esa-sceva/satcom-chunk-collection huggingface dataset:
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### GPS Acquisition  
#### 2.5.1 Acquisition Strategy  
The three main acquisition methods for GNSS signals are the serial search (SS), the parallel frequency search (PFS), and the parallel code-phase search (PCS) [28]. Compared to the PCS and the PFS methods, the acquisition time of the SS method is extremely long, therefore, the SS was not considered. Compared to PCS, PFS has two main drawbacks. Firstly, an extra loss due to the integration before the fast Fourier transform (FFT) and secondly a loss due to the mismatch between the replica code chipping rate and the received code chipping rate, especially important for long integration times [28]. Since the sensitivity is of prime importance in high altitude space applications, these additional losses are not acceptable, and therefore the PCS technique was selected.  
The acquisition structure of receiver is illustrated in Figure 8. In acquisition, the receiver uses coherent accumulations, which are the length of a full navigation data-bit (20 ms for GPS C/A code). The results are then non-coherently accumulated to gain further sensitivity. To reduce the effect of the data-bit transitions a number of different accumulations are formed with different starting points. Ideally, for GPS C/A code we would have 20 accumulations spaced 1 ms apart. However, to limit the impact on the FPGA resources we form 10 accumulations spaced 2 ms to achieve a balance between performance and the available resources.  
Following the results of Section 2.3.2, the sensitivity targeted is 15 dB-Hz, slightly higher than the minimum required to acquire at least four signals. Using the methodology proposed in [27], the theoretical analysis of the acquisition parameters is presented in Table 2. This is used to estimate the total required integration time required for a successful acquisition. In acquisition, the receiver's sampling rate of 40.96 MHz is decimated to 4.096 MHz as a compromise to improve processing performance. The resolution in the code search is one sample so this results a resolution around 14 of a chip. A higher sampling frequency requires more samples to be processed but results in a fine  
Figure 7: Doppler rates of received GPS L1 C/A signals across the MTO.  
resolution in the code search domain. A lower frequency means less samples to process but poorer resolution with higher losses from the code misalignment. In tracking the full sample rate of 40.96 MHz is used to allow precision tracking from narrow correlator spacings. A frequency step of 25 Hz has been selected, again as compromise for the performance. Finally, the maximum tolerable Doppler rate error is defined as the Doppler rate, which implies a shift of one frequency bin during the integration time.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 6 Decomposition Methods
### Primal Decomposition  
To use primal decomposition to solve a problem with the structure of Equation (11), a variable \(\mathbf{y}\in\mathbb{R}^{m}\) can be introduced, which represents the amount of resources allocated to the first subproblem. Obviously, \(-\mathbf{y}\) is allocated in the second subproblem. Our first subproblem is then given by:  
\[\underset{\mathbf{x}_{1}}{min}\;\;f_{1}\left(\mathbf{x}_{1}\right)\] (12a) s.t. \[\mathbf{x}_{1}\in\mathcal{X}_{1},\;\;\mathbf{g}_{1}\left(\mathbf{x}_{1}\right) \leq\mathbf{y} \tag{12b}\]and the second subproblem becomes  
\[\underset{\mathbf{x}_{2}}{\text{\emph{min}}}\;\;f_{2}\left(\mathbf{x}_{2}\right) \tag{13a}\] \[\mathbf{x}_{2}\in\mathcal{X}_{2},\;\;\mathbf{g}_{2}\left(\mathbf{x}_{2}\right) \leq-\mathbf{y} \tag{13b}\]  
Let \(\Psi_{1}\left(\mathbf{y}\right)\) and \(\Psi_{2}\left(\mathbf{y}\right)\) denote the optimal values of the subproblems (12) and (13), respectively. The original problem becomes equivalent to the master problem of:  
\[\underset{\mathbf{y}}{\text{\emph{min}}}\;\;\Psi=\Psi_{1}\left(\mathbf{y} \right)+\Psi_{2}\left(\mathbf{y}\right) \tag{14}\]  
and the subproblems can be solved separately when \(\mathbf{y}\) is fixed.  
