esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Generation of DaMDHAR-Expressing Transgenic Yeast and Their Stress Response to FT  
A cDNA containing the open reading frame (ORF) of _MDHAR_ from the Antarctic hair-grass _D. antarctica_ (_DaMDHAR_) was subcloned into the yeast expression vector p426 GPD, thereby facilitating the constitutive expression of the _DaMDHAR_ gene under the regulation of the yeast _GPD_ promoter (Figure 1A). To determine whether the _DaMDHAR_ gene is effectively expressed in transgenic yeast, immunoblotting was performed. A single signal intensity corresponding to a region within \(\sim\)45 kDa) was detected from transformed yeast cells with the p426 GPD-_DaMDHAR_ construct (DM), whereas there were no intensity in wild-type (WT) yeast cells with vector alone (Figure 1B). To investigate if heterologous _DaMDHAR_ expression is related to stress tolerance in yeast cells under FT conditions, a spotting assay was introduced. The spotting assay demonstrated that DM cells hadacquired tolerance to FT stress compared to WT cells, dependent on the cycle of FT stress from FT1 to FT6, although stress resistance decreased with progression in the FT cycle. There was a distinct difference in both DM and WT cells under FT stress conditions despite there being no difference under normal conditions (Figure 1C). To confirm these results, growth kinetics was performed. In the growth-kinetics assay, DM cells recovered more rapidly than WT cells for the indicated times when exposed to FT2 stress (Figure 1D). These results suggested that _DaMDHAR_ expression in _S. cerevisiae_ effectively enhances stress tolerance when yeast cells are challenged to FT stress.  
Figure 1: Cellular response in cDNA encoding _MDHAR_ (_DaMDHAR_)-expressing transgenic yeast cells to freezing and thawing (FT) stress. (**A**) Schematic diagram of _DaMDHAR_-expression vector constructs for _S. cerevisiae_. _GPDp_, glyceraldehyde-3-phosphate dehydrogenase (GPD) promoter; _DaMDHAR_, the gene encoding MDHAR from Antarctic haigrass _D. antarctica_; _CYC1t_, cytochrome c (CYC1) terminator; _URA3_, uracil gene as a selectable marker; _BamHI_ and _HindIII_, restriction endonuclease. (**B**) _DaMDHAR_ expression at the protein level was performed by immunoblotting assay. Tubulin protein was used as a loading control. (**C**) Stress-response assay under six-repeated FT cycles. Exponential yeast cells (A\({}_{600}\approx 1.5\)) were exposed to six-repeated FT cycles from FT1 to FT6 and then serially diluted to \(10^{-4}\) with Yeast Extract Peritone Dextrose (YPD) broth. Five microliters of the diluted solution were spotted onto the YPD agar broth, incubated at 28 \({}^{\circ}\)C, and then visualized. (**D**) Growth-kinetics assay after FT2 exposure. Exponential yeast cells exposed to repeated FT cycles were harvested by centrifugation and resuspended into YPD broth. Optical density was measured at 600 nm every 1 h for 12 h. WT, wild-type cells with empty vector; DM, _DaMDHAR_-expressing transgenic cells.
## 5 Case-Study: Resource Fitting
### Experiment 2: FL Clients' and Server Collocated vs. Isolated  
In this experiment, we investigate the performance implications when collocating the FL server with the client services. For this, we explore two configurations: (i) the client and server share the same physical node and resources; and (ii) the client and server run on separate nodes. The purpose of this experiment is to establish a baseline for subsequent experiments that involve multiple clients. Our goal is to determine whether it is feasible to schedule the server on the same testbed node as the clients without affecting their performance, or if a dedicated physical node for the server is necessary.
