esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design
### Improving the Communication Complexity of HBFT  
For the PBFT (Practical Byzantine Fault Tolerance) algorithm, communication complexity is an important consideration. Optimizing communication complexity can improve the system's performance and efficiency, reduce resource consumption, and ensure the reliability and stability of the communication process. PBFT is a fault-tolerant consensus algorithm used to solve the Byzantine Fault Tolerance problem in distributed systems. In PBFT, nodes achieve consensus by exchanging messages with each other frequently, so communication complexity directly affects the algorithm's performance. While reducing communication complexity can improve performance and scalability, other factors such as security and fault tolerance need to be considered as well.  
Communication complexity refers to the total amount of communication between nodes during the execution of an algorithm. It can be calculated based on the number of messages exchanged, message sizes, and message transmission patterns. In this paper, the algorithm calculates the communication complexity by considering these factors during the corresponding steps of the algorithm.  
In the proposed algorithm described in Figure 6, assuming the top layer consists of n/3 nodes, the middle layer consists of n nodes, and the bottom layer consists of 3n nodes, the communication count for submitting and distributing tasks can be calculated as follows:  
\[\mathrm{T}_{\mathrm{submit}}=\mathrm{T}_{\mathrm{distribute}}=\mathrm{n}+3 \mathrm{n}+\mathrm{n}/3, \tag{6}\]  
\[\mathrm{T}_{\mathrm{consensus}}=4\mathrm{n}. \tag{7}\]  
Based on the analysis, the communication complexity of the proposed algorithm in this paper can be considered as O(n). Compared to the algorithm that combines PBFT and RAFT, the proposed algorithm in this paper shows significant improvement in terms of communication complexity. Additionally, the algorithm will further benefit from the batch processing operation that will be introduced. The experimental data will be presented in detail in the next section to demonstrate these improvements.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 2 Basic Principles of FL over Wireless Networks
### Energy Consumption Model
#### 2.3.3 Total Energy Consumption Minimization Problem  
The total energy consumption by clients (in Joule) during a CFL or DFL training round can be decomposed into the energy for the local computation process and that for the communication process, which can be expressed as  
\[Etotal=\sum_{k=1}^{K}(Ecom_{k}^{\text{u}}+NcmpEcmp_{k}), \tag{10}\]  
where \(Ncmp\) represents the number of local computation rounds per global iteration.  
To enhance energy efficiency in FL over wireless networks, resource allocation must be optimized to minimize total energy consumption over \(N_{0}\) global training rounds. This challenge can be formulated as the following optimization problem:  
\[\underline{\text{p}}: \min_{p_{k},f_{k},p_{k},a_{k},k^{\prime}} N_{0}\sum_{k=1}^{K}(Econ_{k}^{u}+NcmpEcmp_{k}) \tag{11a}\] \[s.t. 0\leq p_{k}\leq P_{\text{max}},\quad\forall k\in\mathcal{K},\] (11b) \[f_{\text{min}}\leq f_{k}\leq f_{\text{max}},\quad\forall k\in \mathcal{K},\] (11c) \[\sum_{k=1}^{K}B_{k}\leq B_{\text{u}},\] (11d) \[a_{k,k^{\prime}}\in\{0,1\},\quad k,k^{\prime}\in\mathcal{K}, \tag{11e}\]  
where constraints (11b) and (11c) ensure appropriate uplink power and CPU frequency for each client, respectively. Constraint (11d) ensures that the total bandwidth does not exceed the available uplink bandwidth \(B_{u}\) (FDMA), while constraint (11e) enforces link selection criteria for DFL strategies.  
Effective resource allocation for wireless transmission and local computation is critical for reducing energy consumption in FL. Tailored strategies are needed for different wireless network architectures to achieve optimal energy efficiency. Advanced wireless networks, continually evolving, offer innovative approaches to further improve energy efficiency for FL at the edge. The next two sections explore these architectures in detail.
