esa-sceva/satcom-chunk-collection huggingface dataset:
# Responses of Water Use Efficiency to Drought in Southwest China
###### Abstract  
Water use efficiency (WUE) measures the tradeoff between carbon uptake and water consumption in terrestrial ecosystems. It remains unclear how the responses of WUE to drought vary with drought severity. We assessed the spatio-temporal variations of ecosystem WUE and its responses to drought for terrestrial ecosystems in Southwest China over the period 2000-2017. The annual WUE values varied with vegetation type in the region: Forests (3.25 gC kg\({}^{-1}\)H\({}_{2}\)O) \(>\) shrublands (2.00 gC kg\({}^{-1}\)H\({}_{2}\)O) \(>\) croplands (1.76 gC kg\({}^{-1}\)H\({}_{2}\)O) \(>\) grasslands (1.04 gC kg\({}^{-1}\)H\({}_{2}\)O). During the period 2000-2017, frequent droughts occurred in Southwest China, and overall, drought had an enhancement effect on WUE. However, the effects of drought on WUE varied with vegetation type and drought severity. Croplands were the most sensitive to drought, and slight water deficiency led to the decline of cropland WUE. Over grasslands, mild drought increased its WUE while moderate and severe drought reduced its WUE. For forests and shrublands, mild and moderate drought increased their WUE, and only severe drought reduce their WUE, indicating that these ecosystems had stronger resistance to drought. Assessing the patterns and trends of ecosystem WUE and its responses to drought are essential for understanding plant water use strategy and informing ecosystem water management.  
water use efficiency; drought; gross primary production; evapotranspiration; remote sensing; resistance; resilience; photosynthesis; water stress; MODIS 2020 IEEEexample:BSTcontrol
## 3 Results
### Quantitative Validation  
#### 3.1.1 Experiment Configuration  
The EPFL monocular 3D rigid tracking dataset provides a simple CAD model of the object(.obj), several object videos with man-made disturbance, the camera's intrinsic parameters, and the ground truth pose of the camera regarding the object reference system. Among the three test objects of the electric box, can, and door, the can object is made by texture-less and specular material that most approximates to an industrial part. Therefore, the can dataset was chosen to test the proposed method. The can dataset contains four training videos and two test videos. In three training videos, the can is surrounded by 14 ARUCO markers, and in the test videos, three markers are laid on the same side of the can. To acquire enough data to analyze the calibration accuracy, the training videos with 14 markers were selected as the experiment videos. The image size of the three experiment videos is \(1920\times 1080\), and the image frame numbers are 1248, 740, 1003 respectively. The materials provided in the can dataset are shown in Figure 8. The radius and the height of the can are 42 mm and 85 mm, respectively.  
Figure 7: Final optimized \(T_{O}\) and its corresponding AR registration effect.
## 3 Data Fusion and Multimodal Integration
Figure 3 illustrates the late fusion technique, where separate models process each modality independently and then combine their results into a final decision. This approach is particularly effective when the modalities vary greatly from one another. * **Intermediate fusion:** Positioned between early and late fusion, intermediate fusion combines features at various abstraction levels, enhancing the model's ability to learn cross-modal relationships. Unlike early or late fusion, intermediate fusion allows the loss function to influence feature extraction, enabling each modality to improve its own representations in a multimodal setting. Fusion may occur simultaneously or progressively, starting with strongly correlated modalities before incorporating less-related data. Guided fusion can refine this process by letting one modality influence feature extraction in another (e.g., using genomics data to guide histology feature selection). Applications include lung cancer detection through PET and CT fusion, prostate cancer classification with MRI and ultrasound, and multi-omics cancer subtyping for survival analysis. Figure 4 illustrates how intermediate fusion strikes a balance between early and late fusion by combining features during the learning process. This helps capture deeper relationships between different modalities. To make the different multimodal learning approaches easier to understand, Table 1 offers a comparative summary. It outlines the key features, advantages, and drawbacks of techniques like deep learning, transfer learning, hybrid models, ensemble methods, and fusion strategies that are commonly used in fault diagnosis. This comparison acts as a helpful guide for both researchers and industry professionals when choosing the best methods for their specific needs. Figure 3: In the late fusion strategy, individual models are trained on each modality, and their results are combined at the decision-making stage. This approach provides flexibility and modularity, making it easier to handle different types of data. To help clarify the different multimodal learning approaches, Table 1 provides a comparative summary. It highlights the main features, benefits, and limitations of techniques such as deep learning, transfer learning, hybrid models, ensemble methods, and fusion strategies commonly used in fault diagnosis. This comparison serves as a useful reference for both researchers and industry professionals, helping them choose the most suitable methods for their specific applications.
