esa-sceva/satcom-chunk-collection huggingface dataset:
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
## 5 Results and Discussion
### Discussion  
As per the results, it is evident that for the given application, the direct-mapped cache provides around a 2.5% lower hit ratio but has the advantage of utilizing less resources, i.e., RAM and Slice LUTs. On the other hand, set-associative techniques have a better hit ratio achieved at the cost of a higher number of resources.  
Both of these cache techniques can be useful for different Blockchain applications. In many Blockchain scenarios, common key-value pairs exist in the database that represent frequently used transactions [23]. For these applications, a small increase in hit ratio will also significantly improve the overall access delay, as common key-value pairs will be accessed using the cache. Thus, the effective hit ratio will be more than the actual hit ratio in these cases. For other Blockchain applications, where such common key-value pairs do not exist and where the speed of data access is less critical, the direct-mapped cache could be a cost effective caching technique.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Application Layer Transmission Requirements  
This section concerns data transmission requirements between IoT devices or between the devices and the cloud platform. Current application-layer data transmission protocols include Modbus, MQTT (Message Queuing Telemetry Transport), CoAP (Constrained Application Protocol), OPC-UA (OPC Unified Architecture), HTTP protocols, etc. Here, we introduce the first four mentioned protocols.
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 4 Experimental Setup and Datasets
### Experimental Design  
In this experimental study, GloVe embeddings [45] are used for non-BERT methods while for BERT-based methods, pre-trained BERT embeddings are utilized. Dimensions are kept at 300 for both embeddings and hidden state vectors. The learning rate has been kept as 0.001. L2 regularization is used along with a drop-out rate of 0.1 to avoid overfitting. The initialization of weight matrix and biases is carried by sampling from a uniform distribution U(0-0.01, 0.01). Adam optimizer is adopted for model training. The batch size has been kept as 64 with step size being 5. The selections of hyperparameters for our experimental study are based on existing research [7; 33; 37]. Furthermore, the architecture-specific hyperparameters for some deep learning methods are taken from original work. The number of graph convolution layers in ASGCN and ASTCN is kept 2. For CapsNet, the capsule size is 300. For RGAT, GAT, and GAT-BERT, the Deep Biaffine [52] parser is used. The average scores of Accuracy and Macro F1 on test data are reported. The implementation work is carried using the PyTorch framework.
###### Abstract  
Studying how to use the coupling characteristics of net aberration fields induced by different perturbation parameters to realize the wave aberration compensation correction of perturbed telescopes is of great significance for the development of active optics. Based on nodal aberration theory, this paper studies the wave aberration compensation correction method of an unobscured off-axis three-mirror telescope. Specifically, first of all, it theoretically analyzes the coupling effects and compensation relationships of net aberration fields induced by different perturbation parameters of the telescope. Furthermore, it establishes wave aberration correction models with the secondary mirror as the compensator and the third mirror as the compensator for the telescope, respectively. In the end, it verifies the two compensation correction models by simulations. The results show that the tolerance of the secondary mirror compensation correction mode (SMCM) to the perturbation parameter threshold is significantly better than that of the third mirror compensation correction model (TMCM). When the introduced perturbation parameter threshold is small, the correction accuracy of the two models for the wave aberrations is equivalent, and both reach the order of \(10^{-3}\lambda\) (RMS, \(\lambda=632.8\) nm). When the perturbation parameter threshold is increased, the correction accuracy of SMCM can still be maintained at the order of \(10^{-3}\lambda\) but the correction accuracy of TMCM would decrease by an order of magnitude.  