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 3 Short-Landing Performance Analysis  
### Trim for Short Landing  
To land the UAV safely, the sink rate, angle of attack, and velocity must be maintained for a steady approach. However, determining the baseline values for these parameters is challenging. To address this, the target UAV is trimmed at various angles of attack, sink rates, and velocities at a height of 50 m under control input restrictions. The trim speed ranges from 17 to 50 m/s, the angle of attack from 3 to 12 degrees, and the target sink rate from 0 to \(-4\) m/s. The lift fan control is split into two parts: balancing the moment from \(\delta_{\mathrm{trq}}\) and compensating for \(\delta_{e}\), as shown in Figure 8.  
The results in Figure 9 show that the reachable area decreases as the angle of attack increases, and the velocity boundaries move to the left. As the angle of attack increases, the speed boundary decreases, allowing for a lower flight speed for the approach. However, a lower speed comes with a reduced maneuvering area, which increases the risk of the approach phase. Therefore, a balance must be made between the landing speed and flight safety, and the proper angle of attack and flight velocity must be selected during the short landing.  
Figure 7: SSD yaw control moment derivative (per radian); the gray area is the continuous error bar for various angles of attack (angles of attack \(\alpha=-6\)–\(12\) deg).  
Figure 8: Trim procedure for short-landing performance analysis.  
Figure 9: Reachable area for different angles of attack (angle of attack, \(\alpha=3\)–\(12\) deg).
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Scientific and Technical Goals and Payloads
#### 2.4.2 Radiation Test  
As the EFR32 FG12 microcontroller was never used in space before it has been preliminarily radiation tested. Four off-the-shelf development boards of the microcontroller were irradiated by a Cobalt-60 gamma-radiation source. They were exposed to a total dose of 13.12 krad over 20 h. While testing the controllers were powered and exchanged radio messages between each other and performed regular integrity checks of all memories. The test results show no memory errors or communication failures.  
In a subsequent test another four controllers were to be irradiated until failure, but the test had to be aborted after 3.75 h due to time constraints. At the end, a total dose of 33.75 krad has been reached. Again, no errors in memory were observed. After a dose of 20 krad one controller showed less than 1% of radio packet loss, the others none.  
Reference [14] estimates 1.2 krad/year for 1 mm shielding in LEO. With these test results the chip is expected to be suitable for at least 1 year planned mission lifetime with a good safety margin. Due to budget constraints, high-energy proton and ion tests are not planned, but these events are typically less of a concern in a short time LEO mission.
Furthermore, they give a one-sided perspective of the physiological response of its workers by only measuring the reserve heart rate. Respiratory monitoring is another physiological signal that carries insightful information. Previously, it was used to quantify relevant factors in the occupational context, such as mental load and environmental stress [17]. [PERSON] et al. compared the effects of fatiguing dynamic and isometric hand-grip exercises on cardiorespiratory adaptations. They measured HR, blood pressure, and ventilatory gas exchange, finding relevant differences between those two types of exercise only in breathing patterns [18]. In our previous work, we aimed to evaluate respiratory and cardiovascular responses to fatigue-inducing work. To accomplish this goal, a protocol simulating repetitive work was applied in a laboratory setting. In the first study, respiratory inductance plethysmography (RIP) signals were used to explore synchrony metrics between the abdominal (ABD) and rib cage (RC) walls [19]. A second study explored the changes in heart rate variability (HRV) with increased fatigue [20]. In these, the main findings were that throughout the fatigue trials, both the synchrony between the respiratory walls and HRV were significantly reduced. In summary, the prevalent risk quantification tools lack consideration for the cardiorespiratory response, when assembly line tasks are linked to long-term cardiovascular deterioration. The assessment of risk is performed by the workstation measuring its biomechanical load, rather than the individual response to the workload volume. The use of wearable technology has brought the capacity for individual continuous monitoring of workers' adaptations during their shifts. While the previous research has addressed some of the downsides of subjective assessments, they do not fully address the cardiorespiratory influences as they have their main concern on productivity, falling short in exploring other variables other than reserve heart rate; are mostly conducted in laboratory settings; do not include all conditions of realistic settings; and lack actual workers in the industry. This research has the objective of bridging this gap by verifying if there are distinct cardiorespiratory responses to particular workload volumes and ERs of assembly line workers during a period of their shift. Like this, there is the possibility of more personalized work interventions, task management, and health problem prevention. In this paper, operator's adaptations refer to assembly line worker's cardiorespiratory adaptations due to their repetitive tasks. Workload volume is defined by the time in which a complete cycle of work is performed. In the remainder of this document, the Materials and adopted Methods will be detailed in Section 2, followed by the showcase of the results in Section 3, their discussion in Section 4, and finally, the summary of the main findings and contribution.
