esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Over two hours at midday, the solar concentrator enhanced the PV4 panel's performance to be the best at these hours and attained a maximum average of 14.38% electrical efficiency compared to 12.94% for the PV1 panel on Day 5, as shown in Figure 8b. This can be helpful when the LCPV/T-PCM system is used to cover a higher electricity demand at this short time of the day. The LCPV/T-PCM system can provide an 11.06% increase over the conventional (reference) one if used in similar conditions to the case under study during an approximately two-hour period around noon. Otherwise, the PV/T-PCM system can represent the best choice to produce more electricity over the day combined with thermal power output. The enhancement of 5.03% in electrical efficiency of the PV/T-PCM system is consistent with what was reported in reference [15], which used only a passive cooling system with a PCM of 52 \({}^{\circ}\)C melting point filled in a finned container. One of the objectives of this study is to augment the overall efficiency of PV panels by utilizing them as solar collectors in PV/T systems. The effect of combining these systems with a PCM with/without solar low concentration on overall efficiency is reported in Figure 9a for the five days under the investigation. Both PV/T-PCM and LCPV/T-PCM systems showed higher average day-based overall efficiencies, attaining a maximum of 51.2% and 46.5%, respectively, on Day 4, as a result of the higher mass flow rate. Moreover, the relatively lower inlet temperature of the cooling water contributed to these higher efficiencies along with the role of the PCM in releasing the stored heat during the afternoon to the thermal fluid. This minimized the heat loss from the PV/T collector at higher solar  
Figure 8: The average electrical efficiency of the four PV panels: (**a**) over the day from 9:30 to 16:30, and (**b**) around noon from 11:00 to 13:00.  
flux by storing the extra heat by changing the phase of the PCM and recovering it when the solar irradiance started to decline in the afternoon. The solar low concentration magnified the thermal heat recovered from the PV4 panel at noon to reach a maximum overall efficiency of 70.3%, compared to 61.6% for the PV3 panel on Day 5, as shown in Figure 9b. The cumulative effects of low cooling-water temperature, high mass flow rate, use of the PCM, and solar low concentration contributed to reaching this higher overall efficiency.
## 2 The Wear Data Analysis for PCCS and Problem Statement  
### The Pantograph Carbon Contact Strip  
Every subway has a total of two sets of pantographs (single-arm) which are installed on the odd number terminal and even number terminal, respectively. A typical single-arm pantograph for the subway system is shown in Figure 1. The overhead line is staggered for the track in order to guarantee uniform contact strip wear. The PCCS (see Figure 1) is located at the top of the pantograph head. It can clearly be seen that there are four carbon strips on the pantograph in contact with the catenary. If the train operates regularly, the four PCCSs contact with the catenary directly. If the pantograph lifting holding force is under the standard value, the carbon strip and catenary will not cooperate well, which may cause the arc discharge phenomenon due to the separation of carbon strip and catenary during operation, resulting in abrasion of the carbon strip and the catenary. The operation stability may influence the contacting force between one PCCS and the catenary. The overhead line slides over the carbon contact strips from one side to the other side repeatedly. The PCCS gets thinner and thinner gradually due to the wear issue between the catenary and the pantograph.  
The thickness of the pantograph strip of a brand-new pantograph is around 40 mm for the one used in this application. In terms of the Pantograph Maintenance Manual, if the PCCS itself does not have any cracks, it is replaced according to the wear thickness. When the carbon contact strip is thinner than 4.5 mm, the PCCS must be replaced. This inspection work is still carried out by the maintenance staff periodically. It is very desirable to predict the RUL of the PCCS in time so that predictive maintenance can be carried out. For an illustrative purpose, an illustration of wear condition for PCCS is demonstrated in Figure 2.  
Figure 1: The pantograph of the metro vehicle.  
In Figure 2, 40 mm represents the original thickness value, T1 and T2 are the remaining thickness value, the wear loss of the PCCS is the original thickness value minus the smallest value between T1 and T2 (the smallest remaining thickness value is taken as the record standard in practical).
# Robust Segmentation of Partial and Imperfect Dental Arches
## 4 Results and Discussion
### Ablation Study
#### 4.5.2 Impact of Other Factors  
Further experiments were conducted to evaluate the effects of (1) pre-processing with/without die meshes; (2) pre-processing with/without gingiva cleaning for master arches; (3) framework with/without post-processing. The rationale behind these experiments was to identify the procedure that affects the segmentation results the most. The results of the ablation experiments were compared with default settings using die mesh, gingiva cleaning for master arch, and post-processing.  
