esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dust Emission
CARB's PM\({}_{10}\) emission factors were developed in 2003 for a number of crops in California, including almond, cotton, sorghum and wheat, etc. [6]. The highest PM\({}_{10}\) emission level of 40.78 lbs/acre was found for almond mechanical harvesting. This almond emission level is smaller than the PM\({}_{10}\) emission of 65.74 lbs/acre for banagrass. These results suggest that mechanical harvesting of banagrass on Hawaii Island would generate higher dust emission levels than mechanical harvesting of other agricultural crops reported previously. Total annual emissions for PM\({}_{2.5}\) and PM\({}_{10}\) were estimated at 21.84 and 248.18 try. Given the PSD permitting requirement of 250 try for fugitive dust emission [9], the PM\({}_{10}\) emission of 248.18 try is statistically equivalent to this requirement limit which would probably trigger an emission level violation. It should be noted that factors affecting the emission level include biomass harvesting rate, the number of vehicles on the field and precipitation conditions. Sensitivity analysis shows that if the probability for the number of days in a year with precipitation more than 0.01 inch per day decreases by 0.37% (about 1.36 days), ceteris paribus, the total PM\({}_{10}\) emission reaches the calculated 250 try limit which automatically cites a major stationary source of pollution. Additionally, an increase in the emission level can occur from lowering the biomass harvest rate which reduces harvest efficiency and requires more machines on the field. The result of this sensitivity analysis suggests that it is highly likely that the feedstock production area will become a major stationary source of pollution requiring a PSD permit in order to continue operations.
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.3.3 MODIS LAI Data  
Leaf area index (LAI) is an important input parameter for simulating the carbon cycles of forest ecosystems, and is closely related to the photosynthesis, steaming, water utilization, and productivity formation of vegetation [59]. Remote sensing technology is an important method for obtaining a large-scale LAI. However, MODIS LAI data are susceptible to the influences of factors such as the atmosphere, which leads to an irregular reduction in data. To reduce data noise and improve data quality, the locally adjusted cubic-spline capping (LACC) [60] algorithm was used to smooth the clipped MODIS LAI data. Then, the smoothed MODIS LAI data were assimilated by the particle filter (PF) algorithm [61]. The assimilated MODIS LAI data were shown in Figure 1b.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 4 Proposed Scheme-I (Key Recovery System Using Signcryption)
### Session Key Recovery Phase  
This phase describes how to recover a key if the \(Dev_{B}\) requests key recovery as shown in Figure 8.  
* **Step 1:** When \(Dev_{B}\) requests \(KRF\) decryption from Med to recover \(SK\), and sends \(KRF\).
* **Step 2:** Then Med requests \(KRF\) decryption from KRC to recover \(SK\), and sends \(KRF\).
* **Step 3:** KRC upon receiving a request for \(KRF\) decryption, obtains \(KRF_{i}\) pieces after \(KRF\) decrypt with \(sk_{KRC}\). \[D_{sk_{KRC}}(E_{pk_{KRC}}(KRF_{1}\parallel\ldots\parallel KRF_{n}\parallel H (SGN)))\] (24)
* **Step 4:** The obtained \(KRF_{i}\) pieces are sent to each M-KRA to request decryption.
* **Step 5:** The requested M-KRA obtain \(c_{i},r_{i},s_{i},SGN,TT_{i}\) values with \(sk_{KRA_{i}}\). \[D_{sk_{KRA_{i}}}(E_{pk_{KRA_{i}}}(c_{i}\parallel r_{i}\parallel s_{i}\parallel SGN \parallel TT_{i}))\] (25)
* **Step 6:** Among the obtained values, \(c_{i},r_{i},s_{i},SGN\) values are encrypted with \(pk_{KRC}\) and sends to the KRC. \[E_{pk_{KRC}}(c_{i}\parallel r_{i}\parallel s_{i}\parallel SGN)\] (26)
* **Step 7:** KRC compares \(SGN\) obtained by decrypting the received ciphertext with \(sk_{KRC}\) and \(H(SGN)\). If they match, \(c_{i},r_{i},s_{i}\) pieces are collected and \(c,r,s\) are recovered.
