esa-sceva/satcom-chunk-collection huggingface dataset:
# Weather Radar in Complex Orography
## 2 Practical Applications, Needs and Requirements
### Radar System Requirements
The question has to be put in the context of a specific application, taking into account all the different types of uncertainties involved in the end-to-end chain and also considering the sensitivity of the application to errors. The cost-loss ratio is one of many ways of doing this. 8. Information on uncertainties: In modern automatic quantitative applications, it is common practice to take into account information about the uncertainty of the radar product. This is a broad issue, and the requirement depends on the application. For instance, radar rainfall maps and nowcasts need to come as ensembles for hydrological runoff modelling in steep Alpine catchments, in order to deal appropriately with the uncertainties and their space-time covariances in the hydrological model. 9. Vertical scanning: It is essential, for the severity ranking of thunderstorms and the identification of hail, to scan from the ground up to the top of the troposphere with a sufficiently high vertical resolution. Small increments of the elevation angle between successive scans at the lowest sweeps help to improve quantitative precipitation estimates over complex terrain. 10. Continuity: The user expects a certain degree of spatiotemporal continuity in radar products. Sharp artificial discontinuities and artefacts irritate users and may lead to errors in the algorithms that use radar data as input. 11. Availability: On the basis of the users' requirements, radar operators typically aim at an availability of radar data above 99%. 12. Traceability: It is important, in the context of warnings and air traffic control, to record any changes in hardware, software and configuration, to document maintenance work, to archive all calibration values and hardware status parameters and to use calibrated maintenance tools. 13. Product redundancy: It is necessary to run different algorithms in parallel for the same type of product to cope with the weaknesses of a single method and to have redundancy when one of the algorithms is upgraded or does not perform well for various reasons. Dual-polarisation QPE approaches, for instance, result in a higher accuracy, but are more sensitive to small hardware instabilities than a single-polarisation algorithm. It is therefore recommended to run both in parallel. 14. Innovation and verification: Both are essential to be able to satisfy the continuously increasing demands of the users. The information given here corresponds to the situation in the European Alps but can be different in other mountainous regions. The requirements are considered as guidelines for the design, operation and innovation activities of a radar network.
# LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
## 2 Related Work
### Anomaly Detection
[PERSON] et al., argued that it is impractical to build log anomaly detectors to apply to all types of software systems [22], but there are similar aspects among log types of different software systems; they used migration learning to transfer knowledge on the source system to the target system. [PERSON] et al., proposed a BERT-based log anomaly detection method LogBERT [23], which learned the patterns of normal log sequences via two self-supervised training tasks and was able to detect anomalies that deviate from normal log sequences. [PERSON] et al., proposed an unsupervised log sequence anomaly detection network LSADNET based on local information extraction and a global sparse transformer model [24]. Multilayer convolution was applied to capture local correlations between adjacent logs, and a transformer was used to learn global dependencies between distant logs. The shortcomings of these studies are as follows:  
1. Log formats are constantly changing, yet most methods assumed that log templates do not change. The Word2 Vec method was used in most current studies to represent log templates. Word2 Vec is a method to convert words into vectors. Using shallow neural network training, words can be converted into vector form. However, Word2 Vec is a static word embedding method, and the word vectors of corresponding words are fixed without considering the contextual background, thus extracting no semantic information; it is unable to represent the similarity between different templates with the same or similar semantics, as well as effectively solve the problem of multiple meaning of words. 2. When the anomaly detection model is applied to a new system, it takes some time to collect enough logs to train the model, and there is a cold-start problem. In a cross-system setup, the log templates of the two may be completely different yet semantically similar. It is not feasible to use the knowledge of one system to fit the anomaly detection model of the other system. In this paper, we propose the log anomaly detection method LogBD, which uses a pretrained model BERT to extract log semantic information and obtain semantic vectors as features that can better learn the information contained in the logs. Then, a TCN is trained as a feature extractor using a domain adaptation method to extract common features of logs from different systems and build a cross-system log anomaly detection model to detect the source and target anomalies in the system.
