esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Strengths and Limitations  
This paper makes a novel contribution to the literature by providing information about the anthropometric and physiological characteristics of Hungarian junior female rowers through an analysis of how these characteristics vary depending on age, ranking, and stage of sports career. These findings may serve as comparative material for further studies in this field, especially as there are a small number of publications dealing with the issue of differences in anthropometric and physiological characteristics of female rowers representing various age groups. It should be noted that weight categories were not used to classify the rowers in this study due to their relatively young age. The limitations of this study were associated with sampling, time, and logistical constraints. Although the sample was relatively homogeneous, it is likely to be highly representative of Hungarian rowers as a whole. Regarding time and logistical limitations, all measurements were performed within a timeframe of 3 days to minimize disturbances to the athletes' training and changes in their condition. Therefore, due to limited time, this study did not examine heart rate (HR) values (min, avg, and max), lactate anaerobic threshold values, and all indicators of acid-base balance, such as the concentration of lactic acid in the blood, alkaline deficiency or excess, blood pH and current molecular pressure of CO\({}_{2}\). Nonetheless, the study obtained valid results with regards to differences between age groups and VO\({}_{\text{2 max}}\), which is considered the \"gold standard\" for cardiorespiratory fitness. To complement the results of this study, future studies could examine indicators of acid-base balance, i.e., blood pH, partial pressure of CO\({}_{2}\) in arterial blood (pCO\({}_{2}\)), HCO\({}_{3}\)- ion concentration, and alkaline deficiency or excess (BE). Moreover, the authors suggest conducting a repeated study on the same group of athletes during different training periods.
# Design of Optical Tunnel Switching Networks for Big Data Applications
###### Abstract
The three-plane Torus topology optical tunnel switching network with 49 nodes is presented, and its performance is also discussed in Section 4. In Section 5, performance evaluation, traffic management and applications of optical tunnel switching networks are discussed. Finally, some conclusions are made in Section 6.
## 3 Results and Discussion
### Establishment of Optimal Analysis Conditions  
The optimal HPLC analysis conditions were determined by comparing a selection of reverse-phase C18 columns (I. D. 4.6 mm \(\times\) length 250 mm, particle size 5 \(\upmu\)m) such as Phenomenex, Waters, Shiseido, and Younglin Biochrom Co., and column temperatures (30, 35, 40, and 45 \({}^{\circ}\)C) using the four selected marker analytes. The optimal conditions were established to be the use of a Waters' SunFire C18 column (Milford, MI, USA), maintained at 40 \({}^{\circ}\)C, and gradient elution using distilled water and acetonitrile, both containing 0.1% (_v/v_) formic acid (Table S2). The four markers, hyperoside, quercitrin, ginseng Rg1, and 6-gingerol, were eluted at 14.15, 16.04, 17.87, and 31.24 min, respectively. A representative HPLC chromatogram of the standard and test solutions is shown in Figure 1.
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 3 Experiments  
In this Section, to estimate the optical-to-SAR image registration capability of the proposed algorithm, we use the proposed algorithm to compare with OS-SIFT [12] and RIFT [15]. Both of the two comparison algorithms display a good performance for optical and SAR image registration. All methods in this paper are conducted via MATLAB R2020b software.
## Results
### The Removal of Chlorophyll-_a_, MCs and T&O Compounds via Treatment Processes in the DWTP  
#### 2.3.1 Coagulation/Flocculation (C/F+)  
The concentrations of cyanobacteria and the associated metabolites in raw water sampled from the DWTP showed no significant differences from that sampled at Intake-A in the studied EDWS (\(p>0.05\)). As the first step to remove algae and metabolites, the Coagulation/Flocculation effects must be assessed to ensure minimal impacts on the subsequent processes and improve total removal efficiencies of DWTP. In this study, the elimination efficiency of chlorophyll-_a_ from raw water to the C/F+ (+, with 9 mg/L potassium permanganate (KMnO4) and 10 mg/L powdered activated carbon (PAC) in summer and autumn with contact time of 30 min) process ranged from 24.6 to 80.9% (45.6% average) (Figure 2). The removal rates of intracellular MCs and T&O compounds ranged from 9.7 to 89.5% (43.8% average) (Figures 3 and 4). The maximum removal rates of chlorophyll-_a_ and the cell-bound metabolites by the C/F+ process were both observed in summer. The elimination rates of the dissolved MCs and T&O compounds were lower than 45.7%, and the concentrations of some dissolved metabolites even increased after the C/F+ step. The concentrations of EMC-LR, EMC-RR and _d_-ION increased by 55.2%, 42.8% and 45.3% (on average) in winter, respectively, while those of _d_-CYC and _d_-ION increased by 175.1% and 210.7% in summer, respectively.  
