esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Rainfall Simulations by WRF-Only and the Fully Coupled WRF/WRF-Hydro  
#### 3.1.1 The 24 h Accumulation of Rainfall  
Figure 2 shows the 24 h rainfall histogram and cumulative curve. For both WRF-only and the fully coupled WRF/WRF-Hydro, the RE was truly related with the temporal and spatial homogeneousness of storm events. It could be grasped intuitively that the cumulative rainfall of the coupled system was generally closer to the observations than that of WRF-only, except Event 5. Table 6 further provides the relative error (RE), different type rainfall average relative error (ARE) of the 24 h cumulative rainfall curve and 24 h rainfall accumulation values using WRF-only simulation and the fully coupled WRF/WRF-Hydro simulation. For the three types of events, the values of ARE (b) were all less than ARE (a), which might mean that the fully coupled WRF-Hydro could improve the cumulative rainfall compared with WRF-only. The results of ARE values were type 3\(>\)type 2\(>\)type 1, which were reasonable for the inhomogeneous spatial and temporal distribution, led to worse simulations. The RE of Event 1-4 of the fully coupled modeling system were less than WRF-only. In particular, the Event 1 simulated and observed RE was reduced from 0.139 (WRF-only) to 0.038 (the fully coupled WRF/WRF-Hydro). Nevertheless, the fully coupled system cumulative rainfall values of Event 5, an extreme heavy storm mentioned before, were slightly less, with the associated RE values that rose from 0.657 (the fully coupled WRF/WRF-Hydro) to 0.616 (WRF-only).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation/Observation** & **Yes** & **No** \\ \hline Yes & NA & NB \\ No & NC & ND \\ \hline \hline \end{tabular}  
* NA: The hit number of rainfall occurring in both simulation and observation; NB: The hit number of rainfall occurring in simulation but no observation; NC: The hit number of no rainfall occurring in simulation and observation; ND: The hit number of rainfall occurring in observation but no simulation.  
\end{table}
Table 5: Rain/no rain contingency table for the simulation against the observation.
## 1 Introduction
As the focus of decision makers, the final system performance can be quantified into a set of evaluation criteria, such as production efficiency, investment cost, floor space, etc. Thus, when solving this kind of complex problem, the industrial company has shown great interest in choosing between a set of feasible solutions with different final performances, instead of solving sub-problems by breaking down the complete problem. Therefore, we propose to solve TLBP and BAP simultaneously with a multi-objective heuristic approach. We also refer to the real manufacturing system in the automotive sector with four units producing four different kinds of engine blocks to further validate the proposed approach and compare the results on all the four real situations. The four kinds of engine blocks are shown in Figure 1, where part A is the same case study presented in [4]. The four parts have six faces and 351, 242, 421, and 285 machining tasks, respectively. They have 49, 50, 57, and 58 different machining features with 84, 68, 109, and 96 different machining operations, respectively. The rest of this paper is structured as follows: Section 2 presents a literature review on some references from the research area of TLBP, BAP, and the simultaneous approaches to them. Then, the main contribution of this paper is discussed. Section 3 presents the detailed mathematical description of the problem. Section 4 provides the proposed approach, describing in detail the heuristic principles and tuning of the parameters of the proposed approach. Section 5 presents the industrial case study of four engine blocks to compare and validate the proposed approach. At the end, the results and outcomes are summarized in the conclusion. A detailed description of the industrial case study with four manufacturing units is reported to ensure the reproducibility of the proposed approach and the possible usage of the case study as a testbed for other possible approaches.