Table 2 provides the DSC performance result of our framework with different settings. The standard error was used to measure the dispersion as suggested in [44]. We can observe that PTV2 decreases its performance according to different settings. The PTV2-trained models are most sensitive to the presence of the die mesh. The default setting in bold demonstrates the best performance. Based on these results, registration is considered the most important step for the PTV2 segmentation model because the die mesh provided essential information for proper registration. It is difficult to distinguish two consecutive premolars if one of them is missing without space. The performance of PTV2 was decreased in most of these situations due to shifted labels even when it could properly segment the teeth. Although we used the missing teeth augmentation to increase the training samples with missing teeth with space, the augmentation of missing teeth without space was not addressed in this work.  
\begin{table}
\begin{tabular}{c c c} \hline
**Settings** & **Lower** & **Upper** \\ \hline w/o die & \(0.893\pm 0.012\) & \(0.892\pm 0.012\) \\ w/o MC & \(0.932\pm 0.008\) & \(0.916\pm 0.010\) \\ w/o PP & \(0.911\pm 0.011\) & \(0.928\pm 0.009\) \\
**default** & \(\mathbf{0.936\pm 0.008}\) & \(\mathbf{0.948\pm 0.007}\) \\ \hline \end{tabular}
\end{table}
Table 2: Ablation analysis showing the framework performance in terms of DSC for different settings. MC: Master gingiva Cleaning; PP: Post-Processing, \(\pm\) is followed by standard error.  
Figure 16: Impact of registration for full, semi-partial arch, and partial arch with less than 8 teeth compared with published pre-trained MeshSegNet model, and MeshSegNet and PTV2 models trained on our data and integrated in ArchSeg.
# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 4 Experiments and Results
### Ablation Experiments
#### 4.5.3 Ablation Experiments for CAM Module  
The CAM, which is added to skip connections between the encoder and decoder, can fuse high-level features to guide the feature selection of the low-level features. The deeper the layer, the richer the features. For this reason, we studied the number of CAM applied to the skip connection layer.  
As shown in Table 5, when CAM is employed simultaneously on the third and fourth layers, the effect is the best, and the results are 90.40, 85.54, 82.15, and 86.03 for the WT, TC, ET, and average Dice, respectively. We speculated that the shallow semantic information in first layer and second layer is less helpful to the decoder layer. CAM is beneficial to our architecture, but its effectiveness is limited.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Layer Number:Kernel Size**} & **WT** & **TC** & **ET** & **AVG** \\ \hline
**A** & Layer 1:3 & Layer 2:3 & Layer 3:3 & Layer 4:3 & 90.32 & 85.43 & 80.51 & 85.42 \\
**B** & Layer 1:5 & Layer 2:5 & Layer 3:5 & Layer 4:5 & 89.86 & 85.45 & 80.65 & 85.32 \\
**C** & Layer 1:7 & Layer 2:7 & Layer 3:7 & Layer 4:7 & 89.97 & 85.50 & 79.37 & 84.96 \\
**SL\({}_{\text{i}}\)RF** & Layer 1:3 & Layer 2:5 & Layer 3:7 & Layer 4:7 & **90.40** & **85.54** & **82.15** & **86.03** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dice results of ablation experiments of selective kernel for SL\({}_{\text{i}}\)RF module.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **SL\({}_{\text{i}}\)RF** & **AAM** & **CAM** & **DP** & **WT** & **TC** & **ET** & **AVG** \\ \hline
**A** & & \(\surd\) & & & 89.62 & 81.79 & 80.98 & 84.13 \\
**B** & \(\surd\) & \(\surd\) & & & 90.42 & 84.00 & 80.66 & 85.03 \\
**C** & \(\surd\) & \(\surd\) & \(\surd\) & & 90.13 & 84.19 & 81.14 & 85.15 \\
**D** & \(\surd\) & \(\surd\) & & \(\surd\) & **90.83** & 85.09 & 82.00 & 85.97 \\
**SARFNet** & \(\surd\) & \(\surd\) & \(\surd\) & 90.40 & **85.54** & **82.15** & **86.03** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dice result of ablation study of each module in SARFNet.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Layer**} & & & & \\ \cline{2-9}  & **4 th** & **3 rd** & **2 nd** & **1 st** & & & \\ \hline
**A** & & & & & **90.83** & 85.09 & 82.00 & 85.9 \\
**B** & \(\surd\) & & & & 90.65 & 85.31 & 80.00 & 85.32 \\
**C** (SARFNet) & \(\surd\) & \(\surd\) & & & 90.40 & **85.54** & **82.15** & **86.03** \\
**D** & \(\surd\) & \(\surd\) & \(\surd\) & & 89.24 & 84.10 & 81.74 & 85.02 \\
**E** & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 89.71 & 85.53 & 78.66 & 84.63 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dice results of ablation experiments for CAM module.
