esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Methods Utilized for the Reconstruction of High-Resolution MR-PET Images
The motion consistency loss \(\mathcal{L}_{\text{mc}}\) is designed to enforce temporal and spatial coherence across sequential or neighboring image frames in the reconstructed outputs. It is formulated as follows:  
\[\mathcal{L}_{\text{mc}}=\mathbb{E}_{t}\Big{[}\|G_{\theta}(x_{t})-T_{t\to 0}G_{ \theta}(x_{0})\|_{2}^{2}\Big{]},\]  
where  
* \(x_{t}\) is the input data at time frame or spatial position \(t\). * \(G_{\theta}(x_{t})\) is the generator's output (super-resolved image) corresponding to input \(x_{t}\). * \(x_{0}\) is a designated reference frame (e.g., a motion-free frame or an initial frame). * \(T_{t\to 0}\) is a deformation or transformation operator that aligns frame \(t\) to the reference frame 0. This operator is typically learned by a motion correction network or estimated through registration algorithms. * The \(L_{2}\) norm measures the pixel-wise difference between the transformed frame and the reference frame after applying the generator. * The expectation \(\mathbb{E}_{t}\) is computed over all relevant frames \(t\) in a temporal window or spatial neighborhood. This loss penalizes inconsistencies between frames caused by motion, encouraging the generator to produce temporally aligned and artifact-free outputs. It is especially critical in dynamic imaging scenarios and when dealing with motion-corrupted MR-PET data. Training of \(G_{\theta}\), \(D_{\phi}\), and the motion correction network is performed jointly in an end-to-end fashion using stochastic gradient descent. The training dataset consists of paired high-resolution and low-resolution MR-PET images, augmented with random motion perturbations and intensity variations to improve generalization. The optimized sparse sampling patterns described in Section 9 are employed to generate realistic training inputs. The structured joint sparsity regularization \(\mathcal{L}_{\text{sparse}}\) plays a critical role in guiding the super-resolution network toward anatomically plausible outputs [27]. This term enforces consistency between the low-rank structures of the generated MR and PET images, thereby improving cross-modality alignment and reducing modality-specific artifacts. In summary, the proposed method leverages a tightly integrated pipeline combining motion correction, joint sparsity regularization, and adversarial training to achieve well-established MR-PET super-resolution [11; 26; 27]. Experimental results demonstrate substantial improvements over classical SR approaches, with gains of 2-3 dB in PSNR and significant perceptual quality enhancements. The resulting high-resolution imagesexhibit sharper anatomical boundaries, reduced motion artifacts, and improved cross-modality consistency, thereby facilitating more accurate quantitative analysis and clinical interpretation in hybrid MR-PET imaging.
## 3 Model Assumption and Toss-and-Catch Algorithm
### Score-Based Selection Mechanism
#### 3.3.2 Scoring and Ranking
Figure 14 illustrates two scenarios as follows: (1) SBS \(i\) cannot fully handle the OIS's load; and (2) SBS \(i\) can fully handle the OIS's load. In Scenario 1, \(\phi_{i}<\psi\); hence, \(score_{\text{load}}=\frac{33\%}{50\%}\times 100=66\); and in Scenario 2, \(\phi_{i}\geq\psi\); hence, \(score_{\text{load}}=100\). Figure 14: Calculation of \(score_{\text{load}}\). Figure 13: Calculation of \(score_{\text{RSSmax}}\). (4) Total score after weight averaging (\(score_{\text{total}}\))  
After the scoring system has scored all of the SBSs in SBSG1 according to the three-stage scoring process described above, the scores for each stage are weight-averaged to calculate the total score \(score_{\text{total}}\). If the score for any stage is 0 or if SBSG1 is an empty set (i.e., there is no signal coverage), \(score_{\text{total}}\)= 0. Therefore, \(score_{\text{total}}\) can be expressed as  
\[score_{\text{total}}= \begin{cases}k_{\text{hold}}score_{\text{hold}}+k_{ \text{RSSmax}}score_{\text{RSSmax}}+k_{\text{load}}score_{\text{load}},& score_{\text{hold}}\
eq 0,\ score_{\text{RSSmax}}\
eq 0,\ score_{\text{load}}\
eq 0,\\ 0,&\text{otherwise}\end{cases} , \tag{30}\]  
where \(k_{\text{hold}}(0\leq k_{\text{hold}}\leq 1)\), \(k_{\text{RSSmax}}(0\leq k_{\text{RSSmax}}\leq 1)\), and \(k_{\text{load}}(0\leq k_{\text{load}}\leq 1)\) are the weights for \(score_{\text{hold}}\), \(score_{\text{RSSmax}}\), and \(score_{\text{load}}\), respectively, and \(k_{\text{hold}}+k_{\text{RSSmax}}+k_{\text{load}}=1\). As the final step, we rank the SBSs in SBSG1 according to their respective \(score_{\text{total}}\), and the highest-scoring SBS is logged. In the event of a tie in the scores, random ranking is performed. If all of the scores are 0, the overload support mechanism is activated to support the OIS.
