esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 3 IEEE 802.11 ah Overview
### Traffic Indication Map (TIM) Segmentation
Apart from the bitmap indicating the stations that have pending DL data buffered at the AP, TIM beacons also announce the TBTT of the next DTIM beacon. Based on the above timings, time intervals between sequential TIM beacons can be in the range 1.024 ms to 67.10784 s and between DTIM beacons in the range of 1.024 ms to 35.790848 min (for a single page). The TIM beacon for the first TIM group is broadcast together with the DTIM beacon. The term _paged_ in this paper refers to the case that there is at least one data packet buffered at the AP. A station is paged (by TIM) if an AP has pending DL traffic for that station, and, in this case, the TIM group to which the STA belongs has to be paged (by DTIM) as well. For example, if the DTIM pages TIM groups \(g_{x}\) and \(g_{y}\), then all stations assigned to the TIM groups \(g_{x}\) and \(g_{y}\) will have to wake up at their corresponding TSBTT to receive TIM beacons of their groups. All other stations sleep until the next DTIM beacon, unless they have data to transmit. After receiving TIM beacons of their groups, stations assigned to TIM groups \(g_{x}\) and \(g_{y}\) will check in the TIM whether they are paged or not. The ones that are paged will contend for the medium, and the rest will go back to sleep until the next DTIM beacon scheduled at TBTT as illustrated in example in Figure 2.  
TIM segmentation and its possibilities for energy conservation and latency management have been poorly investigated so far. [PERSON] and [PERSON] ([PERSON] and [PERSON], 2012) proposed a method that (selectively and dynamically) temporarily changes the TIM group membership of nodes and rearranges their traffic in order to reduce the number of unnecessary wake-ups of stations that are not paged in their TIM, but must wake up for receiving the TIM beacon because their TIM group was paged by DTIM. Their method aims to group all paged stations together in order to reduce energy consumption for stations with no DL data in that DTIM interval, without causing additional delays to data delivery. However, their method does not improve DL latency. [PERSON] et al. (2017) proposed a delay and energy aware RAW formation scheme where they used TIM segmentation. However, the scheme is only able to broadcast the RAW Parameter Set (RPS) that defines the RAW configuration along with TIM beacon in a single DTIM. They deviated from the TIM specification of the IEEE 802.11 ah standard by using short beacon intervals of variable length (short beacon intervals are announced in DTIM beacons and cannot change between two consecutive DTIM beacons, i.e., in TIM beacons). Furthermore, they only consider UL latency where TIM does not play any significant role, whereas delays in bidirectional communication in IEEE 802.11 ah (namely, RTT optimization) are a completely different problem. So far, only two research papers show results in relation with TIM segmentation. However, TIM segmentation has much more potential to be exploited, requiring additional research.
# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
### Trimap-Based Methods
#### 4.1.2 One-Stream Structure with Additional Modules  
Although the one-stream structure is simple and has relatively low computational complexity, it often lacks sufficient auxiliary information, leading to poor performance in some complex scenarios. To overcome this limitation, some methods have proposed additional modules to augment the model with more contextual and structural information.  
[PERSON] et al. [24] observed that previous methods often resulted in an unclear structure or texture of semi-transparent regions due to the local blurriness of transparent objects. To address this issue, they developed an encoder-decoder network with an additional module named guided contextual attention module. Specifically, their approach involves guiding alpha information from the image context to unknown pixels and using low-level feature information to propagate alpha features. Additionally, they proposed a guided contextual attention module that can distribute opacity information globally by relying on the learned low-level affinity information, leading to an improved performance in semi-transparent regions. These advancements offer a promising approach for improving the matting quality of transparent objects.  
[PERSON] et al. [16] discovered that index-guided decompression performs better than other up-sampling operators in restoring edge details during the decoding process of neural networks. The authors suggested a unification of current up-sampling operators with index functions, which can be treated as a function of the feature map that the network model can adaptively learn from the data. Specifically, they designed a one-stream model with a custom additional module, called IndexNet, which can dynamically predict and learn indexes based on the given inputs. The learned index function can then guide the down-sampling and up-sampling processes in the network to achieve better performance without requiring additional supervision.  