# Novel Janus Fibrous Membranes with Enhanced Directional Water Vapor Transmission
## 3 Results and Discussion
### Fabrication and Morphology of Polyurethane (PU)/Cellulose Acetate (CA) Janus Fibrous Membrane
To explore the influence of the inner diameters of the electrospun needle nozzle on the fibrous morphologies, electrospun needle nozzle with the inner diameters of 0.6 (6G), 0.8 (8G), 1.0 (10G), and 1.2 mm (12G) were equipped during the fabrication process. Figure 3a-d presents the typical SEM images of electrospun PU fibrous membranes with different fiber diameters, which are named as PU-6G, PU-8G, PU-10G, and PU-12G, respectively. The corresponding statistic average diameters (AD) are 380 nm, 669 nm, 1090 nm, and 1110 nm, respectively. Meanwhile, the statistic average fiber diameter of electrospun CA fibrous membranes named CA-6G, CA-8G, CA-10G, and CA-12G are 225 nm, 228 nm, 340 nm, and 354 nm, respectively. Both the PU and CA fiber diameters increased with the increase in inner diameters of the electrospun needle nozzles. Herein, the concentrations of PU and CA were chosen at 12.5 wt %, and the electrospun needle nozzle type was 8G, respectively, under which uniform fibers can be fabricated. When peeled off from the Al foil substrate, a free standing hydrophobic/hydrophilic PU/CA Janus membrane was successfully obtained.
## 3 Results and Discussion
### Surface Roughness  
The results of the surface roughness tests are shown in Figure 7. It shows maps of the topography of the test pieces in their initial state (after sandblasting and mechanical polishing, heat treatment and steam sterilization).  
Figure 7: Maps of the surface topography of the samples generated in Leica Map software: **(a)** PW 1,2, (b) DMLS 1,2, (c) PW 1,2,4, **(d)** DMLS 1,2,4, **(e)** PW 1,2,3, **(f)** DMLS 1,2,3, **(g)** PW 1,2,3,4, **(h)** DMLS 1,2,3,4.  
A comparison of the values of the surface roughness parameters is shown in Figure 8. The results of the roughness measurements are presented according to ISO 25178 [37] for parameter Sa and ISO 21920 [38] for parameter Ra.  
Mechanical surface treatment, including grinding and mechanical polishing, for both groups of samples showed convergent values. Additionally, the samples printed in the initial state are characterized by a lower Sa and Ra parameter compared to the bar after plastic working the samples. In addition, the initial roughness of the samples DMLS printed after sandblasting was also checked--Ra = 11.4 \(\upmu\)m. The ANOVA results indicated (\(p<0.05\)) for samples subjected to heat treatment and mechanical polishing compared to those in the initial state for both test groups.
## 4 Experiment
### Experimental Results
#### 4.4.2 Comparison Experiment with Deep Learning Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Longitude**} & \multicolumn{3}{c}{**Latitude**} \\ \cline{3-8}  & & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline \multirow{3}{*}{RNN [16]} & Train & 0.006257 & 0.008113 & 0.007923 & 0.006818 & 0.008588 & 0.025302 \\  & Validation & 0.005498 & 0.007931 & 0.007111 & 0.007245 & 0.015367 & 0.029939 \\  & Test & 0.00652 & 0.008556 & 0.008362 & 0.00692 & 0.008948 & 0.024227 \\ \hline \multirow{3}{*}{AE-RNN [44]} & Train & 0.004601 & 0.006242 & 0.006486 & 0.007548 & 0.010023 & 0.027295 \\  & Validation & 0.004342 & 0.005721 & 0.005968 & 0.00758 & 0.008988 & 0.029349 \\  & Test & 0.004345 & 0.005603 & 0.006106 & 0.007618 & 0.009273 & 0.029999 \\ \hline \multirow{3}{*}{GRU [10]} & Train & 0.003822 & 0.004338 & 0.003804 & 0.005096 & 0.007405 & 0.019484 \\  & Validation & 0.00385 & 0.003958 & 0.003767 & 0.004990 & 0.010832 & 0.020406 \\  & Test & 0.00388 & 0.005111 & 0.003783 & 0.004931 & **0.006610** & 0.017913 \\ \hline \multirow{3}{*}{LSTM [24]} & Train & 0.00467 & 0.00588 & 0.00664 & 0.007284 & 0.00974 & 0.027391 \\  & Validation & 0.00456 & 0.005394 & 0.006553 & 0.0072 & 0.008636 & 0.028688 \\  & Test & 0.00461 & 0.00559 & 0.006517 & 0.007216 & 0.008583 & 0.026491 \\ \hline \multirow{3}{*}{AE-GRU} & Train & 0.002585 & 0.003945 & 0.003368 & 0.004158 & 0.007416 & 0.0130732 \\  & Validation & 0.002757 & 0.00397 & 0.003635 & 0.0041940 & 0.006921 & 0.0147232 \\ \cline{1-1}  & Test & **0.002627** & **0.00494** & **0.003403** & **0.004313** & 0.007389 & **0.012989** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The prediction results of RNN, AE-RNN, GRU, LSTM, and AE-GRU using MAE, RMSE, and MAPE. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Typhoon** & **Avg APE** & **Max APE** & **Min APE** \\ \hline Tip & 188.08 km & 323.72 km & 67.39 km \\ Meranti & 148.01 km & 247.41 km & 11.56 km \\ Dujuan & 182.32 km & 339.738 km & 54.94 km \\ Haiyan & 137.87 km & 292.00 km & 15.15 km \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Avg APE, max APE, and min APE results of four typhoons in the 72 h forecast.