A subgradient for the optimal value of each subproblem from an optimal dual multiplier associated with the coupling constraint can be obtained. For e.g., if for the first subproblem the optimal solution is \(\Psi_{1}\left(\bar{\mathbf{y}}_{1}\right)\), and \(\bar{\mathbf{y}}_{1}\in\mathbf{dom}\;\left(\Psi_{1}\right)\). If \(\lambda\left(\mathbf{y}\right)\) is an optimal dual vector of variables associated to the constraint set \(\mathbf{g}_{1}\left(\mathbf{x}_{1}\right)\leq\bar{\mathbf{y}}\), then \(-\lambda\left(\mathbf{y}\right)\) is a subgradient of \(\Psi_{1}\) at \(\bar{\mathbf{y}}_{1}\). Therefore, to find a subgradient of \(\Psi\), we solve the two subproblems (12) and (13), to find the optimal solutions \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\) as well as the dual variable vectors \(\lambda_{1}\) and \(\lambda_{2}\) associated with the constraint sets \(\mathbf{g}_{1}\left(\mathbf{x}_{1}\right)\leq\mathbf{y}\) and \(\mathbf{g}_{2}\left(\mathbf{x}_{2}\right)\leq-\mathbf{y}\), respectively. In this case a subgradient would be \(\left(\lambda_{2}-\lambda_{1}\right)\). If \(\bar{\mathbf{y}}\
otin\mathbf{dom}\;\Psi\), a cutting plane can be generated to separate \(\bar{\mathbf{y}}\) from \(\mathbf{dom}\;\Psi\), for use in solving the master problem.  
A primal decomposition algorithm can be summarized as follows:  
**while**_Stopping criteria not satisfied_**do**  
solve subproblems (12) and (13) (possibly in parelle) to obtain \(\mathbf{y}_{1}\), \(\mathbf{y}_{2}\), \(\lambda_{1}\) and \(\lambda_{1}\);  
Update resource allocation:  
\[\mathbf{y}\leftarrow\mathbf{y}-\alpha_{k}\left(\lambda_{2}-\lambda_{1}\right) \tag{15}\]  
**end**  
where \(\alpha_{k}\) is an appropriate step size. At every step of this scheme, we have feasible solutions to the original problem.
## 2 Algorithms
### Traditional PSO  
In the traditional PSO algorithm, each individual has a velocity and position update method [15]  
\[\vec{v}_{t+1}=c_{0}\cdot\vec{v}_{t}+c_{1}\cdot rand(0,1)\cdot(\vec{p}_{\text {global}}-\vec{p}_{t})+c_{2}\cdot rand(0,1)\cdot(\vec{p}_{\text{local}}-\vec{p} _{t}), \tag{3}\]\[\vec{p}_{t+1}=\vec{p}_{t}+\vec{v}_{t+1}, \tag{4}\]  
respectively, where \(c_{0}\) is the momentum constant, and \(c_{1}\) and \(c_{2}\) are the global and local attraction strengths. \(rand(0,1)\) is a randomly generated value from a uniform distribution within the range \([0,1)\). \(rand(0,1)\) varies the degree of attraction to the respective best value every iteration up to \(c_{1}\) and \(c_{2}\), respectively. The parameter labels have been changed for ease of equating to data arrays in code. This is applied to all later algorithms as well, however, these parameters are entirely independent.  
Velocity and position vectors are subject to the dimension-wise limitation  
\[v_{t+1}=\begin{cases}v_{t+1}&\text{if }p_{\text{max}}>p_{t+1}>p_{\text{min}},\\ -v_{t+1}&\text{otherwise},\end{cases} \tag{5}\]  
and  
\[p_{t+1}=\begin{cases}p_{t+1}&\text{if }p_{\text{max}}>p_{t+1}>p_{\text{min}},\\ p_{\text{min}}&\text{if }p_{t+1}\leq p_{\text{min}},\\ p_{\text{max}}&\text{if }p_{\text{max}}\leq p_{t+1}.\end{cases} \tag{6}\]  
Allowing particles to bounce off the upper and lower limits maintains a degree of activity, preventing early termination of exploration due to boundary collisions. Additionally, given that the position is stopped at the respective limit, a measure of performance at the said limit will still be obtained. The general process flow for PSO is shown in Figure 1a. The velocity calculation time complexity is \(O(5D\times N)\), position updates are \(O(D\times N)\), and each dimension limit check is \(O(D\times N)\), resulting in a total time complexity per iteration of \(O(8D\times N)\).
