esa-sceva/satcom-chunk-collection huggingface dataset:
# Android-Based Audio Video Navigation System Forensics: A Case Study
###### Abstract
Furthermore, previous research does not analyse various data sources; hence, the available artefacts are limited. In this article, a digital forensic investigation is performed on four Android-based infotainment systems, also known as audio video navigation (AVN) systems [13]: two Android 4.2.2 Jellybean-based and two Android 4.4.2 KitKat-based. To collect forensic artefacts from the AVN systems, a logical acquisition method is applied to the Jellybean-based AVN systems and a physical acquisition method to the KitKat-based AVN systems. For the collected artefacts, Bluetooth data, navigation data, and system logs are identified and analysed individually. Next, we examine the differences in the storage structure and file location of major digital artefacts depending on the Android version and show the limitations of the individual data analyses. Finally, we construct a timeline of the driver's activities by integrating and analysing the various artefacts containing Bluetooth data, navigation data, and system logs. The main contributions of this work are as follow. * This work provides the methodology for Android-based infotainment system forensics. Depending on the version of the Android operating system, the method of extracting data from the storage device, logically or physically, is determined. * Data are obtained from various sources including Bluetooth, navigation and system logs. Many kinds of artefacts are collected and analysed. * By integrating the analysis results, a timeline of user activities is constructed. A timeline can help an investigator infer the user's behaviour or cause of the accident. * In particular, the data obtained from the system logs provide temporal information and vehicle events, useful to construct a timeline of user activity. The structure of this article is as follows. In Section 2, we analyse the recent trends and problems in digital vehicle forensics. We describe the characteristics of the four target infotainment systems and the forensic procedures in Section 3. In Section 4, we describe the forensic results. Section 5 discusses the analysis of the forensic results. We conclude our work in Section 6.
## 2 Experimental Section
### Sample Descriptions and Sampling Area  
Water and TWA sampling were carried out at the two municipal surface water sites for the city of Corner Brook, NL, Canada: Trout Pond (Lat: 48.92567\({}^{\circ}\)N; Long: \(-57.89312^{\circ}\)E) and Three Mile Pond (Lat: 48.93987\({}^{\circ}\)N; Long: \(-58.00831^{\circ}\)E). The sample sites selected, had been monitored for THMs over a number of years, where values varied between 110 and 160 ppb, and hence THM detection could be expected. Trout Pond samples were taken from left side of the dock extending in the south quadrant of the pond, where there were trees, shrubs, and short grass on the bank. The bottom topography was formed from sand, pebble, and cobble sized material. Three Mile Pond samples were taken from the north-northeast section of the pond in shallow water, where there was a high density of trees and short grass on bank. The bottom topography was silt with an intermediate density of decaying vegetation. All samples were collected at the same depth (1/3 m below water surface) in triplicate and were sampled over a two days period during the fall of the year. Samples were analyzed on site for conductivity, dissolved solids, pH, temperature, and within 12 h of collection for trihalomethanes (THMs). For spot sampling, triplicate real water samples were collected each day over the two days period in the two ponds during the fall of the year. For TWA sampling, triplicate SPME fibers were positioned and left attached on a stand for a two days period that was placed in the pond at the same depth as was used for spot sampling.
# Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types
## 4 Conclusions  
Remote sensing provides a means of monitoring rangeland ecosystems continuously across broad spatiotemporal scales, efficient targeting and resource allocation for management and conservation decisions, as well as the ability to monitor outcomes. Due to the fundamental challenge of mixed pixels, whereby at any spatial resolution pixel level estimates are aggregations of the sub-pixel dynamics occurring on the ground [17], available datasets and products are often too coarse to effectively account for rangeland heterogeneity. Understanding this heterogeneity across space and through time is critical for rangeland conservation and management actions, for example creating grazing plans and strategies, understanding results of herbicide treatments for invasive annuals, or predicting fuel conditions for an upcoming fire year. The methods presented in this paper provide a means of understanding the finer scale phenological and productivity dynamics of rangelands that are missed when using single, pixel level estimates. Key areas of future research are to translate these partitioned values of NDVI, GPP and NPP into metrics (e.g., above ground biomass, pounds of forage, animal unit months, and fuel load) that are more practical and meaningful for rangeland managers. Such dynamics can provide critical insight into the sustainable management and conservation of rangelands.  