* **Step 8:** KRC recovers the \(k\) value using the received ciphertext, public parameters, and recovered \(c,r,s\). \[k=H((pk_{A}\cdot g^{r})^{s\ast k_{KRC}}\ mod\ p)\] (27)
* **Step 9:** Then, KRC divides \(k\) by \(k_{1},k_{2}\).
* **Step 10:** KRC recovers the \(SK\) using the obtained \(k_{1}\) and \(c\). \[D_{k_{1}}(C)=D_{k_{1}}(E_{k_{1}}(SK))=SK\] (28)  
Figure 8: Session key recovery phase of proposed scheme-I.  
* **Step 11:** KRC compares the calculated \(H_{k_{2}}(SK)\) and \(r\) values using the obtained \(K2\).
* **Step 12:** If it matches, KRC sends the recovered \(SK\) to Med.
* **Step 13:** Then, Med sends \(SK\) to \(Dev_{B}\) and the message is decrypted using the received \(SK\).  
\[D_{SK}(C)=D_{SK}(E_{k_{1}}(M))=M \tag{29}\]
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 4 The NoC Workflow
### Pipelines  
Pipelines are divided into routing pipelines and data transmission pipelines.  
1. Routing pipelines are used by the router to perform the routing function and other control information calculation. The routing packet consumes one clock at each node.
2. Data transmission pipelines are the pipelines used by router for the data transmission circuit. The data transmission circuit of each node can be set as a combination circuit or a pipeline register. Along the locked data path, most nodes are set as combination logic while few intermediate nodes are set to pipeline register according to the length of the whole path. The information about which nodes are set as pipeline registers is calculated by routing source.
3. If CRC is acquired, extra clock cycles are needed by the data sender and data receiver according to the port bandwidth. However, when parallel CRC is designed using Galois matrix, the extra cycle cost can be negligible.  
Figure 11 depicts difference in pipelines between PS NoC and PCCNoC. PCCNOC establishes a transferring circuit at first, which take one time unit going through every node. After the circuit is connected, it takes only one time unit to transfer a packet from DS to DR. VC NoC and WH NoC transfer flits by pipeline, each flit is transferred immediately with the former flit. When conflicts happen, VC NoC continues to transfer flits to conflict-adjacent nodes while WH NoC stalls the transfer link until the conflict is resolved. In theory, the transferring time is the same for VC, WH, and PCCNOC. However, PCCNOC can accelerate data transfer after a circuit is established, as only one time unit is required for each packet and the transmission time is guaranteed. Each node only consumes one clock cycle per routing process, while VC PS NoCs and WH PS NoCs need extra cycles for buffering and data-ordering [8].
