esa-sceva/satcom-chunk-collection huggingface dataset:
## WT1 Immunohistochemical Expression in Human Embryonal/Fetal and Neoplastic Tissues
### WT1 Expression in Vascular Tumors  
Vascular lesions are a heterogeneous group of tumors, including benign and malignant tumors, as well as malformations. Vascular tumors and malformations can be diagnostically challenging. Although they may initially appear very similar, they have distinct clinical courses and management. To provide a correct diagnosis, several studies have been conducted to identify immunohistochemical markers. Among the antibodies studied, interesting results have emerged from the study of WT1 cytoplasmic expression. Based on the studies available in the literature that compare vascular lesions, both benign and malignant, with malformations, it emerged that almost all benign lesions, such as capillary hemangiioma, pyogenic granulomas, cherry angiomas and tufted angiomas, are WT1-positive, while lymphangioma and cavern hemangioma are negative. Malignant tumors, such as angiosarcomas, hemangioidotheliomas and Kaposi's sarcomas, are strongly WT1-positive (Figure 13). WT-1 expression in vascular malformations (angiokeratoma/verrucous hemangiioma, combined vascular malformations, venous malformations, glomerous malformations, lymphatic malformations/lymphangioma, telangiectasia and targetoid hemosiderotic hemangioma) is generally completely negative or weak and focal positive. Interestingly, a strong and diffuse WT-1 staining was reported in a case of thrombosed vascular malformation with prominent endothelial hyperplasia [158; 159; 160]. WT1 expression in vascular tumors was also analyzed in fetal tissues, where a moderate to strong staining intensity in the cytoplasm of the endothelial cells of blood vessels was reported [37; 52]. As WT1 staining has been obtained in the cytoplasm of embryonal/fetal endothelial cells, the reported cytoplasmic expression of this marker in many vascular tumors is not at all surprising.  
Figure 12: Congenital/infantile fibrosarcoma with herring-bone configuration (**A**). Neoplastic cells showing diffuse cytoplasmic WT1 (_N-terminus_) expression (**B**).
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 3 Problem Formulation
### OTFS Receiver  
At the receiver, the Wigner transform maps the received signal \(r(t)\) to the TF domain:  
\[Y[m,n]=\int r(t)\cdot g_{rx}^{*}(t-nT)\cdot e^{-2\pi m\Delta f(t-nT)}dt, \tag{5}\]  
where \(g_{rx}(t)\) is the receive pulse shaping function.  
Then, the symplectic finite fourier transform (SFFT) is used to obtain the DD-domain signal:  
\[y[k,l]=\frac{1}{\sqrt{NM}}\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}Y[m,n]\cdot e^{-2\pi \left(\frac{M}{N}-\frac{m}{M}\right)}. \tag{6}\]
## 4 Discussion  
Results from the literature review (method A) prove that the topic is considerably investigated from a theoretical perspective. Similarly, results from tool screening (method B) confirm that designers and contractors have several possibilities to assess the circularity performances of their projects. Nevertheless, it is confirmed what was initially stated, that there is a lack of coordination between policies and tools, which hampers the systematic implementation of CE in constructions. Indeed, few connections among the findings from each method have been found, and a wide room for improvement is detected in both, as well as high advancement potentialities due to the two ends' mutual exchange.  
Looking at the correlation matrix, it emerges that recycled materials--the assessment topic to which rating systems assign on average more weight--are quite well correlated to the main drivers. The issue is mentioned as already established, especially with respect to green demolition (D11), but would highly benefit from further development in the field of urban mining and material bank databases (D8). As for life cycle impact, room for improvement is mentioned especially with regard to methodological issues (D1, D2 and D9), suggesting that a broader, multi-scale and cross-disciplinary approach would help  
Figure 9: Radar charts for relevant drivers: clockwise (**a**–**d**). Light blue represents established areas, while the blue dotted line represents drivers yet to be developed.  
the assessment along with digitalization. Water management and reuse is not deemed as relevant to the topic from the driver's standpoint.  
Waste management emerges as pivotal in this correlation, both for established connections and areas in need of improvement, where it particularly stands out that urban mining and the development of material banks, platforms and markets (D8) will be crucial. On the other hand, it can be generally observed that certain assessment areas have little or no connection with the detected drivers, as in the case of Adaptability and Flexibility. Design for Disassembly, instead, is very little established in practice, but mentioned as an area to be developed further, especially to develop as-a-service business models and Extended the Producer Responsibility (EPR) beyond selling, which is something the European Commission is particularly emphasizing.  
