esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Product/Services
#### 4.2.8 Decentralized Exchanges (DEXs)  
Decentralized exchanges (DEXs) are digital marketplaces that use smart contracts to enable direct cryptocurrency transactions between traders without the involvement of a third-party intermediary in managing their assets [6; 95]. DEXs operate on a decentralized infrastructure that relies on self-executing smart contracts containing specific conditions to be met before a trade can occur. These exchanges facilitate various functions, including creating the liquidity pool between tokens, adding or removing liquidity, and exchanging tokens [59]. DEXs offer liquidity benefits while minimizing market price impact. Liquidity in DEXs is determined by automated market maker (AMM) mechanisms. AMMs enable users to trade tokens while concurrently upholding market liquidity and stability efficiently. A prevalent AMM type sets exchange rates using the formula \"Amount of token X \(\times\) Amount of token Y = constant.\" These AMMs employ smart contracts for token locking and interest payments, offering rates from a few percentage points to triple-digits. They determine pricing through algorithms, ensuring instant quotes despite liquidity pool depth. As AMMs evolve, future designs are expected to lower fees, reduce friction, and enhance liquidity for DeFi users. In addition to providing increased security, DEXs offer greater user control and privacy because traders retain complete control and ownership over their assets throughout the transaction process. Prominent examples of DEXs include _Uniswap_, _SustiSawp_, and _Curve_[57; 91].  
AMM mechanisms, prevalent in DeFi, are less common in TradFi. However, signs suggest adoption within CeFi and TradFi entities. AMMs, offering algorithm-based pricing for instant quotes despite liquidity pool depth, are projected to generate value across DeFi and TradFi. Future advancements are anticipated to further enhance efficiency over time [6; 91; 57].
## Materials and Methods
### Lithium-Ion Batteries Sizing  
In order to estimate the capacity that is required for batteries to supplement the PV output power under a worst-case scenario (\"very cloudy day\") and to bring it up to the amount generated under a best-case scenario (\"sunny day\"), the total additional energy required for one solar day (\(E_{tot}\)) is calculated in Equation (6):  
\[E_{tot}=\int P_{sunny}dt-\int P_{very\ cloudy}dt, \tag{6}\]  
where \(P_{sunny}\) and \(P_{very\ cloudy}\) represent the PV output power (W) on a \"sunny\" and a \"very cloudy\" day, respectively, while \(t\) is the operation time (h) over the entire day. The estimated total additional energy required during one solar day was calculated to be \(\sim\)1.5 kWh (marked in Figure 11 in Appendix A.1).  
An equation that is commonly used to determine the battery capacity and that accurately selects and sizes the battery pack for the stand-alone system is given by Equation (7) [64]:  
\[C_{x}=\frac{E_{tot}}{V_{dc}}.\frac{T_{mut}}{DoD_{max}}, \tag{7}\]  
Figure 2: Variations of the amount of sunlight (plotted as (A): solar irradiance, (B): PV power and (C): temperature) as a function of time on different “solar days”, illustrating a “sunny day”, “partly cloudy day” and “very cloudy day”. Data were taken from the KIT Solar Park on 5 May 2016, 26 May 2016 and 13 Oct 2016, respectively.  
where \(C_{x}\) is the required battery capacity (Ah) at a specified discharge rate \(x\), \(E_{tot}\) is the total energy required over one day, as mentioned above (Wh), \(V_{dc}\) is the dc nominal voltage (V), \(T_{att}\) is the number of days of autonomy and \(DoD_{max}\) is the maximum depth of discharge of the battery (%). Assuming the battery voltage = 24 V, T\({}_{aut}\) = 1 (assuming that there will always be some power generated by the PV panels, even during a worst-case scenario like on a \"very cloudy day\") and \(DoD_{max}\) = 80%, the total battery capacity is calculated to be \(\sim\)84 Ah (energy capacity of 2 kWh). Hence, two batteries connected in parallel exhibit a voltage of 24 V and a nominal battery capacity of 100 Ah (energy capacity of 2.4 kWh), and the discharge rate \(x\) is calculated to be 0.2 C, assuming the maximum required discharge current from the pump is 20 A (detail found in the datasheet [65]).
# An Accurate Platform for Investigating TCP Performance in Wi-Fi Networks
## 3 Emulator-Based Approach for Investigating TCP Performance in a Wi-Fi Network
### Congestion Control Algorithms
#### 3.2.3 Hybrid Algorithms  
_Centre for Advanced Internet Architectures (CAIA) Delay-Gradient (CDG)_[44] uses the delay gradient calculated from the minimum and maximum RTTs as a congestion signal. CDG switches to loss-based operation when it detects that its delay-based mode has no measurable effect.  