Figure 1: Relationships analyzed by redundancy analysis among cyanobacterial density and concentrations of microcystins and taste-and-odor compounds in the eastern drinking water source of Lake Chaohu (Intake-A, -B and -C). GEO: geosmin; MIB: 2-methyl isoborneol; CYC: \(\beta\)-cyclocitral; and ION: \(\beta\)-ionone.
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 4 Antenna Array Design and Scenario Analysis
### Test Scenario and Parameter Configuration  
We validate and compare the performance of the proposed system by testing it on a moving vehicle on an actual roadway. Figure 9a illustrates how the radar is installed. As shown in Figure 9b, the RF panel antenna is oriented to face the direction of vehicle travel, enabling the radar to perform the spatial detection of other vehicles on the road from a side-view perspective.  
Figure 8: Antenna array II. (**a**) RF board. (**b**) Antenna array layout. (**c**) Virtual antenna RX channels’ arrangement.  
The radar data sampling information used in the experiment is shown in Table 2, where the number of distance dimension sampling points is 1024, the number of Doppler sampling points is 256, and the data bit width is 32 bits.
## 2 Proposed System
### Sensors and Embedded System Hardware
#### 2.2.4 Raspberry Pi Zero W  
As shown in Figure 1, the Raspberry Pi Zero W embedded system is used to read and process the data from the DH22 and AMG8833 sensors. We chose the Raspberry Pi Zero W due to its low cost (US$ 25) and the small size of the single-board computer that can perform a simple image processing task for calculating the leaf temperature from the thermal camera. The Raspberry Pi Zero W specifications are a 1 GHz single-core ARMv6 CPU (BCM2835), 512 MB RAM, 802.11 b/g/n wireless LAN, Bluetooth 4.1, 40-pin GPIO, and CSI connector.
## 4 Remarks on the Parallel Implementation  
The parallelization of the product \((E_{n}+D)^{-1}F(x_{n})\) (Step 2 in Algorithm 3) is obvious, because \(E_{n}+D\) is block diagonal. Concerning the product \((E_{n}+D)s_{n}s_{n}^{T}\), observe first that the element \(p_{ij}\) of the product \(ss^{T}\) is \(p_{ij}=s_{i}s_{j}\) and \(s_{n}s_{n}^{T}\) can be computed by a very simple algorithm. Then, because \(E_{n}+D\) is block diagonal, \(E_{n}+D=diag((E_{n}+D)_{1},...,(E_{n}+D)_{p})\) with dimensions \(m_{1},...,m_{p}\), the product \((E_{n}+D)_{i}s_{n}s_{n}^{T}\) gives the \(m_{i}\) lines of \((E_{n}+D)s_{n}s_{n}^{T}\). These products are independent from each other and therefore can be computed in parallel. The same observations can be made for Algorithms 4 and 5.  