Grounded in these overall observations, a specific discussion regarding barriers and drivers as a knowledge basis for further research steps follows; this is graphically synthesized in Figure 10, considering the journey that policies and tools should jointly undertake to advance systematic implementation of circularity in the construction sector.
## 5 Simulation Results
### Packet Delivery  
The total number of packets that the BS has received is the definition of this network performance indicator. Regarding network throughput, the ESO-GJO approach performs better than the LSA, LEACH-IACA, and LEACH-ANT algorithms in Figure 6a (100 nodes). Specifically, ESO-GJO's network throughput outperformed PSO-LSA,EPOA-CHS, LEACH-IACA, and LEACH-ANT by 7.89%, 5.44%, 119.3%, and 133.1%, respectively. The ESO-GJO algorithm outperforms PSO-LSA,EPOA-CHS, LEACH-IACA, and LEACH-ANT in terms of network throughput when Figure 6b (200 nodes) is considered. Specifically, ESO-GJO's network throughput outperformed PSO-LSA,EPOA-CHS, LEACH-IACA, and LEACH-ANT by 6.62%, 4.70%, 167.36%, and 156.22%, respectively. Finally, the ESO-GJO algorithm outperforms PSO-LSA, EPOA-CHS, LEACH-IACA, and LEACH-ANT in terms of network throughput when Figure 6c (300 nodes) is considered. Specifically, ESO-GJO's network throughput outperformed PSO-LSA, EPOA-CHS, LEACH-IACA, and LEACH-ANT by 1.02%, 0.58%, 308.26%, and 149.70%, respectively. These outcomes unequivocally demonstrate the improved network throughput that the ESO-GJO algorithm achieves in many contexts as well as its superiority over the other protocols.  
\begin{table}
\begin{tabular}{c l c c c c} \hline \multicolumn{2}{c}{**No. of Rounds**} & & & & \\ \hline
**Simulation Scenario** & **Protocol** & **FND** & **HND** & **MND** & **LND** \\ \hline \multirow{6}{*}{Number of nodes is 100} & ESO-GJO & 2080 & 2350 & 2383 & 2448 \\  & PSO-LSA & 1805 & 2191 & 2233 & 2332 \\  & EPOA-CHS & 1920 & 2249 & 2290 & 2392 \\  & LEACH-IACA & 755 & 1060 & 1116 & 1214 \\  & LEACH-ANT & 701 & 1023 & 1035 & 1094 \\ \hline \multirow{6}{*}{Number of nodes is 200} & ESO-GJO & 1091 & 2231 & 2287 & 2863 \\  & PSO-LSA & 1698 & 2492 & 2628 & 2402 \\ \cline{1-1}  & EPOA-CHS & 1322 & 2334 & 2424 & 2623 \\ \cline{1-1}  & LEACH-IACA & 775 & 889 & 913 & 1012 \\ \cline{1-1}  & LEACH-ANT & 821 & 925 & 947 & 1038 \\ \hline \multirow{6}{*}{Number of nodes is 300} & ESO-GJO & 683 & 2333 & 2393 & 2737 \\ \cline{1-1}  & PSO-LSA & 1275 & 2230 & 2275 & 2407 \\ \cline{1-1}  & EPOA-CHS & 879 & 2268 & 2343 & 2565 \\ \cline{1-1}  & LEACH-IACA & 483 & 546 & 553 & 584 \\ \cline{1-1}  & LEACH-ANT & 653 & 901 & 934 & 1010 \\ \hline \end{tabular}
\end{table}
Table 2: Round and lifetime.  
Figure 4: Network life cycle. Scenarios with (**a**) 100 nodes; (**b**) 200 nodes; (**c**) 300 nodes.  
Figure 5: Live nodes number in each round. Scenarios with (a) 100 nodes; (b) 200 nodes; (c) 300 nodes.  
Figure 6: Packet delivery in each round. Scenarios with (**a**) 100 nodes; (**b**) 200 nodes; (**c**) 300 nodes.
