esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Verification
### Simulation Verification of the Np/2 Sensors-Based Schemes
#### 4.1.4 Maximum Torque-Speed Characteristics  
The maximum torques of the four-phase SRM drive over a wide speed range from 300 rpm to 3000 rpm are obtained with a large current reference of 70 A. At each speed point, the electrical-cycle-averaged motor torques with different combinations of the turn-on and turn-off angles are derived by simulation, and the maximum torque with the corresponding angle combination is derived. By using the baseline method, the ACR must be smaller than \(\tau_{\mathrm{r}}/2\). By using the proposed method, only the ECR must be smaller than \(\tau_{\mathrm{r}}/2\). The maximum torques, turn-on, and turn-off angles with respect to the speed are shown in Figure 16. Clearly, the maximum torque with the proposed method is much larger than that with the baseline scheme, especially at high speeds. Quantitatively, the maximum torque with the proposal is 153% on average and 334% at the highest.
###### Abstract  
This study quantified the impacts of land-use/cover change (LUCC) on gross primary production (GPP) during 2000-2013 in a typical densely urbanized Chinese city, Wuhan. GPP was estimated at 30-m spatial resolution using annual land cover maps, meteorological data of the baseline year, and the normalized difference vegetation index (NDVI), which was generated with the spatial and temporal adaptive reflectance fusion model (STARFM) based on Landsat and MODIS images. The results showed that approximately 309.95 Gg C was lost over 13 years, which was mainly due to the conversion from cropland to built-up areas. The interannual variation of GPP was affected by the change of vegetation composition, especially the increasing relative fraction of forests. The loss of GPP due to the conversion from forest to cropland fluctuated through the study period, but showed a sharp decrease in 2007 and 2008. The gain of GPP due to the conversion from cropland to forest was low between 2001 and 2009, but increased dramatically between 2009 and 2013. The change rate map showed an increasing trend along the highways, and a decreasing trend around the metropolitan area and lakes. The results indicated that carbon consequences should be considered before land management policies are put forth.  
land-use/cover change; gross primary production; urban areas; spatial and temporal adaptive reflectance fusion model +
Footnote †: journal: Journal of LaTeX Templates
## 2 Study Area  
The study area covers the United Republic of Tanzania. To consider the potential discrepancies in the country's boundaries, we considered a polygon encompassing a buffer zone of approximately 40 km from the national boundary of Tanzania.  
Tanzania lies in Eastern Africa, bordering the Indian Ocean, between Kenya and Mozambique and covering a land area of 945,087 square kilometers. Tanzania has a tropical climate with regional variations due to its topography. A central highland of around 900-1800 m is covering the major part of the country, including the Kilimanjaro mountain range with its highest peak of 5895 m [31].  
Regarding precipitation, the country has a complex seasonality with a mix of bimodal and unimodal rainfall patterns. The south western and central parts of Tanzania have one rainy season from December to April. The north-east of the country, except Lake Victoria, receives rainfall in two periods, from October to December and from March to May. In the area of Lake Victoria, a long rainy season usually lasts from October to May. The west of the country, bordering Lake Tanganyika, has one rainy season from November to April [32].  
Agricultural land covers more than 43% of the country's area and forests cover up to 37%. While agriculture and forestry are both important components of Tanzania's economy, agriculture contributes to the gross domestic product to a much larger extent than the forests do. This might change in the future as the forests' exploitation share is increasing significantly [33]. Tanzania possesses a range of forests with different structures, canopy cover and phenology; evergreen montane forests [34], semi-evergreen coastal forests [35] and dry miombo forests [36]. Despite conservation efforts, more than 5.86 million hectares of Tanzania's forests were lost due to deforestation and degradation between 2000-2015. That represents approximately 10% of the country's forests overall [37]. As bioenergy provides more than 90% of the country's energy needs, the main cause of the forest loss is charcoal production [33].