## 3 Design
### Implementation  
We implemented our proposed scheme by designing a parallel file encryption and centralized decryption framework that integrates post-quantum cryptography (PQC), quantum key distribution (QKD), and AES-based symmetric encryption across a distributed architecture for cloud systems. To support subset-level processing, overlapped encryption and transmission, and centralized decryption, we implemented approximately 270 lines of Python 3.9 code using mpi4 py, oqs-python, qiskit, and pycryptodome.  
Our implementation includes the following key components:  
* **Layered Key Initialization:** We used the Kyber512 algorithm from the Open Quantum Safe (OQS) library [78] to generate a PQC key pair. The AES key is encapsulated using the PQC public key. The PQC secret key is XOR-masked using a QKD session key simulated with Qiskit Aer [62], and optionally split via [PERSON]'s secret sharing for fault-tolerant recovery.
* **Parallel and Overlapped Subset Encryption:** Each slave node performs AES encryption and transmission in parallel by overlapping file reading, encryption, and communication. Encrypted subsets are streamed to the master using non-blocking MPI operations, allowing continuous processing without idle time. This structure improves resource utilization and enables high-throughput distributed encryption.
* **Centralized Decryption Pipeline:** The master node receives encrypted subsets, re-orders them based on subset indices, and reconstructs the AES key by decapsulating the PQC ciphertext and unmasking the PQC secret key. Subsets are decrypted in parallel using a thread pool and merged to restore the original file.  
We open-source our implementation at: [[https://github.com/changjongkim/Hybrid-Encryption-Scheme.git](https://github.com/changjongkim/Hybrid-Encryption-Scheme.git)]([https://github.com/changjongkim/Hybrid-Encryption-Scheme.git](https://github.com/changjongkim/Hybrid-Encryption-Scheme.git)), accessed on 6 July 2025.
## 3 Results
### Sensitivity Analysis  
#### 3.2.1 Temperature Measuring Position  
The influence of the temperature measuring position is studied first. A variation of the measurement position in the numerical model by \(\pm\)1 mm in radial position results in a difference in the PCM temperature of up to 5.2 K and a difference in the temporal mean of the temperature of up to 1.0 K. Varying the position in vertical direction by \(\pm\)1 mm leads to much lower differences of a maximum deviation of less than 0.5 K and only about 0.03 K temporal mean deviation. Table 15 in the Appendix A shows the maximum and mean values as well as the standard deviation of the temperature differences introduced due to the measurement point variation. The outcome that a variation of the measurement position in radial direction has a much higher effect than a vertical variation can be explained by the almost vertical melting front (see Figure 3). The isotherms within the solid and the liquid PCM follow, to a large extent, the shape of the melt front and, therefore, a small and a large temperature gradient result in vertical and radial directions, respectively.
## 4 Methodology
### Data Description, Collection, and Preparation
#### 4.2.3 System Architecture  
The architecture of our proposed approach consists of various steps to fulfill a complete execution. Starting by dividing the dataset into training and testing subsets, the utilized splitting mechanism is the k-fold criteria. In every \(k\) experiment, the training set is split into \(k-(1/k)\) parts while the testing set (remaining) partitions to \(1/k\) part. The process is repeated \(k\) times with a unique set for each training and testing partition. This is done to ensure the maximum diversity for training and testing sets to obtain the best possible model.  
