esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Thyroid US Examination and US-Guided FNA  
Conventional US diagnosis were carried out with Vinno 70 Doppler ultrasound system (VINNO Technology Co., Ltd., Suzhou, China) using a 6-18 MHz high-resolution linear transducer (X6-16L). The mechanical index was set to be 1.2, with a depth of 38 mm, a probe frequency of 10 MHz, a thermal index of soft tissue of 0.8, and a total gain between 95 and 110. The time gain compensation was fixed at near and far fields, and the focus was set at the image center. B-mode US was performed by two experienced sonographers to select the region of interest (ROI) and measure the size of thyroid lesion based on the following protocols: (1) The patients were examined in the supine position; (2) US probe was positioned over patient's thyroid to capture B-mode image in longitudinal section, avoiding the trachea and blood vessels and minimizing the artifacts as much as possible; (3) after a static frozen image was obtained, an ROI was marked on the thyroid lesion with a typical dimensions between 4.5 mm \(\times\) 2 mm and 25 mm \(\times\) 15 mm. The features of individual thyroid nodules were characterized according to their shape, echogenicity and calcification. Echogenicity was divided into hypo- or hyper-echoic (nodules demonstrating hypoechogenicity comparing to surrounding thyroid parenchyma). Calcification (if present) was classified into mixed calcifications, microcalcifications or macrocalcifications including eggshell calcifications. The shape was measured in the transverse direction, and identified as taller or wider. All cases were thereafter assessed with Doppler examination to evaluate the vascularization of thyroid and nodules. A qualitative assessment of thyroid vascularization, which was labeled as either absent or present, was performed in the present work.  
Subsequent US-guided RNAs were applied under the ultrasonographic guidance by an interventional radiologist with a 24-gauge needle. After determining the nodule's position under US guidance, FNA samples were obtained at least quartic inside each thyroid nodule in the US scanning plane, and local anesthesia was applied routinely. The tissue specimen in the needle was placed on glass slides, and then immediately put in 95% ethanol for hematoxylin eosin (HE) staining. Histological diagnoses were made by two experienced pathologists, and the final histological result of the RNAs was classified as either benign or malignant based on standard pathological criteria.
## 3 System Model
### Network Initialization  
The threshold-based secret sharing scheme proposed by [PERSON] can be initialized by a single trusted authority (TA) or a group of TAs. However, the drawback of using a single TA is that if an adversary can access the TA and retrieve the network's private key, this will result in the whole network being compromised. An alternative to this is using multiple TAs working independently and providing multiple partial shares to a joining vehicle. Then, the combined partial share will be the vehicle's private key, while the group private key is not known to any individual.  
The local TA decides the global cryptographic parameters shown in Table 1 and selects the cloud TAs required in the V2X network. The selection of cloud TAs will be based on the type of vehicles you want to support in the V2X network; at the minimum, all vehicles' Home TAs should be part of cloud TAs. Then, upon sharing global cryptographic parameters, each TA (i) initializes itself by generating a large random number as its secret (\(a_{10}\)) with generated witness values as shown in Equations (1)-(7).  
Let the number of dealers be \(d\) and define  
\[f_{1}(x)=a_{1_{0}}+\sum\
olimits_{n=1}^{t-1}(a_{1_{n}}x^{n})\bmod p \tag{1}\]  
\[f_{2}(x)=a_{2_{0}}+\sum\
olimits_{n=1}^{t-1}(a_{2_{n}}x^{n})\bmod p\ldots \tag{2}\]  
\[f_{d}(x)=a_{d_{0}}+\sum\
olimits_{n=1}^{t-1}(a_{d_{n}}x^{n})\bmod p \tag{3}\]  
\[f(x)=\sum\
olimits_{n=1}^{d}f_{n}(x) \tag{4}\]  
\[W_{1}=\left\{\begin{array}{l}g^{a_{10}},\,g^{a_{11}},\ldots,g^{a_{1_{t}}}\\ \\ \end{array}\right\} \tag{5}\]  
\[W_{2}=\left\{\begin{array}{l}g^{a_{20}},\,g^{a_{21}},\ldots,g^{a_{2_{t}}}\\ \\ \end{array}\right\} \tag{6}\]  
\[W_{d}=\left\{\begin{array}{l}g^{a_{d_{0}}},\,g^{a_{d_{1}}},\ldots,g^{a_{d_{t} }}\\ \\ \end{array}\right\} \tag{7}\]  
Then, TAs are ready to enroll vehicles to the V2X network. The network-wide private key is unknown to anyone. Using threshold cryptography, a collaboration of any T number of TAs could compute the network-wide private key. A summary of the network's cryptographic information is shown in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
\begin{tabular}{l} Public information available \\ to everyone \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of network cryptographic information.  