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 2 Framework
### Uncertainty Budget  
We adapt to LST data a theoretical approach for surface temperature uncertainties first applied to SST [30; 31]. This approach has gained much traction, both in the surface temperature community and the wider satellite climate community [18]. It is for example, being adopted in multiple projects: ESA SST CCI ([[http://www.esa-sst-cci.org/](http://www.esa-sst-cci.org/)]([http://www.esa-sst-cci.org/](http://www.esa-sst-cci.org/))); H2020 EUSTACE ([[https://www.eustaceproject.eu/](https://www.eustaceproject.eu/)]([https://www.eustaceproject.eu/](https://www.eustaceproject.eu/))); and ESA DUE GlobTemperature ([[http://www.globtemperature.info/](http://www.globtemperature.info/)]([http://www.globtemperature.info/](http://www.globtemperature.info/))). Each of these projects conforms to a standard uncertainty model for surface temperature across different domains. A simplified version of this approach was first applied to the Advanced Along Track Scanning Radiometer (AATSR) instrument [1]. Here we present the full LST implementation of this uncertainty model, using MODIS as an example case.  
In general three components of uncertainty are provided for each pixel; each component represents the uncertainty due to effects whose errors have distinct correlation properties:  
1. Random (also called uncorrelated), for which there is no correlation of error components between pixels;
2. Locally correlated (also called structured random), for which there is a correlation of error components between pixels that are within the correlation length of the effect;
3. (Large-scale) systematic, for which there is a correlation of error components between all pixels.  
This three-component model applies to all satellite processing levels (Level 1 through to Level 3 and above).
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 8 Conclusions  
We presented a formal approach to analyse time metrics for workflow systems at the level of traces. Our objective is to compute the randomised time metrics of trace components, which represent subsets of workflow traces that satisfy specific temporal patterns. In the model building of our approach, we create compact trace profiles from the workflow specification and the workflow execution data summaries, define trace components using DFA, and build SMC models from the profiles and DFA. Our core computation method is an efficient quantitative verification technique that produces the moments of FPT in an SMC. We demonstrated the applicability of our approach to create a monitoring plan in real-world workflow systems such as AWS Step Functions. The computation time evaluation shows the efficiency of our approach to compute high-order FPT moments for large-workflow instances. In particular, our approach can compute up to the fourth moment for a workflow model with 10,000 states within 70 s.  
In future work, we plan to apply our approach to other real-world workflow systems. We also plan to establish an extension to the current approach to analyse not only time metrics but also other commons QoS metrics, such as reliability and availability. Also, despite its expressive power, encoding temporal properties in DFA is at a very low level. To improve the usability of our approach, we can introduce a high-level expressive notation (such as a notation based on the Linear Temporal Logic) to specify trace components. Finally, as mentioned in Section 5.3, in practice, we usually do not know the exact forms of the probability distributions for time metrics. In order to infer quantiles from moments in the absence of distribution functions with known forms, we plan to develop more advanced means of statistical inference than the standard method of moments for our approach.  
Conceptualization, G.S. and L.L.; methodology, G.S.; software, G.S.; validation, G.S. and L.L.; formal analysis, G.S.; writing--original draft preparation, G.S.; writing--review and editing G.S. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Experiment data and implementation is available by contacting the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 3 Methodology
### Feature Extraction  
For video frames (Image Input) of size 224 \(\times\) 224 with 3 channels, the swin transformer [29] was used to extract feature vectors from the preprocessed data of the MSAD dataset, which is illustrated in Figure 2.  
