esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Chip Reconstructive Spectrometer Based on Parallel Cascaded Micro-Ring Resonators
## 1 Introduction
Compared to previous on-chip programmable photonic filters used as spectrometers, our parallel cascaded MR-based design offers a significantly less complex and more compact solution, with an estimated chip area of approximately 40 \(\times\) 100 \(\upmu\)m\({}^{2}\). The reduced complexity and miniaturization not only minimize the overall size of the spectral reconstruction system but also allow for lower fabrication costs and improved scalability. Additionally, our device provides wide-band spectral computational reconstruction within the wavelength of 1450 nm to 1650 nm, achieving a tunable resolution ranging from 2 nm to 0.2 nm under different numbers of sampling states. The programmable capability of the silicon photonic filter adds flexibility, empowering users to tailor the number of sampling channels to suit different applications, thereby adjusting the resolution, relative error, and computational complexity as needed.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 5 Implementation and Evaluation
### Security Evaluation  
Providing that the registrar service is secure, our solution has the following security properties:  
**The integrity and the authenticity of advertisements are protected.** \(Resource_{URL}\) advertisements are digitally signed by the IoT devices. The public key of the IoT device is included in the DID document, whose integrity is protected by the document proof generated by the DID controller. Therefore, any entity can verify the advertisement signatures. Additionally, through the authorization proof, any entity can verify that a particular IoT device is _authorized_ to advertise this specific \(Resource_{URL}\).  
**Our solution is resilient to IoT devices' key breaches.** A breached IoT device key can be used for generating fake advertisements for the \(Resource_{URL}\) for which the IoT device has been authorized, until the corresponding DID document expires. The impact of this attack is further decreased by including in the document the identifier of the edge node in which the IoT device is attached (it is reminded that only the DID owner can modify a DID document). Then, each edge device can check whether the included identifier matches its own identifier. With this approach, fake advertisements can only be sent from the same location where the legitimate IoT device is located, limiting, in this way, the impact of this attack to a few more valid advertisement messages.  
**Dependence on the trusted registrar service is minimal.** Domain owners rely on the registrar service in order to receive the authorization proof. When this step is completed, domain owners can manage, assign and delegate \(Resource_{URL}\) without relying on the registrars. For example, a domain owner can freely modify the assertion key of a DID document, as opposed, for example, to a solution based on WebPKI, where an owner would require a new \"digital certificate\".
## 6 Complexity Analysis of Image Classification with RRFs and SCNs  
Image classifiers are typically compared in terms of accuracy, precision, recall, F-1, and ROC curves, etc. The complexity analysis of image classification by various trained classifiers remains relatively unexplored. Yet, this type of analysis is fundamental not only to physical run-time performance, but also to energy consumption [20]. In this section, we define a theoretical framework for doing the complexity analysis of image classification with SCNs, RFs, and RRFs. In our analysis, we assume that number addition, subtraction, multiplication, division, and comparison are performed in constant time. For example, the activation of ReLU, which is the maximum of 0 and the output, requires 2 operations, because it is one if-then-else statement.
# Prediction of Injuries in CrossFit Training: A Machine Learning Perspective
## 2 Materials and Methods
### Validation  
The performance of the ML models was evaluated using 10-Fold Cross Validation (10 FCV). 10 FCV is a modification of the holdout method. Initially the dataset was divided into 10 subsets. Each ML model was trained using data from the k-1 folds and the trained model was then validated on the remaining kth fold. This process was repeated until every  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ML Model** & **Hyperparamaeters** \\ \hline LR & C: [0.01, 0.1, 1, 10, 100], penalty: \{’11’, ’12’\} \\ DT & criterion: \{’gini’, ’entropy’\}, min\_samples\_leaf: [1, 2, 3, 4, 5], \\  & min\_samples\_split: [2, 3, 4, 5, 6, 7] \\ KNN & algorithm: \{’auto’, ’ball\_tree’, ’kd\_tree’, ’brute’\}, leaf\_size: [1, 2, 3, 5], \\  & n\_neighbors: [3, 4, 5, 7, 9, 12, 14, 15, 16, 17], weights: \{’uniform’, ’distance’\} \\ SVM & kernel: \{’rbf’, ’linear’, ’sigmoid’\}, \{’c: [0.001, 0.1, 0.1, 10, 25, 50, 100, 1000], \\  & gamma: \{0.01, 0.001, 0.0001, 1\}\times 10^{-5}\} \\ Adaboost & criterion: \{’gini’, ’entropy’\}, min\_samples\_leaf: [1, 2, 3, 4, 5], \\  & min\_samples\_split: [2, 3, 4, 5, 6, 7], n\_estimators: \{10, 15, 20, 25, 27, 30] \\ RF & criterion: \{’gini’, ’entropy’\}, min\_samples\_leaf: [1, 2, 3, 4, 5], \\  & min\_samples\_split: [2, 3, 4, 5, 6, 7], n\_estimators: \{10, 15, 20, 25, 27, 30] \\ \hline \hline \end{tabular}
\end{table}
Table 2: ML hyperparameters tested in our experimentation.  