astonomical telescope; active optics; nodal aberration theory; aberration compensation; misalignments; figure errors +
Footnote †: journal:  
Article
## 6 Results
### Practical Results  
The verification of the computer simulation results is carried by developing a hardware setup with the help of six diodes (RHRG3040) and six transistors (IRF840). The on and off control of a transistor is achieved with a gate supporting circuit that utilizes EXB840 hybrid chip and isolated DC voltage as the main sources. The size of the gate supporting circuit is not comparable to the overall size of six diodes and transistors. The use of input capacitor (1 uF) and output capacitor (4.7 uF) enhances the power quality by suppressing the unwanted components. An inductor of 1 mH reduces the current ripple to enhance its power quality. It stores and releases the power during conducting and non-conducting intervals of PWM control that is a mandatory requirement of voltage step up and down operation. The six gate supporting circuits facilitate the on and off operation of the six transistors. A high-speed signal STM controller produces the required control outputs. These signals have to match with the change in the polarity of the source voltage. This task is achieved by employing the sensing circuit for input polarity detection. The low power output control signals of the STM controller are connected to the six transistor's driving circuits that take the power from six DC sources for the amplification purpose. These outputs of these DC sources are isolated with respect to one another and also with respect to line voltage as this is realized by employing six low-frequency step-down transformers. The output control signals of gate supporting circuits have similar characteristics as generated by the STM controller, but their voltage level is increased to 12 V to 15 V (output level of the DC sources).  
The waveforms of input and output voltage obtained from this setup with the help of an oscilloscope are demonstrated in Figure 13(a),b, validating non-inverted and inverted  
Figure 12: Computer simulation results for output step-up process across \(T_{\tau 5}\), \(D_{5}\) (blue) and \(T_{\tau 6}\), \(D_{6}\) (red) in (**a**) non-inverted form and (**b**) inverted form.  
Figure 13: Computer simulation results for the compensation of voltage sag for depth level of (**a**) 70% and (**b**) 30%.  
buck capabilities. Here the red color of input differentiates the output that is plotted with blue color.  
Figure 15a,b confirm the result of Figure 10a,b in the simulation section. The confirmation of voltage boost capabilities is confirmed by the plots of Figures 16 and 17 for output voltage and the voltage across the transistors operated at high frequencies. All the practical results prove the validity of the computer simulation section and input-output analysis of the operational modes.  
Figure 14: Practically obtained results of output voltage (red) with respect to input voltage (blue) for output step-down process in (**a**) non-inverted form and (**b**) inverted form.  
Figure 15: Practically obtained results for output step-down process across \(T_{r1}\), \(D_{1}\) (blue) and \(T_{r3}\), \(D_{3}\) in (**a**) non-inverted form and (**b**) inverted form.
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 4 Response of FF Ground under Sinusoidal Excitation
### Acceleration along Depths
#### 4.1.2 Amplification Coefficients of Acceleration along Depth  
Variation of peak acceleration with depth can be qualified with the coefficient of acceleration amplification, \(C_{a}\). It is defined as the ratio of the peak acceleration at a monitoring position to that at the bottom of the ground.  
\[C_{a}=\frac{A(z)}{A(0)} \tag{7}\]  
where \(A(z)\) is the peak acceleration at a monitoring position, and \(A(0)\) is the peak acceleration at the bottom. The variation of it along depths are shown in Figure 14.  
In the cases of the Sin-2 and Sin-3, the acceleration amplification coefficients in the model soil increase along depths. In the cases of the Sin-6, Sin-9, and Sin-11, the amplification coefficients first decrease and then increase along depths, but amplification coefficients increase sharply in the surface soil layer. In the cases of the Sin-14 and Sin-17, the amplification coefficients first increase and then decrease and increase again along depths. The acceleration amplification effects of the surface soil layer are similar with the acceleration recordings of two downhole arrays, Treasure Island and La Cienega, instrumented by the Caltrans/CDMG project [21].  
Figure 12: Cont.  
These results show that vibration transmission property of model soil may relatively decrease with the increase of input frequency in the middle soil layer. This is because  
Figure 14: Acceleration amplification coefficients along depths in FF model.  
Figure 12: Acceleration responses with depths in FF model. (**a**) Sin-2. (**b**) Sin-3. (**c**) Sin-6. (**d**) Sin-9. (**e**) Sin-11. (**f**) Sin-14. (**g**) Sin-17.  