## 3 Results
### Quantification of Total Flavonoids  
The linear regression equation was calculated and a good linear relationship was obtained between the standard concentrations (0.05-5 mg/mL) and the absorption of the reaction solution at 750 nm (Table 3), using as standard quercetin calibration. The total flavonoid content of the extracts was expressed as quercetin equivalent (EqQr) in the calibration curve (Scheme 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetable Extract** & **Determinations No.** & **Absorbance** & **EqPir/mL** \\ \hline \multirow{4}{*}{Hydroalcoholic} & 1 & 1.6459 & 17.27 \\  & 2 & 1.6635 & 17.56 \\  & 3 & 1.6589 & 17.48 \\ \cline{2-4}  & & Mean/SD & 17.44 \(\pm\) 0.14 \\ \hline \multirow{4}{*}{Ultrasonicated} & 1 & 1.6349 & 17.10 \\  & 2 & 1.6381 & 17.15 \\ \cline{1-1} \cline{2-4}  & 3 & 1.6225 & 16.90 \\ \cline{1-1} \cline{2-4}  & & Mean/SD & 17.05 \(\pm\) 0.13 \\ \hline \multirow{4}{*}{Aqueous} & 1 & 0.9236 & 5.65 \\  & 2 & 0.9218 & 5.62 \\ \cline{1-1} \cline{2-4}  & 3 & 0.9220 & 5.62 \\ \cline{1-1} \cline{2-4}  & & Mean/SD & 5.63 \(\pm\) 0.01 \\ \hline \end{tabular} SD = Standard deviation.  
\end{table}
Table 2: Results of determinations in Eq Pir/mL of total polyphenol content.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Concentration** & **Absorbance** \\
**mg/mL** & **at 760 nm** \\ \hline
5 & 3.1443 \\
2.5 & 1.6345 \\
1 & 0.8510 \\
0.5 & 0.4407 \\
0.1 & 0.1307 \\
0.05 & 0.0768 \\ \hline \hline \end{tabular} SD = Standard deviation.  
\end{table}
Table 3: Quercetin standard.  
As there are no significant differences in the total phenol content between the hydroalcoholic plant extract and the ultrasonicated hydroalcoholic extract, it was decided to determine the total flavonoid content only for the hydroalcoholic extract.  
Table 4 presents the total flavonoid content of the hydroalcoholic plant extract obtained from _Epilobium parviform_ [PERSON]. which was determined from the regression equation (Scheme 2) of the calibration curve, ranging from 2.84 to 2.9 mg EqQr/g plant powder with an arithmetic mean of 2.87 mg EqQr/g vegetable powder; the content of the aqueous plant extract was determined from the regression equation (Scheme 2) of the calibration curve, ranging from 0.78 to 0.9 mg EqQr/g plant powder with an arithmetic mean of 0.86 mg EqQr/g vegetable powder. The hydroalcoholic plant extract had the highest flavonoid content with a mean of 2.87 Eq Qr/mL, and the aqueous plant extract had the lowest level with a mean of 0.86 Eq Qr/mL.
## 7 Experimental Verification and Analysis
### Inter-Satellite Link Visibility (from Beidou-3 Constellation to Lunarprobe-1) Analysis  
To demonstrate the feasibility of the proposed method, this subsection will conduct a visibility analysis of the inter-satellite link (from Beidou-3 constellation to lunarprobe-1), during the simulation period (from 24 March 2023 04:00:00.000 to 24 April 2023 04:00:00.000).  
The access time and chain access AER between the interstellar link of Beidou-3 constellation and lunarprobe-1 are illustrated in Figure 14 and Figure 15, respectively. The analysis time of the access time and chain access AER are 20 April 2023 23:06:15 and 21 April 2023 09:48:22.  
Figure 14 shows that for the entire BD-3 constellation (a total of 24 satellites), at least 20 satellites can simultaneously provide inter-satellite link signals to the Earth-Moon space targets, and a single GEO satellite can provide inter-satellite link signals for up to 8.5 h continuously. Meanwhile, Figure 15 confirms that the chain access can be available at up to 73,000 km, with the angle ranging from \(-80^{\circ}\) to \(360^{\circ}\). By comparison, similar work has been conducted in [54; 55] to improve the assigning links of the inter-satellite links of GNSSs for the downlink of telemetry data in time and effective ranging under limited facilities and links, with the same simulation scene of BDS-3, and inter-satellite and satellite-ground visibility matrices were calculated. However, the final results in [54] illustrated that the number of visible satellites varied from 6 to 10, and the ratio of the proposed algorithms was \(<\)50% with more than 9 visible satellites, while [55] showed the number of visible satellites through ISL ranging from 10 to 24, with the maximum number of links being 17, and the average number of links was 9.6319.  
which is presented in Figure 16 and Table 6. It can be seen that the Min Duration is 670.669 s (from 24 March 2023 04:00:00.000 to 24 March 2023 04:11:0.669), while the Max Duration is 620,793.171 s (from 24 March 2023 05:28:02.118 to 31 March 2023 09:54:35.289), and the Mean Duration is 36.14332 h, with a Total Duration of 5.782 days.
