esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Blockchain-Based Federated Learning
## 3 Blockchain-Based Federated Learning
### Functions of BFL
#### 3.2.1 Decentralization  
In FL, due to the central aggregation function of the server, once its device is subjected to a single-point attack by adversaries, it poses a significant security risk to the entire learning framework [93; 94; 95; 96]. To enhance the security, trustworthiness, and reliability of the framework, [PERSON] et al. [85] proposed a BFL architecture to improve the security of FL. Basically, for each global model, the framework creates a new channel to store a specific channel ledger, and concurrently creates a \"global model state tree\" to track weight updates of the global model. [PERSON] et al. [97] utilized offline and online blockchains to store temporary training data from a large number of nodes in real-time, a technique based on a distributed multi-layer computing framework. The multi-layer and multi-chain structure effectively reduces the impact of network failures and malicious attacks on FL. [PERSON] et al. [98] developed a framework called PriModChain by integrating differential privacy, FL, the Ethereum blockchain, and smart contracts. It offers privacy, security, and reliability for FL applications in the industrial IoT. However, the operating efficiency of the framework restricts its further development. [PERSON] et al. [87] introduced a novel hybrid blockchain architecture composed of a permissioned blockchain and a local DAG. It aims to enable effective data sharing in vehicular networks, thereby enhancing the reliability of the learning model. [PERSON] et al. [81] introduced a multi-level trust framework using a private blockchain to ensure end-to-end trustworthiness, from observation to learning and verification of local model updates.
## 4 Real-World Use Cases with Sample Results  
In the following section, we present examples of computer vision applications that can interact with the simulation. As shown in Figure 3, the simulator and ROS interface support a wide range of heterogeneous computational platforms. To demonstrate this capability, the applications were executed on efficient and low-power embedded hardware, facilitating the deployment of applications in resource-constrained environments, such as mobile and aerial robots. Please note that the presented results are not meant to set new standards in the presented use scenarios, but rather to comprehensively demonstrate the capabilities of the simulator on a broad range of target applications, using a broad range of hardware. The ability to integrate various commercial off-the-shelf (COTS) devices with the simulator aligns with the principles of the New Space paradigm [45]. The evolution of  
Figure 4: An example image with semantic labels for the bird’s eye view (BEV) task.  
this paradigm necessitates the creation of versatile tools capable of accommodating a wide range of hardware. The simulator can also easily be adapted to other devices, thanks to the use of industry-standard ROS interfaces.  
We based this research on the artificial lunar landscape dataset (ALLD) [36]. It provides real lunar (ALLD (real)) and rendered (ALLD (synthetic)) images with ground truth segmentation masks. Similarly, we generated images from the simulator (our (synthetic)) and included a real subset of images collected at an analogue lunar research station (our (real))--the LunAres Research Station [46], which is a simulation facility for studying the challenges and requirements of space missions. The images were collected in various lighting conditions, using a camera mounted on the rover and directed 45 degrees down.  
To test the simulator's capability for rendering realistic images, a simple test was performed. An image descriptor, in the form of embeddings extracted by a convolutional neural network, was used to encode the images into vectors [47]. Generally, the closer the embedding vectors of two images lie in the embedding vector space, as measured by some metric, the more similar the images are. Using embeddings from single images would not be a good representation of a whole dataset, so after the embeddings were extracted, they were averaged, to represent all the images in a given dataset. The base MobileNetV2 model was used to generate the embedding vectors [48]. The model was pre-trained, using data from the ImageNet dataset [49]. The cosine distances of the averaged embeddings of the LunarSim and ALLD synthetic part to the average embedding of the ALLD real part were 0.347 and 0.360, respectively. This confirms that the LunarSim rendered images are a closer representation of the real images.
