esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Flexible Traceability System of _Agaricus bisporus_ Supply Chain
## 2 Analysis of Traditional Traceability Models  
### Traditional Blockchain-Based Traceability Model for Agricultural Products  
The combination of blockchain and traditional agricultural product traceability models can lead to a more comprehensive and efficient agricultural product traceability system. The decentralized and tamper-proof characteristics of blockchain technology can effectively address these issues [14]. Blockchain technology can be employed to record and store information at various stages of agricultural product cultivation, storage, processing, transportation, and sale. Through smart contracts, it facilitates information sharing and verification, enabling all participants to trace the origin and production process of agricultural products. This, in turn, enhances the security and credibility of agricultural products [15]. The blockchain-based agricultural product traceability model is illustrated in Figure 1.
## 3 Methodology
### Biometrics Mapping Network Based on DNN Architecture
#### 3.2.2 Binary Code Mapping Network  
To effectively learn the mapping between face image and random binary code, we design a robust binary mapping network. In fact, the mapping network is to learn unique binary code, which follows a uniform distribution. In other words, each bit of this binary code has a 50% chance of being 0 or 1. Since the extracted feature vector can represent the uniqueness of each face image, our proposed method only requires a nonlinear project matrix to map the feature vector into the binary code. Assuming that the extracted feature vector can be defined as \(V\) and the nonlinear project matrix can be defined as \(M\), the mapped binary code \(K\) can thus be denoted as:  
\[K=M^{\mathrm{T}}V \tag{1}\]  
Therefore, we can combine a sequence of fully connected (FC) layers with a nonlinear activate function to establish nonlinear mapping, such as Equation (1). The mapping network contains three FC layers (namely FC_1 with 512 dimensions, FC_2 with 2048 dimensions, FC_3 with 512 dimensions) and one tanh layer. For different bio-key lengths, we slightly modify the dimension of the FC_3 layer. Moreover, a dropout strategy [59] is applied to these FC layers with a 0.35 probability to avoid overfitting. The tanh layer is used as the last activation function for generating approximately uniform binary code. This is because the tanh layer is differentiable in the backpropagation learning and close to the signum function.  
It is noted that each element of the mapped real-value \(Y\) through the network may be close to 0 or 1 where \(Y\in\mathbb{R}^{l}\). In this case, we adopt binary quantization to generate binary code from \(Y\). To obtain the uniform distribution of the binary code, we set a dynamic  
Figure 4: The architecture of feature extraction network.  
threshold \(\overline{Y}=\frac{1}{l}\sum_{i=1}^{l}Y_{i}\) where \(Y_{i}\) denotes \(i\)-th element of \(Y\), and \(l\) represents the length of \(Y\). Therefore, the final mapping element \(K_{r}\) of binary code \(K\) can be defined as:  
\[K=[K_{1},\ldots,K_{r}\ldots,K_{l}]=[q(Y_{1}),\ldots,q(Y_{r})\ldots,q(Y_{l})] \tag{2}\]  
Here, quantization function \(q(Y_{r})\) is defined as:  
\[q(Y_{r})=\left\{\begin{array}{ll}1,&if\ Y_{r}\geq\overline{Y}\\ 0,&\text{otherwise}\end{array}\right.\text{where }l\geq r\geq 1 \tag{3}\]
# Variation in Crop Yield Depending on the Tractor Tire Contact Area and Clay Loam Soil Moisture
## 4 Discussion  
When a tractor pulls an implement, maintaining traction force, it induces slippage. To achieve a high net traction coefficient, the optimal volumetric soil moisture content is 15-20%. Beyond this range, traction efficiency decreases. Tractors achieve maximum draft at 30-40% slippage. From an energy efficiency perspective, an 8% slip is considered the best. If the slip is lower, more fuel is wasted due to the tractor's excess weight for countering the slip; if it is higher, the soil is adversely affected [26; 27].  
