esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 5 Summary and Future Directions
### Future Directions
Traditional consensus models like Proof of Work (PoW) or Proof of Stake (PoS) may need to be adapted or supplemented with new protocols that can support secure, verifiable deletion of data across decentralized networks while maintaining system stability. This could involve the introduction of more flexible data management frameworks or hybrid solutions that allow for controlled, reversible actions on certain data points without undermining the overall structure of the blockchain. In addition, attention should be given to how these privacy-focused optimization techniques can be integrated into existing blockchain applications across industries such as finance, healthcare, and supply chain management, where data privacy and compliance are paramount. By innovating in this area, blockchain systems could remain at the forefront of both technological advancement and regulatory compliance, facilitating their widespread adoption in industries that require stringent data privacy standards. * The integration of smart contracts and data storage optimization The application of smart contracts in blockchain is becoming increasingly widespread, but their data storage poses numerous challenges. Future research could explore ways to optimize the storage structure and execution methods of smart contracts to reduce their usage of blockchain storage resources. For instance, off-chain computation and storage technologies could be introduced to alleviate storage pressure on smart contracts and improve execution efficiency. Additionally, techniques such as dynamically adjusting storage strategies, data compression, and optimization for smart contracts are also worth further investigation.
## 4 Results and Discussion
### Impact of a Proposed Redarge Dam on the Infiltration of Water on the Tata Alluvial Aquifer  
#### 4.3.1 Site Choice of the Future Recharge Dam  
The mountainous basin of the Wadi Tata has been suffering from the overexploitation of its alluvial aquifer. To address this issue, we strongly advocate for the construction of a recharge dam upstream of Tata city. This dam would not only minimize the high risk of flooding but also replenish the alluvial aquifer downstream. Ten potential sites for the dam's construction were selected based on hydraulic calculations and bank topography, as illustrated in Figure 8a. The location of the dam was chosen based on the grain size analysis performed on 10 samples of alluvial deposits, where the proportion of fine particles (clays and silts) was low (\(<\)7%), which facilitates the infiltration of floodwaters into these formations. This dam is equipped with a discharge pipe of 800 mm in diameter. This allows a continuous flow rate of 4.3 m\({}^{3}\)/s to be evacuated. To estimate the alluvium's capacity to infiltrate and store water volumes, we used the geoelectric exploration method, as shown in Figure 8b.  
The sizing of the forthcoming dam was conducted through the utilization of the ECRET software. By utilizing the hydraulic continuity equation, the balance of volumes at the inlet and outlet of the dam can be modeled during a specific time interval. This enables the determination of the water volume and level reached within the dam, as well as the requisite duration and corresponding volumes at the dam's valve outlet. Estimation of flooding at a 200-year return period was achieved through statistical adjustment of the maximum flows recorded at the Kasbat Zolite station.  
Consequently, the maximum reservoir height of 9 m (775 m) (Figure 8d), based on the yield flood of a 200-year return period, permits storage of 1,620,000 m\({}^{3}\), occupying an area of 812,000 m\({}^{2}\) (Figure 8a).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Groundwater Recharge (L/s)** & \\ \hline Infiltration of rainwater at the plain & 38.5 \\ \hline Recharge from the edges & 160 \\ \hline Infiltration from surface water & 45.9 \\ \hline Reinfiltration of irrigation water taken from groundwater & 22.04 \\ \hline Total inflow rates into the alluvial aquifer & 277.44 \\ \hline
**Groundwater Discharge (L/s)** & \\ \hline Agricultural withdrawals and Drinking Water Supply & 245.4 \\ \hline Water evaporation at the level of the foam & 25 \\ \hline Downstream flow at the southern limit of the aquifer & 16 \\ \hline Total outflow rates from the alluvial aquifer & 286.4 \\ \hline Water balance of the alluvial aquifer & \(-\)8.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Alluvial groundwater balances in Tata watershed.