## 4 Implementation
### Comparison of Training Loss between FairMOT Models DLA-34 and YOLOv5s  
Figure 6 compares the training results of the two backbones of the FairMOT model, FairMOT + DLA-34 and FairMOT + YOLOv5s. Both models were trained with an input image size of 128 \(\times\) 64, a batch size of 8, and 60 epochs. This graph shows the change in train loss during the training process to identify the point where the training loss is minimized.  
For the FairMOT-DLA-34 model, the initial training loss value starts at 1.955 and decreases sharply in the early stages of training, dropping rapidly until around 10 epochs. After that, it shows a gradual decrease, finally recording a loss value of 0.1067 at 60 epochs. The FairMOT model with the DLA-34 backbone shows a relatively low initial loss value and converges to a stable loss value quickly during the training process.  
On the other hand, the FairMOT-YOLOv5s model starts with an initial training loss value of 2.348 and decreases sharply in the early stages of training, dropping rapidly until around 20 epochs. After that, it shows a gradual decrease, finally recording a loss value of 0.8541 at 60 epochs. The FairMOT model with the YOLOv5s backbone shows a higher initial loss value, a relatively slower training speed, and a higher final loss value.  
The graph visually shows the difference in training speed and performance between the two models. The FairMOT-DLA-34 model shows faster training speed and lower final loss value compared to the FairMOT-YOLOv5s model, indicating superior performance. These results suggest that the DLA-34 backbone is more effective in feature extraction and has faster convergence during training compared to the YOLOv5s backbone. This graph plays an important role in determining the optimal number of epochs for model training and helps identify the optimal training point. This helps prevent overfitting and maximize the generalization performance of the model.  
Figure 6: Comparison of training loss between FairMOT models DLA-34 and YOLOv5s.
# Multifunction RF Systems for Naval Platforms
## 3 MFRF System Design Considerations
### Ideal MFRF System Architecture
To accommodate all RF functions of interest, this implies a required ADC sampling rate capability of 40 gigasamples per second (GSPS), corresponding to the highest frequency of operation listed in Table 1. The ADC in each digital receiver must also have sufficient dynamic range to meet the most stringent dynamic range requirement indicated in Table 1. This corresponds to a dynamic range value of 90 dB for radar functions, which implies about 15 bits of digitization. Once the Rx signal is digitally captured in this way, all other traditional receiver functions, such as filtering and quadrature demodulation, can be done through digital signal processing. Furthermore, the same digitized set of signal data can be processed in parallel in different ways, to meet the needs of different RF functions. For example, a digitized signal data set can be digitally filtered at the frequencies utilized by the VS radar function (assuming that there was a corresponding VS radar transmission to generate radar return signals), and pulse compressed as the first step in the target detection process. Concurrently, for the ES function, banks of bandpass filters spanning the full ES monitoring range can be applied to the same digitized data, using different filter bandwidths to mitigate the effect of system noise on detection of both wideband and narrowband weak emitter signals. This scheme would essentially allow instantaneous searching of threat signals over the entire frequency range of interest, eliminating the need for a separate instantaneous frequency measurement (IFM) receiver typically required in many current ES systems.
