esa-sceva/satcom-chunk-collection huggingface dataset:
### Overview  
IIoT devices facilitate proactive monitoring, condition-based maintenance, automation, alerts, scheduling, forecasting, and predictive analysis, which help the O&M team in decision-making [5; 24]. Surveys by [PERSON] et al. [25] and [PERSON] et al. [26] show that of the total levelized cost of energy (LCOE) in a wind farm, 49% is attributed to the wind turbine energy generation, 29% to O&M functions, 16% to auxiliary systems, and 6% to regular financial costs. To reduce the 29% LCOE attributed to O&M functions, wind farm operators are gradually adopting IEC 61400-25 [27] industry-grade Internet of Things (IIoT) devices alongside other customized IIoT devices to monitor their critical infrastructure [28; 29]. In turn, this significantly reduces the high O&M costs.  
Some common commercial wind turbine monitoring platforms are Windmill Manager (WebNMS), which supports condition-based wind turbine performance monitoring and maintenance; Cloud-based IoT Solution (Qburst), which supports wind turbine parameter monitoring, alerts, and calendar-based maintenance management; Digital WindFarm (General Electric), which supports maintenance strategies, reliability, and availability assessment; EnOS(tm) Wind (Envision), which supports remote regional supervision and maintenance; and MindSphere (Siemens AG innovation), which connects IIoT devices to a cloud platform, harnessing big data from offshore wind farm data-acquisition systems and enhancing operations through the creation of digital twins [30].
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Printer-Associated Problems
#### 3.1.2 Poor Adhesion of the First Layer  
Having a successful first layer that adheres to the build platform is crucial to ensure a good print. The first layer of print that does not stick to the build platform may be due to an incorrect built platform levelling sequence, nozzle height set too high, first layer printing speed too high, and/or poor build platform adhesion due to the surface grease or residues from previous prints [37; 68]. Solutions include the following:  
1. Recalibrating the built platform through active levelling or manual levelling. Run through the first layer calibration to adjust the Z-height to get the optimum distance between the nozzle and the first layer as shown in Figure 6[63; 67].
2. Decrease the first layer print speed. If the first layer speed is set at 50%, it could mean that the first layer will print 50% slower than the rest of the sections.
3. Disable the cooling fan for the first few layers. Ensure that the recommended bed temperature is used.
4. Increase the nozzle temperature by 5 \({}^{\circ}\)C for better adhesion or as recommended in Table 3.
5. Check the extrusion settings to ensure that the right amount of plastic is extruded.
6. Wipe the build surface using 90% isopropyl alcohol (IPA) solution or Acetone to remove residues from previous prints and to keep the surface grease-free. If the problem continues, improve the surface adhesion such as with a PEI sheet, blue painter's tape for PLA, Kapton Tape for ABS or separate agent coatings such as glue stick or hair spray for PETG. Check if the material requires a special surface such as when using PP filaments [3; 69].
7. Increasing the roughness of the surface of the build platform by sanding or creating cleated surfaces [37].
8. Printing an additional structure like a brim or a raft, as discussed in Section 2.3.
# Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application
## 2 GFDM Systems and Channel Model
### Tactile Internet Application for GFDM  
The mission of tactile internet aims for high reliable low-latency services, where the low end-to-end latency must be less than 1 milisecond. This scenario is first introduced in [2]. The requirement for low latency is specified from typical interaction of devices. If the round-trip delays between the command insertion are too large, the response of devices will result in poor QoE. This requirement makes the overall frame size limited. GFDM can satisfy the application by employing a small _MK_ product.  
Table 1 shows the channel and GFDM parameters for tactile internet applications. The GFDM parameters are then used in this paper. In addition, channel parameters are satisfied by using NYUSIM channel model. The detailed channel model is described in the next subsection.
