esa-sceva/satcom-chunk-collection huggingface dataset:
# Tabu Search Aided Multi-Track Detection Scheme for Bit-Patterned Media Recording
## 3 Simulation Results  
[PERSON]'s channel model is considered in this paper as described in [11]. The 2D Gaussian pulse response is described by  
\[h(x,y)=A\exp\left(-\frac{1}{2c^{2}}\left[\left(\frac{x}{\text{PW}_{50\_{\text{ along}}}}\right)^{2}+\left(\frac{y+\Delta_{TMR}}{\text{PW}_{50\_{\text{cross}}}} \right)^{2}\right]\right), \tag{1}\]  
where A = 1 is the peak amplitude and \(c=1/2.3548\) is a constant. As in [11], the replay response of a square island with length a = 11 nm has \(\text{PW}_{50\_{\text{along}}}\) = 19.5 nm and \(\text{PW}_{50\_{\text{cross}}}\) = 24.7 nm. For achieving different areal densities, the islands on rectangular grids are arranged by varying the island periods \(T_{x}\) on the along-track and \(T_{y}\) on the cross-track. In this simulation, the arrangements of islands with \(T_{x}\) = 14 nm and \(T_{y}\) = 15 nm, and \(T_{x}\) = 12.7 nm and \(T_{y}\) = 14 nm are considered, which give areal densities round 3.0 Tb/in\({}^{2}\) and 3.5 Tb/in\({}^{2}\), respectively. The signal-to-noise ratio (SNR) is defined as 10 \(\log_{10}\left(A/\sigma^{2}\right)\), where \(\sigma^{2}\) is the variance of the additive white Gaussian noise. The value of track misregistration (TMR) is defined as  
\[\Delta_{TMR}=\left(TMR_{y}\times T_{y}\ \right)/100, \tag{2}\]  
where \(TMR_{y}\) is the percentage of TMR. For the equalizer, the 1D equalizer with 11 coefficients and the 2D equalizer with 11 \(\times\) 11 array coefficients are used. We set \(\delta\) so that the ratio of the number of bits entering the TS detector to total bits \(\gamma\) is 0.2%, 0.5%, 1%, and 2%, respectively. For the TS detector, we chose the parameters with M = 20 and P = 10.  
Figure  
than 1.0%. When \(\gamma\) is 1.0%, the proposed detection has more than 4 dB gains compared to the simplified multi-track detection at the BER of \(10^{-4}\).  
Figure 5 show the BER performances for the various detection scheme with 10% TMR when an areal density is 3.5 Tb/in\({}^{2}\). Due to the influence of TMR, the BER performances for all detection schemes deteriorated. However, the single-track and joint-track detections show particularly the serious BER performance degradation. The simplified multi-track detection has the more severe BER performance degradation than the proposed detection. When \(\gamma\) is 2.0%, the proposed detection has more than 6 dB gains compared to the simplified multi-track detection at the BER of 2 \(\times\) 10\({}^{-4}\). The BER performances of the proposed detection are saturated when \(\gamma\) is more than 1.0% below the BER of 2 \(\times\) 10\({}^{-5}\).  
Figure 4: BER performances for the various detection schemes with 0% TMR when an areal density is 3.5 Tb/in\({}^{2}\).  
Figure 3: BER performances for the various detection schemes with 0% TMR when an areal density is 3.0 Tb/in\({}^{2}\).
## 2 Method  
### Multi-DOF Myoelectric Hand Prosthesis  
In this study, the fingers of the multi-DOF myoelectric hand prosthesis were designed based on the anatomical structure and motion mechanism of human fingers. Two types of fingers were designed, i.e., the thumb and the other four fingers. As shown in Figure 1a, the thumb consists of one phalange and two joints--namely, the metacarpophalangeal (MCP) joint and trapezicometacarpal (TM) joint. As shown in Figure 1b, the other four fingers consist of three phalanges (the distal phalange (DP), middle phalange (MP), and proximal phalange (PP)), one bone (the metacarpal bone (MB)), and two joints connecting the phalange and the bone (the proximal interphalangeal (PIP) and MCP joints).  