[PERSON] et al. [25] proposed that previous methods had not adequately considered the potential impact of semantic information of the foreground on the matting effect. Therefore, the authors proposed expanding the traditional trimap to a semantic trimap, which includes semantic information. They introduced a patch-based classifier to extract semantic information from the input image and merge it with the original trimap to create the semantic trimap. The semantic trimap and input image are fed into the u-structure for learning. Additionally, a multi-class discriminator and gradient constraint with weights sensitive to the content were introduced. The utilization of a multi-class discriminator regulates alpha mattes at a semantic level, while the inclusion of a gradient constraint with content-sensitive weights balances the various regularization losses. The utilization of a semantic trimap enhances the prior knowledge of the model and reduces the search space of the model when reasoning foreground objects.
## 1 Introduction
[21] reported on an IGZO TFT with a solution-processed Al\({}_{2}\)O\({}_{3}\) gate dielectric, and the threshold voltage shift of the IGZO TFT was +3.66 V under a gate bias of +20 V for 3600 s. Some methods, such as increasing annealing temperature or using a UV/ozone treatment, have been used to solve this problem [22; 23; 24]. [PERSON] et al. [23] reported an In\({}_{2}\)O\({}_{3}\) thin-film transistor using a solution-processed ZrO\({}_{2}\) dielectric. The ZrO\({}_{2}\) film, with a leakage current density of 10\({}^{-9}\) A/cm\({}^{2}\) at 2 MV/cm, was processed by a UV/ozone treatment and annealed at 500 \({}^{\circ}\)C. The threshold voltage shift was 0.22 V for In\({}_{2}\)O\({}_{3}\) TFTs under PBS with a V\({}_{\rm GS}\) value of 1.5 V for 7200 s. Solution-processed amorphous indium-zinc-tin oxide (a-IZTO) thin-film transistors (TFTs) with spin-coated zirconium oxide (ZrO\({}_{\rm x}\)) as the gate insulator were presented by [PERSON] et al. [18]. The ZrO\({}_{\rm x}\) gate insulator was processed without and with UV/O\({}_{3}\) treatment. The threshold voltage shift was 1.13 V for the untreated ZrO\({}_{\rm x}\)-based TFT and was 0.64 V for the UV/O\({}_{3}\)-treated ZrO\({}_{\rm x}\)-based TFTs. [PERSON] et al. [24] reported on a solution-processed zinc-tin-oxide (ZTO)/ZrO\({}_{2}\) TFT with a 90-nm-thick ZrO2 dielectric annealed at 500 \({}^{\circ}\)C for 1 h. The TFT exhibited very small hysteresis windows in both dark and illuminated conditions, and the shift in V\({}_{\rm th}\) was 0.4 V of negative bias-stress under illumination over 5000 s.  
In this paper, solution-processed ZrO\({}_{2}\) dielectric films were demonstrated at a deposition temperature of 400 \({}^{\circ}\)C. The electrical properties of ZrO\({}_{2}\) films were characterized by using metal-insulator-metal (MIM) structures, and the physical properties were measured by X-ray diffraction (XRD), X-ray reflectivity (XRR) and atomic force microscopy (AFM). IGZO-TFTs with solution-processed ZrO\({}_{2}\) dielectrics were fabricated on glass substrates. By decreasing the precursor concentration and increasing the spin-coating times, an optimal thickness of ZrO\({}_{2}\) film was achieved, which showed a smooth surface and reduced internal defects. Consequently, the resulting TFT devices had not only good electrical properties but also improved bias stability. When under positive bias and negative bias stress over 3600 s, the offset of threshold voltage was less than 0.6 V. Compared with the previous reports of other groups, our work proposes a simple and feasible way to reduce the defect state of the insulating thin films without a special thermal annealing process such as UVtreatment. Therefore, it will help improve the bias stability of TFTs and promote the development of solution-method TFTs in practical applications.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 5 V2V Aids toward Sustainable Transportation Systems
### V2V Edge Computing Contributions in ITSs  
There are many benefits to V2V edge computing; therefore, there is an evolution in how cars communicate together in smart ITSs [74]. Taking advantage of edge computing, vehicles can analyze and determine data locally, thus reducing the time lag and enabling real-time decision-making without the central server as the standard. Specifically, this characteristic allows for quick reaction times and reliability, which is important in cases like collision avoidance and traffic management, which are crucial for safety. Also, edge computing allows the processing of the data coming from different sensors of the vehicles, from the cameras to Light Detection and Ranging (LiDARs), without using too much Internet capacity. This has the advantage of increasing the expansiveness of V2V networks while at the same time preserving privacy by reducing the necessity to send data by beating roundson extra links [68]. Furthermore, edge computing creates the possibility of performing the AI algorithms directly on vehicles' boards, which eventually will enable functions such as predictive maintenance and personalized services [75]. In conclusion, V2V edge computing offers the prospect of safer, more effective, and smarter transportation systems, paving the way for ITSs, like smart cities, to evolve into an era of interconnectedness and sustainability.
