esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads
## 2 Materials: Optimal Power Flows Considering Uncertainty Cost Functions
### Optimization Problem Statement
#### 2.1.3 Constraints  
Below are the constraints that are taken into account for optimal power flow [18].  
The active power limits of each generator must be respected:  
\[P_{i,min}\leq P_{gen,i}\leq P_{i,max}\qquad i=1\ldots n_{gen}. \tag{18}\]  
The reactive power limits of each generator must be respected:  
\[Q_{i,min}\leq Q_{gen,i}\leq Q_{i,max}\qquad i=1\ldots n_{gen}. \tag{19}\]  
The voltage limits in each system bar must be respected:  
\[V_{min}\leq V_{k}\leq V_{max}\qquad\quad k=1\ldots n_{bus}. \tag{20}\]  
The chargeability of any line or transformer should not be exceeded. For this restriction, the maximum apparent power of each element, the input power and the output power are taken into account:  
\[S_{p,in}\leq S_{max,p}\qquad p=1\ldots n_{lines} \tag{21}\]  
\[S_{p,out}\leq S_{max,p}\qquad p=1\ldots n_{lines} \tag{22}\]  
\[S_{i,in}\leq S_{max,l}\qquad p=1\ldots n_{transformers} \tag{23}\]  
\[S_{tot}\leq S_{max,l}\qquad p=1\ldots n_{transformers}. \tag{24}\]  
Finally, for each node k of the system, the balance of active and reactive power must be maintained, that is, the load flow [19]:  
\[P_{generated,k}-P_{demanded,k}=\sum_{i=1}^{n_{bus}}|V_{k}||V_{i}||Y_{ki}|cos( \delta_{k}-\delta_{i}-\theta_{ki}) \tag{25}\]\[Q_{generated,k}-Q_{demand,k}=\sum_{i=1}^{m_{max}}|V_{k}||V_{i}||Y_{ki}|sin(\delta_{k }-\delta_{i}-\theta_{ki}). \tag{26}\]  
Methods: Heuristic and Analytical Algorithms to Solve Optimal Power Flows Considering Uncertainty Cost Functions
## 6 Future Studies  
The image after spatial spectrum fusion has the characteristics of both hyperspectral resolution and high spatial resolution, and it has prominent advantages in the assessment of soil, vegetation, and water bodies properties. However, because of the large number of bands, there is also a problem of data redundancy. In future research based on this study, feature bands will be extracted according to the spectral response mechanism of different land cover types and combined with deep learning and machine learning algorithms to improve the classification accuracy. Furthermore, in the quantitative inversion of soil, vegetation, and water elements, the latest technology of feature band optimization will be studied to reduce the cross redundancy of spectral responses of different surface feature elements, so as to enhance the application of hyperspectral remote sensing in the quality assessment of various land use types.  
Conceptualization, P.F. and W.Z.; methodology, W.Z.; software, Y.Z.; validation, P.F., Y.Z. and W.Z.; formal analysis, Y.Z.; investigation, F.M.; resources, Y.Z.; data curation, W.Z.; writing--original draft preparation, P.F.; writing--review and editing, F.M.; visualization, W.Z.; supervision, B.Z.; project administration, F.M.; funding acquisition, P.F., F.M. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research is jointly supported by the National Natural Science Foundation of China (Grant No. 42101388), the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2022 MD070), the Shandong Top Talent Special Foundation, and the Doctoral Foundation of Shandong Jianzhu University (XNBS1708).  
Not applicable.  
Data Availability Statement: The datasets analyzed during the current study are available from the corresponding author on reasonable request.
