esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 5 Evaluation and Discussion of the Proposed Model's Results
### Visual Results  
The proposed Lightweight YOLOv4 model demonstrates strong performance in real-world vial detection scenarios, particularly when identifying single vials in an image. As shown in Figure 6, the model effectively recognizes and labels individual medical vials with high confidence scores, even under varying lighting conditions and perspectives. When vials are presented individually, as in Figure 6b-d, the model consistently achieves accurate classification, with confidence levels above 0.95 for most vial types.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Enhancement** & **Impact on Model Performance** \\ \hline ResBlocks & Improved feature extraction, better gradient \\  & flow, and reduced false positives \\ \hline Spatial Pyramid Pooling (SPP) & Enhanced small object detection with multi- \\  & scale feature extraction \\ \hline Multi-Head YOLO Architecture & More accurate detection at different scales and \\  & better classification precision \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of Model Enhancements on Performance.  
Figure 6: Detection results of the proposed Lightweight YOLOv4 model. (**a**,**_e_**) demonstrate cases where multiple vials are present, leading to misclassification issues, particularly between visually similar vials such as Midazolam and Amiodron. The model struggles to differentiate these vials when they appear together in the same frame. (**b–d**) illustrate successful detections where a single vial is present, achieving high accuracy with confidence scores above 0.95.  
However, when there is more than one vial in a frame Figure 6a,e, the model has trouble classifying them correctly, especially when the vials look alike, like Midazolam and Amiodron. In cases where multiple vials with a similar appearance are closely positioned, the model occasionally assigns incorrect labels, indicating a challenge in differentiating look-alike vials under occlusion and clustered arrangements. This suggests that the model relies heavily on label positioning and vial texture features, which may not be sufficient in dense pharmaceutical settings.  
Overall, these results indicate that while the model performs exceptionally well for single-object detection, further improvements are needed to enhance multi-vial differentiation in complex scenarios.
## 3 Results and Discussion
### Municipal Landfill with Liners Installed-Leakage Investigation
The other distinguished part of the profile is its middle section and the boundary between the landfill body and the soil below the liner. The section, of a depth between 12 and 16 m below the ground level, has an electrical resistivity of more than 160 \(\Omega\)m, which indicates the occurrence of sand deposits. The layer was also observed in the geological profile investigated before the construction of the landfill. The electrical resistivity tests did not indicate leakage at the bottom or slopes of the landfill, since no areas of low electrical resistivity were detected in the sand layer below the landfill. The ground water monitoring and water sample chemical analysis from the standpipe piezometers also did not show any leakage from inside the landfill sealing. Another example is the way in which the electrical resistivity test results were used to determine the integrity of a cut-off wall constructed around the MSW disposal, near a large metropolitan area. A vertical cut-off wall in the form of a slurry wall was constructed around the landfill at the end of the 1990s. The field tests included electrical resistivity tomography (Figure 7), to indicate any possible cut-off wall leakage, as well as points of the material permeability tests. The electrical resistivity tomography was used along the sealing at a distance of approximately 5 m from the cut-off wall, on the outside and on the inside of the separated area. Areas with reduced electrical resistivity were observed (less than 1 \(\Omega\)m) in the profile, on the inside of the cut-off wall (Figure 7). It confirms the occurrence of soils contaminated with the landfill leachate. The electrical resistivity outside the cut-off wall (second profile, Figure 7) is between 20 and 70 \(\Omega\)m for the entire profile. The test results did not show any contamination from the landfill and prove that the cut-off wall material has maintained its properties and is impermeable. Figure 6: The electrical resistivity cross-section of waste disposal with a bottom sealing system. Figure 7: The electrical resistivity cross-section inside (**A**) and outside (**B**) the cut-off wall.
# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 3 Proposed Method
### Emotional Label Generation  
Before building the model, we need to prepare labels for the emotion classification task. However, most abuse detection datasets have no corresponding emotional labels, and manual labeling, while effective, is costly in time and labor. Therefore, we explored automatic labeling of emotion classification.  