## 1 Introduction
The difference between SSWS and PSWS is the former uses the fixed parameter to assign work items, but the parameter is dynamic in the latter. The above-described methods are all based on the CARM architecture, whose algorithms require users to determine the number of computing nodes, which can lead to inefficient execution. To solve this problem, the author in [10] proposed the FLR-mining algorithm, which efficiently allocates the computing nodes. FLR-mining is the fastest algorithm among the five methods. It efficiently considers load balancing and resources, as well as determines the appropriate number of computing nodes automatically. However, FLR-mining and the existing methods assume that the network bandwidth is constant. In practical distributed and many-task running systems, the assumption fails because there are packet collisions caused by many mining tasks running simultaneously. As a result, a method that can consider the varying network bandwidth is necessary. Therefore, we propose the fast distributed mining in changing network bandwidth (FDCNB-MINING) algorithm to rapidly mine frequent patterns under the varying network bandwidth. This algorithm can also determine the appropriate number of computing nodes to efficiently utilize the computing resources and achieve load balancing. Through empirical evaluation, the proposed method is shown to deliver varying network and bandwidth environmental issues. The rest of this paper is structured as follows. We briefly review related work in Section 2. In Section 3, we introduce the FDCNB-MINING algorithm. In Section 4, we compare our proposedalgorithm with FLR-mining under a constant network bandwidth in various simulation conditions, as well as discuss the experimental results. In Section 5, we present the conclusions of this study.
## 2 Materials and Methods
### Protocols Based on Clustering
At TEEN, sensors are sensing the medium continuously, transmitting at a lower frequency only when the sensed measurement is within a range of interest, maintaining a balance between energy efficiency and information precision, working under the concept of clusters. The disadvantages are: TEEN does not rotate roles but uses other mechanisms to reduce energy consumption, so its viability concerning consumption is low. It is also an aspect against scalability by increasing the number of nodes. The TEEN protocol is relevant in this context as it allows the transmission of both event-relevant packets and continuous monitoring and is also based on the formation of clusters. In TEEN, packets are transmitted using a random access protocol. Additionally, the transmission of information is proposed when the measured data are different from those previously sent; therefore, energy consumption is reduced when the conditions to be measured are relatively stable. Hierarchical Power-aware Routing (HPAR) [39]. HPAR protocol divides the network into groups of sensor nodes that are close to each other, configured as a zonal cluster, and treated as an autonomous entity in hierarchical routing through the other zones. In this way, a decrease in energy consumption is achieved. It is the route chosen between nodes, the one with the lowest energy cost, and the best among several routes with minimum consumption. The algorithm selects the path in balance with the improvement in energy consumption. Other enhancements are presented, but they overload the messages and, consequently, the latency increases; therefore, the detriment of the efficiency of their performance is evident. We have supplemented this analysis with a summary of the main characteristics of the cluster-based protocols considered in this work, see Table 2.