# Adaptive Emergency Call Service for Disaster Management
## 4 Adaptive Multi-Hop Emergency Call Protocol: 5G-SOS
### 5G-SOS Adaptive Parameters
#### 4.1.2 Relaying Threshold, \(RS_{\text{th}}\)  
The parameter \(RS_{\text{th}}\) is a critical parameter that determines the balance between the reliability of the communication and the fairness in transmitting distinct emergency calls. If the \(RS_{\text{th}}\) of an emergency call is high, the probability that the call reaches a gNB becomes higher. However, under a dense network and/or a high number of emergency calls, increasing the number of relay thresholds may lead to saturation of network devices in the busy state, and many interferences. Late emergency requests are penalized or rejected since the first emergency requests remain longer in the pending requests list \(\eta\). In the 5G-SOS protocol, a device adjusts the value of its \(RS_{\text{th}}\) according to the real-time conditions following the expression:  
\[RS_{\text{th}}=RS_{\text{th}}^{\text{max}}-\mid\eta\mid. \tag{6}\]
## 3 The Timepix3 Radiation Monitor  
The Timepix3 Radiation Monitor detector for R2E radiation level measurements at CERN accelerators is based on a setup developed for accelerator beam profile and emittance monitoring [35]. It employs a Timepix3 chip [36] manufactured by ADVACAM [37] in Prague, Czech Republic, part of the Timepix detector family [38], designed by the Medipix collaboration [39]. The Timepix is a hybrid semiconductor pixel detector consisting of a sensor chip with a matrix of 256 \(\times\) 256 pixels, 55 \(\times\) 55 \(\upmu\)m\({}^{2}\) each, which is bump-bonded to the Timepix3 readout Application-Specific Integrated Circuit (ASIC) chip. The detection layer for the Medipix detector family can be made of different semiconducting or semi-insulating materials (such as Si, GaAs, CdTe, CZT, etc.) in combination with the same ASIC chip owing to its hybrid structure. The monolithic sensing layer can have various thicknesses, ranging from 100 to 2000 \(\upmu\)m. The Timepix3 Radiation Monitor has a 300 \(\upmu\)m thick silicon layer (typically the minimum thickness required to obtain particle identification capabilities), with metalization and dead layers both on the top and on the bottom due to the manufacturing process. The Timepix3 nominal clock time (frequency) is 25 ns (40 MHz), matching the LHC bunch spacing, which could allow a bunch-by-bunch characterization of the radiation environment. This good timing resolution allows the detector to separate individual particle hits that have moderately high flux rates, here, up to approximately 10\({}^{8}\) particles/(cm\({}^{2}\)-s).
## 3 Discussion  
The charge and discharge of an SC at both low and high constant power are assessed in this section in order to prove the proposed formulation valid. Parameters R, C, \(\text{R}_{\text{TH}}\), and \(\text{C}_{\text{TH}}\) given by Maxwell Technologies(tm) for a 650-F cell are used [63] and shown in Table 1, which also includes the value of the constant a as defined in (16). Moreover, \(\text{U}_{0}\), \(\text{P}\), \(\text{T}_{0}\), and \(\text{T}_{\text{amb}}\) are assumed to be known variables as well.  
The differential equation that enables the calculation of i can be defined by means of a power balance in the discharge:  
\[0=-\text{i}^{3}+\text{C}\cdot\left(\text{P}-\text{R}\cdot\text{i}^{2}\right) \cdot\text{i}\prime. \tag{37}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Electrical Values**} & \multicolumn{2}{c}{**Thermal Values**} & \multicolumn{1}{c}{**Constant**} \\ \hline
**C (F)** & **R (m\(\Omega\))** & \(\text{R}_{\text{TH}}\) (\({}^{\circ}\)**C/W)** & \(\text{C}_{\text{TH}}\) (J\({}^{\circ}\)**C)** & \(\text{T}_{\text{amb}}\) (\({}^{\circ}\)**C)** & **a** \\ \hline
650 & 0.8 & 6.5 & 190 & 20 & 210.53 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the cell under study.  
Three additional differential equations must be solved to compute the internal voltage \(\mathrm{u}\), the energy losses, \(\mathrm{e_{d}}\), and the thermal jump \(\mathrm{\theta}\); once these variables are known, the other remaining electrical variables (external voltage, discharged energy, state of charge, etc.) can be calculated straightforwardly. These three equations are the well-known formula relating \(\mathrm{i}\) and \(\mathrm{u}\), the relation between the power and energy losses, and the thermal Equation (11). Therefore, the equations system to be solved is as follows:  
\[\left\{\begin{array}{c}\mathrm{i}\
u=\frac{\mathrm{i}^{3}}{\mathrm{C}\cdot \left(\mathrm{P}-\mathrm{R}:\mathrm{i}^{2}\right)}\\ \mathrm{u}\
u=-\frac{\mathrm{i}}{\mathrm{i}}\\ \mathrm{e}^{\prime}_{\mathrm{d}}=\mathrm{P}_{\mathrm{d}}=\mathrm{R}:\mathrm{ i}^{2}\\ \mathrm{\theta}\prime=\frac{\mathrm{R}:\mathrm{i}^{2}}{\mathrm{C}_{\mathrm{TH}}} -\frac{\mathrm{\theta}}{\mathrm{R}_{\mathrm{TH}}\mathrm{C}_{\mathrm{TH}}}. \end{array}\right. \tag{38}\]  
As referred to above, once Equation (38) is solved, the thermal jump \(\mathrm{\theta}\) and the other variables are directly obtained. All of them can be expressed as a function of the others.