Here, swin transformers were used for leveraging their hierarchical attention mechanism to capture both local and global spatial patterns. During the forward pass, the model processed the input images, and feature maps were extracted from intermediate layers, providing rich representations for anomaly detection. For mathematical interpretation of input image I, the feature extraction process using Swin-S can be described as:  
\[F=\mathrm{Swin}S(I), \tag{5}\]  
Figure 2: Illustration of feature extraction using swin transformer [29].  
where \(I\) is the input image of size 224 \(\times\) 224 \(\times\) 3 and F is the feature vector whose output feature shape is 768 dimensional vectors (feature vectors). In patch embeddings, the image \(I\) is divided into patches of size 4 \(\times\) 4, and each patch is embedded into a patch token \(T\) as  
\[T=W_{\rm E}I+\rm b_{E}, \tag{6}\]  
where \(W_{\rm E}\) is the learnable embedding weight and \(\rm b_{E}\) is the bias. Then, for hierarchical feature representation, the swin transformer applies Shifted Windows Multi-Head Self-Attention (SW-MSA) in hierarchical stages using:  
\[F(\rm I)=SW\text{-}MSA(MLP(F^{(l-1)})), \tag{7}\]  
where \(F^{(l)}\) is the feature map at layer 1 and SW-MSA is the shifted window-based multi-head self-attention. Then, feature extraction at final layer was performed, where final feature representation F can be taken from the last stage of the swin transformer using:  
\[F=P\rm{ooling}(F^{(l)}), \tag{8}\]  
where L is the last layer and the pooling operation reduces spatial dimensions to a feature vector.  
Finally, the input image 224 \(\times\) 224 \(\times\) 3 was given to the swin transformer, and 768 dimensional vectors as feature vectors were obtained. These embeddings then served as inputs to the anomaly detector model, allowing for effective anomaly detection.
## 3 Multi-Adversarial Joint Distribution Adaptation Network
### Feature Extractor  
The feature extractor of MAJDAN is a 1 DLCNN design based on MobileNet V2, which saves computational costs compared with the standard 1D CNN, and also has good feature extraction capabilities. In the first layer, standard convolution is applied to process the input data. Considering the length of the data, a 46 \(\times\) 1 wide convolutional kernel was chosen to give the model a larger receptive field, capturing features over a broader range and simultaneously reducing the data's length. In subsequent structures, depthwise separable convolution replaces standard convolution to reduce computational costs. The parameters of the feature extractor were determined through empirical values and manual tuning. At the end of the network, global average pooling is used to reduce the dimensionality of the feature maps. Table 1 shows the parameters of 1 DLCNN.  
Figure 4: MAJDAN model structure.  
In our model, the input of the feature extractor \(G_{f}\) is the one-dimensional vibration signal of the source domain and the target domain. During the training phase, data from the two domains are fed into the model in mini-batches. The inputs from the source and target domains are represented as \(x^{s}\) and \(x^{t}\), respectively. Their corresponding output features are denoted as \(F^{s}=G_{f}\Big{(}x^{s};\theta_{f}\Big{)}\) and \(F^{t}=G_{f}\Big{(}x^{t};\theta_{f}\Big{)}\).
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 4 Experiments and Results
### QBR with Reservation Experimental Results
As observed from the figures, the distortion caused by the packet loss affected the measured MOS, where the values achieved without the VQoSRR were significantly lower than those measured compared to the VQoS-R and VQoS-RR. Take, for instance, Figure 12a: on average, MOS without the VQoSRR was about 40% of 5 compared to 61.3% for VQoS-R, and 76% for VQoS-RR, respectively. In addition, Figure 12b presents a similar pattern for HD, where VQoS-RR scored 97.3%, and VQoS-R was 62.7, compared to No_VQoSRR, which achieved 41.3%. Figure 13 shows the MOS average values calculated to measure the quality difference between the three test cases. The MOS measured for No_VQoSRR reached 1.6 for the best effort video, and 2 for SD and 2.1 for HD, which all these values indicate a poor quality video. In terms of VQoS-R, BE MOS was 1.8 (poor quality), and SD MOS increased from 2 (poor quality) to 3 (fair quality), as well as an HD improvement from 2.1 (poor quality) to 3.1 (fair quality). While in VQoS-RR, the MOS remained at 1.6 (poor quality) for BE,  
Figure 12: The MOS values of the tested videos. (**a**) SD resolution; (**b**) HD resolution. Figure 13: The average MOS value of the tested videos. 3.8 (fair to good quality) for SD, and 4.8 (good to excellent) for HD. As we can see from the figure, the VQoS-RR improved the video quality; for example, in SD, the measured MOS increased from 40.0% for No_VQoSRR to 76.0% for VQoS-RR, which implies a 36% increase. While in HD, the measured MOS increased from 41.3% for No_VQoSRR to 97.3% for VQoS-RR, which implies a 56% increase. Therefore, not using the VQoSRR will result in low MOS, which means severe video streaming quality degradation. Additionally, this result indicates that the proposed system succeeded in achieving good user perception. Figure 14a-c represent the correlation of the results obtained from the subjective tests against those obtained from the objective tests for the best effort (BE) video, SD video, and HD video, respectively. In this comparison, we mapped the values of objective SSIM metrics to an equivalent MOS scale (objective MOS). Figure 14: _Cont._  
In Figure 14a, the subjective MOS and objective MOS for BE traffic with and without VQoSRR were slightly correlated, with subjective MOS not exceeding 1.7 and objective MOS reaching around 3. Figure 14b shows that SD in the case of VQoSRR had highly correlated MOS and SSIM results, whereas MOS without VQoSRR had a low quality compared to the objective MOS with a fair quality. Finally, the results of the HD video in Figure 14c demonstrated that there was a poor approximation between SSIM and subjective MOS in both test cases. With VQoSRR, subjective MOS scored higher than objective MOS, while without it, the opposite happened. Further, when comparing this result of HD to SD in Figure 14a, we can see that subjective QoE tests had higher MOS ratings in HD, which reflects that most users prefer watching videos at a high resolution.
