esa-sceva/satcom-chunk-collection huggingface dataset:
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 1 Introduction
This paper introduces a secure data-sharing framework leveraging blockchain, the InterPlanetary File System (IPFS), NFTs, and encryption/decryption technologies to efficiently manage and safeguard large data volumes, thereby optimizing performance and security. The main contributions of this paper are as follows:  
1. A storage architecture is designed in which data and data ownership are separated. A proof of data ownership is generated for each shared piece of data, which is stored on the blockchain and the data itself are stored off the chain. This storage architecture not only ensures the security and integrity of shared data but also reduces the overhead of data storage on the blockchain. 2. A lightweight on-chain/off-chain cooperative data security algorithm based on the Elliptic Curve (ECC) and Temporary Elliptic Curve Diffie Herman (ECDHE) is proposed. With this algorithm, information about shared data is cryptographically preserved in the proof of ownership. This algorithm not only ensures that the plaintext information of shared data wouldn't be revealed, which improves security, but also ensures the performance of the data sharing. 3. A shared data tracking mechanism is designed. With smart contract, the records of data sharing are automatically recorded in the corresponding data ownership proof. The parties involved in the data-sharing process can be traced with this mechanism, and the shared data flow records can be queried as well. The structure of this paper is as follows: Section 2 provides a review of relevant literature on data sharing. Section 3 details the proposed scheme. Section 4 presents and analyzes the experimental results. Section 5 concludes the paper.
# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 3 Methodology  
This study aims to develop a new approach for analysing video from surveillance cameras and extracting dynamic traffic parameters. The model primarily utilised is modified FairMOT (mFairMOT), which employs an encoder-decoder neural network as its backbone architecture. mFairMOT produces detection frames and re-identification objects as its outputs. The detected frames are processed to determine real-time traffic volume and identify abnormal scenarios using logical algorithms. For re-identification, a spatial transformation matrix is computed to convert video coordinates into real-world coordinates. The speed of objects is calculated based on the distance they cover within a given unit of time. Figure 1 displays the flowchart of detection logic that is proposed in this study.  
Figure 1: Flowchart of detection logic.
## 3 Results and Discussion
### Impact of Formation and Friction
(2) Optimize hydraulics by keeping viscosity within 1-2.5 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s and elevating the flow rate to 55-60 L/min, reducing SFED and TREE while preventing laminar flow. (3) For formations with a friction coefficient < 0.15, reducing viscosity may suppress leftward deflection; with a friction coefficient > 0.15, implement dual measures: injecting drag reducers AND moderately increasing viscosity (1-2.5 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s) to promote drill string centralization. (4) In high-modulus zones (>20 GPa), deploy distributed dampers at -30 m intervals, modestly increase flow rate to 60 L/min, implement stepwise RPM elevation, and judiciously elevate viscosity (\(\leq\)2.5 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s)--collectively attenuating impact forces while keeping the equivalent circulating density (ECD) within safe limits via real-time diagnostics.
# High-Harmonic and Terahertz Spectroscopy (HATS): Methods and Applications
## 2 HATS for Atoms
### Attosecond-Resolved THz Generation from Argon
(**b**) Calculated classical and 1D-TDSE results of THz yield modulation with the Coulomb interaction. (**c**) Current simulated by the 3D-CTMC method (green squares). (**d**) THz yield modulations below 10 THz (red squares), 30 THz (green diamonds) and the total residual current (blue triangles) calculated by the 3D-TDSE simulations. Reproduced with permission from [17; 19; 20]. Copyright APS Publishing, 2012, 2014, 2015.  
way, which is easier to carry out than measuring the photoelectrons or ions [52; 53; 54]. The measured absolute optimal phase at 0.8\(\pi\) has significance, which is against the predictions from previously widely-adopted four wave mixing model and (plasma) current model. Our further investigations show that the optimal phase is in fact nearly universal [20], reflecting the particular subcycle ionization dynamics in THz wave generation. The cut-off of high harmonics is mainly contributed by the electrons released at 0.17\(\pi\) after the peak field, while THz generation is mainly contributed by the electrons released before the peak field at \(-0.1\pi\). Without Coulomb field, the latter electrons will directly escape without revisiting the atomic core. Therefore, HATS allows us for the first time to demonstrate the importance of the atomic potential in THz wave generation from atoms and molecules, which was also well known for the solid state systems (see, e.g., [55; 56; 57; 58] and references therein). Based on HATS, we find that subcycle ionization dynamics and the following electron-atom collision, either hard collision or soft collision play important roles in the radiation from atoms driven by strong fields. By manipulating the emission process, which is a near field control and detecting in the far field, both radiation can be used to resolve the electron dynamics in attosecond resolution regardless of the 5-6 order difference of the radiation wavelength.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### CubeSats in Earth Observation and Earth Science Missions
Finally, as highlighted by the success of the QB50 mission, CubeSats can also be used as distributed cost-effective scientific measurement instruments for cutting-edge missions that may shed light to otherwise insufficiently studied regions of the Earth's thermosphere. This success has facilitated the development of a variety of different scientific CubeSat missions which are intended for the performance of cutting-edge research tasks in a cost-effective and efficient manner, supplementing or even substituting the work of more advanced traditional satellite platforms.
