esa-sceva/satcom-chunk-collection huggingface dataset:
# The Evaluation of the Detection of Cr(VI) in Leather
## 4 Discussion
### Recommendations  
1. The addition of an inconclusive boundary as used in BS EN ISO 62321-7-1:2015 for mean values close to the compliance limit, where error measurements straddle the pass-fail boundary, is recommended. This could work by stipulating results are inconclusive, where errors in mean Cr(VI) values overlap with the compliance limit. Samples that fall under this category could be then tested further until a definitive pass or fail can be established.
2. As Cr(III) is a significant interference for quantifying Cr(VI) and needs to be separated, it is recommended the colorimetric method is phased out as a suitable procedure for quantifying Cr(VI) in leather at 3 or 1 mg kg\({}^{-1}\).
3. To minimise the risk of false positives results due to colourants, reporting the UV-visible spectrum of the analyte and confirmation of the presence of Cr(VI) should be made mandatory.
4. It is recommended that the quantity of detectable Cr(VI) in solution be maximised by using the largest injection volumes possible ideally greater than 100 uL, using larger leather sample weights, and minimising dilution factors.
5. It is recommended that the absorptivity coefficient be maximised by reacting eluted Cr(VI) with DPC as a follow-up reaction during IC rather than by directly detecting the CrO\({}_{4}\)\({}^{2-}\) ion. In this case, a reaction time of greater than 300 s is recommended to maximise and stabilise the colour intensity. A quoted molar absorptivity coefficient is recommended to confirm that all calibrations are correct; however, there is currently no agreed value [25]. The speciation of the of the Cr(III)-carbazone complex is currently unconfirmed; therefore, an accurate molar value cannot be derived until speciation is established [26].
6. The use of a DAD and reporting the UV-visible spectrum of the analytes should be made compulsory to avoid false positives from interfering colourants.
7. The LOQ for the chromatographic method should be investigated further with substantial interlaboratory trials to objectively confirm the quantification limit, as demonstrated in BS EN ISO 17075-1:2017 annex B.  
Although recommendations for the established chromatographic methodology have been made, to futureproof Cr(VI) analysis in leather goods, an alternative to spectrophotometric detection should be developed. One option is to consider inductively coupled plasma mass spectrometry (ICP-MS). This technique differentiates the elemental composition by atomic mass rather than photometric response. The ICP ionisation technique destroys interfering organics, whereas interfering inorganic species can be mitigated with the use of a collision cell. Cr(III)-Cr(VI) differentiation can be achieved by using an in-line chromatographic separation prior to ionisation, as described as part of ASTM UOP 1031-19 [27].
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
###### Abstract  
Recently, P2P streaming techniques have been a promising solution to a large-scale live streaming system because of their high scalability and low installation cost. In P2P live streaming systems, however, it is difficult to manage peers' buffers effectively, because they can buffer only a limited amount of data around a live broadcasting time in the main memory and suffer from long playback lag due to the nature of P2P structures. In addition, the number of peers decreases rapidly as the playback position moves further from this time by performing time-shifted viewing. These situations widen the distribution of peers' playback positions, thereby decreasing the degree of data duplication among peers. Moreover, it is hard to use each peer's buffer as the caching area because the buffer area where the chunks that have already been played back are stored can be overwritten at any time by new chunks that will arrive soon. In this paper, we therefore propose a novel buffering scheme to significantly increase data duplication in buffering periods among peers in P2P live and time-shifted streaming systems. In our proposed scheme, the buffer ratio of each peer is adaptively adjusted according to its relative playback position in a group by increasing the ratio of the caching area in its buffer as its playback position moves earlier in time and increasing the ratio of the prefetching area as its playback position moves later. Through extensive simulations, we demonstrate that our proposed adaptive buffering scheme outperforms the conventional buffering technique considerably in terms of startup delay, average jitter ratio, and the ratio of necessary chunks in a buffermap.  