# Computing the Gromov-Wasserstein Distance between Two Surface Meshes Using Optimal Transport
## 2 The Discrete Gromov-Wasserstein Problem
There are many options to solve the regularized linear optimal transport problem in step 2, such as the Sinkhorn algorithm [20; 21] initially proposed for solving the OT problem by [PERSON] [18], or stabilized version of this algorithm [31; 32; 33; 34; 35], or using our own method based on statistical physics [23; 24]. ```
Input:\(N_{1}\) and \(N_{2}\), the size of the two sets of points \(S_{1}\) and \(S_{2}\). The mass vectors \(m_{1}(k)\) and \(m_{2}(l)\), for \(k\in[1,N_{1}]\) and \(l\in[1,N_{2}]\). The distance matrices \(d_{1}(k,l^{\prime})\) and \(d_{2}(l,l^{\prime})\) over all points \((k,l^{\prime})\in S_{1}^{2}\) and \((l,l^{\prime})\in S_{2}^{2}\). Tolerance, TOL; regularization parameter, \(e\); \(N\), maximum number of iterations. Initialize: Initialize transport plan \(G_{0}(k,l)=m_{1}(k)m_{2}(l)\) for\(n=1,\ldots,N\)do (1) Define \"cost matrix\" \(C_{n}(k,l)=\sum\limits_{k^{\prime}=1}^{N_{1}}\sum\limits_{l^{\prime}=1}^{N_{2 }}|d_{1}(k,l^{\prime})-d_{2}(l,l^{\prime})|^{p}G_{n-1}(k^{\prime},l^{\prime})\). (2) Solve \(G_{n}=\operatorname*{argmin}\limits_{G}U(G)=\operatorname*{argmin}\limits_{G} \left(\sum\limits_{l}C_{n}(k,l)G(k,l)+e\sum\limits_{k,l}G(k,l)\log(G(k,l)) \right)\) under the constraints defined in Equation (8). (3) If \(||G_{n}-G_{n-1}||<TOL\), break. endfor Output: The optimal transport plan, \(G_{n}\). ```  
**Algorithm 1** An iterative solver for the regularized GW problem. Algorithm 1 can be seen as applying successive linear approximation to the quadratic GW problem and as such it is expected to be efficient in computing time. There remains difficulties, however, as:  
1. Solving the regularized OT problem in step 2 is difficult when \(e\to 0\) (a necessary condition to get to the real GW distance). 2. Algorithm 1 is basically a fixed point method for which there is no guarantee of convergence. This is discussed in detail in Ref. [22]. 3. There is no easy option within Algorithm 1 to compute a scaling factor between distances within \(S_{1}\) and distances within \(S_{2}\). Those distances may have different scales, however, which can significantly impact the numerical stability of the algorithm. In the following section, we describe a different method for solving the GW problem that attempt to solve at least some of these concerns.