## 4 Reliability Analysis of High-Pressure Tunnel
### Sensitivity Analysis
#### 4.3.2 The Influence of the Same Parameters on Different Failure Modes  
In the high-pressure tunnel's surrounding rock-reinforced concrete lining structure system, the reliability sensitivity of the same random variable under different failure modes is also different. As shown in Figure 12, the sensitivity curves of \(E_{1}\) under the three failure modes were similar, with the sensitivity ranking being g\({}_{2}\)(X) \(>\) g\({}_{1}\)(X) \(>\) g\({}_{3}\)(X). This indicated that with the change in \(E_{1}\) parameters, the main factor affecting the tunnel system was failure mode 2. The change curves of \(c\) and \(\varphi\) in failure mode 1 were almost horizontal, which were most affected by failure mode 3, followed by failure mode 2. In addition, with the change in COV (0.04-0.2), the fluctuation range of the reliability index of \(E_{2}\) in three failure modes was higher than 30%, and the sensitivity was the strongest in failure modes 1 and 3, which had a great influence on the stability of the tunnel.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**COV** & \(E_{1}\) **(GPa)** & **C (MPa)** & \(\varphi\) (\({}^{\circ}\)) & \(E_{2}\) **(GPa)** \\ \hline
0.04 & Inf & 4.2984 & 4.2415 & 4.4046 \\
0.06 & 5.1993 & 4.2908 & 4.1957 & 4.3470 \\
0.08 & 5.0690 & 4.2763 & 3.9998 & 4.2694 \\
0.10 & 4.8250 & 4.2694 & 3.6346 & 4.0671 \\
0.12 & 4.2316 & 4.2519 & 3.3405 & 3.7865 \\
0.14 & 3.7523 & 4.2355 & 3.1399 & 3.5555 \\
0.16 & 3.3333 & 4.2184 & 3.0077 & 3.3527 \\
0.18 & 3.0120 & 4.1490 & 2.9180 & 3.1787 \\
0.20 & 2.7485 & 3.9624 & 2.8574 & 3.0387 \\ \hline \end{tabular}
\end{table}
Table 8: Changes in COV and reliability indexes for failure mode 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**COV** & \(E_{1}\) **(GPa)** & **C (MPa)** & \(\varphi\) & \(E_{2}\) **(GPa)** \\ \hline
0.04 & Inf & 4.6850 & 4.5127 & 5.1993 \\
0.06 & 5.0690 & 4.6575 & 4.3601 & 4.9912 \\
0.08 & 4.9912 & 4.6450 & 3.8746 & 4.6620 \\
0.10 & 4.7983 & 4.6114 & 3.4783 & 4.1781 \\
0.12 & 4.4999 & 4.5566 & 3.2195 & 3.7934 \\
0.14 & 4.1875 & 4.5195 & 3.0572 & 3.4
## 5 Conclusions  
The addition of HA significantly reduced the available metal contents in the soil. The available Cd, Pb, Cu, and Zn was inactivated by maximum percentages (HA14.8 kg\(\cdot\)ha\({}^{-1}\)) of 37%, 39%, 18%, and 29%, respectively. The bioavailable fractions were transformed into nonbioavailable fractions more significantly upon applying HA. Tobacco had an accumulation effect toward Cd, whereas it had only an absorption effect toward Pb, Cu, and Zn. HA distinctly decreased the uptake of toxic elements such as Cd and Pb in tobacco leaves. It is worth noting that HA treatment also inhibited the uptake of mineral elements such as Cu and Zn into tobacco leaves. The metal contents in the upper, middle, and lower leaves were significantly reduced with HA treatment. However, the concentration of Cd in the modified soil far exceeded the standard limit of the Chinese environmental quality standard for soil (0.6 mg\(\cdot\)kg\({}^{-1}\), GB15618-2018), which means that this polluted soil would still not be suitable for agricultural use, even after the addition of HA.  
Conceptualization, Q.R. and C.Z.; methodology, H.H.; project administration, K.Z.; software, K.Z.; supervision, X.N.; validation, C.Z.; writing--original draft, Q.R.; writing--review and editing, C.L. All authors have read and agreed to the published version of the manuscript. The study was supported by the National Natural Science Foundation of China (no. 41461091), the Natural Science Foundation of Guangxi, China (no. 2015 GXNSFEA139001) and the College Young and Middle-aged Teachers' Basic Ability Improvement Project of Guangxi, China (no. KY2016 YB038). No potential conflict of interest was reported by the authors.
## 5 Experiment and Result Analysis
### Experimental Process and Result Analysis  
#### 5.3.1 Construction of the Output Notes  
1. Initialization of the receiver's dual key pair The receiver [PERSON] initializes the dual key pair, i.e., the scan key pair (\(s\), \(S\)) and the spend key pair (\(b\), \(B\)), where the information of the generation element G and the associated key pair parameters are shown in Figure 4.