The front and rear driving wheels displace the topsoil layer depending on the slippage value. According to [PERSON] and [PERSON] [28], a 27% slip can move the soil layer by 35 cm. The soil compaction is greater when the tractor slips than when simply passing through the soil without a draft. The authors also found that as slip increases from 1% to 27%, normal soil pressure rises from 90.6 kPa to 104.4 kPa. Additionally, the maximum shear pressure increases from 19.7 kPa to 42.6 kPa for the front wheels and from 6.0 kPa to 61.6 kPa for the rear wheels at a tire pressure of 160 kPa [28; 29].  
Swedish researchers suggest that good crop emergence requires soil moisture under the seeds to be at least 5% (w/w) higher than the moisture level at which plants wilt. Hard and dense soil beneath the seedbed layer supports capillary rise of water to the seed and results in good seed germination in the absence of rainfall. However, not all seeds end up in the hard and moist soil layer during sowing [18].  
Presumably, shallow soil tillage during the six years of our study produced organic matter on the topsoil, which, contrary to expectations at the beginning of the study, reduced the effect of compaction by slippage. However, at the end of the research, the weeds spread significantly, and soil moisture was lower than in moldboard-ploughed soil. These findings confirm that increasing tire contact area by reducing air pressure and using dual wheels  
Figure 14: Yield of spring wheat dependence on tire contact area.  
effectively minimizes soil compaction impact, resulting in significantly improved spring cereal yields, particularly under wetter soil conditions.  
For future research: contrary to expectations, shallow tillage over a period of 6 years resulted in more surface organic matter and increasingly reduced the impact to the soil of the tractor driving wheels. Shallow tillage, or perhaps the summer-only crop rotation, is thought to be responsible for the higher weed spread, particularly the spread of long-lasting thistles (Cirsium arvene). Contrary to expectations, in the wet weather conditions, the soil moisture was lower in the treatment not ploughed soil than in the moldboard ploughed soil in the neighboring field, presumably due to the formation of water runoff channels.
## 4 Simulation Results
### Peak Communication Rate When the Bandwidth of TDOM Is Nearly 100 KHz  
In this subsection, we will discuss the peak communication rate that the TDOM can reach when it occupies a bandwidth close to that of the VDES, i.e., 100 KHz. When the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(f\) & **Carrier Form** & \(\|R^{-\mathbf{I}}\|\) \\ \hline
1 & sine & 3.1839 \\
20 & cosine & 0.0019 \\
30 & cosine & 0.0018 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Norm of the matrix \(\R^{-1}\) for sine–carrier and cosine–carrier TDOM systems. The total number of carriers within one symbol is 128.  
Figure 8: Communication performance of cosine TDOM system under the maritime multipath channel. The total number of carriers within one symbol is 128.  
Figure 7: Communication performance of TDOM system under the maritime multipath channel. (**a**) \(f=1\), (**b**) \(f=10\), (**c**) \(f=20\), (**d**) \(f=30\).  
bandwidth is increased from 19.2 KHz to nearly 100 KHz, the duration of the TDOM symbol must be decreased. In this subsection, we increase the symbol rate of the TDOM to 24,000 symbols/s. As a result, the bandwidth of the TDOM symbol is 96 KHz, as shown in Figure 9a.  
The system performance under the maritime multipath channel is shown in Figure 9b. The total number of carriers is 128, and the frequency of the carrier \(f\) is 30. We can see that the TDOM system with a bandwidth of 96 KHz can achieve a performance which is similar that of to the TDOM system with a bandwidth of 19.2 KHz. The communication rate of the TDOM system now increases to  
\[2(\mathrm{bit/carrier})\times 128(\mathrm{carrier/symbol})\times 24000( \mathrm{symbol/s})=6.144(\mathrm{Mbit/s}). \tag{32}\]  
Therefore, the communication rate of the TDOM system with a 96 KHz bandwidth can reach 20 times that of the VDES, as shown in Equation (27).  