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 4 Optimization of Detection Algorithms for Vertical Curve Segments
### Algorithm Verification
This method achieves comparable detection accuracy to the acceleration integration method on straight and level sections while demonstrating substantially higher accuracy on vertical curve sections, with maximum detection error \(E_{\text{max}}\) and root mean square \(E_{\text{rmse}}\) reduced by 71.77% and 86.61%, respectively. In summary, a key limitation of the acceleration integration method lies in the inability of the high-pass filter to completely remove low-frequency components introduced by vertical curves. In contrast, the advantage of the reference cancellation method is that it does not require the design of different spatial cutoff frequencies for various track alignments; it achieves consistent detection accuracy using a single high-pass filter cutoff frequency. However, this method also has a notable drawback: if ideal pitch angle data cannot be obtained, the detection accuracy may be compromised.
## 5 Experiments and Results
### K-EmoDB  
Table 3 shows emotion recognition results for K-EmoDB. [PERSON] [33] achieved approximately 67.9% accuracy using 34 acoustic features and LSTM. [PERSON] [30] employed eGeMAPS features and a multi-layer perceptron model to achieve 67.14% accuracy. The study by [PERSON] [58] employed only MFCC as the acoustic feature and achieved 61.32% using the logistic model tree. The studies by [PERSON] [59] and [PERSON] [9] proposed an emotion recognition method by end-to-end learning using a convolution layer. With the recognition using 1D convolution layer and LSTM with the input of time-series signals of speech data, their accuracy was 65.89%, and with the recognition using the LSTM layer by combining 1D convolution layer and 2D convolution layer with the input of Mel-spectrogram, their accuracy was 62.63%. When the basic feature combination proposed in this study was used, the accuracy was 70.51%.  
When emotion recognition was performed by combining the basic feature combination and harmonic features, the accuracy was 75.46%, which improved around 5% compared to that not using the harmonic features. Moreover, when the final emotion of the speech data was produced by determining the label of each section at a shorter interval, high accuracy of 83.81% was obtained.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & \multicolumn{2}{c}{**Features**} & **Algorithm** & **Accuracy** \\ \hline \multirow{8}{*}{[PERSON] (2017)} & \(\bullet\) & 3 Time-domain: zero crossing rate, energy of energy & & \\  & \(\bullet\) & 5 Spectral-domain: spectral centroid, spectral spread, spectral entropy, spectral flux, spectral rolloff & LSTM with CTC loss & 67.90\% \\  & \(\bullet\) & 13 MFCCs & & \\  & \(\bullet\) & 13 Chroma: 12-dimensional chroma vector, standard & & \\  & deviation of chroma vector & & & \\ \hline [PERSON] (2019) & \(\bullet\) & eGeMAPS feature set & Multi-layer perceptron & 67.14\% \\ \hline Zamil (2019) & \(\bullet\) MFCC & Logistic Model Tree & 61.32\% \\ \hline George (2016) & - & 1D Conv layer + LSTM & 65.89\% \\ \hline Jianfeng (2019) & Log mel-spectrogram & 1D + 2D Conv layer + LSTM & 62.63\% \\ \hline \multirow{8}{*}{Proposed} & \(\bullet\) & Spectral: Centroid, Bandwidth, Contrast, Flatness, Rolloff & \\  & \(\bullet\) & MFCC & & \\ \cline{1-1}  & \(\bullet\) & 12 Chroma & LSTM + Voting mechanism & 83.81\% \\ \cline{1-1}  & \(\bullet\) & Harmonic: Inharmonicity, Tristimuls, Harmonic & & \\ \cline{1-1}  & & energy, Noise energy, Noiseness & & \\ \hline \multirow{8}{*}{Proposed} & \(\bullet\) & Spectral: Centroid, Bandwidth, Contrast, Flatness, Rolloff & & \\ \cline{1-1}  & \(\bullet\) & MFCC & & \\ \cline{1-1}  & \(\bullet\) & 12 Chroma & LSTM & 75.46\% \\ \cline{1-1}  & \(\bullet\) & Harmonic: Inharmonicity, Tristimuls, Harmonic & & \\ \cline{1-1}  & & energy, Noise energy, Noiseness & & \\ \hline \multirow{8}{*}{Proposed} & \(\bullet\) & MFCC & & \\ \cline{1-1}  & \(\bullet\) & Spectral: Centroid, Bandwidth, Contrast, Flatness, Rolloff & & \\ \cline{1-1}  & \(\bullet\) & 12 Chroma & LSTM & 70.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results from K-EmoDB.