_Illinois_[45] determines whether to increase or decrease the congestion window following a loss-based method. Moreover, it uses a queuing delay to adjust the size change rate.  
_Westwood_[46] depends on an estimate of the end-to-end bandwidth to control the transmission rate following returning acknowledgment packets (ACKs).  
_Yet Another High-speed TCP (YeAH)_[47] is characterized by two operation modes (slow and fast). YeAH relies on an estimate of the bottleneck queue size.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 2 Filtering Methods  
For speckle suppression of medical ultrasound breast image, we can broadly categorize the filtering algorithms and compare the performances among different filtering algorithms to find the most preferred one. Because of numerous applications and versatility of de-speckling filters, this paper is unable to discuss all proposed filters. In this paper, we discuss eight typical filtering algorithmsaccording to the filter categories discussed above: WTF, Gaussian-Laplace Filter (GLF), Lee Filter (LF), BF, Wiener Filter (WF), PMD, WJD, and TV.
## 1 Introduction  
Solving the problems of scheduling construction projects is currently of interest to many researchers around the world. Repetitive multi-unit projects, which are often found in construction practice, deserve particular attention. Due to their nature, they are an excellent example of construction works for which the optimization of certain processes can bring measurable financial benefits.  
They consist of the implementation of many different buildings with similar technology. Cases of such undertakings may occur in residential, infrastructure, industrial or commercial construction branches. The optimization model of a multi-object project presented in the article is intended to be used for projects consisting in the implementation of many different construction objects with a similar implementation technology, but significantly differing in the size of the works to be performed. For this reason, this model can be treated as a generalization of the approach to scheduling repetitive objects presented, for example, in the LOB method [1] and derivative methods [2]. As a result, the potential application of new implementations can be widely used in industry. As numerous reports show [3; 4; 5], a significant proportion of construction projects are overestimated or underestimated. This may result, inter alia, from incorrect construction of project models. This case hinders proper analysis and preparation of the implementation.  
One of the factors causing large deviations of models from reality may be an insufficient consideration of the human factor. The learning and forgetting effect (LAF) is a phenomenon that allows to describe the increase in the efficiency of construction work groups and, thus, the decrease in the duration of construction works of one type in many different locations. This has been proven in the works of many researchers [6; 7; 8; 9].  
The article presents the application of this phenomenon in a multi-unit construction project. Considering the effect of learning and forgetting is a very important element of the actual work schedule. The article presents an optimization model of such a construction project. The search for the optimal schedule, taking into account the assumed constraints and the objective of the project implementation cost, is an NP-hard optimization task. The model of scheduling a multi-unit project presented in the article can be successfully used during the planning process of such projects by practitioners and decision makers of the construction sector.
## 3 Methodology of FSACM
### Evaluating of Model Performance  
The performance and efficiency of the proposed method were evaluated on the KDD '99 and UNSW-NB15 datasets. In addition to the proposed method, machine learning techniques used in previous studies in this field were also applied to these datasets. During the training phase, the KDD '99 and UNSW-NB15 training datasets were used, while the KDD '99 and UNSW-NB15 test datasets were utilized during the testing phase. Machine learning algorithms have been widely applied in various fields, including network attack analysis and detection, and were therefore assessed based on the datasets used in this study. While no algorithm is universally superior, each algorithm has its own advantages and disadvantages, and the optimal algorithm will depend on several factors such as the data distribution, number of features, and inter feature relationships [40; 41; 42].  
In order to evaluate the efficiency of the proposed model and to compare the performance of various machine learning algorithms, a range of metrics were utilized, including precision, recall, f-measure, accuracy, false positives, and false negatives. These metrics were computed using the confusion matrix. To reduce the overfitting, the datasets were divided into training, validation, and test sets. When performance started to degrade, we stopped the training. Before the classification, the data preprocessing stage was performed efficiently, which degraded the level of overfitting, and the most appropriate features were selected. We also used a large amount of training data to reduce overfitting. Most of the time, using a more diverse dataset can help a model learn the underlying patterns of the data efficiently, which decreases the level of memorizing.
## 3 Validation  
Two types of simulated data sets (type A and B) were created and used for the validation of the algorithm. The first type (A) of data set was constructed to evaluate the ability of the AMPD algorithm to detect peaks in noisy _periodic_ signals. Therefore 5 signals (sampling frequency: 100 Hz, length: 20 s, frequency of the sinus oscillation: 1 Hz) with different amount of Gaussian white noise were used. The signal-to-noise ratios (SNR) of the final signals were 25 dB, 10 dB, 5 dB and 0 dB. Additionally one signal without noise was created. The SNR values were calculated according to the  
Figure 1: Calculation steps of the AMPD algorithm.  