## 5 Results and Discussion
### Emotion Analysis  
We applied the emotion analysis using an NLP tool that calculates the number of times anger, anticipation, disgust, fear, joy, sadness, surprise, and trust emotions are identified in the text. The pie charts shown in Figure 8 summarize the frequency of detecting each emotion in the students' feedback of each lesson. We can conclude that the dominant feelings in all the lessons are trust and anticipation, which are positive feelings that reflect the students' positive feedback about the lessons. Table 4 shows an overview of the number of students who expressed each specific emotion. We can conclude that most of the students experienced and showed the same emotions in their feedback.  
Figure 7: Sentiment analysis for each lesson.
# Carrier Dynamics in InGaN/GaN on the Basis of Different In Concentrations
## 3 Results and Discussions
### TR-FWM Results  
Measurements were carried out at room temperature with various excitation energy densities L. Figure 5 shows the decay of light-induced transient grating (LITG) recorded in the samples. The decay kinetics were nearly exponential and occurred rapidly for small grating periods, in accordance with eq (1). The \(\tau_{\text{R}}\) and D values were determined by plotting the dependence of grating decay rate 1/\(\tau_{\text{G}}\) versus inverse grating period \(\Delta\)N\({}^{-2}\). Similarly, we measured the grating decay rates in InGaN layers grown on different In concentrations. Table 2 presents the determined parameters. The data indicated that lifetime decreased with excitation energy densities. CSI27 and PIN27 showed the longest carrier lifetimes and typical D values for InGaN. Complementary measurements of PL intensity at 325 nm excitation (Figure 4) demonstrated the increased radiative recombination component due to decreased nonradiative recombination rate in InGaN layers. The presence of a high density of defects in the used template may also influence the quality of InGaN layers. The extremely short carrier lifetime of about 150 ps in PIN27 and its approximately 10-fold increase with excitation reflected the high defect density and partial saturation of electron trapping centers by the injected carriers. In CSI27, four power measurement results were provided, thereby showing better quality than the other samples.  
Figure 4: Temperature-dependent photoluminescence (PL) spectra of samples (**a**) SI27, (**b**) CSI27, (**c**) PIN27, and (**d**) PIN15.
## 2 Materials and Methods
### Measures and Instruments  
Personal data were collected in the baseline assessment. Outcome measures were evaluated at four time points: (a) prior to the intervention (1 st baseline assessment--E1), (b) at the 4 th week of the intervention program--E2, (c) at the 8 th week of the intervention program--E3, and (d) at the end of the exercise program at 12 weeks--E4. The outcome measures included:  
1. Balance  
The timed-up-and-go (TUG) test examines mobility and balance in elderly people. The time required for the participant to stand up from the chair, walk a distance of three meters, turn, walk back to the chair, and sit in it is recorded. For the execution, the physiotherapist uses a chair (with back and arms), a tape measure, a tape, and a watch or stopwatch. From the chair, we measure and mark a distance of three meters. As soon as the patient is seated, the timing stops. The time required to perform the test is approximately 10-35 s. The test has good reliability for Parkinson's disease [24].  
2. Functional ability  
The five times sit-to-stand test (FTSST) assesses the functional ability of the lower limbs. It is simple and short and designed for elderly people. The test begins with the participant sitting with their back on the chair, feet shoulder-width apart and arms crossed at chest level. The participant stands up using only the lower limbs and returns to the sitting position without touching the back of the chair 5 times as fast as possible. The test has good reliability in patients with Parkinson's Disease [25].  