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 4 Evaluation
### Evaluation Criteria  
In key component detection models, intersection over union (IoU) and average precision (AP) were selected as evaluation metrics to assess the detection benchmarks. First, IoU is defined as follows:  
\[I_{IoU}=\Big{(}b_{pred},b_{gt}\Big{)}=\frac{Area\Big{(}b_{pred}\cap b_{gt}\Big{)} }{Area\Big{(}b_{pred}\cup b_{gt}\Big{)}} \tag{1}\]  
where \(b_{gt}\) represents the ground-truth bounding box of the object and \(b_{pred}\) represents the detected bounding box. IoU was used to evaluate the accuracy of the predicted bounding boxes.  
The IoU threshold is a predefined constant represented as \(\Omega\). When IoU (\(b_{gt}\), \(b_{pred}\)) > \(\Omega\), the image in \(b_{pred}\) is considered a positive sample (containing an object); otherwise, it is considered a negative sample (background). Based on IoU and the IoU threshold, the precision (\(P\)) and recall (\(R\)) for object detection can be calculated as follows:  
\[P=\frac{T_{TP}}{T_{TP}+F_{FP}} \tag{2}\]  
\[R=\frac{T_{TP}}{T_{TP}+F_{FN}} \tag{3}\]  
where \(T_{TP}\), \(F_{FP}\), and \(F_{FN}\) represent the true positive rate, false positive rate, and false negative rate, respectively. They denote the number of positive samples predicted correctly, the number of negative samples predicted as positive, and the number of background samples incorrectly detected as positive, respectively. When calculating precision and recall, the IoU threshold \(\Omega\) is generally set to 0.5.  
Average precision (\(AP\)) is also a frequently used metric in object detection, and its calculation formula is as follows:  
\[A_{AP}=\int_{0}^{1}P(t)dt \tag{4}\]  
where \(P(t)\) represents the precision at IoU threshold \(\Omega\) = \(t\).  
The mean average precision (\(mAP\)) is used as an evaluation metric, and its mathematical expression is  
\[m_{mAP}=\frac{\sum_{n=1}^{N}A_{AP}^{n}}{N} \tag{5}\]where \(N\) represents the number of object categories, and \(A_{AP}^{n}\) represents the average precision of the algorithm for the \(n\)th class of objects.
## 4 Materials and Methods
### SLC10A1 Sequencing and Genotyping
#### 4.5.2 MALDI-TOF MS-Based Genotyping  
28 known _SLC10A1_ variants were genotyped using MALDI-TOF MS. These comprised all variants described in the literature, as well as promoter and missense exonic variants with minor allele frequencies (MAF) \(\geq\) 0.5% or unknown MAF in Caucasians. Additionally, two missense variants were included, which were located in the first described region important for HBV binding (NTCP p.157-164) [41], rs201339654 and rs199663299. For assay design, the MassARRAY(r) Assay Design v.3.1 program (Sequenom/Agena Bioscience, San Diego, CA, USA) was used.  
Regions of interest were preamplified in 4 multiplex-PCRs using 10 ng of DNA as a template. The iPLEX(r) Gold Reagent Kit (Sequenom/Agena Bioscience) was used for the following allele-specific primer extension reaction. Samples were measured on the MassARRAY(r) Compact System MALDI-TOF MS (Sequenom/Agena Bioscience). For quality control, 10% of the samples were run as duplicates. Information about primer sequences is available upon request.
## Algorithm Based on Heuristic Strategy to Infer Lossy Links in Wireless Sensor Networks  
**[PERSON] 1,2,* and [PERSON] 1**  
1 School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, Shaanxi, China; E-mail: winsoft21 [EMAIL_ADDRESS]  
2 School of Material Science and Engineering, Xi'an Shiyou University, Xi'an 710065, Shaanxi, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-136-691-99580.  
_Received: 7 May 2014; in revised form: 8 July 2014 / Accepted: 11 July 2014 / Published: 29 July 2014_  
**Abstract:** With the maturing of the actual application of wireless sensor networks, network fault management is eagerly demanded. Severe link packet loss affects the performance of wireless sensor networks, so it must be found and repaired. Subject to the constraints on limited resources, lossy link is inferred using end to end measurement and network tomography. The algorithm based on heuristic strategy is proposed. This maps the problem of lossy links inferences to minimal set-cover problems. The performance of inference algorithms is evaluated by simulation, and the simulation results indicate feasibility and efficiency of the method.  