The flexion and extension motions of the human finger are performed by pulling the tendon fixed to the phalange as the muscle contracts. In this study, we use a motor as a muscle and a wire as a tendon to perform each finger flexion motion. The motor is mounted inside the PP of each finger, and the wires are fixed to the worm-wheel gear and the MP. The designed finger flexion motion is performed by pulling the wire affixed to the worm-wheel gear when the motor is driven. The PP of the finger is actuated to \(60^{\circ}\) from the MCP joint by the motor-gear mechanism (see Figure 1b). At this time, the wire affixed to the worm-wheel gear is pulled such that the MP and DP synchronously actuate the PP by \(85^{\circ}\). The extension motion of the finger is performed by a restoring spring mounted between the PP and the MP. The thumb can perform flexion, extension, and rotation motions. The flexion motion of the thumb is performed by the MCP joint using the motor-gear mechanism. Further, the rotation motion is performed by the TM joint using the motor-gear mechanism in the palm of the hand.  
The human hand has 22 DOFs and weighs about 400 g [28]. The proposed hand was developed based on the human hand, as shown in Figure 1c. The developed hand was manufactured using AL 6061 and SUS 304 materials to reduce the total weight and increase the durability of the hand. Its dimensions are 151 \(\times\) 174 \(\times\) 50 mm (W \(\times\) L \(\times\) H). The hand performs various hand gestures and grip motions using the five fingers with the gear-motor-wire mechanism. It has six DOFs; specifically, the thumb has two DOFs, and the other four fingers have one DOF each. Further, this hand, including the motor, weighs about 503 g (see Figure 2). The proposed hand has a faster flexion angular velocity than other commercial multi-DOF myoelectric hand prostheses, and its maximum gripping force is on par with that of other commercial products (see Table 1).  
Figure 1: Structure of the proposed multi-DOF myoelectric hand prosthesis: 3D schematic of the (**a**) thumb, (**b**) other fingers, and (**c**) schematic of the multi-DOF myoelectric hand prosthesis.
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 6 Discussion  
### Antenna Gain and Antenna Correlation  
The antenna gains and losses in the chip-to-AP link and the Augmented MIMO link of our experiment are depicted in Figure 11.  
Taking the losses into account, the overall gain of each antenna of Augmented MIMO is formulated in dB as follows:  
\[G_{\text{augmented}}=G_{\text{FFC}}-L_{\text{prox}}-L_{\text{calbe}}. \tag{3}\]  
In the experiment, \(L_{\text{prox}}\approx 6.8\) dB, and \(L_{\text{calbe}}\approx 2.0\) dB at 2.45 GHz. Although \(G_{\text{FFC}}\) and \(G_{\text{chip}}\) depend on direction and polarity, the maximum values of these gains shown in the datasheets [14; 17] are approximately 3 dBi and roughly equal. Therefore, we assume \(G_{\text{FFC}}\approx G_{\text{chip}}\), and then, \(G_{\text{augmented}}\) becomes approximately 9 dB lower than the original chip antenna gain \(G_{\text{chip}}\). Despite the reduced antenna gain, i.e., \(G_{\text{augmented}}<G_{\text{chip}}\), the throughput was significantly improved by a factor of 2 or more in the LOS environment.  
Assuming a constant noise level, the received signal strength determines the SNR; then, the antenna gain determines the SNR for a fixed transmit power. Decreasing the overall antenna gain with Augmented MIMO reduces the SNR at the receiver.  
Figure 10: Throughput evaluation results for the NLOS environment. Different colored lines indicate different trials. Augmented MIMO (“\(\circ\)” markers) was, on average, 1.5 times faster than the chip-to-AP link (”\(\times\)” markers).  