Figure 13: Phase difference in FF model.  
the high-order vibration modes may be activated and show remarkable effects on the acceleration response of the model ground. Different combinations of modal amplitudes are activated under sinusoidal excitations of different frequency (concluded in Section 4.2). The propagation of seismic waves is also affected by many factors, including soil damping and the superposition effect of seismic wave. Therefore, the amplification mechanism is more complex in the surface soil layer, and acceleration amplification coefficients obviously increase under a higher excitation frequency.  
To sum up, amplification effects of ground surface acceleration are obvious under a higher excitation frequency. Except for the surface soil layer, the soft ground has a damping effect on higher frequency excitations.
# Visual Tracking Using Wang-Landau Reinforcement Sampler
## 1 Introduction
### Basic Idea  
We further enhance prediction accuracy by balancing the exploitation and exploration abilities of reinforcement learning. The exploitation procedure is used to further simulate the movements at the states around the current local optimum, which the tracker has extensively explored. For example, assume that our visual tracker observes that the target in Figure 1 usually moves to the right, up to the current frame. We likely need to further exploit possible states on the right side of the current state. Our visual tracker can accurately predict the target state using exploitation, especially when the target moves smoothly. In contrast, the exploration procedure is used to simulate the movements at states far from the current local optimum, which the tracker has only minimally explored. For example, the target in Figure 1 moves randomly and inconsistently in some cases; thus, we need to explore unvisited states, far from the current state. Our visual tracker can predict the target state using exploration, especially when the target is fast-moving. Traditional reinforcement learning methods experience difficulty in scheduling the exploitation and exploration procedures. In contrast, our visual tracker overcomes this problem by introducing Wang-Landau sampling [7], in which exploitation and exploration compete against each other in a sampling framework and attain the equivalence status.
## 4 ITS Threats Analysis and Classification
### ITS Security Requirements
In this context, ensuring the confidentiality of the broadcast messages is not required, since all of the neighboring vehicles should be able to receive and decode these safety messages. In contrast, infrastructure-vehicle unicast (IVU) and vehicle-vehicle unicast (VVU) employ unicast communications between OBUs and/or RSUs to enable commercial and comfort applications. In this context, it is necessary to ensure the confidentiality of the communications, in addition to the other security requirements that are listed in Table 3.
Secondary metabolites are chemical compounds found in products of natural origin and the three most important groups are terpen (giving rise to monoterenes, sesquiterpenes, diterpenes, triterpenes, and tetraterpenes), phenolic compounds (derived from carbohydrates, such as flavonoids, tannins, and coumarins, among others) and alkaloids (derived from amino acids, the main constituents of proteins) [12]. Among the phenolic compounds, coumarin 1,2-benzopyrone is the simplest representative; it was isolated for the first time from _D. odorata_ fruits by [PERSON] in 1820 [13]. However, more than 1300 coumarins have been identified from natural sources, especially green plants, and their pharmacological and biochemical properties and their therapeutic applications depend on their substitution pattern [14]. In that sense, products of plant origin emerge as a source of active substances with the potential for various biological activities, including antioxidant, insecticidal, fungicidal, herbicidal, and nematicidal action [15]. Pharmacologically, the antioxidant activity has been extensively investigated with a view to replacing products of synthetic origin with those of natural origin for insertion in human food and use in the prevention of pathologies. Scientifically, it has been demonstrated that the progression and onset of various pathologies may be related to oxidative stress in degenerative, cardiac, inflammatory, immunological, and neurological diseases, among others [16]. The DPPH (2,2-diphenyl-1-picrylhydrazyl) method, for example, is widely used to assess antioxidant capacity [17; 18]. The test evaluates the ability of a sample to eliminate or neutralize free radicals, which is monitored using a UV/visible spectrophotometer [19]. Antioxidant activity is usually expressed by the IC50 parameter (inhibitory concentration), which is defined by the antioxidant agent's ability to sequester 50% of the free radicals present in the solution. The inverse relationship between the amount of sample and the antioxidant activity means that the lower the IC50 value is, the greater the antioxidant activity of the product [20]. In this context, chemically characterizing the extracts from different parts of _D. punctata_ and evaluating their potential in terms of antioxidant activity is an interesting strategy because, in addition to the lack of chemical information about this species, the seeds of _Dipteryx_ spp. are used in the food industry, mainly for the manufacture of sweets and ice cream [7; 8]. These facts encourage studies on the capture of free radicals, add economic value to these species, and contribute to the increased demand for them in agroforestry systems. In addition, finding an antioxidant action in fruit residues would open up the possibility of a new destination for this by-product hitherto discarded in the environment, thereby making more efficient use of production. Thus, this study aimed to evaluate the phytochemical profile and antioxidant activity of extracts obtained from leaves, branches, and fruits (residues and seeds) of _Dipteryx punctata_.