## 4 Damage Identification of Beam-like Structures Considering Shear Deformation
### Example 1: Simply Supported Beam with Three Elements
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Beam Theory**} & \multirow{2}{*}{**Iterations**} & **Element 1** & **Element 2** & **Element 3** \\  & & & **(mm)** & **(mm)** & **(mm)** \\ \hline \multirow{2}{*}{1} & [PERSON] & 13 & 0.80 & / & / \\  & [PERSON]–[PERSON] & 8 & 0.89 & / & / \\  & [PERSON] & 12 & / & 1.20 & / \\  & [PERSON]–[PERSON] & 12 & / & 1.21 & / \\  & [PERSON] & 7 & / & / & 1.00 \\  & [PERSON]–[PERSON] & 11 & / & / & 1.08 \\  & [PERSON] & 20 & 0.80 & 1.20 & / \\  & [PERSON]–[PERSON] & 17 & 0.83 & 1.21 & / \\  & [PERSON] & 14 & 0.80 & / & 1.00 \\  & [PERSON]–[PERSON] & 16 & 0.86 & / & 1.06 \\  & [PERSON] & 17 & / & 1.20 & 1.00 \\  & [PERSON]–[PERSON] & 14 & / & 1.21 & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identification results of the simply supported beam with a wide flange cross-section. Similar to previous findings, the data in Table 4 leads to the following conclusions: The damage identification results obtained based on the Timoshenko beam theory are consistent with the actual values of corrosion depth (Table 2). However, the damage identification results obtained based on the Euler-Bernoulli beam theory seem to have greater errors, especially for the identification of Element 2, where the corrosion depths in Scenarios 2, 4, and 6 are 1.28 mm. To further analyze the obtained data, MRE analysis was conducted on the results obtained based on the Euler-Bernoulli beam theory. Table 5 shows the MRE of the damage identification results obtained based on the Euler-Bernoulli beam theory for two simply supported beams with different cross-sectional shapes. From Table 5, it can be observed that for the simply supported beam with rectangular and wide flange cross-section, the largest MREs are 27.70% and 10.93%, respectively. In conclusion, it can be confirmed that the method based on the Timoshenko beam theory effectively improves the accuracy of damage identification results for simply supported beams with two different cross-sectional shapes.
# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 4 The Experiments
### Analysis of GFETrack
#### 4.2.2 The Speed and Number of Parameters  
To analyze the number of parameters and running speed of the proposed GFETrack, we compared the tracking speed and the number of parameters of the proposed tracker with three similar algorithms, TransT [9], STARK-ST50 [25], and SiamRPN++ [36].  
As illustrated in Table 2, the running speed of GFETrack is measured at 55 fps, surpassing the threshold of 25 fps. This indicates that our proposed method is well-suited for real-time object tracking. Additionally, GFETrack achieves a notable reduction of approximately 20% in the number of parameters compared to TransT. The reduction in parameters is attributed to our innovative FSA module, which employs orthogonal random features to implement fast self-attention within self-attention. This involves decomposing the attention matrix into the product of random non-linear functions of the original query and key. By doing so, the FSA module effectively reduces the model's spatial complexity. This reduction in spatial complexity not only contributes to a more streamlined model but also enhances inference speed. The trade-off between speed and accuracy is a critical consideration in tracking scenarios. In the case of GFETrack, the FSA module achieves a delicate balance by reducing the number of parameters without compromising tracking accuracy. Orthogonal random features enable efficient self-attention computations, allowing the model to maintain a high level of accuracy while ensuring rapid inference. Generally, reducing the size and computational complexity of the model will result in an increase in fps, However, for accuracy, it may have the opposite effect. The effect in our article, such as Vot2018, is not very good, and it may also have this impact.
# Development and Research of a Medium Vertical Dipole Electromagnetic Pulse Simulator
## 3 Design and Construction
### Pulsar Design  
The layout of the pulse voltage top feed mode is presented in Figures 1a and 2a. It features a self-developed compact Marx generator with a rated voltage of 300 kV [12; 17] and a peak capacitor [18; 19], both arranged on a movable flatbed car. The car is placed in a tunnel, and its upper surface is flush with the ground, being electrically connected to it through several circularly arranged aluminum plates.  