P2P live streaming; time-shifted viewing; adaptive buffering; prefetching; caching Article  
An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming  
[PERSON], [PERSON], and [PERSON] (2017)
## 3 Application to Immersed Tunnel
### Damping Setting  
Combined with the implicit FEM, the Rayleigh damping matrix is used to consider the energy dissipation under earthquake. The mathematical expression of the Rayleigh damping matrix is shown in Equation (12).  
\[[C]=\theta[M]+\varphi[K], \tag{12}\]  
where \([C]\) is the damping matrix of the soil, \([M]\) and \([K]\) are the mass and stiffness matrix of the soil, respectively, \(\theta\) is the damping constant proportional to the mass, and \(\varphi\) is the damping constant proportional to the stiffness.  
When the vibration damping ratios of the various modes of the calculation system are the same,  
\[\left\{\begin{array}{c}\theta\\ \varphi\end{array}\right\}=\frac{2\xi}{\omega_{m}+\omega_{n}}\begin{bmatrix} \omega_{m}\omega_{n}\\ 1\end{bmatrix}, \tag{13}\]  
Figure 10: Sketch drawing of seabed surface hydrodynamic pressure decomposition.  
where \(\xi\) is the mode damping ratio, \(\omega_{m}\) and \(\omega_{n}\) are the two reference mode shape angular frequencies of the calculation system, and the subscripts m and n represent the order of the corresponding mode shape, respectively.  
Since the fundamental frequency of the soil-immersed tunnel interaction system often differs greatly from the main seismic excitation frequency, the determination of the proportional coefficient in the damping matrix has a great influence on the seismic response calculation results. According to the conclusion of [PERSON] et al. [42], the two reference frequencies are taken as \(f_{1}\) and \(f_{r}\), respectively, where \(f_{1}\) is the first-order mode frequency of the soil-immersed tunnel during horizontal seismic excitation, and \(f_{r}\) is the frequency corresponding to the peak of the spectrum in the input seismic acceleration response spectrum. Here, the values of \(f_{1}\) and \(f_{r}\) are 0.505 Hz and 2.778 Hz, respectively, and \(\omega_{m}\) and \(\omega_{n}\) can be calculated using Equation (14).  
\[\left\{\begin{array}{l}\omega_{m}\\ \omega_{n}\end{array}\right\}=2\pi\begin{bmatrix}f_{1}\\ f_{r}\end{bmatrix}. \tag{14}\]
# Spectroscopic Approach for the On-Line Monitoring of Welding of Tanker Trucks
## 3 Experimental Results and Discussion
### Field Trials
For example, Figure 6e presents the welding voltage and current profiles for Weld Trial #4, where oil was used at the top of the joint to simulate a lack of cleanliness. A visual inspection of the seam (Figure 6, top-left) clearly revealed the effects of the oil during the process. Specifically, the Ar I profile depicted in Figure 6a exhibited a trend with a good correlation with the visual defects to be found in the seam. Both the welding voltage and current also exhibited some perturbations matching these events (Figure 6e). Figure 6: Output monitoring profiles for Weld Trials #4 and #8 using different spectroscopic and electrical parameters. Ratio of Ar I line (738 nm) to Ar I (763 nm) line associated with (**a**) Trial #4 and (**b**) Trial #8. Ratio of H I line (656 nm) to Al I lines at 396 nm (thick blue line) and 670 nm (thin green line) associated with (**c**) Trial #4 and (**d**) Trial #8. Welding voltage (thick blue line) and current (thin green line) associated with (**e**) Trial #4 and (**f**) Trial #8. A less clear situation can be studied in Figure 6b,d,f. In this case, the trial was designed to analyse the effects of joint preparation and variations in the stand-off distance employed in the process. Curiously, the X-ray inspection did not detect any defect, but a \"burnt\" is clearly visible at x \(\approx\) 9600 mm. Both the Ar I (Figure 6b) and the voltage (Figure 6d) profiles show perturbations at this location--more evident in the optical analysis. It is worth mentioning that, in this case, the HI profile did not exhibit any correlation with this event.
## 4 Experimental Results and Analysis
### Physical Experiment Results and Analysis
#### 4.2.2 Single Defect Experiment  
The detection object is an artificial defect in the surface of a used rail, as shown in Figure 9. The direction of detection is from left to right. A wire is tied to the rail, which will change the lift-off of the handcart to simulate the vibration. If no vibration is encountered, the lift-off of the sensor is 2.0 mm, and when the handcart wheel rolls over the wire, the lift-off will fluctuate within 2.0-3.5 mm. The defect is 2.0 mm in width and 1.0 mm in depth. Therefore, a = 1.0 mm, z\({}_{0}\) = 2.0 mm. The distance between a pair of sensors is \(\sqrt{a^{2}+z_{0}^{2}}\), that is 2.
