esa-sceva/satcom-chunk-collection huggingface dataset:
### Scope of Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors** & **Year** & **Objective** & **Merits** & **Demerits** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Reyna et al. [13] & 2018 & To investigate and survey the BC integration in IoT & Critical Analysis of applications & No proposed solution for issues and challenges & Y & N & Y & N & Y & N & N \\ \hline Gupta et al. [14] & 2018 & To provide a survey of BC from security perspective & Elaborated attacks on BC & Discussed threats only in bitcoin-based BC & Y & N & Y & N & Y & N \\ \hline \multirow{4}{*}{Hölbl et al. [16]} & \multirow{4}{*}{2018} & To highlight the difficulties and promising areas for blockchain research in healthcare & Categorically reviewed number of publications year-wise & Review of qualitative to date of publications & Review of qualitative to date of publication & N & Y & Y & N & N & N & N & N \\ \hline \multirow{4}{*}{Gokalp, E. et al. [17]} & \multirow{4}{*}{2018} & To analyze the opportunities and challenges of BC integration in healthcare & Analyzed features and challenges of proposed framework & Abstract view and challenges & & & & & & & & & & \\ \hline \multirow{4}{*}{Chen et al. [6]} & \multirow{4}{*}{2018} & To survey BC usage in different domains & Survey on issues in various applications & Abstract work on technical aspects of BC & & & & & & & & & \\ \hline \multirow{4}{*}{Agbo et al. [18]} & \multirow{4}{*}{2019} & To discuss the ongoing research in BC technology in healthcare & Detailed review of publications in BC in healthcare & Focus is on lighting of research papers & N & Y & Y & Y & N & N & N \\ \hline \multirow{4}{*}{McGhin et al. [19]} & \multirow{4}{*}{2019} & To assess the challenges and opportunities of BC in healthcare applications & Analysis of existing BC healthcare & Not addressed & & & & & & & & & \\ \cline{1-1} \cline{5-12}  & & To provide a review of BC in industry 4.0 and IoT BC integration & Categorically existing performance research directions & Only conceptual discussion on various components of BC & N & N & N & Y & Y & N & N \\ \hline \multirow{4}{*}{Aladi et al. [15]} & \multirow{4}{*}{2019} & To explore BC in industries the opportunities, benefits, and challenges & Benefits of BC in various domains & Focuses on BC domain in general & Y & N & N & N & Y & N & N \\ \hline \multirow{4}{*}{Khezr et al. [21]} & \multirow{4}{*}{2019} & To provide a comprehensive review of BC-based healthcare applications & Comparison of data management, supply chain, IoT medical mechanisms & Potential threats and issues are not discussed in detail & Y & Y & Y & Y & N & Y & N \\ \hline \multirow{4}{*}{Soni et al. [22]} & \multirow{4}{*}{2019} & To survey on working of BC covering threats and futuristic use cases of BC & Compared different types of BC & Only security and privacy issues are focused & Y & N & Y & N & Y & Y & N \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of proposed survey with existing state-of-the-art surveys.
# Three-Dimensional Change of Lip after Two-Jaw Surgery in Facial Asymmetry Using Facial Scanner
###### Abstract  
A facial scanner and three-dimensional computed tomography (CT) were used to evaluate the three-dimensional change in lip asymmetry before and after two-jaw surgery for 22 patients with facial asymmetry (menton deviation > 3 mm). We used the labrale superius (Ls), deviated/non-deviated-side cheilions (Ch-D/Ch-ND), and labrale inferius (Li) to construct the upper and lower lip planes to evaluate the lip asymmetry. A correlation analysis was performed to determine the factors related to the vertical change in the cheilions (\(\Delta\)ChZ-D/ND). In the transverse axis, Ch-D and Li moved to improve the asymmetry after surgery. All landmarks, except the Ls, moved backward in the anteroposterior axis. In the vertical axis, significant upward movement was observed in all hard tissue landmarks; however, there were no significant changes in the soft tissue. In the lip plane, the difference in the height of Ch-D and Ch-ND was significantly reduced (1.38 mm vs. 0.72 mm). In the anteroposterior axis, the \(\Delta\)ChZ-D/ND showed significant correlations with the mandibular setback. In the vertical axis, the \(\Delta\)Ch-ND showed significant correlations with the maxillary impaction of the non-deviated side. The improvement in lip asymmetry post-surgery was mainly achieved by the movement of the lower lip and Ch-D rather than the upper lip and Ch-ND.  
lip asymmetry; two-jaw surgery; three-dimensional CT; facial scanner +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON], K-J- [PERSON], S-H- [PERSON], YJ- [PERSON], S-H- [PERSON], [PERSON], [PERSON]- [PERSON], H.  