**Keywords:** lossy link inference; network tomography; minimal set-cover problem; heuristic strategy
# Method for Enhancing AI Accuracy in Pressure Injury Detection Using Real and Synthetic Datasets
## 4 Experimental Results  
### Experimental Environment  
We evaluated the performance of the PI dataset and extended dataset using the YOLOv8 model. The learning environment was set as follows: the weights used were yolov8-cls.pt, and a weight file for image classification pre-trained on 224 \(\times\) 224 images. Training was performed for 100 epochs, and the batch size was set to 16. The size of the input image was adjusted to 224 pixels. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio to evaluate performance.  
Based on a PI dataset constructed with real images, we generated a synthetic dataset consisting of 500 images per class using a stable diffusion model. To study the impact of synthetic datasets on classification metrics, we considered four types of datasets. The real dataset consisted of 3000 real images. The synthetic dataset contained 3000 synthetic images. The balanced dataset included 3000 images that combined real and synthetic images. The hybrid dataset was 6000 images including all available real and synthetic images. The performance of the trained models was evaluated using these real, synthetic, balanced, and hybrid datasets.
## 4 Conclusions  
Based on smartphone mobile devices, this study develops a new portable train comfort measurement system (DTRCMS). The availability, accuracy, and reliability of the system are verified by comparison with a professional test system in an on-board test. The main conclusions are as follows:  
1. Mobile devices can realize multi-module algorithm integration to meet real-time comfort assessment and visual presentation. The FFT, Sperling algorithm, A-weighting algorithm, and other calculation processes were integrated into the system, and noise and vibration data processing could be performed in real time.
2. The measurement accuracy of the built-in sensor in the mobile phone was verified by a comparative test. The relative error of the noise test data was mainly distributed in 1.5-5%. The overall trend of the three-way acceleration values was the same, and the relative error was 2\(\sim\)10%. The difference in the Sperling index was small, and the relative error was less than 5%.
3. The availability and reliability of the DTRCMS were verified by an on-board application on Chengdu Metro Line 6. During the application process, the decibel value basically remained between 65 and 75 db, which is consistent with the actual listening experience. The Sperling value was between 2 and 3, and the measured data of the two states were roughly consistent with actual human experience.  
In summary, the DTRCMS not only provided almost all train ride comfort-related functions, such as noise and vibration measuring and Sperling index calculation, but it is also more convenient and faster than a traditional test meter. In addition, the challenges in the existing riding comfort evaluation standards and methods were discussed, and an optimization direction was introduced.
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 2 Materials and Methods
### Atmospheric Methane Concentration  
Lastly, satellite-based measurements of the total column-averaged dry-air mole fraction of methane (XCH\({}_{4}\)) for a 26,000 km\({}^{2}\) region surrounding the HPPS lenti zone from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S-5P) satellite were downloaded from the Google Earth Engine repository [79]. The data product represented the two-band retrieval utilizing the 2.3 \(\upmu\)m methane absorption band and the 760 nm oxygen absorption band to determine methane abundance in the atmosphere at 7 \(\times\) 7 km resolution [79,80]. Cloud-free data were available for the months of July and August, 2020. A 1,414 km\({}^{2}\) convex hull was fit around the high-water season reservoir to which a 1 km buffer had been added. This area of interest was removed from the larger 26,000 km\({}^{2}\) region, the remainder considered as the regional background. For comparison, methane abundance was also extracted over a 17,500 km\({}^{2}\) area of intact protected forest in the Xingu National Park located 200 km to the east. We determined the source methane enhancement (\(\Delta\)XCH\({}_{4}\)) by subtracting the mean XCH\({}_{4}\) difference between the reservoir area of interest and the regional background. We estimated emission according to the mass balance method from [81]. The monthly average values of wind speed and surface pressure needed for the calculations were extracted from the ERA5-Land monthly averaged ECMWF climate reanalysis data [82].