\[\frac{Rate_{TDOM}}{Rate_{VDES}}=\frac{6.144(\mathrm{Mbps})}{307.2(\mathrm{Kbps })}=20. \tag{33}\]
## 4 Results and Analysis
### Average Delay (sec)  
Figure 8 shows the delay in the results of the AODV and the proposed technique by means of the time taken by the nodes to deliver the packets to the targeted node on time. The Results shows that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ad Hoc On-Demand** & \multirow{2}{*}{**Local Intrusion**} & \multicolumn{2}{c}{**Hybridization of Particle**} \\
**Distance Vector** & & **Decection (LID)-ADOV** & **Swarm Optimization-Genetic** & **Proposed** \\
**(AODV)** & & **Algorithm (HPSO-GA)** & \\ \hline
59.23 & 66.398 & 69.26 & 72.122 \\
60.18 & 66.98 & 70.21 & 73.44 \\
62.52 & 67.81 & 71.86 & 75.91 \\
63.18 & 68.65 & 72.63 & 76.61 \\
63.98 & 69.35 & 73.34 & 77.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The throughput evaluation values.  
Figure 6: The throughput (kbps).  
the AODV delay of the network is high because the malicious node drops the data packets during the transmission. In our proposed technique, the delay is slightly higher at meager points, as the number of nodes is increasing because the IDS nodes are continuously sending packets to normal nodes for the status. Moreover, the link failure is obvious in MANETs, meaning that the nodes re-transmit the data packets to the destination node again, so the time taken for this may be the reason for the delay. After the deployment of the IDS nodes, however, there are few computations to predict the given answers from the query point. Table 8 shows the average delay with varying number of nodes. It can be seen that at meager points in particular, the proposed DDBG performs much better than other methods.
## 4 Case Study
### Data  
We use historical wind power data from the PJM market to generate wind scenarios, using common scenario-generating methods [20]. Figure 3 shows the wind power curve, which is obtained by averaging the wind power data over various scenarios for a sample day. This averaging process helps to present the trend of the wind power during that day. The maximum ratio of the qualified wind power capacity for regulation to the rated capacity (\(\kappa\)) is 30%, indicating the upper limit of the wind power available for regulation purposes within the given conditions. The anticipated energy prices, encompassing day-ahead and real-time market prices, as well as regulation market capacity clearing prices and performance clearing prices, are sourced from the PJM market [21] and are illustrated in Figure 4. The average mileage ratio of RegD and the performance score are computed based on their historical performance. Given that the interruptible/curtailable (I/C) program is among the most commonly implemented demand response programs currently, this paper uses the I/C program as an example. The incentive is derived from the PJM market as indicated in [22]. For CVaR, the confidence level is set to be 0.9, which is a commonly used value.  
Figure 2: The flowchart for the proposed bidding strategies.
## 3 Architecture
### Task Execution Time and Energy Model
#### 3.5.3 Energy Consumption When Executed on Mobile Devices  
If subtask \(m\) of the task \(k\) is executed on the mobile device, that means \(D_{bm}=0\). In this situation, the mobile device is in a working state. The local execution energy consumption \(E_{bm}^{l}\) of the task  
Figure 2: The fine-grained topology model for task \(k\).  
is determined by its execution time \(T_{km}^{l}\) and the power \(p_{l}\) when the mobile device performs the computation. The execution energy consumption \(E_{km}^{l}\) of the subtask \(m\) can be expressed as follows:  
\[E_{km}^{l}=T_{km}^{l}p_{l} \tag{3}\]
# Affective Stroking: Design Thermal Mid-Air Tactile for Assisting People in Stress Regulation
## 2 Materials and Methods
### Computer-Mediated Stroking Design
#### 2.2.2 Pilot Study  
The purpose of this study is to test which combination of independent parameter variables can best simulate the effect of stroking, providing users with comfort and a sense of pleasantness. Our experimental device consists of an array of 8 fans arranged in four rows, surrounding the outer and upper sides of the arm (Figure 3). To avoid any potential influence from the experimental device, we used KT boards to make a box that covers the device.  