# DP-YOLO: Effective Improvement Based on YOLO Detector
## 3 Approaches
### Petal-like Sample Amplification (PSA)  
The sample matching method of YOLOv5 involves two stages. In the former stage, each detection layer has three anchors of different sizes for every grid. For each ground truth, the ratio of its length to width is calculated with different anchors, and if the ratio falls within the specified threshold range of 0.25-4, the associated anchor is considered a positive sample. The grid where the center point is located is also treated as a positive sample, and its own confidence loss is calculated. Additionally, when the centroid of the ground truth falls into a different region of the grid, the associated anchor of the corresponding adjacent grid will also be expanded as a positive sample. As shown in Figure 2, the center points  
Figure 1: Overview of our work.  
of the ground truth represented by the red, orange, green, and white dots are located in the upper right corner area of their respective grids. Therefore, the associated anchors of the grid above and to the right will also be considered positive samples, as depicted in the three gray boxes in the figure. This process expands the positive samples twice for each case.  
We firmly advocate for sample augmentation across grids to be conducted within the appropriate range. Previous studies have utilized a solid circle centered at the object's center to define positive samples [27]. Moreover, it has been observed that pixels within the theoretical receptive field contribute to the output cell with an asymptotically Gaussian distribution [28]. Integrating the circle-like characteristics of the effective receptive field into the sample selection process would further enhance the quality of positive samples [29]. By incorporating these insights, the sample should be expanded into the positive sample because it can 'feel' the ground truth. Specifically, we draw a circle with the centroid of each grid as the center and a certain length as the radius. If the centroid of the ground truth lies within this circle, the associated anchors of that grid are expanded into positive samples, and this approach resembles the effective receptive field concept. Since the expanded ranges of multiple neighboring grids will form a shape similar to two petals when they overlap, we named it Petal-like Sample Amplification (PSA).
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 2 Materials and Methods
### Hardware  
The wireless sensing system was developed on open-source Arduino-based sensor platform. Radiofrequency at 868 MHz was selected for the communication of the End-nodes with the Coordinator. Usually, in such networks, the battery powered nodes are wirelessly connected to a communication gateway to forward the data [10]. It is important to clarify that the Coordinator in our system was the same dummy device as the End-nodes, however, it was programmed differently to serve the specific task of collecting the data from all the End-nodes of the network. The Coordinator was also equipped with a 4G module for the connection of the wireless local network to the cloud via 4G cellular network. The sensors and parameters measured by the developed system are summarized in Table 1. All sensors have minimum operational temperature range of \(-\)40\(\rightarrow\)+85 \({}^{\circ}\)C, making them suitable for all weather conditions.
# Optimization of DC Energy Storage in Tokamak Poloidal Coils
## 2 Operations of PF Coils in a Tokamak
### Reference Scenario of PF Coils  
The prediction of tokamak operations is a very complex and multidisciplinary task, implying the solution of magnetohydrodynamic (MHD) problems [4]. From the PS point of view, tokamak operations are fully described by a \"scenario\", including the following:  
* The profiles of the current for each \"active\" (supplied) coil.
* A model for the plasma behavior in terms of filaments of equivalent electrical currents.