## 3 Preparation Methods for the Analysis of SVOCs or Thermally Stable Compounds
### QuEChERS  
The dispersive solid-phase extraction (d-SPE) technique employs acetonitrile extraction partitioning and requires few steps, which reduces the time required to complete the extraction and clean-up procedures [44; 45; 46; 47]. The typical QuEChERS methodology was introduced as a green, user-friendly, and cheap approach to meet the challenge of analyzing trace residue of various organic compounds in foods of plant and animal origin, which are complex matrices. In recent years, the QuEChERS methodology has become increasingly popular as a method to determine contaminants in all kinds of food matrices [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83]. The QuEChERS multi-method is predominantly suitable for the analysis of polar analytes. Target analytes for QuEChERS also include multi-class contaminants, such as pesticide residues, N-nitrosamines, veterinary drug residues, prohibited flavor compounds.  
Certain substrates present in foods, such as tea, honeybees, meat, or leek, introduce a heavy matrix interference, which could cause poor peak shape or suppress ionization of analytes or low content target analytes. In this context, further dilution of extracts or the use of a clean-up process, such as USE, multiplug filtration clean-up (m-PFC), robotic clean-up, deep-frozen, SPE methods, are recommended to effectively reduce matrix interference [84; 85; 86]. The m-PFC method provides a more practical and effective way to perform the clean-up process than the conventional d-SPE method, offering a compromise between the clean-up performance of SPE and the convenience of d-SPE [44]. Since the QuEChERS method is mainly based on the penetration of water and acetonitrile into the sample tissue (at room temperature), if samples have poor water solubility, e.g., beeswax, analysis by the common QuEChERS method might not be efficient [18].  
New materials as adsorbents are constantly being developed, among which can be cited tributylamine-functionalized graphene oxide (tri-BuA-rGO), graphitized MWCNTs, and ZrOX cluster [87]. These materials can be used as sorbents in sample preparation since they offer relatively high sensitivity for the analysis of pesticides [67]. Thus, the QuEChERS method is currently a widely used sample preparation method that can be coupled with GC-MS/MS detection and that allows simultaneous determination of a large number of contaminants in a sample.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Classical Deep Learning Approaches in 6G Networks  
Classical deep learning approaches in 6G networks are being adapted to handle the massive data volumes and ultra-low-latency requirements of these next-generation systems. Researchers are exploring techniques such as distributed and federated learning to process data at the network edge and developing more efficient neural network architecturesoptimized for 6G's high-bandwidth, low-latency environments [49; 50]. However, classical approaches face significant challenges in dynamic 6G conditions, including real-time adaptability to rapidly changing network topologies, the ability to process heterogeneous data from diverse IoT devices, and the requirement for energy-efficient computations to support massive connectivity. Additionally, the sheer scale and complexity of 6G networks pose limitations for traditional deep learning models regarding computational resources and training time.
