification techniques without Merkle tree.
## 1 Introduction
### Precursor Autonomous Systems
The system is deployed on research vessels and an example of data usage is described by Reference [15]. The SAS Solar Tracker system from Seabird Electronics consists of three hyperspectral Seabird/HyperSAS radiometers measuring downwelling irradiance, downwelling sky radiance, and upwelling water radiance mounted at fixed zenith angles (180\({}^{\circ}\), 140\({}^{\circ}\), and 40\({}^{\circ}\), respectively) on an azimuthally rotating platform, with compass-based sun-tracking to ensure optimal relative azimuth angle with respect to sun. Power and data are managed via a deck interface unit and a standard PC. The system is used routinely on a few research vessels and ships of opportunity [16]. The WISP-3 is a handheld system, commercially available from Water Insight, with three embedded radiometers measuring water reflectance from downwelling irradiance, sky radiance, and water radiance. Data for monitoring parameters such as chlorophyll a concentration are logged internally and can be downloaded to a PC as processed data. A variant called WISPR station is under development for autonomous operation. An example of data usage and inter-comparison with other systems is described in Reference [17]. The current PANTHYR design will provide hyperspectral reflectance data not available from the Seaprism system and has advantages over the RFLEX, DALEC, Suntracker, and WISP designs because the zenith pointing flexibility allows sky and water radiance measurements to be made by the same radiometer and allows downward pointing to protect instruments when not measuring. Use of a single radiance sensor provides cost saving and reduces the uncertainties associated with spectral and radiometric calibration differences of the two-radiance-sensor systems. The disadvantage of the PANTHYR sequential measurement of water and sky radiance as compared to the simultaneous measurements possible with the two-radiance-sensor systems is considered to be minimal for the good clear sun and sky illumination conditions needed for satellite validation. If illumination conditions are suboptimal, this will be detected by the PANTHYR replicate measurements and will lead to data rejection. The PANTHYR system does not pretend to achieve the very high performance expected of the OSPREY system, but represents a much lower-cost alternative which should be ideal for deployment at multiple sites worldwide by organizations with moderate budgets. All of these systems, including PANTHYR, are developing within a context where hyperspectral spectrometers are rapidly evolving with reduction of size, power, and cost (thanks to mass production for medical and industrial applications), and where pointing systems are becoming more affordable and more easily available as COTS items (thanks to mass production for applications such as video surveillance). Other technologies facilitating PANTHYR development include the massive improvements and cost reductions in data transmission (thanks to mobile phone networks) and in microcomputers.
# Integrated Framework for Bus Timetabling and Scheduling in Multi-Line Operation Mode
## 4 Case Study and Discussion
### Results and Discussion
#### 4.2.1 Description of Results  
The number of initial solution sets is set as 100, and the number of optimization iterations is set as 500. We get 46 Pareto frontier solutions when the computation time is less than 15 min. All the solutions satisfy the constraint. Figure 11 shows the objective function value distribution of the Pareto frontier solutions. These solutions are all better than the actual operational plan.  
We use Euclidean distance to express the effect of Pareto frontier solutions in each iteration. The calculation is shown in Equation (22). The smaller the Euclidean distance, the closer the position of the solution is to the origin. Figure 12 shows how minimum Euclidean distance changes with the number of iterations. The results eventually tend to converge.  
\[E_{it}^{s}=\sqrt{\left(\frac{Z_{1}^{s}}{10000}-0\right)^{2}+\left(\frac{Z_{2}^ {s}}{1000}-0\right)^{2}} \tag{22}\]  
Figure 13 shows a solution in the Pareto frontier solutions. A row represents a trip-link chain, and a panel represents a trip. The number before the \"\(-\)\" is a line number, and the number following the \"\(-\)\" is a departure time (minute timestamp). The object function value (passenger waiting time) is 25,8084 min and the operating cost is CNY 94,045. All operational constraints are met.  
Figure 11: The object function value distributions of Pareto frontier solutions and actual operation plan.  
Figure 12: Minimum Euclidean distance changes with the number of iterations.  
For Solution-A, the optimized objective function value of passenger waiting time is 25,8084 min, and the total passenger number is 52,195. We map the passenger waiting time as a histogram distribution in Figure 14. It can be seen that the waiting time of most passengers is less than 5 min, and the number of passengers waiting for five to twenty minutes gradually decreases.  
