esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Models for Sorting and Logistics Operators
### Formulation of the Collection Problem  
In the collection problem, we have a set \(I\) of customers who require the collection of quantities \(q_{i},\forall i\in I\), of waste material to be sorted/recycled during the time horizon \(T_{c}\) with \(T_{c}\leq T_{s}\). The logistics operator uses a fleet \(H\) of vehicles, which each have a limited capacity \(C\). The cost \(c_{i}^{c}\) measures the expenses for the logistics operator to collect the waste commodity from customer \(i\in I\) in time slot \(t\in T_{c}\). The binary decision variable \(z_{ilt}\in\{0,1\}\) is equal to 1 if customer \(i\in I\) is serviced by vehicle \(h\in H\) in time slot \(t\in T_{c}\), and it is zero otherwise. The resulting collection model P2 is the following:  
\[\min\ Z_{c}= \sum_{i\in I}\sum_{h\in H}\sum_{t\in T_{c}}c_{it}^{c}\cdot z_{ilt}\] (10) s.t. \[\sum_{h\in H}\sum_{t\in T^{c}}z_{ilt}= 1,\forall i\in I, \tag{11}\] \[\sum_{i\in I}q_{i}\cdot z_{ilt}\leq C,\forall h\in H,\forall t\in T_{c},\] (12) \[z_{ilt}\in \{0,1\},\forall i\in I,h\in H,t\in T_{c}. \tag{13}\]  
Equation (22) is the objective cost function. Constraint (11) imposes that each customer has to be serviced only once, while (12) defines capacity restrictions for each vehicle in each timeslot. Constraint (13) defines the domain of variables \(z_{ilt}\). While model P1 is a variant of the lot sizing model and a variant of the model presented in [21], model P2 defines a multiperiod allocation problem which can be considered a variant of the knapsack problem.
## 2 Materials and Methods
### Physical Condition  
The upper limb power of the players was assessed using the overhead medicine-ball-throwing test with tape measure ([PERSON], Wolfenbuttel, Germany) of 0.1 cm precision [4].  
Lower limb power was assessed using the Squat Jump (SJ) and Counter Movement Jump (CMJ). The latest version of the MyJump application [36] was used to measure the jumps and was followed the protocol previously described by [PERSON] et al. [37].  
Speed was measured with a 30 m sprint at the maximum possible intensity. The start and end of the test were determined by two photocells ([PERSON], [PERSON], Bolzano, Italy).  
The agility of the players was evaluated using the T-Half Test following the protocol described by [PERSON] et al. [38]. This test was chosen because, unlike the \(t\)-Test, it is shorter in distance and more representative of the game of handball. The start and end of the test were determined by means of a photocell ([PERSON], [PERSON], Bolzano, Italy).  
Endurance was assessed with the Yo-Yo Intermittent Recovery level 1 test [39]. In addition, with this test, the theoretical VO\({}^{2}\) max was also estimated using the formula described by [PERSON] et al. [40].  
The tests were performed in the following order: medicine ball throw, SJ, CMJ, sprint, T-Half Test and the Yo-Yo test. This order was chosen following the recommendations of the National Strength and Conditioning Association (NSCA), which are based on the metabolic routes required and the fatigue generated by each physical test, leaving a rest between attempts of each test of five minutes so that fatigue interference would have the least possible effect [41]. In addition, this order has also been used in previous research with similar populations [26; 42].  
All players performed two valid attempts for the medicine-ball-throwing, jumping, sprinting and agility tests and a single attempt for the endurance test. In addition, the week before the measurements, a familiarization session with the protocols of each physical test was carried out. On the other hand, before each session of the physical tests, a standardized warm-up was also performed, consisting of 10 min of continuous jogging, dynamic joint mobility exercises and exercises that simulated the tests that they were going to perform in that session at low intensity. The researchers encouraged the players to perform all attempts of the physical tests at the maximum possible intensity.