# Iterative Learning for K-Approval Votes in Crowdsourcing Systems
## 5 Analysis of Algorithms
### Quality of Workers  
We can assume several worker models that reflect how workers behave when they receive tasks to solve. Common methods to generate their responses are simple probabilistic approaches. The basic assumption is that a worker has latent variables, which influences the generating of workers' responses. Latent variables usually represent diligence or expertise of workers, or the level of difficulty of given tasks. A recent model [22] uses a noise model that assumes that workers' responses are considered corrupted by noise from true answer. Here and after, we use \(\mathbb{U}_{D}\) to denote a set of standard \(D\)-dimensional unit vectors. For a fixed \((D,K)\), we model a task \(i\) associated with an unobserved \"correct\" solution \(s_{i}\in\ \mathbb{U}_{D}\). \(\vec{A}_{ij}\) denotes the response vector of each worker \(j\) and can be represented by the sum of \(K\) number of vectors in \(\mathbb{U}_{D}\) (\(K\)-approval setting). Each component of the response vector \(\vec{A}_{ij}\) is binary (1 or 0). The worker \(j\) with reliability \(p_{j}\) has \({}_{D}C_{K}\) choices for a response, and those choices are divided into only two types as follows:  
\[\vec{A}_{ij}(d):d^{\text{th}}\text{component of }\vec{A}_{ij},\ ^{\forall}d\in[1:D]\]  
\[\vec{A}_{ij}\cdot s_{i}= \begin{cases}1&\text{with probability }\big{(}\frac{p_{j}}{{}_{D-1}C_{K-1}}\big{)}\\ 0&\text{with probability }\big{(}\frac{1-p_{j}}{{}_{D-1}C_{K}}\big{)}\end{cases} \]  
From an information theoretical perspective, the quality of workers can be defined as negative entropy with an offset. This offset causes the quality of worker with random response to set to zero. Using the probabilities above, we can express negative entropy and define the quality of worker \(j\) with reliability \(p_{j}\) as:  
\[q_{j}(p_{j})=Q(p_{j})-Q\Big{(}\frac{K}{D}\Big{)},\quad\text{where}\;Q(p)=p\log \Big{[}\frac{p}{D-1}C_{K-1}\Big{]}+(1-p)\log\Big{[}\frac{1-p}{D-1}C_{K}\Big{]}. \tag{3}\]  
According to the quality of each worker, we can divide the workers into three types. \"Reliable workers\" are workers with the a quality close to 1, who make mostly correct answers. At the extreme, workers with a quality close to 0 make arbitrary responses and we define them as \"Non-informative workers\". At the other extreme, there are workers who make wrong answers on purpose and affect the crowdsourcing system badly; they can be regarded as \"Malicious workers\". In our algorithm, since the worker message value \(y_{j}\) is related to the quality, workers with positive \(y_{j}\), negative \(y_{j}\) and \(y_{j}\) close to zero correspond to \"Reliable workers\", \"Malicious workers\", and \"Non-informative workers\", respectively.
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 2 System Model
### Communication Model
#### 2.2.1 The Channel Gain \(h_{k}^{s}\)  
The channel gain from SAT to GU \(k\) in \(A_{SAT}\) is denoted as \(h_{k}^{s}\). According to [34,35], \(h_{k}^{s}\) can be expressed as  
\[h_{k}^{s}=\sqrt{G_{\rm s}G_{\rm k}\left(\frac{c}{4\pi f_{c}d_{k}^{s}}\right)^{2 }}, \tag{1}\]  
where \(d_{k}^{s}\) is the distance between SAT and GU \(k\), \(f_{c}\) is the frequency of operation, \(c\) is the speed of light, and \(G_{\rm s}\) and \(G_{k}\), respectively, denote the antenna gain of SAT and GU \(k\). In Equation (1), \(G_{\rm s}\) can be expressed as [36, 37, 38]  
\[G_{\rm s}(\theta_{s})=G_{\rm s,max}\cdot\left(\frac{J_{1}(u_{s})}{2u_{s}}+36 \frac{J_{3}(u_{s})}{u_{\rm s}^{3}}\right)^{2}, \tag{2}\]  
where \(u_{s}=2.07123\cdot\sin\theta_{s}/\sin\theta_{s,\rm 3 dB}\), \(G_{\rm s,max}\) is the maximum SAT antenna gain, \(\theta_{s}\) is the angle between the spot beam center and the location of GU \(k\) as seen from the SAT, and \(J_{1}(\cdot)\) and \(J_{3}(\cdot)\), respectively, denote Bessel function of the first kind with order 1 and 3. Without loss of generality, each GU is assumed to equip with an omni-directional antenna.  
Figure 1: Satellite–UAV “multiscale explanation” synergy remote sensing scenario.