We designed a 2 (\"Moving Direction\") \(\times\) 3 (\"Duration\") \(\times\) 2 (\"Intensity\") repeated measures experiment. The \"Moving Direction\" variable includes two levels: from the wrist to the elbow, and from the elbow to the wrist. The \"Duration\" variable refers to the time interval between the activation of adjacent fans, with intervals of 1.5 s, 2 s, and 2.5 s. The \"Intensity\" variable controls the air force output of the fans using Arduino's PWM, with levels set at \"200\" and \"255.\"  
We recruited 10 participants, including 7 males and 3 females, aged between 23 and 25. All participants were students from a local university. Each participant signed a consent form, agreeing to participate in the experiment and allowing their data to be used for research purposes.  
The experiment included 12 parameter combinations based on a 2 (\"Moving Direction\") \(\times\) 3 (\"Duration\") \(\times\) 2 (\"Intensity\") design, plus two additional sets without parameters, resulting in 14 sets of wind haptic parameters. These 14 sets were counterbalanced by randomly ordered for each participant to experience.  
Upon entering the experimental environment, participants were asked to choose their dominant hand, and the experimenter adjusted the device's position accordingly. Each participant then inserted their arm into the device's box and closed their eyes. During the experience, the participants needed to report whether they felt a stroking sensation and identify the direction of the stroke. They then evaluated each parameter set on four dimensions: perceived continuity, perceived authenticity, perceived comfort, and perceived pleasantness, using a 7-point Likert scale for their ratings. These are the four metrics commonly measured in previous studies on mediated stroking design [41; 42; 43; 44; 45; 46]. Participants reported their scores, and the experimenter recorded the data. At the end of the experiment, we conducted a brief semi-structured interview to gather qualitative data from the participants.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 1 Introduction
Other methods also include object-based image analysis, which differs in the way pixels are grouped or how homogeneous pixels are converted into objects [22; 32; 33], and the accuracy of the results often depends on the geographical characteristics of the study area [34]. The automated approaches are simple [35; 36], and cost-effective without the need for physical interventions [37]. Some of the research studies used the SCP for application in various land cover classifications, e.g., [38]. In particular, in the heterogeneous Himalayan region of Bhutan, an SCP-based random forest classifier was first employed to great effect to classify satellite imagery for geohazard mapping [17]. In Bhutan, the absence of substantive RS-based studies exacerbates research and development gaps in Earth observation and monitoring. The limitations of such studies have led to knowledge gaps in the decision-making process and pose greater challenges in achieving Sustainable Development Goals (SDGs). To fill this gap, this study explores how RS techniques and image classification algorithms can be used efficiently, especially for LULC mapping, while maintaining the ecological benefits. Examination of existing literature reveals few studies on LULC, for example, one at the national level that presented the LULC map of Bhutan for 2010 [39] and 2016 [40; 41] and the other for the LULC map of Thimbhu district of 2018 [41]. To capitalize on RS technology and GIS-based classification techniques, the proposed study examined the Gelephu region for which land cover information is lacking. Likewise, NDVI mapping and change detection are also performed to evaluate and monitor the health and density of vegetation in Gelephu. The NDVI index is widely used for various applications such as climate change response [42], vegetation degradation dynamics [43], and geo-environmental monitoring and land degradation assessment [44]. In summary, the purpose of the study is to examine the LULC and NDVI change at the local level. This study leverages Sentinel-2 satellite imagery and efficiently utilizes the GIS-based machine learning (RF) algorithm on SCP and provides the first local-scale LULC and NDVI maps for Gelephu. The expansion of urban infrastructure in Gelephu Thromde is also closely examined. The study highlights the change dynamics of LULC and NDVI that can be used for planning and managing sustainable development activities.
