esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
By using spherical hashing to divide the feature space into multiple hash buckets, we limit the global search scope to local neighborhoods, significantly reducing computational costs. In addition, we designed a learnable similarity scoring function that adaptively optimizes the similarity score between LR and Ref images to ensure accurate matching. Secondly, in the process of feature transfer, we propose a progressive multi-scale dynamic aggregation (PDA) module. This module uses a reference aware learnable filter to capture texture information in both spatial and channel domains, enabling more accurate extraction of image features. It prevents the inclusion of irrelevant textures and adaptively aggregates relevant texture information to ensure accurate texture transmission. In addition, gradually adjusting at each feature scale enhances the robustness of the model to large-scale biases. In summary, the main contributions of this article are as follows:  
1. We propose a hash adaptive matching method that reduces the computational cost of global matching and improves matching accuracy between the LR and Ref images through adaptive similarity adjustment. 2. We design dynamic filters that perceive texture information in both spatial and channel domains, optimizing the selection of Ref image features, reducing the introduction of irrelevant textures, and enhancing robustness to large-scale discrepancies through progressive adjustments.
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 4 Computer Algorithms
### Algorithm for Width \(W_{0}\) and the Length \(l_{Z0}\)  
In the design of microstrip filters, the value of the source impedance line, \(Z_{0}\), is known, thus, its width, \(W_{0}\), must be computed. This is performed through Equations (6) or (7). However, their accuracy is around 1 percent [24]. With the purpose of improving accuracy, it was developed the iterative Algorithm 1. This algorithm is important so that there is a good correspondence between the desired impedance of the source impedance, \(Z_{0}\) and the width of input line, \(W_{0}\).  
Figure 5: Transmission responses of two filters designed for \(f_{c}=2\) GHz. In red, a filter constructed following the design steps, where the cutoff frequency is at 2.486 GHz. In black, a filter with frequency shift compensation.  
The logic behind Algorithm 1 is as follows. The function \(W(z_{c},e_{r},h)\) approaches the value of \(W_{0}\). In reverse order, the more accurate function \(Z_{c}(W_{0},f_{c},h,e_{r})\) computes the impedance \(Z\) from \(W_{0}\). The variable \(z_{c}\) is then adjusted by \(\Delta Z\) and used to compute \(W_{0}\) iteratively until \(\Delta Z<0.0001\).  
The physical length of the source input microstrip line, \(l_{20}\), could be selected among the values  
\[l_{Z0}=\frac{\lambda_{g0}}{2^{x-1}},\quad\text{ for }x\in 3,4,5,6, \tag{19}\]  
where \(\lambda_{g0}\) is the guided wavelength of the microstrip source input line, from Equation (3), with \(f=f_{c}\). Note in Equation (19) that the parameter \(x\) must be chosen from a given set of values. The decision of the value of \(x\), then, should be made with a criterion of minimum acceptable length, e.g., 5 mm. The right choice of the value \(x\) allows us to select a line as short as possible but with the necessary length to solder the input connector, in a fabricated PCB.
# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 3 Methodology
### Proposed DataWeighted Fed Approach
#### 3.5.2 Proof
_wFedAvg_ improves _FedAvg_ by calculating the average using weights based on the dataset size of each client and will dynamically update as more clients are added. Doing so will improve the overall accuracy of the trained models because _wFedAvg_ will provide a more accurate representation of the client models (collectively) so that the global model can be updated. This is because having a weighted average can account for the varying dataset sizes of each of the clients, as clients with more data should have more influence on the global model since they have more evidence/proof of conclusions that their models can train on. Overall, this can help offset the accuracy loss due to FL and differential privacy. This _wFedAvg_ aggregation method was created through customizations with the Flower framework when designing the FL setup for this study. The principle is encapsulated in the following formulas. (a) _FedAvg Formula_:  
_FedAvg_ updates the global model by averaging the local model weights of selected clients without considering dataset size:  
\[G_{t}=(1/k)\times\sum w_{i}{}^{(1)}\text{ with }i\in S_{t}, \tag{4}\]  
where \(G_{t}\) is a global model at round \(t\), \(k\) is the number of selected clients (\(|S_{t}|=k\)), \(S_{t}\) is a subset of clients chosen at round \(t\), \(w_{i}{}^{(1)}\) is a local model weight for client \(i\) at round t.  