## 1 Introduction
On the other hand, the proposed methodology optimizes the gateways placement points from pre-established locations by a network planning specialist, imposing a restriction to make the process less computationally expensive, preventing the algorithm from choosing an inappropriate place physically, environmentally, and financially, keeping in mind that we are dealing with a wooded region, which is difficult to access. In addition to performing an adjustment in a regional propagation model through a GA based on measurements performed in the place where the implementation of the IoT network is desired, we also intend to improve the accuracy in the positioning of the gateways to serve the entire coverage area of the smart campus, ensuring a quality signal using SF7 and establishing a conservative communication threshold, that is, ensuring uninterrupted communication with all devices within the smart campus. Thus, this study aims to estimate the signal level of a LoRa network using data measured at the Belem Campus, Federal University of Para (UFPA), a typical highly wooded Amazonian environment, applying an empirical model called UFPA for prediction of the RSSI adjusted through a Genetic Algorithm (GA). To determine the signal coverage radius, define the best positioning of the gateways, maximize the coverage area, and minimize the amount of equipment needed to implement a LoRa network, was used the Evolutionary Particle Swarm Optimization (EPSO) multi-objective optimization technique. Section 2 describes the signal strength measurement campaign conducted on the campus. The methodology used is described in Section 3. In Section 4, the results and a discussion are presented. Finally, Section 5 presents the final considerations of the study and discusses future work.
# Prediction of Building's Thermal Performance Using LSTM and MLP Neural Networks
## 2 Materials and Methods
### Data Acquisition of the Experimental Case Study
#### 2.2.1 Building and HVAC System Description  
The case study building where this new methodology has been proved is a public library located in the city of Vigo, in the north west of Spain. It is a three floor building with connected and large open areas that trigger temperature stratification inside the library. The building has a working floor area of 820 m\({}^{2}\) and the average capacity is 220 persons. This total usable floor area includes 412.3 m\({}^{2}\) on the ground floor, 233.5 m\({}^{2}\) on the first floor and 73.0 m\({}^{2}\) on the second floor. Due to the interconnected spaces and the large window area, the building experiences high thermal inertia.  
The HVAC system is a ground-source heat pump (GSHP) distributed with a radiant floor (RF). The reversible heat pump is a CIATESA IZA 185 with a nominal heating capacity of 45 kW and nominal cooling capacity of 35.7 kW, with a COP of 3.63 for heating and EER of 3.25 for cooling. It has a glycol-water mixture flow rate of 8450 m\({}^{3}\)/h. The water storage tank contains 200 L. The GSHP is composed of six boreholes 100 m deep. The set point temperature for heating is 20 \({}^{\circ}\)C when the library is open and 17 \({}^{\circ}\)C when the library is closed. For cooling, the setpoint temperature is 26 \({}^{\circ}\)C when the library is open and 29 \({}^{\circ}\)C when closed. The tested heating period goes from October to May.  
Further information about the building and its HVAC system can be consulted in [43, 44, 59].  
Figure 3: The variables used as input into the models to predict the thermal demand of the building (left) and the indoor temperatures of the building (right).
# Development of a 3-DOF Cylindrical Ultrasonic Motor Based on Non-Standard Modes
## 2 Configuration of the Motor
### Stator Assembly Analysis and Operating Principle  
#### Layout Rules for PZT  
The vibration mode of the motor is excited by piezoelectric ceramics. The arrangements of PZTs used in motors for ultrasonic frequency vibration are depicted in Figure 3. The red arrows in the diagram represent the direction of polarization. Piezoelectric ceramics are toroidal and use \(d_{33}\)-coupled vibration modes. The PZTs are divided into two groups, producing longitudinal and bending vibrations.  
Figure 3a shows the longitudinal vibration ceramic set. The cylindrical ultrasonic motors most often use odd-order longitudinal vibration mode. This ceramic set is arranged in the middle of the designed stator to improve the excitation efficiency. The longitudinal vibration ceramic set has two ceramic rings and two electrode sheets. PZTs are polarized in uniform thickness directions. The polarization directions of the ceramic rings are opposite or back-to-back, as shown in the diagram. The middle electrode sheet of the two ceramic rings is used to apply a high-frequency A.C. voltage while the other electrode is grounded. The two piezoelectric rings produce displacement in the same direction. Figure 3b presents the bending vibration ceramic set. For this stator design, if a low odd order of longitudinal vibration is used, the inherent frequency of the stator will be higher than the frequency of bending vibration of the same order. Therefore, a higher-order bending vibration should be chosen. The bending vibration ceramic set uses two groups of ceramics with orthogonal distribution: groups A and B. Group A is distributed close to the clamping body, and group B is closer to the end of the motor. The modes produced by voltage excitation of longitudinally vibrating piezoelectric and curved vibrating piezoelectric ceramics are known as mixed modes. Simultaneous excitation of both longitudinal and bending modes  
Figure 3: Piezoelectric ceramic arrangement of the stator: (**a**) longitudinal vibration ceramic set, and (**b**) bending vibration ceramic set.  