## 6 Discussion
It is also important to note that over the years, the IATA LOS concept has undergone a series of additional improvements with the aim of making it more accurate and applicable toairports. Today, IATA defines four different LOS recommendations in its ADRM, i.e., over-design, optimum, sub-optimum, and under-provided. The recommendation is certainly to use the optimum level, i.e., to ensure optimal terminal space that will meet passenger requirements. A large number of airports use this concept to help them determine the requirements for facilities to accommodate the planned throughput. The optimum LOS was also used in this study. It is advisable to use the IATA LOS standards, bearing in mind that passengers have different requirements/needs, and especially taking into account the diversity of airline business models. Examples of check-in area, security control area, and gate holdrooms' area modification/segmentation according to estimated traffic demand and shares of airline business models at Pula Airport passenger terminal are provided. The examples show an adjustment of the airport terminal facilities; for example, for check-in area modification, it can be made from the current 16 counters to a breakdown into two areas, where one contains four counters for full-service/charter carriers (FSC/CC), and the second contains 12 for low-cost carriers (LCC). Pula Airport was chosen for the case study due to the fact that it is becoming an increasingly popular destination for low-cost carriers. Hence, Pula Airport has plans for further expansion and modernization and adjustment to LCC airlines, including adaptation to accommodate the increasing number of passengers while preserving an optimal level of service. Traffic demand and the share of airline business forecasting models were primarily used to demonstrate the possibility of modifying the airport terminal facilities, taking into account the trend of airline business models, using the example of a smaller airport that is striving towards the dominance of the low-cost business model. In this way, other airports (primarily, for example, Croatian smaller coastal airports) that have a strong seasonality and are increasingly experiencing a change in the share of airline business models from FSC dominance to LCC, can apply this methodology to their airport terminal facilities.
## 3 Materials and Methods  
### Raw Data  
To establish a reliable database for the development of OMC, MDD, and UCS prediction models, experimental data of natural and stabilized soils were collected from the published literature with certain selection criteria. This is because a data-driven model is not a common practice in this research area and there is no readily available large enough dataset. To acquire experimental data, databases of Scopus and Web of Science were largely used. Both are abstract and citation databases of the peer-reviewed literature, delivering a complete citation search by giving access to numerous databases. A set of queries that comprise the manuscript's title, topic, abstract, and keywords were carried out on every database to choose works reporting \"stabilized soils\". Any duplicated records from the databases were removed and then manually checked to select suitable ones. A total of 79 scientific papers which have performed experiments in stabilized soil were found and used to gather the data. Indeed, gathering data from the works of the literature was not a straightforward activity. The typical challenges were (i) the use of different measuring units, (ii) some results presented in the form of charts, and (iii) the use of different soil classification methods. Translating all the data in preferred units and format to obtain well-sounded data required great attention and was a time-demanding task.  
Data consisting of 408 observations and 13 features that comprise information regarding the quantity of soils and stabilizers (cement, lime, pozzolans, and fly ash), Arterberg limits (LL, PL, and PI), compaction properties (OMC and MDD), soil classification, and unconfined compressive strength of soils at different ages were collected. The dataset covers a wide range of soils from 12 countries in Africa, America, the Middle East, South Asia, and Oceania.
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 4 Results
### Results Obtained with Feature Selection: Data Set 2  
With average merit > 0, 25 variables (except B4, B13, P4, P11, P12, P13, and P15) are selected from the original 32 variables for ANN and SVM prediction models for dataset 2.
# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 1 Introduction
PMD-YOLO takes YOLOv4-Tiny as a benchmark and further optimizes the network structure by combining with the lightweight network GhostNet. The overall contribution of this paper is summarized as follows. * Based on YOLOv4-Tiny, a lightweight network GhostNet was used for feature extraction. This operation significantly reduced the weight of the model and greatly reduced the space required for network deployment. * Bidirectional feature fusion and depthwise separable convolution were incorporated into the Neck stage; the feature layers of different sizes were further fused. This operation can make full use of the deep semantic features and shallow spatial location features of the pointer meter, thus improving the model detection performance. * The improved receptive field block (RFB) was introduced behind the feature extraction network to enhance the network's attention to meter features by using dilated convolution that can simulate human visual mechanisms. * A new channel attention mechanism was introduced in GhostNet, and two convolutional block attention modules (CBAMs) were introduced in the Neck stage to improve the network's attention to small- and medium-sized meters, so the object detection algorithm has improved the detection performance for small- and medium-sized objects.