esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Gait Feature Extraction Module
#### 3.1.1. Skeleton Graph Representation of Gait  
In our approach, we create a graph representation of a person's gait by estimating the 2D poses for each frame, which we then integrate over the sequence for a complete gait representation. In each frame, key points like the nose, eyes, and knees are detected using a 2D pose estimator. These key points collectively define the human skeleton, which is represented as a graph denoted by \(\mathcal{G}=(\mathcal{V},\mathcal{E})\), where \(\mathcal{V}\) corresponds to the nodes (e.g., joints) and \(\mathcal{E}\) to the edges (e.g., bones).  
For \(2D\) human pose estimation, we utilize YOLO-pose [41] pre-trained on the COCO dataset [42] rather than the more computationally expensive HRNET [43]. YOLO-pose identifies \(N=17\) key points representing the human body's pose.  
These 2D poses are then used to construct the graph. We represent each joint with a \(C\)-dimensional feature vector (\(\mathbf{g}_{t,n}\)) containing the position of the \(n\)-th joint in frame \(t\), (\(n\in[1,N]\) and \(t\in[1,T]\)) and an associated confidence value.  
Thus, the gait for person \(i\) becomes a 3D tensor denoted as  
\[\mathbf{G}^{i}\in\mathbb{R}^{T\times N\times C},\text{ defined as: }\mathbf{G}^{i}=\Big{\{}\mathbf{g}_{t,n}^{i}\in\mathbb{R}^{C}\mid t,n\in \mathbb{Z},1\leq t\leq T,1\leq n\leq N\Big{\}}. \tag{1}\]  
This tensor comprehensively captures the joint-specific feature vectors for each frame (\(t\)) and joint (\(n\)) and across the various dimensions (\(C\)), providing a detailed representation of the gait pattern for person \(i\).  
For a specific frame (\(t\)) and person (\(i\)), the corresponding pose can be extracted from the tensor as a matrix (\(G_{i}^{i}\in\mathbb{R}^{N\times C}\)). It is important to note that in our case, we are working with 2D positions, which implies \(C=3\) (position plus confidence coefficient) due to the inclusion of the confidence value.
# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
## 2 Proposed Asymmetrical Bidirectional Rendering Approach
### Asymmetrical Bidirectional Rendering
In addition, to accommodate some possible depth changes and remove ghosts, the background edge of the selected regions is extended outward by using a morphological operation. Once all the selected regions are extracted, the auxiliary virtual image can be synthesized by the modified 3D warping.
# Real-Time Detection of At-Risk Movements Using Smartwatch IMU Sensors
## 3 Materials and Methods
### Real-Time Inference Algorithms on Smartwatches  
We developed real-time inference apps using 1D CNN models in the time domain and frequency domain, respectively, and compared their performance. By leveraging TensorFlow Lite's 16-bit quantization [16], the 1D CNN model performs efficiently on wearable devices with limited battery capacity.  
Figure 4 shows the algorithm flowchart for the wearable watch application. The app begins by collecting data from start to stop and computes an SVM (signal vector magnitude) from these data. To detect motion, the accelerometer values are continuously read during operation; however, the issue of low battery power is not addressed in this study. Next, it calculates the amount of change in the SVM to limit unnecessary computation by not running inferences when there is no movement. To do this, we use a sliding window method that calculates the change in recent motion using the sum of the absolute differences between consecutive accelerometer values within the last 150 data points, which corresponds to 3 s. In this paper, a value of 5 is set experimentally; it suggests that there is some movement (not static). The app uses a 1D CNN to infer motion in real time and displays the result on the screen. If no motion is detected, it continues to collect data until further changes occur.  
Figure 4: Wearable watch system flowchart.  
Figure 3: One-dimensional convolutional neural network (1D CNN) architecture of the frequency domain.