fold has served as testing set. Then the final performance of the model was calculated by averaging the 10 recorded scores.  
The classification performance of the competing ML models was evaluated with respect to a number of evaluation criteria that are extracted on the basis of the confusion matrix. Specifically, precision, sensitivity, specificity and classification accuracy have been widely utilized in the recent literature to assess the predictive performance of ML techniques in various health applications [45; 46; 47; 48]. The aforementioned metrics are shortly described below.  
_Precision_ is also referred to as Positive Predictive Value (PPV) and is defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
_Sensitivity_ is the proportion of true positives that are correctly identified by the model and is defined by:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{3}\]  
_Specificity_ is the proportion of the true negatives correctly identified by the model and is defined by:  
\[Specificity=\frac{TN}{TN+FP} \tag{4}\]  
A Receiver Operating Characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. AUC stands for \"Area under the Curve\" and provides an aggregate measure of performance across all possible classification thresholds.  
Classification _Accuracy_ (ACC) is the percentage of correct predictions (either positive or negative) over the total number of samples.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
## 3 Experimental Testing and Analysis of the Developed System
### Experimental Tests and Results  
The field experiments were conducted according to the methods specified in GB/T 21961-2008 for maize harvesting machinery. Before the experiments, the moisture content of the maize kernels was determined to be 28-30%. Figure 8 shows the harvesting process. The test area was divided into a stable area of 20 m long, a measurement area of 20 m long, and a parking area of 15 m long. All dropped ears were removed from the measurement area before harvest. After harvest, fallen kernels (including the kernels in the straw), the broken ears, and newly dropped ears (including ears more than 5 cm long) in the same area were collected and weighed to measure the maize picking loss. The maize picking loss rate \(MPL\) (%) was calculated as follows:  
\[MPL=\frac{W_{L}}{W_{Z}}\times 100 \tag{7}\]  
where \(W_{Z}\) is the total kernel weight (g) and \(W_{L}\) is the weight of seeds on the ground (g).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Engine speed (r/min) & 2200 \\  & 7.727 \\ Reduction ratio of wheel reducer Front axle final drive ratio & Operation 7.295 \\  & Road 3.638 \\ Speed ratio at header & 2.7 \\ Front wheel diameter (mm) & 1540 \\ Machine weight (kg) & No load 10,000 Full load 12,000 \\ \hline \hline \end{tabular} Remarks: the speed ratio at header refers to the reduction ratio from the pulling rollers motor to pulling rollers.  
\end{table}
Table 2: Machine parameters.  
Figure 8: Field experiment.
## 3 Initial Configuration Analysis
### Grid Convergence Study  
A grid convergence study [29] is performed using the initial configuration at Ma = 0.85 and \(\alpha\) = 0\({}^{\circ}\). Five different multi-block structured C-H type grids with 1,064,960 cells, 2,075,008 cells, 4,013,056 cells, 8,160,896 cells and 15,883,677 cells, respectively, are employed with the same computing domain and grid topology. Table 4 shows the results of the lift coefficient (C\({}_{\text{L}}\)), drag coefficient (C\({}_{\text{D}}\)), pitching moment coefficient (C\({}_{\text{m}}\)) and simulation time (the accumulated time when the monitoring forces remain constant). The coefficients of the Grid 3, Grid 4, and Grid 5 are almost same. Considering the simulate accuracy and efficient, Grid 3 is chosen in this study. Grid 3 with 4,013,056 cells has a multi-block structured C-H type topology with an O-type mesh around the model (Figure 9). The grid around the model grows by 1.2 with a y+ of 1, and the far field is 20 times as long as the mean aerodynamic chord. For the geometric complexity of the upper surface, the grid is refined in typical regions, such as the leading edges of the fuselage and wing, the inlet and the engine bumps.  
Figure 8: The model of initial configuration.