# Classifying Microscopic Images of Reactive Lymphocytosis Using Two-Step Tandem AI Models
## 1 Introduction
According to the morphological criteria of the Standardization Committee of the Japanese Society for Laboratory Medicine and Hematology, atypical lymphocytes are characterized by a diameter of 16 um or more, a relatively wide cytoplasm, a strongly basophilic color tone compared with normal lymphocytes, and the presence of azurophilic granules and vacuoles in the cytoplasm. The nuclei are round and may be transformed. Chromatin is concentrated, with a mixture of lymphocyte- and para-chromatin-like chromatin. Nucleoi may be present within the nucleus [11]. Because of these various morphological characteristics, [PERSON]'s categorization, as a classical method, was developed as a subcategorization criterion. Quality control surveillance has shown that the recognition of atypical lymphocytes is more complicated compared with other cell categories (e.g., monocytes, normal lymphocytes, lymphoblasts, or erythroblasts), and is frequently judged differently, even by experts. In the past 10 years, numerous clinical application studies on AI technology for mature leukocyte categorization or neoplastic leukocytes diagnosis have reported the high accuracy and usefulness of AI analysis [12; 13; 14]. Recent lymphocytosis experiments are shown in Table 1. Most studies have focused on neoplastic lymphocytes, all achieving accuracy of more than 85% [15; 16; 17; 18; 19; 20; 21; 22]. AI technology for neoplastic lymphocytosis has achieved high performance in clinical diagnosis and been proven to contribute to the diagnosis of neoplastic lymphocytes at an early stage. However, although the highly accurate detection of atypical lymphocytes in hematological screening is essential for the diagnosis of viral infections at an early stage, few studies have been conducted on techniques for atypical lymphocyte screening applications using AI technology [19; 20]. If AI diagnostic technologies for reactive lymphocytosis and their clinical utility can be identified, AI could also contribute to the diagnosis of other hematological diseases with polymorphic changes. Recently, the effectiveness of ensemble models for improving leukocyte recognition accuracy using AI has been reported [23; 24; 25]. Ensemble models are a method of improving accuracy by combining multiple models, mainly used in parallel combinations. Because reactive lymphocytosis involves atypical lymphocytes that display diverse morphologies, it may be more difficult to recognize compared with neoplastic leukocytes, which displays monomorphic changes, when using a single AI model to compare multiple similar cell categories. Therefore, it may be possible to improve recognition accuracy by extracting only similar cell categories and combining multiple models. In the present study, we approached leukocyte categorization using two-step tandem AI models and assessed their clinical usefulness for the identification of reactive lymphocytosis at blood smear screening.
## 2 Materials and Methods
### qRT-Pcr  
#### 2.5.1 Primers Design and Validation, Reference Gene Selection  
Specific primer pairs for _alpha-tubulin (\(\alpha\)-TUB)_, _beta-tubulin (\(\beta\)-TUB)_, _Enolase (ENO)_, _1-deoxy-D-xylulose 5-phosphate synthase (DXS)_, _lycopene \(\beta\)-cyclase (LCYB)_, _chloroplast-localized heat shock protein (HSP70)_, and _chloroplast ribulose phosphate-3-epimerase (RPE)_ were designed based on the sequences of the corresponding genes in the _D. salina_ genome deposited in GenBank. Whereas, primer pairs for _carotene globule protein_ (_CGP_) amplification were designed based on the sequence of the correspondinggene in _Dunaliella bardawil_ genome, as the sequence among _Dunaliella salina_ is not available. All primer sequences were designed using Primer-BLAST from NCBI and Primer 3 Input 4.0. Primer pairs used for _Actin (Act)_ and _18S rRNA_ are those cited by [39; 40], respectively. Primer set and amplicon characteristics are represented in Table 1. The designed primers obeyed to the following criteria: a length between 18-24 nucleotides, an amplicon length from 100 to 200 bp, chosen temperature (Tm) around 60 \({}^{\circ}\)C, and avoiding secondary structures and self- and cross-annealing. The specificity of the designed primer was verified by the blast search function ([[http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov)]([http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov))). Furthermore, the optimal concentrations were set after testing three different primer concentrations ranging from 100 to 200 nM to avoid non-specific products and/or primer-dimer artifacts.  