## 3 Materials and Methods
### Initial AHP-QFD Matrices
#### 3.2.1 Logistic Service Site Design
Other techniques, such as zero-one goal programming, are needed to facilitate decision-making. It is recommended that future studies apply other techniques in conjunction with AHP-QFD to potentially improve decision-making outcomes. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline CR1 & 3 & 9 & 9 & 3 & 9 & 9 & 3 & 9 & 0.300 \\ CR2 & 9 & 9 & 9 & 3 & 3 & 3 & 3 & 9 & 0.060 \\ CR3 & 9 & 9 & 3 & 9 & 3 & 1 & & 0.087 \\ CR4 & 9 & 9 & 3 & 3 & & & & 3 & 0.030 \\ CR5 & & 9 & 3 & & & & & 3 & 0.127 \\ CR6 & 3 & 9 & 3 & & 9 & & & & 0.124 \\ CR7 & & 3 & & 3 & 9 & 9 & & & 0.042 \\ CR8 & & & 9 & 1 & 3 & 3 & & 9 & 0.021 \\ CR9 & & & & & 9 & 9 & 9 & & 0.017 \\ CR10 & & 9 & & 9 & 3 & & 9 & 9 & 0.192 \\ Importance degree & 2.865 & 8.406 & 3.330 & 4.227 & 3.687 & 2.727 & 3.474 & 2.868 & 5.628 \\ Normalize important degree & 7.7 & 22.6 & 8.9 & 11.4 & 9.9 & 7.3 & 9.3 & 7.7 & 15.1 \\ \hline \hline \end{tabular} Note: The list of customer requirements associated with this AHP-QFD include: (CR1) quick/accurate logistic ability, (CR2) comfortable logistics, (CR3) adequate building space, (CR4) proper logistics center site, (CR5) reasonable logistics fee, (CR6) easy to load/unload, (CR7) adequate airport service supply, (CR8) sufficient public facilities, (CR9) stable economic utilities provide, (CR10) quick delivery service capability.  
\end{table} 
Table 3: Original AHP-QFD matrix of site selection for an Airport Cargo Logistics Center [23].
# UHPLC-MS Method for the Analysis of the Molecular Adjuvant Sulfavant [PERSON]
###### Abstract  
A fast and sensitive method that is based on Ultra High Performance Liquid Chromatography coupled with High Resolution Mass Spectrometry (UHPLC-HRMS) for the measurement of Sulfavant A, a molecular adjuvant with a sulfolipid skeleton, is described. The method has been validated over the linearity range of 2.5-2000 ngmL\({}^{-1}\) using a deuterated derivative (d\({}_{30}\)-Sulfavant A) as internal standard. Chromatographic separation is based on a UHPLC Kinetex(r) 2.6 \(\upmu\)m PS C18 column and a gradient of methanol in 0.32 mM ammonium hydroxide solution buffered at pH 8. The lowest limit of quantification of Sulfavant A was 6.5 ngmL\({}^{-1}\). The analytical procedure was tested on an extract of mice lung spiked with 30, 300, and 1500 ng of Sulfavant A. The analysis revealed a precision and accuracy value (as a mean value of all the quality control samples analyzed) of 4.7% and 96% in MeOH and 6.4% and 93.4% in the lung extracts, respectively.  
sulfavant; sulfoquinovosyllacylglycerrols; sulfoglycolipids; mass spectrometry; UHPLC-MS; lipids +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Sorption Process  
The metal ion removal by the means of adsorption process was prepared in a batch system--60 cm\({}^{3}\) polypropylene flasks. Approximately 0.5 g of AP and PAP and 20 cm\({}^{3}\) of Cr(VI) and Cd(II) ions in different concentrations (50, 100, 150, 200, 300 mg/dm\({}^{3}\), respectively) were used for each test in different variants. After 5, 10, 15, 30, 60 and 90 min of shaking on the rotary shaker (150 rpm) after the start of sorption, the portion of the solution was filtered through analytical paper filter and the concentration of residual chromium(VI) and cadmium(II) ions in the filtrate were examined with an AAS method. The maximum sorption capacity was calculated using equation:  
\[q_{e}=\frac{C_{0}-C_{e}\cdot V}{m_{e}\cdot 1000}, \tag{1}\]  
where \(V\) is the volume of solution (cm\({}^{3}\)) and \(m_{e}\) is the mass of sample (g). The tests were conducted in triplicates and results averaged--the mean standard error was not higher than 5%.
