esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Morphological Anomaly Detection Based on Angular Margin Loss
###### Abstract  
Deep anomaly detection aims to identify \"abnormal\" data by utilizing a deep neural network trained on a normal training dataset. In general, industrial visual anomaly detection systems distinguish between normal and \"abnormal\" data through small morphological differences such as cracks and stains. Nevertheless, most existing algorithms emphasize capturing the semantic features of normal data rather than the morphological features. Therefore, they yield poor performance on real-world visual inspection, although they show their superiority in simulations with representative image classification datasets. To address this limitation, we propose a novel deep anomaly detection algorithm based on the salient morphological features of normal data. The main idea behind the proposed algorithm is to train a multiclass model to classify hundreds of morphological transformation cases applied to all the given data. To this end, the proposed algorithm utilizes a self-supervised learning strategy, making unsupervised learning straightforward. Additionally, to enhance the performance of the proposed algorithm, we replaced the cross-entropy-based loss function with the angular margin loss function. It is experimentally demonstrated that the proposed algorithm outperforms several recent anomaly detection methodologies in various datasets.  
anomaly detection; angular margin loss; self-supervised learning; metric learning; morphological transformation
# Nutrition in Pediatric Intensive Care: A Narrative Review
## 10 Examples of Enteral and Parenteral Feeding Prescriptions in PICU  
Example No.1: 6-year-old male patient without previous comorbidities, admitted to PICU for septic shock, intubated and mechanically ventilated.  
1. Nutrition screening and anthropometric measurement--(e.g., Strongkids): * Low risk of malnutrition, weight = 25 kg, height 120 cm.
2. Indirect calorimetry or Schofield equation E and protein target calculation: * E target: 1061 kcal/d, protein target at least: 37.5 g/d.
3. Gastric tube insertion, and after initial stabilization (fluid resuscitation, antibiotics), gastric nutrition started according to local protocol within 24 h from admission: * Isocaloric enteral nutrition formula (1 kcal/1 mL, target 1061 mL/d), bolus form 5 \(\times\) times per day--initial bolus 10% of estimated dose = 1061/5 times per day/10% \(\approx\) 20 mL of initial dose, 40 mL of second dose, 60 mL of third dose, in the case of intolerance (GRV > 3 mL/kg, dose reduction and optimize to tolerance).  
Example No. 2: 11-year-old female patient with severe decompensated [PERSON]'s disease, after being admitted to PICU after subtotalectomy, low risk of refeeding syndrome.  
1. Nutrition screening and anthropometric measurement--(e.g., Strongkids): * High risk of malnutrition, weight = 35 kg, height 140 cm.
2. Indirect calorimetry or Schofield equation E and protein target calculation: * E target: 1276 kcal/d, protein target at least: 52.5 g/d.
3. Gastric tube in situ, severe abdominal distension, enteral nutrition according to the surgeon currently contraindicated: * Total parenteral nutrition (with added micronutrients) via central/peripherally inserted central line with frequently reassessment of the possibility of \"trophic\" enteral feeding.
* If individual PN--53 g of 10% amino acids (525 mL) + 36 g of 20% lipid emulsion (180 mL) + 178 g of 40% glucose (445 mL) + 40 mL of 10% NaCl + 35 mL of 7.5% KCl + 5 mL MgSO4 10% + 5 mL of Ca gluconomicum + multivvitamin + trace elements + 640 mL of aqua pro injecting (Holiday and Segar formula fluids 1875 mL/day).
###### Abstract  
Our investigation focused on assessing the influence of the metamorphic buffer in metamorphic high-electron-mobility transistors (MHEMT) that were grown on GaAs substrates. While an MHEMT exhibited elevated off-state current levels, its direct current (DC) and radio frequency (RF) traits were found to be comparable to those of InP-based lattice-matched high-electron-mobility transistors (LM-HEMTs). However, the Pulsed I-V measurement results confirmed the presence of the fast transient charging effect, leading to a more substantial degradation in drain current observed in MHEMT. In addition, through the low-frequency noise characteristics, it was confirmed that the dominant trapping location was located in the bulk site. The slope of the \(1/f\) noise measurement indicated that the primary trapping site was in proximity to the bulk traps. The carrier-number-fluctuation (CNF) model was employed to extract the bulk trap density (\(N_{\mathrm{f}}\)). For the LM-HEMTs, the value was at \(3.27\times 10^{16}\) eV\({}^{-1}\cdot\)cm\({}^{-3}\), while for the MHEMT, it was \(3.56\times 10^{17}\) eV\({}^{-1}\cdot\)cm\({}^{-3}\).  
reliability; traps; MHEMT; fast transient; pulsed I-V; \(1/f\) (low-frequency) noise Article
# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 5 Discussion
### Ablation Experiment  
This section verifies each model component based on the SYSU data set in all-search and indoor-search modes. A dual-path network without SGA module is used as B, S represents the spatial attention branch, and G represents the global attention branch in the SGA module.  