In order to implement this method, let us suppose that the multiprocessor system has \(p\) processors. A simple idea is to set the system into \(p\) partitions and assign a partition to every processor. In this case the following issue arises: How large should the partitions \(m_{i},\ i=1,...,p\) be in order to obtain a low cumulative computation cost per iteration? In the case of Algorithms 3 and 5, the main processing of processor \(i\) is to inverse a matrix of dimension \(m_{i}\). Therefore every processor has to perform a computation that has a polynomial complexity of order \(m_{i}^{3}\). The cumulative computation cost for all \(p\) processors is \(m_{1}^{3}+m_{2}^{3}+...+m_{p}^{3}\), and, if there is not overlapping, \(m_{1}+m_{2}+...+m_{p}=m\). Further, we use the following elementary inequality  
\[\sum_{i=1}^{p}x_{i}^{q}\leq(p-1)\mu^{q}+(\sum_{i=1}^{p}x_{i}-(p-1)\mu)^{q},\]  
where \(x_{i}\) are positive numbers and \(\mu=\min\{x_{1},...,x_{p}\}\). If \(q=3\) and \(x_{i}=m_{i}\), then  
\[\sum_{i=1}^{p}m_{i}^{3}\leq(p-1)\mu^{3}+(m-(p-1)\mu)^{3}.\]  
We will prove now that if  
\[\frac{m}{(p^{2}-p+1)^{1/3}+p-1}\leq\mu\leq\frac{m}{p}, \tag{3}\]  
then  
\[(p-1)\mu^{3}+(m-(p-1)\mu)^{3}\leq\frac{m^{3}}{p}.\]  
Because \(p\mu\leq m\), it is sufficient to show that \((p-1)\mu^{3}+(m-(p-1)\mu)^{3}\leq p^{2}\mu^{3}\), and this is equivalent with the first part of (3). We obtain the following  
**Propositon 1.**_Suppose that the smallest size of partitions, \(\mu=\min\{m_{1},...,m_{p}\}\), satisfies the condition (3). Then the cumulative computation cost in Algorithms 3 and 5 is less than \(m^{3}/p\)._  
A similar result can be established for Algorithm 4. The condition (3) becomes  
\[\frac{m}{(p+1)^{1/2}+p-1}\leq\mu\leq\frac{m}{p},\]  
and the cumulative computation cost satisfies \(\sum_{i=1}^{p}m_{i}^{2}\leq 2m^{2}/p\).
# Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets
## 2 Task Factorisation  
A full audio transcription task can be described as audio event detection followed by event classification. In order to properly train a full transcription network, we need a large amount of data that is not available in a low-resource dataset. Since it is very hard to train a network to predict full transcription on a low-resource dataset, we factorise the final task of full transcription into intermediate tasks that can predict an intermediate transcription matrix that can later be used to boost the performance of a full transcription network. Figure 1 depicts the overall task factorisation into the intermediate tasks and how they interact with the final task of full transcription. We define a WHEN network that performs audio event detection considering all classes as one general class; in other words, it predicts when any event is present without taking into consideration the different event classes. We also define a WHO network that performs audio tagging without predicting any temporal information. By combining the two different predictions from these networks, we create an intermediate transcription that provides us with the events present in a recording and the times where any of these events could be present in a recording. This intermediate transcription is to be used as supplementary information when training the full transcription network in order to improve its performance by focusing its attention to the classes present in a recording and the time frames that may contain them.  
When using a large enough dataset that provides satisfactory training data and has a a good representation for each different class, many methods have been successful in performing both of the intermediate tasks. A few methods for audio event detection can be found in [13; 14], while for audio tagging in [2; 3; 4; 5; 6]. These tasks are less challenging to train for than a full transcription task. However, using a low-resource dataset can degrade their performance. Hence, in order to achieve a satisfactory performance when training with a low-resource dataset, we propose a few training setups and techniques. The rest of this section describes in detail the task specific setups and techniques that we used.
# Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition
## 2 Methods
### Extraction of Facial Active Patches  
There are two main drawbacks in direct training of the classifier through the whole face: (1) The dimensions of the features are too large and the training time is relatively long; (2) Some regions on the face do not express emotion and contribute little to the representation of facial expressions. Hence, the features obtained from these regions are most likely to introduce noise.  
The face must be partitioned appropriately for micro-expression recognition to be feasible [22]. The FACS criterion quantifies several muscle movements of the face and reveals 57 elementary components of the expression. These elementary components are known as the action units (AUs) and action descriptors (ADs). Similar to other facial expressions, a micro-expression is also a spatial combination of AUs. Each AU describes a local movement of micro-expression. Table 1 defines several relationships between AUs and facial movements [14].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ROIs** & **AUs** & **Facial Movements** \\ \hline R1, R2 & AU1, AU4 & inner eyebrows \\ R3, R4 & AU2 & outer eyebrows \\ R5, R6 & AU5 & upper eyelid \\ R7, R8 & AU7 & lower eyelid \\ R9, R10 & AU6, AU10 & side of the nose \\ R11, R12 & AU6 & cheeks \\ R13, R14 & AU12, AU13 & mouth corner \\ R15 & AU15 & upper lip \\ R16 & AU17 & chin \\ R17 & AU9 & eyebrow center \\ R18 & AU9 & nose root \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationships between AUs and facial movements.  