Figure 2: Schematic diagram of the clamping body.  
can change the trajectory of the stator surface plasmas, thus driving the rotor to rotate in different directions. The specific modes of motion are described in Section 4.
## 3 Materials and Methods
### MOEA/D QoS Optimal Path  
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) [42] decomposes a MOP into a set of scalar optimization subproblems using \(N\) uniformly distributed weight vectors, \(w_{i}\), each of which is iteratively optimized using recombination operators (crossover and mutation). Each normalized weight vector is uniformly distributed in \(T\) regions, called neighboring subproblems, and the objective is to find a Pareto-optimal solution along each reference neighborhood. The child solutions replace the parent solutions of the subproblems if their cost, \(w\), is better than the previous cost. An important aspect of the algorithm is the choice of the decomposition method. According to [PERSON] and [PERSON] [43], three common decomposition methods are implemented: weighted sum, Tchebysheff, and penalty-based boundary interaction. For simplicity, in this paper, we focus only on the weighted sum approach according to Equation (13). The problem is divided into three weight vectors using the weighted sum method, where each neighborhood is assigned a network parameter priority using weight vectors, \(w\) (Algorithm 3, introduces MOEA/D pseudocode).  
```
1:data = parameters // Initial parameter MOEA/D
2:pop = solution // Create an initial population
3:\(W\) (t = 0) // Initialize Weight vectors
4:T = \(w_{i}\)\(e\)\(W(t)\) // Calculate neighborhoods by the weight vectors
5:\(P_{F}\) = [] //create pareto front = empty
6:While (terminal Condition==false) do
7:For neighborhood \(\epsilon\)pop do //everyone Weight vector builds a neighborhood
8:For individualneighborhood do
9:parents = (neighborhood) //selection parents by neighborhood
10: offspring = recombination (data, parents) //crossover
11: offspring = mutation (data, offspring)
12:pop = replacement (parents, offspring)
13:\(P_{F}\) = Insert (Offspring)
14:End
15:End
16:pop = feedback (_Pareto Front_)
17:End
```  
**Algorithm 3** Pseudocode of metaheuristics based on MOEA/D
# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 3 Results and Discussions
### Tests on the Swedish Leaf Dataset
#### 3.2.2 Performance Test of MEQ Method on Swedish Leaf Dataset  
In this section, six commonly used CNN models were used to test the performance of MEQ methods. Table 3 lists the classification results of these six models on the standardized Swedish leaf and the original Swedish leaf. And their validation accuracy and validation loss are shown in Figure 10. It can be seen that the classification performance of almost all tested models has a certain degree of improvement in the standardized dataset. Among them, the MAC values of ResNet18 and ResNet50 models both reach 99.39%. In addition, the MAC values of AlexNet and ResNet101 models both reach 99.70%. Compared with the AC values obtained on the original dataset, their classification results are improved by about 0.30%. For the VGG16 and GoogLeNet models, although their classification results are not significantly improved, it is not difficult to find from Figure 9 that these networks have lower validation loss values on the standardized dataset. Moreover, it can also be found from Figure 10 that the proposed method reduces the fluctuation of the validation accuracy curve of these models during the training process to some extent. Therefore, the above experimental results can prove that using the MEQ method to standardize the dataset is beneficial to the classification and recognition of the CNN model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **AC** & **MAC** \\ \hline AlexNet & 99.39\% & 99.70\% \\ GoogLeNet & 99.39\% & 99.39\% \\ VGG16 & 100\% & 100\% \\ ResNet18 & 99.09\% & 99.39\% \\ ResNet50 & 99.09\% & 99.39\% \\ ResNet101 & 99.39\% & 99.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of classification results of CNN models on the original and standardized Swedish leaf datasets.  
Figure 9: The original image of the Swedish leaf sample (**a**), input images obtained by direct compression (**b**), and input images obtained by MEQ standardization (**c**).  
