esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### MIMOs for PDFU Healing and Prevention (1986-2021)
Overall, the studies analyzed in this review report encouraging and promising results following MIMOs with regard to ulcer healing (range 55.1-100.0%), mean healing time (range from 1 to 2 months) and recurrence rates (range 0.0-13.6%). In the study by [PERSON] et al. (2016), 17/20 lesions (85.0%) completely healed, while the remaining 3/20 (15.0%) improved [30]. Thus, if we consider the other six studies (case report excluded), the healing rate was greater than 90.0% [27; 30; 38; 39; 40; 41]. Healing time was reported in six studies and ranged from 1 to 2 months [27; 30; 38; 39; 40; 41]. Hence, the most important finding of this review is that MIMOs are an effective intervention in PDFU management, not only for promoting healing but also for reducing their recurrence by achieving a better distribution of plantar pressure on the metatarsal bone with a low incidence of complications. Relating to the severity of the ulcers, based on their clinical experience [6; 27], the authors strongly believe that MIMOs can be an alternative valid treatment also for PDFUs with chronic infection, ulcers penetrating deep structures (IIIB), and ulcers with osteomyelitistis of the metatarsal bones or the proximal phalanx. These challenging feature presentations, or evolution of diabetic feet, becoming increasingly frequent, in association to other vascular complications, such as ankle brachial index below 0.5 or flat pulse volume recording at the ankle level, should be considered as relative, but not absolute, contraindications for MIMOs [6; 52]. As the PDFU represents the main access door for the bacteria causing foot osteomyelitisitis, resolving the first promotes the healing of the second, avoiding long-term antibiotic therapy, which causes progressive bone damage in diabetic patients [53]. However, the few absolute contraindications of these percutaneous procedures must be well known by clinicians and surgeons: severe ischemia and gangrene, insufficient vascular perfusion and extensive soft tissue infection presenting as cellulitis of the foot or toe. Finally, it is the opinion of the authors, that the best approach to diabetic foot ulcers remains multidisciplinary, with a team that should include endocrinologists, podiatrists, wound care nurses, vascular, general and orthopedic surgeons, and infectious disease specialists. For this reason, in particular for PDFU management, referral to a diabetic care clinic is advised for the most appropriate indication of treatment [6].
# Verification of Convolutional Neural Network Cephalometric Landmark Identification
## 3 Results  
A customized (human) operator in situ scattergram showing the envelope of detection around each evaluated anatomical landmark depicted as yellow ovals surrounding each \"averaged\" landmark is shown in Figure 3. Since Algoceph(r) was \"taught\" until target accuracy matched ground truth, the 5 trials performed for each landmark resulted in repeated single point localization (Figure 4). Figure 2: Description of phases of algorithm for detection of defined landmarks. Comparison between operator (avgX, avgY) and automatic (algoX, algoY) landmark detection are shown in Table 2. All landmark identification points were found to be statistically similar except; SoftpogY, UpperlipY, OrbitaleX, PTMX, PorionY and BasaleX. For SoftpogY the difference was found to be 2.67 mm \(\pm\) 2.55 mm, whereas for the remaining landmarks the mean recognition error was less than 1.5 mm. Furthermore, comparison of agreement between AI landmark detection and the x and y coordinates of each landmark as selected by the human operators found that 36 out of 42 (85.72%) of these coordinates were found to be highly correlated (r > 0.90). The aforementioned outliers were found to be moderately correlated (r = 0.729-0.891) (Table 2). Figure 3: In situ scattergram of operator selected anatomic landmarks. The range of landmark selection is indicated by the yellow ovals calculated as 2 std dev from the mean, the mean being the determined landmark position. Figure 4: Example of automatic landmark location performed by the AI application of Algoceph. Note that identical outcomes of 5 separate trials provide non-scattered single point outcomes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Landmark X/Y Coordinate**} & \multicolumn{2}{c}{**Differences between Measurement Scores**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Spearman** \\ **Correlation** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Mean Recognition** \\ **Error** \\ \end{tabular} } \\ \hline \multirow{3}{*}{1} & & **Mean** & **Std. Deviation** & \(p\) & \multicolumn{2}{c}{**Mean (mm) \(\pm\) SD**} \\ \cline{2-6}  & Sella avgX & 54.67 & 3.73 & 0.114 & 0.988 ** & 0.14 \(\pm\) 0.39 \\ \cline{2-6}  & Sella algoX & 54.81 & 3.74 & & & \\ \hline \multirow{3}{*}{2} & Sella avgY & 139.04 & 6.61 & 0.959 & 0.903 ** & 0.05 \(\pm\) 1.28 \\ \cline{2-6}  & Sella algoY & 138.99 & 6.51 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Differences and correlations between Algoceph (algoX and algoY) and the operators’ average (avgX and avgY). Note: significant differences are marked in bold, \(p\) < 0.01. ** significant Spearman correlations. r > 0.729, \(p\) < 0.01.