# Concentration Influence of Complexing Agent on Electrodeposited Zn-Ni Alloy
## 1 Introduction
stated that the increase in the concentration of ethylenediaminetetraacetic acid (EDTA) yields more hydrogen evolution [30]. At low EDTA concentration, the refinement of microstructure could be obtained with low Zn content, the coating presented a \(\gamma\)-phase structure with better corrosion resistance. [PERSON] et al. used various amines as complexing agents for Zn-Ni alloy electrodeposition [18]. Amines significantly affected the phase transformation of Zn-Ni alloy and crystal orientation. These complexing agents shifted the deposition potential more negatively and produced pore-free coatings. In this study, we used tetraethylenepentamine (TEPA) as a complexing agent and investigated the effect of TEPA concentration on the electrodeposited Zn-Ni alloy coating. We examined the appearance of Zn-Ni coatings obtained by the Hull cell test with different TEPA concentrations via surface gloss. We also validated the microstructure relating to the variation in TEPA concentrations. Finally, we explored electroplating performance through thickness measurement, determination of Ni content, and partial polarization curves.
# Artificial Heart Valve Scaffold Based on Electrospun PCL/PU Three-Layer Composite Fibers
## 1 Introduction
At present, electrospun fibers based on biocompatible materials can be applied to prepare skin, blood vessels, bone, cartilage, etc. [13; 14]. A conductive electrospun nanofibrous scaffold was engineered by [PERSON] et al. to replicate the electrochemical signals characteristic of the myocardium [15]. The nanofibrous heart valve prepared using polylactic acid/polyaniline (PLA/PANI) provided a biocompatible growth environment for cardiomyocytes, improved cell-cell contact, and led to an increasing number of myotubes. The experimental results demonstrated the better cytocompatibility of PLA/PANI composites compared to pure PLA nanofibers. [PERSON] et al. evaluated the phenotypic characteristics and cardiac regeneration capabilities of human-heart-valve-derived scaffolds (hHVS) in comparison to natural protein/polycaprolactone (NP/PCL) anchored bone marrow mononuclear cells (BMNNCs) [16]. Through mRNA sequencing of BMMNCs cells on hHVS scaffolds and PCL scaffolds, they reached the conclusion that the performance of hHVS scaffolds for BMMNCs was superior to that of PCL scaffolds. [PERSON] et al. investigated the potential of an electrospun poly(carbonate urethane) mesh, which is coated with a poly(ethylene glycol) diacrylate hydrogel, as a synthetic biomaterial for replacing heart valve leaflets. In this context, the mesh offered mechanical support while the hydrogel ensured necessary blood compatibility on the surface [17]. In order to predict the mechanical behavior, a 3D model was established. Finally, it was concluded that the 3D model could optimize blood compatibility without relying on a mechanical response. In a word, previous studies mainly focused on the selection of polymer materials or simply adjusting the fiber structure. However, few studies demonstrated layered heart valve stents made up of artificial and natural polymer materials with different proportions. In this work, a PCL/PU three-layer composite heart valve scaffold including an inner-layer PCL, an intermediate-layer PCL/PU, and an outer-layer PCL was prepared by 3D printing and electrospinning. Structurally, the three well-designed layers could simulate the natural layered structure of human heart valves. Functionally, the mechanical properties and biocompatibility of electrospun heart valve scaffolds was further studied. The composite scaffold prepared in this work showed excellent anisotropic mechanical properties. The results indicate that the composite biomaterials could be well-suited for replacement heart valve application.
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
### State of the Art  
In recent years, researchers have actively applied machine learning and artificial intelligence methods, such as neural networks, clustering, and outlier detection techniques. Machine learning [11; 12] and artificial intelligence [13; 14] play a key role in predicting anomalous data. Deep learning algorithms, including recurrent [15; 16] and convolutional neural networks [17; 18], have been proven effective in processing time series data from sensors. Studies [15; 16; 17; 18] show that combining different neural network architectures can enhance anomaly prediction accuracy and reliability. For example, using autoencoders to identify hidden patterns in data ensures high accuracy in detecting anomalies in complex systems, such as aviation and industrial installations [19; 20].  
Alongside machine learning methods, statistical methods [21; 22] and clustering techniques [22; 23] are widely used for predicting anomalous data. Statistical approaches, such as probability density estimation and principal component analysis, identify deviations from normal data behavior. Clustering methods, like k-means and hierarchical clustering, group data and detect anomalies based on differences between clusters. These methods are often combined with machine learning to enhance prediction quality.  
Research also focuses on the practical integration of anomaly prediction methods into industrial applications [24]. Implementing such systems significantly improves equipment monitoring and diagnostics, preventing failures and reducing downtime. For example, in aviation, anomaly prediction systems help detect potential faults in engines and other critical components, ensuring higher levels of safety and reliability [25]. Similarly, in the energy sector, these systems optimize control processes and reduce the risk of emergencies [26].  