The channel capacity depends on the SNR at the receiver and the channel correlation, as described in Section 2. The experimental system is the case of \(N=2\) in (1). Higher channel correlations reduce \(\lambda_{1}\) and \(\lambda_{2}\), which can cause the rank of \(W\equiv H^{\dagger}H\) to be 1. Lower antenna gains reduce the SNR \(\rho\) at the receiver. Hence, whether antenna gain or antenna correlation is dominant depends on other factors.  
The experiment results of increased throughput indicate that the increase in \(\lambda_{k}\) in (1), i.e., the antenna correlation reduction, contributed more than the reduction in SNR \(\rho\). The distance between the transmitter and the receiver was relatively short, 5.4 m, and the channel included the LOS path as shown in Figure 7b; therefore, the SNR may have been relatively high for both chip-to-AP and Augmented MIMO compared to typical WLAN use cases. This high SNR condition may have reduced the influence of the antenna gain reduction in the Augmented MIMO link.  
In the NLOS experiment, Augmented MIMO also improved throughput, but the ratio of its throughput to that of the chip-to-AP link dropped to 1.5. The SNR in the NLOS experiment is considered to be lower than that in the LOS experiment due to the blocked direct path and the longer distance between the transmitter and receiver.  
The conditions for Augmented MIMO to improve the channel capacity need further investigation, and the limitations of Augmented MIMO should be clarified in future work.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 6 Discussion  
The main lines of research resulting from this work are:  
* The design of new parallelization techniques: It would be interesting to get more efficient parallel implementations.
* Secure extension of the specified protocol: All ACO routing protocols for mobile ad hoc networks are designed without considering the malicious behavior of some nodes, which can be exploited to violate the network security.
* Application of the ACO routing protocols developed for mobile ad hoc networks in other fields: An area of immediate application would be sensor networks given the similarity between both types of networks. Another field of application could be robotics, where frequently, the use of simple local interactions is analyzed to solve complex tasks, such as, for example, navigation in indoor environments.
## 2 Methods
### Ihacres Model  
The IhACRES runoff-precipitation model is a conceptual and lumped model. Previous studies have shown that the IHACRES model requires fewer inputs and is mathematically more straightforward than other hydrological models [24; 34]. Additionally, an investigation of the uncertainty associated with runoff simulation based on different hydrological models showed that the uncertainty of the IHACRES model is comparable to that of other hydrological models [24; 29; 39].  
This model is based on a nonlinear loss module and a linear unit hydrograph. First, the temperature (\(t_{k}\)) and precipitation (\(r_{k}\)), which are based on the nonlinear loss module, are converted to effective rainfall. Subsequently, rainfall is converted to surface runoff based on the linear unit hydrograph module. The equation used in the nonlinear module is based on the following equations:  
\[u_{k}=s_{k}\times r_{k} \tag{11}\]  
\[s_{k}=C\times r_{k}+\left[1+\frac{1}{\tau_{w}(t_{k})}\right]s_{k-1} \tag{12}\]  
\[\tau_{w}(t_{k})=\tau_{w}e^{0.062(R-t_{k})} \tag{13}\]  
\[x_{k}=a^{d}x_{k-1}+b^{d}u_{k-1}+a^{s}x_{k-1}+b^{s}u_{k-1} \tag{14}\]  
where \(s_{k}\) is the wet index for the basin, \(\tau_{w}(t_{k})\) is the control parameter of the \(s_{k}\) value when precipitation does not occur, \(R\) is the reference temperature, \(C\) is a parameter related to runoff and effective rainfall, and \(x_{k}\) is a runoff.  
The parameters \(\tau_{w}\), \(C\), \(a\), and \(b\) are computed based on the calibrated and observed data.