###### Abstract  
Demand response (DR) implementations have recently found wide application areas in the context of smart grids. The effectiveness of these implementations is primarily based on the willingness of end-users to be involved in such programs. In this paper, an interactive and user-friendly interface is presented in order to facilitate and accordingly to increase the participation of end-users in DR programs. The proposed interface has the capability of providing the targeted information about the DR events to end-users and system operators, as well as allowing end-users to interactively monitor and control the progress of their appliances. In addition to its benefits to system operators and thus to the improved operation of power systems, the proposed interface particularly aims to exploit the potential energy-related cost savings by providing the required information and resources to end-users via mobile phone. A separate interface apart from the mentioned end-user oriented interface has also been developed for the system operator to more effectively check the status of DR applications in detail. The capabilities of the proposed concept are evaluated in a real smart home in terms of various aspects.  
user interface; demand response; home energy management; mobile applications; smart grid +
Footnote †: journal: Applied Sciences
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 3 Longitudinal Controller Design
### Acceleration and Braking Mode Switching Controller  
A tolerance zone is added to the design of the longitudinal controller to make the ferry vehicle slowly accelerate and brake so as to prevent the frequent switching of acceleration control and braking control, which impacts the comfort and stability of speed control. When the ferry vehicle releases the acceleration pedal and the brake pedal, it stops under the action of all kinds of resistance, calculates the maximum free deceleration of the ferry vehicle, and obtains the relationship between the deceleration and the speed of the ferry vehicle. The deceleration formula of the ferry vehicle is as follows:  
\[a=\frac{v}{t} \tag{17}\]  
In Formula (17), \(a\) is the free deceleration, \(v\) is the desired speed, and \(t\) is the time taken by the ferry vehicle from the expected speed to the stop.  
The testing of the vehicles was achieved by employing different speeds and using experimental methods, and the relationship between the free deceleration of the ferry vehicle and the speed is shown in Figure 12.  
According to the actual vehicle debugging and experience, the acceleration tolerance interval is taken as \(\Delta a=0.1\,\mathrm{m}/\mathrm{s}^{2}\), and the acceleration and braking switching rules are established through the expected acceleration (\(a_{des}\)) and the maximum free deceleration (\(a\)) as follows:  
At \(a_{des}-a-\Delta a>0\), the driverless ferry vehicle has reached the upper boundary of the tolerance interval and is in the acceleration control interval, so it is necessary to control the acceleration of the vehicle and then input it into the fuzzy PID controller according to the calculated \(a_{des}\); thus, calculate the required motor torque, and the braking control does not work at this time.  
At \(a-\Delta a\leq a_{des}\leq a+\Delta a\), the driverless ferry vehicle has reached the tolerance range, and the vehicle does not do anything in the tolerance zone; that is, it does not accelerate or brake, keeping the original state of the vehicle.  
When \(a_{des}-a+\Delta a<0\), the driverless ferry vehicle reaches below the lower boundary of the tolerance interval and is in the braking control interval, it is necessary to brake the vehicle and then convert the brake oil pressure needed by the vehicle according to the difference between the expected speed and the actual speed, and the acceleration control does not work at this time.  
The curve of the switching rule of acceleration and braking is shown in Figure 13.  
Figure 12: Relationship between vehicle speed and maximum free deceleration.
###### Abstract  
The ecological environment of the Yellow River Basin in China is characterized by drought, which has been exacerbated by global warming. It is critical to keep accurate track of the region's agricultural drought conditions. To enhance the vegetation health index (VHI), the optimal time scale for the standardized precipitation evapotranspiration index (SPEI) was determined by using the maximum correlation coefficient method, and the calculation method for VHI was optimized. The contributions of the vegetation condition index (VCI) and the temperature condition index (TCI) to the VHI were scientifically optimized, leading to the development of the optimal VHI (VHIopt). Soil moisture anomaly (SMA) and the SPEI were employed for assessing the performance of VHIopt. Furthermore, the temporal and spatial evolution of agricultural drought in the Yellow River Basin (YRB) was analyzed using VHIopt. The results indicate the following: (1) In the YRB, the optimal contribution of the VCI to the VHI is lower than that of the TCI. (2) The drought monitoring accuracy of VHIopt in forests, grasslands, croplands, and other vegetation types exceeds that of the original VHI (VHIori). Additionally, it demonstrates a high level of consistency with the SMA and the SPEI03 regarding spatial and temporal characteristics. (3) Agricultural drought in the YRB is gradually diminishing: however, significant regional differences remain. Generally, the findings of this study highlight that VHIopt is better suited to the specific climate and vegetation conditions of the Yellow River Basin, enhancing its effectiveness for agricultural drought monitoring in this region.  
