esa-sceva/satcom-chunk-collection huggingface dataset:
# MEC: A Mesoscale Events Classifier for Oceanographic Imagery
## 2 SST Satellite Data  
The detection and classification of upwelling events is performed by processing SST data acquired by radiometers installed aboard meteorological satellites. In particular, for the analysis described in this paper data from the years 2009 to 2017 from two sources were used: the satellites of the _Metop_ programme of EUMETSAT [42] and the _Aqua_ satellite of NASA [43]. Table 1 outlines the main characteristics of these two sources.  
Data are collected in the form of either NetCDF-4 files or HDF files (the latter only for older files from Aqua) and processed at level L2P in accordance with the Group for High-Resolution Sea Surface Temperature (GHRSST) data processing specification, ([[https://www.ghrsst.org/ghrsst-data-services/products/](https://www.ghrsst.org/ghrsst-data-services/products/)]([https://www.ghrsst.org/ghrsst-data-services/products/](https://www.ghrsst.org/ghrsst-data-services/products/)), accessed on 19 January 2023) meaning that the files contain derived SST data at the same resolution and location as source data from the sensor. Both sources cover the entire surface of Earth, but only files that contain data within our region of interest (latitude between 35\({}^{\circ}\) and 40\({}^{\circ}\) N, longitude between 12\({}^{\circ}\) and 6\({}^{\circ}\) W) were downloaded, resulting in datasets comprising an average of 2-3 images per day. Moreover, the reliability of the data captured by satellite radiometers is often prone to relevant fluctuations--this may happen, for instance, if the cloud coverage in the area is too dense, resulting in partially unreliable, sometimes even completely, sensor measurements (see Figure 1 for an example). This implies that at the finest possible resolution value, the sensor measurements are often unreliable due to noise, or unavailable because of energy dispersion occurring in the radiation propagation process. Therefore, some images have been pre-emptively selected: in particular, images with less than 15% of the expected amount of data (estimated using the declared spatial resolution and the extension of the area of interest) have been discarded.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Sensor** & \
\begin{tabular}{c} **Resolution** \\ **(at Nadir)** \\ \end{tabular} &
\begin{tabular}{c} **Temperature** \\ **Accuracy** \\ \end{tabular} \\ \hline Metop-A (2009-2016) & AVHRR & 1 km & 0.01 \({}^{\circ}\)C \\ Metop-B (2017) & MODIS & 1 km & 0.005 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellite data specifications.
## 3 Materials and Methods
### Methods  
#### 3.4.1 An Overview of MS-WFFNet  
The architecture of MS-WFFNet is shown in Figure 4. It is mainly composed of three key components: the EEMA-VGG16 sub-network, the MSFA module, and the DMR module. First, to initially improve the feature expression ability, MS-WFFNet adopts a structure with a truncated VGG16 as the backbone network, cross-injects EEMA after its five body blocks, and constructs the EEMA-VGG16 sub-network. This strategy effectively reduces the noise interference caused by similar backgrounds, thereby enhancing the model's ability to focus on the object area. Secondly, the MSFA module is introduced to aggregate features containing information of different scales and realize dense connections between these features. In the MSFA module, the feature maps output by the five EEMA modules from the shallow to the deep layers are aggregated step by step. This process combines the inter-feature weighting mechanism in BiFPN with the intra-feature weighting mechanism in LSKA. It not only reduces the interference of irrelevant features but also enhances the expression ability of effective information. At the same time, the local structure and global dependencies are captured to form a multi-scale feature representation. Finally, the four feature maps with decreasing resolutions generated by the MSFA module are, respectively, fed into the DMR module for regressing the corresponding density maps. Combining multiple density map losses for distributed supervision ensures that the network can learn the distribution of object features more rigorously.