PUF could be used to generate a unique id associated with a vehicle. However, direct use of PUF-generated unique id Equation(8) will break the anonymity and the uniqueness of the message in the network.  
\[\text{UID}_{i}=PUF(i) \tag{8}\]  
where i is the node number and _PUF_ is Physical Unclonable Function  
Hence, it is proposed that each vehicle generates a self-identity using a one-way hash algorithm such as SHA-256 (H) with a network name (N) and current time (T) as parameters Equation (9).  
\[\text{ID}_{i}=H\;(\;\text{UID}_{i}+\text{N}+\text{T}) \tag{9}\]
# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 4 Traditional and Modern Processes of Architectural Design
### GAI-Driven Architectural Design Workflow
#### 4.2.5 The Impact of AI on Workflow  
In the field of architectural design, AI not only enhances architectural workflows by streamlining information exchange but also significantly improves the efficiency of task organization through advanced computational capabilities.  
In terms of information exchange, AI algorithms such as Pix2 Pix, developed by a conditional GAN and interactive sketch program based on CNNs, can generate sketches into architectural drawings or models. In combination with VR, architects can fully experience and modify designs in a virtual environment. At the same time, augmented reality and mixed reality technologies build bridges between the real and virtual worlds, further optimizing the way information is exchanged.  
In terms of task organization, the decomposition and organization of architectural design tasks are crucial to achieve efficient design processes. AI analyzes information flow by Design Structural Matrix (DSM) and optimizes the complex matrix using heuristic algorithms such as clustering and genetic algorithms, helping to create cohesive task modules, reducing information loss, and improving design efficiency.
## Appendix B
### Preliminaries--Technical Concepts--Blockchain-Related Concepts  
For the blockchain-related concepts, first, we describe a typical blockchain network and the consensus algorithm that supports it. Then, we describe smart contracts that run on decentralized networks and token-based systems to exchange assets and values within a blockchain network.  
Blockchain network and consensus mechanisms: The blockchain network comprises peers and nodes, that represent entities that execute transactions within the network. Transactions are organized in blocks, cryptographically linked with previous transactions, and are redundantly recorded across peers, ensuring consistency of the blockchain's state among all peers. Generally, blockchain networks fall into two categories: private and public. In private blockchains, permission is required to join the network, while in public blockchains, anyone can join and execute transactions on the network. Before any transaction is accepted into the network, it undergoes validation using a specified consensus method. Public blockchains commonly employ proof-based consensus methods,such as proof of work and proof of stake, along with their variations. In contrast, private blockchains typically use voting-based consensus methods, often based on adaptations of Byzantine fault-tolerant systems.  
Smart contracts and tokenization: The computer programs running on the blockchains are commonly known as smart contracts. Consequently, different rules and conditions can be encoded within a smart contract and are executed without the need to rely on a central entity for coordination. A blockchain application, also referred to as a decentralized application (DApp), can consist of several smart contracts. Smart contracts are also used to realize information assets and value exchange among the network participants. These values and digital assets can be represented in various types of tokens that exist in blockchain networks. Some common examples of tokens are utility and non-fungible tokens (NFTs). Utility tokens are fungible and are used to implement the ownership and transfer of values in blockchain networks. NFTs are commonly used to provide a unique representation of digital assets in a blockchain network.