## 2 Materials and Methods
### Fourier-Transform Infrared (FTIR) Analysis  
To assess ITB's compatibility with the stabilizers and polymers used to prepare nanosponges, FTIR analysis was carried out. Potassium bromide (KBr) was separately mixed with ITB and optimized IBNS4 before being compressed into a disc and scanned from 4000 to 400 cm\({}^{-1}\) to identify the fingerprint region of ITB in IBNS4 (Jasco 4600 Mid-IR FTIR spectrometer, Tokyo, Japan). FTIR is one of the reliable, feasible, and economical techniques for qualitative analysis; the intensity and peak positions directly measure the nature and interactions between the materials [29; 30].
###### Abstract  
This study aimed to estimate the radiofrequency exposure levels in the vicinity of nursery and primary schools at the northwest part of Crete island in Greece. Moreover, the compliance with the exposure limits, according to Greek legislation, was investigated. A total of 396 in situ frequency-selective and broadband measurements were conducted around 69 schools, classified in urban and suburban environments, in the range of 27-3000 MHz (subdivided in seven frequency bands). The measured value of the electric field strength (V/m) was recorded and, subsequently, the exposure ratio was calculated. Statistical analysis was performed in order to analyze and evaluate the data. In addition, a worst-case scenario was examined by considering the highest measured exposure level around each school. The statistical tests indicated that the mean and median values of the exposure ratio, even in the worst-case scenario, were found well below 1 for all frequency bands. The calculated distributions of the electric field measurements demonstrated that almost 90% of the latter were below 1 V/m, with the majority of values lying in the range of 0.5-1 V/m. The main contributors to the total exposure were the mobile communication frequencies and broadcasting, while the exposure was greater in urban than in suburban environments.  
electromagnetic field measurements; exposure assessment; exposure ratio; general public exposure; in situ measurements; nonionizing electromagnetic fields +
Footnote †: journal: _sciences_  
_Applied_  
_sciences_
## 2 Material and Methods
### Sample Collection and Pre-Treatment  
The winemaking waste, consisting of seeds, skins and stalks, was supplied from a local supplier, dried, and stored at \(-20\)\({}^{\circ}\)C until before use. Extraction of quercetin and trans-resveratrol was carried out through the following procedure. A 0.5 g portion of sample was weighed into a screw-capped glass tube. The sample was crushed, pressed further, and extracted with 1 mL of ethanol by ultrasonication (AU-32, Argo Lab, Carpi, Italy) for 15 min and shaking for 24 h at room temperature. After centrifugation at 5000\(\times\) g, the remaining pellet was re-extracted for 24 h using 1 mL of fresh extraction solvent. The procedure was repeated 3 more times. The extracts were combined and 1 mL was sealed in a 15 mL vial filled with a 6.6% ethanol/10% sodium chloride solution at pH 2.3, immediately subjected to SPME using the polyacrylate fiber for 1 h at room temperature under magnetic stirring, and eventually desorbed into the SPME interface. Each analysis was conducted in triplicate.