esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## Appendix B  
Quantitative evaluation indexes of gray mean value, standard deviation, information entropy, average gradient, correlation coefficient, spectral distortion, and bias index.  
\begin{tabular}{c c c} \hline \hline
**Name** & **Expression** & **Feature** \\ \hline Gray mean Value & \(A(Mean)=\frac{1}{M\times N}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N}A(i,j)\) & The closer the average value is to the multi-spectral image average, the smaller the spectral distortion. \\ \hline Standard deviation & \(S(std)=\sqrt{\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\left(A(i,j)-\overline{A}(i,j) \right)^{2}}{M\times N}}\) & The larger the standard deviation, the more scattered the gray level distribution, the larger the contrast of the image, and the difference between the gray level and average gray level of each pixel. \\ \hline Information entropy & \(H(entropy)=-\sum_{i=1}^{M}P_{i}\ln P_{i}\) & The larger the entropy value of the fused image, the more the information of the fused image increases. \\ \hline Average gradient & \(G(grad)=\frac{1}{(M-1)\times(N-1)}\sum_{i=1}^{M-1}\sum_{j=1}^{N-1}.\) & \\ Average gradient & \(M\) and \(N\) are the numbers & The larger the average gradient, the clearer the layers of the image and the higher the sharpness of the image. \\ \hline Correlation coefficient & \(C(f,g)=\frac{\sqrt{\sum_{i=1}^{M-1}\sum_{j=1}^{M-1}\left[\left(f(i)-\epsilon_{ f}\right)\left(g(i,j)-\epsilon_{g}\right)\right]}}{\sqrt{\sum_{i=0}^{M-1}\sum_{j=1}^{M-1} \left[\left(f(i,j)-\epsilon_{g}\right)^{2}\right]}}\) & The larger the correlation coefficient between the fused image and the multi-spectral image indicates that the higher the degree of integration of high-frequency information, the smaller the degree of image spectral variation. \\ \hline Spectral distortion & \(W(warp)=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}F(i,j)-A(i,j)|\) & The greater the degree of distortion, the higher the degree of spectral distortion of the image, and \(A\) (\(i,j\)) is the gray value of the original \\ \hline Bias index & \(D(bras)=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}\frac{F(i,j)-A(i,j)|}{A (i,j)}\) & \\ \hline \end{tabular}  
The smaller the deviation index, the higher the spectral information retention of the image before and after fusion.
# Development of a Lightweight Prosthetic Hand for Patients with Amputated Fingers
## 3 Analysis of the Prosthetic Hand  
### Kinematic Analysis  
#### 3.1.1 Position Analysis  
Because the diameter of the tendon-fixing frame (\(d_{4}\) in Figure 2) is equal to the diameters of the joint frames in the DIP, PIP, and MCP joints, in natural motion, the angles of each of the phalanxes (\(\theta_{i}\), \(i=1,\ 2,\ \text{and}\ 3\)) are changed by an identical magnitude \(\theta\), i.e., \(\theta_{1}=\theta_{2}=\theta_{3}=\theta\), as shown in Figure 7a. If (\(X\), \(Y\)) represents the \(X\)- and \(Y\)-coordinates in the coordinate frame \(O\), then the positions of points \(p_{1},\ p_{2}\), and \(p_{3}\) can be determined as follows:  
\[p_{1}=(l_{1}cos\theta_{1},\ l_{1}sin\theta_{1}), \tag{1}\]  
\[p_{2}=(l_{1}cos\theta_{1}+l_{2}cos(\theta_{1}+\theta_{2}),\ l_{1}sin\theta_{1} +l_{2}sin(\theta_{1}+\theta_{2}))\ \text{and}\]  
\[p_{3}=(l_{1}cos\theta_{1}+l_{2}cos(\theta_{1}+\theta_{2})+l_{3}cos(\theta_{1} +\theta_{2}+\theta_{3}),\]  
\[l_{1}sin\theta_{1}+l_{2}sin(\theta_{1}+\theta_{2})+l_{3}sin(\theta_{1}+\theta_ {2}+\theta_{3})).\]  
Because \(\alpha\) and \(\beta\) (Figure 7b) exist in the self-adaptive grasping motion, the positions of points \(p_{1}\), \(p_{2}\), and \(p_{3}\) are derived as follows:  