## 4 Test Results
### Static Test
In general, the SBAS has much wider coverage, but it has similar or lower positioning accuracy than the local area DGPS whose coverage is within 150 km [29]. Figure 15 shows the horizontal and vertical position errors of the SBAS, DGPS and FKP-DGPS results in the east-north-up (ENU) coordinate. In this test, the DGPS show the position error of the user of the previous VRS-DGPS assumption at Seoul, wherein the correction from the WULJ RS is used. We can easily confirm that the horizontal and vertical results of the FKP-DGPS are much better than those of the SBAS and DGPS. Table 6 lists the detailed numerical results.
## 3 Results and Discussion
As shown in Figure 4, the extract from the optimal conditions also greatly reduced the production of Reactive Oxygen Species (ROS) from the glutamate-induced mouse hippocampal neuronal cells compared to the same amount of the extract from a conventional ethanol extraction because the increase of ROS production is very closely related to oxidative stress within the cells and also indicates the lowest neuroprotective activity [31; 32]. These neuroprotective activities were found to be better than those of other natural resources such as _Aronia melanocarpa_ and _C  
Figure 3: Comparison of the neuroprotective activities of the extracts from the optimal extraction process and a conventional 70% ethanol extraction process by the growth of the glutamate-induced mouse hippocampal neuronal cell line (HT22). * Significant at \(p<0.05\), *** Significant at \(p<0.001\). Figure 2: Comparison of High-Pressure Liquid Chromatography (HPLC) analysis of chlorophyll a contents by different extraction conditions. (**a**) Standard (chlorophyll a); (**b**) optimal extraction conditions; (**c**) conventional extraction conditions. Figure 5 compares the antioxidant activities of the extracts from a conventional extraction condition, 70% ethanol at 8 \({}^{\circ}\)C for 24 h, and the extract from the optimal conditions of the proposed model. Figure 4 clearly illustrates that the extract from the optimal conditions had higher DPPH radical scavenging activities than that from a conventional process, 69.38 \(\pm\) 4.13% vs. 58.25 \(\pm\) 5.23%, respectively. Higher biological activities shown in Figures 3-5 could be attributed by synergistic effects of higher amounts of chlorophyll a and also other bioactive substances in the extract from the optimal condition since this extract contained more biologically active substances than the extract from the conventional process, as already shown in Figure 2. More detailed studies on the effects of chlorophyll a on the neuroprotective activities of the extracts should be undertaken. However, in general this result would suggest that _S. maxima_ extract from proper extraction conditions is a promising candidate for a functional food to prevent memory impairment and related diseases by enhancing neuroprotective activities through an optimal extraction processes due to the high concentrations of chlorophylls and other bioactive components in the optimized extract. Figure 4: The reduction in Reactive Oxygen Species (ROS) production from the glutamate-induced mouse hippocampal neuronal cell line (HT22) by the addition of 100 (\(\mu\)g/mL) of the extracts from the optimal extraction conditions and a conventional extraction process. ** Significant at \(p<0.01\), *** Significant at \(p<0.001\). Figure 5: The \(\alpha\),\(\alpha\)-diphenyl-\(\beta\)-picrylhydrazyl (DPPH) radical scavenging activity of the extracts from the optimal extraction conditions and a conventional extraction process.