Figure 4: Results of the NDVI disaggregation (**a1,b1,c1,d1**), GPP partitioning (**a2,b2,c2,d2**), and NPP partitioning (**a3,b3,c3,d3**) from four example sites across western US rangelands. The red line/bar represent the pixel results before NDVI disaggregation and GPP/NPP partitioning. For each example site, results are for a single pixel for the year 2017. The NLCD classification, pixel coordinates and the PFT breakdown are shown.  
**Author Contributions:** Conceptualization, N.R., B.A. and M.J.; methodology, N.R., B.A., A.M.; formal analysis, N.R., B.A. and A.M.; investigation, N.R., B.A. and A.M.; writing--original draft preparation, N.R., B.A. and A.M.; writing--review and editing, N.R., B.A., M.J., T.E., A.M. and D.N.; visualization, N.R. and A.M.; supervision, B.A.; project administration, B.A.; funding acquisition, B.A. and D.N.  
**Funding:** This work was made possible by the Natural Resources Conservation Service (NRCS) Working Lands for Wildlife in support of sage-grouse and prairie chicken conservation, the NRCS Conservation Effects Assessment Project, the Bureau of Land Management (BLM), and the Intermountain West Joint Venture. The contributions of [PERSON] were supported by the NASA Earth Observing System MODIS project (grant NNX08 AG87A).  
**Acknowledgments:** We thank the Google Earth Engine developers for their support and technical advice.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Study Region and Data Sources
### Sampling and Verification Data  
Survey of crop planting structure was carried out in Hetao Irrigation District during 28 to 30 August 2012 [11]. We obtained 41 sampling sites of maize and 53 sampling sites of sunflower using the Global Positioning System (GPS), which has a positioning accuracy of 2 m-5 m (Figure 1). In order to avoid the appearance of mixed pixels, the area of the sampling site is above 100 m \(\times\) 100 m. From the spatial distribution of the sampling points, sunflower is mainly distributed in Wuyuan County, while summer maize mainly in Hangjinhouqi and Linhe counties. To find out more about the growth situation of these two major crops, we also investigated the local phenology calendar (Table 1).  
Figure 3: Variations of major crop planting areas in the Hetao Irrigation District from 2009 to 2015.  
Figure 2: Land use map for the study area.  
In order to evaluate the accuracy of crop identification, we also carried out field investigations to obtain the verification points in 2014 and 2015. A total of 55 verification points were selected per year and the location of these points in two years were completely consistent (Figure 1). Because of the crop rotation policy in Hetao Irrigation District, the crops in the same location were different in different years. As a result, there were 19 sampling points of summer maize, 23 of sunflower, and 7 of other crops in 2014, while there were 15 of maize, 25 of sunflower, and 15 of other crops in 2015.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 2 SuDoRM-RF++
### Decoder  
The decoder is a transposed convolution network that shares the same stride and kernel size as the encoder. With the latent representation \(\widehat{\mathbf{v}}_{k}\) corresponding to the \(k\)th speaker, the decoder outputs the separated signal \(s^{k}\):  
\[\hat{s}_{k}=\text{Conv}1\text{D}^{T}(\widehat{\mathbf{v}}_{k}). \tag{4}\]  
It is important to note that only one trainable convolution network is used to turn the latent representation back into the time-domain signals. This means that all speakers use the same decoder.