Figure 2: Example of applying the AMPD algorithm to a simulated signal. (**a**) Local maxima scalogram, calculated from scale 1 to \(L\), with \(L=\left\lceil N\,/\,2\right\rceil-1\) and \(N\) the length of the signal (\(N=3000\)); (**b**) Rescaled local maxima scalogram (LMS) according to the detected global minimum as shown in (**c**); (**d**) Calculated row-wise standard deviation of the rescaled LMS; (**e**) and (**f**) Detected peaks.  
formula SNR = 10 log(\(P_{signal}\) / \(P_{noise}\)), with \(P_{signal}\) the power of the periodic signal and \(P_{noise}\) the power of the noise. The second type (B) of data set was constructed to evaluate the ability of the AMPD algorithm to detect peaks in noisy _quasi-periodic_ signals. Therefore, five chirp signals were created, each comprising a sinus oscillation with an increasing frequency (start frequency: 1 Hz, end frequency: 3.8 Hz). As for the type A signals, different amounts of noise were added to the signals leading to the same SNR-values as for the type A signals.  
The AMPD algorithm was implemented in Matlab (The Mathworks, Natick, MA, USA). The results of applying the algorithm to the simulated data sets are visualised in Figures 3 and 4. All peaks were correctly detected (based on visual inspection) for all types of signal characteristics used.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 3 Taxonomy
### Simulation
The proposed HDC-OFDM has the benefits of the power efficiency of DACO-OFDM and the dimming flexibility of DCO-OFDM. In [50], the authors explored the potential of transmitting signals over in-phase and quadrature IQ components separately using two LEDs and Wavelength Division Multiplexing (WDM), namely, IQ-WDM. By sending baseband signals from the IQ components, the higher attenuation due to the higher frequency band can be avoided. In [51], the authors channels with reference to the proposals for the standard mandatory PHY of the IEEE 802.11 bb. In [55] a simplified analysis was done. However, the general case was also presented where ordinary commercial LED bulbs were analyzed in terms of their light output as compared to the receiver threshold. In [71], they also presented a simplified yet general case in which ordinary commercial LED bulbs were analyzed in terms of their light output as compared to the receiver threshold. In [72] the evaluation of three relevant scenarios with RF/LiFi hybrid networks was done to determine the improvement possibilities provided by this technology and was assessed for the potential of the respective realistic developments.
## 4 Numerical Examples
### Example 2--MBB Beam
Therefore, a volume of less than 48%, for example, \(\frac{V_{i}}{V_{0}}=0.35\), is below the curve and therefore violates the compliance constraint, \(\frac{C_{i}}{C_{0}}=1.45\), because it requires compliance 45% higher than the initial one. So, this structural design is not feasible because there is a lack of material to complete it. On the other hand, projecting the compliance ratio, \(\frac{C_{i}}{C_{0}}=1.20\), to a point above the curve, \(\frac{V_{i}}{V_{0}}=0.60\), verified that the amount of volume is more than sufficient for the design's execution, that is, the design is viable but it is oversized. Therefore, the ESO, SERA and SESO structural optimization methods can be used as a tool to improve the performance of structures that are oversized while saving material. In Figure 10, such structures satisfy the design conditions, which are not oversized, and have provided a considerable material saving. In SIMP, this process is continuous. The topology evolves by continuously changing the elasticity modulus. Since the prescribed volume remains constant during the iterative process, the performance's characteristic curve is a straight line (Figure 10(d)). Therefore, the design is feasible above the curve, but it is oversized because the volume used is larger  
Figure 11: Performance characteristic curve for structures with compliance constraints: (**a**) ESO, (**b**) SERA, (**c**) SESO and (**d**) SIMP. than necessary to carry out the design (Figure 9d). On the other hand, below the curve, the design is not feasible, and it is underestimated because there is not enough material to carry it out. This is a SIMP advantage since its convergence meets the Karush-Kuhn-Tucker optimality conditions during the entire iterative process. For the RBTO-3D analysis results' comparison, the volumes, displacements, objective function, and computation time due to each method, for the MBB beam, are listed in Table 2.
## 3 Results  
During the experimental period, there were no unexpected deaths. Animals that bit the suture, had a wound exposed, or had part of the skin removed from the area were selected to be part of the group that was euthanized one week after implant placement. No problems were found during the final sampling, and all the implants could be recovered.  
Regarding the microtomography analysis, no significant difference was observed between the experimental groups.  
In results at one week, when we used non-thermal low-pressure argon plasma (NTLP-ArP) on a mechanized surface, the BIC% was improved; on the other hand, on RBM surfaces we did not obtain an improvement. However, these differences had no statistically significant difference.  
In the second week, our results showed lower BIC% values when NTLP-ArP was used on both surfaces.  
The results in the fourth week showed somewhat higher BIC values when NTLP-ArP was applied. Group IV (RBM-P) showed higher BIC values than the rest of the groups. However, no statistically significant difference was observed between groups or between time intervals (\(p<0.05\)) (Table 1) (Figure 2).  