Moreover, and depending on the training set, the WOA randomly initializes the first solution. The solution consists of, as mentioned earlier, SVM hyperparameter elements (\(C\) and \(\gamma\)) and the weighted features of the dataset elements. Then, the SVM is trained based on this given solution (\(C\), \(\gamma\), and new features value), and during the training inner cross-validation is applied to avoid any overfitting problem. Afterward, the selected \(C\) and \(\gamma\) values alongside the weighted features transmitted to WOA as fitness values (accuracy). All the previous processes are repeated in every fold until the maximum number of iterations in the WOA algorithm is reached. The best solution provided by the WOA is used for the testing of that fold, then the average of all folds was taken. Figure 3 illustrates all the previous processes.
# A Deep Multi-Frame Super-Resolution Network for Dynamic Scenes
## 3 Methods  
### Framework  
Most VSR methods fuse features from different inputs by convolutional networks, which are only suitable for a constant number of fusion features. In our model, however, we used a normalized weighted sum feature fusion strategy because this operation has no limit to the number of fusion features. As a result, our framework could handle different numbers of inputs.  
The framework is shown in Figure 2. The target frame \(I_{0}\), along with other N-1 frames, \(I_{i}(i=1:N-1)\) are sent into the body part to get series of deep features \([F_{0},F_{1}\ldots F_{i}\ldots F_{N-1}]\). Each \(F_{i}\) consists of M features, i.e., \(F_{i}=\{f_{i,j}|1\leq j\leq M\}\). Then these features are aligned to \(F_{0}\) by the SSA module to generate the aligned features \(F_{i}^{a}\). Each feature \(f_{i,j}^{a}\) in \(F_{i}^{a}\) are input to generate the fused feature \(\hat{f}_{j}\), the \(j\)-th feature from the fused features \(\hat{F}\), by a weighted sum operation, as shown in Equation (1):  
\[\hat{f}_{j}=\frac{f_{0,j}+\sum\limits_{i=1}^{N-1}w_{i}\times f_{i,j}^{a}}{1+ \sum\limits_{i=1}^{N-1}w_{i}} \tag{1}\]  
where \(w_{i}\) is the weight of features \(F_{i}^{a}\) from each input frame \(I_{i}\) and is generated by the DW module. Then, the fused features \(\hat{F}\) are fed into the enhancement part and the tail part to generate the final output.  
As for the body part, it consists of one convolutional layer and some resblocks. Each resblock has a basic form of conv-relu-conv, and all the resblocks has the same number of channels and the same resolution. The enhancement part consists of several denseblocks. The denseblock has dense connections between its three resblocks. The tail part consists  
Figure 2: The framework of our method.  
of a pixel shuffle layer and a convolutional layer. In addition, the tail part can be directly added at the end of the body part during the training phase to generate a raw output, which accelerated training.  
According to Equation (1), the fused feature \(\hat{F}\) equals \(F_{0}\) when only one target frame \(l_{0}\) is fed into the model, which serves as the SFSR. When the number of input frames N is fixed, this model turns to VSR. Therefore, our framework is more flexible and was suitable for various situations.