## 2 Mathematical Modeling of DER System  
### System Description  
In the distributed energy system, the conditioning of the output voltage is performed by a DC-AC voltage inverter together with a second-order LC Low Pass filter, as shown in Figure 1. This figure shows the electrical diagram of the inverter used and the polarity of the currents involved. The model to be presented to the distributed energy system disregards the influence of the use of batteries and filter capacitors. The switching circuit (or switching) composed of the IGBT (Insulated Gate Bipolar Transistor) switches fed by the DC link voltage and the PWM (Pulse Width Modulation) modulator is modeled by the average value of the voltage applied to the LC low-pass filter and which will be represented by the input voltage \(V_{DER}=k_{PWM}\ V_{CC}\), where \(k_{PWM}\) is the gain of the modulator and \(V_{CC}\) the voltage of the DC link.
# A Comparison of Energy Management System for a DC Microgrid
## 2 Mathematical Modeling for DC Microgrid Components
### Photovoltaic Modules  
The commonly used solar PV cell model is a single diode model is shown in Figure 2[14]. The module is connected to a micro-grid point through Buck-Boost DC-DC converter. The expression in Equation (3) represents the mathematical behavior of the current in module terminals \(\mathbf{I}_{CDk}^{i}\)[15]. Where the meaning of each term is as follows: the current of the photovoltaic module is represented by \(I_{PV}\), The current of saturation is represented by \(I_{S}\), the DC voltage in the module terminals represented by \(V_{CD\text{ }k}^{i}\), the resistance in series is represented by \(R_{S}\), the number of cells connected in series represented by \(n_{s}\) and the number of cell connected in parallel represented by \(n_{p}\). The term \(R_{S}\) is evaluated from Equation (4), where the open circuit voltage represented by \(V_{\infty}\), the voltage of the point of maximum power represented by \(V_{mpt}\), the current of short circuit electricity represented by \(I_{SC}\), the current maximum power point electricity represented by \(I_{mpt}\). Mathematical expression of \(I_{SC}\) must also be evaluated through Equation (5). Where the current short circuit electricity standard under the conditions of test represented by \(I_{SC,stc}\), irradiance represented by \(G\), irradiance under the test represented by \(G_{stc}\), electricity temperature coefficient represented by \(k_{t}\), temperature of the panel represented by \(\mathfrak{I}\), standard temperature under the test of the panel represented by \(T_{stc}\)\(V_{\infty}\) are evaluated through Equation (6) where standard low open circuit voltage conditions test is represented by \(V_{\infty,stc}\) and temperature coefficient represented by \(k_{\wp}\)[16].  