## 3 Results and Discussion
### Hardness  
The _HD_ of extruded product describes the minimal force necessary to break the sample. The expansion, bulk density and hardness are closely related--a product with poor expansion and high density possesses high hardness [37]. Our experimental data showed that a rise in the feed moisture led to a decrease in the expansion, and an increase in the _BD_ and _HD_ of the extruded product (Figure 1). The effect of the feed moisture on the _HD_ was more pronounced at low amount of chokeberry pomace (10%) and low level of feed moisture (14%) than at 20% chokeberry pomace and 20% feed moisture (Figure 2). A weaker effect than the feed moisture came from the amount of pomace and screw speed. When the amount of chokeberry pomace increased and the working screw speed decreased, the _HD_ decreased. This is probably related to the _WAI_, as the higher amount of chokeberry pomace led to an increase in the number of components able to absorb water (like fibers and proteins), thus the _WAI_ increased and the _HD_ decreased, i.e., the extruded product is softer. Increased screw speed led to a reduction in the duration in the extruder leading to lower expansion, and higher density and hardness. The reduced duration hinders the complete gelatinization of the starch [26]. [PERSON] et al. [35] also found that increase in the feed moisture led to an increase in the _HD_ of the chickpea flour-based snack. According to [PERSON] et al. [33], the response surface plot demonstrated that the decrease in die temperature and level of tomato pomace led to a rise in product hardness. This probably means that the relationship between hardness and the added amount of pomace depends on the type and composition of the pomace.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 3 Attention Mechanism in MDE Methods
### Hybrid Attention Methods
#### 3.4.2 Self-Supervised Training  
[PERSON] et al. presented a self-supervised depth prediction structure consisting of three encoders, i.e., the one based on Transformer to extract global information, and the other two are based on CNN to extract local features from images of different resolutions [19]. All the features of the three encoders are fused by a mask-guided multi-scale fusion block followed by a Transformer-based decoder to generate a depth map. MonoFormer was proposed by [PERSON] et al. to address the generalizability issue of MDE methods [20]. They found that the Transformer-based MDE model outperforms the CNN-based model on the generalizability capability. Inspired by this observation, MonoFormer employs a hybrid encoder consisting of a CNN module followed by Transformers. Moreover, an attention connection module consisting of spatial attention and channel attention is employed to replace the traditional skip connection in the classical encoder-decoder structure. [PERSON] et al. proposed a self-supervised MDE method named MonoViT consisting of a lightweight pose network and a Transformer-CNN hybrid depth estimation network [21]. Unlike the parallel scheme of DepthFormer, they designed a joint CNN and Transformer layer in which three Transformer blocks and a convolutional block are arranged in parallel. Four joint CNN and Transformer layers are cascaded to form the backbone of the depth estimation network. Figure 20 illustrates the structure of the joint CNN and Transformer layer. In addition, MonoViT adopts spatial attention and channel attention in the decoder to fuse the multi-scale features.  
Figure 19: Overview of token attention module used in [79] (image: courtesy to [PERSON] et al. [79]).  
[PERSON] et al. presented a cost volume-based self-supervised MDE method [80]. Cost volume is firstly created by calculating the similarity of the features of the source and target images. Then, cost volume is fed into an encoder consisting of CNN followed by Transformers to extract features. Finally, features are decoded by fusion blocks consisting of spatial attention, channel attention, and upsampling operations. [PERSON] et al. proposed Lite-Mono, a lightweight and self-supervised hybrid model [18]. They investigated the effective combination of CNN and Transformer, designing a consecutive dilated convolutions module and local-global features interaction module; the former extracts rich local features, and the latter captures global characteristics via a self-attentive mechanism. The features extracted by the encoder are then fed to the decoder to generate a high-resolution depth map through multi-scale fusion (Figure 21).  
Figure 21: The proposed consecutive dilated convolutions module (CDC) and local–global features interaction module (LGFI) (image: courtesy to [PERSON] et al. [18]).  
Figure 20: Joint CNN and Transformer layer used in [21] (image: courtesy to [PERSON] et al. [21]).
