esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 4 Obstacle Avoidance Algorithm and Transverse Controller Design
### Coordinated Control Design  
The results of the lateral simulation in Section 4.2 show that different speeds correspond to different parameters of the lateral motion controller, the vehicle speed is an important input of the longitudinal motion controller, and the impact of avoiding obstacles. When the speed is below 18 km/h and the matching predictive time domain is 7, replanning works well. When the vehicle speed is greater than 18 km/h and less than 36 km/h, the corresponding prediction time domain is 10, and the effect of obstacle avoidance replanning is the best. Therefore, the primary vehicle speed information is used to coordinate and regulate the driverless ferry vehicle's horizontal and longitudinal motion. The speed-planning module is designed to predict the two-dimensional time-domain look-up table module, and the optimal predicted time-domain values of different speeds and their corresponding \(N_{p}\) are input into the two-dimensional look-up table module as a horizontal and vertical correlation module. When the vehicle speed is lower than 18 km/h, the corresponding prediction time domain is 7, and when the speed is greater than 18 km/h less than 36 km/h, the corresponding prediction time domain is 10. Figure 18 illustrates the coordinated control structure that is both horizontal and vertical.
## 5 Experimental Results and Discussion
### Comparison
#### 5.2.2 Different Scales of Data  
In response to a data-limited environment, this study randomly subsampled the training set of EdNet at different proportions, constructing subsets containing 70%, 50%, 30%, 10%, and 1% of the entire dataset. For different scales of data, we have compared different methods, based on BERT, ALBERT, and UniLM. Figure 6 illustrates the AUC of various models under different scales of data. The percentages represent the proportion of data in the subsets. \"FT\" refers to fine-tuning while \"PT\" represents prefix-tuning. \"BERT\", \"ALBERT\", and \"UniLM\" represent three pre-trained models.  
The experimental results indicate that the AUC of the fine-tuning methods obviously decreases when the training samples are reduced to 30% of the whole data based on the three pre-trained models. In contrast, the performance of the prefix-tuning method at 30% training sample size decreases less obviously, which is better than the performance of the fine-tuning method using the full 100% dataset. This is because prefix-tuning has better parameter efficiency. A significant performance decrease is observed only when the training samples are reduced to 10% of the total data. It shows that even prefix-tuning methods may face the risk of overfitting, as the model might fail to generalize to unseen data. Moreover, with a 1% training sample, it is evident that the prefix-tuning methods based on the three pre-trained models result in AUC improvement of 0.0645, 0.0912, and 0.0674, respectively, compared to fine-tuning. Due to differences in the number of trainable parameters and  
Figure 5: The experimental result of fine-tunning and prefix-tunning, based on different pre-trained model.  
Figure 6: The AUC for different combinations of pretrained models and approaches under different quantity of data.  
the impact of the prefix sequence on the global context, the advantages of prefix-tuning become more apparent on limited datasets. Therefore, FAP-Prefix is effective in adapting to data-limited scenarios and achieves better predictive performance by reducing the number of iteration parameters.