The equivalent circuit diagram of this mode is illustrated in Figure 4, where \(C_{\mathrm{m}}\) denotes the Marx section capacitance, Rm and \(L_{\mathrm{m}}\) represent the discharging circuit's equivalent resistance and inductance of the Marx generator, \(C_{\mathrm{p}}\) is the peak capacitor, and \(R_{\mathrm{p}}\) and \(L_{\mathrm{p}}\) are the peak circuit's resistance and inductance, respectively. \(R_{\mathrm{L}}\) stands for the load's equivalent impedance, and \(S_{1}\) and \(S_{2}\) denote the Marx section and pulse source output switches, respectively, assuming the values of \(C_{\mathrm{m}}\), \(L_{\mathrm{m}}\), \(R_{\mathrm{m}}\), \(C_{\mathrm{p}}\), \(L_{\mathrm{p}}\), \(R_{\mathrm{p}}\), and \(R_{\mathrm{L}}\) are as follows: \(C_{\mathrm{m}}=0.56\) nF, \(L_{\mathrm{m}}=1.2\) uH, \(R_{\mathrm{m}}=10\)\(\Omega\), \(C_{\mathrm{p}}=160\) pF, \(L_{\mathrm{p}}=150\) nH, \(R_{\mathrm{p}}=10\)\(\Omega\), and \(R_{\mathrm{L}}=50\)\(\Omega\). When \(S_{1}\) is turned on and, approximately 20 ns later, \(S_{2}\) experiences a breakdown, the output voltage waveform of the pulse source is depicted in Figure 4b, and the circuit successfully achieves an ideal double exponential waveform with a rise time of about 2.5 ns and a half-width of approximately 25 ns.  
The pulse voltage bottom feed mode is illustrated in Figures 1b and 2b. The pulser used in this mode is a solid-state 6-stage Marx generator based on the avalanche triode [20], consisting of charging isolation resistors, main capacitors, and avalanche triodes. All components are patch components integrated on a PCB board with a size of 130 mm \(\times\) 44 mm.  
Figure 4: Equivalent circuit and output waveforms of the pulse source based on one-stage pulse compression. (**a**) Equivalent circuit of one-stage pulse compression. (**b**) Simulated output voltage waveforms.  
Figure 3: Top view of ground layout of the developed dipole EMP simulator.  
The output waveform of the pulse source conforms to the double exponential pulse with a rise time of 2.5 \(\pm\) 0.5 ns and a half-width of 23 \(\pm\) 5 ns, meeting the specifications of the IEC standard, with a maximum output amplitude of 2.95 kV and an output impedance of 50 \(\Omega\). The entire setup is positioned in a tunnel underneath the ground directly beneath the antenna, with the upper end of the tunnel covered with an aluminum plate and connected to the ground. The output voltage pulse is fed into the antenna through the ground plate after being transmitted by a 10-m-long cable with an impedance of 50 \(\Omega\).
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 3 Problem Formulation
### Multi-Hop Problem Formulation
#### 3.2.2 Coverage Constraint  
Each grid place must be covered by at least one deployed node or one donor.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **One-hop and Multi-hop Formulation Parameters** \\ \hline i & Pre-deployment donor location \\ \hline j & Number of potential nodes locations \\ \hline k & Number of grids needing to be covered \\ \hline \(R_{o}\) & Overhead or required overhead \\ \hline I & Set of all donor locations \\ \hline J & Set of all potential nodes locations \\ \hline K & Set of the locations on the grid that need to be covered \\ \hline U & Set of the active users in the coverage of a donor or a node \\ \hline \(\alpha_{j}\) & Indicates whether the candidate location is chosen to deploy node \\ \hline \(C_{ik}\) & Indicates whether grid \(k\in K\) can be covered when node deployed at location \(i\in I\) \\ \hline \(C_{jk}\) & Indicates whether grid \(k\in K\) can be covered when node deployed at location \(j\in J\) \\ \hline \(Y_{ij}\) & Indicates whether donor i can provide backhaul when node deploys in j or a donor deployed at \(i\in I\) can provide backhaul to a node located at \(j\in J\) \\ \hline \(Y^{\prime}_{jn}\) & Indicates whether node at \(j\in J\) can provide backhaul to the other node located at \(n\in J\) \\ \hline \(Y^{\prime}_{nm}\) & Indicates whether node at \(n\in J\) can provide backhaul to the other node located at \(m\in J\) \\ \hline \(d_{ij}/d_{pq}\) & Distance between two candidate nodes or two nodes \(p\) and \(q\), where \(p,q\in I\bigcup J\bigcup U\) \\ \hline \(R_{ij}/R_{pq}\) & Data rate between donor i and node j or between two nodes \(p\) and \(q\), where \(p,q\in I\bigcup J\bigcup U\) \\ \hline \(A_{i}/A_{j}/A_{x}\) & Access data rate when donor/node is deployed in i/j or access data rate of a node or a donor \\ \hline \(F_{i}\) & Fixed data rate in of a donor \(i\in I\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: One-hop and multi-hop formulation parameters.  