## 7 Summary of MIR Gas Sensing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Species** & **Bands** & **Wavelength/nm** & **Laser Type** & **Techniques** & **LOD/ppb** & **Refs** & **Applications** \\ \hline CH\({}_{4}\)/C\({}_{2}\)H\({}_{6}\) & \(v_{3}\)/\(v_{10}\) & 3291 & ICL & DAS & 5@1 s & [244] & Atmospheric \\ \cline{2-7}  & \(3337\) & & & & 8@1 s & & \\ \hline CH\({}_{4}\)/C\({}_{2}\)H\({}_{6}\) & \(v_{3}\)/\(v_{10}\) & 3291 & ICL & DAS & 2.7@1 s & [245] & Atmospheric \\ \hline CH\({}_{4}\)/C\({}_{2}\)H\({}_{6}\) & \(v_{3}\)/\(v_{10}\) & 3337 & ICL & WMS-2f & [EMAIL_ADDRESS] s & [246,247] & Atmospheric \\ \hline CH\({}_{4}\)/C\({}_{2}\)H\({}_{6}\) & \(v_{3}\)/\(v_{10}\) & 3337 & ICL & WMS & [EMAIL_ADDRESS] s & [248, 249] & Atmospheric \\ \hline CH\({}_{4}\)/C\({}_{2}\)H\({}_{6}\) & \(v_{3}\)/\(v_{10}\) & 3404 & DFG & CLaDS & 60@100 s & [248] & Technique  research \\ \cline{2-7}  & \(3335.5\) & & & & / & \\ \hline CH\({}_{4}\)/C\({}_{2}\)H\({}_{6}\)/C\({}_{3}\)H\({}_{8}\) & \(v_{3}\)/\(v_{10}\)/\(v_{1}\) & 3345 & ICL & QEPAS & 90@1 s & [249] & Oil and gas  industry  monitoring \\ \cline{2-7}  & \(3\times 10^{3}@1\) s & & & & \(3\times 10^{3}@1\) s & & \\ \hline CH\({}_{4}\)/CO/H\({}_{2}\)CO & \(v_{4}\)/\(v_{1}\)/\(v_{2}\) & 7880/4633 & QCL & WMS-2f & 0.5 for H\({}_{2}\) & [250] & Atmospheric \\ \hline CH\({}_{4}\)/N\({}_{2}\)O & \(v_{4}\)/\(v_{3}\) & 7700 & QCL & DOAS & \(3\times 10^{4}\) & [35] & Remote gas  leakage  detection \\ \hline \hline \end{tabular}  
* \({}^{1}\) LOD: Limit of Detection; \({}^{2}\) HPSDS: Heterodyne phase sensitive dispersion spectroscopy; \({}^{3}\) OF-CEAS: Optical feedback cavity-enhanced absorption spectroscopy; \({}^{4}\) QW-DFB-DL: Quantum wells distributed feedback diode laser; \({}^{5}\) DROPO: Doubly resonant optical parametric oscillator; \({}^{6}\) NICE-OHMS: Noise-immune cavity-enhanced optical heterodyne molecular spectrometry; \({}^{7}\) CLADS: Chirped laser dispersion spectroscopy; \({}^{8}\) DF-RFM: Dual-feedback RF modulation; \({}^{9}\) VECSEL: Vertical-external cavity surface-emitting laser; \({}^{10}\) DIAL: Differential absorption lidar; \({}^{11}\) I-QEFAS: Intracavity Quartz-Enhanced Photocaustic Spectroscopy; \({}^{12}\) QEFAS: Quartz-enhanced photoacoustic spectroscopy; \({}^{13}\) DIAS: Differential absorption spectroscopy; \({}^{14}\) OA-ICOS: Off-axis integrated cavity output spectroscopy;  
\end{table} 
Table 3: Summary of information on multi-component simultaneous detection in the last 10 years.
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 5 Conclusions  
This paper proposes a safety message dissemination scheme considering the high mobility and density of vehicles in urban vehicular network environments. The proposed scheme efficiently performs message dissemination considering both V2V and V2I environments. Specifically, in the V2V environment, the proposed scheme reduces packet loss caused by frequent cluster subscriptions and departures through a cluster management technique. Furthermore, in V2I communication, the proposed scheme reduces duplicate message requests and the number of RSU processing requests through intercluster safety message dissemination. To demonstrate the superiority of the proposed scheme, we conducted experimental evaluations of the cluster management technique and vehicle request algorithm. The performance evaluation results show that the proposed scheme outperformed the existing scheme. In future research, we plan to conduct additional performance  
Figure 11: Duplicate messages removed as a function of density of vehicles.  