Our solution ensures that supply and demand are matched not only on different routes but also over different time periods, as shown in Figure 15. As we can see, the more passengers on the line/time, the higher the heat, the more trips supplied.  
The Gantt diagram corresponding to this solution is shown in Figure 16. The diagram provides a visual representation of Solution-A and clearly shows the vehicle's daily operation schedule. As can be seen from the color transformation, vehicles can shift between multiple lines.  
Figure 14: The histogram distribution of passenger waiting time.  
Figure 13: A demonstration of Solution-A.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 4 Preliminaries
### Requirements  
CrossDeFi establishes fundamental requirements for transferring assets across diverse DeFi systems. These requirements serve as the foundation for defining the CrossDeFi protocol. In CrossDeFi, the DeFi system utilizes a variety of blockchains, and asset transfers are conducted through a finite set \(D\). Each DeFi system in the set \(D=\{\text{DeFi X},\text{DeFi Y},\dots\}\) employs its smart contracts to facilitate asset transfers. Here, any DeFi system in set \(D\) can act as either a source or a destination. Before the transfer of asset \(A\), it must exist only on the source DeFi, and after the transfer, \(A\) must exist solely on the destination DeFi. At no point should \(A\) exist simultaneously on both the source and destination DeFi systems, as this could lead to accidental asset duplication, potentially deflating the asset's value. Therefore, a CrossDeFi \(A\) transfer is deemed successful only if \(A\) is created on the destination DeFi after being burned on the source DeFi by its owner.  
Requirement 1: A user in a DeFi system must burn \(A\) on their blockchain. For example, if user \(UX\) in DeFi \(X\) wishes to transfer \(AX\), then \(AX\) should be burned in DeFi \(X\) if \(UX\subseteq\text{DeFi X}\) and \(AX\subseteq AP_{X}\).  
Requirement 2: When DeFi \(X\) needs to transfer \(AX\), it must first burn \(AX\) from \(AP_{X}\) and send proof to the destination to recreate \(AX\) in the destination asset pool.  
Requirement 3: It is crucial to prevent double-spending at all times. That is, if asset \(A\) is burned on the source blockchain, then \(A\) can be recreated only once at the destination DeFi.  
Requirement 4: A time-bound \(t\) is established for recreating \(A\) at the destination after it has been burned at its source DeFi system. Finality should always be decentralized, with no single actor responsible for ensuring it.  
Requirement 5: After a particular asset, \(A\), is burned on the source DeFi, the source must eventually receive confirmation of the successful recreation of \(A\) on the destination within \(t\). This transfer confirmation ensures that the source blockchain is informed about whether \(A\) has been successfully recreated at the destination or another blockchain.
# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 3 Experiments  
### Data  
#### Training Data  
The training of high-quality TTS models benefits from a sufficient amount of well-curated data. However, the recording and manual annotation of emotional speech are resource-intensive, making the construction of adequately sized, high-quality emotional datasets challenging. Compared to discrete emotion labels or emotion transfer techniques, our approach leverages an emotion recognition model to extract emotion dimensions and applies an emotion-adaptive coordinate transformation to generate an EASV as the emotion style representation.  
In this study, we follow a data curation strategy inspired by [30], filtering and compiling 200 h of high-quality English emotional speech from an initial 600 h corpus that contains abundant expressive content. The construction process consists of the following steps:1. Emotion Classification and Filtering: We apply the pretrained emo2 vec model [59] to assign categorical emotion labels and confidence scores to all audio samples. Only utterances classified as one of the seven target emotions--neutral, happy, angry, sad, disgusted, excited, and fearful--with a confidence score of 1.0 are retained.  
2. Audio Quality Control: To ensure perceptual quality, we evaluate each utterance using the DNSMOS model and keep only those with an overall listening rating (OVLR) above 3.0.  
3. Emotion Representation Extraction: For each utterance, we extract valence--arousal -dominance (VAD) emotion dimensions and apply the emotion-adaptive spherical transformation to derive the Emotion-Adaptive Spherical Vector (EASV) as a continuous emotional representation.  
4. Segment-Level Labeling: To support fine-grained control, we apply a sliding window (0.5 s window, 0.25 s stride) to extract local EASV segments within each utterance.  
5. Automatic Transcription: All retained speech samples are transcribed using the Whisper-Large pretrained speech recognition model [60] to generate accurate text-audio pairs.  