## 5 Result and Discussion
### Gap Analysis and Key Findings  
Although most of the RESOLVD value propositions were pretty aligned with the customers, the research identified some gaps which could be filled through better engagement between the developer and the DSOs.  
* A high importance was given to cybersecurity by DSOs, which was not reflected as strongly in the S&R.
* Not all value propositions were urgently needed.
* Value propositions related to uncontrolled and controlled islands were relevant but not urgent. According to DSOs, these value propositions were expected to become urgent in the span of 5-10 years. Therefore, for these value propositions, the technology developers have more time to mature technology than others.
* For technologies needed withing five years, the technology developers should expedite their innovation process to be market-ready in time.  
The main takeaway and contribution of this paper was the introduction of the sense and response approach in the energy domain combined with the broader survey. This provided a better understanding of the customer's (DSOs) needs and the technology developers' perceptions on their technology value propositions. This two-way communication and interaction minimized the gap and facilitated rapid learning by sensing the most critical challenges and responding accordingly. At a higher level of decision making, e.g., the EU, this could elicit in-depth insight into which direction of research is relevant and should be prioritized to overcome the current and future challenges of the DSOs as well as the right technology based on the need and urgency.
# Generating Super Spatial Resolution Products from Sentinel-2 Satellite Images +
## 2 Materials and Methods  
The development of a DL-based method involves at least three key steps, with some iterations among them:  
* Preparing an appropriate dataset for training and testing.
* Designing and developing a DL model.
* Training and evaluating the model using prepared data.  
Following this approach, we will outline the progression of our proposed model in the same order in order to make the material clearer.
## 4 Proposed Routing Protocol
### Sensor Phase  
In this sensing phase, each UAV node periodically broadcasts its location by hello packet and maintains its neighbor tables. The neighbor topology table of the node is described as \(NT_{i}=\left\{x\in N_{nbr}(i)\mid\left(LS_{i,x},E_{x}^{pre},L_{x},NF_{x}\right)\right\}\), where \(LS_{i,x}\), \(E_{x}^{pre}\), and \(L_{x}\), respectively, represent the link stability between the node \(i\) and its neighbor node \(x\) during a period of time, the predicted residual energy of the neighbor node \(x\) of the node \(i\) in next hello moment, and the geographical location of a node \(x\). \(NF_{x}=(D_{1d},D_{2d},\ldots,D_{8d})\) is the topology information of the neighbor node \(x\), where \(D_{hd}\) is used to denote the farthest distance between the neighbor of node \(i\) and the destination node \(d\) in the \(l\)th region, which is used to estimate the location of the two-hop neighbors of the node in the region, which has already been described in [42].  
\[L_{i_{2}}(x,h)=L_{i}+D_{hd}\bigg{(}\cos\frac{(1+2h)\pi}{8},\sin\frac{(1+2h)\pi}{8} \bigg{)} \tag{5}\]  
In the process of data-packet forwarding, additional information, including the source node \(s\), the last-hop node \(l\), the destination node \(d\) and its position information \(L_{d}\), and the node set \(HVN=\{i,\ldots,l\}\) that has been visited during the packet-forwarding process, are recorded in the data packet. \(NF_{i}\) is to be able to evaluate the ability of the two-hop node of node \(i\) to reach the destination node to avoid forwarding of a packet to a void area. The purpose of recording additional information in a packet is to construct feature values and prevent the formation of loops.
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 2 Related Work
### Motivation of Quantized Convolutional Neural Networks  
When quantum neural networks are trained on large data sets, the problem of the 'barren plateau' (exponential vanishing gradient) will occur. However, researchers have shown that the landscape of QCNN models does not affect this problem when training on large data sets. Therefore, the QCNN architecture can be trained under random initialization of parameters. The QCNN-LSTM framework used in this paper effectively avoids the gradient explosion and gradient disappearance problems of common recurrent network models.  