(b) _Weighted FedAvg (wFedAvg) Formula_:  
To address _FedAvg_'s drawbacks, _wFedAvg_ incorporates weights directly proportional to the dataset sizes of clients (weighted linearly based on dataset size). The global model is updated as a weighted average (3), and the weights are normalized such that  
\[\eta_{i}=|D_{i}|/(\sum|D_{j}|)\text{ with }j\in S_{t}\cup S_{\text{new}} \tag{5}\]  
where S\({}_{\text{new}}\) represents newly added clients in subsequent rounds. Regardless of dataset size, _FedAvg_ assigns equal weight to all selected clients. _wFedAvg_ assigns a higher weight to clients with larger datasets, ensuring their contributions are proportional to the data they provide. As more clients join the FL system, the weight calculation dynamically updates to account for the new clients' dataset sizes. The aggregation remains according to (5). This dynamically weighted approach improves model accuracy by better reflecting the data distribution among clients in the global model updates.
## 3 Results and Discussion
### Experimental Performance of EC
#### 3.2.3 Multi Response Optimization  
For the EC System, the predicted response values of the factors (Y1, Y2, and Y3) were obtained as 101.50%, 94.452%, and 98.866% under optimal operating conditions. Input variables of current and time are the dominant factor in reaction conditions, so the predicted response value shows a higher value. After three experimental runs, the average response values of Y1, Y2, and Y3 were 99.81%, 99.99%, and 99.98%, respectively. All the experimentally attained values are quite close to the predicted response values and show a good correlation (Table 7).
# Surface-Wave Extraction Based on Morphological Diversity of Seismic Events
## 3 New Method: Sparse Representations of Wavefields Based on MCA
### Time-Domain High-Resolution HRT  
Inverse and adjoint HRT in the discrete form can be expressed as [21]:  
\[d(t,x)=\sum_{v}m\Big{(}\tau=\sqrt{t^{2}-x^{2}/v^{2}},v\Big{)} \tag{7}\]  
and  
\[m_{\mathrm{adj}}(\tau,v)=\sum_{x}d\Big{(}t=\sqrt{\tau^{2}+x^{2}/v^{2}},x\Big{)} \tag{8}\]  
where \(d(t,x)\) are the seismic data in the time-offset domain, \(x\) is the offset, \(t\) is two-way time, \(m(\tau,v)\) are the Radon coefficients, \(v\) is the root-mean-square velocity of a reflection, \(\tau\) is the time intercept, and \(m_{\mathrm{adj}}(\tau,v)\) are the low-resolution Radon coefficients. Equations (7) and (8) are represented in matrix-vector form as follows [28]:  
\[\mathbf{d}=\mathbf{L}\mathbf{m} \tag{9}\]  
\[\mathbf{m}_{\mathrm{adj}}=\mathbf{L}^{T}\mathbf{d} \tag{10}\]  
where \(\mathbf{d}\) is a vector of size \(N\times 1\), whose elements are taken trace-wise from the seismic data (\(N=nx\times nt\)), and \(\mathbf{m}\) is a vector of size \(M\times 1\), whose elements are taken velocity-wise from the Radon coefficients (\(M=nv\times nt\)). \(nx\), \(nt\), \(nv\), and \(n\tau\) are the numbers of traces, samples, velocities, and time intercepts, respectively. In Equations (9) and (10), the operators \(\mathbf{L}\) and \(\mathbf{L}^{T}\) are just represented for the summation algorithms shown in Equations (7) and (8), instead of matrices [28].  
High-resolution forward HRT can be inverted from Equation (9) using a sparse constraint satisfying  
\[\left(\mathbf{W}_{m}^{T}\mathbf{W}_{m}\right)^{-1}\mathbf{L}^{T}\mathbf{W}_{ d}^{T}\mathbf{W}_{d}\mathbf{L}\mathbf{m}=\left(\mathbf{W}_{m}^{T}\mathbf{W}_{m} \right)^{-1}\mathbf{L}^{T}\mathbf{W}_{d}^{T}\mathbf{W}_{d}\mathbf{d} \tag{11}\]  
which is solved by the algorithm of a left preconditioned version of conjugate gradient for the normal Equations [28].  