## 3 Beamformer Design
### One-Shot Design with CRI Zero Forcing  
Algorithm 1 has iterative applications of SDR and associated multi-dimensional search, which may require quite a bit of computational load. Here, we propose a CRI zero-forcing base reference scheme implemented in one shot approach with much reduced computation. Algorithm 2 sets the transmit beamformer \(\mathbf{w}_{T,i}\) of the \(i\)-th HDR to the direction of the channel vector \(\mathbf{g}_{i,i}\) and the receive beamformer to the zero-forcing direction of CRI. The beamformer set computation of two HDRs is coupled only through CRI components. This formation considers the second hops (relays to NOMA user channels) as if only a single user is served so that the application of SDR is avoided and lets only the relay receive beamformers to take care of the CRI.
## 4 Experimental Results
### Comparison of the Computational Speed  
The computational speed and AP of the proposed AED-CNN with other reported methods are compared in Table 1. We measure the running times of all the methods using the same machine. Although YOLOv2 [20] and YOLOv2 + ABMS [30] have the fastest computational speed (0.02 s/f), their precision is worse than our approach (over 10%). The computational speed of our method is 0.03s per image, which is very competitive compared to other approaches.
## 4 Results
### Experiment Results  
The results of the baseline models (TF-IDF, CNN, BERT) and fine-tuned GPT 2.0/3.0 models can be seen in Table 3. Each column presents the results for a specific outcome category, and the last column represents the average value across all categories. Identical performance metrics were applied to all models, encompassing precision, recall, F1 score,and accuracy. Precision measures the ratio of correctly identified positive instances to all instances classified as positive by the classifier, while Recall quantifies the ratio of correctly identified positive instances to the total number of relevant samples (i.e., all instances that should have been correctly identified as positive) [56; 57]. The F1 score can be considered as a comprehensive assessment metric that amalgamates Precision and Recall [58].  
Overall, the results show that pretrained GPT-3 with unstructured free-text data is outperformed almost everywhere. With 82.33% accuracy the best performer is the \"Cut\" class (F1 score 0.88). The \"Others\" class presents low F1 scores in all classifiers. Since the \"Others\" class includes different injury scenarios (e.g., overexertion, occupational diseases, and fire) that cannot be included in the other major five accident outcomes, it may be hard to find certain attributes in injury scenarios.  
Figure 5 presents a confusion matrix of the GPT-3.0 fine-tuned model for both combined structured and unstructured data and unstructured data alone. Initially, the authors anticipated that incorporating more data would enhance the prediction performance by providing additional contextual information; the amalgamation of structured and unstructured data did not exhibit superior performance compared to utilizing unstructured data alone. The prediction performance was also affected by the version of GPT models. GPT 3.0 outperforms GPT 2.0 in almost every class. GPT 2.0 showed very low performance in the \"Struck-by\" class.