\[\sum_{j\in J}C_{jk}\alpha_{j}+\sum_{i\in I}C_{ik}\geq 1,\forall k\in K \tag{16}\]
# A Statistical Approach to Discovering Process Regime Shifts and Their Determinants
## 4 Case Studies
### Case 2: Gasoline Price and Impact of COVID-19  
The second case involves the _process_ of _crude oil spot price generation_. This process is generated from a _complex socio-economic system_ involving oil producers, transporters, governments, end-users and several other factors as its components or things. It also includes a complex interaction between these things, which may be in the form of politics, trade and conflicts. The process is socially and economically complex, in which many identified or unidentified process determinants lead to unclear system boundaries and its environment. For now, we treat this system as a blackbox, in which the main output or dependent variable of interest is the weekly WTI oil spot prices. The event log analyzed is for the period of 1 January 2015 to 31 December 2021 (Figure 10), which is obtained via the U.S. Energy Information Administration website ([[https://www.eia.gov/](https://www.eia.gov/)]([https://www.eia.gov/](https://www.eia.gov/)) accessed on 1 February 2022).  
The aim of this mining exercise is twofold. First, we seek to identify all regime shifts. Second, we aim to evaluate the role of COVID-19 deaths in the oil price formation out of many determinants. As the planning horizon is long, we can safely assume several time-dependent determinants, such as population and industrial growth. Accordingly, the minimal base model used has both the level and the trend components, i.e., we use \(p_{i}^{\prime}=t_{i}\alpha_{i}+\varepsilon_{i}\) as suggested in Equation (2a,b). This model, as we recall, has a proxy time index term to capture any trend in the data. We thus used this minimal base model and tested the price data for regime changes. For brevity, we directly present the graphical results in Figure 11. The results indicate twelve regime shifts during the analysis period, the location and confidence intervals of which are indicated in Table 4.  
Figure 10: WTI Spot Price (Dollars/Barrel).  
Figure 9: Fitted model with determinant variable and no regime change detected.  
To test whether the deaths caused by COVID-19 impacted the formation of any of these breaks, we used the COVID deaths log (Figure 12) obtained from Our World in Data ([[https://ourworldindata.org/](https://ourworldindata.org/)]([https://ourworldindata.org/](https://ourworldindata.org/)) accessed on 1 February 2022). We tested the model again with the added determinant variable for COVID-19 and found one regime shift located at week 262 not recognized by the statistical tests. This week corresponds to the second week of January 2020, in which a sharp rise in deaths started to appear. The refitted model with one less segment is shown in Figure 13. Similarly, other determinants can be tested for regime changes.
# Survey on Videos Data Augmentation for Deep Learning Models
## 3 Review on Video Data Augmentation
### Solving the Reality Gap (Simulation + GAN)  
The reality gap is the subtle discrepancy between reality and simulation that prevents DL models to properly learn from simulated images. One way to alleviate the problem is to exploit the recent advancement in generative adversarial networks. GAN models can be used to refine synthetic images to be visually closer to real ones. Recently, [PERSON] et al. [48] used a similar idea in their data augmentation framework for crowd videos. They created two synthetic datasets. The first one is a large synthetic video training set with labels generated using the video game GTAV; the second one is a smaller dataset of synthetic images generated by a CycleGAN. The CycleGAN takes as input real and simulated images and generates realistic images based on the two. CycleGAN generated dataset preserves the labels of the original simulated videos. The large synthetic dataset was used to pretrain a CNN crowd understanding model. The crowd model was then fine-tuned on the smaller refined dataset.  