# Cystic Fibrosis: Overview of the Current Development Trends and Innovative Therapeutic Strategies
## 4 Gene Therapy for CF
### Targeting CFTR
The crucial challenges in gene therapy for CF include the limited levels of gene transfer achieved within the airway epithelial cells and the persistent expression of the transgene [153]. In recent years, gene editing has been suggested as a potential treatment strategy for several genetic diseases, including CF. This therapeutic approach has a permanent effect and permits intervention at the chromosomal level of a specific gene, leading to the correction of all disease-associated mutations. CFTR function can be rescued by either editing the endogenous gene or inserting a wild type CFTR at a safe harbor locus such as _AAV51_. The three most prominent gene editing techniques that have been explored as potential CF treatment approaches are zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and Clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated systems (Cas). ZFN-based gene editing successfully corrected the F508 del mutation in CFTR29 cells; however, the editing efficiency was very low [154]. Using a similar approach, induced pluripotent stem cells (iPSC) from CF patients were corrected and differentiated into airway-like epithelial cells by the TALEN gene editing technique, resulting in restoration of their electrophysiological property when co-cultured with CFBE41o- cells [155]. However, since both techniques are laborious, an alternative approach, the CRISPR/Cas technique, has been proposed. This technique is simple and has a low synthesis cost [156; 157]. Furthermore, a variant of the CRISPR/Cas system that is capable of precise correction without a mutagenic double-strand break is now available [158; 159]. [PERSON] et al. (2020)demonstrated the ability of base editor systems to recover the genetic mutation and functions of CF intestinal organoids [160]. Consequently, the CRISPR/Cas gene editing system may serve as a potential tool for CF treatment in the near future. Figure 4 summarizes gene-based therapeutic approaches for targeting faulty CFTR.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
In the present study, multivariate statistics (PCA) were used in order to describe the effect of preservation conditions (packaging and temperature) on the quality of fresh chicken cuts during refrigerated storage in the range of 0-10 \({}^{\circ}\)C. In the present study, the variability of the obtained data was explained by two components: component 1 explaining 49.35% and component 2 explaining 19.35%. Figure 5a illustrates an inverse relationship between microbial load (i.e., TVC and _Pseudomonas_ spp.) and sensory scoring for overall impression, indicating that the higher the microbial load in chicken samples, the lower the sensory scoring that was reported by the panelists. Among the variables, temperature exhibited a higher correlation with component 2 (R\({}^{2}\) = 0.934). TVC and _Pseudomonas_ spp. load correlated strongly with component 1 (R\({}^{2}\) = 0.971 and 0.936, respectively), while for sensory scoring, the respective correlation with component 1 was described by the value R\({}^{2}\) = 0.945. Figure 5b illustrates the observations plot of the PCA analysis and the clusters formed as a function of temperature (0, 5 and 10\({}^{\circ}\)), indicating that storage temperature plays an important role in the quality of fresh chicken cuts as storage temperature forms, as also indicated by the relatively high E\({}_{\text{a}}\) values presented in Table 2.  
Figure 5: Principal component analysis output indicating the effect of packaging and storage conditions on the quality of fresh chicken cuts. (**a**) Variable plot: “Pack” refers to packaging type, “Temp” refers to storage temperature, “TVC” refers to total viable count load, “Pseudo” refers to _Pseudomonas_ spp. load, “pH” refers to pH value, and “sens” refers to sensory scoring (overall impression). (**b**) Observation plot: Black refers to the control, green refers to MAP, blue refers to EC-flav, red refers to EC-glu, and numbers 0, 5, and 10 refer to storage temperature, i.e., 0, 5, and 10 \({}^{\circ}\)C, respectively.
## 6 Experimental Results
As shown in Figure 22a, the power factor (PF) and THD\({}_{\text{i}}\) characteristics indicate that, under load conditions of 154 W or higher, the power factor (PF) remains above 0.99, and the THD\({}_{\text{i}}\) is below 10%. Specifically, under the rated-load condition (1 kW), a low THD\({}_{\text{i}}\) of 3.79% is observed. Furthermore, based on the efficiency characteristics graph shown in Figure 22b, it was observed that, from a load condition of 273 W onwards, the system achieves an efficiency of 167.9 V\({}_{\text{rms}}\) and load \(\text{P}_{\text{O}}\): 478 W.  
Figure 19: Experimental waveforms of **(a)** the PFC stage (V\({}_{\text{Link}}\), V\({}_{\text{AN}}\), I\({}_{\text{A}}\), I\({}_{\text{B1}}\)) and **(b)** the DC/DC stage (V\({}_{\text{P}}\), I\({}_{\text{P}}\), V\({}_{\text{S}}\), I\({}_{\text{S}}\)) obtained by control of the constant link voltage (V\({}_{\text{o}}\): 565 V\({}_{\text{DC}}\)) in conditions of wind generator voltage V\({}_{\text{LL}}\): 167.9 V\({}_{\text{rms}}\) and load \(\text{P}_{\text{O}}\): 478 W.  