Recently, some studies have merged 3D facial surface scans with CT images [6, 13, 15]. Using the structured light scanning system, texture and color information on the facial skin can be readily obtained at a high resolution without additional radiation hazards, together with advantages such as a short scan time and no hazard to the naked eye [16]. Considering these advantages, the use of a 3D facial scan in conjunction with a 3D CT can be an effective tool in evaluating hard and soft tissue changes after orthognathic surgery [6].  
The purpose of this study was to evaluate the 3D change in lip asymmetry after two-jaw orthognathic surgery in patients with facial asymmetry using a facial scanner (FS) and 3D CT. The null hypothesis was that there would be no significant change in lip asymmetry after two-jaw orthognathic surgery in patients with facial asymmetry.
## 7 Applications
### Co-Location of Tasks in a Data Center  
Yet another application of our balanced-partitioning algorithm is in scheduling of computation tasks within data centers. The cost of intra-network bandwidth in a data center is nonnegligible, and adds up over time. Besides, the underlying network topology may be hierarchical: the machines at a lower-level subtree can have much higher bandwidth and may even be organized as a clique. Consequently the communication cost between machines across different subtrees can be much higher.  
One way to reduce network bandwidth usage is to co-locate related tasks within closer physical proximity and therefore optimize the network traffic. As communication patterns of many applications stay stable over time, one can look into historical logs to construct a graph of communication among tasks. In this graph, each node is an application (or task) and the edge weight between two nodes is the communication cost between them. Machines have limited size or memory, thus we naturally cast the problem as balanced partitioning: We cluster the tasks (subject to a physical limit) and minimize the communication cost across clusters.  
Figure 10: The percentage of improvement in cut value as we increase the allowable imbalance factor.  
Our initial implementation of this technique (within a subset of the overall traffic) resulted in a six-fold increase of co-located tasks.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 3 Results and Discussion  
### Experimental Setup  
Training is performed in a supervised manner on the labels of the Interaction nodes, resulting from the provided training set by merging the MedLine and DrugBank subsets. This amounts to a total of 17,176 training examples, derived from 3395 sentences and 608 documents after the construction of the knowledge graph. Similarly, the evaluation is performed on the RE task test set, where a separate graph with 3057 Interaction nodes and 604 sentences from 159 documents is created. The domain-adaption of the LMs is trained either on the training set sentences (Sentence Level Domain Adaption--SLDA) or training set paragraphs (Document Level Domain Adaption--DLDA) only, in a self-supervised manner using the Masked Language Modeling task.  
Our proposed approach requires the definition of the two main elements of the architecture, the underlying BERT-based LM that will generate the word embeddings and the GNN-based classification module. First, different pre-trained BERT variants were compared, such as the base version of the general domain BERT, the scientific domain SciBERT and the biomedical domain BioBERT. Furthermore, BioBERT, the pre-training of which is in alignment with the DDI domain, was tested on both base and large versions. Additionally, since recent studies show that domain-adapting a LM by pre-training it on the downstream task can potentially offer large gains in the task performance [29], we aligned both SciBERT and BioBERT base to the DDI task corpus and compared their performance.  
Having the features of the nodes generated by the BERT-based LMs, it is then necessary to define and train the classification unit. GIN [30] was chosen as the GNN framework, with node embeddings dimensions \(d_{GNN}=256\) for \(d_{BERT}=768\) and \(d_{GNN}=512\) for \(d_{BERT}=1024\), with the internal \(MLP\) unit consisting of \(l_{GIN}=3\) consecutive layers. Its performance is also compared to the mean GraphSAGE framework [31]. Additionally, the contribution of a single-level \(MLP_{pre}\) of size \(d_{GNN}\) and two-level \(MLP_{post}\) of sizes \(d_{GNN}/2\) and \(d_{GNN}/4\) with a drop-rate of 0.4 and ReLU activations are evaluated.  