When the SiPM is biased above breakdown, working in Geiger mode, it produces a photocurrent proportional to the number of cells activated which flows through the sensor from cathode to anode, either of which can be used as a standard output terminal. Reading out from the cathode will give a negative polarity [33]. In addition to the anode and cathode, a third terminal called the fast output has been developed by On Semiconductors. It is the sum of capacitively coupled outputs from each microcell. The fast output is used to provide information on the number of photons detected since its amplitude is proportional to the number of microcells that are activated. When photons arrive at a pixel, a pulse is generated at its output regardless of the number of photons. If two or more avalanches occur at the same time, the amplitude of the electrical output pulse is equal to the number of avalanches times the amplitude of a single avalanche. Therefore, the height of the output pulse may be said to be proportional to the number of detected photons assuming that noise is neglected [34]. Then, the SiPM detector signal is the summed output of that 2D array of microcells, providing great sensitivity improvement over APD and PIN diodes. In general, SiPMs are operated in Geiger-mode may provide high gain (i.e., >10\({}^{6}\)) at moderate bias voltages (i.e., ~30 V). Operating (bias) voltage (\(V_{bias}\)) of SiPM is defined as the sum of breakdown voltage (\(V_{br}\)) and overvoltage (\(\Delta V\)). The gain of a SiPM sensor is defined as the amount of charge created for each detected photon, and is a function of overvoltage and microcell size and can be calculated from the overvoltage, the microcell capacitance \(C\), and the electron charge, \(q\)[33]:  
\[G=\frac{C\times\Delta V}{q}\]One of our motivations is to investigate the long range capability of laser rangefinder based on 905 nm semiconductor laser and SiPM detector. These two combinations offer a very cost-effective solution for a few km laser rangefinders. Therefore, in this work we study the performance of SiPM based on this configuration.
# XPS on Co\({}_{0.95}\)R\({}_{0.05}\)Fe\({}_{2}\)O\({}_{4}\) Nanoparticles with R = Gd or Ho
###### Abstract  
Given the green synthesis route of preparation and the magnetic properties of these compounds, our goal is to use them for applications in cancer therapy, for hyperthermia, and to prepare magnetoelectric core-shell structures, where the aim is to locally generate electric charges in the body, controllable by relatively low external magnetic fields, to affect only the cancer cells.  
Co\({}_{0.95}\)R\({}_{0.05}\)Fe\({}_{2}\)O\({}_{4}\) nanoparticles were synthesized using a sol-gel approach incorporating bio-based agents and were found to be single phases adopting a cubic _Fd-3m_ structure. XPS shows the presence of Gd\({}^{3+}\) and Ho\({}^{3+}\) ions. The spin-orbit splitting of about 15.4 eV observed in Co 2\(p\) core-level spectra is an indication that Co is predominantly present as Co\({}^{3+}\) state, while the satellite structures located at about 6 eV higher energies than the main lines confirm the existence of divalent Co in Co\({}_{0.95}\)R\({}_{0.05}\)Fe\({}_{2}\)O\({}_{4}\). The positions of the Co 3s and Fe 3s main peaks obtained by curve fitting and the exchange splitting obtained values for Co 3s and Fe 3s levels point to the high Co\({}^{3+}\)/Co\({}^{2+}\) and Fe\({}^{3+}\)/Fe\({}^{2+}\) ratios in both samples. The saturation magnetizations are smaller for the doped samples compared to the pristine ones. For theoretical magnetization calculation, we have considered that the heavy rare earths are in octahedral sites and their magnetic moments are aligned antiparallelly with 3d transition magnetic moments. ZFC-FC curves shows that some nanoparticles remain superparamagnetic, while the rest are ferrimagnetic, ordered at room temperature, and showing interparticle interactions. The \(M_{\rm S}/M\)s ratio at room temperature is below 0.5, indicating the predominance of magnetostatic interactions.  
Academic Editor: [PERSON]  
Received: 24 June 2025  
Revised: 23 July 2025  
Accepted: 24 July 2025  
Published: 25 July 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON].  
Keywords:** cobalt ferrite nanoparticles; heavy rare earths; cation distribution; XPS spectroscopy; magnetic properties +
Footnote †: journal: Applied sciences
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 4 Proposed Protocol
### Configuration Phase  
The Sink/BS node begins flooding the network with a broadcasted \"Hello\" message, to which every node in the network will reply with an acknowledgment message informing their nd energy level.  
With the information obtained in the flooding process, H-kdtree begins the cluster formation process, based on the k-d tree algorithm. Once the clusters are formed, the following step is to select the CH nodes, based on the energy levels obtained during the flooding process. The node with the highest energy is selected as CH node. If two or more nodes have the same energy level, one of them is randomly selected to become a CH node.  
At this point, the Sink/BS node already has information of every cluster, with their respective CH node and member nodes. The next step is configuring static routes. The Sink/BS node sends the CH nodes the static routes information, which is then forwarded to the rest of the nodes in each cluster. The result is the typical LEACH hierarchical routing, using a two-hop topology.  
Figure 7: Clustering using the k-d tree algorithm.