Figure 10: Cont.
## 1 Introduction
[29] monitored salt aggregation of latex particles for a long time with a vertically installed Couette device with a rotating inner cylinder. Their results revealed evidence of cluster rearrangement. On this problem, [PERSON] et al. [30; 31; 32] have reported extensively to demonstrate breakup and rearrangement of clusters using latex suspension and have proposed the analytical method of population balance. Their results clearly show that the size of the floc increased in the beginning, and in some cases, the size decreased after reaching the maximum. However, the condition and reproducibility of their behavior was not fully analyzed. The effectiveness of this method was reviewed by [PERSON] et al. [33] by placing emphasis on the permeability and restructuring process. A variety of studies entailing not only simple shear but also programmed, well-defined flows have been published recently, revealing the frequent appearance of densification processes during flocculation [34; 35]. However, such results are not fully reflected in the design of water treatment processes; therefore, the original question of the effect of the ALT ratio on flocculation remains unanswered. Conventionally, in water purification plants, the main control factors for the flocculation process are the injection rate of the flocculant (PACI) and pH. To increase floc size, flocculation is generally performed with rapid and slow fixed intensities. Large flocs have a higher settling velocity related to the separation load in the settling basin. However, the relation between floc size and settling velocity is complex, and, as the floc grows according to the fractal scaling law, the density decreases with respect to the floc size, following a power-law relationship. Therefore, the authors attempted to develop a progressive flocculation method able to achieve high density and large flocs to simplify solid-liquid separation facilities. We investigated the possibility of high-density and high-settling-velocity flocs by controlling not only PACI and pH, but also agitation intensity. In a previous report [36], the ratio of kaolinite to PACI was fixed under a laminar shear flow (\(G\)-value 29 1/s) to visualize the structural changes in the floc. As a result, the flocs grew rapidly in the initial stage of flocculation but did not grow beyond a certain diameter and were broken up by the flow field. Surrounding fragments produced by the fracturing were incorporated into larger flocs and rearrangements, contributing to densification. Thus, under constant agitation intensity, the flocs were qualitatively confirmed to increase in density through repeated growth, fracture, and rearrangement. It was confirmed that these phenomena tend to occur under a higher shear flow. The purpose of the present investigation is to obtain the characteristics of the floc structure under a constant laminar shear flow by varying the ALT ratio and agitation intensity simultaneously. A further objective is that of understanding the details of the phenomenon of floc densification by rearrangement and determining the conditions under which flocs are easily densified.
## 3 Applications of L-Band SAR Data in Forestry
### Forest Stocks
improved the AGB map of tropical arid forests by integrating LiDAR, ALOS PALSAR, and climate data, and reduced the relative error of biomass estimation by 12.2% [41]. [PERSON] et al. used L-band ALOS data to estimate the AGB of Chinese fir forests in Huangfengqiao Forest Farm, Hunan Province, by extracting multiple rotation thresholds, and realized an estimation accuracy of 77.5% [42]. [PERSON] et al. estimated the biomass of deciduous forests in mountainous areas with three-dimensional (3D) data and found that the season had a significant impact on the estimation results [43]. These studies show that the L-band has the potential to estimate forest stock and biomass, but the accuracy of estimation results is generally lower than 80%, due to inaccurate information of forest vertical structures.
# Failure Life Prediction of Hub Bearing in Composite Tooling
## 2 Derivation of Loads on the Hub Bearing
### Structure of Composite Working Implements  
A composite working implement is a working implementation that performs two or more tasks simultaneously with the tractive force of a tractor. The composite working implement in this study is one that simultaneously performs tillage operation and hilling work. As shown in Figure 1, the composite working implement consists of a three-point hitch connector for mounting working tools on the three-point hitch system of the tractor, disk narrow assemblies for tillage operation for paddy fields or fields, chisel plows for crushing subsoil, a hiller for forming ridges, and the mainframe for mounting components.  
There are 18 disk narrow assemblies in total. Nine versions of assembly are installed at equal intervals in each of the rows in the front and rear of the mainframe, and four chisel plows are mounted in a row in the middle between the front and rear disk narrow assemblies. As shown in Figure 2, a disk narrow assembly consists of a disk narrow that performs tillage operation, a hub bearing that enables the rotational movement of the disk narrow and supports loads and a disk narrow arm for connecting the hub bearing and the frame.