The final dataset contains 200 h of transcribed emotional speech covering seven categories, with the following approximate distribution: Neutral (23.4%), Happy (14.0%), Angry (10.2%), Sad (14.2%), Disgusted (9.6%), Excited (15.6%), and Fearful (13.0%). The overview of the dataset is shown in Table 1.
# An Estimator for the Kinematic Behaviour of a Mobile Robot Subject to Large Lateral Slip
## 3 Experimental Identification of the Estimator
### Tracking Method  
As can be seen in Figure 4, two ArUco markers, a set of open source square fiducial markers, designed for the fast processing of high quality images [24; 25; 26], were fixed to the robot (one per module), while one was fixed to the ground at an arbitrary location. The camera recording the tests was free to move in the environment; its position only affects the accuracy of the measure. The test recordings were then post-processed by a custom marker-tracking algorithm that first searched for a set of markers and then estimated the homogeneous transformation \(\mathbf{T}_{n}^{\rm C}\), a representation of the pose of the \(n^{th}\) marker in the camera reference frame. The poses \((x_{1},y_{1},\varphi_{1})\) and \((x_{2},y_{2},\varphi_{2})\) of the two moving markers were then estimated with respect to the marker fixed to the ground, representing the world reference frame, by applying a concatenation of homogeneous transformations such as \(\mathbf{T}_{n}^{0}=\mathbf{T}_{n}^{\rm C}\,\mathbf{T}_{\rm C}^{0}=\mathbf{T}_ {n}^{\rm C}\,\mathbf{T}_{0}^{\rm C-1}\). With this approach, the camera can move freely, and the tracking results could still adhere to the desired reference frame. As anticipated, the camera position only influenced the accuracy of the measure, since the system used just a single camera; therefore, the picture depth estimation was noisy.  
Figure 4: Experimental setup with a schematic representation of the visual-based tracking system.
## 1 Introduction
The SCs are arranged in groups, either in in-line (aligned) or staggered configuration. This type of arrangement has been used for a long time in heat transfer equipment such as condensers and evaporators in power plants, refrigerators, and air conditioners [21], and is also adopted for cylindrical batteries [22] and SCs [23]. In previous studies, different arrangements of cylindrical batteries and SCs have been compared considering ventilation power, compactness of the stack, and temperature. For example, [PERSON] et al. [24] and [PERSON] et al. [23] investigated a supercapacitor module with staggered arrangement. They chose this arrangement since, considering equal compactness level, it has a higher heat transfer coefficient. [PERSON] et al. [23] used three rows configuration and shown that, for a definite power ventilation, the lowest spacing arrangement provides maximum cooling performance. Recently, [PERSON] et al. [25] conducted another study comparing staggered and aligned configurations and showed that for the first three rows, the staggered arrangement is better. However, for the others rows, the aligned arrangement provides better cooling performance. Therefore, considering the whole module aligned arrangement, it provides better cooling performance and temperature uniformity. [PERSON] et al. [25] used in their experiment 32 batteries (eight rows). It can be concluded that the staggered arrangement is better only for the first three rows. This conclusion verifies the contradiction between the results presented in [23; 25]. Considering all of these observations, we have proposed in this study a novel configuration in order to benefice from the advantages of both these two arrangements. This configuration proposes a mix between the aligned and the staggered arrangements. We tested its advantages and we analyzed the advantages compared with the previous proposed configurations. First of all, the different components of the test bench, the experimental protocol and the numerical model are presented. Then, the experimental results obtained during the natural and forced convection tests are analyzed. Lastly, in order to understand better the thermal and flow behavior in the analyzed SCs stack, a numerical simulation is performed for the forced convection case.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Controller Functions
#### 5.4.5 Network Virtualization  
There are network virtualization technologies such as Virtual Local Area Networks (VLANs), Virtual Private Networks (VPNs), etc. The control plane can build virtual networks on top of real networks by utilizing these network virtualization technologies. Network virtualization will increase network scalability and flexibility, since they can be generated dynamically upon requirement [395]. Furthermore, these virtual networks can be separated from one another to stop unwanted access to sensitive data and ensure high security in the network. Service providers could offer services to several customers on a single physical network thanks to these virtual networks, each of which may have its own set of users and resources [395]. In addition to virtual networks, the control plane can leverage Network Function Virtualization (NFV) to virtualize network services that can be installed as needed, including firewalls, load balancers, routers, etc. [396]. NFV successfully lowers service providers' capital and operating costs.