The application of neural networks for predicting anomalous data in sensor systems is scientifically justified due to their ability to efficiently process large amounts of data and identify complex, nonlinear dependencies [27; 28] that are difficult to detect using traditional methods [29; 30]. Their capability to train from historical data enables adaptive prediction [31], which is crucial in changing, dynamic systems, such as aviation and industrial applications. Moreover, the ability to automatically extract features from data makes neural networks a powerful tool for anomaly detection, minimizing the need for manual tuning and data preprocessing [32]. This enhances anomaly detection accuracy and reliability, which is critical for ensuring the safety and resilience of sensor systems.
## 6 Conclusions  
In this paper, to obtain an accurate slope deformation prediction, we conducted a comparative investigation between the ARIMA statistical method and 10 machine learning approaches based on monitored time-series displacement data of the Huanglianshu landslide located in eastern Chongqing. Four error metrics were used to comparatively analyze the prediction performance of all prediction models. The prediction results and comparative analysis of the model characteristics indicate the following: (1) the displacement data within the deformation area of the Huanglianshu landslide have more complex nonlinear relationships than the displacent data outside the deformation area; (2) machine learning approaches have a higher capacity to capture the nonlinear relationships between the time-series displacement data of slopes than the ARIMA model; and (3) among all the models, the Attention-LSTM model and the transformer model achieved the highest prediction accuracies for slope deformation, and the transformer model was better adapted to predict nonlinear landslide displacements affected by multiple factors. In the future, we will consider more factors that affect slope deformation and employ state-of-the-art deep learning approaches to address multivariable slope deformation prediction problems.  
Conceptualization, N.X. and G.M.; methodology, N.X., Q.Y., Y.S. and G.M.; validation, N.X., Q.Y., Y.S. and G.M.; formal analysis, N.X. and G.M.; investigation, N.X., Q.Y., Y.S. and G.M.; resources, Q.Y. and Y.S.; data curation, N.X., Q.Y. and Y.S.; writing--original draft preparation, N.X., Q.Y., Y.S. and G.M.; writing--review and editing, N.X. and G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of China (Grant No. 11602235 and 42277161).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the editor and the reviewers for their contributions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 4 Integration of Artificial Intelligence in LPBF Defect Sensing
### Integrated ML Techniques and Acoustic/Ultrasonic Sensors
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Material** & \multicolumn{2}{c}{**Sensor Type**} & \multicolumn{1}{c}{**ML Technique(s)**} & \multicolumn{1}{c}{**Defects Observed**} \\ \hline CL 20 ES Stainless Steel [161] & Fiber Bragg grating (FBG) sensor & SCNN, & \begin{tabular}{c} Tubular voids, gas porosity, \\ LOF, insufficient bonding \\ \end{tabular} \\ SS304L [159; 201] & Acoustic emission sensor, PCB microphone & DBN, MLP, SVM & DBN, CNN, SVM & \begin{tabular}{c} Balling, overheating, \\ underheating \\ \end{tabular} \\ IN718 [200] & Airborne resonant acoustic emissions sensor & CNN & \begin{tabular}{c} Balling, LOF pores, keyhole \\ pores, delamination, crack \\ propagation \\ \end{tabular} \\ \hline Ti6 Al4V [204] & \
\begin{tabular}{c} Layerwise electro-optical \\ imagery, acoustic microphone, \\ multi-spectral emissions, scan \\ vectors \\ \end{tabular} & CNN &
\begin{tabular}{c} Keyholing, under-melting, \\ balling \\ \end{tabular} \\ \hline Al92 Mn6 Ce2 [202] & Acoustic emission sensor & LR, SVM, RF, GPC & Gas pores, low fusion, cracks \\ SS316L [203] & Ultrasound microphone & CNN & LOF pores, keyhole pores \\ \hline \hline \end{tabular}
\end{table} 
Table 12: A compiled list of acoustic/ultrasonic sensors integrated with ML methods for detecting LPBF defects. Figure 14: Graphical diagram showing the process of FBG sensors and an SCNN algorithm to locate gas porosities in LPBF parts. **(a)** FBG sensor location inside LPBF printing chamber (left) with DAQ read-out system with SCNN algorithm (right); **(b)** FBR read-out system schematic [161].