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 1 Introduction
### Need for a High-Speed and Low-Area Key-Exchange Design  
The fourth industrial revolution (or industry 4.0) results in the rapid development of technological devices due to the increasing demand for interconnectivity and smart automation of several applications such as healthcare [1], automotive mobile/vehicles [2; 3; 4], smart cards [5; 6], IoT [7; 8; 9; 10] and many more. These applications require the exchange of sensitive information on insecure public channels. In modern health care systems, patients can only collect their medical details from the cloud to obtain records securely, as we know that the cloud is not entirely safe [36; 37]. Thus an authentication framework is required to maintain the security and privacy in the communication system. According to [2], the connected vehicles involve applications, services and emerging technologies that enable internal connectivity among devices present in the vehicle and/or enable communication of the vehicle to external devices and networks, collectively named vehicle-to-everything (V2X) communication. Therefore, V2X needs authentication to start secure communication over the network/cloud. In the case of smart cards, authentication of the card is needed for online payments [5; 6]. Identification of a human using his/her smart ID card is another application where authentication is essential [5]. The authentication is (also) required to start secure communication between the two nodes in IoT related applications/networks. Along with higher security in terms of authentication, the aforementioned applications demand high speed with low hardware resource utilization.  
Consequently, different optimization techniques have been utilized for achieving high-speed and to reduce hardware resources in the architectures reported in [12; 17; 19; 34; 35]. For example, pipelining is utilized in [12] to reduce the critical path and improve the clock frequency. The bit/digit serial multipliers are used in [16; 18] to decrease hardware resources with a considerable decrease in the performance of the design. Towards the latency optimization, multiple modular multipliers are used in [17]. The coprocessor architectures, for performing key-agreement using ECDH, are considered in [34; 35]. A coprocessor implies the integration of FPGA with a host device (e.g., micro-controller/processor) to achieve flexibility while ignoring the area and performance (speed or throughput) parameters [11].  
The limitation of the designs reported in [12; 17; 19] is that they only accelerate/implement ECDH computations. Similarly, the high computation time is the limitation of the designs published in [34; 35; 38]. Therefore, there is a need to present a high-speed ECDH architecture for key agreement with low hardware resource utilization.
# Data Balancing Based on Pre-Training Strategy for Liver Segmentation from CT Scans
## 2 Materials and Methods
### Selection of Training Samples  
Inspired by pre-training strategy, in this work, a pre-training model is used as a sample classifier to classify hard samples and easy samples in training dataset. First, the whole training dataset was divided into two parts (A and B) based on their simple statistics information (e.g., the number of slices in volume, the proportion of positive and negative samples in volume). In this way, the ratio of positive and negative slices in two subsets (A and B) can be guaranteed the same as that of the whole training dataset. Part A is used for the later sample classification and screening, while part B is used for model pre-training. Second, slices in part B are enhanced by flipping and mirroring, and then these enhanced slices are used in model pre-training process. And we get a pre-training model when model is trained to a set iteration (such as \(5\times 10^{5}\) iterations in this work). Third, the pre-training model is used to predict slices in part A, and all slices in part A are simply divided into two categories, hard samples, and easy samples, by their Dice score. Next, the hard slices in part A are enhanced by flipping, and then added to the training dataset (part B) used in pre-training process. Finally, we continue the training process until reaching to the set \(10\times 10^{5}\) iterations, and then get the final segmentation model. Just \(5\times 10^{5}\) iterationsare needed in the final training process if \(5\times 10^{5}\) iterations were done in the pre-training process and the pre-training model structure is consistent with the final model, while \(10\times 10^{5}\) iterations are needed in the final training stage if the pre-training model structure is inconsistent with the final model. In this study, we use the same model structure in pre-training process and final training stage.