## 1 Introduction
Comparing healthy tissues with neoplastic tissues, there is a greater expression of Wt1, Srpk1, Srsf1 and the pro-angiogenic VEGF isoforms. WT1 inhibition regulates negatively the expression of Srpk1 and Srsf1 in endothelial cells, inducing the development of the antiangiogenic VEGF isoform, associated with apoptotic cell death [31; 32]. In the past many authors believed that WT1 immunohistochemical expression, using antibodies directed against the _C-terminal_ portion of the protein [33; 34], was exclusively limited to the nucleus, and that the occasional cytoplasmic staining obtained in some tumors was the result of an artifact (non-specific staining). The variability of staining with anti-WT1 _C-terminus_ antibodies could be due to the use of different clones of the same antibody. Subsequently, with the advent of newly available antibodies against the _N-terminal_ portion of WT1 (clone WT6F-H2), it has been demonstrated that WT1 expression can be found in the nucleus or in the cytoplasm, or concurrently in both the nucleus and cytoplasm [35; 36; 37; 38; 39; 40; 41; 42]. During human ontogenesis, according to the antibodies used, _anti-C_ or _N-terminus_ WT1 protein, [36; 39; 40; 41; 42; 43; 44; 45; 46; 47], nuclear expression can be frequently obtained in metanephric and mesonephirglomeruli, primary sex cords, gonadic stroma and mesothelial and sub-mesothelial cells, while cytoplasmic staining is usually found in developing skeletal muscles, myocardium, radial glia of the spinal cord and cerebral cortex, sympathetic neuroblasts, adrenal cortical cells and the endothelial cells of blood vessels [33; 34; 37; 38; 49; 50; 51; 52]. With regard to neoplastic tissues, WT1 protein has been found in several tumors with variable immunostaining patterns, i.e., exclusively nuclear, cytoplasmic or both, according to the antibodies used (_anti-C_ or _N-terminus_ WT1 protein) [35; 36; 39; 40; 41; 42; 43; 44; 45; 46; 47]. The variable nuclear and cytoplasmic WT1 staining may be explained by assuming that the expression of this transcription factor in some neoplastic tissues (Wilms tumor, ovarian, mesothelial neoplasms, Sertoli cell tumor and rhabdomyosarcoma) mirrors its normal developmental regulation [37; 38; 41]. In addition, our research group have recently demonstrated that WT1 shows an oncofetal expression pattern, being abundantly detected in developing and neoplastic skeletal muscle tissues, while its expression is down-regulated in adult normal skeletal muscle tissues [41; 46]. The present review focuses on the immunohistochemical expression of WT1 in some common and less common tumors (Table S1), emphasizing its potential diagnostic role and usefulness in differential diagnosis. In addition, we analyze WT1 protein expression profiles during human ontogenesis to provide suggestions about its role in the development of organs and tissues, and to establish if its expression in some tumors replicates that observed during the development of tissues from which these tumors arise.
## 3 Results
### The SARSF-RA Results  
The SARSF-RA result is as shown in Figure A2c, and the spatial distribution map is as shown in Figure A1c. It was concluded that: (1) the highest SARSF-RA value was in Versailles Estate, i.e., 4.804; the SARSF-RA of LiRen Garden#2 was the smallest at zero, and this value was related to the processing method of the ASF-RA standardized data. Thus, the Versailles Estate residential area had the strongest comprehensive service capability and the highest value of reasonableness. (2) The SARSF-RA values of HuaHuiXinCun and HuijingYuan were higher, at 2.750 and 2.680, respectively. It is easy to determine from the spatial distribution that the two residential areas are adjacent to each other and are located in the commercial area. Also, these two communities are low-floor mature communities that were built before 2010. The overall service capacity of the low-floor mature residential area located in the commercial center was ranked second. The SARSF-RA values in LiRen Garden#1 and LiRen Garden#2 were the lowest, at 0.280 and 0, respectively. The reason for this phenomenon is that the two communities are located outside of the commercial area of Hudai Town and are far from the Hudai Industrial Zone; thus, there is less infrastructure. Also, LiRen Garden#1 was the most occupied residential area, which resulted in a low SARSF-RA value. There were demolition and resettlement houses in LiRen Garden#1 and LiRen Garden#2, and the number of surrounding supporting facilities was small. However, the number of occupants was large, and it could be concluded that the comprehensive service facilities capacity of the demolition and resettlement housing community outside the commercial area was poor. Based on the existing analysis, the SARSF-RA largely depended on the hierarchical position, the maturity of the location, and the location of the community.  
In additional, Graduated color symbology is used to show a quantitative difference that can classify the SARSF-RA in the experimental area, as shown in Figure A1c. The low-value area was interlaced with the high-value area. Also, it was block-shaped, and the spatial differentiation was evident, which indicated that the comprehensive serviceability was different at different levels and conditions. There were social phenomena that were similar to the classification and differentiation of communities. Some historical, social, economic, geographical, and administration factors have impacted urban planning orientation. From the perspective of the spatial allocation rationality of service facilities, we attempt to consider the future direction and suggestion of urban planning for the experimental area. We suggest that increasing the surrounding commonal service facilities over low SARSF-RA residential areas, such as LiRen Garden#1 and LiRen Garden#2, can attract more residents.