The computational cost of applying operators \(\mathbf{L}\) and \(\mathbf{L}^{T}\) is controlled by the size of the Radon domain [29]. To speed up the implementation, time-domain high-resolution forward HRT is solved in the restricted Radon space [29]  
\[\left(\mathbf{W}_{m_{A}}^{T}\mathbf{W}_{m_{A}}\right)^{-1}\mathbf{L}_{\mathbb{A }}^{T}\mathbf{W}_{d}^{T}\mathbf{W}_{d}\mathbf{L}_{\mathbb{A}}\mathbf{m}_{ \mathbb{A}}=\left(\mathbf{W}_{m_{A}}^{T}\mathbf{W}_{m_{A}}\right)^{-1}\mathbf{ L}_{\mathbb{A}}^{T}\mathbf{W}_{d}^{T}\mathbf{W}_{d}\mathbf{d} \tag{12}\]  
where \(\mathbf{d}\) should be normalized to unity by dividing the maximum of the seismic data [30]. The restricted Radon space is defined as  
\[\mathbb{A}=\left\{(\tau,p):\frac{1}{nx}\left|\mathbf{m}_{\text{adj}}\right|>T\right\} \tag{13}\]  
where \(T\) is the threshold satisfying \(0<T<1\).
## 2 Materials and Methods
### Experiments
#### 2.1.2 OH Radical Generation  
The main oxidant of toluene in our experiments was OH generated by O\({}_{3}\) photolysis in the presence of water vapor in GO:PAM. O\({}_{3}\) was generated by external irradiation of VOC-free air with a mercury lamp (\(\lambda=185\) nm) and its concentration was measured with an O\({}_{3}\) monitor (2B Technologies, Boulder, CO, USA). UV photolysis of O\({}_{3}\) (\(\lambda=254\) nm) generated excited oxygen [O(\({}_{1}\)D)] atoms inside the flow tube (Go:PAM) at a relative humidity of 20-80%. It was previously established that Go:PAM OH exposures can be determined by measuring the residual VOC concentration at the outlet of Go:PAM with a VOC monitor [39; 40]. In this study, the OH radical exposure and concentrations were estimated using Equation (3). Assuming an average atmospheric OH concentration of \(1.5\times 10^{6}\) molecules cm\({}^{-3}\)[41], our experimental conditions correspond to about 0.77 to 1.61 days of atmospheric oxidation aging (See Table S1 in Supplementary Materials).
# Fine-Grained High-Utility Dynamic Fingerprinting Extraction for Network Traffic Analysis
## 4 Fine-Grained High-Utility Dynamic Fingerprinting Extraction  
According to the preprocessing of the network traffic in Section 3, several segments of the network traffic are obtained. The \(N\)-gram model is used to represent the results after the partition, and the label is used to represent the results, as shown in Table 3. In this way, the partition results can retain the complete application fingerprint to the maximum extent. Network behavior is a constantly changing data flow. To obtain the changes in new network behavior in time, it is necessary to obtain application fingerprints dynamically online, which requires that network traffic can only be processed once.  
In Section 4, the problem to be solved is that application fingerprints with typical features can be dynamically obtained. Based on a frequent set mining algorithm, a fine-grained efficient dynamic application fingerprint extraction method Hut is proposed. The Hut algorithm focuses on two parts: one is to calculate the utility of application fingerprints, and the other is to build an \(N\)-gram tree structure to mine application fingerprints with high-utility values according to the tree structure.
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 7 Discussion and Open Issues  
By studying the above papers, it is observed that most of the proposed schemes in IoV use permissioned blockchain frameworks instead of permissionless (Figure 7). In these authorised environments, data tampering and partial pseudonymity seem to be more important, because it is easier to detect malicious users due to some kind of authority presence, than in a complete permissionless system. Additionally, it is important to highlight that two proposed schemes [1; 60] are using access control to enrol their users, providing even more control of participants in the blockchain network.  
With regards to privacy mechanisms, various techniques have been used to protect the involved users and the aggregated data. More specifically, the purpose of the above techniques was to protect either location privacy, identity privacy of involved parties and data exchange. In most papers, it seems that identity privacy is achieved mainly through the use of pseudonyms during communication (e.g., [37; 38]), cloaking/clustering areas for location privacy (e.g., [40; 47]) and homomorphic encryption to ensure data privacy [34; 52]. However, in some papers, some other more advanced techniques, such as zero knowledge proofs (e.g., [39; 45]), ring signatures [33; 41], blind signatures [57; 61], and group signatures [42] are also used to ensure privacy. It is significant here to point out some unique techniques proposed to preserve privacy, such as federating learning [50].  