* The parasitic currents induced in the tokamak \"passive\" elements (vacuum vessel, walls, and so on), characterized by equivalent circuits with internal inductance (depending on geometry) and resistance (material losses).  
In practice, a scenario is characterized by a time-dependent vector containing the current samples in the desired time instants. The voltage evolutions in the same circuits can be derived by modeling the geometric structure of the tokamak poloidal cross-sections and the magnetic interactions among them. All the flowing currents are magnetically coupled by a square matrix \(\underline{M}\) containing the mutual inductances among the active coils, the plasma currents, and the passive elements. For the active coils, the voltage to be produced by the PS systems can be derived from the voltage across the coil terminals, subtracting the voltage drops in the corresponding circuit (including DC bus bars, cryogenic transitions, connections, joints, parasitic effects, and so on).  
The waveforms in Figure 2 were calculated using such a procedure for the DTT operations. However, to have a reference scenario for the PS design, these waveforms contain more phenomena with respect to standard scenarios where only equilibrium situations are considered. These include such transient phases as plasma breakdown, plasma ramp-up, L-H mode transition, and H-L mode transition. The analyses for these phases were carried out by the CREATE team [27; 28; 29] and reprocessed for this paper. Of course, the calculations involved also the other active and passive currents, in particular those in the CS coils, that are not reported in the plots.  
Figure 1: Basic structure of a tokamak (DTT) with emphasis on the magnetic systems.  
Even though other experimental scenarios are possible in DTT [23; 28], the scenario in Figure 2 is the most demanding from an electrical point of view and can be used as a benchmark for the design and the optimization.  
The waveforms in Figure 2 establish the first specifications for the design of each PF PS, namely, the minimum rated current and voltage. It is important to stress that in many cases the plasma breakdown (occurring at 60 s in Figure 2) is supported by other components in series in the PS circuit, such as a switching network unit (SNU) [30], while for the DTT PF coils, all the phases are implemented by the PS converters.
## 2 Materials and Methods
### Microstructural, Physicochemical and Textural Properties  
The imaging of the interior microstructure of the meat analogues was obtained from a scanning electron microscope (SEM, SU 8020, Hitachi, Tokyo, Japan). The meat analogues were cut into small pieces (5 mm \(\times\) 8 mm \(\times\) 0.8 mm) and immersed in glutaraldehyde (2.5%, _v/v_) at 25 \({}^{\circ}\)C for 24 h. The sample was then rinsed with distilled water and dehydrated in a series of graded ethanol solutions then dried out. Finally, after coating with gold, the sample was observed using SEM.  
A Testo205 pH sensor (Testo AG, Lenzkirch, Germany) was used to measure the pH value, and the water activity (_a_w) was measured using a benchtop meter (AquaLab 4 TE, Decagon Devices, Pullman, WA, USA). The color of each sausage analogue was determined using a colorimeter (CM-600d1, Konica Minolta Inc., Tokyo, Japan). The chromaticity parameters L* (lightness), a* (redness), and b* (yellowness) were measured and compared. The instrument was calibrated with standard white and black reference tiles before analysis [10]. Cook yield was determined by measuring the weight of each sausage analogue before and after cooking. The values were calculated by [PERSON] et.al as follows [11]:  
\[\text{Cook yield }(\%)=\frac{\text{Cooked weight}}{\text{Pre- cooked weight}}\times 100 \tag{1}\]  
The results are expressed as a percentage. The texture profiles of the different meat analogue and sausage analogue samples were analyzed using a CT3 texture analyzer (Brookfield, Middleboro, MA, USA). The hardness, springiness, fracture property and viscous force of the sausage analogue were measured using two cycles of compression with the accompanying probe attachment (TA44, cylinder diameter = 4 mm). The test was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatments** & **Egg White Powder** & **Meat Flavour Powder** & **Oil (\%)** & **Red Yeast Rice (\%)** \\  & **(\%)** & **(\%)** & **Oil (\%)** & **Red Yeast Rice (\%)** \\ \hline Control & 0 & 0 & 0 & 0 \\ G1 & 3 & 0.1 & 17 & 0.01 \\ G2 & 3 & 0.2 & 19 & 0.02 \\ G3 & 3 & 0.3 & 21 & 0.03 \\ G4 & 4 & 0.1 & 19 & 0.03 \\ G5 & 4 & 0.2 & 21 & 0.01 \\ G6 & 4 & 0.3 & 17 & 0.02 \\ G7 & 5 & 0.1 & 21 & 0.02 \\ G8 & 5 & 0.2 & 17 & 0.03 \\ G9 & 5 & 0.3 & 19 & 0.01 \\ MMSA & 3 & 0.1 & 19 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key factorial design for mushroom-based meat sausage analogues formulation.  