## 2 Materials and Methods
### Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)  
CEEMDAN is an algorithm based on EMD [31], that can effectively solve the EMD mode mixing and the EEMD residual noise problems. The decomposition process is as follows:  
1. Add white noise \(V^{i}(t)\) with a normal distribution to the original signal \(S(t)\). The signal for the \(i\)th decomposition, where \(i\) denotes the number of decompositions, is represented as \[S^{i}(t)=S(t)+V^{i}(t),\ i=1,\ \cdots,\ I.\] (4)
2. The EMD decomposes the trial signal \(S^{i}(t)\) to obtain \(IMF_{\mbox{1}}^{i}\) correspondingly, so \[IMF_{\mbox{1}}=1/I\sum_{i=1}^{l}IMF_{\mbox{1}}^{i}(t),\ \mbox{residual}\ r_{\mbox{1}}(t)=S(t)-IMF_{\mbox{1}}.\] (5)
3. Add white noise \(V^{i}(t)\) to the residual \(r_{\mbox{1}}(t)\), execute the trial \(i\) times (\(i=1\), \(2\), \(\cdots\), \(l\)), and each trial adopts EMD to decompose \[r_{\mbox{1}}^{i}(t)=x(t)+v^{i}(t)\] (6)to find its first-order component \(IMF_{1}^{i}\). \[IMF_{2}^{i}=\frac{1}{I}\sum_{i=1}^{l}IMF_{1}^{i}(t)\] (7) Residual \[r_{2}\;(t)=S(t)-IMF_{2}\] (8) iv. Then, the signal is further decomposed by EMD to calculate the second IMF mode and the relating residue by repeating the above decomposition process. When the residual cannot be decomposed by EMD, the program ends. The original signal can be represented as: \[S(t)=\sum_{i=1}^{n}IMF_{i}+r_{n}(t).\] (9)
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 4 Results  
The PlaIMoS pilot study was carried out over a 1h period at the University Regional Hospital with the objectives of:  
* relaying information through the WSN Infrastructure using the PlaIMoS-Routing protocol
* locating the indoor location of PlaIMoS remote nodes
* transmitting clinical parameters through the PlaIMoS fixed measurement station
* generating alarms when patient clinical parameters exceeded the present ranges (temperature and patient falls)
* viewing clinical patient information
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 2 Related Works
### Satellite Network-Based Imaging Technology  
There is currently limited research on agricultural imaging based on satellite internet, and most of it focuses on data transmission and image matching. [PERSON] et al. [24] reviewed the literature on using satellite imagery and machine learning to measure sustainable development outcomes, with a particular focus on the impact of data scarcity and noise on model performance. The research showed that satellite technology is better than traditional methods for predicting key sustainable development outcomes. [PERSON] et al. [25] proposed an automated workflow, which can process large volumes of high spatial resolution satellite imagery, to generate actionable information within the framework of smallholder farming. [PERSON] et al. [26] studied the bottlenecks of multispectral imaging precision agriculture technology in U.S. cotton-growing regions. They successfully achieved fast bidirectional data transfer between devices by implementing wireless LAN technology on experimental farms. [PERSON] et al. [27] studied the satellite imaging data transmission scheduling problem and proposed a Specific Time-Evolving Graph (STEG) model that improves the efficiency of image data transmission. [PERSON] et al. [28] [PERSON] studied image matching between satellite images and aerial photographs of agricultural land. They also evaluated the performance of multiple feature points under different times, perspectives, sensors, and altitudes.  
Despite significant progress in predicting and understanding agricultural sustainability outcomes, enhancing data transmission efficiency, and improving image-matching techniques using satellite imagery and machine learning, existing research still falls short in ensuring data integrity, authenticity, and security.