## 4 Discussion  
### Thermal Stability and Uniformity  
In order to verify the thermal stability and uniformity of the NS03 cavity, we conducted long-time CW tests under different \(P_{l}\) conditions, and Figure 15 contains these results, with the points on the graph in steady states. The cavity could be operated stably in CW mode at a \(P_{l}\) of at least 10 W. We compared the variation in cavity temperature with \(P_{l}\), and the results are shown in Figure 16. At the same \(P_{l}\), the \(T_{avg}\) of the Cu- clamped cavity is significantly lower and the cavity permitted a larger \(P_{l}\). In addition, as \(P_{l}\) increases, the temperature difference (\(T_{d}\)) between the equator and the end of beam pipe of the NS04 cavity gradually increases, approaching almost 4 K at \(P_{l}\) of 1 W. In contrast, the \(T_{d}\) of the NS03 cavity could be maintained at around 0.5 K when \(P_{l}\) is below 7 W. At a \(P_{l}\) of 10 W, the maximum temperature difference in the NS03 cavity was 0.8 K. The shaded area in Figure 16 indicates that the thermal stability of the cavity is sufficient to operate the cavity at 10 MV/m. Cold sprayed Cu treatment distinctly improved the thermal stability and thermal uniformity of the cavity. The \(P_{l}\) of the NS03 cavity has a sufficient margin, which indicates that thermal stability
## 6 Experiments, Results, and Analysis
### Dashboard
```
#X_train: datasettraininstances
#feature_vector: featurevectorusedtoissuetheforecast
#relevant_features: listofrelevantfeaturestothegivenforecast
#modelforecastingmodel
#anomaly_detector: some anomalydetector
#n_samples: numberofsamplestodrawfromtheNormaldistribution givenX_train,feature_vector,relevant_features,model,anomaly_detector synthetic_samples=newdictionary() foreachfeatureinrelevant_features:
``` feature_mean=mean(X_train[feature]) feature_std=standard_deviation(X_train[feature]) feature_perturbed_values=normal(feature_mean,3*feature_std,n_samples) synthetic_samples[feature]=feature_perturbed_values ```
#createnewdataset,mergingdatafromnonrelevantfeatures,andperturbedones new_dataset=create_dataset(X_train,synthetic_samples) counterfactual_examples=newlist() foreachfeature_vectorinnew_dataset:
``` y_pred=model.predict(feature_vector) ifnotanomaly_detector.is_anomaly(_pred): counterfactualexamples.append(feature_vector) returncounterfactual_examples ```  
**Listing 1** Algorithm used to compute counterfactual examples. The last dashboard square is devoted to providing a set of values expected to make a better forecast (Figure 5F). To obtain them, we drew ten thousand values following a normal distribution, with a mean equal to the mean of past time series values and a standard deviation equal to three times the standard deviation measured in the past for that same time series. The values were then filtered by performing the same anomaly detection procedure as for the GTSMLFM forecasts, keeping only those not considered anomalous. Figure 5F displays just a subset of them: the minimum, median, and maximum values, and additional four random samples drawn from them. In this work, we used a particular selection of models and algorithms to create the forecasts, detect anomalies, and craft the explanations. However, given the architecture's modular structure, these can be replaced as black boxes, impacting the quality of the content displayed in the dashboard.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## Materials and Methods  
### Trace Organic Contaminants (TrOCs), Lacase and Mediators  
A set of 30 TrOCs comprising 10 pharmaceuticals, four personal care products, six pesticides, four industrial chemicals, five steroid hormones and one phytoestrogen was selected based on their widespread occurrence in surface water bodies (see Supplementary Data Table S1). Key physicochemical properties of the TrOCs including molecular weight, water solubility, hydrophobicity (log D) and volatility (pK\({}_{\rm H}\)) are presented in Table 1. All TrOCs were purchased from Sigma Aldrich (Sydney, NSW, Australia), and were of analytical grade. A stock solution (25 mg/L) containing the mixture of 30 TROCs was prepared in pure methanol, and kept in dark at \(-\)18 \({}^{\circ}\)C.  
Laccase from genetically modified _Aspergillus oryzae_ was supplied by Novozymes Australia Pty Ltd (Sydney, NSW, Australia). According to the supplier, the enzyme has a molecular weight, purity, activity and density of 56 KDA, 10% (\(w/w\)), 150,000 uM\({}_{\rm(DMPT)}\)/min (measured using 2,6-dimethoxy phenol, DMP, as substrate) and 1.12 g/mL, respectively.  