Some previous works have focused on resource creation or sentiment categorization for specific tasks and domains [35; 36; 37]. They constructed dictionaries associated with different psychological traits, judging emotional content by specific words. However, the word-dependent approaches often face the out-of-vocabulary (OOV) problem, and the overly fine-grained emotional dimension of words often hinders the accurate judgment on the emotional polarity of the whole sentence. Therefore, inspired by some transfer learning methods [38], we adopted the pretrained model SKEP ([[https://github.com/baidu/Senta](https://github.com/baidu/Senta)]([https://github.com/baidu/Senta](https://github.com/baidu/Senta))) (accessed on 23 March 2022) [15] as the derivation method of emotion dichotomy labels. SKEP uses the pretrained model with enhanced emotional knowledge to comprehensively surpass state-of-the-art methods in 14 typical emotional analysis tasks.  
However, the abuse of language detection datasets tends to be associated with strong negative emotions, so the probability of negative emotions remained high across all datasets. This resulted in a serious sample disequilibrium, which affected the generalization ability of the model. In order to reduce the influence of the sample distribution, we used the probability of the last SKEP layer output as the soft label of emotion classification rather than 0 or 1, so that the objective of our auxiliary task was to approximate this probability.  
Figure 1: Detailed model structure, which consists of emotional label generation, encoder and decoder. The emotional label generation is a pretrained sentiment analysis model to derive emotion labels. The encoder module encodes the input text and the encoder is initialized with BERT. The decoder module uses TextCNN and BiLSTM for two tasks of abuse detection and emotion classification, and contains a cross-attention interaction mechanism to further improve the effect of abuse detection.
# Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication
## 4 Performance Evaluation
Overall, the sum rate results of SANOCO-D2D in both scenarios show that achievable rate of the weak user can be significantly improved. This gain stems from the fact that the strong users cooperate with the weak users in the system through out-band D2D communication. In this way, fairness in the system is ensured as a more homogeneous QoS is achieved throughout the satellite's coverage area. In addition, the simultaneous out-band satellite transmission and D2D cooperation reduce the delay inherent to two-hop transmissions and the increased sum rate allows more packets to be served, thereby benefiting further performance metrics, such as the average packet delay. Finally, Table 4 summarizes the key characteristics of the proposed scheme and the scheme of [6]. It can be observed that the addition of D2D cooperation improves the overall performance of the wireless transmission at an affordable complexity. Figure 11: Sum rate for scenario 2, \(B_{d}=0.4\) MHz and different elevation angle values. Figure 10: Spectral efficiency efficiency for scenario 2, \(B_{d}=0.4\) MHz and different elevation angle values.
## 4 Societal Benefits and Impact
### Contributions to Smart City and Community Initiatives  
Integrating outdoor safety monitoring enabled by the IoPVT into the larger landscape of urban governance and community development can be a pivotal step toward realizing the vision of truly \"smart\" cities [94]. Smart city initiatives frequently emphasize the importance of connectivity, data-driven decision making, and citizen engagement [95]. By weaving together human activity recognition, environmental sensing, and predictive analytics within immersive virtual platforms, the approach directly aligns with the core principles of sustainable urban planning and collective well-being.
###### Abstract  
Despite the widespread use of Mg and Al alloys among light metals in the automobile and aviation industries, they have low tensile and fatigue strength. Therefore, in the present work, AZ91 Mg and AA6063 Al alloys were coated with a multilayer transition metal nitride film (Cr/CrN/TiCrN/TiCrCN) to increase fatigue and tensile strength. Films with Cr/CrN/TiCrN/TiCrCN microstructure architecture were synthesized on the surfaces of AZ91 Mg and AA6063 Al alloys using the CFUBMS (closed-field unbalanced magnetron sputtering) system, one of the PVD (physical vapor deposition) techniques. Films' structural properties were analyzed by XRD, SEM, and EDAX, whereas mechanical properties were investigated using tensile and rotary bending fatigue testing machines. According to the SEM examination, the Cr, CrN, TiCrN, and TiCrCN multilayer nitride films on the two alloys have a columnar and dense microstructure. The XRD analysis detected Cr (211), CrN (111) and (200), TiN (111), (200) and (222), and TiCN (200) and (311) diffraction peaks. The Cr/CrN/TiCrN/TiCrCN multilayer coating increased the fatigue limit value of AZ91 by 11.22% from 70.26 MPa to 78.15 MPa. The fatigue limit value of AA6063 decreased by 9.79% from 79.71 MPa to 71.9 MPa. After coating, the tensile strength value of AZ91 increased from 137.89 MPa to 139.65 MPa, while the tensile strength of AA6063 decreased from 129.35 MPa to 118.16 MPa.  