# An Improved Method for Named Entity Recognition and Its Application to CEMR+
## 4 Experiments and Analysis
### Comparison of Different Parameters  
On the one hand, the increase of the dimension will increase the complexity of the model, making the model more generalized. On the other hand, the increase of the dimension will bring huge computational cost. To optimize the network structure, we evaluated the impact of four different embedding dimensions on the F1-score. When we evaluated different embeddings, such as word embedding, we fixed the dimensions of other embedding (such as char embedding, etc.) as relatively optimal results. We tried different test embedding dimensions, increasing from 64 to 512 with a step size of \(2^{n}\).  
From the result shown in Figure 6, the dimension of word embedding had a large impact on the F1-score of the model. The F1-score was 89.56% using 64 dimensions of word embedding and was increased to 93.98% using 256 dimensions of word embedding (CCKS2017). Although the 512-dimensional word embedding was 0.13% higher than the 256-dimensional word embedding F1-score, it doubled the computing resources. The dimensions of char embedding and POS embedding had little effect on the F1-score. In order to have lower computational complexity, we selected 64 as their best dimension number. Position embedding also had an impact on the model performance. We found that the larger the dimension of position embedding, the better the performance of the model. However, the larger the dimension, the weaker it was. To balance model performance and computational cost, we chose a dimension of 256.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**CCKS2017**} \\ \cline{2-7}  & **Body** & **Check** & **Disease** & **Signs** & **Treatment** & **Average** \\ \hline Bi-LSTM-CRF [2] & 94.81 & 96.40 & 87.79 & 95.82 & 88.10 & 93.97 \\ ID-CNNs-CRF [9] & 90.89 & 88.28 & 79.67 & 91.04 & 81.27 & 87.47 \\ Attention-ID-CNNs-CRF (ours) & 95.38 & 97.79 & 86.55 & 96.91 & 87.64 & 94.55 \\ \hline \multicolumn{7}{c}{**CCKS2018**} \\ \cline{2-7}
**Model** & **Body** & **Symptom** & **Operation** & **Drug** & **Description** & **Average** \\ \hline Bi-LSTM-CRF [2] & 92.59 & 93.12 & 87.43 & 82.86 & 86.33 & 90.44 \\ ID-CNNs-CRF [9] & 84.37 & 85.81 & 76.59 & 80.42 & 84.61 & 81.34 \\ Attention-ID-CNNs-CRF (ours) & 95.18 & 94.47 & 85.86 & 80.06 & 91.99 & 91.17 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the entity category on the CCKS2017 and CCKS2018 datasets.
## 6 Conclusions  
This study has used the machine learning (ML) approach to intelligently model the intrinsic compressibility index of soils by adopting non-parametric tree-based ensemble learners and meta-heuristic ensembles. Predictions using traditional stand-alone algorithms were also performed and compared with those of the ensemble learners. ML multiclass or multinomial classification was also applied to examine the ability of the classifiers to adequately learn between the soil types and by so doing distinguish between positive and negative classes. The following are the main conclusions from this study:  
1. Among the traditional stand-alone ML regression models, BLR did produce the least accuracy of prediction of intrinsic compressibility index of soil when compared to REG and ANN. Although, a general consideration of the stand-alone ensembles such as REG and BLR would suggest their inability to implicitly detect the complexities of variable combinations due to their basic underlying assumption of linearity.
2. The tree ensembles (RDF and BDT) did generally outperform the stand-alone models with the RDF model having an R\({}^{2}\) of 0.86 and RMSE of 0.48 and the BDT model  
Figure 15: ML Feature importance.  
producing and an R\({}^{2}\) of 0.822 and RMSE of 0.53. The techniques of 'bagging' or 'bootstrapping' inherent in these tree-ensembles certainly enhanced their accuracy in that respect.
3. The technique of ensemble averaging with a meta-heuristic combination of models' hyperparameters through voting and stacking gave the best overall performance in the prediction of the intrinsic compressibility index of soils with an average R\({}^{2}\) of 0.9 and RMSE of 0.34.
4. Sensitive analysis and diagnostic tests used to examine the procedure and outcome of dataset training, testing and validation showed the Monte Carlo method of cross validation as giving the best results for ML prediction.
5. An analysis of the features with direct influence on the ML prediction revealed that the void ratio determined at effective stress of 100 kPa had the most significance followed by soil's Atterberg limits while specific gravity was the variable of least importance in the forecast of intrinsic compressibility index of soils C*c.  