The results are shown in Table 3. We can draw the following conclusions.  
1. In the two modes, compared to the baseline method, MFCNet achieves an improvement in the rank-1 accuracy and mAP. Notably, the rank-1 accuracy increased by 5.76% and 7.56% between the proposed method and other methods. The results show that the SGA module designed in this paper can identify more discriminative features than can other methods to improve the hit rate.
2. The spatial attention branch in SGA module helps improve network performance. In all-search and indoor-search modes, the spatial attention mechanism increases the hit rate for r = 1 by 2-5%. Specifically, the spatial attention mechanism can retain the key information form an image while ignoring noncritical information and interference;
3. The global attention branch in SGA module yields a 3-5% improvement in the hit rate for r = 1 in the two modes. This result verifies that the global attention mechanism can mine feature context, which helps the network extract robust features and improve network performance.
4. As shown in Tables 1-3, for the same method, indoor-search performs better than all-search. This is because images of indoor have less background variation and a more stable light condition and person pose, which makes matching easier. In the first row of the Figure 1, the first two images were taken indoors, and the second two were taken outdoors. We can see that the photos taken indoors are purer than those taken outdoors.
## 2 Materials and Methods
### Accuracy Assessment  
The quantitative results of comparing the output and ground truth labels, identified based on the threshold, were derived from the confusion matrix to verify the constructed models. An instance was categorized into the landslide (positive) class in most cases when the likelihood of occurrence was \(\geq\)0.5; otherwise, the instance was assigned the nonoccurrence (negative or non-landslide) label. Similar to previous classifications, the positive and negative labels in this study were changed from landslide and non-landslide to landslide source and runout classes, respectively. The threshold of 0.5 for classification could also be adjusted by the cost-sensitive analysis (p*).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{2}{c}{**Ground Truth**} \\ \cline{2-4}  & & **Runout** & **Landslide Source** \\ \hline \multirow{2}{*}{Prediction} & Run-out & True Negative (TN) & False Negative (FN) \\  & Landslide source & False Positive (FP) & True Positive (TP) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix used in this study.  
Four commonly used quantitative indices derived from a confusion matrix, namely overall accuracy (OA), user's accuracy (UA), producer's accuracy (PA), and area under the receiver operating characteristic (ROC) curve (AUC), were used to quantitatively evaluate the developed models. OA represents the ratio of samples correctly classified as TN and TP, as illustrated in Equation (13). UA and PA reflect the errors for each class (landslide source and runout) as presented in Equations (14)-(17), where R and S represent the runout and landslide source classes. The false alarm (commission errors) and missing (omission errors) assignments can be calculated as 1-UA and 1-PA, respectively. The AUC is commonly used to assess data mining-based models. The binary classification threshold is used for reclassifying instances, calculating the rates of FPs and TPs to draw the ROC curve. In general, the AUC ranges from 0.5-1, and results larger than 0.7 are acceptable, and larger than 0.8 are excellent [59].  
\[\text{OA}=\frac{\text{TN}+\text{TP}}{\text{TN}+\text{FN}+\text{FP}+\text{TP}} \tag{13}\]  
\[\text{UA}(\text{R})=\frac{\text{TN}}{\text{TN}+\text{FN}} \tag{14}\]  
\[\text{UA}(\text{S})=\frac{\text{TP}}{\text{FP}+\text{TP}} \tag{15}\]  
\[\text{PA}(\text{R})=\frac{\text{TN}}{\text{TN}+\text{FP}}. \tag{16}\]  
\[\text{PA}(\text{S})=\frac{\text{TP}}{\text{FN}+\text{TP}} \tag{17}\]
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 8 Lipase-Catalyzed Synthesis of Sustainable Polyesters and Polyamides from Biobased Monomers
### Biobased Saturated Aliphatic Polyesters
#### 8.1.2 Poly(butylene succinate)  
PBS is normally synthesized via polycondensation of succinic acid or succinic anhydride with 1,4-BDO at elevated temperatures, using a chemical catalyst [249]. It is also promising to synthesize biobased PBS via enzymatic polymerization.  