## 2 Methods
### Absolute Ranging Based on a Pair of Femtosecond Lasers  
The principle of absolute distance measurement based on a pair of femtosecond lasers is shown in Figure 2. Two femtosecond lasers with similar repetition frequencies (\(f_{r}\) and \(f_{r}-\Delta f_{r}\)) are used as the signal laser and the local oscillator (LO), respectively. The signal laser is directed at the reference and the target so that the target TOF is simply determined by the temporal separation of the retroreflected laser pulses from the reference and the target. LO is used to sample the retroreflected signal laser pulse envelopes nonlinearly via sum-frequency generation. Owning to the slightly different repetition rate, the sampling pulses will walk through the signal pulses and equivalently \"stretch\" the time frame by \(N=f_{r}/\Delta f_{r}\)-times. The so-called nonlinear ASOPS process [12] makes the timing of ultrafast signal laser pulses accessible to standard electronics. In order to retrieve the target distance, we only need to measure the TOF of the target and the reference in the \"stretched\" time scale, as well as \(f_{r}\) and \(\Delta f_{r}\). In practice, we do not measure the difference of repetition frequencies \(\Delta f_{r}\) directly; rather, we use the following equation,  
\[\Delta f_{r}=\dfrac{1}{t_{ref2}-t_{ref1}}, \tag{7}\]  
where \(t_{ref1}\), \(t_{ref2}\) are the measured timing of two successive temporally-broadened reference pulses. As a result, one can calculate the absolute distance \(L\) by:\[L=\frac{c}{2n_{g}}t_{TOF}=\frac{c}{2n_{g}}\frac{1}{N}t_{ broaden}=\frac{c}{2n_{g}}\frac{1}{f_{r}}\frac{t_{tar}-t_{ref1}}{t_{ref2}-t_{ ref1}}, \tag{8}\]  
where \(c\) is light velocity in a vacuum, \(n_{g}\) is the group refractive index in air, \(t_{TOF}\) is the real TOF of the femtosecond laser, while \(t_{ broaden}\) and \(t_{tar}\) represent the target-reference TOF and the timing of target pulse in the \"stretched\" time scale, respectively.
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 3 Chitosan-Based Nanocomposites
### Chitosan-Polymer Mixture or Copolymer  
Chitosan is often introduced to other types of polymers to form mixture or copolymers in order to enhance the properties for specific applications (Table 1). Attributed to the chemical reactivity of primary amine groups in chitosan, polymers containing activated carboxyl groups can be covalently linked with chitosan, commonly employing carbodiimide crosslinking reagents to form an amide bond between amine and carboxyl groups [91; 92]. For polymers containing abundant hydroxyl groups, such as cellulose, physical mixture/interaction with chitosan is often used for the preparation of chitosan-polymer hydrogels [93], simple Van der Waal interactions occur that do not change chemical structures. Alternatively, chemical linking of polymers containing hydroxyl groups to chitosan works by the activation of hydroxyl into imidazolyl carbamate intermediates, that react with primary amine groups in chitosan to form \(N\)-alkyl carbamate linkages [94]. Since chitosan is a polycationic molecule, it can easily interact with negatively charged electrolyte/polymers/particles through electrostatic interactions. For instance, a chitosan-nucleic acid nanoparticle complex can be formed by coacervation between the positively charged amine groups and negatively charged phosphate groups in the nucleotide. Similarly, alginic acid/alginate as a natural polysaccharide has a similar chemical structure to chitosan but contains carboxylic instead of amine groups. Due to the opposite charges, chitosan and alginate can easily form electrostatic complexes [95].
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Exploiting FPGA Features
#### 5.4.1 Dynamic Partial Reconfiguration  
Partial reconfiguration (PR) allows for modifying parts of the FPGA while other sections continue to operate, leading to significant energy savings. By only utilizing resources when needed, PR minimizes idle power consumption and reduces the necessity to power the entire FPGA continuously. Similarly, the dynamic partial reconfiguration (DPR) of FPGAs allows them to adjust to different tasks or conditions on the fly, saving energy by updating existing resources to meet new requirements and avoiding unnecessary parallel deployments of all modules [121]. Consequently, PR and DPR not only enhance the flexibility and efficiency of FPGA usage but also contribute to the overall energy efficiency.  
For instance, a reconfigurable approach was applied in [80] to design an epileptic seizure detection system based on multiple feature extractors followed by one classifier. These feature extractors were implemented using DPR to avoid leakage power from different extractor parts when they were inactive. This resulted in a 0.68\(\times\) reduction in resource consumption, thereby reducing associated leakage power. Since leakage power is the energy consumed by a component even when it is inactive, delaying the deployment of that component until it is required can eliminate the associated leakage power of that component.  
In another instance, the authors of [83], in arrhythmia classification using the DCNN model, conducted convolutional operation layer by layer using a reconfigurable convolution accelerator through time-division multiplexing. This approach not only allowed for the optimized use of resources but also a reduction in idle power consumption by those unused accelerators.