[PERSON] was chosen as the optimizer with a learning rate and weight decay equal to \(5\times 10^{-5}\) and \(5\times 10^{-4}\), respectively. Furthermore, the mini-batch training approach is followed, where the Interaction nodes of the training set are divided into 53 batches of size 324, while the number of epochs is equal to 170. Finally, the cross entropy loss function is used in the context of the multi-class classification problem.  
The experiments were conducted on a computer with a single RTX 3090 24 GB graphics card an a 24-core Intel CPU and the LM domain-adaption on a computer with two RTX A6000 48 GB graphics cards and were implemented using the Pytorch library and the Python programming language.
# Post-Disaster Recovery Monitoring with Google Earth Engine
## 2 Materials and Methods
### Reference Data and Land Cover Classes  
We defined the land cover classes based on the local knowledge of the authors about the study area, and by considering the capacity/potential of the Landsat images (i.e., spatial and spectral resolutions)  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Timeline**} & \multirow{2}{*}{**Acquisition Dates**} & \multicolumn{1}{c|}{**Number of**} & \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{**Spatial**} \\  & & & **Collected** & & \\ \hline T0 & Before Haiyan & 2013-05-01 to 2013-10-28 & 68 & & \\ \hline T1 & Event time & 2013-11-10 to 2014-03-31 & 56 & Landsat 7 & \\ \hline T2 & Post-Haiyan 1 & 2014-06-01 to 2014-12-30 & 104 & & \\ \hline T3 & Post-Haiyan 2 & 2015-06-01 to 2015-12-30 & 109 & & \\ \hline T4 & Post-Haiyan 3 & 2016-06-01 to 2016-12-30 & 109 & & \\ \hline \end{tabular}
\end{table}
Table 1: Satellite images used in this study.  
Figure 1: An overview of the Philippines showing the path of Typhoon Haiyan and the location of Leyte island. Pre-event image and an image acquired three days after the disaster for Tacloban.  
to identify different classes through field verification and visual interpretation of high-resolution satellite imagery acquired from various platforms (i.e., WorldView1-3, GeoEye 1, Pleiades) and images available from Google Earth Pro. We identified five land cover classes: forest/trees, built-up, crop land, water body, and other (i.e., non-tree vegetation, bare land, and debris/rubble) (Table 2), and the corresponding regions of interests (ROIs) were collected for training and testing of the classification method using the tools available within GEE.
## 2 Theory
### Tensor Response of Heating and Reorientation
If the material is initially isotropic, then photothermal heating will result in isotropic strain. Similarly, light of any polarization will deposit the same amount of heat, which will cause the material to isotropically expand. As such, the anisotropic behavior of the trans to cis photo-isomerization mechanism and the isotropic behavior of photothermal heating can be used to separate the two mechanisms in a simple experiment that records the stress response for two orthogonally polarized pump beams. There are two cases to treat. The angular hole-burning process, shown in Figure 1, results in the photomechanical response denoted by \(\kappa^{(hole)}\). The molecular reorientational process described in Figure 2 results in the photomechanical response denoted by \(\kappa^{(re)}\). Below we start with a process that includes both heating and molecular reorientation. Then we treat angular hole burning. It is possible that all three mechanisms might be acting together, making the modeling straightforward but more complex. Given the uncertainties in typical data, it is not clear weather such a model can be used to unambiguously determine the relative importance of the mechanisms. We thus focus on molecular reorientation, which builds significantly over time.
## 2 Operating Structure of a Distribution Network Comprising a Micro Energy Grid  
The operating structure of a distribution network comprising a micro energy grid is shown in Figure 2. The energy management module of the distribution network was the \"brain\", and was the key component of the optimal distribution-network operation. The micro energy grid and waste-to-energy power station connected to each node were managed by the distribution-network energy-management module. As the lower level of the distribution network, the micro energy grid provided the necessary electrical measurement data for the distribution network, and provided decision support for the dispatch of the distribution network. The communication network between the upper and lower levels was an optical network, and the communication protocol was IEC61850 (International Electrotechnical Commission) [33]. The micro energy grid included PV power, wind power, microturbines, gas-fired boilers, heat-recovery boilers, lithium-bromide absorption-type refrigerators, battery storage, heating and cooling storage tanks, and air-source heat pumps. The micro energy grid was connected to a node of the distribution network. PV power, wind power, and microturbines were used to satisfy the internal electrical load of the micro energy grid. Heat-recovery boilers, lithium-bromide absorption-type refrigerators, and air-source heat pumps were used to satisfy the internal heating load of the micro energy grid. Battery storage, and heating and cooling storage tanks mainly had roles in the adjustment of the micro energy grid. The energies of cooling, heating, electricity, and gas transformed and supported each other, thus improving the efficiency of the comprehensive energy utilization.  