Figure 10: Number of RSU requests as a function of density of vehicles.  
evaluations using simulation libraries and frameworks that can design and implement real road environments. In additional performance evaluations, we will consider many factors that affect the V2V communication, such as the road capacity, the number of lanes, the size and type of vehicles, terrain, and obstacles, the number of lanes, and speed limits. In addition, we will analyze more in-depth coverage of transportation-related concepts as well as the transportation system's underlying principles, dynamics, and limitations to improve the proposed scheme. Finally, we will consider interference from other external devices in transferring the messages.  
Conceptualization, J.L., D.P., D.C., K.B. and J.Y.; methodology, J.L., D.P., D.C., K.B. and J.Y.; software, D.P.; validation, J.L., D.P., K.B. and J.Y.; formal analysis, J.L., D.P., D.C., K.B. and J.Y.; data curation, J.L. and D.P.; writing\(-\)original draft preparation, J.L. and D.P.; writing\(-\)review and editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT). (No. 2022 RIA2B5B02002456), the Korea Association of University, Research Institute and Industry (AURI) grant funded by the Korean Government (Ministry of SMEs and Startups; MSS). (No. S3047889, HRD program for 2021), and by \"the Ministry of Science and ICT (MSIT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01462) supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methodology
### Data Processing  
The hand kinematics movement was collected at 20 Hz and resampled to 2000 Hz to synchronize with the sEMG signals. The sEMG signals and hand joint angle signals were divided into fragment sequences of 100 ms duration, and the sliding step-length was 0.5 ms. The commonly used feature extraction methods in EMG processing include root mean square value (RMS) [27], mean square value (MSV) [28], envelopes [29], etc. In this paper, RMS was employed as the feature extraction approach, due to its abundant information content and uncomplicated computation process. The RMS feature extraction used a 100 ms processing window size with 0.5 ms stride length. The RMS could be calculated as:  
\[RMS=\sqrt{\frac{1}{N}\sum_{i}^{n}(n_{i}-\tilde{n})} \tag{1}\]  
where \(n_{i}\) represents the values in the window, and \(\tilde{n}\) is the mean value of the window; \(N\) is the length of the window.
###### Abstract
This study aims to bridge this gap by conducting a comprehensive analysis of the R152a cooling system. By evaluating its performance and cooling capacity under different operating conditions, this study seeks to provide valuable insights for optimizing R152a-based refrigeration systems while ensuring environmental sustainability. The main innovations of this study are as follows:  
This study utilized computational fluid dynamics (CFD) to analyze the performance of an R152a heat exchanger in detail. This approach provides valuable insights into the fluid behavior within the exchanger, including the pressure, density, temperature distribution, and impact of temperature on these parameters. Focusing on R152a as an Environmentally Friendly Refrigerant, this research emphasizes R152a as a promising alternative refrigerant with a significantly lower global warming potential (GWP) compared to commonly used refrigerants such as R134a and R404a. This study compares the performance of R152a with other refrigerants such as R134a and R32, highlighting its advantages (higher performance than R134a) and limitations (lower COP than R32). This paper presents a meticulous explanation of the meshing process employed in the CFD analysis. This includes details regarding the software used, element size, skewness ratio, and other relevant parameters, providing valuable information for replicating the analysis. This study introduces the National Institute of Standards and Technology (NIST) and its role in establishing standards for CFD simulations. This highlights the importance of using well-established methods to improve the accuracy and reliability of results.
## 6 Conclusions and Future Works  
To sum up, our study suggests a method to make a system for basketball game broadcasting with player and object tracking along with AI-generated commentary. For the basic setup for detecting players and objects, our study used camera calibration to generate bird's-eye view images of the court. After the court calibration, we detect players and objects with YOLO models, and furthermore, player's actions are classified with R(2+1)D models. Our results show a better performance compared to the previous results found by other studies, reaching an average of 97% in court calibration, 92.5% in player and object detection, and 85.04% in action recognition. Further work can be undertaken to generalize the model to other kinds of sports and to produce better AI-generated commentary by using other LLM models or fine-tuning.  