The lipase-catalyzed polycondensation of PBS was studied by [PERSON] et al. [250], using a two-stage method which is similar to those used for the industrial production but at much lower temperatures (Scheme 24). The solvent-free enzymatic polycondensation with succinic acid gave oligomers. However, by replacing succinic acid with diethyl succinate, the temperature varied two-stage method in diphenyl ether resulted in PBS with a \(\overline{M_{\mathrm{w}}}\) of 38,000 g/mol and a dispersity of 1.39.  
To synthesize PBS with higher molecular weights, another two enzymatic strategies were developed: (1) using cyclic oligomers [251]; and (2) \(co\)-polymerization of succinic acid and 1,4-BDO with succinate anhydride [252]. By using cyclic butylene succinate oligomers in the N435-catalyzed polymerization, PBS with a \(\overline{M_{\mathrm{w}}}\) of up to 130,000 g/mol and a dispersity of 1.6 was obtained. However, under similar reaction conditions, the direct enzymatic polycondensation gave PBS with a lower \(\overline{M_{\mathrm{w}}}\) (45,000 g/mol) and a broader dispersity (3.7) (Scheme 25). On the other hand, the enzymatic \(co\)-polymerization of succinic acid and 1,4-BDO with succinate anhydride resulted in PBS with a \(\overline{M_{\mathrm{w}}}\) of 73,000 g/mol and a dispersity of 1.7 (Scheme 26). However, although high molecular weight PBS can be enzymatically produced via these two approaches, an extra synthesis step is required.  
**Scheme 26. Synthesis of poly(butylene succinate) via N435-catalyzed \(co\)-polymerization of succinic acid and 1,4-butanediol with succinate anhydride [252].**
## 4 Application Scenarios and Platform Extensions
### The Influence of Sampling Frequency on the Control Effect  
To analyze the feature of partial delay, the whole time delay was resolved into intrinsic parts, and a variable-controlling approach was used to reveal the influence factors of each part. The factors of data sampling delay and network transmission delay were analyzed as follows:  
The impact of sampling frequency on data sampling delay \(\Delta t_{s}\): In the physical device, the average sampling delay was decided by sampling frequency. The sampling frequency was set as a variable, and the communication delay was set as a constant. Three different sampling frequencies were set in Environments 2, 3, and 4 at 50 Hz, 20 Hz, and 10 Hz, respectively. While Environment 1 was ideal without delay, Environment 5 presented the case without the control strategy. Environment 1 was compared with Environments 2, 3, and 4. Due to the decreasing frequency, the data transmission delay increased gradually, and the control execution time lagged behind. As shown in Figure 9, three different sampling frequencies caused a delay of 62.3 ms, 94.4 ms, and 144.7 ms, respectively, where the minimum frequency value was reduced by 0.0082 Hz, 0.0124 Hz, and 0.0191 Hz, respectively, when compared to the ideal situation.  
Figure 8: Time-delay comparison among different environments.  
Figure 9: Data sampling delay comparison at different sampling frequencies.  
The impact of sampling frequency on network transmission delay \(\Delta t_{n}\): Due to the limited capacity of the communication network, when the sampling frequency became higher, the queuing delay in the data transmission process, packet loss ratio, and bit error ratio increased, leading to blockages in the communication network. Therefore, the overall delay increased.  
As shown in Figure 10, two sets of network parameters were set: (1) the set where sampling frequency was 50 Hz (relatively low); and (2) the sampling set where frequency was 200 Hz (relatively high). Two sets of environment were simulated, and various sampling frequencies resulted in different delays. Compared to the ideal situation without delay, Environments 1 and 2 caused delays of 80 ms and 420 ms, respectively, and correspondingly the minimum frequency dropped by 0.02 Hz and 0.08 Hz, respectively.