## 3 Interference Mitigation Schemes
### Signal Processing Approach  
In the absence of interference in WBASNs, the signal processing of sensors only needs to rebuild the original signal and remove both noise and electromagnetic interference (EMI) from power lines. The first approach was to reconstruct the waveform of respiratory signals by processing single-channel ECG [46]. The empirical mode decomposition (EMD) in [46] aims to sum the components that approximates theoriginal ECG signal. The EMD algorithm determines instrinsic oscillations by using the charateristics of data. Another approach is to combine ECG and EEG signals in the scenario of heatcare service [47].  
In the presence of interference in WBASNs, each receiver has the ability to remove interference from other systems by either using detectors or changing the structure of the receiver. In [48], the interference in CDMA-based WBASN systems is rejected by using multiuser detection. The multiuser detectors have two different structures which are the decorrelator and the minimum mean square error (MMSE) receivers. The decorrelator has a decision metric to remove correlation, and the MMSE receiver minimizes the mean square error between the transmitted bits and the outputs of the transformation. The system is modeled by both the belt-head on-body transmission and an interference source from other WBASNs. The result of simulation shows that the detectors outperform the conventional receiver using match filters.  
On the other hand, an ultra-low power wake-up receiver (WUR) [49] is implemented in WBASNs to consume low power and avoid interference from wireless devices. In WUR, the transmission modes depend on the type of transmission. That is, the data transmission uses the transceiver's Gaussian frequency shift keying (GFSK) and the wake up transmission uses Gaussian on-off keying (OOK) modulated by pulse width. The WUR filters interference by using the preamble detector.
# Research on the Principle of a New Flexible Screw Conveyor and Its Power Consumption
## 2 Analysis of the Mechanical Properties of a Single Flexible Blade
### The Axial Analysis of a Single Blade  
The flexible blades are the elements of the screw conveyor. As a mechanical element, they must possess adequate strength and rigidity. When the end of the nylon fiber axis is fixed on the shaft, the other end is free, so this structure can be considered as a flexible cantilever beam of nylon fibers. The nylon fibers are thin cylinders, and the cross section of each fiber is circular. The force analysis is shown in the Figure 2.  
Figure 1: A single flexible blade radial force diagram.  
Figure 2: Single flexible helical blade axial force diagram.  
In the Figure 2, \(F_{1}\) is caused by the weight of the material of the uniformly distributed load. Since there is no granule material, the study of the screw conveyor is an idealization. \(f_{3}\) is the radial wall friction preload in the axial direction.  
When the fiber bundle gets bent, resistance to bending internal forces will be produced as well. Depending on the shape of the material, the internal forces resisting deformation are different. The formula of the maximum bending stress of the fiber bundles is:  
\[\sigma_{\text{max}}=\frac{\mu_{1}\text{d}N_{1}l}{0.2d_{\text{s}}^{3}} \tag{2}\]  
The friction along the axial preload is \(f_{3}\), so its maximum deflection \(W_{\text{max}}\) is:  
\[W_{\text{max}}=\frac{64f_{3}l^{3}}{3\pi Ed_{\text{s}}^{4}} \tag{3}\]
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Cultural and Environmental Conditions  
Wheat and soybean canopies were grown in sealed, water-cooled, controlled-environment chambers (Model EGC-13, Environmental Growth Chambers, Chagrin Falls, OH, USA). Two canopies of the same species were grown simultaneously in adjacent chambers. Wheat was seeded into lids containing a 10 mm layer of inert media (Isolite, size CG-2, Sumitomo Corp., Denver, CO, USA) at a density of 1100 plants m\({}^{-2}\). Soybean seedlings were transplanted into closed cell foam plugs in a Styrrofoam lid at a planting density of 60 plants m\({}^{-2}\). The seedling roots grew into a recirculating hydroponic solution after germination. The hydroponic system is described in [PERSON] (2006).  