The most significant differences were observed when carrying out the histological study (Figure 3). At one week, our results did not show differences with a statistical significance (\(p<0.05\)), although the RBM and RBM-P groups showed higher values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experimental Group**} & \multirow{2}{*}{**n**} & \multicolumn{2}{c}{**1 Week**} & \multicolumn{2}{c}{**2 Weeks**} & \multicolumn{2}{c}{**4 Weeks**} \\ \cline{3-8}  & & **BIC (\%)** & **C.I. 95\%** & **BIC (\%)** & **C.I. 95\%** & **BIC (\%)** & **C.I. 95\%** \\ \hline I (M) & 12 & 70.67 & 61.09–80.25 & 76.97 & 73.42–80.52 & 76.16 & 71.05–81.27 \\ II (M-P) & 12 & 75.52 & 71.73–79.31 & 73.48 & 70.09–76.87 & 77.92 & 73.34–82.50 \\ III (RBM) & 12 & 77.85 & 74.38–81.38 & 75.60 & 72.11–79.09 & 75.81 & 71.48–80.14 \\ IV (RBM-P) & 12 & 73.78 & 68.13–79.43 & 74.83 & 69.61–80.05 & 78.80 & 72.95–84.65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: BIC percentages and standard deviation of each experimental group obtained by microtomography at 1, 2, and 4 weeks. No statistically significant difference was observed between groups or between time intervals taking into account the significance value of \(p<0.05\).  
Figure 2: BIC percentages with the C.I. (95%) of each experimental group obtained by microtomography at 1, 2 and 4 weeks.  
At two weeks, both surfaces, mechanized and RBM, showed better BIC when they were treated with NTLP-ArP, showing statistical differences between groups I and II (\(p=0.018\)), between groups I and IV (\(p=0.001\)). Statistical differences also appeared between groups III and IV (\(p=0.035\)).  
At four weeks, no differences were found on the mechanized surface when treated or not treated with NTLP-ArP (groups I and II). On RBM surfaces, higher BIC was found when treated with NTLP-ArP showing statistical differences when comparing group IV with groups I (\(p=0.018\)) and with group II (\(p=0.024\)) (Table 2) (Figure 4).
# PTMD Control on a Benchmark TV Tower under Earthquake and Wind Load Excitations
## 2 Schematic Model of a Pounding Tuned Mass Damper (PTMD)  
Based on the fact that a large amount of energy can be dissipated during impact, a bounding tuned mass damper (PTMD) is developed by setting up a motion limitation around the mass of a tuned mass damper (TMD) device [34]. The basic schematic model of a PTMD device is shown in Figure 1. As shown in the figure, a viscoelastic (VE) layer is attached to the limitation collar. Unlike some limitation collars found in traditional TMD devices, the limitation can not only function to prevent extreme vibration of the mass, but it can also further dissipate vibration energy through the deformation of VE layer during impact. When a certain gap is set between the mass and the limitation,impact will happen between the mass and the VE layer as soon as their relative displacement reaches the gap value. As a result, during operation, this device can absorb vibration energy of the host structure and transfer it to the kinetic energy and potential energy of the attached mass, as well as some heat energy from the impact. The amount of kinetic energy is largely dependent on the weight and the moving speed of the mass; the potential energy is affected by the stiffness of the connection; the amount of impact energy will be dependent on the relative velocity and deformation of the VE layer. Therefore, by changing the mass, stiffness, and gap of the TMD will definitely vary the control efficiency under different cases.
## 5 Discussion and Future Directions
### Methodological Quality and Evidence Gaps  
In this review, several studies seem to suffer from small sample sizes, short follow-up durations, and inconsistent outcome reporting. Most early studies (2011-2018) were single-subject or small case series, limiting generalisability. Many of the studies mentioned in this review were either tested on animals or cadavers, were pilot studies, or were early phase feasibility studies, which explains the lack of sample sizes. For example, the pilot study on upper limb FES therapy included only four participants, limiting statistical significance and generalisability. Similarly, the NEO brain-computer interface was tested on a single patient, making it difficult to draw broader and more reliable conclusions. Many studies fail to report adverse event rates, dropout data, or standardised outcome measures such as ASIA scores or neurophysiological indices, which hinders cross-study comparison and meta-analysis [47]. Moreover, it is common in most of the studies reviewed that device efficacy is often reported without stratification by injury level, chronicity, or comorbidities--factors known to influence rehabilitation outcomes. These findings reinforce the need for standardised protocols, higher-powered studies, and broader inclusion criteria in future research to ensure device efficacy across diverse SCI populations. Future clinical studies may be performed with a larger cohort across a varied geographical area.