2. Construction of the output notes The sender [PERSON] needs to construct the payment note by first calculating the stealth address \(T\) for this transaction, which is shown in Figure 5, including the calculation of the randomness seed, the temporary key pair (\(r\), \(R\)), and the shared key \(c\). Taking the above transaction as an example, the information of the final payment note is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware** & **Configuration** \\ \hline Operating System & Ubuntu 20.04.5 LTS (64 bit) \\ Processor & Intel (R) Core (TM) i7-7700 CPU @ 3.60 GHz (4 cores) \\ Memory & 4 GB \\ Graphics board & AMD Radeon (TM) R7 350 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hardware environment of the experiment.  
After testing, the time consumed for initialization of dual public key pairs, calculation of stealth address and construction of output note for transaction users are 133.096 ms, 9.779 ms and 12.162 ms respectively, all in milliseconds.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
###### Abstract  
_Background_: Thailand's logistics costs significantly exceed global averages. This study examines the novel application of a triple-helix model in factory logistics improvement projects in Northern Thailand from 2022 to 2024, addressing industrial logistics inefficiencies. _Methods_: The project involved 30 factories across various sectors, employing a case study methodology to analyze improvement initiatives in transportation, warehouse/inventory management, and logistics administration. _Results_: Transportation management interventions yielded up to 25% cost savings and improved delivery performance. Warehouse and inventory management upgrades led to inventory cost reductions of up to 55%. Logistics administrative improvements resulted in up to 20% cost savings and enhanced planning capabilities. _Conclusions_: The project demonstrates the effectiveness of the triple-helix model in facilitating knowledge transfer and practical improvements in industrial logistics. These findings are valuable for Thai industries, policymakers, and logistics managers globally, offering a blueprint for similar initiatives in developing economies. The success of this approach provides practical insights for optimizing logistics operations, potentially benefiting supply chain stakeholders, economic planners, and researchers seeking to enhance logistics efficiency and reduce costs in various industrial contexts.  
Keywords:factory logistics improvement; Northern Thailand industry; logistics in Thailand +
Footnote †: journal: _logistics_
## 2 Variables and Material Characteristics
### Mechanical Properties  
To assess the compressive strength (\(f_{\text{ck}}\)) of both NC and UHPFRC, \(\phi\)100 cylindrical specimens were made. The compressive strength experiments were conducted based on ASTM C39M [52]. In the case of the tensile strength (\(f_{\text{f}}\)), a splitting tensile strength experiment was conducted for NC, and a direct tensile strength experiment was conducted for UHPFRC. Both the compressive and tensile strength experiments were conducted on three specimens. The specimen used for determining the splitting tensile strength of NC was a \(\phi\)150 cylinder, referring to ASTM C496 [53]. As shown in Figure 3, the direct tensile strength specimens were fabricated in dog-bone shapes, featuring 2 mm wide and 12.5 mm deep notches on both sides to induce cracking in the center.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (min)** & **Specimen 1** & **Specimen 2** & **Specimen 3** \\ \hline
0 & 0.11 mA & 0.22 mA & 0.53 mA \\ \hline
30 & 0.24 mA & 0.29 mA & 0.63 mA \\ \hline
60 & 0.31 mA & 0.38 mA & 0.66 mA \\ \hline
90 & 0.42 mA & 0.46 mA & 0.72 mA \\ \hline
120 & 0.45 mA & 0.52 mA & 0.74 mA \\ \hline
150 & 0.49 mA & 0.58 mA & 0.78 mA \\ \hline
180 & 0.58 mA & 0.62 mA & 0.82 mA \\ \hline
210 & 0.61 mA & 0.67 mA & 0.86 mA \\ \hline
240 & 0.73 mA & 0.71 mA & 0.90 mA \\ \hline
270 & 0.81 mA & 0.83 mA & 0.93 mA \\ \hline
300 & 0.85 mA & 0.86 mA & 0.95 mA \\ \hline
330 & 0.91 mA & 0.88 mA & 0.97 mA \\ \hline
360 & 0.96 mA & 0.96 mA & 1.13 mA \\ \hline Q (Coulombs) & 12.48 & 13.30 & 17.62 \\ \hline \# \(Q=900\times(l_{0}+2l_{30}+2l_{60}\cdots 2l_{330}+l_{360})\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results for chloride ion penetration.  