The QCNN part uses qubits for quantum computation and represents a variety of possible combinations of 1 and 0, connecting them to provide more processing power than the same number of binary files. By binarizing the parameters and weights in the network, the quantum neural network can reduce the memory occupied by the training while ensuring the training accuracy. By introducing the superposition idea of quantum states into the traditional feed-forward neural network, the activation function of the hidden layer of the neural network is superimposed by multiple S-shaped functions. An S-shaped function can only represent two orders of magnitude and states, and the linearly superposed sigmoid function has different quantum intervals between adjacent functions. The uncertainty of the input data of the training sample can be quantified by the training of the quantum interval because the hidden neural unit can represent more amplitude and state. By using the multi-layer activation function, different data are mapped to different amplitudes, which increases the fuzziness of the network and improves the accuracy and certainty of network pattern recognition.  
In the model input part of the framework, quantum-dense coding converts data into qubits. Quantum entanglement enables one qubit to transmit multiple classical qubits, thus significantly expanding the channel capacity. A qubit contains two orthogonal (10) and 11) or linear combination) states of a two-state system. If the auxiliary entanglement is a Bell state, then two classical pieces of information can be transmitted while transmitting a qubit; the entanglement-assisted classical channel capacity is twice the original.
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 2 Network Model and Problem Description  
### Network Model  
The SU obtains the unused spectrum of the PU by the spectrum sensing algorithm in CWMNs. This paper did not address the specific spectrum sensing algorithm, but focused on how to construct a routing path to satisfy the QoS constraints, and to allocate the unused spectrum resources after the available spectrum is sensed.  
It was assumed that there is a common control channel (CCC) for transmitting the available spectrum information and control information between the SUs [19]. The communication distance of each node is \(T_{R}\), and the interference distance is \(I_{R}\), and \(I_{R}\) = 2 \(\times\)\(T_{R}\).  
We adopted a simple undirected graph \(G\) = (\(V\),\(E\)) model of a CWMN, where \(V\) represents the set of the CR-Mesh node, and \(E\) represents the set of wireless links. The physical distance between node \(v_{i}\) and node \(v_{j}\) is represented by \(d(v_{i}\),\(v_{j})\). As each node is in a different location and the PU uses a spectrum with a different pattern, the sensed available spectrum of each node was not the same. Each node \(v_{i}\in V\) had an available channel set \(K_{i}\), which had been sensed.  
Two CR-Mesh nodes that can communicate with each other must satisfy the following conditions: (1) there are common available channels, \(K_{i}\cap K_{j}\
eq\Phi\); and, (2) the nodes must satisfy the restriction of distance, \(d(v_{i}\),\(v_{j})\) \(<\)\(T_{R}\).  
The set of available channels \(K\) was [1,2,3,4,5] in the current network, and the bandwidth of the channel \(k\) was \(B^{k}\) Mbps. Different channels had different bandwidths, i.e., different channels \(i\) and \(j\), leads to \(B^{i}\
eq B^{j}\). \(x\)(\(u\),\(v\)) = \(k\) represents the wireless link (\(u\),\(v\)) allocated to channel \(k\). \(x\)(\(u\),\(v\)) = 0 represents the wireless link (\(u\),\(v\)) not allocated to any channel. Every wireless link was either allocated only one channel, or was not allocated a channel.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 2 Motivating Scenario, Challenges, and Objectives
### Challenges  
The primary aim of this research is to construct a user mobility model that facilitates the implementation of inference attacks. This makes it possible to determine what types of sensitive information can be extrapolated from the model, which could potentially compromise an individual's privacy. The intent is to subsequently use this model to enhance privacy protections for participants in crowdsourcing ecosystems. To develop this nuanced and context-aware framework, one needs to cope with several key scientific challenges, but mainly the following ones that we aim to address here:  
1. Challenge 1: How to understand and analyze user mobility? This challenge requires the consideration of user mobility in order to understand and analyze movements with precision, which safeguards privacy. It involves analyzing data to recognize the regularity of visits to POIs, the paths taken between these points, and the fluctuations in these routines. Advanced data analytics and modeling techniques, such as graphs, are required to encapsulate this complexity in a mobility model. The model must maintain a high degree of accuracy to be effective for inference attacks while implementing robust privacy-preserving measures to protect individual identities.