## 3 Results and Discussion
### Effect of HP Treatment on Microbial Growth of Sliced Cooked Ham  
For vacuum packed sliced cooked ham, LAB were also the dominant spoilage microorganisms, and their growth rates were estimated for control untreated samples and for HP treated at 500 and 600 MPa (5 min at 25 \({}^{\circ}\)C) (Figure 3, Table 3). Processing of sliced cooked ham at the higher pressure (600 MPa) resulted in significantly (\(p<0.05\)) lower growth rates. At 0 \({}^{\circ}\)C storage, the LAB rate was approximately five times lower compared to the rate of control samples. All the other measured microorganisms (_Pseudomonas_ spp., _Enterobacteriaceae_, Yeasts and molds, _Staphylococcus_ spp., _Brochothrix thermosphacta_) were below the detection limit for HP treated samples throughout the whole storage period at all storage temperatures. _Pseudomonas_ spp. growth was recorded at all storage temperatures for control samples. Nevertheless, their maximum population never exceeded the 5.2 log CFU/g up to a storage of 40 days (for all studied temperatures) (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Storage Temperature**} & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Nitrite Enriched**} & \multicolumn{2}{c}{**HP-Treated**} \\  & **Samples** & \multicolumn{2}{c}{**Samples**} & \multicolumn{2}{c}{**Samples**} \\ \cline{2-6}  & **TVC** & **LAB** & **TVC** & **LAB** & **TVC** & **LAB** \\ \hline
0 \({}^{\circ}\)C & 0.197 \(\pm\) 0.008 & 0.149 \(\pm\) 0.011 & 0.092 \(\pm\) 0.006 & 0.118 \(\pm\) 0.012 & 0.040 \(\pm\) 0.007 & 0.071 \(\pm\) 0.006 \\ \hline
5 \({}^{\circ}\)C & 0.303 \(\pm\) 0.031 & 0.505 \(\pm\) 0.092 & 0.273 \(\pm\) 0.041 & 0.454 \(\pm\) 0.067 & 0.180 \(\pm\) 0.010 & 0.174 \(\pm\) 0.024 \\ \hline
10 \({}^{\circ}\)C & 0.474 \(\pm\) 0.081 & 0.651 \(\pm\) 0.053 & 0.403 \(\pm\) 0.087 & 0.607 \(\pm\) 0.104 & 0.364 \(\pm\) 0.014 & 0.241 \(\pm\) 0.019 \\ \hline
15 \({}^{\circ}\)C & 0.998 \(\pm\) 0.138 & 1.368 \(\pm\) 0.107 & 0.886 \(\pm\) 0.099 & 1.334 \(\pm\) 0.134 & 0.725 \(\pm\) 0.093 & 0.803 \(\pm\) 0.083 \\ \hline E\({}_{\text{a}}\) (kJ/mol) & 69.3 & 89.8 & 94.1 & 101.4 & 123.2 & 100.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Growth rates (days\({}^{-1}\)) for the total viable count and the lactic acid bacteria of Bratwurst sausages, stored at temperatures ranging from 0 to 15 \({}^{\circ}\)C, for control samples, nitrite-enriched samples, and HP-treated (600 MPa/25 \({}^{\circ}\)C/5 min) samples. The effect of temperature on the rates is expressed through the activation energy values, which are also presented.  
Figure 1: Growth of total viable count of Bratwurst sausages during storage at 0, 5, 10, and 15 \({}^{\circ}\)C; (x): control samples, (\(\square\)): nitrite-made samples, (\(\Delta\)): HP-treated samples at 600 MPa and 25 \({}^{\circ}\)C for 5 min.  
Figure 2: Growth of lactic acid bacteria of Bratwurst sausages during storage at 0, 5, 10, and 15 \({}^{\circ}\)C; (x): control samples, (\(\square\)): nitrite-made samples, (\(\Delta\)): HP-treated samples at 600 MPa and 25 \({}^{\circ}\)C for 5 min.
## 2 Materials and Methods
### Cell Compatibility Analysis  
The ACDS was disinfected by immersing it in 70% EtOH under UV light for 1 d. The ACDS was then washed several times with triple-distilled water before use. Next, the disinfected ACDS was immersed in a cell culture medium consisting of Dulbecco's modified Eagle's medium (DMEM, Gibco, Carlsbad, CA, USA) containing 10% fetal bovine serum, 500 U/mL penicillin, and 500 ug/mL streptomycin (Gibco, Carlsbad, CA, USA). After incubating the patch for 2 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\), 1 \(\times\) 10\({}^{5}\) human keratinocyte cell line (HaCaT) cells were incubated for 24 h (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)). Then, 3-(4,5-dimethylthiazol-2-y)l-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt (MTS, Promega, Madison, WI, USA) solution was added to the eluted medium for 4 h (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)). Absorbance at 490 nm was measured using an ELISA reader (Infinite\({}^{\circledR}\) 200 PRO, Tecan Trading AG, Mannedorf, Switzerland) to evaluate the cell viability (\(n=3\)).