## 3 Results and Discussions
### Order Picking Performance  
According to the subset selection, the 10 most efficient subsets are presented in Tables 6 and 7. The picking errors are assumed to occur with a mean of 4.5% (for error \(e_{1}\), \(e_{2}\), and \(e_{3}\)) or 5% (for error \(e_{4}\)) probability, and represent the baseline error probability. In the experiment, multiple scenarios were analyzed to investigate how each routing policy performs in each one of them. Each scenario depicts how picking errors affect the routing policies and picking technologies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Routing Policy** & **Paperless Technology** & **W1** & **W2** & **W3** & **W4** & **Total** \\ \hline \multirow{4}{*}{Traversal} & Barcode Handheld & 120 & 201 & 207 & 177 & 176 \\  & RFID tags Handheld & 121 & 202 & 208 & 178 & 177 \\  & Voice picking & 157 & 261 & 268 & 218 & 226 \\ \hline \multirow{4}{*}{Midpoint} & Barcode Handheld & 133 & 221 & 228 & 191 & 193 \\  & RFID tags Handheld & 127 & 194 & 199 & 173 & 173 \\  & Voice picking & 128 & 195 & 201 & 173 & 174 \\  & Voice picking & 169 & 252 & 260 & 211 & 223 \\ \hline \multirow{4}{*}{Return} & Barcode Handheld & 116 & 181 & 187 & 157 & 160 \\  & RFID tags Handheld & 117 & 182 & 188 & 157 & 161 \\  & Voice picking & 152 & 234 & 240 & 190 & 204 \\ \hline \multirow{4}{*}{Total} & Total & 128 & 199 & 205 & 168 & 175 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The picking rate per hour based on routing heuristics and paperless technologies.
## 3 Experiments and Results
### Experiments
#### 3.2.4 Curriculum Learning  
The next step will be to study the effect of curriculums on the training. To perform this, a new dataset is generated, containing 100,000 normalized samples, using quaternions for the orientation. The dataset is then divided into four curriculums, following two different approaches. In the first, the boundaries for each curriculum are distributed so that each slice encompasses the same distance. In the second, the boundaries are chosen so that each slice contains the same number of samples. Each curriculum is then built containing the previous ones.  
With the idea of reducing training time and of preventing overfitting, a new version of the UpdateLRCurriculumOnPlateau callback (Algorithm 1) is written. In this case, the change of curriculum occurs when a certain threshold is reached for the validation loss. The threshold starts at 0.3 and decreases every time a new curriculum is loaded.  
All possibilities are tested with the SimpleMlp model, and the results can be seen in Table 5 and Figure 13. As before, the plots contain measures taken with the validation data during training, and the table contains the metrics tested on a separate dataset.  
The best results are obtained when the model is training with curriculums distributed by equal distance, using the version of the callback described in Algorithm 1, where the curriculum is changed once the learning rate is at its minimum. However, the difference between metrics for the difference options is minimal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Curriculum Division** & **Threshold** & **MSE** & **Position Error** & **Orientation Error** & **Time** \\ \hline None & No & 0.1286 & 0.1395 & 0.2992 & 19 m 39 s \\ \hline By equal distance & No & **0.1212** & **0.1351** & **0.2753** & 17 m 34 s \\ By equal distance & Yes & 0.1232 & 0.1400 & 0.2764 & 17 m 17 s \\ \hline By equal number of samples & No & 0.1222 & 0.1443 & 0.2889 & 16 m 47 s \\ By equal number of samples & Yes & 0.1229 & 0.1432 & 0.2745 & 17 m 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the different metrics for the SimpleMlp model trained with a dataset with and without curriculums.  
This leads to the threefold conclusion that, within the scope of the tests conducted, the changes applied to the training scheme are not relevant to the results. In the first place, the use of curriculums, as opposed to training with only one dataset, makes only a minor impact, barely improving the metrics. Secondly, the choice of boundaries for the curriculum generation is irrelevant. Finally, using a threshold to decide when to change curriculums does not reduce training time or enhance the performance of the models.  
To sum up, although curriculum training has been shown to improve other training schemes for IK, such as DRL control, the specific strategies tested here do not seem to affect supervised learning. It remains possible that alternative curriculum designs could yield different outcomes.  
Figure 13: Comparison of the different metrics for the SimpleMlp model trained with a dataset with and without curriculums.