AA6063; AZ91; CFUBMS; thin film; fatigue; tensile Article
## 3 Results and Discussion  
The isotopic and elemental profile analysis of chicken and pork meat from different Romanian areas was used, firstly, to verify _the geographical origin_ of meat, and, secondly, to differentiate the samples with respect to _the animal breeding system_ (animals coming fromyard rearing system versus animals coming from conventional/industrial farms). Table 1 presents the isotopic and elemental content (as minimum, maximum, and mean values) for the investigated meat samples. _Isotopic signature of chicken and pork meat_  
_Isotopic signature of hydrogen and oxygen_  
The isotopic composition of hydrogen ranged from \(-58.1\) to \(-28.1\%\) for chicken meat samples and from \(-74.7\) to \(-25.9\%\) for pork meat samples. The \(\delta^{18}\)O values for chicken ranged between \(-6.6\) and \(-0.5\%\), while those for pork ranged between \(-9.6\) and \(-3.7\%\) (mean value \(-6.1\%\)). To compare the results obtained in other parts of Europe, [PERSON], [PERSON], and [PERSON] (2004) [15] reported, for pork samples coming from the northern part of Germany, an average value for \(\delta^{18}\)O of \(-7.1\%\) (min \(-8.5\%\) and max \(-6.0\%\)). The lowest values of \({}^{2}\)H and \({}^{18}\)O from investigated meat samples belong to a pork meat sample coming from Maramures county. This region is situated in the north of the country (\(47^{\circ}39^{\prime}09.00\"\) N, \(23^{\circ}34^{\prime}08.40\"\) E), having a colder climate throughout the year compared to other Romanian areas [16]. The \(\delta^{2}\)H and \(\delta^{18}\)O values of precipitations from this location will be reflected in the animals' drinking water, and then the isotopic fingerprint of the drinking water will be transferred to the animal by consuming the water, proving the fact that isotopic values of \({}^{2}\)H and \({}^{18}\)O are very good indicators for the geographical assignment [11; 17]. As it can be observed in Figure 2, there are chicken meat samples having enriched values of \({}^{2}\)H and \({}^{18}\)O as compared with those of the pork meat samples, but there is also an overlap in the range of values for chicken and pork. The reason for this is that the samples with similar values are from the same county (Cluj) or neighboring regions (Salaj, Alba). _Isotopic Signature of Carbon_  
The \(\delta^{13}\)C value in the animal body is directly influenced by the composition of the animal diet, which is formed of different plant species [11]. The \(\delta^{13}\)C isotopic signature of plants is different as a function of the photosynthetic pathway, C3 or C4. Most plants and herbs (most fruits and vegetables, cereals--barley, oats, wheat) have a C3 photosynthetic cycle. Corn and sugarcane are the best-known examples of C4 plants. For C3 plants, \(\delta^{13}\)C ranges from \(-30\) to \(-23\%\), while for C4 plants, \(\delta^{13}\)C values are higher, from \(-14\) to \(-12\%\) [11]. Thus, the \(\delta^{13}\)C values of the investigated meat samples will depend on the proportion of C3 and C4 plants introduced in the feeding regime of the animal.
# Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels
## 2 Torque Ripple
### Analysis Result of the Torque Ripple  
The torque waveform of the initial model, as shown in Figure 2, was analyzed using Ansys Maxwell, which is electromagnetic field analysis software. The difference between the maximum and minimum value of the torque was 1.686 N\(\cdot\)m, and the average torque value was 25.296 N\(\cdot\)m. Using Equation (1), the torque ripple value of the initial model was calculated as 6.664% [13].  
\[T_{ripple}=\frac{T_{max}-T_{min}}{T_{avg}}\times 100\% \tag{1}\]  
where \(T_{max}\) is the maximum torque, \(T_{min}\) is the minimum torque, and \(T_{avg}\) is the average torque.
# RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System
###### Abstract  
In polymer optical fiber (POF) systems, the nonlinear transfer function of the resonant cavity light emitting diode (RCLED) drastically degrades the communication performance. After investigating the characteristics of the RCLED nonlinear behavior, an improved digital look-up-table (LUT) pre-distorter, based on an adaptive iterative algorithm, is proposed. Additionally, the system parameters, including the bias current, the average electrical power, the LUT size and the step factor are also jointly optimized to achieve a trade-off between the system linearity, reliability and the computational complexity. With the proposed methodology, both the operating point and efficiency of RCLED are enhanced. Moreover, in the practical 50 m POF communication system with the discrete multi-tone (DMT) modulation, the bit error rate performance is improved by over 12 dB when RCLED is operating in the nonlinear region. Therefore, the proposed pre-distorter can both resist the nonlinearity and improve the operating point of RCLED.  
polymer optical fiber communication; optical modulators; nonlinear distortion; digital pre-distorter; discrete multi-tone modulation +
Footnote †: journal: Applied Physics Letters B
## 2 Problem Formulation  
### Computational Domain and Boundary Conditions  
This study used an existing flow frame, which was composed of vertically symmetric flow channels and electrodes, as shown in Figure 1. Only one side of the unit cell was considered, the size of the unit cell was \(392\times 382\times 5\) mm, and the size of the electrode was \(362\times 260\times 5\) mm, which accounted for approximately 63% of the total computational domain. The inlet channel of the flow frame acted as the flow path through which the electrolyte from the manifold was sent to the electrode. The inlet channel was S-shaped, and it contained 90 outlets and guides located at equal intervals to uniformly distribute the electrolyte to the electrode. In terms of the flow direction, the electrolyte entered the electrode through the inlet channel at the bottom and was discharged through the outlet channel at the top, after oxidation and reduction. The outlet channel contained 90 inlets and guides.  
For flow analysis, ANSYS Fluent, a commercial code, was used and the three-dimensional incompressible flow was considered throughout the whole region. In addition, as shown in Figure 2, the Reynolds number was calculated at three locations in the inlet channel, and laminar flow (Re \(<\) 2300) was considered based on a Reynold's number of 537 (the highest value), as shown in Table 1. Since the operating flow rate through the VRFB was 60 L/min for a total of 60 cells, the electrolyte of 1 L/min based on the single cell was used as inlet condition, as shown in Table 2. For the outlet, we used the atmospheric absolute pressure condition. In addition, the non-slip condition was used as the wall condition.  
Figure 1: Computational domain for flow frame of vanadium redox flow battery.
# An Efficient Analytical Approach to Visualize Text-Based Event Logs for Semiconductor Equipment
## 3 Log Analysis System Configuration and DB Design
### Configuration of EventLogs  
EventLogs show what happened in the system based on the source of the event. The history of events is displayed chronologically, making it easy to see what happened from the past to the present. The event log consists of the following:  
* Record the contents of all user actions in the UI. For example, the operator starts or stops Lot with the manual and records everything he or she looks upon a particular screen.
* Records the events that occurred for all modules. For example, if you open and close the valve of an installation or run a command to raise the temperature to a certain temperature, you will record the information in the EventLog that increases the temperature.
* In the EventLog, information about failures in the event of a facility failure and in the event of a failure resolution are also stored.
* Records all information related to scheduler behavior.
* Displays information about each module operating situation.  
The explanation of each item of the content shown as an example in Figure 1 is as follows.  
* Log Time: The Log Time indicated the time stamp when the CTC server receives the event and records the logging on the log file.
* Issue Time: The issue time indicates the time stamp when the issuer reports the events.
* Event ID: Each event has a specific eventID and this ID section shows a number of it.
* Name: It is an identifier of the parent category of each event.
* Issuer: Each event is published by each issuer, and the issuer can be either device level or image level.
* Event Text: Event Text provides more detailed information about which event has occurred.