Inside each chamber, a polished aluminum, reflective side-wall was built around the perimeter of the ~1 m\({}^{2}\) canopy to minimize edge effects and side lighting. The incident photosynthetic photon flux (PPF\({}_{0}\)) was 1600 umol m\({}^{-2}\) s\({}^{-1}\) for wheat and 750 umol m\({}^{-2}\) s\({}^{-1}\) for soybean. Lighting was provided by four, 1000 W high-pressure sodium (HPS) lamps, which were adjusted with neutral density filters to achieve \(\pm\)5% PPF\({}_{0}\) uniformity over the crop surface. PPF\({}_{0}\) was measured at the top of the canopy with a quantum sensor (Model LI-190 SB, LI-COR, Lincoln, NE, USA), and was adjusted daily throughout the life cycle by lowering the canopy platform as the plants grew taller. Longwave radiation emitted by the lamps was removed by a 10 cm deep water filter. The filter consisted of a glass box filled with recirculating, chilled water located below the lamps. The water filter under the lamps was removed over the course of several days during tests that change surface radiation forcing by increasing incident PPF\({}_{0}\) and longwave radiation impinging on the canopy. Advective conditions existed in the chamber because the temperature control system heated the air to maintain the chamber temperature setpoint, and the canopy was exposed to a continuous flow of warm air.  
Air temperature was 21.0 \(\pm\) 0.3 \({}^{\circ}\)C, the barometric pressure was 86 \(\pm\) 0.1 kPa, and chamber CO\({}_{2}\) varied between 400 and 1400 umol mol\({}^{-1}\) to manipulate canopy temperature, LE and H. Relative humidity at night was 50% \(\pm\) 5%. During the day, transpiration humidified the 1300 L of chamber air and daytime relative humidity was 70 \(\pm\) 5%. Wheat was grown under a 20 h light/4 h dark photoperiod and soybean under a 12 h light/12 h dark photoperiod. The canopies grew in a chamber supplied with a constant temperature (T\({}_{\rm Air}\)) and VPD of bulk air surrounding the vegetation (D\({}_{\rm Bulk}\)) as well as a constant wind speed. Thus, boundary layer forcing (T\({}_{\rm Air}\) and D\({}_{\rm Bulk}\)) and surface layer feedbacks (chamber wind speed) were held constant, but in nature they are dictated by diurnal changes in local climate (T\({}_{\rm Air}\), D\({}_{\rm Bulk}\), and wind speed).
# Transition Metal Coordination Compounds as Novel Materials for Dye-Sensitized Solar Cells
## 2 Characterization and Principle of Operation of Dye-Sensitized Solar Cells
### Compounds Used as Dyes
Some Fe\({}^{3+}\) complexes have an unfilled d sub-shell with a high-spin ground state (sextet), which are more commonly used nowadays. When some Fe\({}^{3+}\) complexes of hexacarbene were formed, low-spin ground states were formed (doublet) [33]. Coordination compounds of silver, such as [Ag(L)\({}_{0.5}\)]\({}_{\mathrm{n}}\) (L = azobenzene-4,4'-dicarboxylate) have been reported to enhance the absorption of DSSCs cells in the ultraviolet region. In the case of this compound, the enhancement stems from the co-sensitization of the compound and a Ru-bipyridine dye (N719), allowing the Ag(L)\({}_{0.5}\)]\({}_{\mathrm{n}}\)/N719/TiO\({}_{2}\) system to achieve a PCE of 3.14% [34]. Zn-based coordination compounds use similar ligands, such as 4-[(8-hydroxy-5-quinolin)]azo]-benzenesulfonic acid, with the resulting coordination compound, augmented with N719 dye, achieving a PCE of approx. 8% [35]. Recent literature reports indicate the use of bile acids and bile salts (e.g., sodium cholate and sodium deoxycholate), both of which are naturally occurring anionic biosurfactants, in dye-sensitized solar cells. On the other hand, in the synthesis of inorganic as well as organic materials, they enable charge compensation. The use of bile acids, capable of acting as coadsorbates, influences the efficiency of the cell, among others, by blocking the current leakage (related to the generation of energy losses) or by the solubilization of dyes in solutions, thus facilitating the formation of DSSCs. For example, the use of deoxycholic acid as a coadsorbate with the dye N749 attains a cell conversion coefficient of 8.4%, NKX-2700--7.8%--or N719--6.4% [18].