\[\mathbf{I}_{CDk}^{i}=\left[I_{PV}-I_{S}\left[exp\left(\frac{\frac{V_{CDk}^{i}}{n_{ s}}+\frac{t_{CDk}^{i}}{n_{p}}R_{S}}{V_{CD}^{i}}\right)-1\right)\right]n_{p}; \forall t_{i}\in T \tag{3}\]  
\[R_{S}=\frac{\frac{V_{\infty}-V_{mpt}}{n_{s}}+V_{CD}^{i}\ln\left(\frac{I_{SC}- I_{mpt}}{I_{SC}}\right)}{\frac{I_{mpt}}{n_{p}}} \tag{4}\]  
\[I_{SC}(T,G)=I_{SC,stc}-\frac{G}{G_{stc}}\left[1+\frac{k_{l}}{100}(\mathfrak{I} -T_{stc})\right] \tag{5}\]  
\[V_{\infty}(T)=V_{\infty,stc}\left[1+\frac{k_{\wp}}{100}(\mathfrak{I}-T_{stc})\right] \tag{6}\]  
In this paper, the photovoltaic module parameters involved in Equations (3)-(6) were obtained from the features of the monocrystalline module brand Silfab model SLAM 300 [17].  
Equation (7) describes the power generated in DC terminals of the photovoltaic module to the microgrid.  
\[p_{CDk}^{i}\big{(}V_{CDk}^{i},I_{CDk}^{i}\big{)}=I_{CDk}^{i}V_{CDk}^{i};\forall t _{i}\in T \tag{7}\]  
Figure 2: Schematic model of a photovoltaic module.
# High-Power, High-Beam-Quality, Long-Pulse-Width 532 nm Laser Based on a 4f Optical System
## 5 Conclusions  
Based on a high-power 532 nm laser with frequency doubling in a dual acousto-optic Q-switch cavity, this paper sets out a design for a high-power long-pulse-width 532 nm laser. By using a 4f optical system, it achieves image transfer properties, ensuring that the q-factor remains constant within the laser cavity, thus allowing for an extended-cavity length and longer pulse width. During the experiment, under a 10 kHz repetition frequency condition, there was no significant decrease in the average power compared to the short-cavity power, with a maximum output power of up to 112 W achieved. Due to reflection losses and lens absorption, the power conversion efficiency was 88.2%. A pulse width of 85 ns was also achieved, with a pulse width broadening rate of 45.6% due to losses. The beam quality slightly degraded due to aberrations and misalignment, but the shape remained relatively good.  
Conceptualization, X.L.; methodology, X.L.; software, J.H.; validation, J.H. and H.L.; formal analysis, W.L.; investigation, J.H.; resources, H.L.; data curation, N.X.; writing--original draft preparation, J.H.; writing--review and editing, J.H.; visualization, Y.L. and T.Z.; supervision, [PERSON] and Z.W.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Basic Scientific Research (JCKY2021110B175); Beijing Municipal Natural Science Foundation (1212009); Rapid Support Programme (80914020105).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company AVIC Beijing Aeronautical Manufacturing Technology Research Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Experiment Results and Discussion
### Model Training and Initialization of Parameters  
The proposed model was fine-tuned on five crowd density levels by setting the parameters and hyperparameters. The training and validation batch sizes were set at 40 samples, the learning rate was kept at 0.001 and we used the Adam optimizer. In this approach, the model training performance was kept at its highest while avoiding  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{8}{c}{**Training Samples**} & \multicolumn{8}{c}{**Testing Samples**} \\ \cline{2-13}  & **VL** & **L** & **M** & **H** & **VH** & **Total** & **VL** & **L** & **M** & **H** & **VH** & **Total** \\ \hline UCSD-Ped1 & 2093 & 4171 & 1043 & 614 & 119 & 8040 & 1394 & 2715 & 690 & 480 & 82 & 5360 \\ \hline UCSD-Ped2 & 2113 & 4137 & 1016 & 785 & 107 & 8158 & 1374 & 2749 & 717 & 502 & 98 & 5440 \\ \hline PETS2009 & 49 & 128 & 156 & 114 & 290 & 737 & 46 & 86 & 111 & 70 & 179 & 492 \\ \hline UMN-Plaza1 & 113 & 503 & 123 & 93 & 190 & 1022 & 79 & 335 & 76 & 66 & 126 & 682 \\ \hline UMN-Plaza2 & 197 & 570 & 100 & 189 & 143 & 1199 & 125 & 371 & 75 & 142 & 87 & 800 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of datasets’ splits for training and testing.  