# A Survey on Satellite Communication System Security
## 3 Background
### Main Components for Security  
The three pillars of security are Confidentiality, Integrity, and Availability, known as CIA, the most fundamental elements in safeguarding data within cybersecurity. Failing to protect or inadequately secure any aspects exposes them to attacks or threats that compromise the CIA triad. Therefore, this study categorizes attacks and defenses according to the CIA framework, with detailed discussions in subsequent sections [30].  
**Confidentiality:** Confidentiality generally refers to ensuring that information is accessible and comprehensible only to authorized individuals. It plays a crucial role in preventing unwanted disclosure of information, closely linking it to privacy protection. The most common form of attack related to confidentiality is eavesdropping, which is particularly prevalent in SCSs due to the wireless nature of their transmission. SCSs that are not encrypted due to technical limitations pose significant threats to confidentiality. Encryption is the best method to prevent such breaches, but it may not be sufficient. Additional measures are necessary and will be elaborated on later.  
**Integrity:** Integrity signifies that only authorized individuals have the access and ability to modify information. Integrity is compromised when unauthorized persons alter or access the information. In SCSs, spoofing is a primary method of compromising integrity. Spoofing involves transmitting forged signals to deceive the receiver, thereby breaching integrity. Protective measures include PLS and encryption, which ensure the integrity of communications and enhance their reliability.  
**Availability:** Availability denotes the assured, timely access and usability of information or systems. This implies that information or systems must be properly provided and functional whenever needed to be considered secure regarding availability. SCSs can face availability issues due to different factors such as jamming and DoS/DDoS attacks. A compromise in availability can render SCSs unusable, posing a significant threat, especially in emergencies. Ensuring availability requires traffic control through authentication systems like blockchain or filtering unauthorized signals using PLS.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 2 Related Work
### Scalable Subspace Clustering  
With the exponential increase in data volume, large-scale subspace clustering has become a research hotspot. A few researchers have proposed clustering methods which can extend to large-scale data for single-view. Several methods are based on dictionary learning [41; 42; 43; 44], which construct a dictionary and utilize it to represent each data point by dictionary atoms. Inspired by the development of learning subspace dictionary to express the data points, [PERSON] et al. [45] presented a framework where jointly trained a neural network and approximated the self-expressive coefficients to scale to large datasets. [PERSON] et al. [46] proposed a network to learn the latent representations and subspace bases simultaneously, and the clustering outcomes can be inferred through the learned subspace bases. [PERSON] et al. [47] extracted hierarchy features from single view images and designed an attentive module for each feature map to enhance feature representations. However, these approaches are mainly applied to single-view subspace clustering and difficult to keep a balance between accuracy and efficiency. In multi-view clustering, we can process features from different views and design modules to enhance extensibility.
## 3 Experimental Results  
### Islanding Detection Test for the Individual Inverter  
Figure 5 shows the test results for cases 1 and 2 in Table 4. In Figure 5, the waveform (A) is the line-to-line voltage of one phase of the grid utility section, (B) is the output voltage of the inverter, (C) is the output current of the inverter, and (D) is the frequency. Figure 5 shows the resultant waveform for islanding detection testing under the 20 kW rated power of the PV inverter. The load capacities were 20 kW for R-load, \(-20\) kVA for L-load, and 20 kVA for C-load at that time.  
After turning off the grid side switch, the time until the PV inverter stopped was calculated using the scope waveform. Because the magnitude of the output current of the test inverter was the same in all phases, the waveform of the voltage and current for only one phase was measured. The test results for the PV inverter showed approximately 267.5 ms for the inverter to stop. The PV inverter stopped normally within 0.5 s of the grid code in Korea. The method of detecting islanding in the PV inverter might be estimated using a frequency detection algorithm to detect the frequency of every cycle. The frequency waveform changed to 56.55 Hz from 59.9 Hz in the previous cycle and then decreased continuously to reach the cutoff frequency.  
Figure 6 shows the resultant waveform for testing under EESS inverter-operated discharging mode with 20 kW output power to the grid utility section. The RLC loads were the same as for the PV inverter testing. The time taken for the inverter to stop was approximately 300 ms. In the case of the EESS inverter, the method of detecting whether the frequency was changed was by injecting reactive power at every specific period. The injection period for reactive power of the EESS inverter was about 300 ms, such as for the frequency waveform in Figure 6. In comparison to the previous test results, the islanding detection method of the inverters of manufacturer A and B was different.  