# How Much Does the Dynamic F0 Curve Affect the Expression of Emotion in Utterances?
## 4 Discussion  
Our findings shed light on the intricate role of dynamic F0 contours in speech emotion recognition and highlight both consistencies and discrepancies with previous research. Previously, [8] provided a foundational summary of the acoustic characteristics associated with various emotions. They reported that anger typically exhibits the highest energy and pitch level; disgust is characterized by a low mean pitch, low intensity, and slower speech rate compared to neutral speech; fear correlates with a high pitch level and increased intensity; and sadness is associated with low mean intensity and pitch. Additionally, they emphasized that pitch contour trends are valuable in distinguishing emotions, particularly noting that fear resembles sadness with an almost downward slope in the pitch contour, which helps separate it from joy.  
However, statistics such as the mean and variance of pitch, while informative, are rudimentary and may not capture the complex temporal dynamics of emotional speech. Our study advances this understanding by directly modeling dynamic F0 contours using Generalized Additive Mixed Models (GAMMs) for the classification of eight basic emotions in the RAVDESS corpus.  
One of the key advantages of using GAMMs in our study is the direct modeling of dynamic F0 contours. Unlike traditional methods that might rely on summary statistics, GAMMs allow us to capture the non-linear, temporal patterns in pitch that are critical for differentiating emotions. This approach not only improves classification performance but also enhances interpretability, enabling us to understand which aspects of the pitch contour contribute to recognizing specific emotions.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Additional Factors** & **Deviance Explained** \\ \hline Utterance Duration by Emotions & 69.9\% \\ Statement + Utt. Duration by Emotions & 71.4\% \\ Gender + Utt. Duration by Emotions & 71.4\% \\ Intensity & 73.1\% \\ Intensity + Utt. Duration by Emotions & 76.3\% \\ Intensity + Statement + Utt. Duration by Emotions & 76.3\% \\ Intensity + Statement + Gender + Utt. Duration by Emotions & 76.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 21: Summary of deviance explained by adding additional factors (duration, statement type, gender, and intensity) to Model 5.
## 4 Materials and Methods  
### Identification of SmCCO Family and Sequence Analysis  
The nucleotide and amino acids sequences of the reported _A. thaliana_ CCO amino acid sequences _AtCCD1_ (AT3G63520.1), _AtCCD4_ (AT4G19170.1), _AtCCD7_ (AT2G44990.3), _AtNCED2_ (AT4G18350.1), _AtNCED3_ (AT3G14440.1), and _AtNCED5_ (AT1G30100.1) were employed as a query to search against the _S. milliorrhiza_ genome assembly of our lab by the BLASTn algorithm. PFAM (pfam-legacy.xfam.org) analyzed conserved domains, deleted sequences with incomplete RPE65 domains [62], and identified all members of the SmCCO family. We used online websites to once again determine that each member has a complete conserved domain ([[http://www.ebi.ac.uk/interpro/search](http://www.ebi.ac.uk/interpro/search)]([http://www.ebi.ac.uk/interpro/search](http://www.ebi.ac.uk/interpro/search)) accessed on 9 September 2024) and calculate its molecular weight and isoelectric point ([[https://web.expasy.org/compute_pi/](https://web.expasy.org/compute_pi/)]([https://web.expasy.org/compute_pi/](https://web.expasy.org/compute_pi/)) accessed on 9 September 2024). We used MEGA 6.0 to construct an evolutionary tree for the _S. milliorrhiza_ CCO family by using the neighbor-joining method [63]. We used online websites for motif analysis ([[http://meme-suite.org/tools/meme](http://meme-suite.org/tools/meme)]([http://meme-suite.org/tools/meme](http://meme-suite.org/tools/meme)) accessed on 9 September 2024), gene structure analysis ([[http://gsds.cbi.pku.edu.cn](http://gsds.cbi.pku.edu.cn)]([http://gsds.cbi.pku.edu.cn](http://gsds.cbi.pku.edu.cn)) accessed on 9 September 2024), cis-acting component analysis ([[http://bioinformatics.psb.ugent.be/webtools/plantcare/](http://bioinformatics.psb.ugent.be/webtools/plantcare/)]([http://bioinformatics.psb.ugent.be/webtools/plantcare/](http://bioinformatics.psb.ugent.be/webtools/plantcare/)) accessed on 9 September 2024), and signal peptide prediction, transmembrane domains, and hydrophilicity/hydrophobicity analysis ([[http://www.detailsbio.com/tools/](http://www.detailsbio.com/tools/)]([http://www.detailsbio.com/tools/](http://www.detailsbio.com/tools/)) accessed on 9 September 2024). We predicted the tertiary structure of the SmCCD4 protein by using the SWISS-MODEL online website ([[https://swissmodel.expasy.org/](https://swissmodel.expasy.org/)]([https://swissmodel.expasy.org/](https://swissmodel.expasy.org/)) accessed on 9 September 2024).