vegetation health index; yellow river basin; algorithm optimization; drought monitoring Article
## 3 Results and Discussion
### Odor Profiles of Grain During the Ripening of Rice  
The contribution of each VOC to the overall aroma of rice is influenced not only by its concentration but also its odor threshold. Volatile compounds with an rOAV \(\geq\) 1 are considered to contribute significantly to the overall flavor of rice grains [32]. To identify the key aroma-active compounds in rice grains at various ripening stages, we performed rOAV analyses on all detected VOCs. In total, 65 VOCs with rOAV above 1 were identified as key aromatic compounds in rice grains (Figure 3B), with their threshold values and odor descriptions listed in Table S7. Among these, 7 aroma components had rOAV greater than 1000, 13 had roAVs between 100 and 1000, and 36 had roAV between 1 and 100, detected across the four ripening stages. Additionally, nine aroma components with rOAV greater than 1 were detected only at one or two ripening stages. These findings suggest that these 65 substances play a crucial role in contributing to the flavor profile of rice grains. Combining the differential metabolite analyses in seeds of different maturity levels (Tables S5 and S7), we identified 25 VOCs (with rOAV > 1) that varied significantly with grain maturation and contributed to fragrance in all three rice varieties. Except for methyl salicate, the remaining substances accumulated in large quantities as the grains matured. Notably, (Z)-6-nonenal, (Z,Z)-3,6-nonadienal, and 2-thiophenetthanethiol had rOAVs exceeding 1000 at all four stages of grain development. Additionally, 5-methyl-2-furanethanethiol, 2-methylisoborneol, 3-mercaptoethanol acetate, 2,2,6-trimethyl-cyclohexanone, and 3-octen-2-one had rOAVs greater than 100 at all stages, reaching their maximum levels by 35 days. The accumulation of 3-octen-2-one recorded in mature grains aligns with previous reports [4]. Moreover, other compounds, including 1-(2-thienyl)-ethanoneome, (Z)-3-hexen-1-ol, 2-acetyl-1-pyrroline, 2-furanethanol, benzyl alcohol, (Z)-3,7-dimethyl-1,3,6-ocatrien, 2-methylinaphthalene, naphthalene, (-)-perillaldehyde, (Z)-2-decenal, (E)-4-decenal, propanoic acid hexyl ester, and heptanoic acid ethyl ester, also played important roles in aroma formation during seed ripening despite having roAVs of
###### Abstract  
The storm onset on 7 September 2017, triggered several variations in the ionospheric electron density, causing severe phase fluctuations at polar latitudes in both hemispheres. In addition, although quite rare at high latitudes, clear amplitude scintillations were recorded by two Global Navigation Satellite System receivers during the main phase of the storm. This work attempted to investigate the physical mechanisms triggering the observed amplitude scintillations, with the aim of identifying the conditions favoring such events. We investigated the ionospheric background and other conditions that prevailed when the irregularities formed and moved, following a multi-observations approach. Specifically, we combined information from scintillation parameters and recorded by multi-constellation (GPS, GLONASS and Galileo) receivers located at Concordia station (75.10\({}^{\circ}\)S, 123.35\({}^{\circ}\)E) and SANAE IV base (71.67\({}^{\circ}\)S, 2.84\({}^{\circ}\)W), with measurements acquired by the Special Sensor Ultraviolet Spectrographic Imager on board the Defense Meteorological Satellite Program satellites, the Super Dual Auroral Radar Network, the Swarm constellation and ground-based magnetometers. Besides confirming the high degree of complexity of the ionospheric dynamics, our multi-instrument observation identified the physical conditions that likely favor the occurrence of amplitude scintillations at high latitudes. Results suggest that the necessary conditions for the observation of this type of scintillation in high-latitude regions are high levels of ionization and a strong variability of plasma dynamics. Both of these conditions are typically featured during high solar activity.  