\[p_{1}=(l_{1}cos(\theta_{1}-\alpha),\ l_{1}sin(\theta_{1}-\alpha)),\]  
\[p_{2}=(l_{1}cos(\theta_{1}-\alpha)+l_{2}cos(\theta_{1}-\alpha+\theta_{2}-\beta ),\ l_{1}sin(\theta_{1}-\alpha)+l_{2}sin(\theta_{1}-\alpha+\theta_{2}-\beta))\ \text{and}\]  
\[p_{3}=(l_{1}cos(\theta_{1}-\alpha)+l_{2}cos(\theta_{1}-\alpha+\theta_{2}-\beta ))+l_{3}cos(\theta_{1}-\alpha+\theta_{2}-\beta+\theta_{3}),\]  
\[l_{1}sin(\theta_{1}-\alpha)+l_{2}sin(\theta_{1}-\alpha+\theta_{2}-\beta))+l_{ 3}sin(\theta_{1}-\alpha+\theta_{2}-\beta+\theta
## 3 Simulation Results and Discussion
### Reconfiguration with DGs
#### 3.2.1 Standard IEEE 33-Bus Distribution System  
Three distributed generations at buses of 14, 24, and 30 are proposed to integrate into the standard test system before the reconfiguration. The initial configuration of the system provides a power loss of 71.46 kW with a minimum voltage of 0.9687 pu. After the reconfiguration using three different methods, it can be noticed that the topologies are slightly different with increased minimum voltage (i.e., 0.9774 pu). Additionally, the computation time of MSSOE is faster compared to the existing method as seen in Table 4. The voltage profile of the system is shown in Figure 9. Regarding power loss, it has been reduced dramatically to 57.7 kW due to the reconfiguration.
# Regional Traffic Event Detection Using Data Crowdsourcing
## 3 Proposed Regional Traffic Event Detection Technique
### Data Preprocessing  
Social data are freely expressed by users and are irregular by nature. They vary in terms of structure and form and almost always contain typographical errors, special characters, etc. Analysis techniques that use machine learning are greatly influenced by these data forms. Raw data may incur errors or reduce accuracy during event detection. As such, data refinement processes are needed. The following data refinement methods represent nonstandard data in a standardized form and promote accurate event detection.  
Tokenization is a process that converts complex text into word sets known as tokens. Data consisting of text include spaces, punctuation, mathematical symbols, special characters, and typographical errors. In the proposed system, characters other than Korean letters and numbers are removed, and an N-gram tokenization approach is used to divide each part of the text into words. After this stage, each text in the corpus is expressed as a series of words for additional processing.  
Unnecessary data negatively impact machine learning performance. Therefore, the proposed approach removes unnecessary data, such as noise and stopwords, from the text. Noise refers to mathematical symbols such as '\(\oplus\), '\(\_\)', whereas special characters include punctuation and typographical errors. The Korean language uses consonants and vowels, specifically in patterns of initial consonants, medial vowels, and final consonants. Text that does not follow this pattern is considered meaningless. Therefore, texts containing only consonants or vowels were considered typographical errors and removed. On the other hand, stopwords are words that appear often but are meaningless. For this study, meaningless words such as \"urgent\" or \"first\" were selected and removed.  
Index words or keywords representing sentences are in the form of nouns. Therefore, by extracting nouns to convey meaning accurately, the proposed method can reduce text expressions during machine learning and increase accuracy. In this study, we used a morpheme analyzer to perform noun extraction.
## 4 Comparison between Monitored Data and Numerical Simulation  
This section provides a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.  