## 3 Research on the Effect of Path Spacing and Bulk Material Removal Depth on Residual Error
### Summary and Discussion of Simulation Results  
Through the above three sets of simulation experiments, the influence rules of path spacing and bulk material removal depth on the residual error obtained after deterministic polishing were thoroughly studied and analyzed. When the path spacing was fixed, the larger the bulk material removal depth, the larger the RMS and PV values of residual error. The distribution of surface form error directly reflected the difference in bulk material removal depth across a to-be-polished surface in subsequent corrective polishing. This is a good explanation of the genetic phenomenon of the error distribution during the surface correction process. In the case where the bulk material removal depth was constant, the path spacing also affected the RMS and PV values of residual error approximately linearly. Once the surface form error distribution of the actual workpiece was determined, we could get the variation in bulk material removal depth across the surface and could consider the use of  
Figure 4: The influence of path spacing variations on the (**a,c**) root-mean-square (RMS) and (**b,d**) peak-to-valley (PV) of the residual error resulting from different bulk material removal depths.  
fine-tuning of the path spacing to compensate for the effect of surface form error on the residual error, as well as achieve the purpose of optimizing the residual error distribution. That is to say, small path spacing was used where surface form error was large, and a large path spacing was used where surface form error was small, so that the path spacing adapted to the variation of the surface form error, which was expected to make the optimized residual error distribution obtained more uniform and almost unaffected by the distribution of parent surface form error.
# Annealing Process-Induced Microstructural Variation in NiV/B4C Multilayers
## 2 Materials and Methods  
The NiV/B4C multilayers were deposited on 20 mm \(\times\) 20 mm super-polished silicon wafers by a direct current magnetron sputtering system. The base pressure was 8 \(\times\) 10\({}^{-5}\) Pa during the deposition. The multilayers consisted of 30 NiV/B4C bilayers with the period thickness d = 8 nm and the NiV layer thickness to period thickness ratio of \(\Gamma\) = d\({}_{\rm NiV}\)/d = 0.4. The target materials used for sputtering included a B4C target with 99.5% purity and a NiV target with 99.99% purity. The NiV target was composed of 93% nickel and 7% vanadium. The sputtering powers of the NiV and B4C targets are on the order of tens of watts, with the distance between the sputtering targets and the substrate being approximately 80 cm. Argon was used as the working gas at a pressure of 0.16 Pa during the deposition, ensuring that the deposited atoms have high kinetic energy and form a smooth interface. The thickness of each layer in the multilayer was adjusted by controlling the sputtering time of each target. The deposited samples were annealed for 60 min in a high-vacuum (less than 5 \(\times\) 10\({}^{-4}\) Pa) at temperatures of 70, 140, 220, 290, and 340 \({}^{\circ}\)C, respectively. We also annealed some samples at 340 \({}^{\circ}\)C for different annealing times, specifically 10, 30, 60, and 120 min. The temperature control curve is consistent with previous studies of NiV/B4C multilayers with a period of 3 nm.  
The structures of NiV/B4C multilayers were characterized by X-ray reflectometry (GIXR) and XRD measurements. For the GIXR and XRD experiments, we employed a Bruker D8 DISCOVER X-ray diffractometer with Cu-K\(\alpha\) radiation. The rocking curve scans were performed around the first Bragg peak of the multilayers. The surface roughness of the sample was measured using a Bruker Dimension Icon Atomic Force Microscope (AFM). Each sample was scanned at two different locations, with each scan covering an area of 2 um \(\times\) 2 um, with 512 \(\times\) 512 data points scanned using fast axis X and slow axis Y. The GISAXS measurements were carried out at the BL16B1 beamline station of the Shanghai Synchrotron Radiation Facility (SSRF). The scattering intensity was measured using a two-dimensional Pilatus 3X 2M detector with an X-ray energy of 10 keV. The exposure time was set to 10 s to avoid detector saturation. The small angle of incidence \(\theta_{\rm i}\) was set to 0.35\({}^{\circ}\), between total external reflection and the first Bragg peak. The NiV/B4C multilayers were placed at the sample table [17]. The annealed multilayer was characterized using a JEM-ARM300F transmission electron microscope from JEOL, Tokyo, Japan, equipped with a double spherical aberration corrector. The electron acceleration voltage was 300 kV, and the point resolution was 0.08 nm. The microstructure and crystalline state of the annealed multilayer was analyzed through transmission electron microscopy (TEM) and selected area electron diffraction (SAED) measurements.