GNSS amplitude scintillations; multi-constellation receivers; high-latitude ionosphere; geomagnetic storm; space weather +
Footnote †: journal: _Fennote sensing_
## 1 Introduction  
The task of a vehicle license plate detector and recognizer is to identify a vehicle by its license plate. There are numerous practical applications of these detectors, such as in traffic surveillance, toll collection, etc. [1; 2]. A complete automatic license plate recognition (ALPR) system comprises multiple blocks. First, the plate has to be detected and located. Then, characters can be segmented and recognized for the final or target application.  
Different computer vision algorithms and image processing techniques can be used to detect a license plate and recognize its contents [3; 4; 5; 6; 7; 8]. However, like other computer vision applications, a convolutional neural network can be used for highly accurate localization and recognition. Nonetheless, neural network-based classifiers are normally too computationally expensive, and can be difficult to realize on an embedded platform such as a mobile phone or tablet.  
There are two phases to the utilization of a neural network, (i) Training and (ii) Inference. The training and inference (utilization) phases of a neural network have different requirements. The training phase of a network is computationally and memory expensive due the large number of samples, parallel classifications, and multiple repetitions required for learning. State-of-the-art architectures therefore require a powerful GPU board or even GPU clusters to perform the training phase, which cannot be performed on a mobile platform. However, a neural network in the inference phase typically only classifies single images. By exploiting the computational power of an embedded GPU and simplifying the flow of a trained neural architecture developed for running on desktop and server environments, this can become feasible on a mobile platform. Trained parameters of a neural network can be imported to replicate the same architecture on a mobile platform. By replicating the same flow on a mobile device, a deep neural network can be used in real-time when there is no network connectivity.  
The remainder of the paper is organized as follows: Section 2 reviews the related work in the field of automatic license plate recognition (ALPR) systems using embedded platforms. The architecture of an ALPR system is presented in Section 3. Section 4 discusses the simplified flow of this ALPR system for the mobile platform. Section 5 presents the results, and finally, this work is concluded in Section 6.
###### Abstract  
When natural disasters occur, timely and accurate building damage assessment maps are vital for disaster management responders to organize their resources efficiently. Pairs of pre- and post-disaster remote sensing imagery have been recognized as invaluable data sources that provide useful information for building damage identification. Recently, deep learning-based semantic segmentation models have been widely and successfully applied to remote sensing imagery for building damage assessment tasks. In this study, a two-stage, dual-branch, UNet architecture, with shared weights between two branches, is proposed to address the inaccuracies in building footprint localization and per-building damage level classification. A newly introduced selective kernel module improves the performance of the model by enhancing the extracted features and applying adaptive receptive field variations. The xBD dataset is used to train, validate, and test the proposed model based on widely used evaluation metrics such as F1-score and Intersection over Union (IoU). Overall, the experiments and comparisons demonstrate the superior performance of the proposed model. In addition, the results are further confirmed by evaluating the geographical transferability of the proposed model on a completely unseen dataset from a new region (Bam city earthquake in 2003).  
a _[EMAIL_ADDRESS] Article  
1  
[PERSON], [PERSON]: [PERSON], [PERSON]: [PERSON], [PERSON]: [PERSON]: BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images. _Remote Sens._**2024**, 16, 182. [[https://doi.org/10.3390/s16010182](https://doi.org/10.3390/s16010182)]([https://doi.org/10.3390/s16010182](https://doi.org/10.3390/s16010182))  
10.3390/s16010182
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 4 Application to Seismic Records of Typhoon Periods
### Typhoon Morakot in 2009
This seismic event is recorded in the earthquake catalog of the CWB. The location of the M\({}_{\rm L}\) 3.1 earthquake is 22.88\({}^{\circ}\) N/121.29\({}^{\circ}\) E, and the epicentral distance to the SSLB station is 106 km. It is easy to confuse this record with a landslide case due to the waveform and frequency, since the attributes of this misidentified earthquake event are very close to those of a landslide case.