Figure 3: Landmarks on the face (68 total) and cut the image.  
Considering that micro-expressions only involve certain local muscle movements and AUs, extracting only a few active facial patches instead of the entire image of the face is an effective approach to recognition. The eyebrows and eyes, for example, are involved in nearly all basic emotions [23]. The morphological characteristics of the eyes and brow are important cues of different micro-expressions. The mouth is another key discriminant area for expression recognition. Here we manually choose a frontal neutral face image as a template, and divide the image into 18 ROIs, as shown in Figure 4.  
The patches are separated according to the movements of micro-expressions. Each patch represents the active facial area of the micro-expression. We maintain the same size of each patch and extract the sequences of active patches for subsequent research.
# Air-Based Contactless Wafer Precision Positioning System
## 6 Wafer Control
### Outer Loop Controller  
In the outer loop, two separate controllers have been designed, one for controlling the rotation at lower bandwidth and one for positioning the wafer in \(x\) and \(y\) at a higher bandwidth to maximize the performance of the total system. The parameters of the controller will be discussed later in this section.  
The outer loop could not be identified using the open-loop response because the system consists of a floating mass with viscous damping. The identification of the system could be done by adding stiffness to the mass or through simulation. The simulated open-loop transfer function could be described by the transmissibility of the inner loop controller (\(T_{ILC}\)), the pneumatic gain of the system and the wafer, which is a mass system. This is shown in Equation (5), where \(C_{OLC}\) is the outer loop controller.  
\[PC=\frac{T_{ILC}C_{OLC}}{m_{ wafer}s^{2}} \tag{5}\]  
For the controller, it is decided to use a PD controller with a low pass filter. The low pass filter must be placed before 300 Hz to cancel out the coupling in the inner loop. Because of the high gain of the mass-line, it was chosen to leave out the integrator.  
\[K_{p}=\frac{m_{ wafer}\omega_{bw}^{2}}{k_{pn}*4}; \tag{6}\]  
The gain of the system was calculated using Equation (6). The factor 4 accounts for the frequency range of the derivative part of the controller as given in Table 4, which adds a gain of the same value. To start, the pneumatic gain \(k_{pn}\) values calculated by [PERSON] were used. This gives an initial place to start after which the controller has been tuned iteratively. After the initial measurement, the new controller gain could be calculated to compensate for the lower pneumatic gain.  
Figure 27: The steady state error during control of the wafer in x and y.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**DOF** & **RMS Error** \\ \hline x & 12.4 μm \\ y & 12.9 μm \\ Rz & 3.1 mrad \\ \hline \hline \end{tabular}
\end{table}
Table 5: Wafer position control-steady state performance.
## 3 Background: Merkle Patricia Tries in Ethereum State Management  
The Merkle Patricia Trie (MPT) represents a sophisticated fusion of three fundamental data structures: Merkle trees, radix tries, and Patricia tries. Each component serves a crucial role in achieving Ethereum's state management requirements:  
* Merkle trees provide cryptographic verification capabilities through hierarchical hashing, enabling efficient proof generation and verification [21]. The hash-based structure ensures data integrity while allowing selective disclosure of state information;
* Radix tries optimize storage efficiency by sharing common prefixes among multiple keys [22; 23]. This characteristic is particularly valuable for managing Ethereum addresses, which often share significant prefix patterns due to their derivation from public keys;
* Patricia tries (Practical Algorithm to Retrieve Information Coded in Alphanumeric [22]) enhance the radix trie concept by eliminating single-child nodes through path compression. This optimization significantly reduces the trie's height and memory footprint while maintaining lookup efficiency.  
The synthesis of these structures in Ethereum's MPT creates a data structure uniquely suited for blockchain state management, offering the following:Deterministic root hash computation independent of insertion order;
2. Efficient proof generation for state verification;
3. Optimal storage utilization through prefix sharing and path compression;
4. O(log n) complexity for key operations.  
This foundation enables Ethereum to maintain a cryptographically verifiable mapping between addresses and account states while supporting efficient state updates and proof generation.
