esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Lidar-Assisted Control Field Tests  
Despite the large LAC research literature from the past decade, only a small number of field tests have been reported. However, the results of these experiments show that the types of control improvements revealed by theory and simulation can be demonstrated in the field. As mentioned in Section 2.2, [PERSON] et al. (2012) demonstrated a reduction in yaw error when replacing the wind vane measurements in the yaw controller of the three-bladed 600-kW CART 3 (Controls Advanced Research Turbine), located at the US National Renewable Energy Laboratory (NREL)'s National Wind Technology Center, with wind direction measurements from a nacelle lidar. These yaw control improvements were achieved using the circularly-scanning ZephIR DM CW lidar introduced in Section 2.1. Several lidar-assisted collective pitch control field tests were performed at NREL's National Wind Technology Center as well, all using simple feedforward controllers based on wind speed-to-pitch angle lookup tables. In 2012, [PERSON] et al. (2013) evaluated a feedforward pitch controller on the 600-kW, two-bladed CART 2 wind turbine using measurements from the custom circularly-scanning pulsed lidar system mentioned in Section 2.1. During the same year, a similar feedforward controller was tested on the CART 3 turbine using measurements from a BlueScout Optical Control System (OCS) three-beam pulsed lidar, as described by [PERSON] et al. (2013). Based on these two experiments, [PERSON] et al. (2013) report a reduction in rotor speed standard deviation during the above-rated operation when using LAC, while [PERSON] et al. (2013) additionally reveal a decrease in tower fore-aft loads. [PERSON] et al. (2013) discuss feedforward pitch control experiments on the CART 2 turbine using preview measurements provided by the five-beam pulsed Avent lidar system, described in Section 2.1 as well. By re-tuning the feedback pitch controller with which the feedforward controller is combined, the authors demonstrated the ability to maintain roughly the same rotor speed error as the original baseline feedback controller allowed, but with significant reductions in tower fatigue loads and pitch activity.
## 3 Model Description  
The proposed visual system contains two modules, where one is a motion detection module, and the other is a contrast detection module. The external visual brightness signal is firstly received and smoothed by the ommatidia, and then further transmitted into the neurons with different functions in the motion information detection channel and contrast information detection channel to detect the position information and contrast information of small features, respectively. Finally, the STMD neuron integrates position information and contrast information to separate small objects from background small-target-like objects (see Figure 4b). The information processing of the \(\mathcal{CI}\)STMD network is illustrated in Figure 4a. When entering a video frame, the visual network ascertains the positions of small targets and false features by calculating the brightness change of each pixel over time \(t\) in the motion information detection channel while obtaining contrast information by calculating the brightness information of each pixel and the brightness information of its surrounding image points. The position information and contrast information are further processed in STMD neurons to record motion trajectory and contrast trajectory, and to calculate contrast variance in each contrast trajectory at each time step. Finally, the network uses contrast variance as the criterion for discrimination, combined with threshold setting, to achieve robust detection of small objects. Our idea stems from the following observation: When a small object moves against the background, its contrast information fluctuates significantly over time. In contrast, the contrast information of fake features, which is static relative to the background, changes very little over time. Based on the above findings, we propose a new discrimination method: By calculating the standard deviation of contrast information over time \(t\), small moving objects can be effectively distinguished.
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 6 Implementation and Performance Study
### Hash Generation  
The process for generating the hash associated with a frame is described below:  
1. **Calculating the average pixel value.** We must input an 8-bit greyscale image, so that the input can be understood as a matrix in which elements are positive and lower than 256.
2. **Generating a chain of 1 s and 0 s.** We apply the following function to the values of the matrix, from top to bottom: \[f(pixel)=\begin{cases}0&pixel\leq average\\ 1&pixel>average\end{cases}\]
3. **Converting the chain to hexadecimal.** We consider the previous chain to be a binary number and change its base to 16. The resulting number must have a length of 64 characters, so 0 s may have to be added at the beginning.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 9 Analysis and Discussion
### Algorithm Complexity
#### 9.3.1 Vehicle Grouping Algorithm Complexity  
To find the vehicle group algorithm's time complexity, we must consider the GH-selection process, neighbor selection process, and verification of vehicles to join the group. Let \(V_{n}\) is the number of vehicles in the vicinity that takes part in the GH-selection process. The cost of computation of selection of GH takes \(O(V_{n})\). The neighbor function counts the number of transmission vehicles in the group vicinity. Let \(D_{n}\) is the distance calculation among neighboring vehicles in the group, then time complexity for the neighbor function is \(O(D_{n}*V_{n})\). After this, the GH starts the joining of transmission range vehicles in the group. GH verify each vehicle's authenticity, let \(VF_{n}\) is the cost of the verification process, then the cost of vehicle verification is \(O(VF_{n}*V_{n})\). The time complexity of the vehicle grouping algorithm is given below.  