3. Quality of life  
The [PERSON]'s disease questionnaire (PDQ-39) consists of 39 questions covering eight factors of patient's health. The score ranges from 0-4. A higher score indicates a worse quality of life. It has good internal consistency (Cronbach \(\alpha\) = 0.71-0.94) [26; 27].  
4. Depression  
The hospital anxiety and depression scale (HADS) consists of 14 questions, examining anxiety and depression, 7 questions each, through a 4-point Likert-type scale from 0 (not at all) to 3 (happens to me all the time/often). The total score ranges from 0 to 21 for each factor. Higher scores also indicate increased levels of anxiety-depression. The classification for each factor separately is: 8-10 (mild), 11-14 (moderate), and 15-21 (severe). A score below 8 indicates no clinical findings of anxiety-depression. The scale shows high internal consistency (Cronbach \(\alpha\) = 0.884; anxiety factor 0.829, depression factor 0.840). Also, it has high test-retest reliability (ICC = 0.944) [28].
## 5 Conclusions  
To address traffic bottlenecks caused by emergency events on roadways, this study proposes an MVSPC strategy. This approach facilitates a rapid response to disruptions on long-span bridges and extended road sections, efficiently mitigating the negative impacts of moving bottlenecks. An extended macroscopic traffic flow model is introduced based on the classic MetaNet model. Using model predictive control with this enhanced model, the control variables--subsegment speed and density--are optimized to alleviate congestion and prevent secondary accidents. Under different traffic flow levels, especially in high-demand traffic environments, the MVSPC strategy achieved improvements of 62.44%, 95.32%, and (26.4%, 14.11%, 26.53%, and 32.90%) in efficiency, safety, and emissions, respectively.  
The proposed strategy has some limitations, including model constraints, such as assumptions made for simplified traffic dynamics, and potential challenges in real-world implementation, particularly in high-density traffic and complex road networks.  
Conceptualization, J.Y. and P.W.; methodology, J.Y. and P.W.; case study, J.Y. and P.W.; validation, J.Y.; writing--original draft preparation, J.Y.; writing--review and editing, J.Y. and P.W.; supervision, P.W. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Road speed variations under bottleneck conditions.  
Figure 7: Road speed variations under natural conditions.  
Figure 9: Road speed variations under traffic control conditions.  
**Funding:** National Natural Science Foundation of China (52372321); Scientific Research Project of Shaanxi Provincial Department of Transportation (no. 21-42X).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.
# Improvement of Arsenic Phytoextraction Using Indigenous Bacteria and Mobilizing Agents
## 2 Materials and Methods
### Phytotoxicity Tests  
Phytotoxicity tests based on the inhibition of germination and root extension of _Lepidium sativum_ L. [51] were performed. A quantity of 10 g of As-contaminated soil was saturated with deionized water and placed in a Petri dish; a Whatman #1 filter paper with 10 seeds of _Lepidium sativum_ L. was then placed on it. The plates (five replicates performed simultaneously) were placed in a climatic chamber for 72 h in the dark, at a temperature of 25 \(\pm\) 1 \({}^{\circ}\)C. At the end of the test, the germinated seeds were counted, and root elongation was measured. The germination index (_GI_%) and root elongation inhibition (_IhI_%) were estimated based on the relationships expressed in Equations (1) and (2), where \(G_{s}\) and \(G_{c}\) are, respectively, the average numbers of germinated seeds in the contaminated soil samples and the negative control, while \(L_{s}\) and \(L_{c}\) are the mean root lengths (mm) for the contaminated soil samples and the negative control, respectively.  
\[G1\%=\frac{G_{s}\times L_{s}}{G_{c}\times L_{c}}\times 100 \tag{1}\]  
\[In\%=\frac{L_{c}-L_{s}}{L_{c}}\times 100 \tag{2}\]  
High values of \(G1\%\) indicate reduced phytotoxicity of the contaminated soil, while high values of \(In\%\) are associated with worse soil quality. These indices are useful as part of a preliminary investigation to evaluate the toxicity of soils.