## 3 Materials and Methods
### Framework Overview
#### 3.2.4 Multitask Learning of Regression Prediction and Deep Feature Embedding  
From the above description, it can be seen that the entire network framework includes two loss functions, i.e., regression prediction loss and the triplet loss for training together. By jointly training two loss functions, the features learned by the network can better predict crop yield accurately.  
1. Regression prediction: For a regression task, the regression prediction loss for crop yield prediction is calculated through the mean squared error: \[L_{s}=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}\] (6)  
Figure 5: The structure of the triplet network.  
2. Deep feature embedding learning: The regression prediction loss just penalizes the regression error equally for each county, which cannot enforce the learned deep features discriminatively enough. Thus, inspired by the metric learning strategy, we utilize the triplet loss as the similarity constraint. \[L_{f}=\frac{1}{N}\sum_{i=1}^{N}h\Big{(}D\Big{(}x_{i}^{a},x_{i}^{p}\Big{)}-D(x_{i} ^{a},x_{i}^{n})+\alpha\Big{)}\] (7) where \(x_{i}^{a}\) has similar crop yield with \(x_{i}^{p}\). \(x_{i}^{a}\) and \(x_{i}^{n}\) have large differences in crop yield.
3. Multitask learning: To incorporate the advantages of regression prediction and metric learning, we apply multitask learning to learn more discriminative features for crop yield prediction. The framework is formulated by integrating the two losses above as follows: \[L_{all}=L_{s}+L_{f}\] (8)  
Benefiting from the multitask learning of regression prediction and deep feature embedding, the deep neural network is jointly optimized by propagating the regression prediction error information and the similarity constraint information backward simultaneously. In such a way, more powerful deep features suitable for crop yield prediction are obtained.  
In this paper, we utilize the stochastic gradient descent (SGD) algorithm [19] to solve the minimization problem of the objective function. As the triplet networks have been trained, we only need one branch for inference. That is to say, the triplet loss is removed during testing. And the test samples are directly fed to the backbone network trained to extract the deep embedding features. Then, the output of the network is used to predict the crop yield of each test sample.
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 2 System Model
### The Receiver  
We consider a VLC-MIMO indoor imaging receiver system based on wavelet-OFDM using one receiver (M) of the hybrid space-frequency domain pre-equalization system. The receiver location (scenario-room corner) is given by (0, 0.05 m). The system block diagram is shown in Figure 4.  
Figure 3: Line of Sight (LOS) channel configuration [28].  
* The produced N serials of unipolar data have been used to drive N LED modules, respectively, as shown in Figure 1. The LED radiation light is sensed by an ImR or an ImADR at the detector side. The recipient side of the wavelet-OFDM VLC-MIMO technique is illustrated in Figure 4.
* An A/D conversion is utilized to convert the analog signals to digital. The 3-dB modulation bandwidth of LED is adjusted as 50 MHz.  