# Shared Tuned Mass Dampers for Mitigation of Seismic Pounding
## 3 Numerical Study
### Equivalence of STMD and Viscous Coupling of the Two Buildings  
Based on the discussion above, it is apparent that a very stiff TMD is equivalent, in terms of structural response reduction, to a viscous dashpot coupling the two buildings. To illustrate this point better, we modify the system shown in Figure 1 by lumping the TMD mass on building A, and connecting the two buildings by the same viscous dashpot as that used in the STMD. The resulting system has only two degrees of freedom with masses (\(m_{\text{a}}+m\)) and \(m_{\text{b}}\) connected by a dashpot \(c\). The mass (\(m_{\text{a}}+m\)) is connected to the ground by spring and dashpot \(k_{\text{a}}\) and \(c_{\text{a}}\), while \(m_{\text{b}}\) is connected to the ground by spring and dashpot \(k_{\text{b}}\) and \(c_{\text{b}}\). The peak response of this modified system is divided by the peak response of the system with STMD. The ratio of peak displacement of building A is presented, for the El Centro ground motion, as a function of frequency ratio and damping ratio in Figure 4. When the shared TMD is near resonance with building A, it is more effective than viscous coupling alone. However, as the frequency ratio is increased, the two systems approach each other, and for a frequency ratio greater than about 20, there is no difference between the two systems. This helps us conclude that STMD optimized on the global minima of the cost function is not a tuned mass damper, as the mass is neither tuned to the structure, nor does it play any part in reducing structural response. In this sense, it is illogical to use additional TMD mass and a very stiff spring element, when the same effect can be obtained by just connecting the two buildings with a viscous dashpot.  
Figure 4: The ratio of maximum displacement of building A when the two buildings are connected by a viscous dashpot and an STMD.
## 3 GPS SDR Design for CRPA Using General-Purpose Parallel Processors
### Hardware Parallelism
Since the GPU DMA engine can directly access page-locked memory, the latency of page-locked memory is lower than the latency of conventional pageable memory. CUDA 4.0 Toolkit released in March 2011 [47] allows a simple way to utilize page-locked memory by a new function called 'cudaHostRegister'. Once existing variables allocated in host memory are registered using this function, they can be accessed faster as page-locked memory. Special memory allocations using 'cudaHostAlloc' as in the previous CUDA versions, for example, are not necessary. Thus, the benefit of page-locked memory can be appreciated without significant modification of existing codes. After the parallel executions of CPU, GPU compute engine, and GPU DMA engine on 1 ms incoming data, CPU needs to prepare next 1 ms data by memory copy within ring buffer in host memory, etc. In addition, CPU needs to update graphical user interface (GUI) of the SDR. These remaining tasks take about 0.16 ms as shown in Figure 8. The total processing time for 1 ms data is about 0.75 ms. Hence, the GPS SDR for CRPA demonstrates real-time capability with sufficient timing margin.
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 6 Simulation and Real-Time Results  
In this section, the MG, which is provided in Figure 3, is simulated in MATLAB/Simulink (R2017a, MathWorks, MA, USA, 2017) to investigate the efficiency of the FOSMC method from a systematic perspective. To confirm the applicability of the FOSMC in the context of MGs, the experimental examinations are conducted. The FOSMC-based experimental outcomes of the test MG are compared to the well-known methodologies, such as MPC and SMC. In this application, the real-time HIL testbed is established to take into account the delays and realistic errors that are not considered in the usual off-line simulation. The schematic diagram of the HIL setup is depicted in Figure 5 and the main parts of the setup are given below [34; 36].  
(i): A real-time OPAL-RT simulator is used which simulates the studied MG shown in Figure 3; (ii): For the programming host, a PC is used as the command station to execute the MATLAB/Simulink based-code on the OPAL-RT; (iii): A router is established to connect all the setup devices in the same sub-network. In this application, the OPAL-RT is connected to the DK60 board by Ethernet ports.
# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 4 Experiments and Results
### Results on U-Net Variants  
The performances of U-net variants are shown in Table 2.  
For U-net and U-net + residual blocks [43] with dice loss, large structures bone (BO), muscle (MU), and adipose tissue (AD) are segmented correctly, but most of the small structures are missing (see Table 2). The main reason for this is the extremely imbalanced classes in the dataset.  
To increase the importance of small structures, weighted dice loss is adopted. We set the weights according to the proportions of the classes in the dataset. From Table 2, we can see that the results for small structures are improved, but the accuracy of BO is decreased. One possible reason is that when patch-wise training is used, they are unavoidably cut into several parts for the bones since they account for a large proportion of the image and their positions are in the middle. It is difficult for the neural network to distinguish its pattern because the special structure of bones is scattered into different patches. To improve the network's performance, SE blocks are added, the results of small structures are improved compared with that of no SE blocks. Moreover, the accuracy of BO has a little increasement.  