Figure 12:0 Experimental waveforms of **(a)** the PFC stage (V\({}_{\text{Link}}\), V\({}_{\text{AN}}\), I\({}_{\text{A}}\), I\({}_{\text{B1}}\)) and **(b)** the DC/DC stage (V\({}_{\text{P}}\), I\({}_{\text{P}}\), V\({}_{\text{S}}\), I\({}_{\text{S}}\)) obtained by control of the constant link voltage (V\({}_{\text{o}}\): 565 V\({}_{\text{DC}}\)) in conditions of wind generator voltage V\({}_{\text{LL}}\): 249.3 V\({}_{\text{rms}}\) and load \(\text{P}_{\text{O}}\): 1000 W.  
efficiency of 90.54% within the link voltage control range (V\({}_{\text{Link}}\): 565 V\({}_{\text{DC}}\)). For higher load conditions, the system maintains an efficiency above 90%, reaching a maximum efficiency of 93.61% under the rated load (1 kW). Figure 12:1 Characteristics according to the load variation of 3-phase single-stage AC-DC converter for IPT-based small wind power generation system obtained by link voltage control (V\({}_{\text{Link}}\): 565 V\({}_{\text{DC}}\)): (**a**) power factor and THD\({}_{i}\). (**b**) efficiency, and (**c**) link voltage. As illustrated in Figure 22c, without wireless communication and by controlling only the link voltage (V\({}_{\text{Link}}\)), the output voltage (V\({}_{\text{o}}\)) under the light load (154 W) is 406.3 V, while under the rated load (1 kW), it is 398.2 V, showing a variation rate of 2.025%. Therefore, the experimental results demonstrate that stable power control can be achieved by controlling only the link voltage (V\({}_{\text{Link}}\)) rather than the output voltage via wireless communication for the IPT-based small wind power generation system.
## 4 Experiments
### Ablation Study  
#### 4.5.1 Ablation Experiment with DDM and GRU  
In a previous section, it was noted that some scholars have incorporated GRU modules into news recommendation models and achieved good results. Therefore, it is necessary to compare these two methods here. This article attempted to integrate GRU modules into the news recommendation model. The experimental results are shown in the following table 3.  
From the experimental results, it can be seen that, although adding the GRU module improves the performance compared to only using a multi-head attention mechanism module, this improvement is not significant. However, the performance improvement is quite significant when using the DDM module. We believe that integrating the GRU module does not effectively exploit the model's potential. It exhibits a relatively low effectiveness in extracting features from news headlines and fails to achieve a more ideal overall recommendation effect. Therefore, this article abandoned the approach of incorporating a GRU module.  
Once again, the superiority of the DDM structure over other structures is demonstrated in the data above.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AUC** & **MRR** & **nDCG@5** & **nDCG@10** \\ \hline LibFM & 59.74 & 26.33 & 27.95 & 34.29 \\ DeepFM & 59.89 & 26.21 & 27.74 & 34.06 \\ DKN & 61.75 & 27.05 & 28.90 & 35.38 \\ NPA & 63.21 & 29.11 & 31.70 & 37.81 \\ NAML & 65.50 & 30.39 & 33.08 & 39.31 \\ LSTUR & 64.38 & 29.46 & 31.89 & 38.17 \\ NRMS & 64.83 & 30.01 & 32.52 & 38.92 \\ FIM & 65.02 & 30.26 & 32.91 & 39.10 \\ KIM & 66.25 & 31.62 & 34.97 & 41.16 \\ \hline DDM & **66.65** & **32.05** & **35.32** & **41.58** \\ Improv. & 0.40 & 0.43 & 0.35 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of different methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ways**} & \multicolumn{4}{c}{**Evaluation Indicator**} \\ \cline{2-5}  & **AUC** & **MRR** & **nDCG@5** & **nDCG@10** \\ \hline Pure multi-head attention & 64.83 & 30.01 & 32.52 & 38.92 \\ With incorporation of the GRU module & 65.00 & 30.45 & 33.35 & 39.92 \\ DDM module & **66.65** & **32.05** & **35.32** & **41.58** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment with DDM and GRU.
# A Review of Low-Frequency Active Vibration Control of Seat Suspension Systems
## 2 Seating Systems with Active Suspension  
### Experiments with Prototypes  
Active seat suspension prototypes in several research projects have used electromagnetic, hydraulic, or air actuators to generate a corresponding compensation force for vibration cancellation in a finite number of frequencies, thereby reducing the vibration acceleration amplitude and improving the comfort of the seating system. According to [48], using an active vibration control suspension in a vehicle has better vibration cancellation performance than passive seat suspension. In [49], comparative experiments demonstrated that active and semiactive seat suspensions could improve comfort by approximately 50% compared with passive seat suspensions.  