# [PERSON]'s Law and Perceptual Features for Face Image Quality Assessment
## 2 Literature Review  
### Benford's Law  
The applications of Benford's law are diverse and can be found in various fields, including the following:  
1. Financial auditing: [PERSON]'s law is used as a tool for detecting anomalies and potential fraud in financial statements, such as identifying irregularities in tax returns, accounting records, or expense reports. Deviations from the expected distribution of leading digits can indicate data manipulation [13].
2. Forensic analysis: [PERSON]'s law is applied in forensic investigations, such as detecting fraudulent transactions, money laundering, or identifying irregularities in large datasets related to economic, social, or scientific data [14, 15].
3. Election fraud detection: [PERSON]'s law has been used to analyze election results, particularly in detecting potential irregularities or fraud. Significant deviations from Benford's law's expected distribution could signal suspicious patterns in the reported vote counts [16].
4. Data quality assessment: [PERSON]'s law serves as a tool to assess the integrity and quality of data, ensuring its accuracy and identifying possible errors or anomalies that may require further investigation or correction [17, 18].
5. Natural phenomena analysis: [PERSON]'s law has found applications in various scientific studies, such as analyzing physical constants, earthquake data, population demographics, stock market data, and more, to determine if the observed data align with the expected distribution [19, 20].  
[PERSON]'s law has also found several interesting applications in image processing. Namely, [PERSON]'s law has been employed as a tool for detecting image forgeries or tampering. When an image is manipulated or altered, the distribution of pixel values may deviate from the natural patterns expected in genuine images. By examining the first digit distribution (FDD) of pixel values in different regions of an image, inconsistencies can be detected. Significant deviations from the expected distribution may indicate potential areas of tampering or manipulation. For instance, [PERSON] et al. [21] elaborated a method for detecting unknown JPEG compression levels in semi-fragile watermarked images by examining FDDs of JPEG coefficients. In contrast, [PERSON] et al. [22] utilized FDDs for identifying multiple JPEG compressions of digital images. Further, the authors proved that the proposed method exhibits robustness against scaling and rotation. Similarly, [PERSON] et al. [23] elaborated a method for multiple JPEG compression identification in digital images, but Benford-Fourier analysis was applied. Later, the same authors gave a similar solution for the detection of previous JPEG compression [24]. In [25], the authors empirically corroborated that differentiating between contrast-enhanced and unaltered images is possible by applying Benford's law-inspired features. Similarly, [PERSON] et al. [26] pointed out that [PERSON]'s law-inspired features extracted from discrete cosine transform (DCT) coefficients are suitable for differentiating between morphed and unaltered face images.
## 4 Retrofitted Results
### Energy Results
#### 4.1.4 Renewable Energy and Total Energy Consumption  
The total energy consumption provided by the BEC software in Table 16 reveals that in the base case, the net energy consumption is lower than that of the reference building according to the standard. However, the gap between the standard allowance and net power consumption is considered to be small when the building energy code is revised in stricter terms. In the retrofitted case, the building energy consumption was reduced significantly with respect to the base case by approximately 41% for the envelope, lighting, and air conditioning systems. Concerning renewable energy, the BEC software outputted that the installation of a 70-kWp PV system can generate electricity for usage in buildings up to 87,000 kWh/year. This generating capacity can directly reduce the building energy consumption, resulting in further reduction in the evaluated consumption.