Three monitoring points, A, B, and C, were selected on the canal, bottom, and slope, respectively (as shown in Figure 6a). Their safety-monitoring data from January 2014 to January 2018 were calculated and analyzed. Settlement monitoring points were used to conduct on-site surveys of the settlement displacement of the deep excavated section of the SNWDP (Figure 6b,c). The calculation results were compared and analyzed. Figure 7 shows the correlation curve between the simulated settlement and the monitored settlement of the monitoring points.  
It can be seen from Figure 7 that, (1) during the 5-year operation of the deep excavated canal, the displacement gradually increased from the bottom to the top of the canal, reaching the highest at the top. The maximum difference between the monitored displacement and settlement value and the calculated value is 0.559 mm, and the minimum is 0.02 mm; (2) the canal subsided rapidly during the first two years, and then the settlement slowed down and got close to final settlement; and (3) compared to the displacement curve of the monitored section, the settlement trend is similar. According to the Adj.R-square coefficient and Pearson's correlation coefficient, the calculated data curve is highly fitted to the actually monitored data curve. After five years of operation, the settlements are the same, suggesting that the numerical simulation of the canal's long-term settlements is consistent with the actual project operation.  
Figure 5: Canal seepage system.
## 2 Materials and Methods
### Heart Rate Variability (HRV)
#### Near-infrared spectroscopy (NIRS)  
SmO\({}_{2}\) measurements were obtained using a NIRS sensor (Moxy, Fortiori Design LLC, Minnesota MN, USA), which was placed on the vastus lateralis of the non-dominant leg, 15 cm above the superior border of the patella and 5 cm lateral to the midline of the thigh, aligned with the muscle fibers. The Moxy sensor was set to a sampling frequency of 10 Hz. Data were transmitted via ANT+ technology to Golden Cheetah software (GoldenCheetah version 3.6. CO, USA), allowing real-time visualization for the researchers. The NIRS device was secured with medical adhesive tape, and during measurements, the limb was covered with a black cloth to prevent interference from ambient light. Skinfold was assessed with a skinfold caliper (Harpenden(r), London, UK). The skinfold thickness was less than half the distance between the sensor's emitter and detector (\(\sim\)2.5 cm). The average skinfold thickness was 11.4 \(\pm\) 2.3 mm, which was below the distance between the NIRS light emitter and detector. Measurements were performed by an experienced researcher in skinfold measurements. The resting SmO\({}_{2}\) measurement was conducted by an experienced investigator using the occlusion technique.
# Health Care Resource Use at End of Life in Patients with Advanced Lung Cancer
## Results
### Place of Death  
By the study cut-off date, 77% of SACT patients (\(N=376\)) had died compared with 97% of no-SACT patients (\(N=278\)). Among the patients who died during the study period, only 15% of patients in the no-SACT group died in an acute care hospital and 7% in a nursing hospital, whereas 38% of the SACT group deaths occurred in an acute care hospital and 24% in a nursing hospital (Table 4). The majority of deaths in an acute care hospital occurred after the patients had been admitted via the ED.  
\begin{table}
\begin{tabular}{c c c} \hline
**Health Care Service** & **No-SACT (N = 287)** & **SACT (N = 489)** \\ \hline General practitioner visit & 10 (7–25) & 10 (5–14) \\ \hline Specialist doctor visit & 111 (45–257) & 241 (41–301) \\ \hline Home nurse visit & 105 (36–236) & 98 (17–147) \\ \hline Nursing hospital stay & 228 (67–432) & 121 (25–212) \\ \hline ED visit leading to hospital stay & 416 (193–1664) & 275 (116–552) \\ \hline Hospital stay, total & 547 (238–1783) & 638 (426–1280) \\ \hline incl. intensive care & 297 (66–1860) & 118 (24–165) \\ \hline incl. bed days & 250 (137–604) & 157 (73–335) \\ \hline Prescription drugs & 77 (33–112) & 81 (29–113) \\ \hline SACT administrations & - & 332 (228–511) \\ \hline ED outpatient visit & 34 (8–101) & 20 (7–35) \\ \hline All services & 215 (39–240) & 992 (744–1610) \\ \hline SACT, systemic anti-cancer treatment; ED, emergency department. & & \\ \end{tabular}
\end{table}
Table 3: Average per capita costs in euros (Q25–Q75) of different health care services per person-month.  