Conceptualisation, E.U.E. and S.J.A.; methodology, E.U.E. and S.J.A.; validation, E.U.E. and S.J.A.; formal analysis, E.U.E. and S.J.A.; investigation, E.U.E. and S.J.A.; resources, S.J.A. and C.A.B.; data curation, E.U.E. and S.J.A.; writing--original draft preparation, E.U.E. and S.J.A.; writing--review and editing, E.U.E., S.J.A. and C.A.B.; visualisation, S.J.A. and C.A.B.; supervision, C.A.B.; project administration, C.A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Space Habitat Data Centers--For Future Computing
## 6 Feasibility and Performance Evaluation  
This section discusses feasibility, evaluation results and has two parts. The first and second part presents results on feasibility of accessing asteroid water for SHDC cooling and analysis of performance benefits in the quality of service (QoS), respectively.  
The analysis in this section has two goals. The first goal is to investigate the feasibility of accessing asteroid water to realize zero WUE SHDC operation. This is done with the aim of identifying suitable asteroids, their near-Earth-orbit dates, and determines the maximum, minimum and mean duration of accessing asteroid water resources. The second goal is to examine how the use of SHDC enhances the quality of service from the satellite space computing perspective.
## 3 Resource Allocation Based on a Multi-Agent Reinforcement Learning Algorithm
### Multi-Agent Environment Modeling
#### 3.1.3 Immediate Reward  
The most insightful factor among the Reinforcement Learning algorithms is the potential it holds for defining the reward function \(\mathcal{R}\) for actuating the learning process. The immediate reward can be defined as \(\mathcal{R}:\mathcal{S}\times\mathcal{A}\), and \(r(s,a)\) is the real value immediate reward as the current state \(s\) is exposed to the action \(a\). In the proposed novel CMADDPG algorithm, two reward signals are defined: global reward and immediate local reward. The global rewards received by the agent are responsible for estimating the agent's level of cooperation, while the local rewards are responsible for evaluating the effectiveness of every agent individually.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 6 Discussion
### Future Development Trends
#### 6.4.2 Combination with Deep Learning  
Deep learning is booming recently and has basically replaced previous related technologies. An extraordinary breakthrough has been made in image classification, face recognition, speech recognition, and so on. At the same time, these technologies have also been introduced into the AEC industry, such as face recognition [74], workforce and equipment tracking [75, 76], helmet identification [77, 78], defect detection [72, 73].  
In particular, there have been studies on 3D reconstruction of building using deep learning in the past few years. They have focused on material recognition and classification [79, 80], point cloud segmentation [48, 50, 81], automatic registration of point cloud and BIM [43], camera pose regression [82], structural component recognition [66] and so forth. The topic, however, is still in its infancy and further developments are yet to be expected [6]. First, the success of deep learning depends on the availability of data sets, but there are currently no large-scale data sets available in the field of AEC, especially the labeled data sets. To obtain more accurate results, a variety of images are required especially for those scenes that are difficult to recognize. However, the images in a project are similar, so how to combine many projects in the AEC industry to obtain a comprehensive and rich image database for training needs to be further explored. Second, the images in other industries are so different from those in the AEC industry, and thus the general feature-based object detection algorithms are not well suited for construction engineering structural component detection because of the complex spatial structural relationships (such as adjacency, aggregation, and hole inclusion) between various components in large buildings and the insignificant differences in features such as texture and color. Third, most of the state-of-the-art techniques deal with images that contain a single object, but there are a lot of occlusions, shadows, and messy backgrounds in construction site images. In this scenario, how to design a suitable point cloud segmentation strategy combined with deep learning is a hot research topic.
# Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation
## 2 Materials and Methods
### Single Fitness Function of GSA Algorithm  
In WSNs, the energy proficiency is the most troublesome undertaking. Grouping has been demonstrated to be a powerful way for WSNs to save energy. Nonetheless, most of past cluster approaches flopped pitifully in the determination of CH, coming about in expanded energy utilization [47; 48; 49; 50].  