Figure 4: Examples of crowd scene of different density levels.
# Improved Calculation of Load and Resistance Factors Based on Third-Moment Method
###### Abstract  
Load and resistance factor design (LRFD) is widely used in building codes for reliability design. In the calculation of load and resistance factors, the third-moment method (3M) has been proposed to overcome the shortcomings (e.g., inevitable iterative computation, requirement of probability density functions (PDFs) of random variables) of other methods. With the existing 3M method, the iterative is simplified to one computation, and the PDFs of random variables are not required. In this paper, the computation of load and resistance factors is further simplified to no iterations. Furthermore, the accuracy of the proposed method is proved to be higher than the existing 3M methods. Additionally, with the proposed method, the limitations regarding applicable range in the existing 3M methods are avoided. With several examples, the comparison of the existing 3M method, the ASCE method, the Mori method, and the proposed method is given. The results show that the proposed method is accurate, simple, safe, and saves material.  
load and resistance factors; third-moment method; reliability; iteration computation +
Footnote †: journal: Applied Sciences
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 2 Impedance Characteristics Analysis of D-PMSG
### Impedance Modeling of D-PMSG
#### 2.3.2 Simplified Model of the Converter  
The converter depicted in Figure 3 uses pulse width modulation techniques. The amplitude of output phase voltage could reach as large as \(V_{dc}/2\), correspond modulation factor is 1. Therefore, to get good control performance, it is necessary to take account in this characteristic when designing both system setup parameters and control strategies.  
On one hand, in the design of the grid-connected converter parameters under the unit power factor control, it is worth mentioning that the output voltage, current, PCC voltage, and power grid voltage meet the relationship shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline
**I\({}_{a}\) [\(f\)], \(\mathbf{I_{b}}\) [\(f\)], \(\mathbf{I_{c}}\) [\(f\)]** & \(\mathbf{T(\theta_{1})}\) & \(\mathbf{I_{d}}\) [\(f\)], \(\mathbf{I_{q}}\) [\(f\)]** \\ \hline \(f_{1}\) & \(f_{p}\) & \(f_{1}-f_{p}\) \\ \(f_{1}\) & \(2f_From the following figure, we can conclude that usually \(U_{pwm}<U_{s}\) since the soothing inductance usually smaller than line inductance \(L_{line}\). As a result, it will be praised if the \(dc\) side voltage \(V_{dc}\) is set to be twice as large as \(V_{sa}\), at least.  
On the other hand, modulation factor in PWM should not be larger than 1 the carrier usually uses (\(-1\), 1) triangular wave. Therefore, in control strategy design, the three-phase voltage reference should be divided by \(V_{dc}\)/2, amplitude limiter should be added respectively.  
If the grid-connected GSC of D-PMSG system satisfied the two conditions above, the VSC could be supposed as a voltage-controlled voltage source, and \(v_{ga}\), \(v_{gb}\), \(and\)\(v_{gc}\) would follow \(v_{\textit{arf}}\), \(v_{\textit{pref}}\), and \(v_{\textit{cref}}\). To verify the correctness of the assumption, a simulation experiment was conducted. The structure diagram of experimental circuit is shown in Figure 3. The converter side parameters are listed in Appendix A. The grid side parameters used in this simulation are as follows: \(V_{\text{sa}}\) = 650 V, no resistance, and \(L_{line}\) increases from 0.3 mH to 0.46 mH at 3 s. The converter output voltage compared with reference voltage is depicted in Figure 7.  