## 4 Methodology
### Data Analysis  
We performed a thematic analysis to uncover how organizations perceive and address deepfake threats. The analysis was conducted on the interview transcripts with iterative coding, followed by theme refinement, to ensure a transparent, reproducible process of identifying the core issues in deepfake mitigation. Inspiration for this section was taken from [PERSON]'s guidelines for a thematic analysis [22]. Our five steps were as follows:  
1. Transcribing and reviewing the interviews;
2. Assigning initial descriptive codes (e.g., _awareness level, vendor reliance_);
3. Clustering the codes into themes (e.g., _Organizational Readiness, Mitigation Strategies_);
4. Refining and removing redundant categories;
5. Summarizing the findings in a thematic table (Appendix B).  
Insights from the Thematic Analysis  
Following the thematic analysis, we found the following core issues based on the interviews conducted.  
* **Organizational readiness:** Most view deepfakes as a looming threat but remain reactive due to scarce real-life incidents;
* **Mitigation strategies:** Organizations rely on general security controls (e.g., MFA, phishing filters) rather than dedicated deepfake solutions;
* **Detection gaps:** None deploy specialized deepfake detectors, instead trusting existing security vendors;* **Training limitations:** Deepfake-focused training is uncommon, leaving employees underprepared for AI-driven social engineering.  
Overall, organizations depend on standard cybersecurity measures and vendor trust, indicating a need for targeted deepfake detection and response strategies.
## 2 Materials and Methods
### Root Canal Treatment  
The working length (WL) was determined by inserting a #10 K-File (Dentsply Maillefer, Ballaigues, Switzerland) into the canal until its tip was visible at the apical foramen. Gates Glidden burs (Dentsply Maillefer, Ballaigues, Switzerland) were used in the coronal third, and manual instrumentation was performed using #10, #15, and #20 K-Files until WL was reached. Canal shaping was performed using NiTi Rotate rotary instruments (VDW, Munich, Germany) 15.04, 20.04, 25.06, and a Gold Reciproc motor (VDW, GmbH, Munich, Germany). Torque and rpm settings were set following manufacturer's instructions. Canal instrumentation was performed with gentle motions with an amplitude of 2-3 mm. Dentin trapped in the instrument was removed using an endodontic sponge. A volume of 3 mL of 5% NaOCl (Niclor 5, Ogna, Muggio, Italy) was inserted into the canal through a 5 mL syringe with a 27 G needle. This procedure was repeated until WL was reached. The treatment was considered completed when no remnants were visible on the instrument surface and the irrigant solutions appeared free of canal remnants. After mechanical instrumentation, the tooth was further irrigated with 5% NaOCl for 3 min and 10% EDTA for 1 min to achieve complete removal of the smear layer. A final irrigation with 1 mL of saline solution was performed. Instrumentation times were recorded. CBCT and X-ray images (vestibulolingual, VL, and mesiodistal, MD, projections) were taken before repositioning the samples in numbered containers and placing them in the incubator.  
_Group 1 (GF)_: Samples in group 1 (\(n\) = 11) were obtutated using a carrier-based technique. The canal was dried using sterile paper points. Neosealer Flo was used in combination with Guttafusion (Guttafusion system, VDW, GmbH, Munich, Germany). The sealer was placed in the canal using a syringe positioned 2 mm from the apex. Excess sealer was removed with a paper point. The obturation material used was Guttafusion #25, which consists of a cross-linked gutta-percha core coated with gutta-percha. The carrier-based obtutator was heated in the Thermaprep 2 oven for 1 min and then introduced into the canal with a slow and steady motion until reaching the WL. X-ray (VL) and CBCT were performed on the samples. The samples were reintroduced into the designated numbered containers and placed in an incubator for 30 days.  