## 3 Communication Performance
### Channels  
Examples of channel impulse responses (CIRs) are shown as a function of geotime in the left panel of Figure 5 for the transmissions from the source to Rx# 1 during the five transmissions. The CIRs are unique because of the signals' different times and locations. A snapshot of the CIRs for each transmission at the five locations is shown in the right panel of Figure 5. The approximately 20 ms of delay spread (less than 31.5 ms) in the channels corresponds to intersymbol interference of about 80 symbols at the symbol rate of R = 1/T = 4000 symbols/s. The DJK16 channels varied slowly with time over 2.5 s and exhibits sparse characteristics. We used an IPNLMS algorithm in the analysis because it can yield robust estimates for sparse UWA channels.  
\begin{table}
\begin{tabular}{c c} \hline \hline NFFT size & 512 \\ Number of data symbol & 386 \\ PN length & 126 \\ Bandwidth & 4 kHz \\ PN duration & 31 ms \\ Symbol duration including PN & 128 ms \\ Uncoded data rate (QPSK) & 5580 bps \\ Uncoded spectral efficiency (QPSK) & 1.5 bits/s/Hz \\ Coded data rate (QPSK) & 2790 bps \\ Coded spectral efficiency (QPSK) & 0.70 bits/s/Hz \\ Convolution code & (171, 133) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Single carrier with frequency domain equalization communication system specifications.  
Figure 4: Transmitted signal structure.  
The received SNRs of the five transmissions ranged from 4 dB to 11 dB as shown in Figure 6a. The SNR of the fifth transmission was the lowest, about 4 dB. The estimated SNRs of the bottom receiver element (Rx #8) were slightly higher than the SNRs for the top element (Rx #1).  
Figure 5: Examples of estimated CIRs for the DJK16 data at five transmissions. The left panel shows CIRs varies as a function of geotime, the right panel show a snapshot of the CIRs. (**a,b**) shows the CIRs from 5.2 km; (**c,d**) shows the CIRs from 4.98 km; (**e,f**) shows the CIRs from 5.09 km; (**g,h**) shows the CIRs from 5.18 km; and (**i,j**) shows the CIRs from 5.0 km, respectively.  
The estimated transmitter drifting speed (Doppler estimation) for the five transmissions is shown in Figure 6b. The drifting speed ranged from \(-0.32\) knots to \(0.05\) knots, reflecting the Doppler shift from \(-0.64\) Hz to \(0.01\) Hz at the center frequency of \(6000\) Hz.
## 4 Simulations and Analyses
This suggests that the identified optimal solution is not trapped to a local optimum. Figure 12c shows the curve of the proposed Meta-PPO algorithm's value loss varying with the number of episodes. In the reinforcement learning process, the value function can be used to predict the expected return of a given state. In our proposed Meta-PPO algorithm, a Critic network is employed to estimate this value loss, which represents the disparity between the Critic network's predictions and the actual returns. A greater value loss indicates a greater deviation between the Critic network's predictions and the actual obtained rewards. If this loss keeps increasing, it may indicate that the Critic network is not learning effectively or that the learning rate is not fitting its purpose. As can be seen from Figure 12c, the use of the Meta-PPO algorithm initially results in a high initial value loss, which is because of the neural network with random initialization for value function estimation used in the algorithm. As the episode approaches 100, there's a noticeable drop in the value loss, reaching \(2\times 10^{11}\). This sharp decline is then followed by small  
Figure 11: Comparison of different MAML gradient learning update rates. Figure 12: Meta-PPO algorithm’s convergence curves of different parameters. (**a**) Meta-PPO policy action losses curve; (**b**) Meta-PPO policy entropy curve; (**c**) Meta-PPO value-losses curve. variations. These changes suggest that the value function estimator is gradually becoming more accurate in predicting actual returns. In summary, the Meta-PPO algorithm demonstrates a rapid and efficient learning trend in the initial learning phase, quickly mastering the optimal strategy, and then undergoing fine-tuning and stabilization in subsequent learning. Meanwhile, although DQN can eventually find an effective strategy, it requires a longer time to achieve similar performance. Under the current system model, the Meta-PPO algorithm achieves the maximum normalized throughput when using the clipping ratio \(r(\theta)=0.2\) and the MAML gradient learning update rate \(\alpha=0.1\) as parameters. This curve change reflects the effectiveness and robustness of the two algorithms and further emphasizes the superior performance of Meta-PPO.