## 2 Materials and Methods  
### Materials  
Polyethersulfone (PES) was obtained from Goodfellow (SU30-GL-000111, nominal granule size 3 mm, molecular weight 58,000 g/mol, melt volume rate: 35 cc/10 min at 360 \({}^{\circ}\)C, Pittsburgh, PA, USA). Dichloromethane (DCM, CH\({}_{2}\)Cl\({}_{2}\)), an ACS-grade solvent (VWR BDH, Radnor, PA, USA), was utilized for PES sulfonation. Chlorosulfonic acid (CSA, HSO\({}_{3}\)Cl, >98.0%, Honeywell Fluka, Charlotte, NC, USA) served as the sulfonating agent, while N-Methyl-2-pyrrolidone (NMP, C\({}_{5}\)H\({}_{9}\)NO), another ACS-grade reagent (Fisher Scientific, Hampton, NH, USA), was used to prepare the solvent for membrane fabrication. Membranes were cast using Petri dishes. Sodium chloride (NaCl) and sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)), both laboratory-grade (ACS reagent grade), were purchased from Fisher Scientific (USA) for preparing feed and electrode rinse solutions. Unless stated otherwise, all electrodialysis (ED) experiments were conducted in triplicate.
## 3 Analysis of Simulation Results
### Visibility Analysis
The main reason for this phenomenon is that the smoke is floated up by the high temperature, and then the smoke particles settle down under the influence of gravity, thus reaching dynamic equilibrium. Therefore, the visibility decreases gradually from top to bottom, and the visibility of the upper floor of the fire floor is affected the most. Visibility is most reduced in the room on the fourth floor, corresponding to the location of the room where the fire started. The visibility of stairwells above the third floor is affected, and the closer the stairwells are to the fire source, the more affected they are. Combined with the analysis of the variation of each factor, it was found that temperature and carbon monoxide did not have a significant impact on the evacuation of personnel. The evacuation of personnel in this academic building was mainly limited by visibility, among the above factors. The corresponding available safe egress time (ASET) is 709.2 s.  
Figure 7: Changes in visibility: (**a**) visibility slice (20.4 s); (**b**) visibility slice (30.0 s); (**c**) visibility slice (100.8 s); and (**d**) visibility slice (709.2 s). Whether personnel can evacuate safely depends on two characteristic times: one is the time required for the fire to develop to the point where it poses a safety threat to personnel, that is, the Available Safe Evacuation Time (ASET); the other is the time required for personnel to evacuate to a safe area, that is, the Required Safe Evacuation Time (RSET). In order to ensure that personnel can safely evacuate, it is necessary to require RSET < ASET.
## 3 Results and Discussion
### Analysis of the Results Using Numerically Based Spectral Element Code  
In this section, the numerical results from the SEM are shown. The main idea is to check the experimental results visualized with the numerical studies and to undertake a proper comparison of the results. For this spectral elemental study, a hole with \(\mathcal{O}\) 0.5 cm section was selected to be analyzed. Figure 3a shows the mesh arrangement and PZT position in the SEM approach, and Figure 3b shows the result obtained with the SEM numerical study.  
Thus, a comparative study of numerical analysis and experimental results shows that the hole was detected at a time of 29 \(\upmu\)s in both cases, as shown in Figures 2b and 3b, respectively. The comparison of the experimental and numerical results in Figures 2 and 3 reveals the wave field of the Lamb waves and its change due to the damage zones.  
Figure 2: (**a**) Schematic representation of PZT, damage zones; (**b**) a hole of \(\mathcal{O}\) 0.5 cm detected at 29 \(\upmu\)s; (**c**) BVID 1 detected at 37 \(\upmu\)s; (**d**) BVID 2 detected at 48 \(\upmu\)s.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 2 eLoran Timing Error
### Main Error
#### 2.1.2 Propagation Path Correction Errors  
eLoran timing relies on ground and sky waves. Sky-wave propagation depends on ionospheric reflection, and factors such as solar activity (sunspots, solar flares, geomagnetic activities, sunrise, and sunset), meteorological factors, and solar eclipses affect ionospheric conditions, which lead to propagation errors. However, high-precision timing is primarily achieved through ground-wave propagation, with the primary factor (PF), secondary factor (SF), and ASF being the primary influences on the timing accuracy [24; 25].
# Grid-Connected PV Systems Controlled by Sliding via Wireless Communication
## 6 Conclusions  
In this work, a grid-connected PV system is controlled via wireless. It develops a sliding mode control for the DC/DC converter, that guarantees the achievement of the MPP under all the environmental conditions, and a PI controller for the DC/AC converter, that ensures the power quality requirements of the grid. The use of the Wi-Fi communication allows the system remote control and supervision, with the advantage of reducing costs for the cabling installation. In addition, the wireless communication allows the operation of the system in real-time, facilitating the detection of faults rapidly. The advantage of the Wi-Fi technology is the precision of the acquired data and low latency, guaranteeing robustness. The system has been validated in an experimental platform and the results show the optimum performance of the whole system.  