## 2 Numerical Model
### Grid Division Strategy  
The grid cells used for the numerical simulations were generated using STARCCM+ software, and the grid type was a cut-body grid. Adaptive encryption was used for the train and track surfaces to provide the mesh with an appropriate encryption size for the model area. The minimum mesh size for the train surface was \(5.42\times 10^{-3}\) h and that for the track surface was \(2.72\times 10^{-3}\) h. To simulate the development of the flow field on the train surface, a 20-layer boundary layer mesh with a growth rate of 1.2 and total thickness of 6 mm was applied to the surface of the majorle train. According to [PERSON] study [23], the tunnel surface and ground boundary layer slightly affect the propagation and diffusion of pressure waves in the tunnel. Therefore, the near-wall attached boundary layer grid is not used at the tunnel wall and ground. The wall treatment method corresponds to full y+ wall treatment, with a total grid volume of approximately 50,249,100 and grid volume of approximately 15 million in the overlapping grid area around the car body, as shown in Figure 5.  
Figure 4: Location of measurement points. (**a**) Location of measurement points in the tunnel. (**b**) Layout of measurement points outside the tunnel.  
Figure 5: Grid generation details. (**a**) Longitudinal section of train; (**b**) Internal tunnel cross section; (**c**) Cross section of train.  
The calculations were performed using the SST K-\(\omega\) separated flow simulation based on the DDES turbulence simulation method, with the flow term set in mixed-BCD format while using the flow field equation of state for an ideal gas and separating the fluid temperatures. The boundary conditions in the computational domain were set to free-stream, incoming flow conditions were set to Mach 0, time step was set to \(5.3\times 10^{-5}\) s, and the second order time discrete format was selected. Furthermore, the maximum physical time and number of internal iterations were set to 0.3 s and 25, respectively.
## 4 Methodology
### Proposed Hybrid Framework  
The proposed hybrid framework consists of two major components: a multi-channel convolutional neural network-based encoder (MC-CNN-Encoder) and a Long Short-Term Memory-based decoder (LSTM-Decoder). The framework is connected in a linear structure and trained jointly to minimize the reconstruction error. The CNN layers are an important part of the framework and work as encoder to extract spatial features. The multi-channel structure helps to capture the spatial dependencies in neighboring spatial regions. The reconstruction error of the framework for the given data point is used for anomaly score. Our model design is inspired by the network combining CNN and LSTM into one unified architecture for text prediction problem [49], which shows that LSTM performance can be improved by providing better features. The overall architecture of the framework is illustrated in Figure 3.  
The performance of the designed framework depends on various components and training parameters. The number of layers and units in each layer, the number of kernels used in each convolutional layer, dropout ratio and type and size of pooling operators are all important parameters in the anomaly detection process as they can affect the learning performance and speed. They play an important role in extracting more representative features of the training data. To determine the optimal architecture and the best training parameters for the overall network, including the optimum parameters used to preprocess the raw data such as window size and step size, the characteristics of the input data have to be analyzed carefully. In this study, the number of layers and neurons on each layer are selected accordingly to the best empirical performance of grid search on training dataset.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 3 Prototyping Methodology
### Prototype Development  
Our primary objective was to gather relevant environmental data. The secondary objective was to communicate the data to a smartphone for a better user experience. Therefore, the development started with two mini projects from public libraries to present the opening idea. The first stage included a simple display of data on the LCD, and the second stage communicated the data using Wi-Fi.