Conceptualization, S.J. and A.C.; methodology, S.J., H.K., H.P., and A.C.; software, S.J. and H.P.; validation, S.J., H.K., H.P., and A.C.; formal analysis, S.J., H.K., H.P., and A.C.; investigation, H.K. and A.C.; resources, A.C.; data curation, S.J., H.K., and H.P.; writing--original draft preparation, H.K. and A.C.; writing--review and editing, A.C.; visualization, S.J., H.K., H.P., and A.C.; supervision, A.C.; project administration, A.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Gachon University research fund of 2024 (GCU-202404610001).  
Not applicable.  
The source code used to implement this work will be made available by A.Y.C. upon reasonable request.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Validation--Recent climate
#### 3.1.2 Heat Waves  
In this section, all HW are identified for HIST, and the above-referred HW measures are calculated for each HW by applying the previously defined HW identification criteria that considers Tmax_c (i.e., climatological percentile 90 of Tmax for HIST). As said before, the HW of all the analysis is performed for the period starting on 1 May and ending on 31 October (extended summer).  
The annual cycle of Tmax_c (i.e., threshold) for EOBS, WRF NC, and WRF BC (Figure 5) presents biases relative to EOBS' similar to Tmax and Tmin. These are very large for WRF NC for spring but, again, after BC differences relative to EOBS are minimized.  
All HW are identified in each model (i.e., WRF and CORDEX models) by the application of the respective Tmax_c. Figure 6 shows the (a) NDAYS annual average and (b) Tmax_sum HW average for EOBS, WRF CORDEX average, maximum and minimum (vertical bar) for HIST BC. WRF and CORDEX ensemble average present values very similar to EOBS. However, the CORDEX ensemble shows some inter-model variability. These two HW measures are very important to quantify the impacts of HW, since they represent absolute and accumulated temperature relevant to heat stress. NWAVES and DUR  
Figure 5: Tmax_c (i.e., threshold) for each calendar day for EOBS and WRF NC and BC.  
may not reflect the same. For example, a small NWAVE number in a given year may be associated to a large HW DUR and/or INT. The same is true for many HW of small DUR each.  
INT quantifies the average HW Tmax above Tmax_c and therefore may misrepresent the HW impact in absolute terms. For example, two HW with the same INT may be observed for different Tmax_c. Models simulated an average of approximatelly 10 NDAYS, with each HW contributing with nearly 150 of the accumulated temperature. Figures S1-S3 of the Supplemental material show the same information as Figure 6 but for the remaining HW measures. They show an average of 2.5 HW per year of 4-5 days duration.  
Tmax during a HW is on average nearly 2 \({}^{\circ}\)C above Tmax_c (INT). The HW daily temperature range (RF) is 14.5 \({}^{\circ}\)C on average. The average Tmax_ave of all HW is slight above 33 \({}^{\circ}\)C considering all extended summer months (May to October). In each year, HW contribute, on average, with 350 \({}^{\circ}\)C-day of accumulated temperature (for example one HW with 10 days and Tmax of 35 \({}^{\circ}\)C in each day).
# Spatial Variations in Microbial Compositions in a
## 2 Materials and Methods
### DNA Extraction, PCR and Pyrosequencing Analysis  
The water samples (3 L/each) for microbial analysis were filtered through 0.25-\(\upmu\)m size filter and returned to the laboratory and stored at \(-80\)\({}^{\circ}\)C in a refrigerator until DeoxyriboNucleic Acid (DNA) extraction and pyrosequencing of 16S Ribosomal Ribonucleic Acid (rRNA) analysis were carried out. The DNA of the sampled water was extracted using a FastDNA Spin Kit (Qbiogene, Carlsbad, CA, USA) as specified by the manufacturer. The quality of the extracted DNA was checked by standard agarose gel electrophoresis, and stored at \(-20\)\({}^{\circ}\)C. The DNA concentration was determined using a UV-VIS Spectrophotometer (Mechasys Co. Ltd., Sibeung-si, Korea). The 16S rRNA was also amplified by Polymerase Chain Reaction (PCR) for multiplexed pyrosequencing using barcoded primers. To analyze the taxonomic composition of the water bacteria community, the V1-V3 region of the 16S rRNA gene was chosen for the amplification and subsequent pyrosequencing of the PCR products at the ChunLab Inc. (Seoul, Korea) [41; 42]. The 16S rRNA genes were amplified using forward and inverse primers to distinguish each sample prior to sequencing at the ChunLab Inc. [39; 43]. Pyrosequencing was conducted using 454 Genome Sequencer FLX Titanium Junior (Roche, Branchburg, NJ, USA) by the ChunLab Inc. (Seoul, Korea). Distinct sequences were deposited in the Sequence Read Archive (ChunLab Inc.).