performed for up to 50% compression and the speed was set at 2.00 mm/s. For selecting an optimal mushroom variety and the proportion of mushroom with SPI to mimic real meat, both \(a_{\text{{{m}}}}\) and texture profiles of meat analogues were compared with beef.
## 2 Materials and Methods
### Assessment of Plant Growth-Promoting Ability of S10  
#### 2.3.1 Pot Experiment  
To evaluate the plant growth promotion activity of S10 on wheat plants, a pot experiment was performed. Wheat seeds (Mingxian 169) were surface-disinfected through immersing in 75% ethanol for 2 min, soaked in 2% NaClO for 2 min, and washed five times in SDW. Subsequently, these disinfected seeds were sown in trays containing SDW for 7 days. Then, the same healthy seedlings were selected and cultivated in 10 cm diam pots (substrate was disinfected in autoclaves at 121 \({}^{\circ}\)C for 60 min). Three days after transplantation, wheat plants were inoculated with 30 mL of different concentrations of S10 spore suspension (10\({}^{7}\), 10\({}^{8}\), 10\({}^{9}\) CFU/mL) with root drenching methods for each treatment. Wheat plants treated with 30 mL SDW were used as a control. Simultaneously, each pot was watered with 30 mL SDW once every 5 days. Then, 20 days after planting, the plants were gently taken out of the pots and rinsed with tap water to clear adhering particles. Biometric properties (dry weight, fresh weight, root length, and shoot length) were measured and statistically analyzed at the same time. Three independent experiments were conducted, and each treatment contained five replicate pots, with each replicate pot containing 10 wheat plants.
###### Abstract  
Brain tumors are serious conditions, which can cause great trauma to patients, endangering their health and even leading to disability or death. Therefore, accurate preoperative diagnosis is particularly important. Accurate brain tumor segmentation based on deep learning plays an important role in the preoperative treatment planning process and has achieved good performance. However, one of the challenges involved is an insufficient ability to extract features with a large receptive field in encoder layers and guide the selection of deep semantic information in decoder layers. We propose small kernel convolution with an effective receptive field network (SCENet) based on UNet, which involves a small kernel convolution with effective receptive field shuffle module (SCER) and a channel spatial attention module (CSAM). The SCER module utilizes the inherent properties of stacking convolution to obtain effectively receptive fields and improve the features with a large receptive field extraction ability. CSAM of decoder layers can preserve more detailed features to capture clearer contours of the segmented image by calculating the weights of channels and spaces. An ASPP module is introduced to the bottleneck layer to enlarge the receptive field and can capture multi-scale detailed features. Furthermore, a large number of experiments were performed to evaluate the performance of our model on the BraTS2021 dataset. The SCENet achieved dice coefficient scores of 91.67%, 87.70%, and 83.35% for whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves the state-of-the-art performance compared with more than twelve benchmarks.  
brain tumor segmentation; MRI; medical image; receptive field; attention mechanism; small kernel convolution +
Footnote †: journal: applied sciences