The ideal transfer hub determination really limits the sensor hubs' energy utilization. The proposed research brought about the presentation of most recent energy proficiency executive calculations, MGSA-ORS, with a novel technique for the determination of group head process and changed GSA for ideal forwarder hub determination. In this methodology, numerous boundaries, for example, the closeness to the sink, lingering energy of hubs, and likelihood esteem, are utilized to pick CHs.  
The sink distance is thought to save extreme energy use because of the distance between the two hubs. Moreover, the proposed MGSA thinks about the separation from each hub to its individual CH, as well as the lingering energy of hubs and connection delay, as huge contemplations for the determination of forwarder hubs. Novel strategy plans expand CH proficiency when contrasted with real techniques.  
* The lifetime of a sensor network is broadened, in view of further developed choice of transfer hub and utilization of refreshed GSA.
* Information moving among the sink and CH is improved, despite the fact that CHs are chosen as contingent upon sink closeness to save energy.
* Since hub postponement is an urgent measure for the ideal choice of forwarder hub in MGSA, start to finish, the information transmission time (delay) is reduced.
# Hyperparameter Optimization Using Successive Halving with Greedy Cross Validation
## 2 Materials and Methods
### The Greedy Successive Halving Algorithm
```
Input:\(M\) (set of candidate ML models), \(k\) (number of folds), \(D\) (training dataset), \(h\) (halving factor) Output: Best-performing ML model identified in \(M\) \(N_{max}\leftarrow\pi(D)\) (maximum # of training cases to use per iteration) \(N_{min}\gets 6k\) (minimum # of training cases to use per iteration) \(N_{iter}\leftarrow\lfloor\log_{h}(N_{max}/N_{min})\rfloor+1\) (total # of halving iterations to perform) \(b_{cases}\leftarrow\frac{\ln(N_{max}/N_{min})}{N_{iter}-1}\) (scalar for computing # of training cases to use per iteration) \(b_{models}\leftarrow\frac{\ln(2/n)}{-N_{max}+1}\) (scalar for computing # of best models to select per iteration) for each\(i\in\{0,\ 1,\ \ldots,\ N_{iter}-1\}\)do (for each halving iteration) \(M_{best}=\varnothing\) (the set of best models selected during this iteration) (determine the # of best models to retain after this iteration): if\(i=N_{iter}-1\)then \(N_{models}\gets 1\) else \(N_{models}\leftarrow\min\Big{(}n(M),\ \text{round}\Big{(}n(M)*e^{-(i+1) \cdot b_{models}}\Big{)}\Big{)}\) endif \(N_{cases}\leftarrow\text{round}\Big{(}N_{min}*e^{j\cdot b_{cases}}\Big{)}\) (# of training cases to use during this iteration) \(D^{*}\leftarrow\) random sample of \(N_{cases}\) training cases selected from \(D\) split \(D^{*}\) into \(k\) folds of approximately equal size, s.t.\(D^{*}=\{d_{1},d_{2},\ldots,d_{k}\}\) for each\(m\in M\)do (for each remaining candidate model)  train \(m\) using folds \(\{d_{2},\ldots,d_{k}\}\) \(P_{m}\leftarrow\) performance of \(m\) evaluated using fold \(d_{1}\) \(N_{m}\gets 1\)(number of folds evaluated for \(m\)) while\(n(M_{best})<N_{models}\)do (while more best models remain to be selected) \(m^{*}\leftarrow\) best incompletely evaluated \(m\in M\), per \(P\) \(N_{m^{*}}\gets N_{m^{*}}+1\) train \(m^{*}\) using all folds \(d_{j}\in D^{*}\) where \(j\
eq N_{m^{*}}\) evaluate performance of \(m^{*}\) using fold \(d_{N_{m^{*}}}\) \(P_{m^{*}}\leftarrow\) mean performance of \(m^{*}\) for folds \(\{d_{1},\ldots,d_{N_{m^{*}}}\}\) (if all folds have been evaluated for \(m^{*}\), then add \(m^{*}\) to the set of best models): if\(N_{m^{*}}=k\)then \(M_{best}\gets m^{*}\) endif endwhile \(M\gets M_{best}\) (define the set of remaining candidate models) endfor return the only remaining \(m\in M\)
```  
**Algorithm 1**ML model selection using greedy successive halving.