Conceptualization, A.D.M. and J.M.C.; formal analysis, A.D.M. and J.M.C.; investigation, A.D.M., J.M.C., R.S.H., and J.R.V.; methodology, A.D.M. and J.M.C.; project administration, R.S.H., J.R.V., and F.J.R.-R.; resources, R.S.H., J.R.V., and F.J.R.-R.; validation, A.D.M. and J.M.C.; writing--original draft, A.D.M.; writing--review & editing, A.D.M., J.M.C., R.S.H., J.R.V., and F.J.R.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Retrofitted Results
### Energy Results
#### 4.1.3 Air Conditioning System  
Concerning the air conditioning system, the unit was individually evaluated using the BEC software, as shown in Table 15. The results in the base case show that the currently used fixed-speed air conditioning unit has a SEER equal to the standard value in the case of a 9000-BTU air conditioning unit. Both the 12,000-BTU and 18,000-BTU units failed in meeting the energy code, presenting a SEER lower than the standard allowance. This results were from the aging of the air conditioning unit and the technological limitations when it was installed. Thus, the retrofitted case proceeded by substituting all of the air conditioning units for variable-speed units. This is a recent technology with a higher efficiency. As a result, all of the air conditioning units in the retrofitted case have a significantly higher SEER than the standard level, thereby allowing the retrofitted building to have some margin when the units age and the standard is to be adjusted to stricter levels in the future.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Baseline Case** & **Retrofitted** \\ \hline Total Power (W) & 71,132 & 25,411 \\ Power density (W/m\({}^{2}\)) & 9.530 & 3.405 \\ Power density of energy code (W/m\({}^{2}\)) & 12.000 & 12.000 \\ Status & Passed & Passed \\ \hline \hline \end{tabular}
\end{table}
Table 14: Evaluated results of the lighting system in comparison with building energy code.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Building Energy Code** & **Base Case** & **Status** & **Retrofitted Case** & **Status** \\ \hline OTTV & 30.00 & 45.490 & Failed & 20.242 & Passed \\ RTTV & 10.00 & 9.961 & Passed & 3.679 & Passed \\ \hline \hline \end{tabular}
\end{table}
Table 13: Thermal transfer values (W/m\({}^{2}\)) in comparison with building energy code.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 8 Challenges and Open Research Issues
### Interoperability  
Interoperability in fog-assisted systems is another major challenge that these systems need to overcome. Several concerns arise from the absence of interoperability between deployed equipment and devices. These concerns are:  
* Difficulty integrating and deploying devices and equipment made by different manufacturers, having different types of connectors, using different data formats and supporting different communication protocols.
* A lack of common monitoring platforms to monitor these devices,
* A lack of common interfaces to pull and push information from/to these devices,
* A lack of common techniques and approaches for testing the Application Programming Interfaces (APIs) of these devices,
* Difficulty in using security software offered by third parties to secure devices.  
Currently, non-interoperability in IoT is addressed with the deployment of intermediary components, supporting multiple protocols, to resolve the above issues. An intermediary component, such as An IoT gateway, typically performs several critical functions from translating protocols to aggregating, filtering, encrypting, processing, and managing data. Several efforts investigated the issue of IoT interoperability and proposed intelligent edge gateways [110; 111; 112; 113]. [PERSON] et al. [112] presented a lightweight edge gateway for IoT (LEGIoT) architecture, which uses microservices and thin virtualization technologies to ensure an extensible and flexible solution. By using the socket-proxy framework and container-based virtualization, their solution aims to make edge gateways suitable for various IoT protocols/applications. It also aims to allow optimized management of resources while taking into account their needs in terms of energy efficiency, multi-location, and interoperability. [PERSON] et al. [113] proposed a blockchain-based framework for edge and fog computing called FogBus, where the main components are Fog Gateway Nodes (FGNs). FogBus provides front-end interfaces to users to perform several operations such as accessing back-end services, managing IoT devices, and requesting resources through FGNs, which filter data from different sources, aggregate them, and organize them into a standard format. They forward data to other computation instances in the environment in the case of large processing requirements and exchange data with fog nodes using the Constrained Application Protocol (CoAP) or the Simple Network Management Protocol (SNMP).