# Energy Efficient Transceiver Design for NOMA VLC Downlinks with Finite-Alphabet Inputs
## 3 Transceiver Design for NOMA VLC
### Optimal Power Allocation  
This subsection aims at proposing an optimal power allocation scheme to improve the system's energy efficiency.  
As we know, under high signal-to-noise ratio (SNR) conditions, the error performance of the ML detection is determined by the MED of the received signals. To optimize the performance of the ML detector, our main task is to maximize the MED between any distinct received signals under the power constraint. Hence, the corresponding problem can be stated as follows.  
**Problem 1**.: _For the given channel coefficient \(h_{i}\) and any given positive integer \(K_{i}\), find power allocation coefficients \(p_{i}\), \(i=1,2,\cdots,M\), such that the MED:_  
\[D=\min_{(s_{1}\cdots s_{M})\
eq(\tilde{s}_{1}\cdots\tilde{s}_{M})s_{i}\tilde{ s}_{i}\in\mathcal{S}_{i}}h_{M}\left|\sum_{i=1}^{M}p_{i}s_{i}-\sum_{i=1}^{M}p_{i} \tilde{s}_{i}\right| \tag{6}\]  
_is maximized subject to \(\sum_{i=1}^{M}p_{i}(2^{K_{i}}-1)=1\)._  
The optimal solution to Problem 1 is determined in the following theorem.  
**Theorem 1**.: _Let \(\tilde{p}_{i},i=1,2,\cdots,M\) denote the optimal power allocation coefficient, \(\tilde{D}=\min_{(s_{1}\cdots s_{M})\
eq(\tilde{s}_{1}\cdots s_{M})s_{i}\tilde{ s}_{i}\in\mathcal{S}}h_{M}|\sum_{i=1}^{M}\tilde{p}_{i}s_{i}-\sum_{i=1}^{M} \tilde{p}_{i}\tilde{s}_{i}|\). Then, \(\tilde{p}_{i}\) and \(\tilde{D}\) can be determined in the following._  
\[\left\{\begin{aligned} &\tilde{D}=\frac{h_{M}}{2^{\sum_{j=1}^{M}K_{j}}-1},\\ &\tilde{p}_{1}=\frac{\tilde{D}}{h_{M}},\tilde{p}_{i}=\frac{ \tilde{D}\times 2^{\sum_{j=1}^{i-1}K_{j}}}{h_{M}},i=2,3,\cdots,M.\end{aligned}\right. \tag{7}\]  
The proof of Theorem 1 is postponed to Appendix A.
## 2 Materials and Methods
### Data Sources  
Meteorological data used in this study were obtained from the ERA5-Land monthly mean dataset provided by the European Centre for Medium-Range Weather Forecasts ([[https://cds.climate.opernicus.eu/](https://cds.climate.opernicus.eu/)]([https://cds.climate.opernicus.eu/](https://cds.climate.opernicus.eu/)) (accessed on 20 May 2024)), with a horizontal resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and a temporal resolution of 1 month, covering the period from 1982 to 2022. Digital elevation model (DEM) data were downloaded from the Geospatial Data Cloud (Table 2) platform ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn)) (accessed on 8 May 2024)) with a spatial resolution of 30 m. The boundary of the Yellow River Basin was obtained from the Center for Resource and Environmental Science and Data. Land use data were derived from  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name of Reservoir**} & \multirow{2}{*}{\begin{tabular}{c} **Year of** \\ **Construction** \\ **(YEAR)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Normal** \\ **Storage Level** \\ **(FSL)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean Depth** \\ **(DEP)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Catchment** \\ **Area** \\ **CATCH** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Storage** \\ **Volume (CAP)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Long-Term** \\ **Average Flow** \\ **(DIS)** \\ \end{tabular} } \\ \hline  & & **m** & **m** & **km\({}^{2}\)** & **Billions of m\({}^{3}\)** & **Billions of m\({}^{3}\)** \\ \hline KY (Kangyang) & 2007 & 2033 & 20.2 & 7.35 & 0.288 & 216 \\ GB (Gongbo) & 2004 & 2005 & 103 & 4.85 & 6.2 & 200 \\ SZ (Suzhi) & 2005 & 1900 & 18.6 & 4.5 & 0.455 & 221 \\ HF (Huang) & 2011 & 1880.5 & 17.2 & 3.61 & 0.59 & 221 \\ JS (Ishixia) & 2010 & 1856 & 69.7 & 3.6 & 2.635 & 180 \\ SG (Sigouxia) & 2009 & 1748 & 25.7 & 41.81 & 0.47 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Location map of the study area (**a**) national topographic map; (**b**) topographic map of the study area; (**c**) elevation map.  