# A Waveform and Velocity Ambiguity Resolution Method for Corner Radar
## 5 Conclusions  
This article proposes a FMCW MIMO single-frame multi-mode radar waveform and design concept by studying the common transmission methods and waveforms of automotive millimeter wave radar. Compared with intra-frame single waveforms, it better balances detection distance and resolution and makes the detection of nearby targets more accurate. A target parameter matching based AB wave velocity ambiguity resolution method is also proposed for the velocity ambiguity problem of this waveform, which solves the velocity ambiguity problem under high velocity resolution and can also adapt to future mainstream DDMA transmission modes. The reference waveform configuration and unambiguous method presented in this article have also been experimentally validated based on the Alps Pro radar chip, proving that this method is suitable for automotive radars in current engineering.  
Conceptualization, Y.L. and D.Z.; methodology, Y.L.; validation, Y.L., S.W. and J.L.; formal analysis, D.Z.; investigation, Y.L.; resources, D.Z.; writing--original draft preparation, Y.L. and D.Z.; writing--review and editing, Y.L. and S.W.; visualization, S.W.; supervision, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the Fundamental Research Funds for the Central Universities, and in part by the Jiangsu Agriculture Science and Technology Innovation Fund (JASTIF; Project number: CX (21)3187).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
Figure 19: Range–azimuth of different waveforms. (a) Multi mode. (b) Single mode.  
Figure 18: Range–velocity of different waveforms. (a) Multi mode. (b) Single mode.
## 2 Acoustic Propagation Models  
Simulating UWSN communications requires modeling the acoustic wave's propagation while a node tries to transmit data to another one. There are several models proposed in the literature from the simplest ones based in the sound propagation theory to more elaborate and complex models based on the physics of acoustic sound propagation. In this section, we will describe several acoustic propagation models that represent different approaches to the same problem, but with different degrees of complexity/accuracy. We will present them in order of increasing complexity, so for each approach we will know how propagation acoustics are predicted and what parameters are taken into account for that purpose.
# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 3 Brief Introduction of ACO  
The ACO is employed to address optimization problems by imitating the natural foraging process of ants, which rely on pheromone trails to identify the shortest path from their nest to food sources. During the early phase of this process, ants explore their surroundings randomly. As they traverse different paths, they deposit pheromones along the path. The pheromone concentration increases as more ants follow a particular trail, reinforcing its attractiveness. This positive feedback loop encourages even more ants to choose the route with the strongest pheromone concentration, ultimately resulting in the identification of the shortest path.  