However, as a whole, none of these proposed solutions has been tested in real conditions and their privacy preserving aspect remains in theory (e.g., [33; 36]) or in simulation (e.g., [13; 48]), and only four of them are in a proof of concept stage [40; 52; 60; 61].  
Given the above, technologies that are used in IoV, can only run in limited hardware and must have the minimum energy consumption, while the proposed consensus protocols, the energy efficiency and performance must be tested thoroughly and in scale. Moreover, blockchain is a technology that can surely protect from data alteration but cannot guarantee privacy preservation. An equally important issue is how all these technologies can be integrated to an existing infrastructure and estimate the cost of integration, as many of the proposed schemes do not address any hardware or software requirements. Based on these characteristics, limitations and requirements of the Privacy-Enhancing Technologies (PETs) need to be optimised.
## 3 Interference Mitigation Schemes
### Power Control Approach
#### 3.1.5 PCG Using Social Networks  
An algorithm to mitigate interference in a cyber-physical WBASN system was designed by detecting network topology and applying PCG [21]. In a scenario with people wearing WBASNs, changing their positions changed the network topology, thereby causing interference with other WBASNs. This mechanism includes two algorithms: the power control game and the social contact network consisting of WBASNs.  
In the social contact network consisting of WBASNs, a tool for social networks was devised to build a social interaction network. An interference graph of the overlapped communication range was created in which the vertices were the nodes and the edges were the links between two adjacent nodes. The detection of dynamic interaction in social networks was developed using Bluetooth and acoustic wave technology. The distance between two WBASNs was detected by sending and receiving an acoustic wave, and the WBASN calculated the time delay to estimate the distance between them. A four-state dynamic social interaction prediction algorithm was used to detect the interaction in dynamic topology by applying Markov Chain. The four states of the predictor can be listed as strong interaction, weak interaction, strong noninteraction, and weak noninteraction. Moreover, the channel gain model was developed for three-tier network topology, which is same as in [9]. The channel gain is the function of the distance between the transmitter and the receiver in two different tiers.  
The second algorithm is the PCG based on the output parameters in the first algorithm. Similar to the PCG in [17, 18], the network utility and the transmit power are optimized. The player in this game is the communication link between the transmitter and the corresponding receiver. The SINR at each node is calculated by using the channel gain in the first algorithm. The price value of each player is defined as the function of the SINR, weight, and transmission power. The weight is set according to WBASNs' wireless channel state, power state, constraints on power, and quality of service (QoS) requirements. In the PCG, each WBASN estimates the distance and predicts social interaction among people; then the WBASN updates its power and price values.
## 4 Experiments and Results
### Evaluation Metrics  
To evaluate the performance of proposed model, two evaluation metrics were employed: the area under the curve (AUC) of the receiver operating characteristic (ROC) and the partial-AUC (pAUC). In audio anomaly detection, AUC is a key metric for evaluating the overall performance of a model. It measures the model's ability to distinguish between normal and anomalous samples at different thresholds by calculating the area under the  
Figure 8: Log-mel spectrogram of the collected data.  
Figure 7: Data recording scene [36].  
ROC curve. Mathematically, with the true positive rate (TPR) on the y-axis and false positive rate (FPR) on the x-axis, AUC is defined as  
\[\text{AUC}=\frac{\sum_{i=1}^{N_{-}}\sum_{j=1}^{N_{+}}\mathcal{H}(\mathcal{A}(x _{j}^{+})-\mathcal{A}(x_{i}^{-}))}{N_{-}N_{+}}, \tag{22}\]  
where \(x_{j}^{+}\) and \(x_{i}^{-}\) are normal and anomalous test samples, respectively, \(N_{-}\) and \(N_{+}\) are the numbers of normal and anomalous test samples, \(\mathcal{A}(\cdot)\) is an anomaly score function, and \(\mathcal{H}(x)=1\) when \(x>0\) and 0 otherwise. AUC covers the full range from 0 to 1. A higher AUC (closer to 1) indicates better model performance.  