Gene coding for _Act_, _18S RNA_, _\(\alpha\)-TUB_, and _\(\beta\)-TUB_ was used for the screening of the most stable gene in all experiment conditions as they are known to be classic plant reference gene candidates [41]. Quantification cycle (Cq) values were imported into the comprehensive web-based tool RefFinder ([[http://150.216.56.64/referencegene.php?type=reference](http://150.216.56.64/referencegene.php?type=reference)]([http://150.216.56.64/referencegene.php?type=reference](http://150.216.56.64/referencegene.php?type=reference))), which integrates commonly used reference gene evaluation programs together, including geNorm, NormFinder, Bestkeeper, and the comparative delta Ct methods [41; 42]. Results elaborated by RefFinder were confirmed by GeNorm, which is available on excel file [43].
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 4 Experimental Results and Discussion
### Dataset  
The probes were deployed in Zhengzhou and Hong Kong, China, and Virginia, USA. Traceroute measurements of the acquired IPv6 landmark addresses were performed by probes for a period of a month. The measurement data were collected and combined with methods such as Allys and Mercator [32] for router aliasing and [PERSON]'s [33] anonymous route resolution method for cleaning and filtering the measurement data. We chose the minimum value of delay in the measured data during the long measurement period as the final delay data involved in the experimental training. The hostname data corresponding to the measurement dataset were derived from the public dataset Rapid7 [34]. The IPv6 city-level identification IPs were collected using the method of Zhao [13] for the measured data.  
Based on the demand for fine-grained location accuracy in the field of IP geolocation, we screened a total of 5610 street-level landmarks in three regions at home and abroad, including 2195 in Shanghai, China, 1826 in New York State, USA, and 1589 in Tokyo, Japan,by combining the IPv6 public datasets provided by APNIC [35], an Asian Pacific address distribution organization, and ARIN [36], a North American address distribution organization, through the method of [20]. The region of Shanghai is 6340 square kilometers. The region of New York State is 141,300 square kilometers. The region of Tokyo is 2155 square kilometers. These regions are divided between developed and developing parts of North America and Asia. As shown in Figure 4.  
It is worth noting that we were dealing with the cleaned path data. Therefore, the inaccuracy of the route data caused by route aliases and anonymous routers is a problem we must solve before starting the experiment. Routing aliases were cleaned using the Spring [32] approach. As for anonymous routers, we simply treated them as a \"top-down\" link. The anonymous routing processing scheme of Sarac [33] was used.
## 1 Introduction
Previous devices such as the CP-Walker, Lokomat(r), and the Walkbot-K(r) have been studied in terms of fatigue during gait rehabilitation, showing improvements in physiological parameters such as heart rate (HR) and energy cost during walking [19; 20; 21]. The newly released ATLAS 2030 is a portable device designed to support a child's body from trunk to feet, facilitating both passive and active forward and backward walking, both passively and actively [22]. Previous case series of children with CP levels III and IV or with spinal muscular atrophy have shown that this device can reduce spasticity and improve range of motion. However, there is a lack of information regarding children with level V [23; 24]. The ATLAS 2030 enables gait training for children with GMFCS levels IV and V with limited mobility, giving them the opportunity to interact with their environment. This external walking aid could provide a completely novel experience for these children, in addition to all of the benefits that this therapy can offer [23]. Previous literature has indicated that all of these interventions positively affect motor function, range of motion, and spasticity. However, the studies often fail to distinguish between the different interventions. Furthermore, the techniques and methodologies used in the studies are very different (starting with working position and activity demands), with very different samples and aspects regarding water temperature, number of sessions for each intervention, and GMFCS level, factors that compromise the reliability of the results. Given the current knowledge and recognizing that children with CP levels IV and V have less functionality and more limitations, it is essential to identify which interventions have greater evidence and are therefore more appropriate for the management of this type of patient. Therefore, the main aim of this study was to compare the post-immediate effect of four interventions (RAGT, compared to aquatic therapy, hot water immersion or supported standing) on spasticity, range of motion, and heart rate of children with CP levels IV and V.