In conclusion, by using SE-based methods, U-Net + Res + SE or U-Net + Dense + SE, it can be pointed out that with the help of SE block, average evaluations are apparently increased. During our training, another valuable observation is that the bones are segmented more completely than those on the smaller patch size. The main reason is that a larger patch size is beneficial for the networks to capture the global features. When a larger patch size is used, more parts of bones will be in the same patch. As a result, it can be segmented more correctly.
## 1 Introduction
Section 4 features simulation examples for presentation and analysis. Section 5 provides a summary of this paper. **Notations:**\(R^{n}\) denotes the \(n\)-dimensional real vector. \(R^{m\times n}\) denotes an \(m\times n\) real matrix space. \(I_{n}\) and \(0_{n}\) denote an \(n\)-dimensional identity matrix and zero matrix, respectively. \(1_{n}\) denotes an \(n\)-dimensional column vector with all ones. For any symmetric matrix \(P\), \(P>0\) (\(P<0\)) means \(P\) is a positive (negative) definite matrix. \(\|\cdot\|\) denotes the Euclidean norm. For any \(x\in R^{n}\), \(|x|^{\alpha}=[|x_{1}|^{\alpha},|x_{2}|^{\alpha},\ldots,|x_{n}|^{\alpha}]^{ \mathrm{T}}\), \(\mathrm{sign}(x)=[\mathrm{sign}(x_{1}),\mathrm{sign}(x_{2}),\ldots,\mathrm{ sign}(x_{n})]^{\mathrm{T}}\), \(\mathrm{sig}(x)^{\alpha}=[|x_{1}|^{\alpha}\mathrm{sign}(x_{1}),|x_{2}|^{ \alpha}\mathrm{sign}(x_{2}),\ldots,|x_{n}|^{\alpha}\mathrm{sign}(x_{n})]\), where \(\alpha>0\) and \(\mathrm{sign}(\cdot)\) is the signum function. The Kronecker product of matrices \(X\) and \(Y\) is denoted by \(X\otimes Y\). \(\lambda_{\max}(\mathcal{A})\) and \(\lambda_{\min}(\mathcal{A})\) are the maximum eigenvalue and the minimum eigenvalue of matrix \(\mathcal{A}\), respectively. In this paper, if not clearly stated, matrices are assumed to have compatible dimensions.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Aloha-Based Protocols
#### 5.1.1 Slotted Aloha (S-Aloha)  
The most similar version to Aloha is S-Aloha. It consists of discretizing the channel, where each time slot has the duration of a packet transmission time [46]. The purpose of discretizing is to avoid partial collisions among packets. S-Aloha is used, for example, in the sending of short packets and requests to initiate communications in the DVB-RCS standard [48].  
S-Aloha achieves a low normalized throughput of \(S=0.368\) when the normalized load is \(C=1\). Similar to the case of pure Aloha, a channel load of \(C<10^{-3}\) is supported when the \(PLR=10^{-3}\). In the case of a higher packet loss, \(PLR=10^{-2}\), the normalized load is increased to \(C\simeq 0.01\). Regarding the complexity of implementation, it can be said that S-Aloha adds additional complexity since it requires all the nodes to be synchronized, both on the ground and also on the satellite. Such a synchronization requires us to consider the time margins in order to align the time-slots among nodes that have different delays. Similar to pure Aloha, this protocol proves to be quite inefficient for high channel loads due to the need for retransmissions.  
For IoT aplications, S-Aloha is a good option in scenarios where the offered load is low and the delays between nodes and base station do not have a large variation, but it becomes impractical if the delays imposed on a satellite link are considered. An application of this protocol on the recent Weightless-N IoT technology is reported in [49].