2. Challenge 2: How to integrate dynamic contexts? The flowing nature of user behavior and urban environments requires that the user mobility model remains adaptable to dynamic contexts. Taking into account the dynamic context to accommodate different scenarios and environments is essential for it to remain relevant across various user behaviors and conditions. This adaptability involves not only the integration of dynamic and possibly real-time data but also the capacity to adjust to the evolving nature of urban landscapes and user interactions with them. Techniques such as context-aware computing and adaptive algorithms play a pivotal role in ensuring the model's resilience and relevance.
3. Challenge 3: How to identify POIs? A pivotal challenge is pinpointing POIs to reflect an individual's movements within a user mobility model. This necessitates the precise detection and classification of locations that users frequently visit.
## 5 Evaluation
### System Implementation and Data Preparations
#### 5.1.1 Data Collection  
Our Android application collects raw GNSS measurements and locations returned by GPS. The GPS location indicator has two sources in Android: AndroidLoc, obtained through the Android LocationManager API [37], and GoogleLoc, provided by Google's FusedLocationProviderApi [38]. We record locations and their accuracies (associated with the Location class) for both sources at a frequency of 1 Hz. The ground truth is manually labeled by marking critical points along the way, and the locations between the critical points are linearly interpolated by time, assuming users move at a constant speed. We collect 30 traces at different times covering about 9000 typical location points in the urban area, roughly summarized in the following nine scenarios:  
\begin{tabular}{l l} On street & On void deck \\ blocked on one side by buildings & blocked on two sides by buildings \\ building enclosing & inside a glass-covered building \\ on a sheltered street & in a tunnel \\ inside a room with heavy walls & \\ \end{tabular}  
We randomly select 20 traces for training purposes and assign the remaining traces as testing traces, resulting in a training dataset with about 6600 location points and a testing dataset with about 2300 location points.
## 3 Materials and Methods  
Geological core logging was performed on the three existing boreholes (boreholes OV125149, OV125156, and OV125160) at the Vele Colliery core yard. Thickness of the stratigraphic units was measured perpendicular to the strike of the strata using a measuring tape. The stratigraphic data was used to generate the stratigraphic logs of the Madzaringwe Formation in the above-mentioned boreholes. A total of 28 samples, comprising of 7 coal, 12 mudstones, 6 shales and 3 sandstones from the Madzaringwe Formation in the Vele Colliery were collected and subjected to petrographic, X-ray diffraction (XRD) and X-ray fluorescence (XRF) analyses at the Council of Geoscience, South Africa. Fifty-two representative thin sections of the shale, mudstone and sandstone were prepared and studied using a Nikon Eclipse Polarizing microscope to determine their mineral compositions, texture and arrangement of mineral grains. The Bruker XRD D8 Advance (Model: V22.0.28) was used for the XRD analysis. The XRD measurements were done at 25 \({}^{\circ}\)C room temperature and the samples were scanned at 2\({}^{\circ}\)\(\theta\)/min ranging from 2\({}^{\circ}\) to 70\({}^{\circ}\) (wavelength of 1.5406). The XRD results are expressed in wt. % and it only show crystalline phases, and do not recognize amorphous phases such as carbonaceous organic matter and hydrous iron oxide minerals. The XRF analysis of the major and trace elements was performed using Magi-X Fast spectrometer. Bulk sample preparation consists of drying where necessary, crushing  
Figure 3: Stratigraphic correlation of the main Karoo Basin and the northern sub-basins [20].  
to less than 10 mm, and subsequent milling in a tungsten carbide milling machine to less than 50 \(\upmu\)m. The major elements were analysed on fused beads, while trace elements were executed on pressed powder pellets.  
Different discriminatory plots of the major and trace elements have been used by several researchers [5; 6; 7; 14] to determine the provenance and tectonic setting. These plots were attempted in this study to infer the tectonic provenance of the Madzaringwe Formation. Furthermore, the ternary diagram of Al\({}_{2}\)O\({}_{3}\)-(CaO + Na\({}_{2}\)O)-K\({}_{2}\)O (represented as A-CN-K) was plotted and the chemical index of alteration (CIA), chemical index of weathering (CIW), and plagioclase index of alteration (PIA) were calculated and used to quantify the degree of weathering.