Figure 1: General workflow for the optimal operation of the distribution network comprising the micro energy grid, based on the improved grey wolf optimization (GWO) algorithm.
## 4 Implementation and Experiments
### Datasets
#### 4.2.2 Food Volume Estimator Datasets  
This section will primarily introduce the dataset used in the volume measurement experiment. Its main content is the same as mentioned in Section 3.2, utilizing 3D food  
\begin{table}
\begin{tabular}{c c} \hline
**Developing Environment and Tools** & **Specifications** \\ \hline Central Processing Unit (CPU) & AMD Ryzen 9 5950X 16-Core Processor (AMD, Santa Clara, CA, USA) \\ Graphics Processing Unit (GPU) & NVIDIA RTX A6000 (NVIDIA Corporation, Santa Clara, CA, USA) \\ Random-Access Memory (RAM) & 64G \\ Operating System (OS) & Ubuntu 20.04.6 LTS (Canonical Ltd., London, UK) \\ Python & 3.11.9 \\ PyTorch & 2.3.1 \\ Cuda & V11.6.124 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental environment setup.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Datasets** & **Train** & **Validation** & **Test** \\ \hline Food 101 & 75,750 & 12,625 & 12,625 \\ CNFOOD-241 & 170,843 & 10,472 & 10,472 \\ Taiwanese Food 101 & 20,371 & 2546 & 2546 \\ \hline \end{tabular}
\end{table}
Table 3: List of CLIP fine-tuning datasets.  
models from the NutritionVerse3D [56] dataset. Blender renders these models onto a flat surface, producing precise scene image data. A total of 400 different scene datasets were generated, and based on the experimental results from E-Volume [7], each scene is set to have 40 images to achieve the best prediction results. In our experiment, to compare the test accuracy of different food categories, we further divided the dataset into the following four categories, as detailed in Table 4.  
The above four categories are used to analyze whether there are differences in measuring different types of food using the learning-based MVS method and further explore the direction of subsequent improvement.
## 2 Description of Multi-Agent Model  
In this paper, we have adopted a bottom-up approach to model the search and rescue (SAR). Before we built the conceptual model, we had gathered a lot of detailed information about SAR through the interviews carried out with first responders and experts who specialize in on-site search and rescue in disaster. In this model, shown in Figure 1, two types of active agents are defined, namely victims and rescue teams, and they can act upon their own initiative ([PERSON] et al., 2017). Apart from the active agents, there are passive agents in this model, such as the buried sites. The passive agents are solely reactive. The buried sites are spread randomly in the affected area, and they differ in buried depth. There might exist one or more victims in each site. The victim agents have a property, i.e., survival probability. Without timely rescue, the health condition of a victim could deteriorate continuously, thus the survival probability will decrease with time, and they may be dead as time passes. The rescue teams are agents that can take actions against disastrous situations. They can sense the environment, interact with other agents, and plan the next actions according to their objectives, such as searching for victims, rescuing a buried site, calling for cooperation and so on ([PERSON] et al., 2017).  
The rescue teams search victims in the affected area, and if there are survivors trapped in a buried site within the search radius, rescue teams are able to locate the buried site with a certain probability. The rescue teams who locate the buried site take on the role of auctioneers, and other rescue teams who are not at work presently within the scope of cooperation take on the role of bidders. The interaction takes place mostly between rescue teams, which include auctioneers and bidders, and the interaction is carried out through communication. The communication is modeled as the input that agents receive and the outputs that they produce [28]. Auctioneers would communicate with bidders who are within the scope of cooperation if necessary, sending messages about victims' coordinates, buried depth, etc. The bidders receive the auction messages, calculate the bids, and send them back to the auctioneers. Finally, the auctioneers will determine the winning bids, and send the message to bidders. The rescue operations would start once the cooperation is reached.