## 3 Materials and Methods
### Experimental Datasets
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Long Name** & **Clock** & **Receiver** & **Receiver Firmware** & **Antenna** \\ \hline AGGO & AGGO00 ARG & CESIUM & LEICA GR25 & 3.11 & TRM57971.00 \\ ALBH & ALBH00 CAN & H-MASER & TPS NET-G3A & 4.02 & TRM57971.00 \\ BOR1 & BOR100 POL & H-MASER & TRIMBLE NETR9 & 5.2 & TRM59800.00 \\ BRUX & BRUX00 BEL & MASER & SEPT POLARX4 TR & 2.9.6 & JAVRINGANT\_DM \\ CEBR & CEBR00 ESP & H-MASER & SEPT POLARX4 TR & 2.9.5 & SEPTCHOKE\_MC \\ DLF1 & DLF100 NLD & CESIUM & TRIMBLE NETR9 & 5.15 & LEIAR25.R3 \\ DRAO & DRAO00 CAN & H-MASER & JAVAD TRE\_G3 TH & 3.63 & TRM59800.00 \\ GOP6 & GOP600 CZE & CESIUM & LEICA GRX1200 + GNSS & 9.20/6.405 & LEIAR25.R4 \\ HERES & HERSO0 GR & H-MASER & SEPT POLARXERR & 2.1 & LEIAR25.R3 \\ KIRU & KIRU005 WE & CESIUM & SEPT POLARX4 & 2.9.5 & SEPTCHOKE\_MC \\ KOUR & KOUR00 GCUF & H-MASER & SEPT POLARX4 & 2.9.5 & SEPTCHOKE\_MC \\ MASI & MAST0100 ESP & CESIUM & SEPT POLARX4 & 2.9.5 & LEIAR25.R4 \\ MATE & MATE00 ITA & H-MASER & LEICA GRX1002 GGPR0 & 8.7/1.3823 & LEIAST00 GG \\ MEDI & MEDI00 ITA & H-MASER & LEICA GR10 & 4.02/6.522 & LEIAR20 \\ MGUE & MGUE00 ARG & H-MASER & SEPT POLARX4 & 2.9.5 & LEIAR25.R4 \\ MIZU & MIZU00 PN & CESIUM & JAVAD TRE\_G3 TH & 3.6.7 & JAV\_RINGANT\_G3T \\  & & DEILTA & & \\ MXEA & MXEA00 USA & H-MASER & JAVAD TRE\_G3 TH & 3.6.4 & JAVRINGANT\_DM \\  & & & & \\ NRC1 & NRC100 CAN & H-MASER & JAVAD TRE\_G3 TH & 3.6.3 & AOAD/M\_T \\  & & & & \\ NYA1 & NYA100 NOR & H-MASER & TRIMBLE NETR8 & 48.01 & ASH701073.1 \\ OHH3 & OHH300 ATA & H-MASER & LEICA GR25 & 4.10/6.523 & LEIAR25.R4 \\ ONSA & ONSA005 WE & H-MASER & JAVAD TRE\_G3 TH & 3.512 & AOAD/M\_B \\  & & & & \\ PARK & PARK00 AUS & H-MASER & TRIMBLE NETR9 & 5.15 & ASH701945C\_M \\ PIE1 & PIE100 USA & H-MASER & JAVAD TRE\_G3 TH & 3.6.4 & ASH701945E\_M \\  & & & & \\ PRDS & PRDS00 CAN & H-MASER & DELTA & 3.6.3 & AOAD/M\_T \\ PT11 & & H-MASER & SEPT POLARX4 TR & 2.9.6 & H-MASER \\ REDU & REDU0 BEL & CESIUM & SEPT POLARX4 TR & 2.9.5 & SEPTCHOKE\_MC \\ ROAP & ROAP00 ESP & H-MASER & SEPT POLARX4 TR & 2.9.0 & LEIAR25.R4 \\ SPT0 & SPT0005 WE & H-MASER & JAVAD TRE\_G3 TH & 3.512 & JNSCR\_C146-22-1 \\  & & & & \\ STK2 & STK200 PN & CESIUM & TRMBBLE NETR9 & 4.61 & TRM59800.00 \\ SYDN & SYDN00 AUS & CESIUM & SEPT POLARX4 TR & 2.9.6 & ASH701945C\_M \\ TSK2 & STK200 JPN & H-MASER & TRIMBLE NETR9 & 4.61 & TRM59800.00 \\ USW9 & USW900 USA & H-MASER & NOV GEM6000000000 & TPSK.G5 \\ VILL & VILL100 ESP & CESIUM & SEPT POLARX4 & 2.9.5 & SEPTCHOKE\_MC \\ WTZS & WTZS00 DEU & CESIUM & SEPT POLARX4 TR & 2.9.3 & LEIAR25.R3 \\ WTZZ & WTZZ200 DEU & H-MASER & JAVAD TRE\_G3 TH & 3.6.9 & LEIAR25.R3 \\ YAR2 & YAR200 AUS & H-MASER & SEPT POLARX4 TR & 2.9.6 & AOAD/M\_T \\ YEL2 & YEL200 CAN & H-MASER & SEPT POLARX4 TR & 2.9.6 & LEIAR25.R4 \\ ZECK & ZECK00 RUS & H-MASER & JAVAD TRE\_G3 TH & 3.2.7 & JAVRINGANT\_DM \\  & & & & \\ ZIM3 & ZIM300 CHE & H-MASER & TRIMBLE NETR9 & 5.22 & TRM59800.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The detail information of selected IGS stations.