Among deceased SACT patients, 72 patients received SACT within 30 last days of life (EOL-SACT group), 162 patients more than 100 days before death (>100 SACT) and 142 patients received their last SACT within 31 to 100 days prior to death (31-100 SACT).  
The majority of no-SACT patients (78%) died out-of-hospital, whereas the majority of deaths in the SACT group (62%) occurred under medical supervision in an acute care or nursing hospital. The probability of dying in a hospital setting was highest in the EOL-SACT group and decreased in parallel with the time elapsed from the last SACT administered. We hypothesized that the SACT > 100 group received care similar to that of no-SACT patients, but more no-SACT patients died out-of-hospital and less in nursing hospitals compared with the SACT > 100 group.
## 4 Experiment Design
### Training Procedure
#### 4.3.2 Model Training  
The proposed HAFNetE was implemented using the PyTorch framework and following the design patterns of the PyTorch library Segmentation Models PyTorch (SMP) [34]. Training and evaluation phases were conducted using a NVIDIA GeForce RTX 1080 Ti GPU (11 GB memory). Since data had been previously balanced during the preprocessing phase, a simple non-weighted version of Binary Cross Entropy loss was used. Multiple experiments were carried out to choose the best optimizer for minimizing the loss function (Stochastic Gradient Descent (SGD), Adagrad, Adam). Table 2 shows validation metrics using the different optimization strategies.  
Of all the optimizers, Adam converged to the highest performance metrics, as visible from the percentages reported in Table 2. The observed training curves are shown in Figure 5.  
As stated earlier, the model encoders were initialized with the pre-trained EfficientNet-B0 weights, so a small learning rate \(lr=1\times 10^{-3}\) was used to optimize loss. The learning rate was modulated using different learning rate scheduling strategies, including Cosine Annealing Warm Restart and Multi-step LR. In the end, the simplest one (Multi-step LR) was selected, with learning rate reduced by a factor of \(\gamma=0.1\) at epochs 2 and 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimizer** & **Validation IoU** & **Validation F1-Score** & **Validation Accuracy** \\ \hline SGD & 85.56\% & 92.15\% & 92.07\% \\ Adagrad & 89.76\% & 90.32\% & 91.98\% \\ [PERSON] & 91.58\% & 95.59\% & 96.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation metrics using different optimization strategies.  
Figure 5: Training (blue) and validation (orange) curves obtained using the Adam optimizer. From top-left, clockwise, the four graphs represent the measures of loss, fscore, IoU, and accuracy, respectively.  
The selected \(\gamma\) factor is a standard setting in learning scheduling, while the milestones selected to perform the scheduling steps were found by experiments. The model was trained for 10 epochs for a total time of 50 min/run. A batch size of 20 was selected by a trial-and-error procedure in order to saturate the GPU and, therefore, achieve the maximum training speed given the available hardware acceleration. In order to further increase the overall model performance, the net was fine-tuned for 10 more epochs on a small, augmented subset of the original training set starting from the saved weights of the previous run and continuing the optimization process with a very small learning rate. Results are reported in Table 3.
## 2 Discussion
### Interleukin Receptors (ILR)  
Interleukin receptors (ILRs) are high-affinity receptor proteins that bind interleukins to initiate the immune cascade reactions associated with each type of interleukin molecule [12]. The ILR-encoding genes associated with psoriasis are IL20 RA, IL28 RA, and IL36 RN. IL20 RA encodes a receptor for IL20, a cytokine that may be involved in epidermal function. IL28 RA encodes a receptor complex that interacts with IL28A, IL28B, and IL29 [4]. The expression of these cytokines can be induced by viral infections. IL36 RN encodes the IL36 receptor antagonist protein and the loss of function mutations leads to dysregulated IL36R signaling, which is linked to disease onset [3; 4].  