\[\begin{split} Time\ complexity(Grouping)=O(V_{n})+O(D_{n}*V_{n})+O( VF_{n}*V_{n})\\ =O(1+D_{n}+VF_{n})V_{n}\\ =O(D_{n}+VF_{n})V_{n}\\ =O(D+VF)V(n)\\ =O(n)\end{split} \tag{62}\]
## 3 Results and Discussion
### Phosphorus Removal in Batch Adsorption Experiments
#### 3.2.1 The Effect of the pH  
The results indicated that the phosphorus removal was highest in the pH range of 6-7. The removal was over 94% and the final concentration was lowered to below 1.0 mg/L. The adsorption started to decline at pH 2 and pH 11 (Figure 4).  
The pH directly affects the form of phosphorus in water [38]. Due to a competition between the hydroxide and phosphate ions for the metallic ion, the efficiency of phosphorous removal is controlled by the relative concentrations of these two ions in solution. [PERSON] [50] when describing the phenomenon, reported that the hydroxides during precipitation have the tendency to pick up small particles which would otherwise stay in suspension.  
Several studies pointed out that the pH is a critical factor that affects the adsorption of phosphorus compounds [33; 36; 38; 51]. The presented results are consistent with a previous study performed by [PERSON] et al. (2008) who also noted the best phosphorus removal at pH between 5 and 7 [51]. [PERSON] et al. [49] suggested that the mechanisms of phosphorus removal consist of two processes such as co-precipitation and adsorption of phosphorus compounds that occur simultaneously. They reported a trend linking the pH increase with iron hydroxide's behavior. Briefly, when pH increased, iron hydroxides started to precipitate and further pH increase involved re-solubilization of iron hydroxides due to a build-up of negative charge on their surface. When the pH was higher, the formation of solid iron hydroxides occurred [51].
## 3 Temporal Variations in and Correlations between PM and Meteorological Factors
### Hourly Variations in Pollutant Concentrations
In autumn, PM2.5 and PM10 are seen to have a strong correlation with relative humidity (HUMI), and also exhibit a certain degree of correlation with the U wind component (east-west wind vector) and sea-level atmospheric pressure (PSEA). In winter, the correlation between PM2.5 and PM10 and relative humidity (HU-MI) is observed to significantly increase. Figure 4: Heat map of the overall correlation between pollutants and meteorological factors. A double asterisk (**) indicates the \(p\)-value is less than 0.01. Study on the Correlation between Boundary Layer Height, Atmospheric Stability, and Particulate Matter  
Boundary layer height data for Macau (sourced from ERA5 reanalysis data) and atmospheric stability data (sourced from radiosonde data from Hong Kong) were selected for this study to conduct a correlation analysis with the pollutant data. These two datasets were utilized in conjunction with pollutant data for the correlation study. The research findings indicate that a certain relationship is identified between atmospheric stability and PM2.5 and PM10 air pollution levels in Macau, with correlation coefficients being 0.40 and 0.387, respectively [21]. While boundary layer height is typically somewhat correlated with PM concentrations in many regions [22], the levels of PM in Macau are found to show little relationship with boundary layer height.
## 3 Implementation
### Proposed Method
#### 3.2.1 Quad Key Chaotic System (QKCS)  
The encryption coefficient matrix is shuffled using the four chaotic subsystems to attain these circumstances in QKCS. This confusion also reduces the connection between encryption matrix components, which increases the confidentiality degree for the image of interest. The QKCS involves the following steps:  
1. Using the 3D Gauss system approach with the logistic, Arnold, and sine maps generates the QKCS sequence \(Z=\{zi\}\), given an initial condition with an acceptable step size.