## 3 Results and Discussion
### Analysis of Spectral Characteristics  
The pretreatment spectrums by smoothing of all the paddy samples with different storage statuses are shown in Figure 3. Based on the picture above, it could be seen that the spectral curves distribution trends of all samples were basically consistent within the testing band of 1000-1800 nm, and they exhibited a gradient change trend with different fatty acid contents in the absorbance. With the fatty acid value of paddy increased, the corresponding absorbance displayed a trend from low to high, and the paddy storage status changed from suitable storage to severely unsuitable storage. In other words, the paddy with a high fatty acid value had a relatively strong ability to absorb light, which might be related to the loss of nutrients such as water, protein, starch, and the darkening of its surface color, because the increase in the fatty acid value of paddy indicated a decrease in its storage quality. Furthermore, the test spectrum contained multiple different absorption bands (1116 nm, 1264 nm, 1650 nm), which might be linked to the different internal components and contents of hydrogen groups, such as OH, CH, and NH, in the paddy [38; 39]. Therefore, it is necessary to further analyze whether these spectral features can serve as the basis for establishing classification models for distinguishing the different storage statuses of paddy.
# PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11 bc
## Abbreviations
In Proceedings of the 2022 IEEE 19 th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 8-11 January 2022; pp. 139-143. [CrossRef]
* [16] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON], [PERSON] ACK-Less Rate Adaptation Using Distributional Reinforcement Learning for Reliable IEEE 802.11 bc Broadcast WLANs. _IEEE Access_**2022**, _10_, 58858-58868. [CrossRef]
* [17] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Experimental evaluation of large scale WiFi multicast rate control. In Proceedings of the IEEE INFOCOM 2016--The 35 th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA, 10-14 April 2016; pp. 1-9. [CrossRef]
* [18] [PERSON] [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] AMuSe: Adaptive Multicast Services to Very Large Groups--Project Overview. In Proceedings of the 2016 25 th International Conference on Computer Communication and Networks (ICCCN), Waikoloa, HI, USA, 1-4 August 2016; pp. 1-9. [CrossRef]* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] AMuSe: Large-scale WiFi video distribution--Experimentation on the ORBIT testbed. In Proceedings of the 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), San Francisco, CA, USA, 10-14 April 2016; pp. 854-855. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] [PERSON] Adaptive reliable multicast in 802.11 networks. In Proceedings of the 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 15-18 April 2018; pp. 1-6. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] WBAP: A Wireless Broadcast Access Protocol for the IEEE802.11 bc Enhanced Broadcast Service. In Proceedings of the IEEE Latin-American Conference on Communications, LATINCOM 2022, Rio de Janeiro, Brazil, 30 November-2 December 2022; pp. 1-6. [CrossRef]  
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# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 3 Case Study
### Filter  
The signals from the sensor fusion stage are filtered by the second-order LPF described in Equation (6). The parameters of the filter are chosen as \(\omega_{n_{2}}=60\ \text{rad}/\text{s}\) and \(\zeta_{2}=0.8\), such that a good compromise between the noise reduction and the settling time of the filter is achieved. So, the derived filter is  
\[F_{\bar{\Sigma}^{2}}(s)=\frac{40000}{s^{2}+320s+40000} \tag{27}\]
# A Comparison of Energy Management System for a DC Microgrid
###### Abstract  
This paper investigates the analysis of the energy management system for a DC microgrid. The microgrid consists of a photovoltaic panel and a batteries system that is connected to the microgrid through a bidirectional power converter. The optimization problem is solved by the hybrid internal point method with the genetic algorithms method and particle swarm optimization (PSO) method, by considering forecasting demand and generation for all the elements of the microgrid. The analysis includes a comparison of energy optimization of the microgrid for solar radiation data from two areas of the world and a comparison the efficiency and effectiveness of optimization methods. The efficiency of the algorithm for energy optimization is verified and analyzed through experimental data. The results obtained show that the optimization algorithm can intelligently handle the energy flows to store the largest amount in the batteries and thus have the least amount of charge and discharge cycles for the battery and prolong the useful life.  
microgrid; genetic algorithms; energy management system +
Footnote †: journal: Applied Sciences