## 2 State of the Art
### Application of RFID in a Simulated Production Line for an Industrial Context
Also, Table 2 shows some tests carried out on RFID tags in the context of the industrial production environment and the various types of interference that may exist. \begin{table}
\begin{tabular}{c c c} \hline
**Tag \#** & **Tag Name** & **Read Range (cm)** \\ \hline Tag 1 & Avery Dennison AD-550m5 & 150 \\ \hline Tag 2 & AD-171m5 & 35 \\ \hline Tag 3 & AD-321r6 & 76 \\ \hline Tag 4 & Abracon Metal Tag & 20 \\ \hline Tag 5 & V780-A-JIME-Z3 BLI-10 & 116 \\ \hline Tag 6 & Avery Dennison AD-383u & 90 \\ \hline \end{tabular}
\end{table} 
Table 1: RFID system read-range tests (adapted from [31]). \begin{table}
\begin{tabular}{c c c} \hline
**Tag \#** & **Read Range with Metal** & **Read Range with Cardboard** & **Read Range with** \\
**Interference (cm)** & **Interference (cm)** & **Packet Understand and** \\ \hline Tag 1 & 62 & 130 & 109 \\ \hline Tag 2 & N/A \({}^{*}\) & 25 & 27 \\ \hline Tag 3 & N/A & 65 & 61 \\ \hline Tag 4 & N/A & N/A & N/A \\ \hline Tag 5 & 67 & 110 & 111 \\ \hline Tag 6 & 61 & 90 & 84 \\ \hline \end{tabular} \({}^{*}\)N/A—not applicable.  
\end{table} 
Table 2: Experimental procedures for RFID tags in the context of the production environment and existing interferences (adapted from [31]). Proper selection of an RFID tag requires a good knowledge of the tag standard, the memory structure, and the information that can be written and read. Manufacturing processes that have utilised RFID technologies have used UHF-RFID readers along with second-generation tags, with most of these tags being passive and able to be written anywhere in the supply chain. All the memory banks, apart from the user memory, are used to edit the labels during the production process. In turn, the EPC code associated with these RFID tags provides the primary product labelling function, while the respective RFID standards allow translations between RFID codes and QR codes/barcodes, thus facilitating the ability to transfer location data to parts after a process has been completed. This flexibility in using RFID codes makes it easier to track a part during production and is possible for various types of industrial systems [31]. In addition, each manufacturing station integrated into the factory has its control platform including a programmable logic controller and associated hardware (input/output modules, communication modules, and a human-machine interface module). In addition, the programmable logic control platform is used to monitor and control the operations taking place at the production station in question, to communicate with the RFID readers, and to access specific client software capable of transferring this information to a centralised server system using a database to trace the sequence of the production process, thus ensuring easy traceability of the components manufactured and paving the way for the integration of I4.0 into production systems [31].
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 3 Materials and Methods
### Method
#### 3.2.3 Model Calibration  
The models' boundary parameters and others were determined using the non-linear method of the least squares with Levenberg-Marquardt algorithm for non-linear optimization [39]. Starting from the given initial values of the parameters, the algorithm iteratively reduces the sum of squares of errors between real and modeled values of the explanatory value by sequentially updating model parameters. All model parameters were estimated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Par.** & **The Lower Range Limit** & **The Upper Range Limit** & **Condition** &
\begin{tabular}{c} **The Expected Range** \\ **(Field Data)** \\ \end{tabular} \\ \hline \(\mathrm{v}_{\mathrm{sw}}\) & \(\mathrm{v}_{\mathrm{sw}_{1}}=\mathrm{v}_{5}\) & \(\mathrm{v}_{\mathrm{sw}_{2}}=\mathrm{v}_{95}\) & \(\mathrm{q}\leq 500\,\mathrm{pc/h/lane}\) & \(107\div 113\) km/h \\ \(\mathrm{q}_{\mathrm{max}}\) & \(\mathrm{q}_{\mathrm{max}_{1}}=\mathrm{q}_{95}\) & \(\mathrm{q}_{\mathrm{max}_{2}}=\mathrm{q}_{99}\) & & \(4175\div 4386\) pc/h \\ \(\mathrm{v}_{\mathrm{opt}}\) & \(\mathrm{v}_{\mathrm{opt}_{1}}=\mathrm{v}_{5}\) & \(\mathrm{v}_{\mathrm{opt}_{2}}=\mathrm{v}_{95}\) & \(\mathrm{q}_{\mathrm{max}_{1}}\leq\mathrm{q}\leq\mathrm{q}_{\mathrm{max}_{2}}\) & \(72\div 86\) km/h \\ \(\mathrm{k}_{\mathrm{opt}}\) & \(\mathrm{k}_{\mathrm{opt}_{1}}=\mathrm{k}_{5}\) & \(\mathrm{k}_{\mathrm{opt}_{2}}=\mathrm{k}_{95}\) & \(\mathrm{v}_{\mathrm{opt}_{1}}\leq\mathrm{v}\leq\mathrm{v}_{\mathrm{opt}_{2}}\) & \(47\div 58\) pc/km \\ \(\mathrm{k}_{\mathrm{max}}\) & based on aerial measurements & calculated assuming minimal & n.a. & \(200\div 300\) pc/km \\ \hline \hline \end{tabular}
\end{table}
Table 3: Determining the expected range of boundary parameter values from an averaged representation of traffic conditions.  