The specific processes to perform digital Pre-FDE were fully illustrated in [22]. In the OFDM transmitter (Tx), a Pre-FDE has performed adaptively according to the estimated SNR information.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ions** & **Control** & **PMF 1** & **PMF 2** \\ \hline  & **mg/100 g** & **mg/100 g** & **mg/100 g** \\ \hline Al & 0.32 \(\pm\) 0.06 \({}^{\text{a}}\) & 0.28 \(\pm\) 0.12 \({}^{\text{a}}\) & 0.32 \(\pm\) 0.04 \({}^{\text{a}}\) \\ \hline Ca & 429.00 \(\pm\) 1.88 \({}^{\text{c}}\) & 543.85 \(\pm\) 3.74 \({}^{\text{b}}\) & 613.25 \(\pm\) 2.00 \({}^{\text{a}}\) \\ \hline Cd & 0.02 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.02 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.03 \(\pm\) 0.01 \({}^{\text{a}}\) \\ \hline Fe & 4.98 \(\pm\) 0.25 \({}^{\text{a}}\) & 5.66 \(\pm\) 0.38 \({}^{\text{a}}\) & 5.45 \(\pm\) 0.58 \({}^{\text{a}}\) \\ \hline K & 751.12 \(\pm\) 1.32 \({}^{\text{c}}\) & 1380.25 \(\pm\) 4.76 \({}^{\text{a}}\) & 1074.08 \(\pm\) 6.12 \({}^{\text{b}}\) \\ \hline Mg & 560.50 \(\pm\) 1.80 \({}^{\text{b}}\) & 649.33 \(\pm\) 4.50 \({}^{\text{a}}\) & 544.67 \(\pm\) 2.18 \({}^{\text{b}}\) \\ \hline Mn & 1.61 \(\pm\) 0.07 \({}^{\text{b}}\) & 1.86 \(\pm\) 0.11 \({}^{\text{a}}\) & 1.85 \(\pm\) 0.02 \({}^{\text{a}}\) \\ \hline Mo & 0.07 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.06 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.06 \(\pm\) 0.01 \({}^{\text{a}}\) \\ \hline Na & 80.38 \(\pm\) 0.48 \({}^{\text{c}}\) & 197.03 \(\pm\) 7.01 \({}^{\text{a}}\) & 151.35 \(\pm\) 1.34 \({}^{\text{b}}\) \\ \hline Ni & \textless\)LOQ & \textless\)LOQ & \textless\)LOQ \\ \hline P & 746.83 \(\pm\) 0.87 \({}^{\text{c}}\) & 1064.08 \(\pm\) 6.44 \({}^{\text{a}}\) & 816.97 \(\pm\) 8.81 \({}^{\text{b}}\) \\ \hline Pb & \textless\)LOQ & \textless\)LOQ & \textless\)LOQ \\ \hline S & 291.23 \(\pm\) 2.17 \({}^{\text{c}}\) & 349.90 \(\pm\) 2.02 \({}^{\text{a}}\) & 307.90 \(\pm\) 1.02 \({}^{\text{b}}\) \\ \hline Se & 0.02 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.02 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.01 \(\pm\) 0.01 \({}^{\text{a}}\) \\ \hline Zn & 8.17 \(\pm\) 0.02 \({}^{\text{c}}\) & 9.79 \(\pm\) 0.03 \({}^{\text{a}}\) & 9.03 \(\pm\) 0.05 \({}^{\text{b}}\) \\ \hline \end{tabular} Mean values with \(\pm\)SD values and statistical analysis using [PERSON]’s test at the significance level of \(\alpha\) = 0.05; the results indicated with the same letters do not demonstrate a statistically significant difference (test performed as part of a comparison of the concentrations of individual ions with each other after the application of slowly changing magnetic fields and with a control group); \(\textless\)LOQ—result below the Limit of Quantification.  
\end{table} 
Table 4: Changes in the content of basic micro and macro components and heavy metals under the influence of applied pulsed magnetic fields for sunflower sprouts. Figure 6: Changes in micro–macro elements and heavy metals and the sum thereof under the influence of the application of doses of pulsating magnetic field for cress sprouts.