Two N-OH type redox-mediators, namely 1-hydroxybenzotriazole (HBT) and violuric acid (VA), and one phenolic redox-mediator, namely syringaldehyde (SA), were used in this study (see Supplementary Data Table S2). The selected mediators all follow hydrogen atom transfer (HAT) pathway for TrOC degradation [34], but the oxidation of phenolic and N-OH type redox-mediators by lacase produces highly reactive phenoxyl and aminoxyl radicals, respectively. The mediators were also purchased from Sigma Aldrich (Sydney, NSW, Australia). A separate stock solution (50 mM) of each redox-mediator was prepared, and stored at 4 \({}^{\circ}\)C in dark.
# Development of the Virtual Reality Application:
## 2 Methodology
### Developing and Optimizing a VR Application in Unity3d
#### 2.3.1 Geometry Optimization to Reduce CPU Time  
The first step is geometry and texture size optimization, both of which were already completed in the previous step. Since meshes require CPU time to be drawn, the fewer triangles they contain the better [26]. Textures use both the CPU and GPU, for loading and displaying, respectively. Lowering their size is important to avoid long loading times between scenes and potential VRAM limitations on the graphics card, which could lead to stuttering. Additionally, separating the different items into separate meshes is considered beneficial for the third step of the optimization process, occlusion culling.
## 6 Results
### Results for BFSOOK  
Based on the experiments conducted for OOK, it was evident that for the classification of frequencies the stripes' width is a better deciding factor than the number of stripes. Thus, the max of widths per frame is used to identify the frequency and thus determine the bit.  
Figure 15 shows max widths of subsequent frequencies used for representing logic 0 and 1. 500 Hz and 1000 Hz are the two frequencies used, and the distance of 20 cm is kept constant for testing the thresholding method. Figure 15 also shows that a sliding window size of 5 provided more adaptive thresholding for the determination of bits. Further, by keeping window size constant, the accuracy of the modulation scheme is tested. The frequencies selected follow \(F2=2\times F1\), to enable better classification, where \(F1\) and \(F2\) are frequencies for bit 0 and 1.  
Figure 16 shows the BER in percentage considering 2048 bits at varying distances. Here, \(F1\) frequency is used for representation. It is observed from the result that the BER(%) is 0% for \(F1=200\) Hz viz. \(F2=400\) Hz. This is because the difference between the widths for \(F1\) and \(F2\) frequency is prominent and thus easy to decode. Whereas, due to the elliptic  
Figure 14: OOK BER vs. Distance.  
Figure 13: OOK Sliding Window Thresholding.  
relation between width and frequency, as the frequency increases, there is a decrease in the difference between widths, making bit estimation more prone to errors resulting in an increase in BER.  
BFSOOK is more robust than OOK as the technique is inherently asynchronous and independent of distance. This modulation method provides better accuracy than OOK, even for a distance up to 40 cm considering a smaller transmitter. However, the obtained data rate is lower than OOK as the number of frames required to decode the bit is higher.
# 5G Mobile Phone Network Introduction in Colombia
## 6 5G Deployment Architecture Options from LTE (4G)  
There are two approaches to deploy the 5G mobile network from LTE (4G). The SA (StandAlone) comprises architectures of 5G radio access without any interaction with an existing 4G core, while NSA (Non-StandAlone) comprise architectures that provide 5G built over an existing 4G network. Service providers who want to implement 5G speeds as soon as possible will start with NSA 5G networks aided by existing 4G infrastructure. This allows us to amortize the 4G core networks. Once 5G coverage may be established widely, then StandAlone 5G will be implemented [49].  
The NSA option 3 family does not require 5G Core and provides dual connectivity supporting legacy 4G devices and the 5G devices [50; 51], being a crucial feature connect to gNB (5G base station) and the eNB (4G base station) wirelessly with the user equipment (UE) [52]. It is composed for the master node (eNB) and the secondary node (gNB) and the RAN is connected to the 4G Core (Evolved Packet Core--EPC) [53; 51]. Specifically, in the control plane Option 3x, the eNB is connected to the EPC, and the gNB operates with the eNB. With regard to user plane traffic, EPC can transmit/receive it to/from both the  
Figure 2: Scheme for: (**a**) Distributed-Radio Access Network (D-RAN) architecture. (**b**) Centralized Radio Access Network (C-RAN) architecture. (**c**) C-RAN evolution to V-RAN and O-RAN.  
eNB and the gNB. The gNB can steer the received user plane traffic toward the UE directly wirelessly or indirectly through the eNB [51].