The actuators used for active seat suspensions generally fall into three categories: electromagnetic actuators using linear or conventional rotating motors, hydraulic actuators using hydraulic servos, and air actuators using air springs. Among these three types of actuators, electromagnetic actuators have attracted the most attention because they have good dynamic responses; do not require an additional hydraulic servo system, tubes, or compressors; and have small space requirements. The large output of hydraulic actuators makes them easy to carry with greater mass. The air actuator has a good vibration isolation effect in high frequencies.
## 2 Materials and Methods
### Temperature Measurements  
Disposable K-type thermocouples (RS components) were adopted to measure the temperature variation. Thermocouples were placed into a standardized hole created 1 mm below the cavity floor (Figure 1a). A filtered photocell, obtained from Demetron LED radiometer (Kerr Corporation), was employed to monitor the light power level. The light curing unit EMS Swiss Master Light at an intensity level of 1000 mW/cm\({}^{2}\) and exposure time of 20 s was employed to cure bulk-fill composites. In the case of MOD restoration, the rationale for choosing an exposure time of 20 s relies on the distance between the light guide of the curing unit and the composite (Figure 1a). In fact, dental cusps prevent the positioning of the light guide of the curing unit close to the composite to be cured. Thus, prolonged exposition time has been considered to compensate for the light intensity attenuation due to the distance between the light guide and the composite. The rationale for choosing a halogen-based curing unit relies on the initiator systems characterizing the bulk-fill composites that have been investigated. In fact, some of these composites (see Table 1) contain both camphorquinone (CQ, absorption peak around 460 nm) and ivocerin (IV, absorption peak around 400 nm). Compared to blue LED (Light Emitting Diode) curing units having a very narrow emission spectrum centred around 460 nm, the halogen curing unit has a broad emission spectrum covering the wavelengths between 400 nm and 480 nm [27]. Thus, this curing unit is more appropriate for curing all the bulk-fill composites that have been investigated. To simulate pulp temperature levels, a Thermoblock system (Falc, Genova, Italy) [23] set at 37 \({}^{\circ}\)C was used to keep each sample heated during the test. Before each test, the ThorLab energy meter console PM100D (ThorLabs, Newton, NJ, USA), equipped with a S121C sensor (ThorLabs) and connected to the PMD100D software running under LabView (National Instruments), was employed to measure the power output of the light curing unit.  
Temperature and light intensity data were simultaneously acquired at a speed of 50 p/s up to 300 s using the National Instruments DAC (National Instruments) driven by the Signal Express software (National Instruments).  
Ten specimens for each bulk-fill composite and for each restorative technique were used. Peak temperature data were analysed using ANOVA, followed by [PERSON]'s test at a critical value of 0.05.
# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 4 Results and Discussion
### Evaluation Measure
#### 4.1.4 Kappa-Cohen Index
ResNet-50 predicted one wrong cases for the canker class with one wrong prediction in dot, and two-hundred and seven were correctly predicted. All dot class predictions (205) were correctly predicted in the dot class. The mummification class in this model's predictions had 221 correct predictions in dot. It has one wrong and two wrong predictions in the canker class. ResNet-101 also had higher accuracy results as compared to those of all other models. According to its confusion matrix, it made one wrong prediction for the canker class into the rust class. As compared to ResNet-50, it had one wrong case prediction, but in a different class. Regarding the dot class, ResNet-50 made no wrong predictions, and ResNet-101 made four wrong predictions. In the third case of the mummification class, there were four wrong predictions, and ResNet-50 made three wrong predictions. There were three wrong predictions in the last class, rust. There were 186 correct predictions for ResNet-101. The above analysis of the five models shows that ResNet-50 had an overall high rate of correct predictions; for wrong predictions in the dot class data, it was highly difficult for each model, as it was predicted as the wrong class in most cases. The other classes also misled the models, where the most challenging and least robust class was dot. Therefore, the dot class may need more confident and robust approaches to classify it from other classes. The mummification class had no wrong predictions in ResNet-50 and -101, where it only had a higher rate of wrong predictions in the cases of SqueezeNet with four, where the two remaining models also did not make very many accurate predictions for this class. The healthy class overall in all models remained accurate with no wrong prediction by any model. It made the normal class easily distinguishable by any model. However, the challenge was differentiating the guava disease categories.