# An Architecture for Reliable Transportation of Delicate Goods
## 2 Market Analysis
The BeeBeacons communicate, using Bluetooth Low Energy (BLE) [23; 24], with a mobile hotspot, a gateway for the BLE devices, that sends data to the cloud (Roambee cloud) using a 3G/4G network. The information is updated every 15 min. Besides collecting data periodically, the BeeBeacons can trigger alert notifications whenever the conditions are out of bounds. Normally the gateway is fixed on the warehouse or on the transport vehicle and is the one that contains the GPS function. The devices are attached to the merchandise but can also be used at fixed positions, like warehouses or refrigerators. If the cellular network is not available, the BeeBeacons record the data on the local storage until they get access to a cellular network. Roambee configured the BeeBeacons to continuously advertise, allowing to read the data directly from a mobile terminal without the need to activate the BeeBeacon to put it in advertising mode. The mobile terminal also behaves as a gateway to forward the data to the cloud. Customers can get reports and have access to dashboards using the Roambee Cloud Portal. As an architectural solution, Roambee is the closest solution to the one presented in this paper, but the goals, priorities, target market and technical options are quite distinct. Roambee emphasizes the access to the data in real-time and the relationship between time and geographical location, thus being more oriented to logistics management. The structural requirements and technical options were designed with a focus on the customer,which is the logistic operator, and considering that the distances and transport times are not very long. There is no specific concern with the reliability of the data and other issues, like the autonomy of the devices, or investment and operating costs. It doesn't mean that it is worse, but it was conceived for another situation and goals.
# Model Parallelism Optimization for CNN FPGA Accelerator
## 2 Background and Related Works  
### ShuffleNet  
_ShuffleNet_ is an efficient CNN structure specifically designed for low-end devices such as mobile devices [9]. In order to significantly reduce the computation cost while maintaining accuracy, this new structure utilizes two operations: pointwise group convolution and channel shuffle. Experiments on ImageNet classification and MS COCO object detection illustrated that _ShuffleNet_ has superior performance compared to other structures, e.g., presenting a lower top-1 error (7.8%) than the recent MobileNet structure at a computation performance of 40 MFLOPs [10].  
The _ShuffleNet_ used in this paper consisted of one independent convolutional layer (CL), followed by 16 blocks, ending with a fully connected layer (FC). In addition, there were three max-pooling layers with a step size of 2 and an average pooling layer. The final Softmax layer output a vector of 1000 elements, representing 1000 possible image classes. The _ShuffleNet_ architecture is shown in Table 1.
## Appendix A.2.1 Architecture  
As shown in Figure 11, in UMTS cellular networks, devices are usually referred to as user equipment (UEs). From 3G onwards, the mobile equipment (ME) is usually endowed with a universal SIM (USIM), an advanced SIM which is able to handle several mini applications and has much more memory.  
The UMTS terrestrial radio access network (UTRAN) is partitioned into several radio network subsystems (RNSs). Its organization is similar to that of GSM networks. Each RNS consists of a set of radio elements, called base stations (BSs) or simply NodeBs, and their corresponding controlling elements, called radio network controllers (RNCs).  
The latter part of the architecture, called the core network (CN), covers all the network elements needed for switching and subscriber control.
# Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer
## 4 A Casebook Study
### Results of the Simulation
#### 4.4.2 Hydraulic Head  
The results of hydraulic conductivity were substituted into the finite element model for positive analysis to verify the reasonableness of this simulation. The simulated values of the hydraulic head and leakage volume at the monitoring points were obtained and compared with the corresponding measurements. Absolute and relative errors of hydraulic head and leakage were calculated. A contour of the hydraulic head in the dam site area was also predicted.  
The mathematical expression for the relative error of the hydraulic head is given in Equation (14):  
\[\delta_{H}=\frac{|H_{i}-H|}{\Delta H}\times 100\% \tag{14}\]  
where \(\delta_{H}\) is the relative error of the hydraulic head; \(H_{i}\) and \(H\) represent the simulated and measured values of the hydraulic head, respectively. \(\Delta H\) denotes the difference in water level between the upstream and downstream sides, taken as 148 m.  
Table 5 compares the calculated and measured hydraulic head values at the monitoring points. The calculated values at the monitoring points are relatively close to the measurements. Among them, the simulated values at the piezometers \(P_{DB-24}-P_{DB-27}\) show a very high consistency with the corresponding measurements. The maximum value of the absolute error \(-1.41\) m, and the maximum value of the relative error is 0.95%. Meanwhile, the value of the hydraulic head decreases with the increase of seepage distance.