On the other hand, pAUC (partial AUC) focuses on a more practical low-FPR region (e.g., \(0\leq\text{FPR}\leq\alpha\)), with the formula being  
\[\text{pAUC}=\frac{\sum_{i=1}^{\lfloor\alpha N_{-}\rfloor}\sum_{j=1}^{N_{+}} \mathcal{H}(\mathcal{A}(x_{j}^{+})-\mathcal{A}(x_{i}^{-}))}{\lfloor\alpha N_{- }\rfloor N_{+}}, \tag{23}\]  
where \(\alpha\) is set to 0.1 in this study. By restricting the evaluation range, pAUC more accurately reflects performance in critical operational scenarios, especially in anomaly detection where class imbalance is severe. While optimizing AUC alone may lead to overfitting in high-FPR regions, pAUC ensures improved detection capability at low false positive rates.
## 3 Results and Discussion  
### High-Temperature Performance  
#### 3.1.1 Marshall Stability Test  
The Marshall stability (_MS_) test results for the four types of mixtures are shown in Figure 5. The _MS_ values in all four groups were greater than 3 kN, which is the requirement of the specification. The _MS_ values of the three WER-EA mixtures were higher than that of the pure emulsified asphalt mixture. The _MS_ values of E20-T, E44-T, and E51-T increased by 26.32%, 14.78%, and 25.71%, respectively, based on the MS values of the emulsified asphalt. Among the WER-EA mixtures, the _MS_ value of E20-T was the largest, and the _MS_value of E44-T was the smallest. The _MS_ of the E20-T group was 10.01% higher than that of the E44-T group. This indicates that given the curing agent TETA, the WER system can improve the strength of the emulsified asphalt mixture, and the improvement effect is most obvious when the epoxy value is set at 20.  
As shown in Figure 6, the flow (_FL_) values of the four emulsified asphalts were all in the range of 1.5-4 mm. Compared with the _FL_ value of EA, the _FL_ value of WER-EA was reduced to varying degrees, and the four _FL_ values were within the specification limit. Compared with EA, the _FL_ values of E20-T, E44-T, and E51-T were reduced by 42.13%, 25.28%, and 38.48%, respectively. Among them, the _FL_ value of E20-T was the smallest, being 29.13% lower than that of E44-T. Previous studies indicate that the larger the MS value of the specimen, the smaller the _FL_ value. Combining Figures 5 and 6 indicates that the results in this study obeyed the same patterns observed in previous studies.  
Compared with other Marshall test indexes, the Marshall modulus (_T_, kN/mm) can clearly characterize the high-temperature performance of WER-EA mixtures, and the results are calculated as follows:  
\[T=MS/FL \tag{4}\]  
Figure 5: Stability results of asphalt mixture.  
Figure 6: Flow value results of asphalt mixtures.  
The results were calculated and are presented in Figure 7. It is observed that the Marshall modulus of the pure EA mixture was significantly smaller than that of the WER-EA mixture. The Marshall modulus of \(T_{\text{E20-T}}\) was close to that of \(T_{\text{E51-T}}\), with a difference of only 0.019 kN/mm, and both were larger than that of \(T_{\text{E44-T}}\). The \(T\) value of the E20-T mixture was 42.25% higher than that of the E51-T mixture, but 119.57% higher than that of the EA mixture. A larger \(T\) value reflects lower mixture deformation under the same load, or a higher load under the same deformation. That is, a larger \(T\) value means the mixture has higher strength.  
Based on the _MS_ value, _FL_ value, and \(T\) value, the WER system could significantly improve the strength of the emulsified asphalt mixture, and E20 was more suitable for improving the strength of the WER-EA mixture than the other WERs. This observation is consistent with the asphalt properties shown in the published research, with the E20 group showing higher viscosity under a high temperature.
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 2 Methods and Principles
### CCA and CSEA Modules  
In neural networks, the integration of an attention mechanism within the decoder enables the capture of feature correlations. Utilizing coordinate attention (CA) [28] and squeeze-and-excitation attention (SE) [29], we have developed two innovative attention modules, CCA and CSEA, to enhance the denoising ability and localization of the network. Attention mechanisms help networks to better handle complex tasks and data by optimizing resource allocation, improving model accuracy, enhancing flexibility and adaptability, and reducing redundant information interference. During the downward propagation of the neural network, there will be important information that may be lost due to the maximum pooling operation. To alleviate this problem, we adopt a U-Net-like framework for the joining process in the CCA and CSEA modules, which mainly performs time-consuming convolutional computations in low-resolution space. Among them, the fusion of shallow and deep features is widely used. Such feature fusion not only facilitates a seamless information flow from superficial to in-depth layers but also proves beneficial for maintaining features, supporting efficient gradient backpropagation, and minimizing information loss during subsequent propagation.