The initial values of boundary parameters were treated as the centers of the expected ranges of values that were determined during data preparation. The boundary density for traffic conditions in two-regime models was set as the middle value from the range of expected \(k_{opt}\) values.
# Three-Phase Unbalance Analysis Method Based on Three-Phase Motor Current Instantaneous Information
### Calculation of Coefficient of Variation  
The coefficient of variation (C.V.) is the ratio of the standard deviation value to the mean value, which reflects the degree of variation of each observation. The coefficient of variation is a dimensionless statistic.  
The mean value \(\mu\) is shown in the following equation.  
\[\mu=\frac{\sum_{i=1}^{n}x_{i}}{n} \tag{8}\]  
where \(x_{i}\) is the \(i\)-th data and \(n\) is the data length.  
The sample standard deviation value \(\sigma\) is shown in the following equation.  
\[\sigma=\sqrt{\frac{\sum_{i=1}^{n}(x_{i}-\mu)^{2}}{n-1}} \tag{9}\]  
From the definition of the coefficient of variation, the coefficient of variation \(c_{\text{\tiny{P}}}\) is obtained by associating Equations (8) and (9):  
\[c_{\text{\tiny{P}}}=\frac{\sigma}{\mu} \tag{10}\]  
The coefficient of variation is defined only when the mean is not zero and is usually used when the mean is greater than zero. The coefficient of variation measures the fluctuation of the overall data. By calculating the coefficient of variation of the instantaneous frequency and instantaneous amplitude, the fluctuation status can be analyzed.  
Both instantaneous amplitude and instantaneous frequency are basic features of a signal and can be used to analyze the characteristics and variation patterns of a signal. The coefficient of variation of the instantaneous information (instantaneous frequency and instantaneous amplitude) of the motor current, reflecting the instantaneous amplitude and instantaneous frequency fluctuations, can reflect the symmetry of the three-phase current signal during motor operation.
## 2 Experimental Details
### Fluxes Preparation and A-TIG Welding Process
#### 2.3.2 A-TIG Welding Process  
Two kinds of fluxes were made in this study: nanoscale and microscale Fe\({}_{2}\)O\({}_{3}\) flux. Nanowire flux was mixed with nanoscale Fe\({}_{2}\)O\({}_{3}\) and acetone in different mass ratios (Fe\({}_{2}\)O\({}_{3}\)/acetone = 1:1, 1:5 and 1:10), and microscale Fe\({}_{2}\)O\({}_{3}\) flux was made with microscale Fe\({}_{2}\)O\({}_{3}\) and acetone (commercial microscale Fe\({}_{2}\)O\({}_{3}\) powders, Sigma Aldrich) as shown in Figure 2a. After mixing with a mechanical stirring with a Teflon pududler, the fluxes were applied by using a spray gun on the surface of the HMS steel plates to be welded. The coating thickness was thin enough to mask the joint line. The total quantity of flux required is 5-6 mg. After a few minutes, the flux coating was visibly dry, then the acetone evaporates, leaving a layer of flux adhering to the surface. Figure 2b shows a schematic diagram of activated TIG welding with a robotic stage and Figure 2c is an image of the real experiment set up. The welds were made with a fixed stand-off distance (also named arc length, L) of 3 mm from the tip of the tungsten to the workpiece. The arc was moved along the centerline of the test specimen and welds were carried out in the flat position on a single plate which was coated with the flux. During A-TIG welding, a part of or all of the fluxes were molten and vaporized.  
Figure 1: SEM photo (**a**) and XRD result (**b**) of as-grown Fe\({}_{2}\)O\({}_{3}\) nanowires.  
Figure 2: (**a**) Spray coating of active flux to thick steel plate; (**b**) Schematic figure of A-TIG welding; (**c**) Photo of real experimental set up.  