the China Land Cover Annual Dataset (CLCD) for the years 1985-2022 [32], provided by Landsat. Hydrological data for the Upper Yellow River (UYR) were primarily sourced from the Yellow River Hydrological Yearbook, compiled by the Yellow River Conservancy Commission (Table 1). This yearbook records detailed information on all reservoirs in the Yellow River, including basic data such as name, year of construction, longitude, latitude, altitude, area, storage capacity, average depth, installed capacity, catchment area, main purpose, long-term average flow rate, water residence time, and other key parameters, thus providing reliable foundational data for this study.
## 2 Experimental Validation
### Comparison of Temperature Field  
Based on the analysis carried out on each numerical model, the results of the temperature evolution at the three nodal points are presented. Figure 3 represents the numerical results obtained and compared with Test 4 in the points (A, E, F). All graphics represent the real fire curve used in the laboratory Tf_test4. In the M3 model, the heating curve considered in the cavity was the F_Test4 curve obtained experimentally.  
According to the results, curve A is the one with the highest temperature field because it is located between the gypsum board and the wood interface. Curve E is the one with the lowest temperature profile because it is located inside the wood component.  
Regarding the comparison of results between experimental and numerical tests, model M1 presents a great variation. M2 presents some similarities to the experimental test. M4 is the model that presents behavior closest to the experimental one, approaching the results  
Figure 3: Temperature field. Cavity with (**a**) M1—air mesh; (**b**) M2—radiation; (**c**) M3—heating curve; (**d**) M4—radiation and convection.  
obtained with M3, and both can validate the temperature results of Test 4 [1]. According to the results shown in Figure 3, a quantitative observation was obtained between models M3 and M4.  
Table 1 presents the amount of data on temperatures registered (n), the mean values of temperatures (M), the standard deviation (SD), and the standard error of the mean (SEM) for the models M3 and M4. The SD describes the variability between data of temperature; the SEM estimates the precision and uncertainty of how the study data represents the original population.  
The high value of SD allows us to form a conclusion on the high variability of the temperature values during all exposure fire, which was expected. The SEM indicates that the lower values are obtained in model M4, which permits to identity of model M4 as the chosen.
# Benchmarking GPU Tensor Cores on General Matrix Multiplication Kernels through CUTLASS
## 1 Introduction
This automatic mechanism works well in most cases, but outliers still exist whereby the chosen configuration is not suitable for the user's input. For instance, in big data analysis and processing, the programmer needs various custom kernels to handle incoming data streams and extract key features from them; to this end, one may wish to have a library containing kernels with higher customizability while maintaining performance with minimal programming and maintenance efforts. Realizing the need for a community-driven high-performance GPU kernel library with user customizability, NVIDIA announced CUTLASS, an open-source codebase to provide concepts, primitives, kernels and programming models on top of CUDA. CUTLASS is a template C++ library targeting GEMM, SpGEMM (sparse GEMM) and convolution kernels that are widely used in both AI and HPC applications. With the C++ template, CUTLASS implements zero-cost abstraction and compile-time polymorphism that supports varying data types. More importantly, CUTLASS offers a system of concepts, interfaces and approaches that allows users to easily split and distribute work items into different granularities of resource organizations. Figure 2 demonstrates a representative workflow pipeline in a CUTLASS GEMM kernel. Initially, the whole problem is parallelized by splitting the output matrix into multiple tiles. Each CTA (Cooperative Thread Array, also known as a _thread block_) is responsible for loading, calculating and then storing each tile in  
Figure 1: Floating point number representation formats of tf16 (top, also called e8m7) and tf32 (bottom, also called e8m10). the output matrix. Inside each CTA, 32 consecutive threads are organized as _warps_ and get executed on the _SM_ (Streaming Multiprocessor) in the lock-step fashion. In each iteration, each warp will try to load a tile of input matrix elements from global memory to shared memory, and synchronize at the CTA level to ensure all required data in current iteration are ready in shared memory. Then, the required CTA data in shared memory are further split into warp-level tiles to load into per-thread registers for calculation. Right after the data preparation is complete, the result tile will be computed depending on which hardware units are used (traditional CUDA Core for the SIMT fashion, Tensor Core for WMMA fashion). When the primary calculation is accomplished, some extra steps including scaling, clamping or rounding need to be conducted (called _epilogue_) before the output elements are stored back to global memory. As we can see, the whole pipeline including work item distribution and parallel execution is hierarchically organized corresponding to the hardware architecture, which is the core design of CUTLASS and other GPU-based accelerating algorithms.