Specific variants of ILRs are associated with psoriasis. This includes the rs11209026 variant of IL23R, an SNP involving a substitution of adenine (A) with guanine (G). Similarly, an SNP associated with IL20R (rs17728338) featuring the G allele was associated with an increased risk of psoriasis. Furthermore, the C allele present in the rs7631529 SNP found in the IL36 RN gene was correlated with an increased risk of psoriasis. The common understanding among these SNP variants is that they predispose dysregulated interleukin signaling and thereby increase inflammatory mechanisms associated with disease pathophysiology [3; 4; 9; 10; 12]. Further research is necessary to better understand the implications of the underlying mechanisms of these genetic variations in psoriasis development.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 2 Ambisonic Rendering
### Decoding
The high-frequency attenuation caused by Max \(\mathbf{r_{E}}\) weights was hence negated through amplitude compensation [22; 23] prior to the dual-band crossover. The values for each order were calculated from the root-mean-square (RMS) of the Max \(\mathbf{r_{E}}\) channel weights \(g_{m}\) ([18], p. 183). Table 1 presents the RMS Max \(\mathbf{r_{E}}\) channel weights \(g_{mRMS}\) for Ambisonic orders 1 to 5. Binaural decoders were computed by encoding the HRTFs of each loudspeaker configuration into a continuous SH representation by a summation of the multiplication of each channel of the decoding matrix with the corresponding HRTF such that  
\[D_{K}^{SH}=\sum_{l=1}^{L}H_{l}\times D_{l}, \tag{7}\]  
where \(H\) is the HRTF and \(D\) is the decode matrix. This was repeated for both Basic and Max \(\mathbf{r_{E}}\) weighted decoders. HRIRs in this study were from the Neumann KU 100 dummy head [25]. \begin{table}
\begin{tabular}{c c c c c c} \hline \(\boldsymbol{M}\) & \(\boldsymbol{1}\) & \(\boldsymbol{2}\) & \(\boldsymbol{3}\) & \(\boldsymbol{4}\) & \(\boldsymbol{5}\) \\ \hline \(g_{mRMS}\) & 0.707 & 0.633 & 0.600 & 0.581 & 0.569 \\ \hline \end{tabular}
\end{table} 
Table 1: RMS Max \(\mathbf{r_{E}}\) weightings for Ambisonic orders 1 to 5. Figure 2: Example of the high-frequency differences of decoding with and without Max \(\mathbf{r_{E}}\) weighting-fifth-order binaural Ambisonic rendering (Neumann KU 100 HRIRs) at (\(\theta^{\circ},\phi^{\circ}\)) = (0, 0) (left ear). Crossover filters between the low- and-high frequency decoders were phase matched to avoid unwanted destructive interference around the crossover frequency [22; 26]. Whereas in loudspeaker reproduction, the transition between low and high frequencies should be implemented gradually [26; 27], when rendering Ambisonics binaurally the crossover can be implemented more abruptly and at a higher frequency due to the head's fixed position inside the virtual loudspeaker array. Crossover frequencies were calculated from the integrated D-error, which is the error between a wave field constructed from a single plane wave and an Ambisonically reconstructed plane wave [18; 28; 29]. For Ambisonic orders 1 to 5, dual-band decoder crossover frequencies are presented in Table 2.  
The two binaural decoders were high and low-passed using the above filters and combined. An advantage of using an SH HRTF format binaural decoder is that the decoding matrix takes length \(K\) instead of \(L\), which removes the increased computation of additional convolutions in the case \(L>K\). Binaural rendering \(A\) is then achieved by convolution of the encoded Ambisonic format signals with the binaural decoder:  
\[A=\sum_{k=1}^{K}B_{k}*D_{k}^{SH}, \tag{8}\]  
where \(B_{K}\) is the encoded Ambisonic signal, \(D_{K}^{SH}\) is the dual-band binaural decoder and \(*\) denotes convolution.