## 2 Proposed Methodology
### Capturing Spatiotemporal Features through the DED-ConvLSTM Module
Pyramid dilated convolutions offer significant advantages in the field of spatiotemporal prediction, particularly in enlarging the receptive field without information loss. Compared to traditional CNNs, they obviate the need for pooling to increase the receptive field, thereby avoiding the reduction in image size and the consequent loss of information. As shown in Figure 2, dilated convolutions allow for the observation of a broader range of information without reducing image resolution, addressing the issue of information loss during the process of downsizing and then upsizing in fully convolutional networks. Under the condition of maintaining a constant kernel size in the convolution process, the model is able to extract a greater amount of spatial information. This leads to the development of a more powerful ConvLSTM variant, termed the DED-ConvLSTM model.
# Determining Rotor Blade Multi-Mode Vibration Components +
## 3 Validation of the Algorithm by Numerical Simulation
Table 2 also shows the influence of the number of rotations N on accuracy in calculating the amplitude \(\rm\varepsilon_{iA}\) (Equation (3)) and phase \(\rm\varepsilon_{i\varphi}\) (Equation (4)). The assumed vibration parameters in Table 1 are consistent with the calculated vibration parameters in Table 2, which shows that the algorithm is correct. The accuracy of the calculated vibration velocity amplitude and phase for: frequency 560 Hz is \(\rm\varepsilon_{iA}=-4.7\%\), \(\rm\varepsilon_{i\varphi}=5.0\%\); for 766 Hz, \(\rm\varepsilon_{iA}=5.4\%\), \(\rm\varepsilon_{i\varphi}=-4.8\%\); for 1720 Hz, \(\rm\varepsilon_{iA}=10\%\) and \(\rm\varepsilon_{i\varphi}=7.7\%\) and for 2410 Hz \(\rm\varepsilon_{iA}=-14\%\), \(\rm\varepsilon_{i\varphi}=19\%\). These values were calculated for the number of revolutions N = 20 and the accuracy between calculated and assumed velocities \(\rm\sigma=0.23\ m/s\) (Equation (A18)). The accuracy of the vibration velocity amplitude \(\rm e_{iA}\), phase \(\rm e_{i\varphi}\), and accuracy \(\rm\sigma\) between the calculated and assumed velocities \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\rm\bf\omega_{i}\) (Hz) & \(\rm\bf A_{i}\) (m/s) & \(\rm\varepsilon_{iA}\) (\%) & \(\rm\varphi_{i}\) (deg) & \(\rm\varepsilon_{i\varphi}\) (\%) & \(\rm\bf A_{ix}\) (mm) \\ \hline \multicolumn{6}{c}{N = 20 \(\rm\sigma=0.23\ m/s\)} \\ \hline
560 & 1.5 & \(-4.7\) & 10 & 5.0 & 0.41 \\ \hline
766 & 1.3 & 5.4 & 50 & \(-4.8\) & 0.28 \\ \hline
1720 & 0.6 & 10.0 & 30 & 7.7 & 0.05 \\ \hline
2410 & 0.5 & \(-14.0\) & 20 & 19.0 & 0.03 \\ \hline \multicolumn{6}{c}{N = 25 \(\rm\sigma=0.79\ m/s\)} \\ \hline
560 & 1.5 & \(-1.3\) & 10 & 5.0 & 0.41 \\ \hline
766 & 1.3 & 1.5 & 50 & 0.0 & 0.28 \\ \hline
1720 & 0.6 & \(-8.3\) & 30 & \(-3.0\) & 0.05 \\ \hline
2410 & 0.5 & \(-10.0\) & 20 & 10.8 & 0.03 \\ \hline \multicolumn{6}{c}{N = 50 \(\rm\sigma=0.79\ m/s\)} \\ \hline
560 & 1.5 & \(-1.3\) & 10 & 4.0 & 0.42 \\ \hline
766 & 1.3 & 2.3 & 50 & 0.0 & 0.27 \\ \hline
1720 & 0.6 & \(-8.3\) & 30 & \(-3.0\) & 0.05 \\ \hline