# An Internet of Things Model for Improving Process Management on University Campus
## 2 Materials and Methods
### Architecture of a University Campus
#### 2.1.2 Network Layer  
The network layer is responsible for connecting smart devices to one another or to network devices or servers. This layer has sufficient tools and protocols to allow the transmission of data between devices or servers [31]. Depending on the requirements in each location, several of the traditional communication protocols remain in effect in the IoT, such as local network connections via Ethernet or wireless transmission through mobile connectivity. In larger environments, the operability depends on the connection speeds allowed by the new protocols such as 5G. These are the basis of the long-range connectivity of the IoT [32].  
In the university considered, in addition to the traditional protocols, the protocols that have been designed with respect to the IoT and the communication between devices and services are also used. An example is NFC or Bluetooth 4.0, which are part of the low energy provision precisely because they are intended to be implemented in systems with reduced batteries. Making the physical connection between the end devices is not sufficient to enable effective communication [33]. For such communication to occur, devices must know how to communicate. Figure 3 shows the necessary components that are needed in order to establish effective communication. Communication begins with a message, or information, which is sent from a source to a destination. This process is governed by protocols that must be respected so that the message is sent and understood correctly. Communication protocols include tasks such as encoding that are responsible for converting information into an acceptable form for transmission [34]. This task includes decoding which involves reversing the coding process so that the information is interpreted correctly. The way in which this task works is that the sending device converts messages sent over the network into bits. Each bit is encoded in a pattern of sounds, light waves, or electronic impulses, depending on the network medium used. The host or destination device receives and decodes the signal in order to interpret the message. In addition to this process, the communication task in an IoT environment is governed by a common communication model in a particular data medium. The protocols that govern it are the format and encapsulation of the message, the frame size, segmentation, message timing, flow control, response timeout, and message delivery options.
## 5 Results and Discussion
### [45\({}^{\circ}\)]\({}_{10}\) Model Calculation Results and Validation
#### 5.2.2 Matrix Damage  
The critical load for interface failure was 9108 Nm, and after crack failure at the interface, it instantly extended transversely to the matrix, and matrix failure occurred immediately afterwards. When the load reached 9396.46 Nm, the matrix in the reinforced section of the model failed, as shown in Figure 24. The MDI of the 10 th layer was 1.019, extending inward to the 8 th layer, and the failure of the interface and matrix occurred almost simultaneously. Point b in Figure 15b represents the stiffness reduction corresponding to the appearance of matrix damage; this result is basically consistent with the acoustic emission experimental results. As the load increased, when the torque reached 9554.38 Nm, the matrix damage extended inward from the 10 th layer to the 1 st layer, as shown in Figure 24. The MDI exhibited a decreasing trend from the outer layer to the inner layer; the damaged area also gradually became larger from the 1 st layer to the outer layer, and the damaged area of the 10 th layer was the largest and the most severe, as shown in Figure 25. Point c in Figure 15b shows the corresponding stiffness reduction after the failure of the matrix of all layers, at which time the matrix no longer had the ability to carry and transfer force. Thereby, more load was transferred to the destabilized fibres, which were then carried by these fibres.  
Figure 24: MDI of fibres of the [45\({}^{\circ}\)]\({}_{10}\) model under different loads.  
Figure 23: MDI of the interface element of the [45\({}^{\circ}\)]\({}_{10}\) RVE model: (**a**) M = 1000 Nm; (**b**) M = 3000 Nm; (**c**) M = 5000 Nm; (**d**) M = 7000 Nm; (**e**) M = 9100 Nm; and (**f**) M = 9108 Nm.