## 5 RC Frame Model Used for Model Uncertainty Quantification
### Dynamic Amplification Factor  
Typically, the DAF is defined as the ratio of the maximum dynamic displacement (\(u_{d}\)) to the static displacement (\(u_{s}\)) for an elastic SDOF system under an imposed loading (\(Q_{d}\)) [71]. Similarly, a displacement-based \(DAF_{D}\) can also be defined with regard to the IDA and pushdown curves (Figure 16a):  
\[DAF_{D}=\frac{u_{d}}{u_{s}} \tag{3}\]  
where \(u_{d}\) is the dynamic displacement in the dynamic capacity curve and \(u_{s}\) is the static displacement under the same imposed load in the static capacity curve. Note that \(DAF_{D}\) can only be calculated up to the ultimate dynamic response, even if a higher portion still exists in the static capacity curve (Figure 16a).  
Figure 15: Load–displacement responses: (**a**) Case A; (**b**) Case B; and (**c**) Case C.  
A force-based \(DAF_{F}\)[71; 72] can also be calculated by dividing the static load-bearing capacity by the dynamic load-bearing capacity at the same displacement (Figure 16a):  
\[DAF_{F}=\frac{Q_{s}}{Q_{d}} \tag{4}\]  
where \(Q_{s}\) is the load in the static capacity curve and \(Q_{d}\) is the load in the dynamic capacity curve.  
Based on the previous pushdown and IDA curves, the displacement-based \(DAF_{D}\) is calculated and presented in Figure 16b. Regarding Case A, the \(DAF_{D}\) increases from 1.74 to 4.11 when the imposed load increases. In terms of Case B and Case C, the \(DAF_{D}\) increases from 2.00 to 4.93 and 4.95 when the imposed load increases, respectively. It is worth noting that the results are obtained using a resolution of 0.05 kN/m for the imposed load increment in the IDA. A similar result is obtained for a bilinear SDOF model with different non-linear parameters as studied by [PERSON] [71], in which the \(DAF_{D}\) may increase from 2.00 to infinity as the applied loading increases. The values lower than 2.00 in Case A in Figure 16b can be attributed to the fact that the response in this case is not a single deformation mode as mentioned before. Additionally, yield points (YP), i.e., the moment when the reinforcement starts yielding (see Table 1), in the curves are marked (Figure 16b), for which \(DAF_{D}\) is 2.10, 2.28, and 2.32 for the three cases.  
The force-based \(DAF_{F}\) is calculated and shown in Figure 16c. For Case A, the \(DAF_{F}\) decreases from 2.23 (when the vertical imposed load is 4.0 kN/m in the IDA curve) to 1.10 (at the displacement where first failure occurs) when the displacement increases. In terms of Cases B and C, the \(DAF_{F}\) decreases from 2.02 (at 4.0 kN/m) to 1.08 and 1.04 (at the displacement where first failure occurs), respectively. A similar result is obtained for a 5-storey RC frame subjected to a column removal scenario as studied by [PERSON] and [PERSON] [72], in which the \(DAF_{F}\) decreased from almost 2.70 to 1.11 with the increasing vertical displacement. Again, yield points (YP) in the curves are marked (Figure 16c), for which \(DAF_{F}\) is 1.65, 1.73, and 1.72 for the three cases.