So, in this paper, the switching dynamics process of converter will not be detailed described. The simplified converter model is proposed and verified in this section, which satisfies:  
\[\left\{\begin{array}{l}v_{ga}(t)=v_{\textit{arref}}(t)\\ v_{gb}(t)=v_{\textit{bref}}(t)\\ v_{gc}(t)=v_{\textit{cref}}(t)\end{array}\right. \tag{19}\]  
Figure 6: Phasor analysis of VSC ac side voltage.  
Figure 7: Voltage tracking verification of D-PMSG’s grid side converter (GSC).
## 5 Conclusions  
In this paper, we introduced two adaptive fuzzy logic-based broadcasting schemes integrated with reinforcement learning to address the dynamic challenges inherent in MANETs. Our first proposed method dynamically adjusts fuzzy membership function boundaries and employs reinforcement learning to fine-tune the ratio of broadcasting nodes, while the second proposed method advanced adaptability further by introducing a multi-profile strategy that dynamically selects among specialized parameter sets tailored for various network scenarios. Through an extensive comparative analysis with other state-of-the-art adaptive broadcasting methods, the multi-profile fuzzy logic approach particularly excelled in scenarios demanding sophisticated context sensitivity and energy conservation, demonstrating a powerful mechanism for robust performance under diverse MANET conditions.  
Future research could explore integrating the proposed adaptive broadcasting models into post-disaster response systems, where resilient communication is critical despite rapid topology changes. Another promising direction involves optimizing profile selection through collaborative or federated learning techniques to improve adaptability without centralized control. Additionally, applying these models to energy-constrained sensor networks powered by intermittent energy sources (e.g., solar) may further demonstrate their effectiveness under harsh environmental conditions.  
Conceptualization, A.I. and B.-K.S.; Methodology, J.K.; Formal analysis, Software, J.K.; Validation, Y.-B.P.; Formal analysis, K.-I.K.; Writing --review & editing, K.-I.K.; Project administration, K.-I.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government through MSIT under Grant RS-2022-00165225, and in part by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-II221200, Convergence security core talent training business (Chungnam National University)).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 4: Coverage sensitivity to \(\pm 20\%\) perturbations in \(w_{\text{NN}}\). Shaded bands represent \(\pm 1\%\) variability over 100 Monte Carlo trials for each method.
## 3 Results
### Predictive Validity Assessment of Effective Connectivity
The predictive validity (i.e., the classification accuracy, sensitivity, specificity, correlation coefficient and its significance) of the other six connections are reported in Table 3. We can find that the connections with 100% posterior probability of group difference, i.e., the self-connection of the pre-SMA, and the pre-SMA\(\rightarrow\)ISFG connection, exhibit significant classification ability (\(p<0.05\)) to distinguish drivers from controls (sensitivity = 80%).
# A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem
## 4 MDP Framework for HFSP
### Reinforcement Learning Process for HFSP  
For the deterministic initial sequence HFSP, its pseudo code of solution with reinforcement learning method is shown in Algorithm 1, and the corresponding flow chart is illustrated in Figure 2.  
For the non-deterministic initial sequence HFSP, it is necessary to choose the initial sequence before executing the below pseudo code (i.e., Algorithm 1).  