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Machine Learning Models
#### 3.1.8 Logistic Regression (LR) Classifier  
Logistic Regression (LR) is a popular technique in supervised machine learning, primarily used for binary classification tasks. It can be adapted to handle multi-class classification as well. Unlike a true regression technique, logistic regression employs the logistic (sigmoid) function to represent the likelihood of a binary outcome, such as 0 or 1, as a function of one or more predictor variables. The logistic function converts a linear combination of predictor variables into a probability score between 0 and 1, making it a useful tool for classification [51]. The logistic regression model estimates each predictor's impact on the log odds of the binary outcome, and the coefficients are optimized during the training process to improve the model's performance. Logistic regression employs a threshold, typically 0.5, to categorize data points. Values above the threshold are assigned to one class, while those below the threshold are assigned to the other class.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 5 New Localization Module: GNSS/SLAM Fusion
### Odometry  
In a previous work [42], we described a generic module for low-level control of Ackermann type vehicles, called control logic for easy Ackermann robotization (CLEAR). From this low-level module, we obtain the linear speed and steering angle of the vehicle. For this first implementation of the framework, we developed a module to extract the positions and orientations by fusing the information obtained through CLEAR and an inertial measurement unit (IMU). This module provides as output the standard ROS message for odometry.
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 6 Numerical Results
### Comparative Evaluation
#### 6.3.2 Second Scenario: Why Reinforcement Learning?  
In this scenario, we compare our proposed reinforcement-learning component with four other approaches by modifying the reward function described in Section 3, Equation (1) as follows:  
1. The first alternative approach is defined by reward function as \(r_{m,e}(t)=\dfrac{1}{d_{m,e}}\). The greater distance \(d_{m,e}\) of evacuee \(m\) from evacuation route \(e\) is, the lower the corresponding reward function is (we refer to this strategy as \"Distance Reward\").
2. The second alternative defines the reward function as: \(r_{m,e}(t)=C_{e}\). The greater the \(C_{e}\) (i.e., Capacity) of selected evacuation route \(e\) is, the greater the reward function is as the aforementioned evacuation route fits more evacuees (we refer to this strategy as \"Capacity Reward\").
3. The third alternative approach is defined by reward function \(r_{m,e}(t)=\lambda_{e}\). The greater evacuation rate \(\lambda_{e}\) is, the greater the reward function is (we refer to this strategy as: \"Evac_Rate Reward\").
4. The last approach is defined by reward function \(r_{m,e}(t)=\lambda_{e}\cdot|AvgDistEvac-d_{m,e}|\), where \(AvgDistEvac\) is the average distance of all other evacuees from respective evacuation route \(e\) that evacuee \(m\) selected (we refer to this strategy as \"Avg_Dist Reward\").  
Figure 6 presents the average reward function of all aforementioned approaches. It is observed that our approach presents the best performance (highest average reward function) because it examines and properly combines several features of the other approaches, including capacity \(C_{e}\), evacuation rate \(\lambda_{e}\), and distances of evacuees from evacuation routes \(d_{m,e},\forall m\forall e\). Moreover, we notice that the \"Capacity Reward\" approach had the worst performance as it took into consideration only capacity \(C_{e}\), which is a static feature and common for all evacuees. Among the remaining methods, the one with the evacuation rate as a reward function (\"Evac_Rate Reward\") is the best because its nature is dynamic, and it is constantly updated during the algorithm, implicitly indicating the evacuees how congested evacuation routes are. Finally, approaches that consider evacuee distances from the evacuation routes (i.e., \"Distance Reward\" and \"Avg_Dist Reward\"), given that distances are different for each evacuee, achieved a better distribution of the evacuees to the evacuation routes when compared to the \"Capacity Reward\" alternative.  
Figure 5: Comparative analysis—Scenario 1.