## 3 Results and Discussion
### DCF and PARX Adsorption Assays
#### 3.2.6 Desorption Studies  
The reusability of biomass-derived carbon ACK was then tested up to four cycles of adsorption-desorption by using water as the desorption agent of DCF and PARX. Ultrasonic washing is known to be an effective and easy regeneration method [66], and using water as the washing solvent fulfils the cost and environmental requirements. As shown in Figure 10, the removal efficiency of the pharmaceutical compounds decreased with the consecutive uses of the carbon, this effect more accentuated after the second reuse. This decrease may result from the incomplete removal of DCF and PARX from the carbon surface during the sonication process with water as the solvent; thus, the carbon-active sites become gradually occupied during each cycle [67].  
Figure 10: Influence of the regeneration cycle on the removal efficiency of DCF and PARX with ACK carbon.  
It was previously demonstrated that organic solvents or even acidic solutions performed better in desorbing pharmaceutical compounds from carbon surfaces [68]. Thus, future assays should be performed with different organic solvents that allow simultaneously a high regeneration efficiency without destroying the porous structure and recovery of the pharmaceutical compounds [69].  
Based on these results, no more than one cycle of use of ACK after washing with water should be performed.
## 5 Conclusions  
This study addressed temperature control in a CSTR process by employing nonlinear PID control in conjunction with a neural network. The process dynamics were identified using an RBFNN, whose weights, centers, and widths were continuously updated with an online learning rule. Simultaneously, the gains of the nonlinear PID controller, incorporating saturation commonly observed in real-world systems, were updated online to achieve superior performance. To evaluate the feasibility of the proposed method, a series of simulations were conducted to assess its performance in tracking, disturbance rejection, and parameter variation scenarios. The results of these simulations were then compared with those obtained from an adaptive RBFNN-based PID controller under identical conditions. The simulation results showed significant improvements with the proposed method compared to the APID controller. For setpoint changes, maximum overshoot decreased by 33.7% and settling time decreased by 54.2% for upward/downward transitions, and these parameters decreased by 27.2% and 5.3% for downward/upward transitions, respectively.  
Figure 16: Responses of two methods to increased parameters.  
Figure 17: Responses of two methods to decreased parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Controller**} & \multicolumn{2}{c}{\(Da=0.0864\), \(H=9.6\), \(\beta=0.36\)} & \multicolumn{2}{c}{\(Da=0.0576\), \(H=6.4\), \(\beta=0.24\)} \\ \cline{2-7}  & \(M_{peak}\) & \(t_{ry}\) & \(IAE\) & \(M_{peak}\) & \(t_{ry}\) & \(IAE\) \\ \hline APID & 1.283 & 6.147 & 2.306 & 1.155 & 9.401 & 4.686 \\ \hline ANPID & 1.266 & 4.151 & 1.774 & 1.098 & 7.287 & 3.511 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of parameter variations of two methods.  
Under disturbance changes, reductions were observed in peak magnitude (\(M_{\text{peak}}\)) of 4.9%, in recovery time (\(t_{\text{rxy}}\)) of 23.6%, and in the integral absolute error (\(IAE\)) of 16.2%. Similarly, for parameter variations, the reductions were 3.0% (\(M_{\text{peak}}\)), 26.4% (\(t_{\text{rxy}}\)), and 24.4% (\(IAE\)). Further research could investigate the practical implementation of this algorithm in a physical system, as well as the design of an adaptive nonlinear TDOF PID controller with a first-order filter in the derivative term.  
J.-Y.L., conceptualization, writing--original draft preparation, funding acquisition; G.-G.J., writing--review and editing, software, visualization; G.-B.S., supervision, writing--review and editing, validation. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the project titled \"Development of Advanced Technology for Ocean Energy, Harbor and Offshore Structure (PEA 0321)\" funded by the Korea Institute of Ocean Science and Technology (KIOST), South Korea.  
All data are presented in the article.  
The authors declare no conflicts of interest.