```
0: discount factor \(\gamma\), learning parameter \(\alpha\)  initialize Q arbitrarily (e.g. \(Q(s,a=0;\forall s\in S,\forall a\in A)\) for each episode do \(s\) is initialized as the initial state \(ST_{i,j,l}\), \(ET_{i,j,l}\), \(BT_{j,l}\), \(FT_{j,l}\) are initialized as 0, \(AT_{i,j,l}\) is initialized with the initial sequence. repeat for each state do repeat \(AT_{i,j+1,k}=ET_{i,j,k}\) (\(j=1,2,\ldots,S-1\)) for each workpiece \(J_{i}\)do repeat  choose an action \(a\in A(s)\) based on Q and an exploration strategy  perform action \(a\)  observe the new state \(s^{\prime}\) and receive reward \(r\) \(Q(s,a):=Q(s,a)+\alpha(r+\gamma\underset{a}{\text{max}}Q(s^{\prime}a)-Q(s,a))\) \(i=i+1\) until all workpieces transfer to \(s^{\prime}\)  sort the workpiece with the ending time of machine with ascending until\(s^{\prime}\) is a goal state until episode is the last episode  end if
```  
**Algorithm 1**The Reinforcement Learning Method for HFSP
## 4 Sound Event Classification Systems
### CNN-Based Models
#### 4.1.1 Audio Processing and Data Augmentation  
Due to the varying length of each sound clip in the database, the audio files have been cut into fixed length analysis fragments of 1 s, with 0.5 s hop size. The original sampling frequency of 48 kHz has been kept. All the fragments from the same audio clip inherit the label of the whole clip, so all of them have the same label. All the programming for processing and implementing the classifiers has been done using Python as programming language.  
To feed the CNN branch, the log-Mel spectrogram of each fragment is calculated using the Librosa [46] library, with 128 Mel filters and a window length of 21 ms and a frame shift 10.5 ms (resulting in 94 frames). These log-Mel spectrograms are used as input to the convolutional layers.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Supervised Dataset (SD)**} & \multicolumn{6}{c}{**Unsupervised Dataset (UD)**} \\ \hline \multirow{2}{*}{**Categories**} & **Total** & **Min** & **Max** & **Average** & **Total** & **Total** & **Min** & **Max** & **Average** & **Total** \\ \cline{2-10}  & **Length** & **Length** & **Length** & **Length** (\(\pm\sigma\)) & **Events** & **Length** & **Length** & **Length** (\(\pm\sigma\)) & **Events** \\ \hline car & 1795 & 6 & 123 & \(17.1\pm 17.4\) & 105 & 918 & 6 & 56 & \(14.8\pm 8.4\) & 62 \\ motorbike & 2240 & 6 & 123 & \(21.5\pm 20.1\) & 104 & 511 & 8 & 39 & \(14.6\pm 5.9\) & 35 \\ cleaning-truck & 1164 & 20 & 123 & \(77.6\pm 38.6\) & 15 & 441 & 6 & 123 & \(55.1\pm 39.7\) & 8 \\ voice & 7004 & 6 & 123 & \(71.4\pm 49\) & 98 & 11420 & 7 & 123 & \(88.5\pm 43.5\) & 129 \\ music & 4813 & 6 & 123 & \(104.6\pm 34\) & 46 & 21 & 6 & 15 & \(10.5\pm 4.5\) & 2 \\ dog & 183 & 7 & 29 & \(13.\pm 7.1\) & 14 & 128 & 6 & 24 & \(12.8\pm 6.1\) & 10 \\ storm & 831 & 6 & 123 & \(51.9\pm 48.8\) & 16 & 11 & 11 & 11 & \(11\pm 0\) & 1 \\ impact & 184 & 6 & 26 & \(12.3\pm 6.2\) & 15 & 79 & 6 & 20 & \(11.3\pm 4.3\) & 7 \\ machinery & 501 & 6 & 123 & \(26.4\pm 35.4\) & 19 & 232 & 6 & 123 & \(38.6\pm 39.3\) & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SD and UD events length and distribution by class. Length in seconds.  
The other branch uses OpenL3 embeddings. These embeddings are obtained using Mel-spectrograms calculated as explained before as an input for the embedding model which returns an embedding vector of 512 values.  
Mixup is used as an augmentation technique for the training data. This technique [47] generates a weighted combination of random pairs of vectors from the training data with their corresponding labels. The input vector pairs (both log-Mel spectrograms and embeddings) are combined with a weighting value within the [0, 1] range, sampled from a Beta distribution with \(\alpha\) = 0.4, and the one-hot encoded labels are also combined applying the same weight.