Figure 5: Test results for islanding detect of A (PV inverter).
## 4 Stray Node Detection Protocol and Method of Avoiding Message Collision
### Request-Response-Type Stray Node Detection Protocol
#### 4.2.2 Collision Avoidance Method  
In this protocol, a normal node that has received a REQUEST message from the leader node sends a REPLY message and forwards the REQUEST message. If all normal nodes do this simultaneously, bursty traffic arises. To prevent this, the message transmission timing is varied according to the message type. Figure 8 shows a sequence diagram for this collision avoidance method. To avoid a collision, a node that has received a request message begins to count delay time T3(_i_). When T3(_i_) has elapsed, the node returns a reply message and forwards the request message after T2(_i_). A node that has received a reply message begins to count delay time T2(_i_). When T2(_i_) has elapsed, it forwards the message.  
T3(_i_) = ID \(\times\) _s_d \(\times\) hop limit  
Figure 7: Flowchart for the request response-type stray node detection protocol.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Bee-Sensor-C  
Bee-Sensor-C is an energy-aware and scalable multipath routing protocol based on dynamic cluster and foraging behavior of bees swarm [70]. It adopts an enhanced multipath construction strategy so that it achieves the balance of energy consumption within the network, reduces overheads of routing, improves the network scalability and performance, then its lifetime. The event-driven of Bee-Sensor-C allow to perform a dynamic clustering scheme near an event. During the first phase of Bee-Sensor-C (clusters formation), the HiveHeader into the bee-hive claims to be a CH node when it detects an event within an event area. Nodes nearby the event will become activated and measure the specific perceived attribute, then the nodes having information about the event will join the cluster. The second phase (multipath construction) occurs when CH needs to send data until the sink node. The CH looks whether there are appropriate foragers of any nodes in the cluster for the existing multipath. The last phase consists in the transmission and occurs when the backward scout with unique path ID arrives at the CH and recruits foragers using the waggle dance. For evaluated Bee-Sensor-C, two different scenarios are considered and the number of nodes varies until 500 (first scenario). In the first scenario, there is only one event that occurred in the network whereas in the second scenario several events (at least 2) can be detected. The proposed algorithm is compared to the bee-inspired routing protocol BeeSensor [72], IEEABR [73], and the flood forward ant routing FF-Ant [74]. The obtained results have revealed that Bee-Sensor-C consumes less energy but has the highest packet delivery rate compared to the three other protocols.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 5 Discussion  
The use of a CV-CNN brings unique advantages in modeling both magnitude and phase information simultaneously. This is particularly important in eddy current testing, where defect characteristics are embedded in complex-valued impedance patterns. Compared to real-valued CNNs, CV-CNN better captures subtle feature differences, leading to higher classification performance and clearer reconstructed images.  
From the perspective of model structure and data characteristics, CV-CNN effectively leverages the complex-valued nature of eddy current signals to jointly extract amplitude and phase features, showing strong adaptability to crack-type defects. These defects typically exhibit directional and coherent responses in the complex plane, which aligns well with the feature learning capacity of CV-CNN.  
In terms of methodological innovation, CV-CNN goes beyond the limitations of traditional real-valued architectures by incorporating complex-valued convolution, which significantly enhances its capacity to model phase-dominant features. This complex-valued modeling framework offers a novel approach for deep feature extraction and the decision analysis of eddy current signals, thus opening new avenues for signal processing in nondestructive testing. From the perspective of limitations, the current model primarily targets defects with clear structures and concentrated forms, such as cracks. For diffuse, irregular, or composite defects (such as corrosion or coexisting cracks and corrosion), its identification capabilities require further validation. Such defects often lack regular amplitude-phase structures in eddy current responses, which can lead to reduced model generalization capabilities. Future efforts could explore incorporating more representative data, conducting cross-domain feature fusion, or constructing spatio-temporal hybrid structures to enhance the model's ability to identify complex defects.  
Despite these challenges, the current experimental results have fully demonstrated the application value of CV-CNN in crack-oriented defect recognition tasks, showing its potential for deployment in real-world NDT systems, especially in industries that demand high accuracy in early crack detection, such as aircraft maintenance, rail transit, and energy equipment inspection.  
In terms of future application, CV-CNN is well-suited for high-precision crack detection in industrial settings. Its lightweight structure facilitates integration with embedded sensing systems and allows further expansion toward smart inspection solutions incorporating digital twins and industrial IoT platforms, promoting the intelligent and adaptive evolution of nondestructive testing technologies.
# DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks +
## 2 System Architecture and Simulation Parameters
The nonlinear part includes only the second-order Volterra kernels, and higher order kernels are discarded due to complexity reasons. The considered equalizer can be further simplified for considerations of practical implementation, since ONU receivers are very cost-sensitive. The FFE/DFE filter is expressed by [12]:  
\[y^{\prime}[k]=\sum_{i=0,j=1}^{M-1N}\left(a_{i}y[k-i]-b_{j}y^{\prime}[k-j]\right) +\sum_{i=0,j=0}^{M-1}a_{i,j}y[k-i]y[k-j]-\sum_{i=1,j=1,j\
eq j}^{N}b_{i,j}y^{ \prime}[k-i]y^{\prime}[k-j] \tag{1}\]  
where \(M(N)\) is the FFE(DFE) filter order. \(y(y^{\prime})\) denotes the filter input(output) sample. The first term on the right-hand-side of Equation (1) is the linear part, and the remainder is the nonlinear part. For our simulations, MatLab is used, and all models are self-developed. A Gaussian response lowpass filter (LPF) is used to model the joint frequency response of a driver and an EAM. The Gaussian LPF may cover the effect of an analogue digital-to-analogue convertor (DAC), where it is needed in practical systems. In addition, the optical Duobinary transmitter requires a shaping filter that adopts a fifth-order Bessel filter model. The shaping filter bandwidth is optimized, and the identified optimum value is approximately 17 GHz [11]. The APD-trans-impedance amplifier (TIA) receiver is used for all systems [11], and a LPF model covers the joint frequency response of the ADP-TIA, and an analogue-to-digital convertor (ADC). The APD-TIA thermal noise and shot noise model is based on that presented in [16], and the key parameters include an average APD gain of 4, an amplifier noise figure of 2, an ionization constant of 0.5, and a dark current of 25 nA. The Volterra filter consists of 7-tap symbol-spaced FFE and 5-tap DFE (M = 7, N = 5) which correspond to 12 linear taps and 38 second-order nonlinear taps. Note that in practical implementations, there significant parts of the second-order Volterra kernel terms, having very small weights; thus, the complexity can be reduced significantly by disabling these negligible terms, but without degrading their performance [17]. A feedforward error correction (FEC) RS(255,223) with a threshold bit error rate (BER) of 1.0 \(\times\) 10\({}^{-3}\) is assumed, which has been adopted in 10G Ethernet PON standard. The transmitter output optical power is set to be 10 dBm, and the signal carrier wavelength is 1550 nm. A split-length SMF model is adopted with a fiber loss of 0.2 dB/km, a chromatic dispersion of 17 ps/km/nm, and a nonlinear coefficient \(\gamma\) = 1.3 W\({}^{-1}\)/km. A second-order optical band-pass Gaussian filter is considered to model the MUX/De-MUX with a 40 GHz 3 dB bandwidth, which corresponds to a 50G WDM grid. A responsivity of 3.2 A/W is used for the optical receiver. Note that the optimization of the thresholds is performed in the receiver decoders for all cases.
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 4 Conclusions  
This study proposes an online multi-object tracking method that uses advanced deep learning techniques to train feature models with pedestrian re-identification datasets. To overcome the limitations of single datasets, we applied a single-task multi-dataset joint learning approach to enhance data diversity and model performance. Additionally, we propose an initialization method that combines detector and tracklet confidence, achieving the highest MOTA performance in the comparative analysis. Overall, the proposed method outperformed state-of-the-art techniques, and future work will focus on further improving feature models and tracking accuracy through more complex loss functions and network architectures.  
While this study specifically addresses pedestrian multi-object tracking (MOT) using Single-Task Joint Learning (STJL) in moderate-scale scenarios, future work will focus on scaling the model for larger applications, including environments with higher object counts and multi-camera systems where data volume and tracking complexity are significantly increased. To support real-world deployment, we will leverage scalability and resource efficiency techniques from prior research on surveillance optimization, such as distributed computing, model parallelization, and dynamic resource allocation, to ensure that the model maintains real-time responsiveness even in resource-constrained settings. Additional strategies, including data compression and model pruning, will enable effective operation of the model on edge devices with real-time performance, facilitating smooth integration into existing video management system (VMS) standards across complex surveillance networks. Moreover, extending the model's adaptability in handling diverse object types and responding to unpredictable conditions such as variable weather and lighting will provide a more comprehensive evaluation of its robustness in real-world scenarios. These enhancements can establish our model as a scalable, resilient solution capable of meeting the practical demands of large-scale, dynamic surveillance environments.  
Conceptualization, Y.-K.W., T.-M.P. and C.-E.H.; methodology, Y.-K.W. and C.-E.H.; software, T.-M.P. and C.-E.H.; validation, Y.-K.W., T.-M.P. and C.-E.H.; formal analysis, Y.-K.W., T.-M.P. and C.-E.H.; data curation, C.-E.H.; writing--original draft preparation, C.-E.H.; writing--review and editing, T.-M.P. and Y.-K.W.; supervision, Y.-K.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.