# CFD Based Non-Dimensional Characterization of Energy Dissipation Due to Verticle Slosh
## 4 Dimensional Analysis  
The remainder of the article will consider a slosh system which is representative dimensionally of the Protospace experimental setup. As such we consider the vertical oscillation (SDOF) of a rectangular 2D tank (single Protospace compartment) supported by a mass-spring-damper system with a spring constant \(k\) and damper constant \(c\). All simulations employ air at standard atmospheric conditions as the gas within the liquid-gas slosh system. Therefore the 14 parameters considered relevant to the dimensional analysis are presented in Table 2. Also included are entries of the dimensional matrix for the units of Mass (M), Length (L) and Time (T). The rank 3 dimensional matrix employs the normalisation of length \((h)\), mass \((\rho h^{3})\) and time \((\frac{h}{\rho})\) respectively. A straightforward application of the Buckingham Pi Theorem leads to \(14-3=11\) dimensionless groups:  
\[ \begin{array}{llll}\pi_{1}=\frac{m}{\rho h^{3}}=\tilde{m}&\pi_{2}=\frac{\rho_ {Air}}{\rho}=\tilde{\rho}&\pi_{3}=\frac{\rho vh}{\mu}=Re_{v}\\ \pi_{4}=\frac{v}{\sqrt{\tilde{g}h}}=Fr_{v}&\pi_{5}=\frac{\rho v^{2}h}{\gamma}= We_{v}&\pi_{6}=\frac{k}{\rho h^{3}}\left(\frac{h}{v}\right)^{2}\\ \pi_{7}=\frac{c}{\rho h^{3}}\frac{h}{v}&\pi_{8}=\theta&\pi_{9}=\frac{l}{h}= \tilde{A}\\ \pi_{10}=\frac{\eta_{0}}{h}=\tilde{F}&\pi_{11}=\frac{y_{0}}{h}=\tilde{Y}_{0}& \pi_{9}=\frac{l}{h}=\tilde{A}\end{array}  \tag{13}\]  
We define the \(\pi_{1}\) group as of the order solid to liquid mass ratio \(\tilde{m}\) of the one-degree of freedom system. \(\pi_{2}\) is the density ratio \(\tilde{p}\) of the gas and liquid phase in the tank. \(\pi\) groups 3 to 5 are well known flow physics related dimensionless numbers, namely Reynolds, Froude and Weber. Note that the \(v\) subscript is added to the latter to indicate that the standard definition based on the fluid speed was employed. As we are interested in the energy dissipation of a tank which is supported by a spring-mass-damper system, the fluid non-dimensional numbers are to be written in terms of the resulting excitation. Considering harmonic motion at frequency \(\omega\) enables expressing the reference flow velocity in terms of the slosh-mass-spring-damper system response as:  
\[v\sim\omega h \tag{14}\]  
This is as in this work the magnitude of tank displacement is of a similar order to the tank height. To consolidate the validity of Equation (14) we note that from experimental observation the first two to three cycles of oscillation are representative of the slosh induced energy dissipation potential for the violent slosh under consideration. Hence only the first 3 excitation cycles are considered for the non-dimensional scaling calculations (to follow) as these are representative of peak loads and slosh induced dissipation. We can therefore modify \(\pi\) groups 3 to 5 as follows  
\[\pi_{3}^{\prime}=\frac{\rho\omega h^{2}}{\mu}=Re\qquad\pi_{4}^{\prime}=\omega \sqrt{\frac{h}{g}}=Fr\qquad\pi_{5}^{\prime}=\frac{\rho\omega^{2}h^{3}}{\gamma }=We \tag{15}\]
# Congestion Control in CoAP Observe Group Communication
## 5 Performance Evaluation  
In this section, regarding the Obs, we compared the performance of our CoCo-RED with the default CoAP under the same continuous traffic scenario and burst traffic scenario and then provided a discussion of our evaluations of these mechanisms. We hypothesized that our developed CoCo-RED could control congestion more effectively with low packet loss and response time, yielding better network reliability and application support in group communication.