## 4 Collision Avoidance Algorithm
### Gaussian-Based Virtual Repulsive Force Model
A higher value of \(K\) results in a stronger correction applied to the user command, enabling the drone to reverse or significantly adjust its path when an obstacle is very close. This parameter is critical in tuning the sensitivity of the avoidance system. This model offers several advantages for real-time drone navigation. First, it provides a smooth and continuous force profile, ensuring stable control responses without abrupt fluctuations. The function produces a maximum repulsive force when the obstacle is very close (\(D_{n}\to 0\)) and rapidly decays as the distance increases, which helps the drone prioritize immediate threats while ignoring distant, non-critical objects. As shown in Figure 15, the detection space is categorized into three zones based on the distance to the obstacle and corresponding repulsive force. In the first zone, when the obstacle is approximately 200 cm to 400 cm away (green area in Figure 15), although the obstacle is detected, the generated repulsive force is negligible or zero, resulting in no modification to the control command. This prevents unnecessary drone reactions to distant objects. In the second zone, when the distance is less than 200 cm, a corresponding repulsive force is generated, and the control command is modified accordingly. As the obstacle approaches, the magnitude of the repulsive force increases, prompting the drone to slow down or reverse (yellow area in Figure 15). In the third zone, when the obstacle is very close, at approximately 70 cm or less, a strong repulsive force is generated, causing the drone to move quickly in the opposite direction to avoid collision(red are in Figure 15). The blue solid line represents the generated repulsive force corresponding to each distance across the defined zones, illustrating how the force varies within each region. Additionally, the vertical red and green dashed lines indicate the conceptual boundaries between adjacent zones. In this research, the values of \(\sigma\) and \(K\) were set to 75 and 1050, respectively. Figure 15: Relationship between obstacle distance \(D_{n}\) and the corresponding repulsive force magnitude \(f_{n}\), modeled using a Gaussian function. The force reaches its maximum value when the obstacle is closest to the drone (i.e., \(D_{n}=0\)) and decays exponentially as the distance increases, promoting smooth and reactive collision avoidance behavior. To ensure effective operation in confined indoor environments, the drone was flown at deliberately low speeds, aligned with the system's processing capacity. Although the actual flight speed was not measured, slow movement allowed sufficient time for object detection, force computation, and control adjustment. This strategy contributed to stable and safe navigation in cluttered environments.
## 2 Background and Related Work
### Anomaly Detection and Mitigation
Even if the approaches differ, the reconfiguration process is performed in the same manner. The literature in general has concentrated on MAPE-K [50], where each letter represents one step to overcome system failure by e.g., reconfiguration. MAPE stands for monitoring, analyze, plan and execution; finally, K for knowledge represents data storage, which is used to set and compare monitored data, identify failures by analysis, determine a suitable preventive strategy by planning and implement a new strategy into the fleet or system setup.
## 4 Experiments, Results, and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **RMSE** & **Score** & **Training Samples** & **Training Time [s]** \\ \hline
**ARIMA SVM**[6] & 39.6843 & - & 20631 & - \\
**DCNN**[1] & 18.4480 & 1286.7 & 20631 & - \\
**LSTM**[5] & 16.17 & 338 & 20631 & 714.53 \\
**DCNN**[11] & 12.61 & 273.7 & **17731** & - \\
**WELM**[5] & 13.78 & 267.31 & 20631 & 5.04 \\
**HDNN**[9] & 13.017 & 245 & 20631 & - \\
**Basic COS-ELM** & 15.08 & 221.72 & 20631 & - \\
**ResCNN**[10] & **12.16** & 212.48 & 20631 & - \\
**Proposed** & 13.74 & **199.1712** & **10250** & **5.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance results compared to other recent RUL prediction approaches. Figure 6: Dynamic forgetting function behavior during training. Figure 7: RUL prediction function compared to desired function.
###### Abstract
The issues related to the scintillation and ESF event observed on 26 October 2021, are presented and discussed. We shall first indicate the existence of a plasma bubble in the Taiwan-Philippines sector using the FS7/COSMIC2 Global Positioning System (GPS) or GLObal NAvigation Satellite System (GLONASS) RO observations. We shall verify the latitudinal extent of the tracked plasma bubble using the recorded ionograms from the Vertical Incidence Pulsed Ionospheric Radar (VIPIR) located at Hualien (23.89\({}^{\circ}\)N, 121.55\({}^{\circ}\)E, dip latitude 17\({}^{\circ}\)N), Taiwan. We further discuss the spatial and temporal variabilities of two-dimensional scintillation index maps based on the simultaneous GPS L1-band signal measurements from 133 ground-based receivers located in Taiwan and the surrounding islands. We also operate two high-sampling, software-defined GPS receivers and characterize the targeted plasma irregularities by carrying out spectrum analyses of the received signal. Overall, we summarize the derived plasma irregularity and ESF characteristics and point out a potential precursor for post-sunset scintillation and ESF events.