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Representation** & **Description** & **Range** & **Accuracy** \\ \hline Arduino Mega 2560 & Microcontroller unit & \(-\) & \(-\) \\ Data logger & Arduino shield & \(-\) & \(-\) \\ DS18B20 & Temperature (T\({}_{\mathrm{g}}\)) & \(-\)55 to +125 \({}^{\circ}\)C & \(\pm\)0.5 \({}^{\circ}\)C \\ SHT20 & Temperature (T\({}_{\mathrm{a}}\)) & \(-\)40 to +125 \({}^{\circ}\)C & \(\pm\) 0.3 \({}^{\circ}\)C \\ Humidity (RH) & 0–100\% & \(\pm\) 3\% \\ TSL2561 & Luminosity (I) & 0.1\(-\)40,000 lux & \(-\) \\ TCS34725 & RGB & \(-\) & \(-\) \\ SEN0170 & Wind (v) & 0\(-\)30 m/s & \(\pm\)3\% \\ Adafruit Ultimate & GPS breakout & 22 satellites & \textgreater{}3 m \\ SEN-14262 & Sound detector & \(-\) & \(-\) \\ PMS5003 & Particulate matter & 0.3 \(\upmu\)m\(-\)10 \(\upmu\) & 98\% (at 0.5 \(\upmu\)m) \\ ESP-01 & Wi-Fi module & & \\ Feather Huzzah & Microcontroller unit & \(-\) & \(-\) \\ DHT11 & Temperature (T\({}_{\mathrm{a}}\)) & 0\(-\)50 \({}^{\circ}\)C & \(\pm\)2.0 \({}^{\circ}\)C \\ Humidity (RH) & 20–80\% & \(\pm\)5\% \\ Liquid crystal & 16\(\times\)02 Alphanumerical & \(-\) & \(-\) \\ display (LCD) 1602 & 16\(\times\)02 Alphanumerical & \(-\) & \(-\) \\ display & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of components for the Weather-Gear prototype.
# Historically Based Room Acoustic Analysis and Auralization of a Church in the 1470s
## 2 Background on the Church  
The church targeted for reconstruction in this paper is a Gothic abbey church located in the south of Sweden, built in the 14 th and 15 th century. It played an important religious and cultural role in the Nordic countries in the middle ages. After the Swedish reformation (1527), the monastery was eventually dissolved and the abbey church fell into disrepair and neglect for almost two centuries. Although some artifacts remain and most of the abbey's extensive library has been preserved, the interior space of the church has been significantly altered due to several renovations [36].  
The church itself is oriented west-east, with the chancel in the west. Its nave is divided into three aisles of five bays each, every bay measuring about \(11\times 11\,\text{m}^{2}\). In the west, the central aisle is extended by an apse of approximately the same size as the bays. The church walls and pillars are built of limestone, and the ceiling vaults are of plastered brick. In the present day, the interior walls are of naked stone, but their rough surface indicate that they were originally plastered. It is also known that plaster was removed during a renovation in the 19 th century [36]. A photograph of the modern day church can be seen in Figure 1, and a 3D model of the interior walls and vaulted ceiling is shown in Figure 2.  
Figure 1: Present day church. View from east to west.  
The church and its abbey was the mother abbey of a religious order of both monks and nuns. The monks and nuns lived in separate enclosures, but shared the same church. Both congregations had a specific location within the church where their services were celebrated. The order was characterized by the premiership of women, and is the source of the only known monastical office exclusive for women [37].  
The information about the order guides the acoustic analysis to focus on the dedicated spaces for the monastic congregations, especially the nuns'. These two areas are digitally reconstructed, as described in Section 3.1. The monks' choir and the gallery may both be interpreted as choirs. As the choir often has an important role in the acoustics of a church [32; 33], a deep acoustic analysis is motivated both by religious and acoustic considerations. However, only traces of the structures remain in the church today, and a historically based digital reconstruction is the only practical option to evaluate the historical sound field of the space.  
Below, the processes of digital reconstruction, recording of liturgical elements, and acoustic simulations of the church are presented. The foundations of the historical model are described thoroughly, as one of the goals of the paper is to inspire future researchers in similar projects, and to validate the acoustic results based on historical information.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 4 Definitions and Problem Statement  
The objective of VEPO-S2S is to predict the future trajectory of a vessel based on AIS data. To articulate our approach more clearly, we provide the following definitions:  
[Vessel Trajectory] A trajectory point is defined as a tuple \(x_{t}=(lon_{t},lat_{t},so_{t},cog_{t},dist_{t},\)\(l,w,d,t,\alpha,\beta,\gamma)\) at time \(t\), in which \(x_{t}\) is composed of longitude \(lon_{t}\), latitude \(lat_{t}\), speed \(so_{t}\), course \(cog_{t}\), sealing distance \(dist_{t}\), length \(l\), width \(w\), draft \(d\), type \(t\), Sailing Location Preference \(\alpha\), Voyage Time Preference \(\beta\), and Anchoring Time Preference \(\gamma\), respectively. A vessel trajectory \(X=(x_{t_{0}},x_{t_{1}},\ldots,x_{t_{n}})\) is defined as a chronological sequence, where \(\{t_{i},i=0,1,2,\ldots,n\}\) is a set of timestamps.  