[PERSON] et al. [58] used Unity game engine to augment a thermal imagery video dataset with synthetic images. In order to improve the fidelity of the synthetic images generated in Unity, the authors used a LSGAN model. Starting from a synthetic image and its segmentation mask, the generative network is able to output a refined thermal image.
## 3 Results
### Validation Saddle Pressure Measurement and Bike Fitting
#### 3.6.2 Test Subject B  
Test subject B, with a similar weight to the other two test subjects, exhibited a peak pressure that was nearly 24% lower than that of test subjects A and C in the initial measurement. Consequently, the adapted saddle pad for test subject B was manufactured with a strut thickness reduced by 0.05 mm. The pressure image shown in Figure 12a depicts the initial measurement on the CFRP shell of test subject B. It is notable that the left half of the saddle is loaded to a greater extent than the right half. Furthermore, the measurement indicates the presence of elevated pressures on the anterior aspect of the saddle, which is an unfavorable outcome in this region. As per the recommendations of the bike fitting expert, test subject B exhibits a slight pelvic tilt, which is the probable cause of the unequal peak pressure distribution on the saddle.  
Figure 12b illustrates the pressure distribution of test subject B with the custom saddle pad. In contrast to the objective of reducing pressure peaks, the peak pressure on the right  
Figure 12: Visual representation of both measurements of test subject B; (**a**) color plot with peak pressure values of the initial measurement, (**b**) color plot with peak pressure values of the validation measurement—blue, low pressure to orange, high pressure, and (**c**) histogram of both measurements.  
side increased by 28% and on the right half of the saddle by 37%. However, the analysis of the measurement data indicates a markedly more uniform distribution across both saddle halves compared to the CFRP shell. Additionally, the pressure in the front area has shifted slightly towards the back. Despite this increase in peak pressure, the pressure distribution on the saddle has become more uniform overall.  
The frequency distribution of pressure values between the initial and validation measurements is also largely analogous for test subject B (Figure 12c). In the initial area, the frequencies of the validation measurement exceed those of the initial measurement. Subsequently, a precipitous decline in the values is observed. It is notable that for the range between 126 and 225 mbar, a greater number of values was recorded in the initial measurement. From 401 mbar, the frequencies of the validation measurement are predominant, reaching values above 675 mbar for the first time, indicating an increase in the peak pressure in the validation measurement. In comparison to test subject A, test subject B also exhibits the highest value in the lowest pressure ranges (1-25 mbar). However, the difference between the two measurements is less pronounced for test subject B than for test subject A. In the pressure ranges from 26 to 100 mbar and 101 to 250 mbar, subject B demonstrates a more consistent distribution, whereas subject A exhibits a pronounced decline in frequency. Overall, in the medium and higher pressure ranges (above 250 mbar), test subject B displays a more uniform distribution between the two measurements in comparison to test subject A.
# Robust IoT Activity Recognition via Stochastic and Deep Learning
While previous studies have explored statistical signal processing, machine learning, or deep learning techniques in isolation, our approach uniquely integrates an optimized preprocessing pipeline--featuring carefully calibrated Kalman filtering--with a hybrid model that synergizes statistical and deep learning-based feature extraction. This fusion directly addresses critical gaps in handling noisy sensor data, improving robustness and recognition accuracy in IoT-based human activity recognition systems beyond what prior methods have achieved. Overall, the major contributions can be summarized from the following aspects:  
* We developed a dedicated application for collecting smartphone sensor data, enabling a human activity recognition system that achieved 98.91% accuracy with Random Forest compared to the 91.30% achieved by state-of-the-art TransUNet architecture. * We evaluated both traditional machine learning and deep learning models--including Random Forests, CNNs, LSTMs, and three Transformer-based architectures--establishing statistically significant performance differences (ANOVA: \(F=48.69\), \(p<0.001\)) and identifying computational efficiency tradeoffs critical for resource-constrained IoT deployments. * We conducted systematic sensitivity analyses of Kalman filter parameters and window segmentation lengths, providing empirical evidence for optimal preprocessing configurations and enhancing reproducibility across different IoT environments. In summary, this work not only advances techniques for human activity recognition but also addresses critical issues related to data quality and security in IoT applications, paving the way for more reliable and secure health monitoring systems.