## 4 Experimental Evaluation and Analysis  
### Evaluation Datasets and Mertics  
Our prediction model VTP-GCN was trained and tested on two public vehicle trajectory datasets: I-80 and US-101 in NGSIM [26] which are the most widely used benchmarks for the task of vehicle trajectory prediction. The datasets both contain 45 min vehicle states information (coordinates, velocity, acceleration, etc.) in various traffic scenes with a sampling frequency of 10 Hz, and the rich traffic scenes (mild, moderate, and heavy) are suitable for the training and evaluation of the proposed model. The vehicle trajectory study areas of I-80 and US-101 are shown in Figure 5.  
For the fairness and effectiveness of the comparison experiments, we followed the data processing method available in the literature [22]: the raw data were downsampled to 5 Hz, and then the trajectories were divided into segments of 8 s each, the first 3 s of which were taken as the historical time horizon to model the interaction among vehicles and the last 5 s were taken as the prediction time horizon to calculate the prediction errors between the predicted trajectories and ground truth data. For the purpose of this paper, the division ratio of training set, verification set, and test set was 6:2:2.  
In order to compare the prediction accuracy with the previous excellent vehicle trajectory models, we chose the root mean square error (RMSE) between the prediction trajectory  
Figure 5: Bird view of naturalistic traffic recorded within I-80 (**a**) and US-101 (**b**) freeway study area.  
and the ground truth data as evaluation metrics. The smaller the RMSE, the better the prediction accuracy. At the time \(t_{h}+t^{\prime}\), the calculation formula of RMSE is:  
\[RMSE_{t_{h}+t^{\prime}}=\sqrt{\frac{1}{N}\sum_{n=i}^{N}\left[\left(\hat{x}_{t_{ h}+t^{\prime}}^{n}-x_{t_{h}+t^{\prime}}^{n}\right)^{2}+\left(\hat{y}_{t_{h}+t^{ \prime}}^{n}-y_{t_{h}+t^{\prime}}^{n}\right)^{2}\right]} \tag{19}\]  
where \(x_{t_{h}+t^{\prime}}^{n}\) and \(y_{t_{h}+t^{\prime}}^{n}\) are the true position coordinates of a vehicle \(n\) at the time \(t_{h}+t^{\prime}\). Since the output of the VTP-GCN is the trajectory distributions, we report the smallest RMSE based on 20 random samplings.
## 2 Experimental Setup
### Model to Extract the Elastic Modulus  
In order to extract the elastic modulus of the concrete in contact with the chain, we modelled numerically the dynamic interaction between a chain of particles identical to the one embedded in the transducer and an elastic material that mimics the concrete. The partial differential equation of the motion associated with the propagation of an HNSW in the chain can be determined using Lagrangian description of particle dynamics:  
\[m\partial_{tt}^{2}\mu_{n}=A[\mu_{n-1}-\mu_{n}]_{+}^{3/2}-A[\mu_{n}-\mu_{n+1}]_{ +}^{3/2}. \tag{1}\]  
In Equation (1), \(u_{i}\) is the \(i\)th particle displacement, \([x]_{+}\) denotes \(max(x,\,0)\), and \(A\) the stiffness constant present in the Hertz's law. By solving this equation, the time history of the \(n\)th particle's oscillation is obtained and then the displacements of the particles can be used to compute the approach \(\delta=u_{i+1}-u_{i}\) and then to apply in the Hertzian relationship. More details about the models are available at [38; 41; 42].  
Figure 5 shows the time-of-flight (TOF) of HNSWs as a function of the Young's modulus for a Poisson's ratio equal to 0.20, which is a typical value for concrete. The graph shows that the Young's modulus affects significantly the wave feature when \(E<100\) GPa; moreover, when the modulus of elasticity of the test sample is higher than 25 GPa, small differences in the measurement of the TOF, let say 3%, yields about 60% change in the estimated modulus. More details about the procedure to generate Figure 5 are available at [43].  
In this study, we used Figure 5 to estimate the modulus of the samples by intersecting the experimental TOF to the numerical curve. For illustrative purposes, the figure shows the modulus corresponding to two experimental data, namely 0.5881 ms and 0.5692 ms.  
The dynamic modulus of elasticity \(E_{d}\) was then converted into the static modulus of elasticity \(E_{s}\) using the empirical formula proposed by [PERSON] and [PERSON] [44; 45]:  
\[E_{s}=0.83E_{d}. \tag{2}\]