## 6 Conclusions  
This paper represents an evaluation and comparative study of three routing protocols in MANETs environment, conducted under a series of simulations with varying speed of node scenario. A multipath routing scheme called MRLAM is proposed in which the routing decision is made based on residual energy, link quality, and mobility status of the network nodes. In addition, the MRLAM scheme aggregates multiple parameters into a single metric using the mechanics of the Q-learning process to make an optimal routing decision. The MRLAM routing protocol evaluates the status of nodes during the route computation and topology sensing process to determine the optimal routes in a convergence network. Moreover, the proposed scheme has the ability to cope with link failure and sustains the network lifetime by avoiding nodes with lower residual energy and higher mobility in selecting optimal and stable paths between all pairs of source and destination nodes. The simulation results show that MRLAM has approximately 33% and 23% less energy cost per packet compared to MP-OLSR and MP-OLSRv2 routing schemes, respectively. Moreover, results also corroborate the premise that MRLAM attains better throughput in all scenarios of node speed with successfully delivered packets. In addition, the frequent change in the network topology increase the computational complexity of existing MP-OLSR and MP-OLSRv2 routing processes as the route computation process for discovery of new routes becomes more intricate. In the proposed routing scheme, the Q-learning aggregates multiple parameters related to energy, mobility, and link quality into a comprehensive metric to dramatically reduce the complexity and avoid the control overhead caused by separately broadcasting multiple parameters. Furthermore, the results show that the MRLAM scheme decreases the packet loss ratio up to 30.76% and 24.59%, approximately as compared to MP-OLSR and MP-OLSRv2 routing schemes respectively. Overall, the proposed MRLAM scheme evidently outperforms the conventional MP-OLSR and its extended version MP-OLSRv2 routing schemes in terms of throughput, average end-to-end delay, packet loss ratio, and energy consumption rate.
## 4 Performance and Economic Feasibility of a Retaining Wall Displacement Measurement System
### Economic Feasibility of System
#### 4.2.4 Sensitivity Analysis  
To improve the reliability of the LCC analysis, it was necessary to evaluate the variability of the economic analysis results by changing the LCC analysis variables and assumptions. Consequently, a sensitivity analysis (Appendix B) was conducted by changing five variables (the number of inclinometers, measurement period, system construction cost, system repair cost, and discount rate [Table 11]).  
The sensitivity analysis results (Figure 17) showed that the developed system was economically feasible within the analysis range of all five variables. The yellow bar in Figure 17 shows the results of the economic analysis in Section 4.2.3. Among the five variables, the economic feasibility of the system was the lowest (present worth = $10,074.09, B/C ratio = 1.45, annual measurement cost-saving effect = 31.16%, ROR = 15.02%, and breakeven point = 6.74 years) when the number of inclinometers was 1. When a single inclinometer was installed, measurements were performed only for one side of the retaining wall, and the probability of this case was low. Nevertheless, the retaining wall displacement measurement system had economic benefits relative to the conventional measurement method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Present Worth** & **B/C Ratio** & **Annual Measurement Cost-Saving Effect** & **ROR** & **Breakeven Point** \\ \hline \$56,075.91 & 3.52 & 71.59\% & 61.48\% & 2.71 years \\ \hline \hline \end{tabular}
\end{table}
Table 10: Economic analysis results.  
Figure 17: _Cont._Among the variables considered, the number of inclinometers had the most considerable effect on the economic feasibility. Regarding a site where four inclinometers were installed, the ROR of the retaining wall displacement measurement system increased to 144.87%. The economic feasibility of the system increased as the measurement period increased, showing that the manpower input was larger for the conventional measurement method, since it was based on manpower. The effects of changes in the system construction and system repair costs, as well as the discount rate on the economic analysis results were relatively insignificant, showing that the economic feasibility of the system was not sensitive to these variables. Since the economic feasibility of the system aggravates when system construction costs increase by 351.96% ($42,183.82), the system must be designed and constructed to avoid exceeding this cost level, especially when the cost increases owing to system improvement.