Figure 2: Experimental results for chloride ion penetration.  
The results show that NC has an average compressive strength of 27 MPa and an average splitting tensile strength of 1.8 MPa. The average compressive strengths of the UHPFRC-F series with steel fiber contents of 1.0%, 1.5%, and 2.0% are 132, 145, and 158 MPa, respectively, and the average direct tensile strengths are 10.1, 13.6, and 15.2 MPa, respectively. For the UHPFRC-T and UHPFRC-I series, the average compressive strengths are 132 and 131 MPa, respectively, and the average direct tensile strengths are 11.6 and 11.2 MPa, respectively. The measured results for each experimental variable are illustrated in Figures 4 and 5.  
Figure 4: Compressive strength results according to test parameters.  
Figure 3: Dog-bone-type specimen used for the measurement of direct tensile strength.
# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 2 Materials and Methods
### Detection Method Used in This Study
#### 2.2.2 Bleeding Detection
We adopted a weighted loss function inspired by You Only Look Once (YOLO) [25], combining the detection confidence and bounding box regression terms. Unlike a previous study that used mean squared error (MSE) for spherical targets [20], we regressed all six parameters of the 3D cuboids. During detection, 3 \(\times\) 96 \(\times\) 96 \(\times\) 96 cubic VOIs were extracted from lattice points within the bounding box of the body region, with an interval of 48 voxels in each direction. Each VOI was then fed into the trained 3D object detection model to predict candidate bleeding regions as bounding boxes. The predicted bounding boxes were subsequently decoded and mapped back to the original CT coordinate space.
## 4 Node Activity Scheduling
### Cross Layering Optimization
#### 4.4.1 Cross-layering with the Application Layer
Our variant of SERENA without immediate acknowledgement performs better than TDMA-ASAP, because the node priority in SERENA coloring algorithm is equal to its number of descendants. Thus, nodes with a higher number of descendants tend to color first, taking smaller colors and then reducing the constraints on the colors taken by their descendants. Figure 14 depicts the average number of messages sent by a node during the coloring process of a tree with regard to different application requirements. TDMA-ASAP has not been represented because the coloring algorithm given in [36] is centralized. It turns out that for 100 nodes the additional number of color messages due to the immediate acknowledgement is about 148%, whereas the additional number of color messages due to broadcast is limited to about 13%. Notice that this additional cost is paid only when the coloring is performed. Figure 13: Comparison of the number of colors used in SERENA and TDMA-ASAP.
# Period-1 Motions and Bifurcations of a 3D Brushless DC Motor System with Voltage Disturbance
## 5 Conclusions  
The analytical variations of nonlinear currents and voltages for brushless DC motors were obtained in this paper through the application of the generalized harmonic balance method. The nonlinear model of the brushless DC model was transformed into a Fourier series system with coefficients varying with time slowly. The main conclusion can be summarized as follows:  
(1) The process from period-1 motion evolutions is achieved analytically. The independent period-1 motions and their bifurcations were observed. With the analytical solution, the initial condition for numerical integration can be easily determined and the characteristics of the dynamic system for such brushless DC motors can also be investigated. With the application of the generalized harmonic method, detailed bifurcations revealing the relationship among the independent period motions can be depicted, which is unattainable with numerical integration.  
(2) For the independent period-1 motions, the closed curves in harmonic amplitude are joined up by non-sequence stable and unstable routes. The continuous curves indicate that the unstable motions are connected with cyclic stable motions.  
(3) The harmonic amplitude results depicted that the closer the disturbance frequency is to zero, the more complex the motions are. The numerical illustrations directly show that the analytical and numerical results match very well.  
Applying the generalized harmonic method to other nonlinear differential equations and dynamic systems will be an appropriate subject for future investigations.  