2. Converting the sequence \(Z=\{zi\}\) into the integer sequence \(Z^{*}=\{zi^{*}\}\).
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Model Classification for Blockchain-Based Federated Learning
#### 4.1.3 Digital Signature  
In federated learning, data encryption makes it difficult to identify the quality of model updates, and malicious data owners may launch attacks such as data poisoning and free-riding. However, a blockchain is actually a form of blocks linked by hash values under the control of a consensus mechanism (such as Proof of Work (PoW)), mined by miners using digital signatures to make the linked blocks immune to modifications and changes [81]. Therefore, blockchain-based federated learning can realize data encryption and review, thereby ensuring data security.  
In the digital signature, each node of the blockchain has a pair of public and private key pairs. During the transaction, the transaction content is first hashed at the point of transaction, and the private key is used to encrypt it to form a signature: The transaction party verifies the signature, and only after passing can the next transaction be carried out.  
Data encryption makes it difficult for federated learning to identify the quality of these model updates. Therefore, [PERSON] et al. [18] proposed a blockchain-based encryption gradient audit method to ensure the availability of aggregate gradients and achieve effective data protection. Moreover, [PERSON] et al. [22] adopted the distributed architecture of blockchain by taking advantage of edge computing and federated learning to upload data information to the blockchain for verification, effectively reducing the risk of information disclosure, ensuring the integrity of data transmission between devices and the security of high data communication. Using the advantages of edge computing and federated learning, the distributed architecture of blockchain is adopted to upload the data information to the blockchain for verification, effectively reducing the risk of information disclosure, ensuring the integrity of data transmission between devices and the security of high data communication.  
BLADE-FL [82] is used to overcome the problem of centralized aggregation in traditional federated learning systems. Compared to traditional blockchain-enabled federated learning, BLADE-FL helps to improve privacy, prevent model leakage, and guarantee tamper-resistant model updates in a trusted blockchain network.  
In BlockFL [61], local model updating is performed on data samples available on user devices. Local model updates accumulate on the blockchain in blocks. The global model update is also calculated by the user device based on the latest block, thereby establishing the concept of federated learning on the device. The author considers the scalability, robustness and delay minimization of the global learning model.
## 3 Test and Analytical Methods  
### Unconfined Compressive Strength Test (UCS)  
The compressive strength of RCPB is a critical determinant of their engineering performance, with UCS serving as the primary evaluation parameter. This metric also functions as a fundamental quality control indicator for concrete materials, ensuring structural reliability in practical applications.  
UCS tests were performed on RCPB specimens using a microcomputer-controlled universal testing machine (WDW-10, South Star Testing Technology Co., Ltd, Shanghai, China). Specimens cured for 7, 14, and 28 days were subjected to uniaxial compressive loading at a constant rate of 0.2 mm/min. Real-time compressive stress was monitored until specimen failure [22], after which the ultimate compressive strength was calculated using the initial cross-sectional area. To ensure data reliability, triplicate tests were conducted for each specimen group, and results were reported as mean values.
## 6 Conclusions and Future Work  
Depression detection has become an essential task, as it has multiple risks to individuals, society, and economics. Due to the lack of specialists per patients, this task has become difficult and has escalated, becoming a global problem. Since there is a link between language and signs of depression, social media is used to detect depression, using users' posts. The literature shows that most of these solutions provide a representation of text using objective features, such as the number of pronouns, the count of a certain word, or the use of certain phrases inside the text. However, as far as we know, state-of-the-art proposals do not provide their result in a language close to the human expert, which is essential for helping decision makers in the application area. Hence, in this paper, we proposed a new representation of the text based on sentiment analysis and emotions to provide an understandable representation, allowing to discriminate depressive and non-depressive posts in a language close to that of human experts. The aim was to provide experts the information of social media to aid in the diagnosis of users, who may not know they have depression. We also proposed an understandable model based on our representation and pattern-based classification, obtaining both an understandable and accurate model for human experts. Our proposed model outperforms most of the other five state-of-the-art models for depression detection. Additionally, it provides insights on the sentiment-features that define a depressive or a non-depressive post, providing more information to an expert or even the posts' author. Moreover, based on the statistical tests and F1 and AUC metrics, our model statistically outperforms the Random Forest, Naive Bayes, and AdaBoost models using the parser-tree, VAD and Topics, and BOW representations. However, it obtains similar statistical results to the logistic regression models, using the ensemble of BOWs and handcrafted features representations. Consequently, we can conclude that our proposed model, as well as the representation based on sentiments and emotions, allows for providing the best classification results for predicting depressive posts. In future work, we plan on exploring new representations, including features directly related to the medical symptoms of depression and fuzzy pattern classification. The assessment of psychology experts on the patterns and the subsequent interpretation is also part of our future work on this topic. O.L.-G.; M.A.M.-P.: methodology, formal analysis, and writing--original draft. O.L.-G.; M.A.M.-P.; L.M.G.S. : investigation, formal analysis, and software. O.L.-G.; investigation, writing--review and editing. O.L.-G.; M.A.M.-P.: supervision. M.A.M.-P.: funding acquisition. All authors have read and agreed to the published version of the manuscript. This research was partly supported by the National Council of Science and Technology of Mexico under the scholarship grant 1010773. Not applicable.