_Group 2 (SC)_: Samples in group 2 (\(n\) = 12) were obtutated using the single-cone technique. The canal was dried using sterile paper points. Neosealer Flo was placed in the canal using a syringe positioned 2 mm from the apex. The canal was filled completely until the sealer was visible at the canal orifice. Gutta-percha core #25.06 (Dentsply Maillefer, Ballaigues, Switzerland) was slowly inserted in an apical direction until reaching the WL. X-ray (VL) and CBCT were performed on the samples. The samples were placed back into their designated numbered containers and kept in an incubator for 30 days.
## 3 Theoretical Background
### Local Outlier Factor
Points \(p_{1}\)–\(p_{4}\) have the same RD, and \(p_{5}\) is not a \(k\)-nearest neighbor. One advantage of the LOF algorithm is that it considers both local and global characteristics of the dataset. This means it can achieve a good performance even for datasets in which the outlying samples have different underlying densities. The LOF algorithm can perform two types of detection, outlier detection and novelty detection. Among them, outlier detection is based on unsupervised learning, while novelty detection is semi-supervised. In this study, the LOF algorithm was implemented using the scikit-learn library [47] and worked as an outlier-detection method. Some guidelines for determining the range of the neighborhood size \(k\) are given in [46]. In general, the lower bound on the number of neighbors should be the minimum number of samples in a cluster, and the upper bound should be the maximum number of nearby samples that are likely to be outliers. Unfortunately, such information is usually not available. Even if this information is known, the ideal neighborhood size between the lower and upper bounds remains undetermined. In addition to the neighborhood size, the contamination rate, which defines the ratio of samples in the dataset to be identified as outliers, is another hyperparameter of the LOF algorithm. The above two hyperparameters affect the performance of the LOF algorithm to a great extent; however, limited literature is available regarding the tuning of these parameters in the LOF algorithm for outlier detection. [PERSON] et al. [48] proposed a heuristic methodology to set up the hyperparameters of the LOF algorithm. The key concept is to maximize the difference between predicted outliers and inliers that are nearby the decision boundary.
# Common Data Model and Database System Development for the Korea Biobank Network
## 4 Proposed System
### KBN-CDM Development
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Table** & **Columns** & **Description** \\ \hline BASICINFO & 6 & Donor ID, bank name, sex, birth, job \\ REGISTRATION & 9 & Age at registration, disease date, disease diagnosis \\ SPECIMEN & 7 & Specim collection date, specimen type, number of specimens \\ STAGE & 8 & AJCC\_T, AJCC\_N, AJCC\_M, AJCC\_stage, AJCC pre-op, AJCC post-op \\  & & Height, weight, BMI, blood pressure, pregnancy status, weight loss, \\ BDMEASURE & 19 & ECOG, waist circumference, pulse rate, body temperature, respiration \\  & & rate, head circumference, UBV\_Ar (umbilical cord blood arterial), \\  & & UBV\_Ve (umbilical cord blood vessels) \\ DRINSMOK & 16 & Drinking/smoking experience, duration, quit duration, frequency, amount \\ HISTORY & 10 & History disease, drug history \\ FAMHISTORY & 7 & Family history of the same disease, family relations, family history of allergic reactions \\ CLINEXAM & 10 & Laboratory examination name, unit, result of numeric type, result of character type, date \\ NOTE & 10 & Pathology record, surgical record, radiology report, functional test \\  & & record, record date, record free text \\ EXAM & 8 & Examination name, results, date \\ OPERATION\_DETAIL & 6 & Operation record detail, operation detail results, operation date \\ PATHOLOGY & 6 & Pathology record detail, pathology detail results, pathology date \\ TREATMENT & 7 & Treatment name, treatment detail, treatment date \\ SYMPTOM & 6 & Symptom name, symptom results, symptom onset date \\ SURVEY & 8 & Survey name, survey date, survey result \\ FOLLOWUP & 20 & Renal replacement therapy, recurrence, metastasis, death, cause of death \\  & & death \\ VOCA & 5 & KBN name, KBN code, standard code, standard name, reference \\ \hline \hline \end{tabular}
\end{table} 
Table 1: KBN clinical common data model table. as lung capacity tests and ECG tests or exceptional test results such as the Apgar score of premature babies. The OPERATION_DETAIL table includes surgical operation record details, whereas the PATHOLOGY table includes pathology record details. Data from both tables include information regarding the body part that the surgery was performed on, the size of the tumor, or any other information considered important during surgery or radiology. The TREATMENT table includes non-surgical treatment information, drug treatment information, or any other past treatment that the specific patient has had. The SYMPTOM table includes records of what symptoms caused by the disease the patient has had, such as hepatic coma status and ascites status. Medical survey data that patients have prepared themselves, such as International Prostatism Symptom Score (IPSS) and urination matching, are collected in the survey table. The FOLLOWUP table includes follow-up data of the patient, such as death, disease recurrence, or metastasis. In the VOCA table, internal management codes for terms used in the overall table definition and standard term codes for each term are stored.