Methodology, B.C. and Y.X.; Software, B.C.; Validation, Z.C.; Investigation, B.C.; Data curation, B.C.; Writing--original draft, B.C.; Writing--review & editing, Y.X.; Visualization, Y.J.; Funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Nature Science Foundation of China (Grant No. 12102319), the Fundamental Research Funds for the Central Universities (Grant No. xzy012021004), and the Key R&D and Transformation Plan Project of Qinghai Province (NO. 2023-QY-215).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** No conflicts of interest exist in the submission of this manuscript. I would like to declare, on behalf of my co-authors, that the work described was original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.
## 2 Background and Related Works
### Related Works
#### 2.2.1 Explainability for Input Features  
Several XAI methods [6; 9; 10; 11; 12] have been developed for a specific type of AI model. Methods that explain the DNN model's [6; 11; 12] operation by tapping or backpropagating the inference process, resulting in faster computations than methods with repetitive model inferences. In order to understand a Deep Q-network (DQN), [PERSON] et al. [13] introduced a method that requires manual clustering to generate an explanation in the form of a saliency map. On the other hand, [PERSON] et al. [14] manipulated the RL environment and used the saliency map to verify the counterfactual explanation and showed that the saliency explanation is subjective and insufficient. In that work, several attributive XAI methods were analyzed, but SHAP [4] was not included. [PERSON] et al. [15] highlighted the importance of the baseline data that are required for the SHAP method in the RL problem but did not provide a detailed data selection method nor its verification. For a machine learning problem, SHAP [4] is a model-agnostic method in which it is observed to produce the best results when applied to the telecommunications use case [16]. RL-SHAP [17] is an example where SHAP [4] can be applied to the RL problem to explain the contribution of the actor's input features. A recent survey by [PERSON] et al. [18] also identified that SHAP is highly adopted in RL, especially in the field of vehicle guidance, robotic manipulation, and system control. In the aforementioned methods, they used the entire model to generate the explanation, while our method splits the model into each reward component, and the importance of the input feature is now generated per reward component, thus providing fine-grained explanations.
## 4 DRFM Performance Results
### Effects of Waveform Time-Bandwidth Product  
The last factor evaluated was the time-bandwidth product of the radar waveform received by the DRFM, defined here as:  
\[\varrho=\tau\beta \tag{4}\]  
where \(\tau\) is the pulse width, and \(\beta\) is the bandwidth; in order to understand the effects of \(\varrho\) on the amplitude and I-Q DRFM systems. In both cases of the DRFM, an LFM signal was chosen due to the ease of scaling the time-bandwidth product compared to a Barker code.  
The results for a 10 us pulse with 1, 2, 5, and 10 MHz bandwidths are shown in Figures 15 and 16. The I-Q DRFM clearly illustrates the \(sin(x)/x\) pattern [21], with the expected reduction in main lobe width and decrease in side lobe levels as the bandwidth increases.  
Figure 14: Correlation results for a waveform with a 13-bit Barker code, with a pulse width of 10 μs using a variety of ADC resolutions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**ADC Bits** & **1** & **2** & **4** & **6** \\ \hline SLL—LFM & \(-9\) dB & \(-12\) dB & \(-18\) dB & \(-22\) dB \\ SLL—PSK & \(-3\) dB & \(-6\) dB & \(-12\) dB & \(-12\) dB \\ \hline \hline \end{tabular}
\end{table}
Table 3: ADC resolution DRFM comparison.  
The more interesting effects occur in Figure 15 for the amplitude DRFM, where the replicated envelope pulse is slid over the LFM waveform during the correlation. The result is a ripple over the pulse that changes with the frequency of the LFM waveform. As the frequency increases, the pulse ripple decreases.  
The results of these figures are summarized in Table 4. While not a linear response, the time delay and processing gain loss increase with an increase in signal bandwidth in an envelope DRFM. In a complex DRFM, recording the I/Q signal, the processing gain and time delay remain stable, but the side-lobe levels increase inversely with the signal bandwidth.  
Figure 16: Correlation results for a linear frequency modulated wave with a pulse width of 10 \(\upmu\)s and a variety of bandwidths from an IQ DRFM.  
Figure 15: Correlation results for a linear frequency modulated wave with a pulse width of 10 \(\upmu\)s and a variety of bandwidths from an envelope DRFM.