2410 & 0.5 & \(-10.0\) & 20 & 10.8 & 0.03 \\ \hline \end{tabular}
\end{table} 
Table 2: Influence of rotation number N on blade vibration amplitudes \(\rm A_{i}\), phase \(\rm\varphi_{i}\), blade amplitude \(\rm A_{xi}\) and accuracy \(\rm\varepsilon_{iA}\), \(\rm\varepsilon_{i\varphi}\). (see Equation (A18)) for N = 25 (\(\sigma\) = 0.79 m/s) and N = 50 (\(\sigma\) = 0.79 m/s are higher than for N = 20 (\(\sigma\) = 0.23 m/s). Therefore, N = 50 will be used in further calculations.
## 2 System Model
### Queuing Model  
This section summarizes the standard queuing model. The complete set of assumptions for the queuing model can be found in [32].  
A queuing model is composed of jobs, scheduling policy queues, and servers. The jobs entering the system are described by a renewal process with different attributes. The attributes may be assigned upon arrival or later on. The scheduling policy is responsible for allocating the servers to the jobs and choosing the job to be processed out of the queue when the server is idle. The queuing model of the data packets assumes a renewal reward process [33]. We use the extended [PERSON] [32; 46] notation \(A/B/C-D/P\) to characterize the input random process and the service mechanism.  
As defined below:  
* \(A\) refers to the inter-arrival time of the renewal process.
* \(B\) refers to the random process of service time required by the packets.
* \(C\) refers to the number of servers.
* \(D\) refers to the deadline random process.
* \(P\) refers to the priority or reward random process.  
Each packet arriving at the AP has its destination MS, payload, priority, and deadline. Let \(J_{i}\) (\(i\in\mathbb{N}\)) be the \(i^{\text{th}}\) packet that arrives to the system. Let \(A_{i}\), \(B_{i}\), \(C_{i}\), \(D_{i}\), and \(P_{i}\) be random variables defining the job, \(J_{i}\). The packet arrival time, denoted \(t_{i}\), is defined by:  
\[t_{i}=t_{i-1}+A_{i}=\sum_{j=1}^{i}A_{j}. \tag{1}\]  
Let the tuple \(J_{i}=<a_{i},b_{i},e_{i},p_{i}>\) represent a packet \(i\) with its random parameters where \(a_{i},b_{i},e_{i}\), and \(p_{i}\) are realizations of \(A_{i},B_{i},E_{i}\), and \(P_{i}\).  
Following are the queuing model assumptions:  
* The pair \((A_{i},P_{i})\) is a renewal reward process.
* Packets' deadlines are measured with respect to the end of transmission.
* The reward is obtained only if packets arrived on time (hard real-time system requirements).
* \(b_{i}\), \(e_{i}\), and \(p_{i}\) are known upon arrival of \(J_{i}\) to the queue.
* The scheduling policy is non-preemptive, and forcing idle time is not allowed.  
Let \(S_{t}^{\pi}\) be the set of packets that were successfully delivered to their destinations by policy \(\pi\) up to time \(t\). By definition, the renewal reward process provides a mechanism to analyze the performance of the system. The cumulative rewards' function is a simple way to compare the performance of different algorithms.  
**Definition 1**.: _The cumulative reward function for time \(t\) and policy \(\pi\) is:_  
\[U_{t}^{\pi}=\sum_{J_{i}\in S_{t}^{\pi}}p_{i}. \tag{2}\]  
The objective is to find a policy \(\pi\) that maximizes the cumulative reward function.