###### Abstract
One possible configuration of an OLMEH-based charger with an active rectifier operating under a constant voltage load (CVL) is illustrated in Figure 1. The idea behind this circuit is to short-circuit the OLMEH's secondary winding so to preserve the stored magnetic energy and then release it towards the load under optimal conditions. This helps to increase the power output of the circuit, allows control over the depth of saturation, and, enables the implementation of overcharge and overcurrent protection features. Moreover, the active rectifier allows the application of better maximum power point tracking (MPPT) [17; 18; 19], which can help attain a higher power under variable line current conditions. The circuit in Figure 1 was investigated in the recent literature [19; 20; 21; 22], which suggests several approaches to deal with the challenging problem of core nonlinearity. For instance, its earlier counterpart [19] applied a cot(*) function to accurately model core nonlinearity. Yet, due to the complex nature of such an approximating function, after a few theoretical steps, the final solutions were obtained only numerically. This article aims to develop an approximated, yet complete, nonlinear analytical model and solution for an overhead line magnetic energy harvester with an active rectifier (OLMEH-AR), under a constant voltage load, as shown in Figure 1.  
The proposed model is derived by applying piecewise nonlinear analysis, relying on the Froelich equation to model the B-H characteristics of the magnetic core. The Froelich approximation is based on a first-order rational function. The advantage of the offered approach is that it lends itself to the analytical treatise. The result is a complete closed-form  
Figure 1: Overhead line magnetic energy harvester with an active rectifier feeding a constant voltage load. analytical solution to the problem that is quite accurate, simple, and suited for engineering applications. The rest of the paper is as follows: simulated waveforms, state analysis, and derivation of equivalent circuits are given in Section 2; also, this section presents the analytical model of the OLMEH-AR and derives the expressions for the power transfer window, output current, and the output power and the conditions for maximum power. Experimental measurements of a laboratory prototype are reported in Section 3 and stand in good agreement with the proposed theoretical predictions.
## 2 Related Works
### The Fuzzy Logic-Based Algorithm Used in Routing Selection  
In [19], the authors propose an energy-effective cross-layer routing protocol for wireless sensor networks based on fuzzy logic. In this protocol, for minimizing the consumed energy and maximizing the network lifetime, the algorithm takes the remaining battery reserve capacity, the link quality, and the transmission power of the neighbor nodes into consideration to select the next hop relay nodes, dynamically. In [20], for reducing the average end-to-end delay of the mobile _ad hoc_ network, the authors propose a fuzzy logic-based adaptive cross-layer routing protocol for the delay-sensitive applications. In this algorithm, each node can switch between reactive routing mode and proactive routing mode based on the current node status separately. The algorithm uses the fuzzy logic controller to decide the routing model of each node. The inputs of the fuzzy logic controller are the number of link breaks, the interface queue length, and the type of application for each node. In [26], the authors introduce a new routing algorithm for the wireless sensor network to extend the network lifetime and balance the energy consumption by combining the fuzzy approach and the A-star algorithm together. In this algorithm, the remaining battery power, the number of hops to the destination node, and the traffic loads are taken into consideration to determine an optimal routing path from the source node to the destination node. In [28], to prolong the lifetime of the wireless sensor network, a fuzzy logic-based energy-optimization routing protocol is proposed. In this algorithm, the social welfare function is used to predict inequality of residual energy of neighbor nodes after selecting different next hop nodes. The algorithm computes the degree of energy balance based on the energy inequality. The fuzzy logic system uses the degree of node closeness to sink, and the degree of energy balance to achieve the routing decision. Additionally, in [29], the node density, the delay, and the number of dead nodes are the inputs of the fuzzy logic system to select the next hop relay node to achieve the balanced energy consumption across all of the sensor nodes with minimum delay. More related works can also be found in [30, 31, 32, 33].