## 4 Game Clustering Model  
### Definition of Game Space  
Participant: The active nodes in WSNs, denoted as \(N=\{1,2,3,\ldots,N\}\).  
Strategic space: The actions of nodes are carried out in rounds. In each round, nodes select respective strategies from the strategy set \(S=\{D,ND\}\), \(D\) denotes that the node participates in the CH election, and \(ND\) implies that the node does not partake in the CH election.  
Utility function: The strategies of participants are interdependent, and the benefits of each participant are related to the strategy set of other participants. Accordingly, \(U=\{U(i),\)\(i=1,2,3,\ldots,N\}\) is used to depict the node profit set corresponding to the strategy set in this study.  
In the network, CHs process the data collected from ordinary nodes and transmit it to the BS in the form of data packets. Meanwhile, nodes can choose strategy \(D\) or strategy \(ND\) to maximize their payoffs. If no node chooses strategy \(D\) to become a CH and the data acquired by nodes cannot be sent to the BS, the profits of all nodes equal 0. In the case that at least one node \(j\) selects strategy \(D\) to be a CH, then the payoffs of node \(j\) and any node other than node \(j\) are \(V(i)-C_{CH}(i)\) and \(V(i)-C_{CM}(i)\), respectively, where \(V(i)\) is the gain of the data collected by the node successfully reaching the BS, \(C_{CH}(i)\) is the loss of the node elected as CH, \(C_{CM}(i)\) is the punishment for the node becoming an ordinary node. In the case that \(C_{CM}(i)>C_{CH}(i)\), \(V(i)-C_{CH}(i)>V(i)-C_{CM}(i)\), being elected as CH can obtain more benefits, and all nodes will definitely choose the CH strategy. Therefore, in this paper, the following assumption \(C_{CM}(i)<C_{CH}(i)<V(i)\) is set. Furthermore, the profit function \(U(i)\) of any node \(i\) is defined as  
\[U(i)=\begin{cases}0&if\ s_{j}=ND,\forall j\in N\\ V(i)-C_{CM}(i)&if\ s_{j}=D\\ (i)-C_{CH}(i)&if\ s_{j}=ND\ and\\ &\exists j\in N,s.t.s_{j}=D\end{cases} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Advantage** & **Limitation** \\ \hline \multirow{2}{*}{HGTD} & Satisfactory trade-off between minimizing energy & \multirow{2}{*}{Residual energy excluded from the payoff definition} \\  & consumption and providing the required services & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The corresponding profit matrix is shown in Table 2.  
Based on the above matrix, in the situation that all nodes are selected as CHs or all nodes are elected as ordinary nodes, both the strategy set \(S\) = {\(D\), \(D\),..., \(D\)} and \(S\) = {\(ND\), \(ND\),..., \(ND\)} are not a Nash equilibrium. The overall payoff is the most satisfactory when one node becomes CH and other nodes are ordinary nodes.