Two sets of welding experiments were performed. At first, nanoscale Fe\({}_{2}\)O\({}_{3}\) flux of different concentrations was applied onto the samples, the welding speed was 120 mm/min in this set. After that, a certain ratio should be decided for both nanoscale and microscale fluxes for welding set No. 2, this time the welding speed was 60 mm/min.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 4 Results
### Field Experiment Results  
The positioning system was mounted on a commercial UAV, and equipped by the UTFSM Robotics Research Group (GRAI). The complete system was positioned over a transmission system, whose main parameters are summarized in Table 4. The estimate based on the embedded Kalman Filter was used as position reference of the UAV. Figure 11 presents the resultant images in all stages of the transmission line detection approach. The corresponding results are shown in Figures 12 and 13. It is interesting to note that the estimated position, shown as a magenta dashed line, converges to reference (black line), provided that the transmission lines are visible in the acquired images, as shown in Figures 12 and 13. Otherwise, the system compensates poor estimations using Bayesian methods. In this context, we used the estimator described in Equations (7) and (8) to reduce this negative effect. Table 5 compares the results obtained in the position estimation, after applying two approaches: simple when the system returns the position and hybrid when the sensor fusion expression only with the IMU, GNSS and our approach data. The impact of this solution is shown in Figures 12 and 13, as a cyan dashed line, where it is possible to note that the system determined the UAV position in the hybrid case despite not seeing wires, giving the robustness to the system. In terms of the RMSE, the two approaches show similar  
Figure 10: Comparison of power line detection results: (**a**) ideal lighting conditions; (**b**) brightness increased by 20%; (**c**) brightness increased by 60%; and (**d**) brightness reduced by 50%.
## 4 Results
### Results for Maritime Mobile Service  
#### 4.2.1 Results for MMS with Omnidirectional Antenna  
Interference analysis for the system with the omnidirectional receiver for different distances showed that _l_/_N_ protection criteria is all cases with 7 dB margin. Figure 12 shows distribution functions of _l_/_N_ when interfering from IMT to the System 1 MMS receiver.
# Exposure to NO\({}_{2}\) and PM\({}_{2.5}\) While Commuting: Utility of Low-Cost Sensor
## 3 Results
### Descriptive Statistics
For NO\({}_{2}\) during the heating season, both the average 30.8 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) (median 25.6 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) and the range of concentrations 0.1-109.6 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) are higher compared to the non-heating season, where the average was 22.6 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) (median 20.4 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)), and the range was 0.2-103.3 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\). The larger interquartile range in the heating season indicates greater variability in concentrations, meaning that NO\({}_{2}\) levels vary more during this period. For PM\({}_{2.5}\) concentrations, seemingly contradictory results are observed between the measures of central tendency. During the heating season, the mean concentration is 8.1 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) (median: 3.2 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)), with values ranging from 2.0 to 45.5 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\). During the non-heating season, the mean concentration is 9.9 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) (median: 2.6 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)), and values range from 2.0 to 117.8 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\), indicating a higher mean concentration compared to the heating season but a lower median concentration. The discrepancy between the mean and median values in the non-heating season suggests greater variability and the presence of outliers, which inflated the mean value. A probable explanation for this observation may relate to short-term local sources (e.g., construction activity, resuspended road dust, or increased vehicular emissions during dry and warmer periods) that are more prevalent in the non-heating season. Furthermore, specific meteorological conditions--such as lower relative humidity and less atmospheric stability in warmer months--may contribute to elevated peak concentrations under certain circumstances. Conversely, while the heating season is typically associated with increased emissions from residential heating, atmospheric conditions such as temperature inversions and reduced vertical mixing can suppress dispersion and lead to more consistently elevated background levels, which may explain the higher median concentrations observed during that period. Figure 2: Boxplots of NO\({}_{2}\) by transport mode during non-heating and heating seasons. Boxes represent the 25 th to 75 th percentile, the central dark line is the median, bars outside the box min-max, the cross is the mean value, and the circles are the outliers.
# Review of Traffic Assignment and Future Challenges
## 5 Discussion  
The traffic assignment problem has been extensively addressed in the literature. It has benefited from traffic supply models and demand models. Several resolution methods have been proposed for this problem. They generally share a common algorithmic structure, starting from an initial traffic situation and iterating until a stopping criterion is satisfied. At each iteration, a portion of the traffic is reassigned to less costly routes. In general, resolution methods are designed for transportation planning scenarios where relatively long computation times are acceptable.  
While traffic assignment approaches and associated models have been extensively tested through studies and real-world applications, the advancements in intelligent transportation systems reveal their limitations and call for new contributions. To illustrate the claim, this section is organized into two parts. The first one reviews the major progression in intelligent transportation systems and how emergent technologies question the theories behind traffic assignment. The second part provides promising research directions.