Assuming that the \(k\)th ant moves from position \(i\) to position \(j\) at time \(t\), where position \(i\) refers to the ant's current grid and position \(j\) indicates the unvisited grid to which the ant will move. If there are multiple unvisited grids \(j\) around the current grid \(i\), the transition probability \(P_{ij}^{k}(t)\) is determined using Equation (8), and the grid \(j\) is selected using the roulette model. The following expression describes Equation (8):  
\[P_{ij}^{k}(t)=\left\{\begin{array}{cc}\left[\tau_{ij}(t)\right]^{\alpha}* \left[\eta_{ij}(t)\right]^{\beta}&if\ j\in A_{k},\\ \sum\limits_{S\in A_{k}}\left[\tau_{ij}(t)\right]^{\alpha}*\left[\eta_{ij}(t) \right]^{\beta},&if\ j\in A_{k},\\ 0,&otherwise,\end{array}\right. \tag{8}\]  
where \(\tau_{ij}(t)\) represents the pheromone amount between position \(i\) and position \(j\) at time \(t\); \(\alpha\) and \(\beta\) refer to the pheromone concentration factor and expected heuristic factor, respectively; \(A_{k}\) denotes the collection of unvisited grids around the position \(i\); and \(\eta_{ij}(t)\) indicates the heuristic value that can be determined by Equation (9):  
\[\eta_{ij}(t)=\frac{1}{d_{ij}}, \tag{9}\]  
where \(d_{ij}\) indicates the Euclidean distance between the next position \(j\) and the ending position. Once all ants have finished an iterative search, the pheromone on each path is updated using Equation (10):  
\[\tau_{ij}(t+1)=(1-\rho)\tau_{ij}(t)+\sum\limits_{k=1}^{m}\Delta\tau_{ij}^{k}, \tag{10}\]  
Figure 2: Motion analysis of a DDMR.  
For the purpose of enhancing the performance of ACO and to minimize the number of lost ants, we propose an enhanced variant algorithm of ACO--known as EACI. This algorithm incorporates several key modifications, including an island-based division strategy, a non-uniform pheromone distribution, an improved heuristic function, and an optimized global pheromone updating rule.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 4 Methodology
### Cost Modeling  
The proposed scheme introduces a simplified form of cost modeling to ensure the effective deployment and utilization of all network and computational resources. The field data obtained by the validated mobile sensors are legitimate users, and the respective costs are paid to the sensory service provider. The empirical formulation of the cost model for the proposed system is shown by Equation (12).  
\[Co=h_{12}+\sigma \tag{12}\]  
In Equation (12), the scheme computes the cost \(Co\) using two dependable attributes, \(h_{12}\) and \(\sigma\). The attribute \(h_{12}\) represents the product of the channel capacity size \(\chi\) assigned to the mobile sensors and \(e^{-\psi.in}\) where the variables \(\psi\) and \(in\) represent a change in the unit price and instantaneous channel capacity identified at specific timeslots. The last attribute, \(\sigma\), represents the minimum unit price for channel capacity usage. Based on the cost modeling, the higher the usage of channel capacity by the user, the higher the cost required to be paid by the user. Therefore, the user may opt for a different access point or use a different user (or mobile sensor) to explore alternative solutions to lower resource costs. Increasing the resource cost can significantly contribute towards minimizing the bottleneck condition and assist the system in offering a better quality of experience.  
Equation (12) is related to the cost model for the overall system. Hence, the system also introduces a cost model for individual mobile sensors based on the occupied duration of resource usage, mathematically expressed by Equation (13).  
\[Co_{user}=Co_{1}+Co_{2} \tag{13}\]  
Equation (13) indicates that the user cost \(Co_{user}\) is computed using two cost attributes, \(Co_{1}\) and \(Co_{2}\). The first attribute, \(Co_{1}\), is the product of the system cost \(Co\) (obtained from Equation (12)) and the time stamp for the transmitting sensor, and \(Co_{2}\) represents the same for the receiving sensor. Furthermore, an objective function is designed for this purpose with a target to minimize the cost.  
\[O_{fun}\to arg_{min}(Co_{j}) \tag{14}\]  
The prime conditions used for Equation (14) for the objective function \(O_{fun}\) are multiple viz. (i) there is a limited channel capacity assigned for all APs to a single mobile service; (ii) there is a limited cumulative channel capacity for facilitating the N2N mode of communication; (iii) limited channel capacity is assigned for APs to a single mobile sensor; (iv) only one AP can be used to connect with one mobile sensor (that can also be connected with another mobile sensor at maximum); (v) the trust score of communicating mobile sensors should be greater than the cut-off trust score; (vi) the formation of complete data using data extracted from the access point, AP and N2N itself; and (vii) cumulative latency should not be more than its tolerable latency for accomplishing data transmission. A high-level diagram of the proposed scheme is exhibited in Figure 5.