[Position Sequence] The position of the ship at time \(t\) is defined as a tuple \(y_{t}=(lon_{t},lat_{t})\), and the sequence of positions of the vessel at time \((1,2,\ldots,t)\) is defined as \(Y=(y_{1},y_{2},\ldots,y_{t})\).  
[Vessel Trajectory Prediction] Given an observed trajectory \(X=(x_{1},x_{2},\ldots,x_{t})\) at timestamp \((1,2,3,\ldots,t)\), the objective is to predict the trajectory \(Y=(y_{t+1},y_{t+2},\ldots,y_{t+k})\) at the following timestamps \((t+1,t+2,\ldots,t+k)\).
## 5 Discussion
### Datasets' Performances Evaluation
The lighting conditions have affected all the datasets' performance as shown in the aforementioned tables. EgoGesture and NVGesture were medium performers and they have limitations and advantages but can be improved for use in real-world scenarios if data augmentation is applied to them (removing biases and applying transformations can improve the performance) [46; 47]. In reflecting upon the results (Section 4), it becomes evident that the challenges confronted across the datasets extend beyond the crassness of hand gestures alone. Factors such as the lighting conditions, image resolution, variable length sequences with maximum deviation, and inherent biases present in the datasets play pivotal roles in shaping the model's adaptability and robustness in real-world application scenarios. The sign-language dataset showed some resilience while staying the most effective to different test scenarios. In summary, the comparative evaluation of the six models across the selected hand gesture recognition benchmarks reveals a consistent performance hierarchy: InceptionV4 > EfficientNet > MobileNetV3 > NanoViT > ConvNeXt > EdgeNeXt. Similarly, among the benchmarks, SignLanguage performed best overall, followed by EgoGesture, NVGesture, and Jesture, indicating that dataset quality shaped by balanced class distribution, subject diversity, effective augmentation, minimized bias, consistent sequence lengths, and varied conditions plays a critical role in model generalization. These findings underscore the importance of both architectural efficiency and data quality when designing gesture recognition systems, especially for real-time or resource-constrained environments.
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models
#### 4.4.9 Cost Reduction  
The integration of IoT and Distributed Computing Systems can lead to significant cost reductions across various business operations. IoT devices allow for on-time monitoring and organization of assets, reducing the need for manual inspections and maintenance [46]. For example, IoT sensors can detect equipment malfunctions and trigger automated maintenance processes, minimizing downtime and reducing repair costs [83]. Distributed systems contribute to cost reduction by optimizing resource allocation and improving operational efficiency. They enable businesses to analyze data and identify areas in which costs can be mitigated, such as energy consumption, inventory management, and labor [84; 85]. For instance, distributed systems can analyze energy usage data from IoT sensors and identify patterns that lead to energy waste, allowing businesses to implement energy-saving measures. Additionally, the automation and task scheduling capabilities of distributed systems reduce the need for manual intervention and labor costs, further contributing to overall cost savings.
## 4 GLEAM LE product  
The GLEAM LE product was calculated based on the Priestley and Taylor equation, the different components of land evapotranspiration was estimated separately, including transpiration, bare-soil evaporation, interception loss, open-water evaporation and sublimation [27]. The GLEAM LE product version 3.a used in our study is based on satellite data (soil moisture, vegetation optical depth and snow-water equivalent), reanalysis data (ERA air temperature and net radiation), and a multi-source precipitation product [26]. The GLEAM LE product has a daily temporal resolution and 0.25\({}^{\circ}\) spatial resolution.